From 0e65e9bd85246f8f390c50ac9b27205badc7e7c1 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 17:13:36 +0000 Subject: [PATCH 01/81] add mlfm first attempt --- docs/tutorials/mlfm.ipynb | 1688 +++++++++++++++++ docs/tutorials/mlfm_data/figs/GI.png | Bin 0 -> 7730 bytes docs/tutorials/mlfm_data/figs/mlfm_0_4.png | Bin 0 -> 21697 bytes docs/tutorials/mlfm_data/figs/mlfm_fit.png | Bin 0 -> 42958 bytes docs/tutorials/mlfm_data/figs/mlfm_flow.png | Bin 0 -> 104895 bytes docs/tutorials/mlfm_data/figs/mlfm_iv.png | Bin 0 -> 91317 bytes docs/tutorials/mlfm_data/figs/mlfm_matrix.png | Bin 0 -> 79235 bytes .../tutorials/mlfm_data/figs/mlfm_scatter.png | Bin 0 -> 193764 bytes docs/tutorials/mlfm_data/figs/mlfm_stack.png | Bin 0 -> 122446 bytes pvlib/mlfm.py | 483 +++++ pvlib/mlfm_graphs.py | 380 ++++ 11 files changed, 2551 insertions(+) create mode 100644 docs/tutorials/mlfm.ipynb create mode 100644 docs/tutorials/mlfm_data/figs/GI.png create mode 100644 docs/tutorials/mlfm_data/figs/mlfm_0_4.png create mode 100644 docs/tutorials/mlfm_data/figs/mlfm_fit.png create mode 100644 docs/tutorials/mlfm_data/figs/mlfm_flow.png create mode 100644 docs/tutorials/mlfm_data/figs/mlfm_iv.png create mode 100644 docs/tutorials/mlfm_data/figs/mlfm_matrix.png create mode 100644 docs/tutorials/mlfm_data/figs/mlfm_scatter.png create mode 100644 docs/tutorials/mlfm_data/figs/mlfm_stack.png create mode 100644 pvlib/mlfm.py create mode 100644 pvlib/mlfm_graphs.py diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb new file mode 100644 index 0000000000..df43196568 --- /dev/null +++ b/docs/tutorials/mlfm.ipynb @@ -0,0 +1,1688 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MLFM PVLIB \n", + "### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "#### Tutorial overview.\n", + "\n", + "I) The Loss Factors Model (LFM) |2011 ref 1| quantifies \n", + "normalised losses from module parameters \n", + "(e.g. i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) by analysing the shape \n", + "of the IV curve and comparing it with STC reference values from the datasheet. \n", + "\n", + "II) The Mechanistic performance model (MPM) |2017 ref 2| \n", + "has \"meaningful,independent, robust and normalised\" coefficients \n", + "which fit how the LFM values depend on irradiance, module temperature \n", + "(and windspeed) and time. \n", + " \n", + "These parameters relate to \n", + " - c_1 = \"overall performance quality\" \n", + " - c_2 = \"normalised temperature coefficient\" (units /K) \n", + " - c_3 = \"low light level drop\" due to v_oc and r_sc (r_shunt) \n", + " - c_4 = \"high light level fall\" due to r_oc (~Rseries). \n", + " (optional) \n", + " - c_5 = \"wind speed coefficient\" \n", + " - c_6 = \"low light level drop\" sometimes needed for r_shunt behaviour. \n", + "\n", + "III) This tutorial shows how to take module measured and weather data, \n", + "(either outdoor or IEC 61853-like \n", + "matrix data), normalise it, generate MLFM coefficients, fit them with the MPM then \n", + "analyse module performance looking for loss values, degradation and \n", + "allowing performance predictions. \n", + "\n", + "Fig 1: MLFM overview flow chart of this tutorial. \n", + "![mlfm_flow.png](mlfm_data/figs/mlfm_flow.png) " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "#import pvlib\n", + "from pvlib import *\n", + "\n", + "# Import essential library file with lfm and mpm definitions 79-|\n", + "from mlfm_code import *\n", + "# Import graphics code from separate file\n", + "from mlfm_graphs import *\n", + "\n", + "# STANDARD DEFINITIONS\n", + "\n", + "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", + "plt.rcParams['figure.figsize'] = [7,5] # setup figure size inches\n", + "plt.rcParams.update({'font.size': 12}) # setup fontsize\n", + "plt.linewidth = 1.5 # line width in points\n", + "plt.linestyle = '--' #- # solid line\n", + "plt.marker = 's' #o # the default marker\n", + "plt.markersize = 9 #6 # marker size, in points\n", + "plt.bbox = 1.4 # offset right to not overwrite\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\Users\\\\steve\\\\OneDrive\\\\Documents\\\\_CONS\\\\__Reference\\\\PVPMC\\\\__repository\\\\pvlib-python\\\\docs\\\\tutorials'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", + "root_dir = os.getcwd()\n", + "\n", + "root_dir" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Select MLFM measurement files\n", + "\n", + "File naming conventions can be used to help identify files, for example \n", + "`x81_T1906_D3_Fh.csv` \n", + "\n", + "where \n", + " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", + " - 81 = module id/channel number \n", + " - T1906 = (T)ime started = yymm(dd) \n", + " - D3 = (D)uration in days \n", + " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", + " - etc. \n", + " \n", + " \n", + " Three default files are included (* = version number ) \n", + "(-1) t1_041.csv \n", + "(0) g78_T16_Xall_F10m_R900*.csv \n", + "(1) n05667_Y13_R1k6_fClear*.csv \n", + "(2) x19074001_iec61853*.csv \n", + "\n", + "\n", + "Essential default column names in meas() are :- \n", + "\n", + "meas { \n", + "'date_time', 'module_id', 'poa_global', 'wind_speed', 'temp_air', \n", + "'temp_module', 'v_oc', 'i_sc', 'i_mp', 'v_mp', 'r_sc', 'r_oc' \n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# select one of the following meas files\n", + "meas_file = 0\n", + "\n", + "if meas_file == 0:\n", + " mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv'\n", + "elif meas_file == 1:\n", + " mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv'\n", + "elif meas_file == 2:\n", + " mlfm_meas_file = 'x19074001_iec61853_041.csv'\n", + "\n", + "# optional\n", + "elif meas_file == -1:\n", + " mlfm_meas_file = 't1_041.csv'\n", + " \n", + "# extract module id from filename e.g. 'g78'\n", + "mlfm_mod = mlfm_meas_file.split('_')\n", + "\n", + "mlfm_mod_sel = mlfm_mod[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get ref module data at STC \n", + "\n", + "Get STC Reference module data for the selected module \n", + "(searching for a row with same module id). \n", + " \n", + "Ref values include electrical data and temperature coeffs and must include the following :- \n", + "\n", + "ref { \n", + "'i_sc', 'i_mp', 'v_mp', 'v_oc', \n", + "'alpha_i_sc', 'alpha_i_mp', 'beta_v_mp', 'beta_v_oc', 'gamma_p_mp', \n", + "} \n", + "\n", + "NOTE : Users must add their own data to the reference file \n", + "when they add new meas data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read all ref data " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# user must keep updated with their modules from their measurements\n", + "ref_file_name = (root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_reference_modules.csv')\n", + "\n", + "ref_data = pd.read_csv(ref_file_name, index_col='module_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select stc data from reference database" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "while True:\n", + " try:\n", + " ref_data = ref_data[ref_data.index == mlfm_mod_sel]\n", + " break\n", + "\n", + " except IndexError:\n", + " print(\"You must define module ref data to use this module ...\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# Put relevant data into a dict for easy use\n", + "# ignore any other columns that may be database specific\n", + "# as they aren't needed\n", + "\n", + "ref = dict(\n", + " # module_id = ref_data['index'].values[0],\n", + " i_sc = ref_data['i_sc'].values[0],\n", + " i_mp = ref_data['i_mp'].values[0],\n", + " v_mp = ref_data['v_mp'].values[0],\n", + " v_oc = ref_data['v_oc'].values[0],\n", + " \n", + " alpha_i_sc = ref_data['alpha_i_sc'].values[0],\n", + " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", + " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", + " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", + " gamma_p_mp = ref_data['gamma_p_mp'].values[0],\n", + ")\n", + "\n", + "# create p_mp and ff in case they don't exist\n", + "ref['p_mp'] = ref['i_mp'] * ref['v_mp']\n", + "ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import measured data (outdoor or matrix)\n", + "\n", + "DateTime, Met and Raw module measurements. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read in selected measured file data " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "meas = pd.read_csv(\n", + " root_dir + '\\\\mlfm_data\\\\meas_gtw\\\\' + mlfm_meas_file,\n", + " index_col='date_time'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate useful data columns for meas" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idpoa_globalwind_speedtemp_airblue_fracbeam_fractemp_modulev_oci_sci_mpv_mpr_scr_ocpoa_global_kwm2p_mp
date_time
2016-01-26 07:20:00-07:00782.6664841.4728328.1779790.4549921.1000002.08194033.0406440.0132150.00980924.337320115258.549800608.6809990.0026660.238726
2016-01-26 07:30:00-07:00787.8991431.2977118.2414250.522027-0.1000002.43698537.6440290.0372490.02983229.6249808253.745059150.4612830.0078990.883783
2016-01-26 07:40:00-07:007852.9276720.9554827.7396240.2701540.3002672.59208739.6492060.0728370.06119632.4448684762.54397263.6600280.0529281.985488
\n", + "
" + ], + "text/plain": [ + " module_id poa_global wind_speed temp_air \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 78 2.666484 1.472832 8.177979 \n", + "2016-01-26 07:30:00-07:00 78 7.899143 1.297711 8.241425 \n", + "2016-01-26 07:40:00-07:00 78 52.927672 0.955482 7.739624 \n", + "\n", + " blue_frac beam_frac temp_module v_oc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.454992 1.100000 2.081940 33.040644 \n", + "2016-01-26 07:30:00-07:00 0.522027 -0.100000 2.436985 37.644029 \n", + "2016-01-26 07:40:00-07:00 0.270154 0.300267 2.592087 39.649206 \n", + "\n", + " i_sc i_mp v_mp r_sc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.013215 0.009809 24.337320 115258.549800 \n", + "2016-01-26 07:30:00-07:00 0.037249 0.029832 29.624980 8253.745059 \n", + "2016-01-26 07:40:00-07:00 0.072837 0.061196 32.444868 4762.543972 \n", + "\n", + " r_oc poa_global_kwm2 p_mp \n", + "date_time \n", + "2016-01-26 07:20:00-07:00 608.680999 0.002666 0.238726 \n", + "2016-01-26 07:30:00-07:00 150.461283 0.007899 0.883783 \n", + "2016-01-26 07:40:00-07:00 63.660028 0.052928 1.985488 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# normalise poa_global to kW/m^2\n", + "meas['poa_global_kwm2'] = meas['poa_global'] / 1000\n", + "\n", + "# calculate p_mp as it might be missing\n", + "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", + "\n", + "#show some meas data\n", + "meas.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Count how many mlfm variables are in the data \n", + "usually matrix=4 (i_sc,i_mp,v_mp, v_oc) \n", + "and iv=6 (i_sc,i_mp,v_mp, v_oc + r_sc, r_oc) " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def get_qty_mlfm_vars(dmeas):\n", + " '''\n", + " Find the quantity of MLFM variables in the measured data\n", + " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", + " \n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + " \n", + " Returns\n", + " -------\n", + " qty_mlfm_vars : int\n", + " number of mlfm_values present in data usually\n", + " 2 = (imp, vmp) from mpp tracker\n", + " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", + " 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve.\n", + " '''\n", + " # find how many mlfm variables were measured\n", + " qty_mlfm_vars = 0\n", + " for mlfm_sel in (\n", + " 'i_sc', 'r_sc', 'i_mp',\n", + " 'v_mp', 'r_oc', 'v_oc'\n", + " ):\n", + " if mlfm_sel in dmeas.columns:\n", + " qty_mlfm_vars += 1\n", + " #print(qty_mlfm_vars, mlfm_sel)\n", + " \n", + " return qty_mlfm_vars" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "qty_mlfm_vars = get_qty_mlfm_vars(meas)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Normalise MLFM values norm from meas and ref dataframes \n", + "\n", + "Fig 2 illustrates the loss factors model (LFM). \n", + "It uses the shape and values from dc measurements to quantify the values of each \n", + "of the loss factors going from (1) ref_p_mp to (5) meas_p_mp. \n", + "\n", + "Fig 2: Loss Factors Model : \n", + "![lfm_0_4.png](mlfm_data/figs/mlfm_iv.png) \n", + "\n", + "1) ref_p_mp = Initial datasheet value at STC.\n", + "\n", + "2) Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", + "\n", + "3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", + " - norm_i_sc = measured / expected isc (purple)\n", + " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", + " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", + " \n", + " \n", + "4) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", + " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", + " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", + " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown)\n", + " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", + " \n", + " \n", + "5) These losses cause the performance to fall to pr_dc = meas_p_mp / ref_p_mp \n", + "\n", + "pr_dc = 1/ff * \n", + " (norm_i_sc * norm_r_sc * norm_i_ff ) * \n", + " (norm_v_ffv * norm_r_oc * norm_v_oc_t * norm_temp_corr ) \n", + "\n", + "Note: \n", + "The gamma temperature correction is subtracted from voc for simplicity. \n", + "In reality there will be temperature dependencies for i_sc and ff but they are smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dcpr_dc_temp_corri_mpv_mpi_scv_ocv_oc_temp_corrr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:000.4964970.4452930.7422410.7365870.9263800.7475260.6875640.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:000.6204710.5574730.8008960.7869770.8814090.8516750.7844180.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:000.2080370.1870590.8401720.8182980.2572270.8970410.8266880.8977000.8950180.9359160.914282
\n", + "
" + ], + "text/plain": [ + " pr_dc pr_dc_temp_corr i_mp v_mp \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.496497 0.445293 0.742241 0.736587 \n", + "2016-01-26 07:30:00-07:00 0.620471 0.557473 0.800896 0.786977 \n", + "2016-01-26 07:40:00-07:00 0.208037 0.187059 0.840172 0.818298 \n", + "\n", + " i_sc v_oc v_oc_temp_corr r_sc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.926380 0.747526 0.687564 0.983502 \n", + "2016-01-26 07:30:00-07:00 0.881409 0.851675 0.784418 0.893852 \n", + "2016-01-26 07:40:00-07:00 0.257227 0.897041 0.826688 0.897700 \n", + "\n", + " r_oc i_ff v_ff \n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.760559 0.754692 0.968481 \n", + "2016-01-26 07:30:00-07:00 0.866922 0.896006 0.907783 \n", + "2016-01-26 07:40:00-07:00 0.895018 0.935916 0.914282 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "norm = mlfm_meas_to_norm(meas, ref, qty_mlfm_vars)\n", + "\n", + "# show some normalised data\n", + "norm.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make irradiance and temperature bins for pivot tables \n", + "(Gbin=100W/m^2, Tbin=5C)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# poa_global bin = 100W/m2\n", + "norm['poa_global_bin'] = meas['poa_global'].round(-2)\n", + "\n", + "# temp_module bin = 5C\n", + "norm['temp_module_bin'] = (5 * round(meas['temp_module'] / 5,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Perform sanity checks on meas and norm data \n", + "\n", + "It's easier to sanity check and study normalised data than raw values. \n", + "1) Remove bad, missing, unwanted or outlier data \n", + "2) User defined limits may depend on data scatter and degradation \n", + "3) Can either select on values e.g. '0.5 x stdev from mean'\n", + "4) Possible to select on dates if desired. " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# select by irradiance poa_global range e.g. 100-1100W/m2\n", + "meas = meas[\n", + " (meas['poa_global'] >= 100) &\n", + " (meas['poa_global'] <= 1100)\n", + " ]\n", + "\n", + "# if there's date_time can select by it, i.e. not matrix data\n", + "### better if index is formatted as a date\n", + "if qty_mlfm_vars == 6:\n", + " '''\n", + " # not for matrix as they don't contain dates ###\n", + " # example\n", + " meas = meas[(meas.index > '2016-01-01') &\n", + " (meas.index < '2017-01-01')]\n", + " '''" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# remove specific mlfm values outside limits e.g. <0.5 or >1.5\n", + "norm = norm[((norm['pr_dc'] > 0.5) &\n", + " (norm['pr_dc'] < 1.5))]\n", + "\n", + "# remove all mlfm values outside x~3 stdevs\n", + "if qty_mlfm_vars == 6:\n", + " # only needed for outdoor data as indoor ought to be less scattered\n", + " # remove all mlfm data > x stdev usually 3\n", + " stdevs = 3\n", + "\n", + " for lfm in (\n", + " 'i_sc', 'r_sc', 'i_ff',\n", + " 'v_ff', 'r_oc','v_oc'\n", + " ):\n", + " norm = norm[\n", + " ((norm[lfm] - norm[lfm].mean()) /\n", + " norm[lfm].std()).abs() < stdevs\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filter only matching rows from meas and norm data\n", + "like an inner join but leave data in separate norm and meas frames" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "#drop meas rows that aren't in norm\n", + "meas_not_in_norm = ~meas.index.isin(norm.index)\n", + "meas = meas.drop(meas[meas_not_in_norm].index)\n", + "\n", + "#drop norm rows that aren't in meas\n", + "norm_not_in_meas = ~norm.index.isin(meas.index)\n", + "norm = norm.drop(norm[norm_not_in_meas].index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot normalised MLFM data vs irradiance \n", + "\n", + "For outdoor data - \n", + "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", + "\n", + "For matrix data - \n", + "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", + "\n", + "1. Higher values are always better (unlike measured values such as \n", + " Rseries or Io where lower is better)\n", + "1. Accurate measurements and a stable module result in narrowest lines \n", + "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", + "1. r_oc tends to fall at high light levels ( \\ right) \n", + "1. i_ff and v_ff are usually fairly flat ( - ) \n", + "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", + " and/or snow (if not properly corrected). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fig 3 : Normalised mlfm values vs. irradiance.\n", + "\n", + "All traces should be thin, usually around 0.9 ± 0.1 \n", + "i_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yYGPYoYLQLEGOC4yxskaypbOP2UWsJqGv2dFcvs9AIKIkCPHUUpQyxi42pj1NARF9CyGhMocxNkli/lAISdI3Qoil3wihSdPcRjWUw+HUiUaPISaiFyAkutxfy7jOAI67CmIiCoOQdNSLCVnNIKKPAJzj/3Q4HA6H0xAQ0V0QWr4fhpAoKCWIP4GQ1Pu0+DkNwBrGGK+OweG0AFpaDHFXADaHGBbJhFB5wAsimoaaxgapOp2uXhu3WGrq7KvVcs4SDofD4bQETCYTA/Cby6QVjLEVrmOIqBWA5wGkQXgyKUdPCKXnHGQCiCWiaMZYYZBMbjbs379fo1Kp/gvhie5lUzefc9liA/CT1Wr9e2pqqllqQEsTxOEQSlK5YoRQ7scL8R/bCgAICwtjFRX+Nh+TZuHChc73CxYs8DGSw+FwOM0dIqpkjPWvZdi/AbzHGDtD5LNPjuf1yfE+AsBlJ4gVCsWMVq1aDUpKSipRKBS8XBWnWWO32yknJ2dwSUnJDMjU1W9pjTnKIXTAcqUVgEaJW+RwOBzOlYPYIOQmCM1NasPz+uR4f1len5RK5QPx8fEVXAxzWgIKhYLFx8eXK5XK++XGtDQP8TEAKiLqwhg7Lk5LQU3bYw6Hw+FwgsUNEBJJc0XvcDgAJRH1YIz18xh7CML1aJ34OQVCoull5x0GAMaYXqPRXJb7xrk80Wg0FsaYbEJzY5ZdUxFRKIRYIyURhYpZ657jSBynET+HOkrXMKGF6AYAzxNRGBENAjAKwEeNtR8cDofDuWJYAaGFeh/xtRxCZaOhEmNXA5hKRD1IaCE/H0J3xcsVqiWEhMNpVojnq6zubcyQifkQ2rTOhdBethLAfJfaponiuCRxnsPrWwmhra+Df0AofXMRwKcAZvCSaxwOh8MJNowxE2Ms3/GCEBZRxRi75HntYox9A6HN9/8gtMfOAcCTTTicFkKjhUwwxp6D0KNeinCXcdkAZG87xfqio4NnGYfD4XA4tSNexxzvc+Fy7RKnvQbgtUY2i8PhBIGWllTH4XA4HA6H02KYO3du3IQJE5JqG7d69erWcXFxvXU6Xd9du3ZpMzMzQ66++uoeYWFhfV944YW2jWHrlUxLS6rjcDgcDofDaTEsXrw4359x8+bNa//KK6/kTpo0qQQAxo8fnzRw4MCyP//883CDGsgBwAUxh8PhcDicRuJg1cGoPVV7EipYhSaMwswDQgec6x3au9m1WrdYLI3egCsvLy+kT58+lY7PZ8+eDRk7dmyzOzaXKzxkgsPhcDgcToNzsOpg1M7KnUkVrEIDABWsQrOzcmfSwaqDsqWw6kJCQoLh2Wefje3atWuPiIiIPsOHD+9kMpkIAF599dWYxMTEXnq9vs+NN97YOTs726l6iSj1pZdeapOUlNQrOTnZ8NVXX0XExsb2nj9/fmxUVFRKmzZten/00Uet165dq09OTu6l1+v7zJ07t9bW3I8++mj8qFGjOsrNr6ysJJ1O19dms+Gaa67p0aFDh17XXntt119//TXiqaeeStTpdH0PHjwYEpyjw5GDC2JOUMhak4UlyUuwULEQS5KXIGtNVlObxOFwOJxmxJ6qPQk22Nx0hw02xZ6qPQnB3tbGjRujvv322+MnTpzI+vPPP7VvvvlmzJdffhnxwgsvJHzyySen8vPzMzt06FA9duzYTq7Lpaent96zZ8+fR48e/QMACgsL1VVVVYq8vLyDc+fOPT9z5sykjz/+OOr3338/nJGRceT111+PP3z4sKY+tmq1WmYymX4HgL179x4+c+bMH7/88sux1NTU8pdeeinXZDL93rt37+r6bINTO1wQc+pN1pospE9LhzHHCDDAmGNE+rR0Loo5HA6H48ThGfZ3en2YMWPGheTkZEtsbKztlltuMR44cED78ccfR02YMKFw8ODBJq1Wy5YtW3buwIEDYUePHnVuf+7cufmxsbG28PBwBgAqlYotXrw4LyQkhE2ZMqWopKRENWfOnIuRkZH2/v37V3Xu3Lly//79umDbz2l8uCDm1JuMeRmwmCxu0ywmCzLmZTSRRRwOh8NpboRRmDmQ6fUhPj7eeVHS6XT2iooKZX5+viYpKcnpadXr9fbWrVvbcnJynGETHTt2dLNFr9dbVSoh3So8PNwOAAkJCc51h4aG2svKyriWugzgXyKn3hhzjQFN53A4HM6Vx4DQAeeUUNpdpymhtA8IHXCuMbYfFxdnzsnJccbilpaWKkpKSpRJSUlOgcu77125cEHMqTf6RH1A0zkcDodz5dE7tHfREO2QHIdHOIzCzEO0Q3Iaq8rExIkTi9auXRu9e/dubWVlJc2aNSshJSWlolu3bkH3UHNaHrzsGqfepC1KQ/q0dLewCbVOjbRFaU1oFYfD4XCaG71Dexc1VZm1UaNGlT311FPnJ0yYcFVpaamqX79+5evWrTvVFLZwmh/EGGtqGxqFsLAwVlFRUa91LFy40Pl+wQLeot6VrDVZyJiXAWOuEfpEPdIWpcEw0dDUZnE4HI4sRGRijIU1tR0tkczMzOyUlJSCpraDwwmEzMzMmJSUlGSpedxDzAkKhokGLoA5HA6Hw+G0SHgMMYfD4XA4HE49GDJkSBedTtfX8+XauOOdd96JkhrTuXPnnk1pO0eAe4g5HA6Hw+Fw6sHOnTuP1zZmxowZRTNmzOCtmJsp3EPM4XA4HA6Hw7mi4YKYw+FwOBwOh3NFwwUxh8PhcDgcDueKhgtiDofD4XA4HM4VDRfEHA6Hw+FwOJwrGi6IORwOh8PhcJoZM2fOjI+MjEyJiYlJAYDVq1e3jouL663T6fru2rVL29T2XW7wsmscDofD4XA4zYgTJ06oV6xYEXfy5MmDCQkJVgCYN29e+1deeSV30qRJJU1s3mUJ9xBzOBwOh8NpFJYDUfGAQQGkxgOG5UBUU9skhcViadLtnzx5MkSv11sdYhgA8vLyQvr06VPZlHZdznBBzOFwOBwOp8FZDkTNAZLyAA0DkAdo5gBJwRbFCQkJhmeffTa2a9euPSIiIvoMHz68k8lkIgB49dVXYxITE3vp9fo+N954Y+fs7Gy1YzkiSn3ppZfaJCUl9UpOTjZ89dVXEbGxsb3nz58fGxUVldKmTZveH330Ueu1a9fqk5OTe+n1+j6uneikyM7OVoeGhva7cOGC0jFt165d2sjIyJTq6mqSWmbTpk0Ro0aN6nrp0iW1TqfrO2LEiI46na6vzWbDNddc06NDhw69gnWsODVwQczhcDgcDqfBeR5IqPLQHVWA4nkgIdjb2rhxY9S33357/MSJE1l//vmn9s0334z58ssvI1544YWETz755FR+fn5mhw4dqseOHdvJdbn09PTWe/bs+fPo0aN/AEBhYaG6qqpKkZeXd3Du3LnnZ86cmfTxxx9H/f7774czMjKOvP766/GHDx/WyNmRnJxs6dOnT/nHH38c6Zi2evXq6GHDhhWHhIQwqWVGjx5d9sUXXxxv06aNxWQy/Z6enn7aZDL9DgB79+49fObMmT+Cc5Q4rnBBzOFwOByODET0MRHlEVEpER0jogdlxt1PRDYiKnd53dC41jZv8gFJ4Sg3vT7MmDHjQnJysiU2NtZ2yy23GA8cOKD9+OOPoyZMmFA4ePBgk1arZcuWLTt34MCBsKNHjzq3P3fu3PzY2FhbeHg4AwCVSsUWL16cFxISwqZMmVJUUlKimjNnzsXIyEh7//79qzp37ly5f/9+nS9bxo8fX/T5559HAYDdbsfmzZuj7r33Xt7CuZnBBTGHw+FwOPK8BCCZMdYKwEgALxBRqszYnxlj4S6vHY1mZQsgDjAHMr0+xMfHO4OAdTqdvaKiQpmfn69JSkqqdkzX6/X21q1b23JycpxhEx07dnSzRa/XW1Uqof5AeHi4HQASEhKc6w4NDbWXlZX51FKTJ08uPnDgQHh2drZ669at4UTEhg4dWl7vneQEFV5looWQtSYLGfMyYMw1Qp+oR9qiNBgmGpraLA6Hw7msYYwdcv0ovq4CsL9pLGq5PAucmwMkuYZNhAL2Z4FzjbH9uLg4c05OTojjc2lpqaKkpESZlJTkFLhEkmG99SImJsY2aNAg4+rVqyOPHDmiHT16dJFCwf2RzQ3+jbQAstZkIX1aOow5RoABxhwj0qelI2tNVlObxuFwOC0ZFRHtc3lNkxpERG8TkQnAEQB5AL6WWV9fIioQQyueISLudHJhOlD0OpDTDjATgHaA+XUgZzrQKOEDEydOLFq7dm307t27tZWVlTRr1qyElJSUim7dugXdQ+3JXXfdVfTZZ59Fb926tfXkyZN5uEQzhAviFkDGvAxYTO4lYCwmCzLmZTSRRRwOh3NZYGWM9Xd5rZAaxBj7B4AIAH8FsAFAtcSwnQB6AWgL4E4AdwP4V8OY3XKZDhSdB7LswP7zQFZjiWEAGDVqVNlTTz11fsKECVfFxcWlZGdnh6xbt+5UY2z77rvvLsnJyQmNjo62Xnfddbx0WjOEGJNMcrzsCAsLYxUVFfVax8KFC53vFyxYUF+T/N+uYqHwkM4TAhbYG88ODofDuZwgIhNjLCzAZZYDOMwYW1bLuLsA/IsxJhdv3KLJzMzMTklJKWhqOzicQMjMzIxJSUlJlprHPcQtAH2iPqDpHA6Hw2kwVBBiiGuDAQh+QCqHw2kQuCBuAaQtSoNap3abptapkbYorYks4nA4nMsfImpLRHcRUTgRKYloKIRQiO8lxg4joljxfXcAzwDY3LgWc5qKIUOGdNHpdH09X74ad9xzzz2JUsvcc889iY1pO0eAB/y3ABzVJHiVCQ6Hw2lUGIAZAJZDcCDlAJjNGNtMRIkADgPowRjLBZAG4AMiCgdwAcDHAF5sGrM5jc3OnTuPB7rMJ598kgsgtwHM4dQBLohbCIaJBi6AORwOpxFhjF0CcL3MvFwA4S6fHwfweCOZxuFwggwXxBxe45jD4XA4HM4VTaPFEBPRI2Kdx2oi+qCWsXOIKJ+IjES0iohCXObtIKIql9aYRxvc+BZG1posLElegoWKhViSvMRnvWJe45jD4XA4HM6VTmMm1Z0H8AKAVb4GiUkLcyHEYyUD6ARgocewR1xaY3ZrAFtbLIEKXF7jmMPhcBoXIlIR0Rgiek90FJ0Q/75HRGN5Qw8Op/FpNEHMGNvAGNsEoLCWofcBeI8xdogxVgzg3wDub2DzLhsCFbjGXGNA0zkcDodTd4joIQCnADwE4CSARQCmi39PAvg7gFNENL3JjORwrkCa411oT7iXqskEEEtE0Ywxh5h+iYgWAzgKYB5jbEcj29hsCVTg6hP1gjdZYjqHw+Fwgk5XAAMYY/kS8zYCeJGI2gF4rHHN4nCubJpjHeJwAK4KzfE+Qvz7JIQwigQAKwCkE5FkkXQimuboUW+1WhvK3mZFoE08eI1jDofDaTwYY4/JiGHXMXli1QpOE2GxWGof1AzXzak7zVEQlwNo5fLZ8b4MABhjvzLGyhhj1YyxDwHsAnCb1IoYYyscPepVquboDA8+gQpcw0QDRqwYAX2SHiBAn6THiBUjfFaZCCRpL1g0xTY5HA6nISGirkR0BxFNFv92bWqbGpzjy6OwId6ATxSp2BBvwPHlUcHeREJCguHZZ5+N7dq1a4+IiIg+w4cP72QymQgAXn311ZjExMReer2+z4033tg5OzvbecEkotSXXnqpTVJSUq/k5GTDV199FREbG9t7/vz5sVFRUSlt2rTp/dFHH7Veu3atPjk5uZder+/jq/GGg0cffTT+1ltv7TRq1KiO4eHhfd94440YubH/+9//dL169bo6PDy8b3R0dMqDDz7Y3jFv27Zt4X379u0eERHRJy4urveyZcui63usODU0R5V4CEAKgHXi5xQAF1zCJTzh7TFdqEsTj0BqHDuS9hxxyo6kPddtB5v6bpOXleNwOM0JsanHWgjXt5MQnoS2AnAVEWUCuEusc3x5cXx5FPbPSYK9SnDGVeVpsH9OEgCgy/SiYG5q48aNUd9+++1xrVZrv+6667q/+eabMd27d6964YUXEtLT04+npqZWTp8+vf3YsWM77du3z1mtKj09vfWePXv+DAsLs+/YsSO8sLBQXVVVpcjLyzv45ptvRs+cOTNp8ODBpb///vvhkydPagYNGtRj8uTJRT169DD7smf79u2t33///VMbNmw4XVlZKatZ5syZkzhjxowLDz/8cJHRaFTs27dPCwDHjx/XjBkzpstrr72Wc//99xcXFxcrTp06pQneEeM0miAWs2ZVAJQAlEQUCsDKGPOMZVgNodvPGgB5AOYD+EBcR2sAfwHwAwArgAkAhgCY3fB70HJoyCYevpL2muM2gyXguajmcDhB5H0APwJIY4yZHBOJKAzAsxCueTc2jWkNSNbzCU4x7MBepUDW8wnBFsQzZsy4kJycbAGAW265xXjgwAHtvn37dBMmTCgcPHiwCQCWLVt2Ljo6us/Ro0c13bp1MwPA3Llz82NjY22O9ahUKrZ48eI8lUqFKVOmFD3++ONJc+bMuRgZGWnv379/VefOnSv379+vq00Q9+nTp+Lee+8tAYDw8HAmN06lUrETJ06E5uXlqdq1a2dNS0urAIBVq1ZFDRw4sPShhx4qAoC4uDhbXFxcZT0PE8eFxgyZmA+gEkJJtUni+/lElCjWE04EAMbYNwBeBvA/CG0ycwAsENehhlC67RKAAgD/BDCaMcZrETcSTVGVoj7bDEZZOV6rmcPhBJm/AJjvKoYBgDFWAUEQ/6VJrGpoqvKlPZpy0+tBfHy88x+/TqezV1RUKPPz8zVJSUnVjul6vd7eunVrW05OjjNsomPHjm7CVq/XWx0hl+Hh4XYASEhIcK47NDTUXlZWVquWio+P9ymYHbz//vvZJ06cCOnRo0fPXr16Xf3pp5/qAeDMmTOajh07Vte2PKfuNGbZtecYY+Txeo4xlivWE851GfsaYyyWMdaKMfYAY6xanH6JMXYNYyyCMdaaMXYtY+y7xtoHTuBJew7qEwNc120CwRHwvFYzh8MJMmcA3C4z7zYAl1+4BACExkmLQrnpQSYuLs6ck5PjbPRVWlqqKCkpUSYlJTn/wRM1TASmv+s1GAzV6enppwsKCjIfe+yx/Pvvv/+q0tJSRYcOHcynT58OqX0NnLrSHJPqOM2YulSlqK+HtT6VMOojph3UR1TzZEAOhyPBIwBWEdFPRPQWEb1IRG8S0U8Qmlc93MT2NQyGZ89BEWp3m6YItcPw7LnG2PzEiROL1q5dG717925tZWUlzZo1KyElJaXCES7RHHj77bejzp8/r1IqlYiMjLQCQhjFlClTinbv3t1q5cqVkRaLBfn5+crdu3drm9reywkuiC8DGlN01aUqRX09rHXZpoNglJWrj1ech1pwOBxPGGMZAK4C8CEAC4C2EPJiPgTQhTH2fROa13B0mV6E1NdzENrODBAQ2s6M1Ndzgh0/LMeoUaPKnnrqqfMTJky4Ki4uLiU7Oztk3bp1pxpj2/6ybds2fa9evXrqdLq+jz/+eOLKlStP6XQ61qVLF/P69euPL1u2LDYyMrJvnz59eu7fv1/X1PZeThBjsrHdlxVhYWGsoqKiXutYuLCmg/SCBQt8jGw8PJPGAEHw+SsYG4OFioVCLRBPCFhgb/jjWN+EuLoe4yXJS6SbniTpMTt7dkD7wOFwgg8RmRhjYU1tR0skMzMzOyUlpaCp7eBwAiEzMzMmJSUlWWpecyy7xgmApqj6EChN3Q2vvlU36lLKDuBtsTkcjjxEdDWAeyF0Z42AUGv/EICPGGN/NqVtHM6VCBfELZyWILrSFqVJelhbUje8uojq+t4I8FJvHM7lCRHdDeAdAF8C2ImaOsQpAHYT0XTG2NomNJETIEOGDOmyb9++cM/pM2fOzFu8eHF+XcdyGg8uiFs4Te199Ye6elhbOvW5EWiKBigcDqfReBHAcMbYLs8ZRDQIwBoIjTs4LYSdO3ceb4ixnMaDC+IWTl1FV2N7HxuyWUhzpT43Ai0hFIbD4dSZNgB+k5n3OwDZ1r4cDqdh4IK4hVMX0dXY3scr+dF/XW8EWkIoDIfDqTPfQSi7Np8xdtIxkYiuAvC8OJ/D4TQiXBBfBgQqurbO2tpo3sf6iu8rVUzz+GMO57JmCoC3ARwmIitqYohVADaI8zkcTiPCBfFlQCDiJ2tNFioLpdufN4T3sT6P/usipi8XIcjjjzmcyxfGWDGAu4lIB6ArgHAA5QCOebZz5nA4jQNvzNHCCbT5g69mGA3Rfrk+j/4DbehxOTXCqE8zEt5qmsNpGTDGTIyxA4yxn8S/XAxzOE0E9xC3cAL1wPoSorW1X970wCbYLULXTWOOEZse2ATAt9exPo/+AxXTl1siWlPEH18uHnYOp6VCRBoARxhjnZraFg7nSoIL4hZOoOJHTqBqo7VuwsdTGJkKTE4x7MBusWPrrK0+BVN9Hv0HKqZ5IppAXW9CeKgFh9MsIADJTW0Eh3OlwUMmWjhyIkduetqiNKh1ardpap0aw5YOc36WCj2wVFg8VwUAsvHIDgwTDUi5LwWkJAAAKQkp96X4JbDkbJUT04Eei8uVQI+bAx5qweF4Q0QfE1EeEZUS0TEietDH2DlElE9ERiJaRUQhMuNsci8AlZBuds9pRCwW6Wtec183p+74JYiJqAcRxYrvw4loIRE9KyYEcJqQQMWPP7GpUsKormStyULmh5lgNuH/O7MxZH6Y6Vdcb6BxtHUVgpcbdY0/5h52TnPilxUv4MOxKfjwzt7O12f3D8GpnVsa25SXACQzxloBGAngBSJK9RxEREMBzAWQBsHD2wnAQpl1FgEYDSGhzvPVK7jmNzPOX4zCz5kG/LAvFT9nGnD+YlSwN5GQkGB49tlnY7t27dojIiKiz/DhwzuZTCYCgFdffTUmMTGxl16v73PjjTd2zs7Odl40iCj1pZdeapOUlNQrOTnZ8NVXX0XExsb2nj9/fmxUVFRKmzZten/00Uet165dq09OTu6l1+v7zJ07N642ex599NH4W2+9tdOoUaM6hoeH933jjTdk60xXVlbSlClTOrRt27Z327Zte0+ZMqVDZWUlOeZ//PHHrbt3794jPDy8b4cOHXp98cUXrep7vDgC/oZMfAJgAoALAF4B0A1AFYB3IfRi5zQRdalD7LmMwwPomB6IANJGa53vpeJPGzOu90rtiCdFU7Sa5nDqyqmdW/DbmmWoKMxHWHQclKE6lJ496TWuuqwEu956FgDQacjwRrGNMXbI9aP4ugrAfo+h9wF4zzGeiP4NoePcXInV7gcQ41qD2IHoVSbvRS4Dzl+MwskzSbAzwRlntmhw8kwSACC+bVEwN7Vx48aob7/99rhWq7Vfd9113d98882Y7t27V73wwgsJ6enpx1NTUyunT5/efuzYsZ327dt31LFcenp66z179vwZFhZm37FjR3hhYaG6qqpKkZeXd/DNN9+MnjlzZtLgwYNLf//998MnT57UDBo0qMfkyZOLevToYfZlz/bt21u///77pzZs2HDaVeB68tRTT7Xbv39/2O+//36YiDB8+PDOc+fObbd06dLz//vf/3QzZsxIXr169amRI0eW5ubmqktKSpTBPG5XMsRY7U9miKiEMdaaiAhAPoCeEB7rnGaMtW1gG4NCWFgYq6ioqNc6Fi6sudlfsGBBfU1qEPxJivKMFQUET6rDi7gkeYmkMPJEqVFi1KpRMEw0yK7Tl6d5zMdjAmog4mlnMOHJZPU/3vwYtgw8xWe/iTODKi6d6y/IAykUYHY7wmLayW7n1M4t2L18IWzVVQFtx9c6/YWIzABcH1etYIytkBj3NoD7AWghdJIbwhgr9xiTCeBFxtha8XMMgEsQhG+hx9ieACyMsWMydiUxxnLqvGONQGZmZnZKSkpBQAv9nGmA2aLxmq5Rm3FdStDKASUkJBjmzZt37h//+EcRAEyfPr19aWmpwmq1UlRUlG358uVnAcBoNCqio6P7HDp06I9u3bqZiSh18+bNx0aOHFkGAF999VXE2LFju5SXl/+mUqlQXFysiIqK6puRkXHkxhtvrACAnj17Xj137ty8e++9t0TOnkcffTR+586dEa7CW44OHTr0euWVV85MmDDBCADr169vNXPmzKRz585l3XPPPUlardb+3nvvnQnCYboiyczMjElJSUmWmuevh7iaiCIA9ABwhjFWQEQqAKFBspETBPxNiqrNayuXCJdyXwqOf31cUvDIrdMXtSVsNZZ3mSeTCdTHw86PYcvAU3xWFOThx6VP4eKR33HttPn1Xvev7y2GubzmZprZ7W7b+XHpU06R7PhbV4Jku5Ux1r+2QYyxfxDRPwFcB+AGANUSw8IhNNhw4HgfAaDQY2w7AD/42F6zFsN1RkoM+5peD+Lj450XD51OZ8/Ly1MXFxer+vbtW+KYrtfr7a1bt7bl5OSou3XrZgaAjh07unl69Xq9VaUSpFJ4eLgdABISEpzrDg0NtZeVldUafhofH+/Tg+zg0qVLmquuusp5fnXq1Ml88eJFNQCcO3dOPXToUB7D1kAEEjLxPYQf9pvitH4ATjeEUZy64a+ArC1WtC7CqC5xpq4JW1LbaqyY1mAIb0/vaJfbusjePDRn6lrq7XIreXe54eq1leLotnU4um0dAEATrsdfps51el5dPcoh4XowxmCuKEVYdBzap/4VZ/f/KLteKRwiuD5i2NP2tt37NngIBWPMBuAnIpoEYAaAZR5DyiF0m3PgeF8msbp/AfiUiHYB2ALga8bYuSCb3PzQqM2yHuJGIC4uzpyTk+NMdCwtLVWUlJQok5KSnP+8hAfhwcff9bZp08Z88uTJkP79+1cBwOnTpzVt27a1AIIQP3nyJHdENhB+CWLG2BwiugXCI57/iZPtAOY0mGWcgPFXQPoTKxqoMJJbZ204PIlSnsXGal8se9xyjFioWOgz9CRjXoZgI8GZF27MMWLfO/u89hFoGd7SuoQ+8IS85oWriNWEtYK1ygS71b9EWXO5ET+9+QwuHvkdJ3/4Ctaqml4R1WUlzvcVBXlOEd3U/LZmWaPFFEO4bl4lMf0QgBQAjoOSAuCCZ7gEADDGhopJ6WkAbgMwj4iMEMUxgN2MseDcMTQnktqdc4shBgAF2ZHUrlFuBiZOnFh0//33d5o8eXJh3759q2bNmpWQkpJS4fAONwfuuOOOosWLF7f761//WkFEWLRoUbs777yzEAD+/ve/Xxo5cmTXkSNHltx+++1ljhjivn37BhZvxJHE77JrjLFvAZwgomvFz/sYY983mGWcgPG37FiX27p4pWzUtxqDVIUHf9JCSEmynsUut3WRXEZuuiuBdK3zKbBllnVbvzjOF7WVL6tLF8CGoK7d/njJu6bh1M4t+OKhofhwbAq+eGgoTu3c4gyLqCjIAxiDudzotxh2wGxWHN22zk0MN2cqCvMbZL1E1JaI7hKrKynFShJ3Q3hi6slqAFPFqkyRAOYD+EBu3WKXunTG2AzGWDKAiQBKACwCkEdEnxHRX4K8S01LfNsiXNUhx+kR1qjNuKpDTrAT6uQYNWpU2VNPPXV+woQJV8XFxaVkZ2eHrFu37lRjbNtfFi9enJeSklKRkpLSo3fv3j0MBoNp8eLFeQDwt7/9zfTWW29l/+tf/+rQqlWrvtdff323U6dOBT3c5ErF36S6RACfAugDgDHGwoloLIBbGWOyNRmbE1dCUp0/SVFSY0BA/+n9Mfzt+nlYpMIGMj/MrFsJN/LhIU7SY3b2bMltOryZcomBrsu62u11TCRwXdbfxEPPfVpg9z5vGjN5sDYCOW6u1HUfeCJe3ZFLRiOlGsx2ZdU5DYtph7Hvbgt4OSIyMcbCfMxvA+ALCN5eBYAcAMsYY/8Vr4uHAfRgjOWK4x8F8CSE5Lv1AKYzxqTijWuzqxWAoQCKGWPbA12+MahTUh2H08QEI6nuXQiPcv6KmuSA7wC8Wm/rOEHDn9hfyRrDDNi/Yj8SByXWS4xIhVkkDkp02qON0sJWbYO5vPanU/pEvc9QBofHUircIndXrqxYlZrusDn9oXTZBiSey9YlFMDhLfUUgeZys6SXfON9G7Hh3g2NKhTrGvpQl7hznohXd07t3IKf3pgnGYd7pYlhZUgo+k2c2SDrZoxdAnC9zLxcCIl0rtNeA/BabeslokEARjLGnpSYtxjAJsbY53UymsPh1Al/BfEAAMMZY3YiYgDAGDMSEX8e2syoLfZXTtgwG2sQMeJpz5LkJbUKYkf4hjM+V4L0aelQaVWSQnLf8n2SywBwdsyTojYPseuygcZMO/ZJSgTK4WhmIiUUXeOXSUlgNgZ9Uv2Fc33itgONO+eJeL7xjAO2WcywVfvuDHmloAnXOxP7gl02rpF4GsDbMvN+ADAPwIjGM4dTX4YMGdJl37594Z7TZ86cmbd48eL8uo7lNB7+CuILADoDcNZMJKIeAHIbwihOw+FLyDWGGPHpaRTDJByxzL6Es8VkkRewPqKAHCITcPfUkoJqjQN2XVaqNJ0sBGe76iXJS+oUQuL63XiKal/CWQ65UAW5knsN0e3vckjEq2tNX0+xS0SoLjc6Kzcc/34T7Jaa89+1lNmVjjJEi7s//LGpzagvfQB8IzPvOwDvNZ4pnGCwc+fO4w0xltN4+CuIXwHwFRG9BEBFRHdDuMNd3GCWcRqE2oRcQ4sRf+OC/RabgW4/qSZsQUpU+rMs4BEiUJunmAHHvz6OrDVZdarE4cDx3fhqrV1bqEXWmixsnbUVlYU1nkYpId0Ycb2BeqObW7yxVE3fXW89K9TilfBeypU+cxW7zalyQ3OElCoMnP5sU5sRDFoB0EBocOWJGkKJUw6H04j4W3ZtFREVAZgG4AyEFpXPMMY2NaBtnCDhKSRS7kvB/hX7JUWglBipixCpjwfSl+BzRRuthbXS6pUgKOfpdd2Ov9vwXLauiYOuorPOMP+S+eQ8xr5uNFw90FKhDw0hRgPxRjd1vLGUJ/i3Ncu8EtrsVotT4DoaRxz/fhOKs4+6lSzjBE4wOtM1I44AuAXAZol5t4jzORxOI+Kvhxii+N3UYJZwGgQpIZH5YSZSp6V6CTkpMVIXIeLPMr7ElT9earVOjWFLh3mtS06gaqO1GLZ0mHM7gXjCHcsC3kl8+5bvqzXUwoGcaFaoFSAi2My2WtfhWfPYn206hG5tNwFyx6ShxGgg3uj6xhvXR9BLdndb9jTgR4UeAMjP+tWvcRxpFCo1Bj38/OUihB28DuBdIlJCSKCzE5ECwGgAbwF4tCmN43CuRPwSxEQ0RW4eY2xV8MzhBBs5IXH86+MYsWJErSKhLkKktmVqS76Se5ROSgKzMy9bfVW2kNuvQJLiNOEa+fhfP4WpL0a/PxoAsPG+jX6FboAhIFHsqMpR200AKUiyEcnWWVsbLPlN6lw4Un0Eu6t2o8xehghFBAaGDqxXvLG/gt4tpIHIt+D1UwxzaickojUGTHlSsjNeC06a8wlj7BMiigPwIYAQIioAEAOgCsACxtinTWogh3MF4q+H+F6Pz3EQOvXsAsAFcTPGl5DwpypAXYRIfZOl5B6l+1uX15/9CiQpzmF3feKrHZUgPNEn6Z225u7Kdetw5xMmLOuvqE+flg5tlNYtdthrlRKhFgBkl2mIePMj1UeQYcqAFVYAQJm9DBmmDOg66GDK9W4S4av6hWsljrD2uYi74QAUIcL3bTdrsPudAzi60yjdzY0L3kZDFaJ1E7ydhgy/7ASwFIyx14hoJYDrAERDKGn6M2OstGkt43CuTPyNIf6b5zTRa3x10C3iBJX6tj+uy/JywsvfbTZGYpfUNszlZp92y3qVa/HWqnVqtL+uPU5nnPaa59p17/jX/iceO5IQ/W0QYjFZoNKqoNap/boJqK2zHlD791mXMIXdVbudYtiBFVaEPhMKyyyLX/HGjm1vX/AGWnXPROt+wndKLhX3lCFmKNoeQYXYVoBXcWg6GqrLXEtAFL+BdxThcDhBx+/WzRJ8AGBqkOzgNBBSLZUDKaMV6PJZa7JQXerdmEmpUTqX8adNsWGiAbOzZ2OBfQFmZ8/2qpRQ1zbHrstmzMtA2qI05zaGLR0m2X7amGPEkuQl6HJbFyg1Sq/96j+9v1CBggSh2n+G++cRK0ag6IR0Z1JXEeyvx9X1+Afipa0sqsSIFSOctmmjtVCo5f8FGHOMPtfv6xySagG94d4N2PKPLT5tLLOXSU6332l3s12fpMfA+VE4uvNxt7bFgPDIfd/aKYjs/QtUYZUgchfDDqSmcYKLJlyPsJh2PseERcc1kjXNAyLaS0TjiEiy5S4RaYhoPBHx4PPLhLlz58ZNmDAhqant4PjG3xhiz6umDsAkCH3XOc2Y+npbA10+Y14G7Bbv7lmaCI1kpQO5eE4572J9ErxqW9arlJqL59eYY8Tv7/0Om9U98Y0xhsRBibW2vd5w7wbJ6a6CU84DrY3WQhOukTz+gcRC6xP1bvu5JHmJzxAKUhJatW8la5Pj+5D6nuQ6Iu5bvs9nR8QIRYSkKI5QRLjZLpnotvQp/Lj0KQCAQu21Ck4jowwJxV+mznWGP2x77u9eCYYN2WWuGXMfgOcBvENEvwE4CqAMQqm1rgD6AfgewP1NZSAnuPBmGy0Df2OIrfB+KHwOwN+Daw6nIQi0g1h9lpfzKFYWVeJI9RFsfmozbCZ3UemZoOVLuNaWsOfrMb0/CYKOfZUKRZCqAmG32P1KLpMVu1Fa53u52GnPahqOUAa5MnZSSHn1a/MuMxuTtann+J54MfxFt1bXrt+T7LoZJI+XI5FKWZCPiFAdiv7oiYqsDggznEF0l/1QMBs+xBKf9nIah5CI1kgeeAtO7PjSreycQqWGKlQn20Fu6HP/vSIS5mqDMXYYwFgxqe5mAAYICXXFAFYDuJcxdrEJTWxQ9i7fG7Xz+Z0J5fnlmvC4cPOQZ4ecu2b6NdKP0JoQi8UCtZrfWV9J+CuIO3p8rmCMFQTbGE7zoi4xoLIVIhSEtdq1srG2rgLKl3D1lbBXmwc4kGS/QEIR/BmbtigNm6ds9hLV1aXVyFqT5e2hdjnmgHe5N0+vuK8KFXItnWvzLpOSJG3qclsX7Ht3H+D9IMD5Pflat+fx8vT2KqsqEHPVHkQn7QeUNih4fluzQBmixcDpzzoFbNvufQMWt1dKwpw/MMbyAXzU1HY0JnuX7436ds63SdYqqwIAyvPKNd/O+TYJAIIpihMSEgxTp069+Nlnn0Xn5eVphgwZUvr555+f1ul07NVXX41ZunRpnNFoVKWmppavWrUqJzk52QIARJT64osv5i5fvjzWarXSu+++mz116tSOf//73y+8/fbbcUqlkr322mu5Go2GPfnkkx2Ki4tVM2bMyK/NA/zoo4/Gnzx5MmTz5s3eiSQiJpOJ7r777uQffvhBb7PZkJSUVL1169bjHTp0sF64cEH58MMPd9i5c2erqqoqxYABA8q2b99+MljHiyPgb1JdTkMbwmle1DU0Qc5jWVs5MdcELV/C1VeSX20eYF/Leor/2ioyuCE2zJC7YXCs2x8Ps5Q3Xqrcm2cZOwBex12pUUITofHyKjuozbvs+M48bXo55mVJMezAmGvEmI/GYMO9GxCWkIvInn9AqauE3awBGIMixIIPxqwXYnhlypsRAaSuvS4zx3/CYtrBWl0p2yCElCoQUU2lDRe6DR2Pa6fNd5vGxS0nUHY+vzPBIYYdWKusip3P70wItpd448aNUd9+++1xrVZrv+6667q/+eabMd27d6964YUXEtLT04+npqZWTp8+vf3YsWM77du376hjufT09NZ79uz5MywszL5jx47wwsJCdVVVlSIvL+/gm2++GT1z5sykwYMHl/7++++HT548qRk0aFCPyZMnF/Xo0cPsy57aeOutt6LLysqUZ86cOajVau0///yzLiwszA4AEyZM6BgWFmY/dOjQoVatWtm3b98eVt/jw/FGNqOGiH4kop21vfzdEBE9QkT7iKiaiD6oZewcIsonIiMRrSKiEJd5UUS0kYgqiCiHiO7x1waO//gSl74wTDS4JT+RsvbMJdcucEuSl8h6kR0eU7kkP1khLSbFOeOCPZbtclsXrwSw6tJqrwQ6XzhuGDwT/NySy+SW9eFh9tXu2VFfGBCOe8p9Kc7jTQqCzWITRL24T5unbHazz/FdyX1Hrq2qXantRsERq9zrPiWiU/c5E9uUIWYoQy3uSW68vJkEtfxmZLIBVaE6KENCZRerKMzHgClPQqHyfgysCddj8CP/xqCHnxeS4IgQFtMOf531Eu5bf9BLDHM4daE8v1wykVBuen2YMWPGheTkZEtsbKztlltuMR44cED78ccfR02YMKFw8ODBJq1Wy5YtW3buwIEDYUePHnVuf+7cufmxsbG28PBwBgAqlYotXrw4LyQkhE2ZMqWopKRENWfOnIuRkZH2/v37V3Xu3Lly//79uvraq1arWXFxserw4cMhKpUKf/3rX01RUVH2nJwc9c6dO/UffPBBTps2bWwhISFs+PDh5fXdHscbXx7ilUHe1nkALwAYCkArN4iIhgKYC+BGcZmNABaK0wChi48ZQCyAPgC2EFEmY+xQkO1tEUg1Mege0r3e661PLWFXj+JCxUL5gQQo2ysx4qURALw9nK44RK+vJD9nMpzEdpzTXZpaOEIJpMS/3WKHNlo4Tb0EoEyZNalmFf60iHaNI3bFIaZ94To/88NMp1eX2b0NtJlt2Dprq5t9ct5lfyqRhLWv8f7aTFoUH+qFinOJSFuUhlM7t6C8dB0U/t9TtFxqa+LhJ2Ex7TD23W34cGyK7E3hX2e9BADY9dazbp5chUqN6x56BgDw0xvzwOzeLvyw6DinR9dXqEOTeX1PrwEy5wGmXECXCKQsAjpObBpbOA1CeFy4uTzPW/yGx4XXy7sqRXx8vPMHotPp7Hl5eeri4mJV3759SxzT9Xq9vXXr1racnBx1t27dzADQsWNHN1v0er1VpRKkUnh4uB0AEhISnOsODQ21l5WV1adiFwBgxowZRWfOnNHcc889ncrKypRjxowpWrp06blTp06p9Xq9tU2bNvyRWQNDrJE9NET0AoD2jLH7ZeZ/AiCbMfa0+DkNwBrGWBwRhUFIPOjFGDsmzv8IwDnG2Fyp9TnQaDRs3rx5QdwTDofD4bRknnvuORNjrEkeP4ttmxcAWMQY865V2czJzMzMTklJCSiXyDOGGABUoSr7La/fkhPsGOK33nore/To0WVATQxvSEiIPSoqyrZ8+fKzAFBaWqqIiorqc+jQoT+6detmJqLUrKysP3r16lUNAF999VXE1KlTO164cOEgICTaaTSa1CNHjmQ5BHRqamq3qVOnXvrHP/4ha78/McSuHD16VHPbbbd1eeSRRy6MGTPG2LFjx94XL148EBMTw0VxPcnMzIxJSUlJlprn910NEcUS0QgieoCIpjheQbOyhp4AMl0+ZwKIJaJoCCVpbA4x7DK/p4zN08QwDT/bf3E4HA6H0/AwxmwAHgZQe6ecy4Rrpl9TdMvrt+SEtws3g4DwduHmYIthX0ycOLFo7dq10bt379ZWVlbSrFmzElJSUioc4rYpSU9Pj9izZ4/WarWidevWNpVKxZRKJUtKSrIMGTLE+MADDyReunRJWV1dTVu3bg1vansvR/ytQzwawMcAjkMQn4cA9ALwE4LfujkcgOtzb8f7CIl5jvkRUitijK0AsAIQPMTBNZPD4XA4nHrxIYDpAN5uakMai2umX1PUVGXWRo0aVfbUU0+dnzBhwlWlpaWqfv36la9bt+5UU9jiyfnz59X//Oc/ky5cuKDW6XT2ESNGFM2YMaMQANauXXt6xowZHbp3797LYrHQtddeWzZs2DAeRxxk/AqZIKI/ACxkjH1ORMWMsUgiegBAT8bY4wFtsPaQiUwIj5DWiZ+jARRAqNOYCGAXY0znMv4xADcwxkb42m5YWBirqKgIxFQvFi6siYddsGBBvdYVDJYWL5WdNytyViNaUn88q1rUCsFZAuz418dhzDVCG6WFuczsVs1BoVZINgoJNqQkqLVqmMslHA0k384aAPrP6C/sg58NNtxQAEqVUrKChS9c43/BCKCa/wOuOVuMXZkd3TyrKixJXoKI7p9CFeb9HVortDi77TYAQKurziG6329gtprzQBkSioHTF7jF5l4x9Xj9jQvelAyYJIoZ6ZKA0dkNZh4RNVnIhLj9nwD8BUJd/zNwiR5njA1pKrv8oS4hExxOU+MrZMLfOsSJjLHPPaZ9CCAfQECC2A8OAUgBsE78nALgAmOskIiqAKiIqAtj7LjL/Csyoa4lI1XjeMSKEW7TKgsrpQUm4KycsO+dmmgYKcFpt9hlk+CCCbMxn7ZaK63QRkuL4v0r9tdalk4OAtVJDEf3+w0KlbgcyW/7chPDjAH2ajVABIXGLLl/jAFhYaPcphlzjbDaerkfNwB2qxLFh3o5P5eeTICt2oaolMNQhlQgLKadpNitS8myutQFb1JOrwH2TANsJuGzKUf4DHiL4pRF7mMBQKkTpnuu8/JKvPuv+OJwOE2Mv4L4IhHFMsYuAMgmousgeG39ziEnIpW4PSUAJRGFArAyxqweQ1cD+ICI1gDIAzAfwAcAwBirIKINAJ4nogchVJkYBWCgv3ZcToQgBNXwzsUIQYjE6OaDXI3jEStGYHb2bOeYTQ9sCs4GmVCTN1DhGEwsJkut9X7rArMzyWoPABCVkgmFRhDpzKoEsymgCBFsaG5C119PdG3jfM1nDCg72RFFB/sBANoP/VrS42szabHh3g3YMGmDsxKJNkqLirOJAOBdWUOc7qDibKI4Tfhez351AGkvy7er9gfZuuCXfoIh+T8NJxCdAjQHICXAbILX1p/tZM5zF7iA8Dlznveyjs++xG4gAruFwBj7sKlt4ASHIUOGdNm3b59XbO/MmTPzHI073nnnnajHHnssyXNMfHy8+cSJE9yx18T4GzLxJIATjLH1RDQZQlyuHcCrjLFn/NoQ0XMQMmpdWQghBvkwgB6MsVxx7KMAnoRQnm09gOmOLFwiihKXuRlAIYC5jLFPatv+5RgycaT6CL41fQvm4v4kEG7R3RKU0msNxcsxL0t6SvVJeqcglmqdXFe00VpUl1Y3SuhEsFGHqaGL0ckeCy9vL2oqgDU30SuFv7Z6CV0mVtDzCO+ovBCD0Jhi7+PBgLJTNWIYkD52dqsShb/1cxO5ap0almoLUI/7KYUaCGmlRWVRZZ28u3K/B32MEbOXvl4zQakDBqzwXyD6EryAt9c2kO18ooD0oxkC7qnDb7EBwiqaImSCiO5ljH0kvpdNTGeMBTs/J6jwkAlOS6TeIROMsf+4vF9NRDsAhDHG/vTXCMbYcwCek5ntdlfFGHsNwGsy6ykCMNrf7V7OOERvQ9Qhbiiy1mTJxtK61jgOpHWyLxxNPPwWwwpAG1kjXLrc1iWgkAZ1mBrWKmu9vL6uWEwWzM6ejaw1WdgwaYPX/KjeB9wEHdAyhLCDOnuFCbBXa8CsSqe31ni0N8qy20vXR/bw4gLw2+Prd1y7D+yWmpAeh3c3d1cujm8+AGOeBfpoI9ImHYBh5oPCAh6eUtm64AWt3CfIeWCl8PS4MvE8MuUAP09C1i4DMtZNg7FAD32MEWnjM2AYlOX/dnSJMgLW+7tws0nOS2zKlV5Gbnrz5W7UtGy+V2YMQ/AT1jkcjg/8rTIxG8CnYsgEHJ5cTtPTPaR7sxbAnvjqdOfavlmuzXIgaKO1GLZ0GDbc6y0kZbEDmnANnih4wjlp33L/q/apQlUY8e6IwBIEfeA4JoaJBmybu8RNvJny4pwhEM2JYCbi+XqApdCYkbPlTq/pNSELta49oLG1dpALAIvJ4hL/TjAWtMaGJdcj9481SOx6Fhnr7oCxQA9tuAnAQYBJN8LSx0j8RvwViFIhDSJZuwxIXzkCFrPQQ8FY0BrpK4W8ZacoNuWIXttcQB0lHB5zUY2QlYoLBtUs509IxM/3Aj9PErzA6ijAUuhtrC+B3QxhjN3m8v5vTWkLh8Opwd8Y4hsAvEhEuwGsAbCeMVbaYFZxWhz+Jvz48vwac4x4XvU8mI1BG62VrxAhJsmRknx6YjXhGt8d7OTs8LAxEHFeWVTp1U2vrgl9Ye1z0arnYXx45yqERLRGdKoRCqWwMlVYJSKuOt0o3mB/BK5DuNqr1VBoLPXWjowBdrMGRZkpiOz5h2ysb4Br9Xhf7+ZSQYawb/sA7Ns+AI4DWFku/zRfobQibXyG6MlNq/Hk3vMLDLrkGi9r/G3A+a9rPod3Bi7tqPEIS5CxLs0phh1YzBpkrEurEcRAjQfYVaiKHmaExAO2Ko81M/cxP08SPqujAVYtIdBdxkshlXgXRIgoBEJJtJsARAE4AeBpxthWibH3A3gPgOvJejtjbIef2yK4/HIYYy0vxovDacH4GzIxmohaAxgL4RHPm0T0DYQOcgG43ziXI7IJP4CXKK5NXDoEbmVhJZQapXSFCJe2y748sQ5hW9s4TzxbKactSsOmBzb5FXahjdIK8Z7ijcGYj8b4JcgVagX6PdgPuftXQRt/wln5wSFEq8tKvNogN1ZohD/bcYwhlb1WMSwnsJldKP8mFbpQW3UH/6jbASOygTFHPGxDH3T/12+3KbHh7TFuyxkLWmPTWzdh66pqVJbrnKEOQCtsXf04KssFTzPRIDBG0IabYLOqYK4SxK823IRhk7+BsUAvsUXITpek+rz/Y6U8v/6gDPSmKGBUEMqhXQ8gF8BtANYRkYExli0x/mfG2GB/V05ECQDeBDAEQGuP2VdC43MOp9ngr4cYjLESACsBrCSiRPH95+A/2iuejHkZXmLTYrJg430b3cY4agX7W/HB1xhjrtEptjfet1HSU6yPKQdOr4Fh4kQ3G0jh27NcXVqNrDVZzvUbJhqwddZW2dhnB0qNEtWl1W6xopse2ARViPAzc4ttrdSi4mQ3FJ+4CvpEPfo8FIpzB5+FNsHSrGKAAw1/UKhsTmErtS67WYOKMwkIT86tNZnNgXSsb09UnO0Q+A458WenhGw8QQz7O74xvzzpbdltKlSWC+ecsaA1Nr4zGkSA3V7zr5oxaQ90ZXkYNrx9B7ThJknvNCnsyNplcPcSNyXmwgatNMEYq4B77stXRHQaQCqA7CBsYjkAE4A0AD9AEMbPAfg6COvmcDgB4LcgBgAiGgwhIWAshLJrTV9qgdPkyIVBMBvD5imbwRhzelcrCyuhUCuEmrxFlXWvD8yEzPu0RWm448M7vDzAao0ZaeO2AXuEBlCGiROdAre2JiB2ix0Z8zLcvNuVRXJiWNgBfZsKmC0RqCxx3yG7xQ6zxexV0UClq4Te8DsGjNyOtnFK7NoTBbuNNSsxXGeIwW5V+hS81UUxfiW+OZCO9W3o5pNuT7D9HN/8YEzpMxbbG4XoSfYW+Myu9I4lbmoCSST0RkVErkkCK8QOp5IQUSyArpCvfd+XiAoAFEFInHtJorSoKwMh1PmvICLGGMskoqkAdoPXJ+YEkTvvvDM5ISHBvGzZsgAe3VxZ+JtU938AxkP4D7kWwFDG2IEGtIsTBI5UH2mUChS+wiCkvLx2i92ZuFZbeTVtpAJWkxmWau9T1bV+8YgVI5AxZzWMl8LcM+Jt8LpYGiYacN56Hnvm74H9rHQYhDGnBPhUBbS5ASg/AX30HTAWtPbe9xgjZi9dAgBYOHEBPAVEVO/fENEpGyApsUs4ejoaR08DDSHugpHcRhT4ejRK4MIBA1p1PSoreP1PZuM0DfJfuGQscVNT90oTVsZYf38GEpEaQg7Nh4yxIxJDdgLoBSAHQE8I10orgJd8rNYmjgGAEiJqA6AUQIJ/5nMaCovFArVa3dRmcPxA6ruyWq1QqQLy+frtIQ4HMIkx9mNAa+c0GUeqjyDDlAGr+L+2zF6GDJNQ4SEYotg1iS6QMAgH/sT3qrWEYRM3A3azM2nI80JtMVmQMS8Ds7Nnw0CvQlJYSlwsz40+h1YjW8HY2wh2VircwigkHV0Ujlna+Ay3rHtA9EKPz3BbxljQ2k0EA7WJyYbxKjo8gswOkKL+j/LtZhUUGisABqXCDktVCEgtnFtutYBtClx7TTY63ZHlValAHCFji+t30BDHpLHDGS5vjAV6Z+hE1i4D0t8bDku10BCIiCE1bS+GP+Ced7bl/WHYn3GNM1wDEGKWe177B44f6OZMCuzS56jbZ7dyb3I0cKUJIlJA8PiaATwiNYYxdsrlYxYRPQ/gX/AtiH+FEJe8EcA2CCK6EoD/pW1aGEe3rYvK/Hx5QmVxoUYbGW1OGTf9XLeh44uCuY2EhATD1KlTL3722WfReXl5miFDhpR+/vnnp3U6HXv11Vdjli5dGmc0GlWpqanlq1atyklOTrYAABGlvvjii7nLly+PtVqt9O6772ZPnTq149///vcLb7/9dpxSqWSvvfZarkajYU8++WSH4uJi1YwZM/IdjTekyM7OVnfv3t2Qk5OTGRsbawOAXbt2aW+//fau+fn5B0NCQiS9ITabDU899VS7jz/+OKaqqkpx/fXXG1euXHkmOjraBgDbtm0Lnzt3bvsTJ06EhoWF2Z9++ulzM2fOlAzGf+WVV2I2b94cRURYuXJl7LXXXlv2/fffn8jOzlY/9NBDiXv27AnX6XT2GTNmXJg/f/5FAHj00Ufj//zzz1CNRsO2b9/eOiEhofqLL744+emnn0a+++67sRqNhr399tvZY8aMKQWAAQMGdLvmmmvKd+7c2So7OztkwIABZZ988km2Y5/lkNuPwsJC5YMPPthhx44deq1Wa580aVLBSy+9lKdUKrFs2bLoDz74oE3fvn0rvvjii+j77rvv4rlz5zShoaH2s2fPavbs2RPx6aefnhg9enSZP+eLA3+T6mYEslJO07O7ardTDDuwwordVbt9CmJ/qkV4hhw4wiBIQWB2/zydruXEADgTzxyVI/RJeqTdsRmGa/YL4wZliR5Yb5whGwodYJdovqLQudc3VUfhblQh1FKBPXf+Bd8tvwW2atdQeAZjgR5LZs12XowdF2S3bH5x3qlcPX49EI/IIRlo3UyaYji378MOIga7ndzHSxCuM2Psbe6CxCF2NW3z3eKir4qrRKdE4fuQOmbmKrWPygmBHrRAwxmYy3tO/SBsfOcOr6Q+QIhPrqmW4ZhtA5gSnse+sjzMraqGsaC11+cNb4/BhrfHQB1SjRFTt3iL44avNEEQqkfEAriNMeZvrUN/7sLuRU25k9kAHgMQAWBJwIa2AI5uWxe19/2Xk2wWswIAKosLNHvffzkJAIItijdu3Bj17bffHtdqtfbrrruu+5tvvhnTvXv3qhdeeCEhPT39eGpqauX06dPbjx07ttO+ffuOOpZLT09vvWfPnj/DwsLsO3bsCC8sLFRXVVUp8vLyDr755pvRM2fOTBo8eHDp77//fvjkyZOaQYMG9Zg8eXJRjx49zFJ2JCcnW/r06VP+8ccfRz722GMFALB69eroYcOGFcuJYQB44403oj/77LPo7du3H01ISLCOGzeu49SpUxM3bdp0+vjx45oxY8Z0ee2113Luv//+4uLiYsWpU6c0cut6/PHHC37++edw15AJm82G4cOHdx42bFjJ5s2bT506dUp9yy23dLv66qur7rzzzlIA+P7771t/8sknJ7744ovT48ePTx42bFjXSZMmXXIci3/+859JY8aMcf4gP//88+gtW7Yc69atm3ncuHEdp02blrh58+bTcnb52o8HH3ywQ2lpqfLUqVNZFy9eVA0dOrRru3btLHPmzCkAgIMHD4bdeeedRQUFBQeqq6vpvvvuS/ryyy+j1q9ffzwtLe1EdXV1wP/oA/Mnc1oMZXbpGyO56YB0tYgNkzZg66ytGLZ0GAwDDwKZ85Ax+w5YTK3dlrVb7NBGa2GttLp5e5UapVsMMQCoNRakjVwLfPIooEuEYeAiGLJnexfl9yi15PDAeuKsX2yv9C5B5fAuOco7AYClEI7c9L8M+BVaiwnfOz3QgOvF2DVe0lUYOziVq8dP+9qLiVdNL4SlcYjBGuOUSjvahymw54tboWhdgMieh6DUCXHSrvtAAPr18nZ+uIrds9uGyXryPI9Z4F5jBk2oRayCIDfG34NOILK7eSg5dcdxzkvjcYyZr0uNRNcVic+W6lBseHsMdm4ZCosJQnhUmwqkPd0JhoZt3fwOgKsB3MQYk82sJaJhAH5jjF0gou4AnoGQeC6LmKzueF8J4AWJ9W5hjA2vo+3NiszPlyc4xLADm8WsyPx8eUKwBfGMGTMuODy/t9xyi/HAgQPaffv26SZMmFA4ePBgEwAsW7bsXHR0dJ+jR49qunXrZgaAuXPn5rt6NVUqFVu8eHGeSqXClClTih5//PGkOXPmXIyMjLT379+/qnPnzpX79+/XyQliABg/fnzRunXroh577LECu92OzZs3R73//vun5MYDwNq1a6P/8Y9/XHCs9//+7//Opqam9rRYLKdXrVoVNXDgwNKHHnqoCADi4uJscXFxvrO+Pfjhhx/CioqKVK+88koeAPTo0cN87733Xvr000+jHII4NTW1zPF+3Lhxxdu2bYtctGhRvuuxKCgoUMbExNgAYOzYsYXXXHNNFQC8+OKL56699toeVqv1tFzogtx+WK1WbNmyJernn38+HBkZaY+MjDQ//PDD+Z9++mm0QxC3adPGPG/evIsAoFarGQDcdNNNJbfccksFAOh0uoDjELkgboH448VVrFeg6IkioFj4TFGE0JdCETMhRpgg0REqY94lydCFysJKpP99IzD1Sxiuy5EtvVRZVFlTZszFNlz6CRmLjsNY0KpGOF3rUtx/zzTg0i7g9IfuRfk9kApbAABzuRlZa7KA3T1rbyYgQe9BWeg9KAtLZs32Etyu8ZKncvXYvS8RVlHbMxtBoWrYxC7PZKi6C24CEQNjQJjWghi1Brs/EY9VeRgqziYBYGIljEPO2N/So91REQcg0fv4Sd0g1IaU19hXKa8Fa54HAMnvBoDYuALOcmK1iePAxDAPs2h+EApyahqbGi+FI31eMdAmK6BW2H5vjSgJwEMAqgHkU80P8CEAPwI4DKCH2KwqDcAHRBQO4AKAjwG8GAQz/hqEdTQLKosLJb2YctPrQ3x8vPNiptPp7Hl5eeri4mJV3759SxzT9Xq9vXXr1racnBy1QxB37NjRTdjq9XqrQ9CFh4fbASAhIcG57tDQUHtZWZnPouaTJ08ufvrppxOzs7PVhw4dCiEiNnTo0HJfy1y4cEGdnJzstKVLly5mm81GZ8+eVZ85c0bTsWPHan+OgxynTp3SXLp0SRMREdHHMc1ut1P//v2dXrM2bdo4HzPrdDp7ZGSk17EwGo0KhyDu0KGDm71Wq5Xy8vJUHTp0kEwslduPvLw8lcVioS5dujjX17FjR/OFCxecgcLt2rXzEivt27evV6cqLohbGP7U/M1ak4XifxQDLqcGK2Ko/GclEkISgCFrkPXa68j47I4aT+pdr8OYO0J2u5ZKho1v3wbYzT49tYaJBtGTvKRGbIeUw7DUR51Rmwk4ucJnowCgRkxtXX2r22P3ysJKpE/5DCrNrf41E5DBVZi5xQEzwrYfknChoJUgkZw1dxu6yoG/Atg/4cYY4b47heOwZNZsr2MFECrOJoniuIaMdVHOWFFJ73uAeAppObHr2oVNLoZ72ORvnOuS9j7XF9/HtsbjzIVzU+HII2gIQcwYy4HvLzfcZezjAB4PuhGXEdrIaHNlcYHXD1QbGS3rXQ0mcXFx5pycnBDH59LSUkVJSYkyKSnJebWkBnjMFxMTYxs0aJBx9erVkUeOHNGOHj26SKHw3RgoNjbWkp2d7TxWJ06c0CiVSta+fXtLhw4dzPv27ZPv2iOB534lJyebExISqnNycv4IZD2+OHPmjJu9KpWKtWvXTrbKitx+tGvXzqpSqdjx48c1qampVQCQnZ2tiY2Ndf2evC7AUtMCobm1auLUglzNX9eWyBnzMsAsEueFGfhzwZ/IWrYS6SuGiiJEaBubvmIotOGeXaXccZRc6tLnKNQa9/9fai0J3mBH+1VTDgAm/DXLi+GsXQYsmTUbC++ZjyWzZiNrl++LmmFQFjSh3jeBFrPGxUvojqcH0rnNiQvctqmPMSKsfS4SR2wSusAphMoQpGDIL4ho8CJfdUGptKNbx0LYqkLBGGCrUsuW2LJVhQIgQBMdUIMFRwJV+soR7ufMyhHY8v4wyWMZCGnjM7zPJ4+ERcOgLIx4MB36mBIADPqYEox4MF0Qw6QEOs+A4W/n3cZ4JliqNWZoI/x/qqiPMTo90NIw3DFjI8b8Y4OX/XLjOQ2Drw6YnOZDyrjp55RqjVtpH6VaY08ZN/1cY2x/4sSJRWvXro3evXu3trKykmbNmpWQkpJS4fAONyR33XVX0WeffRa9devW1pMnT641PGTcuHFF77zzTuyRI0c0RqNR8cQTTyQMHz68WK1WY8qUKUW7d+9utXLlykiLxYL8/Hzl7t27fXapadu2reX06dPOm4EbbrihIjw83DZv3ry48vJyslqt2Lt3b+gPP/wgfSH1g/Xr10fv378/tKysTDFv3rz4W2+9tdhXpQe5/VCpVLjtttuK586dm1BcXKw4duyY5q233oq966676tjBxz/8LbuWAuB1AH1Qc0dMABhjLOiPOjjyyP3jd53usz1yrhEZH/eR9KSqNBVQ69Q+O7pZzBocP9ANIx5Md/MWdknNQca8VtiQWwJ99DS/vIeeHj1/QxwC6pYFd0+j1Da3f90XB/KByCEZPkqMNYYH0F9Pr5AQF6a1oF+vfHRKNCKsMsa5X1G9f/Nq62y3KlF00ABs2QMA0Mc9C2O+fz11tOEm2Va+cglQtVUL8PQ2pwz5vdbKApIhGrokYHS28L7NIBisU2S34RDY6R/cVWvXQldBLiSOSX8vrvb4GgcIx7G6MgR2G38wF2yceQScZo0jTrihq0zIMWrUqLKnnnrq/IQJE64qLS1V9evXr3zdunU+Y3mDxd13310ye/bs5Hbt2pmvu+66Wu/MZ82aVXD+/Hn1DTfc0L26upqGDBlSunLlylxACEdYv3798SeeeKL97Nmzk0Vhe27gwIGy650xY0bBuHHjroqIiOjzl7/8pWz79u0nt2zZcuKf//xn++Tk5N5ms5k6duxYtXDhwjrfnIwdO7bw/vvv73j69OnQAQMGlK1atSrb13hf+7Fy5crcBx98MLFTp06GkJAQNmnSpEuzZs0qqKtt/kDMj4rtRHQYwHrUlIRxwhg72TCmBZewsDBWUSFRgSAAFi5c6Hy/YEHT9CSRq9urT9JjdvZsn2Mc44y5JYBULCUxjPnoTj+6sjEsWFNzLKQeVas15hoPnty+yD4qL3HW9g1kOW14BaxmtU87PJcNa5+L6NR9UCgb3ntXWz1fldIGi1kNUvpuEc0YcP/YPwBSuIWZuArAsPZnvBpfmPIS8ax1AXBaCJlJXzHUr+Q2bXiF6H0P9KbAfX2O7wKA5PniSxR7ilsvsT3pAAwzHxRWljlPMgZdQIGsXT2R8flQGC+FgRR2MLvCLRbZc9svP/QvycoYnuepVB1q5/5pCSMe3IrcPyPdbiKkjpP8NI6Ax3mlU2PEihF1CpkgIhNjLKBHz40NEZUxxiKa2g5PMjMzs1NSUhpUoHBaDgMGDOh21113FT766KPN+pzIzMyMSUlJSZaa56+rIg7As8wf9cxpUKTq9qq1hLQ7NjurNqTNfhKbnlC4VXYAhIoPaYvSkPHElzCe9w7r0UcbYQgbAcPeRcja3dtHS2R3sS3nPawtdlfO02ss0AuePxlR4yueFIAgdgrCoY8u8fI0CmIxF1G9D0ARIhzD5lIZwmpTIDnSjpP5Wii1Qhc/kghqYtYQ4B478In7TIcHdeHEBfKd3cRkSsN1OW71nX0ltzlEotRNiG88akaL54Tjvec8T2+z42kBQEhfebubV99r7NsDAcsrMDz2uOAx/jwGsBTKxj0HEvs8bPI30jWo7/pJsEG8MZE7RqSwYcTUr2GYNAAZw0O9jotn9Qsh+ZFHs/lCuLGXTyq+zAhGYh6Hw6kFfwXxhwDugdClh9OEuNXtzTVC306FtDEbnfV6YcqBod3jwMuvYOsLZU5PrzZaK5ROE5dP//tGWCprxK7zEbFY9cEwcAXw4R1In7rOrUucZ2wnUIuwlYOU0Ce1lvZ2x6sFUbMpWVIU+6oJDKUOhsf7AB0n4tQXT+O3zWb8tr6XM7wg7i+/ICT+fJOI4Nq2Gaa14IabjyJaFHHWUCNiUn9z8xgzG6FjW1HYSpSmA3yUp4sx1lQWQQDJbW2rkTb+R789yr7wHe7iv4CWHLv2BhhumIWs3b2RMfveWsvoAe6eZ4e32NND7J7MKYTXqTQWwF4NdJ4OtBkE7Jkme6M24sF0GK7LAs4XwFjwgOSeM0bOpy5y9bYB5rTxSvYek4JdNmKYiDQA5gO4G0A8gPMAPgOwiDFWBQCMMV+NPTjNhCFDhnTZt29fuOf0mTNn5sk17rjnnnsSN23aFO05ffTo0YWffPJJwO0XO3fu3PP8+fNeYayvvvpqzowZMxolLMWTd955J+qxxx5L8pweHx9vPnHihFwL9CbBX0G8GMDPRPQ0hHIyThhjNwbdKo5PDBMNNRcBKdFoM8GQ/B8YCrJllwccoroE+ugaAeAUCIXHoetwHjGjK1GRYUOpj9hOnwJMDmYTvN1SwnxMOnA6WSi2v2daTSk2132Q8/J1vE8Qwzu3YPeG7bBVC/8bKio1+HFvB4QmNHQbdztUSgarLTDRolTanfV+Xfdtx3cGnLoYCkVIFezVoejUtgo33LhfOC4d7wNOvQfY3fNBfHbVk6nxLLtcqB1pr90Dw8BegGolMj7u4xa2kLmzb0BVHRznhL/e5oCT//4Xj/T3N8JSKb1+1ycXnqE+zK502uYmnJWCCLaa1XB8p5XlYeKYr2B4621hbMg85B773b0bG9XczGR918q9P4gLrsl7vn5Ps5cukb1xad4IO60OqXZ2tPPv9+F508XEGwLpCjstkHcAdAMwE0LL5yQAT0Fo3TylCe3iBMjOnTuPB7qMKHrr3Hfck6YUmHv27DkqNX3GjBlFTSXGA8VfQfwFgNMQ2ksGVPyZ08BItCUWpucIj9TFGsPwKFzvLI/28xznNE+BYMo1wXQpAp0W5GNqlw8RUVUsZPQziH9tgC4JafO6In1esXtDDp0Sypc6oVIdBq1FInablDAgBXjAgIx1N7p7eq/LEjyZjmSpX+6rtSSbg1P/W4vf3tmLitIq1F7wvyEgTBx9CJ+lX41qs38/L9IqMcBwDp06GCUf8d8wca/3QjYTcP5rQBkB2N0Tb3160HVJsjcabssVtvbwwBlgeH0iDNco4KroEruedWmrLex/Dd4xxM7ENj/LowUioPUxRiF8p1JCcbrgsFUq1MeBUzgPPgzY5JMKt64eCsPNyYApF1l7r8dvO65xC3ewVIdi84pR4vZuli00UV0Z4myFXFubcLl63P4TrI59gTVFAeAuhoW0bJc47kpYLQrnGM+2zsI490TQhiy51kiMBnCVS4OOw0T0K4AT4IKYw2lU/BXEfQBEM8YapVYgRwKJRhroOFHW2yfAahpfADWi+PQaYP8sr3JokgKhEjj9Zjvs2jocN/2xFmq7KHqZzdky1TB6Is5HbsPvT/2E6nwl9NFGXDfxZxRd2wU7cIf7chAvo6LANQw6CMOgg96mO4R+x4nAz/f6dYhO5eqx+7d2sNmq0dSPlAeknMdPexN9FtoiYhjU/wyO3T4CJ9ELxSuPI2NlmlusrM+qG+Ix8jtO1tHi1nEeOM4nTZTwpViKYLi5FIbH+3vdQAEQzhuPRD7X7bjZ0aYCXVIO+awcsXH5aC+B44pvAS0ttmtaCctTI7J9e5+NBXrnvvqKr876rhUMgxgyPu4Du9U79tdmVYnHRT4vym4TxkATjYx1/WAxq+EQrqSwo32XXGSsS3NW8GjfJRfZf3YEsytAxKDSmGGpDoE23ASbVSV29nPgfpPSsedJnD7UCdK/kcBFbmC4LMMAUipwx4d3egvaT1zXvRWAfChJCy+5lg9AB6DEZZoWQF6TWBMYdrvdTgqFgucWcVoEdrudAMhmrfsriH8E0APAgSDYxAkUR21f1y5uDpHrI6zAic0kiJ+OE4V1/TrF6zE7IH/RZ+cYBh3b4iZqXdd7JD4VlPopHnvtE7cxlj9+w/ZeE7C91wQMOrbF6WEmf7y9ukT39zKi/1SuHr/9EYeKSjWIAu1EFnwcccIVZxJR+FtftOp2RLIlslJpx8B+Z9Ep0Yg2x7bg/b8tgHF9FzCz+7XFZ3KiLhFZP3RE+sqBtYtoUjrDSQAIf11Fr+sNV+a8mjGu8/dM8+mpd4pjUgPXvg9kfg6YtnqNYy62SQtdwTs4bPI36DUoyymhamJ4XRthMOdYw+AjyFjnO/nPVWTXlijoGvIjP5ac348vgV2TuCi/PaEe+M1eJeGYXYnTh66Cayy0sC3hM2PkFMM9r/0Dv/0vFd5ilUEdIjSEcl2X256QDQBJ/IYaruIFszFpD69EUm2trdtbJh8B+IaI3gBwFkAHAA8DWE1EznBExtj3TWSfL/64dOlSjzZt2hi5KOY0d+x2O126dEkPQLYRib+C+DSAb4loI7xjiJ+tu4kcv8ic5y14bSaU/v5PfJH2Cob2ew4Jh94SPYY1/5e8PIcVWTCEzJIUw4D8BSciulQQs1KYcrG7ajfGHvvSSzCr7RYMEsXesYT+AICZW2d7rcLLzrt+hOHRmlAOV9F/KlePXw/Ew2xx9Sw6hIG0iYHBEKKxodqsrIPAZuicLIRKZaxLQ2lBa5RmdwIAsSWyUAYtXGd21g8G4Dy27Jz0DkgKLaUORbHXYfvH7fyr8MFsQmvsNoMAAFnLxHjgQj30sXakjf1aqDwBSD9VkDoH5VC3ci5n3/MgFLaahi9WKKAggJhdIlmtRuhazWqY1DrkRHVBUtFxGAZlYevqW+EtzITPhptLgZT3kXb3m0h/90ZJkQ0wWMxqZ6LeX+/+EVvfGQab1fvfoEJpdUserfE+e58Pju/Hl+B1xFx7l1xz2RMlfNRHri38h1BZHuZj/SSGIsifz0Koh9x871jeYIlkSQ+vxI2+ECoyUvSeC6h1aqEhUMvlIfHv0x7Tp4svQDjYnRrNIj+xWq0P5ufnr8zPz+8F3uSL0/yxA/jDarU+KDfAX0GsA7AFgAbCHawDflfYGMjECUdUFaPMXoZNMTqkDfsG3UO6O5PsJJteTEsH7o+HYZB0s5e08RlIf2+UV1WJm8d/J2taaWhrlNnLZAWz5/Sy0Ei0cpkm3ZxjOHBtbxg6ioM6TsSp/YewZ9MmVJuVaLhwCOF0VintGHCNkHz3wy9JXjWKHcKbNASIpe2IAE1qNIwj+sCe9YmXiK0pg+ZewxkQjkmEIgLmDmaYcr1Fpz6mFOg8Q4gZFkNmimKvQ0TOepQWeF5HBSRFtM0E7J+FzB8SsWXFsJpjnq9E+oqhgN1cI6JdnyoA8rHqUpiLkLUmCxnzLsGY+yRaRZcibfx2GAZlQQEGhcudi2FQFjLWpaGy3LtqxLb1t2H2kCXOb1uuE2FleZgz3twwZBZgq5DxJrtXm7j9wXQgDICEHlOp7W4hCl36SOaLABC82UKimx5SQlGpsjoTEOXOXaXKCps1GOe2r+VrW7fcfGmBHSwkPbyeYT26RBgefxIY2Lumws5lUGWCMdax9lHNk9TU1IsARja1HRxOsJAVxET0CGPsTfHjIsbYiUayieOJTMhAWWgkAMAKK3ZX7RYEsehZkUwCMll81gY2/PUo0CUeGS+egvFSmFfcp+el3qJQY1fX4eh6bp+s6Q4bHezq6h6LLGlnlQIZT3yJqAHfwJi+DJl7IkSPcEN3+BL2rqJSg92/tcfAfmdhOnY1tB2PO2sW280aFGWmQKU04NajtyLDlAEramo6F0IFOrjG/8obCg1a9X0DU/QTkfVilneN6RAr0uZ1AQbMcFtMszEGarsl4AofzFyI/312r19eZWbKdX7fWXuvd6sw4asTYdbe65G+wrEfhNICPb5aOQIE6VhoX6E6sk8m5DAXwTCoUFJku2Ixa/D9ujTYSqXPKXOVxhmH61n32MNKVFeGSDbuABg0oWbcPuUrZGwYDYtZKl6aOTvYNXXce/AIzHss6+H1DOsBYOjYoitKcDicZowvhbEIgEMQ/wagVcObw5FE4vGhQ4wCQNdz+zDo2Bag6kFBPHe8D8ZCmdrAMtMF1FDP+Btmtf2H7OWsNDRS8EyHRmJX1+E4ltAfD/xvoWx6jsNGB47QiesPb4DWapKvYZxnQfaKd3DiVCSaQijYbAr89kccBv/tMLZ4Zvzr1Bi6Ik24AQGwu2q34CVXRGBg6EBUaKMkKwGoNBak3f1LzUY00UDqUudF36vGtA8PWFil4OWXrEgQYvWqFe2Kv3WjK7RRCAeQtSYL6e/+zVm9weFhtUMBw18PQ+ESKmNXhiJj3U1ej/59xULLiXpNnAUMCpCYA6ENN0kKT220tuaDePPoT7k2Y4Ee1J7AzvrzoEv+HJRvxUzQhlfCcMN5bHhHPnlQE2qREdS+aJ6d7EhhQ+qN+5zJlOJU2fHaaO0VLXCJKAXA6xAS1x01bIX6G4zVtYwIh8OpA74E8UkiehXAIQBqIpIsAcMYW9UglnFqcHl8yEw5bmK067l97lUcTDnA6Q+hb/e0TDe6MtnNMHsFMkwZiPcIa3BQoY3G+zc84xTgtx78uCZZTsQzHti8qB1CxnlvS223gCAvhuIG/NJkYthBRaUaKbdlQQFHKTI99O3USHu5pk1s9/P70T3zebfqH1u7DMNN1WsB1JQ+axVjRMzjVTA8+XPNBhyJbD/fC4u2HXZ1uw2Zt/VCq9tb4dbQW52CW9I2bTTCKwu9SqxFxJRB/VIyzl6bgu6HjkJtc4kXV+pQSf7VjbYo1PixyzAMgyDQPUuZWcwafL3xduTN6I2Bx75y3iT90nUkjHne5x0gL8Svn/A/fPXeKNirasIQ1VrCbXd+DYVLQvCwyd9g84pRbjG/So0Sw5YOq1lZyiLY9zzoV2e9VjGlGHjnbmS8I19+rXZ8n5/GQj3Qfyn0iZekm9D46BAoXR6NiTcG0uEjTSmU3dukC8mUWbsMyNgyRdh3jxrMap3a7bsTwmwun3AIP/kUwHoIdYh5SVMOpwnxJYjvAvAEhA46agBSta8YAC6IG4OOE7FtZwz2zN8D+zk7KIEQ+kw1BsVIV39IG7/dK2NdyLCXjwcGhPALz7AGQBBIxl5Poue5TFzvMq9VVbHzGicVD4zZwv/4kHEhznW5Vqxw9XC6Jp4JFRmCcWGvu0AI0wo2upUw0yUBo8UqDDLVP7SGe4TKGpotmDVoifMGJq/D32pW7rGsuvI8Bh38EJX2CTiW0B/fmb7DDtMOVKPa6Xl2FcjGXk8iZP88qO0Wp30WhRoZve7C0YREHIPgiRduWEpAoljfU7kD10/4H7b+d7ibCFSG2HDdxJ/BAC975cpamfPVOJqQiqMJqW7Tle3LYDvjXY3CVXDbRc9vWWgk8qcbENI7DJoXNTVi6I7NMFzzu9vyhkFZsAP4euNImPPVkqLpSHwqcntOwHUTf/YpdNUaM24c/z1SBmRCZzG53fCYq9XODo/uuJ9LqhALbGEasCJ5D7Mmzooj8alIW2TxCodRhVhw4/gMbF93M8oKvB/AOUJTvErqDTmJJY9K3/AKbZ/9Od+DK5z1bcqRNm6b1xMAw82lMLw1G4BvwZu1xj1c6DJpuuEPcQCeZSw4KcEcDqfukD+/QyLKYIy16FTesLAwVlEh0SAiABYurEmGWrBArr1qw7Dtw234ZcYv7j4ELXDHA+vRWzKWk7DNshWH/vUtyi5F1Br3CQCV6jCsuGkRgJowDNfwiLwOf8O4jH8hosq76QwDsFSmgxa1J+gP6qH+5QRCN+yDsqjc2Uq5U6LQjGLn/3ogrOsRtzbFdYExQRQAQqJbbHQZLhVHwGYL7OLvWhbNY2+Ae0QbZVpLW7TxWHHD026xxSqokKarCbOQW7Y0NBLv/8373PJaHsC54/+HVlmLEV5V5PbUwJMIRQSm6IUHPEeqjyD7+IuIXHkMP6+5DsZCPTRxViifa+V20+K6vSXJSyS9m47v1ZPqz6thnWP1uhlzeA8tCjW295rgZqurjQCEpjISObsMwLJhSwAAsyJnec1fZVyFMnuZ046qf1eBnWNQ6W1Q2yyoLAuBvk0F0u76CYZrf/FaHrokZFWke8dya8xIGfK7W13lG8dn4JuUSV776kQLaJdoETMhBlP0U7Dtw23Y+8xe2M7axBvaUISMC0H159WonF3p9ttWhNpx+9TN6Dsw023fq9XhCO2/HFm7e0sK7C5jQnBis9JHtQrBM6vSqmREf2CQknDHh3cITX48yz8qdcCAFdI1rT2QO8f0SXrMzp5dbzvlICITYyzQeJVgbv91APsYY2uaygYOhyPgV5ZSSxfDlwN7n9nr/UCtEtj++c2SgpiB4bqIu3HLkmL//EAKDXZdPd758VhCf29xZS9DuIQYdiAXn8zOMah/OQHd6p9AZqHWqaOV8o97OyAuphThVx9CMDxWrp5lxoBLxeG4KrEIR0uTgcIyIDwUKqhgq6hAWHQc+k2ciU5DhuPUzi34dc2rqC4ogD0qDNdcfRad4iQ8o671kWUqL6gr85CmS/OKLXYLgTDlSjbU6CVzw+KWOClSljgCm2J0bsJbCodABIDuId3xQ/tBOLogFbQAaO167EBgYF72pi1Kw8a/bwRzDZvQAqHPhEpuL2ZCDK4JuwbfPP0NTGdM0MQJntBeA7JQGhqJ3V1vxzEXr7IKKgwMHei+kloSSSMUEThSfcTrGLvua8i4EDeR7yagP5GuEsVMOVCPVWMERtR4M2NcvZ81dZVLQyMRkybsa8a8DEHQKQHYhJsFh+Ats5fhSPURnBh5AhEjvZtzOGw0v2CG7awN+kQ9zE+bkT/QgNJjuV4x+7MiJ8LQEThvPe/2xEj9TCtcGheG1KGd8eeCP52e2C63dcHxr487P1+98GocsxxD5czKWh/SKzVKMMZgt3jfqKrD1Bjx7ghnJ0MA0s2D/EDuKUQLb7rhD4sB/ExET8O7pOmN0otwOJyGwFeViTPwo6waYyyxtjGc+mM7K90QoawgQvDEeNSIJbiHM3hiVreCWaVGWGUhKrTRMPZ6EifbtAJYlcwSgghxxK5KoYmzwpyn9poeZjgD3aq9ILunNYJwzS+IQEPFPdpsCpy5oIdx2XjveYoImEOvAgB0GjIcnYa4JAB6hkMAsCg12N75r8gzrhIEo1zDEF0iuod09xkDnLX3esmGGia1DhgmvYyr2AOEZL7axDAgfG+uVMl8xwxM0utqmGjAeet5/ProrzXhAaGCgFZAAbtLnK9D3Haf2B17b98LjV2D6s+rse3ft+GbZcNACYSIZyOgHx8hf7MA+EwkVUGFZGWyW4WPMnsZMkwZCEEIqlEteQxcBfRUbZTkeVwWGokMUwbSxqZh9sTZwsTTa2Df8ybg8hO0KNT4petI574aJhrcvNOe7DDt8PldhY0Lw8j7RjqPwyrjKhyze9+Uun6X50afQ6uR7qEWVlhxbvQ53HrXrc59vaC4gFtfF2LSj1QfQYYpA3bYobVrnR50TZQGSihRWVgJUhKYjUGfpHdWgPArtleiKoS/6BP10h7ilt10wx++gFDnfyN4DDGH06T48hBPajQrOLWibK+UjMtUtlcJjyUz50mKM0cei2e5tIyrR7pdbFVQwepDDDuEjrHXkwjb+4Rkm4CbJmTgm+W3uCVHRfX7Da2ST/toluhYuuGoNEmf5g4RBcBbkLklMuaiLLR1TUiCuFxEz4dRtGwtMj77q3xTERdcBVnl2sGwmN3tEkqB3QSdTDSOp7AtWFvgFDSuj+BdkfK+RigiJIWbQzTmn3wV/Y6sR0RVMazaeKj7vAwgxt1DXAxUz6lG75DeODf6nKS4LbOXeYUDsLMMpbNKcZPuJklR5TxGrcuQYrgHg45+DVVlHiq0UfixyzDkdfgb0kIHSt4MWGGFilRQMZVXuIqngP6xyzDJOPldXYfDCiu+NX2LbaZtwj7FD0T3ASthOfAEVJXnURYaid+634nEqx5zO28Ghg7ENtM2r30CICnSXY+7503BwNCBXiX9AMDCLDhSfQTdQ7rLim/Hee15swC430S5etC9QlY8cHxXju/n++LvveyW8tj7uil0JW1RmneYSstvuuEPfQBEM8akuyVxOJxGw68Y4suByzWG+Np3rsXQ+4YCANgnCpCMT9gx1VesKYHQ5dxeZ+wwc0l8OtNjOnp2f1HcDnlJ2JoWyhrYqkJRdLAXtBEmhF99uJ57LnSOYwwwW5TQhLeGuTywx6i2qDCUvXyXzzFy4QIAZD1/5s/NqJ5lgksjNihC7Wj9RgxuvvdmN6HwQ+UPbp7ZkugS6ecvBEQXRsMG95sfzxjirDVZkmEM2iVap8hRQw0FFF6Jeds+3OaRnBmKsHFhuFp9NazZq/G3P9xbcNuUIXh5zuOS3n9fMZ7vlryLC4YLkmXNpJZzeC99xl6LLC1eKrlNABiqG+olzByfXZGKk5f6XcjZIIUvu6TwJUSPVB9xJlZK2SO1T0DNuezvdAdSTwc87ZH7fgD4/d3J0RRVJppBDPHXAJ5mjB1oKhs4HI6Av0l1KgjVJvqiplYiAIAxNq1hTAsuLV0QA3BLylG2V+Kaf1/jFMMAUL4xRjacAZBP2HLgVcLNBYtCjWN9Hsev7TpjbMbjbmXZTuXqsfu39rDZajzDSqUdSoUdZkt9mmkI56YjAa9johHLhi1BqyfWQlFULruEW5EqjRKmyYNhubZzwFt3iCk5r5+xt1FS7DmSzUIQgq7qrvjT8qeXp09u2ZAOIdBmar2mRyISVoXVKfIKexdKdrWTS3QDBIHS+cvO2P+P/W6eONIS/vLOX3Bu9Dmv79bBwkkLAKnqBQS0LmwteSOxvHg5LkRfkBX+C+zu56LnjYdrUpynQJK7SZETmIEKVX/X64mcXaEUCiuzSoZN+PKmyq0vBCEgItnwl7oQSqGoYlVu9rh6feUEtePJRSDfR3OhGQjitwCMgxAy4RlD/GyTGMXhXKH4q1Y+hpA1sRUeP1pO4zH0vqFuAtgTqcfArtTW9Wvwsa9ll1XbLehweDm2xy7wKsv22x9xbmIYEGJ3A63sUIN7/VVH57hKtXDdqhyTCt3KH6SbgYRpYA9RQ1FUAXtUGKrG9K+TGAZqHjU7hILXts7JeOPF6dWoRpZFOkku9JlQr8oCap0ayvnSDRyKUewMOymzl8F0xlsM+7IJEEIK9j6zFzaTu/eZVTL8ueBP0EiSPUf00dJ1fSmBnDZtM21z3jxEKCJQjWpQgnTjC0dsqKvgcsUz1MKYY8SGv2/AjsoduPnemyVDCjzDQ/wRc/4iF57gGSaQrEzGn/Y/vey6Xns9AEjua5m9DN+ZhHKIniJUjmpU+5HhERiOc9xhz3nLebebObnj58tOX/M4AAAdgC0ANAA6NLEtHM4Vjb+C+FYAHRhj/L9bU+No5iCRyZ3X4W/YDmDowU/cGho4KAuNlE06AuCzggQgCOqu5/Y5Hys7HjdXVHo/SheoqyD2Xs5mU+C3Q3EAAMu1nVF94gJCdhzx8gZX3n1dnQWwFFZYIfcURU7sOUSiLxxhDZ4e0O9v+94vu+q6bbnkTGOuEa3RGmUyTVmum/gzvlkxzCtkR67ShEMISQl/0hLino3z6bWt+neVZFWV4oXF2HbnNhjUBp+VPDwf79dHDANCuMHS4qVOYR2CENhhhwU1N5Bl9jJk2bOghtr5O/O0q3tId7xb8q7XDZYddvxQ+QMA79CDpsAOu+zNnCe+bjY849457jDGHmhqGzgcjoC/gvgwgCgAXBA3JafXIOu115Hx2R0uSVyvI2rmeWxrG40yexnKRLHq5SlW6tCq7xsgKpf1LMmJIQckrhdwL8uWkPE5KkpKg7KLvrAZa/anatIg2DrHInTDvqB4g31uF9IiUkrs+RKJnjiSmlwfK39f7J8gruu25ZIzHUJarilL0YNdoO2hrTWJT2ofgRrhr0hQIOqZKFy4w/eDptq871mWLByzHMMNuhskQw3kKnC4xopbmMXvkAOH4HP89ZUk5ymSt5m24YfKH3C99np0D+kuu80qVoVvTd/WW7w3Nr7sTVYmN54hLRQiuhrAWACxjLFHiKgbgBDG2MEmNo3DuaKQLsbpzSQAK4noX0Q02fXl74aIKIqINhJRBRHlENE9MuNCiOh1IjpPRMVE9DYRqV3m7yCiKiIqF19H/bWhpZO1bCXSVwwVH12TUKprxVCcfWu926PJYwn9sb3XBJSGRoIBKNdG41y/54COE30KgFNtevi8FJ/K1WPzV51wccEBRDzxGdS/nECEIgK9e55D0J/fSmCPcg/1s1zbGWUv3wXjyqkoe/muBhHDvggZFwLtEi2oPQEkxO+6JrX5i+O7O1J9BN7pisHddufnOkOt8/Douwhpz3OnNDTS2UQjZFwI9Af1aF3YWoiR9nM/XZeLPBgJ67javZ9ynm7X6dWoRoYpA0eqj7iNOVJ9RPZRvUO8me1mxFCMX/YHgypWhe9M33nZ6kltYtjf86O58Kflz1r3uTkjXo/eE69ZZUT0OxHJFEYEiGgOEeUTkZGIVhGRzx8JEY0DsBNAAgDH9TQCwGtB2wkOh+MX/ibVPQ+hjfMfcPdJMcbYEL82RPQpBAE+FUKpmS0ABjLGDnmMWwDgJgCjIJS5TwewjTG2QJy/A8DHjLGV/mzXweWQVLekzRzJOE59TAnoWLLPZR0Z33IJYgDwwP8WynqIpRLnHAlsYVqzGDbh78W69raxwUyOa+7UlrwXbBTrFaj6dxVMZ0x+e3obG+vnVlTOqXSPd/aoouHAsxNfc/ayRigiYLabfXqYLzdCEILpkdOb2gxJakuqI6IwAP8C8AGAXAC3AfgUgIExlu0xdiiA1QBuBHAeQqLcL4yxuT7W/yeAuxljB4iomDEWKTqAzjPG2tRr5zgcTkD4GzIxC0BfxtifddmI+E/lTgC9GGPlAH4ioi8B3AvA85/FCAD/YYwVicsuA/AfAI2vQJsZcp3gjIV6t45jDjzLSv3W/QBC2qXKXox9Jd1JJc45BGtFpQa1e4g92ymHe63PbXQQk+PkcK1iUF9hWNekLQI5a+TWlUC3bb/TDt2dOozQjag1eUsKFVQNHuOqGqdCGIWh/Plyt/JwUt+Pq4e9sW4q6kqZvQxDdUObtWgPNtWodtZODgTPsnOhFOoMO2ksGGMVAJ5zmfQVEZ0GkAog22P4fQDeczh5iOjfANbA+xrnSlsAjv7czOXvlXFycDjNCH8F8QUId8d1pSsAG2PsmMu0TADXS4wluDsHCUB7ItIzxhwFaF8iosUAjgKYxxjbIbVRIpoGYBoAaDSaepjfPNC3U8N43luIaNt6h0F4llBrVVWMQQc/RBd1V6yPCZG8GPuKIZZPnHMg1QIE8KwY4WinrOobA+tvxRLd64Qlgp0c5yCUQsEYQ+nnpV4NIypnCx/qIooZmNASGraAhA6BcNx6vF4Csy7CytEKOlmZjCy77+QpNdQIVYS6Ja951lX2h0CFtGKsAq3Gtqp1nKOhSH1uKhqLCEWEU9DV5WakpeK4UfFXzEp5+qtYFbaZtuG85TxuDA9aV2MVEe1z+byCMbZCbjARxUK4nh2SmN0TwGaXz5kAYokomjEmVw9zPwTH0GqXaXcB2OOP8U3J/v37NSqV6r8ABkN4msvhNAU2AD9Zrda/p6am1qvBjb+C+HUAa0QRetF1BmPslB/LhwPw7KZghBAr5clWALOI6H8QfmQzxek6cZknIST5mSH840gnoj6MsZOeKxL/sa0AhJAJP+xs1qS9PBLpD34Bi0snOLXGjJvHfoP8cwa3pgKDjm3xKqGmtluQcOgt9LpplWQGuVRCFSCES/hPzWHWqG2oCgmDotzdI22zKWA5ZUaX6Y8g+73/wlZd5bZ09Q3dGyw0Qk1qDNQOxOf//lyyikHVv6vq7CWui6i1w16vWrKhFAo1qeskrMrsZfjTXvtDHwssCEVNst4e0x5UQdpmuZsCJZS4Wn2135ULAmGgTOe65kiZvQyrxNbfU/RTsLx4ebMOn6hvuTpXAhGzu6t2y243y5KF+Or4YHmKrYwx724sEoihDGsAfMgYkwqM9rzOOd5HAJATxDMBfEtEUwGEEdE2CIL7Fn9sakoUCsWMVq1aDUpKSipRKBQt/vrKaZnY7XbKyckZXFJSMgNAvQrO+yuI3xL/jvSYzuDfnWE5AE9XTytIV61YBKA1gAMAqgH8F0JDkIsAwBj71WXsh0R0N4S4rjf8sKNFY5hogHXfw/jh49SaKhPjM2AYmIXSY7luglg2/MGUi2xbtuQsz3JqhJrYYf/ig92ifsFAoHLpiz0VleNYfyVuUC/Ab2uWoaIwH/bIMFSOSXWKYRVUiFPE4Zz9XNAuyg7haD8n3UvaVx3f5kgVq4KV1V0I+isiHcfNl/BWQinrIbfB5lMMK6CA3Xd/b0kMaoPQfa+Zh0q44qjzK9WFrrkR7LCOLEsWjhV7VwfxrOdc2w3e7qrdjRo6QUQKAB9BcMQ8IjPM8zrneC+7M4yxI0TUHcDtAL4CcAbAV2JoYbNGqVQ+EB8fX8HFMKcpUSgULD4+vrysrOx+NIYgZoz5W41CjmMQHk11YYwdF6elQOKxE2OsEsI/nEcAZ9jDfsaYdO0rfzK0LhPOHf8/9LnmR/S95keveZ4CWDb8QZfo82LjKKeWkvcHEr/6Ej/ubYu6HV6CxSJ/r2SPCkOZvQydhkxBpyHDAdRcFC2uTQ4sfwb1oqyGGttM2wKu4+vPRbqpaC6eUbnydLWhhtqtVFkgHLMcQ1Zx8L3OgVCXmGo77M1eDDcUjuog5y3nkW3LlmxUUhuN+VskIgLwHoBYALcxxuRO1kMQrmvrxM8pAC74CJcAES1jjM10WcYxfQljbHZ9bW9IGGN6jUYj3xqVw2kkNBqNhTEWVd/11Ffo+oWYmLABwPNEFEZEgyBUkfjIcywRJRBRPAlcC+AZiAl1RNSaiIYSUSgRqYhoIoAhAFqOe6iunF6Dtr/Nl5WmZaGRbp93dR0Oi8Ij7lepA1IW1VosXwUVij6rwI+/xqK+9xqOyGJXmEaJqjH9vezoHtIdU/RTMCtyFqbopyDblh10secQXh0fyYNK4x5upNKY0fGRPK9lHHGznIahrmIY8F0PuLFQkQohEMJsWlpZNFfUqC1PIHhYYUWWJavOwraRG368A+BqACNEh40cqwFMJaIeRBQJYD6E6hS+uF9m+r2BGtkEiPcKHE7TIp6H9dazjSKIRf4BQAsh9OFTADMYY4eIKFGsJ5wojrsKwG4AFQA+BDCXMfatOE8N4AUAlwAUAPgngNGMscu/FnHmPKht0vHiFoUau7oOd34mEHLbD8b2XhNQro0GAwG6JGDACqDjRAwMHQiVzMOBCEUEOn12GmWH/vDbtNoq9xEAe1Q4GABbVBhMkweDXdu9VpHZkF6gUV3WYuSD6dDHlABg0MeUYOSD6RjVZa3bOBVUSFYmY3fV7gazhdOyqWJVsMEGg9qAEN9lZ5s1LanqRWPdoBJREoCHIJQKzXepfz/R89rFGPsGwMsA/gcgR3xJVkcioilENAXCk9MpHq8XIFzfOBxOI+JvDHG9EcuojZaYngshGcHxeSeAZJl1XAJwTcNY2LxhplxJ3xMD8INhEvLaDwY8W9i2fgjo5b1M95DuKPjpR5xY+wlQWAZER6DzhPsxOO3vAIDV3/fxbQsD7NVqKEIssJm0UISYQSr5x+VhMe3Qa9lS2Ta7cgQSpqCCCkooJT2GUolBEVXFMAwqhmGQ++N21/y2UApFDMU0SCLY5UYwk69aIg6PZ0umuYTe+ENjxQ8zxnLg+zFZuOsHxthr8K+phsMDrIG7N5hBqOp0XwBmcq5wjh49qunevbvBbDbvV6t9P+kJZOyVRmN6iDl15Ej1EZSFtpacVxYaifadn3ALNajtYnFq5xacevcdUGGZUOOusAwn334DH97ZG188NBTM7ju5iTRanM+6Azmb7kTe4RFoPWA8SCl9b6UMCUW/iTO9wiH8uaBJebJVUGGobiiG6oY6H5tGKCKQpkvDDbobJMfLlZiToiw0EhGKCAzVDcX12utx1n62Vjs5AorL9N9JKPnXipvTODRyuESDwBj7G2PsbwAWO96LrxsZY3czxn5pahtbMgkJCYZNmza1/BOlGXLw4MGQYcOGdYqMjEyJiIjo07Vr1x7PPfdcrNVac0O9fPnyqBEjRnQEgLvvvjspOTm5l0KhSF22bFm05/oWLlzYNiYmJiUiIqLPuHHjkisrK503oBcuXFDefPPNV2m12r7x8fGG5cuX1ztO2BeN5iHm1J3dVbvRTqIkmkWhRpnhqYC9Jb++txjMJu0NqijwjqF1hZQqDP7Hs+j02XC36ad29haqRRTkgRQKMLsdYTHt0G/iTGfSXKB41mv19CzL7bfneKl6r5Il5pQ6tOr7BqboJwIAVhlXBWyzEkqoSV2vUmotEQYGDTTQKDRBD3UhEG7RCVWomqJ275X2XTZnHCFMq4yrAnra1FxhjM1vahs4HH85dOhQyODBg68eP358wYEDBw4nJSVZMjMzQ+bPnx9fUlKijImJsQHA1q1b9cOGDTMCQO/evU133XVX0bx589p7rm/9+vWtli1b1m7btm1Hk5KSLMOHD+/82GOPxb/99tvnAODBBx9M1Gg0LD8/P/OXX37RjR07tnP//v1N/fv3b5B/yrIuHSI6Q0S5tb0awiiOO2X2MhxL6I/tvSagNDQSDEBpaCS295qAhC7/Cnh95nLPktD+QUo1Bj/yb0mB22nIcIx9dxvuW38Qkz8/gPvWH8TYd7fVWQw7CNSzLDVeytN8LKE/fjBMgkUbD3jEWDvwJbxCKdQrgYpAuEl3E9R0ZT6GqkY1BoYO9DuxTAUVDGpDrclct+huQfeQ7s7v1qA2BMNcTguDQLhafTX+tPzpVgYww5SBI9VSZYE5zZLly6MQH2+AQpGK+HgDguz1Gz16dMe8vDzNXXfd1UWn0/WdP39+LABkZGSE9e3bt3tERESfbt269fjqq6+cHuQBAwZ0mzlzZnzfvn2763S6vjfeeGPn/Px85ciRIzuGh4f37dWr19VHjx51dvciotQXXnihbfv27Q2RkZEpDz30UHubzXeVnWXLlkX369ev+9SpUztERET0ad++veG7774LW7ZsWXRcXFzvqKiolDfeeMPpQS0sLFTecccdyZGRkSnx8fGGJ554op1jG1arFdOmTWsfGRmZ0r59e8OGDRvcmgV4esgfffTR+FGjRnWUsquwsFA5fvz4pDZt2vRu27Zt75kzZ8a7entdefrpp+P79etXvnLlyrNJSUkWAEhJSalOT08/7RDDNpsNP/30U6s77rijFACeeuqpS6NGjSoLCQnxevT8wQcfRN99990F/fv3r2rTpo1t3rx559etWxcDAKWlpYpvvvkm8qWXXjqn1+vtQ4cOLU9LSzOuWrXKy8scLHx5iCe5vL8GQkzTMgiJAkkQyqKtlliOE2QcsbSOkmiu0wPl1M4tfo91eHpJoUDXm8fi2mkt05kh62nuOUvoLSWDrxjm67XXS6+zhdXEdeCoIVxfMkwZfscSK6FEliWrVgF93nIewJXV2Y3jDQOTrDzj6LrYUr3EVxTLl0dhzpwkVIndpfLyNJgzJwkAMH16UTA2sWnTptMJCQnhb731Vvbo0aPLAOD06dPqO++8s8u77757euzYscYvv/yy1aRJk646fPjwH/Hx8VZxuaitW7cej4uLsw4YMKD7tddee/WSJUty1q9ff3r8+PHJ8+bNi//iiy+yHdtJT09vvX///sOlpaXKm2++uevSpUurHn30UZ/JkAcPHgy77777Lr377rtnHn300fjJkyd3uummm4ynT5/O2rp1a8S999571eTJk4v1er39wQcf7FBaWqo8depU1sWLF1VDhw7t2q5dO8ucOXMKXnvttTbfffedfu/evYcjIiLsI0eOvKqux2vChAnJbdu2tZ48efKPsrIyxa233trl9ddfN//rX//y2pddu3a1euaZZ3zGEe7YsSOsQ4cO1e3atas1KeHYsWPakSNHljg+DxgwoLKwsFCVn5+vPHnypEapVKJ3797OxKDevXubfvrppwYLhZH1EDPGfnC8IJSGuZUx9l/G2LeMsf8CGA7ggYYyjFODXCxtXTKt96z6j99jdVGx+OuslzD58wNuYngNhKxHhfh3TcBWND7BimEGappByK2zMWMc5cRkoOW/glUuLJDELEcCZG0COsuShe9M3zVbMXw5xLS2FOTOgeZ6bnA8eP75BKcYdlBVpcDzzyc05GZXrlwZfcMNNxgnTJhgVCqVuOOOO0p79epVsX79eqdn9e677y7o2bNndXR0tO3GG280JiYmVo8ePbpMrVZj3LhxxX/88YfOdZ3/+te/8mNjY21dunQxT58+/cLnn39eq6c7ISGhetasWYUqlQqTJk0qzs/P17z44ovntVotGzNmTKlarWaHDh0KsVqt2LJlS9TLL798LjIy0t6tWzfzww8/nP/pp59GA8CGDRsiZ8yYcbFz586W2NhY25NPPplfl+Ny5swZ1c6dO/UrVqzIbdWqlT0hIcH6yCOPXPjiiy8k96WkpESZkJDgs07ml19+qb/55pv9egxtMpkUkZGRTk9MVFSUDQCMRqOyrKxMGR4e7ual0ev1tvLy8gZrE+5vDHE8hC48rpQDaNCTmCPgK5b2nTVZODUvA2G5RlQk6tFpURpmTJR/pFxdVuL3disK8rDrrWcBwBn6sAbANAAmcUyO+BkAJuLyorYYZjkGhg5EhinDTRwSCCEUgipWhRCEwAKLW2c2FVS4Wn21s1GBPxU2IhQRMNvNkpU1XON5pbbniRXWZl0poi5d7BqLZGUysuwtu8JES+dyuikRy7id89GMquWSn68JaHqQyMnJ0WzdujUyIiLCKYCtVisNGTLE+U82NjbW+Q9bq9Xa27Rp4xR+Op3ObjKZ3IR8cnKy2fX9hQsXao2Vi4mJcVsnAHTo0MG53ZCQEHtZWZkyLy9PZbFYqEuXLs5tdOzY0bmNCxcuqBMTE53zrrrqqjoVZD9x4oTGarVSu3btUhzTGGMUFxcnWeO1devWtnPnzvncz+3bt+uXL1+e48/2dTqdvaSkxClwi4uLFYAgfCMiImwVFRVux7y0tNRLJAcTfwXxlwC+FOsjngXQAcBT4nROI+DwSLryzposnJuWjnCT8BsLzzHi3LR0vAP4FMWBYLdasGfVf5yCeB5qxLADkzj9chPEgPRx92cZwLeQ9mxVKyW0HYlDnkQoIjBFPwUAsLRYulNlNaoxXT9dcntyMDCvrmuO9tmBVNtwdBn8w/JHsxXYwSTbll2nbnWc4FDXp2XNmGwA2UT0kvg09vIhLs6MvDxv8SsjwIJFhw4dzHfccUfhZ5995pdQ84fs7GyNI7krJydHExsbW/cOQx60a9fOqlKp2PHjxzWpqalVju05ttG2bVtLbm6u8zieOnXKrQC6Vqu1u4rJ/Px8Sa3XqVMni0ajYUVFRQf8KcE2aNCg0k2bNkXOmjVLskNhbm6u6tKlS+pBgwZ5ygRJunbtWpmZmakDUAwAe/bs0UVHR1vj4uJsOp2u2mq1UlZWVojBYKgGgIMHD2q7d+/uqzlOvfC3TtJ0AD8DWA7gNwide34Vp3OaiFPzMqA2uf8G1SYLTs3LcB+3cwu+eGgoPhybgrrg6lWW+28StP8ylwm1hWj4E8LhT6iMnGfMVxdAX8uk6dK8ytndqb8TQ3VD3cqPhSAEBrVB1r4bw2/EzMiZGKobKtsE5nKhzF7GxXATcrX66sstfrgjhJydtk1tSNB59tlzCA11f9wTGmrHs8+eC+ZmYmJiLCdOnHCKxKlTpxZu37699fr161tZrVaYTCb66quvIk6ePFnnDOhXX3017tKlS8oTJ06oly9f3vbOO+8MSgw0AKhUKtx2223Fc+fOTSguLlYcO3ZM89Zbb8XeddddhQAwZsyY4nfffbftyZMn1ZcuXVK+/PLLca7L9+jRw/TZZ59FVVdX086dO3Vbt26VrDOalJRkGTRokHHatGkdioqKFP/f3pnHx1WV///9zKRJm6XbFEKb2iRQSoXSBaqiICBbEdn8gmBpWVU2KxV+KAjKJiAuCCgCVgGFFhAEUTYXUBAQVEB2W6hkgUJDG0qXpE2azPn9ce5N7tzcO3NnkkyWed6v121mzj3n3HPPTOd+7nOf8zydnZ289tprJQ899FB5UP0rr7zy3RdeeKH8tNNOm9zY2FgE8Oqrr5YcccQRtWvXro3fd999Y/bZZ58NsVi3tNyyZYu0traKMUa2bt0qra2t4i4OPPHEE5vvvPPOCc8///zINWvWxK+88sqJxxxzzFqA0aNHJ+fNm/fhBRdcMGnDhg2xP//5z2WPPvro2FNOOaXf0oVHEsTGmC3GmPONMTsYY0Y5f8/PkMZS6WfKGoPddLzlb/39If5x06U2nFqalHI7zTsm0jHjwK7LXuHrNddycexSvl5zLbsue4V+c+opYKaXTA8UqN6Lfy7+5enahAn16SXTOW3saSwet5jF4xZz+rjT2a98v4zjCzqHMCE9r3ReYAQJQYZtjGOl99R31g/0EPoUY0yDMeZJY8wVAz2WPuf00z/gmmsamDixHRGYOLGda65p6KsFdS7f+MY3Vl999dUTKyoqZl900UWVU6dO3Xr33XevvOqqqyYmEonZVVVVM3/0ox9VJpPJnBdPfO5zn/twzpw5O8+dO3eXAw44YP3Xv/71Ps0u+Mtf/rKxtLQ0uf322++69957Tz/qqKM+WLx48VqAc845Z82+++67Yffdd99l9uzZOx9++OHrvG2vuuqqVQ0NDSXjxo2bfdFFF0064ogjQuf37rvvrm9vb5ePfvSjM8aOHTv76KOP3iHMLWKXXXZp+/vf//7fxsbG4pkzZ86oqKiYfdRRR+2w++67t4wdO7bzj3/845hDDjkkRZjsvffe08rKynb7z3/+U3buuedWl5WV7fbII49UABx99NEbFi1atPqggw7aqba2dubkyZPbr7766nfdtjfffHPD5s2bY5WVlbNOPPHE7X/4wx829lfINQAxmfLuuhVFDgS+CGxrjDlMROYCo40xf+2vwfUlZWVlpqWlpVd9XHrppV2vL744MCNnXvlGzbWUN6SK4rLJjSRmv0Ss2HkCJRIohCUWwxhDWWK7rljBd5746cCQbMXlY5j/6ycBmLnsFQ479QGKPZbp9tIRPLDkMF7uIzcNJTuiuF/0RZu+JN3xV735Q8a8+n3KNjfTMirB+hnnsXHKYV31XV/nkTKSNtPWwy1jcmwya81ajR9cQCwetzindiLSaowp6+PhZHN8Ab4MzAcmGGNmisjewHbGmLsHalxReOmll+pnzZpVkCmmRWT3V1555dUZM2bk5Ls7HNm6dSvbbrvtrJUrV76aSCTy7v/+0ksvTZg1a1ZNb/qI9CxTRL4GLAZ+CRztFG/GhmEbVs5bQ4ntr9if1V/5AxWJtxi3y6vES63BXrz3vCE3PMYYTvztSylln/jS+Tx1/XdSknZIvIhPfOn8rvfzLnwsRQwDFLduZd6Fj0FUQdzUDHWroK0dSoqhtgoqsw8tuAzru9wITAGuYHj6MWciVz/ngXzMHHr8umVUvXAJdFoXtPLNzZS/cAkUTWJ67Sk9qqcT1pn8pl2rddB+V3SXUEI77QXhCz1UGeIL6i4DDgSuxbokgl2ncw0wqAWxonh5//33i84///x3B0IM9xVRnfu+DuxvjKkXkfOcsuXATv0yKiUSZxwwiUe/3Mk7jS8g8ey+g2WJ7XqUuQvnXlj2E1qaV6dYj13K07hp1BBBnDY1wxsN4KaHbmu37yErUbwMeLSpmcfrVjGlrZ3GkmIudYR1IYriYcNLF3aJ4S46W215bc9PNp2wd/ctb1veI+qH160kaJ/r+nHL+ltoS6oRaLAyDBbUnQTMMcasFZEbnbI6YPuBG5KSK8cdd9yU+++/v8eF7Mgjj2y+4447hnUis6qqqo7zzjtvzUCPozdEFcQVwNvOa9dUMgLo15WhSgbqVvHhhr9lLYbjJSPZbcFZgfu23/tzodnllgGbpozp4aYBsGHKmK6FdaGh2JqaYXldz46TSWsxzkIQ/7OpmevfaKDMEdY1be1c/0YD3wIW5GBtVgYJrSHXjLDyCESJ+hG2L11UDo0sMbAM9bTNDnG6Q5q619ZyeoY5VQYRxpjng8od0Tushe9wJupKlb8D5/vKzgL+1rfDUcIISoZh2tpp2ZjtgkvDitMvZse9P4cAgv31zZRcw40//Kcr9qe9NNXfvqN0BI9esX9KmRuKrQvXMhxGW/C9VVgSkHPqVnWJYZeyZJJz6nJfrDwUE44MO0qnZFcekXRRPdLtixqRIwhvVA4/biKUdHXC2HXErmkTqWRKhT0cyCbBziDnYeDHIlICXT7F3wUeGNBRKUoBElUQfw34vIjUAxUisgL4AnBOfw1M6cYVow1YE0IDcDywNr6V4pLs1oOUjdrK5Xt/LiXNQQtwAukFoBt/+JUFu/LAksP4sHoMRmBT9Rh+v+QwZhwwibpnX6bzieeoe/Zl5jc1p94m163qdpMIoqRnaMqg8z4VOBOYEiKgw8ozETbHZ2bRvgYV071m1hUQL00ti5fa8gEgSkSOoNByRRSxz6h9AqNmFFHEQaUHsXjcYk4be1qPkHbxDDFb9ivfj4NKDwoVxTFiwzrUXQklmSsNHc7BJr5aD4zBWoargfPSNVIUpe+J9KtpjHlPRD4GfAz7n/Vt4F/GmMGbPmoYEZQMwwCs/xmdW6N/BLFYko/sZh/xzm9q5kqP/+0FtVUsrExwIdb/1z2u6xPste2+smBXXvEsoFvQ1MzPfe4Lv3CswTWVCetP3NYGYVatWMwurItw3q3YINjfLCmmJkD8tpYUExhA0cG7EG+RMwflbe3sU1LMEbVV3OlxtzDOsW7EfukD/aKbmtlUt4r5be3s6czjnZWJYZu9r99x/YRfutC6SZROsWI4wH84H0Rxt0hXZ3rJdCa1TcrY3m/pvGndTYEZCF2L9PSS6fyp9U+BY26jjXml8zImYskXfZ0Bcd/Sffusr4HGGLMBOFJEKrE/tW8bY3JKw6soSu+IbEYwNj7bv4B/ichngL2wrhRKPxPmkPTuwy/T0emLKuFg6Ck/k0n4zZRjmN/UzC3L63BtUjVt7dzi+PbeWZngZKftUU3N/HtlIxM6rI/y2nicxTtOSRGNAJeHuC9cWbeKWqe/YzvXUBTvGWfemE6enlbLXpWJHtEnPlVbRUOIP/AFtVX8wiPCATpiMcoDhLWLN+30/KZmvudpP9kj4v3nByF+0Y4bSHnAjcCdzs2FCuIcqF0wYAI4iCgROaIs7suGfUv3TbsQEAhN710Rq0g5ZpQshX2NX/iHZV0EQsPnhdUd6m4SIhL0ZHaNs3XtV4OTouSXSC4TIvKEiOzpvD4PuAu4U0Qu6M/BKZYw78m/3DOPZFuwv2DYw9SqvzzPdW824vdcHAnctKIegK1YMXzrinq26ejs8jXeprOTW5bXMb8p1W85k/vCViC2/qeQ9OVxSW7GrLuE/SsTPOX6GLt9OeLSfyyXOysTfGVaNfUlxSSB+pJiiqZVp12Y57U4X5lGxIfRwy86wA3E24eurFBypS+Tsnh9pOeVzss5yYnrolERqwh1W6iIVQT696YT46eNPY2DSg/KGD7NdUMZBnRgfxbDNne/oih5JKqFeAbwrPP6K8C+WF+np4Er+35YipcrgIUB5WbkB8SKe/5uGhNsNQZINK9mQmdwVIoKY5jf1MydlQmurFtFSUAM45HAdW82plhRG0PcFwRIPvEcSUDGXeYMrhOIQedq2PAzGmU57UB1GnEZZLEFK4rdfdVAPaSNcewVqLn6IDdAV3i5jrb2QGnh9hHDWqWj2DpzjamssZiHL5ksy1HcOTK18VNEERVUsI51Pcq9gjxTKDs/6azZ/nP1WrRdd4thElHCpXagB6AMPqZOnbrLtdde23jooYcOvJ9TgRLVVBADjIjsgM1u919jzNtAYH5spW9ZALiScH5Tc9fitQlzXiDw4VsamgPiD7sIdFk30wnDCZ2dKZbbC2qraIn1HIhrWY4DImJVusTBbIUNP6Ol/UkucBZLVYUcr7qtPWWhXhgNwFlNzXQur0uxMrctr+O4pmaKIOWBbGPAIr505d5zchfeZeqjE3sjM4HuRXZBi+/CFvQJPRfoedtPAE6h56JDXdBXOKSLkBGlzbzSeT2s0CeMOyGwPFNKbn8dL9mkGPeO76xxZw2niBJAV2rmjNtAj3MoU1VVtev9998/pDK2rFy58rVsxfDf/va30n322WdqRUXF7DFjxszeddddP3rdddelWJDOP//87RYtWlS1ZcsWOfjgg7evqqraVUR2f/DBB1PmJ5lMcsYZZ1SNHTt29tixY2effvrpk5MeI9WKFSuKP/GJT0wbNWrUnNra2l2G2vxGIaqF+CngemAi8DsARxwXZNrGgeA6bCKK699ooOm/T3PfP+4jVhRs6RWBrUUjGNGRaj3eGi/ijwvO4rR4nPKO4LZT2tqpe/blNEGdrFBbtryOZcvr6ARumjiBr0yr5rblddG+ULFiOsacxVdqj+dOx1c0nZVZsP65y5bXcd2bjYF+zADfebOxx/r8EnpatCHYB7klFuOCND7IkCqqo/bRjBWqT2NTPbqfSgNwMjCakEWTTp2FTts96faBdvv147p1+K3EfkvyIdh4T2pZLmzCrNC99ZsOqgvZWbMLARG5HYKdp40xJ+R5OMogZevWrYwYkeoe+eijj5Ydfvjh084+++z37rrrrvrKysqOp59+uvR73/vedosXL+66PPzlL38Zc+WVV64C+NSnPrXp7LPPfn/hwoU9Er9cffXVEx555JFxzz///GuxWIwDDjhg2o9+9KO2b37zm2sAjj322O3nzp276bHHHnvzt7/97Zjjjz9+hxUrVrw6adKkYROMPap98STgQ+Bl4BKnbDpWpyl54JN/f4jPnn80v732JJ780y8yxh+++auXsbFiLAb7a7uhfAw3L/ouf9n7c1wwdUra5Ss1be1pBTF0C9Ui4KvvreXL763JECwqlVh82y4xDOFWZv8xt+ns7PItjuOzmIe4gkzo7OwREi7IB/kr06pD3TOCyKYPNzqG9xZlflMzbzz7Mu9HsIDfiM2d7hfOQfh9l4Ms0DeilmUlv+RizS4AVgL/82wtwGeBDwZyUP3LTeNh0q4Q293+vWl8X/Z+5JFH1r733nvFX/ziF3csLS2d8+1vf7sS4LHHHiubM2fO9IqKitk77bTTzl4L6cc//vGdzjrrrElz5syZXlpaOme//fabunr16vjhhx9eW15ePmfGjBkfXbFiRdcjQRHZ/fLLL9928uTJu44bN27WaaedNrkz5Prj8tprr5Xsscce08aOHTt73Lhxsw4//PDatWvXdl02vVbtc845Z9LBBx+8/RFHHFFbXl4+56c//ekEf3/nnXfe5KOOOqr5iiuuWD1x4sSOWCzGpz/96daHH374LbfOmjVr4nV1dSP333//TSNHjjQXXXTR+/PmzdsUC7jW3nHHHRMWLVq0eocddthaW1u7ddGiRU3Lli1LALz88sslr7/+eukPf/jDd8vLy81JJ5304bRp0zYvXbp0WHkJRA271gxc4Ct7qF9GpPTgrb8/xD9uupTOti0RWwjP7v05anfaIyW02puO5fL6ygTT1m/kzPfWptwRJYl+h5R6NPjM+k0ZRbQXv7uBKyLd8bqCO4iyZJI7HNcOv4U2DNf67I8EkY0ADiLXPuY3NaeM3T+uIKKmYPEvwgwKX+cnzLKsKEr/YYy51F8mIjcDFw/AcPLATePh7GrY4lxq3iu27wFO75ObgPvvv7+uqqqq/Gc/+1n9kUceuRGgrq5uxFFHHbXjz3/+87qjjz56/R/+8IfRCxcu3OH111/vsnDef//94x955JE3t9tuu46Pf/zj0/fYY4+PXnvttQ333ntv3THHHFNz4YUXTvrtb39b7x7ngQceGPv888+/vmHDhviBBx447brrrttyzjnnhD41N8Zw3nnnrT744IM3rlu3Ln744Yfv8M1vfnPSLbfc8nZQ/UcffXTsrbfe+tZ9991Xt3nz5pTL4caNG2Mvvvhi+SWXXPJuurn43e9+N3rPPffcUFSUWeqtXLly5G677da18n333Xdv/fa3vz0K4MUXXxw1efLktnHjxnVdbHfZZZfNr732WvaZhQYxofpHRC70vL4sbMvPMAubF5b9JAsxDFNn7s1JjuCqcRZ+1bS18+M3GqCpmfHA16bVsHB6bYp1M4woEUSzEcNbgAfHjw602tbuMZPGkuKM/SXb2gMjRUQZmz+ahNfKvPWJ5yL5LPeWXKJcRKWBVL/lqNEu3AWDfWUpziVZSaY2mgBFKQBeBIZFOI2eXFbVLYZdtsRsef/xy1/+MrHvvvuuP/bYY9fH43E+//nPb5gxY0bLvffeO8atM3/+/LW77LJLWyKR6Nxvv/3WT5kype3II4/cOGLECL7whS+se/XVV1MyBn3jG99YXVlZ2bnjjju2n3766U333HNPWkv3jBkz2j7/+c9vGDVqlJk0aVLH4sWLm5555plQP9zZs2e3HH/88R/G43HKy8tTLsNr166NJ5NJJk+enDYaycMPPzz24IMPXp9+diytra3xcePGdZm5x40b19na2hpLJpNs3LgxVlFRkWICHzNmTOemTZuyeTA86El32zDZ8/oj/T0QJZyW5mhx2kViTJuxD3vss5BZAYKrKJm0ERgqE4GJOa6sWxXox7u2KE5LPJ7RcuvHeP66bZLAilElKdZpv3U0Sra5JHbBXa64x/BbaoPGBPSYq95alnOJcpEANtPT2iv0vGlpxvoeB0UnSYe7oM/1WXb9jt1f+g+I5nN8JnATqb7QmZKVeONEe9s8jfV3biD1XKP0qSi9RUQWYd0GdwXuNMacFFLvJOBm7H9Tl0ONMY+n6Xs/X1Ep8EXg9ZwHPKhZHWJ5CSvvGxoaGoofeeSRcRUVFV0CuKOjQ/bee++uRWyVlZVdvrCjRo1KbrPNNl1is7S0NNna2poi5Gtqatq9r5uamtLmTF+1alXRaaedNuVf//pXeWtrazyZTDJ69OhQP4tJkyaFXgwmTJjQGYvFeOedd0bMmTMn0FrW2dnJU089NfqGG24ItED7KS0t7fzwww+7zvHDDz+Ml5aWJmOxGBUVFUm/+N2wYUOsvLw8vZ/IECNUEBtjzvC8Pjk/w1GCKEtsR8va90L3x4uK+dT+J7L9Tp+0BcZQHias2tr5679eYdfNbV0i1RV/t1aO5+SmD1KEtHH+eXD86B77MtHgiEev4IwDMz3H7jpHT4i1sAV23jH1NjGt67KRzspclkxy+/I6YpAyV8uW17F0eV2gOA660XCP4y0LO0e/K4m3v3Ulxfy+toovVyYwIcfqrVCH1Ax9Ll5buVc03xDQfpmvrUsrcKLTNkhUh2Um9Aprv/BXVw8lD7wLXA7MA0ZlqPuMMWavLPq+2fe+BWshnp9FH0OI7dqtm0RQef/xkY98pP3zn/9881133dVn0Tvq6+uL586duwWs4K6srExrrT377LOrRMS8/PLLr2233Xadt99++9hzzz03LM2AjcwUQkVFRXL27Nmb7rnnnnGHHXZYYGSKJ554oqyqqqot6qK3qVOnbnnhhRdKP/OZz7QCPP/886OmTp26GWD27Nmb33nnnZJ169bFXLeJ1157rfSYY44ZVr7u6Vwmto+y5XOwhcpuC84K3ScSSxXDtjBtf7uGCNJDP9jAV6ZVs6Yo3iU83IVsZ763NisxnMSK6CDBGTY61zoatMDOeLZs3DPCxuZG08hkjY4HHE+gyw3FXeA3v6mZ95/+D8uW16W4qdyyvI5bV9SnlP3ijQYeHD+6xzn6I1TM97m9JNraOfaNBr7oHM/vEpMukUlfY7BCNchlYXGadp2EL+QLc+3I5LIT1i6Ke0VYGLxM7ZTCwRhznzHmfqK78WfTd61vm2GMWWiMqevrYw0OLloFI30XkpFJW953TJgwYevKlSu7ssd86Utfan700UfH3nvvvaM7OjpobW2VBx98sOJ///tfWqtuOq6++urt1qxZE1+5cuWIm266adujjjoqrTjctGlTvKysLDlhwoTOurq6Eddcc014DNQIXHXVVe/89re/TXznO9+pXL16dRzgmWeeGXXooYduD/CHP/xhzIEHHpjiLrF582ZpbW0VgPb2dmltbRU3tNr8+fObf/azn1XW1dWNqK+vH3H99ddvt2DBgmaAmTNntk2fPr31vPPOm9Ta2iq33Xbb2BUrVoxauHBhasDyIU66NVQrgTedv2Hbm/09QAW23/tzofuMSaaK4QiECcrqtnaue7ORhJOdzkuUxXZe4RLDRp/Ixq2huShO3bMvs3R5HZ3GkPT0KZ6tNxhnbK6I7G1/Zckk173ZyC/eaOjK6udlJPRIcOK9+UgXoSKdn3F/+iBHxWCts66AdKOORFUNrcBpTpsgt4+oTHHGMIHu70gM6y4SFNs57qnnr7MwoOwUT9/uWGsIF8pneuoVOe+DUOE9KCgSkec826mZm6RljoisFZE3ROQ7ItLjYZaIxKJsvRzHIOX0D+CaBpjYbv+HTGy37/tmQZ3LN77xjdVXX331xIqKitkXXXRR5dSpU7fefffdK6+66qqJiURidlVV1cwf/ehHlclkMudLwOc+97kP58yZs/PcuXN3OeCAA9Z//etfTxuG9rLLLnv3lVdeKR09evScz372szsedthhvRKTBx54YMtDDz204oknnhi944477jpmzJjZp512WrXrM/zoo4+OOfzww1ME8dSpU2eUlZXt9v7774846qijdiwrK9vtzTffLAY499xz1xx00EHrZ8+evcusWbN2OeCAA9afe+65a9y2d99991svvvhi2fjx4+dcdNFFk2+//fb/DaeQa2CTbAz0GPJCWVmZaWlp6VUfl17avSD44ovzuAi4qZnfnnd0YKi1sooER5/8w/yNJYRcI1S4eIXvUCMXq3USiO8zt+t9kPvDUsddI6gtBM93Eut2MaWtvesz8btTpHO16C83jOHICOBWUt01ziTYXeQMUt1L/P7SYJ1Hl6DuH/lERFqNMWUR614OTE7jQ7w93fdRuwC/AW43xnzPV897rx+KMWZQL1h66aWX6mfNmlWQuQhEZPdXXnnl1RkzZrQN9FiCePvtt4t22223nZuaml4OCrE2HHnppZcmzJo1q6Y3fRTGTA116lax26f+j3hRT9eryTUzB2BA3RhgSzzeayHbG+uv60rhLxvMeH2Fg9wfbl1Rn7Ztuox6bj9FBLt2hLlaZHLD8Ebj6O8oHEOBrVj3EK+lN0gM45RPoNsaHBRTupXuzIbeuumyHIaRz2gdaum2GGPeMsbUGWOSxphXgMuAowOq1gLbO9vXgCeAg4GPOn//BizKz6iV4ci6devil19++TuFIob7ikhrk5zHPmdiQ8G4Tw8BMMbs3T9DU7poa2f7nT7J+++uZMUrf0vZ9eZ/nyYxaSo7Zuk20VdISTEjATIEJe/XMXheG2BtPE5JMsnoPD39yFbIG7p9mN3oHn73B7+bhUsSuvyM/TGYXXeQILzuFOlcLcL2fcoXtzpK3ORCoBnrihHlm+bePmRa1RO0gHEhPSNsuOUJbChD9/nXSOe9tw9vxJFyoI3UjIm5RusIiwzi9uXPkOhfSJlpf9gxs20zQAQ+PPKmZRaRc4C5xpgPnaI3ROQ54DnC76+UQcpxxx035f777+/xg3jkkUc233HHHVEjYPaamTNnts2cOXNQWq8HM1EX618D7Id9oncF9vfoDOCufhqX4qWkGNraeaf+5R67kh3t/Ovff2DHmfuQbGunOR5nbGcnoSsFSophZDGs35T9OETAK9RiMaitguWDZ/2He/Upz1EMh7k/9KVLhzcd9S/eaGBUFosVBboiXDxVUcqB6zd1idRMY0u3gDDTPn8SF0iNDJKObN0whprbRn/fdoVF2HDf++30mSKWB/3Pd63TJ+CENASmYk2V3m9nGVZwu+H3NhFs6T4BG4XkZsD9ZrnC3D1GAthAqjB3hbv7afvD/GUS4P2BYxAqwrqfx0VkJNBhjOnw1fss8IIxpklEpgPfAe7J0P0YrLfMh56yUqdcGaQYY54PKndEb96Er9K3RBXE/wd80hjTKCKXGmOuE5E/AT+nO5Wz0l/UVsEbDaHpmjvWvQ97zGR77AViflMz173Z2JXKeF1RnPFTp0CQqGhqhjcaIESUGeCDeJzEjk50mLpV0NZuhXVtFVQm2FS3KjzMWxr6y294QmcnDRlCt4URNpb+8m0uSybpILrvkldMT3FcG6LSHI/TUhQPnJd0+wyEpuXOFKUj24x8uWTwU/oO91eggWBLdgvdluh0lu4k4eZN9xjpHG78VnJX9IaF5uvn0HvfJjVz3ELgUhG5BRsveGdjTCOwP/ArESkHmoClwJUZ+v418KiIXAu8jY35f5ZTrihKHokqiEux/1kBNotIqTFmuYjM6adxKV4cIVA2OkHLhoCFdQkbveUK7IXDm04440IdV2TUreqyMCOQ6Ojsss7dVZnothIFiJILaqv4XsQUyl46Cf8C9lYs++MfD2Zi2JBr2Y41a+8wsfNyy/I6/Pk2RyeTvDByFNW+yBvp3DDACmk3fF1zURwMJDq7vzvXvdkY6IZx3ZuNgVbgsOgZtzlPIVQUFyauBTuM/jTJGWMuIdzwU+6pdy5wbpbdfxMbselYYBLwHnA98ItsxzkAJJPJpMRiscG+ZEMZ5jjRQnp9sY8qiP8LfAz4F9a36RIR2QDkL8ZToVOZYLeTz+UfN12aksY5XjKyK06xK3qz9q+rTEBlosvC7Kc6Q/PrKxOspTv5hDe6QU1nJ3T09C/OFJWi09mfiyBeWxTvEk7umNxj9UUc41xIJ/Cb43G+u+MUvlm3iqo+CAUXxoSOTpYurwt8xF9iDPuv3xQYczmMJFZIb9NmP99tPJ+zG4O5JKTthM5OtnGeYHitwGEW5yJQS7ESSmh2g0GOMSaJDel900CPJQdeXbNmzc7bbLPNehXFykCRTCZlzZo1Y4BXe9tXVEG8GKtRAM7BPg2roPtJVkZEZDzWpewgYC3wLWPMHQH1SoCrsHfMo4A7gcXGmK3Z9DMcceMRv7DsJ7Q0r6YssR27LTgrJU7xAnJ/dOhamP2hoK7I0G4KqVZpl2qgPsAlwwC/mDiBeR9sCH1Ef9PECRwasj8dbma9zieeC7Rw3/BGPWe+t3ZARHHYMcd2dnIMcF5tFb9aXhfu/90Hx0933tnEeDLAJpG0Cxf9Vmj/WLy4VuDmoniKsPbXieKzrBQWQubfqMGMiJyMXSNZhTUy3W6MuXVgR5WZjo6OL69evfqXq1evnoFGrFIGjiTwakdHx5d721He4hCLyJ3Y/zRfAmYDDwGfMsa85qt3MXAAcAT2Gv0A8CdjzMXZ9ONnSMchziO5rvpOG1O1qbmH7/GyygT/bGru4WphgD+PKefg2dM5rqmZJVm6Pfgtzy2xGN+aVs1PAN5sxHT2TJ4xKCiK8048zuSQG4CBsmyH0RsLfrpz2YJNWRoWZcPQnRJchbHikutVLJs4xP2BiFyIXWd4NfYBXTVwNrDUGDOUdb6iDDkiC2IRqQFm4vGZAohinRWRMmAdMMMY84ZTdjuwyhhzvq/uc8D3jTH3OO+Pc95/JJt+/BSkIA4QooEL6/qAnEMheca4yRE6P61MdIWY8kYcaC+KMzLEegjhQmtLUZyRSRO6cLAv6Y1wHWyiF2wEADfrmkt/j3NNPM64zs60j69aYjFurRzPoR9sGDLRKJT+oRqoz7HtIBDEdcC+vlBs1cDfjTGZvNUURelDosYh/hZwEfAasNmzywBR3BWmAZ2uiHV4CRvXuMfhSL3eCjBZRMZgA5lH7aew8bsqtLXb99AvojhnVw3HfxnsndZPgD/Q7cvsdcXocsHIMsxbOhENwf69uYo+A0g8nlNc5nyL4UznaID18XhXtBKXqOMMsvZGmddEZycLp9emXRRZlkz2Oi7yV5qauSBDeLcENuLBUAsFVyhEceka5JQBa3xlzVh3QUVR8khUH+L/B+xujHk9x+OUA+t9Zeuxfsh+HgEWi8jfsC4TZznlpVn2g5OX/lSA4uLwzF7DkrpVPS2iyaQtH+QX8rAV443QPXafX3JLLEarSNdCrWyQkmKeqq2ipm4Vk9raebekmE3jRzN9zbrABYFp+wLYywm+8tR/+j9hyZjy3GJKk1mYNpQUU51D6DqXkRMnsP79dRQ7c7DWiUKR6TNqLClOWRTpj3zhkm1cZK+oXVcUp6yjs8vPOZ2gPr6pmRs1FNygo5pBnZQjKn8ElonI+difOPe0/jSgo1KUAiSqI3wzuT+VAhu/fbSvbDSwMaDuFcB/gBeBfwD3Y5/cvp9lPxhjlhhj5hpj5hYVRdX+w4QwIdMLgZMvwlaMd5VXJmBaNZtKikkC9SXFfGVaNYt3nEKLP1VlLAbxNMvFnOQie1UmmLzHTGL7zGXyHjOZPq0mbbswR6MGN6XyG/X5yd6XoxjOhAF+XFuFpEkRHUosBhMnQNMHjHF8tgUoTRoe3nYcnRIuxbcAFZ2ddD7xHFfWreLC2qruOY3AlLb2wBTT/rTUCY8YdvFm7HNpBi4LCQXnr6vkl3qGvBgGm6J5I/ZJ5ybsda8Fm9JZUZQ8ElUlfh1Y4gQPf9+7wwlInok3gCIR2dEY86ZTNgvrgpGCMWYz9kdiEXRZeZ83xnSKSOR+Ch4nu11g+SAnUrSLygTllYkevsv/Afby+01DcPKRojiEJSyBtDcPBmj1xQ5uicX4cW0VP2lqhvfWRjnVQc0x0JUUJrL/tTunAU8oypJJTlyzLjXboZd4nJHJZJeLS01bO0uX17GVnq4W6Vwvli2v69rnhn9rjxjnOSjsW1gouExJSVyKgRF0J7RQes9wca41xmwAThCRk4AJwFonFJuiKHkmqoW4GBvm7F/YG3N3i+TMaYxpAe4DLhORMhHZExtF4nZ/XRGpEpFJYtkDm/7y4mz7KXhqq6ylzoubanmQswAboaIaK3qqCU8usgD7RUw6f/eqTMAeM2Gfufav66M8rbr7ZqCkGKbXwp5z0ruPpLl5aC0pZtG0auo9VupF06r5RGXCisEhjuDcWLhzF4Ux5daqvrwu/GYizAWlpNiKaZ9YFuyPT9T4yEGRL0Zi4yVHodH5zOc3NdPgWJnTtZzf1DNRToLU7+4tWNOfwaYuy9VXfMBWfuWBbM5tGPgNpyAipcAMbLbsPUTkUyLyqQEelqIUHFEtxDcAFwB3kbqoLhvOxF4b3sc+iTzDGPOaiEwhNf3lDsBtwLbY7HjnG2P+nKmfHMc09KlbBi9dCK2NUDoFZl0BtQtSMtDlI8pEX9ObeMqBeBbvRaa2KngBnwjltVUcUJlg38pEz8gaQ8AtJRJt7fD358Mtul5EYENLtLphxxpgDPDE+NEc57hXlDoiOkawRToGXOXzWS4FriPku9vUzIK6VRzX1s5aX0bI79RW8Uhlgg+w36VDgKKmZs5xfJ5bS4opd/7/TiB92mOXaqwQj1I3Uz+HYG9K+9oJKIENJg/2h/0merojjcQKZnduhoHfcBcicgI2M107PResD9V8I4oyJIkUdk1EmoBJxpg8OEX2D8My7FrdMl758TU8dtenWb92DGMmrGf/Lz7JruecbUWx0nuamuHNxm5/4ExuFgDPvjwoBN6QwrXG5zJvYe5BuRCLWXEf0f/bAOuK4nQ6Kavbi+KMNNj23pvQgAQ1XjpiMYqmVXd/r4Lqx2IwrZpllQlOxi6sCMIbAzwoRngxdhVyM3bVcidWmEJ60RnUV28YAdzqO07O4RtzZBCEXVsNHG+M+ctAjUFRFEtUC/GPgPNF5EqTr0weSkZe+ckveWDJPLa2WzGxfu1YHlgyD4p+ya7XqCDuE3K1LGfjd6t0u/JkGVIP6Nubjyw/MwHGe9xAUkL8eUMdBkV98VDkjwCTJkrMAqeOKxzHO7uDxGzO6dwD8PYVlOLdxbUoP+zUc0V3DLrcTxIEW9L7/MnQ4KcdeHygB6EoSnQf4rOAS4BNItLo3fpvaEomHls6u0sMu2xtL+axpbMHZkCKJcxneQgsaBwQ0kSd6Df8/vX9RTKZ3qfai7dOhigxXt/5tc7m+tEHicz6NPuzwe3L9Yf2+kovdcrrsT52br0O52+n89c44y0w4RvGd4Afi8iEgR6IohQ6US3EC/t1FEpOrG8ek1W5kkfCLMth0S62GQdNHxSmVdkYWNkIyTw9fHJdGVz/+sFGwEK9FJ54LjWCygCtEyhAa25/8AZwGXCmdN8YCmCMMWniRSqK0tdkFMQiEscuYtvZGNPW/0NSojJm4gjWv9sRWK4MQjItdBxTkbovilgTyX0h22AimwQo7jnH4/YGIpvzLym20UeamqE9zAt3AHn25Whz0dbe072kn7NRKv3C7dhF5L8h9wXriqL0ARkFsRP/txO72FcF8SBi/x8czgNf+R1bN3cLghGjhP1/cPgAjkpJSzqfZP++J55L31eQlTDHtNFDCmPsuXd0Zn8z0NbevWBtMN5I9NZiPUSyUSpdJICLdG2Oogw8UV0mrgXuFpErgXfwRMYxxrzVD+NSIrDrgl0BeOzCx1jfuJ4xU8aw/xX7d5UrQ5x0yVX2mJla5gqgZ18e/oIYeiccMyxwy5psrPQlxfbzyTIleFa0tXdHOonqRtHUPGRDNA5xbgWOx1qJFUUZQKIK4uudvwf6yg12EbEyQOy6YFcVwMOVoGgVmZKr9NbC6Bd3sdjw82vOZo6K4unFqwgUj4jeZ758lt3jRHGj8Id4U9eLfPJxYJGIXAg0eXcYY/YemCEpSmESSRAbY/K0JFtRlC5ySa6Sje9xLNbTmmxMV7xbKhOZ3TaCiMWgZARsHqQeVtm4lew5J3Nc6cG4MM9LMmkXLYZ9b9KEeFNB3O/8wtkURRlgolqIAXCyylUB7xhj3u6fISmK0kW2cZDDrMqV4+GDDT2FdZDY84qhXJJeDGYxDNm5N0B4xsJs+hpoOjqtJTjou5QhxJvSfxhjfp2pjojcYIw5Mx/jUZRCJpIgFpGJ2LTNn8QmOEqIyLPAF40x7/bj+BRFyYZsrcqZxFAuSUYGsxiG6OfiuqZUJnJLGDLYWF7XfR7xOGw7zt4khaFxswcLC7GZrRVF6UeiWohvBF4CDjHGtIhIGXAlNvW8hjRQlMFENlbldAv33L7APnLvz4Vggw0RWL9x8MYq7i2dnfDe2vR1xo/Oz1iUTAxA5hpFKTyiCuK9gInGmK0Ajij+JrCq30amKEr/E2XhniuwvZEIhnt4N2MyC8b+Ih6HHacMvFV6zTqYVpNaptEoBoIh4pejKEObqIJ4HbAz1krsshPwYV8PSFGUPJKNi4Xf8uyPTgBWTFeUwvpN2Y+lLyJaTK8dOCHZ2yQp+8xNfT/Q1umOTnijHt5fF3zzo9EoFEUZRkSNHvED4FERuUpEzhCRq4C/OOWKogxlKhM2rvE+c+3fqOKmMmGjUbjuFSXF9v3s6TBxQvbj6Ivwbisbe99HrkQRw0VpolT6UzbXVtmbhIHkvbXpnwQkk/DmAM55HhCRRSLynIi0icivMtQ9W0RWi8h6EblFREr6Ygh90IeiKBmIGnbtFyLyP+A4YCbwLjDfGPPX/hycoiiDnDB/5Wk13Y/b/Y/Z+zMxxWD3c043PnfRW1EcOrNMST2QdHYGh+eLx62U6+gc6u4V7wKXA/OAUWGVRGQecD6wn9Pmd8ClTllvWNrL9oqiRCBy2DVH/KoAVhQlO6K6WoiEWyOHu8+yl8Eu6qPi/byGsHuFMeY+ABGZC0xOU/VE4GZjzGtO/e8Cy8ggiEXkFGA+MAkrpO8CbnHTORtjzujtOSiKkpmoYdeKgZOA2UC5d58x5oQ+H5WiKMOXML9lgBX1wZbRHafYv9mGgFMGD657xeASxEUi4jVvLzHGLMmxr12A33vevwRUikjCGNMc1EBEfgAcAVwLNABTgHOxa3S+meM4FEXJgagW4l8Ds4AH8KWXVBRFyZp0oeG8Id7ciAveugO92EzJnU5fgpAoUSv6N7JFhzFmbuZqkSgH1nveu68rsPH7gzgJ2M0Y845bICIPAS+gglhR8kpUQXwwUGuM+bAfx6IoSqGTKYayuz9TOmVl8LK8zt70bDMOmj7otvi3tXf7UXufGnifCgxu14tNgDd4s/t6Y5o2GwP2bwTSZExRFKU/iLqEuRHoi9WyiqIovccbJ9lPPK5Z1gY7HU5ikDD3F6849tdxU4sPPl7DPkl1mQU0hblLOFwL3CciB4rIR0XkIOAe4BoR2d7d+m/IiqK4RLUQ3wb8XkSuw+cyoZEmFEXJO5UJm0nOnzwjFsu/v/HECQOXxKNQyePTAREpwl4r40BcREZiXS06fFVvA34lIsuA94BvA7/K0P11zt/P+Mr3B37ivDbOsRVF6UeiCuJFzt8rfeUG0LtXRVHyz7QaGFOR3r80H/7G763tu1Bp4oScHSoh1waSZ1/OVyi3bwMXe94vBC4VkVuA14GdjTGNxpg/Oovk/oYNz3avr10PjDEDHGhaURSXqHGIa/t7IIqiKFmTzufYm3I6KMxbTPouxFm6fmICyYgCV4VwdPLkT2yMuQS4JGS3P+rSj4Ef99tgFEXpNyLHIVYURRmSpAvzFiSUK8fDBxts3XjcitTeuF5EFcNK9gzOUG6REZEpWCvyHHqK62kDMihFKVBUECuKMvxJZ0mOEtIrKBPbUMYv/Icy/lBuQ4t7gOXARcDmAR6LohQ0KogVRSlcMoV5cykpHvrC0UtFKaxuHj4uGnWrhqogng580hij2WYUZYBRh35FUZRM1FZZq+pwYf2m4SOGYSjfrDwA7DPQg1AURS3EiqIomQnyQx4/eni4HAwHhm7c6bOAf4jI/+gZ0vSUgRmSohQmKogVRVGiEOZeMdz8iwczY8phY2vPhZDpErUMbm4FOoH/oj7EijKgqCBWFEXpDWH+xe4iPb9V2ZuuWMmOja2piwHTLYQcGuwHTDLGpEvvrChKHlBBrCiK0htqq4LDt7lCzS/WvMlEiuI9YxiLDC//3r4kmbRieI+ZAz2SvuJlIAGoIFaUAUYFsaIoSm8Ii3OcKWGIS1Nzz7b5yLA3VBle8/JX4M8icis9fYhvGZghKUphooJYURSlt0QN35ZNW7/V2cW/oM8V0Ssb+y7z3mBm6C6gC2IvYBVwkK/cACqIFSWPqCBWFEUZbGRrdXYJE9HDhaG9gK4HxpjPDPQYFEWxqCBWFEUZjGRrdXbrvtlos7f1hr88Ar+8Ad5vgm0r4ctnwoGf7V2fvaUoDlOnDOUFdIGISAI4BNjOGPNDEZkExIwx7wzw0BSloFBBrCiKMlxwRbTXL9mPK3abVluLazIJldt1i95rroLf39tdv2k1fP+79nW2oth7LD8jR8H/+1b0PoehO4iI7APcCzwH7An8ENgROBc4bACHpigFR95SL4nIeBH5nYi0iEiDiBwXUk9E5HIRWSUi60XkcRHZxbP/cRHZIiKbnG1Fvs5BURSlz1m2DGpqrDitqbHv05VHoTIRHInhL49YcesKVNe9omk1XHER7PuxVDHs0rEVfnp1z76OPcy22e8TqX+PPcwK66suCxbDAFs2w5WX2H6isqLeiv3hw7XAscaYg4EOp+yfwMcHbESKUqDkMxfpz4B2oBJYANzoFboevgCcAnwaGA88A9zuq7PIGFPubDv145gVRSl0ogjTdHUy7Tv1VGhosKHWGhrs+zPPDC7PJJbdchEoKrLi9LN727/7fsyK3o6tuc3DhvWpYjdIWHsF9u/vhc6O4L5cTNJakKNijF08OHyoMcY85rx2Y+21o09vFSXv5OU/nYiUAUcBM4wxm4CnROQPwPHA+b7qtcBTxpi3nLZLgbPzMU5FUZQUXMHa2mrfu8IUYMGCzHUgffsLL+ze59LaCjfd1DMWcWurrQ9w8smwdWt3nyefDE8/Db/+dXd/rh/x5j5OgOaK3b7i/SZrJf7p1VZ0B+F16RherhOvi8g8Y8yfPGUHAK8M1IAUpVARk4cA8CIyB/iHMWaUp+xcYB9jzGG+utXA74AvAnXAFcA0Y8yRzv7HgV0AAVYAFxpjHg857qnAqQDFxcW7t7W19eo8Lr300q7XF198ca/6UhRlkLBsmRWajY0wZQpccUW32K2psYLTT3U11NdnrgPB+xIJWLvWWniz/Q0ebok7RACx1uJM7PYx+PENsM/cPjq0tBpjyvqks9yOvwfwIPAQcAxwG9Z3+AhjzL8HalyKUojky2WiHPDf+q8HKgLqvgc8iRW7m7EuFF4L8XnA9kAVsAR4QER2CDqoMWaJMWauMWZuUZE+gVKUgiKKq8OZZ8Lxx4e7JjSGPJ5vaLBtw8SwWyesfXNz7sJ2OIlhsOcTRQwDvPBvuPb7/TuePGKMeRaYCbyGjTtcB3xcxbCi5J98CeJNwGhf2WiC01VeDHwM+AgwErgU+KuIlAIYY/5pjNlojGkzxvwaeBobskZRlEJi2TKYMMEKSxH7etmy7vKFC1OF7sKFtl4sZsXssmXhrgmLF9vXU6aEH//GG8PFsMtwE6+DgQd+N9Aj6DNE5FxjzLvGmB8YY75qjLnKGPOOiJwz0GNTlEIjX4L4DaBIRHb0lM3C3hX7mQX8xhjzjjGmwxjzK2AcsHNI3wbrPqEoSi5kG82gN9EPch2Lv/yAA6zAbfZEHGhuhhNOgFNOSS33Y4wVs6efHi5YXQvupk1QPKwyow19ehtjeXBxUUj5t/M6CkVR8iOIjTEtwH3AZSJSJiJ7AkfQM3oEwL+BL4hIpYjEROR4YASwUkTGisg8ERkpIkUisgDYG/hTQD+KMvTJRXxm0yYsykFYm6D6xx9vxWM2ERjCyk45padV94ADeh7zscd6HgdslIP2gNi7QWzalLlOc3P0/pT8EI8P9Ah6jYjsJyL7AXER+Yz73tm+TPDTU0VR+hNjTF42bAi1+4EWoBE4zimfgnWpmOK8H4kN0fYesAF4ATjY2bcNVjBvBD4EngUOjHL80tJS01suueSSrk1R+p2lS40pLTXG8bI0YN8vXdr7NkuXGlNdnVrPu1VXB/efrk3Qsc44o2edeNyY4uLUMv973XQL2844o5f/sboBWozJzzXQu2F9heuATs/rOuAt4B/A4QMxLt10K+QtL1EmBgNlZWWmpaWlV31olAml16SLaOAnSoQDf5+xWPAjZTeqgVvfGwosjOrq1PEtW2YttpmoroZDDoGf/7w7Lq2i9JZ43H5vb8gibnEGBkGUiduMMScM1PEVRekmn4k5FGXwkg/XhKCFXumSLaSLXuDt1+tOEOZf2dxsjx+LwYknZhbD7nHcJBEVFdHEsNvuxhtVDCt9R3U1dHT0qRgeDKgYVpTBgwpiZXiRq7AN86P19+eG2hJJH67L227ChPCFXm6yhaAxhOH1oQxK7BBGc3N60RxEa6sVt1H8bZXciKX5GR4xAsrL8zeWwUpDQ98v4IyIiIwXkd+JSIuINIjIcSH1ThKRThHZ5Nn2ze9oFUXJFQ3OqwxOMrkWBO2H9BnD3Prjx9v3H3xg227aFJwtbPFim+XL29+NN3bX8bsbeTOJeceRLuKB2+/ixdGFbWenFVFTpmQO+5VvhlvSiP5mxIjujHNB3Hqr/d5PmJD5ewTDe/6DsgTmh59h0ylXArOBh0TkJWNMUJSkZ4wxe+VzcIqi9A3qQ5wF6kOcI+nEbRRhC1BaCkuW2HZBPrClpTBqVLBoSCRShW1/U109+ITqUCYWK2z3i0QCZs8Oj6xRaPh96HtBJh9iESkD1gEzjDFvOGW3A6uMMef76p4EfFkFsaIMTdRCrPQvfvHqt9oG7Rs1Kthi61pfTzyx52P/1tZwwRvFstZXxOPh2cmU7MVtIpHd55eLeC4rg17eLPcrzc0qhr307c1mkYg853m/xBizxPN+GtDpimGHl4B9QvqbIyJrgQ+wYUW/Z4zp6MsBK4rSP6gPsdI3hPnuBvm4uuI2bF+YAHIFcz4C85eWWjGWLZ2d6bObFTIi2YvVLVuyq5+LJbmlBUaOzL5dtojmD+oz0sW9zo4OY8xcz7bEt78cWO8rWw9UBPT1d2AGsC1wFDAf+EZvB6goSn5QQayk0leL0hYutH6P6SIlZGvpicezd3vIRdhWV1v3jOuuy17EuKHKchE/5eU9280nNVrp/IB2uYzxjDNyE/y9IRf3rHxZbrMV3rlQIO5pecNNCnPmmf15lE3AaF/ZaAISZxhj3jLG1BljksaYV4DLgKP7c3CKovQdKoiHM7mk5M0ma5lLWKQDN/1tkKjLJOL8bU4akWoZDhOK3vK34/CnE62wLS2Nfryn3oHqp62/8umn9xyr+z5oDIccEt4u3bhF4KabbDs3isQCgV8ANdj/qTXY915RXFwM++0X7RhebrzRLiqMWj/b/nOtn03d4zLUjdpXpnqZ9ldXp6/j3dcYi95/tvNciBhj/9/0X/SJN7BuFTt6ymYBQQvq/BhAHwsoylBhoDOD5GsbNpnqnjzDmLfjxnRi/z4ZkrVp6VJjThphTB22bh32fbosZ9XVxswntc18wrOWuUBwO7d8k+/j2OTb728X1GZrsTFfS6Tv86ch7cxSe96JRPAxg9ptontu3axuIsZ8pcyYxpht2xnQ5muJ1M/AbbdAjHkfY5Jp5sKfZa4u5OtcR3edWCw1y1um+fZvfVHfnYu6gHZh/R+X5ViifmZu3Uxz7W4/Dfkc3XpfS2SeH7PUmBYJH0um73/U73Km/zf+8q3O3/edzTt3/jr+8vUY0+HM4VZnX9DnFTSGdFsubaJsmX6jQiBCpjrgLuBOoAzYE+sysUtAvc8Clc7r6cCrwMWZ+tdNN90GxzbgA8jXNiwE8ZNnpBduXn5RnFmw+ZmPMZt9bTZjy9OxQHq2S9J9IQ76SOoIFgNJjGkJadMYM2ZhzF6gg/b7BZC7bUwYY5YaY6ptHX+9sHZvx512ie56YXW957V0qZ3n9z1t0rWrw4pmV7C7m//zc7dOTKjICJub9aQKkT86dcPGlST1BiVT/97v2HysgErX//v0FEZh35X1Ad+TdHPj/y7659o7b2H91GFvTjaXpZ+f47Dfq7A6YfPlis2w+Uk3rrD/p39M0y7K9z3ddzTpfA7pbkiSznmFifHOgGOku/nKZhMxuRBREI8H7gdagEbgOKd8CtalYorz/kdAk1PvLazLxIjMH4huuuk2GDYNu5YFAx527Z0imBywoOydOEz2LGR+6kz41I3BDjH1QE3IZ75GYJugcmCbNN+TsHYQ/tAw6WxBcU7StdmMtdNkQxLYIlCa5Xc9iY0+ms16qyRwUhyWdEZvZwA3rOp1wARPedBn2Imdn0bgQeDLEcbon9OoD3O3OPVKItR12YBdcpSuf//xt2K/C0Ft+vLBs8F6f7Zh5zmsX/erkum4SfrH8SzsnI1zzHjIvnw9oO/LY3UANwGHYiVmI3AB1iYblRxDsQ106mZFUQYPGnZtKDEpJLqCv7xmSfhFOl0AhAlZlkfZn+6iGfbtC2uTJHsx7LbLVgy77bINPpAELstCDIM931udv8W+8iBcMVQDfDVNPf8x0r0PI5fgC5nEcNDxR2RRtzcIPZdI9eaY/bUKI+z4QrAYTtemP+jLYxWR+j2uAZY5m5cksETgTN//5dLS7vjliqIoOaKL6oYS74ZcCf3lYcIZoLUPIgv4F+tlwq9Fwyyf6dq0RGgTRL7bxUh/0xFGCaliOCpDcclOYTyUUrIh6IbNv8WBMwz8d39rERbpjgiT38x1iqIMQ1QQDyXqT7VCzUuLU+7lw5CPNQmUX9ez/KkzrTtGGB9I9yruZcvg0ZPh8QboMPbvpgzj3ugc2yWKiEti3TuS2Eeqo3x9pMM4WwfW8ppNnoxc27k05tiukMhFxKuIVlymP27dI5JJ+1fFsKIofYAK4iHDMtjrYSjFCrYk1nf4P2fAXjek1qsIUA8GeGN/up1VHZ46E+bcaH2Tg4RKG/A1AyefbMXwPxfD9VtTQ4GNwPqABtHi9JHNN80AN2L9CDdjH6nGnL+ZhJHr2yhO/ZOxfrZR3cf97YLC0xrP5i9/0Bl3WLvBQtSxBJ1nJiSHNpnGMBQt4Uo/kYfEPIqiFBwqiIcEy4BTgYZuwRYrhcm/9olhYNNiGBGgRpLAtMesJfgpTyD7miXBfrmuECoGbgN+vNUm2zinuWf9kcCH2MV3XrHYibW0RvHS8Lb7M/A14Ep6HkvoviHYQE/h5RdOZVj/xFZnfK7F2V3UF0YZcDr2/Dt94/M+xvUf+1Dn9UZ6zkWmRWZBCV4zCcs2wm9Gwo5Tj51j73mFIRHqhJHMsZ2XbNrnOk5liBHmRK0oipI7KoiHBBdiFZ2XVjALewrc0pC0x3Hspz2501qETy23PsDp/I29ltavAi8C1SF1E9jFSn6fv6+mOS2vMPW2Owgb6TPsWDGn7w+IvqBsG6x1fSHWot1M5m+/a5mOY6d/Y4TjVWPvX7ah+3yi+EwTUieTiC4G2iQ7ITgZO8dhwj7KuDLh/Ux7SzZ95JC5uSAYVjcKp2auoiiKkiUqiIcCJiTFsWAF7p43gnHUR5QLXxnw8xboNOlXs/vfzwwo7xojwaG5hOBvmRuuKihslRsJIN3Y5pP94rUyrNV5PpkjZwS1rYhQL0gExgLKgtpl+7/RPVZ5ms8xqI0b3iyb42UjSg32JiLfYjhdBIahTLZW8iCGjcvJ/sANGWspiqJkiwriocCqDFd5rwiLE+0C6rUOBvnChrUJIpeIDDG6/YKzRbApjHOxek3BiuJcBMKwERX9SL78ffva4jmYLajZ+GQP5vPoE56hZzw2RVGU3qOCeChwXmd2F7psFzW5AsbQ7QccFYN1J1ibRZu+oIzcvr1CuCvGYGewiR3/eIIWEkZpl8txt/RBP0H9pnsfpU1/MdBxkQcNrVgXMkVRlL5l2P98Dguers5ecOZqAW0BYmdEv9C7/rkVZLe4a6DI1a91oMXoYFww5r2RaiX63Hrb5XJOgg3Dl23bTIsHg9yEMh1jC8GLIbM9vpIFGtdQUZS+RwXxUOCKK+DrRA8dFkYUATIF4AaQ/bO7gI8ENuTiCOsZWy5t2vqpb3/7/nQDiBJFqhdT2++4AjWb9M5uu97Mq+uDns3xcv2ehTEKKFoa/fjqd9MHeEO9TEBdKBRF6QsG6yVW8bJgAYw5wy6urqc7dFg2foXvxOHpM2BFBqHblcnu0fT9BTHeYGO0lUYcmAeBrFO1xZyxrE0zpq6+e0Gm9m7ItFyFtwAf5PFGoj8YKJ2Xq3tQ5PrVIJk+mwVEiy0Ig+cDGy40A6egolhRlN6igniocMMNcMhS2LcaigSmVttrQRRWxWFyh41ZPP3RcNcGQ2omOwlxtg2N/lCNFQdLsI66koUFrxq4pbtd1HABI7GZ8mqrYVMfpKXOhQZsKLdc8wU0AouSuWmlTDcD6RgO2kzKcxDj2YSiaCDzlziqGCvJ8thKNNpRv2JFUXqLCuKhxIIFqSlLowiBoNTOm0I+9hZIzWR3BdGtvaVOfbePeiAJ7eXRxvjUIant+HV0MT0FaGy0GeJ661aSLW3Y40Ju/5vc9neSvZ94EvhNxHZ+8duGjauckbG5LTaLMoY+YRMQ4TuWQl9nOjuVaHenbf1wbMWifsWKovQOFcRDmXFp9rluEj1SOwNjQ5RmD+3rsfaGiRnXZeGpE+mRFhpgZAaFarDZ7BY+3PPYUS1/zcCUKXD9B7avbN1JeoO3fS7XZG/7xWQ3nhg2xfRvyHzz4J9L17sl403Hh7ktNosyhj4jW+flvsafNEfJP9kGJVcURUlFBfFQ5t00j18N3W4SUdsFljtW23TuE6OA226Dmhqb/a6mBpa5j5EzXKgEK+r2DEg+IhEfL48GDloLXy61fbmJJ6Ig9C672UhsXGOAB8nemuptf2cOY3FTU2+KcCz/cY+Mw41ZtnMZVGvDPhjoASgDSjHdT6cURVFyQwXxUKb+1HABlU4s15/a07UgyLUihTTuE2XAFS3weAN0GPv30ZMdURzB7aIM+H7QeCM+Xi4BLmiBC1tsX7lQT+7WYlfzH0qwNTVq+6j1/biZ/bId/+RO+Gp1juJ2MP10TGHoBpdWsse7+DaBXXsQ8HRKURQlCwbTVU3Jlr1ugL/v3FMUZxK3e91gXSneidu2Ya4VKTjuE2GiawJQg/1G1QDXb4V/LqbHIruw9lVB4jcLkTMF+Ej06ik0ArXkLoiTWO2eqyYToA6bUro3rpA5/W9uIDdF3Buzel8i2HPYhF3ZqAxe3Bzm1cAZdP+HcW+GveVuvaV0x4t0tzbP67WoGFYUpS8QY4bDUvPMlJWVmZaW3q24uvTSS7teX3zxxb0dUt/x1JlQswQmdVrLcP2pGcRtL9g0AcojhreoB2r8368arIDxU+008LIMu2DJ66MZ4sDaAazDJgnpQRqnV1fXNWKty4Ht80SSbm06qFwS9gf+SvZ3DNkGCu4truWwPWBfCdGCViup5Oow7qcI+BWDTbyKSKsxJtfnSoqiDCPUQjwc2OsG6y8cM+F+w33Fi8f01BVh18tA9+EgFwpvhAovPusy1cDpAe2x19sKArRQqdPG7SNBV8xYg/0f4Fq1x6Q5lxQSZBUarkfbkPBwrgEtVAzHSbWsRSWGFbWZ2vnH5R7vZNJPjLed+5NSTfpVn/7xuceLQtgctgMTsWN2+3LPIUgk+xlUdyGDAP//nSALrvd9gmCfpQSDUQwriqJ4KRroAShDjLvvhrm+srBMbq2JgIhY7kXxQqxZdgpWDIddLBcE7NsTkidAzGd9HAlsKcP6b2TquwbEZ6mOlBfEa8nO5n6yFCvu3bG4WUVybb8MOD6kjzjWMht0/jUEW+jBflhBMdxq0owryLLvkm5+gtplmk/vHITVbQRucDYvDxN83v65ujCkntfaHWY1rc7Qh1uHkP3p9oVRhn2C0tdP+hLAdaiIVRSlUFALsZId5zRb4ekl6Ml4R3Fqko8UvPGG68n+orvAWsODGNkase9cnHX9luxMoZ7idFvSvGI2SluXsPYLCBdBScLPP91q/LA5STdX6foLO0cJaZduTvxzEFY3rDzsycSvSZ2rsHq30e23entIHffmox7r+xpWJ91TkrB9Z4SU/9wZTzor7lJnCyrzW9oTTrn65iqKUlioIFayI62O81xwi/p75Xe2gijXehAuSjNF0MgkTDMlPalO097dH0S6c0uXZjjbOU2EjMsl6BwF+xg+qF2YGFxKzznIxvUGgt1v/J9n1Hq9rZPLvhsytKmn+7tyAz1vCoNuQhfQnepQF6kpilLY6KK6LBi0i+rySdiiuk0JKM821VpvCFpw53cryLZ9EOlcAtx+TiQ4RFyUtu7jdf9j+CjnkuscZNuuN3PtnmMU95hs62fbt6KkoovqFEVxyZuFWETGi8jvRKRFRBpE5LiQeiIil4vIKhFZLyKPi8gu2faj9BPl11l3CC9p3SP6i6gWv6jtE/R0Ik5ncfT282uys1Z629bT/Rg+23PJdQ6ybdebuc7WPSab+r11vVGUzGRzzRGRs0VktXPtukVEBjqNoqIoEcmny8TPsEu9K7FXrhu9QtfDF4BTgE8D44FnsGoh236UfmGB4w6RT/eINGPprS9yV/u12AD/uYq+3ohz/1jqs2ibr3YqPpWCJdI1R0TmAedjQ7rUANsDl/rrKYoyOMmLy4SIlGGjxM4wxrzhlN0OrDLGnO+rex6wuzHmGOf9LsDzxpiR2fTjR10mFEVRFC+ZXCayvHbdAdQbYy5w3u8PLDPGbNdvJ6AoSp+Rr7Br04BO9wfF4SVgn4C6dwHHisg0bP6uE4E/5tAPInIq1vkRwIjI5txPoYsioOOSSy7pg66GJEXYNBiFjM6BzkGhnz8MjzkYJSLPed4vMcYs8bzP5pqzC/B7X71KEUkYYyJmM1IUZaDIlyAuB9b7ytZjUyn4eQ94EliBXan0NrBfDv3g/LAtCdqXKyLynDHGH4m3YCj08wedA9A5KPTzh4KZg2yuOf667usKQAWxogxy8uVDvAkY7SsbDWwMqHsx8DHgI9iIt5cCfxWR0iz7URRFUZTekM01x1/Xfa3XJ0UZAuRLEL8BFInIjp6yWcBrAXVnAb8xxrxjjOkwxvwKmwN25yz7URRFUZTekM015zVnn7dek7pLKMrQIC+C2BjTAtwHXCYiZSKyJ3AEqdEjXP4NfEFEKkUkJiLHAyOAlVn201/0qQvGEKTQzx90DkDnoNDPHwpgDrK85twGfElEdhaRccC3gV/lbbCKovSKvCXmEJHx2LhWB2L9qc43xtwhIlOA14GdjTGNIjISuBr4P6AMWAlcYIz5Y7p+8nISiqIoSkER9drl1D0HOA8YBdwLnG6MaRuYkSuKkg0Fk6lOURRFURRFUYLIZ2IORVEURVEURRl0qCBWFEVRFEVRChoVxBHIJpf9UEVESkTkZuf8NorIf0Tks579+4vIchFpFZG/iUi1Z5+IyPdFpNnZfiAiMjBn0ntEZEcR2SIiSz1lhXT+XxSR/zrf9/+JyKed8oKYAxGpEZGHRWSdiKwWketFpMjZN+zmQEQWichzItImIr/y7cv5fJ15/JvTdrmIHJDH01IURckKFcTRiJTLfohThE2Csg8wBvgOcLdzUZuAXWn9HWA88BzwG0/bU4EjsWGGZgKHAqflbeR9z8+w0U4AKKTzF5EDge8DJ2MTCuwNvFVIcwDcALwPTARmY/9PnDmM5+Bd4HLswrEu+uB87wT+AySAC4Hfisg2/XIGiqIovcUYo1uaDRvpoh2Y5im7HbhqoMeWh3N/GTgKe+H7h29ONgPTnff/AE717P8S8OxAjz/Hc/4icDdwCbDUKSuk8/8H8KWA8kKag/8Ch3je/xD4+XCfA6wo/lVffObYlMdtQIVn/5PYqAsDfq666aabbv5NLcSZCctlP9wsxCmISCX23F/DnutL7j5jY3P+j+45SNnPEJ0fERkNXAb8P9+uQjn/ODAX2EZEVorIO467wCgKZA4crgO+KCKlIlIFfBb4I4U1B9C7890FeMsYszFkv6IoyqBCBXFmssllPywQkRHAMuDXxpjlZJ4D//71QPlQ8J/08V3gZmPM277yQjn/SmwSnKOBT2PdBeZgEwwUyhwAPIEVbhuAd7CuAvdTWHMAvTvfgvvdVBRlaKOCODPZ5LIf8ohIDOsS0g4scoozzYF//2hgkzFmyAS5FpHZwAHANQG7h/35O2x2/v7UGPOeMWYt8GPgEApkDpzv/5+wvrNlwARs6vjvUyBz4KE351tQv5uKogx9VBBnJptc9kMax7JzM9ZSeJQxZquz6zXsObv1yoAd6J6DlP0MzfnZF6gBGkVkNXAucJSIvEBhnD/GmHVYi2iQgCuIOcAuHvsIcL0xps0Y0wzcir0pKJQ5cOnN+b4GbC8iFSH7FUVRBhUqiDNgsstlP9S5EfgocJgxZrOn/HfADBE5Smxq7YuAlx13CoDbgHNEpEpEJmF9cH+Vx3H3BUuwF/vZznYT8BAwj8I4f5dbga+JyLYiMg74OvAgBTIHjlW8DjhDRIpEZCxwItb/dVjOgXOeI4E4EBeRkU6YuZzP11lz8SJwsdPf57GRKO7N57kpiqJEZqBX9Q2FDWs1uh9oARqB4wZ6TP1wjtVYy+AW7ONOd1vg7D8AWI59rP44UONpK8APgA+c7Qc4acGH6oYnykQhnT/Wh/gG4ENgNfATYGSBzcFs5/zWAWuBe4Bth+scON9149su6e35Yp+4PO60XQEcMNDnqptuuukWtokxQ9W9TVEURVEURVF6j7pMKIqiKIqiKAWNCmJFURRFURSloFFBrCiKoiiKohQ0KogVRVEURVGUgkYFsaIoiqIoilLQqCBWFEVRFEVRChoVxIqiKIqiKEpBo4JYUZQhgYh8UkSeEZEnROROERkx0GNSFEVRhgcqiBVFGSo0APsZY/YB3sKmUFcURVGUXqOCWFGGACJSLyIH9HXdbNqKiBGRFhG5Ipe+e4sx5l1jzGbnbQeQdMb1VxHZIiJPDcS4FEVRlKGPCmJFUbJhljHmQgAR+ZaIPOzdKSJvhpR90fN+koi8k+sARKQW+CzwIIAxZj/g9Fz7UxRFURQVxIqi5MrfgT1FJA4gItsBI4DdfGVTnbouhwB/zOWAIjIa+DVwvDGmvRdjVxRFUZQuVBArSgiO+8C3ROR1EVknIreKyEhn30dF5HER+VBEXhORwz3tzheR/4nIRqft5yMebzcR+Y/T7h4R+Y2IXB5SN/T4Dh8LGndvxhfAv7ECeLbzfm/gb8AKX9n/jDHvetodAjzsjKVeRL4hIi877hg3i0iliDzijO9RERnn1C0C7gQuMcasyHHMiqIoitIDFcSKkp4FwDxgB2Aa8G0nusEDwJ+BbYGvActEZCenzf+ATwNjgEuBpSIyMd1BRKQY+B3wK2A8VvgFCtUIxw8ct2df1uMLwrHQ/hMrenH+Pgk85Svrsg47Y98b+Iunq6OAA51xHgY8AlwATMD+Rp3l1JsPfAK4yLkZODbbMSuKoihKECqIFSU91xtj3jbGfABcgRVlewDlwFXGmHZjzF+x/qzzAYwx9zgLwJLGmN8AbwIfz3CcPYAi4CfGmK3GmPuAf6WpG3r8NOOmF+ML4wm6xe+nsYL4SV/ZE576ewMvGWM2esp+aoxpMsasctr+0xjzH2NMG/YmYY4z7tuNMROMMfs6229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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot normalised values vs. irradiance\n", + "plot_mlfm_scatter(meas, norm, mlfm_meas_file, qty_mlfm_vars)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert multiplicative to subtractive losses to show on a stack plot \n", + "\n", + "LFM losses can be analysed as either \n", + "\n", + "- multiplicative \n", + "pr_dc = 1/ff * ( norm(i_sc) * norm(r_sc) * norm(i_ff) * \n", + " norm(v_ff) * norm(r_oc) * norm(v_oc_t) * norm(temp_corr) ). \n", + " \n", + " \n", + " \n", + "- subtractive \n", + "pr_dc = 1/ff - (stack(i_sc) + stack(r_sc) + stack(i_ff) - \n", + " stack(v_ff) + stack(r_oc) + stack(v_oc_t) + stack(temp_corr) ). \n", + " \n", + "Multiplicative losses are easier to understand but to represent them on a graph \n", + "it's easier to show them as a stacked plot where the values are 'translated' \n", + "so the sum of the stacked losses is shown to equate to the product of the \n", + "multiplicative losses." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dci_scr_sci_ffi_vv_ffr_ocv_octemp_module_corr
date_time
2016-01-26 08:10:00-07:000.9874550.0638170.0445290.0359110.010.0739080.098226-0.002454-0.065435
2016-01-26 08:30:00-07:000.8369980.2372830.0325940.0302460.010.0770190.0829280.004324-0.042936
2016-01-26 08:40:00-07:000.9783350.0611030.0655990.0131430.010.0913420.0911430.000727-0.033518
\n", + "
" + ], + "text/plain": [ + " pr_dc i_sc r_sc i_ff i_v \\\n", + "date_time \n", + "2016-01-26 08:10:00-07:00 0.987455 0.063817 0.044529 0.035911 0.01 \n", + "2016-01-26 08:30:00-07:00 0.836998 0.237283 0.032594 0.030246 0.01 \n", + "2016-01-26 08:40:00-07:00 0.978335 0.061103 0.065599 0.013143 0.01 \n", + "\n", + " v_ff r_oc v_oc temp_module_corr \n", + "date_time \n", + "2016-01-26 08:10:00-07:00 0.073908 0.098226 -0.002454 -0.065435 \n", + "2016-01-26 08:30:00-07:00 0.077019 0.082928 0.004324 -0.042936 \n", + "2016-01-26 08:40:00-07:00 0.091342 0.091143 0.000727 -0.033518 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# translate multiplicative to stack losses and add to dataframe df\n", + "stack = mlfm_norm_to_stack(meas, norm, ref, qty_mlfm_vars)\n", + "\n", + "# show some stack losses\n", + "stack.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot stack losses vs. measurement \n", + "\n", + "Fig 4 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", + "\n", + "Fig 4 Stacked losses by measurement \n", + "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", + "\n", + "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", + " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", + " \n", + "- This figure shows a typical c-Si module for four clear days for \n", + " different months July to Oct in AZ. \n", + " \n", + "- In the middle of the days the high irradiance results in the biggest \n", + " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", + " (as the module heats to 60C). \n", + " \n", + "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", + " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", + "\n", + "Stack losses are indicated by their colours \n", + "(from top to bottom for mlfm4=matrix and mlfm6=ivcurve) \n", + "\n", + "`+-----+----+-------+--------+------------+--------------+` \n", + "`| 1 | 2 | 4 | 6 | " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot stack loss vs. time (or measurement) chart\n", + "plot_mlfm_stack(\n", + " dmeas=meas,\n", + " dnorm=norm,\n", + " dstack=stack, # dataframe measurements\n", + " ref=ref, # dataframe reference STC\n", + " mlfm_file_name=mlfm_meas_file, # name of data file\n", + " qty_mlfm_vars=qty_mlfm_vars, # number of mlfm measurements usually 4 or 6\n", + " xaxis_labels=12, # show this many x_labels or 0 to show all\n", + " is_i_sc_self_ref=False, # is isc self referenced?\n", + " is_v_oc_temp_module_corr=True, # is voc temperature corrected?\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fit mechanistic model to measured weather and normalised losses \n", + "\n", + "Perform a Mechanistic Performance Model (MPM) fit to the mlfm parameters \n", + "poa_global (kW/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", + "\n", + "MPM_6 = c_1 + c_2 * (temp_module-25) + c_3 * log10(poa_global_kwm2) + \n", + " c_4 * poa_global_kwm2 + c_5 * wind_speed + c_6 / poa_global_kwm2 \n", + "\n", + "\n", + "Report the fit (coeffs) and error (errs) coefficients. " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", + "mlfm_sel = 'pr_dc' \n", + "\n", + "# FIX THIS WARNING,\n", + "# SettingWithCopyWarning:\n", + "# A value is trying to be set on a copy of a slice from a DataFrame.\n", + "# Try using .loc[row_indexer,col_indexer] = value instead\n", + "# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\n", + "\n", + "norm, cc, ee, coeffs, errs = mlfm_fit(meas, norm, mlfm_sel) # qty_mlfm_vars)\n", + "\n", + "# Fix a bug with fit routine which gives a\n", + "# finite cc[4] even if all the ws data is 0\n", + "# this won't matter until cc is applied to other\n", + "# data with some ws <>0 when it will give bad results\n", + "if np.mean(meas.wind_speed) == 0:\n", + " cc[4] = 0\n", + " c_5 = 0\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show residual fit vs. measured for MLFM parameter " + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_residuals(dmeas, dnorm, fit, title):\n", + " ''' \n", + " Scatter plot residuals to normalised measured\n", + "\n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + " \n", + " \n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + " \n", + " fit : string\n", + " name of fitted variable e.g. 'pr_dc'.\n", + " \n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " '''\n", + " \n", + " fig, ax1 = plt.subplots()\n", + " \n", + " plt.title(title)\n", + "\n", + " plt.ylabel('fit ' + fit + ' * poa_global_kwm2')\n", + " ax1.set_ylim(0, 1.2)\n", + " \n", + " plt.xlabel('meas ' + fit + '* poa_global_kwm2')\n", + " ax1.set_xlim(0, 1.2)\n", + " \n", + " plt.plot(\n", + " dnorm[fit] * dmeas['poa_global_kwm2'],\n", + " dnorm['calc_' + fit] * dmeas['poa_global_kwm2'],\n", + " 'c^',\n", + " label = fit\n", + " )\n", + " \n", + " # plot 1:1 line to show optimum fit\n", + " plt.plot((0,1.2),(0,1.2), 'ko-')\n", + " \n", + " plt.legend(loc='upper left')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fig 6 : fit_mlfm_sel * poa_global vs. measured_mlfm_sel * poa_global" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot fit vs. measured, include a 1:1 line for comparison\n", + "fit_plot = plot_residuals(meas, norm, mlfm_sel, 'residual ' + mlfm_meas_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot heatmap of mean residual vs. tenp_module and poa_global\n", + "\n", + "Show a heatmap of the average residual (meas - fit) error \n", + "for each irradiance (100W/m^2) and tmod bin (5C)." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_heatmap(dmeas, dnorm, fit, y_axis, x_axis, z_axis, title):\n", + " ''' \n", + " Plot a heatmap of Z vs. binned X and Y axes.\n", + " \n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + " \n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + " \n", + " fit : string\n", + " fitted parameter e.g. 'pr_dc'.\n", + " \n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global_bin'.\n", + " \n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module_bin'.\n", + " \n", + " z_axis : string\n", + " value as a colour surface plot e.f. 'diff_pr_dc'.\n", + " \n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " \n", + " '''\n", + " \n", + " df_piv = pd.pivot_table(\n", + " dnorm, \n", + " index = y_axis, # e.g. 'temp_module_bin'\n", + " columns = x_axis, # e.g. 'poa_global_bin'\n", + " values = z_axis, # value to aggregate\n", + " fill_value = 0, # fill empty cells with this ?\n", + " aggfunc = [np.mean], # e.g. min, np.sum, len->count\n", + " margins = False, # grand totals hide\n", + " dropna = True # hide missing rows or columns\n", + " )\n", + "\n", + " fig, ax1 = plt.subplots()\n", + " \n", + " # force z limits to be -2% to +2% if desired\n", + " df_piv = df_piv.clip(lower = -0.02, upper = +0.02)\n", + " \n", + " im = ax1.imshow(\n", + " df_piv,\n", + " cmap='RdYlBu',\n", + " origin='lower'\n", + " )\n", + " \n", + " cbar = ax1.figure.colorbar(\n", + " im, ax=ax1, shrink=0.75, label=z_axis)\n", + " \n", + " #Y AXIS : show only 1 of each y_skip labels\n", + " y_ticks = df_piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = df_piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + " \n", + " # X AXIS : show only 1 of each x_skip labels\n", + " x_ticks = df_piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ###\n", + " ax1.set_title(title)\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + " \n", + " ax1.grid( color='k', linestyle=':', linewidth=1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fig 7 : Residual MLFM fit heatmap vs. poa_global and temp_module." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap\n", + "heatmap_plot = plot_heatmap(\n", + " dnorm = norm,\n", + " dmeas = meas,\n", + " fit = mlfm_sel,\n", + " y_axis = 'temp_module_bin',\n", + " x_axis = 'poa_global_bin',\n", + " z_axis = 'diff_' + mlfm_sel,\n", + " title = 'residual ' + mlfm_meas_file\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read in complete (G,T) Matrix to fill with MLFM predicted values \n", + "\n", + "Read in a matrix with complete values of \n", + "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", + "to predict all MPM values " + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['mid', 'poa_global', 'temp_module', 'wind_speed', 'poa_global_kwm2'], dtype='object')" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in the complete matrix data\n", + "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", + "\n", + "matr['poa_global_kwm2'] = matr['poa_global'] / 1000\n", + "\n", + "matr.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predict performance from MPM fit coefficients \n", + "\n", + "1. generate predicted mpm data \n", + "2. create a pivot table mpm(g,t) \n", + "3. show as a heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "# populate pivot table from predicted mpm data\n", + "matr[mlfm_sel] = mlfm_6(matr, cc[0], cc[1], cc[2], cc[3], cc[4], cc[5])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot heatmap of predicted MLFM values vs. temp_mod and poa_global bins" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", + " vmin=0, vmax=1.2, levels=5):\n", + " ''' \n", + " Plot filled contour plot Z vs. X and Y bins.\n", + " \n", + " Parameters\n", + " ----------\n", + " df : dataframe\n", + " measured or noralised data containing weather columns\n", + " (poa_global, temp_module and wind_speed).\n", + " \n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global'.\n", + " \n", + " y_axis : string \n", + " binned y axis e.g. 'temp_module'.\n", + " \n", + " z_axis : string\n", + " measured value as a colour surface plot.\n", + " \n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " \n", + " vmin, vmax : float\n", + " minimum and maximum values for contour chart ###\n", + "\n", + " \n", + " ''' \n", + " \n", + " piv = pd.pivot_table(\n", + " df,\n", + " index = y_axis,\n", + " columns = x_axis,\n", + " values = z_axis,\n", + " fill_value = 0, # fill empty cells?\n", + " aggfunc = [np.mean], # min, np.sum, len->count\n", + " margins = False, # grand totals\n", + " dropna = True # hide missing rows or columns\n", + " )\n", + " \n", + " piv = piv.clip(vmin, vmax) ###\n", + " \n", + " fig, ax1 = plt.subplots()\n", + "\n", + " cs = plt.contourf(\n", + " piv,\n", + " cmap = 'RdYlBu', # or 'nipy_spectral',\n", + " # origin = 'lower'\n", + " # nchunkint = 1,\n", + " levels = levels, ###\n", + " vmin = vmin, ###\n", + " vmax = vmax ###\n", + " )\n", + " \n", + " cbar = fig.colorbar(cs, ax=ax1)\n", + " cbar.ax.set_ylabel(z_axis,\n", + " rotation=90,\n", + " va='bottom',\n", + " labelpad=+30)\n", + " \n", + " plt.title(title)\n", + "\n", + " y_ticks = piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + "\n", + " # show only 1 of each y_skip labels\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + " \n", + " x_ticks = piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " # show only 1 of each x_skip labels\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + " \n", + " ax1.grid( color='k', linestyle=':', linewidth=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fig 8 : Contour plot of predicted mlfm_sel + vs. poa_global and temp_mod." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df = matr,\n", + " y_axis = 'temp_module',\n", + " x_axis = 'poa_global',\n", + " z_axis = mlfm_sel,\n", + " title = 'matrix predicted ' + mlfm_meas_file,\n", + " vmin = 0.6,\n", + " vmax = 1.05,\n", + " levels = 9\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fig 9 : Contour plot of measured mlfm_sel vs. poa_global and temp_mod." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df = norm,\n", + " y_axis = 'temp_module_bin',\n", + " x_axis = 'poa_global_bin',\n", + " z_axis = mlfm_sel,\n", + " title = 'avg normalised ' + mlfm_meas_file,\n", + " vmin = 0.6,\n", + " vmax = 1.05,\n", + " levels = 9\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References \n", + " \n", + "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", + "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", + "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", + " \n", + ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", + " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", + "What's Most Important for Energy Yield' \n", + "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", + "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", + "\n", + ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", + " 'Choosing the best Empirical Model for predicting energy yield' \n", + " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", + " Canobbio, Switzerland 30-31 March, 2017 \n", + "\n", + ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", + "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", + "benchmark its practical relevance in energy yield prediction' \n", + "PVSC June 2019 Chicago, USA \n", + "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", + "\n", + ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", + "under Real Outdoor and IEC 61853 Test Conditions \n", + "Using High Quality Module IV Measurements' \n", + "36th EU PVSEC Sep 2019 \n", + "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", + "\n", + ".. [5] Steve Ransome (SRCL) \n", + "'How to use the Loss Factors and Mechanistic Performance Models \n", + "effectively with PVPMC/PVLIB' \n", + "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", + "https://pvpmc.sandia.gov/download/7879/ \n", + "\n", + ".. [6] W.Marion et al (NREL) \n", + "'New Data Set for Validating PV Module Performance Models' \n", + "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", + "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", + "\n", + "Many more papers are available at www.steveransome.com \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + 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zHN|S#iDWru-+nobCra)kAYw{RlQQ?LJc%8C+PJucJRF6a;;gg2>tt5$_(XYe!SCB? zOAfB~U~N2Vc-f(NKbW6T;ou_GvSXqTe`!fkM##O~tbK$Y{~>$1=9sw5E%;qVPeNMp zE8KIrG_hwiuFaA{+&tXNz~f-y11$Zs;Lumxo{@mge8o9Tr-XcESHsNuIsh7y_d5I+ zmHi@siT6g%JSVb~b)daWk0kStdI*R%u$#!z5kY_Y_o{TEHlyORI_ literal 0 HcmV?d00001 diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py new file mode 100644 index 0000000000..304ba861e3 --- /dev/null +++ b/pvlib/mlfm.py @@ -0,0 +1,483 @@ +''' +This ``mlfm code`` module contains functions to analyse and predict 79-| +performance of PV modules using the mechanistic performance (MPM) and +loss factors models (LFM) + +Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + +https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules + +https://github.com/python/peps/blob/master/pep-0008.txt + +''' + +import numpy as np +import pandas as pd +import datetime as dt + +import os +import sys + +from scipy.optimize import curve_fit +from scipy import optimize + + + +''' DEFINE REFERENCE MEASUREMENT CONDITIONS ''' +# or use existing definitions in pvlib + +# NAME value comment unit PV_LIB name +# +T_STC = 25.0 # STC temperature [C] temperature_ref +T_HTC = 75.0 # HTC temperature [C] +G_STC = 1.0 # STC irradiance [kW/m^2] +G_LIC = 0.2 # LIC irradiance [kW/m^2] + + + +''' Define standardised MLFM graph colours as a dict ``clr`` ''' + +clr = { + #parameter_clr colour R G B + 'irradiance' : 'darkgreen', # 0 64 0 + 'temp_module' : 'red', # 255 0 0 + 'temp_air' : 'yellow', # 245 245 220 + 'wind_speed' : 'grey', # 127 127 127 + + 'i_sc' : 'purple', # 128 0 128 + 'r_sc' : 'orange', # 255 165 0 + 'i_ff' : 'lightgreen', # 144 238 144 + 'i_mp' : 'green', # 0 255 0 + 'i_v' : 'black', # 0 0 0 # between i and v losses + 'v_ff' : 'cyan', # 0 255 255 + 'v_mp' : 'blue', # 0 0 255 + 'r_oc' : 'pink', # 255 192 203 + 'v_oc' : 'sienna', # 160 82 45 + + 'pr_dc' : 'black', # 0 0 0 +} + + + +def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): + ''' + Convert measured values e.g. meas(i_sc, ... v_oc) + to normalised loss values e.g. norm(i_sc, ... v_oc) + normalising by ref stc values and irradiance. + + Parameters + ---------- + dmeas : dataframe + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values + 'i_sc' .. 'v_oc', temp_module. + + ref : dict + reference stc values e.g. 'v_oc' and + temperature coeffs e.g. 'beta_v_oc' . + + qty_mlfm_vars : int + number of mlfm_values present in data usually + 2 = (imp, vmp) from mpp tracker + 4 = (i_sc, i_mp, v_mp, v_oc) from matrix + 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + + Returns + ------- + dnorm : dataframe + normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + ''' + dnorm = pd.DataFrame() + + # calculate normalised mlfm values depending on number of qty_mlfm_vars + + if qty_mlfm_vars >= 1: # do for all measurements + dnorm['pr_dc'] = (dmeas['p_mp'] / + (ref['p_mp'] * dmeas['poa_global_kwm2'])) + + # temperature corrected + dnorm['pr_dc_temp_corr'] = (dnorm['pr_dc'] * + (1 - ref['gamma_p_mp']*(dmeas['temp_module']-T_STC))) + + if qty_mlfm_vars >= 2: # + dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] + dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] + + if qty_mlfm_vars >= 4: # + dnorm['i_sc'] = (dmeas['i_sc'] / + (dmeas['poa_global_kwm2'] * ref['i_sc'])) + + dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] + + # temperature corrected + dnorm['v_oc_temp_corr'] = (dnorm['v_oc'] * + (1 - ref['beta_v_oc']*(dmeas['temp_module']-T_STC))) + + if qty_mlfm_vars >= 6: # 6,8 IV data + + ''' + create temporary variables (ir, vr) from + intercept of r_sc (at i_sc) with r_oc (at v_oc) + to make maths easier + ''' + + ir = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / + (dmeas['r_sc'] - dmeas['r_oc'])) + + vr = (dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + + # calculate normalised resistances r_sc and r_oc + dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc + dnorm['r_oc'] = vr / dmeas['v_oc'] # norm_r @ roc + + # calculate remaining fill factor losses partitioned to i_ff, v_ff + dnorm['i_ff'] = dmeas['i_mp'] / ir + dnorm['v_ff'] = dmeas['v_mp'] / vr + + return dnorm + + + +def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): + ''' + Predict normalised lfm values e.g. pr_dc, norm(i_sc, ... v_oc) + from poa_global, temp_module, wind_speed and mlfm(c_1 .. c_6). + + Parameters + ---------- + + dmeas : dataframe + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values + 'i_sc' .. 'v_oc', temp_module. + + c_1 to c_6 : float + fitted mlfm coefficients (dependencies) + c_1 - constant + c_2 - temperature coefficient (1/K) + c_3 - low light log irradiance drop (~v_oc) + c_4 - high light linear irradiance drop (~r_s) + c_5 - wind speed dependence (=0 if indoor) + c_6 - inverse irradiance (<= 0). + + Returns + ------- + mlfm_6 : float + predicted performance values for pr_dc, norm(i_sc, .. v_oc) . + ''' + + mlfm_6 = ( + c_1 + # 'constant' lossless + c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient + c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' + c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' + c_5 * dmeas['wind_speed'] + # wind_speed (optional or 0) + c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) + ) + + return mlfm_6 + + + +def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): + ''' + Converts MLFM normalised multiplicative losses norm(i_sc ... v_oc) + to stacked subtractive losses stack(i_sc ... v_oc). + + Ref: + http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf + + current losses : + meas(imp) / ref(i_sc) = + poa_global_kwm2 * (norm(i_sc) * norm(r_sc) * norm(i_ff)) + + voltage losses : + meas(vmp) / ref(v_oc) = + (norm(v_ff) * norm(r_oc) * norm(v_oc)) + + 1/ff_ref = (ref(isc) / ref(imp)) * (ref(voc) / ref(vmp)) + + + Multiplicative losses: + - are easier to use on a scatter plot vs. irradiance or temperature. + + Stacked losses: + - are better to use to see relative loss proportions + (for underperformance limitations) + or vs. time e.g. for degradation. + + - Stacked losses are scaled so they give the correct pr_dc + and are in the right proportion to each other. + + Parameters + ---------- + + dmeas : dataframe + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values + 'i_sc' .. 'v_oc', temp_module.. + + dnorm : dataframe + normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + + ref : dict + reference stc values e.g. 'v_oc' and + temperature coeffs e.g. 'beta_v_oc' . + + qty_mlfm_vars : int + number of mlfm_values present in data usually + 2 = (imp, vmp) from mpp tracker + 4 = (i_sc, i_mp, v_mp, v_oc) from matrix + 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + + Returns + ------- + dstack : dataframe + normalised subtractive lfm loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/ff - sum('i_sc', ... 'v_oc'). + + ''' + + #create an empty pands to put stack results + dstack = pd.DataFrame() + + # create a gap to differentiate i and v losses : gap width~0.01 + gap = 0.01 # + + # calculate reference fill factor (usually between 0.5 and 0.8) + ff_ref = ref['ff'] + + ''' + calculate inverse fill factor ~ 1.25 - 2 as we calculate + i_losses from ref_isc to norm_imp + v_losses from ref_vocc to norm_vmp + ''' + inv_ff = 1 / ff_ref + + if qty_mlfm_vars == 6: # ivcurve + ''' + find factor to transform multiplicative to subtractive losses + correction factor to scale losses to keep 1/ff --> pr_dc + ''' + # product + prod = inv_ff * ( + dnorm['i_sc'] * dnorm['r_sc'] * dnorm['i_ff'] * + dnorm['v_ff'] * dnorm['r_oc'] * dnorm['v_oc'] + ) + + # total + tot = inv_ff + ( + dnorm['i_sc'] + dnorm['r_sc'] + dnorm['i_ff'] + + dnorm['v_ff'] + dnorm['r_oc'] + dnorm['v_oc'] - 6 + ) + + # correction factor + corr = ( inv_ff - prod ) / ( inv_ff - tot ) + + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) + dstack['pr_dc'] = +dnorm['pr_dc'] # initialise + dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr + dstack['r_sc'] = -(dnorm['r_sc'] - 1) * corr + dstack['i_ff'] = -(dnorm['i_ff'] - 1) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_ff'] = -(dnorm['v_ff'] - 1) * corr - gap/2 + dstack['r_oc'] = -(dnorm['r_oc'] - 1) * corr + dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr + + dstack['temp_module_corr'] = ( + - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) + + elif qty_mlfm_vars == 4: # matrix + ''' + find factor to transform multiplicative to subtractive losses + correction factor to scale losses to keep 1/ff --> pr_dc + ''' + + # product + prod = inv_ff * ( + dnorm['i_sc'] * dnorm['i_mp'] * + dnorm['v_mp'] * dnorm['v_oc'] + ) + + #total + tot = inv_ff + ( + dnorm['i_sc'] + dnorm['i_mp'] + + dnorm['v_mp'] + dnorm['v_oc'] - 4 + ) + + #correction factor + corr = ( inv_ff - prod ) / ( inv_ff - tot ) + + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) + dstack['pr_dc'] = + dnorm['pr_dc'] # initialise + dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr + dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_mp'] = -(dnorm['v_mp'] - 1) * corr - gap/2 + dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr + + dstack['temp_module_corr'] = ( + - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) + + return dstack + + + +def mlfm_fit(dmeas, dnorm, mlfm_sel): + ''' + Fit MLFM to normalised data e.g. norm_pr_dc,(norm_i_sc .. norm_v_oc). + + Parameters + ---------- + + dnorm : dataframe + normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + + mlfm_sel : string + mlfm variable being fitted e.g. pr_dc. + + Returns + ------- + dnorm : dataframe + same data but with added mlfm_var fit values calc_mlfm_sel and diff_mlfm_sel. + + cc : list + fit coefficients c_1 to c_6. + + ee : list + error values. + + coeffs : string + formatted coefficients for printing. + + errs + formatted errors for printing. + ''' + + # drop missing data + dnorm = dnorm.dropna() + ''' + ensure correct number of p0 and bounds + https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html + ''' + # define function name + f = mlfm_6 + + # setup initial values and initial boundary conditions + + # initial c1 c2 c3 c4 c5 c6<0 + p0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) + # boundaries + bounds = ([ -2, -2, -2, -2, -2, -2], + [ 2, 2, 2, 2, 2, 0]) + + if True: + popt, pcov = optimize.curve_fit( + f = f, # fit function + xdata = dmeas, # input data + ydata = dnorm[mlfm_sel], # fit parameter + p0 = p0, # initial + bounds = bounds # boundaries + ) + + # get mlfm coefficients + c_1 = popt[0] + c_2 = popt[1] + c_3 = popt[2] + c_4 = popt[3] + c_5 = popt[4] + c_6 = popt[5] + + cc = [c_1, c_2, c_3, c_4, c_5, c_6,] + + # get mlfm error coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + + e_1 = perr[0] + e_2 = perr[1] + e_3 = perr[2] + e_4 = perr[3] + e_5 = perr[4] + e_6 = perr[5] + + ee = [e_1, e_2, e_3, e_4, e_5, e_6,] + + # format coefficients as strings, easier to read in graph title + coeffs = ( + ' {:.4%}'.format(c_1) + + ', {:.4%}'.format(c_2) + + ', {:.4%}'.format(c_3) + + ', {:.4%}'.format(c_4) + + ', {:.4%}'.format(c_5) + + ', {:.4%}'.format(c_6) + ) + ###print ('coeffs = ', mlfm_sel, coeffs) + + errs = ( + ' {:.4%}'.format(e_1) + + ', {:.4%}'.format(e_2) + + ', {:.4%}'.format(e_3) + + ', {:.4%}'.format(e_4) + + ', {:.4%}'.format(e_5) + + ', {:.4%}'.format(e_6) + ) + ###print ('errs = ', mlfm_sel, errs) + + # save fit and error to dataframe + dnorm['calc_' + mlfm_sel] = (mlfm_6(dmeas, c_1,c_2,c_3,c_4,c_5,c_6,)) + + dnorm['diff_' + mlfm_sel] = (dnorm[mlfm_sel] - dnorm['calc_' + mlfm_sel]) + + return(dnorm, cc, ee, coeffs, errs) + + + +""" +## References + +The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) +together known as "MLFM" have been developed by SRCL and Gantner Instruments +(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM + +.. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome + '4AV.2.41 Characterising PV Modules under Outdoor Conditions: +What's Most Important for Energy Yield' +26th EU PVSEC 8 September 2011; Hamburg, Germany. +http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf + +.. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) + 'Choosing the best Empirical Model for predicting energy yield' + 7th PV Energy Rating and Module Performance Modeling Workshop, + Canobbio, Switzerland 30-31 March, 2017. + +.. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) +'Checking the new IEC 61853.1-4 with high quality 3rd party data to +benchmark its practical relevance in energy yield prediction' +PVSC June 2019 [Chicago], USA. +http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf + +.. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) +'5CV.4.35 Quantifying Long Term PV Performance and Degradation +under Real Outdoor and IEC 61853 Test Conditions +Using High Quality Module IV Measurements'. +36th EU PVSEC Sep 2019 [Marseille] + +.. [5] Steve Ransome (SRCL) +'How to use the Loss Factors and Mechanistic Performance Models +effectively with PVPMC/PVLIB' +[PVPMC] Webinar on PV Performance Modeling Methods, Aug 2020. +https://pvpmc.sandia.gov/download/7879/ + +.. [6] W.Marion et al (NREL) +'New Data Set for Validating PV Module Performance Models'. +https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models + +Many more papers are available at www.steveransome.com +""" diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py new file mode 100644 index 0000000000..18f916037b --- /dev/null +++ b/pvlib/mlfm_graphs.py @@ -0,0 +1,380 @@ +''' +This ``mlfm graphs`` module contains functions to display 79-| +performance of PV modules using the mechanistic performance (MPM) and +loss factors models (LFM) + +Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + +https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules + +https://github.com/python/peps/blob/master/pep-0008.txt +''' + +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.colors import LinearSegmentedColormap + + + +''' Define standardised MLFM graph colours as a dict ``clr`` ''' + +clr = { + #parameter_clr colour R G B + 'irradiance' : 'darkgreen', # 0 64 0 + 'temp_module' : 'red', # 255 0 0 + 'temp_air' : 'yellow', # 245 245 220 + 'wind_speed' : 'grey', # 127 127 127 + + 'i_sc' : 'purple', # 128 0 128 + 'r_sc' : 'orange', # 255 165 0 + 'i_ff' : 'lightgreen', # 144 238 144 + 'i_mp' : 'green', # 0 255 0 + 'i_v' : 'black', # 0 0 0 # between i and v losses + 'v_ff' : 'cyan', # 0 255 255 + 'v_mp' : 'blue', # 0 0 255 + 'r_oc' : 'pink', # 255 192 203 + 'v_oc' : 'sienna', # 160 82 45 + + 'pr_dc' : 'black', # 0 0 0 +} + + + +def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): + ''' + Scatter plot normalised MLFM parameters(y) vs. irradiance(x). + + y1_axis : e.g. norm(i_sc, ... v_oc),_temp_module_corr + x_axis : e.g. irradiance, poa_global_kwm2 + y2_axis : e.g. temp_air, temp_module (C/100 to fit graphs). + + Parameters + ---------- + dmeas : dataframe + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values + 'i_sc' .. 'v_oc', temp_module. + + dnorm : dataframe + multiplicative lfm loss values 'i_sc' ... 'v_oc' + where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + + mlfm_file_name : string + mlfm_file_name used in graph title. + + qty_mlfm_vars : int + number of mlfm_values present in data usually + 2 = (imp, vmp) from mpp tracker + 4 = (i_sc, i_mp, v_mp, v_oc) from matrix + 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + ''' + + # offset legend to the right to not overlap graph, use ~1.2 + bbox = 1.2 + + # set x_axis as irradiance + xdata = dmeas['poa_global'] + + fig, ax1 = plt.subplots() + + # get filename without ".csv" for title + ax1.set_title('Plot mlfm scatter ' + + mlfm_file_name[:len(mlfm_file_name)-4]) + + ax1.set_ylabel('normalised mlfm values') + ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line + ax1.set_ylim(0.8, 1.1) # optional normalised y scale + + ax1.set_xlabel('poa_global [W/m$^2$]') + ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC + ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT + ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC + + # plot the mlfm parameters depending on qty_mlfm_vars + if qty_mlfm_vars == 1: # only p_mp + ax1.scatter(xdata, dnorm['pr_dc_temp_corr'], + c=clr['pr_dc'], label='pr_dc_temp_corr') + + if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix + ax1.scatter(xdata, dnorm['i_mp'], c=clr['i_mp'], label='norm_i_mp') + ax1.scatter(xdata, dnorm['v_mp'], c=clr['v_mp'], label='norm_v_mp') + + if qty_mlfm_vars >= 6: # ivcurve + ax1.scatter(xdata, dnorm['i_ff'], c=clr['i_ff'], label='norm_i_ff') + ax1.scatter(xdata, dnorm['v_ff'], c=clr['v_ff'], label='norm_v_ff') + ax1.scatter(xdata, dnorm['r_sc'], c=clr['r_sc'], label='norm_r_sc') + ax1.scatter(xdata, dnorm['r_oc'], c=clr['r_oc'], label='norm_r_oc') + + if qty_mlfm_vars >= 4: # matrix + ax1.scatter(xdata, dnorm['i_sc'], c=clr['i_sc'], label='norm_i_sc') + + ax1.scatter(xdata, dnorm['v_oc_temp_corr'], c=clr['v_oc'], + label='norm_v_oc_temp_corr') + + ax1.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) + + # y2axis plot met on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('temp_module, temp_air (C/100)') # poa_global (kW/m$^2$); + + # set wide limits 0 to 4 so they don't overlap mlfm params + ax2.set_ylim(0, 4) + + ax2.scatter(xdata, + dmeas['temp_module']/100, + c=clr['temp_module'], + label='temp_module C/100') + + # temp_air may not exist particularly for indoor measurements + try: + ax2.scatter(xdata, + dmeas['temp_air']/100, + c=clr['temp_air'], + label='temp_air C/100') + except: + pass + + ax2.legend(bbox_to_anchor=(bbox, 0.5), loc='upper left', borderaxespad=0.) + plt.show() + + + +def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, + xaxis_labels=12, is_i_sc_self_ref=False, is_v_oc_temp_module_corr=True): + + ''' + Plot graph of stacked MLFM losses from intital 1/FF down to pr_dc. + + Parameters + ---------- + dmeas : dataframe + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values + 'i_sc' .. 'v_oc', temp_module. + + dnorm : dataframe + normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + + + dstack : dataframe + normalised subtractive lfm loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/ff - sum('i_sc', ... 'v_oc'). + + ref : dict + reference stc values e.g. 'v_oc' and + temperature coeffs e.g. 'beta_v_oc' . + + mlfm_file_name : string + mlfm_file_name used in graph title. + + qty_mlfm_vars : int + number of mlfm_values present in data usually + 2 = (imp, vmp) from mpp tracker + 4 = (i_sc, i_mp, v_mp, v_oc) from matrix + 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + + xaxis_labels : int + number of xaxis labels to show (~12) or 0 to show all. + + is_i_sc_self_ref : bool + self corrects i_sc to remove angle of incidence, + spectrum, snow or soiling?. + + is_v_oc_temp_module_corr : bool + calc loss due to gamma, subtract from v_oc loss. + + ''' + + # offset legend right, use ~1.2 + bbox = 1.2 + + # select x axis usually date_time + xdata = dmeas.index + fig, ax1 = plt.subplots() + + ax1.set_title('Plot_mlfm_stack : ' + + mlfm_file_name[:len(mlfm_file_name)-4]) + + if qty_mlfm_vars == 6: # iv curve + labels_6 = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_r_oc', + 'stack_v_ff', + '- - -', + 'stack_i_ff', + 'stack_r_sc', + 'stack_i_sc' + ] + + color_map_6 = [ + 'white', # colour to bottom of graph + clr['temp_module'], + clr['v_oc'], + clr['r_oc'], + clr['v_ff'], + clr['i_v'], + clr['i_ff'], + clr['r_sc'], + clr['i_sc'] + ] + + # plot stack in order bottom to top, + # allowing self_ref and temp_module corrections + ax1.stackplot( + xdata, + dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), + dstack['v_oc'] - ( + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), + dstack['r_oc'], + dstack['v_ff'], + dstack['i_v'], + dstack['i_ff'], + dstack['r_sc'], + dstack['i_sc'] * (not is_i_sc_self_ref), + labels = labels_6, + colors = color_map_6 + ) + + if qty_mlfm_vars == 4: # matrix + labels_4 = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_v_mp', + '- - -', + 'stack_i_mp', + 'i_sc' + ] + + color_map_4 = [ + 'white', # colour to bottom of graph + clr['temp_module'], + clr['v_oc'], + clr['v_mp'], + clr['i_v'], + clr['i_mp'], + clr['i_sc'] + ] + + # plot stack in order bottom to top, + # allowing self_ref and temp_module corrections + ax1.stackplot( + xdata, + dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), + dstack['v_oc'] - ( + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), + dstack['v_mp'], + dstack['i_v'], + dstack['i_mp'], + dstack['i_sc'] * (not is_i_sc_self_ref), + labels = labels_4, + colors = color_map_4 + ) + + ax1.axhline(y=1/ref['ff'], c='grey', lw=3) # show initial 1/FF + ax1.axhline(y=1, c='grey', lw=3) # show 100% line + ax1.set_ylabel('stacked mlfm losses') + + # find number of x date values + x_ticks = dmeas.shape[0] + plt.xticks(np.arange(0, x_ticks), rotation=90) + + if (xaxis_labels > 0 and xaxis_labels < x_ticks): + xaxis_skip = np.floor(x_ticks / xaxis_labels) + else: + xaxis_skip = 2 + + # + xax2 = [''] * x_ticks + x_count = 0 + while x_count < x_ticks: + if x_count % xaxis_skip == 0: + ''' + try to reformat any date indexes (not for matrices) + + 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 + y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' + ''' + try: + xax2[x_count] = xdata[x_count][2:13]+'h' + + except IndexError: + xax2[x_count] = xdata[x_count] + + x_count += 1 + + ax1.set_xticklabels(xax2) + ax1.set_ylim(0.6, 1/ref['ff']+0.1) # optional normalised y scale + plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) + + # plot met data on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/100)') + ax2.set_ylim(0, 4) # set so doesn't overlap mlfm params + + plt.plot(xdata, dmeas['poa_global_kwm2'], + c=clr['irradiance'], label='poa_global_kwm2') + plt.plot(xdata, dmeas['temp_module'] / 100, + c=clr['temp_module'], label='temp_module/100') + + # temp_air may not exist particularly for indoor measurements + try: + plt.plot(xdata, dmeas['temp_air']/100, c=clr['temp_air'], label='temp_air/100') + except: + pass + + ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) + ax1.set_xticklabels(xax2, rotation = 90) + plt.show() + + + +""" +## References + +The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) +together known as "MLFM" have been developed by SRCL and Gantner Instruments +(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM + +.. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome + '4AV.2.41 Characterising PV Modules under Outdoor Conditions: +What's Most Important for Energy Yield' +26th EU PVSEC 8 September 2011; Hamburg, Germany. +http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf + +.. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) + 'Choosing the best Empirical Model for predicting energy yield' + 7th PV Energy Rating and Module Performance Modeling Workshop, + Canobbio, Switzerland 30-31 March, 2017. + +.. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) +'Checking the new IEC 61853.1-4 with high quality 3rd party data to +benchmark its practical relevance in energy yield prediction' +PVSC June 2019 [Chicago], USA. +http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf + +.. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) +'5CV.4.35 Quantifying Long Term PV Performance and Degradation +under Real Outdoor and IEC 61853 Test Conditions +Using High Quality Module IV Measurements'. +36th EU PVSEC Sep 2019 [Marseille] + +.. [5] Steve Ransome (SRCL) +'How to use the Loss Factors and Mechanistic Performance Models +effectively with PVPMC/PVLIB' +[PVPMC] Webinar on PV Performance Modeling Methods, Aug 2020. +https://pvpmc.sandia.gov/download/7879/ + +.. [6] W.Marion et al (NREL) +'New Data Set for Validating PV Module Performance Models'. +https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models + +Many more papers are available at www.steveransome.com +""" From a8a359cb3a8d4c47706723f3a66efe29191fe8fd Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 17:28:17 +0000 Subject: [PATCH 02/81] format fix mlfm.py --- pvlib/mlfm.py | 214 ++++++++++++++++++++++++-------------------------- 1 file changed, 101 insertions(+), 113 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 304ba861e3..df87e03137 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -13,16 +13,10 @@ import numpy as np import pandas as pd -import datetime as dt -import os -import sys - -from scipy.optimize import curve_fit from scipy import optimize - ''' DEFINE REFERENCE MEASUREMENT CONDITIONS ''' # or use existing definitions in pvlib @@ -34,9 +28,8 @@ G_LIC = 0.2 # LIC irradiance [kW/m^2] - ''' Define standardised MLFM graph colours as a dict ``clr`` ''' - + clr = { #parameter_clr colour R G B 'irradiance' : 'darkgreen', # 0 64 0 @@ -55,34 +48,33 @@ 'v_oc' : 'sienna', # 160 82 45 'pr_dc' : 'black', # 0 0 0 -} - +} def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): - ''' + ''' Convert measured values e.g. meas(i_sc, ... v_oc) to normalised loss values e.g. norm(i_sc, ... v_oc) normalising by ref stc values and irradiance. - + Parameters ---------- dmeas : dataframe - measured weather data + measured weather data 'poa_global', 'temp_module', 'wind_speed' and measured electrical/thermal values 'i_sc' .. 'v_oc', temp_module. - - ref : dict + + ref : dict reference stc values e.g. 'v_oc' and temperature coeffs e.g. 'beta_v_oc' . - - qty_mlfm_vars : int + + qty_mlfm_vars : int number of mlfm_values present in data usually 2 = (imp, vmp) from mpp tracker 4 = (i_sc, i_mp, v_mp, v_oc) from matrix 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. - + Returns ------- dnorm : dataframe @@ -90,39 +82,39 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). ''' dnorm = pd.DataFrame() - + # calculate normalised mlfm values depending on number of qty_mlfm_vars - + if qty_mlfm_vars >= 1: # do for all measurements dnorm['pr_dc'] = (dmeas['p_mp'] / (ref['p_mp'] * dmeas['poa_global_kwm2'])) - - # temperature corrected + + # temperature corrected dnorm['pr_dc_temp_corr'] = (dnorm['pr_dc'] * (1 - ref['gamma_p_mp']*(dmeas['temp_module']-T_STC))) - + if qty_mlfm_vars >= 2: # dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] - - if qty_mlfm_vars >= 4: # + + if qty_mlfm_vars >= 4: # dnorm['i_sc'] = (dmeas['i_sc'] / (dmeas['poa_global_kwm2'] * ref['i_sc'])) - + dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] - + # temperature corrected dnorm['v_oc_temp_corr'] = (dnorm['v_oc'] * (1 - ref['beta_v_oc']*(dmeas['temp_module']-T_STC))) - - if qty_mlfm_vars >= 6: # 6,8 IV data - - ''' - create temporary variables (ir, vr) from + + if qty_mlfm_vars >= 6: # 6,8 IV data + + ''' + create temporary variables (ir, vr) from intercept of r_sc (at i_sc) with r_oc (at v_oc) - to make maths easier + to make maths easier ''' - + ir = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) @@ -132,7 +124,7 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc dnorm['r_oc'] = vr / dmeas['v_oc'] # norm_r @ roc - + # calculate remaining fill factor losses partitioned to i_ff, v_ff dnorm['i_ff'] = dmeas['i_mp'] / ir dnorm['v_ff'] = dmeas['v_mp'] / vr @@ -140,22 +132,21 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): return dnorm - def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): - ''' + ''' Predict normalised lfm values e.g. pr_dc, norm(i_sc, ... v_oc) from poa_global, temp_module, wind_speed and mlfm(c_1 .. c_6). - + Parameters ---------- - + dmeas : dataframe - measured weather data + measured weather data 'poa_global', 'temp_module', 'wind_speed' and measured electrical/thermal values 'i_sc' .. 'v_oc', temp_module. - - c_1 to c_6 : float + + c_1 to c_6 : float fitted mlfm coefficients (dependencies) c_1 - constant c_2 - temperature coefficient (1/K) @@ -163,13 +154,13 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): c_4 - high light linear irradiance drop (~r_s) c_5 - wind speed dependence (=0 if indoor) c_6 - inverse irradiance (<= 0). - + Returns ------- mlfm_6 : float predicted performance values for pr_dc, norm(i_sc, .. v_oc) . - ''' - + ''' + mlfm_6 = ( c_1 + # 'constant' lossless c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient @@ -181,78 +172,77 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): return mlfm_6 - def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): - ''' + ''' Converts MLFM normalised multiplicative losses norm(i_sc ... v_oc) to stacked subtractive losses stack(i_sc ... v_oc). - + Ref: http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf - - current losses : - meas(imp) / ref(i_sc) = + + current losses : + meas(imp) / ref(i_sc) = poa_global_kwm2 * (norm(i_sc) * norm(r_sc) * norm(i_ff)) - - voltage losses : + + voltage losses : meas(vmp) / ref(v_oc) = (norm(v_ff) * norm(r_oc) * norm(v_oc)) - + 1/ff_ref = (ref(isc) / ref(imp)) * (ref(voc) / ref(vmp)) - - - Multiplicative losses: + + + Multiplicative losses: - are easier to use on a scatter plot vs. irradiance or temperature. - - Stacked losses: + + Stacked losses: - are better to use to see relative loss proportions (for underperformance limitations) or vs. time e.g. for degradation. - + - Stacked losses are scaled so they give the correct pr_dc and are in the right proportion to each other. - + Parameters ---------- dmeas : dataframe - measured weather data + measured weather data 'poa_global', 'temp_module', 'wind_speed' and measured electrical/thermal values 'i_sc' .. 'v_oc', temp_module.. - + dnorm : dataframe normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). - - ref : dict + + ref : dict reference stc values e.g. 'v_oc' and temperature coeffs e.g. 'beta_v_oc' . - - qty_mlfm_vars : int + + qty_mlfm_vars : int number of mlfm_values present in data usually 2 = (imp, vmp) from mpp tracker 4 = (i_sc, i_mp, v_mp, v_oc) from matrix 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. - + Returns ------- dstack : dataframe normalised subtractive lfm loss values 'i_sc' .. 'v_oc' where pr_dc = 1/ff - sum('i_sc', ... 'v_oc'). - + ''' - + #create an empty pands to put stack results dstack = pd.DataFrame() - + # create a gap to differentiate i and v losses : gap width~0.01 - gap = 0.01 # - + gap = 0.01 # + # calculate reference fill factor (usually between 0.5 and 0.8) ff_ref = ref['ff'] - + ''' calculate inverse fill factor ~ 1.25 - 2 as we calculate i_losses from ref_isc to norm_imp @@ -277,9 +267,9 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): dnorm['v_ff'] + dnorm['r_oc'] + dnorm['v_oc'] - 6 ) - # correction factor + # correction factor corr = ( inv_ff - prod ) / ( inv_ff - tot ) - + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) dstack['pr_dc'] = +dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr @@ -289,31 +279,31 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): dstack['v_ff'] = -(dnorm['v_ff'] - 1) * corr - gap/2 dstack['r_oc'] = -(dnorm['r_oc'] - 1) * corr dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr - + dstack['temp_module_corr'] = ( - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) - + elif qty_mlfm_vars == 4: # matrix ''' find factor to transform multiplicative to subtractive losses correction factor to scale losses to keep 1/ff --> pr_dc ''' - + # product prod = inv_ff * ( dnorm['i_sc'] * dnorm['i_mp'] * dnorm['v_mp'] * dnorm['v_oc'] ) - + #total tot = inv_ff + ( dnorm['i_sc'] + dnorm['i_mp'] + dnorm['v_mp'] + dnorm['v_oc'] - 4 ) - + #correction factor corr = ( inv_ff - prod ) / ( inv_ff - tot ) - + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) dstack['pr_dc'] = + dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr @@ -321,46 +311,45 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): dstack['i_v'] = gap dstack['v_mp'] = -(dnorm['v_mp'] - 1) * corr - gap/2 dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr - + dstack['temp_module_corr'] = ( - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) - - return dstack + return dstack def mlfm_fit(dmeas, dnorm, mlfm_sel): - ''' + ''' Fit MLFM to normalised data e.g. norm_pr_dc,(norm_i_sc .. norm_v_oc). - + Parameters ---------- - + dnorm : dataframe normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). - + mlfm_sel : string mlfm variable being fitted e.g. pr_dc. - + Returns - ------- + ------- dnorm : dataframe same data but with added mlfm_var fit values calc_mlfm_sel and diff_mlfm_sel. - + cc : list fit coefficients c_1 to c_6. - + ee : list error values. - + coeffs : string formatted coefficients for printing. - + errs formatted errors for printing. - ''' - + ''' + # drop missing data dnorm = dnorm.dropna() ''' @@ -369,15 +358,15 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): ''' # define function name f = mlfm_6 - + # setup initial values and initial boundary conditions - + # initial c1 c2 c3 c4 c5 c6<0 p0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) # boundaries bounds = ([ -2, -2, -2, -2, -2, -2], [ 2, 2, 2, 2, 2, 0]) - + if True: popt, pcov = optimize.curve_fit( f = f, # fit function @@ -385,8 +374,8 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): ydata = dnorm[mlfm_sel], # fit parameter p0 = p0, # initial bounds = bounds # boundaries - ) - + ) + # get mlfm coefficients c_1 = popt[0] c_2 = popt[1] @@ -394,19 +383,19 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): c_4 = popt[3] c_5 = popt[4] c_6 = popt[5] - + cc = [c_1, c_2, c_3, c_4, c_5, c_6,] # get mlfm error coefficients as sqrt of covariance perr = np.sqrt(np.diag(pcov)) - + e_1 = perr[0] e_2 = perr[1] e_3 = perr[2] e_4 = perr[3] e_5 = perr[4] e_6 = perr[5] - + ee = [e_1, e_2, e_3, e_4, e_5, e_6,] # format coefficients as strings, easier to read in graph title @@ -416,7 +405,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): ', {:.4%}'.format(c_3) + ', {:.4%}'.format(c_4) + ', {:.4%}'.format(c_5) + - ', {:.4%}'.format(c_6) + ', {:.4%}'.format(c_6) ) ###print ('coeffs = ', mlfm_sel, coeffs) @@ -426,22 +415,21 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): ', {:.4%}'.format(e_3) + ', {:.4%}'.format(e_4) + ', {:.4%}'.format(e_5) + - ', {:.4%}'.format(e_6) + ', {:.4%}'.format(e_6) ) ###print ('errs = ', mlfm_sel, errs) - + # save fit and error to dataframe dnorm['calc_' + mlfm_sel] = (mlfm_6(dmeas, c_1,c_2,c_3,c_4,c_5,c_6,)) - + dnorm['diff_' + mlfm_sel] = (dnorm[mlfm_sel] - dnorm['calc_' + mlfm_sel]) - - return(dnorm, cc, ee, coeffs, errs) + return(dnorm, cc, ee, coeffs, errs) """ ## References - + The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) together known as "MLFM" have been developed by SRCL and Gantner Instruments (previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM @@ -464,7 +452,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf .. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) -'5CV.4.35 Quantifying Long Term PV Performance and Degradation +'5CV.4.35 Quantifying Long Term PV Performance and Degradation under Real Outdoor and IEC 61853 Test Conditions Using High Quality Module IV Measurements'. 36th EU PVSEC Sep 2019 [Marseille] From 283ee65210843165e89e2c1e9f2308cb12e8f7fc Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 17:42:00 +0000 Subject: [PATCH 03/81] format fix mlfm.py --- pvlib/mlfm.py | 66 +++++++++++++++++++++++++-------------------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index df87e03137..42ee343925 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -22,32 +22,32 @@ # NAME value comment unit PV_LIB name # -T_STC = 25.0 # STC temperature [C] temperature_ref -T_HTC = 75.0 # HTC temperature [C] -G_STC = 1.0 # STC irradiance [kW/m^2] -G_LIC = 0.2 # LIC irradiance [kW/m^2] +T_STC = 25.0 # STC temperature [C] temperature_ref +T_HTC = 75.0 # HTC temperature [C] +G_STC = 1.0 # STC irradiance [kW/m^2] +G_LIC = 0.2 # LIC irradiance [kW/m^2] -''' Define standardised MLFM graph colours as a dict ``clr`` ''' +''' Define standardised MLFM graph colours as a dict ``clr`` ''' clr = { #parameter_clr colour R G B - 'irradiance' : 'darkgreen', # 0 64 0 - 'temp_module' : 'red', # 255 0 0 - 'temp_air' : 'yellow', # 245 245 220 - 'wind_speed' : 'grey', # 127 127 127 - - 'i_sc' : 'purple', # 128 0 128 - 'r_sc' : 'orange', # 255 165 0 - 'i_ff' : 'lightgreen', # 144 238 144 - 'i_mp' : 'green', # 0 255 0 - 'i_v' : 'black', # 0 0 0 # between i and v losses - 'v_ff' : 'cyan', # 0 255 255 - 'v_mp' : 'blue', # 0 0 255 - 'r_oc' : 'pink', # 255 192 203 - 'v_oc' : 'sienna', # 160 82 45 - - 'pr_dc' : 'black', # 0 0 0 + 'irradiance': 'darkgreen', # 0 64 0 + 'temp_module': 'red', # 255 0 0 + 'temp_air': 'yellow', # 245 245 220 + 'wind_speed': 'grey', # 127 127 127 + + 'i_sc': 'purple', # 128 0 128 + 'r_sc': 'orange', # 255 165 0 + 'i_ff': 'lightgreen', # 144 238 144 + 'i_mp': 'green', # 0 255 0 + 'i_v': 'black', # 0 0 0 # between i and v losses + 'v_ff': 'cyan', # 0 255 255 + 'v_mp': 'blue', # 0 0 255 + 'r_oc': 'pink', # 255 192 203 + 'v_oc': 'sienna', # 160 82 45 + + 'pr_dc': 'black', # 0 0 0 } @@ -85,7 +85,7 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): # calculate normalised mlfm values depending on number of qty_mlfm_vars - if qty_mlfm_vars >= 1: # do for all measurements + if qty_mlfm_vars >= 1: # do for all measurements dnorm['pr_dc'] = (dmeas['p_mp'] / (ref['p_mp'] * dmeas['poa_global_kwm2'])) @@ -93,11 +93,11 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): dnorm['pr_dc_temp_corr'] = (dnorm['pr_dc'] * (1 - ref['gamma_p_mp']*(dmeas['temp_module']-T_STC))) - if qty_mlfm_vars >= 2: # + if qty_mlfm_vars >= 2: # dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] - if qty_mlfm_vars >= 4: # + if qty_mlfm_vars >= 4: # dnorm['i_sc'] = (dmeas['i_sc'] / (dmeas['poa_global_kwm2'] * ref['i_sc'])) @@ -107,7 +107,7 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): dnorm['v_oc_temp_corr'] = (dnorm['v_oc'] * (1 - ref['beta_v_oc']*(dmeas['temp_module']-T_STC))) - if qty_mlfm_vars >= 6: # 6,8 IV data + if qty_mlfm_vars >= 6: # 6,8 IV data ''' create temporary variables (ir, vr) from @@ -255,22 +255,22 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): find factor to transform multiplicative to subtractive losses correction factor to scale losses to keep 1/ff --> pr_dc ''' - # product + # product prod = inv_ff * ( dnorm['i_sc'] * dnorm['r_sc'] * dnorm['i_ff'] * dnorm['v_ff'] * dnorm['r_oc'] * dnorm['v_oc'] ) - # total + # total tot = inv_ff + ( dnorm['i_sc'] + dnorm['r_sc'] + dnorm['i_ff'] + dnorm['v_ff'] + dnorm['r_oc'] + dnorm['v_oc'] - 6 ) - # correction factor + # correction factor corr = ( inv_ff - prod ) / ( inv_ff - tot ) - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) dstack['pr_dc'] = +dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr dstack['r_sc'] = -(dnorm['r_sc'] - 1) * corr @@ -289,22 +289,22 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): correction factor to scale losses to keep 1/ff --> pr_dc ''' - # product + # product prod = inv_ff * ( dnorm['i_sc'] * dnorm['i_mp'] * dnorm['v_mp'] * dnorm['v_oc'] ) - #total + # total tot = inv_ff + ( dnorm['i_sc'] + dnorm['i_mp'] + dnorm['v_mp'] + dnorm['v_oc'] - 4 ) - #correction factor + # correction factor corr = ( inv_ff - prod ) / ( inv_ff - tot ) - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) dstack['pr_dc'] = + dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 From ac9304b1159519c6c55186d760552aca0793796f Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 18:00:00 +0000 Subject: [PATCH 04/81] format fix mlfm.py --- pvlib/mlfm.py | 83 +++++++++++++++++++++++++-------------------------- 1 file changed, 41 insertions(+), 42 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 42ee343925..55d6fa3fdd 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -31,23 +31,23 @@ ''' Define standardised MLFM graph colours as a dict ``clr`` ''' clr = { - #parameter_clr colour R G B - 'irradiance': 'darkgreen', # 0 64 0 - 'temp_module': 'red', # 255 0 0 - 'temp_air': 'yellow', # 245 245 220 - 'wind_speed': 'grey', # 127 127 127 - - 'i_sc': 'purple', # 128 0 128 - 'r_sc': 'orange', # 255 165 0 - 'i_ff': 'lightgreen', # 144 238 144 - 'i_mp': 'green', # 0 255 0 - 'i_v': 'black', # 0 0 0 # between i and v losses - 'v_ff': 'cyan', # 0 255 255 - 'v_mp': 'blue', # 0 0 255 - 'r_oc': 'pink', # 255 192 203 - 'v_oc': 'sienna', # 160 82 45 - - 'pr_dc': 'black', # 0 0 0 + #parameter_clr colour R G B + 'irradiance': 'darkgreen', # 0 64 0 + 'temp_module': 'red', # 255 0 0 + 'temp_air': 'yellow', # 245 245 220 + 'wind_speed': 'grey', # 127 127 127 + + 'i_sc': 'purple', # 128 0 128 + 'r_sc': 'orange', # 255 165 0 + 'i_ff': 'lightgreen', # 144 238 144 + 'i_mp': 'green', # 0 255 0 + 'i_v': 'black', # 0 0 0 # between i and v losses + 'v_ff': 'cyan', # 0 255 255 + 'v_mp': 'blue', # 0 0 255 + 'r_oc': 'pink', # 255 192 203 + 'v_oc': 'sienna', # 160 82 45 + + 'pr_dc': 'black', # 0 0 0 } @@ -91,7 +91,7 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): # temperature corrected dnorm['pr_dc_temp_corr'] = (dnorm['pr_dc'] * - (1 - ref['gamma_p_mp']*(dmeas['temp_module']-T_STC))) + (1 - ref['gamma_p_mp']*(dmeas['temp_module']-T_STC))) if qty_mlfm_vars >= 2: # dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] @@ -99,13 +99,13 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): if qty_mlfm_vars >= 4: # dnorm['i_sc'] = (dmeas['i_sc'] / - (dmeas['poa_global_kwm2'] * ref['i_sc'])) + (dmeas['poa_global_kwm2'] * ref['i_sc'])) dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] # temperature corrected dnorm['v_oc_temp_corr'] = (dnorm['v_oc'] * - (1 - ref['beta_v_oc']*(dmeas['temp_module']-T_STC))) + (1 - ref['beta_v_oc']*(dmeas['temp_module']-T_STC))) if qty_mlfm_vars >= 6: # 6,8 IV data @@ -116,14 +116,14 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): ''' ir = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / - (dmeas['r_sc'] - dmeas['r_oc'])) + (dmeas['r_sc'] - dmeas['r_oc'])) vr = (dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc - dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc - dnorm['r_oc'] = vr / dmeas['v_oc'] # norm_r @ roc + dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc + dnorm['r_oc'] = vr / dmeas['v_oc'] # norm_r @ roc # calculate remaining fill factor losses partitioned to i_ff, v_ff dnorm['i_ff'] = dmeas['i_mp'] / ir @@ -162,12 +162,12 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): ''' mlfm_6 = ( - c_1 + # 'constant' lossless - c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient - c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' - c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' - c_5 * dmeas['wind_speed'] + # wind_speed (optional or 0) - c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) + c_1 + # 'constant' lossless + c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient + c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' + c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' + c_5 * dmeas['wind_speed'] + # wind_speed (optional or 0) + c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) ) return mlfm_6 @@ -238,7 +238,7 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): dstack = pd.DataFrame() # create a gap to differentiate i and v losses : gap width~0.01 - gap = 0.01 # + gap = 0.01 # calculate reference fill factor (usually between 0.5 and 0.8) ff_ref = ref['ff'] @@ -268,7 +268,7 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): ) # correction factor - corr = ( inv_ff - prod ) / ( inv_ff - tot ) + corr = (inv_ff - prod) / (inv_ff - tot) # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) dstack['pr_dc'] = +dnorm['pr_dc'] # initialise @@ -279,7 +279,6 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): dstack['v_ff'] = -(dnorm['v_ff'] - 1) * corr - gap/2 dstack['r_oc'] = -(dnorm['r_oc'] - 1) * corr dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr - dstack['temp_module_corr'] = ( - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) @@ -302,10 +301,10 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): ) # correction factor - corr = ( inv_ff - prod ) / ( inv_ff - tot ) + corr = (inv_ff - prod) / (inv_ff - tot) # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) - dstack['pr_dc'] = + dnorm['pr_dc'] # initialise + dstack['pr_dc'] = + dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 dstack['i_v'] = gap @@ -369,11 +368,11 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): if True: popt, pcov = optimize.curve_fit( - f = f, # fit function - xdata = dmeas, # input data - ydata = dnorm[mlfm_sel], # fit parameter - p0 = p0, # initial - bounds = bounds # boundaries + f = f, # fit function + xdata = dmeas, # input data + ydata = dnorm[mlfm_sel], # fit parameter + p0 = p0, # initial + bounds = bounds # boundaries ) # get mlfm coefficients @@ -384,7 +383,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): c_5 = popt[4] c_6 = popt[5] - cc = [c_1, c_2, c_3, c_4, c_5, c_6,] + cc = [c_1, c_2, c_3, c_4, c_5, c_6 ] # get mlfm error coefficients as sqrt of covariance perr = np.sqrt(np.diag(pcov)) @@ -396,7 +395,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): e_5 = perr[4] e_6 = perr[5] - ee = [e_1, e_2, e_3, e_4, e_5, e_6,] + ee = [e_1, e_2, e_3, e_4, e_5, e_6 ] # format coefficients as strings, easier to read in graph title coeffs = ( From 5c652ce6cfddf0f7f6563e7f15e0cc6d409fae0b Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 18:29:02 +0000 Subject: [PATCH 05/81] format fix mlfm.py --- pvlib/mlfm.py | 117 +++++++++++++++++++++++++++----------------------- 1 file changed, 63 insertions(+), 54 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 55d6fa3fdd..ef214b7cff 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -31,23 +31,23 @@ ''' Define standardised MLFM graph colours as a dict ``clr`` ''' clr = { - #parameter_clr colour R G B - 'irradiance': 'darkgreen', # 0 64 0 - 'temp_module': 'red', # 255 0 0 - 'temp_air': 'yellow', # 245 245 220 - 'wind_speed': 'grey', # 127 127 127 - - 'i_sc': 'purple', # 128 0 128 - 'r_sc': 'orange', # 255 165 0 - 'i_ff': 'lightgreen', # 144 238 144 - 'i_mp': 'green', # 0 255 0 - 'i_v': 'black', # 0 0 0 # between i and v losses - 'v_ff': 'cyan', # 0 255 255 - 'v_mp': 'blue', # 0 0 255 - 'r_oc': 'pink', # 255 192 203 - 'v_oc': 'sienna', # 160 82 45 - - 'pr_dc': 'black', # 0 0 0 + # parameter_clr colour R G B + 'irradiance': 'darkgreen', # 000 064 000 + 'temp_module': 'red', # 255 000 000 + 'temp_air': 'yellow', # 245 245 220 + 'wind_speed': 'grey', # 127 127 127 + + 'i_sc': 'purple', # 128 000 128 + 'r_sc': 'orange', # 255 165 000 + 'i_ff': 'lightgreen', # 144 238 144 + 'i_mp': 'green', # 000 255 000 + 'i_v': 'black', # 000 000 000 between i and v losses + 'v_ff': 'cyan', # 000 255 255 + 'v_mp': 'blue', # 000 000 255 + 'r_oc': 'pink', # 255 192 203 + 'v_oc': 'sienna', # 160 082 045 + + 'pr_dc': 'black', # 000 000 000 } @@ -86,26 +86,30 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): # calculate normalised mlfm values depending on number of qty_mlfm_vars if qty_mlfm_vars >= 1: # do for all measurements - dnorm['pr_dc'] = (dmeas['p_mp'] / + dnorm['pr_dc'] = ( + dmeas['p_mp'] / (ref['p_mp'] * dmeas['poa_global_kwm2'])) # temperature corrected - dnorm['pr_dc_temp_corr'] = (dnorm['pr_dc'] * - (1 - ref['gamma_p_mp']*(dmeas['temp_module']-T_STC))) + dnorm['pr_dc_temp_corr'] = ( + dnorm['pr_dc'] * + (1 - ref['gamma_p_mp']*(dmeas['temp_module']-T_STC))) if qty_mlfm_vars >= 2: # dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] if qty_mlfm_vars >= 4: # - dnorm['i_sc'] = (dmeas['i_sc'] / - (dmeas['poa_global_kwm2'] * ref['i_sc'])) + dnorm['i_sc'] = ( + dmeas['i_sc'] / + (dmeas['poa_global_kwm2'] * ref['i_sc'])) dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] # temperature corrected - dnorm['v_oc_temp_corr'] = (dnorm['v_oc'] * - (1 - ref['beta_v_oc']*(dmeas['temp_module']-T_STC))) + dnorm['v_oc_temp_corr'] = ( + dnorm['v_oc'] * + (1 - ref['beta_v_oc']*(dmeas['temp_module']-T_STC))) if qty_mlfm_vars >= 6: # 6,8 IV data @@ -115,11 +119,13 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): to make maths easier ''' - ir = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / - (dmeas['r_sc'] - dmeas['r_oc'])) + ir = ( + (dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / + (dmeas['r_sc'] - dmeas['r_oc'])) - vr = (dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + vr = ( + dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc @@ -162,12 +168,12 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): ''' mlfm_6 = ( - c_1 + # 'constant' lossless - c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient - c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' - c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' - c_5 * dmeas['wind_speed'] + # wind_speed (optional or 0) - c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) + c_1 + # 'constant' lossless + c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient + c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' + c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' + c_5 * dmeas['wind_speed'] + # wind_speed (optional or 0) + c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) ) return mlfm_6 @@ -234,7 +240,7 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): ''' - #create an empty pands to put stack results + # create an empty pands to put stack results dstack = pd.DataFrame() # create a gap to differentiate i and v losses : gap width~0.01 @@ -267,20 +273,20 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): dnorm['v_ff'] + dnorm['r_oc'] + dnorm['v_oc'] - 6 ) - # correction factor + # correction factor corr = (inv_ff - prod) / (inv_ff - tot) - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) - dstack['pr_dc'] = +dnorm['pr_dc'] # initialise + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) + dstack['pr_dc'] = +dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr dstack['r_sc'] = -(dnorm['r_sc'] - 1) * corr dstack['i_ff'] = -(dnorm['i_ff'] - 1) * corr - gap/2 - dstack['i_v'] = gap + dstack['i_v'] = gap dstack['v_ff'] = -(dnorm['v_ff'] - 1) * corr - gap/2 dstack['r_oc'] = -(dnorm['r_oc'] - 1) * corr dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr dstack['temp_module_corr'] = ( - - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) + -(dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) elif qty_mlfm_vars == 4: # matrix ''' @@ -300,10 +306,10 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): dnorm['v_mp'] + dnorm['v_oc'] - 4 ) - # correction factor + # correction factor corr = (inv_ff - prod) / (inv_ff - tot) - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) dstack['pr_dc'] = + dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 @@ -334,7 +340,8 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): Returns ------- dnorm : dataframe - same data but with added mlfm_var fit values calc_mlfm_sel and diff_mlfm_sel. + same data but with added mlfm_var fit values + calc_mlfm_sel and diff_mlfm_sel. cc : list fit coefficients c_1 to c_6. @@ -368,11 +375,11 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): if True: popt, pcov = optimize.curve_fit( - f = f, # fit function - xdata = dmeas, # input data - ydata = dnorm[mlfm_sel], # fit parameter - p0 = p0, # initial - bounds = bounds # boundaries + f=f, # fit function + xdata=dmeas, # input data + ydata=dnorm[mlfm_sel], # fit parameter + p0=p0, # initial + bounds=bounds # boundaries ) # get mlfm coefficients @@ -383,7 +390,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): c_5 = popt[4] c_6 = popt[5] - cc = [c_1, c_2, c_3, c_4, c_5, c_6 ] + cc = [c_1, c_2, c_3, c_4, c_5, c_6] # get mlfm error coefficients as sqrt of covariance perr = np.sqrt(np.diag(pcov)) @@ -395,7 +402,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): e_5 = perr[4] e_6 = perr[5] - ee = [e_1, e_2, e_3, e_4, e_5, e_6 ] + ee = [e_1, e_2, e_3, e_4, e_5, e_6] # format coefficients as strings, easier to read in graph title coeffs = ( @@ -406,7 +413,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): ', {:.4%}'.format(c_5) + ', {:.4%}'.format(c_6) ) - ###print ('coeffs = ', mlfm_sel, coeffs) + # print ('coeffs = ', mlfm_sel, coeffs) errs = ( ' {:.4%}'.format(e_1) + @@ -416,12 +423,14 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): ', {:.4%}'.format(e_5) + ', {:.4%}'.format(e_6) ) - ###print ('errs = ', mlfm_sel, errs) + # print ('errs = ', mlfm_sel, errs) # save fit and error to dataframe - dnorm['calc_' + mlfm_sel] = (mlfm_6(dmeas, c_1,c_2,c_3,c_4,c_5,c_6,)) + dnorm['calc_' + mlfm_sel] = ( + mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6)) - dnorm['diff_' + mlfm_sel] = (dnorm[mlfm_sel] - dnorm['calc_' + mlfm_sel]) + dnorm['diff_' + mlfm_sel] = ( + dnorm[mlfm_sel] - dnorm['calc_' + mlfm_sel]) return(dnorm, cc, ee, coeffs, errs) From 40170247c5e8f6c7b700af4dde69ac669aadf97a Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 18:33:56 +0000 Subject: [PATCH 06/81] format fix mlfm.py --- pvlib/mlfm.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index ef214b7cff..2c1ed1f44c 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -128,8 +128,8 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc - dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc - dnorm['r_oc'] = vr / dmeas['v_oc'] # norm_r @ roc + dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc + dnorm['r_oc'] = vr / dmeas['v_oc'] # norm_r @ roc # calculate remaining fill factor losses partitioned to i_ff, v_ff dnorm['i_ff'] = dmeas['i_mp'] / ir @@ -168,12 +168,12 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): ''' mlfm_6 = ( - c_1 + # 'constant' lossless - c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient - c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' - c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' - c_5 * dmeas['wind_speed'] + # wind_speed (optional or 0) - c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) + c_1 + # 'constant' lossless + c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient + c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' + c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' + c_5 * dmeas['wind_speed'] + # wind_speed (optional or 0) + c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) ) return mlfm_6 @@ -256,7 +256,7 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): ''' inv_ff = 1 / ff_ref - if qty_mlfm_vars == 6: # ivcurve + if qty_mlfm_vars == 6: # ivcurve ''' find factor to transform multiplicative to subtractive losses correction factor to scale losses to keep 1/ff --> pr_dc @@ -281,14 +281,14 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr dstack['r_sc'] = -(dnorm['r_sc'] - 1) * corr dstack['i_ff'] = -(dnorm['i_ff'] - 1) * corr - gap/2 - dstack['i_v'] = gap + dstack['i_v'] = gap dstack['v_ff'] = -(dnorm['v_ff'] - 1) * corr - gap/2 dstack['r_oc'] = -(dnorm['r_oc'] - 1) * corr dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr dstack['temp_module_corr'] = ( -(dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) - elif qty_mlfm_vars == 4: # matrix + elif qty_mlfm_vars == 4: # matrix ''' find factor to transform multiplicative to subtractive losses correction factor to scale losses to keep 1/ff --> pr_dc @@ -310,10 +310,10 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): corr = (inv_ff - prod) / (inv_ff - tot) # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) - dstack['pr_dc'] = + dnorm['pr_dc'] # initialise + dstack['pr_dc'] = + dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 - dstack['i_v'] = gap + dstack['i_v'] = gap dstack['v_mp'] = -(dnorm['v_mp'] - 1) * corr - gap/2 dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr @@ -340,7 +340,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): Returns ------- dnorm : dataframe - same data but with added mlfm_var fit values + same data but with added mlfm_var fit values calc_mlfm_sel and diff_mlfm_sel. cc : list From a4552906edf6e0a92ee4a6c88a4b04dd8675e96b Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 18:37:12 +0000 Subject: [PATCH 07/81] format fix mlfm.py --- pvlib/mlfm.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 2c1ed1f44c..6d35e955b4 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -124,8 +124,8 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): (dmeas['r_sc'] - dmeas['r_oc'])) vr = ( - dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc @@ -172,7 +172,7 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' - c_5 * dmeas['wind_speed'] + # wind_speed (optional or 0) + c_5 * dmeas['wind_speed'] + # wind_speed (optional|0) c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) ) From 1a2c2187bae10d8156de014c8807d27781f8f6c9 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 18:39:02 +0000 Subject: [PATCH 08/81] format fix mlfm.py --- pvlib/mlfm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 6d35e955b4..03868a7005 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -124,8 +124,8 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): (dmeas['r_sc'] - dmeas['r_oc'])) vr = ( - dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From b0bd28ac2f2840aadcc999484868d70a000dcb89 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 18:40:41 +0000 Subject: [PATCH 09/81] format fix mlfm.py --- pvlib/mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 03868a7005..33e2c7c81f 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -125,7 +125,7 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): vr = ( dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From 3ee9c5066644a2a53400fdc0ed6bf370268357ba Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 19:06:51 +0000 Subject: [PATCH 10/81] format fix mlfm_graphs.py --- pvlib/mlfm_graphs.py | 226 +++++++++++++++++++++---------------------- 1 file changed, 112 insertions(+), 114 deletions(-) diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py index 18f916037b..6ac7dce82f 100644 --- a/pvlib/mlfm_graphs.py +++ b/pvlib/mlfm_graphs.py @@ -12,67 +12,65 @@ import numpy as np import matplotlib.pyplot as plt -from matplotlib.colors import LinearSegmentedColormap - ''' Define standardised MLFM graph colours as a dict ``clr`` ''' clr = { - #parameter_clr colour R G B - 'irradiance' : 'darkgreen', # 0 64 0 - 'temp_module' : 'red', # 255 0 0 - 'temp_air' : 'yellow', # 245 245 220 - 'wind_speed' : 'grey', # 127 127 127 + # parameter_clr colour R G B + 'irradiance': 'darkgreen', # 000 064 000 + 'temp_module': 'red', # 255 000 000 + 'temp_air': 'yellow', # 245 245 220 + 'wind_speed': 'grey', # 127 127 127 - 'i_sc' : 'purple', # 128 0 128 - 'r_sc' : 'orange', # 255 165 0 - 'i_ff' : 'lightgreen', # 144 238 144 - 'i_mp' : 'green', # 0 255 0 - 'i_v' : 'black', # 0 0 0 # between i and v losses - 'v_ff' : 'cyan', # 0 255 255 - 'v_mp' : 'blue', # 0 0 255 - 'r_oc' : 'pink', # 255 192 203 - 'v_oc' : 'sienna', # 160 82 45 + 'i_sc': 'purple', # 128 000 128 + 'r_sc': 'orange', # 255 165 000 + 'i_ff': 'lightgreen', # 144 238 144 + 'i_mp': 'green', # 000 255 000 + 'i_v': 'black', # 000 000 000 between i and v losses + 'v_ff': 'cyan', # 000 255 255 + 'v_mp': 'blue', # 000 000 255 + 'r_oc': 'pink', # 255 192 203 + 'v_oc': 'sienna', # 160 082 045 - 'pr_dc' : 'black', # 0 0 0 -} + 'pr_dc': 'black', # 000 000 000 +} def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): - ''' + ''' Scatter plot normalised MLFM parameters(y) vs. irradiance(x). - + y1_axis : e.g. norm(i_sc, ... v_oc),_temp_module_corr x_axis : e.g. irradiance, poa_global_kwm2 y2_axis : e.g. temp_air, temp_module (C/100 to fit graphs). - + Parameters ---------- dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' - and measured electrical/thermal values + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values 'i_sc' .. 'v_oc', temp_module. dnorm : dataframe multiplicative lfm loss values 'i_sc' ... 'v_oc' - where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). - + where pr_dc = 1/ff * product('i_sc', ... 'v_oc') + mlfm_file_name : string mlfm_file_name used in graph title. - - qty_mlfm_vars : int + + qty_mlfm_vars : int number of mlfm_values present in data usually 2 = (imp, vmp) from mpp tracker 4 = (i_sc, i_mp, v_mp, v_oc) from matrix 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. ''' - + # offset legend to the right to not overlap graph, use ~1.2 bbox = 1.2 - + # set x_axis as irradiance xdata = dmeas['poa_global'] @@ -81,75 +79,76 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): # get filename without ".csv" for title ax1.set_title('Plot mlfm scatter ' + mlfm_file_name[:len(mlfm_file_name)-4]) - + ax1.set_ylabel('normalised mlfm values') - ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line + ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line ax1.set_ylim(0.8, 1.1) # optional normalised y scale - + ax1.set_xlabel('poa_global [W/m$^2$]') - ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC - ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT - ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC + ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC + ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT + ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC # plot the mlfm parameters depending on qty_mlfm_vars - if qty_mlfm_vars == 1: # only p_mp + if qty_mlfm_vars == 1: # only p_mp ax1.scatter(xdata, dnorm['pr_dc_temp_corr'], c=clr['pr_dc'], label='pr_dc_temp_corr') - - if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix + + if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix ax1.scatter(xdata, dnorm['i_mp'], c=clr['i_mp'], label='norm_i_mp') ax1.scatter(xdata, dnorm['v_mp'], c=clr['v_mp'], label='norm_v_mp') - - if qty_mlfm_vars >= 6: # ivcurve + + if qty_mlfm_vars >= 6: # ivcurve ax1.scatter(xdata, dnorm['i_ff'], c=clr['i_ff'], label='norm_i_ff') ax1.scatter(xdata, dnorm['v_ff'], c=clr['v_ff'], label='norm_v_ff') ax1.scatter(xdata, dnorm['r_sc'], c=clr['r_sc'], label='norm_r_sc') ax1.scatter(xdata, dnorm['r_oc'], c=clr['r_oc'], label='norm_r_oc') - - if qty_mlfm_vars >= 4: # matrix + + if qty_mlfm_vars >= 4: # matrix ax1.scatter(xdata, dnorm['i_sc'], c=clr['i_sc'], label='norm_i_sc') - + ax1.scatter(xdata, dnorm['v_oc_temp_corr'], c=clr['v_oc'], label='norm_v_oc_temp_corr') - + ax1.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) - + # y2axis plot met on right y axis ax2 = ax1.twinx() - ax2.set_ylabel('temp_module, temp_air (C/100)') # poa_global (kW/m$^2$); - + ax2.set_ylabel('temp_module, temp_air (C/100)') # poa_global (kW/m$^2$); + # set wide limits 0 to 4 so they don't overlap mlfm params ax2.set_ylim(0, 4) - + ax2.scatter(xdata, dmeas['temp_module']/100, c=clr['temp_module'], label='temp_module C/100') - + # temp_air may not exist particularly for indoor measurements try: ax2.scatter(xdata, - dmeas['temp_air']/100, - c=clr['temp_air'], - label='temp_air C/100') + dmeas['temp_air']/100, + c=clr['temp_air'], + label='temp_air C/100') except: pass - + ax2.legend(bbox_to_anchor=(bbox, 0.5), loc='upper left', borderaxespad=0.) plt.show() - -def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, - xaxis_labels=12, is_i_sc_self_ref=False, is_v_oc_temp_module_corr=True): +def plot_mlfm_stack(dmeas, dnorm, dstack, ref, + mlfm_file_name, qty_mlfm_vars, + xaxis_labels=12, is_i_sc_self_ref=False, + is_v_oc_temp_module_corr=True): - ''' + ''' Plot graph of stacked MLFM losses from intital 1/FF down to pr_dc. - + Parameters ---------- dmeas : dataframe - measured weather data + measured weather data 'poa_global', 'temp_module', 'wind_speed' and measured electrical/thermal values 'i_sc' .. 'v_oc', temp_module. @@ -157,48 +156,47 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, dnorm : dataframe normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). - - - dstack : dataframe + + dstack : dataframe normalised subtractive lfm loss values 'i_sc' .. 'v_oc' where pr_dc = 1/ff - sum('i_sc', ... 'v_oc'). - - ref : dict + + ref : dict reference stc values e.g. 'v_oc' and - temperature coeffs e.g. 'beta_v_oc' . - + temperature coeffs e.g. 'beta_v_oc'. + mlfm_file_name : string mlfm_file_name used in graph title. - - qty_mlfm_vars : int + + qty_mlfm_vars : int number of mlfm_values present in data usually 2 = (imp, vmp) from mpp tracker 4 = (i_sc, i_mp, v_mp, v_oc) from matrix 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. - + xaxis_labels : int number of xaxis labels to show (~12) or 0 to show all. - + is_i_sc_self_ref : bool self corrects i_sc to remove angle of incidence, spectrum, snow or soiling?. - + is_v_oc_temp_module_corr : bool calc loss due to gamma, subtract from v_oc loss. ''' - + # offset legend right, use ~1.2 bbox = 1.2 - + # select x axis usually date_time xdata = dmeas.index fig, ax1 = plt.subplots() - + ax1.set_title('Plot_mlfm_stack : ' + mlfm_file_name[:len(mlfm_file_name)-4]) - - if qty_mlfm_vars == 6: # iv curve + + if qty_mlfm_vars == 6: # iv curve labels_6 = [ 'pr_dc', 'stack_t_mod', @@ -209,10 +207,10 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, 'stack_i_ff', 'stack_r_sc', 'stack_i_sc' - ] - + ] + color_map_6 = [ - 'white', # colour to bottom of graph + 'white', # colour to bottom of graph clr['temp_module'], clr['v_oc'], clr['r_oc'], @@ -221,7 +219,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, clr['i_ff'], clr['r_sc'], clr['i_sc'] - ] + ] # plot stack in order bottom to top, # allowing self_ref and temp_module corrections @@ -237,11 +235,11 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, dstack['i_ff'], dstack['r_sc'], dstack['i_sc'] * (not is_i_sc_self_ref), - labels = labels_6, - colors = color_map_6 + labels=labels_6, + colors=color_map_6 ) - - if qty_mlfm_vars == 4: # matrix + + if qty_mlfm_vars == 4: # matrix labels_4 = [ 'pr_dc', 'stack_t_mod', @@ -250,22 +248,22 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, '- - -', 'stack_i_mp', 'i_sc' - ] - + ] + color_map_4 = [ - 'white', # colour to bottom of graph + 'white', # colour to bottom of graph clr['temp_module'], clr['v_oc'], clr['v_mp'], clr['i_v'], clr['i_mp'], clr['i_sc'] - ] - + ] + # plot stack in order bottom to top, # allowing self_ref and temp_module corrections ax1.stackplot( - xdata, + xdata, dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), dstack['v_oc'] - ( @@ -274,68 +272,68 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, dstack['i_v'], dstack['i_mp'], dstack['i_sc'] * (not is_i_sc_self_ref), - labels = labels_4, - colors = color_map_4 - ) - - ax1.axhline(y=1/ref['ff'], c='grey', lw=3) # show initial 1/FF - ax1.axhline(y=1, c='grey', lw=3) # show 100% line - ax1.set_ylabel('stacked mlfm losses') - + labels=labels_4, + colors=color_map_4 + ) + + ax1.axhline(y=1/ref['ff'], c='grey', lw=3) # show initial 1/FF + ax1.axhline(y=1, c='grey', lw=3) # show 100% line + ax1.set_ylabel('stacked mlfm losses') + # find number of x date values x_ticks = dmeas.shape[0] plt.xticks(np.arange(0, x_ticks), rotation=90) - + if (xaxis_labels > 0 and xaxis_labels < x_ticks): xaxis_skip = np.floor(x_ticks / xaxis_labels) else: xaxis_skip = 2 - - # + + # xax2 = [''] * x_ticks x_count = 0 while x_count < x_ticks: if x_count % xaxis_skip == 0: ''' try to reformat any date indexes (not for matrices) - + 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' - ''' + ''' try: xax2[x_count] = xdata[x_count][2:13]+'h' - + except IndexError: xax2[x_count] = xdata[x_count] x_count += 1 ax1.set_xticklabels(xax2) - ax1.set_ylim(0.6, 1/ref['ff']+0.1) # optional normalised y scale + ax1.set_ylim(0.6, 1/ref['ff']+0.1) # optional normalised y scale plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) - + # plot met data on right y axis ax2 = ax1.twinx() ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/100)') - ax2.set_ylim(0, 4) # set so doesn't overlap mlfm params + ax2.set_ylim(0, 4) # set so doesn't overlap mlfm params - plt.plot(xdata, dmeas['poa_global_kwm2'], + plt.plot(xdata, dmeas['poa_global_kwm2'], c=clr['irradiance'], label='poa_global_kwm2') plt.plot(xdata, dmeas['temp_module'] / 100, c=clr['temp_module'], label='temp_module/100') - + # temp_air may not exist particularly for indoor measurements try: - plt.plot(xdata, dmeas['temp_air']/100, c=clr['temp_air'], label='temp_air/100') + plt.plot(xdata, dmeas['temp_air']/100, + c=clr['temp_air'], label='temp_air/100') except: pass - + ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) - ax1.set_xticklabels(xax2, rotation = 90) + ax1.set_xticklabels(xax2, rotation=90) plt.show() - """ ## References From 95b8963f71c6c48a02f34f29395cf323212e7dd7 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 19:14:14 +0000 Subject: [PATCH 11/81] format fix mlfm_graphs.py --- pvlib/mlfm_graphs.py | 41 ++++++++++++++++++++--------------------- 1 file changed, 20 insertions(+), 21 deletions(-) diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py index 6ac7dce82f..000b034620 100644 --- a/pvlib/mlfm_graphs.py +++ b/pvlib/mlfm_graphs.py @@ -1,5 +1,5 @@ ''' -This ``mlfm graphs`` module contains functions to display 79-| +This ``mlfm graphs`` module contains functions to display 79-| performance of PV modules using the mechanistic performance (MPM) and loss factors models (LFM) @@ -15,7 +15,7 @@ ''' Define standardised MLFM graph colours as a dict ``clr`` ''' - + clr = { # parameter_clr colour R G B 'irradiance': 'darkgreen', # 000 064 000 @@ -37,7 +37,6 @@ } - def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): ''' Scatter plot normalised MLFM parameters(y) vs. irradiance(x). @@ -53,7 +52,7 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): 'poa_global', 'temp_module', 'wind_speed' and measured electrical/thermal values 'i_sc' .. 'v_oc', temp_module. - + dnorm : dataframe multiplicative lfm loss values 'i_sc' ... 'v_oc' where pr_dc = 1/ff * product('i_sc', ... 'v_oc') @@ -69,26 +68,26 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): ''' # offset legend to the right to not overlap graph, use ~1.2 - bbox = 1.2 + bbox = 1.2 # set x_axis as irradiance xdata = dmeas['poa_global'] fig, ax1 = plt.subplots() - + # get filename without ".csv" for title ax1.set_title('Plot mlfm scatter ' + mlfm_file_name[:len(mlfm_file_name)-4]) ax1.set_ylabel('normalised mlfm values') ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line - ax1.set_ylim(0.8, 1.1) # optional normalised y scale + ax1.set_ylim(0.8, 1.1) # optional normalised y scale ax1.set_xlabel('poa_global [W/m$^2$]') ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC - + # plot the mlfm parameters depending on qty_mlfm_vars if qty_mlfm_vars == 1: # only p_mp ax1.scatter(xdata, dnorm['pr_dc_temp_corr'], @@ -104,7 +103,7 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): ax1.scatter(xdata, dnorm['r_sc'], c=clr['r_sc'], label='norm_r_sc') ax1.scatter(xdata, dnorm['r_oc'], c=clr['r_oc'], label='norm_r_oc') - if qty_mlfm_vars >= 4: # matrix + if qty_mlfm_vars >= 4: # matrix ax1.scatter(xdata, dnorm['i_sc'], c=clr['i_sc'], label='norm_i_sc') ax1.scatter(xdata, dnorm['v_oc_temp_corr'], c=clr['v_oc'], @@ -124,9 +123,9 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): c=clr['temp_module'], label='temp_module C/100') - # temp_air may not exist particularly for indoor measurements - try: - ax2.scatter(xdata, + # temp_air may not exist particularly for indoor measurements + try: + ax2.scatter(xdata, dmeas['temp_air']/100, c=clr['temp_air'], label='temp_air C/100') @@ -137,9 +136,9 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): plt.show() -def plot_mlfm_stack(dmeas, dnorm, dstack, ref, +def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, - xaxis_labels=12, is_i_sc_self_ref=False, + xaxis_labels=12, is_i_sc_self_ref=False, is_v_oc_temp_module_corr=True): ''' @@ -152,7 +151,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, 'poa_global', 'temp_module', 'wind_speed' and measured electrical/thermal values 'i_sc' .. 'v_oc', temp_module. - + dnorm : dataframe normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). @@ -322,9 +321,9 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, plt.plot(xdata, dmeas['temp_module'] / 100, c=clr['temp_module'], label='temp_module/100') - # temp_air may not exist particularly for indoor measurements - try: - plt.plot(xdata, dmeas['temp_air']/100, + # temp_air may not exist particularly for indoor measurements + try: + plt.plot(xdata, dmeas['temp_air']/100, c=clr['temp_air'], label='temp_air/100') except: pass @@ -336,7 +335,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, """ ## References - + The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) together known as "MLFM" have been developed by SRCL and Gantner Instruments (previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM @@ -359,7 +358,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf .. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) -'5CV.4.35 Quantifying Long Term PV Performance and Degradation +'5CV.4.35 Quantifying Long Term PV Performance and Degradation under Real Outdoor and IEC 61853 Test Conditions Using High Quality Module IV Measurements'. 36th EU PVSEC Sep 2019 [Marseille] @@ -370,7 +369,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, [PVPMC] Webinar on PV Performance Modeling Methods, Aug 2020. https://pvpmc.sandia.gov/download/7879/ -.. [6] W.Marion et al (NREL) +.. [6] W.Marion et al (NREL) 'New Data Set for Validating PV Module Performance Models'. https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models From 3a64a7f4a228a7847238b0edf98b06da17d57ab2 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 19:15:46 +0000 Subject: [PATCH 12/81] format fix mlfm_graphs.py --- pvlib/mlfm_graphs.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py index 000b034620..8be034a254 100644 --- a/pvlib/mlfm_graphs.py +++ b/pvlib/mlfm_graphs.py @@ -335,7 +335,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, """ ## References - + The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) together known as "MLFM" have been developed by SRCL and Gantner Instruments (previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM From 22283304c48bd5d0f6e6a0ec426bfe0256c30a85 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 19:18:33 +0000 Subject: [PATCH 13/81] format fix mlfm.py --- pvlib/mlfm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 33e2c7c81f..6d35e955b4 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -124,8 +124,8 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): (dmeas['r_sc'] - dmeas['r_oc'])) vr = ( - dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From 0c6051b274e1da4ca1cbc2b47bc06e1130e39b33 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 19:20:05 +0000 Subject: [PATCH 14/81] format fix mlfm.py --- pvlib/mlfm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 6d35e955b4..e08d08db21 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -124,8 +124,8 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): (dmeas['r_sc'] - dmeas['r_oc'])) vr = ( - dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From 3c5f338ffc49b3a4d903fec6d8a3d08f8492fe44 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 19:22:10 +0000 Subject: [PATCH 15/81] format fix mlfm.py --- pvlib/mlfm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index e08d08db21..33e2c7c81f 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -124,8 +124,8 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): (dmeas['r_sc'] - dmeas['r_oc'])) vr = ( - dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From 65a3e62f4c71925ef963aac3a48f30f699c62a6a Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 21:10:57 +0000 Subject: [PATCH 16/81] format fix mlfm.py --- pvlib/mlfm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 33e2c7c81f..98981afd80 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -124,8 +124,8 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): (dmeas['r_sc'] - dmeas['r_oc'])) vr = ( - dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) + (dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From 77a6cc579e04c8d9be1244fa7a94f3cb8f7b1838 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 21:12:53 +0000 Subject: [PATCH 17/81] format fix mlfm.py --- pvlib/mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 98981afd80..c5c38c9be6 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -125,7 +125,7 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): vr = ( (dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From 27bf8a7333de89b680491c2ee620daf6edefdb53 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 21:15:10 +0000 Subject: [PATCH 18/81] format fix mlfm.py --- pvlib/mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index c5c38c9be6..98981afd80 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -125,7 +125,7 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): vr = ( (dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From e98835a4d86f6afd71cad7aac907afd7feecd720 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 21:23:04 +0000 Subject: [PATCH 19/81] format fix mlfm.py --- pvlib/mlfm.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 98981afd80..4f0b686687 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -119,13 +119,11 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): to make maths easier ''' - ir = ( - (dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / - (dmeas['r_sc'] - dmeas['r_oc'])) + ir = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / + (dmeas['r_sc'] - dmeas['r_oc'])) - vr = ( - (dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + vr = ((meas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc From c7c1775fda5775dbf7dc1456fe3ebbe97c305f1f Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 21:25:01 +0000 Subject: [PATCH 20/81] format fix mlfm.py --- pvlib/mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 4f0b686687..b4f5a1bdb7 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -122,7 +122,7 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): ir = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) - vr = ((meas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + vr = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) # calculate normalised resistances r_sc and r_oc From 55e48229958fd4c44a00f3cbf7f4f4ac4ccb9e5f Mon Sep 17 00:00:00 2001 From: steve ransome Date: Fri, 10 Dec 2021 21:44:11 +0000 Subject: [PATCH 21/81] format fix mlfm.ipynb --- docs/tutorials/mlfm.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb index df43196568..ae4d5d5d12 100644 --- a/docs/tutorials/mlfm.ipynb +++ b/docs/tutorials/mlfm.ipynb @@ -47,7 +47,7 @@ "from pvlib import *\n", "\n", "# Import essential library file with lfm and mpm definitions 79-|\n", - "from mlfm_code import *\n", + "from mlfm import *\n", "# Import graphics code from separate file\n", "from mlfm_graphs import *\n", "\n", From 19ed54866ed92aa7c9b46370c60c8afbd06aa607 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Sat, 11 Dec 2021 12:10:53 +0000 Subject: [PATCH 22/81] fix bare except mlfm_graphs.py --- pvlib/mlfm_graphs.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py index 8be034a254..ce0f4d6881 100644 --- a/pvlib/mlfm_graphs.py +++ b/pvlib/mlfm_graphs.py @@ -129,7 +129,7 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): dmeas['temp_air']/100, c=clr['temp_air'], label='temp_air C/100') - except: + except KeyError: pass ax2.legend(bbox_to_anchor=(bbox, 0.5), loc='upper left', borderaxespad=0.) @@ -325,7 +325,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, try: plt.plot(xdata, dmeas['temp_air']/100, c=clr['temp_air'], label='temp_air/100') - except: + except KeyError: pass ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) From b9b64fc1461ee6864fc2ab3d34a37202fd4b133c Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 10:20:39 +0000 Subject: [PATCH 23/81] better formatted mlfm.ipynb --- docs/tutorials/mlfm.ipynb | 411 ++++++++++++++++---------------------- 1 file changed, 171 insertions(+), 240 deletions(-) diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb index ae4d5d5d12..4bc5f9d0f1 100644 --- a/docs/tutorials/mlfm.ipynb +++ b/docs/tutorials/mlfm.ipynb @@ -39,14 +39,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "#import pvlib\n", "from pvlib import *\n", "\n", - "# Import essential library file with lfm and mpm definitions 79-|\n", + "# Import essential library file with lfm and mpm definitions\n", "from mlfm import *\n", "# Import graphics code from separate file\n", "from mlfm_graphs import *\n", @@ -60,30 +60,20 @@ "plt.linestyle = '--' #- # solid line\n", "plt.marker = 's' #o # the default marker\n", "plt.markersize = 9 #6 # marker size, in points\n", - "plt.bbox = 1.4 # offset right to not overwrite\n" + "plt.bbox = 1.4 # offset right to not overwrite" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 59, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'C:\\\\Users\\\\steve\\\\OneDrive\\\\Documents\\\\_CONS\\\\__Reference\\\\PVPMC\\\\__repository\\\\pvlib-python\\\\docs\\\\tutorials'" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", - "root_dir = os.getcwd()\n", + "# import os\n", + "# root_dir = os.getcwd()\n", "\n", - "root_dir" + "# root_dir" ] }, { @@ -121,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -136,8 +126,8 @@ " mlfm_meas_file = 'x19074001_iec61853_041.csv'\n", "\n", "# optional\n", - "elif meas_file == -1:\n", - " mlfm_meas_file = 't1_041.csv'\n", + "# elif meas_file == -1:\n", + "# mlfm_meas_file = 't1_041.csv'\n", " \n", "# extract module id from filename e.g. 'g78'\n", "mlfm_mod = mlfm_meas_file.split('_')\n", @@ -174,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -209,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +213,7 @@ " i_mp = ref_data['i_mp'].values[0],\n", " v_mp = ref_data['v_mp'].values[0],\n", " v_oc = ref_data['v_oc'].values[0],\n", - " \n", + "\n", " alpha_i_sc = ref_data['alpha_i_sc'].values[0],\n", " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", @@ -233,6 +223,7 @@ "\n", "# create p_mp and ff in case they don't exist\n", "ref['p_mp'] = ref['i_mp'] * ref['v_mp']\n", + "\n", "ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc'])" ] }, @@ -254,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -273,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -392,32 +383,16 @@ "" ], "text/plain": [ - " module_id poa_global wind_speed temp_air \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 78 2.666484 1.472832 8.177979 \n", - "2016-01-26 07:30:00-07:00 78 7.899143 1.297711 8.241425 \n", - "2016-01-26 07:40:00-07:00 78 52.927672 0.955482 7.739624 \n", - "\n", - " blue_frac beam_frac temp_module v_oc \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.454992 1.100000 2.081940 33.040644 \n", - "2016-01-26 07:30:00-07:00 0.522027 -0.100000 2.436985 37.644029 \n", - "2016-01-26 07:40:00-07:00 0.270154 0.300267 2.592087 39.649206 \n", + " module_id poa_global ... poa_global_kwm2 p_mp\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 78 2.666484 ... 0.002666 0.238726\n", + "2016-01-26 07:30:00-07:00 78 7.899143 ... 0.007899 0.883783\n", + "2016-01-26 07:40:00-07:00 78 52.927672 ... 0.052928 1.985488\n", "\n", - " i_sc i_mp v_mp r_sc \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.013215 0.009809 24.337320 115258.549800 \n", - "2016-01-26 07:30:00-07:00 0.037249 0.029832 29.624980 8253.745059 \n", - "2016-01-26 07:40:00-07:00 0.072837 0.061196 32.444868 4762.543972 \n", - "\n", - " r_oc poa_global_kwm2 p_mp \n", - "date_time \n", - "2016-01-26 07:20:00-07:00 608.680999 0.002666 0.238726 \n", - "2016-01-26 07:30:00-07:00 150.461283 0.007899 0.883783 \n", - "2016-01-26 07:40:00-07:00 63.660028 0.052928 1.985488 " + "[3 rows x 15 columns]" ] }, - "execution_count": 39, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -444,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -471,10 +446,7 @@ " '''\n", " # find how many mlfm variables were measured\n", " qty_mlfm_vars = 0\n", - " for mlfm_sel in (\n", - " 'i_sc', 'r_sc', 'i_mp',\n", - " 'v_mp', 'r_oc', 'v_oc'\n", - " ):\n", + " for mlfm_sel in ('i_sc', 'r_sc', 'i_mp','v_mp', 'r_oc', 'v_oc'):\n", " if mlfm_sel in dmeas.columns:\n", " qty_mlfm_vars += 1\n", " #print(qty_mlfm_vars, mlfm_sel)\n", @@ -484,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -534,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -633,26 +605,16 @@ "" ], "text/plain": [ - " pr_dc pr_dc_temp_corr i_mp v_mp \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.496497 0.445293 0.742241 0.736587 \n", - "2016-01-26 07:30:00-07:00 0.620471 0.557473 0.800896 0.786977 \n", - "2016-01-26 07:40:00-07:00 0.208037 0.187059 0.840172 0.818298 \n", - "\n", - " i_sc v_oc v_oc_temp_corr r_sc \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.926380 0.747526 0.687564 0.983502 \n", - "2016-01-26 07:30:00-07:00 0.881409 0.851675 0.784418 0.893852 \n", - "2016-01-26 07:40:00-07:00 0.257227 0.897041 0.826688 0.897700 \n", + " pr_dc pr_dc_temp_corr ... i_ff v_ff\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 0.496497 0.445293 ... 0.754692 0.968481\n", + "2016-01-26 07:30:00-07:00 0.620471 0.557473 ... 0.896006 0.907783\n", + "2016-01-26 07:40:00-07:00 0.208037 0.187059 ... 0.935916 0.914282\n", "\n", - " r_oc i_ff v_ff \n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.760559 0.754692 0.968481 \n", - "2016-01-26 07:30:00-07:00 0.866922 0.896006 0.907783 \n", - "2016-01-26 07:40:00-07:00 0.895018 0.935916 0.914282 " + "[3 rows x 11 columns]" ] }, - "execution_count": 42, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -674,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -700,36 +662,34 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "# select by irradiance poa_global range e.g. 100-1100W/m2\n", - "meas = meas[\n", - " (meas['poa_global'] >= 100) &\n", - " (meas['poa_global'] <= 1100)\n", - " ]\n", + "meas = meas[(meas['poa_global'] >= 100) &\n", + " (meas['poa_global'] <= 1100)]\n", "\n", "# if there's date_time can select by it, i.e. not matrix data\n", "### better if index is formatted as a date\n", - "if qty_mlfm_vars == 6:\n", - " '''\n", - " # not for matrix as they don't contain dates ###\n", + "\n", + "# if qty_mlfm_vars == 6:\n", + "\n", + " # not for matrix as they don't contain dates\n", " # example\n", - " meas = meas[(meas.index > '2016-01-01') &\n", - " (meas.index < '2017-01-01')]\n", - " '''" + " # meas = meas[(meas.index > '2016-01-01') &\n", + " # (meas.index < '2017-01-01')]\n" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "# remove specific mlfm values outside limits e.g. <0.5 or >1.5\n", - "norm = norm[((norm['pr_dc'] > 0.5) &\n", - " (norm['pr_dc'] < 1.5))]\n", + "norm = norm[((norm['pr_dc'] > 0.5) & \n", + " (norm['pr_dc'] < 1.5))]\n", "\n", "# remove all mlfm values outside x~3 stdevs\n", "if qty_mlfm_vars == 6:\n", @@ -737,10 +697,7 @@ " # remove all mlfm data > x stdev usually 3\n", " stdevs = 3\n", "\n", - " for lfm in (\n", - " 'i_sc', 'r_sc', 'i_ff',\n", - " 'v_ff', 'r_oc','v_oc'\n", - " ):\n", + " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc','v_oc'):\n", " norm = norm[\n", " ((norm[lfm] - norm[lfm].mean()) /\n", " norm[lfm].std()).abs() < stdevs\n", @@ -757,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -804,19 +761,17 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 73, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -851,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -940,27 +895,23 @@ "" ], "text/plain": [ - " pr_dc i_sc r_sc i_ff i_v \\\n", - "date_time \n", - "2016-01-26 08:10:00-07:00 0.987455 0.063817 0.044529 0.035911 0.01 \n", - "2016-01-26 08:30:00-07:00 0.836998 0.237283 0.032594 0.030246 0.01 \n", - "2016-01-26 08:40:00-07:00 0.978335 0.061103 0.065599 0.013143 0.01 \n", + " pr_dc i_sc ... v_oc temp_module_corr\n", + "date_time ... \n", + "2016-01-26 08:10:00-07:00 0.987455 0.063817 ... -0.002454 -0.065435\n", + "2016-01-26 08:30:00-07:00 0.836998 0.237283 ... 0.004324 -0.042936\n", + "2016-01-26 08:40:00-07:00 0.978335 0.061103 ... 0.000727 -0.033518\n", "\n", - " v_ff r_oc v_oc temp_module_corr \n", - "date_time \n", - "2016-01-26 08:10:00-07:00 0.073908 0.098226 -0.002454 -0.065435 \n", - "2016-01-26 08:30:00-07:00 0.077019 0.082928 0.004324 -0.042936 \n", - "2016-01-26 08:40:00-07:00 0.091342 0.091143 0.000727 -0.033518 " + "[3 rows x 9 columns]" ] }, - "execution_count": 48, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# translate multiplicative to stack losses and add to dataframe df\n", - "stack = mlfm_norm_to_stack(meas, norm, ref, qty_mlfm_vars)\n", + "stack = mlfm_norm_to_stack(norm, ref, qty_mlfm_vars)\n", "\n", "# show some stack losses\n", "stack.head(3)" @@ -1032,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -1040,19 +991,17 @@ "output_type": "stream", "text": [ "C:\\Users\\steve\\OneDrive\\Documents\\_CONS\\__Reference\\PVPMC\\__repository\\pvlib-python\\docs\\tutorials\\mlfm_graphs.py:334: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " ax1.set_xticklabels(xax2, rotation = 90)\n" + " ax1.set_xticklabels(xax2, rotation=90)\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1089,7 +1038,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -1122,7 +1071,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -1151,25 +1100,25 @@ " '''\n", " \n", " fig, ax1 = plt.subplots()\n", - " \n", + "\n", " plt.title(title)\n", "\n", " plt.ylabel('fit ' + fit + ' * poa_global_kwm2')\n", " ax1.set_ylim(0, 1.2)\n", - " \n", + "\n", " plt.xlabel('meas ' + fit + '* poa_global_kwm2')\n", " ax1.set_xlim(0, 1.2)\n", - " \n", + "\n", " plt.plot(\n", " dnorm[fit] * dmeas['poa_global_kwm2'],\n", " dnorm['calc_' + fit] * dmeas['poa_global_kwm2'],\n", " 'c^',\n", " label = fit\n", " )\n", - " \n", + "\n", " # plot 1:1 line to show optimum fit\n", " plt.plot((0,1.2),(0,1.2), 'ko-')\n", - " \n", + "\n", " plt.legend(loc='upper left')\n", " plt.show()" ] @@ -1183,19 +1132,17 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 78, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1216,14 +1163,14 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "def plot_heatmap(dmeas, dnorm, fit, y_axis, x_axis, z_axis, title):\n", - " ''' \n", + " '''\n", " Plot a heatmap of Z vs. binned X and Y axes.\n", - " \n", + "\n", " Parameters\n", " ----------\n", " dmeas : dataframe\n", @@ -1231,53 +1178,52 @@ " 'poa_global', 'temp_module', 'wind_speed'\n", " and measured electrical/thermal values\n", " 'i_sc' .. 'v_oc', temp_module.\n", - " \n", + "\n", " dnorm : dataframe\n", " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", - " \n", + "\n", " fit : string\n", " fitted parameter e.g. 'pr_dc'.\n", - " \n", + "\n", " x_axis : string\n", " binned x axis e.g. 'poa_global_bin'.\n", - " \n", + "\n", " y_axis : string\n", " binned y axis e.g. 'temp_module_bin'.\n", - " \n", + "\n", " z_axis : string\n", " value as a colour surface plot e.f. 'diff_pr_dc'.\n", - " \n", + "\n", " title : string\n", " title for graph e.g. mlfm_meas_file.\n", - " \n", + "\n", " '''\n", - " \n", + "\n", " df_piv = pd.pivot_table(\n", - " dnorm, \n", - " index = y_axis, # e.g. 'temp_module_bin'\n", - " columns = x_axis, # e.g. 'poa_global_bin'\n", - " values = z_axis, # value to aggregate\n", - " fill_value = 0, # fill empty cells with this ?\n", - " aggfunc = [np.mean], # e.g. min, np.sum, len->count\n", - " margins = False, # grand totals hide\n", - " dropna = True # hide missing rows or columns\n", + " dnorm,\n", + " index=y_axis, # e.g. 'temp_module_bin'\n", + " columns=x_axis, # e.g. 'poa_global_bin'\n", + " values=z_axis, # value to aggregate\n", + " fill_value=0, # fill empty cells with this ?\n", + " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", + " margins=False, # grand totals hide\n", + " dropna=True # hide missing rows or columns\n", " )\n", "\n", " fig, ax1 = plt.subplots()\n", - " \n", + "\n", " # force z limits to be -2% to +2% if desired\n", " df_piv = df_piv.clip(lower = -0.02, upper = +0.02)\n", - " \n", + "\n", " im = ax1.imshow(\n", " df_piv,\n", " cmap='RdYlBu',\n", " origin='lower'\n", " )\n", - " \n", - " cbar = ax1.figure.colorbar(\n", - " im, ax=ax1, shrink=0.75, label=z_axis)\n", - " \n", + "\n", + " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", + "\n", " #Y AXIS : show only 1 of each y_skip labels\n", " y_ticks = df_piv.shape[0]\n", " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", @@ -1291,7 +1237,7 @@ "\n", " ax1.set_yticklabels(yax2)\n", " ax1.set_ylabel(y_axis)\n", - " \n", + "\n", " # X AXIS : show only 1 of each x_skip labels\n", " x_ticks = df_piv.shape[1]\n", " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", @@ -1303,14 +1249,13 @@ " if x_count % x_skip == 0:\n", " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", " x_count += 1\n", - "\n", - " ###\n", + " \n", " ax1.set_title(title)\n", "\n", " ax1.set_xticklabels(xax2)\n", " ax1.set_xlabel(x_axis)\n", - " \n", - " ax1.grid( color='k', linestyle=':', linewidth=1)\n" + "\n", + " ax1.grid( color='k', linestyle=':', linewidth=1)" ] }, { @@ -1322,32 +1267,30 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 80, "metadata": {}, "outputs": [ { "data": { - "image/png": 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06WwlFTSDZoXXbN60vakepOa8A+VDeQwQF+MKl8emLgfgsqECoK5s4qxofyKNgfWqmhu3b1IxbTeKrKwHrKSCZtCs8JpvvPNU6Y2SJBXnHSgfNukAEaVhPhZXszhGTVw1uXhW4OorJJJMW0QkXUQmiMiEmTMXFi6eNWnSmdzc+eTkTKdly3QAMjKeoU+fYRQUrKZ+/Y7k5S0jO3sibdrcAEB6+qP065cFQO3a7SgoWE1W1lg6dHAufWlpvRgyZBQA1aq1BmDIkFGkpfWioGA1HTrcSVbWWAoKVlO7djsA+vXLIj39UQDatLmB7OyJ5OUto379jgD06TOMjAy3iNiyZTo5OdPJzZ1PkyadKShYTWbmQK9zArzOadGi5d7nBJifE+B1Ts8++673Ofl+Tjfc8KT5OZ14Yob3Ofl+TgcffLnXOb0+dASXXHg7AOeccS0fvJ9NQcEqdt3BWW5f7P8m117Zg5UFqzm57aV8+cV48vOWsE+DtgA8+fhL3HmrO+ejDj2PiTlTvT+ngoLVSZ1ToOKySdcgRKQbcIKqHhu373/Alqp6ddy+ybhygsMTnt8CGKOq28bty8CVqSxxruy7BrG5pVwImlVTsxKl2ghrEBWYTT1A5OKKmL8Yty8duFhVj4we18DVsD5YVaclPL8Grv7uAao6I9o3GMhT1TtKeu2wSB2oSoRF6oAFm8zEJCJH4IqOvJFw6G3gQBHpKCJbA/cCPyQODlC4PvEW0FNEaojIkbhC4y9b9TNmKrAkaAbNiqoZMyNZkorzDpQPm3IN4mLgLVUtiN+pqktxnkn34WYHhwHnx45HZS4/jHvK1cA2OE+oocBVpbm4JkO9ejtZSQXNoFnhNXerZ59pOxXnHSgfQqqNQGAzJJiYAhaEVBsJxDxTgmbQrAqaMa8lS1Jx3oHyIcwgEsjLW0a9enVMXztoBs1Nrek7g8jPW8Ju9Xbxaus7g0jmvMMMomITZhAJ5ObOD5pBs8pozphhX9AtFecdKB/CAJFAjx6DgmbQrDKa9/d81lQPUnPegfIhmJgCgST4eO6B5pon7DnVXHPVOlvTFsDc3xuYax5Up2YwMVVgwgwigVhKhaAZNMvK/+68yVyza/ojpnrXX2VfkzrzpmvNNQPlQxggEmjZsknQDJom7HtQc3PNQ4z72eIQszyXhRzQ/GBzzUD5EExMgUASBBOTLcHEVLEJM4gEYhk8g2bQLCtnHlhsYcSNZrvaJ5nq1dvR/tp82J67mmsGyocwQCSwcOHw0hsFzaDpwavf/GiuuWDh26Z60+d+YaoH8NmPM8w1A+VDGCASyM7+PmgGTRN++GaMuWZ29kRTvdFfjDPVAxg/5qvSGwUqBWGASKB/f/siJkGzamp+OHSwuaZ1Pwe+kJhcuey8OXiguWagfAiL1IFAEoRFalvCInXFJswgEoiV0wyaQbOsPHhDurlmWlpPU71LL7zVVA/gtvRLzDU3NSKyo4i8LSKrRGSuiHQqoe1NIrJIRFaIyIsislW0fysRGRA9v0BEJopIu4TnthWRaSKyWkQ+FxF7z4YysFkPEBtTk/rUU//PvCb1qaf+n3n95lNP/T/zmtTHHXeweU3qvfbazbwmtYh4n5Pv5zRnziKvc7r1/A5M+mY0vyzOp9NhLoZgeP+n6df7HgCubX8cMyZ/z4KfZzLp69EAvPzEQ7z8xEMAXNbmUBb8PJMZk7/n2vbHAXBLRl8e6/MaALvXP7PwnI5rcz3gguP69XsPgHfe/jI6pzF06HBHdE49GTLkEwCqVzsGgNeHvl948T/3jKv58P3PKShYVei1NPCF17n+qu6cfMqxnHL8xXz1xTjy85bQeE9XDfipxwdy122uz8ccdjYTv5vCnJkzOO3Q5gA889B9PPPQfQCcdmhz5sycwZTvJ3LucUdx7InteOSeO3npaVfn+7gD9mFJfj7jR3/JJR1Odp/zTdfyxkuFhSUrIk8DfwJ1gTTgWRH5V9CIiJwE3AG0BRoCewM9osNbAPOBY4HtgHuA10WkYfTcOrgCaPcAOwITAL/6r5uIYGIKBJIgmJhsqYgmprjSxgeqam6072VgYWJpYxEZAsxR1buix22BV1W1SF9fEfkB6KGqw6Nyy11U9Yi4110GtCiqomZ5sFnPIDaG2F1l0AyaZeXkvewv0rEZghW1/9PUVA/goDo1zTU3MY2B9bHBIWISUFTY+QHRsfh2dUXkX2X1RKRupD2lqOdGJZVnFfM65cIW5d2BisaGDdlBM2iaMHL2MnPN9Ru+NNX7/U/72cvkZSvNNX2ovX1TXffXKq+2a1bPmwKsjdvVT1VjJoaawIqEp6wAahUhldg29n8t4JfYThHZEngVeCludlATWOr5OuVCmEEkELNPB82gWVY+f/dNc83YWoMVrw9931QPYMSbr5tr+rB+3Sr2/+8dXhuwVlVbxm3x9ueVQO0E+dpAQREvm9g29n9hWxGpBryMW9OIz2SYzOuUC2GASGDEiK+DZtA04dvPPjbXtO7nyA/sI6m/+PhDc01fpLp4baWQC2whIvvG7WvG36aheKZEx+LbLVbVXwDEeVQMwC12d1TVv4p7brQG0aiY1ykXwiJ1IJAEYZHaFstF6hq19tT9W9zt1Tbnq64lvq6IvAYocDnQHPgAOEJVpyS0OxkYBBwH5APDgXGxxWwReS56/vGqujLhuTsDM4FLgRE476djVfVwr5PYBIQZRAIxd8igGTTLSvfLinWd32hirq1WnHvG1aZ6ANd2Osdc0w+/2YPHDALgamAbYAkwFLhKVaeISAMRWSkiDQBUdSTwMPA5MDfaugNEMQ1dcQPEouh5K0UkLXruUqAjcB/Oa+ow4Hyrd8OCsEidwBVXtA+aQdOEdhdcZK5p3c9LLre/mJ99UTkFyglU29LmnldVlwNnFLF/Hm5xOX7fY8BjRbSd63pV4uuMAvYrS19TSRggEmjdunnQDJom/PfwI801W7duYap31LGHmuoBtDryaHNNLwSoFowiloR3M4FYxG/QDJplJe1w+/WK3eufaarXJIqctuS4A/ctvVEKEFsTU4CwSB2oQKxct4upXjVZZ6oHsO1i+xTe7LS7vWYKWEZzc81dttnDbJG65vZ76UFHZ3q1/eb9LhUugrsiEmYQCcTy+ATNiqs58AV7P/sX+9vHLPR7xb4uQr8BIyu0HsDgAa+aa3oRrUH4bAE/wjuVwIQJ04NmBdecmGPvJv5dCjQn/DDPXvO7mRVaD2DSdz+Ya3ohAtWr+W0BL4KJKVBhCCamik2FNzHtuLc2P6G3V9sxr6cFE5MHYShNIJY2OmhWXM1Tjr/YXPPktpeaa7Y5+1+ej2XXPNE2XsNaD+CME8snDkLELJI6EBHcXBPo3r1L0Kzgmnfec4255l33XmWu2f3m0+w1u9kG31nrAdza7WZzTS9EwvqCMZv03RSR80Xkp6hK0ywROTra711VKZlKTxtD48Z7WMoFzRRo7rNvQ3PNffe1L+TVeG9bkxlA433qVWg9gEb77GWu6U21an5bwItN9k6JyAnAQ8AluHS2xwA/b0RVJa9KTxtLq1ZdraSCZoo0j/0/exPG0YdfYK7Z6tQH7TWPtL07t9YDOOHIU801fZFq4rUF/Nhki9QiMhYYoKoDEvZ7V1VKptJTImGRuuITFqkrNhV9kbrWzo205ZkPe7XN7n92WKT2YJPMIESkOtAS2FlEZorIAhHpKyLbkFxVpWQqPW0UsRrOlgRNW556fKC55pOPv2Su2ed5+xoTfZ54u0LrATzzxPPmmj6ICLJlda8t4MemMjHVBbYEzgaOxmU3bAF0o2zVm0pqi4iki8gEEZkwc+ZCMjPdhaVJk87k5s4nJ2c6LVumA5CR8Qx9+gwjL+8X6tfvWFg4PuaFk57+aGHQV+3a7aLC8WMLs4CmpfUqLDoTK185ZMgo0tJ6kZf3Cx063ElW1lgKClZTu3Y7wAWRpac/Cjhvn+zsieTlLStM+9CnzzAyMp4BoGXLdHJyppObO58mTTqTl/cLmZkDvc4J8DqnOXMWeZ8T4HVO3377k/c5Adzfsy/39+wLQIum7ZiRO4eJ303hmMPOBuCu2x7ig/c/B6DxnseSn7eEr74YV+jZdP1V3QsD6Xbd4XAKClbxwfvZnHOGq9NyyYW38/rQEQDU3PK/ALw+dASDBgwH4JwzruWD97MpKFjFrju4rMsv9n+Ta690dejbnP0Y2WNzyVv0G/UPcZPWPs+PIqOnC7Rr2e5+cn6YS+7Pi7nvyQ8AyOzzPpl9XGGeJkd3J/fnxeT8MJeW7e53n9PtAwov1PX3upi8vF/I/nJyoYdR+jV9CwPa7rp3sPvujRhHh4493ed08SMMeS3bfU7buGR+Q17LJu3iR9zn1LEnWSPGuc9p53Pd5zRgJOnX9CUvfzltTryT7C8nu+/+Xu597PPE22Tc7ib7LY+4kZzvZjJrxs8cfpArefpw78d4uLfz0jr8oGOYNeNnJn33A8cfcQqL8hdz7+09CweKg/Y6hEV5ixjz5deFHk4Z19yekoC6YGKyZZOYmERkB2A5zpT0UrSvI26A+BLYUlWvjms/GchU1eEJOi2AMaq6bdy+DKC1qpaY5jKYmCo+wcRUsanoJqbadffRQy/wcy3+9H+nBxOTB5tkBqGqvwILcAU4EkmmqlIylZ42itjdtyVB05bYbMKSow49z1wzNkMw1TzixgqtB3D8EaeYa/rhN3sIMwh/NqW/10DgOhHZJZpR3Ai8D7wNHCgiHUVka+Be4IfEBWooXJ94C+gpIjVE5EjgdFy9VxOefz7DSipopkjzf8/2MNd86tnu5prPP5Rmr9n32tIblaMeQJ++9t5bPohAtS2re20BPzblANELGI+bBfwETATuK62qkojcJSLxRW6LrPRk1clatbYtvVHQLFfNmjVr2Gum4txrbm2vWWubCq0HULNWzdIbpQKBatXEaytVKol4KxG5SUQWicgKEXlRRLaKO3ZttA76h4gMSnheQxHRuEpzK0XknrK8BdZssgFCVf9S1atVdXtV3VVVr1fVtdGxUaq6n6puo6qtVXVO3PPuV9V2cY+Xq+oZqlpDVRuo6hDLfrZvb596IGjacu4Z9lHP55xxnblm+4ufsdc8q2eF1gNIO6uLuaYffoODzwCBZ7yViJwE3AG0BRoCe+NqS8fIA3oDL5bwWturas1o6+XTuU1FSNYXqDCEReqKTUVfpN6+XmM9Jr2vV9usHicV+7rJxFuJyBBgjqreFT1uC7yqqrsmtOsN7K6qXeL2NQRm45x07L+sBoSY8wRibqNBs+JqxtxgLbmvp/3dfsy11VSzt+mE2VwPKHR/3eTYmZiSibf6RxxX9H9dEdkpiZ7PjWLDBkaZJSoMYYAIBAKbBa4ktfcAUScWIxVt8e52ZYnNiv1fZGxWAsuAVsCewCHRc8qp2lLRBBNToMIQTEwVm4puYtqhfhNte82zXm2H3922JBOTd7yViEzCOdu8Hj3eCXfhr6Oqv8S1+5eJqYjX3RXIB7ZT1d+9TiTFhBlEArGI3qBZcTVbNG1XeqMkad60xDjLjaLJ0faus00Osk1+aK0HFEZbb2pEYIstq3ltpZBMvNU/4rii/xfHDw5JELtbrzCBGqEeRAJZWQ8ETQ903Ede7d677xTvtjUbNvJqN+KlK6i5fGzpDf9c46UH8H7/i9g2/4vSG+7un/Yr68NHoF7p6c7nrfo/b83nXx/JvHX7ltquQY2vvfSy3n8AtvJzG956XYFXuzfeeZqtq/u1NUUwCYJT1VUiEou3uhyXGuh04Igimg8GBonIq7i7/27AoMIuiWyBu85WB6pHsV7rVHWdiBwG/AbMAHYAngSyVTXRvFVuhBlEAgUFq4OmpebqP+01V66tHJoF/gOUL6tW2l54U9HHlStXmWv6ILZurkXGW4lIgyheoQGAqo4EHgY+B+ZGW/zUsRuwBucK2zn6v1t0bG9gJFAA/Aj8AdjnnS8DYYBIoGvXPkHTkCsfGGmu2fXWwfaad7xmr5mC9/PO620jn1PRxxuusjeteWEYKFdcvJWqzoviFebFtX1MVeuqam1VvURV/4g7lqmqkrBlRseGqupe0WvspqoXqeoi+zdm4wmL1IGNwtdslAziaWLyJgkTkzdJmJh8ScbE5IuviSkZVq7btfRGSVL7P03NFqnr7Lmftr99QOkNgUHXHBWS9XkQZhAJxFJRB00bbvnfp+aaGd3ta0xk9HrLXjPDz6MmGXrfebupXir6eNdtD5lr+iFUq1bNawv4ERapE6hXL5n4lqBZqmYd+7w89Xbd3l6z7nb2mil4P+vutpupXir6uNtu9rW4fRBsFqkDfxNMTIGNIpiY7AgmJhtTz84N99czuw3yatv/isODicmDMNdKIFb5LGjasPtpT5lr1m9mn0K8fsu77TXrn2Oueei+e5nqpaKPjfc81lzTB8M4iEBEMDElMH68fT3dqqw5bmAXc83xH9lnRB7//m32muPt7ftZX9pGcqeij198/Ya5ph/eLqwBT8JQmkBu7vygaak5b7m95ix7T8Dc2UvsNXMXmGv+PHOmqV4q+jhzxhxzTR/E0M014NisBwgRSY8l45o5c2FhZtEmTTqTmzufnJzphSUxMzKeoU+fYfToMYj69TuSl7eM7OyJtGlzAwDp6Y/Sr18WALVrt3OF47PG0qGDq3eQltaLIUNGAVCtWmsAhgwZRVpaL3r0GESHDneSlTXWFY6v7VJF9OuXRXr6owC0aXMD2dkTyctbVmjq6dNnWKFnUcuW6eTkTCc3dz5NmnSmR49BZGYO9DonwOuc7rlngPc5AXTIeIOsr2ZQsOoPtjvO+dP3e2ciXR9w9Z2Ou+pVbn58FHlLCwpNTY+9+m2hZ1OriweSMy2f3Hm/sN85zwGQ+ci7ZD7yrjunI+4md9YicibNoeWJrm5BRvdhXH7zIHdOzTLIW/Qb2WOm0ebMh9053TKYfi+7iOja+99Cwcq1ZH0ymQ6XuNlR2nWDGPLOBHdODVwNiCHvTOD8q126/g6XPE/WJ5MpWLmW2vvf4s7p1TGk3z40+pxuIjv7++hzOif6nF4v9AZq2bIrOTm55ObO59RTXWbozMxBZGYOij6ni6LPKZeWLV2ai9533k7/J58AnAlpcX4eX3/1Jee1OxGAO6+7hiEvOvfNtPansLKggFEfjOCyc9z35PpLL+bd110cR8OoANCQIZ+SltbbnVOHu+K+e6dG3733SU/vQ48eL3mf04zcOYVpTu7v2bcwq26Lpu2YkTuHid9N4ZjDzuaBXk9z120P8dTj7rvZeM9jyc9bwldfjOOU4y92fb6qOwNfeB1rpFo1ry3gR1ikDmwUYZHajrBIbbNYvGujAzTtIb/05Y+d0zwsUnsQhtIEYnf0QdOG2GzCkvRb7COpYzMEU830FERSX3eNqV4q+nj9ZhBJHXCEReoEWrZsEjQNOWR/+7vOls32tNf8bwN7zRS8nwe1ONhULxV9bHGI/SzLl3DxtyWYmAIbRTAx2RFMTDamnt32OUC79PGLsn/wjIOCicmDYGJKILaAHDRtiC1eW1K7ka2ZBShckDbVjBaELTlgt51N9VLRx3o7ls91V0TYcotqXlvAj2BiSmDhwuFB05AFWbbZRwEWTrIfdBaO722vudA+HuDb3J9N9VLRx+lzPepqpIhgYrIlDKUJZGd/HzQtNb+bV3qjZDXHTrPX/HqGvWYK3s9vvvrSVC8VfRz9xThzTR9EoJqI1xbwIwwQCfTvnxU0LTXf+d5e82XbiyRA/yEeFeqS1ez/vrnm0IEvmuqloo8DXyivSGo7LyYR2VFE3haRVSIyV0Q6ldD2JhFZJCIrRORFEdkq7ti1USzWHyIyqIjnthWRaSKyWkQ+FxF7D4wyEBapAxtFWKS2IyxS2ywW797kQL32Wb+07Xe2bVLi64rIUNwN9GW4kqMjgCNUdUpCu5NwZUePA/KAt4FvVPWO6PhZwAbgJGAbVe0S99w6wCzgciAL6AUcraqHe53EJiDMIBKIRQkHTSPNe9+117zKfqBPu26QvWaa/brG9ZdebKqXij5eeuGt5pp+CNXFbytRRaQG0BG4R1VXqupo4D3gwiKaXwwMUNUpqvor7iLfJXZQVd9S1XeAX4p47lnAFFV9Q1XXAplAMxHZL+lTTxFhkTqBU0+1v5tLRnPDW4O82p2yy1/ebX8fMcurXZuFS/jtMr9EeNultfBqd+ohe4BnjWL93S9v0ylHNfJq+/12V3rpARxw0jZM3OrcUttt8Yu//fq/x6Yx+ZdWpbY7aP2r3prnttmOBqs82tfY20svme+msMGrXbtTjvFua4lgtkjdGFivqrlx+yYBRaWpPQB4N6FdXRHZSVWLGhQSnzsp9kBVV4nIrGi//ULbRpD0ACEiuwD/qAKjqrauFeVIp07HVwrNC472uwAkw9mN7Au9dGprH4jVqb3f4JQM7TqWPjgkyykp0Ox01mG2ep3amuoBnHuBveusF5LUAFFHRCbEPe6nqrGpaU1gRUL7FUCtInQS28b+r0XRs4bE5y71fJ1ywdvEJCIni8hCIB+YGbfZu3+UI7GkdBVdc4uzB5lr7jjwK3PN6sc/aa/Z2D419yF1a5trNtvZ/ndebdfLbfWqHWeqB1DrPweaa/ogwBbVq3ltwDJVbRm3xdstVwKJX4jaQEERL5vYNvZ/UW1Le25Jr1MuJLMG8TTOvlZTVavFbdVT1LdyYcOG7Eqhue7NLuaayy852lxz/ajr7TVzHzbXzFn8u7nmpKX2v/MNi16w1dvwmakeQMGfP5preiF+Hkwes4xcYAsR2TduXzNgShFtp0TH4tst9jAv/eu50dpHo2Jep1xIZoDYAXheVVPgGlJxiKW3ruiaQ7+yt+q9Ocu+JsKQT6fba2ZNNNf8cLh96ukPUqA55K1vbfWGfGqqB/D60BHmmj4IUL2aeG0loaqrgLeAniJSQ0SOBE4HXi6i+WDgMhFpKiI7AN2AQYV9EtlCRLYGqgPVRWRrEYmZ9t8GDhSRjlGbe4EfVLVCrD9AcgPEAOCSVHWkojBihL17YEo0c+yL+3y0wL64z4hvZttrfv6TueZXn4w01/zyY3vNEZ9MKr1RMnop+G6O/KAcI6ntAuWuBrYBlgBDgatUdYqINBCRlSLSAEBVRwIPA58Dc6MtPp1tN2ANcAfQOfq/W/TcpThvqfuAX4HDgPPL+h5Y4h0HISJfAYfi3oB/lPRS1WM8np8NHA6si3YtVNUm0bG2OBNWA+BboIuqzi1GZ0fcYHUisAy4U1VLTQJfWeIgfD2TksHXiykZfL2YkqLB7qZyyXgx+bJFClI5HLTeL8FcUuxi78Swap29E0Ot/xxoFgfRsOl/9d5XP/Bqe9nBe4RkfR4kM4N4AUjHjXYDEjZfrlXVmtEWGxzq4KZz9wA7AhOAkn4xTwN/AnWBNOBZETGLXopVU7MkFZqnP2BvtrpglL3ps0O39+w1uw4017yxs73H0XVp55hrdrjQdtG/Q4e7TPUAzjnDPpmiD2K3BhGI8HZzVdWXUtSHwmARABHJBJaJyH6Jtri4AJYDVXUlMFpEYgEsd1h05oor2lvIpFzz8hPs3UcvbmwfKXvFqfYeLVecZ+vqCXDWhfbW044X2WtecWGpk/Xk9K44zVQP4JLL7QdGX0KeJVtKHCBE5EJVfTn6/9Li2qmqb4KYB0TkQWA6cLeqZpNcsEgyASwbRevWza2kUqt5gP3F/MjdtjPXbN3M1mwE0PpQe/PJIUceZa7Z6kh7r7DWR9gG2abiu3n0saUHB6YCw0C5QERpJqYL4v6/sJits+dr3Q7sDdQH+gFZItKIsgWllNQWEUmPEmVNmDlzIZmZzjTRpEnnqHD8dFq2TAcgI+MZ+vQZRv36HalfvyN5ecvIzp5ImzY3AK4cZ79+Lpld7drtKChYTVbW2ELzUVpar0JvpVjcw5Aho0hL60X9+h3p0OHOuMLxru5Cv35ZhWU+27S5gezsieQtX80eVzgL22Pv/cgtL40H4NDbssiZtYzcvBXsf91b7HHFMHoMm0iPYc6jZ//r3iI3bwU5s5Zx6G2un7e8NJ7H3nMuh01f+5b81X8wOv832n/4AwA3jpnBoOn5ADR4eSxNh37LyHm/FJqarvhiWqFnUyxG4s1ZS7jiCzdud+j2Hllf/0zB6j/Zrr0rcN/v/R/p+pjzjDnu5uHsenZ/8patZPfznCXysTe+45bnnFarq4aSk7uE3AW/st/Froxojyc/pseTHwOw34kPkzt7KTk/LqDVmf9z5/RAFrsc3gOA3Y/qRd7iFWR/O4vjOj8HQNdub9LvtW8AOHrveqxaWcCXH31YaEK668pLCz2WYrEPHw5/nTaNXY60Gzufy5cffciqlQUcvXc9AN4aPJDeGc5d97LT2zF+zFcsWZTP8Qc6L8iXnnmSR+9134Pz2x7N1EkTmTNrBkfuXR+AZx++n2cfvh+A9oc1Z86sGUydNJHz27oBJKP7MPo863Jb1W+WQd6i38geM402Zzp33vRbBtPvZbfwu90+11Kwci1ZH39faG5Ku6pfoXdTLE5iyJBPC9NodOhwV9x379Tou/c+6el9qF//HNq0uYns7O/Jy1tG/fru7r9Pn9fJyHCfacuWXcnJyWVG7hyaN3XPv7/n09zf82kAmjc9lRm5c5j43RSOPuxcGu/Zhjtve4QnHx8EwL57tiE/bwlffTGOdsd3AeC6qzJ50TipnwhsWb2a1xbwo9yS9YnISFwCrH2ALVX16rhjk4FMVR2e8JwWwBhV3TZuXwbQWlVLtOOERWpbwiK1HWGR2maxuNGBzfXB4R97tT13v7phkdqDpIZSEdleRNJE5Nbo7/ZleG3FzQqTCRZJJoBlo4jNEixJhWb/T+zjC2KzCUv6vW8fNBWbIVjy1mD7he83B9um5gYKZxJmev3s031bzwy8EahWzW8L+JFMqo3jgDnA9UAr4DpgTuSiWtpztxeRk2JBIiKSBhwDfEQSwSJJBrBsFBMm2F94U6GZM8snUDM5vl+20lwzJ3exveaPC8w1p06yD76b+r295oRJRXp/b7xeCr6bE3PKJxBYCAWDrEkmDmIqzuzzety+c4BeqlriypmI7Ax8AOwHrMctPt+jqp9Ex48H+gJ78nccxJzo2F24HOntosc7Ai8CJ+CSYd0R4iBKJpiY7AgmJlssTUz7HtRcH3vXz/27Q6Odg4nJg2QmW/WAxKLFbwOlutOo6lJVbaWqtVR1e1U9PDY4RMdHqep+qrqNqraODQ7Rsftjg0P0eLmqnqGqNVS1gc/gkAyxRemKrnncvR+aa8YWry057mb7OtexBWlL0s88xVzzstPbld4oSWIL12Z6bW4y1QMKF6LLgzCDsCWZAWIwkBgBc1W0f7Ohe/culULz3nObm2ve3ryBuea9F9nHLNx73Qnmmum32AczXnmbfRBa91s62Op1ty1ABHDXPVeX3igVCFTz3AJ+lBYH8RVuMRncYHKViNwGLMS5q9YF7FcMy5HGjfeoHJr17GMWGm23jblm4923t9dsWMdcc89G+1QKzcaNbONfGje2j1PZZ9+G5po+CLBluPqbUtoM4gX+TqfRH1c79W7gmejv5VGbzYZWrbpWCs3Dbrf3jGr73vfmmodeY29fP7SjfY2JzieaxVoW0ul426hngFYn2ZaFbdXqKlM9gGP+7zxzTR8EP/NSMDH5U+IMItn0GiLyTHw8Q2Vk4UJ7m3kqNOf3t/8RTj3f3hy0YNhl9pqj/cqiJsNHP+SW3ihJRv1oX0tr4aQ+tnoL7V1SZ8z93FzTl3Dxt8XaI9g3qrrC0qeP/R1vKjRjEdKW9E2B++hjb3xnrznAPp30y88+Za750jP2M51YxLWZXh/7mhWxCOpNjYQ1CHOSrkldCpX+rc/L848vGFot3avdZyyl3q22hVk+Zyn1B/tVQTt/7Fle7X598lO2u+JIr7Z3L+nk1e7T/Ef5ZaVfmcwb6vvZ12eu/pkl9buU2q7aKv+v47JF+V53n423959prPt1ql/7Ff/x1sxbUgBbeLRfNs9Pb9Zs77bVdtjBq92i/EVUk7+82lqTCjfkqoxpqg0R+V1V7Yv7GpCKOAjfAaK88R0gksF3gEiGG06wXYBdtMp+0X2/HexNUVutSEFg2YYN5pJrdmhWeqMkqbFlC7N4hP2btdCBH/nNLv9vt+1CHIQHIeg8gVjyPkvuxjb6NVWarS6xTzcx8Gb7tZITjzzeXLPT8faZV484NM1cs+Vx99rqHd+99EZJcuSh9jcPftgtUovIjiLytoisEpG5IlLsSYnITSKySERWiMiLIrKVj46INBQRjSrUxTb7BbYyEExMCTz/fIa55uXUrRSaz91uH9jV7mr7C9AjfW0XagG69bFfL+j7bDdzzecfs60x8Xwf+5oVqThvH8R2fSG+MFlzYISITFLVf0z3ROQkXC2a44A8XPBwD/6uT+Ojs72qrqMCYj2DeMVYb5NTq9a2pTdKkq1TMFFLhWatbf1t4b78Zxv797NmzZrmmjVSoJmK71KtmltXaD2AmrVqmGv6skU18dpKIq4w2T2qulJVRwOxwmSJXAwMUNUpqvor0AvoshE65ojIRSLy34R9zUTE+/WTSdYnInKFiHwmIj9E+44RkcJajapq71S9iWnf3j6i9lEWVgrNDrfauzy+cd915poXnW1vurk+BeVBzzrjRnPN9p0es9VLe9xUD+DsM6431/TBMFlfcYXJiipt/I+CZ9H/dUVkpyR05orIAhEZGJVgtqIXMD9h33ygt69AMrehPYHLcMV+YjkZFuAKAW02TJ9uPwnqw16VQnPaMPuAvq7P2Af0jZlkH7z/7jffm2tOnvq2ueb0cY/Y6n3zkKkewKSp75pr+lJd/DagTqyYWLTFLz6WpYhZ7P9aHjrLcJmx9wQOifa/mtQJl0xtINHVcQWwva9AMgNEF+A0VX2Nv9NvzMZVidtsiFWds2Q4yyqFZo8XvjLX/GroM+aaj95nm7AO4Lmo4pslvXs+b66Z+dBbtnoP2w9i9/W0T6bog1uD8J5BLFPVlnFbvIvjStzFNZ7aQEERL5vYNvZ/QWk6kdlpgqquU9XFwLXAiSJi5Qk6FWfiiudM4CdfgWQWqavjThj+HiBqxu0LBAKBckMwi4MoLEymqrFw+OIKk8UKnr0e126xqv4iImuT0IG/r6tWS+23Ax+IyHnALFz1zraAd+riZOpBvIBbjb8JyAd2Ah4H/lMZ0muEOAhbQhyEHSEOwiYe4aAWh+g7X4zxarvPdtuU+LoiErOUXI7zPvoAOKIIL6aTgUE4L6Z8XEmEcap6R2k6InIY8BswA9gBl+NuF1Vt43USHohIA6ATsAdu/eFVVU1clyiWZExMN+NqQqwAtsPNHPakAq9BiEh6zMY4c+bCQvNRkyadyc2dT07O9MK4h4yMZ+jTZxhNmnSmfv2O5OUtIzt7YmEth/T0RwtLh9au3Y6CgtV8x8rCxeK+5DMmMvel4S4iY/idvuSTwWweZSHfsZI1bOAy3M3EZ/zGC7iKa72Zz1RW8yvruAZX4GcEy3mFpYCLe5jNWvL5kwxmk8FshrOs0NSUwWzy+ZPZrC2MkXiFpYxgOQC7d+hL3tICsr+by3HXODNn1wc/pN873wOw3fGP0fic58gaPaNwsTqt+3sM+dj9Hqof8SAAQz6eQlr39wB4o/e1zBiXzR9rVvHY+YcD8P1Hb/Lh0z0AePXuS+l72QkULF9C30tc4cFv33mJT198FHAxEotmTmX5wjk8f7UrKf7ofQ8XmpCObHY4s2bMYtLESYWxD5l33MsBDVx9quaNDmJR/iLGfjmGs04+A4Bbrs3g5RddBvoj99qNVSsL+OKjD7ihs1uEvvPKS/lwuLvZa7GLMwV/OPx1jtxrNwBu6HwOX3z0AatWFhTuGz74RXpluMX2E9um8+UXE8jLW8peDU4C4InHX+b2W93i8RGHpvFdzk/MyJ1L7W1dbqvePZ8vNDcd1PRMZuTO5bucnwrjJDLuGUKfp119j/oHXE9e/q9kj/6JNh2c2Sv9phfp95LLb1R954spKFhD1siJdIgWrNPSn2HIm2MBqFbnIvc5Df+atCudqadD58fJ+mgiBSvXUHsvV0Sp3+Bs0jMG0uTw22lzxgNkj/mJvEW/Uv+gGwHo88yHZNw7FHCxEjmT5jAjdy7Nmp4OODNSzJTUrOnpzMidy8ScqRx5aCeaNT2dO27tw/8ed59DowYnkJ+3hC+/mMDJbV1U/bVX9uLF/sY5ymxTbVwNbAMsAYYCV0UX9QZRvEIDAFUdCTwMfA7MjbbupelEx/YGRuJMTj8CfwAXlOk9SEBV56nqg6p6TfTXe3CAjYikFpG6uEXq+aq6KKknlyO+M4jc3Pne6bl9ZxD5/Mlu2LqQJqPpO4PInbecxg129GrrO4NYvnAOO9Zv6NXWdwYxa8YsGu3bqNR2ycwg5s6awZ6N9i21XTIziBm5c9m38Z6ltktmBpE7M5/G++xWekPPGUTurEXeKcR9ZxC+5w22M4j/HnyIZn051qttw1pbb5aR1CLyMn+bqopFVS/y0SutHkRRM4yl0VZ4XFXt57PlREHBanPNtdi/PanQLFj9p7nmn2vs38+VK+2XvValQDMV36WClWsrtB7AyoJV5pp+KNWkQsabbUpmxv1fBxenkYWb2TQA2gPeWbpLMzGtA/4qYYsd32zo2tU+SjdmRqromlc+ZF/G9MNnephr3nqtfbR77wx73/1rr/J2N/em6822XnZdM+y99lJx3j4ISjXWe22bK6raI7bh4jBOVdU0Vb1LVTsDpwJNfPVKNDGJiNc8UVXtEwMZExapbQmL1HaERWobU0+zg1voh6P9alHUr7HDZmliikdEVgB1VPWvuH1bAr/4JlUtcQahqnN9trKdRsUiI8Pebz+20FzRNW950jYlOVC4IG1J5h22CesA+txrH0EfW7i2JOOeIbZ60UK0JXfcaj8L90MRNnhtVYSJwP0isg1A9Pc+4HtfAe84iJIWP3wXPCoD9ertZK65A9UrhWa9Ovb5iGruuLO55q672c40AHbe1WPhN0l2q2d/7vV29avJ4K+3vakepOa8fRCU6uVUh6KC0gUYAqwQkV9xrrQTcG6vXiQTB5GYlnNX4GycX+2Nvi9YXgQTky3BxGRHMDHZmHqaH9JMPxkz0qvtLtvU2+xNTDFEZA9ciEK+qs5LOHakqhYbPOIdBxG/+BFtVwHtgNL9DSsR9esnRqaXnVhcQ0XX3L1DX3PNWPyDJc0bHWSuecJBpbu4JkssTsKS+gfYLqbH4h4sadTgBHNNX4KJ6d+o6nxV/TZxcIgo0TOlrPUgvgeOLaNGhWL8ePv8Ob3x8wkvb81xAy4217y4j72Ne+ToT8w1X/3kS3PNMd/YJ34cP8rWK2z8J/b1OkZ/Y5lvLhkUkap18TegxLDBZNJ9H5ewnYYLMZ9axg5WKHJzkwo09CIf+/iCVGjmzl9urrl8ob0Pw88z7GdP82bNLL1RksycYX/uubNsY1Ot9QBmzPCrcW1NbA3CZwsUUuIaQzIziAEJj1fhZhCmoeGpYnnOXK81g97Mpxt+kdRbbVV6G4C3//yFFv/xKx5zX7MbvNrN+OkJ9t3fr+1WO53t1e7ewWfS84QnvNreerBfEFiHPv3oe+txXm3HLPDLgdC9+yPcP+SoUtsdu4d/zYzrHu/Ouae8XGq7rfJGe2ve3+0pThjmUQ+jlv+ibo9H3qH1MR4mtpp+CUF7PDaC1u38yq1u87vfWsmD3ftw0nvlVFWuipmPUk3SqTYqK3vL1mptlvEdIJLBd4BIhrtf9hsgkqHNnvZRwmMW2HpRHbvHL6Z6ALUWfWSumcwA4c22Vhmj41j1m7lktZ06my0WtzjkQP3iG7906Nv9p0mVWaQuCRH5vaSYiBJNTCJSzWez73b5kYoI5ef+stecN9vWHx7guXvsI5Rvuto+vqDv3Teba15/lX2t+PQ7XrPXvLG/rd7VT5nqAaTflGhs2DQIIKz32jZ3ogqge4tIaf7wJU7bSzMxrcMj8ROkwCm/nNgb+2lBo2r2mtvWaFB6oyRpdKC9G2Pzg+09jvY5qLm5ZotDDjTXbPlf+8+oZXPb+lwtD7b33mrZ3L7aoR9K9ZCLCQBVVRGZTNFV8OLblXg8mVQbp+LiHh7AJX6KpfoerqrPeva73AgmJluCiclSNJiYLLQOPuQAHfOt38x62y2bb/YmJhEZDVyuqtM2VsM71QauHsRZqvqJquaq6ifAOcAtG/viFZFYrQZLOv9hrzlpgr2ZpXML++qxDerY35mf16yhuWb9nQ4216zd9DZ7zQaX2urVOcdUD6B2g8vNNX0QcW6uPlsVIRsYKSKZInKZiFwa23wFklk/2A5IdMXZNtrvjYjsKyJrReSVuH1tRWSaiKwWkc9LShIoIjuKyNsiskpE5oqIaUhv3xTE/fX/j73mgS3sayj3/+oHc80ps78x1xw45kdzzWlz7OMgFo7raa855WlbvdnemZ/9NafYr2v4YrUGkcx1RkRuEpFFIrJCRF4Uka18dZK59m0ERwKzcbFqnYELo62zr0AyA8RLwKioSls7EUkHPiKJ3OIRTwPjYw9EpA7wFnAPsCMuV8iwUp7/J1AXSAOeFZEDkuxDsfyEvelkyoYU1ET43X5WMmWcX7nGZBjzpf0A8eO39v0c/cU4c83sb+xjK7LH2IYdZX852VQPIHvMT+aafrg1CJ/NA6/rjIicBNyBq/XcEFclLj6asVidjbj2JYWqtilm8/M7J7kB4jbgSeA84DHgfKBvtN8LETkfV4M1Pm3oWcAUVX1DVdcCmUAzEdmviOfXADoC96jqSlUdDbyHGxVN+IwVVlKFfLLBXnPZUn9/fF8+eb30OIBkGTzAPpL6o9cGm2sOGvB66Y2SpP8Qv+pmSWm+9Jmt3gC/3EVJaQ72S7ltjWBjYkryOnMxMEBVp6jqr0AvXJI8Hx3va9/GIiLbi0iaiNwa/d0+mecnk4tpg6o+p6ptVXV/VT0ueuzlMyYitYGeQKIv5QHApLjXWQXMivYn0hhYr6rxGdMmFdN2o7iF+lZShdy5pb1mo8ZXmWve+Zx9aoghb9m7PN7T3z6Vw7C3nzPXfO9F+2SO7w291VbvLftUG+8NsXeX9sXIxJTMdeYf16/o/7oispOHTjLXvqQRkeOAOcD1QCvgOmCOiHgnSEsqhkFELhGRz0RkevT3kiSe3gs30ibmsqgJ/7ptX0HR7lnJtCUyh00QkQmL+IvhLAMgg9nk8yezWcvduHQIr7CUESynL/lcwyx+ZR1TWU1vXHdfYDGf8RvgFrLXsIEJ61fywF8uWveJv/L5av3vAJz9h/s+fLX+d574K58n/srngb8WMmH9StbohsJF60/W/1YYI3Hvn/P5ccNq/vrzNyZPvAuAxfmjWDDPFXaf9uODrF41j7VrFjN1Ug/mzBxI/oIR5C8YAcDUST1Yu2Yxq1fNY9qPDwKwYN5wFuePAuCKo/7L8sWL+PHbMdx74ZmAi3v4ZJi7G+/cYm8eveEyJnz2EQ9c6UyUT2RcyVdZ7vXPblLXnVPWcJ7IcIXvO511GSNHjKKgYGXhYvRLLwwpjH3ocML5nHlyGvl5i2m612EAPP1Ef+653VUcO+7/2vP9d5OZOeNnDj2wDQBD/vcwQ/73MABXHn8YC2fPYuaPk7jpdPedHnD/vdzYwc2QuxxxIL8sXsTkb8ZwV6fTARcjMTKaYdTf6WAKClby4fufcd6Zrs+XXZTBG69lAbDdVu5G7Y3XsjhwX6d53plX8uH7n1FQsLJw4XrgC8MK4yTanPcU2V/PIG/xCuq3cvv69PuMjF5vA9Dy1EfImTyf3J+XsF20SJ35+IdkPu5yojVp3Zvcn5eQM3k+LU99xH0fu71Cn77uc6zf9Gry8n8le/RU2rTvBbjYh36D3KT7P7t0pqBgDVkjc+hwgXt+2hV9GfKmM7tV29GZuIe8lk3aRe54h7N6kDXiWwoKVhcuSvd7YSTpVz9F2kWP0OaEO8j+4gfy8n6h/l4uc3+fJ94i4/YX3Dn93w3kfDeT3Jn5NDnU+aRkPjSczIfcd6PJobeQOzOfnO9n0/K4bqSlP03GPa/S5+kPonO69u9z6uA++/SbBtDPeDZEcjOIOrFrQ7TFj+ZluSbF/q/loZPU9Wwj6Aukq+phqnquqh4OXIEze3mRTLrvu4GLgD787eZ6E/CKqt5XynObA68CLVT1TxHJBPZR1c4i8j9gS1W9Oq79ZCBTVYcn6LQAxqjqtnH7MoDWqtq+pD74urmO4XeOxM9F0NfN9av1v3N0dT9NXzfX5cvGs2OdVl5tfd1cv8oaztHt/bLZ+rq5vvnau5x9/ulebX3dXL94bzjHdii9n8m4ub7xWhbnnF/iVwhIzs11yDsT6HSGhydlEm6uQ94cQ6ezjyy9oaeb65DXsul0fmu/F/d0cx3y5lg6nX2EV1tLN9eWLZvohHF+Bb+k+vHFvm4y1xkRmQTcp6qvR493Apbh6kE3KEknmWvfxiAivwE7xVt5RGQLYJmqbu+jkcwM4nLgRFXtp6ofqWo/4GTAZx7dGreAM09EFuFcYzuKyHfAFKAwQiuy2zWK9ieSC2whIvHRPc2KabtR+A4OyeA7OCSD7+CQDL6DQzL4Dg7J4DM4JIvP4JAsXoNDspo+g0Myer6DQzKanoODPQq6wW8rmWSuM/+4fkX/L1bVXzx0krn2bQyDgWsS9l0V7fcimQGiBvyrzuUvgE9Vln64E28ebc8BI4CTgLeBA0Wko4hsDdwL/FBUcEdko3sL6CkiNUTkSOB0wGx1NQ37gjAxk5MlE8clfu5lJ2ZGsmSnre2jajvsYx9YFjM3WVJtT/ugx5gJyUxv69NM9QCq7eTtRWmLAqp+W0kyyV1nBgOXiUhTEdkB6IbLcu2j433t20gOBvqIyAIR+VZEFuAsQC1E5MvYVpJAMtlcRwKvisgdwDyciek+nKtriajqavjbf1REVgJrVXVp9Lgjzl72CvAtzkMq1vYu4GhVbRftuhp4EViCG6CuUlWzGcSrNLaSKuTNrew1Wxxq6w8P8OZ0+5xRv6ydba753kz7etwr/rD6Tf7Nhrn/s9dcbpuDa8Pa9031ADb8Yu/s4E3pswNfirzOiEgDXImDpqo6T1VHisjDwOe4m+XhQPfSdABUdWlJ1z4D+kfbRpPMDOJaoAC36r4Sl+p7FW5lPClUNVNVO8c9HqWq+6nqNqraWlXnxB27P25wQFWXq+oZqlpDVRuoqukvZgy/W8oBFC5eW7J82fjSGyVJbEHakjdfe9dc84v37PsZW7i2ZMg7E+w137SNARnyWrapHrg1iPJBYf06v600pWKuM9GgUDO+OpuqPqaqdVW1tqpeoqp/lKYTd7zYa1+Z3w3Vl0rbgMNK0kjGzfV3Vb0IFz29G7Ctql6kqr+V6SwqGBNZZa6Zs8Fe8/ff7KOJc7LtK7V9/IG1pwqM//xjc82RH2Sba4741L7W9IiPJtrqfWB/ozHiY9s+eqNmaxBViRLtgUnVgxCRbYF9cO5Zhahqed0yeBOS9dkSkvVZioZkfRZaLQ/eRyeMftSrrdQ4c7NP1ueDiBRoCRldkyk5ehGwCPgMFw4e2+yT3pcjj+JfhcyXWKyEJbNy7RPoxuIfLOl01mXmmr2uSDPXjMVJWNLh0n72mlHsg5neWbY1rgE6dOpjrumHwvr1flsghlnJ0YeBjlEW182W45LLPejFCdXsNevsXHrJzWQ54VyzjCWFXHSZfUXak86/yFyzy2Xnmmte0cne3fOKi73T6PjpXXayqR7AFRe1Mdf0QgnmI2OSGSD+xKWP3azZ/18Ja8vOAdXsNWvWti/0csChtj72AEcec7i55oGH2ffzqGMPNddsffg+9ppHNrXV86lvnazmkfuba3oTBohkKVNFuXjuAR4TkR6quqxsfdr07HjInlwwvvQpf+3a7fj99w+9NI/o4OfCOe7Dqzi0nZ9JqN8LfkFgxzaqxxez8rzaLl39p1e7C4/5L8Mnz/VqO/xHP+vkdccczlNf+mUgPesAv3WNPXc+kLlLS9esOeIhLz2A+p1eZsUQjxlUmxP9NQ+4hN/nDyy13aoa/hfp+g0OJX956Zlna+SN8tM74HZ+n+L5PjVo7qe5p/9vyBZFq/gAISLDVPW86P9LVLW0L2CJPsnJuLnmAh2AxSKyPto2iMhmZdBLxRfbd3BIBt/BIRl8B4dk8B0cksFncEgWr8EhSXwGh2TxGRySwXtwSEazXAYHnBeTkZtrJeYkEYnNCkoNxFHVErN+JjNAvIyLGmyGy1LYGNg3+rvZ0K+fvT/84rnZ5ppvvWx/8flwqH3xmC/fsg3sAnhpgL1mv4+n22sO+rT0Rkny4gtvmOr1S0FK8lT8hrwxiKSu5HwFfC0ig4GtRWRwUZuvWDIDxE7Avar6o6rOit+SPYOKzIQJ9heKVSvmmGv+NMne13zG5O/NNef+ZF+l7vvv7DVzZtlbTSd8/7O55sQc29iKCZMTkysbaKbgN+RNiIM4BxeZPR23bD+rmM2LZLK5PgZ8r6r21Vo2AS1bNtHxHmsQyeC7BpEMT3muQSSD7xpEMixYbh/8d5ZZVQ/HDh9m2goCksQahC/JrEH44rsGkRSeaxDJUK1aa7s4iOYNdfzH9/q9bt3LNss4CBG5VlX7Rv8PUdUyJe9KZgZxKPBCVAviS/FM9lTZaNPGPlBtytgHzTW7nnmKuebtF3Qw13w0/TxzzQ4n2msed4+93bzNafY1qdsd38VUr8159vWjU/Eb8iKsQYDLjxejzJkYk/FiKnPip8pA9+5dzDV3b3yGuWb6LXeaa6bd4F091pv26Teaa97WzV7z3vOam2t2v8M+gv2ue64uvVESdL/RPg4iFb8hbzZv85EPs0SkDy5l+JYicmlRjVT1RR8x7wEiSuxUIiLyTHzxi8pI48Z7mGtuU9M+jXaDRvY+9vX3amSuWXfPvc01G+1jr9m4nn0wY+NGu5pr7rNvQ1O9xnvvYqoHqfkNebN5L0D7cD5wG3ABsCVF19FWXIbZUkmq5KgH5ZQI3o5Wrbqaa07+yt7UcNFJx5pr3nD68eaa911oX2/g+CPtNQ+99T1zzVbH3W2uecz/2ZrXWrW3T4uRit+QF6qwYYPftpmiqrmqermqngB8oaptiti8w/GTMTH5UGJUXmVg4UL7VNKHnPC4ueaHk+yLEL3yjX320UdG2mcLnTLbXnPBAMs0/I6FP9nHv8yY+7mp3sJx9jcvqfgNebNhs15fSApVbVtWDesZRIWa34lIeqwo+cyZC8nMdLEDTZp0Jjd3Pjk502nZ0lVMzch4hj59htGnzzDq1+9IXt4ysrMnFi64pac/WujfXbt2OwoKVvPr4u+ZNu4JAGZ89xzLFn4NwDfvXwLAsoVfM+O758ibNZJp457g18Xfs37dGsZ96GJTFs/N5ucfBgFuIXvFsmksXZRPu2YutOSVZ5/i8e53AXDhicfw06SJzJ01g45HtOCVZ5+i3yP30++R+wHoeEQL5s6awU+TJnLhiccA8Hj3u3jlWbcI2fnwA/hlcT4/fDO6cDH6ybtuKox96HjQnrz29GN8++lIMi93jg8P3ZDO5+++CcApe9cB4PN33+ShG9x71vemS5n05SjWrlrJdce4FBBfvjWEl++7A3AL1K88cDe/LV3MrSe7Eqkfv9KPNx7vBUDvzqcy96fJLJ77M93Oau1es/fjPNTbDaiHHtSamTN+5vvvJnPcEacCcM/tvejU0ZlVD9irFfl5ixn95deFC9c3XXNHYZzEdp1epmDNX2SNn0eH+51XT9rjXzDkS+flV/0s930Y8uUsDslwdSs63D+KrPHzKFjzF9t1coW/+n08na7PujoMbU7rSfboqeTlL6f+/u5z7NP3fTK6ubYtW99Fzvc/kzszn132ce9T5oNvkvmgex+btLyJ3Jn55Hz/My1bu8/2ztse4cnH3fdg3z3bkJ+3hK++GFe4IH3dVZmF8Q871WxBQcEqPng/m3POcFUFL73wNl4fOgKAWv850J3TuzmkXe8cDjtc1p+sUT9SsHIttQ+43Z3TkLGk3zmMPv0/p815T5H99QzyFq+g/qHOC6hP/8/J6P2OO6fTHiVn8nxyc+fTpIkzEmRmDiz299Snz7DC3xNQ6u/JjDCDQER+ivt/vojMS9jmi8i8kjT+oZdMum+Pzv2uqinIM1x2fN1c3RfbbxnF18117tTX2LOp3x2qr5vr493v4qYe93u19XVz7X/fPVxxdy+vtr5urm883otzbrrHq62vm+s9t/ei10Olaybj5nrLwHE8eknp+ZiScXPN6PYyfXqXHqGdjJvrnbc9wgMP31pqO18314ze79Cn2xl+L+7p5prMb8jUzfWgPXT8Ozf6ve4+t5T5dUVkR2AAcCKwDLizpAJmInITcDt/V567KlZcqCQtEWkIzIZ/FKt5SFX/9WMVkaNUdXT0f7F2aFX9wuscjQeIEnOLlychDsKWEAdhR4iDsBogdtfxb/m52FZrfJvFADEUZ4W5DGgOjACOKKoEsoichMtEcRyQh6tH/Y2q3lGaVtwAsaWqlmhDExEvm6GqegWMWJuYyrEYrQ0xk5Mlk7/KNNeMmZEsub6DbSppcGYka2LmJkta3WK/SB0zIVly9GG2aclbnuZXYCcpzRT8hrxQYN16v62MiEgNoCNwj6qujO7a36NoryGAi4EBqjpFVX8FegFdNlKrJPaI2/YF7gDa4gq9HRc99k4FndQideRTewFQDzcKvga8qNE0pLTET5WB55/PMNfc66Au5pp3PVJqHq6kue6+x8w1L7zbPkjwsb72ms9dZV+74fknLjfXfPLZ7qZ6zz9gH3SYit+QF7E1iE1DY2C9qsZ7i0wCijPrHAC8m9C2rojsBDTw1JorIgp8AtxaVFZtVb0k9r+IvAZcoKrD4/adhUvH4UUyFeUextnP3gJuxdnQbgHs00GWI7Vq2dduqL7F1uaa29a0Lc8JsE0Ne82tt61hrlmzlr1mrW22tNesuY25Zs2atudeq4Z93dxU/Ia88V+krhNzYIm2ZKc9NYEVCftWAMWZ2BPbx/6v5aG1DGgF7AkcEu1/1aOP7YB3Eva9C3inYUjGxNQFaKuqz6rqB6r6HG5B5ZKSn1a5aN/ePkJ5+vgnzDVvvtC+AlrMe8mSp24qMpCzTHQ6y16zw332Nvv25z9srhnzXLKi/WX2yRFS8Rvyxn+AWKaqLeO2fyxQiki2iGgx22hgJZDokFMbKCimZ4ntY/8XlKYVmZ0mqOo6VV0MXAucKCKlOQTNBBK/MFeTRLK+ZExMBfz75AuA35PQqPBMn26/jNK8jb1JZPhY+2yuL3xmW2sAoPdb2eaa4ybba0572t45YPoE+/iX76eOMNWb/rl9MF8qfkNeqJqsLzgpbV3S8WjdYAsR2VdVZ0S7m+FSXBTFlOj463FtF6vqLyKyNkmtmGdRaXFnlwNvi8htwEKgPrAOOKuU5xWSzAziCeAtETlBRPYXkROBN4DHRWTv2JaEXoUk5tttyfzp75hrxuIfLHnlCXtr4XvP218kY3ESlvR4zX7AjcU+WHJ/z6dN9TIft09SmIrfkBfKJouDUNVVOHN7TxGpISJHAqfj6uYUxWDgMhFpKiI7AN2AQT5aInKYiDQRkWrRmsWTQLaqJpqlEvs4EbcgfQHwGNAJ2FdVv/M9z2RmELFV0cSK5G1xHQb3EVVPQjMQCASM2KSL1ODMNS8CS4BfcHENUwBEpAEwFWiqqvNUdWS0jvs5f8dBdPfRAvYG7gd2wVlsPsFd9EtFVf/CFRHaKEzjICoy29bYU/c78HZTzTa32Hu+PLL7MHPNaU38AtWS4fv8Em9eNoqz97MtXrPl8p9Kb5Qs29sn4Bu95DBzzaPqTTLXTAWmcRD776bjBnbxalv9/x7cLOtBWGMdB1HpmTqph7nmizecbq6533nPm2uecmgzc82MU48y1zxgf/s02k0Ot715AGjS9DJzzU5H217TYqkzKrqmF6qwbp3fFvDC28QUTZm6Ay1wblmFqOpmU5d678ZXmmueefuTpTdKkvce8XZl9uaZIfY281uetq9z/fa79vEaWa/eZK/5jv3NxkMvvWaql5X1gKleqjS92VA1LCKbimTWIN4ApgH3AmtS053yZ8OGP8w1/1xrn5aiIAXpM1atXGmuuXaV/bmvLEjB+7lybQo07X8mq40/o4KC1aZ6qdL0YtMGylUJkjEx7Qdcoqrvq+qn8VuqOlcezJtdbK6tjeaT53uba175UAq8T26+1lzzhR6lJ5ZLlquvsr9D7Zph73nT9Sr7aPdHbr/RVK9rV/t6EKnQ9KaKZ3O1xnuRWkReweUSsU1Iv4kIi9S2hEVqO8IitdEideNddFxfvwDS6ic9HRapPUhmBnE98KyIjBCRF+O3VHWuPFgwz77YyReD7e+obnnSfuL28D13mGu+8oi9Hf62W+zvzDPuHWqveZtt9mCAvj27meplZDxjqpcqTW/CDMKUZAaIgcB64CdcVF78Vioi8oqI5IvI7yKSKyKXxx1rKyLTRGS1iHwuInuWoLOjiLwtIqtEZK6ImOaH2HJL+9rENXbY2VyzXh37vEm77LqbueYOO9vX496tXh1zzXq7bm+vudtO5pp16trOYOrVs+9jKjS9CAWDzEnGxFQA1FPV4nKNlPb8A4CZqvqHiOwHZAOnAnNxuUEuB7JwaXCPVtXDi9HxzsEeTzAx2RJMTHYEE5ORiWnfnXXc435ZJKq37xdMTB4kM4P4AdjoW4MoD3rMRUijrREuL8gUVX1DVdcCmUCzaBD5B8Z504tk8kT7HP7Pdz3BXHP3Dn3NNVs3bWSueU2bFuaaDfewrwdR/6Ab7TX3tE9+eOYh+5vq1a9vn4MqFZpehDgIc5Jxc/0M+FhEBgKL4w+oqtc6hIg8g8sKuw0wEfgAuA+X+zymtUpEZuHyp09LkEg2B3vS7HfAbVZShaQ94JOZNznGDbjYXPP1T0eba/YeZu9tNfbbQeaa4z+xrbMAMP7rp8w1+4/4zFRv/Hj7gMtUaHoTzEemJDODOAq33nAi7o49tnmHTarq1bhc5kfjklP9QXJ51ZPKwS4i6bF873/8sYT8BS4T5tRJPVi7ZjGrV81j2o8u0+qCecNZnD+KtWuXMHniXfz1528U/J7LjJ+eAJz767Il7gI6acLNrF+/llkTvuCdB12JwxH/u5OfRruL4WPnurvmn0Z/yIj/3cmv+fN458EbmDXhC/5cs4qnLjoSgB9GDeeT511Z2dczL2f+lAnkLS0onB08NuTbwsXoVpcMJGfaInLnLWe/854nd/5yerzwFT1ecGlW9jvveXLnLSdn2iJaXeJcNm958lMeG/It4GYHS/LzGTf6S7p0OBmA7jddy+svubG9VYO6/DR5Ep+P/ICrO7lI5VvTu/D+m87kdcBOrg7B+28O49b0LgA8es1FfJf9MWtWreSyQ12Rqs/eeIUXMp1ra+8uHflm5Hv8umRR4UxixKDnCheu7z73JGZP+YH8ObMKI6579ehPrx4uBfUB+59Nbu48vsv5icMPvQhwC9QP3Of63HCPU8nLW8oX2TmccJyrVXXVlffzQv+3Aai915UUrFxD1kcT6dDZJfhLu/I5hgz/GoBqu7jzGDL8ay66xi0od+j8OFkfTaRg5Rpq7+WCJvsNziY9coNtc/ytZH8xiby8XwpnCH0eH164IN3ysGvJ+W4GubkLOPxI993I7PkymT1dDrcmTS8jN3cBOd/NoOVhzq24b89uvPa8+8zPPGR/li3KZ+LY0Vx/9mkAPHLbjbz3yiAALjjqEFavLGDMJx9yRxdX57znNZfzydtvAHDM7ju4cxoyirQ0993q0OFOsrLGUlCwmtq127lz6pdFevqj5ObOp02bG8jOnkhe3rLCu/8+fYYVLja3bJlOTs50cnPnF0ZJZ2YOLEzK16RJZ3Jz55OTM52WLdPJzZ0f1aV235369TuSl7eM7OyJtGnj3pP09Efp1y8LUxR0vXptAT/KLReTiDyHS2bVCFdr9eq4Y5OBzPhKSNH+FsAYVd02bl8G0FpV25f0er5rEDN+eoJ997/R6xx81yBez7ycczNf8GrruwZx3DWv8tnTaV5tfdcgunQ4mUHvjfRq67sG0btLR7oN8vMM812DOOG4q/jks2dLbZfMGkSbMx7g83c86hgksQbR5vhb+XzUI6W2S2YN4vqzT+PJN98vtZ3vGkSbNjfw+ee2XmHJaJquQTSqo9/e72d+3OL8wWENwoOkBogo1ewpwK6q+oiI1AOqqeqCpF9Y5AVgFS7n+cWqemS0vwawFDhYVaclPKcG8CtwQCxvuogMBvJixb+LIyxS2xIWqe0Ii9Q2F+pD9t5Jv+3pVyxtywtfCQOEB8mUHD0WmA6k4dJtgMs1XuqtnIjsIiLni0hNEakuIifh0tV+BrwNHCgiHUVk60j7h8TBATYqB3vSpCaSupe5ZtcH7W373W9KQSR1pn0k9VVX2tfCSE9BJHV6KiKpb7vRVC89/VFTvVRpeqHAevXbAl4kWzDoPFU9GVeVCOBb4FCP5ypwFbAANwN4FLhRVd9V1aU4z6T7omOHAefHnigid4lI/NXwatwi9xJgKP/Mm15mtq3RwEqqkLqNmpprHrKffczCAc0PNtfc+wD7DLGHGHvyALRstpe95iH7mms2+W9zU72WLZuY6qVK0wsF1m/w2wJeJBMH8auq7hD9v1xVdxSRasBSVS2nyBh/gonJlmBisiOYmIxMTA131G+6neTV9j9XvFbm1xWRHYEBOMedZcCdqlqsCUJEbgJu5++CQVfFXP9F5Fqch+dBwFBV7ZLw3LbA00AD3I15F1WdW5b++5DMDGJqZBqK53hgsmF/yp1JE24214x5LVmy3fH2Ka9bNbCPeo55N1my0/aJRQ3LTsxjyVRzxzPNNU9qsoepXsyrqaJreqHAXxv8NhueBv4E6uJM789GAcH/Irp23oGrwNkQVyUuPg9NHtAbV1Uu8bl1cKb1e4AdgQmA/Z1kESQTB3EzMEJERgDbiMjzQHvcGsBmw4Et7O3bXZ//2FxzwbvXmGtmT5lprtn3c/taz3Pml+7FkywLJ9vXuV441z7+5e2cqaZ6Cxfa5x5LhaYXkZvrpiAuaPdAVV0JjBaRWNBuUQ4zF+OSncZKkvYCXo21VdW3ov0tgd0TnlsYTBy1yQSWich+Ra3VWpLMDOJo4L84r6MXgdm49YejU9CvcmPl7zPMNedPmWCumT1xnrnmuDEbXbq2WH4aP9Zc84ts75rr3mSPsf+dZX/xg7nmxK9tgxmzs7831UuVph+bNBdTcUG7Rc4gov2TEtrWjTxDS+Mfz42cdWLBxCklmQHiXlXNU9WHVfUaVX0wcm+1TS9Zzixbah9NPHnUW+aa/d/93lzzjcH2iXk/e+MVc80BL7xjrtn/5Wx7zRfsPc2yXrWt0Ne/v3GwWoo0vVDQvzZ4bUCdWBBttKUn+WpJBe0W0T72f3Hty/JaZpS6SC0ix0X/ZgGnARJ3eG9cXqRis69WFJptX0M/OMrW+2XaM5+Y6gHceo592umR2fa28J1X2pvNcts9aKr30OUDTPUABnT9y1yzKmO6SL37Djr2+tZebbe+/Z0SX1dEsik+hc8Y4DqSCNoVkUnAfar6evR4J9zCdh1V/SWuXW9g9/hFahH5H57BxNb4zCAGRNvWONNS7PELwKW4N2qz4ZqJP5tr3nfdFeaac2ba++1f2eXq0hslSdqVz5lrZvycZ675xYBMc81YqouKrFkZ+uiPZwyExzqFqrZWVSlmOwrIBbYQkXgvjGY4E3xRTImOx7ddHD84lMA/nhutfzQq4bXMKHWRWlX3ijo1WFUvSnWHypvjd9neXPPwtn6ud8lQe/sDzTVPaHe8ueapJ9jHQbTevoa55u4H2bssn3rq/1V4zcrQR28UdMOmWaSOkorGgnYvx5UeOB0o7os0GBgkIq8C+TjT/KDYQRHZAnc9rg5Uj4KG16nqOlww8SMi0hFX3qDYYGJrvNcgqsLgAHBm/R3NNduecba55o51WplrdjzPL5d+MnTqaH+xaL+jfVGnRoeeaK7ZqZP9gGutWRn66Isq6F/rvTYjig3aFZEGIrJSRBq4vulI4GHgc1wNnLlAfArhbsAanFdT5+j/btFzSwwmTiXJLFJXCXYfkWOuefye9oPOxHH2bq51t61nrhnLmGrJfjn2N04Dr7KPValWrXWF16wMffTHzsTk9Wqqy1X1DFWtoaoN4oPkVHWeqtZU1Xlx+x5T1bqqWltVL4mrj4OqZhZhysqMOz5KVfdT1W0i89cck5MohWTiIKoEC049xFxz1Nzl5potDn3aXHPxanvb/oYlg8w1px3yr1pSZeaSZ8eYa27YkF3hNStDH71RYBOZmKoKYQaRwNsL7S/mn77zprnm8mXjzTWHD7N3x43VXrAka7l9mo9Z4+y9soYMGVXhNStDH5Mh1IOwJQwQCYxa8pu55jeffmSu+ftvP5prfvKh/Q97xCf2OYGyf1tlrrlgsn1A34gR9oOjtWZl6KM3qvDXer8t4EW5FQza1IQ4CFtCHETAAss4iIPr1tavOvklPqz5xKhQD8KDMINIoMt4+3xE3S69wFxzVm7pFdWSpXNHe0e1WKlPS66caZv1FWDUM/a1yDt08KhQV86alaGPSRHqQZgSFqkTSGtQx1zz1E4Xm2vW2fkoc80LL/UuL+7NFRe2Ntc8t8725pqNj+pgrnnFFSVWwa0QmpWhj95swjiIqsJmPYMQkfRYrpXZq/6gT67z0jk6+0d+XrmWH1asot1XrmZAz6nzef7nxfzfTrU4ZNQPLFr7J2N/KeDsr6cDcNsPc3ll3lIAmoycyMp16/l61MjC2cF9111RuBgdc2v99J03ue+6K2h2+JF0u/QCvh41ktUrC2jf1BUlen/IIB6740YAbj6vPd9/PZq//vyNyRPvAmBx/igWzHOR9NN+fJDVq+axds1ipk7qQc3a+5K/YAT5C0YAMHVSD9auWczqVfOY9qMz1SyYN5zF+W5d4b97t2BR3iLGfDmWM09yhekzrrmVwQNcrqS9d9mXZgf/l49GfFw4k7iyy9WFC9cxF9jhw94qjLju0Plxsj6aSMHKNYXpsvsNzi6sztbmjAeoXr0aeYt+pf5B7jz7PPMhGfc6M1rL47uTM2kOubMW0eRwV6vjqbylPJXn3ueTfpzF7LV/8uOqtZz102wAHpy/mNw1zjvw6B9msvjPv/i2YBUXTnep8e+Zm8+wpb8B8PKNx/PX2lXM+2F04QzhiwGZhQvSMdfWWeM+Zta3rhb3qGduY94Po/lr7SpevtH580//6l3GvPqQO6c2N5CdPZG8vGXUr+/exz59hpGR8Yw7p5bp5ORMJzd3Pjff3BeAzMyBZGa696RJk87k5s4nJ2c6LVu69D8ZGc/Qp4/L3ly/fkfy8paRnT2RNm1uAFyFtn79XH6jtLReFBSsJitrbOGdelpar8KF4ZiL6ZAhowojmjt0uJOsrLEUFKwuTMXdr18W6emP0rp186TOqUmTzqWeU+vWzZM6JytUlQ1/bfDaAn6ENYgEmoycyPSTW3hp+q5BtG/agKypftlXfdcgJk24mWYt/WpC+K5B7L3Lvvy8xC+bre8aRO29ruT32X7pNnzXIA6emMt3LRqX2i6ZNYiXbzyeC58ofZE+mTWI2rXb8fvvtgn7rDXLu4+WaxAtdq6ln5/u56a+w4AvwhqEB8HElIDv4JAMvoNDMvgODsngOzgkg+/gkAw+g0Oy+AwOyWJ94U2FZmXoozfBxGTOZm1i2hhiZiRL3h8yyFxz2RL7tOQxc5Ml/QZnm2vGTEiWTP/qXXNNaxNKKjQrQx99UUA3bPDaAn6EASKBH35bba6Z+8P35pqrV9nPSiZ9Zx+zMGHSbHPNH1evMddcNs8+fceECdMrvGZl6KM3YQ3CnLAGUQZCHIQtIQ6i6mG5BtF8x5r6yYn/9Wq7y7CvwxqEB2EGkUDMa8mSm8+zd/ub8dMT5pox7yZL2pzxgLlmzGPJkg8fu9ZcM+axU5E1K0Mfk0E3qNcW8CMsUidwc2P7jKYX3Xi7ueau9U8x17zl7gxzze63nmGueW09+1iV5qddaq7ZvXuXCq9ZGfroiypsCBd/U8IAkcDeNbYy19x970bmmltvvYu5ZqN99jbXbNxoV3PNhlv9x1xzu132MNds3Ljia1aGPnoTrUEE7AgmpgROHW2/WHnNaW3NNadNedhc88Sj2plrtjqhh7nm2dPsTUzvPXiZuWarVl0rvGZl6GMyBBOTLVVmkXpv2Vp7s6ep5qOH2tta3/7U3tSxw632F/51T9jXxD7+mA9M9XK+OcBUL2CP5SL1f7eroR8c6eeIsseHOWV+XRHZERgAnAgsA+6MLxpURPubgNtxVeiG4yrQ/REduxboAhwEDFXVLnHPawjMBuLTGD+kqikv/h1mEAmMwL4eRCzdhSX9n3zCXPOpSfZJ8J5+or+5Zirez1haiKqmWRn66I9u6jiIp4E/gbpAGvCsiBR5VyIiJ+HKibYFGgJ7A/HT6zygN/BiCa+3fVSlruamGBwgDBD/4lfsc8X/9Zd9gZvF+fnmmotW/VF6o2Q18xeba6bi/czL+6VKalaGPnqjbLI4CBGpgasTfY+qrlTV0cB7wIXFPOViYICqTlHVX4FeuBmD67rqW6r6DlBOb17RBBNTGQgmJluCianqYWliOqjWtvpey3292u6d/cNcnFkoRj9V7ef7WiLSAhirqtvE7bsFOFZV/+XXLiKTgPtVdVj0uA6wFKijqr/EtesN7F6MiSkPFzD+CXCrqsb3PyWEGUQCd2O/ABrLrmrJaUcfYa55zJsTzDWP+z/7GJBUvJ+xzKpVTbMy9DEZkig5ukxVW8Zt3oNDRE0gcSq7Aqjl2T72f3Ht41kGtAL2BA6JnvOqd0/LQBggEricuuaaDfbqZK75wJN9zTX/d2wTc83Hnr7fXDMV7+fzz9vHgFQGzcrQR2/Uz4PJx4tJRLJFRIvZRgMrgdoJT6sNFBQjmdg+9n9x7eNOS1eq6gRVXaeqi4FrgRNFJPH1zQlxEAlsnYIxs1o1+9iKGjV9bjySo9aW1c01a9aqYa6ZivezVq1tq6RmZeijN9EahImUauuSjkdrEFuIyL6qGkuD3AyYUsxTpkTHX49ruzjevJRM92Ld2IjnJsUmmUGIyFYiMkBE5opIgYhMFJF2ccfbisg0EVktIp+LSLGLBSKyo4i8LSKrIj3T28lHWWgpB8DPufYpry875yxzzXM/nGyu2elM+/iCVLyf7dvbl8msDJqVoY++uGyumyYOQlVXAW8BPUWkhogcCZwOvFzMUwYDl4lIUxHZAegGDIodFJEtRGRroDpQXUS2FpEtomOHiUgTEakmIjsBTwLZqmrvrZHApjIxbQHMB44FtgPuAV4XkYbRYs1b0b4dgQlASX5y3q5lG0Mf9rKSKqRps+7mmp9/b38x/+4Cv4LvyTDux8/NNVPxfk6fbp/qvDJoVoY+eqNJrUFYcDUupmEJMBQX1zAFQEQaiMhKEWkAoKojgYeBz4G50Rb/Re4GrMG5wnaO/u8WHdsbGIkzR/0I/AHYF7ovgk0yQKjqKlXNVNU5qrpBVd/HrcofApwFTFHVN1R1LZAJNBOR/RJ1NsK1LGmGY+8YECsLasnj9/c213xgvH1q7od6PWGumYr3M1Y+s6ppVoY++qNs2OC3mbya6nJVPUNVa6hqg/ggOVWdF8UrzIvb95iq1lXV2qp6SSxILjqWqaqSsGVGx4aq6l7R6+ymqhep6iKTkyiFclmkFpG6QGOcXe4AoLAQQTR1mxXtT6QxsF5Vc+P2TSqmbSAQqEKowrp1flvAj00+QIjIljgXrZdUdRrJuYsl5VomIukiMkFEJizir8LZQQazyedPZrO20K31FZYyguV0pA7XMItfWcdUVtMbF138Aov5jN8AuIwZrGEDK36dzKzcZwGYM3Mgy5eNB2DiuGsAWL5sPHNmDmS33U9lVu6zrPh1MuvXr2XShJsBVxVu3mx30zHjpyco+D2Xxfl5HLqvM3P1f/IJet/pMsGedvQRTJ74HT/PmEGb5gdx013dePz+3oUziTbND+LnGTOYPPG7QhfY3nfeXhhx3WTwWPJX/cFXC3/l1HcnAnD9F9MZODUPgPoDvuLaZnvw4ZxlnBetRVw2aipvzHCBbts9lw3AGzMWc9moqQB0OusyRo4YRUHBShrUORCAl14Ywk1XOxt0hxPO58hjDic/bzFN93Lmq6ef6M89t7s+H/d/7fn+u8nMnPEzhx7YBnCzg9gMYeqkHqxds5jVq+YVurYumDecatXdIvXkiXfx15+/UfB7bmH683mzhxRW26tdux0FBavJyhpLhw6uT2lpvRgyxEViV6vWGoAhQ0YxY8YC1+cOd5KVNZaCgtXUru2Wyfr1yyI9/VH3Pre5gezsieTlLaN+fZcevU+fYWRkPAM4F8+cnOnk5s5n6NBPAXdHHburbtKkM7m588nJmV7oDpqR8Uxh9HH9+h3Jy1tGdvbEwrTZ6emPFlZpe+yx173PKS2tV6nnlJl5SVLn1KRJ51LPKTPzkqTOyZING/y2gB+bNFBORKoBQ3AuXqer6l8i8j9gS1W9Oq7dZCBTVYcnPL8FMEZVt43blwG0Lio4JR7fQLkMZnuvQ/gGyk2d1MPbbu4bKNem+UHe6xC+gXIHD/3Wex3CN1Du0APbeK9D+AbK+b6fyQTKNWnS2dx2Xhk0y7uPloFyTbfZWl9t2NCr7cHTpoeCQR5sshmEiAgusVVdoKOqxkpzxdy/Yu1qAI0o2l0sl8i1LG5fSa5lSXML9a2kCtm78ZXmmgPeeMtc8/V2B5lrDnnbvqpbKt7PrCz7wkaVQbMy9NEbDTMIazalielZYH+gvarGFxV+GzhQRDpGbl73Aj9E5qd/sBGuZUmzFvtvz4YN9jmOVq0sNb4maQr+ss9DtbJgVemNkiQV72dBgX0t8sqgWRn66EtYg7BnU8VB7Al0BZoDiyL3r5UikqaqS3GeSfcBvwKHAefHPfcuEfkwTq5Y1zILXsA+uVxsncGSO6+3L5F5wxcpKLd6zV3mmql4P7t27VMlNStDH5MhzCBsCcn6ykBI1mdLSNZX9bBcg9hvq611wK4NvdoeNS+sQfgQcjEl8ApLzTUXzBteeqMkiXk3WXL32JnmmjGPJUtS8X7GPHaqmmZl6KM3YQ3CnJCLKYEdsM9HtOWW25lr1t1tN3PNXVNQj3vX3eyTH6bi/axXb6cqqVkZ+uhLbA0iYEcwMZWBYGKyJZiYqh6WJqYmW26tz+7k9xtvuzg3mJg8CCamBK5hlrnm5In2C7WxYDpLmgwea64ZC5CzJBXvZyxArKppVoY++qIEE5M1VWYG0bJlEx0/vvSaIHl5y6hXr46X5kVP+o2vq1csY9vt/DTb3Xi5V7tfWccOnhbC86f5zUryFv9Ovbp+Keal8eF+mkm8n74ETTvN8u6j5Qyi8RZb65O1/WYQ7X4NMwgfwgwigdzc+eaavy+x18znT3PN3Dn2C/SpeD+DZsXVS5WmD2EGYU8YIBLo0WOQueYPI+3t9W+loLZ5z76jzDVT8X4GzYqrlypNL4IXkznBxFQGfE1MyeBrYkoGXxNTMviamAKBkrA0Me1TfWt9bBs/E9Ppq4KJyYcwg0gglrXTkm+GPWKumYqI76732Od3SsX7GTQrrl6qNL1QWLfeb7Mg2eqWInKTiCwSkRUi8qKIbBXtL7HiZtTGu+qmJWGASKBlyybmmjvtYa+5N/YxC4ccaJ+oMBXvZ9CsuHqp0vShHNYgvKtbishJuGpxbYGGuCpxPaLDxVbcjJ6bbNVNM4KJqQwEE1MgUDYsTUyNZGt9YAu/G+vz1pXNxBRlnf4VODBWwExEXgYWquodRbQfAsxR1buix22BV1V112L0fwB6qOpwEUkHuqjqEXGvvQxoUVRSU0vCDCKBWEEVS167/SRzzcuYYa653cH3mmum4v0MmhVXL1WaPmziGUSy1S3/UTkz+r+uiPwr7Dyh4ua/nltK1U1TQqqNBBYutM/z07HH2+aafWlkrrngy7vNNVPxfgbNiquXKk0fZvPHRxdsyPUN6thaRCbEPe6nqsmYGJKqbllE+9j/teBvl8QiKm7Gnpvog17Sa5kRZhAJZGd/b665eOZEc82fsM+5nz3OPoo8Fe9n0Ky4eqnS9EFVT1bVlp7bgQmP/zE4iEi2iGgx22hgJa4yZjy1geIKtSS2j/1f2D6quPkybl0jPp9/sq9lRhggEujf375O7oyv7TU/+9fNS9np//o4e80UvJ9Bs+LqpUpzU6OqrVVVitmOIvnqlv+onBn9v1hVf4ESK27+67mlVN00ZbMeIEQkXUQmiMiEmTMXehWOf++9B0otsl67djsKClaz4McxfN7frUeNHtyT2TmfAPDKjccAMDvnE0YP7kmbKx7k8/53sODHMfy1dnXhmsSMse8VusB+/NT1LJoxkV9ZV5gPagTLC9OP381cZrOWfP4kg9ncQn2Gs4zhLHP9Zzb5/Mls1nI3c10/WMoIlgOw+9H3kbf4d7K/ncVxFz4POLfWfsO+Bdz6w6uPXkDWZ1PpcOUgANIyhjIk63sAqu/nznNI1vekZQwFoEOHO8nKGktBwepCu3O/flmFbo5t2tzAzTefS17essL8PH36DCtMB92yZTo5OdPJzZ1PkyadAcjMHFjq53Tssc0BvD+nrKyxdOhwpzuntF4MGeICAqtVa+3OacgoatXa1vucsrMnep3T9Onzvc+pT59hXueUnf299zmlpfUq9Zzee++BpM7J53N6770HkjqnyshGVLccDFwmIk1FZAegGzAo7nhxFTchiaqb1gQvpgTS0nrx6qv3eGn6ejGNHtyToy7yWwD29WLqSz7X4pfy29eLKS1jKK/2ucCrra8XUzLvpy9B006zvPto6cW0qRGRHYEXgRNw6wh3qOqQ6FgDYCrQVFXnRftuBm7HVcQcDlypqn9EMQ1zgD+A+ITlXVX11ei5xwN9gT2Bb3FeTXNSfY5hkTqBU0/9P3PN+gfYa7aghrnmqa33t9dMwfsZNCuuXqo0KyKquhw4o5hj83CLy/H7HgMeK6LtXEBKea1RwH4b29eNJcwgykCIgwgEykZlnkFUBTbrNYiNIWbDtSS2JmFJGrmlN0qS2DqDJal4P4NmxdVLlWagfAgDRAIbNmSba3Z+4ktzzVdpbK65ftqD5pqpeD+DZsXVS5VmoHwIA0QCMW8QS2LeTZaM4XdzzZjHkqlmCt7PoFlx9VKlGSgfwgCRwIgRX5trLpxirzmRVeaaI7J/stdMwfsZNCuuXqo0A+VDWKQuA2GROhAoG2GRumITZhAJxIKPLIkF01nyKAvNNWMBcqaaKXg/g2bF1UuVZqB8CHEQCVxxRXvvtr53+3VZycGebS/Y4DfLqZk1lvbtj/Bq60v6TRvMZwbJvJ9Bc9NrVoY+BsqPYGJKoKBgdWHahdIYWi3dq90aNrCN52TNd4BIpp++BM2qp1nefQwmpopNMDElEMtFY8m12GdJTUU/g2bV06wMfQyUH2EGUQZ8ZxDJ4DuDCAQ2B8IMomITZhAJpCLD5Gf8Zq6Zin4GzaqnWRn6GCg/wgCRwIQJ0801f+YPc81U9DNoVj3NytDHQPkRTExlIJiYAoGyEUxMFZtNNoMQkWuj4j1/iMighGNtRWSaiKwWkc+j/OjF6ewoIm+LyCoRmSsinSz7GStoYklv5ptrpqKfQbPqaVaGPgbKj01pYsoDeuMKbBQiInVwlZnuAXYEJgDDStB5GleztS6QBjwrIgdYdbJ79y5WUoWcxU7mmqnoZ9CsepqVoY+B8mOTDRCq+paqvoOrvBTPWcAUVX1DVdcCmUAzEflXcYyoFmtH4B5VXamqo4H3gAut+tm48R5WUoXsxn/MNVPRz6BZ9TQrQx8D5UdFWKQ+AJgUexDVep0V7U+kMbBeVeOLIUwqpu1G1aRu1aqrd63j71hZmPKiL/mFGVZjtRrG8Dt9yacbc3mUhXzHStawgcuYATjvphdYDDgz1FRWe9cFbtWqq2mt49q123HIIelmtY7BmRr++99LzWtS77//Rd7n5Fu/eZ99Onmfk2/95j33PNf7nHw/pz32ONu0JnWrVl3Na1K3atV1s69JXVXY5IvUItIb2F1Vu0SPBwBLVfWOuDZjgP6qOijhuUcDb6jqrnH7rgDSVLV1Sa8bFqkDgYpHWKSu2FSEGcRKoHbCvtpAQRnbbhSxux5LRrDcXDMV/QyaVU+zMvQxUH5UhAFiCtAs9iBaZ2gU7U8kF9hCRPaN29esmLYbRV5e4hJJ2fmV9eaaqehn0Kx6mpWhj4HyY5OZmERkC1z22O7A7sAVwDpgB2AmcCkwAugBHKuqRaYVFZHXAAUuB5oDHwBHqGqJg0QwMQUCFY9gYqrYbMoZRDdgDXAH0Dn6v5uqLsV5Jt0H/AocBpwfe5KI3CUiH8bpXA1sAywBhgJXlTY4JENs4dCSu5lrrpmKfgbNqqdZGfoYKD9CJHUCOTnTOeSQJl6avjOI2axlL7b2aus7g0imn74EzaqnWd59DDOIik1FWIOoUFjnxgfYOgVvcyr6GTSrnmZl6GOg/AgDRALt29uXS0xFedBU9DNoVj3NytDHQPkRTExlICxSBwJlI5iYKjZhBpFALDrUkuEsM9dMRT+DZtXTrAx9DJQfVWYGISJLwcudqA6YX9GDZtCsqJrl3cc9VXVn49cPGFFlBghfRGSC9ZQ3aAbNiqpZGfoYKD+CiSkQCAQCRRIGiEAgEAgUSRgg/k0q3IiCZtCsqJqVoY+BciKsQQQCgUCgSMIMIhAIBAJFEgaIQCAQCBRJGCACgUAgUCRblHcHAn6ISGNc7e1auAp6UxJqc1cIQj9tqSz9DGyehEVqKvaPUEQaAMNwlfNmAStwZVYbAZOA81V1Xvn10BH6aUtl6Wdg86ZKDxCV4UcoIp8COUCmqq6O218DuBdoparHlVf/4voT+mlIZekngIikA11wN1k1cbXjpwADVbV/OXYtUEaq+gBR4X+EIrIS2FFV/yzi2FbAclWtsel79q++hH4aUon6+RBwGtAHd1MVu8lqDtwMZKlqyP9dSanqi9SH4cqero7fqaqrcAPEYeXSq38yH/cDLIpTgIpiZgj9tKWy9PNS4HhVfVFVc1R1pqp+p6ovAifias8HKilVfZE69iN8q4hjFeVHeC0wXERu5t93aAfg6nlXBEI/baks/ZQyHg9UYKq6iaktMBz4kWJ+hKr6Wbl1MEJEdgLO4t823rdV1b7YxEYS+mlLZehnZGLqwL9NTM3428R0R/n1MFAWqvQAAZXjRxgIVGREpCtwEf/+DQ1W1efLs2+BslHlB4jKjohcoKpDy7sfpRH6aUtl6WegchMGiBKoDD9CEflRVQ8s736URuinLZWln4HKTRggSiD8CAOBsiEiv6tq7fLuR2DjqOpuriUSBodAoMycUt4dCGw8YYCoBIhIuoiMFZEVIrI++jtWRCqUj3nopy2VpZ8loaqjy7sPgY2nqsdBVPg0AaVFqorI3hUhUjX005bK0k8o9ATsSEI+M2C4qv5Snn0LlI0qvQZRGdIEiMhS4L+qml/EsXrAD6paZ9P37F99Cf00pBL1sy3wJjCZf8dBHISLJfq8/HoYKAtVfQZxKUX/CL8TkZHAD0B536VVlkjV0E9bKks/nwIuU9V/ZSMQkTOBZ4D9N3mvAiZU9QGiMvwIBwCfiUhxkarlbgaLCP20pbL0c09gRDHHPgBe3YR9CRgTTEyVIE1AZYlUDf20pTL0U0Q+4++MyKvi9tcAuuMyIrcpr/4FykaVHiCgcvwIA4GKiojsCQwFWgA/8/dN1t7A91SAmiqBjafKDxCVhSKq3v2oqjPKt1f/JvTTlkrWz6bE3WRVxH4GkiMMEFTsH2FlqHoHoZ/WVJZ+BjZvqvQAURl+hJWh6l3Un9BPQypLP6HixxIFNp6qPkBU+B9hJSo9GfppSCXqZ4WPJQpsPFU91UYoOWpH6KctlaWfoeToZkxVj4MIJUftCP20pbL0szLEEgU2kqpuYqrMJUd/BN6pSFXvKkt1vkrczwJgKhWon5UlliiwcVTpAQIqx8VCRFrgFs4/AP4ErsL5mX+qqu+XZ9+KQ0T2Ak6NHo5U1Znl2Z/KiIjsA1wIHAhsCywAxgGDVPWv8uxbPCGWaPOlyg8QxSEi1YG7VbVnOffjMqA3oEAezhy2B848eD5wQ2TvLVdE5CdV3T/6/1jgPWBMdPho4PQKMhv7H/C6qo4ptXE5IiJnAK/g3kMBjsV53DUCdgVOUNWfy62DgSpBGCCKIfIUWa2q1cu5H9NwU3gBfgKOUtWx0bGTgIdVtVk5dpGoLwWqWiv6/yugv6oOjh6nAdeo6hHl2ceoL+uA1cASYDDwkqrOLd9e/RsRyQW6xjKhisiJwE2q2k5EbgHaqOqpJYpUAESkQXm7igc2nio9QIhISXfeWwBpFWCAWKGq20X/rwJqavShiUg1nLvj9uXYRaK+FJaWFJElQP2YGSSajS1V1R3Ls49RXwqAusA5OLPIMcBoYBDwZnw+ofJERH4Ddoj7rLcA8lV1ZxHZFlhU0Ut5VpSbrMDGU9W9mDrhsmYuL+JYRflSrxKRLaOL7SD954i+DbChnPqVyJYicglupqPAf4CYnXwLKs77qZFb80vAS1Gw5EXAXUBfERmuql3Ks4MROcD1wP+ixzfi7PoA64F15dCnfyEix5RweKtN1pFASqjqA8Rk4CNVfS/xgIhsDVQE74tPgX2An1T1moRjp+FqVlQEvsVdaMF52jQFxkePjwWml0enSiMyf/QGeovIEfx9DuXNNcC7ItIrerwEOCP6vzHwQnl0qgiygXwqzo1KwJCqbmK6Blioqu8Ucaw6LoiuxybvmCcisjPujrhCeFsVh4hsB2xZEfoZv1ZS0Ym+g/vhZmXTVLVCzBriEZHZOFPs2CKObQ2sCiamykuVHiACgUDZEJE3gK9U9ckijv0HmK6qe236ngUsCANEIBDYaERkS4CKFJcRsKOq52IKBAJlQFX/Km5wEJHqInLvpu5TwI4wgwgEAikhuLlWfqq6F1MgECgDHrFEgUpM+AADgUBZqAyxRIGNJJiYAoHARiMi44FeJcQSrVbVsNZZSQkfXCAQKAuDKP468hdQYeOIAqUTZhCBQCAQKJIwgwgEAoFAkYQBIhAIBAJFEgaIQIVCROaIyPEe7VqLyIKNfI2GIqJRCu2S2mWLyOXFHGsgIiujfEmBwGZJcHMNBDaCKAtszfLuRyCQSsIMIhAIBAJFEgaIQMysc6eITBWRX0VkYOTDjohcISIzRWS5iLwnIvXinvc/EZkvIr+LSI6IHO3xWtuIyEvR6/wkIrcVZyoSka1E5AkRyYu2J6L0DfFt7hKRZdE5pMXtP1VEJkZ9my8imRv59jQSkXEiskJE3hWRHSP9f5ipInNULxEZIyIFIvKxiNTZyNcMBCoEYYAIxEgDTgIa4QrSdBOR44AHgHOB3YC5wGtxzxkPNAd2BIYAb8QGlhLoDjQE9gZOADqX0PZu4PDoNZoBhwLd4o7vCtQB6gMXA/1EpEl0bBWu+M/2wKnAVSJyRil9K4qLgEuBergqbv9Kax1HJ+ASYBdcRb1bNuL1AoEKQxggAjH6qup8VV0O3AdcgBs0XlTV71T1D+BO4P9EpCGAqr6iqr+o6jpV7YMrMdmkGP0Y5wL3q+qvqrqAki+4aUBPVV2iqktxQVcXJrS5R1X/UNUvgBGRPqqaraqTVXWDqv4ADMVVtkuWl1X1x6hW9T3AuSUsTA9U1VxVXQO8jhvYAoFKSxggAjHmx/0/F3fHXC/6HwBVXQn8grtjR0QyIjPRChH5DdgOd0dfEvUSXmt+cQ0TXz+uXzF+jS7c/zouIoeJyOcislREVgBXevStKBLfly1L0FkU9/9qwiJ2oJITBohAjD3i/m8A5EXbnrGdIlID2AlYGK033I67Y99BVbcHVuDKY5ZEPrB7Ma+byD9eP65fMXaI+lTU8SHAe8Aeqrod8JxH34oi8X35Cyj30qmBwKYgDBCBGNeIyO7RIuxdwDDcRfYSEWkeLQ7fD3yrqnOAWjib/FJgi6gwTG2P13kduFNEdhCR+sC1JbQdilsL2Tla8L0XeCWhTQ8R+U80YJ0GvBHtrwUsV9W1InIobn1gY+gsIk1FZFugJ/Cmqq7fSK1AoFIRBohAjCHAx8DP0dZbVT/F2d2H4+78GwHnR+0/Aj4EcnGml7WUbC6K0RNYAMwGRgFvAn8U07Y3MAH4AZgMfBfti7EI+BU3a3gVuFJVp0XHrgZ6ikgBbmB53aNvRfEyLiHdImBr4PqN1AkEKh0hWV8AEZkDXK6qo8rhta8CzlfVjVlADgQCKSTMIAKbFBHZTUSOFJFqkUtqBvB2efcrEAj8mzBABMwRkQ+jPEWJ2124+IDngQLgM+Bd4Jly7GtR/VzpE/QXCGzuBBNTIBAIBIokzCACgUAgUCRhgAgEAoFAkYQBIhAIBAJFEgaIQCAQCBRJGCACgUAgUCRhgAgEAoFAkfw/3OKWKDN+IsQAAAAASUVORK5CYII=\n", + "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot heatmap\n", "heatmap_plot = plot_heatmap(\n", - " dnorm = norm,\n", - " dmeas = meas,\n", - " fit = mlfm_sel,\n", - " y_axis = 'temp_module_bin',\n", - " x_axis = 'poa_global_bin',\n", - " z_axis = 'diff_' + mlfm_sel,\n", - " title = 'residual ' + mlfm_meas_file\n", + " dnorm=norm,\n", + " dmeas=meas,\n", + " fit=mlfm_sel,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis='diff_' + mlfm_sel,\n", + " title='residual ' + mlfm_meas_file\n", ")" ] }, @@ -1364,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -1373,7 +1316,7 @@ "Index(['mid', 'poa_global', 'temp_module', 'wind_speed', 'poa_global_kwm2'], dtype='object')" ] }, - "execution_count": 55, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -1400,12 +1343,12 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "# populate pivot table from predicted mpm data\n", - "matr[mlfm_sel] = mlfm_6(matr, cc[0], cc[1], cc[2], cc[3], cc[4], cc[5])\n" + "matr[mlfm_sel] = mlfm_6(matr, cc[0], cc[1], cc[2], cc[3], cc[4], cc[5])" ] }, { @@ -1417,7 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -1425,62 +1368,61 @@ " vmin=0, vmax=1.2, levels=5):\n", " ''' \n", " Plot filled contour plot Z vs. X and Y bins.\n", - " \n", + "\n", " Parameters\n", " ----------\n", " df : dataframe\n", " measured or noralised data containing weather columns\n", " (poa_global, temp_module and wind_speed).\n", - " \n", + "\n", " x_axis : string\n", " binned x axis e.g. 'poa_global'.\n", - " \n", - " y_axis : string \n", + "\n", + " y_axis : string\n", " binned y axis e.g. 'temp_module'.\n", - " \n", + "\n", " z_axis : string\n", " measured value as a colour surface plot.\n", - " \n", + "\n", " title : string\n", " title for graph e.g. mlfm_meas_file.\n", - " \n", + "\n", " vmin, vmax : float\n", " minimum and maximum values for contour chart ###\n", "\n", - " \n", " ''' \n", " \n", " piv = pd.pivot_table(\n", " df,\n", - " index = y_axis,\n", - " columns = x_axis,\n", - " values = z_axis,\n", - " fill_value = 0, # fill empty cells?\n", - " aggfunc = [np.mean], # min, np.sum, len->count\n", - " margins = False, # grand totals\n", - " dropna = True # hide missing rows or columns\n", + " index=y_axis,\n", + " columns=x_axis,\n", + " values=z_axis,\n", + " fill_value=0, # fill empty cells?\n", + " aggfunc=[np.mean], # min, np.sum, len->count\n", + " margins=False, # grand totals\n", + " dropna=True # hide missing rows or columns\n", " )\n", - " \n", - " piv = piv.clip(vmin, vmax) ###\n", - " \n", + "\n", + " piv = piv.clip(vmin, vmax)\n", + "\n", " fig, ax1 = plt.subplots()\n", "\n", " cs = plt.contourf(\n", " piv,\n", - " cmap = 'RdYlBu', # or 'nipy_spectral',\n", - " # origin = 'lower'\n", - " # nchunkint = 1,\n", - " levels = levels, ###\n", - " vmin = vmin, ###\n", - " vmax = vmax ###\n", + " cmap='RdYlBu', # or 'nipy_spectral',\n", + " # origin='lower'\n", + " # nchunkint=1,\n", + " levels=levels,\n", + " vmin=vmin,\n", + " vmax=vmax\n", " )\n", - " \n", + "\n", " cbar = fig.colorbar(cs, ax=ax1)\n", " cbar.ax.set_ylabel(z_axis,\n", " rotation=90,\n", " va='bottom',\n", " labelpad=+30)\n", - " \n", + "\n", " plt.title(title)\n", "\n", " y_ticks = piv.shape[0]\n", @@ -1497,7 +1439,7 @@ "\n", " ax1.set_yticklabels(yax2)\n", " ax1.set_ylabel(y_axis)\n", - " \n", + "\n", " x_ticks = piv.shape[1]\n", " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", "\n", @@ -1512,7 +1454,7 @@ "\n", " ax1.set_xticklabels(xax2)\n", " ax1.set_xlabel(x_axis)\n", - " \n", + "\n", " ax1.grid( color='k', linestyle=':', linewidth=1)" ] }, @@ -1525,32 +1467,30 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 84, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "contour_plot = plot_contourf(\n", - " df = matr,\n", - " y_axis = 'temp_module',\n", - " x_axis = 'poa_global',\n", - " z_axis = mlfm_sel,\n", - " title = 'matrix predicted ' + mlfm_meas_file,\n", - " vmin = 0.6,\n", - " vmax = 1.05,\n", - " levels = 9\n", + " df=matr,\n", + " y_axis='temp_module',\n", + " x_axis='poa_global',\n", + " z_axis=mlfm_sel,\n", + " title='matrix predicted ' + mlfm_meas_file,\n", + " vmin=0.6,\n", + " vmax=1.05,\n", + " levels=9\n", ")" ] }, @@ -1563,32 +1503,30 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 85, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "contour_plot = plot_contourf(\n", - " df = norm,\n", - " y_axis = 'temp_module_bin',\n", - " x_axis = 'poa_global_bin',\n", - " z_axis = mlfm_sel,\n", - " title = 'avg normalised ' + mlfm_meas_file,\n", - " vmin = 0.6,\n", - " vmax = 1.05,\n", - " levels = 9\n", + " df=norm,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis=mlfm_sel,\n", + " title='avg normalised ' + mlfm_meas_file,\n", + " vmin=0.6,\n", + " vmax=1.05,\n", + " levels=9\n", ")" ] }, @@ -1647,13 +1585,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -1664,8 +1595,8 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", - "language": "python", + "display_name": "Python 3 (Spyder)", + "language": "python3", "name": "python3" }, "language_info": { From f0f38e7582d4427a67d0d29696b027638ff98934 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 10:34:13 +0000 Subject: [PATCH 24/81] better formatted mlfm.ipynb --- docs/tutorials/mlfm.ipynb | 509 ++++---------------------------------- 1 file changed, 42 insertions(+), 467 deletions(-) diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb index 4bc5f9d0f1..3c1f797bc8 100644 --- a/docs/tutorials/mlfm.ipynb +++ b/docs/tutorials/mlfm.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# MLFM PVLIB \n", + "# MLFM PVLIB 211213\n", "### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", "#### Tutorial overview.\n", "\n", @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -65,15 +65,15 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", - "# import os\n", - "# root_dir = os.getcwd()\n", + "import os\n", + "root_dir = os.getcwd()\n", "\n", - "# root_dir" + "root_dir" ] }, { @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -264,139 +264,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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module_idpoa_globalwind_speedtemp_airblue_fracbeam_fractemp_modulev_oci_sci_mpv_mpr_scr_ocpoa_global_kwm2p_mp
date_time
2016-01-26 07:20:00-07:00782.6664841.4728328.1779790.4549921.1000002.08194033.0406440.0132150.00980924.337320115258.549800608.6809990.0026660.238726
2016-01-26 07:30:00-07:00787.8991431.2977118.2414250.522027-0.1000002.43698537.6440290.0372490.02983229.6249808253.745059150.4612830.0078990.883783
2016-01-26 07:40:00-07:007852.9276720.9554827.7396240.2701540.3002672.59208739.6492060.0728370.06119632.4448684762.54397263.6600280.0529281.985488
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" - ], - "text/plain": [ - " module_id poa_global ... poa_global_kwm2 p_mp\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 78 2.666484 ... 0.002666 0.238726\n", - "2016-01-26 07:30:00-07:00 78 7.899143 ... 0.007899 0.883783\n", - "2016-01-26 07:40:00-07:00 78 52.927672 ... 0.052928 1.985488\n", - "\n", - "[3 rows x 15 columns]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# normalise poa_global to kW/m^2\n", "meas['poa_global_kwm2'] = meas['poa_global'] / 1000\n", @@ -419,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -456,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -506,119 +376,9 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pr_dcpr_dc_temp_corri_mpv_mpi_scv_ocv_oc_temp_corrr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:000.4964970.4452930.7422410.7365870.9263800.7475260.6875640.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:000.6204710.5574730.8008960.7869770.8814090.8516750.7844180.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:000.2080370.1870590.8401720.8182980.2572270.8970410.8266880.8977000.8950180.9359160.914282
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" - ], - "text/plain": [ - " pr_dc pr_dc_temp_corr ... i_ff v_ff\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 0.496497 0.445293 ... 0.754692 0.968481\n", - "2016-01-26 07:30:00-07:00 0.620471 0.557473 ... 0.896006 0.907783\n", - "2016-01-26 07:40:00-07:00 0.208037 0.187059 ... 0.935916 0.914282\n", - "\n", - "[3 rows x 11 columns]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "norm = mlfm_meas_to_norm(meas, ref, qty_mlfm_vars)\n", "\n", @@ -636,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -662,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -683,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -714,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -761,20 +521,9 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# scatter plot normalised values vs. irradiance\n", "plot_mlfm_scatter(meas, norm, mlfm_meas_file, qty_mlfm_vars)" @@ -806,109 +555,9 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pr_dci_scr_sci_ffi_vv_ffr_ocv_octemp_module_corr
date_time
2016-01-26 08:10:00-07:000.9874550.0638170.0445290.0359110.010.0739080.098226-0.002454-0.065435
2016-01-26 08:30:00-07:000.8369980.2372830.0325940.0302460.010.0770190.0829280.004324-0.042936
2016-01-26 08:40:00-07:000.9783350.0611030.0655990.0131430.010.0913420.0911430.000727-0.033518
\n", - "
" - ], - "text/plain": [ - " pr_dc i_sc ... v_oc temp_module_corr\n", - "date_time ... \n", - "2016-01-26 08:10:00-07:00 0.987455 0.063817 ... -0.002454 -0.065435\n", - "2016-01-26 08:30:00-07:00 0.836998 0.237283 ... 0.004324 -0.042936\n", - "2016-01-26 08:40:00-07:00 0.978335 0.061103 ... 0.000727 -0.033518\n", - "\n", - "[3 rows x 9 columns]" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# translate multiplicative to stack losses and add to dataframe df\n", "stack = mlfm_norm_to_stack(norm, ref, qty_mlfm_vars)\n", @@ -983,28 +632,9 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\steve\\OneDrive\\Documents\\_CONS\\__Reference\\PVPMC\\__repository\\pvlib-python\\docs\\tutorials\\mlfm_graphs.py:334: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " ax1.set_xticklabels(xax2, rotation=90)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot stack loss vs. time (or measurement) chart\n", "plot_mlfm_stack(\n", @@ -1038,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1071,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1132,20 +762,9 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot fit vs. measured, include a 1:1 line for comparison\n", "fit_plot = plot_residuals(meas, norm, mlfm_sel, 'residual ' + mlfm_meas_file)" @@ -1163,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1267,20 +886,9 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot heatmap\n", "heatmap_plot = plot_heatmap(\n", @@ -1307,20 +915,9 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['mid', 'poa_global', 'temp_module', 'wind_speed', 'poa_global_kwm2'], dtype='object')" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# read in the complete matrix data\n", "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", @@ -1343,7 +940,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1360,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1467,20 +1064,9 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "contour_plot = plot_contourf(\n", " df=matr,\n", @@ -1503,20 +1089,9 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "contour_plot = plot_contourf(\n", " df=norm,\n", From 9336555ec33b4ad6ce52430448db6fe6588d8f55 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 10:38:31 +0000 Subject: [PATCH 25/81] stickler checked mlfm.py --- pvlib/mlfm.py | 197 ++++++++++++++++++++++++-------------------------- 1 file changed, 95 insertions(+), 102 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index b4f5a1bdb7..521b8d54d6 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -1,5 +1,5 @@ ''' -This ``mlfm code`` module contains functions to analyse and predict 79-| +This ``mlfm code`` module contains functions to analyse and predict performance of PV modules using the mechanistic performance (MPM) and loss factors models (LFM) @@ -17,7 +17,7 @@ from scipy import optimize -''' DEFINE REFERENCE MEASUREMENT CONDITIONS ''' +# DEFINE REFERENCE MEASUREMENT CONDITIONS # or use existing definitions in pvlib # NAME value comment unit PV_LIB name @@ -28,7 +28,7 @@ G_LIC = 0.2 # LIC irradiance [kW/m^2] -''' Define standardised MLFM graph colours as a dict ``clr`` ''' +# Define standardised MLFM graph colours as a dict ``clr`` clr = { # parameter_clr colour R G B @@ -113,25 +113,25 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): if qty_mlfm_vars >= 6: # 6,8 IV data - ''' - create temporary variables (ir, vr) from - intercept of r_sc (at i_sc) with r_oc (at v_oc) - to make maths easier - ''' - ir = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / + # create temporary variables (i_r, v_r) from + # intercept of r_sc (at i_sc) with r_oc (at v_oc) + # to make maths easier + + + i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / (dmeas['r_sc'] - dmeas['r_oc'])) - vr = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) # calculate normalised resistances r_sc and r_oc - dnorm['r_sc'] = ir / dmeas['i_sc'] # norm_r @ isc - dnorm['r_oc'] = vr / dmeas['v_oc'] # norm_r @ roc + dnorm['r_sc'] = i_r / dmeas['i_sc'] # norm_r @ isc + dnorm['r_oc'] = v_r / dmeas['v_oc'] # norm_r @ roc # calculate remaining fill factor losses partitioned to i_ff, v_ff - dnorm['i_ff'] = dmeas['i_mp'] / ir - dnorm['v_ff'] = dmeas['v_mp'] / vr + dnorm['i_ff'] = dmeas['i_mp'] / i_r + dnorm['v_ff'] = dmeas['v_mp'] / v_r return dnorm @@ -156,6 +156,7 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): c_2 - temperature coefficient (1/K) c_3 - low light log irradiance drop (~v_oc) c_4 - high light linear irradiance drop (~r_s) + (optional or set at 0) c_5 - wind speed dependence (=0 if indoor) c_6 - inverse irradiance (<= 0). @@ -165,7 +166,7 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): predicted performance values for pr_dc, norm(i_sc, .. v_oc) . ''' - mlfm_6 = ( + mlfm_out = ( c_1 + # 'constant' lossless c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' @@ -174,10 +175,10 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) ) - return mlfm_6 + return mlfm_out -def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): +def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): ''' Converts MLFM normalised multiplicative losses norm(i_sc ... v_oc) to stacked subtractive losses stack(i_sc ... v_oc). @@ -210,12 +211,6 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): Parameters ---------- - dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' - and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module.. - dnorm : dataframe normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). @@ -247,18 +242,18 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): # calculate reference fill factor (usually between 0.5 and 0.8) ff_ref = ref['ff'] - ''' - calculate inverse fill factor ~ 1.25 - 2 as we calculate - i_losses from ref_isc to norm_imp - v_losses from ref_vocc to norm_vmp - ''' + + # calculate inverse fill factor ~ 1.25 - 2 as we calculate + # i_losses from ref_isc to norm_imp + # v_losses from ref_voc to norm_vmp + inv_ff = 1 / ff_ref if qty_mlfm_vars == 6: # ivcurve - ''' - find factor to transform multiplicative to subtractive losses - correction factor to scale losses to keep 1/ff --> pr_dc - ''' + + # find factor to transform multiplicative to subtractive losses + # correction factor to scale losses to keep 1/ff --> pr_dc + # product prod = inv_ff * ( dnorm['i_sc'] * dnorm['r_sc'] * dnorm['i_ff'] * @@ -287,10 +282,10 @@ def mlfm_norm_to_stack(dmeas, dnorm, ref, qty_mlfm_vars): -(dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) elif qty_mlfm_vars == 4: # matrix - ''' - find factor to transform multiplicative to subtractive losses - correction factor to scale losses to keep 1/ff --> pr_dc - ''' + + # find factor to transform multiplicative to subtractive losses + # correction factor to scale losses to keep 1/ff --> pr_dc + # product prod = inv_ff * ( @@ -341,10 +336,10 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): same data but with added mlfm_var fit values calc_mlfm_sel and diff_mlfm_sel. - cc : list + coeff : list fit coefficients c_1 to c_6. - ee : list + err : list error values. coeffs : string @@ -356,86 +351,84 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): # drop missing data dnorm = dnorm.dropna() - ''' - ensure correct number of p0 and bounds - https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html - ''' + + # ensure correct number of p0 and bounds + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html + # define function name - f = mlfm_6 + func = mlfm_6 # setup initial values and initial boundary conditions # initial c1 c2 c3 c4 c5 c6<0 - p0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) # boundaries bounds = ([ -2, -2, -2, -2, -2, -2], [ 2, 2, 2, 2, 2, 0]) - if True: - popt, pcov = optimize.curve_fit( - f=f, # fit function - xdata=dmeas, # input data - ydata=dnorm[mlfm_sel], # fit parameter - p0=p0, # initial - bounds=bounds # boundaries - ) - # get mlfm coefficients - c_1 = popt[0] - c_2 = popt[1] - c_3 = popt[2] - c_4 = popt[3] - c_5 = popt[4] - c_6 = popt[5] - - cc = [c_1, c_2, c_3, c_4, c_5, c_6] - - # get mlfm error coefficients as sqrt of covariance - perr = np.sqrt(np.diag(pcov)) - - e_1 = perr[0] - e_2 = perr[1] - e_3 = perr[2] - e_4 = perr[3] - e_5 = perr[4] - e_6 = perr[5] - - ee = [e_1, e_2, e_3, e_4, e_5, e_6] - - # format coefficients as strings, easier to read in graph title - coeffs = ( - ' {:.4%}'.format(c_1) + - ', {:.4%}'.format(c_2) + - ', {:.4%}'.format(c_3) + - ', {:.4%}'.format(c_4) + - ', {:.4%}'.format(c_5) + - ', {:.4%}'.format(c_6) - ) - # print ('coeffs = ', mlfm_sel, coeffs) - - errs = ( - ' {:.4%}'.format(e_1) + - ', {:.4%}'.format(e_2) + - ', {:.4%}'.format(e_3) + - ', {:.4%}'.format(e_4) + - ', {:.4%}'.format(e_5) + - ', {:.4%}'.format(e_6) - ) - # print ('errs = ', mlfm_sel, errs) + popt, pcov = optimize.curve_fit( + f=func, # fit function + xdata=dmeas, # input data + ydata=dnorm[mlfm_sel], # fit parameter + p0=p_0, # initial + bounds=bounds # boundaries + ) - # save fit and error to dataframe - dnorm['calc_' + mlfm_sel] = ( - mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6)) + # get mlfm coefficients + c_1 = popt[0] + c_2 = popt[1] + c_3 = popt[2] + c_4 = popt[3] + c_5 = popt[4] + c_6 = popt[5] + + coeff = [c_1, c_2, c_3, c_4, c_5, c_6] + + # get mlfm error coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + + e_1 = perr[0] + e_2 = perr[1] + e_3 = perr[2] + e_4 = perr[3] + e_5 = perr[4] + e_6 = perr[5] + + err = [e_1, e_2, e_3, e_4, e_5, e_6] + + # format coefficients as strings, easier to read in graph title + coeffs = ( + ' {:.4%}'.format(c_1) + + ', {:.4%}'.format(c_2) + + ', {:.4%}'.format(c_3) + + ', {:.4%}'.format(c_4) + + ', {:.4%}'.format(c_5) + + ', {:.4%}'.format(c_6) + ) + # print ('coeffs = ', mlfm_sel, coeffs) + + errs = ( + ' {:.4%}'.format(e_1) + + ', {:.4%}'.format(e_2) + + ', {:.4%}'.format(e_3) + + ', {:.4%}'.format(e_4) + + ', {:.4%}'.format(e_5) + + ', {:.4%}'.format(e_6) + ) + # print ('errs = ', mlfm_sel, errs) - dnorm['diff_' + mlfm_sel] = ( - dnorm[mlfm_sel] - dnorm['calc_' + mlfm_sel]) + # save fit and error to dataframe + dnorm['calc_' + mlfm_sel] = ( + mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6)) - return(dnorm, cc, ee, coeffs, errs) + dnorm['diff_' + mlfm_sel] = ( + dnorm[mlfm_sel] - dnorm['calc_' + mlfm_sel]) + return(dnorm, coeff, err, coeffs, errs) -""" -## References +REFS = """ The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) together known as "MLFM" have been developed by SRCL and Gantner Instruments (previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM From 2e77520d147ea6adbffc6eb5798137607dd335c5 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 10:44:10 +0000 Subject: [PATCH 26/81] stickler checked mlfm.py --- pvlib/mlfm.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 521b8d54d6..72d6e2e458 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -113,17 +113,15 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): if qty_mlfm_vars >= 6: # 6,8 IV data - # create temporary variables (i_r, v_r) from # intercept of r_sc (at i_sc) with r_oc (at v_oc) # to make maths easier - i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / - (dmeas['r_sc'] - dmeas['r_oc'])) + (dmeas['r_sc'] - dmeas['r_oc'])) v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = i_r / dmeas['i_sc'] # norm_r @ isc @@ -242,7 +240,6 @@ def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): # calculate reference fill factor (usually between 0.5 and 0.8) ff_ref = ref['ff'] - # calculate inverse fill factor ~ 1.25 - 2 as we calculate # i_losses from ref_isc to norm_imp # v_losses from ref_voc to norm_vmp @@ -286,7 +283,6 @@ def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): # find factor to transform multiplicative to subtractive losses # correction factor to scale losses to keep 1/ff --> pr_dc - # product prod = inv_ff * ( dnorm['i_sc'] * dnorm['i_mp'] * @@ -366,7 +362,6 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): bounds = ([ -2, -2, -2, -2, -2, -2], [ 2, 2, 2, 2, 2, 0]) - popt, pcov = optimize.curve_fit( f=func, # fit function xdata=dmeas, # input data From cd83fb75e41d86f09f8b9da043ae0a7708e5ab5a Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 10:47:05 +0000 Subject: [PATCH 27/81] stickler checked mlfm_graphs.py --- pvlib/mlfm_graphs.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py index ce0f4d6881..926e2b5c98 100644 --- a/pvlib/mlfm_graphs.py +++ b/pvlib/mlfm_graphs.py @@ -1,5 +1,5 @@ ''' -This ``mlfm graphs`` module contains functions to display 79-| +This ``mlfm graphs`` module contains functions to display performance of PV modules using the mechanistic performance (MPM) and loss factors models (LFM) @@ -13,8 +13,7 @@ import numpy as np import matplotlib.pyplot as plt - -''' Define standardised MLFM graph colours as a dict ``clr`` ''' +# Define standardised MLFM graph colours as a dict ``clr`` clr = { # parameter_clr colour R G B @@ -93,7 +92,8 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): ax1.scatter(xdata, dnorm['pr_dc_temp_corr'], c=clr['pr_dc'], label='pr_dc_temp_corr') - if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix + # if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix + if qty_mlfm_vars in (2, 4): # mppt or matrix ax1.scatter(xdata, dnorm['i_mp'], c=clr['i_mp'], label='norm_i_mp') ax1.scatter(xdata, dnorm['v_mp'], c=clr['v_mp'], label='norm_v_mp') @@ -283,7 +283,8 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, x_ticks = dmeas.shape[0] plt.xticks(np.arange(0, x_ticks), rotation=90) - if (xaxis_labels > 0 and xaxis_labels < x_ticks): + # if (xaxis_labels > 0 and xaxis_labels < x_ticks): + if 0 < xaxis_labels < x_ticks: xaxis_skip = np.floor(x_ticks / xaxis_labels) else: xaxis_skip = 2 @@ -293,12 +294,12 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, x_count = 0 while x_count < x_ticks: if x_count % xaxis_skip == 0: - ''' - try to reformat any date indexes (not for matrices) - - 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 - y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' - ''' + # + # try to reformat any date indexes (not for matrices) + # + # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 + # y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' + # try: xax2[x_count] = xdata[x_count][2:13]+'h' @@ -332,9 +333,8 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, ax1.set_xticklabels(xax2, rotation=90) plt.show() - -""" -## References +REFS = """ +References The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) together known as "MLFM" have been developed by SRCL and Gantner Instruments From 789b0917f43a9a7d2c0531db435eaa271820fae2 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 10:49:49 +0000 Subject: [PATCH 28/81] stickler checked mlfm_graphs.py --- pvlib/mlfm_graphs.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py index 926e2b5c98..647753f69e 100644 --- a/pvlib/mlfm_graphs.py +++ b/pvlib/mlfm_graphs.py @@ -92,7 +92,7 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): ax1.scatter(xdata, dnorm['pr_dc_temp_corr'], c=clr['pr_dc'], label='pr_dc_temp_corr') - # if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix + # if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix if qty_mlfm_vars in (2, 4): # mppt or matrix ax1.scatter(xdata, dnorm['i_mp'], c=clr['i_mp'], label='norm_i_mp') ax1.scatter(xdata, dnorm['v_mp'], c=clr['v_mp'], label='norm_v_mp') @@ -283,7 +283,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, x_ticks = dmeas.shape[0] plt.xticks(np.arange(0, x_ticks), rotation=90) - # if (xaxis_labels > 0 and xaxis_labels < x_ticks): + # if (xaxis_labels > 0 and xaxis_labels < x_ticks): if 0 < xaxis_labels < x_ticks: xaxis_skip = np.floor(x_ticks / xaxis_labels) else: From 8cd6e88333c7e5a5558b52c100d2035554b195f5 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 10:51:27 +0000 Subject: [PATCH 29/81] stickler checked mlfm_graphs.py --- pvlib/mlfm_graphs.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py index 647753f69e..cd9612d612 100644 --- a/pvlib/mlfm_graphs.py +++ b/pvlib/mlfm_graphs.py @@ -333,6 +333,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, ax1.set_xticklabels(xax2, rotation=90) plt.show() + REFS = """ References From 4459a4431e158d144b4be865e582181423e22358 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 10:54:45 +0000 Subject: [PATCH 30/81] added mlfm.py and mlfm_graphs.py to __init__.py --- pvlib/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pvlib/__init__.py b/pvlib/__init__.py index ff6b375017..5d09277441 100644 --- a/pvlib/__init__.py +++ b/pvlib/__init__.py @@ -12,6 +12,8 @@ ivtools, location, modelchain, + mlfm, + mlfm_graphs, pvsystem, scaling, shading, From 8db12374432895cdd8444b30e1b39a695ced29f6 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 13 Dec 2021 15:01:07 +0000 Subject: [PATCH 31/81] mlfm.ipynb --- docs/tutorials/mlfm.ipynb | 530 +++++++++++++++++++++++++++++++++++--- 1 file changed, 489 insertions(+), 41 deletions(-) diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb index 3c1f797bc8..8ed7f0cd16 100644 --- a/docs/tutorials/mlfm.ipynb +++ b/docs/tutorials/mlfm.ipynb @@ -39,17 +39,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#import pvlib\n", "from pvlib import *\n", "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", + "import os\n", + "root_dir = os.getcwd()\n", + "\n", + "root_dir\n", + "\n", "# Import essential library file with lfm and mpm definitions\n", - "from mlfm import *\n", + "from mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", "# Import graphics code from separate file\n", - "from mlfm_graphs import *\n", + "from mlfm_graphs import plot_mlfm_scatter, plot_mlfm_stack \n", + "\n", + "#import matplotlib.pyplot as plt\n", "\n", "# STANDARD DEFINITIONS\n", "\n", @@ -65,9 +77,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'C:\\\\Users\\\\steve\\\\OneDrive\\\\Documents\\\\_CONS\\\\__Reference\\\\PVPMC\\\\__repository\\\\pvlib-python\\\\docs\\\\tutorials'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", "import os\n", @@ -111,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -164,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -245,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -264,9 +287,139 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idpoa_globalwind_speedtemp_airblue_fracbeam_fractemp_modulev_oci_sci_mpv_mpr_scr_ocpoa_global_kwm2p_mp
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2016-01-26 07:40:00-07:007852.9276720.9554827.7396240.2701540.3002672.59208739.6492060.0728370.06119632.4448684762.54397263.6600280.0529281.985488
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" + ], + "text/plain": [ + " module_id poa_global ... poa_global_kwm2 p_mp\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 78 2.666484 ... 0.002666 0.238726\n", + "2016-01-26 07:30:00-07:00 78 7.899143 ... 0.007899 0.883783\n", + "2016-01-26 07:40:00-07:00 78 52.927672 ... 0.052928 1.985488\n", + "\n", + "[3 rows x 15 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# normalise poa_global to kW/m^2\n", "meas['poa_global_kwm2'] = meas['poa_global'] / 1000\n", @@ -289,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -376,9 +529,119 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dcpr_dc_temp_corri_mpv_mpi_scv_ocv_oc_temp_corrr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:000.4964970.4452930.7422410.7365870.9263800.7475260.6875640.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:000.6204710.5574730.8008960.7869770.8814090.8516750.7844180.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:000.2080370.1870590.8401720.8182980.2572270.8970410.8266880.8977000.8950180.9359160.914282
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" + ], + "text/plain": [ + " pr_dc pr_dc_temp_corr ... i_ff v_ff\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 0.496497 0.445293 ... 0.754692 0.968481\n", + "2016-01-26 07:30:00-07:00 0.620471 0.557473 ... 0.896006 0.907783\n", + "2016-01-26 07:40:00-07:00 0.208037 0.187059 ... 0.935916 0.914282\n", + "\n", + "[3 rows x 11 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "norm = mlfm_meas_to_norm(meas, ref, qty_mlfm_vars)\n", "\n", @@ -396,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -422,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -443,7 +706,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -474,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -521,9 +784,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# scatter plot normalised values vs. irradiance\n", "plot_mlfm_scatter(meas, norm, mlfm_meas_file, qty_mlfm_vars)" @@ -555,9 +829,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dci_scr_sci_ffi_vv_ffr_ocv_octemp_module_corr
date_time
2016-01-26 08:10:00-07:000.9874550.0638170.0445290.0359110.010.0739080.098226-0.002454-0.065435
2016-01-26 08:30:00-07:000.8369980.2372830.0325940.0302460.010.0770190.0829280.004324-0.042936
2016-01-26 08:40:00-07:000.9783350.0611030.0655990.0131430.010.0913420.0911430.000727-0.033518
\n", + "
" + ], + "text/plain": [ + " pr_dc i_sc ... v_oc temp_module_corr\n", + "date_time ... \n", + "2016-01-26 08:10:00-07:00 0.987455 0.063817 ... -0.002454 -0.065435\n", + "2016-01-26 08:30:00-07:00 0.836998 0.237283 ... 0.004324 -0.042936\n", + "2016-01-26 08:40:00-07:00 0.978335 0.061103 ... 0.000727 -0.033518\n", + "\n", + "[3 rows x 9 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# translate multiplicative to stack losses and add to dataframe df\n", "stack = mlfm_norm_to_stack(norm, ref, qty_mlfm_vars)\n", @@ -632,9 +1006,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\steve\\OneDrive\\Documents\\_CONS\\__Reference\\PVPMC\\__repository\\pvlib-python\\docs\\tutorials\\mlfm_graphs.py:333: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax1.set_xticklabels(xax2, rotation=90)\n" + ] + }, + { + "data": { + "image/png": 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12bdvH1lZWXz55Ze0bt2ayMjIEqXGqmhat27Nnj177FJlbdy4EYvFQuvWrStUltq1a9O7d2/efvvtAtsEQcDf3x+dTmdLh9WnTx/Aen+o1WrWr19va2+xWNi0aZPNSh0aGkpAQIBdG4B169bZ2hRH69atbecm7/5OTk40bNgQgGPHjtlZsOfPnw/A+vXrmTRpUklPhYSExEOKpJiWEZ06daJZs2YMHDiQXbt2cfz4cQYPHkx2djYvvfQSACEhIchkMtasWcONGzdISUmpZKnLngULFvDDDz9w5MgRYmJiWLJkCWlpaTYFr2rVqly7do09e/YQHx9PZmYmISEhqNVqvvnmG86fP8/mzZt5/fXX7SyOAwYMICQkhMcee4xNmzZx8eJFNm/ezLJlywrIMHbsWD799FP69OnD6tWri5S3c+fOjB8/vtDt1apVo3fv3rzyyits27aNqKgoRo4cSWpqqkMr6ty5c+nbty++vr4FtvXt25eFCxfy888/c/HiRXbs2MGrr75KvXr1bJavsiAqKorDhw9z7do1cnJybApCrpUyPDyc/v378+GHH7JmzRrOnj3L+PHjOXXqFKNGjSrRGC+//DJarZaZM2cC4OHhwcKFC/nyyy/ZunUrYD13giDw+eefc/HiRf766y8+/PDDMjvOkpD3+NPT00lMTOTw4cN2L5ADBw7Ey8uLgQMHcuTIEf755x9eeeUVnn76aapWrVqh8oI1qGzVqlV2gVczZszgyJEjnDp1iq+++opXX32VCRMm2PJ/uri4MGrUKCZMmMCqVas4ceIEQ4cOJSsry2alFASBt99+m1mzZrF48WJOnTrFuHHjOHLkCG+++WaJZHvppZdISUnhxRdf5MSJE/z999+8//77vPrqq+h0OgDq1Kljt+Sew8jISLtgwOKuUwkJiYeUSvVwvYcpSRoaikkX1a5dO1u6qFw+++wzMSAgQJTJZKVKF5WXRYsWifm/ul9//VUERKPRWOh++eV1FJTgKEBrz549IiCePXu2WHn/+OMPsWXLlqKbm5uo1WrF2rVri/Pnz7dtz8nJEQcMGCC6u7vbpYtavny5GBERIarVarFBgwbi1q1bRblcLi5cuNC2b1xcnPjcc8+Jnp6eolqtFiMjI23bHR3Ld999J6rVavGvv/4qVN6QkJBig4ji4+PFfv36iVqtVvT29hbff/998cknnxQfffRRu3a55ylvoFReTCaT+PHHH4vVq1cXNRqN6O/vLw4cOFCMiYkpcnxHFBX8FBISUiAtV/7vNCMjQxw9erTo7e0t6vV6sU2bNrZAs+JYsmSJqFAoxL179xbY9vLLL4shISFiSkqKKIrWwJqgoCBRo9GIrVu3FteuXSsC4j///COKYvmni3KUpg0oEIB36tQpsWvXrqJWqxU9PDzEESNG2AVz3el9WBSFpYsSRVHs0qWL2KZNG9vnrl27im5ubqJKpRLr1q0rzps3r8A+OTk54ttvvy36+vqKarVabNWqVYHfH1G0/gYFBweLKpVKrF+/vsMgvaLYs2eP2LJlS1GtVou+vr7iuHHjRJPJVGj7woKfSnKdSkhIPHwIoliBc4gSEg8AZrOZGjVq8Nhjj/H5559XtjgSEhISEhIPDIrim0hIPNxs376dGzdu0LBhQ9LS0pg1axbR0dHF5j+VkJCQkJCQKB2Sj2klo9frC12mTZtW2eIVoEePHoXKm5tI/0HDbDYzdepU6tevT8eOHblw4QL//POPXeqgsqB27dqFntuS+n6WBdOmTSvyuqwolixZUqQcly5dqjBZ7oR75fssKTt27CjyfJe2AMP9xtmzZ9FoNDz77LOFtpk1axZ+fn64uroydOhQuywUEhISZYM0lV/JFJUmyMPD456raX/lyhWysrIcbsvNsShxZ8TExGA0Gh1uc3FxwcfHp0LkSExMJDExsdDtuQE35U1aWlqRUfyhoaEoFPfupM+98n2WlKysLK5cuVLo9sDAQFvFqQeRbt26kZWVRUhICIsXLy6wff369QwePJgtW7YQEBDA448/TosWLfj0008rQVoJiQcXSTGVkJCQkHioWbp0KStWrKBWrVqcO3fOoWI6cOBAQkNDbTNZmzdvZtCgQQXyukpISNwd0lS+hISEhMRDS2pqKh988EGxgYwnTpywq5JXv359rl+/TkJCQnmLKCHxUFFh82CzZ8/mp59+4tixYwwYMICffvqp2H06derEP//8g9FoLHbKTiaTPdDTTBISEhIlQbRYEGSSzSGXzMxMGjdubPs8YsQIRowYYfv8/vvvM2zYMIKDg4vsJz09HVdXV9vn3L/T0tLw9PQsY6krH6PRyOXLl8nOzq5sUSTKCLlcjpubG15eXgWKvdxLVJhiGhAQwHvvvcf69esL9VHMy5IlSzCZTCXuX6vVkpGRcTciSkhISNz3JB07invdepUtxj2DTqdj//79DrcdPnyYTZs2cejQoWL70ev1dtXBcv92dnYuG0HvMS5fvoyzszOhoaF3XVZbovIRRRGj0cj169e5fPkyVapUqWyRCqXCFNMnnngCgP379xdbVjIlJYUpU6bwf//3f7Rs2bIixJOQkJB4IDBLFq4Ss3XrVqKjo20P6fT0dMxmM1FRURw8eNCube3atTly5Aj9+/cH4MiRI/j6+j6Q1lKA7OxsSSl9gBAEAZVKRWBgYIFy1Pca96Qtd8KECbz00kv4+flVtigSEhIS9xWmbCmFUUkZMWIE58+ft5VDHTVqFL169WL9+vUF2g4ePJgFCxYQFRVFUlISU6dOfeBzGUtK6YPHvTyFn8s9J+H+/fvZtWsXr776arFt582bR5MmTWjSpEmppv0lJCQkHlTMkmJaYpycnPDz87Mter0ejUaDt7c3ly5dssuX2717d9555x06duxISEgIISEhTJkypdC+4+Pj+eKLL+jcuTNeXl4olUq8vLzo3LkzM2fO5ObNmxV1mBIS9xX3VBJAi8XCyy+/zFdffVWi/IR5ndh1Ol15iychISFxz2M25FS2CPctkydPtv1dpUoV0tPT7ba/9dZbvPXWW8X2M378eBYvXkzPnj0ZNmwYNWvWxNnZmbS0NE6ePMm2bdto1KgRgwYNkvKg3kMIgsDZs2crLFezhGPuKcU0NTWV/fv38/TTTwPWijsAQUFBLF++nLZt21ameBISEhL3PGapGlGlExAQwLlz51Cr1QW2NWzYkIEDB5Kdnc38+fMrQToJiXubClNMTSYTJpMJs9mM2WwmOzsbhUJhZxl1dXXl6tWrts+xsbE0a9aMAwcO4O3tXVGiSkhISNy3mLMli2llUxJXNI1Gw+jRoytAmocXk8l0T1eHk3BMhfmYTp06Fa1Wy6effsrixYvRarVMnTrVzo9HEAQ7f59cZdTX1xeVSlVRokpISEjcl4gWC+ZCyqBKVDwmk4kNGzYwa9YsPvzwQ2bNmsWGDRukmIi7JDQ0lE8++YRatWrh7u7OkCFDyM7OZuvWrQQFBfHZZ5/h5+fHkCFDiuxnxowZ+Pv7ExAQwI8//mi3LSsrizFjxhASEoKrqytt2rQpUapLibunwl4lJk+ebOe/k5f8fjy5hIaGIlVMlZAoHdnxN9F4STMMDyOiyYxoMle2GBJY00n16dMHURSpV68erq6upKam8tVXXyEIAitXrqRePSnf7J2yZMkS1q9fj06no3fv3kydOpUuXbpw7do1EhMTiYmJwWKxFLr/unXrmDlzJps3b6Zq1aq8+OKLdtvHjh3LiRMn2L17N35+fvz333/3RUT7g4Bk45aQeMBIPHqMgE6dKlsMiUpANJuwGCVr3L3A8OHDGTNmjMNp/dmzZzN06NBCE/9LFM/o0aNt1bomTpzIq6++SpcuXZDJZEyZMsWhf29efvvtN4YMGUKdOnUAq/Hs119/BayB2D/++CP//vsvgYGBALRq1aocj0YiL5L6LyHxgJF06kJliyBRSVjMJsymwq1EEhVHVFQUo0aNcrhtxIgRnDx5soIlerDIW0I2JCTEFp/i7e2NRqMpdv+rV68W6COX+Ph4srOzCQ8PL0OJJUqKpJhKSDxgJJyKIyM2trLFkKgERJMJs1lyf7oXqFmzJnPmzHG4be7cudSsWbOCJXqwiM3zG3fp0iUCAgKAkhcF8Pf3L9BHLl5eXmg0Gs6fP19G0kqUBkkxlZB4wDi5JpGj3/5S2WJIVAKi2YRZMpjeE8yfP58vvviC4OBgevXqxcCBA3n00UepUqUKX3zxBQsWLKhsEe9rvv32Wy5fvkxiYiLTpk2zpZksKf379+enn34iKiqKzMxMu2IJMpmMoUOH8tZbb3H16lXMZjN79uzBIKViqxAkxVRC4gHDbBS4dvhGZYvx0JN+8WKFj2kxmTFLLqb3BA0aNODs2bMsWLCArl27UqtWLbp06cKCBQs4c+YM9evXr2wR72sGDhxIt27dCAsLIywsjPfee69U+/fo0YM33niDTp06ERERQad8fvkzZ86kbt26NG3aFA8PD959990ig6kkyg5BfEDC3nU6HRkZGZUthoREpTM7aAyiBV69+nlli/LQIooi8yLeYsjBSahc3Sps3Myrlzny82pajh9ZYWPe60jPhjvj5MmT96y7QWhoKPPnz6dLly6VLcp9yb383YJkMZWQeOAQLZAY50LyyajKFuWhxZiSzLULbpxcULEuFaLJjFnKFnXPsGHDBgYOHEj9+vUJCwujfv36DBw4kI0bN1a2aBIS9yySYiohUcZU9iSExWx1/r+ydU+lyvEwY0hMBODE8rMVOq5oNks+pvcIs2bN4vnnnyciIoIPPviAefPmMWnSJKpVq8bzzz/PV199VdkiPvBMmzYNvV5fYOnRo0dliyZRBFIeUwmJMiYnKQm1h0eljW+xWBXTpLPXKk2Ghx1DklUxvXhQhzE9DaXeuULGtZjNWKSo/HuCGTNm8M8//1CjRg279U888QQDBgygY8eOvP7665Uk3f1NdHR0idpNmDCBCRMmlK8wEmWOZDGVkChjMvKkHakMci2mSRdTKlWOhxlDYjIAphwlsWs3VNi4otmE2SQppvcCGRkZthRG+fHz8yMzM7OCJZKQuD+QFFOJcuH0j4vIuBRT2WJUCumXr1Tq+GKuxfTSw1szvbLdKQzJqba/L6w9XGHjimbJx/ReoV+/fvTu3ZvNmzdz8+ZNcnJyiI+PZ/PmzTz++OM8+eSTlS2ihMQ9iaSYSpQ5ZoOBVWOOsXWM4+TSDzrpVyo3VZPFYr2tow+5ELt6baXKUllc+O2PSh0/JyXd9vfpjZkVpihbFVPJYnov8P3339OqVSuef/55fH190Wq1+Pr68vzzz9OiRYtCk+9LSDzsSIqpRJljykgnPVlH7IGHMxlxRlxipY5vscgBEEUZMZsPVvj44j0QfXPufwe5tmNHpY1vSL6tmMZfdiHh4IEKGdciReXfM6hUKj755BNbEviYmBgSEhK4fPkyn3zyCSqVqrJFlJC4J5EUU4kyx5ydBUDCZR0WU+Vk+65M5SjjRnqlHTfctpgCxJ9OrvDx47ZtJfXsmQofNy8Wk4UjcyvPWmxIsfcfvLz53woZV5SCn+5J3NzcCAoKws3NrbJFkZC455EUU4kyx5yVDYDJqOTKxk2VIsOWEZNJO3+uUsY2ZZkwJMRXythgr5jeOFPxCnL65Wsc+fa3Ch83LxYzXDteecElORk5dp9jd1dMQJxoNldY8FNl+/HezxgMBuRyeWWLISFxTyIpphJljjk72/b3f59vtPtcUZzblsaBmYsL3V6eD1WTwYwhvjIVU8H2d9w5N27s3l2h42deS+TQsuRKVVxEi0jcGSfMlVTb2pRt/0Jw6WDFvCCYTBZEs0jKmVPlPta/46dXyr19v3Dp0qVCl5iYmPtTsReE8l3uITp06MD8+fMrW4w74n6WHSTFVKIcMOV5WJ3Y7ELsmnUVOr4oityIdub4/9KIP7DfYZucxIRyG9+cYyblfMXXSc9FzKcD7f/y7wodP+NGKknXnLmyofKq24hmEUOWms0vfogxPa3Cxzcb7L+E+FjXcstSkXLqpE3JES1Wi+mxOeUf/HXo1+ucW/p7uY9zvxIaGkrVqlUJDQ0tsNSoUQPhHlPEHgQmT57Ms88+W2nj3+8K4b2CpJhKlDn5rSjHF1dsBSJLdjYWs5yEKy5sffdXh23SY8ovlZUp28L1/afLrf+iEM0WyGeIObZGwJJTcamjMm5Y65IfmlM5bhxgtZgC7Fmk4tL/Kt7X1GgoGIF04c+yfUEzpqexdsB45jb7mXNLlgFgtogYbmZxeHliuftZZyQrOfD9AUxSPk6H+Pv7s3v3boxGY4ElLa3iX5YkJO4XJMVUoswxZ9tPnx7404m4bVsrbHxT5u2I6LO71Rz7ai4ZsbF2bdJjyy/XqDnHwrXDlZMyymIqqIBmZ2iIXVtxyllmgtW/8ugaVaX5+Yp5dLJTKyo+M4HZUHCa9uzqsg0IO/z5D+xdqiErTcuWSf9hyTEiWkQyr2aTcMWF6L9Wlul4+bGYZZz9z43/PviyXMe5X2nSpAmHDh1CLpcXWBQKxf05lX8P8dlnnxEYGIizszORkZGsXr2aadOmsWzZMvR6PfXr1wdg4cKF1KxZE2dnZ8LCwpg7d65dPytXrqRBgwa4uLgQHh7OunUFXyDj4uKoV68eM2fOLFSeiRMnsmPHDkaPHo1er2f06NFFyi8IAt999x3VqlXD2dmZ999/n/Pnz9OyZUtcXFzo378/OTm3fdV/+OEHIiIi8PDw4LHHHuPq1au2bRs3bqRGjRq4uroyevTo+/7akhRTiTInv2IKsP/LNRU2vikjw/Z3TpaaFW9c4+DnP9u1Sb96s+B+ZWT5MeWIRO+zVMqPg1hINoA9M/6pMBmyU63WQpNRyc73FlTYuHnJtZgCnFhnf01UBCYHiumZXeoy88m0mEz8O+/2y9W1C27EH9iH2Wwh87r1/J/+Y1+ZjFWoDLd8mY+tqDx/6nuZuXPn8sQTTzjcplarsVgqP63a/crp06eZPXs2+/btIy0tjfXr11OjRg0mTJjA008/TXp6OkeOHAHAx8eHVatWkZqaysKFC3nzzTc5eND6srp3714GDx7MjBkzSE5OZvv27YSGhtqNFR0dTfv27Rk9ejRjx44tVKaPP/6Ytm3bMnv2bNLT05k9e3axx7Fu3ToOHDjAv//+y/Tp0xkxYgRLliwhNjaW48eP8+uv1hm/LVu2MH78eH777Tfi4uIICQnhmWeeASA+Pp5+/foxdepU4uPjCQ8PZ9euXXdyWu8ZJMX0ISHtwnkMiRWTX9PkQDE9slpdwGpZbuM7UDAvbLP3KU2/UvBcpF04XzbjG0SSrjmTUIh/a3liMeU4XH96lxuX162vEBmMWbcfuIf/klXKVK8lz0x6VrqWA9O+q9DxTcaCiqkhU03M/1aXSf9HZ80l8aqL3bqEY2cQLRYyblgVxrPbyjfwy2K2Pj6uXyw//9mK4tlnn8Xf3x8XFxeqV69eqJ/gTz/9hFwuR6/X25atW7c6bLtq1SpMlZg27kFGLpdjMBiIiorCaDQSGhpKeHi4w7a9evUiPDwcQRBo37493bp1Y8etHMcLFixg6NChdO3aFZlMRmBgIDVq1LDtGxUVRYcOHZgyZQojRowo8+N49913cXFxoXbt2tSpU4du3boRFhaGq6srPXr04NChQwAsWbKEoUOH0qhRI9RqNZ988gl79uwhOjqaNWvWUKtWLZ588kmUSiVvvPEGfn5+ZS5rRSIppg8Juz/4kS+CZnJ+WfkHK5gd+DOajQoOf7Wo3McGx4rpzWj7ZNbp1wr6eKVdLJuHq/mWbnhlW/larBxhMRb+IDwwu3x9PrNvXAfAmHU7qCMnW0X0ylXlOq4j8lpMATbNSOHqli0VNr6pEJ3w/JpDd923KIrs+a7gS1TCycuIZhGzwfqznnjVhYRD5efGkKuYApz5pXzdBsqb8ePHEx0dTWpqKn///TfvvfceBw44LorQsmVL0tPTbUuHDh0cttu3bx8tW7akQYMGTJw4kV27dt33U6z3ChEREXz55ZdMnjwZHx8fnnnmGbup7bysXbuWFi1a4OHhgZubG2vWrCH+VtaU2NjYQhVasCqEgYGB5VY+1tfX1/Z3bmWwvJ/T061uaVevXiUkJMS2Ta/X4+npyZUrV7h69SrBwcG2bYIg2H2+H1FUtgBlhdFoZMqUKZUtxr1LNRW8C4tPnYApJ8p/vMkFV23CyKaK+o7yjZ+Bk/31ESxnpyNZyqJCz2Nu8Bj8lX6TvyrjmpzsePVh9ByuCHn6u9l9XHLmJFT0eWjjAm1ufzSh5IcdO6CiqkG1d4L2BVfvRsnusjgXL7gWWLUZM+zdZff9z/77f/D3/+5+PEe8fzsP59+GJP6+j39/a9eubftbEAQEQeD8+fM0btz4jvvM9WU8duwYa9asYfz48Zw+fZrOnTvTs2dPunfvjpeX113L/rAycOBABg4cSGpqKiNHjuTdd98lIiLCro3BYKBfv3783//9H3369EGpVNK3b1/bC0JwcDDnzxc+UzZ58mTWrVvHwIEDWbp0abG5Z8sr00JAQAAxeQJ2MzIySEhIIDAwEH9/f2LzzEaKomj3+X5EsphKSEhISDz0vPzyyzg5OVGjRg38/f3p2bOnw3aHDh3Cy8uL6tWr89FHHxU7XV+3bl3effddtm/fzpkzZ+jbty8bN26kXr16tGjRgvXrK8bF5kHi9OnTbNmyBYPBgEajQavVIpfL8fX1JTo62ua/m5OTg8FgwNvbG4VCwdq1a9mwYYOtn2HDhrFw4UI2b96MxWLhypUrnDp1OwewUqlk+fLlZGRk8NxzzxXrF+zr68uFCxfK/HgHDhzIwoULOXz4MAaDgQkTJtC8eXNCQ0Pp1asXJ06cYMWKFZhMJr7++muuXbtW5jJUJBWmmM6ePZsmTZqgVqt54YUXCm23dOlSIiMjcXV1xcfHh+eff57U1NSKElNCQkJC4gHCZDLRpEkT2zJv3jyH7b777jvS0tLYsWMHTzzxBGq1ukCbdu3acfz4cW7cuMEff/zBr7/+yowZM0osi6urK/379+fnn3/m6tWrfPPNN3h4eNzxsVU4oli+SwkxGAyMGzcOLy8v/Pz8uHHjBtOmTeOpp54CwNPTk0aNGuHs7MzXX39N//79cXd355dffuGxxx6z9dOsWTNbQJSrqyvt27e3s0wCqFQqVqxYwY0bNxg6dGiRyunrr7/O77//jru7O6+99lopT27hdO7cmY8++oh+/frh7+/P+fPnWbp0KQBeXl4sX76ccePG4enpydmzZ2ndunWZjV0ZCGIFOb2sWLECmUzG+vXrycrK4qeffnLYLjY2Fq1Wi5eXF+np6YwcORJPT0++/vrrIvvX6XRk3GXkbU56Dp84f3JXfdyrPDYhnoa1b0cJfvPB5ySev/tceoJM4LU5y3HTHyc5vQ5fv/wUtfu5c/l7+5v3pb2LUSVYg4EOHn+NVZ95FvADLCvC2rny3Mg3SQzth0d04YnGd+x5j+ijnlzYnoLOR0u752+ydoa+1OPJFDLC2znT4+mFZGb5YAwLIzTN+vD7vzmzuLgz5Y6PpbS4BOpwPygjRWk/5TT88Cp0cbd9TH9b/AUn197dC5/SScm7cz9ELrvtULl1x2RatJiDRnm9QPv9R99k9WcFp6DLgwY/BxLdOwM3o5nBW8YjWKyOv0azMzNfGU9OmuMgsTslsJErDbpesh3f89+fZWX/pnZtqqVn0HPHRHb+O5HN3yjvaJzgpq4MfeNNAPY2m8geT286XbtM3UMzuXSjJ1tW9qDTxweoEv2TbZ8de95jy+y789rS+zkx5vN3bJ+/fm8mvf66QNipRQgm6+/I2UvP8Mv4GoV1USyBjVwZ9tYYBOHWb8dTaaAs/f2Ynzt5NowaNYpatWoVq1wsXbqUGTNmOPRH/d///seqVasKpCfK7b9v37507969VHJVJCdPnqRmzZqVLYZEOXCvf7cVZjF94okn6Nu3L56enkW2Cw4OtvO7kcvlnDtXObkQ72favyTS9/0byJTWr1gmt0/4XaXB3acqCWjoyqtzfsdNfxwAN/1x6vbWIXPwzP2x8UCM7vUAaFTna4Z8vgeZomwuP48wZ/q8F0/NHtYoZYXKqvDGuYUUtRvObklUaxoHgMZVgVJ1Z6l8nvkoioHD3sRdf5RA701ofTMQsfoaVW1QfhWmHCHIwezAzemPWo9gcrvtR9fn6WnU7aOHO3SJ6vZmFh1GpNsppQBqbRZ4BnKuzisF9qlb82fcQu5e0SgJua5eyUo5OZ6NbOuV8jQ6jUgqs3E8qzkz8pvNDHtrDA3rzEHjZrWyKVQFAwDP6ZwQVe7UrH3nOWXrd7Zac3I8GnDAzfpbelPrDIBamYwgF7Bg/1JSNfzug/CC6tsrtgq1jHSVjoQqPWzrqlVZSo+37/xlt9MzO28rpZWMyWQq0vcwF0EQCg1o+uKLLwqtQvTss88yffr0u5JRQuJB5Z70Md25cyeurq44Ozvzxx9/8MYbbzhsN2/ePNv0zIOSlkOlvzNLSi6eEc48/fEFOrSZQv0a31Grhw4Amcz+/ITXvbvKRPUe1/PMK9/irj9mtz6w2jVkqoLtDXIZN3xuBxIE+6wjpKXzXcmg9dTQ9/3rPPvuzzSoOZv+z75FtU6uKNQioqAgVu+BqNAjOhIIcHG+Rt3ay1DplfhWF1AqS6eYuoXoCWvnSrUqS+3WG+VqUFuVhjp1V6LU3d13WhpkcoGCNYcgSaVkZ82nMLtUB0CtjOeJ/mPp+no2alfH58cRgkyg+XMqWjb5jFZNC84u6JxTQK1hdXA1O0XYOmYiIyZ9jncNlwL7lTny28pCrK99AEv9uj/hWc0ZQXZ3gQpuIXoGv/M9fh47EAQLclk2TfvnIFPIkCsL/h6JgsCF6gPwdD5IYOPSW45lShk1I5cDcCS8Jzly68/3NY0TABpVEoIMzIICUbitSAb5rMe39t2d87D69gUplFqBDKWW1WHNMetCbeub1p9FWDtXqrQo3fH51XUlLLD8y6g64saNGyxdupT09HTMZjPr16/n119/pVOnTgXarl27luvXrbMBp06d4qOPPqJPnz4O+42KiqJt27YOt7Vu3ZoTJyogCFWizMmbKizvsiNfYOWOHTsKbStRNPekYtqmTRtSUlK4fPkyb7/9doGEt7mMGDGC/fv3s3//fhSK8kswcLfKokwho+kgDd3HZvDMtHNFKgLNnjHTZKCm1GNo3NQodUqefWcBNUL/z7a+VRdrqh55PotpjbBf6f/RxTs6tpCWrnR79CuctQUt2R6elxxaTAH+9a+BKNy+5IJr3d0Ud4PeZurXmIO7/qht3SP9F6HSWrgS+TxXtVoWdpzMyXoFrXcAzs6x6NQxPDnxOM06bUWlyir52P10vPLRBzz5QkGrh1muYEcj65ju+mN0faVi8sfCLcW0kMjQI25uHK/W125dq6af0uv1GPzquqL3c0KQO95X7aoivL0rA6dF0b37hELHd3a+gUll/eG9WKVzge1adRwvvTeGiI7lPKWf55dtQ0A4Fm2g7bNGeZPRk8dQ/wkn5Oqio2wL7V4ho/OQU7g4nbVb36n9JDwj9MgVjkvAbvMPR5Sp6DxgOwpN6X6zwto646S6gkUbQJTr7ZmnmyoFolyLWpmAIBMwyxQcbGyfCLx+t4KuFSVF466mQe0f7NYpNZCqVJGslLOr3gu29YJg4bmRbzLk1TcZ/tUu+r5/gyrNXWnxvJJaPa3KsSATcPLW4lfXFbdQPTV7uNB50L93LN/dIggCc+bMISgoCHd3d8aOHcuXX35Jnz59uHTpEnq9nkuXLgGwefNm6tWrh06no2fPnjzxxBNMmOD4fsjKyiq09Gh6ejpZWSX/vZG4d8ibKizvkv8lpG3btoW2lSiaezpdVGBgIN27d+eZZ56xVWqoDIZ8soEV3/bh5qnS++TJFDLajzTSrtUHtnX1Hv2UfUsct9fqMmnY5A/2//J4icfQ+zrR/eUzeHmewk0XZbfN3/Mf1K5dsWicEFUeXAl7nKBTC1DIM6gZ9jPRj3/C3mLSiwoyweYPWrOHC32fmYJKkeywraf7SeSqNg63XdaqMXo0svmaNm36M1XCGrN8WiMMKSX3+VM6KTFmGanbaGPB8Z0PUiWyFRfcg0lTWJWOQx4B1HLQj4uTNXqyWhVrdY2LcY+jclYV6X/oVd2Zpn3iaNZgMgAKecGcqWZBwSE3d1p4NESVeIgm9WZxoM5Mrh8v/yA+QQ6WIlKWHPDwoZ7cCcF8W+66kXOpOw6MJhdMZh0nzz2FTp9IUnwgKQkuNG+9BIU8E70mutjx9brL5CiDAFgXUBX39KfxurDMXkZBZMDQd5h55COyEsumElJ+BNlti6lRLuNAnedpcuBzBMtt14PH+r6Dd9A4Lp3w4fTG0r0kdXk1kzrVHCdh94sUUSgdK6ZpCjkGryZUtfzJsx+bSU4OYt/qaiRdMpCVaCjS77pxZ2sO1O0NRpCc14dYELA4BaE2nUeQg0mQc8jNm0ZyDYLZen4b1P0/tmhfQalVlPqcN37ChFJhf+0qNZCitL6BntU70xYBAXvZA702Eui1kfp53E5PtR6Mp/sZvF3/xWhyITmjFt6ulaeUAnh7e7Nt2zaH26pUqWKnSMycObPIspR5adiwIb///jtDhgwpsG3FihU0aNDgjuSVkHjQuacVUyi5r095IVfJ8fPYxoA3E/lr4VAu/VuyB5ggE2g2SEmTVivwcrGvAKRQmtC4qdG6K0m6aP/2pNZm4eF8hG5v9iCwylE2LOqKq5+IMVvAxTeH+GgVl/amIppFIru60qzrTnw8DxSpNHhX02LUuZPi14YNwTUZHFcTRcpJADp3+Yzo/R9w42RBpUnjrsbZV82AN3/i8pWW+PoexVV3uoBSmuXTBk3iEQRTGm66Ezj7ZAKOrb5xvo0IuaWY6rUX0QddZPQX+zlydCCbZ6sRzfYPt1xF0aeWC5Gt03DxSKZq1W2kpFbF33OrwzEiqq7lT6dRts/xaiUZ/l3sgn/AOp1tN5Yynd5jLpCd5UxGmjM52SqMBgUqjZHgsDPk5GipU21+AT84s0t1ZBmXwZKDIJow3cp1FxvQivDEQwiChe4vbOPXyc3ISXessJQVMrmAsYgZ6jSFgm3N3qH1qRUok47abVMqUlEqUmlU+1agYdEuug5xcbpAkjISsCrIJ30iaesge4pMZsQrQk3s3vJRTBHsr6PdXj7U9WyE5uae200EkVZNP6FZIyeWmT8m7bqI2SgSf8axlcslSEfjPqlUqXqMYN/C/URDa8UgVxjRp+tJ1xe0jiS4RxJ4Yzchfn8T4odNccvO8eXMxce5erEKF/ZpaP3UOTbPDyUtLpMWz6uoEfp/iCoPjrsUtDYbdIE4pZ1FpRMxyJzIUMoxutWxvQRq1XGM+Pxv0tKDWTyuntUvsgSxhwENXenUwRr0JAoK/m06jsbn1qDUiOTIrNd5ukKOyb0eyqQjxfaXdzZHqUgtoJSKSjcsWh8w53Bntux7hwkTJtC/f3+SkpJs0dRxcXH88ccffPTRRyxbtqz4TiQkHkIqTDE1mUyYTCbMZjNms5ns7GwUCkWBKfglS5bQtm1bgoODuXTpEhMnTqRz54JTghWFUmeVz11/jMGvjONUT6sz+9Xoquhd09i1xANTthn/Olpi/k3Fq7ozGmeBfi/Ow1V30mGfcoWF9kPTqF59PQf39ePYOg2pV6xRo2q11ZLVssmnAAwfU7DG/NlLA0hN8aFRnSkIQvFPF+8wI5lOzpz1rUOaQsH22gPotNtqwVUpknnmjYX8s+Z5Lh+FlNhMgho7c+m/FGp3l9G9x/so5Bm4Rx512HeOZ2P+iexBvRvVCTr9I4JgoVa9XRzE8Xe2zb86z51zJ92nBc6XrQ93vSaa1s2m0exHF67c7Mj5040xZKrwq3KV0JBtnDvfhcCgXQR63baQejoXbkF31p4nK18i5DU1uvFUXNGVjzzdDtuNUVL+ajSM6ik3OePqTd/932OSWa+Zc27+5NYUCfVfydMfKFj0bt0SKQR3ikwuIBaT5PmImxvnmrzAC//NQ552lky/DujiNpfJ+GplPAal1vb5uIsbbRR6BFNBBc0r1Ejs3jsfS+/nhN5HSdNeF7l2KYDLx9XEHbn14uhAq7nhVYcqeRTTXBTyTAYNexOT2QlBsHD64kAunorEO/AG16J9qdvsX7zcj6LXXChRcE7D2rMxqJvyZv03+fzM52Tms6pfcg0k0MF+GtV16kV+T71IsHRTIpMZCXy/KRcutqNZg88BSAroiNmBb2ym1hsnQOtqIV1lnTJPcYvAO+H2S7G36168Xfcy8ptrpKSEcPliJK6eiWSkuZAS70zUejPZybctynK1nMeG/YFMZkQUZETVf529nl5c1j2DQi2S95Vif7XetNxbvGJaHKdqvcB/3sH4GLLpLsjvTV+zEvLII4+wYMECxowZw9tvv21bHxwczPz58+nWrVslSichce9SYYrp1KlT7SrvLF68mEmTJjF06FBq1apFVFQUVapUISoqinfffZekpCTc3d3p2bMnn3xSeSmcVE63T5FcZqB2+AIAat/SOFo0lmGy6EhKq4N5gBp/j63F9imTW5ArcvBwPkyXTofJSp/OwVsvz2pN8VGtuVPP+RGVbogqF2QZlzC7VEeeegYAJ2cDqTpPYtytx3LcxZmOai8Eg9Vi6K4/xhP9x3LzkWYkp4Sh093gh//a4Rt8A4W88DQr5+q+ymGvIK5o1LjmBBF0a71OV3gkepJKwe+tx5OmkDPkykYE8XaQiFKRSqj/SkL97csbNmtQ+ANPFGRk+nUgwS2c4FMLbf3l5Ht4X9WoMDtHIE87d+tcuSAY7a3ECg93SL1Z6Fh248rUWJwCEczZXNaquay1Hn22S7hNMb3o5IQoyBBEqzITFvgHnV+twc6fdaVyXSgNwi2FTCNqyBYKt0ZmKGTMazmS9tdjyVBqaBa3GZNLJIrUooPixEKUzLxk51FMc+QyjtcdRbUre9Dc2GXXrkHLrZzb0YGMm9lYTKWLxm45REmnjlOQy7KsymIdSGpXj29e6medDnfwwhblGUKVIvrMdcuoFf4jtXLfKBoULYeocud6yKMozDk2lwVBsIAAaUlpJOxLQNtCa7fPSVc3WjiY+s6LTGa1rHu57sOrwe2o+v1BDR22T3PywgtwcssmQ+0MmLnqGoy3g7a+brvwddtF9XwW8fZdqnM5rg0yuQlEAReXWHzddwKQ5duOTf7WModXNSpCncAi3PZbP+vsSstCj6bkxDp7kaKUk6LU0UUup+RhefcmTz75JE8++SSnT58mISEBT09PIiMjK1ssCYl7mgpTTCdPnszkyZMdbsvrw/Pxxx/z8ccfV5BUxaPUFv3OLggWlPI0fNwKWmIKQ64wIZffVsiatf2bg791ARHU6jvzQxQRONDgZY66eRKWnkaaUk3v7eNvjWcmSylguBXFKwoCqb4tcYldgyDefrjkWlQsCnfkqo74+NoHduTc8pkEiKs+mNVBt2sMX9S70My9Hsqko3YRwY64qrE+bhJDH8fz4vLSHWceRS/Lpw0CFuY3sCZMHnWzMer4/xAFhUM/ywzX6rjcUkyz3euhvbETMY+/ZYZrJK63lPmiyPZpzYkqbYnVudL53Fa7ban6AIy3pjgNchkWXVXk6bddUdo0/5gmjbzZt38kZ/715PKBlDK1oMrkAjqjjth/YvHoVnTibqNMYJN/FYKysmng2YRTIR2oczIdWZY1AlsU5LbrQ5QpETX+7Kg/lNCUq4REWfO0nqvzCl6pschEEy6xVut+ltw+MfkWvyBy5O1onE8xreKzhtc+20pcfHsO7urEzWgFVw6kWFNYOTgnSp0ShVpOiwEZtG1ZcLbAXX+UoMZDiN2XYheVn8tpZz0h9d8g8uzvyDIvF3luikOUaxA1PpwNf5x1gVUBeC12PYIxGQCLKEOlUvHVyK94/8j7GLntwpGmUGDwaYnmxm7EPC+IYH/O83Ox9ihOOjuO6E3WWK2ketdMYtU+QDL/efpRT+FsyzNaHC5OZ6gV7vj6j/a1V4gVGotdWrIklRKLNgBZluN65SVBROCSRlt8w/uAnJwcVKrbanVhyqjBYHCYyF9C4mHmnvcxrWyUTmU/mSRXWJArbiumvu47qdK8H5f+TUGtSr6jPo0e9dnl5QNYp2oBcjyboErYj0JpIr+umOAcjNajAcqUUwgme6tojntN3EKccHexBlJZNH4IhniOhXWnUeJhLLoQ/qxq/6BKUCmI82tOlaSjWAoLy8/HptAmPF1KxdToXh+5MQ2ZIYlt1btzQ33bl/WGd12C4/8DueMf+iSXIGQBXdGkXyLBIxLfnBSSPWvjcf0/LEpnkp39ye+9J6o8ABFEC5izECw5HKzamX23qrX8WusR+zGcvDDJbs8jJ/k0xSvd3kdao7xJ25ZTadsS1q2fxvl/1RizLaRcKmES8HyKW0QnVy4fzCY72YCgENg4bROblm3is72fOfRxzM8VjZp5TQYhAAmaEXTYO4MDDV4lOPkSbslnUMfv41CjMVx1cuG8zolrGi0hUWBxCmarfzhOPqFozWYev6WY5sgL/qwcd/OikaCws5CD1UoZ7LuW4CfWkpkTyNkLfagRvpQrNzoiyCxs+6MdXiE5uHimExqxF3+PnQUCcfJSs811YvdpCk0FtSEglNNuo2keF4VfzBqwGMFisAUJlRSja032V3uUi7rbaZhyXKuhjrdaN82iQPChQ1xv3hxVmgqjs71v8c8NnuT5wwIHQzsiAC32TQOFM5cjniL4ZMGgKlFQsM2/WqHyJKqtaeGcnFNI0zgDyWQp5KT7tsL5yt2XvDzr6mP3Wak2Y8k30Z7m3RiVIRxldgKKFPsgzOKwOAWzqNUbZCjvd89SK/Xq1WPo0KE8++yzBAQEFNgeFxfHokWL+Omnn4iKKt25qkx+7levXPt//g/HLmOVQYcOHXj22WcZPnx4ZYvy0CEppsWgLH3mpmKRy03I5fYPqkZdz3DpX180qtIn/za51GB/xKMF1ucGGskVJkSFvQXJLJOT6FGLdP8WhJ9cYPdgTnKPwK86aP3U5Cgb80/Nx2kR8y87vX053mUm1VKTMDp48GeorA9Hi6xkl9U1jZrkkD64Xt6AWR9qC8jKiyhT20VSZ+qDWVm9PUFZmVx0crLlcgRI0roTDCBz/KXt8qtGYnBNfHNyCEtLxuLfgg1BNWnv5E2UewCRydb6whanIARDPGZ9GNFVOnPa3Q8QMAoyGl8/x353d1ufWfmKBCSpdXY+nmuqNqa1kydhZ5Yi5BR0cej+yAR4BERR4MCxNzBkadk2X0dAfSeuHM7AlGXCNViH3keBb4SRa2eUPPLcJlx0MWz63xDSE2QMGvYmxhec+X7yZFQ6SElIISU+hZQLKcjrFf+gFwXBZv066uqKsfm7nHRxRu7pg8bSmAEnXNnh5WvLWn9No8bsUp0zVXuRoZCRoZABSixaf2RZcXaKeS7JSjlpQY/gEru6UDmcVFeoX+M7AFtOy6qj/yxW/rzUqvUXG3jGocU0lxgnDTHhjQjzq0HthFh2+oXz9JFlqOP3Iao8EXISENVeYDYgmNIQZUqyvVugufkfiCZONHiToMTz7POwLxaS7hxqU0wtFhnna9Wi9RNPkH4pHaG2/f2SLZcxv+GTNn9RY4v3OeHsigx4IucZzDIlvmcXIyAiytQca/BqgWpeebl56wVN75JApuq21fGKZ01q3KViatEGEO1kf08p1GYsgr08e6o055pGQ6crpwnOvmFnCXaEqHRFMKYgKt04H97XPtPAfc7OnTv59NNPqV+/Pu7u7kRGRuLs7ExaWhpnzpwhOTmZF154ge3bt1e2qA8UkydP5ty5cyxevLiyRZG4CyTFtBhU5TCzJFeY7CymAHUjf+Dqsx+hURXMNyjmm47Lnc7O8WjE6ZBORDt7cEHnVGC/GFd/QrBmAcj/oDbL5BjlKg54V8EtozdeeSyXV10CCK9/kVUNn6PxjfOc1ek4VacLYFUw9nl64Yh0lVUG0YFiUhg/1+pIQFhrLIiEZqRyQ6Pn0X9nIsrkGFwjSXWuwjXXQIITzpPo7E+yxoVkpZxkZcHk/LlWI7EQi+lNtdWSe1WjJkHpTTWVhgyFjG1+VclQyAjO1JIc0pffq7XFNycHEAuc15iqdYo8niSVFp05J89nJauCq/Gk8TH8rmxDnu4gRB1rhHiTerMAaNVMhiBYuJrQicwsL4J8tjm8Lvo8NYWMbKvXpFKexnPjFpCUXY+l++W0HD6c6+euE1CvoLWmOE66WM+tWSaQIRP4sV6v26WUbvFXo6EkKe09AFN9muEWs9KhYgpw2q8+TYtQTO8EUeUJOYk2f00X7WlrRbESBAVe0DmRqahKkkrBf9V6UU8XyIrqbRh06Ddi/ZvdGkAEQWB1UASNkzoSrXMhQaWgjrZgwvokvS+5qqpZsF5r8W+8Qcyu/yO0dmiB9nmDmA653X7Z+aV6CwA6OPsRkHIZdU4q//gGFdg/L/EqJaIgx1l/wy5X8E7fKkTmcxfIi1iMr2tMrREkOHkU+P4VKhOWfB6gp2+5GWwNqI7edyyPb5tgc7spMK7KnUzPRmjSLmLU+rImj1vQg4CXlxczZ85k2rRp/Pfffxw7dozk5GTc3d0ZN24czZo1Q6msuMIbEhL3E/dz0GOFoNTevQOgqLBXouRyE5eDG2Pwao5F44soUyITTPToMR6Vk4UM/9sR7RZtIOm+rez2N3g1R1R7keYaxha/IIdKKcA5vcut8YwIynyKqSDDLFNwTa1iW0gT/ms2EZNrTUSFjnPO7oTWPsIlJw27/ao5jAJ2ROot87Lllt+AU4JjufJzVaPimkbNv57eXNBpOR85gH31XmReo35c9KzKLk8/FtVsy+qgCJu7giPib/mnFVbpKS8GuYxTztbzk3HL6pmuVPN3RBsylHIu6LSFnteiiFerMAoFFbP1VWpzKqIvFq1/sX3kRn4HeG4hIug3h0opWIPFcsvBArjpoggN2ITb629y9dVXuX7p7goY5OLIX/eyVmM7b7lE+dYFsPnY5ueguxexNYZj1le9a5myvVtyqPG7HKszlNTgXrb1giCi89aArGT37TW19Vo55ObGz7U6kKZQ8H3TgawOirAuwdVYHRQBwAF3DxJUtwIIXQumbIrXutn+Nt26B043bMjli3fmN77VN5CLnmEcC2hcbFuzTEDU+KLT3sSc52c9QyEjxa+17bMou/3SlhT6OEmhj99aryLbuyWiwhmzPswqe73X+SukFju8/QqMZ7WYOv5dSFIpidVqOFd3NADZ3q2IrTEcUaFDlGsw66tyrsZzHK7SioM1n+F8YFmETd2bqFQq2rZty8svv8yECRN46aWXaN26taSUlgGfffYZgYGBODs7ExkZyerVq5k2bRrLli1Dr9dTv359ABYuXEjNmjVxdnYmLCyMuXPn2vWzcuVKGjRogIuLC+Hh4axbt67AWHFxcdSrV6/IHLZLly6lSZMmdutmzZrFY489VuRxpKSkMHjwYLy9vQkJCWHq1KlYLLdf6H744Qeb/LVq1arUnO4VhWQxLQal+u5rN+9t9DreGYlUPfsrQk4SMrkJo0bOzuo90ZqMND37ty2nZJZnfU4ENadZ3GZEQU6SXyvStB7oEvxuBVYIXPFthN4tnAxV0WUG0xRyRIUzCqURMZ/F1CSTYxFkIAi3osq9yanZH5/MZK5p1GTUsJauvK4peVxs8i1nf4tMiemwibW/raX9tPYlP1G3yFUEAPa5exaZMD4vN1QqRIRCLab5MeVTuC/oXIqcLi0JKUoFrsaCD500hYJN/lXY5f02L2772OG0PlgVBMFyNxH7AtciqnHepQoB16FNmo4M5xL6rt4lUS7utEQokBEhl2y5jBVV69DUNYCWBz4v4NtcUkSZij/rPM6NW9dmoKsX/a5stLl86H2Ud11u9E6I0+psf5vz/LReTwElSrsAqJJy1MWjwAtAYZi0vshECxZkdhaHC941aRi7BiwmVrWZRPejvyI3xPN/NdrRNv46rvFViQ7tyargalTJ7MM1tYqAbAPRusKni5Rqo13wkyPWBlQlIkrHyjp9uKZR81x2dy56VeOss4fd74qskFrzEhKFcfr0aWbPns2+ffsICAggOjoas9nMhAkTCkzl+/j4sGrVKsLCwti+fTs9evSgadOmNGrUiL179zJ48GB+//13OnfuTFxcXIFqXdHR0XTr1o2xY8cyYsSIQmV67LHHePHFFzl79izVqln9wX/55RfGjBlT5LG8+uqrpKSkcOHCBRISEujWrRv+/v4MGzaM5cuXM3nyZP766y+aNGnC+fPnH4qXGkkxdYBHmDNylYBKJ6DRG4rf4RZ50+3kptUxudbkXw9P8PSipvsYQlNvID+ThUwp2qwu1RJq4ZUcBYKcRLcIDrt50VShY3PzcVzWOpEtE3gyuz0KswF98hkOeVfBLSeH7BJMmZudApDLcwoopmZBjjnf/gfd3eFW4NTfVezrnJeEBGWuYirn0LpD/Lv23ztSTPNSUqUUrFHmosa3RBZTR9ytUppLnLrw8bMUMnY2Gk3V5Mv4X9mGPO0cJpdI5IYEBEM8Rvd6yIxptuvI5FIDReqpEo8tCnKMt9QSWbVqJJ5ORN2kYqJ+M5RyRF0VhxbjvOzz8OBg56m88s+HgAwhJ4EcjwYoE49gdquFIvl2DXFRkJNQ9SnUhiQytV6ka9zwSb5gU0rBGryVEPo4XheWAqDzAEqQb7SsuarR2KLqjXmiDYW69VClqjC6lF4xLU0wUI7WG0VmQgHFdIe3H+FB3dFkXueCzolljQfT5dJREAT2efhwsOWrNuX30i1f0qKUUrBO5SsEFWbMWChkul4QyPRubrNKr6jW2qGSXZp7XEICQC6XYzAYiIqKwtvbu9Cy5QC9et2eUWnfvj3dunVjx44dNGrUiAULFjB06FC6du0KWKtN5iUqKoqpU6fyySefMGDAgCJlcnJyok+fPvz666988MEHnD17llOnThVpMTWbzSxbtoxDhw7h7OyMs7MzY8aMYdGiRQwbNoz58+fzzjvv0LRpUwAiIiIK7etBQlJMHdCodwING/yMSp5MXHy7AtvzWrWuRwwi2qMqzfdO5Zp/a3xlStLcIlEbkjjv34iLek+bf9ZJZz1ndDraay8i5Kld/29gPTpmJaDMSeWGzpsshYzNzcdxwuW2C8DuoAb4ZmcQonLmikbNFU3JlA2j1heFMgeU+aoVyWSYHSkQt2Q1ykvv5ZGlkCGqvbEIcg5vPYxcUHP6h9PUfLFmoQ+vssaor2JLt6NAgQlTMXuUA3I1FGEdO+juzkF3d3rLVVx0fQ6jXEHdG+eQW0wY5Wqi3QKJSLzIQb8a+Gal0/ig1ffU4hSEPO3srb+DkWXGFuxckGPE+h1mVqtGzJH1VG9SvcwPsTCyXCIwyoq/dswyAaM+hFOhXal76HNOhXahTtYNUt0i8cijmJ6pN5p1AfZT/yGeBf0tj/jXpvMt9129p4mLO2LR16hFulPF1aU2yQREp0CEjEt2ynlS48Yknt6Numn5viBkaTzQZyVhRoazRUuW7HYt9hivSJyM1sSlCSolf93KqpF9B/c5gEJlJHlnMt41vcn0LFiWN5dVNbvbflNKavmVkCiOiIgIvvzySyZPnsyJEyd45JFH+OKLLxy2Xbt2LVOmTOHMmTNYLBYyMzOpW9fqdhQbG0vPnj0LHWfJkiVERETw5JNPlkiugQMHMmbMGD744AN++eUX+vbti5NT4S5h8fHx5OTkEBJyO6lwSEgIV65csckXHv5g+V+XBOmXwgHevjE4qa6gkGcUKD1o0fhwpfpzgNWac9ivJofcPBAFOdecfdle6ykOBjYgS+vNP75BBSwPZplAskcIsjyK4gWdEwvq9+K6dz2u6KxW1LxKKcBFnZZ/Pb1YFlG8v1lesrTeKOQG8uugJkFWwGJaFhj1VbAIClR6X6p8/DFz3p2DNq3ichNm6fyx3PKjU2dUTn5AlaFkFtudfmEcd3XltF7HBfcqbKhSj79Ca3PQ3Z3fwhtxTudErJMLJ+q9TFTdl7kY0g1R5YHFKYg/mo8mKaQvFierkmazEuexmJ5u355jO86gEisuTblRqSvUxzQ/N7wbscM7gO0tPuBfT3+M+iqc8a1FcpXHSK7yGGZ91QJKKUCMU8HrKTrPj79Gl0N6Uhqrp5ZtoFVJMOiswWimPDfcuUaN2Pe//YXtUmaka9xAkGNGRtox++nIf3yD2BBw+wFnuEOFNBeFxsjJnSfZ+0vRpbuuFTF7ICFxNwwcOJCdO3cSExODIAi8++67CPms7waDgX79+jF27FiuX79OcnIyPXv2tJbkxVqFq6iS55MnT8bLy4uBAwdiNjvOL5yXbt26ER8fz+HDh/n1118ZOHBgke29vLxQKpXExMTY1l26dMlmuS1OvgcVSTF1gIeb/dSpqPYiPdBaPi7Jvx2HfMIxeDUjoWo/TjnrMchlxNYYyl5PH467unJGrydZ56jmyq3+VNgpprmsDKlVIC3L3ZLq5IVckYNF4cBiWgLLVmnJ1AViEuVoRo4kpqU1qCFmZ0wxe5UdqU4+WORK5MhJOVc2wT+lxZJSMutwkuq2r9AhNzdrkvJ8P6yxWjW7vAPZ6RPIfz6h/NH6XZY3f5WrGjX/V6sD37cdw8HG49jVbBxgzdhgunVbp7q6IlSpwfVNjoOnygOTQgNZav6b+h+qYur2/OcfiUkmcNjNjSyFnGOhnfjPw4tfanZkdURbVjR9qcTjpivktsIOcoUFi2jhr7l/oTc4TkhfXmTqrMFtxjwp07KcnDi++xxqsXxflLIVWkQEsrMsbFm4pcB2Ryne7hSlysTBLQeZP2k+uhRd8Ts8xBgMBiZOnEhYWBiut9y3NmzYwOzZsytZsvuX06dPs2XLFgwGAxqNBq1Wi1wux9fXl+joaFvwUE5ODgaDAW9vbxQKBWvXrmXDhg22foYNG8bChQvZvHkzFouFK1eucOrU7ee/Uqlk+fLlZGRk8Nxzz9kFJTlCoVDw5JNP8vbbb5OYmGhzESgMuVxO//79mThxImlpacTExPDFF1/w7LPW0ufDhw9n5syZHDhwAFEUOXfunJ0S+6AiTeXnQ5AJuDsft1t3OvJZjnj60zcnldVhLUhSKVhX63Hcjbf9T/8Mve2TaZQJHPYKLnQMmcKC9bllrxiWh69VgpMHPrJLBdJFmYRCpvLvkmS9PzKDkZs1a3IlNJTmffvy5ctfMuvsLDIU5R+Ek6x1wyNFhTpRzfE9J6hZv8athDgVF2CRmZCNUHjyAIcUVt9eFASy5dZtjqZdjTKBHT7WqOlWTsEgU5DD7b4sderw43sfM7br2Ao5B0aFBoNB4NcvfqVx78Yo6hf+E3NZa6+o5WZcMMoE4tWld/AX1R4I2TeQyc04N6yLs7Mzb9R4gy9PfUm6umKm9BOd/fEEjHJ7pdy7aTPEKyIUnfWpVMiQcfmPy4iiSOiToZhkckRBRkqSga2/b+WxWY8VWZb2bpCrcgh/pAemHAVX9l3BrYtbuYzzIPDmm29y5coVlixZQo8ePQCoXbs2b775JqNHj65k6UrHvZIA32AwMG7cOE6ePIlSqaRVq1bMmzcPtVrN4sWL8fT0pGrVqhw8eJCvv/6a/v37YzAY6N27t53PZ7NmzVi4cCFvvvkmFy9exNfXl2+//ZYaNWrY2qhUKlasWMGjjz7K0KFD+fHHH5EVYdQZOHAg7dq14+WXX0ahKF7F+uabb3j11VcJCwtDo9Hw4osvMnToUACeeuopEhISGDhwIFeuXCE0NJRFixbZTf0/iEiKaT7kKjlymX3A04aAEERB4Psmz9jWReu0RFP4FHVRPqBWxVQEyj+6Ls5Jj7/CAMr8UfnlM5Ufp/fGN+cml6pXB0Hgvz//JKxWLVa+u5Iun3cp8/HyE6/REypTcmbTGdLTRDJ2ZmDKMeHaqWB6n/LCItfiJJoxCCUPnCsLslyrocm4bJvKB0ho3JjYs7HosnWka8pfOTPK1WRlWq0Ky6ctZ8CyogMGyhKLyh1Z9g3kCjPptWtR96uv2DVkCGdWnyHgidLnc70TLrj4UA0wKuwVU3O7dkRt2EPE0LILXpBdlDH9xekAfN/me4wyBaIgI/lmBvV69SJ2dSzejxY+c3M3yFUmstt3xF/tyq+fLOGVLq9UmB/5/caff/7JuXPn0Ol0NoUmMDDQ5kcoUXrq1avH3r2O3Uh27txp9/mVV17hlVdeKbSvxx9/nMcff7zA+q1bt9r+1mg0bNq0qUSytW3b1uYqUBLc3d2LLAgwatQoRo0aVeL+HgSkqfw8CAIIcnvLlSjXFGrNulNkCgsyZfH+KmXBFY0WhSwbmVlAI2rQWqzKtEmQYSqHqfyLeleO+9UkJ0/95+Du3Vm1cBU6Y/lP+V3XOGGRK9n8y2biTVomPjaRr17+Co1YDiW8CsGic0GRVvHvfOn6AERBbmcxPdmkCW2GDiUxKrFCZDAoNBjlOtq88AL7Nu9DfaXi/HzNKjcA5AozogwOP/kkbu7uLJu+DIGKifw+q9MhKvSkqe1zfx7v2ZOVc1bmi5e/O/b+cfvBfHLjSUxyuTWLQXwm4oABrJm/pszGyo9SnUNs7Tr8+/bbGDJEtMkPRo378kClUmEy2Qdh3rx5E09Pz0L2kJB4uJEU0zwIgqVg/kN52StTMoUFuaJiFNNsuQxBo+T0ptN80+MbTi+7lYZIkNkl4i4rbqqV3NDb51e9MHo0arWatFNphexVdtxQKTAo9Sg17mRG1AQg8UYia94tv4d0fnL0rmz5uqCPX3mT6OQFgoycfN/rjVdfZe+fRQeplBUZSg3ZooadCxfStE8ffpnwS4WMC2BU5RaUMGOWy8jQ6wn94w8ykjLRxFfMi4lZZk2RFOMWabc+1c0N76qROGWWvmiDI/QZelbNX0WDDh1o2KkT2//YjlGQISIQL9NyvGtXTvx7otz8WhUqAynOLhg0GkJ69+bcP+fKrO+KeomoKJ566imef/55Ll68CFiTtY8ePZpnnnmmmD0l7kX0er3DZceOHYXuU7t2bYf7LFmypAIlv3+QFNM8CIKIkO+MiIryUEzNyOQVo5gCoNNjwcyJvSdYNW8VYJ3KLw+LKYCQ77KKDQuj+YwZ7Pil8Bu37AYX2BFcD32HziTXrk1EnTo0ffRRdv69s8IeeNlqJ9YsWGOzTlcU0S7et/KY2h/nmQYN2LXyX8QT5e9jmqHUkmmyWovNw4ez4387KsRSDmBQuwHWymrirUvwcMeO1B42jPVf3l29+NKwsXoXrrpVKbDeuXt30mPKxp1iyetLCKpei8P//MOhzZsxmbSYRRWiICMtIIAMvZ7mgwdjiSmf6XWlymBzGUl44gnW/ViwWs6dcu7ncziZy0aBvxeYNm0aoaGh1K1bl+TkZKpVq0ZAQACTJk2qbNEk7oD09HSHS9u2bQvd58SJEw73GTRoUAVKfv8gKaZ5EGRiAYupRVH2yoVMYUGooKl8AJw1mCwCcrmcCycuoEvRYZRZrablgliw3z0jR7Jz5W506eWvpMRptMR17cbNkBDOHTvGjS++IOlmEk6minnY5VhkeAYEcHXz1QoZL5czeh2ZziF2Pqa5hHfrxu7fdpe7DGkqNZm30nUd6N6d6vXqcW3PtXIfFyBda603L1eYEPO4T8c98wwrvluB4mLFuFfEOGkxyAtmJIjp3ZsL+y7cdf9OZif+WfEPLkOG2NZ59O1LTqY1hV1igNWfNmH4cNbNLjuFMS8ypWh7ATreqhXpScYyUSb1mXrmvDOHVeNXUYHxiuWKSqXiyy+/JD09nevXr5OWlsasWbNQqaRUWhISjpAU0zxUlGIqKMzIFRUXKGBx1qDs8Sgt586l9bvvMrrWaGRGTZlZTLUWLfJb/6wUDKoyqlRU69SZPyb8USZjFoUcOVfDw7kWZA2BjqlWjbZff40htmKCkQzICXrpJZbPWF4h4+Ul0TkQo7zg+Td37syK71aUu/UyRakkW21VUESZDI8PPmDumLkoKiDOMkljDXCTK8x2Os2Z+vWJqFOHFdNWlLsMuZgc3APR1atz9tDlu+4741QGWq2Wi50729bFt2tHeqoJERk3/a0pq842bMiGJRvK3PooR45FkNvlqw16/HFyYu+mlK71d+S1mq9hMpr4e/7f5GTeXX+VyYULFxwuaWlpXLx40fZZQkKiIJJimgcBEVm+4CeLvOwVU5XKAELFmQPMTkoynF3ZOWwY1594AkOWgfS4nAK14u8EOXIWPb+IXR/t4spfuVGmhVxWvXqx8deN5R6IJBNlZOWrtnHm8cf567O/SP0ntVzHBjCIcg4PHsyF4xfQmSo2x2OS2g2TgxQlx3r3pm7btsRsLN8ceMkKBRma28e8t29fslKySd6RXK7jAiRorEUpZDIjlnz3sf8rr7Dljy0VNkVsKuQeSLXo7vr637xwM3WWLuVKnpQxpxs1IjnJaM1je6uWtlGlolbr1netMOaiETWsGrWK5UOWYxFlmPO8AMU/+ii7f9l9V+4rhnMGMtMyaf3ss/e9NTEiIoJq1aoRERFR6JJbT11CQsIeSTHNh0xx+4EmImBRlL0SpVRnV0h9aOWtdFQ5CrWtdvephg0JrlqV+e/8Hxjv/sffHGVm5+qdLJ21lOVfWC2EogNrEcCxRx9Fq9Wya8auux63KGSi3C4rAMC1wEAObz3KjOEzUKIsFwVFhQqNqCFbUJDq6kqTQYNIO1n+AV95SdQ4O1RM052d0Q4fztLPlpaL9VKfpUefrSdbLiNddfvcWuRyavbpw/t93y93JT3WyQlR7oRcbkQU7Gck9r7wAtXq1uXiqovlKkMuRtHx/S1v0oSsk1kOt5UEjahh++/bOfLII3brTUol6Sarj2letEOGcOnApTseLxclSqY0msKm3zaxfeV2jCYZpjyK6cmmTVk1bxV/vfHXHWUeUKJk+qDpuLi4cOKrr2j50Ud3LXNlYrFYMJvNWCyWQpeSVBKSkHgYkRTTfORVTG9UG4RZXh6KaU6Zp6Cy6x8ll5ZeYmL1iShRYlCoMeRWohEEXJcsISb6GvuXHb3jgCAVKi4uusiHT3xoKwN3/vj5W1PFji+rNBcXai1axKJPFyG/UDY5VAUE9Bl6Tv9w2rZO5sDHFSCsRQvSktL4ttu3LH91eYHKRHeTykctqtn96W6yT2eTdWv8hJEjWT+v4oJuAG5qdIgOpvIBTvTqxY2YGyx9bmmZRWvrjDrUcWpeDHqR4QHD0Rv0ZOV7Mbn5yiuIosilTXevIBVFilLBtfCnkCtMWPLVZTdoNLh8+qnt5amsUaCwe9kxFlK8Ir5ZM/6c8ecdj2O5aqHWI48UePECyHLyKKCYHuzXj3ULNtx1mipVioq4mDjbZ7Mow5znBcgilxPZrh1rF69lx6QdxVb9yo/8mpyEqwkEb9hAsocH28eORSiixriEhMSDi5RgPx/yWw+0Ew3GsM03iL4Xj5T5GCICYiEKoT5LT4Y2446r9DglOjGm6RjSkqyWOtkVGdl5LKYAx1u2pHndRixftJLXn2xLpmdmqceJWxfHV69/BUDb779HOHoUS2YmWRezUIcXnp/vZKdOKBQKXm3yKvOvzr+rpO8CAv9N+4/9G/dz5sgZ3g9+H8/ungiFWGxVffvSKCuLgxs3ErU/inbPtMO5nbNtuzZHS4aqdNWpBAQOzzrMuv9bR1xMHEnXk1BM7QTAqcaNubZ2H33j+mLwrxj/1mQHCksuqW5utH/xRbZ9/jmP7nsUTbO7f+laNGIR21dut33OuZlDpqv9+T/dsCFNH32URR8u4q3ub5VrIvZYtyDchfMOX/xOdOyI/EIcmjgN2f5lUxFJjhzzKTPHtx3HYrFQ66VaQOFT+WcbNeLC+r08Z3rujiqhndx4EuGJJxxuy3T1xGKwHzdbq0XrF0rKthSc2zs73K8kXNl/BUEQaDdmDOj1mMUoa+LnPGjbt4dVq1j+zXIMWQa6Te+GCVMhPVpRokSZpGTx+MXU7dKFvc2bA1b/5Ack9om2bdsWqOGey/bt2x2uv2fZtr98+2/fpHz7LwUdOnTg2WefZfjw4ZUtykOHZDHNh0wBJpcabPYLwigTOOUVZpsSvxvUotoWeCIKAphlaOI0qFDZWTOu/neVxI13lgxdhozP+n5mU0oBLh+6TLZCSU4+C4584GAsFiVrP19b6nGczE5MH2atONOgY0d2jBzJ9m+/5cYbb3Dzws0CVpu8pLm40PqddwBIv3B3qXMUMQp+mfkLZ46cAeCjgR+hz9DjKPgKYOeQIVz/4QdkMhkajYZvRn+DcOb2AyPnWul98XQGHQs/WmizJq1bvI4rMUmA9eFao1s3Xm/0OtobFZM6qjgLePxTTwHw/Rvf37XVVCNq7JRSAGOmkQwH5//i/PkkXElAcbl834XP6t2sU/kOLsFsrZYmI0aw8I2FZZY6LOO/DN5o9Qbfj/+e/837n63f/LlkczGqVNTt3JnYLbGlHkuBgt+//J0jvXs73J7q5o0oyJDlqzqjad6cWSNn3bHVVCWq+Prlr2n2++9smzGDbZMmYVIXdMvY8dZbtP/iCwD+nv83C59YiJPZCc11DarLqgK/o0qUxKyIYUT4CHb+byfZY8fekXxlwbPPPou/vz8uLi5Ur16d+fPnF9p21qxZ+Pn54erqytChQzEYin7pHD58OMOGDbMtvXr14tq1a3TpUv6V8B42Jk+ebKszL3H/Iimm+ZApBC4HtbdZXE64ODO14dS7SjWjvallTOgYRgWMYumApYjIOLPlPKNqj2LpkKVoU24rLWnxaWxbuq3UYzglOHHpt0ucP34egMaPPELLpUs5tecU2XJVganF3QMHUaXTo/z5/Z9o4kpuOXNKcOKvMX9hyDLQpEcPMmfNsm07U68eSdfTsRSiGOayd+JEgkJDObTmUCmOsCD/LPgHAC9vb1rdygd3bMUx7HIF5eNKSAitZ8+m2WefcfPKTd5q/RZKlMiQkXA+oVTj60w6ru29nQqp/dixNOnTh2xZngdwnz7kGHLYt2xfqfq+U/LnkM3PiebNaf/xx1w6c4l149bdkbKiQIH8gpyFzyxEJpPh7etL8PnzVDl7FotFQYaD8x/v60vD55/n5Xovo0/Xl3rMkhKvViKo5VgKOazrgwbx38b/OPXDKVTi3flYO5mdmPrMVNvnuJg4dJlWhc1Ryi4bI0cy66VZOFlKN1VtOmnCr1otMnWOfXVT1c6Igpz8d/ONLl1IvJGI+cSd+TQazxjxDqzCf3kstWah4O+hKJOx7Y03aHer/OOBrQcY6TOSUTVH8XK9l1n/znqUMUo01zTELotlVvtZTB9ufcGt3bIlR9u1uyP5yoLx48cTHR1Namoqf//9N++99x4HDhwo0G79+vV8+umnbN68mejoaC5cuFBsPtLnn3/ebnn33XdZs2YNGzduLK/DkbgHEEURi0Uq03snSIppPmQKa7BQLoKgIC4mjpmDZpb6IS5HTtyfcYyMHElmWiZms5l/1/9LdmIOmenWqcTtK7dz/O/jtn2y0o3sXrMbTZwGfXbJHuDqq2pGVBvBzFEz8fD0pMVvv3Fo9Wr2PP00ickCWQolBlnBh/DWmXOIqF2b2cNmF2sVliHj6oqrfNzrY9b83xpaDx7M/jVrOFO//u1GgkCm1gdLIT6etmN0ciJz3z7W/7wJbXzpLIkCAroca2Tz6oWraTNsGFV//JHDc+fSqEsX9m/YX2jwVS47XnqJHaNH0/zJJzGbzZjOmNBYNOSYSv7ycXPNTUaHjGbGkBm0GjCAFr//zs5PPkH+xBPkaG8f075+/QitVo35k+aXmV9tUZTEErhtwgSa9evHyh9WcnT2UfSZJVcUZcjY+dFOXm3yKv9t/I/mS5bA4cPEhoVxKSICk18Q6YWc/xsvvADAplklqzl9p1j0boUqpqcaNyasZk3mvDuHaxvuPL+qk9mJOf3mkJ6SjpePD62ffZb2n33G3p/3IiCQU8Q9cPiRRxAsMnbM3FHivL4qVHz+wue4dOtWaJsMQYlJpiS/HfxkkyY0fuQR3mz7Jkkbk5AhK7HFWIaMea/Pw7dNG7v1JqGQ3wtBYPvs2bR/7z0Au5rhf8//m1cavsKoWqP4/KXPOXfs3K1dBFwqebq0du3aqG+5wQiCgCAInD9/vkC7n3/+mWHDhlG7dm3c3d15//33+emnn0o9XmBgIEePHr1bsR9qPvvsMwIDA3F2diYyMpLVq1czbdo0li1bhl6vp/6tZ9PChQupWbMmzs7OhIWFMXfuXLt+Vq5cSYMGDXBxcSE8PJx16wrm/o2Li6NevXrMnDmzSJk6dOjAxIkTad26NU5OTkWmBLt69SqPPfYYHh4eRERE8MMPP9i2mc1mpk2bRnh4OM7OzjRu3JjY2NLPstyvVJhiOnv2bJo0aYJareaFWw8oR/z88880btwYFxcXgoKCeOeddwrUGS5P5ArBLjm2QrQqK9Gnojn5w0mcLE4lji6+8MsFPhv2WYH18bFJmC0islt5RL8d++3twIk2nWj0aG9G1R7Fa5GvoUvRFRlFbTps4qU6L9k+B/z9N/8+9RSWWwEwlpr1yBI0mBw8iAxaLQEjR3L83+Osf2c9TolOqEU1mutWm4sMGU5mJw5/cZj/vfw/pg+fTsyZGLRaLec//tihPNcEfYlKnSZ6eRHWoQPvdXivVLk1VXEqXvR7kZ8H/UxIZE12/vAD+x59lEydjnO//86FI9FYjMWPL8pk3Jw8mYjatXm9xevM6z+PHL1HiWTQpej4+NmPMWQZ0Gr17P7lF/7t1w+zQsG+p54iITDQ1takVCL/6y/Ca9Vi+UfL0Vwr31RZxVlMc0m9NW06/4P5TOk4pcRpfrL2ZrF01lIANBoNB/v25abf7brwWUY5mQrHlshTjRsT2aCBdf9TJRquROS/Pww6lwLpovLiNW0aAB8O+JDUf1LvaFo//WQ6h7Yfokn37sRfu8auRYuI6dOHeRPnobFoMBThzmJUqaj/zDMs+nQRI0JGsHfaXq78fgWnJCcUKNBatAgIdi/C19ZfI/ZsLBeefLLQfjNEOSa5Bkff5PU5cwCY8vQUZrWZxd5P9mI+ai42O4XyqpJTB04R89pr9sfgwGKal20ffUT7PA/xwtI/NX/8cVwSEthVxDOhonj55ZdxcnKiRo0a+Pv707NnzwJtTpw4YVN4AOrXr8/169dJSCh8tuXHH3+0W2bPnk2vXr1o0aJFuRzHw8Dp06eZPXs2+/btIy0tjfXr11OjRg0mTJjA008/TXp6OkeOWONDfHx8WLVqFampqSxcuJA333yTgwcPArB3714GDx7MjBkzSE5OZvv27YSGhtqNFR0dTfv27Rk9ejRjS+BusmjRIubNm0daWhoheVK65WfAgAEEBQVx9epVfv/9dyZMmMDmzZsB+OKLL/j1119Zs2YNqamp/Pjjjzg9RMGAFaaYBgQE8N577zF06NAi22VmZvLll18SHx/Pf//9x+bNm4t9SylLZHIwyG9bA+QWOa2WLCGsRg3mvDuHl3xfYly9cegz9AUiT5WXlCRtTEJv0FunqkZbp7m9fHxosmoVNQ4coP0HH2A2i2jbtkcwGGg/fTpVIqpxcbU1jU2yqCL71lRYZlomL1Z9kQOzDqDL0aFL01mj0LP16A16bq65yfu93wdAq9XS+JFHON6qlZ1MWfXrk2nUFOrztuullwirWZO/5//NiIgRzHt8HtOfnI4+U494RmRc7XH8NPUnNi61TjsFhYbSbMoUW/L6/CTp3ElPKdmUoaJVK25evcmSl5aQsSujWIv0zVU3eaeN1T91z7o9eLz1ll0ARqqrK9VffY2ESyUL5jpfqxapGzeiUCg48M8BUl18imyvETXo0nRM6nR76i4iX9oeo0pFgrd3gXGChg5l+8rtTOgwoVzzuJZUyYpq1oz271uvndizsXz/xPdEL4nGyexUoA8nsxO6VB1n5p9hcr/JAPj6+9N82jQMGvtjSc0SydYWruTmLF2Kj58fb7Z+s9SR2/nRmXTc+N8N/n7lb+Tnb1tpjU5aLEWch719+tD81rT0B/0+4MKi0iU612fo+WbUN6jVam7Mnm27BqMjIwkKDUWWISt6Kh+IGTsWtVqNKIr8MvMXZoyYwYjwEUytP5UZHWcQszSGfdP3oU/X45TkxGdDP6Nh587EhoUV2meGKCNL5YZGLBg2dLlqVWo2sQaWXIy6yJIZSxjTYQzjao/DeNCIPkPv8OVk/TfraditGzH5cm4aZcX73W8bM4Za//2H3GjEmJVF+3ffxS8wEDd3dxp360abBQvY/9tvpLi7F9vX3WAymWjSpIltmTdvnsN23333HWlpaezYsYMnnnjCZkHNS3p6Oq6urrbPuX+npRWeFm7RokV2y7p162jVqhW//PLLXR7Zw4tcLsdgMBAVFYXRaCQ0NJTw8HCHbXv16kV4eDiCINC+fXu6detmq2u/YMEChg4dSteuXZHJZAQGBlKjRg3bvlFRUXTo0IEpU6YwYsSIEsn2wgsvULt2bRQKBUql4/skNjaWnTt38tlnn6HRaGjQoAHDhw9n0aJFAMyfP5+pU6cSGRmJIAjUr18fT8/Cg4ofNCosKv+JWw+C/fv3c/ly4dVPXnrptvUvMDCQQYMG8c8//5S7fAAIAjI5ZCtuX0yCKGP3wIHUrFYNmjXDbDaTcC2B4cHDCakewqCJg/AK9WLP73tYOW8lOQb7AJpGXbtybf589lex1s4+1agRk1YMJy0wGLNCwba336Z9aio/TVrAe/XfI9Gs4HzjxqhUKnJyrH0t/GghCz9aiCAIDHp7EEd3HMXNx80WeFIlPJzM3bs54FNQsbpSvz6pKVcwujl+UJoVCjxmzuRCr14AHNpu9fuM3hZNZkomiTduB2I16NgR3n+fbR07FnoK00JDuXzyDDj+jbDjwODBBE6fztYVW9m6YiuPDX+MFo+3wL2FO7KbMvYs3oNcISc9KZ3k+GTWL7GmXlKr1eTk5HDawbRmYqtWXDy4BUpojLjh70/N+vW5GRvL2ehUznz5NwO+G0CWMguFqMAoGFGiRG6Rs/zl5Wz6zToN3ahrVwwffsgVN7cSjXPwxRep8/vvHP/3X1a/vZouM7vcceaFoin5u+a2Dz+k1fnznFizhkPbD1m/+1dhzJwx1Oxbk7Tzaahd1IysP9JuSrbd669zYOpUtukLugCkZJiLVEwvRkbSbsIEbrz2GiqDihz1nSV/12fqmd5rui3w7cyhM4zdPRYTJmRKU9FVzQSB//74g1aDBrH7l1/46vWvmOw/Ge8u3hgxFrqb7JyMPUv3sPKHlSgEBU2/+46d+R6G4Y8/jiHegEFX9PdwKSKC9q+9xrYZM+zWX4u9BrHwxcvWIKLfvvqNnGzrOTIWk9szHQW7w1oW8DHN5ebq1TR4+mkOb91qW5d4I5G3u7yNIAiIosjg8YMxGU34h/uzc8VO/tv4H43WF0x5VuhUfj6imjWz/b3t00/xGDsWGXDA07NAVH95oVAo2L+/ZNHkcrmcNm3asHjxYubMmcNr+SzFer2e1NTbRTpy/3Z2LjzjQYU9vx4iIiIi+PLLL5k8eTInTpzgkUce4YtbgXf5Wbt2LVOmTOHMmTNYLBYyMzOpW7cuYFUQHVnGc1myZAkRERE8WcRMRX6Cg4OLbXP16lU8PDzsrpuQkBDbdRobG1uoov0wcM+ni9q+fTu1a9eukLFEEWRKyJbfPi2Cxfr3yaZNaf3cc+y69UYDEHMmhmnPTyu0v5ZPP82+RYtslVhyMVUNt5vuPv7aa2TPmsVrTV6j4Yb1pLm4oE5JIeLcOeJatCAjI+OWfCKLpy+266tBhw4kzp9PvAOlFOBKlSpk78zBGFz4g3J/z560HzeObZ9+CkBw1apMHzod3yq+ALQaMABllSps++STYh8mN6tW5fzGtfBokc0AyNDrCV22jOTOncnIyODv+X/z9/y/8Q/xJzMtk5TElAL71GjUCHH+fHx37WJ7PsskwPmGDXH7YS68XPz4NplXr6bWr7+SodHx7x9b2PLHFlw9XMkx5NCxX0eO7jpK4o1EMtOsllj/oCBOr1hBhgPFrDDSXFxwfu01Aq5c4X8//g+dq47O73YudXqq4indw373kiU03LyZY92721xmPn/pcxSvKTAZC7rQNO7WjR1ffIFYiOKXlG4mW1O0RXj/0KGEf/89pmQT+JZKXMDqYrL4tcU2pRSsrjbGKCNCLQG5ylQid5Kob76hbnQ0x3bvZnL/yVStVZWJGyeSrrXPFqHP0vPLa7/wz4p/bAp6y2eeYaeD2Z/tM2fSY9OX5IQU70+8bfp0/F57jYj33yc9JoZj27cXSLpuyLJGfLd+7jl2tWxZZH9pogJRkKF2YDEFiPfxIet//6Pdzz9j2b+fcxs3cu2KtVpb7nH93yf/Z7dPzcaNOejgBTCnhIppfhK9vO5ov4rGZDI59DGtXbs2R44coX///gAcOXIEX1/fAtaskpYbDSvCAi5RNAMHDmTgwIGkpqYycuRI3n33XSIiIuzaGAwG+vXrx//93//Rp08flEolffv2tV3vwcHBDr/nXCZPnsy6desYOHAgS5cuRV5Ijui8FJYaLC8BAQEkJiaSlpZmU04vXbpE4C03sFy56tSpU2xfDyL3dPDTwoUL2b9/f6F+HfPmzbNNz5SFH6ooCMgVkJVHMc0b4b3vhx+ou3Mn9bZvt/mH5qVKeDi1b1kIGj/yCIcWLiyglAIY1RpMeaLkE7y9aTx8OGazmQwX69SQQaPhXJ06uJw8SZthwxAEwTamXq9Hr9dTp2VLUubM4VJRb1aCgKh1xliMwrLtk09wTkmh7dy5uP/8M14+vniHRNJw40Z2//KLVWktwQ133d8fQymqZZ1o3pxGM2YQXquWbV1cTJxDpbRJjx7Iv/2W0w0bsn30aIf9Zep0yMJLV+ov3teX7W+8QXaeh0tKYgpZGVms+b81XD5/mcy0TARBQKvV4vfTT6VSSnPZM2AAutWrUSgULJ21lLG1xpbJtL5aVCO/FXBUUh/TvBzq3JkW339Pk+7dbT+qjpTSFv36cfPbbwtVSgGuG2TFXieZOh1pmzZx4+TNUkfGy5BhjjKzdcVWVCoVdVu3pv1771GjYUMWjF0AYFVMi/DxzCXZw4PzGzbYpvUvRl3klYhX2DNlD0dmHeHol0eJmhPF9J7T2fLHFtvDTKvVkjlypMM+RZmM1HTILqTyU36uBQWxc+FCDm/Zgsv167T++WdaLltGkx49aDN0KGq1Glc3N85/8kmxfaULckBAW4QhPkOvZ/srr7Bz4UKuXb5Mgy1bqBIWhsJBtTAAz1tuRfnJcRBMeb9y48YNli5dSnp6OmazmfXr1/Prr7/SqVOnAm0HDx7MggULiIqKIikpialTpzqMmchbkrRatWq2Jf9niTvj9OnTbNmyBYPBgEajQavVIpfL8fX1JTo62hYNn5OTg8FgwNvbG4VCwdq1a9mwYYOtn2HDhrFw4UI2b96MxWLhypUrnDp12wFeqVSyfPlyMjIyeO6558osyj44OJhWrVoxfvx4srOzOXr0KAsWLGDQrewyw4cP5/333+fs2bOIosjRo0eL9GN+0LhnLaZ//fUX48aNY9OmTXgV8pY9YsQIm9+HrpAUKqVBFGTI5CJZed6KxDyKaY5azbHWrQEIvHCB0G3bSJ07l5vR0VTr1YvoceM4UbUqVc+c4WC1aoU+wE3IMOZ7cF558UX8li8nzcM+ACcuOJi4+fMJf/NNMtzciHjvPXL69EGVlMTOIUNKdFwmnWuhPqZ5SXNxYcet89nuqacQAgLYVtpce4JARsOGpdplx0svwahRtB8zhsvr13M+KorgqlUJbtkS0WxGrtcjd3Hh0KRJpObx7yr0OPJMH5aGa9Wr0/7tt9n95ZcYjQWndNtNn865J5/kUD7n+NJwtm5d2k+axLb33yclMYU5j81h5JyRWIIsxSYjz0Ujakg7lIZ7LXeu/nuV32f8TkZaBq9tf43SWkxz2TlsGAwbRs19+3B67z0Ob96Mh5cXZpOJgP/9j2vh4ez19LQF1RVGqkvJ/AVv+Pvz47hFeAW5MPzb4WT7Fp/w3snsxLd9v+X4v8dx9/Cg+g8/2FIY1e3enWNt2qARNciVxkIT3OcnU6fj8JIl1OnYkeP//oshy8Cyr5Y5bBtavTpBrVpxbcwYjhRhyUhIB0MhlZ+KIsnTk12DB1s/PPUUCAJV334bo1rN5TwBdYWR5aTDYhYc+pgWxuGOHRHOniUoOhr3y5dx/uEHBIWCjNhYXFq3Zt8ty2B+cooJfrqfEASBOXPmMGrUKCwWCyEhIXz55Zf06dOHS5cuUatWLaKioqhSpQrdu3fnnXfeoWPHjmRlZdGvXz+mTJlSoM+8CszChQvZtGkTkydPJiQkhJiYGD788EM6d+5ckYdZNtwjCfANBgPjxo3j5MmTKJVKWrVqxbx581Cr1SxevBhPT0+qVq3KwYMH+frrr+nfvz8Gg4HevXvz2GOP2fpp1qyZLSDq4sWL+Pr68u2339r5mapUKlasWMGjjz7K0KFD+fHHHx0apkrLr7/+yqhRowgICMDd3Z0pU6bQtWtXAN566y0MBgPdunUjPj6eGjVq8Oefd14x7n5DEMVS/IqVAe+99x6XL18uMsXGunXreO6551i9ejXNSqhk6HQ625T3nWIyG/ij13eYfpaRplRjEAxos/wZGeD4x/lOWXHyW445hzMpqLvdes+bN62plMpAyc7Lh7GbWKOvwb/ujgOWHBF08SJOKSmcadCg1ON5X7tmF6ldGgSLheZ//EFyeDinGjW6oz7cEhNJzqfgl4YaBw/ieeIEOW5uOG3dirFhQzIDAjjswIJyR4gibUeO5N+ffrIpwNXrV+epMU8RUDcAMUS0Kaly5Ji5Pb2rvaHl2yHfcnTPUeRyOYJMsFk3556Zi9zLmeHuA+5axOpHjnCmfn1cUlJK9DKQS9VTp7iY50e9KBr36MGBdetQqVW8/cPb+D3qZ3esuWTszCAzOZNty7axc/VOANosWFBgKr1Rt26MmvMUXZIPMiV4KD/7NC2x3AqjkRaLF3Nw9GgyM+2D59zc3an/4ouceOst4n2L9z3o890sTj79DGc8/Us8flmgT01lRtY//M+9J2tUd18UpChWHvqcPg3HlOsYaUBZZLwti2fD3RAUFMTZs2fR5vG9zszMpHr16kXGW1Q2J0+epGbNmpUthkQ5cK9/txX22msymTCZTJjNZsxmM9nZ2SgUigJTSFu2bGHQoEH8+eefJVZKywpRJrNG5SerOL3xNG6+bqR4OkFA2Y5jFgSHFp380dxlhQElhlJO8V6uWvWOx7tTpRSs38G/t6oT3Sl3o5SCNUCNXKW4kCo7d4UgsGPePOq88AKn2rfHZDJx5sgZPh5sTcFVq0ktgqoFEdk0kjnvzqHTk50IbxDOss+X2QWjmc1m8upx05+YzvBv30FoKzosyVkacvPTlkYpBUgoxXfvdMu/LseQw8eDP2bAWwPo+HJHDB4G1ElqMq9nknY9jfcff99uvwYdOjj077SMH0/qpYMI7mKJLaa5mJRKdg4ZQo369fH69lssWVmIoohoNGIZPJhteawsxZGgdiE9x0Jjs5kDJfBJKysydTqMyWKhPqZliaO8yBKOsVgsREdH2ykCMTExBfyJJSQkbiHeAZmZmaLBYCjVPpMmTRIBu2XSpEliTEyMqNPpxJiYGFEURbFDhw6iXC4XdTqdbenevXux/Ts5Od3JodhhsBjE5Y99IX7050c2GTsPeVa0PqHKbvn11FxxfOzGMu+3sOWtmB1i7cS4ChtPWkq+tJ07V3RycipwbxS3BIaEiFqtVmz8yCNim/nzxbZz5oiA2Pul58TWRqPY0GSu9GMrbvG6fl1s9803ooenp92xOTk7Ph8qlUpsP2OGGHLmjMP+BLNZHLdwonj+8Evi09cPVtpxNdi8WfSLixM/SEqt8LFnnPpDfCYru9zH+b8jc8p9jDSxbCiLZ8PdMH36dNHPz08cP368+N1334njx48X/f39xc8++6xS5SqOqKioyhbhniSvbpJ32b59+121rUju9e+2RBbTsWPH0r9/f5o1a8bq1at58sknEQSBZcuW0buEFqXJkyczefJkh9vS029HwVZ2ag2ZUiD+UjxyuRyz2Uxmdtm/1VqQ2QU/lTcG5KW2mEpUDDtGjED5wgu0XbCA5MWLuXHxItfj4gpt7+buTp2ZM9k5dCiRhw5xxceHa7f8D8O/+YYLh84yI/Yaf/l4ckhXuqpaFU28jw/bR4/Gr29f6j7zDMd27QKwZT/IpXq9euh9fND178+2F18stD9RJiM5U4ZFkJUo+Km8uBkWhlKppF5qBrgVnkaoPMjKEVCJFWAxvcOo/IeRt99+m7p167J8+XIOHTqEv78/P/74I927dy9+Z4l7jrz6Slm2lbhNiRTTJUuW8OGHHwLw4YcfsnjxYlxdXXnzzTdLrJjeLwhyMAh6WgwYQPKZM6giI8t8DJMgL1C7vjzJFOXk3GFQjET5Y1SprAFgL72EW2Ii7X79FeHCBTJPn0Y+ZAjqXbvIjo5G2bMnp3r3ZuctP8fT+YLM3L74Aqe//sLbkIO/g6j6e5VrQUFc27GDdq++yt4FC8jOzkaj0eDq7o6bry9n9+9HlZNTZH7UXOKNWkRB5rDSWUURFxxMlZwcql27UuFjZxlAWRGKaQkS7Evcpnv37pIiKiFRQkqkmGZmZuLk5ERCQgIXLlygX79+gNVP5kFDUAikelVh988fIogiQRcvlvkYJkFeojyLZUWmRY5BUkzvC5I9PNieP0VPv37WJLvF+I0eeOQR6uj1yC0ifoYc5KKIuYKSmN81gsCOr7/GY9IkGm7axPkOHUj09OT6rVKWJVFKAZK1bliE65hkFffilx+LXI5SrSYiOQ1BFO/a37c0pBnlFaKYZt9l1a6HCaPRyNSpU1m0aBFXr14lICCA5557jokTJxZaqlVC4mGmRIpp9erVWbJkCefOnbOlM4iPj7eLMnxQkCnhGk6IMhki1gotZY1JEAqkiypPMpGTQ8U+qCv6gfzAU8JzGdWiBYpTF6mSlc1jOUb+VN8/Dz5RJiPB25s9A+48q0ByYBUsnK1QVxlH6EVwMpmpKYpEVeB9kGqS41o2qRaLJPMBShdV3rzzzjvs3buX77//3pYu6qOPPiI1NZVZs2ZVtngSEvccJfp1+e6773j99ddRKpX8+OOPAKxfv55uDiqC3PfIRFLcyrcmrQkFxgr8YU8zCxgLSaBdXgRjVU5jJOW0QrHI5SgsIl1ir3PERX9fKaZlQUJEBGbDlgqdkXCETrRqh0+npDPJ3aXCxk02yfGuAItpphSVX2KWL1/OkSNHbNWhIiMjadSoEfXr15cUUwkJB5RIW2natCm7d++2Wzdo0CBblYIHBQEBQQapQSXP93knmAUZxgpU2FJywOigAlV5oRBFGuYYMQsCMeWcT/FeRC+KpFeiQq6wWFBZLFTLyAKP0qV7ut+5ERiI6YyAvkKzMxdEb7EK0DglDSpSMRXUFTKVnylIimlJEQv5PgpbLyHxsFNis8LGjRsZNmyYLdhp//79bNmypdwEqwwsFguCHG6WoMrK3WAS5JgqcGo9TVRUqGKqBV6PvUb/+OQKeUjeawRX8jHLb40fli+6/WEgQ6fDbBZ4MiUdt0r8HnS3xo5Mqdio3Cx3jwq557Kk4KcS89RTT9G7d2/Wr1/PyZMnWbduHX379qV/IVW17mWEcl7uJTp06MD8+fPLfZysrCx69+6Nq6srT93K4f3ee+/h5eWF313kBL+fKZFi+s033/DSSy9RrVo1tm/fDljrRb/33nvlKlyFYwFBJbvrBO3FYRJkFRqckaXTFVtKsixRAR2v3OC5szE0ewiTSAdX8jErblnr/DKzKlWOSkEQMFnkuBktjK5gpTAv+lslKasmpzEks/hyq2VFppd3haSLyhRUNHoI7+07Yfr06XTp0oVXXnmFxo0bM3r0aDp27Mhnn31W2aI9cEyePJlnn322ssUoFb///jvXr18nISGB5cuXExsby+eff05UVBTXrl2rbPEqhRIppl9++SWbNm1i3LhxthqxNWrU4PTp0+UqXEVjNptBUf7vbUbk5FRgcEZ6Kav33C3qPH/3qUTloLIIruRUTYpbSpFPZjZCJVgNa1oseFWitdKIEtEi0Ci18q49ndn6HciB6cfPVti4aV6eKC3lf+7TBRXvXbmBy0M4I1JaVCoVH374IefOnSMzM5Nz587x0UcfoVari99Z4oEnJiaG6tWr26pgxsTE4OnpiY+PTyVLVnmUSDFNS0sjODgYAOGW75zRaHzwUl2IAhURk2QSZBgrMDgjzaXifNwAVHmeVW8dP0fAQ/bwcjNZcK7EY1bcGlshipRvGJ9jqprMDE+tvNrkRkEJMhl1klIrTQanPMqhV5ahwtw7LCoVStFCPXP5huZnyJQ0jU+iS46xXMd5UIiOjubvv//ml19+sVsk7pzPPvuMwMBAnJ2diYyMZPXq1UybNo1ly5ah1+upf6us8sKFC6lZsybOzs6EhYUxd+5cu35WrlxJgwYNcHFxITw8nHXr1hUYKy4ujnr16jFz5sxC5Vm6dClNmjSxWzdr1iweK6Kc8aRJk/jwww9tMs+dO5euXbty9epV9Ho9L7zwQinOyINDidSwdu3a8emnnzJx4kTbuq+//pqOHTuWm2CVgSgIVIQh0yQoMFVguqiKnMYHUHP7ISwHOmbnsERb8daBxmYzV2Uy4io4EEkliviKImmVFAAlt9xWSnxFkfgKlsPPZGLU+Vg+a1ijUlKGGUQFiDLCk9NQiyKGSpBBJdorhuEmM7HK8n/rlQOeOSaeTE3naDkGXaULKtQmM0/EJ7Mi8OG17JSETz75hA8//JDatWvbpVgUBIGBAwdWomT3L6dPn2b27Nns27ePgIAAoqOjMZvNTJgwgXPnzrF48WJbWx8fH1atWkVYWBjbt2+nR48eNG3alEaNGrF3714GDx7M77//TufOnYmLiyMtLc1urOjoaLp168bYsWMZMWJEoTI99thjvPjii5w9e5Zq1aoB8MsvvzBmzJhC95kyZQqCINjJHBkZybPPPsvly5fv5hTd15TYx/TPP/8kNDSUtLQ0IiMjWb58OV988UV5y1ehiDJZhUzlmwQZOQ9wHkBVPuPQ4zeTKlwGmSjyTFIa66LOV/jYaouFQEsFJJMsBEUea51fOVvOHOFvNBGSlkHbSvJBzJGrAQEZEFpJlmt1vun0quXo3iHLc4wyEaqlZ1izAZQjmchQWEQGnrtEWAW4DtzPfP755xw4cID9+/ezY8cO25IbryFReuRyOQaDgaioKIxGI6GhoYSHhzts26tXL8LDwxEEgfbt29OtWzd27Ph/9s47vKmyDeN3VtOR7t3SCZQlG9nQIqCCylA2MgREEVRUFAWUMj4cgCKiAoogQ0EcoAzZG5RdRqFlde+dptnn/f44TZpxkpykSQo1v+vK1eas98065znPuJ9TAIANGzZgypQpGDhwILhcLiIjI9GyZUvtvqmpqUhKSsKiRYvMGqUA4OnpiaFDh+Lnn38GANy5cwe3b9826zF1wQwrwzQ8PBwXLlzAL7/8gp9++gk//vgj/v3330ZXMcbh8UCc4FxUcHhQOVln0ZMQtHGSsaTrMQWAYQ9yEOZkA6EDRSFKJkPbkgr4O3lsN0IQrWy4whBdw7SlXIEEJxvJ4XIFAKBrjdyp42pQ8NygObU9LZEioAGMUzdDw7T2PXEEvXVuAHggaF4hRqyDFRkkPAH4hAIHQEvVo9P+tiEIDAxEbGxsQ0+jUdGsWTOsWrUKycnJCAkJwZgxY5CXl8e47f79+9G9e3cEBATAz88P+/btQ0lJCQAgOzvbpEEL0O3YIyMjMWLECFbzGjdunNYw/emnnzBs2DB4enpa+epcsLaOOBwOunbtipEjR0IqleL06dOOnFeDQLhcgOf4i5jKycVPAC1htPdqGnydcJE29JjyAAx1YmUyADwhkSFaIgMHQFsne+6EFIUohRLCBvLW8XUM0V6VYoxzcgFacG3eYZzMuZ+5BhlPCFKbKjMxuxBNGsCjZxjKH1BUBnc7fx+4hOCdKgleKK+CR+2xeQCCpXLEOrjwS+zmqf2exbvyTM2yatUqTJ8+HRcvXkRWVpbew4XtjBs3DqdPn0ZmZiY4HA7mzp2rrYHRIJfL8cILL2DOnDkoLCxERUUFBg8erNWQjYqKwr17pqNqycnJCAoKwrhx4+jiaAs8+eSTKCkpwdWrV/Hzzz+7UjVshJVhmpiYiDNnzgCgE47HjBmDsWPHYtmyZQ6dnNPhcACeE6ryOVyntiQFgCZqCjFiCWY5oVKZySCbmp3v8HF16VIlRqva4pfWcoVTi5HcKIKuFWI83kChbN0c0+EPctHeyYVIXir6dcdJpE65ETJEznODxmnfqbgMTRrgczD0mPYoKMHTdjbgogkw79Y9DM8uxJu1n7HmrOKpUiPEge99jbtI65mPl9rfM84lBG4NmA5jTxQKBQ4ePIiuXbsiNjZW+4iLi2voqT2ypKWl4ejRo5DL5XB3d4eHhwd4PB5CQ0ORkZEBqva7o1AoIJfLERwcDD6fj/379+PgwYPa40ydOhUbN27EkSNHQFEUcnNzcfv2be16gUCAnTt3QiKRYMKECdrjmoLP52PEiBF49913UVZWpm3h7sI6WFlHN27cQPfu3QEA3333HY4fP45//vkHa9eudejkGgSB4w1TBYfv9N71GgmjZ/NLHD4Wk2H6eGEZop1opITIFPCrDZ9Oyi5EdyeGG90IhWczchGgbiCPqc7/7moKvQpL8KwTvVqetYZp63IxPihzfmW8HHxQOnLdMUqlXh6mMxAS4wvYQDvnfbZSqRAgUyBKLMHYHFrvUPessvlBrl3H00Xm6ak1TNuI7X/jE0+IU/RYncFrr72GZcuWoaqqCkqlUvtQKByX3uEoiIMfbJHL5Xj//fe1IvRFRUVYtmyZVqA+MDAQnTp1gre3N1avXo1Ro0bB398fP/30k17OZ9euXbFx40a89dZb8PX1RWJiIjIzM/XGcnNzw++//46ioiJMmTLFonE6btw4HD58GCNHjtRKQLmwDlbvGkVR4HA4uHfvHgghaNWqFQCgvNz5RS0OxwkeUzWH43yPaa1h8nhhKcJbxDq0Ut3UBaWpSo0sJ1QmA0Cojhene2Ep4qPCACd1vxKqCXgA/Cg1AOd2yOExvPfBUjkmFJViTxPn5IRrDNMYsQQDikqBQOfq6NJdiepMtAW3HuBu+wQccmL3MzeGm5L2FdVAiH0EvASE4GkdQze+Nl1DNxMpttpxeaaEz9ea/t0KS8GLi4TajueU9koVwHfuzbujUKlUeOmll8BzsjpKY6Zdu3Y4f/484zrDNMOZM2di5syZJo81fPhwDB8+3Gj58ePHtf+7u7vj8OHDrObWp08fq9rNJicn6z1PSkr6T1fkAyw9pr1798asWbMwZ84c7Qd47949BAUFOXRyDQFxgt2kBB9yJ1fl+9d6DHkAXnJQaFcj5m4qtzJe6TyvXYhUP78xXua8QhxNCNJf5fwQsqlvVfci591EeuqEztuUVjhd5F8GAXQbHEZIpHglv9Spc2AKQ7cqr7Tb8WdUS/FG6n3tc5FShRBCwNPxO0U7wJMJAD6E6HlmfRVKbM4rstvxO6gpTLXj8RqaOXPm4JNPPrHKWHHh4r8MK8N006ZN8PPzQ7t27bBo0SIAwO3bt/Hmm286dHINAt/xnkwlh+tUHVMAcNe5UHZ0QJ6pLyFoVxvaMyx+0pBULkaok07OfjL9MFmLaue15xTWvtd+ajWCnXwxMuWTiRRLtML7jsZTR5HAXU0hyimj1iHlCACD39fwBzlOrc5nMkwDZAq7fR96MKQFxKkpvRO6hwPyTDmEoL1abXQD1LOozG5jzMsrwqCsxtOKcfXq1UhOToZIJEJ0dLTew8Wjh0gkYnxoJKiYaNOmDeM+27Ztc+LMHx1Yue0CAwONCp2eeeYZh0yooSFOiPYpOHzInZxj6q6jZ9myUgxE2TesO1Qmh79KjRRvL5Me0xfvZuGmyBOf+IrsOrYhnoQYGWFdi8uAmHCHjqtBU/gSpFDhJbEEn/k49vXqYuoHzQPQhAAZTtCa9zTI502UKbDD3Q0KJwnd13AE4EJ/LC6AVmo1zjgp58uw+ElDHEWh2A4h3RYMUY94lQr3DV5fZ6UK+93sd1J7jCLoKFUgReShtzyqir7xUdXzM+6tUqFjmf08yw8DumLvLh59qqutd+zcvHnTATNpvLBy233++ee4evUqAOCff/5BdHQ04uPjce7cOUfOrUEgPMd7MuXg6hVnOIIQQnD8Tl0St67HNKFcbPcq9TEFJUgsr0ITQoykcnTpWeH4YhimjMZwidThbRo1aDymnSqqMDrHuSFJc2bX9IoqtHZCpbOngZj88ht38IIDdTwNERM+uAwRiVZOLAATmnifo+yQ3sEhBAkM7Vbj5MYllcvuZqGPHQv/+tdI0bFaYvQ944FWCagvm2/cQdMKxzYHcDaJiYkmHxoaq6PHhQtbYGWFffHFF1ppiw8++ABvv/025s+fj9mzZztybg0CcULnJwVPgGgKaOtAQ2mkRIrEvGL41Rqguh5TN4rCx8X2Cb15EYKBSiUGZheieVU1JlVVG3W90aW5A9IIuIRgbmU1JtXmlZqSKNqYlmH3sXVJVKnwtlgCt9ocy86FZehUXIahTjTKzBmms2/dx3AH5R3qYugxDa2RYVSh/UK9lhATAThc499xW4nz0jlMSR01sYNx/IRKDS8GYzNGJgcXRK+6uUNxOZop7GiYllaiU1kl4/esl1xer3SRbio14iolDr5lfzgxFwZ24eK/BivDtLKyEr6+vhCLxUhJScHrr7+OqVOnIi0tzdHzczqONkx9CYGCcJFAUfigoNghY3gTgrfuZgMA4msvkO4GF8omUvsYSz9l5ePPf6+DTwiaVlZjeF6xWZmX+Ipqxsrx+vC/sip8cvU2htcW+PiaMIw7FJc5TFezJUXh21sP8ExRmTaMq/GaLb3jPCFtc0FiD5UabRxQqa3JG+6iVuMxNQUBw/v/VE4BgpyU41lNCcDnGr8TAwpK0cJJ2pimDNMoef0M03ZqCrvP32Bc569Q0Z+/gVEea6cbowSKQtuySrQx0U3tg7vZGC2zfawRDeQplcvlmDp1KmJiYuDt7Y2OHTti//79jNtu2rQJPB5PL09Qt3rbhQsX9YeVYRoVFYWzZ89i+/bt6Nu3L3g8Hqqqqhql/AXlQMN0ukSKj0or4KfioIVShbF3s+1eHMMlBN/nFqJpbXHES6WV4BACoYF3NkIqRTAh8LRx/DBC4EUInsnI03pjPVRqtCqrhMiMJ9iNojDSjh7EZhSF92/cAQAkVFWjo5qCr4lUAi6AJIUScyurEWPn9/21kgq0KqtEfFW10Xv9WGkFOjopjcBSBuVjdr74d1arcTYlDcGEYGiVBIMlzIavh0qNmU7qQFXBcWNUAmhdVokjV27ZXR8zgaLwWWmF3jI3E5/3c3mF+LAen8GAGimjtxQA/BVKWi7KIM8z1k4C+NtvZyBaLAEPzFGJVmWV6FOPiEg/O0VxrEWlUiEqKgonTpxAZWUllixZglGjRiEjI4Nx+x49eqC6ulr7SEpKcup8Xbho7LAyTJcvX44RI0bgf//7Hz788EMAwJ49e9C1a1eHTq4hcJTHtIOawtqLN9GxQoxWUhUCa71Kre3claafSo1R9+o00KbcyYQ/AHeDcSKrpfiouBydbBx/VW4hjqU9MPLQearU6GtwkTbkh0upSKAom41iDUJCkKQjA9W0QowZJWXwNWMEzs4uwMSsfDwhlRt5bm3x5HIJwWMUha61BRtRVRKIVMZesbZOEtPmW3gJbUsq8JQdcy2/upuN+MpqDK+RoWOlGIlmCldGZjun0rqSIzD5WUZWS/GqHRUawgjB3ut3MPF+Drx0xjSVY5pQLsYHN+9CZON3/3Ezxr2/QknLRRmcwsbcy0bPeua2DpEr0bG4THtoLxNRiSnpGehqwzklkBB0sGNlvzV4eXkhOTkZsbGx4HK5ePbZZxEXF4dLly41yHxcuPivw8owHTx4MPLy8pCRkYHOnTsDAEaOHIk///yT9UBr1qxBly5dIBQKMXnyZJPb3bhxA0899RSCgoKM+t46A0d5TPvVSMEB3ZowWE6Bz6Xf+jb1CH0ZwiMESQahWk8VLVnkbmCshUqkGFhQgk5ShU15Ye3KxXjcRN5g7zzzKQoeKjWOXb5V74tlN7UaK1PStc/dKAq9i8rhayZcm5RbhGYVYnQVSzDZQOs01ob3oQmALx/kokshrZPJAxDHYDw0s+PnbA4+i/4pOy/dRFs1BR87eA5b1BazvXk/B0m5xWhpxhvYpqzSKZJNlUIvszcZAy3cOFlDclEZmlWIEVojw5KSCnxeXI5QQrR5xkx4qNRIVNqW9/mYmeJBf7kCXGLcQceNotC2nlGKYWUVes+9TLy/AopgdHkVnmD5+gSEYLhcgW9yC52sU2KawsJCpKeno02bNozrr1y5gqCgICQkJGDJkiVQ2aG47FHROOVwOA59uHABsDRMAeDOnTtYvHgxXnnlFSxevBgZGRkQWNFJJSIiAgsWLMCUKVPMbicQCDBq1Chs2LCB9bHtCREYvyX1Cf31UqnQnKLwckYeALoISQme1hjsz+BhMnXSN8cghRJvVNegN8NFN5ii4G5gBPIAtCivwv+upyPJSgORR4jZylk2hm6ERIrm9bxYDhTXwMfA+xdTVY02EpmJPWjcKApJRaWYn56hLQ4DgBZWvg/ehKCVUoUncvQvqkw5lk1rnFN4w+bi7q1Q4fT563i3HiHleIrAjxAE1Brcrcsq4aVSIdZCuL69E5oOSLx9wTXzFWxpp9aggYSgi87v9zGxBO2qqtFGpbbY572rDYVYwYSgBUM1vga/2qp8wpDK0rye4fzHDN4zTzOv7/Vb9zGjoAT/syD75E0I+qjU+O2fa3pRHnujUqnQpUsX7WP9+vUmt1UqlRg/fjwmTZqEli1bGq3v27cvbty4gaKiIvz222/4+eefsXz58nrPcd68efU+hgsXjQVWhulff/2Fzp074/bt2wgICEBaWhq6dOlilcf0+eefx7BhwxAYaL4lX4sWLTB16lSTd6uOhhhc2TmEIFGlsjlHcPm9HKScS0Er3ZO0m7vWeBmUXYABSpXW+J0glWNHZp7V43STSNGzgtmLGaSmjDymGkRKFZ6yMvcvnhCLF142JNh4sfQlBF8Wl2HBtXSjdZ4qNV67/cDiMVqWVSGuUoKd93O0+YgtrDCU3xRLUHj2Kv53L5vV9k/lFNkU4rQWS6F8DT4KJRKLbe8GtfVOJkZIjW8ALJ1QetSYv2mwFd2bIYlIZNZjGlchrneeaUc1hT/uZuGxkgrtsjZllWhVXoU2coXJHFMNPcwYmKZYlVfEeNOjwU+mAJ8we9+6l1Ui0sbXzCcErUv152sqlA/QN2Yj7udg3vU7WFAhxg/5xRgvleu9513UajyhUGLJgxyHV+Hz+XxcvHhR+5g+fTrjdhRFYcKECXBzc8OaNWsYt4mPj0dcXBy4XC7atm2Ljz76CL/++qvZ8RUKBT766CM0b94cXl5eaN68OT788EPIZHW/hQ8++MD2F+jCRSODldr0vHnzsHv3bvTr10+77Pjx45g1axaGDBnisMlZYv369dq7X3uEUwBAbdD5yRPAovu5yPTywNgmoVYdy5sQPF5YauRB5Li5gV97YvdQqbElJQ1b4iLxXqAfuoolGJBdCPeYCMhYhjb8CMHInELEV4oZDdBgtRruatPvz5R7WVgZ6IsCC+OFEYLJVRIMLLFPe8thOQVYGOSHKitDOP0VSr12jIZYYzQPyClE15gI3OZxEcYy93KAUoVVl28BADqzzIsLlMkxrbgc58Mc28bXGvn4boWlaN80Cte4HBCWnwGXEPRRq9GjoAQ9Ckqsnt+7t+5jU7e2yLNT2G6iVIaf3YXop1KhgsvFeR4Pcg8Ps4YpD8DvmXl4NjbS5nF718jQxyBlJaLWC9qlSmLRMO2VXwxBXCSaEIAHgrtcyz6C5hakvrgA/Ck1CKWG4TehV34xrpdVIq5bW1Ra8d43IQTjxBKjgitzHlNdlqTQyi1PijzQJq4Jtvp5Y0VmHhLzinElOAA9863/DjkCQgimTp2KwsJC7Nu3j3U0kMPhWAzDz5gxA2lpaVi9ejViYmKQmZmJjz/+GLm5ufjhhx/sMX0XLhoVrDymOTk56NOnj96y3r17IyfHceEXNkyfPl17F8y3U0cXwxxTEei80JY2VJtOqa5hDGtzhEK95WESKZ6tvchFyuQQUhTaqylMZxHui6UIfnmQi9ZllSa9ouEKlVEoX5cAmQJ3zqVohbifrTXQ+ISASwg+Ly5HD5Uay/OK8d6t+3gip9DivNgQWyXBt1b2xG5JUZhv57DfnPxiNCEEvizCzK0oCr9dtK2Lx8Q7WXhaoXSofi2bHFMNbhSFS6cvoxNFscr3DSIEo+QK/Hjtjs3z85Mr8LEdjBEfQhBPEbyemYeJUhkGV1Sjv05RE9eCsfBMZj5+z8rHLBvks6bUyDD7nmkJsKSCEiNlBkM8VWpcSr2H+6cuoZ+MXeQgodxyCkKwUmOYGuMvV2C8zjlFSAijeoEuc0oq8OmV20bLvayMmERWSzH9Tha23s7AwOxCeKrU6JXvGLk8W5gxYwZu3bqFv/76Cx4eHia3279/PwoL6fPf7du3sWTJEgwdOtTssXft2oU9e/Zg0KBBaN26NQYNGoRdu3Zh165d9nwJLlw0GlgZph06dMDKlSv1ln3++efo0KGDI+bUoBgZprUn7oTyKrShKHxfUIKPS5lzp3QrbXmE4D0Tgu58Hg8CgwtCQlklAglBRG2oc3xZJT6/moYvissxwkSIOYYQPC+pwUAL1c7RMjncVeYvJCKlCvvO38Cp9Ez8eOUWYgjBYIUKk6VyvJV6Dz9fS8eLd7Pgb2ex+FH3ctCNZd5hHEXwTLUUnewsKzPifg7O/nsdPiwKNt4qLjPKa2WLkKKw/1wKphkUktgDzfeJZ2W0lgdgfVomtl1Px6Da18UlBNG1x4unCBJqjZCFxeVYeCcTMfUU6R97LwtN6hlKf0quwPb0B+hcWIbvL9zEm6n3EC+VaW/42LwPwx/kYtHNuxaNM0Peu5uFeDPpL9FiCavvSNvaNICmUgVO3snE1Bopvi4qxe9Z+VhUXoV+Ot/HwQolfFkcM1ipBGUmZeSlnAL0VanwdVEpjtzJwm/ZBQgjBDxCjFoJCwjBRBOpKp423FwFyuToWFxWLxF+R5CZmYl169bh6tWrCAsL0+tjnpWVBZFIhKws+kbkyJEjaNeuHby8vDB48GA8//zzFvNDw8LCUFOjfwMklUoRHu6cFsmNjW3btmk/o0GDBhmt13xmmgcTur3rXc0NHj5YuRm//fZbPPfcc/jyyy8RFRWF7OxseHl5WZVj+qigNjBMvWvPoZ4qNf755xpEShXK3N2wxf8x5HE4CCUEUg4HSTI5XsvMR7qPCNdEnhDzuNrQniF8QrShfA08AJ/nlyCytkhm6p1MeKrUmJ16D9MEfCg6t4GMC5zn8/F2hRjL/bwxRCLFjPuWvYcxEincWaQ6iJQq9K71Yswoq4KQotC3Ng+xvsaIKfiE4J8zV/DA1wvvtWqKX4Vu2nXuhCAUQEulCrPyinEwyA8Lb9rurTOHj0IJX5UKXEJAmQl1dtXJKbSVp/JL0DHAD1fs2P72bNoDzG0aDaUNEXKNof/7+ev4Na4J9gf4YuPFm3jx8TboJa4BAfDAXYhZt0ynT1iDgCL4I+0BnmsRZzF9xBQda2RG+dRNq2sQHxKAdA4HPDNtcXUJkClwJj0TYxJikGVhLjxCME6mYGwHWh96llWgT14xeuSXaI224QAGhQbg5eaxmJdfhCEZ7PLOQxRKEMr0b71LYRmOF5bp5XXKeFy0rhCjZVkVnu/WFkFqNZrJFDjs7WnyRtTLSbq8ziAmJsZsOF63N/qKFSuwYsUKq44/YcIEPP3003j99dfRpEkTZGdn4+uvv8bEiRNx9OhR7XZPPPGE9ZP/DzJ+/HiMHz/e5Pro6GiL/exdvesfblgZpi1btsStW7dw7tw55OfnIyIiAt26dbOqKl+lUkGlUkGtVkOtVkMmk4HP5xuF4AkhkMvlUNTqPspkMnA4HAiFQitelu0Y6piKdC5woloPRoBMgctnruLHhGhMScvU8wB0q5UNMgefEAgYLpwT72RqA7GeOl5EkVKFRfeyEC2WYFHrZph/LR2dYsJR6uaGZiwqq5uKq62WYnn5biauBfrZ3TtpirhKCXb8cw3jurXFDnchZlXXIEquwHs37iJX5IHIaim6FpfB247tFQ3xUahw6dZ9FLoL8WZsBDwI4AaCt/OLsSQiBGIOR6/YxVZalFfh+Pnr6NW1LW5xOVDbYJy5EwIZh4PhcgVayBToUliGrdVSHIoIsXle7moKL97Nwou1z1ffuItqAR9NK8SQ8u0r5tOlsAy94prgN50bEWtgUp9oViVBgkqNdDeuxVC+Lj0KSvBWgC/WBPpCzOGgiOHzmCCVY1JeEfrbKY1FF428mqEn8fHCMvwhkyOukv1NYZBcAcpMPjlgJHOKsXfrvKKzcgvRobQS4RIpOsZEmDyGl4l0ARfGrFu3DgCwbNkyveVr167F2rVrAdC5qvfv2+fGz4WLRx3WiZl8Pt8oz9Qali5dikWLFmmfb926FQsXLsSUKVPQunVrpKamIjo6GpmZmYiLi9Nu5+HhgZiYGJNdOOwNxeeAQwg4ACgOB94mqk+FFIXpt22bE5PHVIMpE6VDredy5oNscEHnyMlYetxirLiwaQiQKZCUa13+Z33hAtj+73VEdWyFpSlpWpHyyNrcwRAHVXRraFtaoQ2XHi+rRKWbAJcDfNGzqBznsgvApdjJMbHBR6HEt/ey8WdYIJb7MIebTBFACLY/yMXayBAkVVZj2p1MAEC4RIrBOfYTsQ/T8fh7OkDmaXF6Ji4+1gyZVhjmE6RyJFWIGbVyo8QSdJNIcVDAtzqlYXbqPbzM5+OL1vH418sDczPy8EqzKLxRXA53NYWJdzIdVj1u7rjWGKUAECxTgNRD/WFQVt3351kz6iCeFlKDXNTx4IFllRAXLlzUYdIwjYqKYiV4q8m9sURycjKSk5MZ1+m63WNjYxtMbJhLCNR8DloSgk5yJXa6u0HkgN7aAooY5ZiyRbcAwlSxkyH2Cxg7hyXX0kx2znEkujl8YRIpwiRSRIklDjHKAKB3fjGq3PjY7O2FjkoV/nYTQEgI5Ay/O09C4AOgBMCRW/fRobgcHUrKUejpAQ+d+QXZqf2kM2hdVokvswqwIjIYp1kULyZQFL6/eNOs6sLYzHys8PNmHcrXxUulwtup91ApdEO4RIqb9SzOIVwuOE7+HofI5PUyTNni5YQxXDx8PCqNAFw82pi8GmzdutWZ83go4IKWi+oqV2D9xVQs8/LA582j7T4OnyLgN4Dh9ajA1uB2Bo4ySjU8kVOIrOxCnAkPAj86HN2ra7AgwNdou7V5xehUVol3WsRqvefBUjmCGQxRKU8FLocHoerh76QyNCMXJUIBCkICTEom9VGpoAQHvWpkFqXAmlaK4QGAa+NXyFOlhqeq/s0QCI8LBUcFIeXc28IQqQxqC6F8e+DpMkxZk5KSgrfeegtXr17VOmEIIeBwONqUNRcuXNRh0jBNTEx05jweCniEQCXgoldVNdwoCtFiCUZbaK9pC3xCzAplu/jvoDHC++UWoWWFGCESKdC2OU6LPKHmABQ4iFaqMCi3EEFSOX67YDlp/8rpXxGZ0Bkxgc0dPX27MCwzHyFyBYbU5jTyCYEnAAGAqWIJPr1yGxSAYk93VsfzIAAPDXtzQ3EBcXU5hELzDUXsjaeKQqUzDFMndPBqLIwdOxYvvPACVq9ebVaKyoULFzT2Ef9sJHAJoOby8FRunTFqi4i4JQSEgG9DqNFF4ya8NqdzQFEZhucWoTVTu1oW6gpqtRK5dy4/MoZpoEyO5zLyMM/PBxIuByPyS3DTV4Tptx9o8y+5AEJZ5hh7gIDXwD8vFVGhOP8ugmKda5iCAxCnGKaOH6OxUFBQgMWLF7t6wbtwwZJHLf3QofBAAPAQ7SBpJA10KL9xe0wV7AUbXBjQpbCU0Shli1qlwoOrJ0C4j9aF8H9Xb2PV5VvonV+MaTpGqbV41DaGaEgUShnuXT5qeUMHYKkq3x54KV0eU7ZMmjQJP/30U0NPw4WLRwaXx9QANdfxFtV/IZT/z5EfEftYL0QHNGvoqTxy1Lfyn1IpoVLIoOQSuD2ijvn6vAcetYLxDYlcUYPSnDugBDxwnWzEESd4Mz2dYPw2Ft5//3306NEDy5YtQ2iofltrXR1TFy5c0LgMUwPUTnhLBBTVqIufCIeDjGun4R0Q/p80TAmXA04D3nio1bS6gFQhhhvXu8Hm0VB4ENLgoSCN11KmlsITtmm12gSHY1Zg3164PKbsGTFiBOLi4jB8+HBXjqkLFyxgZYVVVlZi9erVuHLlilFHhYMHDzpkYg0FxbWvkDgTjd5jyuWAUGoUZd0GHnuyoWfjdCgeF7wGFCDXGEWS6nL4+vwHDVMKqJQUIRQN99qp2s+/vDwfnr4xThuXAHCGs9jLgY0uGhtXr15FaWkp3NyceIPiwsUjDCvDdOTIkVCr1f+JOz4VnBDKb+QeU01yYFFGKgiHY3UvcntAOByAw3G6jiQAKCkFeHaT4rcetYr2mFaVFyDCx/5yZw87HoSCuDQfyjAfCJQNcwOo6VdfnHsHkU40TAHAGTU2ruIn9vTp0wepqano0KFDQ0/FhYtHAlaG6T///IPS0lKrWpA+qqg5TgjlEwJBIzZMSa1lSqkUUPM54DeAcSDj0MaZRwNkq8jkErjzfZw+rgZKRWsjlhVkADFdnT++gAeOimqQGxKAbtfKpdQoKctGuHeTBpkDVRtOz7+Xgg6tBzhxZKL9/TmSR6usrmGJi4vDk08+ieHDhxvlmC5evLiBZuXCxcMLq6t27969cevWLbRr187R82lwKCcYpnyKMuqL3ajQuWpJ5FXw5VrXctMeZN6/BHcvP8QGt3D62EqVDEToB04DNQqgar1Z968cQ4+eY5w+D5laBnAAT9IwN7IeFAUuRaG8uOEMU43IfVFGqtNzjp2lSiS/cQXCxzo6Z7BHmJqaGjzzzDNQKBTIzs5u6OnUiy/Lv3To8d/0f9Ohx7eGpKQkvPjii5g2bRrrfQYNGoQxY8Zg0qRJZrdbsGAB1q5dCz6fj4KCAvzxxx944403UF5ejlOnTqFjx//274qVFbZp0yYMHjwY3bp1M7rj++ijjxwysYZCzeEBcGx+IJ8CBA9RdyN7Q1B3ES7MuQ3f6C5On8Ptc3vh5umDmKEtnO7dUatVUHMJ+A2UZqoxitQKOWooGbycWXwD2mNcUZqD+LDHnDquBi81AYdSozT3LhDfo0HmoAnlgxCoeRynycMRADDRQcve/HNuJxJdhqlFNm7c2NBT+M+QnJyMu3fvNljnyv3791vcJjs7GytXrkRmZiZCQkIAAHPmzMGaNWswdOhQR0/xkYDVGWz+/PnIzs5GYWEh7ty5o33cvXvX0fNzOs7wmAoI5XSBfZXAeXXKupfgayd+BWmAuF9NdTmKM1NB+M7P9aRUShSVZjl9XA1qVV2bQ0lNudPHr6kuw90rx5w+rgYftRoctQr596832ByoWmUEAJCr2TUGsBtcLgjP8b/3UnkFqOJCh4/TGLh16xaWLFmCWbNmAQDS0tJw7dq1Bp6Vi4YgMzMTgYGBWqNUs6xNmzYNOKuHC1Znr+3bt+Pq1av49ddfsWXLFu1j8+bNjp6f03GKjilFnF78VFjywGmC67oeU0l5ESi+c8V7CACllG6SUFyZ49SxAUClViL98mGnj6tBU/wE0AVQzkZcWYz8O1ca5KYAAHxUanApNaSVJVALGmYOakmdeklNTYUTRyaQyapRQxxvDKuIGtL7tx0+zqPOzp070bdvX+Tm5mqvmWKxGG+//XYDz+zR5tNPP0VkZCS8vb3RokUL7N27F8uWLcOOHTsgEonQvn17ALTHulWrVvD29kZ8fDzWrVund5zdu3ejQ4cO8PHxQdOmTfH3338bjZWfn4927dphxYoVZueUlJSE77//3uT6w4cPY+DAgcjLy4NIJMLYsWMhEomgVqvRvn17NG3a1IZ3ovHBymKIj4//TxQ+AYAz3Ht8ioJA7dwcU1lNFSROuFgBADHIn61ROraTlhE6BvidFOd77iiVCpnXTzvFa8U8fp1hWl6Q4fTx5TViAECNusbpYwOAj0qlVWMQyyoaZA6UmO7c5eUfgqpyx3oVCaD9rhEQFOSlQ1xVbH4nO6AmFMrz7jt8nEedjz76CIcOHcLatWvB49E3Su3bt0dKSkoDz+zRJS0tDWvWrMGFCxcgFotx4MABtGzZEvPmzcPo0aNRXV2tfX9DQkKwZ88eVFVVYePGjXjrrbdw+fJlAMD58+cxceJELF++HBUVFTh58iRiY2P1xsrIyEBiYiJmzZqFOXPm1GveAwYMwP79+xEREYHq6mr8/PPPWgnOlJQU3Lt3r17HbyywunJOmDABQ4YMwc8//4yjR4/qPRobzqhoFTRA8ZNapcS91NNOGYuCvje4vCzXKeNq0an+eHD1pNNbc6pUCoAQyDkNI6mj0jFMc+9ccfr4Shl9I1JS0jDpDH5KFVBrmF4783uDzIGSSOAbGI5nDtxEVbFjv//VpAZbv5oOtYALEEAiKUdJvuMNRhUI7henO3ycR52ioiKt945Te27icDja/11YD4/Hg1wuR2pqKpRKJWJjY016G5955hk0bdoUHA4HiYmJePLJJ3Hq1CkAwIYNGzBlyhQMHDgQXC4XkZGRaNmypXbf1NRUJCUlYdGiRZg+fbpTXpsLlobp119/jfz8fMybNw9Tp07VPqypVntkcILH1EPl/KoYtUqBGyd+c4oXjzLIn71/9bjDx9SF6JzwKZUCco5z329NjqdE4vz8TqBOLgoAKguzQDk5pC6vNUxz0y85dVwNAQolSG0B2IOrJxompYADdOr5HDxyCkBVlDh0qPTrJ0CpFJCppAAIeG5CZN8679AxAfoG9IGsEJTC/pGYGq4SSqXc7sdtCDp37owtW7boLdu+fTu6dnW+lFtjoVmzZli1ahWSk5MREhKCMWPGIC8vj3Hb/fv3o3v37ggICICfnx/27duHkhL6N5mdnW02fL5t2zZERkZixIgRDnkdLpgxaaXohhkePHjA+Lh/vxGGcYjjDTf3hjBMlQqoFDJIieNP9sSg61HmjbMgTswzNby3KC7JcNrYAP1eA0B5qfPzWwlg1PpHRjm3+EZRG8p/kOJ8bzUA+MmVUOt8BxVwvueaKxQiuoz+Hghz8vRuluwJ4XFx++xfAIDKikIQQsBXqlGUcdOhN6G63zCZAzzCt64e1h/kEWb16tVYsGABEhMTIZFI8NRTT+HDDz/EF1980dBTe6QZN24cTp8+jczMTHA4HMydO9fICy2Xy/HCCy9gzpw5KCwsREVFBQYPHqxNN4uKijIbPk9OTkZQUBDGjRsHtdrVhtdZmDxz9enTR/t/8+bNnTKZhwFnFMs3hMdUVetFKy564PCxDD2mACB3onFADK5oGTfPOG1sAFDVenryH9xw6rgAGEUsS50cUpdLacNUpZA53VsNAP5yhd7NUY2syulz4Ll7AnFxAIDgO9mg+I4xTCVUDVS1HstqcSk9dhGdX6pw5HvP4SBBGIImlAcq8+x7TlEJOFpjuzHQsmVL3L59GzNnzsTSpUvx0ksv4fr16/+p66q9SUtLw9GjRyGXy+Hu7g4PDw/weDyEhoYiIyMDVG0qj0KhgFwuR3BwMPh8Pvbv36/XRn3q1KnYuHEjjhw5AoqikJubi9u36wr6BAIBdu7cCYlEggkTJmiP68KxmNRG8vPzw549e9C6dWvk5+fjwYMHRkUtAF0Y1ZggHMd79twa4MutqRIuyrqFmKAEh45FMfSJF1eXwN09hGFr+2NkmKacQu/ESU4TmleraMM068Y5kN4vOrcDEoP9k512AVE9nFftKa+uMwSrxEVw9wxz2tgAECCXo0znN1ZRmgf/UF+nzoHn6QU0awYACPo3BXKVDJ4O0JPNuntZ+79CWk17THn0aV1SUwGhm7/dxwQAcIDgVh3R7MPlKFjzuV0Pff/+Ra2x3Rh44403sHr1aowaNUpv+ezZs7Fq1aqGmZSNPCwC+HK5HO+//z5u3boFgUCAnj17Yv369RAKhdi6dSsCAwMRFxeHy5cva997uVyO5557DkOGDNEep2vXrtqCqAcPHiA0NBRff/21Xp6pm5sbfv/9dzz77LOYMmUKfvjhB3CdpBX8X8WkYfrll19i9uzZyMzMBEVRjHkYHA6n8bm3qcaZkK4qoMNt2ann8Xgnx4j4FsuK4O0VpO08pEtB1m0EJzjJMDUwBCm1CgqOCkJ2KdX1RqWgDVOVvAZqPsBXWtjBnjB4TDNvnEOPHmOc1mhAVlOp/b+iNA8hTjZMvRUqEKruO5ifcR1xoa2cOge+yJs2TDkc8GqkUInLAVGo5R2tgHCAG6fqirtoTzUBTyCEv0CEspJsBETY3zAlHA7A4cC3bWcAgDDBvt3Vsm79a9fjNTSbNm3C6tWrjZZv2bLlkTNMHxbatWuH8+eZ86hPn9Yv8p05cyZmzpxp8ljDhw/H8OHDjZYfP35c+7+7uzsOH7YsAai7jymSkpKQk6Of5sXk9PsvY9Iw1f2wvL29IRaLnTaphqVx3glp8h7FZfmgBDxwlfa9oSAATvz6BVr3HIKQyDqPLJfHB5fHx51Lh/FYQl+nGEcUMX5tVdWlCHYPdsLogFLH21Mtq4Ifz9sp45pCIRWDEnDBUzreY0wAyGvqNDxL8u4iIaqTw8c1hNK5Yc66eQ49uo1yagcwnq8f4OEBREYCOTkgeTlAgn0NUxWfA2lVmfa5TFIJAoDv7oHe767A3RP7gAj7t5HOLklHRGgCvFrQguBe3XqApDywS2SA8HnITWuYojl788MPPwAAVCqV9n8N9+/fR1BQUENMy4WLhx5WVlhpaamj5/HQ4KyW1tUcucNz+xUCoEROf3YqnQpXicL+NxnVkEJSVoictIugar1VrUNaoBc3HE+UuUMuqQTlJLFzphzXkgLnFerphiFLCh2f06uHCeuLrth2AlyOXvFVXgPIVQH6hqm8uhKUEzufAQBq9SqhySO8fcvuQ1RJ9Kv9ZbUpFKKoeAR07gm3ykqm3erN3avHQQgFjoBOTXDzDQA49jmbVcrLjYr3HI1cLsfUqVMRExMDb29vdOzY0WxryS+++AJhYWHw9fXFlClTIJczF5RqGtEoFAq9xjRbt27FvXv38OOPPzrqJblwICKRiPGhkaACgFdffZVxm1dffbUBZ/7oYNJjOmHCBFY6a42t+xPXSR7T4oJ78GjymEO9WDevHsK1Iz9j4tub9LQtC/PS4R3Zwb5jXaBP5Pl3riC+fSIAoEn3/gj/6GMgKwvNZ46GTCV1eN92wuFATRmnEhTev45Wsd0cOrYGpbzOMM24eQbNHOC1MoVGh5crcEPzkOaQcSlkZt9CZWURvHyiHDq2WsA1aqYgKSt0iIfeEpTBd6BaVglfJ3quKc1tZ/PmwLFj4P77DzDkRbuOUZyvX00srablybxbtgUAhBdVg/B54Nix2JLwuci+eQ7qAZPB4dddPlQVJRD41N8DmJd5s97HsBaVSoWoqCicOHEC0dHR2LdvH0aNGoXr168bia0fOHAAn3zyCY4ePYqIiAgMHz4cCxcuxCeffGJ03GPH6OYeCxYswNKlS53xUlw4AY0gvjnWrl2LtWvXOmE2jROTVlizZs3QtGlTiw+2rFmzBl26dIFQKMTkyZPNbsv2jtQRcLiO9eoRAAo+XSAkkTsuPYJwuUg99QcAQEoUWsPUTeiBO3Zul0n4XNw5X9fG7cZpelzfrr2AbrQx2H7rPtRUOVbPUcKR448t87TVybrkpl92hkQtKikJirPrqjpzb11wuo4oAMSFNUPXb3ai7b93ANAhdUdTVlWAtGvGnbZqVM7vAKUpwONwuOBweSjKNy0J4wiIxmufQKe1eF24bHc9VUNvNB3WJ/Du1B0AEFJWAyWlYNjTdmrUtOedotTg6nQDlN5JtcvxH1w7aZfjWIOXlxeSk5MRGxsLLpeLZ599FnFxcbh0yTil4Mcff8TUqVPRpk0b+Pv748MPP8SmTZvMHt9llLpwYR0mPaYLFy6060ARERFYsGABDhw4AKnUdFjRmjtSR8DjO7b1ag1HAWl1FSi1CndvnkKntk87ZBw1D3oyMiq1AlGUBzo//TJ2/7nGrp6UKkUlKHWdh6qyIBMCFQXPlo8B3bsDO3dCUCmG57VrQK8Iu4xpCOFz8de69yCXiHHz3J8A6PxWAHjh30JcGt4HFN+xeZaEy8He796HUqrvNaySVzgvz7TW+I69WwiuWo3Af64g6s2RyL59Hu1a9nPo0Bm3zuHOJeObnuKCexCFtnbo2IZoBPaDwmPhznND1q1/0bxJB8ePWxtl0kaja1VLuAolKLkEHJ67fcbhciGvLMOof4twb1BvXCpLh1RcDgJAEEoXm3FiY8G98C/QuaddxgSA7AdXAQBqtRJ8nXOluji/3scmfB5KstLqfZz6UlhYiPT0dLRp08Zo3c2bNzF0aF3xaPv27VFYWIjS0lIEBgY6c5ouXDRaWMetMzIy8Oeff+Knn37Se7Dl+eefx7Bhwyz+eG25I7UnXIFJW90upJz7AxSlhlpchevHdzpMeF6lE8qUycRQqZXoOHMhfH2C0DKiFRSwX6n4/dRzRst81Dy6QrxzZ+0yj23b7TamIYUVOZBLaA907u2LAAD/4CbwD46EZ1YeWl6651APNQDIOEojoxQAMtKcV2WskcryzqozFHr8fhplefcd6rlVCbhIPb2b8fXfSznusHFNQanV8AmKQItSFdpezkBu2gWHdz2TcBQ4e+YnqPmcujxnHTk9csl+3ZhkUKB1jhgeuYVotuckOFweCKWGWrfwLz4e1Tvsl8dIOBykHN0BgO5uphvK9w4ON2qsYS0yooCbpzeaRdUahDXG3yVbUKlU6NKli/axfv16k9sqlUqMHz8ekyZN0pMM0lBdXQ1f3zrpMc3//53iYBcuHA+HsNAp+Pjjj7F48WK0adMGHh4edTtzODh50rrQy4IFC5CTk2PS2Gzfvj3mzZuH0aNHAwBKSkoQHByMkpISs0atm5sb5s+fb9VcXLhw4cJF4+azzz6DRGLZyKUoCuPGjUNVVRV2794NgcA4eta+fXvMnz9fq0laWlqKoKAgi9enR5Fbt26hVSvnyqy5cA4P+2fLyj24cuVKXLp0Ca1bOz4cZ+6O1PCHv379erN3vy5cuHDhwoUlCCGYOnUqCgsLsW/fPkajFADatGmDlJQUrWGakpKC0NBQq43SrKwsREZGgsdzfv65CxcPO6wM08DAQKPqREchEolQVaXTOab2f29v4xy96dOnY/r06QBoj6kLFy5cuHBhLTNmzMCtW7dw+PBhvaigIRMnTsTkyZMxfvx4hIeHY+nSpRaLeZmIjY1FbGwsPvjgA7z88sv1mLmT+cnBFaTjHh6h+aSkJLz44ouYNm1aQ0/lPwcrw3TVqlWYPn06Zs+ejZAQ/e490dHRdp2QrXekAoGg/gVbajU+L6/C2zftX8Er5anxyxf0Cajf+Hko/ecIrt2jcw/HvbMRAqX9fpAEwP7dK1CcSVfKhjfviOiYVmg54316g+++A6ZPR83WHfBsUr+WsqrZL4N/9SooNwH2TnsKZQWZAIDEns8j9p1keqPNm4FJkwAAZb27I2DpV/UaUxcCYPcvi1BZO64ufXlNELezTo8w8/knETZ2JoTBESAwKflpE5ev/43rx34xuT5p7FzEBNu3Qw4TSgEHR7cvw1Nf/W607trc6Wg76GW7C82nZV3AP7u+1Vs28mIpPLPyAADnXx8DEtsU3ToPs/PIdagFPNy8fhhtW/cHh6KQdnonWiz4TG8bVbfu4H9qv++eBsLlYvu616GQ1uUZdn3pPbR6tlYeauBAoLZrjGT9Bngl1E8+TK2QgvdkX71lmc8PxHEUoMfQ15AwUUcrsXlzFPbpgdBJb9RrTKWAg59WvqR9nvj8m4gdN0Wv05j42UHwfneJTcdXKWvAH5gIvPQS8MMPoGRSgCLgenrWa94AHco3R2ZmJtatWwehUIiwsLouZevWrUOfPn3QunVrpKamIjo6Gk8//TTee+899OvXD1KpFC+88AIWLVpk9ZwePHiA7OxsnDhxwup9XZgmOTkZd+/exdatWxt6Ki7qAatqAIVCgYMHD6Jr167aO73Y2FjExcWxHkilUkEmk0GtVkOtVkMmk0HF0Lpy4sSJ2LBhA1JTU1FeXm7zHamtCBwk7vzgTl3hA6VWQa1Wg0MRRLToiOqaMjN7Wo9SAJTmpGufl+c/AFdXBmvaNCAoCOJ/jtdrHGlJPvhXrwKgq46j1XWeBmGwThvKYcOAWo+299XrdVI6dkDJB6NRCgACA+mvmN8PouYC3a6O2FHsn3A5uP3PXrPbZDAUiDkCAgIRhzkM6ZWZC5XAvmYp4XBw/aS+EezlH6w1SgGg4+a9KLiX4rCGEgTAlYt/4srBLZAQWprKS2ys/ME9/69WRsqeyDlKPaMUADg8nXv+0LqOT7LM+t/0atoL6+LzgH6/a6oNziXdukGQVv9Kd6miGl7+IWgZ2QbhTZrT3c0MdK69QyNQVZRt0/GVh2tvIGsr3rnuHnYxStkQExMDQghkMhmqq6u1j/HjxyM6OhrV1dV6Dpi3334bhYWFqKqqwsaNGyEUCm0as3fv3q66iEYMIQQU5fhue40RVobpa6+9hmXLlqGqqgpKpVL7UCjYa+QtXboUHh4e+OSTT7B161Z4eHhg6dKlyMrKgkgkQlZWFgDo3ZHGxMQgJibGpjtSm6AoCCgClZ27xBAAt87t0T5XUypwwEHvrs9iYA4B55Z9NAA1yMsKMH7XdcRH0TnBsuoKcHVzmTgcICEBbhfqVyVccVZfGsi7vE54WBgWWbfCx4fuGw5AUC1BVUFWvcbVpaj4Ptw8vSEU+RqtEzQx9uZT/54FAKiVMqN1tiLnqhmr0XXJSDkJpZ2NQkYI4C1nPhmGXk5FZXWxXYeTcVWQlBXqLXMXeuk9F1SKEUM87W4Ua5DzKdw88RsA1OmVKo1vermEQJZr/05cxSXGN0Z6vzcdL5zqwZ16j1d96YzRMp/b98Dh8iCpNNAK7tcPouup9b4ZlFcUY+hvF9Dtq+3odDFDr+2ulvbtUXPReG6WIBQFzvfr6Cc6Kh6NAaVSiVOnTmHNmjVYunQp1qxZg1OnTkGptJ8qyn+VTz/9FJGRkfD29kaLFi2wd+9eLFu2DDt27IBIJEL79u0BABs3bkSrVq3g7e2N+Ph4rFu3Tu84u3fvRocOHeDj44OmTZvi77//NhorPz8f7dq1w4oVK8zOKSkpCfPnz0evXr3g6emJ+/dNdxzctGkT4uPj4e3tjbi4OGzbtk277rvvvtPOuXXr1rh8+bI1b80jDysLTKVS4aWXXoJIJAKPx9N7sCU5ORmEEL1HcnKyw+5IbYGo1BAQgou7vgalI+Mkq6kys5dl5HwKA85l4pkCAUQBoaBUKviENUH8Bx8DnTrBe/FSrcyPPeAcPwquUoWWl+t+FFzDxgEJCfC5cg1qpe0C3KI9+l5C77w6o0cYYdBlqFZoHABq/j1u85iGkJTLGLHnBvo/0G/C4BcaDUGscZqC523ak1x+eI/dBPdvXztqtMzNU4Qxp/Mw8mIpnk+tgdDLB5Vi+xqFhhAOkJWVAlEFs5EsSn8AZUGOXcfMzbputMyNb5zvHX/yKiqqCo2W24OiorrveXbaBfoftbFhCgDiK/b1XBMAKcd2GC3Xa9Sh4zF1v17/zkb8o0eMlvGkMnj4+ENcXqS/4pln4CapQXVZ/d575f07EFTQ58GAf1NAMbUg7d4dXrt2WW0EV2XehntpOeDvDzRpUq95PiyUlJRgzpw5iIiIwLRp03Do0CHcvn0bhw4dwtSpUxEREYE5c+agpMSxTUcaK2lpaVizZg0uXLgAsViMAwcOoGXLllpFn+rqaqSkpAAAQkJCsGfPHq098dZbb2kNvfPnz2PixIlYvnw5KioqcPLkSaN6moyMDCQmJmLWrFmYM2eOxblt2bIF69evh1gsRkxMDOM2EokEb7zxBvbv3w+xWIyzZ8+iQ4cOAICdO3ciOTkZmzdvRlVVFf78889Gp/hgCVaG6Zw5c/DJJ5+AhbLUIw1RK8GnKNy7dwk1NRX0MhBU3jTuAGINnMN/w/dGGoLOXkaf9CpQlBI+rem7ObRpA15JCWSF9jEY5JWl8Fr5BQAg6Mwl8AS0UW9kmLZqBZ5CiYrsdMNDsKKyMBPe6fphSZ/0Om+Ue1Ss/g4t6vIr3c+ctWlMQwifh5BvN0BQUYXgUxcQHFFniEZEJYAf17SuZ7lm7MJiyGvEoCrKoKyqqP8cuFykHPnZaHlUYAyERXSepffte2jm0wRFefX3lpmcB4eDu9lXcebXL+FZUmFyO149veSGpF04aLTMjWN8w+pzMx3uqfbvF084wFUdwzD/bm03pF69gCjjFqy8K1eMltUHlYCD0hzjz1WvnbOOx9T7xi2ombyNLFHUiOFznfl9FAo9Iak0uPkJCwPat0f1Zes9mRoIAPxT95vlqtWgmDRhO3eGd0Exii6fMl5nBunJQ/Q/LRyfg+0s+vTpg7CwMFy9ehVpaWnYvXs3tm7dit27dyM9PV1bO9G3b1/LB3NhBI/Hg1wuR2pqKpRKJWJjY012onzmmWfQtGlTcDgcJCYm4sknn9T2td+wYQOmTJmCgQMHgsvlIjIyUk+/NjU1FUlJSVi0aJG20NoSkydPRps2bcDn802qOwAAl8vFjRs3IJVKER4erm3o8P333+O9997D448/Dg6Hg2bNmpk0cBsrrAzT1atXIzk5GSKRCNHR0XqPxgRRqcBXqaAChcLawiFCCLiH9lvY0zQqWQ34S+sKAkJOngcfPPB9/OgFjz0GABBfpHMfVYr6tV8tO75XW9zCoSj4BtDeGiPDtE8fAIDi2kWbxpFe+cdombCoFD5B4eDxBeD5+umvfPxx7b++l1OglJvu/sUWKj8bwsI6j0NTHce2f5Om8HIXAbXhHA0cAOX/HAGqKlFTYeBdsgEVj+i0+akjpEy/DedjfxyD6sEdh+VZlkgLcfb31QAAnlyh937r4r3rL1B8+7iKKT4PJVm3jZa7EebTitvvxgVZ9UXOo1CeW3eDJJeIQTgckNBQYOJEo+19Ll0FsWPeV0kZc06l3u9Np2CUSwgqc2xvD1uVc89k8ZqQbyKyFBcHd4bwJFvUAi6CDusbmyolw3mKx6MjQKs+1+sEZ46qoiyEbqwtVNGJqjzqpKSkYM6cOYiMjGRcHxERgXfffRdXa3P0XVhHs2bNsGrVKiQnJyMkJARjxoxBXl4e47b79+9H9+7dERAQAD8/P+zbt0/rqc7OzjbbWn3btm2IjIzEiBEjWM8tiuGG2BAvLy/s2LEDa9euRXh4OJ555hncvn2b1Zz+C7AyTLdu3YrDhw9j37592LJli96jMUFUKgjUdHHE9X/+pNsLEgpBB4+hIs90rog5Sq//A55BkZenitR5n8PDgdatwb1Me2Urrtru2QAAz6P6YWU/NxEAg5w3gDZcRCJ4HTD2eLHB7ThzNWmIRwD4AoYL5MCBQO3dI1dNoSq3/kUgsiP6NwzRR/8Fh0N/pYVNE2jj+M03jfbz+eEHcCsqIDt+oN5zqJGLERLRFO1C68SKm0S1QOTZq3rbuecVwe9aKpQO6HhLuByc+bOuKp7H4QKrVjFu65FTAPlp4372tlBaXcC4XGDC7hOeOWv37kv5ucaFPdoxGDr3uFWJUVmQYbfxb/7zF+NyPY+pTigfAOR3bPccKx+YjnC4cwUAl+H9jY6G79UbzMYkC0hpEdzKK/WWmbyBbt4cntl5KL7Czmtac/kcbWh7eQFOLHJ1NGzlC10yh7Yzbtw4nD59GpmZmeBwOJg7d67+7w6AXC7HCy+8gDlz5qCwsBAVFRUYPHiw9vobFRWFe/dMX4uSk5MRFBSEcePGQa1mVzhpOAdTPPXUUzh06BDy8/PRsmVLrWyYpTn9F2B1lUhMTDT5aEwQEAhqvSmV5QXIKk4D1CpwgoIg32ebt4d73DgfzE0iq2tZCABjxsDn4mVQFAX11Ys2G8HyGjF8UvRz2Hxk9DhGHlM+H+jcGT637qCqyLpiJGl1Bfz+Zfa0imRq8AQMJ1sfH732jMp79a8UVp06rvfcI6cAfsG0h8KdK6Dz1Z56ymg/z5x8iK6kwP1y/cO6lYWZSPzrAjqs3Ymuvs3g5R8CP447vO4Zv6dRfx6FNNe2z9YcZbJSPWUCbrPmQM+egIm7bvmv7FsJm4JwuTi2YznjOjelmRN4daXpddbOgcPBlSPbjJZTnNqM7dqCO0OkV+3TIlYt4CH31gWERTZHSIT+e20qxxQA+NdTbB6Tf41hX29vwMsLQsJhvihGRYGrVqPivm35rfxTx42WKRUmIh61Xk/Ptd9CrTJf4KNWKeG9rTYNpkcPoF8/m+b3MPPLL7/gzTffxPr1640Knl577bUGmtWjT1paGo4ePQq5XA53d3d4eHiAx+MhNDQUGRkZ2mp4hUIBuVyO4OBg8Pl87N+/HwcP1jljpk6dio0bN+LIkSOgKAq5ublazyVAy1Du3LkTEokEEyZMsFuVfWFhIf78809IJBIIhUJt/Q4ATJs2DStWrMClS5dACMHdu3eRmcmsPNNYcWzz6EcQvk4I6tKhreAQCnjqKXiesz43r6o4B0F7jT2SRK0CoPMFHzYMbpViVGSkgldUDNkx28Ju5ecOG4X5fMroSnkjwxQAunQBAEgYwvLmqLpxAVymfOORI+EuUzAbpoCeoSQ4ZV0rW0PUSgW8bxt7j0IEdCMGobcfnV8XGsroOfPMzoObQeGBLSHekrsp8MzMBYcQtNr4B1rwAuAeEsG4LYcQeP7vf1aPYYnU8/v0nnP71t4w9uzJuL3vxStQ1tSvt7eEqoG0sgTgcNDVtxlaRrbRrnNTmDZMVVdtSx1hQspVQFxiHL7T9otv3pxxP8FV++SZyuRi9HaLRv+NBzBozS4EhsVq13F0f4nBwXrSSr6nztokW0UIBe9/LhivWLwY6NMHQiWljRjoUZufxt38g9VjAoByu7HxrzKVitOnDyASwfvuAxTtMd5Pl5LLJ+GVXSt91auXTXN7mFmxYgXee+89AMDatWvRtWtX5Ofna9c/klqb44hjHyyRy+V4//33ERQUhLCwMBQVFWHZsmUYOXIkALopUKdOneDt7Y3Vq1dj1KhR8Pf3x08//YQhQ4Zoj9O1a1dtQZSvry8SExONjEA3Nzf8/vvvKCoqwpQpU+xinFIUhZUrVyIiIgIBAQE4ceIEvvnmGwDAyJEjMX/+fIwbNw7e3t4YNmwYysrsKyn5sMNKYP+/BF/HXS8uzoGsphIeTzwB0bZtkFQWw8s3mPWxJP8ehw/DcqJW6heStW0LhIZCcfwgPIqKwZVan38pk1TB75tvjZZ75xQA4SYM09oWs5579wFPjWI9Fu+4cRU6XnsNeO45uH/3Ofjh7sw76nhMA0+eRUXeffhFGFfOs6Ey5w4C1MYniMBy+r0ThobXLezQAbhtnAupm58KAMqaKriJ/FjPQSXgQl2Yr7cs5G4OJO0701qxGzYY5Z/yU65BXlUOoY8/63HMoRZwcf+y/ufB0+TVMhjkAJ1nK027BkFH242B4sIHEHh4YVCqGP5X/kBFh9ZIayoAIRQEDFJNGpS//ARBD/t4xvJzjD9TAFCplXRleGAgXQCVrZ8H6nX1ml3Gp+7eRtPtdcoUCVUcaGr+ubohdT6fnkvtjZBbpRjluffgH2VdTmV1aQG8S8v1F/L5dC6tQgFh2r/geDJ4TGu9mP5n/kV5zh34N2E22JlQ1lTBLdu4MNNkjni3bsAffwADByJo/QYonh4JN3cvo81qxGXw/fTTugVt27Ke06PCt99+i4MHDyKh9v1fuHAhevfujaNHj2q1U13YRrt27XD+PLOz6PTp03rPZ86ciZkzZ5o81vDhwzF8+HCj5cePH9f+7+7ujsOHDxttY24fc4SHh5ttrvDqq6/i1VdfNbm+sePymBrgplLDXaFGhydGom1Ya4gzbgGJieAAqDpj+Yupi9de5qIpolKBMjwpPf44/PYdgDA3D143rc9Bq7h0kpZcMcD7Ln33x2WS3KpN0va9dpN13p2kshgBB4zTE/DKK0CXLnBXEq0SgBEGoWV5lu15NPJ0Zu1Xn9wigMOBm39Q3cJaGQ5DeAolFPK6IiVlpXV3pQUF6Xhst/7Jxe9GOjzUoLtrPfGE0T4cAOUp9pMsKqkwFlvX3oSYqXJWn7ddGYFwAOXtFAw/mQn/K3R42O9qKrp7xQIABBFNgDFjGPf1uHIVSkn95NcAOpXg0kHmHHelSl5XZNajh/EcikpRXV4/+STC4cBt9Zd6y6IPn9N6LDmGuZ4GEjTyW9Ybx9IshqKp7t2BgABgzBgIlGpwGBQR0KIFwOeDA0D5+3brxky/wbicUGaKmzp1AgAIamQo270NcgPvvFqlROWpA/rnq9qb5MZEcXExmumkkyxatAhvvfUW+vTpg7S0NNa5iC5c/NdwGaYG8NUqxPg1QfuBo9Hp9C0E//onbVC1bAnfn36GmmW1aem9a/C5xVyoQNRqwFDrr2tXuJeUwd3bF8KKKlQVWycf5bZvH+NyYXEZuDw+uG4MXkwdVYWai6eN1zMgvs4Qxo+OBtq1A4KC4D5kOPhuJgxTg4szuWebfFKNuAz+679jXOeTngGB0AMc3WKv2pQFJsQ6FdLKUvZV+pX5DxD43vvwyNb3mAqLSuFXVJtH2b07477uf/zGehxLZKUZew14mp91t25GclnaORw5wvq7bIhYLYHPyXNGr73Z9n3gCtzA8xIBEyYw7ssBUJ1af7HoGiKlUwkY0L3ZwBdfAL7GzRfEDCL11kAUUghv6N8cuRcUw8ufjqgYhdQNbhL4563Pc6VuM+SIDhhA/42OBs/Xz9ggBuiua7VpDUG790JabXwDawrZPwxFTAMHgsszE2wLCNAqEYR9+x34w55C6aK3UHqfNnKL9m1H+Gef123P55vMB36UiYmJwbVr+jcgs2bNQnJyMpKSkiCX10+BxUXDIBKJGB8aCSpbt3VRh8mzy4QJE1jd0W3evNmuE2po+CoVAuJa0ifKzZvBeb+2v/zUqfB8910UpV5ASFtjL4wuKpUCwiXJzCtFIpBWLfWLn4C6UFbfvkBGBqS7t8NnmmUxX4A2kvyZcs+GDQPOnoXQxw9cdwbDVEfWInDdBtQkDoand4DZsXhnGQzYd9/V/useFAZelolQvoEWm8e5c8B46woAKEoN8Y6NCGXwDgOAR24BvPwMusdoDDSGqkqyYyswj+6lLS809j6aQrZrB3zvMSeke1TXhjlN5XhevgaZuBzu3vUL56sFPNw+Z9wKlasxipo0Afr3Bw4a5zl7FJWi4PxRhPV40upx3Q4dhM8RY48rV6GEf2AEeCIfWoXB3x8oN/6cFOfPAI8nWT2uLgX5dTc1ooBQSCpKQGrzNqU1leCIat/biAhaku2MviHq9933UPR+ijHMzAZ5eio8GJb7efqhuqwQXMNzp4FhGnDsFGRvV8HdkynZhxnRwUPGC3W+YzxvH0BqIne4Tx/g1i1w1WpU/rIRHlPetjgeIRS8jzGEG99+G9zL5lvw4okngO20d5anUCLw2GkoL1xBVVQkQtMNPL8dO2oVOxoTkyZNwuHDh7XC6RqmTJkCoVCIDz/8sGEm5qJeVFdXW97Ihm1d1GHSY9qsWTM0bdoUTZs2ha+vL3bt2gW1Wo0mTZqAoijs3r0bfn5+Tpyqc+Cr1PCKaUrLrrRvXxcG1lSbrlyOcgs6hKWXT0GUwaBvyOUCmzaB+PkZF9rUiutq9EWDf/4VFbns9A5r/j3JrG04bx7w7LNw9wkAV8hgLHp60nJVANzE1aj6x7yMUEXuXQTtM7g4xscDM2Zon7qFR0Lg5c18AAPD1OfGbVSXMUsOmZxDZhpCt5oPR/p4GRh8IlHd+2uA/9FT2k41pJDdXAhFwfeImfdKc5Ht25fxgssBUHmrfk0bCIeDs8c3M+pFcnW/DbU6uUx4bLVe7o1SKyFY/pnJ9SKBB7je3vTrZsjbAgDvf+ov8p954wxCI5uhY1BL9L4vQUREncdNUlWq38edIX/Ro7AYFTdtL8QSH2cuUPRT0x5qI8+lQRoLV61GxUX2BYDlWenG5xQOh77pqoXn48vsMQX01CnCNv+MgpN7mLfTHTP7DjwKGbqVdeoErqmoiIZPP6U9tToIqiXwuZUOrmFueCMVmZ8zZ47JTkHjx483267ShYv/MiYN04ULF2of6enp2Lt3L7Zt24Zly5Zh69at2Lt3L9LS6i/587DBV6sgStC5mD/3HP23NuwtysgGZ+Uyxn0VMgmKvloK9x83MR+8Y0fghRdAQIGCwcm5aVO6gjw+HmjaFFy1Gvj8Y4vzFZfkIeBHhurOmBhaq3TIELiLfMF1Z/LvAOjdW/uv73cbIDOR/0cIBeqrz40N4EGD9MLFnPh4eAWHG25FExBAS9totgVQfd50AjgTbHLzfDkMqgAmDDSeSgW5tBqEUOAVsDNMyzNS4V5iJh9VY4yKRIAJSTXO6fqpEpTJS3D/0hF4+QfDy6+uII/D5elr1poRLfe9eRsVVurJ1qRdM9Ll1YVPOOBpmkcMG8a4jWd2HkrvGbcyZQvhciEtL0KfPRfRbv1OhB49hyaVdXMSlxXoG2gm8hc5xxg8kCyg1Cr4Hma+MQksrKCPbfhL0en+pMFj+3bWFb7yEwyauwkJtAxbLXxff+aqfEDvdw4AgZ+tQP5h8xJ4yj93Gi/09ARCQsDjW6ibjY42+fnr4e8PfPCB5e1cuHDxn4FVjuk///yD7gb5ct26dcO5c/btO/0wIFCp4NW2Q90CzevW8fb5XbmOomv6oUyKUqPqiyUI+W03fG8yVAu3bKktxKAIASiDPE0uF5g/n87Nqi2a8b10DWVZ5o3/6rNHICyvMF6h8Wb37w+hyJc5lA/oFeh4FBWj/Mhuo0IoiqJQsGM9ApjSBQwNr+BgeJkLTxpI+Phs+wlKli0axSW5CPraWHnAEF8pgxSPCY8pACiqK6BSyMGtrGA1D+Vh5nxeLbpe0nHjGDcJ3HfI6jxiXWT308FzE6JTtTuGbzuNgFD6xonL59M5exosdNPhLZrP+v0HAJmF4hkeOOD51OZ09uun77nUgfrNuI0rW+RcFRIqAa8HdR7E8Et1BYNVJTn6OqJxcYzHCdx70LiFJwtK76Qw/+YABF2lf/tcQwMx3PhmzTc1DYVHd7EaU3T0uPHC2iIjDTx/f5PvN0JC9H57gmoJwpd+jPxfvjPqbU8IhcILRxH6627j49Sm/3D5LELvT7JIE+ncmVYsaKQQQvAmQ5MPFy5cmIaVYdqxY0fMmzcP0loZI6lUivnz5xvlzjQGBAD4fjp5lhrPi7+/nrcv6M3ZKEqpy1sr2vQlgpiq1TV06KDtQqSm1MwXkBEjaM9KbXiOA8BjzluQVlcwHlKtUsL7j13M42kKPkQiuAcEg+vhybzdwIF6T0O/XAP3GS9rRf7l0moUbV+H8LUbmPc3uDgCgFegsXdIi4GB6Jmdh/JUdiHV6vOnIJDUWNxOo92qhxkDTV5SCKVCCn5ZhcVjS6srELzDQrMFXcO0f3/GTXgqFWpsbNpAuIBo82YMKHZDzK7D4MkV6HeELsTh8gT6BU9mQvkA4J1+D+VpV1mNW3BqH4IOHze7DZ8i4Gm+ayKRXoGdLoEHjkBtYyeiquIsNN2hf3PgczMdAnd63PKCLH2PqYlGA1xCUH3berF7so/BYKtFdDcTfHdP45A6g8cUAIJWrdYv1mKgJO0yRA8YmmAYtNvliXxMh/IBYNIko0Xh36xH0brlKMu4hcJLJ1Bweh/K581C6LtzmY+hNUxZdCwykWOtRyO8hmhQqVQYN27cf06D0oWL+sLKMN20aRPOnDkDX19fhIaGwtfXF6dPn8aPP/7o6Pk5Hb454et27bT/cglByJuzUbR6CfJ/24CwzRY8QC1baitP1Sol8wUkPBwICqKLVmrxKCpGxTljmaryrHRUv/0y8wULqPOYAvDgC02H8ps21fOactUUhOUVkJ4/CaVChrLDuxC23oQwt0E3Jw1eQewNUwAgFy0L/FOUGp4s+3375DBIAQWb1p/lbf4BarkUHlmWPZiVZw/TaRbm0DVMo6PpAhwG/P/YbbE7DhNF334K37MXEXbkLHhyBQBAlP4AQeFxdIhV12MaHGzSONQgXPc1K8F3998YQrsG8CiAp5t/aMJTzVVTqClnr4KgQcEnKDr+F7gGWqkcQuAfQHslpVWlxh5TU57EywxRADMUXT+HgAMMOr46uHuIjEPq/v50GNwAQbUE5Ru/Mgrpa7yYxakXIJr3PvNABgVVPIGbecNUR1hcl9Dtv8J32hSEvDMHYQsWIuCcmfek1jDlsSlWatbMclGTzjm1MVFdXY1BgwZBpVJh06ZNDT0dFy4eKVgJ7MfGxuLs2bPIzs5GXl4ewsPDEW3hYveowmcQbdfStatRdW/I73+yO7BOnptKrYCbwEw1sI5hCgBBq75CgdAdyHyAgJFTUFNWCPXGtfC/ZqbFoI5Ejjt44HqYMEwBWnOzZUtAp2We57HjKPHwgv8mM6oLc+cyXvBN5pgCjJ5L7yPHQKa+bTI/TlJZAvX7b8DnFjt5KaGmKl6XoCDjZbUE/HMRZV98DG9xNeQ1Ygg9mYu3JJXFCP5speUJGF6M+/QBduwwnmdFFQqvnkZoF/aC8+KibATt3MW4rolKiHQe31giqksXIMt021nfG7dRlpWOgNhWJrepzH8Av6vMmpa68FUGLWlNdF8CAFlJAbxDokyuZ4K3YxtabNnFuM6f4w6NqasncO/uTkuVPXhgtE/grr2QTZwJdy/L1fFqtQp+8+aBb0HmRyj0NM4xBejf2fjxRotDt/+KIoUCga+9D15tiLzowjFwTp9A8J/7mQsbAWMJKoHQdI6pZns+H2DIETaXN6wlJASo7U7DKpQvENDG6S0zuswmmkA86qxatQo1NTX4+++/ta0mGwOLOIscevyFZKFDj28NSUlJePHFFzFt2jTW+wwaNAhjxozBJIbohAv2sNYxLS0txfHjx3HixAlER0cjLy8POTm258g9rLiZ03a0pXrUy4s2xnQ8R2q1St+jY0iU/sVaIKlB2EeLELZhM6jxw+E7bjSCjlnQQdMxTD3UHNOhfID2ehpc5HxSbsLj0EFG0X4AtPdn1izGVe6hzB5CAIyGqWd+EQq/X2nSeyjetQ0+t+6YvkAbwtRMwIxhCgABZ2hdSakZL17VmSPsLuCGhqmJAigA8P34Y9NddBio+eVHk3Nocu0ueDwDjylAF91ZQLX7FyhkEsZ1ZZm34f7adFbz46kpfcOUIbdSg7oo3+Q6xu1VcnC/+hKCCuYCvcCyuvkbeQ5NdBbiy+Uo//tXVuOXXDsLN7Fl+Rd3vtBYLgoARo2izwcMhPz+J8Tvv4ai6+eQf/h3BCxeghBzRqmHh1GKAo8vMO8xdXMzmdZgEYEAWLiQNvIBcE21HTakc2fz6xupYdqjRw/cvHkThw7ZVmDnwjaSk5Px4osvNtj4+/fvdxmldoCVYXrixAm0aNEC27Ztw5IlSwAAd+7cwQwdmaDGglmP6YABRhIoFunViz6h6xhkHC5X36NjiI+PXiheF/fScssGmsH+niERli8kBh5wDgC/i1dNbz90qF5FsB7mwndNm9bl7eoQtu0XFB3ZBVVt3mFlYSYK/t6BgiN/IMRSmoThuEyGaWCg6XCuDrIM5qYIACDabTq3kHEeGrp2Nbmpe2k5Kj9jp2dYcucqgnaZ1o8M+OcqRB7eNhmmIX/sgdvTSSi9f0PvBkGpkMJ3+lSTxT6G8JUGHlMTaQwAwLlpXWW+7Mq/Zr/7gel1urJGN35mWl4GbdwMmaTS4vhuW9lpNgvBY/Zc8vlmc539Ll5FyOtvIGzpxxBUM98kaHn+eaNzEU/obt5jCrDL+2QiOppuO6wZi43HFKAL4GrF9o1o0kQvb78x0b9/f/z111+YMmUK6zaVLly4oGFlmM6ePRs7duzA33//DX7tRa9bt24me9U+yvDN5Q+KRGZDk4y0agWMHKlnLHD4fMCSeVmfu76kJL3cLc+Wj1k2ygw0Ri0ycqT18wJoj4uJscI+/gzV781A0fV/IP/pB4R9sgJhS5ZZzukEgMGDgStXgOnTmQ1TPt+ksa+L75dfQi7V94qJS/NRcPBXeKex05U1MkxbtDD7/gcdOWGxQr/o8kkEvPqqWY8thxAEKxlC+RYKoHTxeX0mio/R6SkFZw+g5NAf2jxWNgjkSn3NXDMe06A/97EyCDXId/1idr3PrTrpKyPD1Iwqg6BaAurViRCX5JncpiTtMvwusSuUcqdgujmJBc89YPHMQMOg9sATCM17TAHg2WfZHN0YA2UD1h7TiROBlBTm355B8VZjo0+fPvj777/xyiuvNPRUGiWffvopIiMj4e3tjRYtWmDv3r1YtmwZduzYAZFIhPa136+NGzeiVatW8Pb2Rnx8PNatW6d3nN27d6NDhw7w8fFB06ZN8TdDLUN+fj7atWuHFStWmJ1TUlISvv/+e7Pb3L17F4mJifD19UVQUBBGjx6tXXfz5k0MHDgQAQEBCA0NxbJlzNKUjR1WhmlGRgb611YXa064bm5uULEJaz5iCCwZQVHW5cShVSsjQ4XD5Vm+gBhUy1tF+/Z6Ui3u8SyM6Y4dae8uW2z1vAAmCx44oKW4Ql5/HSG7LUgyGfLhh7TxMWOGaa+2mQIoDR5Fpaj89jMUbP0aFW9PRcHxPyGYPgmhyz5lPxdDw1QkAiIjze5Ss/dXFOw0fUITbN/GykD3K6ky9phGRbHurCOQ1CD0f5+gYMd6hM5bgPDlX7DaT4ObRKoN9wIw6zHlyRWo2LWN1XGr7lzXpluYgl8tgcCDDpUb/b7MGKYArQ4hOcIsOl+enY7AV15hnUrirqBMR0RYfAct4ubGeH7gurtbNhiTklhFDowwaCfMKscUoL+LYWGAodC8UEjrLDdy2rVrh4MMnddc1I+0tDSsWbMGFy5cgFgsxoEDB9CyZUvMmzcPo0ePRnV1NVJS6BvJkJAQ7NmzB1VVVdi4cSPeeustXL5Mt0U+f/48Jk6ciOXLl6OiogInT55ErMF3PSMjA4mJiZg1a5bJhgnW8OGHH+LJJ59EeXk5cnJy8PrrrwMAxGIxBgwYgKeffhp5eXm4e/eu1u76r8HKMG3dujUOHNAXeD58+DDamgmPPapwDFuFGhIVxeyRY8LNjdFDweHxzIfyAaOcT6vo2lUvx5TDJvn+pZeAVavYpSrExdHNAGzFnp4SLhc4fbpOb7ZNG9MpBiy9wiF/7kfY95vgd/kawpKXsEuf0IVJfNyCYRS2aRvCvl6Hgt83aj22FKVGwaZVKFkyB37n2fWX97ufazw+l2uxMl8XDuge5zaYL3CrrKbzHzU0bWr68wAQ+t1GFF03r4dccPYAZH+zS6PwqC1iMvKYtmhh7Ek2IOCn7UbecoW8BtSGb6x6L4Q1MubiJ8A+hmlEBPONhqcn+JZknAICTDYcMIvBPqw9phreeUf/XPjYY8CCBdbP4xEkxtpolAuL8Hg8yOVypKamQqlUIjY2Fk1N5E8/88wzaNq0KTgcDhITE/Hkk09qe9Vv2LABU6ZMwcCBA8HlchEZGYmWOnnPqampSEpKwqJFizB9Ors8e0sIBAJkZmYiLy8P7u7u6F3b/GLPnj0ICwvDO++8A3d3d3h7e6ObTme3/xKsDNOVK1di/PjxmDRpEqRSKV555RVMnjwZy5cvd/T8nAqHx9f22zZJnz7shKM12zJ4yrgcrmWPaXw8s4FjCaFQT/6JNXw+Xb3dpYvlbQcPtv74uthyYTRFTIy+p1cgMF1Q0bSpvjfPUTAZDZaKQGoJXf0Naha+g/zfNqDot40I27QNQUdOsDaMfG6kMRtgtha9WImwokrfMBUKgRdeMLk9B4BP8kKThVcUpYbfypUI/o2lYSo04TEVCo28foa4VYpR9dUyFBzbjYJzB1Fweh8qVy1B4PEzZvczxL2qxlhgX4O9DFMmPD3BZ+PJtCVNqJ++cgTrHFMN7u50XjpAe1BjYizeKDQmampqcO3aNZw9e1bv4cI2mjVrhlWrViE5ORkhISEYM2YM8vKYU3H279+P7t27IyAgAH5+fti3bx9KSkoAANnZ2SYNWgDYtm0bIiMjMWLECLvN/bPPPgMhBF27dkWbNm3www8/sJrLfwlWlk/37t2RkpKCbdu2YcqUKYiKisL58+fRxEDW6FGH4+YGYq4qH6BzK7OygL/+snxAE0Yeh8sxX5UP0MbNyJHAz1Z2yJkwgVEvkTUJCYC5E6anJ1DfnCkTnXhsginn15Th260bUFYG/GI+V7HeMBmmbAx+0Iaa//nLAEsPqSE8uYL5hoZBb9YRuJVX6humAJ1nuHGjyX3cS8uRf2Ifwp8yzluuyLyNAFPKEAx4cGlPHuPvq3lz4J75FqzB+w4B++pXSe1RVmn6RsLBhqmAx8JgfOMN4McfgdsMHeqY8PMzSr/h8my4aR48mI6WNGtmXdrQI87mzZsxa9YsuLm5wUPnt8HhcJBlRsbNhXnGjRuHcePGoaqqCq+88grmzp2LZrVa4RrkcjleeOEFbN68GUOHDoVAIMCwYcNACN15MSoqCvfMnBOSk5Px999/Y9y4cdi+fbtdpL/CwsLw3XffAQBOnz6NAQMGoG/fvoiKisLP1l7vGymsPKaff/45IiMj8d577+Hrr7/G+++/j8jISEyZMsXR83MqHIHAssfU3b0ubGyO1q1NVmPTOaYsvuAMmocWefVV6/fRpU0b01W0AG3c1TeFw56GqcGJCACd18vE6NGMnarsDpNh+tRTdBjVGTSkx7RKYpzD2KePSZkkDeEff4aCE39pheZrxGUoXrEAvFXWRWU8KHpsxoiEtYWLNuJeXGa6Ot4e+s+m8pU9PcFnc17x9KSLBNnSvbuRkgbXlmhORAQwezYd0Rg2zPr9HcSaNWvQpUsXCIVCTJ482eR2mzZtAo/Hg0gk0j7YVNy/9957+O2331BSUoLs7Gztw2WU2k5aWhqOHj0KuVwOd3d3eHh4gMfjITQ0FBkZGdrziEKhgFwuR3BwMPh8Pvbv36+X8zt16lRs3LgRR44cAUVRyM3NxW2dGzaBQICdO3dCIpFgwoQJRo0wbGHnzp1aqU1/f39wOBzweDw8++yzKCgowKpVqyCXyyEWi/Hvv+bz6hsrrM4uP/74I3x9fTF16lQAdP/fF198EcXF1veZfpjhCtxA2FSAP/44faI2/JJyubRm5ZUrwIoVdKEBAxw2oXzAemNiwoT6G14zZgA3btAeFSYsFPGwIjiYNlQkFiRx2MDU0pDJWAVoT56FXE+7YCL/D4mJwB9/OHZsU3f0TvKYcpkMIx6PLq47fdrsvj7r1qMoNwc+z42GfMkHCLbBayxU0L9fxt9XffK2rcA9rxDEVIGRPVpwmtJT5nIh4LPMf3/5ZeDUKXbfR4YiJZs8pgCd1pGXZ9+b03oSERGBBQsW4MCBA9q226bo0aMHTlv4Hhvi5uaGJBPXgkeNh0UAXy6X4/3338etW7cgEAjQs2dPrF+/HkKhEFu3bkVgYCDi4uJw+fJlrF69GqNGjYJcLsdzzz2HITod0Lp27aotiHrw4AFCQ0Px9ddf6+WZurm54ffff8ezzz6LKVOm4IcffrBcI2KGCxcuYPbs2aisrERoaCi+/PJLxNX+Hg4dOoQ333wTixYtglAoxOzZs/+beaaEBXl5eSQhIYHs2LGDqFQqMnLkSDJ48GAik8nY7E4IIaS0tJQMGzaMeHp6kujoaLJt2zbG7WQyGZk9ezYJDw8nfn5+ZMaMGUShUFg8vqenJ+u5mJ3n5h/Zbfj444QAdY/27QnZvJmQDz8k5Omnze6acfpvUnb5H8tjyGSEcDj645h6cDiElJaym7slvvnG9Dhz59pnjFat2L0ucw8ej5DCQuvG/eef+o9r6XHsGPPYb77p+LGFQuaxU1IcPzZASHg48/hvvMH6GGoez+bxb00cSjY935ZUZN41nsORI855DwBCXbtm+jsYHm77sR9/3Ny3m6SuX252vR7ffstuzHXrjHbNPn+M/TgNDNtrw/z588mkSZNMrt+4cSPp1auX1eNv2rSJzJo1ixQXF1u9b0OSmpra0FNw4SAe9s+WldkfHh6OgwcPYu7cuUhKSoJSqcTu3bshZFudDmDmzJlwc3NDYWEhtm3bhhkzZuDmTeOWmp988gkuXryIGzduID09HZcvX8bSpUvZW9r1hICw29AwFNa1K+2xTEiwqBvJ4XDYeUyFQmDQIHbzGTzYfqFiXa+i4WdsD48pYFbfkjWjRplPO2CChY5kvTElzeSM6lxTHlMWVel2wVTrWyu8/6x0a03gLpEBMOExtWfRnQVM6pgC9YtqjBpldjXfjf05GX36sNsuLMxokc0e00ecK1euICgoCAkJCViyZAkrycSEhAT8+eefCA0NBY/HA69WlaUxtSp14cKemLSOfvjhB73HkSNH8Pzzz+Pu3bt4+umnsXnzZm01mSUkEgl+++03LFmyBCKRCL1798aQIUOwZcsWo23/+usvvPHGGwgICEBwcDDeeOMN1uPYA2JJLkrD0KH6eVcafdPevS3q89FV+SxPSmwEdt3dgc3sutKwonnzOgPUMPRoL+PKHoapmWpvkzSkYWqP/EJLmMr9EwrNdh2yG6YMUwsV8fZCWEnLPTGmFISFAf7+TpkHU3czLY8/bpviBkB3nzODQGji/WeiTRt2qS2PuGGqUqnQpUsX7WP9+vU2Hadv3764ceMGioqK8Ntvv+Hnn39mpUwzYcIETJw4ESkpKUhPT0d6ejru3LmD9HTTXeZcPLzo5hjrPjQSVADw6quvMm7zan1rQP4jmDy7MBmNANCyZUts374dAO0VYFMAlZ6eDh6PhwSdC2P79u1x4sQJo20JISCE6D3PyclBZWUlfHW0OQFg/fr12pOM3cT+2SY3BwfTEkAXLtDPNUZHbKzFizCHwzHvUdGlbVu6bZ9YbHqbrl3tW1gTHk4XXn32GW2YXr4M9O8PlJQAzz1nnzHMCK+zxpbcG1/fOsNRqTS/ra2YMkydkd9qzuBp2xa4dcux4xv8RrU4KafQvawS8DETkWjVyrzqhL0wZ5jOnEl3Q7I239jHx6IGsMDdCsMUAH76ybKuMINhynuEDFM+n4+LFy/W+zjxOnnabdu2xUcffYTly5fjgw8+MLtfaWkpFi9ezP6c7+Khprq62uI2a9euxdq1a50wm8aJybPLsWPH7DZIdXW1kVHp6+sLMYOxNWjQIHz55Zfo168f1Go1Vq9eDYDWgTM8xvTp07Wit14Wqn7ZwtpjCtDdj+7doyWIrBEwZ1v8BNAXuObNaePQFI7wRr3yCvD993SovG9f4Ouv6ef2OrnGxNDey1o9Oavx96d7bdtCYCD9nurc4doVU4Zpy5b0uHfuOGZcwHy43hleS1OGadOm9PviqJuBWtyLS4HYINPvQ+vWzjFMzX0OQUF0MZi1hmlCgsXfn0BopVRcu3b0Z2NORusR95g6Cg6Ho+dEMcVLL72ELVu2YOLEiU6YlX0hhLgM6kaGPZQFHA0r6+jgwYNGYYf09HQcOsRO708kEqGqqkpvWVVVFby9vY22nT9/Pjp27IgOHTqgZ8+eGDZsGAQCAUKszSW0ETYnGi3vvAM88wz9vxUSSqxzTDVY8i46wuCIiaHbHgYE0BfQ+Hja02Mvpk0zqVrAivroQQYHs2+SYC0eHuY7gzlqXA3mPKbOSCVg6okO0KoETtCu5FfSN7smq2ZNtMO1O5Z+37bkmbKQu+ILbWggYS49ICKCsRtcYzJMVSoVZDIZ1Go11Go1ZDIZYwRu//79KCwsBADcvn0bS5YswVBN0wAznD9/HtOmTUOLFi3Qt29fvcfDjLu7O0pLS627Jrp4aCGEQKFQIDc3126OPEfB6uwyc+ZMnDx5Um+ZSCTCzJkzWeXJJCQkQKVS4c6dO2hee3JNSUlBG4bQpoeHB9asWYM1a9YAoMP1nTt3dlqiOOviJ4DOK+3cGTh50qrcRQ5YCOzrwmSYauSq4uIckzvI49G6nzU1dCoBYL/CJ4A23tq3B3791bb965Mr2qsXfUPx8cf067Mna9aYNyD69aO9z47C3O+kIQ1TgO5IxkL3sT7wxbQEmckbP4Ye8w7BkmFqQuPYLCwMU4G7DRecgQOBdeusGpNnKirwCLJ06VIsWrRI+3zr1q1YuHAhpkyZgtatWyM1NRXR0dE4cuQIJk+ejOrqaoSGhuLFF1/EvHnzLB7/5Zdfxssvv+zIl+AQmjRpgpycnEYnC/lfhs/nw9fXF0HOqLWoD2xK9318fIyWURRFvL29WZf/jx49mowZM4ZUV1eT06dPEx8fH3Ljxg2j7XJyckhubi6hKIqcO3eONGnShBw4cMDi8e0lF1Xw3TfW7VBTQ8j9+9aNcf4kqUm/xX6H5GRatuXJJwmJiKD/HzqUkOBgQiZPJkSptG7ObKmoIOTyZcccmxBCdu+2XTZnyBDbxy0qov9OnGhfmSA+n5CqKvNjp6c7VqooNtb02M6QjHr/fdPj79rl+PEBsmVMFyKrMCOdFhvrmLHDwgiJjqb/z8gw/z0ghJBmzdgfOy6OlRyc9NJ5y+MaUl1NSEgIPXdDqa5p0xh3ETPJcT2k2OvaYC3fffcdyc3NbZCxXbh4lGEVT46Pj8fRo0f1lh0/flwrCsuGb775BlKpFCEhIRg7diy+/fZbtGnTBllZWRCJRNouGPfu3UPPnj3h5eWFSZMm4ZNPPsGTjg5/6kAIsW4HDw+rCzs4HDMeHSaaNaPD9Tt21KUOPPYYnfvZtKntFb6W8PV1bMGOqQ5NbKjPHZ8mDcDeXoyOHeu8y6aIirJfni4T5r4LzZtb9uTVF3MeU0tFNnaC7+Zu/vf1xBP2HTA0FNiwAdi7F9iyhQ59s3mfLRTN6DFzJqsCR6uq8jV4edGpNYMH099hXUz8/htTKN9RXLhwAT169ECHDh0wf/58nDlzxvrriwsX/0FYnV2Sk5Px/PPPY+rUqWjatCnu3buHjRs3YqOZ/teGBAQEYNeuXUbLo6Oj9arc+vbti4yMDNbHtTcWW5LaAQ6h25+yRtOa1M+PvrhPn04bqzKZ6S5H9oIhv8xuxMfTIX25nP0+vr5AVZV9ZJ/smQIRFwew0dt1d6cLympz1eyOuVC+hwdtnKalOWZswLxhGhPjlAIovsDNfKpMnz6APSXoYmMBXXWSqCh2mrHW5Fiz7FrFE1m4MTLFiBH05+LjA+hWsGtuhA1wGaaWWVebHnH9+nXs27cPH3zwAdLS0tC/f38MHjwYTz/99MMfUnXhogFg5T4ZOnQoDh48CIlEgr1790IikeDAgQOsEr8fNazKMbURLjjg8K3M0Rozhv47cSKdozh0KG0Mde5s/wk6Cx7P+v7lbdvS+Xn26P3u41P/Y2h4/XX2hU2OzPW05D13ZPFPv37miwA5HNq76GD4fDfzHlN7C+0bavtGR7PzmMbG0jcqbNDoJFvCksfeFB070r+rfv3qlkVHm84xdRmmrGnbti3mzp2LkydPIj09HcOGDcOhQ4fQrl07dO/eHQcOHGjoKbpw8VDB+uzStWtXdLUlYf9Rwxq5KBvhEGKdxxSo88BoLjz+/nQo31rD7mEjPh64cYP99nFxtGFu6wVYF3d32iOsUNTvOEKhdcUsbdvW6d/aG0ueOkeK7A8cSEuomSM8HMjJcdwcAPB5AnC4Zk5trVvTRrK9wqqGhmlMDDvDlMul01muXDG/nVDI3jCt781Wv350UeDChWZVFLgeVspS/QehKMpIHcLX1xejRo3CqNoOXhccdR5w4eIRhrVhevXqVZw6dQolJSV6eTKLFy92yMQaCmfkAHHUlPUeUyaskKh6aNERrWZFXJx9jFINPj62a6kCtGHbs6d18j9Dh9o3lKyLJY+pPTzNhggEwI8/AomJlre1R8cvC/C5fPMeU5GI9gZmZtZ/sPh4YNYs/WVsDVMAGDLEvGH6+uu0sci2gUZ9882FQloG7+pVYOVKk5u5DFPL+Pr6olevXlppqG7dukFg4JB43EKXQBcu/ouwOnuuX78evXr1wtGjR/Hpp5/i+vXrWLlyJe7evevo+Tkd4gTxWQ5FwHFUwdKjRrNmtMYlG8LDgXHj7Du+KUF4tkREAJMmmW7FyYQjIw8NYZj26QOMHcuum5ejDFOdXGg+lweOJc+xPYr6oqJog9wwNcMaw3TGDOZ86dat6QLH1auB4cPrP1drEAiA5cvNflYW318X+Pvvv5GUlIQzZ87gueeeg6+vL/r164eFCxfiyJEjkEqlDT1FFy4eSlidPT/77DP8/fff+OOPP+Dh4YE//vgDv/76q9HdX2PAGTmmHNgQym+sTJtmOfwL1IU9WRaBsKa+oc+ICKA2LMeasDD75rfqYslgaNXK/pX5LVuy39Yw7G0PunYFdNr/8dl0VrOHYdq0KdC7t/Hy2Fj273FoKPDii/rtdXv2BN59F/jqq/rP0VbYpg64MEmvXr3w/vvvY+/evSgrK8PZs2cxfPhwpKamYsyYMfD392/oKbpw8VDC6uxZVFSEPn360DtwuaAoCoMGDcJff/3l0Mk1CM4I5fMF4AgcWO3+KCEUWm4tGhxMe+Qc0eGqvh3FIiKs85ZqsLeB7e9PNyuw1F0pOJhdyN0arMlbdYT8WJcudDOI2nC3G4eFN2/6dOsMaiZMfR9jY9lV5Wt45x26Z/2HH9LPW7YEJk+uX2c0Fw8VlZWVyM7ORlZWFjJrU0j69+/fwLNy4eLhhFU8uUmTJsjIyEBsbCwSEhKwe/duBAUFwc2RUkINhDNyTLluQlcoTBdLHaV69aJz+RzhZR49GqhPVawlo9oUzZvbrwCqY0fg33/p94dNqLxvX+DYMfuMDVhnbDrCMO3enU4HOXECaNcOfMJCJ7ZZMzpE/vHHto9rSr84KgpQWyE7p/kOffQRIJU6JQ/XheP59ddfceLECZw8eRLl5eXo1asXevfujYkTJ6Jt27auHvQuXJiAlWH63nvv4datW4iNjcVHH32EESNGQKFQ4Msvv3T0/JwOcUJVPtcWD1tjhsm4EwrpqvnKSqBDB9ob5ojQV5cu9dvfVlUEe3p/16ypM9rZ5K/as2jOWg9sXBydxlBVZZ/x4+LomwuAzsmMi4OfmKXKwrPP1s8wNVXwxufbVoTE5wOffgqUlto+JxcPDaNGjUKrVq0wd+5cjB49GkKhsKGn5MLFIwGrs+fkyZO1/w8aNAjl5eVQKBQQiUSOmlcD4gSPqauiVR8mwzQpifY8/fADbUgplY7JT9R0gQJow0Clsm5/W8PB9jJMhw7Vz9FlYxDZS8u0RQtagN0aTzaXS3s4Dx6s//hcLvDNN/pNINq0QeR5lvJj3bvTNzxlZdaP7eEBOCIUy+XqfyddPLKcPn0aJ0+exI4dO/Dee++hefPm6NOnD/r06YNevXrBx1F55i5cPOKwyjHtaNCmzs3NDSKRCF3q6216CHFGKJ/nMkz1YcrzbN4cePVV2oCLjqYv1o7QbA0OrmsR+tZb1u9fH8OUrQSQOSZMsH6f5s1p72J9aN0a2LgRePtt6/e1lypBr17A00/rL0tIgFdOAbv9uVzbc33bt7ctt9jFf4aePXtqi5/y8/Px1VdfISwsDBs3bkTz5s2NrqsuXLigYWWYMslCEUJw//59u0+ooXFKKN/Ty+FjPFJougHpVjJHRdFdrVq3pg3TFi2AwED7j83j1RmItQV+rPHyYieRxESfPnSBS33gcmlRe1sYPLh+Y3fpAvToYTk/mAl7eYtbtTJeZm0xmq1duNq3t20/F/9JNMVP2dnZyMjIQFlZGYqLixt6Wi5cPJSYjftNnDgRAKBQKLT/a8jIyEAbRxQyNDDOyTF1eUz1CAmhJXOqq4Fdu+hwtCa8/8QT9a+ct0RwMJ3X17w5+45AXl7WNwfQxd29/qkJrVrZLjtVX+OwPnqo9mrJyuStjox0jmHqyNauLhoFusVPN2/eRHR0NPr06YNXXnkFeY8F2AAAmQlJREFUffv2RfNHvWufCxcOwqxh2lTn4qP7P4fDQa9evTBy5EjHzayhcIZclKsiXx8PD1rHsaCAzlf09q4LNWsKWxxJaChw+zbtbROJALHY8j4vvQTk5tZv3PoYpjwe8NRTtu9fX8O0Pka5PQzTiAjg+eeZl7PtPw/Y/hnYW+7LRaNjwYIF6Nu3L959910kJiYi6hHThlUqlcjJyYFMJmvoqbhoZLi7u6NJkyYmtfDNGqYLFy4EAHTv3h1P1eci+AjB5bqMxgYhKQmQyejQ/aVLdR4pW0Pl1vDkk7Tcko8P4OfHzjAdP976QilDTMkNseH11+t0L20dm8sFbO10Vh9vT1QUrbogl9t+jAEDmI1Kaz2mthqYLm+XCwvcvn0bAJCXl4cIhvPYlStXHuo805ycHHh7eyM2NtYlbeXCbhBCUFpaipycHMSZuAayyjF1c3PDgwcPAAAFBQWYNGkSpkyZgoIClkUGjxBcnqtVaIPQpg2dt+jpaX2uZ315+eU6D6Kfn+XtORw6jM7U9cca2rWjK8NtoXlzdnM1RUIC8Mkntu0bHg7Up8e3p2f9uxqZkmoKDrbOY2ptrmhYGH38R8z75aLhePLJJ1FmoPxw/vx5DK5vnreDkclkCAwMdBmlLuwKh8NBYGCgWU88K8P0tddeA682/Pz2229DqVSCw+Fg+vTp9pnpQwTXzaU11yA05MkvOBj44w/6f19fy9t36MBuOzYcOmSdIaWhvqF4Lle/DaY1PP98/dua1ldqyZRB6eVFG75sCQ6m85mjotgZqR060O+b62LtgiXTp0/Hk08+ierqagDA2bNnMWTIEGzYsKGBZ2YZl1HqwhFY+l6xcg/m5uYiOjoaKpUKBw4cQGZmJtzc3BjDE486XJcI8n8TTSENGy/kM8/Yb1yRCFi+nA7Ne3kBEonlfYKD65cGoKFLF9oYS0mxbr/nnqv/2PHxtmuIAqbboHK51qs3DB9OG7NpaZbfi/btaa+pCxcseeONN1BZWYnBgwfjgw8+wOTJk7Ft2zYMGDCgoafmwsVDCSu3h4+PDwoLC3HixAm0bt1aK6yvVCodOrmGwOUx/Y/Dph2kLdqh5pg5k1YiYBPWf/ZZID2dWSrJWjw9gStX6Pac1mAvqSRbCqg4HLo4ztxNsbUqDm+8QReSWdqPy6XTLx7ivEAXDycffvghHn/8cYwePRo7d+50GaWNiNjYWBw+fNjidsePH0cTG1tYZ2RkgMPhQGWhriEpKQnff/+9TWM8TLAyTF9//XU8/vjjGD9+PGbOnAkAOHPmDFraKi7+EPMwe0wJIU5pAAAAlzIvoe9nfbH32l6njPfQYMlYat3atLfOVjgc2ijq29fytoMH1y+3lGnsN99kv72np/08hrZITvXubblgydrOSc2aAf361enpmqJNG/rGxVWR78ICUVFRiI6O1nvs2LEDFEXhxRdf1C5z4eJRJj09HUOHDkVwcDACAgLw1FNPIS0trd7HZRXKnzt3LoYPHw4ej6eVjYqMjGwUlrkhPOHD181FqpDi3V/fxV8pf4HD4WDLlC3ok+DYAqHFfy3GqTuncKfoDm4vvg1fTzvlVLJAppRBLBMj2LsBWjOaM0xjY4FVqxwzbng4rUwA0OH92nw0eHoCNTW0csDBg7Z3mjLHjBnAO+8AChY95u0ljg/YZpgOHmx5nra29AwJwT/BwMLOQIk7sPJfIClfZ/3q1fScHRTKT8lOwYqDK9CzaU+83Odl8F2FmI8sW7dubegpuHDhcCoqKjBkyBBs3LgR3t7eWLx4MYYOHapVpLAV1hUMCQkJelqmCQkJaNu2bb0GfxjhCm0oRHEgFEVh/Pfj8c3xb9AltgvceG7ot7If/rj8h0PGI4RArpTjyO0jeDz2cRRWFWLBrgUOGYuJ1UdWI/itYIS+E4qnvngKRVVFThsbgHljqWNH2zstWSI8nM75nDABGDKkbrmmwHDsWGDPHsARvzker66hgSWsDfubwxbDtHNnWkXBHDYapr8jAz2HAGdDgGJ3oN8zwFvdAG2MIjraYdX4x9OOo8PiDtj6z1a8tu01zNg2wyHjuHAOiYmJrB4ubCM2NhYff/wxWrduDX9/f7z00kvaKu/vvvsOzZo1Q0BAAIYMGYK8vDztfm+++SaioqLg4+ODzp0749SpUxbHkkqlmDRpEvz9/dGqVSt89tlnJkPycrkcs2fPRkREBCIiIjB79mzIDWTxli1bhqCgIMTGxmLbtm3a5Xv37kXHjh3h4+ODqKgoJCcn2/DO1JGfn4927dphxYoVOHbsmJ69NmDAAHTVaQ3du3dv7Nq1CwD93i5fvhzt2rWDl5cXpk6disLCQgwaNAje3t4YMGAAysvLAQBdu3bF1KlTERAQAIFAgLfeegtpaWkoLS2t19xBGgmenp52OU7FySN2OY69+ObYNwTTQFYeWEkIIaRCUkE6Le5Ewt8JJ1XSKruP9+bPbxJMA8E0kO3nt5PXtr5GuC9zSUZJht3HMmTOL3MIpoE8veppMn3zdCJ8VUii34sm5ZJyh4+thaIIad+ekLg4Qjw8CKFbLtCP5GTHjbt8Of03LY2Q7dsJ4fHoMc+eJSQwkJCjRx03NiGEJCURwuEQwuXqv2bdR+fOdfO0BydP6h2fAshNP5BzwfT/RuNHRxOiUlk+7qlTVk/lXtE94vmqO+k2BKRYCFIhAJnei/4d7EyMJmTCBELkchtepGVkChnpsqQLwTSQFQdWkBHfjiD8V/ikqKrIIeP917DXtcEavvzySyKTycxuI5PJyJdffumkGVlPampqQ0/BJDExMaRNmzYkKyuLlJaWkp49e5L58+eTI0eOkMDAQHLp0iUik8nIrFmzSJ8+fbT7bdmyhZSUlBClUklWrFhBQkNDiVQqNTvW3LlzSd++fUlZWRnJzs4mbdu2JZGRkXpzOXToECGEkA8//JB069aNFBYWkqKiItKjRw+yYMECQgghx44dIzwej7z11ltEJpOR48ePE09PT3L79m3t+mvXrhG1Wk1SUlJISEgI+eOPPwghhDx48IAAIEql0uxcExMTyXfffUcePHhAmjdvTtatW0cIIUQqlRJ3d3dSXFxMlEolCQ0NJeHh4aSqqorU1NQQd3d3UlJSon093bp1IwUFBSQnJ4cEBweTjh07ksuXLxOZTEb69etHkk1cC//44w8SFhZmdo4azH2/XLEiA7jWiHMDUKgU4HK4Dgm7FYuLMfe3uejfqj/eGvgWAMDX0xffjP8G3T/ujqV7luLTEZ/abTyKovDlkS+1zztGdUTPpj2x7uQ6fHX0K6wYucJuYxmy++purDi4AjOSZuCrsV+Bx+VhXNdxSFqRhE/2f4JPXrBRc9NaOBwUDRmI3OJMdOgzDJzx4+vW2TOMbcicOfTfhASgqooW8N+8mX7esqX92niaIiqKLqgqLQUKC5m3mTQJsGe3Nx3va4UbMLI/cDiSft4vD9h4Eoip1tm+a1fau2sJK4ufCCGY+dNM8HgC/HpUjiAFPdbX/5zHmVCChS1r8PyLL4Pr5mbVcdny+aHPcTHzIrZM3YIXu7+II7eO4NdLvyLk7RDc/d9dNA2pR/tXFxZZs2YNNm3ahOvXr2Ps2LHYtGmTyW2/+OILfPrpp5BKpXjhhRfw7bffQshQl1BQUIBmzZph8ODBSExMRIsWLeDt7Q2xWIz09HQcP34c+/fvN2r1/TAze/tsXM2+6tAxOkR1wKoxq1htO2vWLG03rfnz5+P1119Hfn4+pkyZgk61Oscff/wx/P39kZGRgdjYWLz44ova/d955x0sXboUaWlpaG+moPOXX37Bt99+C39/f/j7++ONN94w6c3ctm0bvvrqK4TUnoMWLlyIV155BUuWLNFus2TJEgiFQiQmJuKZZ57BL7/8gg8//BBJmlQuAO3atcPYsWNx4sQJDBs2jNX7oSE1NRVLly7Fxx9/jLFjxwKgOy116dIFJ0+eREREBNq1awc/Pz+cOXMGQqEQzZs3R6COmsnrr7+O0Nqc+z59+iAkJETbDGL48OE4cuSI0bg5OTmYOXMmPv/8c6vmy0Q9xQjZU1ZWhuHDh8PLywsxMTH46aefGLcjhGDBggWIjIyEr68vkpKScPPmTWdNE1x3doZpYVUhnvvqOYhmidDkvSbYk7LH7nP57uR3EMvE+HL0l3q6X93iu2Fyz8n44vAXKJPYKLfDwPG049r/hXwhmoY0RVRAFEZ2HonvTn0HqUJqt7F0IYTgo90foUVYC3w5+kvwartvJbZIxEu9XsJnBz5DWkH9E6rZzGPZ3mUIy1+JTqqdiEl5H8c7htZ1+YmMdPgcANAdjRYsAJ54gpY+6tvX8YZpmzb0OOHhpjU6H3vMvvmV4eFAQgIoAMMHACfCgI8uA0suApeCgBf6A3LdMxRbiSwrQ/mXsy7j7xt/Y+GQhWgS3gz4/ntgxgzwBzyJeUXhSFWX4E+veoamzLD76m50iu6EF7vTF832Teoukp8d+Mxh47qgiYiIwIIFCzBlyhSz2x04cACffPIJjhw5goyMDNy/f1/bHdGQZcuW4cqVK2jevDk2bNiAQYMG4bHHHsPgwYPxww8/oGXLlrhy5QqWLl3qiJf0n0C3xWtMTAzy8vKQl5eHGJ2OcCKRCIGBgcitbR+9cuVKtGrVCr6+vvDz80NlZSVKSkrMjpOXl6c3lrnWsobja+alwd/fH15eXozr//33X/Tr1w/BwcHw9fXF2rVrLc6NiW3btiEyMhIjRozQW56YmIjjx4/j5MmTSExMRFJSEk6cOIETJ04YpZWE6hSCenh4GD3XaPJqKC4uxpNPPonXXntNawzXC1Y+VzswZswYMmrUKCIWi8mpU6eIj48PuXHjhtF2O3bsIOHh4eTevXtEpVKR999/n3Ts2NHi8e0Vrqm5c8viNmXVZaTZvGbE8zVP8ubPb5J2ye0IpoGsPb7WLnMghBCFUkGavNuEDFg5gHH9xYyLBNNANpzaYLcxJ/8wmfi94UeyS7PJncI72uWHUw8TTAP59eKvdhtLlz0pewimgfx45kejdQWVBUT4qpDM2DrDIWPrsuXcFoJpIK0/bE2W7llKmrzbhGAayJ9vj6BDyc4ObSkU9F+JxPFjVVURUlxMyIsvEtKypX4IXSQixM2NkNJS+4979Sr5sj2f/i4n1I25K5oOo7/eA4S0akUv/+YbdsekKKum8PpPrxPhq0I6ZeSLL+iFxcWErF5NlF9+QeI/iCdd/9eVUFYelw0bT28kmAYy//f5est503kE00DcXnUjOWU5dh9Xw53CO2Tj6Y3katZVh43R0LC9NsyfP59MmjTJ5PqxY8eSDz74QPv88OHDJDQ0tL7Te2h52EP53377rfb5vn37SHx8PJkyZQp59913tcurq6sJn88nDx48ICdPniTBwcHacDkhhPj5+WnD8KaIjY0lBw4c0D7/7rvvTIby4+Pjyd69e7XrDhw4QGJiYgghdaH86upq7fpRo0aRxYsXa/f9/PPPtakFb775Jhk/fjwhxLpQ/jfffEOGDBlCRowYQVQ6qU8HDx4kbdu2Jc888ww5deoUuXHjBunRowdJSkoiO3fuZHw9hBAyfvx4snDhQr3X379/f+3zsrIy0qFDBzJ37lyzczPE3PfLKR5TiUSC3377DUuWLIFIJELv3r0xZMgQbNmyxWjbBw8eoHfv3oiPjwePx8OLL76I1NRUZ0wTAMB1t9w15oPfP8CDkgc4MPsAVo1ZhfPzzqN/q/5477f3UFBpnzatu67uQk55Dt7o/wbj+k7RnRAfHI9fLv5il/EA4OSdk+jXoh+aBDRBs5C6MGtSiySE+oRix4UddhtLl2X7liEmMAZjuxrfaYX6hGJ8t/HYdHYTKmoqHDK+RC7B0VtHkVmaCQA498E5zH9mPm4k30B7nzi8JD+EXE+wLxCqB4dTD6PpvKbo8XEP/H79L3qhNZ2MbMXbGwgKomWT2rXTX3fqFPDee7Qgvp2RtkrAkj4iDMzn4qX0uuVDs4CZN4E1rYG7456lF7ItlrKiW41CpcDP53/G0A5D4efpB8yaRa8ICgImTwZ/yjS8NeAtnH9wHinZVjYiYMH2C9sR4h2CBc/qFxhK1kiQvjQdakqNVYdX2X1cQggW/7UYLT9siZc2vYQOiztg0g+ToKbUdh+rsXDz5k29kG/79u1RWFhY/0IPFzbx9ddfIycnB2VlZVi2bBlGjx6NcePGYePGjbh69SrkcjnmzZuHbt26ITY2FmKxGHw+H8HBwVCpVFi8eDGqqqosjjNq1Ch8/PHHKC8vR25uLtasWWNy27Fjx2Lp0qUoLi5GSUkJFi9erJc+ANDhfYVCgVOnTmHPnj0YWZseJRaLERAQAHd3d5w/f95kVNkSAoEAO3fuhEQiwYQJE0BRFACgZ8+eSEtLw/nz59G1a1e0adMGmZmZ+Pfff9GXjVQhA1VVVXjqqafQq1cvfGJri2sGnGKYpqeng8fjIUFH/7F9+/aMIfoxY8bg7t27SE9Ph1KpxI8//oinn37aGdMEAHAtGAH3i+9jw5kNeDXxVfRuTvdKFwqE+GbcN5AqpFh+YLld5vHDmR8QExiDwW2Z+ylzOByM6jIKh28dRml1/U+MeRV5uF98H72bGfd/53F5GNF5BP669hdq5DX1HkuX1LxUnL13FrMHzIaAL2DcZkbSDEgVUvx66Ve7jq3hg98/QP/P++Oro1/B080TPh4+AOh83h3v7oeMqDBzRnvaeHMgf6X8hWe/ehaEEBSLi/HCty847GaACZlSho+FtzE49CpGPwHsiefTxmiHDnU5sHZm09lNKJFVYH5xExiak/OvAgIK+MI7A3j8cYcoIuy/sR8l1SWY2KM214+vkyvu7Q2IRBjVZRS4HC5+u/ybXceurKnE6bunMaLzCLgL9NVAhAIhmoc2x7AOw/DjuR+hVNm3mcn3p77Hwj8XYnSX0bgw/wLmPDkHm89txsf7PrbrOA8DKpUKXbp00T7Wr19v03Gqq6vhq9OKWPO/WCy2yzxdWMe4cePw5JNPIj4+HvHx8ViwYAH69++PJUuW4IUXXkB4eDju3buH7du3AwCeeuopDBo0CAkJCYiJiYG7u7vZsLyGjz76CE2aNEFcXBwGDBiAESNGMOYVA8CCBQvQpUsXtGvXDm3btkWnTp2wYEHdTWdYWBj8/f0RERGB8ePHY+3atVo9+G+++QYfffSRVnZp1KhRNr83bm5u+P3331FUVIQpU6aAoih4eXmhU6dOaNOmDdxq8+V79OiBmJgYbU6stfzxxx+4cOECNm7cCJFIpH1kZWXZPHcAzgnlnzx50ijksX79epKYmGi0rVwuJ2+88QYBQHg8HomNjSX3799nPO66detI586dSefOnYmbm5td5qq2EDZ9ZfMrRPiqkOSW5xqtG/71cBL8VjCRyOoXeq2sqSSCVwTknV/eMbvd5czLBNNA1p9YX6/xCCFkx/kdBNNAzt8/z7h+//X9BNNADtw4wLjeVpbuWUowDYzvpwaKokiLBS1I4meJdh1bc+zm85prlQiavNvEaJvk3ckE00Cu51y3+/gadl3ZRTANpNPiTqREXEKUKiV5bOFjxPM1T3Iy7aTDxtUgU8jI06ueJpgG0mJ2JAkeT78fm5IiLe9cD9oubEs6L+lMqL59jKvwY2PJS0lc4vmaJ5G8Nt0h449eN5qEvBVClCrzIbKk5Umk1Yet7Dp28u5kwnmZQy5lXDK5zZ9X/6TTSa7+abdxi6qKiP8b/iRpeZI2pElRFBm7fizhTeeRm7k37TaWUqUk3xz7hoxdP5Yk704m94ru2e3YbLFXKL9du3Zkx44d2uclJSUEgLaaubHxsIfyLYXgHcU333xD+vbt2yBjNyYaPJQvEomMXOZVVVXwZvBALVq0CBcuXEB2djZkMhkWLlyIJ554AjU1xp666dOn4+LFi7h48SL4fPtUxZurypfIJfjp/E8Y8/gYRPgZt0R8e+DbKBYXY/XR1fWaw983/oZSrcTQ9kPNbtchqgOahzS3Szj/5J2T8HTzRIeoDozr+zbvCze+Gw6lHqr3WLr8ceUPdI/vzvh+auBwOBjfbTxOpJ9AbnmuXce/mHERd4ruaJ/7e/obbTPriVnwdPPEJ/sdowygUqsw97e5aBnWEiffO4lAUSD4PD72v7EfkX6ReHnLyw7v+DVl0xT8feNvfDfxO9x+6yxyfgb6RnXFK03z8e/9fx0yZkp2Cq7nXseUXlPACQ0DdIoCIBAASUl4sSYCNYoa/N3M/h3Z5Eo59l3fh2Edh1lU1Xih0wu4lX8L6QXpZrezhu0XtqNfi37oFNPJ5DZPt3kawd7B+Olf28J6TCzbtwxVsip8Pe5rcLn0JYDD4eCrsV/BQ+CB5D+T7TKOXCnHwC8G4rVtr+FQ6iEk/5WMzks740buDbsc39m0adMGKSl16RwpKSkIDQ3Vq2Z20fjIz8/HmTNnQFEU0tLSsHLlSgwfPryhp9WocYphmpCQAJVKhTt36gyAlJQUtGnTxmjblJQUjB49Gk2aNAGfz8fkyZNRXl7uvDxTM/lpv1/+HWKZGC/1eolxfe/mvdE3oS9+PPtjvQyJPdf2IEgUhJ7NelqYKh3OP3r7KIrFxTaPp6bU+P3y7xjYeqDJcLqn0BO9mvbC4VuWewKzJbssG5cyL2F4R8s/8qEdaCPd3obxgZsHAACBIvriEuBlnEcZKArEzH4z8dP5n+xqmGj48eyPSCtIw7Lhy+AlrDPOmgQ0wdyn5yKtIA0n00/afVwN5+6dw0/nf8LC5xZiWp9pQHQ03Jo2x+9v70OwdzBe//l1hxjGm89thoAnwOjHR9OqAE8/XScHFRUFtGyJvkGPIVAUiN+QYffxj6Udg1gm1n63zKFJqdF8X+pLal4qbhfcxgudXjC7nYAvwKDHBuHQrUPaXLH6UCYpw3envsO4ruPQOqK13rpAUSBmD5iNnZd2IjWv/ufbZfuW4XjacWyYtAHFXxQjdXEq3AXueGnjS3Z5LfZCpVJBJpNBrVZDrVZDJpMx9iSfOHEiNmzYgNTUVJSXl2Pp0qWYPHky4zEpimL1cPFwMGjQIL1QtOaxbNkyKBQKvPLKK/D29sYTTzyBoUOH4rXXXmuwuTLNUyQSsWoW8MjgLLft6NGjyZgxY0h1dTU5ffq0yar85ORk0qtXL1JQUEDUajXZvHkz8fT0JOXl5WaP7wwR5X7L+5H4D+LNVueuO7GOYBrI5czLNo8T/0E8eeGbF1htm5KdQjAN5Ntj31re2ASXMi4RTAPZem6r2e3+t+d/BNNASsT2CV19ffRrgmkgt/NvW9yWoigS9k4YGfHtCLuMrWH29tnEe5Y3af1ha4JpIMO/Hs64XU5ZDsE0kE/3f2rX8aUKKYl6L8pk1Xe1rJpEzIkgfT9zXOho7PqxxHuWN6mW1VWLks8+I4TUfZ+P3T5m1zGVKiUJeyeMDF0zlF5w+jQhq1YR0rEjHcbv14+QffsI+egjMnXTVOLzug+RKcwLllvLjK0ziGimiEgV5gW2NTSf15wM/nKwXcZe9OciwnmZQ/LK8yxuu/ns5nqfUzR8uv9Tgmkg17KvMa4vqioibq+6kdd/er1e4xRWFhLP1zzJqLWj9JZrXoulc409sXRtWLhwIQHd4Ev7WLhwIcnMzCReXl4kMzNTu+3KlStJSEgI8fb2JpMnTzYpos/hcAiXyzX50Kx/mHmYQ/kuHn0aPJQP0Im9UqkUISEhGDt2LL799lu0adMGWVlZesmyc+fORfv27dGhQwf4+fnhiy++wG+//QY/Pz9nTZWR7LJsHEs7hsk9J+tpihoyovMICHgC/Hz+Z5vGKaoqwv3i++gW343V9m0j2yIuKK5enpxz988BAPo072N2O02x19l7Z20eS5c/U/5EQmgCWoS1sLitxjv8Z8qfdq3OL60uRYBXADzc6BQOplA+AET6R6JjdEfsuWZfvdot57Yguywb/xv2P8bvlZfQC28NeAsn00/iZq799XxP3zmNn8//jJn9Zup5a/ESHRWY0H0Cgr2D8fmh+osm63Lu3jkUVBZgfLfaBgbdugGtWwM9etDPn3gC6N8fGDwYL3R6AVXSKrt66wHa+96vZT+jwiNTPP3Y0ziWdgwypazeY/966Vf0btYb4X7hFrft36o/ANjl9W+/sB3d47ujbRPm1rbB3sEY0WkEfjz3IyRyic3jrDy4ElKlFIuGLNJbPr7beLRv0h5L9i55aDyGycnJIIToPZKTkxEdHY3/t3feYU2dbRi/w95hyxBQEJCtggPc1j1qrVqtq1q11tptq21t1aptrdpqtcNPW2vV2trtXnXVPQGRpaDsJXuv5Pn+eElYARJICML7u65ckJyTc583OTnnOc96CwsL4Vijh/Dbb7+N9PR05Ofn48cff2ywCObRo0d4+PBhgw/Jcg6HU59WM0zNzc3xzz//oKioCAkJCZgxYwYA1Pvx6+np4ZtvvkFqairy8/Nx586dVq3KbwhJCPnZns82up65oTmGdR+Gv4P/blb482zUWQDAwG6NG4kSBAIBhrgPwX8P/mv2if5q7FXYCm3hYN54hWLvLr2hramtFMO0sLQQ56LP4Wm/p5teuYrJvSajvLJcaWHtjw99jL3X9sJYz1hqnEgq8mUx3nc8LsdcVkoXBAm7Lu+Cl52X1PiQxdygudDR0sH//vuf0nQl/B38N3S0dPDhuNrtimBpCQDQ19HHggELcCzsGDILFG/23BCXYi4BAIZ1H8Ze0NJis1wFBgJCIfDaa4CODtC3L57yeApCfaFSq+ITshIQkxFTrS8Ho7xGoaS8BBcftCxklpqbirDkMLlSCADAztQOHrYetSbAaA4P0h8gOCEYzwU0Xu27YOAC5Jfk48S9E83SKSwtxDfnv8H03tPR3bZ7rWUaGhp4f+z7iE6LxsGQg83a/pOAk5OTXA8Oh1OfVjNMn3T+jfwXNkKbenlZspjYYyJiMmIQmRqpsM6piFMwMzBD76695X7PELchyC7Kxr2U5hUVXH14FYEugY16ggFmpPRy7IXLMZebpVOTW/G3UF5ZrpBh0KdrH+hq6eLC/Qst1geALWe2AACKy4uho8naZxjrNdwSarzveIhJ3OwLdl0iUyNx7eE1VvzTyGdvaWyJZ3s+i5+v/4zyynKlaANASXkJDtw8gKHuQ2t7S+vwXMBzEIlF+CfkH6VpX3xwEd1tuktzewGwvNK+fVnP1BpteXS0dDDBbwIOhhxEpah+7l9zkNwAPtW94RuCugxxHwIdLZ0W55lKpnXs3UX+33ifLn1wO/52i3J9Je3WpvhPaXS9ga4DYWpg2uzowMGQgygqK8IrQ2Tn4U3uNRldLbvi63MN94Nsbxw6dAhLly7FCy+8gDlz5kgfHA6nPtwwlQMiwpnIM3iq+1NNGm8ApF7A5ngELj64iEFug6TTcsrDYHc2nVhzPCqS1IFA50C51u/frT9uxt1ssYF049ENAMzYlBc9bT30c+6nNMPUVN8UAJBbnAsCu+Ab6Ro1uH6AUwCsja2VFs4/cPMANAQamNlvZpPrzuo3C9lF2UozigFWzJecm4x3R73b6Hp+Dn5wsXJRWh/Z4rJinIs+hxGedfqSamiw6V+XLq33nqf9nkZ2Ubb0uGkpZ6LOwMrYCt723nK/x1DXEINcB7X4O5AYpjWnHm0Kfyd/pOenIyU3pemVG+Bo2FH0cuzVZGRES1MLY7zH4GjY0WZFYfbf2A9Hc0cEucgu3tTS1MLsfrNxLvpci8bzpPDxxx9j0aJFEIvF+P3332FhYYGTJ0+qPT2Nw2mrcMNUDu4l30NGQQaGewyXa317M3t42XnhvweKhZwz8jMQkxGD/t36K/Q+JwsnOJo7NivEeO3hNQBAoIt8hmmQSxBKK0pxJ/6Owlo1ufjgIlytXWt7zORgsNtgBCcEI684r0X6ANhMPwDySvKknrjGDFMNDQ2M8x2HE+EnlNLw/MjdIwh0CUQnk05NrjvScyQsjSyx79q+FutKOBVxChZGFhjqPrTR9QQCAab4T8GZqDPILspuse7FBxdRWlGKcT7jZK8gI29vuMdwaAg0cPze8RbrExHORp3FsO7D5LrRrMlo79EITwlHYnZis/VDEkPQ1bIrhAbCpleuwt/JHwBwO/52szRzinJwNfYqxniPkWv98b7j8bjgscJ6jwse42T4STzf53lpKypZzOg7A0SEX2/8qtD2n0R27dqF06dPY/PmzdDR0cHmzZtx+PBhxMXFqXvXOJw2CTdM5UBSdNBYHmBdAl0Cce3hNYU8DpLczYY8DY3Rv1t/XIm9onCo7+rDq9DS1EIvx4Z7KdbVAYDLsc0P55dXlsv2mMnBYLfBEJNYKXmukvD1UPehEBGbirExwxRgF+zc4lxpwVhzSclNwe342xjvO16u9bW1tDHFfwqOhh1VSvFNRWUFjoYdxSjPUY0aEBKmBkxFpahSKXmBF+5fgJamlrSYTh7MDM0Q6BKoFMP0QfoDpOSmKJRGImGU1ygALWsbFZIY0mC/4Ibo4dADGgKNZhumpyNOQ0xijPGRzzCV3KwoenP9x+0/IBKLMKPvjEbXc7dxRw+HHvg7+G+Ftv8kkpubC29v5pnX0dFBRUUF+vTpgwsXlBP54bR/4uLiIBAIZLYxa8m6bRVumMrB2aizcLV2bTIEVpNA50DkFufifrr8fS+vxF6BjpaO1DuiCEEuQUjJTVHYk3M19ip6OvSUVqU3hY3QBs5Wzi3KM70TfwdFZUXNMgz6OfeDtqZ2i8L5GfkZ+O/+fygoLYCrtSv+euUv6RzhRnqNG6ZD3YdCIBC0uABLkg4wwXeC3O+Z2GMiisuLcSbyTIu0AdbDM6swC8/1lm/au16OvdDFootSwvkX7l9AgFNAo3mtshjjPQa342+3qGcvUN2FQtb0u03hZecFO1O7Zn8H+SX5eJDxQKEwPsD6CHvYejTbMD0bdRYm+ibo21W+bh+2prZwtXZV+Dg/fu84XKxc4GMvu+q/JmO8x+Dao2vIL2l6vvInGRcXF+n0297e3vjuu++wd+9emJnJ7gDCkY8uXbrg33+V26mjo/LSSy9hx44dSE1NxdNPPw07OzsIBIJ6Xv2ysjK8+OKLMDExgY2NDb78sna3lpCQEPj7+8PAwAD+/v4ICQlp1v5ww7QJiAhXYq802UqpLpLQuCKetcsxl+Hv5C93+xpZeop4EitFlbgZd1PuML6E/i7N885KuBHH8gT7OfdT+L0Gugbo3aV3iwzTvp/2xeCNg5FTnIMglyAY6xnLFcoHmOfO285bYU9SXQ6HHkZXy65yFdNJGOo+FEa6RjgUeqhF2gBwLOwY9LT1MNJzpFzrS8L5pyNOt6hdV3FZMW7G3cRgt8EKv1eSSiMpXGouV2OvQqgvRHeb7k2vXAeBQIA+XfrgTkLzUlnORJ4BEWGI+xCF3+vv5I/bCc0zTK/EXkG/rv2anOGqJoPcBuHig4tyR30qRZW4cP8CnvKQLxd/pNdIVIoqcS76nNz79CSybt06ZGWxTh6fffYZtm7dinfffbfeRZ3DURcnTpzA2LFjoaGhgdGjR+PPP2V3QFm9ejUePHiA+Ph4nDt3Dhs2bMCJEyznvry8HBMnTsSsWbOQk5ODF154ARMnTkR5ueL1KNwwbYL76feRXZStcHjdvZM7zAzM5DYUyyrKcCv+Fvq7KJZfKsHX3hcGOgYKGcJhyWEoLi+Wu/BJQqBLINLz0xGfFa/obgIArj+8DjtTO9ib2Tfr/YPdBuNW/K1m91mMy4oDAGQWZsLMkHktKsXyGaYAu2Bfib3S7Arx8spynIk6g3E+4xTKcdTV1sVIr5FKCWefDD+JwW6D5faUA6yau0JUgcOhh5ute/XhVVSIKpplmPo7+cNYz7jF3uqrD6+ib9e+cqUwyKKnY088yHiAwtJChd97MvwkjPWMm5Wu4+/kj7S8NIULhvKK83Av5Z7CmgNdByKnOAfhKfL1z70dfxv5JflyR0KCXIJgqGuIU+GnFNqvJ42xY8di0KBBAIC+ffsiJiYGaWlpePbZxlsPchpm9uzZSEhIwIQJE2BkZIQNGzbg2rVrCAoKgqmpKfz8/HD+/Hnp+kOGDMGHH36IoKAgGBkZYcKECcjKysLMmTNhYmKC3r171/IOCgQCbN26Fc7OzrC0tMS7777b5A3a7t270b9/f7z11lswNTWFs7Mzrly5gt27d8PBwQHW1tb46aefpOvn5eVhzpw5sLKygpOTE9atWyfVEIlEeOedd2BpaQlnZ2ccPXq0llZdb/Hq1asxa9YsmfuVl5eH+fPnw9bWFvb29vjwww8hEomky+/evQtTU1N07twZnTp1wiuvvILevWV3DNmzZw8++ugjmJmZwcPDAwsXLsTu3bsBAOfPn0dlZSXefPNN6Orq4vXXX2f5/GcVdyRww7QJpHmfTUwPWhcNDQ3079Zf7oKkkMQQlFeWK+y9lKCtpY0eDj0UKkq6GsuMWEU1JfmokupiRbkZdxN9ushfjV+XQW6DUCmqlO5/cykpL5E21JcYmfKElwe5DkJRWRGCE4KbpXsz7iZKyksUylmWMNB1IBKzE5GUndQsbQCIz4pHVFoURnsr1h+4T9c+sDO1a1Ge6YX7F6Ah0FC4wA9g1dz+Tv64FX+r2foFpQW4l3yvWd56Cb0ce4GIEJoU2vTKdbgSewVBLkENTv3bGM0tgLr+6DqISOFz2CBXZkzJew6TeLKbKqaToKOlg6HuQ3Eqon0bpgDw4MEDfPLJJ1iyZAk++eSTWtNzcxRn7969cHR0xOHDh1FYWIiZM2di3Lhx+PDDD5GdnY1NmzZh8uTJePy4Ou3n119/xd69e5GcnIzY2FgEBgZi3rx5yM7OhoeHBz7+uPZkEH///Tdu3bqFO3fu4ODBg9i1a1eT+3X9+nX4+voiKysLM2bMwPTp03Hz5k3ExMRg3759ePXVV1FYyG5oX3vtNeTl5eHhw4e4cOEC9uzZgx9//BEAsHPnThw5cgTBwcG4desW/vij+SlUL7zwArS0tBATE4Pg4GCcOnUK33//vXT5sWPHMG5cA4WoNcjJyUFKSgr8/KrTkPz8/KRpKuHh4fD19a3lbPH19ZUuVwRumDbBldgrMDc0h3unpmcnqstA14GITotGRn5Gk+tKQoMBTgEK60jo5dgLIYkhcoferj28BhuhDRzNHZteuQY+9j7QEGg0yzDLKcrBg4wHCrWJqkv/bv2hqaGplLZRUsO0ymMqTxqFJK2jueH889HnIRAIMMhtkMLvlXi9WlJ8JSnckRTyyItAIMB43/E4GX6y2d7iiw8uoqdjz0YnMmiMAKcAhCaGNrtdWXBCMMQklntmNVn0dOwJAAp3pmiu51JCcwugrsZehUAgkDu/VEIXyy7obNZZ7uP8TNQZ+Nj7wNrEWm6NUV6jEJMRg0ePHym0b08S+/fvR8+ePXH37l0YGhoiLCwMvXr1wv79+9W9a4rx5pvAkCGqfbz5ZrN2bd++fRg7dqw0HD1ixAgEBATg2LFj0nXmzZsHFxcXCIVCjBkzBi4uLhg+fDi0tLQwdepUBAfXvp4tX74c5ubmcHR0xJtvvolffml6NseuXbti3rx50NTUxLRp05CYmIiVK1dCV1cXI0eOhI6ODmJiYiASiXDgwAF89tlnMDY2RpcuXbB06VLs3bsXAPDbb7/hzTffhIODA8zNzfH+++8363NJT0/H8ePHsWXLFhgaGsLa2hpvvfUWfv21uhvG0aNHMXbs2Ca3JTGohTV6TAuFQhQUFEiX11xWd7kicMO0CaLTouFt792ssJ/EgJHMctMYwQnBMDMwU6jAqi49HXuisKwQMRkxcq0fkhgCf0d/hVvmGOgawN3GvVkeU4m3S5Hm4nUx1jOGX2e/Zhln289vr/VcEsrX0tCq9bcxbE1t0c26W7NDyuejz8PX3hfmhuYKv7eHQw/oaeu1yFt8MvwkHM0dm5VjOcRtCArLChGWHKbwe0ViEW7F3WqRtzKgSwDKKsvkDi/XRXID2JwCQwl2pnawMrZCcKJiN2ZSz2UzDVNDXUN0t+musGF6JfYKfOx9FL4ZEAgECHQOlKt3bFlFGS7HXFY4CiDJtZXnHPmk8uGHH+LYsWM4cOAANmzYgF9//RXHjh3DBx98oO5dazfEx8fj999/h6mpqfRx6dIlpKamStfp1Km6LZ++vn695xLDS4KDQ/W12MnJCSkpTafQ1N2mrNcKCwuRmZmJ8vLyWrN/OTk5ITk5GQCQkpJST785xMfHo6KiAra2ttLPZdGiRcjIYM6y3NxcREVFISio6XOSkRFLc8vPry5WzM/Ph7GxsXR5zWV1lyuC/JnwHZSU3JRme/f8nfyhr6OPSw8u4dlejecTBScEo6djT4WNxJpIPDnBicFws3FrdN2yijJEpkVigp/8VeE16eHQo1kXE8lFLqBL8z3DAGv6fvTu0aZXrEPdwhmJx/TvV/7G95e+h7OVs1zbGdBtAI7cPQIiUug7qxRV4krsFcwfMF/+na6BjpYOApwCmu0xFYvFOB99Hs/0eKZZx5okBP/f/f+kx5u8RKdFo7CssEU3JZKIwq24WwrrA8zLaSu0lat3bEMIBAL0cuylcMTgSuwVaAg0WhQt8Hfyl7avkweRWIRrj65hRp/G2zc1pvf77d+RXZTd6I3U3aS7KK0oVThFw9PWEyb6JrgSewWzA2c3ax/bOgUFBQgMrJ0u1a9fPxQVNS9HXm1s2aLuPahFzfOXg4MDZs+ejZ07dypt+4mJifDy8gIAJCQkwM7OTmnbtrS0hLa2NuLj4+Hp6SnVsLdndRe2trZITKzusJOQkFDr/YaGhiguLpY+T0tLk6nj4OAAXV1dZGZmQkurvrl38uRJPPXUU9DUbHpCHzMzM9ja2iI0NBQjRrBWj6GhodLPyMvLC1988UWta+Ldu3exZMmSJrddF+4xbQQiQkpeCuxNm1eko6Olw/I+m6jgrRRVIiw5rFkX2pp42XlBW1NbrhBjVFoUKkWV8O3s2yytno49kZidqPC88TfjbsKtk5u0uX1z8bH3QUZBhlxpEjWJfRxb67nEMHWzccOGKRvkNtb6d+uPzMJMhdqBAc0vOKtJoEsgbsffblY/0+j0aGQXZSvUQ7QmjhaO8LD1aFZngJtxNwG0zFvubOUMUwPTZueZ3km4I3fP3sbo6dgT91LuoayiTO73XI29Cm9772anMQBAL6deSM1LRWpuatMrg/VszS/Jb9HNNdB0XqvUE+2omCdaQ0MD/br2U0pf4rbK22+/jQ8++AClpez3WlJSghUrVuDtt99W85492XTq1AkPHz4EAMyaNQuHDx/GyZMnIRKJUFpaivPnzyMpqfm5+Bs3bkROTg4SExPx1VdfYdq0acradWhqauK5557DihUrUFBQgPj4eHz55ZfSAqbnnnsOW7duRVJSEnJycrB+/fpa7+/Rowd+/fVXVFRUNJqDamtri5EjR2Lp0qXIz8+HWCxGbGystIeurDB+aWkpysrYea2srEx63ALAnDlzsG7dOuTk5CAqKgo7d+7E3LlzAbACM01NTWzduhVlZWX4+ms25fCwYYq3heSGaSPkFueipLwEdqbNv1Py6+yHkMSQRlsrRaVFoayyDD0dWmaY6mjpwNveW64Q492kuwDQbMNU0iBc0XD+jUc3WuQxkiDZb0VCykSE2MexcLKoDotIQvmKIp1oQMF+rhKPcUtyHINcglAhqmjW7FtXYuSYxIEIuHQJqFG5WZMJvhPw34P/UFJeopD2rbhbMNQ1hLuN4vnaEgQCAQKcAnArTnHDtLisGJGpkejlpATD1KEnKkWVcqcUSDyXzQ3jS5AYfvKG81uauiCvYXo7/jbMDMzQxbKLwhpBLkG4l3yvXfUzdXBwgKOjIxwdHfHtt99iy5YtMDExQadOnSAUCrF582Z899136t7NJ5r3338f69atg6mpKQ4cOICDBw/i008/hZWVFRwcHLBx48ZmTakrYeLEifD390ePHj0wbtw4zJ/fvChXQ2zbtg2GhoZwdnbGgAEDMGPGDLz44osAgIULF2LUqFHw8/NDr1696nVwWLt2LWJjY2FmZoZVq1ZhxoyGIyJ79uxBeXk5PD09YWZmhilTpiA1NRVEhNOnT2P06NpFsPr6+tKwfffu3aUpCQCbXtfFxQVOTk4YPHgw3n33Xen7dXR08M8//2DPnj0wNTXFrl278M8//0BHR0fhz4aH8hshOZflezTXYwowA277he2Iz4pv8KQtCQkqOhuMLHo69MSh0ENNhpjvJt2FrpYuXK1dm6VT0zCVN68sOScZqXmpLfKYSZA08A5LDpNbP6swC3kleRjQbYC01ZXEY6oo7p3cYW5ojiuxV/DigBflft/1R9dhaWSJrpZdm6UL1O5Zq2il9eXYy7AwsoBbp0ZSPb79Fnj1VWD1asDHBxg3rtY0oUHdgrDh5AaEJIYo1NHhZtxN+Dv5Q1OjgbDRv/8C4eGAtzfQowdgIXu62oAuAfji1BcorShVqOdvWHIYxCRWisdUYtwGJwTLZehGpEQgvyS/xYapJN3ndvxtjPdretaw4IRg6GrpwsPGo1l6ZoZmcLZylssw9XdSPF8dYP2MxSTGrbhbGOahuHelLbJvn/KmDubIZuLEiZg4cWKt1xqaTatm6yiA9ZatyfDhwxETU7s2Y+zYsXj99dfl3p+5c+dKvYcA0K1bt3oOqZoeXDMzswaPEy0tLWzevBmbN2+WvlYzJO7s7Izr16/LfG+XLl1q6QqFQnz33Xf1boRu3LghbWNVk8acaLq6uti1a1eDHQp69uyJ27eb12u5JtwwbQRJmLYlRkRNA64xw1RPW69FniQJPR17YtflXUjOSUZn884NrheaFAovOy+FGm7XxMrYCvam9ghNlL9ljsS7qgzDwNrEGtbG1ghLkt9jKikK87b3xtEwlp8qt2FaUAAYGgJVRXAaGhoIdA5UuAjp+sPr6Nu1b4tyiTuZdEJXy67NyjOVtCuSqU8EXLsGSFqJrF7N/n76KWBgADz1FODtLW31dePRDbkN0/LKcoQkhuDVYa/WX1haCjz9NHD6dPVrZmbA4MGsUveNN2qtHuAUgApRBcKSwtC7q/w3ORLvoTKOP2dLZxjrGeNOwh3MR9OelJZMN1wTIz0juHdyl7vR/p2EO/Dt7Nus9lQS/B39pWkYsiirKENYchjeHtG80HQPxx4A2DmpvRimgwcr3qeXw2lt6rbIaivwUH4j3Eu+B4FAAC87r2ZvQ57WSiGJIfDt7NtsI7Em8oa47ybdbXYYX4KnnSciUiPkXl9ixLZUV4JPZx+FQvmS/FJve2/pa7raug2tzigtBXbtAoRCoE4op6djT0SnR8ud65lfko/ItEilpDIEuQQpPPtWZkEmotOi6xtHRUXAzJmAjg4QFASEhAADauSgfvIJa+Pi4wP8/DNsTTqhs1lnuaq1JYSnhKOsskx2O7SLF2sbpQCQkwP88w/T3b0bqOrvB1QXzimaZ3on4Q4sjCxa1PlCgoaGBno49JC7Mv/6o+uwMraSu7iuMfyd/OUK5RMR7iTcaXHuekCXAMRlxTWYT34v5R4qRBXNThfoZNIJNkIbhW5ynyQqKiqwatUqODs7Q09PD87Ozli1alWzZsThqJeXX34ZRkZG9R4vv/yyundNYfr06YMxY8aoezdkwg3TRghLDoOLlQsMdA2avQ0DXQN0s+4mzemsCxEhODG4xfmlEiRG172Uew2uk56fjvT89JYbpraeiEqLkjuPJzQpFF0tu7ao+KMmPvY+CE8Jl85z3xQxGTEQCATwtG1iGtD//Q9YsAA4exYwNmYGKRFw/Dj7W4Wfgx9EYpHceYY3426CiBTuJymLQJdApOWlKTT71rWH1wDI8Nr98guwfz9QWaM36bZtwN27LLzetUbEYNYs4Icf0KdrH1x/JDuUJItGC58iqm5uBg9mBmppHUN/3jzgxReBm2wbjuaOsDSyVDjPNCwpDL72vg17q2/fBt59F3j8GLhxg3lqly1jubY3bgCptQuOejn2QmhiqFzHX3hKOHw7N6KtAP5O/kjJTUFanuxKXAlxmXHILc5tsYdYYnA2dHMtMZJb0oJLkovfHlm2bBn+/fdfbN++HaGhodi+fTvOnj2L5cuXq3vXOA1AROjWrVu917dv347CwsJ6j+3bt8vYCqe5cMO0Ee4l34O3nXfTKzaBl51Xg57F+Kx45BbnttirIcHc0Bx2pnaNhrjDk5khJcnTbC6edp4oKitCYk5i0yuDGabK8pYCbP+Ly4vx8PFDudZPzk2GtbF14x0BRo4EXn4Z+OEHFrqWGGtjxgDp6cBffwFVhrhfZzYDhryenusPmSHXpMc0JwfIz2eeyzt3gM8/By7XLrKSNtpXIJXgVvwtCASCagPif/8DFi8GVq0CnJyAb74BRoxg3skePZiH9KmngLAwZpx99RV734kT6NOlD2Ifx8rdleFm3E2YGZjV9hi+8gogEDCvqIkJcO4cMHw4y2e9cwf46afantvp04HKSlYA1SVAIY8pESEiNaLx6MdnnwGbNgHW1kDfvsDWrcDGjYCpKXvepw+QlgaUsKKvno49UVxejAfpjc/iQ0SISIlo+oZITiSGZlPtqiTe3JYappKb3YZuwCSFTy1NeYpIjWj2xAltmd9//x2HDh3CyJEj4e7ujpEjR+Lvv//Gb7/9pu5d43DaJNwwbYDSilI8yHgAn84tM94AZpjGZMTIDPlKQtHKNtga85hGpkUCADxsm1cQIUFSUBGZGtnkusVl7AIuMeaUQc0CKHnILsqGhaFF/YIZImYURURUh5QnTAC6dwd27GCe0q1b2etTprBljx/DxcoFhrqGcnt6rj+6DrdObvU7Ady/zwqMrlS1zHF1ZakDPXsC/v7Ae+8xzTpjN9Q1VKjNTnBCMNw7ubNpV8+fZwb49u2Alhbw5ZfMUDx1CqiRwC9FQwN4/XVg9mzg/HkEWjNjpbHcw5rUK465dQuQJOMPHcryWGt6E3v2BObMAfrX6Iv58CFw/TqQk4MApwCEp4SjuKy6l19jJOUkoaC0AJ52MozDnByguJgdAzXZtg3w9AQkjbeTkgBbWyCApRJIZ4Bqoh1cYnYiCssKlWaYNmUoSrgTfweaGpotPodZG1vDwsiiUcO0l1OvFnmD/Tr7oUJUgai0qGZvo63SULqNImk4HE5HghumDRCVGgWRWKQ0j6mYxIhOi663TPJac2bhaQhve29EpEQ0OG1kVGoUjPWMW9QGC4D0Ih+R0nSeaXhKOMQkhp+D8gxTLzsvCAQCuQugsoqyYG5oXt8wXb8eGDYMqGoUjEuXgEOHgMhIYOFCYPRooFs3YMMGtvzYMcDaGhqzZ2OwsLtcc6YTEa4/ui47jP/rr2ybY8cC48cDWTK8kDk5QO/ewIcfAmDzxvfp0kchwzQkMYQZU/fvM0O3UyfmiX30CKjTjqRBXnkFyM5G4NZfIRAI5MozrRRVIiIlovq7f/CAjdXJiXkgz54FGmrC/Npr7O+nnwLa2syDam6Opx+UQSQWyT1fveQYrWccVn2XMDYGsrOZFzksjH0ur77KugSEhjJPsnRjEcC1a/Cw8YCulm6TnktJtESmUdwMLIwsYCu0xb3khm8+AXbD5tbJTaHOBbKQ5NnLMkzLK8sRlhymcP/SukgKoEISQlq0nbbI1KlTMWHCBJw8eRKRkZE4ceIEnnnmGTz33HPq3jUOp03CDdMGkHjhlOUxBWR7OKLTo2FtbN3sfpqy8LT1RFllGRKyE2Quj0qLQneb7i3Od7MwsoCVsZXUA9sYEgNCmR5TA10DuFi5yO0xzSrMgoWRBfS0alyor14F1q6tvWLPBtIq3n0XyM1lRUIAsH8/5j3SQWhiaJPej4TsBKTnp9fuX5qTwwy9VavY87w84GjVbFbOztUFP2ZVx8atW6wQ6bXXgIMH0adrH4Qlh8kV/swqzEJCdgJ62PsyI1RPj+VNBgVJOw3IRb9+wNKl0N6zD7NKOsPgr6Yb7cc+jkVZZVl16siCBcxLfeoUM44bw96ehc7fe495davwPXgRAOTOM5UYh/VC+evXs3QNSZ50QABrV1Vzij5fX+DkydqvffEFtLW04W3v3aTHXGIUt6SIsi7e9t6NRkUANJ26oACetqzQse5xfi/5Hsory1uUXwoArtau0NPWk/tG40liw4YNGD58OJYsWQJ/f3+8+uqrGDp0KD7//HN17xqH0ybhhmkD3Eu+Bx0tHXSzqp8ArShundygqaEp0zCNSo1SSpuomlgYsf6PucW5MpdHpkYqzUPraespl8c0NDEURrpGsvPQJLmEX33Filz27q0OnzaBj738lflZRVmwMLSAvo4+Xhr0Ei4tv8SMUkNDoGY/OYNGit2EQiA5GXjuOUAoRGAk643aVBGSJL9U6jElYl64uk22X38dGDSItW16/nn2mcTHM6/dw4eAnR3w9dfApEnoaeWBClGFXKkUEuNpzL1c5gX86ivA0bHJ98lk8WIAwJ59iXhn922giVw5yffjbe/N8nQvXmTGtVvj0+ZK0dNjYf6XXgJcXABTU+hcu4nxBWZNhtElRKRGwMrYCpbGltUvZmYyz+hHH1W/5tmAV1MgYMfIm2+yYqyTJ4Hy8kbzx2tqS8LhysLbzhsRqRENFl6VlJfg4eOHSjNMvey8kFuci9S82gVgyih8AlgEwNuuaSP/SURHRwdr1qxBTEwMiouLERMTg7Vr10JXt4mOIBxOB4Ubpg1wL+Ueutt0b1H/Pwm62rroZt1NpgEXnR6tdMPUWNcYAFBQWlBvWUFpAZJzk1ucXypB0jKqKY9hWHIYfDr7QEOWd27uXGYgvPkm8xLOmcOKYABgzRrgzJnqkHMdfOx9EJMR0+QsREQk9ZgKBAL8b/b/0F/PiRkYL7/M8hzlxdISOHAAWLwYtvdi8XQ8cDeucQPpVvwt6GjpVOcSv/46qwL/4gtmhCclAe+8wwqdLlwArKxYEdDrr7Mws68vq45PTgZ+/x0gwthP9wAk3+xbIYkh0K0EvDb+wAp5WhJGdHEBXngBYg0NiASAeN5c1ue1ASRt1zxsPFiBFREwdariujo6QEwMEBEBgYEB/vgzDxkR8vXzDE8Jrx3GT0wEDh9mntKJE5mxvn8/M4IbomtXYPNmYMYMNt6PPoKXnRdSclMavAkEmGGqrDC+BG97b5SUl+BRZv3fBMBShMQkVlpea0NRnzsJdyDUFyqlDVYPxx4ITWo6+vAkcPbsWbkenI6Dl5dXvUb/HNlww7QBIlMjlXZSByAzRyu7KBuPCx7DvZOSDVM9Zpjml9af4k/ZOa0eNh7ILc5Fen56o+uFp4Q37L2RNWXZ9essdL5qFcurHDCAhbfr4NvZF2ISN+m1KiwrRFllGcwNzatfPHCAGSZz57KiFoAZgfIyYQI0KkU4eBoQfrG10VVDE0PhaesJHS0dZoRWzSOMyZOZx9benlWAN2YYSXj6aeCpp2B88ixGZ2jL1RUgOCEYizLMoJGZCaxbxwqeWsKuXbh29SBGjgY0ikuYMd0A95LvoZtVNxgUlQJbtrAxe7TgxsjWFjh1CrqVYvQ/17DXUIK0Kl5iHN6+zbzFL74IdOkC9OrFPKXPPy+f/vDhLB1hwwYE5rBjtyGvtbIr8iVIfksN5Zk2mLrQTBrKJw9PCYePvY9S2mD5dfZDVmEWknOSW7wtRcnOzsakSZNgaGgIJycn7N+/X+Z6u3fvhqamZq0elrKMjfnz5zf5WLBggYpH1b7p0qUL/v33X3XvhtyEh4djyJAhTa43cuRInDp1Cvfu3cOoUaNgaWkp8/fV1DF75swZdO/eHQYGBhg6dCji4+VvLahuWs0wlfeHX7eBra6uLowVMRaUQEl5CeKy4pRakORl54XYx7G1KvNVUfgEQNonVJbHVHIB7W6rpFC+HAVQjwseI7MwU/bFedcuoLyctWnas4d5065WtUDatIn9vXatev2UlFpvl1bmN1EAtf74egBAoHMgC41XVjIDxcmJeQAFApbzGKpAjltgoDTs73P8OmtU3wB3k+9W59f+8Qf7e/o001cUHR2Wi2ptjd0XgKR7TfcTDU4IxuJwAtzdWQuolqKhAR/fwbhsC5Qa6LLipHzZc52HJYexMP6hQ8zT+N57Ldfv1w/xA3vig9si5L0tYzapGqTmpSKvJI8df0TMKw2wrgtHjtTuBiAvVdsYOOMtfHITiEiQfdyk5KYw7cY8pqIqw1osZoVh//zTpLxkew1VyoenhENTQxOunZo35XBdZFXmExHzRCvJGyz5fdxNlt3zWZUsWbIEOjo6SE9Px88//4zFixcjPFz2ZxsYGFirh6UsY+PRo0dNPh4+lK/NHaf9U1nVlrCoqAi3b9/G4MGDoa2tjeeeew4//PCDzPc0dsxmZmbi2Wefxdq1a5GdnY2AgABMmzat1cbTUlrNMJX3h1+3ge3zzz+Pqc0J+7WAmIwYEJHSjDegujI/KrW6HYrEMFV6KF+v4VB+VFoUtDS1lJI7C1S3nGqsAEpyMavnvYmOrp5N6eOPWSsioHbx0bx5td/z3ntAWZm0WMXF2gX6OvqN5pmm56fj02OfYlrvaRisac+8ZNrarLG8vX31iiNG1G4m3xQCAfDgAb6bHQDzvFLWjF1GGDIjPwNpeWmsKp2I6fboUZ2u0Bx0dYEPPkCn/Aqs3H4dlNywl6m4rBiGYZHoHp9b3TtUCRjrGaOrXXd8Nd2HNb+fMqXe+EvKSxCTEcMM03PnWBpEr5ZPCQoAmTu3YU83wPyr7dWttmQgLT6y8QBWrGCpEB9/zLoueDXTo2huDixdCgD4IBSg4ydka0sq8hvymIaEAJ07s+9EU5Pl3U6axDoDBDdc7W+sZ4wuFl0a9pimRMDV2pV56JWArMr8xwWPkV2UrTRvsMTAlSdnWpkUFRXhzz//xNq1a2FkZIQBAwbg6aefxt69e1t1PzjyM3v2bCQkJGDChAkwMjLChg0bcO3aNQQFBcHU1BR+fn61PNlDhgzBhx9+iKCgIBgZGWHChAnIysrCzJkzYWJigt69eyMuLk66vkAgwNatW+Hs7AxLS0u8++67TU4kExsbi2HDhsHCwgKWlpaYOXMmcnNzpctrenhXr16NKVOmYNasWTAxMcHu3bsBMC9n//79oaurC3d3d8yfPx9eMs5RTR2zf/31F7y8vDB16lTo6elh9erVCA0NRVTUk9GOrVUM0+b+8CXve+GFF1pjN6VIeukpM8QuK0crOj0a2prazW9MnZdXXU1cAxM95jGVFcqPTI2Ei5WLUnJnAcBWaAuhvrBRj2mDhmnNMExAjakqdXWBqCjmXdu1i4X0g4JYOH/vXhbu9vIC8vKgqaEJT1vPRg3TuMw4AMDsfrOr54GX4NvC/rF2dsie9gw2ewP49ltmdNZBUmns29mXFdDcuMEKeVrKG2/gyKcvwy1bhMqhQ9iMRTK4l3IPK+4Qyo0MACX/lvyd/LHNPJV5t0+fZikYNYhKi4KYxOhfImSpE2PHKtYFoBE8uvTEkgFAvrkxyxPOzpa5nsQ47HnrEWuiP2mStO1Wi9i0CSguRrmmAON+OMWKqepqN1aRf+0ay/dNkzGDk68vM+BXrmQ3PIfqdz9orDK/0dSZpsjNZWkldabMrFuZr4o2WNbG1nIVUyqT+/fvQ1NTE241ivH8/Pwa9JgGBwfD0tISbm5uWLt2rdTb1RAODg5wdHSs93B1dcXQoUOxbdu2JrfBqc3evXvh6OiIw4cPo7CwEDNnzsS4cePw4YcfIjs7G5s2bcLkyZPxuMY58ddff8XevXuRnJyM2NhYBAYGYt68ecjOzoaHh0e9eeP//vtv3Lp1C3fu3MHBgwexa9euRveJiPD+++8jJSUFkZGRSExMxOrVqxtc/+DBg5gyZQpyc3Mxc+ZMAMCxY8cwbty4Jsff1DEbHh4OP7/qDjiGhoZwcXFp8Jhua7SKYaroD1/Cn3/+CSsrKwwaNEjm8h07diAgIAABAQFK/WFLDFO3TnJWDcuBq7UrNDU0a+VCRqVGwcXKBVqaTeT7lZczoyc1lYWaHz9mYWNra5nN0A11DSEQCBr0mCozdUAgEEgLoBoiPCUcQn1hdd/U0lJWQLJiBfNYxsfXz3l0dweMjNj/Gzaw4qfDh2sMJErqJWuqMl8yM5WDuQPz7NnYsAXe3tW9SVuAX2c/vNMHKHLtWp1+UANJDqhfZz8WOu7cmRlSSsB0ykx86Q1oP4hp0NiKvnMOExKAopfms64CSsTfyR/JuclInzSG3TDU6TIg8egN3PIL0964UWnaBroGsLHrhp2TPJmH8emnZXqsw1PCYW5oDuEPe5m3/PfflWYcQ18fNwOcYJtdwo6nxNqzoEWkRkjbqtWitJQdAxYWLHxfXs4Ku4iARYuq11u7ln1mMvrMett7Iyotql67sNKKUsQ+jm2ewZiby84py5axG8QaeWl1K/NV0QarqXNJc6isrJReJwICArBjx45aywsLCyGs87sQCoUokFHQN2jQINy7dw8ZGRn4888/8csvv2BjE8f066+/DjMzM6xatQrff/89Vq5cCQsLC8ybNw/Tpk3D1q1b8cEHH7R8oCrnTQBDVPx4s1l7tm/fPowdOxZjx46FhoYGRowYgYCAABw7dky6zrx58+Di4gKhUIgxY8bAxcUFw4cPh5aWFqZOnYrgOhGK5cuXw9zcHI6OjnjzzTfxiwynQ026deuGESNGQFdXF1ZWVnj77bdxoZHc+8DAQDzzzDPQ0NCAvr4+AOD48eMYO3Zsk+Nt6phV5Jhui7SKYdrcD+mnn37CnDlzGkysf+mll3Dr1i3cunULWi0t5qhBdFo0HM0dYaDbSNsgBdHV1oWrtWs9j2mTRmJ2NrtALFnCWgX16MEMUn9/djHbu5d5E2sY5gKBAMZ6xsgvqe0xraisQExGjNIq8iV42Hg0Gn6TFJ5Iv8fz55lnMS+PVZ3L27bI1JTNX75mDXseXj21alpeGjIL6nusADbzDgB0NrVnodMJE1iY9OpVxYqdGsDPwQ9iDSB4RC+23Qe1p6gMTQqFnakdLDUNWDh78GClhdN9O/vi/d5A+EAf5o3Nyam3ju5fB6EBwHThK0rRrImkTdCt3AfMC7x3b60K/bDkMHQr0YbB9dvAW2+xY1eJ+Nj7YKdtLpul6fJlmSH9iJQITIATBP/9x35HmppK3Yf/Vr6EgIlgrbDqXLwkhU/SY7+oiOWnDh/ObjKXLWOTN2hrs1xngM3GJRKxaMidO6zoTyRix39wMEtlAWsZVSmqrDclqqQiX26DsaKCTcF76BA7lxw8WL2sSxepsV036hORGgGhvhC2Qlu5P6umaKhfakvQ0tKSXidu3bqFl+pEK4yMjJBfJz86Pz9fZm2Ds7MzunbtCg0NDfj4+GDlypX4Q5Iz3gC7d+/G8ePHMX/+fIwcORILFizAkSNH8PPPP+Pll1/GkSNHmjR6OI0THx+P33//HaamptLHpUuXkJpa3d6sU42eyfr6+vWeF9ZpUejg4CD938nJCSl16hvqkpGRgenTp8Pe3h4mJiaYNWsWMmVEUWRtHwDCwsJgYmJS73VZNHXMKnJMt0VaxTBtzoeUmJiICxcuYM6cOarevXrI7VUsLmbtY/74o17YSxY1c7QqRZWIyYhpOr/0RgMz60TXmEXq6tV6RTvGusb1PKYPMx+iQlQhv8f06FFWuXzuHLvwe3gAGRn1VvO080R6fjqyi2SHUuuFFU+dYn+XLWMtohRhyxbWd7JTJ1YkkpQknQShIa9pYnYiXMr1YG5syQz9/v2ZgS/xyLaQzmadIdQX4rgbu+vFn3/WWn43qarwackS5u1euFApugArdHO2dsbugdbsePz223rreP13F/ftjSDortwiO4BNyykQCFg/S29v5vHLy5Muf/AoFDtu6TNjUAXJ9z72PniQ/gAl06eyorA6RoKkQGdBSAmgr8+OZyXjZeeF21ZAoY8H8PPPUq+ttDhIkoNZUMBymN97r3pmqTfekL1RDQ1289KzJ/v9jR7NPtdevVgXhzt3qivz64TzG5zlqiEWLWJdBiZOZHm3337LPNCS47Qq/02iJ9l+vRtOJeBp54n8knyk5DZuBCgTNzc3VFZW4kGNG8rQ0FCZuX11EQgETRrRqampMKpzrjE0NJQaOm5ubrVyEdsuWwCcV/Fji9x7U/O4c3BwwOzZs5Gbmyt9FBUV4b0WFFom1oh+JCQkwM6u8ZkS33//fQgEAty9exf5+fnYt29fo8dG3d+NvGF8oOlj1svLC6E1bIKioiLExsbKdUy3BVrFMG3OD3/Pnj0ICgqCs4wWQaqEiBCdFt104dOtW+wC8fbbrCdjzfBbA0gq8yX9BytEFY0bpkTVOXv797Nq6v/9rzonbcIEaQEGAgJqhbotjSzrtXCSpCg06TGNj2cXqfHjWV/RYcOYZzMqis3jXqf6XFoAJcNrmpGfgczCzGrDVCRiuYbjxzPPkaxWUfIwZw67uLu6okcxMwgbMkzvJNzBxzFVHntHR/nbAsmJQCCAt703/iuNA/r0Af76S7qsvLIckamRGKrpwC7wS5cyj6kS6eHQAweRADzzDKuOT6ie8Ut04zq8kooQ8VTLGqA3hLGeEXztnXE36W71xATF1fPXv7TzIgbfz2debkUKy+TEp7MPxCRGZFEyO6b27mUzRVWRUZABZOeg77VYYNYsVrSkZCQh8+Bx/YC7d6U3XhkFGcgpzmHLr19nv6HHj5m3dNkyNpOVPEadoyNw/Dhw7x4r0BOJgG++QXcbd2gINOpV5ksq8uVKRTp7lv3Ga94sLVzIbjJ27GB53VUeVCtjq1qV+RGpym+DJdmessP5jWFoaIhnn30WK1euRFFRES5fvoyDBw9itqQYswbHjx9Hejo7r0ZFRWHt2rWYOHFio9ufMGECJk6ciH///RdRUVH4999/MXnyZEyYMAEAcPXqVXTp0kXp42rvdOrUSdrZYNasWTh8+DBOnjwJkUiE0tJSnD9/HklJSc3e/saNG5GTk4PExER89dVXTVa1FxQUwMjICKampkhOTm4yxaMuR48erRXGJyKUlpaivMrpVVpairKqaElTx+ykSZNw7949/PnnnygtLcWaNWvg6+uL7ipwTqgEaiWmTZtG06dPp8LCQrp06RKZmJjQvXv3Glzfzc2NfvjhB7m3b2BgoIzdpKTsJMIC0Ddnv2l4pYoKooAAImY6EvXrRyQQEBUUEF27RnT0KNGjR0RFRbXe9uuNXwkLQKGJoXQ45DBhAehKzJWGdd56i23fw6P+spAQpkdE1KdP9b4EBBClp9OiT8aS2wq3Wm9Zf2w9YQEotyi38Q+hU6fq7dV8jBvH/h4+XGv1R48fERaAdlzYUW9TZyPPEhaATt47yV44cYJt4/ffG9+HphCLiU6dIgJIvGIFWb9uQQt+WlBvtZLyEtJ9WZdSHSyIRo5smWYjvLz3ZTJ93ZTEr7xCZGEhfT0kIYSwABQ1bTQbd3y80rU/PvQxCRYKqDAqnGl88ol0WcGAfpSlC9p/arsSlLKI6BQRPUtE/YioBxEFkUgMis3QJkr3JfofiIJvExFRTloCVQhA15/prwRt2USlRhEWgH66vIvov//Y+NeskS4/E3GGFvWvOn7v3FHJPlSKKklvsR4t2/8mkb090dChUm0sAP0b8W/t31FWVssEFyxg25k3j9w/dKdJ30yqtXjSN5PI/UP3prezfz/bjrk5UWkp0c6d7DdVkw8+INLUJCosJCKiQRsGUdBnQZRZkElYAPri5Bfy73d8PNGyZUTr1hH99hv7DdchLS+NsAC05fQW+bfbBPJcG7KysmjixIlkYGBADg4O9PPPP1ftcjwZGhpSfNXvdunSpWRtbU0GBgbUtWtX+uijj6i8vLzRbZeUlNDy5cvJ2dmZ9PT0yNnZmZYvX05FVdeH1NRU6fbbEhEREerehUb5559/yMHBgYRCIW3cuJGuXbtGgwYNIjMzM7K0tKSxY8dKP9fBgwfTzp07pe9dsWIFvfDCC9Lnp0+fJhcXF+lzAPTVV19R165dydzcnN5++22qrKxsdH/u3btHvXr1IkNDQ/Lz86NNmzaRvb29dLmTkxOdPn2aiIhWrVpFM2fOlC7Lzc0lS0tLqqiokL726NEjAlDr4eTkJF3e0DFbc0zu7u6kp6dHgwcPpkePHjX9obYijR1frWaYyvvDJyK6cuUKGRgYUH5+vtzbV5Zh+m/Ev4QFoDMRZ2SvEBFBtGgRO6Fv20YUHs4MNcnzmhcgT89aJ99rsdcIC0CHQg7RppObCAtAWYUNXKQKCqq3c+hQ4ztdUED07rv1DEnnGZpUXlF90nzhhxfIdqlt49sqKqpvkPbtSxQZSVRcTGRkRGRiQnTrlvQtIpGIDF4xoLd+fave5r4++zVhASgpO4lIJGIXbTMzdiFUBsOHEwF0t4uQgtb0rrf4Wuw1sn2+ahyffqocTRl8c/YbwgJQ/muLifT0pK//dPknGjgOJBYI2HGjAg4GH6y+ydHXJ3rnHbbg5k0igN7uA7qbeFeBLYqJ6A4RHSOi8cQM0e5EpEHslKFHRKZV/4MyC8zpyF2QqNCGvXbjIBER3ft+IxFAV7evbeEIU4kZxERE2UR0qOpvEVVUxtK6I5qUXWRMRCOIPhlANF6bKD2diIi2ndlG/3UClXd3k2kIKYseH/egMVvGEG3axI6169dp25lthAWgxycOVv+Wnnqq5WIiEdGbb7Lv9oPh5PqBa63F7h+607PfPtv4NtLSiHR12T5tacQIPHaMrfPii0TFxbR432Iyfd2ULkRfICwAHQ87Lt8+Fxczo73meeV4/feKxWIyf8OcXtrzknzblQNlXRtUyWeffabuXahHWzdMVQkAevDgQavpHThwgKZOndpqem2BNmGYqhplnXwkhlRyTnL9hXfvVp9U3367+vWUFNkeRoDol1/YRaCoiFJzUwkLQNvObKMFPy0gyzctG96RiRPZ+7/+Wv6dF4mYJ6JKe8hY0P20+9LFfT/pS8M2DWv4/UVF1Rer7t2ZV/b8eaKcnOp1rl4l6tyZyM2N6PJlpklEvdb0olGbR9Xb5OJ9i0n4mpDEYjHRH3+wbX/3nfxjaorkZKLAQCKAXhuqz3RqsP38dnqzb9V3ERmpPF0qIKKVRJREREfp5qOf6HQ4KP1Ab6ZVdXe99LeldKqzgMT29lKvk3KpoLjMOHJ5H/TztVXM2/1S1UV9zhwq1dUmq3k6tW5Q6hNLRLeJ6AoRvUVEWiQxOqv/NyCi14noGyKKJ+Y9vUlERP8E/0NYAEq4Mp+te5lFOu49PYgKtEDxiVFNjEFMREeJKJqIqrZBfYnoaSLyrbEvw2v8DyISEJFhndeqHiH7iIjowy9nMq/62pYaxxJSiX1eN4kohYjOEtFG2n6+L91P0yIqe5FogDHRtKlSI048YgSRrS3R/fvS30uLyc4mMjGh8EAPEiwUUHFZMRERlZaXkuZLmvTh3x/Kfp9IRLRiBZGWFovyhIc3rlNZSTR3LjumXV1p518s6rLyn5WEBaD4zEY8fZWVRFOmMA/24MFsG0eO1D4/xsTUe9uA9QNowPoBcn4QTfMkGKbGxsbq3oV6cMO09QzTkydP0pUrjURP2yHcMFWA1/a/RkZLjOoZOETEQqSSE2pKSu1l27YRDRlCNG8e0a+/svC+k1P1+v37k0gkIt2Xdemd396hgZ8PpP7rGwhxXrxIpKFB1KsXUUmJ4oN4/XUigGYOBh0JPUJEzBMhfE1Ir+x7pf76paVEb7xBNHt29f42Fp46dYp5TgEWViSiWd/PIodlDvVWHbRhEAV+Flj1ZBCRi4vUaFMaYjGluTtShh4o/cZ/tRYt2f0SxZkISBwY2AKBNGJG6Hpi3sNNxMLYICJ9IgKJxQKSGkXvgajK2z/n/aB64WXFSCKi+0R0kYgOENFWIvIhIhsi0qV6BlkliLYGEmVmEuno0D9B9hSwNqDG9qKJaDURvUhEU4jIi5iBV9e4syWixURUQkS/ETPIZPMw4yFhAejyiefYey9sJMrJoUIjXfqjm5bs3xIREeUT0R+yx0E6VftVc99ciMifiKYS0Qoieo6IRtP+6/70wg/mRJRDlDSVrXtrExERnfGzolItDaLExMY+5EbIrPoM3qAGjWBZj9MmNHjDYBqyph/zoL/xRjP1G+Gjj4gA8n4WdDuOpU/EpMcQFoB+vPRj/fWLiogmT2bH4/PPs9QHeam6US43NiTPySCPjzwaPk9K+OWX6vOJhgaLWIjFzECXvC7jxvvFH18k67es5d+3JngSDFMjIyN170I9uGFa3zBdtGgRGRoa1nssUlE0rD3DDVMFGPHliDoX8iri4tgFBmAXBHl4+LC2d+DsWXL9wJWmfjeVrN+ypvm759d/j1hM5OXFvJLNvZgWFhIBtLw36MtTXxIRUUpOCmEBaOu/W+uv+9NP1fvo50f0+HHTGjk5RBMmMK/LX3/RJ0c+YaHsktrpF5ZvWrLcz/j4FhpojXP52G5K0wPledXOq131clX+7V9/KbjFbGIG3GBioWtZBkhnYgaiIxH505wfzCktyogoE0SpqSQWi2mbvz5VagiYZ1cukoloAxEtIaIBDeha1Hvtx0tOtPM/e/b8sBMLzwI0YIZJjdzbJXXeZ0tErkTkTkSziWgeES0kZgzLj0gkIuNXjWn3/hFsu2ffJ3rvPSKA5rzqU2ftfCKaRcyw16mzP6ZE1IWIjhBRJRGVEzMKzxNRcYP6G09srE6LifyZbevqeyzdBKCDz/SUcySpRLSPmNf2ZSIKJNmf/yhi3/urRLSDiL6ghKyB1HMN6F7SbqIKAdF9HbJ6y4q+WDam2lOobLKyqNLIkH7rCvrp8k9EVJ3T/OftP+uv/8EH7Pf6xReKpzWIxUR//kkE0Dt9QFgA6v5h98bfI4n6jBtHdONG7WVlZUSOjkSj6kdZJLnwecV5iu1jAzwJhin3mHI6Go0dX602JemTQlRalOyK/O+/Z/0DY2Or+2g2RdeurK+hZPaHYcMwuNwCIYkhyCjIkF2Rf+IE68+5ejVrxN4cDA0BMzN0K9PF/fT7ABqoyM/PB4YMqZ4N6NAh1nrK0rJpDVNT1imgXz/guefwVFgWANTqqfi44DEyCzNZpe2+fezFGTOaN6Ym6BI4Ap/2AEzC71e3owLQ8+YjFOppsRmHmoQA/A7gdQAOAFYDuA5gKICTAA4DSAEQCuBvADEAHgAIBnAL6fm9EXlXAzAFUFSA9PgoTL9Xgof9fVkP2gZJAPACgAUAugNYBuAbANEAPABMA7ABwCawlippAAoAlAMoAZCN4MSJeOPXHFCMIWBeAHz/PcoCfHHJIB8BTt4ANlZtU8IvABIB3AcQBWAPgF0AdgCoMU2rHGhoaMDH3gd3H1f1DCxJA339Nf5014Fu735Va2UC+AfAMAD7wD7rVwF8C+ArsM85B8AjAOMAaALQBqAHYDAA/Qb1ve29AQBhSWGAXlVvwsocFO/9ERUCIGnOlCZGkA5gHYBAALMA/ABgO9j3Og/AoqrnoQAeAzgBIBXANgALAbyNkvLvEZwA3Iwj4IYLSK8SjwseY2BcKetRquRODAAAc3MIXnsdUx8BmdfOAwCKyljHDCPdOq3Q/vuPdWwYMYJ1ElG0xZNAADz7LKhrV/TPZj2j9bT1Gl4/IgI4eZK1xDpyBOjdu/ZyHR023fCpU+ycWgPXTq4AgAcZtfuzcjicjoHyutK3A4rKipCYnVh/KtKiImaYjh7Nml0rwvTp7O++fUBMDGbcycX3zuyEK1Nn+XKmIaNViUL06oXJVy/gUBxr7SJp5VSrh+n8+aztVadObNagqvYlcmNkxNrYPPUUem7eA92xbJalXk5sLnRJv8OASjNg3QrWdkrSRFzJ2AptcdDLCB8+1IDVjBlsFh1TU3g/zMF9dxv00tVt5N33wQzMfQB+BTOKBoDNRLIQ9Q01WwCSqUyrt+tj74OE7NPs7SXpKFv3OSzKgbg3Ftd5fwmYsZMAZtzWnOfeBcA1MOOzf63t10ZieDDjza+zH4rLiyF6bAmtoExgdTY0x+jitwhgYo+PwYy+IQCOgHWJa9jQaw6+nX1xPaxqiuHgCxAUFmJrN+CzoBQwA/Q3MKPOHMBPAJTXn9jHnvWxvZdyD4M9qwxAcTbwzxWctQNcXGu2ysoHYAh2A6IJ9n1L2nsZAHgN7LvJATNU5WvG72zlDB0tHdbmqJM+oC8GCPC6Eslu/pTUM7cuGkuXovCLzzFs+yHglVIUlrEm4Ya6htUrnTvHDFJzc+C111qkJxgwAEP/2A8dUSOG6dGjrGetsTE7nzXEwoXAunXAlCmsJ7GTEwA2Sx7AbnIlEzi0d4hI3bsgEyJSap9aDgdo+njnHtMaSLyLtTymIhEz2NLS2N1/szd+H3jpJQw+fx+Dq3pHO5rXmPHo55/ZLCthYWwKwub295Tw6acwK66E14W7AJjH1EjXCPZmVUZWXh7zkL76asvGJhQCmzZBJyMTHwVXz7IEVM8Q02vvMfY57tnToiE1hkAgQCdnT3wytRuQlQWcPg1KTYVLTiVSvWr2z/wXzHt4BMBbAEYDcAcwHsABAO+AGY7nwTym8nsPve298bhQzJ4khcH+z+PY5Q44j55aY61VYAbQXgAXAHiBeWS/BrC26jVPMM9iY8Z0bXw7M0O5HFX6kwEtgzI82wvQEPgC2A/gHJhRplyjFGDTrT4srOpvm/kQJbbWsJ0ABHU7CuapFYMZp5FQplEKAHamdjAzMGMeU8OqWYgyHsAgPhl/dwG87LqDeThnAxCC3Y8/D+A5MC/4CgAhAIoAbAX7TgZAXqMUALQ0teDeyZ3djIn1AQOgZxZgkJSmsigBAMDCAr9N9UePB1nAp59KDVOpxzQ9nfUkdnUFHj5kvV5bwsyZEJaIsP4moKtV5/isqGATGEyaxGb4unmz8aiPvT2wfj3rj1xjIpVu1t0AVJ+POwIDBw5U9y7UQ1NTExUVFereDU47pKKiotHZOrnHtAbxWWxe6K6WNQyZjRuZx+F//5MzHNwAAgGwYQOKDv+FtbczMcgOMNCpakgeGclO6L16MWMxMLAFo6iid2+kutjhtSspKExJkM5mJb37/fprNlvV3Lkt1xoyBPTCC1i+5yd8c/sWs6nAmmSPyDGA4Z+/s4bi9oqFiJsmG8x4EAIg+Nm74kbmKWbPZWSg7LuvoQcgY3BfAPcA/AjgyzrbsAQwECxk+2zVtpqHj70Pfpf0dt+9GQKxGHsG2mCRoRlY6Hw5WPgcVf9/BkA53ggvOy9oCDTw6FdreF3NBqJnYXqvXCTmROLye+eVotEYvp19kSGZ/Kw7UH6qEr96AxWiHtDW/BfMGFe+QQywmxKfzj5sggUDM6AUQGwIxAIgKlAf9mazAFwCoAMgACw94ikAfgDMADgpZT+87Lxw7eE1wK8LBAbA+ExtABXMW6lC0mdMwi9Xb2L6pk2gXp8CqDJM8/LYpAtFRcwjKWz+sS1l5Ejc72aNSXEZOFnXY/rjj+wBAN98I/WANso777Dz0IoVwOTJwM6d0Dc3h4O5Q7sK5ZeXlyM6OhqZmZm1vEXDhrGTZc053dsKpqamSE9Ph729PTQ0uA+LoxzEYjHS09PrTVNfE26Y1kAypaalUVWOpUgEbN3KQvjKmEZSKETauGEI/OE3CMsAfR19los1dSoL9f3zDwurKwOBAOEfv4lhs5bh8bpViDSKxBD3IWxZcDDwySfsouWvnFCZYPNmZP+xDxO3HgTerAC0tRGREoH3HugDQm1g5coWbJ3A8iu1wELoYWA5kZFgnjgHAHHY8YKIrf05IFhzGLqfnsYRB8CqbycAU8FyKQcC+B8AyWw5T4EZJy3Hw9YDjyWGaeYDHPUWwqWvF5jR+xNYCsAkMG+pYQNbaR76Ovpw6+SG6GsieG0FcHERrh2ZhUBnJdzkyIG3vTdEkieLAL2KPKw9YoiPxp+Fsj7fRvXtvLHv+j6QQABBvgDoSii+qInz/UvAjNLnwW4G/FS2D552nvj15q+oFHtAC8DT+XqAs0MzbsgkhkscWJ5rBYAIALfAjv8QsPxjCwDGmNFXBxH7ATpWhv5JX8FABzDSMwKWLWepOrt2Ae5NTH0sLwIB4t1s0f9RBnQ1a0R1zp5lUZcBA1g+qyLh35deAn77jc2Y1q0b8PnncLV2rZWv/iRz6dIlTJ06FWVlZcjPz4eJiQkKCgrg4OAgnbmoLWJpaYmkpCRE15z+msNRAoaGhrBspJaFG6Y1kBim5oZV0xb+9huQmsrmZ1dSnk1xL19off8b3PMAfS09YNJwICmJTSeqLKO0CuvBo/CLyzJM27UfJVPL0X1wd1aMNWMGK1765psmtyE3Zmb4cVpPLN91i80VP306MmLvYVh4DrDkVVaQ1SClAHIB2AAoBjNCz4KFWv8GC8OGy3gf85QyT9w45JeEQF87AdraAPJOQqAFpHwHvOT3btX6+wDMAPNSNjEtazPQ19GHpp4dgBTQGsDTIQ97HM5ULX0PrMBG/vCwovh29sUe+0t4dtkyZPfwQPxP8XhlyCsq06uJib4JizSsegS4mmJ2YWdkFVnjo/GqN0oB5q3OL8lHYnYiHIsIeA7QKxfh7+CemNTzWwD9mtxGS5FMp5lXIoIFAJ/cIqDfGDnf/Qgs3zUMzLuuD5ZSUpdjYL+TwQCSAKShs1kSzHwBjb5i2CAOSzYDhvklwE8/sQIjSXGjkig1M4GBCBCKqy4fpaXAggUsf/zwYcXPlZaWQEgI28+NGwELCywbuwyV4kql7re6eOutt7Bs2TK89dZbMDMzQ3Z2NtasWQMDyRS+bRQNDQ04Ojo2vSKHo2S4f74G2UXZ0NLUYmGwxYuZAde7NwsxKQlNaxsAgEUZYBhyj+Webt3KCoOURiGAX9DNyhl7XAGtsnJ0zwW8TZ1Z8UOPHmye+UYrxRVHe+o0PDABKtatQVZOGsbffgwtkRh4+eU6a4rBqr8zAfwJwBHMmygE8ySuAsu1XAKWE1oJYA2AM2Dezp/BvEm5APLALuYHkZZ3GjqLAcoXAFMAUboAL40DxOJZAG4CmAllhc4bwtCYGbyCIMDMDAhNnAXm4foMqjRKAWaYHtROQf7qFQhNYrnFPRx6qFSzrn5QQTeIJ6fgaNhDaVFSa+DTmWmFJYex4nkR8OI3QGzGDLSGUQqwUD4ApOTnAwB0tcXstwYCJLm/AIBYsJux02D5rc8CcAbwAVieMwCYgOU9Hwbz+O4B68JQDJYO8mvV6xEAcmDzjh6+3cO6D/iIAYM9+5nB+PrrShqdCOzG7n3AgYXwLUuqPLvffgs8egRs28ZueJvLd98Bo0YBy5djVMhjjPMd19KdbhPcv38fb7zxRq3X3nvvPWzevFlNe8ThtG24YVqD7KJsmBuYQ5CaCmzfzl788UdAU3kGhXaVYWpZCujtPwDo6bFQfrOQVdlWCGA+gBkw0J2ARa8bABsBazEQcDueFQZt2QJ4ejZTE2AXVap6ZAD4A8BdjPTRwa9zAO2sSCQd+x2LIoGs3r6Ah8Q7KQaQBdaWaBEAKwBTwKq13cHy/wYD+AjM2PwTLPweVfXaMAAvgXk96+evSSqjBRUEDAIExoRPj/lCQ2Nv1bZVj6iTJ3bN18GJE8vgsBzQ1VoBVYaPayIpgLqXfA/BCcEAgJ6OPVtFG2AFUNcLHyIq5xGKy4ulbZxaA2+7qpZRyWHAsm4ocbPD3ggWXm8tXKxcoK2pjYiMqi4LhgACAsDaj2mCtQQTAugGlnM7EsCnYF0Y5oGF7fPBfldpYEbpeLDuDLPBOjDoo26gS1NDE91tPHD58WMAgJWuBjS++QYYPhzwVvQ7ILDf2zdghYFGVfvZpWof1mP4iotACNBNn7WIw65dQP/+TK8lGBiwbXXqBLzyClBc3LLttRGEQiHyq25WbG1tERERgZycHBQWFqp5zzictgkP5dcguyibhfEvXWIv3L4NeHkpVUPHhnkpez8GBFd/BGbOBExMFNjCI7CK8Uyw0HARAGMw76EFmOEn4RaeHVMMjAF6xwO2R88Cjo7AoEEK7vUtMI/J31V6u8A8OhpgfT0Z3vaA9zYA2wCTRd+jayGQuWgRWEjyb7AQfXCN7ToA8AcwF8BEGbqKXVS1NLXgau2KiGMpcPcqRq9DZfhowkcKbaOlGOuZYKFmJV7LLQOgL+3J2Br4dWYGcGhSKIITg2Fvag8rY6tW0+9s1hliEuPC/QsAqo3F1kBoIISDuQOrzI+IxJ7z24EDr7XqPmhrabM838yqavI1AIZtAPP6A8zraQTWdSEIrH3XWLDGty3z5HvaeuJK1efet5CAlBRg504FthAJ5oXdC3aOQdU+CcGiF13BbngdkZ35I+z94uB8NxO4d4/1XVZWWpCtLXDgAGux9fff7Pz4hPPss8/i2LFjmDFjBubPn4+hQ4dCW1sbU5vtkOBw2jfcMK1BdnGVYRoRAWhotNCrWJdIALPg6JIGOge8NhmsuEquZv0FAHaChfluyFhuBOYpHQhWHOEOYDgAK3xzeAKWTDiKoZp60Dh1muk1mQP2sGp/d4KFCiVFCJpgh0wZWEhRAOBFsIKeKABe2PTPNLzzTAGcSu8iXV8A6zn2ADqDVdDbgzWRNwFrjaT8HKvuNt3xTFQ5vvT9EneTJ8DBzEHpGo1hom8CMYlxKeYS/Dr7QVNDteH7mjiYO0BDoIHknGSEJIa0ahgfYGMHgIsPLkIgEMDLXrk3dU3hY19Vma+lhdD0CAj1mbHamnjaeuJxTFU+9ESA5RW8AWAlmJGnmuPB09YTP1//GVQMmGkSaw81enQT78oF88quRfVvvDOYt1QIlofdq967roSUYGqX9TCmEuDXX9m5UonpThg4kE1O8uOP7cIw3bJli/T/pUuXom/fvigoKMCoUaPUt1McThuGG6Y1yC7Khr2pPXD5Nkvk12tkZpNGqQAr1ikGa56+BqxdEaCpAeYomQ4gaVwDff5EYHlkp8EubDW9EbZg3scMsNy5ZAA9wLyS9dvxVOp5ADiKPqWlrPK/Xr9SEZhRuxzAHQC3UTsfzgDsCusAFk63BgvlXwLQF8xbC7CQI/DvowC8g3PQ+AzInaKHTtrPgoWy/wAL06s2e8TD1gP/hPyDuKw4AKzxfmtioseMs+CEYCweUrexvmoRCAQw0TdBWn4aIlMj8UyPZ1pVXzL2C/cvoJtVNxjrGTfxDuXiY++D0xGnUVFZgbCkMHjbe7d6c3AvOy/cuvg7cAsouzsIui9eaBVdacpCIdh96qxZzGCUyS2wtml/g/2WvcGK8wIB+IB5Rxum0pRFfQypjHk3hw1TbuGmhgbw0UcslE+ktMJTdZOQkIDk5GQ4OjryoiIOpxG4YVqDP17+Azo37wBHpiowQwqBGXSXwGaTiQGb4rAuvgC6o1L0HMSxU6AzFkDlizWWZ4C1gPkVwCGwilsJ3QBMAJuFyB21jTtJqFZ2j8igbhMBbIKGJYBFi1hFDsQA4sGmXtwE5gGVMAzM0B0IZnhagPV/rIkemEe2Pt1tfQGcA+yArlblYJX1O1E9U5Fq8bD1gEgswsUHFwEANkKbVtGVIPEaikmslllrTPVNcS76HERiEfp07dOq2pKxp+Sm4LmA51pVG2CGaYWoAvfT7yMsOQzP93m+1ffB084Tq/WBZ98D9h860nq6VR0BBEVgua1d6kwBilQwQ3QjWOGgKYAxYJMdTIAinlzSZ50WnHJzgJhU4L33WrLrspk3T/nbVBOpqamYPn06rl69CgsLC2RlZSEwMBC//PIL7JRcgMrhtAe4YVoDZytn4NRWQFeX9fmUSTbYST0YzBDdDFYtC7A2LmPBwmFOqJ7v2wasXyagpQkcTQd6+wPWnYaCFT+Eg100SsCMvqfAjFBPsMIDRXJQa9O76wBQvgYENmBT/+FLsEbz96rWmAjm0XQEM3oHNFsLYBfms68AWrpA6Pgv8NpTbzT9JiXiYcsKrX679RssjSyho9XCGbQUpKaXUB2GqVBfiNCkUABA73rGiWqReEyB1i26kiCpzD9+7zjySvJatSuABE9bT0AAHHPVhZ5B63mMJYV/KCxn94COjmDnqr1gOeF3q9YMBEstmI/qaIdiaAi0IC4EzCpLAQsL4HlFbwAI7Jx3FKyQ8S5YhCgdLIKTDObJHQVlzxKmDhYvXgw/Pz8cO3YMhoaGKCoqwgcffICXX34Zhw4dUvfucThtDm6Y1oSIJdyPHMnmeQbATpQE5mF8DywkXZMRYFNKPg8W7m7a83AlExg3EABcwTylZmCh8OGoNmyVh6DcFFhUAGhLmq17gU292BdAbyizhZKXnRcGVACoAP5zqJ+fpmrcOrlJ/29tjyFQbZzpaetJvVitidCAzabhaO6oNm8xAPR0aH3D1L2TOzQ1NLH/xn4AUIth6trJFVqaWjAzbJ3+rRIkU6KiKKzKY1oJwAUsj7QPmKc0EKzoqmW/d4FAAJJ4ZhcuZNX0DUJVesVg7bB+Bes8IOnRWtfbKim40gaQgPZgmF66dAmpqanQ1tYGwJqLb9iwAfZKnwmPw2kfcMO0JqGhQEICsGpV1QtiAOPAqmpFNVacVvX6SACK51ZdlxS9ojtY2L5vM3dYTiznArgKVqT0LNgFSzXUNMYk7YtaE0Pd6kb+257f1ur6EuPMr7MftDRb/+cl1GeGaWt7S4HaHtMejj1aXV9XWxfundylrbJas12VBB0tHbhat14nhpp42nkChWEs88dwOphReAus84Xy0BBoQFOSy7poUSNr/gvWO9gALH0AYDfgemDnPkMwg9USzGj2RnVKEoF5fJ98zMzMEBERAT+/6rZx0dHRMG1Jz1cOpx3DDdOa3LnD/j7lBObN9AJwEsyr+CzY3bsNWlrAcyYScFoOxH9+Hqpu+M74ohU0GGaGZrAV2kJPW0/qvVMXzlbOra4pMc7UEcYHqg1TdXiLJWkMtkJbdDJR7ixm8uLT2QcRqRGwN7Vvda+lhOcCnkNeSV6r63rYeCDUGvBzAViO+jEo2ygFmMc0DoBmN2M4dOlSYwmBdQ75F6ylXWzV67ZgKULvg0WE5FIBy29/8lm2bBmGDx+O+fPnw8nJCfHx8fjxxx+xdu1ade8ah9Mm4YZpTaKjAW1twOEIWBFTDIDJYLmkyjUgU/K0lL7NtsLzfZ6HgY76ptt7+OlDFJapp3m1jdAGNkIbjPWR9wKsXNRpmGppasFAx0At+aUSXKxYNMDFWnVRgaZY/fRqteh62nlCw1zy7BhYAaPycbZ0RkoFYNdNkipSAuBzsFnZ0sAuK/0APAPgVbDm/B2XhQsXwsXFBfv378fdu3dhZ2eHX375BcOUOtsfh9N+4IZpTR5EA1+bABpbql44BZb3qVwDMnVTaqsX5bQmXzzXeh5aWXS1arzdjSox1DVE6qZUtelbG1tDS1NLbR7buUFzMcR9iFq0AWC4x3B8euxTvD/mfbXtg7rw6+yHKduBb2e+gwGuqjFKAaCXUy8UlgXBUEcM4ArYbGzhYOlN08AKmlqvf++TwLBhw7ghyuHIiYCIZM1r+cQhqXZsPiLghDkwOh/ALLA5rLsrZ+c4nFYipygHDzIeqMVj2lYoKC1o9R6qbYXrD6+jl2MvaGtpq1jpWQD/gIXvHcBawrXNhvEtvza0jPLycqxbtw6//PILUlJSYGdnh+nTp2PFihXQa3avbA6n/cIN05qI3gJKhYDhKrTXMDuHw+G0nB0A9gGYAjb7W+v0KW4O6jZM58+fj+joaKxYsUKaY/rZZ5+hW7du2LVrl9r2i8Npq3DDlMPhcDjtFnVfGywsLBAbG1urCj87OxvdunVDdnb76DzA4SgT1c4PWYPs7GxMmjQJhoaGcHJywv79+xtc9+HDhxg/fjyMjY1haWmJZcuWtdZucjgcDqeDocj1afPmzbCxsYFQKMSLL76IsrKyBtcFABsbGxQXF9d6raSkBLa2rTtdMofzpNBqxU9LliyBjo4O0tPTERISgnHjxsHPzw9eXl611isvL8eIESOwZMkSHDhwAJqamrh//35r7SaHw+FwOhjyXp9OnjyJ9evX4+zZs7Czs8OkSZOwatUqrF+/vsFtz549G6NHj8Zrr72Gzp07IzExEd988w3mzJmDs2fPStfjxVEcDqNVQvlFRUUwMzPDvXv34ObGZuaZPXs27O3t6/2gd+zYgb179+LixYsKaag7XMPhcDictkdT1wZFrk8zZsxAly5d8OmnnwIAzpw5g5kzZyItLa3B7Xft2nSXEIFAgIcPH8ozHA6n3dMqHtP79+9DU1NT+qMHAD8/P1y4cKHeuteuXUOXLl0wZswY3Lx5E97e3ti2bRt8fFp/ekEOh8PhtG8UuT6Fh4dj4sSJtdZLT09HVlYWLCxkTwjw6NEjma9zOBzZtIphWlhYCKGw9ixAQqEQBQUF9dZNSkrCuXPncOjQITz11FP46quvMHHiRERFRUFHp3bvzx07dmDHjh0AgOLiYhgaGtbbnqJI8oU0NTUhEomaWFs5cC2uxbW4Fteqr6Wrq9vi7RUXFyMgIED6/KWXXsJLL70kfa7I9anuupL/CwoKGjRMORyOYrSKYWpkZIT8/Pxar+Xn58PYuH6vQX19fQwYMABjxowBALzzzjtYt24dIiMja801DNQ/wSgDiXHr4eGByMhIpW67IbgW1+JaXItr1ddqjfQsRa5PddeV/C9rXQ6H0zxapSrfzc0NlZWVePDggfS10NDQeonlAODr6wuBgPcQ5XA4HI7qUeT65OXlhdDQ0FrrderUiXtLORwl0iqGqaGhIZ599lmsXLkSRUVFuHz5Mg4ePIjZs2fXW3fWrFm4du0a/v33X4hEImzZsgWWlpbw8PBojV3lcDgcTgdCkevTnDlz8MMPPyAiIgI5OTlYt24d5s6d2/o7zeG0Y1qtXdS3336LF198EdbW1rCwsMB3330HLy8vJCQkwNPTExEREXB0dIS7uzv27duHl19+GRkZGejVqxcOHTpUL79UVUyaNAkAMGjQIPz333+tosm1uBbX4lpcq/W1JMh7fRo9ejSWLVuGoUOHoqSkBJMnT8bHH3/cqvvK4bR32s3MTxwOh8PhcDicJ5tWm/mJw+FwOBwOh8NpDG6YcjgcDofD4XDaBK2WY9pWycvLQ3R0NAoLC2u9rurp4TqSLh8rH+uTrsvH2j7HyuFw2h4d2jDdvXs3lixZAiMjIxgYGEhfV/X0cB1Jl4+Vj/VJ1+VjbZ9j5XA4bRTqwNjZ2dGxY8e4bjvTVJcuH2v71OVjbb+6HA6n7dGhq/I7deqElJQUaGpqct12pKkuXT7W9qnLx9p+dTkcTtujQxc/LV++HOvWrYNYLOa67UhTXbp8rO1Tl4+1/epyOJy2R4fzmDo4OEinPCUipKWlQUdHp96UcgkJCVz3CdJUly4fKx+rMuFjVb0uh8Np23S44qd9+/Zx3XaoqS5dPtb2qcvH2n51ORxO26bDeUw5HA6Hw+FwOG2TDpljKhaL8fXXX2Px4sU4dOgQAJbj5Ovrizlz5iAzM1Nl2jExMVi1ahUmT56MMWPGYOHChdi5cycqKipUpqkuXXVoquO7feONN3D58mWlb7ct6l6+fBmpqakAgLKyMnz00Ufw9/eHv78/Vq9ejfLy8nalW5NHjx7hm2++wddff40HDx6oXK8uAQEByM7ObleabeF75XA4bYsO6TF97bXXcOHCBYwePRrHjx9H7969kZ2djXnz5uGnn36CtrY2Dhw4oHTdf/75B7NmzUL//v1BRLhw4QKmTZuG2NhYpKWl4fTp03B2dm4Xuuoaqzq+Wy0tLRgYGMDa2hpz5szBCy+8ACcnJ6VqtBVdV1dX/Pfff7C1tcVrr72G4OBgvP322yAifPXVV/D398fmzZvbha6HhwciIyMBABcuXMCECRPQv39/CAQCXLx4EQcPHlRJ8/c5c+bIfP2PP/7A+PHjoaenhz179jzxmoD6jicOh9OGaaW2VG0KW1tbSk9PJyKipKQk0tDQoOzsbCIiysnJISsrK5Xourq60tmzZ6XPT548SaNHjyYioo0bN9LYsWPbja66xqqO79bIyIiKiopo9+7dNGzYMNLS0qIhQ4bQ7t27qbCwUOl66tQ1NDSU/u/g4EBZWVnS59nZ2WRra9tudI2MjKT/DxgwgH766Sfp83379lFgYKDSNYmI9PT0aNCgQfTxxx/T6tWrpQ8jIyN65513aPXq1e1Ck0h9xxOHw2m7dEjD1MzMjMrLy4mIqLi4mLS0tKTPKysryczMTCW6QqGQxGKx9HlFRQVZWloSEVFRUREZGxu3G111jVUd323dscTHx9PatWvJzc2NjIyM6IUXXlC6prp0PTw86MaNG0RE1K1bN+lNABFRRkYGmZqaKl1TXbo1P18rKyvpcUSk2vPE/fv3aeTIkTRt2jRKSkqSvm5jY1Nr3E+6JpH6jicOh9N26ZA5poGBgVi0aBFOnDiBl19+GX5+fvjiiy9QUFCAL774An5+firR9ff3x9atW6XPt2zZAi8vLwCApqYmtLRU0yRBHbrqGqu6vtuaODo64sMPP0R0dDROnjwJPT09lWu2lu7KlSvx3HPP4ccff8SCBQswfvx47Nu3D/v27cP48eMxY8YMpWuqS7eiogI//vgjdu3aBYFAUCvfsbKyEiKRSOmaAAtvnzx5Es888wyGDRuGTZs2obKyUtpaqb1oAuo7njgcThtG3ZaxOoiLi6OxY8eSh4cH/e9//6PIyEhydHQkDQ0NcnFxodDQUJXoRkZGkpubGxkbG5OxsTG5uLhQWFgYERHdvXuX3n333Xajq66xquO7rRnybU3UpXvq1CkKCgoiHR0dEggEJBAIyMHBgVauXEkVFRXtRnfw4ME0ZMgQ6UPi2SNiqSm9e/dWumZd8vLy6I033iAvLy8yMjJSqfdSXZrqOp44HE7bpEMWP8mCiJCdnV2vubOyEYlE0oKK7t27q8xz2BZ01TXWurTWd9vREIvFSE9Ph76+PkxNTdu9bk3y8vJQUVEBS0vLVtELCQnBhQsXsGjRolbzwLe2Zlv4Xjkcjvrp8IZpTk4OCgoKYGxsDDMzM67bTjTVpduRxsrhcDgcjrLpkDmmFRUV+OCDD2BrawtLS0t06dIFlpaWsLOzw4oVK1TWZ1Oia2dn1+511T3W1vxu1X08tbZuQ5SXl6ukBVhb1OVjbb+6HA5HvXRIw3Tx4sW4evUqfv75Z2RkZKC8vByPHz/Gvn37cO3aNSxevFiluvv27Wv3uuoea2t+t+o+nlpbtyGICHFxca2qqS5dPtb2q8vhcNRLhwzlm5mZIS4uDkKhsN6ynJwcdO3aFbm5uVz3CdNUl25HGqumpmaDy4gIAoFAJdXq6tDlY1Wtpjp1ORxO20U91ShqRk9PD6mpqTIv6GlpaSpL9O9IunysqtVUl665uTl27doFT0/PesvKysrg4+OjdE116fKxqlZTnbocDqft0iEN02XLlmHo0KGYP38+/Pz8IBQKkZ+fj9DQUPzwww947733uO4TqKku3Y40Vn9/f2RmZsLFxaXesrKyMqgqAKMOXT5W1WqqU5fD4bRdOmQoHwBOnjyJPXv2IDw8HIWFhTAyMoKXlxfmzJmDUaNGcd0nVFNduh1lrOHh4dDW1oabm5vM5fHx8XBycmoXunysqtVUpy6Hw2m7dFjDlMPhcDgcDofTtuiQVfkcDofD4XA4nLYHN0w5HA6Hw+FwOG0CbphyOBwOh8PhcNoE3DDlcDgcDofD4bQJOqRhGhMTU6vx+A8//IDJkydj8uTJ2L17d6vvz6RJk1TSgL0m6enpuHXrlvT5kSNH8NVXXyEkJERlmkeOHMHnn3+OO3fuAAC2b9+Op59+GitXrkRpaanKdIuLixEcHIyCgoJ6yy5fvqwyXQn5+fk4evQojhw5gpycHJVqqWOs6vp88/Ly8OOPP2Lp0qVYvHgxPvnkE5w+fVpleg3Bf6/tQ5fD4bRNOmRVvpeXF44cOYKuXbti9erV+OWXX7Bo0SIAwM6dOzFr1iysWLFC6borV66U+fqWLVuwcOFCGBoaYs2aNUrXPXToEGbNmoXKykoMHToUI0eOxIkTJ1BZWYmLFy/it99+w9NPP61UzbVr1+K7777DgAEDcO3aNcyfPx8nTpzA9OnTceDAAfj6+mL79u1K1QSAGzduYNy4cSgvL0dFRQVWr16NZcuWSZebmJggPz9fqZqjR4/GiRMnAAB3797FqFGjYGpqCoFAgKysLJw4cQI9e/ZUqiagnrGqQxNgBu+ECRNgZWUFIkJsbCxGjBiBqKgoODg44O+//4alpaVSNfnvVfW/V3XpcjicNgx1QAwNDaX/Ozs7U1xcnPR5YmIiOTo6qkRXIBBQYGAgzZs3j+bOnSt96Ovr07Rp02ju3Lkq0e3Rowddv36drl+/TgKBgE6cOCFdtnfvXurTp4/SNR0cHCg2NpaIiKKiokhDQ4OSk5OJiCg1NZVsbW2VrklENGDAANqxYwcREQUHB5OPjw8tWrRIutzIyEjpmsbGxtL/R40aRZ999pn0+YYNG2j48OFK1yRSz1jVoUlE5OfnR/v27ZM+3717N82YMYMqKyvpjTfeoOnTpytdk/9eVf97VZcuh8Npu3RIw7Rr164UFRVFROzEmJ+fL11WUFBQy9BQJufPn6cePXrQ8uXLqaioSPq6jY0Npaenq0STiEgoFEr/19PTo8rKSunziooKMjMzU6lmRUUFaWlpkUgkIiIisVhMpqamStckIjI1NSWxWCx9npeXR4MHD6ZZs2aRSCRSuWFqbW1NJSUl0uelpaVkaWmpdE0i9YxVHZpE7DOuqVtRUUEWFhbSfVDF8cR/r6r/vapLl8PhtF06ZI7pG2+8gWnTpuHcuXN45513MGPGDFy6dAmXLl3CjBkzMG7cOJXoDh48GDdv3oSZmRl69eqFP/74QyU6ddHR0YFYLAYADB8+HJqamtJllZWVEIlEStf09fXFypUrERUVhY8++ghdunTBL7/8AgA4cOAAXF1dla4JAAYGBnj8+LH0uYmJCU6cOIHHjx9jypQpKpnisLKyEufOncPZs2ehoaEBgUAgXaahoaGyPDl1jFUdmgDg4eGBv//+W/r8zz//hLOzs3SfVKHLf6+q/72qS5fD4bRh1GsXq4+dO3eSg4MDCQQC6UNXV5deeOGFWh5UVREXF0cTJ06kYcOGkVAoVKkHZujQoRQWFiZz2fHjx6lv375K1wwJCSFPT08yNDSkFStW0JUrV8jExITMzc3JzMyMzp07p3RNIqLnn3+eNm/eXO/1srIyGj9+PGloaChd08nJibp06SJ9XLlyRbrs8uXL5Ovrq3RNIvWMVR2aROxztLS0JG9vb/Ly8iIzMzP677//iIilFDz//PMq0ZXAf6/nlK6pTl0Oh9N26ZDFTxKICElJSUhKSoK+vj7c3d2hr6/fqvtw+PBhnD17FuvWrYOhoWGragNAdHQ0iAjdu3dXuVZOTg4ePnwINzc3GBsbq0Tj8ePHKCwsRNeuXestq6ysxJUrVzBo0CCVaMsiMTERBQUF8PT0VPq21TFWdX6+OTk5uHLlCogIQUFBMDc3V4lOY/Dfq+pRly6Hw2kbdGjDlMPhcDgcDofTduiQOaYA8Ntvv+GNN97Ajh07UFFRUWvZK6+8wnVVSEVFBYYNG9aqmurS5WNVHpJjeOfOna12DO/YsQNBQUEQCoXQ1NSEUChEUFAQdu7cqRI9devKoiMdwxwOR/10SI/ppk2b8PXXX2PixIm4ePEiiAjHjh2Dra0tANX1Yuxoug1RVlYGAwMDlRRxtDVdPlbloI5jePny5Thy5AiWLl0KPz8/CIVC5OfnIyQkBF9++SUmTJiAzz77TKma6tRtiI50DHM4HPXTIQ1TFxcXHD9+HG5ubgCAVatWYd++fTh79iycnJxgbGwsc1Ybris/koppWYjFYiQmJqrkgqMOXT5W1WoC6jmGrayscPfuXanxW5OUlBT4+voiMzNTqZrq0u1IxzCHw2nbaKl7B9TB48eP0a1bN+nzjz/+GFZWVhg4cCBOnz5dq90P120e2dnZ2LRpk8wimfLycowfP17pmurS5WNVrSagnmO4qXt2Vd3Tq0O3Ix3DHA6nbdMhDVMnJyfcvXsXPXr0kL726quvwsDAAEOGDEFZWRnXbSG9evWCvr4+nnrqqXrLysrKVHZRV4cuH6tqNQH1HMPz58/HsGHD6oXUQ0ND8eWXX2LhwoVK11SXbkc6hjkcThunldpStSk2btxIGzdulLls37591LVrV67bQs6dO0c3btyQuUwsFtP58+eVrqkuXT5W1WoSqe+3s337dgoKCiKhUEiampokFAopKCiItm/frhI9del2pGOYw+G0bTpkjimHw+FwOBwOp+3RYdtF1aW1WyZ1RF0+1vapy8faPnU70lg5HE7bgXtMq2jtlkkdUZePtX3q8rG2T92ONFYOh9N24B7TKtRln3ckXT7W9qnLx9o+dTvSWDkcTtuBG6ZVfPDBB1y3HWqqS5ePtX3q8rG2X10Oh9M24KF8TquSkJAABwcHlfVsbSsUFBSAiGBiYqLuXWnXZGdnw9zcXN27weFwOBwlwT2mdWit+b5bc856sViMLVu24NVXX0VYWBgyMjIwefJk9OzZE++++y7Ky8tVoiuLfv36qWS2HAlxcXG1nh84cABTp07FlClTsG/fPpVobtu2DdHR0QCAxMREDBgwAKampjAzM8PgwYORnJysEl0/Pz988skniI+PV8n224omAKSmpmLKlCnw8PDA0qVLkZubi379+sHS0hIODg64deuWSnR/+uknDB48GBYWFtDX14erqytmzpyJqKgolehJ2LFjB4KCgiAUCqGpqQmhUIigoCDs3LlTpbqyaI/nRA6H03bhHtM6tLf5vgFg6dKlCAkJgYaGBu7evYuXX34Znp6eqKiowPr16zF+/HisX79eqZqOjo4yX09JSYGNjQ00NDSQkJCgVE2g9me4fft2rFmzBm+88QYEAgG2bt2K999/H0uWLFGqpq2tLWJiYmBoaIiJEyeiS5cuWLVqFQBg7dq1ePjwIQ4ePKhUTQDQ1dVFUFAQLl68iEGDBmHevHmYPHkyDAwMlK6lTk0AmDhxIoRCIaZPn46ff/4ZUVFRmDRpEl577TV88803OHHiBP777z+lan788cfYu3cv5s+fD7FYjB9//BEzZsyASCTCzp07sX//fgwfPlypmgCwfPlyHDlypF6D/ZCQEHz55ZeYMGECPvvsM6XrNkR7PCdyOJw2TOu1TG07dO3atcGHk5MTaWhoqETX2dmZoqOjpc9XrlxJzs7OFBcXR0RERkZGKtG1t7ennJwcyszMJIFAQDExMdJl4eHhKmlO7u7uToMHD6YTJ07Q+fPn6fz583Tu3DkyNzenv/76S2WNs2t+ht7e3nTlyhXp8+vXr5OHh4dKNCsqKoiIyNramsrKyqTLysvLydzcXOmaRETGxsZERPTo0SP6+OOPqVu3bmRsbEwvvPCCyj5fdWgSEVlYWEg/16KiItLW1qby8nIiYp+xmZmZ0jU7deok/W0SET148IC8vb2JiOjYsWPUs2dPpWsSEVlaWlJKSorMZcnJyWRhYaF0zY52TuRwOG2XDmmYCoVC2rlzJ/3777/1HseOHVPZSdjY2JhEIlGt17Zt20YODg4UFRUlvegrGxMTE5n/S1DFyb+8vJzWrVtHnp6edPDgQenrNjY2lJ6ernQ9CTU/QwsLCxKLxQ0uVxaDBg2iX375hYiIgoKC6Pbt29JloaGhZG9vr3RNItljuXjxIi1cuJBMTU2pS5cu7UKTiBmJjx8/JiKilJQUEggElJubS0REeXl5ZG1trXRNa2trKi0tlT4vKCggGxsbIiISiURkaGiodE0idtw2Zpiq4kano50TORxO20VL3R5bddCR5vsGAEtLS+Tn58PExATbt2+vtezx48cwNDRUuqa2tjZWrFiBGTNm4PXXX8f27duxbds2lRc9lZaWYs6cOQAAkUiE9PR02NjYAAByc3Oho6OjdM3NmzdjzJgxOH78OHr37o3hw4dj0qRJEAgE+Pvvv/Hpp58qXROQ3VZnwIABGDBgALZu3Yp//vmnXWgCwDPPPIMRI0Zg1KhRuHTpEqZPn44XX3wRc+fOxZ49ezBixAila44ZMwZz5szB8uXLIRaLsX79euk5Izc3F0KhUOmaADB//nwMGzasXig/NDQUX375JRYuXKh0zY52TuRwOG0Y9drF6qGjzfe9ZcsWevjwocxl33//Pc2bN08lujX566+/yNXVlXR0dFTqMV29enWtR2RkpHTZwYMH6fnnn1eJbmZmJn3wwQc0bNgwcnNzIz8/P5o9ezadPXtWJXpERGPGjFHZttuSJhFRWVkZff7557R48WK6desW5efn04wZM8jb25vmzZtHWVlZStfMzc2luXPnkqWlJVlaWtKsWbMoOzubiIhiY2Ppt99+U7qmhO3bt1NQUBAJhULS1NQkoVBIQUFBtH37dpXodbRzIofDabvw4idOq1FcXIwHDx7A29sbmpqa6t4dDofD4XA4bYwOGcqXcP/+fYSHh6OgoADGxsbw8vKCm5sb11WxZmsYpW1hrN7e3nB1dVWppixddX2v6jiGW+MzVtdYGyIhIaHBrhftSVOduhwOR42o22WrDuLj46lfv36kr69P3t7e1L9/f/Lx8SEDAwMKDAyk+Ph4rvsEaqpLl4+Vj7W1KC0tVVkhUlvSVKcuh8NRLx3SYzpv3jwMHDgQZ86cqdV/saioCGvWrMHcuXNx9uxZrvuEaapLl4+Vj1WZNNaPVVXFQOrQVKcuh8Npu3TIHFMjIyNkZ2fLrNAuKyuDubk5ioqKuO4TpqkuXT5W1WqqS1ddY9XQ0ICtrS00NGRPzJeSkqL0Zvfq0FSnLofDabt0yClJHRwccOTIEZnLjh07prKcpo6ky8eqWk116fKxqlYTYC2Ufv/9dyQmJtZ7PHjwoN1oqlOXw+G0XTqkx/TMmTOYPHkyvL296035Fx4ejj///FMlc0N3JF0+Vj7WJ11XXWOdOnUqBg4ciNdff73esvLycri7u+PRo0dPvKY6dTkcTtulQxqmAJCVlYW//voL4eHhKCwshJGREby8vDBp0iRYWlpy3SdUU126fKx8rMqioqICAJukorVQh6Y6dTkcTtulwxqmHA6Hw+FwOJy2RYfMMZWFiYkJ122HmurS5WNtn7p8rO1Xl8PhtA24YVqFuhzHHUmXj7V96vKxtk/djjRWDofTduCGKYfD4XA4HA6nTcAN0yoiIiK4bjvUVJcuH2v71OVjbb+6HA6nbdAhZ36qS3R0NE6dOgUAGDVqVKvNg92RdPlYVQ8fa/vTVJduRxorh8NpW3RIj+mQIUMQHBwMAPjrr78QEBCA06dP4/Tp0+jduzcOHTrEdZ9ATXXp8rHysT7puh1prBwOp41DHRBTU1MSi8VEROTr60sXLlyQLrt06RJ5eXlx3SdQU126fKyq1VSXLh+rajXVqcvhcNouHdIwtbS0pKysLOn/FRUV0mWVlZVkZGTEdZ9ATXXp8rGqVlNdunysqtVUpy6Hw2m7dMhQ/tSpU/Hqq6+isLAQs2fPxmeffQYiglgsxvr16+Hj48N1n0BNdenysfKxPum6HWmsHA6njaMWc1jNFBcX08yZM0koFJKvry8JBALS1dUlXV1dcnV1pYiICK77BGqqS5ePlY/1SdftSGPlcDhtmw49Jen9+/dx/fp1JCUlQV9fH76+vhg0aBC0tFTbrKAj6fKx8rE+6bp8rO1zrBwOp23SoQ1TDofD4XA4HE7boUPmmDaGSCTCmjVruG4701SXLh9r+9TlY22/uhwOR71wj2kdysrKYGBgAJFIxHXbkaa6dPlY26cuH2v71eVwOOqlQybwvPjiiw0uq6ys5LpPqKa6dPlYVaupLl0+VtVqqlOXw+G0XTqkYbp//37Mnz8f5ubm9Zap8u68I+nysapWU126fKyq1VSXbkcaK4fDaeOosyWAuggICKCDBw/KXFZSUkICgYDrPoGa6tLlY1Wtprp0+VhVq6lOXQ6H03bpkMVPc+fOhVgslrlMW1sbq1at4rpPoKa6dPlYVaupLl0+VtVqqlOXw+G0XXjxE4fD4XA4HA6nTdAhPaYcDofD4XA4nLYHN0w5HA6Hw+FwOG0CbphyOBwOh8PhcNoE3DDlcDgcDofD4bQJuGHK4XA4HA6Hw2kTcMOUw+FwOBwOh9Mm+D9KZkNCIKfaFgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# plot stack loss vs. time (or measurement) chart\n", "plot_mlfm_stack(\n", @@ -668,7 +1061,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -701,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -762,9 +1155,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# plot fit vs. measured, include a 1:1 line for comparison\n", "fit_plot = plot_residuals(meas, norm, mlfm_sel, 'residual ' + mlfm_meas_file)" @@ -782,7 +1186,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -886,9 +1290,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# plot heatmap\n", "heatmap_plot = plot_heatmap(\n", @@ -915,9 +1330,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['mid', 'poa_global', 'temp_module', 'wind_speed', 'poa_global_kwm2'], dtype='object')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# read in the complete matrix data\n", "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", @@ -940,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -957,7 +1383,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1064,9 +1490,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "contour_plot = plot_contourf(\n", " df=matr,\n", @@ -1089,9 +1526,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "contour_plot = plot_contourf(\n", " df=norm,\n", From 223388e098d77b870c003460457e9ae874e20dd7 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Tue, 14 Dec 2021 09:27:24 -0700 Subject: [PATCH 32/81] remove mlfm_graphs, add matplotlib to optional dependencies, add has_mpl decorator --- pvlib/mlfm.py | 325 +++++++++++++++++++++++++++++++++- pvlib/mlfm_graphs.py | 378 ---------------------------------------- pvlib/tests/conftest.py | 9 + setup.py | 4 +- 4 files changed, 335 insertions(+), 381 deletions(-) delete mode 100644 pvlib/mlfm_graphs.py diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 72d6e2e458..a52d6de682 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -1,6 +1,8 @@ ''' This ``mlfm code`` module contains functions to analyse and predict performance of PV modules using the mechanistic performance (MPM) and +loss factors models (LFM). The module also contains functions to display +performance of PV modules using the mechanistic performance (MPM) and loss factors models (LFM) Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) @@ -8,11 +10,11 @@ https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules https://github.com/python/peps/blob/master/pep-0008.txt - ''' import numpy as np import pandas as pd +import matplotlib.pyplot as plt from scipy import optimize @@ -423,6 +425,327 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): return(dnorm, coeff, err, coeffs, errs) +# Define standardised MLFM graph colours as a dict ``clr`` + +clr = { + # parameter_clr colour R G B + 'irradiance': 'darkgreen', # 000 064 000 + 'temp_module': 'red', # 255 000 000 + 'temp_air': 'yellow', # 245 245 220 + 'wind_speed': 'grey', # 127 127 127 + + 'i_sc': 'purple', # 128 000 128 + 'r_sc': 'orange', # 255 165 000 + 'i_ff': 'lightgreen', # 144 238 144 + 'i_mp': 'green', # 000 255 000 + 'i_v': 'black', # 000 000 000 between i and v losses + 'v_ff': 'cyan', # 000 255 255 + 'v_mp': 'blue', # 000 000 255 + 'r_oc': 'pink', # 255 192 203 + 'v_oc': 'sienna', # 160 082 045 + + 'pr_dc': 'black', # 000 000 000 +} + + +def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): + ''' + Scatter plot normalised MLFM parameters(y) vs. irradiance(x). + + y1_axis : e.g. norm(i_sc, ... v_oc),_temp_module_corr + x_axis : e.g. irradiance, poa_global_kwm2 + y2_axis : e.g. temp_air, temp_module (C/100 to fit graphs). + + Parameters + ---------- + dmeas : dataframe + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values + 'i_sc' .. 'v_oc', temp_module. + + dnorm : dataframe + multiplicative lfm loss values 'i_sc' ... 'v_oc' + where pr_dc = 1/ff * product('i_sc', ... 'v_oc') + + mlfm_file_name : string + mlfm_file_name used in graph title. + + qty_mlfm_vars : int + number of mlfm_values present in data usually + 2 = (imp, vmp) from mpp tracker + 4 = (i_sc, i_mp, v_mp, v_oc) from matrix + 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + ''' + + # offset legend to the right to not overlap graph, use ~1.2 + bbox = 1.2 + + # set x_axis as irradiance + xdata = dmeas['poa_global'] + + fig, ax1 = plt.subplots() + + # get filename without ".csv" for title + ax1.set_title('Plot mlfm scatter ' + + mlfm_file_name[:len(mlfm_file_name)-4]) + + ax1.set_ylabel('normalised mlfm values') + ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line + ax1.set_ylim(0.8, 1.1) # optional normalised y scale + + ax1.set_xlabel('poa_global [W/m$^2$]') + ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC + ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT + ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC + + # plot the mlfm parameters depending on qty_mlfm_vars + if qty_mlfm_vars == 1: # only p_mp + ax1.scatter(xdata, dnorm['pr_dc_temp_corr'], + c=clr['pr_dc'], label='pr_dc_temp_corr') + + # if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix + if qty_mlfm_vars in (2, 4): # mppt or matrix + ax1.scatter(xdata, dnorm['i_mp'], c=clr['i_mp'], label='norm_i_mp') + ax1.scatter(xdata, dnorm['v_mp'], c=clr['v_mp'], label='norm_v_mp') + + if qty_mlfm_vars >= 6: # ivcurve + ax1.scatter(xdata, dnorm['i_ff'], c=clr['i_ff'], label='norm_i_ff') + ax1.scatter(xdata, dnorm['v_ff'], c=clr['v_ff'], label='norm_v_ff') + ax1.scatter(xdata, dnorm['r_sc'], c=clr['r_sc'], label='norm_r_sc') + ax1.scatter(xdata, dnorm['r_oc'], c=clr['r_oc'], label='norm_r_oc') + + if qty_mlfm_vars >= 4: # matrix + ax1.scatter(xdata, dnorm['i_sc'], c=clr['i_sc'], label='norm_i_sc') + + ax1.scatter(xdata, dnorm['v_oc_temp_corr'], c=clr['v_oc'], + label='norm_v_oc_temp_corr') + + ax1.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) + + # y2axis plot met on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('temp_module, temp_air (C/100)') # poa_global (kW/m$^2$); + + # set wide limits 0 to 4 so they don't overlap mlfm params + ax2.set_ylim(0, 4) + + ax2.scatter(xdata, + dmeas['temp_module']/100, + c=clr['temp_module'], + label='temp_module C/100') + + # temp_air may not exist particularly for indoor measurements + try: + ax2.scatter(xdata, + dmeas['temp_air']/100, + c=clr['temp_air'], + label='temp_air C/100') + except KeyError: + pass + + ax2.legend(bbox_to_anchor=(bbox, 0.5), loc='upper left', borderaxespad=0.) + plt.show() + + +def plot_mlfm_stack(dmeas, dnorm, dstack, ref, + mlfm_file_name, qty_mlfm_vars, + xaxis_labels=12, is_i_sc_self_ref=False, + is_v_oc_temp_module_corr=True): + + ''' + Plot graph of stacked MLFM losses from intital 1/FF down to pr_dc. + + Parameters + ---------- + dmeas : dataframe + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values + 'i_sc' .. 'v_oc', temp_module. + + dnorm : dataframe + normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + + dstack : dataframe + normalised subtractive lfm loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/ff - sum('i_sc', ... 'v_oc'). + + ref : dict + reference stc values e.g. 'v_oc' and + temperature coeffs e.g. 'beta_v_oc'. + + mlfm_file_name : string + mlfm_file_name used in graph title. + + qty_mlfm_vars : int + number of mlfm_values present in data usually + 2 = (imp, vmp) from mpp tracker + 4 = (i_sc, i_mp, v_mp, v_oc) from matrix + 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + + xaxis_labels : int + number of xaxis labels to show (~12) or 0 to show all. + + is_i_sc_self_ref : bool + self corrects i_sc to remove angle of incidence, + spectrum, snow or soiling?. + + is_v_oc_temp_module_corr : bool + calc loss due to gamma, subtract from v_oc loss. + + ''' + + # offset legend right, use ~1.2 + bbox = 1.2 + + # select x axis usually date_time + xdata = dmeas.index + fig, ax1 = plt.subplots() + + ax1.set_title('Plot_mlfm_stack : ' + + mlfm_file_name[:len(mlfm_file_name)-4]) + + if qty_mlfm_vars == 6: # iv curve + labels_6 = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_r_oc', + 'stack_v_ff', + '- - -', + 'stack_i_ff', + 'stack_r_sc', + 'stack_i_sc' + ] + + color_map_6 = [ + 'white', # colour to bottom of graph + clr['temp_module'], + clr['v_oc'], + clr['r_oc'], + clr['v_ff'], + clr['i_v'], + clr['i_ff'], + clr['r_sc'], + clr['i_sc'] + ] + + # plot stack in order bottom to top, + # allowing self_ref and temp_module corrections + ax1.stackplot( + xdata, + dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), + dstack['v_oc'] - ( + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), + dstack['r_oc'], + dstack['v_ff'], + dstack['i_v'], + dstack['i_ff'], + dstack['r_sc'], + dstack['i_sc'] * (not is_i_sc_self_ref), + labels=labels_6, + colors=color_map_6 + ) + + if qty_mlfm_vars == 4: # matrix + labels_4 = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_v_mp', + '- - -', + 'stack_i_mp', + 'i_sc' + ] + + color_map_4 = [ + 'white', # colour to bottom of graph + clr['temp_module'], + clr['v_oc'], + clr['v_mp'], + clr['i_v'], + clr['i_mp'], + clr['i_sc'] + ] + + # plot stack in order bottom to top, + # allowing self_ref and temp_module corrections + ax1.stackplot( + xdata, + dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), + dstack['v_oc'] - ( + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), + dstack['v_mp'], + dstack['i_v'], + dstack['i_mp'], + dstack['i_sc'] * (not is_i_sc_self_ref), + labels=labels_4, + colors=color_map_4 + ) + + ax1.axhline(y=1/ref['ff'], c='grey', lw=3) # show initial 1/FF + ax1.axhline(y=1, c='grey', lw=3) # show 100% line + ax1.set_ylabel('stacked mlfm losses') + + # find number of x date values + x_ticks = dmeas.shape[0] + plt.xticks(np.arange(0, x_ticks), rotation=90) + + # if (xaxis_labels > 0 and xaxis_labels < x_ticks): + if 0 < xaxis_labels < x_ticks: + xaxis_skip = np.floor(x_ticks / xaxis_labels) + else: + xaxis_skip = 2 + + # + xax2 = [''] * x_ticks + x_count = 0 + while x_count < x_ticks: + if x_count % xaxis_skip == 0: + # + # try to reformat any date indexes (not for matrices) + # + # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 + # y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' + # + try: + xax2[x_count] = xdata[x_count][2:13]+'h' + + except IndexError: + xax2[x_count] = xdata[x_count] + + x_count += 1 + + ax1.set_xticklabels(xax2) + ax1.set_ylim(0.6, 1/ref['ff']+0.1) # optional normalised y scale + plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) + + # plot met data on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/100)') + ax2.set_ylim(0, 4) # set so doesn't overlap mlfm params + + plt.plot(xdata, dmeas['poa_global_kwm2'], + c=clr['irradiance'], label='poa_global_kwm2') + plt.plot(xdata, dmeas['temp_module'] / 100, + c=clr['temp_module'], label='temp_module/100') + + # temp_air may not exist particularly for indoor measurements + try: + plt.plot(xdata, dmeas['temp_air']/100, + c=clr['temp_air'], label='temp_air/100') + except KeyError: + pass + + ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) + ax1.set_xticklabels(xax2, rotation=90) + plt.show() + + REFS = """ The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) together known as "MLFM" have been developed by SRCL and Gantner Instruments diff --git a/pvlib/mlfm_graphs.py b/pvlib/mlfm_graphs.py deleted file mode 100644 index cd9612d612..0000000000 --- a/pvlib/mlfm_graphs.py +++ /dev/null @@ -1,378 +0,0 @@ -''' -This ``mlfm graphs`` module contains functions to display -performance of PV modules using the mechanistic performance (MPM) and -loss factors models (LFM) - -Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) - -https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules - -https://github.com/python/peps/blob/master/pep-0008.txt -''' - -import numpy as np -import matplotlib.pyplot as plt - -# Define standardised MLFM graph colours as a dict ``clr`` - -clr = { - # parameter_clr colour R G B - 'irradiance': 'darkgreen', # 000 064 000 - 'temp_module': 'red', # 255 000 000 - 'temp_air': 'yellow', # 245 245 220 - 'wind_speed': 'grey', # 127 127 127 - - 'i_sc': 'purple', # 128 000 128 - 'r_sc': 'orange', # 255 165 000 - 'i_ff': 'lightgreen', # 144 238 144 - 'i_mp': 'green', # 000 255 000 - 'i_v': 'black', # 000 000 000 between i and v losses - 'v_ff': 'cyan', # 000 255 255 - 'v_mp': 'blue', # 000 000 255 - 'r_oc': 'pink', # 255 192 203 - 'v_oc': 'sienna', # 160 082 045 - - 'pr_dc': 'black', # 000 000 000 -} - - -def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): - ''' - Scatter plot normalised MLFM parameters(y) vs. irradiance(x). - - y1_axis : e.g. norm(i_sc, ... v_oc),_temp_module_corr - x_axis : e.g. irradiance, poa_global_kwm2 - y2_axis : e.g. temp_air, temp_module (C/100 to fit graphs). - - Parameters - ---------- - dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' - and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module. - - dnorm : dataframe - multiplicative lfm loss values 'i_sc' ... 'v_oc' - where pr_dc = 1/ff * product('i_sc', ... 'v_oc') - - mlfm_file_name : string - mlfm_file_name used in graph title. - - qty_mlfm_vars : int - number of mlfm_values present in data usually - 2 = (imp, vmp) from mpp tracker - 4 = (i_sc, i_mp, v_mp, v_oc) from matrix - 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. - ''' - - # offset legend to the right to not overlap graph, use ~1.2 - bbox = 1.2 - - # set x_axis as irradiance - xdata = dmeas['poa_global'] - - fig, ax1 = plt.subplots() - - # get filename without ".csv" for title - ax1.set_title('Plot mlfm scatter ' + - mlfm_file_name[:len(mlfm_file_name)-4]) - - ax1.set_ylabel('normalised mlfm values') - ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line - ax1.set_ylim(0.8, 1.1) # optional normalised y scale - - ax1.set_xlabel('poa_global [W/m$^2$]') - ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC - ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT - ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC - - # plot the mlfm parameters depending on qty_mlfm_vars - if qty_mlfm_vars == 1: # only p_mp - ax1.scatter(xdata, dnorm['pr_dc_temp_corr'], - c=clr['pr_dc'], label='pr_dc_temp_corr') - - # if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix - if qty_mlfm_vars in (2, 4): # mppt or matrix - ax1.scatter(xdata, dnorm['i_mp'], c=clr['i_mp'], label='norm_i_mp') - ax1.scatter(xdata, dnorm['v_mp'], c=clr['v_mp'], label='norm_v_mp') - - if qty_mlfm_vars >= 6: # ivcurve - ax1.scatter(xdata, dnorm['i_ff'], c=clr['i_ff'], label='norm_i_ff') - ax1.scatter(xdata, dnorm['v_ff'], c=clr['v_ff'], label='norm_v_ff') - ax1.scatter(xdata, dnorm['r_sc'], c=clr['r_sc'], label='norm_r_sc') - ax1.scatter(xdata, dnorm['r_oc'], c=clr['r_oc'], label='norm_r_oc') - - if qty_mlfm_vars >= 4: # matrix - ax1.scatter(xdata, dnorm['i_sc'], c=clr['i_sc'], label='norm_i_sc') - - ax1.scatter(xdata, dnorm['v_oc_temp_corr'], c=clr['v_oc'], - label='norm_v_oc_temp_corr') - - ax1.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) - - # y2axis plot met on right y axis - ax2 = ax1.twinx() - ax2.set_ylabel('temp_module, temp_air (C/100)') # poa_global (kW/m$^2$); - - # set wide limits 0 to 4 so they don't overlap mlfm params - ax2.set_ylim(0, 4) - - ax2.scatter(xdata, - dmeas['temp_module']/100, - c=clr['temp_module'], - label='temp_module C/100') - - # temp_air may not exist particularly for indoor measurements - try: - ax2.scatter(xdata, - dmeas['temp_air']/100, - c=clr['temp_air'], - label='temp_air C/100') - except KeyError: - pass - - ax2.legend(bbox_to_anchor=(bbox, 0.5), loc='upper left', borderaxespad=0.) - plt.show() - - -def plot_mlfm_stack(dmeas, dnorm, dstack, ref, - mlfm_file_name, qty_mlfm_vars, - xaxis_labels=12, is_i_sc_self_ref=False, - is_v_oc_temp_module_corr=True): - - ''' - Plot graph of stacked MLFM losses from intital 1/FF down to pr_dc. - - Parameters - ---------- - dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' - and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module. - - dnorm : dataframe - normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). - - dstack : dataframe - normalised subtractive lfm loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/ff - sum('i_sc', ... 'v_oc'). - - ref : dict - reference stc values e.g. 'v_oc' and - temperature coeffs e.g. 'beta_v_oc'. - - mlfm_file_name : string - mlfm_file_name used in graph title. - - qty_mlfm_vars : int - number of mlfm_values present in data usually - 2 = (imp, vmp) from mpp tracker - 4 = (i_sc, i_mp, v_mp, v_oc) from matrix - 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. - - xaxis_labels : int - number of xaxis labels to show (~12) or 0 to show all. - - is_i_sc_self_ref : bool - self corrects i_sc to remove angle of incidence, - spectrum, snow or soiling?. - - is_v_oc_temp_module_corr : bool - calc loss due to gamma, subtract from v_oc loss. - - ''' - - # offset legend right, use ~1.2 - bbox = 1.2 - - # select x axis usually date_time - xdata = dmeas.index - fig, ax1 = plt.subplots() - - ax1.set_title('Plot_mlfm_stack : ' + - mlfm_file_name[:len(mlfm_file_name)-4]) - - if qty_mlfm_vars == 6: # iv curve - labels_6 = [ - 'pr_dc', - 'stack_t_mod', - 'stack_v_oc', - 'stack_r_oc', - 'stack_v_ff', - '- - -', - 'stack_i_ff', - 'stack_r_sc', - 'stack_i_sc' - ] - - color_map_6 = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['r_oc'], - clr['v_ff'], - clr['i_v'], - clr['i_ff'], - clr['r_sc'], - clr['i_sc'] - ] - - # plot stack in order bottom to top, - # allowing self_ref and temp_module corrections - ax1.stackplot( - xdata, - dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), - dstack['r_oc'], - dstack['v_ff'], - dstack['i_v'], - dstack['i_ff'], - dstack['r_sc'], - dstack['i_sc'] * (not is_i_sc_self_ref), - labels=labels_6, - colors=color_map_6 - ) - - if qty_mlfm_vars == 4: # matrix - labels_4 = [ - 'pr_dc', - 'stack_t_mod', - 'stack_v_oc', - 'stack_v_mp', - '- - -', - 'stack_i_mp', - 'i_sc' - ] - - color_map_4 = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['v_mp'], - clr['i_v'], - clr['i_mp'], - clr['i_sc'] - ] - - # plot stack in order bottom to top, - # allowing self_ref and temp_module corrections - ax1.stackplot( - xdata, - dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), - dstack['v_mp'], - dstack['i_v'], - dstack['i_mp'], - dstack['i_sc'] * (not is_i_sc_self_ref), - labels=labels_4, - colors=color_map_4 - ) - - ax1.axhline(y=1/ref['ff'], c='grey', lw=3) # show initial 1/FF - ax1.axhline(y=1, c='grey', lw=3) # show 100% line - ax1.set_ylabel('stacked mlfm losses') - - # find number of x date values - x_ticks = dmeas.shape[0] - plt.xticks(np.arange(0, x_ticks), rotation=90) - - # if (xaxis_labels > 0 and xaxis_labels < x_ticks): - if 0 < xaxis_labels < x_ticks: - xaxis_skip = np.floor(x_ticks / xaxis_labels) - else: - xaxis_skip = 2 - - # - xax2 = [''] * x_ticks - x_count = 0 - while x_count < x_ticks: - if x_count % xaxis_skip == 0: - # - # try to reformat any date indexes (not for matrices) - # - # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 - # y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' - # - try: - xax2[x_count] = xdata[x_count][2:13]+'h' - - except IndexError: - xax2[x_count] = xdata[x_count] - - x_count += 1 - - ax1.set_xticklabels(xax2) - ax1.set_ylim(0.6, 1/ref['ff']+0.1) # optional normalised y scale - plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) - - # plot met data on right y axis - ax2 = ax1.twinx() - ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/100)') - ax2.set_ylim(0, 4) # set so doesn't overlap mlfm params - - plt.plot(xdata, dmeas['poa_global_kwm2'], - c=clr['irradiance'], label='poa_global_kwm2') - plt.plot(xdata, dmeas['temp_module'] / 100, - c=clr['temp_module'], label='temp_module/100') - - # temp_air may not exist particularly for indoor measurements - try: - plt.plot(xdata, dmeas['temp_air']/100, - c=clr['temp_air'], label='temp_air/100') - except KeyError: - pass - - ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) - ax1.set_xticklabels(xax2, rotation=90) - plt.show() - - -REFS = """ -References - -The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) -together known as "MLFM" have been developed by SRCL and Gantner Instruments -(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM - -.. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome - '4AV.2.41 Characterising PV Modules under Outdoor Conditions: -What's Most Important for Energy Yield' -26th EU PVSEC 8 September 2011; Hamburg, Germany. -http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf - -.. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) - 'Choosing the best Empirical Model for predicting energy yield' - 7th PV Energy Rating and Module Performance Modeling Workshop, - Canobbio, Switzerland 30-31 March, 2017. - -.. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) -'Checking the new IEC 61853.1-4 with high quality 3rd party data to -benchmark its practical relevance in energy yield prediction' -PVSC June 2019 [Chicago], USA. -http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf - -.. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) -'5CV.4.35 Quantifying Long Term PV Performance and Degradation -under Real Outdoor and IEC 61853 Test Conditions -Using High Quality Module IV Measurements'. -36th EU PVSEC Sep 2019 [Marseille] - -.. [5] Steve Ransome (SRCL) -'How to use the Loss Factors and Mechanistic Performance Models -effectively with PVPMC/PVLIB' -[PVPMC] Webinar on PV Performance Modeling Methods, Aug 2020. -https://pvpmc.sandia.gov/download/7879/ - -.. [6] W.Marion et al (NREL) -'New Data Set for Validating PV Module Performance Models'. -https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models - -Many more papers are available at www.steveransome.com -""" diff --git a/pvlib/tests/conftest.py b/pvlib/tests/conftest.py index b3e9fcd5a1..4eba16c923 100644 --- a/pvlib/tests/conftest.py +++ b/pvlib/tests/conftest.py @@ -150,6 +150,15 @@ def has_numba(): requires_siphon = pytest.mark.skipif(not has_siphon, reason='requires siphon') +try: + import matplotlib.pyplot as plt + has_mpl = True +except ImportError: + has_mpl = False + +requires_mpl = pytest.mark.skipif(not has_mpl, reason='requires matplotlib') + + try: import netCDF4 # noqa: F401 has_netCDF4 = True diff --git a/setup.py b/setup.py index b2cbf486d5..9dc3219f63 100755 --- a/setup.py +++ b/setup.py @@ -53,8 +53,8 @@ 'requests-mock', 'pytest-timeout', 'pytest-rerunfailures', 'pytest-remotedata'] EXTRAS_REQUIRE = { - 'optional': ['cython', 'ephem', 'netcdf4', 'nrel-pysam', 'numba', - 'pvfactors', 'siphon', 'statsmodels', + 'optional': ['cython', 'ephem', 'matplotlib', 'netcdf4', 'nrel-pysam', + 'numba', 'pvfactors', 'siphon', 'statsmodels', 'cftime >= 1.1.1'], 'doc': ['ipython', 'matplotlib', 'sphinx == 3.1.2', 'pydata-sphinx-theme', 'sphinx-gallery', 'docutils == 0.15.2', From 89627017ecddf820a24a200a12688c1ebd0f92cd Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 6 Jan 2022 16:16:09 -0700 Subject: [PATCH 33/81] docstring edits to trigger build --- pvlib/mlfm.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index a52d6de682..b9e8c4cc0c 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -554,19 +554,19 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, is_v_oc_temp_module_corr=True): ''' - Plot graph of stacked MLFM losses from intital 1/FF down to pr_dc. + Plot graph of stacked MLFM losses from initial 1/FF down to pr_dc. Parameters ---------- - dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' + dmeas : DataFrame + Measured weather data. Must include 'poa_global_kwm2' and + 'temp_module', may include 'temp_air'. and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module. + 'i_sc' .. 'v_oc', temp_module (cwh: I don't see these are referenced). dnorm : dataframe - normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + Normalised multiplicative LFM loss values 'i_sc' .. 'v_oc' + where pr_dc = 1/FF * product('i_sc', ... 'v_oc'). dstack : dataframe normalised subtractive lfm loss values 'i_sc' .. 'v_oc' @@ -585,15 +585,15 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, 4 = (i_sc, i_mp, v_mp, v_oc) from matrix 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. - xaxis_labels : int - number of xaxis labels to show (~12) or 0 to show all. + xaxis_labels : int, default 12 + Number of xaxis labels to show. Use 0 to show all. - is_i_sc_self_ref : bool - self corrects i_sc to remove angle of incidence, + is_i_sc_self_ref : bool, default False + Self corrects i_sc to remove angle of incidence, spectrum, snow or soiling?. - is_v_oc_temp_module_corr : bool - calc loss due to gamma, subtract from v_oc loss. + is_v_oc_temp_module_corr : bool, default True + Calculate loss due to gamma, subtract from v_oc loss. ''' From a505e31c83ef0f82701af52049257b0d5a6c1c65 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 6 Jan 2022 16:33:14 -0700 Subject: [PATCH 34/81] adjust matplotlib import --- pvlib/mlfm.py | 10 ++++++++-- pvlib/tests/conftest.py | 2 +- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index b9e8c4cc0c..834b4b880c 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -14,8 +14,6 @@ import numpy as np import pandas as pd -import matplotlib.pyplot as plt - from scipy import optimize @@ -477,6 +475,10 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): 4 = (i_sc, i_mp, v_mp, v_oc) from matrix 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. ''' + try: + import matplotlib.pyplot as plt + except ImportError: + print('mlfm requires matplotlib') # offset legend to the right to not overlap graph, use ~1.2 bbox = 1.2 @@ -596,6 +598,10 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, Calculate loss due to gamma, subtract from v_oc loss. ''' + try: + import matplotlib.pyplot as plt + except ImportError: + print('mlfm requires matplotlib') # offset legend right, use ~1.2 bbox = 1.2 diff --git a/pvlib/tests/conftest.py b/pvlib/tests/conftest.py index 4eba16c923..2b5559d7ba 100644 --- a/pvlib/tests/conftest.py +++ b/pvlib/tests/conftest.py @@ -151,7 +151,7 @@ def has_numba(): reason='requires siphon') try: - import matplotlib.pyplot as plt + import matplotlib.pyplot as plt # noqa: F401 has_mpl = True except ImportError: has_mpl = False From d01db5f95b2e0d49de1320fe17bdffe5c7bf5935 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 6 Jan 2022 17:55:57 -0700 Subject: [PATCH 35/81] remove mlfm_graphs --- pvlib/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pvlib/__init__.py b/pvlib/__init__.py index 5d09277441..2142853a25 100644 --- a/pvlib/__init__.py +++ b/pvlib/__init__.py @@ -13,7 +13,6 @@ location, modelchain, mlfm, - mlfm_graphs, pvsystem, scaling, shading, From 66130f2e7844576612cadbd14fa8eb0cb0ee6201 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 13 Jan 2022 16:28:18 -0700 Subject: [PATCH 36/81] test functions --- pvlib/tests/test_mlfm.py | 159 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 159 insertions(+) create mode 100644 pvlib/tests/test_mlfm.py diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py new file mode 100644 index 0000000000..bedd8eda57 --- /dev/null +++ b/pvlib/tests/test_mlfm.py @@ -0,0 +1,159 @@ +''' +written : 220111 Steve Ransome (SRCL) +tests functions from mlfm.py + +TESTED +def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): + +def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): + +def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): + +NOT TESTED YET +def mlfm_fit(dmeas, dnorm, mlfm_sel): + +def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): + +def plot_mlfm_stack(dmeas, dnorm, dstack, ref, + mlfm_file_name, qty_mlfm_vars, + xaxis_labels=12, is_i_sc_self_ref=False, + is_v_oc_temp_module_corr=True): +''' + +''' +Define tolerance for checking. +Most values are normalised ~1 +0.000001 is probably good +''' + +import pandas as pd +from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack +from numpy.testing import assert_allclose # assert_almost_equal, +import pytest + + +tolerance = 0.000001 + +qty_mlfm_vars = 6 # check all 6 mlfm params from iv curves + +@pytest.fixture +def reference(): + # get reference module STC values for normalisation + ref = dict( + module_id='g78', + i_sc=5.35, + i_mp=4.9, + v_mp=36.8, + v_oc=44.2, + alpha_i_sc=0.0005, + alpha_i_mp=0, # often not known, not used here + beta_v_mp=0, # often not known, not used here + beta_v_oc=-0.0035, + gamma_p_mp=-0.0045, # = alpha_i_mp + beta_v_mp + delta_ff=0, # often not known, not used here + ) + # create p_mp and ff + ref['p_mp'] = ref['i_mp'] * ref['v_mp'] + ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc']) + return ref + + +@pytest.fixture +def measured(): + # get measured data + data_meas = { + # 'date_time': ['2016-03-23 09:00:00-07:00'], + 'module_id': [78], + 'poa_global': [591.3868886], + 'wind_speed': [4.226408028], + # 'temp_air': [17.42457581], + 'temp_module': [27.82861328], + 'v_oc': [43.52636044], + 'i_sc': [3.14995479], + 'i_mp': [2.949264766], + 'v_mp': [35.76882896], + 'r_sc': [674.5517322], + 'r_oc': [1.355690858], + } + + meas = pd.DataFrame(data_meas) + + # create p_mp and ff in case they don't exist + meas['poa_global_kwm2'] = meas['poa_global'] / 1000 + meas['p_mp'] = meas['i_mp'] * meas['v_mp'] + meas['ff'] = meas['p_mp'] / (meas['i_sc'] * meas['v_oc']) + + return meas + + +@pytest.fixture +def normalized(): + data_norm_target = { + # 'date_time': ['2016-03-23 09:00:00-07:00'], + 'pr_dc': [0.989242790817207], + 'pr_dc_temp_corr': [1.00183462464583], + 'i_mp': [0.93628796685047], + 'v_mp': [0.821773945683017], + 'i_sc': [0.995586151149719], + 'v_oc': [0.98475928597285], + 'v_oc_temp_corr': [0.994508547151521], + 'r_sc': [0.981487711004909], + 'r_oc': [0.903706382978424], + 'i_ff': [0.953947722780796], + 'v_ff': [0.909337325885234], + } + + norm_target = pd.DataFrame(data_norm_target) + + return norm_target + + +@pytest.fixture +def stacked(): + # get stack data + data_stack_target = { + # 'date_time': ['2016-03-23 09:00:00-07:00'], + 'pr_dc': [0.989242790817207], + 'i_sc': [0.0052435168594609], + 'r_sc': [0.0219920307073518], + 'i_ff': [0.049708690806242], + 'i_v': [0.01], + 'v_ff': [0.102704472076433], + 'r_oc': [0.114393859291095], + 'v_oc': [0.0181055001343123], + 'temp_module_corr': [0.0115818228244058], + } + + stack_target = pd.DataFrame(data_stack_target) + + return stack_target + + +@pytest.fixture +def mlfm_6_coeffs(): + # test mlfm coefficients + c_1 = +1.0760136800094817 + c_2 = -0.004619443769147978 + c_3 = +0.018343135214876096 + c_4 = -0.07613482929987923 + c_5 = -0.0006626101399079871 + c_6 = -0.014752223616684625 + expected = 0.9859917396312191 + + return c_1, c_2, c_3, c_4, c_5, c_6, expected + + +def test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized): + norm_calc = mlfm_meas_to_norm(measured, reference, 6) + assert_allclose(norm_calc, normalized, atol=1e-6) + + +def test_mlfm_6(measured, mlfm_6_coeffs): + c_1, c_2, c_3, c_4, c_5, c_6, expected = mlfm_6_coeffs + result = mlfm_6(measured, c_1, c_2, c_3, c_4, c_5, c_6) + assert_allclose(expected, result[0], atol=1e-6) + + +def test_mlfm_norm_to_stack(normalized, reference, stacked): + stack_calc = mlfm_norm_to_stack(normalized, reference, 6) + assert_allclose(stack_calc, stacked, atol=1e-6) From b45ab620ab93cf38df1b91f5825de95a5629aed8 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Sat, 15 Jan 2022 13:57:28 -0700 Subject: [PATCH 37/81] add test for mlfm_fit --- pvlib/tests/test_mlfm.py | 105 ++++++++++++++++++++++++++++----------- 1 file changed, 77 insertions(+), 28 deletions(-) diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index bedd8eda57..9a76bdb246 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -1,33 +1,7 @@ -''' -written : 220111 Steve Ransome (SRCL) -tests functions from mlfm.py - -TESTED -def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): - -def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): - -def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): - -NOT TESTED YET -def mlfm_fit(dmeas, dnorm, mlfm_sel): - -def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): - -def plot_mlfm_stack(dmeas, dnorm, dstack, ref, - mlfm_file_name, qty_mlfm_vars, - xaxis_labels=12, is_i_sc_self_ref=False, - is_v_oc_temp_module_corr=True): -''' - -''' -Define tolerance for checking. -Most values are normalised ~1 -0.000001 is probably good -''' +import numpy as np import pandas as pd -from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack +from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit from numpy.testing import assert_allclose # assert_almost_equal, import pytest @@ -143,6 +117,72 @@ def mlfm_6_coeffs(): return c_1, c_2, c_3, c_4, c_5, c_6, expected +@pytest.fixture +def matrix_data(): + # sample ghi, tmod, ws and pr_dc to fit + # this data selectable from mlfm.ipynb + # --- + # select one of the following meas files + # meas_file = 2 # <<< change from 0 to 2 + # --- + + return pd.DataFrame(np.array( + [[0.1, 15, 0, 0.935774123487434], + [0.2, 15, 0, 0.978281104560968], + [0.4, 15, 0, 1.00721377598511], + [0.6, 15, 0, 1.02254628193195], + [0.8, 15, 0, 1.02710983555693], + [1.0, 15, 0, 1.02655910642259], + [0.1, 25, 0, 0.907539559416693], + [0.2, 25, 0, 0.94849519081601], # LIC + [0.4, 25, 0, 0.980840831523425], + [0.6, 25, 0, 0.994311717861206], + [0.8, 25, 0, 0.998914055228048], + [1.0, 25, 0, 1], # STC + [1.1, 25, 0, 0.998984571122331], + [0.1, 50, 0, 0.833074775054297], + [0.2, 50, 0, 0.879615265280794], + [0.4, 50, 0, 0.908004964318957], + [0.6, 50, 0, 0.920260626745268], + [0.8, 50, 0, 0.925496431895749], + [1.0, 50, 0, 0.927551970214086], + [1.1, 50, 0, 0.926324993653569], + [0.1, 75, 0, 0.746819733167856], + [0.2, 75, 0, 0.792739683524666], + [0.4, 75, 0, 0.826481538938877], + [0.6, 75, 0, 0.842744854690247], + [0.8, 75, 0, 0.847735029475644], + [1.0, 75, 0, 0.849053676698728], + [1.1, 75, 0, 0.849039573519871]]), + columns=[ + 'poa_global_kwm2', 'temp_module', 'wind_speed', 'pr_dc']) + + +@pytest.fixture +def mlfm_6_fit(): + # fit matrix + ''' + Excel fit GRG linear values + c_1 = +1.0573318761708000 + c_2 = -0.0030251199627269 + c_3 = +0.1228522267570000 + c_4 = -0.0545505400372862 + c_5 = 0 + c_6 = -0.002394779219883 + rmse = 0.280% + ''' + c_1 = +1.0579328401731174 + c_2 = -0.0030248261647759975 + c_3 = +0.12378885001559799 + c_4 = -0.05521716508715758 + c_5 = +0.000 + c_6 = -0.0023546463713093836 + expected = 0.9845007615699125 + + cc_target = [c_1, c_2, c_3, c_4, c_5, c_6] + return c_1, c_2, c_3, c_4, c_5, c_6, expected, cc_target + + def test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized): norm_calc = mlfm_meas_to_norm(measured, reference, 6) assert_allclose(norm_calc, normalized, atol=1e-6) @@ -157,3 +197,12 @@ def test_mlfm_6(measured, mlfm_6_coeffs): def test_mlfm_norm_to_stack(normalized, reference, stacked): stack_calc = mlfm_norm_to_stack(normalized, reference, 6) assert_allclose(stack_calc, stacked, atol=1e-6) + + +def test_mlfm_fit(matrix_data, mlfm_6_fit): + c_1, c_2, c_3, c_4, c_5, c_6, expected, cc_target = mlfm_6_fit + # choose which parameter to fit - usually pr_dc + mlfm_sel = 'pr_dc' + norm, cc_fit, ee, coeffs, errs = mlfm_fit( + matrix_data, matrix_data, mlfm_sel) + assert_allclose(cc_fit, cc_target, atol=1e-3) \ No newline at end of file From 060054e757bf6b642c21a9945a421adf287d6845 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 23 Feb 2022 10:12:20 -0700 Subject: [PATCH 38/81] stickler --- pvlib/tests/test_mlfm.py | 57 ++++++++++++++++++++-------------------- 1 file changed, 29 insertions(+), 28 deletions(-) diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 9a76bdb246..a401841e81 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -10,6 +10,7 @@ qty_mlfm_vars = 6 # check all 6 mlfm params from iv curves + @pytest.fixture def reference(): # get reference module STC values for normalisation @@ -127,33 +128,33 @@ def matrix_data(): # --- return pd.DataFrame(np.array( - [[0.1, 15, 0, 0.935774123487434], - [0.2, 15, 0, 0.978281104560968], - [0.4, 15, 0, 1.00721377598511], - [0.6, 15, 0, 1.02254628193195], - [0.8, 15, 0, 1.02710983555693], - [1.0, 15, 0, 1.02655910642259], - [0.1, 25, 0, 0.907539559416693], - [0.2, 25, 0, 0.94849519081601], # LIC - [0.4, 25, 0, 0.980840831523425], - [0.6, 25, 0, 0.994311717861206], - [0.8, 25, 0, 0.998914055228048], - [1.0, 25, 0, 1], # STC - [1.1, 25, 0, 0.998984571122331], - [0.1, 50, 0, 0.833074775054297], - [0.2, 50, 0, 0.879615265280794], - [0.4, 50, 0, 0.908004964318957], - [0.6, 50, 0, 0.920260626745268], - [0.8, 50, 0, 0.925496431895749], - [1.0, 50, 0, 0.927551970214086], - [1.1, 50, 0, 0.926324993653569], - [0.1, 75, 0, 0.746819733167856], - [0.2, 75, 0, 0.792739683524666], - [0.4, 75, 0, 0.826481538938877], - [0.6, 75, 0, 0.842744854690247], - [0.8, 75, 0, 0.847735029475644], - [1.0, 75, 0, 0.849053676698728], - [1.1, 75, 0, 0.849039573519871]]), + [[0.1, 15, 0, 0.935774123487434], + [0.2, 15, 0, 0.978281104560968], + [0.4, 15, 0, 1.00721377598511], + [0.6, 15, 0, 1.02254628193195], + [0.8, 15, 0, 1.02710983555693], + [1.0, 15, 0, 1.02655910642259], + [0.1, 25, 0, 0.907539559416693], + [0.2, 25, 0, 0.94849519081601], # LIC + [0.4, 25, 0, 0.980840831523425], + [0.6, 25, 0, 0.994311717861206], + [0.8, 25, 0, 0.998914055228048], + [1.0, 25, 0, 1], # STC + [1.1, 25, 0, 0.998984571122331], + [0.1, 50, 0, 0.833074775054297], + [0.2, 50, 0, 0.879615265280794], + [0.4, 50, 0, 0.908004964318957], + [0.6, 50, 0, 0.920260626745268], + [0.8, 50, 0, 0.925496431895749], + [1.0, 50, 0, 0.927551970214086], + [1.1, 50, 0, 0.926324993653569], + [0.1, 75, 0, 0.746819733167856], + [0.2, 75, 0, 0.792739683524666], + [0.4, 75, 0, 0.826481538938877], + [0.6, 75, 0, 0.842744854690247], + [0.8, 75, 0, 0.847735029475644], + [1.0, 75, 0, 0.849053676698728], + [1.1, 75, 0, 0.849039573519871]]), columns=[ 'poa_global_kwm2', 'temp_module', 'wind_speed', 'pr_dc']) @@ -205,4 +206,4 @@ def test_mlfm_fit(matrix_data, mlfm_6_fit): mlfm_sel = 'pr_dc' norm, cc_fit, ee, coeffs, errs = mlfm_fit( matrix_data, matrix_data, mlfm_sel) - assert_allclose(cc_fit, cc_target, atol=1e-3) \ No newline at end of file + assert_allclose(cc_fit, cc_target, atol=1e-3) From 9092c7228ba4b72cbbb706d4dd232d70123aca3c Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 23 Feb 2022 10:32:59 -0700 Subject: [PATCH 39/81] add test for a plot function --- pvlib/mlfm.py | 9 +++++++++ pvlib/tests/test_mlfm.py | 18 +++++++++++++----- 2 files changed, 22 insertions(+), 5 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 834b4b880c..a7753da850 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -474,6 +474,12 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): 2 = (imp, vmp) from mpp tracker 4 = (i_sc, i_mp, v_mp, v_oc) from matrix 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + + Returns + ------- + fig : Figure + Instance of matplotlib.figure.Figure + ''' try: import matplotlib.pyplot as plt @@ -549,6 +555,9 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): ax2.legend(bbox_to_anchor=(bbox, 0.5), loc='upper left', borderaxespad=0.) plt.show() + return fig + + def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index a401841e81..81d9f0a674 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -1,9 +1,10 @@ import numpy as np import pandas as pd -from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit +from pvlib import mlfm from numpy.testing import assert_allclose # assert_almost_equal, import pytest +from .conftest import requires_mpl tolerance = 0.000001 @@ -185,18 +186,18 @@ def mlfm_6_fit(): def test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized): - norm_calc = mlfm_meas_to_norm(measured, reference, 6) + norm_calc = mlfm.mlfm_meas_to_norm(measured, reference, 6) assert_allclose(norm_calc, normalized, atol=1e-6) def test_mlfm_6(measured, mlfm_6_coeffs): c_1, c_2, c_3, c_4, c_5, c_6, expected = mlfm_6_coeffs - result = mlfm_6(measured, c_1, c_2, c_3, c_4, c_5, c_6) + result = mlfm.mlfm_6(measured, c_1, c_2, c_3, c_4, c_5, c_6) assert_allclose(expected, result[0], atol=1e-6) def test_mlfm_norm_to_stack(normalized, reference, stacked): - stack_calc = mlfm_norm_to_stack(normalized, reference, 6) + stack_calc = mlfm.mlfm_norm_to_stack(normalized, reference, 6) assert_allclose(stack_calc, stacked, atol=1e-6) @@ -204,6 +205,13 @@ def test_mlfm_fit(matrix_data, mlfm_6_fit): c_1, c_2, c_3, c_4, c_5, c_6, expected, cc_target = mlfm_6_fit # choose which parameter to fit - usually pr_dc mlfm_sel = 'pr_dc' - norm, cc_fit, ee, coeffs, errs = mlfm_fit( + norm, cc_fit, ee, coeffs, errs = mlfm.mlfm_fit( matrix_data, matrix_data, mlfm_sel) assert_allclose(cc_fit, cc_target, atol=1e-3) + + +@requires_mpl +def test_plot_mlfm_scatter(measured, normalized): + import matplotlib.pyplot as plt + fig = mlfm.plot_mlfm_scatter(measured, normalized, 'title_string', 2) + assert isinstance(fig, plt.Figure) From e79e8f31970f30ccf797aa8b93616a8a280ffe39 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Mon, 9 May 2022 15:21:06 -0600 Subject: [PATCH 40/81] remove extra line --- pvlib/mlfm.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index a7753da850..83f288396b 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -558,7 +558,6 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): return fig - def plot_mlfm_stack(dmeas, dnorm, dstack, ref, mlfm_file_name, qty_mlfm_vars, xaxis_labels=12, is_i_sc_self_ref=False, From a55915ac3eabfa3c2680216a62a7db1881438295 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Mon, 9 May 2022 16:25:39 -0600 Subject: [PATCH 41/81] fix test, start on notebook file --- docs/tutorials/mlfm.ipynb | 60 ++++++++++++++++++++++++++++----------- pvlib/tests/test_mlfm.py | 4 +-- 2 files changed, 45 insertions(+), 19 deletions(-) diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb index 8ed7f0cd16..2450d30e1e 100644 --- a/docs/tutorials/mlfm.ipynb +++ b/docs/tutorials/mlfm.ipynb @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -57,11 +57,7 @@ "root_dir\n", "\n", "# Import essential library file with lfm and mpm definitions\n", - "from mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", - "# Import graphics code from separate file\n", - "from mlfm_graphs import plot_mlfm_scatter, plot_mlfm_stack \n", - "\n", - "#import matplotlib.pyplot as plt\n", + "from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit, plot_mlfm_scatter, plot_mlfm_stack \n", "\n", "# STANDARD DEFINITIONS\n", "\n", @@ -77,16 +73,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'C:\\\\Users\\\\steve\\\\OneDrive\\\\Documents\\\\_CONS\\\\__Reference\\\\PVPMC\\\\__repository\\\\pvlib-python\\\\docs\\\\tutorials'" + "'c:\\\\python\\\\ransome\\\\pvlib-python\\\\docs\\\\tutorials'" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -134,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -187,9 +183,27 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'c:\\\\python\\\\ransome\\\\pvlib-python\\\\docs\\\\tutorials\\\\mlfm_data\\\\ref\\\\mlfm_reference_modules.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# user must keep updated with their modules from their measurements\u001b[39;00m\n\u001b[0;32m 2\u001b[0m ref_file_name \u001b[38;5;241m=\u001b[39m (root_dir \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mmlfm_data\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mref\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mmlfm_reference_modules.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m ref_data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref_file_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodule_id\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m 306\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 307\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[0;32m 308\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m 309\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m 310\u001b[0m )\n\u001b[1;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:680\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 665\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 666\u001b[0m dialect,\n\u001b[0;32m 667\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 676\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m 677\u001b[0m )\n\u001b[0;32m 678\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:575\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 572\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 574\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 575\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 577\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 578\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:933\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 930\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 932\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m--> 933\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1217\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1213\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1214\u001b[0m \u001b[38;5;66;03m# error: No overload variant of \"get_handle\" matches argument types\u001b[39;00m\n\u001b[0;32m 1215\u001b[0m \u001b[38;5;66;03m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[39;00m\n\u001b[0;32m 1216\u001b[0m \u001b[38;5;66;03m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[39;00m\n\u001b[1;32m-> 1217\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[call-overload]\u001b[39;49;00m\n\u001b[0;32m 1218\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1219\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1220\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1221\u001b[0m \u001b[43m 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\u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\common.py:789\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 784\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 785\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 786\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 787\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 788\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 789\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 791\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 792\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 793\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 794\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 795\u001b[0m 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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m mlfm_sel \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpr_dc\u001b[39m\u001b[38;5;124m'\u001b[39m \n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# FIX THIS WARNING,\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# SettingWithCopyWarning:\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# A value is trying to be set on a copy of a slice from a DataFrame.\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# Try using .loc[row_indexer,col_indexer] = value instead\u001b[39;00m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m norm, cc, ee, coeffs, errs \u001b[38;5;241m=\u001b[39m mlfm_fit(\u001b[43mmeas\u001b[49m, norm, mlfm_sel) \u001b[38;5;66;03m# qty_mlfm_vars)\u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Fix a bug with fit routine which gives a\u001b[39;00m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# finite cc[4] even if all the ws data is 0\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;66;03m# this won't matter until cc is applied to other\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;66;03m# data with some ws <>0 when it will give bad results\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmean(meas\u001b[38;5;241m.\u001b[39mwind_speed) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", + "\u001b[1;31mNameError\u001b[0m: name 'meas' is not defined" + ] + } + ], "source": [ "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", "mlfm_sel = 'pr_dc' \n", @@ -1618,8 +1644,8 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -1632,7 +1658,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.4" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 81d9f0a674..d3819b012c 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -169,7 +169,7 @@ def mlfm_6_fit(): c_2 = -0.0030251199627269 c_3 = +0.1228522267570000 c_4 = -0.0545505400372862 - c_5 = 0 + c_5 = 0 # this is in conflict with the data which include wind_speed c_6 = -0.002394779219883 rmse = 0.280% ''' @@ -177,7 +177,7 @@ def mlfm_6_fit(): c_2 = -0.0030248261647759975 c_3 = +0.12378885001559799 c_4 = -0.05521716508715758 - c_5 = +0.000 + c_5 = +0.735430328541581 c_6 = -0.0023546463713093836 expected = 0.9845007615699125 From ccd25a77692e3c1d8886bab2f94acfac573a11f5 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Tue, 10 May 2022 16:29:24 -0600 Subject: [PATCH 42/81] fix mlfm_fit --- pvlib/mlfm.py | 135 ++++++++++++++++++--------------------- pvlib/tests/test_mlfm.py | 8 ++- 2 files changed, 68 insertions(+), 75 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 83f288396b..25cc7deb22 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -134,48 +134,6 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): return dnorm -def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6): - ''' - Predict normalised lfm values e.g. pr_dc, norm(i_sc, ... v_oc) - from poa_global, temp_module, wind_speed and mlfm(c_1 .. c_6). - - Parameters - ---------- - - dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' - and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module. - - c_1 to c_6 : float - fitted mlfm coefficients (dependencies) - c_1 - constant - c_2 - temperature coefficient (1/K) - c_3 - low light log irradiance drop (~v_oc) - c_4 - high light linear irradiance drop (~r_s) - (optional or set at 0) - c_5 - wind speed dependence (=0 if indoor) - c_6 - inverse irradiance (<= 0). - - Returns - ------- - mlfm_6 : float - predicted performance values for pr_dc, norm(i_sc, .. v_oc) . - ''' - - mlfm_out = ( - c_1 + # 'constant' lossless - c_2 * (dmeas['temp_module'] - T_STC) + # temperature coefficient - c_3 * np.log10(dmeas['poa_global_kwm2']) + # low light drop, 'v_oc' - c_4 * dmeas['poa_global_kwm2'] + # high light drop 'rs' - c_5 * dmeas['wind_speed'] + # wind_speed (optional|0) - c_6 / dmeas['poa_global_kwm2'] # rsh (optional but < 0) - ) - - return mlfm_out - - def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): ''' Converts MLFM normalised multiplicative losses norm(i_sc ... v_oc) @@ -312,44 +270,77 @@ def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): return dstack -def mlfm_fit(dmeas, dnorm, mlfm_sel): +def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): ''' - Fit MLFM to normalised data e.g. norm_pr_dc,(norm_i_sc .. norm_v_oc). + Predict normalised lfm values e.g. pr_dc, norm(i_sc, ... v_oc) + from poa_global, temp_module, wind_speed and mlfm(c_1 .. c_6). Parameters ---------- - dnorm : dataframe - normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + dmeas : dataframe + measured weather data + 'poa_global', 'temp_module', 'wind_speed' + and measured electrical/thermal values + 'i_sc' .. 'v_oc', temp_module. - mlfm_sel : string - mlfm variable being fitted e.g. pr_dc. + c_1 to c_6 : float + fitted mlfm coefficients (dependencies) + c_1 - constant + c_2 - temperature coefficient (1/K) + c_3 - low light log irradiance drop (~v_oc) + c_4 - high light linear irradiance drop (~r_s) + (optional or set at 0) + c_5 - wind speed dependence (=0 if indoor) + c_6 - inverse irradiance (<= 0). Returns ------- - dnorm : dataframe - same data but with added mlfm_var fit values - calc_mlfm_sel and diff_mlfm_sel. + mlfm_6 : float + predicted performance values for pr_dc, norm(i_sc, .. v_oc) . + ''' + mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ + c_3 * np.log10(dmeas['poa_global_kwm2']) + \ + c_4 * dmeas['poa_global_kwm2'] + c_6 / dmeas['poa_global_kwm2'] + if 'wind_speed' in dmeas.columns: + mlfm_out += c_5 * dmeas['wind_speed'] + return mlfm_out - coeff : list - fit coefficients c_1 to c_6. - err : list - error values. +def mlfm_fit(data, var_to_fit): + ''' + Fit MLFM to data. + + Parameters + ---------- - coeffs : string - formatted coefficients for printing. + data : DataFrame + Must include columns 'poa_global', 'temp_module', 'wind_speed'. + May include optional column 'wind_speed'. + Must include column names var_to_fit. - errs - formatted errors for printing. + var_to_fit : string + variable being fitted e.g. 'pr_dc'. + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients c_1 to c_6. + + resid : Series + Residuals of the fitted model. ''' # drop missing data - dnorm = dnorm.dropna() + data = data.dropna() - # ensure correct number of p0 and bounds - # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html + c5_zero = 'wind_speed' not in data.columns + # if wind_speed is not present, add it + if c5_zero: + data['wind_speed'] = 0. # define function name func = mlfm_6 @@ -364,8 +355,8 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): popt, pcov = optimize.curve_fit( f=func, # fit function - xdata=dmeas, # input data - ydata=dnorm[mlfm_sel], # fit parameter + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter p0=p_0, # initial bounds=bounds # boundaries ) @@ -378,6 +369,9 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): c_5 = popt[4] c_6 = popt[5] + if c5_zero: + c_5 = 0. + coeff = [c_1, c_2, c_3, c_4, c_5, c_6] # get mlfm error coefficients as sqrt of covariance @@ -403,7 +397,7 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): ) # print ('coeffs = ', mlfm_sel, coeffs) - errs = ( + err = ( ' {:.4%}'.format(e_1) + ', {:.4%}'.format(e_2) + ', {:.4%}'.format(e_3) + @@ -414,13 +408,10 @@ def mlfm_fit(dmeas, dnorm, mlfm_sel): # print ('errs = ', mlfm_sel, errs) # save fit and error to dataframe - dnorm['calc_' + mlfm_sel] = ( - mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5, c_6)) - - dnorm['diff_' + mlfm_sel] = ( - dnorm[mlfm_sel] - dnorm['calc_' + mlfm_sel]) + pred = mlfm_6(data, c_1, c_2, c_3, c_4, c_5, c_6) + resid = pred - data[var_to_fit] - return(dnorm, coeff, err, coeffs, errs) + return pred, coeff, resid # Define standardised MLFM graph colours as a dict ``clr`` diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index d3819b012c..137393adf7 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -177,7 +177,7 @@ def mlfm_6_fit(): c_2 = -0.0030248261647759975 c_3 = +0.12378885001559799 c_4 = -0.05521716508715758 - c_5 = +0.735430328541581 + c_5 = 0. c_6 = -0.0023546463713093836 expected = 0.9845007615699125 @@ -205,8 +205,10 @@ def test_mlfm_fit(matrix_data, mlfm_6_fit): c_1, c_2, c_3, c_4, c_5, c_6, expected, cc_target = mlfm_6_fit # choose which parameter to fit - usually pr_dc mlfm_sel = 'pr_dc' - norm, cc_fit, ee, coeffs, errs = mlfm.mlfm_fit( - matrix_data, matrix_data, mlfm_sel) + # drop wind_speed since it's always zero + matrix_data = matrix_data.drop(columns=['wind_speed']) + predictions, cc_fit, residuals = mlfm.mlfm_fit( + matrix_data, mlfm_sel) assert_allclose(cc_fit, cc_target, atol=1e-3) From 9396d05b60e3a4d8c4795c7fd8e5e5e82118b394 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 18 May 2022 14:57:43 -0600 Subject: [PATCH 43/81] docstring work, add iPython notebook probably broken --- docs/tutorials/mlfm.ipynb | 231 +++++++++++++++++++++----------------- pvlib/mlfm.py | 72 +++++++----- 2 files changed, 174 insertions(+), 129 deletions(-) diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb index 2450d30e1e..29a4574ce7 100644 --- a/docs/tutorials/mlfm.ipynb +++ b/docs/tutorials/mlfm.ipynb @@ -39,12 +39,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#import pvlib\n", - "from pvlib import *\n", + "#from pvlib import *\n", "\n", "import numpy as np\n", "import pandas as pd\n", @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -82,7 +82,7 @@ "'c:\\\\python\\\\ransome\\\\pvlib-python\\\\docs\\\\tutorials'" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -183,27 +183,11 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'c:\\\\python\\\\ransome\\\\pvlib-python\\\\docs\\\\tutorials\\\\mlfm_data\\\\ref\\\\mlfm_reference_modules.csv'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# user must keep updated with their modules from their measurements\u001b[39;00m\n\u001b[0;32m 2\u001b[0m ref_file_name \u001b[38;5;241m=\u001b[39m (root_dir \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mmlfm_data\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mref\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mmlfm_reference_modules.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m ref_data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref_file_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmodule_id\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m 306\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 307\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[0;32m 308\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m 309\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m 310\u001b[0m )\n\u001b[1;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:680\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 665\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 666\u001b[0m dialect,\n\u001b[0;32m 667\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 676\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m 677\u001b[0m )\n\u001b[0;32m 678\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:575\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 572\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 574\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 575\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m 577\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 578\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:933\u001b[0m, in 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\u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1226\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1227\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1228\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\io\\common.py:789\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 784\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 785\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 786\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 787\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 788\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 789\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 791\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 792\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 793\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 794\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 795\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 796\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 797\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 798\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'c:\\\\python\\\\ransome\\\\pvlib-python\\\\docs\\\\tutorials\\\\mlfm_data\\\\ref\\\\mlfm_reference_modules.csv'" - ] - } - ], + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "# user must keep updated with their modules from their measurements\n", "ref_file_name = (root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_reference_modules.csv')\n", @@ -220,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -236,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -301,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -420,16 +404,32 @@ "" ], "text/plain": [ - " module_id poa_global ... poa_global_kwm2 p_mp\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 78 2.666484 ... 0.002666 0.238726\n", - "2016-01-26 07:30:00-07:00 78 7.899143 ... 0.007899 0.883783\n", - "2016-01-26 07:40:00-07:00 78 52.927672 ... 0.052928 1.985488\n", + " module_id poa_global wind_speed temp_air \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 78 2.666484 1.472832 8.177979 \n", + "2016-01-26 07:30:00-07:00 78 7.899143 1.297711 8.241425 \n", + "2016-01-26 07:40:00-07:00 78 52.927672 0.955482 7.739624 \n", + "\n", + " blue_frac beam_frac temp_module v_oc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.454992 1.100000 2.081940 33.040644 \n", + "2016-01-26 07:30:00-07:00 0.522027 -0.100000 2.436985 37.644029 \n", + "2016-01-26 07:40:00-07:00 0.270154 0.300267 2.592087 39.649206 \n", "\n", - "[3 rows x 15 columns]" + " i_sc i_mp v_mp r_sc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.013215 0.009809 24.337320 115258.549800 \n", + "2016-01-26 07:30:00-07:00 0.037249 0.029832 29.624980 8253.745059 \n", + "2016-01-26 07:40:00-07:00 0.072837 0.061196 32.444868 4762.543972 \n", + "\n", + " r_oc poa_global_kwm2 p_mp \n", + "date_time \n", + "2016-01-26 07:20:00-07:00 608.680999 0.002666 0.238726 \n", + "2016-01-26 07:30:00-07:00 150.461283 0.007899 0.883783 \n", + "2016-01-26 07:40:00-07:00 63.660028 0.052928 1.985488 " ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -456,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -493,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -543,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -642,16 +642,26 @@ "" ], "text/plain": [ - " pr_dc pr_dc_temp_corr ... i_ff v_ff\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 0.496497 0.445293 ... 0.754692 0.968481\n", - "2016-01-26 07:30:00-07:00 0.620471 0.557473 ... 0.896006 0.907783\n", - "2016-01-26 07:40:00-07:00 0.208037 0.187059 ... 0.935916 0.914282\n", + " pr_dc pr_dc_temp_corr i_mp v_mp \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.496497 0.445293 0.742241 0.736587 \n", + "2016-01-26 07:30:00-07:00 0.620471 0.557473 0.800896 0.786977 \n", + "2016-01-26 07:40:00-07:00 0.208037 0.187059 0.840172 0.818298 \n", + "\n", + " i_sc v_oc v_oc_temp_corr r_sc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.926380 0.747526 0.687564 0.983502 \n", + "2016-01-26 07:30:00-07:00 0.881409 0.851675 0.784418 0.893852 \n", + "2016-01-26 07:40:00-07:00 0.257227 0.897041 0.826688 0.897700 \n", "\n", - "[3 rows x 11 columns]" + " r_oc i_ff v_ff \n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.760559 0.754692 0.968481 \n", + "2016-01-26 07:30:00-07:00 0.866922 0.896006 0.907783 \n", + "2016-01-26 07:40:00-07:00 0.895018 0.935916 0.914282 " ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -673,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -699,7 +709,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -720,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -751,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -798,18 +808,31 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -843,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -932,16 +955,20 @@ "" ], "text/plain": [ - " pr_dc i_sc ... v_oc temp_module_corr\n", - "date_time ... \n", - "2016-01-26 08:10:00-07:00 0.987455 0.063817 ... -0.002454 -0.065435\n", - "2016-01-26 08:30:00-07:00 0.836998 0.237283 ... 0.004324 -0.042936\n", - "2016-01-26 08:40:00-07:00 0.978335 0.061103 ... 0.000727 -0.033518\n", + " pr_dc i_sc r_sc i_ff i_v \\\n", + "date_time \n", + "2016-01-26 08:10:00-07:00 0.987455 0.063817 0.044529 0.035911 0.01 \n", + "2016-01-26 08:30:00-07:00 0.836998 0.237283 0.032594 0.030246 0.01 \n", + "2016-01-26 08:40:00-07:00 0.978335 0.061103 0.065599 0.013143 0.01 \n", "\n", - "[3 rows x 9 columns]" + " v_ff r_oc v_oc temp_module_corr \n", + "date_time \n", + "2016-01-26 08:10:00-07:00 0.073908 0.098226 -0.002454 -0.065435 \n", + "2016-01-26 08:30:00-07:00 0.077019 0.082928 0.004324 -0.042936 \n", + "2016-01-26 08:40:00-07:00 0.091342 0.091143 0.000727 -0.033518 " ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1020,25 +1047,19 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\steve\\OneDrive\\Documents\\_CONS\\__Reference\\PVPMC\\__repository\\pvlib-python\\docs\\tutorials\\mlfm_graphs.py:333: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " ax1.set_xticklabels(xax2, rotation=90)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1075,32 +1096,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'meas' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m mlfm_sel \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpr_dc\u001b[39m\u001b[38;5;124m'\u001b[39m \n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# FIX THIS WARNING,\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# SettingWithCopyWarning:\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# A value is trying to be set on a copy of a slice from a DataFrame.\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# Try using .loc[row_indexer,col_indexer] = value instead\u001b[39;00m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m norm, cc, ee, coeffs, errs \u001b[38;5;241m=\u001b[39m mlfm_fit(\u001b[43mmeas\u001b[49m, norm, mlfm_sel) \u001b[38;5;66;03m# qty_mlfm_vars)\u001b[39;00m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Fix a bug with fit routine which gives a\u001b[39;00m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# finite cc[4] even if all the ws data is 0\u001b[39;00m\n\u001b[0;32m 14\u001b[0m \u001b[38;5;66;03m# this won't matter until cc is applied to other\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;66;03m# data with some ws <>0 when it will give bad results\u001b[39;00m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmean(meas\u001b[38;5;241m.\u001b[39mwind_speed) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n", - "\u001b[1;31mNameError\u001b[0m: name 'meas' is not defined" - ] - } - ], + "outputs": [], "source": [ "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", "mlfm_sel = 'pr_dc' \n", "\n", - "# FIX THIS WARNING,\n", - "# SettingWithCopyWarning:\n", - "# A value is trying to be set on a copy of a slice from a DataFrame.\n", - "# Try using .loc[row_indexer,col_indexer] = value instead\n", - "# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\n", + "# combine measured and normalized data into a single DataFrame\n", + "data = meas.copy()\n", + "data[mlfm_sel] = norm[mlfm_sel]\n", "\n", - "norm, cc, ee, coeffs, errs = mlfm_fit(meas, norm, mlfm_sel) # qty_mlfm_vars)\n", + "predictions, cc, residuals = mlfm_fit(data, mlfm_sel)\n", "\n", "# Fix a bug with fit routine which gives a\n", "# finite cc[4] even if all the ws data is 0\n", @@ -1120,7 +1127,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1181,17 +1188,41 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ + { + "ename": "KeyError", + "evalue": "'calc_pr_dc'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'calc_pr_dc'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [23]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# plot fit vs. measured, include a 1:1 line for comparison\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m fit_plot \u001b[38;5;241m=\u001b[39m \u001b[43mplot_residuals\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmeas\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlfm_sel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresidual \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmlfm_meas_file\u001b[49m\u001b[43m)\u001b[49m\n", + "Input \u001b[1;32mIn [22]\u001b[0m, in \u001b[0;36mplot_residuals\u001b[1;34m(dmeas, dnorm, fit, title)\u001b[0m\n\u001b[0;32m 32\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmeas \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m fit \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m* poa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 33\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_xlim(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1.2\u001b[39m)\n\u001b[0;32m 35\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\n\u001b[0;32m 36\u001b[0m dnorm[fit] \u001b[38;5;241m*\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m---> 37\u001b[0m \u001b[43mdnorm\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcalc_\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfit\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m*\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m 38\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc^\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 39\u001b[0m label \u001b[38;5;241m=\u001b[39m fit\n\u001b[0;32m 40\u001b[0m )\n\u001b[0;32m 42\u001b[0m \u001b[38;5;66;03m# plot 1:1 line to show optimum fit\u001b[39;00m\n\u001b[0;32m 43\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot((\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1.2\u001b[39m),(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1.2\u001b[39m), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mko-\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3623\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3625\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3626\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3627\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3628\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[1;31mKeyError\u001b[0m: 'calc_pr_dc'" + ] + }, { "data": { - "image/png": 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+ "image/png": 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gPDAlIh6pKyAzM+s8lc5ZRMRw4DBgCPBtSbdJOrfWyMzMrGNUShaSVgBGAWuRhilfEY8+a2a22KjaDfWnwvTTiJhbX0hmZtZpqiaLj0fE7GKBpA9GxLQaYjIzsw5T9XcWF0laozEj6SPAWfWEZGZmnaZqsvgUcJmk90raFTgN2LW+sMzMrJNU/VHeNEmfBa4BXgV2ioh5tUZmZmYdo9tkIely3rnZEcCywAvALyQREXvUGZyZmXWGspbF9/skCjMz62jdJouIuL7KRiTdEhFbtVi2M/ATYBDw84j4bpflo4CzgWF5nRMiYnKV/ZqZWd+oeoK7zJBmhZIGkQYd3AUYA+wraUyX1f4DuDAiPgDsA5zRppjMzKxN2pUsokX5FsCsiJgdEa8DvwLGNXnuCvnxisDjbYrJzMzapF3JopU1gDmF+bm5rOhE4ABJc4HJwGeabUjSUZKmS5o+b54vxDIz60vtShbqxXP3BSZGxEjSbzfOlfSuuCJiQkSMjYixI0aM6MXuzMysp9qVLA5sUf4YsGZhfmQuKzocuBAgIm4hnf8Y3qa4zMysDbpNFpLmS3qxyTRf0ouN9SLi3habmAaMlrSOpMGkE9iTuqzzKLBj3t/fkZKF+5nMzDpI2aWzQ3uz8Yh4U9KxwNWky2LPioiZkk4CpkfEJOCLwM8kfYF0svuQiGh1wtzMzPpBj+6hLek9FC6TjYhHy56TfzMxuUvZ1wuP7wO26UkcZmbWt6re/GgPSX8BHgauBx4BrqoxLjMz6yBVT3CfDGwJPBgR65DOMUytLSozM+soVZPFGxHxLLCEpCUiYgowtsa4zMysg1Q9Z/G8pOWBG4HzJD0NvFxfWGZm1kmqtizGAQuAzwO/Ax4Cdq8pJjMz6zBVb370sqT3ksZ6+htwde6WMjOzxUDVq6GOAG4DPgF8Epgq6bA6AzMzs85R9ZzFl4EPNFoTklYBbgbOqiswMzPrHFXPWTwLzC/Mz89lZma2GCi7B/dx+eEs4FZJvyUNyTEOuKfm2MzMrEOUdUM1xoZ6KE8Nv60nHDMz60RlAwl+sziff2tBRLxUZ1BmZtZZql4NtZGkO4GZwExJt0vasN7QzMysU1Q9wT0BOC4i1oqItcjDitcXlpmZdZKqyWK5PB4UABFxHbBcLRGZmVnHqfo7i9mSvgacm+cPAGbXE5KZmXWaqi2Lw4ARwKV5GpHLzMxsMVB1bKjngM/WHIuZmXWosh/lXU76EV5TEbFH2yMyM7OOU9ay+H6fRGFmZh2t7Ed51/dVIGZm1rkqnbOQNIN3d0e9AEwHvuV7W5iZDWxVL529CngLOD/P7wMsCzwJTMR3zTMzG9CqJoudImKzwvwMSXdExGaSDqgjMDMz6xxVf2cxSNIWjRlJHwQG5dk32x6VmZl1lKotiyOAsxqjzpJufnS4pOWA/6wlMjMz6xhVf5Q3DdhY0op5/oXC4gslHRwRZ9cRoJmZ9b+q3VBAShJdEkXD51o9R9LOkh6QNEvSCS3W+RdJ90maKen8ZuuYmVn/qdoNVUZNC6VBwOnAR4G5wDRJkyLivsI6o4F/B7aJiOckvadNMZmZWZv0qGXRjVZDgmwBzIqI2RHxOvAr0v27i44ETs/jTxERT7cpJjMza5N2JYumLQtgDWBOYX5uLitaD1hP0k2SpkrauekOpKMkTZc0fd68eb2P2MzMKmtXsripF89dEhgNbA/sC/xM0rCuK0XEhIgYGxFjR4wY0YvdmZlZT1U+ZyHpn4ANgSGNsog4Kf89tsXTHgPWLMyPzGVFc4FbI+IN4GFJD5KSx7SqsZmZWb0qtSwkjQf2Bj5D6nLaC1irwlOnAaMlrSNpMGmYkEld1rmM1KpA0nBSt5Tvwmdm1kGqdkNtHREHAc9FxDeBrUhf6t2KiDeBY4GrgfuBCyNipqSTJDXuhXE18Kyk+4ApwJc9MKGZWWep2g21IP99RdLqwLPAalWeGBGTgcldyr5eeBzAcXkyM7MOVDVZXJFPOp8K3EG6VPZndQVlZmadpepwHyfnh5dIugIY0uKX3GZmNgBVvfnRUsDRwHa56DpJZ+YrmMzMbICr2g3138BSwBl5/sBcdkQdQZmZWWepmiw+GBGbFOb/KOnuOgIyM7POU/XS2bckva8xI2ld0m1WzcxsMVC1ZfFlYIqk2aQf5a0FHFpbVGZm1lGqXg11bR5KfP1c9EBEvFZfWGZm1kmqXg01BDgG2Jb0G4sbJY2PiFfrDM7MzDpD1W6oc0j33f6vPL8fcC5pjCgzMxvgqiaLjSJiTGF+Sh7LyczMFgNVr4a6Q9KWjRlJHwKm1xOSmZl1mqoti82BmyU9mudHAQ9ImkEaC/Dva4nOzMw6QtVk0fRWp2ZmtnioeunsX+sOxMzMOle77sFtZmYDmJOFmZmVKk0WknbKf3esPxwzM+tEVVoWH5G0DbB9zbGYmVmH6jZZSPoGsDTwB2CwpK93t76ZmQ1M3SaLiPgm8GfgRODPEXFSXwRlZmadpUo31AoR8T1gaN3BmJlZZypNFhHx4/z3tNqjMTOzjuRLZ83MrJSThZmZlaqULCQtJ2mJwvwSkpatLywzM+skVVsW1wLF5LAs6XJaMzNbDFRNFkMi4qXGTH5cqWUhaWdJD0iaJemEbtbbU1JIGlsxJjMz6yNVk8XLkjZrzEjaHFhQ9iRJg4DTgV2AMcC+ksY0WW8o8Dng1orxmJlZH6p6P4vPAxdJehwQ8F5g7wrP2wKYFRGzAST9ChgHdL0l68nA94AvV4zHzMz6UNX7WUyTtAGwfi56ICLeqPDUNYA5hfm5wIeKK+QWy5oRcaWklslC0lHAUQCjRo2qEraZmbVJt8lC0idaLFpPEhFxaW92nq+w+iFwSNm6ETEBmAAwduzY6M1+zcysZ8paFrvnv+8BtiZdFSVgB+BmoCxZPAasWZgfmcsahgIbAddJgtS9NUnSHhExvUoFzMysft0mi4g4FEDSNcCYiHgiz68GTKyw/WnAaEnrkJLEPsB+he2/AAxvzEu6DviSE4WZWWepejXUmo1EkT0FlJ44iIg3gWOBq4H7gQsjYqakkyTt0eNozcysX1S9GupaSVcDF+T5van4o7yImAxM7lLW9L4YEbF9xXjMzKwPVb0a6lhJ/wxsl4smRMRv6gvLzMw6SdWWBTk5NE0Qkm6JiK3aFpWZmXWUdo06O6RN2zEzsw7UrmTh3z2YmQ1gvp+FmZmValeyUJu2Y2ZmHag0WUgaJGlKyWoHtikeMzPrQKXJIiLeAt6WtGI369zb1qjMzKyjVL109iVghqTfAy83CiPis7VEZWZmHaVqsriU8kEDzcxsgKr6C+6zJQ0GNiBdJvtARLxea2RmZtYxKiULSbsCZwIPka58WkfSv0bEVXUGZ2ZmnaFqN9QPgR0iYhaApPcBVwJOFmZmi4Gqv7OY30gU2Wxgfg3xmJlZB6raspguaTJwIemcxV7AtMZtV3t7e1UzM+tsVZPFENINjz6S5+cBy5Buuxr4SikzswGt6tVQh9YdiJmZdS4PJGhmZqWcLMzMrFSlZCFpnSplZmY2MFVtWVzSpOzidgZiZmadq9sT3JI2ADYEVmxcJputgG+lama22Ci7Gmp9YDdgGOky2Yb5wJE1xWRmZh2m22QREb8Ffitpq4i4pY9iMjOzDlPWDXV8RJwC7Cdp367LfT8LM7PFQ1k31H357/S6AzEzs85Vliz2Bq4AhkXET/ogHjMz60Bll85uLml14DBJK0lauThV2YGknSU9IGmWpBOaLD9O0n2S7pF0raS1FqYiZmZWn7KWxXjgWmBd4HbSjY8aIpe3JGkQcDrwUWAuaaTaSRFxX2G1O4GxEfGKpKOBU0gtGjMz6xDdtiwi4rSI+DvgrIhYNyLWKUzdJopsC2BWRMzOt2H9FTCuyz6mRMQreXYqMHIh6mFmZjWq9AvuiDh6Ibe/BjCnMD83l7VyOL77nplZx6l6P4vaSToAGMs798zouvwo4CiAUaNG9WFkZmZW96izjwFrFuZH5rL/R9JOwFeBPSLitWYbiogJETE2IsaOGDGilmDNzKy5upPFNGC0pHUkDQb2ASYVV5D0AeBMUqJ4uuZ4zMxsIdSaLCLiTeBY4GrgfuDCiJgp6SRJe+TVTgWWBy6SdJekSS02Z2Zm/aT2cxYRMRmY3KXs64XHO9Udg5mZ9Y7vlGdmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUrUnC0k7S3pA0ixJJzRZvrSkX+flt0pau+6YzMysZ2pNFpIGAacDuwBjgH0ljemy2uHAcxHxfuBHwPfqjMnMzHqu7pbFFsCsiJgdEa8DvwLGdVlnHHB2fnwxsKMk1RyXmZn1wJI1b38NYE5hfi7woVbrRMSbkl4AVgGeKa4k6SjgqDz7mqR7a4m4MwynS/0HGNdv0TWQ6wYDv37rL+wT604WbRMRE4AJAJKmR8TYfg6pNq7fom0g128g1w0Wj/ot7HPr7oZ6DFizMD8ylzVdR9KSwIrAszXHZWZmPVB3spgGjJa0jqTBwD7ApC7rTAIOzo8/CfwxIqLmuMzMrAdq7YbK5yCOBa4GBgFnRcRMSScB0yNiEvAL4FxJs4C/kRJKmQm1Bd0ZXL9F20Cu30CuG7h+LckH8WZmVsa/4DYzs1JOFmZmVqqjk8VAHyqkQv2Ok3SfpHskXStprf6Ic2GU1a2w3p6SQtIidblilfpJ+pf8/s2UdH5fx9gbFT6boyRNkXRn/nzu2h9xLgxJZ0l6utVvtZSclut+j6TN+jrG3qhQv/1zvWZIulnSJpU2HBEdOZFOiD8ErAsMBu4GxnRZ5xhgfH68D/Dr/o67zfXbAVg2Pz56Ualflbrl9YYCNwBTgbH9HXeb37vRwJ3ASnn+Pf0dd5vrNwE4Oj8eAzzS33H3oH7bAZsB97ZYvitwFSBgS+DW/o65zfXbuvC53KVq/Tq5ZTHQhwoprV9ETImIV/LsVNLvVBYFVd47gJNJY4G92pfBtUGV+h0JnB4RzwFExNN9HGNvVKlfACvkxysCj/dhfL0SETeQrrxsZRxwTiRTgWGSVuub6HqvrH4RcXPjc0kPvlc6OVk0GypkjVbrRMSbQGOokEVBlfoVHU462lkUlNYtN+3XjIgr+zKwNqny3q0HrCfpJklTJe3cZ9H1XpX6nQgcIGkuMBn4TN+E1id6+r+5KKv8vbLIDPexOJN0ADAW+Eh/x9IOkpYAfggc0s+h1GlJUlfU9qQjtxskbRwRz/dnUG20LzAxIn4gaSvSb6U2ioi3+zswq0bSDqRksW2V9Tu5ZTHQhwqpUj8k7QR8FdgjIl7ro9h6q6xuQ4GNgOskPULqF560CJ3krvLezQUmRcQbEfEw8CApeSwKqtTvcOBCgIi4BRhCGoRvIKj0v7kok/T3wM+BcRFR6Tuzk5PFQB8qpLR+kj4AnElKFItSn3e3dYuIFyJieESsHRFrk/pN94iIhR7krI9V+WxeRmpVIGk4qVtqdh/G2BtV6vcosCOApL8jJYt5fRplfSYBB+WrorYEXoiIJ/o7qHaRNAq4FDgwIh6s/MT+PnNfclZ/V9IR2UPAV3PZSaQvFkgf0IuAWcBtwLr9HXOb6/cH4CngrjxN6u+Y21W3LutexyJ0NVTF906krrb7gBnAPv0dc5vrNwa4iXSl1F3Ax/o75h7U7QLgCeANUgvwcOBTwKcK793pue4zFsHPZln9fg48V/hemV5lux7uw8zMSnVyN5SZmXUIJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WNuBJeiT/MK7VcknaPk8dMxClpImSPlmyTrd1a7L+IZJ+2pt9LixJa+ZhzRvDtn+ujv1YPZwsbJGVh3jp7TaWASYCG5KGIJmYy6z93gS+GBFjSEO8fFrSmH6OySpysrCWJK0t6c/5aPNBSedJ2imPpPoXSVvk9ZbLN1y5Ld8MZ1zh+TdKuiNPW+fy1STdIOkuSfdK+nCTfT8i6ZR8g5bbJL0/l0+UNF7SrcApLeJeRdI1+ej156Rf5DaWHZRv/HK3pHMjYgHpXiGHAYeS7tGwoMv2TpR0rqRbcr2PzOWSdGquwwxJe+fy5ZVuVnVHLm82PHtx+19TutHQnyRdIOlLTdbZMb+2M/JrvXRh8fFNXqfdlW4IdqekP0hatbsYWsR1cn69PyTp0lw2TtICSYMlDZE0O5dfJ+lHkqZLul/SByVdml+vbwFExBMRcUd+PB+4n4E7muvA098/TffUuROwNulocGPSgcXtwFmkL99xwGV5ve8AB+THw0jDRCwHLAsMyeWjycMKAF/knSEkBgFDm+z7kcI6BwFX5McTgSuAQd3EfRrw9fz4n0j3XhhOaj08CAzPy1YGlgH+Bzg2T/8DLNNleyeShrVYJm9nDrA6sCfw+1yHVUnjJa1GGnF2hfzc4aThaNQi1g+ShlwYQhpg8S/Alwp1/WReNgdYL5efA3y+5HVaqbFP4AjgB/nxIcBPu3ntGvs8FRif3+slgdl5+fdJY0dtQxoF+YJcfh3wvfz4c6T7W6wGLE0acmKVJp+tRxuvk6fOnzxEuZV5OCJmAEiaCVwbESFpBukfHuBjwB6FI+IhwCjSF8ZPJW0KvEUaTA/Sl81ZkpYiJZy7Wuz7gsLfHxXKL4qIt7qJeTvgEwARcaWkxo1e/iE/95m87G+5XofxzvDvp0f+Nuvit5FaHAskTSHdIGhb0pflW8BTkq4nfflfBXxH0nbA26Sj51WBJ5tsd5u87VeBVyVd3mSd9UnvQ2PQt7OBTwM/zvPNXqeRwK+VbtozGHi4yXZb+Rrp7mlH5fk3JT2kNGDgFqQxr7YjJckbC89rDDY4A5gZefC93PpYkzwitKTlgUtICe/FHsRl/cjdUFamOCz624X5t3nnfigC9oyITfM0KiLuB75AGghxE9L9OAbD/93JazvSsM8TJR3UYt/R4vHLvajPu3eSXJenVoOldS3vblC1/YERwOYRsSnpNRjS+0hbavY6/RepBbEx8K893P80YHNJKxfKbiDdgvMN0gCX2+apmCyKn42un5slAfIBwiXAeRFxaQ9isn7mZGHtcDXwGSldSaQ0tDqk+4s8EemGOAeSjkSRtBbwVET8jDQC5mYttrt34e8tPYjnBmC/vK9dSF0yAH8E9pK0Sl62cvOnNzUu99GvQhp6fBrpi3JvSYMkjSAlwNtI9X46It5QusHMWt1s9yZg97zt5YHdmqzzALB243wE6bW8vrC82eu0Iu/cg+FgeuZ3wHeBKyUNzWU3Ap8HbomIeaQ7Uq4P3Ft1o/nz8Qvg/oj4YQ9jsn7mbihrh5NJXSL3KN0F72HSl94ZwCW55fA73mkRbA98WdIbwEukvvZmVpJ0D+kodd8exPNN4ILcbXYzqW+ciJgp6dvA9ZLeAu6k+t367gGmkM5BnBwRj0v6DbAV6XxGAMdHxJOSzgMuz11104E/t9poREyTNClv/ylSF84LXdZ5VdKhwEVKV4BNI51PaGj2Op2Y13+OlCTXqVjPxj4vyolikqRdgVtJXWk3FF6P93bTEmtmG1KimyHprlz2lYiY3JPYrH94iHLrSEp30BvbOL/Qz7GcCLwUEd+vafvLR8RLkpYlfRkfFfmqIbNO4ZaFWf+boPR7gyHA2U4U1oncsrBFVu6a6for4Jsi4tP9EU938rmOa5ss2jEq3gO5zfF8FdirS/FFEfHtvo7FFg1OFmZmVspXQ5mZWSknCzMzK+VkYWZmpZwszMys1P8CsUW/daNQlqgAAAAASUVORK5CYII=\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 25cc7deb22..765bb53b3b 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -271,37 +271,45 @@ def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): - ''' - Predict normalised lfm values e.g. pr_dc, norm(i_sc, ... v_oc) - from poa_global, temp_module, wind_speed and mlfm(c_1 .. c_6). + r''' + Predict normalised LFM values from data in ``dmeas``. - Parameters - ---------- + The normalized LFM values are given by - dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' - and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module. + .. math:: - c_1 to c_6 : float - fitted mlfm coefficients (dependencies) - c_1 - constant - c_2 - temperature coefficient (1/K) - c_3 - low light log irradiance drop (~v_oc) - c_4 - high light linear irradiance drop (~r_s) - (optional or set at 0) - c_5 - wind speed dependence (=0 if indoor) - c_6 - inverse irradiance (<= 0). + c_1 + c_2 * (T_m - 25) + c_3 * \log10(G_{POA}) + c_4 * G_{POA} + + c_5 * WS + c_6 / G_{POA} + + where :math:`G_{POA}` is global plane-of-array (POA) irradiance in kW/m2, + :math:`T_m` is module temperature in C and :math:`WS` is wind speed in + m/s. + + Parameters + ---------- + dmeas : DataFrame + Must include columns: + * 'poa_global' global plane of array irradiance [kW/m^2] + * 'temp_module' module temperature [C] + May include optional column: + * 'wind_speed' wind speed [m/s]. + + c_1 : float + Constant term in model- constant + c_2 - temperature coefficient (1/K) + c_3 - low light log irradiance drop (~v_oc) + c_4 - high light linear irradiance drop (~r_s) + c_5 - wind speed dependence (=0 if indoor) + c_6 - inverse irradiance (<= 0). Returns ------- - mlfm_6 : float - predicted performance values for pr_dc, norm(i_sc, .. v_oc) . + mlfm_6 : Series + Predicted values. ''' mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ - c_3 * np.log10(dmeas['poa_global_kwm2']) + \ - c_4 * dmeas['poa_global_kwm2'] + c_6 / dmeas['poa_global_kwm2'] + c_3 * np.log10(dmeas['poa_global']) + \ + c_4 * dmeas['poa_global'] + c_6 / dmeas['poa_global'] if 'wind_speed' in dmeas.columns: mlfm_out += c_5 * dmeas['wind_speed'] return mlfm_out @@ -313,14 +321,16 @@ def mlfm_fit(data, var_to_fit): Parameters ---------- - data : DataFrame - Must include columns 'poa_global', 'temp_module', 'wind_speed'. - May include optional column 'wind_speed'. - Must include column names var_to_fit. + Must include columns: + * 'poa_global' global plane of array irradiance [kW/m^2] + * 'temp_module' module temperature [C] + Must include column named ``var_to_fit``. + May include optional column: + * 'wind_speed' wind speed [m/s]. var_to_fit : string - variable being fitted e.g. 'pr_dc'. + Column name in ``data`` containing variable being fit. Returns ------- @@ -328,10 +338,14 @@ def mlfm_fit(data, var_to_fit): Values predicted by the fitted model. coeff : list - Model coefficients c_1 to c_6. + Model coefficients ``c_1`` to ``c_6``. resid : Series Residuals of the fitted model. + + See also + -------- + mlfm_6 ''' # drop missing data From 4a820b8fec37a24a63f5a7fa98fcb81d73894bc7 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 18 May 2022 15:26:23 -0600 Subject: [PATCH 44/81] use poa_global_kwm2 --- pvlib/mlfm.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 765bb53b3b..8265305e47 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -289,7 +289,7 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): ---------- dmeas : DataFrame Must include columns: - * 'poa_global' global plane of array irradiance [kW/m^2] + * 'poa_global_kwm2' global plane of array irradiance [kW/m^2] * 'temp_module' module temperature [C] May include optional column: * 'wind_speed' wind speed [m/s]. @@ -308,8 +308,8 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): Predicted values. ''' mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ - c_3 * np.log10(dmeas['poa_global']) + \ - c_4 * dmeas['poa_global'] + c_6 / dmeas['poa_global'] + c_3 * np.log10(dmeas['poa_global_kwm2']) + \ + c_4 * dmeas['poa_global_kwm2'] + c_6 / dmeas['poa_global_kwm2'] if 'wind_speed' in dmeas.columns: mlfm_out += c_5 * dmeas['wind_speed'] return mlfm_out @@ -323,7 +323,7 @@ def mlfm_fit(data, var_to_fit): ---------- data : DataFrame Must include columns: - * 'poa_global' global plane of array irradiance [kW/m^2] + * 'poa_global_kwm2' global plane of array irradiance [kW/m^2] * 'temp_module' module temperature [C] Must include column named ``var_to_fit``. May include optional column: From b908b98f300b1bb865644da77ef9b108917e1ba7 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 19 May 2022 13:18:16 -0600 Subject: [PATCH 45/81] add doc references --- docs/sphinx/source/reference/pv_modeling.rst | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/docs/sphinx/source/reference/pv_modeling.rst b/docs/sphinx/source/reference/pv_modeling.rst index d1ae5a6559..d8c2c80f89 100644 --- a/docs/sphinx/source/reference/pv_modeling.rst +++ b/docs/sphinx/source/reference/pv_modeling.rst @@ -177,6 +177,25 @@ Utilities for working with IV curve data ivtools.utils.rectify_iv_curve +Loss Factors model +^^^^^^^^^^^^^^^^^^ + +.. autosummary:: + :toctree: generated/ + + mlfm.mlfm_6 + mlfm.mlfm_meas_to_norm + mlfm.mlfm_norm_to stack + +Functions for fitting the Loss Factors model + + mlfm.mlfm_fit + +Utilities for plotting + + mlfm.plot_mlfm_scatter + mlfm.plot_mlfm_stack + Other ----- From 62c027e0fef703dcc531876781cccfc568d020c4 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 19 May 2022 13:38:50 -0600 Subject: [PATCH 46/81] loosen atol for test_mlfm_fit, docstring work --- docs/sphinx/source/reference/pv_modeling.rst | 6 ++++++ pvlib/tests/test_mlfm.py | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/sphinx/source/reference/pv_modeling.rst b/docs/sphinx/source/reference/pv_modeling.rst index d8c2c80f89..79d26c52d7 100644 --- a/docs/sphinx/source/reference/pv_modeling.rst +++ b/docs/sphinx/source/reference/pv_modeling.rst @@ -189,10 +189,16 @@ Loss Factors model Functions for fitting the Loss Factors model +.. autosummary:: + :toctree: generated/ + mlfm.mlfm_fit Utilities for plotting +.. autosummary:: + :toctree: generated/ + mlfm.plot_mlfm_scatter mlfm.plot_mlfm_stack diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 137393adf7..019e413b53 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -209,7 +209,7 @@ def test_mlfm_fit(matrix_data, mlfm_6_fit): matrix_data = matrix_data.drop(columns=['wind_speed']) predictions, cc_fit, residuals = mlfm.mlfm_fit( matrix_data, mlfm_sel) - assert_allclose(cc_fit, cc_target, atol=1e-3) + assert_allclose(cc_fit, cc_target, atol=5e-3) @requires_mpl From ecdef89a8edbc5de50a79b503832286fe60a0cbe Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 19 May 2022 13:39:13 -0600 Subject: [PATCH 47/81] docstring work --- pvlib/mlfm.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 8265305e47..d2611fafce 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -289,11 +289,12 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): ---------- dmeas : DataFrame Must include columns: + * 'poa_global_kwm2' global plane of array irradiance [kW/m^2] * 'temp_module' module temperature [C] May include optional column: - * 'wind_speed' wind speed [m/s]. + * 'wind_speed' wind speed [m/s]. c_1 : float Constant term in model- constant c_2 - temperature coefficient (1/K) From 87e79e160d1b333d2ea3e80b0b731d5510551f11 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 19 May 2022 16:16:45 -0600 Subject: [PATCH 48/81] rework mlfm_meas_to_norm --- pvlib/mlfm.py | 119 ++++++++++++++++++++++----------------- pvlib/tests/test_mlfm.py | 3 +- 2 files changed, 70 insertions(+), 52 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index d2611fafce..2fd118ec05 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -51,29 +51,48 @@ } -def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): +def mlfm_meas_to_norm(dmeas, ref): ''' - Convert measured values e.g. meas(i_sc, ... v_oc) - to normalised loss values e.g. norm(i_sc, ... v_oc) - normalising by ref stc values and irradiance. + Convert measured power, current and voltage to normalized values. Parameters ---------- - dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' - and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module. + dmeas : DataFrame + Measurements. Must include columns: + + * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] + * `'temp_module'` module temperature [C] + * `'p_mp'` - power at maximum power point [kW] + * `'temp_module'` - module temperature [C] + + May include optional columns: + + * `'i_sc'` - current at short circuit condition [A] + * `'v_oc'` - voltage at open circuit condition [V] + * `'i_mp'` - current at maximum power point [A]. Must be accompanied + by `'i_sc'`. + * `'v_mp'` - voltage at maximum power point [V]. Must be accompanied + by `'v_oc'`. + * `'r_sc'` - inverse of slope of IV curve at short circuit condition. + Requires both `'i_sc'` and `'v_oc'`. [V/A] + * `'r_oc'` - slope of IV curve at open circuit condition. Requires + both `'i_sc'` and `'v_oc'` [A/V] ref : dict - reference stc values e.g. 'v_oc' and - temperature coeffs e.g. 'beta_v_oc' . + Reference values. Must include: - qty_mlfm_vars : int - number of mlfm_values present in data usually - 2 = (imp, vmp) from mpp tracker - 4 = (i_sc, i_mp, v_mp, v_oc) from matrix - 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + * `'p_mp'` - Power at maximum power point at Standard Test Condition + (STC). [kW] + * `'gamma_p_mp'` - Temperature coefficient of power at STC. [W/C] + + May include: + + * `'i_sc'` - Current at short circuit at STC. Required if `'i_sc'` is + present in `'dmeas'`. [A] + * `'v_oc'` - Voltage at open circuit at STC. Required if `'V_oc'` is + present in `'dmeas'`. [A] + * `'beta_v_oc'` - Temperature coefficient of open circuit voltage at + STC. Required if `'v_oc'` is present in `'dmeas'`. [V/C] Returns ------- @@ -83,36 +102,29 @@ def mlfm_meas_to_norm(dmeas, ref, qty_mlfm_vars): ''' dnorm = pd.DataFrame() - # calculate normalised mlfm values depending on number of qty_mlfm_vars - - if qty_mlfm_vars >= 1: # do for all measurements - dnorm['pr_dc'] = ( - dmeas['p_mp'] / - (ref['p_mp'] * dmeas['poa_global_kwm2'])) + dnorm['pr_dc'] = ( + dmeas['p_mp'] / + (ref['p_mp'] * dmeas['poa_global_kwm2'])) - # temperature corrected - dnorm['pr_dc_temp_corr'] = ( - dnorm['pr_dc'] * - (1 - ref['gamma_p_mp']*(dmeas['temp_module']-T_STC))) + # temperature corrected + dnorm['pr_dc_temp_corr'] = ( + dnorm['pr_dc'] * + (1 - ref['gamma_p_mp']*(dmeas['temp_module'] - T_STC))) - if qty_mlfm_vars >= 2: # - dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] - dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] - - if qty_mlfm_vars >= 4: # - dnorm['i_sc'] = ( - dmeas['i_sc'] / - (dmeas['poa_global_kwm2'] * ref['i_sc'])) + if 'i_sc' in dmeas.columns: + dnorm['i_sc'] = dmeas['i_sc'] / dmeas['poa_global_kwm2'] / ref['i_sc'] + if 'i_mp' in dmeas.columns: + dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] + if 'v_oc' in dmeas.columns: dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] - + if 'v_mp' in dmeas.columns: + dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] # temperature corrected - dnorm['v_oc_temp_corr'] = ( - dnorm['v_oc'] * - (1 - ref['beta_v_oc']*(dmeas['temp_module']-T_STC))) - - if qty_mlfm_vars >= 6: # 6,8 IV data + dnorm['v_oc_temp_corr'] = dnorm['v_oc'] * \ + (1 - ref['beta_v_oc']*(dmeas['temp_module'] - T_STC)) + if all(c in dmeas.columns for c in ['i_sc', 'v_oc', 'r_sc', 'r_oc']): # create temporary variables (i_r, v_r) from # intercept of r_sc (at i_sc) with r_oc (at v_oc) # to make maths easier @@ -278,8 +290,8 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): .. math:: - c_1 + c_2 * (T_m - 25) + c_3 * \log10(G_{POA}) + c_4 * G_{POA} - + c_5 * WS + c_6 / G_{POA} + c_1 + c_2 (T_m - 25) + c_3 \log10(G_{POA}) + c_4 G_{POA} + + c_5 WS + c_6 / G_{POA} where :math:`G_{POA}` is global plane-of-array (POA) irradiance in kW/m2, :math:`T_m` is module temperature in C and :math:`WS` is wind speed in @@ -290,18 +302,23 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): dmeas : DataFrame Must include columns: - * 'poa_global_kwm2' global plane of array irradiance [kW/m^2] - * 'temp_module' module temperature [C] + * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] + * `'temp_module'` module temperature [C] May include optional column: - * 'wind_speed' wind speed [m/s]. + * `'wind_speed'` wind speed [m/s]. c_1 : float - Constant term in model- constant - c_2 - temperature coefficient (1/K) - c_3 - low light log irradiance drop (~v_oc) - c_4 - high light linear irradiance drop (~r_s) - c_5 - wind speed dependence (=0 if indoor) - c_6 - inverse irradiance (<= 0). + Constant term in model + c_2 : float + Temperature coefficient in model (1/K) + c_3 : float + Coefficient for low light log irradiance drop. + c_4 : float + Coefficient for high light linear irradiance drop. + c_5 : float, default 0 + Coefficient for wind speed dependence + c_6 : float, default 0 + Coefficient for dependence on inverse irradiance. Returns ------- diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 019e413b53..5633251ae7 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -186,7 +186,7 @@ def mlfm_6_fit(): def test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized): - norm_calc = mlfm.mlfm_meas_to_norm(measured, reference, 6) + norm_calc = mlfm.mlfm_meas_to_norm(measured, reference) assert_allclose(norm_calc, normalized, atol=1e-6) @@ -209,6 +209,7 @@ def test_mlfm_fit(matrix_data, mlfm_6_fit): matrix_data = matrix_data.drop(columns=['wind_speed']) predictions, cc_fit, residuals = mlfm.mlfm_fit( matrix_data, mlfm_sel) + # atol is large due to different behavior in conda_linux Python 3.6 env. assert_allclose(cc_fit, cc_target, atol=5e-3) From 435463d74e46ee79ab28d2f1f148acbe0656433b Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 26 May 2022 11:13:22 -0600 Subject: [PATCH 49/81] rework mlfm_norm_to_stack --- pvlib/mlfm.py | 158 ++++++++++++++++++++++----------------- pvlib/tests/test_mlfm.py | 12 +-- 2 files changed, 96 insertions(+), 74 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 2fd118ec05..8f849fc293 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -96,9 +96,21 @@ def mlfm_meas_to_norm(dmeas, ref): Returns ------- - dnorm : dataframe - normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + dnorm : DataFrame + Normalised values. + * `'pr_dc'` is `'p_mp'` normalied by reference `'p_mp'` and + `'poa_global_kwm2'` + * `'pr_dc_temp_corr'` is `'pr_dc'` adjusted to 25C. + * Columns `'i_sc'`, `'i_mp'`, `'v_oc'`, `'v_mp'`, `'v_oc_temp_corr'`, + `'r_sc'`, `'r_oc'`, `'i_ff'`, `'v_ff'` are returned when the + the corresponding optional columns are included in `'dmeas'`. + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 ''' dnorm = pd.DataFrame() @@ -146,10 +158,9 @@ def mlfm_meas_to_norm(dmeas, ref): return dnorm -def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): +def mlfm_norm_to_stack(dnorm, ff): ''' - Converts MLFM normalised multiplicative losses norm(i_sc ... v_oc) - to stacked subtractive losses stack(i_sc ... v_oc). + Converts normalised values to stacked subtractive normalized losses. Ref: http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf @@ -165,69 +176,75 @@ def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): 1/ff_ref = (ref(isc) / ref(imp)) * (ref(voc) / ref(vmp)) - Multiplicative losses: - - are easier to use on a scatter plot vs. irradiance or temperature. - - Stacked losses: - - are better to use to see relative loss proportions - (for underperformance limitations) - or vs. time e.g. for degradation. + Normalized values can reveal losses via scatter plots vs. irradiance or + temperature. - - Stacked losses are scaled so they give the correct pr_dc - and are in the right proportion to each other. + Stacked subtractive losses can show relative loss proportions. Stacked + losses partition the difference between the normalized power and the + power that corresponds to the reference fill factor. Parameters ---------- + dnorm : DataFrame + Normalised values. Must include columns: - dnorm : dataframe - normalised multiplicative lfm loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/ff * product('i_sc', ... 'v_oc'). + * `'pr_dc'` normalized power at the maximum power point. + * `'i_sc'` normalized short circuit current. + * `'i_mp'` normalized current at maximum power point. + * `'v_oc'` normalized open circuit voltage. + * `'v_mp'` normalized voltage at maximum power point. + * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. - ref : dict - reference stc values e.g. 'v_oc' and - temperature coeffs e.g. 'beta_v_oc' . + May include optional columns: - qty_mlfm_vars : int - number of mlfm_values present in data usually - 2 = (imp, vmp) from mpp tracker - 4 = (i_sc, i_mp, v_mp, v_oc) from matrix - 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + * `'v_ff'` normalized multiplicative loss in fill factor apportioned + to voltage. + * `'i_ff'` normalized multiplicative loss in fill factor apportioned + to current. + * `'r_oc'` normalized slope of IV curve at open circuit. + * `'r_sc'` normalized slope of IV curve at short circuit. + + ff : float + Reference value of fill factor for IV curve at STC conditions. Returns ------- - dstack : dataframe - normalised subtractive lfm loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/ff - sum('i_sc', ... 'v_oc'). + dstack : DataFrame + Stacked subtractive normalized losses. + + See also + -------- + mlfm_meas_to_norm + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 ''' - # create an empty pands to put stack results + # create an empty DataFrame to put stack results dstack = pd.DataFrame() # create a gap to differentiate i and v losses : gap width~0.01 gap = 0.01 - # calculate reference fill factor (usually between 0.5 and 0.8) - ff_ref = ref['ff'] - - # calculate inverse fill factor ~ 1.25 - 2 as we calculate - # i_losses from ref_isc to norm_imp - # v_losses from ref_voc to norm_vmp - - inv_ff = 1 / ff_ref + inv_ff = 1 / ff - if qty_mlfm_vars == 6: # ivcurve + if all(c in dnorm.columns for c in ['v_ff', 'r_oc', 'i_ff', 'r_sc']): - # find factor to transform multiplicative to subtractive losses - # correction factor to scale losses to keep 1/ff --> pr_dc + # include effects of series and shunt resistances in stacked losses + # find factor to transform multiplicative to subtractive losses + # correction factor to scale losses to keep 1/ff --> pr_dc - # product + # product prod = inv_ff * ( dnorm['i_sc'] * dnorm['r_sc'] * dnorm['i_ff'] * dnorm['v_ff'] * dnorm['r_oc'] * dnorm['v_oc'] ) - # total + # total tot = inv_ff + ( dnorm['i_sc'] + dnorm['r_sc'] + dnorm['i_ff'] + dnorm['v_ff'] + dnorm['r_oc'] + dnorm['v_oc'] - 6 @@ -248,36 +265,34 @@ def mlfm_norm_to_stack(dnorm, ref, qty_mlfm_vars): dstack['temp_module_corr'] = ( -(dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) - elif qty_mlfm_vars == 4: # matrix + return dstack - # find factor to transform multiplicative to subtractive losses - # correction factor to scale losses to keep 1/ff --> pr_dc + # subtractive losses without series and shunt resistance effects + # find factor to transform multiplicative to subtractive losses + # correction factor to scale losses to keep 1/ff --> pr_dc - # product - prod = inv_ff * ( - dnorm['i_sc'] * dnorm['i_mp'] * - dnorm['v_mp'] * dnorm['v_oc'] - ) + prod = inv_ff * ( + dnorm['i_sc'] * dnorm['i_mp'] * + dnorm['v_mp'] * dnorm['v_oc'] + ) - # total - tot = inv_ff + ( - dnorm['i_sc'] + dnorm['i_mp'] + - dnorm['v_mp'] + dnorm['v_oc'] - 4 - ) + tot = inv_ff + ( + dnorm['i_sc'] + dnorm['i_mp'] + + dnorm['v_mp'] + dnorm['v_oc'] - 4 + ) - # correction factor - corr = (inv_ff - prod) / (inv_ff - tot) + corr = (inv_ff - prod) / (inv_ff - tot) - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) - dstack['pr_dc'] = + dnorm['pr_dc'] # initialise - dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr - dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 - dstack['i_v'] = gap - dstack['v_mp'] = -(dnorm['v_mp'] - 1) * corr - gap/2 - dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr + # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) + dstack['pr_dc'] = + dnorm['pr_dc'] # initialise + dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr + dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_mp'] = -(dnorm['v_mp'] - 1) * corr - gap/2 + dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr - dstack['temp_module_corr'] = ( - - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) + dstack['temp_module_corr'] = ( + - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) return dstack @@ -324,7 +339,14 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): ------- mlfm_6 : Series Predicted values. - ''' + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + ''' mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ c_3 * np.log10(dmeas['poa_global_kwm2']) + \ c_4 * dmeas['poa_global_kwm2'] + c_6 / dmeas['poa_global_kwm2'] diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 5633251ae7..0b05b9130a 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -4,7 +4,7 @@ from pvlib import mlfm from numpy.testing import assert_allclose # assert_almost_equal, import pytest -from .conftest import requires_mpl +from .conftest import requires_mpl, assert_frame_equal tolerance = 0.000001 @@ -68,10 +68,10 @@ def normalized(): # 'date_time': ['2016-03-23 09:00:00-07:00'], 'pr_dc': [0.989242790817207], 'pr_dc_temp_corr': [1.00183462464583], - 'i_mp': [0.93628796685047], - 'v_mp': [0.821773945683017], 'i_sc': [0.995586151149719], + 'i_mp': [0.93628796685047], 'v_oc': [0.98475928597285], + 'v_mp': [0.821773945683017], 'v_oc_temp_corr': [0.994508547151521], 'r_sc': [0.981487711004909], 'r_oc': [0.903706382978424], @@ -187,7 +187,7 @@ def mlfm_6_fit(): def test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized): norm_calc = mlfm.mlfm_meas_to_norm(measured, reference) - assert_allclose(norm_calc, normalized, atol=1e-6) + assert_frame_equal(norm_calc, normalized, atol=1e-6) def test_mlfm_6(measured, mlfm_6_coeffs): @@ -197,8 +197,8 @@ def test_mlfm_6(measured, mlfm_6_coeffs): def test_mlfm_norm_to_stack(normalized, reference, stacked): - stack_calc = mlfm.mlfm_norm_to_stack(normalized, reference, 6) - assert_allclose(stack_calc, stacked, atol=1e-6) + stack_calc = mlfm.mlfm_norm_to_stack(normalized, reference['ff']) + assert_frame_equal(stack_calc, stacked, atol=1e-6) def test_mlfm_fit(matrix_data, mlfm_6_fit): From 075a5ea8eb65f60d084d3f503a2597850243df3c Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 26 May 2022 15:19:15 -0600 Subject: [PATCH 50/81] reword plotting functions --- pvlib/mlfm.py | 335 ++++++++++++++++++--------------------- pvlib/tests/test_mlfm.py | 2 +- 2 files changed, 153 insertions(+), 184 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 8f849fc293..9904900e6c 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -63,7 +63,6 @@ def mlfm_meas_to_norm(dmeas, ref): * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] * `'temp_module'` module temperature [C] * `'p_mp'` - power at maximum power point [kW] - * `'temp_module'` - module temperature [C] May include optional columns: @@ -158,7 +157,7 @@ def mlfm_meas_to_norm(dmeas, ref): return dnorm -def mlfm_norm_to_stack(dnorm, ff): +def mlfm_norm_to_stack(dnorm, fill_factor): ''' Converts normalised values to stacked subtractive normalized losses. @@ -204,7 +203,7 @@ def mlfm_norm_to_stack(dnorm, ff): * `'r_oc'` normalized slope of IV curve at open circuit. * `'r_sc'` normalized slope of IV curve at short circuit. - ff : float + fill_factor : float Reference value of fill factor for IV curve at STC conditions. Returns @@ -230,7 +229,7 @@ def mlfm_norm_to_stack(dnorm, ff): # create a gap to differentiate i and v losses : gap width~0.01 gap = 0.01 - inv_ff = 1 / ff + inv_ff = 1 / fill_factor if all(c in dnorm.columns for c in ['v_ff', 'r_oc', 'i_ff', 'r_sc']): @@ -468,32 +467,9 @@ def mlfm_fit(data, var_to_fit): return pred, coeff, resid -# Define standardised MLFM graph colours as a dict ``clr`` - -clr = { - # parameter_clr colour R G B - 'irradiance': 'darkgreen', # 000 064 000 - 'temp_module': 'red', # 255 000 000 - 'temp_air': 'yellow', # 245 245 220 - 'wind_speed': 'grey', # 127 127 127 - - 'i_sc': 'purple', # 128 000 128 - 'r_sc': 'orange', # 255 165 000 - 'i_ff': 'lightgreen', # 144 238 144 - 'i_mp': 'green', # 000 255 000 - 'i_v': 'black', # 000 000 000 between i and v losses - 'v_ff': 'cyan', # 000 255 255 - 'v_mp': 'blue', # 000 000 255 - 'r_oc': 'pink', # 255 192 203 - 'v_oc': 'sienna', # 160 082 045 - - 'pr_dc': 'black', # 000 000 000 -} - - -def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): +def plot_mlfm_scatter(dmeas, dnorm, title): ''' - Scatter plot normalised MLFM parameters(y) vs. irradiance(x). + Scatterplot of normalised values (y) vs. irradiance (x). y1_axis : e.g. norm(i_sc, ... v_oc),_temp_module_corr x_axis : e.g. irradiance, poa_global_kwm2 @@ -501,84 +477,98 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): Parameters ---------- - dmeas : dataframe - measured weather data - 'poa_global', 'temp_module', 'wind_speed' - and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module. + dmeas : DataFrame + Measurements. Must include columns: + + * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] + * `'temp_module'` module temperature [C] + + May include optional columns: - dnorm : dataframe - multiplicative lfm loss values 'i_sc' ... 'v_oc' - where pr_dc = 1/ff * product('i_sc', ... 'v_oc') + * `'temp_air'` - air temperature [C] + + dnorm : DataFrame + Normalised values. May include columns: - mlfm_file_name : string - mlfm_file_name used in graph title. + * `'pr_dc_temp_corr'` normalized power at the maximum power point. + * `'i_sc'` normalized short circuit current. + * `'i_mp'` normalized current at maximum power point. + * `'v_mp'` normalized voltage at maximum power point. + * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. + * `'v_ff'` normalized multiplicative loss in fill factor apportioned + to voltage. + * `'i_ff'` normalized multiplicative loss in fill factor apportioned + to current. + * `'r_oc'` normalized slope of IV curve at open circuit. + * `'r_sc'` normalized slope of IV curve at short circuit. - qty_mlfm_vars : int - number of mlfm_values present in data usually - 2 = (imp, vmp) from mpp tracker - 4 = (i_sc, i_mp, v_mp, v_oc) from matrix - 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + title : string + Title for the figure. Returns ------- fig : Figure Instance of matplotlib.figure.Figure + See also + -------- + mlfm_meas_to_norm ''' try: import matplotlib.pyplot as plt except ImportError: - print('mlfm requires matplotlib') + raise ImportError('plot_mlfm_scatter requires matplotlib') # offset legend to the right to not overlap graph, use ~1.2 bbox = 1.2 # set x_axis as irradiance - xdata = dmeas['poa_global'] + xdata = dmeas['poa_global_kwm2'] / 1000. fig, ax1 = plt.subplots() - # get filename without ".csv" for title - ax1.set_title('Plot mlfm scatter ' + - mlfm_file_name[:len(mlfm_file_name)-4]) + ax1.set_title(title) - ax1.set_ylabel('normalised mlfm values') + ax1.set_ylabel('Normalised values') ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line ax1.set_ylim(0.8, 1.1) # optional normalised y scale - ax1.set_xlabel('poa_global [W/m$^2$]') + ax1.set_xlabel('Plane of array irradiance [W/m$^2$]') ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC - # plot the mlfm parameters depending on qty_mlfm_vars - if qty_mlfm_vars == 1: # only p_mp - ax1.scatter(xdata, dnorm['pr_dc_temp_corr'], - c=clr['pr_dc'], label='pr_dc_temp_corr') - - # if (qty_mlfm_vars == 2) or (qty_mlfm_vars == 4): # mppt or matrix - if qty_mlfm_vars in (2, 4): # mppt or matrix - ax1.scatter(xdata, dnorm['i_mp'], c=clr['i_mp'], label='norm_i_mp') - ax1.scatter(xdata, dnorm['v_mp'], c=clr['v_mp'], label='norm_v_mp') - - if qty_mlfm_vars >= 6: # ivcurve - ax1.scatter(xdata, dnorm['i_ff'], c=clr['i_ff'], label='norm_i_ff') - ax1.scatter(xdata, dnorm['v_ff'], c=clr['v_ff'], label='norm_v_ff') - ax1.scatter(xdata, dnorm['r_sc'], c=clr['r_sc'], label='norm_r_sc') - ax1.scatter(xdata, dnorm['r_oc'], c=clr['r_oc'], label='norm_r_oc') + lines = {'pr_dc_temp_corr': 'pr_dc', + 'i_mp': 'i_mp', + 'v_mp': 'v_mp', + 'i_sc': 'i_sc', + 'r_sc': 'r_sc', + 'r_oc': 'r_oc', + 'i_ff': 'i_ff', + 'v_ff': 'v_ff', + 'v_oc_temp_corr': 'v_oc'} + labels = {'pr_dc_temp_corr': 'pr_dc_temp-corr', + 'i_mp': 'norm_i_mp', + 'v_mp': 'norm_v_mp', + 'i_sc': 'norm_i_sc', + 'r_sc': 'norm_r_sc', + 'r_oc': 'norm_r_oc', + 'i_ff': 'norm_i_ff', + 'v_ff': 'norm_v_ff', + 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} - if qty_mlfm_vars >= 4: # matrix - ax1.scatter(xdata, dnorm['i_sc'], c=clr['i_sc'], label='norm_i_sc') - - ax1.scatter(xdata, dnorm['v_oc_temp_corr'], c=clr['v_oc'], - label='norm_v_oc_temp_corr') + # plot the mlfm parameters depending on qty_mlfm_vars + for k in lines.keys(): + try: + ax1.scatter(xdata, dnorm[k], c=lines[k], label=labels[k]) + except KeyError: + pass ax1.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) # y2axis plot met on right y axis ax2 = ax1.twinx() - ax2.set_ylabel('temp_module, temp_air (C/100)') # poa_global (kW/m$^2$); + ax2.set_ylabel('Temperature (C/100)') # poa_global (kW/m$^2$); # set wide limits 0 to 4 so they don't overlap mlfm params ax2.set_ylim(0, 4) @@ -603,58 +593,64 @@ def plot_mlfm_scatter(dmeas, dnorm, mlfm_file_name, qty_mlfm_vars): return fig -def plot_mlfm_stack(dmeas, dnorm, dstack, ref, - mlfm_file_name, qty_mlfm_vars, +def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, xaxis_labels=12, is_i_sc_self_ref=False, is_v_oc_temp_module_corr=True): ''' - Plot graph of stacked MLFM losses from initial 1/FF down to pr_dc. + Plot stacked subtractive losses. Parameters ---------- dmeas : DataFrame - Measured weather data. Must include 'poa_global_kwm2' and - 'temp_module', may include 'temp_air'. - and measured electrical/thermal values - 'i_sc' .. 'v_oc', temp_module (cwh: I don't see these are referenced). + Measurements. Must include columns: + + * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] + * `'temp_module'` module temperature [C] - dnorm : dataframe - Normalised multiplicative LFM loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/FF * product('i_sc', ... 'v_oc'). + May include optional columns: - dstack : dataframe - normalised subtractive lfm loss values 'i_sc' .. 'v_oc' - where pr_dc = 1/ff - sum('i_sc', ... 'v_oc'). + * `'temp_air'` - air temperature [C] - ref : dict - reference stc values e.g. 'v_oc' and - temperature coeffs e.g. 'beta_v_oc'. + dnorm : DataFrame + Normalised values. Must contain column `'pr_dc'`. + + dstack : DataFrame + Stacked subtractive losses. Must contain columns `'v_oc'`, `'v_mp'`, + `'i_v'`, `'i_mp'`, `'i_sc'`, `'temp_module_corr'`. If optional columns + `'r_oc'` and `'v_ff'` are present, these columns are plotted instead + of `'v_mp'`. If optional columns `'r_sc'` and `'i_ff'` are present, + these columns are plotted instead of `'i_mp'`. - mlfm_file_name : string - mlfm_file_name used in graph title. + fill_factor : float + Reference value of fill factor for IV curve at STC conditions. - qty_mlfm_vars : int - number of mlfm_values present in data usually - 2 = (imp, vmp) from mpp tracker - 4 = (i_sc, i_mp, v_mp, v_oc) from matrix - 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve. + title : string + Title for the figure. xaxis_labels : int, default 12 Number of xaxis labels to show. Use 0 to show all. is_i_sc_self_ref : bool, default False Self corrects i_sc to remove angle of incidence, - spectrum, snow or soiling?. + spectrum, snow or soiling. is_v_oc_temp_module_corr : bool, default True Calculate loss due to gamma, subtract from v_oc loss. + Returns + ------- + fig : Figure + Instance of matplotlib.figure.Figure + + See also + -------- + mlfm_norm_to_stack ''' try: import matplotlib.pyplot as plt except ImportError: - print('mlfm requires matplotlib') + raise ImportError('plt_mlfm_stack requires matplotlib') # offset legend right, use ~1.2 bbox = 1.2 @@ -663,90 +659,62 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, xdata = dmeas.index fig, ax1 = plt.subplots() - ax1.set_title('Plot_mlfm_stack : ' + - mlfm_file_name[:len(mlfm_file_name)-4]) - - if qty_mlfm_vars == 6: # iv curve - labels_6 = [ - 'pr_dc', - 'stack_t_mod', - 'stack_v_oc', - 'stack_r_oc', - 'stack_v_ff', - '- - -', - 'stack_i_ff', - 'stack_r_sc', - 'stack_i_sc' - ] - - color_map_6 = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['r_oc'], - clr['v_ff'], - clr['i_v'], - clr['i_ff'], - clr['r_sc'], - clr['i_sc'] - ] - - # plot stack in order bottom to top, - # allowing self_ref and temp_module corrections - ax1.stackplot( - xdata, - dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), - dstack['r_oc'], - dstack['v_ff'], - dstack['i_v'], - dstack['i_ff'], - dstack['r_sc'], - dstack['i_sc'] * (not is_i_sc_self_ref), - labels=labels_6, - colors=color_map_6 - ) - - if qty_mlfm_vars == 4: # matrix - labels_4 = [ - 'pr_dc', - 'stack_t_mod', - 'stack_v_oc', - 'stack_v_mp', - '- - -', - 'stack_i_mp', - 'i_sc' - ] - - color_map_4 = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['v_mp'], - clr['i_v'], - clr['i_mp'], - clr['i_sc'] - ] - - # plot stack in order bottom to top, - # allowing self_ref and temp_module corrections - ax1.stackplot( - xdata, - dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), - dstack['v_mp'], - dstack['i_v'], - dstack['i_mp'], - dstack['i_sc'] * (not is_i_sc_self_ref), - labels=labels_4, - colors=color_map_4 - ) - - ax1.axhline(y=1/ref['ff'], c='grey', lw=3) # show initial 1/FF + ax1.set_title(title) + + ydata = [dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), + dstack['v_oc'] - ( + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), + dstack['v_mp'], + dstack['i_v'], + dstack['i_mp'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_v_mp', + '- - -', + 'stack_i_mp', + 'stack_i_sc'] + color_map = [ + 'white', # colour to bottom of graph + clr['temp_module'], + clr['v_oc'], + clr['v_mp'], + clr['i_v'], + clr['i_mp'], + clr['i_sc']] + + if all([c in dstack.columns for c in ['v_ff', 'r_oc']]): + # replace v_mp with v_ff and r_oc + ydata.pop(3) + ydata.insert(2, dstack['v_ff']) + ydata.insert(2, dstack['r_oc']) + labels.pop(3) + labels.insert(2, 'stack_v_ff') + labels.insert(2, 'stack_r_oc') + color_map.pop(3) + color_map.insert(2, clr['v_ff']) + color_map.insert(2, clr['r_oc']) + + if all([c in dstack.columns for c in ['i_ff', 'r_sc']]): + # replace i_mp with i_ff and r_sc + ydata.pop(-1) + ydata.append(dstack['r_sc']) + ydata.append(dstack['i_ff']) + labels.pop(-1) + labels.append('stack_r_sc') + labels.append('stack_i_ff') + color_map.pop(-1) + color_map.append(clr['r_scf']) + color_map.append(clr['i_ff']) + + # plot stack in order bottom to top, + # allowing self_ref and temp_module corrections + ax1.stackplot(xdata, *tuple(ydata), labels=labels, colors=color_map) + + ax1.axhline(y=1/fill_factor, c='grey', lw=3) # show initial 1/FF ax1.axhline(y=1, c='grey', lw=3) # show 100% line ax1.set_ylabel('stacked mlfm losses') @@ -780,7 +748,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, x_count += 1 ax1.set_xticklabels(xax2) - ax1.set_ylim(0.6, 1/ref['ff']+0.1) # optional normalised y scale + ax1.set_ylim(0.6, 1/fill_factor + 0.1) # optional normalised y scale plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) # plot met data on right y axis @@ -802,7 +770,8 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, ref, ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) ax1.set_xticklabels(xax2, rotation=90) - plt.show() + + return fig REFS = """ diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 0b05b9130a..fd08419728 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -216,5 +216,5 @@ def test_mlfm_fit(matrix_data, mlfm_6_fit): @requires_mpl def test_plot_mlfm_scatter(measured, normalized): import matplotlib.pyplot as plt - fig = mlfm.plot_mlfm_scatter(measured, normalized, 'title_string', 2) + fig = mlfm.plot_mlfm_scatter(measured, normalized, 'title_string') assert isinstance(fig, plt.Figure) From bc8fb58e56f17732cdef76c30ff10d9d0d2c4db7 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 23 Jun 2022 08:55:50 -0600 Subject: [PATCH 51/81] formatting --- pvlib/mlfm.py | 95 ++++++++++++---------------------------- pvlib/tests/test_mlfm.py | 2 +- 2 files changed, 30 insertions(+), 67 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 9904900e6c..e64f8e0c66 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -97,7 +97,7 @@ def mlfm_meas_to_norm(dmeas, ref): ------- dnorm : DataFrame Normalised values. - * `'pr_dc'` is `'p_mp'` normalied by reference `'p_mp'` and + * `'pr_dc'` is `'p_mp'` normalised by reference `'p_mp'` and `'poa_global_kwm2'` * `'pr_dc_temp_corr'` is `'pr_dc'` adjusted to 25C. * Columns `'i_sc'`, `'i_mp'`, `'v_oc'`, `'v_mp'`, `'v_oc_temp_corr'`, @@ -192,7 +192,7 @@ def mlfm_norm_to_stack(dnorm, fill_factor): * `'i_mp'` normalized current at maximum power point. * `'v_oc'` normalized open circuit voltage. * `'v_mp'` normalized voltage at maximum power point. - * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. + * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. May include optional columns: @@ -414,57 +414,18 @@ def mlfm_fit(data, var_to_fit): bounds=bounds # boundaries ) - # get mlfm coefficients - c_1 = popt[0] - c_2 = popt[1] - c_3 = popt[2] - c_4 = popt[3] - c_5 = popt[4] - c_6 = popt[5] - if c5_zero: - c_5 = 0. - - coeff = [c_1, c_2, c_3, c_4, c_5, c_6] + popt[4] = 0. - # get mlfm error coefficients as sqrt of covariance + # get error of mlfm coefficients as sqrt of covariance perr = np.sqrt(np.diag(pcov)) - e_1 = perr[0] - e_2 = perr[1] - e_3 = perr[2] - e_4 = perr[3] - e_5 = perr[4] - e_6 = perr[5] - - err = [e_1, e_2, e_3, e_4, e_5, e_6] - - # format coefficients as strings, easier to read in graph title - coeffs = ( - ' {:.4%}'.format(c_1) + - ', {:.4%}'.format(c_2) + - ', {:.4%}'.format(c_3) + - ', {:.4%}'.format(c_4) + - ', {:.4%}'.format(c_5) + - ', {:.4%}'.format(c_6) - ) - # print ('coeffs = ', mlfm_sel, coeffs) - - err = ( - ' {:.4%}'.format(e_1) + - ', {:.4%}'.format(e_2) + - ', {:.4%}'.format(e_3) + - ', {:.4%}'.format(e_4) + - ', {:.4%}'.format(e_5) + - ', {:.4%}'.format(e_6) - ) - # print ('errs = ', mlfm_sel, errs) - # save fit and error to dataframe - pred = mlfm_6(data, c_1, c_2, c_3, c_4, c_5, c_6) + pred = mlfm_6(data, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], + popt[6]) resid = pred - data[var_to_fit] - return pred, coeff, resid + return pred, popt, resid, perr def plot_mlfm_scatter(dmeas, dnorm, title): @@ -494,7 +455,7 @@ def plot_mlfm_scatter(dmeas, dnorm, title): * `'i_sc'` normalized short circuit current. * `'i_mp'` normalized current at maximum power point. * `'v_mp'` normalized voltage at maximum power point. - * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. + * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. * `'v_ff'` normalized multiplicative loss in fill factor apportioned to voltage. * `'i_ff'` normalized multiplicative loss in fill factor apportioned @@ -538,24 +499,26 @@ def plot_mlfm_scatter(dmeas, dnorm, title): ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC - lines = {'pr_dc_temp_corr': 'pr_dc', - 'i_mp': 'i_mp', - 'v_mp': 'v_mp', - 'i_sc': 'i_sc', - 'r_sc': 'r_sc', - 'r_oc': 'r_oc', - 'i_ff': 'i_ff', - 'v_ff': 'v_ff', - 'v_oc_temp_corr': 'v_oc'} - labels = {'pr_dc_temp_corr': 'pr_dc_temp-corr', - 'i_mp': 'norm_i_mp', - 'v_mp': 'norm_v_mp', - 'i_sc': 'norm_i_sc', - 'r_sc': 'norm_r_sc', - 'r_oc': 'norm_r_oc', - 'i_ff': 'norm_i_ff', - 'v_ff': 'norm_v_ff', - 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} + lines = { + 'pr_dc_temp_corr': 'pr_dc', + 'i_mp': 'i_mp', + 'v_mp': 'v_mp', + 'i_sc': 'i_sc', + 'r_sc': 'r_sc', + 'r_oc': 'r_oc', + 'i_ff': 'i_ff', + 'v_ff': 'v_ff', + 'v_oc_temp_corr': 'v_oc'} + labels = { + 'pr_dc_temp_corr': 'pr_dc_temp-corr', + 'i_mp': 'norm_i_mp', + 'v_mp': 'norm_v_mp', + 'i_sc': 'norm_i_sc', + 'r_sc': 'norm_r_sc', + 'r_oc': 'norm_r_oc', + 'i_ff': 'norm_i_ff', + 'v_ff': 'norm_v_ff', + 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} # plot the mlfm parameters depending on qty_mlfm_vars for k in lines.keys(): @@ -770,7 +733,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) ax1.set_xticklabels(xax2, rotation=90) - + return fig diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index fd08419728..5540368932 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -207,7 +207,7 @@ def test_mlfm_fit(matrix_data, mlfm_6_fit): mlfm_sel = 'pr_dc' # drop wind_speed since it's always zero matrix_data = matrix_data.drop(columns=['wind_speed']) - predictions, cc_fit, residuals = mlfm.mlfm_fit( + predictions, cc_fit, residuals, perr = mlfm.mlfm_fit( matrix_data, mlfm_sel) # atol is large due to different behavior in conda_linux Python 3.6 env. assert_allclose(cc_fit, cc_target, atol=5e-3) From 8b44cc99f7c5a2e421d660937f5b9f7e90b47ac4 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 23 Jun 2022 09:04:02 -0600 Subject: [PATCH 52/81] count to 6 not 7 --- pvlib/mlfm.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index e64f8e0c66..49ac9a1a8f 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -421,8 +421,7 @@ def mlfm_fit(data, var_to_fit): perr = np.sqrt(np.diag(pcov)) # save fit and error to dataframe - pred = mlfm_6(data, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], - popt[6]) + pred = mlfm_6(data, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5]) resid = pred - data[var_to_fit] return pred, popt, resid, perr From 1790dd2eadee7d279cb665136d3cc0d5cefb0553 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 23 Jun 2022 09:20:46 -0600 Subject: [PATCH 53/81] fix scatterplot function --- pvlib/mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 49ac9a1a8f..a522aa200a 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -522,7 +522,7 @@ def plot_mlfm_scatter(dmeas, dnorm, title): # plot the mlfm parameters depending on qty_mlfm_vars for k in lines.keys(): try: - ax1.scatter(xdata, dnorm[k], c=lines[k], label=labels[k]) + ax1.scatter(xdata, dnorm[k], c=clr[lines[k]], label=labels[k]) except KeyError: pass From feabd50817503b24681dfb371e4e281db794a694 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 29 Jun 2022 12:44:54 -0600 Subject: [PATCH 54/81] updates from SR --- docs/tutorials/mlfm_220627_0.ipynb | 2069 +++++++++++++++++++ docs/tutorials/mlfm_220627_1.ipynb | 2069 +++++++++++++++++++ docs/tutorials/mlfm_220627_2.ipynb | 2960 ++++++++++++++++++++++++++++ pvlib/mlfm.py | 47 +- pvlib/tests/test_mlfm.py | 4 +- 5 files changed, 7124 insertions(+), 25 deletions(-) create mode 100644 docs/tutorials/mlfm_220627_0.ipynb create mode 100644 docs/tutorials/mlfm_220627_1.ipynb create mode 100644 docs/tutorials/mlfm_220627_2.ipynb diff --git a/docs/tutorials/mlfm_220627_0.ipynb b/docs/tutorials/mlfm_220627_0.ipynb new file mode 100644 index 0000000000..d985a08589 --- /dev/null +++ b/docs/tutorials/mlfm_220627_0.ipynb @@ -0,0 +1,2069 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0) MLFM PVLIB \n", + "##### ver:220627\n", + "#### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "##### Additions from Cliff Hansen (Sandia)\n", + "\n", + "## Tutorial overview.\n", + "\n", + "I) The Loss Factors Model (LFM) |2011 ref 1| quantifies \n", + "normalised losses from module parameters \n", + "(e.g. i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) by analysing the shape \n", + "of the IV curve and comparing it with STC reference values from the datasheet. \n", + "\n", + "II) The Mechanistic performance model (MPM) |2017 ref 2| \n", + "has \"meaningful,independent, robust and normalised\" coefficients \n", + "which fit how the LFM values depend on irradiance, module temperature \n", + "(and windspeed) and time. \n", + " \n", + "These parameters relate to \n", + " - c_1 = \"overall performance quality\" \n", + " - c_2 = \"normalised temperature coefficient\" (units /K) \n", + " - c_3 = \"low light level drop\" due to v_oc and r_sc (r_shunt) \n", + " - c_4 = \"high light level fall\" due to r_oc (~Rseries). \n", + " (optional) \n", + " - c_5 = \"wind speed coefficient\" \n", + " - c_6 = \"low light level drop\" sometimes needed for r_shunt behaviour. \n", + "\n", + "III) This tutorial shows how to take module measured and weather data, \n", + "(either outdoor or IEC 61853-like \n", + "matrix data), normalise it, generate MLFM coefficients, fit them with the MPM then \n", + "analyse module performance looking for loss values, degradation and \n", + "allowing performance predictions. \n", + "\n", + "![mlfm_data/figs/mlfm_flow.png](mlfm_data/figs/mlfm_flow.png) \n", + "\n", + "Fig 1: MLFM overview flow chart of this tutorial. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#import pvlib\n", + "from pvlib import *\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", + "import os\n", + "root_dir = os.getcwd()\n", + "\n", + "root_dir\n", + "\n", + "# Import essential library file with lfm and mpm definitions\n", + "from mlfm_220627 import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", + "# Import graphics code \n", + "from mlfm_220627 import plot_mlfm_scatter, plot_mlfm_stack \n", + "\n", + "# STANDARD DEFINITIONS\n", + "\n", + "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", + "plt.rcParams['figure.figsize'] = [7,5] # setup figure size inches\n", + "plt.rcParams.update({'font.size': 12}) # setup fontsize\n", + "plt.linewidth = 1.5 # line width in points\n", + "plt.linestyle = '--' #- # solid line\n", + "plt.marker = 's' #o # the default marker\n", + "plt.markersize = 9 #6 # marker size, in points\n", + "plt.bbox = 1.4 # offset right to not overwrite" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "##root_dir ##SR##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Select MLFM measurement \n", + "\n", + "Three default files are included (* = version number ) \n", + "\n", + "(0) g78_T16_Xall_F10m_R900*.csv \n", + "(1) n05667_Y13_R1k6_fClear*.csv \n", + "(2) x19074001_iec61853*.csv \n", + "\n", + "Essential default column names in meas() are :- \n", + "\n", + "meas { \n", + "'date_time', 'module_id', \n", + "'poa_global', 'wind_speed', 'temp_air', 'temp_module', \n", + "'v_oc', 'i_sc', 'i_mp', 'v_mp', \n", + "'r_sc', 'r_oc' <-- optional \n", + "}\n", + "\n", + "\n", + "File naming conventions can be used to help identify files, for example \n", + "`x81_T1906_D3_Fh.csv` \n", + "\n", + "where \n", + " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", + " - 81 = module id/channel number \n", + " - T1906 = (T)ime started = yymm(dd) \n", + " - D3 = (D)uration in days \n", + " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", + " - etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nNotes for Cliff Hansen 220624t17\\n\\nMy comments are marked ##SR##\\n\\nI can't get the stacked plot chart to work option 0. section\\nKeyError: 'v_mp'\\n\\n\"" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Notes for Cliff Hansen 220624t17\n", + "\n", + "My comments are marked ##SR##\n", + "\n", + "I can't get the stacked plot chart to work option 0. section\n", + "KeyError: 'v_mp'\n", + "\n", + "\"\"\"\n", + "\n", + "##meas.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# select one of the following meas files\n", + "################\n", + "meas_file = 0 #\n", + "################\n", + "\n", + "if meas_file == 0:\n", + " mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", + " # 6 measured LFM variables\n", + " # date_time, module_id, \n", + " # poa_global, wind_speed, temp_air, blue_frac, beam_frac, temp_module, \n", + " # v_oc, i_sc, i_mp, v_mp, \n", + " # r_sc,\tr_oc\n", + "\n", + "elif meas_file == 1:\n", + " mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", + " # 4 measured LFM variables \n", + " # date_time, module_id,\tpoa_global,\ttemp_module,\n", + " # i_sc,\tp_mp, i_mp,\tv_mp, v_oc,\tff,\t\n", + " # temp_air,\trelative_humidity, pressure, precipitation, dni, ghi, dhi, soil, wind_speed\n", + " \n", + "elif meas_file == 2:\n", + " mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", + " # 4 measured LFM variables\n", + " # date_time, module_id,\ttemp_module, poa_global,\n", + " # i_sc, v_oc, i_mp,\tv_mp, p_mp, \n", + " # wind_speed\n", + " \n", + "##SR##\n", + "#elif meas_file == 3:\n", + "# mlfm_meas_file = 'x19074005_iec61853.csv' \n", + "\n", + "# optional\n", + "# elif meas_file == -1:\n", + "# mlfm_meas_file = 't1_041.csv'\n", + " \n", + "# extract module id from filename e.g. 'g78'\n", + "mlfm_mod = mlfm_meas_file.split('_')\n", + "\n", + "mlfm_mod_sel = mlfm_mod[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Get ref module data at STC \n", + "\n", + "Get STC Reference module data for the selected module \n", + "(searching for a row with same module id). \n", + " \n", + "Ref values include electrical data and temperature coeffs and must include the following :- \n", + "\n", + "ref { \n", + "'i_sc', 'i_mp', 'v_mp', 'v_oc', \n", + "'alpha_i_sc', 'alpha_i_mp', 'beta_v_mp', 'beta_v_oc', 'gamma_p_mp', \n", + "} \n", + "\n", + "NOTE : Users must add their own data to the reference file \n", + "when they add new meas data. \n", + "\n", + "\n", + "If alpha_i_mp and beta_v_mp are not known \n", + "use the following approximations :- \n", + " alpha_i_mp = 0 \n", + " beta_v_mp = gamma_p_mp \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read all reference module data " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# user must keep updated with their modules from their measurements\n", + "ref_file_name = (root_dir + '\\\\mlfm_data\\\\ref\\\\' + 'mlfm_reference_modules.csv')\n", + "\n", + "ref_data = pd.read_csv(ref_file_name, index_col='module_id')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) Select module stc data from reference database" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "while True:\n", + " try:\n", + " ref_data = ref_data[ref_data.index == mlfm_mod_sel]\n", + " break\n", + "\n", + " except IndexError:\n", + " print(\"You must define module ref data to use this module ...\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Put relevant data into a dict for easy use\n", + "# ignore any other columns that may be database specific\n", + "# as they aren't needed\n", + "\n", + "ref = dict(\n", + " # module_id = ref_data['index'].values[0],\n", + " i_sc = ref_data['i_sc'].values[0],\n", + " i_mp = ref_data['i_mp'].values[0],\n", + " v_mp = ref_data['v_mp'].values[0],\n", + " v_oc = ref_data['v_oc'].values[0],\n", + "\n", + " alpha_i_sc = ref_data['alpha_i_sc'].values[0],\n", + " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", + " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", + " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", + " gamma_p_mp = ref_data['gamma_p_mp'].values[0],\n", + ")\n", + "\n", + "# create p_mp and ff in case they don't exist\n", + "ref['p_mp'] = ref['i_mp'] * ref['v_mp']\n", + "\n", + "ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4) Import measured data (outdoor or matrix)\n", + "\n", + "DateTime, Met and Raw module measurements. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5) Read in selected measured file data " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "meas = pd.read_csv(\n", + " root_dir + '\\\\mlfm_data\\\\meas_gtw\\\\' + mlfm_meas_file,\n", + " index_col='date_time'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6) Calculate useful data columns for meas" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2016-01-26 07:40:00-07:007852.9276720.9554827.7396240.2701540.3002672.59208739.6492060.0728370.06119632.4448684762.54397263.6600280.0529281.985488
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" + ], + "text/plain": [ + " module_id poa_global ... poa_global_kwm2 p_mp\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 78 2.666484 ... 0.002666 0.238726\n", + "2016-01-26 07:30:00-07:00 78 7.899143 ... 0.007899 0.883783\n", + "2016-01-26 07:40:00-07:00 78 52.927672 ... 0.052928 1.985488\n", + "\n", + "[3 rows x 15 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# normalise poa_global to kW/m^2\n", + "meas['poa_global_kwm2'] = meas['poa_global'] / 1000\n", + "\n", + "# calculate p_mp as it might be missing\n", + "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", + "\n", + "#show some meas data\n", + "meas.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7) Count how many independent mlfm variables are in the data \n", + "usually matrix=4 (i_sc, i_mp, v_mp, v_oc) \n", + "and iv=6 (i_sc, i_mp, v_mp, v_oc + r_sc, r_oc) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_qty_mlfm_vars(dmeas):\n", + " '''\n", + " Find the quantity of MLFM variables in the measured data\n", + " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", + " \n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + " \n", + " Returns\n", + " -------\n", + " qty_mlfm_vars : int\n", + " number of mlfm_values present in data usually\n", + " 2 = (imp, vmp) from mpp tracker\n", + " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", + " 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve.\n", + " '''\n", + " # find how many mlfm variables were measured\n", + " qty_mlfm_vars = 0\n", + " for mlfm_sel in ('i_sc', 'r_sc', 'i_mp', 'v_mp', 'r_oc', 'v_oc'):\n", + " if mlfm_sel in dmeas.columns:\n", + " qty_mlfm_vars += 1\n", + " #print(qty_mlfm_vars, mlfm_sel)\n", + " \n", + " return qty_mlfm_vars" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qty_mlfm_vars = get_qty_mlfm_vars(meas)\n", + "\n", + "qty_mlfm_vars ##SR##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8) Normalise MLFM values norm from meas and ref dataframes \n", + "\n", + "Fig 2 illustrates the loss factors model (LFM). \n", + "It uses the shape and values from dc measurements to quantify the values of each \n", + "of the loss factors going from (1) ref_p_mp to (5) meas_p_mp. \n", + "\n", + "![mlfm_data/figs/mlfm_iv.png](mlfm_data/figs/mlfm_iv.png) \n", + "\n", + "Fig 2: Loss Factors Model : \n", + "\n", + "1) ref_p_mp = Initial datasheet value at STC.\n", + "\n", + "2) Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", + "\n", + "3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", + " - norm_i_sc = measured / expected isc (purple)\n", + " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", + " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", + " \n", + " \n", + "4) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", + " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", + " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", + " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown)\n", + " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", + " \n", + " \n", + "5) These losses cause the performance to fall to pr_dc = meas_p_mp / ref_p_mp \n", + "\n", + "pr_dc = 1/ff * \n", + " (norm_i_sc * norm_r_sc * norm_i_ff ) * \n", + " (norm_v_ffv * norm_r_oc * norm_v_oc_t * norm_temp_corr ) \n", + "\n", + "Note: \n", + "The gamma temperature correction is subtracted from voc for simplicity. \n", + "In reality there will be temperature dependencies for i_sc and ff but they are smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_global_kwm2temp_modulepr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:000.0026662.0819400.4964970.4452930.9263800.7422410.7475260.7365870.6875640.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:000.0078992.4369850.6204710.5574730.8814090.8008960.8516750.7869770.7844180.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:000.0529282.5920870.2080370.1870590.2572270.8401720.8970410.8182980.8266880.8977000.8950180.9359160.914282
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" + ], + "text/plain": [ + " poa_global_kwm2 temp_module ... i_ff v_ff\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 0.002666 2.081940 ... 0.754692 0.968481\n", + "2016-01-26 07:30:00-07:00 0.007899 2.436985 ... 0.896006 0.907783\n", + "2016-01-26 07:40:00-07:00 0.052928 2.592087 ... 0.935916 0.914282\n", + "\n", + "[3 rows x 13 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "norm = mlfm_meas_to_norm(meas, ref) ##SR##, qty_mlfm_vars)\n", + "\n", + "# show some normalised data\n", + "norm.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9) Make irradiance and temperature bins for pivot tables \n", + "(Gbin=100W/m^2, Tbin=5C)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# poa_global bin e.g. 100, 200 .. 1100W/m2\n", + "norm['poa_global_bin'] = meas['poa_global'].round(-2)\n", + "\n", + "# temp_module bin e.g. 5, 10 .. 75C\n", + "norm['temp_module_bin'] = (5 * round(meas['temp_module'] / 5,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10) Perform sanity checks on meas and norm data \n", + "\n", + "It's easier to sanity check and study normalised data than raw values. \n", + "1) Remove bad, missing, unwanted or outlier data \n", + "2) User defined limits may depend on data scatter and degradation \n", + "3) Can either select on values e.g. '0.5 x stdev from mean' \n", + "4) Possible to select on dates if desired. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# select by irradiance poa_global range e.g. 100-1100W/m2\n", + "meas = meas[(meas['poa_global'] >= 100) &\n", + " (meas['poa_global'] <= 1100)]\n", + "\n", + "# if there's date_time can select by it, i.e. not matrix data\n", + "### better if index is formatted as a date\n", + "\n", + "# if qty_mlfm_vars == 6:\n", + "\n", + " # not for matrix as they don't contain dates\n", + " # example\n", + " # meas = meas[(meas.index > '2016-01-01') &\n", + " # (meas.index < '2017-01-01')]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# remove specific mlfm values outside limits e.g. <0.5 or >1.5\n", + "norm = norm[((norm['pr_dc'] > 0.5) & \n", + " (norm['pr_dc'] < 1.5))]\n", + "\n", + "# remove all mlfm values outside x~3 stdevs\n", + "if qty_mlfm_vars == 6:\n", + " # only needed for outdoor data as indoor ought to be less scattered\n", + " # remove all mlfm data > x stdev usually 3\n", + " stdevs = 3\n", + "\n", + " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc','v_oc'):\n", + " norm = norm[\n", + " ((norm[lfm] - norm[lfm].mean()) /\n", + " norm[lfm].std()).abs() < stdevs\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 11) Filter only matching rows from meas and norm data\n", + "like an inner join but leave data in separate norm and meas frames" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "#drop meas rows that aren't in norm\n", + "meas_not_in_norm = ~meas.index.isin(norm.index)\n", + "meas = meas.drop(meas[meas_not_in_norm].index)\n", + "\n", + "#drop norm rows that aren't in meas\n", + "norm_not_in_meas = ~norm.index.isin(meas.index)\n", + "norm = norm.drop(norm[norm_not_in_meas].index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 12) Plot normalised MLFM data vs irradiance \n", + "\n", + "For outdoor data - \n", + "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", + "\n", + "For matrix data - \n", + "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", + "\n", + "1. Higher values are always better (unlike measured values such as \n", + " Rseries or Io where lower is better)\n", + "1. Accurate measurements and a stable module result in narrowest lines \n", + "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", + "1. r_oc tends to fall at high light levels ( \\ right) \n", + "1. i_ff and v_ff are usually fairly flat ( - ) \n", + "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", + " and/or snow (if not properly corrected). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 13) : Normalised mlfm values vs. irradiance. Fig 3.\n", + "\n", + "All traces should be thin, usually around 0.9 ± 0.1 \n", + "i_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idpoa_globalwind_speedtemp_airblue_fracbeam_fractemp_modulev_oci_sci_mpv_mpr_scr_ocpoa_global_kwm2p_mp
date_time
2016-01-26 08:10:00-07:0078207.7803440.6760598.2593690.3887580.6545559.17198244.2920191.0514410.97660737.8831301022.1792813.6481630.20778036.996924
2016-01-26 08:30:00-07:0078314.4320340.8565478.9720310.4305500.74718414.17019744.0319351.3311721.25293637.8762081069.8048382.4835500.31443247.456451
2016-01-26 08:40:00-07:0078364.1616110.5891409.5725250.4451930.76949816.85355044.1726951.8470611.71715537.412432395.6073661.9624800.36416264.242948
2016-01-26 08:50:00-07:0078414.4448540.52661410.1409910.4572800.81681718.93064944.1008502.1216671.98034337.078834507.5955731.6607800.41444573.428792
2016-01-26 09:00:00-07:0078462.1142701.28421310.1871340.4668100.81964519.76313844.1725742.3720332.21974936.923490451.1426101.6184550.46211481.960880
\n", + "
" + ], + "text/plain": [ + " module_id poa_global ... poa_global_kwm2 p_mp\n", + "date_time ... \n", + "2016-01-26 08:10:00-07:00 78 207.780344 ... 0.207780 36.996924\n", + "2016-01-26 08:30:00-07:00 78 314.432034 ... 0.314432 47.456451\n", + "2016-01-26 08:40:00-07:00 78 364.161611 ... 0.364162 64.242948\n", + "2016-01-26 08:50:00-07:00 78 414.444854 ... 0.414445 73.428792\n", + "2016-01-26 09:00:00-07:00 78 462.114270 ... 0.462114 81.960880\n", + "\n", + "[5 rows x 15 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "meas.head() ##SR## check what's there" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot normalised values vs. irradiance\n", + "fig_scatter = plot_mlfm_scatter(meas, norm, mlfm_meas_file) ##SR##, qty_mlfm_vars) add scatter\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 3 : MLFM multiplicative factors vs. irradiance\n", + "\n", + "\n", + "## 14) Convert multiplicative to subtractive losses to show on a stack plot \n", + "\n", + "LFM losses can be analysed as either \n", + "\n", + "- multiplicative \n", + "pr_dc = 1/ff * ( norm(i_sc) * norm(r_sc) * norm(i_ff) * \n", + " norm(v_ff) * norm(r_oc) * norm(v_oc_t) * norm(temp_corr) ). \n", + " \n", + " \n", + " \n", + "- subtractive \n", + "pr_dc = 1/ff - (stack(i_sc) + stack(r_sc) + stack(i_ff) - \n", + " stack(v_ff) + stack(r_oc) + stack(v_oc_t) + stack(temp_corr) ). \n", + " \n", + "Multiplicative losses are easier to understand but to represent them on a graph \n", + "it's easier to show them as a stacked plot where the values are 'translated' \n", + "so the sum of the stacked losses is shown to equate to the product of the \n", + "multiplicative losses." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dci_scr_sci_ffi_vv_ffr_ocv_octemp_module_corr
date_time
2016-01-26 08:10:00-07:000.9874550.0638170.0445290.0359110.010.0739080.098226-0.002454-0.065435
2016-01-26 08:30:00-07:000.8369980.2372830.0325940.0302460.010.0770190.0829280.004324-0.042936
2016-01-26 08:40:00-07:000.9783350.0611030.0655990.0131430.010.0913420.0911430.000727-0.033518
\n", + "
" + ], + "text/plain": [ + " pr_dc i_sc ... v_oc temp_module_corr\n", + "date_time ... \n", + "2016-01-26 08:10:00-07:00 0.987455 0.063817 ... -0.002454 -0.065435\n", + "2016-01-26 08:30:00-07:00 0.836998 0.237283 ... 0.004324 -0.042936\n", + "2016-01-26 08:40:00-07:00 0.978335 0.061103 ... 0.000727 -0.033518\n", + "\n", + "[3 rows x 9 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# translate multiplicative to stack losses and add to dataframe df\n", + "stack = mlfm_norm_to_stack(norm, fill_factor = ref['ff']) ##SR## ref) ###, qty_mlfm_vars)\n", + "\n", + "# show some stack losses\n", + "stack.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15) Plot stack losses vs. measurement \n", + "\n", + "Fig 4 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", + "\n", + "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", + "\n", + "Fig 4 Stacked losses by measurement \n", + "\n", + "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", + " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", + " \n", + "- This figure shows a typical c-Si module for four clear days for \n", + " different months July to Oct in AZ. \n", + " \n", + "- In the middle of the days the high irradiance results in the biggest \n", + " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", + " (as the module heats to 60C). \n", + " \n", + "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", + " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", + "\n", + "Stack losses are indicated by their colours \n", + "(from top to bottom for mlfm4=matrix and mlfm6=ivcurve) \n", + "\n", + "`+-----+----+-------+--------+------------+--------------+` \n", + "`| 1 | 2 | 4 | 6 | \u001b[0m\n fig_stack = plot_mlfm_stack(\n", + " File \u001b[0;32m~\\OneDrive\\Documents\\_CONS\\__Reference\\PVPMC\\__repository\\pvlib-python\\docs\\tutorials\\mlfm_220627.py:653\u001b[0m in \u001b[0;35mplot_mlfm_stack\u001b[0m\n dstack['v_mp'],\n", + " File \u001b[0;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001b[0m in \u001b[0;35m__getitem__\u001b[0m\n indexer = self.columns.get_loc(key)\n", + "\u001b[1;36m File \u001b[1;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001b[1;36m in \u001b[1;35mget_loc\u001b[1;36m\u001b[0m\n\u001b[1;33m raise KeyError(key) from err\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m\u001b[1;31m:\u001b[0m 'v_mp'\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot stack loss vs. time (or measurement) chart\n", + "fig_stack = plot_mlfm_stack(\n", + " dmeas=meas,\n", + " dnorm=norm,\n", + " dstack=stack, # dataframe measurements\n", + " fill_factor=ref['ff'], # ref=ref, # dataframe reference STC\n", + " title='title', #\n", + " # mlfm_file_name=mlfm_meas_file, # name of data file\n", + " # qty_mlfm_vars=qty_mlfm_vars, # number of mlfm measurements usually 4 or 6\n", + " xaxis_labels=12, # show this many x_labels or 0 to show all\n", + " is_i_sc_self_ref=False, # is isc self referenced?\n", + " is_v_oc_temp_module_corr=True, # is voc temperature corrected?\n", + ")\n", + "\n", + "##SR## added stack = \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 17) Fit mechanistic model to measured weather and normalised losses \n", + "\n", + "Perform a Mechanistic Performance Model (MPM) fit to the mlfm parameters \n", + "poa_global (kW/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", + "\n", + "MPM_6 = c_1 + c_2 * (temp_module-25) + c_3 * log10(poa_global_kwm2) + \n", + " c_4 * poa_global_kwm2 + c_5 * wind_speed + c_6 / poa_global_kwm2 \n", + "\n", + "\n", + "Report the fit (coeffs) and error (errs) coefficients. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", + "mlfm_sel = 'pr_dc' \n", + "\n", + "# FIX THIS WARNING,\n", + "# SettingWithCopyWarning:\n", + "# A value is trying to be set on a copy of a slice from a DataFrame.\n", + "# Try using .loc[row_indexer,col_indexer] = value instead\n", + "# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\n", + "\n", + "cc, coeffs, ee, errs = mlfm_fit(norm, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", + "\n", + "# Fix a bug with fit routine which gives a\n", + "# finite cc[4] even if all the ws data is 0\n", + "# this won't matter until cc is applied to other\n", + "# data with some ws <>0 when it will give bad results\n", + "if np.mean(meas.wind_speed) == 0:\n", + " cc[4] = 0\n", + " c_5 = 0\n", + "\n", + "norm['calc_' + mlfm_sel] = cc \n", + "\n", + "norm['diff_' + mlfm_sel] = norm[mlfm_sel] - norm['calc_' + mlfm_sel] \n", + "\n", + "norm.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# cc\n", + "# date_time\n", + "# 2013-01-04 08:15:12-06:00 0.877861\n", + "# 2013-01-04 08:20:12-06:00 0.899799\n", + "\n", + "# coeffs\n", + "# array([ 1.27323780e+00, -4.79820818e-03, 6.18587637e-01, -2.83390574e-01,\n", + "# 0.00000000e+00, -2.74667887e-10])\n", + "\n", + "# ee\n", + "# date_time\n", + "# 2013-01-04 08:15:12-06:00 -0.166853\n", + "# 2013-01-04 08:20:12-06:00 -0.149638\n", + "\n", + "# errs\n", + "# array([0.03774048, 0.00015903, 0.08827109, 0.04484896, 0. ,\n", + "# 0.00641688])\n", + "\n", + "#mlfm_meas_file\n", + "#norm.columns\n", + "#fit\n", + "norm.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 18) Show residual fit vs. measured for MLFM parameter " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_fit(dnorm, fit, title):\n", + " \n", + " ''' \n", + " Scatter plot fit to normalised measured\n", + "\n", + " Parameters\n", + " ---------- \n", + " \n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + " \n", + " fit : string\n", + " name of fitted variable e.g. 'pr_dc'.\n", + " \n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " '''\n", + " \n", + " fig, ax1 = plt.subplots()\n", + "\n", + " plt.title(title)\n", + "\n", + " plt.ylabel('fit ' + fit + ' * poa_global_kwm2')\n", + " ax1.set_ylim(0, 1.2)\n", + "\n", + " plt.xlabel('meas ' + fit + '* poa_global_kwm2')\n", + " ax1.set_xlim(0, 1.2)\n", + "\n", + " plt.plot(\n", + " dnorm[fit] * dnorm['poa_global_kwm2'],\n", + " dnorm['calc_' + fit] * dnorm['poa_global_kwm2'], ##SR##\n", + " 'c^',\n", + " label = fit\n", + " )\n", + "\n", + " # plot 1:1 line to show optimum fit\n", + " plt.plot((0,1.2),(0,1.2), 'ko-')\n", + "\n", + " plt.legend(loc='upper left')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fig 6 : fit_mlfm_sel * poa_global vs. measured_mlfm_sel * poa_global" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot fit vs. measured, include a 1:1 line for comparison\n", + "fit_plot = plot_fit(norm, mlfm_sel, 'fit ' + mlfm_meas_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 19) Plot heatmap of mean residual vs. tenp_module and poa_global\n", + "\n", + "Show a heatmap of the average residual (meas - fit) error \n", + "for each irradiance (100W/m^2) and tmod bin (5C)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_heatmap(dmeas, dnorm, fit, y_axis, x_axis, z_axis, title):\n", + " '''\n", + " Plot a heatmap of Z vs. binned X and Y axes.\n", + "\n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + "\n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + "\n", + " fit : string\n", + " fitted parameter e.g. 'pr_dc'.\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global_bin'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module_bin'.\n", + "\n", + " z_axis : string\n", + " value as a colour surface plot e.f. 'diff_pr_dc'.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " '''\n", + "\n", + " df_piv = pd.pivot_table(\n", + " dnorm,\n", + " index=y_axis, # e.g. 'temp_module_bin'\n", + " columns=x_axis, # e.g. 'poa_global_bin'\n", + " values=z_axis, # value to aggregate\n", + " fill_value=0, # fill empty cells with this ?\n", + " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", + " margins=False, # grand totals hide\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " # force z limits to be -2% to +2% if desired\n", + " df_piv = df_piv.clip(lower = -0.02, upper = +0.02)\n", + "\n", + " im = ax1.imshow(\n", + " df_piv,\n", + " cmap='RdYlBu',\n", + " origin='lower'\n", + " )\n", + "\n", + " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", + "\n", + " #Y AXIS : show only 1 of each y_skip labels\n", + " y_ticks = df_piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = df_piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " # X AXIS : show only 1 of each x_skip labels\n", + " x_ticks = df_piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + " \n", + " ax1.set_title(title)\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid( color='k', linestyle=':', linewidth=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 20) Residual MLFM fit heatmap vs. poa_global and temp_module. Fig 7 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot heatmap\n", + "heatmap_plot = plot_heatmap(\n", + " dnorm=norm,\n", + " dmeas=meas,\n", + " fit=mlfm_sel,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis='diff_' + mlfm_sel,\n", + " title='residual ' + mlfm_meas_file\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 21) Read in complete (G,T) Matrix to fill with MLFM predicted values \n", + "\n", + "Read in a matrix with complete values of \n", + "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", + "to predict all MPM values " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in the complete matrix data\n", + "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", + "\n", + "matr['poa_global_kwm2'] = matr['poa_global'] / 1000\n", + "\n", + "matr\n", + "\n", + "#matr['poa_global'] = matr['poa_global'] /1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 22) Predict performance from MPM fit coefficients \n", + "\n", + "1. generate predicted mpm data \n", + "2. create a pivot table mpm(g,t) \n", + "3. show as a heat map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#105.68%\t-0.42%\t12.85%\t-5.71%\n", + "coeffs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# populate pivot table from predicted mpm data\n", + "matr[mlfm_sel] = mlfm_6(matr, coeffs[0], coeffs[1], coeffs[2], coeffs[3], coeffs[4], coeffs[5])\n", + "\n", + "#matr[mlfm_sel] = mlfm_6(matr, coeffs)\n", + "\n", + "matr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 23) Plot heatmap of predicted MLFM values vs. temp_mod and poa_global bins" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", + " vmin=0, vmax=1.2, levels=5):\n", + " ''' \n", + " Plot filled contour plot Z vs. X and Y bins.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : dataframe\n", + " measured or noralised data containing weather columns\n", + " (poa_global, temp_module and wind_speed).\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module'.\n", + "\n", + " z_axis : string\n", + " measured value as a colour surface plot.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " vmin, vmax : float\n", + " minimum and maximum values for contour chart ###\n", + "\n", + " ''' \n", + " \n", + " piv = pd.pivot_table(\n", + " df,\n", + " index=y_axis,\n", + " columns=x_axis,\n", + " values=z_axis,\n", + " fill_value=0, # fill empty cells?\n", + " aggfunc=[np.mean], # min, np.sum, len->count\n", + " margins=False, # grand totals\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " piv = piv.clip(vmin, vmax)\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " cs = plt.contourf(\n", + " piv,\n", + " cmap='RdYlBu', # or 'nipy_spectral',\n", + " # origin='lower'\n", + " # nchunkint=1,\n", + " levels=levels,\n", + " vmin=vmin,\n", + " vmax=vmax\n", + " )\n", + "\n", + " cbar = fig.colorbar(cs, ax=ax1)\n", + " cbar.ax.set_ylabel(z_axis,\n", + " rotation=90,\n", + " va='bottom',\n", + " labelpad=+30)\n", + "\n", + " plt.title(title)\n", + "\n", + " y_ticks = piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + "\n", + " # show only 1 of each y_skip labels\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " x_ticks = piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " # show only 1 of each x_skip labels\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid( color='k', linestyle=':', linewidth=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", + "\n", + "matr2 = matr[matr['temp_module'] >= 10]\n", + "\n", + "matr2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 24) Contour plot of predicted mlfm_sel + vs. poa_global and temp_mod. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "contour_plot = plot_contourf(\n", + " df=matr2,\n", + " y_axis='temp_module',\n", + " x_axis='poa_global',\n", + " z_axis=mlfm_sel,\n", + " title='matrix predicted ' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 25) Contour plot of measured mlfm_sel vs. poa_global and temp_mod. Fig 9" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "contour_plot = plot_contourf(\n", + " df=norm,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis=mlfm_sel,\n", + " title='avg normalised ' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.1,\n", + " levels=9\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 26) References \n", + " \n", + "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", + "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", + "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", + " \n", + ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", + " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", + "What's Most Important for Energy Yield' \n", + "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", + "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", + "\n", + ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", + " 'Choosing the best Empirical Model for predicting energy yield' \n", + " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", + " Canobbio, Switzerland 30-31 March, 2017 \n", + "\n", + ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", + "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", + "benchmark its practical relevance in energy yield prediction' \n", + "PVSC June 2019 Chicago, USA \n", + "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", + "\n", + ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", + "under Real Outdoor and IEC 61853 Test Conditions \n", + "Using High Quality Module IV Measurements' \n", + "36th EU PVSEC Sep 2019 \n", + "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", + "\n", + ".. [5] Steve Ransome (SRCL) \n", + "'How to use the Loss Factors and Mechanistic Performance Models \n", + "effectively with PVPMC/PVLIB' \n", + "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", + "https://pvpmc.sandia.gov/download/7879/ \n", + "\n", + ".. [6] W.Marion et al (NREL) \n", + "'New Data Set for Validating PV Module Performance Models' \n", + "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", + "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", + "\n", + "Many more papers are available at www.steveransome.com \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stop\n", + "\n", + "# delete below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr2.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#norm.describe()\n", + "meas.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "whos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm.to_csv('\\\\mlfm_data\\\\export\\\\'+'norm.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "meas.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'meas.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stack.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'stack.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ref_data.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\' + 'ref_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stack.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ref" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "root_dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (Spyder)", + "language": "python3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc-autonumbering": true, + "toc-showmarkdowntxt": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/mlfm_220627_1.ipynb b/docs/tutorials/mlfm_220627_1.ipynb new file mode 100644 index 0000000000..d985a08589 --- /dev/null +++ b/docs/tutorials/mlfm_220627_1.ipynb @@ -0,0 +1,2069 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0) MLFM PVLIB \n", + "##### ver:220627\n", + "#### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "##### Additions from Cliff Hansen (Sandia)\n", + "\n", + "## Tutorial overview.\n", + "\n", + "I) The Loss Factors Model (LFM) |2011 ref 1| quantifies \n", + "normalised losses from module parameters \n", + "(e.g. i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) by analysing the shape \n", + "of the IV curve and comparing it with STC reference values from the datasheet. \n", + "\n", + "II) The Mechanistic performance model (MPM) |2017 ref 2| \n", + "has \"meaningful,independent, robust and normalised\" coefficients \n", + "which fit how the LFM values depend on irradiance, module temperature \n", + "(and windspeed) and time. \n", + " \n", + "These parameters relate to \n", + " - c_1 = \"overall performance quality\" \n", + " - c_2 = \"normalised temperature coefficient\" (units /K) \n", + " - c_3 = \"low light level drop\" due to v_oc and r_sc (r_shunt) \n", + " - c_4 = \"high light level fall\" due to r_oc (~Rseries). \n", + " (optional) \n", + " - c_5 = \"wind speed coefficient\" \n", + " - c_6 = \"low light level drop\" sometimes needed for r_shunt behaviour. \n", + "\n", + "III) This tutorial shows how to take module measured and weather data, \n", + "(either outdoor or IEC 61853-like \n", + "matrix data), normalise it, generate MLFM coefficients, fit them with the MPM then \n", + "analyse module performance looking for loss values, degradation and \n", + "allowing performance predictions. \n", + "\n", + "![mlfm_data/figs/mlfm_flow.png](mlfm_data/figs/mlfm_flow.png) \n", + "\n", + "Fig 1: MLFM overview flow chart of this tutorial. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#import pvlib\n", + "from pvlib import *\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", + "import os\n", + "root_dir = os.getcwd()\n", + "\n", + "root_dir\n", + "\n", + "# Import essential library file with lfm and mpm definitions\n", + "from mlfm_220627 import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", + "# Import graphics code \n", + "from mlfm_220627 import plot_mlfm_scatter, plot_mlfm_stack \n", + "\n", + "# STANDARD DEFINITIONS\n", + "\n", + "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", + "plt.rcParams['figure.figsize'] = [7,5] # setup figure size inches\n", + "plt.rcParams.update({'font.size': 12}) # setup fontsize\n", + "plt.linewidth = 1.5 # line width in points\n", + "plt.linestyle = '--' #- # solid line\n", + "plt.marker = 's' #o # the default marker\n", + "plt.markersize = 9 #6 # marker size, in points\n", + "plt.bbox = 1.4 # offset right to not overwrite" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "##root_dir ##SR##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Select MLFM measurement \n", + "\n", + "Three default files are included (* = version number ) \n", + "\n", + "(0) g78_T16_Xall_F10m_R900*.csv \n", + "(1) n05667_Y13_R1k6_fClear*.csv \n", + "(2) x19074001_iec61853*.csv \n", + "\n", + "Essential default column names in meas() are :- \n", + "\n", + "meas { \n", + "'date_time', 'module_id', \n", + "'poa_global', 'wind_speed', 'temp_air', 'temp_module', \n", + "'v_oc', 'i_sc', 'i_mp', 'v_mp', \n", + "'r_sc', 'r_oc' <-- optional \n", + "}\n", + "\n", + "\n", + "File naming conventions can be used to help identify files, for example \n", + "`x81_T1906_D3_Fh.csv` \n", + "\n", + "where \n", + " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", + " - 81 = module id/channel number \n", + " - T1906 = (T)ime started = yymm(dd) \n", + " - D3 = (D)uration in days \n", + " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", + " - etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nNotes for Cliff Hansen 220624t17\\n\\nMy comments are marked ##SR##\\n\\nI can't get the stacked plot chart to work option 0. section\\nKeyError: 'v_mp'\\n\\n\"" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Notes for Cliff Hansen 220624t17\n", + "\n", + "My comments are marked ##SR##\n", + "\n", + "I can't get the stacked plot chart to work option 0. section\n", + "KeyError: 'v_mp'\n", + "\n", + "\"\"\"\n", + "\n", + "##meas.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# select one of the following meas files\n", + "################\n", + "meas_file = 0 #\n", + "################\n", + "\n", + "if meas_file == 0:\n", + " mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", + " # 6 measured LFM variables\n", + " # date_time, module_id, \n", + " # poa_global, wind_speed, temp_air, blue_frac, beam_frac, temp_module, \n", + " # v_oc, i_sc, i_mp, v_mp, \n", + " # r_sc,\tr_oc\n", + "\n", + "elif meas_file == 1:\n", + " mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", + " # 4 measured LFM variables \n", + " # date_time, module_id,\tpoa_global,\ttemp_module,\n", + " # i_sc,\tp_mp, i_mp,\tv_mp, v_oc,\tff,\t\n", + " # temp_air,\trelative_humidity, pressure, precipitation, dni, ghi, dhi, soil, wind_speed\n", + " \n", + "elif meas_file == 2:\n", + " mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", + " # 4 measured LFM variables\n", + " # date_time, module_id,\ttemp_module, poa_global,\n", + " # i_sc, v_oc, i_mp,\tv_mp, p_mp, \n", + " # wind_speed\n", + " \n", + "##SR##\n", + "#elif meas_file == 3:\n", + "# mlfm_meas_file = 'x19074005_iec61853.csv' \n", + "\n", + "# optional\n", + "# elif meas_file == -1:\n", + "# mlfm_meas_file = 't1_041.csv'\n", + " \n", + "# extract module id from filename e.g. 'g78'\n", + "mlfm_mod = mlfm_meas_file.split('_')\n", + "\n", + "mlfm_mod_sel = mlfm_mod[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Get ref module data at STC \n", + "\n", + "Get STC Reference module data for the selected module \n", + "(searching for a row with same module id). \n", + " \n", + "Ref values include electrical data and temperature coeffs and must include the following :- \n", + "\n", + "ref { \n", + "'i_sc', 'i_mp', 'v_mp', 'v_oc', \n", + "'alpha_i_sc', 'alpha_i_mp', 'beta_v_mp', 'beta_v_oc', 'gamma_p_mp', \n", + "} \n", + "\n", + "NOTE : Users must add their own data to the reference file \n", + "when they add new meas data. \n", + "\n", + "\n", + "If alpha_i_mp and beta_v_mp are not known \n", + "use the following approximations :- \n", + " alpha_i_mp = 0 \n", + " beta_v_mp = gamma_p_mp \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read all reference module data " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# user must keep updated with their modules from their measurements\n", + "ref_file_name = (root_dir + '\\\\mlfm_data\\\\ref\\\\' + 'mlfm_reference_modules.csv')\n", + "\n", + "ref_data = pd.read_csv(ref_file_name, index_col='module_id')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) Select module stc data from reference database" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "while True:\n", + " try:\n", + " ref_data = ref_data[ref_data.index == mlfm_mod_sel]\n", + " break\n", + "\n", + " except IndexError:\n", + " print(\"You must define module ref data to use this module ...\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Put relevant data into a dict for easy use\n", + "# ignore any other columns that may be database specific\n", + "# as they aren't needed\n", + "\n", + "ref = dict(\n", + " # module_id = ref_data['index'].values[0],\n", + " i_sc = ref_data['i_sc'].values[0],\n", + " i_mp = ref_data['i_mp'].values[0],\n", + " v_mp = ref_data['v_mp'].values[0],\n", + " v_oc = ref_data['v_oc'].values[0],\n", + "\n", + " alpha_i_sc = ref_data['alpha_i_sc'].values[0],\n", + " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", + " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", + " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", + " gamma_p_mp = ref_data['gamma_p_mp'].values[0],\n", + ")\n", + "\n", + "# create p_mp and ff in case they don't exist\n", + "ref['p_mp'] = ref['i_mp'] * ref['v_mp']\n", + "\n", + "ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4) Import measured data (outdoor or matrix)\n", + "\n", + "DateTime, Met and Raw module measurements. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5) Read in selected measured file data " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "meas = pd.read_csv(\n", + " root_dir + '\\\\mlfm_data\\\\meas_gtw\\\\' + mlfm_meas_file,\n", + " index_col='date_time'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6) Calculate useful data columns for meas" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idpoa_globalwind_speedtemp_airblue_fracbeam_fractemp_modulev_oci_sci_mpv_mpr_scr_ocpoa_global_kwm2p_mp
date_time
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2016-01-26 07:30:00-07:00787.8991431.2977118.2414250.522027-0.1000002.43698537.6440290.0372490.02983229.6249808253.745059150.4612830.0078990.883783
2016-01-26 07:40:00-07:007852.9276720.9554827.7396240.2701540.3002672.59208739.6492060.0728370.06119632.4448684762.54397263.6600280.0529281.985488
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" + ], + "text/plain": [ + " module_id poa_global ... poa_global_kwm2 p_mp\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 78 2.666484 ... 0.002666 0.238726\n", + "2016-01-26 07:30:00-07:00 78 7.899143 ... 0.007899 0.883783\n", + "2016-01-26 07:40:00-07:00 78 52.927672 ... 0.052928 1.985488\n", + "\n", + "[3 rows x 15 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# normalise poa_global to kW/m^2\n", + "meas['poa_global_kwm2'] = meas['poa_global'] / 1000\n", + "\n", + "# calculate p_mp as it might be missing\n", + "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", + "\n", + "#show some meas data\n", + "meas.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7) Count how many independent mlfm variables are in the data \n", + "usually matrix=4 (i_sc, i_mp, v_mp, v_oc) \n", + "and iv=6 (i_sc, i_mp, v_mp, v_oc + r_sc, r_oc) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_qty_mlfm_vars(dmeas):\n", + " '''\n", + " Find the quantity of MLFM variables in the measured data\n", + " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", + " \n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + " \n", + " Returns\n", + " -------\n", + " qty_mlfm_vars : int\n", + " number of mlfm_values present in data usually\n", + " 2 = (imp, vmp) from mpp tracker\n", + " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", + " 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve.\n", + " '''\n", + " # find how many mlfm variables were measured\n", + " qty_mlfm_vars = 0\n", + " for mlfm_sel in ('i_sc', 'r_sc', 'i_mp', 'v_mp', 'r_oc', 'v_oc'):\n", + " if mlfm_sel in dmeas.columns:\n", + " qty_mlfm_vars += 1\n", + " #print(qty_mlfm_vars, mlfm_sel)\n", + " \n", + " return qty_mlfm_vars" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qty_mlfm_vars = get_qty_mlfm_vars(meas)\n", + "\n", + "qty_mlfm_vars ##SR##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8) Normalise MLFM values norm from meas and ref dataframes \n", + "\n", + "Fig 2 illustrates the loss factors model (LFM). \n", + "It uses the shape and values from dc measurements to quantify the values of each \n", + "of the loss factors going from (1) ref_p_mp to (5) meas_p_mp. \n", + "\n", + "![mlfm_data/figs/mlfm_iv.png](mlfm_data/figs/mlfm_iv.png) \n", + "\n", + "Fig 2: Loss Factors Model : \n", + "\n", + "1) ref_p_mp = Initial datasheet value at STC.\n", + "\n", + "2) Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", + "\n", + "3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", + " - norm_i_sc = measured / expected isc (purple)\n", + " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", + " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", + " \n", + " \n", + "4) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", + " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", + " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", + " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown)\n", + " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", + " \n", + " \n", + "5) These losses cause the performance to fall to pr_dc = meas_p_mp / ref_p_mp \n", + "\n", + "pr_dc = 1/ff * \n", + " (norm_i_sc * norm_r_sc * norm_i_ff ) * \n", + " (norm_v_ffv * norm_r_oc * norm_v_oc_t * norm_temp_corr ) \n", + "\n", + "Note: \n", + "The gamma temperature correction is subtracted from voc for simplicity. \n", + "In reality there will be temperature dependencies for i_sc and ff but they are smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_global_kwm2temp_modulepr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:000.0026662.0819400.4964970.4452930.9263800.7422410.7475260.7365870.6875640.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:000.0078992.4369850.6204710.5574730.8814090.8008960.8516750.7869770.7844180.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:000.0529282.5920870.2080370.1870590.2572270.8401720.8970410.8182980.8266880.8977000.8950180.9359160.914282
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" + ], + "text/plain": [ + " poa_global_kwm2 temp_module ... i_ff v_ff\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 0.002666 2.081940 ... 0.754692 0.968481\n", + "2016-01-26 07:30:00-07:00 0.007899 2.436985 ... 0.896006 0.907783\n", + "2016-01-26 07:40:00-07:00 0.052928 2.592087 ... 0.935916 0.914282\n", + "\n", + "[3 rows x 13 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "norm = mlfm_meas_to_norm(meas, ref) ##SR##, qty_mlfm_vars)\n", + "\n", + "# show some normalised data\n", + "norm.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9) Make irradiance and temperature bins for pivot tables \n", + "(Gbin=100W/m^2, Tbin=5C)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# poa_global bin e.g. 100, 200 .. 1100W/m2\n", + "norm['poa_global_bin'] = meas['poa_global'].round(-2)\n", + "\n", + "# temp_module bin e.g. 5, 10 .. 75C\n", + "norm['temp_module_bin'] = (5 * round(meas['temp_module'] / 5,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10) Perform sanity checks on meas and norm data \n", + "\n", + "It's easier to sanity check and study normalised data than raw values. \n", + "1) Remove bad, missing, unwanted or outlier data \n", + "2) User defined limits may depend on data scatter and degradation \n", + "3) Can either select on values e.g. '0.5 x stdev from mean' \n", + "4) Possible to select on dates if desired. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# select by irradiance poa_global range e.g. 100-1100W/m2\n", + "meas = meas[(meas['poa_global'] >= 100) &\n", + " (meas['poa_global'] <= 1100)]\n", + "\n", + "# if there's date_time can select by it, i.e. not matrix data\n", + "### better if index is formatted as a date\n", + "\n", + "# if qty_mlfm_vars == 6:\n", + "\n", + " # not for matrix as they don't contain dates\n", + " # example\n", + " # meas = meas[(meas.index > '2016-01-01') &\n", + " # (meas.index < '2017-01-01')]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# remove specific mlfm values outside limits e.g. <0.5 or >1.5\n", + "norm = norm[((norm['pr_dc'] > 0.5) & \n", + " (norm['pr_dc'] < 1.5))]\n", + "\n", + "# remove all mlfm values outside x~3 stdevs\n", + "if qty_mlfm_vars == 6:\n", + " # only needed for outdoor data as indoor ought to be less scattered\n", + " # remove all mlfm data > x stdev usually 3\n", + " stdevs = 3\n", + "\n", + " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc','v_oc'):\n", + " norm = norm[\n", + " ((norm[lfm] - norm[lfm].mean()) /\n", + " norm[lfm].std()).abs() < stdevs\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 11) Filter only matching rows from meas and norm data\n", + "like an inner join but leave data in separate norm and meas frames" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "#drop meas rows that aren't in norm\n", + "meas_not_in_norm = ~meas.index.isin(norm.index)\n", + "meas = meas.drop(meas[meas_not_in_norm].index)\n", + "\n", + "#drop norm rows that aren't in meas\n", + "norm_not_in_meas = ~norm.index.isin(meas.index)\n", + "norm = norm.drop(norm[norm_not_in_meas].index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 12) Plot normalised MLFM data vs irradiance \n", + "\n", + "For outdoor data - \n", + "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", + "\n", + "For matrix data - \n", + "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", + "\n", + "1. Higher values are always better (unlike measured values such as \n", + " Rseries or Io where lower is better)\n", + "1. Accurate measurements and a stable module result in narrowest lines \n", + "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", + "1. r_oc tends to fall at high light levels ( \\ right) \n", + "1. i_ff and v_ff are usually fairly flat ( - ) \n", + "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", + " and/or snow (if not properly corrected). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 13) : Normalised mlfm values vs. irradiance. Fig 3.\n", + "\n", + "All traces should be thin, usually around 0.9 ± 0.1 \n", + "i_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idpoa_globalwind_speedtemp_airblue_fracbeam_fractemp_modulev_oci_sci_mpv_mpr_scr_ocpoa_global_kwm2p_mp
date_time
2016-01-26 08:10:00-07:0078207.7803440.6760598.2593690.3887580.6545559.17198244.2920191.0514410.97660737.8831301022.1792813.6481630.20778036.996924
2016-01-26 08:30:00-07:0078314.4320340.8565478.9720310.4305500.74718414.17019744.0319351.3311721.25293637.8762081069.8048382.4835500.31443247.456451
2016-01-26 08:40:00-07:0078364.1616110.5891409.5725250.4451930.76949816.85355044.1726951.8470611.71715537.412432395.6073661.9624800.36416264.242948
2016-01-26 08:50:00-07:0078414.4448540.52661410.1409910.4572800.81681718.93064944.1008502.1216671.98034337.078834507.5955731.6607800.41444573.428792
2016-01-26 09:00:00-07:0078462.1142701.28421310.1871340.4668100.81964519.76313844.1725742.3720332.21974936.923490451.1426101.6184550.46211481.960880
\n", + "
" + ], + "text/plain": [ + " module_id poa_global ... poa_global_kwm2 p_mp\n", + "date_time ... \n", + "2016-01-26 08:10:00-07:00 78 207.780344 ... 0.207780 36.996924\n", + "2016-01-26 08:30:00-07:00 78 314.432034 ... 0.314432 47.456451\n", + "2016-01-26 08:40:00-07:00 78 364.161611 ... 0.364162 64.242948\n", + "2016-01-26 08:50:00-07:00 78 414.444854 ... 0.414445 73.428792\n", + "2016-01-26 09:00:00-07:00 78 462.114270 ... 0.462114 81.960880\n", + "\n", + "[5 rows x 15 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "meas.head() ##SR## check what's there" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot normalised values vs. irradiance\n", + "fig_scatter = plot_mlfm_scatter(meas, norm, mlfm_meas_file) ##SR##, qty_mlfm_vars) add scatter\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 3 : MLFM multiplicative factors vs. irradiance\n", + "\n", + "\n", + "## 14) Convert multiplicative to subtractive losses to show on a stack plot \n", + "\n", + "LFM losses can be analysed as either \n", + "\n", + "- multiplicative \n", + "pr_dc = 1/ff * ( norm(i_sc) * norm(r_sc) * norm(i_ff) * \n", + " norm(v_ff) * norm(r_oc) * norm(v_oc_t) * norm(temp_corr) ). \n", + " \n", + " \n", + " \n", + "- subtractive \n", + "pr_dc = 1/ff - (stack(i_sc) + stack(r_sc) + stack(i_ff) - \n", + " stack(v_ff) + stack(r_oc) + stack(v_oc_t) + stack(temp_corr) ). \n", + " \n", + "Multiplicative losses are easier to understand but to represent them on a graph \n", + "it's easier to show them as a stacked plot where the values are 'translated' \n", + "so the sum of the stacked losses is shown to equate to the product of the \n", + "multiplicative losses." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dci_scr_sci_ffi_vv_ffr_ocv_octemp_module_corr
date_time
2016-01-26 08:10:00-07:000.9874550.0638170.0445290.0359110.010.0739080.098226-0.002454-0.065435
2016-01-26 08:30:00-07:000.8369980.2372830.0325940.0302460.010.0770190.0829280.004324-0.042936
2016-01-26 08:40:00-07:000.9783350.0611030.0655990.0131430.010.0913420.0911430.000727-0.033518
\n", + "
" + ], + "text/plain": [ + " pr_dc i_sc ... v_oc temp_module_corr\n", + "date_time ... \n", + "2016-01-26 08:10:00-07:00 0.987455 0.063817 ... -0.002454 -0.065435\n", + "2016-01-26 08:30:00-07:00 0.836998 0.237283 ... 0.004324 -0.042936\n", + "2016-01-26 08:40:00-07:00 0.978335 0.061103 ... 0.000727 -0.033518\n", + "\n", + "[3 rows x 9 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# translate multiplicative to stack losses and add to dataframe df\n", + "stack = mlfm_norm_to_stack(norm, fill_factor = ref['ff']) ##SR## ref) ###, qty_mlfm_vars)\n", + "\n", + "# show some stack losses\n", + "stack.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15) Plot stack losses vs. measurement \n", + "\n", + "Fig 4 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", + "\n", + "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", + "\n", + "Fig 4 Stacked losses by measurement \n", + "\n", + "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", + " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", + " \n", + "- This figure shows a typical c-Si module for four clear days for \n", + " different months July to Oct in AZ. \n", + " \n", + "- In the middle of the days the high irradiance results in the biggest \n", + " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", + " (as the module heats to 60C). \n", + " \n", + "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", + " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", + "\n", + "Stack losses are indicated by their colours \n", + "(from top to bottom for mlfm4=matrix and mlfm6=ivcurve) \n", + "\n", + "`+-----+----+-------+--------+------------+--------------+` \n", + "`| 1 | 2 | 4 | 6 | \u001b[0m\n fig_stack = plot_mlfm_stack(\n", + " File \u001b[0;32m~\\OneDrive\\Documents\\_CONS\\__Reference\\PVPMC\\__repository\\pvlib-python\\docs\\tutorials\\mlfm_220627.py:653\u001b[0m in \u001b[0;35mplot_mlfm_stack\u001b[0m\n dstack['v_mp'],\n", + " File \u001b[0;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001b[0m in \u001b[0;35m__getitem__\u001b[0m\n indexer = self.columns.get_loc(key)\n", + "\u001b[1;36m File \u001b[1;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001b[1;36m in \u001b[1;35mget_loc\u001b[1;36m\u001b[0m\n\u001b[1;33m raise KeyError(key) from err\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m\u001b[1;31m:\u001b[0m 'v_mp'\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot stack loss vs. time (or measurement) chart\n", + "fig_stack = plot_mlfm_stack(\n", + " dmeas=meas,\n", + " dnorm=norm,\n", + " dstack=stack, # dataframe measurements\n", + " fill_factor=ref['ff'], # ref=ref, # dataframe reference STC\n", + " title='title', #\n", + " # mlfm_file_name=mlfm_meas_file, # name of data file\n", + " # qty_mlfm_vars=qty_mlfm_vars, # number of mlfm measurements usually 4 or 6\n", + " xaxis_labels=12, # show this many x_labels or 0 to show all\n", + " is_i_sc_self_ref=False, # is isc self referenced?\n", + " is_v_oc_temp_module_corr=True, # is voc temperature corrected?\n", + ")\n", + "\n", + "##SR## added stack = \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 17) Fit mechanistic model to measured weather and normalised losses \n", + "\n", + "Perform a Mechanistic Performance Model (MPM) fit to the mlfm parameters \n", + "poa_global (kW/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", + "\n", + "MPM_6 = c_1 + c_2 * (temp_module-25) + c_3 * log10(poa_global_kwm2) + \n", + " c_4 * poa_global_kwm2 + c_5 * wind_speed + c_6 / poa_global_kwm2 \n", + "\n", + "\n", + "Report the fit (coeffs) and error (errs) coefficients. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", + "mlfm_sel = 'pr_dc' \n", + "\n", + "# FIX THIS WARNING,\n", + "# SettingWithCopyWarning:\n", + "# A value is trying to be set on a copy of a slice from a DataFrame.\n", + "# Try using .loc[row_indexer,col_indexer] = value instead\n", + "# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\n", + "\n", + "cc, coeffs, ee, errs = mlfm_fit(norm, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", + "\n", + "# Fix a bug with fit routine which gives a\n", + "# finite cc[4] even if all the ws data is 0\n", + "# this won't matter until cc is applied to other\n", + "# data with some ws <>0 when it will give bad results\n", + "if np.mean(meas.wind_speed) == 0:\n", + " cc[4] = 0\n", + " c_5 = 0\n", + "\n", + "norm['calc_' + mlfm_sel] = cc \n", + "\n", + "norm['diff_' + mlfm_sel] = norm[mlfm_sel] - norm['calc_' + mlfm_sel] \n", + "\n", + "norm.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# cc\n", + "# date_time\n", + "# 2013-01-04 08:15:12-06:00 0.877861\n", + "# 2013-01-04 08:20:12-06:00 0.899799\n", + "\n", + "# coeffs\n", + "# array([ 1.27323780e+00, -4.79820818e-03, 6.18587637e-01, -2.83390574e-01,\n", + "# 0.00000000e+00, -2.74667887e-10])\n", + "\n", + "# ee\n", + "# date_time\n", + "# 2013-01-04 08:15:12-06:00 -0.166853\n", + "# 2013-01-04 08:20:12-06:00 -0.149638\n", + "\n", + "# errs\n", + "# array([0.03774048, 0.00015903, 0.08827109, 0.04484896, 0. ,\n", + "# 0.00641688])\n", + "\n", + "#mlfm_meas_file\n", + "#norm.columns\n", + "#fit\n", + "norm.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 18) Show residual fit vs. measured for MLFM parameter " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_fit(dnorm, fit, title):\n", + " \n", + " ''' \n", + " Scatter plot fit to normalised measured\n", + "\n", + " Parameters\n", + " ---------- \n", + " \n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + " \n", + " fit : string\n", + " name of fitted variable e.g. 'pr_dc'.\n", + " \n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " '''\n", + " \n", + " fig, ax1 = plt.subplots()\n", + "\n", + " plt.title(title)\n", + "\n", + " plt.ylabel('fit ' + fit + ' * poa_global_kwm2')\n", + " ax1.set_ylim(0, 1.2)\n", + "\n", + " plt.xlabel('meas ' + fit + '* poa_global_kwm2')\n", + " ax1.set_xlim(0, 1.2)\n", + "\n", + " plt.plot(\n", + " dnorm[fit] * dnorm['poa_global_kwm2'],\n", + " dnorm['calc_' + fit] * dnorm['poa_global_kwm2'], ##SR##\n", + " 'c^',\n", + " label = fit\n", + " )\n", + "\n", + " # plot 1:1 line to show optimum fit\n", + " plt.plot((0,1.2),(0,1.2), 'ko-')\n", + "\n", + " plt.legend(loc='upper left')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fig 6 : fit_mlfm_sel * poa_global vs. measured_mlfm_sel * poa_global" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot fit vs. measured, include a 1:1 line for comparison\n", + "fit_plot = plot_fit(norm, mlfm_sel, 'fit ' + mlfm_meas_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 19) Plot heatmap of mean residual vs. tenp_module and poa_global\n", + "\n", + "Show a heatmap of the average residual (meas - fit) error \n", + "for each irradiance (100W/m^2) and tmod bin (5C)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_heatmap(dmeas, dnorm, fit, y_axis, x_axis, z_axis, title):\n", + " '''\n", + " Plot a heatmap of Z vs. binned X and Y axes.\n", + "\n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + "\n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + "\n", + " fit : string\n", + " fitted parameter e.g. 'pr_dc'.\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global_bin'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module_bin'.\n", + "\n", + " z_axis : string\n", + " value as a colour surface plot e.f. 'diff_pr_dc'.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " '''\n", + "\n", + " df_piv = pd.pivot_table(\n", + " dnorm,\n", + " index=y_axis, # e.g. 'temp_module_bin'\n", + " columns=x_axis, # e.g. 'poa_global_bin'\n", + " values=z_axis, # value to aggregate\n", + " fill_value=0, # fill empty cells with this ?\n", + " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", + " margins=False, # grand totals hide\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " # force z limits to be -2% to +2% if desired\n", + " df_piv = df_piv.clip(lower = -0.02, upper = +0.02)\n", + "\n", + " im = ax1.imshow(\n", + " df_piv,\n", + " cmap='RdYlBu',\n", + " origin='lower'\n", + " )\n", + "\n", + " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", + "\n", + " #Y AXIS : show only 1 of each y_skip labels\n", + " y_ticks = df_piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = df_piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " # X AXIS : show only 1 of each x_skip labels\n", + " x_ticks = df_piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + " \n", + " ax1.set_title(title)\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid( color='k', linestyle=':', linewidth=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 20) Residual MLFM fit heatmap vs. poa_global and temp_module. Fig 7 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plot heatmap\n", + "heatmap_plot = plot_heatmap(\n", + " dnorm=norm,\n", + " dmeas=meas,\n", + " fit=mlfm_sel,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis='diff_' + mlfm_sel,\n", + " title='residual ' + mlfm_meas_file\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 21) Read in complete (G,T) Matrix to fill with MLFM predicted values \n", + "\n", + "Read in a matrix with complete values of \n", + "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", + "to predict all MPM values " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# read in the complete matrix data\n", + "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", + "\n", + "matr['poa_global_kwm2'] = matr['poa_global'] / 1000\n", + "\n", + "matr\n", + "\n", + "#matr['poa_global'] = matr['poa_global'] /1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 22) Predict performance from MPM fit coefficients \n", + "\n", + "1. generate predicted mpm data \n", + "2. create a pivot table mpm(g,t) \n", + "3. show as a heat map" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#105.68%\t-0.42%\t12.85%\t-5.71%\n", + "coeffs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# populate pivot table from predicted mpm data\n", + "matr[mlfm_sel] = mlfm_6(matr, coeffs[0], coeffs[1], coeffs[2], coeffs[3], coeffs[4], coeffs[5])\n", + "\n", + "#matr[mlfm_sel] = mlfm_6(matr, coeffs)\n", + "\n", + "matr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 23) Plot heatmap of predicted MLFM values vs. temp_mod and poa_global bins" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", + " vmin=0, vmax=1.2, levels=5):\n", + " ''' \n", + " Plot filled contour plot Z vs. X and Y bins.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : dataframe\n", + " measured or noralised data containing weather columns\n", + " (poa_global, temp_module and wind_speed).\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module'.\n", + "\n", + " z_axis : string\n", + " measured value as a colour surface plot.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " vmin, vmax : float\n", + " minimum and maximum values for contour chart ###\n", + "\n", + " ''' \n", + " \n", + " piv = pd.pivot_table(\n", + " df,\n", + " index=y_axis,\n", + " columns=x_axis,\n", + " values=z_axis,\n", + " fill_value=0, # fill empty cells?\n", + " aggfunc=[np.mean], # min, np.sum, len->count\n", + " margins=False, # grand totals\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " piv = piv.clip(vmin, vmax)\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " cs = plt.contourf(\n", + " piv,\n", + " cmap='RdYlBu', # or 'nipy_spectral',\n", + " # origin='lower'\n", + " # nchunkint=1,\n", + " levels=levels,\n", + " vmin=vmin,\n", + " vmax=vmax\n", + " )\n", + "\n", + " cbar = fig.colorbar(cs, ax=ax1)\n", + " cbar.ax.set_ylabel(z_axis,\n", + " rotation=90,\n", + " va='bottom',\n", + " labelpad=+30)\n", + "\n", + " plt.title(title)\n", + "\n", + " y_ticks = piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + "\n", + " # show only 1 of each y_skip labels\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " x_ticks = piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " # show only 1 of each x_skip labels\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid( color='k', linestyle=':', linewidth=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", + "\n", + "matr2 = matr[matr['temp_module'] >= 10]\n", + "\n", + "matr2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 24) Contour plot of predicted mlfm_sel + vs. poa_global and temp_mod. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "contour_plot = plot_contourf(\n", + " df=matr2,\n", + " y_axis='temp_module',\n", + " x_axis='poa_global',\n", + " z_axis=mlfm_sel,\n", + " title='matrix predicted ' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 25) Contour plot of measured mlfm_sel vs. poa_global and temp_mod. Fig 9" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "contour_plot = plot_contourf(\n", + " df=norm,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis=mlfm_sel,\n", + " title='avg normalised ' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.1,\n", + " levels=9\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 26) References \n", + " \n", + "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", + "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", + "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", + " \n", + ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", + " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", + "What's Most Important for Energy Yield' \n", + "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", + "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", + "\n", + ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", + " 'Choosing the best Empirical Model for predicting energy yield' \n", + " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", + " Canobbio, Switzerland 30-31 March, 2017 \n", + "\n", + ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", + "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", + "benchmark its practical relevance in energy yield prediction' \n", + "PVSC June 2019 Chicago, USA \n", + "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", + "\n", + ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", + "under Real Outdoor and IEC 61853 Test Conditions \n", + "Using High Quality Module IV Measurements' \n", + "36th EU PVSEC Sep 2019 \n", + "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", + "\n", + ".. [5] Steve Ransome (SRCL) \n", + "'How to use the Loss Factors and Mechanistic Performance Models \n", + "effectively with PVPMC/PVLIB' \n", + "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", + "https://pvpmc.sandia.gov/download/7879/ \n", + "\n", + ".. [6] W.Marion et al (NREL) \n", + "'New Data Set for Validating PV Module Performance Models' \n", + "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", + "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", + "\n", + "Many more papers are available at www.steveransome.com \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stop\n", + "\n", + "# delete below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr2.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#norm.describe()\n", + "meas.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "whos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm.to_csv('\\\\mlfm_data\\\\export\\\\'+'norm.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "meas.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'meas.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stack.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'stack.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ref_data.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\' + 'ref_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stack.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ref" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "root_dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (Spyder)", + "language": "python3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc-autonumbering": true, + "toc-showmarkdowntxt": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/mlfm_220627_2.ipynb b/docs/tutorials/mlfm_220627_2.ipynb new file mode 100644 index 0000000000..c97a5fd96f --- /dev/null +++ b/docs/tutorials/mlfm_220627_2.ipynb @@ -0,0 +1,2960 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0) MLFM PVLIB \n", + "##### ver:220627\n", + "#### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "##### Additions from Cliff Hansen (Sandia)\n", + "\n", + "## Tutorial overview.\n", + "\n", + "I) The Loss Factors Model (LFM) |2011 ref 1| quantifies \n", + "normalised losses from module parameters \n", + "(e.g. i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) by analysing the shape \n", + "of the IV curve and comparing it with STC reference values from the datasheet. \n", + "\n", + "II) The Mechanistic performance model (MPM) |2017 ref 2| \n", + "has \"meaningful,independent, robust and normalised\" coefficients \n", + "which fit how the LFM values depend on irradiance, module temperature \n", + "(and windspeed) and time. \n", + " \n", + "These parameters relate to \n", + " - c_1 = \"overall performance quality\" \n", + " - c_2 = \"normalised temperature coefficient\" (units /K) \n", + " - c_3 = \"low light level drop\" due to v_oc and r_sc (r_shunt) \n", + " - c_4 = \"high light level fall\" due to r_oc (~Rseries). \n", + " (optional) \n", + " - c_5 = \"wind speed coefficient\" \n", + " - c_6 = \"low light level drop\" sometimes needed for r_shunt behaviour. \n", + "\n", + "III) This tutorial shows how to take module measured and weather data, \n", + "(either outdoor or IEC 61853-like \n", + "matrix data), normalise it, generate MLFM coefficients, fit them with the MPM then \n", + "analyse module performance looking for loss values, degradation and \n", + "allowing performance predictions. \n", + "\n", + "![mlfm_data/figs/mlfm_flow.png](mlfm_data/figs/mlfm_flow.png) \n", + "\n", + "Fig 1: MLFM overview flow chart of this tutorial. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#import pvlib\n", + "from pvlib import *\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", + "import os\n", + "root_dir = os.getcwd()\n", + "\n", + "root_dir\n", + "\n", + "# Import essential library file with lfm and mpm definitions\n", + "from mlfm_220627 import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", + "# Import graphics code \n", + "from mlfm_220627 import plot_mlfm_scatter, plot_mlfm_stack \n", + "\n", + "# STANDARD DEFINITIONS\n", + "\n", + "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", + "plt.rcParams['figure.figsize'] = [7,5] # setup figure size inches\n", + "plt.rcParams.update({'font.size': 12}) # setup fontsize\n", + "plt.linewidth = 1.5 # line width in points\n", + "plt.linestyle = '--' #- # solid line\n", + "plt.marker = 's' #o # the default marker\n", + "plt.markersize = 9 #6 # marker size, in points\n", + "plt.bbox = 1.4 # offset right to not overwrite" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "##root_dir ##SR##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) Select MLFM measurement \n", + "\n", + "Three default files are included (* = version number ) \n", + "\n", + "(0) g78_T16_Xall_F10m_R900*.csv \n", + "(1) n05667_Y13_R1k6_fClear*.csv \n", + "(2) x19074001_iec61853*.csv \n", + "\n", + "Essential default column names in meas() are :- \n", + "\n", + "meas { \n", + "'date_time', 'module_id', \n", + "'poa_global', 'wind_speed', 'temp_air', 'temp_module', \n", + "'v_oc', 'i_sc', 'i_mp', 'v_mp', \n", + "'r_sc', 'r_oc' <-- optional \n", + "}\n", + "\n", + "\n", + "File naming conventions can be used to help identify files, for example \n", + "`x81_T1906_D3_Fh.csv` \n", + "\n", + "where \n", + " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", + " - 81 = module id/channel number \n", + " - T1906 = (T)ime started = yymm(dd) \n", + " - D3 = (D)uration in days \n", + " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", + " - etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nNotes for Cliff Hansen 220624t17\\n\\nMy comments are marked ##SR##\\n\\nI can't get the stacked plot chart to work option 0. section\\nKeyError: 'v_mp'\\n\\n\"" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Notes for Cliff Hansen 220624t17\n", + "\n", + "My comments are marked ##SR##\n", + "\n", + "I can't get the stacked plot chart to work option 0. section\n", + "KeyError: 'v_mp'\n", + "\n", + "\"\"\"\n", + "\n", + "##meas.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# select one of the following meas files\n", + "################\n", + "meas_file = 1 #\n", + "################\n", + "\n", + "if meas_file == 0:\n", + " mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", + " # 6 measured LFM variables\n", + " # date_time, module_id, \n", + " # poa_global, wind_speed, temp_air, blue_frac, beam_frac, temp_module, \n", + " # v_oc, i_sc, i_mp, v_mp, \n", + " # r_sc,\tr_oc\n", + "\n", + "elif meas_file == 1:\n", + " mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", + " # 4 measured LFM variables \n", + " # date_time, module_id,\tpoa_global,\ttemp_module,\n", + " # i_sc,\tp_mp, i_mp,\tv_mp, v_oc,\tff,\t\n", + " # temp_air,\trelative_humidity, pressure, precipitation, dni, ghi, dhi, soil, wind_speed\n", + " \n", + "elif meas_file == 2:\n", + " mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", + " # 4 measured LFM variables\n", + " # date_time, module_id,\ttemp_module, poa_global,\n", + " # i_sc, v_oc, i_mp,\tv_mp, p_mp, \n", + " # wind_speed\n", + " \n", + "##SR##\n", + "#elif meas_file == 3:\n", + "# mlfm_meas_file = 'x19074005_iec61853.csv' \n", + "\n", + "# optional\n", + "# elif meas_file == -1:\n", + "# mlfm_meas_file = 't1_041.csv'\n", + " \n", + "# extract module id from filename e.g. 'g78'\n", + "mlfm_mod = mlfm_meas_file.split('_')\n", + "\n", + "mlfm_mod_sel = mlfm_mod[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) Get ref module data at STC \n", + "\n", + "Get STC Reference module data for the selected module \n", + "(searching for a row with same module id). \n", + " \n", + "Ref values include electrical data and temperature coeffs and must include the following :- \n", + "\n", + "ref { \n", + "'i_sc', 'i_mp', 'v_mp', 'v_oc', \n", + "'alpha_i_sc', 'alpha_i_mp', 'beta_v_mp', 'beta_v_oc', 'gamma_p_mp', \n", + "} \n", + "\n", + "NOTE : Users must add their own data to the reference file \n", + "when they add new meas data. \n", + "\n", + "\n", + "If alpha_i_mp and beta_v_mp are not known \n", + "use the following approximations :- \n", + " alpha_i_mp = 0 \n", + " beta_v_mp = gamma_p_mp \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read all reference module data " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# user must keep updated with their modules from their measurements\n", + "ref_file_name = (root_dir + '\\\\mlfm_data\\\\ref\\\\' + 'mlfm_reference_modules.csv')\n", + "\n", + "ref_data = pd.read_csv(ref_file_name, index_col='module_id')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3) Select module stc data from reference database" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "while True:\n", + " try:\n", + " ref_data = ref_data[ref_data.index == mlfm_mod_sel]\n", + " break\n", + "\n", + " except IndexError:\n", + " print(\"You must define module ref data to use this module ...\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Put relevant data into a dict for easy use\n", + "# ignore any other columns that may be database specific\n", + "# as they aren't needed\n", + "\n", + "ref = dict(\n", + " # module_id = ref_data['index'].values[0],\n", + " i_sc = ref_data['i_sc'].values[0],\n", + " i_mp = ref_data['i_mp'].values[0],\n", + " v_mp = ref_data['v_mp'].values[0],\n", + " v_oc = ref_data['v_oc'].values[0],\n", + "\n", + " alpha_i_sc = ref_data['alpha_i_sc'].values[0],\n", + " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", + " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", + " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", + " gamma_p_mp = ref_data['gamma_p_mp'].values[0],\n", + ")\n", + "\n", + "# create p_mp and ff in case they don't exist\n", + "ref['p_mp'] = ref['i_mp'] * ref['v_mp']\n", + "\n", + "ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4) Import measured data (outdoor or matrix)\n", + "\n", + "DateTime, Met and Raw module measurements. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5) Read in selected measured file data " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "meas = pd.read_csv(\n", + " root_dir + '\\\\mlfm_data\\\\meas_gtw\\\\' + mlfm_meas_file,\n", + " index_col='date_time'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6) Calculate useful data columns for meas" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idpoa_globaltemp_modulei_scp_mpi_mpv_mpv_ocfftemp_airrelative_humiditypressureprecipitationdnighidhisoilwind_speedpoa_global_kwm2
date_time
2013-01-04 08:05:12-06:00n0566724.04.70.13614.8698140.125538.803345.851278.014.982.21007.100.018.118.31.000.0240
2013-01-04 08:10:12-06:00n0566733.05.20.17886.5305350.165739.411846.408978.725.082.91007.001.624.925.01.000.0330
2013-01-04 08:15:12-06:00n05667204.76.51.102445.3492271.054842.993250.078082.155.281.91007.10353.453.031.51.000.2047
\n", + "
" + ], + "text/plain": [ + " module_id poa_global ... wind_speed poa_global_kwm2\n", + "date_time ... \n", + "2013-01-04 08:05:12-06:00 n05667 24.0 ... 0 0.0240\n", + "2013-01-04 08:10:12-06:00 n05667 33.0 ... 0 0.0330\n", + "2013-01-04 08:15:12-06:00 n05667 204.7 ... 0 0.2047\n", + "\n", + "[3 rows x 19 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# normalise poa_global to kW/m^2\n", + "meas['poa_global_kwm2'] = meas['poa_global'] / 1000\n", + "\n", + "# calculate p_mp as it might be missing\n", + "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", + "\n", + "#show some meas data\n", + "meas.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7) Count how many independent mlfm variables are in the data \n", + "usually matrix=4 (i_sc, i_mp, v_mp, v_oc) \n", + "and iv=6 (i_sc, i_mp, v_mp, v_oc + r_sc, r_oc) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_qty_mlfm_vars(dmeas):\n", + " '''\n", + " Find the quantity of MLFM variables in the measured data\n", + " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", + " \n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + " \n", + " Returns\n", + " -------\n", + " qty_mlfm_vars : int\n", + " number of mlfm_values present in data usually\n", + " 2 = (imp, vmp) from mpp tracker\n", + " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", + " 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve.\n", + " '''\n", + " # find how many mlfm variables were measured\n", + " qty_mlfm_vars = 0\n", + " for mlfm_sel in ('i_sc', 'r_sc', 'i_mp', 'v_mp', 'r_oc', 'v_oc'):\n", + " if mlfm_sel in dmeas.columns:\n", + " qty_mlfm_vars += 1\n", + " #print(qty_mlfm_vars, mlfm_sel)\n", + " \n", + " return qty_mlfm_vars" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qty_mlfm_vars = get_qty_mlfm_vars(meas)\n", + "\n", + "qty_mlfm_vars ##SR##" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8) Normalise MLFM values norm from meas and ref dataframes \n", + "\n", + "Fig 2 illustrates the loss factors model (LFM). \n", + "It uses the shape and values from dc measurements to quantify the values of each \n", + "of the loss factors going from (1) ref_p_mp to (5) meas_p_mp. \n", + "\n", + "![mlfm_data/figs/mlfm_iv.png](mlfm_data/figs/mlfm_iv.png) \n", + "\n", + "Fig 2: Loss Factors Model : \n", + "\n", + "1) ref_p_mp = Initial datasheet value at STC.\n", + "\n", + "2) Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", + "\n", + "3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", + " - norm_i_sc = measured / expected isc (purple)\n", + " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", + " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", + " \n", + " \n", + "4) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", + " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", + " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", + " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown)\n", + " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", + " \n", + " \n", + "5) These losses cause the performance to fall to pr_dc = meas_p_mp / ref_p_mp \n", + "\n", + "pr_dc = 1/ff * \n", + " (norm_i_sc * norm_r_sc * norm_i_ff ) * \n", + " (norm_v_ffv * norm_r_oc * norm_v_oc_t * norm_temp_corr ) \n", + "\n", + "Note: \n", + "The gamma temperature correction is subtracted from voc for simplicity. \n", + "In reality there will be temperature dependencies for i_sc and ff but they are smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_global_kwm2temp_modulepr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corr
date_time
2013-01-04 08:05:12-06:000.02404.70.9568560.8895291.0393760.9221160.9150110.8462880.865476
2013-01-04 08:10:12-06:000.03305.20.9332120.8691660.9930690.9267340.9261400.8492290.877238
2013-01-04 08:15:12-06:000.20476.51.0447140.9777230.9870680.9568210.9993610.8585250.950057
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" + ], + "text/plain": [ + " poa_global_kwm2 ... v_oc_temp_corr\n", + "date_time ... \n", + "2013-01-04 08:05:12-06:00 0.0240 ... 0.865476\n", + "2013-01-04 08:10:12-06:00 0.0330 ... 0.877238\n", + "2013-01-04 08:15:12-06:00 0.2047 ... 0.950057\n", + "\n", + "[3 rows x 9 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "norm = mlfm_meas_to_norm(meas, ref) ##SR##, qty_mlfm_vars)\n", + "\n", + "# show some normalised data\n", + "norm.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9) Make irradiance and temperature bins for pivot tables \n", + "(Gbin=100W/m^2, Tbin=5C)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# poa_global bin e.g. 100, 200 .. 1100W/m2\n", + "norm['poa_global_bin'] = meas['poa_global'].round(-2)\n", + "\n", + "# temp_module bin e.g. 5, 10 .. 75C\n", + "norm['temp_module_bin'] = (5 * round(meas['temp_module'] / 5,0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 10) Perform sanity checks on meas and norm data \n", + "\n", + "It's easier to sanity check and study normalised data than raw values. \n", + "1) Remove bad, missing, unwanted or outlier data \n", + "2) User defined limits may depend on data scatter and degradation \n", + "3) Can either select on values e.g. '0.5 x stdev from mean' \n", + "4) Possible to select on dates if desired. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# select by irradiance poa_global range e.g. 100-1100W/m2\n", + "meas = meas[(meas['poa_global'] >= 100) &\n", + " (meas['poa_global'] <= 1100)]\n", + "\n", + "# if there's date_time can select by it, i.e. not matrix data\n", + "### better if index is formatted as a date\n", + "\n", + "# if qty_mlfm_vars == 6:\n", + "\n", + " # not for matrix as they don't contain dates\n", + " # example\n", + " # meas = meas[(meas.index > '2016-01-01') &\n", + " # (meas.index < '2017-01-01')]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# remove specific mlfm values outside limits e.g. <0.5 or >1.5\n", + "norm = norm[((norm['pr_dc'] > 0.5) & \n", + " (norm['pr_dc'] < 1.5))]\n", + "\n", + "# remove all mlfm values outside x~3 stdevs\n", + "if qty_mlfm_vars == 6:\n", + " # only needed for outdoor data as indoor ought to be less scattered\n", + " # remove all mlfm data > x stdev usually 3\n", + " stdevs = 3\n", + "\n", + " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc','v_oc'):\n", + " norm = norm[\n", + " ((norm[lfm] - norm[lfm].mean()) /\n", + " norm[lfm].std()).abs() < stdevs\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 11) Filter only matching rows from meas and norm data\n", + "like an inner join but leave data in separate norm and meas frames" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "#drop meas rows that aren't in norm\n", + "meas_not_in_norm = ~meas.index.isin(norm.index)\n", + "meas = meas.drop(meas[meas_not_in_norm].index)\n", + "\n", + "#drop norm rows that aren't in meas\n", + "norm_not_in_meas = ~norm.index.isin(meas.index)\n", + "norm = norm.drop(norm[norm_not_in_meas].index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 12) Plot normalised MLFM data vs irradiance \n", + "\n", + "For outdoor data - \n", + "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", + "\n", + "For matrix data - \n", + "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", + "\n", + "1. Higher values are always better (unlike measured values such as \n", + " Rseries or Io where lower is better)\n", + "1. Accurate measurements and a stable module result in narrowest lines \n", + "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", + "1. r_oc tends to fall at high light levels ( \\ right) \n", + "1. i_ff and v_ff are usually fairly flat ( - ) \n", + "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", + " and/or snow (if not properly corrected). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 13) : Normalised mlfm values vs. irradiance. Fig 3.\n", + "\n", + "All traces should be thin, usually around 0.9 ± 0.1 \n", + "i_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idpoa_globaltemp_modulei_scp_mpi_mpv_mpv_ocfftemp_airrelative_humiditypressureprecipitationdnighidhisoilwind_speedpoa_global_kwm2
date_time
2013-01-04 08:15:12-06:00n05667204.76.51.102445.3492271.054842.993250.078082.155.281.91007.10353.453.031.51.000.2047
2013-01-04 08:20:12-06:00n05667238.08.41.293752.9648951.234442.907450.093281.735.380.81007.20395.165.236.71.000.2380
2013-01-04 08:25:12-06:00n05667272.410.31.489860.8225671.419442.850950.111981.475.679.71007.30435.678.241.61.000.2724
2013-01-04 08:30:12-06:00n05667209.110.71.151046.3717081.095642.325449.463181.465.978.91007.20284.372.244.91.000.2091
2013-01-04 08:35:12-06:00n05667204.310.71.123545.1791771.068042.302649.434681.345.778.61007.20264.675.547.31.000.2043
\n", + "
" + ], + "text/plain": [ + " module_id poa_global ... wind_speed poa_global_kwm2\n", + "date_time ... \n", + "2013-01-04 08:15:12-06:00 n05667 204.7 ... 0 0.2047\n", + "2013-01-04 08:20:12-06:00 n05667 238.0 ... 0 0.2380\n", + "2013-01-04 08:25:12-06:00 n05667 272.4 ... 0 0.2724\n", + "2013-01-04 08:30:12-06:00 n05667 209.1 ... 0 0.2091\n", + "2013-01-04 08:35:12-06:00 n05667 204.3 ... 0 0.2043\n", + "\n", + "[5 rows x 19 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "meas.head() ##SR## check what's there" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot normalised values vs. irradiance\n", + "fig_scatter = plot_mlfm_scatter(meas, norm, mlfm_meas_file) ##SR##, qty_mlfm_vars) add scatter\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 3 : MLFM multiplicative factors vs. irradiance\n", + "\n", + "\n", + "## 14) Convert multiplicative to subtractive losses to show on a stack plot \n", + "\n", + "LFM losses can be analysed as either \n", + "\n", + "- multiplicative \n", + "pr_dc = 1/ff * ( norm(i_sc) * norm(r_sc) * norm(i_ff) * \n", + " norm(v_ff) * norm(r_oc) * norm(v_oc_t) * norm(temp_corr) ). \n", + " \n", + " \n", + " \n", + "- subtractive \n", + "pr_dc = 1/ff - (stack(i_sc) + stack(r_sc) + stack(i_ff) - \n", + " stack(v_ff) + stack(r_oc) + stack(v_oc_t) + stack(temp_corr) ). \n", + " \n", + "Multiplicative losses are easier to understand but to represent them on a graph \n", + "it's easier to show them as a stacked plot where the values are 'translated' \n", + "so the sum of the stacked losses is shown to equate to the product of the \n", + "multiplicative losses." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dci_sci_mpi_vv_mpv_octemp_module_corr
date_time
2013-01-04 08:15:12-06:001.0447140.0159550.0482710.010.1695430.000788-0.060828
2013-01-04 08:20:12-06:001.0494370.0046120.0518600.010.1729450.000416-0.054896
2013-01-04 08:25:12-06:001.052938-0.0030060.0538710.010.175514-0.000047-0.048840
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" + ], + "text/plain": [ + " pr_dc i_sc ... v_oc temp_module_corr\n", + "date_time ... \n", + "2013-01-04 08:15:12-06:00 1.044714 0.015955 ... 0.000788 -0.060828\n", + "2013-01-04 08:20:12-06:00 1.049437 0.004612 ... 0.000416 -0.054896\n", + "2013-01-04 08:25:12-06:00 1.052938 -0.003006 ... -0.000047 -0.048840\n", + "\n", + "[3 rows x 7 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# translate multiplicative to stack losses and add to dataframe df\n", + "stack = mlfm_norm_to_stack(norm, fill_factor = ref['ff']) ##SR## ref) ###, qty_mlfm_vars)\n", + "\n", + "# show some stack losses\n", + "stack.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 15) Plot stack losses vs. measurement \n", + "\n", + "Fig 4 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", + "\n", + "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", + "\n", + "Fig 4 Stacked losses by measurement \n", + "\n", + "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", + " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", + " \n", + "- This figure shows a typical c-Si module for four clear days for \n", + " different months July to Oct in AZ. \n", + " \n", + "- In the middle of the days the high irradiance results in the biggest \n", + " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", + " (as the module heats to 60C). \n", + " \n", + "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", + " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", + "\n", + "Stack losses are indicated by their colours \n", + "(from top to bottom for mlfm4=matrix and mlfm6=ivcurve) \n", + "\n", + "`+-----+----+-------+--------+------------+--------------+` \n", + "`| 1 | 2 | 4 | 6 | " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot stack loss vs. time (or measurement) chart\n", + "fig_stack = plot_mlfm_stack(\n", + " dmeas=meas,\n", + " dnorm=norm,\n", + " dstack=stack, # dataframe measurements\n", + " fill_factor=ref['ff'], # ref=ref, # dataframe reference STC\n", + " title='title', #\n", + " # mlfm_file_name=mlfm_meas_file, # name of data file\n", + " # qty_mlfm_vars=qty_mlfm_vars, # number of mlfm measurements usually 4 or 6\n", + " xaxis_labels=12, # show this many x_labels or 0 to show all\n", + " is_i_sc_self_ref=False, # is isc self referenced?\n", + " is_v_oc_temp_module_corr=True, # is voc temperature corrected?\n", + ")\n", + "\n", + "##SR## added stack = \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 17) Fit mechanistic model to measured weather and normalised losses \n", + "\n", + "Perform a Mechanistic Performance Model (MPM) fit to the mlfm parameters \n", + "poa_global (kW/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", + "\n", + "MPM_6 = c_1 + c_2 * (temp_module-25) + c_3 * log10(poa_global_kwm2) + \n", + " c_4 * poa_global_kwm2 + c_5 * wind_speed + c_6 / poa_global_kwm2 \n", + "\n", + "\n", + "Report the fit (coeffs) and error (errs) coefficients. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_global_kwm2temp_modulepr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrpoa_global_bintemp_module_bin
date_time
2013-01-04 08:15:12-06:000.20476.51.0447140.9777230.9870680.9568210.9993610.8585250.950057200.05.0
2013-01-04 08:20:12-06:000.23808.41.0494370.9890550.9962820.9541620.9996650.8565510.955411200.010.0
2013-01-04 08:25:12-06:000.272410.31.0529380.9992891.0024130.9527451.0000380.8551040.960835300.010.0
2013-01-04 08:30:12-06:000.209110.71.0457900.9939541.0088970.9518680.9870900.8556960.949448200.010.0
2013-01-04 08:35:12-06:000.204310.71.0428340.9911451.0079300.9506010.9865220.8557290.948901200.010.0
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poa_global_kwm2temp_modulepr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrpoa_global_bintemp_module_bincalc_pr_dcdiff_pr_dc
count1433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.000000
mean0.68850028.9060010.9250180.9371760.9418410.9414040.9703760.8335040.980062688.34612728.8729940.9244200.000598
std0.28205910.5393650.0836880.0853080.0754530.0057570.0247850.0138270.023198284.06882610.6034700.0725100.055453
min0.100300-3.9000000.5284790.5389200.5988010.8761070.8742230.8069930.887511100.000000-5.0000000.000000-0.187850
25%0.46680021.9000000.9025650.9412020.9422420.9377430.9524030.8217470.971589500.00000020.0000000.900437-0.016455
50%0.75460030.1000000.9315790.9633510.9651740.9405620.9727300.8316060.989182800.00000030.0000000.939210-0.002625
75%0.93190035.9000000.9802690.9832740.9836060.9449070.9877610.8451300.996239900.00000035.0000000.9662300.017487
max1.07830053.7000001.0858211.0330451.0339320.9619081.0269110.8970881.0067321100.00000055.0000001.0383691.042834
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" + ], + "text/plain": [ + " poa_global_kwm2 temp_module ... calc_pr_dc diff_pr_dc\n", + "count 1433.000000 1433.000000 ... 1433.000000 1433.000000\n", + "mean 0.688500 28.906001 ... 0.924420 0.000598\n", + "std 0.282059 10.539365 ... 0.072510 0.055453\n", + "min 0.100300 -3.900000 ... 0.000000 -0.187850\n", + "25% 0.466800 21.900000 ... 0.900437 -0.016455\n", + "50% 0.754600 30.100000 ... 0.939210 -0.002625\n", + "75% 0.931900 35.900000 ... 0.966230 0.017487\n", + "max 1.078300 53.700000 ... 1.038369 1.042834\n", + "\n", + "[8 rows x 13 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", + "mlfm_sel = 'pr_dc' \n", + "\n", + "# FIX THIS WARNING,\n", + "# SettingWithCopyWarning:\n", + "# A value is trying to be set on a copy of a slice from a DataFrame.\n", + "# Try using .loc[row_indexer,col_indexer] = value instead\n", + "# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\n", + "\n", + "cc, coeffs, ee, errs = mlfm_fit(norm, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", + "\n", + "# Fix a bug with fit routine which gives a\n", + "# finite cc[4] even if all the ws data is 0\n", + "# this won't matter until cc is applied to other\n", + "# data with some ws <>0 when it will give bad results\n", + "if np.mean(meas.wind_speed) == 0:\n", + " cc[4] = 0\n", + " c_5 = 0\n", + "\n", + "norm['calc_' + mlfm_sel] = cc \n", + "\n", + "norm['diff_' + mlfm_sel] = norm[mlfm_sel] - norm['calc_' + mlfm_sel] \n", + "\n", + "norm.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['poa_global_kwm2', 'temp_module', 'pr_dc', 'pr_dc_temp_corr', 'i_sc',\n", + " 'i_mp', 'v_oc', 'v_mp', 'v_oc_temp_corr', 'poa_global_bin',\n", + " 'temp_module_bin', 'calc_pr_dc', 'diff_pr_dc'],\n", + " dtype='object')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# cc\n", + "# date_time\n", + "# 2013-01-04 08:15:12-06:00 0.877861\n", + "# 2013-01-04 08:20:12-06:00 0.899799\n", + "\n", + "# coeffs\n", + "# array([ 1.27323780e+00, -4.79820818e-03, 6.18587637e-01, -2.83390574e-01,\n", + "# 0.00000000e+00, -2.74667887e-10])\n", + "\n", + "# ee\n", + "# date_time\n", + "# 2013-01-04 08:15:12-06:00 -0.166853\n", + "# 2013-01-04 08:20:12-06:00 -0.149638\n", + "\n", + "# errs\n", + "# array([0.03774048, 0.00015903, 0.08827109, 0.04484896, 0. ,\n", + "# 0.00641688])\n", + "\n", + "#mlfm_meas_file\n", + "#norm.columns\n", + "#fit\n", + "norm.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 18) Show residual fit vs. measured for MLFM parameter " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_fit(dnorm, fit, title):\n", + " \n", + " ''' \n", + " Scatter plot fit to normalised measured\n", + "\n", + " Parameters\n", + " ---------- \n", + " \n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + " \n", + " fit : string\n", + " name of fitted variable e.g. 'pr_dc'.\n", + " \n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " '''\n", + " \n", + " fig, ax1 = plt.subplots()\n", + "\n", + " plt.title(title)\n", + "\n", + " plt.ylabel('fit ' + fit + ' * poa_global_kwm2')\n", + " ax1.set_ylim(0, 1.2)\n", + "\n", + " plt.xlabel('meas ' + fit + '* poa_global_kwm2')\n", + " ax1.set_xlim(0, 1.2)\n", + "\n", + " plt.plot(\n", + " dnorm[fit] * dnorm['poa_global_kwm2'],\n", + " dnorm['calc_' + fit] * dnorm['poa_global_kwm2'], ##SR##\n", + " 'c^',\n", + " label = fit\n", + " )\n", + "\n", + " # plot 1:1 line to show optimum fit\n", + " plt.plot((0,1.2),(0,1.2), 'ko-')\n", + "\n", + " plt.legend(loc='upper left')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fig 6 : fit_mlfm_sel * poa_global vs. measured_mlfm_sel * poa_global" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot fit vs. measured, include a 1:1 line for comparison\n", + "fit_plot = plot_fit(norm, mlfm_sel, 'fit ' + mlfm_meas_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 19) Plot heatmap of mean residual vs. tenp_module and poa_global\n", + "\n", + "Show a heatmap of the average residual (meas - fit) error \n", + "for each irradiance (100W/m^2) and tmod bin (5C)." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_heatmap(dmeas, dnorm, fit, y_axis, x_axis, z_axis, title):\n", + " '''\n", + " Plot a heatmap of Z vs. binned X and Y axes.\n", + "\n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measured weather data\n", + " 'poa_global', 'temp_module', 'wind_speed'\n", + " and measured electrical/thermal values\n", + " 'i_sc' .. 'v_oc', temp_module.\n", + "\n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + "\n", + " fit : string\n", + " fitted parameter e.g. 'pr_dc'.\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global_bin'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module_bin'.\n", + "\n", + " z_axis : string\n", + " value as a colour surface plot e.f. 'diff_pr_dc'.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " '''\n", + "\n", + " df_piv = pd.pivot_table(\n", + " dnorm,\n", + " index=y_axis, # e.g. 'temp_module_bin'\n", + " columns=x_axis, # e.g. 'poa_global_bin'\n", + " values=z_axis, # value to aggregate\n", + " fill_value=0, # fill empty cells with this ?\n", + " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", + " margins=False, # grand totals hide\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " # force z limits to be -2% to +2% if desired\n", + " df_piv = df_piv.clip(lower = -0.02, upper = +0.02)\n", + "\n", + " im = ax1.imshow(\n", + " df_piv,\n", + " cmap='RdYlBu',\n", + " origin='lower'\n", + " )\n", + "\n", + " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", + "\n", + " #Y AXIS : show only 1 of each y_skip labels\n", + " y_ticks = df_piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = df_piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " # X AXIS : show only 1 of each x_skip labels\n", + " x_ticks = df_piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + " \n", + " ax1.set_title(title)\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid( color='k', linestyle=':', linewidth=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 20) Residual MLFM fit heatmap vs. poa_global and temp_module. Fig 7 " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap\n", + "heatmap_plot = plot_heatmap(\n", + " dnorm=norm,\n", + " dmeas=meas,\n", + " fit=mlfm_sel,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis='diff_' + mlfm_sel,\n", + " title='residual ' + mlfm_meas_file\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 21) Read in complete (G,T) Matrix to fill with MLFM predicted values \n", + "\n", + "Read in a matrix with complete values of \n", + "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", + "to predict all MPM values " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpoa_global_kwm2
id
1matrix100000.1
2matrix100500.1
3matrix1001000.1
4matrix1001500.1
5matrix1002000.1
..................
176matrix12005001.2
177matrix12005501.2
178matrix12006001.2
179matrix12006501.2
180matrix12007001.2
\n", + "

180 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2\n", + "id \n", + "1 matrix 100 0 0 0.1\n", + "2 matrix 100 5 0 0.1\n", + "3 matrix 100 10 0 0.1\n", + "4 matrix 100 15 0 0.1\n", + "5 matrix 100 20 0 0.1\n", + ".. ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2\n", + "177 matrix 1200 55 0 1.2\n", + "178 matrix 1200 60 0 1.2\n", + "179 matrix 1200 65 0 1.2\n", + "180 matrix 1200 70 0 1.2\n", + "\n", + "[180 rows x 5 columns]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in the complete matrix data\n", + "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", + "\n", + "matr['poa_global_kwm2'] = matr['poa_global'] / 1000\n", + "\n", + "matr\n", + "\n", + "#matr['poa_global'] = matr['poa_global'] /1000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 22) Predict performance from MPM fit coefficients \n", + "\n", + "1. generate predicted mpm data \n", + "2. create a pivot table mpm(g,t) \n", + "3. show as a heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.27323780e+00, -4.79820818e-03, 6.18587637e-01, -2.83390574e-01,\n", + " 0.00000000e+00, -2.74667887e-10])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#105.68%\t-0.42%\t12.85%\t-5.71%\n", + "coeffs" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpoa_global_kwm2pr_dc
id
1matrix100000.10.746266
2matrix100500.10.722275
3matrix1001000.10.698284
4matrix1001500.10.674293
5matrix1002000.10.650302
.....................
176matrix12005001.20.862194
177matrix12005501.20.838203
178matrix12006001.20.814212
179matrix12006501.20.790221
180matrix12007001.20.766230
\n", + "

180 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2 pr_dc\n", + "id \n", + "1 matrix 100 0 0 0.1 0.746266\n", + "2 matrix 100 5 0 0.1 0.722275\n", + "3 matrix 100 10 0 0.1 0.698284\n", + "4 matrix 100 15 0 0.1 0.674293\n", + "5 matrix 100 20 0 0.1 0.650302\n", + ".. ... ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2 0.862194\n", + "177 matrix 1200 55 0 1.2 0.838203\n", + "178 matrix 1200 60 0 1.2 0.814212\n", + "179 matrix 1200 65 0 1.2 0.790221\n", + "180 matrix 1200 70 0 1.2 0.766230\n", + "\n", + "[180 rows x 6 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# populate pivot table from predicted mpm data\n", + "matr[mlfm_sel] = mlfm_6(matr, coeffs[0], coeffs[1], coeffs[2], coeffs[3], coeffs[4], coeffs[5])\n", + "\n", + "#matr[mlfm_sel] = mlfm_6(matr, coeffs)\n", + "\n", + "matr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 23) Plot heatmap of predicted MLFM values vs. temp_mod and poa_global bins" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", + " vmin=0, vmax=1.2, levels=5):\n", + " ''' \n", + " Plot filled contour plot Z vs. X and Y bins.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : dataframe\n", + " measured or noralised data containing weather columns\n", + " (poa_global, temp_module and wind_speed).\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module'.\n", + "\n", + " z_axis : string\n", + " measured value as a colour surface plot.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " vmin, vmax : float\n", + " minimum and maximum values for contour chart ###\n", + "\n", + " ''' \n", + " \n", + " piv = pd.pivot_table(\n", + " df,\n", + " index=y_axis,\n", + " columns=x_axis,\n", + " values=z_axis,\n", + " fill_value=0, # fill empty cells?\n", + " aggfunc=[np.mean], # min, np.sum, len->count\n", + " margins=False, # grand totals\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " piv = piv.clip(vmin, vmax)\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " cs = plt.contourf(\n", + " piv,\n", + " cmap='RdYlBu', # or 'nipy_spectral',\n", + " # origin='lower'\n", + " # nchunkint=1,\n", + " levels=levels,\n", + " vmin=vmin,\n", + " vmax=vmax\n", + " )\n", + "\n", + " cbar = fig.colorbar(cs, ax=ax1)\n", + " cbar.ax.set_ylabel(z_axis,\n", + " rotation=90,\n", + " va='bottom',\n", + " labelpad=+30)\n", + "\n", + " plt.title(title)\n", + "\n", + " y_ticks = piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + "\n", + " # show only 1 of each y_skip labels\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " x_ticks = piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " # show only 1 of each x_skip labels\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid( color='k', linestyle=':', linewidth=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpoa_global_kwm2pr_dc
id
3matrix1001000.10.698284
4matrix1001500.10.674293
5matrix1002000.10.650302
6matrix1002500.10.626311
7matrix1003000.10.602320
.....................
176matrix12005001.20.862194
177matrix12005501.20.838203
178matrix12006001.20.814212
179matrix12006501.20.790221
180matrix12007001.20.766230
\n", + "

156 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2 pr_dc\n", + "id \n", + "3 matrix 100 10 0 0.1 0.698284\n", + "4 matrix 100 15 0 0.1 0.674293\n", + "5 matrix 100 20 0 0.1 0.650302\n", + "6 matrix 100 25 0 0.1 0.626311\n", + "7 matrix 100 30 0 0.1 0.602320\n", + ".. ... ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2 0.862194\n", + "177 matrix 1200 55 0 1.2 0.838203\n", + "178 matrix 1200 60 0 1.2 0.814212\n", + "179 matrix 1200 65 0 1.2 0.790221\n", + "180 matrix 1200 70 0 1.2 0.766230\n", + "\n", + "[156 rows x 6 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", + "\n", + "matr2 = matr[matr['temp_module'] >= 10]\n", + "\n", + "matr2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 24) Contour plot of predicted mlfm_sel + vs. poa_global and temp_mod. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bGBhUeqy0tBSA9MM9ePBg9OrVCwcPHuScrJubm0ID9ZaWlgr1q6SkBCEhITAyMsKzZ89U/oMl5TaTMTIygpmZGfdcZtft27e5ghQZMj1Vtcu2oSilpaUYMGAAvL29K5yzsbFRuz8mJiYV+lf2NWrcuDFevHghd01RUREyMzMr/KAqS48ePQBIfzw1adIEjo6OAIB27drJXdeuXTskJiYCQJXXuLm54eLFixU0rl+/jri4OBw5cqRaG6tC9rlWZ5pE2WrKTp064fnz51i3bp3CjhMAd3+bNm0gkUjw0UcfISIiQu4HjSIYGhriww8/xKlTp/D48WN06NCBO1fda3v06FGldMrSpk0bREdHK31faWkppk6dWqGADABXrPTNN9/gzz//xI4dO+Dq6gpLS0ssXrwYb9++lbte0e8QGY0bN0ZqaqrcMdlz2Wf62rVrCAoK4orDZH8TTk5O8PLywv79+5XSLE9Vf69GRka4cOECQkNDcfXqVQQGBmL58uX4/fff8eGHHwIApk+fjjVr1uDHH3/EiRMnUL9+fQwdOlSt/tQ2NF5V++TJE7x+/RqbNm1Cv3790LZtW7x580buy1f25VxVubcirF27FrGxsQgJCUFYWBh++OEHhe4rW9qflZWFp0+fom3btlVe37lzZwBAUlISWrduLfdwdnYGIP2yef36NffLGADS09MRGxtbZbvt2rWDmZkZLl26VOU1JiYmFV4jT09PREdHo0mTJhX6Y2dnx/UnJCRE7r7yz1WlZ8+e+Oeff+SmjFy5cgWlpaXVZhPCw8MBAE2bNgUAvP/++wCAmJgYuetiYmLg5OQEQGqrubl5tdeUZf/+/XB3d8d7772ntF1CUVpaioKCApXvnzVrFnJzcyv9oaQI+/btw4QJE9CvXz/uPQCAFi1aoFmzZgq/tooyZcoUXL9+vcopLVVNR/H09MTDhw/h7Oxc4XMtyx7cunULkydPxvjx4+Hh4YFWrVpV+zemKD179uQ+wzIuXrwICwsLdOrUCYB02k5ERAT3OHDgAADg0qVLWLNmjcrainwPGBgYoGvXrlixYgVu3bqFPn364NChQ9z5iRMnQiwW4/z58zh69CgmTZrE5sgqC59538qqQAcMGECmT58ud2zIkCFk8uTJhJD/ijM+++wz8uzZM3L16lXi6elJDAwMyKFDhwgh0vEkKysrsnTpUpKSksKNR1U1LlD+eFBQEDEyMuIKkv744w9ibGxM/vnnnyptkRX+dO7cmdy8eZM8fPiQjBgxgtjb23OVc5VV+hFCyMyZM4mDgwM5cuQIiYuLIxEREcTPz498//33hBDpGKOHhwfp2rUruXv3LgkPDyeDBw+usTho5cqVxNLSknh7e5OYmBgSERFBNm/ezJ0fPnw46devH0lMTCSvX78mJSUlJDU1lTRu3JgMHjyY3Lp1i8THx5Pg4GCyYsUKEhISQgiRVicaGBiQFStWkJiYGHLq1CnSsmVLhatqy3L06FFS9mMlFotJ06ZNyQcffEAiIiLI9evXiZOTk9z41KFDh8iRI0dIVFQUefbsGTl06BCxtbWtMO4ybtw44ujoSM6fP09iY2PJ8uXLiampKXn8+DF3zdKlS4m1tTU5ceIEefbsGfnxxx+JoaEhuXbtmlxbaWlpxNjYmPz8889V2lcT1Y1bpaSkkPDwcHL+/HkCgBw6dIiEh4eTlJQU7pqvvvqKe0+ioqLI1q1bSZ06dcjOnTsV0q/q87dz507SsGFD8vbtW+5YeHg4CQ8PJyNGjCDdunXjnlfV1tdff03q169PQkNDuWN79+4lRkZGZO/eveTZs2fk8OHDxNzcnBw8eFCh/lb2ehUWFnKFcdu2bSOhoaEkISGBXLhwgXz00Ufkp59+qrR/jx8/JlZWVmTSpEnk7t275Pnz5+T69evkyy+/JCKRiBBCyNixY0mbNm3I3bt3SXR0NPHy8iJ169Ylffr04dqp7DurJpKSkoi1tTWZOXMmiYqKIn/++Sdp0KABWbZsmVK2EyItcgoPDydz584lzZo1494XWWEkIYS0adOG7Nmzh3te3fdASEgIWb9+Pblz5w5JTEwkV69eJY0bNyarVq2S0x09ejTp2LEjV6zFUA6NO05CpFVdrVu3JqampqRjx46co5M5TkIIOXz4MHFyciJ16tSpMB2lPGWPZ2RkkKZNm5Kvv/5a7po5c+YQJycnuWkSZZH9oV66dIm4uroSExMT4unpKfdFUtUXV3FxMfnhhx9ImzZtiLGxMWnYsCHp3bs3VzhAiLRoZNCgQcTU1JQ0adKE/PTTTzVORyktLSU//fQTcXFxIcbGxsTe3l7OuYSGhpJ3332XmJmZyRUIJCQkkEmTJhFbW1tiYmJCmjdvTiZPnsxNByGEkBMnTpBWrVoRExMT0rVrV3L69GleHCchhDx9+pQMGjSImJubkwYNGpA5c+bIle0fPnyYtG/fnlhaWhILCwvi5uZGfvjhB7miI0IIycnJIQsWLCB2dnbEysqK9OrViysgK/vaf/fdd6RJkybE3NycvPvuu9y0kbJs2bKFWFpayjkXZanOcVY2/QKAXPHW+PHjSfPmzYmJiQlp2LAh6dGjBzl27JjC+lV9/sRiMalfv77cl2VlfSn7PlXW1rfffktsbGzkfmDu3r2btGrVipiamhI3Nzelipmqer2KiorITz/9RDp37kwsLCyItbU16dixI9m0aRN58+ZNlf17+PAhGTlyJKlXrx4xMzMjzs7OZPbs2SQjI4MQInVwgwcPJhYWFsTBwYGsXr2azJw5U23HSQgh//zzD+nevTsxNTUljRo1IsuXLyfFxcVK296iRYtK35eyxT3lPzfVfQ9ERUWRYcOGkUaNGnF/60uWLJFzxIQQ7u+7fOEmQzEMCCk3aMcAIF2VZdasWSguLtZ0VxgMBoOhRWh8jJPBYDAYDF2CiuOUrRkqexgZGeGLL77gzl+7dg2urq6wsLBAv379uCpJBoM2wcHBFT6vZR/BwcFU+pGUlFRtP44dO0alH4qiLa+buuiLHbTw9vaGp6cnTE1NMWPGjGqv/fHHH+Hg4AAbGxvMnDlTrgguISEBw4cPR/369eHg4IAFCxZodbaPeqo2JycHjRo1wl9//YXevXsjPT0dzs7OOHDgAEaMGIHvvvsOwcHBerFIO0P3yMvLw8uXL6s836RJE5ibmwvej+LiYiQkJFR5vlGjRrC2tha8H4qiLa+buuiLHbQ4deoUDA0NcenSJeTl5cHf37/S6y5duoRp06bh+vXrcHR0xOjRo/Hee+9xmyIMHz4c9vb22LdvH7KysjBo0CDMnj0bX375JUVrFIe64zx8+DDWrVsHkUgEAwMD+Pr6wt/fH7dv3wYgday2trYIDw+Hq6srza4xGAwGQwVWrVqFf//9t0rHOWnSJDg5OWHz5s0ApFnGyZMnc/Nf27Ztix07dmD48OEApHNws7Oz1Z7vKhTUxzgPHz6MadOmcRN4o6Oj5VYbsrS0hLOzs0qTohkMBoOhfZT/nvfw8EBaWhoyMjIAAAsXLkRAQAByc3Px8uVLXLhwQasXZaA66zUpKQk3b96En58fd0wikXAT8WXY2NhALBZXuN/X1xe+vr4AgAdhYbCBEeqjDl6gEA4wRimAdBShCUyQgWLUgQFsYIQkFMARJigCQRaK0RgmSEcxTGEAaxghAQVoDlPkoxTZKIEDjPEKRbCAIaxghHgUoCVMkYYiGACwhzFSUYS6MIIZDJGEAjjBFGKUoAAEtqiDFBSiHurAGAZIRiGawxRvUYJiEDREHbxEIWxhDEMAqShCM5jgDaQ5fZlN5jCENYwEtUmCEuSilLPJEIAtjNWyydLcAIn5hXCxMMWrQqlN9iZ1EJtbgBZmJiglBC8Li9Da3BSivALY1DGCrXEdPM3Nh7O5KQpLCdIKi9HK3AQvC4pgbmiIBsZGeJyTjzYWZsgpKcWb4mK0MDPBi/wiWNcxRL06RojKyUd7SzNkFZdAXFyKZmbGSMwvRP06dWBpZIiY3HzYGtdBHQMD5JWWoompMZ7nFaKRSR2YGBpAlFcAVwszpBcVo4gQNDYxxrO8AjQxMYahgbxNhqZGaGxthsevxHBuYIESAiS9zYWrrTVeZufB2NAQ9lameJSWjQbmxrAxM0aKOB/vNLRC0ttcWBjXga2FCSJT36JDo7oQFxQjPbcQzg0skfAmFzZmdVDf3AThKW/RqbEN3uQV4m1+MZzqW0CUmQNbCxNYm9bBo7RseDjYID23ELlFxWhuY4HoV9loUc8CpnUM8fS1BB0a1cUrSQGKSkvRpK45nqaL0dzGAkYGgCgzF+3srZEizgcAhW1ytbNCQXEpZ9PTdDFsLUwFsykuQ4LG1macTbYWJjAyMBDUpvLvk52FKSxNjASzqfz7lFRYB+np6bx899at1w7FRTk1X1iOlk5WcqnpOXPmYM6cOUq3I5FIuJXKgP9WLROLxWjYsCH69OmDX375BXXr1kVJSQmmT5+OUaNGKa1DC6qO88iRI+jVqxdatmzJHbOysqqwEXF2dnal4zdl37QmBqbYBidB+1ueeOSjJcxqvlBH9fjQ7Pqecc0XlSFKkof2VvTGjNTVazLESel7HiS/xbuONjVfyBO09TShWRtsfP9cas0XKUhxUQ5c2y9T+j7DkoO4f/++2vrlv+dl/7e2tkZpaSmGDBmCuXPn4vbt25BIJJg5cyaWLVuGrVu3qq0tBFRTtUeOHMH06dPljrm5uSEyMpJ7npOTA5FIBDc3t2rb0sQ8GjPKqrT11NVU1mkCgKURXRtV1WsyxEklpwkA1qZGNV/EI7T1NKFZG2zUJ8p/z0dGRqJRo0Zo2LAhMjMz8eLFCyxYsACmpqZo2LAhPv30U/z1118a7HH1UPvWun37Nl6+fIlPPvlE7vjo0aMRFRWFwMBA5OfnY/369XB3d6+xMCgVRUJ2t1K2o+pqO33QU0dTFacJAHNiklS6T1WU0ZM5S1Udpoyxx9X/xa7NeprQrA026gLFxcXIz89HSUkJSkpKkJ+fX+k0kmnTpsHPzw+PHz/GmzdvsHHjRm76iq2tLVq2bAkfHx8UFxcjKysLhw8frrDTljZBrap27ty5yM3NrXQnhatXr2LBggVITExEt27d4O/vX+PC0a0MzLARLQTqLUMZVHWa2oq6jpLB4IP3z6XykiYFAAvLFoKkateuXVthW701a9Zg5syZaNeuHR4/fsxt+7hz50788MMPyMvLw9ixY7Fv3z5uB5+IiAgsWrQIkZGRMDIyQr9+/fDzzz/D3t5e6T7TQGeX3KtvUAc/w5mqZiDSMRa2equnqqY6jnP3i1f4shm9P47q9IRymBuDYrGqr0vNF+qoniY0a4ONuuA4aytsLxmGWuh6tMmiSwaDoSw6G3GyVK3m0WWnyRwmQ9thEaf2orOLvL9AIXXNxYjXaz1lNPlymoMi4mq+iEeGxSZQd5rue4L0Wk8TmrXBRob2orOpWgfQj3aWoIle6ymqyWek6dumOW9tVYfMWQZ60h0zBoDASZ56racJzdpgI0N70dmIs1QDmvmUVWnrKaLJd3o2p0R4G8tGmOKCEsH1ykNbk9moP5oM7URnHWe6BuZxHkCaXuvVpCnEmOaq+GTe2yxL+bTsgnMPBdWrDNqazEb90WRoJ6w4iKEQulYIxIp/GLoOKw7SXnQ24swA/U1Of8VrvdarSlNIp7klkb/1OGVU5zSXX3rMu15N0NZkNuqPJkM70dnioDowoK5ZH3TXqqStV5mm0JGmvTF/H0FFoszG1nQXzdeEJrNRfzQZ2glL1TKqRJfSsyw1y9A3WKpWe9HZVG0SCqhrzodIr/XKatJymj3DYtS6X9lF2FvtuKqWnirQ1mQ26o8mQzvR2YizuYEpvqe8H+cbFKM+xew2bT2Z5pD36O2PmVZYhEYmqjlpVaLMZHE+HCmn3GhrMhv1Q5NFnNqLzkacRaDv71Mor1ZEWw8A6rWlO3c0IU95G9XZ6utZRo5K96kDbU1mo/5oMrQTnXWcWRqoqj2FDL3WA4Dd/77Saj11xzI3BcWqdb8uaDIb9UeToZ3obKqWFQfxi7YXArHiH0Ztg6VqtRedjTjTNRBx6uvKQWWd5qrnwq7kUx5F9Ph0mvPP0l/9hbYms1F/NBnaiU45Tl9fX3h6esLT0xP5KEUg0gFId/RIQSHikY+VSAQgnch/HpkApJWib1CMx8jFRrwAIHVK15EFAPBCHPJQigeQYDteAgC8kYIQZAMAJkOaoiEg8EYKAGA7XuIBJMhDKbwg3eHjOrI4Z7cRL/AYuXiDYq5S9TwyuQUGViIR8chHCgq5HUkCkS5nU0MYCW5TauscfBX3LwBgztNE1DEAJCUl6HjvCQAgIC2Tc26To+Nx920O0gqLuGpYv+R0bhGDUY9EiJLkIT6vgNv1ZPeLV9j9QpqOHRQRh/i8AkRJ8jDqkfQ1eZFfCL9kqc09w2KQVliEu29zMDla+ppsLsyGX1gSAMB+y0WIC4pxPiYNY0+EAgBmBIbj5COpfRbrzgMATj56iRmB4QCAsSdCcT4mDeKCYthvuYh3HevBLyyJ+xIc4v8PbiVkIFmcz1VN7rr9nJvs3sM3GA+S3yIuQ8LtjrExKBYb/z9t574nCHEZEjxIfosevsEApBPld91+DkBaiencwBK3EjIwxP8f6Xt39iGvNgGQsyk4IVNwm5LF+XI2xaRLBLWp/Pv0rmM9wW0q/z61s7cW1Kby7xNDe2Gp2lqOtqZoWWqWUdthqVrtRWdXDkrQwDxOL8TBD+/ojV5lTrPjvSeI6NpWME1F9IR0mvZbLuLVt0MFa18bNPnWMx1Y8+fBZvAuvL28kDdNvvQKrj7hTVMTnx2GdqKzEaeTgRk2U44481AKc4rZbaH1KnOckpISWBnRW+qvrB6NKFNcUAxrU7q/F2lrKqOniFNUSDO3ENYWJry0pSm9mpws7feRRZzai85GnJrYq/IJcvEurPRCr6oU7d3sXAyoby2IZnV6tFKztxIy8EGbRlS0NKUp0+PLKSpCUPgLjOjprNN6Nb1el0NEGNHTmdcolqGb6KzjzAb9TWWv4y1VxymUXnXjmifTMqk6zj9JPqYN6UBN7+CDJOqOk4Zm2S99/ytPMGZ+X0H1yvPLmUiqjpO2XlnNqhysNjvUOpbGqOfpqPR92XcF6IweoLOpWlYcpDraUhDECoBUh2Y0yVAPVR0qn6nauvat4fnJdqXvy767kaVqK0GnpqOU5RWKqGvKpqLosl5NTlM2NUVoZE5TVrpPC9p6fGmaDmwr96iOyevOqa2nLLQ1dcnG8u+dIu8hQ7vR2VSthQZ8fidY6rUeAPSrL3wqumykOczFXnC9stDWU1VTnS/WD3rQTWFqQlMfbNTFlC9DCkvV1iK0IUXL0rOVwyIQRnm6LrrBUrVais6mauM1MI9TttqOLuop6jTfuRPNm2Z5KnOashVXaEFbrypNIdN2Ru8r/wWpa5q1wUaG9sIizlqCpqPN2h5psoiSoSws4tRedDbilGhgOopsnVdd01PGaZ5Jz+JFsyzVOU3ZWp+0oKkniyYDKf80PX6F/hgZbc3aYCNDe9FZx5mrgQUQwkF3I1vaegBw442E1/ZqijQvxNLd/5OGXvn06/nbIsE1y0JbTxOatcFGhvbCUrV6jiZTtLUpPctSsQy+Yala7UVnI85UDczjlG3PpSt6qjjNOU8T1dJUFtkWTbqqp0hxz8hlp3jVrAnaeprQrA02MrQXnZ3HWRf0FiKX0R82eq0HAOMbNeClHUWjzZnvNudFT1H40lMmwpw90oMXTW3V04RmbbCRob3orOM000Cw3BYWOqOnaoq2W131bVQmRdvbqaHaesqgjp6q6di+nZqprKkLeprQrA02MrQXnU3VJmlgHucC0C0OoK0HAL3C1Js7quy4pvPOq2rpKYsqeurOtWw6ykfle3VBTxOatcFGhvZC1XEGBASgbdu2sLS0hLOzM4KDgwEA165dg6urKywsLNCvXz8kJtY8zuYEU6G7WwGam1iro6dOQZA6m1irUgxEe2NgZfT4WpyA5gbPmtDThGZtsJGhvVBznFeuXMGyZctw6NAhiMVi3Lp1C61atUJ6ejrGjBmDDRs2IDMzE56enhg/fnyN7Yk1sq1YltbrqVtFG5CWqdJ9qlbQ+oUlqXSfqiiix/dqPr5nInlrSxv1NKFZG2xkaC/UHOeaNWuwevVqvPfeezA0NESTJk3QpEkTnDp1Cm5ubvjkk09gZmaGtWvXIjIyEk+fPq22vQLQn0XznHJ6mLYeAETl5Ct9jzrTTh4kZ6l8L596Qu5aEfY0jfc2tUlPE5q1wUaG9kLFcZaUlOD+/ft4/fo1WrdujaZNm2LBggXIy8tDdHQ0PDz+q1aTpXGjo6tfM9VWA3VNs0B3A2Rl9fiYs7mxlXKb3ao7V/PnEe5q3a+uHo0tnvYvHSxo+5rW04RmbbCRob1QcZxpaWkoKirCH3/8geDgYERERCA8PBwbN26ERCKBjY38tAsbGxuIxeIK7fj6+sLT0xOenp5IRAECkQ4AWIx4pKAQ8cjHSkjHR3/Fa5yHNO04HyK8QTEeIxcb8QIAcABpXCrUC3HIQykeQMLNnfRGCrfknWyx9W8Qz+2RuR0v8QAS5KEUXogDIE2tHoD0V+lGvMBj5OINijH//4t8ziMTv+I1AGAlEhGPfKSgEIsRDwAIRLqcTd/9/zWK2rTqeTKXau147wkkJSW49kbMzc38Ku5fbkk92WLuZ9KzuD045zxNxPDIOEhKStDxnnR5sYC0TKx6nix9HaLjcfdtDtIKi9AzLAZNhjhh1+3nWH7pMQCgh28wHiS/RVyGBO57gqSvQ1AsNgZJXz/3PUGIy5DgQfJb9PCVjm+/s/Madt1+DgBoteMqksX5uJWQgSH+/0jtPPuQS6/ab7kIcUExzsekcfMxZwSGc8voyRZTP/noJbcH5tgToTgfkwZxQTHst1zEEP9/4BeWhC8fvIDpwLbo/0UAgsKTkJwu4Yo/dgaEYon3DQBAF6+jCItJRWxSJlwn+gEA1h0MwbqDIQAA14l+iE3KRFhMKrp4HQUALPG+gZ0B0v41HeWDnvOOISg8Cf2/CAAAzN16mUv72QzeBXFuIc6GiLh5gpPXneOWd5MtLH78yhNuP8iRy07hbIgI4txC2AzeBUCaRpy79TIAoMHQ3YLblJwukbOp6SgfQW0q/z71/yJAcJvKv0+9Pz8uqE3l3yeG9kJl5aA3b96gQYMG8Pf3x/Tp0wEAgYGB2LhxI3r37o2ioiLs3buXu75Dhw5Yu3Ytxo4dW2WbjQ1MsAMthe66HI+Ri3YUp6Qoo8fXCkF33+agm03N+4DytSrQrYQMqlNS/mloib6d6M4dDQpPoqpJW08TmrXBRrZykPZCJeKsX78+mjZtCgMDgwrn3NzcEBn536B7Tk4ORCIR3Nzcqm3TGBXbEprGMNFKPT6X1XMyr1mTz6X0Wjeks1m3LCXr0oyfBR6UgbYms1F/NBnaCbXioE8//RR79uzBq1ev8ObNG/z000/48MMPMXr0aERFRSEwMBD5+flYv3493N3d4erqWm17ySik1PP/WAW6y9HR1gOAMY+eV3ue7/Vne/n+zWt7lVF2DLPrrKOC65WHtiazUX80GdoJtUXei4qKsHDhQhw/fhxmZmYYN24ctm7dCjMzM1y9ehULFixAYmIiunXrBn9/fzg5OVXbHlvkXQrNRdx1bdF2tvA6Q5dhqVrthVrEaWxsjL179yIrKwupqanYvXs3zMzMAAADBw7E06dPkZeXh6CgoBqdJgC81cA8Tllhjr7qAYBfcnqlx4VymrLCIL6pymnKikFoQluT2ag/mgztRGeX3CvWwDzON5SddU16QkSbr4qKKxwTMtJMESs/b7Q6appekpzO736jikBbk9moP5rajre3Nzw9PWFqaooZM2ZUe+2PP/4IBwcH2NjYYObMmSgo+G+eemZmJkaPHg1LS0u0aNECx48fF7jn6sH249RhaKRpdSk9y1KzDH1CF1K1p06dgqGhIS5duoS8vDz4+/tXet2lS5cwbdo0XL9+HY6Ojhg9ejTee+89fP/99wCAiRMnorS0FH5+foiIiMAHH3yA27dv11gkqil0NuJ8qYHioJWUi3Wq0xPKaY569N/C8jScpmw+pzoos4iBbA4fTWhrMhv1R1PbGTNmDEaNGoWGDaufUnb48GF4eXnBzc0N9evXx3fffcc52ZycHAQGBmLDhg2wsrJCr169MHLkSBw9qr2vt85uK2YLekUxMrRl5SAhI82NLaUrB9GKNL0/VG/lIGWjzH1LB6mlpwq0NZmN+qPJFxZ1TdF5QCul7/vt3Gt4enpyz+fMmYM5c+Yo3U50dDQ++ugj7rmHhwfS0tKQkZGBpKQkGBkZwcXFRe78zZs3ldahhc46Tk2EyrT3ANXEnqOWRoZU07PWpqptSK5qWtZagXmqfENbk9moP5qaxs7Ojpd0cfkV4mT/F4vFSq0epy3obKo2FUXUNWXL8WlST+hxzc9fpAjafnnGHlf+j1KdscyRy/6n8r26osls1B9NfcHKygrZ2dncc9n/ra2tK5yTnbe2tqbaR2XQWcfZjPIqPgCoL/FHW6/JECc8/KIvVU1l9dQtAHp6wkut+3VBk9moP5r6QvkV4iIjI9GoUSM0bNgQLi4uKC4uRlxcnNx5bS0MAnTYcb5BxWkTQiNbgF1TejSqaGULstNCUT2+djGRLfpNE9qazEb90dR2iouLkZ+fj5KSEpSUlCA/Px/FxRW/m6dNmwY/Pz88fvwYb968wcaNG7npK5aWlhgzZgxWr16NnJwchISE4M8//8TUqVMpW6M4Ous4GfyizdNO2DQTBkM72bhxI8zNzfH999/j119/hbm5OTZu3IikpCRYWVkhKUm6s9HQoUOxdOlS9OvXDy1atECLFi2wbt06rp29e/ciLy8P9vb2mDhxInx8fLQ64mTzOHUEoaNNbXSczGEyajN8zuN0aO2GqVtPKH3fjc0z2ZJ7laCzVbUvNDCPczHiqY47yvRoOk33PUFUxzmr0hPKabpO9KM+VkVbk6aeocd7AACXTl8gNnwPFc2yeqWRd6hpauKzw9BOdNZxOmhgHucSNNE7vfKRZuAkz8ovFIjyekJHmWd+GC1o+9qgyZeezCkqwrnfvuVFU1k9ZfqorpPVxGeHoZ3orOMs1YBmPmXVfJRS3f0EAMQFdNfjLatHIzUrzqOfqaCtqYieMg5HIU1JHq/tCaGnrpPVxGeHoZ3obHFQugbmcR5Aml7pVTauueDcQ0E1K9Pjq2JWEeZtvUJFR5Oa87xvw9DjvWoffDNn4X7e29SkXmWvWdnXlVG7YcVBWoyQ0aa2FAOxAiD1YV/k2gOfY66sOEh70dmIM0MD8zh/xWtqWl3fM8aWxFRqejKWX3pMTct0YFss8b5BTQ8AdT0hNGuKHhevOMyrniLQ1tRWG2lF+AzNolOO09fXF56envD09EQeSrkFAhYjHikoRDzyuR1FfsVrbiPo+RDhDYrxGLnYiBcApGnQ68gCAHghDnkoxQNIuGXuvJGCEEiXgZoM6ST9tyiCN6RL0m3HSzyABHkohRekK15cRxaXXt2IF3iMXLxBMeZDuuPIeWRyznclEhGPfKSgEIsRD0C64EEg0tH1PWMMiohDHQBRkjxux5ItiancRtM9w2KQVliEu29zMDlaev+q58kISJPa3PHeE0hKSnDtjRhznkpfk6/i/sWZ9Cw0GeIEi3XnAQAnH73EjMBwAMDYE6HIyi+CuKAY9lsuAgD8wpIw/6w0fTvE/x/cSshAsjgfrXZcBSDdiFrmbHv4BuNB8lvEZUjgvidI+joExXKLHLjvCUJchgQPkt+i1wnpr9iwp2ncBsFNR/kgOV2CoPAk9P8iAAAwd+tl+J6RrjhiM3gXxLmFOBsiwshlp6TvzbpzOH7lCQDA6H3ptknHrzzB5HXnAAAjl53C2RARxLmFsBm8C462VvA9E4m5Wy8DAPp/EYCg8CQkp0vQdJQPAOmGxTJn18XrKMJiUhGblAnXiX4ApBPhZZPhXSf6ITYpE2ExqdzuGUu8b8jZZGlurJZNhh7vISCmCFN23YGhx3sYMW4Lzl64D7E4D9aOUwAAvoeuYM6X+wAAp8/fQ1BwFJJTMuHoMhsAsGPPGe6Lv3PvpQgLFyE2Lhkunb4AAKzdfBJrN58E8P8Vq3HJCAsXoXPvpQCkTmPHnjMAAEeX2UhOyURQcBT6Dl8NAAi58xS+h6QpaWvHKRCL83D2wn2MGLcFADBp5k84/pt0JxyDuh9L36ffgjFp5k8AUKNNfYevlrPJsXF9wW2a8+U+OZsaNLBS2SabYd4w9HgPBx6IMc8vAoYe76H/t39V+9ljaC8sVauF6PucTZaerR4WoTBKI++wVK0Wo1MRZ1mSUFDzRTwjixyFpKzT7BkWw3v7NTlNWSQpFOWdpuyXNi1o6ymiyXdaTxZl0oS2pr7byH48aTc6Ox3FUQOLvAsd4ZaPNE91UH7/vOpQJNL8e04vXjXLUlmkee8A3fUoaetVpin0l+L9mz8I2r42aNYGGxnai85GnEWgn2FOobxaUYIG5o09y8gRpN2q0rOxLzIF0asK2noA8MzEgWqhSOyzZME1NK1ZG2xkaC866zizNFBVewoZgrVd2bjm7n9f8da+ouOamwTYHaW6Mc31B2/zrlcdtPTKOsq1W36joimDtp4mNGuDjQzthRUHaQE0VgfSVEFQbSsEYmNTDL7w7LeFFQdpKTobcaZrIOKkvXLQquf8pIaUcZqyqSd8oIjTlE0LoYVQetWlYWVTKmhBW08TmrXBRob2orPFQaYwoK7ZCqa8t1ldtNne0kzt9pWNNN91rKe2JqB4pNnZtREveorCt54iEaZnJ2deNbVNTxOatcFGhvbCUrUaRh/nbOp7epalYxk0YKla7UVnU7UJGpjHKVshiC9qcpod7z1Rq31VnKZsxSBVUdZp2gzepZaesqijp2pVrGwlHFrQ1tOEZm2wkaG96GyqtrkAadOa8AZ/qRpFIs2/O7uo3L6qkabo64Eqa6oSaf57+jOV9VRBFT11I8zkmF/Uul/b9TShWRtsZGgvOhtx0t4bEwCeIJeq3t1sunoAcCtBtSk3qqZng8JfqHSfqiiqx+e8y6C/o9VuQ5v1NKFZG2xkaC866zizQXfDZQC4jre8tKPouObJNNUm66szrnnwQZLK96rCL/+/2Lm26AmxSIFsoXBa0NbThGZtsJGhvbDiIMro45xNfSgGYgU/DG2Dz+Kgdzp0xM4/lV+Het24oaw4qBJ0NuJ8hSLqmrItxWjxVdy/St+jrtOUbTGmKOo6Tdn2X7Qoq0drGTzZNlO0oK2nCc3aYCNDe9HZ4iALDfj8TrBU635lo81+9a2Uup6PSHOYi73C1/IRaX7Qg+7cuA96OFOPLj8c2lmv9TShWRtsZGgvLFVLCX1L0epiepalYxm6BEvVai86m6qN18A8zsngfwH06njnjuJVfHw5TYt152u8hk+nafT+dt7aqg6Z0zSo+zEVvbLQ1mQ26o8mQzvR2VRtSw3M4zwG1eZVqhptxr3nptJ96pC75oNqz/MdaZYEL+G1vfKUjzJJ9h+C6lUGbU1mo/5oMrQTahFn3759YWZmBisrK1hZWaFNmzbcuWvXrsHV1RUWFhbo168fEhMTa2xPooHpKCHIVvoedVK0Z9KzFLqOzxTtyUcvqzwnRHr2+BX1VkeqjspSs8d/CxZMrypoazIb9UeToZ1QTdV6e3tDIpFAIpEgJiYGAJCeno4xY8Zgw4YNyMzMhKenJ8aPH19jW7kaWAAhHMJs8lwVN95IaryG73HNC7GV7wEq1Jjm+dsi3tusrlL23MUw3vVqgrYms1F/NBnaCbXioL59+2LKlCmYNWuW3HFfX1/4+/vj9m3pBsM5OTmwtbVFeHg4XF1dq2xPF4qD9KUgSJcKgVgBEENfYMVB2gvViPPbb7+Fra0tevbsiaCgIABAdHQ0PDw8uGssLS3h7OyM6OiKhTG+vr7w9PSEp6cnElGAQKQDABYjHikoRDzysRLSNO+veI3zkK68Mx8ivEExHiMXGyFdcu0A0nAdWQCki7fnoRQPIMF2SFOV3kjhUrOyoqAVSOTmcm7HSzyABHko5RZ/v44sbs/OjXiBu29zkFZYhJ5h0ujaLzkdWxJTAQCjHokQJclDfF4BBkVI79/94hV2v5BGfIMi4jAlOh5RkjyMeiSNyrYkpsIvWWpzz7AYGPRwwK2EDAzx/0dq59mH8AuTrvxjv+UixAXFOB+ThrEnQgFI52jKUrGyIqCTj15yczfHnghFr1/+hrigmFvs/XBuIbeHZf8vAhAUnoTkdAmajvIBAOwMCMUS7xsAgC5eRxEWk4rYpEy4TvQDAKw7GIJ1B0MAAK4T/RCblImwmFR08ToKAGg97hfsDJD2r+koHySnSxAUnoT+XwQAkO6f6fv/q/3YDN4FcW4hzoaIMHLZKel7s+4cjl95AkOP97hCo+O/BXNz7kaM24KzF+5DLM6DteMUjBi3Bb6HrnB7K/YdvhpBwVFITsmEo8tsAMCOPWeweMVhAEDn3ksRFi5CbFwyXDp9AQBYu/kk1m4+CQBw6fQFYuOSERYuQufeS6WfxxWHsWPPGQCAo8tsDBq5DkHBUeg7fDUA6b6OslVorB2nQCzOw9kL9zFi3BYA0vmCsrSgrCClOpsAyNnUsMUMwW1KTsmUs6lFu3mC2lT+fRoxbovgNpV/n4aN2SioTeXfJ4b2Qi3ivHv3Ltq1awcTExMEBARgwYIFiIiIwObNm2FnZ4fvv/+eu7Znz56YPXs2ZsyYUWV7DgYm2ImWFHr+Hw8gwbtQfG6luhHntTdiDKhvXek5oSLN8zFp+KCNdM9KGpHm2RARRvRUby6nMlHm2Qv3MWKYp1p6ykJbk9moH5os4tReqEWc3bp1g7W1NUxNTTF9+nT07NkTf/31F6ysrJCdLV90k52dDWvryh2GDDMNzKRpCwuqet3q0tUDgN5ODQHQS8/27dRMrfuVTc327UW/Upm2JrNRfzQZ2onG5nEaGBiAEAI3NzdERv638HZOTg5EIhHc3Kr/kCZpYB7nAiheyMLH+GavsMrnjQo5rum88yrVMU1ZyldZVF0qz7HNbJX01IG2JrNRfzQZ2gkVx5mVlYVLly4hPz8fxcXFOHbsGG7duoUhQ4Zg9OjRiIqKQmBgIPLz87F+/Xq4u7tXWxgEAE4amMfph3eo6kV0pV+U8/baV3T1Li9U+h51CoDEyb+qfK+uaDIb9UeToZ1QcZxFRUVYtWoV7OzsYGtriz179uD06dNo06YN7OzsEBgYiJUrV6J+/fq4e/cuAgICamxTrJFtxbKo6gVUsq2YkNGm6cC2XCEOLZTR42NB9tqwHRWzUX80GdoJFcdpZ2eH0NBQiMViZGVl4c6dOxg0aBB3fuDAgXj69Cny8vIQFBQEJyenGtssAP0ldp9TTg9H5eTLPacx9STsaZrgGqro8TXN5H44//NGtU2T2ag/mgztROGqWkIIDhw4gBMnTiA9PR0PHz7ErVu3kJqainHjxgndzwpo+zxOIeZwCh1taiNsXiajtsKqarUXhSPO1atXw8/PD3PmzEFSknSuYNOmTfHDDz8I1rnqSEEhdU3ZHNCa4MtpTo6O5/5Py2nK5k/Sojo9IZymbI4eTWhrMhv1R5OhnSi8yLu/vz/Cw8Nha2uLzz77DADQsmVLPH/+XLDOVUc9DaxPPwYNqep92VTxvTH5YvXMHhrXEzLKXPst/ewIbU0aeq/q9JJ7vnDVBryq01Nw3ar07Iv/FlxTE58dhnaisPcpKSmBlZV08r+BgQEAQCKRcMdoYwwD6pqNYUJVz8lcqkczRevSrIFgWpVRXk/o1KxLa0dB29cGTXX1yjtFRXBuTXdD8vJ6yvRZVSeric8OQztROFU7fPhwfP311ygokBbIEELw3XffYcSIEYJ1rjqSNZCqXYWad23hkzGPnlPdnBoAus46qhE9PipmFcGzzzLBNTStWZ3eqzq9anyowuBeQ1TtLnU9RV6Dyl4LTXx2GNqJwsVB2dnZmDZtGi5evIiioiKYmZlh8ODBOHLkSI2r/AiBNhcH8VkYVBsKglgBEH+o6vgYqiFkipgVB2kvCkecdevWxenTp5GYmIg7d+5AJBLhf//7n0acJgC81cA8Ttmi8dXBp9P8w1q4rdOqcpqyBddp8eOtyrcxEwrZot76ollZhOSzS7XVmNSBtqa22Mhn1M7QHaod4ywtrfjFbWdnBzs7O7nzhob0V+4r1sA8zjeUnXWKOL/mi3gmOb3mPUD5wtDjPSSfpLsTRHLKG6p6fGsq8qWcmpLKm56i0NbUdhurep9oFDExhKfaVK2hoSFXCFQZhBAYGBigpIR+9KetqVq+Ik59T9Gy9KxisOildlCZQ9WVVG1mZia8vLxw+fJl2NraYsuWLZg0aVKF6woKCrB8+XKcPHkSeXl5mDhxInbt2gVjY2MUFBTg888/x9WrV5GZmYnWrVtj8+bNGDZsmNJ9pkG1oWJ8fDyeP39e5UN2XhO81EBx0ErKxUE9fIN5b7MmpynbJ1NIyjpN2d6ItKCtp6wmHym/QT0Gq3SfOtDW1DcbdTndO3/+fJiYmCAtLQ3Hjh3DZ599Vul+yt9//z3u37+PqKgoxMbG4sGDB9i4cSMAoLi4GM2aNcPNmzfx9u1bbNiwAePGjUNCQgJlaxSj2lRtixbaF9HJsAX/K/PUxCw0oqIjiza9P3SnoleWfUsH1XyRGpSPNH13zRVUrzy09WrSFOILcrv3Nt7b1DbN2mCjLpCTk4PAwEBERUXBysoKvXr1wsiRI3H06FG5PZYB4OzZs1i2bBkaNJBOQfvyyy+xbNkyrFu3DpaWlli7di137YcffoiWLVsiLCxMoSVYaaPwPM6pU6dWmbY9cuQIbx1SFE3sh1bTHqB8L7NnbWrEa3uKpGitzYWbq1pZetbaylwwvcqgrVdek0YkYWVNf241bc3aYCOfWBoTdGus/JDa69ev4en53+bdc+bMwZw5c7jnsbGxMDIygouLC3fMw8MDN2/erNAWIQRlRwYJIfj333/x9u1b2NjYyF2blpaG2NjYGreX1BQK+5/WrVvD2dmZe1haWuLChQvcrwfapKKIuuZ2vBRco+zY5tjj9MvARy77nyDtVjWm+eG4LYLoVQVtvVd1emHY+J+opt+mjJ1KRUeTmrXBRm3Azs4O9+/f5x5lnSYgXQSnvNOzsbGBWCyu0NawYcOwa9cuvH79Gqmpqdi9ezcAIDc3V+66oqIiTJ48GdOnT69xe0lNoXDEuWbNmgrHvLy8sG7dOl47pCjNKK/iAwA70JKq3sMv+vLWlqIFQU9PePGmKaO6QqDY8D2861UHLb2yTvKfh7epaGpKTxOatcFGXcDKygrZ2dlyx7Kzsyudprhy5UpkZWWhY8eOMDU1xezZsxEeHg57+/+WFi0tLcXUqVNhYmICb29vwfuvKmplPDt27FhpSE6DNyimrhmIdKp6G4NieWlHmSradQdDeNGUUVP17NrNJ3nVqwmh9SqLLLdtpDs2RltPE5q1wUZdwMXFBcXFxYiLi+OORUZGVppiNTc3h7e3N16+fInnz5+jYcOG6Ny5M4yMpENShBB4eXkhLS0NgYGBMDamX8eiKApHnNevX5d7npubi4CAALRr1473TtVWaC+vJzS1ZcqJLlVAMhh8YmlpiTFjxmD16tU4cOAAIiIi8Oeff+L27YrR+cuXL2FgYIDGjRvj7t272LBhA/z8/Ljzn332GZ48eYKrV6/C3Jx+LYIyKBxxenl5yT2WL18OADhx4oRgnSuPr68vPD094enpiQIQLgJcjHikoBDxyOemjPyK19xKP/MhwhsU4zFyua3BDiAN15EltQ1xyEMpHkDCjWN6IwUhkKYgJkMa+TnABN5IASAd73wACfJQCi/Eoet7xghIy8Sq58nSe6LjcfdtDtIKi9AzLAYA4Jecji2J0knUox6JECXJQ3xeAQZFSH+tbQyK5aJM9z1BGN/BEQ+S33LTUpZfeoxdt6XTf1rtuIpkcT5uJWRgiP8/UjvPPoRfmHTLN/stFyEuKMZlcxOMXHZK2qd153D8yhMAgNH72wEAx688weR15wAAI5edwrttHCDOLYTN4F3S1/xMJOZuvQxAugVYUHgSktMlaDpKuorKzoBQLPG+AUA6lSUsJhWxSZlwnXEMgDTCk0V5Lp2+QGxcMsLCRdwUDbEkn1tZx9FlNpJTMhEUHMVt4TTny33wPXQFAGDtOAVicR7OXriPEf8/Vjlp5k84/pv09TGo+7HUpt+CMWnmTwCAEeO24OyF+xCL82DtOAVrV4yH76ErmPPlPgDSraKCgqOQnJIJR5fZAKQr/SxeIV2YoXPvpQgLFyE2Lhkunb6Qs+lVnV5wfncpRHEiRD6I5KYrrFm+hltlxr2VB6bOnIqQWyEYPWQ0AGDx/CU44ied9tPK3hkSsQSXzl/mxtDmzfgMgSel71kjCwcAQODJU5g3Q7or0ZSxU3Hp/GVIxBK0spcudn7E7ygWz18CALgdfBsht0KQmpwK91YeAKSr3qxZLh1uGdRjMCIfREIUJ0J3d+nuNNs2buMiqu7uPWq0KTU5Vc6m1JQ0QW0aPWS0nE3frPpGcJvKv0+fLfxMUJvKv0+6wt69e5GXlwd7e3tMnDgRPj4+cHNzQ1JSEqysrLhtKEUiEXr06AFLS0tMnz4d33//PQYPlr4XiYmJ2L9/PyIiIuDg4AArKytYWVnh2LFjmjStShReq1bbMDYwxGG8Q1VzMeKrHOdUt6K2smjTfU+Q2uOcyi524DrRT+1xTmUiTZdOX1Ad5+RDT9kIs7t7D6rjY7T1NKFZG2wc/v4HvC2A0PFdD1wOuazRPugTSi+5VxmaWHLPQQPzOJegCVW9wEmeNV9UDaqsEHTmh9FqaSqbnj3327dq6SmLqnrqpGN/DaS74wxtPU1o1gYbGdpLtY6zTp061S65J0MTS+4Jt/x51eRTVhUXqP66qrqsnjhP9RWZVBnTFEvyVNZTBWX1+Bi/lIjprf+rCT1NaNYGGxnai8JL7u3Zswd9+vTBxYsX8eTJE1y8eBH9+vXTWMlwugbmcR5AWqXHhUjTAsCCcw/ValcV5m29otJ9qhYCzVm4X6X7VEVRPT7nXS5Z8A0v7WirniY0a4ONDO1F4THO1q1b4/79+6hXrx537M2bN/D09IRIJBKqf1WiTYu8C+U4VYX2Iu76Uj3LqmMZ2gQb49ReFB6cfPv2bYUVHnJzc/H27VveO6UIGRqYx/krXlPVW37psdL3qOs0ZRWyiqKu05RVr9KiMj2hV/WhXSWpiapMZiOjNqHwPM7p06dj4MCBWLRoEZo1a4YXL15g9+7dmD59upD9q5I6qHnslW/qg9+1Y4Hqo83G1ma869WEo63i63HyEWk6Nq6vdhuq6tGKMB0aO1DR0ZSeJjRrg40M7UXhVG1paSl8fX3x+++/Izk5GY0bN8a4ceMwe/ZsbuUHmmhLqlab0rQ0U7S6nJ5lKVmGLsBStdqLwqlaQ0NDzJs3D9euXcOTJ09w/fp1zJs3TyNOEwCSUEBdcz7ojuW22qH8xrPqIlvYoDr4dJqyRQdo8KpOLzi4zKemJ0M2uV1f9TShWRtsZAhHQUEBiorkC0yLiopQUKCYX1FqAuahQ4fQv39/tGnTBv3798ehQ4eUuZ1XHDWwyDvfEW5N0ebfcxSPjPiKNu8doLsDxP2bPwiuUXYM8/LflwTXKw9tTWaj/mgyhGHQoEEICwuTOxYWFoYhQ4YodL/CjnPTpk34/vvvMWHCBOzevRsTJkzA1q1bsWnTJuV6zBNFoL/gUQpUn+OoCs8ychS6js8UbeyLzGrP852ijX2WzGt75SmflhU9o18BTluT2ag/mgxhePToEbp16yZ3rGvXroiMjFTofoUd54EDB3D58mXMmTMHQ4YMwZw5c3Dx4kX4+voq12OeyNJAVe0pZMg953vj6vJs4ml3FGVYf7DqJcWEGNdcu+U33tuUUdlY5vZN2wXTqwramsxG/dFkCIONjQ3S0uTn5aelpcHS0lKh+xUuDrK3t0dCQgIsLCy4YxKJBK1atcKrV6+U6DI/aENxkDqOk6+iIFoFQbpUDMSKfxj6ACsOEo7FixcjPDwcu3fvRqtWrSASifD111+jQ4cO2LlzZ433KxxxDh06FJMnT0ZMTAzy8vLw9OlTTJ8+XeGcMN+kayDirGrlIKGYf5b+ykGynVDKIqTTlO1Swhc1OU3ZzhQ0oa3JbNQfTYYwbNq0CW3btkXXrl1hbW2N9957D23atMHmzZsVul9hx+nt7Q1ra2t4eHjAysoKHTt2hKWlJfbsobezRVlMNTCPsxVMeWlH0WjzXcd61Z4XItrs7NpI7rnQkaZnJ2de2lF0EQOPd+lXRtLWZDbqjyZDGMzMzPDzzz8jJycHqampkEgk8Pb2hpmZYnPnld5WrLS0FOnp6bC1tdXIrigyNJ2q1XSalkaKVlfSsyw1y9BHWKqWX54/f67Qda1atarxGoVXDgKkS+w9e/YMEokEz54944736NFDmWZ4IUED8zi9EAc/inuA2m+5iFffDqWmBwA2g3fh7eWF1JymteMUiJN/VeleVRxmK3tnPH9FeT4uZU1mo/5oMvijdevWMDAwACGE2/VLFjeW3QVMkd2+FHacR44cwYIFC2BiYgJzc3PuuIGBAbfDN02a85Q2VQZvqJ9WVCbaFH09sNLjQkab/57+jGqkmRzzi0r3qRplPhQpVm7OJ7Q1mY36o8ngj7L7Sx86dAhXr17F2rVr0aJFCyQmJmL9+vUYMGCAQm0pnGtdunQpAgMDkZ6ejhcvXnAPZZ1mXFwczMzMMGXKFO7YtWvX4OrqCgsLC/Tr1w+JiYk1tkN7b0wAeILcmi/ikVsJGRWOCZ2ivZVlUfNFPBL0d7RS16u7IHvIraqn2wgFbU1mo/5oMoThu+++w4EDB/DOO+/AxMQE77zzDvbv349Vq1YpdL/CjtPExAR9+/ZVtZ8c8+fPR5cuXbjn6enpGDNmDDZs2IDMzEx4enpi/PjxNbaTDfqbZ1+HdCcYoedvyjj4gG4kb+jxHnwPqbYfp6ooo8fHWObRg0fVbkPbNZmN+qPJEIbS0lIkJCTIHUtMTFQoTQsoURx0+PBh3L9/H2vWrIGtra3SHQWAgIAAnDp1Cu3atcOzZ8/w66+/wtfXF/7+/rh9W/prLicnB7a2tggPD4erq2uVbWmyOEhVx6luUZCQ0aY2FwKx4h9GbYQVBwnHtm3bsHPnTnz66afcbl/+/v5YtGgRli5dWuP9CkecLi4uOHPmDBo1agQjIyMYGRnB0NBQ4UXes7OzsXr1auzYsUPueHR0NDw8/ivztrS0hLOzM6KjK6bwfH194enpCU9PTyShAIFIBwAsRjxSUIh45GMlpGneX/Ea5yFdPm4+RHiDYjxGLjbiBQDpnMzryAIgLfrJQykeQILteAkA8EYKQpANAJgM6Qo+a5AEb6QAAOY8TcS1N2JISkrQ8d4TAEBAWiZWPZcuITc5Oh533+YgrbAIPcNiAAC7bj/n9tjs4RuMB8lvEZchgfueIADAxqBYbPz/1YLc9wRh7PFQPEh+ix6+wQCke2XuDAgFIF2MPTldgqDwJPT/IgCAdA6m7xnpOIzN4F0Q5xbibIgII5edkvZp3TkcvyLtq9H70lVQjl95gsnrzgEARozbgj5Dv4NYnAdrR2kq3ffQFW6uZd/hqxEUHIXklExucfYde85we1x27r0UYeEixMYlw6XTFwCAtZtPYu3mkwAAl05fIDYuGWHhInTuLf1wunb+Ejv2nAEgXfA9OSUTQcFR6Dt8NQBg6sLTOOIn/aXfyt4ZErEEl85fxpSx0jV15834DIEnpfY1spBu+xR48hTmzfgMADBl7FRcOn8ZErEEreydMW/GZzjid5Sbkzd6yGiE3ApBanIqt4i3zy4fbu/FQT0GI/JBJERxInR3lxbBbdu4Dds2bgMAdHfvAVGcCJEPIjGox2Dp52T5Gvjski6W797KA9PHz0DIrRCMHjIagHQ+IJ82AZCzya1Fe8FtSk1OlbOpW/v3BLWp/Ps0b8ZngttU/n3ymjxLUJvKv098UgcS2Bf/rfRDX/nmm29w6NAhpKWl4cyZM0hNTcXBgwcVcpqAEhFn69atMXHiRIwfP16uOAgAnJ1rLppZuHAhHB0dsWzZMqxdu5aLOL28vGBnZ4fvv/+eu7Znz56YPXs2ZsyYUWV79gbG+Ak1lw3zSQiy0RN1qUWcJx+9xPgOTQDQizaP/xaMSePeF0yrPFXpCRVlBp48hbHjxwjStrZoMhv1Q5PPaM/zXWfcv7lV+fv6bdHLiFMRPvjgA5w/f77ScwpX1WZkZGD9+vVyZbuKEhERgatXryI8PLzCOSsrK2RnZ8sdy87OhrW1dbVtWgmwqXRN0HSaAKg4zfLQdJpV6QmZmqX9ZasJTSH17qZU/nfXtPcnuJsimKxSet0aC1P/oInPDkNzBAcHV3lOYcf56aef4ujRo5g2bZrSHQgKCkJCQgKaN28OQLrGbUlJCR4/fox58+bh8OHD3LU5OTkQiURwc3Orts14DczjnIxYxKH6fvGJxbrzyF3zgaAa5cc2Dep+DJL9h6Ca1ekJPZ7ZyMIBabmpgmpoWlNVvaqcoiKMdLbDGdFrle/nU08ZO5Rxspr47DC0E4VTtb169cK9e/fQsmVLNGokvyzbrVu3qr03NzdXLqrcvn07EhIS4OMjHV9o3bo1Dh48iA8++ABr1qzBzZs3cefOnWrb1ERxkCZWC6otBUGsAEhY1HGKDClCRbJVwVK1mqVu3boVsqEyFI44Z8+ejdmzZ6vUAQsLC7ldVaysrGBmZgY7OzsAQGBgIBYsWIApU6agW7duCAgIqLFNiQamo5xJz8JI23pK36eq0zz56CWmUUzTApoZ4xw46Vtqevo8NiZzjjfPBKLPyLGC65WFtqYmbNy6/3SlmrQdKkPzKOw4p0+fXuM1n3/+Ofbu3VvjdWvXrpV7PnDgQDx9+lTRrgAAcjWwAMKNNxKVHKeqXMrOh/KJccWoKto8dzGMmuN8VacX/rh8DAMnUZEDAFy5cIW64xRKs6ooMvT6ZepOhbamNtlY1fvAHKpuU10yVulF3qujutCWb2inajUxd1Of07QsNas8LN2qHyjqUFmqVhhKSkowc+ZM+Pr6wtS06qVbt2zZgm+/rTwbxuv2Jjz64BpJRRE1LZnTnPO05qUA+cJ0YFtu/iXfVOc0R4zbIohmWco6TdmcOFrQ1lNH826KkdxDUTbMnqySnjrQ1tRlG8u/r8q+vwz1MDIywuXLl2vc3asqpwkouTtKTagyVUVV6mpgOsr4Rg2o6s0eyf/+fzVFmnM+HcS7ZlnKR5pTZ9J1ZLT1lNHk68tzyAShEvzao6mPNjLnSY+vvvoKa9aswbp162BsrHw2kVfHSRMzfoPlKimbou1WV7kF0NWtpO3bqZlK96tD3170ptsAQM/edLeko61XnaZQX5Ttu/UUpF1t0qwNNjKEY8+ePUhNTcXOnTthZ2cnt92YIhuX8Oo4aaZqkyjM4yw/rtkrLBYRXelVuTYd5YO3lxfy1p4i45qObWarvD9mTVQ2runu7EF1j0PaemU1aUUUn/Zoj5MPE6hoaUqzNtjIEI5ff1XvO47XsK3sVmFC4yTwfpyVFQPRdJoAqDtNAFSdJgDqToy23t0UI5yITKCahtPElzttzdpgI0M4unfvjmvXrmHWrFkYPnw4Zs2ahatXr6Jbt24K3a+U4zx48CAGDRoENzc3DBo0CH5+fnJRpmxBAxqIBZzHWVUFbUBapsJt8LHggWzBdpoIsa1YdRW0skW0aUFDr3zBx8UTRwTXLAttPU1o1gYbGcLx2Wef4fr169i9ezdCQ0Oxe/du3Lx5E59//rlC9yu1kfUPP/yAMWPGYNu2bRg7diy2b9+OZcuWqdx5dSiAMGnh6qadROXkC6JZFWFP03hpR5mpJ/fD+Y3Iapp2EvmA7o8DIfWqqo589ihCMM3KoK2nCc3aYCNDOE6fPo1z585h2LBhaNeuHYYNG4bTp0/j9OnTCt2v8DxOe3t7PHjwAE2bNuWOvXjxAu+++y5ev6a3RqUMoeZx8rFJtbbN3dTUnM3aMleTVUMyhGDduKFsHqdAuLm54cqVK3B0dOSOvXz5EoMHD650S8vyKBxxWltbV9ixxNraGnXr1lWiu/yRgkLe26zJaU6OjuddsyzlnaZsn011UNZpyvbBVBdFnaZs70Na8KWnzPy7FZM+4kVTUWjraUKzNtjIEI6pU6di6NCh+OWXX3DhwgX4+vpi+PDhmDZtGq5fv849qkLhqtpFixZhzJgxWL58OZo2bYoXL15g27Zt+Oqrr/D8+XPuulat6OyRWY/nmTSKRJpfNrXnVbMmVs9Ub+qEKpHm2m/HqaUJKBdpLlm5RG09ZVBXT5XocuKXim2Oyxe09TShWRtsZAjH/v37AQCbN2+WO75v3z7s27cPgHRdgrK+rSwKe5+FC6UVnjdu3JA7fu3aNXz55ZecUEkJnfUZjcHfYguKpmedzE1qvIbPXVBcmtFdcAEAXFo71nxRNSibnnVuXfMm6Hyiqp466VjHlnRtpK2nCc3aYCNDOOLj1cseKpyqLS0trfFBy2kCQDJPqVplxjTHPKr814dQdJ2legWoquOann1UL/ZSZUxzcK8hKuupgrJ6fCyHtnjUQLXu13Y9TWjWBhsZ2guvi7zThI/iID4KgcqjLftu0i4I0qdCIFbsw9AGWHGQ9qJwxJmUlAQvLy+8++67cHFxkXtogrca2I/TLzm92vN8O82dAaEqtaeO09yx54zS96jjNH120Zv7W5OeUItt/+9AzVvt6bKeJjRrg40M7UXhMc5PPvkErq6uWL9+PczNzYXsk0IUqzmPU5Vo81VRsVqaypKcLlH6HnUjzeSUN0pdr26kmZqSqtb9fOgJHWFmvqJrI209TWjWBhsZ2ovCEefTp09x6NAhfPjhhxgwYIDcgxa+vr7w9PSEp6cnikAQCGkEuBjxSEEh4pGPlZBu/fUrXuM8pCv9zIcIb1CMx8jFRrxA1/eMsep5MrcSUMd7TyApKcG1N2Ju67Cv4v7FmfQsAMA7d6TzetwszfBV3L8ApFuMXXsjhqSkBB3vPUGTIU7wC0vC/LMPAQBD/P/BrYQMJIvz0WrHVQDArtvPsfzSYwBAD99gPEh+i7gMCVwn+gEA1h0MwbqDIQAA14l+mDPSA2ExqejiJR3rXOJ9g4tCm47yQXK6BEHhSdy0lblbL3Mr/1g7ToFYnIezF+5zW4VNmvkTjv8WDAAwqPsxAOD4b8GYNPMnANItxfq+7waxOA/WjtLlE30PXcGcL6VVZn2Hr0ZQcBSSUzLh6DIbr+r0gs8uH6xZvgYAMKjHYEQ+iIQoToTu7tKK4G0bt2Hbxm0AgO7uPSCKEyHyQSQG9RjMva+yKNC9lQdSk1MRciuEmzayeP4SbrWfVvbOkIgluHT+MrdV17wZnyHwpHT7tUYWDgCAwJOnMG/GZwCkW3pdOn8ZErEEreydse77dTjidxRTP/0Gd1OMMKDfGDy6E4KMtFTM6N4egDSy8NssnZbz1cgBePYoEi/jRZg3QLoc1/FdW3F8lzTtNW9AN7yMF+HZo0h8NVL6t+C3eTUXnczo3h6jvD7Hozsh3HQG7xVfc6vQjHd3Qq5EgnvXLnHbVm1fNBc3zwQCAEY62wEAbp4JxPZFcwFIt7e6d+0SciUSjHd3AiBd1cZ7xdcAAFFUpOA2ZaSlytmUJ5EIatOKSR/J2eS1Yr3gNpV/nyZ+uVRQm8q/TwztReExzilTpsDLywv9+vUTuk8KYWpgiEN4R6V7VR3bHPVIhNMdKq+sE2Jss4vXUYT6Kb4NFh/jmp17L0XYrZrHQvga0xzUYzCu3L7MS1uK0KPLYPx45ho1PUD6pU5Tk7aeJjRrg426MsaZmZkJLy8vXL58Gba2ttiyZQsmTZpU4TpCCL777jscOnQIEokEnTp1ws8//ww3N/kdmeLi4tChQwd8/PHHai/GLhQKp2p3796NHj16wNnZGY0aNZI7d/DgQd47VhO24L+wpyY2tlRvqoay7Fuq+N6YfBUD+e6aW+M1fBYCbffexltbNXE3xQjzN+2kpieDtiazUX80dYH58+fDxMQEaWlpiIiIwAcffAAPD48KDvH333/HwYMH8ffff6NFixZYtWoVpk6digcPHlRor0uXLjRNUBqFU7WffvopjIyM0LZtWzRp0kTuoQno7MYpj6VR5apCVdJaKzBvlG+sraofv+a7etbK2orX9iqjbNGPuZXweuWhrcls1B9NbScnJweBgYHYsGEDrKys0KtXL4wcORJHj1acShcfH49evXqhVatWMDIywpQpU/D48WO5awICAlCvXj2qQ4CqoLD/uX79Ou7cuYPt27djw4YNcg9NkIoile5TZwrKnJiaNzjlk5HL/qfQdXxOPfnw/8dDK0OIKSeysUqhKF/4s2FWxRSS0NDWZDbqj6a2ExsbCyMjI7nZFR4eHpWu9zphwgQ8e/YMsbGxKCoqwuHDhzF06FDufHZ2NlavXo0dO3ZQ6bs6KJyqdXd3R0ZGRoX1ajVFM9CPxq50VG1MVVWenvCiqgcAseF7Kj0u1DzNfx7eFqTdqipl9127K4heddDWZDbqjyZv5OagNPKO0re9fv0anp6e3PM5c+Zgzpw53HOJRAIbGxu5e2xsbCAWiyu01bhxY7z//vto06YNjIyM0KxZM7n1YL/77jt4eXmhWbNmSveTNgpHnP3798fgwYOxZcsWHDx4UO6hCd6A7tQQANj94lWFY0IueCCrsK0Ovhc6WLv5ZIVjQi5uIKu45ZPqppfIqixpQluT2ag/mprGzs4O9+/f5x5lnSYAWFlZITs7W+5YdnZ2pQHWunXrEBoaihcvXiA/Px9r1qxB//79kZubi4iICFy9ehVfffWVoPbwhcIR599//40mTZrg8mX5CkgDAwPMnDmT944JgRArBWkSTW0Xpq2wFX8YDLq4uLiguLgYcXFxeOcdaUYuMjKyQmGQ7Pj48eO5rSlnzJiBRYsW4fHjx/j777+RkJCA5s2bA5BGsiUlJXj8+HGF4iFtoFYtuce349T0vps0HKeuLKXHnCZD3+B1OoqrA+4dUL6eoOuiGzX2YcKECTAwMMCBAwcQERGB4cOH4/bt2xWc57p163DlyhUEBgbCzs4Ox44dw7x58/Dy5UuYmJjIRa7bt29HQkICfHx8YGdnp3S/hUap4tSMjAwcPXoU27ZJ02vJycn4999/BelYTbwQYD/OmhgUEcdLO4o6TdnCCJUhlNN06fQF938aTlO2UIKqKLtMnmxyPE1oazIb9UdTF9i7dy/y8vJgb2+PiRMnwsfHB25ubkhKSoKVlRWSkqRFlcuWLYOHhwc6duyIevXq4ccff0RgYCDq1asHCwsLODg4cA8rKyuYmZlppdMElIg4b968ibFjx8LT0xMhISEQi8W4efMmtm/fjrNnzwrdzwo0MzDFD3BS6h51I874vAK0NDflngu9oHtsUiZcmle+tZhQjjM2Lhku7zhSizRFcSI4v0Nvq6+X8SI0obw9FG1NZqN+aOpKxFkbUWoj65MnT2LAgAGoX78+AKBbt264d++eYJ2rjlIlr+cjTZtT8p8qjV1QxHmVR9VCpmjFkjyq6VmJWPn1eNVJy+ZJlNdTF9qazEb90WRoJwqnahMSErhJqQYG0k2kTUxMUFxMv7oVANJVnMepDqvik6nqzdt6pcIxocc1Zy48Jmj75Vmy4Bulrld3LPPnlV+rdb8uaDIb9UeToZ0o7DjbtWuHS5cuyR27evUqOnTowHunFKGJBuZxVrVOrVAos04tH7yq04vqurEAlNLjowCI9vqmmtBkNuqPJkM7Udhx7ty5E5MnT8b06dORl5eHuXPnYsaMGVyhEG0yNDCPc0uidFshWptVL/G+IfdcyGhTlp6V7XRCC0X0+NwnUxM7T9DWZDbqjyZDO1HYcQYHB+Phw4dwc3PDzJkz0bJlS9y7dw/BwcFC9q9K6sBA4Wv5moZib6zwkDAvONr+tzYmrTmbDo0dqOgoqsf3NJMG9nTt04Qms1F/NBnaicJVtXXr1q2wQgQANGjQAJmZmbx3rCaUmcfJ5/xNTc3dpBFtahNsXiajtsOqarWXGkMo2VqCJSUluHHjBsr62efPn2ts7dokFFDX7BkWgwRKaVpAuln1v6c/o+o03Vt54OHzSMH0ylOZnpBOc0b39vD/J0qw9rVBUwi9WxHVF8b5zBqIzw5c5VVTXb3eHfndBlATnx2GdlKj4/Tyki40np+fL7e0noGBARwcHLBnT+WLgguNo4LFQXxGm6c6tOKtLUVQ5Reiulz++1LNFwmkRyPK3HGa3pe7pjSV1avJKSrC1K3H1W6Dbz1l7FLEyWris8PQTmp0nPHx8QCAadOm4ciRIyoLTZkyBdeuXUNOTg4cHBywdOlSzJo1CwBw7do1zJ8/H0lJSejWrRv8/f3RokX1adgi0F8pUNKh8sUIakLVFG3si0w0HTBQpXsVobIUreiZCA6O9MZyZHq0UrPJ8SI0bER3rIq2pkyPD4eoKJnJibBqYK+zeoq8VklRoZgy5SPeNBm6i8LFQeo4TQD49ttvkZCQgOzsbJw5cwarVq1CWFgY0tPTMWbMGGzYsAGZmZnw9PTE+PHja2wvSwNVtZuCYqnqrf+Nflpo+6btVPVWr9lBdTzzxG76O1zQ0LwVkcw9fDZvpOo0AeD2b/v0Wk+mWfZ1Lvtg1C40ssh7TEwM+vbti127diErKwv+/v64fVu6L2NOTg5sbW0RHh4OV1fXKttQtDhIGwqDVI049b0giBUAqQ77stYN1BlnZcVB2otSi7yry+effw4LCwu4urqicePGGD58OKKjo+Hh4cFdY2lpCWdn50p3EC9LugIRJ99Oc/7Zh0rfp47TnPOlML+qq3Oai+cvEUSzPDKn6b2C7mostPX40lQmwrnss15tPWWhrakrNrIIVT+h6jj37t0LsViM4OBgjBkzBqampkrtIO7r6wtPT094enoiH6UIRDoAYDHikYJCxCMfK5EIAPgVr+GXLD3fMywGaYVFuPs2B5OjpWO2q54nIyBNOo2m470nkJSU4NobMeY8ld7/Vdy/OJOeBQB4547UiZcSYEZgOABg7IlQnI9Jg7igGPZbLgIA/MKSOOc6xP8f3ErIQHK6BE1H+QAAdgaEcosadPE6irCYVMQmZXK7oKw7GMJtXu3S6Qs0a9IQYeEidO69VGrnisPYsecMAMDRZTaSUzIRFByFvsOlE7PnfLkPvoeky/RZO06BWJyHsxfuY8S4LQCASTN/wvHfpPNuG1lIx9wCT57CvBmfAQCmjJ0KY2NjSMQStLKXrpJ0xO8o50xHDxmNkFshSE1OhXsr6Y8dn10+3CIGg3oMRuSDSIjiRNyuJ9s2buM2q+7u3gOiOBGOXY7CVyOlyzem/ZuE/x3YC0BatZiRlopHd0KwYpJ0LMl7xde4eEI6TDDe3Qm5EgnuXbuEDbMnAwC2L5qLm2cCAQAjnaU7Kdw8E4jti+YCADbMnox71y4hVyLBeHcntO7QERdPHOGc2YpJH+HRnRBkpKViRvf2AID/HdjLTXb/auQAPHsUiZfxIm53jOO7tnKbGs8b0A0v40V49iiSs8lv82o5mxq3aKm0Tfv3HMCtiGSMdLbDrYhkPAn+C+d+XA4AOLX5C4hCg1CYl4Ndk7sDACIv/8F9sb+Ivo+kqFBIMl/BZ5Z0jDz0zGHc8Jem4Y9+MwGposfITE6A34IRAICQkz4IOSn9nPotGIHM5ASkih7j6DcTAAA3/Lcj9Mxh6Xs+ayAkma+QFBWKgNXS4sGMf58j8vIfAIBdk7ujMC8HotAgnNos3W3n3I/L8ST4L6l9Y6WfHWVsCljtJWdTI+d2gtt02We9nE0NmznzZtOtiGR8PnIYfv31T/x1PQITPdvhVkQydqzfwhZa0AE0th/nvHnz0K5dO4hEIhQVFWHv3r3cuQ4dOmDt2rUYO3ZslfcrkqrlK+LUxNxNodK0mk7RsvRs5bAohFGeG5tnslStlkI14ixLcXExRCIR3NzcEBn53zy+nJwc7nh1JGhgHqcsslQUdZ2mteMUle5XB1mkKQSVOc3x7k6C6VUGbb2qNIVM3cmiG5rQ1qwNNjK0FyqO89WrVwgICIBEIkFJSQkuXbqEEydOoH///hg9ejSioqIQGBiI/Px8rF+/Hu7u7tUWBgFAc5hWe14IRF8LNzWkMpJjfuG1PUWizYciYRY/qCrSPHSbbuUwbT2ZJs0xLpoLEWhKszbYyNBeqDhOAwMD+Pj4oGnTpqhfvz6WLFmCn376CR999BHs7OwQGBiIlStXon79+rh79y4CAgJqbDNf6R05VaNsmvZWQobC96mzvJ6MoL+rL5BSBkVTtCG3bvOmKaO69GzU3RDe9aqDpp7MSZ48cYaaJgC8iAqlqqcJzdpgI0N7oeI47ezscPPmTWRlZSE7OxuPHj3C7NmzufMDBw7E06dPkZeXh6CgIDg5OdXYZjZKBOxx5Rx8kCS4RtmxTVmhD02OHjzKa3s1jWleClBvfrCy0NArH1VGXgkUXLMstPU0oVkbbGRoLxorDlIXWsVB+jB3U5MFQbWlGIgV9zD4hhUHaS8aKw5Sl1coqva8EE5TNhWlJvhympNm/qRSO2VR1mnKpqbwgSJOUzZthBZ86ykyZimbnkAL2nqa0KwNNjK0F7obTPKIhQZ8/jAXemtxAsCHQztT1QOAQcMG8dKOopFml/6DedFTFL70lIkwnT1786KprXqa0KwNNjK0F71N1bI0reZStPqcnmUpWQYt+EzVvutYDyFzlP8+eP9cKkvVVoLOpmrjBZ7HWZnTtFh3vsb7+HSaBnU/VqktdZCtKKQqyjpN2Wo/tFBFT91pJLJVZWhBW08TmrXBRob2wiLOKmDRpvLoW6TJokuGJmERp/aisxGnRAPTUU4+elnteT7mbpZFtq6ssqjjNANPnlLpPlWdpmydWVooosf3IgWy9UxpQVtPE5q1wUaG9qKzjjNXwAUQqoo2L8S+EkSvqmjz3MUwQfSq48oF5eeOqhNphl6/rPK9fOoJuaqP6P4t3tvUJj1NaNYGGxnaC0vVVoIup2lpp2h1PT3L0rEMbYWlarUXnY04U6uZx8nnPpxlGXui6iW3hHCasu3AaDJlrOKTpPlwmrKttGgh06O5L6JsGypa0NbThGZtsJGhveis46wLYSKd6qLNme82F0SzKuZ8qtycSj6izakzFXOcfEWaQyZM46UdRWna9QPqUabHoKq3x9MHPU1o1gYbGdqLzi6AYKYBn9/bqSFVvb69qt9arSx8pWh79u5R4zV8pmfbd+vJW1vVIXOWzdp3oaJXFtqazEb90WRoJzobcSYJMI+zprFN552Vbysk1NimY5vZ1Z4XAnfn6ueq8T2m+WmP9ry2VxllI0yfWXS3htOEJrNRfzQZ2oleFgepOsapq0VBtAqCdK0QiBX+MHQZVhykvehsxCnWwDxOv7CK24rxPXezLIpsK8a30zziV/m2YkI5zYsnhNnmqyqnGXn5D0H0qoO2JrNRfzQZ2onOOs4C8BsoKxJtPkjO4k1PkWjzfriINz1FiXwQWeGYkJHms0cRvLZXU7Vsmugxr3qKQFuT2ag/mgztRKccp6+vLzw9PeHp6YliEAQiHQCwGPFIQSHikY+VSAQAbElMhV+y9HzPsBikFRbh7tscTI6OBwCsep6MgLRMAEDHe08gLijG+Zg0bsrJjMBwbqUg2Rq1vZ0acluLjT0RivMxaRDnFsJm8C5p/85EYu5W6QT7/l8EICg8CcnpEjQd5QMA2BkQiiXeNwAAnXsvRVi4CLFxyXDpJC1zX7v5JNZuPgkAcOn0BZZ8MRJh4SJ07r1UaueKw9ix5wwAwNFlNh6+ao2QWyEYPWS09Pz8JVzE2MreGRKxBJfOX+ammMyb8Rm3MpBsTdrAk6e4rcSmjJ2KwcMHQyKWoJW9MwBg3c5j8F7xNQBgxaSP8OhOCDLSUjGju3Rs8n8H9sJv82oAwFcjB+DZo0i8jBdh3oBuAIDju7bi+K6tUv0B3fAyXoRnjyLx1cgBAABzKyv878Be6WvevT0y0lLx6E4IVkz6CADgveJrLiod7+6EXIkE965d4qaVbF80l1sNSLYO7ZPgv7gtoE5t/gKi0CAU5uVg1+TuGPzZakRe/gOXfdYDAAJWeyEpKhSSzFfcGFbomcO44b8dAHD0mwlIFT1GZnIC/BaMAACEnPRByEnpe+q3YAQykxOQKnqMo99MAADc8N+O0DOHAUjHxXqMn4ekqFAErPYCAFz2Wc9FL7smd0dhXg5EoUHcdIdzPy7nVqmRrY9anU0A5GzKTEkU3CZJ5is5m2R9EMqm8u/T4M9WC25T+fep74zFgtpU/n1iaC86O8ZpbmAIP7xT6TlVxjgViTiH+P+DSzO6c8+FHtvsO3w1gv5aX+V5IcY2Rw8Zjf9d+h8AOmOaKyZ9hM3H/1SrDWXGMgNWe2HCej+19JSFtiazUT802Rin9qJTEWdZ6mlgJs3Kvi5U9dZ+O67Kc0IVBC1ZuQQAvUKgiV8uVet+ZQuAeoybp5aeKtDWZDbqjyZDO9FZx2kMg0qPC7VqEAC0bmgpWNuV4dLasdLjQlbROrd2plo969jSWeV7VamabeBY9TKNQkFbk9moP5oM7URnHWcyCnlrS9FpKL18/+b+T2MKimefZSppqEO/HkOo6i0epfzcOHWWyzu6dJJK96kDbU1mo/5oMrQTnR3jrGoep1Djm+XR1NxNoedsavtcTTY3k1FbYGOc2ovORpxvNTCPc9ft51T1ZBW0tLibYsRVuNJCUT2+FmWXVVHShLYms1F/NBnaic46zmKe53EqQoo4HwC9aDM55Y3ccyGjTVmkmfkqVTCNylBEj88oU5L5mre2tFWT2ag/mgzthKVqoXyqVhNp2tqYomVpWUZthqVqtRed3R3lJU/FQco4zR6+wQj7bY5KOqo4zc69lyLs1laV9JShrNP8auQA/HjmmuCaNekJ5TSPfjMBU7cFCNK2tmjS1gu79hyhvy9Gl092UNMM/X0x5u37HzU9QDOfHYZ2orOO0xbCTTupCu8P3anq+e6aC4BOilbG/E07BdOqjPJ6QkeZg+atFrR9bdDkSy/smuJj+q59PudFUxk9ZfrXeUArtTU18dlhaCc66zg1MThrbUo3nWltZU5VD5AugacpPRqpWRNzC8E1NK2piJ4yTkcRjEzMeG2Pbz0+nKwmPju6QGZmJry8vHD58mXY2tpiy5YtmDSp4tSdefPm4ddff+WeFxUVwcTEBGKxmDsWEBCAdevWISkpCQ4ODvD398f7779PxQ5l0FnHmYoi6pofn36Ip+OV38xW1bHND8dtwd+PwlW6VxEqG9fcMGsS9l27K5hmZXqTdtBLuf1v8xfw8j5LTU8TmidWzcN7k+lWRz88v4mqppB6VTnZO8c+5zT5iGD1hfnz58PExARpaWmIiIjABx98AA8PD7i5ucldt2/fPuzbt497PmPGDBga/hcCXblyBcuWLcPJkyfRtWtXpKSkULNBWWp9cZAyY5y0i4Jopmg1BSsAUh++o0eG6vDpUHWhOCgnJwf169dHVFQUXFykS5JOnToVTZo0wffff1/tfQ4ODjh37hz69OkDAOjRowe8vLzg5eVV5X3ags5OR3mDYrXbULaadt3BELU1lWHbxm1U9QBwO5nQ4FZEMrd7BS1o6wmhGXbtudyjPPH36Bew0NbUVhvLvzdVvUe0KcouwMtLCUo/Xr9+ze1I5enpCV9fX7l2Y2NjYWRkxDlNAPDw8EB0dHS1/QkMDISdnR169+4NACgpKcH9+/fx+vVrtG7dGk2bNsWCBQuQl5fH/4vBAzqbqqWN6cC2gAqOU71o8x+V7q0JbYg2WaSpONrwxctQj6reQ21P+drZ2VUbcUokEtjY2Mgds7GxkRu3rIzDhw9j2rRpMDCQrjmelpaGoqIi/PHHHwgODoaxsTE++ugjbNy4EZs2bVLfEJ6p1alalqbVDMxpVg9zlIzOA1rxmqrtYGWO/3VQfkOFCUVm1fYhPDwcPXv2RG5uLndsx44dCAoKwtmzlY/rv3jxAi1btkRsbCxatZL+cHjz5g0aNGgAf39/TJ8+HYA0Kt24cSPCw4Wr81AVnU3VvuBxkXdFcZ2o3F586q5L2929h1r3V0ZNTlO2AbVQlHeask2HaUFbTxFNvtN6d47RnRqiCU19t1FXfjy5uLiguLgYcXFx3LHIyMgKhUFlOXLkCHr06ME5TQCoX78+mjZtykWg2o7ORpzNDEzxA5zkjglVGCSLNmOTMuHSvIHC7asbbYriRHB+R/Vtt8qjSKT5Ml6EJmps9VUdlUWamckJaODoJIheZdDWq0xT6C/F3KyXsKjXRFANTWvWBhuz727U+ogTACZMmAADAwMcOHAAERERGD58OG7fvl2l82zTpg2WLVuGmTNnyh1fvXo1Lly4gPPnz8PY2BgjR45E3759sWHDBqX7LTQ6O8ZZqub9quyIIs6jG+VKxBKqegCQJxFGs6r0bGFebqXHhYK2HgA8vBkHa3t1P7GKU1KYT01LU5q1wUZdYe/evZg5cybs7e3RsGFD+Pj4wM3NDUlJSWjXrh0eP36M5s2bAwD++ecf/Pvvv/jkk08qtPPdd98hPT0dLi4uMDMzw7hx47By5Ura5igElVRtQUEBvLy80KJFC1hbW6NTp064cOECd/7atWtwdXWFhYUF+vXrh8TExBrbTNfAPM55W68ofC0fY5tLFnyjUhuVoei45s8rv+ZNU0Z1Y5pX9q3nXa86aOrJUq9Pb9KdU0lbTxOatcFGXaFBgwY4ffo0cnJykJSUxC1+0Lx5c0gkEs5pAkD37t2Rk5MDa2vrCu0YGxtj7969yMrKQmpqKnbv3g0zM7oLaygKlVRtTk4Otm3bhhkzZqB58+b466+/MHHiRDx69AhWVlZwdnbGgQMHMGLECHz33XcIDg7GnTt3qm2zsuIgbSoM0raiIE0VBNW2QiBdGZtiaD+6kqqtjVCJOC0tLbF27Vo4OTnB0NAQH374IVq2bImwsDCcOnUKbm5u+OSTT2BmZoa1a9ciMjIST58+rbbNDB7mcSpCWae5xPsGFU0Za5av4aUdZZym32b+1uNUxGne8N/Om54iCKVXXWHPs5BDgmhWBW09TWjWBhsZ2otGqmrT0tIQGxsLNzc3REdHw8PDgztnaWkJZ2fnSifQ+vr6chNx81CKQKQDABYjHikoRJQkD6MeiQAAWxJT4ZcsPd8zLAZphUW4+zYHk6PjAQDzzz6EX1gSAMB+y0WIC4pxPiYNY0+EAgBmBIbj5KOXAACj96VftmmZOZi87hwAYOSyUzgbIoI4txA2g3dJ+3cmEnO3Xoahx3voO3w1goKjkJySCUeX2QCkG1MvXiHdDLdz76UICxchNi4ZLp2+AAB88/0/3KIH3d17oE4dY0Q+iMSgHoMBSB2pzy7pZHr3Vh5ITU5FyK0QjB4yWvo6zF+CI35HAQCt7J0hEUuw59er2DB7MgBg+6K5uHkmUNp/ZzsAwM0zgdi+SLqY/IbZkyF5+xa5EgnGuzsBAC6eOALvFdL07YpJH+HRnRBkpKViRvf2AKQbUcuc7VcjB+DZo0i8jBdheq/OAKST/2ULAPgtGIHM5ASkih7j6DcTpK+p6DG3QbDPrIGQZL5CUlQoAlZLVw+57LMekZf/AADsmtwdhXk5EIUG4dRm6Wt27sfleBL8l9S+sdLP0ZPgv3Dux+UAgFObv4AoNAiFeTnYNbk7rBrYIfLyH7jsI03ZBqz2QlJUKCSZr+AzayAA6YbFMgd79JsJSBU9RmZyAlcdW9amn2cORXBgMMSvRAj9fTEA6RdsUsSf0mv9Z8LQ2AxvXkYh/PQqAMDToL1Ijr4MALj1y0QUF+YhPSEUD89L56tFX9mJtNhbAIAbe6XvbVrsLURfkS6I//D8JqQnhKK4MA+3fpkIAEiOvoynQdJU4uv4u3jzMgoFOZkI8ZcWYCRF/Ml98Yf+vhjiVyLkZr3kKkXj7wVwE/zvHPscuVkvq7WpICdTzqas1KeC2hR+epWcTSaWDQS3qfz7VMfMWlCbyr9PDO2FelVtUVERhg0bBmdnZ+zfvx9eXl6ws7OTW56pZ8+emD17NmbMmFFlO+qkamtTmlYTKVp9T8+ydCyDBixVq71QjThLS0sxdepUmJiYwNvbGwBgZWWF7Oxsueuys7MrHTwuSxIKBOtnVTQdRXe5NvdWHjVfVA2qOE1ZJKkqyjpNWZRHC3X0VJ1nKYsmaEFbTxOatcFGhvZCbToKIQReXl5IS0vDX3/9BWNjaXTo5uaGw4cPc9fl5ORAJBJVO4EWABxhImh/gYrR5r0DU2u8h89o8/Lfl1RqC1A90txx+qrKmqpEmlO3HldZTxVU0VM3wvT8hO44Lm09TWjWBhsZ2gu1iPOzzz7DkydPcPbsWZib/7fP5OjRoxEVFYXAwEDk5+dj/fr1cHd3h6ura7XtFYH+ug2xLzKp6omeiajqAUByPF3NzOSapx5pQo/PlXxys+imrmnraUKzNtjI0F6oOM7ExETs378fERERcHBwgJWVFaysrHDs2DHY2dkhMDAQK1euRP369XH37l0EBNS8C0EWparasqw/eLva83yPbW7fpNovXHXGNU/sVm13FFXHNW//tq/mi3ikJj0hdrNICKW7kwdtPU1o1gYbGdqLzi65R6M4SNnCIG0pCqJdEKQPxUCs4IehbbDiIO1FZxd5T9dAxDl362WqeovnL1H6HnWdpmzqiaKo6zRl00JoUV6Pxn6JsukHtKCtpwnN2mAjQ3vR2bVqTSHsKvqVRZudXRtVeb0Q0abHu8pV1fIRabbu0FHha/mINBs5t1O7DWX1aEeXde1a67WeJjRrg40M7aXWpWr1OU1LM0Wri+lZlo5l6BIsVau96GyqNkED8zhlKwTRopW94h90vpymbMWg6uDTae6a3J23tqpD5jRlK7jQhLYms1F/NBnaic5GnE4GZthMOeIU5xbC2qLi/FGhok2JWAIrayuF2uLLceZKJLCwqlqT70izMC8HJuaWvLZZlvJRZnFhHuqYmFdxtTDQ1mQ26ocmizi1F52NOPPV3pFTeYLCX1DVC7lV/fQXGXymaKPuhlR5Toj07IuoUN7blFFZajYrOUowvaqgrcls1B9Nhnais44zGyWCtV3V+OYvZyIrHBNybPPowaM1XsP3uOalgCOVHhdqTDPySiDvbVZXKStbtJsmtDWZjfqjydBOdDZVK2RxkDKFQZouCqJREKRLhUCsAIihL7BUrfaisxHnKxRR15RtKUaLeTM+q/a8EE5TtsWYDKGdpmz7Lz5QxGnKtnyiCW1NZqP+aDK0E52dx2mhAZ//QQ/5X2xCR5uDhg1SqX116NJ/MPd/GpGms2dvtdtQJsq0beGptp6y0NZkNuqPJkM70ZtUbW1L0wqdotWV9CxLzTL0FZaq1V50NlUbr4F5nEbv091WqJGFQ6XHhXSaI53tqDrN7WNV23NU1aXybuwdrZKeOtDWZDbqjyZf5OQQ3LtTpPSDUTks4iyHohGnvkabuhBpsiiTURvgM+KsrJhSEXZ2tmYRZyXobMQpEXA6SlUcv/KEql7gyVMVjgntNJ8E/yVY+5WhjB4fC7Knxd5S635d0GQ26o8mQzvRWceZq4EFEM7flm7yrGq0qSxXLlyRe05j6onoPt0vB0X1+Ioy0xPp/3qmrcls1B9NhnbCUrVl0PY0bW1M0bK0LKO2wlK12otORZy+vr7w9PSEp6cnElGAQKQDABYjHvF5BYiS5GHUI2lUuCUxFX7J0vM9w2KQVliEWwkZGOL/DwBg/tmH8AtLAgDYb7kIcUExzoaIMHKZND06ed05LjUrKwrqPPMIN5dzxLgtOHvhPsTiPFg7TpH279AVzPlyHwCg7/DVCAqOQnJKJhxdZgMAfHb5YM3yNQCAQT0GI/JBJERxInR37wEA2LZxG7Zt3AYA6O7eA6OHjkHkg0gM6jEYd1OM4Ld5Nf53QLon4Izu7ZGRlopHd0KwYtJHAKR7aV48IV35Z7y7E3IlEty7dgkbZk8GIJ2jefOMdKWekc52AICbZwKxdPo0AMCpzV/g6NJJKMzL4RZfj7z8B7eHZcBqLyRFhUKS+Qo+swYCAELPHMYNf+nrc/SbCUgVPUZmcgL8FowAAISc9EHISR8AgN+CEchMTkCq6DGOfjMBAPDLZ8MReuaw9PWZNRCSzFdIigpFwGovhF17jqdBe7kVW279MhHFhXlITwjFw/ObAEjn1slSaLLijbTYW9ycu4fnNyE9IRTFhXm49ctEPDy/CcnRl7m9FcNPr8Kbl1EoyMlEiP9MAEBSxJ94FnJIat/viyF+JUJu1kvcOfY5ACD+XgDi7wUAAO4c+xy5WS8hfiVC6O+LAQDPQg4hKeJPqf3+MxHx5xq8eRmF8NOrAIB3mwDI2RTsN1VwmwpyMuVsun1ktqA2lX+fHp7fJLhN5d+nyLPrBbWp/PvE0F50NuJ0MDDBTrTkntOIOM+GiPDR55MV6l95VIk2L52/jCEfSOdV0oo2RaFBcO7SVzCt8lSmJ2SUmZ4QClunLoK1rw2azEb90GQRp/aiUxFnWcw00PW+nZpR1evZWxqJ0kzRNmtP98uovJ7Qqdl6ju0FbV8bNJmN+qPJ0E50duWgJCXncSq6nVh1NB3lA3Ga+ivdKIq7swdORCZQ0wOk6dKFx/7RiB6N8czbh2ei9+wTgutoUpOWXtb9/350Rd7/Gh6e9JakK6tXz9ORiqYmPjsM7URnU7XKFgfxMYdTE0VBtaEgiBUAaS9lnaOuQsux8g1L1WovOpuqFWtgHqfvoSs1X8Qj63YeE6ztqpxm5OU/BNOsjLO791HVqw3bUSmrl3U/ucqHoqS/+lvZbqqFMnrV2aeMnWxbMYYMnU3VFoDfQFmRaPO+3z7M+ZTewuvPHkUAE6dR0wOANNFjalph154j+/UzOGJwzRfzBG09TWiW16MRNebmJAmuIaReTa9RPU9HjXx2GNoJS9X+P9qWptX3FC1Lz/KPPqRVdRUh0sEsVau96GyqNgWF1DX7Dl9NVU82P5MmAau9BNco6zRlc+ZoQVtPCM2a0o1xT37iVU8RaGtqm418pLsZuoPOpmrraaDra78dp9T16q4UNPHLpWrdXxk1RZs9xs3jXbMs5SNNpy4TBNUrD209dTRV/dJ1aDJcpfvUgbamLtlY3fuoq4VLtR2ddZzGMKCu6dKazodclqZ1bKn8/nnVoUiKtoGj8ukcRaksPWtRj+4XB209RTT5jkrMzOx5bU8bNfXFxuree51NB9YCdPa9SaaYqpWNb3r2WUZNEwAWjxpIVQ8Aji6dJEi7VY1p3v99iSB6VUFbr6wmrVTe0+itvLepbZq1wUaG9sKKg/6f6oqDVCkM0raiIE0WBNXWQiA2vsVQB8OSg6w4SEvR2YjzLY/zOBXdFWXHnjO8aSqCbEF3dVHGacoWXOeLmpymbJFtWtDQKx9JpqVcFVyzLLT1NKFZG2xkaC86O8ZZzPM8TkVITnlDVS/zVSpVPQCQZL7mrS1FIs3CnEze9BRBKL3qosuioreCaGqLniY0a4ONDO2lVqRqac/h1KY0raZStLUlPcvSsQyhYKla7UVnU7UvKRUHlXWanXvzPz2kOr4aOYCqHgBun0x1UMZpyvZGpAVfesoU9jyN+p4XTUWhracJzdpgI0N70dlUrS1q3n+Tb3x3za3xGnXnbpZl/ib1dptQJdocNE+9RR6UjTRd+3yulp6yqKOnanTZvKUwlcraoqcJzdpgI0N70VnHqYlQ2drKXLC2K0vTmltZqdyeqilaE3MLlTVVSc8amZiprKcKquipm441NDRV635t19OEZm2wkaG9UPM/3t7e8PT0hKmpKWbMmCF37tq1a3B1dYWFhQX69euHxMTEGttLRZFAPa2aD8dtoaq3YRb9X7j/2/yFSvepOqb58Pwmle5TFUX1+Jxn+TyW7g4wtPU0oVkbbNQVMjMzMXr0aFhaWqJFixY4fvx4ldc+f/4cH374IaytrWFra4ulS/8b/kpISMDw4cNRv359ODg4YMGCBSguLqZhgtJQKw46deoUDA0NcenSJeTl5cHf3x8AkJ6eDmdnZxw4cAAjRozAd999h+DgYNy5c6fa9vgqDuKzMEhbioJoFwTpUyEQK/ZhaAu6Uhw0ceJElJaWws/PDxEREfjggw9w+/ZtuLm5yV1XWFiItm3bYv78+Zg7dy6MjIwQGxsLd3d3AMDw4cNhb2+Pffv2ISsrC4MGDcLs2bPx5ZdfKt1voaEWcY4ZMwajRo1Cw4YN5Y6fOnUKbm5u+OSTT2BmZoa1a9ciMjIST58+rba9N6D/S2Tt5pNU9Y7vor9SSchJH6WuV9dpxt8LUOt+vvSEXJA75d/zgrSrLXqa0KwNNuoCOTk5CAwMxIYNG2BlZYVevXph5MiROHr0aIVr/f394ejoiK+//hqWlpYwMzPjnCYAxMfHY9y4cTAzM4ODgwOGDh2K6OhomuYojMbHOKOjo+Hh4cE9t7S0hLOzM6Kjo+Hq6ip3ra+vL3x9fQEAeaZ1sLO99X8nq8vcnqtmPmR15wAAN7j/vX79Gueu0IuuXr9+jbiQ69T0ZJo3RKHU9Epfv0b23Wca1xPyF6RJndcwLEkTUEGzeprQrA021hQ8KIPLkL7YmZ6u9H15eXnw9PTkns+ZMwdz5szhnsfGxsLIyAguLi7cMQ8PD9y8ebNCW3fu3IGTkxOGDRuG0NBQtG/fHnv27EGHDh0AAAsXLkRAQAD69u2LN2/e4MKFC9iwYYPSfaaBxh2nRCKBnZ2d3DEbGxuIxeIK15Z90zw9PanPL6KtyWzUfT1NaDIb9UOzrMNSl4sXL/LWVlkkEglsbGzkjlX1/f3vv//ixo0bOHPmDAYMGIBdu3bho48+wtOnT2FiYoI+ffrgl19+Qd26dVFSUoLp06dj1KhRgvRbXTQ+j9PKygrZ2dlyx7Kzs2FtbV3FHQwGg8HQBpT5/jY3N0evXr0wbNgwmJiYYMmSJcjIyMCTJ09QWlqKIUOGYMyYMcjJyUF6ejrevHmDZcvobqyhKBp3nG5uboiMjOSe5+TkQCQSVRhYZjAYDIZ24eLiguLiYsTFxXHHIiMjK/3+dnd3h4FB5dtBZmZm4sWLF1iwYAFMTU3RsGFDfPrpp/jrr78E67s6UHOcxcXFyM/PR0lJCUpKSpCfn4/i4mKMHj0aUVFRCAwMRH5+PtavXw93d/cK45vlKZtnpwVtTWaj7utpQpPZqB+amrBRWSwtLTFmzBisXr0aOTk5CAkJwZ9//ompU6dWuHbKlCm4c+cOrl69ipKSEvz000+wtbVF27ZtYWtri5YtW8LHxwfFxcXIysrC4cOH5epftApCiTVr1hAAco81a9YQQgi5cuUKadOmDTEzMyN9+vQh8fHxtLrFYDAYDDXIyMggH330EbGwsCDNmjUjx44dI4QQkpiYSCwtLUliYiJ3bWBgIHF2dibW1takT58+JCoqijsXHh5O+vTpQ+rVq0caNmxIPv74Y5KWlkbdHkXQ2UXeGQwGg8HQBBof42QwGAwGQ5dgjpPBYDAYDCVgjpPBYDAYDCVgjpPBYDAYDCVgjlOL8PX1RY8ePWBjYwMjIyPY2NigR48e+OWXXzTdNV7Qd/sAZqO+kJGRAV9fXyxcuBAzZ87EwoUL4evri4yMDE13jaEFaHzJPUXx9fWFv78/oqOjIZFIYGVlBTc3N3z66aeYPXu2prunNsuWLcO5c+ewePFieHh4wMbGBtnZ2YiIiMDOnTvx/PlzbNlCd1szPtF3+wBmo77YeO3aNXz88cfo0KEDPDw84OjoiOzsbBw7dgzLly9HYGAg+vXrp+luMjSJpufDKMLSpUtJu3btiJ+fH7l//z6Ji4sjYWFhxM/Pj7i5uZHly5druotqY2trS5KTkys99/LlS9KwYUPKPeIXfbePEGajvtjYtm1bEhgYWOm5U6dOEVdXV8o9YmgbOhFxHjx4EA8fPkTjxo3ljr/77rsYOnQo3N3ddf5XLqlhOm1N57UdfbcPYDYqcl4XSExMxAcffFDpueHDh2Py5MmUe8TQNnTCcdaGP1YvLy/079+/QgosMjISO3fu1Pl0tL7bBzAb9cXGbt26YdWqVVi7di0sLS254zk5OVi3bh26deumwd4xtAJNhbrKsHTpUuLq6kp++eUXcu/ePRITE0NCQ0PJgQMHSLt27ciyZcs03UVe2LdvH+nRowexsbEhRkZGxMbGhvTo0YPs27dP013jBX23jxBmoz6QkJBAunfvTszMzEi7du1I9+7diZubGzE3Nyfdu3eXW0KOUTvRmSX39u/fjyNHjlQoDpo2bRrmzp2r6e4xGAw9IzY2Fo8fP5b7vnnnnXc03S2GFqAzjrO2EBsbi+joaIjFYlhbW6N9+/Z69ceq7/YBzEYGQ9/RiTFOGfr8x5qUlITx48cjMjISzs7O3NiRSCSCh4cHAgIC0Lx5c013U2X03T6A2agvNgL6P/2NoSaazRQrRmJiInnvvfeIubk5ad++PenZsyfp0KEDsbCw0Jsxh/79+5NvvvmG5OTkyB2XSCRk6dKlpF+/fhrqGT/ou32EMBv1xcbaMP2NoR46kaodMGAAOnfujLVr18LCwoI7npOTg/Xr1yM0NBTXr1/XYA/Vx8rKCpmZmTAxMalwrqCgAA0aNEBOTo4GesYP+m4fwGzUFxvt7Owqnf4GAMnJyXB3d0d6eroGesbQFnRiyb27d+9i48aNck4TkO4+vn79ety9e1dDPeOPZs2a4dy5c5We++uvv3Q+/aXv9gHMRn2xsaZYQgdiDYbA6MQYp+yPdcyYMRXO6csfq7e3N8aOHYudO3dWWMosOjoagYGBmu6iWui7fQCzUV9srA1zVRnqoROp2mvXrmHs2LFo3759lX+s/fv313Q31SYjIwOnTp2SK0ho3749Ro0aBVtbW013T20qs8/NzQ2jR4/WC/uA2mmjtbU12rVrp1c2sulvjOrQCccJ1I4vpPDwcIhEIgwfPhwmJibw8fHB8+fPMWDAAHz44Yea7h6vxMfH4/z58wCAoUOHonXr1hruEUNRnj17hqNHjyIqKgq5ublo2rQpunbtihkzZsDY2FjT3WMwBEdnHGdVlJSUYNOmTVi9erWmu6IWfn5+WLVqFQwMDODo6IgxY8bgxYsXKC4uRkBAAHbt2oWZM2dqupsq07ZtWzx58gQAcPPmTYwcORI9e/YEAAQHB+PPP//U+azBwoULMW7cOM4ufeT06dOYMmUKevbsCUIIbt68ifHjx0MkEiE1NRVXrlxBq1atNN1NQUlKStKL4SGGGmiompc38vPziaGhoaa7oTZt2rQhMTEx5OnTp8TAwICEhIRw5y5evEjc3d012Dv1sbKy4v7fq1cvcvjwYe75r7/+Srp3766JbvGKkZERsba2Js7OzmTdunUkISFB013inXfeeYdcv36de37p0iUydOhQQggh27ZtI8OHD9dU16igL983DPXQiYizukiruLgYx44dQ0lJCcUe8Y+NjQ3evn0LQFotLJFIYGBgAAAoLS1FgwYNkJWVpcEeqkfdunWRnZ0NALC3t8fLly+5tF5JSQns7OyQmZmpyS6qjbW1NdLS0vD777/jyJEjuHXrFnr16oUZM2bg448/llswXFepV68e3rx5w302i4uL0bhxY7x+/Rq5ublwcHDg3mdd5datW1WeKygowNChQ3X++4ahHjpRVXv8+HF4eXmhQYMGFc7pywfY0tISRUVFMDY2xowZM7gvJgDIy8uDoaFOzByqkqKiIhw6dAiEEBgYGKCwsJBznMXFxXrxPhoYGMDCwgLTp0/H9OnTkZSUhCNHjmDz5s1YsGABxo4dC39/f013Uy06d+6M3bt3Y+HChQCAn376CW5ubgAAIyMj1KmjE18p1dK3b180btxY5//mGAKi4YhXITw9Pcmff/5Z6bm8vDxiYGBAuUf8M2XKFPL48eNKzwUEBJA+ffrQ7RDP9OnTh/Tt25d73Lt3jzt36dIl0qVLFw32jh+sra2rPBcSEkLmzp1LsTfC8OTJE+Li4kKsra25tPSjR48IIYQ8fPiQfPPNNxruofo4OTnJDZWUJS8vj6VqGbqRqv3555/RpEkTjBo1qsK5kpISbNy4EWvWrKHfMUq8fv0aBgYGelM9XJ63b9+iqKhI5+2ztraGWCzWdDcEp6SkBE+fPgUhBK6urnoRZZblk08+wfvvv48vv/yywrnCwkK0adMG8fHxGugZQ1vQCcfJYDAYtCgqKgIANrWGUSUsic9gMBhlMDY2rtJplpSUYP369ZR7xNA2WMTJYDAYClJQUAALCwu9KGZjqI5+DU4wGAyGmtQ0/Y3BYI6TwWAwylAbpr8x1IOlahkMBqMMXbp0wXfffYeRI0dWOJefnw8LCwuUlpZqoGcMbYEVBzEYDEYZZsyYUaVjNDY21uupbwzFYBEng8FgMBhKwCJOBoPBYDCUgDlOBoPBYDCUgDlOBkMFnJyccPXq1RqvCwoKQtOmTVXSSEhIgIGBAZsCwWBoGcxxMhgMBoOhBMxxMhgMBoOhBMxxMnQGJycnbNmyBe3atUP9+vXx6aefIj8/HwDwyy+/oHXr1mjQoAFGjhyJ5ORk7r6FCxeiWbNmqFu3Ljp37ozg4OAatfLy8jB9+nTUr18fbdu2xdatW6tMuRYUFGDRokVwdHSEo6MjFi1ahIKCArlrNm/eDFtbWzg5OeHYsWPc8fPnz6NTp06oW7cumjVrhrVr16rwyjAYDJowx8nQKY4dO4ZLly5BJBIhNjYWGzduxPXr1/Htt9/it99+Q0pKClq0aIEJEyZw93Tp0gURERHIzMzEpEmT8Mknn3AOtyrWrVuHhIQEPH/+HFeuXMGvv/5a5bWbNm3CnTt3EBERgcjISNy7dw8bN27kzqempiI9PR0vX77E4cOHMWfOHMTExACQbmB+5MgRZGVl4fz58/Dx8cHp06fVe5EYDIawaGgfUAZDaVq0aEF8fHy45+fPnyetWrUiM2fOlNtAWSwWkzp16pD4+PhK26lXrx6JiIioVqtly5bk4sWL3PNffvmFNGnSRK4vV65cIYQQ0qpVK3L+/Hnu3MWLF0mLFi0IIYTcuHGDGBkZEYlEwp3/5JNPyPr16yvVXbhwIVm0aBEhhJD4+HgCgBQVFVXbVwaDQRcWcTJ0imbNmnH/b9GiBZKTk5GcnIwWLVpwx62srNCwYUO8fPkSALBjxw60bdsWNjY2qFevHt6+fYv09PRqdZKTk+W0yv6/smvL6sv6JaN+/fqwtLSs9Pzdu3fRr18/2NnZwcbGBvv27auxbwwGQ7Mwx8nQKV68eMH9PykpiRtXTExM5I7n5OQgIyMDTZo0QXBwMH744Qf89ttvePPmDbKysmBjYwNSw4JZjRs3xr///lupbnnK68v6JePNmzfIycmp9PykSZMwcuRIvHjxAm/fvsW8efNq7BuDwdAszHEydIqff/4Z//77LzIzM7F582aMHz8ekyZNwqFDhxAREYGCggKsWLEC3bp1g5OTE8RiMerUqQM7OzsUFxdj/fr1yM7OrlFn3Lhx2LJlC968eYOXL1/C29u7ymsnTpyIjRs34vXr10hPT8f69esxZcoUuWvWrFmDwsJCBAcH49y5c/jkk08AAGKxGA0aNICZmRnu3buH48ePq/cCMRgMwWGOk6FTTJo0CYMHD0arVq3QqlUrrFq1CgMGDMCGDRswduxYNG7cGCKRCAEBAQCAIUOGYNiwYXBxcUGLFi1gZmZWbdpVxurVq9G0aVO0bNkSAwcOxMcffwxTU9NKr121ahU8PT3h7u6ODh064N1338WqVau48w4ODqhfvz4cHR0xefJk7Nu3D66urgCAvXv3YvXq1bC2tsb69esxbtw4Hl4lBoMhJGyRd4bO4OTkhAMHDmDgwIHUtX18fBAQEICbN29S12YwGNoFizgZjEpISUlBSEgISktLERMTgx07dmD06NGa7haDwdACmONk1FqGDRsGKyurCo/NmzejsLAQc+fOhbW1Nfr374+PPvoIn3/+uaa7zGAwtACWqmUwGAwGQwlYxMlgMBgMhhIwx8lgMBgMhhIwx8lgMBgMhhIwx8lgMBgMhhIwx8lgMBgMhhIwx8lgMBgMhhL8H9Ut/ss4wzemAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df=matr2,\n", + " y_axis='temp_module',\n", + " x_axis='poa_global',\n", + " z_axis=mlfm_sel,\n", + " title='matrix predicted ' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 25) Contour plot of measured mlfm_sel vs. poa_global and temp_mod. Fig 9" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df=norm,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis=mlfm_sel,\n", + " title='avg normalised ' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.1,\n", + " levels=9\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 26) References \n", + " \n", + "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", + "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", + "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", + " \n", + ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", + " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", + "What's Most Important for Energy Yield' \n", + "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", + "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", + "\n", + ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", + " 'Choosing the best Empirical Model for predicting energy yield' \n", + " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", + " Canobbio, Switzerland 30-31 March, 2017 \n", + "\n", + ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", + "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", + "benchmark its practical relevance in energy yield prediction' \n", + "PVSC June 2019 Chicago, USA \n", + "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", + "\n", + ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", + "under Real Outdoor and IEC 61853 Test Conditions \n", + "Using High Quality Module IV Measurements' \n", + "36th EU PVSEC Sep 2019 \n", + "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", + "\n", + ".. [5] Steve Ransome (SRCL) \n", + "'How to use the Loss Factors and Mechanistic Performance Models \n", + "effectively with PVPMC/PVLIB' \n", + "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", + "https://pvpmc.sandia.gov/download/7879/ \n", + "\n", + ".. [6] W.Marion et al (NREL) \n", + "'New Data Set for Validating PV Module Performance Models' \n", + "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", + "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", + "\n", + "Many more papers are available at www.steveransome.com \n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'stop' is not defined", + "output_type": "error", + "traceback": [ + "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", + "\u001b[1;36m Input \u001b[1;32mIn [35]\u001b[1;36m in \u001b[1;35m\u001b[1;36m\u001b[0m\n\u001b[1;33m stop\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m\u001b[1;31m:\u001b[0m name 'stop' is not defined\n" + ] + } + ], + "source": [ + "stop\n", + "\n", + "# delete below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr2.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#norm.describe()\n", + "meas.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "whos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm.to_csv('\\\\mlfm_data\\\\export\\\\'+'norm.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "meas.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'meas.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stack.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'stack.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ref_data.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\' + 'ref_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stack.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "norm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ref" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "root_dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "matr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (Spyder)", + "language": "python3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc-autonumbering": true, + "toc-showmarkdowntxt": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index a522aa200a..4756b8e6ac 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -6,6 +6,7 @@ loss factors models (LFM) Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) +Thanks to Cliff Hansen (Sandia National Laboratories) https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules @@ -62,7 +63,7 @@ def mlfm_meas_to_norm(dmeas, ref): * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] * `'temp_module'` module temperature [C] - * `'p_mp'` - power at maximum power point [kW] + * `'p_mp'` - power at maximum power point [W] May include optional columns: @@ -73,16 +74,16 @@ def mlfm_meas_to_norm(dmeas, ref): * `'v_mp'` - voltage at maximum power point [V]. Must be accompanied by `'v_oc'`. * `'r_sc'` - inverse of slope of IV curve at short circuit condition. - Requires both `'i_sc'` and `'v_oc'`. [V/A] - * `'r_oc'` - slope of IV curve at open circuit condition. Requires - both `'i_sc'` and `'v_oc'` [A/V] + Requires both `'i_sc'` and `'v_oc'`. [Ohm] + * `'r_oc'` - inverse slope of IV curve at open circuit condition. + Requires both `'i_sc'` and `'v_oc'` [Ohm] ref : dict Reference values. Must include: * `'p_mp'` - Power at maximum power point at Standard Test Condition - (STC). [kW] - * `'gamma_p_mp'` - Temperature coefficient of power at STC. [W/C] + (STC). [W] + * `'gamma_pdc'` - Temperature coefficient of power at STC. [1/C] May include: @@ -91,7 +92,7 @@ def mlfm_meas_to_norm(dmeas, ref): * `'v_oc'` - Voltage at open circuit at STC. Required if `'V_oc'` is present in `'dmeas'`. [A] * `'beta_v_oc'` - Temperature coefficient of open circuit voltage at - STC. Required if `'v_oc'` is present in `'dmeas'`. [V/C] + STC. Required if `'v_oc'` is present in `'dmeas'`. [1/C] Returns ------- @@ -120,7 +121,7 @@ def mlfm_meas_to_norm(dmeas, ref): # temperature corrected dnorm['pr_dc_temp_corr'] = ( dnorm['pr_dc'] * - (1 - ref['gamma_p_mp']*(dmeas['temp_module'] - T_STC))) + (1 - ref['gamma_pdc']*(dmeas['temp_module'] - T_STC))) if 'i_sc' in dmeas.columns: dnorm['i_sc'] = dmeas['i_sc'] / dmeas['poa_global_kwm2'] / ref['i_sc'] @@ -161,20 +162,6 @@ def mlfm_norm_to_stack(dnorm, fill_factor): ''' Converts normalised values to stacked subtractive normalized losses. - Ref: - http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf - - current losses : - meas(imp) / ref(i_sc) = - poa_global_kwm2 * (norm(i_sc) * norm(r_sc) * norm(i_ff)) - - voltage losses : - meas(vmp) / ref(v_oc) = - (norm(v_ff) * norm(r_oc) * norm(v_oc)) - - 1/ff_ref = (ref(isc) / ref(imp)) * (ref(voc) / ref(vmp)) - - Normalized values can reveal losses via scatter plots vs. irradiance or temperature. @@ -209,7 +196,16 @@ def mlfm_norm_to_stack(dnorm, fill_factor): Returns ------- dstack : DataFrame - Stacked subtractive normalized losses. + Stacked subtractive normalized losses. Includes columns: + + * `'pr_dc'` equal to `dnorm['pr_dc']`. + * `'i_sc'` + * `'r_sc'` + * `'i_mp'` + * `'i_v'` + * `'v_mp'` + * `'v_oc'` + * `'temp_module_corr'` See also -------- @@ -253,6 +249,7 @@ def mlfm_norm_to_stack(dnorm, fill_factor): corr = (inv_ff - prod) / (inv_ff - tot) # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # accounting for series and shunt resistance losses dstack['pr_dc'] = +dnorm['pr_dc'] # initialise dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr dstack['r_sc'] = -(dnorm['r_sc'] - 1) * corr @@ -318,9 +315,11 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] * `'temp_module'` module temperature [C] + May include optional column: * `'wind_speed'` wind speed [m/s]. + c_1 : float Constant term in model c_2 : float @@ -414,6 +413,8 @@ def mlfm_fit(data, var_to_fit): bounds=bounds # boundaries ) + # if data has no wind_speed measurements then c_5 coefficient is + # meaningless but a non-zero value may have been returned. if c5_zero: popt[4] = 0. diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 5540368932..4a322bf9d9 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -24,8 +24,8 @@ def reference(): alpha_i_sc=0.0005, alpha_i_mp=0, # often not known, not used here beta_v_mp=0, # often not known, not used here - beta_v_oc=-0.0035, - gamma_p_mp=-0.0045, # = alpha_i_mp + beta_v_mp + beta_voc=-0.0035, # 1/C + gamma_pdc=-0.0045, # = alpha_i_mp + beta_v_mp delta_ff=0, # often not known, not used here ) # create p_mp and ff From 0325b81962f610f0a1f0780bfb3a178ffb92e1fd Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 29 Jun 2022 13:51:50 -0600 Subject: [PATCH 55/81] typo fix --- pvlib/mlfm.py | 4 ++-- pvlib/tests/test_mlfm.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 4756b8e6ac..1954770e0d 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -6,7 +6,7 @@ loss factors models (LFM) Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) -Thanks to Cliff Hansen (Sandia National Laboratories) +Thanks to Cliff Hansen (Sandia National Laboratories) https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules @@ -199,7 +199,7 @@ def mlfm_norm_to_stack(dnorm, fill_factor): Stacked subtractive normalized losses. Includes columns: * `'pr_dc'` equal to `dnorm['pr_dc']`. - * `'i_sc'` + * `'i_sc'` * `'r_sc'` * `'i_mp'` * `'i_v'` diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 4a322bf9d9..95b144f46c 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -24,7 +24,7 @@ def reference(): alpha_i_sc=0.0005, alpha_i_mp=0, # often not known, not used here beta_v_mp=0, # often not known, not used here - beta_voc=-0.0035, # 1/C + beta_v_oc=-0.0035, # 1/C gamma_pdc=-0.0045, # = alpha_i_mp + beta_v_mp delta_ff=0, # often not known, not used here ) From d4256ae65953a0f4c6c7ea36688b0ceb9d6823a8 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 29 Jun 2022 14:05:51 -0600 Subject: [PATCH 56/81] docstring edits --- pvlib/mlfm.py | 40 ++++++++++++++++++++++------------------ 1 file changed, 22 insertions(+), 18 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 1954770e0d..74eb87ce74 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -88,22 +88,23 @@ def mlfm_meas_to_norm(dmeas, ref): May include: * `'i_sc'` - Current at short circuit at STC. Required if `'i_sc'` is - present in `'dmeas'`. [A] - * `'v_oc'` - Voltage at open circuit at STC. Required if `'V_oc'` is - present in `'dmeas'`. [A] + present in ``dmeas``. [A] + * `'v_oc'` - Voltage at open circuit at STC. Required if `'v_oc'` is + present in ``dmeas``. [A] * `'beta_v_oc'` - Temperature coefficient of open circuit voltage at - STC. Required if `'v_oc'` is present in `'dmeas'`. [1/C] + STC. Required if `'v_oc'` is present in ``dmeas``. [1/C] Returns ------- dnorm : DataFrame Normalised values. - * `'pr_dc'` is `'p_mp'` normalised by reference `'p_mp'` and + + * `'pr_dc'` is `'p_mp'` normalised by reference `'p_mp'` and \ `'poa_global_kwm2'` * `'pr_dc_temp_corr'` is `'pr_dc'` adjusted to 25C. * Columns `'i_sc'`, `'i_mp'`, `'v_oc'`, `'v_mp'`, `'v_oc_temp_corr'`, `'r_sc'`, `'r_oc'`, `'i_ff'`, `'v_ff'` are returned when the - the corresponding optional columns are included in `'dmeas'`. + the corresponding optional columns are included in ``dmeas``. References ---------- @@ -321,15 +322,15 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): * `'wind_speed'` wind speed [m/s]. c_1 : float - Constant term in model + Constant term in model. c_2 : float - Temperature coefficient in model (1/K) + Temperature coefficient in model. [1/C] c_3 : float Coefficient for low light log irradiance drop. c_4 : float Coefficient for high light linear irradiance drop. c_5 : float, default 0 - Coefficient for wind speed dependence + Coefficient for wind speed dependence. c_6 : float, default 0 Coefficient for dependence on inverse irradiance. @@ -361,11 +362,15 @@ def mlfm_fit(data, var_to_fit): ---------- data : DataFrame Must include columns: + * 'poa_global_kwm2' global plane of array irradiance [kW/m^2] * 'temp_module' module temperature [C] + Must include column named ``var_to_fit``. + May include optional column: - * 'wind_speed' wind speed [m/s]. + + * 'wind_speed' wind speed [m/s]. var_to_fit : string Column name in ``data`` containing variable being fit. @@ -432,9 +437,8 @@ def plot_mlfm_scatter(dmeas, dnorm, title): ''' Scatterplot of normalised values (y) vs. irradiance (x). - y1_axis : e.g. norm(i_sc, ... v_oc),_temp_module_corr - x_axis : e.g. irradiance, poa_global_kwm2 - y2_axis : e.g. temp_air, temp_module (C/100 to fit graphs). + Electrical quantities are plotted on the left y-axis, and temperature + quantities are plotted on the right y-axis. Parameters ---------- @@ -557,7 +561,7 @@ def plot_mlfm_scatter(dmeas, dnorm, title): def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, - xaxis_labels=12, is_i_sc_self_ref=False, + xaxis_labels=0, is_i_sc_self_ref=False, is_v_oc_temp_module_corr=True): ''' @@ -591,15 +595,15 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, title : string Title for the figure. - xaxis_labels : int, default 12 - Number of xaxis labels to show. Use 0 to show all. + xaxis_labels : int, default 0 + Number of x-axis labels to show. Default 0 shows all. is_i_sc_self_ref : bool, default False - Self corrects i_sc to remove angle of incidence, + Self-correct `'i_sc'` to remove angle of incidence, spectrum, snow or soiling. is_v_oc_temp_module_corr : bool, default True - Calculate loss due to gamma, subtract from v_oc loss. + Calculate loss due to temperature and subtract from `'v_oc'` loss. Returns ------- From 83414289dbe9f143ea73a1a906d3be0cf070ff57 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 29 Jun 2022 14:07:03 -0600 Subject: [PATCH 57/81] remove old tutorial --- docs/tutorials/mlfm.ipynb | 1699 ------------------------------------- 1 file changed, 1699 deletions(-) delete mode 100644 docs/tutorials/mlfm.ipynb diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb deleted file mode 100644 index 29a4574ce7..0000000000 --- a/docs/tutorials/mlfm.ipynb +++ /dev/null @@ -1,1699 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MLFM PVLIB 211213\n", - "### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", - "#### Tutorial overview.\n", - "\n", - "I) The Loss Factors Model (LFM) |2011 ref 1| quantifies \n", - "normalised losses from module parameters \n", - "(e.g. i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) by analysing the shape \n", - "of the IV curve and comparing it with STC reference values from the datasheet. \n", - "\n", - "II) The Mechanistic performance model (MPM) |2017 ref 2| \n", - "has \"meaningful,independent, robust and normalised\" coefficients \n", - "which fit how the LFM values depend on irradiance, module temperature \n", - "(and windspeed) and time. \n", - " \n", - "These parameters relate to \n", - " - c_1 = \"overall performance quality\" \n", - " - c_2 = \"normalised temperature coefficient\" (units /K) \n", - " - c_3 = \"low light level drop\" due to v_oc and r_sc (r_shunt) \n", - " - c_4 = \"high light level fall\" due to r_oc (~Rseries). \n", - " (optional) \n", - " - c_5 = \"wind speed coefficient\" \n", - " - c_6 = \"low light level drop\" sometimes needed for r_shunt behaviour. \n", - "\n", - "III) This tutorial shows how to take module measured and weather data, \n", - "(either outdoor or IEC 61853-like \n", - "matrix data), normalise it, generate MLFM coefficients, fit them with the MPM then \n", - "analyse module performance looking for loss values, degradation and \n", - "allowing performance predictions. \n", - "\n", - "Fig 1: MLFM overview flow chart of this tutorial. \n", - "![mlfm_flow.png](mlfm_data/figs/mlfm_flow.png) " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "#import pvlib\n", - "#from pvlib import *\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", - "import os\n", - "root_dir = os.getcwd()\n", - "\n", - "root_dir\n", - "\n", - "# Import essential library file with lfm and mpm definitions\n", - "from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit, plot_mlfm_scatter, plot_mlfm_stack \n", - "\n", - "# STANDARD DEFINITIONS\n", - "\n", - "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", - "plt.rcParams['figure.figsize'] = [7,5] # setup figure size inches\n", - "plt.rcParams.update({'font.size': 12}) # setup fontsize\n", - "plt.linewidth = 1.5 # line width in points\n", - "plt.linestyle = '--' #- # solid line\n", - "plt.marker = 's' #o # the default marker\n", - "plt.markersize = 9 #6 # marker size, in points\n", - "plt.bbox = 1.4 # offset right to not overwrite" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'c:\\\\python\\\\ransome\\\\pvlib-python\\\\docs\\\\tutorials'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", - "import os\n", - "root_dir = os.getcwd()\n", - "\n", - "root_dir" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Select MLFM measurement files\n", - "\n", - "File naming conventions can be used to help identify files, for example \n", - "`x81_T1906_D3_Fh.csv` \n", - "\n", - "where \n", - " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", - " - 81 = module id/channel number \n", - " - T1906 = (T)ime started = yymm(dd) \n", - " - D3 = (D)uration in days \n", - " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", - " - etc. \n", - " \n", - " \n", - " Three default files are included (* = version number ) \n", - "(-1) t1_041.csv \n", - "(0) g78_T16_Xall_F10m_R900*.csv \n", - "(1) n05667_Y13_R1k6_fClear*.csv \n", - "(2) x19074001_iec61853*.csv \n", - "\n", - "\n", - "Essential default column names in meas() are :- \n", - "\n", - "meas { \n", - "'date_time', 'module_id', 'poa_global', 'wind_speed', 'temp_air', \n", - "'temp_module', 'v_oc', 'i_sc', 'i_mp', 'v_mp', 'r_sc', 'r_oc' \n", - "}\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# select one of the following meas files\n", - "meas_file = 0\n", - "\n", - "if meas_file == 0:\n", - " mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv'\n", - "elif meas_file == 1:\n", - " mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv'\n", - "elif meas_file == 2:\n", - " mlfm_meas_file = 'x19074001_iec61853_041.csv'\n", - "\n", - "# optional\n", - "# elif meas_file == -1:\n", - "# mlfm_meas_file = 't1_041.csv'\n", - " \n", - "# extract module id from filename e.g. 'g78'\n", - "mlfm_mod = mlfm_meas_file.split('_')\n", - "\n", - "mlfm_mod_sel = mlfm_mod[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get ref module data at STC \n", - "\n", - "Get STC Reference module data for the selected module \n", - "(searching for a row with same module id). \n", - " \n", - "Ref values include electrical data and temperature coeffs and must include the following :- \n", - "\n", - "ref { \n", - "'i_sc', 'i_mp', 'v_mp', 'v_oc', \n", - "'alpha_i_sc', 'alpha_i_mp', 'beta_v_mp', 'beta_v_oc', 'gamma_p_mp', \n", - "} \n", - "\n", - "NOTE : Users must add their own data to the reference file \n", - "when they add new meas data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Read all ref data " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# user must keep updated with their modules from their measurements\n", - "ref_file_name = (root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_reference_modules.csv')\n", - "\n", - "ref_data = pd.read_csv(ref_file_name, index_col='module_id')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Select stc data from reference database" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "while True:\n", - " try:\n", - " ref_data = ref_data[ref_data.index == mlfm_mod_sel]\n", - " break\n", - "\n", - " except IndexError:\n", - " print(\"You must define module ref data to use this module ...\")\n", - " break" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Put relevant data into a dict for easy use\n", - "# ignore any other columns that may be database specific\n", - "# as they aren't needed\n", - "\n", - "ref = dict(\n", - " # module_id = ref_data['index'].values[0],\n", - " i_sc = ref_data['i_sc'].values[0],\n", - " i_mp = ref_data['i_mp'].values[0],\n", - " v_mp = ref_data['v_mp'].values[0],\n", - " v_oc = ref_data['v_oc'].values[0],\n", - "\n", - " alpha_i_sc = ref_data['alpha_i_sc'].values[0],\n", - " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", - " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", - " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", - " gamma_p_mp = ref_data['gamma_p_mp'].values[0],\n", - ")\n", - "\n", - "# create p_mp and ff in case they don't exist\n", - "ref['p_mp'] = ref['i_mp'] * ref['v_mp']\n", - "\n", - "ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import measured data (outdoor or matrix)\n", - "\n", - "DateTime, Met and Raw module measurements. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Read in selected measured file data " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "meas = pd.read_csv(\n", - " root_dir + '\\\\mlfm_data\\\\meas_gtw\\\\' + mlfm_meas_file,\n", - " index_col='date_time'\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate useful data columns for meas" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2016-01-26 07:40:00-07:007852.9276720.9554827.7396240.2701540.3002672.59208739.6492060.0728370.06119632.4448684762.54397263.6600280.0529281.985488
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" - ], - "text/plain": [ - " module_id poa_global wind_speed temp_air \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 78 2.666484 1.472832 8.177979 \n", - "2016-01-26 07:30:00-07:00 78 7.899143 1.297711 8.241425 \n", - "2016-01-26 07:40:00-07:00 78 52.927672 0.955482 7.739624 \n", - "\n", - " blue_frac beam_frac temp_module v_oc \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.454992 1.100000 2.081940 33.040644 \n", - "2016-01-26 07:30:00-07:00 0.522027 -0.100000 2.436985 37.644029 \n", - "2016-01-26 07:40:00-07:00 0.270154 0.300267 2.592087 39.649206 \n", - "\n", - " i_sc i_mp v_mp r_sc \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.013215 0.009809 24.337320 115258.549800 \n", - "2016-01-26 07:30:00-07:00 0.037249 0.029832 29.624980 8253.745059 \n", - "2016-01-26 07:40:00-07:00 0.072837 0.061196 32.444868 4762.543972 \n", - "\n", - " r_oc poa_global_kwm2 p_mp \n", - "date_time \n", - "2016-01-26 07:20:00-07:00 608.680999 0.002666 0.238726 \n", - "2016-01-26 07:30:00-07:00 150.461283 0.007899 0.883783 \n", - "2016-01-26 07:40:00-07:00 63.660028 0.052928 1.985488 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# normalise poa_global to kW/m^2\n", - "meas['poa_global_kwm2'] = meas['poa_global'] / 1000\n", - "\n", - "# calculate p_mp as it might be missing\n", - "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", - "\n", - "#show some meas data\n", - "meas.head(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Count how many mlfm variables are in the data \n", - "usually matrix=4 (i_sc,i_mp,v_mp, v_oc) \n", - "and iv=6 (i_sc,i_mp,v_mp, v_oc + r_sc, r_oc) " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def get_qty_mlfm_vars(dmeas):\n", - " '''\n", - " Find the quantity of MLFM variables in the measured data\n", - " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", - " \n", - " Parameters\n", - " ----------\n", - " dmeas : dataframe\n", - " measured weather data\n", - " 'poa_global', 'temp_module', 'wind_speed'\n", - " and measured electrical/thermal values\n", - " 'i_sc' .. 'v_oc', temp_module.\n", - " \n", - " Returns\n", - " -------\n", - " qty_mlfm_vars : int\n", - " number of mlfm_values present in data usually\n", - " 2 = (imp, vmp) from mpp tracker\n", - " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", - " 6 = (i_sc, i_mp, v_mp, v_oc, r_sc, r_oc) from iv curve.\n", - " '''\n", - " # find how many mlfm variables were measured\n", - " qty_mlfm_vars = 0\n", - " for mlfm_sel in ('i_sc', 'r_sc', 'i_mp','v_mp', 'r_oc', 'v_oc'):\n", - " if mlfm_sel in dmeas.columns:\n", - " qty_mlfm_vars += 1\n", - " #print(qty_mlfm_vars, mlfm_sel)\n", - " \n", - " return qty_mlfm_vars" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "qty_mlfm_vars = get_qty_mlfm_vars(meas)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Normalise MLFM values norm from meas and ref dataframes \n", - "\n", - "Fig 2 illustrates the loss factors model (LFM). \n", - "It uses the shape and values from dc measurements to quantify the values of each \n", - "of the loss factors going from (1) ref_p_mp to (5) meas_p_mp. \n", - "\n", - "Fig 2: Loss Factors Model : \n", - "![lfm_0_4.png](mlfm_data/figs/mlfm_iv.png) \n", - "\n", - "1) ref_p_mp = Initial datasheet value at STC.\n", - "\n", - "2) Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", - "\n", - "3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", - " - norm_i_sc = measured / expected isc (purple)\n", - " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", - " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", - " \n", - " \n", - "4) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", - " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", - " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", - " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown)\n", - " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", - " \n", - " \n", - "5) These losses cause the performance to fall to pr_dc = meas_p_mp / ref_p_mp \n", - "\n", - "pr_dc = 1/ff * \n", - " (norm_i_sc * norm_r_sc * norm_i_ff ) * \n", - " (norm_v_ffv * norm_r_oc * norm_v_oc_t * norm_temp_corr ) \n", - "\n", - "Note: \n", - "The gamma temperature correction is subtracted from voc for simplicity. \n", - "In reality there will be temperature dependencies for i_sc and ff but they are smaller." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pr_dcpr_dc_temp_corri_mpv_mpi_scv_ocv_oc_temp_corrr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:000.4964970.4452930.7422410.7365870.9263800.7475260.6875640.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:000.6204710.5574730.8008960.7869770.8814090.8516750.7844180.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:000.2080370.1870590.8401720.8182980.2572270.8970410.8266880.8977000.8950180.9359160.914282
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" - ], - "text/plain": [ - " pr_dc pr_dc_temp_corr i_mp v_mp \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.496497 0.445293 0.742241 0.736587 \n", - "2016-01-26 07:30:00-07:00 0.620471 0.557473 0.800896 0.786977 \n", - "2016-01-26 07:40:00-07:00 0.208037 0.187059 0.840172 0.818298 \n", - "\n", - " i_sc v_oc v_oc_temp_corr r_sc \\\n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.926380 0.747526 0.687564 0.983502 \n", - "2016-01-26 07:30:00-07:00 0.881409 0.851675 0.784418 0.893852 \n", - "2016-01-26 07:40:00-07:00 0.257227 0.897041 0.826688 0.897700 \n", - "\n", - " r_oc i_ff v_ff \n", - "date_time \n", - "2016-01-26 07:20:00-07:00 0.760559 0.754692 0.968481 \n", - "2016-01-26 07:30:00-07:00 0.866922 0.896006 0.907783 \n", - "2016-01-26 07:40:00-07:00 0.895018 0.935916 0.914282 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "norm = mlfm_meas_to_norm(meas, ref, qty_mlfm_vars)\n", - "\n", - "# show some normalised data\n", - "norm.head(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Make irradiance and temperature bins for pivot tables \n", - "(Gbin=100W/m^2, Tbin=5C)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# poa_global bin = 100W/m2\n", - "norm['poa_global_bin'] = meas['poa_global'].round(-2)\n", - "\n", - "# temp_module bin = 5C\n", - "norm['temp_module_bin'] = (5 * round(meas['temp_module'] / 5,0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Perform sanity checks on meas and norm data \n", - "\n", - "It's easier to sanity check and study normalised data than raw values. \n", - "1) Remove bad, missing, unwanted or outlier data \n", - "2) User defined limits may depend on data scatter and degradation \n", - "3) Can either select on values e.g. '0.5 x stdev from mean'\n", - "4) Possible to select on dates if desired. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# select by irradiance poa_global range e.g. 100-1100W/m2\n", - "meas = meas[(meas['poa_global'] >= 100) &\n", - " (meas['poa_global'] <= 1100)]\n", - "\n", - "# if there's date_time can select by it, i.e. not matrix data\n", - "### better if index is formatted as a date\n", - "\n", - "# if qty_mlfm_vars == 6:\n", - "\n", - " # not for matrix as they don't contain dates\n", - " # example\n", - " # meas = meas[(meas.index > '2016-01-01') &\n", - " # (meas.index < '2017-01-01')]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# remove specific mlfm values outside limits e.g. <0.5 or >1.5\n", - "norm = norm[((norm['pr_dc'] > 0.5) & \n", - " (norm['pr_dc'] < 1.5))]\n", - "\n", - "# remove all mlfm values outside x~3 stdevs\n", - "if qty_mlfm_vars == 6:\n", - " # only needed for outdoor data as indoor ought to be less scattered\n", - " # remove all mlfm data > x stdev usually 3\n", - " stdevs = 3\n", - "\n", - " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc','v_oc'):\n", - " norm = norm[\n", - " ((norm[lfm] - norm[lfm].mean()) /\n", - " norm[lfm].std()).abs() < stdevs\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Filter only matching rows from meas and norm data\n", - "like an inner join but leave data in separate norm and meas frames" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "#drop meas rows that aren't in norm\n", - "meas_not_in_norm = ~meas.index.isin(norm.index)\n", - "meas = meas.drop(meas[meas_not_in_norm].index)\n", - "\n", - "#drop norm rows that aren't in meas\n", - "norm_not_in_meas = ~norm.index.isin(meas.index)\n", - "norm = norm.drop(norm[norm_not_in_meas].index)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot normalised MLFM data vs irradiance \n", - "\n", - "For outdoor data - \n", - "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", - "\n", - "For matrix data - \n", - "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", - "\n", - "1. Higher values are always better (unlike measured values such as \n", - " Rseries or Io where lower is better)\n", - "1. Accurate measurements and a stable module result in narrowest lines \n", - "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", - "1. r_oc tends to fall at high light levels ( \\ right) \n", - "1. i_ff and v_ff are usually fairly flat ( - ) \n", - "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", - " and/or snow (if not properly corrected). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fig 3 : Normalised mlfm values vs. irradiance.\n", - "\n", - "All traces should be thin, usually around 0.9 ± 0.1 \n", - "i_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# scatter plot normalised values vs. irradiance\n", - "plot_mlfm_scatter(meas, norm, mlfm_meas_file, qty_mlfm_vars)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert multiplicative to subtractive losses to show on a stack plot \n", - "\n", - "LFM losses can be analysed as either \n", - "\n", - "- multiplicative \n", - "pr_dc = 1/ff * ( norm(i_sc) * norm(r_sc) * norm(i_ff) * \n", - " norm(v_ff) * norm(r_oc) * norm(v_oc_t) * norm(temp_corr) ). \n", - " \n", - " \n", - " \n", - "- subtractive \n", - "pr_dc = 1/ff - (stack(i_sc) + stack(r_sc) + stack(i_ff) - \n", - " stack(v_ff) + stack(r_oc) + stack(v_oc_t) + stack(temp_corr) ). \n", - " \n", - "Multiplicative losses are easier to understand but to represent them on a graph \n", - "it's easier to show them as a stacked plot where the values are 'translated' \n", - "so the sum of the stacked losses is shown to equate to the product of the \n", - "multiplicative losses." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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pr_dci_scr_sci_ffi_vv_ffr_ocv_octemp_module_corr
date_time
2016-01-26 08:10:00-07:000.9874550.0638170.0445290.0359110.010.0739080.098226-0.002454-0.065435
2016-01-26 08:30:00-07:000.8369980.2372830.0325940.0302460.010.0770190.0829280.004324-0.042936
2016-01-26 08:40:00-07:000.9783350.0611030.0655990.0131430.010.0913420.0911430.000727-0.033518
\n", - "
" - ], - "text/plain": [ - " pr_dc i_sc r_sc i_ff i_v \\\n", - "date_time \n", - "2016-01-26 08:10:00-07:00 0.987455 0.063817 0.044529 0.035911 0.01 \n", - "2016-01-26 08:30:00-07:00 0.836998 0.237283 0.032594 0.030246 0.01 \n", - "2016-01-26 08:40:00-07:00 0.978335 0.061103 0.065599 0.013143 0.01 \n", - "\n", - " v_ff r_oc v_oc temp_module_corr \n", - "date_time \n", - "2016-01-26 08:10:00-07:00 0.073908 0.098226 -0.002454 -0.065435 \n", - "2016-01-26 08:30:00-07:00 0.077019 0.082928 0.004324 -0.042936 \n", - "2016-01-26 08:40:00-07:00 0.091342 0.091143 0.000727 -0.033518 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# translate multiplicative to stack losses and add to dataframe df\n", - "stack = mlfm_norm_to_stack(norm, ref, qty_mlfm_vars)\n", - "\n", - "# show some stack losses\n", - "stack.head(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot stack losses vs. measurement \n", - "\n", - "Fig 4 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", - "\n", - "Fig 4 Stacked losses by measurement \n", - "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", - "\n", - "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", - " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", - " \n", - "- This figure shows a typical c-Si module for four clear days for \n", - " different months July to Oct in AZ. \n", - " \n", - "- In the middle of the days the high irradiance results in the biggest \n", - " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", - " (as the module heats to 60C). \n", - " \n", - "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", - " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", - "\n", - "Stack losses are indicated by their colours \n", - "(from top to bottom for mlfm4=matrix and mlfm6=ivcurve) \n", - "\n", - "`+-----+----+-------+--------+------------+--------------+` \n", - "`| 1 | 2 | 4 | 6 | " - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot stack loss vs. time (or measurement) chart\n", - "plot_mlfm_stack(\n", - " dmeas=meas,\n", - " dnorm=norm,\n", - " dstack=stack, # dataframe measurements\n", - " ref=ref, # dataframe reference STC\n", - " mlfm_file_name=mlfm_meas_file, # name of data file\n", - " qty_mlfm_vars=qty_mlfm_vars, # number of mlfm measurements usually 4 or 6\n", - " xaxis_labels=12, # show this many x_labels or 0 to show all\n", - " is_i_sc_self_ref=False, # is isc self referenced?\n", - " is_v_oc_temp_module_corr=True, # is voc temperature corrected?\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fit mechanistic model to measured weather and normalised losses \n", - "\n", - "Perform a Mechanistic Performance Model (MPM) fit to the mlfm parameters \n", - "poa_global (kW/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", - "\n", - "MPM_6 = c_1 + c_2 * (temp_module-25) + c_3 * log10(poa_global_kwm2) + \n", - " c_4 * poa_global_kwm2 + c_5 * wind_speed + c_6 / poa_global_kwm2 \n", - "\n", - "\n", - "Report the fit (coeffs) and error (errs) coefficients. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", - "mlfm_sel = 'pr_dc' \n", - "\n", - "# combine measured and normalized data into a single DataFrame\n", - "data = meas.copy()\n", - "data[mlfm_sel] = norm[mlfm_sel]\n", - "\n", - "predictions, cc, residuals = mlfm_fit(data, mlfm_sel)\n", - "\n", - "# Fix a bug with fit routine which gives a\n", - "# finite cc[4] even if all the ws data is 0\n", - "# this won't matter until cc is applied to other\n", - "# data with some ws <>0 when it will give bad results\n", - "if np.mean(meas.wind_speed) == 0:\n", - " cc[4] = 0\n", - " c_5 = 0\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Show residual fit vs. measured for MLFM parameter " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_residuals(dmeas, dnorm, fit, title):\n", - " ''' \n", - " Scatter plot residuals to normalised measured\n", - "\n", - " Parameters\n", - " ----------\n", - " dmeas : dataframe\n", - " measured weather data\n", - " 'poa_global', 'temp_module', 'wind_speed'\n", - " and measured electrical/thermal values\n", - " 'i_sc' .. 'v_oc', temp_module.\n", - " \n", - " \n", - " dnorm : dataframe\n", - " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", - " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", - " \n", - " fit : string\n", - " name of fitted variable e.g. 'pr_dc'.\n", - " \n", - " title : string\n", - " title for graph e.g. mlfm_meas_file.\n", - " '''\n", - " \n", - " fig, ax1 = plt.subplots()\n", - "\n", - " plt.title(title)\n", - "\n", - " plt.ylabel('fit ' + fit + ' * poa_global_kwm2')\n", - " ax1.set_ylim(0, 1.2)\n", - "\n", - " plt.xlabel('meas ' + fit + '* poa_global_kwm2')\n", - " ax1.set_xlim(0, 1.2)\n", - "\n", - " plt.plot(\n", - " dnorm[fit] * dmeas['poa_global_kwm2'],\n", - " dnorm['calc_' + fit] * dmeas['poa_global_kwm2'],\n", - " 'c^',\n", - " label = fit\n", - " )\n", - "\n", - " # plot 1:1 line to show optimum fit\n", - " plt.plot((0,1.2),(0,1.2), 'ko-')\n", - "\n", - " plt.legend(loc='upper left')\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fig 6 : fit_mlfm_sel * poa_global vs. measured_mlfm_sel * poa_global" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'calc_pr_dc'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\_libs\\index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\_libs\\index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'calc_pr_dc'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [23]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# plot fit vs. measured, include a 1:1 line for comparison\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m fit_plot \u001b[38;5;241m=\u001b[39m \u001b[43mplot_residuals\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmeas\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlfm_sel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresidual \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmlfm_meas_file\u001b[49m\u001b[43m)\u001b[49m\n", - "Input \u001b[1;32mIn [22]\u001b[0m, in \u001b[0;36mplot_residuals\u001b[1;34m(dmeas, dnorm, fit, title)\u001b[0m\n\u001b[0;32m 32\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmeas \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m fit \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m* poa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 33\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_xlim(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1.2\u001b[39m)\n\u001b[0;32m 35\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(\n\u001b[0;32m 36\u001b[0m dnorm[fit] \u001b[38;5;241m*\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m---> 37\u001b[0m \u001b[43mdnorm\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcalc_\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfit\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m*\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[0;32m 38\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc^\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 39\u001b[0m label \u001b[38;5;241m=\u001b[39m fit\n\u001b[0;32m 40\u001b[0m )\n\u001b[0;32m 42\u001b[0m \u001b[38;5;66;03m# plot 1:1 line to show optimum fit\u001b[39;00m\n\u001b[0;32m 43\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot((\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1.2\u001b[39m),(\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1.2\u001b[39m), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mko-\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", - "File \u001b[1;32mc:\\Users\\cliff\\Anaconda3\\envs\\ransome\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3623\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3625\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3626\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3627\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3628\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[1;31mKeyError\u001b[0m: 'calc_pr_dc'" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot fit vs. measured, include a 1:1 line for comparison\n", - "fit_plot = plot_residuals(meas, norm, mlfm_sel, 'residual ' + mlfm_meas_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot heatmap of mean residual vs. tenp_module and poa_global\n", - "\n", - "Show a heatmap of the average residual (meas - fit) error \n", - "for each irradiance (100W/m^2) and tmod bin (5C)." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_heatmap(dmeas, dnorm, fit, y_axis, x_axis, z_axis, title):\n", - " '''\n", - " Plot a heatmap of Z vs. binned X and Y axes.\n", - "\n", - " Parameters\n", - " ----------\n", - " dmeas : dataframe\n", - " measured weather data\n", - " 'poa_global', 'temp_module', 'wind_speed'\n", - " and measured electrical/thermal values\n", - " 'i_sc' .. 'v_oc', temp_module.\n", - "\n", - " dnorm : dataframe\n", - " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", - " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", - "\n", - " fit : string\n", - " fitted parameter e.g. 'pr_dc'.\n", - "\n", - " x_axis : string\n", - " binned x axis e.g. 'poa_global_bin'.\n", - "\n", - " y_axis : string\n", - " binned y axis e.g. 'temp_module_bin'.\n", - "\n", - " z_axis : string\n", - " value as a colour surface plot e.f. 'diff_pr_dc'.\n", - "\n", - " title : string\n", - " title for graph e.g. mlfm_meas_file.\n", - "\n", - " '''\n", - "\n", - " df_piv = pd.pivot_table(\n", - " dnorm,\n", - " index=y_axis, # e.g. 'temp_module_bin'\n", - " columns=x_axis, # e.g. 'poa_global_bin'\n", - " values=z_axis, # value to aggregate\n", - " fill_value=0, # fill empty cells with this ?\n", - " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", - " margins=False, # grand totals hide\n", - " dropna=True # hide missing rows or columns\n", - " )\n", - "\n", - " fig, ax1 = plt.subplots()\n", - "\n", - " # force z limits to be -2% to +2% if desired\n", - " df_piv = df_piv.clip(lower = -0.02, upper = +0.02)\n", - "\n", - " im = ax1.imshow(\n", - " df_piv,\n", - " cmap='RdYlBu',\n", - " origin='lower'\n", - " )\n", - "\n", - " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", - "\n", - " #Y AXIS : show only 1 of each y_skip labels\n", - " y_ticks = df_piv.shape[0]\n", - " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", - " yax2 = [''] * y_ticks\n", - " y_skip = 2\n", - " y_count = 0\n", - " while y_count < y_ticks:\n", - " if y_count % y_skip == 0:\n", - " yax2[y_count] = df_piv.index[y_count]\n", - " y_count += 1\n", - "\n", - " ax1.set_yticklabels(yax2)\n", - " ax1.set_ylabel(y_axis)\n", - "\n", - " # X AXIS : show only 1 of each x_skip labels\n", - " x_ticks = df_piv.shape[1]\n", - " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", - "\n", - " xax2 = [''] * x_ticks\n", - " x_skip = 2\n", - " x_count = 0\n", - " while x_count < x_ticks:\n", - " if x_count % x_skip == 0:\n", - " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", - " x_count += 1\n", - " \n", - " ax1.set_title(title)\n", - "\n", - " ax1.set_xticklabels(xax2)\n", - " ax1.set_xlabel(x_axis)\n", - "\n", - " ax1.grid( color='k', linestyle=':', linewidth=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fig 7 : Residual MLFM fit heatmap vs. poa_global and temp_module." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot heatmap\n", - "heatmap_plot = plot_heatmap(\n", - " dnorm=norm,\n", - " dmeas=meas,\n", - " fit=mlfm_sel,\n", - " y_axis='temp_module_bin',\n", - " x_axis='poa_global_bin',\n", - " z_axis='diff_' + mlfm_sel,\n", - " title='residual ' + mlfm_meas_file\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read in complete (G,T) Matrix to fill with MLFM predicted values \n", - "\n", - "Read in a matrix with complete values of \n", - "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", - "to predict all MPM values " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['mid', 'poa_global', 'temp_module', 'wind_speed', 'poa_global_kwm2'], dtype='object')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# read in the complete matrix data\n", - "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", - "\n", - "matr['poa_global_kwm2'] = matr['poa_global'] / 1000\n", - "\n", - "matr.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predict performance from MPM fit coefficients \n", - "\n", - "1. generate predicted mpm data \n", - "2. create a pivot table mpm(g,t) \n", - "3. show as a heat map" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# populate pivot table from predicted mpm data\n", - "matr[mlfm_sel] = mlfm_6(matr, cc[0], cc[1], cc[2], cc[3], cc[4], cc[5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot heatmap of predicted MLFM values vs. temp_mod and poa_global bins" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", - " vmin=0, vmax=1.2, levels=5):\n", - " ''' \n", - " Plot filled contour plot Z vs. X and Y bins.\n", - "\n", - " Parameters\n", - " ----------\n", - " df : dataframe\n", - " measured or noralised data containing weather columns\n", - " (poa_global, temp_module and wind_speed).\n", - "\n", - " x_axis : string\n", - " binned x axis e.g. 'poa_global'.\n", - "\n", - " y_axis : string\n", - " binned y axis e.g. 'temp_module'.\n", - "\n", - " z_axis : string\n", - " measured value as a colour surface plot.\n", - "\n", - " title : string\n", - " title for graph e.g. mlfm_meas_file.\n", - "\n", - " vmin, vmax : float\n", - " minimum and maximum values for contour chart ###\n", - "\n", - " ''' \n", - " \n", - " piv = pd.pivot_table(\n", - " df,\n", - " index=y_axis,\n", - " columns=x_axis,\n", - " values=z_axis,\n", - " fill_value=0, # fill empty cells?\n", - " aggfunc=[np.mean], # min, np.sum, len->count\n", - " margins=False, # grand totals\n", - " dropna=True # hide missing rows or columns\n", - " )\n", - "\n", - " piv = piv.clip(vmin, vmax)\n", - "\n", - " fig, ax1 = plt.subplots()\n", - "\n", - " cs = plt.contourf(\n", - " piv,\n", - " cmap='RdYlBu', # or 'nipy_spectral',\n", - " # origin='lower'\n", - " # nchunkint=1,\n", - " levels=levels,\n", - " vmin=vmin,\n", - " vmax=vmax\n", - " )\n", - "\n", - " cbar = fig.colorbar(cs, ax=ax1)\n", - " cbar.ax.set_ylabel(z_axis,\n", - " rotation=90,\n", - " va='bottom',\n", - " labelpad=+30)\n", - "\n", - " plt.title(title)\n", - "\n", - " y_ticks = piv.shape[0]\n", - " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", - "\n", - " # show only 1 of each y_skip labels\n", - " yax2 = [''] * y_ticks\n", - " y_skip = 2\n", - " y_count = 0\n", - " while y_count < y_ticks:\n", - " if y_count % y_skip == 0:\n", - " yax2[y_count] = piv.index[y_count]\n", - " y_count += 1\n", - "\n", - " ax1.set_yticklabels(yax2)\n", - " ax1.set_ylabel(y_axis)\n", - "\n", - " x_ticks = piv.shape[1]\n", - " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", - "\n", - " # show only 1 of each x_skip labels\n", - " xax2 = [''] * x_ticks\n", - " x_skip = 2\n", - " x_count = 0\n", - " while x_count < x_ticks:\n", - " if x_count % x_skip == 0:\n", - " xax2[x_count] = piv.columns.levels[1][x_count]\n", - " x_count += 1\n", - "\n", - " ax1.set_xticklabels(xax2)\n", - " ax1.set_xlabel(x_axis)\n", - "\n", - " ax1.grid( color='k', linestyle=':', linewidth=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fig 8 : Contour plot of predicted mlfm_sel + vs. poa_global and temp_mod." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "contour_plot = plot_contourf(\n", - " df=matr,\n", - " y_axis='temp_module',\n", - " x_axis='poa_global',\n", - " z_axis=mlfm_sel,\n", - " title='matrix predicted ' + mlfm_meas_file,\n", - " vmin=0.6,\n", - " vmax=1.05,\n", - " levels=9\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fig 9 : Contour plot of measured mlfm_sel vs. poa_global and temp_mod." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "contour_plot = plot_contourf(\n", - " df=norm,\n", - " y_axis='temp_module_bin',\n", - " x_axis='poa_global_bin',\n", - " z_axis=mlfm_sel,\n", - " title='avg normalised ' + mlfm_meas_file,\n", - " vmin=0.6,\n", - " vmax=1.05,\n", - " levels=9\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References \n", - " \n", - "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", - "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", - "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", - " \n", - ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", - " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", - "What's Most Important for Energy Yield' \n", - "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", - "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", - "\n", - ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", - " 'Choosing the best Empirical Model for predicting energy yield' \n", - " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", - " Canobbio, Switzerland 30-31 March, 2017 \n", - "\n", - ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", - "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", - "benchmark its practical relevance in energy yield prediction' \n", - "PVSC June 2019 Chicago, USA \n", - "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", - "\n", - ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", - "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", - "under Real Outdoor and IEC 61853 Test Conditions \n", - "Using High Quality Module IV Measurements' \n", - "36th EU PVSEC Sep 2019 \n", - "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", - "\n", - ".. [5] Steve Ransome (SRCL) \n", - "'How to use the Loss Factors and Mechanistic Performance Models \n", - "effectively with PVPMC/PVLIB' \n", - "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", - "https://pvpmc.sandia.gov/download/7879/ \n", - "\n", - ".. [6] W.Marion et al (NREL) \n", - "'New Data Set for Validating PV Module Performance Models' \n", - "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", - "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", - "\n", - "Many more papers are available at www.steveransome.com \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - }, - "toc-autonumbering": true, - "toc-showmarkdowntxt": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} From c3bf44a396e6ff5612d35ee111ade6bdf3cd5833 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Wed, 29 Jun 2022 16:26:32 -0600 Subject: [PATCH 58/81] working on notebooks --- docs/tutorials/mlfm_220627_0.ipynb | 207 ++--- docs/tutorials/mlfm_220627_2.ipynb | 1301 +++++++++++----------------- pvlib/mlfm.py | 19 +- 3 files changed, 576 insertions(+), 951 deletions(-) diff --git a/docs/tutorials/mlfm_220627_0.ipynb b/docs/tutorials/mlfm_220627_0.ipynb index d985a08589..917a56910a 100644 --- a/docs/tutorials/mlfm_220627_0.ipynb +++ b/docs/tutorials/mlfm_220627_0.ipynb @@ -43,13 +43,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ - "#import pvlib\n", - "from pvlib import *\n", - "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -61,9 +58,9 @@ "root_dir\n", "\n", "# Import essential library file with lfm and mpm definitions\n", - "from mlfm_220627 import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", + "from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", "# Import graphics code \n", - "from mlfm_220627 import plot_mlfm_scatter, plot_mlfm_stack \n", + "from pvlib.mlfm import plot_mlfm_scatter, plot_mlfm_stack \n", "\n", "# STANDARD DEFINITIONS\n", "\n", @@ -79,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -131,7 +128,7 @@ "\"\\nNotes for Cliff Hansen 220624t17\\n\\nMy comments are marked ##SR##\\n\\nI can't get the stacked plot chart to work option 0. section\\nKeyError: 'v_mp'\\n\\n\"" ] }, - "execution_count": 3, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -152,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -242,13 +239,6 @@ "ref_data = pd.read_csv(ref_file_name, index_col='module_id')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -258,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -274,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -293,7 +283,7 @@ " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", - " gamma_p_mp = ref_data['gamma_p_mp'].values[0],\n", + " gamma_p_mp = ref_data['gamma_pdc'].values[0],\n", ")\n", "\n", "# create p_mp and ff in case they don't exist\n", @@ -320,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -339,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -458,16 +448,32 @@ "" ], "text/plain": [ - " module_id poa_global ... poa_global_kwm2 p_mp\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 78 2.666484 ... 0.002666 0.238726\n", - "2016-01-26 07:30:00-07:00 78 7.899143 ... 0.007899 0.883783\n", - "2016-01-26 07:40:00-07:00 78 52.927672 ... 0.052928 1.985488\n", + " module_id poa_global wind_speed temp_air \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 78 2.666484 1.472832 8.177979 \n", + "2016-01-26 07:30:00-07:00 78 7.899143 1.297711 8.241425 \n", + "2016-01-26 07:40:00-07:00 78 52.927672 0.955482 7.739624 \n", + "\n", + " blue_frac beam_frac temp_module v_oc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.454992 1.100000 2.081940 33.040644 \n", + "2016-01-26 07:30:00-07:00 0.522027 -0.100000 2.436985 37.644029 \n", + "2016-01-26 07:40:00-07:00 0.270154 0.300267 2.592087 39.649206 \n", "\n", - "[3 rows x 15 columns]" + " i_sc i_mp v_mp r_sc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.013215 0.009809 24.337320 115258.549800 \n", + "2016-01-26 07:30:00-07:00 0.037249 0.029832 29.624980 8253.745059 \n", + "2016-01-26 07:40:00-07:00 0.072837 0.061196 32.444868 4762.543972 \n", + "\n", + " r_oc poa_global_kwm2 p_mp \n", + "date_time \n", + "2016-01-26 07:20:00-07:00 608.680999 0.002666 0.238726 \n", + "2016-01-26 07:30:00-07:00 150.461283 0.007899 0.883783 \n", + "2016-01-26 07:40:00-07:00 63.660028 0.052928 1.985488 " ] }, - "execution_count": 9, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -494,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -531,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -540,7 +546,7 @@ "6" ] }, - "execution_count": 11, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -595,127 +601,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 37, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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poa_global_kwm2temp_modulepr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:000.0026662.0819400.4964970.4452930.9263800.7422410.7475260.7365870.6875640.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:000.0078992.4369850.6204710.5574730.8814090.8008960.8516750.7869770.7844180.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:000.0529282.5920870.2080370.1870590.2572270.8401720.8970410.8182980.8266880.8977000.8950180.9359160.914282
\n", - "
" - ], - "text/plain": [ - " poa_global_kwm2 temp_module ... i_ff v_ff\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 0.002666 2.081940 ... 0.754692 0.968481\n", - "2016-01-26 07:30:00-07:00 0.007899 2.436985 ... 0.896006 0.907783\n", - "2016-01-26 07:40:00-07:00 0.052928 2.592087 ... 0.935916 0.914282\n", - "\n", - "[3 rows x 13 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyError", + "evalue": "'gamma_pdc'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[1;32mIn [37]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m norm \u001b[38;5;241m=\u001b[39m \u001b[43mmlfm_meas_to_norm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmeas\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m##SR##, qty_mlfm_vars)\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# show some normalised data\u001b[39;00m\n\u001b[0;32m 4\u001b[0m norm\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m3\u001b[39m)\n", + "File \u001b[1;32mc:\\python\\ransome\\pvlib-python\\pvlib\\mlfm.py:125\u001b[0m, in \u001b[0;36mmlfm_meas_to_norm\u001b[1;34m(dmeas, ref)\u001b[0m\n\u001b[0;32m 118\u001b[0m dnorm[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpr_dc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 119\u001b[0m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp_mp\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m\n\u001b[0;32m 120\u001b[0m (ref[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp_mp\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[0;32m 122\u001b[0m \u001b[38;5;66;03m# temperature corrected\u001b[39;00m\n\u001b[0;32m 123\u001b[0m dnorm[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpr_dc_temp_corr\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 124\u001b[0m dnorm[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpr_dc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m--> 125\u001b[0m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m \u001b[43mref\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgamma_pdc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m*\u001b[39m(dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtemp_module\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m-\u001b[39m T_STC)))\n\u001b[0;32m 127\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_sc\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m dmeas\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[0;32m 128\u001b[0m dnorm[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_sc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_sc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m ref[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_sc\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[1;31mKeyError\u001b[0m: 'gamma_pdc'" + ] } ], "source": [ @@ -2045,8 +1944,8 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -2059,7 +1958,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.4" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false diff --git a/docs/tutorials/mlfm_220627_2.ipynb b/docs/tutorials/mlfm_220627_2.ipynb index c97a5fd96f..82115b54a9 100644 --- a/docs/tutorials/mlfm_220627_2.ipynb +++ b/docs/tutorials/mlfm_220627_2.ipynb @@ -43,13 +43,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ - "#import pvlib\n", - "from pvlib import *\n", - "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -61,9 +58,9 @@ "root_dir\n", "\n", "# Import essential library file with lfm and mpm definitions\n", - "from mlfm_220627 import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", + "from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", "# Import graphics code \n", - "from mlfm_220627 import plot_mlfm_scatter, plot_mlfm_stack \n", + "from pvlib.mlfm import plot_mlfm_scatter, plot_mlfm_stack \n", "\n", "# STANDARD DEFINITIONS\n", "\n", @@ -79,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -131,7 +128,7 @@ "\"\\nNotes for Cliff Hansen 220624t17\\n\\nMy comments are marked ##SR##\\n\\nI can't get the stacked plot chart to work option 0. section\\nKeyError: 'v_mp'\\n\\n\"" ] }, - "execution_count": 3, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -152,13 +149,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "# select one of the following meas files\n", "################\n", - "meas_file = 1 #\n", + "meas_file = 2 #\n", "################\n", "\n", "if meas_file == 0:\n", @@ -232,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -242,13 +239,6 @@ "ref_data = pd.read_csv(ref_file_name, index_col='module_id')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -258,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -274,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -293,7 +283,7 @@ " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", - " gamma_p_mp = ref_data['gamma_p_mp'].values[0],\n", + " gamma_pdc = ref_data['gamma_pdc'].values[0],\n", ")\n", "\n", "# create p_mp and ff in case they don't exist\n", @@ -320,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -339,7 +329,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -364,22 +354,13 @@ " \n", " \n", " module_id\n", - " poa_global\n", " temp_module\n", + " poa_global\n", " i_sc\n", - " p_mp\n", + " v_oc\n", " i_mp\n", " v_mp\n", - " v_oc\n", - " ff\n", - " temp_air\n", - " relative_humidity\n", - " pressure\n", - " precipitation\n", - " dni\n", - " ghi\n", - " dhi\n", - " soil\n", + " p_mp\n", " wind_speed\n", " poa_global_kwm2\n", " \n", @@ -395,99 +376,67 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " 2013-01-04 08:05:12-06:00\n", - " n05667\n", - " 24.0\n", - " 4.7\n", - " 0.1361\n", - " 4.869814\n", - " 0.1255\n", - " 38.8033\n", - " 45.8512\n", - " 78.01\n", - " 4.9\n", - " 82.2\n", - " 1007.1\n", - " 0\n", - " 0.0\n", - " 18.1\n", - " 18.3\n", - " 1.0\n", - " 0\n", - " 0.0240\n", - " \n", - " \n", - " 2013-01-04 08:10:12-06:00\n", - " n05667\n", - " 33.0\n", - " 5.2\n", - " 0.1788\n", - " 6.530535\n", - " 0.1657\n", - " 39.4118\n", - " 46.4089\n", - " 78.72\n", - " 5.0\n", - " 82.9\n", - " 1007.0\n", - " 0\n", - " 1.6\n", - " 24.9\n", - " 25.0\n", - " 1.0\n", + " 0\n", + " 19074001\n", + " 15\n", + " 100\n", + " 0.595\n", + " 65.78\n", + " 0.543\n", + " 55.56\n", + " 30.16908\n", " 0\n", - " 0.0330\n", - " \n", - " \n", - " 2013-01-04 08:15:12-06:00\n", - " n05667\n", - " 204.7\n", - " 6.5\n", - " 1.1024\n", - " 45.349227\n", - " 1.0548\n", - " 42.9932\n", - " 50.0780\n", - " 82.15\n", - " 5.2\n", - " 81.9\n", - " 1007.1\n", + " 0.1\n", + " \n", + " \n", + " 1\n", + " 19074001\n", + " 15\n", + " 200\n", + " 1.183\n", + " 67.79\n", + " 1.093\n", + " 57.70\n", + " 63.06610\n", " 0\n", - " 353.4\n", - " 53.0\n", - " 31.5\n", - " 1.0\n", + " 0.2\n", + " \n", + " \n", + " 2\n", + " 19074001\n", + " 15\n", + " 400\n", + " 2.354\n", + " 69.65\n", + " 2.185\n", + " 59.42\n", + " 129.83270\n", " 0\n", - " 0.2047\n", + " 0.4\n", " \n", " \n", "\n", "" ], "text/plain": [ - " module_id poa_global ... wind_speed poa_global_kwm2\n", - "date_time ... \n", - "2013-01-04 08:05:12-06:00 n05667 24.0 ... 0 0.0240\n", - "2013-01-04 08:10:12-06:00 n05667 33.0 ... 0 0.0330\n", - "2013-01-04 08:15:12-06:00 n05667 204.7 ... 0 0.2047\n", + " module_id temp_module poa_global i_sc v_oc i_mp v_mp \\\n", + "date_time \n", + "0 19074001 15 100 0.595 65.78 0.543 55.56 \n", + "1 19074001 15 200 1.183 67.79 1.093 57.70 \n", + "2 19074001 15 400 2.354 69.65 2.185 59.42 \n", "\n", - "[3 rows x 19 columns]" + " p_mp wind_speed poa_global_kwm2 \n", + "date_time \n", + "0 30.16908 0 0.1 \n", + "1 63.06610 0 0.2 \n", + "2 129.83270 0 0.4 " ] }, - "execution_count": 9, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -514,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -551,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -560,7 +509,7 @@ "4" ] }, - "execution_count": 11, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -615,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -639,8 +588,6 @@ " \n", " \n", " \n", - " poa_global_kwm2\n", - " temp_module\n", " pr_dc\n", " pr_dc_temp_corr\n", " i_sc\n", @@ -658,62 +605,58 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " 2013-01-04 08:05:12-06:00\n", - " 0.0240\n", - " 4.7\n", - " 0.956856\n", - " 0.889529\n", - " 1.039376\n", - " 0.922116\n", - " 0.915011\n", - " 0.846288\n", - " 0.865476\n", - " \n", - " \n", - " 2013-01-04 08:10:12-06:00\n", - " 0.0330\n", - " 5.2\n", - " 0.933212\n", - " 0.869166\n", - " 0.993069\n", - " 0.926734\n", - " 0.926140\n", - " 0.849229\n", - " 0.877238\n", - " \n", - " \n", - " 2013-01-04 08:15:12-06:00\n", - " 0.2047\n", - " 6.5\n", - " 1.044714\n", - " 0.977723\n", - " 0.987068\n", - " 0.956821\n", - " 0.999361\n", - " 0.858525\n", - " 0.950057\n", + " 0\n", + " 0.936042\n", + " 0.908517\n", + " 1.007988\n", + " 0.912605\n", + " 0.936845\n", + " 0.844634\n", + " 0.913971\n", + " \n", + " \n", + " 1\n", + " 0.978361\n", + " 0.949591\n", + " 1.002058\n", + " 0.923922\n", + " 0.965471\n", + " 0.851158\n", + " 0.941899\n", + " \n", + " \n", + " 2\n", + " 1.007065\n", + " 0.977451\n", + " 0.996976\n", + " 0.928207\n", + " 0.991961\n", + " 0.853123\n", + " 0.967743\n", " \n", " \n", "\n", "" ], "text/plain": [ - " poa_global_kwm2 ... v_oc_temp_corr\n", - "date_time ... \n", - "2013-01-04 08:05:12-06:00 0.0240 ... 0.865476\n", - "2013-01-04 08:10:12-06:00 0.0330 ... 0.877238\n", - "2013-01-04 08:15:12-06:00 0.2047 ... 0.950057\n", + " pr_dc pr_dc_temp_corr i_sc i_mp v_oc v_mp \\\n", + "date_time \n", + "0 0.936042 0.908517 1.007988 0.912605 0.936845 0.844634 \n", + "1 0.978361 0.949591 1.002058 0.923922 0.965471 0.851158 \n", + "2 1.007065 0.977451 0.996976 0.928207 0.991961 0.853123 \n", "\n", - "[3 rows x 9 columns]" + " v_oc_temp_corr \n", + "date_time \n", + "0 0.913971 \n", + "1 0.941899 \n", + "2 0.967743 " ] }, - "execution_count": 12, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -735,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -761,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -782,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -813,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -860,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -885,22 +828,13 @@ " \n", " \n", " module_id\n", - " poa_global\n", " temp_module\n", + " poa_global\n", " i_sc\n", - " p_mp\n", + " v_oc\n", " i_mp\n", " v_mp\n", - " v_oc\n", - " ff\n", - " temp_air\n", - " relative_humidity\n", - " pressure\n", - " precipitation\n", - " dni\n", - " ghi\n", - " dhi\n", - " soil\n", + " p_mp\n", " wind_speed\n", " poa_global_kwm2\n", " \n", @@ -916,145 +850,97 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " 2013-01-04 08:15:12-06:00\n", - " n05667\n", - " 204.7\n", - " 6.5\n", - " 1.1024\n", - " 45.349227\n", - " 1.0548\n", - " 42.9932\n", - " 50.0780\n", - " 82.15\n", - " 5.2\n", - " 81.9\n", - " 1007.1\n", - " 0\n", - " 353.4\n", - " 53.0\n", - " 31.5\n", - " 1.0\n", - " 0\n", - " 0.2047\n", - " \n", - " \n", - " 2013-01-04 08:20:12-06:00\n", - " n05667\n", - " 238.0\n", - " 8.4\n", - " 1.2937\n", - " 52.964895\n", - " 1.2344\n", - " 42.9074\n", - " 50.0932\n", - " 81.73\n", - " 5.3\n", - " 80.8\n", - " 1007.2\n", - " 0\n", - " 395.1\n", - " 65.2\n", - " 36.7\n", - " 1.0\n", - " 0\n", - " 0.2380\n", - " \n", - " \n", - " 2013-01-04 08:25:12-06:00\n", - " n05667\n", - " 272.4\n", - " 10.3\n", - " 1.4898\n", - " 60.822567\n", - " 1.4194\n", - " 42.8509\n", - " 50.1119\n", - " 81.47\n", - " 5.6\n", - " 79.7\n", - " 1007.3\n", - " 0\n", - " 435.6\n", - " 78.2\n", - " 41.6\n", - " 1.0\n", + " 0\n", + " 19074001\n", + " 15\n", + " 100\n", + " 0.595\n", + " 65.78\n", + " 0.543\n", + " 55.56\n", + " 30.16908\n", " 0\n", - " 0.2724\n", - " \n", - " \n", - " 2013-01-04 08:30:12-06:00\n", - " n05667\n", - " 209.1\n", - " 10.7\n", - " 1.1510\n", - " 46.371708\n", - " 1.0956\n", - " 42.3254\n", - " 49.4631\n", - " 81.46\n", - " 5.9\n", - " 78.9\n", - " 1007.2\n", + " 0.1\n", + " \n", + " \n", + " 1\n", + " 19074001\n", + " 15\n", + " 200\n", + " 1.183\n", + " 67.79\n", + " 1.093\n", + " 57.70\n", + " 63.06610\n", " 0\n", - " 284.3\n", - " 72.2\n", - " 44.9\n", - " 1.0\n", + " 0.2\n", + " \n", + " \n", + " 2\n", + " 19074001\n", + " 15\n", + " 400\n", + " 2.354\n", + " 69.65\n", + " 2.185\n", + " 59.42\n", + " 129.83270\n", " 0\n", - " 0.2091\n", - " \n", - " \n", - " 2013-01-04 08:35:12-06:00\n", - " n05667\n", - " 204.3\n", - " 10.7\n", - " 1.1235\n", - " 45.179177\n", - " 1.0680\n", - " 42.3026\n", - " 49.4346\n", - " 81.34\n", - " 5.7\n", - " 78.6\n", - " 1007.2\n", + " 0.4\n", + " \n", + " \n", + " 3\n", + " 19074001\n", + " 15\n", + " 600\n", + " 3.532\n", + " 70.65\n", + " 3.292\n", + " 60.06\n", + " 197.71752\n", " 0\n", - " 264.6\n", - " 75.5\n", - " 47.3\n", - " 1.0\n", + " 0.6\n", + " \n", + " \n", + " 4\n", + " 19074001\n", + " 15\n", + " 800\n", + " 4.706\n", + " 71.35\n", + " 4.398\n", + " 60.21\n", + " 264.80358\n", " 0\n", - " 0.2043\n", + " 0.8\n", " \n", " \n", "\n", "" ], "text/plain": [ - " module_id poa_global ... wind_speed poa_global_kwm2\n", - "date_time ... \n", - "2013-01-04 08:15:12-06:00 n05667 204.7 ... 0 0.2047\n", - "2013-01-04 08:20:12-06:00 n05667 238.0 ... 0 0.2380\n", - "2013-01-04 08:25:12-06:00 n05667 272.4 ... 0 0.2724\n", - "2013-01-04 08:30:12-06:00 n05667 209.1 ... 0 0.2091\n", - "2013-01-04 08:35:12-06:00 n05667 204.3 ... 0 0.2043\n", + " module_id temp_module poa_global i_sc v_oc i_mp v_mp \\\n", + "date_time \n", + "0 19074001 15 100 0.595 65.78 0.543 55.56 \n", + "1 19074001 15 200 1.183 67.79 1.093 57.70 \n", + "2 19074001 15 400 2.354 69.65 2.185 59.42 \n", + "3 19074001 15 600 3.532 70.65 3.292 60.06 \n", + "4 19074001 15 800 4.706 71.35 4.398 60.21 \n", "\n", - "[5 rows x 19 columns]" + " p_mp wind_speed poa_global_kwm2 \n", + "date_time \n", + "0 30.16908 0 0.1 \n", + "1 63.06610 0 0.2 \n", + "2 129.83270 0 0.4 \n", + "3 197.71752 0 0.6 \n", + "4 264.80358 0 0.8 " ] }, - "execution_count": 17, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -1065,17 +951,19 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 101, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1113,7 +1001,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -1158,50 +1046,54 @@ " \n", " \n", " \n", - " 2013-01-04 08:15:12-06:00\n", - " 1.044714\n", - " 0.015955\n", - " 0.048271\n", + " 0\n", + " 0.936042\n", + " -0.009381\n", + " 0.097640\n", " 0.01\n", - " 0.169543\n", - " 0.000788\n", - " -0.060828\n", + " 0.177468\n", + " 0.074172\n", + " -0.026863\n", " \n", " \n", - " 2013-01-04 08:20:12-06:00\n", - " 1.049437\n", - " 0.004612\n", - " 0.051860\n", + " 1\n", + " 0.978361\n", + " -0.002460\n", + " 0.085913\n", " 0.01\n", - " 0.172945\n", - " 0.000416\n", - " -0.054896\n", + " 0.172866\n", + " 0.041262\n", + " -0.028168\n", " \n", " \n", - " 2013-01-04 08:25:12-06:00\n", - " 1.052938\n", - " -0.003006\n", - " 0.053871\n", + " 2\n", + " 1.007065\n", + " 0.003671\n", + " 0.082151\n", " 0.01\n", - " 0.175514\n", - " -0.000047\n", - " -0.048840\n", + " 0.173297\n", + " 0.009758\n", + " -0.029400\n", " \n", " \n", "\n", "" ], "text/plain": [ - " pr_dc i_sc ... v_oc temp_module_corr\n", - "date_time ... \n", - "2013-01-04 08:15:12-06:00 1.044714 0.015955 ... 0.000788 -0.060828\n", - "2013-01-04 08:20:12-06:00 1.049437 0.004612 ... 0.000416 -0.054896\n", - "2013-01-04 08:25:12-06:00 1.052938 -0.003006 ... -0.000047 -0.048840\n", + " pr_dc i_sc i_mp i_v v_mp v_oc \\\n", + "date_time \n", + "0 0.936042 -0.009381 0.097640 0.01 0.177468 0.074172 \n", + "1 0.978361 -0.002460 0.085913 0.01 0.172866 0.041262 \n", + "2 1.007065 0.003671 0.082151 0.01 0.173297 0.009758 \n", "\n", - "[3 rows x 7 columns]" + " temp_module_corr \n", + "date_time \n", + "0 -0.026863 \n", + "1 -0.028168 \n", + "2 -0.029400 " ] }, - "execution_count": 19, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -1281,17 +1173,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 86, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1331,7 +1225,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -1355,8 +1249,6 @@ " \n", " \n", " \n", - " poa_global_kwm2\n", - " temp_module\n", " pr_dc\n", " pr_dc_temp_corr\n", " i_sc\n", @@ -1378,98 +1270,92 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " 2013-01-04 08:15:12-06:00\n", - " 0.2047\n", - " 6.5\n", - " 1.044714\n", - " 0.977723\n", - " 0.987068\n", - " 0.956821\n", - " 0.999361\n", - " 0.858525\n", - " 0.950057\n", - " 200.0\n", - " 5.0\n", - " \n", - " \n", - " 2013-01-04 08:20:12-06:00\n", - " 0.2380\n", - " 8.4\n", - " 1.049437\n", - " 0.989055\n", - " 0.996282\n", - " 0.954162\n", - " 0.999665\n", - " 0.856551\n", - " 0.955411\n", - " 200.0\n", - " 10.0\n", - " \n", - " \n", - " 2013-01-04 08:25:12-06:00\n", - " 0.2724\n", - " 10.3\n", - " 1.052938\n", - " 0.999289\n", - " 1.002413\n", - " 0.952745\n", - " 1.000038\n", - " 0.855104\n", - " 0.960835\n", - " 300.0\n", - " 10.0\n", - " \n", - " \n", - " 2013-01-04 08:30:12-06:00\n", - " 0.2091\n", - " 10.7\n", - " 1.045790\n", - " 0.993954\n", - " 1.008897\n", - " 0.951868\n", - " 0.987090\n", - " 0.855696\n", - " 0.949448\n", - " 200.0\n", - " 10.0\n", - " \n", - " \n", - " 2013-01-04 08:35:12-06:00\n", - " 0.2043\n", - " 10.7\n", - " 1.042834\n", - " 0.991145\n", - " 1.007930\n", - " 0.950601\n", - " 0.986522\n", - " 0.855729\n", - " 0.948901\n", - " 200.0\n", - " 10.0\n", + " 0\n", + " 0.936042\n", + " 0.908517\n", + " 1.007988\n", + " 0.912605\n", + " 0.936845\n", + " 0.844634\n", + " 0.913971\n", + " 100\n", + " 15.0\n", + " \n", + " \n", + " 1\n", + " 0.978361\n", + " 0.949591\n", + " 1.002058\n", + " 0.923922\n", + " 0.965471\n", + " 0.851158\n", + " 0.941899\n", + " 200\n", + " 15.0\n", + " \n", + " \n", + " 2\n", + " 1.007065\n", + " 0.977451\n", + " 0.996976\n", + " 0.928207\n", + " 0.991961\n", + " 0.853123\n", + " 0.967743\n", + " 400\n", + " 15.0\n", + " \n", + " \n", + " 3\n", + " 1.022415\n", + " 0.992350\n", + " 0.997258\n", + " 0.932050\n", + " 1.006203\n", + " 0.850106\n", + " 0.981637\n", + " 600\n", + " 15.0\n", + " \n", + " \n", + " 4\n", + " 1.026992\n", + " 0.996792\n", + " 0.996553\n", + " 0.934552\n", + " 1.016173\n", + " 0.843868\n", + " 0.991363\n", + " 800\n", + " 15.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " poa_global_kwm2 ... temp_module_bin\n", - "date_time ... \n", - "2013-01-04 08:15:12-06:00 0.2047 ... 5.0\n", - "2013-01-04 08:20:12-06:00 0.2380 ... 10.0\n", - "2013-01-04 08:25:12-06:00 0.2724 ... 10.0\n", - "2013-01-04 08:30:12-06:00 0.2091 ... 10.0\n", - "2013-01-04 08:35:12-06:00 0.2043 ... 10.0\n", + " pr_dc pr_dc_temp_corr i_sc i_mp v_oc v_mp \\\n", + "date_time \n", + "0 0.936042 0.908517 1.007988 0.912605 0.936845 0.844634 \n", + "1 0.978361 0.949591 1.002058 0.923922 0.965471 0.851158 \n", + "2 1.007065 0.977451 0.996976 0.928207 0.991961 0.853123 \n", + "3 1.022415 0.992350 0.997258 0.932050 1.006203 0.850106 \n", + "4 1.026992 0.996792 0.996553 0.934552 1.016173 0.843868 \n", "\n", - "[5 rows x 11 columns]" + " v_oc_temp_corr poa_global_bin temp_module_bin \n", + "date_time \n", + "0 0.913971 100 15.0 \n", + "1 0.941899 200 15.0 \n", + "2 0.967743 400 15.0 \n", + "3 0.981637 600 15.0 \n", + "4 0.991363 800 15.0 " ] }, - "execution_count": 21, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -1480,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -1504,8 +1390,6 @@ " \n", " \n", " \n", - " poa_global_kwm2\n", - " temp_module\n", " pr_dc\n", " pr_dc_temp_corr\n", " i_sc\n", @@ -1522,174 +1406,166 @@ " \n", " \n", " count\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", - " 1433.000000\n", + " 27.000000\n", + " 27.000000\n", + " 27.000000\n", + " 27.000000\n", + " 27.000000\n", + " 27.000000\n", + " 27.000000\n", + " 27.000000\n", + " 27.000000\n", + " 27.000000\n", + " 2.700000e+01\n", " \n", " \n", " mean\n", - " 0.688500\n", - " 28.906001\n", - " 0.925018\n", - " 0.937176\n", - " 0.941841\n", - " 0.941404\n", - " 0.970376\n", - " 0.833504\n", - " 0.980062\n", - " 688.346127\n", - " 28.872994\n", - " 0.924420\n", - " 0.000598\n", + " 0.922290\n", + " 0.964306\n", + " 1.007518\n", + " 0.921644\n", + " 0.926516\n", + " 0.832990\n", + " 0.962283\n", + " 581.481481\n", + " 42.222222\n", + " 0.922290\n", + " -3.745975e-15\n", " \n", " \n", " std\n", - " 0.282059\n", - " 10.539365\n", - " 0.083688\n", - " 0.085308\n", - " 0.075453\n", - " 0.005757\n", - " 0.024785\n", - " 0.013827\n", - " 0.023198\n", - " 284.068826\n", - " 10.603470\n", - " 0.072510\n", - " 0.055453\n", + " 0.078215\n", + " 0.038515\n", + " 0.008416\n", + " 0.011069\n", + " 0.066698\n", + " 0.013716\n", + " 0.036399\n", + " 358.455058\n", + " 23.588350\n", + " 0.078160\n", + " 2.919170e-03\n", " \n", " \n", " min\n", - " 0.100300\n", - " -3.900000\n", - " 0.528479\n", - " 0.538920\n", - " 0.598801\n", - " 0.876107\n", - " 0.874223\n", - " 0.806993\n", - " 0.887511\n", + " 0.746677\n", + " 0.856461\n", + " 0.996553\n", + " 0.896040\n", + " 0.777618\n", + " 0.806040\n", + " 0.872546\n", " 100.000000\n", - " -5.000000\n", - " 0.000000\n", - " -0.187850\n", + " 15.000000\n", + " 0.753834\n", + " -7.157428e-03\n", " \n", " \n", " 25%\n", - " 0.466800\n", - " 21.900000\n", - " 0.902565\n", - " 0.941202\n", - " 0.942242\n", - " 0.937743\n", - " 0.952403\n", - " 0.821747\n", - " 0.971589\n", - " 500.000000\n", - " 20.000000\n", - " 0.900437\n", - " -0.016455\n", + " 0.849048\n", + " 0.948074\n", + " 1.000054\n", + " 0.915451\n", + " 0.877241\n", + " 0.823270\n", + " 0.941010\n", + " 200.000000\n", + " 25.000000\n", + " 0.849028\n", + " -1.246825e-03\n", " \n", " \n", " 50%\n", - " 0.754600\n", - " 30.100000\n", - " 0.931579\n", - " 0.963351\n", - " 0.965174\n", - " 0.940562\n", - " 0.972730\n", - " 0.831606\n", - " 0.989182\n", - " 800.000000\n", - " 30.000000\n", - " 0.939210\n", - " -0.002625\n", + " 0.926241\n", + " 0.974878\n", + " 1.006971\n", + " 0.925326\n", + " 0.936845\n", + " 0.833784\n", + " 0.975621\n", + " 600.000000\n", + " 50.000000\n", + " 0.924557\n", + " 1.418281e-04\n", " \n", " \n", " 75%\n", - " 0.931900\n", - " 35.900000\n", - " 0.980269\n", - " 0.983274\n", - " 0.983606\n", - " 0.944907\n", - " 0.987761\n", - " 0.845130\n", - " 0.996239\n", + " 0.996683\n", + " 0.994383\n", + " 1.013776\n", + " 0.930878\n", + " 0.987048\n", + " 0.844251\n", + " 0.990444\n", " 900.000000\n", - " 35.000000\n", - " 0.966230\n", - " 0.017487\n", + " 62.500000\n", + " 0.996118\n", + " 2.014444e-03\n", " \n", " \n", " max\n", - " 1.078300\n", - " 53.700000\n", - " 1.085821\n", - " 1.033045\n", - " 1.033932\n", - " 0.961908\n", - " 1.026911\n", - " 0.897088\n", - " 1.006732\n", + " 1.026992\n", + " 1.000051\n", + " 1.026623\n", + " 0.934552\n", + " 1.023294\n", + " 0.853123\n", + " 1.003213\n", " 1100.000000\n", - " 55.000000\n", - " 1.038369\n", - " 1.042834\n", + " 75.000000\n", + " 1.030609\n", + " 6.910501e-03\n", " \n", " \n", "\n", "" ], "text/plain": [ - " poa_global_kwm2 temp_module ... calc_pr_dc diff_pr_dc\n", - "count 1433.000000 1433.000000 ... 1433.000000 1433.000000\n", - "mean 0.688500 28.906001 ... 0.924420 0.000598\n", - "std 0.282059 10.539365 ... 0.072510 0.055453\n", - "min 0.100300 -3.900000 ... 0.000000 -0.187850\n", - "25% 0.466800 21.900000 ... 0.900437 -0.016455\n", - "50% 0.754600 30.100000 ... 0.939210 -0.002625\n", - "75% 0.931900 35.900000 ... 0.966230 0.017487\n", - "max 1.078300 53.700000 ... 1.038369 1.042834\n", + " pr_dc pr_dc_temp_corr i_sc i_mp v_oc v_mp \\\n", + "count 27.000000 27.000000 27.000000 27.000000 27.000000 27.000000 \n", + "mean 0.922290 0.964306 1.007518 0.921644 0.926516 0.832990 \n", + "std 0.078215 0.038515 0.008416 0.011069 0.066698 0.013716 \n", + "min 0.746677 0.856461 0.996553 0.896040 0.777618 0.806040 \n", + "25% 0.849048 0.948074 1.000054 0.915451 0.877241 0.823270 \n", + "50% 0.926241 0.974878 1.006971 0.925326 0.936845 0.833784 \n", + "75% 0.996683 0.994383 1.013776 0.930878 0.987048 0.844251 \n", + "max 1.026992 1.000051 1.026623 0.934552 1.023294 0.853123 \n", "\n", - "[8 rows x 13 columns]" + " v_oc_temp_corr poa_global_bin temp_module_bin calc_pr_dc \\\n", + "count 27.000000 27.000000 27.000000 27.000000 \n", + "mean 0.962283 581.481481 42.222222 0.922290 \n", + "std 0.036399 358.455058 23.588350 0.078160 \n", + "min 0.872546 100.000000 15.000000 0.753834 \n", + "25% 0.941010 200.000000 25.000000 0.849028 \n", + "50% 0.975621 600.000000 50.000000 0.924557 \n", + "75% 0.990444 900.000000 62.500000 0.996118 \n", + "max 1.003213 1100.000000 75.000000 1.030609 \n", + "\n", + " diff_pr_dc \n", + "count 2.700000e+01 \n", + "mean -3.745975e-15 \n", + "std 2.919170e-03 \n", + "min -7.157428e-03 \n", + "25% -1.246825e-03 \n", + "50% 1.418281e-04 \n", + "75% 2.014444e-03 \n", + "max 6.910501e-03 " ] }, - "execution_count": 22, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", + "# choose which normalised mlfm parameter to model e.g. pr_dc or i_sc,...,v_oc \n", "mlfm_sel = 'pr_dc' \n", "\n", - "# FIX THIS WARNING,\n", - "# SettingWithCopyWarning:\n", - "# A value is trying to be set on a copy of a slice from a DataFrame.\n", - "# Try using .loc[row_indexer,col_indexer] = value instead\n", - "# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\n", - "\n", - "cc, coeffs, ee, errs = mlfm_fit(norm, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", + "# add selected variable to measured data frame. We do this to ensure that data indexes match.\n", + "meas_temp = meas.copy()\n", + "meas_temp[mlfm_sel] = norm[mlfm_sel]\n", "\n", - "# Fix a bug with fit routine which gives a\n", - "# finite cc[4] even if all the ws data is 0\n", - "# this won't matter until cc is applied to other\n", - "# data with some ws <>0 when it will give bad results\n", - "if np.mean(meas.wind_speed) == 0:\n", - " cc[4] = 0\n", - " c_5 = 0\n", + "cc, coeffs, residuals, errors = mlfm_fit(meas_temp, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", "\n", "norm['calc_' + mlfm_sel] = cc \n", "\n", @@ -1700,19 +1576,19 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['poa_global_kwm2', 'temp_module', 'pr_dc', 'pr_dc_temp_corr', 'i_sc',\n", - " 'i_mp', 'v_oc', 'v_mp', 'v_oc_temp_corr', 'poa_global_bin',\n", - " 'temp_module_bin', 'calc_pr_dc', 'diff_pr_dc'],\n", + "Index(['pr_dc', 'pr_dc_temp_corr', 'i_sc', 'i_mp', 'v_oc', 'v_mp',\n", + " 'v_oc_temp_corr', 'poa_global_bin', 'temp_module_bin', 'calc_pr_dc',\n", + " 'diff_pr_dc'],\n", " dtype='object')" ] }, - "execution_count": 23, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -1751,17 +1627,19 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ - "def plot_fit(dnorm, fit, title):\n", + "def plot_fit(dmeas, dnorm, fit, title):\n", " \n", " ''' \n", " Scatter plot fit to normalised measured\n", "\n", " Parameters\n", - " ---------- \n", + " ----------\n", + " dmeas : dataframe\n", + " measurements, must include 'poa_global_kwm2'\n", " \n", " dnorm : dataframe\n", " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", @@ -1785,8 +1663,8 @@ " ax1.set_xlim(0, 1.2)\n", "\n", " plt.plot(\n", - " dnorm[fit] * dnorm['poa_global_kwm2'],\n", - " dnorm['calc_' + fit] * dnorm['poa_global_kwm2'], ##SR##\n", + " dnorm[fit] * dmeas['poa_global_kwm2'],\n", + " dnorm['calc_' + fit] * dmeas['poa_global_kwm2'], ##SR##\n", " 'c^',\n", " label = fit\n", " )\n", @@ -1807,23 +1685,25 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 91, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "# plot fit vs. measured, include a 1:1 line for comparison\n", - "fit_plot = plot_fit(norm, mlfm_sel, 'fit ' + mlfm_meas_file)" + "fit_plot = plot_fit(meas, norm, mlfm_sel, 'fit ' + mlfm_meas_file)" ] }, { @@ -1838,7 +1718,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -1942,17 +1822,19 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 93, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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Tv6YtnBYdi9VN17+6yUyPjo/CuOrNEJEJGN/wb4HbVLWoeAWlyg4z0B7AznTdpjC/xXMjcxlgH3ApJS65PDNx6w95bTyLb0h/8pmKF1ZLe58AezcfxOpBzTPS95p/b0tipOQctv/x/LTy++QNt4E9ym9LDIziOWCfs1jx27i09wuwS/nEyKXpYa9KF7Lm908y0vcWzcxX4757ncevaz5Pe7+n122Z9j5TDLG4v4jEx3/uq3/N7FUR4wcezxqMT3IiFaO6+HYVo33axLrEfg7BBO45HxOm9CngLf4eVc8JO8xAmykyNchmkkwMspkkU4NsJsnUIJspMjXIZpJMDbKZJBODbEZIfY92ZRI/2nxMJql4KlG43UJi20qYKGUaJdYorp/1wAeq+h0UpJFcKSJ7qWUSlEwQ9mhLyGhWu5ZgzWuz/TJ07ffZnOSNyhgD+w92LcGKAf0SQ0aXffr2HeZagjX9+37oWkJKCMn3Z1N078kBdoqPF44xdirMZWtmVFdYu5lArXgrZKBWXP10jAFVjDIViSkMtCXkRza6lmDNlJX5riVYkT1/lWsJ1kydnEqSnbLD5OxZyRuVMbIn+fcAlp39Q/JGZQEhLe49auJPD8HEZa8gIvUw+YjfKKT568A9IlJFRCpjMpC9FtVlYZJZ3CEiu4rJzwwmBjWYHNuXRy5AO2NC344vC7NZCANtiWlVkFnNH54/q4ZrCVb0vjkxmVHZ57mX/pu8URmiV++HXUuwpk/f9q4lWPNyn+TJ1MsKaQxYcSsm6cUvmH3TW1R1poicFS0Jx+iDSSU4AxMrekR0DDXJKRpjkqOsxsR6bxwdR1VHY2Jyj4jOcxSQsbSItoSBtoR05WfXEqy55KOpriVY0eCRkckblTEanX9N8kZliAvObe1agjUNzrnDtQRrzmuQmGSrjJLGNHlq0oQ2VtUKqnqYqg6Ojo9T1Ypx7VRVO6jqvlHpEG9lrCbd58mquruqnqSqUxLO87KqVlHVfVT1UlUtM1/OwRiqhFzBfq4lWHP/SYe7lmDFI1fVcS3Bmg4P3+VaghUPP+LfQPvIo/9xLcGajo/c6FpCakhIk5dOwkBbQg5mF9cSrDlqr91dS7CiRuW9XEuw5sjqmXF1yhRHVU+/61umqVHjUNcSrKnuiWYBJEN+1Tsi4Z0sIQ+zyLUEa875cLJrCVacdt9w1xKsObdeI9cSrKh3un8uVHVPvcm1BGv+WdefGW25cpK0BFIjzGhLSC+OdC3Bmh+a/dO1BCt+7ne1awnWzFrwrWsJViz4yT+f1MVLPnAtwZpFi/1wSRIRyoc0eWkjvJMlZATpj9KTaV6YXmZsBFLi2WF+BX8A6NWjn2sJVvR4rjBvi7LNs93fdi3Bmuee9ce/Osxo00eY0ZaQ39jiWoI1y9Ztci3Bitzf1ruWYM2ypctdS7Biae4K1xKsyc1d6VqCNUt90RwtHQfSg/w1RvP2yxGym2Yi1nEmueSmzMQ6zhSZinWcSTIV6ziTZCLWcSbJVKzjTJKpWMeZ4vS6LcmeNDttI+M+VWroObe8lLTdBx3Pz04SgjFAWDouMQ95aAz1rw+yXUuwom4H/4yhGvzzUtcSrDijbjPXEqw59ZRWriVYc9qpfrgkiQg77Vw+aQmkhl+PbWUQLyNDnelXZKiX2/hlvAXwbK8nXEuw4sWX/YsM1buPh5Ghet/nWkLKhKXj9BEG2hKym4eLAhU9exLdc/fM5OXNJBX3rOBaghV77rmHawnW+Ki5oi+aBZsQi4Ek+DdKlDGeYYlrCdZc/YlfVryX/fcL1xKsaXq5J/6SEVc0vsu1BGsaXerP7DBG40Z+zMKF5BbHYcabOmFGW0K6458BUPbVdV1LsGL2C1e4lmDNxO+/dC3Bihmz/PNJ/WGOP64yMWb98I5rCSkhAjsFP9q0Ed7JEvI+npjrx/Hf7IWuJVjR+Z0pyRuVMbo91sO1BCu6dunjWoI1nTu94lqCNV0693ctITVCZKi0Ema0gUAgEEhAKBdiHaeN4Edbhgl+tJkn+NFmnuBHm3nS7Ud7QNVj9bKHXk3abkCbfwY/2hTYrh9ZRKS1iEwSkUnL+KNgmbcdC1jKJhawocAPdhArCsIp3sZ8fmMzs1hXkG+2P8sZzWoAbmQu69nKZPJpyVwAXmQpX/E7AM3IAeArfudFlgLGaGoy+axnKzdGrxnNavpjIgh15WdmsY7f2MxtzAdMqMSHvjG//+uDbKauyGPe6nWc/M5EwCwBx5aBT35nIvNWr2PqirwCP9mHvplfEG7xmDe/ZunajYzLXc1Br4wD4M5xObw2OxeAQ14bT96mzYxctJKrP5kBQKvRs3l3ntG3d78xALw7bzmtRs8G4OpPZjBy0UryNm3mkNfGA9Dvsznc3HsCYPLIZn2/lNxf13HoTWZv6tlh33PvwO8A4x+bPX8lOblrOPb2IYBZJo4tFR97+xByctdQ7eZ3C3xp7x34XUFIxkNveofcX9eR9f3Sgpy1N/eeQL/P5hjNzQeRt/4Phk/6mcv++zkAzXuM4a1xPwKw05WvAfDe20Np3eJOAJpecSOjRnxOXl4+h+1/PAAD+w/m7lsfAEye2fFjvmFp7nKOq3YaYMItdryvq7nmf17K1MkzqFPjTOoefw5glpFjS8l1jz+HeXN/ZOrkGQW+th3v61oQsvG4aqexNHc548d8U5DT9u5bH2Bgf7Mfedj+x5OXt5YRH42lSWTAdP11D/L2W+b6d9/5ZADefmsk11/3IABNGt/FiI/Gkpe3lgP2OQuAAf2GcNvNRvMF57bmyMMbkpu7gmqHXQiYkIz3tX8WMD62k7NnMzdnESccdzlglppjy80nHHc5c3MWMTl7doE/7n3tny0I63hIlcvJzV1JVtaUghyybVo/Td++Ju7vXpUuJC9vHcOHf0WjRiYxerNmXRg8+DMAypc7G4DBgz+jWbMu5nNodD+HVLmcvLx17FXJaO7bdxhtWj9tPodz7iArawq5uSs5pIrR/Gz3t7m33YuA8cHNzp5DTs7PHHO0yQ/eudMrBcvRxx1zNTk5PzE5+4cC39cO9/YsCKFY9ZBG5OauYEzW5IIcs7e0eZL+fT8EYN+9ziMvby0fDR9fYAB1XbNHObTyvwHYpfwZALw1+FOua2Ye9ho3as9Hw8eTl7eWffc6D4D+fT/kljZPAiaX7ZisyeTmrqDqISZpxXPPDqbDvT1JO9EebbISSI0woy0hS9mUsVR5mZrRzlu9jqP2Tr+bQaZmtDm5azKWKi9TM9p5c3/kqOpHZKTvTMxo5+YsonqNzKz4ZGpGm5Pzc8ZS5WVqRpuT8xM1ahyW9n7TPaP9R7Vj9cpOrydt9/INdcOMNgX8Wh8pg2xgq2sJ1uT/4Vd85rz1f7iWYE1+nl/LpXl561xLsMZHzfneaA7GTukkzP1LSGzp1yfuHJ/jWoIVt/T52rUEa+657UHXEqxoe0tX1xKsubnN064lWHPLzd1cS0gJCVbHaSUMtCXkcc8MrADGXH6yawlWTHzKr7jBAKO/9is+84SJb7qWYM13kzxxlYnj2++SGxiVCQTK71w+aUmpK5F9ReQDEVkrIotEpGkR7UREuonIqqh0ExGJq68jItkisi76WaeQPnYRkdkisnhbLz0ThIG2hAzCv/RiMQMrX4gZT/lEzDjKF2KGTz4RM2zyiYwYLmWANEeG6gVsAg4EmgEvi0jNQtq1BhoDtYFawKVAGzADKDAUGATsAwwEhkbH42kPZe9LOQy0JWQf/IobDHDQHpkx3soUlffZ3bUEaw462K9kEwdXPsC1BGsqV97ftQRrDvZFc5qWjkWkAtAE6Kiq+ao6HhgGXFdI8+uB7qq6WFWXAN2BG6K6+hiboh6qulFVexqVNIg7VzWgOfDfbb3sTBEG2hJyCfu6lmDN7bUyY6mZKe5pdLxrCdbcdtdNriVYcdfdhX3vlW3uaXeNawnW3H1PoaumZZI0zWhrAJtVNd4wZBpQ2Iy2ZlRXWLuawHT9q5vM9IR+XgAeBNanIqw0CQNtCYn5vPrEMW/6ZVwU88H1iZiPrS/E/Gd9IuYf6xMx/9eyjgjsVL5c0gLsH4tVEJXWCV1VhCjAwJ+sAfYs5LQVo7r4dhWjfdrEur/0IyKXA+VVtUwG7Q7uPSXEt2hTAF82Psm1BCu+7eafMdQXXw1zLcGKr74Z5FqCNRO/6+dagjVfTxzgWkJKCEL51GasK5P40eYDlRKOVQLyUmhbCchXVRWRIvuJlqefAi5ORbALwoy2hCxlk2sJ1sxbU+ZWVoolJzfxQbbsM3/uAtcSrJg3d5FrCdbk5PzsWoI1cz3SnKal4xxgJxGpHnesNjCzkLYzo7rC2s0EasVbIWMMpmYC1YHDgXEisgwYAhwsIstE5PBURGaaMNCWkCGsci3BmicnL3QtwYou/5vqWoI1T3Xt4VqCFV279HUtwZounT1xlYnjsS6ezGgFyokkLclQ1bWYga+LiFQQkXrAZcAbhTR/HbhHRKqISGWgHfBaVJcFbAHuEJFdRaRtdHw08D1wKFAnKq2A5dHvZeLJJiwdl5CH8cuwCGDEv+u4lmDF6C4NXUuwZthnb7uWYMWnX/g30I7+0g9XmXg+H93LtYSU2WmntM3DbgVeAX4BVgG3qOpMETkLGKmqFaN2fYAjgBnR3/2jY6jqJhFpHB17EpgNNFbV2JListjJRORXYKuqFhxzTZjRlhAvI0ON8ysyVCxJgU/EkhD4QizBgE/EEgj4RCxBQFnHRIZKXlJBVX9V1caqWkFVD1PVwdHxcXGDLGrooKr7RqVDvJWxqk5R1ZNVdXdVPUlVC01UrapZqnpIyd6B9BJmtCXkCHZ1LcGaE/evmLxRGeLkI/dzLcGaOied4FqCFSedfJxrCdacfMrRriVYc/LJx7iWkCJC+RSWhgOpEQbaEtKAvV1LsOaGYyu7lmDFTef794V6fSt//CUBbrzpCtcSrGnd2g9XmXhatW7sWkJKCIRYxmnEeulYRP4hIkfEl0wI84VYblmfiOWO9YW9m/vnehLLZesLsTy1PhHLQ+sTsTyzZR0LP9pACqQ8oxWRi4ABwEGYB54YCh7GIUwTL3KkawnWzG56umsJVvzc72rXEqyZueAb1xKs+PGnUa4lWLN4SZmMTVAsixYPdS0hZcKMNn3YPJL0Ah4DKqpqubiyww6yALPxJb/kn4xfutq1BCuyZpYZ48GU+WqsXwPt2DHZriVYk5VVqC1MmWaML5olrUkFdnhsBtp9gD6q6le0gwwz+m9Rwco+r/2w1LUEK/p/Nse1BGteH/CWawlWvNJ/iGsJ1vTr51cqQoD+/fyY0Qqwc/lySUsgNWzeqQHAfzIlxFfupYprCda8c6FfFrFDH/BjXyuewUP8CEwQ4/0Pe7iWYM2wYX64ysTz4TA/XJJC4vf0YjPQno7JI5gjImPjS6bE+cCL+DU7BGg1erZrCVY07zHGtQRrWre407UEK66/7kHXEqxp1qyLawnWXNfsUdcSUiYMtOnDxr2nf1QCcZxIBdcSrLnwML9S+11ysn/Rty64uEHyRmWIhhf7Z3V8ySX/dC3BmosvqedaQkqIhIE0nchf0/ttv1Sucby26fmuaxlWDH30S9cSrLjtZf+y7FTeZw/XEqzx7ftvRd5G1xKsqXVwYqKYss21553NzKmT03ZnHFGztnZ5J7kl+nUnVM5Okr0nQJKlYxG5Lu73lkWVzMssu3Rq6F9EnSkTb3MtwYpWJx/mWoI1Fx+xv2sJVlxUzS+9ANfV8s8+ovYBhaVhLYMIlC8nSUsgNZItHV/Ln1kWriuijWICRu+QdBo5y7UEa06s609gc4D+2T+5lmDNxz+udC3BilEL/NIL8Mb0Ja4lWDNtRWFpWMseAill5wmkRrEzWlW9OO73c4oofm1GpZnpX37kWoI1v678zrUEK74d+aFrCdZ8OfQ91xKs8E0vwIQR/gWs+Pj9/7mWkCLBjzadWDlCicjeItJMRNpHP/fOkC5vyJnon0Xs76u/dy3BimnjvnAtwZqJoz91LcGKbz3TCzB17OeuJVgz9lM/InCZEIyStARSI2VjKBFpgEngOwdYBBwGHAM0UdUy/00YjKEyTzCGKh18m0gEY6jMk25jqKNOqKPdP/wsabvGR/0jGEOlgM2M9kWgtaqepqpXqerpwE2Y0Iw7LIMfvdW1BGvm57zsWoIVPe/yL05KJ8+y9zx6o196Abq3vd61BGtub/Z/riWkRGyPNlkJpIbNQFsZeD/h2AeYJAM7LCc39OMfJ579DzjTtQQrzr7Cv0Gg4bUtXEuwwje9AOdc2cy1BGuatPDnoTEMtOnDZqB9A0j0C7kFeD19cvzj8Fp1XUuwpmKl6q4lWHHMyf4FJjjhND8CE8SodbpfegGOPfUM1xKsObWeH4FBRGDncpK0BFIjmR/tuLgwiycC3UVksYh8KyKLgWej4zss3Zv/y7UEa76f4le4vXYXnepagjXX/dOvfLTNTvdLL8Ad557kWoI15x1fw7WElCknyUsgNZL50SaGXOyXKSG+8uCQSa4lWFP7lGddS7Ci1zi/YjMDvD9jkWsJVnzwvV96Afp9k+NagjVfL/QjNrqQvqVhEdkXk5TmAmAl8ICqDi6knQBPAq2iQ/2B+zWy2BWROlE/xwKzgRtVdWpU1x64HqganeMlVS0zGRyS+dEOTKXE2ovIS5mXXLaY9LEvfnF/svKX8a4lWDFmyJuuJVgz8q2ByRuVIT4e7JdegNHvDXItwZr3Xvcntk8a92h7AZuAA4FmmOQ0NQtp1xpoDNQGagGXAm0ARGQXYCgwCJOydSAwNDoOxn6rRVR3EdBWRK6xvugMke6Egs3T3F+ZJ3fuTNcSrFm31q9IS4tmTXctwZq5M6a6lmCFb3oBFsz0776YNdWPxO8isFM5SVqS9yMVgCZAR1XNV9XxwDAKjzR4PdBdVRer6hKgO3BDVFcfswLbQ1U3qmpPzODaAEBVn1LVyaq6WVXnYAblMmN4kO6BdodbtW90Z2fXEqw5rJpfVrwtHu7mWoI1dzzxnGsJVtz5X7/0Atz46FOuJVjzyLMvuJaQMmnao60BbFbV+HX+aUBhM9qaUV1h7WoC02PLyBHTC+snWoI+Cygzs6B0D7Q7RiqgOF7t4J8v39zZPVxLsOKp1le5lmDNfdc2ci3BivbX+KUX4PGWV7qWYM2NlzV0LSElLPxo9xeRSXGldUJXFYHfE46tAQrLrlAxqotvVzEaOBPriuunE2ZsezWFSy0VbPLRBgqhfnO/MuEAHFTl4uSNyhCNWt/tWoI1ze7s4FqCFc3v8ksvwBW33ONagjU3d/DF4j/lPdiVSSJD5QOJYbYqAYVlV0hsWwnIV1UVkZT6EZG2mL3as1S1zIQkC0vHJWS/Koe7lmDNbrv9w7UEKw6qWs21BGuqVDvStQQrDvFML8BBVY9wLcGaqkce5VpCSqRrjxbIAXYSkXjn/doUvqw7M6orrN1MoFY0u41RK76fKGXr/cC5qro4FXGlRboHWv/MAEtI3zv8iwz1w0y/9rYea/5v1xKsufOy81xLsOL2Rn7pBXjkWr9WZgCanne2awkpk449WlVdi4mR30VEKohIPeAy/ky/Gs/rwD0iUkVEKgPtgNeiuixgC3CHiOwazVwBRgOISDPgCeB8Vf1xW685U6Q80IrhJhEZLSLTo2Nni0jBBpqq3lLM67NEZIOI5EdlTlxdUxFZJCJrReTDyO+qqH7qiEi2iKyLftZJ9Roywb1v+pe954QTn3AtwYrun/jnqzzomzJjh5ESg7/1Sy/AC19Mdi3Bms+/n+taQkqkOdbxrcDuwC/AW8AtqjpTRM6KloRj9AGGAzOA74ER0TFUdRPG9acFsBpoCTSOjgN0BfYDvosbY3pv8xsQh4i0EJFaCcdqi0hROdr/hs2MtgtwI9AXk7kHYDFwn0UfbVW1YlSOBoj8qfpgzL0PBNYBhfrjpuBLVepMeL/M7LenzPKlfqUX++SNvq4lWDOkv1+5Nt7v55degI8HpuV7tFQZ+FJP1xJSprwkL6mgqr+qamNVraCqh8WCVajqOFWtGNdOVbWDqu4blQ7xVsaqOkVVT1bV3VX1JFWdEldXTVV3jhtfKqrqzWl6Kx4Dfk449jNmcE8Jm4H2BuDfqvo2f1oXLwBKulHSDBiuqmNVNR/oCFwhIoVZk9WnGF8qF+T9+ourU28zf/yRaLxXtlm9YrlrCdasWr7MtQQrfNMLft4XK5Z5EhlKYKdy5ZKWHYRKFG45vXeqHdi8U+UxVmHw50BbMe5YKvxXRFaKyFciUj869hffKVWdj4kiUlhQ0JR9qUqLC2+ymdCXDQ45rIlrCVZcfU9H1xKsuemhx1xLsKL1w37pBWh676OuJVhzb5f/upaQEmbpOMQ6jpiFCboRz+WYMJApYTPQfgw8KyK7QoFT8GOYNfVUuA8z+62CWX4eLiJHYucfZdMWEWkd8+/6dckivhz0IgA9WzVk5eKF5M6dSZ/bjS/eJ/26FSwDP9PsX/y+6hcWTJ9Y4Cc77PlHC8ItPnHFKWxct5Y533zJf5uY7D3vdWvP9C8/AqBTw+MAmP7lR7zXrT1g8tbO+eZLNq5byxNXGGv4SR//j2HPmy+LVztcz4LpE/l91S8808wkKli+9HMW/2QyE/7w/ZOsW/sTG9YvZ9Y0EyRj6eIRLF08AoBZ0zqzYf1y1q39iR++fxKAxT+9X7BMPGPKg/yxaTV5v+cwbZJxi/hpweCCcIzTJt3Dli0bWPPbjIJ8tQvnvcqvK78DYMpE48b068rvWDjPvE/zc15mzW8z2LJlQ0GfY4a8yetdzcPHU62v4odJX7N6xTLaXWiu+ZM3+vLOs+ZLvUuzi1k4ezrLFv3IQ5ebax7a51mG9jGxmB+6/F8sW/QjDzc5hy7NjOHLO88+VrCU3O7CU1i9Yhk/TPq6wNf29a73FYRsvO2sY9mwNp+pYz8ryGnb98Hb+XbkhwC0OtnsgHw59D263Wnc/zq1asq3X4xiXX4eTU6oCphwij0fNC5G913biOnfjGfV8qU0P9083w3p34t+j5uHgTsaNWDujKncfGE9WjUw98agHt0Y1MME3WjVoC6Lf5zH3BlTuaORWYjp93jHgqXm5qfXZNXypUz/ZnyBL27PB+8uCOnY5ISqrMvP45vPRxXkkH3yztZ8OfQ9AC6qtn/BNT0ZXdOjNzblm8/NNV1+vLmmjwcP5PkHzDW1v6YRLc+py6rlS2l6mrmm9/v1om9Xc01tLzXXtPjHedx4jrmmN3p0443omm48589ranupuaa+XTsWLEfffu5J/PbLMmZ/N6HA93VA5w4FIRRvOr0G69fmMznr04Icsy/ddxsTRnwAwHW1qgAwYcQHvHSfuQ+7t72edhefwfq1+dx0unkmH/3eIAZ0Nm5Kj7e8ktnfTeC3X5Zxe5R84OOBvRn8jPnf6Xj1RSyYNZ2lC+fT/lKTNnLIS90Z8lJ3AC49rQ4L589l1rQpXHOuybjzzCMPFCz9nnd8dX5ZtpTvvhpX4Bvb5Z7bC0Is/vPwg1mbn0fWJx8X5KC9v01LGp5kkjfUPsB8ZX38/v+4v01L8z41+z+yPvmYtfl5/PPwgwETsrHLPbeb9/myhnz31Th+WbaU8443RrwDX+rJM488QPpJvj+7A6XJuw/oLyLvi8hTIjIEE3O5XaodyF8nh8U0FKmE2RNtCOwMbAA+BVqoamE+Ucn6G4XZ7D4P+EpVn4qrywPqq2p2wmvuxliVXRx3bDiQpardiztf5RrHa5ue79rKTEru3JlUrp6ZCfXQR7/MSL/r1v7EHhUOS97QkttevjTtfQIsnD2dw4+tlbzhNlB5nz0y0u/cGVOpfkKdjPSdiZlEJvWuyMuMO+OCWdOpdlxm7otaBye6bKaHWdOmcFzt9Cc8u/a8s5k5dXLa7owTTjpZh435Kmm7Iyrtnp3Ej3a7QESqAtcCh2L2Z99U1cR92yJJOWCFqv4OXC4iB2KMoX5W1ZJs7ChmheIvvlMicgSwK8b/KpGZQDsRkbjl41qYoNVO2GX3Cq5Ovc2UK7erawlW7LZHxeSNyhi7V/BLs296AXbbw7//vT0q+vE+C0p52eEC/RWJqi7CZBbaJpLloy2XWIAVQDbwS9yxYhGRvUXkQhHZTUR2inyezgZGAW8Cl0am3hUw1s1DipglZ1GML5ULBncq0qOpzPJjjl/Wmi9Ey74+0amVX/GkY8vQPvHs7f6FP729qT9+9yJbk5btFRF5Q0ReT1ZS7S/ZILkZ+KOYEqtPxs4YU+gVmFyBt2N8oHJUdSZwM2bA/QWz33pr3AWPFJEHISVfqlLnjv4jXZ16mzmutl9GJI9/4J+vcv/RE11LsGLAl37pBXh6uF/pHgGGfzvVtYSUEJRybElatmPmAfOjsgYz7pTHuLSWwwTdWJ1qZ8kG2moYA6aiSqy+WFR1haqeqqp7qureqnq6qn4WVz848q+qoKqXqeqvcXUNVfWJuL+L9KVyQczAyidiBlS+EDOO8omY8ZMvvOGZXqDAcMknXn7Kn2AxO/KMVlU7xwrGA+YSVW2mqg+qanPgEuDoVPsrdo82WpcOBAKBwA6FUk42uxZRVjgd+Cbh2LfAP1PtIGVjKBF5gyLS4Klqi1T72d44p3nb5I3KGAcfcolrCVZc1sa/LC3N7/LLv/o6z/QCXHFryt4VZYZbPMneE1s6DgAwBXhCRB5R1fUisjvQGZiaagc2frTxa9bzgbUYV59fi3vR9k7PVn7kl4wn5ofrCzEfW5+I+dD6Qsw/1idi/q8+celpdVxLSA3ZsZeOE7gBqAesEZHlmD3bMzG2Qilh497zt29nERkA+GVZk2aadnrZtQRrjqiRrhCgpcPtPfyLJ92p/2DXEqzoPMAvvQD3vDDQtQRrXhicfl/+TCHsMANpsajqQuAMETkUqAwsVdWf4tuISD1VLdLxuKSJ36cC/k030sim9WtdS7Bm69Yykw85JTass4nyWTZYv9Yvzb7pBdiwzr//vXX5vrzPSjlJxaFkxyEKUFFUkIqR/D0xfQE2afIaJJR/Y3IFzrIRu70xvKd/E/qfFvg1e3m96/2uJVjzwkN+7Ss//6BfegFeicIt+kSXe+5wLSElBKWcbE1aAgUUG5XLZkY7IOHvtZgZ7bWWgrYr2rzwnmsJ1hxzvF8D1yNvfuxagjU9hzmLobJNvDjcL70Aj70zyrUEa97+YpxrCSkTlo6tKDaMVsoz2ijfX3w5XlWbq+qCkmv0l0/6+ed/GEtU4AuxJAQ+EUsy4AuxBAI+EUsQ4BOZSQCQCTQYQ6WRYme0qYRXBFDVHfYd33Pff7iWYM3OO+/lWoIVex9woGsJ1ux34EGuJVjhm17w87444KCDXUtICQl7tGkl2dLxZpJMiSPKp0GLl5zRxL84vAcefJ5rCVZceF1r1xKsuaLVba4lWNHkJr/0Alx8vV/W8wDX3+rHHi1AubB0HEsHWw1YpKrFORYXu0drE4LxdmAMcBFwbPTzS8C/iA1pJJY71idmTPHDaT5GLJetT8Ry1fpCLA+tT8TyzPpELI+sDwhbk5btnShL3AySTDhVtdCc6DGKHWhVdVGsAPcAV6jqZ1EygM+A/wPutZO+fdE6AzluM80xNf2y1uw46CPXEqx5fujnriVY8cIwv/QCdHnLPyO5wZ+PdS0hJUTM0nGyklpfsq+IfCAia0VkkYgUmipKDN1EZFVUukUzylh9HRHJFpF10c86qb62hEzBxDveZmwiQ+0FJGbJ3iM6vsOyaslC1xKs2bDhF9cSrFi2yD97uyUL5ruWYMViz/QCLFv0o2sJ1iyaP8+1hBRJq3tPL2ATcCDQDHhZRApbQmmNyZJTG5Nn/FKgDYCI7AIMBQYB+wADgaHR8WJfmwaygFEi0klEbhSRlrGSagc2A+1A4HMRaS0iDUWkNfBJdHyHJWuQs5zz28yyJX7NBIb1fc61BGvefP4p1xKsGNTDL70AQ172L6tTb5+y96Rh6TjKMd4E6Kiq+ao6HhgGXFdI8+uB7qq6WFWXAN0x4Q8B6mNsinqo6kZV7YnZF22QwmtLSj1gASY4U/NI+3XR7ylh40fbARPv+GqiMFTAi0A/iz62O/7zlH/PGdWPvcu1BCs69P2fawnWdHtrmGsJVjz9tl96AR56xT8f9gFD/chfLZF7TwrsLyKT4v7uq6p94/6uAWxW1Zy4Y9MoPKJgzaguvl3NuLrp0Z5pjOnR8VFJXlsiVPWckvZh40e7VVV7q+q5qnqsqjaI/t6hUzwMez5Ehso0r3f1L7NMzwfvdi3Biucf8EsvwAAvI0Pd7lpCiqS8R7tSVU+JK30TOqoI/J5wbA1QmPFQxaguvl3FaK81sS6xn+JeW2JEZG8RaSYi7aOfe9u83mbpGBH5j4iMFpE50U//fFvSTOXq/llr7lHhMNcSrKh6XC3XEqypfkId1xKs8E0vQLWa/t0Xx9U50bWElBHdmrSkQD5/jwFcCchLoW0lID+axSbrp7jXlggRaQAsBO4ATsV44CwUkXNT7cMmH+1DmLRA3YFFQFWgg4hUVtXHLXQ7ocKuO1H36PQHl6h7dOa8mx4Zk6kBsVCjvxKzfqdVGen32loXAZnpe82mRPu+9FDnrusz0i/Afrum33DpnPvOxGS/TD87b/0tI/02e7g2xvMiA6xclJFuT7h2N/gj/StKu2sG/j/SE4coB9hJRKqr6tzoWG1gZiFtZ0Z1EwtpNxNoJyISN3jWwhhaJXttSXkRaK2qBXtYIvJ/0bmPSaUDmxltK+ACVe2rqp9ESwQXYay9dlianFDVtQRrKh1wlWsJVhy4Tz3XEqypfqBf98V+e5d4G6rUqbTv5a4lWFOpui8BK9QMtMlKsl5U1wJDgC4iUkFE6gGXAW8U0vx14B4RqSIilYF2mMQ1YCx/twB3iMiuIhKb4YxO4bUlpTKQGLf2AyDlcGo2A20FYEXCsVXA7hZ9bHe88fX3riVYs+TH11xLsGLeT5+6lmDNlHl+3RcLf/bPV3nJojddS7BmyRRPrLsV2LoleUmNWzHjxC/AW8AtqjpTRM4Skfi8gX2A4Zhliu+BEdExVHUTxn2nBbAaaAk0jo4X+9o08AaQGDrtFszgnhI2A+0o4E0ROVpEdheRYzCuPZ9Y9LHdMePbInP9llmyxvo1CIwbMyl5ozLGhHF+3Rdjsia7lmBN1pjpriVYkzVhjmsJKaKwdWvykkpPqr+qamNVraCqh6nq4Oj4OFWtGNdOVbWDqu4blQ7xe6yqOkVVT1bV3VX1JFWdkuprS8iJQHcRWSwi34rIYswW6okiMjZWiuvAZqBti9l4no7ZeJ6KSZXnixldRhj5VsoPNWWGfq/4lV7slf5DXEuwZtArft0XA/p/6FqCNf36++EqE0+/Qf6kyUvH0vF2Qj/M1ulDwEvRz5uA/pj0sbFSJGI76EcZffbHmHV7805XP6GO9hz2hWsZVlx08NeuJVixfqdDXEuwZs2mQ11LsCYTxlCZJFPGUBklQ8ZQmeLUix5n0rRF6Qo5yCknHaWTvkoeEET2uCxbVf0LRp5mROQlVb21qHpb9549gOOBo4DTReQMETmjhBq9ptud/tmCNbv+adcSrLjhOl9yeP7JrTf4dV+0aO5fPtpm1z3pWoI1zW7t71pC6mzZnLwEYhQbJcrGvacFxsx5E7A+rkoBvxwz00jdBhe4lmDNJQ1PdS3BioYXn+1agjXnNfTrvrj4kjNdS7DmkotPcy3BmkvO88X3VyFtW5w7BMWuJqS8dCwiy4Droqw93hGWjjNPWDouHcLScSmwoy8dn3ikThqTfMVA9roqLB0DIvK7qiYG1CjAZul4E8aXKRDHxUfs71qCNeV2v9S1BCsq7OxPNJ0YlSv4dV/supN/s8Nyu1zkWoI15SqnK6FMKRCModKGzUDbEXhWRPz6BskwH/+40rUEa7auH+5aghVr/5iSvFEZI3etX/fFxs3fupZgzdZNflnPA2zNTZdrZ4ZRRbdsTloCBRS7mmAz0OYAjYDlIrIlKltFZIdOKvDlUP8yiAx+O8u1BCveecs/N44h7/h1X7z9ln/u8IPf+tK1BGsGD5mYvFGZID2RoXxFRN6J+z2VmP6Diqu0GWjfwETCqI1JfVQDqE4JM8/7zsTR/kUtGjHyO9cSrBj5cbG+4GWSz0f6dV98PGK8awnWjPjYv1n4iM89CrKhmrxsv1wYl/nn+WSNVfWW4uptjKF+A/ZNY7SNUiUYQ2WeYAxVOgRjqFJgRzeGqlNNv/sseQrQcv/4z3ZpDCUiw4EDMCu51wBvF9ZOVVuk0p/NjPZVTFb5QBydWmUmE04madSki2sJVlzZ+E7XEqxpcaVf98Xll7VzLcGaRo39ywXdqMWLriWkhuqO7kf7fxh31jkYF9b5RZSUSNmPFqgLtI3S5S2Pr1BV/xwd00TDa1N6oClT3NTSL2vNlq2ucC3BmuYt/bovbmzV2LUEa25q1dC1BGtuan6Wawkpotv1HmwKtFLVFwFEpKaqdi5JZzYDbb+oBOI44TT/UrjVP/t41xKsOOtf/q1MnXGWX/fFv+qf5FqCNfX/5Uvwhz+pf8bRriWkzo490D6OmdEC/LuknaU80KrqwGRtksV73B657p/H8/4Mv/ZzqhxxA7+v+F/yhmWEow67gOW/+ZUN58Sjjmfucn/ui8MP/TerVvtlxVulajN+//UD1zKsqHJiB36f29O1jNTw0xwnXfwoIt0xyeN3FpGWhTVS1VdS6cxmRpsKzTG5B3cYfBtkAa8GWcC7QRbwapAFvBtkAe8GWcCvQXb73oNNxtVAB+BaYGcKt09SIKWB1iqpQAqkzerNF0a+lXSiX+boO8AvR/9X+r3vWoI1g17x677o38+/Qatv/49dS7Cm7yCPXNV2YD9aVc1R1Vaqej4wRlXPKaQ0SLW/dA+0O9xaw9wZU11LsGbS5HmuJVgxOXuWawnWTJs81bUEK7KzZ7uWYM2k7LmuJVgzaZonKx2avsTvvqOq55a0D+t8tMV2liSwskuCH23mCX60pUPwoy0FdnQ/2hMO1e8+vCtpu3JH3bu9+tHOVtVjo99/5u+TSAFUVVPKXLddLx2LSGsRmSQik3IXLWBQj24AtGpQl8U/zmPujKnc0cjM/vs93pEh/XsB0Pz0mqxavpTp34znvmsbAdDzwbsLlombnFCVdfl5fPvFKK4+qTpg8tLGwjHGEg18OfS9gny1nVo15dsvRrEuP48mJ1QFzLJzzwfvBuC+axsx/ZvxrFq+lOan1wSge48PaHffAABOOeMusifPI2fuEo4+wQQm79R1MJ26Dgbg6BPakDN3CdmT53HKGXcB0O6+AXTvYZYEq1S7ntzcVWSNncE+B10NQOvbXixYRq50wFXk5a1j+IiJBX62za5/uiBcYywRweC3swry2TZq0oXhIyaSl7eOSgdcBZhl3rY3PwbARee2YuyYSSzN/YUjDzsfgOefe53723cHoF7dpkzJnsXcnEXUPu4yAB7v0pvHu/QGoPZxlzE3ZxH16l5LvbrGL/X+9t15/rnXATjysPNZmvsLY8dM4qJzWwHQ9ubHCpaaD9ynHnl5a/n4ozEFvrg3XPdAQUjHWLKCIe+8V5A/tsWVTfn041Hk5+VR/UDzOQ16ZSDt25rPqclFjZgwdjzLli7lxCPN59T7+V50vt/kc72wXgOmT5nKxWefz5m16wLwzOPdeOZxc++dWbsu8+fOY/qUqVxYz9x7ne/vSO/nzb134pE1WbZ0KRPGjqfJRebea9/27oKl6OoHViUvby0fDR9X4PvaonnHghCKseQAb7/1SUGO2csva8dHw8eRl7eW/fY+BzBLxbfc/AQA5ze4hVNPak5u7goOP/QSAJ579k063GsC4pxetwWTs2eTk/MTNY+9EoDHOvfjsc7GCaHmsVeSk/MTk7Nnc3pd49bU4d7nee7ZN829V7WpuffGTOOc89qbe++W5wuWfivte7m59z76psA3ttl1TxaEWIwlDxj81pcFOWgbNX6UE05sY+69fS8HzFJy61uM5nPOa0/WmGnk5q6iSlVz73R/7n3adegLwCmntSV78lxychZz9HE3AtCpyxt06vIGAEef2ZGc+cvJnr6IUy58HIB2nd+le2+TvKzKiR3IXbaarAlzOKeJuZ9bt3+jYGm4UvU7yMvfwPBPpxX4zja7tT/HnW2uL5ZcYPCQiQU5ahu1eJHhn04jL38DlarfYa5p0FhatzeazmnSnawJc8hdtpoqJ3Yw19T7M9p1fpe0owqbtyQv2y83xf3eHLNHG19ix1Ii3TPal5OFonJFpma0078ZT63TM5PLM1Mz2qyxM6h/9glp7zdTM9qxYyZxdoZcfDI1o50wdjxnnJ2Z+yITM9oxWdn8q/7Jae8XMjejzRozjfr/qp2RvjM1o82aMCcjLj5pn9Eef4h+927bpO3KHfdAiWe0IrIvMAC4AFgJPKCqg4toK8CTQKvoUH/g/ljEQhGpE/V1LDAbuFFVp0Z17YHrgarReV5S1aeLOE9KUX1U9ZFU2llZHUcmztcClYFcTFiqV2IXWVYH2UxSpdqRriVYU+Ooyq4lWFG9ekqrM2WKI6r7dV9Ur+Hfe1yjun9bFTWOONC1hNSI7dGWDr0waVgPBOoAI0RkmqrOLKRta6AxJua+Ap8BC4DeIrILMBToAbwEtAGGikh1Vd2EWXFtAUwHjgQ+FZGfVbWw8IrxT+C7AU2A74BFwGGYAE4pW2mmvHQsIk8B9wFDgPbRSe4FuqXax/bInZed51qCNafWu8e1BCvOPL2ZawnWNDzTr/vijNNucC3BmlP/ebtrCdac2vAJ1xJSpxSMoUSkAmYQ66iq+ao6HhhG0cuy1wPdVXWxqi4BugM3RHX1MZPHHqq6UVV7YgbXBgCq+pSqTlbVzao6BzMoFxpZRlX/EytRH9eqaj1VbaqqZ2LiH6eMzYz2BuAkVV0cOyAiI4DJGH+jHZJB3xT20FW2WbLAL9eT+T995lqCNVPm+3VfLPx5hGsJ1ixZVOjqYplmyZSnXEtIDVXYnJIf7f4iMinu776q2tfiTDWAzaqaE3dsGvCvItrXjOrj29aMq5uekPhmenT8Lz6N0RL0WUAqCYIbAolP+8Mw8f9TwsYYKi8qicd+t+hjuyNmQOUTMQMpX4gZP/lEzLjJF2KGSz7R/Tn//KtjxlResFWTF1ipqqfEFZtBFqAifx9D1gB7FtN+TULbitHAmVhXXF+dMONfKoPlPOC2hGO3kKGkAj2AISLyJLAYs4bdHnhORI6INVLVHy369J5Vy5e5lmBN7tJfXUuwYmnuCtcSrFm+1K/7YmnuStcSrMldusq1BGtyl692LSE1lHQtDWdR9Oz0K+B2INEltBJ/n9TFyE9oXwnIV1UVkcS6QvsSkbaYvdqzVHVjsmvAGF59ICIdgCVAFWAzkHK2E5t8tKm866qq5VM9eWkS/GgzT/CjLR2CH20psKP70R57sH73WqHhff9CudOfKJHVcbRH+xtQU1XnRsdeB3JV9f5C2k8AXlXVftHfLYHWqnq6iFyACYl4aJwV8iKgjaqOimvfBTjbZlIoIjsDp2MMgZcCX6vqH6m+PuWlY1Utl0Ipk4NsJon54fpEzM/WF2I+tD4R85H1hZj/q0+cclpy95OyRswnt8yjim7ZnLSU/DS6FmNg20VEKohIPeAy4I0iXvI6cI+IVBGRykA74LWoLgvYAtwhIrtGM1eA0QAi0gx4AjjfduVVVf9Q1XGq+o6qjrUZZCH9ASt2OG5//FnXEqzp86JfX1AvvvywawnWPP2iX/fFSy8/4FqCNX1evtO1BGv6PN3ctYTUKb0QjLcCuwO/AG8Bt8Rce0TkrGhJOEYfYDgwA/geGBEdI3LhaYxZFl4NtAQaR8cBugL7Ad+JSH5UeqfrIooj5T1aETkMeBQ4EbPpXICq1kizLm/YvULF5I3KGHvuubtrCVZU3LOCawnWVKjo133h43u8Z0W/7mOAPSvs5lpC6mwtndD1qvorZoAsrG4cceNNtCTcgSI8XVR1ClBo5BVVrVZSrduKzYz2XczA/AjG4iq+7LB0auXfsualV6QU9KTMcGXjO1xLsOb6K/26Ly6/zC/faoBLo3CNPnHp9S8mb1QWCEkF0oqN1fExwD9Vt+PcSNtA/9ETXUuwZs6MVFzHyg7TZg11LcGa8dP8ui9mzn7PtQRr5swa4FqCNXPGP+ZaQmooqfrRBlLAZkY7nKLNtHdYYokKfCKWiMAXYkkGfCKWRMAXYgkCfCKWAMAnOj0z3LWEFAkz2nRiM6O9A5ggIvOB5fEVqprcDjwQCAQC/hAG0rRh40c7FDgKGAmsj69T1Y7pl5Zegh9t5gl+tKVD8KMtBXZ0P9rqB+jE55LHYyh/ad/tMh9turFZOm4AnK6q96pqx/iSKXE+0KpBXdcSrInls/WFWK5an4jlovWFWJ5Zn4jlkfWJo8/05OsyFhkqLB2nBZul4+kYH6SiQmPtkHTq79d+J8DwISmlUCwzvPdhT9cSrBn4nl/3xQdD/fL7BRj+YWfXEqwZPtAXH/ZSTZO33WMz0I7G5O97lb/v0b6SVlUesX5tfvJGZYy8vPXJG5Uh8vPWupZgzdp8v+4LH9/jvHy/7mOAvLUbXEtInTDQpg2bpeMzMQGVL8DkCowVj0KdpJ8XHvLP/7BNW098+SLa3tLVtQRr2rf167649Zb/upZgTZtbnnctwZo27Qe5lpAaCvrH1qQlkBopz2hV9ZxMCvGVnsNGu5ZgzaQJPVxLsOKriX4twwJ88pVf98U3E/1LRTjpW78eGAEmffKQawmpoQpbSicy1I6AVaxjEdlPRK4TkfbR35VFxD9T0zTS73FPjBviaHefX47+97fv7lqCNZ3v9+u+6HCvf7PDdh1sU5+6p13nd11LSAkFdKsmLYHUsIl1/C/gfWASUA94GqgO3AtcmhF1aaTCLlupe3D696G+O2rfjPSbSSofvG9G+pUnMjO4VJ41P2N9H3RTZm7do/ZZwUFrMpOYXHMXp73PQ/iFnaZ+lPZ+Ada/m5koWQdMW8j6js9lpO9dMnS/HXzUdLYcWDPt/erOaY77rIQZbRqxTfx+tap+ISIxx7hvAb/8GNLMbXe1di3BmnZ3Xe5aghV31jvStQRr2t18vmsJVtzT/J+uJVhzZ93DXUuw5u57rnUtITVU0T+2uFax3WCzdHy4qsYiPsQedTZhN1hvd9SsdqprCdZUqXa9awlWHPHMZ64lWFPlxEKTi5RZDrkoMzPDTHJkryzXEqw57BBPfMJjM9pkJZASNgPtLBG5MOHYeZi8gDssn3+VmeW2TPLdV375TI5vfZZrCdZ8N/JB1xKsmPhGK9cSrBnf4nTXEqz5ZqJH9hFbNXkJpITNQHsP8KaIDAR2F5E+mMz27TMhzBfmz/vRtQRrcublupZgxbxVfvmkAuT8uDx5ozJEzqJVriVYM/e3da4lWDM35yfXElJDQbdo0hJIDZuB9iygFjATeAVYgNmf9W+6kUae6trDtQRrOvuWvScrx7UEazp392ulo0vfsa4lWPPE+HmuJVjTpYsnsX1U4Y8tyUsgJWz2Vx9R1WeAp+IPisjDgF9rkWlk2KfvuJZgzZef+hWc4JP/nOFagjVfvt/OtQQrRvdt4VqCNaOa+meH+cVof3x/g/tO+kg6oxWRBiLSACgvIufE/o5KK3bw2Md333a/awnWtL7Nn392gNuGTXctwZrW7f3Kldrmcb9m4ABtR810LcGam9t4kqc4GEOllVSWjgdEZTfMknHs7/5AS+D2jKnzgDon1XItwZpTTjrKtQQrTqq8l2sJ1pxSu6prCVacfGxl1xKsOemgSq4lWHPyyce4lpAiKQyyaRpoRWRfEflARNaKyCIRaVpMWxGRbiKyKirdRETi6uuISLaIrIt+1imkj11EZLaIpN8hvQiSLh2rajUAEXldVf1bX8ow199Y5D1RZml940WuJVhx4yl+DVoArZuf7VqCFa2vOMm1BGta1vEvl/BNrf1x7ylFP9peGFfRA4E6wAgRmaaqhS1ZtAYaA7WNSj7D2Av1FpFdgKGYmA8vAW2AoSJSXVU3xfXRHlgB7JmJiymMlI2hwiBbOFUPOM61BGsqHXCVawlW/OPxka4lWFOp+h2uJVix19meLGnGceBzn7uWYM0+e/kRyES1dEIwikgFoAnQUVXzVXU8MAyTsKYwrge6q+piVV0CdAduiOrqYyaPPVR1o6r2BASTSz12vmqYRDilaqhiFes48He+/zEz4eUyyZIfX3MtwYr59/rx5RTPkilPJW9Uhlg88i7XEqyZd2t91xKs+WnxUNcSUiTlpeP9RWRSXLENlVcD2Kyq8a4F04Ci4lTWjOoLa1sTmK6q8U8A0xP6egF4ECjVHIthoC0hX439xrUEa7LGfu9aghVjF/rn45k1YY5rCVZkZS9yLcGacT//6lqCNWOyJruWkBpKqgErVqrqKXHFNtNDReD3hGNrKHpZt2JUH9+2YrRPm1j3l75E5HKgvKp+YKmxxISBtoS8/opfPqkA/V4Z5VqCFa9M8m8Q6DdonGsJVvT7wJMBII5XppaaLUva6N9vmGsJqZGmfLQikiUiWkQZD+QDiVZtlSjamyWxfSUgP5rFFtlXtET9FOBkTycMtCVk8PueOKDHMez9R1xLsOL9Zv75Sw57va1rCVYMe+4a1xKsee9K/wy4Phzmy5aCwpatyUuyXlTrq6oUUc4EcoCdRKR63MtqYwIjFcbMqL6wtjOBWvFWyPwZZKk6cDgwTkSWAUOAg0VkmYgcnvRCSkgYaEtI6+v9825qdv3TriVYccN7/s22mt3a37UEK5o9NMS1BGtuGDYteaMyxnXNOrmWkBqlZAylqmsxg14XEakgIvWAy4CiHNFfB+4RkSoiUhlohwkFDJAFbAHuEJFdRST2tDsa+B44FGPVXAdoBSyPfv+5xBeShB068046uKDhua4lWHNJQ78yDjWscaBrCdZccp5f/tWXnFU9eaMyRsOjDnAtwZqLL/EoylnpBaS4FROj4RdgFXBLzLVHRM4CRqpqxahtH+AI/kxm0z86hqpuEpHG0bEngdlA4zjXnmWxE4rIr8BWVS04lknCQFtCrrymsWsJ1jS9pr5rCVZcXauKawnWNL3Cr+Xuphed4FqCNVcf51+QjWubXuBaQmoobE1hDzYtp1L9FeMbW1jdOIyRU+xvBTpEpbD2U4CTUzhnFnCIvdpto9SWjkWkbWT+vVFEXos7fni0MZ4fVzoW08/hIvJlFPnjBxE5r1QuoAj2392/YArldr/UtQQr9njUv/CA5Sq3cS3BivKnPOZagjUVun3iWoI1O5c/07WElFBNvmwcYiGnTmnOaHOBrsCFwO6F1O+tqptT6Oct4Gvg4qi8F0X+WJE2pRasXO+fRezW9cNdS7BiXed/u5ZgzdbcPq4lWLFlUpHPtmWWtfclpscu+/yxZbxrCSmzNcQyThulNqNV1SGq+iFmDX6bEJEawEnAo6q6XlXfx6zVN0mPSnvee/tDV6feZga/neVaghXvTF/iWoI1g4f4Fchk8KgZyRuVMd6Z5VdeZYC3Bn/qWkJqlJIx1I5CWbI6XiQii0XkVRHZv4g2NYEfVTXex6q4KCIZ59ORX7g69TYzYuR3riVYMTLHryTqACM+9yvj0Ihxc11LsGbkPCeLWCXi4xETXEtICVVl6x9bkpZAapSFgXYlcCpQFbOJvSfwZhFti438kYiItI6FBlswbyHduj4HQN0T6jNv7o9MnTyDBmdcAkDH+x6jVw8T1KRmtVNZmruc8WO/ptEFVwMmHd7AASY4RdUDjiMvL59RIz4nP38tYNx8YrPb2L7te29/WOD+07RJS0aN+Jy8vPyC+MgDBwwuSLPX6IKrGT/2a5bmLqdmNWMV3L3HB7S7bwAAp5xxF9mT55EzdwlHn2D2/zp1HUynKIn70Se0IWfuErInz+OUM+4CoN19A+jewwRBqVLtenJzV5E1dga5S01Enda3vUjfASZ4RaUDriIvbx3DR0ykUZMugHEDis1+Y/u6g9/OKnAPatSkC8NHTCQvb11B/OQBkxYVpLW78NUJjF2wktzfN3DEM58B8PxX87l/1CwAzug9jsm5q5m7Mp9aPb8EoOuXc+j6pYmqVKvnl8xdmc8dZxzBGb1NAIj7R83i+a/mA3DEM5+R+/sGxi5YyYWvmi+w24ZNZ0AU4OIfj48kb+NmRsxZTpM3zQzzhvcmF8yQY3u/g4dMLHDHadTiRYZ/Oo28/A0F8Yr7DhpbkPbunCbdyZowh9xlq6lyorHH6N77M9p1ftd8Thc+Tvb0RTza7lKOPtMsx3Z6ZjidnjHL9Uef2ZGc+cvJnr6IUy583HxOnd+le2/z/lQ5sQO5y1aTNWEO5zTpbj6n9m/Qd5BJzF6p+h3krd3I8LE5NLr7bfM5PTSkYEYa22sdPGpGgctOo7vfZvjYHPLWbiyIadx3yOSC1HgNWr/OTZefRO6KPA65yPyPPDvoa+59zmg6tXk/smcvJWfRKo65ohcAnfuMoXOfMQAcc0UvchatInv2Uk5t3g+Ae5/7jGcHfQ3Akb2yWJq3gbE//cpFg83n0HbUTF6ZarwqDnzuc/I2bubjeb9wZeTKdcOwaQUz1the7Duzcgtceq58bzJXHXcweRs3F8Q8fmXqzwWp8y4aPJGxP/3K0rwNHNkry9x7Exdy/+gfAKj32tdMWbaGub+upXZfc289Pn4ej0fJ5I875hpycn4iO/sH6p7aEoD2977Ac8++BcBhh1xGbu5KxmRN5twGxovk5jbd6NfXhFjcZ6/zyctbx0fDx9O4kblPrmvWqcDqOLZX+9bgTwtcfho36sBHw8eTl7euICZyv75DC1LrndugLWOyJpObu5LDDjHJCZ579i3a3/sCmSDMaNOH/DUsZCmcUKQrcIiq3lBE/UHAUqBSwsw1FkLrcVU9Lu7YCwCqWqxDa52Ta+kXX6XfqKZpk5YZC1qxL5mZFTVq0iUjQSs2PJWZfckmb07MWNCK3W7KjGFYoxYvZixoheamPyJSo7vfzljQig3vZmYZ/cr3JmcsaMUuT2Rmz7pxow4ZCVpxWt0byZ70gyRvmRp19q2on12Q3EXtH+98na2qp6TrvNsrZWFGm0hs5C9M20zgCBGJn8EWF0Uk47Ro6V+avJta+pUmr6WHafJuan6WawlW3HS5f1GWWtYpNe+MtNHqpkauJaRMmNGmj9J079lJRHYDygPlRWS36NhpInK0iJQTkf2AnkCWqiYuERNleJgKPBq9/nJMiK33S+s6Eql39umuTr3N1D/7eNcSrDj78P1cS7Cm/hlHu5ZgRf2T/XuYOevQfV1LsOZf9f14oFFVtvyxNWkJpEZpzmgfxqQmuh+TD3B9dOwIYBQmiPT3wEbg2tiLRKS3iPSO6+ca4BTgN0z0jytdufYAHH+EX4EJAKoccYNrCVYcGe3v+kRs/9YXDmnYw7UEa456Kcu1BGtie6tlnmB1nFZKzY9WVTsBnYqofquY192c8PdCTILfMsGiFbNcS7Dm9xX/cy3Bil8eauhagjW/z+3pWoIVa8be51qCNcvvdhqrZpv4bY0/D41hIE0fZXGP1itilsg+EbM09oUBHqbJi1kJ+0LfIf4lbohZLftEzCq5rKMKukWTlkBqhIG2hEyd7Je/JMCkyfNcS7Bicu7ftuvLPJOm+fVwkD3bv+APk5cl5gsv+2Rn/+BaQmoEP9q0EpIKlJDnej3pWoI1fXv5lSu1VyO/MuEA9H36OtcSrOjzkH9hLl+8yFmcmm2mdx9/lujD0nH6CDPaEhILaOET51zwgGsJVsSCUfhELNiELzRo/bprCdbEgl/4RCy4RVlHFbZu1aQlkBphRltCOjx8l2sJ1jz6sF++vw/Vr+FagjWPtvNrhvhI67NdS7DmwTOPci3BmkceaelaQsqEPdj0EQbaEnLkUUe4lmBNjaP8yuN51H4VkzcqY9Q4wq9k9TWq+uerXH2fPVxLsKZ6jcNcS0gN1VLLR7sjEJaOS8h59fyauQCcWu8e1xKsODOKResTpzZ8wrUEK+pe19+1BGvOfP0b1xKsOb3uja4lpEbwo00rYUZbQmYu8CsTDsCSBQNdS7Dix3vPdy3BmiVT0h/PNpMsHnW3awnWzL+tvmsJ1vy02BP3HoIxVDoJM9oSEsv44xOxjD6+EMvW4xOxbDy+EMu04xPPT1zoWoI1sew/ZZ4QgjGthIG2hCxb6l+u1FiaPF9YmrfRtQRrcpevdi3BitwV+a4lWLM0f4NrCdbk5q50LSElFNi6NXlJByKyr4h8ICJrRWSRiBRprSmGbiKyKirdRETi6uuISLaIrIt+1kl4/UkiMlZE8kVkuYjcmZ6rKJ4w0JaQx7plJp1WJunezZN9oognLzoueaMyRvdH/8+1BCueudu/5fknGxzjWoI1Tz9TbDbPsoOW3kAL9AI2AQcCzYCXRaQoJ+nWQGNM1rZawKVAGwAR2QUYCgwC9gEGAkOj44jI/pi4+n2A/YCjgE/TdhXFEAbaEhJLHO8TscTwvhBL+u4TsaTuvhBL2O4T9V7zb7k7lkTeB0pjoBWRCkAToKOq5qvqeGAYUFTEl+uB7qq6WFWXAN2BG6K6+hi7ox6qulFVewICNIjq7wE+UdU3o/o8VZ1d8qtIThhoS8izL/oXGarPi344zcd4sdEJriVY0+fp5q4lWNHby8hQ/q10vNzbj6xOqrB5S/IC7C8ik+JKa8tT1QA2RylQY0wDiprR1ozqC2tbE5iuqvFWXNPj6k8HfhWRCSLyi4gMF5FS8bcKVsclpOKeFVxLsGbPPXd3LcGKPXfx7zbds8JuriVYseceu7iWYE1FH++LPf3w/Y3t0abASlU9pQSnqggkBq1eA+xZTPs1CW0rRvu0iXWJfR0CnAScD8wAnsJkjqu3reJTJcxoS0jTK/xZCopx6RVdXEuwoslg/1yoLr3+RdcSrGh099uuJVhz5Xv+ZRxq3MiPGW269mhFJEtEtIgyHsgHKiW8rBImP3lhJLavBORHs9hkfa0HPlDV71R1A9AZOENE9kp+JSUjDLQlZOKMLNcSrJkzo49rCVZMv+Mc1xKsmTP+MdcSrPhhyG2uJVgzrfVZriVYM+sHfx5o0jHQqmp9VZUiyplADrCTiFSPe1ltYGYRXc6M6gtrOxOoFW+FjDGYitVPx0zWC+Qlv4L0EAbaEtKt63OuJVjTqatfOXS7fjnHtQRrOj0z3LUEKzr3GeNagjWPj/cr3SNAl84DXEtICVXYvDl5Kfl5dC0wBOgiIhVEpB5wGfBGES95HbhHRKqISGWgHfBaVJcFbAHuEJFdRSRmjDI6+vkqcHnkArQz0BEYr6oZz8Mpf9033n4RkRVAJpKE7g/44Rz3J75p9k0v+KfZN70QNMdTVVUPSFdnR++ym/Y94PCk7ernzsku4R4tIrIv8Apm73QVcL+qDo7qzgJGqmrF6G8BugGtopf3B+6LGUCJyInRseOA2cCNqjol7ly3AA8DewDjgVtV9eeS6E/pGneUgTZTiMikkt5opY1vmn3TC/5p9k0vBM2Z5Oidd9Pe+x+etF2DZSUfaHcE/DPbCwQCgUDGCZOw9BEG2kAgEAj8hdgebSA9hIG25PiXVcA/zb7pBf80+6YXguaMksYQizs8YY82EAgEAn+h+k676fMVqyZtd8manLBHmwJhRhsIBAKBv6JhRptOwkAbCAQCgb8Q9mjTSxhoA4FAIPA3toZdxbQRBtpAmUJEjsWkyKqJCQaehwmh9kZppbQKBHZ0LJIKBFIgDLSWiMgFmPyHiQPBq6r6mUNp3iMi1wIvY/JRjsVk3qiEiWc6QURuVtV3HErcbojSmd2AuY8rYgKyx+5j/5LTllFEZCdMPtTE74vRqlp2F2fDHm1aCQOtBSJyN9AB6Ae8z18HgoEi0k1Vn3co0XeeAC5R1a8SK6IYqG8CYaAtISLyJHApJmn2NP68j+tg4sgeoaoPuFO4fSAitYGhmOTj0/nzfb4TUBG5TFWnO5RYJErYo00nwb3HAhHJBRqo6g+F1B0DfKmqB5e+su0DEckHDlDV9YXU7QH8Eot5Gth2orjftVR1aSF1lTHJs/cvfWXbFyLyHfC6qr5QSF1b4Iay6hojIqMwcZmTsVJVL8q0Ht8JA60FIrIGOFRVExMVIyJ7A4tUNeO5DbdXROQDYAPwsKrOjzt+JNAF2ENVL3elb3tBRFYCJxQz0M5Q1f1KX9n2hYisBfZW1T8KqdsF+E1VK5S+skBpE9Lk2fE+MFxEzhWRA0RkFxHZX0TOBT4A3nOsz3daRj9niUi+iORGs9yZmOW3lkW/NGDBAGC0iLQSkVNFpIaInCIiNwKfYbZGAiVnNnBLEXVtovrADkCY0VoQPYV2BloAB2O2MgRYismT+KiqbnKncPsgWiauwZ9GOjmqus6tqu0LEWmDuY8TjaFeV9U+LrVtL4hIHeBDoDx/3aOthcmb2lhVp7nSFyg9wkC7jURLxRWBfFVd7VbN9oWIVMAMtDErzZwoQXQg4BVRgvFzMPlR4x9osgpbUg5sn4SBdhsQkRr81Vz/e1Wd61aV/0QPLy8DVwCb+HMGsAtm2f628FCTHopwO/keY9AX7E0DgTQSBloLROQwjHtJbWA+fw4ER2LcJK5R1Z/cKfQbERkCrAc6quqPccePwCzZV1DVK1zp214oxu2kFmY7pMy6nfhG8FcOQBhorRCRL4BsoFP8nmG01PkIcKqqNnClz3dEJA84sLD92Og9Xqaqe5a+su0Ln91OfCKZvzIwPPgr7xiEgdaCyAJ238IMnkRkV+DXYK6/7YjIQqBZMQErBqtq8txdgWIJbielQ/BXDsQIkaHs+Bn4NzCkkLqLgbBsXDIeBEaKyDD+PgP4N3CzO2nbFTG3k56F1AW3k/QhJawPbCeEGa0Fkb/s+xijkcSBoCbQRFVHOxO4HSAiNYGm/HVP63vMbHaWS23bC8W4ndQGNhPcTtKCiHQDGvH3pePa/Ll0fL87hYHSIgy0lojIfhir2ETjhg9UdaVLbdsrIjIJuEBVf3WtZXshcjupz98faMYEt5P0EfyVAxAG2rQhIuWBh1S1i2stviIirxdR1QQYAWxQ1RalKGm7RETKAbdivvxHqerQaPbVEDPzukdVV7jUGAhsT4QQjOljJ+BR1yI85/+AqsA8jPtUrGwFFkW/B0rO80BrjO9sVxF5BTga6AhUAP5mjRxIP5G7YGAHIMxoLYi+kIpiJ4zFbPnS0rO9ISLVgReB3zCzqtzo+FKgtqr+4lLf9kKUhaqOqv4iIlUwRnz7q+pvUdCQHFX9h1OR2zmRl8K68H2xYxCsju1oignIXtheYfiHKSFRdK0LReQa4EsR6Qf0wARRCKSP3TAPM2Du5a2YvUMws9zwvZAGROTsYqp3LTUhAeeEGa0FkaP/Y6o6rJC63TBPqGE5Pg2ISCVMarzzMMvJR4YZbXoQkRHAcuB/wLWYvdr3gF4Yt5+GqnqOO4XbByKyFZNwZGsRTSqHGe2OQRhoLRCR24AlqvphIXXlMXlUO5e6sO2YyBXlX0AfVd3gWM52gYhUBV4CDsesGIwDRgGHAguAK0IIxpIjIgsw20kTCqnbDVgbBtodgzDQBgIBREQwUc9WudayvSAi7wLjVPVvgUGiCFxzVLVa6SsLlDZhoA0EAoEMEPkqE/ySA2E/MRAIBDKAqv5R1CArIuVF5JHS1hRwQ5jRBgKBQCkT3Ht2LIIZfyAQCGSAFPzuAzsI4cMOBAKBzBD87gNAWDoOBAKBjBD87gMxwoccCAQCmeE1iv6O/QMIPvc7CGFGGwgEAoFABgkz2kAgEAgEMkgYaAOBQCAQyCBhoA0ELBGRhSJyXgrt6ovI4m08x+EioiJSrGeAiGSJSKsi6g4TkfwoDncgEHBEcO8JBLZTVPUnoKJrHYHAjk6Y0QYCgUAgkEHCQBvwgmi59gERmSUiv4nIq5EvIiJyk4jME5FfRWSYiFSOe93zIvKziPwuItkiclYK59pdRAZG55ktIh2KWgIWkV1FpIeI5EalRxReL77NgyKyMrqGZnHHLxGRKZG2n0Wk0za+PUeKyMSon6Eism/U/1+Wn6Nl5sdE5CsRyRORT0Vk/208ZyAQSJEw0AZ8ohlwIXAkUAN4WEQaAP8FrgIOBhYBb8e95jugDrAvMBh4NzZAF8OjmFytRwDnA82LafsQcHp0jtpAXeDhuPqDgP2BKsD1QF8ROTqqWwu0APYGLgFuEZHGSbQVRgugJeb6NwN/S8sWR1PgP8A/gF2Ae7fhfIFAwIIw0AZ84kVV/VlVfwUeB67FDL6vqOpkVd0IPAD8U0QOB1DVQaq6SlU3q2p3YFfg6CL6j3EV8ISq/qaqiyl+4GoGdFHVX1R1BSYIwXUJbTqq6kZVHQOMiPpHVbNUdYaqbo0Srb+FSXJvyxuq+r2qrgU6AlcVYwD1qqrmqOp64H+YB4RAIJBBwkAb8Imf435fBFSOyqLYQVXNB1ZhZpCIyL3R8u8aEVkN7IWZYRZH5YRz/VxUw8Tzx+mK8Vs0AP6tXkROE5EvRWSFiKwBbk5BW2Ekvi87F9PPsrjf1xGMpQKBjBMG2oBPHBr3+2FAblSqxg6KSAVgP2BJtB/bATOD3EdV9wbWAJLkPEuBQ4o4byJ/OX+crhj7RJoKqx8MDAMOVdW9gN4paCuMxPflD2DlNvQTCAQyQBhoAz5xm4gcEhn7PAS8g1lu/Y+I1ImMkJ4AvlXVhcCemD3LFcBOUaLtSimc53/AAyKyj4hUAdoW0/YtzF7xAZFh0SPAoIQ2nUVkl2jg/zfwbnR8T+BXVd0gInUx+6fbQnMROU5E9gC6AO+p6pZt7CsQCKSZMNAGfGIw8CnwIzAf6Kqqn2P2Jd/HzESPBK6J2n8CjAJyMEuqGyh+GThGF2AxsAD4HHgP2FhE267AJGA6MAOYHB2LsQz4DTOLfRO4WVV/iOpuBbqISB5mgP5fCtoK4w1MAPtlwG7AHdvYTyAQyAAhqUDAC0RkIdAqGlhL+9y3ANeo6rYYKgUCgR2cMKMNBBIQkYNFpJ6IlItccdoBH7jWFQgE/CQMtIEdEhEZGcUBTiwPYvxL+wB5wGhgKPCSQ62F6cxPJfhGIBBwT1g6DgQCgUAgg4QZbSAQCAQCGSQMtIFAIBAIZJAw0AYCgUAgkEHCQBsIBAKBQAYJA20gEAgEAhkkDLSBQCAQCGSQ/weh/TVHYLpMWgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1982,7 +1864,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -2133,7 +2015,7 @@ "[180 rows x 5 columns]" ] }, - "execution_count": 28, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -2162,17 +2044,17 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1.27323780e+00, -4.79820818e-03, 6.18587637e-01, -2.83390574e-01,\n", - " 0.00000000e+00, -2.74667887e-10])" + "array([ 1.05793203, -0.00302483, 0.12378713, -0.05521624, 0.58767661,\n", + " -0.00235475])" ] }, - "execution_count": 29, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -2184,7 +2066,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -2233,7 +2115,7 @@ " 0\n", " 0\n", " 0.1\n", - " 0.746266\n", + " 0.980696\n", " \n", " \n", " 2\n", @@ -2242,7 +2124,7 @@ " 5\n", " 0\n", " 0.1\n", - " 0.722275\n", + " 0.965572\n", " \n", " \n", " 3\n", @@ -2251,7 +2133,7 @@ " 10\n", " 0\n", " 0.1\n", - " 0.698284\n", + " 0.950448\n", " \n", " \n", " 4\n", @@ -2260,7 +2142,7 @@ " 15\n", " 0\n", " 0.1\n", - " 0.674293\n", + " 0.935324\n", " \n", " \n", " 5\n", @@ -2269,7 +2151,7 @@ " 20\n", " 0\n", " 0.1\n", - " 0.650302\n", + " 0.920200\n", " \n", " \n", " ...\n", @@ -2287,7 +2169,7 @@ " 50\n", " 0\n", " 1.2\n", - " 0.862194\n", + " 0.923891\n", " \n", " \n", " 177\n", @@ -2296,7 +2178,7 @@ " 55\n", " 0\n", " 1.2\n", - " 0.838203\n", + " 0.908767\n", " \n", " \n", " 178\n", @@ -2305,7 +2187,7 @@ " 60\n", " 0\n", " 1.2\n", - " 0.814212\n", + " 0.893643\n", " \n", " \n", " 179\n", @@ -2314,7 +2196,7 @@ " 65\n", " 0\n", " 1.2\n", - " 0.790221\n", + " 0.878519\n", " \n", " \n", " 180\n", @@ -2323,7 +2205,7 @@ " 70\n", " 0\n", " 1.2\n", - " 0.766230\n", + " 0.863395\n", " \n", " \n", "\n", @@ -2333,22 +2215,22 @@ "text/plain": [ " mid poa_global temp_module wind_speed poa_global_kwm2 pr_dc\n", "id \n", - "1 matrix 100 0 0 0.1 0.746266\n", - "2 matrix 100 5 0 0.1 0.722275\n", - "3 matrix 100 10 0 0.1 0.698284\n", - "4 matrix 100 15 0 0.1 0.674293\n", - "5 matrix 100 20 0 0.1 0.650302\n", + "1 matrix 100 0 0 0.1 0.980696\n", + "2 matrix 100 5 0 0.1 0.965572\n", + "3 matrix 100 10 0 0.1 0.950448\n", + "4 matrix 100 15 0 0.1 0.935324\n", + "5 matrix 100 20 0 0.1 0.920200\n", ".. ... ... ... ... ... ...\n", - "176 matrix 1200 50 0 1.2 0.862194\n", - "177 matrix 1200 55 0 1.2 0.838203\n", - "178 matrix 1200 60 0 1.2 0.814212\n", - "179 matrix 1200 65 0 1.2 0.790221\n", - "180 matrix 1200 70 0 1.2 0.766230\n", + "176 matrix 1200 50 0 1.2 0.923891\n", + "177 matrix 1200 55 0 1.2 0.908767\n", + "178 matrix 1200 60 0 1.2 0.893643\n", + "179 matrix 1200 65 0 1.2 0.878519\n", + "180 matrix 1200 70 0 1.2 0.863395\n", "\n", "[180 rows x 6 columns]" ] }, - "execution_count": 30, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -2371,7 +2253,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -2471,7 +2353,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -2520,7 +2402,7 @@ " 10\n", " 0\n", " 0.1\n", - " 0.698284\n", + " 0.950448\n", " \n", " \n", " 4\n", @@ -2529,7 +2411,7 @@ " 15\n", " 0\n", " 0.1\n", - " 0.674293\n", + " 0.935324\n", " \n", " \n", " 5\n", @@ -2538,7 +2420,7 @@ " 20\n", " 0\n", " 0.1\n", - " 0.650302\n", + " 0.920200\n", " \n", " \n", " 6\n", @@ -2547,7 +2429,7 @@ " 25\n", " 0\n", " 0.1\n", - " 0.626311\n", + " 0.905076\n", " \n", " \n", " 7\n", @@ -2556,7 +2438,7 @@ " 30\n", " 0\n", " 0.1\n", - " 0.602320\n", + " 0.889952\n", " \n", " \n", " ...\n", @@ -2574,7 +2456,7 @@ " 50\n", " 0\n", " 1.2\n", - " 0.862194\n", + " 0.923891\n", " \n", " \n", " 177\n", @@ -2583,7 +2465,7 @@ " 55\n", " 0\n", " 1.2\n", - " 0.838203\n", + " 0.908767\n", " \n", " \n", " 178\n", @@ -2592,7 +2474,7 @@ " 60\n", " 0\n", " 1.2\n", - " 0.814212\n", + " 0.893643\n", " \n", " \n", " 179\n", @@ -2601,7 +2483,7 @@ " 65\n", " 0\n", " 1.2\n", - " 0.790221\n", + " 0.878519\n", " \n", " \n", " 180\n", @@ -2610,7 +2492,7 @@ " 70\n", " 0\n", " 1.2\n", - " 0.766230\n", + " 0.863395\n", " \n", " \n", "\n", @@ -2620,22 +2502,22 @@ "text/plain": [ " mid poa_global temp_module wind_speed poa_global_kwm2 pr_dc\n", "id \n", - "3 matrix 100 10 0 0.1 0.698284\n", - "4 matrix 100 15 0 0.1 0.674293\n", - "5 matrix 100 20 0 0.1 0.650302\n", - "6 matrix 100 25 0 0.1 0.626311\n", - "7 matrix 100 30 0 0.1 0.602320\n", + "3 matrix 100 10 0 0.1 0.950448\n", + "4 matrix 100 15 0 0.1 0.935324\n", + "5 matrix 100 20 0 0.1 0.920200\n", + "6 matrix 100 25 0 0.1 0.905076\n", + "7 matrix 100 30 0 0.1 0.889952\n", ".. ... ... ... ... ... ...\n", - "176 matrix 1200 50 0 1.2 0.862194\n", - "177 matrix 1200 55 0 1.2 0.838203\n", - "178 matrix 1200 60 0 1.2 0.814212\n", - "179 matrix 1200 65 0 1.2 0.790221\n", - "180 matrix 1200 70 0 1.2 0.766230\n", + "176 matrix 1200 50 0 1.2 0.923891\n", + "177 matrix 1200 55 0 1.2 0.908767\n", + "178 matrix 1200 60 0 1.2 0.893643\n", + "179 matrix 1200 65 0 1.2 0.878519\n", + "180 matrix 1200 70 0 1.2 0.863395\n", "\n", "[156 rows x 6 columns]" ] }, - "execution_count": 32, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -2657,17 +2539,19 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 99, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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hks9T/OOG1LXkPiJyQ4rXqWHQbcwFROROEZkhZq3fYSnK3itmrd/tIjJURCrFnVshZs3h2P0JdXaw76FaZyrCWuASVZ3kZIYuwySCjAYewSwOkAsLrlssriIiB5MwJSOBdXrgdJpyh4jU4OepJclYoap2B5ksceZT/4QZl66iZkpQsnIdMNNnzsPkJozCZLf3dM6vwCwVGIm50kGMxXXCLOX2hfP8KsyqKSPBbO8FbBSR5qqauASXxVKucb7s7e4bKXB+PJT7HxBeo6rvQfGC+QfsYhTHzcAQNctQIiKPYJIPS1t0I7QEMcZ5M2YljpirezJxKwep6g6MB5q40o7FYrFYosl+3/PO/0cmrHX8hohsEJH/SpItAsOErx6nM3Z5DmZR5xjVMVMe4tkG1EhyfVfMslpUrCCnH16pIvWqHcLCzTtocmgVfvwJVhXt5sSjD2X15p1UPLgCR9aszLzVW2levya79/1EwdZdNKtXg+837aDqIQdTp0YlZn+/hV8cfRhFu39gQ9EejqtbneUbdnBo1YocXqcmMxcVcHrz+mzevottRbs5tkEtlq7eTJ3DqlKj6iHMXbqeU5vVY8PWHezcvY9G9Q7j2+830aBOdSofcjALV2yk5XFHsm5zEXt/+Ilj6tZk4YoNNKp3GBUqCEtXbaZF07rkbzQ/kBvUqcH8Zes57pja/PSTsnLtVk5sfASr1m/nkIMrcGTt6sxduo4TG9dh994fyN9YxAkND2fl2q1UrVyRIw6rxjeL19Ly5IYUFu1m46btHHdsPZavXM+hNatSu1Z1Zs5ZzumnHMvmLUVs276TYxvVZenytdQ5vCY1qldmbt73nPqLxmzYtJ2du/bS6Og6fLu0gAb1DqNypYosXJxPy5Mbsm7DNvbu+4FjGhzOwsVraHRMHSpUqMDS5Wtp0fwY8tduMTbVq8X8Ras47th6/PTTT6xctZETmx3FqvxNHFLxYI484lDm5n3Pic0asHvPPvLXbuWE4+qzcvVGqlY5hCMOr8k381ZYm6xN5cammXOWb1TVI9z47j3syJb6w57MHfAd21bEdnmKMVhVB5ehCdUx3+sxYv/XwOxqdANmWlxsis4nTtRxaxm0PMfvUG1nYLKqLo87VoRZqDuemiQJszg3bDBA1YMP0oXXH7hnbbVf1XetsQAHn3Jc8f+tb3qVZe/fWUppd2l906vMGH5r6oKJNG1eds3zejFjfL8yXx8FvRVzSt3ExRPNRV/8I3VBF/X8fE2D0Gx9Xi++n/ecr3p+v6YVDr9xpVt1/bCnkJbtM1/o6KsPbt6tqq1Tl0xJ4vd87P/Y2sTx64v/Q0Ruxiy+MtoFbdfxO1R7E/BawrE8zIoZAIjZzLYpP6+DmZRjqqe7qlbZie80AV7+26Wea2al17R5Vp0mwMv/vCV1IRfJdb0gNK2N0dfLQfb7nnf+X6eqm0oor5S+BGOg+NZxikhbzAotiXvFjQJaiEgnZ0+9B4G5qRKDDkrScre9zURqVE1n/fgA9FzoMIs1ffhBUp70gtC0NkZfLyqIyMHO9/ZBwEEiUtnJ/E5kOHCLiJzkrN/dC7NCWGyK1Zkicohz/V8wy3+m2uUqMPz0OG/GbIuzXwhWzaLhnTDrRW7BrM14barKvtu2y4s2FpPobQL85s8jPNUsk55LHWax5vVPu1pfedcLQtPaGH29CNELsyxiT+BG5/9ecfONGwKo6jjMUpOfY9byXYnZFhLMOOdLmO//NZhF6y8uxRsNnMguuXfqETX088tPL37u5dhmKHG5w7RYLOGiwuE3znRpfJHqhx2rZRzjdK0NuURkl9xbu2Nv6kIu02fwxHDoedhp9nn8Xc/qLo96QWhaG6OvZwk3ObEYebnxNq2XabFYLIGTE6HanO84bYdpCTG7a7YIugmhovL2+a7UY0O14SWyHufCzTs8qbe0TvOETi/y7bt3eKJbot5s/+aqAZzQ5j6+nfaU1YuwZiZ6bnV6LU/qyNwFH7pSV9T17A+J3CeyHWeTQ6sA3k9BiWf0P6/xTYumzRn9zt/803MY/WYPqxdBzfgv63c+fJndNf3dYOjd959PXcjqWXKEyHacP/7kfp2pQrSFO/1NSCos2p26UMQ1c13PDc1MPZjCQm+iMWHSzHU9S7iJbFbtqqLdvnqbALf9Y4w/Qs6Y5m1/HuKPXhx+a+a6XirN3TVbpHxkyp23P5JNc8uE35q5rmcJN5FODpr2j8tcqy80CUE2EShnsGNdlmyoWrGVTQ4KKZH1OPP37PNds8ez//NWIKHT7NH7DW/1kuC3Zq7oleYp9vyLv6vO+K0XhGau61nCTWTHOCse7H+f3+CIA3Y6c48knmaDerW80ysBvzWjplcWL7J+A1d2hgqtXhCaua5nCTeRDdWe3ri2ft2rgyt1BR6mteHZ0GHDrJagsaHa8BLZUO281Vt91zzqkmfdr7SUTvOok/zb+zMozaD13ErIKY0mDS90tb6w6QWhmet6lnAT2VBt8/qJe1+XjUy8zemvubwnXwpPc/pn/mfy+a3pp97umi34Ytrb7K5Z1zdNgMlf+zuO67deEJq5rmcJN5H1OHfv82AiZwoWf+/iLjdphGcXLytwTy9N/Nb0Ui+ZF7l0yUrP9ErCb01rY/T1LOEmsh1nwdbs9+PMdGzz4VcmZa0JpD2m+fAT77mjlwF+a7qtlyrc+mjfQa7qpYPfmtbG6OtZwk25Tg4KJCnIJgK5ik3iseQqNjkovETK4xSRriIyQ0RmzFu9lb4fzgPgpL+PYfHa7cxauZlfPvIJAPe//Q3P/HcRAI3ue5/8rbuY+O06LnjyMwBuHz6NwaNmAVCz/RMU7tjD6C8W0/HPIwC4odco3hxndjmo0KYfAOfd/m9u6DUKgI5/HsHoLxZTuGMPNds/AcDgUbPo+phZXejcbsOZMHMF+RsKi5OKnh73XfE8wtbn9WLm7OUsXlrACW3uA8yef7F9/05ocx/X3PI8M2cvp/V5vQAzB/HpFz4GTJJLfsEWJkxewLkdTfu63juEwa+NNzY1vJXCwl2MHjeLjs7u9Td0fYE33/nS2HT4jQC8+c6X3ND1BWPT9U/T4bePU1i4i5oNbzU2vTaervealW/O7diPCZMXkF+wpTjJ5ukXPs7IpsVLC/azqWW7v2Vk0wY5lncnbeLSmwazu2YLunTuyYi3zPVVK7YCYMRbH9Olc08AOl1xF2M+mkhh4Q7q1mpL9259GfLKO3Tv1heADuffwqSJ08nPX1+cADLgmeHF8/batrmOWTMXsGTxSlqe1BGAfn1fol/fl0z7T+rIksUrmTVzAW3bXAeYOX8DnhkOmKSS33f+G5MmTqfD+WaMPNYGgLq12lJYuIMxH02k0xV3AWRsE7CfTU2OucBzm/Lz1+9nU6sWV3hqU+J96t6tr+c2xd+nmlVbe25T4n2yhJfIepyNDq+mSx/vWObry+JtDh41i65XnlY2wTJ4moNfG0/Xm88rm14Z8VszlZ7bHuWQV97hlj/+1tU6w6ZpbYy+HliPM8xEtuPMNlTra5jWhmczwoZfLRbbcYaZSIVq45n9/ZYyX1vWTjMWks2ILDrNWLjUT/zWrNnwVk/nUCYSC5n5id+a1sbo61nCTWTncf7i6MN811wz5u7MLsjS01yT5/8egH5pxjrIZas+80UvxrLvPV5vOASa1sbo61nCTWQ9zqLdP5TpumxCtBNmZTCXy4Xw7IQpC7OuI2yaiV7lpIkzPNVLxG+9IDStjdHXc5uK1SpSr3WDjB+W5ES249xQtMd3zVdGfeOv3vDPfdXzSrO0MOzQV991Xa80/NYLQtPaGH09S7gpV8lBviUE2WQgwCb5WCzZ4GZyUK2jmum5t7+Y8XWjel9ok4OSEFmPc/mGHb5rxuZwloqLnWZsfqWfZKuZaZJPbM6bX/itF4SmtTH6epZwE9nkoEOrVvRd89Kzji+9gMue5qUXnepqfV5qltW7vPiSs8t0XVnxWy8ITWtj9PUs4abchGo9D9OWw/CsDcVaLN5hQ7XhJbKh2lkryz6Ps6zElt47AI86zdiyeH6Sjqab8y1jS5X5hd96QWhaG6OvZwk3kQ3VntaoVtpl3fI2f5rW68CDHnqaP2163bO6M9X0yrvcuW+2J/WGRS8ITWtj9PUs4SayHufmHXt914wt+l6Mx+HZ2ILsfpKo6fVqPrFFsv3Cb70gNK2N0dezhJvIdpzbdu5Lq5ybY5tjJi/5+YkPY5pj/uvvvNGYpl/L3wGM/dilPU5DqheEprUx+nqWcJPzyUGeJAXlaCKQTfaxWMKDTQ4KL5H1OJeuL/Jds+OfR/jaacb20fSaeO8yttegX+S6XhCa1sbo61nCTWQ7ziOqV0pZxm1v84+3/8bV+lLq3XSu5xqJXuYfbu3kuWZ50gtC09oYfT1LuIlsVm31yj43vWlz2tfd5atk+zNP9KzuksKyZ5/jb1Qm1/WC0LQ2Rl/PEm589ThF5FoRWSgiO0RkmYi0c46fLyKLRGSniHwuIo1S1TVv9dZSz3sxtnnUyX9yvU6/9VIl/TRteKHrmqWR63pBaFobo69nCTe+dZwiciHwOPB7oAZwNvCdiNQB3gN6A7WBGcCIVPW1apj+PM6sccY1t3//qn+aHuilk/yzfou/U2ByXS8ITWtj9PUs4cZPj/NhoK+qfq2qP6nqGlVdA1wF5KnqSFXdDfQBThGRUrNwNhaWvK2YV8vrDX5tvCf1eq2XydSSIa+844pmuuS6XhCa1sbo61nCjS8dp4gcBLQGjhCRpSKyWkQGikgV4GRgTqysqu4AljnHS2Tn3rJtZJ0NM2Yvj5xeplNMZs1ckLWm1QtW09oYfT1LuPHL4zwSqAj8FmgHtAJOBXoB1YFtCeW3YcK5+yEiXUVkhojM2Puj0vfDeQCc9PcxLF67nVkrN/PLRz4BoMez/+PpN74G4KhLniV/QyETZq7g3G7DAej62BgGj5oFQM32T1C4Yw+jv1hsppxgthB7c9x8aNq8eP3W9meeWLztVsfrn2b0uFkUFu6iZsNbAeMhdr13CADnduzHhMkLyC/YwlEn3QnA0y98TI/ebwDQ+rxezJy9nMVLCzihzX0A9Hn8Xfo8bjbMPaHNfdzX/RJmzl5O6/PMUn89er/B0y+YFUyOOulO8gu2MGHyAs7taNbQ7XrvkGIvtWajrmyQYxnz0cTiVPounXsWr4ASW3tzxFsfF2+Z1OmKu7jksnMoLNxB3VptAfNLu3u3vgB0OP8WJk2cTn7+epo4Yz4DnhlOz7+YaTNt21zHrJkLWLJ4JS1P6ghAv74v0a/vSwC0PKkjSxavZNbMBbRtcx0ANWpUY8Az5p40aXgh+fnrmTRxOh3OvwWA7t36Fv/ar1urLYWFOzK2acxHE4ttemHQg57b1PMvT+9n098f7OapTYn3aemSlZ7blHifYm3wyqbE+/TCoAc9tyn+Po0cMc5zmxLvkyW8+LIAgojUAjYDXVT1NedYJ0zHOQmoqKp3xJWfB/RR1RK3Xa9RuaJuHvjbA467HqaNm7d5bsd+fP5hkvVqPaKsetksZNDh/Fv45LMhZb7e6gWvaW2Mvh7YBRDCjC9zOlR1i4isBuJ76dj/ecDNsYMiUg1o6hwvkfqHVXG7mQeSsNjBQ/df5b1mFnpurPzz9we7ZV2H1QtW09oYfT1LuPEzOehfwJ9EpK7jgd4LfASMAlqISCcRqQw8CMxV1UWlVVa54oFN93rPzWZN63taf1n13FxX9rjjU84EcpVc1wtC09oYfT1LuPGz43wEmA4sBhYC3wCPquoGoBPwKLAF+CVwbarKFhVs966lkHRpvTPO7+2tZhn03F5f9qxf3eBqfeVdLwhNa2P09SzhJmcWefdybDOM2AXZLZbcxo5xhpfIrlW7bvtu7yovodOMZbT6RUl6XnaasSxDv8h1vSA0rY3R14sSIlJbREY5K8KtFJHrSyh3mIi8JiLrnUefhPNtRWSaiBSKyFwROcsXA8pAZNeq3ffDT8X/ez22GSN/7RZfdErS88PLLMjf4LlGedILQtPaGH29iPECsBcz7bAVMEZE5qhqYoLnM0BVoDFQF/hMRFaq6r9EpDYwGuiGWUnuOmC0iDRRVX+/eNMgJ0K1uR6mtWFZS1j5vqhZ0E0ILQ2rL87q+iiEap1ZEFuAFqq62Dn2b2CNqvZMKLsRuFhVpzvPH3CetxORy4DHVfXkuPKLnWP+zgNKg8h6nAu9Sg4qpdNsfV4vZozv541uCXqTZ7zvmx6YieRfTnvL6kVYM109Nzu9q9q35b0J/q3nGgW9MP2oqFy1IieekvmsgFFQR0RmxB0arKqD4543A36IdZoOc4BzSqhSEv5vUcK5ZOdDQ2Q7zkaHVwP8C9MCvPzPW1IXcondNVvw3MuP+aYXY+BL/mYO57qeH5qJX9C9/vmK71/afZ8daPVyk40pvN7qQKIXk3TlN2Ac0FNEbsaEdf+ACd0CfAU0EJHrgHeA6zHz+asmqSdwIttxVkj8beIGKUK0NapX9kD0QGKh2Ro1qvmiF4/fmrmul61mWTrAatWTfWd5i9+aua4XIYqAmgnHagKFScreBTwPLAE2AW9hxjJR1U0icjnwFGbM9BPgU2C1N83Ojshm1S5bX+Srtwnwm+v9XUOy0xX+7v8ZhGau65Wm+X1Rs5SPstDt2k7ZNDcSmrmuFyEWAweLyPFxx04hycpvqrpZVW9Q1XrOWGYFYFrc+Ymqeoaq1gY6A83jz4eJSCcHzRjZ3b0KQ5IQZBOBcocwjXFZokfzWlVdSw6qf/zJ+od/vp3xdY91bJGyDSLyH8wSqrdismo/BtomZtWKSFNgq/O4CPg3cE6snIicCswHqgB9gTNU9cyMG+0DkfU4C3bs810ztnOJVyR2mrGdHfzEb81c0EvlLT7f37+EsiD0gtDMdb2IcQems1uPCb/erqp5ItJORIriyp0OzMOEcf8B3JDQud4PbARWAfWBK/1ofFmI7BhnrmE9zfBjPUiL5UBUdTNwRZLjX2CSh2LP3wZKdHtV9Tov2ucFkQ3Vtj6xgU4f7lKWa8BhWttphg/bSVqCJiqh2vJIZEO185et910ztuG0m5TWacY25PUTvzWD1nMzKackOrRu6Wp9YdMLQjPX9SzhJrKh2uOOqe1ORRl4m6Pf7OGOpkMqT/Pd9593VS8d/Nb0U+/7omY8/+Zovi86PnVhFxn0H2/HxoPWC0Iz1/Us4SayHedPP/kfYi4scm9h+XTCs4WFO1zTSxe/Nb3US+Y57ihKNr3MW/zWtDZGX88SbiIbql25dmv2lWQ4tnnbn91ZMjHdMc07b3/EFb1M8FvTTb10wq0P3nOna3rp4remtTH6epZwU76TgwJICrKJQO5ik3gsuYpNDgovkfU4V633aJH3UujR+42srs+00+z5F39XKgpCM1O9bJN4+vfqmbqQy/itaW2Mvp4l3ESq4xSRriIyQ0RmbC/aTZ/BEwE4odOLLF65iZkLC2h906sA9Hj2fzz9xtcAHHXJs+RvKGTCzBWc281sSNt14BQGvzYegJoNb6WwcBejx82io7Os3g1dX+DNd8xuCBUOvxGAdeu3cUPXFwDoeP3TjB43i8LCXdRseCsAg18bT9d7TTj33I79mDB5AfkFWzjqpDvZXbMFA54ZXtxRtG1zHbNmLmDJ4pXFmZ79+r5UPEG/5UkdObjiwcyauYC2bcz0pp5/ebp4Q90mDS8kP389kyZOp8P5xvPu3q0vQ155B4C6tdpSWLiDMR9NpNMVdwHQpXNPRrxlNseuWrEVACPe+pgunc2XQqcr7mLr1u0UFu6gbq22AAx55R26d+sLQIfzb2HSxOnk56+nScMLATK2acnilfvZNGvmglJtuvnWATw58Au+L2pGnVpnUVRYyPixY4qXQOtxaxdGjxwBQPNaZj3o0SNH0OPWLoBZKm382DEUFRZy2jF1qVuvPiOGDaH3PWbVqc6XdWDq5EmsK8in3YlNABg6cEDxF+VV7dsyf/Ysli9dUpxZ+Xz/fsUT4ju0bsnypUuYP3sWV7U3r1n/Xj0ZOnAAAO1ObEKVqtWYOnkSnS8z2+D1vqc7I4aZ98lpx9TN2iZgP5s+GzPac5vWFeTvZ9OsqV95alPifapbr77nNsXfpzdeGeS5TYn3yRJeym+o1scwrQ3Ppo8NvVosBhuqDS+R8jjjmbt0XdkvLmOnedRJmScIZNNpxrw6P/Fbs0nDCz2bP5mM2C97P/Fb09oYfT1LuInsdJQTG9fxXXP6Z5llgGbraU7+Orsx1TBrxjrJEZ995YtejHfGT/ZVLwhNa2P09SzhJrIe5+69P5TtwixCtIuXFaRd1o3w7NIlK7OuI0yayTzLFcuWeqaXDL/1gtC0NkZfzxJuIttx5m8sSl3IZR5+4r20yrk1pvlo30Gu1BO0Zmlh2IH9H3VdrzT81gtC09oYfT1LuClfyUE+JQTZZCCb5GOxZItNDgovkfU4XVk5KENiU01Kw81OMzYNxE+y1cw0ySeWmu8XfusFoWltjL6eJdxENjmoauWKvmu2bnVsqefd9jRPO/0kV+vzSjMb77JFq9PKfG0U9ILQtDZGX88SbspPqNbjMG15DM/acKzF4h02VBteIhuq/WbxWt81YysEJeJVpxlbvcdPUmm6PecytqKKX/itF4SmtTH6epZwE9lQbcvjMngju+Rtrsk7cO9ILz3NZd//z7O6M9X0yructGCZJ/WGRS8ITWtj9PUs4SayHmfhzr2+a06YsnC/516HZydNnOFp/eloer2iz7TJkzyrOwx6QWhaG6OvZwk3ke04N27dmV5BF8c2Xxn+efH/foxpDn3V/13nh776rq9L4L392lDPNYLUC0LT2hh9PbepWqkirZodkfHDkpzcTw7yICkoVxOBbLKPxRIe3EwOatqilfZ/978ZX3d18yNtclASIutxLs/f4rvmDV1f8LXTjG335TXx3mVsSyS/yHW9IDStjdHXs4SbyHach1avnLqQy97mRZf/xtX6UnHxJWd7rpHoZbbvcLHnmuVJLwhNa2P09SzhJrdDtS52nLkWnrVhWYsl3NhQbXiJrMc5c1GKnUo8GNusWrGV63X6rZcq6Se2k71f5LpeEJrWxujrWcKNbx2niEwQkd0iUuQ8vo07d72IrBSRHSLyvojUTlXf6c3re9vgOGLe5s59s33T9EIvHS9z0ZY0s5VdItf1gtC0NkZfzxJu/PY471TV6s7jBAARORl4GegMHAnsBF5MVdHm7btKPunR8noj3vrYk3q91stkasnokSNc0UyXXNcLQtPaGH09S7gJQ6j2BmC0qk5S1SKgN3CViNQo7aJtRbt9aVw8Yz/2dxK0G3qZjmVO+GRs1ppWL1hNa2P09Szhxu+O8x8islFEpohIe+fYycCcWAFVXQbsBQ74xheRriIyQ0Rm/PiT0mfwRABO6PQii1duYubCAlr/8XUAevR+g6dfMB7bUSfdSX7BFiZMXsC5HfsBZouwwa+NB8watIWFuxg9bhYdr38aMFNP3nznS3bXbFE81njxJWcXTxHpdMVdjPloIoWFO4rXdx3yyjvF23J1OP8WJk2cTn7+epo0vBCAAc8Mp+dfTP1t21zHrJkLWLJ4JS1P6ghAv74v0a/vSwC0PKkjf+99O7NmLqBtm+sA6PmXpxnwzHAAmjS8kPz89UyaOJ0O55skqe7d+jLklXcAqFPrLBYU1Gf82DF0u7aTeU1u7VL8yzk2ZjN65IjiVPtu13bi0k6/o6iwsHhtzhHDhhRvqdT5sg5MnTyJdQX5tDuxCQBDBw6gfy/zmlzVvi3zZ89i+dIldGjdEoDn+/fj+f7mNe/QuiXLly5h/uxZXNXevGZH1KvH0IEDAGh3YhPWFeQzdfIkOl/WATDbOY0YZrZzO+2YuhQVFmZs0/ixY4ptevrVYZ7b1L9Xz/1suv+Rxzy1KfE+rV9b4LlNifepavVqntqUeJ+efnWY5zbF36fPP/nYc5sS75MlvPiWVSsivwQWYDrFa4GBQCtgMDBSVQfFlV0D3KCqE0qq77AalXXL+L8ceMLlMG18Nm2nK+7i3fefc7X+0iiLXrbZst2u7cSg//i3YlGu6wWhaW2Mvh7YrNow45vHqapTVbVQVfeo6mvAFOASoAiomVC8JlBYWn11DvM+yy1xCsofbu3kuWY2em5MMbn65j9kXYfVC1bT2hh9PUu4CXJ3FAUEyANOiR0UkSZAJWBxaRfXqHrIgQc93nPz7HP8/eGVrp6bczLbnOX9ogvlSS8ITWtj9PUs4cYXj1NEDhORDiJSWUQOFpEbgLOBccAbwG9EpJ2IVAP6Au+paqke59yl6z1tc7IFD5o6Y5V+kY6e2wsZnH1SU1frK+96QWhaG6OvZwk3fnmcFYF+QHPgR2ARcIWqLgYQkW6YDvRw4FPg96kqPLVZvf0PeOxtAqzf8qXnGunqebXyz6xV3v4gKW96QWhaG6OvZwk3vnicqrpBVc9Q1Rqqepiq/kpV/xd3/k1Vbaiq1VT1clXdnKrODVt3eNbekpbXi2Ws+kUyPa+3+4plEfpFrusFoWltjL6eJdyk3XGK4Y8iMl5E5jrHzhaRq71rXsns3L3v5yc+eJsAs2Yu8EWnJD0/1pedP3uW5xrlSS8ITWtj9PUs4Sbt6Sgi8ghwIfAsMEhVD3MSeUaq6uneNTE5+y3y7uEUlDBgF2S3hJG560pNQ7A4tDyy1LVcSsRORwkvmYRquwCXqep/MBmxAMuBJm43Kh2+/X6TJ/WW1mnGFhrwiw7n3+J7pxmb/G31oquZjt7cdYVZP+Lp0/lKr8xJSpT03Hh9LeEik+SggzBzLuHnjrN63DFfaVCnuvnHpzAtwN8f7Oab1vdFzbj1L4/6phfjzp5/t3oR00z8kr2k672+f/H+7s77rJ6l3JBJx/kx8E8RuRfMmCfwCDDai4alovIh7icEpwrRHnd8I9c1kxHzMhs3Pc4XvXj81sx1vWw1y9IB1m/s/9QJvzVzXc8SbjIJ1f4ZqA9sAw7FeJqNgL960K6ULFyx0VdvE+CsX93guUZ8aPa3553luV4ifmvmul5Jml6G6/7224uybXLoNXNdzxJuMl6rVkSOBBoCq1R1rSetSoPWJzbQ6VOedK2+sCQE2USg3MCOUVmyxc3EHJsc5C6lepwiUiHxAWwAZgLr4475zrrte3zXjO1M4hWJnWZs5wY/8VszF/RSeYqjh77kumZp+K0XhGau61nCTaqBwh/4OREoGeKcP8i1FqXJ3n0/+C1JQf4Gz+pO5mmuX1vgmV5J+K0ZJb2yepFb1vsbmPFbLwjNXNeLEiJSGxgCXARsBP6mqm8mKXcYMAC42Dn0oqr2iTv/OdACs1b5cuBBVf3A08aXkVJDtSKSVjaMqq50rUVp0rpVE50+/hFX6go6TGvDs+HChlktYSAqoVoReQsTvbwFs1XkGKCtquYllPsXZibGzUBd4DOgn6r+yznfEligqj8421B+CjRTVf89iBSUGmZV1ZXpPPxqbDwLF6/xXTO2obSblNZpxjbc9RO/NYPW82P+3F+v8ndzAL/1gtDMdb2o4GzM0QnorapFqjoZ+BDonKT4b4AnVHWnqq7AeKnF+7Wp6lxVjYUSFbPG+TFetr+spD2nQ0T+TQlhW1W9ybUWpUmjY+q4Uk8m3ubAl3q7ohkjlafZ99mBruqlg9+afurNXVfITb2f8N2jvK3vUzmtF4Rmruu5TZWKFcq6glEdEZkR93ywqg6Oe94M+CG2YYfDHOCcEuqThP/3+wIWkY+ACzDh2k+AeO3QkMlkyKUJz+sBv8XsauI7FSr4n5NUo0Y11+pKJzxbrXrZlurKBr81vdRL1kFWrlbdM72S8FvT2hh9vRCxMUWotjqwPeHYNiDZB3sc0FNEbgaOxHibVeMLqOplIlIR03meqKo/lbnlHpJ276OqDyc8bscM8gYyM3jp8uwH6zMd2+x0xZ+y1oT0xzS7XdvJFb1M8FvTTb10Qq6Pd7vRNb108VvT2hh9vQhRBNRMOFYTSPYBvAvYBSwBPgDeAlYnFlLVfao6FrhIRDq621x3yHge534XixwMbFbVxBfOc9xIDgoiKcgmArmHTeKx5DJuJge1OPU0fffzKRlfl2qheWeMcwtwsqoucY4NB/JVtWdpdYvIY8Cxqpo0eUREPgXGqOozGTfcYzLZVuy8hMdlwDDA3722HPLXbsnq+rJ0mv36ZjeXK9NO8/n+/bLSKwt+a2aql20Sz9vPu7doRlg1rY3R14sKqroDeA/oKyLVRORM4HLg34llRaSpiBwuIgeJyMVAV6Cfc665iFwsIlVEpKKI3AicDUz0z5r0yWSgcEjCo79z3P1U0xIQka4iMkNEZmzeUkSfx98F4IQ297F4aQEzZy+n9Xm9AOjR+w2efuFjAI466U7yC7YwYfICzu1ovqi7d+tbvFF03VptKSzcwZiPJtLpirsA6NK5JyPeMtdXrdgKgLx5S+jS2fyI6nTFXYz5aCKFhTuoW8tkag555R26d+sLmJ1NJk2cTn7+epo0vJDvi5oxdOAA+vcy11/Vvi3zZ89i+dIldGjdEjCdSKwj6dC6JVs3b2L+7FnFmaD9e/UsnsDf7sQmrCvIZ+rkScW7YfS+p3vxhrunHVOXosJCxo8dUxwO7XFrF0aPHAFA81pmaGH0yBH0uLULYMKmy5cuoaiwkNOOqQuYDXx739MdMLtuTJ08iXUF+bQ70WyKk6lNy5cu2c+myeM/LdWm7l278s/nXmDuukJaHXUEu4qKmDH+E/o7obMBPboxebR5H1zd/EhT5+h3GdDDLMjfv9uNzBj/CbuKirjpNNPmT0cM5+XePQCz60Xe1ClsXreW29qZNo8e+hLD+z8EmGzK7+bPIX/5Mu7u8GvAfInGvkjv7vBr8pcv47v5c4ozL4f3f6h4wvxt7Vqya0cReVOnFO+w8XLvHnw6wiymcdNpTVy3acKo/3hu0+Z1a/ezaebn//XUJj/uU6JN8ffp/cEDfLcpQtwBVAHWY8Kvt6tqnoi0E5H4TUBOB+Zhwrj/AG6Im7IiQB+njg3A3cA1qhrKjVCzCtUGSbahWj/DtDY8mz42/GqxGKIQqi2vZLzkXgnL8PnO/EWrynxtWTvNlidlPk6dTacZ89r8xG/Nc1u18HUPwpg34id+a1obo69nCTfZLrkXw/cl9447tp7fkrz7/vMZlc/W0xz0n3ezuj7MmrFO8q+DXvdFL4bfekFoWhujr2cJN6k6zmPj/r8UM2/zH8BKft5SzP9vd+Cnn8o2vSebEG1h4Y60y7oRnt1R5H/Y0kvNZB7l7h3+7oPut14QmtbG6OtZwk3aS+5h9uO8SlX/p6qLVfV/wO+AQLZGX7lqo++ad96e3piqW2OaD95zpyv1BK1ZWhj25Qf9ffv4rReEprUx+nqWcJN2cpCIbABOUdX8uGNHAXNU1Z317zKgLMlBfiQE2UQgg03ysViywyYHhZdMEnteAz51poRcLCJdMWsJvuZN00pnVf4m3zV7/uVpX/Vi0zyipJlpko/fqfdBpPpbG62eJbfIZK3a+zHr1V4DNAAKgIHAKx60KyWHVMyk6e5Qv8ERpZ5329usW6++q/V5pZmNd1mrrr9JXn7rBaFpbYy+niXclJt5nF6HactjiNaGYy0W77ChWu8QkUrAT6q6L+5YRaCCqu5JdX1GczBF5PciMl5EvnX+/j7zJrvD3Lzvfdds0jD5nnxedZqx1Xn8JJWm23MuYyum+IXfekFoWhujr2fxnP9hVjKK53TM8GNKMkkO+jtwE/A0P09HuRd4XVUfTbe1bnHKyQ119hf/SKusW95mfv56GjSou98xLz3NdQX5HFm/gWf1Z6LplXe5ed1aah/pXxjMb70gNK2N0dcD63F6iYhsAWprXAfoLOazSVVrpbo+E4/zVuAiVR2sqp84m5n+H2ahXt/ZvWdf6kIus3TJyv2eex2eXbEscQtU74nX9GNFn4IVyzyrOwx6QWhaG6OvZ/GcbZg9QeM5Ekhrsn4mHWc1zOK78WzCLO7rO/lrt6ZVzs2xzUf7Dir+348xzYH9fXfkGdj/Ud+WvwMYOfApX3SC0gtC09oYfT2L57wLvCkiLUSkqoj8AhgOvJ3OxZmEaodjdvXuCXyPCdU+CuxU1c5laXk2pJsc5EVSUK4mAtlkH4slPNhQrXeISGXMsOPvgUrAbuBfwH2qujvV9Zl4nHditoOZi9n1ezbGrf1TZk12h5WrU68c5Han2b1bX187zdh2Xl4T72HGtjzyi1zXC0LT2hh9PYu3qOpuVe2OiaTWA6qr6p3pdJqQwTxOVd0O3CQiXYA6wEZVLduCsS5Qtcohvms2bnGur3otWp3maf3JPMymLU7xVLO86QWhaW2Mvp7FfUSktCkDNUQEAFX9LmVdmczjFJGqwHFA9fjjqvpl2pW4RDqhWjc9zlwLz9qwrMUSbmyo1l1E5CfMbl/Cz7t+ifO3uCNU1ZS7faUdqhWRm4C1wHhgRNzjP+nW4SbfzFtR6nkvxjZPO6Zu6kIh10uV+BPbqd4vcl0vCE1rY/T1LO6jqhVU9SBVrYCZJfIf4ASgMtAceBO4JZ26MkkOWgt0dnZFKTMicjwwD3hHVW90jl2P2a6sDmZi6h9UdXNp9ZzasrHOmlBy1qkX3mZRYSHVa9Rwrd5UuK2Xjpe5q6iIKtWrpyznFrmuF4SmtTH6emA9Ti8RkdXA8aq6K+5YVWCxqh6d6vpMkoP2AhMybuGBvABMjz0RkZOBl4HOmHk0O4EXU1VSWFTyGK5Xy+tNmzzJk3q91stkeknetMw/XNmQ63pBaFobo69n8ZwKQOOEY42AlGHa2MXp0hv4p4iUeQsxEbkW2Ap8Fnf4BmC0qk5S1SJH5yoRKdXV2rhpe1mbkRHxY5tvvzbUF0239MoyH/PTt/+dlWam5LpeEJrWxujrWTznGWC8iDwmIreLyGOYfumZdC7OJFT7a0xMON6NFUDTGUwVkZrADOA8THz5OFW9UUQ+AL5U1cfjyhYB56jqzJLqKyk5yG1vM6pJQTb5x2KJNjZU6y0i8n/A7/h5t6+3VXVcOtdm4nH+G7OywilAM+dxvPM3HR4Bhqjq6oTj1THLH8WzDbPYwn44e4HOEJEZc/O+p8/j7wJwQpv7WLy0gJmzl9O2zXWA2TtzwDPDAbM4e37+eiZNnE6H883Yb/dufRnyyjsA1K3VlsLCHYz5aCKdrrgLgC6dezLirY8BaF6rKgDXXnQuPW7tAkC3azsxfuwYigoLi5N4RgwbUjz3svNlHZg6eRLrCvKLF04fOnBA8X6XV7Vvy/zZs1i+dAkdWpsFpJ/v34/n+/cDoEPrltx2TSfmz57FVe3bAmavzKEDBwBmMfZ1BflMnTyJzpd1AMy8z38+9wJz1xVy02lN2FVUxIzxn9C/240ADOjRjcmjzWt2dXOz2tTk0e8yoEc3U3+3G3noxsvZVVRUnAzx6YjhxXPY+nS+krypU9i8bm3xotejh75UvFfhX6+6kO/mzyF/+TLu7vBrAN5+/knefv5JAO7u8Gvyly/ju/lz+OtVZsH8ey4+k9FDXwLMQtqb160lb+oU+nS+EjDz5z4dYe5jWW2aMf6TYpsG9OjmuU3D+z+0n01PdL/ZU5sS79OtbU/23KbE+/SnC3/pqU2J92lAj26e2xR/n649+SjPbUq8T25ySIU9NKy+OONHLqOq41T1FlW9WFX/kNhpisiYkq7NxOM8YFHcdBGRVsAbwKmquldE+rC/xzlFVZ+IK18ItC/N42zSqK4u++ZAr9rLKSijR47gN7+7xrX6U5Gpnhte5uTR73LWbzplXY/VC07T2hh9PXDX4zzt9JN1ytQ3M76uasVWOetxpkJEtqtqzaTnMug4/wnMVtXhZWjAPZjl+WLf7NUxg7ALgXFAI1W9wSnbBFgEHK6qJfYEyUK15TVMa8OyFkvuYTvOYCmt48wkVNsGeNXZi3NS/CONawcDTYFWzmMQMAbogPFEfyMi7USkGtAXeK+0ThNg5pzlGTQ9c5J1mrGQrV+ko+d2pxkLOflFrusFoWltjL6eJdykveQe8IrzyBhV3YmZZgIUJ//sVtUNwAYR6YbpQA8HPsUsvFsqp59y7H7PvZqCEs+iLTtTF/JJzysv8+1F6zypt7zqBaFpbYy+niXcpO1xquprJT1iZUQk5fxLp64+scUPnOdvqmpDVa2mqpenWvwAYPOWonSbnjElhWhHjxzhmWa6el5v+RVLdvCLXNcLQtPaGH09SyiQkk5kEqpNhxtTF3GHbdt/9sb88DYBJnwy1hedkvT8GMucOSGrhaGsXgg0rY3R17N4h4gcJCKviUilFEUfK7GOMiTJltagQlX1ZU26+OSgXE8Kssk/ljAxe3HifvaWdGjV7IiMytvkIO8QkQKgoaruK8v1bnuc7vXCKVi6fK0n9ZbWaXa71t909G7XdvK904zNUbN60dVMpTd78YasHsl4+xF/9o6Nsp4br7PFNZ4BHhaRimW5OJPkoFBR53CTJexXmBbg6pv/4JvW3HWFtLn8Ot/0YlxwdWerFyHNZF+wTc+63Pcv3lM7/M7qWaLEnzAbWP9ZRDYQt92YqjZMdbHbHWeJg6luU6N6ZdfrTBWibXPW2a5rJiPmZZ7c5kxf9OLxWzPX9bLRLGvn17DFGWW6Lhv81sx1PYvnZBUGcjtU+7rL9ZXI3LzvffU2Ac4+qannGvGh2dvObum5XiJ+a+a6XkmaXobqnv/9udk0NxKaua5n8ZyvgPOBV4GPnb8XAFPTuTij5CAR+QNwHWZR3HzMou9Dy7IMX7a0btVEv5gxyrX6wpAQZJOAcgc7RmXJlsc6trDJQR4hIkMwm1g/CqzEbCn2ALBEVVOOyaXtcYrIE8BfgfeAvwDvAvcBj5d2nVes31LyfpxeMWLYEF/1YgtM57Jm1PXS8Ra/GTfSVc1U+K0XhGau61k85wrgMlUdq6oLVHUscLlzPCWZhGq7AOer6kuq+rGqDgIuIo1Vfrxg585dqQulSbre5vzZs1zTTCSZt7ls/hzP9ErCb80o6ZU1nFqwNK/MmmXBb70gNHNdz+I5a4HENU2rYLYXS0kmi7wvA05T1W1xxw4DZqqq94N/CZQ19JCMoMO0NkQbLmyY1RIGbKjWO0SkJ3A98DywGjgG6A68CUyPlVPV8cmuz8TjfBZ4T0QuFJETReQiYCTwjIg0iT3KZkbmLFm8wi+pYmL7XrpJaZ1mbF9AP/FbM0g9v+bPvf5AF9frDJNeEJq5rmfxnNswez4/ALwI/A2oCXQDhjiPV0u6OJPpKAOcv4npZecDzzn/K2a7MM+p3yCzVThKIhNv886ef3dFM0YqT/N3d97nql46+K3pl16sQzz1ilt99yjbXXdHTusFoZnrehZvUdVjU5cqmbQ7TlV1e+pKVlSqdIjvmo2bHudaXemEZ+s39j0C7ruml3rJOsjaDRp7plcSfmtaG6OvZwk3oeoMM2HRouz348x0bPO3552VtSakP6b5t99e5IpeJvit6aZeOiHXf/W4xjW9dPFb09oYfT1LuMkkOagh8BBwKlA9/pyq+p5d40ZyUBBJQTYRyD1sEo8ll7HJQeElE49zJCa0+yBwe8LDd9av25TV9WXpNIcOHJC6UClk2mmOHvpSVnplwW/NTPTcSOKZOmpYma7LBr81rY3R17OEm0w6zubA71X1I1X9LP7hVeMSEZGuIjJDRGZs2LCZfn3Nl27LkzqyZPFKZs1cQNs2ZmH0nn95mgHPmMnuTRpeSH7+eiZNnE6H828BoPc93YsXNDjtmLoUFRYyfuyY4h1QetzapXgj6ea1zHSfrydNoMetXQCzc8n4sWMoKizktGPqAmaBhN73mF0UOl/WgamTJ7GuIJ92JzZh7rpCRg99ieH9HwLgr1ddyHfz55C/fBl3d/g1AG8//yRvP/8kAHd3+DWrliziu/lz+OtVFwIwvP9DxR3Nbe1asnndWvKmTinOFH25d4/iCf43ndaEXUVFzBj/SfFuGQN6dCvekPfq5kcCZoPeAT26AWZXjUUzv2ZXURE3nWYSpD8dMZyXe/cATEZq3tQpbF63ltvamWXkMrUpf/my/WyaMua9Um167K7u/Ou5F5m9eANPXdOGPTt3sGTahOLdKt5/6n7yJo4xZTuaJRjzJo7h/afuN/qPdGfJtAns2bmDp65pQ+HmDXwzbiQfD+wDmGzJlfOmUbhpPc91MXlvU0cN49Mhps1D772agqV5bFqzgkHdLgVg0psvMOnNFwAY1O1SNq1ZQcHSPIbee7V5zYY8WfxF+1yXc9m0ZiUr500rzsz8eGCf4gn1btgE7GfTtA//7blNhZvW72fT3PEfeGpT4n0q3LzBc5vi79Pnw5/13KbE+2QJL5mEal8Hhqjq5942KT2yDdX6Gaa14dn0seFXi8UQlVCtiNTGTN+4CNgI/E1VDxBzNo4eAFwJVASmAN1UdY1zvijhkirAi6r6p4wb7jGZeJx3AS+JyBgRGRr/8KpxpbFo4XdlvrasneZV7dtmfE02nWbMK/MTvzXvuvRcX/cgjHkbfuK3prUx+noR4wVgL3AkcAOmnzg5Sbm7gV8DLTHrnW/BLEAAgKpWjz0wW37twgwRho5M5nH+C/gRWIgxKFAaNqrvu2bfZwdmVD5bT/O2vk9ldX1YNeM7yIu7P+S5Xjx+6wWhaW2Mvl5UEJFqQCeghaoWAZNF5EOgM9AzofixwCequs65dgTwzxKq7gSsB77wpOFZkknHeR7QQFVDEXc86KCyzaTJJkRbrXqNtMu6EZ6tXK166kIu46VmMo/ykCrVPNNLht96QWhaG6OvFyGaAT+o6uK4Y3OAc5KUHQIMEJEGwFaMdzq2hHpvBoYHsfNWOmTS+8wFDveqIZmybOkq3zVjiUOpcGtM8/FuWe21GgrNVGHYkU6ihV/4rReEprUx+npuU+HHXVTePj/jB1AnlpDpPLomVF0d2J5wbBtmObtElgCrgDXONScCfRMLiUgjTMf7WnZWe0cmyUGPANdgQrbr4s+pqu/jnGUZ7PYjIcgmAhlsko/Fkh1uJge1btVEp49/JOPrKhx+Y6ltEJFTgSmqWjXuWA+gvar+JqHs60A14BZgB3A/ZmuvXyaU6wVcqKrJvNZQkInHeRbml8JFmPh17OG/WwQUFKz3XfP5/v1KPe92pxmbxuEn2WpmmuQTmy7gF37rBaFpbYy+XoRYDBwsIsfHHTsFSLYPWytgmKpuVtU9mMSgNiJSJ6HcTYTY24TM1qpNXNzdYgGsd2mxlFdUdYeIvAf0FZFbMZ3j5UCyKQjTgZtEZAKwE7gDyFfVjbECItIWOIqQZtPGSDtUCyAihwOXAPVU9UlnkLeCqq72qoElkWmo1uswbXkM0doO02LxjiiEaqF4HudQ4EJgE9BTVd8UkXbAWGd6Saz/eM4pdwgwH/izqk6Lq+tloKqqds64sT6SdqhWRM4BvsVkQj3oHD4e8H9dOGBB3lLfNTu0bpn0uFedZmz1HT9Jpen2nMvYKi9+4bdeEJrWxujrRQkn9HqFqlZT1YaxxQ9U9YtYp+k836SqN6hqXVU9TFXPiu80nTK3hb3ThMySg74B7lPVz0Rki6rWEpHKwEpVPdLTVibh5BbH6YzZ76ZV1i1vc/nSJRx73PH7HfPS08xfvowGx/q7zVdJml55l5vWrODwoxp7UncY9ILQtDZGXw+i43GWRzKZx9k4bl3aWG+7N8M6XOPHH3/yXXNH0f6dpNfh2d07Eleg8p54TT9CsXt37fBcI0i9IDStjdHXs4SbTLJqF4hIh4RjFwDzXGxP2ny/siC9ci6ObT54z53F//sxpvnyg/d5rpFM06/l7wDGvvCwLzpB6QWhaW2Mvp4l3GQSqv0lMMZ5XA0MB34DXK6q0z1rYQmkmxzkRVJQriYC2WQfiyU82FBteMnE42yHWZw3D5NBtRxo4xz3nTWr16Ys43an2b9XT187zdh2XV4T72H6vaVRrusFoWltjL6eJdxkMj75oKo+BTwRf9BZ5aGkhXo9o2LFin5L8mO12r7q1apbz9P6k3mYNWof4almedMLQtPaGH09S7hJGaoVkfOcf0cDlwESd7oJ0FtVG3nTvJJJJ1TrpseZa+FZG5a1WMKNDdWGl3RCtUOcR2VMiDb2/FXgD0Agm4zOm7e41PNejG3e1i75PE6v8EIvVeJPbCd6v8h1vSA0rY3R17OEm0ySg4ar6k1lFjIL/J6PWeR3LfCEqr7qnDsfsxlqQ2Aq0EVVV5ZW3y9aNtNps0pelckLb3PzurXUPtLb8Gk8buul42UWblpPjcPruqZZ3vWC0LQ2Rl8PrMcZZtJODsqm03T4B2YuaE2gI9BPRE53Fvh9D+gN1AZmACNSVbZnz94Sz3m1vF7BimWe1Ou1XibTSzbnr3BFM11yXS8ITWtj9PUs4aZsu0GXAVXNc1bEB7OAggJNgauAPFUdqaq7gT7AKSLSvLT6CvL9GaOLH9scOfApXzTd0ivLfMwv3noxK81MyXW9IDStjdHXs4SbjBZ5z1pM5EWgC1AF+AY4G3gUOERVb48rNx94SFVLXFOvpOQgt73NqCYF2eQfiyXa2FBtePHN4wRQ1TswO4O3w4Rn92B2EN+WUDTpDuIi0jW2E/n8eYvp19esL9/ypI4sWbySWTMXcFV7s5tN/149GTpwAADtTmzCuoJ8pk6eROfLzOJHve/pzohhQwA47Zi6FBUWMn7sGLpd2wmAHrd2YfRIEzG+urlZivfhm69iQI9upv5uNzJj/CfsKiriptOaAPDpiOG83LsHAH06X0ne1ClsXre2OMln9NCXiudm/vWqC/lu/hzyly8rXlj97eefLN4P8+4Ov+aZe/7Id/Pn8NerLgTMvM7RQ43Nt7VryeZ1a8mbOoU+na8E4OXePfjXcy8ye/EGnrqmDXt27mDJtAm87exe//5T95M3cQwAj3VsAUDexDG8/9T9Rv+R7rz1YFf27NzBU9e0AeCbcSP5eGAfAF5/oAsr502jcNP64mSJqaOGFc9xG3rv1RQszWPTmhXFi2JPevOF4r0MB3W7lE1rVlCwNI+h914NwCt/upKpo4YBJgGjcNN6Vs6bxusPdAHg44F9+GacGcsuq01Lpk0otilWn5c2fTrkyf1s+uCpv3pqU+J9GnBze89tSrxPL9/+G09tSrxPHw/s47lN8fep/5WtPLcp8T5ZwouvHud+wiKDgAWYcG1Fp1ONnZsH9CnN42zYqIF+u2zsAce9nILy6YjhXHBNtkO96ZOpnhte5jfjRnLq//0u63qsXnCa1sbo64H1OMNMkB3nq8AOzEpEN6vqmc7xasAG4DRVXVTS9clCteU1TGvDshZL7mE7zvDiS6hWROqKyLUiUl1EDnIWi78O+AwYBbQQkU7ONmUPAnNL6zQB5swu9XTWJOs0YyFZv0il58Vi7LGQkl/kul4QmtbG6OtZwo0vHqeIHAG8A5yC6axXAs+p6ivO+QuAgUAjfp7HuaK0Olud2ly/mv7zrBU/vM1dRUVUqV49SWlvKE3PKy9zz84dVKpazZO6y6NeEJrWxujrgfU4w4wvHqeqblDVc5xdv2uq6i9inaZz/lNVba6qVVS1fapOE6CwcKdn7S0pRJs3bYpnmunqeb3l1/fz/d3oJtf1gtC0NkZfzxJufM2qdZONG7cU/+/VggeJfPr2v33RKUnPj7HMbz4peTUmqxcNTWtj9PUs4Saw5KBsiU8OyvWkIJv8YwkLC+ekt4G8pWROPKV+WuVsqDa8RNbjXLF8NeBvpxmbw+kXfbr+3vdOMzYHzepFV7M0vYVzCrJ6lMT0tx/zwpSc1Mv2tbYETyb7cYaKQw89YH0Ezzm9/YW+ac1evIHjzzjHN70Yfmvmup6XmiV9uVate4rvX7z1TviV1Qsze3bDMm9nIpQnIh+qzcU9N21o1mI9Dsuo3he6F6o9sYFOH35LxtdVaNPPhmqTENlQ7TezFviWFBQjtvSel8R3mrFlvPzEb81c10um6XWYblRv/yIjQWnmup4l3ETa43zrsxmu1RcGb9N6mrmD9Rgt2WI9zvASWY9zwybv5nGWxOTRJS6dmzXJOs3YAtJ+4rdm1PXS8RhXzRnvqmYq/NYLQjPX9SzhJrIdZ+G2xA1Vyk663ubMCf9zTTOekjzNJdMneqJXGn5rRkUvm5Dq2m+/LpNmWfFbLwjNXNezhJvIhmpbnHqavvu5Oyv5BB2mtSHacGHDrJYwYEO14SWyHufK75b5rtm/242u11lapxnb989P/NYMUs+v+XNfvd7b9TrDpBeEZq7rWcJNZOdx1j68jiv1ZOJtXnB1Z1c0Y6TyNE/t4O/+f0Fo+qUX6xDrNDvfd4+ycetLclovCM1c17OEm8h2nNWq+78AwsltznStrnTCsw1bnOGaXrr4remVXkmdY51jT/FErzT81rQ2Rl/PEm4iG6r9Nm9e1nVkOrZ529kts9aE9Mc0n//9ua7oZYLfmm7qpRNuHffEta7ppYvfmtbG6OtZwk25Tg4KIinIJgK5h03iseQyNjkovETW49yycWNW15el0/x0xPCsNDPtNL8ZF8D2UD5rZqLnRhLP8un+z431W9PaGH09S7iJbMe5a5f/CyAsmz+nzNeWxdMsWJpXZr2y4rdmaXpeZLtuzV/sSj1h1rQ2Rl/PEm4i1XGKSFcRmSEiM/bt28fz/fsB0KF1S5YvXcL82bO4qn1bAPr36snQgQMAaHdiE9YV5DN18iQ6X9YBgJd79yj2IG86rQm7ioqYMf6T4iknA3p0K14pKLZG7clt2hZvLda/243MGP8Ju4qKuOm0JoDxSF/u3QOAPp2vJG/qFDavW8sf2pq1SqeOGsanQ54EYOi9V1OwNI9Na1YwqNulAEx68wUmvfkCAIO6Xcovr+xCwdI8ht57tal/yJNMHTUMgOe6nEvhpvWsnDeN1x/oAsDHA/sUe3BPXdOGPTt3sGTahOIpGO8/dX/xyjmx9VPzJo4p3obq7Ue6c3yb9uzZuYOnrmkDGI/w44F9AHj9gS6snDeNwk3rea7LuWWyadOaFfvZdEiVavvZNOuLeUx6fxyD77re6H/wTPGv/dGPdGTfnp0ULPqqeHrA9LcfK17VJbae6Ko544u3gfrq9d4ULPqKfXt2MvqRjpx6+b0snz6Gbz54BoAvhvRgw/I57Nq+kbFPXAPAkikjmTd2EACfv3QHW9YspnDjav77rHmdF44fzsLx5r3z32e7ULhxNVvWLObzl+4AYN7YQSyZYu7D2Ceuofm5ndmwfA5fDOnhiU3AfjYVbVztuU27tm/cz6ZYG7yyKfE+nXr5vZ7bFH+fVs/93HObEu+TJbxEdoyzWvUaOmv1+jJdW9axzT6dr6TPv0dldE02Y5qvP9CFGx8bVubro6A5+K7raXfL077pfTGkh696QWhaG6OvB3aMM8xEdjpK3frp7aLuJr+7876MymebCNTuujuyuj6smvFh1+bn3eS5Xjx+6wWhaW2Mvp4l3ES246xUqVKZrssmk7Z+46Zpl3Uje7Z2g8ZZ1xEmzWTjlNUPP8ozvWT4rReEprUx+nqWcBOpMc54ln3r/27mf/vtRWmVc2vKyb96XONKPUFqpkrumTDI3yX3/NYLQtPaGH09S7iJ7BhnWeZx+jFv087TNNg5lhZLdtgxzvASWY9z4/qyJQZlw+ihL5V63u1OM5Zt6ifZaJZl6kgsq9Ev/NYLQtPaGH09S7iJ7BjnD/v2ZVTeDW9zy/q1JZ7zwtMs3Oy/91oWzWy8y93bN5X52ijoBaFpbYy+niXclJtQrddh2vIWorWhWIvFW2yoNrxENlQbRHLQX6+6MOlxrzrN2CIBfpJK0+09K2OT0f3Cb70gNK2N0dezhJvIdpwNjmmYdlm3vM3b+j51wDEvPc2Luz/kWd2ZaHq5yXOrjve4XmeY9ILQtDZGXy9KiEhtERklIjtEZKWIXF9CuUoiMkhE1onIZhEZLSJHxZ2/01kZbo+IDPPNgDIQ2Y7zoIMO8l2zcrXq+z33Ojx7SJVqntafStOrzjKegytV9bT+oPWC0LQ2Rl8vYrwA7AWOBG4AXhKRk5OUuxv4NdASaABsAZ6PO58P9AOGetpaF4hsx7nyu2VplXNzbPNxZx1b8GdMc+Qj/s8de73Xbb50mDG+er2XLzpB6QWhaW2Mvl5UEJFqQCegt6oWqepk4EOgc5LixwKfqOo6Vd0NjACKO1hVfU9V3wdCn4mV88lBXiQF5WIikE32sVjChZvJQac3rq1f9+qQ8XWH/PE/K4H4PRwHq+rg2BMRORWYoqpV447dB5yjqr+Jr0tEWgMDgN8BW4FXgfWqek9CuX7A0araJeMG+0RkPc71a1N/0bvdab79/JO+dpqxXUW8Jr7TjO0m4Re5rheEprUx+nohYqOqto57DE44Xx3YnnBsG1AjSV1LgFXAGueaE4G+bjfYDyLbcQbB2k07gm6Cq/gZkrVYLDlJEVAz4VhNIJnX8gJQCTgcqAa8B4z1tHUekdOhWjc9zlwLz9oO02IJNyEJ1ZbaBmeMcwtwsqoucY4NB/JVtWdC2fnA31X1A+f5Yc61R6jqxrhyNlTrFUsWLij1vBdjm7HNmf3CC71UXmZsE2C/yHW9IDStjdHXiwqqugPjOfYVkWoiciZwOfDvJMWnAzeJyKEiUhG4A9PBbgQQkYNFpDJwEHCQiFQWkVCubhdZj7PZSSfr6C9nlHjeC29z05oVHH5UY9fqTYWbeul6mIUbV1OjztGuaFq9YDStjdHXg2h4nGDmcWKmkFyIyYjtqapvikg7YKyqVnfKHQ4855Q7BJgP/FlVpznn+wCJE8kfVtU+GTfcY3zpzUWkEvAicAFQG1gG/E1Vxzrnz8fEvxsCU4EuqrqytDp//PHHEs95tbze3l3+jnG6pZdJWPaHPTtd0bR6wWlaG6OvFyVUdTNwRZLjX2CSh2LPN2HmeZZUTx+gj+sN9AC/QrUHY7KpzgEOBXoBb4tIYxGpg3H1e2M61RmY+T2lkr/qe+9aG0f82ObYFx72RdMtvbIk/8z+8NmsNDMl1/WC0LQ2Rl/PEm4CC9WKyFzgYUyGVRdVbescr4aZN3Sqqpa4IG1JyUFue5tRTQqyyT8WS7SJSqi2PBJIcpCIHAk0A/IwK0fMiZ1zBpuXEbeiRNx1XZ21DGcsXpDH8/37AdChdUuWL13C/NmzihdiH97/oeL9M29r15LN69aSN3UKfTpfCcDLvXvw6QgzN+um05qwq6iIGeM/ob+zOtCAHt2YPPpdAB7r2AKA/zx8O+8/dT8Abz/SnSXTJrBn5w6euqYNAN+MG8nHA/sA8PoDXVg5bxqFm9bzXJdzAbPX5adDngTMYuoFS/PYtGZFcRLQpDdfKJ67OajbpYwe0IuCpXnFC69/OuTJ4v0yn+tyLoWb1rNy3jRef6ALAB8P7MPHr77KwjkFjH6kI/v27KRg0Vd89XpvAKa//Rir5owHYFRv8zqtmjOe6W8/BsBXr/fm6zceZN+enYx+pCMAy6eP4ZsPngHgiyE92LB8Dru2b2TsE9cAZp/CeWMHAWYh7C1rFlO4cXVxMsXC8cOL58D999kuFG5czZY1i4sXzf78xTuK9zoc+8Q17Nq+kQ3L5/DFkB7mNf3gGZZPHwNQZpsKFn1VbNO8sYM8t2ne2EH72fTNB894alPifRr75HWe25R4nz4b+EdPbUq8T7H2eGlT/H16/6GLPbcp8T5ZwovvHqeTTTUWWKaqt4nIEGBDfOqyiEwBXlHVYSXVU++oo3Vi3pIDjns5BWXqqGH88sourtWfikz03PIwl0wZyfFn/s6VuqxeMJrWxujrgfU4w4yvHaeIVADexEyQvVxV94nIAKCiqt4RV24e0EdV3y2prmSh2vIaprVhWYsl97AdZ3jxLVQrIgIMwayg30lV9zmn8oBT4spVA5o6x0vk2/nzPGqpIVmnGQu5+kUqPS9W/omFjPwi1/WC0LQ2Rl/PEm588zhFZBDQCrhAVYvijh8BLAX+AIzBJAydo6q/Kq2+5i1+oR9Mnlb83A9vs3DTemocXtdVndIoTc8rL3PX9o1UqVnHk7rLo14QmtbG6OuB9TjDjC8ep4g0Am7DdJxrRaTIedygqhsw29I8ill+6ZfAtanq3LNnj2ftLSlEuzl/hWea6ep5vb5s0aY1ntVdHvWC0LQ2Rl/PEm586ThVdaWqiqpWVtXqcY83nPOfqmpzVa2iqu1VdUWqOtcX/Nx5eLXgQSJfvPWiLzol6fkxlrnI510gcl0vCE1rY/T1LOEmskvuxScH5fpi7jb5xxIG1s7ID7oJOUW91g1KPW9DteEllAvopkNs5SC/vE0w8yQvubOPb3pv9r2fUy+/1zc9MHPX/NTMdb0gNJPped3pLZs9lKat/uCpRq7p2R8i0SWyHWeVKlVTF3KZ+scdsCaDZyycU8BhDZr5phfDb81c1/NSs6Qv3gr76vr+pVz9sGOtnqXcEPlQbS6GaW1o1mK9EctXH9xsQ7UhJbL7cS6cO8fXMC1QvLSel8R3mrFluvzEb81c10vUXDsjP61HNkz96LZsmxx6zVzXs4SbyHqcJ7VspQ+P/K9r9aXjbe7ZuYNKVau5pplIoqe5b89OKlbyNyTtt2Yu6iV2fD/u28VBFat4qhmkXhCaua4H1uMMM5H1ODdv2+a75vfzp3tWd7Lw7Mblc5KU9Ba/NaOul463uG1TiZv8eILfekFo5rqeJdxEtuMs3LzJtbrSHdv85pORrmnGU9KY5ooZH3uiVxp+a0ZFL5uQ6voVE8qkWVb81gtCM9f1LOEmsqHapi1aaf933QnVBpkUZBOBwoVNyrGEBRuqDS+R9TjXrV7pu2ZsL06/iO3rl8uaQel5kZRTEotnvORJvWHRC0Iz1/Us4Say8zir1ajpSj2ZeJvHn3GOK5oxUnmb9U4odZ17T/Bb0y+9WKdY+ZATfPcqa9VrldN6QWjmup4l3ES246x+aC3fNU8+51LX6konRHvMKee5ppcufmt6pVdS53jE0b/2RK80/Na0NkZfz21+2rGPHV/bYSG3iGyo9ru87LMjMx3bfKxji6w1If1xzVG9L3RFLxP81nRLL92Q61cf3OyKXib4rWltjL6eJdyU6+SgIJKCbDKQe9hEHksu42Zy0KlH1NDPLz894+tqDZlok4OSEFmPs2jblqyuL0unmTdxTFaamXaaq+aMz0qvLPitmYmeG4k8G1Z/VabrssFvTWtj9PUs4SayHeeOwu2+ay6ZPrHM15bF01z77ddl1isrfmuWpudFxuuWtbNdqSfMmtbG6OtZwk2kOk4R6SoiM0Rkhv74I28//yQAd3f4NfnLl/Hd/Dn89SozZja8/0OMHmpSyG9r15LN69aSN3UKfTpfCZgtwr4ZZxY0eOqaNuzZuYMl0ybw9iPdATP1JOZhxsY2jz/jnOIpKW8/0p0l0yawZ+eO4jVsvxk3ko8H9gHg9Qe6sHLeNAo3reefN5wNwJIpI5k3dhAAn790B1vWLKZw42r++2wXABaOH85CZ8Pc/z7bhebn3cSWNYv5/KU7AJg3dhBLppg2j33iGnZt38iG5XP4YkgPo//BMyyfbto8+pGO7Nuzk4JFX/HV670BMxUj5uHFxhZXzRlfPEXjq9d7c3TLc9m3Z2fx+qrLp4/hmw+eAeCLIT3YsHwOu7ZvZOwT15TJpsKNq/ezqXKN2sU2fdTvd3z/RR7fjvuMz579E2C2c1q34nPArBf6475dbF77DYu+Nm1aPOOlYm8gNg61YfVXxdMHFn39DJvXfsOP+3Yx9aPbaNb6dtat+Jxls4cCkDf5H2zbuJC9u7YwY9zdAOQvHcuK+W8BMHfCgxRtXc6uorV886m596sWjWLVolHmNf/0fnYVraVo63LmTngQgBXz3yJ/6VgAZoy7m8YnX8u2jQvJm/wPT2wC9rNp3+6tntu0d9eW/Ww66OBKntqUeJ+atb7dc5vi79OWtbM9tynxPlnCS2THOKvVqKnDZiwt07VlHdt8+5HuXN37hYyuyWZM86vXe/PrGx8p8/VR0Jww8C80/5V/e1Uu+voZX/WC0LQ2Rl8P7BhnmInsdJQatQ/3XfPUDr/LqHy2iUCNW1+S1fVh1YwPu9Zt3N5zvXj81gtC09oYfT1LuIlsx1mlWvUyXZdNJm3DFmekXdaN7Nk6x56SdR1h0kw2Tnno4c0900uG33pBaFobo69nCTeRGuOMZ+W3C3zXfP7356ZVzq0pJ+OeuNaVeoLUTJXcM+OTe1zVS4XfekFoWhujr2cJN5Ed4yzLPE4/5m3aeZoGO8fSYskOO8YZXiLrcW7f4t62YukSy8ItCbc7zViGrJ9ko1mWqSOxrEW/8FsvCE1rY/T1LOEmsh3nnl07MyrvhrdZsDSvxHNeeJpb8xe7XqcXmtnMsyzaurxM15UVv/WC0LQ2Rl/PEm7KTajWyzBteQzP2lCsxeItNlQbXiLrceYvL9sczmx4/YEuvurFFjYIk6bb+1bGJpv7hd96QWhaG6OvZwk3kZ2OUqtuvbTLuuVttrvujgOOeeltNj/vJs/qzkTTS+/y6OZXeFZ3GPSC0LQ2Rl/PEm4i23FWrFTJd83aDRrv99zrEG31w4/ytP5Umn6EY6tUS/8HUBT1gtC0NkZfzxJuIhuqXbMsvSQWN8c2/9XjmuL//RjXnDCou+caiXz23O2uh2NLY+7EPr7oBKUXhKa1Mfp6lnCT88lBXiQF5WIykE32sVjChU0OCi+R9Ti3blyfsozbnebUUcN87TRju4Z4TXynGdstwi9yXS8ITWtj9PUs4SayY5w//vCD75orv13OL5r4p7d7u7eLPCTzMvfu3uqpZnnTC0LT2hh9PUu4yelQrZseZ66FZ21o1mIJNzZUG14iG6pdnSI5yIuxzdjmy37hhV6qxJ/YJr9+ket6QWhaG6OvZwk3kQ3VHtHgaN+0Yt5mq473+Kbptl66HmaTVr93TdPqBaNpbYy+niXc+OZxisidIjJDRPaIyLCEc+eLyCIR2Skin4tIo1T1VTjooBLPebW83sGVqnpSr9d6mYRlDzq4iiuaVi84TWtj9PWihIjUFpFRIrJDRFaKyPUllBsrIkVxj70iMi/u/AoR2RV3PrPtr3zEz1BtPtAPGBp/UETqAO8BvYHawAxgRKrK1q78zoMmHkj82OZXr/fyRdMtvbLMx1z09T+z0syUXNcLQtPaGH29iPECsBc4ErgBeElETk4spKoXq2r12AP4EkicOvCbuDIXed7yMuJ7cpCI9AOOVtUuzvOuQBdVbes8rwZsBE5V1UUl1VNScpDb3mZUk4Js8o/FEm2ikBzkfF9vAVqo6mLn2L+BNaras5TrGgPLgKaqusI5tgK4VVU/zbihPhOG5KCTgTmxJ6q6A/OCHvCLJZ4t69d63KwDWTh+eCT0suk0Vy0aVeZrrV44NK2N0deLEM2AH2KdpsMcUnx/AzcBX8Q6zTjeEJENIvJfETnFxXa6Shg8ziHAhvhfJyIyBXhFVYclXNsV6Oo8bQHM96PNcdTBeMO5qheEZq7rBaFpbYy+HsAJqlrDjYpEZBzGhkypDOyOez5YVQfH1dsOGKmq9eKO/RG4QVXbl9KepUC/+O94ETkTmAUIcLfzaK6qW8vQbk8JQ1ZtEVAz4VhNoDCxoHPDBgOIyAy/5xf5rWltjL5eEJrWxujrxTTdqktV/8+tuhJI+/s7hoicBdQD3ok/rqpT4p7+Q0RuBtoBo91pqnuEIVSbBxS75E7MvKlz3GKxWCzhZTFwsIgcH3fsFEr//r4ZeE9Vi1LUrRjvM3T4OR3lYBGpDBwEHCQilUXkYGAU0EJEOjnnHwTmlpYYZLFYLJbgcXJS3gP6ikg1J9x6OfDvZOVFpApwNTAs4XhDETlTRA5x+oa/YELLU5JUEzh+epy9gF1AT+BG5/9eqroB6AQ8isnO+iVwbRr1DU5dxHX81rQ2Rl8vCE1rY/T1gtIsC3cAVYD1wFvA7aqaJyLtRCTRq7wC2Ap8nnC8BvASpg9YA/wfcLGqertgdxmJ7Fq1FovFYrEEQRjGOC0Wi8ViiQy247RYLBaLJQNsx2mxWCwWSwaEYR6npZwgIicCnTGritTAzPXKA/6tqguDbJvFYrGki+04Q4KIXAR04cBO5V+q+r8Am+YKInIdJmvuQ2ASsA0zUfoU4EsR6aaqKRf3Dzu5fh+h3Nh4MHAeB9o4XlV/CLJtluCJTFZtLn9YReRe4H7gFcw6j/Gdyh+Bx1V1QHAtzB4RWQ7cmLA6SOzcmcAbqtrY94a5SDm5j+XBxlOADzCT7+fys40tMZPyL1fVucG10BI0keg4c/3DKiL5wHnJFn0QkebA56pa3/+WuYczn+sIVd2V5FxVYL2z1VBkKSf3sTzYOB0YrqrPJzl3J2Y3J1+X37OEi6h0nDn9YRWRbcAxqro9ybnDgJWqeqjvDXMRERmFWSy6l6ouizveFOgLVFXVK4NqnxuUk/tYHmzcARymqvuSnDsE2KKq1fxvmSUsRCWrthpmI+xkrAWq+tgWL3gXGC0i54vIEc6yU3VE5HzMkoTvpLg+CvzB+bvA2d093/FC8zAhsT+UfGlkKA/3sTzYuBC4vYRztznnLeWYqHicQzELv/dl/zGHUzBL+X2nqrcE18LscH7FPozZo64+Py9uXAAMBx5S1b3BtdA9nLBsM6A6ZmeFxaq6M9hWuUN5uI/lxMZWwPuYdbUTxzh/BK5Q1TklXW/JfaLSceb8hzWGE+6qDhSFcR+6bHF2v2nGzwlei52FonOKXL+PkNs2ikhF4FzgJH7+kZcHTEgWwrWULyLRccaT4x/WZuyfNTxfVZcE2yp3cO7bS8BVwF5+/hV/CCb81z1X7mcu38cY5cFGi6UkItVx5uqHVUQaAiMwoedl/NypNMVkEV+rqt8H18LsEZH3MDvi9FbV7+KON8FEE6qp6lVBtc8Nysl9zHkbAUSkKz9Pf4v3OP+lqq8E2DRLCIhEx5nrH1YR+QyYCfSJH+9zwpoPAmeo6nlBtc8NRKQQODLZeKZj51pVreF/y9yjnNzH8mBjf+A3wNPsP/2tFfBnYLSq/i2wBloCJyodZ05/WJ3s0trJxmlFpBKwOerp7yKyArihlAUQ3lTVRr43zEXKyX0sDzZuAFqqakGScw2Auapax/+WWcJCVJbc+yVmU9P9PqyqukNEHgQ2B9Ms11gFXIbZST2RS4DIetNxPACMFZEPOfBX/GVAt+Ca5hrl4T6WBxsly/OWHCcqHWeuf1jvBN4VkT9zYKdyMtApuKa5g6q+KSJzgOuBM/l53Gg+0FZVFwTZPpfI+ftI+bBxCDBeRBJDtadgQrV2jLOcE5VQ7fmYzMv5lPBhVdXxgTXQBUTkcEzGaXwywnzgfVXdGGTbvMBJCrrEeTpOVZcG2R63KOE+5gGjcuU+JrGxEFhAbtl4G2b6W+J9HK6qLwfZNkvwRKLjhNz/QhKRUzHJTh9jpmvcDjQBPlXVMUG2zQ1EZKGqnuj8fw5ml5TYeGc7zMLZkf7xU14QkeMw28O1wKzatRqYBgyzcxwt5YHIdJwlISIHAX9X1b5Bt6WsiMgtQD/Mwg75mJD0MZhQ+rXA3ao6NLgWZo+IFMayZkXkC+AVVR3uPL8BM4+zbZBtzBYRGQC8nSwBKlcQkSuA1zE/egQ4B5Px3hSoB1wYP90oFxGRhlHO4rdkTy50nJWAnap6UNBtKSsisgjoiPkiWgicpapfOuc6AE+o6ikBNjFrRGS7qtZ0/l8PHBXzTpwfPxtUtXaQbcwWEfkB2Amsx6xo9Zqqrgy2Ve4iIouB21T1c+f5RcC9qnqxiNwHnKuqlwbaSA/Jhe8bS/ZEouN01qotiYMx0xwi+0YWkW2xHSWcnRmqq3NjRKQCJsX/sACbmDUisgu4A/Pj4B9Ak9hSe86X0foc2FWjEON1/RYzPnY2MBkYBryTC0sLishWoFbc+/NgoEBVj3DWIV4b+4EUVUTk7FJOV8KMyUf2+8aSPVHJqr0ek+mWbNpJLryBd4hIRccDG6b7/5qpAvwUULvcZCqmMwGTSHISMN15fg7wbRCNchl1OsfXgNdEpBFmLPABYKCIvKuqXYJsoAvMBO4CYvvf3oPJNQCzAPoPAbTJbSZg1sHOhc+dxQOi4nFOBx5R1Q+TnKuMCZ1EZYu0AxCRfwOPqeoB2xWJyDXA7ara3veG+YSIHApUjHqSV3w4Osm5tsBNqhrp+api9r/9ALPZApiw9BWqOl9EfgF0VtX7A2ugC4jIckwU68sk5yoDO6zHWb6JSsfZHVijqu8nOXcQZnPkh31vmA+IyBEYTybSnUp5ID4BKpdxPnPNnaffqmoueJnFiMhI4AtVfS7JuUMwNh/rf8ssYSESHafFYrH4hbOlGHZqjaUkIhvetFgsFi9Q1X0ldZoicpCzzKelHGM9TovFYkkTOx3FAtHJqrVYLBZfSGP6m6WcY98EFovFsj+5Pv3NkiU2VGuxWCxx5Pr0N0v22JtvsVgs+zOMkr8b9wE5OfXNkj7W47RYLBaLJQOsx2mxWCwWSwbYjtNisVgslgywHafFUgZEZIWIXJBGufYisrqMGo1FRJ0dSCwWS0iwHafFYrFYLBlgO06LxWKxWDLAdpyWyOCER/8mIgtEZIuI/MuZV4eI/FFElorIZhH5UEQaxF03QERWich2EZkpIu3S0KoiIq85OgtF5P6SQq4iUklEnhWRfOfxrLM0W3yZB0Rko2PDDXHHLxWRb5y2rRKRPmV+gSwWiy/YjtMSNW4AOgBNgWZALxE5D/gHcDVmn8iVwH/irpkOtAJqA28CI2Mdbik8BDQGmgAXAjeWUvbvwK8cjVOANkCvuPP1gDrAUcDNwGAROcE5twOzwfdhwKXA7SJyRYq2WSyWALEdpyVqDFTVVaq6GXgUuA7TmQ5V1Vmqugf4G/BrEWkMoKqvq+omVf1BVZ8GKgEnlFB/jKsxm4tvUdXVwAF7M8ZxA9BXVder6gbMBPnOCWV6q+oeVZ0IjHHqR1UnqOo8Vf1JVecCbwHnpP1qWCwW37EdpyVqrIr7fyXQwHmsjB1U1SJgE8bDQ0Tuc8Kt20RkK3AoxgMsjQYJWqtKKpioH9euGFtUdUey8yLySxH5XEQ2iMg2oFsabbNYLAFiO05L1Dgm7v+GQL7zaBQ7KCLVgMOBNc545v0YD6+Wqh4GbAMkhU4BcHQJuonspx/Xrhi1nDYlO/8m8CFwjKoeCgxKo20WiyVAbMdpiRrdReRoEamNGVscgQlv/l5EWjlJOY8BU1V1BVAD+AHYABzsbEJcMw2dt4G/iUgtETkKuLOUsm9hxlqPEJE6wIPA6wllHhaRQ5yO/DJgpHO8BrBZVXeLSBvMzhwWiyXE2I7TEjXeBP4LfAcsA/qp6qdAb+BdjKfYFLjWKf8JMA5YjAmR7qb0sGuMvsBqYDnwKfAOsKeEsv2AGcBcYB4wyzkWYy2wBeNlvgF0U9VFzrk7gL4iUojpcN9Oo20WiyVA7CLvlsggIiuAW52O0m/t24FrVdUm7lgs5RzrcVosSRCR+iJypohUcKaO9ABGBd0ui8USPLbjtJRbRGSsiBQleTwAHAK8DBQC44EPgBeDbK/FYgkHNlRrsVgsFksGWI/TYrFYLJYMsB2nxWKxWCwZYDtOi8VisVgywHacFovFYrFkgO04LRaLxWLJANtxWiwWi8WSAf8PhZAWqTHIQMMAAAAASUVORK5CYII=\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2693,17 +2577,19 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 100, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2767,177 +2653,12 @@ "\n", "Many more papers are available at www.steveransome.com \n" ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'stop' is not defined", - "output_type": "error", - "traceback": [ - "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", - "\u001b[1;36m Input \u001b[1;32mIn [35]\u001b[1;36m in \u001b[1;35m\u001b[1;36m\u001b[0m\n\u001b[1;33m stop\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m\u001b[1;31m:\u001b[0m name 'stop' is not defined\n" - ] - } - ], - "source": [ - "stop\n", - "\n", - "# delete below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr2.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#norm.describe()\n", - "meas.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "whos" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm.to_csv('\\\\mlfm_data\\\\export\\\\'+'norm.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "meas.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'meas.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stack.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'stack.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ref_data.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\' + 'ref_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stack.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ref" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "root_dir" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -2950,7 +2671,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.4" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 74eb87ce74..eff8ae0384 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -363,8 +363,8 @@ def mlfm_fit(data, var_to_fit): data : DataFrame Must include columns: - * 'poa_global_kwm2' global plane of array irradiance [kW/m^2] - * 'temp_module' module temperature [C] + * 'poa_global_kwm2' global plane of array irradiance. [kW/m^2] + * 'temp_module' module temperature. [C] Must include column named ``var_to_fit``. @@ -386,6 +386,9 @@ def mlfm_fit(data, var_to_fit): resid : Series Residuals of the fitted model. + coeff_err : list + Standard deviation of error in each model coefficient. + See also -------- mlfm_6 @@ -410,7 +413,7 @@ def mlfm_fit(data, var_to_fit): bounds = ([ -2, -2, -2, -2, -2, -2], [ 2, 2, 2, 2, 2, 0]) - popt, pcov = optimize.curve_fit( + coeff, pcov = optimize.curve_fit( f=func, # fit function xdata=data, # input data ydata=data[var_to_fit], # fit parameter @@ -421,16 +424,18 @@ def mlfm_fit(data, var_to_fit): # if data has no wind_speed measurements then c_5 coefficient is # meaningless but a non-zero value may have been returned. if c5_zero: - popt[4] = 0. + coeff[4] = 0. # get error of mlfm coefficients as sqrt of covariance perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) # save fit and error to dataframe - pred = mlfm_6(data, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5]) + pred = mlfm_6(data, coeff[0], coeff[1], coeff[2], coeff[3], coeff[4], + coeff[5]) resid = pred - data[var_to_fit] - return pred, popt, resid, perr + return pred, coeff, resid, coeff_err def plot_mlfm_scatter(dmeas, dnorm, title): @@ -488,7 +493,7 @@ def plot_mlfm_scatter(dmeas, dnorm, title): bbox = 1.2 # set x_axis as irradiance - xdata = dmeas['poa_global_kwm2'] / 1000. + xdata = dmeas['poa_global_kwm2'] fig, ax1 = plt.subplots() From 2bb9fe23541eac9d8755245b41b8f9ac2c900123 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 30 Jun 2022 11:08:11 -0600 Subject: [PATCH 59/81] edits to notebooks, fix stack plot function --- docs/tutorials/mlfm_220627_0.ipynb | 1652 +++++++++++++++++++++------- docs/tutorials/mlfm_220627_2.ipynb | 294 ++--- pvlib/mlfm.py | 112 +- 3 files changed, 1405 insertions(+), 653 deletions(-) diff --git a/docs/tutorials/mlfm_220627_0.ipynb b/docs/tutorials/mlfm_220627_0.ipynb index 917a56910a..528109aeae 100644 --- a/docs/tutorials/mlfm_220627_0.ipynb +++ b/docs/tutorials/mlfm_220627_0.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -74,15 +74,6 @@ "plt.bbox = 1.4 # offset right to not overwrite" ] }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "##root_dir ##SR##" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -119,74 +110,47 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"\\nNotes for Cliff Hansen 220624t17\\n\\nMy comments are marked ##SR##\\n\\nI can't get the stacked plot chart to work option 0. section\\nKeyError: 'v_mp'\\n\\n\"" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"\n", - "Notes for Cliff Hansen 220624t17\n", - "\n", - "My comments are marked ##SR##\n", - "\n", - "I can't get the stacked plot chart to work option 0. section\n", - "KeyError: 'v_mp'\n", - "\n", - "\"\"\"\n", - "\n", - "##meas.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 41, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# select one of the following meas files\n", - "################\n", - "meas_file = 0 #\n", - "################\n", - "\n", - "if meas_file == 0:\n", - " mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", - " # 6 measured LFM variables\n", - " # date_time, module_id, \n", - " # poa_global, wind_speed, temp_air, blue_frac, beam_frac, temp_module, \n", - " # v_oc, i_sc, i_mp, v_mp, \n", - " # r_sc,\tr_oc\n", - "\n", - "elif meas_file == 1:\n", - " mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", - " # 4 measured LFM variables \n", - " # date_time, module_id,\tpoa_global,\ttemp_module,\n", - " # i_sc,\tp_mp, i_mp,\tv_mp, v_oc,\tff,\t\n", - " # temp_air,\trelative_humidity, pressure, precipitation, dni, ghi, dhi, soil, wind_speed\n", - " \n", - "elif meas_file == 2:\n", - " mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", - " # 4 measured LFM variables\n", - " # date_time, module_id,\ttemp_module, poa_global,\n", - " # i_sc, v_oc, i_mp,\tv_mp, p_mp, \n", - " # wind_speed\n", - " \n", - "##SR##\n", - "#elif meas_file == 3:\n", - "# mlfm_meas_file = 'x19074005_iec61853.csv' \n", "\n", - "# optional\n", - "# elif meas_file == -1:\n", - "# mlfm_meas_file = 't1_041.csv'\n", + "mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", + "# File contains: \n", + "# - date_time, module_id, \n", + "# - poa_global (W/m2)\n", + "# - wind_speed (m/s)\n", + "# - temp_air (C)\n", + "# - blue_frac\n", + "# - beam_frac\n", + "# - temp_module (C) \n", + "# 6 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - r_sc, r_oc\n", + "\n", + "# mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", + "# File contains: \n", + "# - date_time, module_id, \n", + "# - poa_global (W/m2)\n", + "# - wind_speed - all zeros (m/s)\n", + "# - temp_air (C)\n", + "# - temp_module (C) \n", + "# - unused environmental quantities: relative_humidity, precipitation, dni, ghi, dhi, soil\n", + "# 4 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - unused IV curve quantities: p_mp, ff\n", + " \n", + "# mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", + "# File contains: \n", + "# - date_time : integers in this file\n", + "# - module_id, \n", + "# - temp_module (C) \n", + "# - poa_global (W/m2)\n", + "# - wind_speed - all zeros (m/s)\n", + "# - 4 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - unused IV curve quantities: p_mp\n", " \n", "# extract module id from filename e.g. 'g78'\n", "mlfm_mod = mlfm_meas_file.split('_')\n", @@ -229,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +247,7 @@ " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", - " gamma_p_mp = ref_data['gamma_pdc'].values[0],\n", + " gamma_pdc = ref_data['gamma_pdc'].values[0],\n", ")\n", "\n", "# create p_mp and ff in case they don't exist\n", @@ -310,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -329,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -473,7 +437,7 @@ "2016-01-26 07:40:00-07:00 63.660028 0.052928 1.985488 " ] }, - "execution_count": 34, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -500,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -537,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -546,7 +510,7 @@ "6" ] }, - "execution_count": 36, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -601,20 +565,127 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 10, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'gamma_pdc'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[1;32mIn [37]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m norm \u001b[38;5;241m=\u001b[39m \u001b[43mmlfm_meas_to_norm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmeas\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m##SR##, qty_mlfm_vars)\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;66;03m# show some normalised data\u001b[39;00m\n\u001b[0;32m 4\u001b[0m norm\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m3\u001b[39m)\n", - "File \u001b[1;32mc:\\python\\ransome\\pvlib-python\\pvlib\\mlfm.py:125\u001b[0m, in \u001b[0;36mmlfm_meas_to_norm\u001b[1;34m(dmeas, ref)\u001b[0m\n\u001b[0;32m 118\u001b[0m dnorm[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpr_dc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 119\u001b[0m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp_mp\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m\n\u001b[0;32m 120\u001b[0m (ref[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp_mp\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[0;32m 122\u001b[0m \u001b[38;5;66;03m# temperature corrected\u001b[39;00m\n\u001b[0;32m 123\u001b[0m dnorm[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpr_dc_temp_corr\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 124\u001b[0m dnorm[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpr_dc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m--> 125\u001b[0m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m \u001b[43mref\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgamma_pdc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m*\u001b[39m(dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtemp_module\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m-\u001b[39m T_STC)))\n\u001b[0;32m 127\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_sc\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m dmeas\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[0;32m 128\u001b[0m dnorm[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_sc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_sc\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m dmeas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoa_global_kwm2\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m ref[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi_sc\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - 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pr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:000.4964970.4452930.9263800.7422410.7475260.7365870.6875640.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:000.6204710.5574730.8814090.8008960.8516750.7869770.7844180.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:000.2080370.1870590.2572270.8401720.8970410.8182980.8266880.8977000.8950180.9359160.914282
\n", + "
" + ], + "text/plain": [ + " pr_dc pr_dc_temp_corr i_sc i_mp \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.496497 0.445293 0.926380 0.742241 \n", + "2016-01-26 07:30:00-07:00 0.620471 0.557473 0.881409 0.800896 \n", + "2016-01-26 07:40:00-07:00 0.208037 0.187059 0.257227 0.840172 \n", + "\n", + " v_oc v_mp v_oc_temp_corr r_sc \\\n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.747526 0.736587 0.687564 0.983502 \n", + "2016-01-26 07:30:00-07:00 0.851675 0.786977 0.784418 0.893852 \n", + "2016-01-26 07:40:00-07:00 0.897041 0.818298 0.826688 0.897700 \n", + "\n", + " r_oc i_ff v_ff \n", + "date_time \n", + "2016-01-26 07:20:00-07:00 0.760559 0.754692 0.968481 \n", + "2016-01-26 07:30:00-07:00 0.866922 0.896006 0.907783 \n", + "2016-01-26 07:40:00-07:00 0.895018 0.935916 0.914282 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -634,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -660,28 +731,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "# select by irradiance poa_global range e.g. 100-1100W/m2\n", + "# select by irradiance poa_global range e.g. 100-1100 W/m2\n", "meas = meas[(meas['poa_global'] >= 100) &\n", - " (meas['poa_global'] <= 1100)]\n", - "\n", - "# if there's date_time can select by it, i.e. not matrix data\n", - "### better if index is formatted as a date\n", - "\n", - "# if qty_mlfm_vars == 6:\n", - "\n", - " # not for matrix as they don't contain dates\n", - " # example\n", - " # meas = meas[(meas.index > '2016-01-01') &\n", - " # (meas.index < '2017-01-01')]\n" + " (meas['poa_global'] <= 1100)]" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -712,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -759,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -914,39 +975,63 @@ "" ], "text/plain": [ - " module_id poa_global ... poa_global_kwm2 p_mp\n", - "date_time ... \n", - "2016-01-26 08:10:00-07:00 78 207.780344 ... 0.207780 36.996924\n", - "2016-01-26 08:30:00-07:00 78 314.432034 ... 0.314432 47.456451\n", - "2016-01-26 08:40:00-07:00 78 364.161611 ... 0.364162 64.242948\n", - "2016-01-26 08:50:00-07:00 78 414.444854 ... 0.414445 73.428792\n", - "2016-01-26 09:00:00-07:00 78 462.114270 ... 0.462114 81.960880\n", + " module_id poa_global wind_speed temp_air \\\n", + "date_time \n", + "2016-01-26 08:10:00-07:00 78 207.780344 0.676059 8.259369 \n", + "2016-01-26 08:30:00-07:00 78 314.432034 0.856547 8.972031 \n", + "2016-01-26 08:40:00-07:00 78 364.161611 0.589140 9.572525 \n", + "2016-01-26 08:50:00-07:00 78 414.444854 0.526614 10.140991 \n", + "2016-01-26 09:00:00-07:00 78 462.114270 1.284213 10.187134 \n", + "\n", + " blue_frac beam_frac temp_module v_oc \\\n", + "date_time \n", + "2016-01-26 08:10:00-07:00 0.388758 0.654555 9.171982 44.292019 \n", + "2016-01-26 08:30:00-07:00 0.430550 0.747184 14.170197 44.031935 \n", + "2016-01-26 08:40:00-07:00 0.445193 0.769498 16.853550 44.172695 \n", + "2016-01-26 08:50:00-07:00 0.457280 0.816817 18.930649 44.100850 \n", + "2016-01-26 09:00:00-07:00 0.466810 0.819645 19.763138 44.172574 \n", + "\n", + " i_sc i_mp v_mp r_sc \\\n", + "date_time \n", + "2016-01-26 08:10:00-07:00 1.051441 0.976607 37.883130 1022.179281 \n", + "2016-01-26 08:30:00-07:00 1.331172 1.252936 37.876208 1069.804838 \n", + "2016-01-26 08:40:00-07:00 1.847061 1.717155 37.412432 395.607366 \n", + "2016-01-26 08:50:00-07:00 2.121667 1.980343 37.078834 507.595573 \n", + "2016-01-26 09:00:00-07:00 2.372033 2.219749 36.923490 451.142610 \n", "\n", - "[5 rows x 15 columns]" + " r_oc poa_global_kwm2 p_mp \n", + "date_time \n", + "2016-01-26 08:10:00-07:00 3.648163 0.207780 36.996924 \n", + "2016-01-26 08:30:00-07:00 2.483550 0.314432 47.456451 \n", + "2016-01-26 08:40:00-07:00 1.962480 0.364162 64.242948 \n", + "2016-01-26 08:50:00-07:00 1.660780 0.414445 73.428792 \n", + "2016-01-26 09:00:00-07:00 1.618455 0.462114 81.960880 " ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "meas.head() ##SR## check what's there" + "meas.head()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -984,7 +1069,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1073,23 +1158,27 @@ "" ], "text/plain": [ - " pr_dc i_sc ... v_oc temp_module_corr\n", - "date_time ... \n", - "2016-01-26 08:10:00-07:00 0.987455 0.063817 ... -0.002454 -0.065435\n", - "2016-01-26 08:30:00-07:00 0.836998 0.237283 ... 0.004324 -0.042936\n", - "2016-01-26 08:40:00-07:00 0.978335 0.061103 ... 0.000727 -0.033518\n", + " pr_dc i_sc r_sc i_ff i_v \\\n", + "date_time \n", + "2016-01-26 08:10:00-07:00 0.987455 0.063817 0.044529 0.035911 0.01 \n", + "2016-01-26 08:30:00-07:00 0.836998 0.237283 0.032594 0.030246 0.01 \n", + "2016-01-26 08:40:00-07:00 0.978335 0.061103 0.065599 0.013143 0.01 \n", "\n", - "[3 rows x 9 columns]" + " v_ff r_oc v_oc temp_module_corr \n", + "date_time \n", + "2016-01-26 08:10:00-07:00 0.073908 0.098226 -0.002454 -0.065435 \n", + "2016-01-26 08:30:00-07:00 0.077019 0.082928 0.004324 -0.042936 \n", + "2016-01-26 08:40:00-07:00 0.091342 0.091143 0.000727 -0.033518 " ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# translate multiplicative to stack losses and add to dataframe df\n", - "stack = mlfm_norm_to_stack(norm, fill_factor = ref['ff']) ##SR## ref) ###, qty_mlfm_vars)\n", + "stack = mlfm_norm_to_stack(norm, fill_factor = ref['ff']) \n", "\n", "# show some stack losses\n", "stack.head(3)" @@ -1162,38 +1251,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ - { - "ename": "KeyError", - "evalue": "'v_mp'", - "output_type": "error", - "traceback": [ - "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", - " File \u001b[0;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001b[0m in \u001b[0;35mget_loc\u001b[0m\n return self._engine.get_loc(casted_key)\n", - " File \u001b[0;32mpandas\\_libs\\index.pyx:136\u001b[0m in \u001b[0;35mpandas._libs.index.IndexEngine.get_loc\u001b[0m\n", - " File \u001b[0;32mpandas\\_libs\\index.pyx:163\u001b[0m in \u001b[0;35mpandas._libs.index.IndexEngine.get_loc\u001b[0m\n", - " File \u001b[0;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001b[0m in \u001b[0;35mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0m\n", - "\u001b[1;36m File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001b[1;36m in \u001b[1;35mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;36m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m\u001b[1;31m:\u001b[0m 'v_mp'\n", - "\nThe above exception was the direct cause of the following exception:\n", - "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", - " Input \u001b[0;32mIn [20]\u001b[0m in \u001b[0;35m\u001b[0m\n fig_stack = plot_mlfm_stack(\n", - " File \u001b[0;32m~\\OneDrive\\Documents\\_CONS\\__Reference\\PVPMC\\__repository\\pvlib-python\\docs\\tutorials\\mlfm_220627.py:653\u001b[0m in \u001b[0;35mplot_mlfm_stack\u001b[0m\n dstack['v_mp'],\n", - " File \u001b[0;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001b[0m in \u001b[0;35m__getitem__\u001b[0m\n indexer = self.columns.get_loc(key)\n", - "\u001b[1;36m File \u001b[1;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001b[1;36m in \u001b[1;35mget_loc\u001b[1;36m\u001b[0m\n\u001b[1;33m raise KeyError(key) from err\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m\u001b[1;31m:\u001b[0m 'v_mp'\n" - ] - }, { "data": { - "image/png": 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BQYEWLlyo2tpabd68ud/a0tLSvu8nT56s5cuX64MPPhj6qQEAiCHmFVxLS4tSU1OVl5fXd8zr9Q54BXenQ4cOyePxDPhv1dXV8vl88vl8am9vT2BkAABiixm4cDgsl8vV75jL5VJnZ+c9f+6tt96S3+/XmjVrBvz3kpIS+f1++f1+ZWZmJjAyAACxxXyK0uFwKBQK9TsWCoXkdDrv+jN79+7V2rVrdeDAAWVkZNz/lAAAJCjmFVxeXp4ikYhaW1v7jgUCgbs+9bh//369+OKLevfddzV9+vShmxQAgATEDJzdbldRUZHWr1+vrq4uNTU1qaGhQStXroxa+/7772v58uWqr6/X008/nZSBAQCIR1wfE6isrFR3d7eysrK0bNkyVVVVyePxKBgMyuFwKBgMSpI2btyozz//XD/4wQ/kcDjkcDg0b968pJ4AAAADifkanCRNmDBBe/fujTo+ZcoUhcPhvsd8JAAAMFpwqy4AgJEIHADASAQOAGAkAgcAMBKBAwAYicABAIxE4AAARiJwAAAjETgAgJEIHADASAQOAGAkAgcAMBKBAwAYicABAIxE4AAARiJwAAAjETgAgJEIHADASAQOAGAkAgcAMBKBAwAYicABAIxE4AAARiJwAAAjETgAgJEIHADASAQOAGAkAgcAMBKBAwAYicABAIxE4AAARiJwAAAjETgAgJEIHADASAQOAGAkAgcAMBKBAwAYicABAIxE4AAARiJwAAAjETgAgJEIHADASAQOAGAkAgcAMFJcgevo6NDixYtlt9uVm5ur3bt333Xt1q1b5Xa75XK5tGrVKt24cWPIhgUAIF5xBa6srEw2m01tbW2qq6tTaWmpmpubo9a999572rx5sw4ePKizZ8/qzJkzqqioGPKhAQCIJWbgurq6VF9fr40bN8rhcKigoEALFy5UbW1t1Nqamhq98MIL8ng8Gj9+vN544w3t3LkzGXMDAHBPMQPX0tKi1NRU5eXl9R3zer0DXsE1NzfL6/X2W9fW1qYrV64M0bgAAMQnLdaCcDgsl8vV75jL5VJnZ2fMtbe/7+zs1MSJE/utra6uVnV1tSTpxIkT8vl8iU//kGtvb1dmZuZIj/HAYd8Gj70bHPZtcE6dOnVfPx8zcA6HQ6FQqN+xUCgkp9MZc+3t7wdaW1JSopKSEkmSz+eT3+9PbHKwb4PEvg0eezc47Nvg3O+FT8ynKPPy8hSJRNTa2tp3LBAIyOPxRK31eDwKBAL91mVnZ0ddvQEAkGwxA2e321VUVKT169erq6tLTU1Namho0MqVK6PWPv/883rzzTd18uRJXb16VZs2bVJxcXEy5gYA4J7i+phAZWWluru7lZWVpWXLlqmqqkoej0fBYFAOh0PBYFCSNHfuXJWXl2v27NnKzc1Vbm6uNmzYEPP3336qEolh3waHfRs89m5w2LfBud99S7EsyxqiWQAAGDW4VRcAwEgEDgBgpGEJHPeyHLx4966mpkZPPfWUxo4dq5ycHJWXlysSiQzztKNHIn9zt82ZM0cpKSnsW5z7dubMGS1YsEBOp1MZGRkqLy8fxklHl3j3zbIsrVu3TpMnT5bL5VJhYeGAN814WOzYsUM+n09jxoyJ+YbEQbXBGgZLly61nnvuOauzs9NqbGy0xo4da504cSJq3f79+62srCzrxIkTVkdHh/XMM89Yr7766nCMOGrFu3eVlZXWoUOHrBs3blgXLlywvvGNb1i//vWvR2Di0SHefbtt165d1qxZsyxJVk9PzzBOOrrEu283btywpk2bZm3ZssUKh8NWd3e3FQgERmDi0SHefXvnnXesSZMmWZ9++qkViUSstWvXWk8++eQITDw61NfXW3/5y1+sl156yfrJT35y13WDbUPSAxcOh60vfOEL1r///e++YytWrBhwuGXLllm//OUv+x4fOHDAys7OTvaIo1Yie3enLVu2WAsWLEjmeKNWovv2v//9z/ryl79sffzxxw914BLZtz/84Q9WQUHBcI43aiWyb5s3b7Z+/OMf9z0+ceKENWbMmGGZczR7/fXX7xm4wbYh6U9Rci/LwUtk7+506NChAT+M/zBIdN9ee+01lZaWyu12D9eIo1Ii+3b48GFNnTpV8+bNU0ZGhgoLC3X8+PHhHHfUSGTfli5dqtOnT6ulpUU9PT2qqanR3Llzh3PcB9Jg25D0wA3VvSwfRons3f/vrbfekt/v15o1a5I53qiVyL75/X41NTXp5ZdfHq7xRq1E9u3ChQt6++239corr+jixYuaP3++Fi1apJs3bw7XuKNGIvs2adIkzZo1S/n5+UpPT9eePXu0devW4Rr1gTXYNiQ9cMm6l+XDIJG9u23v3r1au3at9u3bp4yMjGSPOCrFu2+3bt3S6tWrtX37dqWlxbwtq/ES+XtLT09XQUGB5s2bJ5vNpjVr1ujKlSv65JNPhmvcUSORfduwYYOOHDmi8+fP6/r166qoqNCcOXN07dq14Rr3gTTYNiQ9cNzLcvAS2TtJ2r9/v1588UW9++67mj59+nCNOerEu2+hUEh+v19LliyR2+3WzJkzJUk5OTlqbGwc1plHg0T+3mbMmKGUlJThHG/USmTfAoGAlixZopycHKWlpam4uFhXr17VyZMnh3PkB86g2zAULxDGsmTJEmvp0qVWOBy2Pvzww7u+w2jfvn1Wdna21dzcbHV0dFizZ89+6N9FGe/eHTx40JowYYL1j3/8YwSmHH3i2bdbt25Zly5d6vv617/+ZUmyLly4YN24cWOEJh9Z8f69nTp1ykpPT7f+9re/WZFIxPrd735nTZs2jX2LsW+/+tWvrG9/+9vWf/7zH6u3t9f685//bD3yyCPW1atXh3/oUaCnp8fq7u621q5da61YscLq7u4e8E1eg23DsATuypUr1qJFi6xHHnnEeuyxx6y6ujrLsizr3Llzlt1ut86dO9e3dsuWLVZWVpbldDqt4uJi6/r168Mx4qgV794VFhZaqamplt1u7/uaO3fuSI4+ohL5m7vts88+e6jfRWlZie1bfX299aUvfclyOp3WM888c8+PYZgu3n3r7u62Vq9ebbndbsvpdFpPPvmktW/fvpEcfURVVFRYkvp9VVRUDFkbuBclAMBI3KoLAGAkAgcAMBKBAwAYicABAIxE4AAARiJwAAAjETgAgJEIHADASAQOAGCk/wvuieb8BunrZwAAAABJRU5ErkJggg==\n", + "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1211,8 +1281,7 @@ " is_i_sc_self_ref=False, # is isc self referenced?\n", " is_v_oc_temp_module_corr=True, # is voc temperature corrected?\n", ")\n", - "\n", - "##SR## added stack = \n" + "\n" ] }, { @@ -1233,96 +1302,458 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrr_scr_oci_ffv_ffpoa_global_bintemp_module_bin
date_time
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2016-01-26 08:40:00-07:000.9783350.9424700.9480540.9296690.9993820.8469580.9708870.9442320.9225160.9845760.918096400.015.0
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pr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrr_scr_oci_ffv_ffpoa_global_bintemp_module_bincalc_pr_dcdiff_pr_dc
count698.000000698.000000698.000000698.000000698.000000698.000000698.000000698.000000698.000000698.000000698.000000698.000000698.000000650.0000006.500000e+02
mean0.8968350.9740870.9871560.9253660.9332930.8012960.9972470.9791810.8836290.9451370.906705725.50143344.9785100.8916292.169205e-17
std0.0613830.0252510.0188450.0063640.0332810.0228480.0228770.0099920.0195610.0113930.006917277.63624812.5839890.0567871.701377e-02
min0.7768280.7962080.7913230.8885100.8594630.7627950.9067770.8917500.8469370.9235480.895678100.00000010.0000000.781006-2.062191e-01
25%0.8505190.9600870.9785000.9210640.9010590.7831190.9869600.9754510.8675260.9367920.901756500.00000035.0000000.848248-6.255877e-03
50%0.8942830.9734510.9838090.9256000.9350410.7980871.0053580.9822460.8816700.9443200.904805800.00000045.0000000.8907756.265103e-04
75%0.9444140.9892160.9940250.9297280.9591820.8187821.0138040.9854730.9005110.9523660.9099421000.00000055.0000000.9324216.148308e-03
max1.1123051.0579351.0851990.9412271.0141500.8601991.0243830.9984190.9293871.0087190.9347211100.00000070.0000001.0474598.363585e-02
\n", + "
" + ], + "text/plain": [ + " pr_dc pr_dc_temp_corr i_sc i_mp v_oc \\\n", + "count 698.000000 698.000000 698.000000 698.000000 698.000000 \n", + "mean 0.896835 0.974087 0.987156 0.925366 0.933293 \n", + "std 0.061383 0.025251 0.018845 0.006364 0.033281 \n", + "min 0.776828 0.796208 0.791323 0.888510 0.859463 \n", + "25% 0.850519 0.960087 0.978500 0.921064 0.901059 \n", + "50% 0.894283 0.973451 0.983809 0.925600 0.935041 \n", + "75% 0.944414 0.989216 0.994025 0.929728 0.959182 \n", + "max 1.112305 1.057935 1.085199 0.941227 1.014150 \n", + "\n", + " v_mp v_oc_temp_corr r_sc r_oc i_ff \\\n", + "count 698.000000 698.000000 698.000000 698.000000 698.000000 \n", + "mean 0.801296 0.997247 0.979181 0.883629 0.945137 \n", + "std 0.022848 0.022877 0.009992 0.019561 0.011393 \n", + "min 0.762795 0.906777 0.891750 0.846937 0.923548 \n", + "25% 0.783119 0.986960 0.975451 0.867526 0.936792 \n", + "50% 0.798087 1.005358 0.982246 0.881670 0.944320 \n", + "75% 0.818782 1.013804 0.985473 0.900511 0.952366 \n", + "max 0.860199 1.024383 0.998419 0.929387 1.008719 \n", + "\n", + " v_ff poa_global_bin temp_module_bin calc_pr_dc diff_pr_dc \n", + "count 698.000000 698.000000 698.000000 650.000000 6.500000e+02 \n", + "mean 0.906705 725.501433 44.978510 0.891629 2.169205e-17 \n", + "std 0.006917 277.636248 12.583989 0.056787 1.701377e-02 \n", + "min 0.895678 100.000000 10.000000 0.781006 -2.062191e-01 \n", + "25% 0.901756 500.000000 35.000000 0.848248 -6.255877e-03 \n", + "50% 0.904805 800.000000 45.000000 0.890775 6.265103e-04 \n", + "75% 0.909942 1000.000000 55.000000 0.932421 6.148308e-03 \n", + "max 0.934721 1100.000000 70.000000 1.047459 8.363585e-02 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# choose which normalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", + "mlfm_sel = 'pr_dc' \n", + "\n", + "# add selected variable to measured data frame. We do this to ensure that data indexes match.\n", + "meas_temp = meas.copy()\n", + "meas_temp[mlfm_sel] = norm[mlfm_sel]\n", + "\n", + "cc, coeffs, ee, errs = mlfm_fit(meas_temp, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", + "\n", + "norm['calc_' + mlfm_sel] = cc \n", + "\n", + "norm['diff_' + mlfm_sel] = norm[mlfm_sel] - norm['calc_' + mlfm_sel] \n", + "\n", + "norm.describe()" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [ - "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", - "mlfm_sel = 'pr_dc' \n", - "\n", - "# FIX THIS WARNING,\n", - "# SettingWithCopyWarning:\n", - "# A value is trying to be set on a copy of a slice from a DataFrame.\n", - "# Try using .loc[row_indexer,col_indexer] = value instead\n", - "# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\n", - "\n", - "cc, coeffs, ee, errs = mlfm_fit(norm, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", - "\n", - "# Fix a bug with fit routine which gives a\n", - "# finite cc[4] even if all the ws data is 0\n", - "# this won't matter until cc is applied to other\n", - "# data with some ws <>0 when it will give bad results\n", - "if np.mean(meas.wind_speed) == 0:\n", - " cc[4] = 0\n", - " c_5 = 0\n", - "\n", - "norm['calc_' + mlfm_sel] = cc \n", - "\n", - "norm['diff_' + mlfm_sel] = norm[mlfm_sel] - norm['calc_' + mlfm_sel] \n", - "\n", - "norm.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# cc\n", - "# date_time\n", - "# 2013-01-04 08:15:12-06:00 0.877861\n", - "# 2013-01-04 08:20:12-06:00 0.899799\n", - "\n", - "# coeffs\n", - "# array([ 1.27323780e+00, -4.79820818e-03, 6.18587637e-01, -2.83390574e-01,\n", - "# 0.00000000e+00, -2.74667887e-10])\n", - "\n", - "# ee\n", - "# date_time\n", - "# 2013-01-04 08:15:12-06:00 -0.166853\n", - "# 2013-01-04 08:20:12-06:00 -0.149638\n", - "\n", - "# errs\n", - "# array([0.03774048, 0.00015903, 0.08827109, 0.04484896, 0. ,\n", - "# 0.00641688])\n", - "\n", - "#mlfm_meas_file\n", - "#norm.columns\n", - "#fit\n", - "norm.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 18) Show residual fit vs. measured for MLFM parameter " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ - "def plot_fit(dnorm, fit, title):\n", + "## 18) Show residual fit vs. measured for MLFM parameter " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_fit(dmeas, dnorm, fit, title):\n", " \n", " ''' \n", " Scatter plot fit to normalised measured\n", "\n", " Parameters\n", " ---------- \n", - " \n", + " dmeas : dataframe\n", + " measurements, must include 'poa_global_kwm2'\n", + "\n", " dnorm : dataframe\n", " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", @@ -1345,8 +1776,8 @@ " ax1.set_xlim(0, 1.2)\n", "\n", " plt.plot(\n", - " dnorm[fit] * dnorm['poa_global_kwm2'],\n", - " dnorm['calc_' + fit] * dnorm['poa_global_kwm2'], ##SR##\n", + " dnorm[fit] * dmeas['poa_global_kwm2'],\n", + " dnorm['calc_' + fit] * dmeas['poa_global_kwm2'], ##SR##\n", " 'c^',\n", " label = fit\n", " )\n", @@ -1367,12 +1798,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# plot fit vs. measured, include a 1:1 line for comparison\n", - "fit_plot = plot_fit(norm, mlfm_sel, 'fit ' + mlfm_meas_file)" + "fit_plot = plot_fit(meas, norm, mlfm_sel, 'fit ' + mlfm_meas_file)" ] }, { @@ -1387,7 +1831,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1491,9 +1935,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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midpoa_globaltemp_modulewind_speedpoa_global_kwm2
id
1matrix100000.1
2matrix100500.1
3matrix1001000.1
4matrix1001500.1
5matrix1002000.1
..................
176matrix12005001.2
177matrix12005501.2
178matrix12006001.2
179matrix12006501.2
180matrix12007001.2
\n", + "

180 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2\n", + "id \n", + "1 matrix 100 0 0 0.1\n", + "2 matrix 100 5 0 0.1\n", + "3 matrix 100 10 0 0.1\n", + "4 matrix 100 15 0 0.1\n", + "5 matrix 100 20 0 0.1\n", + ".. ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2\n", + "177 matrix 1200 55 0 1.2\n", + "178 matrix 1200 60 0 1.2\n", + "179 matrix 1200 65 0 1.2\n", + "180 matrix 1200 70 0 1.2\n", + "\n", + "[180 rows x 5 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# read in the complete matrix data\n", "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", @@ -1547,25 +2157,201 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.06820115e+00, -4.53440355e-03, 4.79013950e-03, -7.02941856e-02,\n", + " -6.31677174e-04, -1.54196576e-02])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#105.68%\t-0.42%\t12.85%\t-5.71%\n", + "# show model coefficients\n", "coeffs" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpoa_global_kwm2pr_dc
id
1matrix100000.11.015545
2matrix100500.10.992873
3matrix1001000.10.970201
4matrix1001500.10.947529
5matrix1002000.10.924857
.....................
176matrix12005001.20.858018
177matrix12005501.20.835346
178matrix12006001.20.812674
179matrix12006501.20.790002
180matrix12007001.20.767330
\n", + "

180 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2 pr_dc\n", + "id \n", + "1 matrix 100 0 0 0.1 1.015545\n", + "2 matrix 100 5 0 0.1 0.992873\n", + "3 matrix 100 10 0 0.1 0.970201\n", + "4 matrix 100 15 0 0.1 0.947529\n", + "5 matrix 100 20 0 0.1 0.924857\n", + ".. ... ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2 0.858018\n", + "177 matrix 1200 55 0 1.2 0.835346\n", + "178 matrix 1200 60 0 1.2 0.812674\n", + "179 matrix 1200 65 0 1.2 0.790002\n", + "180 matrix 1200 70 0 1.2 0.767330\n", + "\n", + "[180 rows x 6 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# populate pivot table from predicted mpm data\n", "matr[mlfm_sel] = mlfm_6(matr, coeffs[0], coeffs[1], coeffs[2], coeffs[3], coeffs[4], coeffs[5])\n", "\n", - "#matr[mlfm_sel] = mlfm_6(matr, coeffs)\n", - "\n", "matr" ] }, @@ -1578,7 +2364,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1678,9 +2464,175 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpoa_global_kwm2pr_dc
id
3matrix1001000.10.970201
4matrix1001500.10.947529
5matrix1002000.10.924857
6matrix1002500.10.902185
7matrix1003000.10.879513
.....................
176matrix12005001.20.858018
177matrix12005501.20.835346
178matrix12006001.20.812674
179matrix12006501.20.790002
180matrix12007001.20.767330
\n", + "

156 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2 pr_dc\n", + "id \n", + "3 matrix 100 10 0 0.1 0.970201\n", + "4 matrix 100 15 0 0.1 0.947529\n", + "5 matrix 100 20 0 0.1 0.924857\n", + "6 matrix 100 25 0 0.1 0.902185\n", + "7 matrix 100 30 0 0.1 0.879513\n", + ".. ... ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2 0.858018\n", + "177 matrix 1200 55 0 1.2 0.835346\n", + "178 matrix 1200 60 0 1.2 0.812674\n", + "179 matrix 1200 65 0 1.2 0.790002\n", + "180 matrix 1200 70 0 1.2 0.767330\n", + "\n", + "[156 rows x 6 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", "\n", @@ -1698,9 +2650,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "contour_plot = plot_contourf(\n", " df=norm,\n", @@ -1786,160 +2764,6 @@ "\n", "Many more papers are available at www.steveransome.com \n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stop\n", - "\n", - "# delete below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr2.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#norm.describe()\n", - "meas.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "whos" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm.to_csv('\\\\mlfm_data\\\\export\\\\'+'norm.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "meas.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'meas.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stack.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'stack.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ref_data.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\' + 'ref_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stack.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ref" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "root_dir" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/tutorials/mlfm_220627_2.ipynb b/docs/tutorials/mlfm_220627_2.ipynb index 82115b54a9..8179b07b4d 100644 --- a/docs/tutorials/mlfm_220627_2.ipynb +++ b/docs/tutorials/mlfm_220627_2.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -74,15 +74,6 @@ "plt.bbox = 1.4 # offset right to not overwrite" ] }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "##root_dir ##SR##" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -119,75 +110,48 @@ }, { "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"\\nNotes for Cliff Hansen 220624t17\\n\\nMy comments are marked ##SR##\\n\\nI can't get the stacked plot chart to work option 0. section\\nKeyError: 'v_mp'\\n\\n\"" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"\n", - "Notes for Cliff Hansen 220624t17\n", - "\n", - "My comments are marked ##SR##\n", - "\n", - "I can't get the stacked plot chart to work option 0. section\n", - "KeyError: 'v_mp'\n", - "\n", - "\"\"\"\n", - "\n", - "##meas.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 70, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# select one of the following meas files\n", - "################\n", - "meas_file = 2 #\n", - "################\n", - "\n", - "if meas_file == 0:\n", - " mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", - " # 6 measured LFM variables\n", - " # date_time, module_id, \n", - " # poa_global, wind_speed, temp_air, blue_frac, beam_frac, temp_module, \n", - " # v_oc, i_sc, i_mp, v_mp, \n", - " # r_sc,\tr_oc\n", - "\n", - "elif meas_file == 1:\n", - " mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", - " # 4 measured LFM variables \n", - " # date_time, module_id,\tpoa_global,\ttemp_module,\n", - " # i_sc,\tp_mp, i_mp,\tv_mp, v_oc,\tff,\t\n", - " # temp_air,\trelative_humidity, pressure, precipitation, dni, ghi, dhi, soil, wind_speed\n", - " \n", - "elif meas_file == 2:\n", - " mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", - " # 4 measured LFM variables\n", - " # date_time, module_id,\ttemp_module, poa_global,\n", - " # i_sc, v_oc, i_mp,\tv_mp, p_mp, \n", - " # wind_speed\n", - " \n", - "##SR##\n", - "#elif meas_file == 3:\n", - "# mlfm_meas_file = 'x19074005_iec61853.csv' \n", "\n", - "# optional\n", - "# elif meas_file == -1:\n", - "# mlfm_meas_file = 't1_041.csv'\n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", + "# File contains: \n", + "# - date_time, module_id, \n", + "# - poa_global (W/m2)\n", + "# - wind_speed (m/s)\n", + "# - temp_air (C)\n", + "# - blue_frac\n", + "# - beam_frac\n", + "# - temp_module (C) \n", + "# 6 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - r_sc, r_oc\n", + "\n", + "# mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", + "# File contains: \n", + "# - date_time, module_id, \n", + "# - poa_global (W/m2)\n", + "# - wind_speed - all zeros (m/s)\n", + "# - temp_air (C)\n", + "# - temp_module (C) \n", + "# - unused environmental quantities: relative_humidity, precipitation, dni, ghi, dhi, soil\n", + "# 4 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - unused IV curve quantities: p_mp, ff\n", " \n", + "mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", + "# File contains: \n", + "# - date_time : integers in this file\n", + "# - module_id, \n", + "# - temp_module (C) \n", + "# - poa_global (W/m2)\n", + "# - wind_speed - all zeros (m/s)\n", + "# - 4 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - unused IV curve quantities: p_mp\n", + "\n", "# extract module id from filename e.g. 'g78'\n", "mlfm_mod = mlfm_meas_file.split('_')\n", "\n", @@ -229,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -310,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -329,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -436,7 +400,7 @@ "2 129.83270 0 0.4 " ] }, - "execution_count": 75, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -463,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -500,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -509,7 +473,7 @@ "4" ] }, - "execution_count": 77, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -564,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -656,13 +620,13 @@ "2 0.967743 " ] }, - "execution_count": 78, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "norm = mlfm_meas_to_norm(meas, ref) ##SR##, qty_mlfm_vars)\n", + "norm = mlfm_meas_to_norm(meas, ref)\n", "\n", "# show some normalised data\n", "norm.head(3)" @@ -678,11 +642,11 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "# poa_global bin e.g. 100, 200 .. 1100W/m2\n", + "# poa_global bin e.g. 100, 200 .. 1100 W/m2\n", "norm['poa_global_bin'] = meas['poa_global'].round(-2)\n", "\n", "# temp_module bin e.g. 5, 10 .. 75C\n", @@ -704,28 +668,18 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "# select by irradiance poa_global range e.g. 100-1100W/m2\n", + "# select by irradiance poa_global range e.g. 100-1100 W/m2\n", "meas = meas[(meas['poa_global'] >= 100) &\n", - " (meas['poa_global'] <= 1100)]\n", - "\n", - "# if there's date_time can select by it, i.e. not matrix data\n", - "### better if index is formatted as a date\n", - "\n", - "# if qty_mlfm_vars == 6:\n", - "\n", - " # not for matrix as they don't contain dates\n", - " # example\n", - " # meas = meas[(meas.index > '2016-01-01') &\n", - " # (meas.index < '2017-01-01')]\n" + " (meas['poa_global'] <= 1100)]" ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -756,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -803,7 +757,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -940,25 +894,25 @@ "4 264.80358 0 0.8 " ] }, - "execution_count": 83, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "meas.head() ##SR## check what's there" + "meas.head()" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, "metadata": { @@ -969,7 +923,7 @@ ], "source": [ "# scatter plot normalised values vs. irradiance\n", - "fig_scatter = plot_mlfm_scatter(meas, norm, mlfm_meas_file) ##SR##, qty_mlfm_vars) add scatter\n" + "fig_scatter = plot_mlfm_scatter(meas, norm, mlfm_meas_file)\n" ] }, { @@ -1001,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1093,7 +1047,7 @@ "2 -0.029400 " ] }, - "execution_count": 85, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1173,14 +1127,14 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -1203,8 +1157,7 @@ " is_i_sc_self_ref=False, # is isc self referenced?\n", " is_v_oc_temp_module_corr=True, # is voc temperature corrected?\n", ")\n", - "\n", - "##SR## added stack = \n" + "\n" ] }, { @@ -1225,7 +1178,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1355,7 +1308,7 @@ "4 0.991363 800 15.0 " ] }, - "execution_count": 87, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1366,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1552,7 +1505,7 @@ "max 6.910501e-03 " ] }, - "execution_count": 88, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1574,50 +1527,6 @@ "norm.describe()" ] }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['pr_dc', 'pr_dc_temp_corr', 'i_sc', 'i_mp', 'v_oc', 'v_mp',\n", - " 'v_oc_temp_corr', 'poa_global_bin', 'temp_module_bin', 'calc_pr_dc',\n", - " 'diff_pr_dc'],\n", - " dtype='object')" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# cc\n", - "# date_time\n", - "# 2013-01-04 08:15:12-06:00 0.877861\n", - "# 2013-01-04 08:20:12-06:00 0.899799\n", - "\n", - "# coeffs\n", - "# array([ 1.27323780e+00, -4.79820818e-03, 6.18587637e-01, -2.83390574e-01,\n", - "# 0.00000000e+00, -2.74667887e-10])\n", - "\n", - "# ee\n", - "# date_time\n", - "# 2013-01-04 08:15:12-06:00 -0.166853\n", - "# 2013-01-04 08:20:12-06:00 -0.149638\n", - "\n", - "# errs\n", - "# array([0.03774048, 0.00015903, 0.08827109, 0.04484896, 0. ,\n", - "# 0.00641688])\n", - "\n", - "#mlfm_meas_file\n", - "#norm.columns\n", - "#fit\n", - "norm.columns" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1627,7 +1536,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1685,14 +1594,14 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": { @@ -1718,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1822,14 +1731,14 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Tv6YtnBYdi9VN17+6yUyPjo/CuOrNEJEJGN/wb4HbVLWoeAWlyg4z0B7AznTdpjC/xXMjcxlgH3ApJS65PDNx6w95bTyLb0h/8pmKF1ZLe58AezcfxOpBzTPS95p/b0tipOQctv/x/LTy++QNt4E9ym9LDIziOWCfs1jx27i09wuwS/nEyKXpYa9KF7Lm908y0vcWzcxX4757ncevaz5Pe7+n122Z9j5TDLG4v4jEx3/uq3/N7FUR4wcezxqMT3IiFaO6+HYVo33axLrEfg7BBO45HxOm9CngLf4eVc8JO8xAmykyNchmkkwMspkkU4NsJsnUIJspMjXIZpJMDbKZJBODbEZIfY92ZRI/2nxMJql4KlG43UJi20qYKGUaJdYorp/1wAeq+h0UpJFcKSJ7qWUSlEwQ9mhLyGhWu5ZgzWuz/TJ07ffZnOSNyhgD+w92LcGKAf0SQ0aXffr2HeZagjX9+37oWkJKCMn3Z1N078kBdoqPF44xdirMZWtmVFdYu5lArXgrZKBWXP10jAFVjDIViSkMtCXkRza6lmDNlJX5riVYkT1/lWsJ1kydnEqSnbLD5OxZyRuVMbIn+fcAlp39Q/JGZQEhLe49auJPD8HEZa8gIvUw+YjfKKT568A9IlJFRCpjMpC9FtVlYZJZ3CEiu4rJzwwmBjWYHNuXRy5AO2NC344vC7NZCANtiWlVkFnNH54/q4ZrCVb0vjkxmVHZ57mX/pu8URmiV++HXUuwpk/f9q4lWPNyn+TJ1MsKaQxYcSsm6cUvmH3TW1R1poicFS0Jx+iDSSU4AxMrekR0DDXJKRpjkqOsxsR6bxwdR1VHY2Jyj4jOcxSQsbSItoSBtoR05WfXEqy55KOpriVY0eCRkckblTEanX9N8kZliAvObe1agjUNzrnDtQRrzmuQmGSrjJLGNHlq0oQ2VtUKqnqYqg6Ojo9T1Ypx7VRVO6jqvlHpEG9lrCbd58mquruqnqSqUxLO87KqVlHVfVT1UlUtM1/OwRiqhFzBfq4lWHP/SYe7lmDFI1fVcS3Bmg4P3+VaghUPP+LfQPvIo/9xLcGajo/c6FpCakhIk5dOwkBbQg5mF9cSrDlqr91dS7CiRuW9XEuw5sjqmXF1yhRHVU+/61umqVHjUNcSrKnuiWYBJEN+1Tsi4Z0sIQ+zyLUEa875cLJrCVacdt9w1xKsObdeI9cSrKh3un8uVHVPvcm1BGv+WdefGW25cpK0BFIjzGhLSC+OdC3Bmh+a/dO1BCt+7ne1awnWzFrwrWsJViz4yT+f1MVLPnAtwZpFi/1wSRIRyoc0eWkjvJMlZATpj9KTaV6YXmZsBFLi2WF+BX8A6NWjn2sJVvR4rjBvi7LNs93fdi3Bmuee9ce/Osxo00eY0ZaQ39jiWoI1y9Ztci3Bitzf1ruWYM2ypctdS7Biae4K1xKsyc1d6VqCNUt90RwtHQfSg/w1RvP2yxGym2Yi1nEmueSmzMQ6zhSZinWcSTIV6ziTZCLWcSbJVKzjTJKpWMeZ4vS6LcmeNDttI+M+VWroObe8lLTdBx3Pz04SgjFAWDouMQ95aAz1rw+yXUuwom4H/4yhGvzzUtcSrDijbjPXEqw59ZRWriVYc9qpfrgkiQg77Vw+aQmkhl+PbWUQLyNDnelXZKiX2/hlvAXwbK8nXEuw4sWX/YsM1buPh5Ghet/nWkLKhKXj9BEG2hKym4eLAhU9exLdc/fM5OXNJBX3rOBaghV77rmHawnW+Ki5oi+aBZsQi4Ek+DdKlDGeYYlrCdZc/YlfVryX/fcL1xKsaXq5J/6SEVc0vsu1BGsaXerP7DBG40Z+zMKF5BbHYcabOmFGW0K6458BUPbVdV1LsGL2C1e4lmDNxO+/dC3Bihmz/PNJ/WGOP64yMWb98I5rCSkhAjsFP9q0Ed7JEvI+npjrx/Hf7IWuJVjR+Z0pyRuVMbo91sO1BCu6dunjWoI1nTu94lqCNV0693ctITVCZKi0Ema0gUAgEEhAKBdiHaeN4Edbhgl+tJkn+NFmnuBHm3nS7Ud7QNVj9bKHXk3abkCbfwY/2hTYrh9ZRKS1iEwSkUnL+KNgmbcdC1jKJhawocAPdhArCsIp3sZ8fmMzs1hXkG+2P8sZzWoAbmQu69nKZPJpyVwAXmQpX/E7AM3IAeArfudFlgLGaGoy+axnKzdGrxnNavpjIgh15WdmsY7f2MxtzAdMqMSHvjG//+uDbKauyGPe6nWc/M5EwCwBx5aBT35nIvNWr2PqirwCP9mHvplfEG7xmDe/ZunajYzLXc1Br4wD4M5xObw2OxeAQ14bT96mzYxctJKrP5kBQKvRs3l3ntG3d78xALw7bzmtRs8G4OpPZjBy0UryNm3mkNfGA9Dvsznc3HsCYPLIZn2/lNxf13HoTWZv6tlh33PvwO8A4x+bPX8lOblrOPb2IYBZJo4tFR97+xByctdQ7eZ3C3xp7x34XUFIxkNveofcX9eR9f3Sgpy1N/eeQL/P5hjNzQeRt/4Phk/6mcv++zkAzXuM4a1xPwKw05WvAfDe20Np3eJOAJpecSOjRnxOXl4+h+1/PAAD+w/m7lsfAEye2fFjvmFp7nKOq3YaYMItdryvq7nmf17K1MkzqFPjTOoefw5glpFjS8l1jz+HeXN/ZOrkGQW+th3v61oQsvG4aqexNHc548d8U5DT9u5bH2Bgf7Mfedj+x5OXt5YRH42lSWTAdP11D/L2W+b6d9/5ZADefmsk11/3IABNGt/FiI/Gkpe3lgP2OQuAAf2GcNvNRvMF57bmyMMbkpu7gmqHXQiYkIz3tX8WMD62k7NnMzdnESccdzlglppjy80nHHc5c3MWMTl7doE/7n3tny0I63hIlcvJzV1JVtaUghyybVo/Td++Ju7vXpUuJC9vHcOHf0WjRiYxerNmXRg8+DMAypc7G4DBgz+jWbMu5nNodD+HVLmcvLx17FXJaO7bdxhtWj9tPodz7iArawq5uSs5pIrR/Gz3t7m33YuA8cHNzp5DTs7PHHO0yQ/eudMrBcvRxx1zNTk5PzE5+4cC39cO9/YsCKFY9ZBG5OauYEzW5IIcs7e0eZL+fT8EYN+9ziMvby0fDR9fYAB1XbNHObTyvwHYpfwZALw1+FOua2Ye9ho3as9Hw8eTl7eWffc6D4D+fT/kljZPAiaX7ZisyeTmrqDqISZpxXPPDqbDvT1JO9EebbISSI0woy0hS9mUsVR5mZrRzlu9jqP2Tr+bQaZmtDm5azKWKi9TM9p5c3/kqOpHZKTvTMxo5+YsonqNzKz4ZGpGm5Pzc8ZS5WVqRpuT8xM1ahyW9n7TPaP9R7Vj9cpOrydt9/INdcOMNgX8Wh8pg2xgq2sJ1uT/4Vd85rz1f7iWYE1+nl/LpXl561xLsMZHzfneaA7GTukkzP1LSGzp1yfuHJ/jWoIVt/T52rUEa+657UHXEqxoe0tX1xKsubnN064lWHPLzd1cS0gJCVbHaSUMtCXkcc8MrADGXH6yawlWTHzKr7jBAKO/9is+84SJb7qWYM13kzxxlYnj2++SGxiVCQTK71w+aUmpK5F9ReQDEVkrIotEpGkR7UREuonIqqh0ExGJq68jItkisi76WaeQPnYRkdkisnhbLz0ThIG2hAzCv/RiMQMrX4gZT/lEzDjKF2KGTz4RM2zyiYwYLmWANEeG6gVsAg4EmgEvi0jNQtq1BhoDtYFawKVAGzADKDAUGATsAwwEhkbH42kPZe9LOQy0JWQf/IobDHDQHpkx3soUlffZ3bUEaw462K9kEwdXPsC1BGsqV97ftQRrDvZFc5qWjkWkAtAE6Kiq+ao6HhgGXFdI8+uB7qq6WFWXAN2BG6K6+hiboh6qulFVexqVNIg7VzWgOfDfbb3sTBEG2hJyCfu6lmDN7bUyY6mZKe5pdLxrCdbcdtdNriVYcdfdhX3vlW3uaXeNawnW3H1PoaumZZI0zWhrAJtVNd4wZBpQ2Iy2ZlRXWLuawHT9q5vM9IR+XgAeBNanIqw0CQNtCYn5vPrEMW/6ZVwU88H1iZiPrS/E/Gd9IuYf6xMx/9eyjgjsVL5c0gLsH4tVEJXWCV1VhCjAwJ+sAfYs5LQVo7r4dhWjfdrEur/0IyKXA+VVtUwG7Q7uPSXEt2hTAF82Psm1BCu+7eafMdQXXw1zLcGKr74Z5FqCNRO/6+dagjVfTxzgWkJKCEL51GasK5P40eYDlRKOVQLyUmhbCchXVRWRIvuJlqefAi5ORbALwoy2hCxlk2sJ1sxbU+ZWVoolJzfxQbbsM3/uAtcSrJg3d5FrCdbk5PzsWoI1cz3SnKal4xxgJxGpHnesNjCzkLYzo7rC2s0EasVbIWMMpmYC1YHDgXEisgwYAhwsIstE5PBURGaaMNCWkCGsci3BmicnL3QtwYou/5vqWoI1T3Xt4VqCFV279HUtwZounT1xlYnjsS6ezGgFyokkLclQ1bWYga+LiFQQkXrAZcAbhTR/HbhHRKqISGWgHfBaVJcFbAHuEJFdRaRtdHw08D1wKFAnKq2A5dHvZeLJJiwdl5CH8cuwCGDEv+u4lmDF6C4NXUuwZthnb7uWYMWnX/g30I7+0g9XmXg+H93LtYSU2WmntM3DbgVeAX4BVgG3qOpMETkLGKmqFaN2fYAjgBnR3/2jY6jqJhFpHB17EpgNNFbV2JListjJRORXYKuqFhxzTZjRlhAvI0ON8ysyVCxJgU/EkhD4QizBgE/EEgj4RCxBQFnHRIZKXlJBVX9V1caqWkFVD1PVwdHxcXGDLGrooKr7RqVDvJWxqk5R1ZNVdXdVPUlVC01UrapZqnpIyd6B9BJmtCXkCHZ1LcGaE/evmLxRGeLkI/dzLcGaOied4FqCFSedfJxrCdacfMrRriVYc/LJx7iWkCJC+RSWhgOpEQbaEtKAvV1LsOaGYyu7lmDFTef794V6fSt//CUBbrzpCtcSrGnd2g9XmXhatW7sWkJKCIRYxmnEeulYRP4hIkfEl0wI84VYblmfiOWO9YW9m/vnehLLZesLsTy1PhHLQ+sTsTyzZR0LP9pACqQ8oxWRi4ABwEGYB54YCh7GIUwTL3KkawnWzG56umsJVvzc72rXEqyZueAb1xKs+PGnUa4lWLN4SZmMTVAsixYPdS0hZcKMNn3YPJL0Ah4DKqpqubiyww6yALPxJb/kn4xfutq1BCuyZpYZ48GU+WqsXwPt2DHZriVYk5VVqC1MmWaML5olrUkFdnhsBtp9gD6q6le0gwwz+m9Rwco+r/2w1LUEK/p/Nse1BGteH/CWawlWvNJ/iGsJ1vTr51cqQoD+/fyY0Qqwc/lySUsgNWzeqQHAfzIlxFfupYprCda8c6FfFrFDH/BjXyuewUP8CEwQ4/0Pe7iWYM2wYX64ysTz4TA/XJJC4vf0YjPQno7JI5gjImPjS6bE+cCL+DU7BGg1erZrCVY07zHGtQRrWre407UEK66/7kHXEqxp1qyLawnWXNfsUdcSUiYMtOnDxr2nf1QCcZxIBdcSrLnwML9S+11ysn/Rty64uEHyRmWIhhf7Z3V8ySX/dC3BmosvqedaQkqIhIE0nchf0/ttv1Sucby26fmuaxlWDH30S9cSrLjtZf+y7FTeZw/XEqzx7ftvRd5G1xKsqXVwYqKYss21553NzKmT03ZnHFGztnZ5J7kl+nUnVM5Okr0nQJKlYxG5Lu73lkWVzMssu3Rq6F9EnSkTb3MtwYpWJx/mWoI1Fx+xv2sJVlxUzS+9ANfV8s8+ovYBhaVhLYMIlC8nSUsgNZItHV/Ln1kWriuijWICRu+QdBo5y7UEa06s609gc4D+2T+5lmDNxz+udC3BilEL/NIL8Mb0Ja4lWDNtRWFpWMseAill5wmkRrEzWlW9OO73c4oofm1GpZnpX37kWoI1v678zrUEK74d+aFrCdZ8OfQ91xKs8E0vwIQR/gWs+Pj9/7mWkCLBjzadWDlCicjeItJMRNpHP/fOkC5vyJnon0Xs76u/dy3BimnjvnAtwZqJoz91LcGKbz3TCzB17OeuJVgz9lM/InCZEIyStARSI2VjKBFpgEngOwdYBBwGHAM0UdUy/00YjKEyTzCGKh18m0gEY6jMk25jqKNOqKPdP/wsabvGR/0jGEOlgM2M9kWgtaqepqpXqerpwE2Y0Iw7LIMfvdW1BGvm57zsWoIVPe/yL05KJ8+y9zx6o196Abq3vd61BGtub/Z/riWkRGyPNlkJpIbNQFsZeD/h2AeYJAM7LCc39OMfJ579DzjTtQQrzr7Cv0Gg4bUtXEuwwje9AOdc2cy1BGuatPDnoTEMtOnDZqB9A0j0C7kFeD19cvzj8Fp1XUuwpmKl6q4lWHHMyf4FJjjhND8CE8SodbpfegGOPfUM1xKsObWeH4FBRGDncpK0BFIjmR/tuLgwiycC3UVksYh8KyKLgWej4zss3Zv/y7UEa76f4le4vXYXnepagjXX/dOvfLTNTvdLL8Ad557kWoI15x1fw7WElCknyUsgNZL50SaGXOyXKSG+8uCQSa4lWFP7lGddS7Ci1zi/YjMDvD9jkWsJVnzwvV96Afp9k+NagjVfL/QjNrqQvqVhEdkXk5TmAmAl8ICqDi6knQBPAq2iQ/2B+zWy2BWROlE/xwKzgRtVdWpU1x64HqganeMlVS0zGRyS+dEOTKXE2ovIS5mXXLaY9LEvfnF/svKX8a4lWDFmyJuuJVgz8q2ByRuVIT4e7JdegNHvDXItwZr3Xvcntk8a92h7AZuAA4FmmOQ0NQtp1xpoDNQGagGXAm0ARGQXYCgwCJOydSAwNDoOxn6rRVR3EdBWRK6xvugMke6Egs3T3F+ZJ3fuTNcSrFm31q9IS4tmTXctwZq5M6a6lmCFb3oBFsz0776YNdWPxO8isFM5SVqS9yMVgCZAR1XNV9XxwDAKjzR4PdBdVRer6hKgO3BDVFcfswLbQ1U3qmpPzODaAEBVn1LVyaq6WVXnYAblMmN4kO6BdodbtW90Z2fXEqw5rJpfVrwtHu7mWoI1dzzxnGsJVtz5X7/0Atz46FOuJVjzyLMvuJaQMmnao60BbFbV+HX+aUBhM9qaUV1h7WoC02PLyBHTC+snWoI+Cygzs6B0D7Q7RiqgOF7t4J8v39zZPVxLsOKp1le5lmDNfdc2ci3BivbX+KUX4PGWV7qWYM2NlzV0LSElLPxo9xeRSXGldUJXFYHfE46tAQrLrlAxqotvVzEaOBPriuunE2ZsezWFSy0VbPLRBgqhfnO/MuEAHFTl4uSNyhCNWt/tWoI1ze7s4FqCFc3v8ksvwBW33ONagjU3d/DF4j/lPdiVSSJD5QOJYbYqAYVlV0hsWwnIV1UVkZT6EZG2mL3as1S1zIQkC0vHJWS/Koe7lmDNbrv9w7UEKw6qWs21BGuqVDvStQQrDvFML8BBVY9wLcGaqkce5VpCSqRrjxbIAXYSkXjn/doUvqw7M6orrN1MoFY0u41RK76fKGXr/cC5qro4FXGlRboHWv/MAEtI3zv8iwz1w0y/9rYea/5v1xKsufOy81xLsOL2Rn7pBXjkWr9WZgCanne2awkpk449WlVdi4mR30VEKohIPeAy/ky/Gs/rwD0iUkVEKgPtgNeiuixgC3CHiOwazVwBRgOISDPgCeB8Vf1xW685U6Q80IrhJhEZLSLTo2Nni0jBBpqq3lLM67NEZIOI5EdlTlxdUxFZJCJrReTDyO+qqH7qiEi2iKyLftZJ9Roywb1v+pe954QTn3AtwYrun/jnqzzomzJjh5ESg7/1Sy/AC19Mdi3Bms+/n+taQkqkOdbxrcDuwC/AW8AtqjpTRM6KloRj9AGGAzOA74ER0TFUdRPG9acFsBpoCTSOjgN0BfYDvosbY3pv8xsQh4i0EJFaCcdqi0hROdr/hs2MtgtwI9AXk7kHYDFwn0UfbVW1YlSOBoj8qfpgzL0PBNYBhfrjpuBLVepMeL/M7LenzPKlfqUX++SNvq4lWDOkv1+5Nt7v55degI8HpuV7tFQZ+FJP1xJSprwkL6mgqr+qamNVraCqh8WCVajqOFWtGNdOVbWDqu4blQ7xVsaqOkVVT1bV3VX1JFWdEldXTVV3jhtfKqrqzWl6Kx4Dfk449jNmcE8Jm4H2BuDfqvo2f1oXLwBKulHSDBiuqmNVNR/oCFwhIoVZk9WnGF8qF+T9+ourU28zf/yRaLxXtlm9YrlrCdasWr7MtQQrfNMLft4XK5Z5EhlKYKdy5ZKWHYRKFG45vXeqHdi8U+UxVmHw50BbMe5YKvxXRFaKyFciUj869hffKVWdj4kiUlhQ0JR9qUqLC2+ymdCXDQ45rIlrCVZcfU9H1xKsuemhx1xLsKL1w37pBWh676OuJVhzb5f/upaQEmbpOMQ6jpiFCboRz+WYMJApYTPQfgw8KyK7QoFT8GOYNfVUuA8z+62CWX4eLiJHYucfZdMWEWkd8+/6dckivhz0IgA9WzVk5eKF5M6dSZ/bjS/eJ/26FSwDP9PsX/y+6hcWTJ9Y4Cc77PlHC8ItPnHFKWxct5Y533zJf5uY7D3vdWvP9C8/AqBTw+MAmP7lR7zXrT1g8tbO+eZLNq5byxNXGGv4SR//j2HPmy+LVztcz4LpE/l91S8808wkKli+9HMW/2QyE/7w/ZOsW/sTG9YvZ9Y0EyRj6eIRLF08AoBZ0zqzYf1y1q39iR++fxKAxT+9X7BMPGPKg/yxaTV5v+cwbZJxi/hpweCCcIzTJt3Dli0bWPPbjIJ8tQvnvcqvK78DYMpE48b068rvWDjPvE/zc15mzW8z2LJlQ0GfY4a8yetdzcPHU62v4odJX7N6xTLaXWiu+ZM3+vLOs+ZLvUuzi1k4ezrLFv3IQ5ebax7a51mG9jGxmB+6/F8sW/QjDzc5hy7NjOHLO88+VrCU3O7CU1i9Yhk/TPq6wNf29a73FYRsvO2sY9mwNp+pYz8ryGnb98Hb+XbkhwC0OtnsgHw59D263Wnc/zq1asq3X4xiXX4eTU6oCphwij0fNC5G913biOnfjGfV8qU0P9083w3p34t+j5uHgTsaNWDujKncfGE9WjUw98agHt0Y1MME3WjVoC6Lf5zH3BlTuaORWYjp93jHgqXm5qfXZNXypUz/ZnyBL27PB+8uCOnY5ISqrMvP45vPRxXkkH3yztZ8OfQ9AC6qtn/BNT0ZXdOjNzblm8/NNV1+vLmmjwcP5PkHzDW1v6YRLc+py6rlS2l6mrmm9/v1om9Xc01tLzXXtPjHedx4jrmmN3p0443omm48589ranupuaa+XTsWLEfffu5J/PbLMmZ/N6HA93VA5w4FIRRvOr0G69fmMznr04Icsy/ddxsTRnwAwHW1qgAwYcQHvHSfuQ+7t72edhefwfq1+dx0unkmH/3eIAZ0Nm5Kj7e8ktnfTeC3X5Zxe5R84OOBvRn8jPnf6Xj1RSyYNZ2lC+fT/lKTNnLIS90Z8lJ3AC49rQ4L589l1rQpXHOuybjzzCMPFCz9nnd8dX5ZtpTvvhpX4Bvb5Z7bC0Is/vPwg1mbn0fWJx8X5KC9v01LGp5kkjfUPsB8ZX38/v+4v01L8z41+z+yPvmYtfl5/PPwgwETsrHLPbeb9/myhnz31Th+WbaU8443RrwDX+rJM488QPpJvj+7A6XJuw/oLyLvi8hTIjIEE3O5XaodyF8nh8U0FKmE2RNtCOwMbAA+BVqoamE+Ucn6G4XZ7D4P+EpVn4qrywPqq2p2wmvuxliVXRx3bDiQpardiztf5RrHa5ue79rKTEru3JlUrp6ZCfXQR7/MSL/r1v7EHhUOS97QkttevjTtfQIsnD2dw4+tlbzhNlB5nz0y0u/cGVOpfkKdjPSdiZlEJvWuyMuMO+OCWdOpdlxm7otaBye6bKaHWdOmcFzt9Cc8u/a8s5k5dXLa7owTTjpZh435Kmm7Iyrtnp3Ej3a7QESqAtcCh2L2Z99U1cR92yJJOWCFqv4OXC4iB2KMoX5W1ZJs7ChmheIvvlMicgSwK8b/KpGZQDsRkbjl41qYoNVO2GX3Cq5Ovc2UK7erawlW7LZHxeSNyhi7V/BLs296AXbbw7//vT0q+vE+C0p52eEC/RWJqi7CZBbaJpLloy2XWIAVQDbwS9yxYhGRvUXkQhHZTUR2inyezgZGAW8Cl0am3hUw1s1DipglZ1GML5ULBncq0qOpzPJjjl/Wmi9Ey74+0amVX/GkY8vQPvHs7f6FP729qT9+9yJbk5btFRF5Q0ReT1ZS7S/ZILkZ+KOYEqtPxs4YU+gVmFyBt2N8oHJUdSZwM2bA/QWz33pr3AWPFJEHISVfqlLnjv4jXZ16mzmutl9GJI9/4J+vcv/RE11LsGLAl37pBXh6uF/pHgGGfzvVtYSUEJRybElatmPmAfOjsgYz7pTHuLSWwwTdWJ1qZ8kG2moYA6aiSqy+WFR1haqeqqp7qureqnq6qn4WVz848q+qoKqXqeqvcXUNVfWJuL+L9KVyQczAyidiBlS+EDOO8omY8ZMvvOGZXqDAcMknXn7Kn2AxO/KMVlU7xwrGA+YSVW2mqg+qanPgEuDoVPsrdo82WpcOBAKBwA6FUk42uxZRVjgd+Cbh2LfAP1PtIGVjKBF5gyLS4Klqi1T72d44p3nb5I3KGAcfcolrCVZc1sa/LC3N7/LLv/o6z/QCXHFryt4VZYZbPMneE1s6DgAwBXhCRB5R1fUisjvQGZiaagc2frTxa9bzgbUYV59fi3vR9k7PVn7kl4wn5ofrCzEfW5+I+dD6Qsw/1idi/q8+celpdVxLSA3ZsZeOE7gBqAesEZHlmD3bMzG2Qilh497zt29nERkA+GVZk2aadnrZtQRrjqiRrhCgpcPtPfyLJ92p/2DXEqzoPMAvvQD3vDDQtQRrXhicfl/+TCHsMANpsajqQuAMETkUqAwsVdWf4tuISD1VLdLxuKSJ36cC/k030sim9WtdS7Bm69Yykw85JTass4nyWTZYv9Yvzb7pBdiwzr//vXX5vrzPSjlJxaFkxyEKUFFUkIqR/D0xfQE2afIaJJR/Y3IFzrIRu70xvKd/E/qfFvg1e3m96/2uJVjzwkN+7Ss//6BfegFeicIt+kSXe+5wLSElBKWcbE1aAgUUG5XLZkY7IOHvtZgZ7bWWgrYr2rzwnmsJ1hxzvF8D1yNvfuxagjU9hzmLobJNvDjcL70Aj70zyrUEa97+YpxrCSkTlo6tKDaMVsoz2ijfX3w5XlWbq+qCkmv0l0/6+ed/GEtU4AuxJAQ+EUsy4AuxBAI+EUsQ4BOZSQCQCTQYQ6WRYme0qYRXBFDVHfYd33Pff7iWYM3OO+/lWoIVex9woGsJ1ux34EGuJVjhm17w87444KCDXUtICQl7tGkl2dLxZpJMiSPKp0GLl5zRxL84vAcefJ5rCVZceF1r1xKsuaLVba4lWNHkJr/0Alx8vV/W8wDX3+rHHi1AubB0HEsHWw1YpKrFORYXu0drE4LxdmAMcBFwbPTzS8C/iA1pJJY71idmTPHDaT5GLJetT8Ry1fpCLA+tT8TyzPpELI+sDwhbk5btnShL3AySTDhVtdCc6DGKHWhVdVGsAPcAV6jqZ1EygM+A/wPutZO+fdE6AzluM80xNf2y1uw46CPXEqx5fujnriVY8cIwv/QCdHnLPyO5wZ+PdS0hJUTM0nGyklpfsq+IfCAia0VkkYgUmipKDN1EZFVUukUzylh9HRHJFpF10c86qb62hEzBxDveZmwiQ+0FJGbJ3iM6vsOyaslC1xKs2bDhF9cSrFi2yD97uyUL5ruWYMViz/QCLFv0o2sJ1iyaP8+1hBRJq3tPL2ATcCDQDHhZRApbQmmNyZJTG5Nn/FKgDYCI7AIMBQYB+wADgaHR8WJfmwaygFEi0klEbhSRlrGSagc2A+1A4HMRaS0iDUWkNfBJdHyHJWuQs5zz28yyJX7NBIb1fc61BGvefP4p1xKsGNTDL70AQ172L6tTb5+y96Rh6TjKMd4E6Kiq+ao6HhgGXFdI8+uB7qq6WFWXAN0x4Q8B6mNsinqo6kZV7YnZF22QwmtLSj1gASY4U/NI+3XR7ylh40fbARPv+GqiMFTAi0A/iz62O/7zlH/PGdWPvcu1BCs69P2fawnWdHtrmGsJVjz9tl96AR56xT8f9gFD/chfLZF7TwrsLyKT4v7uq6p94/6uAWxW1Zy4Y9MoPKJgzaguvl3NuLrp0Z5pjOnR8VFJXlsiVPWckvZh40e7VVV7q+q5qnqsqjaI/t6hUzwMez5Ehso0r3f1L7NMzwfvdi3Biucf8EsvwAAvI0Pd7lpCiqS8R7tSVU+JK30TOqoI/J5wbA1QmPFQxaguvl3FaK81sS6xn+JeW2JEZG8RaSYi7aOfe9u83mbpGBH5j4iMFpE50U//fFvSTOXq/llr7lHhMNcSrKh6XC3XEqypfkId1xKs8E0vQLWa/t0Xx9U50bWElBHdmrSkQD5/jwFcCchLoW0lID+axSbrp7jXlggRaQAsBO4ATsV44CwUkXNT7cMmH+1DmLRA3YFFQFWgg4hUVtXHLXQ7ocKuO1H36PQHl6h7dOa8mx4Zk6kBsVCjvxKzfqdVGen32loXAZnpe82mRPu+9FDnrusz0i/Afrum33DpnPvOxGS/TD87b/0tI/02e7g2xvMiA6xclJFuT7h2N/gj/StKu2sG/j/SE4coB9hJRKqr6tzoWG1gZiFtZ0Z1EwtpNxNoJyISN3jWwhhaJXttSXkRaK2qBXtYIvJ/0bmPSaUDmxltK+ACVe2rqp9ESwQXYay9dlianFDVtQRrKh1wlWsJVhy4Tz3XEqypfqBf98V+e5d4G6rUqbTv5a4lWFOpui8BK9QMtMlKsl5U1wJDgC4iUkFE6gGXAW8U0vx14B4RqSIilYF2mMQ1YCx/twB3iMiuIhKb4YxO4bUlpTKQGLf2AyDlcGo2A20FYEXCsVXA7hZ9bHe88fX3riVYs+TH11xLsGLeT5+6lmDNlHl+3RcLf/bPV3nJojddS7BmyRRPrLsV2LoleUmNWzHjxC/AW8AtqjpTRM4Skfi8gX2A4Zhliu+BEdExVHUTxn2nBbAaaAk0jo4X+9o08AaQGDrtFszgnhI2A+0o4E0ROVpEdheRYzCuPZ9Y9LHdMePbInP9llmyxvo1CIwbMyl5ozLGhHF+3Rdjsia7lmBN1pjpriVYkzVhjmsJKaKwdWvykkpPqr+qamNVraCqh6nq4Oj4OFWtGNdOVbWDqu4blQ7xe6yqOkVVT1bV3VX1JFWdkuprS8iJQHcRWSwi34rIYswW6okiMjZWiuvAZqBti9l4no7ZeJ6KSZXnixldRhj5VsoPNWWGfq/4lV7slf5DXEuwZtArft0XA/p/6FqCNf36++EqE0+/Qf6kyUvH0vF2Qj/M1ulDwEvRz5uA/pj0sbFSJGI76EcZffbHmHV7805XP6GO9hz2hWsZVlx08NeuJVixfqdDXEuwZs2mQ11LsCYTxlCZJFPGUBklQ8ZQmeLUix5n0rRF6Qo5yCknHaWTvkoeEET2uCxbVf0LRp5mROQlVb21qHpb9549gOOBo4DTReQMETmjhBq9ptud/tmCNbv+adcSrLjhOl9yeP7JrTf4dV+0aO5fPtpm1z3pWoI1zW7t71pC6mzZnLwEYhQbJcrGvacFxsx5E7A+rkoBvxwz00jdBhe4lmDNJQ1PdS3BioYXn+1agjXnNfTrvrj4kjNdS7DmkotPcy3BmkvO88X3VyFtW5w7BMWuJqS8dCwiy4Droqw93hGWjjNPWDouHcLScSmwoy8dn3ikThqTfMVA9roqLB0DIvK7qiYG1CjAZul4E8aXKRDHxUfs71qCNeV2v9S1BCsq7OxPNJ0YlSv4dV/supN/s8Nyu1zkWoI15SqnK6FMKRCModKGzUDbEXhWRPz6BskwH/+40rUEa7auH+5aghVr/5iSvFEZI3etX/fFxs3fupZgzdZNflnPA2zNTZdrZ4ZRRbdsTloCBRS7mmAz0OYAjYDlIrIlKltFZIdOKvDlUP8yiAx+O8u1BCveecs/N44h7/h1X7z9ln/u8IPf+tK1BGsGD5mYvFGZID2RoXxFRN6J+z2VmP6Diqu0GWjfwETCqI1JfVQDqE4JM8/7zsTR/kUtGjHyO9cSrBj5cbG+4GWSz0f6dV98PGK8awnWjPjYv1n4iM89CrKhmrxsv1wYl/nn+WSNVfWW4uptjKF+A/ZNY7SNUiUYQ2WeYAxVOgRjqFJgRzeGqlNNv/sseQrQcv/4z3ZpDCUiw4EDMCu51wBvF9ZOVVuk0p/NjPZVTFb5QBydWmUmE04madSki2sJVlzZ+E7XEqxpcaVf98Xll7VzLcGaRo39ywXdqMWLriWkhuqO7kf7fxh31jkYF9b5RZSUSNmPFqgLtI3S5S2Pr1BV/xwd00TDa1N6oClT3NTSL2vNlq2ucC3BmuYt/bovbmzV2LUEa25q1dC1BGtuan6Wawkpotv1HmwKtFLVFwFEpKaqdi5JZzYDbb+oBOI44TT/UrjVP/t41xKsOOtf/q1MnXGWX/fFv+qf5FqCNfX/5Uvwhz+pf8bRriWkzo490D6OmdEC/LuknaU80KrqwGRtksV73B657p/H8/4Mv/ZzqhxxA7+v+F/yhmWEow67gOW/+ZUN58Sjjmfucn/ui8MP/TerVvtlxVulajN+//UD1zKsqHJiB36f29O1jNTw0xwnXfwoIt0xyeN3FpGWhTVS1VdS6cxmRpsKzTG5B3cYfBtkAa8GWcC7QRbwapAFvBtkAe8GWcCvQXb73oNNxtVAB+BaYGcKt09SIKWB1iqpQAqkzerNF0a+lXSiX+boO8AvR/9X+r3vWoI1g17x677o38+/Qatv/49dS7Cm7yCPXNV2YD9aVc1R1Vaqej4wRlXPKaQ0SLW/dA+0O9xaw9wZU11LsGbS5HmuJVgxOXuWawnWTJs81bUEK7KzZ7uWYM2k7LmuJVgzaZonKx2avsTvvqOq55a0D+t8tMV2liSwskuCH23mCX60pUPwoy0FdnQ/2hMO1e8+vCtpu3JH3bu9+tHOVtVjo99/5u+TSAFUVVPKXLddLx2LSGsRmSQik3IXLWBQj24AtGpQl8U/zmPujKnc0cjM/vs93pEh/XsB0Pz0mqxavpTp34znvmsbAdDzwbsLlombnFCVdfl5fPvFKK4+qTpg8tLGwjHGEg18OfS9gny1nVo15dsvRrEuP48mJ1QFzLJzzwfvBuC+axsx/ZvxrFq+lOan1wSge48PaHffAABOOeMusifPI2fuEo4+wQQm79R1MJ26Dgbg6BPakDN3CdmT53HKGXcB0O6+AXTvYZYEq1S7ntzcVWSNncE+B10NQOvbXixYRq50wFXk5a1j+IiJBX62za5/uiBcYywRweC3swry2TZq0oXhIyaSl7eOSgdcBZhl3rY3PwbARee2YuyYSSzN/YUjDzsfgOefe53723cHoF7dpkzJnsXcnEXUPu4yAB7v0pvHu/QGoPZxlzE3ZxH16l5LvbrGL/X+9t15/rnXATjysPNZmvsLY8dM4qJzWwHQ9ubHCpaaD9ynHnl5a/n4ozEFvrg3XPdAQUjHWLKCIe+8V5A/tsWVTfn041Hk5+VR/UDzOQ16ZSDt25rPqclFjZgwdjzLli7lxCPN59T7+V50vt/kc72wXgOmT5nKxWefz5m16wLwzOPdeOZxc++dWbsu8+fOY/qUqVxYz9x7ne/vSO/nzb134pE1WbZ0KRPGjqfJRebea9/27oKl6OoHViUvby0fDR9X4PvaonnHghCKseQAb7/1SUGO2csva8dHw8eRl7eW/fY+BzBLxbfc/AQA5ze4hVNPak5u7goOP/QSAJ579k063GsC4pxetwWTs2eTk/MTNY+9EoDHOvfjsc7GCaHmsVeSk/MTk7Nnc3pd49bU4d7nee7ZN829V7WpuffGTOOc89qbe++W5wuWfivte7m59z76psA3ttl1TxaEWIwlDxj81pcFOWgbNX6UE05sY+69fS8HzFJy61uM5nPOa0/WmGnk5q6iSlVz73R/7n3adegLwCmntSV78lxychZz9HE3AtCpyxt06vIGAEef2ZGc+cvJnr6IUy58HIB2nd+le2+TvKzKiR3IXbaarAlzOKeJuZ9bt3+jYGm4UvU7yMvfwPBPpxX4zja7tT/HnW2uL5ZcYPCQiQU5ahu1eJHhn04jL38DlarfYa5p0FhatzeazmnSnawJc8hdtpoqJ3Yw19T7M9p1fpe0owqbtyQv2y83xf3eHLNHG19ix1Ii3TPal5OFonJFpma0078ZT63TM5PLM1Mz2qyxM6h/9glp7zdTM9qxYyZxdoZcfDI1o50wdjxnnJ2Z+yITM9oxWdn8q/7Jae8XMjejzRozjfr/qp2RvjM1o82aMCcjLj5pn9Eef4h+927bpO3KHfdAiWe0IrIvMAC4AFgJPKCqg4toK8CTQKvoUH/g/ljEQhGpE/V1LDAbuFFVp0Z17YHrgarReV5S1aeLOE9KUX1U9ZFU2llZHUcmztcClYFcTFiqV2IXWVYH2UxSpdqRriVYU+Ooyq4lWFG9ekqrM2WKI6r7dV9Ur+Hfe1yjun9bFTWOONC1hNSI7dGWDr0waVgPBOoAI0RkmqrOLKRta6AxJua+Ap8BC4DeIrILMBToAbwEtAGGikh1Vd2EWXFtAUwHjgQ+FZGfVbWw8IrxT+C7AU2A74BFwGGYAE4pW2mmvHQsIk8B9wFDgPbRSe4FuqXax/bInZed51qCNafWu8e1BCvOPL2ZawnWNDzTr/vijNNucC3BmlP/ebtrCdac2vAJ1xJSpxSMoUSkAmYQ66iq+ao6HhhG0cuy1wPdVXWxqi4BugM3RHX1MZPHHqq6UVV7YgbXBgCq+pSqTlbVzao6BzMoFxpZRlX/EytRH9eqaj1VbaqqZ2LiH6eMzYz2BuAkVV0cOyAiI4DJGH+jHZJB3xT20FW2WbLAL9eT+T995lqCNVPm+3VfLPx5hGsJ1ixZVOjqYplmyZSnXEtIDVXYnJIf7f4iMinu776q2tfiTDWAzaqaE3dsGvCvItrXjOrj29aMq5uekPhmenT8Lz6N0RL0WUAqCYIbAolP+8Mw8f9TwsYYKi8qicd+t+hjuyNmQOUTMQMpX4gZP/lEzLjJF2KGSz7R/Tn//KtjxlResFWTF1ipqqfEFZtBFqAifx9D1gB7FtN+TULbitHAmVhXXF+dMONfKoPlPOC2hGO3kKGkAj2AISLyJLAYs4bdHnhORI6INVLVHy369J5Vy5e5lmBN7tJfXUuwYmnuCtcSrFm+1K/7YmnuStcSrMldusq1BGtyl692LSE1lHQtDWdR9Oz0K+B2INEltBJ/n9TFyE9oXwnIV1UVkcS6QvsSkbaYvdqzVHVjsmvAGF59ICIdgCVAFWAzkHK2E5t8tKm866qq5VM9eWkS/GgzT/CjLR2CH20psKP70R57sH73WqHhff9CudOfKJHVcbRH+xtQU1XnRsdeB3JV9f5C2k8AXlXVftHfLYHWqnq6iFyACYl4aJwV8iKgjaqOimvfBTjbZlIoIjsDp2MMgZcCX6vqH6m+PuWlY1Utl0Ipk4NsJon54fpEzM/WF2I+tD4R85H1hZj/q0+cclpy95OyRswnt8yjim7ZnLSU/DS6FmNg20VEKohIPeAy4I0iXvI6cI+IVBGRykA74LWoLgvYAtwhIrtGM1eA0QAi0gx4AjjfduVVVf9Q1XGq+o6qjrUZZCH9ASt2OG5//FnXEqzp86JfX1AvvvywawnWPP2iX/fFSy8/4FqCNX1evtO1BGv6PN3ctYTUKb0QjLcCuwO/AG8Bt8Rce0TkrGhJOEYfYDgwA/geGBEdI3LhaYxZFl4NtAQaR8cBugL7Ad+JSH5UeqfrIooj5T1aETkMeBQ4EbPpXICq1kizLm/YvULF5I3KGHvuubtrCVZU3LOCawnWVKjo133h43u8Z0W/7mOAPSvs5lpC6mwtndD1qvorZoAsrG4cceNNtCTcgSI8XVR1ClBo5BVVrVZSrduKzYz2XczA/AjG4iq+7LB0auXfsualV6QU9KTMcGXjO1xLsOb6K/26Ly6/zC/faoBLo3CNPnHp9S8mb1QWCEkF0oqN1fExwD9Vt+PcSNtA/9ETXUuwZs6MVFzHyg7TZg11LcGa8dP8ui9mzn7PtQRr5swa4FqCNXPGP+ZaQmooqfrRBlLAZkY7nKLNtHdYYokKfCKWiMAXYkkGfCKWRMAXYgkCfCKWAMAnOj0z3LWEFAkz2nRiM6O9A5ggIvOB5fEVqprcDjwQCAQC/hAG0rRh40c7FDgKGAmsj69T1Y7pl5Zegh9t5gl+tKVD8KMtBXZ0P9rqB+jE55LHYyh/ad/tMh9turFZOm4AnK6q96pqx/iSKXE+0KpBXdcSrInls/WFWK5an4jlovWFWJ5Zn4jlkfWJo8/05OsyFhkqLB2nBZul4+kYH6SiQmPtkHTq79d+J8DwISmlUCwzvPdhT9cSrBn4nl/3xQdD/fL7BRj+YWfXEqwZPtAXH/ZSTZO33WMz0I7G5O97lb/v0b6SVlUesX5tfvJGZYy8vPXJG5Uh8vPWupZgzdp8v+4LH9/jvHy/7mOAvLUbXEtInTDQpg2bpeMzMQGVL8DkCowVj0KdpJ8XHvLP/7BNW098+SLa3tLVtQRr2rf167649Zb/upZgTZtbnnctwZo27Qe5lpAaCvrH1qQlkBopz2hV9ZxMCvGVnsNGu5ZgzaQJPVxLsOKriX4twwJ88pVf98U3E/1LRTjpW78eGAEmffKQawmpoQpbSicy1I6AVaxjEdlPRK4TkfbR35VFxD9T0zTS73FPjBviaHefX47+97fv7lqCNZ3v9+u+6HCvf7PDdh1sU5+6p13nd11LSAkFdKsmLYHUsIl1/C/gfWASUA94GqgO3AtcmhF1aaTCLlupe3D696G+O2rfjPSbSSofvG9G+pUnMjO4VJ41P2N9H3RTZm7do/ZZwUFrMpOYXHMXp73PQ/iFnaZ+lPZ+Ada/m5koWQdMW8j6js9lpO9dMnS/HXzUdLYcWDPt/erOaY77rIQZbRqxTfx+tap+ISIxx7hvAb/8GNLMbXe1di3BmnZ3Xe5aghV31jvStQRr2t18vmsJVtzT/J+uJVhzZ93DXUuw5u57rnUtITVU0T+2uFax3WCzdHy4qsYiPsQedTZhN1hvd9SsdqprCdZUqXa9awlWHPHMZ64lWFPlxEKTi5RZDrkoMzPDTHJkryzXEqw57BBPfMJjM9pkJZASNgPtLBG5MOHYeZi8gDssn3+VmeW2TPLdV375TI5vfZZrCdZ8N/JB1xKsmPhGK9cSrBnf4nTXEqz5ZqJH9hFbNXkJpITNQHsP8KaIDAR2F5E+mMz27TMhzBfmz/vRtQRrcublupZgxbxVfvmkAuT8uDx5ozJEzqJVriVYM/e3da4lWDM35yfXElJDQbdo0hJIDZuB9iygFjATeAVYgNmf9W+6kUae6trDtQRrOvuWvScrx7UEazp392ulo0vfsa4lWPPE+HmuJVjTpYsnsX1U4Y8tyUsgJWz2Vx9R1WeAp+IPisjDgF9rkWlk2KfvuJZgzZef+hWc4JP/nOFagjVfvt/OtQQrRvdt4VqCNaOa+meH+cVof3x/g/tO+kg6oxWRBiLSACgvIufE/o5KK3bw2Md333a/awnWtL7Nn392gNuGTXctwZrW7f3Kldrmcb9m4ABtR810LcGam9t4kqc4GEOllVSWjgdEZTfMknHs7/5AS+D2jKnzgDon1XItwZpTTjrKtQQrTqq8l2sJ1pxSu6prCVacfGxl1xKsOemgSq4lWHPyyce4lpAiKQyyaRpoRWRfEflARNaKyCIRaVpMWxGRbiKyKirdRETi6uuISLaIrIt+1imkj11EZLaIpN8hvQiSLh2rajUAEXldVf1bX8ow199Y5D1RZml940WuJVhx4yl+DVoArZuf7VqCFa2vOMm1BGta1vEvl/BNrf1x7ylFP9peGFfRA4E6wAgRmaaqhS1ZtAYaA7WNSj7D2Av1FpFdgKGYmA8vAW2AoSJSXVU3xfXRHlgB7JmJiymMlI2hwiBbOFUPOM61BGsqHXCVawlW/OPxka4lWFOp+h2uJVix19meLGnGceBzn7uWYM0+e/kRyES1dEIwikgFoAnQUVXzVXU8MAyTsKYwrge6q+piVV0CdAduiOrqYyaPPVR1o6r2BASTSz12vmqYRDilaqhiFes48He+/zEz4eUyyZIfX3MtwYr59/rx5RTPkilPJW9Uhlg88i7XEqyZd2t91xKs+WnxUNcSUiTlpeP9RWRSXLENlVcD2Kyq8a4F04Ci4lTWjOoLa1sTmK6q8U8A0xP6egF4ECjVHIthoC0hX439xrUEa7LGfu9aghVjF/rn45k1YY5rCVZkZS9yLcGacT//6lqCNWOyJruWkBpKqgErVqrqKXHFNtNDReD3hGNrKHpZt2JUH9+2YrRPm1j3l75E5HKgvKp+YKmxxISBtoS8/opfPqkA/V4Z5VqCFa9M8m8Q6DdonGsJVvT7wJMBII5XppaaLUva6N9vmGsJqZGmfLQikiUiWkQZD+QDiVZtlSjamyWxfSUgP5rFFtlXtET9FOBkTycMtCVk8PueOKDHMez9R1xLsOL9Zv75Sw57va1rCVYMe+4a1xKsee9K/wy4Phzmy5aCwpatyUuyXlTrq6oUUc4EcoCdRKR63MtqYwIjFcbMqL6wtjOBWvFWyPwZZKk6cDgwTkSWAUOAg0VkmYgcnvRCSkgYaEtI6+v9825qdv3TriVYccN7/s22mt3a37UEK5o9NMS1BGtuGDYteaMyxnXNOrmWkBqlZAylqmsxg14XEakgIvWAy4CiHNFfB+4RkSoiUhlohwkFDJAFbAHuEJFdRST2tDsa+B44FGPVXAdoBSyPfv+5xBeShB068046uKDhua4lWHNJQ78yDjWscaBrCdZccp5f/tWXnFU9eaMyRsOjDnAtwZqLL/EoylnpBaS4FROj4RdgFXBLzLVHRM4CRqpqxahtH+AI/kxm0z86hqpuEpHG0bEngdlA4zjXnmWxE4rIr8BWVS04lknCQFtCrrymsWsJ1jS9pr5rCVZcXauKawnWNL3Cr+Xuphed4FqCNVcf51+QjWubXuBaQmoobE1hDzYtp1L9FeMbW1jdOIyRU+xvBTpEpbD2U4CTUzhnFnCIvdpto9SWjkWkbWT+vVFEXos7fni0MZ4fVzoW08/hIvJlFPnjBxE5r1QuoAj2392/YArldr/UtQQr9njUv/CA5Sq3cS3BivKnPOZagjUVun3iWoI1O5c/07WElFBNvmwcYiGnTmnOaHOBrsCFwO6F1O+tqptT6Oct4Gvg4qi8F0X+WJE2pRasXO+fRezW9cNdS7BiXed/u5ZgzdbcPq4lWLFlUpHPtmWWtfclpscu+/yxZbxrCSmzNcQyThulNqNV1SGq+iFmDX6bEJEawEnAo6q6XlXfx6zVN0mPSnvee/tDV6feZga/neVaghXvTF/iWoI1g4f4Fchk8KgZyRuVMd6Z5VdeZYC3Bn/qWkJqlJIx1I5CWbI6XiQii0XkVRHZv4g2NYEfVTXex6q4KCIZ59ORX7g69TYzYuR3riVYMTLHryTqACM+9yvj0Ihxc11LsGbkPCeLWCXi4xETXEtICVVl6x9bkpZAapSFgXYlcCpQFbOJvSfwZhFti438kYiItI6FBlswbyHduj4HQN0T6jNv7o9MnTyDBmdcAkDH+x6jVw8T1KRmtVNZmruc8WO/ptEFVwMmHd7AASY4RdUDjiMvL59RIz4nP38tYNx8YrPb2L7te29/WOD+07RJS0aN+Jy8vPyC+MgDBwwuSLPX6IKrGT/2a5bmLqdmNWMV3L3HB7S7bwAAp5xxF9mT55EzdwlHn2D2/zp1HUynKIn70Se0IWfuErInz+OUM+4CoN19A+jewwRBqVLtenJzV5E1dga5S01Enda3vUjfASZ4RaUDriIvbx3DR0ykUZMugHEDis1+Y/u6g9/OKnAPatSkC8NHTCQvb11B/OQBkxYVpLW78NUJjF2wktzfN3DEM58B8PxX87l/1CwAzug9jsm5q5m7Mp9aPb8EoOuXc+j6pYmqVKvnl8xdmc8dZxzBGb1NAIj7R83i+a/mA3DEM5+R+/sGxi5YyYWvmi+w24ZNZ0AU4OIfj48kb+NmRsxZTpM3zQzzhvcmF8yQY3u/g4dMLHDHadTiRYZ/Oo28/A0F8Yr7DhpbkPbunCbdyZowh9xlq6lyorHH6N77M9p1ftd8Thc+Tvb0RTza7lKOPtMsx3Z6ZjidnjHL9Uef2ZGc+cvJnr6IUy583HxOnd+le2/z/lQ5sQO5y1aTNWEO5zTpbj6n9m/Qd5BJzF6p+h3krd3I8LE5NLr7bfM5PTSkYEYa22sdPGpGgctOo7vfZvjYHPLWbiyIadx3yOSC1HgNWr/OTZefRO6KPA65yPyPPDvoa+59zmg6tXk/smcvJWfRKo65ohcAnfuMoXOfMQAcc0UvchatInv2Uk5t3g+Ae5/7jGcHfQ3Akb2yWJq3gbE//cpFg83n0HbUTF6ZarwqDnzuc/I2bubjeb9wZeTKdcOwaQUz1the7Duzcgtceq58bzJXHXcweRs3F8Q8fmXqzwWp8y4aPJGxP/3K0rwNHNkry9x7Exdy/+gfAKj32tdMWbaGub+upXZfc289Pn4ej0fJ5I875hpycn4iO/sH6p7aEoD2977Ac8++BcBhh1xGbu5KxmRN5twGxovk5jbd6NfXhFjcZ6/zyctbx0fDx9O4kblPrmvWqcDqOLZX+9bgTwtcfho36sBHw8eTl7euICZyv75DC1LrndugLWOyJpObu5LDDjHJCZ579i3a3/sCmSDMaNOH/DUsZCmcUKQrcIiq3lBE/UHAUqBSwsw1FkLrcVU9Lu7YCwCqWqxDa52Ta+kXX6XfqKZpk5YZC1qxL5mZFTVq0iUjQSs2PJWZfckmb07MWNCK3W7KjGFYoxYvZixoheamPyJSo7vfzljQig3vZmYZ/cr3JmcsaMUuT2Rmz7pxow4ZCVpxWt0byZ70gyRvmRp19q2on12Q3EXtH+98na2qp6TrvNsrZWFGm0hs5C9M20zgCBGJn8EWF0Uk47Ro6V+avJta+pUmr6WHafJuan6WawlW3HS5f1GWWtYpNe+MtNHqpkauJaRMmNGmj9J079lJRHYDygPlRWS36NhpInK0iJQTkf2AnkCWqiYuERNleJgKPBq9/nJMiK33S+s6Eql39umuTr3N1D/7eNcSrDj78P1cS7Cm/hlHu5ZgRf2T/XuYOevQfV1LsOZf9f14oFFVtvyxNWkJpEZpzmgfxqQmuh+TD3B9dOwIYBQmiPT3wEbg2tiLRKS3iPSO6+ca4BTgN0z0jytdufYAHH+EX4EJAKoccYNrCVYcGe3v+kRs/9YXDmnYw7UEa456Kcu1BGtie6tlnmB1nFZKzY9WVTsBnYqofquY192c8PdCTILfMsGiFbNcS7Dm9xX/cy3Bil8eauhagjW/z+3pWoIVa8be51qCNcvvdhqrZpv4bY0/D41hIE0fZXGP1itilsg+EbM09oUBHqbJi1kJ+0LfIf4lbohZLftEzCq5rKMKukWTlkBqhIG2hEyd7Je/JMCkyfNcS7Bicu7ftuvLPJOm+fVwkD3bv+APk5cl5gsv+2Rn/+BaQmoEP9q0EpIKlJDnej3pWoI1fXv5lSu1VyO/MuEA9H36OtcSrOjzkH9hLl+8yFmcmm2mdx9/lujD0nH6CDPaEhILaOET51zwgGsJVsSCUfhELNiELzRo/bprCdbEgl/4RCy4RVlHFbZu1aQlkBphRltCOjx8l2sJ1jz6sF++vw/Vr+FagjWPtvNrhvhI67NdS7DmwTOPci3BmkceaelaQsqEPdj0EQbaEnLkUUe4lmBNjaP8yuN51H4VkzcqY9Q4wq9k9TWq+uerXH2fPVxLsKZ6jcNcS0gN1VLLR7sjEJaOS8h59fyauQCcWu8e1xKsODOKResTpzZ8wrUEK+pe19+1BGvOfP0b1xKsOb3uja4lpEbwo00rYUZbQmYu8CsTDsCSBQNdS7Dix3vPdy3BmiVT0h/PNpMsHnW3awnWzL+tvmsJ1vy02BP3HoIxVDoJM9oSEsv44xOxjD6+EMvW4xOxbDy+EMu04xPPT1zoWoI1sew/ZZ4QgjGthIG2hCxb6l+u1FiaPF9YmrfRtQRrcpevdi3BitwV+a4lWLM0f4NrCdbk5q50LSElFNi6NXlJByKyr4h8ICJrRWSRiBRprSmGbiKyKirdRETi6uuISLaIrIt+1kl4/UkiMlZE8kVkuYjcmZ6rKJ4w0JaQx7plJp1WJunezZN9oognLzoueaMyRvdH/8+1BCueudu/5fknGxzjWoI1Tz9TbDbPsoOW3kAL9AI2AQcCzYCXRaQoJ+nWQGNM1rZawKVAGwAR2QUYCgwC9gEGAkOj44jI/pi4+n2A/YCjgE/TdhXFEAbaEhJLHO8TscTwvhBL+u4TsaTuvhBL2O4T9V7zb7k7lkTeB0pjoBWRCkAToKOq5qvqeGAYUFTEl+uB7qq6WFWXAN2BG6K6+hi7ox6qulFVewICNIjq7wE+UdU3o/o8VZ1d8qtIThhoS8izL/oXGarPi344zcd4sdEJriVY0+fp5q4lWNHby8hQ/q10vNzbj6xOqrB5S/IC7C8ik+JKa8tT1QA2RylQY0wDiprR1ozqC2tbE5iuqvFWXNPj6k8HfhWRCSLyi4gMF5FS8bcKVsclpOKeFVxLsGbPPXd3LcGKPXfx7zbds8JuriVYseceu7iWYE1FH++LPf3w/Y3t0abASlU9pQSnqggkBq1eA+xZTPs1CW0rRvu0iXWJfR0CnAScD8wAnsJkjqu3reJTJcxoS0jTK/xZCopx6RVdXEuwoslg/1yoLr3+RdcSrGh099uuJVhz5Xv+ZRxq3MiPGW269mhFJEtEtIgyHsgHKiW8rBImP3lhJLavBORHs9hkfa0HPlDV71R1A9AZOENE9kp+JSUjDLQlZOKMLNcSrJkzo49rCVZMv+Mc1xKsmTP+MdcSrPhhyG2uJVgzrfVZriVYM+sHfx5o0jHQqmp9VZUiyplADrCTiFSPe1ltYGYRXc6M6gtrOxOoFW+FjDGYitVPx0zWC+Qlv4L0EAbaEtKt63OuJVjTqatfOXS7fjnHtQRrOj0z3LUEKzr3GeNagjWPj/cr3SNAl84DXEtICVXYvDl5Kfl5dC0wBOgiIhVEpB5wGfBGES95HbhHRKqISGWgHfBaVJcFbAHuEJFdRSRmjDI6+vkqcHnkArQz0BEYr6oZz8Mpf9033n4RkRVAJpKE7g/44Rz3J75p9k0v+KfZN70QNMdTVVUPSFdnR++ym/Y94PCk7ernzsku4R4tIrIv8Apm73QVcL+qDo7qzgJGqmrF6G8BugGtopf3B+6LGUCJyInRseOA2cCNqjol7ly3AA8DewDjgVtV9eeS6E/pGneUgTZTiMikkt5opY1vmn3TC/5p9k0vBM2Z5Oidd9Pe+x+etF2DZSUfaHcE/DPbCwQCgUDGCZOw9BEG2kAgEAj8hdgebSA9hIG25PiXVcA/zb7pBf80+6YXguaMksYQizs8YY82EAgEAn+h+k676fMVqyZtd8manLBHmwJhRhsIBAKBv6JhRptOwkAbCAQCgb8Q9mjTSxhoA4FAIPA3toZdxbQRBtpAmUJEjsWkyKqJCQaehwmh9kZppbQKBHZ0LJIKBFIgDLSWiMgFmPyHiQPBq6r6mUNp3iMi1wIvY/JRjsVk3qiEiWc6QURuVtV3HErcbojSmd2AuY8rYgKyx+5j/5LTllFEZCdMPtTE74vRqlp2F2fDHm1aCQOtBSJyN9AB6Ae8z18HgoEi0k1Vn3co0XeeAC5R1a8SK6IYqG8CYaAtISLyJHApJmn2NP68j+tg4sgeoaoPuFO4fSAitYGhmOTj0/nzfb4TUBG5TFWnO5RYJErYo00nwb3HAhHJBRqo6g+F1B0DfKmqB5e+su0DEckHDlDV9YXU7QH8Eot5Gth2orjftVR1aSF1lTHJs/cvfWXbFyLyHfC6qr5QSF1b4Iay6hojIqMwcZmTsVJVL8q0Ht8JA60FIrIGOFRVExMVIyJ7A4tUNeO5DbdXROQDYAPwsKrOjzt+JNAF2ENVL3elb3tBRFYCJxQz0M5Q1f1KX9n2hYisBfZW1T8KqdsF+E1VK5S+skBpE9Lk2fE+MFxEzhWRA0RkFxHZX0TOBT4A3nOsz3daRj9niUi+iORGs9yZmOW3lkW/NGDBAGC0iLQSkVNFpIaInCIiNwKfYbZGAiVnNnBLEXVtovrADkCY0VoQPYV2BloAB2O2MgRYismT+KiqbnKncPsgWiauwZ9GOjmqus6tqu0LEWmDuY8TjaFeV9U+LrVtL4hIHeBDoDx/3aOthcmb2lhVp7nSFyg9wkC7jURLxRWBfFVd7VbN9oWIVMAMtDErzZwoQXQg4BVRgvFzMPlR4x9osgpbUg5sn4SBdhsQkRr81Vz/e1Wd61aV/0QPLy8DVwCb+HMGsAtm2f628FCTHopwO/keY9AX7E0DgTQSBloLROQwjHtJbWA+fw4ER2LcJK5R1Z/cKfQbERkCrAc6quqPccePwCzZV1DVK1zp214oxu2kFmY7pMy6nfhG8FcOQBhorRCRL4BsoFP8nmG01PkIcKqqNnClz3dEJA84sLD92Og9Xqaqe5a+su0Ln91OfCKZvzIwPPgr7xiEgdaCyAJ238IMnkRkV+DXYK6/7YjIQqBZMQErBqtq8txdgWIJbielQ/BXDsQIkaHs+Bn4NzCkkLqLgbBsXDIeBEaKyDD+PgP4N3CzO2nbFTG3k56F1AW3k/QhJawPbCeEGa0Fkb/s+xijkcSBoCbQRFVHOxO4HSAiNYGm/HVP63vMbHaWS23bC8W4ndQGNhPcTtKCiHQDGvH3pePa/Ll0fL87hYHSIgy0lojIfhir2ETjhg9UdaVLbdsrIjIJuEBVf3WtZXshcjupz98faMYEt5P0EfyVAxAG2rQhIuWBh1S1i2stviIirxdR1QQYAWxQ1RalKGm7RETKAbdivvxHqerQaPbVEDPzukdVV7jUGAhsT4QQjOljJ+BR1yI85/+AqsA8jPtUrGwFFkW/B0rO80BrjO9sVxF5BTga6AhUAP5mjRxIP5G7YGAHIMxoLYi+kIpiJ4zFbPnS0rO9ISLVgReB3zCzqtzo+FKgtqr+4lLf9kKUhaqOqv4iIlUwRnz7q+pvUdCQHFX9h1OR2zmRl8K68H2xYxCsju1oignIXtheYfiHKSFRdK0LReQa4EsR6Qf0wARRCKSP3TAPM2Du5a2YvUMws9zwvZAGROTsYqp3LTUhAeeEGa0FkaP/Y6o6rJC63TBPqGE5Pg2ISCVMarzzMMvJR4YZbXoQkRHAcuB/wLWYvdr3gF4Yt5+GqnqOO4XbByKyFZNwZGsRTSqHGe2OQRhoLRCR24AlqvphIXXlMXlUO5e6sO2YyBXlX0AfVd3gWM52gYhUBV4CDsesGIwDRgGHAguAK0IIxpIjIgsw20kTCqnbDVgbBtodgzDQBgIBREQwUc9WudayvSAi7wLjVPVvgUGiCFxzVLVa6SsLlDZhoA0EAoEMEPkqE/ySA2E/MRAIBDKAqv5R1CArIuVF5JHS1hRwQ5jRBgKBQCkT3Ht2LIIZfyAQCGSAFPzuAzsI4cMOBAKBzBD87gNAWDoOBAKBjBD87gMxwoccCAQCmeE1iv6O/QMIPvc7CGFGGwgEAoFABgkz2kAgEAgEMkgYaAOBQCAQyCBhoA0ELBGRhSJyXgrt6ovI4m08x+EioiJSrGeAiGSJSKsi6g4TkfwoDncgEHBEcO8JBLZTVPUnoKJrHYHAjk6Y0QYCgUAgkEHCQBvwgmi59gERmSUiv4nIq5EvIiJyk4jME5FfRWSYiFSOe93zIvKziPwuItkiclYK59pdRAZG55ktIh2KWgIWkV1FpIeI5EalRxReL77NgyKyMrqGZnHHLxGRKZG2n0Wk0za+PUeKyMSon6Eism/U/1+Wn6Nl5sdE5CsRyRORT0Vk/208ZyAQSJEw0AZ8ohlwIXAkUAN4WEQaAP8FrgIOBhYBb8e95jugDrAvMBh4NzZAF8OjmFytRwDnA82LafsQcHp0jtpAXeDhuPqDgP2BKsD1QF8ROTqqWwu0APYGLgFuEZHGSbQVRgugJeb6NwN/S8sWR1PgP8A/gF2Ae7fhfIFAwIIw0AZ84kVV/VlVfwUeB67FDL6vqOpkVd0IPAD8U0QOB1DVQaq6SlU3q2p3YFfg6CL6j3EV8ISq/qaqiyl+4GoGdFHVX1R1BSYIwXUJbTqq6kZVHQOMiPpHVbNUdYaqbo0Srb+FSXJvyxuq+r2qrgU6AlcVYwD1qqrmqOp64H+YB4RAIJBBwkAb8Imf435fBFSOyqLYQVXNB1ZhZpCIyL3R8u8aEVkN7IWZYRZH5YRz/VxUw8Tzx+mK8Vs0AP6tXkROE5EvRWSFiKwBbk5BW2Ekvi87F9PPsrjf1xGMpQKBjBMG2oBPHBr3+2FAblSqxg6KSAVgP2BJtB/bATOD3EdV9wbWAJLkPEuBQ4o4byJ/OX+crhj7RJoKqx8MDAMOVdW9gN4paCuMxPflD2DlNvQTCAQyQBhoAz5xm4gcEhn7PAS8g1lu/Y+I1ImMkJ4AvlXVhcCemD3LFcBOUaLtSimc53/AAyKyj4hUAdoW0/YtzF7xAZFh0SPAoIQ2nUVkl2jg/zfwbnR8T+BXVd0gInUx+6fbQnMROU5E9gC6AO+p6pZt7CsQCKSZMNAGfGIw8CnwIzAf6Kqqn2P2Jd/HzESPBK6J2n8CjAJyMEuqGyh+GThGF2AxsAD4HHgP2FhE267AJGA6MAOYHB2LsQz4DTOLfRO4WVV/iOpuBbqISB5mgP5fCtoK4w1MAPtlwG7AHdvYTyAQyAAhqUDAC0RkIdAqGlhL+9y3ANeo6rYYKgUCgR2cMKMNBBIQkYNFpJ6IlItccdoBH7jWFQgE/CQMtIEdEhEZGcUBTiwPYvxL+wB5wGhgKPCSQ62F6cxPJfhGIBBwT1g6DgQCgUAgg4QZbSAQCAQCGSQMtIFAIBAIZJAw0AYCgUAgkEHCQBsIBAKBQAYJA20gEAgEAhkkDLSBQCAQCGSQ/weh/TVHYLpMWgAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "
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" ] }, "metadata": { @@ -1864,7 +1773,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2015,7 +1924,7 @@ "[180 rows x 5 columns]" ] }, - "execution_count": 94, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2044,7 +1953,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2054,19 +1963,19 @@ " -0.00235475])" ] }, - "execution_count": 95, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "#105.68%\t-0.42%\t12.85%\t-5.71%\n", + "# show model coefficients\n", "coeffs" ] }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2230,7 +2139,7 @@ "[180 rows x 6 columns]" ] }, - "execution_count": 96, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2239,8 +2148,6 @@ "# populate pivot table from predicted mpm data\n", "matr[mlfm_sel] = mlfm_6(matr, coeffs[0], coeffs[1], coeffs[2], coeffs[3], coeffs[4], coeffs[5])\n", "\n", - "#matr[mlfm_sel] = mlfm_6(matr, coeffs)\n", - "\n", "matr" ] }, @@ -2253,7 +2160,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -2353,7 +2260,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2517,7 +2424,7 @@ "[156 rows x 6 columns]" ] }, - "execution_count": 98, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2539,14 +2446,14 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": { @@ -2577,14 +2484,14 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -2653,6 +2560,13 @@ "\n", "Many more papers are available at www.steveransome.com \n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index eff8ae0384..c22bd9b5d5 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -624,6 +624,69 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, except ImportError: raise ImportError('plt_mlfm_stack requires matplotlib') + stack6 = ['i_sc', 'r_sc', 'i_ff', 'i_v', 'v_ff', 'r_oc', 'v_oc'] + stack4 = ['i_sc', 'i_mp', 'i_v', 'v_mp', 'v_oc'] + + if all([c in dstack.columns for c in stack6]): + # data order from bottom to top + ydata = [dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), + dstack['v_oc'] - ( + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), + dstack['r_oc'], + dstack['v_ff'], + dstack['i_v'], + dstack['i_ff'], + dstack['r_sc'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_r_oc', + 'stack_v_ff', + '- - -', + 'stack_i_ff', + 'stack_r_sc', + 'stack_i_sc'] + color_map = [ + 'white', # colour to bottom of graph + clr['temp_module'], + clr['v_oc'], + clr['r_oc'], + clr['v_ff'], + clr['i_v'], + clr['i_ff'], + clr['r_sc'], + clr['i_sc']] + + if all([c in dstack.columns for c in stack4]): + # data order from bottom to top + ydata = [dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), + dstack['v_oc'] - ( + dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), + dstack['v_mp'], + dstack['i_v'], + dstack['i_mp'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_v_mp', + '- - -', + 'stack_i_mp', + 'stack_i_sc'] + color_map = [ + 'white', # colour to bottom of graph + clr['temp_module'], + clr['v_oc'], + clr['v_mp'], + clr['i_v'], + clr['i_mp'], + clr['i_sc']] + # offset legend right, use ~1.2 bbox = 1.2 @@ -633,55 +696,6 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, ax1.set_title(title) - ydata = [dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), - dstack['v_mp'], - dstack['i_v'], - dstack['i_mp'], - dstack['i_sc'] * (not is_i_sc_self_ref)] - labels = [ - 'pr_dc', - 'stack_t_mod', - 'stack_v_oc', - 'stack_v_mp', - '- - -', - 'stack_i_mp', - 'stack_i_sc'] - color_map = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['v_mp'], - clr['i_v'], - clr['i_mp'], - clr['i_sc']] - - if all([c in dstack.columns for c in ['v_ff', 'r_oc']]): - # replace v_mp with v_ff and r_oc - ydata.pop(3) - ydata.insert(2, dstack['v_ff']) - ydata.insert(2, dstack['r_oc']) - labels.pop(3) - labels.insert(2, 'stack_v_ff') - labels.insert(2, 'stack_r_oc') - color_map.pop(3) - color_map.insert(2, clr['v_ff']) - color_map.insert(2, clr['r_oc']) - - if all([c in dstack.columns for c in ['i_ff', 'r_sc']]): - # replace i_mp with i_ff and r_sc - ydata.pop(-1) - ydata.append(dstack['r_sc']) - ydata.append(dstack['i_ff']) - labels.pop(-1) - labels.append('stack_r_sc') - labels.append('stack_i_ff') - color_map.pop(-1) - color_map.append(clr['r_scf']) - color_map.append(clr['i_ff']) - # plot stack in order bottom to top, # allowing self_ref and temp_module corrections ax1.stackplot(xdata, *tuple(ydata), labels=labels, colors=color_map) From ed5f8c2dd3c8fc5ddfc666ed3a1d681e8856292b Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 30 Jun 2022 14:11:48 -0600 Subject: [PATCH 60/81] fix up last notebook --- docs/tutorials/mlfm_220627_1.ipynb | 1975 +++++++++++++++++++--------- 1 file changed, 1332 insertions(+), 643 deletions(-) diff --git a/docs/tutorials/mlfm_220627_1.ipynb b/docs/tutorials/mlfm_220627_1.ipynb index d985a08589..0d4b5a73ff 100644 --- a/docs/tutorials/mlfm_220627_1.ipynb +++ b/docs/tutorials/mlfm_220627_1.ipynb @@ -43,13 +43,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "#import pvlib\n", - "from pvlib import *\n", - "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", @@ -61,9 +59,9 @@ "root_dir\n", "\n", "# Import essential library file with lfm and mpm definitions\n", - "from mlfm_220627 import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", + "from pvlib.mlfm import mlfm_meas_to_norm, mlfm_6, mlfm_norm_to_stack, mlfm_fit\n", "# Import graphics code \n", - "from mlfm_220627 import plot_mlfm_scatter, plot_mlfm_stack \n", + "from pvlib.mlfm import plot_mlfm_scatter, plot_mlfm_stack \n", "\n", "# STANDARD DEFINITIONS\n", "\n", @@ -77,15 +75,6 @@ "plt.bbox = 1.4 # offset right to not overwrite" ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "##root_dir ##SR##" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -122,74 +111,47 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"\\nNotes for Cliff Hansen 220624t17\\n\\nMy comments are marked ##SR##\\n\\nI can't get the stacked plot chart to work option 0. section\\nKeyError: 'v_mp'\\n\\n\"" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"\n", - "Notes for Cliff Hansen 220624t17\n", - "\n", - "My comments are marked ##SR##\n", - "\n", - "I can't get the stacked plot chart to work option 0. section\n", - "KeyError: 'v_mp'\n", - "\n", - "\"\"\"\n", - "\n", - "##meas.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# select one of the following meas files\n", - "################\n", - "meas_file = 0 #\n", - "################\n", - "\n", - "if meas_file == 0:\n", - " mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", - " # 6 measured LFM variables\n", - " # date_time, module_id, \n", - " # poa_global, wind_speed, temp_air, blue_frac, beam_frac, temp_module, \n", - " # v_oc, i_sc, i_mp, v_mp, \n", - " # r_sc,\tr_oc\n", - "\n", - "elif meas_file == 1:\n", - " mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", - " # 4 measured LFM variables \n", - " # date_time, module_id,\tpoa_global,\ttemp_module,\n", - " # i_sc,\tp_mp, i_mp,\tv_mp, v_oc,\tff,\t\n", - " # temp_air,\trelative_humidity, pressure, precipitation, dni, ghi, dhi, soil, wind_speed\n", - " \n", - "elif meas_file == 2:\n", - " mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", - " # 4 measured LFM variables\n", - " # date_time, module_id,\ttemp_module, poa_global,\n", - " # i_sc, v_oc, i_mp,\tv_mp, p_mp, \n", - " # wind_speed\n", - " \n", - "##SR##\n", - "#elif meas_file == 3:\n", - "# mlfm_meas_file = 'x19074005_iec61853.csv' \n", "\n", - "# optional\n", - "# elif meas_file == -1:\n", - "# mlfm_meas_file = 't1_041.csv'\n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' \n", + "# File contains: \n", + "# - date_time, module_id, \n", + "# - poa_global (W/m2)\n", + "# - wind_speed (m/s)\n", + "# - temp_air (C)\n", + "# - blue_frac\n", + "# - beam_frac\n", + "# - temp_module (C) \n", + "# 6 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - r_sc, r_oc\n", + "\n", + "mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' \n", + "# File contains: \n", + "# - date_time, module_id, \n", + "# - poa_global (W/m2)\n", + "# - wind_speed - all zeros (m/s)\n", + "# - temp_air (C)\n", + "# - temp_module (C) \n", + "# - unused environmental quantities: relative_humidity, precipitation, dni, ghi, dhi, soil\n", + "# 4 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - unused IV curve quantities: p_mp, ff\n", + " \n", + "# mlfm_meas_file = 'x19074001_iec61853_041.csv' \n", + "# File contains: \n", + "# - date_time : integers in this file\n", + "# - module_id, \n", + "# - temp_module (C) \n", + "# - poa_global (W/m2)\n", + "# - wind_speed - all zeros (m/s)\n", + "# - 4 measured IV curve quantities\n", + "# - v_oc, i_sc, i_mp, v_mp, \n", + "# - unused IV curve quantities: p_mp\n", " \n", "# extract module id from filename e.g. 'g78'\n", "mlfm_mod = mlfm_meas_file.split('_')\n", @@ -232,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -242,13 +204,6 @@ "ref_data = pd.read_csv(ref_file_name, index_col='module_id')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -258,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -274,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -293,7 +248,7 @@ " beta_v_oc = ref_data['beta_v_oc'].values[0],\n", " alpha_i_mp = ref_data['alpha_i_mp'].values[0],\n", " beta_v_mp = ref_data['beta_v_mp'].values[0],\n", - " gamma_p_mp = ref_data['gamma_p_mp'].values[0],\n", + " gamma_pdc = ref_data['gamma_pdc'].values[0],\n", ")\n", "\n", "# create p_mp and ff in case they don't exist\n", @@ -320,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -339,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -365,19 +320,23 @@ " \n", " module_id\n", " poa_global\n", - " wind_speed\n", - " temp_air\n", - " blue_frac\n", - " beam_frac\n", " temp_module\n", - " v_oc\n", " i_sc\n", + " p_mp\n", " i_mp\n", " v_mp\n", - " r_sc\n", - " r_oc\n", + " v_oc\n", + " ff\n", + " temp_air\n", + " relative_humidity\n", + " pressure\n", + " precipitation\n", + " dni\n", + " ghi\n", + " dhi\n", + " soil\n", + " wind_speed\n", " poa_global_kwm2\n", - " p_mp\n", " \n", " \n", " date_time\n", @@ -396,78 +355,116 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 2016-01-26 07:20:00-07:00\n", - " 78\n", - " 2.666484\n", - " 1.472832\n", - " 8.177979\n", - " 0.454992\n", - " 1.100000\n", - " 2.081940\n", - " 33.040644\n", - " 0.013215\n", - " 0.009809\n", - " 24.337320\n", - " 115258.549800\n", - " 608.680999\n", - " 0.002666\n", - " 0.238726\n", - " \n", - " \n", - " 2016-01-26 07:30:00-07:00\n", - " 78\n", - " 7.899143\n", - " 1.297711\n", - " 8.241425\n", - " 0.522027\n", - " -0.100000\n", - " 2.436985\n", - " 37.644029\n", - " 0.037249\n", - " 0.029832\n", - " 29.624980\n", - " 8253.745059\n", - " 150.461283\n", - " 0.007899\n", - " 0.883783\n", - " \n", - " \n", - " 2016-01-26 07:40:00-07:00\n", - " 78\n", - " 52.927672\n", - " 0.955482\n", - " 7.739624\n", - " 0.270154\n", - " 0.300267\n", - " 2.592087\n", - " 39.649206\n", - " 0.072837\n", - " 0.061196\n", - " 32.444868\n", - " 4762.543972\n", - " 63.660028\n", - " 0.052928\n", - " 1.985488\n", + " 2013-01-04 08:05:12-06:00\n", + " n05667\n", + " 24.0\n", + " 4.7\n", + " 0.1361\n", + " 4.869814\n", + " 0.1255\n", + " 38.8033\n", + " 45.8512\n", + " 78.01\n", + " 4.9\n", + " 82.2\n", + " 1007.1\n", + " 0\n", + " 0.0\n", + " 18.1\n", + " 18.3\n", + " 1.0\n", + " 0\n", + " 0.0240\n", + " \n", + " \n", + " 2013-01-04 08:10:12-06:00\n", + " n05667\n", + " 33.0\n", + " 5.2\n", + " 0.1788\n", + " 6.530535\n", + " 0.1657\n", + " 39.4118\n", + " 46.4089\n", + " 78.72\n", + " 5.0\n", + " 82.9\n", + " 1007.0\n", + " 0\n", + " 1.6\n", + " 24.9\n", + " 25.0\n", + " 1.0\n", + " 0\n", + " 0.0330\n", + " \n", + " \n", + " 2013-01-04 08:15:12-06:00\n", + " n05667\n", + " 204.7\n", + " 6.5\n", + " 1.1024\n", + " 45.349227\n", + " 1.0548\n", + " 42.9932\n", + " 50.0780\n", + " 82.15\n", + " 5.2\n", + " 81.9\n", + " 1007.1\n", + " 0\n", + " 353.4\n", + " 53.0\n", + " 31.5\n", + " 1.0\n", + " 0\n", + " 0.2047\n", " \n", " \n", "\n", "" ], "text/plain": [ - " module_id poa_global ... poa_global_kwm2 p_mp\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 78 2.666484 ... 0.002666 0.238726\n", - "2016-01-26 07:30:00-07:00 78 7.899143 ... 0.007899 0.883783\n", - "2016-01-26 07:40:00-07:00 78 52.927672 ... 0.052928 1.985488\n", + " module_id poa_global temp_module i_sc \\\n", + "date_time \n", + "2013-01-04 08:05:12-06:00 n05667 24.0 4.7 0.1361 \n", + "2013-01-04 08:10:12-06:00 n05667 33.0 5.2 0.1788 \n", + "2013-01-04 08:15:12-06:00 n05667 204.7 6.5 1.1024 \n", "\n", - "[3 rows x 15 columns]" + " p_mp i_mp v_mp v_oc ff \\\n", + "date_time \n", + "2013-01-04 08:05:12-06:00 4.869814 0.1255 38.8033 45.8512 78.01 \n", + "2013-01-04 08:10:12-06:00 6.530535 0.1657 39.4118 46.4089 78.72 \n", + "2013-01-04 08:15:12-06:00 45.349227 1.0548 42.9932 50.0780 82.15 \n", + "\n", + " temp_air relative_humidity pressure \\\n", + "date_time \n", + "2013-01-04 08:05:12-06:00 4.9 82.2 1007.1 \n", + "2013-01-04 08:10:12-06:00 5.0 82.9 1007.0 \n", + "2013-01-04 08:15:12-06:00 5.2 81.9 1007.1 \n", + "\n", + " precipitation dni ghi dhi soil wind_speed \\\n", + "date_time \n", + "2013-01-04 08:05:12-06:00 0 0.0 18.1 18.3 1.0 0 \n", + "2013-01-04 08:10:12-06:00 0 1.6 24.9 25.0 1.0 0 \n", + "2013-01-04 08:15:12-06:00 0 353.4 53.0 31.5 1.0 0 \n", + "\n", + " poa_global_kwm2 \n", + "date_time \n", + "2013-01-04 08:05:12-06:00 0.0240 \n", + "2013-01-04 08:10:12-06:00 0.0330 \n", + "2013-01-04 08:15:12-06:00 0.2047 " ] }, - "execution_count": 9, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -494,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -531,16 +528,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "6" + "4" ] }, - "execution_count": 11, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -548,7 +545,7 @@ "source": [ "qty_mlfm_vars = get_qty_mlfm_vars(meas)\n", "\n", - "qty_mlfm_vars ##SR##" + "qty_mlfm_vars" ] }, { @@ -595,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -619,8 +616,6 @@ " \n", " \n", " \n", - " poa_global_kwm2\n", - " temp_module\n", " pr_dc\n", " pr_dc_temp_corr\n", " i_sc\n", @@ -628,10 +623,6 @@ " v_oc\n", " v_mp\n", " v_oc_temp_corr\n", - " r_sc\n", - " r_oc\n", - " i_ff\n", - " v_ff\n", " \n", " \n", " date_time\n", @@ -642,78 +633,58 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " 2016-01-26 07:20:00-07:00\n", - " 0.002666\n", - " 2.081940\n", - " 0.496497\n", - " 0.445293\n", - " 0.926380\n", - " 0.742241\n", - " 0.747526\n", - " 0.736587\n", - " 0.687564\n", - " 0.983502\n", - " 0.760559\n", - " 0.754692\n", - " 0.968481\n", - " \n", - " \n", - " 2016-01-26 07:30:00-07:00\n", - " 0.007899\n", - " 2.436985\n", - " 0.620471\n", - " 0.557473\n", - " 0.881409\n", - " 0.800896\n", - " 0.851675\n", - " 0.786977\n", - " 0.784418\n", - " 0.893852\n", - " 0.866922\n", - " 0.896006\n", - " 0.907783\n", - " \n", - " \n", - " 2016-01-26 07:40:00-07:00\n", - " 0.052928\n", - " 2.592087\n", - " 0.208037\n", - " 0.187059\n", - " 0.257227\n", - " 0.840172\n", - " 0.897041\n", - " 0.818298\n", - " 0.826688\n", - " 0.897700\n", - " 0.895018\n", - " 0.935916\n", - " 0.914282\n", + " 2013-01-04 08:05:12-06:00\n", + " 0.956856\n", + " 0.889529\n", + " 1.039376\n", + " 0.922116\n", + " 0.915011\n", + " 0.846288\n", + " 0.865476\n", + " \n", + " \n", + " 2013-01-04 08:10:12-06:00\n", + " 0.933212\n", + " 0.869166\n", + " 0.993069\n", + " 0.926734\n", + " 0.926140\n", + " 0.849229\n", + " 0.877238\n", + " \n", + " \n", + " 2013-01-04 08:15:12-06:00\n", + " 1.044714\n", + " 0.977723\n", + " 0.987068\n", + " 0.956821\n", + " 0.999361\n", + " 0.858525\n", + " 0.950057\n", " \n", " \n", "\n", "" ], "text/plain": [ - " poa_global_kwm2 temp_module ... i_ff v_ff\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 0.002666 2.081940 ... 0.754692 0.968481\n", - "2016-01-26 07:30:00-07:00 0.007899 2.436985 ... 0.896006 0.907783\n", - "2016-01-26 07:40:00-07:00 0.052928 2.592087 ... 0.935916 0.914282\n", + " pr_dc pr_dc_temp_corr i_sc i_mp \\\n", + "date_time \n", + "2013-01-04 08:05:12-06:00 0.956856 0.889529 1.039376 0.922116 \n", + "2013-01-04 08:10:12-06:00 0.933212 0.869166 0.993069 0.926734 \n", + "2013-01-04 08:15:12-06:00 1.044714 0.977723 0.987068 0.956821 \n", "\n", - "[3 rows x 13 columns]" + " v_oc v_mp v_oc_temp_corr \n", + "date_time \n", + "2013-01-04 08:05:12-06:00 0.915011 0.846288 0.865476 \n", + "2013-01-04 08:10:12-06:00 0.926140 0.849229 0.877238 \n", + "2013-01-04 08:15:12-06:00 0.999361 0.858525 0.950057 " ] }, - "execution_count": 12, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -735,7 +706,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -761,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -782,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -813,7 +784,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -860,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -886,19 +857,23 @@ " \n", " module_id\n", " poa_global\n", - " wind_speed\n", - " temp_air\n", - " blue_frac\n", - " beam_frac\n", " temp_module\n", - " v_oc\n", " i_sc\n", + " p_mp\n", " i_mp\n", " v_mp\n", - " r_sc\n", - " r_oc\n", + " v_oc\n", + " ff\n", + " temp_air\n", + " relative_humidity\n", + " pressure\n", + " precipitation\n", + " dni\n", + " ghi\n", + " dhi\n", + " soil\n", + " wind_speed\n", " poa_global_kwm2\n", - " p_mp\n", " \n", " \n", " date_time\n", @@ -917,137 +892,193 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 2016-01-26 08:10:00-07:00\n", - " 78\n", - " 207.780344\n", - " 0.676059\n", - " 8.259369\n", - " 0.388758\n", - " 0.654555\n", - " 9.171982\n", - " 44.292019\n", - " 1.051441\n", - " 0.976607\n", - " 37.883130\n", - " 1022.179281\n", - " 3.648163\n", - " 0.207780\n", - " 36.996924\n", - " \n", - " \n", - " 2016-01-26 08:30:00-07:00\n", - " 78\n", - " 314.432034\n", - " 0.856547\n", - " 8.972031\n", - " 0.430550\n", - " 0.747184\n", - " 14.170197\n", - " 44.031935\n", - " 1.331172\n", - " 1.252936\n", - " 37.876208\n", - " 1069.804838\n", - " 2.483550\n", - " 0.314432\n", - " 47.456451\n", - " \n", - " \n", - " 2016-01-26 08:40:00-07:00\n", - " 78\n", - " 364.161611\n", - " 0.589140\n", - " 9.572525\n", - " 0.445193\n", - " 0.769498\n", - " 16.853550\n", - " 44.172695\n", - " 1.847061\n", - " 1.717155\n", - " 37.412432\n", - " 395.607366\n", - " 1.962480\n", - " 0.364162\n", - " 64.242948\n", - " \n", - " \n", - " 2016-01-26 08:50:00-07:00\n", - " 78\n", - " 414.444854\n", - " 0.526614\n", - " 10.140991\n", - " 0.457280\n", - " 0.816817\n", - " 18.930649\n", - " 44.100850\n", - " 2.121667\n", - " 1.980343\n", - " 37.078834\n", - " 507.595573\n", - " 1.660780\n", - " 0.414445\n", - " 73.428792\n", - " \n", - " \n", - " 2016-01-26 09:00:00-07:00\n", - " 78\n", - " 462.114270\n", - " 1.284213\n", - " 10.187134\n", - " 0.466810\n", - " 0.819645\n", - " 19.763138\n", - " 44.172574\n", - " 2.372033\n", - " 2.219749\n", - " 36.923490\n", - " 451.142610\n", - " 1.618455\n", - " 0.462114\n", - " 81.960880\n", + " 2013-01-04 08:15:12-06:00\n", + " n05667\n", + " 204.7\n", + " 6.5\n", + " 1.1024\n", + " 45.349227\n", + " 1.0548\n", + " 42.9932\n", + " 50.0780\n", + " 82.15\n", + " 5.2\n", + " 81.9\n", + " 1007.1\n", + " 0\n", + " 353.4\n", + " 53.0\n", + " 31.5\n", + " 1.0\n", + " 0\n", + " 0.2047\n", + " \n", + " \n", + " 2013-01-04 08:20:12-06:00\n", + " n05667\n", + " 238.0\n", + " 8.4\n", + " 1.2937\n", + " 52.964895\n", + " 1.2344\n", + " 42.9074\n", + " 50.0932\n", + " 81.73\n", + " 5.3\n", + " 80.8\n", + " 1007.2\n", + " 0\n", + " 395.1\n", + " 65.2\n", + " 36.7\n", + " 1.0\n", + " 0\n", + " 0.2380\n", + " \n", + " \n", + " 2013-01-04 08:25:12-06:00\n", + " n05667\n", + " 272.4\n", + " 10.3\n", + " 1.4898\n", + " 60.822567\n", + " 1.4194\n", + " 42.8509\n", + " 50.1119\n", + " 81.47\n", + " 5.6\n", + " 79.7\n", + " 1007.3\n", + " 0\n", + " 435.6\n", + " 78.2\n", + " 41.6\n", + " 1.0\n", + " 0\n", + " 0.2724\n", + " \n", + " \n", + " 2013-01-04 08:30:12-06:00\n", + " n05667\n", + " 209.1\n", + " 10.7\n", + " 1.1510\n", + " 46.371708\n", + " 1.0956\n", + " 42.3254\n", + " 49.4631\n", + " 81.46\n", + " 5.9\n", + " 78.9\n", + " 1007.2\n", + " 0\n", + " 284.3\n", + " 72.2\n", + " 44.9\n", + " 1.0\n", + " 0\n", + " 0.2091\n", + " \n", + " \n", + " 2013-01-04 08:35:12-06:00\n", + " n05667\n", + " 204.3\n", + " 10.7\n", + " 1.1235\n", + " 45.179177\n", + " 1.0680\n", + " 42.3026\n", + " 49.4346\n", + " 81.34\n", + " 5.7\n", + " 78.6\n", + " 1007.2\n", + " 0\n", + " 264.6\n", + " 75.5\n", + " 47.3\n", + " 1.0\n", + " 0\n", + " 0.2043\n", " \n", " \n", "\n", "" ], "text/plain": [ - " module_id poa_global ... poa_global_kwm2 p_mp\n", - "date_time ... \n", - "2016-01-26 08:10:00-07:00 78 207.780344 ... 0.207780 36.996924\n", - "2016-01-26 08:30:00-07:00 78 314.432034 ... 0.314432 47.456451\n", - "2016-01-26 08:40:00-07:00 78 364.161611 ... 0.364162 64.242948\n", - "2016-01-26 08:50:00-07:00 78 414.444854 ... 0.414445 73.428792\n", - "2016-01-26 09:00:00-07:00 78 462.114270 ... 0.462114 81.960880\n", + " module_id poa_global temp_module i_sc \\\n", + "date_time \n", + "2013-01-04 08:15:12-06:00 n05667 204.7 6.5 1.1024 \n", + "2013-01-04 08:20:12-06:00 n05667 238.0 8.4 1.2937 \n", + "2013-01-04 08:25:12-06:00 n05667 272.4 10.3 1.4898 \n", + "2013-01-04 08:30:12-06:00 n05667 209.1 10.7 1.1510 \n", + "2013-01-04 08:35:12-06:00 n05667 204.3 10.7 1.1235 \n", + "\n", + " p_mp i_mp v_mp v_oc ff \\\n", + "date_time \n", + "2013-01-04 08:15:12-06:00 45.349227 1.0548 42.9932 50.0780 82.15 \n", + "2013-01-04 08:20:12-06:00 52.964895 1.2344 42.9074 50.0932 81.73 \n", + "2013-01-04 08:25:12-06:00 60.822567 1.4194 42.8509 50.1119 81.47 \n", + "2013-01-04 08:30:12-06:00 46.371708 1.0956 42.3254 49.4631 81.46 \n", + "2013-01-04 08:35:12-06:00 45.179177 1.0680 42.3026 49.4346 81.34 \n", "\n", - "[5 rows x 15 columns]" + " temp_air relative_humidity pressure \\\n", + "date_time \n", + "2013-01-04 08:15:12-06:00 5.2 81.9 1007.1 \n", + "2013-01-04 08:20:12-06:00 5.3 80.8 1007.2 \n", + "2013-01-04 08:25:12-06:00 5.6 79.7 1007.3 \n", + "2013-01-04 08:30:12-06:00 5.9 78.9 1007.2 \n", + "2013-01-04 08:35:12-06:00 5.7 78.6 1007.2 \n", + "\n", + " precipitation dni ghi dhi soil wind_speed \\\n", + "date_time \n", + "2013-01-04 08:15:12-06:00 0 353.4 53.0 31.5 1.0 0 \n", + "2013-01-04 08:20:12-06:00 0 395.1 65.2 36.7 1.0 0 \n", + "2013-01-04 08:25:12-06:00 0 435.6 78.2 41.6 1.0 0 \n", + "2013-01-04 08:30:12-06:00 0 284.3 72.2 44.9 1.0 0 \n", + "2013-01-04 08:35:12-06:00 0 264.6 75.5 47.3 1.0 0 \n", + "\n", + " poa_global_kwm2 \n", + "date_time \n", + "2013-01-04 08:15:12-06:00 0.2047 \n", + "2013-01-04 08:20:12-06:00 0.2380 \n", + "2013-01-04 08:25:12-06:00 0.2724 \n", + "2013-01-04 08:30:12-06:00 0.2091 \n", + "2013-01-04 08:35:12-06:00 0.2043 " ] }, - "execution_count": 17, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "meas.head() ##SR## check what's there" + "meas.head()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { - 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1085,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -1111,11 +1142,9 @@ " \n", " pr_dc\n", " i_sc\n", - " r_sc\n", - " i_ff\n", + " i_mp\n", " i_v\n", - " v_ff\n", - " r_oc\n", + " v_mp\n", " v_oc\n", " temp_module_corr\n", " \n", @@ -1128,69 +1157,65 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " 2016-01-26 08:10:00-07:00\n", - " 0.987455\n", - " 0.063817\n", - " 0.044529\n", - " 0.035911\n", + " 2013-01-04 08:15:12-06:00\n", + " 1.044714\n", + " 0.015955\n", + " 0.048271\n", " 0.01\n", - " 0.073908\n", - " 0.098226\n", - " -0.002454\n", - " -0.065435\n", + " 0.169543\n", + " 0.000788\n", + " -0.060828\n", " \n", " \n", - " 2016-01-26 08:30:00-07:00\n", - " 0.836998\n", - " 0.237283\n", - " 0.032594\n", - " 0.030246\n", + " 2013-01-04 08:20:12-06:00\n", + " 1.049437\n", + " 0.004612\n", + " 0.051860\n", " 0.01\n", - " 0.077019\n", - " 0.082928\n", - " 0.004324\n", - " -0.042936\n", + " 0.172945\n", + " 0.000416\n", + " -0.054896\n", " \n", " \n", - " 2016-01-26 08:40:00-07:00\n", - " 0.978335\n", - " 0.061103\n", - " 0.065599\n", - " 0.013143\n", + " 2013-01-04 08:25:12-06:00\n", + " 1.052938\n", + " -0.003006\n", + " 0.053871\n", " 0.01\n", - " 0.091342\n", - " 0.091143\n", - " 0.000727\n", - " -0.033518\n", + " 0.175514\n", + " -0.000047\n", + " -0.048840\n", " \n", " \n", "\n", "" ], "text/plain": [ - " pr_dc i_sc ... v_oc temp_module_corr\n", - "date_time ... \n", - "2016-01-26 08:10:00-07:00 0.987455 0.063817 ... -0.002454 -0.065435\n", - "2016-01-26 08:30:00-07:00 0.836998 0.237283 ... 0.004324 -0.042936\n", - "2016-01-26 08:40:00-07:00 0.978335 0.061103 ... 0.000727 -0.033518\n", + " pr_dc i_sc i_mp i_v v_mp \\\n", + "date_time \n", + "2013-01-04 08:15:12-06:00 1.044714 0.015955 0.048271 0.01 0.169543 \n", + "2013-01-04 08:20:12-06:00 1.049437 0.004612 0.051860 0.01 0.172945 \n", + "2013-01-04 08:25:12-06:00 1.052938 -0.003006 0.053871 0.01 0.175514 \n", "\n", - "[3 rows x 9 columns]" + " v_oc temp_module_corr \n", + "date_time \n", + "2013-01-04 08:15:12-06:00 0.000788 -0.060828 \n", + "2013-01-04 08:20:12-06:00 0.000416 -0.054896 \n", + "2013-01-04 08:25:12-06:00 -0.000047 -0.048840 " ] }, - "execution_count": 19, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# translate multiplicative to stack losses and add to dataframe df\n", - "stack = mlfm_norm_to_stack(norm, fill_factor = ref['ff']) ##SR## ref) ###, qty_mlfm_vars)\n", + "# translate multiplicative to stack losses and add to dataframe\n", + "stack = mlfm_norm_to_stack(norm, fill_factor = ref['ff'])\n", "\n", "# show some stack losses\n", "stack.head(3)" @@ -1263,38 +1288,19 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 57, "metadata": {}, "outputs": [ - { - "ename": "KeyError", - "evalue": "'v_mp'", - "output_type": "error", - "traceback": [ - "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", - " File \u001b[0;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3621\u001b[0m in \u001b[0;35mget_loc\u001b[0m\n return self._engine.get_loc(casted_key)\n", - " File \u001b[0;32mpandas\\_libs\\index.pyx:136\u001b[0m in \u001b[0;35mpandas._libs.index.IndexEngine.get_loc\u001b[0m\n", - " File \u001b[0;32mpandas\\_libs\\index.pyx:163\u001b[0m in \u001b[0;35mpandas._libs.index.IndexEngine.get_loc\u001b[0m\n", - " File \u001b[0;32mpandas\\_libs\\hashtable_class_helper.pxi:5198\u001b[0m in \u001b[0;35mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0m\n", - "\u001b[1;36m File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5206\u001b[1;36m in \u001b[1;35mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;36m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m\u001b[1;31m:\u001b[0m 'v_mp'\n", - "\nThe above exception was the direct cause of the following exception:\n", - "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n", - " Input \u001b[0;32mIn [20]\u001b[0m in \u001b[0;35m\u001b[0m\n fig_stack = plot_mlfm_stack(\n", - " File \u001b[0;32m~\\OneDrive\\Documents\\_CONS\\__Reference\\PVPMC\\__repository\\pvlib-python\\docs\\tutorials\\mlfm_220627.py:653\u001b[0m in \u001b[0;35mplot_mlfm_stack\u001b[0m\n dstack['v_mp'],\n", - " File \u001b[0;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\frame.py:3505\u001b[0m in \u001b[0;35m__getitem__\u001b[0m\n indexer = self.columns.get_loc(key)\n", - "\u001b[1;36m File \u001b[1;32m~\\anaconda3a\\envs\\spyder\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3623\u001b[1;36m in \u001b[1;35mget_loc\u001b[1;36m\u001b[0m\n\u001b[1;33m raise KeyError(key) from err\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m\u001b[1;31m:\u001b[0m 'v_mp'\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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pr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrpoa_global_bintemp_module_bin
date_time
2013-01-04 08:15:12-06:001.0447140.9777230.9870680.9568210.9993610.8585250.950057200.05.0
2013-01-04 08:20:12-06:001.0494370.9890550.9962820.9541620.9996650.8565510.955411200.010.0
2013-01-04 08:25:12-06:001.0529380.9992891.0024130.9527451.0000380.8551040.960835300.010.0
2013-01-04 08:30:12-06:001.0457900.9939541.0088970.9518680.9870900.8556960.949448200.010.0
2013-01-04 08:35:12-06:001.0428340.9911451.0079300.9506010.9865220.8557290.948901200.010.0
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" + ], + "text/plain": [ + " pr_dc pr_dc_temp_corr i_sc i_mp \\\n", + "date_time \n", + "2013-01-04 08:15:12-06:00 1.044714 0.977723 0.987068 0.956821 \n", + "2013-01-04 08:20:12-06:00 1.049437 0.989055 0.996282 0.954162 \n", + "2013-01-04 08:25:12-06:00 1.052938 0.999289 1.002413 0.952745 \n", + "2013-01-04 08:30:12-06:00 1.045790 0.993954 1.008897 0.951868 \n", + "2013-01-04 08:35:12-06:00 1.042834 0.991145 1.007930 0.950601 \n", + "\n", + " v_oc v_mp v_oc_temp_corr poa_global_bin \\\n", + "date_time \n", + "2013-01-04 08:15:12-06:00 0.999361 0.858525 0.950057 200.0 \n", + "2013-01-04 08:20:12-06:00 0.999665 0.856551 0.955411 200.0 \n", + "2013-01-04 08:25:12-06:00 1.000038 0.855104 0.960835 300.0 \n", + "2013-01-04 08:30:12-06:00 0.987090 0.855696 0.949448 200.0 \n", + "2013-01-04 08:35:12-06:00 0.986522 0.855729 0.948901 200.0 \n", + "\n", + " temp_module_bin \n", + "date_time \n", + "2013-01-04 08:15:12-06:00 5.0 \n", + "2013-01-04 08:20:12-06:00 10.0 \n", + "2013-01-04 08:25:12-06:00 10.0 \n", + "2013-01-04 08:30:12-06:00 10.0 \n", + "2013-01-04 08:35:12-06:00 10.0 " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "norm.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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pr_dcpr_dc_temp_corri_sci_mpv_ocv_mpv_oc_temp_corrpoa_global_bintemp_module_bincalc_pr_dcdiff_pr_dc
count1433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001433.0000001.433000e+03
mean0.9250180.9371760.9418410.9414040.9703760.8335040.980062688.34612728.8729940.9250185.139703e-07
std0.0836880.0853080.0754530.0057570.0247850.0138270.023198284.06882610.6034700.0682914.837319e-02
min0.5284790.5389200.5988010.8761070.8742230.8069930.887511100.000000-5.0000000.605121-1.878487e-01
25%0.9025650.9412020.9422420.9377430.9524030.8217470.971589500.00000020.0000000.900437-1.645589e-02
50%0.9315790.9633510.9651740.9405620.9727300.8316060.989182800.00000030.0000000.939209-2.623649e-03
75%0.9802690.9832740.9836060.9449070.9877610.8451300.996239900.00000035.0000000.9662291.748713e-02
max1.0858211.0330451.0339320.9619081.0269110.8970881.0067321100.00000055.0000001.0383682.907101e-01
\n", + "
" + ], + "text/plain": [ + " pr_dc pr_dc_temp_corr i_sc i_mp v_oc \\\n", + "count 1433.000000 1433.000000 1433.000000 1433.000000 1433.000000 \n", + "mean 0.925018 0.937176 0.941841 0.941404 0.970376 \n", + "std 0.083688 0.085308 0.075453 0.005757 0.024785 \n", + "min 0.528479 0.538920 0.598801 0.876107 0.874223 \n", + "25% 0.902565 0.941202 0.942242 0.937743 0.952403 \n", + "50% 0.931579 0.963351 0.965174 0.940562 0.972730 \n", + "75% 0.980269 0.983274 0.983606 0.944907 0.987761 \n", + "max 1.085821 1.033045 1.033932 0.961908 1.026911 \n", + "\n", + " v_mp v_oc_temp_corr poa_global_bin temp_module_bin \\\n", + "count 1433.000000 1433.000000 1433.000000 1433.000000 \n", + "mean 0.833504 0.980062 688.346127 28.872994 \n", + "std 0.013827 0.023198 284.068826 10.603470 \n", + "min 0.806993 0.887511 100.000000 -5.000000 \n", + "25% 0.821747 0.971589 500.000000 20.000000 \n", + "50% 0.831606 0.989182 800.000000 30.000000 \n", + "75% 0.845130 0.996239 900.000000 35.000000 \n", + "max 0.897088 1.006732 1100.000000 55.000000 \n", + "\n", + " calc_pr_dc diff_pr_dc \n", + "count 1433.000000 1.433000e+03 \n", + "mean 0.925018 5.139703e-07 \n", + "std 0.068291 4.837319e-02 \n", + "min 0.605121 -1.878487e-01 \n", + "25% 0.900437 -1.645589e-02 \n", + "50% 0.939209 -2.623649e-03 \n", + "75% 0.966229 1.748713e-02 \n", + "max 1.038368 2.907101e-01 " + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# choose which no0rmalised mlfm parameter to model e.g. pr_dc or i_sc..v_oc \n", "mlfm_sel = 'pr_dc' \n", "\n", - "# FIX THIS WARNING,\n", - "# SettingWithCopyWarning:\n", - "# A value is trying to be set on a copy of a slice from a DataFrame.\n", - "# Try using .loc[row_indexer,col_indexer] = value instead\n", - "# TRY TO DO A BETTER METHOD THAN JUST HIDING IT\n", - "\n", - "cc, coeffs, ee, errs = mlfm_fit(norm, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", + "# add selected variable to measured data frame. We do this to ensure that data indexes match.\n", + "meas_temp = meas.copy()\n", + "meas_temp[mlfm_sel] = norm[mlfm_sel]\n", "\n", - "# Fix a bug with fit routine which gives a\n", - "# finite cc[4] even if all the ws data is 0\n", - "# this won't matter until cc is applied to other\n", - "# data with some ws <>0 when it will give bad results\n", - "if np.mean(meas.wind_speed) == 0:\n", - " cc[4] = 0\n", - " c_5 = 0\n", + "cc, coeffs, ee, errs = mlfm_fit(meas_temp, mlfm_sel) # qty_mlfm_vars)##SR## norm\n", "\n", "norm['calc_' + mlfm_sel] = cc \n", "\n", @@ -1373,36 +1695,6 @@ "norm.describe()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# cc\n", - "# date_time\n", - "# 2013-01-04 08:15:12-06:00 0.877861\n", - "# 2013-01-04 08:20:12-06:00 0.899799\n", - "\n", - "# coeffs\n", - "# array([ 1.27323780e+00, -4.79820818e-03, 6.18587637e-01, -2.83390574e-01,\n", - "# 0.00000000e+00, -2.74667887e-10])\n", - "\n", - "# ee\n", - "# date_time\n", - "# 2013-01-04 08:15:12-06:00 -0.166853\n", - "# 2013-01-04 08:20:12-06:00 -0.149638\n", - "\n", - "# errs\n", - "# array([0.03774048, 0.00015903, 0.08827109, 0.04484896, 0. ,\n", - "# 0.00641688])\n", - "\n", - "#mlfm_meas_file\n", - "#norm.columns\n", - "#fit\n", - "norm.columns" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1412,17 +1704,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ - "def plot_fit(dnorm, fit, title):\n", + "def plot_fit(dmeas, dnorm, fit, title):\n", " \n", " ''' \n", " Scatter plot fit to normalised measured\n", "\n", " Parameters\n", " ---------- \n", + " dmeas : dataframe\n", + " measurements, must include 'poa_global_kwm2'\n", " \n", " dnorm : dataframe\n", " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", @@ -1446,8 +1740,8 @@ " ax1.set_xlim(0, 1.2)\n", "\n", " plt.plot(\n", - " dnorm[fit] * dnorm['poa_global_kwm2'],\n", - " dnorm['calc_' + fit] * dnorm['poa_global_kwm2'], ##SR##\n", + " dnorm[fit] * dmeas['poa_global_kwm2'],\n", + " dnorm['calc_' + fit] * dmeas['poa_global_kwm2'], ##SR##\n", " 'c^',\n", " label = fit\n", " )\n", @@ -1468,12 +1762,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# plot fit vs. measured, include a 1:1 line for comparison\n", - "fit_plot = plot_fit(norm, mlfm_sel, 'fit ' + mlfm_meas_file)" + "fit_plot = plot_fit(meas, norm, mlfm_sel, 'fit ' + mlfm_meas_file)" ] }, { @@ -1488,7 +1795,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -1592,10 +1899,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], - "source": [ + "outputs": [ + { + "data": { + "image/png": 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midpoa_globaltemp_modulewind_speedpoa_global_kwm2
id
1matrix100000.1
2matrix100500.1
3matrix1001000.1
4matrix1001500.1
5matrix1002000.1
..................
176matrix12005001.2
177matrix12005501.2
178matrix12006001.2
179matrix12006501.2
180matrix12007001.2
\n", + "

180 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2\n", + "id \n", + "1 matrix 100 0 0 0.1\n", + "2 matrix 100 5 0 0.1\n", + "3 matrix 100 10 0 0.1\n", + "4 matrix 100 15 0 0.1\n", + "5 matrix 100 20 0 0.1\n", + ".. ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2\n", + "177 matrix 1200 55 0 1.2\n", + "178 matrix 1200 60 0 1.2\n", + "179 matrix 1200 65 0 1.2\n", + "180 matrix 1200 70 0 1.2\n", + "\n", + "[180 rows x 5 columns]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# read in the complete matrix data\n", "matr = pd.read_csv(root_dir + '\\\\mlfm_data\\\\ref\\\\mlfm_matrix.csv', index_col = 'id')\n", @@ -1648,19 +2121,197 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.27322965e+00, -4.79822729e-03, 6.18579737e-01, -2.83381845e-01,\n", + " -4.16977322e-01, -1.74639665e-09])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#105.68%\t-0.42%\t12.85%\t-5.71%\n", + "# show model coefficients\n", "coeffs" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpoa_global_kwm2pr_dc
id
1matrix100000.10.746267
2matrix100500.10.722276
3matrix1001000.10.698285
4matrix1001500.10.674294
5matrix1002000.10.650303
.....................
176matrix12005001.20.862196
177matrix12005501.20.838205
178matrix12006001.20.814213
179matrix12006501.20.790222
180matrix12007001.20.766231
\n", + "

180 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2 pr_dc\n", + "id \n", + "1 matrix 100 0 0 0.1 0.746267\n", + "2 matrix 100 5 0 0.1 0.722276\n", + "3 matrix 100 10 0 0.1 0.698285\n", + "4 matrix 100 15 0 0.1 0.674294\n", + "5 matrix 100 20 0 0.1 0.650303\n", + ".. ... ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2 0.862196\n", + "177 matrix 1200 55 0 1.2 0.838205\n", + "178 matrix 1200 60 0 1.2 0.814213\n", + "179 matrix 1200 65 0 1.2 0.790222\n", + "180 matrix 1200 70 0 1.2 0.766231\n", + "\n", + "[180 rows x 6 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# populate pivot table from predicted mpm data\n", "matr[mlfm_sel] = mlfm_6(matr, coeffs[0], coeffs[1], coeffs[2], coeffs[3], coeffs[4], coeffs[5])\n", @@ -1679,7 +2330,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -1779,9 +2430,175 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpoa_global_kwm2pr_dc
id
3matrix1001000.10.698285
4matrix1001500.10.674294
5matrix1002000.10.650303
6matrix1002500.10.626312
7matrix1003000.10.602321
.....................
176matrix12005001.20.862196
177matrix12005501.20.838205
178matrix12006001.20.814213
179matrix12006501.20.790222
180matrix12007001.20.766231
\n", + "

156 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed poa_global_kwm2 pr_dc\n", + "id \n", + "3 matrix 100 10 0 0.1 0.698285\n", + "4 matrix 100 15 0 0.1 0.674294\n", + "5 matrix 100 20 0 0.1 0.650303\n", + "6 matrix 100 25 0 0.1 0.626312\n", + "7 matrix 100 30 0 0.1 0.602321\n", + ".. ... ... ... ... ... ...\n", + "176 matrix 1200 50 0 1.2 0.862196\n", + "177 matrix 1200 55 0 1.2 0.838205\n", + "178 matrix 1200 60 0 1.2 0.814213\n", + "179 matrix 1200 65 0 1.2 0.790222\n", + "180 matrix 1200 70 0 1.2 0.766231\n", + "\n", + "[156 rows x 6 columns]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", "\n", @@ -1799,9 +2616,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "contour_plot = plot_contourf(\n", " df=matr2,\n", @@ -1824,9 +2654,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "contour_plot = plot_contourf(\n", " df=norm,\n", @@ -1887,166 +2730,12 @@ "\n", "Many more papers are available at www.steveransome.com \n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stop\n", - "\n", - "# delete below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr2.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#norm.describe()\n", - "meas.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "whos" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm.to_csv('\\\\mlfm_data\\\\export\\\\'+'norm.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "meas.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'meas.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stack.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\'+'stack.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ref_data.to_csv(root_dir + '\\\\mlfm_data\\\\export\\\\' + 'ref_data.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stack.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "norm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ref" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "root_dir" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "matr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -2059,7 +2748,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.4" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false From 9764f13017aa134569bf761967241fd5634ef436 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 30 Jun 2022 15:49:31 -0600 Subject: [PATCH 61/81] fix plot_mlfm_stack test --- pvlib/mlfm.py | 5 +++-- pvlib/tests/test_mlfm.py | 17 ++++++++++++++++- 2 files changed, 19 insertions(+), 3 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index c22bd9b5d5..fbff001f9e 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -691,7 +691,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, bbox = 1.2 # select x axis usually date_time - xdata = dmeas.index + xdata = dmeas.index.values fig, ax1 = plt.subplots() ax1.set_title(title) @@ -727,9 +727,10 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, # try: xax2[x_count] = xdata[x_count][2:13]+'h' - except IndexError: xax2[x_count] = xdata[x_count] + except TypeError: # xdata can't be subscripted + xax2[x_count] = xdata[0] x_count += 1 diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 95b144f46c..83dcb6a90a 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -216,5 +216,20 @@ def test_mlfm_fit(matrix_data, mlfm_6_fit): @requires_mpl def test_plot_mlfm_scatter(measured, normalized): import matplotlib.pyplot as plt - fig = mlfm.plot_mlfm_scatter(measured, normalized, 'title_string') + fig = mlfm.plot_mlfm_scatter(measured, normalized, 'norm plot') + assert isinstance(fig, plt.Figure) + + +@requires_mpl +def test_plot_mlfm_stack(measured, normalized, stacked, reference): + # stacked plot requires at least index length of 2 + m = measured.append(measured) + m.index = [0, 1] + n = normalized.append(normalized) + n.index = [0, 1] + s = stacked.append(stacked) + s.index = [0, 1] + import matplotlib.pyplot as plt + fig = mlfm.plot_mlfm_stack(m, n, s, reference['ff'], 'stacked plot') + plt.show() assert isinstance(fig, plt.Figure) From 39b9493f63863886bb2db9a0420f0f389c0e94a4 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Thu, 30 Jun 2022 15:49:47 -0600 Subject: [PATCH 62/81] minor edits --- docs/tutorials/mlfm_220627_0.ipynb | 38 ++++++++++++++++-------------- 1 file changed, 20 insertions(+), 18 deletions(-) diff --git a/docs/tutorials/mlfm_220627_0.ipynb b/docs/tutorials/mlfm_220627_0.ipynb index 528109aeae..6a6e5c3392 100644 --- a/docs/tutorials/mlfm_220627_0.ipynb +++ b/docs/tutorials/mlfm_220627_0.ipynb @@ -1252,7 +1252,9 @@ { "cell_type": "code", "execution_count": 18, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -1487,7 +1489,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1709,7 +1711,7 @@ "max 0.934721 1100.000000 70.000000 1.047459 8.363585e-02 " ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1740,7 +1742,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1798,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1831,7 +1833,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1935,7 +1937,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1977,7 +1979,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2128,7 +2130,7 @@ "[180 rows x 5 columns]" ] }, - "execution_count": 37, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2157,7 +2159,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2167,7 +2169,7 @@ " -6.31677174e-04, -1.54196576e-02])" ] }, - "execution_count": 39, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2179,7 +2181,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2343,7 +2345,7 @@ "[180 rows x 6 columns]" ] }, - "execution_count": 41, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -2364,7 +2366,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -2464,7 +2466,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -2628,7 +2630,7 @@ "[156 rows x 6 columns]" ] }, - "execution_count": 43, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2650,7 +2652,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2688,7 +2690,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 31, "metadata": {}, "outputs": [ { From 219259966222506be62b3d58d64a197a5b2fdffc Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Mon, 11 Jul 2022 18:36:01 -0600 Subject: [PATCH 63/81] improve coverage --- pvlib/tests/test_mlfm.py | 47 +++++++++++++++++++++++++++++++--------- 1 file changed, 37 insertions(+), 10 deletions(-) diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 83dcb6a90a..348a9117d8 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -85,7 +85,7 @@ def normalized(): @pytest.fixture -def stacked(): +def stacked6(): # get stack data data_stack_target = { # 'date_time': ['2016-03-23 09:00:00-07:00'], @@ -105,6 +105,24 @@ def stacked(): return stack_target +@pytest.fixture +def stacked4(): + data_stack_target = { + # 'date_time': ['2016-03-23 09:00:00-07:00'], + 'pr_dc': [0.989242790817], + 'i_sc': [0.0054355995322], + 'i_mp': [0.0734605702031], + 'i_v': [0.01], + 'v_mp': [0.214483151855], + 'v_oc': [0.0187687482844], + 'temp_module_corr': [0.012006092936], + } + + stack_target = pd.DataFrame(data_stack_target) + + return stack_target + + @pytest.fixture def mlfm_6_coeffs(): # test mlfm coefficients @@ -196,9 +214,14 @@ def test_mlfm_6(measured, mlfm_6_coeffs): assert_allclose(expected, result[0], atol=1e-6) -def test_mlfm_norm_to_stack(normalized, reference, stacked): +def test_mlfm_norm_to_stack(normalized, reference, stacked6, stacked4): stack_calc = mlfm.mlfm_norm_to_stack(normalized, reference['ff']) - assert_frame_equal(stack_calc, stacked, atol=1e-6) + assert_frame_equal(stack_calc, stacked6, atol=1e-6) + # test without 'i_ff', 'r_sc', 'v_ff', 'r_oc' + # v_mp = v_ff * r_oc and i_mp = i_ff * r_sc + norm = normalized.drop(columns=['i_ff', 'r_sc', 'v_ff', 'r_oc']) + short_stack_calc = mlfm.mlfm_norm_to_stack(norm, reference['ff']) + assert_frame_equal(short_stack_calc, stacked4, check_less_precise=True) def test_mlfm_fit(matrix_data, mlfm_6_fit): @@ -221,15 +244,19 @@ def test_plot_mlfm_scatter(measured, normalized): @requires_mpl -def test_plot_mlfm_stack(measured, normalized, stacked, reference): +def test_plot_mlfm_stack(measured, normalized, stacked6, stacked4, reference): # stacked plot requires at least index length of 2 - m = measured.append(measured) + m = pd.concat([measured, measured]) m.index = [0, 1] - n = normalized.append(normalized) + n = pd.concat([normalized, normalized]) n.index = [0, 1] - s = stacked.append(stacked) - s.index = [0, 1] + s6 = pd.concat([stacked6, stacked6]) + s6.index = [0, 1] + import matplotlib.pyplot as plt + fig = mlfm.plot_mlfm_stack(m, n, s6, reference['ff'], 'stacked 6 plot') + assert isinstance(fig, plt.Figure) + s4 = pd.concat([stacked4, stacked4]) + s4.index = [0, 1] import matplotlib.pyplot as plt - fig = mlfm.plot_mlfm_stack(m, n, s, reference['ff'], 'stacked plot') - plt.show() + fig = mlfm.plot_mlfm_stack(m, n, s4, reference['ff'], 'stacked 4 plot') assert isinstance(fig, plt.Figure) From e63f22c1955ff4148412e484b82a098434aff609 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Tue, 12 Jul 2022 08:10:57 -0600 Subject: [PATCH 64/81] non-interactive plots --- pvlib/tests/conftest.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pvlib/tests/conftest.py b/pvlib/tests/conftest.py index 2b5559d7ba..c9d5398060 100644 --- a/pvlib/tests/conftest.py +++ b/pvlib/tests/conftest.py @@ -152,6 +152,8 @@ def has_numba(): try: import matplotlib.pyplot as plt # noqa: F401 + import matplotlib + matplotlib.use('agg') has_mpl = True except ImportError: has_mpl = False From e22b0fcb649907b4bdba6913188eb1ae6057ea2d Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Fri, 22 Jul 2022 15:27:21 -0600 Subject: [PATCH 65/81] use poa_global W/m2 --- pvlib/mlfm.py | 40 ++++++++++++++------------- pvlib/tests/test_mlfm.py | 58 ++++++++++++++++++++-------------------- 2 files changed, 50 insertions(+), 48 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index fbff001f9e..990f8d295e 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -61,7 +61,7 @@ def mlfm_meas_to_norm(dmeas, ref): dmeas : DataFrame Measurements. Must include columns: - * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] + * `'poa_global'` global plane of array irradiance [W/m^2] * `'temp_module'` module temperature [C] * `'p_mp'` - power at maximum power point [W] @@ -99,8 +99,8 @@ def mlfm_meas_to_norm(dmeas, ref): dnorm : DataFrame Normalised values. - * `'pr_dc'` is `'p_mp'` normalised by reference `'p_mp'` and \ - `'poa_global_kwm2'` + * `'pr_dc'` is `'p_mp'` normalised (divided) by reference `'p_mp'` \ + and by `'poa_global'` in kW/m^2. * `'pr_dc_temp_corr'` is `'pr_dc'` adjusted to 25C. * Columns `'i_sc'`, `'i_mp'`, `'v_oc'`, `'v_mp'`, `'v_oc_temp_corr'`, `'r_sc'`, `'r_oc'`, `'i_ff'`, `'v_ff'` are returned when the @@ -117,7 +117,7 @@ def mlfm_meas_to_norm(dmeas, ref): dnorm['pr_dc'] = ( dmeas['p_mp'] / - (ref['p_mp'] * dmeas['poa_global_kwm2'])) + (ref['p_mp'] * dmeas['poa_global'] / G_STC)) # temperature corrected dnorm['pr_dc_temp_corr'] = ( @@ -125,7 +125,8 @@ def mlfm_meas_to_norm(dmeas, ref): (1 - ref['gamma_pdc']*(dmeas['temp_module'] - T_STC))) if 'i_sc' in dmeas.columns: - dnorm['i_sc'] = dmeas['i_sc'] / dmeas['poa_global_kwm2'] / ref['i_sc'] + dnorm['i_sc'] = dmeas['i_sc'] / (dmeas['poa_global'] * G_STC) \ + / ref['i_sc'] if 'i_mp' in dmeas.columns: dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] @@ -314,8 +315,8 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): dmeas : DataFrame Must include columns: - * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] - * `'temp_module'` module temperature [C] + * `'poa_global'` global plane of array irradiance. [W/m^2] + * `'temp_module'` module temperature. [C] May include optional column: @@ -347,8 +348,9 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): 36th EU PVSEC, Marseille, France. September 2019 ''' mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ - c_3 * np.log10(dmeas['poa_global_kwm2']) + \ - c_4 * dmeas['poa_global_kwm2'] + c_6 / dmeas['poa_global_kwm2'] + c_3 * np.log10(dmeas['poa_global'] / G_STC) + \ + c_4 * dmeas['poa_global'] / G_STC + \ + c_6 / dmeas['poa_global_kwm2'] / G_STC if 'wind_speed' in dmeas.columns: mlfm_out += c_5 * dmeas['wind_speed'] return mlfm_out @@ -363,7 +365,7 @@ def mlfm_fit(data, var_to_fit): data : DataFrame Must include columns: - * 'poa_global_kwm2' global plane of array irradiance. [kW/m^2] + * 'poa_global' global plane of array irradiance. [W/m^2] * 'temp_module' module temperature. [C] Must include column named ``var_to_fit``. @@ -450,7 +452,7 @@ def plot_mlfm_scatter(dmeas, dnorm, title): dmeas : DataFrame Measurements. Must include columns: - * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] + * `'poa_global'` global plane of array irradiance [W/m^2] * `'temp_module'` module temperature [C] May include optional columns: @@ -492,8 +494,8 @@ def plot_mlfm_scatter(dmeas, dnorm, title): # offset legend to the right to not overlap graph, use ~1.2 bbox = 1.2 - # set x_axis as irradiance - xdata = dmeas['poa_global_kwm2'] + # set x_axis as irradiance in kW/m2 + xdata = dmeas['poa_global'] / G_STC fig, ax1 = plt.subplots() @@ -503,7 +505,7 @@ def plot_mlfm_scatter(dmeas, dnorm, title): ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line ax1.set_ylim(0.8, 1.1) # optional normalised y scale - ax1.set_xlabel('Plane of array irradiance [W/m$^2$]') + ax1.set_xlabel('Plane of array irradiance [kW/m$^2$]') ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC @@ -540,7 +542,7 @@ def plot_mlfm_scatter(dmeas, dnorm, title): # y2axis plot met on right y axis ax2 = ax1.twinx() - ax2.set_ylabel('Temperature (C/100)') # poa_global (kW/m$^2$); + ax2.set_ylabel('Temperature (C/100)') # set wide limits 0 to 4 so they don't overlap mlfm params ax2.set_ylim(0, 4) @@ -577,7 +579,7 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, dmeas : DataFrame Measurements. Must include columns: - * `'poa_global_kwm2'` global plane of array irradiance [kW/m^2] + * `'poa_global'` global plane of array irradiance [W/m^2] * `'temp_module'` module temperature [C] May include optional columns: @@ -743,10 +745,10 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/100)') ax2.set_ylim(0, 4) # set so doesn't overlap mlfm params - plt.plot(xdata, dmeas['poa_global_kwm2'], - c=clr['irradiance'], label='poa_global_kwm2') + plt.plot(xdata, dmeas['poa_global'] / G_STC, + c=clr['irradiance'], label='poa_global (kW/m^2)') plt.plot(xdata, dmeas['temp_module'] / 100, - c=clr['temp_module'], label='temp_module/100') + c=clr['temp_module'], label='temp_module / 100') # temp_air may not exist particularly for indoor measurements try: diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 348a9117d8..08ee796754 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -55,7 +55,7 @@ def measured(): meas = pd.DataFrame(data_meas) # create p_mp and ff in case they don't exist - meas['poa_global_kwm2'] = meas['poa_global'] / 1000 + # meas['poa_global_kwm2'] = meas['poa_global'] / 1000 meas['p_mp'] = meas['i_mp'] * meas['v_mp'] meas['ff'] = meas['p_mp'] / (meas['i_sc'] * meas['v_oc']) @@ -147,35 +147,35 @@ def matrix_data(): # --- return pd.DataFrame(np.array( - [[0.1, 15, 0, 0.935774123487434], - [0.2, 15, 0, 0.978281104560968], - [0.4, 15, 0, 1.00721377598511], - [0.6, 15, 0, 1.02254628193195], - [0.8, 15, 0, 1.02710983555693], - [1.0, 15, 0, 1.02655910642259], - [0.1, 25, 0, 0.907539559416693], - [0.2, 25, 0, 0.94849519081601], # LIC - [0.4, 25, 0, 0.980840831523425], - [0.6, 25, 0, 0.994311717861206], - [0.8, 25, 0, 0.998914055228048], - [1.0, 25, 0, 1], # STC - [1.1, 25, 0, 0.998984571122331], - [0.1, 50, 0, 0.833074775054297], - [0.2, 50, 0, 0.879615265280794], - [0.4, 50, 0, 0.908004964318957], - [0.6, 50, 0, 0.920260626745268], - [0.8, 50, 0, 0.925496431895749], - [1.0, 50, 0, 0.927551970214086], - [1.1, 50, 0, 0.926324993653569], - [0.1, 75, 0, 0.746819733167856], - [0.2, 75, 0, 0.792739683524666], - [0.4, 75, 0, 0.826481538938877], - [0.6, 75, 0, 0.842744854690247], - [0.8, 75, 0, 0.847735029475644], - [1.0, 75, 0, 0.849053676698728], - [1.1, 75, 0, 0.849039573519871]]), + [[100., 15, 0, 0.935774123487434], + [200., 15, 0, 0.978281104560968], + [400., 15, 0, 1.00721377598511], + [600., 15, 0, 1.02254628193195], + [800., 15, 0, 1.02710983555693], + [1000., 15, 0, 1.02655910642259], + [100., 25, 0, 0.907539559416693], + [200., 25, 0, 0.94849519081601], # LIC + [400., 25, 0, 0.980840831523425], + [600., 25, 0, 0.994311717861206], + [800., 25, 0, 0.998914055228048], + [1000., 25, 0, 1], # STC + [1100., 25, 0, 0.998984571122331], + [100., 50, 0, 0.833074775054297], + [200., 50, 0, 0.879615265280794], + [400., 50, 0, 0.908004964318957], + [600., 50, 0, 0.920260626745268], + [800., 50, 0, 0.925496431895749], + [1000., 50, 0, 0.927551970214086], + [1100., 50, 0, 0.926324993653569], + [100., 75, 0, 0.746819733167856], + [200., 75, 0, 0.792739683524666], + [400., 75, 0, 0.826481538938877], + [600., 75, 0, 0.842744854690247], + [800., 75, 0, 0.847735029475644], + [1000., 75, 0, 0.849053676698728], + [1100., 75, 0, 0.849039573519871]]), columns=[ - 'poa_global_kwm2', 'temp_module', 'wind_speed', 'pr_dc']) + 'poa_global', 'temp_module', 'wind_speed', 'pr_dc']) @pytest.fixture From 3ba24ff3a3d5f6c68ae1f60e6a15250143be4ee4 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Fri, 22 Jul 2022 15:32:35 -0600 Subject: [PATCH 66/81] typo --- pvlib/mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 990f8d295e..5cc0ae59ea 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -350,7 +350,7 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ c_3 * np.log10(dmeas['poa_global'] / G_STC) + \ c_4 * dmeas['poa_global'] / G_STC + \ - c_6 / dmeas['poa_global_kwm2'] / G_STC + c_6 / dmeas['poa_global'] / G_STC if 'wind_speed' in dmeas.columns: mlfm_out += c_5 * dmeas['wind_speed'] return mlfm_out From 93ee8601e43587632361d96c6f26e9fc2bfe73e9 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Fri, 22 Jul 2022 16:50:50 -0600 Subject: [PATCH 67/81] another typo --- pvlib/mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 5cc0ae59ea..1c776fbd16 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -125,7 +125,7 @@ def mlfm_meas_to_norm(dmeas, ref): (1 - ref['gamma_pdc']*(dmeas['temp_module'] - T_STC))) if 'i_sc' in dmeas.columns: - dnorm['i_sc'] = dmeas['i_sc'] / (dmeas['poa_global'] * G_STC) \ + dnorm['i_sc'] = dmeas['i_sc'] / (dmeas['poa_global'] / G_STC) \ / ref['i_sc'] if 'i_mp' in dmeas.columns: dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] From 60f1fe41fef5634ad33aab4fcd80da2a82e0e831 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Fri, 22 Jul 2022 17:48:58 -0600 Subject: [PATCH 68/81] fix G_STC --- pvlib/mlfm.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 1c776fbd16..c83f6e9e69 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -25,7 +25,7 @@ # T_STC = 25.0 # STC temperature [C] temperature_ref T_HTC = 75.0 # HTC temperature [C] -G_STC = 1.0 # STC irradiance [kW/m^2] +G_STC = 1000.0 # STC irradiance [W/m^2] G_LIC = 0.2 # LIC irradiance [kW/m^2] @@ -115,9 +115,8 @@ def mlfm_meas_to_norm(dmeas, ref): ''' dnorm = pd.DataFrame() - dnorm['pr_dc'] = ( - dmeas['p_mp'] / - (ref['p_mp'] * dmeas['poa_global'] / G_STC)) + dnorm['pr_dc'] = dmeas['p_mp'] / ref['p_mp'] \ + / (dmeas['poa_global'] / G_STC) # temperature corrected dnorm['pr_dc_temp_corr'] = ( @@ -349,8 +348,8 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): ''' mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ c_3 * np.log10(dmeas['poa_global'] / G_STC) + \ - c_4 * dmeas['poa_global'] / G_STC + \ - c_6 / dmeas['poa_global'] / G_STC + c_4 * (dmeas['poa_global'] / G_STC) + \ + c_6 / (dmeas['poa_global'] / G_STC) if 'wind_speed' in dmeas.columns: mlfm_out += c_5 * dmeas['wind_speed'] return mlfm_out From 0e2a0181343f5e014c099f7d7402ff3e6a4f3f27 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Fri, 22 Jul 2022 17:59:07 -0600 Subject: [PATCH 69/81] add data files for tutorials --- .../meas_gtw/g78_T16_Xall_F10m_R900_041.csv | 908 +++++++++ .../meas_gtw/n05667_Y13_R1k6_fClear_041.csv | 1616 +++++++++++++++++ .../meas_gtw/x19074001_iec61853_041.csv | 28 + docs/tutorials/mlfm_data/ref/mlfm_matrix.csv | 181 ++ .../mlfm_data/ref/mlfm_reference_modules.csv | 18 + 5 files changed, 2751 insertions(+) create mode 100644 docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041.csv create mode 100644 docs/tutorials/mlfm_data/meas_gtw/n05667_Y13_R1k6_fClear_041.csv create mode 100644 docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041.csv create mode 100644 docs/tutorials/mlfm_data/ref/mlfm_matrix.csv create mode 100644 docs/tutorials/mlfm_data/ref/mlfm_reference_modules.csv diff --git a/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041.csv b/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041.csv new file mode 100644 index 0000000000..4ed397d0f5 --- /dev/null +++ b/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041.csv @@ -0,0 +1,908 @@ +date_time,module_id,poa_global,wind_speed,temp_air,blue_frac,beam_frac,temp_module,v_oc,i_sc,i_mp,v_mp,r_sc,r_oc +2016-01-26 07:20:00-07:00,78,2.666484317,1.472831997,8.177978516,0.454991652,1.1,2.081939697,33.04064421,0.013215447,0.009809045,24.33732,115258.5498,608.680999 +2016-01-26 07:30:00-07:00,78,7.899142696,1.297711339,8.241424561,0.522026664,-0.1,2.436985474,37.64402934,0.037248728,0.02983236,29.62497997,8253.745059,150.461283 +2016-01-26 07:40:00-07:00,78,52.92767243,0.955482493,7.739624023,0.270154323,0.300267162,2.592086792,39.6492057,0.072837131,0.061195743,32.44486777,4762.543972,63.66002837 +2016-01-26 07:50:00-07:00,78,104.9430478,0.62178426,6.727676392,0.306793868,0.570422814,4.082763672,42.70262294,0.215963967,0.1043503,40.52055001,335.2229575,20.30366921 +2016-01-26 08:00:00-07:00,78,153.4330542,0.410855412,7.471725464,0.352624445,0.624202994,6.691146851,43.92516586,0.401482968,0.2831102,41.82102001,145.7231242,10.27448789 +2016-01-26 08:10:00-07:00,78,207.780344,0.676059248,8.259368896,0.388758434,0.654555168,9.171981812,44.29201888,1.051441221,0.976606838,37.8831301,1022.179281,3.64816346 +2016-01-26 08:20:00-07:00,78,216.1854545,0.254440298,8.958572388,0.419448916,0.725788201,11.9732666,44.18390797,1.201683005,1.126675792,38.21257622,457.923977,3.080113759 +2016-01-26 08:30:00-07:00,78,314.4320338,0.856546629,8.97203064,0.430550146,0.74718442,14.17019653,44.03193485,1.331171973,1.252935622,37.87620839,1069.804838,2.48354974 +2016-01-26 08:40:00-07:00,78,364.1616107,0.58913996,9.572525024,0.445192536,0.769498154,16.85355042,44.17269549,1.847060911,1.717155082,37.41243227,395.6073655,1.962480341 +2016-01-26 08:50:00-07:00,78,414.4448538,0.526614105,10.14099121,0.457279734,0.816816815,18.9306488,44.1008495,2.121667348,1.980342542,37.0788336,507.5955731,1.660780338 +2016-01-26 09:00:00-07:00,78,462.1142704,1.284213332,10.18713379,0.466809799,0.819644581,19.76313782,44.17257403,2.3720334,2.219748978,36.92349031,451.1426099,1.618454729 +2016-01-26 09:10:00-07:00,78,510.2908602,0.195159288,12.86024475,0.474304138,0.837084913,24.29801941,43.72170172,2.612981553,2.441520192,36.35140926,459.5001701,1.519881831 +2016-01-26 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b/docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041.csv @@ -0,0 +1,28 @@ +date_time,module_id,temp_module,poa_global,i_sc,v_oc,i_mp,v_mp,p_mp,wind_speed +0,19074001,15,100,0.595,65.78,0.543,55.56,30.16,0 +1,19074001,15,200,1.183,67.79,1.093,57.7,63.06,0 +2,19074001,15,400,2.354,69.65,2.185,59.42,129.85,0 +3,19074001,15,600,3.532,70.65,3.292,60.06,197.74,0 +4,19074001,15,800,4.706,71.35,4.398,60.21,264.83,0 +5,19074001,15,1000,5.891,71.85,5.503,60.12,330.86,0 +6,19074001,25,100,0.599,63.95,0.547,53.47,29.25,0 +7,19074001,25,200,1.183,66.01,1.09,56.07,61.14,0 +8,19074001,25,400,2.365,67.92,2.19,57.75,126.45,0 +9,19074001,25,600,3.542,68.96,3.298,58.31,192.28,0 +10,19074001,25,800,4.718,69.68,4.4,58.54,257.56,0 +11,19074001,25,1000,5.903,70.21,5.506,58.54,322.3,0 +12,19074001,25,1100,6.488,70.44,6.055,58.49,354.17,0 +13,19074001,50,100,0.602,59.36,0.541,49.64,26.85,0 +14,19074001,50,200,1.199,61.52,1.096,51.75,56.7,0 +15,19074001,50,400,2.379,63.56,2.214,52.88,117.06,0 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+module_id,source,site,manufacturer,technology,module_type,module_serial,i_sc,i_mp,v_mp,v_oc,alpha_i_sc,alpha_i_mp,beta_v_mp,beta_v_oc,gamma_pdc,delta_ff,irradiance_sensor_id,temp_sensor_id,comments +t1,test,test,test,test,test,test,10,8,25,30,0.0005,0.0001,-0.004,-0.0035,-0.0045,-0.0075,1,1,tes +g31,gantner ,tempe,Sanyo,hit,HIP-210NKHEI, ,5.57,5.09,41.3,50.9,0.0002998,0,0,-0.002459,-0.003,,5,121,https://www.gantner-instruments.com/ +g72,gantner ,tempe,FirstSolar,CdTe,FS-380, ,1.88,1.65,48.5,60.8,0.0004,0,0,-0.0027,-0.0025,,6,119,https://www.gantner-instruments.com/ +g78,gantner ,tempe,TrinaSolar,csi,TSM-180DC01, ,5.35,4.9,36.8,44.2,0.0005,0,0,-0.0035,-0.0045,,5,115,https://www.gantner-instruments.com/ +g81,gantner ,tempe,MiaSole,CIS/CIGS,MR-107, ,6.46,5.33,20,25.4,-0.0003,0,0,-0.0036,-0.0044,,5,124,https://www.gantner-instruments.com/ +n0188,nrel,cocoa,Manufacturer 2,msi,Model C,118,2.73,2.522,18.16,22.07,0.000426165,0.00000298,-0.004133773,-0.003298414,-0.004137609,,n/a,n/a,https://www.nrel.gov/docs/fy14osti/61610.pdf +n05667,nrel,eugene,Manufacturer 6,hit,Model G,5667,5.456,5.1,41.58,50.11,0.000349521,-0.000108326,-0.003372754,-0.002666783,-0.003466138,,n/a,n/a,https://www.nrel.gov/docs/fy14osti/61610.pdf +n75669,nrel,golden,Manufacturer 3,cdte,Model D,75669,1.177,1.02,64.12,87.66,0.000388,0.00037,-0.00247,-0.00231,-0.00214,,n/a,n/a,https://www.nrel.gov/docs/fy14osti/61610.pdf +x19074001,cfv,indoor,Panasonic,HIT,VBHN325SA 16,W0JH9NH02089,5.902849487,5.50600091,58.53700524,70.21442506,0.000254238,-4.31E-05,-0.00297779,-0.002441497,-0.002940601,-0.000867764,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ +x19074002,cfv,indoor,LG,,LG320N1K-A5,,10.35517229,9.788568757,32.72393392,40.13233059,0.000292305,-0.000192674,-0.003839589,-0.002909917,-0.004001761,-0.001512487,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ +x19074003,cfv,indoor,Hanwah Q Cells,,Q.PLUS BFR-G4-1 280,,9.488280901,8.9279505,31.12943344,38.75616482,0.000377717,-0.000163595,-0.00381762,-0.002876365,-0.003953902,-0.001574468,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ +x19074004,cfv,indoor,Jinko Solar,,JKM260P-60,,8.98846222,8.436453776,30.64339913,37.69974132,0.000373517,-0.000160436,-0.004092276,-0.003117413,-0.004226894,-0.001618429,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ +x19074005,cfv,indoor,Canadian Solar,cSi,CS6K-275M,1.16E+13,9.29909371,8.81042239,31.48246048,38.29373004,0.000350383,-0.000165735,-0.004038306,-0.003075087,-0.004181404,-0.001560515,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ +x19074006,cfv,indoor,Canadian Solar,,CS6K-270P,,9.150327478,8.600350021,31.10742638,38.07417146,0.000376151,-0.000142885,-0.003951002,-0.003052053,-0.003981806,-0.001530851,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ +x19074007,cfv,indoor,Mission Solar,,MSE300SQ5T,,9.425221741,8.945631878,31.9608779,39.37453464,0.000329992,-0.000168207,-0.003848512,-0.002852233,-0.004141737,-0.001596551,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ +x19074008,cfv,indoor,Hanwha Q CELLS,,Q.Peak-G4.1 300,,9.590035307,9.078192813,31.93130479,39.53341539,0.00031755,-0.000189238,-0.00387389,-0.002865072,-0.004034699,-0.001618217,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ +x19074009,cfv,indoor,itek Energy,,IT-360-SE72,,9.635601996,9.128439457,38.85677238,47.51235266,0.000355585,-0.000115779,-0.003804129,-0.002844126,-0.003902273,-0.001529759,n/a,n/a,https://pvpmc.sandia.gov/download/7701/ From 50b1de9415151c5c9bf6cdc903d2e075d3ba0e01 Mon Sep 17 00:00:00 2001 From: Cliff Hansen Date: Fri, 22 Jul 2022 18:10:40 -0600 Subject: [PATCH 70/81] extra space --- pvlib/mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index c83f6e9e69..9e5e549d67 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -349,7 +349,7 @@ def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ c_3 * np.log10(dmeas['poa_global'] / G_STC) + \ c_4 * (dmeas['poa_global'] / G_STC) + \ - c_6 / (dmeas['poa_global'] / G_STC) + c_6 / (dmeas['poa_global'] / G_STC) if 'wind_speed' in dmeas.columns: mlfm_out += c_5 * dmeas['wind_speed'] return mlfm_out From 2e1758fd3ad99f82065469cd1e1877b6a565ee0b Mon Sep 17 00:00:00 2001 From: steve ransome Date: Sun, 4 Dec 2022 16:54:01 +0000 Subject: [PATCH 71/81] Updated MLFM Dec 2022 --- docs/tutorials/mlfm.ipynb | 2277 +++++++++++++++++ docs/tutorials/mlfm_data/figs/GI.png | Bin 0 -> 7730 bytes docs/tutorials/mlfm_data/figs/flow_1024.png | Bin 0 -> 317113 bytes .../mlfm_data/figs/lfm_220914t15.png | Bin 0 -> 120018 bytes docs/tutorials/mlfm_data/figs/losses.png | Bin 0 -> 22124 bytes docs/tutorials/mlfm_data/figs/mlfm_fit.png | Bin 0 -> 42958 bytes docs/tutorials/mlfm_data/figs/mlfm_matrix.png | Bin 0 -> 79235 bytes 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"cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MLFM for PVLIB \n", + "ver: 221114t17\n", + "### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "Corrections and additions for comments by : \n", + "Cliff Hansen, Kevin Anderson, Anton Driesse and Mark Campanelli\n", + "\n", + "## Tutorial overview.\n", + "see details for each function in mlfm.py\n", + "\n", + "I) The Loss Factors Model (LFM) 2011 ref [1] quantifies normalised losses \n", + "from module parameters (e.g. pr_dc, i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) \n", + "by analysing module measurements or the shape of the IV curve and comparing \n", + "it with STC reference values from the datasheet. \n", + "\n", + "II) The Mechanistic performance model (MPM) 2017 ref [2] has \"meaningful, \n", + "independent, robust and normalised\" coefficients which fit how the LFM values \n", + "depend on irradiance, module temperature (and windspeed) and time. \n", + "\n", + "III) This tutorial shows how to take module measured and weather data, \n", + "(either outdoor or IEC 61853-like matrix data), normalise it, generate MLFM \n", + "coefficients, fit them with the MPM then analyse module performance looking for \n", + "loss values, degradation and allowing performance predictions as shown in fig 2. \n", + "\n", + "Fig 1 illustrates the loss factors model (LFM). \n", + "\n", + "Depending on the number of measurements available the LFM is defined \n", + "with a suffix number x = 1..12 LFM_n as in ref [4] - \n", + "\n", + "It uses the shape and values from dc measurements to quantify the values of each \n", + "of the loss factors (coloured arrors on the y=current or x=voltage axes\n", + "going from (1) ref\\_p\\_mp to (6) meas\\_p\\_mp. \n", + "\n", + "![mlfm_data/figs/lfm_220914t15.png](mlfm_data/figs/lfm_220914t15.png) \n", + "\n", + "Fig 1: Loss Factors Model \n", + "\n", + "\n", + "![mlfm_data/figs/flow_1024.png](mlfm_data/figs/flow_1024.png) \n", + "\n", + "Fig 2: MLFM overview flow chart of this tutorial. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explanations of the Loss factors model in fig 1.\n", + "\n", + "1) ref_p_mp = Initial datasheet value at STC.\n", + "\n", + "Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", + "\n", + "2->3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", + " - norm_i_sc = measured / expected isc corrected for poa_global (purple)\n", + " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", + " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", + " \n", + " \n", + "4->5) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", + " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", + " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown) \n", + " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", + " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", + " \n", + " \n", + "6) These losses cause the performance to fall to pr_dc (= meas_p_mp / ref_p_mp) \n", + "\n", + "pr_dc = 1/ff \\* \n", + " (norm_i_sc \\* norm_r_sc \\* norm_i_ff ) \\* \n", + " (norm_v_ff \\* norm_r_oc \\* norm_v_oc_t \\* norm_temp_corr ) \n", + "\n", + "Note: \n", + "The gamma temperature correction is just subtracted from voc for simplicity. \n", + "In reality there will be temperature dependencies for i_sc and ff but they are smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from pvlib.mlfm import meas_to_norm, mpm_a_fit, meas_to_stack_lin, mpm_b_fit\n", + "from pvlib.mlfm import mpm_a_calc, mpm_b_calc\n", + "\n", + "from pvlib.mlfm import plot_scatter, plot_stack # , mpm_calc\n", + "\n", + "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", + "import os\n", + "root_dir = os.getcwd()\n", + "\n", + "# uncomment to see root dir\n", + "# print(root_dir)\n", + "\n", + "# STANDARD DEFINITIONS (also in mlfm.py)\n", + "G_STC = 1000.0 # STC irradiance [W/m^2]\n", + "T_STC = 25.0 # STC temperature [C] temperature_ref\n", + "\n", + "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", + "plt.rcParams['figure.figsize'] = [7, 5] # setup fig size inches ~[7, 5]\n", + "plt.rcParams.update({'font.size': 12}) # setup fontsize ~12\n", + "plt.linewidth = 1.5 # line width in points ~1.5\n", + "plt.linestyle = '--' # solid line ~'--'\n", + "plt.marker = 's' # the default marker square ~'s'\n", + "plt.markersize = 9 # marker size, in points ~9\n", + "plt.bbox = 1.4 # offset --> to not overwrite ~1.4\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get user choices " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# save graphs as png files to the output directory?\n", + "save_figs = True" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# select which mpm to model : must be 'a original 2017' or 'b advanced 2022'\n", + "mpm_sel = 'b' # 'a' or 'b'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [A] Select MLFM measurement data file\n", + "\n", + "Three default files are included (\\* = version number ) \n", + "\n", + "(0) g78\\_T16\\_Xall\\_F10m\\_R900\\*.csv (6 LFM params)\n", + "(1) n05667\\_Y13\\_R1k6\\_fClear\\*.csv (4 LFM params) \n", + "(2) x19074001\\_iec61853\\*.csv (4 LFM params) \n", + "\n", + "Essential default column names in meas( ) are :- \n", + "\n", + "meas { \n", + "'date\\_time', 'module\\_id', \n", + "'poa\\_global', 'temp\\_module', \n", + "'v\\_oc', 'i\\_sc', 'i\\_mp', 'v\\_mp', \n", + "'r\\_sc', 'r\\_oc', <-- optional for LFM_6 \n", + "'wind\\_speed', 'temp\\_air', <-- optional \n", + "}\n", + "\n", + "\n", + "File naming conventions can be used to help identify files, for example \n", + "`x81_T1906_D3_Fh.csv` \n", + "\n", + "where \n", + " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", + " - 81 = module id/channel number \n", + " - T1906 = (T)ime started = yymm(dd) \n", + " - D3 = (D)uration in days \n", + " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", + " - etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# select one of the files in ''\\pvlib-python\\docs\\tutorials\\mlfm_data\\meas_gtw'\n", + "\n", + "mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' # 0 LFM_6 outdoor\n", + "# mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' # 1 LFM_4 outdoor\n", + "# mlfm_meas_file = 'x19074001_iec61853_041.csv' # 2 LFM_4 indoor\n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041_4param.csv' # 3 #0 no rsc,roc\n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R1_041.csv' # 4 #0 test record\n", + "\n", + "# extract module id from filename e.g. 'g78'\n", + "mlfm_mod = mlfm_meas_file.split('_')\n", + "\n", + "mlfm_mod_sel = mlfm_mod[0]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import measured data (outdoor or matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "meas = pd.read_csv(\n", + " # root_dir + '/mlfm_data/meas_gtw/' + mlfm_meas_file,\n", + " os.path.join(root_dir, 'mlfm_data', 'meas_gtw', mlfm_meas_file),\n", + " index_col='date_time'\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [B] Read all reference datasheet values at STC" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# user must keep updated with their modules from their measurements\n", + "ref_file_name = os.path.join(root_dir, 'mlfm_data', 'ref', 'mlfm_reference_modules.csv')\n", + "\n", + "ref_data = pd.read_csv(\n", + " ref_file_name, index_col='module_id')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select module stc data from reference database" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " ref_data = ref_data[\n", + " ref_data.index == mlfm_mod_sel]\n", + "\n", + "except IndexError:\n", + " print(\"You must define module ref data to use this module ...\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'i_sc': 5.35,\n", + " 'i_mp': 4.9,\n", + " 'v_mp': 36.8,\n", + " 'v_oc': 44.2,\n", + " 'alpha_i_sc': 0.0005,\n", + " 'beta_v_oc': -0.0035,\n", + " 'alpha_i_mp': 0.0,\n", + " 'beta_v_mp': 0.0,\n", + " 'gamma_pdc': -0.0045,\n", + " 'p_mp': 180.32,\n", + " 'ff': 0.762549161}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Put relevant data into a dict for easy use\n", + "# ignore any other columns that may be database specific\n", + "# as they aren't needed\n", + "\n", + "ref = dict(\n", + " # module_id=ref_data['module_id'].values[0],\n", + " i_sc=ref_data['i_sc'].values[0],\n", + " i_mp=ref_data['i_mp'].values[0],\n", + " v_mp=ref_data['v_mp'].values[0],\n", + " v_oc=ref_data['v_oc'].values[0],\n", + "\n", + " alpha_i_sc=ref_data['alpha_i_sc'].values[0],\n", + " beta_v_oc=ref_data['beta_v_oc'].values[0],\n", + " alpha_i_mp=ref_data['alpha_i_mp'].values[0],\n", + " beta_v_mp=ref_data['beta_v_mp'].values[0],\n", + " gamma_pdc=ref_data['gamma_pdc'].values[0],\n", + "\n", + " p_mp=ref_data['p_mp'].values[0],\n", + " ff=ref_data['ff'].values[0],\n", + ")\n", + "\n", + "# uncomment to show ref data\n", + "ref\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate useful data columns for meas" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idpoa_globalwind_speedtemp_airblue_fracbeam_fractemp_modulev_oci_sci_mpv_mpr_scr_ocp_mppr_dcv_oc_temp_corrpr_dc_temp_corr
count907.0907.000000907.000000907.000000907.000000845.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000
mean78.0580.4669852.40828226.9266260.5287710.73671639.98639840.4241873.0371572.80241232.6643531864.66826623.11563591.8459070.83831842.5464310.896654
std0.0368.8251601.3301508.9461270.0516730.30515115.1552562.6500731.9522641.8092612.8857187268.89577597.53471458.1057610.1613333.4228980.178494
min78.01.2628310.1951594.1494140.270154-0.1000000.40155027.1151250.0085680.00675417.440980-99.0000000.9776380.1177920.14965326.9101360.151773
25%78.0223.3910871.35182620.0364840.5135490.75418329.46702639.4442631.1653521.03978731.190638432.5881371.11543436.1143880.80990741.9259820.939101
50%78.0642.1992032.13381826.7346340.5321750.87877940.21743840.8502753.4241873.18212432.860113592.2265671.344735106.4547410.87554544.0700190.966653
75%78.0930.8452453.27728635.7742310.5433220.90983650.93165642.2322044.8761314.50720134.336062977.3155613.163062143.7046760.93052744.7292550.984946
max78.01118.1409889.01842042.5605010.7162241.10000069.43444844.8254135.8794725.44034141.821020121808.0889001165.006272182.8753951.41527445.2777151.407537
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" + ], + "text/plain": [ + " module_id poa_global ... v_oc_temp_corr pr_dc_temp_corr\n", + "count 907.0 907.000000 ... 907.000000 907.000000\n", + "mean 78.0 580.466985 ... 42.546431 0.896654\n", + "std 0.0 368.825160 ... 3.422898 0.178494\n", + "min 78.0 1.262831 ... 26.910136 0.151773\n", + "25% 78.0 223.391087 ... 41.925982 0.939101\n", + "50% 78.0 642.199203 ... 44.070019 0.966653\n", + "75% 78.0 930.845245 ... 44.729255 0.984946\n", + "max 78.0 1118.140988 ... 45.277715 1.407537\n", + "\n", + "[8 rows x 17 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculate p_mp and pr_dc as they might be missing\n", + "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", + "\n", + "meas['pr_dc'] = (meas['p_mp'] / ref['p_mp']\n", + " / (meas['poa_global'] / G_STC))\n", + "\n", + "# temperature corrected v_c and pr_dc\n", + "meas['v_oc_temp_corr'] = \\\n", + " (meas['v_oc'] * (1 - ref['beta_v_oc']*(meas['temp_module'] - T_STC)))\n", + "\n", + "meas['pr_dc_temp_corr'] = \\\n", + " (meas['pr_dc'] * (1 - ref['gamma_pdc']*(meas['temp_module'] - T_STC)))\n", + "\n", + "# show some meas data\n", + "meas.describe()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select LFM_n model by counting variables in the meas data \n", + "usually LFM_4 = matrix (i\\_sc, i\\_mp, v\\_mp, v\\_oc) \n", + "and LFM_6 = iv (i\\_sc, i\\_mp, v\\_mp, v\\_oc + r\\_sc, r\\_oc) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_qty_lfm_vars(dmeas):\n", + " \"\"\"Find the quantity of LFM variables in the measured data.\n", + "\n", + " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", + "\n", + " Parameters\n", + " ----------\n", + " dmeas: DataFrame\n", + " Measured weather and module electrical values per time or measurement\n", + "\n", + " Returns\n", + " -------\n", + " qty_lfm_vars : int\n", + " number of lfm_values present in data usually\n", + "\n", + " 2 = ( i_mp, v_mp ) from mpp tracker\n", + " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", + " 6 = (i_sc, r_sc, i_mp, v_mp, r_oc, v_oc) from iv curve.\n", + "\n", + " \"\"\"\n", + " # find how many lfm variables were measured\n", + " qty_lfm_vars = 0\n", + " for lfm_sel in ('i_sc', 'r_sc', 'i_mp', 'v_mp', 'r_oc', 'v_oc'):\n", + " if lfm_sel in dmeas.columns:\n", + " qty_lfm_vars += 1\n", + " # print(qty_lfm_vars, lfm_sel)\n", + "\n", + " return qty_lfm_vars\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "qty_lfm_vars = get_qty_lfm_vars(meas)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [C] Normalise LFM values from meas and ref to norm dataframes \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_globaltemp_modulewind_speedpr_dcpr_dc_temp_corri_sci_mpv_ocv_oc_temp_corrv_mpr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:002.6664842.0819401.4728320.4964970.4452930.9263800.7422410.7475260.6875640.7365870.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:007.8991432.4369851.2977110.6204710.5574730.8814090.8008960.8516750.7844180.7869770.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:0052.9276722.5920870.9554820.2080370.1870590.2572270.8401720.8970410.8266880.8182980.8977000.8950180.9359160.914282
\n", + "
" + ], + "text/plain": [ + " poa_global temp_module ... i_ff v_ff\n", + "date_time ... \n", + "2016-01-26 07:20:00-07:00 2.666484 2.081940 ... 0.754692 0.968481\n", + "2016-01-26 07:30:00-07:00 7.899143 2.436985 ... 0.896006 0.907783\n", + "2016-01-26 07:40:00-07:00 52.927672 2.592087 ... 0.935916 0.914282\n", + "\n", + "[3 rows x 14 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "norm = meas_to_norm(meas, ref)\n", + "\n", + "# show some normalised data\n", + "norm.head(3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make irradiance and temperature bins for pivot tables " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# poa_global bin e.g. 100, 200 .. 1100W/m2\n", + "norm['poa_global_bin'] = \\\n", + " norm['poa_global'].round(-2)\n", + "\n", + "# temp_module bin e.g. 5, 10 .. 75C\n", + "norm['temp_module_bin'] = \\\n", + " (5 * round(norm['temp_module'] / 5, 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [D] Perform sanity checks on meas and norm data \n", + "\n", + "It's easier to sanity check and study normalised data than raw values. \n", + "1) Remove bad, missing, unwanted or outlier data \n", + "2) User defined limits may depend on data scatter and degradation \n", + "3) Can either select on values e.g. '0.5 x stdev from mean' \n", + "4) Possible to select on dates if desired. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# select by irradiance poa_global range e.g. 100-1100 W/m2\n", + "norm = norm[(norm['poa_global'] >= 100) &\n", + " (norm['poa_global'] <= 1100)]\n", + "\n", + "# remove specific lfm values outside limits e.g. <0.5 or >1.5\n", + "norm = norm[((norm['pr_dc'] > 0.5) &\n", + " (norm['pr_dc'] < 1.5))]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# remove all mlfm values outside x~3 stdevs\n", + "if qty_lfm_vars == 6:\n", + " # only needed for outdoor data as indoor ought to be less scattered\n", + " # remove all mlfm data > x stdev usually 3\n", + " stdevs = 3\n", + "\n", + " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc', 'v_oc'):\n", + " norm = norm[\n", + " ((norm[lfm] - norm[lfm].mean()) /\n", + " norm[lfm].std()).abs() < stdevs\n", + " ]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter only matching rows from meas and norm data\n", + "like an inner join but leave data in separate norm and meas frames" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# drop meas rows that aren't in norm\n", + "meas_not_in_norm = ~meas.index.isin(norm.index)\n", + "meas = meas.drop(meas[meas_not_in_norm].index)\n", + "\n", + "# drop norm rows that aren't in meas\n", + "norm_not_in_meas = ~norm.index.isin(meas.index)\n", + "norm = norm.drop(norm[norm_not_in_meas].index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [E] Plot normalised LFM data vs irradiance \n", + "\n", + "For outdoor data - \n", + "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", + "\n", + "For matrix data - \n", + "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", + "\n", + "1. Higher values are always better (unlike measured values such as \n", + " Rseries or Io where lower is better)\n", + "1. Accurate measurements and a stable module result in narrowest lines \n", + "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", + "1. r_oc tends to fall at high light levels ( \\ right) \n", + "1. i_ff and v_ff are usually fairly flat ( - ) \n", + "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", + " and/or snow (if not properly corrected). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalised lfm values vs. irradiance.\n", + "\n", + "All traces should be thin, smoot lines usually around 0.9 ± 0.1 \n", + "i\\_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot normalised values vs. irradiance\n", + "fig_scatter = plot_scatter(\n", + " norm, mlfm_meas_file, qty_lfm_vars, save_figs)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [E] : LFM multiplicative factors (y) vs. poa irradiance (x)\n", + "\n", + "\n", + "# [F] Convert multiplicative to subtractive losses for a stack plot \n", + "\n", + " Multiplicative losses are easier to understand but to represent them on a graph \n", + "it's easier to show them as a stacked plot where the values are 'translated' \n", + "so the sum of the stacked losses is shown to equate to the product of the \n", + "multiplicative losses.\n", + "\n", + "LFM losses can be analysed as either \n", + "\n", + "- multiplicative pr_dc = 1/ff * PRODUCT(norm(i_sc), ... \\* stack(v_oc_t), stack(temp_corr) ). \n", + "\n", + "- subtractive pr_dc = 1/ff - SUM(stack(i_sc), ... stack(v_oc_t), stack(temp_corr) ). \n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# translate multiplicative to stack losses and add to\n", + "# dataframe stack add a gap between i and v losses\n", + "\n", + "stack = meas_to_stack_lin(meas, ref, qty_lfm_vars, gap=0.0) # gap = 0.01\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [G] Plot stack losses vs. measurement \n", + "\n", + "Fig 3 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", + "\n", + "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", + "\n", + "Fig 3 Stacked losses by measurement \n", + "\n", + "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", + " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", + " \n", + "- This figure shows a typical c-Si module for four clear days for \n", + " different months July to Oct in AZ. \n", + " \n", + "- In the middle of the days the high irradiance results in the biggest \n", + " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", + " (as the module heats to 60C). \n", + " \n", + "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", + " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", + "\n", + "Stack losses are indicated by their colours \n", + "(from top to bottom for lfm_4=matrix and lfm_6=ivcurve) \n", + "\n", + "![mlfm_data/figs/losses.png](mlfm_data/figs/losses.png) \n", + "\n", + "Graph options : \n", + "\n", + "is_i_sc_self_ref : boolean \n", + " = self corrects i_sc to remove angle of incidence, spectrum, \n", + " snow or soiling. \n", + " \n", + "is_v_oc_temp_module_corr : boolean \n", + " = calc temperature loss due to gamma, subtract from voc loss " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot stack loss vs. time (or measurement) chart\n", + "fig_stack = plot_stack(\n", + " dstack=stack, # dataframe measurements\n", + " fill_factor=ref['ff'], # dataframe reference STC\n", + " title=mlfm_meas_file, #\n", + " xaxis_labels=12, # show num x_labels or 0 to show all\n", + " is_i_sc_self_ref=False, # is isc self referenced?\n", + " save_figs=save_figs # save the figure?\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [G] Stacked loss values (y) s. date and time (outdoor) or matrix measurement (x)\n", + "\n", + "# [H] Fit mpm to measured weather and normalised losses \n", + "\n", + "Perform a Mechanistic Performance Model (MPM) fit to the lfm parameters \n", + "poa_global (W/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", + "\n", + "\n", + "mpm_a = c_1 +c_2\\*(t_mod-25) +c_3\\*log10(g) +c_4\\*g +c_5\\*ws +c_6\\/g (deprecated) \n", + "\n", + "mpm_b = c_1 +c_2\\*(t_mod–25) +c_3\\*log10(g)\\*(t_k\\/t_stc_k) +c_4\\*g +c_5\\*ws\n", + "\n", + "\n", + "Report the fit (coeffs) and error (errs) coefficients. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choose which normalised lfm parameter to model e.g. pr_dc or i_sc..v_oc " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "lfm_sel = 'pr_dc'" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_globaltemp_modulewind_speedpr_dcpr_dc_temp_corri_sci_mpv_ocv_oc_temp_corrv_mpr_scr_oci_ffv_ffpoa_global_bintemp_module_bincalc_pr_dcdiff_pr_dc
count689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000641.0000006.410000e+02
mean730.79300445.2791542.5948670.8963680.9749270.9877810.9253740.9329060.9979370.8006500.9796790.8831720.9446390.906453731.93033445.3047900.891053-4.655127e-08
std269.19498612.2608701.3430590.0611190.0229330.0163850.0061600.0331600.0220200.0222320.0084230.0192540.0103170.006508272.84443912.3176230.0573571.343506e-02
min101.71417514.0554810.1951590.7768280.8637610.9480540.9001430.8594630.9067770.7627950.9272780.8469370.9235480.895678100.00000015.0000000.779638-6.024074e-02
25%523.88674636.6580051.5112440.8499280.9605340.9785800.9210560.9004440.9878770.7826540.9757270.8672060.9367120.901710500.00000035.0000000.846298-5.849096e-03
50%795.67516244.9637912.3996230.8941720.9737600.9839250.9255980.9348081.0055830.7977230.9824500.8813470.9442940.904725800.00000045.0000000.8896676.002181e-04
75%966.30895955.1749123.4830680.9426490.9892610.9940590.9297260.9588891.0138550.8179060.9855040.8994820.9520680.9098861000.00000055.0000000.9340735.668780e-03
max1098.46229169.4344489.0184201.1123051.0579351.0851990.9389211.0141501.0243830.8563010.9984190.9293870.9845760.9267611100.00000070.0000001.0385757.621357e-02
\n", + "
" + ], + "text/plain": [ + " poa_global temp_module ... calc_pr_dc diff_pr_dc\n", + "count 689.000000 689.000000 ... 641.000000 6.410000e+02\n", + "mean 730.793004 45.279154 ... 0.891053 -4.655127e-08\n", + "std 269.194986 12.260870 ... 0.057357 1.343506e-02\n", + "min 101.714175 14.055481 ... 0.779638 -6.024074e-02\n", + "25% 523.886746 36.658005 ... 0.846298 -5.849096e-03\n", + "50% 795.675162 44.963791 ... 0.889667 6.002181e-04\n", + "75% 966.308959 55.174912 ... 0.934073 5.668780e-03\n", + "max 1098.462291 69.434448 ... 1.038575 7.621357e-02\n", + "\n", + "[8 rows x 18 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# add selected variable to measured data frame to ensure data indexes match.\n", + "meas_temp = meas.copy()\n", + "meas_temp[lfm_sel] = norm[lfm_sel]\n", + "\n", + "\n", + "if mpm_sel == 'b':\n", + " cc, coeffs, ee, errs = mpm_b_fit(meas_temp, lfm_sel)\n", + "else:\n", + " cc, coeffs, ee, errs = mpm_a_fit(meas_temp, lfm_sel)\n", + "\n", + "# store calculated value of LFM variable\n", + "norm['calc_' + lfm_sel] = cc\n", + "\n", + "# store residual difference of LFM variable\n", + "norm['diff_' + lfm_sel] = norm[lfm_sel] - norm['calc_' + lfm_sel]\n", + "\n", + "norm.describe()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [I] Plot heatmap of mean residual vs. temp_module and poa_global\n", + "\n", + "Show a heatmap of the average residual (meas - fit) error \n", + "for each irradiance (100W/m^2) and tmod bin (5C)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_heatmap(dnorm, fit, y_axis, x_axis, z_axis,\n", + " title, save_figs, clip=0.02,):\n", + " \"\"\"Plot a heatmap of Z vs. binned X and Y axes.\n", + "\n", + " Parameters\n", + " ----------\n", + " dnorm : dataframe\n", + " Normalised multiplicative loss values (values approx 1).\n", + "\n", + " fit : string\n", + " fitted parameter e.g. 'pr_dc'.\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global_bin'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module_bin'.\n", + "\n", + " z_axis : string\n", + " value as a colour surface plot e.f. 'diff_pr_dc'.\n", + "\n", + " clip : value\n", + " clipping of z axis usually 0.02\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " \"\"\"\n", + " df_piv = pd.pivot_table(\n", + " dnorm,\n", + " index=y_axis, # e.g. 'temp_module_bin'\n", + " columns=x_axis, # e.g. 'poa_global_bin'\n", + " values=z_axis, # value to aggregate\n", + " fill_value=0, # fill empty cells with this ?\n", + " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", + " margins=False, # grand totals hide\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " # force z limits to be -2% to +2% if desired\n", + " df_piv = df_piv.clip(lower=-clip, upper=+clip)\n", + "\n", + " im = ax1.imshow(\n", + " df_piv,\n", + " cmap='RdYlBu',\n", + " origin='lower'\n", + " )\n", + "\n", + " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", + "\n", + " # Y AXIS : show only 1 of each y_skip labels\n", + " y_ticks = df_piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = df_piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " # X AXIS : show only 1 of each x_skip labels\n", + " x_ticks = df_piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_title(title)\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid(color='k', linestyle=':', linewidth=1)\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(\n", + " os.path.join('mlfm_data', 'output', 'heatmap_' + title[:len(title)-4]),\n", + " dpi=300\n", + " )\n", + " \n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Residual LFM fit heatmap vs. poa_global and temp_module" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap\n", + "heatmap_plot = plot_heatmap(\n", + " dnorm=norm,\n", + " fit=lfm_sel,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis='diff_' + lfm_sel,\n", + " clip=0.025,\n", + " title='residual_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [I] Residual LFM fit heatmap vs. poa_global bin (x) and temp_module bin (y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_fit(dmeas, dnorm, fit, title, save_figs, coeffs):\n", + " \"\"\"Scatter plot fit to normalised measured.\n", + " \n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measurements, must include 'poa_global_kwm2'\n", + "\n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + "\n", + " fit : string\n", + " name of fitted variable e.g. 'pr_dc'.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " \n", + " \"\"\"\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " plt.title(title)\n", + "\n", + " plt.ylabel('fit ' + fit + ' * poa_global kW/m^2')\n", + " ax1.set_ylim(0, 1.2)\n", + "\n", + " plt.xlabel('meas ' + fit + '* poa_global_kW/m^2')\n", + " ax1.set_xlim(0, 1.2)\n", + "\n", + " plt.plot(\n", + " dnorm[fit] * dmeas['poa_global'] / G_STC,\n", + " dnorm['calc_' + fit] * dmeas['poa_global'] / G_STC,\n", + " 'c^',\n", + " label=fit\n", + " )\n", + "\n", + " # plot 1:1 line to show optimum fit\n", + " plt.plot((0, 1.2), (0, 1.2), 'ko-')\n", + " plt.plot((0, 1.0), (1.0, 1.0), 'ko-')\n", + " plt.plot((1.0, 1.0), (0, 1.0), 'ko-')\n", + "\n", + " plt.legend(loc='upper left')\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(\n", + " os.path.join('mlfm_data', 'output', 'fit_meas_' + title[:len(title)-4]),\n", + " dpi=300\n", + " )\n", + " \n", + "\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot fit vs. measured, include a 1:1 line for comparison\n", + "fit_plot = plot_fit(dmeas=meas,\n", + " dnorm=norm,\n", + " fit=lfm_sel,\n", + " title='fit_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " save_figs=save_figs,\n", + " coeffs=coeffs\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [J] scatter plot of 'fit_lfm_sel * poa_global# (y) vs. 'measured_lfm_sel * poa_global' (x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [K] Read in complete (G,T) Matrix to fill with MLFM predicted values \n", + "\n", + "Read in a matrix with complete values of \n", + "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", + "to predict all MPM values " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# read in the complete matrix data\n", + "matr = pd.read_csv(os.path.join(root_dir, 'mlfm_data', 'ref', 'mlfm_matrix.csv'),\n", + " index_col='id')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predict performance from MPM fit coefficients \n", + "\n", + "1. generate predicted mpm data \n", + "2. create a pivot table mpm(g,t) \n", + "3. show as a heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('b', array([ 1.13725529, -0.00454229, 0.1838458 , -0.15716391, 0.01 ]))" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show model coefficients\n", + "mpm_sel, coeffs" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpr_dc
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" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed pr_dc\n", + "id \n", + "1 matrix 100 0 0 1.066666\n", + "2 matrix 100 5 0 1.040871\n", + "3 matrix 100 10 0 1.015077\n", + "4 matrix 100 15 0 0.989282\n", + "5 matrix 100 20 0 0.963488" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# populate pivot table from predicted mpm data\n", + "if mpm_sel == 'b':\n", + " matr[lfm_sel] = mpm_b_calc(matr, *coeffs)\n", + "else:\n", + " matr[lfm_sel] = mpm_a_calc(matr, *coeffs)\n", + "\n", + "matr.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [L] Plot heatmap of predicted LFM values vs. temp_mod, poa_global bins" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", + " vmin=0, vmax=1.2, levels=5,\n", + " save_figs=False):\n", + " \"\"\"Plot filled contour plot Z vs. X and Y bins.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : dataframe\n", + " measured or normalised data containing weather columns\n", + " (poa_global, temp_module and wind_speed).\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module'.\n", + "\n", + " z_axis : string\n", + " measured value as a colour surface plot.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " vmin, vmax : float\n", + " minimum and maximum values for contour chart ###\n", + " \n", + " \"\"\"\n", + " piv = pd.pivot_table(\n", + " df,\n", + " index=y_axis,\n", + " columns=x_axis,\n", + " values=z_axis,\n", + " fill_value=0, # fill empty cells?\n", + " aggfunc=[np.mean], # min, np.sum, len->count\n", + " margins=False, # grand totals\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " piv = piv.clip(vmin, vmax)\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " cs = plt.contourf(\n", + " piv,\n", + " cmap='RdYlBu', # or 'nipy_spectral',\n", + " # origin='lower'\n", + " # nchunkint=1,\n", + " levels=levels,\n", + " vmin=vmin,\n", + " vmax=vmax\n", + " )\n", + "\n", + " cbar = fig.colorbar(cs, ax=ax1)\n", + " cbar.ax.set_ylabel(z_axis,\n", + " rotation=90,\n", + " va='bottom',\n", + " labelpad=+30)\n", + "\n", + " plt.title(title)\n", + " # # get_yaxis().set_major_formatter(FormatStrFormatter('%.2f'))\n", + "\n", + " y_ticks = piv.shape[0]\n", + "\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + "\n", + " # show only 1 of each y_skip labels\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " x_ticks = piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " # show only 1 of each x_skip labels\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid(color='k', linestyle=':', linewidth=1)\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(os.path.join('mlfm_data', 'output', 'contourf_'+ title[:len(title)-4])\n", + " , dpi=300\n", + " ) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", + "\n", + "matr2 = matr[matr['temp_module'] >= 10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contour plot of predicted lfm_sel + vs. poa_global and temp_mod. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df=matr2,\n", + " y_axis='temp_module',\n", + " x_axis='poa_global',\n", + " z_axis=lfm_sel,\n", + " title='matrix predicted_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [L1] Contour plot (colours) of predicted lfm_sel vs. poa_global (x) and temp_mod (y)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df=norm,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis=lfm_sel,\n", + " title='avg normalised_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [L2] Contour plot (colours) of measured lfm_sel vs. poa_global (x) and temp_mod (y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References \n", + " \n", + "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", + "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", + "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", + " \n", + ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", + " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", + "What's Most Important for Energy Yield' \n", + "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", + "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", + "\n", + ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", + " 'Choosing the best Empirical Model for predicting energy yield' \n", + " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", + " Canobbio, Switzerland 30-31 March, 2017 \n", + "\n", + ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", + "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", + "benchmark its practical relevance in energy yield prediction' \n", + "PVSC June 2019 Chicago, USA \n", + "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", + "\n", + ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", + "under Real Outdoor and IEC 61853 Test Conditions \n", + "Using High Quality Module IV Measurements' \n", + "36th EU PVSEC Sep 2019 \n", + "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", + "\n", + ".. [5] Steve Ransome (SRCL) \n", + "'How to use the Loss Factors and Mechanistic Performance Models \n", + "effectively with PVPMC/PVLIB' \n", + "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", + "https://pvpmc.sandia.gov/download/7879/ \n", + "\n", + ".. [6] W.Marion et al (NREL) \n", + "'New Data Set for Validating PV Module Performance Models' \n", + "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", + "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", + "\n", + ".. [7] Steve Ransome (SRCL)\n", + "'Benchmarking PV performance models with high quality IEC 61853 Matrix\n", + "measurements (Bilinear interpolation, SAPM, PVGIS, MLFM and 1-diode)'\n", + "http://www.steveransome.com/pubs/2206_PVSC49_philadelphia_4_presented.pdf\n", + "\n", + ".. [8] Juergen Sutterlueti (Gantner Instruments)\n", + "'Advanced system monitoring and artificial intelligent data-driven analytics \n", + "to serve GW-scale photovoltaic power plant and energy storage requirements'\n", + "https://pvpmc.sandia.gov/download/8574/\n", + "\n", + "Many more papers are available at www.steveransome.com \n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# =============================================================================================" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IPython 8.2.0\n", + "IPython.core.release 8.2.0\n", + "PIL 9.0.1\n", + "PIL.Image 9.0.1\n", + "PIL._version 9.0.1\n", + "_cffi_backend 1.15.0\n", + "_csv 1.0\n", + "_ctypes 1.1.0\n", + "_decimal 1.70\n", + "_pydev_bundle.fsnotify 0.1.5\n", + "_pydevd_frame_eval.vendored.bytecode 0.13.0.dev\n", + "argparse 1.1\n", + "backcall 0.2.0\n", + "certifi 2021.10.08\n", + "cffi 1.15.0\n", + "cftime 1.6.0\n", + "cftime._cftime 1.6.0\n", + "chardet 4.0.0\n", + "chardet.version 4.0.0\n", + "charset_normalizer 2.0.4\n", + "charset_normalizer.version 2.0.4\n", + "cloudpickle 2.0.0\n", + "colorama 0.4.4\n", + "csv 1.0\n", + "ctypes 1.1.0\n", + "cycler 0.10.0\n", + "dateutil 2.8.2\n", + "debugpy 1.5.1\n", + "decimal 1.70\n", + "decorator 5.1.1\n", + "defusedxml 0.7.1\n", + "distutils 3.9.12\n", + "entrypoints 0.4\n", + "executing 0.8.3\n", + "executing.version 0.8.3\n", + "h5py 3.6.0\n", + "http.server 0.6\n", + "idna 3.3\n", + "idna.idnadata 14.0.0\n", + "idna.package_data 3.3\n", + "ipaddress 1.0\n", + "ipykernel 6.9.1\n", + "ipykernel._version 6.9.1\n", + "jedi 0.17.2\n", + "json 2.0.9\n", + "jupyter_client 7.1.2\n", + "jupyter_client._version 7.1.2\n", + "jupyter_core 4.9.2\n", + "jupyter_core.version 4.9.2\n", + "kiwisolver 1.3.2\n", + "logging 0.5.1.2\n", + "matplotlib 3.5.1\n", + "matplotlib.backends.backend_qt 5.9.2\n", + "matplotlib.backends.qt_compat 5.9.2\n", + "matplotlib.backends.qt_editor._formlayout 1.0.10\n", + "netCDF4 1.5.8\n", + "netCDF4._netCDF4 1.5.8\n", + "numpy 1.21.6\n", + "numpy.core 1.21.6\n", + "numpy.core._multiarray_umath 3.1\n", + "numpy.lib 1.21.6\n", + "numpy.linalg._umath_linalg 0.1.5\n", + "packaging 21.3\n", + "packaging.__about__ 21.3\n", + "pandas 1.4.2\n", + "parso 0.7.0\n", + "pickleshare 0.7.5\n", + "pkg_resources._vendor.appdirs 1.4.3\n", + "pkg_resources._vendor.more_itertools 8.12.0\n", + "pkg_resources._vendor.packaging 21.3\n", + "pkg_resources._vendor.packaging.__about__ 21.3\n", + "pkg_resources._vendor.pyparsing 2.2.1\n", + "pkg_resources.extern.appdirs 1.4.3\n", + "pkg_resources.extern.more_itertools 8.12.0\n", + "pkg_resources.extern.packaging 21.3\n", + "pkg_resources.extern.pyparsing 2.2.1\n", + "platform 1.0.8\n", + "prompt_toolkit 3.0.20\n", + "psutil 5.8.0\n", + "pure_eval 0.2.2\n", + "pure_eval.version 0.2.2\n", + "pvlib 0.9.1\n", + "pvlib.version 0.9.1\n", + "pydevd 2.6.0\n", + "pygments 2.11.2\n", + "pyparsing 3.0.4\n", + "pytz 2021.3\n", + "re 2.2.1\n", + "requests 2.27.1\n", + "requests.__version__ 2.27.1\n", + "requests.packages.chardet 4.0.0\n", + "requests.packages.idna 3.3\n", + "requests.packages.idna.idnadata 14.0.0\n", + "requests.packages.idna.package_data 3.3\n", + "requests.packages.urllib3 1.26.8\n", + "requests.packages.urllib3._version 1.26.8\n", + "requests.packages.urllib3.connection 1.26.8\n", + "requests.packages.urllib3.packages.six 1.16.0\n", + "requests.packages.urllib3.util.ssl_match_hostname 3.5.0.1\n", + "requests.utils 2.27.1\n", + "scipy 1.8.0\n", + "scipy._lib.decorator 4.0.5\n", + "scipy.linalg._fblas b'$Revision: $'\n", + "scipy.linalg._flapack b'$Revision: $'\n", + "scipy.linalg._flinalg b'$Revision: $'\n", + "scipy.linalg._interpolative b'$Revision: $'\n", + "scipy.optimize.__nnls b'$Revision: $'\n", + "scipy.optimize._cobyla b'$Revision: $'\n", + "scipy.optimize._lbfgsb b'$Revision: $'\n", + "scipy.optimize._minpack 1.10 \n", + "scipy.optimize._minpack2 b'$Revision: $'\n", + "scipy.optimize._slsqp b'$Revision: $'\n", + "scipy.sparse.linalg._eigen.arpack._arpack b'$Revision: $'\n", + "scipy.sparse.linalg._isolve._iterative b'$Revision: $'\n", + "scipy.special._specfun b'$Revision: $'\n", + "setuptools 61.2.0\n", + "setuptools._distutils 3.9.12\n", + "setuptools._vendor.more_itertools 8.8.0\n", + "setuptools._vendor.ordered_set 3.1\n", + "setuptools._vendor.packaging 21.3\n", + "setuptools._vendor.packaging.__about__ 21.3\n", + "setuptools._vendor.pyparsing 2.2.1\n", + "setuptools.extern.more_itertools 8.8.0\n", + "setuptools.extern.ordered_set 3.1\n", + "setuptools.extern.packaging 21.3\n", + "setuptools.extern.pyparsing 2.2.1\n", + "setuptools.version 61.2.0\n", + "six 1.16.0\n", + "socketserver 0.4\n", + "socks 1.7.1\n", + "spyder_kernels 1.10.2\n", + "spyder_kernels._version 1.10.2\n", + "stack_data 0.2.0\n", + "stack_data.version 0.2.0\n", + "traitlets 5.1.1\n", + "traitlets._version 5.1.1\n", + "urllib.request 3.9\n", + "urllib3 1.26.8\n", + "urllib3._version 1.26.8\n", + "urllib3.connection 1.26.8\n", + "urllib3.packages.six 1.16.0\n", + "urllib3.util.ssl_match_hostname 3.5.0.1\n", + "wcwidth 0.2.5\n", + "xmlrpc.client 3.9\n", + "zlib 1.0\n", + "zmq 22.3.0\n", + "zmq.sugar 22.3.0\n", + "zmq.sugar.version 22.3.0\n" + ] + } + ], + "source": [ + "## TEST CODE CAN DELETE AFTER HERE \n", + "\n", + "if False: \n", + " # save data to csv\n", + " meas.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'meas.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " norm.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'norm.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " matr.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'matr.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " ref_data.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'ref_data.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " \n", + "if False:\n", + " print(coeffs[0],coeffs[1],coeffs[2],coeffs[3],coeffs[4], coeffs[5], )#coeffs[6],)\n", + " \n", + "\n", + "if False:\n", + " n= norm.loc['2016-03-23 09:00:00-07:00']\n", + "\n", + " print('\\n n= \\n', n)\n", + "\n", + " s = stack.loc['2016-03-23 09:00:00-07:00']\n", + "\n", + " print('\\n s= \\n', s)\n", + "\n", + "if True:\n", + " import sys \n", + " \n", + " for name, module in sorted(sys.modules.items()): \n", + " if hasattr(module, '__version__'): \n", + " print (name, module.__version__ )" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.8.0'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import scipy\n", + "scipy.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (Spyder)", + "language": "python3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "toc-autonumbering": true, + "toc-showmarkdowntxt": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/mlfm_data/figs/GI.png b/docs/tutorials/mlfm_data/figs/GI.png new file mode 100644 index 0000000000000000000000000000000000000000..e872822df5ce928e552dd7ed717441655b47d8f6 GIT binary patch literal 7730 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zdH?N%b-hphe_~!(G{S!W0B4sokP%RUdOmrwUCR|#X!#Vgi$nwZq#(YN)qsNHkKccq zb=&)Z#j3H3Na#T~qcX}*l8#}VHS-ZesD&Oz6dH@3V6O&edYkBD97uhZQ&yYFO_At+vlONZq?$_N)Wp1r zaFn>gib|fVl>>Q4NO`fmg$Cz%&IeUo296l*nZ6h%Zayml{hK_y5!661_PA$qI>6O2 zLF6_zjU6j*6X(hBWBiIVQI&r?DyZxw`_s@%dAqdPiWoq(+(kk_;vK}>QXB((<_i1L z+Y=4Jf*f_F4?q|6A@Fu=F+BHfM}1kf*n_iV3MNfDbSgm5OTeRKA_2klp$c&$)4@@u zhuVpOSMLFdlDG#cA3AKS*DP#-$&JT8`qhrX%ciVqeZ=Y4u^T;mSD{onOv;)u_{e zd@)*Cy=l3U@tQrdT7~q1q_qUMq!Q1Ukdoie(mT88>%r1;UKsw?Kn)Xa`Hwy@q0Gi7 zW{wXboRGMSS?8xgxuv3Y3U*s-%l=w95(Il|Vq^XN{nfH4N~cvMWn`)`Pj(C=e3qG2 zM2lnnv|NzevUp|4Ljwq#*52OElu?LjtCI)(5|?c1aNI7nFQtH43R^!CcxkZR@8e;lFuJaKzu&J3E{zlCN9xHK-dFe*k8 z%lz|+SDMEe3p@InSTVL5Yoi83XH zv&)*K7jPb8n1Tf6=R6C1xLpzyVFljv8qYRzz~EVxcm0oU%+?^}(XK8*zzZ{(YB;@JZO!Q16&Ga<^mSRU>x=LDit`TQ!&tT2vepIcmu7 zP~He0{RVj@8Q4Nm|KgS%@o;EuU?lR^WPFoXsd;l2*m6~DeM^gA`bdh z0UR*XxXM7@G1f##_1k=4=)Qx|XE&q_@0Ke_*&wJXJ_)Ie2##vyeV5 z@-)7(2f0}hM=+{>%);U|B1A^vMQv?Qi$@Qr7EN)1=x$uHX91EBq}i3qGxUE4Cj-0T zZ)vj)nGR{tpxWj7FLr4AAhLN7qfW4}>)P9Ml0%3rAjevQRNa*juyj9A*3) gs-Z6| (can include normalised losses for : + soiling, reflectivity vs. aoi, spectrum <- affecting i_sc, + current mismatch/shading, rollover, + clipping etc.) + + This file just contains - +LFM_6 : 'measurements with r_sc and r_oc' + e.g. iv curves with good smooth data. + +LFM_4 : 'measurements without r_sc or r_oc' + e.g. indoor matrix measurements or iv curves without smoooth data. + +II) The Mechanistic performance model (MPM) 2017 ref [2] +has "meaningful,independent, robust and normalised" coefficients +which fit how the LFM values depend on irradiance, module temperature +(and windspeed) and time. + +Two MPM versions have been included here : + +mpm_a : (mpm_original 2017 ref [2] now deprecated) + The original model to fit normalised parameters such as + pr_dc, v_oc, r_sc, v_mp, i_mp, ff ... + with an extra low light coefficient c_6 to help fit data with + unusual low light performance and/or poor measurements. + c_5 is only used if there is windspeed data, otherwise it is ignored + + mpm_a = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + +mpm_b : (GI name 'mpm_advanced' 2022 ref [7]) + Is an improved model to fit normalised parameters such as + pr_dc, v_oc, r_sc, v_mp, i_mp, ff ... + It better fits precise measurements (see CFV and GI) where the + low light data is measured well and has an improvement for even + better v_oc fitting [ref 7 : 2022 PVSC PHILADELPHIA] + c_5 is only used if there is windspeed data, otherwise it is ignored + + mpm_b = c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + +for mpm_a and mpm_b : + g = (G_POA (W/m^2) / G_STC=1000 (W/m^2)) --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + +Note that both mpm_a or mpm_b can be used with either LFM_6 or LFM_4 + + A later MPM version (not detailed here) can be used to model clipping and +other effects [See ref [8] Sutterlueti et al PVPMC 2022] 'mpm professional' + +Using DATAFRAMES or SERIES for variables +---------------------------------------- + +Many pvlib functions pass series of weather data separately for parameters e.g. + poa_global, temp_module, wind_speed +and measurements such as + pr_dc or p_mp + +This mlfm code keeps all its met and measurement data in dataframes - + meas, norm etc. e.g. + +meas.columns + Index(['module_id', 'poa_global', 'wind_speed', 'temp_air', + 'temp_module', 'v_oc', 'i_sc', 'i_mp', 'v_mp', 'r_sc', + 'r_oc', 'p_mp', 'pr_dc', 'v_oc_temp_corr', 'pr_dc_temp_corr'], + dtype='object') + + It's easier when modelling all 6 or more measurement parameters in one +frame and then use an lfm_sel var to choose which to analyse +e.g. lfm_sel = 'pr_dc' + +If individual series are needed to interface with existing code and +methodolgies they can be created by the following + + +#pvlib series <-- mlfm dataframe + poa_global = meas['poa_global'] + temp_module = meas['temp_module'] + wind_speed = meas['wind_speed'] + pr_dc = meas['pr_dc'] + +# mlfm dataframe <-- pvlib series + meas['poa_global'] = poa_global + meas['temp_module'] = temp_module + meas['wind_speed'] = wind_speed + meas['pr_dc'] = pr_dc + +DATAFRAME DEFINITIONS (for this python file and tutorials) +---------------------------------------------------------- + +A full definition is given here to keep the code in each function shorter + +dmeas : DataFrame +----------------- + Measured weather and module electrical values per time or measurement + + Parameters [units] + ---------- + Index either - + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``module_id`` - unique identifier to match data in ref [alpha num] + + Weather measurements - + + * ``poa_global`` - global plane of array irradiance [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/s] + + [optional weather] + + * ``temp_air`` - air temperature optional [C] + + /Columns as needed by LFM_4 and/or LFM_6/ : + + * ``i_sc`` | 4 6 | current at short circuit condition [A] + * ``i_mp`` | 4 6 | current at maximum power point [A] + * ``v_mp`` | 4 6 | voltage at maximum power point [V] + * ``v_oc`` | 4 6 | voltage at open circuit condition [V] + + * ``r_sc`` | 6 | -1/ (dI/dV|V=0) of IV curve at short circuit [Ohm] + * ``r_oc`` | 6 | -1/(dI/dV|I=0) of IV curve at open circuit [Ohm] + + Optional columns include + + * ``p_mp`` - power at maximum power point = i_mp * v_mp [W] + +ref : dict +---------- + Reference electrical and thermal datasheet module values at STC. + + Parameters [units] + ---------- + Index + * ``module_id`` - unique identifier to match data in dmeas [alpha num] + + * ``p_mp`` - Max Power at Standard Test Condition (STC). [W] + * ``i_sc`` - Current at short circuit at STC. [A] + * ``i_mp`` - Current at max power at STC. [A] + * ``v_mp`` - Voltage at max power at STC. [V] + * ``v_oc`` - Voltage at open circuit at STC. [V] + * ``ff`` - Fill Factor [1] + + * ``gamma_pdc`` - Temperature coefficient of max power point + power at STC. [1/C] + * ``beta_v_oc`` - Temperature coefficient of open circuit + voltage at STC. [1/C] + [optional thermal] + + * ``alpha_i_sc`` - Temperature coefficient of short circuit + current STC. [1/C] + + * ``alpha_i_mp`` - Temperature coefficient of max power point + current at STC. [1/C] + + * ``beta_v_mp`` - Temperature coefficient of max power point + voltage at STC. [1/C] + + [optional ID related] + * ``source`` - Data Source [alpha num] + * ``site`` - Sitename [alpha num] + * ``manufacturer`` - Module manufacturer [alpha num] + * ``technology`` - Module technology e.g. cSi, HIT, CdTe [alpha num] + * ``module_type`` - Type ID e.g. ABC-123 [alpha num] + * ``module_serial`` - Serial number [alpha num] + * ``comments`` - General comments [alpha num] + + +dnorm : DataFrame +----------------- + Normalised multiplicative loss factors per parameter to model fall from + start 1/ref_ff to meas pr_dc where - + + LFM_6 - multiplicative + pr_dc = 1/ff * ( norm(i_sc) *norm(r_sc) *norm(i_ff) + *norm(v_ff) *norm(r_oc) *norm(v_oc_t) *norm(temp_corr) ). + + LFM_4 - multiplicative + pr_dc = 1/ff * ( norm(i_sc) *norm(i_mp) + *norm(v_mp) *norm(v_oc_t) *norm(temp_corr) ). + + Parameters [units] + ---------- + Index (copied from dmeas) either + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``poa_global`` - global plane of array [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/ + + |Columns as used by LFM_4 and/or LFM_6| : + + * ``pr_dc``| 4 6 | Performance ratio dc. + pr_dc = meas_p_mp / ref_p_mp /(poa_global/G_STC) [%] + * ``pr_dc_temp_corr`` + | 4 6 | pr_dc adjusted to 25C by gamma_p_mp. + * ``i_sc`` | 4 6 | loss due to current at short circuit condition [%] + * ``v_oc`` | 4 6 | Loss due to voltage at open circuit condition [%] + * ``v_oc_temp_corr`` + | 4 6 | v_oc adjusted to 25C by gamma_p_mp (not beta_v_oc) + for simplicity + + * ``i_mp`` | 4 | Loss due to current part of ff [%] + * ``v_mp`` | 4 | Loss due to voltage part of ff [%] + + * ``r_sc`` | 6 | Loss due to r_sc ~r_shunt [%] + * ``i_ff`` | 6 | Loss due to r_sc corrected current part of ff [%] + * ``v_ff`` | 6 | Loss due to r_oc corrected voltage part of ff [%] + * ``r_oc`` | 6 | Loss due to r_oc related to r_series [%] + +dstack : DataFrame +------------------ + Stacked subtractive normalized loss factors per parameter to model fall + from start 1/ref_ff to meas pr_dc where - + + LFM_6 - subtractive losses + pr_dc = 1/ff - (stack(i_sc) +stack(r_sc) +stack(i_ff) + +stack(v_ ff) +stack(r_oc) +stack(v_oc_t) +stack(temp_corr)) + + LFM_4 - subtractive losses + pr_dc = 1/ff - (stack(i_sc) +stack(i_mp) + +stack(v_mp) +stack(v_oc_t) +stack(temp_corr) ). + + Parameters [units] + ---------- + Index (copied from dmeas) + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``poa_global`` - global plane of array irradiance [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/ + + |Columns as needed by LFM_4 and/or LFM_6| : + + * ``pr_dc`` equal to `dnorm['pr_dc']` + + * ``i_sc`` | 4 6 | loss due to current at short circuit condition [%] + * ``v_oc`` | 4 6 | Loss due to voltage at open circuit condition [%] + * ``v_oc_temp_corr`` + | 4 6 | v_oc adjusted to 25C by gamma_p_mp (not beta_v_oc) + for simplicity + + * ``i_mp`` | 4 | Loss due to current part of ff [%] + * ``v_mp`` | 4 | Loss due to voltage part of ff [%] + + * ``r_sc`` | 6 | Loss due to r_sc ~r_shunt [%] + * ``i_ff`` | 6 | Loss due to r_sc corrected current part of ff [%] + * ``v_ff`` | 6 | Loss due to r_oc corrected voltage part of ff [%] + * ``r_oc`` | 6 | Loss due to r_oc related to r_series [%] +""" + +# DEFINE REFERENCE MEASUREMENT CONDITIONS +# or use existing definitions in pvlib. These might not all have +# been used in this code but are included for completeness + +# NAME value # comment unit PV_LIB name + +T_STC = 25.0 # STC temperature [C] temperature_ref +G_STC = 1000.0 # STC irradiance [W/m^2] + +# not all yet used below , added here for completeness +T_LIC = 25.0 # LIC temperature [C] +G_LIC = 200.0 # LIC irradiance [W/m^2] + +T_HTC = 75.0 # HTC temperature [C] +G_HTC = 1000.0 # HTC irradiance [W/m^2] + +T_PTC = 55.0 # HTC temperature [C] +G_PTC = 1000.0 # HTC irradiance [W/m^2] + +G_LTC = 500.0 # HTC irradiance [W/m^2] +T_LTC = 15.0 # LTC temperature [C] + +G_NOCT = 800 # NOCT irradiance [W/m^2] +T_NOCT = 45 # NOCT temperature [C] + +T_MAX = 100 # maximum temperature on right y axis + +T0C_K = 273.15 # 0C to Kelvin +T25C_K = 298.15 # 25C to Kelvin + +# Define standardised LFM graph colours as a dict ``CLR`` +CLR = { + # parameter_CLR colour R G B + 'irradiance': 'darkgreen', # 000 064 000 + 'temp_module': 'red', # 255 000 000 + 'temp_air': 'yellow', # 245 245 220 + 'wind_speed': 'grey', # 127 127 127 + + 'i_sc': 'purple', # 128 000 128 + 'r_sc': 'orange', # 255 165 000 + 'i_ff': 'lightgreen', # 144 238 144 + 'i_mp': 'green', # 000 255 000 + 'i_v': 'black', # 000 000 000 between i and v losses + 'v_ff': 'cyan', # 000 255 255 + 'v_mp': 'blue', # 000 000 255 + 'r_oc': 'pink', # 255 192 203 + 'v_oc': 'sienna', # 160 082 045 + + 'pr_dc': 'black', # 000 000 000 +} + + +def meas_to_norm(dmeas, ref): + """ + Convert measured P(W), I(A), V(V), R(Ohms) to values normalized to STC. + + Parameters + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + ref : dict + Reference electrical and thermal datasheet module values at STC. + + Returns + ------- + dnorm : DataFrame + Normalised multiplicative loss values (values approx 1). + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + 'Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements' + 36th EU PVSEC, Marseille, France. September 2019. + + """ + dnorm = pd.DataFrame() + + # copy weather data to meas dataframe for ease of use later + dnorm['poa_global'] = dmeas['poa_global'] + dnorm['temp_module'] = dmeas['temp_module'] + dnorm['wind_speed'] = dmeas['wind_speed'] + + dnorm['pr_dc'] = dmeas['p_mp']/ref['p_mp'] / (dmeas['poa_global']/G_STC) + + # calc temperature corrected pr_dc + dnorm['pr_dc_temp_corr'] = ( + dnorm['pr_dc'] + * (1 - ref['gamma_pdc']*(dmeas['temp_module'] - T_STC))) + + # calculate normalised loss coefficients + if 'i_sc' in dmeas.columns: + dnorm['i_sc'] = (dmeas['i_sc'] / ref['i_sc'] + / (dmeas['poa_global'] / G_STC)) + + if 'i_mp' in dmeas.columns: + dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] + + if 'v_oc' in dmeas.columns: + dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] + + # temperature corrected + dnorm['v_oc_temp_corr'] = ( + dnorm['v_oc'] + * (1 - ref['beta_v_oc']*(dmeas['temp_module'] - T_STC))) + + if 'v_mp' in dmeas.columns: + dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] + + if all(c in dmeas.columns for c in ['i_sc', 'v_oc', 'r_sc', 'r_oc']): + ''' LFM_6 including r_sc and r_oc + + create temporary variables (i_r, v_r) from the + intercept of r_sc (at i_sc) with r_oc (at v_oc) + to make maths easier ''' + + i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) + / (dmeas['r_sc'] - dmeas['r_oc'])) + + v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] + * dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + + # calculate normalised resistances r_sc and r_oc + dnorm['r_sc'] = i_r / dmeas['i_sc'] # norm_r @ isc + dnorm['r_oc'] = v_r / dmeas['v_oc'] # norm_r @ roc + + # calculate remaining fill factor losses partitioned to i_ff, v_ff + dnorm['i_ff'] = dmeas['i_mp'] / i_r + dnorm['v_ff'] = dmeas['v_mp'] / v_r + + return dnorm + + +def mpm_a_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): + """ + Predict norm LFM values from weather data (g,t,w) in ``dmeas``. + + const temp_coeff low_light high_light wind extra + | | | | | | + norm = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters [units] + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + c_1 : float + Constant term in model. [%] + c_2 : float + Temperature coefficient in model. [1/C] + c_3 : float + Coefficient for low light log irradiance drop. [suns] + c_4 : float + Coefficient for high light linear irradiance drop. [1/suns] + c_5 : float, default 0 + Coefficient for wind speed dependence optional. [1/(m/s)] + c_6 : float, default 0 [suns] + Coefficient for dependence on inverse irradiance. + + Returns + ------- + mpm_a_out : Series + Predicted values of mpm coefficient. + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + + """ + mpm_a_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * np.log10(dmeas['poa_global'] / G_STC) + + c_4 * (dmeas['poa_global'] / G_STC) + + c_6 / (dmeas['poa_global'] / G_STC) + ) + + if 'wind_speed' in dmeas.columns: + mpm_a_out += c_5 * dmeas['wind_speed'] + + return mpm_a_out + + +def mpm_a_fit(data, var_to_fit): + """ + Fit mpm_a to normalised measured data 'var_to_fit' using mpm_a model. + + const temp_coeff low_light high_light wind extra + | | | | | | + fit = = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters + ---------- + data : DataFrame (see norm) + Normalised multiplicative loss values (values approx 1). + + var_to_fit : string + Column name in ``data`` containing variable being fitted. + e.g. pr_dc, i_mp, v_mp, v_oc ... + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients ``c_1`` to ``c_6``. + + resid : Series + Residuals of the fitted model. + + coeff_err : list + Standard deviation of error in each model coefficient. + + See Also + -------- + mpm_a_calc + + """ + # drop any missing data + data = data.dropna() + + c5_zero = 'wind_speed' not in data.columns + # if wind_speed is not present, add it and force it to 0 + if c5_zero: + data['wind_speed'] = 0. + + # define function name + func = mpm_a_calc + + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 c6<0 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2, 0]) + + coeff, pcov = optimize.curve_fit( + f=func, # fit function + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter + p0=p_0, # initial + bounds=bounds, # boundaries + # full_output=True + ) + + # if data had no wind_speed measurements then c_5 coefficient is + # meaningless but a non-zero value may have been returned. + if c5_zero: + coeff[4] = 0. + + # get error of mpm coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) + + # save fit and error to dataframe + pred = mpm_a_calc(data, *coeff) + + resid = pred - data[var_to_fit] + + return pred, coeff, resid, coeff_err + + +def mpm_b_fit(data, var_to_fit): + """ + Fit mpm_b to normalised measured data 'var_to_fit' using mpm_b model. + + const temp_coeff low_light improvement high_light ws + | | | | | | + fit =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters + ---------- + data : DataFrame (see norm) + Normalised multiplicative loss values (values approx 1). + + var_to_fit : string + Column name in ``data`` containing variable being fitted. + e.g. pr_dc, i_mp, v_mp ... + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients ``c_1`` to ``c_5``. + + resid : Series + Residuals of the fitted model. + + coeff_err : list + Standard deviation of error in each model coefficient. + + See Also + -------- + mpm_a + + """ + # drop missing data + data = data.dropna() + + # define function name + func = mpm_b_calc + + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2]) + + coeff, pcov = optimize.curve_fit( + f=func, # fit function + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter + p0=p_0, # initial + bounds=bounds, # boundaries + # full_output=True + ) + + # get error of mpm coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) + + # save fit and error to dataframe + pred = mpm_b_calc(data, *coeff) + + resid = pred - data[var_to_fit] + + # fvec = infodict["fvec"] + + return pred, coeff, resid, coeff_err + + +def mpm_b_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0.): + """ + Predict normalised LFM values from weather data (g,t,w) in ``dmeas``. + + const temp_coeff low_light improvement high_light ws + | | | | | | + norm =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters [units] + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + c_1 : float + Constant term in model. [%] + c_2 : float + Temperature coefficient in model. [1/C] + c_3 : float + Coefficient for low light log irradiance drop. [suns] + c_4 : float + Coefficient for high light linear irradiance drop. [1/suns] + c_5 : float, default 0 + Coefficient for wind speed dependence optional. [1/(m/s)] + + Returns + ------- + mpm_b_out : Series + Predicted values of mpm coefficient. + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + + """ + mpm_b_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * ((np.log10(dmeas['poa_global'] / G_STC) + * (dmeas['temp_module'] + T0C_K) / T25C_K)) + + c_4 * (dmeas['poa_global'] / G_STC) + ) + + return mpm_b_out + + +def plot_scatter(dnorm, title, qty_lfm_vars, save_figs=False): + """ + Scatterplot of normalised values (y) vs. irradiance (x). + + Electrical quantities are plotted on the left y-axis, temperature + quantities are plotted on the right y-axis. + + Parameters + ---------- + dnorm : DataFrame + Normalised multiplicative loss values (values approx 1). + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + title : string + Title for the figure. + + qty_lfm_vars : int + number of lfm_vars : 6=iv with rsc, roc ; 4=indoor + + save_figs : boolean + save a high resolution png file of figure + + Returns + ------- + fig : Figure + Instance of matplotlib.figure.Figure + + See Also + -------- + meas_to_norm + + """ + try: + import matplotlib.pyplot as plt + except ImportError: + raise ImportError('plot_scatter requires matplotlib') + + # offset legend to the right to not overlap graph, use ~1.2 + bbox = 1.2 + + # set x_axis as irradiance in W/m2 + xdata = dnorm['poa_global'] + + fig, ax1 = plt.subplots() + + ax1.set_title(title) + + ax1.set_ylabel('Normalised values') + ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line + + # optional normalised y scale usually ~0.8 to 1.1 + ax1.set_ylim(0.8, 1.1) + + ax1.set_xlabel('Plane of array irradiance [W/m$^2$]') + ax1.axvline(x=G_STC, c='grey', linewidth=3) # show 1000W/m^2 STC + ax1.axvline(x=G_NOCT, c='grey', linewidth=3) # show 800W/m^2 NOCT + ax1.axvline(x=G_LIC, c='grey', linewidth=3) # show 200W/m^2 LIC + + # check which lines to plot + if qty_lfm_vars == 6: + # LFM_6 + lines = { + 'pr_dc_temp_corr': 'pr_dc', + 'i_sc': 'i_sc', + 'r_sc': 'r_sc', + 'r_oc': 'r_oc', + 'i_ff': 'i_ff', + 'v_ff': 'v_ff', + 'v_oc_temp_corr': 'v_oc'} + + labels = { + 'pr_dc_temp_corr': 'pr_dc_temp_corr', + 'i_sc': 'norm_i_sc', + 'r_sc': 'norm_r_sc', + 'r_oc': 'norm_r_oc', + 'i_ff': 'norm_i_ff', + 'v_ff': 'norm_v_ff', + 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} + + elif qty_lfm_vars == 4: + # LFM_4 + lines = { + 'pr_dc_temp_corr': 'pr_dc', + 'i_mp': 'i_mp', + 'v_mp': 'v_mp', + 'i_sc': 'i_sc', + 'v_oc_temp_corr': 'v_oc'} + + labels = { + 'pr_dc_temp_corr': 'pr_dc_temp_corr', + 'i_mp': 'norm_i_mp', + 'v_mp': 'norm_v_mp', + 'i_sc': 'norm_i_sc', + 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} + + # plot the LFM parameters depending on qty_lfm_vars + for k in lines.keys(): + try: + ax1.scatter(xdata, dnorm[k], c=CLR[lines[k]], label=labels[k]) + except KeyError: + pass + + ax1.legend(bbox_to_anchor=(bbox, 1), + loc='upper left', borderaxespad=0.) + + # y2axis plot met on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('Temperature (C/100)') + + # set wide limits 0 to 4 so they don't overlap with LFM params + ax2.set_ylim(0, 4) + + ax2.scatter(xdata, + dnorm['temp_module']/T_MAX, + c=CLR['temp_module'], + label='temp_module C/' + str(T_MAX)) + + # temp_air may not exist particularly for indoor measurements + try: + ax2.scatter(xdata, + dnorm['temp_air']/T_MAX, + c=CLR['temp_air'], + label='temp_air C/' + str(T_MAX)) + except KeyError: + pass + + # make second legend box low enough ~0.1 not to overlap first box + ax2.legend(bbox_to_anchor=(bbox, 0.1), + loc='upper left', borderaxespad=0.) + + if save_figs: + # remove '.csv', high resolution= 300 dots per inch + plt.savefig(os.path.join('mlfm_data', 'output', + 'scatter_' + title[:len(title)-4]), dpi=300) + + plt.show() + + return fig + + +def plot_stack(dstack, fill_factor, title, + xaxis_labels=0, is_i_sc_self_ref=False, + save_figs=False + ): + """ + Plot stacked subtractive losses from 1/ref_ff down to pr_dc. + + Parameters + ---------- + dstack : DataFrame + Stacked subtractive losses. + + fill_factor : float + Reference value of fill factor for IV curve at STC conditions. + + title : string + Title for the figure. + + xaxis_labels : int, default 0 + Number of x-axis labels to show. Default 0 shows all. + + is_i_sc_self_ref : bool, default False + Self-correct ``i_sc`` to remove angle of incidence, + spectrum, snow or soiling. + + save_figs : boolean + save a high resolution png file of figure + + # is_v_oc_temp_module_corr : bool, default True + # Calculate loss due to temperature and subtract from ``v_oc`` loss. + + Returns + ------- + fig : Figure + Instance of matplotlib.figure.Figure + + See Also + -------- + norm_to_stack + + """ + try: + import matplotlib.pyplot as plt + except ImportError: + raise ImportError('plt_stack requires matplotlib') + + # label names for LFM_6 + stack6 = ['i_sc', 'r_sc', 'i_ff', 'i_v', + 'v_ff', 'r_oc', 'v_oc_temp_corr'] + + if all([c in dstack.columns for c in stack6]): + + # data order from bottom to top + ydata = [dstack['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['v_oc_temp_corr'], + dstack['temp_module_corr'], + dstack['r_oc'], + dstack['v_ff'], + dstack['i_v'], + dstack['i_ff'], + dstack['r_sc'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_r_oc', + 'stack_v_ff', + '- - -', + 'stack_i_ff', + 'stack_r_sc', + 'stack_i_sc'] + + color_map = [ + 'white', # colour to bottom of graph + CLR['temp_module'], + CLR['v_oc'], + CLR['r_oc'], + CLR['v_ff'], + CLR['i_v'], + CLR['i_ff'], + CLR['r_sc'], + CLR['i_sc']] + + stack4 = ['i_sc', 'i_mp', 'i_v', + 'v_mp', 'v_oc_temp_corr'] + + if all([c in dstack.columns for c in stack4]): + + # data order from bottom to top + ydata = [dstack['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['v_oc_temp_corr'], + dstack['temp_module_corr'], + dstack['v_mp'], + dstack['i_v'], + dstack['i_mp'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_v_mp', + '- - -', + 'stack_i_mp', + 'stack_i_sc'] + + color_map = [ + 'white', # colour to bottom of graph + CLR['temp_module'], + CLR['v_oc'], + CLR['v_mp'], + CLR['i_v'], + CLR['i_mp'], + CLR['i_sc']] + + # offset legend right, use ~1.2 + bbox = 1.2 + + # select x axis usually date_time + xdata = dstack.index.values + fig, ax1 = plt.subplots() + + ax1.set_title(title) + + # plot stack in order bottom to top, + # allowing self_ref and temp_module corrections + ax1.stackplot(xdata, *tuple(ydata), labels=labels, colors=color_map) + + ax1.axhline(y=1/fill_factor, c='grey', lw=3) # show initial 1/FF + ax1.axhline(y=1, c='grey', lw=3) # show 100% line + ax1.set_ylabel('stacked lfm losses') + + # find number of x date values + x_ticks = dstack.shape[0] + plt.xticks(np.arange(0, x_ticks), rotation=90) + + # if (xaxis_labels > 0 and xaxis_labels < x_ticks): + if 0 < xaxis_labels < x_ticks: + xaxis_skip = np.floor(x_ticks / xaxis_labels) + else: + xaxis_skip = 2 + + # + xax2 = [''] * x_ticks + x_count = 0 + while x_count < x_ticks: + if x_count % xaxis_skip == 0: + # + # try to reformat any date indexes (not for matrices) + # + # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 + # y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' + # + try: + xax2[x_count] = xdata[x_count][2:13]+'h' + except IndexError: + xax2[x_count] = xdata[x_count] + except TypeError: # xdata can't be subscripted + xax2[x_count] = xdata[0] + + x_count += 1 + + ax1.set_xticklabels(xax2) + ax1.set_ylim(0.6, 1/fill_factor + 0.1) # optional normalised y scale + plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) + + # plot met data on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/ ' + str(T_MAX)) + ax2.set_ylim(0, 4) # set so doesn't overlap lfm params + + plt.plot(xdata, dstack['poa_global'] / G_STC, + c=CLR['irradiance'], label='poa_global (kW/m^2)') + plt.plot(xdata, dstack['temp_module'] / T_MAX, + c=CLR['temp_module'], label='temp_module / ' + str(T_MAX)) + + # temp_air may not exist particularly for indoor measurements + try: + plt.plot(xdata, dstack['temp_air']/100, + c=CLR['temp_air'], label='temp_air/ ' + str(T_MAX)) + except KeyError: + pass + + ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) + ax1.set_xticklabels(xax2, rotation=90) + + # remove '.csv', high resolution= 300 dots per inch + plt.savefig(os.path.join('mlfm_data', 'output', + 'stack_' + title[:len(title)-4]), dpi=300) + + return fig + + +def meas_to_stack_lin(dmeas, ref, qty_lfm_vars, gap=0.01): + """ + Convert measured values to stacked subtractive normalized losses. + + Stacked subtractive losses show the relative loss proportions + from max possible "ref_i_sc * ref_v_oc" (1/reference fill factor) + to the measured normalized power. + + This version is done in a linear fashion so that LFM4 and LFM6 give the + same answers for Isc and Voc and the loss(i_mp)=loss(r_sc)+loss(i_ff) + + Parameters + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + ref : dict + Reference electrical and thermal datasheet module values at STC. + + gap : float + create a gap to differentiate i and v losses ~ 0.01 + + qty_lfm_vars : int + number of lfm_vars : 6=iv with rsc, roc ; 4=without rsc, roc + + Returns + ------- + dstack : DataFrame + Stacked subtractive normalized losses + + See Also + -------- + meas_to_norm + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + """ + # create an empty DataFrame to put stack results + dstack = pd.DataFrame() + + # copy weather data for ease of use + dstack['poa_global'] = dmeas['poa_global'] + dstack['temp_module'] = dmeas['temp_module'] + dstack['wind_speed'] = dmeas['wind_speed'] + + # ref['p_mp'] = ref['i_mp'] * ref['v_mp'] + + # ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc']) + + # ref['ff'] = (ref['i_mp']*ref['v_mp'])/(ref['i_sc']*ref['v_oc']) + inv_ff = 1 / ref['ff'] + + dstack['pr_dc'] = dmeas['pr_dc'] + + # Find linear values on i and v axes normalised to i_mp, v_mp + lin_i_ratio = ref['i_sc']/ref['i_mp'] + lin_v_ratio = ref['v_oc']/ref['v_mp'] + + lin_i_sc = dmeas['i_sc']/ref['i_mp']/(dmeas['poa_global']/G_STC) + + lin_v_oc = dmeas['v_oc']/ref['v_mp'] + lin_v_oc_temp_corr = dmeas['v_oc_temp_corr']/ref['v_mp'] + + # transform multiplicative to subtractive losses find + # correction factor to scale losses to keep 1/ff --> pr_dc + + if qty_lfm_vars == 6: + # subtractive losses with series and shunt resistance effects + i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / + (dmeas['r_sc'] - dmeas['r_oc'])) + + v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + + lin_i_r = i_r/ref['i_mp'] / (dmeas['poa_global']/G_STC) + lin_i_ff = dmeas['i_mp'] / ref['i_mp']/(dmeas['poa_global']/G_STC) + + lin_v_ff = dmeas['v_mp'] / ref['v_mp'] + lin_v_r = v_r / ref['v_mp'] + + sub_i = lin_i_ratio - lin_i_ff # current drop + sub_v = lin_v_ratio - lin_v_ff # voltage drop + + # correction factor mult --> lin loss + corr = (inv_ff - dstack['pr_dc']) / (sub_i + sub_v) + + # put 6 LFM values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # accounting for series and shunt resistance losses + + dstack['i_sc'] = (lin_i_ratio-lin_i_sc) * corr + dstack['r_sc'] = (lin_i_sc-lin_i_r) * corr + dstack['i_ff'] = (lin_i_r-lin_i_ff) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_ff'] = (lin_v_r-lin_v_ff) * corr - gap/2 + dstack['r_oc'] = (lin_v_oc-lin_v_r) * corr + dstack['v_oc_temp_corr'] = (lin_v_oc_temp_corr-lin_v_oc) * corr + dstack['temp_module_corr'] = (lin_v_ratio-lin_v_oc_temp_corr) * corr + + if qty_lfm_vars == 4: + + lin_i_mp = dmeas['i_mp'] / ref['i_mp'] / (dmeas['poa_global']/G_STC) + lin_v_mp = dmeas['v_mp'] / ref['v_mp'] + + sub_i = lin_i_ratio - lin_i_mp # current drop + sub_v = lin_v_ratio - lin_v_mp # voltage drop + + # correction factor mult --> lin loss + corr = (inv_ff-dstack['pr_dc']) / (sub_i + sub_v) + + # put 4 LFM values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # accounting for series and shunt resistance losse + + dstack['i_sc'] = (lin_i_ratio-lin_i_sc) * corr + dstack['i_mp'] = (lin_i_sc-lin_i_mp) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_mp'] = (lin_v_oc-lin_v_mp) * corr - gap/2 + dstack['v_oc_temp_corr'] = (lin_v_oc_temp_corr-lin_v_oc) * corr + dstack['temp_module_corr'] = (lin_v_ratio-lin_v_oc_temp_corr) * corr + + return dstack + + +""" +The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) +together known as "MLFM" have been developed by SRCL and Gantner Instruments +(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM + +.. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome + '4AV.2.41 Characterising PV Modules under Outdoor Conditions: +What's Most Important for Energy Yield' +26th EU PVSEC 8 September 2011; Hamburg, Germany. +http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf + +.. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) + 'Choosing the best Empirical Model for predicting energy yield' + 7th PV Energy Rating and Module Performance Modeling Workshop, + Canobbio, Switzerland 30-31 March, 2017. + +.. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) +'Checking the new IEC 61853.1-4 with high quality 3rd party data to +benchmark its practical relevance in energy yield prediction' +PVSC June 2019 [Chicago], USA. +http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf + +.. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) +'5CV.4.35 Quantifying Long Term PV Performance and Degradation +under Real Outdoor and IEC 61853 Test Conditions +Using High Quality Module IV Measurements'. +36th EU PVSEC Sep 2019 [Marseille] + +.. [5] Steve Ransome (SRCL) +'How to use the Loss Factors and Mechanistic Performance Models +effectively with PVPMC/PVLIB' +[PVPMC] Webinar on PV Performance Modeling Methods, Aug 2020. +https://pvpmc.sandia.gov/download/7879/ + +.. [6] W.Marion et al (NREL) +'New Data Set for Validating PV Module Performance Models'. +https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models +Many more papers are available at www.steveransome.com + +.. [7] Steve Ransome (SRCL) +'Benchmarking PV performance models with high quality IEC 61853 Matrix +measurements (Bilinear interpolation, SAPM, PVGIS, MLFM and 1-diode)' +http://www.steveransome.com/pubs/2206_PVSC49_philadelphia_4_presented.pdf + +.. [8] Juergen Sutterlueti (Gantner Instruments) +'Advanced system monitoring and artificial intelligent data-driven analytics +to serve GW-scale photovoltaic power plant and energy storage requirements' +https://pvpmc.sandia.gov/download/8574/ + +""" From 5d518d4ecc14bd851101c5f26df87966b0c2bc82 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 12 Dec 2022 18:40:26 +0000 Subject: [PATCH 72/81] Merge branch 'mlfm' of https://github.com/steve-ransome/pvlib-python into mlfm # Conflicts: # pvlib/mlfm.py --- docs/tutorials/mlfm.ipynb | 2277 --- docs/tutorials/mlfm_0.html | 16931 ++++++++++++++++ docs/tutorials/mlfm_0.ipynb | 1913 ++ docs/tutorials/mlfm_2.html | 16931 ++++++++++++++++ docs/tutorials/mlfm_2.ipynb | 1913 ++ ...rmalised_mb_g78_T16_Xall_F10m_R900_041.png | Bin 199950 -> 0 bytes ...redicted_mb_g78_T16_Xall_F10m_R900_041.png | Bin 177554 -> 0 bytes ...meas_fit_mb_g78_T16_Xall_F10m_R900_041.png | Bin 165041 -> 0 bytes ...residual_mb_g78_T16_Xall_F10m_R900_041.png | Bin 198417 -> 0 bytes .../scatter_g78_T16_Xall_F10m_R900_041.png | Bin 336062 -> 0 bytes .../stack_g78_T16_Xall_F10m_R900_041.png | Bin 415187 -> 0 bytes .../mlfm_data/ref/mlfm_reference_modules.csv | 39 +- pvlib/mlfm.py | 859 +- pvlib/tests/test_mlfm.py | 29 +- 14 files changed, 37953 insertions(+), 2939 deletions(-) delete mode 100644 docs/tutorials/mlfm.ipynb create mode 100644 docs/tutorials/mlfm_0.html create mode 100644 docs/tutorials/mlfm_0.ipynb create mode 100644 docs/tutorials/mlfm_2.html create mode 100644 docs/tutorials/mlfm_2.ipynb delete mode 100644 docs/tutorials/mlfm_data/output/contourf_avg normalised_mb_g78_T16_Xall_F10m_R900_041.png delete mode 100644 docs/tutorials/mlfm_data/output/contourf_matrix predicted_mb_g78_T16_Xall_F10m_R900_041.png delete mode 100644 docs/tutorials/mlfm_data/output/fit_meas_fit_mb_g78_T16_Xall_F10m_R900_041.png delete mode 100644 docs/tutorials/mlfm_data/output/heatmap_residual_mb_g78_T16_Xall_F10m_R900_041.png delete mode 100644 docs/tutorials/mlfm_data/output/scatter_g78_T16_Xall_F10m_R900_041.png delete mode 100644 docs/tutorials/mlfm_data/output/stack_g78_T16_Xall_F10m_R900_041.png diff --git a/docs/tutorials/mlfm.ipynb b/docs/tutorials/mlfm.ipynb deleted file mode 100644 index eaa6d6884c..0000000000 --- a/docs/tutorials/mlfm.ipynb +++ /dev/null @@ -1,2277 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MLFM for PVLIB \n", - "ver: 221114t17\n", - "### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", - "Corrections and additions for comments by : \n", - "Cliff Hansen, Kevin Anderson, Anton Driesse and Mark Campanelli\n", - "\n", - "## Tutorial overview.\n", - "see details for each function in mlfm.py\n", - "\n", - "I) The Loss Factors Model (LFM) 2011 ref [1] quantifies normalised losses \n", - "from module parameters (e.g. pr_dc, i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) \n", - "by analysing module measurements or the shape of the IV curve and comparing \n", - "it with STC reference values from the datasheet. \n", - "\n", - "II) The Mechanistic performance model (MPM) 2017 ref [2] has \"meaningful, \n", - "independent, robust and normalised\" coefficients which fit how the LFM values \n", - "depend on irradiance, module temperature (and windspeed) and time. \n", - "\n", - "III) This tutorial shows how to take module measured and weather data, \n", - "(either outdoor or IEC 61853-like matrix data), normalise it, generate MLFM \n", - "coefficients, fit them with the MPM then analyse module performance looking for \n", - "loss values, degradation and allowing performance predictions as shown in fig 2. \n", - "\n", - "Fig 1 illustrates the loss factors model (LFM). \n", - "\n", - "Depending on the number of measurements available the LFM is defined \n", - "with a suffix number x = 1..12 LFM_n as in ref [4] - \n", - "\n", - "It uses the shape and values from dc measurements to quantify the values of each \n", - "of the loss factors (coloured arrors on the y=current or x=voltage axes\n", - "going from (1) ref\\_p\\_mp to (6) meas\\_p\\_mp. \n", - "\n", - "![mlfm_data/figs/lfm_220914t15.png](mlfm_data/figs/lfm_220914t15.png) \n", - "\n", - "Fig 1: Loss Factors Model \n", - "\n", - "\n", - "![mlfm_data/figs/flow_1024.png](mlfm_data/figs/flow_1024.png) \n", - "\n", - "Fig 2: MLFM overview flow chart of this tutorial. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Explanations of the Loss factors model in fig 1.\n", - "\n", - "1) ref_p_mp = Initial datasheet value at STC.\n", - "\n", - "Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", - "\n", - "2->3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", - " - norm_i_sc = measured / expected isc corrected for poa_global (purple)\n", - " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", - " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", - " \n", - " \n", - "4->5) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", - " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", - " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown) \n", - " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", - " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", - " \n", - " \n", - "6) These losses cause the performance to fall to pr_dc (= meas_p_mp / ref_p_mp) \n", - "\n", - "pr_dc = 1/ff \\* \n", - " (norm_i_sc \\* norm_r_sc \\* norm_i_ff ) \\* \n", - " (norm_v_ff \\* norm_r_oc \\* norm_v_oc_t \\* norm_temp_corr ) \n", - "\n", - "Note: \n", - "The gamma temperature correction is just subtracted from voc for simplicity. \n", - "In reality there will be temperature dependencies for i_sc and ff but they are smaller." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pvlib.mlfm import meas_to_norm, mpm_a_fit, meas_to_stack_lin, mpm_b_fit\n", - "from pvlib.mlfm import mpm_a_calc, mpm_b_calc\n", - "\n", - "from pvlib.mlfm import plot_scatter, plot_stack # , mpm_calc\n", - "\n", - "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", - "import os\n", - "root_dir = os.getcwd()\n", - "\n", - "# uncomment to see root dir\n", - "# print(root_dir)\n", - "\n", - "# STANDARD DEFINITIONS (also in mlfm.py)\n", - "G_STC = 1000.0 # STC irradiance [W/m^2]\n", - "T_STC = 25.0 # STC temperature [C] temperature_ref\n", - "\n", - "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", - "plt.rcParams['figure.figsize'] = [7, 5] # setup fig size inches ~[7, 5]\n", - "plt.rcParams.update({'font.size': 12}) # setup fontsize ~12\n", - "plt.linewidth = 1.5 # line width in points ~1.5\n", - "plt.linestyle = '--' # solid line ~'--'\n", - "plt.marker = 's' # the default marker square ~'s'\n", - "plt.markersize = 9 # marker size, in points ~9\n", - "plt.bbox = 1.4 # offset --> to not overwrite ~1.4\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get user choices " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# save graphs as png files to the output directory?\n", - "save_figs = True" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# select which mpm to model : must be 'a original 2017' or 'b advanced 2022'\n", - "mpm_sel = 'b' # 'a' or 'b'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [A] Select MLFM measurement data file\n", - "\n", - "Three default files are included (\\* = version number ) \n", - "\n", - "(0) g78\\_T16\\_Xall\\_F10m\\_R900\\*.csv (6 LFM params)\n", - "(1) n05667\\_Y13\\_R1k6\\_fClear\\*.csv (4 LFM params) \n", - "(2) x19074001\\_iec61853\\*.csv (4 LFM params) \n", - "\n", - "Essential default column names in meas( ) are :- \n", - "\n", - "meas { \n", - "'date\\_time', 'module\\_id', \n", - "'poa\\_global', 'temp\\_module', \n", - "'v\\_oc', 'i\\_sc', 'i\\_mp', 'v\\_mp', \n", - "'r\\_sc', 'r\\_oc', <-- optional for LFM_6 \n", - "'wind\\_speed', 'temp\\_air', <-- optional \n", - "}\n", - "\n", - "\n", - "File naming conventions can be used to help identify files, for example \n", - "`x81_T1906_D3_Fh.csv` \n", - "\n", - "where \n", - " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", - " - 81 = module id/channel number \n", - " - T1906 = (T)ime started = yymm(dd) \n", - " - D3 = (D)uration in days \n", - " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", - " - etc. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# select one of the files in ''\\pvlib-python\\docs\\tutorials\\mlfm_data\\meas_gtw'\n", - "\n", - "mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' # 0 LFM_6 outdoor\n", - "# mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' # 1 LFM_4 outdoor\n", - "# mlfm_meas_file = 'x19074001_iec61853_041.csv' # 2 LFM_4 indoor\n", - "# mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041_4param.csv' # 3 #0 no rsc,roc\n", - "# mlfm_meas_file = 'g78_T16_Xall_F10m_R1_041.csv' # 4 #0 test record\n", - "\n", - "# extract module id from filename e.g. 'g78'\n", - "mlfm_mod = mlfm_meas_file.split('_')\n", - "\n", - "mlfm_mod_sel = mlfm_mod[0]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import measured data (outdoor or matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "meas = pd.read_csv(\n", - " # root_dir + '/mlfm_data/meas_gtw/' + mlfm_meas_file,\n", - " os.path.join(root_dir, 'mlfm_data', 'meas_gtw', mlfm_meas_file),\n", - " index_col='date_time'\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [B] Read all reference datasheet values at STC" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# user must keep updated with their modules from their measurements\n", - "ref_file_name = os.path.join(root_dir, 'mlfm_data', 'ref', 'mlfm_reference_modules.csv')\n", - "\n", - "ref_data = pd.read_csv(\n", - " ref_file_name, index_col='module_id')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Select module stc data from reference database" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " ref_data = ref_data[\n", - " ref_data.index == mlfm_mod_sel]\n", - "\n", - "except IndexError:\n", - " print(\"You must define module ref data to use this module ...\")\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'i_sc': 5.35,\n", - " 'i_mp': 4.9,\n", - " 'v_mp': 36.8,\n", - " 'v_oc': 44.2,\n", - " 'alpha_i_sc': 0.0005,\n", - " 'beta_v_oc': -0.0035,\n", - " 'alpha_i_mp': 0.0,\n", - " 'beta_v_mp': 0.0,\n", - " 'gamma_pdc': -0.0045,\n", - " 'p_mp': 180.32,\n", - " 'ff': 0.762549161}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Put relevant data into a dict for easy use\n", - "# ignore any other columns that may be database specific\n", - "# as they aren't needed\n", - "\n", - "ref = dict(\n", - " # module_id=ref_data['module_id'].values[0],\n", - " i_sc=ref_data['i_sc'].values[0],\n", - " i_mp=ref_data['i_mp'].values[0],\n", - " v_mp=ref_data['v_mp'].values[0],\n", - " v_oc=ref_data['v_oc'].values[0],\n", - "\n", - " alpha_i_sc=ref_data['alpha_i_sc'].values[0],\n", - " beta_v_oc=ref_data['beta_v_oc'].values[0],\n", - " alpha_i_mp=ref_data['alpha_i_mp'].values[0],\n", - " beta_v_mp=ref_data['beta_v_mp'].values[0],\n", - " gamma_pdc=ref_data['gamma_pdc'].values[0],\n", - "\n", - " p_mp=ref_data['p_mp'].values[0],\n", - " ff=ref_data['ff'].values[0],\n", - ")\n", - "\n", - "# uncomment to show ref data\n", - "ref\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Calculate useful data columns for meas" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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module_idpoa_globalwind_speedtemp_airblue_fracbeam_fractemp_modulev_oci_sci_mpv_mpr_scr_ocp_mppr_dcv_oc_temp_corrpr_dc_temp_corr
count907.0907.000000907.000000907.000000907.000000845.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000907.000000
mean78.0580.4669852.40828226.9266260.5287710.73671639.98639840.4241873.0371572.80241232.6643531864.66826623.11563591.8459070.83831842.5464310.896654
std0.0368.8251601.3301508.9461270.0516730.30515115.1552562.6500731.9522641.8092612.8857187268.89577597.53471458.1057610.1613333.4228980.178494
min78.01.2628310.1951594.1494140.270154-0.1000000.40155027.1151250.0085680.00675417.440980-99.0000000.9776380.1177920.14965326.9101360.151773
25%78.0223.3910871.35182620.0364840.5135490.75418329.46702639.4442631.1653521.03978731.190638432.5881371.11543436.1143880.80990741.9259820.939101
50%78.0642.1992032.13381826.7346340.5321750.87877940.21743840.8502753.4241873.18212432.860113592.2265671.344735106.4547410.87554544.0700190.966653
75%78.0930.8452453.27728635.7742310.5433220.90983650.93165642.2322044.8761314.50720134.336062977.3155613.163062143.7046760.93052744.7292550.984946
max78.01118.1409889.01842042.5605010.7162241.10000069.43444844.8254135.8794725.44034141.821020121808.0889001165.006272182.8753951.41527445.2777151.407537
\n", - "
" - ], - "text/plain": [ - " module_id poa_global ... v_oc_temp_corr pr_dc_temp_corr\n", - "count 907.0 907.000000 ... 907.000000 907.000000\n", - "mean 78.0 580.466985 ... 42.546431 0.896654\n", - "std 0.0 368.825160 ... 3.422898 0.178494\n", - "min 78.0 1.262831 ... 26.910136 0.151773\n", - "25% 78.0 223.391087 ... 41.925982 0.939101\n", - "50% 78.0 642.199203 ... 44.070019 0.966653\n", - "75% 78.0 930.845245 ... 44.729255 0.984946\n", - "max 78.0 1118.140988 ... 45.277715 1.407537\n", - "\n", - "[8 rows x 17 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# calculate p_mp and pr_dc as they might be missing\n", - "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", - "\n", - "meas['pr_dc'] = (meas['p_mp'] / ref['p_mp']\n", - " / (meas['poa_global'] / G_STC))\n", - "\n", - "# temperature corrected v_c and pr_dc\n", - "meas['v_oc_temp_corr'] = \\\n", - " (meas['v_oc'] * (1 - ref['beta_v_oc']*(meas['temp_module'] - T_STC)))\n", - "\n", - "meas['pr_dc_temp_corr'] = \\\n", - " (meas['pr_dc'] * (1 - ref['gamma_pdc']*(meas['temp_module'] - T_STC)))\n", - "\n", - "# show some meas data\n", - "meas.describe()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Select LFM_n model by counting variables in the meas data \n", - "usually LFM_4 = matrix (i\\_sc, i\\_mp, v\\_mp, v\\_oc) \n", - "and LFM_6 = iv (i\\_sc, i\\_mp, v\\_mp, v\\_oc + r\\_sc, r\\_oc) " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def get_qty_lfm_vars(dmeas):\n", - " \"\"\"Find the quantity of LFM variables in the measured data.\n", - "\n", - " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", - "\n", - " Parameters\n", - " ----------\n", - " dmeas: DataFrame\n", - " Measured weather and module electrical values per time or measurement\n", - "\n", - " Returns\n", - " -------\n", - " qty_lfm_vars : int\n", - " number of lfm_values present in data usually\n", - "\n", - " 2 = ( i_mp, v_mp ) from mpp tracker\n", - " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", - " 6 = (i_sc, r_sc, i_mp, v_mp, r_oc, v_oc) from iv curve.\n", - "\n", - " \"\"\"\n", - " # find how many lfm variables were measured\n", - " qty_lfm_vars = 0\n", - " for lfm_sel in ('i_sc', 'r_sc', 'i_mp', 'v_mp', 'r_oc', 'v_oc'):\n", - " if lfm_sel in dmeas.columns:\n", - " qty_lfm_vars += 1\n", - " # print(qty_lfm_vars, lfm_sel)\n", - "\n", - " return qty_lfm_vars\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "qty_lfm_vars = get_qty_lfm_vars(meas)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [C] Normalise LFM values from meas and ref to norm dataframes \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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poa_globaltemp_modulewind_speedpr_dcpr_dc_temp_corri_sci_mpv_ocv_oc_temp_corrv_mpr_scr_oci_ffv_ff
date_time
2016-01-26 07:20:00-07:002.6664842.0819401.4728320.4964970.4452930.9263800.7422410.7475260.6875640.7365870.9835020.7605590.7546920.968481
2016-01-26 07:30:00-07:007.8991432.4369851.2977110.6204710.5574730.8814090.8008960.8516750.7844180.7869770.8938520.8669220.8960060.907783
2016-01-26 07:40:00-07:0052.9276722.5920870.9554820.2080370.1870590.2572270.8401720.8970410.8266880.8182980.8977000.8950180.9359160.914282
\n", - "
" - ], - "text/plain": [ - " poa_global temp_module ... i_ff v_ff\n", - "date_time ... \n", - "2016-01-26 07:20:00-07:00 2.666484 2.081940 ... 0.754692 0.968481\n", - "2016-01-26 07:30:00-07:00 7.899143 2.436985 ... 0.896006 0.907783\n", - "2016-01-26 07:40:00-07:00 52.927672 2.592087 ... 0.935916 0.914282\n", - "\n", - "[3 rows x 14 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "norm = meas_to_norm(meas, ref)\n", - "\n", - "# show some normalised data\n", - "norm.head(3)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make irradiance and temperature bins for pivot tables " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# poa_global bin e.g. 100, 200 .. 1100W/m2\n", - "norm['poa_global_bin'] = \\\n", - " norm['poa_global'].round(-2)\n", - "\n", - "# temp_module bin e.g. 5, 10 .. 75C\n", - "norm['temp_module_bin'] = \\\n", - " (5 * round(norm['temp_module'] / 5, 0))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [D] Perform sanity checks on meas and norm data \n", - "\n", - "It's easier to sanity check and study normalised data than raw values. \n", - "1) Remove bad, missing, unwanted or outlier data \n", - "2) User defined limits may depend on data scatter and degradation \n", - "3) Can either select on values e.g. '0.5 x stdev from mean' \n", - "4) Possible to select on dates if desired. " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# select by irradiance poa_global range e.g. 100-1100 W/m2\n", - "norm = norm[(norm['poa_global'] >= 100) &\n", - " (norm['poa_global'] <= 1100)]\n", - "\n", - "# remove specific lfm values outside limits e.g. <0.5 or >1.5\n", - "norm = norm[((norm['pr_dc'] > 0.5) &\n", - " (norm['pr_dc'] < 1.5))]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# remove all mlfm values outside x~3 stdevs\n", - "if qty_lfm_vars == 6:\n", - " # only needed for outdoor data as indoor ought to be less scattered\n", - " # remove all mlfm data > x stdev usually 3\n", - " stdevs = 3\n", - "\n", - " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc', 'v_oc'):\n", - " norm = norm[\n", - " ((norm[lfm] - norm[lfm].mean()) /\n", - " norm[lfm].std()).abs() < stdevs\n", - " ]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Filter only matching rows from meas and norm data\n", - "like an inner join but leave data in separate norm and meas frames" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# drop meas rows that aren't in norm\n", - "meas_not_in_norm = ~meas.index.isin(norm.index)\n", - "meas = meas.drop(meas[meas_not_in_norm].index)\n", - "\n", - "# drop norm rows that aren't in meas\n", - "norm_not_in_meas = ~norm.index.isin(meas.index)\n", - "norm = norm.drop(norm[norm_not_in_meas].index)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [E] Plot normalised LFM data vs irradiance \n", - "\n", - "For outdoor data - \n", - "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", - "\n", - "For matrix data - \n", - "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", - "\n", - "1. Higher values are always better (unlike measured values such as \n", - " Rseries or Io where lower is better)\n", - "1. Accurate measurements and a stable module result in narrowest lines \n", - "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", - "1. r_oc tends to fall at high light levels ( \\ right) \n", - "1. i_ff and v_ff are usually fairly flat ( - ) \n", - "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", - " and/or snow (if not properly corrected). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Normalised lfm values vs. irradiance.\n", - "\n", - "All traces should be thin, smoot lines usually around 0.9 ± 0.1 \n", - "i\\_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# scatter plot normalised values vs. irradiance\n", - "fig_scatter = plot_scatter(\n", - " norm, mlfm_meas_file, qty_lfm_vars, save_figs)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig [E] : LFM multiplicative factors (y) vs. poa irradiance (x)\n", - "\n", - "\n", - "# [F] Convert multiplicative to subtractive losses for a stack plot \n", - "\n", - " Multiplicative losses are easier to understand but to represent them on a graph \n", - "it's easier to show them as a stacked plot where the values are 'translated' \n", - "so the sum of the stacked losses is shown to equate to the product of the \n", - "multiplicative losses.\n", - "\n", - "LFM losses can be analysed as either \n", - "\n", - "- multiplicative pr_dc = 1/ff * PRODUCT(norm(i_sc), ... \\* stack(v_oc_t), stack(temp_corr) ). \n", - "\n", - "- subtractive pr_dc = 1/ff - SUM(stack(i_sc), ... stack(v_oc_t), stack(temp_corr) ). \n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# translate multiplicative to stack losses and add to\n", - "# dataframe stack add a gap between i and v losses\n", - "\n", - "stack = meas_to_stack_lin(meas, ref, qty_lfm_vars, gap=0.0) # gap = 0.01\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [G] Plot stack losses vs. measurement \n", - "\n", - "Fig 3 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", - "\n", - "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", - "\n", - "Fig 3 Stacked losses by measurement \n", - "\n", - "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", - " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", - " \n", - "- This figure shows a typical c-Si module for four clear days for \n", - " different months July to Oct in AZ. \n", - " \n", - "- In the middle of the days the high irradiance results in the biggest \n", - " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", - " (as the module heats to 60C). \n", - " \n", - "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", - " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", - "\n", - "Stack losses are indicated by their colours \n", - "(from top to bottom for lfm_4=matrix and lfm_6=ivcurve) \n", - "\n", - "![mlfm_data/figs/losses.png](mlfm_data/figs/losses.png) \n", - "\n", - "Graph options : \n", - "\n", - "is_i_sc_self_ref : boolean \n", - " = self corrects i_sc to remove angle of incidence, spectrum, \n", - " snow or soiling. \n", - " \n", - "is_v_oc_temp_module_corr : boolean \n", - " = calc temperature loss due to gamma, subtract from voc loss " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot stack loss vs. time (or measurement) chart\n", - "fig_stack = plot_stack(\n", - " dstack=stack, # dataframe measurements\n", - " fill_factor=ref['ff'], # dataframe reference STC\n", - " title=mlfm_meas_file, #\n", - " xaxis_labels=12, # show num x_labels or 0 to show all\n", - " is_i_sc_self_ref=False, # is isc self referenced?\n", - " save_figs=save_figs # save the figure?\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig [G] Stacked loss values (y) s. date and time (outdoor) or matrix measurement (x)\n", - "\n", - "# [H] Fit mpm to measured weather and normalised losses \n", - "\n", - "Perform a Mechanistic Performance Model (MPM) fit to the lfm parameters \n", - "poa_global (W/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", - "\n", - "\n", - "mpm_a = c_1 +c_2\\*(t_mod-25) +c_3\\*log10(g) +c_4\\*g +c_5\\*ws +c_6\\/g (deprecated) \n", - "\n", - "mpm_b = c_1 +c_2\\*(t_mod–25) +c_3\\*log10(g)\\*(t_k\\/t_stc_k) +c_4\\*g +c_5\\*ws\n", - "\n", - "\n", - "Report the fit (coeffs) and error (errs) coefficients. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choose which normalised lfm parameter to model e.g. pr_dc or i_sc..v_oc " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "lfm_sel = 'pr_dc'" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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poa_globaltemp_modulewind_speedpr_dcpr_dc_temp_corri_sci_mpv_ocv_oc_temp_corrv_mpr_scr_oci_ffv_ffpoa_global_bintemp_module_bincalc_pr_dcdiff_pr_dc
count689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000689.000000641.0000006.410000e+02
mean730.79300445.2791542.5948670.8963680.9749270.9877810.9253740.9329060.9979370.8006500.9796790.8831720.9446390.906453731.93033445.3047900.891053-4.655127e-08
std269.19498612.2608701.3430590.0611190.0229330.0163850.0061600.0331600.0220200.0222320.0084230.0192540.0103170.006508272.84443912.3176230.0573571.343506e-02
min101.71417514.0554810.1951590.7768280.8637610.9480540.9001430.8594630.9067770.7627950.9272780.8469370.9235480.895678100.00000015.0000000.779638-6.024074e-02
25%523.88674636.6580051.5112440.8499280.9605340.9785800.9210560.9004440.9878770.7826540.9757270.8672060.9367120.901710500.00000035.0000000.846298-5.849096e-03
50%795.67516244.9637912.3996230.8941720.9737600.9839250.9255980.9348081.0055830.7977230.9824500.8813470.9442940.904725800.00000045.0000000.8896676.002181e-04
75%966.30895955.1749123.4830680.9426490.9892610.9940590.9297260.9588891.0138550.8179060.9855040.8994820.9520680.9098861000.00000055.0000000.9340735.668780e-03
max1098.46229169.4344489.0184201.1123051.0579351.0851990.9389211.0141501.0243830.8563010.9984190.9293870.9845760.9267611100.00000070.0000001.0385757.621357e-02
\n", - "
" - ], - "text/plain": [ - " poa_global temp_module ... calc_pr_dc diff_pr_dc\n", - "count 689.000000 689.000000 ... 641.000000 6.410000e+02\n", - "mean 730.793004 45.279154 ... 0.891053 -4.655127e-08\n", - "std 269.194986 12.260870 ... 0.057357 1.343506e-02\n", - "min 101.714175 14.055481 ... 0.779638 -6.024074e-02\n", - "25% 523.886746 36.658005 ... 0.846298 -5.849096e-03\n", - "50% 795.675162 44.963791 ... 0.889667 6.002181e-04\n", - "75% 966.308959 55.174912 ... 0.934073 5.668780e-03\n", - "max 1098.462291 69.434448 ... 1.038575 7.621357e-02\n", - "\n", - "[8 rows x 18 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# add selected variable to measured data frame to ensure data indexes match.\n", - "meas_temp = meas.copy()\n", - "meas_temp[lfm_sel] = norm[lfm_sel]\n", - "\n", - "\n", - "if mpm_sel == 'b':\n", - " cc, coeffs, ee, errs = mpm_b_fit(meas_temp, lfm_sel)\n", - "else:\n", - " cc, coeffs, ee, errs = mpm_a_fit(meas_temp, lfm_sel)\n", - "\n", - "# store calculated value of LFM variable\n", - "norm['calc_' + lfm_sel] = cc\n", - "\n", - "# store residual difference of LFM variable\n", - "norm['diff_' + lfm_sel] = norm[lfm_sel] - norm['calc_' + lfm_sel]\n", - "\n", - "norm.describe()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [I] Plot heatmap of mean residual vs. temp_module and poa_global\n", - "\n", - "Show a heatmap of the average residual (meas - fit) error \n", - "for each irradiance (100W/m^2) and tmod bin (5C)." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_heatmap(dnorm, fit, y_axis, x_axis, z_axis,\n", - " title, save_figs, clip=0.02,):\n", - " \"\"\"Plot a heatmap of Z vs. binned X and Y axes.\n", - "\n", - " Parameters\n", - " ----------\n", - " dnorm : dataframe\n", - " Normalised multiplicative loss values (values approx 1).\n", - "\n", - " fit : string\n", - " fitted parameter e.g. 'pr_dc'.\n", - "\n", - " x_axis : string\n", - " binned x axis e.g. 'poa_global_bin'.\n", - "\n", - " y_axis : string\n", - " binned y axis e.g. 'temp_module_bin'.\n", - "\n", - " z_axis : string\n", - " value as a colour surface plot e.f. 'diff_pr_dc'.\n", - "\n", - " clip : value\n", - " clipping of z axis usually 0.02\n", - "\n", - " title : string\n", - " title for graph e.g. mlfm_meas_file.\n", - "\n", - " \"\"\"\n", - " df_piv = pd.pivot_table(\n", - " dnorm,\n", - " index=y_axis, # e.g. 'temp_module_bin'\n", - " columns=x_axis, # e.g. 'poa_global_bin'\n", - " values=z_axis, # value to aggregate\n", - " fill_value=0, # fill empty cells with this ?\n", - " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", - " margins=False, # grand totals hide\n", - " dropna=True # hide missing rows or columns\n", - " )\n", - "\n", - " fig, ax1 = plt.subplots()\n", - "\n", - " # force z limits to be -2% to +2% if desired\n", - " df_piv = df_piv.clip(lower=-clip, upper=+clip)\n", - "\n", - " im = ax1.imshow(\n", - " df_piv,\n", - " cmap='RdYlBu',\n", - " origin='lower'\n", - " )\n", - "\n", - " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", - "\n", - " # Y AXIS : show only 1 of each y_skip labels\n", - " y_ticks = df_piv.shape[0]\n", - " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", - " yax2 = [''] * y_ticks\n", - " y_skip = 2\n", - " y_count = 0\n", - " while y_count < y_ticks:\n", - " if y_count % y_skip == 0:\n", - " yax2[y_count] = df_piv.index[y_count]\n", - " y_count += 1\n", - "\n", - " ax1.set_yticklabels(yax2)\n", - " ax1.set_ylabel(y_axis)\n", - "\n", - " # X AXIS : show only 1 of each x_skip labels\n", - " x_ticks = df_piv.shape[1]\n", - " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", - "\n", - " xax2 = [''] * x_ticks\n", - " x_skip = 2\n", - " x_count = 0\n", - " while x_count < x_ticks:\n", - " if x_count % x_skip == 0:\n", - " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", - " x_count += 1\n", - "\n", - " ax1.set_title(title)\n", - "\n", - " ax1.set_xticklabels(xax2)\n", - " ax1.set_xlabel(x_axis)\n", - "\n", - " ax1.grid(color='k', linestyle=':', linewidth=1)\n", - "\n", - " if save_figs:\n", - " # remove '.csv', high resolution= 300 dots per inch\n", - " plt.savefig(\n", - " os.path.join('mlfm_data', 'output', 'heatmap_' + title[:len(title)-4]),\n", - " dpi=300\n", - " )\n", - " \n", - " plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Residual LFM fit heatmap vs. poa_global and temp_module" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot heatmap\n", - "heatmap_plot = plot_heatmap(\n", - " dnorm=norm,\n", - " fit=lfm_sel,\n", - " y_axis='temp_module_bin',\n", - " x_axis='poa_global_bin',\n", - " z_axis='diff_' + lfm_sel,\n", - " clip=0.025,\n", - " title='residual_m' + mpm_sel + '_' + mlfm_meas_file,\n", - " save_figs=save_figs\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig [I] Residual LFM fit heatmap vs. poa_global bin (x) and temp_module bin (y)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_fit(dmeas, dnorm, fit, title, save_figs, coeffs):\n", - " \"\"\"Scatter plot fit to normalised measured.\n", - " \n", - " Parameters\n", - " ----------\n", - " dmeas : dataframe\n", - " measurements, must include 'poa_global_kwm2'\n", - "\n", - " dnorm : dataframe\n", - " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", - " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", - "\n", - " fit : string\n", - " name of fitted variable e.g. 'pr_dc'.\n", - "\n", - " title : string\n", - " title for graph e.g. mlfm_meas_file.\n", - " \n", - " \"\"\"\n", - " fig, ax1 = plt.subplots()\n", - "\n", - " plt.title(title)\n", - "\n", - " plt.ylabel('fit ' + fit + ' * poa_global kW/m^2')\n", - " ax1.set_ylim(0, 1.2)\n", - "\n", - " plt.xlabel('meas ' + fit + '* poa_global_kW/m^2')\n", - " ax1.set_xlim(0, 1.2)\n", - "\n", - " plt.plot(\n", - " dnorm[fit] * dmeas['poa_global'] / G_STC,\n", - " dnorm['calc_' + fit] * dmeas['poa_global'] / G_STC,\n", - " 'c^',\n", - " label=fit\n", - " )\n", - "\n", - " # plot 1:1 line to show optimum fit\n", - " plt.plot((0, 1.2), (0, 1.2), 'ko-')\n", - " plt.plot((0, 1.0), (1.0, 1.0), 'ko-')\n", - " plt.plot((1.0, 1.0), (0, 1.0), 'ko-')\n", - "\n", - " plt.legend(loc='upper left')\n", - "\n", - " if save_figs:\n", - " # remove '.csv', high resolution= 300 dots per inch\n", - " plt.savefig(\n", - " os.path.join('mlfm_data', 'output', 'fit_meas_' + title[:len(title)-4]),\n", - " dpi=300\n", - " )\n", - " \n", - "\n", - "\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot fit vs. measured, include a 1:1 line for comparison\n", - "fit_plot = plot_fit(dmeas=meas,\n", - " dnorm=norm,\n", - " fit=lfm_sel,\n", - " title='fit_m' + mpm_sel + '_' + mlfm_meas_file,\n", - " save_figs=save_figs,\n", - " coeffs=coeffs\n", - " )\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig [J] scatter plot of 'fit_lfm_sel * poa_global# (y) vs. 'measured_lfm_sel * poa_global' (x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [K] Read in complete (G,T) Matrix to fill with MLFM predicted values \n", - "\n", - "Read in a matrix with complete values of \n", - "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", - "to predict all MPM values " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# read in the complete matrix data\n", - "matr = pd.read_csv(os.path.join(root_dir, 'mlfm_data', 'ref', 'mlfm_matrix.csv'),\n", - " index_col='id')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Predict performance from MPM fit coefficients \n", - "\n", - "1. generate predicted mpm data \n", - "2. create a pivot table mpm(g,t) \n", - "3. show as a heat map" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('b', array([ 1.13725529, -0.00454229, 0.1838458 , -0.15716391, 0.01 ]))" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# show model coefficients\n", - "mpm_sel, coeffs" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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midpoa_globaltemp_modulewind_speedpr_dc
id
1matrix100001.066666
2matrix100501.040871
3matrix1001001.015077
4matrix1001500.989282
5matrix1002000.963488
\n", - "
" - ], - "text/plain": [ - " mid poa_global temp_module wind_speed pr_dc\n", - "id \n", - "1 matrix 100 0 0 1.066666\n", - "2 matrix 100 5 0 1.040871\n", - "3 matrix 100 10 0 1.015077\n", - "4 matrix 100 15 0 0.989282\n", - "5 matrix 100 20 0 0.963488" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# populate pivot table from predicted mpm data\n", - "if mpm_sel == 'b':\n", - " matr[lfm_sel] = mpm_b_calc(matr, *coeffs)\n", - "else:\n", - " matr[lfm_sel] = mpm_a_calc(matr, *coeffs)\n", - "\n", - "matr.head()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# [L] Plot heatmap of predicted LFM values vs. temp_mod, poa_global bins" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", - " vmin=0, vmax=1.2, levels=5,\n", - " save_figs=False):\n", - " \"\"\"Plot filled contour plot Z vs. X and Y bins.\n", - "\n", - " Parameters\n", - " ----------\n", - " df : dataframe\n", - " measured or normalised data containing weather columns\n", - " (poa_global, temp_module and wind_speed).\n", - "\n", - " x_axis : string\n", - " binned x axis e.g. 'poa_global'.\n", - "\n", - " y_axis : string\n", - " binned y axis e.g. 'temp_module'.\n", - "\n", - " z_axis : string\n", - " measured value as a colour surface plot.\n", - "\n", - " title : string\n", - " title for graph e.g. mlfm_meas_file.\n", - "\n", - " vmin, vmax : float\n", - " minimum and maximum values for contour chart ###\n", - " \n", - " \"\"\"\n", - " piv = pd.pivot_table(\n", - " df,\n", - " index=y_axis,\n", - " columns=x_axis,\n", - " values=z_axis,\n", - " fill_value=0, # fill empty cells?\n", - " aggfunc=[np.mean], # min, np.sum, len->count\n", - " margins=False, # grand totals\n", - " dropna=True # hide missing rows or columns\n", - " )\n", - "\n", - " piv = piv.clip(vmin, vmax)\n", - "\n", - " fig, ax1 = plt.subplots()\n", - "\n", - " cs = plt.contourf(\n", - " piv,\n", - " cmap='RdYlBu', # or 'nipy_spectral',\n", - " # origin='lower'\n", - " # nchunkint=1,\n", - " levels=levels,\n", - " vmin=vmin,\n", - " vmax=vmax\n", - " )\n", - "\n", - " cbar = fig.colorbar(cs, ax=ax1)\n", - " cbar.ax.set_ylabel(z_axis,\n", - " rotation=90,\n", - " va='bottom',\n", - " labelpad=+30)\n", - "\n", - " plt.title(title)\n", - " # # get_yaxis().set_major_formatter(FormatStrFormatter('%.2f'))\n", - "\n", - " y_ticks = piv.shape[0]\n", - "\n", - " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", - "\n", - " # show only 1 of each y_skip labels\n", - " yax2 = [''] * y_ticks\n", - " y_skip = 2\n", - " y_count = 0\n", - " while y_count < y_ticks:\n", - " if y_count % y_skip == 0:\n", - " yax2[y_count] = piv.index[y_count]\n", - " y_count += 1\n", - "\n", - " ax1.set_yticklabels(yax2)\n", - " ax1.set_ylabel(y_axis)\n", - "\n", - " x_ticks = piv.shape[1]\n", - " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", - "\n", - " # show only 1 of each x_skip labels\n", - " xax2 = [''] * x_ticks\n", - " x_skip = 2\n", - " x_count = 0\n", - " while x_count < x_ticks:\n", - " if x_count % x_skip == 0:\n", - " xax2[x_count] = piv.columns.levels[1][x_count]\n", - " x_count += 1\n", - "\n", - " ax1.set_xticklabels(xax2)\n", - " ax1.set_xlabel(x_axis)\n", - "\n", - " ax1.grid(color='k', linestyle=':', linewidth=1)\n", - "\n", - " if save_figs:\n", - " # remove '.csv', high resolution= 300 dots per inch\n", - " plt.savefig(os.path.join('mlfm_data', 'output', 'contourf_'+ title[:len(title)-4])\n", - " , dpi=300\n", - " ) \n" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", - "\n", - "matr2 = matr[matr['temp_module'] >= 10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Contour plot of predicted lfm_sel + vs. poa_global and temp_mod. " - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "contour_plot = plot_contourf(\n", - " df=matr2,\n", - " y_axis='temp_module',\n", - " x_axis='poa_global',\n", - " z_axis=lfm_sel,\n", - " title='matrix predicted_m' + mpm_sel + '_' + mlfm_meas_file,\n", - " vmin=0.7,\n", - " vmax=1.05,\n", - " levels=9,\n", - " save_figs=save_figs\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig [L1] Contour plot (colours) of predicted lfm_sel vs. poa_global (x) and temp_mod (y)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "contour_plot = plot_contourf(\n", - " df=norm,\n", - " y_axis='temp_module_bin',\n", - " x_axis='poa_global_bin',\n", - " z_axis=lfm_sel,\n", - " title='avg normalised_m' + mpm_sel + '_' + mlfm_meas_file,\n", - " vmin=0.7,\n", - " vmax=1.05,\n", - " levels=9,\n", - " save_figs=save_figs\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig [L2] Contour plot (colours) of measured lfm_sel vs. poa_global (x) and temp_mod (y)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References \n", - " \n", - "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", - "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", - "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", - " \n", - ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", - " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", - "What's Most Important for Energy Yield' \n", - "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", - "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", - "\n", - ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", - " 'Choosing the best Empirical Model for predicting energy yield' \n", - " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", - " Canobbio, Switzerland 30-31 March, 2017 \n", - "\n", - ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", - "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", - "benchmark its practical relevance in energy yield prediction' \n", - "PVSC June 2019 Chicago, USA \n", - "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", - "\n", - ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", - "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", - "under Real Outdoor and IEC 61853 Test Conditions \n", - "Using High Quality Module IV Measurements' \n", - "36th EU PVSEC Sep 2019 \n", - "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", - "\n", - ".. [5] Steve Ransome (SRCL) \n", - "'How to use the Loss Factors and Mechanistic Performance Models \n", - "effectively with PVPMC/PVLIB' \n", - "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", - "https://pvpmc.sandia.gov/download/7879/ \n", - "\n", - ".. [6] W.Marion et al (NREL) \n", - "'New Data Set for Validating PV Module Performance Models' \n", - "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", - "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", - "\n", - ".. [7] Steve Ransome (SRCL)\n", - "'Benchmarking PV performance models with high quality IEC 61853 Matrix\n", - "measurements (Bilinear interpolation, SAPM, PVGIS, MLFM and 1-diode)'\n", - "http://www.steveransome.com/pubs/2206_PVSC49_philadelphia_4_presented.pdf\n", - "\n", - ".. [8] Juergen Sutterlueti (Gantner Instruments)\n", - "'Advanced system monitoring and artificial intelligent data-driven analytics \n", - "to serve GW-scale photovoltaic power plant and energy storage requirements'\n", - "https://pvpmc.sandia.gov/download/8574/\n", - "\n", - "Many more papers are available at www.steveransome.com \n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# =============================================================================================" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IPython 8.2.0\n", - "IPython.core.release 8.2.0\n", - "PIL 9.0.1\n", - "PIL.Image 9.0.1\n", - "PIL._version 9.0.1\n", - "_cffi_backend 1.15.0\n", - "_csv 1.0\n", - "_ctypes 1.1.0\n", - "_decimal 1.70\n", - "_pydev_bundle.fsnotify 0.1.5\n", - "_pydevd_frame_eval.vendored.bytecode 0.13.0.dev\n", - "argparse 1.1\n", - "backcall 0.2.0\n", - "certifi 2021.10.08\n", - "cffi 1.15.0\n", - "cftime 1.6.0\n", - "cftime._cftime 1.6.0\n", - "chardet 4.0.0\n", - "chardet.version 4.0.0\n", - "charset_normalizer 2.0.4\n", - "charset_normalizer.version 2.0.4\n", - "cloudpickle 2.0.0\n", - "colorama 0.4.4\n", - "csv 1.0\n", - "ctypes 1.1.0\n", - "cycler 0.10.0\n", - "dateutil 2.8.2\n", - "debugpy 1.5.1\n", - "decimal 1.70\n", - "decorator 5.1.1\n", - "defusedxml 0.7.1\n", - "distutils 3.9.12\n", - "entrypoints 0.4\n", - "executing 0.8.3\n", - "executing.version 0.8.3\n", - "h5py 3.6.0\n", - "http.server 0.6\n", - "idna 3.3\n", - "idna.idnadata 14.0.0\n", - "idna.package_data 3.3\n", - "ipaddress 1.0\n", - "ipykernel 6.9.1\n", - "ipykernel._version 6.9.1\n", - "jedi 0.17.2\n", - "json 2.0.9\n", - "jupyter_client 7.1.2\n", - "jupyter_client._version 7.1.2\n", - "jupyter_core 4.9.2\n", - "jupyter_core.version 4.9.2\n", - "kiwisolver 1.3.2\n", - "logging 0.5.1.2\n", - "matplotlib 3.5.1\n", - "matplotlib.backends.backend_qt 5.9.2\n", - "matplotlib.backends.qt_compat 5.9.2\n", - "matplotlib.backends.qt_editor._formlayout 1.0.10\n", - "netCDF4 1.5.8\n", - "netCDF4._netCDF4 1.5.8\n", - "numpy 1.21.6\n", - "numpy.core 1.21.6\n", - "numpy.core._multiarray_umath 3.1\n", - "numpy.lib 1.21.6\n", - "numpy.linalg._umath_linalg 0.1.5\n", - "packaging 21.3\n", - "packaging.__about__ 21.3\n", - "pandas 1.4.2\n", - "parso 0.7.0\n", - "pickleshare 0.7.5\n", - "pkg_resources._vendor.appdirs 1.4.3\n", - "pkg_resources._vendor.more_itertools 8.12.0\n", - "pkg_resources._vendor.packaging 21.3\n", - "pkg_resources._vendor.packaging.__about__ 21.3\n", - "pkg_resources._vendor.pyparsing 2.2.1\n", - "pkg_resources.extern.appdirs 1.4.3\n", - "pkg_resources.extern.more_itertools 8.12.0\n", - "pkg_resources.extern.packaging 21.3\n", - "pkg_resources.extern.pyparsing 2.2.1\n", - "platform 1.0.8\n", - "prompt_toolkit 3.0.20\n", - "psutil 5.8.0\n", - "pure_eval 0.2.2\n", - "pure_eval.version 0.2.2\n", - "pvlib 0.9.1\n", - "pvlib.version 0.9.1\n", - "pydevd 2.6.0\n", - "pygments 2.11.2\n", - "pyparsing 3.0.4\n", - "pytz 2021.3\n", - "re 2.2.1\n", - "requests 2.27.1\n", - "requests.__version__ 2.27.1\n", - "requests.packages.chardet 4.0.0\n", - "requests.packages.idna 3.3\n", - "requests.packages.idna.idnadata 14.0.0\n", - "requests.packages.idna.package_data 3.3\n", - "requests.packages.urllib3 1.26.8\n", - "requests.packages.urllib3._version 1.26.8\n", - "requests.packages.urllib3.connection 1.26.8\n", - "requests.packages.urllib3.packages.six 1.16.0\n", - "requests.packages.urllib3.util.ssl_match_hostname 3.5.0.1\n", - "requests.utils 2.27.1\n", - "scipy 1.8.0\n", - "scipy._lib.decorator 4.0.5\n", - "scipy.linalg._fblas b'$Revision: $'\n", - "scipy.linalg._flapack b'$Revision: $'\n", - "scipy.linalg._flinalg b'$Revision: $'\n", - "scipy.linalg._interpolative b'$Revision: $'\n", - "scipy.optimize.__nnls b'$Revision: $'\n", - "scipy.optimize._cobyla b'$Revision: $'\n", - "scipy.optimize._lbfgsb b'$Revision: $'\n", - "scipy.optimize._minpack 1.10 \n", - "scipy.optimize._minpack2 b'$Revision: $'\n", - "scipy.optimize._slsqp b'$Revision: $'\n", - "scipy.sparse.linalg._eigen.arpack._arpack b'$Revision: $'\n", - "scipy.sparse.linalg._isolve._iterative b'$Revision: $'\n", - "scipy.special._specfun b'$Revision: $'\n", - "setuptools 61.2.0\n", - "setuptools._distutils 3.9.12\n", - "setuptools._vendor.more_itertools 8.8.0\n", - "setuptools._vendor.ordered_set 3.1\n", - "setuptools._vendor.packaging 21.3\n", - "setuptools._vendor.packaging.__about__ 21.3\n", - "setuptools._vendor.pyparsing 2.2.1\n", - "setuptools.extern.more_itertools 8.8.0\n", - "setuptools.extern.ordered_set 3.1\n", - "setuptools.extern.packaging 21.3\n", - "setuptools.extern.pyparsing 2.2.1\n", - "setuptools.version 61.2.0\n", - "six 1.16.0\n", - "socketserver 0.4\n", - "socks 1.7.1\n", - "spyder_kernels 1.10.2\n", - "spyder_kernels._version 1.10.2\n", - "stack_data 0.2.0\n", - "stack_data.version 0.2.0\n", - "traitlets 5.1.1\n", - "traitlets._version 5.1.1\n", - "urllib.request 3.9\n", - "urllib3 1.26.8\n", - "urllib3._version 1.26.8\n", - "urllib3.connection 1.26.8\n", - "urllib3.packages.six 1.16.0\n", - "urllib3.util.ssl_match_hostname 3.5.0.1\n", - "wcwidth 0.2.5\n", - "xmlrpc.client 3.9\n", - "zlib 1.0\n", - "zmq 22.3.0\n", - "zmq.sugar 22.3.0\n", - "zmq.sugar.version 22.3.0\n" - ] - } - ], - "source": [ - "## TEST CODE CAN DELETE AFTER HERE \n", - "\n", - "if False: \n", - " # save data to csv\n", - " meas.to_csv(\n", - " os.path.join('mlfm_data', 'output', 'meas.csv'),\n", - " sep=';',\n", - " quotechar='\"',\n", - " encoding='utf-8',\n", - " decimal='.'\n", - " )\n", - "\n", - " norm.to_csv(\n", - " os.path.join('mlfm_data', 'output', 'norm.csv'),\n", - " sep=';',\n", - " quotechar='\"',\n", - " encoding='utf-8',\n", - " decimal='.'\n", - " )\n", - "\n", - " matr.to_csv(\n", - " os.path.join('mlfm_data', 'output', 'matr.csv'),\n", - " sep=';',\n", - " quotechar='\"',\n", - " encoding='utf-8',\n", - " decimal='.'\n", - " )\n", - "\n", - " ref_data.to_csv(\n", - " os.path.join('mlfm_data', 'output', 'ref_data.csv'),\n", - " sep=';',\n", - " quotechar='\"',\n", - " encoding='utf-8',\n", - " decimal='.'\n", - " )\n", - "\n", - " \n", - "if False:\n", - " print(coeffs[0],coeffs[1],coeffs[2],coeffs[3],coeffs[4], coeffs[5], )#coeffs[6],)\n", - " \n", - "\n", - "if False:\n", - " n= norm.loc['2016-03-23 09:00:00-07:00']\n", - "\n", - " print('\\n n= \\n', n)\n", - "\n", - " s = stack.loc['2016-03-23 09:00:00-07:00']\n", - "\n", - " print('\\n s= \\n', s)\n", - "\n", - "if True:\n", - " import sys \n", - " \n", - " for name, module in sorted(sys.modules.items()): \n", - " if hasattr(module, '__version__'): \n", - " print (name, module.__version__ )" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'1.8.0'" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import scipy\n", - "scipy.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - }, - "toc-autonumbering": true, - "toc-showmarkdowntxt": false - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/tutorials/mlfm_0.html b/docs/tutorials/mlfm_0.html new file mode 100644 index 0000000000..d381e9bf6a --- /dev/null +++ b/docs/tutorials/mlfm_0.html @@ -0,0 +1,16931 @@ + + + + + +mlfm_0 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/tutorials/mlfm_0.ipynb b/docs/tutorials/mlfm_0.ipynb new file mode 100644 index 0000000000..11df072f9a --- /dev/null +++ b/docs/tutorials/mlfm_0.ipynb @@ -0,0 +1,1913 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MLFM for PVLIB \n", + "ver: 221212t18\n", + "### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "Corrections and additions for comments by : \n", + "Cliff Hansen, Kevin Anderson, Anton Driesse and Mark Campanelli\n", + "\n", + "## Tutorial overview.\n", + "see details for each function in mlfm.py\n", + "\n", + "I) The Loss Factors Model (LFM) 2011 ref [1] quantifies normalised losses \n", + "from module parameters (e.g. pr_dc, i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) \n", + "by analysing module measurements or the shape of the IV curve and comparing \n", + "it with STC reference values from the datasheet. \n", + "\n", + "II) The Mechanistic performance model (MPM) 2017 ref [2] has \"meaningful, \n", + "independent, robust and normalised\" coefficients which fit how the LFM values \n", + "depend on irradiance, module temperature (and windspeed) and time. \n", + "\n", + "III) This tutorial shows how to take module measured and weather data, \n", + "(either outdoor or IEC 61853-like matrix data), normalise it, generate MLFM \n", + "coefficients, fit them with the MPM then analyse module performance looking for \n", + "loss values, degradation and allowing performance predictions as shown in fig 2. \n", + "\n", + "Fig 1 illustrates the loss factors model (LFM). \n", + "\n", + "Depending on the number of measurements available the LFM is defined \n", + "with a suffix number x = 1..12 LFM_n as in ref [4] - \n", + "\n", + "It uses the shape and values from dc measurements to quantify the values of each \n", + "of the loss factors (coloured arrors on the y=current or x=voltage axes\n", + "going from (1) ref\\_p\\_mp to (6) meas\\_p\\_mp. \n", + "\n", + "![mlfm_data/figs/lfm_220914t15.png](mlfm_data/figs/lfm_220914t15.png) \n", + "\n", + "Fig 1: Loss Factors Model \n", + "\n", + "\n", + "![mlfm_data/figs/flow_1024.png](mlfm_data/figs/flow_1024.png) \n", + "\n", + "Fig 2: MLFM overview flow chart of this tutorial. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explanations of the Loss factors model in fig 1.\n", + "\n", + "1) ref_p_mp = Initial datasheet value at STC.\n", + "\n", + "Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", + "\n", + "2->3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", + " - norm_i_sc = measured / expected isc corrected for poa_global (purple)\n", + " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", + " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", + " \n", + " \n", + "4->5) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", + " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", + " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown) \n", + " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", + " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", + " \n", + " \n", + "6) These losses cause the performance to fall to pr_dc (= meas_p_mp / ref_p_mp) \n", + "\n", + "pr_dc = 1/ff \\* \n", + " (norm_i_sc \\* norm_r_sc \\* norm_i_ff ) \\* \n", + " (norm_v_ff \\* norm_r_oc \\* norm_v_oc_t \\* norm_temp_corr ) \n", + "\n", + "Note: \n", + "The gamma temperature correction is just subtracted from voc for simplicity. \n", + "In reality there will be temperature dependencies for i_sc and ff but they are smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# import mlfm \n", + "\n", + "from pvlib.mlfm import meas_to_norm, mpm_a_fit, mpm_b_fit, meas_to_stack_lin\n", + "from pvlib.mlfm import mpm_a_calc, mpm_b_calc\n", + "\n", + "from pvlib.mlfm import plot_scatter, plot_stack # , mpm_calc\n", + "\n", + "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", + "import os\n", + "root_dir = os.getcwd()\n", + "\n", + "# uncomment to see root dir\n", + "# print(root_dir)\n", + "\n", + "# STANDARD DEFINITIONS (also in mlfm.py)\n", + "G_STC = 1000.0 # STC irradiance [W/m^2]\n", + "T_STC = 25.0 # STC temperature [C] temperature_ref\n", + "\n", + "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", + "plt.rcParams['figure.figsize'] = [7, 5] # setup fig size inches ~[7, 5]\n", + "plt.rcParams.update({'font.size': 12}) # setup fontsize ~12\n", + "plt.linewidth = 1.5 # line width in points ~1.5\n", + "plt.linestyle = '--' # solid line ~'--'\n", + "plt.marker = 's' # the default marker square ~'s'\n", + "plt.markersize = 9 # marker size, in points ~9\n", + "plt.bbox = 1.4 # offset --> to not overwrite ~1.4\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get user choices " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# save graphs as png files to the output directory?\n", + "save_figs = True" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# select which mpm to model : must be 'a original 2017' or 'b advanced 2022'\n", + "mpm_sel = 'b' # 'a' or 'b'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [A] Select MLFM measurement data file\n", + "\n", + "Three default files are included (\\* = version number ) \n", + "\n", + "(0) g78\\_T16\\_Xall\\_F10m\\_R900\\*.csv (6 LFM params) \n", + "(1) n05667\\_Y13\\_R1k6\\_fClear\\*.csv (4 LFM params) \n", + "(2) x19074001\\_iec61853\\*.csv (4 LFM params) \n", + "\n", + "(Some variants are added to the IEC 61853 with fewer data points \n", + "or added scatter to test the fit algorithms)\n", + "\n", + "Essential default column names in meas( ) are :- \n", + "\n", + "meas { \n", + "'date\\_time', 'module\\_id', \n", + "'poa\\_global', 'temp\\_module', \n", + "'v\\_oc', 'i\\_sc', 'i\\_mp', 'v\\_mp', \n", + "'r\\_sc', 'r\\_oc', <-- optional for LFM_6 \n", + "'wind\\_speed', 'temp\\_air', <-- optional \n", + "}\n", + "\n", + "\n", + "File naming conventions can be used to help identify files, for example \n", + "`x81_T1906_D3_Fh.csv` \n", + "\n", + "where \n", + " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", + " - 81 = module id/channel number \n", + " - T1906 = (T)ime started = yymm(dd) \n", + " - D3 = (D)uration in days \n", + " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", + " - etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment just one line to select a file from \n", + "# directory ''\\pvlib-python\\docs\\tutorials\\mlfm_data\\meas_gtw'\n", + "\n", + "# PTS COMMENTS \n", + "\n", + "# 0) LFM 6 outdoor Gantner Instruments \n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' # 900 <<< raw data with rsc and roc\n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041_4param.csv' # 900 deleted rsc,roc\n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R1_041.csv' # 1 test record only\n", + "\n", + "# 1) LFM 4 outdoor NREL \n", + "# mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' # 1600 <<< raw data no rsc,roc measured \n", + "\n", + "# 2) IEC 61853 CFV : either raw data or fewer points and/or added scatter error\n", + "mlfm_meas_file = 'x19074001_iec61853_041.csv' # 27 <<< raw data no rsc,roc measured \n", + "# mlfm_meas_file = 'x19074001_iec61853_041_6pts.csv' # 6 raw but fewer points\n", + "# mlfm_meas_file = 'x19074001_iec61853_041_rand5pc.csv' # 27 rand 5% rmse\n", + "# mlfm_meas_file = 'x19074001_iec61853_041_rand1pc.csv' # 27 rand 1% rmse\n", + "# mlfm_meas_file = 'x19074001_iec61853_041_rand5pc_6pts.csv' # 6 rand 5% rmse fewer points\n", + "\n", + "\n", + "# extract module id from filename e.g. 'g78'\n", + "mlfm_mod = mlfm_meas_file.split('_')\n", + "\n", + "mlfm_mod_sel = mlfm_mod[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import measured data (outdoor or matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "meas = pd.read_csv(\n", + " # root_dir + '/mlfm_data/meas_gtw/' + mlfm_meas_file,\n", + " os.path.join(root_dir, 'mlfm_data', 'meas_gtw', mlfm_meas_file),\n", + " index_col='date_time'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [B] Read all reference datasheet values at STC" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# user must keep updated with their modules from their measurements\n", + "\n", + "\n", + "ref_file_name = os.path.join(root_dir, 'mlfm_data', 'ref', 'mlfm_reference_modules.csv')\n", + "\n", + "ref_data = pd.read_csv(\n", + " ref_file_name, index_col='module_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select module stc data from reference database" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " ref_data = ref_data[\n", + " ref_data.index == mlfm_mod_sel]\n", + "\n", + "except IndexError:\n", + " print(\"You must define module ref data to use this module ...\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Put relevant data into a dict for easy use\n", + "# ignore any other columns that may be database specific\n", + "# as they aren't needed\n", + "\n", + "ref = dict(\n", + " # module_id=ref_data['module_id'].values[0],\n", + " i_sc=ref_data['i_sc'].values[0],\n", + " i_mp=ref_data['i_mp'].values[0],\n", + " v_mp=ref_data['v_mp'].values[0],\n", + " v_oc=ref_data['v_oc'].values[0],\n", + "\n", + " alpha_i_sc=ref_data['alpha_i_sc'].values[0],\n", + " beta_v_oc=ref_data['beta_v_oc'].values[0],\n", + " alpha_i_mp=ref_data['alpha_i_mp'].values[0],\n", + " beta_v_mp=ref_data['beta_v_mp'].values[0],\n", + " gamma_pdc=ref_data['gamma_pdc'].values[0],\n", + "\n", + " p_mp= ref_data['p_mp'].values[0],\n", + " \n", + " \n", + " ff=ref_data['ff'].values[0],\n", + ")\n", + "\n", + "# uncomment to show ref data\n", + "# ref" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate useful data columns for meas" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idtemp_modulepoa_globali_scv_oci_mpv_mpp_mpwind_speedpr_dcv_oc_temp_corrpr_dc_temp_corr
count27.027.00000027.00000027.00000027.00000027.00000027.00000027.00000027.027.00000027.00000027.000000
mean19074001.042.222222581.4814813.45092665.0548153.19440754.227778175.6372600.00.92229067.5661480.964306
std0.023.588350358.4550582.1237154.6831761.9747404.486576110.6985940.00.0782152.5557010.038515
min19074001.015.000000100.0000000.59500054.6000000.54100044.32000024.0657600.00.74667761.2652870.856461
25%19074001.025.000000200.0000001.20300061.5950001.09450049.87500062.0912000.00.84904866.0724550.948074
50%19074001.050.000000600.0000003.54200065.7800003.29800054.250000177.9447800.00.92624168.5026700.974878
75%19074001.062.500000900.0000005.33750069.3050004.94650058.400000269.2324200.00.99668369.5434330.994383
max19074001.075.0000001100.0000006.57800071.8500006.06100060.210000354.1569500.01.02699270.4400001.000051
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" + ], + "text/plain": [ + " module_id temp_module ... v_oc_temp_corr pr_dc_temp_corr\n", + "count 27.0 27.000000 ... 27.000000 27.000000\n", + "mean 19074001.0 42.222222 ... 67.566148 0.964306\n", + "std 0.0 23.588350 ... 2.555701 0.038515\n", + "min 19074001.0 15.000000 ... 61.265287 0.856461\n", + "25% 19074001.0 25.000000 ... 66.072455 0.948074\n", + "50% 19074001.0 50.000000 ... 68.502670 0.974878\n", + "75% 19074001.0 62.500000 ... 69.543433 0.994383\n", + "max 19074001.0 75.000000 ... 70.440000 1.000051\n", + "\n", + "[8 rows x 12 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculate p_mp and pr_dc as they might be missing\n", + "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", + "\n", + "meas['pr_dc'] = (meas['p_mp'] / ref['p_mp']\n", + " / (meas['poa_global'] / G_STC))\n", + "\n", + "# temperature corrected v_c and pr_dc\n", + "meas['v_oc_temp_corr'] = \\\n", + " (meas['v_oc'] * (1 - ref['beta_v_oc']*(meas['temp_module'] - T_STC)))\n", + "\n", + "meas['pr_dc_temp_corr'] = \\\n", + " (meas['pr_dc'] * (1 - ref['gamma_pdc']*(meas['temp_module'] - T_STC)))\n", + "\n", + "# show some meas data\n", + "meas.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select LFM_n model by counting variables in the meas data \n", + "usually LFM_4 = matrix (i\\_sc, i\\_mp, v\\_mp, v\\_oc) \n", + "and LFM_6 = iv (i\\_sc, i\\_mp, v\\_mp, v\\_oc + r\\_sc, r\\_oc) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_qty_lfm_vars(dmeas):\n", + " \"\"\"Find the quantity of LFM variables in the measured data.\n", + "\n", + " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", + "\n", + " Parameters\n", + " ----------\n", + " dmeas: DataFrame\n", + " Measured weather and module electrical values per time or measurement\n", + "\n", + " Returns\n", + " -------\n", + " qty_lfm_vars : int\n", + " number of lfm_values present in data usually\n", + "\n", + " 2 = ( i_mp, v_mp ) from mpp tracker\n", + " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", + " 6 = (i_sc, r_sc, i_mp, v_mp, r_oc, v_oc) from iv curve.\n", + "\n", + " \"\"\"\n", + " # find how many lfm variables were measured\n", + " qty_lfm_vars = 0\n", + " for lfm_sel in ('i_sc', 'r_sc', 'i_mp', 'v_mp', 'r_oc', 'v_oc'):\n", + " if lfm_sel in dmeas.columns:\n", + " qty_lfm_vars += 1\n", + " # print(qty_lfm_vars, lfm_sel)\n", + "\n", + " return qty_lfm_vars" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "qty_lfm_vars = get_qty_lfm_vars(meas)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [C] Normalise LFM values from meas and ref to norm dataframes \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_globaltemp_modulewind_speedpr_dcpr_dc_temp_corri_sci_mpv_ocv_oc_temp_corrv_mp
date_time
01001500.9360420.9085171.0079880.9126050.9368450.9139710.844634
12001500.9783610.9495911.0020580.9239220.9654710.9418990.851158
24001501.0070650.9774510.9969760.9282070.9919610.9677430.853123
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" + ], + "text/plain": [ + " poa_global temp_module ... v_oc_temp_corr v_mp\n", + "date_time ... \n", + "0 100 15 ... 0.913971 0.844634\n", + "1 200 15 ... 0.941899 0.851158\n", + "2 400 15 ... 0.967743 0.853123\n", + "\n", + "[3 rows x 10 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "norm = meas_to_norm(meas, ref)\n", + "\n", + "# show some normalised data\n", + "norm.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make irradiance and temperature bins for pivot tables " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# poa_global bin e.g. 100, 200 .. 1100W/m2\n", + "norm['poa_global_bin'] = \\\n", + " norm['poa_global'].round(-2)\n", + "\n", + "# temp_module bin e.g. 5, 10 .. 75C\n", + "norm['temp_module_bin'] = \\\n", + " (5 * round(norm['temp_module'] / 5, 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [D] Perform sanity checks on meas and norm data \n", + "\n", + "It's easier to sanity check and study normalised data than raw values. \n", + "1) Remove bad, missing, unwanted or outlier data \n", + "2) User defined limits may depend on data scatter and degradation \n", + "3) Can either select on values e.g. '0.5 x stdev from mean' \n", + "4) Possible to select on dates if desired. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# select by irradiance poa_global range e.g. 100-1100 W/m2\n", + "norm = norm[(norm['poa_global'] >= 100) &\n", + " (norm['poa_global'] <= 1100)]\n", + "\n", + "# remove specific lfm values outside limits e.g. <0.5 or >1.5\n", + "norm = norm[((norm['pr_dc'] > 0.5) &\n", + " (norm['pr_dc'] < 1.5))]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# remove all mlfm values outside x~3 stdevs\n", + "if qty_lfm_vars == 6:\n", + " # only needed for outdoor data as indoor ought to be less scattered\n", + " # remove all mlfm data > x stdev usually 3\n", + " stdevs = 3\n", + "\n", + " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc', 'v_oc'):\n", + " norm = norm[\n", + " ((norm[lfm] - norm[lfm].mean()) /\n", + " norm[lfm].std()).abs() < stdevs\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter only matching rows from meas and norm data\n", + "like an inner join but leave data in separate norm and meas frames" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# drop meas rows that aren't in norm\n", + "meas_not_in_norm = ~meas.index.isin(norm.index)\n", + "meas = meas.drop(meas[meas_not_in_norm].index)\n", + "\n", + "# drop norm rows that aren't in meas\n", + "norm_not_in_meas = ~norm.index.isin(meas.index)\n", + "norm = norm.drop(norm[norm_not_in_meas].index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [E] Plot normalised LFM data vs irradiance \n", + "\n", + "For outdoor data - \n", + "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", + "\n", + "For matrix data - \n", + "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", + "\n", + "1. Higher values are always better (unlike measured values such as \n", + " Rseries or Io where lower is better)\n", + "1. Accurate measurements and a stable module result in narrowest lines \n", + "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", + "1. r_oc tends to fall at high light levels ( \\ right) \n", + "1. i_ff and v_ff are usually fairly flat ( - ) \n", + "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", + " and/or snow (if not properly corrected). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalised lfm values vs. irradiance.\n", + "\n", + "All traces should be thin, smoot lines usually around 0.9 ± 0.1 \n", + "i\\_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot normalised values vs. irradiance\n", + "fig_scatter = plot_scatter(\n", + " norm, mlfm_meas_file, qty_lfm_vars, save_figs)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [E] : LFM multiplicative factors (y) vs. poa irradiance (x)\n", + "\n", + "\n", + "# [F] Convert multiplicative to subtractive losses for a stack plot \n", + "\n", + " Multiplicative losses are easier to understand but to represent them on a graph \n", + "it's easier to show them as a stacked plot where the values are 'translated' \n", + "so the sum of the stacked losses is shown to equate to the product of the \n", + "multiplicative losses.\n", + "\n", + "LFM losses can be analysed as either \n", + "\n", + "- multiplicative pr_dc = 1/ff * PRODUCT(norm(i_sc), ... \\* stack(v_oc_t), stack(temp_corr) ). \n", + "\n", + "- subtractive pr_dc = 1/ff - SUM(stack(i_sc), ... stack(v_oc_t), stack(temp_corr) ). \n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# translate multiplicative to stack losses and add to\n", + "# dataframe stack add a gap between i and v losses\n", + "\n", + "stack = meas_to_stack_lin(meas, ref, qty_lfm_vars, gap=0.0) # gap = 0.01\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [G] Plot stack losses vs. measurement \n", + "\n", + "Fig 3 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", + "\n", + "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", + "\n", + "Fig 3 Stacked losses by measurement \n", + "\n", + "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", + " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", + " \n", + "- This figure shows a typical c-Si module for four clear days for \n", + " different months July to Oct in AZ. \n", + " \n", + "- In the middle of the days the high irradiance results in the biggest \n", + " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", + " (as the module heats to 60C). \n", + " \n", + "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", + " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", + "\n", + "Stack losses are indicated by their colours \n", + "(from top to bottom for lfm_4=matrix and lfm_6=ivcurve) \n", + "\n", + "![mlfm_data/figs/losses.png](mlfm_data/figs/losses.png) \n", + "\n", + "Graph options : \n", + "\n", + "is_i_sc_self_ref : boolean \n", + " = self corrects i_sc to remove angle of incidence, spectrum, \n", + " snow or soiling. \n", + " \n", + "is_v_oc_temp_module_corr : boolean \n", + " = calc temperature loss due to gamma, subtract from voc loss " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot stack loss vs. time (or measurement) chart\n", + "fig_stack = plot_stack(\n", + " dstack=stack, # dataframe measurements\n", + " fill_factor=ref['ff'], # dataframe reference STC\n", + " title=mlfm_meas_file, #\n", + " xaxis_labels=12, # show num x_labels or 0 to show all\n", + " is_i_sc_self_ref=False, # is isc self referenced?\n", + " save_figs=save_figs # save the figure?\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [G] Stacked loss values (y) s. date and time (outdoor) or matrix measurement (x)\n", + "\n", + "# [H] Fit mpm to measured weather and normalised losses \n", + "\n", + "Perform a Mechanistic Performance Model (MPM) fit to the lfm parameters \n", + "poa_global (W/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", + "\n", + "\n", + "mpm_a = c_1 +c_2\\*(t_mod-25) +c_3\\*log10(g) +c_4\\*g +c_5\\*ws +c_6\\/g (deprecated) \n", + "\n", + "mpm_b = c_1 +c_2\\*(t_mod–25) +c_3\\*log10(g)\\*(t_k\\/t_stc_k) +c_4\\*g +c_5\\*ws\n", + "\n", + "\n", + "Report the fit (coeffs) and error (errs) coefficients. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choose which normalised lfm parameter to model e.g. pr_dc or i_sc..v_oc " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "lfm_sel = 'pr_dc'" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nfev = 22 \n", + " \n", + " mesg = `ftol` termination condition is satisfied. \n", + " \n", + " ier = 2 NOTE : if ier in (1,2,3,4) then fit found\n" + ] + } + ], + "source": [ + "# add selected variable to measured data frame to ensure data indexes match.\n", + "meas_temp = meas.copy()\n", + "meas_temp[lfm_sel] = norm[lfm_sel]\n", + "\n", + "# try to fit measurement data and print outputs \n", + "\n", + "\"\"\"\n", + "# full_outputboolean, optional\n", + "If True, this function returns additioal information: \n", + " infodict, mesg, and ier.\n", + " \n", + "https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html\n", + "\n", + "mesgstr (returned only if full_output is True)\n", + "A string message giving information about the solution.\n", + "\n", + "ierint (returnned only if full_output is True)\n", + "An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. \n", + "Otherwise, the solution was not found. In either case, \n", + "the optional output variable mesg gives more information.\n", + "\"\"\"\n", + "\n", + "try:\n", + " \n", + " if mpm_sel == 'a':\n", + " cc, coeffs, ee, errs, infodict, mesg, ier = mpm_a_fit(meas_temp, lfm_sel) \n", + " \n", + " if mpm_sel == 'b':\n", + " cc, coeffs, ee, errs, infodict, mesg, ier = mpm_b_fit(meas_temp, lfm_sel) \n", + " \n", + " \n", + " # store calculated value of LFM variable\n", + " norm['calc_' + lfm_sel] = cc\n", + "\n", + " # store residual difference of LFM variable\n", + " norm['diff_' + lfm_sel] = norm[lfm_sel] - norm['calc_' + lfm_sel]\n", + " \n", + " # show infodict data, uncomment fvec to show per row\n", + " print('nfev =', infodict['nfev'], '\\n \\n', \n", + " # 'fvec =', infodict['fvec'],'\\n \\n',\n", + " 'mesg = ', mesg, '\\n \\n',\n", + " 'ier = ', ier, \"NOTE : if ier in (1,2,3,4) then fit found\")\n", + " \n", + "except:\n", + " print(\"CAN'T FIT DATA\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [I] Plot heatmap of mean residual vs. temp_module and poa_global\n", + "\n", + "Show a heatmap of the average residual (meas - fit) error \n", + "for each irradiance (100W/m^2) and tmod bin (5C)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_heatmap(dnorm, fit, y_axis, x_axis, z_axis,\n", + " title, save_figs, clip=0.02,):\n", + " \"\"\"Plot a heatmap of Z vs. binned X and Y axes.\n", + "\n", + " Parameters\n", + " ----------\n", + " dnorm : dataframe\n", + " Normalised multiplicative loss values (values approx 1).\n", + "\n", + " fit : string\n", + " fitted parameter e.g. 'pr_dc'.\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global_bin'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module_bin'.\n", + "\n", + " z_axis : string\n", + " value as a colour surface plot e.f. 'diff_pr_dc'.\n", + "\n", + " clip : value\n", + " clipping of z axis usually 0.02\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " \"\"\"\n", + " df_piv = pd.pivot_table(\n", + " dnorm,\n", + " index=y_axis, # e.g. 'temp_module_bin'\n", + " columns=x_axis, # e.g. 'poa_global_bin'\n", + " values=z_axis, # value to aggregate\n", + " fill_value=0, # fill empty cells with this ?\n", + " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", + " margins=False, # grand totals hide\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " # force z limits to be -2% to +2% if desired\n", + " df_piv = df_piv.clip(lower=-clip, upper=+clip)\n", + "\n", + " im = ax1.imshow(\n", + " df_piv,\n", + " cmap='RdYlBu',\n", + " origin='lower'\n", + " )\n", + "\n", + " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", + "\n", + " # Y AXIS : show only 1 of each y_skip labels\n", + " y_ticks = df_piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = df_piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " # X AXIS : show only 1 of each x_skip labels\n", + " x_ticks = df_piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_title(title)\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid(color='k', linestyle=':', linewidth=1)\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(\n", + " os.path.join('mlfm_data', 'output', 'heatmap_' + title[:len(title)-4]),\n", + " dpi=300\n", + " )\n", + " \n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Residual LFM fit heatmap vs. poa_global and temp_module" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zTJkyhdu5W7t2bZiZmeHChQsK7z1//jx3LR0dHbRo0aLQOq1bt+Y1ov8S+DzXzZs3x+XLl4v80dWhQwcQQhT6MiUlBa9fv0bt2rV5a8nKyoJEIlEYgUskEmhpaRX7g+7FixdITk7mRoZFkZ+fz/tZkrtUfXrP58+fR4UKFbhn6P79+wqfu7e3NwDgwoULWLx4Ma/rFIaNjQ10dXULPAufIpFI0LJlS8yfPx83btxAp06dsHv3bu68k5MTMjIycObMGezbtw/Dhg1TXwwACSApI1FaGCqE6iS5hvl8PfLly5ekWrVq5LvvviM3btwgz549I/7+/mT+/PkkICCAEELI/PnzybFjx8jjx49JZGQkmTp1KpFKpeTNmzeEkIJrxhs2bCB6enrEx8eHREZGknXr1pGKFSsqrBk/fvyYlClThsybN488ffqUHDlyhFhYWCisGc2aNYtUqVKF+Pn5kSdPnpAFCxYQAwMDYm5uzrXzJWvGn659FbZO/ffffxMAJCoqihAiWzOWSCSkefPm5Pr16+TevXukd+/epEqVKgV2oBZGTk4OadiwIRkyZAghRLbeXKtWLTJ16lSuzrt370hoaCgJDQ0lzZs3J/369SOhoaEKu7qjoqLInj17yJMnT0hwcDCZNGkSKVu2LDl37hxXJy8vjzRr1oy0bNmSBAUFkcDAQNK8eXPSunVrhV3ohPy3Ns53t3Bha7kbN24k5cuXJ/v27SOPHj0ic+bMITo6OiQsLIyr8+effxItLS3y66+/ksePH5P169cTLS0tcvbsWa5ORkYGd//VqlUjU6ZMIaGhodxnoIzPP1s+z/W9e/dI+fLlydChQ8mdO3e45/DWrVuEEEKysrKIhYUFadeuHQkODibh4eHE0dGRWFtbK6wZR0REkNDQUDJhwgRSs2ZN7j7evXtHCJGtF+vq6pJRo0aRBw8ekMePH5OffvqJaGlpceuft27dImvXriUhISEkNjaWnD9/njRp0oTUrl2b27vw8uVLsmjRIhIYGEhiY2NJWFgYmTNnDpFIJOT48eO8+ik+Pp7o6+sTFxcX8uDBA3Ly5ElibGxM5syZw7tv+d43IYTUq1eP/Pbbb9zfCxYsIHp6emTLli3kyZMnJCwsjKxYsYIQItuD4O7uTgIDA0lcXBy5fPkyqVatGlm4cKHCdfv160eaNGlCABTqjaAqKuibk5bdvZUWtmasOr5pY0wIIbGxsWTYsGGkcuXKREdHh9SqVYsMHz6cxMTEEEIIcXd3J7a2tkRPT48YGBiQjh07En9/f+79nxvjvLw8Mm/ePFKpUiVSoUIFMmDAgAKuTYQQsnPnTmJhYUF0dXWJo6MjOXjwoIJhfPPmDRk0aBDR19cnxsbGZPLkyWThwoVUjLGWlha5cOECqV+/PtHR0SH29vYFNuAUxcSJE4mFhQX340V+3bJly5KTJ08SQgh59uwZAVCgfHqvT548Ic2bNycVKlQgenp6xMHBgVy/fr3A9RISEsjAgQOJVCol+vr6ZPDgwSQpKalAvR49epD27dvzugdCit5YtXr1alKzZk2io6ND7OzsyPnz5wvU2b17N7GysiLa2trE2tqa7Nu3T+G8/LP5vBTl/vQ5hX22yp5rQggJCgoiXbt2JRUqVCBSqZT7ESMnOjqa9O7dm0ilUlK5cmUycOBAEh8fX6BfCtP+6TN19epV0qlTJ1KxYkViYGBAWrVqxX32hBASEhJC2rRpQ4yMjIiOjg6pU6cOmThxIklMTOTqpKamkj59+pBq1aoRbW1tYmpqSrp166bwY4wPf//9N2nTpg0pV64cMTU1JXPnziUfP34U1Ld87xsAWbx4Mfd3fn4++fXXX4m1tTXR1tYmVapU4TZgPXjwgPTs2ZNzoaxVqxb5+eefFYw7ITL3JwCkYcOGgu5bKHoGtUmr73crLcwYqw4JIQIX/xgMBoPxVSOtaIFGHZYorfch8TfOtZBRMr653dQMBoPBUA7boKVZvukNXF8Ttra2kEqlhZaJEyeq7bo9e/Ys8ro9e/ZU23W/JYrqX6lUihUrVtCWVyrw9/cvtp/8/f1pSxQXbAOXxmEj46+Es2fPFnCPkmNgYKC263p7eyMnJ6fQc5/vUmZ8GWFhYUWeMzY21pyQUoy9vX2x/VS9enXNifkakEj4+RkzVAYzxl8JJQ0G8qWwLzn1w7ICKad8+fKsn1SIRMKmqTUNM8YMBoPBKACbhtYs34wxNq5cCTVrFR9A4EtIfpUMkyomyit+AR/y1bOkn/IqCZWqmKq83Qr5r1XeJgAkJafD1ER9U+3qQJ2aydtMtbSblJ4LUwNd5RUFQnI+qrxNAEjKfg/TCjpqaVtdTiavct6jSnnVa47PyEVKrgpjU0pYhC1N880Y45q1auL8TeExkWmSlFWOtgRB2OXsoi1BOCL07Pt4/hZtCYLIvZdMW4Jg8j8UHw62tOHwJ7+czkKQlGVrxpqE7aYuIft37aUtQTDHfMRlNL18xWV8AHFq9vaPpi1BELsjEmhLEMyeRyLR/O+aMYtNrTmYMS4h4XfDaEsQzMPwL8tGRIvge8Jy6JYGgu8/py1BMCFxabQlCCI0OZ22BMGEvlbPEoOqkUC5WxNbU1Yt30wELrtmTdg0tZph09SagU1Tqx8xTlOHJmeorD2DKpawH7ROab30oGUsApeKYCPjEjLAsQ9tCYIZ94O4gnF0HvIbbQmCEaPmbuuv0pYgiO9PiGuGBwB6nQ6jLYEfEqCMdhmlhaE6vpkNXOpi1vzZtCUIZuIv82lLEMTiGerK2ao+xKjZ7X+2tCUIYl6L2rQlCGZus9q0JfCETUNrGmaMS0gdq7q0JQimVl1xBUewrlOFtgTBiFGzlak+bQmCsKxYgbYEwVgaiiQqHQv6oXHYPEMJ6dm+G20JghnevSNtCYJo8T/la1eljRa919OWIJg2Ky/RliCITn+Ib62y84m7tCXwQgJAUqaM0sJQHaw3S0hodARtCYK5dD+KtgRBvLjjQVuCYF7cdqctQTBxq8W1/yFydDvaEgTzeHgb2hL4IZGgbNkySgsfUlNT0a9fP+jp6cHc3By+vr5F1t24cSOqVq0KQ0NDuLi44N27d7zaOXDggEJikAoVKkAikSAkROZ/vWTJEmhrayvUiYmJ+cLOUQ/MGJeQ7Zu20pYgGJ9tm2lLEMR6Lz/aEgSzfoe4NkMBwMZLT2hLEMRvYeJzefvtnjhc3iQq9DOeMmUKdHR0kJSUhAMHDmDSpEmIiCg4iLlw4QJWrVqFK1euIDY2FjExMVi8eDGvdoYPH47MzEyubNu2DXXq1EGzZs249w8ZMkShTp06dUrYS6qFGeMSkpT4krYEwSS/TKQtQRAJSW9pSxCMGDUnvi08+1ZpJTHrPW0JgnmZLR7NZcpIlBZlZGVl4dixY/Dw8IBUKkX79u3Rp08f7Nu3r0DdvXv3YuzYsbC1tYWRkRHc3NywZ88ewe3I23J2doZEIp51b2aMS8jiVeKbQp3lvpK2BEGsd+tHW4Jg1i/8gbYEwawZ2IS2BEGsaCeujYgAsLy1SDZ8SlRjjCMjI6GlpQVra2vumJ2dXaEj44iICNjZ2SnUS0pKQkpKiqB24uLicOPGDTg7OyscP3XqFIyNjWFrawtPT09e3aBJmDEuIT3adaEtQTDDunWgLUEQ9r3W0pYgGHsRbjprtUJcG7g6/nGHtgTBdFJDDGl1IIEEZctqKS3Jycmwt7fnipeXl0I7mZmZMDQ0VDhmaGiIjIyCAUo+ryt/nZGRIagdHx8fdOjQARYWFtyxwYMH49GjR0hOTsaOHTvg7u6OgwcPCu8YNcJcm0rI2i0baEsQzML14loz/n3VUNoSBPP7yiG0JQjGc3hz2hIEsalTfdoSBLOpvbXySqUAiQQow2NN2MTEpNgIXFKpFOnpimFL09PToa9f0I3u87ry1/r6+oLa8fHxwfz5irEUbGxsuNdt27bF9OnTcfToUTg5ORVzd5qFjYxLiJ5USluCYMSmWV9PXGFBAXFqlupq05YgCKmO+LIKSbXFo1kV09TW1tb4+PEjoqL+8+AIDw+HrW3BADO2trYIDw9XqGdqaopKlSrxbicgIAAJCQkYOHBgsbokEona0mR+KcwYl5BRA4fRliCYacMH0ZYgiN4uXsorlTJ6j91BW4JgftjqT1uCIAafuUdbgmCGXHhAWwI/JFBJogg9PT30798fixYtQlZWFgICAnDy5EmMHDmyQF1nZ2fs3LkTDx8+RFpaGpYtW4bRo0cLamfv3r0YMGBAgRHzyZMnkZaWBkIIbt++jc2bN6Nv375f3j9qgBnjEnIz/DZtCYI5GRhGW4IgnlxbSFuCYJ5cXUBbgmAeun9PW4IgQoe3pi1BMCFDWtKWwAuJCv2Mt23bhpycHFSpUgVOTk7w9PSEra0t4uPjIZVKER8vc1FzdHTE7Nmz0blzZ5ibm8Pc3BxLly5V2o6c3NxcHDlyBKNGjSqg4dChQ7C0tIS+vj6cnZ0xZ86cQuvRhBnjErJu+WraEgSzfc0K2hIEsWTjOdoSBCNGze6nRDJq+5cVt5/RliCYlSGxtCXwQgLZmrGywgdjY2OcOHECWVlZiI+Px7BhstnEWrVqITMzE7Vq1eLqzpw5E0lJSUhPT8fu3btRrlw5pe3I0dXVxZs3b9C1a9cCGg4ePIiUlBRkZmbi8ePHmDZt2hf0inphG7gYDAaDoci/rk0MzcHyGZdiWD5jDSDCx5/lM1Y/33o+48rmDdB3nvL/z+HeP7J8xiqCTVOXkPZ24lgD+pS+rZvQliCIeg7LaEsQTL3Oy2lLEIzNorO0JQii6YFA2hIE0/ywOPaYSCSAVtkySgtDdXzVvenl5cU5oz+Lfsat77a3a4noqKe4FxrGBe1YOteNizPdtK4tXiYm4taNmxjgKAue/8vUn7B/114AgJWpOTIzMnDx7HlUNasGAJg82hXHDx8FAJjpVQYAHD98FJNHuwIAnAcOw8Wz55GZkQErU3MAwP5de/HL1J8AAAMc++DWjZt4mZiIpnVlmxJ8tm3G+kXzAMgCdTwMD0VcdBRnTLevWcGt//Zt3QRx0VF4GB7KBfVYv2geF4e6eyMrvHqZiOAAf+gbVgQAeMz6Ecd8ZL9+21lUQ1ZmBq5fOIvpI2S7redNdMG5Y0dkfVJFtjvx3LEjmDfRBQAwfcQgXL9wFlmZGWhnIesHL99bcJ17CADQechvuPZ3FBKS3qJ6CzeZJi8/zPL4E4AsmEfI/eeIjHnFGdwlG89x6631HJYhMuYVVs7pzQX+mOXxJxerunoLNyQkvcW1v6PQechvAADXuYfg5SsbORrYzEZGZi5OXX6APv/uyB7+4174npD9ki9jPh0A4HsyBMOn+QAA+ozdgVOXHyAjMxcGtnP+u6d5hwveU8tFsnvacRWzlp2Q3dP/1iHk/nNsXtKfM8gK99R5OSJjXiHk/nMuMMisZSe4WNbVWy4qeE/zDv93T7ZzkJH7AafvJXC7n0fuDMTB23EAAJ2Jss/r4O04jNwpM1Y/bPXH6XsJyMj9AOPpxwEA3v7RmLRf1g/d1l/F9Sev4DWyBczn/AVAFqd69tEwALJgIHfjUhGZlMEZbPdTD7g1ZptFZxGZlIG7calc4JDZR8O4WNfWewKQmPUO/i/S8P2JUADAtGuPsTsiAQBgtuMGMt5/xLnY19wOaZdLETgSmSS7522yvjkSmQSXS7KIS4PP3MNUu5rIeP8RZjtuAAB2RyRg2rXHAIDvT4TC/0UaErPewXpPAABZLOv5AU8ByAKGhL7KQNSbbM6or7j9jFuHbn74Np6+yUZYcgYXqGNBYDQXW7r+gb9l95TwBr1Oy/ppun8k9jyS3VONPTdl9xT3GkMu3AcAjPN7hDENZP9PKu64DgD442kSxvk9AgAMuXAf5+JeI+P9R9TYcxMAsOdRAqb7RwIAep0Og3/CGyRmvUP9A3/L7unecywIjIY6UNWaMYMfbJq6hNwLDUPjpk1U3i6gvmnqh+GhsLFrqvJ21TVNHXL/OZo3qqmWttU1Ta1Ozeqapr4bl4pm5sYqb1dd09ShrzK4H4mqRl3T1GHJGWhionrNqp6mrmLRAAOX+Citd/u3SWyaWkV81SNjTfDL1Jm0JQhm2azSt5OwOCb8O9IWExP+HUmLiUkHxBGqUc70649pSxDM9JuRtCXwRHnAD7bBS7Ww3dQl5EKA+NL7+V4WV3CH4DO/0JYgmODTP9OWIJig+d1pSxDEjUEtaEsQzPV+4gg5KlszFk+0sK8BNjIuIUvnutGWIBj5OrRYkK8xiwn5GrKYkK8RiwX5+q+YUNf6rsqRsDVjTcNGxiXEtFpV2hIEY1K1Gm0JgjAzNVReqZQhRs3VDMvTliCIano6tCUIpmoFcWiWgE1DaxpmjEvIxOlTaEsQjPNkca0Zz3IVX5rKWeM705YgmJ+616MtQRA/NqmlvFIp48fGatqIqGpY0A+Nw6apS4jcDUlMdG9kRVuCIORuUWJC7vYkJuRuTWJB7rIkJuQuSaUdCYCyWmWUFobqYCPjEnLu5mXaEgRz4NIN2hIEcUeEm6HunJpFW4Jg/p4nrg1c1wfZ05YgmKs/NKMtgR9sZKxx2E+bEhITJZINGZ8QHy2ujS+RMa9oSxCMGDVHJanOT1UTPH2TTVuCYJ6+zaEtgRcSSKBVRnlhqA5mjEvI+hVraEsQzPa14sratPTX87QlCEaMmj1OR9CWIIiVd2JpSxDMqruxtCXwQiJh09Sahk1Tl5Bj58W1zgYA3ifEld7v6uEfaUsQjBg1X54lrk1nZ39QfRQ5dXPmf01oS+ANm6bWLOynTQmRx5YWEx6zxGUoXEUYgctVjBG49osrrKE8DrWYkMeZFgMsApdmYca4hNg1a0JbgmDUEZdandg3Fp8Li726YmmrkebmRrQlCKKpiQFtCYJpWllKWwIvJBKgTBnlhQ+pqano168f9PT0YG5uDl9f3yLrbty4EVWrVoWhoSFcXFzw7t07Xu3ExsZCIpFAKpVyxcPDgztPCMGcOXNQqVIlVKpUCbNnz0ZpS8vApqlLyAiXUbQlCGaAswttCYJwHdaWtgTBiFHzuA51aUsQxBhbM9oSBDO6gTg0SyCBtorWhKdMmQIdHR0kJSUhLCwMvXr1gp2dHWxtFd1CL1y4gFWrVsHPzw9mZmbo168fFi9ejFWrVvFu582bNyhbtqBZ8/LywokTJxAeHg6JRILu3bujTp06mDhxokruURWwkXEJkadDFBPydIdiwcBmNm0JgpGnXxQT8vSKYkGeOlFMyFMjlnokqpmmzsrKwrFjx+Dh4QGpVIr27dujT58+2LdvX4G6e/fuxdixY2FrawsjIyO4ublhz549gtspjL1792LWrFmoUaMGqlevjlmzZnFtlxYEG+NXr14hJiZGoXzLhD59QFuCYC7eF8+6FQC8uO1OW4JgXgQtpS1BMHGre9OWIIgno8Q3+/BoWGvaEnghAVBGIlFalBEZGQktLS1YW1tzx+zs7BARUXDnfkREBOzs7BTqJSUlISUlhXc75ubmqFGjBsaMGYPXr18X23ZhGmjC2xifP38e1atXR7Vq1WBpackVKytxRXNSNbf8xRcFKDhAXFmbrgWKyy8aEKfm65HqyTusLm4mvKEtQTA3E9/QlsAbPiPj5ORk2Nvbc8XLy0uhjczMTBgaKsZpNzQ0REZGQZ/2z+vKX2dkZChtp3Llyrhz5w7i4uIQEhKCjIwMDB8+vNi2MzMzS9W6MW9jPGXKFLi5uSEzMxP5+flcycvLU6e+Us/+XcoTcJc2ju/bTVuCIHb43qItQTA7Dooj7OGnePuLK4DN7ogE2hIEs+dxIm0JvJBIJNAuW0ZpMTExQXBwMFdcXV0V2pFKpUhPT1c4lp6eDn19/QLX/Lyu/LW+vr7SdqRSKezt7VG2bFmYmppiy5YtuHjxIveewtqWSqWQ8BjdawrexjgtLQ0TJkxA+fLiyuyibnyOFr0zsLSyaf8ftCUI4q9drsorlTL+2jmetgTBnJjSgbYEQRzp1Zi2BMEc7tGItgTeqGKa2traGh8/fkRUVBR3LDw8vMDmLQCwtbVFeHi4Qj1TU1NUqlRJUDsAOCMrH/kW1nZR76UFb2M8duxY7N4trhGVJpg8WnyGYt5Ece2mHv7jXtoSBDN8mvhmTEbuDKQtQRAul0rXmh8fxvk9oi2BFxIVbeDS09ND//79sWjRImRlZSEgIAAnT57EyJEjC9R1dnbGzp078fDhQ6SlpWHZsmUYPXo0r3aCgoLw5MkT5OfnIyUlBdOmTYODgwM3Ne3s7IwNGzbgxYsXSEhIwPr167m2Swu8jXFgYCAmTZoEa2trdOzYUaF8y3Tr+R1tCYLp2N2RtgRB9Opaun7B8kGMmr9vJK5d9o7mlWlLEEyPWsa0JfBGVUE/tm3bhpycHFSpUgVOTk7w9PSEra0t4uPjIZVKER8fDwBwdHTE7Nmz0blzZ5ibm8Pc3BxLly5V2g4AxMTEwNHREfr6+mjYsCHKlSuHgwcPcu+dMGECevfujUaNGqFhw4bo1asXJkyYoMLeKjkSwnMFe+/eokcno0aVfl/b2jaNsWDfadoyBOE5WVx6l/oOpi1BMC2qlZ4NHHypUDaVtgRBZH8UjwGSY6DzgrYEQbRvNQx3Qx6qrD0LWzssPag8bO5mlz4IDhZX5LbSCu+gH2IwuDRwtTeHV3AcbRmCCL09BU1bbqUtgzd9LE3w11Nx7fStplcFiVniytxkWK4+3r4TT4hJMfaxnnZTZH0IpS1DKRKw2NSaplhjvG/fPm5OfteuXUXWc3ER1xqkKhGbIQYgKkMMQHSGGIDojAQAURliQJx9LAZDDACQgKVI1DDFrhl/Oue+b9++Qsv+/fvVLrI0E3T+BG0Jgkl9fYe2BEFc/+sYbQmCOX5YfJr/OHSKtgRBiLGPD/OY+i0NqCroB4M/xRrjs2fPcq+vXr1aaPHz81O7yNLMff8rtCUIJv2NuKKG3bl6kbYEwVw+d4m2BMGcP3uNtgRBiLGPz50VSQhPiQRlyyovDNUhKFHEmzdvcObMGSQkJMDMzAy9evVCxYoV1SRNHIxb/httCYKpbTmGtgRB/Lzxd9oSBLNtz3baEgSz02c9bQmCEGMf79m3krYEXshHxgzNwdu1yc/PD7Vr18bmzZtx584d/Pbbb6hduzauXBHfyFCVbPlJfOvl0ZGetCUIwmP8cOWVShnOA0fQliCYIf1KTwYbPoixjwf+MJ22BN6UkSgvDNXBe2Q8depUeHl5YfDg/9xX/vjjD0yZMgWPH4tr44cq6dBvGG0Jgqls0p62BEH0GOpMW4JgRrgUDGpQ2hk9VlyuaWLsY5dx/WlL4AUbGWse3iPjhIQEDBgwQOFYv3798PLlS5WLEhP1mosjC8unSA3EldyjYat2tCUIpm0H8Wlu36klbQmCEGMfd+hkT1sCPyRA2TISpYWhOngbY2dnZ2zdqugS4+npCWdn8Y1aVMkvPcX1BQYAD0Ln05YgiDHtGtKWIJgmluKJQSynfm1xRdMTYx9b1hJHxD4JAC2JRGlhqI5ip6k7dOjABdzOz8+Hp6cn1qxZg+rVq+PFixdISkpC69biGxmqkt9uqC7qjaaws99AW4IgDofH0pYgmKdJz2hLEMyLlLu0JQhCjH2clCaelKts4KtZijXG48aNU/h7/HjxZaJRNzeO+6Jjf3GtG79+dROVq4hn3fj8IR84imzdeN8uH4x0EZfm3d6HMWbcENoyeCPGPt614xhcxg9QXpEyEjA/Yk1TrDEWGgJz8uTJ2LZtW4kEiY24R/cAiMsYZ2fF05YgiKf3wwCRGePwu+EYKbKN9qEhD0RljMXYx3dDHorDGP+7ZszQHLwTRfDBwMCgQALo0gJLFKF+WKIIzcASRaifbz1RRH27pvA+d01pvRl9urJEESqC9wYuPqjQrouGda7iGUnIiXr0K20Jgpg/rC9tCYLp7/gDbQmC6dVdXK5CYuxjx67jlFcqJTA/Y80iKAKXMiTf4BpDb9cZtCUIpmr172lLEITTtNm0JQjm5/m/0JYgmLkLp9KWIAgx9vH8ReIIrML8jDWPSo3xt4ipeR3aEgSjq1uFtgRBmFnUpS1BMHWsxKfZ0qo2bQmCEGMfW1nVoi2BFxIJ8yPWNGyauoQsH/k/2hIE8zhiDW0JgpjVrxttCYJxbN+dtgTBOLQdSFuCIMTYx+1biye0K8vapFlUaoxHjBBfrNiSsva8uNIRAkCjpitoSxDEnlviyjIFAGHR92lLEMyTWH/aEgQhxj6OjhdPpilVrRmnpqaiX79+0NPTg7m5OXx9fYusu3HjRlStWhWGhoZwcXHBu3fveLUTGBiI7t27w9jYGCYmJhg0aBASExO580uWLIG2tjakUilXYmJihHeKGuFtjAkh2LFjB7p06YLGjRsDAG7cuIEjR45wdTw9i05A4ODgAF1dXa4j6tWrx527cuUK6tevjwoVKqBz586Ii4srsh0hH6wmuLjfi+r1v4SkxMu0JQjiT2/xuctt3yQ+zb9t3EVbgiDE2MebNvrQlsALVeYznjJlCnR0dJCUlIQDBw5g0qRJiIiIKFDvwoULWLVqFa5cuYLY2FjExMRg8eLFvNpJS0uDq6srYmNjERcXB319fYwZo5idbsiQIcjMzORKnTqqW2L08fHBvXv3FI6Fh4dj3759vNvgbYwXLVqEnTt3wtXVFfHxMj/VGjVqYPXq1bwvtmXLFq4jnjx5AgB4/fo1+vfvDw8PD6SmpsLe3h5DhhS9Q5nvB6sp3iYnUbv2l/Lhw1vaEgSR+kp88c9fJopR8yvaEgQhxj5OTEimLYEXEgmgXUaitCgjKysLx44dg4eHB6RSKdq3b48+ffoUaqT27t2LsWPHwtbWFkZGRnBzc8OePXt4tdOzZ08MGjQIBgYGqFChAqZOnYqAAM1FO3Nzc0PNmjUVjtWsWRMLFy7k3QZvY7xnzx6cPn0aQ4cO5XZNW1hYlHiof/z4cdja2mLQoEHQ1dXFkiVLEB4eXmgmKCEfrKYY9JMbtWt/KTVqlf6gA58ydr47bQmCWbJKfJqXr5lLW4IgxNjHq9bOoi2BN3xGxsnJybC3t+eKl5fiTGFkZCS0tLRgbW3NHbOzsyt0ABUREQE7OzuFeklJSUhJSRHUDiCbtbW1tVU4durUKRgbG8PW1rbYWdwvIT09HQYGBgrHDA0N8ebNG95t8DbGeXl5kEqlAP5zYcrMzOSO8WHevHmoXLky2rVrh2vXrgEo+AHo6emhbt26hXay0A9EEywb0Yvatb+Uxw9W0ZYgiJ/6dqUtQTDftRPfprOOrcWR3k+OGPu4XUtxROuTTVMrLyYmJggODuaKq6urQjuZmZkwNDRUOGZoaIiMjIwC1/y8rvx1RkaGoHbu3bsHd3d3rF27ljs2ePBgPHr0CMnJydixYwfc3d1x8OBBwf1SFDY2Njh27JjCsT///BMNGjTg3QZvY/z9999j5syZ3II6IQRubm7o3bs3r/evXr0aMTExePHiBVxdXdG7d29ER0eX6MMqri4AeHl5cb/YXj2PxV+/bwQALOzvgKS4GMQ9us8Z0z82enDrv784tsCb5CQ8Cf6bC+qxb/lc3DguW5/+saMNcrMyEX7jMnR0ywMAvBf8iKDzJwAArvbmAICg8yfgveBHAMCWn1wQfuMycrMy8WNHGwCyuNb7lstGI+tch+BJ8N94k5yEXxxbAJCt7f4TL/uAHz9YheyseOTmJOFh+FIAQOI/Z5D4zxkAwMPwpcjNSUJ2VjxnbP+JP8atD98PnY8P798gIz2S65/4Z754/eomACA8eCby8nLxNu0+oiNlvxpjn+5G6mvZBrXQ21MAAKmv7yD26W4AQHSkJ96m3UdeXi7Cg2cCkMWR3rJA9nr+sL64HxiAlKSXGN1WlnnpT+9t2LliEQCZkX36IBwvnkVjYrdWAADfTWvgu0m223tit1Z48SwafcZM5AzyzhWLuDXk0W0bIiXpJe4HBnCBQbYsmInzh2TrckPsaiM7MxO3r1yAx3jZLtZ1P03A9b9kfdrH0gQAcPzwMUweLfP/dB44AhfPXkBmRiYsTS1kn/0uH/w8VTai6e/4A27dCMDLxJdoUleWNWj7pm1YMld2T9+164bw0HBMn/0T2tnJkqisW74G65bL7qmdXWtER0UjPDScMyZL5i7i1j+b1G2El4kvcetGABfU4ueps7Bvl+yeLE0tkJGRiXOn/TCkn0zzWOdZ+OPQKQCAYbn6AIA/Dp3CWGeZ5iH9JuLcaT9kZGSieqVmAGRxqKdNks3q9Oo+Ev7Xg+C2dAbq1e4AQLZ+vGC27Dnq2Lo/Qu8+wNPIZ2hm2wMAsNLjN6z0+A0A0My2B55GPkPo3QecQV8wexW3Bs3nnjIzMnHx7AU4D5RtAp08eiKOH5Z9TtX0qhT6OQ0YOrDEn1N0VHShn5OdTV9ERcYhNOQhZ0Dn/rKeW/OtW6s7EhNe4cb1YC6Qx9SJHti1Q6bZ1KgdMjKycPb0dQz8YToAYPTIeejzQ2cAgJ52UwDA4YPnMHrkPADAwB+m4+zp68jIyIKpkSw95K4dxzB1ogcAWcCQG9eDkZjwCnVryXaSb9rog7m/rIfqUT4q5rNmLJVKC0RlTE9Ph76+vtK68tf6+vq823n69Cl69uyJTZs2oUOHDtxxGxsbmJmZQUtLC23btsX06dNx9OhR5d3Ak9WrV2PcuHEYMGAAZs+ejf79+2Ps2LFYv57/Z8M7HGZ6ejqcnZ1x/vx5fPjwAbq6uvjuu+/g4+NTaMcqw9HREb169cLTp0/x4cMHhZjWjRo1wpIlSwrkTw4NDUW7du2QnZ3NHVu/fj2uXbuGU6dOFXs9dYXDTIqLUZuvsbrCYebmJEG3vKnK21VXOMwXz6JRXU2+xuoKhxkdFY26avKDVVc4zKeRz2BpbaHydtUVDlOdfayucJhRkXGwsjZXebuqDofZuFlznLpxS2m9AQ7tig2HmZWVBSMjI0RERMDKSpZH3dnZGWZmZli1SnGGbtiwYbCwsMDy5csBAH5+fhg2bBhevnzJq524uDh06tQJc+fOxcSJxQdXWb16NYKCgnD8+HGl98iX+Ph4+Pr64vnz56hZsyaGDx9eYB25OHiPjA0MDHDixAnExcUhMDAQ0dHR+PPPP7/IEAOyqW5CCGxtbREeHs4dz8rKQnR0dIH5fgCwtrbGx48fERUVxR0LDw8vtK6m+O0nkUWqBxATuZ22BEF4jBfH1N6nOA8Ujz+pnMH9xBEdSo4Y+3jgD9NoS+AJgQT5Sosy9PT00L9/fyxatAhZWVkICAjAyZMnMXJkwdCrzs7O2LlzJx4+fIi0tDQsW7YMo0eP5tXOixcv0KVLF0yZMqVQQ3zy5EmkpaWBEILbt29j8+bN6NtXtWF2a9Wqhblz52Lr1q2YO3euIEMMKBkZ5+cr72wAKFOmeJv+5s0bBAUFoVOnTihbtiwOHz4MV1dX3L17F8bGxrC0tMSuXbvQq1cvLF68GNevX0dgYGChbck3kHl7eyMsLAzff/89bt26pdQgs0QR6oclitAMLFGE+vnWE0U0btYMZ/yV+53369RJaaKI1NRUuLi44NKlS6hUqRJWrVqFYcOGIT4+HjY2Nnj48CFq1ZJFJtuwYQNWr16NnJwcDBgwANu3b0e5cuWKbQcAli5diiVLlkBPT0/h2pmZmQAAJycnXLx4Ee/evUONGjUwefJkTJtWsh9GI0eO5BUC2seHnztbsVa0bNmy0NbWLrLIzyvjw4cPWLhwIUxMTFC5cmX89ttvOHHiBOrVqwcTExMcO3YMCxYsgJGREYKCgnDo0CHuvStWrEDPnj25v7dt24acnBxUqVIFTk5O8PT0pDoylq9Diwn5OrNYkK8hiwn52qOYkK8BiwUx9vFyd3HMSklAUAZ5SgsfjI2NceLECWRlZSE+Pp4zoLVq1UJmZiZniAFg5syZSEpKQnp6Onbv3s0Z4uLaAYDFixeDEKLgRyw3xABw8OBBpKSkIDMzE48fPy6xIQYAS0tL1K1bF3Xr1oWhoSFOnDiBvLw81KhRA/n5+Th58iQqVqzIu71iY1M/e/aspHoByHbc3blTdKSqbt26FerKBADz589X+Fv+gTAYDAZDXRCUkXykLaJU82lAkh49euDMmTMKm8Zu3rwJDw8P3u2pNJ9xaYZNU6sfNk2tGdg0tfr51qep7Zo1wYUA5ZH6/tfBkeUzhsyr5/Xr1wozxR8+fEClSpUK7AIvCt4buEaOHAlnZ+dCy7fMwv4OtCUIRu4aJRbkbk9iQu4uIybkbktiQYx9bGcjjtzcqpym/hZo2rQp5s+fj5ycHABATk4OFixYgCZNmvBug3cKRUtLS4W/X758iaNHj2L4cPHtaFQlP4osni8A1LEW165Ztx10449/CT5HD9CWIJgjf4pjPVOOGPv46InNtCXwQwI2TS2APXv2YNiwYTA0NISRkRHS0tJgb28vKHcCb2P86fy4nLFjx2LpUnGNslRNbnYWbQmCyc9/p7xSKSInK1N5pVLGp5tHxEJGprieZTH2cWaGWPqYQCLh503DAGrXro1bt27h+fPnSEhIQLVq1RQ2pgFAQEAA2rVrV2QbJUqh2KRJE1y/fr0kTYgeeQQtMRH/TFwjza3/RvQSE79MFU8MYjnTJy+iLUEQYuzjqZOW0ZbAGwnylBaGIjVr1kSrVq0KGGIACl5BhcF7ZOzn56fwd3Z2Ng4dOgQbGxu+TXyVLNwvLjchAKjfUFw/IDaevEJbgmAu8tj8Utq4Eai6aESaQIx9HHBbHD+EJSAow0bGKkXZXmneI+OxY8cqlLlzZV/oqgy2LUb+2Mh/63ppQR7vWizIY1mLCXkMZDEhj0UtFsTYx+qJI60OZK5NygqDP8oChPAeGavK5/hrw9BE9TGe1Y22tqHySqUI4ypVaUsQTNVqYtRchbYEQYixj6uZmdCWwAsJwKahNUyxxlhV4TC/Zr4b4aq8UinDtJq4Us/1GzeZtgTBTJwuPs0/iizOuhj7ePpPYnEFZRu4NE2JwmHKy7eMPN2hmLgfOl95pVKEPP2imJCn7RMT8vSJYkGMfSxPfSgGyiBfaWHI1oJjYmKQl1f8TIKyNWPe4TDPnDmDo0ePYt68eTA3N0dcXBxWr15dIM3ht4bYonoBQH3b2bQlCGL9n+LbqHP+5iXaEgRz7Zbq8rtqAjH28c1AcfhGSyQsHCZfJBIJGjVqhIyMjGLrKTtfrDE2N/8v7+aGDRsQHBzMBb62traGvb097O3tMWnSJJ6yvz6S4mJQUWTrxrm5r6CtU5G2DN4kPItGJVNxrQ/GREWLbk3zaVQsqpmJ51kWYx9HRcWjmpk41ubZmjF/mjZtisjISNSvX/+L2+C92Pv27VtkZ2crHMvOzsbbt2+/+OJfA6e8fqUtQTAvX5ylLUEQBzeLLzvPuhVraUsQzKplW2hLEIQY+3iFSLI24V/XJmWFIcPBwQGOjo5YsmQJdu7ciV27dnGFL7x3U48aNQrdunXDjBkzULNmTTx//hybN2/GqFGjvkj818LPXodpSxCMVYMZtCUIYoXvSdoSBHP8/AnaEgRz5tI+2hIEIcY+Pn/Fm7YE3kjYmjBvAgICYGFhUSAIlkQigYsLv42RvEfGa9aswbRp03D48GHMnDkThw4dwtSpU7FmjfhGLaqEReBSP1tEGIHrZxFGh5o2yY22BEGIsY+nThRHXAJZ0I8PSgtDxtWrVwstnwfLKg7exrhMmTKYOHEirly5gkePHsHPzw8TJ06ElpbWF4n/WjBv0Ji2BMFU0CsYqq00Y9moCW0JgrFrZkdbgmCaNhfXrnUx9nGz5mKJWChzbVJW+JCamop+/fpBT08P5ubmxSZP2LhxI6pWrQpDQ0O4uLjg3bt3vNu5cuUK6tevjwoVKqBz586Ii4v7724IwZw5c1CpUiVUqlQJs2fPVrq7WShv3rzBgQMHsHbtWhw4cABv3rwR9H5B+Yx3796Nffv24cWLF6hevTpGjhyJMWPGCNVMheY1jXBremfaMgShM9iBtgRBvK/elrYEwSTliOXL8T8+5ovLr/9VlrgSkwCAjpa4+njEd53wMOyuytqzt6+H4DvK17ftW/yiNJ+xk5MT8vPzsXPnToSFhaFXr164desWbG1tFepduHABzs7O8PPzg5mZGfr164fWrVtj1apVStt5/fo16tatC29vb/Tu3Rtubm7w9/dHYGAgAOD333/Hhg0bcOXKFUgkEnTv3h3Tpk3DxImqyWDn5+eH/v37o169ejA3N0d8fDweP36MY8eOoWvXrrza4P3ELV++HKtWrcLQoUOxefNmDB06FGvWrMHy5cu/+Aa+BiovPEVbgmAMbMTl2lS5YifaEgRjU008u5LlNK4ujuhQcrpa1aAtQTAd6pjRlsAfkq+8KCErKwvHjh2Dh4cHpFIp2rdvjz59+mDfvoL7E/bu3YuxY8fC1tYWRkZGcHNzw549e3i1c/z4cdja2mLQoEHQ1dXFkiVLEB4ejsePH3Ntz5o1CzVq1ED16tUxa9Ysrm1VMHXqVHh5eSEoKAhHjhxBYGAgduzYgSlTpvBug/cGLm9vb1y7dk3B3alHjx7o2LEjFixYIEz5V8SzhY60JQjmxW132hIE8ey5uHZ/A8DtyKe0JQjm1uMY2hIE8VfoI9oSBHP+3hPaEvhBCJCv3LUpOTkZ9vb23N+urq5wdf0vKmFkZCS0tLRgbW3NHbOzsys0219ERAT69u2rUC8pKQkpKSmIj48vtp2IiAjY2f23bKGnp4e6desiIiIC9evXL3Dezs4OERERSu+PLwkJCQVibvTr1w/jx4/n3QbvkXFWVhZMTBR/OVeqVAk5OTm8L/Y1ciPmNW0JgrkWKC5DceNaCG0Jggn096ctQTBBN2/QliCIu7du0pYgmJAAEWnmMTI2MTFBcHAwVz41xIAs57ShoWIsfENDw0IDYHxeV/46IyNDaTtCzxsaGiIzM1Nl68bOzs7YunWrwjFPT084O/MPf8rbGDs6OmL48OF48uQJcnJy8PjxY4waNQo9evTgr/grZGdgLG0Jgtnhe4u2BEHs9P6TtgTB+O7m719YWji0R1yaT+7fQ1uCYI7v201bAk8IkJ+vvChBKpUiPT1d4Vh6ejr09fWV1pW/1tfXV9qO0PPp6emQSqVKMynx5e7du9w0eKtWrVCjRg3MmjULoaGh6NixI1eKg/c09ZYtWzB16lTY2dnhw4cP0NbWxuDBg7F58+YS34iYOe7ShrYEwfy1S1zJLY6f3EhbgmB2/SGu0JIAsOOwuFJrrvMRn4//r/uP0JbAHx5rwsqwtrbGx48fERUVBSsrKwBAeHh4gc1bAGBra4vw8HAMHjyYq2dqaopKlSpBV1e32HZsbW2xd+9erq2srCxER0crnA8PD0fLli2L1fCljB8/XtCUdGHwHhkbGBjAx8cH2dnZSExMRHZ2Nnx8fLjwmN8qzgfu0JYgmOE/7lVeqRThPGIhbQmC+dFlNG0JgpkxVlwBfBZNHkdbgmDmTxRJZixCgPyPyosS9PT00L9/fyxatAhZWVkICAjAyZMnMXLkyAJ1nZ2dsXPnTjx8+BBpaWlYtmwZRo8ezaudfv364cGDBzh27Bhyc3Ph7u6Oxo0bc+EpnZ2dsWHDBrx48QIJCQlYv34917YqGDVqlNISFBRUbBuC9u9nZ2fjwYMHePr0KQIDA3Hr1i3cuiWuKU9V872NuGLjAkCvrqr7RagJvu/VnrYEwXR17ElbgmC6iExzu27iWyLr0F1EGz5VME0NANu2bUNOTg6qVKkCJycneHp6wtbWFvHx8ZBKpYiPjwcgWwqdPXs2OnfuDHNzc5ibm2Pp0qVK2wEAExMTHDt2DAsWLICRkRGCgoJw6NAh7r0TJkxA79690ahRIzRs2BC9evXChAkTVNhZytm/f3+x53n7Gfv4+GDq1KnQ0dFB+fLl/2tAIuE6szTD/IzVD/Mz1gzMz1j9fPN+xs3qIvi68uiK9p1XKvUzZsjQ19cvNnMT7ydu9uzZOHbsGF6/fo3nz59zRQyGWJ3o/iK+zUVlzKfTliAI3bLiyxltrq9HW4Jg6hqWV16pFNHGrCJtCYJpbmpAWwI/CFTiZ8z4D2WbxXhv4NLR0YGDg0NJ9Xx15K7tR1uCYPLjNtGWIIjcj+Jbl4/LyKItQTDRb8Xlpvh3whvaEgQTkpSuvFKpgIDwWBNmqA7eI2MPDw/MnDkTr1+Lz69WnRwKfU5bgmB8T4hrWunQwfO0JQjmxBHx7fT9649DyiuVIi4c/4O2BMGcOyaS3dQEKlszZshQtiLM2xhbW1vjr7/+gqmpKbS0tKClpYUyZcp884kizj58SVuCYM5cUV3kGU1w9oyIAiX8y5Xz52hLEIyfyDQHXL5AW4Jg/C+J5YclYdPUShgyZAj3evdu5f7jI0aMKPY87w1clpaWcHJywpAhQxQ2cAFA3bp1+TRBFbaBS/2wDVyagW3gUj/f/AauJha4c3mp0notHDd/sxu4KlasiLS0NEgkEhgYGBQIOiIU3k9cSkoK3N3d0bBhQ9StW1ehfMv03/U3bQmC6ePiRVuCIPr3/Ym2BMG4DBpIW4Jgxg8ZoLxSKeJn5yHKK5UyZowYTFsCT1TjZ/w106FDB7Rp0wbOzs7Izc2Fs7NzoYUvvDdwjRkzBvv27RPU+LfA2Na1aUsQzPhh4hrBjh0nvk1yw8aIJLjDJwwdLS7NfUeMpi1BMP1HiiPlLLdmzCiSP/74A0ePHkVcXBwkEkmJB6a8jfHt27exZcsWLF++HKamiunhbtwQV4B5VdKxTmXaEgTj0NqStgRBdHRoTluCYFp36EBbgmBatS8+dm5po1lb8QWDad5OLJrJN78mrAxvb29MnToVgCxr1OLFi0vUHm9jrIrYm18jFsvO4/Wy3rRlCKJ6y0VIf6jcob+0YFHze7x+UzDlWmmmpbUlHiYm0ZYhiLb16+Dei2TaMnjTp2kDXIn6h7YMQTg2rgf/mATaMvjBjHGxLFiwgDPGp0+fLnF7vI3xqFHK49ZOnjwZ27ZtK5EgsSE2QwxAVIYYgOgMMQDRGWIAojLEAERniAGIyBATIE95PuNvmbp162LWrFmwtbXFhw8fsGtX4VnPXFz4Lf+odMugstibXyPegc9oSxCMl8hSKHrvOE5bgmB8i/iPWZo5uHsnbQmCOCHGFIo+YkmhCObapIRDhw7h7du3OHjwID58+IB9+/YVKEJsokqNsaoSNYuJu/+8oS1BMMH3xBXC9G7II9oSBHMvVHVuJprivsg0Pw4PpS1BMA/FpJkZ42KxtraGt7c3Ll26hE6dOuHq1asFip+fH+/2ePsZ80EVvlbqgvkZqx/mZ6wZmJ+x+vnm/Ywb1cSdv2Yqrddi0IFv1s9Y1YjriROIl5cX7O3tYW9vj6evM+FxUTbCarj6IqKSM3D3nzS0+fUqAGDOqfv49XoUAMDC4xwS3ubgenQyunv6AwAmHw3lpqQrLzyFjNwPOPMwEWaLZQv3zgfucKEx5ckjDoU+5/Id99/1N848TERG7gdUXngKgGyKe/JR2S/l7p7+uB6djIS3ObDwkEVCWu/lh1kesrbse61FyP3niIx5hXoOywAASzaew5KNsrr1HJYhMuYVQu4/h32vtQCAWR5/Yr2X7JdZ9RZuSEh6i2t/R8Go4RwAgOvcQ9yUtYHNbGRk5uLU5QecH/LwH/dyoTPlySV8TwRz+ZD7uHjh1OUHyMjMhYHNbNk97TiOyROXy+6pywRcvxaChIRkWNSUpef7dcN+zPl5IwCgTcuRuBvyCFGRcWjYQObj6rHUCx5LZddv2GAAoiLj0LrFSLRpKctbOufnjfh1g2zqx6JmTyQkJOP6tRB07yJLhzZ54nJuWrtyxU7IyMjCmVM3OF9l5xELufCa8gQUJ44c5vIPuwwaiMtnzyIzIwM21WReA767dmHuj7KNGkN6OuJv/xtISkxECyuZK4PX5k3wmDcXANCrQzvcDw1FH4eOcGhiBwDYuGI5Nq6Q9YlDEzvEREXhfmgoenVoJ7vneXPhtVkWL7yFVV0kJSbib/8bGNJTlm5v7o9TuWlvm2qmyMzIwJVzZzi/4BljR3GhLOXJHv764xCXn3j8kAG4cu4MMjMy0Li6CQDZlPT8aVMAAMN6fYdA/xsY2K0T2tSzkH2Ov/2KFQtkz0mfjm1xP/Qunj2NQtdmjQAAm1Yuw6aVsuewa7NGePY0CvdD76JPR9kPshUL5sD7t18BAL2b1kfyy0TcveWPyQN6AQBW/TKdm2bualUDWZkZ8L94jvMdXjR5HBfuUp4Q4sLxP7gcxj87D8Hwzm2QlZmBrlY1ZJ/j/j1Y9YvsOZ08oBfu3vJH8stE9G4qy2nru/03bF66AAAwukcnPL4XhvjopxjcXrZb33vdSnivWwkA6NemKeKio/AoPBTDu8t2mW9YPB/7PH8DAPRobI3kl4kIDvCHa7/vAQDLZk3jpqE71DFDVmYGblw4x/kWz5/ogoHt7QH8lzDi3LEjXI7jGSMG48aFc8jKzECHOmYAZNPay2ZNAwC49vsewQGye+rR2BoAsM/zN2xYPB+qhwAfPyov3zANGjTgXtesWRO1atVSKPJjfFHpyFhZiiiaqGtkfD06GZ3qmqi8XUB9I+Nrf0fBoY2VyttV18j4+rUQdFKTe5O6RsZ/+99Amw7qcRVS18g40P8GWqtBs7pGxndv+aNZW/W4kKlrZBwc4A/7dqrXrPKRccMauHNsmtJ6LYYf+WZHxjdv3kT79jJXtevXi95k2qlTJ17t8d5NzQdlsTe/RqwqS2lLEIx1nSq0JQjCypr/r8vSQh1L1f/YUTcWluLyP69ZR1x6AcC8rog0s6AfxXLx4kVcvHhRaT2+xljQz79du3ahe/fusLW1Rffu3bFz506FTVuenp5CmvsqaLf5Gm0Jgmnxv3W0JQiiXSvxRX37X0exBHf4jx8c2tGWIAiXnuLaAwLIRrCigBCNZW1KTU1Fv379oKenB3Nzc/j6+hZbf+PGjahatSoMDQ3h4uKCd+/+m3kprq3AwEB0794dxsbGMDExwaBBg5CYmMidX7JkCbS1tSGVSrkSExNTpI7nz59zJSoqCqtWrcKVK1fw9OlT+Pn5YdWqVYiKiuLdD7yN8ezZs7F69Wr0798fa9euxYABA7Bu3TrMmTOH98W+Rp659aQtQTAv7njQliCIZ8/FlU0IAO5ERdOWIJi/n4jLTe9U6GPaEgRz4V4kbQn80dCa8ZQpU6Cjo4OkpCQcOHAAkyZNQkRE4ZnlLly4wBm92NhYxMTEKES+Kq6ttLQ0uLq6IjY2FnFxcdDX18eYMYrhSYcMGYLMzEyu1KlTp0jdu3fv5gohBAcPHkRAQAB8fX1x8+ZNHDokLCUpb2O8Z88eXLlyBZMmTcL333+PiRMn4uLFi7xSR33NyDd9iQn5pi6xIN+wJSbkG7LEhHzDlVjw3f4bbQmCkW8AK/VoaGSclZWFY8eOwcPDA1KpFO3bt0efPn2wb9++Quvv3bsXY8eOha2tLYyMjODm5oY9e/bwaqtnz54YNGgQDAwMUKFCBUydOhUBAQElvgcAOHfuHH744QeFY3379sXZs2d5t8HbGOvr60NfX7/AMQMDA94X+xpJTM+lLUEwCUlvaUsQRGKCuCJDAUDSJ9NfYuHVS3Fpfp0kvlziyWLq43yivJSQyMhIaGlpwdramjtmZ2dX5Mg4IiICdnZ2CnWTkpKQkpIiuK0bN27A1tZW4dipU6dgbGwMW1tbQcuulpaW2Lp1q8Kxbdu2CUoewXsD14wZM9C/f3/MnTsXNWrUwPPnz7F27Vr89NNPCvPqxQ3rv0ZW925EW4Jg1ruJKwvS6nXiS6HotnIVbQmCmb98NW0Jgpi2eDltCYKZuXQFbQn8IITXNHRycjLs7e25v11dXeHq6sr7MpmZmTA0NFQ4ZmhoWKRXzuf15a8zMjIEtXXv3j24u7vj5MmT3LHBgwfD1dUVpqamCAoKwoABA1CxYkU4OTkpvQ9vb2/069cPa9asQfXq1fHixQuULVsWx4/zjx7Ie2Q8ffp0XL16FT169ICtrS0cHR1x5coVTJs2DZaWlrC0tISVlfh2kJYUuZ+ymJD7IYsFuY+xmJD7EIsJuY+wWBjdQySboT5B7rMsBgjJV1pMTEwQHBzMlc8NsYODAyQSSaGlffv2kEqlBQJFpaenF5iFlfN5fflrfX193m09ffoUPXv2xKZNm9Dhk+xqNjY2MDMzg5aWFtq2bYvp06fj6NGjvPqqadOmiIqKwsGDBzFz5kz4+voiKioKzZo14/V+QMDIOJ9tcy+UrQOb0JYgmN9XDaUtQRBbPdUR1EC9rNq8hbYEwSzfJC7Nc9eKb11+4TqRaJavGZeQa9euFXs+KysLHz9+RFRUFDeYCw8PLzB9LMfW1hbh4eEYPHgwV9fU1BSVKlWCrq6u0rbi4uLQrVs3uLm5YeTI4n/kSyQSQSGetbW1FYy7UL7qCFyaQL+cSl21NYK+XjnaEgShr1+BtgTB6EnF538uLWI0UlqpoCe+Pq4gpudCAxu49PT00L9/fyxatAhZWVkICAjAyZMnizSUzs7O2LlzJx4+fIi0tDQsW7YMo0eP5tXWixcv0KVLF0yZMgUTJ04s0PbJkyeRlpYGQghu376NzZs3o2/fviW+R77wNsbx8fEYO3YsmjVrBmtra4XyLdNv19+0JQim97/hLsVCv77KY+SWNlwGDaQtQTDjBvenLUEQP48aQluCYOShMUs9hAAf85QXFbBt2zbk5OSgSpUqcHJygqenJzeajY+Ph1QqRXy8LLmNo6MjZs+ejc6dO8Pc3Bzm5uZYunQpr7a8vb0RExODpUuXKvgSyzl06BAsLS2hr68PZ2dnzJkzh1fqYFXBOxxmq1atUL9+fQwaNAjly5dXONe1a1e1iFMlLFGE+mGJIjQDSxShfr75RBH1THHbU/nGpZazb36z4TBVDe8n7vHjx9i9ezf+97//oWvXrgrlW0aefEJMyJNLiAV54ggxIU8MISbkiR/Egjypg5j4fa1YdlNDYxG4GDJ4G+PevXsXGwybwWAwGF8LmguHyZDBe5o6NTUVbdu2Rd26dWFqaqpwbte/6d1KM2yaWv2waWrNwKap1c83P01tZYLbmwYorddyUTCbplYRvJ+4MWPGQEtLCw0aNED16tUVyrdMw9XKs3aUNuT5kMWCPNexmJDnMhYT8lzFYkGeh1hM9GvTlLYE/mggAhfjP3j75fj5+SEhIaFIZ+xvlT9d2tCWIJhTu/hHyCkN/HlyA20Jgtn1B79gAaUJ7yP8owWVBtbtPUxbgmB+3X+EtgR+yNeMGRqD98i4cePGSElJUacWUZLxTjWZSzRJhsimDTMysmlLEExWZiZtCYLJLCIEYWklO0t8fZwtmueCrRlrGt7GuEuXLvjuu++wcuVK7Nq1S6F8y0w5GkZbgmAmzBWW2os2UyaJZAfqJ8ydNpW2BMEsmC4uzat+mU5bgmCW/SwSzQQgH/KUFobq4L2Bq3Pnwjc/SSQS+PmV/pR8bAOX+mEbuDQD28Clfr75DVx1KyNoRS+l9VqtjWAbuFQE7yfu6tWrhRYxGGJ1MufUfdoSBDPL40/aEgQx5+eNtCUIxmPeXNoSBLNiwRzaEgSxeekC2hIEs2GxSOKsEwLk8SgMlSEosHJKSgrOnj2Lly9f4pdffkFCQgLy8/NRo0YNdelTGWn/5ODYL4XntSwJyXiDYzdU3y4ADLWtqpZ2zfAOJOKxytsteydU5W0CQPXUSJQ9sVV5xS+gWj/1bGazMieoViFcLW1nfKimlnbNa0hRsZzqcwSblM9ReZsA0LBOBdhWTlBL21LJP2ppt0md92hqHKjyditoqXYtmgAgbLe0RuE9Mr5+/Trq1auHAwcOwN3dHQAQFRWFSZMmqU2cGOgFY9oSBDOzfxPaEgQxs09D2hIE89PMYbQlCGbKjPG0JQjix5/G0JYgmFk/iSRmeT6A93nKC0Nl8DbGM2bMwOHDh3H+/HmULSsbULdq1Qq3b99WmzgxMAXRtCUIpsaIvbQlCKLmePG5sJjX6ENbgmBsLFrRliAIa3Px5TOuXkss6UsJSL7ywlAdvKepY2NjuTjUEokEAKCjo4OPH8Xn2qNKlsGctgTB3N4kkl/n/xK0ujdtCYL5+/ZO2hIEcyXgL9oSBHH97z9oSxDMnUD1LLeoHAK2JqxheI+MbWxscOHCBYVjly9fRqNG4orao2oS8Z62BMFEvnhDW4IgIhPe0pYgmKjI57QlCCY66hltCYJ4GhVLW4JgIqPUsxatFlgELo3C2xhv2LABw4cPx6hRo5CTk4MJEyZg9OjRWLt2rTr1lXqOQ3yBUNwPiMsVwf1IGG0JgvFwF9/IeM2yX2lLEMRKD5GMMj9hqfs+2hL4QQjzM9YwvI2xv78/7t27B1tbW7i4uMDCwgK3b9+Gv7+/OvWVehaiJm0JgvFb3Ze2BEH4ufekLUEwl/3EZyj+uiSuYDBnL4tr7wMAXL2yjrYEfsinqZlrk8bgbYzd3d1hZmaG2bNnY+vWrZg7dy5q1KiBZcvElXRA1XgjibYEwUzYfI22BEFM3H6LtgTBTJqwirYEwfw0eR5tCYKYNmkxbQmCcZ0oHp95TW3gSk1NRb9+/aCnpwdzc3P4+voWW3/jxo2oWrUqDA0N4eLignfv/gsqU1xbsbGxkEgkkEqlXPHw8PjvfgnBnDlzUKlSJVSqVAmzZ88Gz5hYKkHpBi55UI+8vDxcvXpVQVxMTMw3nziiDsrRliCY5lZVaEsQRPO6lWhLEEzz5vVpSxBME5FlbWra3Ja2BMHYN7emLYEfGtzANWXKFOjo6CApKQlhYWHo1asX7OzsYGtb8PO9cOECVq1aBT8/P5iZmaFfv35YvHgxVq1axbutN2/ecB5Bn+Ll5YUTJ04gPDwcEokE3bt3R506dTBx4kT13fwnKA2HaWFhAQCIj49HrVq1/nujRIKqVati7ty56NOn9Ltx1JHoim7n89CzXWlLEATJVk9wB3WSr6agH+pEXUE/1IV2GfE9F+oK+qEuWrSaguCQSJW117x6RQRMVu461v7PFyUKh5mVlQUjIyM8ePAA1tayHyojR45E9erVOQP7KcOGDUPt2rWxYoUsXv2VK1cwfPhwvHz5UmlbsbGxsLCwwIcPHwo1xm3btsXo0aPh6ir7Tti5cyd27NiBwEDVB2kpDKXT1M+ePcOzZ88wfPhw7vWzZ88QExODW7duicIQq5OxiKItQTCGA7xpSxBExRH7aUsQjLFhN9oSBFOrsriCq5gZ29OWIBgDIxHt18jLV15KSGRkJLS0tDjjCQB2dnaIiCg8qmFERATs7OwU6iYlJSElJYV3W+bm5qhRowbGjBmD169fF9t2UTrUAe81Yx8fH3XqEC1bUJe2BMH8s8+ZtgRBPN8xhLYEwcT9c5K2BMFEPNPMCEBVPIm7TluCYF7EH6QtgReE8FszTk5Ohr29PVe8vLwEXSczMxOGhoYKxwwNDZFRRDrPz+vLX2dkZChtq3Llyrhz5w7i4uIQEhKCjIwMDB8+vNi2MzMzNbZuLK7UJKWQRxBfrt1r99UTz1ddXItQfbxkdXP9mnridKuTgBviMsY3r4sv+t+16+qJV656+CWKMDExQXBwMFfkU7xyHBwcIJFICi3t27eHVCpFenq6wnvS09OL3Iv0eX35a319faVtSaVS2Nvbo2zZsjA1NcWWLVtw8eJF7j2FtS2VSrkgV+qGGeMS4gfxBaTYce4hbQmC8L70hLYEwXjvEN/I2GenOEZtcnZ7iy8C1w7vs7Ql8IMA5EO+0qKMa9eugRBSaLl58yasra3x8eNHREX9t9wXHh5e6OYtALC1tUV4eLhCXVNTU1SqVElwW3IjKx/5FtZ2Ue9VB8wYl5CfUZ22BMH8teR72hIEcXKe+NZfT/wlvmA4vsfFFajkyIlttCUI5q8THsorlQYINBKBS09PD/3798eiRYuQlZWFgIAAnDx5EiNHjiy0vrOzM3bu3ImHDx8iLS0Ny5Ytw+jRo3m1FRQUhCdPniA/Px8pKSmYNm0aHBwcuKlpZ2dnbNiwAS9evEBCQgLWr1/Pta0JmDEuIVuQSFuCYIavvkRbgiBG/Cq+tcGRw0XoA+s8nbYEQbiM/IW2BMEMH7mStgSeEI1s4AKAbdu2IScnB1WqVIGTkxM8PT25EWl8fDykUini4+MBAI6Ojpg9ezY6d+4Mc3NzmJubY+nSpbzaiomJgaOjI/T19dGwYUOUK1cOBw/+Nxs0YcIE9O7dG40aNULDhg3Rq1cvTJgwQSX3yAdB+YwZBWkKPdoSBNOrpbhcvHo1F1+Us+97taMtQTDffd+FtgRBOH4vvqxNvb4XSWYsorl8xsbGxjhx4kSh52rVqoXMTMVczTNnzsTMmTMFt+Xk5AQnJ6cidUgkEqxZswZr1qzhpVvVMGNcQtrBgLYEwQzrLJLAA//i1KEObQmCcRr2HW0Jghk4VERuNwAGO/2PtgTBDHMSyQ+ef9eMGZqDTVOXkOFQnaO9ptD63pO2BEGUHbiHtgTB6Gi1pS1BMJV0LWhLEISBjg1tCYIpoy2eH2kkjygtDNWhMWO8ZcsW2Nvbo1y5cgqL4srihX6O0Dim6uYAxDXKBIC8s5NoSxDEx6OjaUsQzPs88cXTTskVVwrF9Pfi8goAgPwPF2lL4AUhyn2MNTWN/a2gMWNsZmaGhQsXwsXFpdDzb968QWZmJjIzM+Hm5lZkO5/GHj1w4AAmTZqk0SgpnxOAdOWVShm+V8U1mj/oH0NbgmAO+orjS/dTjh4SlzvWkYOnaUsQjO9BP9oSeJOfR5QWhurQmDHu378/fvjhB1Sq9OVB/7OysnDs2DF4eHhAKpWiffv26NOnD/bto5cjNBRZ1K79pZy5HUdbgiDOhDynLUEwZ88E0JYgmItnxWMoAOD8WfHtsj9zNoi2BF4QAuR/zFNaGKqj1KwZFxUv9FOExjHVBFMhrqD9AHBgTnfaEgSxf4b4ds3uO7BUeaVShpfPJtoSBLFrn/h8uQ/sE0maSqJ8vZitGasW6sZYWbzQTxEax9TLy4uLmfoSH3AMMiM/C8+QiPd4hlwsgGyUuB/JOINUAMAURCMNH/EQ2VgG2ajMG0nwwxsAsuQQOcjHXWRiEqIByPyN5VPW8k1dAUjn/JDX4QXuIhM5yOeSS/jhDZcPeRme4yGykYaPmPJvmxuOh+HnHbK1xxbT/kBIVDIi/3mD+uNk6+RL99/B0v13AAD1x/ki8p83CIlKRotpsshEP++4hQ3HwwAANUbsRUJKFq7dewGTIbsAyPIae/0bjctwgDcyst/jVFAs+iyRRQkavvoSN6Ut3/TlezWS81Pus+QsTgXFIiP7PZd8YselJ1z+4S6LzuHag0QkpGaj5vjDsnv66wF+3ivT3HL2KYREv0Zkwls0+PG47J4Oh2LpYVkoyQY/Hkdkwls4LDyLlrNPye5p7x1s+OsBAKDm+MNISM3GtQeJ6LLoHABZ7uMd/0bsqjhiPzJyPuBU8HP0XXkZgMxnWT7tLd8YdtD3IucX/EOfX3D61E1kZGRxyR68vU5w+Ym7dZmC69fuIiEhGeY1ZElSNm7wxeyfNwMAWrUYg7shj9Gty1TY1JfF1HZf6g33pbL+sak/BJGR8bgb8hitWowBAMz+eTM2bpB9puY1+iAhIRnXr91Fty5TAMhyI3t7nQAgS0CRkZGJ82cuY1j/sQBk/sHyKWb5Jqyjh05yfsPD+o/F+TOXkZGRySWD2Ovty+Uv7tN9KG5eD0T/70fAxkLmerP11x1wmyPLVd6lTW+E3b2Pp1ExaNmwMwBgtcevWO3xq+xzbNgZT6NiEHb3Prq06Q0AcJuzDFt/3QEAsDbvhMSEV/C/fhvfdxsFQJaLeLf3EQCyhA8ZGVk4d/oqBv8wGYDMh1g+DS3fqHXk4GnOt3jwD5Ph0GYwMjKyuIQRu72PcDmOv+82Cv7XbyMx4RWszWU/5n7buBvzZ68GAHRsNRChdyMQFRmLpjY9AQAr3LdghfsWAEA9mzGIjPwHISGRsG8p0zTrl9+xfuNRAED1WkORkJCCa9fD0bnrz7LPYeJGeO04I9Ns1BcZGdk4dfpv9PlBtuw2fORKNG8h268h38jle9CP8z3u84MbTp3+GxkZ2VxCCa8dZ7gcyJ27/oxr18ORkJCC6rWGAgDWbzyKWb/8DnXA1ow1i9IUiqpm4cKF+Oeff7Bnz55Cz798+RLVqlXD27dvYWCg6DYUGhqKdu3aITv7v3jQ69evx7Vr13Dq1Klir6uuFIp3kYlmkKq8XUB9KRRPBcWid6vaKm9XXSkUTwU/R2979fgaqyuF4ulTN/G/3u3V0ra6UiieP3MZjr1UH+1MXSkUz52+ip7/66yWttWVQvHU6b/R+39tVN6uqlMoNjGW4tJ3jZXW6/n0fYlSKDL+g/rI+HM+jxf6KUJjj2qCBqhA7dpfikMjM9oSBOFgW5W2BMF0cmhKW4Jg2nVsTVuCINp3aklbgmAcOtkpr1QKIATI+5ivtDBUh8aM8cePH5Gbm4u8vDzk5eUhNzcXHz9+VBov9FOExjHVBFP/nVIWEzVGiisdpnyKW0yY1xBXAA0AsLUQlzGuZy6+vQTVaxUdAap0wdaMNY3GjPGyZctQvnx5rFq1Cvv370f58uWxbNkypfFCV6xYgZ49e3J/Fxd7lAY7YUXt2l/K22PjaEsQxJv9I2hLEEzq28u0JQgm/vUD2hIEkZAqvunR9DSRuI/xzGfMUB0aM8ZLliwpkEJryZIlcHJywrNnz5CVlYXExET4+PigatX/piXnz5+Pc+fOcX/LY49mZWUhPj4ew4YN09QtFIp8U5eY8BJZCsUdYkyh+O+GKzGx15tuAB2hyDeAiQn5Bq/SDiFA/od8pYWhOkrdmrHYiME72hIEExL1irYEQYREp9CWIJiQkMe0JQgm7O592hIEERpCz6XxS1HlJiv1QkDy85UWhurQ+G5qWqhrN7U6UdduanWhrt3U6kRdu6nVibp2U6sLde2mVifq2k2tLlS9m7qxQQWcblVPab3+aVpsN7WKYCPjEiL3QxYTXeaIZN3qX+Q+xGJC7iMsJvp0H0pbgiDkPstiQu6TLAbYmrFmYSkUS0h/fHl4T1osGm5PW4IgFg1uQluCYNwWjaUtQTCzF86gLUEQ89zE94Nn8SJ6nh9CIATIY2vCGoWNjEtINejQliAY6+oVaUsQhLVZQTe30o6VtXqClKiTulbiSqFoaVWbtgTBWFvVoC2BHxrM2iQ0E9/GjRtRtWpVGBoawsXFBe/e/bdvp7i2Dhw4oJAdsEKFCpBIJAgJCQEg22Ssra2tUCcmRnNJapgxLiELIa6kCwDQcvpR2hIE0WpO8dHVSiNtWopvZNy1XR/aEgTRqc0g2hIE06K1eEbzmvIzFpKJ78KFC1i1ahWuXLmC2NhYxMTEYPHixbzaGj58OJcZMDMzE9u2bUOdOnXQrFkz7v1DhgxRqFOnTh2V3CMfmDEuIVtRl7YEwfyzX1xrbc93DKEtQTBx//xFW4JgHj4TR0YhOZFx4sva9CL+EG0J/NCQn7HQTHx79+7F2LFjYWtrCyMjI7i5uXGhlb+kLWdnZy7qI22YMS4h8uQSYkKePEIsyBNDiAl54gcxIU/sIBZ+27ibtgTByBNNlHY05WcsNBNfREQE7OzsFOomJSUhJSVFUFtxcXG4ceMGnJ2dFY6fOnUKxsbGsLW1haenZ0lvTxBsA1cJSYP4cnompGQrr1SKSEgTn2tMYkLhaUBLMy8Tk2hLEERiorj85QEgIUEsPvME+TxGvskpybC3/29DqKurK1xd+bsLCs3E93l9+euMjAxBbfn4+KBDhw6wsPhvn8TgwYPh6uoKU1NTBAUFYcCAAahYsSKcnDQTwpSNjEvICJjQliCYdePb0pYgiHWjWtCWIJg166bRliAYj9ULaUsQxIo1c2hLEMz6tRNoS+AFAZCfr7yYmJggODiYK58bYgcHB0gkkkJL+/btIZVKkZ6ervCe9PR06OvrF6rr8/ry1/r6+oLa8vHxwahRist1NjY2MDMzg5aWFtq2bYvp06fj6FHNzWQwY1xCFohwA5c837FYkOcyFhPyXMViQp6LWCx0bDWQtgTByHMjl3oIP2OsjGvXrhUIgywvN2/eFJyJz9bWFuHh4Qp1TU1NUalSJd5tBQQEICEhAQMHFv/8SCSSQrMHqgtmjEvIOJjSliCY7T860JYgCM8Jqs//qm48t4tv1LZh6wraEgSxyXMpbQmC+d1zBm0JvCAAPuYpLyVFaCY+Z2dn7Ny5Ew8fPkRaWhqWLVuG0aNHC2pr7969GDBgQIER88mTJ5GWlgZCCG7fvo3Nmzejb1/NZV9jxriE6IqwC/XLa9OWIAix6QUAqb748lxL9fVoSxCEVCouvQCgL5bnQkUjYz4Ul4kvPj4eUqkU8fHxAABHR0fMnj0bnTt3hrm5OczNzbF06VJebQFAbm4ujhw5UmCKGgAOHToES0tL6Ovrw9nZGXPmzCm0nrpgsalLyCw8w3qoJ1iCumJT1x/ni8feqs92pa7Y1A1+PI5Hv/VXS9vqik1tU38IHj5WTx5mdcWmbtmwM24/uKrydtUVm7qpTU+EPlRPqFR1xaauZzMGTx6qfhe4qmNTN9DVhU/N2krrTTKUstjUKoLtpi4h6jLE6kQdhlidqMsQqxN1GWJ1og5DrE7UZYjViToMsVogqhv5MvghvjnWUsYxiM+FZen+O7QlCGLp4VDaEgTjvtSbtgTBrPb4lbYEQaxw30JbgmCWuPvQlsALAuDjR+WFoTq+mWnqypUro3bt2ipvNzk5GSYm4nJvEptmsekFmGZNIDa9gPo0x8bG4vVr1Q0M6uvoYodpbaX1fjJl09Sq4puZplblg/op9vb2onsYxaZZbHoBplkTiE0vIB7Ncj9jhub4Zowxg8FgMHjC1ow1DjPGDAaDwVCAAMhTUVYmBj+YMS4hQuKwlhbEpllsegGmWROITS8gIs1sZKxxvpkNXAwGg8Hgh3VZXWw2UB6XYWEdfVGsgYsBNjJmMBgMhgJsA5fmYcaYwWAwGAoQwvyINQ0zxgwGg8FQhK0ZaxxmjBkMBoNRgHy2m0ijMGPMYDAYDAXYmrHmYbGpGaUKLy8vtG3bFoaGhtDS0oKhoSHatm2LHTt20Jb2VcH6WTOkpKTAy8sL06dPh4uLC6ZPnw4vLy+kpKTQllYs8jVjFptaczBjLBD2JaY+5syZg02bNmHcuHHw8/PDkydPcPXqVYwbNw6bNm3CvHnzaEv8KmD9rBmuXLkCS0tL7N+/H/n5+TAzMwMhBAcOHICVlRWuXi3dWbI0lc+YIYP5GQtgzpw5OH36NGbNmgU7OzsYGhoiPT0dYWFh2LBhA3r37o2VK1fSlilaTExMcO/ePVSrVjBfb0JCAho3bqy2GOPfEqyfNYONjQ2WLVuG/v0LpgD9888/MX/+fDx69IiCMuU4OjryegYqV66M8+fPa0DR1w8zxgJgX2LqpXLlyrh//36R/duoUaNSP70nBlg/awY9PT2kpqaiXLlyBc69e/cORkZGyM7OpqCMURph09QCUPa7hf2uKRljx45Fly5d4O3tjTt37iAyMhLBwcHYuXMnunfvjvHjx9OW+FXA+lkztGrVCgsXLkRWVpbC8aysLLi5uaFVq1aUlDFKI2xkLIA5c+bgr7/+KjBNHR4ezk1Tr1q1irZMUfP777/Dx8cHERERyMzMhFQqha2tLZydnTFhwgTa8r4aWD+rn7i4ODg5OSE0NBR16tThvi9iYmLQpEkTHDp0CLVq1aItk1FKYMZYIOxLjMFgCCEyMhIPHz5U+L6wsrKiLYtRymDGmFHqiIyMREREBDIyMqCvr4+GDRuyLy81wPqZwSg9sKAfXwD7ElMP8fHxGDJkCMLDw1G3bl1uWi86Ohp2dnZsWk9FsH7WHF5eXtizZ0+BmbQxY8awtXmGAswYC4B9iamXMWPGoEOHDrhy5QoqVKjAHc/KyoK7uztGjx4NPz8/igq/Dlg/awZlrpAxMTHMFZLBwaapBdC1a1c0b94cS5YsKfRL7M6dO+xLrARIpVKkpqZCR0enwLl3797B2Ni4wM5UhnBYP2sG5grJEAJzbRJAUFAQli1bpmCIAZk/obu7O4KCgigp+zqoWbMmTp8+Xei5s2fPslkHFcH6WTMwV0iGENg0tQDkX2KFRdRhX2IlZ8uWLRgwYAA2bNhQYFovIiICx44doy3xq4D1s2aQ+3MX5QrJ1owZn8KmqQVw5coVDBgwAA0bNizyS6xLly60ZYqa169f488//1TY8NKwYUP88MMPqFy5Mm15Xw0pKSk4fvw462c1w1whGXxhxlgghX2J2draol+/fuxLTE3Y29vj4sWLMDY2pi3lqyA/Px/btm1DREQEHB0d0bdvX8yZMwfnzp1DkyZNsGHDBvYsMxgahhljFZGXl4fly5dj0aJFtKWIFmdn50KPHzt2DL169YKuri58fHw0rOrr48cff8T169fh6OiIc+fOoUWLFkhNTcWYMWOwd+9eaGtr4/Dhw7RlfvXEx8ezpS0GBzPGKuLdu3eoUKEC8vLyaEsRLeXLl0fLli3RtWtXhc0t69atw8SJEyGVSrF48WKKCr8OzMzMEBYWhipVquDFixeoVasWXr9+DSMjI7x58wbW1tZ49eoVbZlfNez7gvE5bAOXAFxcXIo895Fl2i4x9+7dw9SpU/Hw4UOsX78e1atXBwBs374dv/zyC6pUqUJZ4ddBbm4ujIyMAADGxsYoU6YMpFIpAEBfX589yyrixo0bRZ579+6dBpUwxAAzxgLw9fXF2LFjC127ZL9wS46VlRUuXLiAQ4cOoUuXLhg/fjxmzJgBiURCW9pXRZs2bTBhwgQMHjwYBw8ehJ2dHdavX48pU6bA09MTdnZ2tCV+FTg4OKBatWooU4Z5kDKUw6apBdCiRQu4ubmhT58+Bc7l5uaiQoUKyM/Pp6Ds6yM9PR2LFi3C5cuXERcXh+joaDYyVhFxcXGYPHkynj17hhkzZqBjx47o0aMH/vnnH1hYWOD48eNo3LgxbZmix8LCAgcOHEDbtm0LnMvNzYWenh77Ec/gYCNjAYwePbpIY6utrc3WM1WIgYEBfv31V4SFheH69eswMDCgLemrwdzcHGfOnFE4Fhsbi9TUVFSqVImSqq8Pe3t7BAcHF2qMy5QpwzZvMRRgI2MGg8FQAx8+fAAg+6HOYCiDLWYwGAyGGtDW1i7SEOfl5cHd3V3DihilGTYyZjAYDA3DXJsYn8PWjBkMBkMNMFdIhhCYMWYwGAw1wFwhGUJg09QMBoOhBpgrJEMIbAMXg8FgqAHmCskQAhsZMxgMBoNBGTYyZjAYDAaDMswYMxgMBoNBGWaMGQyB1K5dG5cvX1Za79q1a6hRo8YXXSM2NhYSiUSpC4yDgwO8vb0LPRcfHw+pVMp27jIYIoC5NjEYXym1atVCZmYmbRkMBoMHbGTMYDAYDAZlmDFmiILatWtj5cqVsLGxgZGREcaMGYPc3FwAwI4dO2BpaQljY2P06dMHCQkJ3PumT5+OmjVrwsDAAM2bN4e/v7/Sa+Xk5GDUqFEwMjJCgwYNsGbNmiKnm9+9e4cZM2bAzMwMZmZmmDFjRoHE8StWrEDlypVRu3ZtHDhwgDt+5swZNG3aFAYGBqhZsyaWLFnyBT0DREdHo2XLljA0NETfvn2RmpoKoOBUt4ODA9zc3NCuXTvo6+vju+++w+vXr7/omgwGQ7UwY8wQDQcOHMCFCxcQHR2NyMhILFu2DH5+fpg3bx6OHDmCxMREmJubY+jQodx7WrRogbCwMKSmpmLYsGEYNGgQZ8SLYunSpYiNjUVMTAwuXbqE/fv3F1l3+fLlCAwMRFhYGMLDw3H79m0sW7aMO//y5Uu8fv0aL168wN69e+Hq6oonT54AAPT09ODj44M3b97gzJkz8PT0xIkTJwT3i4+PD3bt2oWEhASULVsW06ZNK7Kur68vdu/ejVevXuH9+/dYt26d4OsxGAw1QBgMEWBubk48PT25v8+cOUPq1KlDXFxcyC+//MIdz8jIIGXLliXPnj0rtJ2KFSuSsLCwYq9lYWFBzp8/z/29Y8cOUr16dQUtly5dIoQQUqdOHXLmzBnu3Pnz54m5uTkhhJCrV68SLS0tkpmZyZ0fNGgQcXd3L/S606dPJzNmzCCEEPLs2TMCgHz48KFYrZ06dSJz5szh/o6IiCDa2trk48ePBdro1KkT8fDw4Opu3bqV9OjRo9j2GQyGZmAjY4ZoqFmzJvfa3NwcCQkJSEhIgLm5OXdcKpWiUqVKePHiBQBg/fr1aNCgAQwNDVGxYkW8fftW6dRsQkKCwrU+fV1Y3U+vL9clx8jICHp6eoWeDwoKQufOnWFiYgJDQ0Ns3779i6aNP++XDx8+FNlO1apVudcVKlRgG7wYjFICM8YM0fD8+XPudXx8PLdOGxcXxx3PyspCSkoKqlevDn9/f6xevRpHjhxBWloa3rx5A0NDQxAlQeeqVauGf/75p9Drfs7n15frkpOWloasrKxCzw8bNgx9+vTB8+fP8fbtW0ycOFGptsL4vF+0tbVRuXJlwe0wGAx6MGPMEA1bt27FP//8g9TUVKxYsQJDhgzBsGHDsHv3boSFheHdu3eYP38+WrVqhdq1ayMjIwNly5aFiYkJPn78CHd3d6Snpyu9zuDBg7Fy5UqkpaXhxYsX2LJlS5F1nZycsGzZMiQnJ+P169dwd3fHiBEjFOosXrwY79+/h7+/P06fPo1BgwYBADIyMmBsbAxdXV3cvn0bvr6+X9Qv+/fvx8OHD5GdnY1FixZh4MCB0NLS+qK2GAwGHZgxZoiGYcOG4bvvvkOdOnVQp04dLFy4EF27doWHhwcGDBiAatWqITo6GocOHQIA9OjRAz179oS1tTXMzc2hq6tb7JSznEWLFqFGjRqwsLBAt27dMHDgQJQrV67QugsXLoS9vT0aN26MRo0aoVmzZli4cCF3vmrVqjAyMoKZmRmGDx+O7du3o379+gCAbdu2YdGiRdDX14e7uzsGDx78Rf0ycuRIjB49GlWrVkVubi42b978Re0wGAx6sEQRDFFQu3ZteHt7o1u3bhq/tqenJw4dOoTr169r/NoMBuPbgI2MGYzPSExMREBAAPLz8/HkyROsX78e/fr1oy2LwWB8xTBjzPgm6dmzJ6RSaYGyYsUKvH//HhMmTIC+vj66dOmCvn37YvLkydS0FqZTKpXyCmDCYDDEAZumZjAYDAaDMmxkzGAwGAwGZZgxZjAYDAaDMswYMxgMBoNBGWaMGQwGg8GgDDPGDAaDwWBQhhljBoPBYDAo83+lSBnPpI2+BQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap\n", + "heatmap_plot = plot_heatmap(\n", + " dnorm=norm,\n", + " fit=lfm_sel,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis='diff_' + lfm_sel,\n", + " clip=0.025,\n", + " title='residual_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [I] Residual LFM fit heatmap vs. poa_global bin (x) and temp_module bin (y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_fit(dmeas, dnorm, fit, title, save_figs, coeffs):\n", + " \"\"\"Scatter plot fit to normalised measured.\n", + " \n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measurements, must include 'poa_global_kwm2'\n", + "\n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + "\n", + " fit : string\n", + " name of fitted variable e.g. 'pr_dc'.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " \n", + " \"\"\"\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " plt.title(title)\n", + "\n", + " plt.ylabel('fit ' + fit + ' * poa_global kW/m^2')\n", + " ax1.set_ylim(0, 1.2)\n", + "\n", + " plt.xlabel('meas ' + fit + '* poa_global_kW/m^2')\n", + " ax1.set_xlim(0, 1.2)\n", + "\n", + " plt.plot(\n", + " dnorm[fit] * dmeas['poa_global'] / G_STC,\n", + " dnorm['calc_' + fit] * dmeas['poa_global'] / G_STC,\n", + " 'c^',\n", + " label=fit\n", + " )\n", + "\n", + " # plot 1:1 line to show optimum fit\n", + " plt.plot((0, 1.2), (0, 1.2), 'yo-')\n", + " \n", + " # plot LIC, NOCT and STC irradiances\n", + " for x in (0.2, 0.8,1): \n", + " plt.plot((0, x), (x, x), 'k--')\n", + " plt.plot((x, x), (x, 0), 'k--')\n", + "\n", + " plt.legend(loc='upper left')\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(\n", + " os.path.join('mlfm_data', 'output', 'fit_meas_' + title[:len(title)-4]),\n", + " dpi=300\n", + " )\n", + " \n", + "\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot fit vs. measured, include a 1:1 line for comparison\n", + "fit_plot = plot_fit(dmeas=meas,\n", + " dnorm=norm,\n", + " fit=lfm_sel,\n", + " title='fit_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " save_figs=save_figs,\n", + " coeffs=coeffs\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [J] scatter plot of 'fit_lfm_sel * poa_global# (y) vs. 'measured_lfm_sel * poa_global' (x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [K] Read in complete (G,T) Matrix to fill with MLFM predicted values \n", + "\n", + "Read in a matrix with complete values of \n", + "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", + "to predict all MPM values " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# read in the complete matrix data\n", + "matr = pd.read_csv(os.path.join(root_dir, 'mlfm_data', 'ref', 'mlfm_matrix.csv'),\n", + " index_col='id')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predict performance from MPM fit coefficients \n", + "\n", + "1. generate predicted mpm data \n", + "2. create a pivot table mpm(g,t) \n", + "3. show as a heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('b', array([ 1.06780656, -0.0028452 , 0.14860936, -0.07080871, 0.01 ]))" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show model coefficients\n", + "mpm_sel, coeffs" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpr_dc
id
1matrix100000.995707
2matrix100500.978989
3matrix1001000.962271
4matrix1001500.945553
5matrix1002000.928834
6matrix1002500.912116
7matrix1003000.895398
8matrix1003500.878680
9matrix1004000.861962
10matrix1004500.845244
\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed pr_dc\n", + "id \n", + "1 matrix 100 0 0 0.995707\n", + "2 matrix 100 5 0 0.978989\n", + "3 matrix 100 10 0 0.962271\n", + "4 matrix 100 15 0 0.945553\n", + "5 matrix 100 20 0 0.928834\n", + "6 matrix 100 25 0 0.912116\n", + "7 matrix 100 30 0 0.895398\n", + "8 matrix 100 35 0 0.878680\n", + "9 matrix 100 40 0 0.861962\n", + "10 matrix 100 45 0 0.845244" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# populate pivot table from predicted mpm data\n", + "if mpm_sel == 'a':\n", + " matr[lfm_sel] = mpm_a_calc(matr, *coeffs) # not mpm_sel\n", + " \n", + "if mpm_sel == 'b':\n", + " matr[lfm_sel] = mpm_b_calc(matr, *coeffs) # not mpm_sel\n", + "\n", + "\n", + "matr.head(10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [L] Plot heatmap of predicted LFM values vs. temp_mod, poa_global bins" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", + " vmin=0, vmax=1.2, levels=5,\n", + " save_figs=False):\n", + " \"\"\"Plot filled contour plot Z vs. X and Y bins.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : dataframe\n", + " measured or normalised data containing weather columns\n", + " (poa_global, temp_module and wind_speed).\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module'.\n", + "\n", + " z_axis : string\n", + " measured value as a colour surface plot.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " vmin, vmax : float\n", + " minimum and maximum values for contour chart ###\n", + " \n", + " \"\"\"\n", + " piv = pd.pivot_table(\n", + " df,\n", + " index=y_axis,\n", + " columns=x_axis,\n", + " values=z_axis,\n", + " fill_value=0, # fill empty cells?\n", + " aggfunc=[np.mean], # min, np.sum, len->count\n", + " margins=False, # grand totals\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " piv = piv.clip(vmin, vmax)\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " cs = plt.contourf(\n", + " piv,\n", + " cmap='RdYlBu', # or 'nipy_spectral',\n", + " # origin='lower'\n", + " # nchunkint=1,\n", + " levels=levels,\n", + " vmin=vmin,\n", + " vmax=vmax\n", + " )\n", + "\n", + " cbar = fig.colorbar(cs, ax=ax1)\n", + " cbar.ax.set_ylabel(z_axis,\n", + " rotation=90,\n", + " va='bottom',\n", + " labelpad=+30)\n", + "\n", + " plt.title(title)\n", + " # # get_yaxis().set_major_formatter(FormatStrFormatter('%.2f'))\n", + "\n", + " y_ticks = piv.shape[0]\n", + "\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + "\n", + " # show only 1 of each y_skip labels\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " x_ticks = piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " # show only 1 of each x_skip labels\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid(color='k', linestyle=':', linewidth=1)\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(os.path.join('mlfm_data', 'output', 'contourf_'+ title[:len(title)-4])\n", + " , dpi=300\n", + " ) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", + "\n", + "matr2 = matr[matr['temp_module'] >= 10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contour plot of predicted lfm_sel + vs. poa_global and temp_mod. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df=matr2,\n", + " y_axis='temp_module',\n", + " x_axis='poa_global',\n", + " z_axis=lfm_sel,\n", + " title='matrix predicted_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [L1] Contour plot (colours) of predicted lfm_sel vs. poa_global (x) and temp_mod (y)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df=norm,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis=lfm_sel,\n", + " title='avg normalised_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [L2] Contour plot (colours) of measured lfm_sel vs. poa_global (x) and temp_mod (y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References \n", + " \n", + "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", + "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", + "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", + " \n", + ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", + " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", + "What's Most Important for Energy Yield' \n", + "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", + "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", + "\n", + ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", + " 'Choosing the best Empirical Model for predicting energy yield' \n", + " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", + " Canobbio, Switzerland 30-31 March, 2017 \n", + "\n", + ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", + "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", + "benchmark its practical relevance in energy yield prediction' \n", + "PVSC June 2019 Chicago, USA \n", + "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", + "\n", + ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", + "under Real Outdoor and IEC 61853 Test Conditions \n", + "Using High Quality Module IV Measurements' \n", + "36th EU PVSEC Sep 2019 \n", + "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", + "\n", + ".. [5] Steve Ransome (SRCL) \n", + "'How to use the Loss Factors and Mechanistic Performance Models \n", + "effectively with PVPMC/PVLIB' \n", + "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", + "https://pvpmc.sandia.gov/download/7879/ \n", + "\n", + ".. [6] W.Marion et al (NREL) \n", + "'New Data Set for Validating PV Module Performance Models' \n", + "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", + "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", + "\n", + ".. [7] Steve Ransome (SRCL)\n", + "'Benchmarking PV performance models with high quality IEC 61853 Matrix\n", + "measurements (Bilinear interpolation, SAPM, PVGIS, MLFM and 1-diode)'\n", + "http://www.steveransome.com/pubs/2206_PVSC49_philadelphia_4_presented.pdf\n", + "\n", + ".. [8] Juergen Sutterlueti (Gantner Instruments)\n", + "'Advanced system monitoring and artificial intelligent data-driven analytics \n", + "to serve GW-scale photovoltaic power plant and energy storage requirements'\n", + "https://pvpmc.sandia.gov/download/8574/\n", + "\n", + "Many more papers are available at www.steveransome.com \n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# =============================================================================================" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "## TEST CODE CAN DELETE AFTER HERE IF NOT NEEDED \n", + "\n", + "test = False\n", + "\n", + "if test: \n", + " # save meas data to csv\n", + " meas.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'meas.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " # save norm data to csv\n", + " norm.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'norm.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " # save matr data to csv\n", + " matr.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'matr.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " # save ref data to csv\n", + " ref_data.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'ref_data.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " \n", + "if test:\n", + " # print mlfm fit coeffs\n", + " print(coeffs[0],coeffs[1],coeffs[2],coeffs[3],coeffs[4],) # coeffs[5], )#coeffs[6],)\n", + " \n", + "\n", + "if test:\n", + " # only works with mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.\n", + " #\n", + " # check data for test \n", + " \n", + " n= norm.loc['2016-03-23 09:00:00-07:00']\n", + "\n", + " print('\\n n= \\n', n)\n", + "\n", + " \n", + " n= norm.loc['2016-03-23 09:00:00-07:00']\n", + "\n", + " print('\\n n= \\n', n)\n", + "\n", + " s = stack.loc['2016-03-23 09:00:00-07:00']\n", + "\n", + " print('\\n s= \\n', s)\n", + "\n", + "\n", + "if test:\n", + " # show all versions\n", + " import sys \n", + " \n", + " for name, module in sorted(sys.modules.items()): \n", + " if hasattr(module, '__version__'): \n", + " print (name, module.__version__ )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# whos\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "toc-autonumbering": true, + "toc-showmarkdowntxt": false + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/tutorials/mlfm_2.html b/docs/tutorials/mlfm_2.html new file mode 100644 index 0000000000..cbe1a7025c --- /dev/null +++ b/docs/tutorials/mlfm_2.html @@ -0,0 +1,16931 @@ + + + + + +mlfm_2 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/tutorials/mlfm_2.ipynb b/docs/tutorials/mlfm_2.ipynb new file mode 100644 index 0000000000..11df072f9a --- /dev/null +++ b/docs/tutorials/mlfm_2.ipynb @@ -0,0 +1,1913 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MLFM for PVLIB \n", + "ver: 221212t18\n", + "### Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "Corrections and additions for comments by : \n", + "Cliff Hansen, Kevin Anderson, Anton Driesse and Mark Campanelli\n", + "\n", + "## Tutorial overview.\n", + "see details for each function in mlfm.py\n", + "\n", + "I) The Loss Factors Model (LFM) 2011 ref [1] quantifies normalised losses \n", + "from module parameters (e.g. pr_dc, i_sc, r_sc, i_mp, v_mp, r_oc and v_oc) \n", + "by analysing module measurements or the shape of the IV curve and comparing \n", + "it with STC reference values from the datasheet. \n", + "\n", + "II) The Mechanistic performance model (MPM) 2017 ref [2] has \"meaningful, \n", + "independent, robust and normalised\" coefficients which fit how the LFM values \n", + "depend on irradiance, module temperature (and windspeed) and time. \n", + "\n", + "III) This tutorial shows how to take module measured and weather data, \n", + "(either outdoor or IEC 61853-like matrix data), normalise it, generate MLFM \n", + "coefficients, fit them with the MPM then analyse module performance looking for \n", + "loss values, degradation and allowing performance predictions as shown in fig 2. \n", + "\n", + "Fig 1 illustrates the loss factors model (LFM). \n", + "\n", + "Depending on the number of measurements available the LFM is defined \n", + "with a suffix number x = 1..12 LFM_n as in ref [4] - \n", + "\n", + "It uses the shape and values from dc measurements to quantify the values of each \n", + "of the loss factors (coloured arrors on the y=current or x=voltage axes\n", + "going from (1) ref\\_p\\_mp to (6) meas\\_p\\_mp. \n", + "\n", + "![mlfm_data/figs/lfm_220914t15.png](mlfm_data/figs/lfm_220914t15.png) \n", + "\n", + "Fig 1: Loss Factors Model \n", + "\n", + "\n", + "![mlfm_data/figs/flow_1024.png](mlfm_data/figs/flow_1024.png) \n", + "\n", + "Fig 2: MLFM overview flow chart of this tutorial. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explanations of the Loss factors model in fig 1.\n", + "\n", + "1) ref_p_mp = Initial datasheet value at STC.\n", + "\n", + "Multiply by 1/FF to get to (ref_i_sc * ref_v_oc) to start to analyse current and voltage losses \n", + "\n", + "2->3) Three 'current' losses get from ref_i_sc to norm_i_mp\n", + " - norm_i_sc = measured / expected isc corrected for poa_global (purple)\n", + " - norm_r_sc = loss caused by 'shunt resistance' slope at i_sc (orange)\n", + " - norm_i_ff = loss caused by 'current part' of fill factor (green). \n", + " \n", + " \n", + "4->5) Three 'voltage' losses (plus a temperature coefficient) get from from ref_v_oc to norm_v_mp \n", + " - norm_temp_corr = optional temp correction subtracted from v_oc (red). \n", + " - norm_v_oc_t = measured / expected v_oc temp_corrected (brown) \n", + " - norm_r_oc = loss caused by 'series resistance' slope at v_oc (pink)\n", + " - norm_v_ff = loss caused by 'voltage part' of fill factor (blue)\n", + " \n", + " \n", + "6) These losses cause the performance to fall to pr_dc (= meas_p_mp / ref_p_mp) \n", + "\n", + "pr_dc = 1/ff \\* \n", + " (norm_i_sc \\* norm_r_sc \\* norm_i_ff ) \\* \n", + " (norm_v_ff \\* norm_r_oc \\* norm_v_oc_t \\* norm_temp_corr ) \n", + "\n", + "Note: \n", + "The gamma temperature correction is just subtracted from voc for simplicity. \n", + "In reality there will be temperature dependencies for i_sc and ff but they are smaller." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# import mlfm \n", + "\n", + "from pvlib.mlfm import meas_to_norm, mpm_a_fit, mpm_b_fit, meas_to_stack_lin\n", + "from pvlib.mlfm import mpm_a_calc, mpm_b_calc\n", + "\n", + "from pvlib.mlfm import plot_scatter, plot_stack # , mpm_calc\n", + "\n", + "# FIND AND SHOW INSTALLATION CURRENT WORKING DIRECTORY\n", + "import os\n", + "root_dir = os.getcwd()\n", + "\n", + "# uncomment to see root dir\n", + "# print(root_dir)\n", + "\n", + "# STANDARD DEFINITIONS (also in mlfm.py)\n", + "G_STC = 1000.0 # STC irradiance [W/m^2]\n", + "T_STC = 25.0 # STC temperature [C] temperature_ref\n", + "\n", + "# https://matplotlib.org/stable/tutorials/introductory/customizing.html\n", + "plt.rcParams['figure.figsize'] = [7, 5] # setup fig size inches ~[7, 5]\n", + "plt.rcParams.update({'font.size': 12}) # setup fontsize ~12\n", + "plt.linewidth = 1.5 # line width in points ~1.5\n", + "plt.linestyle = '--' # solid line ~'--'\n", + "plt.marker = 's' # the default marker square ~'s'\n", + "plt.markersize = 9 # marker size, in points ~9\n", + "plt.bbox = 1.4 # offset --> to not overwrite ~1.4\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get user choices " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# save graphs as png files to the output directory?\n", + "save_figs = True" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# select which mpm to model : must be 'a original 2017' or 'b advanced 2022'\n", + "mpm_sel = 'b' # 'a' or 'b'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [A] Select MLFM measurement data file\n", + "\n", + "Three default files are included (\\* = version number ) \n", + "\n", + "(0) g78\\_T16\\_Xall\\_F10m\\_R900\\*.csv (6 LFM params) \n", + "(1) n05667\\_Y13\\_R1k6\\_fClear\\*.csv (4 LFM params) \n", + "(2) x19074001\\_iec61853\\*.csv (4 LFM params) \n", + "\n", + "(Some variants are added to the IEC 61853 with fewer data points \n", + "or added scatter to test the fit algorithms)\n", + "\n", + "Essential default column names in meas( ) are :- \n", + "\n", + "meas { \n", + "'date\\_time', 'module\\_id', \n", + "'poa\\_global', 'temp\\_module', \n", + "'v\\_oc', 'i\\_sc', 'i\\_mp', 'v\\_mp', \n", + "'r\\_sc', 'r\\_oc', <-- optional for LFM_6 \n", + "'wind\\_speed', 'temp\\_air', <-- optional \n", + "}\n", + "\n", + "\n", + "File naming conventions can be used to help identify files, for example \n", + "`x81_T1906_D3_Fh.csv` \n", + "\n", + "where \n", + " - x = source e.g. (G)antner, (N)rel, (S)andia, matri(X), ... \n", + " - 81 = module id/channel number \n", + " - T1906 = (T)ime started = yymm(dd) \n", + " - D3 = (D)uration in days \n", + " - Fh = (F)requency e.g. (h)ours or (10m)10 minutes \n", + " - etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment just one line to select a file from \n", + "# directory ''\\pvlib-python\\docs\\tutorials\\mlfm_data\\meas_gtw'\n", + "\n", + "# PTS COMMENTS \n", + "\n", + "# 0) LFM 6 outdoor Gantner Instruments \n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.csv' # 900 <<< raw data with rsc and roc\n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041_4param.csv' # 900 deleted rsc,roc\n", + "# mlfm_meas_file = 'g78_T16_Xall_F10m_R1_041.csv' # 1 test record only\n", + "\n", + "# 1) LFM 4 outdoor NREL \n", + "# mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' # 1600 <<< raw data no rsc,roc measured \n", + "\n", + "# 2) IEC 61853 CFV : either raw data or fewer points and/or added scatter error\n", + "mlfm_meas_file = 'x19074001_iec61853_041.csv' # 27 <<< raw data no rsc,roc measured \n", + "# mlfm_meas_file = 'x19074001_iec61853_041_6pts.csv' # 6 raw but fewer points\n", + "# mlfm_meas_file = 'x19074001_iec61853_041_rand5pc.csv' # 27 rand 5% rmse\n", + "# mlfm_meas_file = 'x19074001_iec61853_041_rand1pc.csv' # 27 rand 1% rmse\n", + "# mlfm_meas_file = 'x19074001_iec61853_041_rand5pc_6pts.csv' # 6 rand 5% rmse fewer points\n", + "\n", + "\n", + "# extract module id from filename e.g. 'g78'\n", + "mlfm_mod = mlfm_meas_file.split('_')\n", + "\n", + "mlfm_mod_sel = mlfm_mod[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import measured data (outdoor or matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "meas = pd.read_csv(\n", + " # root_dir + '/mlfm_data/meas_gtw/' + mlfm_meas_file,\n", + " os.path.join(root_dir, 'mlfm_data', 'meas_gtw', mlfm_meas_file),\n", + " index_col='date_time'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [B] Read all reference datasheet values at STC" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# user must keep updated with their modules from their measurements\n", + "\n", + "\n", + "ref_file_name = os.path.join(root_dir, 'mlfm_data', 'ref', 'mlfm_reference_modules.csv')\n", + "\n", + "ref_data = pd.read_csv(\n", + " ref_file_name, index_col='module_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select module stc data from reference database" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " ref_data = ref_data[\n", + " ref_data.index == mlfm_mod_sel]\n", + "\n", + "except IndexError:\n", + " print(\"You must define module ref data to use this module ...\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Put relevant data into a dict for easy use\n", + "# ignore any other columns that may be database specific\n", + "# as they aren't needed\n", + "\n", + "ref = dict(\n", + " # module_id=ref_data['module_id'].values[0],\n", + " i_sc=ref_data['i_sc'].values[0],\n", + " i_mp=ref_data['i_mp'].values[0],\n", + " v_mp=ref_data['v_mp'].values[0],\n", + " v_oc=ref_data['v_oc'].values[0],\n", + "\n", + " alpha_i_sc=ref_data['alpha_i_sc'].values[0],\n", + " beta_v_oc=ref_data['beta_v_oc'].values[0],\n", + " alpha_i_mp=ref_data['alpha_i_mp'].values[0],\n", + " beta_v_mp=ref_data['beta_v_mp'].values[0],\n", + " gamma_pdc=ref_data['gamma_pdc'].values[0],\n", + "\n", + " p_mp= ref_data['p_mp'].values[0],\n", + " \n", + " \n", + " ff=ref_data['ff'].values[0],\n", + ")\n", + "\n", + "# uncomment to show ref data\n", + "# ref" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculate useful data columns for meas" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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module_idtemp_modulepoa_globali_scv_oci_mpv_mpp_mpwind_speedpr_dcv_oc_temp_corrpr_dc_temp_corr
count27.027.00000027.00000027.00000027.00000027.00000027.00000027.00000027.027.00000027.00000027.000000
mean19074001.042.222222581.4814813.45092665.0548153.19440754.227778175.6372600.00.92229067.5661480.964306
std0.023.588350358.4550582.1237154.6831761.9747404.486576110.6985940.00.0782152.5557010.038515
min19074001.015.000000100.0000000.59500054.6000000.54100044.32000024.0657600.00.74667761.2652870.856461
25%19074001.025.000000200.0000001.20300061.5950001.09450049.87500062.0912000.00.84904866.0724550.948074
50%19074001.050.000000600.0000003.54200065.7800003.29800054.250000177.9447800.00.92624168.5026700.974878
75%19074001.062.500000900.0000005.33750069.3050004.94650058.400000269.2324200.00.99668369.5434330.994383
max19074001.075.0000001100.0000006.57800071.8500006.06100060.210000354.1569500.01.02699270.4400001.000051
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" + ], + "text/plain": [ + " module_id temp_module ... v_oc_temp_corr pr_dc_temp_corr\n", + "count 27.0 27.000000 ... 27.000000 27.000000\n", + "mean 19074001.0 42.222222 ... 67.566148 0.964306\n", + "std 0.0 23.588350 ... 2.555701 0.038515\n", + "min 19074001.0 15.000000 ... 61.265287 0.856461\n", + "25% 19074001.0 25.000000 ... 66.072455 0.948074\n", + "50% 19074001.0 50.000000 ... 68.502670 0.974878\n", + "75% 19074001.0 62.500000 ... 69.543433 0.994383\n", + "max 19074001.0 75.000000 ... 70.440000 1.000051\n", + "\n", + "[8 rows x 12 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# calculate p_mp and pr_dc as they might be missing\n", + "meas['p_mp'] = meas['i_mp'] * meas['v_mp']\n", + "\n", + "meas['pr_dc'] = (meas['p_mp'] / ref['p_mp']\n", + " / (meas['poa_global'] / G_STC))\n", + "\n", + "# temperature corrected v_c and pr_dc\n", + "meas['v_oc_temp_corr'] = \\\n", + " (meas['v_oc'] * (1 - ref['beta_v_oc']*(meas['temp_module'] - T_STC)))\n", + "\n", + "meas['pr_dc_temp_corr'] = \\\n", + " (meas['pr_dc'] * (1 - ref['gamma_pdc']*(meas['temp_module'] - T_STC)))\n", + "\n", + "# show some meas data\n", + "meas.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select LFM_n model by counting variables in the meas data \n", + "usually LFM_4 = matrix (i\\_sc, i\\_mp, v\\_mp, v\\_oc) \n", + "and LFM_6 = iv (i\\_sc, i\\_mp, v\\_mp, v\\_oc + r\\_sc, r\\_oc) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def get_qty_lfm_vars(dmeas):\n", + " \"\"\"Find the quantity of LFM variables in the measured data.\n", + "\n", + " (e.g. I_MP+V_MP=2, MATRIX=4, IV_CURVE=6).\n", + "\n", + " Parameters\n", + " ----------\n", + " dmeas: DataFrame\n", + " Measured weather and module electrical values per time or measurement\n", + "\n", + " Returns\n", + " -------\n", + " qty_lfm_vars : int\n", + " number of lfm_values present in data usually\n", + "\n", + " 2 = ( i_mp, v_mp ) from mpp tracker\n", + " 4 = (i_sc, i_mp, v_mp, v_oc) from matrix\n", + " 6 = (i_sc, r_sc, i_mp, v_mp, r_oc, v_oc) from iv curve.\n", + "\n", + " \"\"\"\n", + " # find how many lfm variables were measured\n", + " qty_lfm_vars = 0\n", + " for lfm_sel in ('i_sc', 'r_sc', 'i_mp', 'v_mp', 'r_oc', 'v_oc'):\n", + " if lfm_sel in dmeas.columns:\n", + " qty_lfm_vars += 1\n", + " # print(qty_lfm_vars, lfm_sel)\n", + "\n", + " return qty_lfm_vars" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "qty_lfm_vars = get_qty_lfm_vars(meas)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [C] Normalise LFM values from meas and ref to norm dataframes \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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poa_globaltemp_modulewind_speedpr_dcpr_dc_temp_corri_sci_mpv_ocv_oc_temp_corrv_mp
date_time
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24001501.0070650.9774510.9969760.9282070.9919610.9677430.853123
\n", + "
" + ], + "text/plain": [ + " poa_global temp_module ... v_oc_temp_corr v_mp\n", + "date_time ... \n", + "0 100 15 ... 0.913971 0.844634\n", + "1 200 15 ... 0.941899 0.851158\n", + "2 400 15 ... 0.967743 0.853123\n", + "\n", + "[3 rows x 10 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "norm = meas_to_norm(meas, ref)\n", + "\n", + "# show some normalised data\n", + "norm.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make irradiance and temperature bins for pivot tables " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# poa_global bin e.g. 100, 200 .. 1100W/m2\n", + "norm['poa_global_bin'] = \\\n", + " norm['poa_global'].round(-2)\n", + "\n", + "# temp_module bin e.g. 5, 10 .. 75C\n", + "norm['temp_module_bin'] = \\\n", + " (5 * round(norm['temp_module'] / 5, 0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [D] Perform sanity checks on meas and norm data \n", + "\n", + "It's easier to sanity check and study normalised data than raw values. \n", + "1) Remove bad, missing, unwanted or outlier data \n", + "2) User defined limits may depend on data scatter and degradation \n", + "3) Can either select on values e.g. '0.5 x stdev from mean' \n", + "4) Possible to select on dates if desired. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# select by irradiance poa_global range e.g. 100-1100 W/m2\n", + "norm = norm[(norm['poa_global'] >= 100) &\n", + " (norm['poa_global'] <= 1100)]\n", + "\n", + "# remove specific lfm values outside limits e.g. <0.5 or >1.5\n", + "norm = norm[((norm['pr_dc'] > 0.5) &\n", + " (norm['pr_dc'] < 1.5))]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# remove all mlfm values outside x~3 stdevs\n", + "if qty_lfm_vars == 6:\n", + " # only needed for outdoor data as indoor ought to be less scattered\n", + " # remove all mlfm data > x stdev usually 3\n", + " stdevs = 3\n", + "\n", + " for lfm in ('i_sc', 'r_sc', 'i_ff', 'v_ff', 'r_oc', 'v_oc'):\n", + " norm = norm[\n", + " ((norm[lfm] - norm[lfm].mean()) /\n", + " norm[lfm].std()).abs() < stdevs\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter only matching rows from meas and norm data\n", + "like an inner join but leave data in separate norm and meas frames" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# drop meas rows that aren't in norm\n", + "meas_not_in_norm = ~meas.index.isin(norm.index)\n", + "meas = meas.drop(meas[meas_not_in_norm].index)\n", + "\n", + "# drop norm rows that aren't in meas\n", + "norm_not_in_meas = ~norm.index.isin(meas.index)\n", + "norm = norm.drop(norm[norm_not_in_meas].index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [E] Plot normalised LFM data vs irradiance \n", + "\n", + "For outdoor data - \n", + "LFM values norm() should be narrow, smooth lines (around 70-120% on the yaxis).\n", + "\n", + "For matrix data - \n", + "LFM values norm() should be close, almost parallel lines (around 70-120% on the yaxis).\n", + "\n", + "1. Higher values are always better (unlike measured values such as \n", + " Rseries or Io where lower is better)\n", + "1. Accurate measurements and a stable module result in narrowest lines \n", + "1. v_oc and r_sc tend to fall at low light levels ( / left) \n", + "1. r_oc tends to fall at high light levels ( \\ right) \n", + "1. i_ff and v_ff are usually fairly flat ( - ) \n", + "1. i_sc may vary the most due to spectral sensitivity, soiling, shading \n", + " and/or snow (if not properly corrected). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalised lfm values vs. irradiance.\n", + "\n", + "All traces should be thin, smoot lines usually around 0.9 ± 0.1 \n", + "i\\_sc may be more scattered if there is uncorrected soiling, spectral and angle of incidence ###" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# scatter plot normalised values vs. irradiance\n", + "fig_scatter = plot_scatter(\n", + " norm, mlfm_meas_file, qty_lfm_vars, save_figs)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [E] : LFM multiplicative factors (y) vs. poa irradiance (x)\n", + "\n", + "\n", + "# [F] Convert multiplicative to subtractive losses for a stack plot \n", + "\n", + " Multiplicative losses are easier to understand but to represent them on a graph \n", + "it's easier to show them as a stacked plot where the values are 'translated' \n", + "so the sum of the stacked losses is shown to equate to the product of the \n", + "multiplicative losses.\n", + "\n", + "LFM losses can be analysed as either \n", + "\n", + "- multiplicative pr_dc = 1/ff * PRODUCT(norm(i_sc), ... \\* stack(v_oc_t), stack(temp_corr) ). \n", + "\n", + "- subtractive pr_dc = 1/ff - SUM(stack(i_sc), ... stack(v_oc_t), stack(temp_corr) ). \n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# translate multiplicative to stack losses and add to\n", + "# dataframe stack add a gap between i and v losses\n", + "\n", + "stack = meas_to_stack_lin(meas, ref, qty_lfm_vars, gap=0.0) # gap = 0.01\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [G] Plot stack losses vs. measurement \n", + "\n", + "Fig 3 Shows how to quantify losses by loss parameters stack(i_sc, .. v_oc). \n", + "\n", + "![stack5D_0_4.png](mlfm_data/figs/mlfm_stack.png) \n", + "\n", + "Fig 3 Stacked losses by measurement \n", + "\n", + "- It plots them in a stacked format from the lossless limit 1/ff (top) \n", + " subtracting each loss value in turn until it reaches pr_dc (bottom). \n", + " \n", + "- This figure shows a typical c-Si module for four clear days for \n", + " different months July to Oct in AZ. \n", + " \n", + "- In the middle of the days the high irradiance results in the biggest \n", + " losses being due to r_oc (red, ~rseries, pink) and temp_module \n", + " (as the module heats to 60C). \n", + " \n", + "- Early mornings/late afternoons there is a slight Isc gain (purple, \n", + " top, due to spectral mismatch) but an Isc loss mid day due to soiling. \n", + "\n", + "Stack losses are indicated by their colours \n", + "(from top to bottom for lfm_4=matrix and lfm_6=ivcurve) \n", + "\n", + "![mlfm_data/figs/losses.png](mlfm_data/figs/losses.png) \n", + "\n", + "Graph options : \n", + "\n", + "is_i_sc_self_ref : boolean \n", + " = self corrects i_sc to remove angle of incidence, spectrum, \n", + " snow or soiling. \n", + " \n", + "is_v_oc_temp_module_corr : boolean \n", + " = calc temperature loss due to gamma, subtract from voc loss " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot stack loss vs. time (or measurement) chart\n", + "fig_stack = plot_stack(\n", + " dstack=stack, # dataframe measurements\n", + " fill_factor=ref['ff'], # dataframe reference STC\n", + " title=mlfm_meas_file, #\n", + " xaxis_labels=12, # show num x_labels or 0 to show all\n", + " is_i_sc_self_ref=False, # is isc self referenced?\n", + " save_figs=save_figs # save the figure?\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [G] Stacked loss values (y) s. date and time (outdoor) or matrix measurement (x)\n", + "\n", + "# [H] Fit mpm to measured weather and normalised losses \n", + "\n", + "Perform a Mechanistic Performance Model (MPM) fit to the lfm parameters \n", + "poa_global (W/m$^2$), temp_module (C), wind_speed (ms$^-$$^1$). \n", + "\n", + "\n", + "mpm_a = c_1 +c_2\\*(t_mod-25) +c_3\\*log10(g) +c_4\\*g +c_5\\*ws +c_6\\/g (deprecated) \n", + "\n", + "mpm_b = c_1 +c_2\\*(t_mod–25) +c_3\\*log10(g)\\*(t_k\\/t_stc_k) +c_4\\*g +c_5\\*ws\n", + "\n", + "\n", + "Report the fit (coeffs) and error (errs) coefficients. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choose which normalised lfm parameter to model e.g. pr_dc or i_sc..v_oc " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "lfm_sel = 'pr_dc'" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nfev = 22 \n", + " \n", + " mesg = `ftol` termination condition is satisfied. \n", + " \n", + " ier = 2 NOTE : if ier in (1,2,3,4) then fit found\n" + ] + } + ], + "source": [ + "# add selected variable to measured data frame to ensure data indexes match.\n", + "meas_temp = meas.copy()\n", + "meas_temp[lfm_sel] = norm[lfm_sel]\n", + "\n", + "# try to fit measurement data and print outputs \n", + "\n", + "\"\"\"\n", + "# full_outputboolean, optional\n", + "If True, this function returns additioal information: \n", + " infodict, mesg, and ier.\n", + " \n", + "https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html\n", + "\n", + "mesgstr (returned only if full_output is True)\n", + "A string message giving information about the solution.\n", + "\n", + "ierint (returnned only if full_output is True)\n", + "An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. \n", + "Otherwise, the solution was not found. In either case, \n", + "the optional output variable mesg gives more information.\n", + "\"\"\"\n", + "\n", + "try:\n", + " \n", + " if mpm_sel == 'a':\n", + " cc, coeffs, ee, errs, infodict, mesg, ier = mpm_a_fit(meas_temp, lfm_sel) \n", + " \n", + " if mpm_sel == 'b':\n", + " cc, coeffs, ee, errs, infodict, mesg, ier = mpm_b_fit(meas_temp, lfm_sel) \n", + " \n", + " \n", + " # store calculated value of LFM variable\n", + " norm['calc_' + lfm_sel] = cc\n", + "\n", + " # store residual difference of LFM variable\n", + " norm['diff_' + lfm_sel] = norm[lfm_sel] - norm['calc_' + lfm_sel]\n", + " \n", + " # show infodict data, uncomment fvec to show per row\n", + " print('nfev =', infodict['nfev'], '\\n \\n', \n", + " # 'fvec =', infodict['fvec'],'\\n \\n',\n", + " 'mesg = ', mesg, '\\n \\n',\n", + " 'ier = ', ier, \"NOTE : if ier in (1,2,3,4) then fit found\")\n", + " \n", + "except:\n", + " print(\"CAN'T FIT DATA\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [I] Plot heatmap of mean residual vs. temp_module and poa_global\n", + "\n", + "Show a heatmap of the average residual (meas - fit) error \n", + "for each irradiance (100W/m^2) and tmod bin (5C)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_heatmap(dnorm, fit, y_axis, x_axis, z_axis,\n", + " title, save_figs, clip=0.02,):\n", + " \"\"\"Plot a heatmap of Z vs. binned X and Y axes.\n", + "\n", + " Parameters\n", + " ----------\n", + " dnorm : dataframe\n", + " Normalised multiplicative loss values (values approx 1).\n", + "\n", + " fit : string\n", + " fitted parameter e.g. 'pr_dc'.\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global_bin'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module_bin'.\n", + "\n", + " z_axis : string\n", + " value as a colour surface plot e.f. 'diff_pr_dc'.\n", + "\n", + " clip : value\n", + " clipping of z axis usually 0.02\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " \"\"\"\n", + " df_piv = pd.pivot_table(\n", + " dnorm,\n", + " index=y_axis, # e.g. 'temp_module_bin'\n", + " columns=x_axis, # e.g. 'poa_global_bin'\n", + " values=z_axis, # value to aggregate\n", + " fill_value=0, # fill empty cells with this ?\n", + " aggfunc=[np.mean], # e.g. min, np.sum, len->count\n", + " margins=False, # grand totals hide\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " # force z limits to be -2% to +2% if desired\n", + " df_piv = df_piv.clip(lower=-clip, upper=+clip)\n", + "\n", + " im = ax1.imshow(\n", + " df_piv,\n", + " cmap='RdYlBu',\n", + " origin='lower'\n", + " )\n", + "\n", + " cbar = ax1.figure.colorbar(im, ax=ax1, shrink=0.75, label=z_axis)\n", + "\n", + " # Y AXIS : show only 1 of each y_skip labels\n", + " y_ticks = df_piv.shape[0]\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = df_piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " # X AXIS : show only 1 of each x_skip labels\n", + " x_ticks = df_piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = df_piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_title(title)\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid(color='k', linestyle=':', linewidth=1)\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(\n", + " os.path.join('mlfm_data', 'output', 'heatmap_' + title[:len(title)-4]),\n", + " dpi=300\n", + " )\n", + " \n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Residual LFM fit heatmap vs. poa_global and temp_module" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot heatmap\n", + "heatmap_plot = plot_heatmap(\n", + " dnorm=norm,\n", + " fit=lfm_sel,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis='diff_' + lfm_sel,\n", + " clip=0.025,\n", + " title='residual_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [I] Residual LFM fit heatmap vs. poa_global bin (x) and temp_module bin (y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_fit(dmeas, dnorm, fit, title, save_figs, coeffs):\n", + " \"\"\"Scatter plot fit to normalised measured.\n", + " \n", + " Parameters\n", + " ----------\n", + " dmeas : dataframe\n", + " measurements, must include 'poa_global_kwm2'\n", + "\n", + " dnorm : dataframe\n", + " normalised multiplicative lfm loss values 'i_sc' .. 'v_oc'\n", + " where pr_dc = 1/ff * product('i_sc', ... 'v_oc').\n", + "\n", + " fit : string\n", + " name of fitted variable e.g. 'pr_dc'.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + " \n", + " \"\"\"\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " plt.title(title)\n", + "\n", + " plt.ylabel('fit ' + fit + ' * poa_global kW/m^2')\n", + " ax1.set_ylim(0, 1.2)\n", + "\n", + " plt.xlabel('meas ' + fit + '* poa_global_kW/m^2')\n", + " ax1.set_xlim(0, 1.2)\n", + "\n", + " plt.plot(\n", + " dnorm[fit] * dmeas['poa_global'] / G_STC,\n", + " dnorm['calc_' + fit] * dmeas['poa_global'] / G_STC,\n", + " 'c^',\n", + " label=fit\n", + " )\n", + "\n", + " # plot 1:1 line to show optimum fit\n", + " plt.plot((0, 1.2), (0, 1.2), 'yo-')\n", + " \n", + " # plot LIC, NOCT and STC irradiances\n", + " for x in (0.2, 0.8,1): \n", + " plt.plot((0, x), (x, x), 'k--')\n", + " plt.plot((x, x), (x, 0), 'k--')\n", + "\n", + " plt.legend(loc='upper left')\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(\n", + " os.path.join('mlfm_data', 'output', 'fit_meas_' + title[:len(title)-4]),\n", + " dpi=300\n", + " )\n", + " \n", + "\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot fit vs. measured, include a 1:1 line for comparison\n", + "fit_plot = plot_fit(dmeas=meas,\n", + " dnorm=norm,\n", + " fit=lfm_sel,\n", + " title='fit_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " save_figs=save_figs,\n", + " coeffs=coeffs\n", + " )\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [J] scatter plot of 'fit_lfm_sel * poa_global# (y) vs. 'measured_lfm_sel * poa_global' (x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [K] Read in complete (G,T) Matrix to fill with MLFM predicted values \n", + "\n", + "Read in a matrix with complete values of \n", + "Irradiance (G=100,200 .. 1100,1200) and module temperature (T=0,5 .. 65,70) \n", + "to predict all MPM values " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# read in the complete matrix data\n", + "matr = pd.read_csv(os.path.join(root_dir, 'mlfm_data', 'ref', 'mlfm_matrix.csv'),\n", + " index_col='id')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predict performance from MPM fit coefficients \n", + "\n", + "1. generate predicted mpm data \n", + "2. create a pivot table mpm(g,t) \n", + "3. show as a heat map" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('b', array([ 1.06780656, -0.0028452 , 0.14860936, -0.07080871, 0.01 ]))" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# show model coefficients\n", + "mpm_sel, coeffs" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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midpoa_globaltemp_modulewind_speedpr_dc
id
1matrix100000.995707
2matrix100500.978989
3matrix1001000.962271
4matrix1001500.945553
5matrix1002000.928834
6matrix1002500.912116
7matrix1003000.895398
8matrix1003500.878680
9matrix1004000.861962
10matrix1004500.845244
\n", + "
" + ], + "text/plain": [ + " mid poa_global temp_module wind_speed pr_dc\n", + "id \n", + "1 matrix 100 0 0 0.995707\n", + "2 matrix 100 5 0 0.978989\n", + "3 matrix 100 10 0 0.962271\n", + "4 matrix 100 15 0 0.945553\n", + "5 matrix 100 20 0 0.928834\n", + "6 matrix 100 25 0 0.912116\n", + "7 matrix 100 30 0 0.895398\n", + "8 matrix 100 35 0 0.878680\n", + "9 matrix 100 40 0 0.861962\n", + "10 matrix 100 45 0 0.845244" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# populate pivot table from predicted mpm data\n", + "if mpm_sel == 'a':\n", + " matr[lfm_sel] = mpm_a_calc(matr, *coeffs) # not mpm_sel\n", + " \n", + "if mpm_sel == 'b':\n", + " matr[lfm_sel] = mpm_b_calc(matr, *coeffs) # not mpm_sel\n", + "\n", + "\n", + "matr.head(10)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [L] Plot heatmap of predicted LFM values vs. temp_mod, poa_global bins" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_contourf(df, x_axis, y_axis, z_axis, title,\n", + " vmin=0, vmax=1.2, levels=5,\n", + " save_figs=False):\n", + " \"\"\"Plot filled contour plot Z vs. X and Y bins.\n", + "\n", + " Parameters\n", + " ----------\n", + " df : dataframe\n", + " measured or normalised data containing weather columns\n", + " (poa_global, temp_module and wind_speed).\n", + "\n", + " x_axis : string\n", + " binned x axis e.g. 'poa_global'.\n", + "\n", + " y_axis : string\n", + " binned y axis e.g. 'temp_module'.\n", + "\n", + " z_axis : string\n", + " measured value as a colour surface plot.\n", + "\n", + " title : string\n", + " title for graph e.g. mlfm_meas_file.\n", + "\n", + " vmin, vmax : float\n", + " minimum and maximum values for contour chart ###\n", + " \n", + " \"\"\"\n", + " piv = pd.pivot_table(\n", + " df,\n", + " index=y_axis,\n", + " columns=x_axis,\n", + " values=z_axis,\n", + " fill_value=0, # fill empty cells?\n", + " aggfunc=[np.mean], # min, np.sum, len->count\n", + " margins=False, # grand totals\n", + " dropna=True # hide missing rows or columns\n", + " )\n", + "\n", + " piv = piv.clip(vmin, vmax)\n", + "\n", + " fig, ax1 = plt.subplots()\n", + "\n", + " cs = plt.contourf(\n", + " piv,\n", + " cmap='RdYlBu', # or 'nipy_spectral',\n", + " # origin='lower'\n", + " # nchunkint=1,\n", + " levels=levels,\n", + " vmin=vmin,\n", + " vmax=vmax\n", + " )\n", + "\n", + " cbar = fig.colorbar(cs, ax=ax1)\n", + " cbar.ax.set_ylabel(z_axis,\n", + " rotation=90,\n", + " va='bottom',\n", + " labelpad=+30)\n", + "\n", + " plt.title(title)\n", + " # # get_yaxis().set_major_formatter(FormatStrFormatter('%.2f'))\n", + "\n", + " y_ticks = piv.shape[0]\n", + "\n", + " plt.yticks(np.arange(0, y_ticks), rotation=0)\n", + "\n", + " # show only 1 of each y_skip labels\n", + " yax2 = [''] * y_ticks\n", + " y_skip = 2\n", + " y_count = 0\n", + " while y_count < y_ticks:\n", + " if y_count % y_skip == 0:\n", + " yax2[y_count] = piv.index[y_count]\n", + " y_count += 1\n", + "\n", + " ax1.set_yticklabels(yax2)\n", + " ax1.set_ylabel(y_axis)\n", + "\n", + " x_ticks = piv.shape[1]\n", + " plt.xticks(np.arange(0, x_ticks), rotation=90)\n", + "\n", + " # show only 1 of each x_skip labels\n", + " xax2 = [''] * x_ticks\n", + " x_skip = 2\n", + " x_count = 0\n", + " while x_count < x_ticks:\n", + " if x_count % x_skip == 0:\n", + " xax2[x_count] = piv.columns.levels[1][x_count]\n", + " x_count += 1\n", + "\n", + " ax1.set_xticklabels(xax2)\n", + " ax1.set_xlabel(x_axis)\n", + "\n", + " ax1.grid(color='k', linestyle=':', linewidth=1)\n", + "\n", + " if save_figs:\n", + " # remove '.csv', high resolution= 300 dots per inch\n", + " plt.savefig(os.path.join('mlfm_data', 'output', 'contourf_'+ title[:len(title)-4])\n", + " , dpi=300\n", + " ) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# REMOVE LOW TEMPERATURE DATA WHICH MAY CONTAIN SNOW\n", + "\n", + "matr2 = matr[matr['temp_module'] >= 10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contour plot of predicted lfm_sel + vs. poa_global and temp_mod. " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df=matr2,\n", + " y_axis='temp_module',\n", + " x_axis='poa_global',\n", + " z_axis=lfm_sel,\n", + " title='matrix predicted_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [L1] Contour plot (colours) of predicted lfm_sel vs. poa_global (x) and temp_mod (y)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "contour_plot = plot_contourf(\n", + " df=norm,\n", + " y_axis='temp_module_bin',\n", + " x_axis='poa_global_bin',\n", + " z_axis=lfm_sel,\n", + " title='avg normalised_m' + mpm_sel + '_' + mlfm_meas_file,\n", + " vmin=0.7,\n", + " vmax=1.05,\n", + " levels=9,\n", + " save_figs=save_figs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig [L2] Contour plot (colours) of measured lfm_sel vs. poa_global (x) and temp_mod (y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References \n", + " \n", + "The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) \n", + "together known as \"MLFM\" have been developed by SRCL and Gantner Instruments \n", + "(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM \n", + " \n", + ".. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome \n", + " '4AV.2.41 Characterising PV Modules under Outdoor Conditions: \n", + "What's Most Important for Energy Yield' \n", + "26th EU PVSEC 8 September 2011; Hamburg, Germany \n", + "http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf \n", + "\n", + ".. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) \n", + " 'Choosing the best Empirical Model for predicting energy yield' \n", + " 7th PV Energy Rating and Module Performance Modeling Workshop, \n", + " Canobbio, Switzerland 30-31 March, 2017 \n", + "\n", + ".. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) \n", + "'Checking the new IEC 61853.1-4 with high quality 3rd party data to \n", + "benchmark its practical relevance in energy yield prediction' \n", + "PVSC June 2019 Chicago, USA \n", + "http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf\n", + "\n", + ".. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) \n", + "'5CV.4.35 Quantifying Long Term PV Performance and Degradation \n", + "under Real Outdoor and IEC 61853 Test Conditions \n", + "Using High Quality Module IV Measurements' \n", + "36th EU PVSEC Sep 2019 \n", + "http://www.steveransome.com/PUBS/1909_5CV4_35_PVSEC36_Marseille_Ransome_PPT.pdf\n", + "\n", + ".. [5] Steve Ransome (SRCL) \n", + "'How to use the Loss Factors and Mechanistic Performance Models \n", + "effectively with PVPMC/PVLIB' \n", + "PVPMC Webinar on PV Performance Modeling Methods, Aug 2020 \n", + "https://pvpmc.sandia.gov/download/7879/ \n", + "\n", + ".. [6] W.Marion et al (NREL) \n", + "'New Data Set for Validating PV Module Performance Models' \n", + "https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models \n", + "https://www.nrel.gov/docs/fy14osti/61610.pdf\n", + "\n", + ".. [7] Steve Ransome (SRCL)\n", + "'Benchmarking PV performance models with high quality IEC 61853 Matrix\n", + "measurements (Bilinear interpolation, SAPM, PVGIS, MLFM and 1-diode)'\n", + "http://www.steveransome.com/pubs/2206_PVSC49_philadelphia_4_presented.pdf\n", + "\n", + ".. [8] Juergen Sutterlueti (Gantner Instruments)\n", + "'Advanced system monitoring and artificial intelligent data-driven analytics \n", + "to serve GW-scale photovoltaic power plant and energy storage requirements'\n", + "https://pvpmc.sandia.gov/download/8574/\n", + "\n", + "Many more papers are available at www.steveransome.com \n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# =============================================================================================" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "## TEST CODE CAN DELETE AFTER HERE IF NOT NEEDED \n", + "\n", + "test = False\n", + "\n", + "if test: \n", + " # save meas data to csv\n", + " meas.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'meas.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " # save norm data to csv\n", + " norm.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'norm.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " # save matr data to csv\n", + " matr.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'matr.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " # save ref data to csv\n", + " ref_data.to_csv(\n", + " os.path.join('mlfm_data', 'output', 'ref_data.csv'),\n", + " sep=';',\n", + " quotechar='\"',\n", + " encoding='utf-8',\n", + " decimal='.'\n", + " )\n", + "\n", + " \n", + "if test:\n", + " # print mlfm fit coeffs\n", + " print(coeffs[0],coeffs[1],coeffs[2],coeffs[3],coeffs[4],) # coeffs[5], )#coeffs[6],)\n", + " \n", + "\n", + "if test:\n", + " # only works with mlfm_meas_file = 'g78_T16_Xall_F10m_R900_041.\n", + " #\n", + " # check data for test \n", + " \n", + " n= norm.loc['2016-03-23 09:00:00-07:00']\n", + "\n", + " print('\\n n= \\n', n)\n", + "\n", + " \n", + " n= norm.loc['2016-03-23 09:00:00-07:00']\n", + "\n", + " print('\\n n= \\n', n)\n", + "\n", + " s = stack.loc['2016-03-23 09:00:00-07:00']\n", + "\n", + " print('\\n s= \\n', s)\n", + "\n", + "\n", + "if test:\n", + " # show all versions\n", + " import sys \n", + " \n", + " for name, module in sorted(sys.modules.items()): \n", + " if hasattr(module, '__version__'): \n", + " print (name, module.__version__ )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# whos\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + 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zdH?N%b-hphe_~!(G{S!W0B4sokP%RUdOmrwUCR|#X!#Vgi$nwZq#(YN)qsNHkKccq zb=&)Z#j3H3Na#T~qcX}*l8#}VHS-ZesD&Oz6dH@3V6O&edYkBD97uhZQ&yYFO_At+vlONZq?$_N)Wp1r zaFn>gib|fVl>>Q4NO`fmg$Cz%&IeUo296l*nZ6h%Zayml{hK_y5!661_PA$qI>6O2 zLF6_zjU6j*6X(hBWBiIVQI&r?DyZxw`_s@%dAqdPiWoq(+(kk_;vK}>QXB((<_i1L z+Y=4Jf*f_F4?q|6A@Fu=F+BHfM}1kf*n_iV3MNfDbSgm5OTeRKA_2klp$c&$)4@@u zhuVpOSMLFdlDG#cA3AKS*DP#-$&JT8`qhrX%ciVqeZ=Y4u^T;mSD{onOv;)u_{e zd@)*Cy=l3U@tQrdT7~q1q_qUMq!Q1Ukdoie(mT88>%r1;UKsw?Kn)Xa`Hwy@q0Gi7 zW{wXboRGMSS?8xgxuv3Y3U*s-%l=w95(Il|Vq^XN{nfH4N~cvMWn`)`Pj(C=e3qG2 zM2lnnv|NzevUp|4Ljwq#*52OElu?LjtCI)(5|?c1aNI7nFQtH43R^!CcxkZR@8e;lFuJaKzu&J3E{zlCN9xHK-dFe*k8 z%lz|+SDMEe3p@InSTVL5Yoi83XH zv&)*K7jPb8n1Tf6=R6C1xLpzyVFljv8qYRzz~EVxcm0oU%+?^}(XK8*zzZ{(YB;@JZO!Q16&Ga<^mSRU>x=LDit`TQ!&tT2vepIcmu7 zP~He0{RVj@8Q4Nm|KgS%@o;EuU?lR^WPFoXsd;l2*m6~DeM^gA`bdh z0UR*XxXM7@G1f##_1k=4=)Qx|XE&q_@0Ke_*&wJXJ_)Ie2##vyeV5 z@-)7(2f0}hM=+{>%);U|B1A^vMQv?Qi$@Qr7EN)1=x$uHX91EBq}i3qGxUE4Cj-0T zZ)vj)nGR{tpxWj7FLr4AAhLN7qfW4}>)P9Ml0%3rAjevQRNa*juyj9A*3) gs-Z>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a import numpy as np import pandas as pd from scipy import optimize -<<<<<<< HEAD +# import pvlib + import os """ -ver : 221114t18 +ver : 221212t09 ``mlfm.py`` module contains functions to analyse, fit, predict and display performance of PV modules using the mechanistic performance model (MPM) and loss factors model (LFM). @@ -35,8 +18,6 @@ https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules https://github.com/python/peps/blob/master/pep-0008.txt -version : 221030t10 - OVERVIEW I) The Loss Factors Model (LFM) 2011 ref [1] quantifies @@ -338,24 +319,6 @@ # Define standardised LFM graph colours as a dict ``CLR`` CLR = { # parameter_CLR colour R G B -======= - -# DEFINE REFERENCE MEASUREMENT CONDITIONS -# or use existing definitions in pvlib - -# NAME value comment unit PV_LIB name -# -T_STC = 25.0 # STC temperature [C] temperature_ref -T_HTC = 75.0 # HTC temperature [C] -G_STC = 1000.0 # STC irradiance [W/m^2] -G_LIC = 0.2 # LIC irradiance [kW/m^2] - - -# Define standardised MLFM graph colours as a dict ``clr`` - -clr = { - # parameter_clr colour R G B ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a 'irradiance': 'darkgreen', # 000 064 000 'temp_module': 'red', # 255 000 000 'temp_air': 'yellow', # 245 245 220 @@ -375,83 +338,28 @@ } -<<<<<<< HEAD def meas_to_norm(dmeas, ref): """ Convert measured P(W), I(A), V(V), R(Ohms) to values normalized to STC. -======= -def mlfm_meas_to_norm(dmeas, ref): - ''' - Convert measured power, current and voltage to normalized values. ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a Parameters ---------- dmeas : DataFrame -<<<<<<< HEAD Measured weather and module electrical values per time or measurement. Contains 'poa_global', 'temp_module' and optional 'wind_speed' ref : dict Reference electrical and thermal datasheet module values at STC. -======= - Measurements. Must include columns: - - * `'poa_global'` global plane of array irradiance [W/m^2] - * `'temp_module'` module temperature [C] - * `'p_mp'` - power at maximum power point [W] - - May include optional columns: - - * `'i_sc'` - current at short circuit condition [A] - * `'v_oc'` - voltage at open circuit condition [V] - * `'i_mp'` - current at maximum power point [A]. Must be accompanied - by `'i_sc'`. - * `'v_mp'` - voltage at maximum power point [V]. Must be accompanied - by `'v_oc'`. - * `'r_sc'` - inverse of slope of IV curve at short circuit condition. - Requires both `'i_sc'` and `'v_oc'`. [Ohm] - * `'r_oc'` - inverse slope of IV curve at open circuit condition. - Requires both `'i_sc'` and `'v_oc'` [Ohm] - - ref : dict - Reference values. Must include: - - * `'p_mp'` - Power at maximum power point at Standard Test Condition - (STC). [W] - * `'gamma_pdc'` - Temperature coefficient of power at STC. [1/C] - - May include: - - * `'i_sc'` - Current at short circuit at STC. Required if `'i_sc'` is - present in ``dmeas``. [A] - * `'v_oc'` - Voltage at open circuit at STC. Required if `'v_oc'` is - present in ``dmeas``. [A] - * `'beta_v_oc'` - Temperature coefficient of open circuit voltage at - STC. Required if `'v_oc'` is present in ``dmeas``. [1/C] ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a Returns ------- dnorm : DataFrame -<<<<<<< HEAD Normalised multiplicative loss values (values approx 1). Contains 'poa_global', 'temp_module' and optional 'wind_speed' -======= - Normalised values. - - * `'pr_dc'` is `'p_mp'` normalised (divided) by reference `'p_mp'` \ - and by `'poa_global'` in kW/m^2. - * `'pr_dc_temp_corr'` is `'pr_dc'` adjusted to 25C. - * Columns `'i_sc'`, `'i_mp'`, `'v_oc'`, `'v_mp'`, `'v_oc_temp_corr'`, - `'r_sc'`, `'r_oc'`, `'i_ff'`, `'v_ff'` are returned when the - the corresponding optional columns are included in ``dmeas``. ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a References ---------- .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) -<<<<<<< HEAD 'Quantifying Long Term PV Performance and Degradation under Real Outdoor and IEC 61853 Test Conditions Using High Quality Module IV Measurements' 36th EU PVSEC, Marseille, France. September 2019. @@ -476,31 +384,11 @@ def mlfm_meas_to_norm(dmeas, ref): dnorm['i_sc'] = (dmeas['i_sc'] / ref['i_sc'] / (dmeas['poa_global'] / G_STC)) -======= - "Quantifying Long Term PV Performance and Degradation under Real Outdoor - and IEC 61853 Test Conditions Using High Quality Module IV Measurements" - 36th EU PVSEC, Marseille, France. September 2019 - ''' - dnorm = pd.DataFrame() - - dnorm['pr_dc'] = dmeas['p_mp'] / ref['p_mp'] \ - / (dmeas['poa_global'] / G_STC) - - # temperature corrected - dnorm['pr_dc_temp_corr'] = ( - dnorm['pr_dc'] * - (1 - ref['gamma_pdc']*(dmeas['temp_module'] - T_STC))) - - if 'i_sc' in dmeas.columns: - dnorm['i_sc'] = dmeas['i_sc'] / (dmeas['poa_global'] / G_STC) \ - / ref['i_sc'] ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a if 'i_mp' in dmeas.columns: dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] if 'v_oc' in dmeas.columns: dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] -<<<<<<< HEAD # temperature corrected dnorm['v_oc_temp_corr'] = ( @@ -522,24 +410,6 @@ def mlfm_meas_to_norm(dmeas, ref): v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) -======= - if 'v_mp' in dmeas.columns: - dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] - # temperature corrected - dnorm['v_oc_temp_corr'] = dnorm['v_oc'] * \ - (1 - ref['beta_v_oc']*(dmeas['temp_module'] - T_STC)) - - if all(c in dmeas.columns for c in ['i_sc', 'v_oc', 'r_sc', 'r_oc']): - # create temporary variables (i_r, v_r) from - # intercept of r_sc (at i_sc) with r_oc (at v_oc) - # to make maths easier - - i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / - (dmeas['r_sc'] - dmeas['r_oc'])) - - v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a # calculate normalised resistances r_sc and r_oc dnorm['r_sc'] = i_r / dmeas['i_sc'] # norm_r @ isc @@ -551,15 +421,144 @@ def mlfm_meas_to_norm(dmeas, ref): return dnorm - -<<<<<<< HEAD -def mpm_a_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): +''' +def mpm_fit(data, var_to_fit, mpm_sel): + + print ("var_to_fit, mpm_sel = ", var_to_fit, mpm_sel) """ - Predict norm LFM values from weather data (g,t,w) in ``dmeas``. + Fit mpm to normalised measured data 'var_to_fit' using mpm_sel model. + mpm_sel == a : const temp_coeff low_light high_light wind extra | | | | | | - norm = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + fit = = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + mpm_sel == b: + const temp_coeff low_light improvement high_light ws + | | | | | | + fit =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters + ---------- + data : DataFrame (see norm) + Normalised multiplicative loss values (values approx 1). + + var_to_fit : string + Column name in ``data`` containing variable being fitted. + e.g. pr_dc, i_mp, v_mp, v_oc ... + + mpm_sel : char + MPM version 'a' or 'b' + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients ``c_1`` to ``c_6``. + + resid : Series + Residuals of the fitted model. + + coeff_err : list + Standard deviation of error in each model coefficient. + + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html + + infodict : dict + a dictionary of optional outputs with keys + nfev - The number of function calls. + fvec - The function values evaluated at the solution. + etc. + + mesg : string + A string message giving information about the solution. + + ier : int + An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. + Otherwise, the solution was not found. + + """ + # drop any missing data + data = data.dropna() + + c5_zero = 'wind_speed' not in data.columns + # if wind_speed is not present, add it and force it to 0 + if c5_zero: + data['wind_speed'] = 0. + + # define function name + func = mpm_calc # (data, mpm_sel, *coeff) + + + if mpm_sel == 'a': + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 c6<0 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2, 0]) + + else : # if mpm_sel == 'b': + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2]) + + coeff, pcov, infodict, mesg, ier = optimize.curve_fit( + f=func, # fit function + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter + p0=p_0, # initial + bounds=bounds, # boundaries + full_output=True + ) + + # if data had no wind_speed measurements then c_5 coefficient is + # meaningless but a non-zero value may have been returned. + if c5_zero: + coeff[4] = 0. + + # get error of mpm coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) + + # save fit and error to dataframe + pred = mpm_calc(data, mpm_sel, *coeff) + + resid = pred - data[var_to_fit] + + return pred, coeff, resid, coeff_err, infodict, mesg, ier + + + +def mpm_calc(dmeas, mpm_sel, c_1, c_2, c_3, c_4, c_5=0., c_6=0.,): + + print("mpm_sel, c_1, c_2, c_3, c_4, = ", mpm_sel, c_1, c_2, c_3, c_4,) + + """ + Predict norm LFM values from weather data (g,t,w) in ``dmeas``. + + if mpm_sel == 'a': + const temp_coeff low_light high_light wind extra + | | | | | | + norm = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + if mpm_sel == 'b': + const temp_coeff low_light improvement high_light ws + | | | | | | + norm =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + where : g = G_POA (W/m^2) / G_STC --> 'suns' @@ -571,6 +570,10 @@ def mpm_a_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): dmeas : DataFrame Measured weather and module electrical values per time or measurement. Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + mpm_sel : + mpm_sel : char + MPM version 'a' or 'b' c_1 : float Constant term in model. [%] @@ -587,62 +590,82 @@ def mpm_a_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): Returns ------- - mpm_a_out : Series + mpm_out : Series Predicted values of mpm coefficient. -======= -def mlfm_norm_to_stack(dnorm, fill_factor): - ''' - Converts normalised values to stacked subtractive normalized losses. - Normalized values can reveal losses via scatter plots vs. irradiance or - temperature. + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + + """ + + # print ('mpm_sel = ', mpm_sel) + + + if mpm_sel == 'a': + mpm_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * np.log10(dmeas['poa_global'] / G_STC) + + c_4 * (dmeas['poa_global'] / G_STC) + + c_6 / (dmeas['poa_global'] / G_STC) + ) + + if 'wind_speed' in dmeas.columns: + mpm_out += c_5 * dmeas['wind_speed'] - Stacked subtractive losses can show relative loss proportions. Stacked - losses partition the difference between the normalized power and the - power that corresponds to the reference fill factor. + else : # if mpm_sel == 1: + mpm_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * ((np.log10(dmeas['poa_global'] / G_STC) + * (dmeas['temp_module'] + T0C_K) / T25C_K)) + + c_4 * (dmeas['poa_global'] / G_STC) + ) + + return mpm_out - Parameters - ---------- - dnorm : DataFrame - Normalised values. Must include columns: - * `'pr_dc'` normalized power at the maximum power point. - * `'i_sc'` normalized short circuit current. - * `'i_mp'` normalized current at maximum power point. - * `'v_oc'` normalized open circuit voltage. - * `'v_mp'` normalized voltage at maximum power point. - * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. +''' +def mpm_a_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): + """ + Predict norm LFM values from weather data (g,t,w) in ``dmeas``. - May include optional columns: + const temp_coeff low_light high_light wind extra + | | | | | | + norm = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g - * `'v_ff'` normalized multiplicative loss in fill factor apportioned - to voltage. - * `'i_ff'` normalized multiplicative loss in fill factor apportioned - to current. - * `'r_oc'` normalized slope of IV curve at open circuit. - * `'r_sc'` normalized slope of IV curve at short circuit. + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) - fill_factor : float - Reference value of fill factor for IV curve at STC conditions. + Parameters [units] + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + c_1 : float + Constant term in model. [%] + c_2 : float + Temperature coefficient in model. [1/C] + c_3 : float + Coefficient for low light log irradiance drop. [suns] + c_4 : float + Coefficient for high light linear irradiance drop. [1/suns] + c_5 : float, default 0 + Coefficient for wind speed dependence optional. [1/(m/s)] + c_6 : float, default 0 [suns] + Coefficient for dependence on inverse irradiance. Returns ------- - dstack : DataFrame - Stacked subtractive normalized losses. Includes columns: - - * `'pr_dc'` equal to `dnorm['pr_dc']`. - * `'i_sc'` - * `'r_sc'` - * `'i_mp'` - * `'i_v'` - * `'v_mp'` - * `'v_oc'` - * `'temp_module_corr'` - - See also - -------- - mlfm_meas_to_norm ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a + mpm_a_out : Series + Predicted values of mpm coefficient. References ---------- @@ -650,7 +673,6 @@ def mlfm_norm_to_stack(dnorm, fill_factor): "Quantifying Long Term PV Performance and Degradation under Real Outdoor and IEC 61853 Test Conditions Using High Quality Module IV Measurements" 36th EU PVSEC, Marseille, France. September 2019 -<<<<<<< HEAD """ mpm_a_out = ( @@ -688,165 +710,6 @@ def mpm_a_fit(data, var_to_fit): var_to_fit : string Column name in ``data`` containing variable being fitted. e.g. pr_dc, i_mp, v_mp, v_oc ... -======= - ''' - - # create an empty DataFrame to put stack results - dstack = pd.DataFrame() - - # create a gap to differentiate i and v losses : gap width~0.01 - gap = 0.01 - - inv_ff = 1 / fill_factor - - if all(c in dnorm.columns for c in ['v_ff', 'r_oc', 'i_ff', 'r_sc']): - - # include effects of series and shunt resistances in stacked losses - # find factor to transform multiplicative to subtractive losses - # correction factor to scale losses to keep 1/ff --> pr_dc - - # product - prod = inv_ff * ( - dnorm['i_sc'] * dnorm['r_sc'] * dnorm['i_ff'] * - dnorm['v_ff'] * dnorm['r_oc'] * dnorm['v_oc'] - ) - - # total - tot = inv_ff + ( - dnorm['i_sc'] + dnorm['r_sc'] + dnorm['i_ff'] + - dnorm['v_ff'] + dnorm['r_oc'] + dnorm['v_oc'] - 6 - ) - - # correction factor - corr = (inv_ff - prod) / (inv_ff - tot) - - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) - # accounting for series and shunt resistance losses - dstack['pr_dc'] = +dnorm['pr_dc'] # initialise - dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr - dstack['r_sc'] = -(dnorm['r_sc'] - 1) * corr - dstack['i_ff'] = -(dnorm['i_ff'] - 1) * corr - gap/2 - dstack['i_v'] = gap - dstack['v_ff'] = -(dnorm['v_ff'] - 1) * corr - gap/2 - dstack['r_oc'] = -(dnorm['r_oc'] - 1) * corr - dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr - dstack['temp_module_corr'] = ( - -(dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) - - return dstack - - # subtractive losses without series and shunt resistance effects - # find factor to transform multiplicative to subtractive losses - # correction factor to scale losses to keep 1/ff --> pr_dc - - prod = inv_ff * ( - dnorm['i_sc'] * dnorm['i_mp'] * - dnorm['v_mp'] * dnorm['v_oc'] - ) - - tot = inv_ff + ( - dnorm['i_sc'] + dnorm['i_mp'] + - dnorm['v_mp'] + dnorm['v_oc'] - 4 - ) - - corr = (inv_ff - prod) / (inv_ff - tot) - - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) - dstack['pr_dc'] = + dnorm['pr_dc'] # initialise - dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr - dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 - dstack['i_v'] = gap - dstack['v_mp'] = -(dnorm['v_mp'] - 1) * corr - gap/2 - dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr - - dstack['temp_module_corr'] = ( - - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) - - return dstack - - -def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): - r''' - Predict normalised LFM values from data in ``dmeas``. - - The normalized LFM values are given by - - .. math:: - - c_1 + c_2 (T_m - 25) + c_3 \log10(G_{POA}) + c_4 G_{POA} - + c_5 WS + c_6 / G_{POA} - - where :math:`G_{POA}` is global plane-of-array (POA) irradiance in kW/m2, - :math:`T_m` is module temperature in C and :math:`WS` is wind speed in - m/s. - - Parameters - ---------- - dmeas : DataFrame - Must include columns: - - * `'poa_global'` global plane of array irradiance. [W/m^2] - * `'temp_module'` module temperature. [C] - - May include optional column: - - * `'wind_speed'` wind speed [m/s]. - - c_1 : float - Constant term in model. - c_2 : float - Temperature coefficient in model. [1/C] - c_3 : float - Coefficient for low light log irradiance drop. - c_4 : float - Coefficient for high light linear irradiance drop. - c_5 : float, default 0 - Coefficient for wind speed dependence. - c_6 : float, default 0 - Coefficient for dependence on inverse irradiance. - - Returns - ------- - mlfm_6 : Series - Predicted values. - - References - ---------- - .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) - "Quantifying Long Term PV Performance and Degradation under Real Outdoor - and IEC 61853 Test Conditions Using High Quality Module IV Measurements" - 36th EU PVSEC, Marseille, France. September 2019 - ''' - mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ - c_3 * np.log10(dmeas['poa_global'] / G_STC) + \ - c_4 * (dmeas['poa_global'] / G_STC) + \ - c_6 / (dmeas['poa_global'] / G_STC) - if 'wind_speed' in dmeas.columns: - mlfm_out += c_5 * dmeas['wind_speed'] - return mlfm_out - - -def mlfm_fit(data, var_to_fit): - ''' - Fit MLFM to data. - - Parameters - ---------- - data : DataFrame - Must include columns: - - * 'poa_global' global plane of array irradiance. [W/m^2] - * 'temp_module' module temperature. [C] - - Must include column named ``var_to_fit``. - - May include optional column: - - * 'wind_speed' wind speed [m/s]. - - var_to_fit : string - Column name in ``data`` containing variable being fit. ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a Returns ------- @@ -862,7 +725,6 @@ def mlfm_fit(data, var_to_fit): coeff_err : list Standard deviation of error in each model coefficient. -<<<<<<< HEAD See Also -------- mpm_a_calc @@ -873,23 +735,10 @@ def mlfm_fit(data, var_to_fit): c5_zero = 'wind_speed' not in data.columns # if wind_speed is not present, add it and force it to 0 -======= - See also - -------- - mlfm_6 - ''' - - # drop missing data - data = data.dropna() - - c5_zero = 'wind_speed' not in data.columns - # if wind_speed is not present, add it ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a if c5_zero: data['wind_speed'] = 0. # define function name -<<<<<<< HEAD func = mpm_a_calc # setup initial values and initial boundary conditions @@ -900,63 +749,38 @@ def mlfm_fit(data, var_to_fit): bounds = ([-2, -2, -2, -2, -2, -2], [+2, +2, +2, +2, +2, 0]) - coeff, pcov = optimize.curve_fit( + """ + # full_outputboolean, optional + If True, this function returns additioal information: + infodict, mesg, and ier. + """ + + coeff, pcov, infodict, mesg, ier = optimize.curve_fit( f=func, # fit function xdata=data, # input data ydata=data[var_to_fit], # fit parameter p0=p_0, # initial bounds=bounds, # boundaries - # full_output=True + full_output=True ) # if data had no wind_speed measurements then c_5 coefficient is -======= - func = mlfm_6 - - # setup initial values and initial boundary conditions - - # initial c1 c2 c3 c4 c5 c6<0 - p_0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) - # boundaries - bounds = ([ -2, -2, -2, -2, -2, -2], - [ 2, 2, 2, 2, 2, 0]) - - coeff, pcov = optimize.curve_fit( - f=func, # fit function - xdata=data, # input data - ydata=data[var_to_fit], # fit parameter - p0=p_0, # initial - bounds=bounds # boundaries - ) - - # if data has no wind_speed measurements then c_5 coefficient is ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a # meaningless but a non-zero value may have been returned. if c5_zero: coeff[4] = 0. -<<<<<<< HEAD # get error of mpm coefficients as sqrt of covariance -======= - # get error of mlfm coefficients as sqrt of covariance ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a perr = np.sqrt(np.diag(pcov)) coeff_err = list(perr) # save fit and error to dataframe -<<<<<<< HEAD pred = mpm_a_calc(data, *coeff) -======= - pred = mlfm_6(data, coeff[0], coeff[1], coeff[2], coeff[3], coeff[4], - coeff[5]) ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a resid = pred - data[var_to_fit] - return pred, coeff, resid, coeff_err + return pred, coeff, resid, coeff_err, infodict, mesg, ier -<<<<<<< HEAD def mpm_b_fit(data, var_to_fit): """ Fit mpm_b to normalised measured data 'var_to_fit' using mpm_b model. @@ -1012,13 +836,13 @@ def mpm_b_fit(data, var_to_fit): bounds = ([-2, -2, -2, -2, -2], [+2, +2, +2, +2, +2]) - coeff, pcov = optimize.curve_fit( + coeff, pcov, infodict, mesg, ier = optimize.curve_fit( f=func, # fit function xdata=data, # input data ydata=data[var_to_fit], # fit parameter p0=p_0, # initial bounds=bounds, # boundaries - # full_output=True + full_output=True ) # get error of mpm coefficients as sqrt of covariance @@ -1032,7 +856,7 @@ def mpm_b_fit(data, var_to_fit): # fvec = infodict["fvec"] - return pred, coeff, resid, coeff_err + return pred, coeff, resid, coeff_err, infodict, mesg, ier def mpm_b_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0.): @@ -1094,66 +918,28 @@ def plot_scatter(dnorm, title, qty_lfm_vars, save_figs=False): Scatterplot of normalised values (y) vs. irradiance (x). Electrical quantities are plotted on the left y-axis, temperature -======= -def plot_mlfm_scatter(dmeas, dnorm, title): - ''' - Scatterplot of normalised values (y) vs. irradiance (x). - - Electrical quantities are plotted on the left y-axis, and temperature ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a quantities are plotted on the right y-axis. Parameters ---------- -<<<<<<< HEAD dnorm : DataFrame Normalised multiplicative loss values (values approx 1). Contains 'poa_global', 'temp_module' and optional 'wind_speed' -======= - dmeas : DataFrame - Measurements. Must include columns: - - * `'poa_global'` global plane of array irradiance [W/m^2] - * `'temp_module'` module temperature [C] - - May include optional columns: - - * `'temp_air'` - air temperature [C] - - dnorm : DataFrame - Normalised values. May include columns: - - * `'pr_dc_temp_corr'` normalized power at the maximum power point. - * `'i_sc'` normalized short circuit current. - * `'i_mp'` normalized current at maximum power point. - * `'v_mp'` normalized voltage at maximum power point. - * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. - * `'v_ff'` normalized multiplicative loss in fill factor apportioned - to voltage. - * `'i_ff'` normalized multiplicative loss in fill factor apportioned - to current. - * `'r_oc'` normalized slope of IV curve at open circuit. - * `'r_sc'` normalized slope of IV curve at short circuit. ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a title : string Title for the figure. -<<<<<<< HEAD qty_lfm_vars : int number of lfm_vars : 6=iv with rsc, roc ; 4=indoor save_figs : boolean save a high resolution png file of figure -======= ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a Returns ------- fig : Figure Instance of matplotlib.figure.Figure -<<<<<<< HEAD See Also -------- meas_to_norm @@ -1163,27 +949,12 @@ def plot_mlfm_scatter(dmeas, dnorm, title): import matplotlib.pyplot as plt except ImportError: raise ImportError('plot_scatter requires matplotlib') -======= - See also - -------- - mlfm_meas_to_norm - ''' - try: - import matplotlib.pyplot as plt - except ImportError: - raise ImportError('plot_mlfm_scatter requires matplotlib') ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a # offset legend to the right to not overlap graph, use ~1.2 bbox = 1.2 -<<<<<<< HEAD # set x_axis as irradiance in W/m2 xdata = dnorm['poa_global'] -======= - # set x_axis as irradiance in kW/m2 - xdata = dmeas['poa_global'] / G_STC ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a fig, ax1 = plt.subplots() @@ -1191,7 +962,6 @@ def plot_mlfm_scatter(dmeas, dnorm, title): ax1.set_ylabel('Normalised values') ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line -<<<<<<< HEAD # optional normalised y scale usually ~0.8 to 1.1 ax1.set_ylim(0.8, 1.1) @@ -1247,50 +1017,11 @@ def plot_mlfm_scatter(dmeas, dnorm, title): ax1.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) -======= - ax1.set_ylim(0.8, 1.1) # optional normalised y scale - - ax1.set_xlabel('Plane of array irradiance [kW/m$^2$]') - ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC - ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT - ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC - - lines = { - 'pr_dc_temp_corr': 'pr_dc', - 'i_mp': 'i_mp', - 'v_mp': 'v_mp', - 'i_sc': 'i_sc', - 'r_sc': 'r_sc', - 'r_oc': 'r_oc', - 'i_ff': 'i_ff', - 'v_ff': 'v_ff', - 'v_oc_temp_corr': 'v_oc'} - labels = { - 'pr_dc_temp_corr': 'pr_dc_temp-corr', - 'i_mp': 'norm_i_mp', - 'v_mp': 'norm_v_mp', - 'i_sc': 'norm_i_sc', - 'r_sc': 'norm_r_sc', - 'r_oc': 'norm_r_oc', - 'i_ff': 'norm_i_ff', - 'v_ff': 'norm_v_ff', - 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} - - # plot the mlfm parameters depending on qty_mlfm_vars - for k in lines.keys(): - try: - ax1.scatter(xdata, dnorm[k], c=clr[lines[k]], label=labels[k]) - except KeyError: - pass - - ax1.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a # y2axis plot met on right y axis ax2 = ax1.twinx() ax2.set_ylabel('Temperature (C/100)') -<<<<<<< HEAD # set wide limits 0 to 4 so they don't overlap with LFM params ax2.set_ylim(0, 4) @@ -1298,20 +1029,10 @@ def plot_mlfm_scatter(dmeas, dnorm, title): dnorm['temp_module']/T_MAX, c=CLR['temp_module'], label='temp_module C/' + str(T_MAX)) -======= - # set wide limits 0 to 4 so they don't overlap mlfm params - ax2.set_ylim(0, 4) - - ax2.scatter(xdata, - dmeas['temp_module']/100, - c=clr['temp_module'], - label='temp_module C/100') ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a # temp_air may not exist particularly for indoor measurements try: ax2.scatter(xdata, -<<<<<<< HEAD dnorm['temp_air']/T_MAX, c=CLR['temp_air'], label='temp_air C/' + str(T_MAX)) @@ -1327,21 +1048,11 @@ def plot_mlfm_scatter(dmeas, dnorm, title): plt.savefig(os.path.join('mlfm_data', 'output', 'scatter_' + title[:len(title)-4]), dpi=300) -======= - dmeas['temp_air']/100, - c=clr['temp_air'], - label='temp_air C/100') - except KeyError: - pass - - ax2.legend(bbox_to_anchor=(bbox, 0.5), loc='upper left', borderaxespad=0.) ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a plt.show() return fig -<<<<<<< HEAD def plot_stack(dstack, fill_factor, title, xaxis_labels=0, is_i_sc_self_ref=False, save_figs=False @@ -1353,36 +1064,6 @@ def plot_stack(dstack, fill_factor, title, ---------- dstack : DataFrame Stacked subtractive losses. -======= -def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, - xaxis_labels=0, is_i_sc_self_ref=False, - is_v_oc_temp_module_corr=True): - - ''' - Plot stacked subtractive losses. - - Parameters - ---------- - dmeas : DataFrame - Measurements. Must include columns: - - * `'poa_global'` global plane of array irradiance [W/m^2] - * `'temp_module'` module temperature [C] - - May include optional columns: - - * `'temp_air'` - air temperature [C] - - dnorm : DataFrame - Normalised values. Must contain column `'pr_dc'`. - - dstack : DataFrame - Stacked subtractive losses. Must contain columns `'v_oc'`, `'v_mp'`, - `'i_v'`, `'i_mp'`, `'i_sc'`, `'temp_module_corr'`. If optional columns - `'r_oc'` and `'v_ff'` are present, these columns are plotted instead - of `'v_mp'`. If optional columns `'r_sc'` and `'i_ff'` are present, - these columns are plotted instead of `'i_mp'`. ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a fill_factor : float Reference value of fill factor for IV curve at STC conditions. @@ -1394,7 +1075,6 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, Number of x-axis labels to show. Default 0 shows all. is_i_sc_self_ref : bool, default False -<<<<<<< HEAD Self-correct ``i_sc`` to remove angle of incidence, spectrum, snow or soiling. @@ -1403,20 +1083,12 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, # is_v_oc_temp_module_corr : bool, default True # Calculate loss due to temperature and subtract from ``v_oc`` loss. -======= - Self-correct `'i_sc'` to remove angle of incidence, - spectrum, snow or soiling. - - is_v_oc_temp_module_corr : bool, default True - Calculate loss due to temperature and subtract from `'v_oc'` loss. ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a Returns ------- fig : Figure Instance of matplotlib.figure.Figure -<<<<<<< HEAD See Also -------- norm_to_stack @@ -1437,36 +1109,13 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, ydata = [dstack['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), dstack['v_oc_temp_corr'], dstack['temp_module_corr'], -======= - See also - -------- - mlfm_norm_to_stack - ''' - try: - import matplotlib.pyplot as plt - except ImportError: - raise ImportError('plt_mlfm_stack requires matplotlib') - - stack6 = ['i_sc', 'r_sc', 'i_ff', 'i_v', 'v_ff', 'r_oc', 'v_oc'] - stack4 = ['i_sc', 'i_mp', 'i_v', 'v_mp', 'v_oc'] - - if all([c in dstack.columns for c in stack6]): - # data order from bottom to top - ydata = [dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a dstack['r_oc'], dstack['v_ff'], dstack['i_v'], dstack['i_ff'], dstack['r_sc'], dstack['i_sc'] * (not is_i_sc_self_ref)] -<<<<<<< HEAD -======= ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a labels = [ 'pr_dc', 'stack_t_mod', @@ -1477,7 +1126,6 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, 'stack_i_ff', 'stack_r_sc', 'stack_i_sc'] -<<<<<<< HEAD color_map = [ 'white', # colour to bottom of graph @@ -1499,33 +1147,11 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, ydata = [dstack['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), dstack['v_oc_temp_corr'], dstack['temp_module_corr'], -======= - color_map = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['r_oc'], - clr['v_ff'], - clr['i_v'], - clr['i_ff'], - clr['r_sc'], - clr['i_sc']] - - if all([c in dstack.columns for c in stack4]): - # data order from bottom to top - ydata = [dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a dstack['v_mp'], dstack['i_v'], dstack['i_mp'], dstack['i_sc'] * (not is_i_sc_self_ref)] -<<<<<<< HEAD -======= ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a labels = [ 'pr_dc', 'stack_t_mod', @@ -1534,7 +1160,6 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, '- - -', 'stack_i_mp', 'stack_i_sc'] -<<<<<<< HEAD color_map = [ 'white', # colour to bottom of graph @@ -1544,26 +1169,12 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, CLR['i_v'], CLR['i_mp'], CLR['i_sc']] -======= - color_map = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['v_mp'], - clr['i_v'], - clr['i_mp'], - clr['i_sc']] ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a # offset legend right, use ~1.2 bbox = 1.2 # select x axis usually date_time -<<<<<<< HEAD xdata = dstack.index.values -======= - xdata = dmeas.index.values ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a fig, ax1 = plt.subplots() ax1.set_title(title) @@ -1574,17 +1185,10 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, ax1.axhline(y=1/fill_factor, c='grey', lw=3) # show initial 1/FF ax1.axhline(y=1, c='grey', lw=3) # show 100% line -<<<<<<< HEAD ax1.set_ylabel('stacked lfm losses') # find number of x date values x_ticks = dstack.shape[0] -======= - ax1.set_ylabel('stacked mlfm losses') - - # find number of x date values - x_ticks = dmeas.shape[0] ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a plt.xticks(np.arange(0, x_ticks), rotation=90) # if (xaxis_labels > 0 and xaxis_labels < x_ticks): @@ -1619,7 +1223,6 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, # plot met data on right y axis ax2 = ax1.twinx() -<<<<<<< HEAD ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/ ' + str(T_MAX)) ax2.set_ylim(0, 4) # set so doesn't overlap lfm params @@ -1632,27 +1235,12 @@ def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, try: plt.plot(xdata, dstack['temp_air']/100, c=CLR['temp_air'], label='temp_air/ ' + str(T_MAX)) -======= - ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/100)') - ax2.set_ylim(0, 4) # set so doesn't overlap mlfm params - - plt.plot(xdata, dmeas['poa_global'] / G_STC, - c=clr['irradiance'], label='poa_global (kW/m^2)') - plt.plot(xdata, dmeas['temp_module'] / 100, - c=clr['temp_module'], label='temp_module / 100') - - # temp_air may not exist particularly for indoor measurements - try: - plt.plot(xdata, dmeas['temp_air']/100, - c=clr['temp_air'], label='temp_air/100') ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a except KeyError: pass ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) ax1.set_xticklabels(xax2, rotation=90) -<<<<<<< HEAD # remove '.csv', high resolution= 300 dots per inch plt.savefig(os.path.join('mlfm_data', 'output', 'stack_' + title[:len(title)-4]), dpi=300) @@ -1788,12 +1376,6 @@ def meas_to_stack_lin(dmeas, ref, qty_lfm_vars, gap=0.01): """ -======= - return fig - - -REFS = """ ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) together known as "MLFM" have been developed by SRCL and Gantner Instruments (previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM @@ -1830,7 +1412,6 @@ def meas_to_stack_lin(dmeas, ref, qty_lfm_vars, gap=0.01): .. [6] W.Marion et al (NREL) 'New Data Set for Validating PV Module Performance Models'. https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models -<<<<<<< HEAD Many more papers are available at www.steveransome.com .. [7] Steve Ransome (SRCL) @@ -1843,8 +1424,4 @@ def meas_to_stack_lin(dmeas, ref, qty_lfm_vars, gap=0.01): to serve GW-scale photovoltaic power plant and energy storage requirements' https://pvpmc.sandia.gov/download/8574/ -======= - -Many more papers are available at www.steveransome.com ->>>>>>> 3107769a0c59d61b66940ec85392605cec41b73a """ diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 08ee796754..7514867931 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -1,11 +1,13 @@ - import numpy as np import pandas as pd + from pvlib import mlfm -from numpy.testing import assert_allclose # assert_almost_equal, + import pytest -from .conftest import requires_mpl, assert_frame_equal +from conftest import requires_mpl, assert_frame_equal + +from numpy.testing import assert_allclose tolerance = 0.000001 @@ -260,3 +262,24 @@ def test_plot_mlfm_stack(measured, normalized, stacked6, stacked4, reference): import matplotlib.pyplot as plt fig = mlfm.plot_mlfm_stack(m, n, s4, reference['ff'], 'stacked 4 plot') assert isinstance(fig, plt.Figure) + + +""" + +remove + +reference() +measured() +normalized() +stacked6() +stacked4() +mlfm_6_coeffs() +matrix_data() +mlfm_6_fit() +test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized) +test_mlfm_6(measured, mlfm_6_coeffs) +test_mlfm_norm_to_stack(normalized, reference, stacked6, stacked4) +test_mlfm_fit(matrix_data, mlfm_6_fit) +test_plot_mlfm_scatter(measured, normalized) +test_plot_mlfm_stack(measured, normalized, stacked6, stacked4, reference) +""" \ No newline at end of file From 775b3acf00dc8bf0f0a3080e688108c58c3f625a Mon Sep 17 00:00:00 2001 From: steve ransome Date: Mon, 12 Dec 2022 18:52:38 +0000 Subject: [PATCH 73/81] Add files via upload --- pvlib/mlfm.py | 2231 +++++++++++++++++++++++++++++++------------------ 1 file changed, 1427 insertions(+), 804 deletions(-) diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 9e5e549d67..5851b56871 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -1,804 +1,1427 @@ -''' -This ``mlfm code`` module contains functions to analyse and predict -performance of PV modules using the mechanistic performance (MPM) and -loss factors models (LFM). The module also contains functions to display -performance of PV modules using the mechanistic performance (MPM) and -loss factors models (LFM) - -Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) -Thanks to Cliff Hansen (Sandia National Laboratories) - -https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules - -https://github.com/python/peps/blob/master/pep-0008.txt -''' - -import numpy as np -import pandas as pd -from scipy import optimize - - -# DEFINE REFERENCE MEASUREMENT CONDITIONS -# or use existing definitions in pvlib - -# NAME value comment unit PV_LIB name -# -T_STC = 25.0 # STC temperature [C] temperature_ref -T_HTC = 75.0 # HTC temperature [C] -G_STC = 1000.0 # STC irradiance [W/m^2] -G_LIC = 0.2 # LIC irradiance [kW/m^2] - - -# Define standardised MLFM graph colours as a dict ``clr`` - -clr = { - # parameter_clr colour R G B - 'irradiance': 'darkgreen', # 000 064 000 - 'temp_module': 'red', # 255 000 000 - 'temp_air': 'yellow', # 245 245 220 - 'wind_speed': 'grey', # 127 127 127 - - 'i_sc': 'purple', # 128 000 128 - 'r_sc': 'orange', # 255 165 000 - 'i_ff': 'lightgreen', # 144 238 144 - 'i_mp': 'green', # 000 255 000 - 'i_v': 'black', # 000 000 000 between i and v losses - 'v_ff': 'cyan', # 000 255 255 - 'v_mp': 'blue', # 000 000 255 - 'r_oc': 'pink', # 255 192 203 - 'v_oc': 'sienna', # 160 082 045 - - 'pr_dc': 'black', # 000 000 000 -} - - -def mlfm_meas_to_norm(dmeas, ref): - ''' - Convert measured power, current and voltage to normalized values. - - Parameters - ---------- - dmeas : DataFrame - Measurements. Must include columns: - - * `'poa_global'` global plane of array irradiance [W/m^2] - * `'temp_module'` module temperature [C] - * `'p_mp'` - power at maximum power point [W] - - May include optional columns: - - * `'i_sc'` - current at short circuit condition [A] - * `'v_oc'` - voltage at open circuit condition [V] - * `'i_mp'` - current at maximum power point [A]. Must be accompanied - by `'i_sc'`. - * `'v_mp'` - voltage at maximum power point [V]. Must be accompanied - by `'v_oc'`. - * `'r_sc'` - inverse of slope of IV curve at short circuit condition. - Requires both `'i_sc'` and `'v_oc'`. [Ohm] - * `'r_oc'` - inverse slope of IV curve at open circuit condition. - Requires both `'i_sc'` and `'v_oc'` [Ohm] - - ref : dict - Reference values. Must include: - - * `'p_mp'` - Power at maximum power point at Standard Test Condition - (STC). [W] - * `'gamma_pdc'` - Temperature coefficient of power at STC. [1/C] - - May include: - - * `'i_sc'` - Current at short circuit at STC. Required if `'i_sc'` is - present in ``dmeas``. [A] - * `'v_oc'` - Voltage at open circuit at STC. Required if `'v_oc'` is - present in ``dmeas``. [A] - * `'beta_v_oc'` - Temperature coefficient of open circuit voltage at - STC. Required if `'v_oc'` is present in ``dmeas``. [1/C] - - Returns - ------- - dnorm : DataFrame - Normalised values. - - * `'pr_dc'` is `'p_mp'` normalised (divided) by reference `'p_mp'` \ - and by `'poa_global'` in kW/m^2. - * `'pr_dc_temp_corr'` is `'pr_dc'` adjusted to 25C. - * Columns `'i_sc'`, `'i_mp'`, `'v_oc'`, `'v_mp'`, `'v_oc_temp_corr'`, - `'r_sc'`, `'r_oc'`, `'i_ff'`, `'v_ff'` are returned when the - the corresponding optional columns are included in ``dmeas``. - - References - ---------- - .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) - "Quantifying Long Term PV Performance and Degradation under Real Outdoor - and IEC 61853 Test Conditions Using High Quality Module IV Measurements" - 36th EU PVSEC, Marseille, France. September 2019 - ''' - dnorm = pd.DataFrame() - - dnorm['pr_dc'] = dmeas['p_mp'] / ref['p_mp'] \ - / (dmeas['poa_global'] / G_STC) - - # temperature corrected - dnorm['pr_dc_temp_corr'] = ( - dnorm['pr_dc'] * - (1 - ref['gamma_pdc']*(dmeas['temp_module'] - T_STC))) - - if 'i_sc' in dmeas.columns: - dnorm['i_sc'] = dmeas['i_sc'] / (dmeas['poa_global'] / G_STC) \ - / ref['i_sc'] - if 'i_mp' in dmeas.columns: - dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] - - if 'v_oc' in dmeas.columns: - dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] - if 'v_mp' in dmeas.columns: - dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] - # temperature corrected - dnorm['v_oc_temp_corr'] = dnorm['v_oc'] * \ - (1 - ref['beta_v_oc']*(dmeas['temp_module'] - T_STC)) - - if all(c in dmeas.columns for c in ['i_sc', 'v_oc', 'r_sc', 'r_oc']): - # create temporary variables (i_r, v_r) from - # intercept of r_sc (at i_sc) with r_oc (at v_oc) - # to make maths easier - - i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / - (dmeas['r_sc'] - dmeas['r_oc'])) - - v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * - dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) - - # calculate normalised resistances r_sc and r_oc - dnorm['r_sc'] = i_r / dmeas['i_sc'] # norm_r @ isc - dnorm['r_oc'] = v_r / dmeas['v_oc'] # norm_r @ roc - - # calculate remaining fill factor losses partitioned to i_ff, v_ff - dnorm['i_ff'] = dmeas['i_mp'] / i_r - dnorm['v_ff'] = dmeas['v_mp'] / v_r - - return dnorm - - -def mlfm_norm_to_stack(dnorm, fill_factor): - ''' - Converts normalised values to stacked subtractive normalized losses. - - Normalized values can reveal losses via scatter plots vs. irradiance or - temperature. - - Stacked subtractive losses can show relative loss proportions. Stacked - losses partition the difference between the normalized power and the - power that corresponds to the reference fill factor. - - Parameters - ---------- - dnorm : DataFrame - Normalised values. Must include columns: - - * `'pr_dc'` normalized power at the maximum power point. - * `'i_sc'` normalized short circuit current. - * `'i_mp'` normalized current at maximum power point. - * `'v_oc'` normalized open circuit voltage. - * `'v_mp'` normalized voltage at maximum power point. - * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. - - May include optional columns: - - * `'v_ff'` normalized multiplicative loss in fill factor apportioned - to voltage. - * `'i_ff'` normalized multiplicative loss in fill factor apportioned - to current. - * `'r_oc'` normalized slope of IV curve at open circuit. - * `'r_sc'` normalized slope of IV curve at short circuit. - - fill_factor : float - Reference value of fill factor for IV curve at STC conditions. - - Returns - ------- - dstack : DataFrame - Stacked subtractive normalized losses. Includes columns: - - * `'pr_dc'` equal to `dnorm['pr_dc']`. - * `'i_sc'` - * `'r_sc'` - * `'i_mp'` - * `'i_v'` - * `'v_mp'` - * `'v_oc'` - * `'temp_module_corr'` - - See also - -------- - mlfm_meas_to_norm - - References - ---------- - .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) - "Quantifying Long Term PV Performance and Degradation under Real Outdoor - and IEC 61853 Test Conditions Using High Quality Module IV Measurements" - 36th EU PVSEC, Marseille, France. September 2019 - ''' - - # create an empty DataFrame to put stack results - dstack = pd.DataFrame() - - # create a gap to differentiate i and v losses : gap width~0.01 - gap = 0.01 - - inv_ff = 1 / fill_factor - - if all(c in dnorm.columns for c in ['v_ff', 'r_oc', 'i_ff', 'r_sc']): - - # include effects of series and shunt resistances in stacked losses - # find factor to transform multiplicative to subtractive losses - # correction factor to scale losses to keep 1/ff --> pr_dc - - # product - prod = inv_ff * ( - dnorm['i_sc'] * dnorm['r_sc'] * dnorm['i_ff'] * - dnorm['v_ff'] * dnorm['r_oc'] * dnorm['v_oc'] - ) - - # total - tot = inv_ff + ( - dnorm['i_sc'] + dnorm['r_sc'] + dnorm['i_ff'] + - dnorm['v_ff'] + dnorm['r_oc'] + dnorm['v_oc'] - 6 - ) - - # correction factor - corr = (inv_ff - prod) / (inv_ff - tot) - - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) - # accounting for series and shunt resistance losses - dstack['pr_dc'] = +dnorm['pr_dc'] # initialise - dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr - dstack['r_sc'] = -(dnorm['r_sc'] - 1) * corr - dstack['i_ff'] = -(dnorm['i_ff'] - 1) * corr - gap/2 - dstack['i_v'] = gap - dstack['v_ff'] = -(dnorm['v_ff'] - 1) * corr - gap/2 - dstack['r_oc'] = -(dnorm['r_oc'] - 1) * corr - dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr - dstack['temp_module_corr'] = ( - -(dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) - - return dstack - - # subtractive losses without series and shunt resistance effects - # find factor to transform multiplicative to subtractive losses - # correction factor to scale losses to keep 1/ff --> pr_dc - - prod = inv_ff * ( - dnorm['i_sc'] * dnorm['i_mp'] * - dnorm['v_mp'] * dnorm['v_oc'] - ) - - tot = inv_ff + ( - dnorm['i_sc'] + dnorm['i_mp'] + - dnorm['v_mp'] + dnorm['v_oc'] - 4 - ) - - corr = (inv_ff - prod) / (inv_ff - tot) - - # put mlfm values in a stack from pr_dc (bottom) to 1/ff_ref (top) - dstack['pr_dc'] = + dnorm['pr_dc'] # initialise - dstack['i_sc'] = -(dnorm['i_sc'] - 1) * corr - dstack['i_mp'] = -(dnorm['i_mp'] - 1) * corr - gap/2 - dstack['i_v'] = gap - dstack['v_mp'] = -(dnorm['v_mp'] - 1) * corr - gap/2 - dstack['v_oc'] = -(dnorm['v_oc'] - 1) * corr - - dstack['temp_module_corr'] = ( - - (dnorm['v_oc'] - dnorm['v_oc_temp_corr']) * corr) - - return dstack - - -def mlfm_6(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): - r''' - Predict normalised LFM values from data in ``dmeas``. - - The normalized LFM values are given by - - .. math:: - - c_1 + c_2 (T_m - 25) + c_3 \log10(G_{POA}) + c_4 G_{POA} - + c_5 WS + c_6 / G_{POA} - - where :math:`G_{POA}` is global plane-of-array (POA) irradiance in kW/m2, - :math:`T_m` is module temperature in C and :math:`WS` is wind speed in - m/s. - - Parameters - ---------- - dmeas : DataFrame - Must include columns: - - * `'poa_global'` global plane of array irradiance. [W/m^2] - * `'temp_module'` module temperature. [C] - - May include optional column: - - * `'wind_speed'` wind speed [m/s]. - - c_1 : float - Constant term in model. - c_2 : float - Temperature coefficient in model. [1/C] - c_3 : float - Coefficient for low light log irradiance drop. - c_4 : float - Coefficient for high light linear irradiance drop. - c_5 : float, default 0 - Coefficient for wind speed dependence. - c_6 : float, default 0 - Coefficient for dependence on inverse irradiance. - - Returns - ------- - mlfm_6 : Series - Predicted values. - - References - ---------- - .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) - "Quantifying Long Term PV Performance and Degradation under Real Outdoor - and IEC 61853 Test Conditions Using High Quality Module IV Measurements" - 36th EU PVSEC, Marseille, France. September 2019 - ''' - mlfm_out = c_1 + c_2 * (dmeas['temp_module'] - T_STC) + \ - c_3 * np.log10(dmeas['poa_global'] / G_STC) + \ - c_4 * (dmeas['poa_global'] / G_STC) + \ - c_6 / (dmeas['poa_global'] / G_STC) - if 'wind_speed' in dmeas.columns: - mlfm_out += c_5 * dmeas['wind_speed'] - return mlfm_out - - -def mlfm_fit(data, var_to_fit): - ''' - Fit MLFM to data. - - Parameters - ---------- - data : DataFrame - Must include columns: - - * 'poa_global' global plane of array irradiance. [W/m^2] - * 'temp_module' module temperature. [C] - - Must include column named ``var_to_fit``. - - May include optional column: - - * 'wind_speed' wind speed [m/s]. - - var_to_fit : string - Column name in ``data`` containing variable being fit. - - Returns - ------- - pred : Series - Values predicted by the fitted model. - - coeff : list - Model coefficients ``c_1`` to ``c_6``. - - resid : Series - Residuals of the fitted model. - - coeff_err : list - Standard deviation of error in each model coefficient. - - See also - -------- - mlfm_6 - ''' - - # drop missing data - data = data.dropna() - - c5_zero = 'wind_speed' not in data.columns - # if wind_speed is not present, add it - if c5_zero: - data['wind_speed'] = 0. - - # define function name - func = mlfm_6 - - # setup initial values and initial boundary conditions - - # initial c1 c2 c3 c4 c5 c6<0 - p_0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) - # boundaries - bounds = ([ -2, -2, -2, -2, -2, -2], - [ 2, 2, 2, 2, 2, 0]) - - coeff, pcov = optimize.curve_fit( - f=func, # fit function - xdata=data, # input data - ydata=data[var_to_fit], # fit parameter - p0=p_0, # initial - bounds=bounds # boundaries - ) - - # if data has no wind_speed measurements then c_5 coefficient is - # meaningless but a non-zero value may have been returned. - if c5_zero: - coeff[4] = 0. - - # get error of mlfm coefficients as sqrt of covariance - perr = np.sqrt(np.diag(pcov)) - coeff_err = list(perr) - - # save fit and error to dataframe - pred = mlfm_6(data, coeff[0], coeff[1], coeff[2], coeff[3], coeff[4], - coeff[5]) - resid = pred - data[var_to_fit] - - return pred, coeff, resid, coeff_err - - -def plot_mlfm_scatter(dmeas, dnorm, title): - ''' - Scatterplot of normalised values (y) vs. irradiance (x). - - Electrical quantities are plotted on the left y-axis, and temperature - quantities are plotted on the right y-axis. - - Parameters - ---------- - dmeas : DataFrame - Measurements. Must include columns: - - * `'poa_global'` global plane of array irradiance [W/m^2] - * `'temp_module'` module temperature [C] - - May include optional columns: - - * `'temp_air'` - air temperature [C] - - dnorm : DataFrame - Normalised values. May include columns: - - * `'pr_dc_temp_corr'` normalized power at the maximum power point. - * `'i_sc'` normalized short circuit current. - * `'i_mp'` normalized current at maximum power point. - * `'v_mp'` normalized voltage at maximum power point. - * `'v_oc_temp_corr'` normalized open circuit voltage adjusted to 25C. - * `'v_ff'` normalized multiplicative loss in fill factor apportioned - to voltage. - * `'i_ff'` normalized multiplicative loss in fill factor apportioned - to current. - * `'r_oc'` normalized slope of IV curve at open circuit. - * `'r_sc'` normalized slope of IV curve at short circuit. - - title : string - Title for the figure. - - Returns - ------- - fig : Figure - Instance of matplotlib.figure.Figure - - See also - -------- - mlfm_meas_to_norm - ''' - try: - import matplotlib.pyplot as plt - except ImportError: - raise ImportError('plot_mlfm_scatter requires matplotlib') - - # offset legend to the right to not overlap graph, use ~1.2 - bbox = 1.2 - - # set x_axis as irradiance in kW/m2 - xdata = dmeas['poa_global'] / G_STC - - fig, ax1 = plt.subplots() - - ax1.set_title(title) - - ax1.set_ylabel('Normalised values') - ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line - ax1.set_ylim(0.8, 1.1) # optional normalised y scale - - ax1.set_xlabel('Plane of array irradiance [kW/m$^2$]') - ax1.axvline(x=1.0, c='grey', linewidth=3) # show 1000W/m^2 STC - ax1.axvline(x=0.8, c='grey', linewidth=3) # show 800W/m^2 NOCT - ax1.axvline(x=0.2, c='grey', linewidth=3) # show 200W/m^2 LIC - - lines = { - 'pr_dc_temp_corr': 'pr_dc', - 'i_mp': 'i_mp', - 'v_mp': 'v_mp', - 'i_sc': 'i_sc', - 'r_sc': 'r_sc', - 'r_oc': 'r_oc', - 'i_ff': 'i_ff', - 'v_ff': 'v_ff', - 'v_oc_temp_corr': 'v_oc'} - labels = { - 'pr_dc_temp_corr': 'pr_dc_temp-corr', - 'i_mp': 'norm_i_mp', - 'v_mp': 'norm_v_mp', - 'i_sc': 'norm_i_sc', - 'r_sc': 'norm_r_sc', - 'r_oc': 'norm_r_oc', - 'i_ff': 'norm_i_ff', - 'v_ff': 'norm_v_ff', - 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} - - # plot the mlfm parameters depending on qty_mlfm_vars - for k in lines.keys(): - try: - ax1.scatter(xdata, dnorm[k], c=clr[lines[k]], label=labels[k]) - except KeyError: - pass - - ax1.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) - - # y2axis plot met on right y axis - ax2 = ax1.twinx() - ax2.set_ylabel('Temperature (C/100)') - - # set wide limits 0 to 4 so they don't overlap mlfm params - ax2.set_ylim(0, 4) - - ax2.scatter(xdata, - dmeas['temp_module']/100, - c=clr['temp_module'], - label='temp_module C/100') - - # temp_air may not exist particularly for indoor measurements - try: - ax2.scatter(xdata, - dmeas['temp_air']/100, - c=clr['temp_air'], - label='temp_air C/100') - except KeyError: - pass - - ax2.legend(bbox_to_anchor=(bbox, 0.5), loc='upper left', borderaxespad=0.) - plt.show() - - return fig - - -def plot_mlfm_stack(dmeas, dnorm, dstack, fill_factor, title, - xaxis_labels=0, is_i_sc_self_ref=False, - is_v_oc_temp_module_corr=True): - - ''' - Plot stacked subtractive losses. - - Parameters - ---------- - dmeas : DataFrame - Measurements. Must include columns: - - * `'poa_global'` global plane of array irradiance [W/m^2] - * `'temp_module'` module temperature [C] - - May include optional columns: - - * `'temp_air'` - air temperature [C] - - dnorm : DataFrame - Normalised values. Must contain column `'pr_dc'`. - - dstack : DataFrame - Stacked subtractive losses. Must contain columns `'v_oc'`, `'v_mp'`, - `'i_v'`, `'i_mp'`, `'i_sc'`, `'temp_module_corr'`. If optional columns - `'r_oc'` and `'v_ff'` are present, these columns are plotted instead - of `'v_mp'`. If optional columns `'r_sc'` and `'i_ff'` are present, - these columns are plotted instead of `'i_mp'`. - - fill_factor : float - Reference value of fill factor for IV curve at STC conditions. - - title : string - Title for the figure. - - xaxis_labels : int, default 0 - Number of x-axis labels to show. Default 0 shows all. - - is_i_sc_self_ref : bool, default False - Self-correct `'i_sc'` to remove angle of incidence, - spectrum, snow or soiling. - - is_v_oc_temp_module_corr : bool, default True - Calculate loss due to temperature and subtract from `'v_oc'` loss. - - Returns - ------- - fig : Figure - Instance of matplotlib.figure.Figure - - See also - -------- - mlfm_norm_to_stack - ''' - try: - import matplotlib.pyplot as plt - except ImportError: - raise ImportError('plt_mlfm_stack requires matplotlib') - - stack6 = ['i_sc', 'r_sc', 'i_ff', 'i_v', 'v_ff', 'r_oc', 'v_oc'] - stack4 = ['i_sc', 'i_mp', 'i_v', 'v_mp', 'v_oc'] - - if all([c in dstack.columns for c in stack6]): - # data order from bottom to top - ydata = [dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), - dstack['r_oc'], - dstack['v_ff'], - dstack['i_v'], - dstack['i_ff'], - dstack['r_sc'], - dstack['i_sc'] * (not is_i_sc_self_ref)] - labels = [ - 'pr_dc', - 'stack_t_mod', - 'stack_v_oc', - 'stack_r_oc', - 'stack_v_ff', - '- - -', - 'stack_i_ff', - 'stack_r_sc', - 'stack_i_sc'] - color_map = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['r_oc'], - clr['v_ff'], - clr['i_v'], - clr['i_ff'], - clr['r_sc'], - clr['i_sc']] - - if all([c in dstack.columns for c in stack4]): - # data order from bottom to top - ydata = [dnorm['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr), - dstack['v_oc'] - ( - dstack['temp_module_corr'] * (is_v_oc_temp_module_corr)), - dstack['v_mp'], - dstack['i_v'], - dstack['i_mp'], - dstack['i_sc'] * (not is_i_sc_self_ref)] - labels = [ - 'pr_dc', - 'stack_t_mod', - 'stack_v_oc', - 'stack_v_mp', - '- - -', - 'stack_i_mp', - 'stack_i_sc'] - color_map = [ - 'white', # colour to bottom of graph - clr['temp_module'], - clr['v_oc'], - clr['v_mp'], - clr['i_v'], - clr['i_mp'], - clr['i_sc']] - - # offset legend right, use ~1.2 - bbox = 1.2 - - # select x axis usually date_time - xdata = dmeas.index.values - fig, ax1 = plt.subplots() - - ax1.set_title(title) - - # plot stack in order bottom to top, - # allowing self_ref and temp_module corrections - ax1.stackplot(xdata, *tuple(ydata), labels=labels, colors=color_map) - - ax1.axhline(y=1/fill_factor, c='grey', lw=3) # show initial 1/FF - ax1.axhline(y=1, c='grey', lw=3) # show 100% line - ax1.set_ylabel('stacked mlfm losses') - - # find number of x date values - x_ticks = dmeas.shape[0] - plt.xticks(np.arange(0, x_ticks), rotation=90) - - # if (xaxis_labels > 0 and xaxis_labels < x_ticks): - if 0 < xaxis_labels < x_ticks: - xaxis_skip = np.floor(x_ticks / xaxis_labels) - else: - xaxis_skip = 2 - - # - xax2 = [''] * x_ticks - x_count = 0 - while x_count < x_ticks: - if x_count % xaxis_skip == 0: - # - # try to reformat any date indexes (not for matrices) - # - # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 - # y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' - # - try: - xax2[x_count] = xdata[x_count][2:13]+'h' - except IndexError: - xax2[x_count] = xdata[x_count] - except TypeError: # xdata can't be subscripted - xax2[x_count] = xdata[0] - - x_count += 1 - - ax1.set_xticklabels(xax2) - ax1.set_ylim(0.6, 1/fill_factor + 0.1) # optional normalised y scale - plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) - - # plot met data on right y axis - ax2 = ax1.twinx() - ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/100)') - ax2.set_ylim(0, 4) # set so doesn't overlap mlfm params - - plt.plot(xdata, dmeas['poa_global'] / G_STC, - c=clr['irradiance'], label='poa_global (kW/m^2)') - plt.plot(xdata, dmeas['temp_module'] / 100, - c=clr['temp_module'], label='temp_module / 100') - - # temp_air may not exist particularly for indoor measurements - try: - plt.plot(xdata, dmeas['temp_air']/100, - c=clr['temp_air'], label='temp_air/100') - except KeyError: - pass - - ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) - ax1.set_xticklabels(xax2, rotation=90) - - return fig - - -REFS = """ -The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) -together known as "MLFM" have been developed by SRCL and Gantner Instruments -(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM - -.. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome - '4AV.2.41 Characterising PV Modules under Outdoor Conditions: -What's Most Important for Energy Yield' -26th EU PVSEC 8 September 2011; Hamburg, Germany. -http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf - -.. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) - 'Choosing the best Empirical Model for predicting energy yield' - 7th PV Energy Rating and Module Performance Modeling Workshop, - Canobbio, Switzerland 30-31 March, 2017. - -.. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) -'Checking the new IEC 61853.1-4 with high quality 3rd party data to -benchmark its practical relevance in energy yield prediction' -PVSC June 2019 [Chicago], USA. -http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf - -.. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) -'5CV.4.35 Quantifying Long Term PV Performance and Degradation -under Real Outdoor and IEC 61853 Test Conditions -Using High Quality Module IV Measurements'. -36th EU PVSEC Sep 2019 [Marseille] - -.. [5] Steve Ransome (SRCL) -'How to use the Loss Factors and Mechanistic Performance Models -effectively with PVPMC/PVLIB' -[PVPMC] Webinar on PV Performance Modeling Methods, Aug 2020. -https://pvpmc.sandia.gov/download/7879/ - -.. [6] W.Marion et al (NREL) -'New Data Set for Validating PV Module Performance Models'. -https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models - -Many more papers are available at www.steveransome.com -""" +"""Analyse, fit + predict PV performance measurements using MPM & LFM.""" +import numpy as np +import pandas as pd +from scipy import optimize + +# import pvlib + +import os + +""" +ver : 221212t09 +``mlfm.py`` module contains functions to analyse, fit, predict and display +performance of PV modules using the mechanistic performance model (MPM) and +loss factors model (LFM). + +Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) +Comments : Cliff Hansen, Kevin Anderson, Anton Driesse and Mark Campanelli +https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules +https://github.com/python/peps/blob/master/pep-0008.txt + +OVERVIEW + +I) The Loss Factors Model (LFM) 2011 ref [1] quantifies +normalised losses from module parameters (e.g. pr_dc, i_sc, r_sc, i_mp, +v_mp, r_oc and v_oc) by analysing module measurements or the shape of the +IV curve and comparing it with STC reference values from the datasheet. + + Depending on the number of measurements available the LFM is defined +with a suffix number x = 1..12 LFM_n as in ref [4] - + + parameters modelled +|LFM_1 | ``p_mp`` | +|LFM_2 | ``i_mp``, ``v_mp``, | +|LFM_4 | ``i_sc``, ``i_mp``, ``v_mp``, ``v_oc`` | +|LFM_6 | ``i_sc``, ``r_sc``, ``i_mp``, ``v_mp``, ``r_oc``, ``v_oc`` | + +|LFM_>6| (can include normalised losses for : + soiling, reflectivity vs. aoi, spectrum <- affecting i_sc, + current mismatch/shading, rollover, + clipping etc.) + + This file just contains - +LFM_6 : 'measurements with r_sc and r_oc' + e.g. iv curves with good smooth data. + +LFM_4 : 'measurements without r_sc or r_oc' + e.g. indoor matrix measurements or iv curves without smoooth data. + +II) The Mechanistic performance model (MPM) 2017 ref [2] +has "meaningful,independent, robust and normalised" coefficients +which fit how the LFM values depend on irradiance, module temperature +(and windspeed) and time. + +Two MPM versions have been included here : + +mpm_a : (mpm_original 2017 ref [2] now deprecated) + The original model to fit normalised parameters such as + pr_dc, v_oc, r_sc, v_mp, i_mp, ff ... + with an extra low light coefficient c_6 to help fit data with + unusual low light performance and/or poor measurements. + c_5 is only used if there is windspeed data, otherwise it is ignored + + mpm_a = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + +mpm_b : (GI name 'mpm_advanced' 2022 ref [7]) + Is an improved model to fit normalised parameters such as + pr_dc, v_oc, r_sc, v_mp, i_mp, ff ... + It better fits precise measurements (see CFV and GI) where the + low light data is measured well and has an improvement for even + better v_oc fitting [ref 7 : 2022 PVSC PHILADELPHIA] + c_5 is only used if there is windspeed data, otherwise it is ignored + + mpm_b = c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + +for mpm_a and mpm_b : + g = (G_POA (W/m^2) / G_STC=1000 (W/m^2)) --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + +Note that both mpm_a or mpm_b can be used with either LFM_6 or LFM_4 + + A later MPM version (not detailed here) can be used to model clipping and +other effects [See ref [8] Sutterlueti et al PVPMC 2022] 'mpm professional' + +Using DATAFRAMES or SERIES for variables +---------------------------------------- + +Many pvlib functions pass series of weather data separately for parameters e.g. + poa_global, temp_module, wind_speed +and measurements such as + pr_dc or p_mp + +This mlfm code keeps all its met and measurement data in dataframes - + meas, norm etc. e.g. + +meas.columns + Index(['module_id', 'poa_global', 'wind_speed', 'temp_air', + 'temp_module', 'v_oc', 'i_sc', 'i_mp', 'v_mp', 'r_sc', + 'r_oc', 'p_mp', 'pr_dc', 'v_oc_temp_corr', 'pr_dc_temp_corr'], + dtype='object') + + It's easier when modelling all 6 or more measurement parameters in one +frame and then use an lfm_sel var to choose which to analyse +e.g. lfm_sel = 'pr_dc' + +If individual series are needed to interface with existing code and +methodolgies they can be created by the following + + +#pvlib series <-- mlfm dataframe + poa_global = meas['poa_global'] + temp_module = meas['temp_module'] + wind_speed = meas['wind_speed'] + pr_dc = meas['pr_dc'] + +# mlfm dataframe <-- pvlib series + meas['poa_global'] = poa_global + meas['temp_module'] = temp_module + meas['wind_speed'] = wind_speed + meas['pr_dc'] = pr_dc + +DATAFRAME DEFINITIONS (for this python file and tutorials) +---------------------------------------------------------- + +A full definition is given here to keep the code in each function shorter + +dmeas : DataFrame +----------------- + Measured weather and module electrical values per time or measurement + + Parameters [units] + ---------- + Index either - + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``module_id`` - unique identifier to match data in ref [alpha num] + + Weather measurements - + + * ``poa_global`` - global plane of array irradiance [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/s] + + [optional weather] + + * ``temp_air`` - air temperature optional [C] + + /Columns as needed by LFM_4 and/or LFM_6/ : + + * ``i_sc`` | 4 6 | current at short circuit condition [A] + * ``i_mp`` | 4 6 | current at maximum power point [A] + * ``v_mp`` | 4 6 | voltage at maximum power point [V] + * ``v_oc`` | 4 6 | voltage at open circuit condition [V] + + * ``r_sc`` | 6 | -1/ (dI/dV|V=0) of IV curve at short circuit [Ohm] + * ``r_oc`` | 6 | -1/(dI/dV|I=0) of IV curve at open circuit [Ohm] + + Optional columns include + + * ``p_mp`` - power at maximum power point = i_mp * v_mp [W] + +ref : dict +---------- + Reference electrical and thermal datasheet module values at STC. + + Parameters [units] + ---------- + Index + * ``module_id`` - unique identifier to match data in dmeas [alpha num] + + * ``p_mp`` - Max Power at Standard Test Condition (STC). [W] + * ``i_sc`` - Current at short circuit at STC. [A] + * ``i_mp`` - Current at max power at STC. [A] + * ``v_mp`` - Voltage at max power at STC. [V] + * ``v_oc`` - Voltage at open circuit at STC. [V] + * ``ff`` - Fill Factor [1] + + * ``gamma_pdc`` - Temperature coefficient of max power point + power at STC. [1/C] + * ``beta_v_oc`` - Temperature coefficient of open circuit + voltage at STC. [1/C] + [optional thermal] + + * ``alpha_i_sc`` - Temperature coefficient of short circuit + current STC. [1/C] + + * ``alpha_i_mp`` - Temperature coefficient of max power point + current at STC. [1/C] + + * ``beta_v_mp`` - Temperature coefficient of max power point + voltage at STC. [1/C] + + [optional ID related] + * ``source`` - Data Source [alpha num] + * ``site`` - Sitename [alpha num] + * ``manufacturer`` - Module manufacturer [alpha num] + * ``technology`` - Module technology e.g. cSi, HIT, CdTe [alpha num] + * ``module_type`` - Type ID e.g. ABC-123 [alpha num] + * ``module_serial`` - Serial number [alpha num] + * ``comments`` - General comments [alpha num] + + +dnorm : DataFrame +----------------- + Normalised multiplicative loss factors per parameter to model fall from + start 1/ref_ff to meas pr_dc where - + + LFM_6 - multiplicative + pr_dc = 1/ff * ( norm(i_sc) *norm(r_sc) *norm(i_ff) + *norm(v_ff) *norm(r_oc) *norm(v_oc_t) *norm(temp_corr) ). + + LFM_4 - multiplicative + pr_dc = 1/ff * ( norm(i_sc) *norm(i_mp) + *norm(v_mp) *norm(v_oc_t) *norm(temp_corr) ). + + Parameters [units] + ---------- + Index (copied from dmeas) either + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``poa_global`` - global plane of array [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/ + + |Columns as used by LFM_4 and/or LFM_6| : + + * ``pr_dc``| 4 6 | Performance ratio dc. + pr_dc = meas_p_mp / ref_p_mp /(poa_global/G_STC) [%] + * ``pr_dc_temp_corr`` + | 4 6 | pr_dc adjusted to 25C by gamma_p_mp. + * ``i_sc`` | 4 6 | loss due to current at short circuit condition [%] + * ``v_oc`` | 4 6 | Loss due to voltage at open circuit condition [%] + * ``v_oc_temp_corr`` + | 4 6 | v_oc adjusted to 25C by gamma_p_mp (not beta_v_oc) + for simplicity + + * ``i_mp`` | 4 | Loss due to current part of ff [%] + * ``v_mp`` | 4 | Loss due to voltage part of ff [%] + + * ``r_sc`` | 6 | Loss due to r_sc ~r_shunt [%] + * ``i_ff`` | 6 | Loss due to r_sc corrected current part of ff [%] + * ``v_ff`` | 6 | Loss due to r_oc corrected voltage part of ff [%] + * ``r_oc`` | 6 | Loss due to r_oc related to r_series [%] + +dstack : DataFrame +------------------ + Stacked subtractive normalized loss factors per parameter to model fall + from start 1/ref_ff to meas pr_dc where - + + LFM_6 - subtractive losses + pr_dc = 1/ff - (stack(i_sc) +stack(r_sc) +stack(i_ff) + +stack(v_ ff) +stack(r_oc) +stack(v_oc_t) +stack(temp_corr)) + + LFM_4 - subtractive losses + pr_dc = 1/ff - (stack(i_sc) +stack(i_mp) + +stack(v_mp) +stack(v_oc_t) +stack(temp_corr) ). + + Parameters [units] + ---------- + Index (copied from dmeas) + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``poa_global`` - global plane of array irradiance [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/ + + |Columns as needed by LFM_4 and/or LFM_6| : + + * ``pr_dc`` equal to `dnorm['pr_dc']` + + * ``i_sc`` | 4 6 | loss due to current at short circuit condition [%] + * ``v_oc`` | 4 6 | Loss due to voltage at open circuit condition [%] + * ``v_oc_temp_corr`` + | 4 6 | v_oc adjusted to 25C by gamma_p_mp (not beta_v_oc) + for simplicity + + * ``i_mp`` | 4 | Loss due to current part of ff [%] + * ``v_mp`` | 4 | Loss due to voltage part of ff [%] + + * ``r_sc`` | 6 | Loss due to r_sc ~r_shunt [%] + * ``i_ff`` | 6 | Loss due to r_sc corrected current part of ff [%] + * ``v_ff`` | 6 | Loss due to r_oc corrected voltage part of ff [%] + * ``r_oc`` | 6 | Loss due to r_oc related to r_series [%] +""" + +# DEFINE REFERENCE MEASUREMENT CONDITIONS +# or use existing definitions in pvlib. These might not all have +# been used in this code but are included for completeness + +# NAME value # comment unit PV_LIB name + +T_STC = 25.0 # STC temperature [C] temperature_ref +G_STC = 1000.0 # STC irradiance [W/m^2] + +# not all yet used below , added here for completeness +T_LIC = 25.0 # LIC temperature [C] +G_LIC = 200.0 # LIC irradiance [W/m^2] + +T_HTC = 75.0 # HTC temperature [C] +G_HTC = 1000.0 # HTC irradiance [W/m^2] + +T_PTC = 55.0 # HTC temperature [C] +G_PTC = 1000.0 # HTC irradiance [W/m^2] + +G_LTC = 500.0 # HTC irradiance [W/m^2] +T_LTC = 15.0 # LTC temperature [C] + +G_NOCT = 800 # NOCT irradiance [W/m^2] +T_NOCT = 45 # NOCT temperature [C] + +T_MAX = 100 # maximum temperature on right y axis + +T0C_K = 273.15 # 0C to Kelvin +T25C_K = 298.15 # 25C to Kelvin + +# Define standardised LFM graph colours as a dict ``CLR`` +CLR = { + # parameter_CLR colour R G B + 'irradiance': 'darkgreen', # 000 064 000 + 'temp_module': 'red', # 255 000 000 + 'temp_air': 'yellow', # 245 245 220 + 'wind_speed': 'grey', # 127 127 127 + + 'i_sc': 'purple', # 128 000 128 + 'r_sc': 'orange', # 255 165 000 + 'i_ff': 'lightgreen', # 144 238 144 + 'i_mp': 'green', # 000 255 000 + 'i_v': 'black', # 000 000 000 between i and v losses + 'v_ff': 'cyan', # 000 255 255 + 'v_mp': 'blue', # 000 000 255 + 'r_oc': 'pink', # 255 192 203 + 'v_oc': 'sienna', # 160 082 045 + + 'pr_dc': 'black', # 000 000 000 +} + + +def meas_to_norm(dmeas, ref): + """ + Convert measured P(W), I(A), V(V), R(Ohms) to values normalized to STC. + + Parameters + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + ref : dict + Reference electrical and thermal datasheet module values at STC. + + Returns + ------- + dnorm : DataFrame + Normalised multiplicative loss values (values approx 1). + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + 'Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements' + 36th EU PVSEC, Marseille, France. September 2019. + + """ + dnorm = pd.DataFrame() + + # copy weather data to meas dataframe for ease of use later + dnorm['poa_global'] = dmeas['poa_global'] + dnorm['temp_module'] = dmeas['temp_module'] + dnorm['wind_speed'] = dmeas['wind_speed'] + + dnorm['pr_dc'] = dmeas['p_mp']/ref['p_mp'] / (dmeas['poa_global']/G_STC) + + # calc temperature corrected pr_dc + dnorm['pr_dc_temp_corr'] = ( + dnorm['pr_dc'] + * (1 - ref['gamma_pdc']*(dmeas['temp_module'] - T_STC))) + + # calculate normalised loss coefficients + if 'i_sc' in dmeas.columns: + dnorm['i_sc'] = (dmeas['i_sc'] / ref['i_sc'] + / (dmeas['poa_global'] / G_STC)) + + if 'i_mp' in dmeas.columns: + dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] + + if 'v_oc' in dmeas.columns: + dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] + + # temperature corrected + dnorm['v_oc_temp_corr'] = ( + dnorm['v_oc'] + * (1 - ref['beta_v_oc']*(dmeas['temp_module'] - T_STC))) + + if 'v_mp' in dmeas.columns: + dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] + + if all(c in dmeas.columns for c in ['i_sc', 'v_oc', 'r_sc', 'r_oc']): + ''' LFM_6 including r_sc and r_oc + + create temporary variables (i_r, v_r) from the + intercept of r_sc (at i_sc) with r_oc (at v_oc) + to make maths easier ''' + + i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) + / (dmeas['r_sc'] - dmeas['r_oc'])) + + v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] + * dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + + # calculate normalised resistances r_sc and r_oc + dnorm['r_sc'] = i_r / dmeas['i_sc'] # norm_r @ isc + dnorm['r_oc'] = v_r / dmeas['v_oc'] # norm_r @ roc + + # calculate remaining fill factor losses partitioned to i_ff, v_ff + dnorm['i_ff'] = dmeas['i_mp'] / i_r + dnorm['v_ff'] = dmeas['v_mp'] / v_r + + return dnorm + +''' +def mpm_fit(data, var_to_fit, mpm_sel): + + print ("var_to_fit, mpm_sel = ", var_to_fit, mpm_sel) + """ + Fit mpm to normalised measured data 'var_to_fit' using mpm_sel model. + + mpm_sel == a : + const temp_coeff low_light high_light wind extra + | | | | | | + fit = = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + mpm_sel == b: + const temp_coeff low_light improvement high_light ws + | | | | | | + fit =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters + ---------- + data : DataFrame (see norm) + Normalised multiplicative loss values (values approx 1). + + var_to_fit : string + Column name in ``data`` containing variable being fitted. + e.g. pr_dc, i_mp, v_mp, v_oc ... + + mpm_sel : char + MPM version 'a' or 'b' + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients ``c_1`` to ``c_6``. + + resid : Series + Residuals of the fitted model. + + coeff_err : list + Standard deviation of error in each model coefficient. + + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html + + infodict : dict + a dictionary of optional outputs with keys + nfev - The number of function calls. + fvec - The function values evaluated at the solution. + etc. + + mesg : string + A string message giving information about the solution. + + ier : int + An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. + Otherwise, the solution was not found. + + """ + # drop any missing data + data = data.dropna() + + c5_zero = 'wind_speed' not in data.columns + # if wind_speed is not present, add it and force it to 0 + if c5_zero: + data['wind_speed'] = 0. + + # define function name + func = mpm_calc # (data, mpm_sel, *coeff) + + + if mpm_sel == 'a': + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 c6<0 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2, 0]) + + else : # if mpm_sel == 'b': + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2]) + + coeff, pcov, infodict, mesg, ier = optimize.curve_fit( + f=func, # fit function + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter + p0=p_0, # initial + bounds=bounds, # boundaries + full_output=True + ) + + # if data had no wind_speed measurements then c_5 coefficient is + # meaningless but a non-zero value may have been returned. + if c5_zero: + coeff[4] = 0. + + # get error of mpm coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) + + # save fit and error to dataframe + pred = mpm_calc(data, mpm_sel, *coeff) + + resid = pred - data[var_to_fit] + + return pred, coeff, resid, coeff_err, infodict, mesg, ier + + + +def mpm_calc(dmeas, mpm_sel, c_1, c_2, c_3, c_4, c_5=0., c_6=0.,): + + print("mpm_sel, c_1, c_2, c_3, c_4, = ", mpm_sel, c_1, c_2, c_3, c_4,) + + """ + Predict norm LFM values from weather data (g,t,w) in ``dmeas``. + + if mpm_sel == 'a': + const temp_coeff low_light high_light wind extra + | | | | | | + norm = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + if mpm_sel == 'b': + const temp_coeff low_light improvement high_light ws + | | | | | | + norm =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters [units] + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + mpm_sel : + mpm_sel : char + MPM version 'a' or 'b' + + c_1 : float + Constant term in model. [%] + c_2 : float + Temperature coefficient in model. [1/C] + c_3 : float + Coefficient for low light log irradiance drop. [suns] + c_4 : float + Coefficient for high light linear irradiance drop. [1/suns] + c_5 : float, default 0 + Coefficient for wind speed dependence optional. [1/(m/s)] + c_6 : float, default 0 [suns] + Coefficient for dependence on inverse irradiance. + + Returns + ------- + mpm_out : Series + Predicted values of mpm coefficient. + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + + """ + + # print ('mpm_sel = ', mpm_sel) + + + if mpm_sel == 'a': + mpm_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * np.log10(dmeas['poa_global'] / G_STC) + + c_4 * (dmeas['poa_global'] / G_STC) + + c_6 / (dmeas['poa_global'] / G_STC) + ) + + if 'wind_speed' in dmeas.columns: + mpm_out += c_5 * dmeas['wind_speed'] + + else : # if mpm_sel == 1: + mpm_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * ((np.log10(dmeas['poa_global'] / G_STC) + * (dmeas['temp_module'] + T0C_K) / T25C_K)) + + c_4 * (dmeas['poa_global'] / G_STC) + ) + + return mpm_out + + +''' +def mpm_a_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): + """ + Predict norm LFM values from weather data (g,t,w) in ``dmeas``. + + const temp_coeff low_light high_light wind extra + | | | | | | + norm = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters [units] + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + c_1 : float + Constant term in model. [%] + c_2 : float + Temperature coefficient in model. [1/C] + c_3 : float + Coefficient for low light log irradiance drop. [suns] + c_4 : float + Coefficient for high light linear irradiance drop. [1/suns] + c_5 : float, default 0 + Coefficient for wind speed dependence optional. [1/(m/s)] + c_6 : float, default 0 [suns] + Coefficient for dependence on inverse irradiance. + + Returns + ------- + mpm_a_out : Series + Predicted values of mpm coefficient. + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + + """ + mpm_a_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * np.log10(dmeas['poa_global'] / G_STC) + + c_4 * (dmeas['poa_global'] / G_STC) + + c_6 / (dmeas['poa_global'] / G_STC) + ) + + if 'wind_speed' in dmeas.columns: + mpm_a_out += c_5 * dmeas['wind_speed'] + + return mpm_a_out + + +def mpm_a_fit(data, var_to_fit): + """ + Fit mpm_a to normalised measured data 'var_to_fit' using mpm_a model. + + const temp_coeff low_light high_light wind extra + | | | | | | + fit = = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters + ---------- + data : DataFrame (see norm) + Normalised multiplicative loss values (values approx 1). + + var_to_fit : string + Column name in ``data`` containing variable being fitted. + e.g. pr_dc, i_mp, v_mp, v_oc ... + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients ``c_1`` to ``c_6``. + + resid : Series + Residuals of the fitted model. + + coeff_err : list + Standard deviation of error in each model coefficient. + + See Also + -------- + mpm_a_calc + + """ + # drop any missing data + data = data.dropna() + + c5_zero = 'wind_speed' not in data.columns + # if wind_speed is not present, add it and force it to 0 + if c5_zero: + data['wind_speed'] = 0. + + # define function name + func = mpm_a_calc + + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 c6<0 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2, 0]) + + """ + # full_outputboolean, optional + If True, this function returns additioal information: + infodict, mesg, and ier. + """ + + coeff, pcov, infodict, mesg, ier = optimize.curve_fit( + f=func, # fit function + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter + p0=p_0, # initial + bounds=bounds, # boundaries + full_output=True + ) + + # if data had no wind_speed measurements then c_5 coefficient is + # meaningless but a non-zero value may have been returned. + if c5_zero: + coeff[4] = 0. + + # get error of mpm coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) + + # save fit and error to dataframe + pred = mpm_a_calc(data, *coeff) + + resid = pred - data[var_to_fit] + + return pred, coeff, resid, coeff_err, infodict, mesg, ier + + +def mpm_b_fit(data, var_to_fit): + """ + Fit mpm_b to normalised measured data 'var_to_fit' using mpm_b model. + + const temp_coeff low_light improvement high_light ws + | | | | | | + fit =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters + ---------- + data : DataFrame (see norm) + Normalised multiplicative loss values (values approx 1). + + var_to_fit : string + Column name in ``data`` containing variable being fitted. + e.g. pr_dc, i_mp, v_mp ... + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients ``c_1`` to ``c_5``. + + resid : Series + Residuals of the fitted model. + + coeff_err : list + Standard deviation of error in each model coefficient. + + See Also + -------- + mpm_a + + """ + # drop missing data + data = data.dropna() + + # define function name + func = mpm_b_calc + + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2]) + + coeff, pcov, infodict, mesg, ier = optimize.curve_fit( + f=func, # fit function + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter + p0=p_0, # initial + bounds=bounds, # boundaries + full_output=True + ) + + # get error of mpm coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) + + # save fit and error to dataframe + pred = mpm_b_calc(data, *coeff) + + resid = pred - data[var_to_fit] + + # fvec = infodict["fvec"] + + return pred, coeff, resid, coeff_err, infodict, mesg, ier + + +def mpm_b_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0.): + """ + Predict normalised LFM values from weather data (g,t,w) in ``dmeas``. + + const temp_coeff low_light improvement high_light ws + | | | | | | + norm =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters [units] + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + c_1 : float + Constant term in model. [%] + c_2 : float + Temperature coefficient in model. [1/C] + c_3 : float + Coefficient for low light log irradiance drop. [suns] + c_4 : float + Coefficient for high light linear irradiance drop. [1/suns] + c_5 : float, default 0 + Coefficient for wind speed dependence optional. [1/(m/s)] + + Returns + ------- + mpm_b_out : Series + Predicted values of mpm coefficient. + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + + """ + mpm_b_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * ((np.log10(dmeas['poa_global'] / G_STC) + * (dmeas['temp_module'] + T0C_K) / T25C_K)) + + c_4 * (dmeas['poa_global'] / G_STC) + ) + + return mpm_b_out + + +def plot_scatter(dnorm, title, qty_lfm_vars, save_figs=False): + """ + Scatterplot of normalised values (y) vs. irradiance (x). + + Electrical quantities are plotted on the left y-axis, temperature + quantities are plotted on the right y-axis. + + Parameters + ---------- + dnorm : DataFrame + Normalised multiplicative loss values (values approx 1). + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + title : string + Title for the figure. + + qty_lfm_vars : int + number of lfm_vars : 6=iv with rsc, roc ; 4=indoor + + save_figs : boolean + save a high resolution png file of figure + + Returns + ------- + fig : Figure + Instance of matplotlib.figure.Figure + + See Also + -------- + meas_to_norm + + """ + try: + import matplotlib.pyplot as plt + except ImportError: + raise ImportError('plot_scatter requires matplotlib') + + # offset legend to the right to not overlap graph, use ~1.2 + bbox = 1.2 + + # set x_axis as irradiance in W/m2 + xdata = dnorm['poa_global'] + + fig, ax1 = plt.subplots() + + ax1.set_title(title) + + ax1.set_ylabel('Normalised values') + ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line + + # optional normalised y scale usually ~0.8 to 1.1 + ax1.set_ylim(0.8, 1.1) + + ax1.set_xlabel('Plane of array irradiance [W/m$^2$]') + ax1.axvline(x=G_STC, c='grey', linewidth=3) # show 1000W/m^2 STC + ax1.axvline(x=G_NOCT, c='grey', linewidth=3) # show 800W/m^2 NOCT + ax1.axvline(x=G_LIC, c='grey', linewidth=3) # show 200W/m^2 LIC + + # check which lines to plot + if qty_lfm_vars == 6: + # LFM_6 + lines = { + 'pr_dc_temp_corr': 'pr_dc', + 'i_sc': 'i_sc', + 'r_sc': 'r_sc', + 'r_oc': 'r_oc', + 'i_ff': 'i_ff', + 'v_ff': 'v_ff', + 'v_oc_temp_corr': 'v_oc'} + + labels = { + 'pr_dc_temp_corr': 'pr_dc_temp_corr', + 'i_sc': 'norm_i_sc', + 'r_sc': 'norm_r_sc', + 'r_oc': 'norm_r_oc', + 'i_ff': 'norm_i_ff', + 'v_ff': 'norm_v_ff', + 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} + + elif qty_lfm_vars == 4: + # LFM_4 + lines = { + 'pr_dc_temp_corr': 'pr_dc', + 'i_mp': 'i_mp', + 'v_mp': 'v_mp', + 'i_sc': 'i_sc', + 'v_oc_temp_corr': 'v_oc'} + + labels = { + 'pr_dc_temp_corr': 'pr_dc_temp_corr', + 'i_mp': 'norm_i_mp', + 'v_mp': 'norm_v_mp', + 'i_sc': 'norm_i_sc', + 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} + + # plot the LFM parameters depending on qty_lfm_vars + for k in lines.keys(): + try: + ax1.scatter(xdata, dnorm[k], c=CLR[lines[k]], label=labels[k]) + except KeyError: + pass + + ax1.legend(bbox_to_anchor=(bbox, 1), + loc='upper left', borderaxespad=0.) + + # y2axis plot met on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('Temperature (C/100)') + + # set wide limits 0 to 4 so they don't overlap with LFM params + ax2.set_ylim(0, 4) + + ax2.scatter(xdata, + dnorm['temp_module']/T_MAX, + c=CLR['temp_module'], + label='temp_module C/' + str(T_MAX)) + + # temp_air may not exist particularly for indoor measurements + try: + ax2.scatter(xdata, + dnorm['temp_air']/T_MAX, + c=CLR['temp_air'], + label='temp_air C/' + str(T_MAX)) + except KeyError: + pass + + # make second legend box low enough ~0.1 not to overlap first box + ax2.legend(bbox_to_anchor=(bbox, 0.1), + loc='upper left', borderaxespad=0.) + + if save_figs: + # remove '.csv', high resolution= 300 dots per inch + plt.savefig(os.path.join('mlfm_data', 'output', + 'scatter_' + title[:len(title)-4]), dpi=300) + + plt.show() + + return fig + + +def plot_stack(dstack, fill_factor, title, + xaxis_labels=0, is_i_sc_self_ref=False, + save_figs=False + ): + """ + Plot stacked subtractive losses from 1/ref_ff down to pr_dc. + + Parameters + ---------- + dstack : DataFrame + Stacked subtractive losses. + + fill_factor : float + Reference value of fill factor for IV curve at STC conditions. + + title : string + Title for the figure. + + xaxis_labels : int, default 0 + Number of x-axis labels to show. Default 0 shows all. + + is_i_sc_self_ref : bool, default False + Self-correct ``i_sc`` to remove angle of incidence, + spectrum, snow or soiling. + + save_figs : boolean + save a high resolution png file of figure + + # is_v_oc_temp_module_corr : bool, default True + # Calculate loss due to temperature and subtract from ``v_oc`` loss. + + Returns + ------- + fig : Figure + Instance of matplotlib.figure.Figure + + See Also + -------- + norm_to_stack + + """ + try: + import matplotlib.pyplot as plt + except ImportError: + raise ImportError('plt_stack requires matplotlib') + + # label names for LFM_6 + stack6 = ['i_sc', 'r_sc', 'i_ff', 'i_v', + 'v_ff', 'r_oc', 'v_oc_temp_corr'] + + if all([c in dstack.columns for c in stack6]): + + # data order from bottom to top + ydata = [dstack['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['v_oc_temp_corr'], + dstack['temp_module_corr'], + dstack['r_oc'], + dstack['v_ff'], + dstack['i_v'], + dstack['i_ff'], + dstack['r_sc'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_r_oc', + 'stack_v_ff', + '- - -', + 'stack_i_ff', + 'stack_r_sc', + 'stack_i_sc'] + + color_map = [ + 'white', # colour to bottom of graph + CLR['temp_module'], + CLR['v_oc'], + CLR['r_oc'], + CLR['v_ff'], + CLR['i_v'], + CLR['i_ff'], + CLR['r_sc'], + CLR['i_sc']] + + stack4 = ['i_sc', 'i_mp', 'i_v', + 'v_mp', 'v_oc_temp_corr'] + + if all([c in dstack.columns for c in stack4]): + + # data order from bottom to top + ydata = [dstack['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['v_oc_temp_corr'], + dstack['temp_module_corr'], + dstack['v_mp'], + dstack['i_v'], + dstack['i_mp'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_v_mp', + '- - -', + 'stack_i_mp', + 'stack_i_sc'] + + color_map = [ + 'white', # colour to bottom of graph + CLR['temp_module'], + CLR['v_oc'], + CLR['v_mp'], + CLR['i_v'], + CLR['i_mp'], + CLR['i_sc']] + + # offset legend right, use ~1.2 + bbox = 1.2 + + # select x axis usually date_time + xdata = dstack.index.values + fig, ax1 = plt.subplots() + + ax1.set_title(title) + + # plot stack in order bottom to top, + # allowing self_ref and temp_module corrections + ax1.stackplot(xdata, *tuple(ydata), labels=labels, colors=color_map) + + ax1.axhline(y=1/fill_factor, c='grey', lw=3) # show initial 1/FF + ax1.axhline(y=1, c='grey', lw=3) # show 100% line + ax1.set_ylabel('stacked lfm losses') + + # find number of x date values + x_ticks = dstack.shape[0] + plt.xticks(np.arange(0, x_ticks), rotation=90) + + # if (xaxis_labels > 0 and xaxis_labels < x_ticks): + if 0 < xaxis_labels < x_ticks: + xaxis_skip = np.floor(x_ticks / xaxis_labels) + else: + xaxis_skip = 2 + + # + xax2 = [''] * x_ticks + x_count = 0 + while x_count < x_ticks: + if x_count % xaxis_skip == 0: + # + # try to reformat any date indexes (not for matrices) + # + # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 + # y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' + # + try: + xax2[x_count] = xdata[x_count][2:13]+'h' + except IndexError: + xax2[x_count] = xdata[x_count] + except TypeError: # xdata can't be subscripted + xax2[x_count] = xdata[0] + + x_count += 1 + + ax1.set_xticklabels(xax2) + ax1.set_ylim(0.6, 1/fill_factor + 0.1) # optional normalised y scale + plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) + + # plot met data on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/ ' + str(T_MAX)) + ax2.set_ylim(0, 4) # set so doesn't overlap lfm params + + plt.plot(xdata, dstack['poa_global'] / G_STC, + c=CLR['irradiance'], label='poa_global (kW/m^2)') + plt.plot(xdata, dstack['temp_module'] / T_MAX, + c=CLR['temp_module'], label='temp_module / ' + str(T_MAX)) + + # temp_air may not exist particularly for indoor measurements + try: + plt.plot(xdata, dstack['temp_air']/100, + c=CLR['temp_air'], label='temp_air/ ' + str(T_MAX)) + except KeyError: + pass + + ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) + ax1.set_xticklabels(xax2, rotation=90) + + # remove '.csv', high resolution= 300 dots per inch + plt.savefig(os.path.join('mlfm_data', 'output', + 'stack_' + title[:len(title)-4]), dpi=300) + + return fig + + +def meas_to_stack_lin(dmeas, ref, qty_lfm_vars, gap=0.01): + """ + Convert measured values to stacked subtractive normalized losses. + + Stacked subtractive losses show the relative loss proportions + from max possible "ref_i_sc * ref_v_oc" (1/reference fill factor) + to the measured normalized power. + + This version is done in a linear fashion so that LFM4 and LFM6 give the + same answers for Isc and Voc and the loss(i_mp)=loss(r_sc)+loss(i_ff) + + Parameters + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + ref : dict + Reference electrical and thermal datasheet module values at STC. + + gap : float + create a gap to differentiate i and v losses ~ 0.01 + + qty_lfm_vars : int + number of lfm_vars : 6=iv with rsc, roc ; 4=without rsc, roc + + Returns + ------- + dstack : DataFrame + Stacked subtractive normalized losses + + See Also + -------- + meas_to_norm + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real Outdoor + and IEC 61853 Test Conditions Using High Quality Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + """ + # create an empty DataFrame to put stack results + dstack = pd.DataFrame() + + # copy weather data for ease of use + dstack['poa_global'] = dmeas['poa_global'] + dstack['temp_module'] = dmeas['temp_module'] + dstack['wind_speed'] = dmeas['wind_speed'] + + # ref['p_mp'] = ref['i_mp'] * ref['v_mp'] + + # ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc']) + + # ref['ff'] = (ref['i_mp']*ref['v_mp'])/(ref['i_sc']*ref['v_oc']) + inv_ff = 1 / ref['ff'] + + dstack['pr_dc'] = dmeas['pr_dc'] + + # Find linear values on i and v axes normalised to i_mp, v_mp + lin_i_ratio = ref['i_sc']/ref['i_mp'] + lin_v_ratio = ref['v_oc']/ref['v_mp'] + + lin_i_sc = dmeas['i_sc']/ref['i_mp']/(dmeas['poa_global']/G_STC) + + lin_v_oc = dmeas['v_oc']/ref['v_mp'] + lin_v_oc_temp_corr = dmeas['v_oc_temp_corr']/ref['v_mp'] + + # transform multiplicative to subtractive losses find + # correction factor to scale losses to keep 1/ff --> pr_dc + + if qty_lfm_vars == 6: + # subtractive losses with series and shunt resistance effects + i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / + (dmeas['r_sc'] - dmeas['r_oc'])) + + v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + + lin_i_r = i_r/ref['i_mp'] / (dmeas['poa_global']/G_STC) + lin_i_ff = dmeas['i_mp'] / ref['i_mp']/(dmeas['poa_global']/G_STC) + + lin_v_ff = dmeas['v_mp'] / ref['v_mp'] + lin_v_r = v_r / ref['v_mp'] + + sub_i = lin_i_ratio - lin_i_ff # current drop + sub_v = lin_v_ratio - lin_v_ff # voltage drop + + # correction factor mult --> lin loss + corr = (inv_ff - dstack['pr_dc']) / (sub_i + sub_v) + + # put 6 LFM values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # accounting for series and shunt resistance losses + + dstack['i_sc'] = (lin_i_ratio-lin_i_sc) * corr + dstack['r_sc'] = (lin_i_sc-lin_i_r) * corr + dstack['i_ff'] = (lin_i_r-lin_i_ff) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_ff'] = (lin_v_r-lin_v_ff) * corr - gap/2 + dstack['r_oc'] = (lin_v_oc-lin_v_r) * corr + dstack['v_oc_temp_corr'] = (lin_v_oc_temp_corr-lin_v_oc) * corr + dstack['temp_module_corr'] = (lin_v_ratio-lin_v_oc_temp_corr) * corr + + if qty_lfm_vars == 4: + + lin_i_mp = dmeas['i_mp'] / ref['i_mp'] / (dmeas['poa_global']/G_STC) + lin_v_mp = dmeas['v_mp'] / ref['v_mp'] + + sub_i = lin_i_ratio - lin_i_mp # current drop + sub_v = lin_v_ratio - lin_v_mp # voltage drop + + # correction factor mult --> lin loss + corr = (inv_ff-dstack['pr_dc']) / (sub_i + sub_v) + + # put 4 LFM values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # accounting for series and shunt resistance losse + + dstack['i_sc'] = (lin_i_ratio-lin_i_sc) * corr + dstack['i_mp'] = (lin_i_sc-lin_i_mp) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_mp'] = (lin_v_oc-lin_v_mp) * corr - gap/2 + dstack['v_oc_temp_corr'] = (lin_v_oc_temp_corr-lin_v_oc) * corr + dstack['temp_module_corr'] = (lin_v_ratio-lin_v_oc_temp_corr) * corr + + return dstack + + +""" +The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) +together known as "MLFM" have been developed by SRCL and Gantner Instruments +(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM + +.. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome + '4AV.2.41 Characterising PV Modules under Outdoor Conditions: +What's Most Important for Energy Yield' +26th EU PVSEC 8 September 2011; Hamburg, Germany. +http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf + +.. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) + 'Choosing the best Empirical Model for predicting energy yield' + 7th PV Energy Rating and Module Performance Modeling Workshop, + Canobbio, Switzerland 30-31 March, 2017. + +.. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) +'Checking the new IEC 61853.1-4 with high quality 3rd party data to +benchmark its practical relevance in energy yield prediction' +PVSC June 2019 [Chicago], USA. +http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf + +.. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) +'5CV.4.35 Quantifying Long Term PV Performance and Degradation +under Real Outdoor and IEC 61853 Test Conditions +Using High Quality Module IV Measurements'. +36th EU PVSEC Sep 2019 [Marseille] + +.. [5] Steve Ransome (SRCL) +'How to use the Loss Factors and Mechanistic Performance Models +effectively with PVPMC/PVLIB' +[PVPMC] Webinar on PV Performance Modeling Methods, Aug 2020. +https://pvpmc.sandia.gov/download/7879/ + +.. [6] W.Marion et al (NREL) +'New Data Set for Validating PV Module Performance Models'. +https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models +Many more papers are available at www.steveransome.com + +.. [7] Steve Ransome (SRCL) +'Benchmarking PV performance models with high quality IEC 61853 Matrix +measurements (Bilinear interpolation, SAPM, PVGIS, MLFM and 1-diode)' +http://www.steveransome.com/pubs/2206_PVSC49_philadelphia_4_presented.pdf + +.. [8] Juergen Sutterlueti (Gantner Instruments) +'Advanced system monitoring and artificial intelligent data-driven analytics +to serve GW-scale photovoltaic power plant and energy storage requirements' +https://pvpmc.sandia.gov/download/8574/ + +""" From 5482c95826664d3c2521aa2948d6e9e7d278e6aa Mon Sep 17 00:00:00 2001 From: steve ransome Date: Tue, 13 Dec 2022 11:49:52 +0000 Subject: [PATCH 74/81] 13 Dec 2022 Versions after comments --- docs/tutorials/mlfm_0.ipynb | 308 ++++++++++++++++++------------------ pvlib/mlfm.py | 34 ++-- 2 files changed, 171 insertions(+), 171 deletions(-) diff --git a/docs/tutorials/mlfm_0.ipynb b/docs/tutorials/mlfm_0.ipynb index 11df072f9a..d927c85847 100644 --- a/docs/tutorials/mlfm_0.ipynb +++ b/docs/tutorials/mlfm_0.ipynb @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -203,10 +203,10 @@ "# mlfm_meas_file = 'n05667_Y13_R1k6_fClear_041.csv' # 1600 <<< raw data no rsc,roc measured \n", "\n", "# 2) IEC 61853 CFV : either raw data or fewer points and/or added scatter error\n", - "mlfm_meas_file = 'x19074001_iec61853_041.csv' # 27 <<< raw data no rsc,roc measured \n", + "# mlfm_meas_file = 'x19074001_iec61853_041.csv' # 27 <<< raw data no rsc,roc measured \n", "# mlfm_meas_file = 'x19074001_iec61853_041_6pts.csv' # 6 raw but fewer points\n", "# mlfm_meas_file = 'x19074001_iec61853_041_rand5pc.csv' # 27 rand 5% rmse\n", - "# mlfm_meas_file = 'x19074001_iec61853_041_rand1pc.csv' # 27 rand 1% rmse\n", + "mlfm_meas_file = 'x19074001_iec61853_041_rand1pc.csv' # 27 rand 1% rmse\n", "# mlfm_meas_file = 'x19074001_iec61853_041_rand5pc_6pts.csv' # 6 rand 5% rmse fewer points\n", "\n", "\n", @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -381,105 +381,105 @@ " 19074001.0\n", " 42.222222\n", " 581.481481\n", - " 3.450926\n", - " 65.054815\n", - " 3.194407\n", - " 54.227778\n", - " 175.637260\n", + " 3.451762\n", + " 65.341513\n", + " 3.191874\n", + " 54.327655\n", + " 175.784279\n", " 0.0\n", - " 0.922290\n", - " 67.566148\n", - " 0.964306\n", + " 0.923192\n", + " 67.867073\n", + " 0.965356\n", " \n", " \n", " std\n", " 0.0\n", " 23.588350\n", " 358.455058\n", - " 2.123715\n", - " 4.683176\n", - " 1.974740\n", - " 4.486576\n", - " 110.698594\n", + " 2.126455\n", + " 4.838053\n", + " 1.976012\n", + " 4.450850\n", + " 110.950879\n", " 0.0\n", - " 0.078215\n", - " 2.555701\n", - " 0.038515\n", + " 0.077022\n", + " 2.916070\n", + " 0.038490\n", " \n", " \n", " min\n", " 19074001.0\n", " 15.000000\n", " 100.000000\n", - " 0.595000\n", - " 54.600000\n", - " 0.541000\n", - " 44.320000\n", - " 24.065760\n", + " 0.586188\n", + " 54.257739\n", + " 0.538878\n", + " 44.791292\n", + " 24.156797\n", " 0.0\n", - " 0.746677\n", - " 61.265287\n", - " 0.856461\n", + " 0.749502\n", + " 60.881244\n", + " 0.859701\n", " \n", " \n", " 25%\n", " 19074001.0\n", " 25.000000\n", " 200.000000\n", - " 1.203000\n", - " 61.595000\n", - " 1.094500\n", - " 49.875000\n", - " 62.091200\n", + " 1.216783\n", + " 61.819074\n", + " 1.100956\n", + " 50.166468\n", + " 61.966794\n", " 0.0\n", - " 0.849048\n", - " 66.072455\n", - " 0.948074\n", + " 0.849704\n", + " 66.158148\n", + " 0.951905\n", " \n", " \n", " 50%\n", " 19074001.0\n", " 50.000000\n", " 600.000000\n", - " 3.542000\n", - " 65.780000\n", - " 3.298000\n", - " 54.250000\n", - " 177.944780\n", + " 3.577538\n", + " 65.709173\n", + " 3.258584\n", + " 54.298885\n", + " 178.676199\n", " 0.0\n", - " 0.926241\n", - " 68.502670\n", - " 0.974878\n", + " 0.930519\n", + " 68.745275\n", + " 0.976175\n", " \n", " \n", " 75%\n", " 19074001.0\n", " 62.500000\n", " 900.000000\n", - " 5.337500\n", - " 69.305000\n", - " 4.946500\n", - " 58.400000\n", - " 269.232420\n", + " 5.325936\n", + " 69.072708\n", + " 4.935815\n", + " 58.162051\n", + " 270.721147\n", " 0.0\n", - " 0.996683\n", - " 69.543433\n", - " 0.994383\n", + " 0.979747\n", + " 69.800996\n", + " 0.992304\n", " \n", " \n", " max\n", " 19074001.0\n", " 75.000000\n", " 1100.000000\n", - " 6.578000\n", - " 71.850000\n", - " 6.061000\n", - " 60.210000\n", - " 354.156950\n", + " 6.616982\n", + " 72.945871\n", + " 6.098910\n", + " 61.126873\n", + " 357.769360\n", " 0.0\n", - " 1.026992\n", - " 70.440000\n", - " 1.000051\n", + " 1.039431\n", + " 71.534675\n", + " 1.023443\n", " \n", " \n", "\n", @@ -488,18 +488,18 @@ "text/plain": [ " module_id temp_module ... v_oc_temp_corr pr_dc_temp_corr\n", "count 27.0 27.000000 ... 27.000000 27.000000\n", - "mean 19074001.0 42.222222 ... 67.566148 0.964306\n", - "std 0.0 23.588350 ... 2.555701 0.038515\n", - "min 19074001.0 15.000000 ... 61.265287 0.856461\n", - "25% 19074001.0 25.000000 ... 66.072455 0.948074\n", - "50% 19074001.0 50.000000 ... 68.502670 0.974878\n", - "75% 19074001.0 62.500000 ... 69.543433 0.994383\n", - "max 19074001.0 75.000000 ... 70.440000 1.000051\n", + "mean 19074001.0 42.222222 ... 67.867073 0.965356\n", + "std 0.0 23.588350 ... 2.916070 0.038490\n", + "min 19074001.0 15.000000 ... 60.881244 0.859701\n", + "25% 19074001.0 25.000000 ... 66.158148 0.951905\n", + "50% 19074001.0 50.000000 ... 68.745275 0.976175\n", + "75% 19074001.0 62.500000 ... 69.800996 0.992304\n", + "max 19074001.0 75.000000 ... 71.534675 1.023443\n", "\n", "[8 rows x 12 columns]" ] }, - "execution_count": 9, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -533,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -569,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -586,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -641,39 +641,39 @@ " 100\n", " 15\n", " 0\n", - " 0.936042\n", - " 0.908517\n", - " 1.007988\n", - " 0.912605\n", - " 0.936845\n", - " 0.913971\n", - " 0.844634\n", + " 0.940226\n", + " 0.912578\n", + " 1.020792\n", + " 0.902408\n", + " 0.940634\n", + " 0.917668\n", + " 0.843822\n", " \n", " \n", " 1\n", " 200\n", " 15\n", " 0\n", - " 0.978361\n", - " 0.949591\n", - " 1.002058\n", - " 0.923922\n", - " 0.965471\n", - " 0.941899\n", - " 0.851158\n", + " 0.962432\n", + " 0.934130\n", + " 0.991252\n", + " 0.934837\n", + " 0.970955\n", + " 0.947249\n", + " 0.831821\n", " \n", " \n", " 2\n", " 400\n", " 15\n", " 0\n", - " 1.007065\n", - " 0.977451\n", - " 0.996976\n", - " 0.928207\n", - " 0.991961\n", - " 0.967743\n", - " 0.853123\n", + " 1.000800\n", + " 0.971371\n", + " 1.002495\n", + " 0.912154\n", + " 0.988403\n", + " 0.964271\n", + " 0.861077\n", " \n", " \n", "\n", @@ -682,14 +682,14 @@ "text/plain": [ " poa_global temp_module ... v_oc_temp_corr v_mp\n", "date_time ... \n", - "0 100 15 ... 0.913971 0.844634\n", - "1 200 15 ... 0.941899 0.851158\n", - "2 400 15 ... 0.967743 0.853123\n", + "0 100 15 ... 0.917668 0.843822\n", + "1 200 15 ... 0.947249 0.831821\n", + "2 400 15 ... 0.964271 0.861077\n", "\n", "[3 rows x 10 columns]" ] }, - "execution_count": 12, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -710,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -738,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -753,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -780,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -827,12 +827,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -870,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -922,14 +922,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 41, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -979,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -988,7 +988,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1063,7 +1063,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1171,12 +1171,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1208,7 +1208,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1272,12 +1272,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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t0aMHrly5ItT+8uXLMDc3h4mJSbnl0dHRgqL3qRcvXggVmLKuCFeuXIG7u7tgW2/fvkVkZKTgeaGhoYiNjRV8a+/RoweKiopw48YNQZv09HQ8ePCgVt/sq6Kvrw9TU1OEh4ejRYsW5X4UFBTQpk0bKCgolNtXZWRlZdG1a1eEh4cLLQ8PD6/xnbQ5OTnljjLKjv6r+rL09OlTKCoqVnlXd1lhKCgoqDaHhYUFjIyMKvz7KPtdfPPNN+V+9yNGjEDXrl0RHByMDh06VLud2ggICMDEiRPh7OwMW1tbWFpaIiIiokavoa6uDmNj43IFvTYF/tPuZ+np6QgLC0Pr1q0BAJ06dYK/v3+1X6zLdOrUCampqQgKCqq0jby8PAYNGoStW7fixYsXyM3NFep6NGXKFFy+fBnh4eG4cOECpk6d+mVvjGPUa1WMmjVrhsTERNy/fx8tW7aEkpISlJSUxLa9lJQUzJs3D/Pnz8ebN2+wZs0azJw5U6RTUrdv38bmzZvh5+eHrl274uDBgxg/fjwcHR3RrVs3AKXf7hMTExEfHw+g9JSgiooKDAwMBN/yz549Cw0NDVhaWiI8PBxLly6FsbExli5dKtjWwoUL0b17d6xatQqTJ0/Gw4cP8fPPP2PXrl3lch04cACdO3eu8IOvbdu2Qo+Tk5MBlJ4WNDIyAgAMGDAAHTt2xKRJk/Dzzz+DMYZ58+ahW7du6NOnDwDAysoKI0eOxNy5c/Hrr79CXV0dbm5uMDY2hrOzs+D1Y2JikJqaKjiyCgkJQXJyMszMzKClpVXtPv7cxo0bMWPGDGhoaOCbb76BrKwsQkNDcenSJRw4cAAqKiqC09+KioqCLikXL14U3GC1cuVKDBs2DD/++CMmTJiAkJAQbNy4EfPnzxdsJzs7W5C5sLAQiYmJCA4OhoqKimDwi2+++QabN29Gly5d0Lt3b8TFxWHBggVo37694EvWrl27YGZmBhsbG/B4PFy5cgUeHh6YN2+e4Kjp7NmzyMnJQceOHaGqqorIyEi4u7vDxMQE/fr1q3af8Hg8LF26FG5ubmjdujXs7e1x+PBhPHv2DAcPHgQAaGhoQENDQ+h5GhoayM7OFvqbEOV9fwlra2ucO3cOY8aMgYqKCnbu3In4+Hjo6+vX6HUWL16MNWvWoFWrVujWrRvOnz8Pf3//cu2Cg4MBAKmpqcjOzhY8/vR0Lo/Hw7Jly7Bz505oampi1apVUFZWFlyiWLZsGbp27YqJEydi8eLF0NTUxJMnT2BiYgIHBwc8fPgQU6ZMwdGjR9GlSxf069cPvXr1grOzM3bu3In27dsjPj4eoaGh+O677/Drr7+Cz+ejS5cu0NDQwPXr15GVlSV0VmPw4MHQ0tLCuHHjoKqqiiFDhtRsRzcUXF4Qbeyqu3GnsLCQjR8/nmlqata4C8inZsyYwfr06SO0bPbs2axHjx6Cx2VdQJYsWcK0tLSYiooKmzZtGsvJyal2mykpKczExIQtWrRIaPmsWbOYhYWF4NZ9d3f3ct1aPn9fe/fuZWZmZkxWVpYZGBiwOXPmsA8fPpTb5j///MPat2/P5OTkmJmZGduxY0e5NnFxcUxaWpodOnSo2vfAWOU31MTHx7OxY8cyFRUVpqqqypycnFhSUpJQm8zMTDZjxgymqanJFBUV2ddff80iIyOF2lTU7QdAhd1EKlLR7/avv/5i3bp1Y4qKikxVVZXZ2tqy9evXC9bz+Xzm6enJrKysmKysLNPT02Njx44Veo0//viD2djYMHl5edaiRQu2efNmoW47Zfvl859P/6aKi4vZxo0bmZWVFVNQUGCGhoZswoQJLDo6WtBm69atzMrKiikqKjI1NTXWsWNH5u3tLXQziJ+fH+vatStTV1dn8vLyzNLSks2ZM4fFxMSItI/K/PTTT8zU1JTJyckxW1tbdvny5RrvW1Hed1U+//+7TExMDBs4cCBTUlJiBgYGbO3atWz69OlCr1vWBeRTn9/IV1JSwlauXMm0tbWZkpISGzNmTIVdQCp6D59+dJflvHLlCmvVqhWTk5Nj9vb25W66efDgAevfvz9TUlJiKioqrEuXLuzBgwdC+6qsWxRjpf9PfP/998zAwIDJysoyCwsLtnnzZsYYY2fPnmUODg5MQ0ODKSoqMhsbmwr/P12wYAEDwL7//vuqd3YDxmPsCy48EUIIaRAOHz6M7777rsbdfoho6JokIYQQUol6K5J79+6Fvb095OXl4eLiUmm7I0eOoFOnTlBTU4OJiQmWLVsmMd+Q7ty5I3S7/+c/d+7cEct2Y2Jiqtzu8ePHxbLdpuT48eNV7uOYmBiuIzYIc+bMqXQf2djY1GuWqn5fmzZtqtcspOGqt9Otf/75J6SkpHDlyhXk5eXh8OHDFbbz8vJC27Zt0bVrV3z48AEjRozAt99+ixUrVtRHTLHKy8vDu3fvKl1vbGwMRUXFOt9ucXExoqKiKl2vr69frnsFqZmsrCyhwcA/Z2FhQbM7AHj//j0yMzMrXCcrKwtzc/N6y1JV1xYtLa0vuhmLSJ56vya5evVqxMXFVVokP7dz507cvHkTfn5+4g1GCCGEfKbBX5MMCAio99MwhBBCCNDA+0n6+PggKChIaL66T3l7e8Pb2xsAEBYW1vgGziWEEFLnsrIeC/6dmAhkZHz5CdMGWyT//vtvrFixAv7+/pWO5jFr1izMmjULAGBvb1/l6BCEEEIkW3FxJl69WoDExP8VyQoG66qRBnm69fLly5g5cyb8/PyExrAkhBBCKpKefgdBQbZITDwCbe1vICVVN6Ob1VuRLC4uRn5+PkpKSlBSUoL8/PwKu3bcuHEDEydOxNmzZyucaZ4QQggpw+cX4PXrFQgO7gNAGh063EW7dn/B2tob8vK1v1u63orkhg0boKioiC1btuDYsWNQVFTEhg0bBH34yvqReXh4ICMjA0OGDCk3fRAhhBBSJjv7Pzx+3BWxsT/B0HAm7O2Doa5eOmWYvv5EODhEQVW1U622ITHD0tE1SUIIaRoY4yMuzhNv3qyEjIwGrK1/hY7OsArb1rY2NNgbd+oKn89HcnIy0tPT63XaqsZIQUEBJiYm5eauJISQhiI/PwZhYS5IT78Jbe2RsLY+CDk5XbFtT+KLZFxcHHg8HiwsLCArKyvWSU4bM8YYUlJSEBcXh2bNmnEdhxBChDDGkJR0HJGR8wDwYW39KwwMpon9M13ii2ROTg6sra3LTShLhPF4PGhra5ebYZ4QQrhWVJSKiIg5+PDhNNTUeqB166NQVLSsl21LfJEEQAVSRHSUTQhpaFJTryIszAVFRclo1mwzzMyWgseTrrftN4kiSQghpHEpKcnFmzfL8e7dXigptUG7dhegqtqh3nPQIZaE4vF4Vc5yQAghDVVmZhAeP+6Ed+/2wsRkITp1esxJgQToSJIQQkgDwecXIyZmC6Kj10NOzgC2tv7Q1OzPaSYqko1EcXExzUdICJFYubmvEBY2GZmZ/0JPbwJattwLWVlNrmPR6dbqJBQUoM/Tp0gsKBDL61tYWGDz5s1o06YNNDU1MW3aNOTn5+PWrVswMTHBTz/9BAMDA0ybNq3K19m2bRsMDQ1hZGSE3377TWhdXl4eFi9eDHNzc6irq6Nnz57Iy8sTy/shhJCaYIwhPt4bQUG2yM0NQ+vWv6NNm+MNokACdCRZLY+oKNzNyIBHdDT2WVmJZRvHjx/HlStXoKysjOHDh2PDhg0YMGAAEhMTkZqaiujoaPD5/Eqff/nyZWzfvh3Xr19Hs2bNMHPmTKH1S5YswcuXL3Hv3j0YGBjgwYMHdMcvIYRzBQWJCA//DqmpF6CpOQDW1j5QUDDhOpYQ+qSsQkJBAXySksAH4JOYKLajye+//x6mpqbQ0tLCqlWr8PvvvwMo7bqyfv16yMvLQ1FRsdLnnzp1CtOmTUPbtm2hrKyMdevWCdbx+Xz89ttv2L17N4yNjSEtLY3u3btDXl5eLO+FEEJE8eHD3wgKaof09Oto0WIP2re/0uAKJEBFskoeUVHgfxzatoQxeERHi2U7pqamgn+bm5sjPj4eAKCrqwsFBYVqnx8fH1/uNcokJycjPz8fzZs3r8PEhBDyZYqLMxEWNh0vX46CvLwZOnV6DBOTH8DjNcxy1DBTNQBlR5GFH4tkIWNiO5qMjY0V/DsmJgZGRkYARO/cb2hoWO41yujo6EBBQQGvX7+uo7SEEPJl0tPvCuZ8NDNbhY4d70NZuQ3XsapERbISnx5FlhHX0eS+ffsQFxeH1NRUbNq0Cc7OzjV6vpOTEw4fPoyQkBDk5uZi/fr1gnVSUlKYPn06Fi1ahPj4eJSUlOD+/fsoENOpY0II+RyfX4g3b1YiOLg3ACl06HAHlpYbICUlx3W0alGRrMT9zEzBUWSZQsZwLyOjzrc1YcIEDBw4EJaWlrC0tMTq1atr9PzBgwdjwYIF6NevH1q0aIF+/foJrd++fTvatWuHzp07Q0tLC8uXL6/yRiBCCKkrOTkv8eRJV8TEbEGQ7Dcwa/8Q6urduY4lMomfTzI0NBStW7fmIJFoLCwscOjQIQwYMIDrKAAa/v4ihDQOpXM+7v4456M6bqisw7q01phjZCS2ngIVqe18knQkSQghpE7l58fi2bOv8Pr1ImhpfQ0z2yBsyrARe08BcaAi2Uhs2rQJKioq5X4GDx7MdTRCCAFQNufjCTx61A6ZmQ9gbX0Ibdv+jc3vcuulp4A40GACHIuKihKpnZubG9zc3MQbhhBCvlDpnI+u+PDhJNTUun+c87F5pT0F1pibw6AR9NemI0lCCCG1kpp6DY8etUNy8lk0a7YJHToEQFGxtG92ffYUEIcmcSTJGKMJhUUgIfdwEULqSemcjyvw7t3PUFJqjXbt/KCq2lGoTX32FBAHiS+SsrKyyMvLg5KSEtdRGryioiKaaYQQIpKsrMcIDZ2E3NwwGBvPh6XlZkhLlx8+82nnzhykqzsSf7pVT08P7969Q25uLh0pVYHP5yMpKQnq6upcRyGENGB8fjGiozfiyZNuKC7OQvv219CypWeFBVISSPxhg5qaGoDS8U2Lioo4TtOwKSsrQ0dHh+sYhJAGKi/vNUJDJyMz8z709MajZct9DWZKK3GR+CIJlBbKsmJJCCGkZhhjSEg4hFevFkJKShatW5+Avv54rmPViyZRJAkhhHyZwsIkhId/h5SUf6Ch0R+tWh1ukFNaiQsVSUIIIRVKTj6H8PCZKC7ORIsWnjA2brhTWokLFUlCCCFCiouz8OrVQiQm/goVlQ6wszvW4Ke0EhcqkoQQQgQyMgIRGjoZ+fnRMDNzg4WFe6OY0kpcqEgSQggBn1+IqKh1iIn5CQoKFujQIQDq6j24jsW5pnVymRBCCBIKCtDn6VPBbBz/m/NxMwwNp8PePpgK5Ed0JEkIIU2MR1QU7mZkwCPqLVYoXcabNysgI6OGtm3PQUdnBNfxGhQqkoQQ0oSUzcqhhQ9okbAYr/EEKppD0b71r5CT0+c6XoNDRZIQQpoQj6go9Ob743vsggyKsQ1LYKHwHfZRgawQFUlCCGki4nISoZfgCifcwH+wwWasRDyMoZCUhDUWFo1ifsf6Vm837uzduxf29vaQl5eHi4tLlW137doFAwMDqKurY/r06Sj4eHGZEELIl0lN9cfzx3boids4hBmYj92IhzEAoJDPbzTzO9a3eiuSRkZGWL16NaZPn15luytXrmDLli24fv06oqKi8ObNG7i7u9dTSkIIkSwlJXmIjJyP58+/QhZTxDzsw3FMAh/SgjZ8AD6JiYK7Xcn/1FuRHD16NL755htoa2tX2e7IkSOYMWMGbGxsoKmpiTVr1uDw4cP1E5IQQhqRz7tyfC4r6wkeP+6Ed+/2wNj4/zC2ZwjCHWdjrqEh5D6biL6EMTqarECDuyb58uVLjBw5UvDY1tYWSUlJSElJqbLAhoeHw9HRUWiZk5MTXF1dkZubiyFDhpR7jouLC1xcXJCcnIyxY8eWWz937lw4OzsjNjYWkydPLrd+8eLFGD58OMLDwzF79uxy61evXo0BAwYgODgYCxYsKLd+06ZN6N69O+7duwc3N7dy6z09PWFnZwd/f39s2LCh3PoDBw7A2toafn5+2LFjR7n1vr6+MDU1xcmTJ+Hl5VVu/ZkzZ6Cjo4PDhw9X+EXk4sWLUFJSwv79+3Hq1Kly62/dugUA2L59O/755x+hdYqKirh06RIAwMPDA9evXxdar62tjbNnzwIAVq5cifv37wutNzExwbFjxwAACxYsQHBwsNB6KysreHt7AwBmzZqFiIgIofV2dnbw9PQEAEyaNAlxcXFC6x0cHLB582YAwJgxY5CSkiK0vn///lizZg0AYPDgwcjLyxNaP2zYMCxZsgQAyv3dAfS3R397ngDE/7dn36sX4gsL0UleHi0VS+dzdHJywpw5sxARsQFJSRshK6uH9u2vQkvrK8Fr3M/MROFn8+sWMoZ7GRkgwhpckczOzhaa+Lfs31lZWeWKpLe3t+CPleaKJIQ0JQkFBUj4+LmXWFgIc3l5yElJobDwA4KD+8DF5R7k5XVx794LyMpqCT33aefOXERulHiMffZ14jOnTp1CYGAgbGxsMG3aNMjKygrWubq6Yv/+/TXa4OrVqxEXF1fpKVRbW1usWrUKTk5OAICUlBTo6OggOTm5yiNJe3t7BAUF1SgLIYQ0Vq7h4fg1MRGFjEGOx8N3BgZYpXoHr14tAI8ng+XLjSAnpyc46m6qalsbqrwmuX37dixbtgwA8Msvv6BLly5ISEgQrC87JVGXbGxs8OzZM8HjZ8+eQV9fv9prmYQQ0lSUDQhQdspUiaXCJGEGIiJmQk2tKzp3fgE5OT2OU0qGKoukl5cXrl69it27d+PJkycYMWIEevbsieiPF3erOQgVUlxcjPz8fJSUlKCkpAT5+fkoLi4u127KlCn49ddfERISgrS0NGzYsKHaLiOEENKUeERFgf/x87c7AvEbpqMTHiJYbSVsba9BQcGU44SSo8oi+eHDB7Ro0ULweP369Vi4cCF69eqF8PBw8D67O6oqGzZsgKKiIrZs2YJjx45BUVERGzZsQExMDFRUVBATEwMAGDRoEJYtW4a+ffvC3Nwc5ubmWL9+/Re+PUIIkTz3MzMhzXKwGNuxEauRDB3MxgEcKRnV5CZFFrcqr0m2a9cOvr6+sLOzE1r+22+/YdWqVUhNTW0wHf3pmiQhpKnIyLj3cc7HtzAzWwELi3Xl5nwsu1/E1dWVi4gNRm1rQ5V3t06dOhX+/v7liuT06dMhLy8vuE2ZEEKI+JXO+bgeMTFboKBgBju7AGho9KywbVMvjnWl2rtbGws6kiSESLKcnBCEhk5CdvZTGBhMR4sWuyAjo1Zp+9zcXACAkpJSfUVskMR6JEkIIYRbjPHx7t1evHmzHNLSKrCx+Qu6ut9U+7yyQSyaeheQ2qr2Ci9jDPPnz6+PLIQQQj6Rnx+H58+/xqtX86GpOQCdO/8nUoEkdafKIllcXIwJEyYgNTW1vvIQQkiTU9EYrElJfyAoqB0yMu7Dysobbduep0mROVBpkczOzsbgwYNRXFxMA4wTQogYeURF4W5GBjyio1FUlIaQkIkIDR0PJaVWsLcPhpHRzBp1uSN1p9Jrkp6ensjNzcXly5chLS1dWTNCCCG1UDZ6Dh/Ak4QLeJC8AyVFSWjWbANMTZdDSopuHeFSpUeSDg4OePnyJa5du1afeQghpEnxiIqCDD8frtiHzWwRUvny6NjxX5ibr6pVgSybaYbUTqW/gf79+8PPzw/Ozs44ceJEhdMBEUII+XLBWVm4nhCAn7ERFojGnxgF35I5CJVrC9VavjYVyLpR5Y07vXr1wuXLlyucr44QQsiXY6wER58vx164Qhk5WIqt+Bn/h1zI18nkx8nJyUhOTq6DpE1btcfy7du3x9WrV+sjCyGENAl5eW/wPGQSRhTdxy30wU4sQhZKBwaoq8mPyybzpn6StSPSCW9zc3Nx5yCEEInHGENi4m949WoB8vg87MQqXEF/yPGk4GpoiH1WVlxHJJ8R+apwbm4uXr16hezsbKHl3bt3r/NQhBAiaQoL3yM8fBZSUs5BSa0PJmbNQyx0S9cxBp/ERKwxN4eBvDzHScmnRCqSR48exffffw85OTkoKioKlvN4PMEUV4QQQiqWnOyH8PDvUFycgebNd2JzziAkZb0H8L+hs0sYg0d0NB1NNjAiFclly5bh7Nmz+Oqrr8SdhxBCJEZxcTZev16EhISDUFa2ha3tdaiotMX9R49Q+NncEnV1LZLULZGKpJycHHUBIYSQGsjIuP9xzsc3n8z5WHoq9WnnzmLf/ty5c8W+jaZApCmsPTw8sGjRIrqdmBBCqsHnF+Ht2zV4+rQngBLY2d2GpeVmQYGsL87OznB2dq7XbUoikYqklZUVzp8/D319fUhLS0NaWhpSUlI0XB0hhHwiJycUT544IDp6AwwMpsLe/hk0NHpxkiU2NhaxsbGcbFuSiHS6dfLkyZgyZQqcnZ2FbtwhhBBSNufjPrx5s+zjnI9/Qld3FKeZJk+eDID6SdaWSEUyJSUFP/74I41CTwghnykoeIewsGlIS7sGLa2hsLY+BHl5A65jkToi0unWadOmwdfXV9xZCCGkUXn//iQePWqHjIxAWFn9gnbt/KhAShiRjiQfPnyIvXv3YuPGjdDXF570MyAgQCzBCCGkoSoqSkNk5Pd4//4EVFW7onVrXygpteQ6FhEDkYrkzJkzMXPmTHFnIYSQBi8t7TrCwlxQUJAAC4sfYWa2kuZ8lGBV/mYPHTqEIUOGYOrUqfWVhxBCGqSSkny8feuGuLhdUFS0RseO96GmJv7+jl9q8eLFXEeQCFVek3z06BEcHBxgZ2eHVatWITAwEOyzUSIIIUSSJBQUoM/Tp0gsKBAsy8oKxuPH9oiL2wVj4+9hb/+kQRdIABg+fDiGDx/OdYxGr8oieeDAAURHR8PX1xdqampYuXIlDAwMMGHCBBw7dowGFyCESByPqCjczciAR3Q0GCtBTMxPePKkC4qLU9G+/WW0bPkzpKWVuI5ZrfDwcISHh3Mdo9HjsRoeGmZkZODKlSu4cOECrl27BjMzM6xfvx5ff/21uDKKxN7eHkFBQZxmIIQ0bgkFBbB88AD5fD4seEn4Q2UP8rLuQVf3W1hZeUFWVpvriCIrG0q0qfeTrG1tqPKaJJ/Ph5SU8MGmuro6nJyc4OTkBKD0lCwhhEgCj6go8Pl8DMIl/MB+Rma2NNq18oW+/kTqJ95EVVkk1dXV0aNHD/Tu3Ru9e/dG165dISsrK9Smcz0M1EsIIeKWUFCAPxPDsRrb0At38RR28IQbHmiOoALZhFV5TfLy5ctwdHREYGAghg8fDnV1dfTt2xfu7u64fv068vLy6isnIYSI1aEwH3gxF3TFA+zHXCzGDiRCDx7R0VxHIxyq8kiyR48e6NGjB1asWAHGGJ49e4aAgADcuXMH+/fvR1ZWFvLz8+srKyGE1LnSOR8Xo1eaN17DEouxHW9hCYDmeCQiDiYAlN6wExsbi5iYGER//GbVv39/sQUjhBBx+3TOR1PTZejd7EfMqOcprcRl9erVXEeQCFUWyTNnzuD27dsICAhAWloaevTogZ49e2LKlClo164dnacnhDRKfH4RoqN/RHT0JqTx9GFrcxWWugO4jlWnBgyQrPfDlSqLpJOTE1q3bo3ly5fD2dkZ8vKS8Q2LENJ05eSEITR0ErKzH+Ot4ij8X94MTEkzwz5drpPVreDgYACAnZ0dpzkauyqL5N27dxEQEICTJ09i2bJlaNmyJXr16oVevXqhR48eUFNTq6+chBBSK4yxj3M+LoWUlDKMrP7A4FeGyAcfPomJWGNuDgMJOhBYsGABAOonWVtV3t3avXt3rFixAhcuXEBCQgJ+/vlnGBgYwMfHBy1btkSHDh3qKychhHyxgoJ4PH8+CK9e/QANjX7o3Pk/eGbZgf9xLJUSxuguVlIhkeaTBP53405sbCyioqKQmpqKDx8+iLyh1NRUjBo1CsrKyjA3N8eJEycqbMcYw+rVq2FsbAx1dXU4Ojri5cuXIm+HEEI+9f79aTx61BYZGXfRsqUX2rX7B6nQhE9SEgo/FslCxuCTmCg0XishQDVF8syZM/jhhx9ga2sLHR0dzJ8/H+/fv8fs2bMREhKCuLg4kTc0b948yMnJISkpCcePH8fcuXMrLH6nT5/Gb7/9hjt37iA1NRUODg6YPHlyzd8ZIaRJKypKR2joZISEOEFRsSXs7Z9CSmcaHIODsfL1a8FRZBk6miQVqfKa5OrVq9G7d28sXboUffr0gamp6RdtJCcnB2fPnsV///0HFRUV9OzZEyNGjICvry+2bNki1Pbt27fo2bMnLC1L+ylNmjQJu3bt+qLtEkKaprS0mwgLm4qCgnhYWKyHmZkbpKRksCQ8HHczMvBSRkZwFFmG+kSSilRZJMPCwgAA8fHxMDIyKrf+6dOnIl2XjIiIgLS0NKysrATLbG1tcfv27XJtx40bh5MnTyIiIgLNmjXDkSNHMGjQoGq3ER4eLhjQt4yTkxNcXV2Rm5uLIUOGlHuOi4sLXFxckJycjLFjx5ZbP3fuXDg7OyM2NrbCo9nFixdj+PDhCA8Px+zZs8utX716NQYMGIDg4GDBRfRPbdq0Cd27d8e9e/fg5uZWbr2npyfs7Ozg7++PDRs2lFt/4MABWFtbw8/PDzt27Ci33tfXF6ampjh58iS8vLzKrT9z5gx0dHRw+PBhHD58uNz6ixcvQklJCfv378epU6fKrS+7IWD79u34559/hNYpKiri0qVLAAAPDw9cv35daL22tjbOnj0LAFi5ciXu378vtN7ExATHjh0DUHoDQtmdemWsrKzg7e0NAJg1axYiIiKE1tvZ2cHT0xNA6Retz896ODg4YPPmzQCAMWPGICUlRWh9//79sWbNGgDA4MGDy40uNWzYMCxZsgQAyv3dAfS3x9XfHmN8ALFYvz4KiopWQnM+JhQUwCcpCXwAuXw+EhwcJOpGnc9t2rSJ6wgSQaTBBAYOHIiAgABoaWkJlj18+BAjR45EQkJCtc/Pzs6Gurq60DJ1dXVkZWWVa2toaIhevXrB2toa0tLSMDU1xY0bNyp8XW9vb8EHZVFRkShvhRAioUpKspGbGwoZmVwsX24EBQV93L79v7GlPaKiyt2os++TL+6Spnv37lxHkAxMBLt372adOnViWVlZjDHGAgMDmb6+Prtw4YIoT2dPnjxhioqKQsu2b9/Ohg0bVq6tm5sbc3BwYLGxsayoqIj5+PgwCwsLlpOTU+U2OnXqJFIWQohk4fOLWXT0FnbrliwLDDRgycmXWJ8+fVifPn0EbeLz85nC7dsMN28KfhRv32YJ+fncBRezwMBAFhgYyHUMztW2Noh0d+v//d//YeTIkRgyZAguXbqEUaNG4dixYxWeRqqIlZUViouLERkZKVj27Nkz2NjYlGv77NkzODs7w8TEBDIyMnBxcUFaWhpCQkJELPuEkKYiLy8KwcF98ebNCmhrj0Dnzv9BW7v85ZlPjyLLSPqNOm5ubhWeSic1I3IXkDVr1qBz585wdnbG6dOnazTkkbKyMkaPHo21a9ciJycHgYGBOHfuXIXXWjp37ozTp08jKSkJfD4fvr6+KCoqQosWLUTeHiFEsjHGkJBwGEFB7ZGd/QytWh2Fjc3pSidFvp+ZSTfqkC9S6TVJU1PTcmOz8vl88Pl8TJo0SbAsJiZGpA3t378f06dPh56eHrS1teHl5QUbGxvExMSgTZs2CAkJgZmZGZYvX47379/Dzs4OOTk5aNGiBc6ePQsNDY0ve4eEEIlSWPgBERGzkZz8F9TV+6B16yNQUDCv8jlPad5b8oUqLZJldxbWFS0tLfz999/llpuZmSE7O1vwWEFBAfv27cO+ffvqdPuEkMYvJeUCwsJmoLg4DZaW22Bqugg8XvkTYk5OThykI5Ko0iLZp0+f+sxBCCGVKinJwevXSxAf/wuUldvB1vYqVFTaV9re1dW1HtMRSSbyfJKEEMKFzMwHCA2dhLy81zA1XYpmzTwgVc2cj7m5uQAAJSWl+ojYIJX1Eya1Q0WSENIglc75uAHR0RshL28MO7ub0NAQ7QxX2Z33TXkGDJoiq25QkSSENDi5ueEIDZ2ErKwg6OtPRcuWuyEjo179E4mAv78/AJp8ubaoSBJCGoz4/HzsCl6HYYV7IC2lBBubM9DVHcN1rEapbDhBKpK1U2mRnDx5crkuIBU5evRonQYihDRNBQXxuP3UGUML7iJevjdGd/wD8vKGXMciTVylRZI67xNC6sv792cQFjEbmsU52IUFuFb4DfpCCwZcByNNXqVF0t3dvT5zEEKaoOLiDERG/oCkJF+kyLbDUizDW5hADqjVAOQuLi51mpM0XSJfkywsLER4eDiSk5PBPhneqV+/fmIJRgiRbGlptz7O+fgO2iarMexdX+R8HCmzkDH4JCZijbn5F01nRUWS1BWRiuTdu3fx7bffoqCgAJmZmVBTU0NWVhZMTU3x5s0bcWckhEiQkpJ8vH27GnFxO6Go2AIdOwZiRYIGipAI4H9fwGsznVVycjIAQEdHp65iNzoHDhzgOoJEEKlILly4EMuWLcPChQuhqamJ1NRU/Pjjj026oy4hpOays58jNHQScnJewMhoLpo33wZpaWXcD39UpwOQl01k3ZT7SVpbW3MdQSKIVCQjIiIwf/58oWUrVqxAs2bNBLOzE0JIZRgrQWzsTrx9uxqyslpo1+4itLUHC9bTAOR1z8/PDwAwfPhwjpM0biIVSXV1dWRmZkJDQwOGhoYICQmBtra20MDkhBBSkby8KISFTUVGRgB0dEbDyuoA5OSa7mnQ+rJjxw4AVCRrS6T5JEePHo2LFy8CAGbMmIG+ffuiU6dO+Pbbb8UajhDSeDHGkJh45OOcj0/RqtUR2NicoQJJGhWRjiQ/HSh38eLF6Nq1K7KysvD111+LKxchpBErLEz+OOfjn1BX741WrY5AUdGC61iE1FiNhqV79+4d4uPj0axZMxgbG4srEyGkEUtJuYTw8OkoKkr9OOfjQvB40vWaYe7cufW6PSK5RCqSMTExmDhxIu7fvw8tLS2kpqaiW7duOH78OMzNq54RnBDSNJTO+bgU8fFeUFZuh/btr1Q556M4OTs7c7JdInlEuiY5depUdOrUCRkZGXj//j3S09PRuXNnTJ06Vdz5CCENSEJBAfo8fYrEggKh5ZmZDxAU1AHx8b/A1HQJOnZ8yFmBBIDY2FjExsZytv2GwNfXF76+vlzHaPR4jH3WOakCampqSElJgaysrGBZYWEhtLW1kZWVJdaAorK3t0dQUBDXMQiRaK7h4TiQkIA5RkbYZ2X1cc7HjYiO3gB5eWO0anUEmpqOXMeEo2NphqbcT5KUqm1tEOlIslu3bnj48KHQsqCgIDg4OHzxhgkhjUtCQQF8kpLAB+CTmIjo9Bd4+rQHoqPXQ19/Ajp3ft4gCiQpdfLkSZw8eZLrGI1epdck165dK/h38+bNMWTIEAwdOhSmpqaIjY3FxYsXMWHChHoJSQjhnkdUFPiMAWAYzP8Lkc9+gYK0Etq0OQU9PeoO1tB4eXkBoOuztVVpkfz8fP7o0aMBAO/fv4e8vDxGjRqF/Px88aYjhDQIZUeRKiwZy7AVXfEQQawLnG3/gJ5qM67jESI2lRZJHx+f+sxBCGnAPKKi0J1/C/+HHZBHATwxH5fwDd4mFGGfKtfpCBEfkftJRkZG4vfff8e7d+9gbGyM8ePHo2XLluLMRghpAIqLM9DswyI44SJC0QqbsRKxMAOALx6AXNwWL17MdQQiIUQqkn5+fpg4cSKGDRsGc3NzhIeHw97eHr6+vhgxYoS4MxJCOJKefhuhoVPQufgdzM3d0dt8FeZKyVb/RI7ReKWkrohUJN3c3HDu3Dn07dtXsOzWrVv4/vvvqUgSIoH4/AK8fbsGsbHbBXM+qql15TqWyMLDwwE07emizpw5w3UEiSBSkYyLi0OvXr2ElvXs2RNxcXFiCUUI4Y7wnI9z0Lz5dkhLK3Mdq0Zmz54NoGn3k2zKE07XJZH6SdrZ2QmmXSmzc+dO2NnZiSMTIYQDjJUgJmY7Hj/ujMLC92jX7gKsrLwaXYEkpQ4fPozDhw9zHaPRE+lI0svLC8OHD8fu3bsF/SSVlZVx/vx5cecjhNSD/PxohIZORUbGbejojPo456Mu17FILZQVSBcXF05zNHYiFclWrVohNDQU9+/fR0JCAoyMjNC1a1ehYeoIIY0PYwxJSb6IjPwBAIO1tQ8MDKaCx+NxHY2QBkHkLiAyMjLlrksSQhqv0jkf5yA5+SzU1Xt9nPORBgYg5FOVFklTU1ORvk3GxMTUaSBCiPilpFxGePg0FBWlwNLyJ5iaLq73OR/FafXq1VxHIBKi0iJ57Nix+sxBCKkHpXM+LkN8/H4oK7dF+/aXoaJiy3WsOjdgwACuIxAJUWmR7NOnT33mIITUoYSCAowLCcHJNm1gIC8PAMjMfIjQ0MnIy4uEicliNGu2AdLSChwnFY/g4GAAaNJ34F+8eJHrCBJBpGuSn84I8il5eXmYmJhg0KBB0NfXr9NghJAv5xEVhbsZGfCIjsbPLSwRE7MJUVE/Ql7eCLa216Gp2bf6F2nEFixYAKBp95NUUlLiOoJEEKmfZEREBH766SfcvHkTr169ws2bN/HTTz/h6dOn8PLygqWlJS5fvlzla6SmpmLUqFFQVlaGubk5Tpw4UWnbN2/eYNiwYVBVVYWOjg6WLVtWs3dFSBP26byPVxMe4eGT7oiKcoe+/gTY2z+X+AJJSu3fvx/79+/nOkajJ9KRJJ/Pxx9//IFRo0YJlp07dw4nTpzAv//+iyNHjmDFihUYNGhQpa8xb948yMnJISkpCcHBwRg6dChsbW1hY2Mj1K6wsBBfffUV5s2bh5MnT0JaWhoRERFf+PYIaXo8oqLA5/MxHOfhyryQmSMPO5rzsck5deoUAMDV1ZXjJI2bSEeSV65cKTdG67Bhw3Dp0iUAwKRJk/D69etKn5+Tk4OzZ8/Cw8MDKioq6NmzJ0aMGAFfX99ybQ8fPgwjIyMsWrQIysrKUFBQQPv27WvynghpMhIKCtDn6VMkFhQIHv+dGIL1WIFF2IUXaIsZ+BV8dRpjmZAvIVKRbN68uWCW6zK//PILmjdvDgBITk6GsnLlQ1dFRERAWloaVlZWgmW2trZ4+fJlubb//vsvLCwsMHjwYOjo6MDR0REvXrwQ6c0Q0tR8eu0RAH4L9YYXc0EHPMVu/B+WYSveQ0ewnhBSMyKdbj106BBGjx6Nn376CcbGxnj37h2kpaXx559/Aigdcd/Dw6PS52dnZ0NdXV1ombq6OrKyssq1jYuLw82bN3H+/Hn0798fu3fvxsiRIxEWFgY5OTmhtt7e3vD29gYAfPjwQZS3QojE+PTa48mEV5hR5IEe6ccQBmtsgptgzsdCxhrsvI/ismnTJq4jEAkhUpHs2LEjIiMj8e+//yI+Ph6GhoZwcHAQDEvXu3dvWFpaVvp8FRUVZGZmCi3LzMyEqmr5Kc0VFRXRs2dPDB48GACwZMkSbNiwAaGhobC1Fe7PNWvWLMyaNQsAYG9vL8pbIURieERFgc8Y2uMZVrLNyPjwARbma9DbfA3mNII5H8Wpe/fuXEcgEkLkYelkZWWrHJauTZs25QphGSsrKxQXFyMyMhItW7YEADx79qzcTTsA0L59ewQGBooai5AmKaGgAMcSYzGNHYITTiEeRljE24tLRtMh1cQLJADcu3cPQNMulk25+0tdEumapCgYY5WuU1ZWxujRo7F27Vrk5OQgMDAQ586dw+TJk8u1nTRpEv7991/4+/ujpKQEnp6e0NHRQevWresqKiGN3p7IK/BkczAOJ/EPhmEmDiIEbeja40dubm5wc3PjOgaRAHVWJKsb53X//v3Iy8uDnp4exo8fDy8vL9jY2CAmJgYqKiqCMWCtra1x7NgxzJkzB5qamjh37hzOnz9f7nokIU0RY3zExu5A/+Sx0EAqVmITdmER8qHYJK89kspt374d27dv5zpGo8djVR0C1oCamlqlp1vrg729PYKCgjjbPiHilp8fg7CwqUhPvwUdnW9gZeVNcz5WwtHREUDTPuVI+6BUbWuDyNckCSHcKJ3z8TgiI+cB4MPa+jcYGLjQnI+E1IM6K5J1dEBKCPlEUVEKIiLm4sOH01BX74lWrY7SnI+E1KM6K5IhISF19VKENHkJBQVY9ewQphdtRElxMiwtt8DUdIlEzfkoTp6enlxHIBJCpCKZmZmJdevW4fbt20hOThY6aiy74cbU1FQ8CQlpYkpKcvHPs5mYknsCKTIt0a/jRaiq2nEdq1FpylNklVFUVOQ6gkQQ6e5WV1dXPHnyBGvXrkVqaip+/vlnmJmZYeHCheLOR0iTkpn5CP8+skPL3BM4hW8xpWQfcuSo+1NN+fv7w9/fn+sYnLp06ZJgfG3y5US6u1VPTw+hoaHQ1taGhoYG0tPT8e7dOwwfPhxPnjypj5zVortbSWPG5xcL5nzMldLFOv4yPEIHyPF4+M7QEPs+GfeYVI/u7CRlalsbRDqS5PP5grFXVVRUkJ6eDkNDQ7x69eqLN0wIKZWbG4mnT3siKsodqjrfYgo7hEfoAKB03FWfxETBLB+EiMrDw6PKMbWJaEQqkra2trh9+zYAoFevXpg3bx7mzp0rNKsHIaRmGGOIjz+AoCA75OVFoE2bP/Cr7DpkQUWoXQljNJIOqbHr16/j+vXrXMdo9EQqkgcPHoSFhQUAYM+ePVBQUEB6ejqOHj0qzmyESKyCgkS8eDEMERFzoK7eA507v4CenjPuZ2ai8LMrIDSSDiHcEenu1k9n+NDV1cWvv/4qtkCESLoPH/5CePhM8Pk5aNFiD4yN54HHK/2++rRzZ47TEUI+JXI/SR8fH/j6+uLdu3cwNjbG5MmTMW3aNHFmI0SiFBdn4tWrBUhM9IGKSke0bn0Mysp056o4HDhwgOsIREKIVCQ3btyIo0ePYvHixTA3N0d0dDS2bt2K+Ph4rFq1StwZCWn00tPvICxsCvLzY2Buvhrm5msgJUWD9ouLtbU11xE4p62tzXUEiSBSF5BmzZrh1q1bMDc3FyyLjo5G7969Ed1AbiigLiCkoUgoKMC4kBCcbNMGerLA27fuiI3dCgUFS7Ru7Qt1dQeuI0o8Pz8/AMDw4cM5TkK4Vi8DnOfk5EBXV3i2AW1tbeTl5X3xhgmRVB5RUbibkYHdr65hdO5q5OQ8g6HhLDRvvgMyMirVvwCptR07dgCgIklqT6S7WwcNGoSJEyciPDwceXl5CAsLw9SpU/H111+LOx8hjUpCQQEOJyZgDE6h34fRyC9MQNu2frC2PkAFktSrlStXYuXKlVzHaPREKpJ79+6FqqoqbG1toaysLPjvzz//LO58hDQqO17fwya2CK7wwiN0wZ9a56CjM4zrWKQJun//Pu7fv891jEZPpNOtampqOHr0KA4fPozk5GTo6OhASkqk+kpIk8AYQ8S7I3B8/wOkUIKtWIpLGAzFD4VYblkAA3l5riMSQr6AyF1AIiMjcerUKcTHx8PIyAhOTk5o2bKlOLMR0igUFaV+nPPxFKLQFpuwEgkwAvC/0XJo7FVCGieRDgdPnDiBDh064Pnz51BWVsaLFy/QsWNHnDhxQtz5CGnQUlOv4tGjdkhO/gv/yM7DfHgKCiRAo+VwxdfXF76+vlzHIBJApCPJ1atX4+LFi+jdu7dg2Z07dzB58mRMmDBBbOEIaahKSnLx5s1yvHu3F0pKbdCu3T/oo9oB27kORgDQ/LYAYGJiwnUEiSBSkczKyoKDg3Dfrm7duiEnJ0csoQhpyDIzgxAWNhm5uWEwMVmAZs02QVqaJrhtSE6ePAkAcHZ25jgJd44dO8Z1BIkg0unWRYsWwc3NDfn5+QCAvLw8rFq1CosWLRJrOEIaEj6/GFFRG/D0qQNKSrJha+uPFi12UYFsgLy8vODl5cV1DCIBRDqS3L9/PxITE7F7925oamoiLS0NjDEYGhoK/SHGxMSILSghXMrNfYWwsMnIzPwXenoT0LLlXsjKanIdi5BKLViwAADg6enJaY7GTqQiSYftpKlijCEh4SBevVoIKSk5tG79O/T1x3Edi5BqBQcHcx1BIohUJPv06SPuHIQ0OAUFiQgP/w6pqRegqTkA1tY+UFCgmyEIaUpE7idJSFPy4cPfiIiYiZKS7HJzPhJCmg4qkoR8org46+Ocj799nPPRF8rKbbiORWrozJkzXEcgEoKKJCEfpaff/TjnYzTMzFbBwmItzfnYSOno6HAdgXNWNMpTnai2SD58+BBdunQp929CJAWfX4ioKHfExPwEBYVm6NDhDtTVu3Mdi9TC4cOHAQAuLi6c5uCSt7c31xEkQrVF0t/fHwkJCZCWlsbz58+pSBKJkpPzEqGhk5CdHQxDw+/QvPlOyMioch2L1BIVSVJXqiySMTExcHZ2xpw5cyAlJQUvLy/ExMTAzMysvvIRIhaM8REXtxtv3qyEjIwa2rY9Bx2dEVzHIqTOzJo1CwAdUdZWlUXSx8cHABAXFwcejyfoL7l27VrxJyNETPLzYxEW5oL09BvQ1h4Ba+uDkJPT4zoWIXUqIiKC6wgSocoi6e7ujrt376KoqAg8Hg/9+vVDz5496ysbIXWKMYb3739HRIQrGCuGtfUhGBhMB4/H4zoaIaSBqvaa5J07d7Bq1SowxrB7924qkqRRKp3z0RUfPpyEmlp3tG59FIqKzbmORQhp4KotkitXrqzw34Q0Fqmp1xAW5oKiovdo1mwTzMyWgceT5joWEaOLFy9yHYFIiHobQiQ1NRWjRo2CsrIyzM3NRZqwuV+/fuDxeCguLq6HhEQSJBQUoM/Tp0gsKEBJSR4iI/8Pz58PhIyMOjp2fABz85VUIJsAJSUlKCkpcR2DU3Z2drCzs+M6RqNXb4MJzJs3D3JyckhKSkJwcDCGDh0KW1tb2NjYVNj++PHjVBxJjXlEReFuRgZ+jryAkbmrkJsbBmPj+bC03ExTWjUh+/fvBwC4urpynIQ7NPtH3aiXI8mcnBycPXsWHh4eUFFRQc+ePTFixAj4+vpW2D4jIwPr16/H1q1b6yMekRAJBQU4kvgO43EMfZOdUFichfbtr6FlS08qkE3MqVOncOrUKa5jEAkg0pFkcHAwtLW1YWpqKlgWExODtLQ02NraVvv8iIgISEtLCw2TZGtri9u3b1fY3s3NDXPnzoWBgYEo8UgTl1BQgHEhIWgvnYRtbAna4CVuoh8ytLbAU6sz1/EI4cSkSZMA0FSHtSXSkeSkSZNQVFQktKyoqAiTJ08WaSPZ2dlQV1cXWqauro6srKxybYOCghAYGIgffvih2tf19vaGvb097O3t8eHDB5GyEMnj8fYt1DKOY0jqKJgiGh5YjR+xBt7v85BYUMB1PEI4ERcXh7i4OK5jNHoiFcmYmBhYWloKLWvevDmioqJE2oiKigoyMzOFlmVmZkJVVXj4Lz6fD1dXV+zevRsyMtUf5M6aNQtBQUEICgqCrq6uSFmIZInNioF54nQsxg6EoA1m4FfcQH8AQAlj8IiO5jghIaQxE6lImpiY4MmTJ0LLnjx5AiMjI5E2YmVlheLiYkRGRgqWPXv2rNxNO5mZmQgKCoKzszMMDAzQuXNnwfbv3Lkj0rZI05GcfA4vn3ZABwRhL+ZhKbbhA/43ck4hY7iXkcFhQkJIYyfSNcmFCxdi5MiRWLZsGZo3b47Xr19j+/btWLVqlUgbUVZWxujRo7F27VocOnQIwcHBOHfuHO7duyfUTl1dHfHx8YLHsbGx6NKlCx4/fkxHikSgdM7HhUhM/BVxaIkN2IFoWAAAFKWk8KZrVxjIy3MbknDq1q1bXEcgEkKkIjlz5kxoaGjg119/RWxsLExNTbFjxw6MHTtW5A3t378f06dPh56eHrS1teHl5QUbGxvExMSgTZs2CAkJgZmZmdDNOvn5+QAAfX19kU6/EsmXkRGI0NDJyM+PRojybCzNGYvcT/6My06x7qO59EgT5+DgwHUEicBjjDGuQ9QFe3t7BAUFcR2DiEnpnI/rPs75aIHWrY/CMUIOwTk55draKSvjaWe6q7Up2759OwBgyZIlHCchXKttbaj08Oy3334T6QWmT5/+xRsnpDoJBQX44cU/WMw2oCBHeM7Hp1QHSSX++ecfAFQkSe1VWiQ/7ejPGENgYCAMDAxgamqK2NhYJCYmomfPnlQkidgwxscfL3/ErOxtyJBShX3bv6GjM5LrWIQ0CmPGjAEAnD17luMkjVulRfLmzZuCf//www/45ptvsGDBAsGy3bt34/Xr12INR5qu/Pw4PA+dgg6ZN3EPDtjLluGJ6iCuYxHSaKSkpHAdQSKIdDfMsWPHkJycLLTs+++/h46ODvbs2SOWYKTpSkr6HZGRrsgrKcReLMF5DIEcpOiGHEJIvROpn6SBgQHOnz8vtMzPzw96ejSbO6k7RUVpCAkZj9DQCZBRaIXZOIjzGAqAh0LG4JOYSCPoEJEoKipCUZHG6yW1J9KR5J49ezB27Fhs27YNpqamiImJQUhICE6fPi3ufKSJSE31/zjnYxKaNduArXnf4F1OMoD/3XxN3TuIqC5dusR1BCIhRCqSX331Fd68eYOLFy8iPj4eQ4cOxdChQ6GtrS3ufETClZTk4c2bFXj3bg+UlFqhXbtzUFXthHuPHqHws95JNIIOIaLr378/1xEkQp31k1RTUys3Pmt9on6SjUfZrB1HzArw/vV05OaGwtj4/2BpuYWmtCJ1wsPDAwCwZs0ajpMQrtW2NtTZfJISMiYBqQcb3r6GWcY+vHnRC8XFGWjf/ipattxNBZLUmevXr+P69etcxyASoM7GeuPxeHX1UkSCRWeEok2iM2zwH26jH75rfwxaKoZcxyJE4gwePBgAXZ+tLRoQldQLxhgSE39DRMT/wRw8eGA17vIG4H18FvZZUZEkpK7l5eVxHUEi1NnpVkIqU1j4Hv/99w3Cw7/DS9YK0/EbbqA/desghDR4dE2SiFVy8nk8etQWqalX8FRtJVZgu9CcjzQxMhEHbW1tuvue1IlqT7eWlJTAysoKISEhkK9ijj46700+VVychdevFyEh4RBUVOxgZ3cTC0JyUQDhWTuoWwcRBxqvlNSVaouktLQ0pKWlkZ+fX2WR7NmzZ50GI41XRsa9j3M+voWZ2QpYWKyHlJQczdpBSD0aNmwY1xEkgkg37ixYsABOTk5wc3ODiYmJ0J2slpaWYgtHGpfSOR/XIyZmCxQUzGBnFwANDfryROrfypUrAQCbN2/mOAl3aJqwuiFSkfz+++8BANeuXRNazuPxUFJSUvepSKOTkxOC0NBJyM5+CgOD6WjRYhdkZNS4jkWaqPv373MdgUgIkYokn88Xdw7SSDHGx7t3e/HmzXJIS6vAxuYv6Op+w3UsQpo8R0dHAMCtW7c4zdHY1aif5Lt37xAfHw9jY2MYGRmJKxNpJPLz4xAePg1paf7Q1h4Ga+tDkJPT5zoWIYTUGZG6gMTExKBXr14wNzfH0KFDYWZmhp49eyKabt1vst6/P4mgoHbIyLgPKytvtG17ngokIUTiiFQkp06dik6dOiEjIwPv379Heno6OnfujKlTp4o7H2lgSud8nIiQkHFQUmoFe/tgGBnNpGEJSYNiYmICExMTrmMQCSDS6dbHjx/j6tWrkJWVBQCoqKjgp59+os66TUxa2nWEhbmgsDARFhYeMDNbASkpGtmQNDzHjh3jOgKRECJ9wnXr1g0PHz5Ejx49BMuCgoLg4OAgtmCk4SgpycPbt26Ii/OEoqI1OnS4DzU1e65jEUKq4OTkxHUEiSBSkWzevDmGDBmCoUOHwtTUFLGxsbh48SImTJiAtWvXCtr9+OOPYgtKuJGV9RShoZOQmxsCY+MfPs75qMR1LEKqtGDBAgCAp6cnpzm45OrqynUEiSBSkczPz8fo0aMBAO/fv4e8vDxGjRqFvLw8xMbGAqCpsiQNYyWIidmKqCh3yMrqon37K9DSGsh1LEJEEhwczHUEzuXm5gIAlJToS21tiFQkfXx8xJ2DNCB5eW8QGjoFmZmB0NX9FlZWv0BWVovrWISQGhgyZAgA6idZW3TXBREom/Px1asFAKTRuvUx6OlNoLMEhJAmi4okAVA652N4+CykpJyDhkZftGp1GAoKZlzHIoQQTlGRJEhO9kN4+HcoLs5A8+Y7YWIyHzwezcdNGi8rKyuuIxAJIVKRTExMhIGBgcjLSeNQXJz9cc7Hg1BWtoWt7XWoqLTlOhYhtebt7c11BCIhRCqSVlZWyMzMLLe8TZs2SE1NrfNQRPwyMu5/nPPxDUxNl6NZs/WQkqp8vlBCSOPi4uLCdQSJIFKRZIyVW5aZmQkpKTol19jw+UWIjv4R0dGbPs75eBsaGr24jkVInZo1axaApn1ESUWyblRZJE1NTcHj8ZCXlwczM+GbOFJSUjB+/HixhiN1KycnFKGhk5Gd/RgGBtPQooUnzflIJFJERATXETiXnJwMANDR0eE4SeNWZZE8duwYGGMYMmQIfH19Bct5PB709fVhbW0t9oCk9krnfNyHN2+WfZzz8U/o6o7iOhYhRIzGjh0LgPpJ1laVRbJPnz4ASr+R0KgNjVNBwTuEhU1DWto1aGkNhbX1IcjL081WhBAiikqL5MaNG7Fq1SoAwJYtWyp9ARqvteF6//4kIiLmgs8vgJXVLzA0nEUDAxBCSA1UeudNXFyc4N+xsbEV/nzapjqpqakYNWoUlJWVYW5ujhMnTlTY7siRI+jUqRPU1NRgYmKCZcuWobi4uAZviRQVpQvmfFRUtPo45+NsKpCkybCzs4OdnR3XMYgEqPRI0sbGRvDvVatWoUWLFrXa0Lx58yAnJ4ekpCQEBwdj6NChsLW1FdoOUDoor6enJ7p27YoPHz5gxIgR2L59O1asWFGr7UuqhIICjAsJwck2bWAgL4+0tBsIC5uKgoIEWFj8CDOzlTTnI2lymvLsH6Ru8VhF/TsAqKurIyMjAwCgpqZWYT9JUeXk5EBTUxP//fefYCSMyZMnw9jYuMpTuQCwc+dO3Lx5E35+flW2s7e3R1BQ0BdnbKxcw8NxICEBrobamC/tg7i4XVBUtEbr1r5QU+vMdTxCCEdOnjwJAHB2duY4CbdqWxsqPcRo3rw5Fi9eDBsbGxQVFeG3336rsN306dOr3UhERASkpaWFhoqytbXF7du3q31uQEBAuaPNioSHh8PR0VFomZOTE1xdXZGbmysYEf9TLi4ucHFxQXJysuBOsE/NnTsXzs7OiI2NxeTJk8utX7x4MYYPH47w8HDMnj273PrVq1djwIABCA4OFsxv96lNmzahe/fuuHfvHtzc3Mqt9/T0hJ2dHfz9/bFhw4Zy6z1+/hm/paZC795ZXDt1EGM8C2BkNA/Nm2+lOR9JkzZp0iQApXfoN1VNvTjWlUqL5B9//IGtW7fi999/R1FRkVAXkDI8Hk+kIpmdnQ11dXWhZerq6sjKyqryeT4+PggKCsKhQ4cqXO/t7S3oLFxUVFRtDknjFReDMYrnYY1D+OkVH9OWWuHto71cxyKEczW5X0JSlc31a2pqynGSxq3S062f6t+/P65fv/7FG3n69Cl69OghmAQUAHbs2IFbt25Vehr177//xuzZs+Hv74927dpVu42mdro1OiMMfk+d0BYvcAt9sGFBMviQQfyDBzCQp+HlSNNWdlapKfcRpH1Qqra1QaRx5WpTIIHSsV+Li4sRGRkpWPbs2bNKT6NevnwZM2fOhJ+fn0gFsqlIKChAnydPEBbrjYjgTrDAG2yEG9bDHSWQAQPgER3NdUxCCJEY9TL4qrKyMkaPHo21a9ciJycHgYGBOHfuXIXX+W7cuIGJEyfi7Nmz6NKlS33EazS2vn6CrzLnI/H1bLzltcIM/Ap/fAXgf107bqelcReQEEIkTL2NUL5//37k5eVBT08P48ePh5eXF2xsbBATEwMVFRXExMQAADw8PJCRkYEhQ4ZARUUFKioqGDx4cH3FbLAiE/9C7/dD0QUPcJDninvaR5DOEx45hwegj6YmNwEJaUAcHBzg4ODAdQwiAeqtA52Wlhb+/vvvcsvNzMyQnZ0teHzz5s36itQolM75uBgJCd5IQ3Msxna8Q3OopqWj8NPLyY6OYADufey2Q0hTtnnzZq4jEAlBvcwboLIBAg6bZuH96xnIy3uN0xiPg3BBEeQAxpDL5yPBweF/N+l81v2FENK0LV68mOsIEoGKZAO04W0kWmTsQlTGcSjIm+KG1lEcSjNF0SdHjiWMwSM6Gvs+9j0tu3OYBqInBBgzZgwA4OzZsxwn4c7w4cO5jiARqEg2MFHpz2Gb6AQrhOMaBsG1/VH4hUShkOUItStkTOjUatlgCU39dm9CgNL5bpu68PBwAKApDWuJimQDwRjDu3f78OrVEuhDHu5Yh395jkh/l4annWl4OUJIzZSNAkZfnGun3u5uJZUrKIjH8+eD8OrVD3gKO0zHbwhAHxQyBp/ERCQWFHAdkRBCmiQqkhx7//40Hj1qi4yMu3istg6rsQWp0BasL7v2SAghpP7R6VaOFBWl49WrH5CUdAyqql3QurUv5r/MQCGqvvZICKle//79uY5AJAQVSQ6kpd38OOdjPCws1sPMzA1SUjJ4WotLjy4uLnWWj5DGbs2aNVxHIBKCimQ9KinJx9u3qxAXtxOKilbo2PF+nc35SEWSEPKp1atXcx1BIlCRrCfZ2c8QEjIRubkvxTLnY3JyMgBAR0enzl6TkMaqbCjLS5cucZyEOwMGDOA6gkSgIilmjJUgNnYH3r5dDVlZbbRrdwna2oPqfDtlk0bT7d6EAHl5eVxH4FxwcDAAwM7OjtMcjR0VSTHKy4tCWNgUZGTcgY7OGFhbH4CsrHb1TySEkFpasGABAPriXFtUJMWAMYbExCN49er/APDQqtVR6OtPAo/Hq/a5hBBCGg4qknWssPADIiJmIzn5L6ir90br1kehoGDOdSxCCCFfgIpkLZXN2HGyTRvIZvsjLGwGiovTYGm5DaamC8HjSXMdkZAmZ9iwYVxHIBKCimQtJBQUoFNQENKLMnHh2VY0z/0DysrtYGt7FSoq7es1y9y5c+t1e4Q0ZEuWLOE6ApEQVCRrYcWbN9Aoeoat2ASj3HhoGS1C2xabICUlX+9ZnJ2d632bhJCGa9OmTVxHkAhUJGuo7PTq7ubmkE7agp9xDB+gi6XYiW4Ygn0cFEgAiI2NBQCYmppysn1CGhLHj5OQN+U7O7t37851BIlARbKGPKKiEJXxAsFPJ2IKQnEZX2MvvkcOVBCamIg15uYwkK//Qjl58mQATftDgRDyP/fu3QNAxbK2qEiKKKGgAKNfvIBx9nEcgBcKWOmcjwHoI2hTzOfDIzoa+6ysOExKCCGAm5sbAPriXFtUJEW07fUDjMpegi54hAfogq1YJjSlFQAUATRjByGESBAqkiKIiP8dju/nQA4F2IUFOI8RAMoPDGCnrIynnetmwHJCCCHcoyJZheLiDERG/oCkJF/EoxU2YSViYSZYL8fj4TtDQzq9SkgD4+TkxHUEIiGoSFYiLe3Wxzkf3+EYXHAYE1Hy2e5qSBMiL168mOsIhDQYrq6uXEcgEoKK5GdK53xc/XHOxxbw1z4B31R9lDAmaNMQjyCHDx/OdQRCGozc3FwAgJJS3U1H19h4enpyHUEiUJH8RHb2c4SGTkJOzgsYGc1F8+bbMOdJCApZjlC7hnQEWSY8PBwAYG1tzXESQrg3ZMgQAE37zk6aIqtuUJFE2ZyPOz/O+aiFdu0uQlu7dNLWxnIjzuzZswE07Q8FQsj/+Pv7A6DJl2uryRfJ0jkfpyIjIwA6OqNhZXUAcnI6XMcihJBa2bBhAwAqkrXVZIskYwxJSUcRGfkDAKBVqyPQ159Mcz4SQggRaJJFsrAwGRERc5CcfBbq6r3QqtVRKCpacB2LEEJIA9PkimRKyiWEh09HUVEKLC23wtR0Ec35SIiEcXFx4ToCkRBNpkiWlOTg9euliI/3grJyW7RvfxkqKrZcx6ozq1ev5joCIQ0GFUlSV5pEkczMfIDQ0MnIy3sFU9MlsLDwgLS0Atex6hRdnCfkf5KTkwEAOjpN9ya8AwcOcB1BIkh0keTzixAdvRHR0RsgL28MW9sb0NR05DqWWAQHBwOgvlGEAMDYsWMBNO0uUdRnum5IbJHMzQ1HaOhkZGU9gr7+ZLRs+TNkZNS5jiU2CxYsANC0PxQIIf/j5+cHgEbjqi2p+tpQamoqRo0aBWVlZZibm+PEiROVtt21axcMDAygrq6O6dOno6CgQOTtMMbw7t1+BAV1QF7ea7RpcwqtWx+V6AJJCCGf27FjB3bs2MF1jEav3orkvHnzICcnh6SkJBw/fhxz587Fy5cvy7W7cuUKtmzZguvXryMqKgpv3ryBu7t7ta+flfUY9+6ZICjIDpGR86Cu3hudO7+Ant634ng7hBBCmoB6KZI5OTk4e/YsPDw8oKKigp49e2LEiBHw9fUt1/bIkSOYMWMGbGxsoKmpiTVr1uDw4cMibaew8B1ycp5DX38q2re/BHl5ozp+J4QQQpqSerkmGRERAWlpaVh9MmuGra0tbt++Xa7ty5cvMXLkSKF2SUlJSElJgba2tkjbS0+/RSPnENKEzZ07l+sIRELUS5HMzs6GurrwNUF1dXVkZWVV27bs31lZWeWKpLe3N7y9vQEAiYnAxzG+AURDVdW+7t5AI/HhwwfY2ze99/2pDx8+QFdXl+sYnKP9ULoPtm3bxnUMTtFnAhAWFlar59dLkVRRUUFmZqbQsszMTKiqqlbbtuzfFbWdNWsWZs2aBQCwt7dHUFBQXcZudGgf0D4oQ/uB9gFA+wBArb8k1Ms1SSsrKxQXFyMyMlKw7NmzZ7CxsSnX1sbGBs+ePRNqp6+vL/KpVkIIIaSu1EuRVFZWxujRo7F27Vrk5OQgMDAQ586dw+TJk8u1nTJlCn799VeEhIQgLS0NGzZsoCGmCCGEcKLeuoDs378feXl50NPTw/jx4+Hl5QUbGxvExMRARUUFMTExAIBBgwZh2bJl6Nu3L8zNzWFubo7169dX+/plp12bMtoHtA/K0H6gfQDQPgBqvw94jDFWR1kIIYQQiVJvR5KEEEJIY0NFkhBCCKlEoymS9TX2a0Mn6n44cuQIOnXqBDU1NZiYmGDZsmUoLi6u57TiUZO/hTL9+vUDj8drkvvgzZs3GDZsGFRVVaGjo4Nly5bVY1LxEXUfMMawevVqGBsbQ11dHY6OjhUOidkY7d27F/b29pCXl6/2BkdJ/VwUdR986WdioymS4h77tbEQdT/k5ubC09MTycnJePDgAa5fv47t27dzkLjuiboPyhw/flxiimMZUfdBYWEhvvrqK/Tr1w+JiYmIi4vDpEmTOEhc90TdB6dPn8Zvv/2GO3fuIDU1FQ4ODhXeWd8YGRkZYfXq1Zg+fXqV7ST5c1HUffDFn4msEcjOzmaysrIsPDxcsGzSpEls+fLl5dqOHz+erVy5UvDY39+f6evr10tOcavJfvjcjh072LBhw8QZr17UdB+kp6ezli1bsvv37zMArKioqL6iik1N9sGBAwdYz5496zNevajJPtiyZQv79ttvBY//++8/Ji8vXy8568uqVavY1KlTK10vyZ+LZarbB58T9TOxURxJVjb2a0XfGl++fAlbW1uhdmVjvzZ2NdkPnwsICKhw8IbGpqb7wM3NDXPnzoWBgUF9RRS7muyDf//9FxYWFhg8eDB0dHTg6OiIFy9e1GdcsajJPhg3bhxevXqFiIgIFBUV4ciRIxg0aFB9xuWcJH8ufilRPxMbxaTL4hr7tbGpyX74lI+PD4KCgnDo0CFxxqsXNdkHQUFBCAwMxO7duxEXF1dfEcWuJvsgLi4ON2/exPnz59G/f3/s3r0bI0eORFhYGOTk5Oorcp2ryT4wNDREr169YG1tDWlpaZiamuLGjRv1FbVBkOTPxS9Rk8/ERnEkKa6xXxubmuyHMn///TdWrFiBS5cuQUdHR9wRxU7UfcDn8+Hq6ordu3dDRqZRfBcUWU3+DhQVFdGzZ08MHjwYcnJyWLJkCVJSUhAaGlpfccWiJvtg/fr1ePToEWJjY5Gfnw93d3f069cPubm59RWXc5L8uVhTNf1MbBRFksZ+LVWT/QAAly9fxsyZM+Hn54d27drVV0yxEnUfZGZmIigoCM7OzjAwMEDnzp0BACYmJrhz5069Zq5rNfk7aN++vUROG1eTffDs2TM4OzvDxMQEMjIycHFxQVpaGkJCQuozMqck+XOxJr7oM7EW10nrlbOzMxs3bhzLzs5md+/eZWpqauy///4r1+7SpUtMX1+fvXz5kqWmprK+ffuKdGNLYyHqfrh+/TrT0tJit2/f5iCleImyD/h8PktISBD8PHz4kAFgcXFxrKCggKPkdUfUv4OwsDCmqKjIrl27xoqLi9nOnTuZpaVlk9oH69atYz169GCJiYmspKSEHT16lCkpKbG0tLT6D13HioqKWF5eHluxYgWbNGkSy8vLq/DmNEn+XBR1H3zpZ2KjKZIpKSls5MiRTElJiZmamrLjx48zxhiLjo5mysrKLDo6WtB2x44dTE9Pj6mqqjIXFxeWn5/PVew6J+p+cHR0ZNLS0kxZWVnwM2jQIC6j15ma/C2Uefv2rcTc3cpYzfbB2bNnWfPmzZmqqirr06dPhYWkMRJ1H+Tl5TFXV1dmYGDAVFVVWYcOHdilS5e4jF5n3N3dGQChH3d39yb1uSjqPvjSz0Qau5UQQgipRKO4JkkIIYRwgYokIYQQUgkqkoQQQkglqEgSQgghlaAiSQghhFSCiiQhhBBSCSqShNRAVFSURM1L+bl169aJPJWWhYUF/P39v2g7jo6O1Y6befjwYfTs2fOLXl+czp49i23btkns3wARRkWSEDGqbiJcUnPFxcVQUVHBw4cPBcuOHz8OHo9XblmrVq2Enjtw4EBcvXr1i7d98uRJfPfddzh+/DimT5+Oz7uZL1myBC1btoSqqipatWqFo0ePfvG2SMNARZI0eXV9RMAYw5w5cxAdHQ0ASElJwaxZs5CTk1On22mqZGRk4ODggNu3bwuWBQQEoFWrVuWW9e7dW/A4JycHjx8/Rp8+fb5ou/7+/liwYAGuXbuGgIAAvHnzBsuWLRNqo6ysDD8/P2RkZODIkSOYP38+7t2790XbIw0DFUlSaxYWFti2bRvat28PZWVlzJgxA0lJSRg8eDBUVVUxYMAApKWlCdr/+++/6N69OzQ0NGBra4tbt24J1vn4+KB169ZQVVWFpaUlDhw4IFiXnJyMYcOGQUNDA1paWujVqxf4fH6FmXg8Hvbs2QNLS0vo6Ohg6dKlgraHDx9Gjx49sHDhQmhpaWHdunWVvreSkhIsWbIEOjo6sLS0xIULF4TWp6amYtq0aTAyMoKmpia++eYb8Hg8rFy5Eu7u7rhz5w7mzp2L77//HsrKyuVef926dRg7diycnZ2hqqqKjh07Cg1EHRoaCkdHR2hoaMDGxgbnz58XrLtw4QI6dOgANTU1mJqaVvk+PnX06FGYm5tDW1sbHh4eVZ42PX/+PGxsbKChoQFHR8dys4c8evQIbdq0gaamJqZNm4b8/HwAQFpaGoYNGwZdXV1oampi2LBhtZ6ubOnSpejZsycyMjLQu3dvBAQECNbduXMHy5cvL7fs0yJ5/fp19OjRA/Ly8li3bh2+/fZbTJo0CaqqqmjXrh0iIiKwefNm6OnpwdTUVOiIMygoCLNnz8aVK1dgb28PNTU1XLlyBU+ePBGa3X79+vVo1aoVpKSk0LVrV/Tq1Qv379+v1fsmHBPnmHqkaTA3N2ddu3ZliYmJLC4ujunq6rIOHTqwJ0+esPz8fNa3b1+2bt06xhhjcXFxTEtLi124cIGVlJSwq1evMi0tLfb+/XvGGGP//PMPe/XqFePz+ezWrVtMUVGRPX78mDHG2IoVK9js2bNZYWEhKywsZAEBAYzP51eYCQBzdHRkKSkpLDo6mrVs2ZIdPHiQMcaYj48Pk5aWZnv27GFFRUUsNze30vfm5eXFrK2tWUxMDEtJSWGOjo5CY8AOGTKEOTk5sdTUVFZYWMhu3brFGGMsKiqKubi4sGbNmjEnJyf24sWLCl/f3d2dycjIsNOnT7PCwkK2bds2ZmFhIXiPzZs3Zxs3bmQFBQXs+vXrTEVFhYWFhTHGGLt58yZ7/vw5KykpYc+ePWN6enrsr7/+qvJ39fLlS6asrMzu3LnDCgoK2OLFi5mMjAy7du2aIM/EiRMZY4yFh4czJSUldvXqVVZYWMh++ukn1rx5c8Hg6Obm5szGxkawb7p3785WrVrFGGMsOTmZnTlzhuXk5LDMzEw2duxYNnLkSEGOPn36CH4flfHx8WE9evRgJSUl7LvvvmMDBw5kOTk5jDHGbt26xTQ1NVlJSQn78OEDMzMzYzk5OUxPT0+wjMfjCY1dOnv2bPbLL78I3qe8vDy7fPkyKyoqYpMnT2YWFhZsw4YNrLCwkHl7ezMLC4sq81UnNzeXGRgYSMw4sU0VFUlSa+bm5uzYsWOCx6NHj2Zz5swRPN6zZ4/gA3LLli1s0qRJQs8fOHAgO3z4cIWvPXLkSObp6ckYY2zNmjVsxIgRLDIystpMAIQ+nPbt28f69evHGCv98DU1NRXpvfXt25d5eXkJHl+5ckVQJOPj4xmPx2OpqalCz+Hz+Wz27NksKiqKTZ06lX348IHNnDlT8AH/KXd3d9a1a1fB45KSEmZgYMACAgJYQEAA09fXZyUlJYL148aNY+7u7hVmnT9/PluwYEGV72f9+vVs3Lhxgsc5OTlMVla2wiL5448/sm+//VYom5GREbt58yZjrPT3/um+uXDhArO0tKxwu0+fPmUaGhqCx6IWyS5dujAnJyc2evRooZlL8vLymLy8PAsODmZ//vknmzBhAmOMsa5duwqWfV7kzMzMWExMjOB9DhgwQLDu/PnzTFlZmRUXFzPGGMvMzGQAajVTyJQpU9jXX39d6Rc50jjQ6VZSJ/T19QX/VlRULPc4OzsbABAdHY3Tp09DQ0ND8HP37l0kJCQAAC5duoRu3bpBS0sLGhoauHjxIpKTkwGUnm5r0aIFBg4cCEtLS2zZsqXKTKampoJ/m5ubIz4+vsJ1VYmPjy/3OmViY2OhpaUFTU1NoefweDz88ssvgrY6Ojrw9vaGkpJStTmlpKRgYmKC+Ph4wbalpP73v6m5uTnevXsHAHjw4AH69u0LXV1dqKur45dffhHsK1Hfj5KSUqVzCsbHxwu9XykpKZiamgq2/3n2T/dxbm4uZs+eDXNzc6ipqaF3795IT09HSUlJlfk+9+rVK5w7dw7u7u6Qk5MTLFdQUECXLl0QEBCAgIAA9OrVCwDQs2dPwbJPT7W+ePFCcFq6zOd/ozo6OpCWlhY8BiD4u62ppUuX4r///sOpU6ckcj7PpoSKJKlXpqammDx5MtLT0wU/OTk5WLFiBQoKCjBmzBgsWbIESUlJSE9Px5AhQwR3EKqqqmLHjh148+YN/Pz8sHPnTly/fr3SbcXGxgr+HRMTAyMjI8FjUT+4DA0Ny73Op+8lNTUV6enplT7/8OHD1W7j09fn8/mIi4uDkZERjIyMEBsbK3TdNSYmBsbGxgCACRMmYMSIEYiNjUVGRgbmzJlT7m7Lit7Pp9cG8/LykJKSUmFbIyMjwc1HQOkNSbGxsYLtf5790328Y8cOhIeH48GDB8jMzBRcK6wu3+dat24NHx8fDB48GOHh4ULryq5L3rlzR1Ake/XqJVj2aZG8ePEihg4dWqNtfyl3d3dcunQJV69ehZqaWr1sk4gPFUlSryZNmgQ/Pz9cuXIFJSUlyM/Px61btxAXF4fCwkIUFBRAV1cXMjIygg+aMv/88w9evXoFxhjU1NQgLS0t+OZfkW3btiEtLQ2xsbHYvXs3nJ2da5zXyckJe/bsQVxcHNLS0oSOXg0NDTF48GC4uroiLS0NRUVFQjeOiOrx48f4888/UVxcDE9PT8jLy6Nbt27o2rUrlJWVsXXrVhQVFeHWrVvw8/PDuHHjAABZWVnQ0tKCgoICHj58iBMnTlS7rbFjx8LPzw/37t1DYWEh3N3dKy1cTk5OuHDhAq5fv46ioiLs2LED8vLy6N69u6DNvn37EBcXh9TUVGzatEmwj7OysqCoqAgNDQ2kpqZi/fr1Nd4vZcaPH49NmzZhwIABeP36tWB57969cfPmTcTGxqJNmzYASo8kb926heDgYKEieeHCBQwZMuSLM4hq8+bNOHHiBK5du1bpETppXKhIknplamqKc+fOYdOmTdDV1YWpqSm2bdsGPp8PVVVV7NmzB05OTtDU1MSJEycwYsQIwXMjIyMxYMAAqKiowMHBAa6urnB0dKx0WyNHjkSnTp1gZ2eHoUOHYsaMGTXOO3PmTHz99dewtbVFx44dMXr0aKH1vr6+kJWVRatWraCnpwdPT88ab2PkyJE4efIkNDU14evriz///BOysrKQk5PD+fPncenSJejo6MDV1RVHjx4V9P3bv38/1q5dC1VVVfz4449wcnKqdls2Njb4+eefMW7cOBgaGkJVVRV6enqQl5cv19ba2hrHjh3DDz/8AB0dHfj5+cHPz0/otOeECRMEp78tLS2xevVqAMCCBQuQl5cHHR0ddOvWDYMGDarxfvnU1KlTsXbtWvTr1w9RUVEAgO7duyMjIwNdu3YVnBnQ1taGrq4u9PT00LJlSwBARkYGQkNDhYq7uLi5uSEmJgYtW7aEiooKVFRUsGnTJrFvl4gPTbpMJBKPx0NkZCRatGjBdZQqrVu3Dq9evcKxY8c42X52djY0NDQQGRmJZs2acZJB3E6dOoUzZ87g1KlTXEchjRAdSRLSxPj5+SE3Nxc5OTlYsmQJ2rVrBwsLC65jiY2GhgYWLlzIdQzSSFGRJE3enDlzBKfGPv2ZM2cO19G+yPHjxyt8PzY2NgCAc+fOCW4MioyMxB9//MHZHZj1se8HDhwIBweHOns90rTQ6VZCCCGkEnQkSQghhFSCiiQhhBBSCSqShBBCSCWoSBJCCCGVoCJJCCGEVIKKJCGEEFKJ/wfal+fsEJu3OAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -1317,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1339,16 +1339,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "('b', array([ 1.06780656, -0.0028452 , 0.14860936, -0.07080871, 0.01 ]))" + "('b', array([ 1.05189853, -0.00280426, 0.13154376, -0.05404106, 0.01 ]))" ] }, - "execution_count": 27, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1360,7 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1406,7 +1406,7 @@ " 100\n", " 0\n", " 0\n", - " 0.995707\n", + " 0.996087\n", " \n", " \n", " 2\n", @@ -1414,7 +1414,7 @@ " 100\n", " 5\n", " 0\n", - " 0.978989\n", + " 0.979860\n", " \n", " \n", " 3\n", @@ -1422,7 +1422,7 @@ " 100\n", " 10\n", " 0\n", - " 0.962271\n", + " 0.963633\n", " \n", " \n", " 4\n", @@ -1430,7 +1430,7 @@ " 100\n", " 15\n", " 0\n", - " 0.945553\n", + " 0.947405\n", " \n", " \n", " 5\n", @@ -1438,7 +1438,7 @@ " 100\n", " 20\n", " 0\n", - " 0.928834\n", + " 0.931178\n", " \n", " \n", " 6\n", @@ -1446,7 +1446,7 @@ " 100\n", " 25\n", " 0\n", - " 0.912116\n", + " 0.914951\n", " \n", " \n", " 7\n", @@ -1454,7 +1454,7 @@ " 100\n", " 30\n", " 0\n", - " 0.895398\n", + " 0.898723\n", " \n", " \n", " 8\n", @@ -1462,7 +1462,7 @@ " 100\n", " 35\n", " 0\n", - " 0.878680\n", + " 0.882496\n", " \n", " \n", " 9\n", @@ -1470,7 +1470,7 @@ " 100\n", " 40\n", " 0\n", - " 0.861962\n", + " 0.866269\n", " \n", " \n", " 10\n", @@ -1478,7 +1478,7 @@ " 100\n", " 45\n", " 0\n", - " 0.845244\n", + " 0.850041\n", " \n", " \n", "\n", @@ -1487,19 +1487,19 @@ "text/plain": [ " mid poa_global temp_module wind_speed pr_dc\n", "id \n", - "1 matrix 100 0 0 0.995707\n", - "2 matrix 100 5 0 0.978989\n", - "3 matrix 100 10 0 0.962271\n", - "4 matrix 100 15 0 0.945553\n", - "5 matrix 100 20 0 0.928834\n", - "6 matrix 100 25 0 0.912116\n", - "7 matrix 100 30 0 0.895398\n", - "8 matrix 100 35 0 0.878680\n", - "9 matrix 100 40 0 0.861962\n", - "10 matrix 100 45 0 0.845244" + "1 matrix 100 0 0 0.996087\n", + "2 matrix 100 5 0 0.979860\n", + "3 matrix 100 10 0 0.963633\n", + "4 matrix 100 15 0 0.947405\n", + "5 matrix 100 20 0 0.931178\n", + "6 matrix 100 25 0 0.914951\n", + "7 matrix 100 30 0 0.898723\n", + "8 matrix 100 35 0 0.882496\n", + "9 matrix 100 40 0 0.866269\n", + "10 matrix 100 45 0 0.850041" ] }, - "execution_count": 28, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1525,7 +1525,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1632,7 +1632,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1650,12 +1650,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Xr144ePAg42SdnZ2ZVG11yPrlU1JSgrt370JbWxuxsbEghNTqy4WUG+jX1taWSvNJ7Lp3716FwX+JXm21y7YhK5LqvZ07d1Y4Z2ZmpnB/ylL+OVd2rOyzr4ryn3FNxMbGIjk5GVpaWoiNja3giGVh3rx5mDt3LlJTU2FqagpCCDZu3AgHBwcAgI2NDQAgNTVV6odaWlpahR+GAODv74+YmBj4+vpKHb9//z6ysrLQpUsX5pjk86hfvz4CAwPRs2dP2NjYMEVHZbUAMHqVXSN5X1mf2KC0tBRmZmZSwyUSdHV1mX9X9/dka2sLAHBycpI67uTkhMTERJn7EhcXhy1btuDBgwfo1q0bAMDFxQXh4eH47bffGMdZWT8FAgESEhKkjterV4+punZxcUF2djaWL18uk+O0sbHBzZs3Kxwv+/dx8+ZN3L9/H3p6elLXTJs2DWvXrkVUVFSNOtVR3We+YsUKTJo0CVevXoW/vz/WrVuHxYsXY+3atUwfNm7ciJCQEOjp6SEsLAxHjhxRqD91BaVU1b548QLp6en49ddf4erqinbt2iE7O1vqy1PyP2TZMnt58fT0RHR0NO7evYuQkBBs2LBBpvvKTmt5//49oqKi0K5duyqvl3whJiUloVWrVlIvyZews7Mz0tPTmYgPEI+HRUdHV9muk5MT9PX1ce3atSqv0dXVrfAZCQQCREZGokmTJhX6Y2lpyfTn7t27UveVf88l5acO3b9/v9rPuCx5eXkYP348vvrqK2zduhVz5syR+lzlQUtLCzY2NjAyMsKpU6cAAF9++SUA8XPV09OT+vxLS0tx8+bNSqPzffv2wcXFBZ999pnU8ZEjR+LZs2cICwtjXiNGjEC3bt0QFhaGTp06ARBH3eWf9dWrV2FnZ8dEcj179sSNGzekfohcvXoVhoaGTDtsIxAI8P79exQUFFT4e5L8oOjSpQsiIyOrnEbRu3dvAOLouywvX75EixYtZO6LpNag7BgvIP4xW92Pr5KSEjx9+hTNmjWrtv3S0lIUFhbK1JeePXsiPj5e6m/vxYsXeP36NfP3cejQIYSHh0s9e0BcG3Dx4kWZdKqiS5cu1X43AIC9vT3mzJmDc+fOwcvLC3v27GHOOTs7o3PnzvD19YWvry86duwolS2iVIOiud7KqkD79+9Ppk2bJnXMzc2NGcNKT08nenp65LvvviOxsbHk5s2bRCAQEC0tLSYnX1JSQoyNjcnixYtJSkoKM2ZU1ZhV+eMBAQFEW1ubXL58mRBCyLlz54iOjg65f/9+lbYcOnSIaGlpkS5dupDAwEDy9OlTMnz4cGJlZcWMTUmqasszffp0Ym1tTXx9fUlMTAwJCwsjPj4+zBhCaWkp6dChA+natSt5+PAhCQ0NJYMGDSImJibVVtX+8ssvxMjIiOzcuZO8fPmShIWFkXXr1jHnhwwZQlxdXUliYiJJT08nJSUlJDU1ldjY2JBBgwaR27dvk/j4eBIUFER+/vlncvfuXUIIIY8fPyZaWlrk559/Ji9fviTnz58nLVu2lHuMs/xYjba2doVxFT09PWaMWDLGaWtrS44fP05evnxJVqxYQbS0tJhxvJqYPn06adWqFcnJySGEEDJ8+HDSuXNnqbHIyMhIEhoaSmbNmkWaNWtGQkNDSWhoqNQ1v//+OwkLCyMvXrwgf/zxB9HX168w9jp//nxiYWFBLl68SCIiIsi0adNIgwYNSHJystR1aWlpREdHh+zatUsmGyobe3z06BGpX78++fnnn8mLFy/IkSNHiL6+PtmzZw9zTVJSEjExMSHTp08nERER5K+//iINGzYkS5YskWpLYu/w4cNJt27dmPeyUvbZlpaWkgEDBpDWrVuT8+fPk7i4OPL48WOyfft24u3tTQgRVwy3adOGdOrUidy4cYO8evWK3Lx5k5w6dYppc+zYscTW1pZcunSJREdHk6VLlxI9PT3y/Plz5prExEQSGhpK9u/fTwCQa9eukdDQUJKZmUkIIaSoqIi0adOGdO3aldy5c4e8evWK+Pj4ED09PbJp0yZCiLgCeOHCheTOnTskPj6ePHz4kHz11VdET09PqkJ+5cqV5MaNGyQuLo5ERUURb29vYmJiQr7//nuZPqOSkhLSuXNn5v/pBw8ekC5dupDPPvuMlJaWyvTZymo3IYRMmTKFTJkyhXl/7do1Uq9ePTJ//nwSHh5OoqKiyKFDh0hUVBQRCoVkzpw55NatW+TVq1fkyZMnpE+fPqRXr15Sutu2bSOWlpbE1tZWqlKdUj1KcZyEEHL27FnSqlUroqenRzp27Mg4urJfukeOHCEtWrQg9evXrzAdpTxlj2dmZpKmTZuSH374QeqamTNnkhYtWlQocpAgcYrXrl0jjo6ORFdXlwgEAhIcHFzhmvIUFxeTDRs2kLZt2xIdHR3SqFEj8vnnn0sVbMTHx5OBAwcSPT090qRJE/LHH3/UOB2ltLSU/PHHH6RNmzZER0eHWFlZka+++oo5HxwcTDp37kz09fWlpqMkJCSQiRMnEgsLC6Krq0uaN29OJk2aRF69esXce/LkSWJvb090dXVJ165dyYULF3hznL6+vqRPnz5ET0+P2NnZMWXyNXH69Gmiq6tLHj9+zBzLyMggtra2ZOHChcwxOzu7ClN1yn4+hBAycOBA0qBBA6Krq0s++eQTxgmU5ePHj+Snn34ijRs3Jnp6eqRHjx5Sfw8SfvvtN2JkZEQ+fPggkx1VFe38888/xMXFhXlmmzdvrnDN/fv3Sffu3Ymenh5p3LgxWbp0aYWCjspsl+d3cvlnm5eXR5YsWUJatGhBdHR0SOPGjYmbmxu5desWc01KSgqZMmUKadSoEdHT0yNt27aV+lvIzc0l8+bNI5aWlsTY2Jj06tWLKQYs+7lU1u+y7cTFxZFx48YRa2troq+vT9q2bUt+//13ZopOXl4ecXNzI40bNyY6OjrE1taWDB8+XMppEkLIggULiIODA9HX1ycNGjQgnTt3Jjt37pSrOCY5OZl89dVXxNjYmJiYmJCxY8eStLQ0uT5bWe3u06cP6dOnj9R9V69eJZ999hnR19cnpqampG/fviQuLo7k5+eTCRMmMNPjLC0tydixY0lSUpLU/enp6URHR4fUr1+fpKamymx3XUeLEDkHlzSYw4cP45tvvpFaXYRCoVAolLKo/cpBFAqFQqHwCS+O09jYWOqlra2N//u//2PO37p1C46OjjA0NISrq6tcVXYUbpg9e3aF5yZ5OTs7c6a7bt26KnXLT6+h1I4vvviiys+3/CICdZnq/g7XrVun7O6pBDt37oRAIICenh48PDyqvXbr1q2wtraGmZkZpk+fLlWE1bdvX+jr6zOfb3XT9FQB3lO1ubm5aNy4MS5fvozPP/8cGRkZcHBwwIEDBzB8+HCsWLGCWe2GojzevXsntfh2WXR0dCqs1sQWWVlZyMrKqvK8Ou6Ao2q8ffsW+fn5lZ4zMDCoMF+zrhIbG1vluYYNG6Jhw4Y89kY1OX/+POrVq4dr164hPz8fhw8frvS6a9euYerUqfD394etrS1GjRqFzz77jJmH2rdvX0yePFlpO0PJCyvzOOXh3LlzsLKyYsrTz58/D2dnZ4wZMwaAeAqJhYUFoqKi4OjoyHf3KP/DysoKVlZWvOvSLyTuoY5RNuiPtJqRLCrz+PHjand0OXLkCGbMmMFkqyRzTKtbwEGV4X2M88iRI5g6dSozcTcyMlJq9RkjIyM4ODhUu64khUKhUNSH8t/zHTp0QFpamtQa1suWLYOFhQV69uwp83aSyoLXiDMpKQmBgYHMwucAIBKJmEn5EszMzCAUCivc7+3tzSyXFRoSAisDXVgb6eJFVi7szQxQUgq8FhWgrbkh3ooKoVNPC1aGuojMzoWjjSkKikqR8j4fbaxNkJSZC0Pd+rAw0UNYUjY+adoAooJipIsK0crKGPHpuTAz1EFDI108ScxGZztzvHongpaWFlpaGiEuMw8WDQxhYqiLp7Hv0KmNNdLf5yKvoAh21g3wMikTthbG0NetjxcJGXBp1RhpWSJ8LC5FMytTvEhIh511A9Srp4XY11lo72CF5AyxzbYWJoiIewdTYz1YmBkiMfU92rWwxOt3OdCtXw+NGxrjaWwa2rWwQMHHYiRniNC2eSMkpr6Hob4OLBsYITQ6FS6trCDM+4iM93lo5WCL+MR3MDM1RENzY4SEx6NLh5bIyhbhQ04eWtpZITY+FfXq1YNdMws8jUxCp09aID0zB3n5H2HX1AIvY1Nga90A+no6eBGdDBfn5khL/4CPRcVoZtsIL6Lfwq6ZBerVq4fY+FS0d2yG5NRssU3W5oiIeo1WLa1RWlqKxNcZaNemCaJikmHewAiNLc3wNDIJ7drYoqCwCMmp79G2lQ0S32TA0EAXlo1MEfosAS7OzSEUFSAjMwetWlrLZJNFI1OYGOvjaWQSGluaQUdHm1ObXidnQlenPmOTubkRGpgacmZT+ef07PlrtGhuwalN5Z/Ti+i3sGhkwplN5Z+TRSMTaGvX49Sm8s/JysIURkb6nNlU/jklvMlBRkYGK9+9DRq7oLiw4ndqTdg1MWQ2PwCAmTNnYubMmXK3IxKJmFXLgP9WMBMKhWjUqBE2bNgAJycn6Orq4tSpUxg+fDjCwsKYBWRUDV4dp6+vL3r16oWWLVsyx4yNjSuMpeXk5MDExKTC/WUfWtsGRnj41acVrqkMo89sFOj1fzxJzEJnu4ao34GfFE7IixR0acdC3x1kT3mHhMWjS8eWNV/IEpqupwxNVbOxwLQ963pPQp6jcxenmi9UE039nIgKxz4dtJW19osLhXDpu1ru+z6+2S7TrkU1Uf57XvJvyfe8ZPlEQLwU4MmTJ3H58mWpIlJVgnfHuXTpUqljzs7OUusj5ubmIi4ursbKTW0Zk8xsOU0AMNarz5vTBAATQ92aL6oJOZwmAJgYs7t5cV3XU4YmF3o1OT9daxMUmHJTMFYZJia1WyxfVTW5+HGhSjg7OyM8PJxZ4D88PByNGzeucps1LS0tudev5hPexjjv3buHt2/fMkVAEkaNGoWIiAj4+fmhoKAAXl5ecHFxqbEw6NWHyqsCuWSUN7+VvsN/OF37mx0c5XaaADB84ubaa9YCTddThqasegWm7WV+1cTokfxGBnzrKUtT1SkuLkZBQQFKSkpQUlKCgoKCSheQmTp1Knx8fPD8+XNkZ2dj7dq1zPSV9+/f49q1a8y9x48fx+3bt+Hm5sazNbLD23SUWbNmIS8vD0ePHq1w7ubNm5g3bx4SExPRrVs3HD58uMaFnztZmuDfL7tUew2b0SYAXqNNhaiFw6RoBpoeudQlen82g5U0KQAYN2jJSarW09MTq1dLt7tq1SpMnz4dTk5OeP78ObMRwJYtW7Bhwwbk5+dj9OjR2Lt3L/T09JCeno4hQ4YgKioK2tracHR0xJo1azBw4EC5+8sXvEWc+/btq9RpAsCAAQMQFRWF/Px8BAQEyLRbQmpuzduPsUn9Dq3g6R3Iq2at9BR0mp4b/BS6n+rxo1ldZLjWa08Nd7MP35p1wUZ1wNPTE0S85jnz8vT0RPPmzSESiaS24/vhhx+QlpaGnJwcHDp0iNlqzdLSEsHBwRAKhXj//j0ePHig0k4TUMI8Tr5gO9pUeWiUqXHQ6JFCUU3UdpH3mlK1bDpOlU/RUqep1lAHSakMdUjV1lXUdpH3F1m5VZ7jKtpsO3o3J+0qpMey02zb9UdW26vrehJNRQpv5MXFaQTrbaqaZl2wkaK6qG2q1t7MoOaLWKBstHlxyzheNGXS4yjKvHhiESft1iW98s7w3N/7ONcsi9+FHbzqKUOzLthIUV3U1nGWlFZ+nMuxTWEevwVJVepxmJoVigo4a1tT9WqKGoXCqrMjXMC3njI064KNFNVFbVO1r3n4wi0/tjnrt0uca9aox/F45qwffGq+qI7ryZtqnffdGoU15YFvPWVo1gUbKaqLRhUHafS8TVoApBRo4Q5FWdDiINVFbSPOt6LCmi9SgMqc5qI/bnCqWaUej05z0YrjvGmpoh4XhTtLf+J35SC+9ZShWRdspKguajvGqVNPS+o9H/M2bS0rLjzPqZ6jA++Rpq21eZ3S4yOitLG1rPkiNdZThmZdsJGiumhMqlbj5m3S1Cwn0NQrRV2gqVrVRW1TtRFl5nHytUpQkyF/8KIjcZpNnObxo1cGvjX50CuberVvzv9SXnxrUhs1R5OimqhtqratmSEn7VYXbQYfmcGJZpV6t/iv4uNbkyu9qiLLOw/4HVNVhia1UXM0KaqJ2kachVVN5OSQ6KRM7kXKpGij41K41ysH35ps6slS1BMbk8ianqzwrUlt1BxNimqito4z5X+LA/C5mPvq/be5FSg3rrn69/Pc6lUC35qK6slbAfur116F9GoD35rURs3RpKgmal8cpDFFQbQYSGZogQ+lLkCLg1QXtY04XwsLed86bOY6jlYOqsJpzlzI76o6ytCUVY+tuZVzZ3spdL86aFIbNUeTopqoleP09vaGQCCAQCBAbnEJvP5+BgBw+uUSolNz8CQxC93WXAMALD4Tiq3XowAAdj9eQPL7fAS+TMOAjbcAAN/5PsKB27EAgIbzziG/VTNcDIrGiB9OAwAmLf8TJ65GAADqdV0LACgtLcWk5X8CAEb8cBoXg6IhzC2Ead/fxf378wnjXF1n+yIgJAHJ6UKmGnfz8QfMogaCqQcQ8iIF0cSM2bHDc4Mfs+lx264/opltQ4SExUPQbzkA8eT9zbsuAxBXoyanZCPgznO4jhD3b+ZCH3gf8QcAmDb/BkJhPi5efYIRE8UTtyfN3IUT5+6JbWo0GQBw4tw9TJq5S2zTxM3Q1dGGUJgP0+bfiG064s84N9cRaxFw5zmSU7KZatjNuy4ziwoI+i1HSFg8omNTqrQpOjZFyqaExHdV2lRg2h4zFp/HrtPi52hl3gNCYS4u/ROI0SO/BwB4TFmK0yfF9xvqdAQAnD55GR5TlgIARo/8Hpf+CYRQmAsr8x7o3MUJPvvPMV+Cbv1n4HZgMJKT3zFVk9u2+jKT3Xt0nYAnIc8RE53I7I6x1msPs6mxi9MIxEQn4knIc/ToOgGAeKL8tq2+AMSVmA4OzXA7MBhu/cXFZXNne8Fn/znWbAIgZVPQ7cec25Sc/E7KppdR8ZzaVP45de7ixLlN5Z+Tk7MDpzaVf04U1UVtU7VdWjTEg+VurLWnlDQtTc9WgKZhKRQxNFWruqhVxFmWsKRs1tqS1WlKIku+kER9mqxp2vwbTvemLI/klz+f8K1JbdQcTYpqorbzOD9p2oB3zbeX5rPXmAzR5ttI/vf/40tT4iTjXt/iRU9CXBK/6w0rQ5PaqDmaFNVEbSNOUUExK+3Ik6INeMLSPC4ZU7QBd1+woycHXGuWjyxvB/KbBuJbTxma1EbN0aSoJmrrONM53h2lMvb/Gap4I3KMa+73/VdxPTnhSrOqVOzBA36c6FUF33rK0KQ2ao4mW+gY6cBaYCv3i1I5dbo4iPeCoDpYDESLfSiU2sFmcZB5kzZw/W633PclXVhKi4MqQW0jzvj03JovYhnJVJRaUQunKZkmwidsacpa7CMp3ecLvvWUoUlt1BxNimqitsVBZoY6Ct1fm2hzaK/WtROrZaQ5dFCn2ukpgKKa8kaYXwz5XCE9eeFbTxma1EbN0aSoJnU2VctbmraOpGdpSpZCYReaqlVd1DZV+ySx9vM4a+s0JSsIyYyCTlOyug+fyKup6PxLyYorfMG3njI0qY2ao0lRTdQ2VdvZzpx3zdJHy/nVyzzGq548mmxFmHlFYay0o6p6ytCkNmqOJkU1UduIMyv3I++akrVrZYKFFK1kXVk+qUmT7RV+JGt98gXfesrQpDZqjiZFNVFbx/khr6hW9ykytnnpToxsF7I0rnnpOgvzRlnS5GpJvCuXOd7jVMl6ytCkNmqOJkU1qXPFQZwXBWlYMRAt+qFQlAMtDlJd1DbijH0nkvseRZ2mZMuxKmHZaUq2A+MTiSZfi65LtmjiC771lKFJbdQcTYpqoraO09JYj3fNb0dVM8eRg0jz26murLdZE9Nme/AaZU7/ZjRvWsrQU4YmtVFzNCmqidpW1Rrry9d1NlK0fTvbVX6Co/Rs357tOGm3MiTO8vM+/K7I9HkfgUbrKUOT2qg5mhTVRG0jzmdv3vOu2WTotooHORzTbOL8f5y1XZayEabD/3ai5wtN11OGJrVRczQpqgmvjvPUqVNo164djIyM4ODggKCgIADArVu34OjoCENDQ7i6uiIxsebtuzo2l30eJ1sFQTkBi6UPcFwIlJN0gNP2KxvHfJfN7xQYTddThia1UXM0KaoJb47zxo0bWLJkCQ4dOgShUIjbt2/D3t4eGRkZcHd3x5o1a5CVlQWBQIBx48bV2F6GkP9txbz/fMKv3hF/ztquahzTZ/85zjTrop4yNKmNmqNJUU14c5yrVq3CypUr8dlnn6FevXpo0qQJmjRpgvPnz8PZ2RljxoyBvr4+PD09ER4ejqioqGrby/so20bWbE4/efwi5b83PEw7eRwWz3qbNVXLPgl5zrpmdWi6njI0qY2ao0lRTXiZx1lSUgIDAwN4eXnhwIEDKCgowMiRI7Fx40YsXboUHz9+xJ49e5jr27dvj9WrV2P06Kqr2GSdx8nJvE01natJ52RSKOoDncepuvAScaalpaGoqAjnzp1DUFAQwsLCEBoairVr10IkEsHMzEzqejMzMwiFwgrteHt7QyAQQCAQIPz1e3j9/QwA4PTLJUSn5uBJYha6rbkGAFh8JhTbIjIAAE2G/IHkdCECQhLgOtsXADBz3SUm9Wra93cIcwtxMSiamas5afmfzBJ7ksXdncbuwaQNtwCI5ztevPoEQmE+TJt/I+7fEX/MXOgDAHAdsRYBd54jOSUbTZzmAQA277qMRSuOAwAE/ZYjJCwe0bEpaNv1RwCA5wY/eG4Q7zLftuuP6DZwJULC4iHoJ14jd9GK49i8S7zsVxOneUhOyUbAnedwHSHu38yFPkx617T5NxAK83Hx6hOMmLgZBabt4TFlKbNsmGTB6tMnLzP7DI4e+T0EHUdDKMyFlXkPAOL01NzZXgAAt/4zcDswGMnJ72D/v0KJbVt9sfQn8dzPHl0n4EnIc8REJ8LFaQQAYK3XHqz1Ev8ocnEagZjoRDwJeY4eXScAAFq3GIRtW8XPxL75QCQnv8PtwGC49Z8BAJg724tJkVmZ94BQmItL/wQyc+pksenSP4GMTW79Z3Bu09KfNkvZ1LfXFE5tKv+cbCx6cW5T+edk32wApzaVf05u/WdwblP559S/jwenNpV/ThTVhZeIMzs7Gw0bNsThw4cxbdo0AICfnx/Wrl2Lzz//HEVFRdi9+79fQ5988gk8PT2rjTjbWJsicu3QanXZjjYDUkrRt5cTq21Wq3fnucJ68kaZtwOD8XmfTxXSpHrK1aQ2aoYmjThVF14iTnNzczRt2hRaWloVzjk7OyM8PJx5n5ubi7i4ODg7O1fbpr5O9V1nPUXr4Ig2DjbstlkDiurVJjXbqnUVc1U5QtP1lKFJbdQcTYpqwltx0Ndff40dO3bg3bt3yM7Oxh9//IFhw4Zh1KhRiIiIgJ+fHwoKCuDl5QUXFxc4OlY/jhiVksNTz//j0/4r1EJPkeXyen02qVb31RZN11OGpqbamCRqw7y6d5sq9b7siyuU8blSVBPeFnkvKirC/PnzceLECejr62Ps2LH4/fffoa+vj5s3b2LevHlITExEt27dcPjwYbRo0aLa9qorDuIi2lQXaAEQhQ+4dFCaSHPjaLnvoala1YW3iFNHRwe7d+/G+/fvkZqaiu3bt0NfXx8AMGDAAERFRSE/Px8BAQE1Ok0ASMsp4LjHFZEU5qiiHluLskuKJfhC0/WUoSmPXlVRW02v8hzcWcmqWhzCt56immx8xhTVQW2X3CsqLq30OJfRZnJqNrtt14CsemxGmSnJ6ay1RfX410wStcHLxGLev5zfpabUfBGL8K2nLE11ICsrC6NGjYKRkRHs7Oxw4sSJSq8rLCzEwoULYWtrC3Nzc8yZMwdFRf/tq/zixQv069cPZmZmaNWqFf7880++TJAbjduPs66laWlqtu5Bo5G6waQBvdUiVTthwgSUlpbCx8cHYWFhGDp0KO7du1ehwHP16tW4efMm/vrrL5SUlGD48OFwc3PD6tWrUVxcDCcnJ8yePRvz589HYGAghg8fjtDQULRpo3p/72obcb6opDiIa6cpmU/JF9XpcbVfpmSOG19oul5tNBWNEt379qhtV2sN35p1wUZ1IDc3F35+flizZg2MjY3Rq1cvjBgxAkePHq1w7cWLF/H999+jYcOGsLS0xPfff4+DBw8CAKKiopCcnIyFCxdCW1sb/fr1Q8+ePSttRxVQ223F7BoZ8a65b8sMldDjMsrcuYffymFN16tKk8uo0euPnZy1rSqadcFGNtE31EG7DvJPbwvZnw6B4L/t1GbOnImZM2cy76Ojo6GtrS0VFXbo0AGBgYEV2iKEoGyCkxCCN2/e4MOHD6gs8UkIQUREhNx95gO1dZz1yk0J5SNFa2Ksz65GDZTX4yMta2LC7w8STdaTOEehthaSRK150zUyNuFNS1madcFGVcDS0rLaVK08K7998cUX2LZtG1xdXVFSUoLt27cDAPLy8uDo6AgrKyts3LgRCxcuxL///ovAwEC4urqyaxBLqG2qNu6diHfN4RP5XQqrrB5fY5mjR/KzB6gm6lWVUp09vuoVsLiAbz1laNYFG9UBY2Nj5ORID5vl5OTAxKTij4xffvkFnTp1QseOHdGjRw+MHDkSOjo6sLKygo6ODi5cuIBLly7B2toamzdvxtixY9G0aVO+TJELjSgO0vSCIFoApHrQAh0K17BZHGTT2hnTt5yR+75rq6dV24fc3FyYm5sjMjISrVuLsypTp06Fra0t1q9fX23b3t7eOHToEO7fv1/p+R49emDatGmYNWuW3P3mGrWNOFPe5/OuKVmAnS+W/xHEu9OULIpN9f5D0WkcO9avlVtTEfjWU4ZmXbBRHTAyMoK7uztWrlyJ3Nxc3L17F3/99RemTJlS4dq3b98iOTkZhBA8ePAAa9aswerVq5nzT58+RUFBAfLy8rBp0yakpKTAw8ODR2tkR20dpwROtg1TAWiUqTzoRHQKRXZ2796N/Px8WFlZYcKECdizZw+cnZ2RlJQEY2NjJCUlAQDi4uLQo0cPGBkZYdq0aVi/fj0GDRrEtHP06FHY2NjAysoKt27dwo0bN6Cnp6css6pF7VO1mpimpU6TP6hjpKgq6pCqrauobcQZ+fYD79GmZN9MLinrNCX7CvIJ35p867Vz/Ir3aNJN4MKLjrL0lKFZF2ykqC5qOx3FwcqYg0arjzYvnljEvmYZykeafhd2cKpXGXxrcq1X3jnuPcXvOLUyNKmNmqNJUU3U1nESXV3eNYUifheWFwpzedVThiYXetVFkrmiivPLuIZvTWqj5mhSVBO1TdUmpr5nt0EZxjZn/eDDrmYZKhvXnPfdGs70qoJvTbb0ZE2/rlwwjxU9eeBbk9qoOZoU1URti4ME7WwR7MviEnhKLAqixUC1gxb2UDQZWhykuqhtxPn6XcVF3muNjE5z0Yrj7Gn+j+qc5tKf+F2pSBma8uopWtizfvnSWt2nCHxrUhs1R5OimqiV4/T29oZAIIBAIECOqACe3uKFhNuO3o3oxEyEvEiBYOoBAMCiP25g8/EHAIAmQ/5AcroQASEJcJ0t3uR35rpL8P7zCQDAtPk3EArzcfHqE4z43zJ3k2buwolz9wAA9RpNBgCkvfuASTN3AQBGTNyMi1efQCjMh2nzb8T9O+KPmQvF6VzXEWsRcOc5klOy0cRJnOLZvOsy43wF/ZbjXkw9xEQnMpWla732MBP0XZxGoL5OfTwJec7srrH0p83MJsX2zQciOfkdbgcGw62/OPKeO9sLPvvPAQCszHtAKMzFpX8CMXrk9wAAjylLcfqkeHNsQ52OAIDTJy/DY4r4C2H0yO/x/n0OhMJcWJmLd4Lw2X8Oc2d7AQDc+s/A7cBgJCe/g33zgQDEmyZLnF+PrhPwJOR5tTbFRCdK2fQk5HmNNm3cGYQkURtYmPeCSCiE/5VLzPJni77xwMWzpwEAjuaGAICLZ09j0TceAMTLpPlfuQSRUIjOzaxgZW2D04d9sGLBXADAlGFueHjnNtJSktG7nT0A8YbFki9J9749EBH2BPGxMUxV5Y71a5nJ8G4CF8THxiAi7Amze8b65UuZTY97t7OHgaERHt65jSnDxCtdrVgwF6cPi/9OOjezUtgmAFI23bp0kXOb0lKSpWx68vA+pzaVf05W1jac21T+OZmZN+TUpvLPiaK60FStklK0ND1bNTQFS6HQVK0qo1YRZ1mexqbxrimJHBVFVqcpier4hG9NiR5fcyslv+z5hG9NaqPmaFJUE7WdjtKuhYXijcgZbQbfUrwCVJ5I884D9sdUVUkzSdQGp2/dR5LIljfNc/53eNNSlia1UXM0KaqJ2kacBR+LedeMjktR6H5507OxMYkK6dUGPjTLRpYJcbGc65WFbz1laFIbNUeTopqoreNMzlBwP85ajG2u/v18reVqM6b5q9feWuvVFi41K0vF7lz/K2d6lcG3njI0qY2ao0lRTepucRCPRUF1vRCIFvtQKPJDi4NUF7WNOBVaOaiWTlMy1UQeFHGakmkgfMKWpqzFPpLSfL7gW08ZmtRGzdGkqCZqWxxkqK/Du6agY0te9Tp3ceJVjw1NeaPL9h07K6QnL3zrKUOT2qg5mhTVpO6lammKlhNoOpZCYReaqlVd1DZVGxqdyrumZIUgWWDDaUpW7+ETeTUVnXspWVGFL/jWU4YmtVFzNCmqidqmal1a1eKPWMFo822kbHtHshVpxiXdYKUdLjTZijBvP49jpR1V1VOGJrVRczQpqonaRpzCvI+8awbcfVHjNWymZ28H8p8iqUmT7dV9Ht25zVpbqqinDE1qo+ZoUlQTtXWcGe/z5LuBhbHN/b7/Vnue7THNgwf433G+Mk0ul8M7c+Qg622qkp4yNKmNmqNJUU3qTnEQx0VBmlgIRAt+KBTlwWZxkEP7jljvd13u+zZMGkKLgypBbSPO+ORs2S9myWlKthQrD1dOU7LdF594TFnKy2LrEiRbMPEF33rK0KQ2ao4mRTVRW8dpZqzPu+bQQZ0qHOMy0vxiyOectV0ZSaI2+LTfOF41+7p9odF6ytCkNmqOJkU10fxULYcpWk1Jz9KULIWietBUreqithFnSJRiO5XUhnqNJjP/5sNpGup05FyjvNOU7GTPF5qupwxNaqPmaFJUE7V1nF0cbWq+iOVoszTzGAD+Is28ojBO268s0ozKlrNaWUE0XU8ZmtRGzdGkqCa8Oc6+fftCX18fxsbGMDY2Rtu2bZlzt27dgqOjIwwNDeHq6orExJr3hMzKyeeyu5Vy4tw9XtOzp09e5qTd6op/Lp49zYlmVWi6njI0qY2ao0lRTXiNOHfu3AmRSASRSISXL18CADIyMuDu7o41a9YgKysLAoEA48bVXKDyQVRQ/QUcjG1euh7KepvVceUy+xOuaxrPDLh2hXXNuqynDE1qo+ZoUlQT3oqD+vbti8mTJ+Obb6TXe/X29sbhw4dx7949AEBubi4sLCwQGhoKR8eqnV+NxUEcOE51LgaiBUAUinpBi4NUF14jzmXLlsHCwgI9e/ZEQEAAACAyMhIdOnRgrjEyMoKDgwMiIyMr3O/t7Q2BQACBQIDwmDR4egcCANqO3o3oxEyEvEiBYOoBwMERi1Ycx+Zd4lRnE6d5SE7JRsCd53AdsRaAeG9N7yP+AMSLtwuF+bh49QlGTNwMQDxn88Q5sTOv12gyCkzbo/un45m5laNHfo9L/wRCKMxlFkb32X+O2c/Srf8M3A4MRnLyO9g3HwgA2LbVF0t/Erffo+sEPAl5jpjoRLg4jQAArPXag7VeewAALk4jMHjAN3gS8hw9uk4AACz9aTO2bfUFANg3H4jk5He4HRgMt/7iHxBzZ3vBZ/85AOLF2oXCXFz6JxBDhi0DIJ6HJkk3SQodLp49zcxPmz1+NL7q1wsioZBZ0Pr0YR9mH8Ipw9zw8M5tpKUko3c7ewDAwZ3bsH65+DNx79sDEWFPEB8bAzeBCwBgx/q12LFe/Jm7CVwQHxuDiLAncO8r/swGdHTCwZ3bAAC929kjLSUZD+/cxpRhbgDEeyCePizeB7VzMyuIhEL4X7mE2eNHy2yT/5VLjE2zx4/m3Kb1y5dK2fT1yKGc2lT+OXWzb8q5TeWfk+snbTm1qfxzmj1+NOc2lX9O33z1Jac2lX9OFNWFt4jz4cOHcHJygq6uLk6dOoV58+YhLCwM69atg6WlJdavX89c27NnT3z77bfw8PCosr1WzRoi5nwVG8tyFG1e+icQQ4f1Yb3tqlBUrzZRpv+VS+j3xdBaa1I95WtSGzVDk0acqgtvEWe3bt1gYmICPT09TJs2DT179sTly5dhbGyMnJwcqWtzcnJgYmJSbXsmhrqVn+AwRft5HwHrbVeHInq1Tc127cXvoguarqcMTWqj5mhSVBOlTUfR0tICIQTOzs4IDw9njufm5iIuLg7Ozs7V3v809h3XXayAw/9Srqqsp+hyeZ87OdT6XqqnGprURs3RpKgmvDjO9+/f49q1aygoKEBxcTGOHz+O27dvw83NDaNGjUJERAT8/PxQUFAALy8vuLi4VFsYBACd2lhXPMhxQdC77Hust18d8uqxUQD05DW/P0g0XU8ZmtRGzdGkqCa8OM6ioiIsX74clpaWsLCwwI4dO3DhwgW0bdsWlpaW8PPzwy+//AJzc3M8fPgQp06dqrHN9Pe5PPRcGknhjarpsbkou6QYgi80XU8ZmtRGzdGkqCb1+RCxtLREcHBwlecHDBiAqKgoudrMKyiSPsDD9JMnIc8x41vWZapEFj22p5lEhD3BOMixXRvVUzlNVbLxaZqQE73b9x6i3Rdj5brHpXH1dRM1oYzPlaKayFxVSwjBgQMHcPLkSWRkZODp06e4ffs2UlNTMXasfH/AbFBhHmcdm7dJ52VS2IYrJ1fXUNRBS6BVtaqLzKnalStXwsfHBzNnzkRSUhIAoGnTptiwYQNnnauOl0mZ/73hyWlK5kvyRVV6XDpNyRw2vtB0Pb41n6YJ4T5oAJ6mCeV+KYLnlFEsWaCaevJo1uazZ/NZULhH5lTt4cOHERoaCgsLC3z33XcAgJYtW+LVq1ecda46bC2Medf8ZeVsperxEWXOW/oL5xp1SY9NTVm/UMfM+5EVPXngW1PTbaTOU7WR2XGWlJTA2FjsrLS0tAAAIpGIOcY3+rr/6zqPKdpWre1Y16qOsnp8pWZbOLTiRaeu6MmjydaXpU0L/qdN8K1ZF2ykqC4yp2qHDBmCH374AYWFhQDEY54rVqzA8OHDOetcdbxIyOBds9dnk3jXY7NiVha+6teLN626oFdWk6/03LKvBrHWlqpq1gUbKaqLzMVBOTk5mDp1Kq5evYqioiLo6+tj0KBB8PX1rXGVHy4QtLNF8N2NrLerSgVBtABIPaFpNgobsFmYQ4uD2EXmiNPU1BQXLlxAYmIiHjx4gLi4OPz5559KcZoAkJYl4l1TssA6HySJ2jALUPMJ35rqridL5Hjx4B5WNWuCbz1laNYFGymqS7VjnKWlpRWOWVpawtLSUup8vXr8r9z3kWix3mZN0WZKcjrrmpUhiTTfpabwolcWvjXVSa+2kWT2u9Raa6qDnjI064KNFNWl2lRtvXr1mEKgyiCEQEtLCyUlJZx0rjoEHe0R7L+G1TZVIU1L07PKh6ZaKaqAuqRqs7KyMGPGDFy/fh0WFhb47bffMHHixArXFRYWYunSpTh9+jTy8/MxYcIEbNu2DTo6OgCAyZMn49atW8jNzYW1tTUWL15cYf9mVaHaUDE+Ph6vXr2q8iU5rwxeRL9ltT1ZnKZkX0yuKO80JfsG8gnfmsrW42MO3RJ3fjcH4FtPGZp1wUZ1Ye7cudDV1UVaWhqOHz+O7777rtL9lNevX4/Hjx8jIiIC0dHRePLkCdauXcucX7ZsGRISEpCTk4O///4by5cvR0hICJ+myEy1qVo7O36nX8iDXTML3jV37lnBWduVRZpef+zkTK8q+NbkU+9pmhBTV/zOe0Q5y2uTRuspQ7Mu2KgO5Obmws/PDxERETA2NkavXr0wYsQIHD16VGqPZQC4ePEilixZgoYNGwIAvv/+eyxZsgSrV68GAKkdsbS0tKClpYW4uDh06dKFP4NkROZ5nFOmTKkybevry1/RjAQ2x1VlTdGamBixplmWqtKzRsb8F17xrcm1XnknqW/E/7xjvjWpjZqjyRYGOvVqtRRgeno6BIL/9gWeOXMmZs6cybyPjo6GtrY22rT57zusQ4cOCAwMrNAWIQRlRwYJIXjz5g0+fPgAMzMzAMCcOXNw+PBh5Ofno1OnThgyZIjcfeYDmb1Pq1at4ODgwLyMjIxw5coV5tcD38TG8z9QP3rk/7HeZnVjmrPHj2Zdryb41uRCr7q064bZk1nXqwm+NamNmqOpbCwtLfH48WPmVdZpAuJFcCROT4KZmRmEwor/733xxRfYtm0b0tPTkZqaiu3btwMA8vLymGt2794NoVCIoKAguLu7Q09PjwOrFEfmeZyV8fjxY6xevRoXL15ks08ywVZxkDILgmghEDvQYh6KJsJmcVD7Tp3h9+9due+raaH50NBQ9OzZU8r5bd68GQEBARX8Qn5+Pn766Sf8+eef0NPTw7fffotVq1YhPz8f2traFdqePXs2nJyc8P3338vdb65RKN/ZsWPHSkNyPkhOzeZdc60Xe/O4ZHGaO9avrfEatuFbs7Z6tS3mObOD/UUzVE2T2qg5mqpOmzZtUFxcjJiYGOZYeHi41HilBAMDA+zcuRNv377Fq1ev0KhRI3Tp0qVSpwkAxcXFiIuL46zviiDzGKe/v7/U+7y8PJw6dQpOTk6sd4ovlBVt0khTfmhUSaGoHkZGRnB3d8fKlStx4MABhIWF4a+//sK9e/cqXPv27VtoaWnBxsYGDx8+xJo1a+DjI94c/N27d/D398ewYcNgYGCAmzdv4uTJkzhx4gTfJsmEzBHnjBkzpF5Lly4FAJw8eZKzzpXH29sbAoEAAoEAefmF8NzgBwBo2/VHRMemICQsHoJ+ywEAi1Ycx+ZdlwEATZzmITklGwF3nsN1hDjCmbnQBz77zwEArMx7QCjMxaV/AjF6pDgt4DFlKU6fFN9vqNMRANC6tR08pojtHj3ye1z6JxBCYS6szMVTHHz2n8Pc2V4AxFuC3Q4MRnLyO9g3F5exb9vqizkLDgIQT4uICHuC+NgYuAlcAIijL0kE5iZwwbCvxiEi7AkzhWL98qXMyje929kjLSUZD+/cZratWrFgLrNLfedmVhAJhfC/cokZR1z0jQcunj0NAHA0NwQAXDx7Gou+8QAgHm907tAJIqEQnZtZARDver9iwVwA4u2xHt65jbSUZPRuZw9AvBLP+uVLZbYpPjZGyqZckahKm56mCTF35kxs2b4LADC1sz3yRSI89r+G9f8bb9q2aDbuXBT/HYx1bAwAuHPRD9sWiXeWWT97Mh77X0O+SISpne0x9v9+ws3Tvti3YhEA8VZRkQ/vIistFbN6i/t88eAe+K5fBUA8BeFVRDiS4+Mw3607AHHkIYk+5rt1R3J8HF5FhDPTFXzXr2JWmZnV2wUDxk5B5MO7zLZU+1Ysws3TvqzZBEDKpueP7nFuU1ZaqpRN2e9SObWp/HMa+38/cW5T+ec0/OvvOLWp/HNSF3bv3o38/HxYWVlhwoQJ2LNnD5ydnZGUlARjY2NmG8q4uDj06NEDRkZGmDZtGtavX49Bg8Tr/2ppaWHPnj1o2rQpzM3N8eOPP+KPP/7Al19+qUzTqkShMU5loq+vg/zkw7W+vzbRpovTCDx9/netNeWNNN0ELrj2+Gmt9WoD35rl9biOLOe7dce2a/c51VC2JrVRMzTVYYyzriL3knuVoYwl91q1tOZd0+/CDl719p7y41VPGZoLd/rymoZdsvcYb1rK0qQ2ao4mRTWp1nHWr1+/2iX3JChjyT1ZnXpl1HZsUyjMrbVmbcY1c0X8j+vxpSlxlgW5/C7Wz7eeMjSpjZqjSVFNZF5yb8eOHejTpw+uXr2KFy9e4OrVq3B1dcXOnfyvbgMAia/5349z3ne1m/5S22KglQvm1eo+ReBSs7JK2H0rf+RMrzL41lOGJrVRczQpqonMY5ytWrXC48eP0aBBA+ZYdnY2BAKBUkqGazuPk+9KWlpBSytiKZTaQMc4VReZByc/fPggNckVEE9J+fDhA+udkoXXyZm8ay79abNc1yvqNCXVqnzCpqYs8yz5riBURsUitVH99ZSlSVFNZJ7HOW3aNAwYMAALFixAs2bN8Pr1a2zfvh3Tpk3jsn9Voqsjc9cZFI02bWwtZb6WjUjTytpG4Tb41pQ3ujS34rfIi289ZWhSGzVHk6KayJyqLS0thbe3N86ePYvk5GTY2Nhg7Nix+Pbbb6tc+YFLapOq5StNWxfTszQdS6GwC03Vqi4yp2rr1auH2bNn49atW3jx4gX8/f0xe/ZspThNAHgamSTX9Ww4TclCBtXBptOULDLAJ/JqKrqHpWTiN1/wracMTWqj5mhSuKGwsBBFRUVSx4qKilBYWCjT/XJNwDx06BD69euHtm3bol+/fjh06JA8t7NKuza2vGveeXC82vNsR5rn/O+w2h6bmmxt+vzbOfl3pVcnPWVoUhs1R5PCDQMHDqywSXZISAjc3Nxkul9mx/nrr79i/fr1GD9+PLZv347x48fj999/x6+//ipfj1mioLCo5osk17KUoo2NSazyHBfp2YS4WNbbVESztgurV0dKAr8V2XzrKUOT2qg5mhRuePbsGbp16yZ1rGvXrggPD5fpfpkd54EDB3D9+nXMnDkTbm5umDlzJq5evQpvb2/5eswSyanvedf81Wtvpce5GtPcuZ7/HyWVabLtLMtyducmTtpVFT1laFIbNUeTwg1mZmZIS0uTOpaWlgYjIyOZ7pe5OMjKygoJCQkwNDRkjolEItjb2+Pdu3dydJkdZC0O4rogSJMLgWjBD4WiPGhxEHcsWrQIoaGh2L59O+zt7REXF4cffvgBn3zyCbZs2VLj/TJHnIMHD8akSZPw8uVL5OfnIyoqCtOmTZM5J8w2iW/4XzlIsvOJBK6dpmRXEj6ZO3MmpxFmeSQ7RfAF33rK0KQ2ao4mhRt+/fVXtGvXDl27doWJiQk+++wztG3bFuvWrZPpfpkd586dO2FiYoIOHTrA2NgYHTt2hJGREXbs4HfhcwmGBro1XsN2tNm5y397j/IRabbv2JlzDQkSZ+nQvgNvmgA0Xk8ZmtRGzdGkcIO+vj527dqF3NxcpKamQiQSYefOndDX15fpfrm3FSstLUVGRgYsLCyUsiuKhJpStVymaDUtPUtTshSK6kFTtezy6tUrma6zt695Sp5cni8vLw8RERGIjY3FgwcPcO/evUp3+uaD0GcJvGtamffg1WlKNpPmispSspINd/lC0/WUoUlt1BxNCnu0atUKrVu3Zv4r+XfZ961bt5apLZnXrfP19cW8efOgq6sLAwMD5riWlhazwzefuDg3r/IcV9FmwPN4TtqtitvPuSt/ryrK3Heb342zNV1PGZrURs3RpLBH2a0oDx06hJs3b8LT0xN2dnZITEyEl5cX+vfvL1NbMkecixcvhp+fHzIyMvD69WvmJa/TjImJgb6+PiZPnswcu3XrFhwdHWFoaAhXV1ckJlY9X1KCUFQgl66iJIna4NGd27xqcqFXU+FP5CP50zmKoOl6ytCkNmqOJoUbVqxYgQMHDqB169bQ1dVF69atsW/fPixfvlym+2V2nLq6uujbt29t+8kwd+5cfPrpp8z7jIwMuLu7Y82aNcjKyoJAIMC4ceNqbCcjM6fS41yObZ45cpCztvnQk2Us8+aZo6xq1nU9ZWhSGzVHk8INpaWlSEhIkDqWmJiIkpISme6XuTjoyJEjePz4MVatWgULCwu5OwoAp06dwvnz5+Hk5ITY2FgcO3YM3t7eOHz4MDNWmpubCwsLC4SGhsLR0bHKtqoqDuLCcap7MRAt/qFQ1A9aHMQdGzduxJYtW/D1118zu30dPnwYCxYswOLFi2u8X+aIs02bNvj777/RuHFjaGtrQ1tbG/Xq1ZN5kfecnBysXLkSmzdL72kZGRmJDh3+K/M2MjKCg4MDIiMjK7Th7e0NgUAAgUCAp5FJ8NzgBwBo2/VHRMem4F5MPfToOgGAeO/MbVt9AYgXZ09OfofbgcFw6z8DgHhOps/+cwDERT9CYS4u/ROI0SO/BwB4TFmK0ycvAwAczcWLPowf5IpF33gAAGaPHw3/K5cgEgqZIp7Th32YuZdThrnh4Z3bSEtJZhZOP7hzG7PfpXvfHogIe4L42Bi4CcSLR+9YvxY71q8FALgJXDBr3GhEhD2Be98eAMR7ZR7cuQ2AeDH2tJRkPLxzG1OGiefSrlgwF6cP+wAQFxaJhEIcPHkO62eL0+LbFs3GnYviz2ysY2MAwJ2Lfti2aLa4/dmTsWryl8gXiZhCiJunfZn5a55TRiHy4V1kpaUyC15fPLiH2adwiftAvIoIR3J8HOa7dQcAnNmxEWd2bAQAzHfrjuT4OLyKCMcSd/GC+Qu+6ImLB/cAEC+inZWWisiHd+E5ZRQA8dy5m6fFz3FqZ3vki0R47H9NLpse+19jbNq2aDbnNvmuXyVl0+9zp3FqU/nn9E0PZ85tKv+c/m9gN05tKv+cti2azblN5Z/T5vkzOLWp/HNiE916hWhuHC33S1P56aefcOjQIaSlpeHvv/9GamoqDh48KJPTBOSIOFu1aoUJEyZg3LhxUsVBAODg4FDj/fPnz4etrS2WLFkCT09PJuKcMWMGLC0tsX79eubanj174ttvv4WHh0eV7dnbWSEudKvUMa6jzYtnT2P4mJrTyGyhiF5to8w7F/3Qa/joWt1L9VRDk9qoGZpsRpyduzjj7sMTct/X+7MZGhlxysLQoUNx6dKlSs/JXFWbmZkJLy8vaGlpyd2BsLAw3Lx5E6GhoRXOGRsbIydHerwyJycHJiYm1bbZ0NxY6j0fe23y6TRrq6doWpbvLyNN11OGJrVRczQpyiMoKKjKczKnar/++mscPVq7wfGAgAAkJCSgefPmsLa2xqZNm+Dn54fOnTvD2dlZakX63NxcxMXFwdnZudo2Q8K5nxpSfmxTkrLlC3n12BjLlKSc+ELT9ZShSW3UHE2KaiJzqrZXr1549OgRWrZsicaNpf+Abt+uftpEXl6eVFS5adMmJCQkYM8e8fhCq1atcPDgQQwdOhSrVq1CYGAgHjx4UG2bZYuDuIo21aUoiBb/UCiaB03VKhdTU9MK2VAJMkec3377Lfbv34+ff/4ZM2bMkHrVhKGhIaytrZmXsbEx9PX1YWlpCUtLS/j5+eGXX36Bubk5Hj58iFOnTtXYZla2SNau14rKnObFs6c51ayNHttOU1LswBearqcMTWqj5mhSVBOZHee0adOqfEmYM2eOTG15enri2LFjzPsBAwYgKioK+fn5CAgIQIsWLWps40NOHgB+xjYlBFy7wptWTXpc7WASEnCD9Tbrsp4yNKmNmqNJUR7VJWPlXuS9OqoLbdlGkqqti/M2aWqWQtF8aKqWG0pKSjB9+nR4e3tDT0+vyut+++03LFu2rNJzrG5vwqIPrpHY+FReo01APHdTmXp87JMpmaPGF5qupwxNaqPmaFLYR1tbG9evX69xd6+qnCYgx3QUWajNVJXaYtHIlJN2q4s2x06bzolmTXp8RpgDxk7hTasu6ClDkw29sOh0ua536PWl3PcogiJ6HdtY1uo+ZfztULhh4cKFWLVqFVavXg0dHR2571fbVG3HTo64H8x+sU51jlMkFMK4hvmlbCISCvEqjzc5AEC+SAQDY+OaL6R6KqcpcSSFebnQMzTiXK8sfGuqmo21dcbVQVO13NGsWTOkpqZCW1sblpaW0NLSAiFE5t2+WI04+UzVPnsaw3qbNY1tfu7kgCev37GuWxW9HO3h+0S2zVfZYtbnLrxqaroeW5ryRFc7vnbFj6cfKaQnL3xrqpqNfEe/FMUoW5xaG1iNOL/77jtmbibX1PYXVHWoUlEQLQCqG/CZ3qSoF9dWT6MRJ0d8/PgRa9euxcmTJ5GcnAxbW1uMHz8ev/zyC/T19Wu8X67ioIMHD2LgwIFwdnbGwIED4ePjIxVl8uU0ASAjI5vV9mRxmpIF1LlG4jQlC0zzCd+amqwXFp2OsOh0HNq+m/l3+RcXhF49y0m7qqRZF2ykcMd3330Hf39/bN++HcHBwdi+fTsCAwNlnlIp10bWGzZsgLu7OzZu3IjRo0dj06ZNWLJkSa07rwh5efm8a0aEPeFco2ykGRcRXs2V3MC3pqboVecUU2Ir7vTDJXzrKUOzLthI4Y4LFy7gn3/+wRdffAEnJyd88cUXuHDhAi5cuCDT/TKnaq2srPDkyRM0bdqUOfb69Wt07twZ6en8p5vYTNWqSoqWpmdVH5papfAFTdVyh7OzM27cuAFbW1vm2Nu3bzFo0KBKt7Qsj8wRp4mJSYUdS0xMTGBqys20kJqIiU7gXVOy7yUXVOY0JfsC8gnfmqqqx2Zq9djPHrW6r7bwracMzbpgI4U7pkyZgsGDB2P//v24cuUKvL29MWTIEEydOhX+/v7MqypkrqpdsGAB3N3dsXTpUjRt2hSvX7/Gxo0bsXDhQrx69V/FoL29vWIWyYiNLTvVaPJEm/OW/sKKZnmqijTHzPuRE73q4FtTFfS4jiJ7T5Bt3ERd9ZShWRdspHDHvn37AADr1q2TOr53717s3bsXgHhdgrK+rSwyO8758+cDAP7991+p47du3cL333/PCJWUlMjapELo6enyolOWFg6tWG+zuvSsTYuaNwhnG741+dQLi06HsLQB7+nWhrYtNFpPGZp1wUYKd8THK7Ytpcyp2tLS0hpffDlNAIiKUnw/TnnHNr/q10thzbLUNKa57KtBrOrJAt+aXOuVT7MeWsTvZuTK0KQ2ao4mRTVhdR4nn7BRHKTMoiBaCMQNtHiHoinQ4iDVReaIMykpCTNmzEDnzp3Rpk0bqZcyeJeWqdD9tXGaB3duU0hTgqxO8+JB/ubFKktTUT15i3ce/nlYIb3awLcmtVFzNCmqicxjnGPGjIGjoyO8vLxgYGDAZZ9koqioiHfNd6kpCrchT6SZ/S5VYT154VuzNnqKRJXCLP4jUr41qY2ao0lRTWSOOKOionDo0CEMGzYM/fv3l3rxhbe3NwQCAQQCAQoKP2KtlzhacXEagZjoRDwJeY4eXScAAJb+tBnbtopXibFvPhDJye9wOzAYbv1nIEnUBisWzGVWAurczAoioRD+Vy4xW3kt+sYDF8+KF5F3NDcEADh36IRF33gAEG/55X/lEkRCITo3swIgXlloxYK5AMRTVx7euY20lGT0bieuNP71t9/hu34VAGCJ+0C8ighHcnwc5rt1BwCc2bERZ3ZsBADMd+uOAeOm4lVEOJa4DwQA+K5fxURos3q7ICstFZEP7zJTLPatWMSsjDO1sz3yRSI89r/GbIe0bdFsZhf7sY6NAYh3td+2aDYA8bZJTl17IF8kwtTO4j7fPO2LfSsWARBP5Yh8eBdZaamY1dsFgDhilMem5Pg4KZskbVRnk2TlncmdWuJhWAJiHgXgzBrx53xh02JEBl4CAKwbId5mLjLwEi5sWizWXzMXMY8CUJiXi03jumLAjJ8QevUsLu/0BCCeYpD47BGEme+w3cMVgDiyuOkj7vPBhWOREhuJzLcJ2Dt7KADg9olduH1iFwBg7+yhyHybgJTYSBxcOFb8mflsZKKT7R6u6DZyGhKfPWKmM1ze6cmsQrNpXFcU5uUqZBMAKZtS4yI5t0mY+U7Kpo/5uZzaVP45DZjxE+c2lX9OvSfM4dSm8s+JorrIPMY5efJkzJgxA66urlz3SSYMDQ2QmfNA7vsUGdd079sD5wPu1ere2oxpLnEfiA3n+d11nm/NyvS4HKc8uHAspm89w1n7qqBJbdQMTXUZ48zKysKMGTNw/fp1WFhY4LfffsPEiRMrXEcIwYoVK3Do0CGIRCJ06tQJu3btgrOzMwDAuNwOQvn5+ZgzZw527Nghd7+5RuZU7fbt29GjRw84ODigcePGUucOHjzIesdqormdDe+aXn/srNV9tS0EmuW1qVb3KQLfmhI9vop6vpi7ihcdZWpSGzVHUx2YO3cudHV1kZaWhrCwMAwdOhQdOnRgHKKEs2fP4uDBg7hz5w7s7OywfPlyTJkyBU+eiJcyFYlEzLW5ublo3LgxxowZw6stsiKz4/z666+hra2Ndu3aqcQYp7a2XOvTA1C8itbIWP69OBWpntU34nffSL41w6LTkZn+ETk8VsLqGvC7h6MyNKmNmqOp6uTm5sLPzw8REREwNjZGr169MGLECBw9ehTr16+XujY+Ph69evViFsmZPHkytm7dWmm7586dg5WVFXr37s25DbVBZu/j7++PBw8eYNOmTVizZo3USxnExb7mXVMy/ikrik452fC/sUk+4VqzfAXs2f+NF/EF33rK0KQ2ao6mqhMdHQ1tbW2p2RUdOnSodL3X8ePHIzY2FtHR0SgqKsKRI0cwePDgSts9cuQIpk6dCi0tLc76rggyR5wuLi7IzMyssF6tsnBylm8VHzbmbF57/FTma9mYp7nt2n2F21AVzapSsbP3XuJEryr41lOGJrVRczTZol5JPvRzIuS+Lz09HQKBgHk/c+ZMzJw5k3kvEolgZmYmdY+ZmRmEworffzY2Nujduzfatm0LbW1tNGvWrNL1YJOSkhAYGAgfH362cawNMkec/fr1w6BBg/Dbb7/h4MGDUi9lkJLyjnfNHevXynQdW4sbSKpR+YRNTVnmV0qqHvmCbz1laFIbNUdT2VhaWuLx48fMq6zTBMQFPTk5OVLHcnJyKg2wVq9ejeDgYLx+/RoFBQVYtWoV+vXrh7y8PKnrfH190atXL7Rs2ZJ9g1hC5ojzzp07aNKkCa5fvy51XEtLC9OnT2e9Y2zC5wpBdEUgunoPhVJXaNOmDYqLixETE4PWrVsDAMLDwysUBkmOjxs3jtma0sPDAwsWLMDz58+lolpfX18sXbqUHwNqSZ1Yco8vx1mXnSZ1lhQKu7A5HUXQ0R7B/vLXo3w6aGuNfRg/fjy0tLRw4MABhIWFYciQIbh3714F57l69WrcuHEDfn5+sLS0xPHjxzF79my8ffsWDRo0AADcu3cPAwcORGpqqsoMC1aGXKWpmZmZOHr0KDZuFKfzkpOT8ebNG046VhPPI2Nluo5Np+kmcKnyHBdOU7KIAJ/Iq6nIPpUAmMnqfMG3njI0qY2ao6kO7N69G/n5+bCyssKECROwZ88eODs7IykpCcbGxkhKSgIALFmyBB06dEDHjh3RoEEDbN26FX5+fozTBMRFQe7u7irtNAE5Is7AwECMHj0aAoEAd+/ehVAoRGBgIDZt2oSLFy9y3c8KOLdvhcdhfjVex6bjjI+NQctWrSsc5yrSTI6Pg21Lfrf5kkWTzegy820CGjVpwVp7qqanDE1qo2ZoqkvEWReRayPr06dPo3///jA3NwcAdOvWDY8ePeKsc9VRUlJa4zVsp2hzRRUdJJfp2YJcUc0X8ajJRTr2Y34u622qkp4yNKmNmqNJUU1kTtUmJCQw69JK5tbo6uqiuLiYm57VQFKi4guuy8vKBfOk3nM9prlv5Y+cti+Lpjw7j9SGK7tWc9KuqugpQ5PaqDmaFNVE5lRtz549sXLlSri5uaFhw4bIysrC9evXsW7dOgQEBHDczYrUVBzEdUGQphcC0WIfCkW50FSt6iJzxLllyxZMmjQJ06ZNQ35+PmbNmgUPDw+mUIhv3r7hf8ut9cvFJdJ8OU3JriN8ERadjk1LFvPqNPneCUIZO09QG9VfT1maFNVEZscZFBSEp0+fwtnZGdOnT0fLli3x6NEjBAUFcdm/KtHR0anyHFfRppW1Da+RprmVNW9aEmdp0tCSN826oKcMTWqj5mhSVBOZU7WmpqYVVogAwKRt+aaqVC2XKVpNTM/SlCyFoprQVK3qUmPE6e/vD39/f5SUlODff/9l3vv7++PAgQNKm2/z7Fk0r3pP04TM5s18wbVeZU5TsqEuX2i6njI0qY2ao0lRTWqMOCXrBSYlJaF58+b/3ailBWtrayxduhQjRozgtpeV8IlLGzx6clbqGNfRZlZaKho25i99ypVedVGmMPMdTBpZsa5ZV/WUoUlt1AxNGnGqLjVGnPHx8YiPj8ekSZOYf8fHx+PVq1e4d++ezE5z8uTJsLGxgampKdq0aYMDBw4w527dugVHR0cYGhrC1dUViYmJNbZXWPhRJl02kKRoUxLieNPkQk+WaSVZyQmsataEpuspQ5PaqDmaFNVE5uIgX19fhYSWLVuGhIQE5OTk4O+//8by5csREhKCjIwMuLu7Y82aNcjKyoJAIMC4ceNqbC8lWdoBcBVtlh3XPLtzEycaVcGmnqxjmUEnd7OmSfWUo0lt1BxNimqilEXeX758ib59+2Lbtm14//49Dh8+jHv37gEQ7yhuYWGB0NBQODo6VtlG+eIgPhynOkKLfygU9YSmalUXuRZ5V5Q5c+bA0NAQjo6OsLGxwZAhQxAZGYkOHTow1xgZGcHBwaHSHcTLkpSU/N+/eXKa+1Ys4kSnKhTRq+1qP5d3etZaszZoup4yNKmNmqNJUU14dZy7d++GUChEUFAQ3N3doaenJ9cO4t7e3hAIBBAIBMgV5WOt1x4A4l1L4mNjEBH2BO59ewAQL1ZwcOc2AEDvdvZIS0nGwzu3MWWYGwBgxYK5OH1YvMN452ZWEAmF8L9yCbPHjwYALPrGA3cuiheRH+vYGABASkuxbdFscfuzJ+Ox/zXki0SY2tkeAHDztC/j7DynjELkw7vISktlqmMvHtzDLGqwxH0gXkWEIzk+jtmR5MyOjcxG0vPduqORtS1eRYRjiftAAOIFES4eFNs8q7cLstJSEfnwLjynjAIgdrQ3T/uKFzIY1xWFebmIeRSAM2vmAgAubFqMyEDxLvbrRrQHAEQGXsKFTYvF+mvmol79+ijMy8WmcV0BAKFXzzJfGMd+9kDis0cQZr5jKgwf/nmYmRh+cOFYpMRGIvNtArOTxO0Tu5gNgPfOHorMtwlIiY3EwYVjAQDv097g4Z+HAYirFoWZ75D47BGO/ewBQPxlFXpVXARWW5tiHgUwNtm0cubcpps+G6VsMrdpzqlN5Z9TUkQw5zaVf06Zb15xalP552TTyplzm8o/J8vmrTi1qfxzoqguStuPc/bs2XByckJcXByKioqwe/d/4weffPIJPD09MXr06Crvl6RqaYr2P2halkLRHGiqVnXhNeIsS3FxMeLi4uDs7Izw8HDmeG5uLnO8OsLDorjuYgUkkaWq6bG5CLvklzFfaLqeMjSpjZqjSVFNeIk43717B39/fwwbNgwGBga4efMm3N3dceLECfTo0QOtWrXCwYMHMXToUKxatQqBgYF48OBBtW127OSI0/+GctLfqqLNfJEIBsbGnGjWVo/tKLMwLxd6hkastlmX9ZShSW3UDE0acaouvEScWlpa2LNnD5o2bQpzc3P8+OOP+OOPP/Dll1/C0tISfn5++OWXX2Bubo6HDx/i1KlTNbYpFObx0HNpIh/dVRk9rrb6SooIZr3NuqynDE1qo+ZoUlQTXhynpaUlAgMD8f79e+Tk5ODZs2f49ttvmfMDBgxAVFQU8vPzERAQgBYtWtTY5rv0D5z0tbqxzZtnjnKiKa8el2OZodfO1nwR1VNpTWqj5mhSVBOlFQcpSvtOneH3L/sRoCoXBdHiHwql7kBTtaqL0oqDFOVNQgLrbdbkNCVTUfhCosdVWrYyJKX0fKHpesrQpDZqjiZFNVFbx2lSbu4nH3TpO5B3Pb6jzNaf9qF6aq5JbdQcTYpqQlO1/0PVUrQ0LUuh1G1YTdW2s0Ww7wy57/v0uys0VVsJahtxRoZxMxWlOiQrCHGNxGlKViPhE741NV1PGZpc670IT6nwWjeifaXHK3uxQV14jhTVhUacUK1ok0aalNrAlkOqK7TrYKPsLtQIjThVl/rK7kBt+ZCdzUo78jjNOxf90Gt41csAKkp5pxkZeAnOfYZyplcZfGtqup4imrV1hq/D/dGsQ79a3Vtb+NZUVK82n215TXVwvhRuUFvHKfzAzTzO6ggJuMGZ46ws0owJDuT9S55vTU3XK6/JR2SY+vIB746Tb01VsLE2z5I6W82gTqdqVSVFS9OzmgNNmVLYIunCUpqqVVHUtjgo8VUc75rrZ09mvc3qnKZk+yI+4VtTXfXkKXy5f2wFK5qywreeMjTrgo0U1UVtU7UNG1kodH9tos0BY6copFmemiLNTm5jWNWTBb41VVmPreixhWAIK+2oqp4yNOuCjRTVRW0dp5GxCe+azl17staWLOnZ5u0/ZU1PVvjWVAU9rtOrFi07cNq+svWUoVkXbKSoLmqbqn0Z+azW99Z2bHPW5y611iyLrGOaO752ZUVPHvjW5FPvRXgKtk3ty8m8wuq4+vt4zjWUqacMzbpgI0V1qZPFQcosCqKFQPxBC3Uo6gwtDlJd1DbizM7IqNV9ijjNm6d9a30vIL/TDL2qhK2TeNZkU0+WSDI++BJrerLCtya1UXM0KaqJ2jrO/Hz+N7KOiwiv9b21iTRTYiNrrVdb+NZURK82Kdf3ydG11qstfGtSGzVHk6KaqJXj9Pb2hkAggEAgQFFREXasXwsAcBO4ID42BhFhT+DetwcAYP3ypTi4cxsAoHc7e6SlJOPk31fhOWUUAGDfikVMBDm1sz3yRSI89r/GTDnZtmg27lz0A/DfGrXOXXswW32tnz0Zj/2vIV8kwtTO9gDEEem+FYsAAJ5TRiHy4V1kpaVieg/xGpcP/zyMmz4bAQAHF45FSmwkMt8mYO9s8eT42yd24faJXQCAvbOHotsoD6TERuLgwrHi9n024uGfhwEA2z1cIcx8h8Rnj3DsZw8AwOWdnkwEt2lcVxTm5SLmUQAzBePCpsWIDBT/apasuxkZeInZLunMmrlo3bUvCvNysWlcVwDiiPDyTk8AwLGfPZD47BGEme+w3cO1VjZlvk2QsknXwEhmm37/6lM8fRQH/9PncfCnbwAAwWfW4XW4PwDgzxXi3Wteh/sj+Mw6AOIpBClR91FUmIeLa0ag05cLER98CaF/bQUABPksQnp8OPJzMnDl93EAgJi7Z/Hsyl4AwL975iD7bTSEGW9w/Q9xn174++KFv/hv5/ofHhBmvEH222j8u2cOAODZlb2IuSvu85Xfx8HRdQrS48MR5CP+2wj9aysTvVxcMwJFhXlIibrPTHeQ1yYAUjaJMt5wblN+ToaUTZI+cGVT+efU6cuFnNtU/jm1HzyLU5vKPyeK6qK2Y5xGxiZ48uadXPcoOrbpOWUUPI/+Kdc9ioxpHvvZA5PXHa71/eqgWZ0eF2OUQT6L0HvGZtbbVSVNaqNmaNIxTtVFbaejWNnIt3QVGwVBY+b9KNf1ihYC9Z4wR6H71UGzrB4fxTyO/aZyrqFsTWqj5mhSVBO1dZx6enoyX8tWFa1NCweZr2WjerahbQuF21BlzRfhKcjPMUAej9Wvxo2a8KalLE1qo+ZoUlQTtRrjLEvcyyjeNZd9NUim69iacnJo0ThW2lE1zbLFPAF7+V1yj289ZWhSGzVHk6KaqO0Yp6zzOPmes0nnaVYOnVNJocgHHeNUXdQ24sx4J19hEBtcPLin2vNsO01JtSmfsK1Z01QRSVUjX/CtpwxNaqPmaFJUE7Ud4ywuKqrxGrajzex3qVWe4yLSFGbxH72yoSlPdFmQk6mwnjzwracMTWqj5mhSVBONTtXylaal6VmaiqVQ2IamalUXtU3V1lQcxIXTXOI+sMIxLp2mZJEAPpFXU9GF0iWT0fmCbz1laFIbNUeTopqobarWtllz3jVneW1i/s1HlPnF3FWca9RGk83osuOIBay1pYp6ytCkNmqOJkU1UduIU1tbu8pzXKVo9Y2MAfCXmtU1MOJFRxZNrrbhqq9nyGp7qqanDE1qo+ZoqgNZWVkYNWoUjIyMYGdnhxMnTlR6HSEEy5cvR5MmTWBmZoa+ffsiMvK/tap37twJgUAAPT09eHh48NT72qG2jjPxVVylx7kc19wwezKv45ln1/A/b6y8Jtd7Vt4/tpyztlVBTxma1EbN0VQH5s6dC11dXaSlpeH48eP47rvvpByihLNnz+LgwYMICgpCVlYWunfvjilTpjDnbW1tsXz5ckyfPp3P7tcKjSsO4tJx1pUiIFroQ6EoH3UoDsrNzYW5uTkiIiLQpk0bAMCUKVPQpEkTrF+/XuraDRs2ICQkBGfOnAEAREZGokuXLigoKJC6bvny5Xjz5g0OHz4sd3/5Qm0jznepFb/cuXaakl0++IJvvRfhKTi/dQO/mv6K7XGq6nrK0KQ2ao4mW5D8AhSHx8r9Sk9PZ3akEggE8Pb2lmo3Ojoa2trajNMEgA4dOlQacY4fPx6xsbGIjo5GUVERjhw5gsGDB3NuOxeobXEQhT1ohEmhUCrD0tKy2ohTJBLBzMxM6piZmRmEwopBjI2NDXr37o22bdtCW1sbzZo1g7+/P+t95gO1dZxW1tK7o/CRov18Ir9jjlzrVeYw2/G8A4Sm6ylDk9qoOZqqjrGxMXJycqSO5eTkwMTEpMK1q1evRnBwMF6/fg1ra2scO3YM/fr1Q2RkJAwN1avwSm1TtTEvnvOuKdmcWRP0qooyJZsA84Wm6ylDk9qoOZqqTps2bVBcXIyYmBjmWHh4OJydnStcGx4ejnHjxqFp06aoX78+PDw8kJ2djefP+f8uVxS1dZx29v9t8cVXQdCYFfyOOXKhV1OVbPfJa1nXrA5N11OGJrVRczRVHSMjI7i7u2PlypXIzc3F3bt38ddff0lVy0r49NNPcfbsWaSlpaG0tBRHjx5FUVERWrVqBQAoLi5GQUEBSkpKUFJSgoKCAhQXF/NtkkyoreMsKSnhXfNjfq7a6sk6raS4MI81TVnQdD1laFIbNUdTHdi9ezfy8/NhZWWFCRMmYM+ePXB2dkZSUhKMjY2RlJQEAFiyZAk6dOiAjh07okGDBti6dSv8/PzQoEEDAMDatWthYGCA9evX49ixYzAwMMDatar5Y4UXx1lYWIgZM2bAzs4OJiYm6NSpE65cucKcv3XrFhwdHWFoaAhXV1ckJibW2Gbya/HD4HP6yZVdqznTqgy29OQp/gn7+w9WNKme8jSpjZqjqQ40bNgQFy5cQG5uLpKSkjBx4kQAQPPmzSESidC8uXiVN319fezatQspKSnIycnBkydPpKpqPT09QQiRenl6eirDpBrhZR5nbm4uNm7cCA8PDzRv3hyXL1/GhAkT8OzZMxgbG8PBwQEHDhzA8OHDsWLFCgQFBeHBgwfVttm+U2esOnWV036r+7xNWi1LoagvbM7j7NKiIR4sd5P7vu57Y+gi75XAS8RpZGQET09PtGjRAvXq1cOwYcPQsmVLhISE4Pz583B2dsaYMWOgr68PT09PhIeHIyqq+kXcU9++5bTPlTnNmz4bOdVkS0+R1X6eXdlbq/tqi6brKUOT2qg5mhTVRCljnGlpaYiOjoazszMiIyPRoUMH5pyRkREcHBwqnUDr7e3NTMTN+fABZ3aIHct8t+5Ijo/Dq4hwZgcT3/WrmI2nZ/V2QVZaKiIf3oXnlFEAgH0rFuHmafGE5qmd7ZEvEuGx/zWs/9+yehc2LUZk4CUAwLoR7QEAudkZuLBpMQDgzJq5iHkUgMK8XGwa1xUAEHr1LC7v9AQAHPvZA4nPHkGY+Q7bPVwBiDeJljjDgwvHIiU2EplvE5jq2dsndjGLHuydPRTa2tpIiY1kdiy56bOR2Wh6u4crhJnvkPjsEY797AEAOOG1GJcPHAAAXFwzAkWFeUiJuo/7x1YAAILPrMPrcPG8qT9XiD+n1+H+CD6zDgBw/9gKfCwQoagwDxfXjAAAxAdfQuhfWwEAQT6LkB4fjvycDFz5fRwA8ea+ki+Uf/fMQfbbaAgz3jAViC/8fZmJ49f/8IAw4w2y30YzO01kJ0czGwRf+X0c8nMykB4fjiCfReLP9K+tiA++pJBNKVH3GZv0TRtxbtOzK3ulbNLW1efUpvLPKfnFPc5tKv+cMpMiObWp/HPSN23EuU3ln5OOoQmnNpV/ThTVhfcl94qKivDFF1/AwcEB+/btw4wZM2BpaSm1PFPPnj3x7bffVrvQr0P7jljvd52TPqpjipamZSkUzYKmalUXXiPO0tJSTJkyBbq6uti5cycA+SbQliXxZcWIlA2qc5qSyJEvZNFjexF2yS9fvtB0PWVoUhs1R5OimvDmOAkhmDFjBtLS0uDn5wcdHR0AgLOzM8LDw5nrcnNzERcXV+kE2rI0cWhT7Xku+HrzaZXR42rXkr6z+Z2rqul6ytCkNmqOJkU14c1xfvfdd3jx4gUuXrwIAwMD5vioUaMQEREBPz8/FBQUwMvLCy4uLnB0dKy2vaLCQtb7WFOKNis5gXVNefW43uZLlMlt0VVd01OGJrVRczQpqgkvjjMxMRH79u1DWFgYrK2tYWxsDGNjYxw/fhyWlpbw8/PDL7/8AnNzczx8+BCnTp2qsc3sd6k89FyaoJO7larHxzhmFM87QGi6njI0qY2ao0lRTdR2P062i4NUuSCIFv5QKHUPWhykuqjtknvpya9515RMNeGLE16LeXeaklJ5qqe+mtRGzdGkqCZq6zj1DNjbhkbWaNOmVfUFS2zyIjwFDWz5L4DiW1PT9ZShSW3UHE2KalLnU7WqmKKlqVkKhUJTtaqL2kac8S+e8a4pWSGIS8o6TclqI3zCt6am6ylDk9qoOZoU1URtI86WTp/g9z9vKdSGvNFmYV4u9AyNFNKsjvKRZlFhHnT0+N0ZnW9NTddThia1UTM0acSpuqhtxJmfK+JdMykimLO2K0vPZsSHV3Ilt/Ctqel6ytCkNmqOJkU1qa/sDtQWYVamQvfXZmwz9NpZtO7aVyHdyqhqTDPh8WXYOHZnXa86+NbUdD0+NFMfJ0u9f/ngPLREdpzpVQbfmi8fnNe450hRH9Q2VatocZCqFAXRQiCKhPIOkMIt1gJbZXehWmiqVnVR24gz7U1ire+trdO8sGkxRv74e611yyKLwww+sw6fjv2ZFT1Z4VtTk/UkjjD68R60EXzHi6Yy9JShyYaevD9UymqqutOlcIvaOk4jE1PeNVt/2oeVdmSNMq3bfsaKnjzwranuerJ8+Zpbd2RVU9X0lKGpbBtldbrUwWomaus4jc3Ma3WfIila5z5Da32vBHlSs8069FNYT1741lQHPUVTqJZN+R0X41tPGZrqYqM8fzvUyaoPaltV+ypS/go3Rcc1141or9D98o5nSnaV5xO+NVVBL/VxcrUvRbn/1zSF21BlPWVoaqKNbP/dlaU0twi5D1LkflEqp04VBymzIIgWASkfWnxDUSc+vtnOWmFOJ0sT/PtlF7nvGxAmosVBlaC2EafoQ7Zc17PhNCMDL9Xqvto6zdfh/rW6TxH41uRSr7Jf8Olv7nOmVxV8a1IbNUeTopqorePMFebwrhkTHCj3PYpEmqkvH9T6XnXRZEtP1hRrdmoYK3rywLcmtVFzNCmqSZ1I1SorRUvTs9xB064UTYemalUXtYo4vb29IRAIIBAIkBAViTM7NgIA5rt1R3J8HF5FhGOJu7j4w3f9Klw8uAcAsN3DFcLMd0h89gjHfvYAIN5bM/TqWQDixdsL83IR8ygAZ9bMBSCesylJzUqKgnwWfIULmxYDAM6smYuYRwEozMtlFn8PvXqW2bPT+/uJSI8PR35OBq78Pg4AEHP3LJ5d2QsA+HfPHGS/jYYw4w2u/yHu0wt/X7z43y7z1//wQNDBH5H9Nhr/7pkDAHh2ZS9i7or7fOX3ccjPyUB6fDiCfBaJ9f/aivhgcZ8vrhmBosI8pETdx/1jKwCI5zBKUqOSIpnX4f4IPrMOAHD/2Ar8u3ceigrzmAWt44MvMfsQBvksUtgmYcYbKZuubZkqk02pj5Pxl+dwvL0fh+f/XELUA3Gfoh/vYVJokuKN9Df3Ef1Y/OyjHmxFVmooSory8fCfWYh6sBVpCf8iLuwgACDyzm/4kPECH/Oz8fjqfABAcuwVJEScBAA8DVgJ0ft45ItSEXpT/OxfR/2J11F/ivt3czHyRakQvY/H04CVAICEiJNIjr0CAHh8dT6e3/0dHzJeIPLObwCAuLCDSEv4FwDw8J9ZKCnKR1ZqaK1tAiBlU/DlOZzb9DE/W8qmkOs/cGpT+ecU9WAr5zaVf04v7m3i1Kbyz4miuqhtxGlt1xI7rj+s8To2o82YRwEyLbnHVqSZEnWf9yW++NasTI/LaDIrNRQNrTtx1r4qaFIbNUOTRpyqi9rO4zQwMq7xGrZTtM3bf1rjNWymZy1admCtLVXVtGjZgde0q1kjR960lKVJbdQcTYpqolap2rIkvnzOu+aOr12rPc/2mObV38ez2p4qaUoKeC7/No4XPQmPry3gVU8ZmtRGzdGkqCZqm6qtqTiIz4IgWgQkG7Sgh0KRHZqqVV3UNuLMyVZsW7HaICkmKguXTlNS6MMnbGvWND1EUnzBF3zrKUOT2qg5mhTVRG0dZ2F+XpXnuIo2U2Ijpd5zHWm+T47mtH2uNOVZNkz0Pl5hPXngW08ZmtRGzdGkqCYamarlI01L07PS0DQshcIuNFWruqhtxJkcH1vpcS6dpmQOKF9OUzKXkU/k1VR0QWrJnDm+4FtPGZrURs3RpKgmajsdxdzKusIxriPN3hPm8BppOvabypuWPJpsRpdNHUey1pYq6ilDk9qoOZoU1URtHaeOnh7vmpnvDWDA4/7Zxo2a8CdWgyZXqVgDo4o/gLiEbz1laFIbNUeTopqobar2bZx0EQvX0eaL8BQE7J3LqUZ5+NYrr8nV3oBleRroyVnbqqCnDE1qo+ZoUlQTjSkO4sNx1gVokQ+FohrQ4iDVRW0jzvcZ75h/8+U0JYuR8wWfepLIUrLoNV9oup4yNKmNmqNJUU3UdoyzpLgYAL+RZkEOv4su8KFXPsL8WPCec826pKcMTWqj5mhSVBO1T9XSFG3toWlZCkV1oala1UVtU7Vv4qJ5d5qSPST5giu96gp+JHsV8oWm6ylDk9qoOZoU1URtU7WWtk151+w4YoFa68kSYdp3/JpVzbqupwxNaqPmaFJUE7V1nIXF3GaYK0vR1tcz5FSTKz15UrLa9Q1Y0aR6ytOkNmqOJkU14S1Vu3PnTggEAujp6cHDw0Pq3K1bt+Do6AhDQ0O4uroiMTGxxvayU5I46mnV3D+2XK30ajMHM+rBFoU05UXT9ZShSW3UHE11ICsrC6NGjYKRkRHs7Oxw4sSJSq+bPXs2jI2NmZeenh5MTEyY83379oW+vj5zvm3btnyZIDe8FQedP38e9erVw7Vr15Cfn4/Dhw8DADIyMuDg4IADBw5g+PDhWLFiBYKCgvDgwYNq27Np7YzpW85w0ld1LwiiRT8UivqjLsVBEyZMQGlpKXx8fBAWFoahQ4fi3r17cHZ2rvY+Dw8P1KtXDwcPHgQgdpyTJ0/GN998I3c/+Ya3iNPd3R0jR45Eo0aNpI6fP38ezs7OGDNmDPT19eHp6Ynw8HBERUVV254ok5vCoOqc5gt/X0402dRT1Gm+jvpTofupnvI1qY2ao6nq5Obmws/PD2vWrIGxsTF69eqFESNG4OjRozLdN23aNJ56yi5KH+OMjIxEhw4dmPdGRkZwcHBAZGQkHB0dpa719vaGt7c3AKAoLwfXVvP7oeelpyMp57nG6gFAvfx0fHzzmuqpsSa1UTM0awoe5KFxl54YEJYh9335+fkQCATM+5kzZ2LmzJnM++joaGhra6NNmzbMsQ4dOiAwMLDadv38/GBpaYnPP/9c6viyZcuwdOlStG3bFr/++iv69u0rd5/5QOmOUyQSwdLSUuqYmZkZhEJhhWvLPjSBQMD7/CK+NamN6q+nDE1qo2ZolnVYinL16lXW2iqLSCSCmZmZ1LGqvr/LcuTIEUydOhVaWlrMsQ0bNsDJyQm6uro4deoUhg8fjrCwMDg4OHDSd0VQ+jxOY2Nj5OTkSB3LycmRGjSmUCgUiupRm+/v169fIzAwEFOnSm9h2K1bN5iYmEBPTw/Tpk1Dz549cfnyZU76rShKd5zOzs4IDw9n3ufm5iIuLq7GgWUKhUKhKJc2bdqguLgYMTExzLHw8PBqv799fX3Ro0cP2NvbV9u2lpYWVHVhO94cZ3FxMQoKClBSUoKSkhIUFBSguLgYo0aNQkREBPz8/FBQUAAvLy+4uLhUGN8sT9k8O1/wrUltVH89ZWhSGzVDUxk2youRkRHc3d2xcuVK5Obm4u7du/jrr78wZcqUKu/x9fWtMCXx/fv3uHbtGuMXjh8/jtu3b8PNzY1jC2oJ4YlVq1YRAFKvVatWEUIIuXHjBmnbti3R19cnffr0IfHx8Xx1i0KhUCgKkJmZSb788ktiaGhImjVrRo4fP04IISQxMZEYGRmRxMRE5tp79+4RQ0NDkpOTI9XGu3fviEAgIMbGxsTMzIx069aNXL9+nVc75EFtF3mnUCgUCkUZKH2Mk0KhUCgUdYI6TgqFQqFQ5IA6TgqFQqFQ5IA6TgqFQqFQ5IA6ThXC29sbPXr0gJmZGbS1tWFmZoYePXpg//79yu4aK2i6fQC1UVPIzMyEt7c35s+fj+nTp2P+/Pnw9vZGZmamsrtGUQGUvuSerHh7e+Pw4cOIjIyESCSCsbExnJ2d8fXXX+Pbb79VdvcUZsmSJfjnn3+waNEidOjQAWZmZsjJyUFYWBi2bNmCV69e4bffflN2N2uNptsHUBs1xcZbt27hq6++wieffIIOHTrA1tYWOTk5OH78OJYuXQo/Pz+4uroqu5sUZaLs+TCysHjxYuLk5ER8fHzI48ePSUxMDAkJCSE+Pj7E2dmZLF26VNldVBgLCwuSnJxc6bm3b9+SRo0a8dwjdtF0+wihNmqKje3atSN+fn6Vnjt//jxxdHTkuUcUVUMtIs6DBw/i6dOnsLGxkTreuXNnDB48GC4uLmr/K5fUMJ22pvOqjqbbB1AbZTmvDiQmJmLo0KGVnhsyZAgmTZrEc48oqoZaOM668D/rjBkz0K9fvwopsPDwcGzZskXt09Gabh9AbdQUG7t164bly5fD09MTRkZGzPHc3FysXr0a3bp1U2LvKCqBskJdeVi8eDFxdHQk+/fvJ48ePSIvX74kwcHB5MCBA8TJyYksWbJE2V1khb1795IePXoQMzMzoq2tTczMzEiPHj3I3r17ld01VtB0+wihNmoCCQkJpHv37kRfX584OTmR7t27E2dnZ2JgYEC6d+8utYQcpW6iNkvu7du3D76+vhWKg6ZOnYpZs2Ypu3sUCkXDiI6OxvPnz6W+b1q3bq3sblFUALVxnHWF6OhoREZGQigUwsTEBO3bt9eo/1k13T6A2kihaDpqMcYpQZP/Z01KSsK4ceMQHh4OBwcHZuwoLi4OHTp0wKlTp9C8eXNld7PWaLp9ALVRU2wENH/6G0VBlJsplo3ExETy2WefEQMDA9K+fXvSs2dP8sknnxBDQ0ONGXPo168f+emnn0hubq7UcZFIRBYvXkxcXV2V1DN20HT7CKE2aoqNdWH6G0Ux1CJV279/f3Tp0gWenp4wNDRkjufm5sLLywvBwcHw9/dXYg8Vx9jYGFlZWdDV1a1wrrCwEA0bNkRubq4SesYOmm4fQG3UFBstLS0rnf4GAMnJyXBxcUFGRoYSekZRFdRiyb2HDx9i7dq1Uk4TEO8+7uXlhYcPHyqpZ+zRrFkz/PPPP5Weu3z5stqnvzTdPoDaqCk21hRLqEGsQeEYtRjjlPzP6u7uXuGcpvzPunPnTowePRpbtmypsJRZZGQk/Pz8lN1FhdB0+wBqo6bYWBfmqlIUQy1Stbdu3cLo0aPRvn37Kv9n7devn7K7qTCZmZk4f/68VEFC+/btMXLkSFhYWCi7ewpTmX3Ozs4YNWqURtgH1E0bTUxM4OTkpFE20ulvlOpQC8cJ1I0vpNDQUMTFxWHIkCHQ1dXFnj178OrVK/Tv3x/Dhg1TdvdYJT4+HpcuXQIADB48GK1atVJyjyiyEhsbi6NHjyIiIgJ5eXlo2rQpunbtCg8PD+jo6Ci7exQK56iN46yKkpIS/Prrr1i5cqWyu6IQPj4+WL58ObS0tGBrawt3d3e8fv0axcXFOHXqFLZt24bp06cru5u1pl27dnjx4gUAIDAwECNGjEDPnj0BAEFBQfjrr7/UPmswf/58jB07lrFLE7lw4QImT56Mnj17ghCCwMBAjBs3DnFxcUhNTcWNGzdgb2+v7G5ySlJSkkYMD1EUQEnVvKxRUFBA6tWrp+xuKEzbtm3Jy5cvSVRUFNHS0iJ3795lzl29epW4uLgosXeKY2xszPy7V69e5MiRI8z7Y8eOke7duyujW6yira1NTExMiIODA1m9ejVJSEhQdpdYp3Xr1sTf3595f+3aNTJ48GBCCCEbN24kQ4YMUVbXeEFTvm8oiqEWEWd1kVZxcTGOHz+OkpISHnvEPmZmZvjw4QMAcbWwSCSClpYWAKC0tBQNGzbE+/fvldhDxTA1NUVOTg4AwMrKCm/fvmXSeiUlJbC0tERWVpYyu6gwJiYmSEtLw9mzZ+Hr64vbt2+jV69e8PDwwFdffSW1YLi60qBBA2RnZzN/m8XFxbCxsUF6ejry8vJgbW3NPGd15fbt21WeKywsxODBg9X++4aiGGpRVXvixAnMmDEDDRs2rHBOU/6AjYyMUFRUBB0dHXh4eDBfTACQn5+PevXUYuZQlRQVFeHQoUMghEBLSwsfP35kHGdxcbFGPEctLS0YGhpi2rRpmDZtGpKSkuDr64t169Zh3rx5GD16NA4fPqzsbipEly5dsH37dsyfPx8A8Mcff8DZ2RkAoK2tjfr11eIrpVr69u0LGxsbtf9/jsIhSo54ZUIgEJC//vqr0nP5+flES0uL5x6xz+TJk8nz588rPXfq1CnSp08ffjvEMn369CF9+/ZlXo8ePWLOXbt2jXz66adK7B07mJiYVHnu7t27ZNasWTz2hhtevHhB2rRpQ0xMTJi09LNnzwghhDx9+pT89NNPSu6h4rRo0UJqqKQs+fn5NFVLUY9U7a5du9CkSROMHDmywrmSkhKsXbsWq1at4r9jPJGeng4tLS2NqR4uz4cPH1BUVKT29pmYmEAoFCq7G5xTUlKCqKgoEELg6OioEVFmWcaMGYPevXvj+++/r3Du48ePaNu2LeLj45XQM4qqoBaOk0KhUPiiqKgIAOjUGkqV0CQ+hUKhlEFHR6dKp1lSUgIvLy+ee0RRNWjESaFQKDJSWFgIQ0NDjShmo9QezRqcoFAoFAWpafobhUIdJ4VCoZShLkx/oygGTdVSKBRKGT799FOsWLECI0aMqHCuoKAAhoaGKC0tVULPKKoCLQ6iUCiUMnh4eFTpGHV0dDR66htFNmjESaFQKBSKHNCIk0KhUCgUOaCOk0KhUCgUOaCOk0KpBS1atMDNmzdrvC4gIABNmzatlUZCQgK0tLToFAgKRcWgjpNCoVAoFDmgjpNCoVAoFDmgjpOiNrRo0QK//fYbnJycYG5ujq+//hoFBQUAgP3796NVq1Zo2LAhRowYgeTkZOa++fPno1mzZjA1NUWXLl0QFBRUo1Z+fj6mTZsGc3NztGvXDr///nuVKdfCwkIsWLAAtra2sLW1xYIFC1BYWCh1zbp162BhYYEWLVrg+PHjzPFLly6hU6dOMDU1RbNmzeDp6VmLT4ZCofAJdZwUteL48eO4du0a4uLiEB0djbVr18Lf3x/Lli3DmTNnkJKSAjs7O4wfP56559NPP0VYWBiysrIwceJEjBkzhnG4VbF69WokJCTg1atXuHHjBo4dO1bltb/++isePHiAsLAwhIeH49GjR1i7di1zPjU1FRkZGXj79i2OHDmCmTNn4uXLlwDEG5j7+vri/fv3uHTpEvbs2YMLFy4o9iFRKBRuUdI+oBSK3NjZ2ZE9e/Yw7y9dukTs7e3J9OnTpTZQFgqFpH79+iQ+Pr7Sdho0aEDCwsKq1WrZsiW5evUq837//v2kSZMmUn25ceMGIYQQe3t7cunSJebc1atXiZ2dHSGEkH///Zdoa2sTkUjEnB8zZgzx8vKqVHf+/PlkwYIFhBBC4uPjCQBSVFRUbV8pFAq/0IiTolY0a9aM+bednR2Sk5ORnJwMOzs75rixsTEaNWqEt2/fAgA2b96Mdu3awczMDA0aNMCHDx+QkZFRrU5ycrKUVtl/V3ZtWX1JvySYm5vDyMio0vMPHz6Eq6srLC0tYWZmhr1799bYNwqFolyo46SoFa9fv2b+nZSUxIwrJiYmMsdzc3ORmZmJJk2aICgoCBs2bMCZM2eQnZ2N9+/fw8zMDKSGBbNsbGzw5s2bSnXLU15f0i8J2dnZyM3NrfT8xIkTMWLECLx+/RofPnzA7Nmza+wbhUJRLtRxUtSKXbt24c2bN8jKysK6deswbtw4TJw4EYcOHUJYWBgKCwvx888/o1u3bmjRogWEQiHq168PS0tLFBcXw8vLCzk5OTXqjB07Fr/99huys7Px9u1b7Ny5s8prJ0yYgLVr1yI9PR0ZGRnw8vLC5MmTpa5ZtWoVPn78iKCgIPzzzz8YM2YMAEAoFKJhw4bQ19fHo0ePcOLECcU+IAqFwjnUcVLUiokTJ2LQoEGwt7eHvb09li9fjv79+2PNmjUYPXo0bGxsEBcXh1OnTgEA3Nzc8MUXX6BNmzaws7ODvr5+tWlXCStXrkTTpk3RsmVLDBgwAF999RX09PQqvXb58uUQCARwcXHBJ598gs6dO2P58uXMeWtra5ibm8PW1haTJk3C3r174ejoCADYvXs3Vq5cCRMTE3h5eWHs2LEsfEoUCoVL6CLvFLWhRYsWOHDgAAYMGMC79p49e3Dq1CkEBgbyrk2hUFQLGnFSKJWQkpKCu3fvorS0FC9fvsTmzZsxatQoZXeLQqGoANRxUuosX3zxBYyNjSu81q1bh48fP2LWrFkwMTFBv3798OWXX2LOnDnK7jKFQlEBaKqWQqFQKBQ5oBEnhUKhUChyQB0nhUKhUChyQB0nhUKhUChyQB0nhUKhUChyQB0nhUKhUChyQB0nhUKhUChy8P8STZBmk+Xf/QAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -1687,12 +1687,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1889,8 +1889,8 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", + "display_name": "Python 3 (Spyder)", + "language": "python3", "name": "python3" }, "language_info": { diff --git a/pvlib/mlfm.py b/pvlib/mlfm.py index 7e54ab7da8..0dceb735b5 100644 --- a/pvlib/mlfm.py +++ b/pvlib/mlfm.py @@ -423,7 +423,7 @@ def meas_to_norm(dmeas, ref): ''' def mpm_fit(data, var_to_fit, mpm_sel): - + print ("var_to_fit, mpm_sel = ", var_to_fit, mpm_sel) """ Fit mpm to normalised measured data 'var_to_fit' using mpm_sel model. @@ -451,7 +451,7 @@ def mpm_fit(data, var_to_fit, mpm_sel): var_to_fit : string Column name in ``data`` containing variable being fitted. e.g. pr_dc, i_mp, v_mp, v_oc ... - + mpm_sel : char MPM version 'a' or 'b' @@ -468,20 +468,20 @@ def mpm_fit(data, var_to_fit, mpm_sel): coeff_err : list Standard deviation of error in each model coefficient. - - # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html - + + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html + infodict : dict a dictionary of optional outputs with keys - nfev - The number of function calls. + nfev - The number of function calls. fvec - The function values evaluated at the solution. etc. - + mesg : string A string message giving information about the solution. - + ier : int - An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. + An integer flag. If it is equal to 1, 2, 3 or 4, the solution was found. Otherwise, the solution was not found. """ @@ -543,9 +543,9 @@ def mpm_fit(data, var_to_fit, mpm_sel): def mpm_calc(dmeas, mpm_sel, c_1, c_2, c_3, c_4, c_5=0., c_6=0.,): - + print("mpm_sel, c_1, c_2, c_3, c_4, = ", mpm_sel, c_1, c_2, c_3, c_4,) - + """ Predict norm LFM values from weather data (g,t,w) in ``dmeas``. @@ -553,12 +553,12 @@ def mpm_calc(dmeas, mpm_sel, c_1, c_2, c_3, c_4, c_5=0., c_6=0.,): const temp_coeff low_light high_light wind extra | | | | | | norm = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g - + if mpm_sel == 'b': const temp_coeff low_light improvement high_light ws | | | | | | norm =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws - + where : g = G_POA (W/m^2) / G_STC --> 'suns' @@ -570,7 +570,7 @@ def mpm_calc(dmeas, mpm_sel, c_1, c_2, c_3, c_4, c_5=0., c_6=0.,): dmeas : DataFrame Measured weather and module electrical values per time or measurement. Contains 'poa_global', 'temp_module' and optional 'wind_speed'. - + mpm_sel : mpm_sel : char MPM version 'a' or 'b' @@ -601,10 +601,10 @@ def mpm_calc(dmeas, mpm_sel, c_1, c_2, c_3, c_4, c_5=0., c_6=0.,): 36th EU PVSEC, Marseille, France. September 2019 """ - + # print ('mpm_sel = ', mpm_sel) - + if mpm_sel == 'a': mpm_out = ( c_1 @@ -751,7 +751,7 @@ def mpm_a_fit(data, var_to_fit): """ # full_outputboolean, optional - If True, this function returns additioal information: + If True, this function returns additioal information: infodict, mesg, and ier. """ From d879abc3c84a9355590e7883eeba0c4473d9aa20 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Tue, 13 Dec 2022 15:08:36 +0000 Subject: [PATCH 75/81] Add files via upload --- docs/tutorials/mlfm_data/ref/mlfm_matrix.csv | 362 +++++++++---------- 1 file changed, 181 insertions(+), 181 deletions(-) diff --git a/docs/tutorials/mlfm_data/ref/mlfm_matrix.csv b/docs/tutorials/mlfm_data/ref/mlfm_matrix.csv index 4da506fad8..fcf59e818f 100644 --- a/docs/tutorials/mlfm_data/ref/mlfm_matrix.csv +++ b/docs/tutorials/mlfm_data/ref/mlfm_matrix.csv @@ -1,181 +1,181 @@ -id,mid,poa_global,temp_module,wind_speed -1,matrix,100,0,0 -2,matrix,100,5,0 -3,matrix,100,10,0 -4,matrix,100,15,0 -5,matrix,100,20,0 -6,matrix,100,25,0 -7,matrix,100,30,0 -8,matrix,100,35,0 -9,matrix,100,40,0 -10,matrix,100,45,0 -11,matrix,100,50,0 -12,matrix,100,55,0 -13,matrix,100,60,0 -14,matrix,100,65,0 -15,matrix,100,70,0 -16,matrix,200,0,0 -17,matrix,200,5,0 -18,matrix,200,10,0 -19,matrix,200,15,0 -20,matrix,200,20,0 -21,matrix,200,25,0 -22,matrix,200,30,0 -23,matrix,200,35,0 -24,matrix,200,40,0 -25,matrix,200,45,0 -26,matrix,200,50,0 -27,matrix,200,55,0 -28,matrix,200,60,0 -29,matrix,200,65,0 -30,matrix,200,70,0 -31,matrix,300,0,0 -32,matrix,300,5,0 -33,matrix,300,10,0 -34,matrix,300,15,0 -35,matrix,300,20,0 -36,matrix,300,25,0 -37,matrix,300,30,0 -38,matrix,300,35,0 -39,matrix,300,40,0 -40,matrix,300,45,0 -41,matrix,300,50,0 -42,matrix,300,55,0 -43,matrix,300,60,0 -44,matrix,300,65,0 -45,matrix,300,70,0 -46,matrix,400,0,0 -47,matrix,400,5,0 -48,matrix,400,10,0 -49,matrix,400,15,0 -50,matrix,400,20,0 -51,matrix,400,25,0 -52,matrix,400,30,0 -53,matrix,400,35,0 -54,matrix,400,40,0 -55,matrix,400,45,0 -56,matrix,400,50,0 -57,matrix,400,55,0 -58,matrix,400,60,0 -59,matrix,400,65,0 -60,matrix,400,70,0 -61,matrix,500,0,0 -62,matrix,500,5,0 -63,matrix,500,10,0 -64,matrix,500,15,0 -65,matrix,500,20,0 -66,matrix,500,25,0 -67,matrix,500,30,0 -68,matrix,500,35,0 -69,matrix,500,40,0 -70,matrix,500,45,0 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+177,matrix,1200,55,0 +178,matrix,1200,60,0 +179,matrix,1200,65,0 +180,matrix,1200,70,0 From ceafccfcfc0ed882c686ed51dc8723241b1613f5 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Tue, 13 Dec 2022 15:14:04 +0000 Subject: [PATCH 76/81] Add files via upload --- .../meas_gtw/g78_T16_Xall_F10m_R1_041.csv | 2 + .../meas_gtw/g78_T16_Xall_F10m_R900_041.csv | 1816 ++++----- .../g78_T16_Xall_F10m_R900_041_4param.csv | 908 +++++ .../meas_gtw/n05667_Y13_R1k6_fClear_041.csv | 3232 ++++++++--------- .../meas_gtw/x19074001_iec61853_041.csv | 56 +- .../meas_gtw/x19074001_iec61853_041_6pts.csv | 7 + .../x19074001_iec61853_041_rand1pc.csv | 28 + .../x19074001_iec61853_041_rand2pc.csv | 28 + .../x19074001_iec61853_041_rand5pc.csv | 28 + .../x19074001_iec61853_041_rand5pc_6pts.csv | 7 + 10 files changed, 3560 insertions(+), 2552 deletions(-) create mode 100644 docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R1_041.csv create mode 100644 docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041_4param.csv create mode 100644 docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_6pts.csv create mode 100644 docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand1pc.csv create mode 100644 docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand2pc.csv create mode 100644 docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand5pc.csv create mode 100644 docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand5pc_6pts.csv diff --git a/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R1_041.csv b/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R1_041.csv new file mode 100644 index 0000000000..5bd5ff2946 --- /dev/null +++ b/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R1_041.csv @@ -0,0 +1,2 @@ +date_time,module_id,poa_global,wind_speed,temp_air,blue_frac,beam_frac,temp_module,v_oc,i_sc,i_mp,v_mp,r_sc,r_oc +2016-03-23 09:00:00-07:00,78,591.3868886,4.226408028,17.42457581,0.515025523,0.895491146,27.82861328,43.52636044,3.14995479,2.949264766,35.76882896,674.5517322,1.355690858 diff --git a/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041.csv b/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041.csv index 4ed397d0f5..bfc3b6bbcc 100644 --- a/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041.csv +++ b/docs/tutorials/mlfm_data/meas_gtw/g78_T16_Xall_F10m_R900_041.csv @@ -1,908 +1,908 @@ -date_time,module_id,poa_global,wind_speed,temp_air,blue_frac,beam_frac,temp_module,v_oc,i_sc,i_mp,v_mp,r_sc,r_oc -2016-01-26 07:20:00-07:00,78,2.666484317,1.472831997,8.177978516,0.454991652,1.1,2.081939697,33.04064421,0.013215447,0.009809045,24.33732,115258.5498,608.680999 -2016-01-26 07:30:00-07:00,78,7.899142696,1.297711339,8.241424561,0.522026664,-0.1,2.436985474,37.64402934,0.037248728,0.02983236,29.62497997,8253.745059,150.461283 -2016-01-26 07:40:00-07:00,78,52.92767243,0.955482493,7.739624023,0.270154323,0.300267162,2.592086792,39.6492057,0.072837131,0.061195743,32.44486777,4762.543972,63.66002837 -2016-01-26 07:50:00-07:00,78,104.9430478,0.62178426,6.727676392,0.306793868,0.570422814,4.082763672,42.70262294,0.215963967,0.1043503,40.52055001,335.2229575,20.30366921 -2016-01-26 08:00:00-07:00,78,153.4330542,0.410855412,7.471725464,0.352624445,0.624202994,6.691146851,43.92516586,0.401482968,0.2831102,41.82102001,145.7231242,10.27448789 -2016-01-26 08:10:00-07:00,78,207.780344,0.676059248,8.259368896,0.388758434,0.654555168,9.171981812,44.29201888,1.051441221,0.976606838,37.8831301,1022.179281,3.64816346 -2016-01-26 08:20:00-07:00,78,216.1854545,0.254440298,8.958572388,0.419448916,0.725788201,11.9732666,44.18390797,1.201683005,1.126675792,38.21257622,457.923977,3.080113759 -2016-01-26 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11:00:00-07:00,78,909.7273511,2.819398711,15.3289032,0.508918361,0.89133442,36.52726746,43.2420164,4.771606859,4.431889894,34.62225988,241.5181897,1.059123786 -2016-01-26 11:10:00-07:00,78,930.8391264,3.539826502,15.08152771,0.509800796,0.889984989,37.17326355,43.17568462,4.876980742,4.524938155,34.5065212,250.6222944,1.076740402 -2016-01-26 11:20:00-07:00,78,952.6737668,1.471229307,16.1421814,0.510905034,0.89843852,39.84060669,42.80318453,4.99081633,4.632586909,33.99881312,257.0917648,1.06960044 -2016-01-26 11:30:00-07:00,78,969.1016458,3.216223014,15.56474304,0.511764908,0.895266394,37.5763855,43.02998202,5.070439916,4.71250813,34.19248561,321.207612,1.062183214 -2016-01-26 11:40:00-07:00,78,984.0401724,2.314265622,15.75379944,0.512683518,0.896001535,39.8258667,42.82641556,5.159710789,4.785545544,33.9279814,259.0715545,1.055930891 -2016-01-26 11:50:00-07:00,78,998.6700025,2.27805573,16.03964233,0.512984671,0.892168544,40.92881775,42.70342155,5.226481851,4.841456621,33.77313345,227.7611736,1.035844097 -2016-01-26 12:00:00-07:00,78,1009.877341,0.853702719,16.13128662,0.51359921,0.886740809,41.62097168,42.61719933,5.285078086,4.894073926,33.65156054,257.6905084,1.041174819 -2016-01-26 12:10:00-07:00,78,1017.589242,3.262165692,16.44403076,0.514105918,0.88010677,43.04243469,42.4634818,5.332421256,4.93086387,33.46746965,205.8807495,1.045472024 -2016-01-26 12:20:00-07:00,78,1023.53711,2.374909041,16.62347412,0.514370431,0.883760394,44.0569458,42.27680023,5.355384136,4.952586803,33.26020712,250.6001616,1.021661282 -2016-01-26 12:30:00-07:00,78,1026.415587,0.870606015,16.97019958,0.514954525,0.906067171,42.64125061,42.4040031,5.368770078,4.965982452,33.37857165,265.6540531,1.041993248 -2016-01-26 12:40:00-07:00,78,1027.951635,1.795874946,17.45854187,0.515078616,0.904041651,44.75102234,42.1646632,5.372309817,4.967214632,33.13084081,326.5088919,1.049415663 -2016-01-26 12:50:00-07:00,78,1027.873214,1.897133715,17.16822815,0.515548749,0.907229168,44.08578491,42.16804414,5.374051843,4.966549197,33.13752895,315.340138,1.045823019 -2016-01-26 13:00:00-07:00,78,1022.574354,1.366645927,17.71104431,0.515974689,0.903363809,44.21331787,42.13209756,5.342528453,4.940040894,33.11927452,329.5847048,1.048639853 -2016-01-26 13:10:00-07:00,78,1015.830343,3.414574745,18.02252197,0.515817814,0.904341523,43.16035461,42.32391597,5.321339783,4.917547455,33.32720144,228.6968851,1.056174111 -2016-01-26 13:20:00-07:00,78,1011.932781,3.491960946,17.92254456,0.515408422,0.902809562,39.37147522,42.79103059,5.283454898,4.896424984,33.83247273,271.6647023,1.047228033 -2016-01-26 13:30:00-07:00,78,999.3117599,2.765364354,18.11096191,0.515070114,0.900313349,39.30482483,42.72939776,5.220125496,4.841925897,33.80060105,284.7079089,1.04989949 -2016-01-26 13:40:00-07:00,78,986.9773159,4.003462675,18.64352417,0.514496019,0.898920057,39.77716064,42.63285696,5.158909379,4.789488967,33.71337008,399.9254302,1.057417932 -2016-01-26 13:50:00-07:00,78,966.7793281,5.263109962,18.61148071,0.514355881,0.895679496,38.985672,42.71819111,5.05932487,4.693711508,33.88787062,278.159932,1.042858888 -2016-01-26 14:00:00-07:00,78,954.1827267,1.430693795,18.68966675,0.513497899,0.89455273,41.38639832,42.38288726,4.988799042,4.625554899,33.55837107,330.6979975,1.078548664 -2016-01-26 14:10:00-07:00,78,934.8078407,2.864540569,18.8588562,0.512874482,0.892806921,41.52548218,42.27771659,4.886954319,4.527403933,33.50575,360.2748414,1.087624005 -2016-01-26 14:20:00-07:00,78,911.8316398,3.7804766,18.8319397,0.512049419,0.889955972,39.97006226,42.46665678,4.772465541,4.419507263,33.81102072,304.6103487,1.078035925 -2016-01-26 14:30:00-07:00,78,888.2996565,5.129286381,18.9908905,0.510596069,0.892839523,38.85557556,42.57955167,4.654643886,4.308083603,33.99817064,244.2817712,1.086187066 -2016-01-26 14:40:00-07:00,78,863.5743749,1.675950098,19.22544861,0.509621336,0.890524471,41.29347229,42.30046712,4.526945926,4.193077875,33.7665931,366.635177,1.102555521 -2016-01-26 14:50:00-07:00,78,829.0086592,0.779801155,18.90052124,0.508482706,0.886039212,40.19885254,42.29759713,4.348506237,4.030778452,33.86807994,340.9583709,1.146635909 -2016-01-26 15:00:00-07:00,78,796.1356952,2.054309277,19.11329651,0.506920234,0.878195257,38.67036438,42.41272239,4.18191321,3.880030246,34.07242339,340.8715384,1.173677917 -2016-01-26 15:10:00-07:00,78,767.9465447,3.387537659,19.43373108,0.50487961,0.876784041,38.28262329,42.42909619,4.033510141,3.745647268,34.16498144,357.2189985,1.242885445 -2016-01-26 15:20:00-07:00,78,733.407779,2.11943862,18.95628357,0.502637411,0.872984611,36.4433136,42.60346475,3.856087693,3.584474349,34.44885627,396.4683641,1.269012518 -2016-01-26 15:30:00-07:00,78,695.9438221,1.479240521,19.05433655,0.4999721,0.876319236,36.53688049,42.46456064,3.660500228,3.402207511,34.46078589,470.455575,1.259606262 -2016-01-26 15:40:00-07:00,78,653.4165987,1.734671127,20.33096313,0.49640071,0.871706329,38.29736328,42.13097294,3.444051166,3.197692751,34.23575015,483.4685221,1.312761595 -2016-01-26 15:50:00-07:00,78,609.9141613,1.71444322,20.01373291,0.491962853,0.858749184,36.81437683,42.27056401,3.219809483,2.989914732,34.50589639,439.7797136,1.360559931 -2016-01-26 16:00:00-07:00,78,568.5699126,0.500017558,20.21047974,0.486797011,0.853972831,34.209198,42.49663384,2.999263818,2.788047619,34.84982018,519.8280878,1.488722433 -2016-01-26 16:10:00-07:00,78,524.4403751,0.562543203,21.38777161,0.481083706,0.845724139,35.33779907,42.18125778,2.767495862,2.572645544,34.65055387,496.4633357,1.583931977 -2016-01-26 16:20:00-07:00,78,479.7378031,0.781083028,20.03936768,0.474298838,0.837578486,33.36901855,42.30428569,2.534235887,2.351400039,34.98826351,523.0605724,1.684404678 -2016-01-26 16:30:00-07:00,78,429.339206,0.549725876,21.63322449,0.465958447,0.816728728,32.15197754,42.23115882,2.267617982,2.105032667,35.03611004,608.5513413,1.836710552 -2016-01-26 16:40:00-07:00,78,370.7711198,0.818374185,21.32368469,0.456051782,0.790276452,31.30923462,42.18191901,1.956985046,1.81560762,35.15428319,660.5594336,1.914339427 -2016-01-26 16:50:00-07:00,78,305.4096408,0.874451073,21.53453064,0.446201874,0.771149479,28.68611145,42.16882586,1.609061489,1.488576409,35.40672959,759.7175657,2.435840511 -2016-01-26 17:00:00-07:00,78,280.0941278,1.428691149,21.17308044,0.422114235,0.756427069,27.4005127,41.69485644,1.161249702,0.5355422,26.10387002,59.49574134,8.943940996 -2016-01-26 17:10:00-07:00,78,229.8256157,1.683280631,19.70930481,0.397640252,0.670621075,23.45846558,41.76282553,0.711983362,0.209714203,39.08170998,58.9644509,14.68643914 -2016-01-26 17:20:00-07:00,78,172.6462956,1.579658166,19.65675354,0.365865319,0.636626294,21.40122986,41.21018232,0.529158062,0.4446949,37.05372,493.2273595,5.892224729 -2016-01-26 17:30:00-07:00,78,117.9388936,1.213316191,19.32221985,0.341359839,0.49248775,19.03511047,41.50438216,0.581969433,0.51708543,35.63190758,605.6675899,6.452198899 -2016-01-26 17:40:00-07:00,78,67.05283209,0.789374686,18.86975098,0.275045318,0.430205001,15.5365448,37.82157066,0.086909285,0.073512226,30.91155516,3512.058186,48.14892659 -2016-01-26 17:50:00-07:00,78,10.61600297,1.234344919,18.0199585,0.513020139,-0.060860409,13.26463318,36.39362434,0.046133839,0.03770362,28.74545001,6913.935642,117.0919692 -2016-01-26 18:00:00-07:00,78,3.702710775,1.38739449,17.55018616,0.431763266,0.15352507,12.13861084,33.05673137,0.018910427,0.01490116,24.11611002,17922.37706,366.4066582 -2016-02-24 07:00:00-07:00,78,5.177433453,1.721973591,12.28152466,0.483678046,-0.024971679,8.341400146,35.0835656,0.024553511,0.019202563,26.63313999,15540.21025,246.595412 -2016-02-24 07:10:00-07:00,78,11.5175071,3.115484342,12.08157349,0.549381363,-0.010471237,8.395874023,37.93258873,0.055854206,0.045948241,30.50876515,5633.87217,86.03751699 -2016-02-24 07:20:00-07:00,78,27.80082781,2.043854878,12.13925171,0.471357972,0.209839663,9.02394104,39.1002497,0.089052995,0.07538541,32.24543001,4142.495758,49.02888991 -2016-02-24 07:30:00-07:00,78,25.65740691,2.676482373,12.22064209,0.652858914,0.073466684,9.463577271,39.88422917,0.126003083,0.109773152,33.21069152,4635.170025,33.24994705 -2016-02-24 07:40:00-07:00,78,34.32688304,2.235597828,12.16809082,0.660894462,0.058616436,9.891693115,41.85119317,0.255931768,0.2094555,38.11349999,549.6237664,10.00175183 -2016-02-24 07:50:00-07:00,78,42.1342725,3.183176941,13.02366638,0.667544737,0.375429188,11.19844055,42.58306795,0.34618495,0.2551464,39.80466003,240.9285914,11.22086714 -2016-02-24 08:00:00-07:00,78,57.4646918,2.348512751,13.49598694,0.622028921,0.763690629,14.85401367,43.08002637,0.64803157,0.3540377,41.42246,138.9849535,6.935867773 -2016-02-24 08:10:00-07:00,78,277.8477468,3.746670149,14.38616943,0.466296766,0.74516808,16.34341431,43.52560564,1.263328196,1.167620999,37.41584001,595.9489746,2.992501697 -2016-02-24 08:20:00-07:00,78,329.7967823,2.821321171,14.82901978,0.476283764,0.802842995,18.98191833,43.72808836,1.729664969,1.618431961,36.87171285,1599.986518,2.073783027 -2016-02-24 08:30:00-07:00,78,379.8570463,4.704783568,15.27186584,0.484329597,0.81097259,20.97120056,43.71605018,1.994054575,1.867783812,36.73815781,1210.715235,2.002367042 -2016-02-24 08:40:00-07:00,78,429.6551805,4.085254683,15.64164734,0.490092392,0.831379691,22.87013245,43.67311704,2.255281431,2.11180413,36.46003184,1002.370457,1.812645813 -2016-02-24 08:50:00-07:00,78,479.7450812,3.76613677,16.00695801,0.494830876,0.840170358,24.12882996,43.69556578,2.512065164,2.357828009,36.35269927,851.2675288,1.664522155 -2016-02-24 09:00:00-07:00,78,532.0591515,3.573632883,16.42416382,0.498675366,0.846927111,25.98223877,43.64714454,2.785732663,2.608089809,36.18224146,872.4572448,1.457234642 -2016-02-24 09:10:00-07:00,78,576.7007111,3.259602017,16.87149902,0.502309364,0.852554716,28.20417786,43.47015718,3.024042137,2.833519725,35.77431516,800.738612,1.335221797 -2016-02-24 09:20:00-07:00,78,622.9198615,5.24464428,16.70614624,0.50561303,0.862670612,28.5688324,43.56816149,3.251036461,3.046950563,35.7744005,683.1208503,1.323711634 -2016-02-24 09:30:00-07:00,78,663.6294192,5.758348913,16.87085632,0.508453293,0.872705099,29.26483154,43.59209999,3.45462896,3.23712494,35.69154541,639.6117048,1.27708077 -2016-02-24 09:40:00-07:00,78,700.8459222,5.666903537,17.32524109,0.510646637,0.870732087,30.37226868,43.52486175,3.634374949,3.40785836,35.53606481,727.842233,1.296560913 -2016-02-24 09:50:00-07:00,78,742.6578622,5.35263225,17.7610321,0.513163147,0.879114335,31.55789185,43.45028327,3.840667777,3.596090019,35.35455778,1188.483892,1.206681908 -2016-02-24 10:00:00-07:00,78,780.1462756,6.979463333,17.47776794,0.51524733,0.887880614,31.95715332,43.51334168,4.030492211,3.781668089,35.22426131,739.5281759,1.174184001 -2016-02-24 10:10:00-07:00,78,814.562538,9.018419674,17.75975037,0.516654274,0.885632082,32.43013,43.50877653,4.20668595,3.93747685,35.14330558,658.2310965,1.15099663 -2016-02-24 10:20:00-07:00,78,845.2160894,4.546327111,18.23272705,0.517580351,0.885223667,32.66020203,43.59102646,4.471096422,4.166482881,35.1148898,297.0495614,1.080950935 -2016-02-24 10:30:00-07:00,78,878.728437,7.803954408,18.38973999,0.51874366,0.88650428,34.01116943,43.40419129,4.641026986,4.322034356,34.83500104,375.1046574,1.071396917 -2016-02-24 10:40:00-07:00,78,905.8421368,6.628422313,18.59994507,0.520097289,0.892378298,35.39611816,43.32097944,4.781546766,4.450103914,34.66499149,324.1838238,1.085502839 -2016-02-24 10:50:00-07:00,78,939.942397,5.168900513,19.00883484,0.521015181,0.896509092,37.25016785,43.12769845,4.937104225,4.584668516,34.4039653,312.9416453,1.073458811 -2016-02-24 11:00:00-07:00,78,965.1502756,5.217326889,18.81015015,0.522217919,0.898066416,37.7673645,43.13064399,5.064310829,4.710463721,34.26810309,294.2813327,1.015221757 -2016-02-24 11:10:00-07:00,78,988.4618653,4.561747718,19.28120422,0.523166272,0.902844961,39.76626587,42.85783637,5.195540143,4.819881515,33.92735372,291.7776558,1.050775612 -2016-02-24 11:20:00-07:00,78,1006.496769,3.541748961,19.04600525,0.523988576,0.904498115,39.62719727,42.84142142,5.286343529,4.907857543,33.85928289,319.2157573,1.044200197 -2016-02-24 11:30:00-07:00,78,1025.435644,3.204166065,19.66444397,0.524070848,0.903580289,42.5028125,42.57085501,5.382985115,4.986670638,33.53496364,298.3720561,1.023561038 -2016-02-24 11:40:00-07:00,78,1040.910612,6.134985541,19.47923279,0.525038715,0.901877261,41.64276123,42.62675784,5.456736421,5.056389374,33.55774408,347.2269516,1.033348633 -2016-02-24 11:50:00-07:00,78,1052.718367,3.421984836,19.83042908,0.525885365,0.90455181,43.16355896,42.43173303,5.517638572,5.112743065,33.31705464,467.6205747,1.028934631 -2016-02-24 12:00:00-07:00,78,1064.151372,3.049193546,20.48220825,0.526427656,0.90592271,46.9223175,41.99285647,5.587390997,5.154995987,32.83962022,275.2988101,1.029912393 -2016-02-24 12:10:00-07:00,78,1072.036897,4.320216822,20.06820679,0.526994686,0.906437325,45.94369507,42.0723156,5.619341019,5.19213685,32.88817417,328.694575,1.029591833 -2016-02-24 12:20:00-07:00,78,1080.826659,2.513058521,21.07695007,0.527378368,0.907882416,47.60612488,41.94542386,5.67139434,5.22825736,32.74812844,235.4279313,1.007428533 -2016-02-24 12:30:00-07:00,78,1082.548789,4.574685604,20.60333252,0.527647829,0.90642208,44.96379089,42.24996836,5.677857513,5.24503636,33.05663818,311.7698361,1.004178394 -2016-02-24 12:40:00-07:00,78,1084.650631,4.873935892,21.31407166,0.527953767,0.908575659,46.38845825,42.12430603,5.693980038,5.252523329,32.92275793,236.9647519,1.022171283 -2016-02-24 12:50:00-07:00,78,1086.539356,5.918047777,20.96478882,0.527926325,0.910979957,45.20732117,42.27198522,5.694487083,5.253116935,33.07888656,245.0978795,0.982410609 -2016-02-24 13:00:00-07:00,78,1081.319046,2.910243035,21.98122253,0.528315991,0.907812931,47.2587738,41.9887014,5.674627825,5.225362155,32.78645906,247.8530202,1.024850046 -2016-02-24 13:10:00-07:00,78,1077.700856,3.539826502,22.32795715,0.527556388,0.909217929,46.96333313,41.97452965,5.64681696,5.222196528,32.7142762,429.9695494,1.027823835 -2016-02-24 13:20:00-07:00,78,1068.960099,4.973232108,22.37089539,0.527321466,0.90449019,46.25131226,42.06743615,5.593933519,5.167318644,32.90986549,318.0848331,1.031753961 -2016-02-24 13:30:00-07:00,78,1055.851549,4.030659576,22.08570862,0.527155978,0.90495767,46.26925659,41.8870342,5.53098412,5.099721361,32.76591007,289.2436166,1.020063785 -2016-02-24 13:40:00-07:00,78,1044.48494,4.930734183,21.99148621,0.526200169,0.906479594,44.10244751,42.18121866,5.463787735,5.061830961,33.06773082,455.6151105,1.022993004 -2016-02-24 13:50:00-07:00,78,1030.817847,4.437137176,22.89833069,0.525645375,0.906807654,46.4083252,42.02845365,5.408589845,4.992158567,32.96526761,272.7317243,1.003664185 -2016-02-24 14:00:00-07:00,78,1009.492763,5.148753282,23.32772827,0.525241407,0.903450413,48.49438477,41.53421124,5.294378457,4.891380624,32.50430781,470.3707677,1.067262667 -2016-02-24 14:10:00-07:00,78,991.1516192,2.071132245,22.06584167,0.524555495,0.902790868,47.27735901,41.66231375,5.203058743,4.805829308,32.69941898,432.2689679,1.040735135 -2016-02-24 14:20:00-07:00,78,970.8213198,4.184029824,22.61442566,0.523404602,0.898529485,45.14900208,41.90351262,5.100330973,4.723631861,32.99045516,445.7816937,1.072444713 -2016-02-24 14:30:00-07:00,78,947.1911701,1.254893015,23.084198,0.522568969,0.898779945,46.52304077,41.75043728,4.981886317,4.607959767,32.89871574,410.5660271,1.087540353 -2016-02-24 14:40:00-07:00,78,919.4210831,5.855081663,23.17456055,0.521694546,0.896833506,43.32698059,42.05068359,4.827417587,4.483472893,33.27693337,573.2017002,1.09807174 -2016-02-24 14:50:00-07:00,78,892.1211533,4.343888992,22.33308411,0.520642055,0.89246791,42.61882019,42.0663909,4.694493143,4.357651055,33.35975836,492.3366067,1.115198788 -2016-02-24 15:00:00-07:00,78,858.9860595,1.969312797,23.24697876,0.519544166,0.889523672,43.54745483,41.91414396,4.53067377,4.19782161,33.37184963,536.795452,1.168699758 -2016-02-24 15:10:00-07:00,78,828.1439134,3.825738737,23.04253662,0.51823814,0.891533629,41.56265259,42.092424,4.35860714,4.046243353,33.62570323,1068.812887,1.188827119 -2016-02-24 15:20:00-07:00,78,792.5860629,2.345068208,23.00343933,0.51643075,0.88926891,42.46884705,41.91151592,4.1781461,3.87780996,33.53353104,691.9933174,1.149700605 -2016-02-24 15:30:00-07:00,78,755.9844227,1.531752725,23.27133179,0.514337091,0.879663253,42.07983398,41.88835498,3.993987682,3.712509319,33.6033129,795.6635598,1.206230085 -2016-02-24 15:40:00-07:00,78,717.6021052,2.34883287,23.30145264,0.51230011,0.876306037,41.29154968,41.85533361,3.795955731,3.528275299,33.66804645,679.5775495,1.293048263 -2016-02-24 15:50:00-07:00,78,675.9319907,2.985666683,23.47384644,0.510311865,0.874227311,40.32574463,41.88593654,3.577985506,3.328776383,33.80433992,656.8669436,1.340886887 -2016-02-24 16:00:00-07:00,78,633.7328138,1.149267764,23.52832031,0.507367399,0.864590441,39.23817444,41.98041443,3.355812445,3.121108056,34.10799603,739.4417974,1.38477593 -2016-02-24 16:10:00-07:00,78,589.5593595,1.139855026,25.39520264,0.504341049,0.865634728,41.23451233,41.5964493,3.129370783,2.907446529,33.82777965,735.0932929,1.461024557 -2016-02-24 16:20:00-07:00,78,543.4014975,1.570325475,23.8961792,0.500637182,0.854241131,37.58535767,41.42127095,2.000431358,1.035346,38.97150998,220.4721527,1.898801078 -2016-02-24 16:30:00-07:00,78,493.3777618,1.750372387,23.9666748,0.496909492,0.84797912,35.86459351,41.92019618,2.405743486,1.932032001,36.97831998,154.3408064,1.723578209 -2016-02-24 16:40:00-07:00,78,446.2473081,1.684041568,24.11857605,0.49200494,0.834320198,34.3681488,41.99972564,2.367453751,2.203580047,34.71317719,876.1598954,1.769187702 -2016-02-24 16:50:00-07:00,78,397.4191709,0.934373089,24.13972473,0.485658767,0.822428536,33.22160339,41.93777492,2.109884204,1.96471935,34.78196393,803.9582338,1.931511344 -2016-02-24 17:00:00-07:00,78,347.2094905,1.417235602,23.94552612,0.478092715,0.809968232,31.2316864,41.96114483,1.842231101,1.715357217,34.92979388,744.8628109,2.156183404 -2016-02-24 17:10:00-07:00,78,295.8410436,1.512085428,24.04358704,0.469345772,0.777333299,30.23576355,41.79798296,1.564642939,1.455315238,34.89561146,674.7773685,2.28580931 -2016-02-24 17:20:00-07:00,78,245.0433953,1.069278214,23.74301147,0.457886521,0.763923875,28.76686096,41.6376357,1.292343789,1.196250067,34.98115846,689.806312,2.94979502 -2016-02-24 17:30:00-07:00,78,194.6055831,1.568282596,24.23457336,0.442222635,0.72627801,27.34924316,41.3818701,1.017970446,0.940414545,34.84747419,795.5293282,3.327405663 -2016-02-24 17:40:00-07:00,78,144.9838337,1.423764336,23.68533325,0.422854249,0.688555742,25.42660522,40.74362206,0.485503749,0.3252111,38.46275998,344.8233177,6.796455967 -2016-02-24 17:50:00-07:00,78,100.0532036,1.27383898,23.46743774,0.397718122,0.626754318,23.17199707,40.18048904,0.243706998,0.148643301,37.79282999,162.2175071,11.48899906 -2016-02-24 18:00:00-07:00,78,39.64045578,0.999983554,23.38796997,0.397588231,0.542882348,20.58282471,37.94774664,0.10735681,0.093660449,31.94474121,3423.534757,39.21315601 -2016-02-24 18:10:00-07:00,78,25.22870065,1.191085358,22.57789612,0.364085613,0.09247119,18.42434692,36.83590638,0.068607309,0.057938887,29.86177693,4961.941119,69.39887323 -2016-02-24 18:20:00-07:00,78,7.056378301,0.56422601,21.93763733,0.484663269,-0.1,17.06889343,34.57939339,0.032430266,0.02643184,25.97795,8758.363923,176.3191947 -2016-02-24 18:30:00-07:00,78,2.144727457,0.421589906,21.48326111,0.401295536,-0.1,15.68009949,28.03092546,0.009886181,0.007547792,19.51531999,50096.55696,947.3359976 -2016-03-23 06:20:00-07:00,78,2.294134714,3.353210343,15.15843201,0.493979001,0.350387206,12.43789673,30.76976158,0.012190494,0.00891145,22.73151999,97916.23386,745.1368014 -2016-03-23 06:30:00-07:00,78,6.909372528,3.756363192,15.16035461,0.584140272,0.359975244,12.2244873,35.74918498,0.034639905,0.027591179,27.75883232,8429.55737,174.0457861 -2016-03-23 06:40:00-07:00,78,14.88830705,3.352769874,15.08921814,0.6176721,0.5624809,12.49749756,38.2226149,0.080311391,0.065744792,31.3440801,3798.801827,56.40143721 -2016-03-23 06:50:00-07:00,78,30.54699355,3.676894368,15.15971375,0.495124394,0.671268506,12.83909607,39.9183407,0.164914813,0.14018709,33.72423997,3309.132376,24.98775274 -2016-03-23 07:00:00-07:00,78,54.054548,3.139957893,15.05653381,0.48065403,0.692260741,12.79551697,40.4671763,0.163183124,0.145283819,35.77213076,2766.095002,18.09065983 -2016-03-23 07:10:00-07:00,78,79.1644258,1.448237817,14.81491394,0.483883608,0.739706482,12.31100464,41.94638463,0.373381875,0.3225181,37.05788,650.6757756,10.17737859 -2016-03-23 07:20:00-07:00,78,106.4618437,3.019793322,15.0110321,0.494531286,0.797736034,14.31503296,42.44809048,0.588956461,0.549842458,36.56939339,1880.859002,6.203761176 -2016-03-23 07:30:00-07:00,78,164.0746546,2.52771847,15.15779114,0.48391654,0.809297353,15.59614563,42.81885471,0.792271075,0.723883709,37.24299867,516.0901631,4.585789352 -2016-03-23 07:40:00-07:00,78,209.1545908,3.326534168,15.27891541,0.488718396,0.825700394,16.81317139,43.13094755,1.041630848,0.978840789,36.93667597,1615.346562,3.553673012 -2016-03-23 07:50:00-07:00,78,257.8651105,3.623260944,15.54551697,0.493820415,0.838492485,18.43652344,43.39367469,1.387858125,1.298315114,36.68985381,1454.636853,2.302554239 -2016-03-23 08:00:00-07:00,78,306.7498251,3.135031289,15.69804382,0.497964236,0.862073537,20.17330933,43.45706894,1.65103706,1.542636632,36.63863194,823.9652022,2.36789351 -2016-03-23 08:10:00-07:00,78,355.5582812,2.156890011,16.00631714,0.502520426,0.865605564,21.86073303,43.47648812,1.91294386,1.786342188,36.5441397,832.4852761,2.084210814 -2016-03-23 08:20:00-07:00,78,404.0066526,1.753857024,16.48440552,0.50586522,0.87899067,23.19570923,43.51948487,2.172265564,2.034341358,36.34598663,902.7653861,1.873275174 -2016-03-23 08:30:00-07:00,78,452.0018338,2.220096546,16.56387329,0.50839684,0.885359258,25.40673828,43.41051697,2.427276303,2.271936577,36.11560235,875.7459494,1.713284863 -2016-03-23 08:40:00-07:00,78,501.165507,1.944999271,16.73562622,0.510553707,0.885239293,27.00123596,43.35528014,2.680949501,2.5066541,35.94125163,788.7915705,1.594883425 -2016-03-23 08:50:00-07:00,78,546.7746989,2.632502387,16.91700745,0.5131323,0.898893035,27.77670288,43.38960913,2.916761564,2.726155017,35.85359226,845.8850696,1.504007286 -2016-03-23 09:00:00-07:00,78,591.3868886,4.226408028,17.42457581,0.515025523,0.895491146,27.82861328,43.52636044,3.14995479,2.949264766,35.76882896,674.5517322,1.355690858 -2016-03-23 09:10:00-07:00,78,637.0376787,5.129366498,17.58927917,0.516975775,0.898582773,28.91682434,43.47936033,3.374078838,3.157272635,35.6182822,663.3392451,1.359566779 -2016-03-23 09:20:00-07:00,78,678.9925297,4.353778266,17.87997077,0.518397204,0.90407798,30.05762437,43.45018134,3.584201971,3.361208372,35.44961025,155.9562049,1.242986272 -2016-03-23 09:30:00-07:00,78,722.7342233,4.556060457,17.79115295,0.519965464,0.913132391,31.52713013,43.36074477,3.819413708,3.568250622,35.27351178,715.3999921,1.214620688 -2016-03-23 09:40:00-07:00,78,761.758565,1.91619996,18.48971558,0.521103279,0.908514075,33.79263306,43.10955833,4.018899123,3.757043373,34.828154,530.3495294,1.230942905 -2016-03-23 09:50:00-07:00,78,795.0406738,4.71824253,18.88769531,0.523271297,0.907227068,34.284823,43.10053172,4.181736983,3.905778415,34.74374433,749.9754759,1.160094596 -2016-03-23 10:00:00-07:00,78,828.0140861,3.684184528,18.80822754,0.525108131,0.911166339,35.35061646,43.05429109,4.348355722,4.059135247,34.61391395,460.7073845,1.175194184 -2016-03-23 10:10:00-07:00,78,866.0877807,5.741685899,18.80630493,0.525238436,0.913605017,34.76676941,43.17451421,4.539684688,4.233957528,34.63835299,734.391971,1.115127501 -2016-03-23 10:20:00-07:00,78,896.278269,4.536393229,18.92103271,0.526072921,0.913480223,34.79240417,43.21588002,4.692893291,4.372251636,34.60824811,509.7383962,1.093673569 -2016-03-23 10:30:00-07:00,78,930.8513639,5.585552137,19.80992126,0.525573781,0.919628495,37.55844116,42.90382833,4.870571938,4.531356307,34.20373683,569.360075,1.080590696 -2016-03-23 10:40:00-07:00,78,952.5831545,4.593231613,18.94218445,0.526397848,0.913740335,36.79386902,43.09880871,4.987311613,4.634056282,34.33891159,510.8835716,1.068801521 -2016-03-23 10:50:00-07:00,78,987.2443314,3.904326553,19.86567688,0.525606446,0.9176834,36.81117249,43.14071332,5.159771315,4.801821897,34.22562128,427.6585471,1.050381749 -2016-03-23 11:00:00-07:00,78,1016.214976,5.736158453,19.98553467,0.525506608,0.925853705,37.23222351,43.14604817,5.303016871,4.942699347,34.15741403,563.5132392,1.03540107 -2016-03-23 11:10:00-07:00,78,1034.352767,4.609933952,20.42453003,0.526289641,0.924078798,39.16319275,42.87976143,5.410183097,5.021402535,33.83979676,365.2119268,1.036507535 -2016-03-23 11:20:00-07:00,78,1057.759938,4.3906334,20.02398682,0.526772071,0.908026447,38.37811401,43.03730627,5.525247301,5.133125786,33.94922832,519.8675267,1.022265191 -2016-03-23 11:30:00-07:00,78,1034.707957,3.643168244,20.57705688,0.528566201,0.868936463,40.65708923,42.65145689,5.423504525,5.035738724,33.58246436,1197.134621,1.032900186 -2016-03-23 11:40:00-07:00,78,1029.928544,3.736496405,20.72253418,0.52860781,0.838718492,43.0712738,42.34028633,5.344945336,4.994854654,33.30587075,576.4314714,1.041254301 -2016-03-23 11:50:00-07:00,78,1069.143863,4.611175731,20.49758911,0.528115921,0.850927676,40.56993103,42.80572055,5.502803889,5.163731007,33.69986078,4278.496193,1.017760495 -2016-03-23 12:00:00-07:00,78,1095.517759,2.257187306,20.86865234,0.529414629,0.897237373,43.87878418,42.37007713,5.699945425,5.278662744,33.16105592,993.9393615,0.99732164 -2016-03-23 12:10:00-07:00,78,1116.105284,4.509396446,20.67831421,0.529951855,0.920527948,39.90213013,42.88308434,5.872695497,5.4403415,33.61469028,410.8269623,0.977638431 -2016-03-23 12:20:00-07:00,78,1115.987694,4.728376321,21.1179657,0.530699573,0.929736442,41.06724548,42.76915185,5.84649431,5.419361215,33.49621053,538.2049248,0.985455723 -2016-03-23 12:30:00-07:00,78,1112.616894,3.960643513,20.76226807,0.53167819,0.924872469,43.60961914,42.38482256,5.838306574,5.403658247,33.1049037,414.89409,0.985894725 -2016-03-23 12:40:00-07:00,78,1118.140988,3.205167563,21.51274109,0.531438591,0.929783592,42.0920105,42.6416558,5.879471524,5.426997925,33.36419267,253.0831928,1.005299723 -2016-03-23 12:50:00-07:00,78,1113.594298,3.061771011,22.06968689,0.531471352,0.929936542,44.93431091,42.22199837,5.837950582,5.411526873,32.88476965,851.7433911,0.989294526 -2016-03-23 13:00:00-07:00,78,1106.624916,4.66601091,21.31791687,0.531949338,0.930599548,42.22979736,42.63705131,5.802654015,5.376447601,33.3758187,450.3242978,0.991176423 -2016-03-23 13:10:00-07:00,78,1098.462291,3.483068375,21.48005676,0.531524599,0.929873779,42.54576111,42.52128926,5.760543382,5.334066455,33.2865341,502.5198365,1.013904266 -2016-03-23 13:20:00-07:00,78,1087.266932,2.25530494,20.92633057,0.531643125,0.92873897,45.06761169,42.09887967,5.704283633,5.270663802,32.87461195,533.8822286,0.990380885 -2016-03-23 13:30:00-07:00,78,1077.967844,3.647494004,21.2922821,0.53088174,0.927443315,41.04289246,42.73936349,5.647601522,5.239291065,33.56688875,386.1097998,1.01452185 -2016-03-23 13:40:00-07:00,78,1061.975995,4.00009755,21.80433655,0.530385036,0.927759325,43.87365723,42.31089076,5.579063614,5.159774358,33.16603424,497.9653909,1.026334835 -2016-03-23 13:50:00-07:00,78,1045.452572,3.822774477,22.23887634,0.529628634,0.92708913,44.42288208,42.2459066,5.495496088,5.087429038,33.13379211,545.6525893,1.039702717 -2016-03-23 14:00:00-07:00,78,1027.944302,3.427632423,21.50889587,0.529175786,0.928125436,42.64573669,42.41829155,5.39811087,4.998318275,33.36841979,474.3954065,1.021341976 -2016-03-23 14:10:00-07:00,78,1006.898507,2.15368533,21.96904236,0.528986644,0.924279806,43.7102356,42.2378972,5.285192512,4.898786587,33.22861115,493.6368066,1.051669541 -2016-03-23 14:20:00-07:00,78,984.0989489,4.436376169,22.51957703,0.528085991,0.926109252,45.29255676,41.98292477,5.169668372,4.795766061,33.02048979,941.506799,1.067751208 -2016-03-23 14:30:00-07:00,78,957.4555627,3.085603695,22.62339783,0.527694505,0.921056374,43.99221802,42.14021153,5.030493323,4.667761352,33.2540677,592.1251444,1.050648349 -2016-03-23 14:40:00-07:00,78,928.1958841,2.025629897,22.5221405,0.526900528,0.920339979,43.89031982,42.10314541,4.876146004,4.527324578,33.30283455,594.3123017,1.089173835 -2016-03-23 14:50:00-07:00,78,897.1405857,3.390141078,22.62211609,0.526361993,0.917307958,41.15632629,42.45301538,4.725534842,4.390741223,33.72600081,517.2403908,1.103966234 -2016-03-23 15:00:00-07:00,78,865.389593,2.512497492,22.53175354,0.525155252,0.917266242,40.83909607,42.38291161,4.559551646,4.231540003,33.81932308,477.710506,1.099911837 -2016-03-23 15:10:00-07:00,78,830.6377026,4.393958012,23.45077515,0.524771742,0.912776824,40.21743774,42.37761336,4.384814878,4.072243397,33.90732427,533.2826455,1.118542261 -2016-03-23 15:20:00-07:00,78,797.4977138,2.653250741,21.9927655,0.523571174,0.910649461,38.91261292,42.46432532,4.212588257,3.920534669,34.0725276,620.2470382,1.207581419 -2016-03-23 15:30:00-07:00,78,759.7765956,1.277243569,22.43305969,0.522779645,0.908723975,38.59089661,42.43091648,4.025521663,3.744698689,34.13955245,589.8136015,1.235088464 -2016-03-23 15:40:00-07:00,78,717.6119135,2.618723307,22.33372498,0.521321765,0.90189927,38.22494507,42.41113336,3.79895329,3.542648793,34.22830802,767.0611053,1.232347443 -2016-03-23 15:50:00-07:00,78,678.7173071,2.088315776,23.30209351,0.520263484,0.899094329,38.16085815,42.32221882,3.602351187,3.347559888,34.33321283,686.4435839,1.270176895 -2016-03-23 16:00:00-07:00,78,633.4290767,2.558961245,23.11367798,0.518553969,0.891417468,36.68171692,42.43324547,3.363804547,3.130302612,34.55793694,711.391151,1.379552066 -2016-03-23 16:10:00-07:00,78,592.0164454,2.92622495,23.50909424,0.517688043,0.892221532,35.81332397,42.43413199,3.144407152,2.933888189,34.66736641,906.5092988,1.373402515 -2016-03-23 16:20:00-07:00,78,546.3190519,6.418854827,23.66162109,0.515171745,0.880062637,35.13847351,42.34693401,2.910804234,2.712663026,34.68447608,970.8435188,1.447264291 -2016-03-23 16:30:00-07:00,78,498.921601,3.404320464,23.87182617,0.512521539,0.885914183,34.55143738,42.28905079,2.658216992,2.481512305,34.74259535,804.7496867,1.53172041 -2016-03-23 16:40:00-07:00,78,453.3663458,5.39292776,23.19314575,0.5105552,0.876740661,32.92552185,42.31975538,2.415906514,2.250846331,35.00645986,1076.290402,1.74682616 -2016-03-23 16:50:00-07:00,78,403.3133691,3.294129591,23.39758301,0.508050663,0.86145841,31.79885864,42.27150326,2.148852503,2.001342303,35.08993956,797.7819785,1.776867577 -2016-03-23 17:00:00-07:00,78,355.6537792,4.337079953,23.56292725,0.504835361,0.858109051,30.44020081,42.20289814,1.897544023,1.768856161,35.12604901,866.4628781,2.115707504 -2016-03-23 17:10:00-07:00,78,289.0379896,2.689900612,23.76800537,0.504817934,0.83823755,29.63717651,41.91371111,1.536813182,1.430102594,35.0228965,711.9494182,2.307633953 -2016-03-23 17:20:00-07:00,78,258.5094994,2.954743956,23.52511597,0.495919918,0.834605066,28.34773254,41.86451766,1.371158406,1.272233747,35.16041816,659.7156014,2.796901879 -2016-03-23 17:30:00-07:00,78,210.9037819,4.550171751,23.4039917,0.489617026,0.804980373,26.51994324,41.71715716,1.108383233,1.026461437,35.14369911,846.3946337,3.394316365 -2016-03-23 17:40:00-07:00,78,161.9408673,2.253742553,23.48153687,0.486916311,0.781572233,25.34906006,41.35352358,0.847357514,0.781323821,34.91911736,1309.802713,4.384522232 -2016-03-23 17:50:00-07:00,78,117.9682543,1.992985108,23.26556396,0.48130764,0.753572837,23.66226196,40.9224957,0.600336932,0.550832069,34.66719775,2043.577865,6.779076951 -2016-03-23 18:00:00-07:00,78,78.31435574,4.127032044,22.78553772,0.484468382,0.67292116,22.32090759,40.23212034,0.393243987,0.352428996,34.1014121,2218.530365,9.377480556 -2016-03-23 18:10:00-07:00,78,34.851115,6.285792324,22.34910583,0.5635917,0.41772141,21.09873962,38.508699,0.165030447,0.144077305,31.98099294,3255.322846,23.60125297 -2016-03-23 18:20:00-07:00,78,21.06151898,1.9553329,22.2625885,0.585977962,0.218500583,20.1842041,37.5099901,0.103160228,0.087287225,30.77030415,2460.321107,44.96304629 -2016-03-23 18:30:00-07:00,78,14.39099748,2.63206129,22.16645813,0.498016361,0.246328491,19.39848328,36.42583301,0.064691845,0.05341154,29.43899001,3295.284276,76.65279429 -2016-03-23 18:40:00-07:00,78,7.38960488,3.120771856,21.81651306,0.54312342,-0.1,18.81976318,34.45170772,0.033967462,0.027006808,26.53465152,7256.529944,166.760552 -2016-03-23 18:50:00-07:00,78,2.000196777,1.523100785,21.5851593,0.485308847,1.1,18.42819214,28.30634888,0.010190445,0.007392438,20.59537,25969.80832,893.1703434 -2016-04-18 05:50:00-07:00,78,3.349959488,1.483446141,16.52990723,0.528856668,0.216160412,12.19821167,32.97184492,0.017462518,0.01329588,24.39824,30836.39197,436.6733343 -2016-04-18 06:00:00-07:00,78,8.754199744,1.263705189,16.10437012,0.590191231,-0.087133407,11.83291626,36.67485608,0.044023389,0.03504536,29.51353998,7964.34157,103.748579 -2016-04-18 06:10:00-07:00,78,15.15284752,1.360077169,16.51196289,0.584405601,0.424961658,12.09246826,38.21443843,0.076158987,0.064304462,30.99053752,4818.453329,57.77666904 -2016-04-18 06:20:00-07:00,78,21.40448952,1.388756409,16.74523926,0.603121618,0.576930481,12.67565918,38.99775936,0.109207344,0.094138891,32.16889505,4438.401165,38.45315654 -2016-04-18 06:30:00-07:00,78,27.44809493,1.683921148,16.87213989,0.636191243,0.663142003,13.38768005,39.54898114,0.14605315,0.125319905,33.0381152,2491.660799,28.15895197 -2016-04-18 06:40:00-07:00,78,41.83786867,0.840484458,17.30665588,0.602643095,0.703686178,14.08752441,40.33171823,0.219702696,0.189663121,34.28970747,2548.271762,18.18075625 -2016-04-18 06:50:00-07:00,78,59.41467824,1.342252842,17.98214722,0.573683409,0.701791745,14.9155426,40.94062097,0.310187246,0.278111853,34.67365431,2366.402292,12.4764568 -2016-04-18 07:00:00-07:00,78,87.0771217,2.053948436,18.22439575,0.546157805,0.761232622,16.19793701,41.61381751,0.452053616,0.405013516,36.15584665,623.7092554,7.171297076 -2016-04-18 07:10:00-07:00,78,123.1372044,1.670543142,18.68774414,0.535192567,0.794608353,18.05200195,42.04344094,0.678057576,0.625613009,35.66020787,2180.033445,5.555456167 -2016-04-18 07:20:00-07:00,78,161.1741038,1.013080976,19.40104675,0.529766912,0.819185863,19.93874084,42.30048891,0.890452805,0.825633164,35.82176392,2047.132801,3.758812524 -2016-04-18 07:30:00-07:00,78,203.7456948,1.177346231,19.5785675,0.527028343,0.827162144,22.08634949,42.42605448,1.121477215,1.044228237,35.84667981,1701.384176,3.373838539 -2016-04-18 07:40:00-07:00,78,246.7826584,3.473775708,19.6388092,0.526578216,0.847181514,23.73724365,42.54013543,1.361984576,1.268473434,35.8484447,1247.151578,2.558572711 -2016-04-18 07:50:00-07:00,78,292.7201267,1.964265704,19.74583435,0.527073992,0.852334915,25.29779053,42.61239187,1.602883586,1.493489032,35.81949628,1158.522394,2.4235424 -2016-04-18 08:00:00-07:00,78,339.9828737,2.491228273,20.44631958,0.528373417,0.869840757,27.12877808,42.64036415,1.853825154,1.733088351,35.60750304,1010.034236,2.142053028 -2016-04-18 08:10:00-07:00,78,385.3323558,2.428222345,21.34996033,0.528666996,0.864513848,29.60192871,42.52331892,2.100253641,1.962339364,35.37233142,1001.778943,1.93249366 -2016-04-18 08:20:00-07:00,78,432.1467797,4.659682293,21.67808533,0.529493414,0.878289057,30.85612488,42.56645525,2.345126577,2.190319917,35.28959972,994.7475741,1.773767776 -2016-04-18 08:30:00-07:00,78,476.4524813,2.885248759,21.99020691,0.530242122,0.878275729,31.84371948,42.59884302,2.581680771,2.408958577,35.22181597,842.4170157,1.642564113 -2016-04-18 08:40:00-07:00,78,521.3807099,3.878811202,22.28053284,0.530547443,0.882624609,33.5010376,42.51652229,2.816157709,2.631667825,34.91823146,806.4090373,1.551253555 -2016-04-18 08:50:00-07:00,78,564.9296496,3.049754505,22.53752136,0.531544841,0.889040864,34.29251343,42.55083409,3.043091582,2.843375017,34.83251152,902.9249727,1.46838484 -2016-04-18 09:00:00-07:00,78,607.8931793,2.99415811,22.74388123,0.53201033,0.894313196,36.44459534,42.42705429,3.264012517,3.044104785,34.60307693,763.011027,1.341233349 -2016-04-18 09:10:00-07:00,78,647.774722,3.579200422,23.38604736,0.532257134,0.901322571,38.51976013,42.23305611,3.469197235,3.229834832,34.30090895,689.0394162,1.356729585 -2016-04-18 09:20:00-07:00,78,687.9063502,4.278399297,23.39630127,0.533520425,0.908243657,40.08862305,42.13155916,3.675833943,3.426989406,33.99546991,559.9209415,1.31411278 -2016-04-18 09:30:00-07:00,78,726.4307509,2.737205909,22.92076111,0.533890261,0.904228531,41.09864807,42.01899189,3.86750398,3.599230696,33.80364731,564.2668056,1.224332412 -2016-04-18 09:40:00-07:00,78,761.2196297,1.730985884,23.4559021,0.53448319,0.907183114,42.98539734,41.86887092,4.049545927,3.765000792,33.56335288,505.7048771,1.199177643 -2016-04-18 09:50:00-07:00,78,795.6457281,2.758314195,24.07371521,0.534911524,0.908304045,45.13298035,41.63899268,4.226304751,3.922016716,33.24092482,595.3199009,1.179655548 -2016-04-18 10:00:00-07:00,78,830.7895665,1.503754025,25.4727478,0.534871871,0.910624443,47.92849731,41.31748807,4.410346328,4.088815611,32.73078663,602.4426244,1.166852349 -2016-04-18 10:10:00-07:00,78,857.8494216,1.8132185,23.97052002,0.535310184,0.913261164,46.53713989,41.50468077,4.544678507,4.217896343,32.84798292,493.7504361,1.176694528 -2016-04-18 10:20:00-07:00,78,893.3656003,1.283451975,24.50309753,0.535043643,0.916123034,47.14790344,41.49522918,4.704658473,4.362797043,32.76774446,635.9128065,1.120918565 -2016-04-18 10:30:00-07:00,78,920.9472583,1.361198598,24.42811584,0.535319761,0.915255466,49.03977966,41.30523756,4.847864487,4.486550421,32.49817717,468.4744146,1.112470731 -2016-04-18 10:40:00-07:00,78,942.6052653,1.830562265,25.30163574,0.536008898,0.917384071,51.862854,41.01888008,4.965006176,4.58089493,32.15591145,397.8212683,1.106118025 -2016-04-18 10:50:00-07:00,78,966.3089594,1.326030646,24.73765564,0.535581786,0.919526286,50.09402466,41.18101323,5.078886326,4.689718107,32.25781541,443.3707894,1.071982459 -2016-04-18 11:00:00-07:00,78,989.9733555,0.673735715,26.02133179,0.536066218,0.920172342,52.98054504,40.82855784,5.198998088,4.789704033,31.83498247,406.9849605,1.084265501 -2016-04-18 11:10:00-07:00,78,1006.055826,2.060357309,26.50071716,0.536477017,0.91970782,57.01937866,40.32467916,5.278782209,4.862212584,31.22631543,492.4357914,1.086737954 -2016-04-18 11:20:00-07:00,78,1023.507675,2.73187865,25.77331543,0.536488298,0.922814936,54.42572021,40.6882719,5.371146798,4.951045677,31.54976405,407.870599,1.074024123 -2016-04-18 11:30:00-07:00,78,1037.43941,0.813927935,26.23283386,0.536588573,0.921175489,54.07965088,40.78721638,5.433415954,5.008685775,31.6204049,489.6391792,1.066249881 -2016-04-18 11:40:00-07:00,78,1049.325503,1.418597102,25.90020752,0.536610271,0.922717648,56.48614502,40.53011842,5.497688553,5.058755254,31.32672209,422.7596601,1.06962726 -2016-04-18 11:50:00-07:00,78,1056.579144,2.84042737,25.77651978,0.5379794,0.923681101,55.1749115,40.74764138,5.53254451,5.096621937,31.5248882,476.945948,1.042807776 -2016-04-18 12:00:00-07:00,78,1065.055314,2.842630413,26.34883118,0.537476917,0.925262546,56.55151367,40.66080253,5.575508894,5.131519956,31.41746447,492.2818006,1.04298513 -2016-04-18 12:10:00-07:00,78,1069.303042,1.711919708,25.9899292,0.538156773,0.923814253,54.86152649,40.85026198,5.59137724,5.151237653,31.60131267,447.9533873,1.035957568 -2016-04-18 12:20:00-07:00,78,1071.63273,2.139986716,27.06340027,0.53825614,0.926352188,56.16506958,40.6423508,5.608181667,5.155272834,31.39276648,353.2109219,1.023579258 -2016-04-18 12:30:00-07:00,78,1074.099689,3.49144008,27.37423706,0.538127335,0.927151552,54.82371521,40.83722882,5.620110889,5.170070801,31.58090699,378.1148624,1.053269363 -2016-04-18 12:40:00-07:00,78,1069.469665,4.41282379,26.49623108,0.538152934,0.924853721,51.50267029,41.24019151,5.591968395,5.163420944,31.99525269,489.0875165,1.032258844 -2016-04-18 12:50:00-07:00,78,1068.653887,0.527775767,26.77885437,0.537804154,0.926618338,53.24330139,40.99944746,5.595059862,5.155610179,31.75030306,420.9332166,1.029491146 -2016-04-18 13:00:00-07:00,78,1060.829568,3.189426139,26.35395813,0.537733751,0.925270677,51.8878479,41.23266911,5.551257927,5.122195926,32.00876456,465.6782746,1.04764414 -2016-04-18 13:10:00-07:00,78,1055.609045,1.452804417,26.69810486,0.536996433,0.925380912,53.59129333,40.89869214,5.526652733,5.09175444,31.68339043,533.6157002,1.05503587 -2016-04-18 13:20:00-07:00,78,1040.499093,2.892418639,27.50561523,0.536974172,0.921770984,55.18772888,40.62713945,5.454937971,5.020244786,31.4377266,318.737561,1.073693053 -2016-04-18 13:30:00-07:00,78,1030.489571,2.350755678,27.24221802,0.536561204,0.920941427,53.054245,41.05664159,5.390699665,4.974389666,31.91284342,545.7379929,1.051775279 -2016-04-18 13:40:00-07:00,78,1014.282141,5.092355367,27.20952759,0.536569106,0.920019968,50.82783508,41.26180434,5.309004886,4.896518185,32.21239569,529.0100564,1.074741362 -2016-04-18 13:50:00-07:00,78,996.6587276,3.557010312,27.77029419,0.536594549,0.92109355,50.64260864,41.27357126,5.215700023,4.814261154,32.28437346,503.4863228,1.075752763 -2016-04-18 14:00:00-07:00,78,976.4997341,1.64923334,28.62138367,0.536112151,0.919750579,54.80833191,40.58853173,5.124872944,4.711680337,31.6355317,401.7655157,1.100912428 -2016-04-18 14:10:00-07:00,78,954.8417178,3.351007788,28.1272583,0.535632698,0.917444649,52.68444824,40.84796995,5.010638928,4.618594017,31.95095505,429.4704829,1.068974099 -2016-04-18 14:20:00-07:00,78,927.1278035,2.62581286,27.78503418,0.53468693,0.916855523,50.62338257,41.1519775,4.862555339,4.49408354,32.3352313,521.301744,1.114810174 -2016-04-18 14:30:00-07:00,78,906.9665741,3.865753524,28.12405396,0.534558627,0.913471083,50.43945313,41.11016077,4.758016022,4.394106373,32.35059592,444.6835606,1.108312089 -2016-04-18 14:40:00-07:00,78,876.5555634,3.612646451,27.74594116,0.534451259,0.911959577,47.74969482,41.34343855,4.605785712,4.263105562,32.6624336,793.0624509,1.139217615 -2016-04-18 14:50:00-07:00,78,849.3291503,1.505716648,28.06829834,0.533601427,0.913083928,49.73577881,41.09183505,4.458644138,4.12819714,32.48163115,622.6078611,1.161130205 -2016-04-18 15:00:00-07:00,78,818.1048003,2.1766771,27.87988281,0.533434832,0.909135354,49.85562134,41.00058826,4.301592023,3.983255487,32.46593111,601.7630901,1.222136732 -2016-04-18 15:10:00-07:00,78,788.8378344,2.676001601,28.95848083,0.532760336,0.908615099,49.47813416,40.94305095,4.140198483,3.828381822,32.57433845,759.470397,1.249008211 -2016-04-18 15:20:00-07:00,78,753.9682988,4.272391149,28.55793762,0.532164296,0.90526133,44.97789001,41.46043105,3.959452823,3.671862004,33.18501929,625.7024515,1.271670101 -2016-04-18 15:30:00-07:00,78,712.5237122,3.273461005,28.49513245,0.531487883,0.898928308,44.24920654,41.44894104,3.757371669,3.487924378,33.27792484,843.1828055,1.26013412 -2016-04-18 15:40:00-07:00,78,667.0738371,2.020583292,29.46286011,0.530444454,0.891507036,44.26074219,41.36745166,3.529499311,3.274422582,33.30367677,609.4823241,1.357751411 -2016-04-18 15:50:00-07:00,78,629.0343061,2.205997417,28.96360779,0.530115925,0.888548406,43.88967896,41.3370658,3.330754012,3.096697848,33.37578856,601.4214064,1.352969187 -2016-04-18 16:00:00-07:00,78,587.175643,3.315919326,28.44258118,0.529440172,0.880136213,41.52932739,41.57131638,3.105954956,2.886265778,33.817012,879.5461872,1.472527415 -2016-04-18 16:10:00-07:00,78,544.6018529,1.71228006,28.8886261,0.528037008,0.874916679,40.70515442,41.47452775,2.885395301,2.681301743,33.82836292,866.9183194,1.541562773 -2016-04-18 16:20:00-07:00,78,502.6059556,2.190055456,28.81108093,0.527339974,0.870109053,41.02366638,41.31974134,2.666568449,2.479788068,33.7765832,877.4059936,1.631073968 -2016-04-18 16:30:00-07:00,78,458.8879367,2.301287781,29.26803589,0.526632579,0.873549639,40.00915527,41.2888451,2.436355507,2.264734092,33.87980574,956.792055,1.736755096 -2016-04-18 16:40:00-07:00,78,413.3963531,1.933062952,28.90272522,0.525651152,0.857799355,38.37490845,41.28152063,2.192017275,2.034641616,34.0886183,1090.577477,1.883558352 -2016-04-18 16:50:00-07:00,78,367.6450503,2.025309289,28.7348175,0.524691619,0.859546768,37.57061768,41.16443541,1.950426181,1.808594691,34.07952263,919.4161591,2.074176515 -2016-04-18 17:00:00-07:00,78,322.4721739,3.762852461,29.41607666,0.524297996,0.847938399,36.76951599,41.05769205,1.707971141,1.583004383,34.09351025,711.6416162,2.151417017 -2016-04-18 17:10:00-07:00,78,275.520525,2.445205268,28.61433411,0.524139626,0.839146579,34.65718079,41.06983189,1.456290062,1.349403976,34.22599539,665.5629595,2.650509658 -2016-04-18 17:20:00-07:00,78,230.2371619,1.289701033,29.2718811,0.523866062,0.831356131,34.09384155,40.80806274,1.213806911,1.119401077,34.19049277,662.749279,2.847528343 -2016-04-18 17:30:00-07:00,78,187.4720905,3.862188701,29.14498108,0.525104374,0.814611677,32.76850891,40.55422559,0.983069038,0.904010608,34.05630057,851.3021205,3.805337821 -2016-04-18 17:40:00-07:00,78,147.1640406,0.643093851,29.18984436,0.52853962,0.795802636,31.19706726,40.25442313,0.75657037,0.5671825,36.30924002,1019.974032,4.391788109 -2016-04-18 17:50:00-07:00,78,109.4578466,1.009075614,29.17061951,0.534919702,0.785879952,29.79675293,39.77010384,0.521045771,0.4091192,35.84603998,178.4801445,6.731107442 -2016-04-18 18:00:00-07:00,78,78.26044657,2.547265347,29.00462341,0.544647979,0.737591553,28.48936462,39.12187062,0.330904859,0.2513146,35.75034997,362.0266371,11.91296732 -2016-04-18 18:10:00-07:00,78,51.58766287,0.958606568,28.98283386,0.57140403,0.730104101,27.10185242,38.3777771,0.206854648,0.179150353,33.49612403,1120.182806,18.80755866 -2016-04-18 18:20:00-07:00,78,35.44392265,2.0542293,29.13407898,0.603566474,0.656724217,26.13221741,37.70249047,0.163425556,0.143174599,31.31230404,2062.156121,26.51270816 -2016-04-18 18:30:00-07:00,78,26.17920058,1.741600377,28.6643219,0.599376531,0.642565644,24.95426941,37.26076411,0.126935438,0.109586469,30.64133971,3188.892079,35.49038681 -2016-04-18 18:40:00-07:00,78,19.66762786,1.480642604,28.34388733,0.573404254,0.504007158,24.60307312,36.62107891,0.09659526,0.082060186,29.84923139,4235.294385,48.13172611 -2016-04-18 18:50:00-07:00,78,11.78207517,1.182993888,28.24839783,0.555100024,0.059590689,23.99615479,35.50746689,0.061645981,0.05149556,28.04816999,4999.240138,71.44789704 -2016-04-18 19:00:00-07:00,78,5.704112894,1.585145728,27.76773071,0.531150554,0.000789363,23.27581787,33.16126959,0.029240153,0.02272064,25.49861003,8895.688158,218.9625716 -2016-04-18 19:10:00-07:00,78,1.262830837,1.457410483,27.34539795,0.477447649,0.915569079,22.8400116,27.11512498,0.008567831,0.006753765,17.44098006,-99.00000038,1165.006272 -2016-05-12 05:20:00-07:00,78,1.287324132,1.626602481,20.34378052,0.429934297,0.359136395,17.52391052,27.65495699,0.009232154,0.006909118,20.08666009,-99.0000004,863.4870762 -2016-05-12 05:30:00-07:00,78,4.271043539,1.35166565,20.71868896,0.557782632,0.201080606,17.52326965,33.30804567,0.023273703,0.01821538,24.83323999,18054.0098,285.075547 -2016-05-12 05:40:00-07:00,78,11.40234629,0.541715082,20.72702026,0.577135628,-0.031802226,17.14002991,36.4280473,0.055482323,0.045492665,29.16824088,6203.474845,84.0362474 -2016-05-12 05:50:00-07:00,78,21.4779878,1.180350864,21.17756653,0.561643143,0.37374564,16.81445313,38.36685842,0.113110353,0.097458782,31.54970206,4341.204317,36.71059562 -2016-05-12 06:00:00-07:00,78,34.23868142,0.532662766,22.06071472,0.564090645,0.500182408,17.19578552,39.3903195,0.182308154,0.160404106,32.89871259,3800.13963,21.69809695 -2016-05-12 06:10:00-07:00,78,43.24402484,1.042121267,22.83744812,0.565682986,0.474631418,18.04046631,39.75933239,0.225523486,0.20081951,33.33315915,3095.040313,15.0923138 -2016-05-12 06:20:00-07:00,78,48.28795052,1.069598752,23.02138672,0.579894602,0.607545859,19.09407043,39.70887146,0.24326108,0.217299346,33.35901685,2995.898739,15.73878852 -2016-05-12 06:30:00-07:00,78,57.70724166,1.799159185,22.93293762,0.587944348,0.585644481,20.18484497,39.96398361,0.297474216,0.267900286,33.66719659,3222.107518,12.81718586 -2016-05-12 06:40:00-07:00,78,79.47549616,1.863928526,23.95257568,0.567937487,0.582575501,21.59861755,40.39359457,0.378466893,0.345541011,34.7302784,1129.284471,8.28635181 -2016-05-12 06:50:00-07:00,78,102.4467376,1.632210463,24.52809143,0.549648854,0.602102376,23.14764404,40.74697034,0.516386607,0.4302299,36.12362997,415.2243904,6.972885533 -2016-05-12 07:00:00-07:00,78,130.8610848,1.219044034,24.0397406,0.540526663,0.672996221,23.95898438,41.21029335,0.715785873,0.65678848,34.80283896,1313.709583,4.238080245 -2016-05-12 07:10:00-07:00,78,162.3426143,1.500069271,24.75816345,0.534550502,0.718771845,25.48812866,41.42944367,0.888990982,0.822302821,34.96971017,1226.916477,3.764734161 -2016-05-12 07:20:00-07:00,78,166.4608186,2.120600352,24.91453552,0.547714611,0.707978227,26.98329163,41.25051482,0.904727538,0.836268113,34.77028301,1426.305859,3.710179085 -2016-05-12 07:30:00-07:00,78,242.4440513,0.909459069,26.23667908,0.528684582,0.751258456,29.33532715,41.66615053,1.326175059,1.235179744,35.00742713,1224.794327,2.601282755 -2016-05-12 07:40:00-07:00,78,257.2135447,1.146944789,26.22962952,0.53681929,0.695992503,30.05438232,41.65587054,1.399517397,1.295886051,34.95500365,924.6011599,2.756846995 -2016-05-12 07:50:00-07:00,78,297.0242574,1.607256071,26.7346344,0.532950711,0.726874387,31.9680481,41.67068072,1.612949031,1.496506255,34.84626027,855.4061299,2.504136848 -2016-05-12 08:00:00-07:00,78,379.8080137,1.125355102,27.43511963,0.529742609,0.782268605,35.23780823,41.68790224,2.053635519,1.917532984,34.56932883,822.0503888,1.959080507 -2016-05-12 08:10:00-07:00,78,419.4228169,1.091068508,27.99267578,0.529988891,0.783109393,37.40463257,41.58710843,2.271676255,2.112922543,34.34984928,652.2392768,1.720576234 -2016-05-12 08:20:00-07:00,78,449.6280636,1.290021152,27.85488892,0.530383684,0.770182342,38.9356842,41.5588713,2.433691113,2.257389448,34.22977352,506.6888157,1.770052946 -2016-05-12 08:30:00-07:00,78,516.6184479,1.736113024,28.46308899,0.529492276,0.802773574,41.04353333,41.50986389,2.787029575,2.593099904,33.91853834,487.1365898,1.498298469 -2016-05-12 08:40:00-07:00,78,524.51875,1.824714351,29.89801025,0.533154537,0.793136646,44.01464844,41.08822837,2.807425242,2.613261556,33.47261438,539.3690687,1.505575209 -2016-05-12 08:50:00-07:00,78,537.6738574,1.164328577,29.75894165,0.53315183,0.727280554,45.38996887,40.91761942,2.877656429,2.662882555,33.27286572,397.7377887,1.5618013 -2016-05-12 09:00:00-07:00,78,640.0777456,2.798289726,30.03259277,0.529072306,0.763432399,44.59913635,41.3954852,3.412590766,3.176995573,33.39464898,399.6456749,1.329055161 -2016-05-12 09:10:00-07:00,78,691.201232,2.308097449,30.98814392,0.531191219,0.80492799,46.07122803,41.30626635,3.691770819,3.423247892,33.17162199,366.1335803,1.232620889 -2016-05-12 09:20:00-07:00,78,747.638328,3.262165692,31.48547363,0.53149757,0.81025975,50.00302124,40.88889526,3.985879871,3.687448239,32.59531603,342.1662988,1.279245782 -2016-05-12 09:30:00-07:00,78,745.2718083,0.977792814,31.10670471,0.531603261,0.757096931,51.61802673,40.75554001,3.987442298,3.672658729,32.45383686,288.4960064,1.276517804 -2016-05-12 09:40:00-07:00,78,812.8722891,3.148930301,30.91123962,0.531496204,0.773832518,50.51635742,41.10254007,4.330354165,4.004525491,32.56239984,277.0910154,1.14976199 -2016-05-12 09:50:00-07:00,78,581.3306522,1.432576231,30.75358582,0.539367749,0.644369246,48.5123291,40.63527526,3.169517945,2.870109886,32.80925585,267.4672211,1.45711069 -2016-05-12 10:00:00-07:00,78,670.8366126,2.12432527,31.29833984,0.53782762,0.678361496,51.27836609,40.59369597,3.524639088,3.284639008,32.52082316,525.3713111,1.331084696 -2016-05-12 10:10:00-07:00,78,782.5297437,3.074788805,30.83818054,0.536789154,0.758327394,49.9934082,40.97420696,4.193682657,3.853403148,32.56287211,283.5726772,1.218326067 -2016-05-12 10:20:00-07:00,78,896.109291,1.849147808,31.48867798,0.534742082,0.818453249,54.284729,40.60308745,4.765061809,4.377943796,31.8329383,283.0459799,1.145704799 -2016-05-12 10:30:00-07:00,78,838.4229818,0.9739875,31.70977783,0.537321298,0.809977164,56.14968872,40.24522754,4.439078019,4.104859004,31.62991449,462.4705023,1.181428853 -2016-05-12 10:40:00-07:00,78,945.28522,1.256895731,31.59121704,0.536390559,0.87151125,58.17614746,40.14119798,4.991833765,4.595460113,31.18541323,307.0282216,1.142776946 -2016-05-12 10:50:00-07:00,78,964.4373202,3.306386308,32.48396301,0.536640466,0.877075181,58.66705322,40.09364634,5.086080809,4.682981806,31.08374552,428.88493,1.11078551 -2016-05-12 11:00:00-07:00,78,989.1722537,4.043597323,32.20388794,0.536628204,0.87090805,60.30065918,40.07373724,5.215730271,4.796299791,31.00351186,410.1281975,1.096371434 -2016-05-12 11:10:00-07:00,78,980.5663294,1.731546563,32.54804993,0.536979343,0.849694285,60.55059814,40.03373116,5.192209357,4.757624074,30.98248518,284.3018094,1.108917881 -2016-05-12 11:20:00-07:00,78,744.4511173,1.995227826,32.64994812,0.542578526,0.714825626,59.68029785,39.60224745,4.024315919,3.659201531,31.21045058,261.6007867,1.295779577 -2016-05-12 11:30:00-07:00,78,906.116458,2.466754792,32.87617493,0.53821824,0.807788854,54.1725769,40.6315713,4.741469731,4.374106376,31.86369655,386.4983959,1.096160552 -2016-05-12 11:40:00-07:00,78,1015.119955,1.928055743,33.74713135,0.537569538,0.880856905,59.6232605,40.08806817,5.325100399,4.896171257,30.97139462,410.9996715,1.082710568 -2016-05-12 11:50:00-07:00,78,1016.802889,1.646990413,34.25790894,0.538336276,0.90235091,62.87890625,39.70072558,5.344723631,4.899854999,30.5694916,337.9783191,1.089100818 -2016-05-12 12:00:00-07:00,78,1028.453858,2.090959009,34.23675537,0.538250579,0.911575195,62.64819336,39.6877268,5.39444249,4.946421775,30.52395666,430.1975153,1.091135167 -2016-05-12 12:10:00-07:00,78,1028.649777,1.782616801,34.44697571,0.53839352,0.909352462,63.88381958,39.46379263,5.394333988,4.943621982,30.28329266,438.4019192,1.094450455 -2016-05-12 12:20:00-07:00,78,1031.562537,3.625223846,34.74049377,0.53860163,0.911977948,64.02609253,39.55271308,5.4109227,4.956596067,30.38155106,381.4173868,1.073417022 -2016-05-12 12:30:00-07:00,78,1026.9276,2.352198064,34.67448425,0.538898238,0.910190868,62.36621094,39.74247911,5.385711053,4.93863289,30.58607325,443.6648047,1.086029771 -2016-05-12 12:40:00-07:00,78,1026.300417,1.873340774,35.74346924,0.53881345,0.909436545,62.92504883,39.62269569,5.383491267,4.931815313,30.46558377,378.309285,1.071743471 -2016-05-12 12:50:00-07:00,78,1018.58871,1.495062271,35.28266907,0.539035633,0.909252484,62.56359863,39.60535338,5.339879543,4.899662832,30.46214383,512.3818176,1.092805214 -2016-05-12 13:00:00-07:00,78,1015.154257,1.768998094,35.93252563,0.538443331,0.91157333,64.38049316,39.35428814,5.323005893,4.87486177,30.21831709,431.0924254,1.101195209 -2016-05-12 13:10:00-07:00,78,1005.737339,2.762440047,36.39076233,0.538509608,0.911321383,64.61761475,39.23396244,5.273426648,4.824994872,30.13059381,588.8228355,1.072407039 -2016-05-12 13:20:00-07:00,78,996.1050614,1.716686427,36.12863159,0.538393784,0.909836107,63.15960693,39.46004911,5.224348577,4.791054569,30.38342543,520.8382594,1.109544479 -2016-05-12 13:30:00-07:00,78,984.5007236,4.340244262,36.50291443,0.537911941,0.913169169,65.10467529,39.12093994,5.162076789,4.726399687,30.07577476,478.3095817,1.132444438 -2016-05-12 13:40:00-07:00,78,967.6930697,0.782324667,37.35463501,0.537960741,0.912378466,66.06921387,39.03372057,5.077794455,4.649900736,30.0292467,490.1610851,1.113701779 -2016-05-12 13:50:00-07:00,78,952.3552804,1.47683722,37.23735046,0.537699187,0.911857164,66.01281738,38.98291992,4.999132963,4.578072421,30.02836928,544.845999,1.118745491 -2016-05-12 14:00:00-07:00,78,938.3868612,2.300727032,36.64582825,0.536977443,0.90910338,62.54501343,39.4092043,4.920972728,4.522086233,30.48600127,527.673024,1.110126591 -2016-05-12 14:10:00-07:00,78,914.3695297,2.638430209,36.93678284,0.536790663,0.906564984,61.09921265,39.58148476,4.801101118,4.415273868,30.72291372,541.3318672,1.122743702 -2016-05-12 14:20:00-07:00,78,891.9790701,1.130161565,37.61932373,0.536674102,0.906627329,61.93875122,39.41137863,4.687087288,4.301265586,30.67014373,445.3639345,1.193088286 -2016-05-12 14:30:00-07:00,78,862.9447997,1.096635908,37.0643158,0.536475284,0.902294958,61.44207764,39.48105321,4.537348705,4.170032462,30.81767815,495.8426251,1.213893907 -2016-05-12 14:40:00-07:00,78,837.6267197,1.065353109,36.92076111,0.536350814,0.902269542,59.84628296,39.52428589,4.405318529,4.053019301,30.92561225,662.9441373,1.229864551 -2016-05-12 14:50:00-07:00,78,808.0095696,2.453096341,37.25785828,0.536032897,0.898004425,60.44229126,39.56318503,4.25692961,3.920418475,31.03792187,735.9828286,1.25143784 -2016-05-12 15:00:00-07:00,78,776.6235749,1.944438312,36.63877869,0.535665586,0.895366556,58.1857605,39.75557672,4.093350449,3.777330654,31.3206094,633.5466388,1.275281342 -2016-05-12 15:10:00-07:00,78,744.1326309,1.604091693,37.40783691,0.535228775,0.898307241,59.59762573,39.44706929,3.932127862,3.621373711,31.10425151,692.2459879,1.306946308 -2016-05-12 15:20:00-07:00,78,712.4501863,0.780041507,38.90812683,0.534892638,0.893843169,59.55148315,39.32703288,3.765412736,3.470944237,31.05387506,656.9006124,1.344735179 -2016-05-12 15:30:00-07:00,78,678.8103945,1.741840448,37.99230957,0.534389831,0.89280807,56.8187561,39.59369559,3.582661145,3.302059792,31.50573612,656.0913882,1.376685355 -2016-05-12 15:40:00-07:00,78,639.8425934,1.331277299,37.4238562,0.534668526,0.891812099,54.47442627,39.76069375,3.380873454,3.121974855,31.77236995,614.8930611,1.368695448 -2016-05-12 15:50:00-07:00,78,599.8799615,3.083881284,36.90666199,0.533851665,0.886304323,52.92286682,40.0329147,3.179811804,2.943646356,32.13944141,875.7340451,1.473419917 -2016-05-12 16:00:00-07:00,78,558.736821,1.695136484,38.58256531,0.533522145,0.881281879,54.68399048,39.58716053,2.96649248,2.742995638,31.81063964,990.7694119,1.479460018 -2016-05-12 16:10:00-07:00,78,517.9094617,1.457570856,37.99807739,0.533270614,0.876190998,52.87672424,39.64284369,2.752193622,2.54246602,32.0604718,982.0371768,1.545482314 -2016-05-12 16:20:00-07:00,78,475.8744137,1.002466623,37.38155396,0.532905203,0.867469857,51.6436615,39.73463356,2.529381569,2.339729329,32.27360304,886.1498065,1.714753611 -2016-05-12 16:30:00-07:00,78,433.5871824,0.472139628,38.28070068,0.533084171,0.86611529,50.57595825,39.63181353,2.305996926,2.132327425,32.28449068,991.6861892,1.829952731 -2016-05-12 16:40:00-07:00,78,390.0847634,2.295119399,39.19523621,0.532772268,0.858589823,49.86843872,39.49410746,2.078942832,1.922343197,32.26005245,1047.82284,1.98631093 -2016-05-12 16:50:00-07:00,78,346.9155066,1.984173073,38.80302429,0.532986929,0.849617921,48.1925354,39.5482994,1.850961699,1.712430343,32.43455328,1047.184112,2.1762052 -2016-05-12 17:00:00-07:00,78,304.4541906,0.89956563,37.96218872,0.533214601,0.853995315,46.01034546,39.61584284,1.619930531,1.496337107,32.71113167,902.4551191,2.412828353 -2016-05-12 17:10:00-07:00,78,260.8269957,2.846876266,37.38347717,0.53422726,0.841676356,43.56411743,39.58883915,1.385896689,1.27817385,32.79942277,717.0273502,2.553057372 -2016-05-12 17:20:00-07:00,78,217.4788606,1.529669752,38.29544067,0.536483948,0.830059018,42.31311035,39.3631612,1.156231103,0.8467322,35.66252999,683.1146536,3.26855583 -2016-05-12 17:30:00-07:00,78,177.7783953,1.161444504,37.69494629,0.538776394,0.821800771,41.1146698,38.60978926,0.396078321,0.274928099,35.81912001,92.78648858,7.770309505 -2016-05-12 17:40:00-07:00,78,140.5620948,1.388996132,37.74108887,0.545927141,0.807525389,38.82289124,37.96790378,0.274685713,0.2520392,33.65856002,1916.485985,12.18012399 -2016-05-12 17:50:00-07:00,78,106.5892604,1.201019101,38.03588867,0.556414867,0.800148376,37.07585144,37.69644382,0.253996213,0.233136991,32.88889182,3640.137731,13.88983835 -2016-05-12 18:00:00-07:00,78,77.79988615,1.480121459,38.16278076,0.574212172,0.775557678,35.11924744,37.28689268,0.235813176,0.213364931,31.75044939,2789.390361,16.34248744 -2016-05-12 18:10:00-07:00,78,55.38005664,1.62592201,37.37899109,0.603879754,0.771648652,34.19317627,37.01357022,0.214002718,0.190844858,30.92380973,2431.563507,16.79987069 -2016-05-12 18:20:00-07:00,78,41.35280574,1.202621302,37.34822937,0.633572679,0.725243432,33.60870361,36.81653838,0.191854637,0.168183031,30.55760773,1287.762863,19.38513482 -2016-05-12 18:30:00-07:00,78,35.63257255,1.047127708,37.42962646,0.680852969,0.627329197,32.98640442,36.61569062,0.167830516,0.146608213,30.19879794,2205.096626,22.57091251 -2016-05-12 18:40:00-07:00,78,30.68169747,1.111856955,36.96369934,0.645443343,0.515502941,32.3865509,36.35895107,0.144102711,0.125189616,29.86806355,2638.770329,26.67710862 -2016-05-12 18:50:00-07:00,78,23.93513818,0.745233977,36.77079773,0.642108357,0.555088406,32.19491577,35.84797048,0.115527838,0.098831573,29.34693749,2892.215271,38.63359891 -2016-05-12 19:00:00-07:00,78,17.08563713,1.318139574,35.56594849,0.615818717,0.395643832,31.44061279,35.1664029,0.085531053,0.071960405,28.33823529,3417.605226,48.01883154 -2016-05-12 19:10:00-07:00,78,9.670398017,0.848055063,35.68643188,0.57760452,0.081245219,30.49211121,33.83881722,0.051208989,0.041965859,26.60922463,5611.818832,97.55812979 -2016-05-12 19:20:00-07:00,78,4.814864606,1.398810153,34.50657654,0.52409488,-0.1,29.4692688,31.09450128,0.02317459,0.01778388,23.31235001,13174.07855,284.190229 -2016-06-15 05:10:00-07:00,78,1.561709758,0.914866305,23.06881714,0.518004205,0.754182522,18.01867676,28.68993257,0.011079634,0.00832456,20.96455002,121808.0889,768.0751971 -2016-06-15 05:20:00-07:00,78,5.468979107,1.299754428,22.80989075,0.589726774,-0.1,18.09686279,34.06327397,0.02877231,0.022545216,26.04094105,11123.97924,217.3601565 -2016-06-15 05:30:00-07:00,78,11.36558795,0.439053881,22.85218811,0.636105279,0.101364417,18.15454102,36.18369383,0.054362436,0.044835094,28.74558262,6220.498483,85.64971662 -2016-06-15 05:40:00-07:00,78,17.09057811,1.603691317,23.01304932,0.670928196,-0.057506836,18.19812012,37.36713203,0.083793077,0.070750424,30.41615105,4326.615185,57.90655911 -2016-06-15 05:50:00-07:00,78,23.11446559,0.931489156,23.55395508,0.678233568,0.631899123,18.99281311,37.9706189,0.111905936,0.09651211,31.198415,4264.98725,36.24013901 -2016-06-15 06:00:00-07:00,78,27.44322755,0.626471071,24.17881775,0.678926341,0.642580594,19.4478302,38.3426145,0.13278396,0.115723391,31.68273124,4322.849022,29.79649241 -2016-06-15 06:10:00-07:00,78,31.05160878,1.283131298,24.85301208,0.680105179,0.694729814,20.01950073,38.4911989,0.14857693,0.130217134,31.93161311,4153.054378,26.26780192 -2016-06-15 06:20:00-07:00,78,34.83151668,0.579246171,25.57208252,0.666143049,0.763383418,20.35211182,38.65972956,0.163399987,0.143566504,32.18846812,4007.581301,23.74963648 -2016-06-15 06:30:00-07:00,78,37.66581043,0.984281804,26.1469574,0.666911047,0.772986998,20.81994629,38.80708582,0.179551056,0.158625771,32.35973651,4398.499468,21.41352951 -2016-06-15 06:40:00-07:00,78,48.41286448,0.557456366,27.63827515,0.649424663,0.789327209,21.14552307,39.08465196,0.197058328,0.176596283,33.07228655,2490.068038,18.41533175 -2016-06-15 06:50:00-07:00,78,65.40909464,1.328513576,26.28987122,0.607957079,0.816383656,22.17671204,39.31281484,0.210744102,0.190703758,34.04306389,3400.069462,12.02405571 -2016-06-15 07:00:00-07:00,78,90.96236365,1.031306097,26.68016052,0.580436585,0.831901324,24.20637512,40.0123217,0.258452236,0.2166016,36.52840997,595.9442437,11.81375018 -2016-06-15 07:10:00-07:00,78,121.3562695,1.217682045,26.40202332,0.56476932,0.847229624,26.01364136,40.72553904,0.674265446,0.618098451,34.36535784,1498.194905,5.558485141 -2016-06-15 07:20:00-07:00,78,157.9870404,1.634813882,26.8134613,0.554805093,0.853555427,27.58123779,41.01017975,0.860548595,0.794092421,34.55756957,1367.472845,4.352012368 -2016-06-15 07:30:00-07:00,78,195.2793143,1.71876905,26.78462219,0.548696356,0.866498785,29.04371643,41.22451748,1.056316914,0.979250313,34.68559943,1572.542787,3.226625395 -2016-06-15 07:40:00-07:00,78,234.5265616,2.210363341,26.74873352,0.545450023,0.876879815,30.55619812,41.36279369,1.263298175,1.171802941,34.73584297,1317.143081,3.020550689 -2016-06-15 07:50:00-07:00,78,276.7012821,1.912234481,27.51522827,0.543165904,0.870020933,32.34040833,41.43459208,1.48048241,1.378714846,34.57842383,1312.913296,2.228037764 -2016-06-15 08:00:00-07:00,78,318.0085701,2.470640293,27.6434021,0.542416235,0.886590872,33.80032349,41.49761204,1.692419652,1.576571158,34.54859771,1011.842422,2.329485177 -2016-06-15 08:10:00-07:00,78,360.3131271,2.405830929,27.90808105,0.541395191,0.887076538,34.85328674,41.61661356,1.907906242,1.778154521,34.55498531,944.7714495,2.102460557 -2016-06-15 08:20:00-07:00,78,401.1527606,2.9819813,28.01959229,0.540439053,0.891363879,36.7631073,41.56478172,2.120099655,1.973926434,34.40484353,983.3852155,1.935796879 -2016-06-15 08:30:00-07:00,78,441.9798622,2.790118279,28.3881073,0.540160316,0.898724991,38.35183716,41.51076698,2.331103172,2.170591661,34.24804307,865.2083576,1.798483716 -2016-06-15 08:40:00-07:00,78,482.6798876,2.009447794,29.25650024,0.539731858,0.904903911,40.37637329,41.39433278,2.539668175,2.368936857,33.91620243,927.2529535,1.6896004 -2016-06-15 08:50:00-07:00,78,524.4231599,3.688350054,29.66986084,0.539758087,0.907381998,41.32295227,41.38518718,2.752162978,2.561739291,33.79504032,759.6689937,1.523124417 -2016-06-15 09:00:00-07:00,78,562.8057718,3.177529774,29.92556763,0.539597591,0.912386122,42.51435364,41.37782014,2.950738445,2.746564248,33.70040037,878.9672121,1.450830067 -2016-06-15 09:10:00-07:00,78,599.2650897,1.936707823,30.55299377,0.53936138,0.91562274,44.72154236,41.15996785,3.136760377,2.91120485,33.37886134,685.8059649,1.403611346 -2016-06-15 09:20:00-07:00,78,636.0210782,2.087594862,30.15307617,0.539032079,0.919309914,45.53160095,41.14385354,3.321287458,3.091617006,33.17866065,777.9295305,1.355062041 -2016-06-15 09:30:00-07:00,78,673.6342032,1.115582013,31.14451599,0.538352772,0.919565524,46.68327332,41.08500557,3.514048623,3.266821315,33.03024852,745.8898036,1.316628117 -2016-06-15 09:40:00-07:00,78,707.4943887,2.398861656,32.24491272,0.538690201,0.92172827,49.75500488,40.80349706,3.68842195,3.421237979,32.64840423,597.791781,1.335734964 -2016-06-15 09:50:00-07:00,78,737.4399715,2.389568989,31.54827881,0.539116784,0.925541662,50.65863037,40.75158694,3.84165894,3.559905611,32.52492988,552.5527133,1.258984981 -2016-06-15 10:00:00-07:00,78,769.0782877,2.058114312,32.04174805,0.538597901,0.92549795,52.742771,40.55898553,4.003149047,3.702024307,32.24317291,526.9448808,1.235403024 -2016-06-15 10:10:00-07:00,78,797.5074945,0.98400143,33.30557251,0.538847634,0.927381931,55.12236023,40.26163936,4.148334844,3.836989935,31.80460959,568.7174428,1.256474107 -2016-06-15 10:20:00-07:00,78,824.496262,2.919776053,32.82618713,0.539040494,0.926792622,54.51416016,40.44311797,4.284717896,3.962459246,31.91540759,529.9106883,1.200030685 -2016-06-15 10:30:00-07:00,78,848.310047,2.270565871,33.06266785,0.539042456,0.92862988,57.09051514,40.08325116,4.412577895,4.071270789,31.48979441,369.2654301,1.187920138 -2016-06-15 10:40:00-07:00,78,872.5184299,2.680007243,33.36901855,0.539020628,0.930972008,57.693573,40.12468345,4.525474823,4.174688084,31.46849333,522.6645825,1.205473563 -2016-06-15 10:50:00-07:00,78,893.4121578,2.718699854,33.19725159,0.538634157,0.93235457,58.00375366,40.1711502,4.630538343,4.271554979,31.46227471,540.2567636,1.137844576 -2016-06-15 11:00:00-07:00,78,912.1158246,2.602741462,33.68881226,0.538442632,0.927148159,58.6427002,40.08351088,4.724923831,4.354275924,31.31796476,491.02932,1.147077244 -2016-06-15 11:10:00-07:00,78,927.2650101,1.501270607,33.22417053,0.53844112,0.927989399,57.62628174,40.17633353,4.80882932,4.428357198,31.37660778,571.3851021,1.131621715 -2016-06-15 11:20:00-07:00,78,944.8981579,2.524554092,34.3598175,0.537813705,0.927116523,61.20367432,39.74635465,4.895663543,4.50329942,30.83668901,489.3397715,1.165004442 -2016-06-15 11:30:00-07:00,78,957.0317606,1.310368991,34.67640686,0.538169555,0.928120184,60.93063354,39.73066423,4.956759846,4.559909753,30.78861026,352.5705125,1.127756438 -2016-06-15 11:40:00-07:00,78,969.0600017,3.792893621,33.44720459,0.538000819,0.927465794,56.86491699,40.34252301,5.009157069,4.615505987,31.43604576,577.836874,1.117183213 -2016-06-15 11:50:00-07:00,78,973.8320352,3.077752925,33.85800171,0.538723718,0.925972877,59.14837646,40.02301832,5.043430621,4.639040227,31.04591021,470.7182222,1.09805116 -2016-06-15 12:00:00-07:00,78,981.1127452,2.970765964,34.798172,0.5387541,0.927573505,59.31884766,39.97709465,5.077688222,4.671180311,30.97569362,421.7946648,1.141151488 -2016-06-15 12:10:00-07:00,78,989.8434822,1.641983832,35.34548462,0.538103208,0.927415022,59.87640381,39.92023825,5.112550309,4.704183486,30.90135253,930.7727412,1.109671708 -2016-06-15 12:20:00-07:00,78,995.7179808,5.424931752,35.58773804,0.537890194,0.927854795,58.03900146,40.16579731,5.142645491,4.735874507,31.13295563,549.4405895,1.105417369 -2016-06-15 12:30:00-07:00,78,997.173234,4.393277192,35.91586304,0.537226706,0.930022305,61.83685303,39.74179113,5.152083287,4.731057075,30.70285555,549.0131029,1.077470131 -2016-06-15 12:40:00-07:00,78,994.1525354,2.407233291,35.27241638,0.537160022,0.927952675,59.90524292,39.92825038,5.137774152,4.725231888,30.89304564,491.4708104,1.107732183 -2016-06-15 12:50:00-07:00,78,991.124706,3.992006779,35.65118408,0.536836075,0.928012643,61.01269531,39.74405086,5.123383793,4.704220307,30.71779858,527.7840055,1.110888445 -2016-06-15 13:00:00-07:00,78,986.842612,2.602541344,36.24847412,0.537067643,0.927689235,61.28314209,39.68818816,5.091150852,4.681527786,30.68001139,632.9059914,1.109338704 -2016-06-15 13:10:00-07:00,78,972.3181067,2.399622872,36.17990112,0.537343606,0.921895049,62.13101196,39.72500698,5.017907309,4.618987668,30.76674579,675.8252802,1.121439333 -2016-06-15 13:20:00-07:00,78,967.1516962,4.031901425,35.98764038,0.537274445,0.919459176,58.52670288,40.08916309,4.99202521,4.599001524,31.12480053,797.2112009,1.103608683 -2016-06-15 13:30:00-07:00,78,958.4157974,3.468328449,36.95152283,0.536784896,0.920080044,59.52264404,39.90426905,4.945143807,4.555535726,30.96461311,418.5854935,1.124051393 -2016-06-15 13:40:00-07:00,78,938.310897,2.902993038,36.37217712,0.537416064,0.920637393,62.30148315,39.50018828,4.848185551,4.457258392,30.6170323,524.1893561,1.144785171 -2016-06-15 13:50:00-07:00,78,924.8961349,3.213538988,36.67915344,0.537147559,0.919334654,62.18869019,39.59044852,4.779464042,4.385774088,30.78601643,450.6190991,1.155642151 -2016-06-15 14:00:00-07:00,78,908.0664628,1.543568484,36.5221405,0.536947799,0.916467134,58.83880615,39.9431186,4.690024037,4.315413761,31.20387735,582.1957007,1.18154612 -2016-06-15 14:10:00-07:00,78,887.6161905,2.747219349,37.12968445,0.536800965,0.919519537,59.52648926,39.77091526,4.594769047,4.218939846,31.08443022,362.9649677,1.202577044 -2016-06-15 14:20:00-07:00,78,867.9568896,1.251608497,36.55482483,0.536578258,0.91710636,59.67453003,39.6951013,4.484769517,4.125841906,31.0551452,661.6513296,1.213449468 -2016-06-15 14:30:00-07:00,78,845.6031607,3.862949638,36.9105072,0.536363492,0.917623017,59.73348999,39.57888355,4.372837537,4.020581199,31.00567096,420.7062729,1.201499543 -2016-06-15 14:40:00-07:00,78,821.4609245,4.976316649,36.83296204,0.536621867,0.917290674,57.90057373,39.83961161,4.243242335,3.911965524,31.32686275,504.5890156,1.213201097 -2016-06-15 14:50:00-07:00,78,796.6746581,1.541846422,37.3956543,0.536179318,0.916134707,57.90570068,39.76294547,4.119500441,3.795942059,31.32415361,524.6060657,1.265540508 -2016-06-15 15:00:00-07:00,78,763.9265436,0.755167441,37.72122192,0.536386993,0.913078043,57.70126343,39.68126211,3.958091392,3.645174325,31.33393163,428.1979683,1.295424905 -2016-06-15 15:10:00-07:00,78,734.880045,4.149983232,37.98205566,0.535953589,0.914235947,57.88775635,39.56954687,3.80339081,3.511145924,31.2740963,547.0604953,1.33737374 -2016-06-15 15:20:00-07:00,78,699.8807373,2.777661514,37.03804016,0.536505254,0.911433659,55.41139221,39.84876141,3.626328389,3.344354666,31.73269481,634.3374755,1.364144388 -2016-06-15 15:30:00-07:00,78,668.4873633,2.522912077,37.16300964,0.536175313,0.906950211,55.36012268,39.70161431,3.461374567,3.195137006,31.67036211,641.2204938,1.350478149 -2016-06-15 15:40:00-07:00,78,632.9268629,3.178651272,37.87503052,0.536074113,0.904012843,53.90533447,39.75698555,3.270863659,3.02126469,31.81860621,647.4516236,1.463208642 -2016-06-15 15:50:00-07:00,78,596.1343921,2.674800195,37.11878967,0.536553094,0.900201332,52.08010864,39.91928251,3.076284978,2.845454592,32.0867626,830.720886,1.446776342 -2016-06-15 16:00:00-07:00,78,557.0072926,1.95260948,38.09164429,0.535532153,0.896519801,52.03460693,39.87764169,2.88070338,2.66794545,32.14659722,718.698642,1.569543711 -2016-06-15 16:10:00-07:00,78,517.2088357,0.917109861,36.97779846,0.535928182,0.891096606,50.93998718,39.82844563,2.682212934,2.474456566,32.28576623,679.9496339,1.64623615 -2016-06-15 16:20:00-07:00,78,479.1057068,2.648203648,37.50012207,0.536786794,0.890387535,50.50033569,39.7628828,2.485452922,2.29727329,32.30951673,670.1040105,1.651947418 -2016-06-15 16:30:00-07:00,78,437.3205604,2.077020463,37.93783569,0.537306233,0.888877839,48.89237976,39.76537603,2.266556073,2.094940109,32.44061106,792.9407657,1.858767111 -2016-06-15 16:40:00-07:00,78,394.7490154,1.000103275,38.24993896,0.538281585,0.879939798,48.9289093,39.60447977,2.04629162,1.892144515,32.3927587,944.9653183,2.014408535 -2016-06-15 16:50:00-07:00,78,353.5470247,2.133818474,38.37298828,0.539496493,0.872645842,47.09086609,39.60767719,1.8288194,1.687996498,32.59697992,892.5662719,2.195042615 -2016-06-15 17:00:00-07:00,78,313.1018602,2.763722269,37.8526001,0.541437625,0.869477222,45.38612366,39.62196546,1.616197155,1.490849177,32.71367098,762.9122468,2.429501903 -2016-06-15 17:10:00-07:00,78,269.4573396,3.079034449,37.94937134,0.543661737,0.862273088,44.13513184,39.45642904,1.393475818,1.284043718,32.6693065,743.774861,2.760991586 -2016-06-15 17:20:00-07:00,78,228.9534906,2.524834257,37.96987915,0.546262116,0.8487267,42.91041565,39.29856312,1.179313506,1.084910376,32.62283964,702.2693363,3.195235484 -2016-06-15 17:30:00-07:00,78,189.0227773,1.358594969,37.9666748,0.550783896,0.841556155,41.66134644,38.86745935,0.958722417,0.4942008,24.18405001,132.3984399,7.319573354 -2016-06-15 17:40:00-07:00,78,152.5879252,2.608148698,37.66033936,0.558518833,0.82716097,38.56782532,37.708032,0.261226465,0.237281,33.42532999,1161.792452,10.6217123 -2016-06-15 17:50:00-07:00,78,118.8942333,1.960500833,37.70135498,0.568498629,0.815145756,36.97523499,37.29432849,0.243128419,0.220469491,32.02169092,2286.894349,13.072669 -2016-06-15 18:00:00-07:00,78,87.62096117,2.017338658,38.10189819,0.585912281,0.797626215,36.22155762,37.17392521,0.228028165,0.206743496,31.78539849,2654.141291,12.48663163 -2016-06-15 18:10:00-07:00,78,65.8157459,2.289872398,37.69558716,0.610775693,0.768196185,36.06967163,36.9835731,0.216912451,0.194521233,31.47646334,1681.131238,17.83730899 -2016-06-15 18:20:00-07:00,78,48.50593348,1.862286022,37.30335999,0.646433427,0.749009691,36.31063843,36.69345871,0.206539238,0.182184035,30.75956478,1035.426416,15.43309345 -2016-06-15 18:30:00-07:00,78,41.62474388,2.142149527,36.98997498,0.695035519,0.725528878,35.71270752,36.47877497,0.192196934,0.168528802,30.07112607,2023.591516,19.59037317 -2016-06-15 18:40:00-07:00,78,38.30519399,2.639311147,37.10853577,0.697454585,0.70070738,35.05451965,36.36521996,0.174374024,0.152080702,29.96040695,2145.044901,21.69872815 -2016-06-15 18:50:00-07:00,78,33.11183345,3.001127592,36.7797699,0.665499652,0.603639184,34.71998596,36.12043355,0.155309336,0.134666408,29.70188777,2355.460542,28.03948335 -2016-06-15 19:00:00-07:00,78,27.86947713,4.895725767,36.36063904,0.675349634,0.599980341,34.05859375,35.87428866,0.134336016,0.115391354,29.38127935,2370.655311,29.02785109 -2016-06-15 19:10:00-07:00,78,23.19037457,2.679446493,36.04275513,0.664246218,-0.022333123,33.78109741,35.3440884,0.107079809,0.091354909,28.64568131,3303.256914,37.32492251 -2016-06-15 19:20:00-07:00,78,15.77262356,3.376002065,35.67874146,0.635903893,-0.012636586,33.17481995,34.58447356,0.076985181,0.06427247,27.71575976,3762.927,54.02224029 -2016-06-15 19:30:00-07:00,78,10.62091635,2.302329303,35.35061646,0.604582063,-0.09418201,32.45960999,33.24019567,0.047472625,0.03811786,26.25053,5242.623842,106.5430862 -2016-06-15 19:40:00-07:00,78,4.239244581,3.281431845,35.26152039,0.567664779,-0.1,32.2442688,30.33714745,0.021637324,0.01648923,22.73152,19714.73101,250.7472391 -2016-07-12 05:20:00-07:00,78,1.904643485,0.356260305,25.76754761,0.530952389,0.385939524,21.56913757,28.73573278,0.011745461,0.00889419,20.13262,19805.08055,719.8262171 -2016-07-12 05:30:00-07:00,78,4.981468694,1.258698259,26.48405457,0.613048861,-0.1,22.09211731,33.40099231,0.029006593,0.022777128,25.54074885,14274.51203,190.686811 -2016-07-12 05:40:00-07:00,78,11.32398063,1.010838258,26.52122498,0.653271576,0.093122952,22.36128235,35.51154633,0.055253689,0.045337298,28.30700122,5364.943274,93.7132277 -2016-07-12 05:50:00-07:00,78,17.20814961,1.747768968,24.90107727,0.677189724,0.39837304,22.59968567,36.66224811,0.083266867,0.070639672,29.69434411,4902.412334,57.47333566 -2016-07-12 06:00:00-07:00,78,23.00178908,0.877735591,25.4708252,0.67771192,0.626301384,22.80989075,37.4127284,0.113684466,0.097909644,30.72141187,4714.695909,40.53424195 -2016-07-12 06:10:00-07:00,78,26.40205091,0.909418836,26.52955627,0.679000967,0.629369107,23.08612061,37.79591398,0.135001795,0.117834968,31.1528706,4478.092227,25.89639233 -2016-07-12 06:20:00-07:00,78,30.49062769,1.048209672,27.43768311,0.675882559,0.693778996,23.93334961,37.91089082,0.151414574,0.1325625,31.37347174,3777.670332,29.29426723 -2016-07-12 06:30:00-07:00,78,33.36167058,0.879578074,28.35350037,0.671260963,0.752363296,24.57423401,38.0221839,0.166354714,0.146214647,31.50322892,3957.4177,23.1020869 -2016-07-12 06:40:00-07:00,78,40.33131943,1.50916161,29.51669312,0.675056385,0.772845881,25.06514404,38.22367826,0.186779746,0.165886705,31.96831394,2502.79361,22.90375252 -2016-07-12 06:50:00-07:00,78,54.46613104,0.740948031,30.48570251,0.632219991,0.786287304,25.61566162,38.5750581,0.202697937,0.18356879,33.0845388,2509.316918,16.63954765 -2016-07-12 07:00:00-07:00,78,75.36491039,0.781844035,30.9471283,0.598720462,0.815818849,26.82627869,39.19161146,0.229177191,0.2030168,35.24076997,1026.785913,12.88212639 -2016-07-12 07:10:00-07:00,78,101.7141747,0.75516758,31.7706604,0.576738184,0.827255491,28.81620789,39.9672217,0.577064706,0.526243849,33.64437348,2051.908749,6.454602353 -2016-07-12 07:20:00-07:00,78,134.9327315,0.866159624,32.88322449,0.563190257,0.847904901,30.64399719,40.26212878,0.758312339,0.697010255,33.86094616,1606.587212,4.895448757 -2016-07-12 07:30:00-07:00,78,170.549331,1.163447499,32.73005676,0.555984384,0.857297526,32.60188293,40.43664722,0.959491191,0.885372383,33.94520456,1453.166997,3.897423006 -2016-07-12 07:40:00-07:00,78,208.6230256,0.868202434,32.83964539,0.551015107,0.864005667,34.6007843,40.55027879,1.166700383,1.078382851,33.96047182,1416.077893,2.957088057 -2016-07-12 07:50:00-07:00,78,248.649329,1.222248017,33.67727661,0.548270981,0.875098035,36.65800476,40.60772633,1.384243998,1.285224659,33.80616818,1354.067046,2.772377012 -2016-07-12 08:00:00-07:00,78,290.9734291,1.639900859,32.69096375,0.546906026,0.870933716,38.44029236,40.66863636,1.605138742,1.490728745,33.75559814,1362.525375,2.439137665 -2016-07-12 08:10:00-07:00,78,330.4386042,1.573610133,32.53715515,0.546110373,0.883295405,40.27191162,40.69976328,1.820643439,1.690216663,33.68353531,1184.984405,1.924220652 -2016-07-12 08:20:00-07:00,78,373.2991645,1.566760582,33.10047913,0.545874572,0.890645915,41.9465332,40.70543856,2.04094036,1.89493753,33.57084288,1004.037734,1.79515457 -2016-07-12 08:30:00-07:00,78,414.3860404,1.155516752,33.2485199,0.545381064,0.896992373,43.22956848,40.6917702,2.258917459,2.100498196,33.35059911,830.1137728,1.855032121 -2016-07-12 08:40:00-07:00,78,454.8557534,1.842378723,33.2049408,0.544853518,0.894836615,44.91059875,40.62632993,2.47174012,2.297976054,33.17921502,859.4119658,1.64630997 -2016-07-12 08:50:00-07:00,78,494.2009831,2.137142736,33.98681641,0.544422929,0.902389262,47.16456604,40.48743095,2.674121926,2.484406714,32.9331222,761.3531569,1.643270678 -2016-07-12 09:00:00-07:00,78,532.7769007,1.133245965,34.36366272,0.544515813,0.904760544,49.26280212,40.36911431,2.875875871,2.663656911,32.71076391,742.966993,1.56464676 -2016-07-12 09:10:00-07:00,78,570.7795445,0.801270562,34.62513733,0.544605615,0.909062783,50.67208862,40.26646445,3.073479235,2.851396852,32.42869022,758.8904608,1.391059301 -2016-07-12 09:20:00-07:00,78,608.5619327,0.426596836,36.14529419,0.54463475,0.916023309,52.5690918,40.06455182,3.264692288,3.024372535,32.13109551,739.933875,1.442191447 -2016-07-12 09:30:00-07:00,78,642.1992035,1.079612332,35.72744751,0.544510479,0.91347377,54.1187439,39.94247213,3.438820313,3.182123641,31.91814518,838.1412292,1.400285409 -2016-07-12 09:40:00-07:00,78,675.9271049,1.013642005,37.02906799,0.544576775,0.915691625,56.05484009,39.77866649,3.610047501,3.333800448,31.65997932,673.5062867,1.323400318 -2016-07-12 09:50:00-07:00,78,708.2072613,1.399611253,35.6031189,0.545363249,0.916421994,57.05078125,39.77198124,3.780524269,3.485502664,31.56950969,550.50238,1.288103471 -2016-07-12 10:00:00-07:00,78,740.8744982,2.903754115,37.53216553,0.545591625,0.9205124,58.71832275,39.62116955,3.950650941,3.645187696,31.25835295,570.818564,1.264949076 -2016-07-12 10:10:00-07:00,78,769.4824822,1.317858989,36.823349,0.545417704,0.919472239,59.53033447,39.65491316,4.089031767,3.774178275,31.21735414,634.0432922,1.236296744 -2016-07-12 10:20:00-07:00,78,796.8877185,1.633492266,37.21427917,0.54552134,0.922548803,60.60058594,39.39520152,4.229075393,3.89432335,30.88505006,604.3418374,1.264456658 -2016-07-12 10:30:00-07:00,78,821.5834001,1.802323493,37.47961426,0.545498466,0.922547212,60.93063354,39.54205447,4.354530814,4.007984862,30.96614065,565.1844306,1.242984624 -2016-07-12 10:40:00-07:00,78,843.219619,1.785580362,37.50524902,0.545741572,0.926807188,62.53988647,39.37171165,4.469077191,4.106164015,30.74392991,526.7312665,1.18960039 -2016-07-12 10:50:00-07:00,78,870.6125627,1.180871311,37.82183838,0.545352763,0.927659036,61.92080688,39.48449237,4.60178233,4.227714526,30.78218321,545.3354528,1.171347846 -2016-07-12 11:00:00-07:00,78,884.5268978,2.582032923,37.34758667,0.545781084,0.928953097,62.11947632,39.4800192,4.678637489,4.299712787,30.73062467,618.6867001,1.162123583 -2016-07-12 11:10:00-07:00,78,910.7464268,3.755922723,37.65008545,0.545022709,0.934377699,62.48477173,39.51284234,4.804804086,4.417971903,30.64671815,953.7924309,1.129241181 -2016-07-12 11:20:00-07:00,78,920.1829055,3.124817661,38.45630859,0.546199135,0.930063937,62.78341675,39.48765338,4.849950387,4.457903925,30.59944276,473.4094326,1.145102367 -2016-07-12 11:30:00-07:00,78,939.6508974,1.288979421,37.82824707,0.545383011,0.934426093,65.32064819,39.13928776,4.95139517,4.539715762,30.19307548,539.8170729,1.173273767 -2016-07-12 11:40:00-07:00,78,952.3797921,1.888962477,38.25762939,0.545311001,0.934249308,65.69815063,39.0997182,5.017171911,4.597477115,30.12126315,468.0674853,1.139778915 -2016-07-12 11:50:00-07:00,78,960.238404,2.490307172,38.40375977,0.545448704,0.933150033,65.36743164,39.17539205,5.060004782,4.636164351,30.17858699,724.1099197,1.134214069 -2016-07-12 12:00:00-07:00,78,967.7347231,1.561713768,37.52703857,0.545958045,0.934732895,64.10620117,39.44124426,5.092295072,4.67091989,30.42641022,473.3178757,1.11773673 -2016-07-12 12:10:00-07:00,78,975.1965013,1.470508534,38.64857483,0.545449348,0.932642146,66.41015625,38.93548894,5.136385864,4.697232285,29.90018581,523.6859613,1.13066192 -2016-07-12 12:20:00-07:00,78,977.9597534,1.620313819,38.29415894,0.545330192,0.934152456,64.24398804,39.2720525,5.150111753,4.715135797,30.22652256,390.1213772,1.115277832 -2016-07-12 12:30:00-07:00,78,979.5178195,1.918803449,39.35545349,0.545573104,0.930828338,65.31744385,39.02949275,5.149668768,4.710992946,29.98121483,406.7199726,1.128321664 -2016-07-12 12:40:00-07:00,78,977.817661,0.602318058,39.20292664,0.545306326,0.929152519,68.26678467,38.72185669,5.14366473,4.701670817,29.6763599,461.2552031,1.157198363 -2016-07-12 12:50:00-07:00,78,978.5698315,1.774325212,39.87265015,0.54531391,0.928794395,69.43444824,38.54182597,5.150851098,4.695668012,29.49419536,439.6059353,1.162061472 -2016-07-12 13:00:00-07:00,78,975.0348382,3.115885067,38.97541809,0.544908648,0.931751975,67.94506836,38.79995583,5.125956581,4.689125929,29.76275223,553.9335896,1.143948833 -2016-07-12 13:10:00-07:00,78,969.6160878,1.489654896,39.77972412,0.545191452,0.927230163,66.18841553,38.99034123,5.101617622,4.663406786,29.98119388,439.9603023,1.131944971 -2016-07-12 13:20:00-07:00,78,958.4746383,2.259109765,40.21679688,0.545402067,0.928193556,67.37466431,38.75362539,5.040310933,4.607211757,29.76772517,415.9762617,1.128344529 -2016-07-12 13:30:00-07:00,78,951.0790067,0.905853873,40.56288147,0.544825169,0.927183277,68.05593872,38.64549478,4.998722068,4.568895501,29.66967563,521.1687907,1.174856715 -2016-07-12 13:40:00-07:00,78,938.2545588,2.203233135,40.48533203,0.544649558,0.926689927,65.72186279,39.08758578,4.937601752,4.522570053,30.14387857,399.9999839,1.175313534 -2016-07-12 13:50:00-07:00,78,921.9197672,1.141297133,39.53042603,0.544870263,0.922786334,65.37191772,38.93638841,4.855678592,4.445135901,30.03701871,351.2202934,1.157797573 -2016-07-12 14:00:00-07:00,78,904.7887503,3.517636042,39.98672485,0.544995261,0.922717,65.18734741,38.95664762,4.761193879,4.36235447,30.11530063,468.9942515,1.15026896 -2016-07-12 14:10:00-07:00,78,882.7802094,1.002386715,40.80705261,0.544976678,0.920859837,66.76263428,38.71782657,4.649690201,4.2562305,29.92672806,540.7106428,1.18573814 -2016-07-12 14:20:00-07:00,78,863.0158597,3.763253046,40.85447693,0.544904656,0.915755948,66.28326416,38.85841502,4.549372947,4.162190684,30.18404837,455.1199895,1.199306556 -2016-07-12 14:30:00-07:00,78,840.137816,0.63215896,40.81282043,0.544833778,0.914961709,64.3215332,39.06940394,4.427349719,4.060811879,30.44891048,572.2632931,1.207315885 -2016-07-12 14:40:00-07:00,78,818.4036792,1.296870634,41.3639679,0.544376378,0.913850809,65.1315918,38.84578341,4.314188042,3.95430949,30.30177293,568.942008,1.262077414 -2016-07-12 14:50:00-07:00,78,790.8270083,1.235987074,41.079422,0.544718195,0.911289465,64.72848511,38.90603685,4.176694764,3.832114925,30.42245451,628.6409808,1.215748848 -2016-07-12 15:00:00-07:00,78,766.1998931,3.488075444,41.37870789,0.544287908,0.908700299,61.58178711,39.20410017,4.047499783,3.720927965,30.78829687,596.4898159,1.256920762 -2016-07-12 15:10:00-07:00,78,733.7042105,6.213012747,42.02536011,0.544337437,0.906384839,60.32116699,39.23019151,3.881665279,3.570970763,30.90508937,538.6470773,1.284924185 -2016-07-12 15:20:00-07:00,78,709.9147163,2.626333237,42.15032959,0.544211823,0.911368301,59.43292236,39.30925674,3.764145094,3.457812931,31.04125891,350.2398377,1.34870337 -2016-07-12 15:30:00-07:00,78,676.2308512,5.105894515,41.82092285,0.544227709,0.907402826,57.21420288,39.52015227,3.589265763,3.298101925,31.42272691,361.9728454,1.381957563 -2016-07-12 15:40:00-07:00,78,641.0086472,4.743357156,41.13389587,0.545066467,0.902543007,56.07983398,39.56312483,3.398302235,3.128741683,31.56243378,504.0832068,1.37055335 -2016-07-12 15:50:00-07:00,78,603.3415026,2.9827824,41.99075317,0.545754411,0.898128245,56.96618652,39.31221743,3.199691982,2.945180486,31.41687943,408.408091,1.42407635 -2016-07-12 16:00:00-07:00,78,567.5042703,0.926562482,41.64083862,0.544804416,0.900867755,57.85891724,39.09494535,3.026982004,2.785155495,31.28730895,483.2879281,1.540940599 -2016-07-12 16:10:00-07:00,78,530.376199,1.766354372,41.73953247,0.543797112,0.9002796,56.68289185,39.07050536,2.831687197,2.608063147,31.37002642,490.8863199,1.608751994 -2016-07-12 16:20:00-07:00,78,491.1362389,4.241588842,42.5605011,0.543727656,0.896047039,55.90423584,39.06229919,2.628052426,2.412689452,31.54821234,501.4743208,1.698623083 -2016-07-12 16:30:00-07:00,78,453.4079715,3.039420247,42.11636353,0.544535874,0.8936067,53.15550232,39.26928345,2.42584755,2.231001874,31.8698403,482.9846395,1.782586445 -2016-07-12 16:40:00-07:00,78,412.5683472,3.752918439,42.23556519,0.544941086,0.886876546,52.20059204,39.22222049,2.209792764,2.034704652,31.92663709,594.6165685,1.711902694 -2016-07-12 16:50:00-07:00,78,371.7534463,1.392762191,42.00164795,0.545581451,0.890416569,51.5750885,39.06554819,1.991299556,1.83301984,31.87731782,585.1366546,1.955123869 -2016-07-12 17:00:00-07:00,78,328.8854607,2.086873529,42.04074097,0.546766994,0.879978301,50.59967041,38.96991566,1.764436305,1.621821626,31.90894367,533.6165426,2.27068734 -2016-07-12 17:10:00-07:00,78,285.4932891,3.157141353,41.9715271,0.547784928,0.865646451,48.50527954,39.00160198,1.533446734,1.408058103,32.14207875,551.7616971,2.226784193 -2016-07-12 17:20:00-07:00,78,245.9179126,1.955533787,41.95742798,0.550138794,0.871235383,47.13380432,38.87991323,1.318213016,1.209784133,32.13037777,563.6596739,2.892337335 -2016-07-12 17:30:00-07:00,78,206.871479,2.67860509,41.91897583,0.553398814,0.868946193,46.09173584,38.63328276,1.101950639,0.770212,34.94620003,646.2531682,3.139197855 -2016-07-12 17:40:00-07:00,78,168.1486201,1.861164384,41.69659424,0.559094069,0.860025349,43.52502014,37.60931979,0.332178818,0.2253186,34.90063004,170.4346358,9.331957229 -2016-07-12 17:50:00-07:00,78,133.1614118,4.886353123,42.08239746,0.567457807,0.844275273,41.50753784,36.43124689,0.226948797,0.206260555,31.28785397,2594.502559,15.86211739 -2016-07-12 18:00:00-07:00,78,99.84989635,2.497957544,41.36460876,0.581089873,0.826080512,40.61158752,36.4885956,0.217093397,0.196261961,31.25040314,2309.568492,12.73470436 -2016-07-12 18:10:00-07:00,78,72.85153217,1.575892875,41.63699341,0.601041025,0.827083667,39.90020752,36.24470203,0.205510358,0.185066075,30.66951829,2703.708698,16.10198638 -2016-07-12 18:20:00-07:00,78,53.73856425,1.507599293,41.36460876,0.631957436,0.782867225,39.46313354,36.06086605,0.193507048,0.173221584,30.31661303,2565.439843,20.22682889 -2016-07-12 18:30:00-07:00,78,39.30480012,2.119798902,41.41523743,0.666620069,0.764275096,39.34391785,35.78951867,0.183924972,0.161061022,29.52770671,1685.940694,22.89242304 -2016-07-12 18:40:00-07:00,78,35.76731327,2.731317831,40.57954407,0.716224088,0.767475243,38.68894958,35.63437207,0.167048122,0.145314828,29.26492891,2266.152968,22.62610305 -2016-07-12 18:50:00-07:00,78,31.43375754,4.1022777,40.44815979,0.698920295,0.670710655,38.46528137,35.38703206,0.149684782,0.129221108,29.00070389,2031.884136,28.85591326 -2016-07-12 19:00:00-07:00,78,26.65686582,4.345090678,40.07644653,0.696636745,0.630910516,37.88912964,35.13810663,0.130241686,0.1125149,28.39453,2433.144582,29.96496859 -2016-07-12 19:10:00-07:00,78,21.37996862,3.031729641,39.8508606,0.677941505,0.547120543,37.47064209,34.68842264,0.106709179,0.09079195,28.09182454,3055.482124,41.90930678 -2016-07-12 19:20:00-07:00,78,15.91714504,3.144163932,38.96772766,0.64391665,0.191331213,36.76054382,34.07128827,0.080811625,0.067724865,27.25959072,3694.890002,57.08394038 -2016-07-12 19:30:00-07:00,78,9.440131605,4.532868429,38.76777649,0.61775948,-0.061531381,36.35038147,32.6550507,0.0485804,0.03996488,25.17089999,5750.782362,100.6775674 -2016-07-12 19:40:00-07:00,78,4.613995668,2.32540084,38.23135376,0.573725865,-0.1,35.65118408,29.97143643,0.022692823,0.01730052,22.42783,12138.78106,290.6113344 -2016-08-29 06:00:00-07:00,78,4.780590555,1.121950373,27.55496216,0.593860479,-0.1,23.40270996,32.53074368,0.024620463,0.019345053,24.40160069,13472.69486,260.2410593 -2016-08-29 06:10:00-07:00,78,10.17505006,1.867172671,27.67608643,0.617620014,0.158143026,24.35762024,35.00875356,0.052403673,0.04293955,27.82751626,6541.230794,98.70669478 -2016-08-29 06:20:00-07:00,78,16.24543984,2.226264789,27.58700562,0.605203098,-0.000153009,24.60243225,36.23732748,0.082001927,0.06967186,29.25003001,4910.513253,58.26866478 -2016-08-29 06:30:00-07:00,78,21.14480723,2.329246528,27.73953247,0.632762518,0.024881276,25.14782715,36.97605348,0.114831522,0.096522425,30.367909,2162.384227,40.6774026 -2016-08-29 06:40:00-07:00,78,34.58898522,1.952129337,27.39794922,0.624690943,0.038914865,25.34585571,38.07959926,0.184738549,0.1563418,32.02584999,2567.145818,21.19484802 -2016-08-29 06:50:00-07:00,78,48.20951125,2.31606822,27.56329346,0.59263217,0.019383004,26.08414246,38.87888249,0.274548544,0.243248893,32.67311454,1909.595953,13.77939998 -2016-08-29 07:00:00-07:00,78,77.89542104,1.349903354,28.44258118,0.561304745,-0.026874258,26.93714905,39.64614264,0.418964758,0.3690205,33.99207,1008.887885,8.940309088 -2016-08-29 07:10:00-07:00,78,109.6587155,0.937417396,28.78352356,0.545624127,0.01108184,28.07727051,40.12475602,0.574649833,0.526580018,33.85369124,1213.531587,6.412690522 -2016-08-29 07:20:00-07:00,78,146.463433,2.27032566,29.03923035,0.540698895,0.016755822,29.6852417,40.52768415,0.792177957,0.731235368,34.13880711,1243.766031,4.667419493 -2016-08-29 07:30:00-07:00,78,186.3256166,0.866319998,29.23406982,0.538080348,0.014363494,31.48995972,40.75985627,1.023232018,0.945136858,34.24910624,1090.414427,3.669784041 -2016-08-29 07:40:00-07:00,78,227.8290441,1.771521536,29.91595459,0.537264578,-0.032873867,33.63433838,40.85027493,1.246870041,1.153869113,34.23760815,956.158117,3.039524848 -2016-08-29 07:50:00-07:00,78,271.7722688,1.403536918,30.42481995,0.537386179,-0.003566889,35.62298584,40.88740598,1.482637935,1.377439523,34.04511926,895.7708807,2.609917234 -2016-08-29 08:00:00-07:00,78,317.540704,3.423667154,30.75614929,0.538532037,-0.01345765,37.13032532,40.94933275,1.726043436,1.604955979,33.98128083,755.8203623,2.293921656 -2016-08-29 08:10:00-07:00,78,361.9593212,3.039099639,30.95353699,0.539478389,0.002517518,38.37170715,41.02056016,1.961673041,1.822803464,33.932029,679.6147562,1.936902733 -2016-08-29 08:20:00-07:00,78,403.5485489,1.984973894,31.92254639,0.539449196,-0.00326719,40.71989441,40.88545464,2.181779544,2.026883605,33.69637706,654.0979512,1.89777182 -2016-08-29 08:30:00-07:00,78,450.4511561,0.556294704,33.12739563,0.540023231,-0.010584262,43.68844604,40.67993662,2.428610752,2.255335527,33.2604974,532.6767015,1.755300458 -2016-08-29 08:40:00-07:00,78,495.5777418,3.595702783,32.76530457,0.540759562,-0.025913379,44.42800903,40.78980708,2.660030699,2.470504412,33.25051971,497.4203698,1.566967243 -2016-08-29 08:50:00-07:00,78,536.8874957,4.100515265,32.58073425,0.541182676,-0.017894575,44.82344055,40.853695,2.876700597,2.667834533,33.21052006,504.1732676,1.564829223 -2016-08-29 09:00:00-07:00,78,575.7698279,2.923260969,32.91014099,0.541717932,-0.017972491,46.0904541,40.79397293,3.075158447,2.852607597,33.03788878,524.286987,1.491752546 -2016-08-29 09:10:00-07:00,78,617.8242717,1.277203476,33.92721558,0.541808557,-0.012665751,48.35467529,40.61537752,3.294400963,3.055207448,32.66701154,414.394291,1.430804967 -2016-08-29 09:20:00-07:00,78,656.2068285,4.095188146,33.86505127,0.542176684,-0.000459697,49.40507507,40.62687125,3.493293253,3.23449397,32.583151,370.2844933,1.383551185 -2016-08-29 09:30:00-07:00,78,695.7698939,1.413270053,33.88555908,0.543817023,0.001666186,50.48751831,40.56532023,3.691113366,3.415513832,32.40805979,334.8682662,1.338743687 -2016-08-29 09:40:00-07:00,78,728.6895074,2.052146117,34.53349304,0.544089174,0.000226575,53.4797821,40.21210928,3.85978609,3.562908606,31.96644226,325.4689556,1.267023581 -2016-08-29 09:50:00-07:00,78,763.5223767,1.724817222,34.79881287,0.544432744,0.000453185,54.9589386,40.06274205,4.041076331,3.730058765,31.65909889,325.4277811,1.238002666 -2016-08-29 10:00:00-07:00,78,795.6751624,1.492698924,35.80499268,0.544185951,-0.004199165,56.44192505,39.99631502,4.202724083,3.876177412,31.50226477,309.4430008,1.218305679 -2016-08-29 10:10:00-07:00,78,826.6300125,0.889030905,36.29141235,0.544361786,-0.00853681,58.86315918,39.70778476,4.365918065,4.012067117,31.13496586,268.6957079,1.19996795 -2016-08-29 10:20:00-07:00,78,855.2968099,2.70524187,36.01263428,0.544344635,-0.012055408,59.66171265,39.79318535,4.50617992,4.145618962,31.14429578,287.7507952,1.159174218 -2016-08-29 10:30:00-07:00,78,880.4848509,4.143975223,36.2612915,0.544591732,-0.014684624,60.4871521,39.70359588,4.635839403,4.259579083,30.98243295,275.7710803,1.165256 -2016-08-29 10:40:00-07:00,78,913.3529292,1.540043964,36.46125793,0.545608242,-0.015329857,61.32479858,39.68791071,4.765648136,4.369262019,30.90530215,252.4209885,1.181250261 -2016-08-29 10:50:00-07:00,78,934.6314466,0.807118616,36.82078552,0.54569852,0.923743636,62.97824097,39.39560502,4.873199103,4.472459273,30.49592448,314.0690839,1.174395123 -2016-08-29 11:00:00-07:00,78,957.2497896,3.10442938,36.95921326,0.545621334,0.923442317,62.17779541,39.43873034,4.983380651,4.572540869,30.47784574,391.8788244,1.133684598 -2016-08-29 11:10:00-07:00,78,975.135268,3.203845667,36.47984314,0.545535284,0.926420791,62.98464966,39.42630824,5.080101376,4.654931689,30.41608069,276.1785275,1.152084143 -2016-08-29 11:20:00-07:00,78,992.0286024,3.791571725,37.66290283,0.545891622,0.925842351,62.865448,39.43737057,5.163149057,4.73269741,30.38014364,303.507264,1.116239018 -2016-08-29 11:30:00-07:00,78,1007.768157,2.615158344,36.59584045,0.546094361,0.928570681,62.68792725,39.50239832,5.236489855,4.797139919,30.41617787,335.6508512,1.0858617 -2016-08-29 11:40:00-07:00,78,1018.571568,0.647219634,38.62550354,0.546312921,0.92741322,66.95169067,38.92131688,5.296017715,4.843202756,29.79449133,413.43593,1.118268347 -2016-08-29 11:50:00-07:00,78,1029.561144,4.015318947,37.59625244,0.546538204,0.929791917,65.26937866,39.28784295,5.350142878,4.891701564,30.13791224,261.7431369,1.085276601 -2016-08-29 12:00:00-07:00,78,1035.362016,0.916028176,37.36617188,0.546779038,0.929079692,67.64770508,39.11138894,5.380821147,4.911343078,29.9456412,252.118225,1.086461561 -2016-08-29 12:10:00-07:00,78,1042.676935,0.870765969,38.5639801,0.546752323,0.92969796,68.25396729,38.82621616,5.399552711,4.931666895,29.64234705,553.2159298,1.092308777 -2016-08-29 12:20:00-07:00,78,1043.531983,3.307467643,37.59048462,0.546924856,0.922248545,66.10189819,39.25361309,5.416637736,4.947782937,30.07582842,291.3461273,1.077218011 -2016-08-29 12:30:00-07:00,78,1047.449052,1.302318173,39.29777527,0.546477197,0.930998174,68.01748657,38.80828965,5.436275206,4.956435791,29.60277325,307.3934637,1.10952277 -2016-08-29 12:40:00-07:00,78,1044.012114,0.614574635,38.31530762,0.546652337,0.926054334,67.32852173,38.92245525,5.420549437,4.938214815,29.73666038,283.0134416,1.106214212 -2016-08-29 12:50:00-07:00,78,1039.408958,2.658938072,39.1683197,0.546801751,0.923036838,67.69256348,38.99958447,5.392355624,4.920782015,29.83083532,272.0258679,1.085944555 -2016-08-29 13:00:00-07:00,78,1035.352217,1.526024812,39.0728302,0.546388202,0.924047063,66.16213989,39.0897252,5.370980045,4.905223022,29.92638797,336.4040472,1.105011444 -2016-08-29 13:10:00-07:00,78,1024.86237,2.93703998,38.7094574,0.546288645,0.921460832,64.34204102,39.47386987,5.318213133,4.863518598,30.34473342,287.3175626,1.101728533 -2016-08-29 13:20:00-07:00,78,1014.416909,1.351985838,38.60179138,0.545883911,0.920952374,67.10806274,39.10849751,5.264673931,4.805636747,30.00661229,290.0476712,1.115589342 -2016-08-29 13:30:00-07:00,78,1005.20087,1.372133768,39.31892395,0.545388007,0.922613016,66.75366211,38.92433472,5.206142814,4.761372109,29.84472875,413.9118196,1.124709828 -2016-08-29 13:40:00-07:00,78,990.0982235,2.434991151,39.10423279,0.545503501,0.919384346,65.45587158,39.11982504,5.129670054,4.693731809,30.07907327,418.6025109,1.13005505 -2016-08-29 13:50:00-07:00,78,972.1147627,1.591834766,38.34799194,0.545054128,0.919979067,61.96630859,39.40183413,5.041605742,4.624589826,30.41081912,513.6380433,1.128804584 -2016-08-29 14:00:00-07:00,78,949.9006695,3.133389275,38.29031372,0.544953145,0.916089149,60.61019897,39.51143408,4.923221955,4.521641547,30.5844916,504.2170247,1.140460012 -2016-08-29 14:10:00-07:00,78,930.280556,1.01985048,40.06491089,0.543758912,0.914594891,65.27963257,38.84869857,4.823580848,4.417351876,29.97333015,467.3485731,1.164629499 -2016-08-29 14:20:00-07:00,78,906.1165132,1.954372125,38.90235901,0.544295247,0.91238946,63.65823364,39.11530321,4.699254125,4.316214549,30.29638223,519.7652119,1.173667264 -2016-08-29 14:30:00-07:00,78,879.4045881,1.398049146,38.84596252,0.543178282,0.909344824,62.92312622,39.10413157,4.559867091,4.182696137,30.41264713,549.0055724,1.165350618 -2016-08-29 14:40:00-07:00,78,855.3873854,1.196853225,39.06898499,0.543243858,0.91364404,60.26669312,39.38755076,4.444494732,4.081304968,30.76983544,486.6328076,1.195716396 -2016-08-29 14:50:00-07:00,78,826.7402232,0.821257979,39.13948059,0.542853416,0.914889717,59.81680298,39.39108544,4.296133976,3.950065044,30.85899926,534.1243822,1.249689175 -2016-08-29 15:00:00-07:00,78,798.3404046,2.762640724,38.92863464,0.543399461,0.913061518,57.81213379,39.59930978,4.144588128,3.819554643,31.13314592,592.787924,1.232807728 -2016-08-29 15:10:00-07:00,78,762.6183974,0.643574414,40.02838135,0.542334518,0.908184573,60.29681396,39.21071107,3.968038127,3.645901772,30.83468474,434.6876111,1.313625388 -2016-08-29 15:20:00-07:00,78,729.7135793,1.174542555,39.5496521,0.54243286,0.900881227,59.79885864,39.26027412,3.79500345,3.49944221,30.9768504,606.2916331,1.294865834 -2016-08-29 15:30:00-07:00,78,695.1893513,3.788087158,39.60540771,0.542141891,0.902318103,57.27764893,39.40532487,3.625777021,3.342094359,31.28230888,647.9551441,1.322460156 -2016-08-29 15:40:00-07:00,78,656.6012239,2.688057991,39.24906921,0.541285557,0.898881922,56.10418701,39.49568403,3.427069174,3.165918008,31.47711728,748.9032615,1.416257189 -2016-08-29 15:50:00-07:00,78,614.5000109,0.955001581,39.65539551,0.541454649,0.895038951,55.95550537,39.39951239,3.210389568,2.964569321,31.49557774,727.4331893,1.475464886 -2016-08-29 16:00:00-07:00,78,576.9579091,1.918562888,39.21702576,0.540479973,0.893107538,53.77201843,39.59177487,3.016638563,2.789577079,31.78668492,823.3089511,1.468647571 -2016-08-29 16:10:00-07:00,78,533.3942477,0.693362849,39.06065369,0.537651111,0.883502591,52.985672,39.51504611,2.786260643,2.580846004,31.82796358,813.9407277,1.605219814 -2016-08-29 16:20:00-07:00,78,490.9377991,2.822482623,39.20613098,0.538188264,0.873809108,52.59921265,39.42333623,2.568671269,2.374756922,31.93209254,1009.920807,1.707403559 -2016-08-29 16:30:00-07:00,78,446.8205727,0.940742288,40.32574463,0.537073292,0.873499362,51.55970764,39.3910282,2.337208445,2.162188801,32.0250241,964.6358023,1.723359371 -2016-08-29 16:40:00-07:00,78,402.2354893,2.504927237,39.68615723,0.537210006,0.866710234,50.20169067,39.4935196,2.10739075,1.95060932,32.24428549,856.1617764,1.854853355 -2016-08-29 16:50:00-07:00,78,357.1921919,2.42974394,39.61309814,0.536784037,0.861772318,48.96287537,39.44145655,1.866121668,1.729114031,32.32143962,993.7196183,2.024652131 -2016-08-29 17:00:00-07:00,78,311.7887822,1.133005195,41.02879333,0.536184676,0.854390811,48.67384338,39.1680598,1.633147707,1.50523107,32.26180012,836.453215,2.405728925 -2016-08-29 17:10:00-07:00,78,267.3162558,3.553004321,40.18795776,0.535208732,0.845413432,47.37477112,39.01322216,1.397468546,1.286842088,32.23143961,603.7182918,2.752913395 -2016-08-29 17:20:00-07:00,78,223.0446351,2.95206056,39.94699097,0.535153697,0.831395586,45.03044128,39.0148645,1.164004007,1.070396949,32.34737608,601.5026728,2.966882347 -2016-08-29 17:30:00-07:00,78,181.4801308,2.512777727,39.77844238,0.535409492,0.833902176,43.24302673,38.81328801,0.9426219,0.864214475,32.25942315,779.486314,3.573867412 -2016-08-29 17:40:00-07:00,78,141.8286153,0.995657025,39.57144165,0.538253004,0.79231463,42.0836792,38.47209928,0.733644811,0.668172064,32.04689943,916.7771136,5.593474162 -2016-08-29 17:50:00-07:00,78,105.3962106,1.844421392,40.04055786,0.543230652,0.794905969,40.72886658,37.98825152,0.536312338,0.482758035,31.75651404,1381.102804,6.067228557 -2016-08-29 18:00:00-07:00,78,72.2832086,1.409624834,39.28752136,0.553732982,0.758445258,39.23561096,37.383737,0.373103584,0.2743931,33.66684002,1224.569849,11.24166428 -2016-08-29 18:10:00-07:00,78,48.36629778,1.273398441,39.38877869,0.5734852,0.730806735,37.83721924,36.46753164,0.205886418,0.1579817,32.33323002,523.0515258,19.40278088 -2016-08-29 18:20:00-07:00,78,30.61067427,2.284744838,38.70497131,0.610469407,0.663165477,36.9874115,35.5704686,0.141966296,0.123347339,29.30077714,1863.435369,26.40604898 -2016-08-29 18:30:00-07:00,78,21.198698,3.185500405,38.48002625,0.641849491,0.51605839,36.19528198,34.75959927,0.100053817,0.085123342,28.09744135,3522.275539,39.68590331 -2016-08-29 18:40:00-07:00,78,14.37138997,3.800183712,38.27108765,0.588680311,-0.06255563,35.64413452,33.99958102,0.073592924,0.061405542,27.14973444,4023.676642,63.18476037 -2016-08-29 18:50:00-07:00,78,8.369585091,2.272888846,37.86157227,0.62351991,-0.1,34.98466492,32.28594264,0.040446278,0.032442108,24.92106421,5904.271537,142.1669751 -2016-08-29 19:00:00-07:00,78,3.087848112,2.866022559,37.45526123,0.561282446,0.300465461,34.293797,28.74457924,0.016256504,0.01213936,21.40410997,38336.87795,473.0834204 -2016-09-04 06:00:00-07:00,78,3.161346398,0.785769629,24.32493591,0.561848195,0.182237506,19.41770935,31.34167519,0.016849619,0.01281255,23.32810001,20675.07573,397.5741712 -2016-09-04 06:10:00-07:00,78,8.656198962,0.72160162,24.35185242,0.625185698,-0.1,19.56318665,35.18020664,0.04248261,0.034460313,27.4984319,6601.786496,116.9303333 -2016-09-04 06:20:00-07:00,78,14.31011072,0.566068282,24.07756042,0.584320691,0.482127524,19.89579773,36.7514358,0.072673005,0.061125208,29.62185748,4748.176749,67.63114878 -2016-09-04 06:30:00-07:00,78,21.10560139,0.481352108,24.85237122,0.615564261,0.659155679,20.10217285,37.79439054,0.111670999,0.093500528,31.16283979,2871.807558,37.2699519 -2016-09-04 06:40:00-07:00,78,32.24218348,0.964294109,25.62976074,0.601101911,0.693322804,20.99427795,38.79690236,0.18511547,0.156987678,32.81439676,2857.810018,21.32055438 -2016-09-04 06:50:00-07:00,78,55.04430896,1.031546588,26.09248047,0.56105836,0.766582925,22.62788391,39.61074494,0.302187401,0.26678269,33.54055944,2753.440714,11.18387913 -2016-09-04 07:00:00-07:00,78,79.8184759,0.96966153,26.44752502,0.538970354,0.735248004,23.91348267,40.1027366,0.417765227,0.379670564,33.8716646,1459.709998,8.852776496 -2016-09-04 07:10:00-07:00,78,117.843322,1.278885794,26.82820129,0.527533464,0.812583369,25.26959229,40.62637219,0.561729947,0.518490901,34.91138937,892.2104031,5.612124965 -2016-09-04 07:20:00-07:00,78,158.1952702,1.725057643,26.9691925,0.524506,0.819352499,27.20504761,41.09419723,0.872724897,0.806550394,34.65290279,1373.876385,4.284142134 -2016-09-04 07:30:00-07:00,78,201.0632374,1.22581284,27.6081543,0.523844963,0.842700531,29.40646362,41.25381947,1.105946282,1.024110657,34.69784264,1133.332545,3.418680848 -2016-09-04 07:40:00-07:00,78,246.2216865,1.180150467,28.49320984,0.524185075,0.850222313,31.787323,41.29266301,1.351093531,1.252741078,34.62043639,1111.823034,2.84369815 -2016-09-04 07:50:00-07:00,78,292.7470491,1.002907162,29.04692078,0.525650829,0.872084869,34.23739624,41.26700544,1.596147168,1.484245453,34.36032906,994.3939346,2.454283276 -2016-09-04 08:00:00-07:00,78,339.6423599,1.320743132,29.3302002,0.527274758,0.876683225,36.5138092,41.24200526,1.847175621,1.717366609,34.21044978,1033.772484,2.019604876 -2016-09-04 08:10:00-07:00,78,385.5601471,1.267790459,29.23022461,0.527894642,0.881443544,37.78274536,41.3529275,2.097973071,1.949940654,34.20222536,772.5715135,1.952893108 -2016-09-04 08:20:00-07:00,78,432.6930575,2.606346169,29.77111816,0.528896788,0.882130024,39.08628845,41.38605916,2.343973091,2.181510704,34.00420635,685.3125448,1.692425721 -2016-09-04 08:30:00-07:00,78,478.4492276,1.833486431,30.71513367,0.529568477,0.891427831,41.43702698,41.24322858,2.583034913,2.401717651,33.74417203,644.8480537,1.676898435 -2016-09-04 08:40:00-07:00,78,523.8867457,1.552901454,30.56324768,0.530409053,0.899629611,43.33851624,41.16413533,2.820957854,2.62080603,33.54779267,578.3891462,1.57444316 -2016-09-04 08:50:00-07:00,78,569.0524912,1.608497361,30.81639099,0.530655071,0.910764532,45.20732117,41.07100324,3.059440453,2.837704242,33.32700091,556.678143,1.496354908 -2016-09-04 09:00:00-07:00,78,611.7636719,1.793551412,31.49700928,0.53110648,0.909395646,47.26966858,40.94037942,3.28360764,3.046343501,32.99470011,442.0542224,1.378258192 -2016-09-04 09:10:00-07:00,78,655.2146752,1.28008741,31.77963257,0.531323535,0.916996067,48.74946594,40.87246245,3.502142407,3.246549949,32.81979437,408.1749491,1.323420992 -2016-09-04 09:20:00-07:00,78,695.6768341,2.01213147,32.27375183,0.531543855,0.918446511,50.38113403,40.74395303,3.71343217,3.436353388,32.58198311,399.1258945,1.334193939 -2016-09-04 09:30:00-07:00,78,734.5615954,3.257759674,31.71939087,0.533071775,0.924301896,50.69259949,40.80835084,3.904181671,3.611444557,32.54395145,442.7438463,1.296121198 -2016-09-04 09:40:00-07:00,78,771.0356166,1.457570437,32.0821228,0.533668209,0.921225925,51.61418152,40.7702442,4.090214288,3.780881905,32.41150207,437.6646141,1.260341387 -2016-09-04 09:50:00-07:00,78,807.0662742,1.190805123,32.02444458,0.533963979,0.920237516,52.62677002,40.70130038,4.265522171,3.944530469,32.18119836,448.3423412,1.192573756 -2016-09-04 10:00:00-07:00,78,838.7977421,1.066033719,32.72428894,0.534180959,0.925753458,54.53530884,40.51451467,4.432717149,4.092676328,31.90281076,477.5066878,1.17908034 -2016-09-04 10:10:00-07:00,78,868.7236346,4.003983052,32.66789246,0.534222817,0.926896109,53.50413513,40.61039172,4.589828274,4.236916994,31.92497675,410.619658,1.131581073 -2016-09-04 10:20:00-07:00,78,898.147433,2.781746783,32.64353943,0.534607801,0.928412675,53.96878052,40.60124555,4.730835562,4.366402721,31.83777353,495.6842152,1.139436488 -2016-09-04 10:30:00-07:00,78,925.1533789,2.006123043,32.42948914,0.534783607,0.926885902,54.64746094,40.51802972,4.876115252,4.493058439,31.6792879,343.8994974,1.127784468 -2016-09-04 10:40:00-07:00,78,950.375924,1.511244304,33.06651306,0.535406517,0.928426764,57.26611328,40.24234831,4.994192148,4.604743332,31.27605852,523.4767402,1.145080671 -2016-09-04 10:50:00-07:00,78,973.2514282,3.073747493,33.53372192,0.535266676,0.929678437,59.53674316,40.04213053,5.119118771,4.709174887,31.01667501,467.5373317,1.094092503 -2016-09-04 11:00:00-07:00,78,994.1990929,3.849571352,33.69393921,0.535618496,0.931762977,60.27438354,39.99003668,5.227381498,4.803068538,30.90928902,443.2833205,1.101885393 -2016-09-04 11:10:00-07:00,78,1013.490893,2.682690918,33.84967041,0.535747041,0.932654367,60.15646362,40.01234234,5.312837068,4.886209482,30.88023446,524.5715263,1.093270852 -2016-09-04 11:20:00-07:00,78,1034.12251,4.427083363,34.41749573,0.534997419,0.934513449,58.41455078,40.31238843,5.422151044,4.980642511,31.14307934,408.1477756,1.063696117 -2016-09-04 11:30:00-07:00,78,1046.481475,4.824027736,34.29571838,0.535562378,0.934292043,57.02642822,40.44959093,5.479350909,5.038293397,31.24732586,406.0855171,1.053474267 -2016-09-04 11:40:00-07:00,78,1058.220516,1.769478726,34.38993835,0.535990457,0.934924015,61.07485962,39.89521972,5.548910679,5.086472536,30.64315806,386.9702571,1.071761006 -2016-09-04 11:50:00-07:00,78,1067.674026,1.317739128,35.6300354,0.536182525,0.933592722,63.9331665,39.63932758,5.596085291,5.122812615,30.36392688,436.8289791,1.076840878 -2016-09-04 12:00:00-07:00,78,1075.770514,1.615747567,35.26728821,0.53619542,0.936120923,63.66464233,39.55775838,5.635289976,5.153621076,30.25924318,436.1801054,1.056888523 -2016-09-04 12:10:00-07:00,78,1077.198661,2.103016097,35.50378418,0.536963691,0.931471608,61.88876343,39.82614255,5.63401732,5.160249905,30.52819565,406.6650086,1.052069666 -2016-09-04 12:20:00-07:00,78,1083.406184,2.327764048,36.1055603,0.536803882,0.93132664,62.66549683,39.74370628,5.671965619,5.186649364,30.43663696,399.4792898,1.07079616 -2016-09-04 12:30:00-07:00,78,1084.699663,3.823175062,35.30574036,0.536942311,0.933677138,61.64266968,39.92401748,5.667879206,5.19611465,30.61036412,426.3774692,1.066392219 -2016-09-04 12:40:00-07:00,78,1080.547406,3.288801983,35.5512085,0.537283116,0.931633966,63.4697876,39.73143662,5.652146991,5.172375086,30.4269459,517.509535,1.076019168 -2016-09-04 12:50:00-07:00,78,1073.974765,4.066589023,36.03314209,0.537132454,0.930930393,63.66143799,39.5504568,5.62251819,5.141813668,30.25585295,465.1871027,1.062716557 -2016-09-04 13:00:00-07:00,78,1071.005602,1.998112039,35.82037354,0.536388541,0.929379365,63.17242432,39.83383078,5.598018415,5.128255135,30.55348478,418.2983275,1.067665673 -2016-09-04 13:10:00-07:00,78,1064.021517,1.984733753,36.49778748,0.535959475,0.930525911,64.01968384,39.53282812,5.566919664,5.090587275,30.27492312,454.1084577,1.080087242 -2016-09-04 13:20:00-07:00,78,1052.221076,2.330928915,36.70863342,0.535631107,0.931382051,62.37133789,39.68561532,5.496994018,5.040568528,30.46246634,492.9468494,1.083858302 -2016-09-04 13:30:00-07:00,78,1035.886294,3.70605424,36.37217285,0.535796424,0.926530997,62.70266724,39.65992827,5.422841723,4.960438328,30.48303303,403.521365,1.095374034 -2016-09-04 13:40:00-07:00,78,1021.023695,2.241606046,36.20233154,0.53524028,0.926428358,61.90158081,39.77435394,5.344160412,4.902473881,30.6267313,499.4942998,1.076493062 -2016-09-04 13:50:00-07:00,78,1004.693807,3.349004933,36.68171692,0.534384049,0.926522718,61.48501587,39.72674254,5.26251834,4.828890032,30.62269465,520.0499753,1.103699669 -2016-09-04 14:00:00-07:00,78,982.1122041,4.479114795,37.57958984,0.53487754,0.924372949,63.57876587,39.46576748,5.140669443,4.716250669,30.42394068,531.2184749,1.11676458 -2016-09-04 14:10:00-07:00,78,958.6608131,2.533205822,36.80091858,0.534409568,0.922285188,60.60250854,39.82626149,5.022994124,4.614495204,30.84668124,435.8520144,1.124054596 -2016-09-04 14:20:00-07:00,78,931.6181089,4.03582667,37.5847168,0.534242482,0.919313752,61.75418091,39.68200405,4.88238179,4.489882282,30.77601439,514.4675091,1.138209989 -2016-09-04 14:30:00-07:00,78,905.482043,4.109527837,36.95729065,0.533669632,0.917829129,58.28509521,40.08854725,4.74740831,4.36741437,31.31324678,503.1295145,1.146760881 -2016-09-04 14:40:00-07:00,78,880.908653,2.5392139,38.29351807,0.533142899,0.919792631,61.82659912,39.40085364,4.625568329,4.243075479,30.68338926,448.6442019,1.171305357 -2016-09-04 14:50:00-07:00,78,853.3858085,3.578599719,38.18200684,0.532729822,0.915261805,60.31219482,39.63820423,4.480982999,4.118813961,30.99446844,579.5261866,1.227121184 -2016-09-04 15:00:00-07:00,78,824.8956076,2.886851099,37.67123413,0.531677025,0.919594296,58.94970581,39.68215057,4.327086679,3.9857997,31.13374589,592.1096639,1.239292816 -2016-09-04 15:10:00-07:00,78,786.2313043,2.105259164,38.43323853,0.532071642,0.914831028,56.38296509,39.91212498,4.135783789,3.806261904,31.46149937,421.8700497,1.268760264 -2016-09-04 15:20:00-07:00,78,751.7244296,5.615192642,37.58792114,0.531388066,0.915053465,55.31141663,39.96623162,3.961147864,3.65790676,31.61047706,686.1968232,1.302291424 -2016-09-04 15:30:00-07:00,78,715.4193772,3.613367295,38.00769043,0.530868751,0.912086483,54.52954102,39.99958246,3.773868664,3.481576941,31.7929805,668.3563983,1.334691113 -2016-09-04 15:40:00-07:00,78,673.6489065,4.563430734,38.32684326,0.530839162,0.90853694,52.15637207,40.22952881,3.564362556,3.287765605,32.14544769,492.1740509,1.32017418 -2016-09-04 15:50:00-07:00,78,633.2722625,2.112549115,38.02883911,0.529795351,0.905111384,52.68637085,39.99838513,3.353308589,3.097614648,32.01796541,637.9403088,1.42872575 -2016-09-04 16:00:00-07:00,78,591.2056088,2.863899213,38.44349243,0.528914332,0.906583751,51.90899658,39.94615019,3.136412806,2.898764928,32.08462491,686.9199114,1.483729937 -2016-09-04 16:10:00-07:00,78,545.5351836,1.484447429,38.76905823,0.528056115,0.904960277,51.96731567,39.79960376,2.896370536,2.677371187,32.06252542,745.5051748,1.568698296 -2016-09-04 16:20:00-07:00,78,502.4712333,4.045800576,37.76287842,0.526883303,0.893881439,49.03016663,40.0676342,2.66786367,2.465408085,32.5311605,754.6062713,1.653324006 -2016-09-04 16:30:00-07:00,78,458.1995758,1.828599852,37.66546631,0.525234555,0.889245434,49.02696228,39.84425146,2.434712388,2.249279589,32.43625051,871.747151,1.768943676 -2016-09-04 16:40:00-07:00,78,411.3875625,0.882181562,38.8299408,0.524300198,0.884543047,48.42388916,39.74564727,2.18804769,2.02429098,32.43941574,820.6885658,1.804121675 -2016-09-04 16:50:00-07:00,78,363.7108402,2.795045092,39.23176575,0.523444668,0.888980386,48.03039551,39.55998208,1.937164818,1.789038513,32.39341834,636.1507785,1.972522692 -2016-09-04 17:00:00-07:00,78,317.1878975,3.434001273,38.25250244,0.52214618,0.879248422,45.64312744,39.63436608,1.682936136,1.552442245,32.69012711,620.2548167,2.054328082 -2016-09-04 17:10:00-07:00,78,271.7208071,1.456569219,38.73252869,0.52081601,0.850234343,44.0383606,39.54611614,1.435912272,1.323498286,32.73217744,616.4194523,2.690575827 -2016-09-04 17:20:00-07:00,78,224.0441216,1.106849886,38.33901978,0.519408559,0.84852696,42.22210693,39.43103433,1.184622872,1.090323421,32.7433377,642.6819353,3.186926921 -2016-09-04 17:30:00-07:00,78,179.6524176,3.372957758,38.07498169,0.518584396,0.82804171,40.60197449,39.19314847,0.942707685,0.864866907,32.62355807,847.2583818,3.938992473 -2016-09-04 17:40:00-07:00,78,137.7351527,0.526493895,38.05767822,0.519581018,0.812610098,39.19395447,38.87540037,0.721199851,0.655543194,32.53937367,1135.147535,5.064867091 -2016-09-04 17:50:00-07:00,78,99.98462787,2.220336827,37.71353149,0.523482445,0.783486152,37.7923584,38.35748223,0.515721913,0.460813999,32.26483289,1534.480511,8.021543873 -2016-09-04 18:00:00-07:00,78,68.38305169,1.587949754,37.72891235,0.531326639,0.798495364,36.33370972,37.69130668,0.344182245,0.3001301,31.74577999,1584.813824,10.6561739 -2016-09-04 18:10:00-07:00,78,43.16316569,1.452723672,37.25080872,0.553066903,0.731595891,35.07951355,36.80133946,0.214452925,0.1796275,30.97809999,1718.451797,17.59210995 -2016-09-04 18:20:00-07:00,78,24.64076037,1.657044296,37.26042175,0.610823706,0.679993218,33.86248779,35.68264643,0.123315079,0.1031246,29.44479002,2068.759562,31.92348869 -2016-09-04 18:30:00-07:00,78,16.98765475,1.915599047,36.79579163,0.572271067,0.499706587,32.81080627,34.82026907,0.082721294,0.06947761,28.04572758,3691.999669,55.98458611 -2016-09-04 18:40:00-07:00,78,9.359318455,1.61863178,35.59799194,0.608776917,-0.012919269,31.84307861,33.55227158,0.050397214,0.041030114,26.37048688,4636.626743,99.40040977 -2016-09-04 18:50:00-07:00,78,4.59929233,2.480814317,35.1615448,0.559154819,-0.1,31.22911926,30.68897949,0.022335569,0.01731778,22.99791999,19622.1738,297.9518121 -2016-10-03 06:20:00-07:00,78,3.195150607,0.922156535,19.38566589,0.563797162,0.066360213,15.85635315,32.84628248,0.01917678,0.0152119,23.70720003,18561.42435,375.6359908 -2016-10-03 06:30:00-07:00,78,10.64175499,1.113338876,19.1799469,0.597678844,-0.1,15.74290466,36.32313122,0.049482767,0.04091427,28.61825001,5242.303712,109.7916827 -2016-10-03 06:40:00-07:00,78,18.09069492,0.609207423,19.23762512,0.643099266,0.051430383,15.79930115,37.8081332,0.083554838,0.07153612,30.51819,4024.547004,51.33000653 -2016-10-03 06:50:00-07:00,78,24.41693272,0.839282703,19.23506165,0.683074797,-0.006983076,16.31970215,40.17425299,0.196607054,0.1347996,37.20204998,273.7561336,18.52789629 -2016-10-03 07:00:00-07:00,78,105.9221056,0.954400599,19.6118927,0.466044477,0.007699845,16.7984314,39.14966835,0.154195664,0.136082636,32.83312272,2911.875503,22.30805272 -2016-10-03 07:10:00-07:00,78,125.1655366,0.274828789,19.75352478,0.488468794,0.027158803,17.04646301,39.66456087,0.177932777,0.157045965,34.17526183,1153.6965,22.71425063 -2016-10-03 07:20:00-07:00,78,184.3774721,1.292904946,20.03359985,0.489234232,0.020963365,17.90459534,39.67780377,0.193998393,0.174408373,34.09965954,2181.278676,17.71838106 -2016-10-03 07:30:00-07:00,78,224.1615554,1.006311751,20.62384033,0.492461863,0.037719224,17.78153992,40.67276729,0.309889529,0.282822828,35.26068635,1116.586998,11.2655082 -2016-10-03 07:40:00-07:00,78,252.9605877,1.324508563,21.20320129,0.499836561,0.012622375,20.39697266,43.11938837,1.383759811,1.28305088,36.85746658,378.6272269,2.480456041 -2016-10-03 07:50:00-07:00,78,298.6725151,0.721761645,21.86585999,0.505988805,-0.003307544,23.07971191,42.89856548,1.670601191,1.559914321,36.07807782,1073.04659,2.332671205 -2016-10-03 08:00:00-07:00,78,345.5740164,1.388796363,22.38819885,0.510460814,-0.005322139,25.44967651,42.62858078,1.930697415,1.804129203,35.55622703,777.7485098,2.076062714 -2016-10-03 08:10:00-07:00,78,396.6176063,2.2282279,22.79899597,0.512853892,-0.004361441,29.46478271,42.46268116,2.213444488,2.067207082,35.24668053,685.9302985,1.744536636 -2016-10-03 08:20:00-07:00,78,442.1111121,1.772763105,22.6073761,0.51659587,-0.005151791,32.53330994,42.34793465,2.459660717,2.295900934,35.01140264,758.0157307,1.613117077 -2016-10-03 08:30:00-07:00,78,486.1193465,1.309287515,22.91499329,0.518811171,-0.008034824,34.5905304,42.24609414,2.693708437,2.518898931,34.68785885,764.6325252,1.461041715 -2016-10-03 08:40:00-07:00,78,532.2498865,1.202541115,23.38668823,0.520251056,-0.011379294,37.33348083,42.02528093,2.936229384,2.738385594,34.35286847,730.5452164,1.518244751 -2016-10-03 08:50:00-07:00,78,575.4077027,1.042841831,24.80302429,0.522805424,-0.00354478,40.16168213,41.76734794,3.163402431,2.941108759,33.97748511,626.565472,1.450716449 -2016-10-03 09:00:00-07:00,78,622.838591,0.733818384,23.83273315,0.522881038,-0.002288758,40.90638733,41.74222726,3.405040807,3.164593186,33.82557034,674.9226776,1.327711447 -2016-10-03 09:10:00-07:00,78,662.4117226,1.216720361,24.25636292,0.524617852,-0.005702989,43.25648499,41.50137254,3.602887989,3.351379314,33.398026,690.0271524,1.343615642 -2016-10-03 09:20:00-07:00,78,697.3186964,2.739568977,23.91027832,0.527306028,-0.006084509,41.43766785,41.81784485,3.77573071,3.512060036,33.63475598,610.0301019,1.297505989 -2016-10-03 09:30:00-07:00,78,733.2331392,2.308537709,24.05127869,0.52741141,0.003230065,41.70236206,41.82880079,3.954321451,3.677019896,33.54390653,537.9654207,1.221433499 -2016-10-03 09:40:00-07:00,78,770.1828075,0.248993388,24.70240784,0.52877412,-0.002786605,45.33100891,41.51075553,4.133999509,3.834325568,33.11741979,610.6183434,1.141218557 -2016-10-03 09:50:00-07:00,78,795.3691767,1.454486456,24.32557678,0.527547443,-0.009729536,45.51493835,41.46920298,4.242516129,3.935129805,33.0373041,700.5686835,1.182457487 -2016-10-03 10:00:00-07:00,78,821.1673139,1.545531317,25.0869397,0.526423558,-0.002166144,46.19491577,41.44335593,4.36194216,4.049474864,32.87375272,588.2585344,1.20499314 -2016-10-03 10:10:00-07:00,78,862.3798241,2.921658698,24.5453949,0.527474351,-0.006422428,45.02146912,41.72728616,4.562692316,4.235815815,33.06668654,503.7787279,1.172191621 -2016-10-03 10:20:00-07:00,78,901.0153502,3.858022965,24.60499573,0.528301548,-0.006048683,45.79052734,41.64972069,4.754009953,4.409228854,32.89415518,531.9182633,1.118250748 -2016-10-03 10:30:00-07:00,78,923.4111972,2.361850804,24.85429382,0.528701734,-0.004240186,47.17481995,41.53894373,4.866353415,4.507257944,32.72123696,517.9934313,1.081557003 -2016-10-03 10:40:00-07:00,78,950.100943,2.854686733,25.10360718,0.529412509,-0.000146211,47.35746765,41.60803664,5.000071306,4.625248921,32.72415163,485.6756738,1.091158655 -2016-10-03 10:50:00-07:00,78,969.9042195,2.677964712,25.54837036,0.530257289,-0.00791368,49.14552307,41.33871322,5.101553206,4.719646224,32.3884693,529.1225284,1.09054636 -2016-10-03 11:00:00-07:00,78,982.6156917,3.395989132,25.32406616,0.530253737,-0.000960535,50.05172729,41.2462691,5.176443529,4.778237697,32.25542392,572.7450088,1.086606556 -2016-10-03 11:10:00-07:00,78,1012.900121,0.56843156,25.60220337,0.531558935,0.001152523,50.19656372,41.30095064,5.325843899,4.916260764,32.23739496,524.6922237,1.093346566 -2016-10-03 11:20:00-07:00,78,1015.662385,2.124165036,25.68551636,0.531763204,-0.002352786,51.32579041,41.16261777,5.337704703,4.932573668,32.02987021,631.1638268,1.02307321 -2016-10-03 11:30:00-07:00,78,1044.954941,3.626985792,26.08542297,0.532148942,-0.00436728,50.46636963,41.37637905,5.492136494,5.07425088,32.1766664,426.5551295,1.055899157 -2016-10-03 11:40:00-07:00,78,1051.458342,1.50119042,26.12260071,0.533120398,-0.003156399,50.92332458,41.28460946,5.525965859,5.102659199,32.06515832,469.8162647,1.050969471 -2016-10-03 11:50:00-07:00,78,1067.687473,2.058915691,26.97688293,0.532856821,-0.000762641,50.1055603,41.37481481,5.607321457,5.179114101,32.10922125,510.7963765,1.044955598 -2016-10-03 12:00:00-07:00,78,1076.156525,3.561055488,26.80513,0.532190344,0.001488547,50.7150354,41.34556069,5.655319146,5.21741099,32.0637651,453.0300426,1.026096287 -2016-10-03 12:10:00-07:00,78,1070.96872,2.585157556,26.77372742,0.532890954,-0.003073598,51.49113464,41.20405589,5.62432688,5.189104896,31.92687848,653.479926,1.047280925 -2016-10-03 12:20:00-07:00,78,1082.4751,4.59523384,27.2819519,0.532174788,-0.01050071,50.54711914,41.36805557,5.677145154,5.242640397,32.07602971,533.7443996,1.043041041 -2016-10-03 12:30:00-07:00,78,1075.434513,4.035666436,27.69467163,0.533201229,0.902750018,51.862854,41.19185328,5.645024374,5.206545765,31.90206378,436.9931782,1.050734657 -2016-10-03 12:40:00-07:00,78,1074.057246,4.163722149,26.95701599,0.532712822,0.904065245,49.27626038,41.51491405,5.631659954,5.212064219,32.23718194,509.6695689,1.045772517 -2016-10-03 12:50:00-07:00,78,1057.933524,5.58819551,27.35372925,0.533207545,0.89985784,49.73834229,41.40889924,5.548822626,5.122615508,32.18323461,501.7561462,1.05251093 -2016-10-03 13:00:00-07:00,78,1017.941471,4.372929074,27.07044983,0.533068852,0.893119697,48.91288757,41.47655397,5.332256981,4.924103483,32.41845337,575.6723946,1.069924326 -2016-10-03 13:10:00-07:00,78,1043.122745,4.917235547,27.90808105,0.531954547,0.900062717,50.22091675,41.35391157,5.479528016,5.058369563,32.15377454,383.2598249,1.06023431 -2016-10-03 13:20:00-07:00,78,1028.055181,4.157313206,27.70043945,0.53233776,0.905204344,49.28843689,41.48109499,5.395372723,4.973662538,32.3942501,419.5896978,1.04230395 -2016-10-03 13:30:00-07:00,78,1014.811832,3.751877197,27.9446106,0.5316914,0.903691095,48.78086853,41.56076076,5.319271128,4.912150106,32.5100777,509.0190557,1.027488113 -2016-10-03 13:40:00-07:00,78,996.2753082,3.028405029,28.32209778,0.531659524,0.90259224,48.66999634,41.49623234,5.213902068,4.814782603,32.496506,374.4089318,1.080565294 -2016-10-03 13:50:00-07:00,78,975.9219241,1.143700364,28.07983398,0.531230055,0.903280507,48.30084229,41.52780785,5.106063489,4.721178479,32.57997222,478.2059096,1.080278947 -2016-10-03 14:00:00-07:00,78,955.7872126,2.845714535,28.14585205,0.530502159,0.893238324,49.27754211,41.32059966,5.006575156,4.627135377,32.42205935,585.4531382,1.081572433 -2016-10-03 14:10:00-07:00,78,928.1570722,2.751305318,27.73825073,0.530595643,0.885987146,46.52047729,41.64397637,4.869139875,4.507144111,32.82390669,585.0190385,1.086512904 -2016-10-03 14:20:00-07:00,78,899.5198971,3.186141271,28.50794983,0.529919782,0.880989223,46.40512085,41.60972247,4.725410268,4.379380323,32.85246504,616.7324521,1.093526658 -2016-10-03 14:30:00-07:00,78,862.4158127,2.223861488,28.85786438,0.529051315,0.87855314,46.80375671,41.48241091,4.540454082,4.20821937,32.82594086,761.5963249,1.177197301 -2016-10-03 14:40:00-07:00,78,831.7615376,2.226865771,28.36503601,0.528172177,0.875138178,45.65658569,41.6071998,4.396903526,4.072438412,33.10282044,603.6830874,1.158083002 -2016-10-03 14:50:00-07:00,78,796.0809388,2.602661415,28.41886902,0.527357732,0.878779102,45.09645081,41.59832121,4.224759758,3.914398578,33.17923488,591.039553,1.223436558 -2016-10-03 15:00:00-07:00,78,750.862622,3.696641293,28.25352478,0.526773351,0.860546162,44.9221344,41.471076,3.999051953,3.711621962,33.16440455,643.1382268,1.26586239 -2016-10-03 15:10:00-07:00,78,721.4261212,2.333412054,28.69444275,0.524312147,0.856847142,45.50468445,41.33979581,3.861776872,3.580390033,33.09900765,638.8400852,1.248190171 -2016-10-03 15:20:00-07:00,78,678.4070285,1.450921772,28.94630432,0.5236672,0.86094418,43.2353363,41.59210966,3.641374148,3.383571667,33.47331637,570.1301784,1.280680456 -2016-10-03 15:30:00-07:00,78,635.9175159,2.59589212,28.97514343,0.521761682,0.852892436,42.69636536,41.51135431,3.424187473,3.18475279,33.49790551,695.9799522,1.385049287 -2016-10-03 15:40:00-07:00,78,593.2530593,1.434218735,28.80467224,0.519766848,0.843450618,42.26889038,41.45495675,3.201816111,2.970825265,33.64350724,676.2759812,1.443278055 -2016-10-03 15:50:00-07:00,78,553.2970239,3.83110567,28.70533752,0.516221381,0.845740772,40.77436829,41.5429052,2.99487591,2.785499928,33.82629232,786.8293398,1.439097323 -2016-10-03 16:00:00-07:00,78,510.2085598,3.20741049,28.94374084,0.51345959,0.841183844,39.25675964,41.59827189,2.769108821,2.575790526,34.00510552,859.9908243,1.593306103 -2016-10-03 16:10:00-07:00,78,470.1164258,1.429332225,28.60151672,0.510112369,0.837702101,37.52127075,41.67768125,2.552763827,2.376980158,34.19683955,791.8396699,1.680761124 -2016-10-03 16:20:00-07:00,78,426.3085175,2.565850751,28.64894104,0.506317392,0.832911677,36.16836548,41.70603202,2.314432618,2.152431507,34.44031567,966.763576,1.695250089 -2016-10-03 16:30:00-07:00,78,379.897741,6.534253238,28.44194031,0.502262584,0.835809338,34.87635803,41.66845702,2.066364344,1.92116031,34.52533274,877.2946999,1.9760345 -2016-10-03 16:40:00-07:00,78,331.5803688,1.896292521,28.5085907,0.497174477,0.82251906,33.21903992,41.64343421,1.801920687,1.674964164,34.63027528,712.276017,2.044043247 -2016-10-03 16:50:00-07:00,78,285.5677931,1.809093137,28.77326965,0.490379567,0.811615362,32.64866638,41.42559972,1.547786396,1.436446561,34.55165752,654.9797418,2.504343072 -2016-10-03 17:00:00-07:00,78,237.9159835,2.969644535,28.38938904,0.483032016,0.775060562,31.15861511,41.27489196,1.286940336,1.189522332,34.61840593,630.0422249,2.715150744 -2016-10-03 17:10:00-07:00,78,190.7859944,3.223272474,28.29389954,0.474906181,0.744017806,29.98516846,40.99112672,1.023219688,0.943232159,34.45457149,728.3394893,3.320591916 -2016-10-03 17:20:00-07:00,78,140.6853758,1.215999448,28.36824036,0.467367733,0.726994528,28.61689758,40.51652648,0.736575605,0.671555343,34.20019588,692.1070932,5.041530545 -2016-10-03 17:30:00-07:00,78,102.3222591,1.734270123,28.27146912,0.45052652,0.626241637,27.25823975,40.02926798,0.526523801,0.477718222,33.75174527,1231.137793,6.968070695 -2016-10-03 17:40:00-07:00,78,66.71127301,2.051985813,27.88308716,0.434563733,0.548230766,25.56118774,38.49470687,0.207407089,0.184709744,33.00287323,1669.900262,17.3226796 -2016-10-03 17:50:00-07:00,78,32.38500054,4.510437688,27.16467285,0.444152258,0.258097993,24.51335144,37.39819654,0.12541148,0.107460491,31.45613172,2187.377149,34.68957059 -2016-10-03 18:00:00-07:00,78,13.52215717,3.966010935,26.90959167,0.539371328,-0.080783277,23.75839233,35.45771908,0.060185557,0.049934664,28.23854703,5172.066957,82.17552677 -2016-10-03 18:10:00-07:00,78,5.774914139,1.433137469,26.32704163,0.547106104,0.616614249,22.7111969,32.66250591,0.025110505,0.019574184,24.71914638,10872.48122,263.9587847 -2016-11-18 07:00:00-07:00,78,5.512831281,0.657954407,8.784896851,0.555955125,-0.1,4.334625244,35.80425504,0.024532121,0.019225836,27.1976469,9941.521568,228.7317788 -2016-11-18 07:10:00-07:00,78,11.75947719,1.018769075,8.83744812,0.580844184,0.100851237,4.362182617,38.6423591,0.054879458,0.045374985,31.19586332,6225.99086,88.80865811 -2016-11-18 07:20:00-07:00,78,88.77567525,0.572797554,8.968826294,0.304784114,0.477204897,5.036392212,42.02915254,0.137550336,0.08096088,38.73620003,434.058854,38.38648841 -2016-11-18 07:30:00-07:00,78,136.9825241,0.777277853,9.350143433,0.353441945,0.583017868,8.237579346,43.21441717,0.241517597,0.1149141,41.05335,126.6169447,28.03712652 -2016-11-18 07:40:00-07:00,78,181.2169976,1.043442814,10.34671021,0.391533578,0.683788247,11.45799255,43.90549107,1.008505616,0.929744285,37.79879855,718.0738222,3.776824336 -2016-11-18 07:50:00-07:00,78,222.9461264,1.312772292,11.436203,0.425365949,0.719569005,14.13751221,43.91642725,1.29438411,1.189089428,37.6056748,908.9306451,2.66804215 -2016-11-18 08:00:00-07:00,78,285.0642419,1.423724103,12.09246826,0.437342023,0.7189927,15.9095459,43.78619752,1.343316313,1.260762531,37.74712314,463.298638,2.763923702 -2016-11-18 08:10:00-07:00,78,334.938689,1.211112868,12.46481323,0.453749552,0.796535274,18.22567749,43.88318868,1.786028554,1.620265367,37.63431979,472.722479,2.19699794 -2016-11-18 08:20:00-07:00,78,386.9819886,1.619553021,12.61669922,0.464915102,0.79837982,20.39505005,43.81742619,2.082640204,1.945313661,36.84719403,532.1374266,1.938231105 -2016-11-18 08:30:00-07:00,78,433.6418093,0.999382292,13.29475403,0.473892385,0.817167262,22.52278137,43.72929913,2.365736386,2.212972259,36.4764112,531.3832681,1.754297908 -2016-11-18 08:40:00-07:00,78,482.6812027,2.073495383,14.00036621,0.481430005,0.847629377,24.05063782,43.70371935,2.621865016,2.451981758,36.30544615,563.3556415,1.623022184 -2016-11-18 08:50:00-07:00,78,527.758412,2.992155743,14.81364014,0.487453763,0.853835057,25.90982056,43.62884972,2.859109715,2.668770109,36.13160446,466.8827805,1.440698743 -2016-11-18 09:00:00-07:00,78,571.938917,2.71401416,15.95697021,0.492603272,0.858068424,27.78759766,43.5187622,3.085758203,2.885708995,35.79382513,396.9169311,1.449221889 -2016-11-18 09:10:00-07:00,78,613.2207601,5.002752962,16.28573608,0.496641596,0.865393657,29.365448,43.44712805,3.294074978,3.074625141,35.61968135,386.1918907,1.324189149 -2016-11-18 09:20:00-07:00,78,653.2463793,3.708457681,16.63885498,0.500112009,0.869994213,30.2492218,43.456665,3.495495895,3.264012081,35.52524842,350.5742088,1.277117247 -2016-11-18 09:30:00-07:00,78,690.7382509,3.290043902,16.83496704,0.502797478,0.87553941,30.6625824,43.48296979,3.689115812,3.439150163,35.47892804,325.5510355,1.288824274 -2016-11-18 09:40:00-07:00,78,728.8107564,4.913270068,17.15092468,0.505794619,0.879581641,32.19683838,43.34563376,3.868445228,3.60516411,35.23381028,317.1474128,1.207345633 -2016-11-18 09:50:00-07:00,78,763.9413635,4.70266155,17.39445496,0.508169229,0.884084464,32.93513489,43.3041304,4.042119272,3.765913848,35.02077359,329.0352766,1.227582539 -2016-11-18 10:00:00-07:00,78,796.1118027,4.780127518,17.58158875,0.510154997,0.882777356,33.90927124,43.23957064,4.197257022,3.919145357,34.86867084,301.8182123,1.12555513 -2016-11-18 10:10:00-07:00,78,828.3669971,4.986370043,17.63221741,0.511848878,0.892156687,34.39955139,43.24517202,4.353927181,4.058653079,34.79065873,311.5997677,1.143588276 -2016-11-18 10:20:00-07:00,78,856.2412791,5.597128104,18.05007935,0.512997691,0.894597026,34.51426697,43.23065807,4.49746119,4.189097242,34.71922592,361.1757684,1.121508754 -2016-11-18 10:30:00-07:00,78,881.0116319,3.767658924,18.51086426,0.514671546,0.892880444,36.83360291,43.0067052,4.635538846,4.304377813,34.4282839,373.8505081,1.088752365 -2016-11-18 10:40:00-07:00,78,903.8417454,6.142555727,18.77297974,0.515821333,0.894645078,37.49884033,42.93913476,4.743976947,4.408338293,34.298209,429.2751812,1.051918285 -2016-11-18 10:50:00-07:00,78,925.4539109,4.446229655,18.81463623,0.516589562,0.894144093,38.18457031,42.83011475,4.849795128,4.505915096,34.12003646,399.5126364,1.045943635 -2016-11-18 11:00:00-07:00,78,943.6538745,5.74284798,19.25236511,0.517909779,0.900106312,38.89146423,42.79894822,4.943211608,4.598742088,33.96523095,375.4286577,1.061064826 -2016-11-18 11:10:00-07:00,78,957.9841398,5.466148295,19.59074402,0.51903934,0.89536218,39.37275696,42.76929033,5.022988519,4.66764725,33.90021424,308.3020965,1.051779494 -2016-11-18 11:20:00-07:00,78,971.5769231,3.429474975,19.91951294,0.519640029,0.900239619,39.45159363,42.70360078,5.084381289,4.724387969,33.80096523,393.915616,1.068387467 -2016-11-18 11:30:00-07:00,78,982.7544636,3.408205826,19.91694641,0.520275495,0.899452495,39.66629028,42.72283759,5.145114305,4.779415216,33.79673532,490.639711,1.060209083 -2016-11-18 11:40:00-07:00,78,991.5960444,4.331832742,20.53796387,0.520649334,0.89548434,41.45625427,42.51005389,5.190698312,4.821362282,33.54920563,432.1731349,1.044758161 -2016-11-18 11:50:00-07:00,78,997.2697484,2.013773485,20.50463867,0.521268946,0.900784532,42.15994263,42.39641356,5.22329092,4.844616412,33.41871314,486.3351189,1.061144638 -2016-11-18 12:00:00-07:00,78,1001.355224,3.487955444,21.00965637,0.521499861,0.903178506,41.72735596,42.39944887,5.242066065,4.85961392,33.40978282,345.0249361,1.05792365 -2016-11-18 12:10:00-07:00,78,1003.634388,2.175114783,21.35380554,0.521541214,0.901998129,43.59103394,42.23135185,5.255727937,4.864983896,33.23373124,370.0252626,1.061041099 -2016-11-18 12:20:00-07:00,78,1004.302392,4.20117361,21.75819397,0.52150667,0.901634264,44.83882141,42.05479419,5.2538793,4.86366598,33.05614074,350.9026216,1.043131197 -2016-11-18 12:30:00-07:00,78,999.7390272,1.960941022,21.59989929,0.52165842,0.900682557,45.69631958,41.92704121,5.235954182,4.842868451,32.94136661,358.7882191,1.066048565 -2016-11-18 12:40:00-07:00,78,997.2003577,2.966480227,22.21195984,0.521280911,0.905479739,46.16030884,41.79426463,5.21185397,4.822205992,32.80854366,452.7648558,1.070267846 -2016-11-18 12:50:00-07:00,78,990.4269499,3.592698919,22.32539368,0.521121955,0.905172248,45.73220825,41.79645974,5.179495022,4.793684522,32.83091278,434.1722047,1.075483363 -2016-11-18 13:00:00-07:00,78,980.9301523,2.26143281,21.93891907,0.520908063,0.905011508,43.64678955,42.00457018,5.12187085,4.746058143,33.07150542,540.2526757,1.07684542 -2016-11-18 13:10:00-07:00,78,969.4468484,2.705041403,22.93934631,0.520249721,0.903297171,45.47840881,41.80359609,5.063165155,4.683785306,32.89887774,460.6981464,1.084940745 -2016-11-18 13:20:00-07:00,78,955.5199761,2.335775122,23.18481445,0.519552208,0.90027395,47.05241394,41.55275884,4.988882201,4.616904597,32.6756815,467.202028,1.061265765 -2016-11-18 13:30:00-07:00,78,940.0617199,1.54124523,23.50076294,0.518575077,0.897284658,47.3004303,41.530051,4.908546386,4.541725997,32.68919523,532.8363117,1.10655781 -2016-11-18 13:40:00-07:00,78,921.9517712,0.296979435,23.83850098,0.517529793,0.896533983,47.37733459,41.37144677,4.815243362,4.454169199,32.57967612,475.9949981,1.119839553 -2016-11-18 13:50:00-07:00,78,903.4382945,0.485757915,23.85067749,0.516572762,0.893793788,49.09617615,41.18249884,4.718493455,4.361466122,32.43960683,474.3507143,1.134510962 -2016-11-18 14:00:00-07:00,78,879.3516194,2.23299427,23.30593872,0.515278573,0.896678636,46.99601746,41.31851351,4.589006513,4.246989469,32.63812864,546.3090432,1.154341323 -2016-11-18 14:10:00-07:00,78,853.1270909,3.362423172,24.18202209,0.51396554,0.895038735,48.10473633,41.14717133,4.455581063,4.123227588,32.52996684,592.2265675,1.123077268 -2016-11-18 14:20:00-07:00,78,823.4593036,1.180791333,24.40504456,0.511913831,0.894366272,46.49612427,41.26401014,4.305921743,3.97911364,32.80149848,613.4379958,1.18644344 -2016-11-18 14:30:00-07:00,78,794.5418148,1.743402975,24.74150085,0.510633479,0.884015054,46.58841309,41.24796342,4.163924673,3.854138029,32.86011252,615.4210174,1.20977184 -2016-11-18 14:40:00-07:00,78,759.8609642,1.828359221,23.95513916,0.508364554,0.879217734,44.48248291,41.42784637,3.990958947,3.699754153,33.13036207,579.5561057,1.233560194 -2016-11-18 14:50:00-07:00,78,724.0469691,2.042693356,24.1736908,0.506338554,0.866874671,41.1499176,41.81457833,3.808985499,3.537364528,33.6115266,529.866898,1.217497123 -2016-11-18 15:00:00-07:00,78,686.8300643,2.428302672,23.97692871,0.503249267,0.870166449,40.3699646,41.83574425,3.625228506,3.368547658,33.72984628,588.039545,1.24926611 -2016-11-18 15:10:00-07:00,78,647.6009624,0.796704451,24.47425842,0.500356012,0.868432037,40.19564819,41.78936643,3.428209821,3.184099923,33.86436738,738.7903406,1.328330433 -2016-11-18 15:20:00-07:00,78,603.9654597,1.263905377,24.40119934,0.496831766,0.86149528,38.60435486,41.89993759,3.217122768,2.98864121,34.08304504,670.1121105,1.332444524 -2016-11-18 15:30:00-07:00,78,562.8093114,0.717876563,25.27087402,0.49222466,0.85420867,38.79789734,41.70123671,2.997247221,2.785952433,33.9951244,755.6973987,1.442887654 -2016-11-18 15:40:00-07:00,78,518.5930301,1.180671053,24.91004944,0.487094915,0.857954633,37.93399048,41.74122057,2.762606191,2.571065557,34.14796159,911.3545286,1.470080768 -2016-11-18 15:50:00-07:00,78,474.1505292,1.189202783,25.59899902,0.480909369,0.834389892,37.81671143,41.56839242,2.529266859,2.354176554,34.09386829,1028.211601,1.720561192 -2016-11-18 16:00:00-07:00,78,427.1949628,2.174794315,24.46913147,0.472511209,0.826941984,34.29187622,41.8608228,2.278452139,2.119196809,34.6097615,1088.439505,1.652410092 -2016-11-18 16:10:00-07:00,78,377.8472969,1.331718187,24.45054626,0.462351564,0.788690259,32.25580811,41.94117938,2.015283813,1.877991112,34.82176384,1148.526513,2.040693502 -2016-11-18 16:20:00-07:00,78,329.5427894,1.391760134,24.54988098,0.449451842,0.796480173,31.7437439,41.76011991,1.755439484,1.633716656,34.76288054,1229.865873,1.985461345 -2016-11-18 16:30:00-07:00,78,193.5636133,1.018048162,24.37364197,0.432361515,0.765295534,30.07745361,41.67375064,1.483794554,1.375703204,34.91333513,1219.458636,2.434397807 -2016-11-18 16:40:00-07:00,78,226.653929,1.72001055,24.37043762,0.409185114,0.605541887,28.14777161,40.01823371,0.202163193,0.1649383,37.08271001,750.6258054,15.43663147 -2016-11-18 16:50:00-07:00,78,172.5345329,0.93609585,24.58769226,0.379212835,0.64091381,24.86711121,40.69555451,0.51348104,0.4733146,36.08551,1207.490153,5.570880697 -2016-11-18 17:00:00-07:00,78,117.8858561,0.741108055,24.50437927,0.336642962,0.39046222,23.33605957,40.85697163,0.588959223,0.538383859,34.65095722,749.8671938,6.338091018 -2016-11-18 17:10:00-07:00,78,56.74404649,0.912383096,23.26428223,0.309741908,0.402973616,20.77893066,37.3155623,0.075581374,0.062622054,31.53074945,2078.254582,56.23388229 -2016-11-18 17:20:00-07:00,78,7.576207811,0.916028107,22.31770325,0.577772458,-0.1,18.23529053,35.11959664,0.039924526,0.03186923,27.73705002,5809.233361,135.8241805 -2016-11-18 17:30:00-07:00,78,3.428946352,1.121029132,22.32539368,0.507812619,1.1,16.54336548,31.68799771,0.016246525,0.0122947,23.72542001,23224.75316,454.1792542 -2016-12-18 07:20:00-07:00,78,2.480802064,1.138693364,4.149414063,0.561937842,,0.620101929,33.30080152,0.013134897,0.009840512,24.51384153,23996.38723,681.5999818 -2016-12-18 07:30:00-07:00,78,7.496509761,1.254332266,4.316680908,0.56215657,,0.401550293,38.29331567,0.040318129,0.032612675,30.07766602,6980.507132,137.4083451 -2016-12-18 07:40:00-07:00,78,54.10512226,1.393322661,4.849899292,0.290731685,,0.743148804,41.30133478,0.09575784,0.078875961,35.88868737,1270.704055,43.60488395 -2016-12-18 07:50:00-07:00,78,111.2977188,1.571927466,5.46321106,0.315130162,,3.539942627,43.11840363,0.144060186,0.1222332,39.29588002,1144.993352,25.13292145 -2016-12-18 08:00:00-07:00,78,166.4036413,1.239872435,6.00604248,0.352797195,,6.764846802,43.94550197,0.244101793,0.156825198,41.73776,186.1316533,20.46813606 -2016-12-18 08:10:00-07:00,78,201.4905003,3.978027301,6.54309082,0.389905437,,9.068161011,44.53011818,1.193324175,0.749153001,41.38602,972.5939443,3.322600141 -2016-12-18 08:20:00-07:00,78,258.6547904,2.665147036,7.041702271,0.41563137,,10.5793457,44.632657,1.361525591,1.273750917,38.52874491,665.6558436,2.728302343 -2016-12-18 08:30:00-07:00,78,326.2769355,3.785282783,7.685791016,0.426633682,,11.97647095,44.67922334,1.473467235,1.384472613,39.01361776,391.4515391,2.213807291 -2016-12-18 08:40:00-07:00,78,374.7073597,3.038138514,7.97354126,0.441233041,,14.05548096,44.82541343,2.036268684,1.903816896,37.86856281,638.149563,1.958457047 -2016-12-18 08:50:00-07:00,78,421.7502387,3.448781641,8.190155029,0.453104385,,16.10757446,44.75747767,2.301764583,2.155880712,37.64991568,866.9737425,1.775306685 -2016-12-18 09:00:00-07:00,78,470.3527984,4.312445959,8.558654785,0.462054906,,17.69245911,44.7201997,2.560892597,2.404474798,37.37663675,659.7938982,1.630560948 -2016-12-18 09:10:00-07:00,78,514.1219097,3.53013339,8.771438599,0.469542523,,19.34529114,44.64964687,2.788718602,2.617031381,37.20101875,634.8985062,1.528997846 -2016-12-18 09:20:00-07:00,78,557.3362519,2.559762555,9.15852356,0.475922,,21.64476013,44.469503,3.02087914,2.833130184,36.9030078,555.9628372,1.444530457 -2016-12-18 09:30:00-07:00,78,596.5959379,1.981529491,9.700707397,0.481090067,,23.72891235,44.26397565,3.226235097,3.017570879,36.60025025,551.2874746,1.314892677 -2016-12-18 09:40:00-07:00,78,637.9576721,2.319152271,10.31082153,0.485306347,,25.9213562,44.0711566,3.433751218,3.21604893,36.19166399,480.0639794,1.272680532 -2016-12-18 09:50:00-07:00,78,676.0276683,2.504446674,10.76905823,0.488868537,,27.24926758,43.94065973,3.624770211,3.392969719,35.96356319,492.0029235,1.296491028 -2016-12-18 10:00:00-07:00,78,711.2738172,1.741119674,11.08885193,0.492237271,,28.56626892,43.87761103,3.801648767,3.551484428,35.81050039,404.6274594,1.205299803 -2016-12-18 10:10:00-07:00,78,745.574389,3.528851448,11.38813782,0.495812576,,29.35134888,43.85229712,3.965124726,3.702403952,35.71011416,395.1194631,1.180487921 -2016-12-18 10:20:00-07:00,78,776.4215203,2.893980676,11.50541687,0.497973346,,30.12808228,43.80829266,4.123786834,3.855911609,35.49163225,411.7126882,1.199081729 -2016-12-18 10:30:00-07:00,78,804.9407311,4.825229351,12.11361694,0.499885957,,31.73861694,43.59690254,4.270701055,3.988108308,35.20014952,454.1154501,1.139323696 -2016-12-18 10:40:00-07:00,78,832.3911117,4.64017537,12.27383423,0.501377142,,31.31500244,43.68637425,4.403155856,4.116713637,35.22742472,520.8050961,1.122471041 -2016-12-18 10:50:00-07:00,78,855.4061868,4.775881455,12.27832031,0.502729891,,31.54058838,43.7354938,4.522407362,4.22712817,35.21854276,406.4641703,1.072303626 -2016-12-18 11:00:00-07:00,78,875.7130917,3.281111866,12.7884613,0.504208238,,33.25557434,43.50886219,4.633689686,4.320535843,34.93137299,375.3132719,1.102448768 -2016-12-18 11:10:00-07:00,78,896.3849592,4.017000985,12.63528442,0.505321317,,33.64138794,43.53705238,4.739022869,4.415053055,34.90446769,415.2144823,1.057025046 -2016-12-18 11:20:00-07:00,78,912.8171182,4.738670485,12.88395691,0.506130099,,34.23034668,43.51394457,4.82254152,4.490026316,34.83958811,325.7535828,1.081048902 -2016-12-18 11:30:00-07:00,78,926.7797957,3.302420829,12.92304993,0.507189044,,35.24485779,43.38800237,4.894363119,4.554376914,34.66866652,348.8812845,1.074008006 -2016-12-18 11:40:00-07:00,78,940.5884721,3.021796038,14.06317139,0.507323981,,37.70327759,43.17414487,4.964269023,4.610312411,34.41933814,266.2856559,1.073967917 -2016-12-18 11:50:00-07:00,78,950.8640967,2.884607753,14.44512939,0.507680534,,39.2099762,43.03186034,5.022507427,4.663512056,34.17582439,236.0334472,1.072952602 -2016-12-18 12:00:00-07:00,78,956.218912,4.60048133,13.84976196,0.5081152,,37.55075073,43.07574498,5.040364282,4.691255211,34.2136714,342.7575592,1.067572993 -2016-12-18 12:10:00-07:00,78,961.3116637,3.543231301,13.76579285,0.508298407,,37.8276062,43.04824568,5.071417187,4.715576398,34.16545229,317.4892574,1.067149305 -2016-12-18 12:20:00-07:00,78,962.7378998,4.651350961,14.49319458,0.508203955,,38.90684509,42.96533881,5.079382,4.723169752,34.06778592,313.3208636,1.071666962 -2016-12-18 12:30:00-07:00,78,964.0226713,3.850292126,14.19775391,0.508296404,,38.9690094,42.85416003,5.088187376,4.727837248,33.96036354,335.2971232,1.041953427 -2016-12-18 12:40:00-07:00,78,960.1733173,3.151574093,14.86619568,0.508312792,,39.54260254,42.85499152,5.067303241,4.705308038,33.96706017,289.7959351,1.07004816 -2016-12-18 12:50:00-07:00,78,959.697976,1.741920775,15.43785095,0.50791937,,41.10505676,42.68423084,5.059264198,4.693554283,33.79897035,274.9523005,1.05104876 -2016-12-18 13:00:00-07:00,78,953.4566864,3.332742504,15.61857605,0.507541301,,41.60879517,42.64411637,5.020083153,4.665950858,33.76511401,316.7349302,1.073553465 -2016-12-18 13:10:00-07:00,78,944.812739,0.693362709,15.89865051,0.506972165,,41.82284546,42.49202952,4.974424576,4.614485671,33.64670077,342.5635507,1.057354961 -2016-12-18 13:20:00-07:00,78,932.5587922,1.935826745,15.5282135,0.506601006,,41.69210815,42.3940783,4.911240985,4.562295425,33.56990969,318.2106218,1.093114125 -2016-12-18 13:30:00-07:00,78,917.9253501,2.102174624,15.80314636,0.506189691,,39.70346069,42.66691553,4.838446526,4.484839818,33.96578246,351.5611013,1.093914924 -2016-12-18 13:40:00-07:00,78,902.9346951,1.263264092,16.11013794,0.505163053,,41.57354736,42.36798417,4.758552477,4.418163569,33.62201791,339.2593581,1.108028713 -2016-12-18 13:50:00-07:00,78,885.6778186,2.587079806,15.8300647,0.503231223,,38.53514099,42.68599985,4.670421356,4.335751668,34.06541099,401.5461499,1.112676753 -2016-12-18 14:00:00-07:00,78,865.8925219,2.468797881,16.73370361,0.501876494,,39.67333984,42.58425335,4.568223604,4.240121854,34.01178672,340.2675292,1.088877375 -2016-12-18 14:10:00-07:00,78,843.3679305,3.559293333,15.92556763,0.500184469,,36.82270813,42.77209363,4.442669246,4.135320475,34.26887572,530.0348268,1.141118413 -2016-12-18 14:20:00-07:00,78,817.0227547,3.814122537,16.10757446,0.49864301,,34.89109802,43.03181354,4.302425057,4.009208319,34.59746254,400.2101424,1.128928132 -2016-12-18 14:30:00-07:00,78,789.1844131,3.717309739,15.73136902,0.496470656,,33.21199036,43.14845875,4.1663233,3.884024165,34.79231308,432.9073069,1.172578817 -2016-12-18 14:40:00-07:00,78,761.4283077,3.343877862,16.04669189,0.493376793,,33.03190613,43.13983705,4.015606664,3.746260603,34.85535339,397.613394,1.196289734 -2016-12-18 14:50:00-07:00,78,728.9135783,2.868866189,16.61706543,0.490391938,,32.51216125,43.17799244,3.846600439,3.588286332,35.06791408,578.6128417,1.216212304 -2016-12-18 15:00:00-07:00,78,694.9701518,2.951539345,16.26202393,0.487219084,,30.41648865,43.3834576,3.668681826,3.426211331,35.36326389,565.3044616,1.210534125 -2016-12-18 15:10:00-07:00,78,656.1113688,3.635477778,16.47927856,0.483448999,,29.85955811,43.35244458,3.476356784,3.249752915,35.43496017,620.5654456,1.236978789 -2016-12-18 15:20:00-07:00,78,617.409212,2.055991106,16.65039063,0.478452507,,30.15563965,43.20836213,3.279187078,3.062882499,35.37849956,587.8245323,1.337821096 -2016-12-18 15:30:00-07:00,78,577.4016402,2.994638742,16.61065674,0.472730901,,28.57203674,43.30346254,3.074644351,2.877693311,35.57941572,571.1506298,1.385997442 -2016-12-18 15:40:00-07:00,78,533.1517928,3.792773131,16.50170898,0.46674519,,27.69210815,43.33953044,2.840633709,2.659443037,35.82644759,959.062362,1.46122379 -2016-12-18 15:50:00-07:00,78,490.3153841,1.748529486,16.63757324,0.45836134,,27.30053711,43.22473995,2.617294433,2.449782224,35.82767883,832.3213402,1.479852563 -2016-12-18 16:00:00-07:00,78,443.3264349,1.772522894,16.72601318,0.448448399,,25.79446411,43.26985012,2.373534795,2.22349573,35.98591239,833.839295,1.643668206 -2016-12-18 16:10:00-07:00,78,395.792802,1.648192098,16.99327087,0.436426472,,25.68743896,43.07315472,2.116464048,1.981831937,35.91383786,1062.695702,1.797786068 -2016-12-18 16:20:00-07:00,78,345.6331096,1.711438796,16.73498535,0.419377777,,23.73083496,43.1009758,1.849362383,1.727679983,36.18031235,1069.886822,1.990084175 -2016-12-18 16:30:00-07:00,78,293.1095371,1.076568235,16.59463501,0.399416065,,21.71846008,43.08112628,1.565515742,1.462336527,36.29726337,1290.493007,2.504533446 -2016-12-18 16:40:00-07:00,78,223.7375382,2.591005121,16.37225342,0.376733961,,20.08166504,42.75271958,1.081942458,0.889022599,38.73080002,212.5505853,2.6460012 -2016-12-18 16:50:00-07:00,78,129.5310557,2.364534899,16.22293091,0.365123997,,17.77192688,41.71327176,0.531921423,0.4625263,37.02514,330.5115273,5.026514199 -2016-12-18 17:00:00-07:00,78,39.22753848,0.807038569,15.78392029,0.431233258,,14.57394409,39.53756985,0.16309775,0.125699682,34.44361851,776.2755406,25.40646005 -2016-12-18 17:10:00-07:00,78,21.03275741,1.897013504,15.08473206,0.432028369,,12.29626465,38.44500976,0.085957867,0.073560957,31.75288685,4353.715851,53.0316205 -2016-12-18 17:20:00-07:00,78,7.712316022,1.656002984,14.98091125,0.517470372,,11.04975891,36.15789226,0.038123801,0.0310234,27.91891003,7482.580345,146.8728836 -2016-12-18 17:30:00-07:00,78,2.455111073,1.245680116,14.72454834,0.555397197,,9.927581787,30.927837,0.012009201,0.009101328,21.89337997,17515.17239,709.5084175 +date_time,module_id,poa_global,wind_speed,temp_air,blue_frac,beam_frac,temp_module,v_oc,i_sc,i_mp,v_mp,r_sc,r_oc +2016-01-26 07:20:00-07:00,78,2.666484317,1.472831997,8.177978516,0.454991652,1.1,2.081939697,33.04064421,0.013215447,0.009809045,24.33732,115258.5498,608.680999 +2016-01-26 07:30:00-07:00,78,7.899142696,1.297711339,8.241424561,0.522026664,-0.1,2.436985474,37.64402934,0.037248728,0.02983236,29.62497997,8253.745059,150.461283 +2016-01-26 07:40:00-07:00,78,52.92767243,0.955482493,7.739624023,0.270154323,0.300267162,2.592086792,39.6492057,0.072837131,0.061195743,32.44486777,4762.543972,63.66002837 +2016-01-26 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10:20:00-07:00,78,776.4215203,2.893980676,11.50541687,0.497973346,,30.12808228,43.80829266,4.123786834,3.855911609,35.49163225 +2016-12-18 10:30:00-07:00,78,804.9407311,4.825229351,12.11361694,0.499885957,,31.73861694,43.59690254,4.270701055,3.988108308,35.20014952 +2016-12-18 10:40:00-07:00,78,832.3911117,4.64017537,12.27383423,0.501377142,,31.31500244,43.68637425,4.403155856,4.116713637,35.22742472 +2016-12-18 10:50:00-07:00,78,855.4061868,4.775881455,12.27832031,0.502729891,,31.54058838,43.7354938,4.522407362,4.22712817,35.21854276 +2016-12-18 11:00:00-07:00,78,875.7130917,3.281111866,12.7884613,0.504208238,,33.25557434,43.50886219,4.633689686,4.320535843,34.93137299 +2016-12-18 11:10:00-07:00,78,896.3849592,4.017000985,12.63528442,0.505321317,,33.64138794,43.53705238,4.739022869,4.415053055,34.90446769 +2016-12-18 11:20:00-07:00,78,912.8171182,4.738670485,12.88395691,0.506130099,,34.23034668,43.51394457,4.82254152,4.490026316,34.83958811 +2016-12-18 11:30:00-07:00,78,926.7797957,3.302420829,12.92304993,0.507189044,,35.24485779,43.38800237,4.894363119,4.554376914,34.66866652 +2016-12-18 11:40:00-07:00,78,940.5884721,3.021796038,14.06317139,0.507323981,,37.70327759,43.17414487,4.964269023,4.610312411,34.41933814 +2016-12-18 11:50:00-07:00,78,950.8640967,2.884607753,14.44512939,0.507680534,,39.2099762,43.03186034,5.022507427,4.663512056,34.17582439 +2016-12-18 12:00:00-07:00,78,956.218912,4.60048133,13.84976196,0.5081152,,37.55075073,43.07574498,5.040364282,4.691255211,34.2136714 +2016-12-18 12:10:00-07:00,78,961.3116637,3.543231301,13.76579285,0.508298407,,37.8276062,43.04824568,5.071417187,4.715576398,34.16545229 +2016-12-18 12:20:00-07:00,78,962.7378998,4.651350961,14.49319458,0.508203955,,38.90684509,42.96533881,5.079382,4.723169752,34.06778592 +2016-12-18 12:30:00-07:00,78,964.0226713,3.850292126,14.19775391,0.508296404,,38.9690094,42.85416003,5.088187376,4.727837248,33.96036354 +2016-12-18 12:40:00-07:00,78,960.1733173,3.151574093,14.86619568,0.508312792,,39.54260254,42.85499152,5.067303241,4.705308038,33.96706017 +2016-12-18 12:50:00-07:00,78,959.697976,1.741920775,15.43785095,0.50791937,,41.10505676,42.68423084,5.059264198,4.693554283,33.79897035 +2016-12-18 13:00:00-07:00,78,953.4566864,3.332742504,15.61857605,0.507541301,,41.60879517,42.64411637,5.020083153,4.665950858,33.76511401 +2016-12-18 13:10:00-07:00,78,944.812739,0.693362709,15.89865051,0.506972165,,41.82284546,42.49202952,4.974424576,4.614485671,33.64670077 +2016-12-18 13:20:00-07:00,78,932.5587922,1.935826745,15.5282135,0.506601006,,41.69210815,42.3940783,4.911240985,4.562295425,33.56990969 +2016-12-18 13:30:00-07:00,78,917.9253501,2.102174624,15.80314636,0.506189691,,39.70346069,42.66691553,4.838446526,4.484839818,33.96578246 +2016-12-18 13:40:00-07:00,78,902.9346951,1.263264092,16.11013794,0.505163053,,41.57354736,42.36798417,4.758552477,4.418163569,33.62201791 +2016-12-18 13:50:00-07:00,78,885.6778186,2.587079806,15.8300647,0.503231223,,38.53514099,42.68599985,4.670421356,4.335751668,34.06541099 +2016-12-18 14:00:00-07:00,78,865.8925219,2.468797881,16.73370361,0.501876494,,39.67333984,42.58425335,4.568223604,4.240121854,34.01178672 +2016-12-18 14:10:00-07:00,78,843.3679305,3.559293333,15.92556763,0.500184469,,36.82270813,42.77209363,4.442669246,4.135320475,34.26887572 +2016-12-18 14:20:00-07:00,78,817.0227547,3.814122537,16.10757446,0.49864301,,34.89109802,43.03181354,4.302425057,4.009208319,34.59746254 +2016-12-18 14:30:00-07:00,78,789.1844131,3.717309739,15.73136902,0.496470656,,33.21199036,43.14845875,4.1663233,3.884024165,34.79231308 +2016-12-18 14:40:00-07:00,78,761.4283077,3.343877862,16.04669189,0.493376793,,33.03190613,43.13983705,4.015606664,3.746260603,34.85535339 +2016-12-18 14:50:00-07:00,78,728.9135783,2.868866189,16.61706543,0.490391938,,32.51216125,43.17799244,3.846600439,3.588286332,35.06791408 +2016-12-18 15:00:00-07:00,78,694.9701518,2.951539345,16.26202393,0.487219084,,30.41648865,43.3834576,3.668681826,3.426211331,35.36326389 +2016-12-18 15:10:00-07:00,78,656.1113688,3.635477778,16.47927856,0.483448999,,29.85955811,43.35244458,3.476356784,3.249752915,35.43496017 +2016-12-18 15:20:00-07:00,78,617.409212,2.055991106,16.65039063,0.478452507,,30.15563965,43.20836213,3.279187078,3.062882499,35.37849956 +2016-12-18 15:30:00-07:00,78,577.4016402,2.994638742,16.61065674,0.472730901,,28.57203674,43.30346254,3.074644351,2.877693311,35.57941572 +2016-12-18 15:40:00-07:00,78,533.1517928,3.792773131,16.50170898,0.46674519,,27.69210815,43.33953044,2.840633709,2.659443037,35.82644759 +2016-12-18 15:50:00-07:00,78,490.3153841,1.748529486,16.63757324,0.45836134,,27.30053711,43.22473995,2.617294433,2.449782224,35.82767883 +2016-12-18 16:00:00-07:00,78,443.3264349,1.772522894,16.72601318,0.448448399,,25.79446411,43.26985012,2.373534795,2.22349573,35.98591239 +2016-12-18 16:10:00-07:00,78,395.792802,1.648192098,16.99327087,0.436426472,,25.68743896,43.07315472,2.116464048,1.981831937,35.91383786 +2016-12-18 16:20:00-07:00,78,345.6331096,1.711438796,16.73498535,0.419377777,,23.73083496,43.1009758,1.849362383,1.727679983,36.18031235 +2016-12-18 16:30:00-07:00,78,293.1095371,1.076568235,16.59463501,0.399416065,,21.71846008,43.08112628,1.565515742,1.462336527,36.29726337 +2016-12-18 16:40:00-07:00,78,223.7375382,2.591005121,16.37225342,0.376733961,,20.08166504,42.75271958,1.081942458,0.889022599,38.73080002 +2016-12-18 16:50:00-07:00,78,129.5310557,2.364534899,16.22293091,0.365123997,,17.77192688,41.71327176,0.531921423,0.4625263,37.02514 +2016-12-18 17:00:00-07:00,78,39.22753848,0.807038569,15.78392029,0.431233258,,14.57394409,39.53756985,0.16309775,0.125699682,34.44361851 +2016-12-18 17:10:00-07:00,78,21.03275741,1.897013504,15.08473206,0.432028369,,12.29626465,38.44500976,0.085957867,0.073560957,31.75288685 +2016-12-18 17:20:00-07:00,78,7.712316022,1.656002984,14.98091125,0.517470372,,11.04975891,36.15789226,0.038123801,0.0310234,27.91891003 +2016-12-18 17:30:00-07:00,78,2.455111073,1.245680116,14.72454834,0.555397197,,9.927581787,30.927837,0.012009201,0.009101328,21.89337997 diff --git a/docs/tutorials/mlfm_data/meas_gtw/n05667_Y13_R1k6_fClear_041.csv b/docs/tutorials/mlfm_data/meas_gtw/n05667_Y13_R1k6_fClear_041.csv index 7cc5b5fece..c200c034ff 100644 --- a/docs/tutorials/mlfm_data/meas_gtw/n05667_Y13_R1k6_fClear_041.csv +++ b/docs/tutorials/mlfm_data/meas_gtw/n05667_Y13_R1k6_fClear_041.csv @@ -1,1616 +1,1616 @@ -date_time,module_id,poa_global,temp_module,i_sc,p_mp,i_mp,v_mp,v_oc,ff,temp_air,relative_humidity,pressure,precipitation,dni,ghi,dhi,soil,wind_speed -2013-01-04 08:05:12-06:00,n05667,24,4.7,0.1361,4.8684,0.1255,38.8033,45.8512,78.01,4.9,82.2,1007.1,0,0,18.1,18.3,1,0 -2013-01-04 08:10:12-06:00,n05667,33,5.2,0.1788,6.5303,0.1657,39.4118,46.4089,78.72,5,82.9,1007,0,1.6,24.9,25,1,0 -2013-01-04 08:15:12-06:00,n05667,204.7,6.5,1.1024,45.3502,1.0548,42.9932,50.078,82.15,5.2,81.9,1007.1,0,353.4,53,31.5,1,0 -2013-01-04 08:20:12-06:00,n05667,238,8.4,1.2937,52.966,1.2344,42.9074,50.0932,81.73,5.3,80.8,1007.2,0,395.1,65.2,36.7,1,0 -2013-01-04 08:25:12-06:00,n05667,272.4,10.3,1.4898,60.8224,1.4194,42.8509,50.1119,81.47,5.6,79.7,1007.3,0,435.6,78.2,41.6,1,0 -2013-01-04 08:30:12-06:00,n05667,209.1,10.7,1.151,46.3729,1.0956,42.3254,49.4631,81.46,5.9,78.9,1007.2,0,284.3,72.2,44.9,1,0 -2013-01-04 08:35:12-06:00,n05667,204.3,10.7,1.1235,45.1776,1.068,42.3026,49.4346,81.34,5.7,78.6,1007.2,0,264.6,75.5,47.3,1,0 -2013-01-04 08:50:12-06:00,n05667,258.3,12.8,1.4203,56.9459,1.3445,42.3548,49.6118,80.81,5.7,78.1,1007.3,0,337.2,96.8,49.5,1,0 -2013-01-04 08:55:12-06:00,n05667,372.3,12.9,2.0529,83.85,1.9587,42.8097,50.3039,81.2,5.4,79.5,1007.4,0,498.5,129.1,52.8,1,0 -2013-01-04 09:00:12-06:00,n05667,407.7,14.6,2.2527,91.4999,2.1463,42.6307,50.2297,80.86,5,79.9,1007.4,0,537.6,146,57.7,1,0 -2013-01-04 09:05:12-06:00,n05667,190.2,13.6,1.0489,41.4664,0.9953,41.6617,48.7755,81.05,4.5,80.6,1007.4,0,167.1,93.6,64.9,1,0 -2013-01-04 09:10:12-06:00,n05667,359.7,13,1.9773,80.1503,1.8782,42.675,50.1471,80.83,4.3,81.5,1007.3,0,423,148.7,70.2,1,0 -2013-01-04 09:15:12-06:00,n05667,533.2,15.8,2.9297,119.4426,2.7935,42.7567,50.6181,80.54,4.2,81.9,1007.4,0,663.6,200.4,70,1,0 -2013-01-04 09:20:12-06:00,n05667,453.9,15.4,2.5028,101.1774,2.3784,42.5393,50.2115,80.51,4.2,82.5,1007.6,0,540.1,176.8,65.2,1,0 -2013-01-04 09:25:12-06:00,n05667,267.1,14.9,1.4653,58.563,1.3946,41.9913,49.262,81.13,4.4,82.7,1007.7,0,257.2,118.7,63.4,1,0 -2013-01-04 09:30:12-06:00,n05667,439,15.8,2.4122,97.3753,2.2917,42.4907,50.1588,80.48,4.7,81.9,1007.6,0,499.1,180.8,67.8,1,0 -2013-01-04 09:35:12-06:00,n05667,582,17.9,3.2032,128.7131,3.0349,42.4108,50.3813,79.76,4.9,81.1,1007.7,0,683.1,233.5,72.2,1,0 -2013-01-04 09:40:12-06:00,n05667,520.4,18.1,2.8781,115.5213,2.7311,42.299,50.1335,80.06,5,81,1007.7,0,577.7,217.4,75.3,1,0 -2013-01-04 09:45:12-06:00,n05667,646.8,20.9,3.5582,141.8391,3.3723,42.0597,50.1874,79.43,5.3,80.3,1007.6,0,752.5,265.9,74.1,1,0 -2013-01-04 09:50:12-06:00,n05667,654.7,22.3,3.5986,142.4343,3.4081,41.7934,49.9388,79.26,5.6,79.2,1007.6,0,766.3,266.5,64.1,1,0 -2013-01-04 09:55:12-06:00,n05667,600.1,22.2,3.2928,130.0167,3.115,41.7389,49.7594,79.35,5.8,78.9,1007.7,0,697.9,245,54.4,1,0 -2013-01-04 10:00:12-06:00,n05667,665.7,23.6,3.6725,144.7154,3.4755,41.6385,49.7978,79.13,6.1,77.9,1007.9,0,775.7,267,48.9,1,0 -2013-01-04 10:05:12-06:00,n05667,680,23.1,3.7383,147.1766,3.5355,41.6277,49.8225,79.02,6.3,77.4,1007.8,0,787,274.2,46.3,1,0 -2013-01-04 10:10:12-06:00,n05667,694.4,25.3,3.8227,149.8462,3.6123,41.4818,49.725,78.83,6.5,76.6,1007.8,0,793.9,281.4,45.5,1,0 -2013-01-04 10:15:12-06:00,n05667,709.8,26.1,3.9065,152.8871,3.6927,41.4029,49.6749,78.79,7.4,72.9,1007.7,0,802.6,290.7,46.1,1,0 -2013-01-04 10:20:12-06:00,n05667,723.4,27.9,3.9805,154.8632,3.7602,41.1851,49.4967,78.6,7.7,72.2,1007.7,0,806.8,298.7,47.1,1,0 -2013-01-04 10:25:12-06:00,n05667,736.2,27.5,4.0517,156.8424,3.8199,41.0597,49.3978,78.36,7.8,71.2,1007.7,0,811.7,307.3,48.2,1,0 -2013-01-04 10:30:12-06:00,n05667,751.6,28.9,4.139,159.7639,3.9018,40.9458,49.3256,78.25,7.4,72.2,1007.8,0,819.7,316,48.9,1,0 -2013-01-04 10:35:12-06:00,n05667,762.6,29.2,4.1965,161.621,3.956,40.8542,49.2508,78.2,8,70.5,1007.6,0,823.1,322,48.6,1,0 -2013-01-04 10:40:12-06:00,n05667,762.8,30.2,4.1986,161.4075,3.9562,40.7989,49.2026,78.13,8,70.4,1007.6,0,812.5,322.7,47.7,1,0 -2013-01-04 10:45:12-06:00,n05667,779.8,29.1,4.2829,165.0018,4.0383,40.8595,49.2911,78.16,7.9,69.7,1007.4,0,828.6,332,46.1,1,0 -2013-01-04 10:50:12-06:00,n05667,794.3,30.1,4.3728,167.631,4.1143,40.7436,49.2235,77.88,7.7,71,1007.3,0,840.6,340,45.3,1,0 -2013-01-04 10:55:12-06:00,n05667,803.5,31,4.4164,169.2311,4.1539,40.7403,49.2326,77.83,7.9,71,1007.2,0,844.7,345.5,45.2,1,0 -2013-01-04 11:00:12-06:00,n05667,813.6,31.3,4.4793,171.1884,4.211,40.6529,49.1662,77.73,8.6,68.4,1007.1,0,850.1,351.8,45.3,1,0 -2013-01-04 11:05:12-06:00,n05667,820.9,30.2,4.516,173.1551,4.2522,40.7214,49.2618,77.83,8.9,67.1,1006.9,0,852.2,356.6,45.5,1,0 -2013-01-04 11:10:12-06:00,n05667,829.6,30.8,4.5516,174.5412,4.2878,40.7068,49.2586,77.85,8.5,68.4,1006.8,0,855.8,362.2,45.6,1,0 -2013-01-04 11:15:12-06:00,n05667,837.2,31.8,4.5992,175.4184,4.3295,40.5172,49.0888,77.7,8.8,67.5,1006.7,0,859.5,366.9,45.4,1,0 -2013-01-04 11:20:12-06:00,n05667,842.6,32.6,4.633,176.1003,4.356,40.4267,49.0205,77.54,9.3,65.9,1006.7,0,860.9,370.5,45.6,1,0 -2013-01-04 11:25:12-06:00,n05667,851,32.3,4.6757,177.596,4.3936,40.4211,49.0205,77.48,9.2,65.6,1006.5,0,865.2,375.4,45.5,1,0 -2013-01-04 11:30:12-06:00,n05667,857.5,32.5,4.7175,179.0146,4.4326,40.3855,49.0081,77.43,9.1,66.4,1006.4,0,869.1,379.7,45.4,1,0 -2013-01-04 11:35:12-06:00,n05667,862.1,32.7,4.7407,180.1501,4.4542,40.4449,49.068,77.44,9.2,66.4,1006.3,0,871.3,383.1,45.5,1,0 -2013-01-04 11:40:12-06:00,n05667,866.6,32.6,4.7602,180.6873,4.4734,40.3918,49.027,77.42,9.4,65.6,1006.2,0,873.2,386,45.4,1,0 -2013-01-04 11:45:12-06:00,n05667,869.6,32.2,4.7798,181.1018,4.4901,40.3335,48.9737,77.37,9.7,65,1006.1,0,874.2,388,45.2,1,0 -2013-01-04 11:50:12-06:00,n05667,873.9,33.7,4.7966,181.5834,4.5085,40.2762,48.9229,77.38,10.2,63.9,1006,0,877,390.8,45.1,1,0 -2013-01-04 11:55:12-06:00,n05667,875.3,35.7,4.805,180.9269,4.5136,40.0844,48.7486,77.24,10.6,62.2,1005.9,0,877.4,391.7,45,1,0 -2013-01-04 12:05:12-06:00,n05667,877.9,35.2,4.8146,181.0087,4.5222,40.0264,48.6998,77.2,11.1,59.6,1005.7,0,876.7,394.2,45.6,1,0 -2013-01-04 12:10:12-06:00,n05667,883,35.8,4.8583,182.1459,4.5556,39.9826,48.6647,77.04,11,60.1,1005.6,0,882.1,396.5,45.3,1,0 -2013-01-04 12:15:12-06:00,n05667,883.4,36.8,4.8567,182.138,4.5605,39.9385,48.625,77.13,11.8,57.9,1005.5,0,881.5,397,45.9,1,0 -2013-01-04 12:25:12-06:00,n05667,849.2,34.4,4.6737,175.6544,4.3882,40.029,48.6393,77.27,10.3,61.3,1005.4,0,838.5,383.1,48.5,1,0 -2013-01-04 12:30:12-06:00,n05667,882.9,34.1,4.8534,182.3551,4.5565,40.0208,48.6998,77.15,10.3,61.8,1005.2,0,880.9,396.9,46.2,1,0 -2013-01-04 12:35:12-06:00,n05667,877.9,34.2,4.8271,181.7657,4.5315,40.1119,48.7675,77.21,10.4,62.2,1005.1,0,878.4,393.5,45.4,1,0 -2013-01-04 12:40:12-06:00,n05667,872,37.2,4.7957,179.8876,4.5064,39.918,48.5782,77.22,10.6,61.6,1004.9,0,874.4,391.1,45.4,1,0 -2013-01-04 12:45:12-06:00,n05667,869.9,36.2,4.7951,179.3583,4.4958,39.8945,48.5483,77.05,10.7,61.8,1004.8,0,873.6,389.4,45.4,1,0 -2013-01-04 12:50:12-06:00,n05667,864.9,34.7,4.7662,179.2634,4.4763,40.047,48.6914,77.25,10.6,62.4,1004.7,0,870.7,386.7,46.1,1,0 -2013-01-04 12:55:12-06:00,n05667,860.6,35.3,4.7394,178.3594,4.4488,40.0917,48.7141,77.25,10.8,61.7,1004.5,0,867.6,384.3,46.6,1,0 -2013-01-04 13:00:12-06:00,n05667,853.6,34.6,4.7072,177.1318,4.42,40.0749,48.6901,77.29,11.1,61.5,1004.5,0,862.8,380.2,46.7,1,0 -2013-01-04 13:05:12-06:00,n05667,843.2,34.5,4.6528,175.6953,4.3692,40.2125,48.8013,77.38,11.3,60.4,1004.5,0,855.5,375.9,47.4,1,0 -2013-01-04 13:10:12-06:00,n05667,838.7,33.7,4.6176,175.2312,4.3396,40.3797,48.9581,77.51,11.2,60.6,1004.4,0,852.2,372,47.8,1,0 -2013-01-04 13:15:12-06:00,n05667,834,32.5,4.5875,174.7836,4.3137,40.5187,49.0706,77.64,11.3,61,1004.4,0,851.7,369.2,48.1,1,0 -2013-01-04 13:20:12-06:00,n05667,823.6,31,4.5387,173.9203,4.2673,40.7563,49.2748,77.77,11.4,61,1004.3,0,845.5,364.3,48.4,1,0 -2013-01-04 13:25:12-06:00,n05667,815.6,31.3,4.4901,172.4637,4.2294,40.7774,49.2872,77.93,11.3,61.1,1004.3,0,838.4,359.4,50,1,0 -2013-01-04 13:30:12-06:00,n05667,811.8,32.4,4.4577,170.8499,4.194,40.7364,49.2371,77.84,11.3,61.4,1004.3,0,839.6,358.1,52.3,1,0 -2013-01-04 13:35:12-06:00,n05667,798.5,31,4.4077,169.1238,4.1487,40.7653,49.2417,77.92,11.4,61.3,1004.1,0,833.6,349.8,49.7,1,0 -2013-01-04 13:40:12-06:00,n05667,789,30.6,4.3547,167.1982,4.0989,40.7913,49.2456,77.97,11.5,61.5,1004.1,0,831.5,344.9,49.7,1,0 -2013-01-04 13:45:12-06:00,n05667,779.6,29.6,4.3039,165.6572,4.0519,40.8838,49.3152,78.05,11.1,62,1004,0,828.5,339.2,49.3,1,0 -2013-01-04 13:50:12-06:00,n05667,768.7,27.8,4.2427,164.1628,3.9963,41.0791,49.4739,78.21,11,63.1,1003.9,0,823.9,332.5,48.6,1,0 -2013-01-04 13:55:12-06:00,n05667,755.3,28.9,4.1677,161.053,3.9268,41.0139,49.3757,78.26,11.1,64,1003.8,0,817,324.3,47.7,1,0 -2013-01-04 14:00:12-06:00,n05667,736.8,29,4.0721,157.1783,3.8376,40.957,49.2976,78.3,11.3,64.2,1003.8,0,803.2,314.1,47.3,1,0 -2013-01-04 14:05:12-06:00,n05667,733.6,29.4,4.0631,156.8653,3.8268,40.9916,49.3204,78.28,11.3,63.9,1003.7,0,806.6,310.1,47.4,1,0 -2013-01-04 14:10:12-06:00,n05667,722.7,28.9,3.997,154.5668,3.7684,41.0161,49.3204,78.41,11.2,64,1003.7,0,800.8,303.9,48.5,1,0 -2013-01-04 14:15:12-06:00,n05667,709.3,30.1,3.9295,151.7279,3.7017,40.9886,49.2709,78.37,11.3,64.4,1003.6,0,793.6,296.2,48.8,1,0 -2013-01-04 14:20:12-06:00,n05667,694.2,29.7,3.8464,148.5665,3.6253,40.9806,49.2235,78.47,11.4,63.6,1003.5,0,785.3,288.8,49.4,1,0 -2013-01-04 14:25:12-06:00,n05667,683.5,30.1,3.7933,146.6547,3.5764,41.0057,49.2261,78.54,11.5,63.5,1003.5,0,781.7,282.3,50.6,1,0 -2013-01-04 14:30:12-06:00,n05667,665.8,30,3.6969,142.7397,3.4865,40.9402,49.1376,78.58,11.7,62.8,1003.4,0,768.9,273,51,1,0 -2013-01-04 14:35:12-06:00,n05667,650,28.6,3.6184,140.0512,3.4116,41.0515,49.202,78.67,11.5,62.7,1003.4,0,756,262.4,50,1,0 -2013-01-04 14:40:12-06:00,n05667,621.3,28.3,3.4498,133.8368,3.2529,41.1436,49.228,78.81,11.4,63.1,1003.3,0,734,248.4,48.5,1,0 -2013-01-04 14:45:12-06:00,n05667,578.6,29.1,3.2266,124.7469,3.044,40.9818,48.9951,78.91,11.7,62.6,1003.3,0,688.4,228.1,47,1,0 -2013-01-04 14:50:12-06:00,n05667,553.4,27.5,3.0785,119.5135,2.9044,41.1496,49.083,79.09,11.7,62.6,1003.3,0,662.7,216.7,48,1,0 -2013-01-04 14:55:12-06:00,n05667,415,24.9,2.3239,90.7725,2.2027,41.21,48.8377,79.98,11.4,64.3,1003.2,0,477.6,165.5,48.4,1,0 -2013-01-04 15:00:12-06:00,n05667,288.2,21.6,1.5997,62.3339,1.5125,41.2114,48.5424,80.27,11.2,65.9,1003.1,0,307.3,120.3,48.1,1,0 -2013-01-04 15:05:12-06:00,n05667,336.5,20,1.8606,73.4307,1.7601,41.7195,49.1567,80.28,11,66.9,1003.1,0,389.2,137.4,49.3,1,0 -2013-01-04 15:10:12-06:00,n05667,171,20.1,0.9487,36.4434,0.8967,40.6413,47.733,80.48,11.1,67.4,1003.1,0,144.7,80.3,49.5,1,0 -2013-01-04 15:15:12-06:00,n05667,503,21.9,2.8058,111.3261,2.6563,41.9105,49.7177,79.8,11.1,67.7,1003.1,0,649.3,186.6,52.5,1,0 -2013-01-04 15:20:12-06:00,n05667,496.5,22.6,2.7727,109.5876,2.6237,41.7676,49.5601,79.75,11.1,67.6,1003.1,0,662.7,183.4,53.4,1,0 -2013-01-04 15:25:12-06:00,n05667,469,23.1,2.6218,103.3246,2.4821,41.6278,49.3735,79.82,11.1,67.6,1003,0,631.7,175.2,57.8,1,0 -2013-01-04 15:30:12-06:00,n05667,471,23,2.6372,103.9003,2.4936,41.6667,49.4246,79.71,11,68,1002.8,0,641.6,175.9,63.7,1,0 -2013-01-04 15:35:12-06:00,n05667,421.6,23,2.3572,92.6792,2.232,41.5239,49.1699,79.96,11.1,67.8,1002.7,0,572,158,64.3,1,0 -2013-01-04 15:40:12-06:00,n05667,301.2,21.8,1.6866,65.8968,1.5958,41.295,48.675,80.27,11.1,67.7,1002.6,0,379.6,119.7,62,1,0 -2013-01-04 15:45:12-06:00,n05667,293.7,21.4,1.6568,64.849,1.5664,41.4012,48.7578,80.28,11.3,67.4,1002.8,0,380.1,111.1,57.6,1,0 -2013-01-04 15:50:12-06:00,n05667,251.8,20.4,1.407,54.9461,1.3316,41.2638,48.546,80.44,11.3,67.6,1002.8,0,326.7,97.3,54.8,1,0 -2013-01-04 15:55:12-06:00,n05667,179.4,18.7,1.0022,38.7803,0.9458,41.0035,48.1118,80.43,11.2,68,1002.9,0,209.3,74.8,50.2,1,0 -2013-01-04 16:00:12-06:00,n05667,154.6,16.7,0.8596,33.3015,0.8115,41.0374,48.089,80.56,10.9,69.1,1002.9,0,180.7,63.1,43.8,1,0 -2013-01-04 16:05:12-06:00,n05667,72.7,14.9,0.394,14.6387,0.3682,39.7591,46.6694,79.61,10.7,69.6,1002.8,0,47.7,39.1,35,1,0 -2013-01-04 16:10:12-06:00,n05667,118.9,14.2,0.6604,25.4646,0.6222,40.9284,47.8698,80.55,10.6,69.8,1002.9,0,161.9,41.4,28.1,1,0 -2013-01-04 16:15:12-06:00,n05667,83.2,13.4,0.3758,14.6905,0.3581,41.0252,46.9544,83.24,10.5,70,1002.9,0,101.3,30.7,23.7,1,0 -2013-01-04 16:20:12-06:00,n05667,66.4,12.7,0.2607,9.0249,0.2223,40.5949,46.1141,75.07,10.4,70.6,1002.9,0,96.4,27.8,22.3,1,0 -2013-01-04 16:25:12-06:00,n05667,34.9,11.9,0.1716,6.1808,0.1603,38.5581,45.3327,79.47,10.3,71.1,1002.9,0,20.5,22.7,21.4,1,0 -2013-01-04 16:30:13-06:00,n05667,26.5,11.4,0.1356,4.7142,0.1247,37.8142,44.8104,77.61,10.3,71.5,1002.9,0,6.7,18.6,18.2,1,0 -2013-02-24 07:20:12-06:00,n05667,28.4,1.2,0.1574,5.7674,0.1455,39.629,46.6125,78.61,1.3,86.7,1011.5,0,6.5,25.2,24.8,1,0 -2013-02-24 07:25:12-06:00,n05667,50.4,1.8,0.2383,9.0528,0.2231,40.5796,47.4844,79.99,1.4,86.4,1011.5,0,62.2,33.9,27.9,1,0 -2013-02-24 07:30:12-06:00,n05667,58.2,2.1,0.289,11.0778,0.2705,40.9487,47.8555,80.1,1.7,85.5,1011.6,0,73.6,38.7,30.7,1,0 -2013-02-24 07:35:12-06:00,n05667,41.6,2.3,0.2255,8.4558,0.2097,40.329,47.2688,79.33,1.7,85.4,1011.6,0,13.5,35.6,34.4,1,0 -2013-02-24 07:40:12-06:00,n05667,45,2.5,0.2485,9.3568,0.2308,40.5488,47.4021,79.43,1.7,85.6,1011.7,0,4,40.8,40.9,1,0 -2013-02-24 07:45:12-06:00,n05667,49,2.6,0.2721,10.3,0.253,40.7094,47.622,79.49,1.2,86.4,1011.7,0,1.5,45.6,45.9,1,0 -2013-02-24 07:50:12-06:00,n05667,54.3,2.5,0.2994,11.4379,0.2795,40.9274,47.8321,79.86,0.7,88,1011.7,0,0.6,51.7,52.3,1,0 -2013-02-24 07:55:12-06:00,n05667,67.6,2.7,0.3761,14.5989,0.3527,41.3951,48.2881,80.38,0.7,89.4,1011.7,0,1.1,65.9,66.7,1,0 -2013-02-24 08:00:12-06:00,n05667,81.9,3.1,0.4566,17.8944,0.4287,41.7404,48.6257,80.59,0.9,89,1011.6,0,4.7,75.9,76.3,1,0 -2013-02-24 08:05:12-06:00,n05667,72.5,3.1,0.389,15.0822,0.3646,41.3626,48.266,80.33,0.6,89.7,1011.6,0,16.4,64.1,61.9,1,0 -2013-02-24 08:10:12-06:00,n05667,119.2,3.3,0.6077,24.3751,0.5769,42.2498,49.1695,81.57,0.9,89.8,1011.7,0,110.8,77.8,57.1,1,0 -2013-02-24 08:15:12-06:00,n05667,90.8,4.1,0.4682,18.3626,0.4411,41.6294,48.5275,80.81,1.3,89.2,1011.7,0,69.2,66.9,53.8,1,0 -2013-02-24 08:20:12-06:00,n05667,62.5,4.1,0.3379,12.9358,0.3158,40.9587,47.8328,80.05,1.4,88.1,1011.7,0,2.4,63.3,63.6,1,0 -2013-02-24 08:25:12-06:00,n05667,89.1,4.4,0.4879,19.1585,0.4594,41.701,48.558,80.87,1.6,87.9,1011.5,0,6.9,85.9,85.5,1,0 -2013-02-24 08:30:12-06:00,n05667,134.1,5,0.7351,29.5685,0.6975,42.3933,49.3305,81.54,2.1,86.3,1011.5,0,2.7,128.8,129.8,1,0 -2013-02-24 08:35:12-06:00,n05667,157.9,5.3,0.843,34.0492,0.7995,42.5856,49.5649,81.49,2.5,85,1011.4,0,7.6,138.7,138.6,1,0 -2013-02-24 08:40:12-06:00,n05667,206.7,5.8,1.095,44.7404,1.0422,42.9278,49.9546,81.79,2.8,83.4,1011.3,0,30,158,152.5,1,0 -2013-02-24 08:45:12-06:00,n05667,242.3,6.8,1.2691,51.9074,1.2074,42.9902,50.1392,81.57,3.1,81.3,1011.3,0,54,172.4,160.2,1,0 -2013-02-24 08:50:12-06:00,n05667,272.9,7.9,1.4306,58.6219,1.3635,42.9922,50.2106,81.61,3.3,79.7,1011.3,0,78.8,183.8,163,1,0 -2013-02-24 08:55:12-06:00,n05667,347,7.7,1.8238,75.2562,1.7388,43.2812,50.6424,81.48,3.4,79.4,1011.3,0,191.2,209.5,151.7,1,0 -2013-02-24 09:05:12-06:00,n05667,408.1,9.7,2.1829,90.4363,2.0892,43.2881,50.7956,81.56,3.8,77.4,1011.3,0,307.7,245.1,139.1,1,0 -2013-02-24 09:10:12-06:00,n05667,577.9,12.8,3.0999,127.3876,2.9514,43.1624,51.0506,80.5,4,77.1,1011.3,0,603.8,350,131.6,1,0 -2013-02-24 09:15:12-06:00,n05667,569.2,14.2,3.063,124.9181,2.9136,42.8735,50.7579,80.35,4.3,76.7,1011.2,0,598,349.1,124.1,1,0 -2013-02-24 09:20:12-06:00,n05667,605,14.7,3.2535,131.9379,3.0861,42.7519,50.7169,79.96,4.5,74.4,1011.2,0,634.7,373.6,127.1,1,0 -2013-02-24 09:25:12-06:00,n05667,618.6,15.4,3.3042,133.7893,3.1363,42.6585,50.6402,79.96,4.6,74.3,1011.2,0,622.1,383.6,134.8,1,0 -2013-02-24 09:30:12-06:00,n05667,632.9,16.2,3.3681,135.9991,3.1955,42.5601,50.5576,79.87,4.7,73.2,1011.2,0,614.5,391.7,139.6,1,0 -2013-02-24 09:40:12-06:00,n05667,654.7,18.1,3.4964,140.5516,3.3151,42.3969,50.458,79.67,5.1,72.7,1011.1,0,602,410.6,152,1,0 -2013-02-24 09:45:12-06:00,n05667,692.9,19.2,3.7097,148.5822,3.5153,42.2668,50.4112,79.45,5,73,1011.1,0,621.9,437.9,165.4,1,0 -2013-02-24 09:50:12-06:00,n05667,654.8,19,3.4994,140.0328,3.3144,42.2494,50.311,79.54,5.4,72.2,1011.1,0,550.8,425,179,1,0 -2013-02-24 09:55:12-06:00,n05667,588.6,19.9,3.159,126.1818,2.9947,42.1346,50.073,79.77,5.4,72,1011,0,427.7,383.3,189.4,1,0 -2013-02-24 10:00:12-06:00,n05667,512.7,17.1,2.7516,110.3001,2.6082,42.289,50.0535,80.09,5.5,70.3,1011,0,324.6,341.1,190.3,1,0 -2013-02-24 10:05:12-06:00,n05667,671.3,18.9,3.5966,144.1028,3.4059,42.3096,50.4112,79.48,5.5,70.9,1011,0,520.1,430.3,184.3,1,0 -2013-02-24 10:10:12-06:00,n05667,746.8,19.7,3.9889,159.285,3.7757,42.1873,50.4346,79.18,5.6,70.4,1010.9,0,597.2,474.8,187.3,1,0 -2013-02-24 10:15:12-06:00,n05667,736.1,19.7,3.9456,157.4007,3.7373,42.1163,50.3683,79.2,5.9,69.3,1010.9,0,553.9,460.4,189.4,1,0 -2013-02-24 10:20:12-06:00,n05667,521.8,19.8,2.771,109.7033,2.6206,41.8611,49.651,79.74,6.1,68.9,1010.9,0,332.8,338,173,1,0 -2013-02-24 10:25:12-06:00,n05667,786.2,19.3,4.2174,168.6309,3.9909,42.2541,50.5959,79.03,6,68.3,1010.9,0,627.4,481.6,163.1,1,0 -2013-02-24 10:30:12-06:00,n05667,875.7,21.4,4.6926,185.8871,4.4301,41.9599,50.4815,78.47,6.2,68.4,1010.8,0,733.4,532.9,155.7,1,0 -2013-02-24 10:35:12-06:00,n05667,835.3,23.3,4.4801,176.2238,4.2283,41.6768,50.138,78.45,6.3,65.4,1010.8,0,677.3,512.2,159,1,0 -2013-02-24 10:40:12-06:00,n05667,814.8,24.6,4.3662,171.2153,4.1228,41.5288,49.9539,78.5,6.6,65.7,1010.8,0,643,500.2,160.8,1,0 -2013-02-24 10:45:12-06:00,n05667,787.7,25,4.2243,165.2234,3.9855,41.4559,49.8225,78.5,6.7,66.5,1010.7,0,617.4,483.1,153,1,0 -2013-02-24 10:50:12-06:00,n05667,828.3,25.3,4.4459,173.7052,4.192,41.4374,49.8973,78.3,6.7,63.8,1010.7,0,669.7,504,141.1,1,0 -2013-02-24 10:55:12-06:00,n05667,945.5,25.2,5.0891,198.0632,4.7919,41.3332,50.0391,77.78,6.8,64.3,1010.6,0,801.7,572.9,133.1,1,0 -2013-02-24 11:00:12-06:00,n05667,1000.3,25.6,5.3757,208.4461,5.0637,41.1647,50.0008,77.55,7.2,64.9,1010.6,0,852.5,615.5,142.6,1,0 -2013-02-24 11:05:12-06:00,n05667,880.1,26.9,4.7232,182.832,4.4503,41.0832,49.6684,77.94,7.3,65.3,1010.6,0,694.7,555.9,167.2,1,0 -2013-02-24 11:10:12-06:00,n05667,806.4,26.8,4.3181,167.2042,4.0663,41.1199,49.5402,78.16,7.5,64.7,1010.5,0,603.6,519.3,178.4,1,0 -2013-02-24 11:15:12-06:00,n05667,843.2,28.4,4.5425,175.6894,4.2811,41.0382,49.5604,78.04,7.1,62.9,1010.3,0,625.1,523.5,167.7,1,0 -2013-02-24 11:20:12-06:00,n05667,892.5,28,4.7972,185.3663,4.5162,41.0444,49.6534,77.82,7.4,62.9,1010.3,0,693,548.8,150.4,1,0 -2013-02-24 11:25:12-06:00,n05667,997,29.3,5.3763,206.327,5.0539,40.8257,49.6593,77.28,7.2,64.3,1010.2,0,804.1,608.2,142.5,1,0 -2013-02-24 11:30:12-06:00,n05667,998.4,29.4,5.3855,205.8825,5.0587,40.6983,49.5461,77.16,7.4,63.1,1010.2,0,790.1,612.3,152.1,1,0 -2013-02-24 11:35:12-06:00,n05667,861.6,30,4.6363,177.7069,4.3609,40.7499,49.3009,77.75,8.2,61.1,1010.1,0,632.7,537.8,167.3,1,0 -2013-02-24 11:40:12-06:00,n05667,899.3,30.8,4.8478,185.2004,4.5579,40.6328,49.2748,77.53,8.2,61.6,1010,0,653.6,561.3,177,1,0 -2013-02-24 11:45:12-06:00,n05667,868,29.2,4.6851,179.8368,4.408,40.7978,49.3718,77.75,8.5,56.5,1009.9,0,615.8,540.7,176.2,1,0 -2013-02-24 11:50:12-06:00,n05667,882.7,30.6,4.7541,181.9635,4.4723,40.6868,49.2976,77.64,7.4,60,1009.9,0,643.6,552.8,169.6,1,0 -2013-02-24 11:55:12-06:00,n05667,928.3,30.8,5.0052,190.2261,4.7007,40.4677,49.1669,77.3,8,62.3,1009.8,0,690.9,576.5,163.6,1,0 -2013-02-24 12:00:12-06:00,n05667,960.6,32.4,5.1751,196.5363,4.86,40.4395,49.2183,77.16,8.3,57.6,1009.7,0,736.8,598.2,156.4,1,0 -2013-02-24 12:05:12-06:00,n05667,927.9,32.3,4.9927,188.8696,4.6872,40.2944,48.9951,77.21,8.1,58.6,1009.6,0,706.7,582.9,157.9,1,0 -2013-02-24 12:10:12-06:00,n05667,880.6,33.1,4.7465,179.6942,4.4559,40.3274,48.9346,77.37,7.9,58.6,1009.5,0,649,552.7,161.2,1,0 -2013-02-24 12:15:12-06:00,n05667,875.3,32.4,4.7329,179.629,4.4486,40.3787,48.9893,77.47,7.4,60.2,1009.5,0,627.8,548.3,168.3,1,0 -2013-02-24 12:20:12-06:00,n05667,914.7,33.4,4.9371,186.5388,4.632,40.2715,48.9561,77.18,8,58.9,1009.5,0,669,574.6,170.4,1,0 -2013-02-24 12:25:12-06:00,n05667,831.8,33.2,4.4868,170.1365,4.2183,40.3334,48.8455,77.63,8.4,59,1009.4,0,590.3,525.2,168.2,1,0 -2013-02-24 12:30:12-06:00,n05667,872.5,32.3,4.7179,179.2694,4.4359,40.4136,49.0153,77.52,8,60.4,1009.3,0,645.6,542.9,152.2,1,0 -2013-02-24 12:35:12-06:00,n05667,966.5,32.1,5.2288,197.8695,4.9067,40.3264,49.1226,77.04,8.4,57.7,1009.2,0,767,598.2,133.5,1,0 -2013-02-24 12:40:12-06:00,n05667,1016.7,35.4,5.4901,206.0161,5.1462,40.0326,48.9412,76.67,9,57.8,1009.1,0,854.5,628.1,112.5,1,0 -2013-02-24 12:45:12-06:00,n05667,1035.3,34,5.5831,209.6938,5.2342,40.0624,49.0094,76.64,10,54.9,1009,0,883.2,638.1,106.6,1,0 -2013-02-24 12:50:12-06:00,n05667,996.2,36.2,5.3775,201.5735,5.0432,39.9691,48.8338,76.76,9.9,51.9,1008.9,0,843.1,614.5,108.3,1,0 -2013-02-24 13:00:12-06:00,n05667,1002,36,5.4113,201.0928,5.0632,39.7169,48.5977,76.47,8.7,55.3,1008.7,0,861.4,621.2,106.8,1,0 -2013-02-24 13:05:12-06:00,n05667,966.2,35.2,5.2333,194.9744,4.9047,39.7524,48.5749,76.7,9.3,54.6,1008.6,0,819.3,597.2,110.4,1,0 -2013-02-24 13:10:12-06:00,n05667,970.9,32.5,5.2352,196.767,4.9086,40.0863,48.8924,76.87,10.3,53.2,1008.5,0,830.4,603.4,112.2,1,0 -2013-02-24 13:15:12-06:00,n05667,970.1,35.3,5.2396,196.5185,4.9134,39.9963,48.8124,76.84,10.6,52.1,1008.5,0,832,604.6,115.7,1,0 -2013-02-24 13:20:12-06:00,n05667,959.2,36.6,5.1873,194.5016,4.8624,40.0013,48.7922,76.85,10.4,50.2,1008.4,0,816.7,595.9,119.1,1,0 -2013-02-24 13:25:12-06:00,n05667,959.5,34.4,5.193,194.7729,4.8703,39.9916,48.7844,76.88,10.4,50.3,1008.4,0,817.6,597,123,1,0 -2013-02-24 13:30:12-06:00,n05667,950.3,37.8,5.1462,191.8533,4.8238,39.7721,48.5528,76.78,10.5,48.5,1008.3,0,813.8,595.5,127,1,0 -2013-02-24 13:35:12-06:00,n05667,921.4,35.7,4.989,186.1656,4.6778,39.7973,48.5171,76.91,9.4,51.7,1008.2,0,772.6,576.8,135.1,1,0 -2013-02-24 13:40:12-06:00,n05667,907.7,36.4,4.9098,183.8515,4.6055,39.9197,48.599,77.05,9.1,53.9,1008.2,0,769.5,568.1,131.7,1,0 -2013-02-24 13:45:12-06:00,n05667,910.3,35.3,4.9323,184.7334,4.6257,39.9366,48.6218,77.03,9.4,51.9,1008.1,0,785.7,567.7,126.7,1,0 -2013-02-24 13:50:12-06:00,n05667,869,34.8,4.7134,176.967,4.4208,40.0301,48.6309,77.2,9.4,52.1,1008,0,747.8,540.4,124.9,1,0 -2013-02-24 13:55:12-06:00,n05667,894.3,37.2,4.8433,181.1742,4.5428,39.8819,48.5411,77.06,9.8,52.1,1007.8,0,783.6,558.4,127.3,1,0 -2013-02-24 14:00:12-06:00,n05667,892.1,36.3,4.8507,181.6049,4.5493,39.9197,48.5756,77.07,10.1,50.3,1007.7,0,784.9,555,127.6,1,0 -2013-02-24 14:05:12-06:00,n05667,880.2,35.1,4.7699,179.2759,4.4768,40.0455,48.668,77.23,9.9,49.9,1007.6,0,784.4,550.1,128.4,1,0 -2013-02-24 14:10:12-06:00,n05667,880.7,31.8,4.7665,180.667,4.4723,40.3973,49.014,77.33,9.7,50,1007.5,0,789.3,549.4,129.9,1,0 -2013-02-24 14:15:12-06:00,n05667,860.3,32.7,4.6784,177.212,4.3926,40.3432,48.921,77.43,9.6,50.3,1007.4,0,754.9,533.7,137.9,1,0 -2013-02-24 14:25:12-06:00,n05667,852.6,35.9,4.6615,174.5552,4.3712,39.9334,48.5223,77.17,10.1,49.9,1007.2,0,792.3,515.7,112.2,1,0 -2013-02-24 14:30:12-06:00,n05667,836,35.6,4.5604,171.0183,4.2755,39.9996,48.5476,77.24,10.3,49.2,1007.1,0,809.7,509.5,103.4,1,0 -2013-02-24 14:35:12-06:00,n05667,833.9,36.4,4.5372,170.1769,4.2581,39.9659,48.4923,77.35,10.5,50.3,1007,0,834.2,505.5,94.6,1,0 -2013-02-24 14:40:12-06:00,n05667,817.5,33.9,4.4407,167.6559,4.1713,40.1928,48.6842,77.55,11.3,48.9,1006.9,0,834.4,492.3,88.8,1,0 -2013-02-24 14:45:12-06:00,n05667,801.4,34.9,4.3595,164.3032,4.0928,40.1449,48.5925,77.56,11.4,47.6,1006.8,0,825.5,480.1,87.7,1,0 -2013-02-24 14:50:12-06:00,n05667,783.5,32.5,4.2498,161.1426,3.9936,40.3497,48.7616,77.76,11.3,48.5,1006.8,0,823.7,469.2,85.9,1,0 -2013-02-24 14:55:12-06:00,n05667,763.9,32.5,4.1485,158.0008,3.8995,40.5185,48.8657,77.94,11.7,45.4,1006.8,0,818.9,453.1,79.8,1,0 -2013-02-24 15:00:12-06:00,n05667,712.9,33,3.8791,147.3678,3.6496,40.3796,48.6439,78.1,12,46.2,1006.7,0,750.4,418.6,83.9,1,0 -2013-02-24 15:05:12-06:00,n05667,721,28.8,3.91,149.9791,3.6811,40.7433,49.0029,78.28,12.4,45.2,1006.6,0,782.9,427.1,85.3,1,0 -2013-02-24 15:10:12-06:00,n05667,710.2,27.5,3.8495,148.5448,3.6264,40.9623,49.1831,78.46,12,43.2,1006.5,0,781.2,420.7,87.9,1,0 -2013-02-24 15:15:12-06:00,n05667,690.9,26,3.7361,144.9067,3.5208,41.1577,49.3392,78.61,11.6,45.6,1006.5,0,770,409.4,89.1,1,0 -2013-02-24 15:20:12-06:00,n05667,649.8,24.9,3.5036,136.6021,3.3045,41.3378,49.403,78.92,11.4,45.9,1006.4,0,724.8,387.3,93.9,1,0 -2013-02-24 15:25:12-06:00,n05667,636.5,26.3,3.438,134.0174,3.2441,41.3109,49.3672,78.96,11.4,45,1006.3,0,720.2,377.2,93.4,1,0 -2013-02-24 15:30:12-06:00,n05667,551.8,25.2,2.9869,116.3385,2.8213,41.2355,49.1129,79.31,11.6,46.5,1006.2,0,601.7,325.7,95.2,1,0 -2013-02-24 15:35:12-06:00,n05667,573.6,23.2,3.0801,120.8164,2.9075,41.5532,49.4648,79.3,11.5,45.6,1006.2,0,655.7,340.8,96.9,1,0 -2013-02-24 15:40:12-06:00,n05667,554,23.7,2.9879,117.5156,2.8243,41.6094,49.4583,79.52,11.5,46.5,1006.1,0,641,325.8,95.2,1,0 -2013-02-24 15:45:12-06:00,n05667,480.2,23.6,2.6073,102.1275,2.4645,41.4396,49.148,79.7,11.8,44.4,1005.9,0,549.7,282.7,91.9,1,0 -2013-02-24 15:50:12-06:00,n05667,508.9,21.8,2.7335,107.6113,2.5847,41.6334,49.3906,79.71,11.6,44.4,1005.8,0,621,297.2,89,1,0 -2013-02-24 15:55:12-06:00,n05667,474.2,23,2.5571,100.6144,2.4184,41.6038,49.2962,79.82,11.7,46.8,1005.9,0,597.2,279.5,86.6,1,0 -2013-02-24 16:00:12-06:00,n05667,467.6,22.3,2.5227,99.2495,2.3842,41.6283,49.3114,79.79,11.7,46.2,1005.9,0,604.2,273.8,86.1,1,0 -2013-02-24 16:05:12-06:00,n05667,457.1,21.1,2.4605,97.1979,2.329,41.7336,49.3813,80,11.7,44.6,1005.7,0,597.4,266.3,88.2,1,0 -2013-02-24 16:10:12-06:00,n05667,420.2,20.1,2.2513,89.1289,2.1312,41.8216,49.3913,80.16,11.5,45.7,1005.7,0,545.6,246.6,91,1,0 -2013-02-24 16:15:12-06:00,n05667,296.8,19.7,1.5889,62.1739,1.5018,41.3982,48.7141,80.33,11.6,46.3,1005.6,0,324.7,183.8,95.7,1,0 -2013-02-24 16:20:12-06:00,n05667,313.1,18.7,1.6709,65.9477,1.581,41.7117,49.0517,80.46,11.3,47.3,1005.5,0,363.3,192.2,98.5,1,0 -2013-02-24 16:25:12-06:00,n05667,311.2,18.2,1.6573,65.4099,1.5671,41.7396,49.0599,80.45,11.3,47.6,1005.4,0,379.9,190.6,97.8,1,0 -2013-02-24 16:30:12-06:00,n05667,262.3,18,1.3918,54.7209,1.3159,41.5842,48.7976,80.57,11.3,49,1005.4,0,305.4,164.2,94,1,0 -2013-02-24 16:35:12-06:00,n05667,212.9,17.2,1.1294,44.2103,1.0689,41.3614,48.4866,80.74,11.1,51,1005.3,0,213,135.7,90,1,0 -2013-02-24 16:40:12-06:00,n05667,167.7,16.8,0.8867,34.2717,0.8358,41.0058,48.0198,80.49,11.3,50.9,1005.3,0,140.1,116.2,88.2,1,0 -2013-02-24 16:45:12-06:00,n05667,131.7,15.7,0.695,26.5994,0.6535,40.7059,47.6988,80.23,11.1,50.6,1005.2,0,79.3,99.1,84.6,1,0 -2013-02-24 16:50:12-06:00,n05667,101.5,14.3,0.536,20.3192,0.5025,40.437,47.3387,80.08,10.9,52.1,1005.2,0,28.4,83.1,78.6,1,0 -2013-02-24 16:55:12-06:00,n05667,93.2,13.7,0.4929,18.651,0.4624,40.3393,47.2762,80.04,10.9,52.7,1005.2,0,26.9,76.9,73,1,0 -2013-02-24 17:00:12-06:00,n05667,79.3,12.9,0.4226,15.8529,0.3947,40.1624,47.036,79.74,10.7,53.1,1005.2,0,13.3,68.6,67.1,1,0 -2013-02-24 17:05:12-06:00,n05667,66.6,12.4,0.3567,13.2472,0.3322,39.8793,46.7534,79.43,10.4,54.8,1005.1,0,3.6,60.3,60.1,1,0 -2013-02-24 17:10:12-06:00,n05667,54.9,12,0.2944,10.8197,0.2738,39.511,46.4112,79.19,10.4,55.5,1005.1,0,1.4,51.3,51.4,1,0 -2013-02-24 17:15:12-06:00,n05667,44.8,11.7,0.2435,8.8185,0.2253,39.143,46.0498,78.64,10.4,56.5,1005,0,0.9,42.8,43,1,0 -2013-02-24 17:20:12-06:00,n05667,37.7,11.2,0.206,7.3808,0.1901,38.8164,45.7282,78.34,10.2,56.7,1005,0,1,36.2,36.3,1,0 -2013-02-24 17:25:12-06:00,n05667,30.8,11,0.1699,5.9989,0.1562,38.4103,45.3654,77.82,10.2,57.6,1004.9,0,0.1,30.2,30.3,1,0 -2013-02-24 17:30:13-06:00,n05667,23.7,10.7,0.1327,4.587,0.1214,37.7828,44.8243,77.13,10.1,57.4,1005,0,0.4,23.3,23.4,1,0 -2013-03-07 06:50:12-06:00,n05667,27.3,2,0.1133,4.0957,0.1055,38.8057,45.8679,78.78,2.7,83.7,998.4,0,130.2,13.3,9,1,0 -2013-03-07 06:55:12-06:00,n05667,44.5,2.1,0.1839,6.8673,0.1715,40.033,46.8616,79.68,2.9,82.9,998.4,0,213.6,21.5,11.6,1,0 -2013-03-07 07:00:12-06:00,n05667,37,2.4,0.1665,6.1471,0.1549,39.6887,46.5702,79.29,2.9,82.8,998.4,0,130.5,21.7,14.3,1,0 -2013-03-07 07:05:12-06:00,n05667,20.4,2.3,0.107,3.767,0.0979,38.4887,45.618,77.16,2.9,82.3,998.4,0,5.9,17.6,17.4,1,0 -2013-03-07 07:10:12-06:00,n05667,26.9,2.3,0.1377,4.9765,0.127,39.1787,46.2131,78.2,2.8,83.1,998.4,0,15.1,21.7,20.6,1,0 -2013-03-07 07:15:12-06:00,n05667,104.5,2.7,0.4773,19.0172,0.453,41.9843,48.8332,81.6,2.8,82.9,998.4,0,331.6,58.8,24.2,1,0 -2013-03-07 07:20:12-06:00,n05667,123.3,3.3,0.5765,23.114,0.5472,42.2428,49.1168,81.63,3,82.7,998.3,0,359.3,68.5,25.8,1,0 -2013-03-07 07:25:12-06:00,n05667,175.9,4.1,0.8307,33.8647,0.7917,42.7747,49.6944,82.03,3.2,81.2,998.3,0,518,96.5,27.5,1,0 -2013-03-07 07:30:12-06:00,n05667,197.7,4.6,0.9411,38.6001,0.8994,42.918,49.8915,82.21,3.2,81.7,998.4,0,548.9,110.5,29.3,1,0 -2013-03-07 07:35:12-06:00,n05667,220,5.2,1.0575,43.3659,1.0087,42.9925,50.0288,81.97,3.4,81.6,998.3,0,576.1,124.8,31.2,1,0 -2013-03-07 07:40:12-06:00,n05667,243.3,5.9,1.1777,48.351,1.1225,43.0757,50.1453,81.87,3.7,80.4,998.4,0,603.1,139.7,32.9,1,0 -2013-03-07 07:45:12-06:00,n05667,267.2,6,1.3043,53.6302,1.2422,43.175,50.2948,81.75,3.6,80.5,998.4,0,629.2,154.6,34.5,1,0 -2013-03-07 07:50:12-06:00,n05667,291.1,7,1.437,59.2296,1.3716,43.1824,50.3696,81.83,3.7,80.3,998.3,0,654.1,170,35.9,1,0 -2013-03-07 07:55:12-06:00,n05667,315,7.2,1.5695,64.7388,1.4974,43.2344,50.458,81.75,3.8,80.3,998.3,0,675.7,185.5,37.3,1,0 -2013-03-07 08:00:12-06:00,n05667,338.6,7.8,1.6969,70.0869,1.6201,43.26,50.5589,81.69,3.8,80.1,998.4,0,695.5,201,38.7,1,0 -2013-03-07 08:05:12-06:00,n05667,362.2,8.5,1.8294,75.4694,1.7457,43.2316,50.6037,81.52,4.1,78.9,998.4,0,713.9,216.5,39.9,1,0 -2013-03-07 08:10:12-06:00,n05667,385.8,8.9,1.9637,80.8361,1.8699,43.2296,50.648,81.28,4.2,78.3,998.4,0,730.9,232.1,41,1,0 -2013-03-07 08:15:12-06:00,n05667,409.3,11,2.0863,85.8565,1.9913,43.1167,50.5745,81.37,4.5,77.2,998.5,0,745.2,247.5,42.1,1,0 -2013-03-07 08:20:12-06:00,n05667,432.3,12,2.2148,90.7992,2.1145,42.9408,50.4665,81.24,4.8,75.6,998.5,0,759.7,263.1,43.2,1,0 -2013-03-07 08:25:12-06:00,n05667,455.2,14.1,2.3451,95.4693,2.2339,42.7371,50.339,80.87,4.9,75.3,998.5,0,774.2,278.2,43.7,1,0 -2013-03-07 08:35:12-06:00,n05667,500.8,15.9,2.6042,105.3028,2.477,42.5118,50.1985,80.55,5.4,74.4,998.5,0,799.9,309.2,45.6,1,0 -2013-03-07 08:40:12-06:00,n05667,522,16.9,2.7246,109.7547,2.5897,42.3813,50.1322,80.35,5.3,72.4,998.5,0,811.1,324.6,46.2,1,0 -2013-03-07 08:45:12-06:00,n05667,543.9,18.5,2.851,114.3046,2.7079,42.212,50.0294,80.14,5.3,73.9,998.5,0,821.1,339.3,46.9,1,0 -2013-03-07 08:50:12-06:00,n05667,565.4,18.5,2.9664,119.0526,2.8188,42.2354,50.0866,80.13,4.8,74.3,998.4,0,830.3,354.7,48.1,1,0 -2013-03-07 08:55:12-06:00,n05667,586.1,18.2,3.0823,123.7257,2.9298,42.2295,50.1322,80.07,4.7,76,998.4,0,839.1,369.3,48.7,1,0 -2013-03-07 09:00:12-06:00,n05667,607.3,19.4,3.2062,127.9839,3.0387,42.1182,50.0717,79.72,4.8,76.6,998.3,0,847.2,383.4,49.2,1,0 -2013-03-07 09:05:12-06:00,n05667,627.4,19.3,3.3157,132.2746,3.1439,42.0732,50.0639,79.68,5.1,75.8,998.4,0,855.1,397.8,50.1,1,0 -2013-03-07 09:10:12-06:00,n05667,648,20.3,3.4275,136.6266,3.2506,42.0314,50.0775,79.6,5,77.4,998.3,0,863.5,411.8,50.5,1,0 -2013-03-07 09:15:12-06:00,n05667,667.4,20.1,3.5328,140.5851,3.3478,41.9932,50.0723,79.47,5.3,75.7,998.3,0,869.7,425.5,51.2,1,0 -2013-03-07 09:20:12-06:00,n05667,687,20.1,3.6432,144.8482,3.4513,41.9686,50.099,79.36,5.2,76.2,998.3,0,875.4,438.4,51.6,1,0 -2013-03-07 09:25:12-06:00,n05667,707,20.5,3.7443,149.0668,3.5479,42.0154,50.1725,79.35,5.1,75.7,998.3,0,882.7,452.6,52.3,1,0 -2013-03-07 09:30:12-06:00,n05667,725.9,19.9,3.8472,152.9262,3.6439,41.968,50.1679,79.23,5.3,75.2,998.3,0,889.2,465.8,52.8,1,0 -2013-03-07 09:35:12-06:00,n05667,744.3,20.6,3.953,156.7864,3.7412,41.9083,50.1497,79.09,5.3,74.3,998.3,0,895.3,478.5,53.2,1,0 -2013-03-07 09:40:12-06:00,n05667,762.9,21.2,4.0495,160.2999,3.829,41.8644,50.153,78.93,5.4,74.1,998.4,0,901,491.4,53.6,1,0 -2013-03-07 09:45:12-06:00,n05667,779,22.7,4.141,163.5423,3.919,41.7308,50.0704,78.88,5.7,73.9,998.4,0,905.3,502.9,54,1,0 -2013-03-07 09:50:12-06:00,n05667,797.1,23.5,4.2457,166.6601,4.0076,41.5857,49.9448,78.59,6.2,72.2,998.4,0,911.9,515.8,54.5,1,0 -2013-03-07 09:55:12-06:00,n05667,813.5,25.1,4.3249,169.1074,4.0888,41.3582,49.7711,78.56,6.3,72.8,998.4,0,915.4,526.9,55,1,0 -2013-03-07 10:00:12-06:00,n05667,828.1,27.4,4.4121,171.0488,4.1608,41.1095,49.5578,78.23,7,70.8,998.4,0,917.4,537.1,55.3,1,0 -2013-03-07 10:05:12-06:00,n05667,844.2,28.9,4.5056,173.5935,4.2428,40.9144,49.4153,77.97,7.8,70.5,998.3,0,922.4,548.1,55.5,1,0 -2013-03-07 10:10:12-06:00,n05667,859.1,29.5,4.5905,176.196,4.3212,40.7748,49.3087,77.84,7.9,66.3,998.2,0,926.5,558.9,55.8,1,0 -2013-03-07 10:15:12-06:00,n05667,873.6,31.2,4.6734,178.3652,4.3984,40.5518,49.1343,77.68,8,66.9,998.2,0,929.8,569.3,56,1,0 -2013-03-07 10:20:12-06:00,n05667,887.3,29.4,4.7457,181.076,4.4626,40.5764,49.1825,77.58,7.7,66.2,998.2,0,931.4,578.8,56.8,1,0 -2013-03-07 10:25:12-06:00,n05667,900.5,29.3,4.812,184.3893,4.5288,40.7148,49.3386,77.66,7.5,67.4,998.2,0,932.9,588.5,57.5,1,0 -2013-03-07 10:35:12-06:00,n05667,926.6,31,4.9596,188.8208,4.6591,40.5276,49.2156,77.36,7.7,66.7,998.2,0,940.2,606.4,57.7,1,0 -2013-03-07 10:40:12-06:00,n05667,938.6,31.5,5.0181,190.761,4.7186,40.4278,49.1461,77.35,8.3,66.4,998.1,0,942.1,614.8,58.2,1,0 -2013-03-07 10:45:12-06:00,n05667,948.8,33.7,5.0815,192.078,4.7691,40.2753,49.014,77.12,8.9,63.2,998.1,0,943.1,622.2,58.8,1,0 -2013-03-07 10:50:12-06:00,n05667,959.9,34.9,5.139,193.4529,4.8246,40.0975,48.8709,77.03,9.2,64.9,998.1,0,946.8,630.4,59.2,1,0 -2013-03-07 10:55:12-06:00,n05667,968.8,36.6,5.1978,194.3683,4.8732,39.8853,48.6771,76.82,10,61.3,998.1,0,947.3,636.7,59,1,0 -2013-03-07 11:00:12-06:00,n05667,980.1,33.3,5.2493,197.123,4.922,40.0493,48.8517,76.87,9.1,61.6,998.1,0,950,645.1,59.7,1,0 -2013-03-07 11:05:12-06:00,n05667,989.3,34.9,5.3009,198.2466,4.9705,39.8843,48.7213,76.76,9.2,62.8,998,0,951.5,650.9,59.6,1,0 -2013-03-07 11:10:12-06:00,n05667,997.4,33.7,5.3418,200.9438,5.0102,40.107,48.9522,76.84,9.3,63.6,997.9,0,951.5,657.2,60.9,1,0 -2013-03-07 11:15:12-06:00,n05667,1005.5,31.7,5.3773,203.4785,5.0461,40.3236,49.174,76.95,9.1,63.6,997.9,0,951,663.2,62.4,1,0 -2013-03-07 11:20:12-06:00,n05667,1011.8,32.4,5.416,205.199,5.0827,40.3721,49.2371,76.95,9.4,62.9,998,0,950.5,667.1,62.6,1,0 -2013-03-07 11:25:12-06:00,n05667,1018.9,30.7,5.4442,207.2381,5.1108,40.5491,49.4075,77.04,9.1,63.6,998,0,951.4,672.3,63.4,1,0 -2013-03-07 11:30:12-06:00,n05667,1025.7,30.5,5.4806,208.9386,5.1478,40.5877,49.4589,77.08,9.1,62.5,998,0,953.8,677.3,63.1,1,0 -2013-03-07 11:35:12-06:00,n05667,1035.3,32.1,5.5363,210.0419,5.1959,40.4243,49.323,76.92,9.3,62.4,998,0,959.7,683.7,62.6,1,0 -2013-03-07 11:40:12-06:00,n05667,1041.4,32.4,5.5741,210.8954,5.2269,40.3478,49.2696,76.79,9.5,61.7,997.9,0,962.1,688.7,62.5,1,0 -2013-03-07 11:45:12-06:00,n05667,1045.8,32.5,5.5972,211.3704,5.2514,40.2505,49.1896,76.77,9.7,61.5,997.8,0,962.3,690.9,62.1,1,0 -2013-03-07 11:50:12-06:00,n05667,1054,31.9,5.6466,212.7798,5.2876,40.2412,49.1883,76.61,9.8,59.9,997.8,0,964.9,696,63.1,1,0 -2013-03-07 11:55:12-06:00,n05667,1057.6,32.3,5.6673,213.1012,5.304,40.1773,49.1317,76.53,9.9,59.6,997.8,0,964.5,697.5,62.9,1,0 -2013-03-07 12:00:12-06:00,n05667,1064.7,31.8,5.6931,215.2032,5.3422,40.2835,49.254,76.75,9.6,57.3,997.7,0,970.3,703.2,63.1,1,0 -2013-03-07 12:05:12-06:00,n05667,1067.6,31.7,5.714,215.4874,5.3526,40.2584,49.2306,76.6,9.6,58.1,997.7,0,970.2,704.9,62.8,1,0 -2013-03-07 12:10:12-06:00,n05667,1067.1,32.6,5.7136,215.1378,5.3539,40.1837,49.1552,76.6,10,57.4,997.6,0,969.4,705.3,62.7,1,0 -2013-03-07 12:15:12-06:00,n05667,1074.2,31.6,5.7451,217.7614,5.3873,40.4215,49.4004,76.73,10.1,56.4,997.5,0,975.1,710.2,63.4,1,0 -2013-03-07 12:20:12-06:00,n05667,1072.8,33.3,5.7388,217.3713,5.385,40.3661,49.3549,76.75,10.2,56.4,997.5,0,973.5,708.8,63,1,0 -2013-03-07 12:25:12-06:00,n05667,1074.4,31.3,5.7516,217.6334,5.3933,40.3526,49.3425,76.69,10.2,56.6,997.4,0,974.8,709.8,63.3,1,0 -2013-03-07 12:30:12-06:00,n05667,1075.5,33.2,5.7557,217.2588,5.3979,40.249,49.2404,76.66,10,55.1,997.4,0,976.4,710.4,63,1,0 -2013-03-07 12:35:12-06:00,n05667,1076.3,32.8,5.7555,217.9102,5.3958,40.3852,49.3731,76.68,9.9,55.2,997.2,0,977.4,710.7,63.6,1,0 -2013-03-07 12:40:12-06:00,n05667,1072.5,32.8,5.7404,217.1159,5.3832,40.332,49.3191,76.69,10.2,53.4,997.2,0,977.3,709.2,62.7,1,0 -2013-03-07 12:45:12-06:00,n05667,1070.9,33.2,5.7344,215.7827,5.3745,40.1495,49.1265,76.6,10.5,52.4,997.1,0,977.5,707.7,62.7,1,0 -2013-03-07 12:50:12-06:00,n05667,1068.6,32.6,5.7192,215.6719,5.3618,40.2237,49.1968,76.65,10.6,50.7,997.1,0,977.8,707,63.1,1,0 -2013-03-07 12:55:12-06:00,n05667,1064,33.3,5.6981,215.0339,5.342,40.2536,49.2235,76.67,10.5,52.7,997.1,0,975.1,703.5,63.3,1,0 -2013-03-07 13:00:12-06:00,n05667,1057.4,33.1,5.6657,213.8916,5.315,40.2428,49.1987,76.73,10.7,52.6,997.1,0,972,700,63.9,1,0 -2013-03-07 13:05:12-06:00,n05667,1053.2,33.2,5.6436,213.214,5.2935,40.2785,49.2274,76.75,10.9,51.7,997,0,970.4,696.7,64.3,1,0 -2013-03-07 13:10:12-06:00,n05667,1050.4,31.9,5.6268,212.8788,5.2839,40.2884,49.2274,76.85,11,52.1,996.9,0,971,693.8,64.2,1,0 -2013-03-07 13:15:12-06:00,n05667,1042.9,31.3,5.5942,211.9752,5.2478,40.3934,49.308,76.85,10.9,49.4,996.8,0,968.6,689.6,64.6,1,0 -2013-03-07 13:20:12-06:00,n05667,1040.8,32.8,5.5825,210.89,5.2357,40.279,49.1974,76.79,11.1,48.9,996.8,0,970.3,686.4,64.2,1,0 -2013-03-07 13:25:12-06:00,n05667,1028.6,32,5.5155,209.5913,5.1788,40.4708,49.3633,76.98,11,47.6,996.8,0,963.5,678.9,65.1,1,0 -2013-03-07 13:30:12-06:00,n05667,1015.8,31.2,5.4467,207.4031,5.1168,40.5337,49.3984,77.09,10.9,47.4,996.8,0,956.4,671,65.3,1,0 -2013-03-07 13:35:12-06:00,n05667,1009.1,33.1,5.4201,205.7777,5.089,40.4359,49.2846,77.03,11.4,43.8,996.8,0,956.3,665.6,65,1,0 -2013-03-07 13:40:12-06:00,n05667,1000.7,33.6,5.382,203.7158,5.0491,40.3467,49.1942,76.94,11.8,40.6,996.6,0,955.1,660,65,1,0 -2013-03-07 13:45:12-06:00,n05667,993.8,31.6,5.3389,202.2273,5.0088,40.3748,49.2059,76.98,11.8,41.8,996.5,0,954.2,655.3,65.5,1,0 -2013-03-07 13:50:12-06:00,n05667,981.4,32.8,5.2785,200.1525,4.9556,40.3891,49.2013,77.07,11.4,42,996.5,0,953.5,646.9,63.4,1,0 -2013-03-07 13:55:12-06:00,n05667,970.6,32.5,5.2175,199.2606,4.9024,40.6455,49.4153,77.29,11.7,42.1,996.5,0,952.6,639.7,63,1,0 -2013-03-07 14:00:12-06:00,n05667,955.8,33.4,5.1459,194.8994,4.8317,40.3381,49.0882,77.16,11.9,44.4,996.4,0,948.1,631,62.8,1,0 -2013-03-07 14:05:12-06:00,n05667,941,32.6,5.0708,192.7437,4.7588,40.5026,49.2202,77.23,11.7,46,996.4,0,942.8,621.9,62.6,1,0 -2013-03-07 14:10:12-06:00,n05667,926.9,30.2,4.9845,190.656,4.6922,40.6328,49.323,77.55,11.6,45.3,996.3,0,939,612.8,62.7,1,0 -2013-03-07 14:15:12-06:00,n05667,915.5,29.9,4.9318,188.399,4.6315,40.6776,49.3366,77.43,11.7,46.3,996.4,0,937.5,605,62.4,1,0 -2013-03-07 14:20:12-06:00,n05667,905.9,30,4.8786,187.1916,4.588,40.7998,49.4218,77.64,11.7,46.6,996.4,0,936.6,598.9,63.6,1,0 -2013-03-07 14:25:12-06:00,n05667,896.9,29.7,4.8366,185.3487,4.5475,40.7586,49.3783,77.61,11.9,46.6,996.3,0,932.3,591.9,66.4,1,0 -2013-03-07 14:30:12-06:00,n05667,875.8,28.4,4.7202,181.8913,4.4413,40.9545,49.5162,77.82,11.7,43.5,996.3,0,928.7,578.6,62.8,1,0 -2013-03-07 14:35:12-06:00,n05667,858.8,30.3,4.6328,177.854,4.3557,40.8324,49.3626,77.77,12.1,43.7,996.4,0,926.3,567,60.6,1,0 -2013-03-07 14:40:12-06:00,n05667,841.5,29.1,4.5379,174.5531,4.2705,40.874,49.3672,77.92,12.2,41.6,996.3,0,919.7,555.5,60.7,1,0 -2013-03-07 14:50:12-06:00,n05667,812.4,28.6,4.3818,169.2478,4.127,41.0096,49.4465,78.12,12.1,40.5,996.4,0,914.7,534.4,59.4,1,0 -2013-03-07 14:55:12-06:00,n05667,792.3,28.6,4.2744,165.2858,4.0287,41.0269,49.429,78.23,12.2,43,996.4,0,905.7,520.9,59.4,1,0 -2013-03-07 15:00:12-06:00,n05667,774.7,29.1,4.1858,161.432,3.942,40.9518,49.3119,78.21,12.2,43.9,996.4,0,900.5,508.4,58.7,1,0 -2013-03-07 15:05:12-06:00,n05667,757.6,28.6,4.0927,157.6374,3.8515,40.9293,49.2566,78.2,12.3,43.7,996.3,0,897.6,496.8,57.9,1,0 -2013-03-07 15:10:12-06:00,n05667,738.1,29.7,3.9922,153.5079,3.7553,40.8776,49.1506,78.23,12.7,43.5,996.4,0,890.9,483.1,57,1,0 -2013-03-07 15:15:12-06:00,n05667,715.3,26.5,3.8685,150.1887,3.644,41.2148,49.4511,78.51,12.3,43.6,996.2,0,874.5,467.5,58.1,1,0 -2013-03-07 15:20:12-06:00,n05667,704.1,26.4,3.7941,147.8873,3.5779,41.3332,49.5038,78.74,12.1,45.2,996.3,0,879.8,460.9,58.4,1,0 -2013-03-07 15:25:12-06:00,n05667,685.4,25.8,3.6932,143.8138,3.4838,41.281,49.4349,78.77,12.4,43.8,996.2,0,874.2,447.2,57.3,1,0 -2013-03-07 15:30:12-06:00,n05667,668.8,26.1,3.5974,140.4132,3.3941,41.3701,49.4804,78.88,12.1,44.9,996.2,0,869.6,435.7,57.5,1,0 -2013-03-07 15:35:12-06:00,n05667,649.3,24.2,3.4855,136.7395,3.2894,41.5695,49.6241,79.06,12,45.2,996.1,0,862.9,422,57,1,0 -2013-03-07 15:40:12-06:00,n05667,631.2,23.9,3.3882,133.0409,3.1988,41.5903,49.6059,79.16,12.1,45.1,996.2,0,858.2,409.3,56.3,1,0 -2013-03-07 15:45:12-06:00,n05667,609.6,24.8,3.2764,128.1523,3.0903,41.4697,49.4446,79.11,12.4,43.5,996.2,0,849.1,394.3,55.4,1,0 -2013-03-07 15:50:12-06:00,n05667,591.1,24.6,3.1712,124.273,2.9924,41.5291,49.4609,79.23,12.2,44.7,996.2,0,843.7,381.3,55,1,0 -2013-03-07 15:55:12-06:00,n05667,570.4,22.9,3.0549,120.1658,2.884,41.6669,49.5337,79.41,12.1,44.1,996.1,0,833.8,366.3,54.3,1,0 -2013-03-07 16:00:12-06:00,n05667,543.9,23.5,2.9139,114.2769,2.7506,41.546,49.3731,79.43,12.3,44.6,996.1,0,815.3,347,52.5,1,0 -2013-03-07 16:05:12-06:00,n05667,529.8,22.8,2.8271,111.4077,2.6724,41.6875,49.4771,79.65,12.1,43,996.1,0,816.6,336.8,52.1,1,0 -2013-03-07 16:10:12-06:00,n05667,507.9,22.9,2.7133,106.5009,2.5617,41.574,49.3314,79.57,12.3,42.4,996,0,804.3,320.7,51.2,1,0 -2013-03-07 16:15:12-06:00,n05667,487.8,24,2.5983,101.9192,2.4538,41.5351,49.2462,79.65,12.4,43.9,996.1,0,794.1,306.6,51.4,1,0 -2013-03-07 16:20:12-06:00,n05667,474.7,22.7,2.5199,98.9818,2.3801,41.5873,49.267,79.73,12.4,42.3,996.1,0,785.7,296.5,54.2,1,0 -2013-03-07 16:25:12-06:00,n05667,460.6,21,2.4346,96.2945,2.3044,41.7875,49.4212,80.03,12.1,41,996.1,0,778.2,287.3,57.7,1,0 -2013-03-07 16:30:12-06:00,n05667,87.1,18.3,0.4484,16.5098,0.4191,39.3976,46.3016,79.52,11.9,41.4,996.2,0,17.9,64.5,60.2,1,0 -2013-03-07 16:35:12-06:00,n05667,105,15.8,0.5488,20.7255,0.5153,40.2192,47.1104,80.16,11.9,41.2,996.3,0,39.8,75.3,65.6,1,0 -2013-03-07 16:40:12-06:00,n05667,132.6,15,0.6976,26.7785,0.6557,40.8386,47.8054,80.29,11.6,42.9,996.3,0,80.8,96.8,77.2,1,0 -2013-03-07 16:45:12-06:00,n05667,126.3,14.4,0.6694,25.679,0.6291,40.8197,47.7879,80.28,11.5,43.9,996.4,0,54.5,98.8,86.5,1,0 -2013-03-07 16:50:12-06:00,n05667,253.3,15,1.3365,53.0716,1.2635,42.0021,49.1623,80.77,11.4,44.6,996.5,0,380.5,175.9,91.1,1,0 -2013-03-07 16:55:12-06:00,n05667,337.4,16.5,1.7394,69.2714,1.6442,42.1307,49.4524,80.53,11.6,42.9,996.5,0,659.4,229.6,91.7,1,0 -2013-03-07 17:00:12-06:00,n05667,301.7,18.1,1.537,60.6427,1.4517,41.7737,49.0257,80.48,11.8,45,996.6,0,643.5,204.2,79.3,1,0 -2013-03-07 17:05:12-06:00,n05667,272.3,17.1,1.367,53.7345,1.2907,41.6312,48.8254,80.51,11.7,43.8,996.5,0,624.5,179.7,67.5,1,0 -2013-03-07 17:10:12-06:00,n05667,247.6,17.1,1.2291,48.1357,1.1593,41.5196,48.6198,80.55,11.7,45.2,996.5,0,597.5,159.4,60.8,1,0 -2013-03-07 17:15:12-06:00,n05667,225.4,16.8,1.1084,43.2388,1.0449,41.3807,48.4655,80.49,11.9,45.3,996.5,0,571.8,142.4,56.6,1,0 -2013-03-07 17:20:12-06:00,n05667,204.6,16,0.993,38.6586,0.9352,41.3361,48.3475,80.52,11.6,45,996.5,0,542.7,128.9,55.2,1,0 -2013-03-07 17:25:13-06:00,n05667,56.9,15.2,0.3159,11.481,0.2936,39.1093,46.0108,78.99,11.6,44.5,996.6,0,1.9,55.1,55.6,1,0 -2013-03-07 17:30:12-06:00,n05667,52.1,13.9,0.291,10.5753,0.2699,39.1797,46.0329,78.93,11.4,45.8,996.6,0,0.2,54.3,54.7,1,0 -2013-03-07 17:35:12-06:00,n05667,46.8,13.2,0.265,9.5892,0.2454,39.0763,45.9832,78.69,11.3,44.3,996.7,0,0.4,51.3,51.5,1,0 -2013-03-07 17:40:12-06:00,n05667,41.3,12.9,0.2375,8.529,0.2193,38.8843,45.7818,78.45,11.3,44.7,996.7,0,1.2,48.2,48.3,1,0 -2013-03-07 17:45:12-06:00,n05667,35,12.9,0.1916,6.7059,0.1747,38.378,45.307,77.27,11.4,46.1,996.8,0,17.8,41,40.3,1,0 -2013-03-07 17:50:12-06:00,n05667,34,12.6,0.124,4.1553,0.1109,37.4806,44.3508,75.56,11.3,44.3,996.9,0,110.5,32,28.2,1,0 -2013-04-23 05:45:12-06:00,n05667,23.7,3.2,0.114,4.0213,0.1044,38.5312,45.5947,77.38,4.1,69.9,1006.7,0,278.7,45.2,24.3,1,0 -2013-04-23 05:50:13-06:00,n05667,25,3.3,0.1221,4.3254,0.1118,38.6753,45.7046,77.52,3.9,71.1,1006.8,0,156.5,41.9,28,1,0 -2013-04-23 05:55:12-06:00,n05667,33.4,3.4,0.148,5.3459,0.1364,39.1876,46.1454,78.26,3.9,67.7,1006.8,0,353.7,69.3,32.4,1,0 -2013-04-23 06:00:12-06:00,n05667,37.7,3.5,0.1615,5.8713,0.149,39.3966,46.3193,78.47,4,65.9,1006.8,0,392.9,82.2,35.3,1,0 -2013-04-23 06:05:12-06:00,n05667,41.1,3.7,0.1745,6.3962,0.1618,39.5229,46.4299,78.94,4,65.7,1006.9,0,433.3,96.9,38.6,1,0 -2013-04-23 06:10:12-06:00,n05667,46.6,3.9,0.189,6.9653,0.1754,39.7019,46.6029,79.09,4.3,64.1,1006.9,0,466.9,111.4,41.5,1,0 -2013-04-23 06:15:12-06:00,n05667,56.2,4.2,0.2125,7.9386,0.1985,39.9854,46.8286,79.79,4.4,64.1,1006.8,0,463.8,120.8,44.4,1,0 -2013-04-23 06:20:12-06:00,n05667,67.2,4.5,0.2445,9.1756,0.2281,40.2297,47.081,79.71,4.8,63,1006.8,0,454.3,129.2,47.4,1,0 -2013-04-23 06:25:12-06:00,n05667,80.5,4.9,0.2862,10.8711,0.2683,40.5206,47.3509,80.22,5.4,61.4,1006.6,0,469.1,142.3,50.6,1,0 -2013-04-23 06:30:12-06:00,n05667,92.4,5.4,0.3296,12.5948,0.3092,40.7392,47.574,80.33,5.7,59.3,1006.6,0,475.6,153.7,53.4,1,0 -2013-04-23 06:35:12-06:00,n05667,113.3,6.1,0.4048,15.6527,0.3807,41.115,47.9038,80.73,5.7,59.4,1006.6,0,540,178.8,56.4,1,0 -2013-04-23 06:40:12-06:00,n05667,132.7,6.7,0.488,19.0627,0.4607,41.3801,48.2206,81.01,5.7,59,1006.7,0,571.6,197.2,58.8,1,0 -2013-04-23 06:45:12-06:00,n05667,153.7,7.2,0.5804,22.8422,0.5487,41.6305,48.4957,81.16,5.9,57.1,1006.6,0,614.7,218.7,60.4,1,0 -2013-04-23 06:50:12-06:00,n05667,167.3,7.8,0.6525,25.7344,0.6162,41.7636,48.6102,81.13,5.6,58.2,1006.6,0,618,230.6,62.1,1,0 -2013-04-23 06:55:12-06:00,n05667,189.3,8.4,0.759,30.1533,0.7192,41.9288,48.8513,81.32,6,57.1,1006.6,0,635.8,247.5,64.3,1,0 -2013-04-23 07:00:12-06:00,n05667,209.4,9,0.8655,34.5172,0.8205,42.0693,49.0245,81.35,6,55.7,1006.6,0,662.3,267.1,65.8,1,0 -2013-04-23 07:05:12-06:00,n05667,230.9,9.6,0.9666,38.808,0.9193,42.2161,49.1804,81.63,6,57.4,1006.5,0,670.3,279.6,66,1,0 -2013-04-23 07:10:12-06:00,n05667,268.6,9.6,1.1225,45.3605,1.0698,42.4027,49.4128,81.78,5.9,56.4,1006.5,0,728.3,309.6,66.5,1,0 -2013-04-23 07:15:12-06:00,n05667,298.6,10.8,1.2471,50.5293,1.191,42.4255,49.5136,81.83,6.1,56.2,1006.4,0,745.7,327.4,67.2,1,0 -2013-04-23 07:20:12-06:00,n05667,343.4,11.5,1.3763,55.7571,1.3105,42.5477,49.5832,81.71,6.5,55.3,1006.4,0,758.6,344.5,68.4,1,0 -2013-04-23 07:25:12-06:00,n05667,324.1,11.7,1.4831,60.2994,1.4164,42.5713,49.6567,81.88,6.6,53,1006.4,0,769.1,361.1,69.7,1,0 -2013-04-23 07:30:12-06:00,n05667,346.1,12.6,1.6069,65.2071,1.5301,42.6162,49.6905,81.66,6.7,53.9,1006.4,0,780.6,377.9,70.6,1,0 -2013-04-23 07:40:12-06:00,n05667,403.5,14.8,1.8441,74.9624,1.7673,42.4175,49.6749,81.83,7.2,55.8,1006.5,0,800.9,410.7,73.1,1,0 -2013-04-23 07:45:12-06:00,n05667,456.3,15.7,1.9687,79.9779,1.887,42.3827,49.7205,81.71,7.3,55,1006.6,0,812.8,428.8,73.9,1,0 -2013-04-23 07:50:12-06:00,n05667,430,16.7,2.0877,84.2063,1.9916,42.2806,49.6834,81.18,7.6,53.7,1006.5,0,822,445.4,74.5,1,0 -2013-04-23 07:55:12-06:00,n05667,449.3,17.8,2.2029,88.2204,2.094,42.1299,49.6535,80.65,8.2,52,1006.4,0,829.5,470.7,81.6,1,0 -2013-04-23 08:00:12-06:00,n05667,472.3,18.9,2.3304,92.9225,2.2097,42.0518,49.6157,80.37,8.4,48.2,1006.3,0,840.1,477.2,74.6,1,0 -2013-04-23 08:05:12-06:00,n05667,493.2,18.9,2.448,97.3498,2.3192,41.9762,49.5904,80.19,8.4,45.1,1006.4,0,846.4,494.9,77.4,1,0 -2013-04-23 08:10:12-06:00,n05667,514.6,19.6,2.5656,101.9509,2.4298,41.9594,49.6216,80.08,8.4,44.7,1006.3,0,853.1,509.8,77,1,0 -2013-04-23 08:15:12-06:00,n05667,535.4,21.3,2.6848,106.6165,2.5518,41.7805,49.5266,80.18,9.1,43.2,1006.3,0,857.6,524.1,77.9,1,0 -2013-04-23 08:20:12-06:00,n05667,567.5,23,2.7925,109.8292,2.643,41.5541,49.3328,79.72,9.9,42.8,1006.3,0,860.9,538.6,79.1,1,0 -2013-04-23 08:25:12-06:00,n05667,572,22.6,2.8731,112.9401,2.7188,41.5402,49.3497,79.65,10,43.4,1006.3,0,851.6,549.1,83.9,1,0 -2013-04-23 08:30:12-06:00,n05667,591.4,22.6,3.0094,118.4759,2.8439,41.659,49.4967,79.54,9.8,43.2,1006.3,0,866.2,568.5,83.3,1,0 -2013-04-23 08:35:12-06:00,n05667,610.7,22.8,3.1201,122.5425,2.9459,41.597,49.4941,79.35,9.8,43.5,1006.3,0,871,582.3,83.4,1,0 -2013-04-23 08:40:12-06:00,n05667,629.1,23.8,3.2208,126.5152,3.0451,41.5466,49.4922,79.37,10.1,43.4,1006.3,0,874.9,596.2,84.2,1,0 -2013-04-23 08:45:12-06:00,n05667,650.9,24.9,3.3416,130.7773,3.1595,41.3924,49.3959,79.23,10.5,43.6,1006.2,0,885.2,612.5,83.4,1,0 -2013-04-23 08:50:12-06:00,n05667,670.8,24.1,3.4435,134.974,3.2555,41.46,49.4889,79.2,9.9,44.9,1006.2,0,892.2,628.7,84.2,1,0 -2013-04-23 08:55:12-06:00,n05667,694.5,24.3,3.5697,139.6949,3.3746,41.3956,49.4883,79.08,10,46.2,1006.1,0,905.5,647.1,83.4,1,0 -2013-04-23 09:00:12-06:00,n05667,714.2,25.3,3.6785,143.5754,3.4713,41.3604,49.4798,78.88,10.1,46.2,1006.1,0,911.7,662.3,83.8,1,0 -2013-04-23 09:05:12-06:00,n05667,730.4,25.3,3.7653,147.0357,3.5558,41.3506,49.5065,78.88,10,46.8,1006.1,0,914.1,674.8,84.4,1,0 -2013-04-23 09:10:12-06:00,n05667,750.6,24.7,3.8688,151.4321,3.6557,41.4233,49.632,78.87,9.9,44,1006.1,0,920.1,689.5,84.9,1,0 -2013-04-23 09:15:12-06:00,n05667,766.6,24.6,3.9583,155.1171,3.7419,41.4543,49.6951,78.86,10,45.2,1006.1,0,923.2,701.7,85.6,1,0 -2013-04-23 09:20:12-06:00,n05667,785.2,26.2,4.0567,158.6972,3.8349,41.3823,49.6645,78.77,10.2,44.8,1006.1,0,928.8,715.5,85.9,1,0 -2013-04-23 09:25:12-06:00,n05667,800.7,26.5,4.1489,161.5594,3.9182,41.2328,49.5644,78.57,10.5,45.3,1006.1,0,1.5,728.1,730,1,0 -2013-04-23 09:30:12-06:00,n05667,817.9,26.4,4.2418,164.9869,4.0025,41.2208,49.5897,78.43,10.5,44.9,1006.1,0,1.2,692.8,741.8,1,0 -2013-04-23 09:35:12-06:00,n05667,833.3,27.9,4.3213,167.3761,4.0814,41.0094,49.4167,78.38,10.5,46.3,1006.1,0,937.9,750.9,86.4,1,0 -2013-04-23 09:40:12-06:00,n05667,850.6,26.9,4.4137,170.8366,4.1641,41.0264,49.4713,78.24,10.4,45.2,1006,0,942.2,764.2,87,1,0 -2013-04-23 09:45:12-06:00,n05667,865.6,28.8,4.4917,173.5313,4.2354,40.9712,49.4362,78.15,10.5,46.6,1006,0,944.5,775,86.9,1,0 -2013-04-23 09:50:12-06:00,n05667,878.8,28.9,4.5697,176.2332,4.3097,40.8924,49.3959,78.07,11,46.3,1006,0,946.8,785.3,87.4,1,0 -2013-04-23 09:55:12-06:00,n05667,892.5,28.6,4.6423,178.9569,4.3757,40.8975,49.4252,78,11.1,44.9,1006.1,0,948.1,795.3,87.9,1,0 -2013-04-23 10:00:12-06:00,n05667,904.5,30.3,4.7137,180.4283,4.4401,40.6361,49.206,77.79,11.4,45.9,1006.1,0,948.3,804.4,88.4,1,0 -2013-04-23 10:05:12-06:00,n05667,919.5,27.3,4.7879,184.7376,4.5135,40.9301,49.5201,77.92,10.9,45.4,1006,0,951.6,816.4,89.4,1,0 -2013-04-23 10:10:12-06:00,n05667,929.6,26.8,4.8395,187.4177,4.5629,41.0738,49.6671,77.97,10.8,45.6,1006,0,950.1,824,90.4,1,0 -2013-04-23 10:15:12-06:00,n05667,941.5,28.5,4.9065,189.1331,4.6238,40.9045,49.539,77.81,11.2,48.5,1006,0,950.9,832.4,91,1,0 -2013-04-23 10:20:12-06:00,n05667,952.5,29.6,4.9715,191.3274,4.6822,40.8629,49.5195,77.72,11.2,48.8,1006.1,0,951.7,840.2,91.2,1,0 -2013-04-23 10:25:12-06:00,n05667,964.6,31.3,5.0312,193.1725,4.7411,40.7439,49.4317,77.67,11.9,47,1006.1,0,955.4,849.8,91.5,1,0 -2013-04-23 10:30:12-06:00,n05667,974.1,32.9,5.0871,193.9767,4.7904,40.4929,49.2157,77.48,12.6,46.2,1006,0,956.1,857.3,91.7,1,0 -2013-04-23 10:35:12-06:00,n05667,988.1,31.7,5.1573,197.0353,4.8522,40.6078,49.353,77.41,12,45.5,1006,0,960.6,867.2,92.2,1,0 -2013-04-23 10:40:12-06:00,n05667,998.3,32.3,5.2182,198.2864,4.9073,40.406,49.1816,77.26,12.3,45.1,1005.9,0,963.1,874.6,91.5,1,0 -2013-04-23 10:45:12-06:00,n05667,1007.1,32.5,5.2645,199.7009,4.9526,40.3225,49.1136,77.24,12.2,45,1005.8,0,962.4,881.2,93,1,0 -2013-04-23 10:50:12-06:00,n05667,1028.9,30.2,5.3661,203.9994,5.0476,40.415,49.2456,77.2,12.7,40.4,1005.6,0,968.5,899.7,100.6,1,0 -2013-04-23 10:55:12-06:00,n05667,1026.5,33.6,5.3641,203.2186,5.0414,40.3096,49.1318,77.11,12.4,43.1,1005.6,0,965.1,894.4,94.4,1,0 -2013-04-23 11:00:12-06:00,n05667,1027.1,36.2,5.3835,202.1412,5.0559,39.9816,48.8287,76.9,13.4,42,1005.5,0,959.7,892.9,93.1,1,0 -2013-04-23 11:05:12-06:00,n05667,1032.1,35.9,5.4177,202.2562,5.083,39.7904,48.6602,76.72,14,40.1,1005.4,0,959.4,897.4,93.9,1,0 -2013-04-23 11:10:12-06:00,n05667,1033.6,35.1,5.4241,202.8464,5.0892,39.8586,48.7279,76.75,13.5,41.5,1005.4,0,955.7,900.7,95.9,1,0 -2013-04-23 11:15:12-06:00,n05667,1039.2,33.4,5.4557,204.8925,5.1186,40.0286,48.9009,76.8,13.6,41.3,1005.3,0,951.2,903.3,98,1,0 -2013-04-23 11:20:12-06:00,n05667,1043.9,34.7,5.4873,204.974,5.1456,39.8345,48.7298,76.66,14.1,41.1,1005.1,0,950,905.1,98.1,1,0 -2013-04-23 11:25:12-06:00,n05667,1047.4,34.5,5.4992,206.143,5.1595,39.9539,48.8424,76.75,13.9,41.2,1005.1,0,949.7,908.3,98.6,1,0 -2013-04-23 11:30:12-06:00,n05667,1050.8,35,5.5304,206.4732,5.1853,39.8191,48.7233,76.63,14.4,39.3,1005,0,952.3,911.2,96.4,1,0 -2013-04-23 11:35:12-06:00,n05667,1057.8,37.8,5.5687,207.0476,5.2185,39.6755,48.6056,76.49,15.1,40.1,1005,0,954.1,913.9,95.3,1,0 -2013-04-23 11:40:12-06:00,n05667,1065.8,37.7,5.6107,207.7913,5.2548,39.5429,48.4976,76.36,14.8,40.3,1004.9,0,962.5,922.1,94.3,1,0 -2013-04-23 11:45:12-06:00,n05667,1068.8,35.8,5.6249,209.6325,5.2683,39.7909,48.7324,76.48,14.8,36.3,1004.9,0,962.2,924,94.6,1,0 -2013-04-23 11:50:12-06:00,n05667,1075.8,36.3,5.6551,211.1725,5.3013,39.8339,48.7903,76.54,15,37.5,1004.8,0,969.6,929.8,92.6,1,0 -2013-04-23 11:55:12-06:00,n05667,1077.8,36.1,5.6644,211.1455,5.3068,39.7877,48.7435,76.47,15.3,37.4,1004.7,0,971.7,932.8,92.1,1,0 -2013-04-23 12:00:12-06:00,n05667,1072.2,37.2,5.6488,210.4523,5.2921,39.767,48.7253,76.46,15.2,37.9,1004.6,0,964.2,926.8,91.8,1,0 -2013-04-23 12:05:12-06:00,n05667,1072.6,34.7,5.6439,211.5265,5.2918,39.9723,48.9149,76.62,15.1,37.6,1004.5,0,964.4,927.9,92.2,1,0 -2013-04-23 12:10:12-06:00,n05667,1075.8,34.5,5.6611,212.0048,5.3088,39.9349,48.8866,76.6,15.3,37.2,1004.4,0,968.2,930.5,90.9,1,0 -2013-04-23 12:15:12-06:00,n05667,1073.6,35.6,5.6519,211.2948,5.2942,39.9109,48.8645,76.51,15.4,35.8,1004.4,0,968.7,929.7,90.8,1,0 -2013-04-23 12:20:12-06:00,n05667,1073.3,34.6,5.6476,211.9412,5.2928,40.0431,48.9842,76.61,15.6,34.4,1004.3,0,967.5,929,91.2,1,0 -2013-04-23 12:25:12-06:00,n05667,1078.3,34.2,5.6714,213.0835,5.3148,40.0926,49.0362,76.62,15.6,34.6,1004.3,0,974.2,933.6,90.4,1,0 -2013-04-23 12:30:12-06:00,n05667,1070.7,34.8,5.6433,211.1754,5.2899,39.9207,48.8677,76.58,15.7,38,1004.2,0,970.2,928.1,89.6,1,0 -2013-04-23 12:35:12-06:00,n05667,1068.3,35.2,5.626,210.3848,5.2729,39.8993,48.8385,76.57,15.7,36.8,1004.2,0,970.5,927,90.2,1,0 -2013-04-23 12:40:12-06:00,n05667,1064,36,5.6084,209.5988,5.2567,39.8728,48.8046,76.58,15.9,36.6,1004,0,967.7,922.9,90.1,1,0 -2013-04-23 12:45:12-06:00,n05667,1058.8,35.7,5.5848,208.4772,5.2322,39.8449,48.7695,76.54,16,36.6,1004.1,0,966.1,919.2,90.1,1,0 -2013-04-23 12:50:12-06:00,n05667,1053.7,36.5,5.5617,207.4234,5.2111,39.8038,48.711,76.56,16.5,36,1003.9,0,964.9,915.8,89.9,1,0 -2013-04-23 12:55:12-06:00,n05667,1048.8,36.5,5.5356,205.6091,5.1873,39.6367,48.5503,76.5,17.2,33.5,1004,0,964.8,912.9,89.6,1,0 -2013-04-23 13:00:12-06:00,n05667,1048.3,34.9,5.5237,206.9525,5.1761,39.9825,48.869,76.67,16.4,32.9,1003.9,0,967.5,914.1,91.2,1,0 -2013-04-23 13:05:12-06:00,n05667,1049.1,34.4,5.5187,206.9989,5.1729,40.0163,48.9048,76.7,16.5,32.4,1003.8,0,973.6,915.9,91.2,1,0 -2013-04-23 13:10:12-06:00,n05667,1034.3,33.8,5.4434,205.2803,5.1088,40.182,49.0291,76.92,16.4,33.1,1003.9,0,961.9,903.9,92.5,1,0 -2013-04-23 13:15:12-06:00,n05667,1026.2,33.7,5.4031,204.194,5.0727,40.2535,49.0824,77,16.6,31.3,1003.8,0,961.3,898.2,90.9,1,0 -2013-04-23 13:20:12-06:00,n05667,1021.8,31.8,5.3739,203.1914,5.0421,40.2989,49.1234,76.97,16.8,30.3,1003.8,0,964.1,896.1,90.7,1,0 -2013-04-23 13:25:12-06:00,n05667,1014,31.7,5.3346,202.1633,5.011,40.344,49.1494,77.1,16.8,33.2,1003.7,0,963,890.7,90.9,1,0 -2013-04-23 13:30:12-06:00,n05667,1003.8,33.7,5.286,199.6468,4.9619,40.2361,49.0297,77.03,16.9,35.6,1003.7,0,959.2,883.1,91.2,1,0 -2013-04-23 13:35:12-06:00,n05667,994.5,33,5.237,198.3088,4.9175,40.3275,49.1012,77.12,17.1,35.3,1003.7,0,957,877.1,91.6,1,0 -2013-04-23 13:40:12-06:00,n05667,983.9,31.2,5.1701,196.3529,4.8594,40.4067,49.1481,77.27,17,36,1003.5,0,953.2,868.8,92.1,1,0 -2013-04-23 13:45:12-06:00,n05667,978.7,32,5.1476,195.2574,4.8375,40.3633,49.1025,77.25,17.2,35.6,1003.4,0,958.2,866.4,91.2,1,0 -2013-04-23 13:50:12-06:00,n05667,971.1,31.7,5.0961,193.5849,4.7906,40.4094,49.1227,77.33,17.3,34,1003.5,0,960.1,861.8,91,1,0 -2013-04-23 13:55:12-06:00,n05667,956.6,32.2,5.0397,190.096,4.7297,40.192,48.8801,77.17,17.8,32.9,1003.5,0,953.6,848.1,89.5,1,0 -2013-04-23 14:00:12-06:00,n05667,953.1,31,4.9974,189.9945,4.6986,40.4366,49.1149,77.41,17.8,30.3,1003.4,0,961.4,848.1,89.6,1,0 -2013-04-23 14:05:12-06:00,n05667,933.7,29.3,4.9019,187.3034,4.6095,40.6339,49.2613,77.57,17.4,32.5,1003.5,0,950.5,834.4,91.2,1,0 -2013-04-23 14:10:12-06:00,n05667,917.8,30.3,4.8199,184.5977,4.5381,40.6772,49.2613,77.75,17.5,33.3,1003.6,0,939.9,819.1,91.7,1,0 -2013-04-23 14:15:12-06:00,n05667,912.7,32.4,4.7908,181.7901,4.4991,40.4057,49.003,77.44,18,31.2,1003.6,0,949.4,816.9,90.6,1,0 -2013-04-23 14:20:12-06:00,n05667,891.1,30.8,4.6832,178.5956,4.4023,40.5683,49.113,77.65,17.7,32.1,1003.5,0,935.4,800.4,92,1,0 -2013-04-23 14:25:12-06:00,n05667,874,31.6,4.6038,175.0062,4.3252,40.4621,48.9777,77.61,17.8,27.3,1003.6,0,930.6,786.7,90.3,1,0 -2013-04-23 14:30:12-06:00,n05667,859.7,32.1,4.5207,172.0712,4.2531,40.458,48.9425,77.77,18,27.9,1003.6,0,930.3,777.1,89.1,1,0 -2013-04-23 14:35:12-06:00,n05667,836,32.5,4.4112,167.1833,4.1467,40.3171,48.7591,77.73,18,29.3,1003.5,0,916.2,755.9,87.4,1,0 -2013-04-23 14:40:12-06:00,n05667,824.4,34.8,4.3547,164.0483,4.0902,40.1075,48.5237,77.64,18.2,27.5,1003.5,0,922.6,748,83.4,1,0 -2013-04-23 14:45:12-06:00,n05667,811,35.3,4.2843,160.6499,4.024,39.9233,48.3233,77.6,18.8,26.9,1003.4,0,920.4,735.4,81.4,1,0 -2013-04-23 14:50:12-06:00,n05667,796.2,35.3,4.1942,157.3024,3.9386,39.9388,48.2934,77.66,18.5,28.2,1003.4,0,922.2,728.4,81.9,1,0 -2013-04-23 14:55:12-06:00,n05667,776.9,33.8,4.0912,154.1639,3.8438,40.1066,48.4241,77.82,18.7,25.8,1003.2,0,913.5,713,81.8,1,0 -2013-04-23 15:00:12-06:00,n05667,763.8,33.1,4.0183,152.2695,3.7791,40.2922,48.5822,78,18.2,26,1003.2,0,916.6,704.8,80.9,1,0 -2013-04-23 15:05:12-06:00,n05667,746.5,30.1,3.9067,149.2753,3.6804,40.5596,48.7832,78.33,18.3,19.5,1002.9,0,912.8,692.7,80.6,1,0 -2013-04-23 15:10:12-06:00,n05667,725.5,32.7,3.8099,144.4656,3.5825,40.3256,48.5113,78.16,18.5,24.4,1002.9,0,905,674.9,78.8,1,0 -2013-04-23 15:15:12-06:00,n05667,707.5,33.8,3.7159,140.1759,3.4942,40.1169,48.2771,78.14,18.7,23.5,1002.7,0,902,661,77.3,1,0 -2013-04-23 15:20:12-06:00,n05667,693.6,33.2,3.6295,137.4867,3.4166,40.2411,48.365,78.32,18.7,23.7,1002.6,0,905.3,652,76.2,1,0 -2013-04-23 15:25:12-06:00,n05667,673.4,32.7,3.5227,133.7472,3.311,40.3947,48.4729,78.33,18.7,24.5,1002.5,0,897,636,76.2,1,0 -2013-04-23 15:30:12-06:00,n05667,653.4,32.6,3.41,129.5468,3.2064,40.4024,48.4293,78.44,18.7,25.3,1002.4,0,889.3,619.8,75.8,1,0 -2013-04-23 15:35:12-06:00,n05667,634.2,32.3,3.305,125.2817,3.1079,40.3109,48.2823,78.51,19.1,22.1,1002.3,0,884.5,605.7,75.2,1,0 -2013-04-23 15:40:12-06:00,n05667,614.7,30.4,3.1901,121.5107,3.0007,40.4945,48.4272,78.65,19.2,20.4,1002.2,0,876.7,591.7,75.8,1,0 -2013-04-23 15:45:12-06:00,n05667,594.2,30.7,3.077,117.444,2.8949,40.5692,48.4471,78.78,19.3,20.7,1002.2,0,870.8,576.3,74.9,1,0 -2013-04-23 15:50:12-06:00,n05667,576.1,29.7,2.9735,113.7267,2.7975,40.6524,48.488,78.88,19.2,21.7,1002,0,866.3,562.6,74.5,1,0 -2013-04-23 15:55:12-06:00,n05667,553.7,29,2.8465,109.1306,2.681,40.7048,48.485,79.07,19.3,23.4,1002,0,822,546.2,74.8,1,0 -2013-04-23 16:00:12-06:00,n05667,533.8,29,2.7348,104.9145,2.5745,40.7518,48.48,79.13,19.2,24,1002,0,848.6,530.9,74.4,1,0 -2013-04-23 16:05:12-06:00,n05667,511.2,28.7,2.6094,99.9441,2.4546,40.7167,48.3844,79.16,19.2,24.5,1001.9,0,836.1,512.5,73.9,1,0 -2013-04-23 16:10:12-06:00,n05667,488.9,28.7,2.4921,95.5495,2.3476,40.7007,48.3129,79.36,19.1,24.9,1001.9,0,829.3,497.5,73.4,1,0 -2013-04-23 16:15:12-06:00,n05667,469.2,27.6,2.3799,91.2604,2.2375,40.7875,48.3513,79.31,19.2,23.4,1001.8,0,824.3,482.4,71.9,1,0 -2013-04-23 16:20:12-06:00,n05667,451.1,26.5,2.2694,87.501,2.1382,40.9219,48.4511,79.58,19,24.2,1001.8,0,822.4,469.2,71,1,0 -2013-04-23 16:25:12-06:00,n05667,431.5,25.9,2.1549,83.0858,2.0291,40.9479,48.4176,79.63,19,25,1001.8,0,817.3,454.2,69.8,1,0 -2013-04-23 16:30:12-06:00,n05667,410.5,25.7,2.0344,78.3552,1.9149,40.9184,48.3227,79.71,18.9,25.9,1001.8,0,808.9,437.6,68.6,1,0 -2013-04-23 16:35:12-06:00,n05667,389.3,25.7,1.9129,73.6182,1.8006,40.8844,48.2497,79.76,18.9,26.6,1001.7,0,800.1,421.1,67.3,1,0 -2013-04-23 16:40:12-06:00,n05667,368.1,26.2,1.7929,68.7189,1.6858,40.7639,48.0892,79.7,19,26.9,1001.7,0,792.8,404.4,65.5,1,0 -2013-04-23 16:45:12-06:00,n05667,349.1,25.6,1.6791,64.3237,1.5782,40.758,48.03,79.76,19.1,27.1,1001.6,0,787.6,389.8,64.3,1,0 -2013-04-23 16:50:12-06:00,n05667,328.3,25.1,1.5594,59.6388,1.4657,40.6905,47.9079,79.83,19.1,27.5,1001.5,0,777.9,373.4,63.2,1,0 -2013-04-23 16:55:12-06:00,n05667,307.9,24.6,1.4386,54.9652,1.3522,40.6495,47.8163,79.91,19.2,26.3,1001.3,0,767.1,357.5,62.8,1,0 -2013-04-23 17:00:12-06:00,n05667,286.9,23.9,1.3168,50.2841,1.2376,40.6297,47.7223,80.02,19,26.4,1001.4,0,752.4,340.8,62.5,1,0 -2013-04-23 17:05:12-06:00,n05667,265.3,23.4,1.1939,45.4922,1.1215,40.5643,47.6257,80.01,19,26.4,1001.4,0,734.8,323.1,62.2,1,0 -2013-04-23 17:10:12-06:00,n05667,243.4,22.9,1.0743,40.777,1.0083,40.4406,47.4558,79.99,19,26.8,1001.4,0,710.5,303.5,62.1,1,0 -2013-04-23 17:15:12-06:00,n05667,224.7,22.7,0.9687,36.6542,0.9088,40.3311,47.2935,80.01,19,26.6,1001.4,0,698.4,288.2,61.4,1,0 -2013-04-23 17:20:12-06:00,n05667,206.6,22,0.8653,32.6313,0.8115,40.209,47.1352,80,19,26.1,1001.2,0,682.5,272.8,61.9,1,0 -2013-04-23 17:25:12-06:00,n05667,186.6,21.8,0.7598,28.5143,0.7123,40.0337,46.9252,79.97,18.9,26.5,1001.4,0,661.3,254.7,59.9,1,0 -2013-04-23 17:30:12-06:00,n05667,167.4,21.4,0.6624,24.6917,0.6197,39.8468,46.7024,79.81,18.8,26.9,1001.4,0,637.1,236.6,58.6,1,0 -2013-04-23 17:35:12-06:00,n05667,150.4,21.1,0.5755,21.2976,0.5375,39.6228,46.452,79.66,18.9,26.4,1001.4,0,625.3,221.8,56.6,1,0 -2013-04-23 17:40:12-06:00,n05667,132.1,20.8,0.4915,18.0347,0.4585,39.3319,46.1625,79.49,18.7,26.9,1001.2,0,605.8,205.3,54.5,1,0 -2013-04-23 17:45:12-06:00,n05667,115.5,20.6,0.4174,15.1579,0.3883,39.0343,45.8406,79.22,18.8,28.4,1001.4,0,589.4,189.7,52.1,1,0 -2013-04-23 17:50:12-06:00,n05667,97.6,20.3,0.3488,12.5074,0.3234,38.672,45.488,78.82,18.7,27.4,1001.3,0,561.9,173,50.4,1,0 -2013-04-23 17:55:12-06:00,n05667,82.3,20,0.2919,10.3995,0.2714,38.3141,45.1498,78.91,18.6,27.3,1001.1,0,539.2,158.4,49,1,0 -2013-04-23 18:00:12-06:00,n05667,68.2,19.8,0.246,8.7501,0.2302,38.0146,44.7888,79.4,18.5,29.1,1001.3,0,512.4,143.2,47.1,1,0 -2013-04-23 18:05:12-06:00,n05667,55.9,19.5,0.2126,7.5145,0.1992,37.7268,44.5401,79.37,18.4,30,1001.3,0,482.1,128.3,45.3,1,0 -2013-04-23 18:10:12-06:00,n05667,45.6,19.3,0.1898,6.5383,0.1748,37.4089,44.2372,77.88,18.4,30.5,1001.2,0,439.9,112.1,43,1,0 -2013-04-23 18:15:12-06:00,n05667,42,19.1,0.1763,6.0581,0.1624,37.303,44.1254,77.87,18.4,28.7,1001.1,0,236.2,86.7,40.6,1,0 -2013-04-23 18:20:12-06:00,n05667,33.2,18.8,0.163,5.5832,0.1503,37.1407,44.0239,77.79,18.2,30.7,1001.1,0,359.2,84.1,37.9,1,0 -2013-04-23 18:25:12-06:00,n05667,29.8,18.6,0.1504,5.1106,0.1384,36.939,43.8658,77.44,18.1,31.4,1001.1,0,299.3,71.6,35,1,0 -2013-04-23 18:30:13-06:00,n05667,26.3,18.4,0.1364,4.5956,0.1251,36.7319,43.6889,77.11,18,30.2,1001,0,280.3,58.8,31.6,1,0 -2013-04-23 18:35:13-06:00,n05667,23.3,18.2,0.1217,4.0504,0.111,36.5029,43.4339,76.63,18,31.2,1001.1,0,37.2,29.9,28.6,1,0 -2013-05-01 05:35:12-06:00,n05667,23.6,2.7,0.1236,4.3954,0.1131,38.8641,45.8802,77.5,3.1,78.4,1012.4,0,244.3,44.7,25.5,0.995,0 -2013-05-01 05:40:12-06:00,n05667,27.6,2.7,0.1391,5.0222,0.1284,39.1163,46.1138,78.29,3.1,78.3,1012.5,0,289,55.9,29.1,0.995,0 -2013-05-01 05:45:12-06:00,n05667,31.8,2.7,0.1541,5.6156,0.1425,39.4133,46.3092,78.7,3.2,78.1,1012.5,0,330.6,68.1,32.5,0.995,0 -2013-05-01 05:50:12-06:00,n05667,36,2.9,0.1687,6.1971,0.1564,39.6261,46.465,79.07,3.6,77.1,1012.6,0,368.7,81.1,35.8,0.995,0 -2013-05-01 05:55:12-06:00,n05667,40.2,3,0.1822,6.7266,0.1689,39.829,46.6494,79.12,3.7,75.8,1012.6,0,406,94.8,38.8,0.995,0 -2013-05-01 06:00:12-06:00,n05667,43.9,3.2,0.1937,7.1852,0.1799,39.9449,46.7362,79.39,3.7,75.8,1012.6,0,438.2,108.6,41.6,0.995,0 -2013-05-01 06:05:12-06:00,n05667,46.8,3.2,0.205,7.6472,0.1909,40.0558,46.8553,79.62,3.5,76.2,1012.6,0,470.8,123.6,44.4,0.995,0 -2013-05-01 06:10:12-06:00,n05667,53.6,3.3,0.2225,8.4315,0.2079,40.5628,47.0738,80.51,3.5,77.4,1012.7,0,497.4,137.7,46.6,0.995,0 -2013-05-01 06:15:12-06:00,n05667,65.3,3.6,0.2509,9.7992,0.2371,41.327,47.3782,82.43,3.7,76.5,1012.7,0,519.5,152.3,49.2,0.995,0 -2013-05-01 06:20:12-06:00,n05667,79.1,3.9,0.286,11.3697,0.2733,41.6001,47.6358,83.46,3.8,76.1,1012.6,0,544.3,167.7,51.4,0.995,0 -2013-05-01 06:25:12-06:00,n05667,94.4,4.4,0.3359,13.3308,0.3205,41.5936,47.8576,82.92,4,75.2,1012.8,0,564.7,183.2,54.1,0.995,0 -2013-05-01 06:30:12-06:00,n05667,111.3,4.7,0.398,15.745,0.3788,41.5703,48.1002,82.24,4,74.1,1012.9,0,589.6,200.4,56.5,0.995,0 -2013-05-01 06:35:12-06:00,n05667,128.8,5.1,0.4685,18.4515,0.4435,41.6038,48.3396,81.47,3.9,74.9,1012.9,0,612,217.3,58.6,0.995,0 -2013-05-01 06:40:12-06:00,n05667,146.3,5.6,0.5464,21.5362,0.5164,41.7085,48.5614,81.17,4.1,74.7,1013,0,631,233.5,60.2,0.995,0 -2013-05-01 06:45:12-06:00,n05667,165,6.3,0.6328,25.0608,0.598,41.9112,48.7767,81.19,4.2,74.3,1013,0,651.3,250.4,61.8,0.995,0 -2013-05-01 06:50:12-06:00,n05667,184.3,6.8,0.7246,28.8273,0.6853,42.0633,48.9659,81.25,4.2,73.6,1013.1,0,671.3,268.2,63.3,0.995,0 -2013-05-01 06:55:12-06:00,n05667,203.4,7.5,0.8222,32.8514,0.7786,42.1922,49.1277,81.33,4.2,74.2,1013.2,0,685.2,284.6,65,0.995,0 -2013-05-01 07:00:12-06:00,n05667,223.9,8.3,0.9275,37.1349,0.878,42.2932,49.2583,81.28,4.7,73.2,1013.2,0,700.1,301.1,66.6,0.995,0 -2013-05-01 07:10:12-06:00,n05667,264.9,9.5,1.1432,45.9914,1.0851,42.3849,49.4473,81.36,4.9,71.2,1013.2,0,723.7,334.4,70.5,0.995,0 -2013-05-01 07:15:12-06:00,n05667,295.9,10.2,1.2602,50.7184,1.1945,42.4603,49.5722,81.19,5.2,70.3,1013.2,0,738.6,351.4,70.8,0.995,0 -2013-05-01 07:20:12-06:00,n05667,316.6,10.3,1.3755,55.566,1.3056,42.5581,49.7107,81.26,5.1,69.5,1013.1,0,750.6,368.5,72.4,0.995,0 -2013-05-01 07:25:12-06:00,n05667,326.1,11.2,1.4935,60.5377,1.4226,42.5549,49.781,81.42,5,70.1,1013.2,0,763.3,385.2,73.4,0.995,0 -2013-05-01 07:30:12-06:00,n05667,346.2,11.8,1.6112,65.6415,1.5402,42.6197,49.8512,81.72,5.2,70.2,1013.3,0,774,402.4,74.6,0.995,0 -2013-05-01 07:35:12-06:00,n05667,367.5,12.5,1.7337,70.3209,1.65,42.6182,49.8889,81.3,5.4,69.4,1013.3,0,786.2,419.9,75.3,0.995,0 -2013-05-01 07:40:12-06:00,n05667,388.6,12.9,1.8536,75.0539,1.7623,42.5877,49.898,81.15,5.8,67.1,1013.2,0,796.7,436.1,75.3,0.995,0 -2013-05-01 07:45:12-06:00,n05667,417.4,12.8,1.9732,79.7702,1.8717,42.6182,50.0073,80.84,5.2,68.4,1013.2,0,807,452.6,76.5,0.995,0 -2013-05-01 07:50:12-06:00,n05667,433.1,13.4,2.0904,84.5238,1.9826,42.6321,50.0808,80.74,5.3,68.5,1013.2,0,815.3,469.2,77.9,0.995,0 -2013-05-01 07:55:12-06:00,n05667,492.8,14.4,2.2071,89.1998,2.0969,42.5388,50.0333,80.78,5.8,68.1,1013.2,0,824.4,486,78.6,0.995,0 -2013-05-01 08:00:12-06:00,n05667,486.6,15.4,2.3257,93.799,2.2089,42.4637,50.0262,80.62,5.8,67.5,1013.1,0,832,502.5,79.3,0.995,0 -2013-05-01 08:05:12-06:00,n05667,505.1,17.2,2.4423,98.1284,2.32,42.2962,49.913,80.5,6.4,66.4,1013.1,0,837.6,516.5,79.9,0.995,0 -2013-05-01 08:10:12-06:00,n05667,513,18.2,2.5612,102.1555,2.4243,42.1374,49.7992,80.09,6.9,64.1,1013.2,0,845.1,532.9,81.2,0.995,0 -2013-05-01 08:15:12-06:00,n05667,533.7,18.3,2.676,106.6994,2.5332,42.1199,49.8122,80.05,6.8,63.1,1013.1,0,850.7,549,82.1,0.995,0 -2013-05-01 08:20:12-06:00,n05667,554.8,19.2,2.7925,111.3103,2.6449,42.085,49.8343,79.99,6.7,62.4,1013.1,0,859,564,82.2,0.995,0 -2013-05-01 08:25:12-06:00,n05667,575.6,19.3,2.9054,115.7448,2.7492,42.1013,49.9013,79.83,6.9,62.3,1013.1,0,863,579.7,84.2,0.995,0 -2013-05-01 08:30:12-06:00,n05667,592.5,20.1,3.0047,119.6479,2.8474,42.0207,49.8421,79.89,7.2,62.8,1013.1,0,869.7,595.1,84.6,0.995,0 -2013-05-01 08:35:12-06:00,n05667,613.2,20.6,3.1211,123.9816,2.9516,42.0055,49.8857,79.63,7.4,61.5,1013.1,0,875,610.5,85.6,0.995,0 -2013-05-01 08:40:12-06:00,n05667,631.3,20.4,3.2202,127.8379,3.0479,41.943,49.8649,79.61,7.6,59.7,1013,0,879.7,624.9,85.9,0.995,0 -2013-05-01 08:45:12-06:00,n05667,650,21.2,3.3304,131.9515,3.1498,41.8924,49.8694,79.45,7.3,61.8,1013,0,883.6,638.9,86.8,0.995,0 -2013-05-01 08:50:12-06:00,n05667,669.6,20.9,3.4366,136.1169,3.2501,41.8814,49.8935,79.39,7.3,61.6,1013,0,890.1,653.9,87.1,0.995,0 -2013-05-01 08:55:12-06:00,n05667,688.1,21.4,3.539,140.1034,3.3446,41.889,49.9481,79.26,7.5,61.4,1012.9,0,894.9,668.2,87.7,0.995,0 -2013-05-01 09:00:12-06:00,n05667,707.5,21.9,3.6419,144.0213,3.4459,41.7944,49.9039,79.24,7.8,62.2,1013,0,900.5,682.4,88,0.995,0 -2013-05-01 09:05:12-06:00,n05667,724.5,21.3,3.7363,147.8663,3.5315,41.8706,50.0099,79.14,7.8,60,1013,0,904,696.3,88.4,0.995,0 -2013-05-01 09:10:12-06:00,n05667,741.6,21.8,3.8291,151.1472,3.6217,41.7337,49.9097,79.09,8.1,59.9,1013,0,908.1,708.7,88.7,0.995,0 -2013-05-01 09:15:12-06:00,n05667,758.5,23.3,3.9212,154.4324,3.7066,41.6642,49.8603,78.99,7.9,62.2,1013,0,911.5,721.4,89.7,0.995,0 -2013-05-01 09:20:12-06:00,n05667,772.4,25.8,4.0083,156.9793,3.7834,41.4918,49.7491,78.72,8.4,61.7,1013,0,911.5,732,90.8,0.995,0 -2013-05-01 09:25:12-06:00,n05667,789.1,24.1,4.0891,160.3619,3.8656,41.4842,49.7784,78.78,9.1,60,1012.9,0,912.7,744.1,92.2,0.995,0 -2013-05-01 09:30:12-06:00,n05667,805.6,24.3,4.1767,163.9924,3.9481,41.5369,49.8447,78.77,8.8,60.2,1012.9,0,916.8,756.6,92.6,0.995,0 -2013-05-01 09:35:12-06:00,n05667,821.8,25,4.263,166.8316,4.0289,41.4091,49.7699,78.63,9.1,59.9,1013,0,922.6,769.8,92.2,0.995,0 -2013-05-01 09:40:12-06:00,n05667,838.5,25.2,4.3571,170.049,4.1138,41.3366,49.7484,78.45,9.3,59,1013,0,926.9,782.4,92.7,0.995,0 -2013-05-01 09:45:12-06:00,n05667,852.3,24.9,4.4258,173.2317,4.1803,41.4399,49.8532,78.51,8.9,59.8,1013,0,927.2,793,94,0.995,0 -2013-05-01 09:50:12-06:00,n05667,865.2,25.7,4.5015,175.6369,4.2491,41.3351,49.7933,78.36,9.1,60.4,1013,0,928.6,802.9,94.3,0.995,0 -2013-05-01 09:55:12-06:00,n05667,880.6,24.8,4.5769,179.011,4.324,41.3997,49.8948,78.39,9.4,59.1,1012.9,0,933.1,814.5,94.6,0.995,0 -2013-05-01 10:00:12-06:00,n05667,893.2,25.9,4.6499,181.1302,4.3848,41.3086,49.8252,78.18,9.5,60.1,1013,0,933.4,823.5,95.3,0.995,0 -2013-05-01 10:05:12-06:00,n05667,905.8,26.2,4.7209,183.4917,4.4506,41.2283,49.7738,78.09,9.8,59.1,1012.9,0,935.6,833.4,95.6,0.995,0 -2013-05-01 10:10:12-06:00,n05667,918.1,27.7,4.7823,185.3616,4.513,41.0732,49.6548,78.06,10.1,58.3,1012.9,0,937.7,843.2,96.4,0.995,0 -2013-05-01 10:15:12-06:00,n05667,930.4,25.5,4.8454,188.2376,4.5738,41.1553,49.7523,78.08,10,57.6,1012.8,0,939.3,852.6,97.2,0.995,0 -2013-05-01 10:20:12-06:00,n05667,941.5,26.9,4.909,190.5519,4.6301,41.1554,49.7764,77.98,10.3,56.6,1012.9,0,941.6,860.4,96.5,0.995,0 -2013-05-01 10:25:12-06:00,n05667,952.2,28.7,4.9685,192.3549,4.6863,41.0464,49.7036,77.89,10.5,56.6,1012.8,0,944.6,868.1,96.1,0.995,0 -2013-05-01 10:30:12-06:00,n05667,963.9,28.7,5.0338,193.7544,4.7393,40.8827,49.554,77.67,11,54.8,1012.7,0,947.6,876.6,95.8,0.995,0 -2013-05-01 10:35:12-06:00,n05667,975.2,28.2,5.0936,196.0166,4.795,40.8792,49.5865,77.61,10.7,55.7,1012.6,0,951.9,886,95.4,0.995,0 -2013-05-01 10:40:12-06:00,n05667,983,28.5,5.1371,197.1478,4.8409,40.725,49.4557,77.6,11.1,55.1,1012.5,0,950.2,890.5,95.7,0.995,0 -2013-05-01 10:45:12-06:00,n05667,992.5,29.5,5.1876,198.6102,4.8857,40.6512,49.4044,77.49,11.2,56,1012.4,0,951.9,898.1,96.1,0.995,0 -2013-05-01 10:50:12-06:00,n05667,1002.7,30.3,5.2465,200.4065,4.9349,40.6099,49.3907,77.34,11.8,53.6,1012.5,0,956.1,905.6,95.3,0.995,0 -2013-05-01 10:55:12-06:00,n05667,1009.7,29.5,5.2769,202.6322,4.9739,40.739,49.5201,77.54,11.5,54.9,1012.4,0,955.9,910.4,95.9,0.995,0 -2013-05-01 11:00:12-06:00,n05667,1018.2,29.6,5.3219,203.9482,5.0078,40.7259,49.5208,77.39,11.7,53.6,1012.3,0,957,916.9,97.2,0.995,0 -2013-05-01 11:05:12-06:00,n05667,1021.2,30.9,5.3519,204.0572,5.0341,40.5353,49.3517,77.26,12,53.6,1012.3,0,953.9,918.3,97.4,0.995,0 -2013-05-01 11:10:12-06:00,n05667,1027.8,30.4,5.3855,205.1493,5.0646,40.5062,49.3393,77.21,12.7,49.2,1012.2,0,954.4,923.3,97.4,0.995,0 -2013-05-01 11:15:12-06:00,n05667,1033.2,31.2,5.4181,206.0558,5.0902,40.4812,49.3126,77.12,12.7,50.5,1012.2,0,955.3,927.8,97.8,0.995,0 -2013-05-01 11:20:12-06:00,n05667,1040.7,30.1,5.4515,207.9856,5.1248,40.5845,49.4349,77.18,12.1,50.9,1012.2,0,957.5,932.9,97.8,0.995,0 -2013-05-01 11:25:12-06:00,n05667,1042.6,30.4,5.4657,208.485,5.1379,40.578,49.4336,77.16,12.5,51.9,1012.2,0,954.5,934.4,98.7,0.995,0 -2013-05-01 11:30:12-06:00,n05667,1045.6,32.7,5.4891,207.9868,5.1551,40.3459,49.2177,76.99,12.8,51.8,1012.1,0,954.6,936.5,98.7,0.995,0 -2013-05-01 11:35:12-06:00,n05667,1049.5,32.7,5.516,207.6131,5.1751,40.1174,49.0089,76.8,13.6,49.7,1012,0,954.9,938.9,98.6,0.995,0 -2013-05-01 11:40:12-06:00,n05667,1052.7,32.3,5.5286,208.3081,5.1878,40.1533,49.046,76.82,13.3,48.6,1012,0,955,941.6,99.1,0.995,0 -2013-05-01 11:45:12-06:00,n05667,1052.1,33,5.5202,208.3549,5.1918,40.1317,49.0277,76.99,13.3,50.6,1012,0,950.5,940.9,100.4,0.995,0 -2013-05-01 11:50:12-06:00,n05667,1057.8,32.3,5.5599,210.6774,5.2187,40.3697,49.2697,76.91,13.6,47.3,1012.1,0,955,946,100.4,0.995,0 -2013-05-01 11:55:12-06:00,n05667,1058.4,30.6,5.5552,211.3136,5.2182,40.4958,49.3848,77.03,13.2,49,1012,0,952.4,946.1,101.6,0.995,0 -2013-05-01 12:00:12-06:00,n05667,1058.6,29.9,5.5624,212.1966,5.2239,40.6201,49.4974,77.07,13.2,49.2,1012.1,0,951,946.2,102.1,0.995,0 -2013-05-01 12:05:12-06:00,n05667,1058.2,29.5,5.5641,212.1765,5.2239,40.6165,49.5039,77.03,13.4,47.9,1011.9,0,950.8,946.5,102.3,0.995,0 -2013-05-01 12:10:12-06:00,n05667,1056.7,29.4,5.5561,212.5277,5.2195,40.7179,49.5995,77.12,13.4,48.5,1011.8,0,948.1,944.8,102.8,0.995,0 -2013-05-01 12:15:12-06:00,n05667,1055.9,31.9,5.5506,211.3546,5.2165,40.5167,49.3889,77.1,13.7,47.9,1011.9,0,949.9,944.5,101.1,0.995,0 -2013-05-01 12:20:12-06:00,n05667,1059,31.5,5.566,212.1363,5.2295,40.565,49.4453,77.08,14,47.2,1011.8,0,954.8,947.8,100.2,0.995,0 -2013-05-01 12:25:12-06:00,n05667,1057.2,31,5.5573,211.9221,5.2205,40.5945,49.4792,77.07,14.1,46.8,1011.7,0,955,947.3,99.9,0.995,0 -2013-05-01 12:30:12-06:00,n05667,1053.4,31.9,5.54,210.0964,5.202,40.3872,49.2736,76.96,14.2,47.2,1011.7,0,952.4,943.4,99.8,0.995,0 -2013-05-01 12:35:12-06:00,n05667,1050.4,31.4,5.5256,209.6379,5.19,40.3929,49.2814,76.99,14.3,46.6,1011.7,0,952.5,941.5,99.3,0.995,0 -2013-05-01 12:40:12-06:00,n05667,1046.5,32.5,5.511,208.6273,5.1715,40.3417,49.2157,76.92,14.5,46.5,1011.8,0,952.7,938.5,98.3,0.995,0 -2013-05-01 12:45:12-06:00,n05667,1043.6,34,5.4994,207.3579,5.1615,40.1742,49.0525,76.87,15.1,46.7,1011.7,0,954.1,936.3,97.3,0.995,0 -2013-05-01 12:50:12-06:00,n05667,1037.3,32.9,5.4655,206.5859,5.1318,40.2563,49.1097,76.97,15,45.1,1011.6,0,950.9,932.2,97.8,0.995,0 -2013-05-01 12:55:12-06:00,n05667,1031.2,34.4,5.4412,204.7053,5.1039,40.1077,48.9712,76.82,15.5,43.7,1011.8,0,949.9,927.7,97.1,0.995,0 -2013-05-01 13:00:12-06:00,n05667,1025.8,33.3,5.4024,204.6588,5.0738,40.3364,49.1637,77.06,15.3,44,1011.8,0,947.5,923.6,97.8,0.995,0 -2013-05-01 13:05:12-06:00,n05667,1018,34.1,5.3699,202.4182,5.0418,40.1481,48.9738,76.97,15.3,46.1,1011.7,0,946.4,918.5,97.1,0.995,0 -2013-05-01 13:10:12-06:00,n05667,1012.6,33.3,5.3374,201.8874,5.0118,40.2827,49.0928,77.05,15.3,44.7,1011.6,0,948,916.2,96.3,0.995,0 -2013-05-01 13:15:12-06:00,n05667,1006.3,31.8,5.3056,200.7229,4.9811,40.297,49.0967,77.06,15.6,45.5,1011.6,0,947.2,910.7,95.8,0.995,0 -2013-05-01 13:20:12-06:00,n05667,997.7,32.6,5.2621,198.7154,4.9389,40.2348,49.0115,77.05,15.9,45.3,1011.5,0,946.4,904.7,94.4,0.995,0 -2013-05-01 13:25:12-06:00,n05667,990.4,34.5,5.2209,196.7548,4.9008,40.1475,48.9159,77.04,15.9,43.5,1011.6,0,947.4,899.6,93.2,0.995,0 -2013-05-01 13:30:12-06:00,n05667,979.1,33.6,5.166,195.055,4.8526,40.1963,48.9425,77.15,15.9,45.2,1011.5,0,943.6,892.2,93.2,0.995,0 -2013-05-01 13:35:12-06:00,n05667,972.8,32.7,5.1283,194.3286,4.8182,40.332,49.0577,77.24,16,43.7,1011.5,0,944.5,886.7,92.2,0.995,0 -2013-05-01 13:40:12-06:00,n05667,963.4,31.3,5.0766,193.0627,4.771,40.4659,49.1682,77.35,15.8,44.3,1011.4,0,943.7,880.6,92.3,0.995,0 -2013-05-01 13:45:12-06:00,n05667,951.6,32.7,5.0241,190.0426,4.7171,40.2876,48.977,77.23,16.2,43.6,1011.4,0,940.5,871.6,91.5,0.995,0 -2013-05-01 13:50:12-06:00,n05667,940.7,32,4.9596,188.5451,4.6609,40.4527,49.1012,77.42,15.9,44.4,1011.4,0,938.3,865,91.8,0.995,0 -2013-05-01 13:55:12-06:00,n05667,928.9,32.8,4.9003,185.8782,4.6072,40.3449,48.9868,77.43,16.3,43.9,1011.4,0,937.1,856.5,90.7,0.995,0 -2013-05-01 14:00:12-06:00,n05667,918.3,32,4.8413,184.0081,4.5512,40.4307,49.0434,77.5,16.3,42.9,1011.2,0,936.5,848.9,90.3,0.995,0 -2013-05-01 14:05:12-06:00,n05667,905,32.5,4.772,180.6261,4.482,40.3003,48.8814,77.43,16.9,42.6,1011.2,0,932.8,838.6,89.8,0.995,0 -2013-05-01 14:10:12-06:00,n05667,893.3,31.9,4.7087,178.7121,4.4259,40.3786,48.9354,77.56,16.9,42.3,1011.1,0,931.3,829.9,89.5,0.995,0 -2013-05-01 14:15:12-06:00,n05667,879.6,32,4.6404,176.3869,4.3611,40.4458,48.979,77.61,16.6,43.5,1011.1,0,927.3,819.6,89.5,0.995,0 -2013-05-01 14:20:12-06:00,n05667,866.1,32.2,4.5663,173.5248,4.2956,40.3956,48.8964,77.72,17.1,41.9,1011.1,0,924.8,809.5,88.7,0.995,0 -2013-05-01 14:25:12-06:00,n05667,852.9,32.3,4.4968,171.0757,4.2291,40.4517,48.9276,77.76,16.9,41.4,1011.1,0,923.7,799.6,87.9,0.995,0 -2013-05-01 14:30:12-06:00,n05667,838.2,32.2,4.4147,168.1657,4.1522,40.5002,48.9347,77.84,16.8,42,1011.1,0,920.2,788.6,88,0.995,0 -2013-05-01 14:35:12-06:00,n05667,822.5,31,4.3332,165.2547,4.0739,40.5638,48.9647,77.89,17.2,41,1010.9,0,915.9,776.2,87.3,0.995,0 -2013-05-01 14:40:12-06:00,n05667,808.6,30.3,4.2563,162.8322,4.0067,40.64,49.0043,78.07,17.1,40.9,1010.9,0,914.4,766.3,86.8,0.995,0 -2013-05-01 14:45:12-06:00,n05667,792.9,30.1,4.1734,159.6962,3.927,40.6666,49.005,78.08,17.4,40.8,1010.7,0,909.6,754.6,87.3,0.995,0 -2013-05-01 14:50:12-06:00,n05667,777.5,29.6,4.0855,157.1624,3.8494,40.8275,49.1156,78.32,17.1,40.5,1010.8,0,905.4,743.5,88,0.995,0 -2013-05-01 14:55:12-06:00,n05667,760.3,30.6,3.9966,153.1169,3.7615,40.7061,48.9601,78.25,17.3,40.3,1010.8,0,900.8,730.1,87.7,0.995,0 -2013-05-01 15:00:12-06:00,n05667,740.7,30.1,3.8885,149.095,3.657,40.7702,48.9835,78.28,17.3,40.5,1010.6,0,891.5,715.2,88.2,0.995,0 -2013-05-01 15:05:12-06:00,n05667,719.2,29.2,3.7745,144.7049,3.5508,40.7533,48.9061,78.39,17.8,38.4,1010.2,0,879.9,697.9,88.3,0.995,0 -2013-05-01 15:10:12-06:00,n05667,707.5,28.5,3.7078,142.9342,3.4893,40.963,49.0915,78.53,17.3,39.9,1010.6,0,882.4,689.5,87.9,0.995,0 -2013-05-01 15:15:12-06:00,n05667,692,28.4,3.6213,139.6634,3.4093,40.9649,49.0518,78.62,17.4,39.7,1010.5,0,883.9,679.2,86.7,0.995,0 -2013-05-01 15:20:12-06:00,n05667,672.6,27.9,3.5148,135.422,3.3097,40.9165,48.9621,78.69,17.8,39.3,1010.4,0,876.1,664.9,87.2,0.995,0 -2013-05-01 15:25:12-06:00,n05667,647.6,28.1,3.376,130.135,3.1793,40.9315,48.9061,78.82,17.7,39.3,1010.5,0,858,644.5,88.8,0.995,0 -2013-05-01 15:30:12-06:00,n05667,636.1,28.1,3.3105,127.5797,3.117,40.9306,48.8755,78.85,18.1,38.4,1010.3,0,862.1,636.9,88.7,0.995,0 -2013-05-01 15:35:12-06:00,n05667,615,28.1,3.1873,122.9552,2.9988,41.0019,48.9016,78.89,17.7,39.5,1010.5,0,856.1,623.4,89.3,0.995,0 -2013-05-01 15:40:12-06:00,n05667,601,28.4,3.1111,120.0138,2.9304,40.9553,48.8311,79,18,38.3,1010.5,0,854.1,610,87.7,0.995,0 -2013-05-01 15:45:12-06:00,n05667,582,27.7,3.0074,115.8998,2.8283,40.9782,48.8116,78.95,18.1,38.7,1010.4,0,848.5,594.3,85.8,0.995,0 -2013-05-01 15:50:12-06:00,n05667,561.7,27.2,2.8902,111.8382,2.7207,41.1067,48.8764,79.17,17.9,38.4,1010.4,0,842.1,578.8,84.7,0.995,0 -2013-05-01 15:55:12-06:00,n05667,543.1,26.9,2.7844,107.4712,2.62,41.0197,48.7565,79.16,18.2,38.6,1010.2,0,838.6,564.1,82.6,0.995,0 -2013-05-01 16:00:12-06:00,n05667,523.6,26.9,2.674,103.313,2.5205,40.9884,48.6841,79.36,18.3,39.3,1010.2,0,833.9,549.4,81.6,0.995,0 -2013-05-01 16:05:12-06:00,n05667,501.7,26.4,2.5599,98.9172,2.4099,41.0462,48.6925,79.36,18.2,38.9,1010.1,0,827.6,534.2,80.9,0.995,0 -2013-05-01 16:10:12-06:00,n05667,482,26.6,2.4454,94.5012,2.3046,41.0055,48.5828,79.54,18.3,39.4,1010.2,0,820.5,518.6,80.1,0.995,0 -2013-05-01 16:15:12-06:00,n05667,462.8,25.6,2.333,90.2813,2.197,41.093,48.6294,79.58,18.2,39,1010.1,0,813.8,503.9,80,0.995,0 -2013-05-01 16:20:12-06:00,n05667,442.6,25.7,2.2199,85.6871,2.09,40.9995,48.4974,79.59,18.4,38.1,1010.1,0,806.8,488.1,78.6,0.995,0 -2013-05-01 16:25:12-06:00,n05667,422.1,26,2.0995,80.9721,1.9786,40.923,48.3641,79.74,18.5,38.1,1010.1,0,798.8,471.8,77.6,0.995,0 -2013-05-01 16:30:12-06:00,n05667,402.4,25,1.9832,76.5651,1.8685,40.9765,48.3649,79.82,18.4,38.2,1010,0,790.4,456.8,77.3,0.995,0 -2013-05-01 16:35:12-06:00,n05667,379.8,24.5,1.8559,71.5777,1.7468,40.9764,48.3165,79.82,18.4,38.2,1010,0,772.9,437.5,77.2,0.995,0 -2013-05-01 16:40:12-06:00,n05667,353.3,24,1.7082,65.8195,1.6067,40.966,48.2348,79.88,18.3,37.4,1010,0,744.5,413.8,77.3,0.995,0 -2013-05-01 16:45:12-06:00,n05667,330,23.9,1.5777,60.6588,1.4833,40.8939,48.1173,79.91,18.4,36.7,1010.1,0,724.8,393.7,76.5,0.995,0 -2013-05-01 16:50:12-06:00,n05667,315,23.3,1.4869,57.1558,1.3977,40.893,48.0661,79.97,18.4,36.3,1010,0,725.3,383,75.9,0.995,0 -2013-05-01 16:55:12-06:00,n05667,299.7,22.9,1.3937,53.5141,1.3098,40.8581,47.9938,80,18.3,37.1,1010,0,727.2,372.8,75.5,0.995,0 -2013-05-01 17:00:12-06:00,n05667,276.4,23.1,1.2633,48.3229,1.1871,40.7059,47.7943,80.03,18.5,38.1,1010.1,0,703.5,351.6,74.1,0.995,0 -2013-05-01 17:05:12-06:00,n05667,259.8,22.9,1.1677,44.4477,1.0953,40.5811,47.6299,79.92,18.6,38,1010,0,699,338.9,73.7,0.995,0 -2013-05-01 17:10:12-06:00,n05667,239.2,22.8,1.0525,39.9628,0.9873,40.4789,47.4824,79.96,18.5,38.5,1010,0,678.1,320.7,73.5,0.995,0 -2013-05-01 17:15:12-06:00,n05667,224.2,22.2,0.9633,36.4684,0.9031,40.38,47.3462,79.96,18.6,37.9,1009.9,0,680,311.1,73.1,0.995,0 -2013-05-01 17:20:12-06:00,n05667,192.4,22,0.8076,30.3832,0.7571,40.1284,47.0371,79.98,18.6,38.9,1009.9,0,604.9,274.2,71.6,0.995,0 -2013-05-01 17:25:12-06:00,n05667,188.1,21.8,0.7682,28.8169,0.7197,40.0395,46.9452,79.9,18.6,38.8,1010,0,647.7,279.2,72,0.995,0 -2013-05-01 17:30:12-06:00,n05667,169.4,21.4,0.6788,25.3011,0.6343,39.8866,46.7063,79.8,18.5,37.9,1009.9,0,621.5,261.8,72.2,0.995,0 -2013-05-01 17:35:12-06:00,n05667,151.5,21.4,0.5945,21.9849,0.5545,39.6461,46.4422,79.63,18.6,38.1,1010,0,597.7,244.9,71.7,0.995,0 -2013-05-01 17:40:12-06:00,n05667,135.5,20.9,0.5185,19.0653,0.4835,39.4291,46.2588,79.49,18.4,37.7,1010,0,581.1,230.6,71.2,0.995,0 -2013-05-01 17:45:12-06:00,n05667,120.3,20.7,0.4516,16.4662,0.4203,39.1748,45.9739,79.31,18.4,37.1,1010,0,572.3,217.9,69.6,0.995,0 -2013-05-01 17:50:12-06:00,n05667,106.4,20.4,0.3927,14.1868,0.3646,38.9116,45.7235,79,18.4,36.8,1010,0,574.5,208.9,68.8,0.995,0 -2013-05-01 17:55:12-06:00,n05667,73.4,20.2,0.2903,10.2883,0.2689,38.2574,45.1108,78.55,18.4,36.9,1010,0,349.5,144.6,64.6,0.995,0 -2013-05-01 18:00:12-06:00,n05667,55.9,19.8,0.2341,8.1978,0.2165,37.8652,44.6912,78.37,18.3,36.2,1009.9,0,224.3,107.7,59.6,0.995,0 -2013-05-01 18:05:12-06:00,n05667,51.3,19.5,0.2171,7.61,0.2017,37.7202,44.5891,78.62,18.2,37,1010,0,258.9,108.8,57.3,0.995,0 -2013-05-01 18:10:12-06:00,n05667,46.2,19.3,0.2041,7.0991,0.1888,37.5992,44.4259,78.31,18.1,37.4,1010.1,0,252.7,101.3,55,0.995,0 -2013-05-01 18:15:12-06:00,n05667,45.1,19.1,0.2034,7.0754,0.188,37.6262,44.4909,78.19,18.1,36.8,1010,0,226.3,92.4,54.4,0.995,0 -2013-05-01 18:20:12-06:00,n05667,45.2,19,0.205,7.1174,0.1891,37.6418,44.5182,78,18,37.7,1009.9,0,282.3,96.7,53.5,0.995,0 -2013-05-01 18:25:12-06:00,n05667,44.2,18.9,0.2042,7.0999,0.1885,37.658,44.539,78.08,17.9,37.8,1010,0,307.4,95.7,53.2,0.995,0 -2013-05-01 18:30:12-06:00,n05667,39.9,18.8,0.1933,6.6821,0.1779,37.5521,44.4317,77.82,17.8,38.6,1010,0,127.3,65.6,50.2,0.995,0 -2013-05-01 18:35:12-06:00,n05667,34.7,18.7,0.1761,6.0567,0.1622,37.3507,44.1813,77.86,17.8,38.7,1010,0,19.9,47.7,45.5,0.995,0 -2013-05-01 18:40:13-06:00,n05667,27.5,18.4,0.1453,4.9008,0.1328,36.8963,43.8184,76.96,17.7,39.8,1010,0,33.7,40.7,37,0.995,0 -2013-05-01 18:45:13-06:00,n05667,22.7,18,0.1219,4.0629,0.1113,36.5099,43.4574,76.71,17.6,40.4,1010,0,15.9,31.5,30.4,0.995,0 -2013-06-09 05:30:12-06:00,n05667,32.4,13.4,0.1543,5.3635,0.142,37.7686,44.6573,77.86,12.5,71,999.6,0,456.5,105,35.7,0.994,0 -2013-06-09 05:35:12-06:00,n05667,35.4,13.5,0.1624,5.6814,0.1502,37.8343,44.7784,78.13,12.6,70.4,999.6,0,491,119.1,37.5,0.994,0 -2013-06-09 05:40:12-06:00,n05667,38.1,13.6,0.1698,5.9559,0.1568,37.9726,44.822,78.25,12.7,70,999.7,0,521.5,133.4,39.3,0.994,0 -2013-06-09 05:45:12-06:00,n05667,41.1,13.7,0.177,6.2188,0.1634,38.0524,44.943,78.17,12.6,70.2,999.7,0,549.4,148.1,41,0.994,0 -2013-06-09 05:50:12-06:00,n05667,44.3,13.8,0.1826,6.4436,0.1688,38.1671,45.0119,78.4,12.5,70.8,999.7,0,574.8,163,42.4,0.994,0 -2013-06-09 05:55:12-06:00,n05667,47.3,13.8,0.1887,6.6871,0.1749,38.2347,45.0353,78.7,12.8,69.5,999.7,0,592.1,176.5,43.9,0.994,0 -2013-06-09 06:00:12-06:00,n05667,50.5,14,0.1952,6.9282,0.1811,38.2588,45.142,78.61,12.8,69.6,999.7,0,611.6,191.2,45.4,0.994,0 -2013-06-09 06:05:12-06:00,n05667,52.5,14.1,0.2018,7.1799,0.1871,38.3652,45.2097,78.7,12.7,70.1,999.7,0,626.6,205.8,47.1,0.994,0 -2013-06-09 06:10:12-06:00,n05667,56.6,14,0.2099,7.495,0.1949,38.4528,45.302,78.82,12.6,70,999.8,0,643.9,222.1,49.3,0.994,0 -2013-06-09 06:15:12-06:00,n05667,67.3,13.9,0.2386,8.7453,0.2242,39.0112,45.6123,80.35,12.6,70.6,999.8,0,659.7,237.3,50.7,0.994,0 -2013-06-09 06:20:12-06:00,n05667,81.3,14.2,0.2811,10.2089,0.2603,39.2147,45.8991,79.12,12.7,70.5,999.9,0,677.6,253,51.6,0.994,0 -2013-06-09 06:25:12-06:00,n05667,96.8,14.2,0.326,11.9937,0.3032,39.5519,46.212,79.61,12.5,71.4,999.8,0,692.2,269.2,53,0.994,0 -2013-06-09 06:30:12-06:00,n05667,112.8,14.3,0.379,14.1226,0.3546,39.8284,46.5327,80.07,12.3,71.8,999.8,0,702.1,284.5,54.8,0.994,0 -2013-06-09 06:35:12-06:00,n05667,129.2,14.8,0.443,16.6887,0.4167,40.0467,46.8117,80.48,12.5,71.8,999.9,0,714.4,299.8,56,0.994,0 -2013-06-09 06:40:12-06:00,n05667,146.5,15.1,0.514,19.51,0.4849,40.2367,47.0797,80.62,12.7,71.2,999.9,0,728.7,316.1,56.7,0.994,0 -2013-06-09 06:45:12-06:00,n05667,163.8,15.2,0.5918,22.6336,0.5593,40.4654,47.3333,80.8,12.6,71.8,999.8,0,737.8,331.6,57.9,0.994,0 -2013-06-09 06:50:12-06:00,n05667,182,15.6,0.6814,26.1673,0.6435,40.6624,47.5356,80.78,12.8,71.4,999.8,0,749.7,347.8,58.9,0.994,0 -2013-06-09 06:55:12-06:00,n05667,199.4,16.2,0.7684,29.6246,0.726,40.8025,47.7264,80.78,13.2,70.5,999.9,0,757.7,363,60.1,0.994,0 -2013-06-09 07:00:12-06:00,n05667,218.3,16.2,0.8657,33.5455,0.8184,40.9904,47.9554,80.8,13,69.9,999.8,0,768.5,379.5,61,0.994,0 -2013-06-09 07:05:12-06:00,n05667,236.7,16.6,0.9663,37.5449,0.913,41.121,48.1288,80.73,13.1,70,999.9,0,777.1,395.6,62.3,0.994,0 -2013-06-09 07:10:12-06:00,n05667,255.4,16.8,1.0723,41.7891,1.0135,41.2306,48.2875,80.71,13.1,69.8,999.8,0,779.4,409.6,64.3,0.994,0 -2013-06-09 07:15:12-06:00,n05667,274.7,17.7,1.1843,46.0318,1.1155,41.2658,48.3656,80.36,13.4,69.5,999.9,0,767.2,427.1,76.5,0.994,0 -2013-06-09 07:20:12-06:00,n05667,295,17.6,1.2971,50.6772,1.2244,41.3904,48.5028,80.55,13.4,68.9,999.9,0,792.6,442.8,68.9,0.994,0 -2013-06-09 07:25:12-06:00,n05667,314.5,18.3,1.403,54.9103,1.3251,41.4377,48.6245,80.49,13.3,69.7,999.9,0,812.3,462.3,67.5,0.994,0 -2013-06-09 07:30:12-06:00,n05667,332.5,18.9,1.5054,58.9646,1.4224,41.4544,48.683,80.46,13.5,69.8,999.9,0,805.4,471.5,69,0.994,0 -2013-06-09 07:35:12-06:00,n05667,352.4,19.6,1.6195,63.6124,1.5339,41.4709,48.7448,80.58,13.8,68.6,999.9,0,805.4,487.2,73.6,0.994,0 -2013-06-09 07:40:12-06:00,n05667,376.4,20.6,1.7365,67.9457,1.6411,41.4028,48.7311,80.29,14,68.7,999.9,0,825.5,506,70.9,0.994,0 -2013-06-09 07:45:12-06:00,n05667,388.3,20.3,1.8328,71.7956,1.7312,41.4728,48.8359,80.21,13.8,68,999.8,0,828.7,518.9,70.6,0.994,0 -2013-06-09 07:50:12-06:00,n05667,409.8,21.1,1.9554,76.6332,1.8474,41.4812,48.8788,80.18,14.1,68.1,999.8,0,842.9,536.5,68.8,0.994,0 -2013-06-09 07:55:12-06:00,n05667,428.5,22.5,2.0696,80.6905,1.9512,41.3537,48.8151,79.87,14.5,66.1,999.8,0,851.7,551.7,68.1,0.994,0 -2013-06-09 08:00:12-06:00,n05667,447.4,22.6,2.1762,84.9722,2.054,41.3692,48.8801,79.88,14.5,65.6,999.8,0,859.2,567.2,68.6,0.994,0 -2013-06-09 08:05:12-06:00,n05667,466.6,23.1,2.2823,89.3273,2.1565,41.4224,48.9679,79.93,14.5,66.5,999.8,0,862.9,581.8,68.6,0.994,0 -2013-06-09 08:10:12-06:00,n05667,505.2,22.8,2.3929,93.781,2.2631,41.4399,49.0304,79.93,14.5,65.3,999.8,0,870.8,598.7,69.4,0.994,0 -2013-06-09 08:15:12-06:00,n05667,520.1,24.2,2.4924,97.2899,2.3566,41.2842,48.9094,79.81,15,65.2,999.8,0,872.7,610.2,69.9,0.994,0 -2013-06-09 08:20:12-06:00,n05667,524.4,23.9,2.6026,101.7651,2.4608,41.3541,49.0512,79.71,14.7,65,999.8,0,876.7,626.5,72.2,0.994,0 -2013-06-09 08:25:12-06:00,n05667,540.5,24.1,2.7021,105.5858,2.5522,41.3702,49.0759,79.62,15.1,63.8,999.7,0,874.5,638.3,74.3,0.994,0 -2013-06-09 08:30:12-06:00,n05667,556.3,25.3,2.7963,108.9308,2.6423,41.225,48.99,79.52,15.1,63.5,999.8,0,876.9,650.2,74,0.994,0 -2013-06-09 08:35:12-06:00,n05667,575.5,26.5,2.9084,112.7208,2.7411,41.1228,48.9198,79.23,15.4,63.2,999.8,0,885.2,665.7,73.4,0.994,0 -2013-06-09 08:40:12-06:00,n05667,592.8,26.8,3.0059,116.3352,2.8343,41.0454,48.8879,79.17,15.6,61.7,999.8,0,887.4,679.4,74.4,0.994,0 -2013-06-09 08:45:12-06:00,n05667,610,25.7,3.0995,120.4783,2.9264,41.1696,49.044,79.26,15.2,62.7,999.8,0,888.5,693.5,77.7,0.994,0 -2013-06-09 08:50:12-06:00,n05667,625.3,25.3,3.1869,124.0296,3.008,41.2336,49.1591,79.17,15.3,63,999.7,0,888,703.3,77.9,0.994,0 -2013-06-09 08:55:12-06:00,n05667,644.7,25.8,3.295,128.1174,3.1094,41.2029,49.1708,79.08,15.5,62.7,999.7,0,898.1,720.6,77.3,0.994,0 -2013-06-09 09:00:12-06:00,n05667,662.4,26.8,3.393,131.955,3.2014,41.2174,49.2222,79.01,15.5,62.3,999.8,0,903.8,734,76.9,0.994,0 -2013-06-09 09:05:12-06:00,n05667,680,26.9,3.4919,135.3239,3.2897,41.1359,49.1715,78.81,15.6,62.6,999.7,0,910.7,749.1,76.4,0.994,0 -2013-06-09 09:10:12-06:00,n05667,694.1,26.6,3.5687,138.1659,3.3631,41.0826,49.137,78.79,15.6,63,999.5,0,911.6,759.9,77.8,0.994,0 -2013-06-09 09:15:12-06:00,n05667,710.3,28.5,3.6622,141.3066,3.4544,40.906,49.0369,78.69,15.9,63,999.7,0,914.4,771.8,77.7,0.994,0 -2013-06-09 09:20:12-06:00,n05667,726,28.1,3.7504,144.6091,3.5363,40.8927,49.0557,78.6,16.3,60.1,999.5,0,919.3,785.4,77.7,0.994,0 -2013-06-09 09:25:12-06:00,n05667,741.7,28.9,3.8391,147.7406,3.6151,40.8677,49.0577,78.44,16.3,60.4,999.6,0,921.1,796.4,78.1,0.994,0 -2013-06-09 09:30:12-06:00,n05667,756,29.3,3.9158,150.8176,3.6893,40.8796,49.111,78.42,16.1,60.5,999.6,0,923.7,808.2,78.5,0.994,0 -2013-06-09 09:35:12-06:00,n05667,769.7,29.5,3.9933,153.3553,3.7635,40.7484,49.0212,78.34,16.6,59.5,999.4,0,925.4,818.8,79.2,0.994,0 -2013-06-09 09:40:12-06:00,n05667,783.2,29.2,4.063,156.1969,3.8266,40.8189,49.1084,78.28,16.5,58.4,999.5,0,925.7,828.8,80.3,0.994,0 -2013-06-09 09:45:12-06:00,n05667,798,30.2,4.1439,158.3848,3.9019,40.5914,48.9256,78.12,16.7,59.6,999.5,0,930.5,839.5,78.9,0.994,0 -2013-06-09 09:50:12-06:00,n05667,810,30,4.2075,161.0927,3.9628,40.6514,48.9946,78.15,16.7,58.1,999.4,0,930.4,848.6,80.2,0.994,0 -2013-06-09 09:55:12-06:00,n05667,824.4,29.7,4.2847,164.4065,4.037,40.7247,49.1156,78.12,16.6,58.4,999.4,0,934.7,859.6,80,0.994,0 -2013-06-09 10:00:12-06:00,n05667,836.2,30.4,4.3546,166.3048,4.0986,40.5762,48.9848,77.96,16.9,58,999.4,0,937.3,868.6,79.3,0.994,0 -2013-06-09 10:05:12-06:00,n05667,848.9,30.6,4.4212,168.8425,4.1612,40.5756,49.0076,77.92,17,59.2,999.3,0,939.2,877.4,79.6,0.994,0 -2013-06-09 10:10:12-06:00,n05667,859.8,31.5,4.4826,170.471,4.2192,40.4035,48.8807,77.8,17.2,57.7,999.3,0,939.7,885.8,80.2,0.994,0 -2013-06-09 10:15:12-06:00,n05667,870.2,33.4,4.5364,171.814,4.2683,40.2539,48.7468,77.7,17.6,57.4,999.3,0,941.4,893.3,79.5,0.994,0 -2013-06-09 10:20:12-06:00,n05667,880,32.9,4.589,174.2026,4.3174,40.3492,48.8599,77.69,17.5,56.4,999.4,0,941.4,900,80.3,0.994,0 -2013-06-09 10:25:12-06:00,n05667,891.7,30.9,4.6457,177.1101,4.3734,40.4974,49.0304,77.75,17.4,56.8,999.3,0,944.4,909.4,80.5,0.994,0 -2013-06-09 10:30:12-06:00,n05667,900.8,33.5,4.7031,178.0558,4.4246,40.2422,48.804,77.57,17.6,56.5,999.4,0,946.4,916.3,79.4,0.994,0 -2013-06-09 10:35:12-06:00,n05667,912.3,32.3,4.7558,180.5744,4.4738,40.3625,48.9432,77.58,17.7,55.1,999.3,0,950.5,925.5,79.1,0.994,0 -2013-06-09 10:40:12-06:00,n05667,922,33.8,4.8169,182.1412,4.5269,40.2352,48.845,77.41,17.6,57.4,999.3,0,952.4,932.5,79.4,0.994,0 -2013-06-09 10:45:12-06:00,n05667,927.9,33.9,4.8494,182.8543,4.5582,40.1159,48.7572,77.34,18.2,55.6,999.3,0,950.7,935.7,78.8,0.994,0 -2013-06-09 10:50:12-06:00,n05667,936.6,33.8,4.8989,184.0047,4.5982,40.0163,48.6681,77.18,18.4,54.2,999.2,0,953.3,942.6,78.4,0.994,0 -2013-06-09 10:55:12-06:00,n05667,945.1,33.2,4.9419,187.2427,4.6451,40.3098,48.9653,77.38,17.9,54.6,999.2,0,957.1,950.3,78,0.994,0 -2013-06-09 11:00:12-06:00,n05667,953,34.5,4.985,188.1272,4.6832,40.1708,48.8482,77.26,18.1,54.9,999.3,0,960.4,956.3,76.6,0.994,0 -2013-06-09 11:05:12-06:00,n05667,960.1,35.2,5.0219,189.3554,4.7145,40.1649,48.858,77.17,18.1,54.4,999.3,0,961.9,961.6,76.2,0.994,0 -2013-06-09 11:10:12-06:00,n05667,966.2,35.9,5.0623,190.3613,4.756,40.0258,48.7409,77.15,18.5,53.2,999.2,0,962.6,965.7,75.9,0.994,0 -2013-06-09 11:15:12-06:00,n05667,970.6,34.4,5.0839,191.3466,4.7727,40.0921,48.8007,77.13,18.4,52.7,999,0,960.8,968.7,77.1,0.994,0 -2013-06-09 11:20:12-06:00,n05667,978.6,32.8,5.1191,193.4985,4.8087,40.2391,48.9595,77.21,18.4,53.4,998.9,0,965.1,976,76.7,0.994,0 -2013-06-09 11:25:12-06:00,n05667,982.1,35,5.1406,193.8261,4.8271,40.1536,48.8853,77.13,18.3,55.1,999.1,0,965.1,977.5,75.8,0.994,0 -2013-06-09 11:30:12-06:00,n05667,986.3,36.3,5.1753,193.2059,4.8541,39.803,48.5575,76.88,18.9,54.4,999,0,965.9,980.8,75.9,0.994,0 -2013-06-09 11:35:12-06:00,n05667,991.7,35.1,5.1986,194.8092,4.8796,39.923,48.6876,76.97,19,51.3,998.8,0,969.1,985.6,74.5,0.994,0 -2013-06-09 11:40:12-06:00,n05667,994.2,35.6,5.205,195.5593,4.8866,40.0199,48.7734,77.03,18.8,51.8,998.9,0,970.2,988.4,74.4,0.994,0 -2013-06-09 11:45:12-06:00,n05667,997.1,36.1,5.2291,195.4652,4.9044,39.8547,48.6297,76.87,19.1,51.5,998.8,0,972,990.4,73,0.994,0 -2013-06-09 11:50:12-06:00,n05667,1001.2,34.3,5.2503,197.3929,4.9276,40.0585,48.843,76.97,18.9,51.8,998.8,0,973.6,994.3,73.7,0.994,0 -2013-06-09 11:55:12-06:00,n05667,999.3,35.4,5.2429,196.7039,4.9176,40,48.7721,76.93,19.1,52.7,998.8,0,970.1,992.6,74.3,0.994,0 -2013-06-09 12:00:12-06:00,n05667,1002.8,36.3,5.266,196.486,4.9376,39.7934,48.588,76.79,19.5,51.7,998.7,0,973.7,995.4,72.6,0.994,0 -2013-06-09 12:05:12-06:00,n05667,1004,35.4,5.267,197.7688,4.9422,40.0165,48.7975,76.95,19.1,52.7,998.7,0,975.3,997,72.5,0.994,0 -2013-06-09 12:10:12-06:00,n05667,1006.2,35.4,5.283,198.018,4.9573,39.9446,48.737,76.91,19.2,51.6,998.6,0,978.5,999.2,71.7,0.994,0 -2013-06-09 12:15:12-06:00,n05667,1004,35.5,5.2735,197.2734,4.9455,39.8891,48.6804,76.84,19.5,51.9,998.6,0,974.4,996.2,72.3,0.994,0 -2013-06-09 12:20:12-06:00,n05667,1001.6,37.7,5.2641,196.2795,4.9384,39.7457,48.549,76.8,19.6,51.4,998.5,0,972.7,994.4,72.5,0.994,0 -2013-06-09 12:25:12-06:00,n05667,998,35.9,5.2505,195.1067,4.924,39.6238,48.4332,76.72,20.1,50.6,998.4,0,971.4,991.5,71.9,0.994,0 -2013-06-09 12:30:12-06:00,n05667,1001.8,35.7,5.2666,196.1449,4.9347,39.7477,48.5425,76.72,20.1,47.7,998.2,0,978.6,996.6,70.2,0.994,0 -2013-06-09 12:35:12-06:00,n05667,998,34.4,5.2405,196.0273,4.9161,39.8747,48.6583,76.88,19.9,48.1,998.2,0,975.3,993.6,71.3,0.994,0 -2013-06-09 12:40:12-06:00,n05667,996.1,35.3,5.2322,196.0297,4.9046,39.9689,48.7298,76.89,19.9,46.4,998.3,0,976.8,992.5,70.8,0.994,0 -2013-06-09 12:45:12-06:00,n05667,994.8,37.2,5.227,194.4142,4.901,39.6679,48.4541,76.76,20.3,48.8,998.2,0,978.1,990.8,70.3,0.994,0 -2013-06-09 12:50:12-06:00,n05667,989.6,38.7,5.2051,192.0743,4.8775,39.3797,48.1705,76.61,20.8,48.4,998.2,0,976.9,986,68.7,0.994,0 -2013-06-09 12:55:12-06:00,n05667,987.7,36.2,5.1812,192.9024,4.8571,39.7157,48.4723,76.81,20.6,46,998.2,0,978.5,986.4,69.8,0.994,0 -2013-06-09 13:00:12-06:00,n05667,983.6,36.1,5.1457,192.5025,4.8282,39.8705,48.618,76.95,20.4,48,998.1,0,974.7,982.5,71.6,0.994,0 -2013-06-09 13:05:12-06:00,n05667,974.8,35.7,5.1104,191.1096,4.7949,39.8569,48.588,76.97,20.6,46.1,998,0,973.8,977.9,70.9,0.994,0 -2013-06-09 13:10:12-06:00,n05667,969.2,37.5,5.0908,189.556,4.7693,39.7451,48.4658,76.83,20.4,48.6,998.1,0,972,972.4,71.2,0.994,0 -2013-06-09 13:15:12-06:00,n05667,963.9,36.2,5.0587,188.9663,4.746,39.8156,48.5198,76.99,20.5,48.1,997.8,0,971.6,969.3,71.6,0.994,0 -2013-06-09 13:20:12-06:00,n05667,955.3,38.1,5.0102,186.6435,4.7052,39.6677,48.363,77.03,20.9,47.5,998,0,968.8,961.6,71.1,0.994,0 -2013-06-09 13:25:12-06:00,n05667,948.7,36,4.9754,185.9353,4.6684,39.8285,48.5009,77.05,20.9,45.6,997.9,0,969.3,958.2,71.3,0.994,0 -2013-06-09 13:30:12-06:00,n05667,941,37.4,4.9414,184.1395,4.632,39.7535,48.4098,76.98,20.6,47.2,997.8,0,968.1,952.3,71.2,0.994,0 -2013-06-09 13:35:12-06:00,n05667,936.3,35.1,4.9134,183.8938,4.6082,39.9061,48.5484,77.09,20.7,45.3,997.8,0,972,950.3,70.3,0.994,0 -2013-06-09 13:40:12-06:00,n05667,926.7,37.9,4.8601,181.3642,4.5633,39.7441,48.3799,77.13,21.1,43.6,997.8,0,970.3,941.6,68.9,0.994,0 -2013-06-09 13:45:12-06:00,n05667,916.8,37,4.8075,179.6653,4.5139,39.8027,48.4189,77.18,21,45,997.7,0,966.9,934.4,69.8,0.994,0 -2013-06-09 13:50:12-06:00,n05667,906.3,37.3,4.7598,177.162,4.4627,39.6988,48.2778,77.1,21.2,44.4,997.7,0,965.1,926.7,69.8,0.994,0 -2013-06-09 13:55:12-06:00,n05667,900.3,37.1,4.7221,175.6195,4.4278,39.663,48.242,77.09,21.4,42.9,997.7,0,969.8,923.8,68.9,0.994,0 -2013-06-09 14:00:12-06:00,n05667,887.7,36.9,4.6542,173.5844,4.3655,39.7632,48.2979,77.22,21.4,42.4,997.6,0,964.6,913.8,69.1,0.994,0 -2013-06-09 14:05:12-06:00,n05667,874.6,35.6,4.5866,171.4594,4.3035,39.8414,48.3552,77.31,21.4,42.7,997.3,0,961.1,903.9,69,0.994,0 -2013-06-09 14:10:12-06:00,n05667,867.4,35.8,4.5448,169.8817,4.2629,39.8517,48.3526,77.31,21.4,41.4,997.4,0,965.4,899.6,67.7,0.994,0 -2013-06-09 14:15:12-06:00,n05667,860.9,34.3,4.5003,169.4548,4.2283,40.0763,48.5484,77.56,21.1,42.3,997.4,0,970.5,897.1,67.4,0.994,0 -2013-06-09 14:20:12-06:00,n05667,849,35.1,4.4373,166.4268,4.1674,39.9352,48.3903,77.51,21.4,41.9,997.3,0,970.1,887.9,66.3,0.994,0 -2013-06-09 14:25:12-06:00,n05667,834.7,35.6,4.3676,163.6047,4.0949,39.9528,48.3675,77.45,21.3,42.5,997.4,0,967.2,878.1,66.5,0.994,0 -2013-06-09 14:30:12-06:00,n05667,824.8,34.6,4.3082,161.7066,4.0441,39.9857,48.3754,77.59,21.7,39.5,997.1,0,969.1,871.7,66,0.994,0 -2013-06-09 14:35:12-06:00,n05667,811.4,34.4,4.237,159.4817,3.9781,40.0894,48.4547,77.68,21.4,41.1,997.2,0,965.6,861.1,66.5,0.994,0 -2013-06-09 14:40:12-06:00,n05667,799,33.6,4.1645,156.93,3.9105,40.1308,48.4615,77.76,21.5,40.4,997.1,0,968.1,854.5,65.9,0.994,0 -2013-06-09 14:45:12-06:00,n05667,779.4,34.9,4.0664,153.1768,3.8184,40.115,48.4048,77.82,21.7,39.8,997.1,0,955.8,836.6,66.3,0.994,0 -2013-06-09 14:50:12-06:00,n05667,772.2,33.9,4.0152,151.6355,3.7729,40.1906,48.4654,77.92,21.8,37.6,997,0,966,834.1,64.9,0.994,0 -2013-06-09 14:55:12-06:00,n05667,752.7,34.6,3.9182,147.6067,3.6811,40.0982,48.3162,77.97,22,37.7,997,0,956.7,817,64.6,0.994,0 -2013-06-09 15:00:12-06:00,n05667,741.3,33.8,3.851,145.1538,3.6172,40.1285,48.3136,78.02,22,36.1,996.8,0,961,810.2,63.7,0.994,0 -2013-06-09 15:05:12-06:00,n05667,727.5,34.2,3.7731,141.9806,3.5436,40.0665,48.2378,78.01,21.9,35.6,997,0,961.7,799.5,62.8,0.994,0 -2013-06-09 15:10:12-06:00,n05667,710.1,34,3.6786,138.4332,3.455,40.0674,48.1983,78.08,22,35.3,996.9,0,957.9,786.7,62.6,0.994,0 -2013-06-09 15:15:12-06:00,n05667,695.2,33.7,3.5969,135.8065,3.3772,40.2132,48.294,78.18,21.9,35.7,996.9,0,956.7,775.1,62.2,0.994,0 -2013-06-09 15:20:12-06:00,n05667,677.7,34.4,3.4992,131.7112,3.2858,40.0852,48.1361,78.2,22.1,36.6,996.8,0,953.3,761.5,61.5,0.994,0 -2013-06-09 15:25:12-06:00,n05667,662.4,32.8,3.4104,128.6846,3.2019,40.1907,48.2087,78.27,22,35.5,996.6,0,951.8,750.6,61.5,0.994,0 -2013-06-09 15:30:12-06:00,n05667,645.7,33.3,3.3182,125.1299,3.1158,40.1594,48.1369,78.34,22.3,32.9,996.7,0,951.7,737.9,60.1,0.994,0 -2013-06-09 15:35:12-06:00,n05667,626.7,31.7,3.2104,121.7502,3.0184,40.3363,48.2604,78.58,22,33.3,996.6,0,945.5,723.3,60.4,0.994,0 -2013-06-09 15:40:12-06:00,n05667,609.6,31.4,3.1134,118.1529,2.9278,40.356,48.2467,78.66,22,32,996.5,0,941.5,710,59.9,0.994,0 -2013-06-09 15:45:12-06:00,n05667,589.2,31.3,2.9993,113.8829,2.8199,40.386,48.2261,78.73,21.9,32.3,996.5,0,933.5,694.1,61,0.994,0 -2013-06-09 15:50:12-06:00,n05667,570.6,31.3,2.8992,110.017,2.7252,40.3699,48.168,78.78,22,35.2,996.4,0,927.1,678,60.4,0.994,0 -2013-06-09 15:55:12-06:00,n05667,556.2,31.5,2.811,106.3767,2.6398,40.2974,48.041,78.77,22.5,34.7,996.5,0,931.4,668.6,59.5,0.994,0 -2013-06-09 16:00:12-06:00,n05667,537.8,30.6,2.7028,102.7009,2.5397,40.4388,48.1394,78.93,22.2,33.4,996.4,0,926.3,654,59.6,0.994,0 -2013-06-09 16:05:12-06:00,n05667,520.7,30.6,2.6039,98.8408,2.446,40.4096,48.0649,78.97,22.2,34.5,996.5,0,927.4,641.9,58.4,0.994,0 -2013-06-09 16:10:12-06:00,n05667,499.3,31.2,2.483,94.0721,2.3324,40.3329,47.9346,79.04,22.4,33.8,996.4,0,917.5,623.3,57.9,0.994,0 -2013-06-09 16:15:12-06:00,n05667,478,30.1,2.369,89.8038,2.2248,40.3645,47.9203,79.11,22.3,33,996.3,0,910.8,608.2,58.4,0.994,0 -2013-06-09 16:20:12-06:00,n05667,459.7,29.3,2.2602,85.8681,2.123,40.4473,47.9557,79.22,22.1,33.5,996.3,0,906.4,594,58.4,0.994,0 -2013-06-09 16:25:12-06:00,n05667,438.6,28.8,2.1388,81.2925,2.0092,40.4606,47.9229,79.31,22.2,30.3,996.2,0,897.6,576.6,57.7,0.994,0 -2013-06-09 16:30:12-06:00,n05667,419.3,29.7,2.0284,76.8292,1.9052,40.3269,47.7106,79.39,22.4,31.4,996.2,0,892.7,560.4,56.9,0.994,0 -2013-06-09 16:35:12-06:00,n05667,399.7,29.6,1.9169,72.461,1.8,40.2561,47.6258,79.37,22.5,33.2,996.1,0,886.1,544.8,57.1,0.994,0 -2013-06-09 16:40:12-06:00,n05667,380.7,28.3,1.8009,68.2297,1.6918,40.3297,47.6413,79.52,22.3,30,995.8,0,877.9,529.6,57.7,0.994,0 -2013-06-09 16:45:12-06:00,n05667,361.5,28,1.6883,63.9394,1.5863,40.3064,47.5718,79.61,22.4,31,996,0,873.5,514.4,56.7,0.994,0 -2013-06-09 16:50:12-06:00,n05667,342,28.3,1.5753,59.523,1.4783,40.2648,47.488,79.57,22.2,32.6,996,0,866,497.7,56.5,0.994,0 -2013-06-09 16:55:12-06:00,n05667,323.3,27.7,1.4638,55.2423,1.3735,40.2205,47.3761,79.66,22.2,33,995.9,0,862,483,56,0.994,0 -2013-06-09 17:00:12-06:00,n05667,302.6,27.7,1.3492,50.5773,1.2631,40.0434,47.1574,79.49,22.6,34.2,995.8,0,851.5,464.8,54.8,0.994,0 -2013-06-09 17:05:12-06:00,n05667,283.7,26.9,1.2377,46.4147,1.1585,40.0629,47.1078,79.6,22.3,33.1,995.7,0,843.1,449.3,54.9,0.994,0 -2013-06-09 17:10:12-06:00,n05667,265,27.1,1.1304,42.2779,1.0581,39.9561,46.9746,79.62,22.2,35,995.9,0,837.6,433.8,54.1,0.994,0 -2013-06-09 17:15:12-06:00,n05667,244.6,26.1,1.0167,37.9539,0.952,39.8684,46.8367,79.7,22.2,34.1,995.8,0,822.7,415.5,53.9,0.994,0 -2013-06-09 17:20:12-06:00,n05667,225.4,25.9,0.9115,33.9313,0.8532,39.7686,46.6769,79.75,22.1,34,995.8,0,811.3,398.4,53.2,0.994,0 -2013-06-09 17:25:12-06:00,n05667,206.2,25.6,0.8123,30.0248,0.7589,39.5653,46.4465,79.58,22.2,34.8,995.7,0,801.7,381.4,52.2,0.994,0 -2013-06-09 17:30:12-06:00,n05667,188.8,25.3,0.721,26.5483,0.6731,39.4446,46.2874,79.55,22,35.1,995.7,0,795,367,52,0.994,0 -2013-06-09 17:35:12-06:00,n05667,170.1,25.1,0.6309,23.0335,0.5875,39.2059,46.0292,79.31,22,36.5,995.7,0,782.2,349.1,50.8,0.994,0 -2013-06-09 17:40:12-06:00,n05667,152.8,24.7,0.5467,19.8258,0.509,38.9474,45.7554,79.26,22.1,35.7,995.7,0,772.2,334.1,50.5,0.994,0 -2013-06-09 17:45:12-06:00,n05667,135.6,24.5,0.4718,16.9007,0.4367,38.6998,45.4835,78.76,22,36.8,995.8,0,759.5,317.8,49.9,0.994,0 -2013-06-09 17:50:12-06:00,n05667,118.4,24.1,0.4018,14.2781,0.3715,38.4387,45.1947,78.62,21.9,35.6,995.6,0,744.4,301.1,49.5,0.994,0 -2013-06-09 17:55:12-06:00,n05667,102.1,23.7,0.3402,12.097,0.3169,38.1776,44.9026,79.2,21.7,36.2,995.7,0,731.9,285.2,48.6,0.994,0 -2013-06-09 18:00:12-06:00,n05667,86.2,23.7,0.2864,10.2064,0.2683,38.0404,44.539,80,21.7,36.5,995.7,0,718.2,269.3,47.5,0.994,0 -2013-06-09 18:05:12-06:00,n05667,72.1,23.5,0.2446,8.7393,0.2311,37.8228,44.2841,80.67,21.8,35.8,995.6,0,704.5,253.1,46.1,0.994,0 -2013-06-09 18:10:12-06:00,n05667,59.6,23.3,0.2125,7.4329,0.1997,37.2257,43.968,79.54,21.7,35.3,995.5,0,688,237.5,45.3,0.994,0 -2013-06-09 18:15:12-06:00,n05667,55.3,23.1,0.2029,6.9517,0.1876,37.0469,43.8528,78.12,21.7,35.8,995.6,0,670.1,221.7,44.4,0.994,0 -2013-06-09 18:20:12-06:00,n05667,53.8,22.9,0.1972,6.7403,0.1823,36.9661,43.8099,78.02,21.6,36.5,995.5,0,654.5,206.6,43,0.994,0 -2013-06-09 18:25:12-06:00,n05667,50.9,22.9,0.1919,6.554,0.1775,36.9273,43.7696,78.03,21.5,37.2,995.6,0,635.1,191.6,42,0.994,0 -2013-06-09 18:30:13-06:00,n05667,47.3,22.9,0.1862,6.3325,0.1719,36.844,43.7052,77.81,21.6,37.4,995.5,0,614.7,176.1,40.3,0.994,0 -2013-06-09 18:35:12-06:00,n05667,43.8,23,0.1799,6.117,0.1662,36.8009,43.6401,77.93,21.6,38.2,995.6,0,593.5,160.8,38.4,0.994,0 -2013-06-09 18:40:13-06:00,n05667,40.8,23,0.1742,5.8836,0.1604,36.6906,43.5634,77.52,21.6,38.8,995.7,0,572.4,146.7,37.1,0.994,0 -2013-06-09 18:45:13-06:00,n05667,37.9,23,0.1668,5.6229,0.1536,36.6174,43.4489,77.6,21.5,38.6,995.6,0,550.4,133,35.5,0.994,0 -2013-06-09 18:50:12-06:00,n05667,34.9,23.4,0.1588,5.3234,0.146,36.4547,43.3546,77.32,21.4,39.8,995.7,0,518.8,118.1,33.6,0.994,0 -2013-06-09 18:55:13-06:00,n05667,31.5,23.5,0.1487,4.9597,0.1366,36.3009,43.2167,77.2,21.4,40.6,995.7,0,485.5,103.5,31.5,0.994,0 -2013-06-09 19:00:12-06:00,n05667,28.3,23.5,0.1392,4.6213,0.1277,36.1809,43.0879,77.05,21.3,41.3,995.7,0,447.2,89.6,29.5,0.994,0 -2013-06-09 19:05:13-06:00,n05667,25.8,23.3,0.131,4.3171,0.1198,36.0436,42.9555,76.7,21,45.1,995.7,0,12.7,29.9,28.4,0.994,0 -2013-06-09 19:10:13-06:00,n05667,23.1,22.9,0.1196,3.9141,0.1091,35.8901,42.781,76.51,20.8,47.2,995.7,0,0.6,25.8,26.1,0.994,0 -2013-06-09 19:15:13-06:00,n05667,20.5,21.8,0.1081,3.5176,0.0987,35.6547,42.6189,76.36,20.6,48.3,995.7,0,-0.2,23.4,23.7,0.994,0 -2013-07-21 05:30:12-06:00,n05667,23.6,14.1,0.1202,4.0891,0.1103,37.0745,44.0076,77.31,13.9,76.8,996,0,402.8,65.1,23.9,0.989,0 -2013-07-21 05:35:13-06:00,n05667,26.4,14.1,0.1304,4.4603,0.1198,37.2311,44.1481,77.5,13.8,77.1,996.1,0,434.1,76.8,26.3,0.989,0 -2013-07-21 05:40:12-06:00,n05667,29.5,14,0.1386,4.7754,0.1276,37.4226,44.3034,77.79,13.7,76.4,996,0,463.1,89.4,28.8,0.989,0 -2013-07-21 05:45:13-06:00,n05667,32.5,14.5,0.1475,5.0972,0.1359,37.5149,44.4096,77.79,13.8,75.9,996,0,492.2,102.1,30.7,0.989,0 -2013-07-21 05:50:12-06:00,n05667,35.4,14.7,0.1564,5.4196,0.1441,37.622,44.554,77.78,14.2,75.4,996,0,519,115.2,32.5,0.989,0 -2013-07-21 05:55:12-06:00,n05667,38.9,14.6,0.1644,5.7181,0.1514,37.773,44.6457,77.92,13.9,76.1,996.1,0,543.6,129.4,34.7,0.989,0 -2013-07-21 06:00:12-06:00,n05667,42.3,14.6,0.1713,5.9934,0.1584,37.8329,44.7201,78.23,13.9,75.8,996,0,569.1,143.8,36.4,0.989,0 -2013-07-21 06:05:12-06:00,n05667,45.8,14.6,0.1781,6.2543,0.1647,37.9757,44.8181,78.34,14.2,74.6,996,0,592.3,158.6,38,0.989,0 -2013-07-21 06:10:12-06:00,n05667,48.4,14.7,0.1845,6.498,0.1708,38.0467,44.8896,78.44,14,74.8,996,0,615.7,173.9,39.4,0.989,0 -2013-07-21 06:15:12-06:00,n05667,50.2,14.7,0.1912,6.7566,0.1772,38.1403,44.9709,78.58,14,74.9,996,0,631.9,188.7,40.9,0.989,0 -2013-07-21 06:20:12-06:00,n05667,59.1,14.9,0.2069,7.5491,0.195,38.7066,45.1979,80.71,13.8,76.1,996,0,646.3,203.9,43,0.989,0 -2013-07-21 06:25:12-06:00,n05667,72.3,15,0.2495,9.0549,0.2334,38.7931,45.4835,79.8,13.9,74.9,996.1,0,656.7,218.9,45.4,0.989,0 -2013-07-21 06:30:12-06:00,n05667,87.1,15.4,0.2936,10.684,0.2737,39.0406,45.7814,79.48,14.1,74.9,996.2,0,671.5,234,46.5,0.989,0 -2013-07-21 06:35:12-06:00,n05667,103.2,15.7,0.3432,12.6218,0.321,39.3242,46.063,79.84,14.2,74.2,996.2,0,689.2,250.3,47.4,0.989,0 -2013-07-21 06:40:12-06:00,n05667,119.6,16.2,0.4017,14.9217,0.3771,39.5666,46.3739,80.11,14.7,72.6,996.1,0,707.4,266.3,47.8,0.989,0 -2013-07-21 06:45:12-06:00,n05667,137.3,16.8,0.4715,17.6507,0.4432,39.8218,46.6458,80.25,14.9,72.1,996.1,0,721.8,283,49.1,0.989,0 -2013-07-21 06:50:12-06:00,n05667,155,17.1,0.5491,20.648,0.5157,40.04,46.8995,80.17,14.8,71.7,996.1,0,732.3,299,50.4,0.989,0 -2013-07-21 06:55:12-06:00,n05667,173.1,17.4,0.6341,24.0024,0.5965,40.2416,47.0966,80.37,14.9,71.6,996.2,0,743.7,315.1,51.5,0.989,0 -2013-07-21 07:00:12-06:00,n05667,191.6,17.7,0.7221,27.5256,0.6806,40.4413,47.3493,80.51,14.9,71.7,996.2,0,754.6,331.5,52.5,0.989,0 -2013-07-21 07:05:12-06:00,n05667,210.7,18.3,0.8185,31.36,0.7725,40.5959,47.5168,80.64,14.9,72.2,996.2,0,767.7,348.1,52.9,0.989,0 -2013-07-21 07:10:12-06:00,n05667,230.1,18.5,0.9202,35.4313,0.8696,40.7443,47.7464,80.64,15.1,70.5,996.2,0,778.1,365.2,54.2,0.989,0 -2013-07-21 07:15:12-06:00,n05667,249,19.4,1.0242,39.4875,0.9676,40.8109,47.8563,80.57,15.3,70.3,996.3,0,787.2,381,54.9,0.989,0 -2013-07-21 07:20:12-06:00,n05667,268.4,20,1.1296,43.6708,1.068,40.8887,47.9584,80.61,15.4,69.8,996.4,0,793.4,396.5,56,0.989,0 -2013-07-21 07:25:12-06:00,n05667,289.9,20.6,1.2399,47.9778,1.1721,40.9333,48.0482,80.54,15.3,70,996.4,0,802.1,413.5,57.3,0.989,0 -2013-07-21 07:30:12-06:00,n05667,308.8,20.6,1.3486,52.3543,1.2764,41.0173,48.177,80.58,15.3,69.7,996.3,0,810.7,429.7,58,0.989,0 -2013-07-21 07:35:12-06:00,n05667,328.7,20.7,1.4616,56.846,1.3825,41.1182,48.3058,80.51,15.1,70.6,996.3,0,819.8,446.4,58.3,0.989,0 -2013-07-21 07:40:12-06:00,n05667,348.2,21.8,1.5757,61.1688,1.4892,41.0749,48.3207,80.34,15.4,70.6,996.3,0,827.3,460.9,58.2,0.989,0 -2013-07-21 07:45:12-06:00,n05667,368.9,22.5,1.6868,65.4665,1.5943,41.0618,48.3695,80.24,15.2,70.9,996.4,0,832.2,476,59.1,0.989,0 -2013-07-21 07:50:12-06:00,n05667,396.9,23.5,1.7989,69.7429,1.7004,41.0147,48.3584,80.17,16,68.5,996.4,0,837.9,491.2,59.8,0.989,0 -2013-07-21 07:55:12-06:00,n05667,410,23.8,1.9118,74.2018,1.8086,41.0275,48.4313,80.14,15.7,69.8,996.3,0,845.3,507.8,60.8,0.989,0 -2013-07-21 08:00:12-06:00,n05667,451.4,25.1,2.0245,78.3704,1.9151,40.9224,48.3747,80.02,16.2,67.9,996.3,0,851.4,522.5,60.9,0.989,0 -2013-07-21 08:05:12-06:00,n05667,470.2,26.3,2.1371,82.4203,2.0183,40.8365,48.3077,79.84,16.7,66.7,996.3,0,856.5,537.6,61.2,0.989,0 -2013-07-21 08:10:12-06:00,n05667,468.6,26.2,2.2449,86.7634,2.1239,40.851,48.3884,79.87,16.7,66.5,996.3,0,860.5,553,62.2,0.989,0 -2013-07-21 08:15:12-06:00,n05667,485.7,26.9,2.3607,90.926,2.2289,40.7933,48.3871,79.6,16.6,67.1,996.2,0,867.5,568.6,62.3,0.989,0 -2013-07-21 08:20:12-06:00,n05667,504.6,27.7,2.4729,95.1844,2.3343,40.7761,48.4157,79.5,16.6,66.8,996.3,0,872.9,583.8,62.3,0.989,0 -2013-07-21 08:25:12-06:00,n05667,522.3,28.1,2.579,99.0729,2.4341,40.7017,48.3773,79.41,17.3,65.1,996.3,0,876.5,597.7,63.3,0.989,0 -2013-07-21 08:30:12-06:00,n05667,559.6,28.7,2.6805,102.9148,2.5308,40.6651,48.3871,79.35,17,65.8,996.2,0,879.9,612.3,64.1,0.989,0 -2013-07-21 08:35:12-06:00,n05667,565.5,29.5,2.7865,107.0566,2.6338,40.6466,48.415,79.36,17,65.9,996.1,0,885.7,626.7,63.9,0.989,0 -2013-07-21 08:40:12-06:00,n05667,575.6,30.1,2.8887,110.4158,2.7246,40.5262,48.3539,79.05,17.4,64.8,996.1,0,885.3,638.8,64.9,0.989,0 -2013-07-21 08:45:12-06:00,n05667,593.2,29.9,2.9849,114.4337,2.8163,40.6325,48.4788,79.08,17.4,64.8,996,0,889.5,652.4,66.3,0.989,0 -2013-07-21 08:50:12-06:00,n05667,611.7,31.2,3.096,118.1403,2.9172,40.4976,48.3936,78.85,17.8,64.4,996,0,892.5,666.9,67.2,0.989,0 -2013-07-21 08:55:12-06:00,n05667,629.1,31.8,3.1882,121.4411,3.003,40.4399,48.3819,78.73,17.7,63.9,996,0,893.8,679.1,67.8,0.989,0 -2013-07-21 09:00:12-06:00,n05667,646.7,32.2,3.2952,125.017,3.1022,40.2994,48.2908,78.56,18,63.7,996,0,898.1,693.7,68.2,0.989,0 -2013-07-21 09:05:12-06:00,n05667,661.6,32.8,3.3763,128.3677,3.1797,40.371,48.389,78.57,18.3,62.6,996,0,897.9,705,69.8,0.989,0 -2013-07-21 09:10:12-06:00,n05667,680.1,32.5,3.4755,132.1123,3.2733,40.3601,48.4157,78.51,18.2,63.8,995.9,0,903.6,719.4,69.5,0.989,0 -2013-07-21 09:15:12-06:00,n05667,696.1,32.9,3.5655,135.2777,3.3574,40.292,48.3806,78.42,18.5,62.7,995.8,0,906.3,731.5,69.5,0.989,0 -2013-07-21 09:20:12-06:00,n05667,712.5,33.7,3.6552,138.6228,3.4421,40.2723,48.4111,78.34,18.5,62,995.8,0,908.5,744,70.8,0.989,0 -2013-07-21 09:25:12-06:00,n05667,728.3,34.2,3.7458,141.4524,3.5243,40.1368,48.3175,78.16,18.8,61,995.8,0,912.7,756.5,70,0.989,0 -2013-07-21 09:30:12-06:00,n05667,744.4,34.4,3.8332,144.6461,3.6059,40.1135,48.3227,78.09,18.9,60.9,995.8,0,915.9,768.4,70.3,0.989,0 -2013-07-21 09:35:12-06:00,n05667,758.7,34.5,3.9126,147.4265,3.6757,40.1083,48.3552,77.92,19,60.4,995.8,0,917.3,779.6,70.9,0.989,0 -2013-07-21 09:40:12-06:00,n05667,773.9,34.6,3.9945,150.6033,3.758,40.0755,48.3571,77.97,19.2,59.5,995.8,0,920.3,791.1,70.8,0.989,0 -2013-07-21 09:45:12-06:00,n05667,786.8,37.1,4.0712,152.171,3.8261,39.7719,48.0963,77.71,19.6,60,995.8,0,921.4,800.7,70.7,0.989,0 -2013-07-21 09:50:12-06:00,n05667,800.7,36.2,4.1448,154.7502,3.893,39.751,48.1015,77.62,19.5,58.8,995.7,0,923.9,811.3,71,0.989,0 -2013-07-21 09:55:12-06:00,n05667,815.3,36,4.2129,158.2073,3.9652,39.8987,48.2713,77.8,19.7,58.3,995.7,0,926.7,822.2,71,0.989,0 -2013-07-21 10:00:12-06:00,n05667,828.8,36,4.2891,160.5941,4.0317,39.8327,48.2258,77.64,19.8,57.5,995.6,0,929.9,832.5,70.4,0.989,0 -2013-07-21 10:05:12-06:00,n05667,842.2,36.6,4.3677,163.1835,4.1021,39.7804,48.2186,77.48,20.2,56.1,995.5,0,933.4,842.4,70.3,0.989,0 -2013-07-21 10:10:12-06:00,n05667,854.6,37.1,4.4284,165.676,4.162,39.8065,48.2667,77.51,20.4,55,995.5,0,935.2,851.6,70.6,0.989,0 -2013-07-21 10:15:12-06:00,n05667,867.7,37.3,4.5017,168.0474,4.2243,39.7807,48.27,77.34,20.4,54.5,995.4,0,937.9,861,70,0.989,0 -2013-07-21 10:20:12-06:00,n05667,878.6,37.4,4.5612,170.188,4.2794,39.7692,48.2804,77.28,20.7,53.7,995.5,0,939.6,868.3,69.4,0.989,0 -2013-07-21 10:25:12-06:00,n05667,891.7,37.9,4.6284,172.5863,4.3446,39.724,48.2602,77.27,21.2,51.5,995.4,0,943.9,878.8,68.7,0.989,0 -2013-07-21 10:30:12-06:00,n05667,901.3,38.6,4.6844,173.8019,4.3963,39.5334,48.1145,77.11,21.4,51.4,995.3,0,946.2,886.1,67.9,0.989,0 -2013-07-21 10:35:12-06:00,n05667,912.4,38.9,4.7459,176.0953,4.4523,39.5519,48.1496,77.06,21.6,49.8,995.3,0,948.1,894.1,67.7,0.989,0 -2013-07-21 10:40:12-06:00,n05667,922.4,39,4.7938,177.3812,4.4975,39.4398,48.0657,76.98,21.9,48.4,995.3,0,951.5,902.1,66.6,0.989,0 -2013-07-21 10:45:12-06:00,n05667,930,41.2,4.8421,177.7807,4.539,39.1673,47.8095,76.79,22,49,995.2,0,950.8,907.7,66.6,0.989,0 -2013-07-21 10:50:12-06:00,n05667,940.4,40.3,4.8946,179.8922,4.5884,39.206,47.8674,76.78,22.5,45.7,995.1,0,953.4,915.9,66.7,0.989,0 -2013-07-21 10:55:12-06:00,n05667,947.5,40.4,4.9361,181.4287,4.6252,39.2265,47.9096,76.72,22.5,45.4,995.2,0,953.8,920.9,66.7,0.989,0 -2013-07-21 11:00:12-06:00,n05667,955.5,39.9,4.9781,183.3537,4.6661,39.2949,47.9948,76.74,22.9,44.6,995.2,0,954.1,926.4,67.3,0.989,0 -2013-07-21 11:05:12-06:00,n05667,963.6,40.1,5.0234,184.8198,4.7034,39.2949,48.0098,76.63,22.6,46.3,995.1,0,956.3,933.2,67.3,0.989,0 -2013-07-21 11:10:12-06:00,n05667,969.5,41.6,5.0536,184.9247,4.7324,39.0764,47.7952,76.56,23.3,43.8,995.1,0,956.2,937,66.9,0.989,0 -2013-07-21 11:15:12-06:00,n05667,974.4,41.1,5.0796,186.5181,4.7593,39.1904,47.9292,76.61,23.4,43.7,995,0,954.7,941.3,68.5,0.989,0 -2013-07-21 11:20:12-06:00,n05667,982.1,41.4,5.1256,187.6579,4.8021,39.0782,47.8491,76.52,23.4,43.3,995,0,957.2,945.8,67.3,0.989,0 -2013-07-21 11:25:12-06:00,n05667,988.2,41,5.1505,189.5215,4.8252,39.2771,48.041,76.59,23.5,42.4,995.1,0,958.2,951.2,67.9,0.989,0 -2013-07-21 11:30:12-06:00,n05667,993.2,40.6,5.177,190.1072,4.8514,39.1858,47.9597,76.57,23.5,43.2,995.1,0,959.6,955,67.4,0.989,0 -2013-07-21 11:35:12-06:00,n05667,997.7,41.6,5.2055,190.8321,4.8736,39.156,47.9402,76.47,23.6,42.5,995.1,0,959.5,957.9,67.6,0.989,0 -2013-07-21 11:40:12-06:00,n05667,1001.7,41.8,5.2272,190.9587,4.8966,38.9984,47.8075,76.41,24,42.4,994.9,0,960.7,960.8,66.7,0.989,0 -2013-07-21 11:45:12-06:00,n05667,1004.3,41.8,5.2502,191.5226,4.9137,38.9769,47.7874,76.34,24.1,41.1,994.9,0,961.2,963.5,66.8,0.989,0 -2013-07-21 11:50:12-06:00,n05667,1006.9,43.5,5.2672,191.0401,4.928,38.7663,47.602,76.19,24.3,41.5,994.9,0,961.1,964.9,66.6,0.989,0 -2013-07-21 11:55:12-06:00,n05667,1009.9,42.6,5.2798,191.8808,4.9373,38.8632,47.6852,76.21,24.3,40.7,995,0,960.8,967,67.2,0.989,0 -2013-07-21 12:00:12-06:00,n05667,1011.3,42.9,5.2851,192.3566,4.9466,38.8863,47.7145,76.28,24.3,41.5,994.9,0,959.8,967.8,67.3,0.989,0 -2013-07-21 12:05:12-06:00,n05667,1012.6,42.7,5.2985,192.6473,4.9567,38.8663,47.6904,76.24,25,39.8,994.8,0,959.7,968.8,67.4,0.989,0 -2013-07-21 12:10:12-06:00,n05667,1012.5,42.7,5.3003,193.2025,4.9557,38.9856,47.8114,76.24,24.9,39.4,994.8,0,960.2,969.5,67,0.989,0 -2013-07-21 12:15:12-06:00,n05667,1015.2,41.4,5.3093,194.2185,4.9693,39.0837,47.9096,76.35,24.8,38.7,994.7,0,963.1,972.2,66.5,0.989,0 -2013-07-21 12:20:12-06:00,n05667,1015.3,43,5.3168,193.3515,4.9716,38.8916,47.7269,76.2,25,39.3,994.7,0,963.1,971.3,65.9,0.989,0 -2013-07-21 12:25:12-06:00,n05667,1015.6,42.7,5.3167,193.3283,4.9716,38.8868,47.7249,76.19,25.3,38.4,994.7,0,964.8,972.6,65.5,0.989,0 -2013-07-21 12:30:12-06:00,n05667,1014.8,44,5.3128,192.6208,4.9659,38.7887,47.6091,76.15,25.6,38.2,994.7,0,964.8,971.8,65.2,0.989,0 -2013-07-21 12:35:12-06:00,n05667,1010.2,44.5,5.2966,191.5831,4.9493,38.7094,47.5538,76.06,25.9,37,994.7,0,961.8,967.7,65,0.989,0 -2013-07-21 12:40:12-06:00,n05667,1010.6,42.2,5.2883,192.725,4.9456,38.9688,47.7913,76.26,25.8,36.2,994.5,0,963.7,969.5,65.4,0.989,0 -2013-07-21 12:45:12-06:00,n05667,1008.9,43,5.2813,192.0246,4.9391,38.8787,47.6995,76.23,25.6,38.5,994.7,0,964.7,967.9,64.9,0.989,0 -2013-07-21 12:50:12-06:00,n05667,1006.1,42.8,5.2701,191.3599,4.9285,38.8274,47.6547,76.2,25.8,37.3,994.7,0,965.1,966.5,64.4,0.989,0 -2013-07-21 12:55:12-06:00,n05667,1002.1,42.6,5.2422,190.7435,4.9027,38.9056,47.706,76.27,25.8,38.2,994.7,0,963.9,963.7,65.1,0.989,0 -2013-07-21 13:00:12-06:00,n05667,998.8,42.7,5.2252,190.0817,4.8867,38.8978,47.6982,76.27,26.1,36.6,994.6,0,964.8,960.7,63.8,0.989,0 -2013-07-21 13:05:12-06:00,n05667,994,41.9,5.1974,189.5116,4.8612,38.9844,47.7691,76.33,26,37.6,994.5,0,963.8,957.4,64.2,0.989,0 -2013-07-21 13:10:12-06:00,n05667,988.9,44.4,5.1757,187.7427,4.8392,38.7959,47.5766,76.24,26.2,36.5,994.6,0,965,954.2,63.3,0.989,0 -2013-07-21 13:15:12-06:00,n05667,984.5,42.5,5.1412,187.4427,4.8117,38.9552,47.7243,76.39,26.2,35.4,994.6,0,964,951,63.7,0.989,0 -2013-07-21 13:20:12-06:00,n05667,978.3,43.2,5.1177,185.8566,4.7861,38.8327,47.5877,76.31,26.7,35.8,994.6,0,964.8,946.6,62.4,0.989,0 -2013-07-21 13:25:12-06:00,n05667,972,42.9,5.0811,184.3013,4.7526,38.7791,47.5265,76.32,26.7,34.2,994.6,0,965.2,942.8,62.3,0.989,0 -2013-07-21 13:30:12-06:00,n05667,965.3,43.4,5.0416,183.7635,4.7198,38.9345,47.6631,76.47,26.7,34.5,994.6,0,963.8,937.4,62.5,0.989,0 -2013-07-21 13:35:12-06:00,n05667,954.9,44.5,4.993,180.7313,4.67,38.7002,47.4127,76.34,26.8,34.8,994.6,0,959.2,929.3,63,0.989,0 -2013-07-21 13:40:12-06:00,n05667,948.2,43.1,4.953,180.3336,4.6324,38.9291,47.6202,76.46,26.8,34.4,994.5,0,958.8,924.5,64,0.989,0 -2013-07-21 13:45:12-06:00,n05667,941.6,43.3,4.9174,178.8863,4.5999,38.8891,47.5714,76.47,27.1,33.1,994.4,0,960.4,919.7,62.6,0.989,0 -2013-07-21 13:50:12-06:00,n05667,930.3,42.2,4.8548,176.8649,4.5472,38.8953,47.5499,76.62,27,35.2,994.4,0,956.9,911.2,62.5,0.989,0 -2013-07-21 13:55:12-06:00,n05667,921,43.2,4.8086,175.3328,4.5023,38.9427,47.5831,76.63,26.9,33.8,994.4,0,956.2,904.9,62.3,0.989,0 -2013-07-21 14:00:12-06:00,n05667,911.1,43.2,4.7574,172.7578,4.4494,38.8274,47.4368,76.55,27.3,33.4,994.3,0,956,897.8,61.3,0.989,0 -2013-07-21 14:05:12-06:00,n05667,901.6,44.1,4.7072,170.8249,4.4046,38.7831,47.3802,76.59,27.4,35,994.3,0,956,891.1,61.3,0.989,0 -2013-07-21 14:10:12-06:00,n05667,891.1,43.6,4.6468,168.5569,4.3486,38.7608,47.3366,76.63,27.5,34.2,994.3,0,954.5,883.3,60.8,0.989,0 -2013-07-21 14:15:12-06:00,n05667,878.9,42.5,4.5856,167.2911,4.2908,38.9879,47.5194,76.77,27.4,33.7,994.2,0,952.9,875.1,61,0.989,0 -2013-07-21 14:20:12-06:00,n05667,869.5,41.5,4.5279,165.7917,4.2419,39.0843,47.5935,76.93,27.4,33.8,994.2,0,953.1,869.5,61,0.989,0 -2013-07-21 14:25:12-06:00,n05667,854.9,42.8,4.4558,162.1708,4.1729,38.8629,47.3581,76.85,27.7,34.2,994.1,0,949.4,857.9,60.7,0.989,0 -2013-07-21 14:30:12-06:00,n05667,842.4,42.8,4.3922,159.7585,4.1118,38.8537,47.3151,76.88,27.8,33.8,994.1,0,947.7,848.7,60.4,0.989,0 -2013-07-21 14:35:12-06:00,n05667,827.3,41.5,4.3068,157.782,4.0354,39.0993,47.5103,77.11,27.9,32.9,994,0,941.8,837.2,61.3,0.989,0 -2013-07-21 14:40:12-06:00,n05667,814.8,44,4.2466,154.5929,3.9766,38.876,47.2703,77.01,28.1,34.2,994,0,941.4,827.6,60.5,0.989,0 -2013-07-21 14:45:12-06:00,n05667,803,42.6,4.1772,151.9837,3.9098,38.8725,47.2338,77.03,28.5,31.7,994,0,942.1,819.4,59.3,0.989,0 -2013-07-21 14:50:12-06:00,n05667,790.8,41.4,4.1063,150.3196,3.8484,39.0599,47.4101,77.21,28.2,31.4,993.8,0,943.3,811.2,59.2,0.989,0 -2013-07-21 14:55:12-06:00,n05667,773.9,40.9,4.0163,146.997,3.7609,39.086,47.3802,77.25,28.3,31.9,993.7,0,937.1,797.6,59.2,0.989,0 -2013-07-21 15:00:12-06:00,n05667,763.2,41.8,3.9537,144.9444,3.7037,39.1352,47.401,77.34,28.4,32,993.8,0,940.9,790.8,58.7,0.989,0 -2013-07-21 15:05:12-06:00,n05667,745,41.7,3.8554,141.275,3.6134,39.0978,47.3294,77.42,28.4,32.4,993.8,0,934.2,775.6,58.6,0.989,0 -2013-07-21 15:10:12-06:00,n05667,727.8,41.7,3.7658,137.6328,3.5273,39.0194,47.2169,77.4,28.7,32.7,993.8,0,929.7,762,57.8,0.989,0 -2013-07-21 15:15:12-06:00,n05667,713.3,42.2,3.6885,134.5428,3.4533,38.9605,47.1272,77.4,28.8,32.7,993.7,0,930.3,751.6,56.9,0.989,0 -2013-07-21 15:20:12-06:00,n05667,698.3,40.6,3.5989,131.8113,3.3753,39.0523,47.1831,77.62,28.7,32.7,993.6,0,928.1,740.5,56.9,0.989,0 -2013-07-21 15:25:12-06:00,n05667,681.9,40.5,3.5046,128.6005,3.2882,39.11,47.202,77.74,28.6,32,993.6,0,925.2,727.9,56.5,0.989,0 -2013-07-21 15:30:12-06:00,n05667,666.3,41.4,3.4212,125.1328,3.2062,39.0286,47.0979,77.66,28.9,31.2,993.6,0,922.7,716.2,55.4,0.989,0 -2013-07-21 15:35:12-06:00,n05667,647.4,40.6,3.3156,121.3158,3.1086,39.0261,47.0231,77.81,28.9,31.6,993.4,0,920.3,701.5,54.9,0.989,0 -2013-07-21 15:45:12-06:00,n05667,612.5,40.6,3.1221,114.2128,2.9254,39.0417,46.9724,77.88,29.1,31.7,993.5,0,914,674.9,54.2,0.989,0 -2013-07-21 15:50:12-06:00,n05667,594,41.6,3.0208,110.3069,2.8282,39.0023,46.881,77.89,29,30.9,993.4,0,910.6,660.7,53.6,0.989,0 -2013-07-21 15:55:12-06:00,n05667,578,39.8,2.9189,107.2687,2.7365,39.1995,47.0153,78.17,29,30.7,993.4,0,909.4,649.2,53.9,0.989,0 -2013-07-21 16:00:12-06:00,n05667,559,39.9,2.8167,103.5846,2.6404,39.2302,47.0112,78.23,28.8,30,993.4,0,906,634.4,52.6,0.989,0 -2013-07-21 16:05:12-06:00,n05667,541.8,38.7,2.7126,100.0155,2.5442,39.3116,47.0565,78.35,28.9,31.2,993.3,0,903.3,621.2,52.5,0.989,0 -2013-07-21 16:10:12-06:00,n05667,520.8,37.7,2.5942,95.8922,2.433,39.4127,47.0974,78.49,28.7,31.7,993.3,0,893.2,604.1,53.4,0.989,0 -2013-07-21 16:15:12-06:00,n05667,502.2,37.4,2.4953,92.217,2.3401,39.4068,47.0342,78.57,28.7,31.6,993.3,0,893,591.7,52.4,0.989,0 -2013-07-21 16:20:12-06:00,n05667,481.9,37,2.385,88.227,2.2392,39.4008,47.0049,78.7,28.7,31.1,993.2,0,888.9,576.8,51.9,0.989,0 -2013-07-21 16:25:12-06:00,n05667,461.3,37,2.2677,83.7803,2.1274,39.3819,46.9159,78.75,28.8,31.6,993.3,0,881.8,560.5,51.5,0.989,0 -2013-07-21 16:30:12-06:00,n05667,442,37.3,2.1582,79.4983,2.0244,39.2694,46.7681,78.76,29,30.8,993.2,0,876.9,544.6,50.8,0.989,0 -2013-07-21 16:35:14-06:00,n05667,423.3,36.6,2.0449,75.49,1.9183,39.3528,46.8042,78.88,28.9,29.4,993.2,0,872.1,529.8,50.2,0.989,0 -2013-07-21 16:40:12-06:00,n05667,403.8,36.2,1.9343,71.3355,1.8127,39.3534,46.7585,78.87,28.8,31.5,993.2,0,866.8,515,50.1,0.989,0 -2013-07-21 16:45:12-06:00,n05667,384.1,35.8,1.8178,66.9729,1.7041,39.3009,46.646,78.98,29,31.2,993.1,0,861.7,499.1,49.1,0.989,0 -2013-07-21 16:50:12-06:00,n05667,364.4,35.9,1.704,62.5788,1.5951,39.2331,46.5209,78.94,29,31.6,993,0,855.7,483.2,48.3,0.989,0 -2013-07-21 16:55:12-06:00,n05667,346.2,35,1.5923,58.6213,1.4921,39.2887,46.5364,79.11,28.8,30.6,992.9,0,851,469.3,48.7,0.989,0 -2013-07-21 17:00:12-06:00,n05667,325.4,34.5,1.4762,54.2493,1.3823,39.2467,46.4322,79.14,28.8,31,993,0,840.8,451.2,48,0.989,0 -2013-07-21 17:05:12-06:00,n05667,306.8,34.2,1.3659,50.0796,1.2781,39.1828,46.3229,79.15,28.8,31.4,993,0,837.4,436.7,47,0.989,0 -2013-07-21 17:10:12-06:00,n05667,285.9,33.9,1.2477,45.6075,1.1669,39.0835,46.1813,79.15,28.8,31,992.9,0,825,419,47.1,0.989,0 -2013-07-21 17:15:12-06:00,n05667,266.4,34,1.1382,41.451,1.0643,38.9473,45.9905,79.18,29,30.3,992.9,0,817.7,402.5,46.1,0.989,0 -2013-07-21 17:20:12-06:00,n05667,248.5,33.2,1.0327,37.5555,0.9658,38.8836,45.8464,79.32,28.8,30.8,992.8,0,812.3,388.2,45.7,0.989,0 -2013-07-21 17:25:12-06:00,n05667,228.1,32.9,0.9237,33.4095,0.8629,38.7186,45.6743,79.19,28.8,29.9,992.8,0,799.3,370.5,45.1,0.989,0 -2013-07-21 17:30:12-06:00,n05667,209.7,32.5,0.8223,29.6559,0.768,38.6121,45.5103,79.24,28.7,30.8,992.9,0,791.3,354.8,44.5,0.989,0 -2013-07-21 17:35:12-06:00,n05667,191.6,32.3,0.7271,26.0404,0.678,38.4093,45.2415,79.17,28.8,30.1,992.8,0,785.1,339.5,43.4,0.989,0 -2013-07-21 17:40:12-06:00,n05667,172.1,32.1,0.6362,22.6121,0.5922,38.1804,45.0256,78.94,28.8,31.3,992.7,0,768.6,321.2,43,0.989,0 -2013-07-21 17:45:12-06:00,n05667,154.3,31.8,0.5506,19.4025,0.5115,37.9354,44.7491,78.74,28.9,31.4,992.8,0,757.1,305.2,42.4,0.989,0 -2013-07-21 17:50:12-06:00,n05667,136.9,31.5,0.4719,16.4978,0.4381,37.6567,44.4571,78.64,28.8,31.2,992.8,0,744.6,288.8,41.7,0.989,0 -2013-07-21 17:55:12-06:00,n05667,119.5,31.2,0.4003,13.823,0.3701,37.3508,44.1351,78.23,28.8,30.6,992.9,0,733.4,273.3,40.9,0.989,0 -2013-07-21 18:00:12-06:00,n05667,102.6,30.7,0.3352,11.4974,0.3105,37.0312,43.8073,78.29,28.6,31.9,992.9,0,717.2,256.5,40.1,0.989,0 -2013-07-21 18:05:13-06:00,n05667,86.6,30.7,0.2787,9.5679,0.2602,36.7764,43.4587,79,28.7,31.7,993,0,707.5,241.2,38.4,0.989,0 -2013-07-21 18:10:13-06:00,n05667,72,30.4,0.2323,7.9882,0.2186,36.5417,43.1165,79.74,28.6,31.4,993,0,690.6,225.6,38.1,0.989,0 -2013-07-21 18:15:13-06:00,n05667,58.8,30.1,0.1946,6.6857,0.1849,36.1654,42.777,80.33,28.5,31.7,993,0,670.8,209.3,37.1,0.989,0 -2013-07-21 18:20:13-06:00,n05667,50.3,29.7,0.1783,5.8621,0.1641,35.7206,42.5474,77.29,28.3,32.4,992.9,0,653.3,193.7,36,0.989,0 -2013-07-21 18:25:13-06:00,n05667,48.6,29.5,0.1721,5.6496,0.1583,35.69,42.4713,77.29,28.3,31.6,992.9,0,636.8,178.6,34.6,0.989,0 -2013-07-21 18:30:12-06:00,n05667,46.1,29.4,0.1667,5.4604,0.1534,35.606,42.4485,77.17,28.3,32.1,993,0,618.9,163.9,33.3,0.989,0 -2013-07-21 18:35:13-06:00,n05667,42.5,29.2,0.1598,5.2185,0.1469,35.5248,42.3783,77.06,28.1,32.1,992.8,0,598.6,149.1,31.8,0.989,0 -2013-07-21 18:40:13-06:00,n05667,39.1,29.1,0.154,5.0017,0.141,35.474,42.3002,76.8,28.1,32.2,993,0,580.2,135.2,30,0.989,0 -2013-07-21 18:45:13-06:00,n05667,35.7,29.1,0.1458,4.7242,0.1336,35.3548,42.1916,76.82,28,32.7,993,0,563.5,121.8,28.1,0.989,0 -2013-07-21 18:50:13-06:00,n05667,32.4,29,0.1386,4.4825,0.1271,35.2671,42.0945,76.83,28,32.8,993.1,0,538.2,108.4,26.5,0.989,0 -2013-07-21 18:55:13-06:00,n05667,29,29.2,0.1307,4.1823,0.1191,35.1165,41.9737,76.22,27.7,33.8,993.1,0,505.8,94.2,24.7,0.989,0 -2013-07-21 19:00:13-06:00,n05667,25.1,29.3,0.1214,3.8622,0.1106,34.9167,41.8144,76.11,27.6,34.7,993.1,0,471,81.1,23,0.989,0 -2013-07-21 19:05:13-06:00,n05667,21.7,29.2,0.1107,3.4994,0.1009,34.6825,41.6199,75.96,27.5,36.7,993.1,0,436.4,68.6,21,0.989,0 -2013-08-20 05:50:13-06:00,n05667,21.2,14.3,0.1025,3.4103,0.0931,36.6187,43.5991,76.34,13.8,74.1,1000.8,0,274.3,39.6,20.1,0.995,0 -2013-08-20 05:55:13-06:00,n05667,27.3,14.3,0.1288,4.3909,0.1181,37.177,44.1172,77.25,13.8,74.2,1000.9,0,320.1,52.2,24.7,0.995,0 -2013-08-20 06:00:13-06:00,n05667,35.6,14.4,0.1646,5.7403,0.1518,37.8024,44.6223,78.18,13.8,73.9,1001,0,363.8,68.2,31.6,0.995,0 -2013-08-20 06:05:13-06:00,n05667,40.4,14.7,0.1788,6.2593,0.1649,37.9675,44.783,78.17,13.9,73.9,1001,0,408.2,84.7,37.5,0.995,0 -2013-08-20 06:10:13-06:00,n05667,42,15,0.183,6.409,0.1687,37.9896,44.8233,78.13,14,73.6,1001,0,441.6,93.6,35.9,0.995,0 -2013-08-20 06:15:13-06:00,n05667,49.9,15.1,0.2099,7.449,0.1945,38.3011,45.0796,78.74,14.2,73,1001,0,479.4,111.7,42,0.995,0 -2013-08-20 06:20:13-06:00,n05667,67.4,15.4,0.2719,9.8986,0.2546,38.8837,45.6643,79.72,14.3,73.1,1001,0,504.4,132.2,51.3,0.995,0 -2013-08-20 06:25:12-06:00,n05667,81.6,15.6,0.3164,11.5766,0.296,39.1161,45.9434,79.63,14.6,72.2,1000.9,0,530.4,145.1,51.8,0.995,0 -2013-08-20 06:30:12-06:00,n05667,116,16.3,0.4733,17.6977,0.4439,39.8681,46.7031,80.06,14.7,71.8,1001,0,546.4,181.7,77.4,0.995,0 -2013-08-20 06:35:12-06:00,n05667,123.2,16.9,0.4976,18.6131,0.4671,39.8522,46.677,80.14,15,70.4,1001,0,496.9,183.3,81,0.995,0 -2013-08-20 06:40:12-06:00,n05667,145.8,17.4,0.5775,21.7924,0.5434,40.1023,46.9587,80.37,15,69.9,1001.1,0,573.5,212.8,85.9,0.995,0 -2013-08-20 06:45:12-06:00,n05667,125.2,17.4,0.5547,20.7267,0.5184,39.9846,46.8364,79.78,14.9,70.6,1001.1,0,304.2,173.2,102,0.995,0 -2013-08-20 06:50:12-06:00,n05667,104.3,17.7,0.4916,18.2366,0.4592,39.7097,46.5528,79.69,14.9,70.8,1001.1,0,90.6,123.6,102,0.995,0 -2013-08-20 06:55:12-06:00,n05667,111.7,17.5,0.5142,19.4318,0.4876,39.8535,46.6757,80.96,14.7,71.7,1001.1,0,141.2,133.9,97.3,0.995,0 -2013-08-20 07:05:12-06:00,n05667,229,18.1,0.9724,37.6141,0.9191,40.9264,47.9425,80.69,14.7,71.2,1001.2,0,650.8,271,76.9,0.995,0 -2013-08-20 07:15:12-06:00,n05667,274.5,19.1,1.1818,46.0174,1.1196,41.1003,48.1809,80.82,14.8,70.7,1001.1,0,709.7,304.1,70.6,0.995,0 -2013-08-20 07:20:12-06:00,n05667,291,19.7,1.2838,49.9168,1.2144,41.1043,48.2297,80.62,15.2,68.4,1001.2,0,683.2,314.8,80.3,0.995,0 -2013-08-20 07:25:12-06:00,n05667,324.5,19.7,1.443,56.7443,1.3731,41.3249,48.5191,81.05,15,68.8,1001.2,0,662.7,330.9,94.6,0.995,0 -2013-08-20 07:30:12-06:00,n05667,346.9,19.8,1.5419,61.0054,1.4718,41.4484,48.6505,81.33,15.2,68.4,1001.2,0,703.8,346.7,85.1,0.995,0 -2013-08-20 07:35:12-06:00,n05667,341.1,21.4,1.5895,62.4834,1.5142,41.2652,48.4436,81.15,15.7,67.3,1001.2,0,721.9,355.3,76,0.995,0 -2013-08-20 07:50:12-06:00,n05667,408.7,23.2,1.9619,76.8102,1.8652,41.1799,48.6017,80.55,15.8,66.5,1001.1,0,769.9,411.1,79.1,0.995,0 -2013-08-20 07:55:12-06:00,n05667,417.4,23.8,2.0351,79.3141,1.9287,41.1228,48.575,80.23,15.8,66.9,1001,0,796.3,424.3,69.1,0.995,0 -2013-08-20 08:00:12-06:00,n05667,434.7,23.6,2.1357,83.3184,2.0244,41.1564,48.6596,80.17,15.9,66.5,1000.9,0,804.2,437.6,67.4,0.995,0 -2013-08-20 08:05:12-06:00,n05667,466.7,23.8,2.2382,87.4178,2.125,41.1383,48.6817,80.23,16,66,1000.9,0,806,449.5,67.1,0.995,0 -2013-08-20 08:10:12-06:00,n05667,472.2,25.3,2.3505,91.1988,2.2219,41.0446,48.6336,79.78,16.2,65.9,1000.9,0,815.4,464.5,66.5,0.995,0 -2013-08-20 08:15:12-06:00,n05667,491.9,26.5,2.4676,95.3762,2.329,40.9523,48.5822,79.56,16.6,64.4,1000.9,0,825.1,479.7,65.9,0.995,0 -2013-08-20 08:20:12-06:00,n05667,512.1,26.6,2.6119,101.046,2.4695,40.9176,48.6173,79.58,16.6,64.2,1000.8,0,832.7,495.3,65.8,0.995,0 -2013-08-20 08:25:12-06:00,n05667,549.2,27.6,2.6953,104.2222,2.5463,40.9305,48.6589,79.47,16.6,64.3,1000.8,0,840.2,510.6,66.4,0.995,0 -2013-08-20 08:30:12-06:00,n05667,559.7,27.7,2.8044,108.3317,2.6482,40.9075,48.6759,79.36,16.7,64.3,1000.7,0,846,525.8,67.2,0.995,0 -2013-08-20 08:35:12-06:00,n05667,571.8,28.5,2.9208,112.5488,2.7552,40.8489,48.6674,79.18,16.9,64.1,1000.8,0,851,539.9,67.6,0.995,0 -2013-08-20 08:40:12-06:00,n05667,591.8,28.4,3.0304,116.7497,2.8617,40.7967,48.6563,79.18,16.9,64.3,1000.8,0,858.3,555.6,67.9,0.995,0 -2013-08-20 08:45:12-06:00,n05667,611.5,28.7,3.1407,120.6661,2.9654,40.691,48.6095,79.04,17,63.9,1000.7,0,865.7,570.1,67.6,0.995,0 -2013-08-20 08:50:12-06:00,n05667,631,29.8,3.252,124.39,3.0645,40.5904,48.5536,78.78,17.2,63.2,1000.6,0,871.8,584.8,67.8,0.995,0 -2013-08-20 08:55:12-06:00,n05667,651.3,29.7,3.3633,128.8258,3.1714,40.6209,48.6303,78.76,17.5,62.2,1000.6,0,879,600.5,68.5,0.995,0 -2013-08-20 09:00:12-06:00,n05667,668.8,31.6,3.4584,131.9657,3.2623,40.4523,48.5061,78.67,17.6,62.6,1000.6,0,882.4,612.8,68.7,0.995,0 -2013-08-20 09:05:12-06:00,n05667,686.8,33.2,3.5603,134.9736,3.357,40.2064,48.324,78.45,18.3,60.3,1000.6,0,886.4,626.3,69.4,0.995,0 -2013-08-20 09:10:12-06:00,n05667,703,33.3,3.6556,138.5595,3.4432,40.241,48.3838,78.34,18.5,58.2,1000.5,0,886.4,638.4,71.3,0.995,0 -2013-08-20 09:15:12-06:00,n05667,722.1,33.6,3.7531,142.224,3.5374,40.2062,48.3975,78.3,18.2,60,1000.5,0,892.3,653.3,72,0.995,0 -2013-08-20 09:20:12-06:00,n05667,737.2,32.8,3.8365,145.3753,3.6142,40.2229,48.4384,78.23,18.4,58.8,1000.5,0,894.8,664.7,72.2,0.995,0 -2013-08-20 09:25:12-06:00,n05667,753.8,35,3.9309,148.2643,3.695,40.1258,48.3656,77.98,18.6,58.6,1000.5,0,898.3,676.6,72.1,0.995,0 -2013-08-20 09:30:12-06:00,n05667,771.8,32.8,4.0222,151.9686,3.7866,40.1336,48.4209,78.03,18.8,56.6,1000.3,0,904.3,691.1,72.4,0.995,0 -2013-08-20 09:35:12-06:00,n05667,788.1,33.8,4.1149,155.1868,3.8702,40.0979,48.4215,77.89,18.7,57.9,1000.4,0,907.6,703.1,72.7,0.995,0 -2013-08-20 09:40:12-06:00,n05667,803.8,34.1,4.1977,158.0378,3.9505,40.0045,48.3669,77.84,19,56.6,1000.3,0,911.8,715,72.8,0.995,0 -2013-08-20 09:45:12-06:00,n05667,821.5,34.1,4.2903,162.3509,4.0381,40.2048,48.5998,77.86,18.9,57,1000.3,0,912.8,727.9,76.9,0.995,0 -2013-08-20 09:50:12-06:00,n05667,836.9,36,4.374,163.9581,4.1161,39.8336,48.2654,77.66,19.3,56.1,1000.3,0,916.1,739.3,77,0.995,0 -2013-08-20 09:55:12-06:00,n05667,850.4,35,4.4434,167.4819,4.1837,40.0316,48.4892,77.73,19.3,55.5,1000.3,0,919,749.4,76.4,0.995,0 -2013-08-20 10:00:13-06:00,n05667,859.5,36.5,4.5027,168.4791,4.2334,39.7978,48.2843,77.49,19.6,55.5,1000.2,0,916.5,756.3,76.5,0.995,0 -2013-08-20 10:05:12-06:00,n05667,875,37.1,4.5815,170.9624,4.3072,39.6925,48.2147,77.4,19.9,53.4,1000.2,0,920.6,767.8,77.7,0.995,0 -2013-08-20 10:10:12-06:00,n05667,887.3,37.4,4.6576,173.5691,4.3737,39.6848,48.2316,77.26,20.2,51.5,1000.2,0,920.7,777,78.4,0.995,0 -2013-08-20 10:15:12-06:00,n05667,898.5,37.8,4.7142,175.7939,4.4271,39.7087,48.2719,77.25,20.4,49.9,1000.2,0,922.1,785.3,78.6,0.995,0 -2013-08-20 10:20:12-06:00,n05667,913,37.5,4.7861,178.6865,4.4948,39.7538,48.361,77.2,20.3,50.2,1000,0,925.5,796.7,79.8,0.995,0 -2013-08-20 10:30:12-06:00,n05667,931.9,38.2,4.8966,181.9529,4.5969,39.5813,48.2368,77.03,20.5,48.1,1000,0,922.7,810.3,82.7,0.995,0 -2013-08-20 10:35:12-06:00,n05667,948.5,37.4,4.977,185.7591,4.6729,39.7522,48.4287,77.07,20.7,46.7,999.8,0,931.9,823.5,81.1,0.995,0 -2013-08-20 10:40:12-06:00,n05667,961.7,37,5.0473,188.5653,4.7383,39.7963,48.4989,77.03,20.9,46.3,999.7,0,934.5,834.2,84.8,0.995,0 -2013-08-20 10:45:12-06:00,n05667,978.1,38.8,5.1297,190.5525,4.8198,39.5355,48.2832,76.94,21.2,45.8,999.7,0,935.6,846.7,90.1,0.995,0 -2013-08-20 10:50:12-06:00,n05667,988.6,38.1,5.1937,193.0248,4.8701,39.635,48.3981,76.79,21.2,44.5,999.6,0,939.4,853.8,89.5,0.995,0 -2013-08-20 10:55:12-06:00,n05667,992.3,38.2,5.2154,193.2263,4.8911,39.5058,48.296,76.71,21.5,44.1,999.6,0,939.7,855.7,85.3,0.995,0 -2013-08-20 11:00:12-06:00,n05667,995.8,40,5.2363,192.9694,4.9097,39.3039,48.1041,76.61,21.5,44.5,999.5,0,936.3,857.4,84.8,0.995,0 -2013-08-20 11:05:12-06:00,n05667,1002.9,38.4,5.2703,195.4256,4.9421,39.5428,48.3571,76.68,21.7,43.4,999.3,0,938.2,863.2,84.8,0.995,0 -2013-08-20 11:10:12-06:00,n05667,1008.2,38.4,5.2993,196.4188,4.9681,39.5364,48.35,76.66,22.1,41.8,999.3,0,935.8,866.6,85.9,0.995,0 -2013-08-20 11:15:12-06:00,n05667,1011,38.8,5.3188,196.5419,4.983,39.4422,48.2531,76.58,21.9,42.8,999.3,0,934.2,868.6,85.5,0.995,0 -2013-08-20 11:20:12-06:00,n05667,1018.6,39.1,5.3635,198.0191,5.0251,39.4063,48.2433,76.53,22.2,41.7,999.2,0,937.8,875.1,85,0.995,0 -2013-08-20 11:25:12-06:00,n05667,1027.9,39.1,5.4162,199.818,5.0722,39.3948,48.2485,76.46,22.4,41.8,999.1,0,941.3,882.1,86,0.995,0 -2013-08-20 11:30:12-06:00,n05667,1030.6,39.7,5.4281,200.583,5.0853,39.4436,48.2993,76.51,22.4,41,999.1,0,938.9,883.2,86.3,0.995,0 -2013-08-20 11:35:12-06:00,n05667,1030.5,39.9,5.4391,200.1015,5.0923,39.2949,48.1718,76.37,22.5,41.5,999.1,0,933,881.8,87.1,0.995,0 -2013-08-20 11:40:12-06:00,n05667,1030.4,39.5,5.4332,199.7334,5.0858,39.2729,48.1431,76.36,22.7,41,999,0,926.1,881.9,90.2,0.995,0 -2013-08-20 11:45:12-06:00,n05667,1034.2,41.4,5.4526,199.7947,5.1026,39.1554,48.0306,76.29,23,39.8,999,0,928.7,884.8,89.3,0.995,0 -2013-08-20 11:50:12-06:00,n05667,1038.8,42.2,5.4849,199.7304,5.1287,38.944,47.8498,76.1,23.2,40,999,0,932.9,889.4,88.7,0.995,0 -2013-08-20 11:55:12-06:00,n05667,1042.2,42.6,5.5085,200.5245,5.1522,38.9199,47.8355,76.1,23.8,38.8,999,0,930.2,890.7,90.8,0.995,0 -2013-08-20 12:00:12-06:00,n05667,1048.7,42,5.5348,202.0695,5.1809,39.0025,47.9233,76.18,23.5,38.4,998.8,0,938.4,896,87.7,0.995,0 -2013-08-20 12:05:12-06:00,n05667,1050.2,39.3,5.5469,203.6694,5.1908,39.2362,48.1588,76.24,23.5,38.2,998.7,0,936.3,897.1,89.2,0.995,0 -2013-08-20 12:10:12-06:00,n05667,1048,40.5,5.5362,203.163,5.1811,39.2126,48.1217,76.26,23.6,37.7,998.8,0,930,895.1,92.4,0.995,0 -2013-08-20 12:15:12-06:00,n05667,1042.3,42.2,5.5101,201.4755,5.1619,39.0313,47.9467,76.26,24.1,38.2,998.8,0,917.9,889,96.3,0.995,0 -2013-08-20 12:20:12-06:00,n05667,1046,42.7,5.5337,201.279,5.1726,38.9126,47.8355,76.04,24.3,36.9,998.7,0,918.8,890.4,97.6,0.995,0 -2013-08-20 12:25:12-06:00,n05667,1055.4,40.8,5.5745,203.698,5.2127,39.0774,48.0137,76.1,24.2,35.6,998.5,0,925.2,899.9,101.6,0.995,0 -2013-08-20 12:30:12-06:00,n05667,1054.2,39.7,5.5671,204.1653,5.2097,39.1892,48.1041,76.24,24.2,36.5,998.4,0,922.9,899.1,102.8,0.995,0 -2013-08-20 12:35:12-06:00,n05667,1041.2,42.4,5.5168,200.6251,5.1527,38.9357,47.8478,76,24.3,36.7,998.5,0,914,888.4,101.5,0.995,0 -2013-08-20 12:40:12-06:00,n05667,1040.7,42.5,5.5105,200.3942,5.1521,38.8959,47.8088,76.06,24.8,34.4,998.4,0,916.8,886.2,97.6,0.995,0 -2013-08-20 12:45:12-06:00,n05667,1038.8,41.5,5.4986,200.7073,5.1456,39.0054,47.9155,76.18,24.6,35.1,998.3,0,925.7,890.3,95.9,0.995,0 -2013-08-20 12:50:12-06:00,n05667,1037.5,41,5.4841,200.9018,5.1327,39.1414,48.0365,76.26,24.4,36,998.4,0,932.3,890.8,92.6,0.995,0 -2013-08-20 12:55:12-06:00,n05667,1024.1,41,5.4106,198.5913,5.0644,39.2131,48.0794,76.34,24.6,36.8,998.4,0,917.3,881.1,97.5,0.995,0 -2013-08-20 13:00:12-06:00,n05667,1019.9,40.9,5.3852,197.7169,5.0435,39.202,48.0527,76.41,24.6,36.3,998.3,0,920.8,878.7,94.8,0.995,0 -2013-08-20 13:05:12-06:00,n05667,1014.7,41.2,5.364,196.6191,5.0224,39.1487,47.9929,76.38,24.7,36.9,998.2,0,918.7,873.9,94.7,0.995,0 -2013-08-20 13:10:12-06:00,n05667,1017.3,39.6,5.3628,197.8927,5.0274,39.3625,48.2017,76.56,24.8,35.1,998,0,930.8,876.9,90.2,0.995,0 -2013-08-20 13:15:12-06:00,n05667,1005.1,40,5.2982,195.2397,4.9695,39.2874,48.1048,76.6,25,35,998,0,926.1,869.1,89.8,0.995,0 -2013-08-20 13:20:12-06:00,n05667,994.1,42.7,5.2613,191.9063,4.9215,38.9932,47.8004,76.31,25.5,35.3,998.1,0,920.4,860.9,90.7,0.995,0 -2013-08-20 13:25:12-06:00,n05667,986.9,40.9,5.2147,190.6426,4.8799,39.0671,47.8563,76.39,25.7,33.4,998,0,916.9,855.8,92.1,0.995,0 -2013-08-20 13:30:12-06:00,n05667,977.5,39.8,5.1594,190.1197,4.8367,39.3074,48.0696,76.66,25.4,32.4,997.7,0,908.8,849.6,96.4,0.995,0 -2013-08-20 13:35:12-06:00,n05667,972.3,39.9,5.1373,189.0584,4.8151,39.2636,48.0137,76.65,25.6,32.6,997.8,0,916.1,846.1,91.4,0.995,0 -2013-08-20 13:40:12-06:00,n05667,960.5,39.4,5.0704,186.7492,4.7498,39.3177,48.0417,76.66,25.6,32.7,997.9,0,907,838.6,95.9,0.995,0 -2013-08-20 13:45:12-06:00,n05667,952.3,40,5.027,185.0589,4.7127,39.2681,47.976,76.73,25.8,31.4,997.7,0,906.2,832.6,96.1,0.995,0 -2013-08-20 13:50:12-06:00,n05667,933.9,42,4.9417,180.8175,4.6294,39.0587,47.749,76.63,26,31.4,997.8,0,890.6,817.3,99.2,0.995,0 -2013-08-20 13:55:12-06:00,n05667,927,41.1,4.9035,179.5282,4.5919,39.0967,47.7652,76.65,26.2,30.2,997.6,0,891.2,813,99.3,0.995,0 -2013-08-20 14:00:13-06:00,n05667,920.5,40.3,4.8533,178.3715,4.5486,39.2148,47.8557,76.8,26.2,29.9,997.6,0,902.8,811.7,95.1,0.995,0 -2013-08-20 14:05:12-06:00,n05667,904.8,40.4,4.7828,176.3344,4.4834,39.3303,47.9259,76.93,26.2,28.7,997.3,0,888.3,795.3,96.3,0.995,0 -2013-08-20 14:10:12-06:00,n05667,883,39.3,4.6658,172.2691,4.3762,39.3654,47.9252,77.04,26.3,29.2,997.4,0,867.2,779.6,103.3,0.995,0 -2013-08-20 14:15:12-06:00,n05667,877.4,39.3,4.6348,171.4978,4.3516,39.4102,47.9565,77.16,26.4,29.8,997.3,0,873.2,773.7,99.9,0.995,0 -2013-08-20 14:20:12-06:00,n05667,868.9,40,4.5908,169.315,4.3034,39.344,47.8804,77.03,26.6,28.3,997.3,0,883.2,768.9,94.6,0.995,0 -2013-08-20 14:25:12-06:00,n05667,861,40.7,4.5498,167.4442,4.2618,39.2897,47.8036,76.99,26.8,27.6,997.3,0,884.4,763.4,96,0.995,0 -2013-08-20 14:30:12-06:00,n05667,832.5,40.4,4.4007,162.0933,4.124,39.3046,47.7574,77.13,26.8,27.6,997.2,0,849.1,739,105,0.995,0 -2013-08-20 14:35:12-06:00,n05667,823.6,39.5,4.3466,160.3139,4.0764,39.3273,47.7646,77.22,26.9,27.3,997.2,0,853.7,734.7,104.5,0.995,0 -2013-08-20 14:40:12-06:00,n05667,806.1,38.7,4.2516,157.639,3.9888,39.5208,47.8979,77.41,26.7,27.2,997.1,0,841.9,720.7,107.6,0.995,0 -2013-08-20 14:45:12-06:00,n05667,785.7,38.9,4.1442,153.3565,3.8898,39.4258,47.7672,77.47,27.1,27,997,0,834.4,705.8,105.7,0.995,0 -2013-08-20 14:50:12-06:00,n05667,772.7,39,4.081,150.8717,3.826,39.4332,47.7392,77.44,27.1,26.5,996.9,0,827.3,694.5,107.5,0.995,0 -2013-08-20 14:55:12-06:00,n05667,760.1,39.8,4.0073,148.1932,3.7583,39.4306,47.7139,77.5,27.1,26.3,997,0,833,686.8,104.3,0.995,0 -2013-08-20 15:00:12-06:00,n05667,745.7,39.5,3.9318,145.0977,3.686,39.3642,47.6169,77.5,27.1,26.3,996.8,0,836.9,676,99.4,0.995,0 -2013-08-20 15:05:12-06:00,n05667,739.4,39,3.8877,144.202,3.6536,39.4688,47.6885,77.78,27.3,24.2,996.7,0,851.4,672.7,94.7,0.995,0 -2013-08-20 15:10:12-06:00,n05667,716.3,39,3.7694,139.6628,3.5392,39.4616,47.6358,77.78,27.3,24.9,996.8,0,835.3,655,97.1,0.995,0 -2013-08-20 15:15:12-06:00,n05667,696.1,39.6,3.666,135.2216,3.4389,39.3216,47.4711,77.7,27.5,26.1,996.8,0,825.5,639.2,96.8,0.995,0 -2013-08-20 15:20:12-06:00,n05667,680.4,39.6,3.574,131.8648,3.3552,39.3015,47.4181,77.81,27.7,24.7,996.7,0,824.5,626.8,94.4,0.995,0 -2013-08-20 15:25:12-06:00,n05667,664.4,38.6,3.4849,129.0744,3.2706,39.4655,47.5273,77.93,27.4,25.7,996.6,0,828,618,92.5,0.995,0 -2013-08-20 15:30:12-06:00,n05667,644.2,37.5,3.3705,125.488,3.1668,39.6256,47.6434,78.14,27.2,25.7,996.6,0,813.8,600.8,93.5,0.995,0 -2013-08-20 15:35:12-06:00,n05667,636.7,37.2,3.3249,123.7081,3.1236,39.604,47.603,78.16,27.4,24.9,996.4,0,835.8,597.6,86.7,0.995,0 -2013-08-20 15:40:12-06:00,n05667,612.7,37.3,3.1917,118.7678,2.9973,39.6252,47.5663,78.23,27.4,24.2,996.5,0,817.8,577.4,87.3,0.995,0 -2013-08-20 15:45:12-06:00,n05667,595,36.4,3.091,115.2429,2.9024,39.7059,47.6001,78.33,27.3,25.5,996.3,0,810.7,564.7,88.6,0.995,0 -2013-08-20 15:50:12-06:00,n05667,573.7,36.1,2.9717,111.0344,2.7913,39.7793,47.6149,78.47,27.3,25.1,996.2,0,803.5,548.5,86.9,0.995,0 -2013-08-20 15:55:12-06:00,n05667,555.2,37.1,2.8693,107.002,2.6971,39.6729,47.4769,78.55,27.5,25.3,996.3,0,798.7,532.8,84.4,0.995,0 -2013-08-20 16:00:13-06:00,n05667,537.8,36.6,2.7656,103.0685,2.5975,39.6806,47.4222,78.59,27.5,24.5,996.2,0,797.9,521,83.3,0.995,0 -2013-08-20 16:05:12-06:00,n05667,516.5,35.9,2.6467,98.5621,2.4865,39.6394,47.3281,78.68,27.6,24.9,996.1,0,779.2,504,86.6,0.995,0 -2013-08-20 16:10:12-06:00,n05667,493.5,36,2.5175,93.6723,2.3646,39.6141,47.2597,78.73,27.6,27,996.2,0,764.9,485.2,85.8,0.995,0 -2013-08-20 16:15:12-06:00,n05667,474.7,35.9,2.4153,89.8665,2.2675,39.6333,47.2388,78.76,27.5,26.2,996.1,0,761,472.2,85.1,0.995,0 -2013-08-20 16:20:12-06:00,n05667,453.7,34.9,2.2911,85.4418,2.1523,39.6971,47.2362,78.95,27.5,25.6,996.1,0,757.4,455.7,80.8,0.995,0 -2013-08-20 16:25:12-06:00,n05667,437.7,34.8,2.1998,81.9929,2.0671,39.6647,47.1818,79,27.6,25.9,996,0,762.6,445.2,78.7,0.995,0 -2013-08-20 16:30:12-06:00,n05667,412.5,34.8,2.0617,76.788,1.9378,39.6254,47.0757,79.12,27.6,26.1,996.1,0,736.8,424.1,80.2,0.995,0 -2013-08-20 16:35:12-06:00,n05667,388.9,34.7,1.93,71.7085,1.813,39.5528,46.9443,79.15,27.7,26.4,996,0,714.9,405.2,81.7,0.995,0 -2013-08-20 16:40:12-06:00,n05667,372.9,33.6,1.8296,68.1467,1.7193,39.637,46.9781,79.28,27.6,25.9,996,0,720.1,392.8,77.3,0.995,0 -2013-08-20 16:45:12-06:00,n05667,350.5,33,1.7058,63.4057,1.6007,39.6115,46.9038,79.25,27.5,26.4,995.9,0,699.8,374.1,77.4,0.995,0 -2013-08-20 16:50:12-06:00,n05667,327.9,33,1.5777,58.5766,1.4806,39.563,46.7958,79.34,27.5,26.5,995.9,0,684,355.5,75.5,0.995,0 -2013-08-20 16:55:12-06:00,n05667,310.9,33.3,1.4794,54.7381,1.3876,39.4479,46.6317,79.34,27.6,26.6,996,0,682.6,342.5,73.2,0.995,0 -2013-08-20 17:00:12-06:00,n05667,288.9,32.4,1.353,50.0579,1.2694,39.4336,46.5762,79.44,27.5,26.1,995.9,0,656.9,324.8,75.1,0.995,0 -2013-08-20 17:05:12-06:00,n05667,270,31.7,1.2488,46.1063,1.1713,39.3642,46.458,79.47,27.5,26.8,995.5,0,641.5,308.6,74.3,0.995,0 -2013-08-20 17:10:12-06:00,n05667,251.3,32,1.1421,42.0118,1.0708,39.2332,46.2918,79.46,27.5,28,995.9,0,632.9,293.7,72.3,0.995,0 -2013-08-20 17:15:12-06:00,n05667,231.4,31.3,1.0313,37.8193,0.9663,39.1397,46.1105,79.53,27.4,27.4,995.7,0,612.3,276.4,71.5,0.995,0 -2013-08-20 17:20:12-06:00,n05667,215.6,30.9,0.941,34.4273,0.8813,39.0662,46.0292,79.48,27.3,27.5,995.8,0,606.6,263,69.2,0.995,0 -2013-08-20 17:25:13-06:00,n05667,194.2,30.7,0.8289,30.1457,0.7756,38.8659,45.7879,79.42,27.3,27.2,995.8,0,583.1,244.1,67.8,0.995,0 -2013-08-20 17:30:12-06:00,n05667,175.1,30.3,0.7283,26.3337,0.6805,38.6959,45.5687,79.35,27.2,27.4,995.9,0,555.8,226.2,65.6,0.995,0 -2013-08-20 17:35:12-06:00,n05667,158.4,29.8,0.6421,23.1073,0.5999,38.5187,45.3521,79.35,27.2,27.6,995.7,0,545.7,211.5,62.1,0.995,0 -2013-08-20 17:40:13-06:00,n05667,142,29.5,0.5602,20.0153,0.5227,38.2928,45.1212,79.19,27.1,27.8,995.9,0,520.5,195.6,61.2,0.995,0 -2013-08-20 17:45:12-06:00,n05667,126.7,29.3,0.4881,17.3105,0.4547,38.0719,44.8285,79.11,27.1,26.8,995.8,0,502.2,181.5,59.5,0.995,0 -2013-08-20 17:50:12-06:00,n05667,111.8,29,0.4238,14.8921,0.3939,37.8071,44.5976,78.79,27,27.4,995.8,0,487.4,167.5,56.7,0.995,0 -2013-08-20 17:55:12-06:00,n05667,97.8,28.8,0.3733,13.012,0.3465,37.5496,44.3582,78.58,27,27.2,995.7,0,474.1,155.9,54.7,0.995,0 -2013-08-20 18:00:12-06:00,n05667,84.2,28.5,0.3251,11.2621,0.302,37.2905,44.0896,78.57,26.9,27.2,995.8,0,449.4,141.3,53,0.995,0 -2013-08-20 18:05:13-06:00,n05667,72.1,28.3,0.2834,9.7961,0.2646,37.0216,43.8294,78.87,26.9,28.1,995.8,0,446.5,130.2,49.5,0.995,0 -2013-08-20 18:10:13-06:00,n05667,60.5,28.1,0.2489,8.5708,0.2332,36.7558,43.5803,79.02,26.9,27.4,995.7,0,417.2,115.7,46.6,0.995,0 -2013-08-20 18:15:13-06:00,n05667,50.6,27.8,0.2231,7.524,0.2062,36.4906,43.298,77.9,26.7,27.7,995.8,0,389.1,101.6,43.1,0.995,0 -2013-08-20 18:20:13-06:00,n05667,45.9,27.5,0.2069,6.9366,0.1908,36.3596,43.2108,77.59,26.6,26.4,995.8,0,102.1,52.2,40,0.995,0 -2013-08-20 18:25:13-06:00,n05667,37.4,27.4,0.1912,6.3895,0.1764,36.2318,43.0528,77.61,26.5,24.5,995.7,0,15.9,40.5,36.5,0.995,0 -2013-08-20 18:30:13-06:00,n05667,32.9,27,0.1753,5.8121,0.1611,36.0684,42.9123,77.26,26.5,25.1,995.8,0,11.3,34.9,32.8,0.995,0 -2013-08-20 18:35:13-06:00,n05667,29.2,26.7,0.158,5.1992,0.1451,35.8425,42.6742,77.1,26.5,25,995.9,0,19,36.5,29.1,0.995,0 -2013-08-20 18:40:13-06:00,n05667,25,26.5,0.1391,4.526,0.1273,35.549,42.4615,76.62,26.3,24.5,995.9,0,20.3,30.9,24.9,0.995,0 -2013-08-20 18:45:13-06:00,n05667,20.8,26.2,0.1204,3.8627,0.1096,35.2325,42.1103,76.21,26.1,24.3,995.9,0,77.7,22.9,21.2,0.995,0 -2013-09-01 06:05:13-06:00,n05667,23.1,14.7,0.1066,3.5602,0.0974,36.5708,43.5718,76.64,14.8,79.1,994.7,0,190,35.5,20.7,1,0 -2013-09-01 06:10:13-06:00,n05667,28.8,15.2,0.1233,4.1706,0.1129,36.9412,43.888,77.04,15,78.3,994.6,0,324.4,53.5,23.6,1,0 -2013-09-01 06:15:13-06:00,n05667,37.2,15.3,0.1414,4.8902,0.131,37.3411,44.1663,78.29,15.1,77.8,994.6,0,360.9,64.9,26.2,1,0 -2013-09-01 06:20:12-06:00,n05667,48.3,15.5,0.1714,5.9979,0.159,37.7303,44.5935,78.49,15.1,78,994.6,0,396.9,77.4,28.8,1,0 -2013-09-01 06:25:12-06:00,n05667,61.3,16,0.2134,7.5609,0.198,38.1902,45.0118,78.72,15.6,76.2,994.7,0,431.5,90.7,31.4,1,0 -2013-09-01 06:30:12-06:00,n05667,76,16.5,0.2658,9.5681,0.2477,38.6266,45.4386,79.22,15.9,74.6,994.8,0,464.2,104.8,33.8,1,0 -2013-09-01 06:35:12-06:00,n05667,91.1,16.9,0.3253,11.8586,0.3041,39.0017,45.8035,79.6,16.2,73.6,994.8,0,488.1,118.1,36,1,0 -2013-09-01 06:40:12-06:00,n05667,107.3,17.4,0.3952,14.5833,0.3708,39.3242,46.1612,79.94,16.2,73.1,994.8,0,514.6,132.4,37.9,1,0 -2013-09-01 06:45:12-06:00,n05667,124.5,17.9,0.4745,17.6503,0.4455,39.6191,46.4657,80.06,16.6,71.9,994.8,0,539,147.1,39.9,1,0 -2013-09-01 06:50:12-06:00,n05667,128.2,18.4,0.5186,19.4088,0.4885,39.7309,46.5827,80.35,16.8,71.5,994.8,0,457.8,140.7,42.5,1,0 -2013-09-01 06:55:12-06:00,n05667,160.8,18.8,0.6633,24.9586,0.6219,40.1352,47.0205,80.02,16.9,70.4,994.8,0,586.3,178.9,44,1,0 -2013-09-01 07:00:12-06:00,n05667,179.3,19.8,0.7667,29.3012,0.7272,40.2923,47.2099,80.96,17.1,70.4,994.7,0,612.2,193.6,43.4,1,0 -2013-09-01 07:05:12-06:00,n05667,198.9,20.5,0.8742,33.7065,0.8318,40.523,47.3761,81.38,17.5,69.1,994.7,0,632.6,209.4,44.4,1,0 -2013-09-01 07:10:12-06:00,n05667,218.7,21.7,0.9832,37.8328,0.9325,40.5723,47.4783,81.04,17.4,69.5,994.7,0,652.1,225.5,45.4,1,0 -2013-09-01 07:15:12-06:00,n05667,239.1,21.8,1.0827,41.6068,1.027,40.511,47.576,80.78,17,70.5,994.7,0,669.3,242,46.7,1,0 -2013-09-01 07:20:12-06:00,n05667,257.8,22.1,1.1907,45.6292,1.1239,40.5977,47.6774,80.37,16.6,72.7,994.6,0,679.4,256.1,47.7,1,0 -2013-09-01 07:25:12-06:00,n05667,279.4,22.6,1.3122,50.4341,1.2403,40.6631,47.8283,80.36,16.7,72.5,994.7,0,697.8,273.6,49,1,0 -2013-09-01 07:30:12-06:00,n05667,300,23.1,1.4285,54.9498,1.3498,40.7083,47.9129,80.28,16.8,72.9,994.7,0,712.2,290,50,1,0 -2013-09-01 07:35:12-06:00,n05667,320.4,24,1.5488,59.5847,1.4632,40.7232,47.9922,80.16,17.1,72,994.6,0,724.3,305.6,50.8,1,0 -2013-09-01 07:40:12-06:00,n05667,341.3,24.5,1.6691,64.2219,1.5773,40.7164,48.0404,80.09,17.2,71.2,994.6,0,737.5,322,51.6,1,0 -2013-09-01 07:45:12-06:00,n05667,362.2,24.6,1.7869,68.8647,1.6901,40.7456,48.1119,80.1,17.1,71,994.6,0,749.9,338.4,52.5,1,0 -2013-09-01 07:50:12-06:00,n05667,382.2,25.4,1.9047,73.3395,1.8001,40.7429,48.147,79.97,17.3,70.8,994.6,0,759.6,353.8,53,1,0 -2013-09-01 07:55:12-06:00,n05667,404,26.2,2.0285,78.0746,1.9173,40.7204,48.1789,79.89,17.5,70.1,994.4,0,771.3,369.8,53.5,1,0 -2013-09-01 08:00:12-06:00,n05667,424.5,26.4,2.146,82.5896,2.0302,40.6799,48.1939,79.86,17.6,69.4,994.3,0,781,385.2,53.9,1,0 -2013-09-01 08:05:12-06:00,n05667,445.1,26.5,2.2649,87.1405,2.1397,40.7253,48.2843,79.68,17.5,69.5,994.3,0,789.7,401.3,55,1,0 -2013-09-01 08:10:12-06:00,n05667,466.8,27,2.3814,91.5857,2.2497,40.7095,48.3233,79.59,17.7,69.4,994.3,0,797.1,416.3,55.8,1,0 -2013-09-01 08:15:12-06:00,n05667,488,27.8,2.5045,96.1647,2.3649,40.6634,48.324,79.46,17.8,69.5,994.2,0,806.7,432.1,56.3,1,0 -2013-09-01 08:20:12-06:00,n05667,506.4,29.2,2.6129,99.902,2.4661,40.5109,48.216,79.3,18.3,67,994.2,0,811.4,445.9,56.9,1,0 -2013-09-01 08:25:12-06:00,n05667,526.7,30.4,2.7224,103.8288,2.5692,40.4127,48.1737,79.17,18.5,66.4,994.2,0,818.7,460.9,57.5,1,0 -2013-09-01 08:30:12-06:00,n05667,546.4,30.9,2.8397,107.9338,2.6772,40.316,48.1126,79,18.7,65.7,994.2,0,825.4,475.7,58.2,1,0 -2013-09-01 08:35:12-06:00,n05667,565,31.5,2.9455,111.6167,2.7765,40.1999,48.0599,78.85,18.9,63.8,994.3,0,829.3,489.6,59.4,1,0 -2013-09-01 08:40:12-06:00,n05667,583.1,33.5,3.0475,115.1126,2.8723,40.0768,47.9805,78.73,19,64.3,994.3,0,832.9,502.7,60.2,1,0 -2013-09-01 08:45:12-06:00,n05667,602,33.2,3.1553,118.8205,2.9696,40.0128,47.9487,78.54,19.2,63.7,994.3,0,838.1,516.8,60.9,1,0 -2013-09-01 08:50:12-06:00,n05667,620.8,34.8,3.2616,122.3912,3.0695,39.8733,47.8706,78.39,19.5,62.6,994.3,0,844.4,531.1,61.3,1,0 -2013-09-01 08:55:12-06:00,n05667,638,35.7,3.3593,125.628,3.1599,39.7566,47.7861,78.26,19.6,61.4,994.3,0,847.7,543.3,61.7,1,0 -2013-09-01 09:00:12-06:00,n05667,651.9,37.1,3.4387,127.8565,3.2321,39.5589,47.6286,78.07,20.3,59.6,994.3,0,846.3,553.6,62.5,1,0 -2013-09-01 09:05:12-06:00,n05667,673.3,36.7,3.5567,132.3266,3.3432,39.5804,47.708,77.98,21,56.7,994.3,0,853.9,569,63.6,1,0 -2013-09-01 09:10:12-06:00,n05667,691.8,38,3.6586,135.7811,3.4384,39.4898,47.6475,77.89,21.3,55.3,994.3,0,854.2,581.9,66.9,1,0 -2013-09-01 09:15:13-06:00,n05667,707.9,40.1,3.7511,138.3456,3.5229,39.2702,47.4777,77.68,21.4,55,994.4,0,857.2,596.6,70.1,1,0 -2013-09-01 09:20:12-06:00,n05667,730.5,41.3,3.88,142.1043,3.6393,39.0471,47.319,77.4,21.2,56,994.4,0,865.6,614.3,72.5,1,0 -2013-09-01 09:25:12-06:00,n05667,743.6,40.5,3.9495,144.6779,3.7058,39.0405,47.3477,77.37,21.3,55.3,994.4,0,866.1,621.9,70.2,1,0 -2013-09-01 09:30:12-06:00,n05667,758.3,41.7,4.0278,147.498,3.7782,39.039,47.3763,77.3,20.7,56.6,994.3,0,874.5,632,66,1,0 -2013-09-01 09:35:12-06:00,n05667,773.3,41.7,4.1107,150.8804,3.8554,39.1344,47.494,77.28,21.1,55.2,994.3,0,878.4,644,67,1,0 -2013-09-01 09:40:12-06:00,n05667,791.1,39.9,4.1984,154.1446,3.9405,39.1183,47.5109,77.28,21.1,54.3,994.2,0,887.4,657.8,65.1,1,0 -2013-09-01 09:45:12-06:00,n05667,807.3,41.9,4.2887,157.0717,4.0242,39.0314,47.4699,77.15,21.7,52.3,994.2,0,893.1,668.7,64.1,1,0 -2013-09-01 09:50:12-06:00,n05667,823.4,41.5,4.3823,160.5165,4.1066,39.0875,47.5389,77.05,21.7,51.3,994.1,0,896.3,680.3,65.1,1,0 -2013-09-01 09:55:12-06:00,n05667,839,41.6,4.4492,163.4249,4.1805,39.092,47.5877,77.19,21.3,52.2,994.2,0,899,691.9,65.9,1,0 -2013-09-01 10:00:12-06:00,n05667,852.8,42.5,4.5374,165.334,4.2533,38.8717,47.4088,76.86,21.7,50.6,994.1,0,904.3,701.8,64.5,1,0 -2013-09-01 10:05:12-06:00,n05667,866.8,44.3,4.6166,167.5746,4.3245,38.7503,47.3242,76.7,21.8,50.2,994.1,0,910.1,711.5,63.6,1,0 -2013-09-01 10:10:12-06:00,n05667,879.6,45.6,4.6881,168.7632,4.387,38.4692,47.092,76.44,22.8,48,994,0,913.6,721.2,63.1,1,0 -2013-09-01 10:15:12-06:00,n05667,891.7,46.2,4.7552,171.2347,4.454,38.4452,47.0953,76.46,23,45.7,994,0,916.5,730.8,62.9,1,0 -2013-09-01 10:20:12-06:00,n05667,903.8,46.8,4.8194,173.3495,4.5112,38.4266,47.0992,76.37,23.4,44,993.9,0,921,739.7,62.3,1,0 -2013-09-01 10:25:12-06:00,n05667,917.6,45.8,4.8903,176.1979,4.5738,38.5229,47.2052,76.33,24,42.8,993.9,0,926.3,749.6,61.7,1,0 -2013-09-01 10:30:12-06:00,n05667,926,47.1,4.9448,176.9319,4.6211,38.2881,47.014,76.11,24.1,41.3,993.9,0,926.7,756.2,61.6,1,0 -2013-09-01 10:35:12-06:00,n05667,937.6,45.3,4.9963,179.1974,4.6729,38.3481,47.0966,76.15,23.2,44.1,993.8,0,928.8,765.6,61.8,1,0 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11:15:12-06:00,n05667,1005.5,51.8,5.378,189.5454,5.0141,37.8021,46.7076,75.46,25.5,35.4,993.6,0,947.7,815.8,58.8,1,0 -2013-09-01 11:20:12-06:00,n05667,1011.5,50.1,5.3989,190.5775,5.0342,37.8569,46.7694,75.48,26.5,33.8,993.5,0,947.5,819.9,59.4,1,0 -2013-09-01 11:25:12-06:00,n05667,1014.3,50.2,5.4171,191.8629,5.0544,37.9596,46.8806,75.55,26,34.2,993.5,0,946.5,822.1,59.8,1,0 -2013-09-01 11:30:12-06:00,n05667,1021,50.9,5.4533,192.7795,5.0856,37.9072,46.8436,75.47,25.7,35,993.4,0,949.1,826.8,59.2,1,0 -2013-09-01 11:35:12-06:00,n05667,1022.7,48.6,5.4676,193.9497,5.097,38.0517,46.986,75.5,26.1,33.7,993.4,0,947,828.9,59.7,1,0 -2013-09-01 11:40:12-06:00,n05667,1027.9,49.2,5.498,195.4987,5.1271,38.1305,47.0732,75.54,26,34.6,993.3,0,950.2,833,59.6,1,0 -2013-09-01 11:45:12-06:00,n05667,1029.8,49.3,5.5071,195.4921,5.1362,38.0617,47.0003,75.53,25.6,35.1,993.3,0,948.8,834.3,59.8,1,0 -2013-09-01 11:50:12-06:00,n05667,1034.9,48,5.5299,197.6922,5.1601,38.3115,47.2566,75.65,25.3,35.2,993.2,0,953.6,840.1,60.7,1,0 -2013-09-01 11:55:12-06:00,n05667,1034.6,51.3,5.5395,195.2882,5.1615,37.8355,46.7993,75.33,25.3,35.4,993.1,0,950,837.7,58.9,1,0 -2013-09-01 12:00:12-06:00,n05667,1040,49.8,5.5647,196.28,5.1852,37.8539,46.8338,75.31,26.2,32.8,992.9,0,954.9,841.5,58.4,1,0 -2013-09-01 12:05:12-06:00,n05667,1040.7,49.6,5.5617,196.3188,5.1849,37.8635,46.8462,75.35,26.7,31.4,993,0,954.9,842.3,58.4,1,0 -2013-09-01 12:10:12-06:00,n05667,1042.7,48.4,5.5759,196.7627,5.1947,37.8778,46.8579,75.31,26.6,32.4,992.8,0,957.5,844.1,58.2,1,0 -2013-09-01 12:15:12-06:00,n05667,1040.8,49.1,5.5717,196.5978,5.1922,37.8639,46.8462,75.32,26.6,32.9,992.8,0,956.5,842.2,58.3,1,0 -2013-09-01 12:20:12-06:00,n05667,1040.6,49.1,5.5667,197.1495,5.187,38.0083,46.975,75.39,26.5,33.8,992.8,0,956.1,844,59.3,1,0 -2013-09-01 12:25:12-06:00,n05667,1036.9,49.6,5.5579,196.7371,5.1803,37.9777,46.9307,75.43,25.9,34.7,992.8,0,954.2,841.9,60,1,0 -2013-09-01 12:30:12-06:00,n05667,1037.9,47.5,5.5525,197.3374,5.1707,38.1647,47.1083,75.44,25.3,36.9,992.8,0,953.6,841.7,60.4,1,0 -2013-09-01 12:35:12-06:00,n05667,1032.7,50.5,5.5387,194.7671,5.1571,37.7668,46.7323,75.25,26.1,35.6,992.7,0,952.4,837,58.5,1,0 -2013-09-01 12:40:12-06:00,n05667,1028.7,50.2,5.518,194.6593,5.1413,37.8619,46.8162,75.35,26.5,33.5,992.7,0,953.2,835.4,58.7,1,0 -2013-09-01 12:45:12-06:00,n05667,1024.8,52.2,5.4985,192.8516,5.1218,37.653,46.6166,75.24,26.7,33.5,992.7,0,952.9,831.5,57.4,1,0 -2013-09-01 12:50:12-06:00,n05667,1020.3,52.3,5.4751,191.5743,5.0985,37.5746,46.5281,75.2,27.4,31.7,992.6,0,952.4,828.8,57.1,1,0 -2013-09-01 12:55:12-06:00,n05667,1016,51.7,5.459,190.5282,5.0806,37.5011,46.4442,75.15,27.3,32,992.6,0,950.9,825.3,57.1,1,0 -2013-09-01 13:00:12-06:00,n05667,1010.5,53.7,5.4283,188.8988,5.0517,37.393,46.3362,75.1,27.4,31.5,992.5,0,950.8,822.3,57,1,0 -2013-09-01 13:05:12-06:00,n05667,1003.5,53,5.3908,187.6417,5.0177,37.3959,46.3187,75.15,27.6,32,992.5,0,948.2,817.6,57.4,1,0 -2013-09-01 13:10:12-06:00,n05667,998,51.7,5.3587,187.9232,4.9921,37.6443,46.5483,75.34,27.2,32.6,992.4,0,948.2,814,57.7,1,0 -2013-09-01 13:15:12-06:00,n05667,991.7,52.7,5.3254,187.2111,4.9617,37.7309,46.6075,75.43,27.3,32.5,992.4,0,945.6,809.8,57.8,1,0 -2013-09-01 13:20:12-06:00,n05667,985.6,51.1,5.2815,185.5696,4.923,37.6943,46.5678,75.45,26.9,32.8,992.4,0,946.9,805.1,57.4,1,0 -2013-09-01 13:25:12-06:00,n05667,974.8,50.9,5.2376,185.031,4.8839,37.8863,46.7284,75.6,26.7,32.9,992.3,0,942.9,797.3,57.9,1,0 -2013-09-01 13:30:12-06:00,n05667,966.3,50.6,5.1907,182.5756,4.8371,37.7446,46.5704,75.53,27.1,34.5,992.1,0,937.7,791,57.5,1,0 -2013-09-01 13:35:12-06:00,n05667,959.2,48.5,5.1397,182.6829,4.7952,38.0971,46.8871,75.81,26.4,35.7,992.1,0,939.7,787.4,60,1,0 -2013-09-01 13:40:12-06:00,n05667,949.4,47.3,5.0953,180.9259,4.7503,38.0869,46.8663,75.77,26.5,35.3,992,0,937.2,779.5,59.1,1,0 -2013-09-01 13:45:12-06:00,n05667,938,50.4,5.0446,177.8954,4.6984,37.8629,46.6289,75.63,26.5,34.7,992,0,936,770.4,57.9,1,0 -2013-09-01 13:50:12-06:00,n05667,931.9,48.9,4.9999,177.138,4.6623,37.9937,46.7343,75.81,26.6,35.8,991.9,0,937.4,766.3,58.2,1,0 -2013-09-01 13:55:12-06:00,n05667,920.6,47.5,4.937,175.6,4.6077,38.1097,46.8221,75.96,26.8,33.8,991.8,0,935.8,759,58.2,1,0 -2013-09-01 14:00:12-06:00,n05667,908.1,47.9,4.8667,173.9717,4.5517,38.2209,46.9001,76.22,26.5,34.8,991.9,0,933,749.7,58.1,1,0 -2013-09-01 14:05:12-06:00,n05667,896.7,45.2,4.8056,172.3706,4.492,38.3731,47.0205,76.28,26.5,34.7,991.8,0,931.3,741.4,58.3,1,0 -2013-09-01 14:10:12-06:00,n05667,882.7,46.9,4.7348,169.3828,4.423,38.2955,46.9112,76.26,26.5,35.7,991.8,0,927.1,730.7,57.8,1,0 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14:50:12-06:00,n05667,767.7,46.1,4.1143,147.6192,3.8465,38.3776,46.7551,76.74,27.3,34.2,991.4,0,902.5,647.3,55.9,1,0 -2013-09-01 14:55:12-06:00,n05667,751.7,46.5,4.0319,144.426,3.7699,38.3102,46.6406,76.8,27.5,34.3,991.4,0,900.2,635,55.1,1,0 -2013-09-01 15:00:12-06:00,n05667,735.1,46.9,3.9372,141.463,3.6841,38.3985,46.6836,76.96,27.5,34.1,991.3,0,895,621.8,55.3,1,0 -2013-09-01 15:05:12-06:00,n05667,720.2,43.1,3.8521,139.37,3.6048,38.6619,46.9132,77.12,27.4,33.8,991.1,0,893.8,612.8,55.5,1,0 -2013-09-01 15:10:12-06:00,n05667,704.4,42,3.7597,137.0061,3.5196,38.9264,47.1175,77.34,27.1,34.9,991.2,0,890.8,601.5,55.5,1,0 -2013-09-01 15:15:12-06:00,n05667,681.6,42.5,3.6574,133.3842,3.4282,38.9084,47.055,77.5,27.2,35.3,991.2,0,886.1,587.5,54.7,1,0 -2013-09-01 15:20:12-06:00,n05667,668.8,44.8,3.5682,129.1357,3.3427,38.632,46.7656,77.39,27.6,34.9,991.2,0,884.1,574.1,53.5,1,0 -2013-09-01 15:25:12-06:00,n05667,649.3,44.8,3.4611,125.2584,3.242,38.6356,46.7217,77.46,28,33.7,991.2,0,877.4,559.5,53,1,0 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16:05:12-06:00,n05667,492.8,39.8,2.5666,94.1839,2.4058,39.1492,46.8331,78.36,27.7,33.9,991,0,822.1,442.6,51.3,1,0 -2013-09-01 16:10:12-06:00,n05667,473.4,39.1,2.4557,90.1884,2.3019,39.1802,46.8184,78.44,27.8,33.5,990.9,0,817.3,428,50.1,1,0 -2013-09-01 16:15:12-06:00,n05667,452.5,39.5,2.3466,86.0324,2.1993,39.1189,46.7083,78.49,28.1,33.3,990.9,0,812.8,413.4,48.7,1,0 -2013-09-01 16:20:12-06:00,n05667,432.1,38.4,2.2275,81.8759,2.0897,39.1815,46.7073,78.69,28.4,33,990.8,0,805.5,398.5,47.8,1,0 -2013-09-01 16:25:12-06:00,n05667,412.5,38.9,2.1103,77.6507,1.9804,39.2089,46.6851,78.82,28.4,33,990.9,0,799.3,383.7,47.1,1,0 -2013-09-01 16:30:12-06:00,n05667,392.3,37.6,1.9913,73.3243,1.8688,39.2369,46.668,78.9,28.4,33.3,990.9,0,790,368.3,47,1,0 -2013-09-01 16:35:12-06:00,n05667,371,35.7,1.8695,69.012,1.7543,39.3389,46.7101,79.03,28.1,34.8,990.9,0,779.4,352.2,46.2,1,0 -2013-09-01 16:40:12-06:00,n05667,349.5,34.9,1.7399,64.6203,1.6349,39.5262,46.8219,79.32,27.6,36.3,991.1,0,766.2,336.7,46.5,1,0 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17:20:12-06:00,n05667,189.6,29.6,0.8125,29.6937,0.7609,39.0228,45.9224,79.58,25.6,40.3,991.3,0,661.4,209.4,38.5,1,0 -2013-09-01 17:25:12-06:00,n05667,170,29.6,0.7067,25.6587,0.6617,38.7747,45.654,79.53,25.7,40.8,991.4,0,641.3,192.6,37.1,1,0 -2013-09-01 17:30:12-06:00,n05667,152.7,28.3,0.6134,22.1552,0.573,38.6681,45.4991,79.39,25.4,40.6,991.4,0,625.6,178.4,36.1,1,0 -2013-09-01 17:35:12-06:00,n05667,134.6,27.9,0.519,18.5766,0.4835,38.4199,45.2214,79.15,25.1,41.3,991.5,0,605,162.7,34.5,1,0 -2013-09-01 17:40:12-06:00,n05667,117.6,27.4,0.433,15.3342,0.4026,38.0905,44.861,78.94,25.1,41.1,991.5,0,582.2,147.3,32.8,1,0 -2013-09-01 17:45:12-06:00,n05667,101.5,26.9,0.3549,12.4297,0.3293,37.75,44.5085,78.68,24.9,41.4,991.5,0,557.3,132.7,31.6,1,0 -2013-09-01 17:50:12-06:00,n05667,86.2,26.5,0.2866,9.8876,0.2649,37.321,44.0811,78.26,24.8,41.7,991.6,0,532.8,118,29.8,1,0 -2013-09-01 17:55:13-06:00,n05667,69.2,26.1,0.2272,7.7284,0.2099,36.8187,43.6135,78,24.6,42.2,991.7,0,505.2,103.9,28,1,0 -2013-09-01 18:00:13-06:00,n05667,53.8,25.7,0.1765,5.9509,0.1638,36.3315,43.0931,78.24,24.4,42.7,991.8,0,471,90.1,26.1,1,0 -2013-09-01 18:05:13-06:00,n05667,42.6,25.4,0.1391,4.6313,0.1295,35.7677,42.6306,78.1,24.3,43.2,991.8,0,437.2,76.1,23.8,1,0 -2013-09-01 18:10:13-06:00,n05667,30.5,25.1,0.1163,3.7516,0.1063,35.2988,42.2176,76.41,24.2,43.4,991.9,0,402.8,63.5,21.5,1,0 -2013-09-01 18:15:13-06:00,n05667,25.5,24.8,0.1028,3.2751,0.0934,35.0692,41.9557,75.93,24,43.6,991.9,0,363.8,51.5,19.1,1,0 -2013-10-06 06:30:13-06:00,n05667,42.5,7.8,0.1696,6.1294,0.1575,38.9113,45.7185,79.06,7.7,85.4,999.6,0,202.9,20.1,11.3,1,0 -2013-10-06 06:35:12-06:00,n05667,60.7,8,0.2484,9.2712,0.2333,39.7322,46.4842,80.29,7.7,85.6,999.6,0,269.5,29.3,14.1,1,0 -2013-10-06 06:40:12-06:00,n05667,77,8.3,0.3256,12.3905,0.3074,40.3049,47.0423,80.89,7.7,85.1,999.6,0,308.1,38.6,17,1,0 -2013-10-06 06:45:12-06:00,n05667,100.3,8.6,0.4355,16.854,0.4124,40.8652,47.6238,81.25,7.6,85.7,999.6,0,370.9,50.9,19.8,1,0 -2013-10-06 06:50:12-06:00,n05667,120.8,8.9,0.5379,21.0261,0.5102,41.208,47.9881,81.46,7.8,85.1,999.7,0,413.7,62.8,22.3,1,0 -2013-10-06 06:55:12-06:00,n05667,142.9,9.3,0.6548,25.7505,0.6206,41.4954,48.342,81.35,8.2,83.6,999.6,0,454.9,75.3,24.6,1,0 -2013-10-06 07:00:12-06:00,n05667,165.3,10.2,0.7646,30.2617,0.7265,41.6552,48.5653,81.49,8.6,81.6,999.7,0,492.5,88.7,26.8,1,0 -2013-10-06 07:05:12-06:00,n05667,188.6,10.6,0.8829,35.0278,0.8373,41.835,48.7488,81.39,9.2,78.9,999.7,0,531.3,103.3,28.7,1,0 -2013-10-06 07:10:12-06:00,n05667,187.3,11.2,0.8892,35.2024,0.8431,41.7555,48.6811,81.33,9.2,79.2,999.8,0,486.3,105.4,30.3,1,0 -2013-10-06 07:15:12-06:00,n05667,236.2,11.4,1.1302,45.3409,1.0775,42.0782,49.0852,81.73,9,80,999.8,0,593.4,133,32.8,1,0 -2013-10-06 07:20:12-06:00,n05667,256.3,11.8,1.242,49.7445,1.1801,42.1513,49.2234,81.37,8.7,81.3,999.8,0,609.9,146,34.4,1,0 -2013-10-06 07:25:12-06:00,n05667,250.6,11.8,1.2353,49.4826,1.1751,42.1089,49.1837,81.45,8.4,82,999.9,0,561.2,146.3,35.7,1,0 -2013-10-06 07:30:12-06:00,n05667,302.6,12.2,1.5122,60.9131,1.4378,42.3654,49.5246,81.34,8.2,82.8,999.8,0,657,176.4,37.5,1,0 -2013-10-06 07:35:12-06:00,n05667,325.3,12.9,1.6507,66.4061,1.5652,42.4263,49.5955,81.12,8.3,82.6,999.8,0,675.8,191.4,38.9,1,0 -2013-10-06 07:40:12-06:00,n05667,347,13.2,1.7849,72.0742,1.6935,42.5601,49.7158,81.22,8.4,82.1,999.8,0,692.2,206,40.1,1,0 -2013-10-06 07:45:12-06:00,n05667,369.9,13.8,1.9047,77.1681,1.8099,42.6362,49.7789,81.39,8.6,81.6,999.8,0,708.4,220.9,41.2,1,0 -2013-10-06 07:50:12-06:00,n05667,392,13.9,2.0251,82.0163,1.9306,42.4831,49.8244,81.29,8.7,81.1,999.8,0,723.5,236,42.3,1,0 -2013-10-06 07:55:12-06:00,n05667,415.2,14.4,2.136,86.7301,2.0412,42.4889,49.8628,81.43,8.7,81.1,999.8,0,738.8,251.2,43.3,1,0 -2013-10-06 08:00:12-06:00,n05667,438.2,15,2.2781,91.7692,2.168,42.3289,49.801,80.89,8.9,81.2,999.8,0,752.7,266.1,44.2,1,0 -2013-10-06 08:05:12-06:00,n05667,460.3,17.1,2.3943,96.0211,2.279,42.1336,49.6878,80.71,9.1,80.6,999.8,0,765.1,280.6,44.9,1,0 -2013-10-06 08:10:12-06:00,n05667,481.9,18.5,2.513,100.3114,2.391,41.9541,49.548,80.56,9.3,80.4,999.8,0,776.7,295.4,45.7,1,0 -2013-10-06 08:15:13-06:00,n05667,503.9,19.9,2.642,104.8655,2.5104,41.7727,49.4491,80.27,9.5,79.9,999.8,0,787.8,310.1,46.6,1,0 -2013-10-06 08:20:12-06:00,n05667,527.9,20.9,2.7545,109.1423,2.6182,41.6853,49.3958,80.22,9.7,78.7,999.8,0,797.6,324.5,47.3,1,0 -2013-10-06 08:25:12-06:00,n05667,548.4,21.2,2.8781,113.7059,2.7327,41.6096,49.3802,80.01,9.9,78.6,999.8,0,807.6,339.4,48.1,1,0 -2013-10-06 08:30:12-06:00,n05667,568.2,22.9,2.9945,117.7038,2.8422,41.4124,49.2318,79.84,10.3,77.8,999.9,0,816.8,353.4,48.8,1,0 -2013-10-06 08:35:12-06:00,n05667,588.9,24.2,3.1228,122.0485,2.9573,41.2698,49.1531,79.51,10.7,76.7,1000,0,825.4,367.7,49.5,1,0 -2013-10-06 08:40:12-06:00,n05667,609.4,25.4,3.2354,126.004,3.0646,41.1162,49.0458,79.41,11.2,74.2,1000,0,832.2,381,50.3,1,0 -2013-10-06 08:45:12-06:00,n05667,628.3,27,3.337,129.4594,3.157,41.0075,48.9834,79.2,11.2,74.4,1000.1,0,837.6,393.9,51.2,1,0 -2013-10-06 08:50:12-06:00,n05667,646.6,27.7,3.4408,132.7823,3.2484,40.8765,48.8968,78.92,11.6,74.1,1000.1,0,843.3,406.9,52,1,0 -2013-10-06 08:55:12-06:00,n05667,665.9,28.6,3.5451,136.3683,3.3498,40.7095,48.7869,78.85,12,72,1000.1,0,848.6,419.8,52.8,1,0 -2013-10-06 09:00:12-06:00,n05667,684.6,29.1,3.652,139.9642,3.4485,40.5873,48.7043,78.69,12.4,71.9,1000.2,0,856.4,433.3,53.3,1,0 -2013-10-06 09:05:12-06:00,n05667,702.7,30.3,3.7539,143.4464,3.5424,40.4938,48.6627,78.53,12.7,68.9,1000.2,0,859.9,445.4,54,1,0 -2013-10-06 09:10:12-06:00,n05667,723.5,31.2,3.8648,147.3903,3.6438,40.4492,48.6666,78.36,12.8,68.9,1000.3,0,869,459.6,54.7,1,0 -2013-10-06 09:15:12-06:00,n05667,741.2,32.3,3.9568,150.0971,3.7287,40.2551,48.5033,78.21,13.3,67.5,1000.2,0,874,471.8,55.2,1,0 -2013-10-06 09:20:12-06:00,n05667,759.1,32.9,4.0561,153.5492,3.8215,40.1809,48.4675,78.11,13.5,65.7,1000.1,0,878.9,484.2,56,1,0 -2013-10-06 09:25:12-06:00,n05667,776.3,32,4.1467,157.4034,3.9044,40.3148,48.6275,78.06,13.2,67.1,1000.1,0,882.8,496.6,57.5,1,0 -2013-10-06 09:30:12-06:00,n05667,792.1,31.1,4.234,161.193,3.989,40.4095,48.7628,78.07,13.3,67.5,1000.1,0,885.1,507.9,58.5,1,0 -2013-10-06 09:35:12-06:00,n05667,808.7,30.4,4.3085,164.3804,4.0633,40.4554,48.835,78.13,13.6,66.4,1000.1,0,888.3,519.2,59.5,1,0 -2013-10-06 09:40:12-06:00,n05667,822.8,31.9,4.3997,166.8584,4.1388,40.3155,48.7349,77.82,13.8,67,1000,0,890.4,529.3,60.8,1,0 -2013-10-06 09:45:12-06:00,n05667,836.2,32.8,4.48,168.754,4.2146,40.04,48.4955,77.67,14.3,64.4,999.9,0,893,539.5,61.9,1,0 -2013-10-06 09:50:12-06:00,n05667,849.9,33,4.5396,171.0421,4.2773,39.9881,48.4805,77.72,14.5,63.3,999.9,0,894.1,549.1,62.8,1,0 -2013-10-06 09:55:12-06:00,n05667,863.8,35.4,4.6222,172.8322,4.3432,39.7936,48.3212,77.38,15,62.1,1000,0,896.6,558.8,63.4,1,0 -2013-10-06 10:00:12-06:00,n05667,878,37,4.71,174.9907,4.421,39.5814,48.1598,77.15,15.4,61,999.9,0,900.8,568.6,63.7,1,0 -2013-10-06 10:05:12-06:00,n05667,891.6,39.6,4.7803,176.3927,4.485,39.3293,47.9452,76.96,15.9,59.5,999.9,0,903.6,577.9,64.3,1,0 -2013-10-06 10:10:12-06:00,n05667,904,41,4.8515,177.5071,4.5485,39.0256,47.6856,76.73,16.4,56.8,999.9,0,906.1,586.4,64.7,1,0 -2013-10-06 10:15:12-06:00,n05667,916.1,42.4,4.914,179.5036,4.607,38.963,47.6394,76.68,17.4,53.5,999.8,0,907.9,595,65.6,1,0 -2013-10-06 10:20:12-06:00,n05667,926.7,41.9,4.9754,181.4318,4.6602,38.9319,47.6394,76.55,18,51.2,999.8,0,907.6,602.7,67,1,0 -2013-10-06 10:25:12-06:00,n05667,937.2,42.7,5.031,182.941,4.7117,38.8273,47.5523,76.47,17.8,52,999.8,0,908.1,610.1,68.2,1,0 -2013-10-06 10:30:12-06:00,n05667,947,43.8,5.087,184.0121,4.7589,38.667,47.4183,76.28,17.8,52,999.7,0,908.1,616.9,69.2,1,0 -2013-10-06 10:35:13-06:00,n05667,955.5,43.9,5.1343,185.4653,4.8021,38.6218,47.3877,76.23,18.7,49.4,999.7,0,908.4,623.5,70.4,1,0 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11:15:12-06:00,n05667,1027.7,41.7,5.5021,199.9963,5.1436,38.8825,47.7897,76.06,18.8,50.3,999.2,0,935.1,675,70.8,1,0 -2013-10-06 11:20:12-06:00,n05667,1031.2,41.1,5.5252,200.3851,5.1679,38.7752,47.6999,76.03,19.1,48.8,999.2,0,934.8,677.2,70.5,1,0 -2013-10-06 11:25:12-06:00,n05667,1035.5,42.3,5.5482,200.4696,5.1835,38.6749,47.6115,75.89,19.3,48.7,999.2,0,935.6,680.2,70.6,1,0 -2013-10-06 11:30:12-06:00,n05667,1039.9,43,5.5706,201.0309,5.2048,38.6242,47.5705,75.86,19.7,47.4,999.2,0,936.4,683,70.7,1,0 -2013-10-06 11:35:12-06:00,n05667,1041.9,45.7,5.5865,200.3051,5.214,38.4168,47.3701,75.69,20.1,46.9,999.1,0,936.3,683.8,70.5,1,0 -2013-10-06 11:40:12-06:00,n05667,1044.3,45.2,5.6001,200.4176,5.2251,38.357,47.3148,75.64,20.4,45.4,998.9,0,936.6,686.4,70.9,1,0 -2013-10-06 11:45:12-06:00,n05667,1044.9,42.9,5.5996,201.7309,5.2317,38.5592,47.5145,75.82,20.3,45.1,998.8,0,934.4,687.6,71.8,1,0 -2013-10-06 11:50:12-06:00,n05667,1043.6,45.1,5.5981,200.7844,5.2274,38.4102,47.3708,75.72,20.7,45.3,998.8,0,932.2,686.2,71.7,1,0 -2013-10-06 11:55:12-06:00,n05667,1044.4,44.2,5.6012,201.2605,5.2274,38.501,47.4612,75.71,20.6,44.3,998.7,0,932.8,687.7,72.1,1,0 -2013-10-06 12:00:12-06:00,n05667,1046.5,44,5.6169,201.8726,5.2452,38.4874,47.4554,75.73,20.9,44.2,998.6,0,933.7,689,72.5,1,0 -2013-10-06 12:05:12-06:00,n05667,1043.2,44.3,5.5991,201.4607,5.2303,38.5181,47.4827,75.78,21.1,43.5,998.5,0,929.9,687.2,73.3,1,0 -2013-10-06 12:10:12-06:00,n05667,1044.3,45.8,5.6048,200.6567,5.2325,38.3478,47.3096,75.67,21.3,42.8,998.5,0,930.8,687.9,74.2,1,0 -2013-10-06 12:15:12-06:00,n05667,1038.8,46.6,5.58,199.293,5.2084,38.2638,47.2244,75.63,21.3,43.5,998.4,0,924.7,684.3,75.3,1,0 -2013-10-06 12:20:12-06:00,n05667,1035.2,48.1,5.5717,197.4636,5.1938,38.0194,46.9759,75.44,21.6,42.9,998.3,0,923.5,681.6,74.9,1,0 -2013-10-06 12:25:13-06:00,n05667,1030.9,49.6,5.5542,196.3641,5.1752,37.9436,46.9102,75.37,21.9,42,998.2,0,921.7,679.4,75.1,1,0 -2013-10-06 12:30:13-06:00,n05667,1024.7,47.6,5.5247,196.0929,5.152,38.0619,47.0072,75.51,21.9,41.7,998.1,0,918.3,676,75.3,1,0 -2013-10-06 12:35:12-06:00,n05667,1025.1,47,5.5226,196.5853,5.1514,38.1618,47.1002,75.58,22,41.3,998.1,0,921.6,676.1,75.3,1,0 -2013-10-06 12:40:12-06:00,n05667,1013.9,47.8,5.4676,194.2022,5.0989,38.087,47.0072,75.56,22.5,42.2,998.1,0,915.1,667.8,74.3,1,0 -2013-10-06 12:45:12-06:00,n05667,1009.1,45.3,5.4348,195.0582,5.0758,38.4294,47.3217,75.84,22.8,39.8,998,0,913,664.3,75.3,1,0 -2013-10-06 12:50:12-06:00,n05667,1001.4,45.8,5.3926,193.455,5.0356,38.4177,47.294,75.85,22.7,40.4,998,0,909,658.8,75.5,1,0 -2013-10-06 12:55:12-06:00,n05667,995.8,46.8,5.373,191.8562,5.0144,38.2607,47.1405,75.75,23,39.9,998.1,0,908.9,654.9,75.2,1,0 -2013-10-06 13:00:13-06:00,n05667,988.6,47.1,5.3314,190.101,4.9733,38.2245,47.0846,75.73,23.1,37,998,0,906.9,650.2,75.7,1,0 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13:40:12-06:00,n05667,923.5,42.5,4.9738,180.7267,4.6517,38.8514,47.5334,76.44,25.5,24.2,997.7,0,902.9,604.3,70.6,1,0 -2013-10-06 13:45:12-06:00,n05667,914.2,42.9,4.9226,179.3485,4.6061,38.9373,47.5972,76.55,25.3,24.4,997.7,0,903,597.6,70.3,1,0 -2013-10-06 13:50:12-06:00,n05667,902,44.7,4.8641,176.5293,4.5498,38.7992,47.4404,76.5,25.7,22.5,997.6,0,901.5,589.5,69.6,1,0 -2013-10-06 13:55:12-06:00,n05667,889.3,42.2,4.7913,174.5694,4.4846,38.9267,47.5341,76.65,25.5,22.7,997.7,0,896.2,580.1,69.9,1,0 -2013-10-06 14:00:12-06:00,n05667,880.2,42,4.7337,172.9274,4.431,39.0265,47.6128,76.73,25.6,21.7,997.6,0,897.3,573.3,69.5,1,0 -2013-10-06 14:05:12-06:00,n05667,864.9,40,4.6509,170.7669,4.3563,39.1997,47.7448,76.9,25.5,21.1,997.6,0,892.6,563,69.2,1,0 -2013-10-06 14:10:12-06:00,n05667,849.5,40,4.5648,168.1625,4.2798,39.2924,47.8073,77.06,25.4,22,997.6,0,885.3,552,69.7,1,0 -2013-10-06 14:15:12-06:00,n05667,831.4,41,4.4786,164.4709,4.1969,39.1888,47.6524,77.07,25.6,22,997.6,0,878,539.8,69.3,1,0 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14:55:12-06:00,n05667,690.7,42.1,3.7187,136.1469,3.4884,39.0286,47.2113,77.55,26.2,20.6,997.3,0,826.5,443,66.4,1,0 -2013-10-06 15:00:12-06:00,n05667,669.8,39.3,3.6026,132.6245,3.382,39.2144,47.3377,77.77,26.3,20.5,997.3,0,814.4,428.5,66.3,1,0 -2013-10-06 15:05:12-06:00,n05667,651.7,40.2,3.5113,129.1191,3.2945,39.1927,47.2716,77.79,26.3,20.7,997.3,0,809.2,416.1,65.6,1,0 -2013-10-06 15:10:12-06:00,n05667,631.3,39.9,3.3972,125.1944,3.1889,39.2596,47.2951,77.92,26.3,20.8,997.3,0,799.6,402.3,65.4,1,0 -2013-10-06 15:15:12-06:00,n05667,612.3,38.6,3.2907,121.8303,3.0916,39.4065,47.3869,78.13,26.1,21.1,997.2,0,792.5,389.4,64.7,1,0 -2013-10-06 15:20:12-06:00,n05667,587.8,38.4,3.1563,116.8894,2.9656,39.4154,47.3419,78.23,25.7,22,997.1,0,778.1,373,63.7,1,0 -2013-10-06 15:25:12-06:00,n05667,565.1,36.2,3.0246,112.6525,2.843,39.624,47.497,78.42,25.8,21.9,997,0,762.6,358.1,64,1,0 -2013-10-06 15:30:12-06:00,n05667,545.5,36.9,2.9174,108.6229,2.7425,39.6071,47.4344,78.49,25.9,22,997,0,753.5,344.3,63.3,1,0 -2013-10-06 15:35:12-06:00,n05667,526.5,35.6,2.8083,104.8796,2.6409,39.7138,47.4809,78.65,25.8,21.4,997,0,745.3,331.7,63,1,0 -2013-10-06 15:40:12-06:00,n05667,502.1,35.2,2.6751,100.0757,2.516,39.7761,47.4821,78.79,25.8,21.2,996.9,0,730.9,316.4,62.4,1,0 -2013-10-06 15:45:12-06:00,n05667,478.2,33.4,2.5465,95.7717,2.3974,39.9489,47.602,79.01,25.5,20.6,996.8,0,715.5,301.2,61.9,1,0 -2013-10-06 15:50:12-06:00,n05667,454.4,34.4,2.4165,90.6572,2.274,39.8676,47.4698,79.03,25.5,22.2,996.9,0,699.9,284.9,60.4,1,0 -2013-10-06 15:55:12-06:00,n05667,434.3,34.4,2.3028,86.3008,2.1681,39.8046,47.356,79.14,25.4,22.5,996.8,0,691.7,271.3,58.7,1,0 -2013-10-06 16:00:12-06:00,n05667,410.7,34.1,2.1721,81.3535,2.0458,39.7662,47.2628,79.25,25.3,22.8,996.8,0,675.5,255.5,57.2,1,0 -2013-10-06 16:05:12-06:00,n05667,386.9,33.4,2.0332,76.2895,1.9159,39.8193,47.2548,79.4,25.3,23.3,996.8,0,655.6,240.1,56.4,1,0 -2013-10-06 16:10:12-06:00,n05667,360.9,33.6,1.8936,70.8278,1.7835,39.7117,47.0859,79.44,25.2,24.2,996.8,0,631.8,222.7,54.8,1,0 -2013-10-06 16:15:12-06:00,n05667,338.3,33.5,1.7637,65.9491,1.6619,39.6829,47.0221,79.52,25.2,25,996.9,0,614.5,208.3,53.6,1,0 -2013-10-06 16:20:12-06:00,n05667,315.5,33,1.638,61.1487,1.5423,39.6466,46.9242,79.55,25.2,25.6,996.9,0,595.9,193.4,52.1,1,0 -2013-10-06 16:25:12-06:00,n05667,291.7,31.8,1.502,56.1297,1.4146,39.6788,46.8878,79.7,24.9,26.1,996.9,0,571.4,177.8,50.5,1,0 -2013-10-06 16:30:12-06:00,n05667,269.5,31,1.3748,51.3243,1.2946,39.6445,46.8093,79.75,24.7,26,997,0,548.7,163.3,49,1,0 -2013-10-06 16:35:12-06:00,n05667,245.9,30.3,1.2421,46.3287,1.1687,39.6397,46.7353,79.81,24.4,26.6,997,0,523.1,148.5,47.2,1,0 -2013-10-06 16:40:13-06:00,n05667,221.7,29.8,1.1075,41.229,1.0423,39.5543,46.5911,79.9,24.2,27,997,0,493.4,132.7,44.5,1,0 -2013-10-06 16:45:12-06:00,n05667,203.1,29,1.0027,37.2696,0.9441,39.4771,46.4606,80,24.1,28.1,997.1,0,477.2,120.3,42.1,1,0 -2013-10-06 16:50:12-06:00,n05667,179.2,28,0.8721,32.3164,0.821,39.363,46.312,80.01,23.6,29.1,997.2,0,440.7,105.4,39.7,1,0 -2013-10-06 16:55:13-06:00,n05667,156.4,26.5,0.7482,27.6312,0.7036,39.2691,46.1368,80.04,23.2,29,997.1,0,404.4,91.9,37.3,1,0 -2013-10-06 17:00:12-06:00,n05667,132.8,25.6,0.6277,23.0328,0.5893,39.0866,45.9566,79.84,22.6,30.3,997.3,0,362.1,77.6,34.2,1,0 -2013-10-06 17:05:13-06:00,n05667,94.7,24.5,0.3655,12.3538,0.3108,39.7528,44.9685,75.16,22.4,30.7,997.3,0,253.3,56.3,30.8,1,0 -2013-10-06 17:10:13-06:00,n05667,34.6,23.3,0.1899,6.269,0.1694,37.0067,43.5316,75.83,22,31.9,997.3,0,39.5,31.2,27.3,1,0 -2013-10-06 17:15:13-06:00,n05667,32.2,22.3,0.1509,5.075,0.1393,36.4371,43.2011,77.87,21.6,33,997.3,0,115,33.1,22.4,1,0 -2013-11-21 07:40:12-06:00,n05667,66.7,-4.1,0.3287,12.8631,0.3039,42.325,48.8019,80.19,-2.9,72.1,1012.6,0,89.8,21.5,16.1,1,0 -2013-11-21 07:45:13-06:00,n05667,197.7,-2.2,1.0722,45.2605,1.0283,44.0137,50.9077,82.92,-2.7,71.8,1012.7,0,404.4,45.4,18.2,1,0 -2013-11-21 07:50:12-06:00,n05667,227.8,-0.5,1.2435,52.3387,1.1908,43.9533,50.9322,82.64,-2.5,71.5,1012.7,0,450.3,56.1,20.4,1,0 -2013-11-21 07:55:12-06:00,n05667,254.5,0.6,1.3861,58.601,1.3333,43.9514,50.988,82.92,-2.2,70.8,1012.6,0,483.9,67,22.5,1,0 -2013-11-21 08:00:12-06:00,n05667,284.7,1.5,1.5488,65.5061,1.4893,43.9851,51.0751,82.81,-2,70.2,1012.7,0,521.5,78.9,24.6,1,0 -2013-11-21 08:05:12-06:00,n05667,313.5,2.2,1.7054,71.4062,1.6195,44.0928,51.1521,81.86,-2,67.9,1012.7,0,556.1,91.3,26.6,1,0 -2013-11-21 08:10:12-06:00,n05667,343,2.6,1.8337,77.54,1.7625,43.9935,51.2236,82.55,-1.8,69,1012.8,0,589.9,104.1,28.4,1,0 -2013-11-21 08:15:12-06:00,n05667,370.4,3.7,1.9852,83.5962,1.9042,43.9,51.2009,82.24,-1.4,66.8,1012.7,0,616.8,116.5,29.9,1,0 -2013-11-21 08:20:12-06:00,n05667,398.4,4.7,2.1434,89.5568,2.0388,43.9259,51.1814,81.64,-1.1,65.2,1012.8,0,644.5,129.6,31.5,1,0 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09:10:12-06:00,n05667,619,17.8,3.3138,133.2517,3.1477,42.3332,50.1959,80.11,0.1,59.5,1013.2,0,790,251.3,44.1,1,0 -2013-11-21 09:15:12-06:00,n05667,638.3,18.6,3.4224,136.8756,3.2455,42.1739,50.1146,79.81,-0.6,62.7,1013.3,0,801.5,263.3,44.8,1,0 -2013-11-21 09:20:12-06:00,n05667,661.1,19.6,3.5458,141.3802,3.3616,42.0579,50.0482,79.67,-0.1,62.9,1013.2,0,815.6,274.9,45.2,1,0 -2013-11-21 09:25:12-06:00,n05667,682.1,19.6,3.647,145.86,3.461,42.1436,50.1816,79.7,0,60.2,1013.1,0,828.5,287,45.9,1,0 -2013-11-21 09:30:12-06:00,n05667,700.5,19.4,3.7498,149.7633,3.5537,42.1424,50.218,79.53,0.2,60.2,1013.2,0,837.3,297.7,46.4,1,0 -2013-11-21 09:35:12-06:00,n05667,721,19.6,3.8559,153.8411,3.6545,42.096,50.2069,79.47,0.7,58.3,1013.3,0,849.5,309.4,46.7,1,0 -2013-11-21 09:40:12-06:00,n05667,735.5,21.7,3.9411,156.6134,3.7359,41.9215,50.0749,79.36,0.7,57.6,1013.4,0,853.4,319,47.5,1,0 -2013-11-21 09:45:12-06:00,n05667,747.4,21.5,3.9994,158.5642,3.7871,41.8695,50.0515,79.21,1,58.3,1013.3,0,856.3,327.9,48.6,1,0 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10:25:12-06:00,n05667,862.9,25.6,4.6118,180.2035,4.3566,41.363,49.8082,78.45,1.6,56.4,1013.4,0,902.6,397.3,51.7,1,0 -2013-11-21 10:30:12-06:00,n05667,872,25.5,4.6618,181.731,4.399,41.312,49.777,78.31,1.5,55.5,1013.4,0,905.2,403.7,51.9,1,0 -2013-11-21 10:35:12-06:00,n05667,882.7,24.9,4.7034,184.0636,4.4444,41.4151,49.8875,78.44,1.3,55.5,1013.5,0,908.5,410.8,52.2,1,0 -2013-11-21 10:40:12-06:00,n05667,893.4,22.3,4.7686,187.075,4.4998,41.574,50.0632,78.36,0.8,56.8,1013.4,0,914.3,417.9,52.4,1,0 -2013-11-21 10:45:12-06:00,n05667,898.4,24.5,4.7947,187.3958,4.5309,41.3594,49.8726,78.37,1,59.1,1013.4,0,914.4,422.3,52.3,1,0 -2013-11-21 10:50:12-06:00,n05667,907.3,24.6,4.8512,189.2719,4.5749,41.3716,49.9097,78.17,1.2,58,1013.5,0,915.6,427.4,52.8,1,0 -2013-11-21 11:00:12-06:00,n05667,927,21.4,4.9315,194.5983,4.6599,41.7603,50.3013,78.45,1.1,55.4,1013.4,0,925.2,439.8,53.8,1,0 -2013-11-21 11:05:12-06:00,n05667,931.9,20.8,4.9658,196.4588,4.6892,41.8963,50.4359,78.44,1,56.1,1013.5,0,925.2,443.2,53.8,1,0 -2013-11-21 11:10:12-06:00,n05667,937.8,21.8,4.9885,197.0862,4.7138,41.8105,50.3611,78.45,1.3,56.3,1013.5,0,929,447.4,53.5,1,0 -2013-11-21 11:15:12-06:00,n05667,943,22.2,5.0301,198.2167,4.7485,41.7429,50.3175,78.31,1.7,55.3,1013.6,0,928.9,451,53.9,1,0 -2013-11-21 11:20:12-06:00,n05667,950,21.7,5.0575,199.7628,4.7757,41.829,50.4099,78.35,1.7,55.5,1013.5,0,932.1,455.2,54.2,1,0 -2013-11-21 11:25:12-06:00,n05667,953.8,21.6,5.0727,200.2959,4.7945,41.7764,50.3643,78.4,2,55,1013.5,0,933.4,457.9,54,1,0 -2013-11-21 11:30:12-06:00,n05667,955.3,22.2,5.0912,200.0249,4.7995,41.676,50.2661,78.16,2.3,54.6,1013.5,0,932.6,459.1,54,1,0 -2013-11-21 11:35:12-06:00,n05667,958.4,23.2,5.1097,200.4546,4.821,41.5791,50.1881,78.17,2.7,52.1,1013.4,0,933.7,460.8,53.6,1,0 -2013-11-21 11:40:12-06:00,n05667,959.2,22,5.1007,201.2612,4.8238,41.7228,50.339,78.38,2.7,51.8,1013.3,0,933.3,462,53.8,1,0 -2013-11-21 11:45:13-06:00,n05667,964.9,22.5,5.1368,202.5271,4.8476,41.7784,50.3865,78.25,2.8,51.1,1013.3,0,938.4,464.9,53.5,1,0 -2013-11-21 11:50:12-06:00,n05667,963,23.1,5.1363,201.8817,4.8439,41.6774,50.2837,78.17,3,51.4,1013.3,0,936.9,464.8,53.3,1,0 -2013-11-21 11:55:12-06:00,n05667,964.3,22.2,5.1456,202.6071,4.8527,41.7514,50.3591,78.19,3.2,50.1,1013.2,0,937.4,465.7,53.4,1,0 -2013-11-21 12:00:12-06:00,n05667,963.5,23.9,5.1412,201.1938,4.8484,41.4971,50.1289,78.07,3.2,51.5,1013.1,0,935.8,464.5,53.3,1,0 -2013-11-21 12:05:12-06:00,n05667,963.1,23.7,5.1406,201.6266,4.8472,41.5964,50.2099,78.12,3.4,49.2,1013,0,936.4,464.5,53.1,1,0 -2013-11-21 12:10:12-06:00,n05667,958.4,23.1,5.1236,201.0471,4.8327,41.6018,50.2206,78.13,3.5,50,1013,0,934.8,463.4,53.3,1,0 -2013-11-21 12:15:12-06:00,n05667,958.6,23.1,5.1198,201.2586,4.8266,41.6974,50.2974,78.16,3.6,49,1012.9,0,935.5,462.8,53.1,1,0 -2013-11-21 12:20:12-06:00,n05667,959.3,22.5,5.1128,201.2917,4.8256,41.7133,50.3188,78.24,3.6,49.3,1012.8,0,937.5,462.2,52.9,1,0 -2013-11-21 12:25:12-06:00,n05667,954.8,23.3,5.1026,200.6937,4.8131,41.6978,50.2876,78.21,3.8,49.3,1012.8,0,935.5,459.5,52.6,1,0 -2013-11-21 12:30:12-06:00,n05667,949.7,24.3,5.0857,198.4241,4.7963,41.3706,49.9747,78.07,4.2,49.3,1012.7,0,933.3,456.1,52,1,0 -2013-11-21 12:35:12-06:00,n05667,946.2,23.7,5.0552,197.1401,4.7679,41.3477,49.9532,78.07,4.4,47.3,1012.5,0,934.4,454.8,52.2,1,0 -2013-11-21 12:40:12-06:00,n05667,938.1,25.8,5.023,195.56,4.7365,41.2883,49.8726,78.07,4.4,48,1012.5,0,929.2,450.1,52.3,1,0 -2013-11-21 12:45:12-06:00,n05667,933.7,24,5.0036,195.0359,4.712,41.3916,49.9552,78.03,4.4,47.2,1012.5,0,928.3,446.9,52.2,1,0 -2013-11-21 12:50:12-06:00,n05667,928.3,25.6,4.9701,193.739,4.6818,41.3815,49.9194,78.09,4.6,47.6,1012.4,0,927,443.1,51.8,1,0 -2013-11-21 12:55:12-06:00,n05667,918.8,25.5,4.9247,191.7704,4.6406,41.3247,49.8732,78.08,4.9,46.5,1012.3,0,925,438.9,51.5,1,0 -2013-11-21 13:00:12-06:00,n05667,910.7,25.2,4.8841,190.1106,4.6054,41.2796,49.7965,78.17,5.1,45,1012.3,0,920.6,433.5,51.3,1,0 -2013-11-21 13:05:12-06:00,n05667,903.9,25.4,4.8506,188.6957,4.5684,41.3045,49.8056,78.11,5,44.5,1012.2,0,918.1,428.6,51.2,1,0 -2013-11-21 13:10:12-06:00,n05667,895.5,24,4.8055,187.9975,4.531,41.4911,49.9656,78.3,5.3,44.3,1012.1,0,918.2,423.8,50.5,1,0 -2013-11-21 13:15:12-06:00,n05667,886.6,23.5,4.7591,186.5945,4.4876,41.5796,50.0521,78.33,5.3,43,1012,0,915.5,418.1,50.2,1,0 -2013-11-21 13:20:12-06:00,n05667,875.7,23.4,4.7052,184.4089,4.4357,41.5742,50.0124,78.37,5.3,43.9,1012,0,911.1,411.8,50.1,1,0 -2013-11-21 13:25:12-06:00,n05667,863.1,23.9,4.6363,181.4261,4.3725,41.4924,49.9077,78.41,5.6,43.8,1012.1,0,905,403.8,49.7,1,0 -2013-11-21 13:30:12-06:00,n05667,851.2,21.6,4.5679,180.0966,4.3185,41.7036,50.0775,78.73,5.6,44.1,1012,0,900.2,397.1,49.7,1,0 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16:15:12-06:00,n05667,138.4,7.9,0.3278,11.3311,0.2663,42.5515,47.2368,73.19,6,42.4,1012.2,0,331.2,34.2,15.4,1,0 -2013-11-21 16:20:12-06:00,n05667,48.7,6.8,0.1202,3.9722,0.1004,39.5788,45.0336,73.37,5.9,41.9,1012.3,0,68.9,17.7,13.2,1,0 -2013-12-04 07:55:12-06:00,n05667,68.4,-5.5,0.2758,10.7586,0.2548,42.2199,48.6562,80.16,-4.8,79.4,1006.2,0,45.6,23.4,18,1,0 -2013-12-04 08:00:12-06:00,n05667,219.2,-3.9,0.9426,39.7457,0.9007,44.1254,50.8733,82.88,-4.7,78.1,1006.2,0,342.8,62.4,21.9,1,0 -2013-12-04 08:05:12-06:00,n05667,259.1,-2.3,1.1319,47.6829,1.0812,44.1002,50.9721,82.64,-4.3,76.3,1006.3,0,391.1,75.7,25.1,1,0 -2013-12-04 08:10:12-06:00,n05667,297.6,-1.2,1.311,55.5646,1.2567,44.2141,51.094,82.95,-4.1,76.6,1006.4,0,436.3,89.9,27.2,1,0 -2013-12-04 08:15:12-06:00,n05667,336.5,-0.4,1.4896,63.1967,1.4305,44.1779,51.1671,82.91,-3.8,75.8,1006.5,0,476.8,104.4,29.5,1,0 -2013-12-04 08:20:12-06:00,n05667,369.4,0.7,1.6453,69.8173,1.5813,44.1524,51.2334,82.82,-3.5,74.5,1006.6,0,511,118.7,31.9,1,0 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09:05:12-06:00,n05667,524.2,8.5,2.8047,116.1659,2.6766,43.3999,51.0409,81.15,-2.7,71.5,1006.9,0,700,187.1,40.2,1,0 -2013-12-04 09:10:12-06:00,n05667,545.4,8.9,2.9221,120.6107,2.7869,43.2785,50.9745,80.97,-2.2,68.7,1007,0,711.9,198.1,41.6,1,0 -2013-12-04 09:15:12-06:00,n05667,568.4,9.2,3.0465,125.4631,2.9039,43.2056,50.9374,80.85,-2.1,68,1007,0,727.4,209.8,42.6,1,0 -2013-12-04 09:20:12-06:00,n05667,588.8,10.5,3.1624,129.5259,3.0089,43.0473,50.821,80.59,-2.2,67.3,1007,0,740.8,221,43.5,1,0 -2013-12-04 09:25:12-06:00,n05667,604,11.9,3.2459,132.456,3.0882,42.8907,50.7163,80.46,-2,67.4,1007.1,0,747.5,230.5,44.6,1,0 -2013-12-04 09:30:12-06:00,n05667,627.2,12,3.3691,137.275,3.2059,42.8192,50.7026,80.36,-2.1,68.2,1007.1,0,764.3,242.7,45.5,1,0 -2013-12-04 09:35:12-06:00,n05667,643.7,14.1,3.4647,140.4532,3.291,42.6776,50.5979,80.12,-1.9,67.1,1007.1,0,773.3,252.2,45.7,1,0 -2013-12-04 09:40:12-06:00,n05667,663.4,15.4,3.5722,144.0668,3.3897,42.501,50.4684,79.91,-1.8,67.1,1007.1,0,785.2,262.8,46.3,1,0 -2013-12-04 09:45:13-06:00,n05667,679.5,17.5,3.6573,146.9845,3.4729,42.3237,50.3474,79.82,-1.4,66.2,1007.2,0,793.3,272.4,47,1,0 -2013-12-04 09:50:12-06:00,n05667,694.3,19,3.7352,149.4571,3.5423,42.1924,50.231,79.66,-1,64.9,1007.3,0,798.6,281.4,48,1,0 -2013-12-04 10:00:12-06:00,n05667,724.7,21.8,3.9067,154.7732,3.7029,41.7974,49.9279,79.35,-0.9,64.7,1007.2,0,814.1,299.6,48.9,1,0 -2013-12-04 10:05:12-06:00,n05667,740.7,21.4,3.998,157.9558,3.7855,41.726,49.896,79.18,-0.6,62.9,1007.3,0,820.4,308.3,49.9,1,0 -2013-12-04 10:10:12-06:00,n05667,754.6,21.6,4.064,160.4363,3.8499,41.6733,49.8914,79.13,-0.8,64.6,1007.3,0,825.6,317.2,51.2,1,0 -2013-12-04 10:15:12-06:00,n05667,768.8,22.1,4.1454,163.564,3.923,41.6937,49.9305,79.02,-0.1,60.9,1007.2,0,832.5,325.3,51.5,1,0 -2013-12-04 10:20:12-06:00,n05667,779.5,22.5,4.1951,165.7611,3.9731,41.7214,49.9786,79.06,-0.4,62.6,1007.2,0,835.6,332.1,51.9,1,0 -2013-12-04 10:25:12-06:00,n05667,788.8,23.8,4.2527,167.0684,4.0198,41.5613,49.842,78.82,0,61.1,1007.3,0,836.2,339.1,53.3,1,0 -2013-12-04 10:30:12-06:00,n05667,802.1,22.7,4.3205,169.9758,4.0881,41.5785,49.8887,78.86,0.3,59.3,1007.3,0,841.7,346.8,54.2,1,0 -2013-12-04 10:35:12-06:00,n05667,810.2,24.2,4.3661,171.2983,4.1294,41.4822,49.8095,78.77,0,61.9,1007.3,0,842.8,351.8,54.4,1,0 -2013-12-04 10:40:12-06:00,n05667,824.8,23.6,4.447,174.7324,4.203,41.5735,49.9279,78.7,-0.2,60,1007.3,0,851.5,360.1,54.6,1,0 -2013-12-04 10:45:12-06:00,n05667,832.1,23,4.4736,176.3906,4.2312,41.6877,50.0424,78.79,-0.5,60.8,1007.2,0,851.5,365.1,55.4,1,0 -2013-12-04 10:50:12-06:00,n05667,837.6,22.4,4.5062,177.6127,4.2583,41.7094,50.0755,78.71,-0.6,60,1007.1,0,851.2,369.4,55.6,1,0 -2013-12-04 10:55:12-06:00,n05667,844.9,21.6,4.542,179.3733,4.2939,41.7744,50.1523,78.74,-0.7,60.9,1007,0,852.7,374,55.7,1,0 -2013-12-04 11:00:12-06:00,n05667,855.2,22.9,4.5968,181.2003,4.3454,41.6996,50.1035,78.68,-0.8,61.8,1007,0,858.7,380.2,56.2,1,0 -2013-12-04 11:05:12-06:00,n05667,862.3,22.3,4.6333,182.6705,4.3792,41.7136,50.1367,78.64,-0.6,61.1,1006.8,0,860.3,384.7,56.8,1,0 -2013-12-04 11:10:12-06:00,n05667,872.1,21.8,4.6899,185.4145,4.4323,41.8325,50.2635,78.66,-0.9,61.8,1006.8,0,867.1,390.5,56.9,1,0 -2013-12-04 11:15:12-06:00,n05667,877.9,22,4.7073,185.5074,4.4471,41.7142,50.1445,78.59,-0.8,61.3,1006.7,0,869.5,393.7,56.7,1,0 -2013-12-04 11:20:12-06:00,n05667,882.2,23.4,4.741,186.3772,4.4819,41.584,50.0638,78.52,-0.8,62.3,1006.6,0,871.2,397,56.4,1,0 -2013-12-04 11:25:12-06:00,n05667,884.1,23.8,4.7542,185.9308,4.4872,41.4358,49.9194,78.34,0,58.1,1006.6,0,871.9,398.6,55.8,1,0 -2013-12-04 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index b67ebb4a95..76a05814d0 100644 --- a/docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041.csv +++ b/docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041.csv @@ -1,28 +1,28 @@ -date_time,module_id,temp_module,poa_global,i_sc,v_oc,i_mp,v_mp,p_mp,wind_speed -0,19074001,15,100,0.595,65.78,0.543,55.56,30.16,0 -1,19074001,15,200,1.183,67.79,1.093,57.7,63.06,0 -2,19074001,15,400,2.354,69.65,2.185,59.42,129.85,0 -3,19074001,15,600,3.532,70.65,3.292,60.06,197.74,0 -4,19074001,15,800,4.706,71.35,4.398,60.21,264.83,0 -5,19074001,15,1000,5.891,71.85,5.503,60.12,330.86,0 -6,19074001,25,100,0.599,63.95,0.547,53.47,29.25,0 -7,19074001,25,200,1.183,66.01,1.09,56.07,61.14,0 -8,19074001,25,400,2.365,67.92,2.19,57.75,126.45,0 -9,19074001,25,600,3.542,68.96,3.298,58.31,192.28,0 -10,19074001,25,800,4.718,69.68,4.4,58.54,257.56,0 -11,19074001,25,1000,5.903,70.21,5.506,58.54,322.3,0 -12,19074001,25,1100,6.488,70.44,6.055,58.49,354.17,0 -13,19074001,50,100,0.602,59.36,0.541,49.64,26.85,0 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b/docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand1pc.csv new file mode 100644 index 0000000000..62ffc18666 --- /dev/null +++ b/docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand1pc.csv @@ -0,0 +1,28 @@ +date_time,module_id,temp_module,poa_global,i_sc,v_oc,i_mp,v_mp,p_mp,wind_speed +0,19074001,15,100,0.602557908,66.04604926,0.543752916,55.73108297,30.3039389,0 +1,19074001,15,200,1.170242456,68.1750458,1.093985604,56.70940442,62.03927202,0 +2,19074001,15,400,2.367030485,69.40014068,2.1590955,59.75887347,129.0251148,0 +3,19074001,15,600,3.500676296,70.97044673,3.264833315,59.63981583,194.7140576,0 +4,19074001,15,800,4.71490535,70.73167576,4.384503058,61.12687296,268.0109614,0 +5,19074001,15,1000,5.874275465,72.94587119,5.556625816,59.32675419,329.6565739,0 +6,19074001,25,100,0.605655846,64.0995778,0.538877871,54.29888464,29.26046738,0 +7,19074001,25,200,1.185849602,65.70917319,1.08396994,57.0996608,61.89431588,0 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+26,19074001,75,1100,6.604861088,56.83179966,6.21249486,54.80049027,340.4477641,0 diff --git a/docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand5pc_6pts.csv b/docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand5pc_6pts.csv new file mode 100644 index 0000000000..38781cd6a5 --- /dev/null +++ b/docs/tutorials/mlfm_data/meas_gtw/x19074001_iec61853_041_rand5pc_6pts.csv @@ -0,0 +1,7 @@ +date_time,module_id,temp_module,poa_global,i_sc,v_oc,i_mp,v_mp,p_mp,wind_speed +5,19074001,15,1000,5.914092571,72.74673871,5.818529195,56.54862664,329.029835,0 +7,19074001,25,200,1.17935404,66.81760116,1.017463181,59.89152215,60.93741863,0 +9,19074001,25,600,3.465935936,76.73483611,3.206194621,65.82265194,211.0402326,0 +11,19074001,25,1000,5.672220175,65.28318713,5.509716612,58.49968302,322.3166753,0 +12,19074001,25,1100,6.322854056,66.05136101,6.124626301,54.89003941,336.180979,0 +18,19074001,50,1000,6.085239512,68.95042163,5.662503076,54.32720586,307.6279703,0 From 711fae4756809f34f508ca30ec663eda3e60f47d Mon Sep 17 00:00:00 2001 From: steve ransome Date: Tue, 13 Dec 2022 15:22:23 +0000 Subject: [PATCH 77/81] Add files via upload --- pvlib/tests/test_mlfm.py | 549 +++++++++++++++++++-------------------- 1 file changed, 264 insertions(+), 285 deletions(-) diff --git a/pvlib/tests/test_mlfm.py b/pvlib/tests/test_mlfm.py index 7514867931..5a4d0cbe20 100644 --- a/pvlib/tests/test_mlfm.py +++ b/pvlib/tests/test_mlfm.py @@ -1,285 +1,264 @@ -import numpy as np -import pandas as pd - -from pvlib import mlfm - -import pytest - -from conftest import requires_mpl, assert_frame_equal - -from numpy.testing import assert_allclose - -tolerance = 0.000001 - -qty_mlfm_vars = 6 # check all 6 mlfm params from iv curves - - -@pytest.fixture -def reference(): - # get reference module STC values for normalisation - ref = dict( - module_id='g78', - i_sc=5.35, - i_mp=4.9, - v_mp=36.8, - v_oc=44.2, - alpha_i_sc=0.0005, - alpha_i_mp=0, # often not known, not used here - beta_v_mp=0, # often not known, not used here - beta_v_oc=-0.0035, # 1/C - gamma_pdc=-0.0045, # = alpha_i_mp + beta_v_mp - delta_ff=0, # often not known, not used here - ) - # create p_mp and ff - ref['p_mp'] = ref['i_mp'] * ref['v_mp'] - ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc']) - return ref - - -@pytest.fixture -def measured(): - # get measured data - data_meas = { - # 'date_time': ['2016-03-23 09:00:00-07:00'], - 'module_id': [78], - 'poa_global': [591.3868886], - 'wind_speed': [4.226408028], - # 'temp_air': [17.42457581], - 'temp_module': [27.82861328], - 'v_oc': [43.52636044], - 'i_sc': [3.14995479], - 'i_mp': [2.949264766], - 'v_mp': [35.76882896], - 'r_sc': [674.5517322], - 'r_oc': [1.355690858], - } - - meas = pd.DataFrame(data_meas) - - # create p_mp and ff in case they don't exist - # meas['poa_global_kwm2'] = meas['poa_global'] / 1000 - meas['p_mp'] = meas['i_mp'] * meas['v_mp'] - meas['ff'] = meas['p_mp'] / (meas['i_sc'] * meas['v_oc']) - - return meas - - -@pytest.fixture -def normalized(): - data_norm_target = { - # 'date_time': ['2016-03-23 09:00:00-07:00'], - 'pr_dc': [0.989242790817207], - 'pr_dc_temp_corr': [1.00183462464583], - 'i_sc': [0.995586151149719], - 'i_mp': [0.93628796685047], - 'v_oc': [0.98475928597285], - 'v_mp': [0.821773945683017], - 'v_oc_temp_corr': [0.994508547151521], - 'r_sc': [0.981487711004909], - 'r_oc': [0.903706382978424], - 'i_ff': [0.953947722780796], - 'v_ff': [0.909337325885234], - } - - norm_target = pd.DataFrame(data_norm_target) - - return norm_target - - -@pytest.fixture -def stacked6(): - # get stack data - data_stack_target = { - # 'date_time': ['2016-03-23 09:00:00-07:00'], - 'pr_dc': [0.989242790817207], - 'i_sc': [0.0052435168594609], - 'r_sc': [0.0219920307073518], - 'i_ff': [0.049708690806242], - 'i_v': [0.01], - 'v_ff': [0.102704472076433], - 'r_oc': [0.114393859291095], - 'v_oc': [0.0181055001343123], - 'temp_module_corr': [0.0115818228244058], - } - - stack_target = pd.DataFrame(data_stack_target) - - return stack_target - - -@pytest.fixture -def stacked4(): - data_stack_target = { - # 'date_time': ['2016-03-23 09:00:00-07:00'], - 'pr_dc': [0.989242790817], - 'i_sc': [0.0054355995322], - 'i_mp': [0.0734605702031], - 'i_v': [0.01], - 'v_mp': [0.214483151855], - 'v_oc': [0.0187687482844], - 'temp_module_corr': [0.012006092936], - } - - stack_target = pd.DataFrame(data_stack_target) - - return stack_target - - -@pytest.fixture -def mlfm_6_coeffs(): - # test mlfm coefficients - c_1 = +1.0760136800094817 - c_2 = -0.004619443769147978 - c_3 = +0.018343135214876096 - c_4 = -0.07613482929987923 - c_5 = -0.0006626101399079871 - c_6 = -0.014752223616684625 - expected = 0.9859917396312191 - - return c_1, c_2, c_3, c_4, c_5, c_6, expected - - -@pytest.fixture -def matrix_data(): - # sample ghi, tmod, ws and pr_dc to fit - # this data selectable from mlfm.ipynb - # --- - # select one of the following meas files - # meas_file = 2 # <<< change from 0 to 2 - # --- - - return pd.DataFrame(np.array( - [[100., 15, 0, 0.935774123487434], - [200., 15, 0, 0.978281104560968], - [400., 15, 0, 1.00721377598511], - [600., 15, 0, 1.02254628193195], - [800., 15, 0, 1.02710983555693], - [1000., 15, 0, 1.02655910642259], - [100., 25, 0, 0.907539559416693], - [200., 25, 0, 0.94849519081601], # LIC - [400., 25, 0, 0.980840831523425], - [600., 25, 0, 0.994311717861206], - [800., 25, 0, 0.998914055228048], - [1000., 25, 0, 1], # STC - [1100., 25, 0, 0.998984571122331], - [100., 50, 0, 0.833074775054297], - [200., 50, 0, 0.879615265280794], - [400., 50, 0, 0.908004964318957], - [600., 50, 0, 0.920260626745268], - [800., 50, 0, 0.925496431895749], - [1000., 50, 0, 0.927551970214086], - [1100., 50, 0, 0.926324993653569], - [100., 75, 0, 0.746819733167856], - [200., 75, 0, 0.792739683524666], - [400., 75, 0, 0.826481538938877], - [600., 75, 0, 0.842744854690247], - [800., 75, 0, 0.847735029475644], - [1000., 75, 0, 0.849053676698728], - [1100., 75, 0, 0.849039573519871]]), - columns=[ - 'poa_global', 'temp_module', 'wind_speed', 'pr_dc']) - - -@pytest.fixture -def mlfm_6_fit(): - # fit matrix - ''' - Excel fit GRG linear values - c_1 = +1.0573318761708000 - c_2 = -0.0030251199627269 - c_3 = +0.1228522267570000 - c_4 = -0.0545505400372862 - c_5 = 0 # this is in conflict with the data which include wind_speed - c_6 = -0.002394779219883 - rmse = 0.280% - ''' - c_1 = +1.0579328401731174 - c_2 = -0.0030248261647759975 - c_3 = +0.12378885001559799 - c_4 = -0.05521716508715758 - c_5 = 0. - c_6 = -0.0023546463713093836 - expected = 0.9845007615699125 - - cc_target = [c_1, c_2, c_3, c_4, c_5, c_6] - return c_1, c_2, c_3, c_4, c_5, c_6, expected, cc_target - - -def test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized): - norm_calc = mlfm.mlfm_meas_to_norm(measured, reference) - assert_frame_equal(norm_calc, normalized, atol=1e-6) - - -def test_mlfm_6(measured, mlfm_6_coeffs): - c_1, c_2, c_3, c_4, c_5, c_6, expected = mlfm_6_coeffs - result = mlfm.mlfm_6(measured, c_1, c_2, c_3, c_4, c_5, c_6) - assert_allclose(expected, result[0], atol=1e-6) - - -def test_mlfm_norm_to_stack(normalized, reference, stacked6, stacked4): - stack_calc = mlfm.mlfm_norm_to_stack(normalized, reference['ff']) - assert_frame_equal(stack_calc, stacked6, atol=1e-6) - # test without 'i_ff', 'r_sc', 'v_ff', 'r_oc' - # v_mp = v_ff * r_oc and i_mp = i_ff * r_sc - norm = normalized.drop(columns=['i_ff', 'r_sc', 'v_ff', 'r_oc']) - short_stack_calc = mlfm.mlfm_norm_to_stack(norm, reference['ff']) - assert_frame_equal(short_stack_calc, stacked4, check_less_precise=True) - - -def test_mlfm_fit(matrix_data, mlfm_6_fit): - c_1, c_2, c_3, c_4, c_5, c_6, expected, cc_target = mlfm_6_fit - # choose which parameter to fit - usually pr_dc - mlfm_sel = 'pr_dc' - # drop wind_speed since it's always zero - matrix_data = matrix_data.drop(columns=['wind_speed']) - predictions, cc_fit, residuals, perr = mlfm.mlfm_fit( - matrix_data, mlfm_sel) - # atol is large due to different behavior in conda_linux Python 3.6 env. - assert_allclose(cc_fit, cc_target, atol=5e-3) - - -@requires_mpl -def test_plot_mlfm_scatter(measured, normalized): - import matplotlib.pyplot as plt - fig = mlfm.plot_mlfm_scatter(measured, normalized, 'norm plot') - assert isinstance(fig, plt.Figure) - - -@requires_mpl -def test_plot_mlfm_stack(measured, normalized, stacked6, stacked4, reference): - # stacked plot requires at least index length of 2 - m = pd.concat([measured, measured]) - m.index = [0, 1] - n = pd.concat([normalized, normalized]) - n.index = [0, 1] - s6 = pd.concat([stacked6, stacked6]) - s6.index = [0, 1] - import matplotlib.pyplot as plt - fig = mlfm.plot_mlfm_stack(m, n, s6, reference['ff'], 'stacked 6 plot') - assert isinstance(fig, plt.Figure) - s4 = pd.concat([stacked4, stacked4]) - s4.index = [0, 1] - import matplotlib.pyplot as plt - fig = mlfm.plot_mlfm_stack(m, n, s4, reference['ff'], 'stacked 4 plot') - assert isinstance(fig, plt.Figure) - - -""" - -remove - -reference() -measured() -normalized() -stacked6() -stacked4() -mlfm_6_coeffs() -matrix_data() -mlfm_6_fit() -test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized) -test_mlfm_6(measured, mlfm_6_coeffs) -test_mlfm_norm_to_stack(normalized, reference, stacked6, stacked4) -test_mlfm_fit(matrix_data, mlfm_6_fit) -test_plot_mlfm_scatter(measured, normalized) -test_plot_mlfm_stack(measured, normalized, stacked6, stacked4, reference) -""" \ No newline at end of file +import numpy as np +import pandas as pd + +from pvlib import mlfm + +import pytest + +from conftest import requires_mpl, assert_frame_equal + +from numpy.testing import assert_allclose + +tolerance = 0.000001 + +qty_mlfm_vars = 6 # check all 6 mlfm params from iv curves + + +@pytest.fixture +def reference(): + # get reference module STC values for normalisation + ref = dict( + module_id='g78', + i_sc=5.35, + i_mp=4.9, + v_mp=36.8, + v_oc=44.2, + alpha_i_sc=0.0005, + alpha_i_mp=0, # often not known, not used here + beta_v_mp=0, # often not known, not used here + beta_v_oc=-0.0035, # 1/C + gamma_pdc=-0.0045, # = alpha_i_mp + beta_v_mp + delta_ff=0, # often not known, not used here + ) + # create p_mp and ff + ref['p_mp'] = ref['i_mp'] * ref['v_mp'] + ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc']) + return ref + + +@pytest.fixture +def measured(): + # get measured data + data_meas = { + # 'date_time': ['2016-03-23 09:00:00-07:00'], + 'module_id': [78], + 'poa_global': [591.3868886], + 'wind_speed': [4.226408028], + # 'temp_air': [17.42457581], + 'temp_module': [27.82861328], + 'v_oc': [43.52636044], + 'i_sc': [3.14995479], + 'i_mp': [2.949264766], + 'v_mp': [35.76882896], + 'r_sc': [674.5517322], + 'r_oc': [1.355690858], + } + + meas = pd.DataFrame(data_meas) + + # create p_mp and ff in case they don't exist + # meas['poa_global_kwm2'] = meas['poa_global'] / 1000 + meas['p_mp'] = meas['i_mp'] * meas['v_mp'] + meas['ff'] = meas['p_mp'] / (meas['i_sc'] * meas['v_oc']) + + return meas + + +@pytest.fixture +def normalized(): + data_norm_target = { + # 'date_time': ['2016-03-23 09:00:00-07:00'], + 'pr_dc': [0.989242790817207], + 'pr_dc_temp_corr': [1.00183462464583], + 'i_sc': [0.995586151149719], + 'i_mp': [0.93628796685047], + 'v_oc': [0.98475928597285], + 'v_mp': [0.821773945683017], + 'v_oc_temp_corr': [0.994508547151521], + 'r_sc': [0.981487711004909], + 'r_oc': [0.903706382978424], + 'i_ff': [0.953947722780796], + 'v_ff': [0.909337325885234], + } + + norm_target = pd.DataFrame(data_norm_target) + + return norm_target + + +@pytest.fixture +def stacked6(): + # get stack data + data_stack_target = { + # 'date_time': ['2016-03-23 09:00:00-07:00'], + 'pr_dc': [0.989242790817207], + 'i_sc': [0.0052435168594609], + 'r_sc': [0.0219920307073518], + 'i_ff': [0.049708690806242], + 'i_v': [0.01], + 'v_ff': [0.102704472076433], + 'r_oc': [0.114393859291095], + 'v_oc': [0.0181055001343123], + 'temp_module_corr': [0.0115818228244058], + } + + stack_target = pd.DataFrame(data_stack_target) + + return stack_target + + +@pytest.fixture +def stacked4(): + data_stack_target = { + # 'date_time': ['2016-03-23 09:00:00-07:00'], + 'pr_dc': [0.989242790817], + 'i_sc': [0.0054355995322], + 'i_mp': [0.0734605702031], + 'i_v': [0.01], + 'v_mp': [0.214483151855], + 'v_oc': [0.0187687482844], + 'temp_module_corr': [0.012006092936], + } + + stack_target = pd.DataFrame(data_stack_target) + + return stack_target + + +@pytest.fixture +def mlfm_6_coeffs(): + # test mlfm coefficients + c_1 = +1.0760136800094817 + c_2 = -0.004619443769147978 + c_3 = +0.018343135214876096 + c_4 = -0.07613482929987923 + c_5 = -0.0006626101399079871 + c_6 = -0.014752223616684625 + expected = 0.9859917396312191 + + return c_1, c_2, c_3, c_4, c_5, c_6, expected + + +@pytest.fixture +def matrix_data(): + # sample ghi, tmod, ws and pr_dc to fit + # this data selectable from mlfm.ipynb + # --- + # select one of the following meas files + # meas_file = 2 # <<< change from 0 to 2 + # --- + + return pd.DataFrame(np.array( + [[100., 15, 0, 0.935774123487434], + [200., 15, 0, 0.978281104560968], + [400., 15, 0, 1.00721377598511], + [600., 15, 0, 1.02254628193195], + [800., 15, 0, 1.02710983555693], + [1000., 15, 0, 1.02655910642259], + [100., 25, 0, 0.907539559416693], + [200., 25, 0, 0.94849519081601], # LIC + [400., 25, 0, 0.980840831523425], + [600., 25, 0, 0.994311717861206], + [800., 25, 0, 0.998914055228048], + [1000., 25, 0, 1], # STC + [1100., 25, 0, 0.998984571122331], + [100., 50, 0, 0.833074775054297], + [200., 50, 0, 0.879615265280794], + [400., 50, 0, 0.908004964318957], + [600., 50, 0, 0.920260626745268], + [800., 50, 0, 0.925496431895749], + [1000., 50, 0, 0.927551970214086], + [1100., 50, 0, 0.926324993653569], + [100., 75, 0, 0.746819733167856], + [200., 75, 0, 0.792739683524666], + [400., 75, 0, 0.826481538938877], + [600., 75, 0, 0.842744854690247], + [800., 75, 0, 0.847735029475644], + [1000., 75, 0, 0.849053676698728], + [1100., 75, 0, 0.849039573519871]]), + columns=[ + 'poa_global', 'temp_module', 'wind_speed', 'pr_dc']) + + +@pytest.fixture +def mlfm_6_fit(): + # fit matrix + ''' + Excel fit GRG linear values + c_1 = +1.0573318761708000 + c_2 = -0.0030251199627269 + c_3 = +0.1228522267570000 + c_4 = -0.0545505400372862 + c_5 = 0 # this is in conflict with the data which include wind_speed + c_6 = -0.002394779219883 + rmse = 0.280% + ''' + c_1 = +1.0579328401731174 + c_2 = -0.0030248261647759975 + c_3 = +0.12378885001559799 + c_4 = -0.05521716508715758 + c_5 = 0. + c_6 = -0.0023546463713093836 + expected = 0.9845007615699125 + + cc_target = [c_1, c_2, c_3, c_4, c_5, c_6] + return c_1, c_2, c_3, c_4, c_5, c_6, expected, cc_target + + +def test_mlfm_meas_to_norm(mlfm_6_coeffs, reference, measured, normalized): + norm_calc = mlfm.mlfm_meas_to_norm(measured, reference) + assert_frame_equal(norm_calc, normalized, atol=1e-6) + + +def test_mlfm_6(measured, mlfm_6_coeffs): + c_1, c_2, c_3, c_4, c_5, c_6, expected = mlfm_6_coeffs + result = mlfm.mlfm_6(measured, c_1, c_2, c_3, c_4, c_5, c_6) + assert_allclose(expected, result[0], atol=1e-6) + + +def test_mlfm_norm_to_stack(normalized, reference, stacked6, stacked4): + stack_calc = mlfm.mlfm_norm_to_stack(normalized, reference['ff']) + assert_frame_equal(stack_calc, stacked6, atol=1e-6) + # test without 'i_ff', 'r_sc', 'v_ff', 'r_oc' + # v_mp = v_ff * r_oc and i_mp = i_ff * r_sc + norm = normalized.drop(columns=['i_ff', 'r_sc', 'v_ff', 'r_oc']) + short_stack_calc = mlfm.mlfm_norm_to_stack(norm, reference['ff']) + assert_frame_equal(short_stack_calc, stacked4, check_less_precise=True) + + +def test_mlfm_fit(matrix_data, mlfm_6_fit): + c_1, c_2, c_3, c_4, c_5, c_6, expected, cc_target = mlfm_6_fit + # choose which parameter to fit - usually pr_dc + mlfm_sel = 'pr_dc' + # drop wind_speed since it's always zero + matrix_data = matrix_data.drop(columns=['wind_speed']) + predictions, cc_fit, residuals, perr = mlfm.mlfm_fit( + matrix_data, mlfm_sel) + # atol is large due to different behavior in conda_linux Python 3.6 env. + assert_allclose(cc_fit, cc_target, atol=5e-3) + + +@requires_mpl +def test_plot_mlfm_scatter(measured, normalized): + import matplotlib.pyplot as plt + fig = mlfm.plot_mlfm_scatter(measured, normalized, 'norm plot') + assert isinstance(fig, plt.Figure) + + +@requires_mpl +def test_plot_mlfm_stack(measured, normalized, stacked6, stacked4, reference): + # stacked plot requires at least index length of 2 + m = pd.concat([measured, measured]) + m.index = [0, 1] + n = pd.concat([normalized, normalized]) + n.index = [0, 1] + s6 = pd.concat([stacked6, stacked6]) + s6.index = [0, 1] + import matplotlib.pyplot as plt + fig = mlfm.plot_mlfm_stack(m, n, s6, reference['ff'], 'stacked 6 plot') + assert isinstance(fig, plt.Figure) + s4 = pd.concat([stacked4, stacked4]) + s4.index = [0, 1] + import matplotlib.pyplot as plt + fig = mlfm.plot_mlfm_stack(m, n, s4, reference['ff'], 'stacked 4 plot') + assert isinstance(fig, plt.Figure) From f74cebd04e78f39dfd9f88870103201fd2658901 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Tue, 13 Dec 2022 21:58:13 +0000 Subject: [PATCH 78/81] Add files via upload --- mlfm.py | 1226 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1226 insertions(+) create mode 100644 mlfm.py diff --git a/mlfm.py b/mlfm.py new file mode 100644 index 0000000000..ef68f4b600 --- /dev/null +++ b/mlfm.py @@ -0,0 +1,1226 @@ +"""Analyse, fit + predict PV performance measurements using MPM & LFM.""" +import numpy as np +import os +import pandas as pd +from scipy import optimize + +# import pvlib + +""" +ver : 221213t17 <-- delete when finalised + +``mlfm.py`` module contains functions to analyse, fit, predict and display +performance of PV modules using the mechanistic performance model (MPM) and +loss factors model (LFM). + +Authors : Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) +Comments : Cliff Hansen, Kevin Anderson, Anton Driesse and Mark Campanelli +https://pvlib-python.readthedocs.io/en/stable/variables_style_rules.html#variables-style-rules +https://github.com/python/peps/blob/master/pep-0008.txt + +OVERVIEW + +I) The Loss Factors Model (LFM) 2011 ref [1] quantifies +normalised losses from module parameters (e.g. pr_dc, i_sc, r_sc, i_mp, +v_mp, r_oc and v_oc) by analysing module measurements or the shape of the +IV curve and comparing it with STC reference values from the datasheet. + + Depending on the number of measurements available the LFM is defined +with a suffix number x = 1..12 LFM_n as in ref [4] - + + parameters modelled +|LFM_1 | ``p_mp`` | +|LFM_2 | ``i_mp``, ``v_mp``, | +|LFM_4 | ``i_sc``, ``i_mp``, ``v_mp``, ``v_oc`` | +|LFM_6 | ``i_sc``, ``r_sc``, ``i_ff`, ``v_ff`, ``r_oc``, ``v_oc`` | + +|LFM_>6| (can include normalised losses for : + soiling, reflectivity vs. aoi, spectrum <- affecting i_sc, + current mismatch/shading, rollover, + clipping etc.) + + This file just contains - +LFM_6 : 'measurements with r_sc and r_oc' + e.g. iv curves with good smooth data. + +LFM_4 : 'measurements without r_sc or r_oc' + e.g. indoor matrix measurements or iv curves without smoooth data. + +II) The Mechanistic performance model (MPM) 2017 ref [2] +has "meaningful,independent, robust and normalised" coefficients +which fit how the LFM values depend on irradiance, module temperature +(and windspeed) and time. + +Two MPM versions have been included here : + +mpm_a : (mpm_original 2017 ref [2] now deprecated) + The original model to fit normalised parameters such as + pr_dc, v_oc, r_sc, v_mp, i_mp, ff ... + with an extra low light coefficient c_6 to help fit data with + unusual low light performance and/or poor measurements. + c_5 is only used if there is windspeed data, otherwise it is ignored + + mpm_a = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + +mpm_b : (GI name 'mpm_advanced' 2022 ref [7]) + Is an improved model to fit normalised parameters such as + pr_dc, v_oc, r_sc, v_mp, i_mp, ff ... + It better fits precise measurements (see CFV and GI) where the + low light data is measured well and has an improvement for even + better v_oc fitting [ref 7 : 2022 PVSC PHILADELPHIA] + c_5 is only used if there is windspeed data, otherwise it is ignored + + mpm_b = c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + +for mpm_a and mpm_b : + g = (G_POA (W/m^2) / G_STC=1000 (W/m^2)) --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + +Note that both mpm_a or mpm_b can be used with either LFM_6 or LFM_4 + + A later MPM version (not detailed here) can be used to model clipping and +other effects [See ref [8] Sutterlueti et al PVPMC 2022] 'mpm professional' + +The pairs of functions "mpm_a_calc and mpm_b_calc", and +"mpm_a_fit and mom_b_fit" should probably be merged but so far I haven't +found a way to do this as they call each other and at least one combination +breaks. + +Using DATAFRAMES or SERIES for variables +---------------------------------------- + +Many pvlib functions pass series of weather data separately for parameters e.g. + poa_global, temp_module, wind_speed +and measurements such as + pr_dc or p_mp + +This mlfm code keeps all its met and measurement data in dataframes - + meas, norm etc. e.g. + +meas.columns + Index(['module_id', 'poa_global', 'wind_speed', 'temp_air', + 'temp_module', 'v_oc', 'i_sc', 'i_mp', 'v_mp', 'r_sc', + 'r_oc', 'p_mp', 'pr_dc', 'v_oc_temp_corr', 'pr_dc_temp_corr'], + dtype='object') + + It's easier when modelling all 6 or more measurement parameters in one +frame and then use an lfm_sel var to choose which to analyse +e.g. lfm_sel = 'pr_dc' + +If individual series are needed to interface with existing code and +methodolgies they can be easily created by the following + + +#pvlib series <-- mlfm dataframe + poa_global = meas['poa_global'] + temp_module = meas['temp_module'] + wind_speed = meas['wind_speed'] + pr_dc = meas['pr_dc'] + +# mlfm dataframe <-- pvlib series + meas['poa_global'] = poa_global + meas['temp_module'] = temp_module + meas['wind_speed'] = wind_speed + meas['pr_dc'] = pr_dc + +DATAFRAME DEFINITIONS (for this python file and tutorials) +---------------------------------------------------------- + +A full definition is given here to keep the code in each function shorter + +dmeas : DataFrame +----------------- + Measured weather and module electrical values per time or measurement + + Parameters [units] + ---------- + Index either - + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``module_id`` - unique identifier to match data in ref [alpha num] + + Weather measurements - + + * ``poa_global`` - global plane of array irradiance [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/s] + + [optional weather] + + * ``temp_air`` - air temperature optional [C] + + /Columns as needed by LFM_4 and/or LFM_6/ : + + * ``i_sc`` | 4 6 | current at short circuit condition [A] + * ``i_mp`` | 4 6 | current at maximum power point [A] + * ``v_mp`` | 4 6 | voltage at maximum power point [V] + * ``v_oc`` | 4 6 | voltage at open circuit condition [V] + + * ``r_sc`` | 6 | -1/ (dI/dV|V=0) of IV curve at short circuit [Ohm] + * ``r_oc`` | 6 | -1/(dI/dV|I=0) of IV curve at open circuit [Ohm] + + Optional columns include + + * ``p_mp`` - power at maximum power point = i_mp * v_mp [W] + +ref : dict +---------- + Reference electrical and thermal datasheet module values at STC. + + Parameters [units] + ---------- + Index + * ``module_id`` - unique identifier to match data in dmeas [alpha num] + + * ``p_mp`` - Max Power at Standard Test Condition (STC). [W] + * ``i_sc`` - Current at short circuit at STC. [A] + * ``i_mp`` - Current at max power at STC. [A] + * ``v_mp`` - Voltage at max power at STC. [V] + * ``v_oc`` - Voltage at open circuit at STC. [V] + * ``ff`` - Fill Factor [1] + + * ``gamma_pdc`` - Temperature coefficient of max power point + power at STC. [1/C] + * ``beta_v_oc`` - Temperature coefficient of open circuit + voltage at STC. [1/C] + [optional thermal] + + * ``alpha_i_sc`` - Temperature coefficient of short circuit + current STC. [1/C] + + * ``alpha_i_mp`` - Temperature coefficient of max power point + current at STC. [1/C] + + * ``beta_v_mp`` - Temperature coefficient of max power point + voltage at STC. [1/C] + + [optional ID related] + * ``source`` - Data Source [alpha num] + * ``site`` - Sitename [alpha num] + * ``manufacturer`` - Module manufacturer [alpha num] + * ``technology`` - Module technology e.g. cSi, HIT, CdTe [alpha num] + * ``module_type`` - Type ID e.g. ABC-123 [alpha num] + * ``module_serial`` - Serial number [alpha num] + * ``comments`` - General comments [alpha num] + + +dnorm : DataFrame +----------------- + Normalised multiplicative loss factors per parameter to model fall from + start 1/ref_ff to meas pr_dc where - + + LFM_6 - multiplicative + pr_dc = 1/ff * ( norm(i_sc) *norm(r_sc) *norm(i_ff) + *norm(v_ff) *norm(r_oc) *norm(v_oc_t) *norm(temp_corr) ). + + LFM_4 - multiplicative + pr_dc = 1/ff * ( norm(i_sc) *norm(i_mp) + *norm(v_mp) *norm(v_oc_t) *norm(temp_corr) ). + + Parameters [units] + ---------- + Index (copied from dmeas) either + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``poa_global`` - global plane of array [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/ + + |Columns as used by LFM_4 and/or LFM_6| : + + * ``pr_dc``| 4 6 | Performance ratio dc. + pr_dc = meas_p_mp / ref_p_mp /(poa_global/G_STC) [%] + * ``pr_dc_temp_corr`` + | 4 6 | pr_dc adjusted to 25C by gamma_p_mp. + * ``i_sc`` | 4 6 | loss due to current at short circuit condition [%] + * ``v_oc`` | 4 6 | Loss due to voltage at open circuit condition [%] + * ``v_oc_temp_corr`` + | 4 6 | v_oc adjusted to 25C by gamma_p_mp (not beta_v_oc) + for simplicity + + * ``i_mp`` | 4 | Loss due to current part of ff [%] + * ``v_mp`` | 4 | Loss due to voltage part of ff [%] + + * ``r_sc`` | 6 | Loss due to r_sc ~r_shunt [%] + * ``i_ff`` | 6 | Loss due to r_sc corrected current part of ff [%] + * ``v_ff`` | 6 | Loss due to r_oc corrected voltage part of ff [%] + * ``r_oc`` | 6 | Loss due to r_oc related to r_series [%] + +dstack : DataFrame +------------------ + Stacked subtractive normalized loss factors per parameter to model fall + from start 1/ref_ff to meas pr_dc where - + + LFM_6 - subtractive losses + pr_dc = 1/ff - (stack(i_sc) +stack(r_sc) +stack(i_ff) + +stack(v_ ff) +stack(r_oc) +stack(v_oc_t) +stack(temp_corr)) + + LFM_4 - subtractive losses + pr_dc = 1/ff - (stack(i_sc) +stack(i_mp) + +stack(v_mp) +stack(v_oc_t) +stack(temp_corr) ). + + Parameters [units] + ---------- + Index (copied from dmeas) + date_time : usually for external measurements or + measurement_number : for indoor measurements e.g. IEC 61853 + + * ``poa_global`` - global plane of array irradiance [W/m^2] + * ``temp_module`` - module temperature [C] + * ``wind_speed`` - wind speed optional [m/ + + |Columns as needed by LFM_4 and/or LFM_6| : + + * ``pr_dc`` equal to `dnorm['pr_dc']` + + * ``i_sc`` | 4 6 | loss due to current at short circuit condition [%] + * ``v_oc`` | 4 6 | Loss due to voltage at open circuit condition [%] + * ``v_oc_temp_corr`` + | 4 6 | v_oc adjusted to 25C by gamma_p_mp (not beta_v_oc) + for simplicity + + * ``i_mp`` | 4 | Loss due to current part of ff [%] + * ``v_mp`` | 4 | Loss due to voltage part of ff [%] + + * ``r_sc`` | 6 | Loss due to r_sc ~r_shunt [%] + * ``i_ff`` | 6 | Loss due to r_sc corrected current part of ff [%] + * ``v_ff`` | 6 | Loss due to r_oc corrected voltage part of ff [%] + * ``r_oc`` | 6 | Loss due to r_oc related to r_series [%] +""" + +# DEFINE REFERENCE MEASUREMENT CONDITIONS +# or use existing definitions in pvlib. These might not all have +# been used in this code but are included for completeness + +# NAME value # comment unit PV_LIB name + +T_STC = 25.0 # STC temperature [C] temperature_ref +G_STC = 1000.0 # STC irradiance [W/m^2] + +# not all yet used below , added here for completeness +T_LIC = 25.0 # LIC temperature [C] +G_LIC = 200.0 # LIC irradiance [W/m^2] + +T_HTC = 75.0 # HTC temperature [C] +G_HTC = 1000.0 # HTC irradiance [W/m^2] + +T_PTC = 55.0 # HTC temperature [C] +G_PTC = 1000.0 # HTC irradiance [W/m^2] + +G_LTC = 500.0 # HTC irradiance [W/m^2] +T_LTC = 15.0 # LTC temperature [C] + +G_NOCT = 800 # NOCT irradiance [W/m^2] +T_NOCT = 45 # NOCT temperature [C] + +T_MAX = 100 # maximum temperature on right y axis + +T0C_K = 273.15 # 0C to Kelvin +T25C_K = 298.15 # 25C to Kelvin + +# Define standardised LFM graph colours as a dict ``CLR`` +CLR = { + # parameter_CLR colour R G B + 'irradiance': 'darkgreen', # 000 064 000 + 'temp_module': 'red', # 255 000 000 + 'temp_air': 'yellow', # 245 245 220 + 'wind_speed': 'grey', # 127 127 127 + + 'i_sc': 'purple', # 128 000 128 + 'r_sc': 'orange', # 255 165 000 + 'i_ff': 'lightgreen', # 144 238 144 + 'i_mp': 'green', # 000 255 000 + 'i_v': 'black', # 000 000 000 between i and v losses + 'v_ff': 'cyan', # 000 255 255 + 'v_mp': 'blue', # 000 000 255 + 'r_oc': 'pink', # 255 192 203 + 'v_oc': 'sienna', # 160 082 045 + + 'pr_dc': 'black', # 000 000 000 +} + + +def meas_to_norm(dmeas, ref): + """ + Convert measured P(W), I(A), V(V), R(Ohms) to values normalized to STC. + + Parameters + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + ref : dict + Reference electrical and thermal datasheet module values at STC. + + Returns + ------- + dnorm : DataFrame + Normalised multiplicative loss values (values approx 1). + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + 'Quantifying Long Term PV Performance and Degradation under Real + Outdoor and IEC 61853 Test Conditions Using High Quality Module + IV Measurements' 36th EU PVSEC, Marseille, France. September 2019. + + """ + dnorm = pd.DataFrame() + + # copy weather data to meas dataframe for ease of use later + dnorm['poa_global'] = dmeas['poa_global'] + dnorm['temp_module'] = dmeas['temp_module'] + dnorm['wind_speed'] = dmeas['wind_speed'] + + dnorm['pr_dc'] = dmeas['p_mp']/ref['p_mp'] / (dmeas['poa_global']/G_STC) + + # calc temperature corrected pr_dc + dnorm['pr_dc_temp_corr'] = ( + dnorm['pr_dc'] + * (1 - ref['gamma_pdc']*(dmeas['temp_module'] - T_STC))) + + # calculate normalised loss coefficients + if 'i_sc' in dmeas.columns: + dnorm['i_sc'] = (dmeas['i_sc'] / ref['i_sc'] + / (dmeas['poa_global'] / G_STC)) + + if 'i_mp' in dmeas.columns: + dnorm['i_mp'] = dmeas['i_mp'] / dmeas['i_sc'] + + if 'v_oc' in dmeas.columns: + dnorm['v_oc'] = dmeas['v_oc'] / ref['v_oc'] + + # temperature corrected + dnorm['v_oc_temp_corr'] = ( + dnorm['v_oc'] + * (1 - ref['beta_v_oc']*(dmeas['temp_module'] - T_STC))) + + if 'v_mp' in dmeas.columns: + dnorm['v_mp'] = dmeas['v_mp'] / dmeas['v_oc'] + + if all(c in dmeas.columns for c in ['i_sc', 'v_oc', 'r_sc', 'r_oc']): + ''' LFM_6 including r_sc and r_oc + + create temporary variables (i_r, v_r) from the + intercept of r_sc (at i_sc) with r_oc (at v_oc) + to make maths easier ''' + + i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) + / (dmeas['r_sc'] - dmeas['r_oc'])) + + v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] + * dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + + # calculate normalised resistances r_sc and r_oc + dnorm['r_sc'] = i_r / dmeas['i_sc'] # norm_r @ isc + dnorm['r_oc'] = v_r / dmeas['v_oc'] # norm_r @ roc + + # calculate remaining fill factor losses partitioned to i_ff, v_ff + dnorm['i_ff'] = dmeas['i_mp'] / i_r + dnorm['v_ff'] = dmeas['v_mp'] / v_r + + return dnorm + + +def mpm_a_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0., c_6=0.): + """ + Predict norm LFM values from weather data (g,t,w) in ``dmeas``. + + const temp_coeff low_light high_light wind extra + | | | | | | + norm = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters [units] + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + c_1 : float + Constant term in model. [%] + c_2 : float + Temperature coefficient in model. [1/C] + c_3 : float + Coefficient for low light log irradiance drop. [suns] + c_4 : float + Coefficient for high light linear irradiance drop. [1/suns] + c_5 : float, default 0 + Coefficient for wind speed dependence optional. [1/(m/s)] + c_6 : float, default 0 [suns] + Coefficient for dependence on inverse irradiance. + + Returns + ------- + mpm_a_out : Series + Predicted values of mpm coefficient. + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real + Outdoor and IEC 61853 Test Conditions Using High Quality + Module IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + + """ + mpm_a_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * np.log10(dmeas['poa_global'] / G_STC) + + c_4 * (dmeas['poa_global'] / G_STC) + + c_6 / (dmeas['poa_global'] / G_STC) + ) + + if 'wind_speed' in dmeas.columns: + mpm_a_out += c_5 * dmeas['wind_speed'] + + return mpm_a_out + + +def mpm_a_fit(data, var_to_fit): + """ + Fit mpm_a to normalised measured data 'var_to_fit' using mpm_a model. + + const temp_coeff low_light high_light wind extra + | | | | | | + fit = = c_1 +c_2*(t_mod-25) +c_3*log10(g) +c_4*g +c_5*ws +c_6/g + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters + ---------- + data : DataFrame (see norm) + Normalised multiplicative loss values (values approx 1). + + var_to_fit : string + Column name in ``data`` containing variable being fitted. + e.g. pr_dc, i_mp, v_mp, v_oc ... + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients ``c_1`` to ``c_6``. + + resid : Series + Residuals of the fitted model. + + coeff_err : list + Standard deviation of error in each model coefficient. + + See Also + -------- + mpm_a_calc + + """ + # drop any missing data + data = data.dropna() + + c5_zero = 'wind_speed' not in data.columns + # if wind_speed is not present, add it and force it to 0 + if c5_zero: + data['wind_speed'] = 0. + + # define function name + func = mpm_a_calc + + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 c6<0 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01, -0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2, 0]) + + """ + # full_outputboolean, optional + If True, this function returns additioal information: + infodict, mesg, and ier. + """ + + coeff, pcov, infodict, mesg, ier = optimize.curve_fit( + f=func, # fit function + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter + p0=p_0, # initial + bounds=bounds, # boundaries + full_output=True + ) + + # if data had no wind_speed measurements then c_5 coefficient is + # meaningless but a non-zero value may have been returned. + if c5_zero: + coeff[4] = 0. + + # get error of mpm coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) + + # save fit and error to dataframe + pred = mpm_a_calc(data, *coeff) + + resid = pred - data[var_to_fit] + + return pred, coeff, resid, coeff_err, infodict, mesg, ier + + +def mpm_b_fit(data, var_to_fit): + """ + Fit mpm_b to normalised measured data 'var_to_fit' using mpm_b model. + + const temp_coeff low_light improvement high_light ws + | | | | | | + fit =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters + ---------- + data : DataFrame (see norm) + Normalised multiplicative loss values (values approx 1). + + var_to_fit : string + Column name in ``data`` containing variable being fitted. + e.g. pr_dc, i_mp, v_mp ... + + Returns + ------- + pred : Series + Values predicted by the fitted model. + + coeff : list + Model coefficients ``c_1`` to ``c_5``. + + resid : Series + Residuals of the fitted model. + + coeff_err : list + Standard deviation of error in each model coefficient. + + See Also + -------- + mpm_a + + """ + # drop missing data + data = data.dropna() + + # define function name + func = mpm_b_calc + + # setup initial values and initial boundary conditions + # init c1 c2 c3 c4 c5 + + p_0 = (1.0, 0.01, 0.01, 0.01, 0.01) + # boundaries + bounds = ([-2, -2, -2, -2, -2], + [+2, +2, +2, +2, +2]) + + coeff, pcov, infodict, mesg, ier = optimize.curve_fit( + f=func, # fit function + xdata=data, # input data + ydata=data[var_to_fit], # fit parameter + p0=p_0, # initial + bounds=bounds, # boundaries + full_output=True + ) + + # get error of mpm coefficients as sqrt of covariance + perr = np.sqrt(np.diag(pcov)) + coeff_err = list(perr) + + # save fit and error to dataframe + pred = mpm_b_calc(data, *coeff) + + resid = pred - data[var_to_fit] + + # fvec = infodict["fvec"] + + return pred, coeff, resid, coeff_err, infodict, mesg, ier + + +def mpm_b_calc(dmeas, c_1, c_2, c_3, c_4, c_5=0.): + """ + Predict normalised LFM values from weather data (g,t,w) in ``dmeas``. + + const temp_coeff low_light improvement high_light ws + | | | | | | + norm =c_1 +c_2*(t_mod–25) +c_3*log10(g)*(t_k/t_stc_k) +c_4*g +c_5*ws + + where : + g = G_POA (W/m^2) / G_STC --> 'suns' + t_mod = module temperature (C) + ws = windspeed (ms^-1) + + Parameters [units] + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + c_1 : float + Constant term in model. [%] + c_2 : float + Temperature coefficient in model. [1/C] + c_3 : float + Coefficient for low light log irradiance drop. [suns] + c_4 : float + Coefficient for high light linear irradiance drop. [1/suns] + c_5 : float, default 0 + Coefficient for wind speed dependence optional. [1/(m/s)] + + Returns + ------- + mpm_b_out : Series + Predicted values of mpm coefficient. + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real + Outdoor and IEC 61853 Test Conditions Using High Quality Module + IV Measurements" + 36th EU PVSEC, Marseille, France. September 2019 + + """ + mpm_b_out = ( + c_1 + + c_2 * (dmeas['temp_module'] - T_STC) + + c_3 * ((np.log10(dmeas['poa_global'] / G_STC) + * (dmeas['temp_module'] + T0C_K) / T25C_K)) + + c_4 * (dmeas['poa_global'] / G_STC) + ) + + return mpm_b_out + + +def plot_scatter(dnorm, title, qty_lfm_vars, save_figs=False): + """ + Scatterplot of normalised values (y) vs. irradiance (x). + + Electrical quantities are plotted on the left y-axis, temperature + quantities are plotted on the right y-axis. + + Parameters + ---------- + dnorm : DataFrame + Normalised multiplicative loss values (values approx 1). + Contains 'poa_global', 'temp_module' and optional 'wind_speed' + + title : string + Title for the figure. + + qty_lfm_vars : int + number of lfm_vars : 6=iv with rsc, roc ; 4=indoor + + save_figs : boolean + save a high resolution png file of figure + + Returns + ------- + fig : Figure + Instance of matplotlib.figure.Figure + + See Also + -------- + meas_to_norm + + """ + try: + import matplotlib.pyplot as plt + except ImportError: + raise ImportError('plot_scatter requires matplotlib') + + # offset legend to the right to not overlap graph, use ~1.2 + bbox = 1.2 + + # set x_axis as irradiance in W/m2 + xdata = dnorm['poa_global'] + + fig, ax1 = plt.subplots() + + ax1.set_title(title) + + ax1.set_ylabel('Normalised values') + ax1.axhline(y=1, c='grey', linewidth=3) # show 100% line + + # optional normalised y scale usually ~0.8 to 1.1 + ax1.set_ylim(0.8, 1.1) + + ax1.set_xlabel('Plane of array irradiance [W/m$^2$]') + ax1.axvline(x=G_STC, c='grey', linewidth=3) # show 1000W/m^2 STC + ax1.axvline(x=G_NOCT, c='grey', linewidth=3) # show 800W/m^2 NOCT + ax1.axvline(x=G_LIC, c='grey', linewidth=3) # show 200W/m^2 LIC + + # check which lines to plot + if qty_lfm_vars == 6: + # LFM_6 + lines = { + 'pr_dc_temp_corr': 'pr_dc', + 'i_sc': 'i_sc', + 'r_sc': 'r_sc', + 'r_oc': 'r_oc', + 'i_ff': 'i_ff', + 'v_ff': 'v_ff', + 'v_oc_temp_corr': 'v_oc'} + + labels = { + 'pr_dc_temp_corr': 'pr_dc_temp_corr', + 'i_sc': 'norm_i_sc', + 'r_sc': 'norm_r_sc', + 'r_oc': 'norm_r_oc', + 'i_ff': 'norm_i_ff', + 'v_ff': 'norm_v_ff', + 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} + + elif qty_lfm_vars == 4: + # LFM_4 + lines = { + 'pr_dc_temp_corr': 'pr_dc', + 'i_mp': 'i_mp', + 'v_mp': 'v_mp', + 'i_sc': 'i_sc', + 'v_oc_temp_corr': 'v_oc'} + + labels = { + 'pr_dc_temp_corr': 'pr_dc_temp_corr', + 'i_mp': 'norm_i_mp', + 'v_mp': 'norm_v_mp', + 'i_sc': 'norm_i_sc', + 'v_oc_temp_corr': 'norm_v_oc_temp_corr'} + + # plot the LFM parameters depending on qty_lfm_vars + for k in lines.keys(): + try: + ax1.scatter(xdata, dnorm[k], c=CLR[lines[k]], label=labels[k]) + except KeyError: + pass + + ax1.legend(bbox_to_anchor=(bbox, 1), + loc='upper left', borderaxespad=0.) + + # y2axis plot met on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('Temperature (C/100)') + + # set wide limits 0 to 4 so they don't overlap with LFM params + ax2.set_ylim(0, 4) + + ax2.scatter(xdata, + dnorm['temp_module']/T_MAX, + c=CLR['temp_module'], + label='temp_module C/' + str(T_MAX)) + + # temp_air may not exist particularly for indoor measurements + try: + ax2.scatter(xdata, + dnorm['temp_air']/T_MAX, + c=CLR['temp_air'], + label='temp_air C/' + str(T_MAX)) + except KeyError: + pass + + # make second legend box low enough ~0.1 not to overlap first box + ax2.legend(bbox_to_anchor=(bbox, 0.1), + loc='upper left', borderaxespad=0.) + + if save_figs: + # remove '.csv', high resolution= 300 dots per inch + plt.savefig(os.path.join('mlfm_data', 'output', + 'scatter_' + title[:len(title)-4]), dpi=300) + + plt.show() + + return fig + + +def plot_stack(dstack, fill_factor, title, + xaxis_labels=0, is_i_sc_self_ref=False, + save_figs=False + ): + """ + Plot stacked subtractive losses from 1/ref_ff down to pr_dc. + + Parameters + ---------- + dstack : DataFrame + Stacked subtractive losses. + + fill_factor : float + Reference value of fill factor for IV curve at STC conditions. + + title : string + Title for the figure. + + xaxis_labels : int, default 0 + Number of x-axis labels to show. Default 0 shows all. + + is_i_sc_self_ref : bool, default False + Self-correct ``i_sc`` to remove angle of incidence, + spectrum, snow or soiling. + + save_figs : boolean + save a high resolution png file of figure + + # is_v_oc_temp_module_corr : bool, default True + # Calculate loss due to temperature and subtract from ``v_oc`` loss. + + Returns + ------- + fig : Figure + Instance of matplotlib.figure.Figure + + See Also + -------- + norm_to_stack + + """ + try: + import matplotlib.pyplot as plt + except ImportError: + raise ImportError('plt_stack requires matplotlib') + + # label names for LFM_6 + stack6 = ['i_sc', 'r_sc', 'i_ff', 'i_v', + 'v_ff', 'r_oc', 'v_oc_temp_corr'] + + if all([c in dstack.columns for c in stack6]): + + # data order from bottom to top + ydata = [dstack['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['v_oc_temp_corr'], + dstack['temp_module_corr'], + dstack['r_oc'], + dstack['v_ff'], + dstack['i_v'], + dstack['i_ff'], + dstack['r_sc'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_r_oc', + 'stack_v_ff', + '- - -', + 'stack_i_ff', + 'stack_r_sc', + 'stack_i_sc'] + + color_map = [ + 'white', # colour to bottom of graph + CLR['temp_module'], + CLR['v_oc'], + CLR['r_oc'], + CLR['v_ff'], + CLR['i_v'], + CLR['i_ff'], + CLR['r_sc'], + CLR['i_sc']] + + stack4 = ['i_sc', 'i_mp', 'i_v', + 'v_mp', 'v_oc_temp_corr'] + + if all([c in dstack.columns for c in stack4]): + + # data order from bottom to top + ydata = [dstack['pr_dc'] + (dstack['i_sc'] * (is_i_sc_self_ref)), + dstack['v_oc_temp_corr'], + dstack['temp_module_corr'], + dstack['v_mp'], + dstack['i_v'], + dstack['i_mp'], + dstack['i_sc'] * (not is_i_sc_self_ref)] + + labels = [ + 'pr_dc', + 'stack_t_mod', + 'stack_v_oc', + 'stack_v_mp', + '- - -', + 'stack_i_mp', + 'stack_i_sc'] + + color_map = [ + 'white', # colour to bottom of graph + CLR['temp_module'], + CLR['v_oc'], + CLR['v_mp'], + CLR['i_v'], + CLR['i_mp'], + CLR['i_sc']] + + # offset legend right, use ~1.2 + bbox = 1.2 + + # select x axis usually date_time + xdata = dstack.index.values + fig, ax1 = plt.subplots() + + ax1.set_title(title) + + # plot stack in order bottom to top, + # allowing self_ref and temp_module corrections + ax1.stackplot(xdata, *tuple(ydata), labels=labels, colors=color_map) + + ax1.axhline(y=1/fill_factor, c='grey', lw=3) # show initial 1/FF + ax1.axhline(y=1, c='grey', lw=3) # show 100% line + ax1.set_ylabel('stacked lfm losses') + + # find number of x date values + x_ticks = dstack.shape[0] + plt.xticks(np.arange(0, x_ticks), rotation=90) + + # if (xaxis_labels > 0 and xaxis_labels < x_ticks): + if 0 < xaxis_labels < x_ticks: + xaxis_skip = np.floor(x_ticks / xaxis_labels) + else: + xaxis_skip = 2 + + # + xax2 = [''] * x_ticks + x_count = 0 + while x_count < x_ticks: + if x_count % xaxis_skip == 0: + # + # try to reformat any date indexes (not for matrices) + # + # 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 + # y y y y - m m - d d t h h : m m : s s --> yy-mm-dd hh'h' + # + try: + xax2[x_count] = xdata[x_count][2:13]+'h' + except IndexError: + xax2[x_count] = xdata[x_count] + except TypeError: # xdata can't be subscripted + xax2[x_count] = xdata[0] + + x_count += 1 + + ax1.set_xticklabels(xax2) + ax1.set_ylim(0.6, 1/fill_factor + 0.1) # optional normalised y scale + plt.legend(bbox_to_anchor=(bbox, 1), loc='upper left', borderaxespad=0.) + + # plot met data on right y axis + ax2 = ax1.twinx() + ax2.set_ylabel('poa_global (kW/m^2), temp_module (C/ ' + str(T_MAX)) + ax2.set_ylim(0, 4) # set so doesn't overlap lfm params + + plt.plot(xdata, dstack['poa_global'] / G_STC, + c=CLR['irradiance'], label='poa_global (kW/m^2)') + plt.plot(xdata, dstack['temp_module'] / T_MAX, + c=CLR['temp_module'], label='temp_module / ' + str(T_MAX)) + + # temp_air may not exist particularly for indoor measurements + try: + plt.plot(xdata, dstack['temp_air']/100, + c=CLR['temp_air'], label='temp_air/ ' + str(T_MAX)) + except KeyError: + pass + + ax2.legend(bbox_to_anchor=(bbox, 0.3), loc='upper left', borderaxespad=0.) + ax1.set_xticklabels(xax2, rotation=90) + + # remove '.csv', high resolution= 300 dots per inch + plt.savefig(os.path.join('mlfm_data', 'output', + 'stack_' + title[:len(title)-4]), dpi=300) + + return fig + + +def meas_to_stack_lin(dmeas, ref, qty_lfm_vars, gap=0.01): + """ + Convert measured values to stacked subtractive normalized losses. + + Stacked subtractive losses show the relative loss proportions + from max possible "ref_i_sc * ref_v_oc" (1/reference fill factor) + to the measured normalized power. + + This version is done in a linear fashion so that LFM4 and LFM6 give the + same answers for Isc and Voc and the loss(i_mp)=loss(r_sc)+loss(i_ff) + + Parameters + ---------- + dmeas : DataFrame + Measured weather and module electrical values per time or measurement. + Contains 'poa_global', 'temp_module' and optional 'wind_speed'. + + ref : dict + Reference electrical and thermal datasheet module values at STC. + + gap : float + create a gap to differentiate i and v losses ~ 0.01 + + qty_lfm_vars : int + number of lfm_vars : 6=iv with rsc, roc ; 4=without rsc, roc + + Returns + ------- + dstack : DataFrame + Stacked subtractive normalized losses + + See Also + -------- + meas_to_norm + + References + ---------- + .. [1] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) + "Quantifying Long Term PV Performance and Degradation under Real + Outdoor and IEC 61853 Test Conditions Using High Quality Module + IV Measurements" 36th EU PVSEC, Marseille, France. September 2019 + """ + # create an empty DataFrame to put stack results + dstack = pd.DataFrame() + + # copy weather data for ease of use + dstack['poa_global'] = dmeas['poa_global'] + dstack['temp_module'] = dmeas['temp_module'] + dstack['wind_speed'] = dmeas['wind_speed'] + + # ref['p_mp'] = ref['i_mp'] * ref['v_mp'] + + # ref['ff'] = ref['p_mp'] / (ref['i_sc'] * ref['v_oc']) + + # ref['ff'] = (ref['i_mp']*ref['v_mp'])/(ref['i_sc']*ref['v_oc']) + inv_ff = 1 / ref['ff'] + + dstack['pr_dc'] = dmeas['pr_dc'] + + # Find linear values on i and v axes normalised to i_mp, v_mp + lin_i_ratio = ref['i_sc']/ref['i_mp'] + lin_v_ratio = ref['v_oc']/ref['v_mp'] + + lin_i_sc = dmeas['i_sc']/ref['i_mp']/(dmeas['poa_global']/G_STC) + + lin_v_oc = dmeas['v_oc']/ref['v_mp'] + lin_v_oc_temp_corr = dmeas['v_oc_temp_corr']/ref['v_mp'] + + # transform multiplicative to subtractive losses find + # correction factor to scale losses to keep 1/ff --> pr_dc + + if qty_lfm_vars == 6: + # subtractive losses with series and shunt resistance effects + i_r = ((dmeas['i_sc'] * dmeas['r_sc'] - dmeas['v_oc']) / + (dmeas['r_sc'] - dmeas['r_oc'])) + + v_r = ((dmeas['r_sc'] * (dmeas['v_oc'] - dmeas['i_sc'] * + dmeas['r_oc']) / (dmeas['r_sc'] - dmeas['r_oc']))) + + lin_i_r = i_r/ref['i_mp'] / (dmeas['poa_global']/G_STC) + lin_i_ff = dmeas['i_mp'] / ref['i_mp']/(dmeas['poa_global']/G_STC) + + lin_v_ff = dmeas['v_mp'] / ref['v_mp'] + lin_v_r = v_r / ref['v_mp'] + + sub_i = lin_i_ratio - lin_i_ff # current drop + sub_v = lin_v_ratio - lin_v_ff # voltage drop + + # correction factor mult --> lin loss + corr = (inv_ff - dstack['pr_dc']) / (sub_i + sub_v) + + # put 6 LFM values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # accounting for series and shunt resistance losses + + dstack['i_sc'] = (lin_i_ratio-lin_i_sc) * corr + dstack['r_sc'] = (lin_i_sc-lin_i_r) * corr + dstack['i_ff'] = (lin_i_r-lin_i_ff) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_ff'] = (lin_v_r-lin_v_ff) * corr - gap/2 + dstack['r_oc'] = (lin_v_oc-lin_v_r) * corr + dstack['v_oc_temp_corr'] = (lin_v_oc_temp_corr-lin_v_oc) * corr + dstack['temp_module_corr'] = (lin_v_ratio-lin_v_oc_temp_corr) * corr + + if qty_lfm_vars == 4: + + lin_i_mp = dmeas['i_mp'] / ref['i_mp'] / (dmeas['poa_global']/G_STC) + lin_v_mp = dmeas['v_mp'] / ref['v_mp'] + + sub_i = lin_i_ratio - lin_i_mp # current drop + sub_v = lin_v_ratio - lin_v_mp # voltage drop + + # correction factor mult --> lin loss + corr = (inv_ff-dstack['pr_dc']) / (sub_i + sub_v) + + # put 4 LFM values in a stack from pr_dc (bottom) to 1/ff_ref (top) + # accounting for series and shunt resistance losse + + dstack['i_sc'] = (lin_i_ratio-lin_i_sc) * corr + dstack['i_mp'] = (lin_i_sc-lin_i_mp) * corr - gap/2 + dstack['i_v'] = gap + dstack['v_mp'] = (lin_v_oc-lin_v_mp) * corr - gap/2 + dstack['v_oc_temp_corr'] = (lin_v_oc_temp_corr-lin_v_oc) * corr + dstack['temp_module_corr'] = (lin_v_ratio-lin_v_oc_temp_corr) * corr + + return dstack + + +""" +The Loss Factors Model (LFM) and Mechanistic Performance Model (MPM) +together known as "MLFM" have been developed by SRCL and Gantner Instruments +(previously Oerlikon Solar and Tel Solar) since 2011 MLFM and 2017 MPM + +.. [1] J. Sutterlueti(now Gantner Instruments) and S. Ransome + '4AV.2.41 Characterising PV Modules under Outdoor Conditions: +What's Most Important for Energy Yield' +26th EU PVSEC 8 September 2011; Hamburg, Germany. +http://www.steveransome.com/pubs/2011Hamburg_4AV2_41.pdf + +.. [2] Steve Ransome and Juergen Sutterlueti(Gantner Instruments) + 'Choosing the best Empirical Model for predicting energy yield' + 7th PV Energy Rating and Module Performance Modeling Workshop, + Canobbio, Switzerland 30-31 March, 2017. + +.. [3] S. Ransome and J. Sutterlueti (Gantner Instruments) +'Checking the new IEC 61853.1-4 with high quality 3rd party data to +benchmark its practical relevance in energy yield prediction' +PVSC June 2019 [Chicago], USA. +http://www.steveransome.com/PUBS/1906_PVSC46_Chicago_Ransome.pdf + +.. [4] Steve Ransome (SRCL) and Juergen Sutterlueti (Gantner Instruments) +'5CV.4.35 Quantifying Long Term PV Performance and Degradation +under Real Outdoor and IEC 61853 Test Conditions +Using High Quality Module IV Measurements'. +36th EU PVSEC Sep 2019 [Marseille] + +.. [5] Steve Ransome (SRCL) +'How to use the Loss Factors and Mechanistic Performance Models +effectively with PVPMC/PVLIB' +[PVPMC] Webinar on PV Performance Modeling Methods, Aug 2020. +https://pvpmc.sandia.gov/download/7879/ + +.. [6] W.Marion et al (NREL) +'New Data Set for Validating PV Module Performance Models'. +https://www.researchgate.net/publication/286746041_New_data_set_for_validating_PV_module_performance_models +Many more papers are available at www.steveransome.com + +.. [7] Steve Ransome (SRCL) +'Benchmarking PV performance models with high quality IEC 61853 Matrix +measurements (Bilinear interpolation, SAPM, PVGIS, MLFM and 1-diode)' +http://www.steveransome.com/pubs/2206_PVSC49_philadelphia_4_presented.pdf + +.. [8] Juergen Sutterlueti (Gantner Instruments) +'Advanced system monitoring and artificial intelligent data-driven analytics +to serve GW-scale photovoltaic power plant and energy storage requirements' +https://pvpmc.sandia.gov/download/8574/ + +""" From 050071f54419bf70516bf3d17617284f01f0d540 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Tue, 13 Dec 2022 22:09:04 +0000 Subject: [PATCH 79/81] Add files via upload From 0faed51bd0556813b0c6364997f6edf228b1e473 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Tue, 13 Dec 2022 22:33:32 +0000 Subject: [PATCH 80/81] Add files via upload --- mlfm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlfm.py b/mlfm.py index ef68f4b600..97c09154cf 100644 --- a/mlfm.py +++ b/mlfm.py @@ -7,7 +7,7 @@ # import pvlib """ -ver : 221213t17 <-- delete when finalised +ver : 221213t22 <-- delete when finalised ``mlfm.py`` module contains functions to analyse, fit, predict and display performance of PV modules using the mechanistic performance model (MPM) and From 30cc5ec32052aa993d5335fcf6b2e547e50f4194 Mon Sep 17 00:00:00 2001 From: steve ransome Date: Tue, 13 Dec 2022 22:36:43 +0000 Subject: [PATCH 81/81] Add files via upload

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