diff --git a/appveyor.yml b/appveyor.yml index 0055415380..acf0b50139 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -27,7 +27,7 @@ install: - cmd: conda info -a # install depenencies - - cmd: conda create -n test_env --yes --quiet python=%PYTHON_VERSION% pip numpy scipy pandas nose pytest pytz ephem numba + - cmd: conda create -n test_env --yes --quiet python=%PYTHON_VERSION% pip numpy scipy pandas nose pytest pytz ephem numba siphon -c conda-forge - cmd: activate test_env - cmd: python --version - cmd: conda list diff --git a/ci/requirements-py27.yml b/ci/requirements-py27.yml index e025c16fa6..2ede99444d 100644 --- a/ci/requirements-py27.yml +++ b/ci/requirements-py27.yml @@ -1,4 +1,7 @@ name: test_env +channels: + - defaults + - http://conda.anaconda.org/conda-forge dependencies: - python=2.7 - numpy @@ -7,6 +10,7 @@ dependencies: - pytz - ephem - numba + - siphon - pytest - pytest-cov - nose diff --git a/ci/requirements-py34.yml b/ci/requirements-py34.yml index d1931ce0a8..42f1daddbe 100644 --- a/ci/requirements-py34.yml +++ b/ci/requirements-py34.yml @@ -1,4 +1,7 @@ name: test_env +channels: + - defaults + - conda-forge dependencies: - python=3.4 - numpy @@ -7,6 +10,7 @@ dependencies: - pytz - ephem - numba + - siphon - pytest - pytest-cov - nose diff --git a/ci/requirements-py35.yml b/ci/requirements-py35.yml index 52aae6f9be..dbde37fe93 100644 --- a/ci/requirements-py35.yml +++ b/ci/requirements-py35.yml @@ -1,4 +1,7 @@ name: test_env +channels: + - defaults + - conda-forge dependencies: - python=3.5 - numpy @@ -7,6 +10,7 @@ dependencies: - pytz - ephem - numba + - siphon - pytest - pytest-cov - nose diff --git a/docs/environment.yml b/docs/environment.yml index ed7c4027e0..030b14facb 100644 --- a/docs/environment.yml +++ b/docs/environment.yml @@ -1,4 +1,7 @@ name: pvlibdocs +channels: + - defaults +# - conda-forge # needed for siphon, but causes path too short problems on rtd dependencies: - python=2.7 - mock # needed for local python 2.7 builds @@ -13,6 +16,7 @@ dependencies: - numpydoc - matplotlib - seaborn +# - siphon - sphinx=1.3.5 # same versions as rtd - sphinx_rtd_theme=0.1.7 - docutils=0.12 diff --git a/docs/sphinx/source/forecasts.rst b/docs/sphinx/source/forecasts.rst new file mode 100644 index 0000000000..501194a43a --- /dev/null +++ b/docs/sphinx/source/forecasts.rst @@ -0,0 +1,477 @@ +.. _forecasts: + +*********** +Forecasting +*********** + +pvlib-python provides a set of functions and classes that make it easy +to obtain weather forecast data and convert that data into a PV power +forecast. Users can retrieve standardized weather forecast data relevant +to PV power modeling from NOAA/NCEP/NWS models including the GFS, NAM, +RAP, HRRR, and the NDFD. A PV power forecast can then be obtained using +the weather data as inputs to the comprehensive modeling capabilities of +PVLIB-Python. Standardized, open source, reference implementations of +forecast methods using publicly available data may help advance the +state-of-the-art of solar power forecasting. + +pvlib-python uses Unidata's `Siphon +`_ library to simplify access +to forecast data hosted on the Unidata `THREDDS catalog +`_. + +This document demonstrates how to use pvlib-python to create a PV power +forecast using these tools. The `forecast +`_ and `forecast_to_power +`_ Jupyter notebooks +provide additional example code. + +.. warning:: + + The forecast module algorithms and features are highly experimental. + The API may change, the functionality may be consolidated into an io + module, or the module may be separated into its own package. + +.. note:: + + This documentation is difficult to reliably build on readthedocs. + If you do not see images, try building the documentation on your + own machine or see the notebooks linked to above. + + +Accessing Forecast Data +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The Siphon library provides access to, among others, forecasts from the +Global Forecast System (GFS), North American Model (NAM), High +Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), and National +Digital Forecast Database (NDFD) on a Unidata THREDDS server. +Unfortunately, many of these models use different names to describe the +same quantity (or a very similar one), and not all variables are present +in all models. For example, on the THREDDS server, the GFS has a field +named +``Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average``, +while the RAP has a field named +``Total_cloud_cover_entire_atmosphere_single_layer``, and a +similar field in the HRRR is named +``Total_cloud_cover_entire_atmosphere``. + +PVLIB-Python aims to simplify the access of the model fields relevant +for solar power forecasts. Model data accessed with PVLIB-Python is +returned as a pandas DataFrame with consistent column names: +``temp_air, wind_speed, total_clouds, low_clouds, mid_clouds, +high_clouds, dni, dhi, ghi``. To accomplish this, we use an +object-oriented framework in which each weather model is represented by +a class that inherits from a parent +:py:class:`~pvlib.forecast.ForecastModel` class. +The parent :py:class:`~pvlib.forecast.ForecastModel` class contains the +common code for accessing and parsing the data using Siphon, while the +child model-specific classes (:py:class:`~pvlib.forecast.GFS`, +:py:class:`~pvlib.forecast.HRRR`, etc.) contain the code necessary to +map and process that specific model's data to the standardized fields. + +The code below demonstrates how simple it is to access and plot forecast +data using PVLIB-Python. First, we set up make the basic imports and +then set the location and time range data. + +.. ipython:: python + + import pandas as pd + import matplotlib.pyplot as plt + import datetime + + # seaborn makes the plots look nicer + import seaborn as sns; sns.set_color_codes() + + # import pvlib forecast models + from pvlib.forecast import GFS, NAM, NDFD, HRRR, RAP + + # specify location (Tucson, AZ) + latitude, longitude, tz = 32.2, -110.9, 'US/Arizona' + + # specify time range. + start = pd.Timestamp(datetime.date.today(), tz=tz) + end = start + pd.Timedelta(days=7) + + irrad_vars = ['ghi', 'dni', 'dhi'] + + +Next, we instantiate a GFS model object and get the forecast data +from Unidata. + +.. ipython:: python + + # GFS model, defaults to 0.5 degree resolution + # 0.25 deg available + model = GFS() + + # retrieve data. returns pandas.DataFrame object + raw_data = model.get_data(latitude, longitude, start, end) + + print(raw_data.head()) + +It will be useful to process this data before using it with pvlib. For +example, the column names are non-standard, the temperature is in +Kelvin, the wind speed is broken into east/west and north/south +components, and most importantly, most of the irradiance data is +missing. The forecast module provides a number of methods to fix these +problems. + +.. ipython:: python + + data = raw_data + + # rename the columns according the key/value pairs in model.variables. + data = model.rename(data) + + # convert temperature + data['temp_air'] = model.kelvin_to_celsius(data['temp_air']) + + # convert wind components to wind speed + data['wind_speed'] = model.uv_to_speed(data) + + # calculate irradiance estimates from cloud cover. + # uses a cloud_cover to ghi to dni model or a + # uses a cloud cover to transmittance to irradiance model. + # this step is discussed in more detail in the next section + irrad_data = model.cloud_cover_to_irradiance(data['total_clouds']) + data = data.join(irrad_data, how='outer') + + # keep only the final data + data = data.ix[:, model.output_variables] + + print(data.head()) + +Much better. + +The GFS class's +:py:func:`~pvlib.forecast.GFS.process_data` method combines these steps +in a single function. In fact, each forecast model class +implements its own ``process_data`` method since the data from each +weather model is slightly different. The ``process_data`` functions are +designed to be explicit about how the data is being processed, and users +are **strongly** encouraged to read the source code of these methods. + +.. ipython:: python + + data = model.process_data(raw_data) + + print(data.head()) + +Users can easily implement their own ``process_data`` methods on +inherited classes or implement similar stand-alone functions. + +The forecast model classes also implement a +:py:func:`~pvlib.forecast.ForecastModel.get_processed_data` method that +combines the :py:func:`~pvlib.forecast.ForecastModel.get_data` and +:py:func:`~pvlib.forecast.ForecastModel.process_data` calls. + +.. ipython:: python + + data = model.get_processed_data(latitude, longitude, start, end) + + print(data.head()) + + +Cloud cover and radiation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +All of the weather models currently accessible by pvlib include one or +more cloud cover forecasts. For example, below we plot the GFS cloud +cover forecasts. + +.. ipython:: python + + # plot cloud cover percentages + cloud_vars = ['total_clouds', 'low_clouds', + 'mid_clouds', 'high_clouds'] + data[cloud_vars].plot(); + plt.ylabel('Cloud cover %'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('GFS 0.5 deg forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig gfs_cloud_cover.png width=6in + plt.legend(); + +However, many of forecast models do not include radiation components in +their output fields, or if they do then the radiation fields suffer from +poor solar position or radiative transfer algorithms. It is often more +accurate to create empirically derived radiation forecasts from the +weather models' cloud cover forecasts. + +PVLIB-Python provides two basic ways to convert cloud cover forecasts to +irradiance forecasts. One method assumes a linear relationship between +cloud cover and GHI, applies the scaling to a clear sky climatology, and +then uses the DISC model to calculate DNI. The second method assumes a +linear relationship between cloud cover and atmospheric transmittance, +and then uses the Liu-Jordan [Liu60]_ model to calculate GHI, DNI, and +DHI. + +*Caveat emptor*: these algorithms are not rigorously verified! The +purpose of the forecast module is to provide a few exceedingly simple +options for users to play with before they develop their own models. We +strongly encourage pvlib users first read the source code and second +to implement new cloud cover to irradiance algorithms. + +The essential parts of the clear sky scaling algorithm are as follows. +Clear sky scaling of climatological GHI is also used in Larson et. al. +[Lar16]_. + +.. code-block:: python + + solpos = location.get_solarposition(cloud_cover.index) + cs = location.get_clearsky(cloud_cover.index, model='ineichen') + # offset and cloud cover in decimal units here + # larson et. al. use offset = 0.35 + ghi = (offset + (1 - offset) * (1 - cloud_cover)) * ghi_clear + dni = disc(ghi, solpos['zenith'], cloud_cover.index)['dni'] + dhi = ghi - dni * np.cos(np.radians(solpos['zenith'])) + +The figure below shows the result of the total cloud cover to +irradiance conversion using the clear sky scaling algorithm. + +.. ipython:: python + + # plot irradiance data + data = model.rename(raw_data) + irrads = model.cloud_cover_to_irradiance(data['total_clouds'], how='clearsky_scaling') + irrads.plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('GFS 0.5 deg forecast for lat={}, lon={} using "clearsky_scaling"' + .format(latitude, longitude)); + @savefig gfs_irrad_cs.png width=6in + plt.legend(); + + +The essential parts of the Liu-Jordan cloud cover to irradiance algorithm +are as follows. + +.. code-block:: python + + # cloud cover in percentage units here + transmittance = ((100.0 - cloud_cover) / 100.0) * 0.75 + # irrads is a DataFrame containing ghi, dni, dhi + irrads = liujordan(apparent_zenith, transmittance, airmass_absolute) + +The figure below shows the result of the Liu-Jordan total cloud cover to +irradiance conversion. + +.. ipython:: python + + # plot irradiance data + irrads = model.cloud_cover_to_irradiance(data['total_clouds'], how='liujordan') + irrads.plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('GFS 0.5 deg forecast for lat={}, lon={} using "liujordan"' + .format(latitude, longitude)); + @savefig gfs_irrad_lj.png width=6in + plt.legend(); + + +Most weather model output has a fairly coarse time resolution, at least +an hour. The irradiance forecasts have the same time resolution as the +weather data. However, it is straightforward to interpolate the cloud +cover forecasts onto a higher resolution time domain, and then +recalculate the irradiance. + +.. ipython:: python + + resampled_data = data.resample('5min').interpolate() + resampled_irrads = model.cloud_cover_to_irradiance(resampled_data['total_clouds'], how='clearsky_scaling') + resampled_irrads.plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('GFS 0.5 deg forecast for lat={}, lon={} resampled' + .format(latitude, longitude)); + @savefig gfs_irrad_high_res.png width=6in + plt.legend(); + +Users may then recombine resampled_irrads and resampled_data using +slicing :py:func:`pandas.concat` or :py:meth:`pandas.DataFrame.join`. + +We reiterate that the open source code enables users to customize the +model processing to their liking. + +.. [Lar16] Larson et. al. "Day-ahead forecasting of solar power output + from photovoltaic plants in the American Southwest" Renewable + Energy 91, 11-20 (2016). + +.. [Liu60] B. Y. Liu and R. C. Jordan, The interrelationship and + characteristic distribution of direct, diffuse, and total solar + radiation, *Solar Energy* **4**, 1 (1960). + + +Weather Models +~~~~~~~~~~~~~~ + +Next, we provide a brief description of the weather models available to +pvlib users. Note that the figures are generated when this documentation +is compiled so they will vary over time. + +GFS +--- +The Global Forecast System (GFS) is the US model that provides forecasts +for the entire globe. The GFS is updated every 6 hours. The GFS is run +at two resolutions, 0.25 deg and 0.5 deg, and is available with 3 hour +time resolution. Forecasts from GFS model were shown above. Use the GFS, +among others, if you want forecasts for 1-7 days or if you want forecasts +for anywhere on Earth. + + +HRRR +---- +The High Resolution Rapid Refresh (HRRR) model is perhaps the most +accurate model, however, it is only available for ~15 hours. It is +updated every hour and runs at 3 km resolution. The HRRR excels in +severe weather situations. A major upgrade to the HRRR model is expected +in Spring, 2016. See the `NOAA ESRL HRRR page +`_ for more information. Use the +HRRR, among others, if you want forecasts for less than 24 hours. +The HRRR model covers the continental United States. + +.. ipython:: python + + model = HRRR() + data = model.get_processed_data(latitude, longitude, start, end) + + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('HRRR 3 km forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig hrrr_irrad.png width=6in + plt.legend(); + + +RAP +--- +The Rapid Refresh (RAP) model is the parent model for the HRRR. It is +updated every hour and runs at 40, 20, and 13 km resolutions. Only the +20 and 40 km resolutions are currently available in pvlib. It is also +excels in severe weather situations. A major upgrade to the RAP model is +expected in Spring, 2016. See the `NOAA ESRL HRRR page +`_ for more information. Use the +RAP, among others, if you want forecasts for less than 24 hours. +The RAP model covers most of North America. + +.. ipython:: python + + model = RAP() + data = model.get_processed_data(latitude, longitude, start, end) + + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('RAP 13 km forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig rap_irrad.png width=6in + plt.legend(); + + +NAM +--- +The North American Mesoscale model covers, not surprisingly, North +America. It is updated every 6 hours. pvlib provides access to 20 km +resolution NAM data with a time horizon of up to 4 days. + +.. ipython:: python + + model = NAM() + data = model.get_processed_data(latitude, longitude, start, end) + + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('NAM 20 km forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig nam_irrad.png width=6in + plt.legend(); + + +NDFD +---- +The National Digital Forecast Database is not a model, but rather a +collection of forecasts made by National Weather Service offices +across the country. It is updated every 6 hours. +Use the NDFD, among others, for forecasts at all time horizons. +The NDFD is available for the United States. + +.. ipython:: python + + model = NDFD() + data = model.get_processed_data(latitude, longitude, start, end) + + data[irrad_vars].plot(); + plt.ylabel('Irradiance ($W/m^2$)'); + plt.xlabel('Forecast Time ({})'.format(tz)); + plt.title('NDFD forecast for lat={}, lon={}' + .format(latitude, longitude)); + @savefig ndfd_irrad.png width=6in + plt.legend(); + + +PV Power Forecast +~~~~~~~~~~~~~~~~~ + +Finally, we demonstrate the application of the weather forecast data to +a PV power forecast. Please see the remainder of the pvlib documentation +for details. + +.. ipython:: python + + from pvlib.pvsystem import PVSystem, retrieve_sam + from pvlib.tracking import SingleAxisTracker + from pvlib.modelchain import ModelChain + + sandia_modules = retrieve_sam('sandiamod') + cec_inverters = retrieve_sam('cecinverter') + module = sandia_modules['Canadian_Solar_CS5P_220M___2009_'] + inverter = cec_inverters['SMA_America__SC630CP_US_315V__CEC_2012_'] + + # model a big tracker for more fun + system = SingleAxisTracker(module_parameters=module, + inverter_parameters=inverter, + modules_per_string=15, + strings_per_inverter=300) + + # fx is a common abbreviation for forecast + fx_model = GFS() + fx_data = fx_model.get_processed_data(latitude, longitude, start, end) + + # use a ModelChain object to calculate modeling intermediates + mc = ModelChain(system, fx_model.location) + + # extract relevant data for model chain + irradiance = fx_data[['ghi', 'dni', 'dhi']] + weather = fx_data[['wind_speed', 'temp_air']] + mc.run_model(fx_data.index, irradiance=irradiance, weather=weather); + +Now we plot a couple of modeling intermediates and the forecast power. +Here's the forecast plane of array irradiance... + +.. ipython:: python + + mc.total_irrad.plot(); + @savefig poa_irrad.png width=6in + plt.ylabel('Plane of array irradiance ($W/m**2$)'); + +...the cell and module temperature... + +.. ipython:: python + + mc.temps.plot(); + @savefig pv_temps.png width=6in + plt.ylabel('Temperature (C)'); + +...and finally AC power... + +.. ipython:: python + + mc.ac.plot(); + plt.ylim(0, None); + @savefig ac_power.png width=6in + plt.ylabel('AC Power (W)'); + diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst index c36136510e..45f10ecedf 100644 --- a/docs/sphinx/source/index.rst +++ b/docs/sphinx/source/index.rst @@ -76,6 +76,7 @@ Contents contributing timetimezones clearsky + forecasts modules classes comparison_pvlib_matlab diff --git a/docs/sphinx/source/modules.rst b/docs/sphinx/source/modules.rst index 3ae22dd0f4..e0a23ea8ac 100644 --- a/docs/sphinx/source/modules.rst +++ b/docs/sphinx/source/modules.rst @@ -17,6 +17,14 @@ clearsky :undoc-members: :show-inheritance: +forecast +---------------- + +.. automodule:: pvlib.forecast + :members: + :undoc-members: + :show-inheritance: + irradiance ----------------- @@ -56,7 +64,7 @@ solarposition :members: :undoc-members: :show-inheritance: - + tmy -------------------- @@ -80,4 +88,3 @@ tools :members: :undoc-members: :show-inheritance: - \ No newline at end of file diff --git a/docs/sphinx/source/whatsnew/v0.4.0.txt b/docs/sphinx/source/whatsnew/v0.4.0.txt index 7748db31cf..7c38c8fe14 100644 --- a/docs/sphinx/source/whatsnew/v0.4.0.txt +++ b/docs/sphinx/source/whatsnew/v0.4.0.txt @@ -59,6 +59,8 @@ Enhancements `ModelChain refactor gist `_ for more discussion about new features in ModelChain. (:issue:`143`, :issue:`194`) +* Added ``forecast.py`` module for solar power forecasts. + (:issue:`86`, :issue:`124`, :issue:`180`) Bug fixes @@ -117,3 +119,4 @@ Code Contributors * Will Holmgren * Jonathan Chambers * Mitchell Lee +* Derek Groenendyk diff --git a/docs/tutorials/forecast.ipynb b/docs/tutorials/forecast.ipynb new file mode 100644 index 0000000000..9c0353e8c0 --- /dev/null +++ b/docs/tutorials/forecast.ipynb @@ -0,0 +1,6239 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Forecast Tutorial\n", + "\n", + "This tutorial will walk through forecast data from Unidata forecast model data using the forecast.py module within pvlib.\n", + "\n", + "Table of contents:\n", + "1. [Setup](#Setup)\n", + "2. [Intialize and Test Each Forecast Model](#Instantiate-GFS-forecast-model)\n", + "\n", + "This tutorial has been tested against the following package versions:\n", + "* Python 3.5.2\n", + "* IPython 5.0.0\n", + "* pandas 0.18.0\n", + "* matplotlib 1.5.1\n", + "* netcdf4 1.2.1\n", + "* siphon 0.4.0\n", + "\n", + "It should work with other Python and Pandas versions. It requires pvlib >= 0.3.0 and IPython >= 3.0.\n", + "\n", + "Authors:\n", + "* Derek Groenendyk (@moonraker), University of Arizona, November 2015\n", + "* Will Holmgren (@wholmgren), University of Arizona, November 2015, January 2016, April 2016, July 2016" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/holmgren/git_repos/pvlib-python/pvlib/forecast.py:22: UserWarning: The forecast module algorithms and features are highly experimental. The API may change, the functionality may be consolidated into an io module, or the module may be separated into its own package.\n", + " 'module, or the module may be separated into its own package.')\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "try:\n", + " import seaborn as sns\n", + " sns.set(rc={\"figure.figsize\": (12, 6)})\n", + "except ImportError:\n", + " print('We suggest you install seaborn using conda or pip and rerun this cell')\n", + "\n", + "# built in python modules\n", + "import datetime\n", + "import os\n", + "\n", + "# python add-ons\n", + "import numpy as np\n", + "import pandas as pd\n", + "try:\n", + " import netCDF4\n", + " from netCDF4 import num2date\n", + "except ImportError:\n", + " print('We suggest you install netCDF4 using conda rerun this cell')\n", + "\n", + "# for accessing UNIDATA THREDD servers\n", + "from siphon.catalog import TDSCatalog\n", + "from siphon.ncss import NCSS\n", + "\n", + "import pvlib\n", + "from pvlib.forecast import GFS, HRRR_ESRL, NAM, NDFD, HRRR, RAP" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2016-07-27 00:00:00-07:00 2016-08-03 00:00:00-07:00\n" + ] + } + ], + "source": [ + "# Choose a location and time.\n", + "# Tucson, AZ\n", + "latitude = 32.2\n", + "longitude = -110.9 \n", + "tz = 'US/Arizona'\n", + "\n", + "start = pd.Timestamp(datetime.date.today(), tz=tz) # today's date\n", + "end = start + pd.Timedelta(days=7) # 7 days from today\n", + "print(start, end)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GFS (0.5 deg)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pvlib.forecast import GFS, HRRR_ESRL, NAM, NDFD, HRRR, RAP " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GFS model, defaults to 0.5 degree resolution\n", + "fm = GFS()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average \\\n", + "2016-07-27 09:00:00-07:00 0.0 \n", + "2016-07-27 12:00:00-07:00 0.0 \n", + "2016-07-27 15:00:00-07:00 70.0 \n", + "2016-07-27 18:00:00-07:00 377.0 \n", + "2016-07-27 21:00:00-07:00 960.0 \n", + "2016-07-28 00:00:00-07:00 835.0 \n", + "2016-07-28 03:00:00-07:00 151.0 \n", + "2016-07-28 06:00:00-07:00 75.0 \n", + "2016-07-28 09:00:00-07:00 0.0 \n", + "2016-07-28 12:00:00-07:00 0.0 \n", + "2016-07-28 15:00:00-07:00 150.0 \n", + "2016-07-28 18:00:00-07:00 425.0 \n", + "2016-07-28 21:00:00-07:00 970.0 \n", + "2016-07-29 00:00:00-07:00 729.0 \n", + "2016-07-29 03:00:00-07:00 145.0 \n", + "2016-07-29 06:00:00-07:00 72.0 \n", + "2016-07-29 09:00:00-07:00 0.0 \n", + "2016-07-29 12:00:00-07:00 0.0 \n", + "2016-07-29 15:00:00-07:00 150.0 \n", + "2016-07-29 18:00:00-07:00 434.0 \n", + "2016-07-29 21:00:00-07:00 980.0 \n", + "2016-07-30 00:00:00-07:00 850.0 \n", + "2016-07-30 03:00:00-07:00 152.0 \n", + "2016-07-30 06:00:00-07:00 76.0 \n", + "2016-07-30 09:00:00-07:00 0.0 \n", + "2016-07-30 12:00:00-07:00 0.0 \n", + "2016-07-30 15:00:00-07:00 140.0 \n", + "2016-07-30 18:00:00-07:00 418.0 \n", + "2016-07-30 21:00:00-07:00 960.0 \n", + "2016-07-31 00:00:00-07:00 669.0 \n", + "2016-07-31 03:00:00-07:00 95.0 \n", + "2016-07-31 06:00:00-07:00 48.0 \n", + "2016-07-31 09:00:00-07:00 0.0 \n", + "2016-07-31 12:00:00-07:00 0.0 \n", + "2016-07-31 15:00:00-07:00 90.0 \n", + "2016-07-31 18:00:00-07:00 348.0 \n", + "2016-07-31 21:00:00-07:00 940.0 \n", + "2016-08-01 00:00:00-07:00 772.0 \n", + "2016-08-01 03:00:00-07:00 88.0 \n", + "2016-08-01 06:00:00-07:00 44.0 \n", + "2016-08-01 09:00:00-07:00 0.0 \n", + "2016-08-01 12:00:00-07:00 0.0 \n", + "2016-08-01 15:00:00-07:00 100.0 \n", + "2016-08-01 18:00:00-07:00 343.0 \n", + "2016-08-01 21:00:00-07:00 900.0 \n", + "2016-08-02 00:00:00-07:00 746.0 \n", + "2016-08-02 03:00:00-07:00 94.0 \n", + "2016-08-02 06:00:00-07:00 47.0 \n", + 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03:00:00-07:00 306.000000 \n", + "2016-07-29 06:00:00-07:00 303.600006 \n", + "2016-07-29 09:00:00-07:00 301.500000 \n", + "2016-07-29 12:00:00-07:00 299.500000 \n", + "2016-07-29 15:00:00-07:00 310.100006 \n", + "2016-07-29 18:00:00-07:00 323.899994 \n", + "2016-07-29 21:00:00-07:00 327.600006 \n", + "2016-07-30 00:00:00-07:00 319.600006 \n", + "2016-07-30 03:00:00-07:00 307.299988 \n", + "2016-07-30 06:00:00-07:00 306.299988 \n", + "2016-07-30 09:00:00-07:00 303.700012 \n", + "2016-07-30 12:00:00-07:00 302.100006 \n", + "2016-07-30 15:00:00-07:00 308.299988 \n", + "2016-07-30 18:00:00-07:00 321.600006 \n", + "2016-07-30 21:00:00-07:00 323.899994 \n", + "2016-07-31 00:00:00-07:00 312.000000 \n", + "2016-07-31 03:00:00-07:00 305.899994 \n", + "2016-07-31 06:00:00-07:00 302.399994 \n", + "2016-07-31 09:00:00-07:00 301.200012 \n", + "2016-07-31 12:00:00-07:00 299.899994 \n", + "2016-07-31 15:00:00-07:00 303.799988 \n", + "2016-07-31 18:00:00-07:00 315.799988 \n", + "2016-07-31 21:00:00-07:00 321.200012 \n", + "2016-08-01 00:00:00-07:00 313.299988 \n", + "2016-08-01 03:00:00-07:00 305.399994 \n", + "2016-08-01 06:00:00-07:00 303.100006 \n", + "2016-08-01 09:00:00-07:00 301.600006 \n", + "2016-08-01 12:00:00-07:00 300.399994 \n", + "2016-08-01 15:00:00-07:00 305.200012 \n", + "2016-08-01 18:00:00-07:00 317.000000 \n", + "2016-08-01 21:00:00-07:00 322.000000 \n", + "2016-08-02 00:00:00-07:00 313.299988 \n", + "2016-08-02 03:00:00-07:00 305.000000 \n", + "2016-08-02 06:00:00-07:00 303.600006 \n", + "2016-08-02 09:00:00-07:00 302.000000 \n", + "2016-08-02 12:00:00-07:00 300.600006 \n", + "2016-08-02 15:00:00-07:00 306.000000 \n", + "2016-08-02 18:00:00-07:00 318.299988 \n", + "2016-08-02 21:00:00-07:00 322.700012 \n", + "2016-08-03 00:00:00-07:00 313.899994 \n", + "2016-08-03 03:00:00-07:00 305.299988 \n", + "2016-08-03 06:00:00-07:00 303.399994 \n", + "\n", + " Total_cloud_cover_boundary_layer_cloud_Mixed_intervals_Average \\\n", + "2016-07-27 09:00:00-07:00 0.0 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00:00:00-07:00 0.0 \n", + "2016-08-03 03:00:00-07:00 0.0 \n", + "2016-08-03 06:00:00-07:00 0.0 \n", + "\n", + " Total_cloud_cover_convective_cloud \\\n", + "2016-07-27 09:00:00-07:00 0.0 \n", + "2016-07-27 12:00:00-07:00 0.0 \n", + "2016-07-27 15:00:00-07:00 0.0 \n", + "2016-07-27 18:00:00-07:00 0.0 \n", + "2016-07-27 21:00:00-07:00 0.0 \n", + "2016-07-28 00:00:00-07:00 0.0 \n", + "2016-07-28 03:00:00-07:00 31.0 \n", + "2016-07-28 06:00:00-07:00 0.0 \n", + "2016-07-28 09:00:00-07:00 0.0 \n", + "2016-07-28 12:00:00-07:00 0.0 \n", + "2016-07-28 15:00:00-07:00 0.0 \n", + "2016-07-28 18:00:00-07:00 0.0 \n", + "2016-07-28 21:00:00-07:00 0.0 \n", + "2016-07-29 00:00:00-07:00 0.0 \n", + "2016-07-29 03:00:00-07:00 0.0 \n", + "2016-07-29 06:00:00-07:00 0.0 \n", + "2016-07-29 09:00:00-07:00 0.0 \n", + "2016-07-29 12:00:00-07:00 0.0 \n", + "2016-07-29 15:00:00-07:00 0.0 \n", + "2016-07-29 18:00:00-07:00 0.0 \n", + "2016-07-29 21:00:00-07:00 0.0 \n", + "2016-07-30 00:00:00-07:00 0.0 \n", + 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09:00:00-07:00 0.0 \n", + "2016-08-02 12:00:00-07:00 0.0 \n", + "2016-08-02 15:00:00-07:00 0.0 \n", + "2016-08-02 18:00:00-07:00 0.0 \n", + "2016-08-02 21:00:00-07:00 0.0 \n", + "2016-08-03 00:00:00-07:00 0.0 \n", + "2016-08-03 03:00:00-07:00 0.0 \n", + "2016-08-03 06:00:00-07:00 0.0 \n", + "\n", + " Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average \\\n", + "2016-07-27 09:00:00-07:00 20.0 \n", + "2016-07-27 12:00:00-07:00 25.0 \n", + "2016-07-27 15:00:00-07:00 99.0 \n", + "2016-07-27 18:00:00-07:00 68.0 \n", + "2016-07-27 21:00:00-07:00 25.0 \n", + "2016-07-28 00:00:00-07:00 20.0 \n", + "2016-07-28 03:00:00-07:00 22.0 \n", + "2016-07-28 06:00:00-07:00 44.0 \n", + "2016-07-28 09:00:00-07:00 66.0 \n", + "2016-07-28 12:00:00-07:00 68.0 \n", + "2016-07-28 15:00:00-07:00 57.0 \n", + "2016-07-28 18:00:00-07:00 30.0 \n", + "2016-07-28 21:00:00-07:00 2.0 \n", + "2016-07-29 00:00:00-07:00 25.0 \n", + "2016-07-29 03:00:00-07:00 6.0 \n", + "2016-07-29 06:00:00-07:00 3.0 \n", + 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100.0 \n", + "2016-08-01 15:00:00-07:00 100.0 \n", + "2016-08-01 18:00:00-07:00 99.0 \n", + "2016-08-01 21:00:00-07:00 96.0 \n", + "2016-08-02 00:00:00-07:00 97.0 \n", + "2016-08-02 03:00:00-07:00 100.0 \n", + "2016-08-02 06:00:00-07:00 100.0 \n", + "2016-08-02 09:00:00-07:00 100.0 \n", + "2016-08-02 12:00:00-07:00 100.0 \n", + "2016-08-02 15:00:00-07:00 100.0 \n", + "2016-08-02 18:00:00-07:00 99.0 \n", + "2016-08-02 21:00:00-07:00 86.0 \n", + "2016-08-03 00:00:00-07:00 90.0 \n", + "2016-08-03 03:00:00-07:00 100.0 \n", + "2016-08-03 06:00:00-07:00 100.0 \n", + "\n", + " Total_cloud_cover_high_cloud_Mixed_intervals_Average \\\n", + "2016-07-27 09:00:00-07:00 17.0 \n", + "2016-07-27 12:00:00-07:00 22.0 \n", + "2016-07-27 15:00:00-07:00 96.0 \n", + "2016-07-27 18:00:00-07:00 64.0 \n", + "2016-07-27 21:00:00-07:00 25.0 \n", + "2016-07-28 00:00:00-07:00 20.0 \n", + "2016-07-28 03:00:00-07:00 21.0 \n", + "2016-07-28 06:00:00-07:00 43.0 \n", + "2016-07-28 09:00:00-07:00 66.0 \n", + "2016-07-28 12:00:00-07:00 68.0 \n", + "2016-07-28 15:00:00-07:00 57.0 \n", + "2016-07-28 18:00:00-07:00 30.0 \n", + "2016-07-28 21:00:00-07:00 0.0 \n", + "2016-07-29 00:00:00-07:00 0.0 \n", + "2016-07-29 03:00:00-07:00 0.0 \n", + "2016-07-29 06:00:00-07:00 0.0 \n", + "2016-07-29 09:00:00-07:00 0.0 \n", + "2016-07-29 12:00:00-07:00 0.0 \n", + "2016-07-29 15:00:00-07:00 0.0 \n", + "2016-07-29 18:00:00-07:00 1.0 \n", + "2016-07-29 21:00:00-07:00 0.0 \n", + "2016-07-30 00:00:00-07:00 0.0 \n", + "2016-07-30 03:00:00-07:00 1.0 \n", + "2016-07-30 06:00:00-07:00 32.0 \n", + "2016-07-30 09:00:00-07:00 99.0 \n", + "2016-07-30 12:00:00-07:00 100.0 \n", + "2016-07-30 15:00:00-07:00 58.0 \n", + "2016-07-30 18:00:00-07:00 34.0 \n", + "2016-07-30 21:00:00-07:00 23.0 \n", + "2016-07-31 00:00:00-07:00 54.0 \n", + "2016-07-31 03:00:00-07:00 48.0 \n", + "2016-07-31 06:00:00-07:00 55.0 \n", + "2016-07-31 09:00:00-07:00 100.0 \n", + "2016-07-31 12:00:00-07:00 100.0 \n", + "2016-07-31 15:00:00-07:00 99.0 \n", + "2016-07-31 18:00:00-07:00 97.0 \n", + "2016-07-31 21:00:00-07:00 70.0 \n", + "2016-08-01 00:00:00-07:00 82.0 \n", + "2016-08-01 03:00:00-07:00 100.0 \n", + "2016-08-01 06:00:00-07:00 99.0 \n", + "2016-08-01 09:00:00-07:00 100.0 \n", + "2016-08-01 12:00:00-07:00 100.0 \n", + "2016-08-01 15:00:00-07:00 100.0 \n", + "2016-08-01 18:00:00-07:00 99.0 \n", + "2016-08-01 21:00:00-07:00 96.0 \n", + "2016-08-02 00:00:00-07:00 97.0 \n", + "2016-08-02 03:00:00-07:00 100.0 \n", + "2016-08-02 06:00:00-07:00 100.0 \n", + "2016-08-02 09:00:00-07:00 100.0 \n", + "2016-08-02 12:00:00-07:00 100.0 \n", + "2016-08-02 15:00:00-07:00 100.0 \n", + "2016-08-02 18:00:00-07:00 99.0 \n", + "2016-08-02 21:00:00-07:00 86.0 \n", + "2016-08-03 00:00:00-07:00 90.0 \n", + "2016-08-03 03:00:00-07:00 100.0 \n", + "2016-08-03 06:00:00-07:00 100.0 \n", + "\n", + " Total_cloud_cover_low_cloud_Mixed_intervals_Average \\\n", + "2016-07-27 09:00:00-07:00 0.0 \n", + "2016-07-27 12:00:00-07:00 0.0 \n", + "2016-07-27 15:00:00-07:00 0.0 \n", + "2016-07-27 18:00:00-07:00 0.0 \n", + "2016-07-27 21:00:00-07:00 0.0 \n", + "2016-07-28 00:00:00-07:00 0.0 \n", + "2016-07-28 03:00:00-07:00 0.0 \n", + "2016-07-28 06:00:00-07:00 0.0 \n", + "2016-07-28 09:00:00-07:00 0.0 \n", + "2016-07-28 12:00:00-07:00 0.0 \n", + "2016-07-28 15:00:00-07:00 0.0 \n", + "2016-07-28 18:00:00-07:00 0.0 \n", + "2016-07-28 21:00:00-07:00 0.0 \n", + "2016-07-29 00:00:00-07:00 0.0 \n", + "2016-07-29 03:00:00-07:00 0.0 \n", + "2016-07-29 06:00:00-07:00 0.0 \n", + "2016-07-29 09:00:00-07:00 0.0 \n", + "2016-07-29 12:00:00-07:00 0.0 \n", + "2016-07-29 15:00:00-07:00 0.0 \n", + "2016-07-29 18:00:00-07:00 0.0 \n", + "2016-07-29 21:00:00-07:00 0.0 \n", + "2016-07-30 00:00:00-07:00 0.0 \n", + "2016-07-30 03:00:00-07:00 0.0 \n", + "2016-07-30 06:00:00-07:00 0.0 \n", + "2016-07-30 09:00:00-07:00 0.0 \n", + "2016-07-30 12:00:00-07:00 0.0 \n", + "2016-07-30 15:00:00-07:00 0.0 \n", + "2016-07-30 18:00:00-07:00 0.0 \n", + "2016-07-30 21:00:00-07:00 0.0 \n", + "2016-07-31 00:00:00-07:00 4.0 \n", + "2016-07-31 03:00:00-07:00 2.0 \n", + "2016-07-31 06:00:00-07:00 1.0 \n", + "2016-07-31 09:00:00-07:00 0.0 \n", + "2016-07-31 12:00:00-07:00 0.0 \n", + "2016-07-31 15:00:00-07:00 0.0 \n", + "2016-07-31 18:00:00-07:00 0.0 \n", + "2016-07-31 21:00:00-07:00 0.0 \n", + "2016-08-01 00:00:00-07:00 2.0 \n", + "2016-08-01 03:00:00-07:00 6.0 \n", + "2016-08-01 06:00:00-07:00 3.0 \n", + "2016-08-01 09:00:00-07:00 0.0 \n", + "2016-08-01 12:00:00-07:00 0.0 \n", + "2016-08-01 15:00:00-07:00 0.0 \n", + "2016-08-01 18:00:00-07:00 0.0 \n", + "2016-08-01 21:00:00-07:00 0.0 \n", + "2016-08-02 00:00:00-07:00 1.0 \n", + "2016-08-02 03:00:00-07:00 3.0 \n", + "2016-08-02 06:00:00-07:00 2.0 \n", + "2016-08-02 09:00:00-07:00 0.0 \n", + "2016-08-02 12:00:00-07:00 0.0 \n", + "2016-08-02 15:00:00-07:00 0.0 \n", + "2016-08-02 18:00:00-07:00 0.0 \n", + "2016-08-02 21:00:00-07:00 0.0 \n", + "2016-08-03 00:00:00-07:00 10.0 \n", + "2016-08-03 03:00:00-07:00 2.0 \n", + "2016-08-03 06:00:00-07:00 1.0 \n", + "\n", + " Total_cloud_cover_middle_cloud_Mixed_intervals_Average \\\n", + "2016-07-27 09:00:00-07:00 7.0 \n", + "2016-07-27 12:00:00-07:00 6.0 \n", + "2016-07-27 15:00:00-07:00 77.0 \n", + "2016-07-27 18:00:00-07:00 44.0 \n", + "2016-07-27 21:00:00-07:00 0.0 \n", + "2016-07-28 00:00:00-07:00 0.0 \n", + "2016-07-28 03:00:00-07:00 1.0 \n", + "2016-07-28 06:00:00-07:00 0.0 \n", + "2016-07-28 09:00:00-07:00 0.0 \n", + "2016-07-28 12:00:00-07:00 0.0 \n", + "2016-07-28 15:00:00-07:00 0.0 \n", + "2016-07-28 18:00:00-07:00 0.0 \n", + "2016-07-28 21:00:00-07:00 2.0 \n", + "2016-07-29 00:00:00-07:00 25.0 \n", + "2016-07-29 03:00:00-07:00 6.0 \n", + "2016-07-29 06:00:00-07:00 3.0 \n", + "2016-07-29 09:00:00-07:00 0.0 \n", + "2016-07-29 12:00:00-07:00 0.0 \n", + "2016-07-29 15:00:00-07:00 0.0 \n", + "2016-07-29 18:00:00-07:00 0.0 \n", + "2016-07-29 21:00:00-07:00 0.0 \n", + "2016-07-30 00:00:00-07:00 2.0 \n", + "2016-07-30 03:00:00-07:00 0.0 \n", + "2016-07-30 06:00:00-07:00 24.0 \n", + "2016-07-30 09:00:00-07:00 14.0 \n", + "2016-07-30 12:00:00-07:00 53.0 \n", + "2016-07-30 15:00:00-07:00 0.0 \n", + "2016-07-30 18:00:00-07:00 0.0 \n", + "2016-07-30 21:00:00-07:00 0.0 \n", + "2016-07-31 00:00:00-07:00 34.0 \n", + "2016-07-31 03:00:00-07:00 23.0 \n", + "2016-07-31 06:00:00-07:00 29.0 \n", + "2016-07-31 09:00:00-07:00 7.0 \n", + "2016-07-31 12:00:00-07:00 5.0 \n", + "2016-07-31 15:00:00-07:00 1.0 \n", + "2016-07-31 18:00:00-07:00 0.0 \n", + "2016-07-31 21:00:00-07:00 0.0 \n", + "2016-08-01 00:00:00-07:00 0.0 \n", + "2016-08-01 03:00:00-07:00 2.0 \n", + "2016-08-01 06:00:00-07:00 1.0 \n", + "2016-08-01 09:00:00-07:00 0.0 \n", + "2016-08-01 12:00:00-07:00 0.0 \n", + "2016-08-01 15:00:00-07:00 0.0 \n", + "2016-08-01 18:00:00-07:00 0.0 \n", + "2016-08-01 21:00:00-07:00 0.0 \n", + "2016-08-02 00:00:00-07:00 1.0 \n", + "2016-08-02 03:00:00-07:00 0.0 \n", + "2016-08-02 06:00:00-07:00 0.0 \n", + "2016-08-02 09:00:00-07:00 0.0 \n", + "2016-08-02 12:00:00-07:00 0.0 \n", + "2016-08-02 15:00:00-07:00 0.0 \n", + "2016-08-02 18:00:00-07:00 0.0 \n", + "2016-08-02 21:00:00-07:00 0.0 \n", + "2016-08-03 00:00:00-07:00 1.0 \n", + "2016-08-03 03:00:00-07:00 0.0 \n", + "2016-08-03 06:00:00-07:00 0.0 \n", + "\n", + " Wind_speed_gust_surface \\\n", + "2016-07-27 09:00:00-07:00 1.0 \n", + "2016-07-27 12:00:00-07:00 3.2 \n", + "2016-07-27 15:00:00-07:00 1.8 \n", + "2016-07-27 18:00:00-07:00 4.2 \n", + "2016-07-27 21:00:00-07:00 5.6 \n", + "2016-07-28 00:00:00-07:00 5.1 \n", + "2016-07-28 03:00:00-07:00 3.0 \n", + "2016-07-28 06:00:00-07:00 3.7 \n", + "2016-07-28 09:00:00-07:00 2.0 \n", + "2016-07-28 12:00:00-07:00 1.5 \n", + "2016-07-28 15:00:00-07:00 2.2 \n", + "2016-07-28 18:00:00-07:00 4.4 \n", + "2016-07-28 21:00:00-07:00 4.8 \n", + "2016-07-29 00:00:00-07:00 5.1 \n", + "2016-07-29 03:00:00-07:00 3.8 \n", + "2016-07-29 06:00:00-07:00 3.1 \n", + "2016-07-29 09:00:00-07:00 2.4 \n", + "2016-07-29 12:00:00-07:00 1.9 \n", + "2016-07-29 15:00:00-07:00 2.9 \n", + "2016-07-29 18:00:00-07:00 2.2 \n", + "2016-07-29 21:00:00-07:00 3.8 \n", + "2016-07-30 00:00:00-07:00 3.5 \n", + "2016-07-30 03:00:00-07:00 3.8 \n", + "2016-07-30 06:00:00-07:00 3.0 \n", + "2016-07-30 09:00:00-07:00 2.7 \n", + "2016-07-30 12:00:00-07:00 6.3 \n", + "2016-07-30 15:00:00-07:00 2.5 \n", + "2016-07-30 18:00:00-07:00 1.5 \n", + "2016-07-30 21:00:00-07:00 2.5 \n", + "2016-07-31 00:00:00-07:00 5.4 \n", + "2016-07-31 03:00:00-07:00 8.0 \n", + "2016-07-31 06:00:00-07:00 6.0 \n", + "2016-07-31 09:00:00-07:00 5.8 \n", + "2016-07-31 12:00:00-07:00 2.7 \n", + "2016-07-31 15:00:00-07:00 3.6 \n", + "2016-07-31 18:00:00-07:00 3.4 \n", + "2016-07-31 21:00:00-07:00 2.8 \n", + "2016-08-01 00:00:00-07:00 3.5 \n", + "2016-08-01 03:00:00-07:00 5.3 \n", + "2016-08-01 06:00:00-07:00 5.8 \n", + "2016-08-01 09:00:00-07:00 2.9 \n", + "2016-08-01 12:00:00-07:00 1.5 \n", + "2016-08-01 15:00:00-07:00 4.4 \n", + "2016-08-01 18:00:00-07:00 1.5 \n", + "2016-08-01 21:00:00-07:00 1.5 \n", + "2016-08-02 00:00:00-07:00 3.3 \n", + "2016-08-02 03:00:00-07:00 5.2 \n", + "2016-08-02 06:00:00-07:00 1.9 \n", + "2016-08-02 09:00:00-07:00 3.2 \n", + "2016-08-02 12:00:00-07:00 0.4 \n", + "2016-08-02 15:00:00-07:00 2.6 \n", + "2016-08-02 18:00:00-07:00 2.0 \n", + "2016-08-02 21:00:00-07:00 1.6 \n", + "2016-08-03 00:00:00-07:00 2.4 \n", + "2016-08-03 03:00:00-07:00 5.5 \n", + "2016-08-03 06:00:00-07:00 3.8 \n", + "\n", + " u-component_of_wind_isobaric \\\n", + "2016-07-27 09:00:00-07:00 -0.78 \n", + "2016-07-27 12:00:00-07:00 0.51 \n", + "2016-07-27 15:00:00-07:00 1.45 \n", + "2016-07-27 18:00:00-07:00 1.96 \n", + "2016-07-27 21:00:00-07:00 3.02 \n", + "2016-07-28 00:00:00-07:00 1.58 \n", + "2016-07-28 03:00:00-07:00 1.36 \n", + "2016-07-28 06:00:00-07:00 -3.36 \n", + "2016-07-28 09:00:00-07:00 -1.52 \n", + "2016-07-28 12:00:00-07:00 -0.53 \n", + "2016-07-28 15:00:00-07:00 -1.87 \n", + "2016-07-28 18:00:00-07:00 -2.96 \n", + "2016-07-28 21:00:00-07:00 -3.13 \n", + "2016-07-29 00:00:00-07:00 -2.16 \n", + "2016-07-29 03:00:00-07:00 -1.22 \n", + "2016-07-29 06:00:00-07:00 -3.11 \n", + "2016-07-29 09:00:00-07:00 -2.28 \n", + "2016-07-29 12:00:00-07:00 -1.85 \n", + "2016-07-29 15:00:00-07:00 -2.18 \n", + "2016-07-29 18:00:00-07:00 -1.77 \n", + "2016-07-29 21:00:00-07:00 -1.77 \n", + "2016-07-30 00:00:00-07:00 -2.35 \n", + "2016-07-30 03:00:00-07:00 -3.33 \n", + "2016-07-30 06:00:00-07:00 -2.45 \n", + "2016-07-30 09:00:00-07:00 -1.12 \n", + "2016-07-30 12:00:00-07:00 2.46 \n", + "2016-07-30 15:00:00-07:00 1.98 \n", + "2016-07-30 18:00:00-07:00 1.41 \n", + "2016-07-30 21:00:00-07:00 2.47 \n", + "2016-07-31 00:00:00-07:00 5.26 \n", + "2016-07-31 03:00:00-07:00 5.72 \n", + "2016-07-31 06:00:00-07:00 3.28 \n", + "2016-07-31 09:00:00-07:00 2.63 \n", + "2016-07-31 12:00:00-07:00 2.09 \n", + "2016-07-31 15:00:00-07:00 2.17 \n", + "2016-07-31 18:00:00-07:00 1.56 \n", + "2016-07-31 21:00:00-07:00 1.99 \n", + "2016-08-01 00:00:00-07:00 1.89 \n", + "2016-08-01 03:00:00-07:00 3.94 \n", + "2016-08-01 06:00:00-07:00 2.77 \n", + "2016-08-01 09:00:00-07:00 2.03 \n", + "2016-08-01 12:00:00-07:00 1.37 \n", + "2016-08-01 15:00:00-07:00 2.19 \n", + "2016-08-01 18:00:00-07:00 1.06 \n", + "2016-08-01 21:00:00-07:00 1.32 \n", + "2016-08-02 00:00:00-07:00 3.64 \n", + "2016-08-02 03:00:00-07:00 3.90 \n", + "2016-08-02 06:00:00-07:00 1.89 \n", + "2016-08-02 09:00:00-07:00 2.48 \n", + "2016-08-02 12:00:00-07:00 0.30 \n", + "2016-08-02 15:00:00-07:00 1.03 \n", + "2016-08-02 18:00:00-07:00 1.10 \n", + "2016-08-02 21:00:00-07:00 1.49 \n", + "2016-08-03 00:00:00-07:00 2.84 \n", + "2016-08-03 03:00:00-07:00 3.59 \n", + "2016-08-03 06:00:00-07:00 0.77 \n", + "\n", + " v-component_of_wind_isobaric \n", + "2016-07-27 09:00:00-07:00 0.61 \n", + "2016-07-27 12:00:00-07:00 3.19 \n", + "2016-07-27 15:00:00-07:00 0.39 \n", + "2016-07-27 18:00:00-07:00 -3.74 \n", + "2016-07-27 21:00:00-07:00 -5.07 \n", + "2016-07-28 00:00:00-07:00 -5.09 \n", + "2016-07-28 03:00:00-07:00 -2.70 \n", + "2016-07-28 06:00:00-07:00 -1.62 \n", + "2016-07-28 09:00:00-07:00 1.35 \n", + "2016-07-28 12:00:00-07:00 1.41 \n", + "2016-07-28 15:00:00-07:00 0.20 \n", + "2016-07-28 18:00:00-07:00 -1.27 \n", + "2016-07-28 21:00:00-07:00 -0.77 \n", + "2016-07-29 00:00:00-07:00 -2.39 \n", + "2016-07-29 03:00:00-07:00 -3.61 \n", + "2016-07-29 06:00:00-07:00 0.18 \n", + "2016-07-29 09:00:00-07:00 0.64 \n", + "2016-07-29 12:00:00-07:00 -0.34 \n", + "2016-07-29 15:00:00-07:00 1.22 \n", + "2016-07-29 18:00:00-07:00 -0.30 \n", + "2016-07-29 21:00:00-07:00 -1.27 \n", + "2016-07-30 00:00:00-07:00 -1.92 \n", + "2016-07-30 03:00:00-07:00 -1.84 \n", + "2016-07-30 06:00:00-07:00 0.20 \n", + "2016-07-30 09:00:00-07:00 2.16 \n", + "2016-07-30 12:00:00-07:00 4.45 \n", + "2016-07-30 15:00:00-07:00 1.71 \n", + "2016-07-30 18:00:00-07:00 -0.75 \n", + "2016-07-30 21:00:00-07:00 -3.51 \n", + "2016-07-31 00:00:00-07:00 -1.74 \n", + "2016-07-31 03:00:00-07:00 0.71 \n", + "2016-07-31 06:00:00-07:00 4.39 \n", + "2016-07-31 09:00:00-07:00 3.15 \n", + "2016-07-31 12:00:00-07:00 0.64 \n", + "2016-07-31 15:00:00-07:00 -2.59 \n", + "2016-07-31 18:00:00-07:00 -3.25 \n", + "2016-07-31 21:00:00-07:00 -3.70 \n", + "2016-08-01 00:00:00-07:00 -2.24 \n", + "2016-08-01 03:00:00-07:00 -0.23 \n", + "2016-08-01 06:00:00-07:00 -2.10 \n", + "2016-08-01 09:00:00-07:00 -1.31 \n", + "2016-08-01 12:00:00-07:00 -0.72 \n", + "2016-08-01 15:00:00-07:00 -3.48 \n", + "2016-08-01 18:00:00-07:00 -2.31 \n", + "2016-08-01 21:00:00-07:00 -1.75 \n", + "2016-08-02 00:00:00-07:00 -2.27 \n", + "2016-08-02 03:00:00-07:00 -2.40 \n", + "2016-08-02 06:00:00-07:00 0.18 \n", + "2016-08-02 09:00:00-07:00 -0.87 \n", + "2016-08-02 12:00:00-07:00 -0.20 \n", + "2016-08-02 15:00:00-07:00 -2.05 \n", + "2016-08-02 18:00:00-07:00 -2.92 \n", + "2016-08-02 21:00:00-07:00 -1.49 \n", + "2016-08-03 00:00:00-07:00 -1.87 \n", + "2016-08-03 03:00:00-07:00 -3.36 \n", + "2016-08-03 06:00:00-07:00 -2.32 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = fm.process_data(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AAIIiord/HGpWh8ftWrX16LoO0S1gfHLq56be1QgAONHXiXbf/J/d5eLnwB54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\n/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gIV7/6bhAl3kg3UN1t6/Wc3u5rZULQNg1rPNYMm02x3sXzDMcq27+K2mv2EOhtR+PkR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\n/f0pLy+nrq6O4cOH8+GHH7Jly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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data[['ghi', 'dni', 'dhi']].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cs = fm.location.get_clearsky(data.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ORYG4OtDTzx0c2wsYDWekRNn66SdEBFrvwhF5F9ZNP2GDIJY7u0lny6iKwlDS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ZKGUiV4oT+5inswiDHBJtiLNQnFW3v3xjuQM4KshXiTLRYhdn5tcCS4krwExaxAmHIPykEl2J38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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "cs['ghi'].plot(ax=ax, label='ineichen')\n", + "data['ghi'].plot(ax=ax, label='gfs+liujordan')\n", + "ax.set_ylabel('ghi')\n", + "ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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hhbcBuHRe2aiOz9aVXX6yrt0wv7dRbRBPpnnxjRrGBZxMLXaP6hidDquh1iMw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\nV0dsbCzh4eF89NFHT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "cs['dni'].plot(ax=ax, label='ineichen')\n", + "data['dni'].plot(ax=ax, label='gfs+liujordan')\n", + "ax.set_ylabel('ghi')\n", + "ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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temp_airwind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-07-27 09:00:00-07:0028.4500120.990202548.587787502.227154216.96167620.00.07.017.0
2016-07-27 12:00:00-07:0027.4500123.230511816.331724418.898830411.42662525.00.06.022.0
2016-07-27 15:00:00-07:0032.3500061.501533283.79025416.471511270.47898199.00.077.096.0
2016-07-27 18:00:00-07:0046.0500184.222464121.21107563.614451103.55886768.00.044.064.0
2016-07-27 21:00:00-07:0049.0500185.9012970.0000000.0000000.00000025.00.00.025.0
2016-07-28 00:00:00-07:0043.4500125.3295870.0000000.0000000.00000020.00.00.020.0
2016-07-28 03:00:00-07:0032.2500003.0231770.0000000.0000000.00000022.00.01.021.0
2016-07-28 06:00:00-07:0030.0500183.73014726.70851356.33930622.93952944.00.00.043.0
2016-07-28 09:00:00-07:0028.2500002.032954359.05534197.910661294.53206966.00.00.066.0
2016-07-28 12:00:00-07:0026.7500001.506320543.17375780.878427465.07326068.00.00.068.0
2016-07-28 15:00:00-07:0035.2500001.880665500.073846139.622548387.41166457.00.00.057.0
2016-07-28 18:00:00-07:0047.8500063.220947172.984831294.29342691.91664030.00.00.030.0
2016-07-28 21:00:00-07:0051.9500123.2233210.0000000.0000000.0000002.00.02.00.0
2016-07-29 00:00:00-07:0042.8500063.2214440.0000000.0000000.00000025.00.025.00.0
2016-07-29 03:00:00-07:0032.8500063.8105770.0000000.0000000.0000006.00.06.00.0
2016-07-29 06:00:00-07:0030.4500123.11520535.389974205.63667022.0717133.00.03.00.0
2016-07-29 09:00:00-07:0028.3500062.368122627.065713777.980844115.4051560.00.00.00.0
2016-07-29 12:00:00-07:0026.3500061.880984972.105901751.801227246.8591020.00.00.00.0
2016-07-29 15:00:00-07:0036.9500122.498159787.537299761.928667173.7193201.00.00.00.0
2016-07-29 18:00:00-07:0050.7500001.795244211.095144554.30433959.5671241.00.00.01.0
2016-07-29 21:00:00-07:0054.4500122.1784860.0000000.0000000.0000001.00.00.00.0
2016-07-30 00:00:00-07:0046.4500123.0346170.0000000.0000000.0000002.00.02.00.0
2016-07-30 03:00:00-07:0034.1499943.8045370.0000000.0000000.0000001.00.00.01.0
2016-07-30 06:00:00-07:0033.1499942.45815026.88676681.09116021.80889335.00.024.032.0
2016-07-30 09:00:00-07:0030.5500182.433105222.92029812.180688214.92563399.00.014.099.0
2016-07-30 12:00:00-07:0028.9500125.084692339.76323518.356744322.073372100.00.053.0100.0
2016-07-30 15:00:00-07:0035.1499942.616200492.742372133.536646385.34488258.00.00.058.0
2016-07-30 18:00:00-07:0048.4500121.597060163.581654257.74127693.68632834.00.00.034.0
2016-07-30 21:00:00-07:0050.7500004.2919690.0000000.0000000.00000023.00.00.023.0
2016-07-31 00:00:00-07:0038.8500065.5403250.0000000.0000000.00000059.04.034.054.0
2016-07-31 03:00:00-07:0032.7500005.7638960.0000000.0000000.00000050.02.023.048.0
2016-07-31 06:00:00-07:0029.2500005.48000921.3326589.02794820.78683656.01.029.055.0
2016-07-31 09:00:00-07:0028.0500184.103584218.23497811.054507210.994422100.00.07.0100.0
2016-07-31 12:00:00-07:0026.7500002.185795339.27861818.330290321.633356100.00.05.0100.0
2016-07-31 15:00:00-07:0030.6499943.378905281.30854216.266367268.24930799.00.01.099.0
2016-07-31 18:00:00-07:0042.6499943.60501076.6445180.00000076.64451897.00.00.097.0
2016-07-31 21:00:00-07:0048.0500184.2012020.0000000.0000000.00000070.00.00.070.0
2016-08-01 00:00:00-07:0040.1499942.9308190.0000000.0000000.00000082.02.00.082.0
2016-08-01 03:00:00-07:0032.2500003.9467070.0000000.0000000.000000100.06.02.0100.0
2016-08-01 06:00:00-07:0029.9500123.47604711.5173380.00000011.51733899.03.01.099.0
2016-08-01 09:00:00-07:0028.4500122.415988217.61045810.969046210.440868100.00.00.0100.0
2016-08-01 12:00:00-07:0027.2500001.547676338.78290218.303375321.183270100.00.00.0100.0
2016-08-01 15:00:00-07:0032.0500184.111752275.51494515.356925263.208588100.00.00.0100.0
2016-08-01 18:00:00-07:0043.8500062.54159473.0082880.00000073.00828899.00.00.099.0
2016-08-01 21:00:00-07:0048.8500062.1920080.0000000.0000000.00000096.00.00.096.0
2016-08-02 00:00:00-07:0040.1499944.2898140.0000000.0000000.00000097.01.01.097.0
2016-08-02 03:00:00-07:0031.8500064.5793010.0000000.0000000.000000100.03.00.0100.0
2016-08-02 06:00:00-07:0030.4500121.89855210.8853380.00000010.885338100.02.00.0100.0
2016-08-02 09:00:00-07:0028.8500062.628174216.98227610.882675209.884165100.00.00.0100.0
2016-08-02 12:00:00-07:0027.4500120.360555338.27577718.275978320.722835100.00.00.0100.0
2016-08-02 15:00:00-07:0032.8500062.294210274.82759315.300263262.590277100.00.00.0100.0
2016-08-02 18:00:00-07:0045.1499943.12032172.0423750.00000072.04237599.00.00.099.0
2016-08-02 21:00:00-07:0049.5500182.1071780.0000000.0000000.00000086.00.00.086.0
2016-08-03 00:00:00-07:0040.7500003.4003680.0000000.0000000.00000090.010.01.090.0
2016-08-03 03:00:00-07:0032.1499944.9170820.0000000.0000000.000000100.02.00.0100.0
2016-08-03 06:00:00-07:0030.2500002.44444310.4742400.00000010.474240100.01.00.0100.0
\n", + "
" + ], + "text/plain": [ + " temp_air wind_speed ghi dni \\\n", + "2016-07-27 09:00:00-07:00 28.450012 0.990202 548.587787 502.227154 \n", + "2016-07-27 12:00:00-07:00 27.450012 3.230511 816.331724 418.898830 \n", + "2016-07-27 15:00:00-07:00 32.350006 1.501533 283.790254 16.471511 \n", + "2016-07-27 18:00:00-07:00 46.050018 4.222464 121.211075 63.614451 \n", + "2016-07-27 21:00:00-07:00 49.050018 5.901297 0.000000 0.000000 \n", + "2016-07-28 00:00:00-07:00 43.450012 5.329587 0.000000 0.000000 \n", + "2016-07-28 03:00:00-07:00 32.250000 3.023177 0.000000 0.000000 \n", + "2016-07-28 06:00:00-07:00 30.050018 3.730147 26.708513 56.339306 \n", + "2016-07-28 09:00:00-07:00 28.250000 2.032954 359.055341 97.910661 \n", + "2016-07-28 12:00:00-07:00 26.750000 1.506320 543.173757 80.878427 \n", + "2016-07-28 15:00:00-07:00 35.250000 1.880665 500.073846 139.622548 \n", + "2016-07-28 18:00:00-07:00 47.850006 3.220947 172.984831 294.293426 \n", + "2016-07-28 21:00:00-07:00 51.950012 3.223321 0.000000 0.000000 \n", + "2016-07-29 00:00:00-07:00 42.850006 3.221444 0.000000 0.000000 \n", + "2016-07-29 03:00:00-07:00 32.850006 3.810577 0.000000 0.000000 \n", + "2016-07-29 06:00:00-07:00 30.450012 3.115205 35.389974 205.636670 \n", + "2016-07-29 09:00:00-07:00 28.350006 2.368122 627.065713 777.980844 \n", + "2016-07-29 12:00:00-07:00 26.350006 1.880984 972.105901 751.801227 \n", + "2016-07-29 15:00:00-07:00 36.950012 2.498159 787.537299 761.928667 \n", + "2016-07-29 18:00:00-07:00 50.750000 1.795244 211.095144 554.304339 \n", + "2016-07-29 21:00:00-07:00 54.450012 2.178486 0.000000 0.000000 \n", + "2016-07-30 00:00:00-07:00 46.450012 3.034617 0.000000 0.000000 \n", + "2016-07-30 03:00:00-07:00 34.149994 3.804537 0.000000 0.000000 \n", + "2016-07-30 06:00:00-07:00 33.149994 2.458150 26.886766 81.091160 \n", + "2016-07-30 09:00:00-07:00 30.550018 2.433105 222.920298 12.180688 \n", + "2016-07-30 12:00:00-07:00 28.950012 5.084692 339.763235 18.356744 \n", + "2016-07-30 15:00:00-07:00 35.149994 2.616200 492.742372 133.536646 \n", + "2016-07-30 18:00:00-07:00 48.450012 1.597060 163.581654 257.741276 \n", + "2016-07-30 21:00:00-07:00 50.750000 4.291969 0.000000 0.000000 \n", + "2016-07-31 00:00:00-07:00 38.850006 5.540325 0.000000 0.000000 \n", + "2016-07-31 03:00:00-07:00 32.750000 5.763896 0.000000 0.000000 \n", + "2016-07-31 06:00:00-07:00 29.250000 5.480009 21.332658 9.027948 \n", + "2016-07-31 09:00:00-07:00 28.050018 4.103584 218.234978 11.054507 \n", + "2016-07-31 12:00:00-07:00 26.750000 2.185795 339.278618 18.330290 \n", + "2016-07-31 15:00:00-07:00 30.649994 3.378905 281.308542 16.266367 \n", + "2016-07-31 18:00:00-07:00 42.649994 3.605010 76.644518 0.000000 \n", + "2016-07-31 21:00:00-07:00 48.050018 4.201202 0.000000 0.000000 \n", + "2016-08-01 00:00:00-07:00 40.149994 2.930819 0.000000 0.000000 \n", + "2016-08-01 03:00:00-07:00 32.250000 3.946707 0.000000 0.000000 \n", + "2016-08-01 06:00:00-07:00 29.950012 3.476047 11.517338 0.000000 \n", + "2016-08-01 09:00:00-07:00 28.450012 2.415988 217.610458 10.969046 \n", + "2016-08-01 12:00:00-07:00 27.250000 1.547676 338.782902 18.303375 \n", + "2016-08-01 15:00:00-07:00 32.050018 4.111752 275.514945 15.356925 \n", + "2016-08-01 18:00:00-07:00 43.850006 2.541594 73.008288 0.000000 \n", + "2016-08-01 21:00:00-07:00 48.850006 2.192008 0.000000 0.000000 \n", + "2016-08-02 00:00:00-07:00 40.149994 4.289814 0.000000 0.000000 \n", + "2016-08-02 03:00:00-07:00 31.850006 4.579301 0.000000 0.000000 \n", + "2016-08-02 06:00:00-07:00 30.450012 1.898552 10.885338 0.000000 \n", + "2016-08-02 09:00:00-07:00 28.850006 2.628174 216.982276 10.882675 \n", + "2016-08-02 12:00:00-07:00 27.450012 0.360555 338.275777 18.275978 \n", + "2016-08-02 15:00:00-07:00 32.850006 2.294210 274.827593 15.300263 \n", + "2016-08-02 18:00:00-07:00 45.149994 3.120321 72.042375 0.000000 \n", + "2016-08-02 21:00:00-07:00 49.550018 2.107178 0.000000 0.000000 \n", + "2016-08-03 00:00:00-07:00 40.750000 3.400368 0.000000 0.000000 \n", + "2016-08-03 03:00:00-07:00 32.149994 4.917082 0.000000 0.000000 \n", + "2016-08-03 06:00:00-07:00 30.250000 2.444443 10.474240 0.000000 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-07-27 09:00:00-07:00 216.961676 20.0 0.0 7.0 \n", + "2016-07-27 12:00:00-07:00 411.426625 25.0 0.0 6.0 \n", + "2016-07-27 15:00:00-07:00 270.478981 99.0 0.0 77.0 \n", + "2016-07-27 18:00:00-07:00 103.558867 68.0 0.0 44.0 \n", + "2016-07-27 21:00:00-07:00 0.000000 25.0 0.0 0.0 \n", + "2016-07-28 00:00:00-07:00 0.000000 20.0 0.0 0.0 \n", + "2016-07-28 03:00:00-07:00 0.000000 22.0 0.0 1.0 \n", + "2016-07-28 06:00:00-07:00 22.939529 44.0 0.0 0.0 \n", + "2016-07-28 09:00:00-07:00 294.532069 66.0 0.0 0.0 \n", + "2016-07-28 12:00:00-07:00 465.073260 68.0 0.0 0.0 \n", + "2016-07-28 15:00:00-07:00 387.411664 57.0 0.0 0.0 \n", + "2016-07-28 18:00:00-07:00 91.916640 30.0 0.0 0.0 \n", + "2016-07-28 21:00:00-07:00 0.000000 2.0 0.0 2.0 \n", + "2016-07-29 00:00:00-07:00 0.000000 25.0 0.0 25.0 \n", + "2016-07-29 03:00:00-07:00 0.000000 6.0 0.0 6.0 \n", + "2016-07-29 06:00:00-07:00 22.071713 3.0 0.0 3.0 \n", + "2016-07-29 09:00:00-07:00 115.405156 0.0 0.0 0.0 \n", + "2016-07-29 12:00:00-07:00 246.859102 0.0 0.0 0.0 \n", + "2016-07-29 15:00:00-07:00 173.719320 1.0 0.0 0.0 \n", + "2016-07-29 18:00:00-07:00 59.567124 1.0 0.0 0.0 \n", + "2016-07-29 21:00:00-07:00 0.000000 1.0 0.0 0.0 \n", + "2016-07-30 00:00:00-07:00 0.000000 2.0 0.0 2.0 \n", + "2016-07-30 03:00:00-07:00 0.000000 1.0 0.0 0.0 \n", + "2016-07-30 06:00:00-07:00 21.808893 35.0 0.0 24.0 \n", + "2016-07-30 09:00:00-07:00 214.925633 99.0 0.0 14.0 \n", + "2016-07-30 12:00:00-07:00 322.073372 100.0 0.0 53.0 \n", + "2016-07-30 15:00:00-07:00 385.344882 58.0 0.0 0.0 \n", + "2016-07-30 18:00:00-07:00 93.686328 34.0 0.0 0.0 \n", + "2016-07-30 21:00:00-07:00 0.000000 23.0 0.0 0.0 \n", + "2016-07-31 00:00:00-07:00 0.000000 59.0 4.0 34.0 \n", + "2016-07-31 03:00:00-07:00 0.000000 50.0 2.0 23.0 \n", + "2016-07-31 06:00:00-07:00 20.786836 56.0 1.0 29.0 \n", + "2016-07-31 09:00:00-07:00 210.994422 100.0 0.0 7.0 \n", + "2016-07-31 12:00:00-07:00 321.633356 100.0 0.0 5.0 \n", + "2016-07-31 15:00:00-07:00 268.249307 99.0 0.0 1.0 \n", + "2016-07-31 18:00:00-07:00 76.644518 97.0 0.0 0.0 \n", + "2016-07-31 21:00:00-07:00 0.000000 70.0 0.0 0.0 \n", + "2016-08-01 00:00:00-07:00 0.000000 82.0 2.0 0.0 \n", + "2016-08-01 03:00:00-07:00 0.000000 100.0 6.0 2.0 \n", + "2016-08-01 06:00:00-07:00 11.517338 99.0 3.0 1.0 \n", + "2016-08-01 09:00:00-07:00 210.440868 100.0 0.0 0.0 \n", + "2016-08-01 12:00:00-07:00 321.183270 100.0 0.0 0.0 \n", + "2016-08-01 15:00:00-07:00 263.208588 100.0 0.0 0.0 \n", + "2016-08-01 18:00:00-07:00 73.008288 99.0 0.0 0.0 \n", + "2016-08-01 21:00:00-07:00 0.000000 96.0 0.0 0.0 \n", + "2016-08-02 00:00:00-07:00 0.000000 97.0 1.0 1.0 \n", + "2016-08-02 03:00:00-07:00 0.000000 100.0 3.0 0.0 \n", + "2016-08-02 06:00:00-07:00 10.885338 100.0 2.0 0.0 \n", + "2016-08-02 09:00:00-07:00 209.884165 100.0 0.0 0.0 \n", + "2016-08-02 12:00:00-07:00 320.722835 100.0 0.0 0.0 \n", + "2016-08-02 15:00:00-07:00 262.590277 100.0 0.0 0.0 \n", + "2016-08-02 18:00:00-07:00 72.042375 99.0 0.0 0.0 \n", + "2016-08-02 21:00:00-07:00 0.000000 86.0 0.0 0.0 \n", + "2016-08-03 00:00:00-07:00 0.000000 90.0 10.0 1.0 \n", + "2016-08-03 03:00:00-07:00 0.000000 100.0 2.0 0.0 \n", + "2016-08-03 06:00:00-07:00 10.474240 100.0 1.0 0.0 \n", + "\n", + " high_clouds \n", + "2016-07-27 09:00:00-07:00 17.0 \n", + "2016-07-27 12:00:00-07:00 22.0 \n", + "2016-07-27 15:00:00-07:00 96.0 \n", + "2016-07-27 18:00:00-07:00 64.0 \n", + "2016-07-27 21:00:00-07:00 25.0 \n", + "2016-07-28 00:00:00-07:00 20.0 \n", + "2016-07-28 03:00:00-07:00 21.0 \n", + "2016-07-28 06:00:00-07:00 43.0 \n", + "2016-07-28 09:00:00-07:00 66.0 \n", + "2016-07-28 12:00:00-07:00 68.0 \n", + "2016-07-28 15:00:00-07:00 57.0 \n", + "2016-07-28 18:00:00-07:00 30.0 \n", + "2016-07-28 21:00:00-07:00 0.0 \n", + "2016-07-29 00:00:00-07:00 0.0 \n", + "2016-07-29 03:00:00-07:00 0.0 \n", + "2016-07-29 06:00:00-07:00 0.0 \n", + "2016-07-29 09:00:00-07:00 0.0 \n", + "2016-07-29 12:00:00-07:00 0.0 \n", + "2016-07-29 15:00:00-07:00 0.0 \n", + "2016-07-29 18:00:00-07:00 1.0 \n", + "2016-07-29 21:00:00-07:00 0.0 \n", + "2016-07-30 00:00:00-07:00 0.0 \n", + "2016-07-30 03:00:00-07:00 1.0 \n", + "2016-07-30 06:00:00-07:00 32.0 \n", + "2016-07-30 09:00:00-07:00 99.0 \n", + "2016-07-30 12:00:00-07:00 100.0 \n", + "2016-07-30 15:00:00-07:00 58.0 \n", + "2016-07-30 18:00:00-07:00 34.0 \n", + "2016-07-30 21:00:00-07:00 23.0 \n", + "2016-07-31 00:00:00-07:00 54.0 \n", + "2016-07-31 03:00:00-07:00 48.0 \n", + "2016-07-31 06:00:00-07:00 55.0 \n", + "2016-07-31 09:00:00-07:00 100.0 \n", + "2016-07-31 12:00:00-07:00 100.0 \n", + "2016-07-31 15:00:00-07:00 99.0 \n", + "2016-07-31 18:00:00-07:00 97.0 \n", + "2016-07-31 21:00:00-07:00 70.0 \n", + "2016-08-01 00:00:00-07:00 82.0 \n", + "2016-08-01 03:00:00-07:00 100.0 \n", + "2016-08-01 06:00:00-07:00 99.0 \n", + "2016-08-01 09:00:00-07:00 100.0 \n", + "2016-08-01 12:00:00-07:00 100.0 \n", + "2016-08-01 15:00:00-07:00 100.0 \n", + "2016-08-01 18:00:00-07:00 99.0 \n", + "2016-08-01 21:00:00-07:00 96.0 \n", + "2016-08-02 00:00:00-07:00 97.0 \n", + "2016-08-02 03:00:00-07:00 100.0 \n", + "2016-08-02 06:00:00-07:00 100.0 \n", + "2016-08-02 09:00:00-07:00 100.0 \n", + "2016-08-02 12:00:00-07:00 100.0 \n", + "2016-08-02 15:00:00-07:00 100.0 \n", + "2016-08-02 18:00:00-07:00 99.0 \n", + "2016-08-02 21:00:00-07:00 86.0 \n", + "2016-08-03 00:00:00-07:00 90.0 \n", + "2016-08-03 03:00:00-07:00 100.0 \n", + "2016-08-03 06:00:00-07:00 100.0 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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OKRr0oFypo1Cu2ryi7qCi2USqNRU/eHEcr55cwMX5ot3LGUjkooJz0zKu2B5D0N+dxpZs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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data['temp_air'].plot()\n", + "plt.ylabel('temperature (%s)' % fm.units['temp_air'])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cloud_vars = ['total_clouds', 'low_clouds', 'mid_clouds', 'high_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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llwGwcuVKvvzlL3PNNdewatUqmpqaJDBKpHDIJBPweFzxVKIbqJECDEKIVpqi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ez+NpAsKSwJJaTiDUkgEEiAmFA3oWwCCYTi3o0xbhgjuUpWsSRLGpXRzGja/f0x0ljCTZJHc0fW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\ndgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot()\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('GFS 0.5 deg')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "total_cloud_cover = data['total_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Rbi57PXjKtXZIIi1EeyOJdAK1sUQaMGRyhxACZ6d2mJ1zMdPTUSsrIL5dVTTO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "total_cloud_cover.plot(color='r', linewidth=2)\n", + "plt.ylabel('Total cloud cover' + ' (%s)' % fm.units['total_clouds'])\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('GFS 0.5 deg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GFS (0.25 deg)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# GFS model at 0.25 degree resolution\n", + "fm = GFS(resolution='quarter')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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9HwkJOS6CvhlxoR1kDzdr9hh8Eph8Kh98uL1d29hXWIW9RP/+cN7UXAwnCSQ6\nKildL2cr6mB261SKS+o5vENfuHXg2HTSUmNO+thRI9MA8NncIe+W1xElFQ38/vUNHCizkRRr4bYL\nc6ncUkbZ4XqirGauuXE0Z03KRpZ7Z/Oc5cuX88wzz3TpPhYuXMh3333XoW3MmjWLI0c6f86dCIya\ncdXpBzafSQ+MGucYWRW9hbdowCAIQnN+n/5lICKiaXHRrJxEJFkiGolte0+/4J3QPnanD7+qYWrs\nSneGZYwajRmVTr88/Qto6c5yNh5TtlRT68RZrp+bzhPzINosPi6CsRdkA1B7oJZd7SiBXfrOZmRA\nirUwoQvXjhrZ2La7xtUlgYiqqqxctj3YanzaabKPQwf30TOaGuzdX93p4+kq320vw+VRGN4/nqsH\np7Dly0LcLh99sxOY+fMJZPZPCPcQw643d1Q9M88k7eS22bEAfnME0JQxsngcYES07BYEoQV/oJtV\nRGTTodRkNpCcGUvF4XoOF9WgaaJtd1eosenzLAwSoJ2ZGaNGV08fyt+O2vFWOPjPFwWkp8WQma6X\nMX24XP8iq1gM5AW62QltM/ncbHZvL4caF6tX7SY3JyFYHns66zYcRqv36IufXts57blPZuTwFNZ+\nugeTBnsKqhk+pHMXkf74kz0Y3HoJ3cxZJy+hayTLMqZYC1q9h+3bjzKshyxqXVnnwgyk2H3sPFSK\nJMFZk3MYd26/bnWsfmLti2wp69y1tsamj2ThBa1bT+j111/n008/xWg0ctZZZ3Hvvfcybdo0Pv/8\nc6qqqrjwwgv5z3/+Q2RkJLNnz+ajjz464XYOHTrE7373O3w+H5GRkS2yUYqisHDhQg4fPoymafzs\nZz/jiiuuYN68efzhD38gJyeH9957j6qqKu68806effZZvvvuO9LS0qir00s4N2/ezJNPPonJZCIi\nIoLnnnuOqKiodr9GZ+6ZpB089gYsgGaOBJoCIxrsWOIN1Du82J1eYqLM4RukIAjdRmNgFBVpanH7\nsBGpVByux+Lxc7iigX6nKEcR2qfWrncQlTWQpKYGGGeqeTeN5a8v/gejx8+H727l9v85F6NR5vDu\nCozAyPGZ4R5ijzZr9mjeXLIOk6Ly0l++h1aWUUk+FSMQ3y+uy8sYDbKMOT4CtdZN/rYjnRoYHSqu\no3RXBQZg0IRM0pKjW/W8zOwESrYdpaI0NN3yOkNNlYMRSDhqXFhjzFxyzXAysjqntfqZ4uDBg/z4\n44+8//7X4DiqAAAgAElEQVT7yLLM3Xffzdq1aznrrLPYvHkzxcXFDB48OBgYTZo06aTbevLJJ7nj\njjs4//zz+eqrr9i9e3fwvqVLl5KUlMRTTz2Fw+Hgpz/9KRMnTjzhdnbs2MGmTZv48MMPaWhoYNq0\naQCsXr2aK664gltuuYU1a9Zgs9lEYNRZfHa9HEGL0AMjQ3QMSBL+Bjt9+0RSWNZASaWDYf1FYCQI\nvZ2maWh+FZCwRrUMjLIH9eGbz/cRC2zbVykCoy5QY3M3daSzGLvVld6uYDYbmTVvHO/9bQMmn8pb\nb26i34BEjCr4ZIkLJmWHe4g9WlxsBOOn5LDt6yJMfsDf+hU9FVniumtHdN3gmuk/MIkDG0upLLV1\n2jb9qsrKj7ZjJFBCd0nrG3iMG5NBybaj4FTweBUsrcy0hYumaVDvwYhEat9YrvjpSCK76cXu1mZ2\nusLu3bu56KKLglnDcePGsX//fi677DLWrl1LaWkp9957L6tXr0aWZWbOnHnSbR04cIDRo0cDcNFF\nFwHwySefAFBYWMh5550HgNVqZeDAgRw+fLjF87XA6roHDx5k5Ei9DX50dDSDBul/p3fccQdLlizh\nlltuIS0tjTFjxnTod+/ef8Ehpjj0UjkpUo80JaMRQ3Q0frud7FiJwjK9M90wUX8qCL2e4leDkzQj\njskYRVnNRCdG0lDjYu/uCq6eLOZ+dLYauyd4AjtT5xcdK6WPlQuvGsa3q3ZDrZvCTUcwAFlDk7ts\nwn9vcv7E/mRmxFJT17Z22OPH9kXzh2adw7Mn9KVoYwkGj0JdvZv4uIgOb3PFyt0Y3X4U9MxZW2Sm\nx+IzSJj8Glvyy5g4IavD4+lKNqcPk6oBEqMn9O22QVG4DRs2jPz8fPx+P7Iss3HjRq699lrOPfdc\nlixZQlRUFFOmTOEvf/kLZrM5GLCcSG5uLtu3b+fcc89l1apV1Nc3ZRcHDhzIxo0bueSSS2hoaKCg\noIC+fftisViorKwkJyeHXbt2kZqaSm5uLu+++y4ATqeT/fv1xZRXrlzJjBkzWLBgAS+//DJLly7l\nf/6n/UFl7zibtJLi1Fdwlpul4AyxcfjtdjIj9YhVdKYTBAHA6fE3ZSxOcJV06IhUNn57EHeNC4fb\nhzXCdNxjhPY70zvSnUzeiFRKSus5tFkPihTgsktywz2sM0Z2vwSy+7XtOX0So6isDM0ip4nxkfjN\nRoxeP+s3lnDZ1I6994eK6yjbU4kBGHJWX1L6WNu8DWtSFN4KB/v2VHb7wKiy1kVjA+7Y+MiwjqU7\ny87OZty4cdx4441omsb48eO55JJLAMjIyCAzUy/dzcnJoU+fPqfc1m9+8xsefvhh/u///o+oqCie\neuopdu7cCcANN9zAQw89xJw5c/B4PNx5550kJiYyb948fv/735ORkUFqqj53cujQoUyePJkZM2aQ\nnJwc3G9eXh4PPvggkZGRGAwG/vCHP3Tod+89Z5NW0AKBkdHadGAwxsXhLS0h1aSvRyI60wmCAODy\nKMGM0YnmtwwalsLGbw8SB+worOacEWkhHd+ZrnkpXW8KjACmXzaYN8psuMoaSBuUhFVc9e5VkjJj\nqD9Qx8H9VdDBwOifn+3RP0cx5nYHWQNyk9hT4aC+vPt/P6qoddKYY4tLEIHRiVx33XXBn3/2s58d\nd//ixYuDP7emrXe/fv144403Wtz2xBNPBH/+f//v/x33nClTpjBlypTjbp8/fz7z589vcVtGRgZL\nly497Thaq3edTU5DcuttuU0xTZMODYEGDAmyvpZRSZUDVdOQz/B6dkEQTs3lUYJfzE8UGMUnRmGK\nMoHTR/72oyIw6mQ1tt5XStfczfPGsf9ADRMn9KemVlQy9CZ5eel8e6AOT52+ftDpusedTEFRNWqd\n/r1n+tXD2j2e8WMz2P3DIQw+P3V1LuK7cSamvKIBGQnJKPfK40ZX8fl8/PznPz9urmdOTg6PPvpo\nmEbVPuKvohnJox8gLDHNMkaBtYzMrgZirfHYHF6q690kd+MPviAIXe90gRFA/9wk9ucfpbKkXlxQ\n6USqplHX4KFxtmdvyxiB3ip58MA+GIxiblFvM3xIMl9JuzFqsGtvJSOHta9N+5p/7UNCwpAYSXa/\n9s+djo2JCJb3bdhyhEsvGtjubXW1yir9IkJEtMiydiaTycTbb78d7mF0CnFEbcbg1SdcWmKbOkg1\ntuxWbPX0TdYDphJRTicIvV6LUjrTiQOjUXn6goxRisqho53XRaq3szm8+FWNKKP+ulvElV+hF5Fl\nGUu8XhCWn1/Wrm3sK6xCrXejoXH5tCEdHlNCml5pc7Cwey/0ag801ojphKYVwplJBEbNGH36ByYq\nrikwaiyl89fX0zfQ1180YBAEweH2YUDPAJ0sY5SaGYtklLEgsSn/aCiHd0ZrXMPIGnjdzb0wYyT0\nbjmD9Inn1aXta/rw5RcFSEgYE6Po36/ja/gMHa5nrVw1rg5vqyu5G/RjR5/ktjeZEHoHERg1Y1b0\nD4xXNfPlJ7txNHiCpXRKfT2ZImMkCEKAy6k3ZEGWTrqGjiRJJPfVjyGH9nfvK6k9SY1Nv4gVaRIZ\nI6F3OntCXzQ0DF6Fmrq2BSON2SIVjcundzxbBDB6ZCp+wKRqHCqp65Rtdjavzw8+fVHu1NTWLWAr\n9D4iMArwKX4sfr3BQuEhB3t3lLN7W1kwY6SX0omMkSAIOqdTP17IhlPPG8obo5fTaXYP9sBzhI6p\nsekXsSyy/tqLjJHQ28TFRuC3GJGQWL+xpE3PXfMvPVtkToqif9+OZ4sATEYDklVfkmDr1vaV93W1\nynp3sFV3fGLUKR8r9F4iMApocHgwawoqEr7AheDKMnswY+SvryejjxUJOFrjRPGr4RusIAhh53T5\nAJBPM/k9JzcJTQIrElt2lodiaGe8xlI6YyBTJzJGQm+U3DcWaFs2ek9BFZotkC26YminjictS/++\ndKS4tlO321kqap3BwEi06m6/u++++7jb3nvvPV544YU2beeFF17ocJvt++67jw0bNnRoG8cSgVGA\no1afGO01mPH59BWsK8vtyFYrGAyoLhcmzU9yQiR+VaOs2hnO4QqCEGZut34FxXiSxguNjEYD0YFF\nE3eLwKhT1Nj1UjqDvu62yBgJvVJeXgYA3no3frV1F2u/+rfeic7cx0q/QJlv541Hz44rNm+rxxNK\n5RUOjEggS0REigW32+u5554L9xC6lDibBDjr9QmMiikCn1cPjBx2Ly6HF2NcHEpNDf5AOV1FrYvS\nygayUkSNqiD0Vh6PgsTpAyOAoSNS2PT1AewVDaJtdydoLKVD1SOj3tiuWxCGDkpitQQmDXbsqmD0\nyFOvlbZ7XyWazYMGXHFF58wtam5gdgL/DIxn5+5K8ka0r414V6kIzA83R5lOOi+0O9n1h8eo3bS5\nU7eZMH4cwx9+8JSPWb58OV999RVut5uqqirmzZvHmjVrKCgo4Le//S2PPPII3333HRs3buTxxx8n\nPj4eWZYZM2bMSbdZU1PDAw88gM2mJyGefPLJFvc/+eSTbNq0CUmSuOqqq5g3bx4LFy7kyiuvZNKk\nSXz77bd89tlnPPHEE7zzzjssW7aM5ORkampqADh48CALFy7EaDSiaRrPPPMMqant+/sTZ5MAd52d\nCMBvtgQDI4DKow0YYvXASKnXW3Zv3ldJiZhnJAi9mtfrx8LJO9I1NyovnY1fHyBK1Sg4WMuQnMSu\nH+AZrDaQMVIV/VgtSumE3kiWZSITI1GqXezYXnbawOjrfwfmFvWJom9m52aLGsdjiY9ArXWzc+fR\nbhcY1dU4MQHRolX3aTkcDv72t7/x2Wef8eabb7J06VLWr1/Pm2++GXzMo48+yosvvki/fv34/e9/\nf8rtLVmyhKlTpzJr1iy2bt3K9u3bg/d9/fXXlJaW8v7776MoCnPnzuWcc8454Xaqq6t56623+PTT\nTwGYMWMGAN9//z2jR4/mN7/5DRs2bMBut4vAqKPc9kBgZIlsERhVHLWTEReHB/Db6slMzgJEZzpB\n6O28HgULYG5FYBQZZcYYbcbf4GXz5lIRGHWAqmrU2vUmFo3HapExEnqrAYP6sK/6MNVlp27bvWtv\nJZpdzxZN66ROdCfSb0AiBzcdofpI+9qIdyWn3UsckNBDGi+cLrPTpfsePhyAmJgYBgwYAEBsbCwe\njyf4mOrqavr16wfAuHHjKC4uPun2Dhw4wPXXXw/AmDFjGDNmTHBOUmFhIePHjwfAaDSSl5fH/v37\nWzxf0/TqgOLiYgYPHozRqB/zR40aBcDMmTN5+eWXue2224iNjeXee+9t9+8u5hgF+OyBDJAlEm+L\njJG9aZHX+qZFXktFYCQIvZoSmIsY0cov5f0GJgFQfrh7trLtKeodXlRNIybSiOJTkaTWlTMKwpno\n7Al9UdEwev1U15x87nNjtsiSHE3fjM7PFjUaPzYTAMmtBDt3dgeqpuEPzAtNTRPTIE6nNaWGaWlp\nFBUVAbTIAJ1Ibm4u+fn5AGzYsIGnn366xX2bNm0CwOfzsWXLFnJycjCbzVRWVgKwa9cuAPr3709B\nQQFerxe/3x+8ffXq1UyYMIE33niDyy+/nFdeeaWNv3ETcZktQHEEAp3IqGNK6ewY+jYFRikJkRgN\nMtU2D063QpS4UikIvZISWA/DEtG6Sbxnnd2XA9vKMLoVam1uEmJFOUd7NDZeSIqOAJcTS4SxR8wX\nEISuEBNtQY0wYnT7+XFjCdMvG3zcY3buqUBq8KICV3RhtgggpY8Vn1HCpGhs3lbGpHP7d+n+Wqu+\nwYtZ0wCJPn3E4q4d0Xi8/f3vf89vf/tbYmJisFqtxMWdPOD+xS9+waJFi1i5ciWyLPPYY4+xYsUK\nAKZMmcK6deuYPXs2Pp+P6dOnM2zYMGbOnMmiRYtYtWoV2dnZACQmJnL77bcza9YsEhMTsVr193LU\nqFEsWLCAJUuWoKoqixYtavfvJ77VB/gd+pUWOTIKn0u/qmAyG3A2ePFGBlp22+oxyDIZfaIoLm/g\nSJWD3E7u6iIIQs+gKnpgFNnKiyNJSVZUs4zBq/LjxsNMu3hQVw7vjFUbaLyQEKUHpGYxv0jo5VL6\nxlGzv4biwhO37f5m9X4kICLZSmZ6bJePJybZirusgf37KrtNYFRZ5xKtulvpuuuuC/48efJkJk+e\nDMDQoUN59dVXg/fl5eWxbNmyVm0zMTGRl156qcVtd955Z/DnBQsWHPeckSNHsnLlyuNunzFjRnBu\nUXPvvvtuq8ZyOuKMEqC6AoGR1YrmBINBIjkthiPFddRrVgzoGSOAvsnRFJc3UFLZIAIjQeiF/KqK\npupXHyPb0PY1KTOO2gO1HCioBhEYtUtNYA2j2AgjTsT8IkEYOzaDNftr8NV78KsqBrlplsT2XeVN\n2aIrO3fdopMZNDiZ7WUN2LpRk6qjVQ5MSCCBNcZy+icI7XLXXXdRH/iuDPrcoNjYWF588cUwjqpt\nxBmlkTsQGEXptacmsyEYGNV6TPRBzxiBHhiBaMAgCL2V2+sPTtBsS8Zi7NgMvjxQi6/Wjd+vYjCI\naZ5tVWPTS+miLSaciIyRIOTmJPJ5oE32th3ljAusJwSwds1+ZCAixUpGWkxIxjNudDrbvjmASdGo\nqHKQ0g1K144e1ZtBGETpbZd6/vnnwz2EDhNn5QDJE1gwMFL/AJvMRpIDE/RqGvRuGI0Zo8xAAwbR\nslsQeieXW6Fxun9r2nU3GjyoD4oMJmDr9qNdMrYzXWPGKMqkn75Exkjo7WRZJipJ77S2c3tZ8Pb8\nneXIDh8qMD1E2SKAqCgzaoR+XNy89UjI9nsqtdX6xe8okS0STkMERgEGjwsAOUo/uJjMBlLS9asr\n1dV6i0t/fT2apgUzRqWVDcEWgoIg9B5OT/sCI0mSiErSL6xs29I9vjD0NI1rGFkC2TaRMRIEGDgk\nGYDao02VLN9+qbc8jkyxkp4ammxRoz4Z+lymk817CrWGQKY5XswvEk5DBEYBRp9+FdIQ0RQYxcZH\nYrYYcDp9+KIS0BQF1eUkPtqMNcKIw61Q19B92lEKghAaLo/SVErXhsAIYMx4vZ2to6IBn+I/zaOF\nY9UEmi+YZb0cprVdAQXhTHb2+ExUwOjzU1HZELZsUaMRI/TFZt11blRVDfn+j+V1+ABIThWtuoVT\nE4ER+uQwk6KfbI0Regtdk8mAJOkNGAAc8X0BPWskSRKZzbJGgiD0Li6vv10ZI4CxeWn4ZAmjBuvW\nH+78wZ3B/KpKfeBilBxI1otSOkEAa5QZLdKIhMT6TSV8uyaQLUqNJi3E2SKAEcOSUdDnPRUerA35\n/ptzexVkvx6cheO1EHoWERgBXkUlwh9YzddkDvxP/7LTGBjZo1KA5p3pxDwjQeitmmeM2hoYybJM\nUpZeZrJza9lpHi00V9+gL+4aazUHF9i1iFI6QQAgNdAlt2hHObIzkC26KvTZIgCDLGOM0b9P5eeH\ndz5lVZ072Ko7PjEqrGPpCZYvX87ixYtb3PbrX/8aRVFO+pxJkyZ1aJ8XX3wxXm/7K7C8Xi8XX3xx\nh8bQSARGgMPpxaLqaVa/FFgb45jAyGaMB0CxNTZgEJ3pBKG3crWYY9T2L+aTzs8BwG/zYAvMmRFO\nr7HxQmKMBW9gFXuzyBgJAgDjxulluiZFT6dGpUWTlhy+0rGM/gkAHD1cF7YxABytdmAGNCA6VjRf\naI9nnnkGo7HrjrUd7RSoaVqndRsUZxTAYWtARsMnG1EDB5TGq8CNDRjq1KhgAwZonjESgZEg9DbN\nA6O2zjEC6N8vHsViwOjx8823B7l6eniu6gLYGzx8sbqA2NgILr04N2zjaI3aQGCUEGPB49EDI5Ex\nEgTdwJxEfLKESdXwA1eGYW5Rc2Py0jmyoxzN4cOn+DEZ236s7AxlR+1ISEgWQ49aIuHdV39k/+6K\nTt1m7rAU5vzXOad93JYtW7jtttuora1l9uzZvPTSS3z++eccPXqUBx54AJPJREZGBqWlpbz11lt4\nvV7uv/9+jhw5QkJCAs899xwGw4nf76+++iq4rtHw4cN59NFHg43MSktLWbRoUXBe2u9+9zuGDBnC\npEmT+O677wC47777uPHGGxkxYgT3338/drudrKys4PbfeecdPv74Y2RZZtSoUTz44INteo16zl9I\nF3LV6f3tfaYIfIHyjMarwDFxEVgijHhUAx5DVFPL7j76VZgjVU5UVXSmE4TexOlWMKBfnTKa2ney\n7zeoDwAH91Z22rjawmZ38+7Srbzxwn+o2FNFwfrDfPOfQ2EZS2s1rmGUGBshMkaCcAIxKfpF2+j0\naFLCmC0C/QKQT5YwAPk7ysM2jurAlIcIqzlsY+hpzGYzf/vb33j++ed58803g9mYP//5z8yfP583\n33yTcePGBR/vdDr59a9/zbvvvovNZmPXrl0n3K7f7+ePf/wjr7zyCsuWLaN///4cPXo0uP0nn3yS\nn/3sZ7z99ts8+OCDLFq06LhtND72vffeY/Dgwbz99tvMnj07eP+KFSt4+OGHee+99xg4cGCbm3+I\nMwrgstmJBPwmCz5vY2Ckf9lpbMBQcrAWe0QfUgKldFERRpJiLVTbPJTXOklPCv8CZoIghIbLpZfe\nSrKELLcvfT9lcg7v7CjH6PFTXFJPv8D8gK5Wb3Pz6Wd7qD5YhxH9JKBIYNQktnxzgOycBPqnxYZk\nLG3V2JEuMcZC/UG9PEdkjAShyfXXj+KHHw9z0eTscA8FgMjESJQqJ7t3lzN+TEZYxlBf78IMxMZH\nhGX/7dWazE5XGT58OADJycm4XK5gMFJYWMjYsWMBGD9+PKtWrQIgLi6O9PT04HPc7hOXiNfW1hIf\nH09Cgl5medttt7W4v6ioiAkTJgAwdOhQysuPD6gbA52DBw9y4YUXApCXlxcs9Xv88cd57bXXKCkp\nYezYsW1eVifkGSNFUfj1r3/N7Nmzuemmmzhw4ADFxcXMmTOHm266iUcffTTUQ8Jj08vh/JZIfJ6W\ngREQXOjVZklCsdmCtzd1phMNGAShN2kMjAym9h9C4+MikGP1K5jffXegU8Z1KvU2N+/8Ywtv/986\n6gNBkWI2MGpKDnfcNxksBizAe//YhtPt6/LxtEfjGkYJsU2ldCJjJAhNYqItXD41F3M75j52hewB\niQDUloVv2oGnQT+eJfURF7Bb62TzdQYPHszmzZsB2Lp162kff6ykpCRsNhu2wHfpP/3pT+Tn5wfv\nHzhwIBs2bABg9+7d9OmjV1YoioLL5cLr9bJ/v95xMTc3ly1btgCwa9euYHOI999/n0cffZS3336b\nnTt3Bh/TWiH/5HzzzTeoqsp7773HDz/8wLPPPovP5+O+++5jwoQJPPLII6xevZpLLrkkZGPy2vRS\nOs0S2VRKZ2oeGAU601mS8NfvC96emWwlv7CaksoGJgxNCdl4BUEIL7dbwQwYO1gzPywvnd3fHaLq\ncD2qqiLLnX+tqq7ezSef7abuUD0GwIAeEI07rx/nnp0V3Oc114/i43e2EuNReHnpNu6eN77d2bCu\n0tR8oamUTmSMBKH7OmtcJgXrD2PwKjQ4vESHuJxNVTU0jwJIZKR3z0x4T9AY+Nx///0sWrSI119/\nnejoaEym49eRO1WQJEkSjzzyCL/4xS8wGAwMHz6cvLy84P2//e1veeihh3jttddQFIXHH38cgFtu\nuYUbbriBrKwsMjP1JiOzZ8/mt7/9LXPnziUnJwezWf/bGjx4MHPmzMFqtZKWltZi+60R8jNKdnY2\nfr8fTdOw2+0YjUa2bdsWTJ1dcMEF/PDDDyENjHwOJwBSZCQ+b+AqpOX4wMhmScJXWx+8vW+wM53I\nGAlCb+LxBAKjDmSMAM6f2I/t3x3C5NfI31nOmFHpnTNAoK7OxSef7aGuuFlAZDEw/rz+TDyr73FB\nWGZWPMPHZbB78xG0Mjsffb2f6y8e1Gnj6QyNzRfiokwoioosSx1+DwRB6Drx8ZEoRgMmRWVrfhmT\nzu0f0v3X2j00hmJJySJj1BrXXXdd8Gez2cyXX34Z/PfWrVt5/PHHycrK4oMPPghmjRobI4Dewe5U\nJk+ezOTJk1vctmbNGgAyMzN57bXXjnvO/PnzmT9//nG3/+///u9xt82cOZOZM2eecgynEvLAyGq1\nUlJSwrRp06irq+Oll15i48aNLe632+0hHZPfoQc2cqT1uDlG0KwBgzsSp9OPpqpIshwMjMQir4LQ\nu3gbS247mK0wGQ1YU6x4KhxsXF/SKYGRX1VZ+kE+NQfqWgREE87P5tyzs0753MlTcykurIF6NzvW\nl9AvPZazh6V2eEydwa+q1DV4kICoQEbfHGHstBatgiB0jZg+UbiPNlC4vyrkgVF5jTMYGMXG9aw5\nRt1Reno6v/rVr4iMjMRgMPDYY4+d8HH5+fk89dRTweNzYzvt6dOnt2iU0B2FPDB64403mDx5Mvfe\ney/l5eXMmzcPn6+pnt3hcBAbe/p0Z0JCVIfLWBrJXr1uPSoxlsY5WsnJMSQnN62QnNkvnqJ9VdjM\nicRbNMzxMcQnWDHIEhV1LmLiIokIQU1v8zEJ4SHeg+4hnO+DP7CKenS0pcPjuPjSwfzznS24Kh3E\nxEQS0cE5M+/8Ywv1gaBIjTQy5bLBXHTBwFY/f87Pz+KV//2WVE3i/U93Mzw3mZyMEzeGCOV7UFHr\nRNMgMdZCXGwkAFFRZvF5RByTugPxHpzciFHpbDpagL3K2aWv04m2bcsvQ0ZCMsmkZ8R32b57iwkT\nJvDhhx+e9nF5eXm8/fbbIRhR5wt5YBQXFxfsHBETE4OiKAwfPpz169dz9tlns3btWiZOnHja7dTW\nOjttTEogY6SaInA59JV3HU4vlZVNmau4JH21ZLsliYqiUixZelCWlhhFaZWD/D3l5HRx/WpyckyL\nMQmhJ96D7iHc74M3MPFflunwOPplxuAzSJj8Gis+2cHUKa0PYo5Vb3Ozd1MJRiB9RArXXq13FmrL\nGI2BcrtN3x8iU9H44yv/4aFbzyY6smUteajfg8ISvYw5zmqm7Ij+s9Ek9/rPY7g/C4J4D05nyKAk\nNv57H7JHoehAFTHRnb/I6sneg6IDNQCYIk0dfo9E8Ns7hLw4+5ZbbmHnzp3MnTuXW2+9lfvvv5+H\nH36Y559/ntmzZ6MoCtOmTQvpmCS3HmSZY6x4T1BKB5CcGphnFNEnuJYR6A0YQCz0Kgi9haZp+H16\nxigi4viJp20lyzIp/fUrmXu3d2ytj+UrdmLUwGeWuaYDizuOP68/iclWIpCItHl56eMd+Nu4FkRn\nqwl0pEuMiQgGpmbReEEQur242AgUkwEJia35ZSHdd33gIrpVlNEJrRTys0pUVNQJJ0uFM+Ume/QT\nriU25oRzjABS0ps60/nq6oK3ZyZHw+4K0bJbEHoJj8+PhAZIndYRbfLkHD4uqkFr8FBT5yIxPrLN\n2yg6WIPjiA0ZiQsuHdShDncGg8zUq4ax7M1NpKqw52Aty74uZFYYmzE0rmGUEGPB09iRTrTqFoQe\nIbZPFK6yBgr3VzP5vOyQ7ddl9xIJJCZGhWyfQs8m2vkARp8eGEXGxTS16z4mMIqOtWCWVXyGCOzV\nTenYvoGMkWjAIAi9g8vjp/HocOxxor0y02NRI0zISHyztn1rGv1z1W69lj4+olOaOPRJjWbC+fpE\n6Rwk/r3+MP/ZebTD222vYMYoNiIYGImMkSD0DDkD9fVobCG+iKwE1pxLSxdlcELr9PrASNM0TIp+\nJTIyNhq/oiJJYDS2fGkkSSLRqndmqKpqWtFXtOwWhN7F5VEwoHfaMVk6JzACyB6qf3Eo2V/V5uf+\n8GMxssOHH7jm2hGdNqaxE/vRJzUaCxJZSLzxzz0cOhqeuRS1gYxRYqwlWEonMkaC0DOMHZ2GhobR\n58dmd5/+CZ3A6VYwqPr3trTU6JDsU+j5en1g5PWpWPx6wwUpQs/+mMyGE7aATUrQ5xNU25tq7ZPi\nIt2ymEEAACAASURBVLCYDdQ7vNid3hCMWBCEcHJ5lOCBs/lC0B01ZVIOfsDoVSk6WNPq5/kUPxsD\nWabE7Hgy0jrvyqjBIHPxVUORZYkUJCIVlec/ysfmCP2xrnFx1+aldCJjJAg9Q2xM0zyjLdtCk3mu\nrHPS2OYhLkGU0gmt0+sDI4fbR4Sqn+RVk/4ROll5TGMDhlp308lYliT69mlswCCyRoJwptMzRjpz\nJ5XSAcREWzDG6xOEv//+YKuft+qzPZj8Gj4JruvEbFGjpORozpqcDUCuwUC9zcP/rdiB4g9tM4bm\nzRc8ImMkCD1ObGDqwYHCtmfF26O0zI4BCU2WxLFCaLVeHxg12F2YND9+SUbR9JfDdJL1iFKyEgCo\n16xojQseITrTCUJv4vL6mzJGnbx22cgx+tyg2lIbaiu6wNXUuSjdVQnA0Al9ieyELnknMuacLFLS\nYzD4NQYaDew7XMffPt7RJfs6EcWvYmvwIkkQF23GK5ovCEKPMzA3CQBbVectt3Iq5RV62a8xUhwn\nhNbr9YGRq94GgM9owRdowXuy8pi49CRMigufZMJef/w8o8LS+hM+TxCEM0fzjFFnNV9oNHFCFooE\nJhU2bT1y2sev+GgHRkCxGLjkogGdOpbmZFnmoiuHIhsk4hSNBFnik+8PUBsob+tqdQ0eNPQ1jIwG\nOZgxEqV0gtBzjMlLD84zqrd1/TyjmkAAFhXT+esmCWeuXh8YOev1Kwp+U8RJW3U3MlitxHr12v/y\nktrg7WNy9UnTWwuqcAVO2IIgnJmc7q4LjIxGmZhA96Stm0pP+dh9hVW4KxrQ0Ljw8sEdas/dGol9\nrJw9OQeAHGQMQE0IvtxAU6vuxFi91FBkjASh54mJtqCYA/OMQrCekSNw3IhLaPvyB0Lv1esDI49N\nL3/zW04fGEmyTBz64ysON02O7hMfyeCseLyKyqa9lV08YkEQwsntbWq+0JlzjBqdM7EfAJ5qFy63\n76SP++LTPUhIGBOjGDU8tdPHcSKjz84iNSMWg6qRhYQtRA1nGucXJQSu/AbnGImMkSD0KHGN84z2\nV3f5vrxO/fiZkiI60gmt1+sDI69dD3Q0SyQ+b6A84xRfdhIs+getsqxly9rzRqYB8MOO0K7qLAhC\naDm7sJQOYNjgZHxGPSOz9vtDJ3zMN98dxOBU8APXXtf5DRdORpYlLrpyKEiQjERNjSsk+20s2UuM\naZkxMouMkSD0KAMC84zsXTzPyK+qSIF1KTMyYrt0X/+fvTMPkuSqzv2XS2XtVb3PvmlmJFkCLdZo\nJIERg0FGiOU9gx7GRiZ4YCsM8QhbggiwwZYxDgsMhAIvAmHigRFysEmGZzbZwyYkSyMBWtCKpNln\nenrv2qtyu++Pe7Oquqera8vMqso6vwhFqHu6s7Jrybzf/c75DhEshl4YGXmRJBeNNRzuWs8Y3+zA\nwmJlRQDDvvOmEFJlPHd8GQsZf8pLCILwnxVx3R4IIwDYtIsHvbzw1MxZ/6brJh5/kAumyd1jmJr0\ndzd0dDwGJcpDHjIZf4TRYt0MIwCoCCeNHCOCGCwuvWgz7zMyLSx7uFZayJShif+fEC4VQbTC0Asj\nq8CFkRyNQm9SSgcA8ZEYNLME3QSyy7UPdSyi4tK9E2AAHnq6d9PhCcJNTp7O4MGHT/T6NPqKkoc9\nRg6vvHoXGBikoo7ZVWmX3/6PZ3g8tyzhf/6PCzx5/GY4ATV5n+YZLdXNMDJNC5bFICsSFHXob2EE\nMVAk4hosp8/o8eYBM50yPZuHCglMAiJRb9I6iWAy9HcVu8TtXCUer+sxarwLGRpJI1nhGfxzZxqV\n051Z4SYRxKDyrW/8Co/96EV8997nen0qfUOpbEKCBFmRPAs8mJpMwI5pkCDhpz87Uv3+3EIBM8/z\n2vyXXLENYZfjwltFC3NhVCw27oFyEyfkYSwVqQUvhNU1B3ETBNHfpEXPz5EXWx9k3S6nRbuDTNcJ\nok2GXhgxIYxCiTphtM40ezWVRqrCFyarhdGFu8aQioUwvVDE0VX/RhCDiC2a3I88Oo3pGXpPA0BZ\nlHEp61wn3GDPBVMAgOnDtQTMb//7U1AAWBEVB8TQ1V7gpMGVSj4Jo2qPUbgW1U39RQQxkDh9RvkF\n7/qM5ud5NVA4oTX5SYJYydALI6nMa+S1RKIqjJzd0LVQ0mkky2sLI0WWccUFNdeIIAYdWcwYVQDc\n87UnWho6GnT0SvMNFDe4+mU7YAEImTaefX4eTz87C32+AAaGV193nufx3OvhDJKtlL0fT2BaNrKF\n2nDXSpkS6QhikLn0ok2wnT6jZW/6FHOi1SE1EvHk+ERwGXphpOj8wxNO1YSRuq5jNIJUXSnd6pI5\np5zu0NMzMC1aRBKDi67zXhoGBguAXDTwvXt/3evT6jm6k165zgaKG8RiGrQxPn/joQeP4Yc/eA4S\nJGiTCfzGuZOePnYzojEujByR6CVOf9FIIgxFlqFXaIYRQQwy8ZgGW1MhQcIvPOozKud5/+PEBAUv\nEO1BwkgIo0g62VJct5JOI2yVoNll6BUL2VW7Hds3JLBlMo58ycCTh72rnyUIr1kUiUEWJOy4mAv+\no4+fwanpbC9Pq+cYBt/w8MOxuPiyLQCAwukc1LIFC8Dv+hjP3YhEnJenGIYF2+N+ymp/kTPDiIa7\nEsTAk57iguWYR+skJjZQNm+iqG6iPYZaGDHGEDL5bmR8JNV0wCsAqCn+IUuVuWs0u2qekSRJeNmF\nNNOIGHyWlrjoZ4qE17/2XNixEBQA//714S2pMy0bTDjBXgx3Xc2+SzfDkGoX6o3nTmB8LOb54zbD\nESUKgILHfUbVRLqUmGHk9BhRKR1BDCx79k4A8KbPqFA2EBIbNhs30nBXoj2GWhiVdQsRi990Q8lE\nS3HdciQCKRxBsuyU0+XP+pkrL9wICcBjL8yjsM7keoLoZzLCMZIUGbIs43ffehEsAErJxHe+N5wp\ndfXDXf1YmCuyjNGtfDPGUCS86Q3ne/6YraBpNWGU8ziZrj54ASDHiCCCwMUv3Sj6jGwsLLkrjk7P\n5hGCBAYgkaIeI6I9hloYFcsmwjavQ1VWDHhd/4arplJVx2h1AAPAZ21csHMUpsXwyDOzLp81QfhD\nVgzVlEP8MrF5YxK7Lt0EADj+5AxOnsr07Nx6RdmH4a6reeMbLkB4QwLX/I8LqoKk1zj9VVwYeTvL\naCm7UhiRY0QQg099n9Fjj7tbXXPqtCj31hSK6ibaZqiFUb5YQcTmu51yLNZSKR0gkulEZPf8zNkB\nDABwlTPT6ClKpyMGk4IY3hmqW4C+7pq9sOO8pO5b3/zV0JXUlSpWbbirx6l0DiPpCN71v/f1PHCh\nHkeUyACynjtGtRlGADlGBBEURjbwPqOjLvcZzc3ySh4tRoNdifYZamFUyvAPj65okGS5FtfdRBip\nIoAhGpagVyxkls6Om/zNcycRDil44WQGsy7bxAThB0XhBNTPi5FlGW9560UwwUvq/t93n+3R2fWG\nYr1j5HEqXT/jbB4pALIFbx2jxazTY7SylI4cI4IYbHbv5Zs9hUV310jLYk2WSIddPS4xHAy1MCpm\nuN1qaREwxlqK6wYAJZUGAIxF+c+vVU4X0VRcdh7/0NNMI2IQqYim+mh05a7bxg1J7LlsMwDg5FOz\nOHZy2fdz6xWligkFvDSjX8raeoEjSnwppXMco+TK8AVyjAhisLn0oo2wAYRMhgUXxVHJCWwZo6hu\non2GWhiVs9wxsrQITMMRRTJkef2aVDXNhdGIwncl5qbPFkZArZzuwafOrFluRxD9jF7mn4lY/OzJ\n4a999R6whAYFwP8bopK6Ul34gl89Rv2IVu8YeVhKZ5g2skUDsiQhLd6H5BgRRDCIRkKwhfP+Sxf7\njMwSv0ZsokQ6ogOGWhjpOS6MWDha6y9qoW9AFY5RyuKO01qOEQD8xvZRjCbDmFsu44UhbFQnBhtT\nzPVKJM4WRrIs4/rf4yV1atnCt77zjM9n1xtWCCOfeoz6kRWOkYeldI5bNJLUqhtWFXKMCCIwjIh5\nRsdd6jMyLRuyGKmwZUvalWMSw8VQCyMjL6K2I9GWorodFOEYJUtzAIC5mfyajpAsS7jywg0AqJyO\nGDxsk99cUg3iTqcmEzj38q0AgNNPz+Ho8SXfzq1XlHqQSteP1HqMJGQLFc8eZ6ka1V17D+rV8AVq\nrCaIQWfvue72Gc0uFqEBYABGRqOuHJMYLoZaGFkF/kGU2kikA2qldGpuAfFkGIZuYXnx7AAGANVh\nr488MwvDtNw4bYLwByGMRkYaz4F47av3AEleUvcfdz8JK+AldSXdqptjNLzCSJKk6rUyV/CulM4J\nXhgTwQuMsapjNMzPP0EEBT7PCAhZDHMLha6Pd/J0BhIkMFWCogz1EpfokKF+19hF/iFUVgij5uUZ\nTviCmclgUtSwNiqn2zKZwI4NSRQrJh5/YcGN0yYIX5CFCTo6sv6u2/Vvu5iX1FUs/Pu3n/b+xHrI\nSsdouEu5nFK2oofhC05U96iYYWSZNmyLQVFlqCoJI4IYdOr7jB59rPs+o5kzvBJIJUeZ6JChFkas\nxF0eNZGoDndtFtUN8AGvAGDlspjcsL4wAoCXOTONqJyOGBAqOu+lYWBIp9aPPJ0cj+P8K7YBAM48\nN4/DR92dSdFPUI9RjYhYeOi6BdPyxilcXFVKV+0vouAFgggMo2Iddfxo9+XYiwu8EiiapKhuojOG\nWhhJFS6MtESirVI6SVUhJxKAbWM8zW/QjZLpAOCKCzZAliT86vACsh5H2waJw8eXkBU7xoS/LIo5\nEBYkKHLzy8Q1r9oNKRWGAuC79zwV2JK6IqXSVXHmW/HIbm/K6ZZWldJVE+koeIEgAsNeMdqk6EKf\nUSEjXOYx6i8iOmOohZEshFE4FYcuErhaXew4yXSjEf57czM52PbakdypuIaXnDMGy2Z4+OmZbk97\nKDh5KoMf/Ntj+PL//XmvT2UoWRLCiCnrR9fX87/edjFMCVB1Cz++74hXp9ZTyhWretEc9h6XcF0y\nnVdDXmuldGKGUZkcI4IIGhdfWOszmp3Ld3UsXczfm5yiqG6iM4ZaGCk6v+lGU6m24rqBWgBDSC8g\nkQrDNGxk1tntoHK69njmuTneQCnmERD+six26iW19UvE+FgMsUkeveqUMwSNUtmoOkbNBkEHnXDE\n+yGvq8MXqsEL5BgRRGCIRFTYEX49feyJztdIjDFA59UKWymqm+iQoRVGNmMImfymGx1J1oRRi7vA\nTgCDlclgcmMSADC7Tp/RJXsmEA2rOHomh9Pz3SevBJ3ZWdFACaBA5Ye+kxO9HUqbi3/Hca2UvUsq\n6yWligkJEmSFEo+cuGw+5NX9z6huWMiXDCiyhFRs5XBXcowIIliMOX1GRzrvUc0UdITAK3c2iPlI\nBNEuQ3tnL1cshC1+Mw/V9xi16RiZdcLo0E8P48lfnoJlnt1foYUUXH4+r6N98ClyjZqRW6rFn5+a\nzvbwTIaTgphNo7bZR+O4CHolmNH0jmMx7P1FwGrHyH0hvJTn78GRRLg63FUnx4ggAokzz6i4tPbo\nk1Y4cSoDGRIsWRr61FCic4ZWGBXLBiI2F0ZKvBbXrbX4YXKGvJrZDC64ZBPGJuMo5HT87D+fx113\nHMKTvzgFc9Xcope9ZBMAXk5nrzEQlqih181GmZ0lh81vSqJnJBxt7+biJJWZRvCEkc0YTINverR6\nnQgyK3qMPHCMVpfRAXWOEQkjgggU9fOMznTYZzQtQrDkIe//JLpjaIVRvmQgIhwjuc0Br0AtfMHK\nZBCNaXjru/bhd/7nBUIgVfCz/3oe/3bHIfzqFyerAmnP1jQm0hEs5Sp47lj3sZRBxbJtKHWu20JA\n+1X6mbLo7YpE25sFERU/b+nBE0b1wQvkGNU7RpInQ14XsytnGAE1x4hK6QgiWIQ1FUxcUx57vLN5\nRgtzfBM1ktBcOy9i+BhaYVTKFaDAhiUrkENa28Ko3jEC+CT43edPCYF0IcaFg3T/f72Af/vcITzx\n85OwTRtXXShCGKicriEnT2ZXvDEzy51b60RnGGIBGo+3d4OJxYUwWqOcdNCpn2HUyryzoOMIIxne\nOEZLzgyjVKT6PXKMCCK4jG7kfUYnOpxnlBNR3akmQ8kJYj2GVhgVM9yqNUIiBrbduO50zTGqhwuk\nSfyvd+3Da3/3QkxMJVDI63jg4Au4645DmGKABODnz82hEsBddTc4enx5xdelPIUv+I0p3puJNofk\nJRL855kVvFLRkk4zjOoJh2vhC16k0tWGu57tGGnkGBFE4DhPzDMqddhnVBYl4BOTFLxAdM7QCqNK\njtei2hoXRobRWSmduUoYOUiShHPOm8T1//syXPvml2BiQwLFvI4nHjyO35QVjOgWfv4szTRaizNn\neNiC4TRcl4KZcNbP2KKXJtmuMHIcpgAOeC1VTCqlq6N+wGvWg1K6pezKGUYAOUYEEWReeuEGWBB9\nRjONU34bYYuNk02bUi6fGTFMDK8wynLHyA5zy7XdUjo5HgcUBXaxANtovCiQJAm7zp3A9e+8DNe+\nhQsk2WbYDhmP/uxod39EQMks8t2i2HgMAMAC2Mjf91hc2IymI01+cCUpIaSk4OmiFaV0lHh09hwj\n5nKgTNUxWiN8gRwjgggeYU0FRODPo0+012dUqZhQbX4N2raFhBHROUMrjPS8SDqLrBJGLcZ1S7IM\nNcU/fFa2eZy0JEnYtZcLpCt/Zw8AgOV02AHcWe+WiojpPWfPOABAsRksep58RRZr3LHRWFu/lxbC\nSAEL3Hu7SD1GK3CEkQoJummj4vIGhhO+sFYpHTlGBBFMxkSf0ck2+4yOnlyGAgmWBMRiFL5AdM7Q\nCiOrwB0jKcoXftW47jZ2IpUm5XRrIUkSLr5kMyzw4aUzcxRFXY9t25DE5OrfOH8SJgAZEmbpefKN\nUtmAAoCBIZVs7wYTDquwAEiQUAxYCSSl0q3E6TFSecUrsi7OMqoYFgplE4osIVkXAEKOEUEEm3PP\nmwIAlJbKbf3eMSfpt82h5ASxmqEVRnaBR0Ar8ZXCqJ0FTzWAIdu6MAIAWZbBRM7+8y8stPW7QWd2\noQgFgAVgw2QcTOVv0dPT7dcbE52xKBpfLUmCLLd/ibDFQjkj5tAEBV5Kx/84EkZAWFzDnGciV3Av\ngMFJpBtNhiFL/DlnjFFcN0EEnIsumOJ9RjbDPd9+quXKg9OneeWOFm9vxARBrGZohRErc2GkxuOw\nTBu2zSDLEhSl9aekE8fIISoiaE+dav93g8yRI3zXh4VkyLIMVZTMzM2TY+QXSyIenYnwi7YRC9lc\nLljCqEjhCytwwhckUXbpZmT3WmV0psGv06oqQ1GH9tZFEIFG01SMbOdrq5ln5vC5zz6E5Uxz92hR\nrBESqfb6YgliNcN7dynzxV8okWg7qtuhU8cIACY38Dra5XkaXlpPdddHDGgLx/juT6bD+E6ifbLC\n6ZE6XXwqXBjlXXQQ+gEKX1iJqipQFAkS+I0k52Ip3ZozjKi/iCCGghv+4FLsvIy3HEg5HV++4yE8\n9uT6sx/zQjyNjbfXF0sQqxlaYSRV+Icokkp2VEYH1A157cAx2rFjBABgBGzx2C1LC1wojozxi1tS\nJFLlAlaW1c9kxaJU6bBWWxaCqhCw93apYtWEEdWxAwBC4bohry6+3ovVqO664AWnv4iEEUEEntdd\ncy5e93sXwVAlhGzgge88g2/c82TD0jpDbMxs3Jj08zSJADK0wkjVHWGU6FgYObOMVg95bYVzd4+D\ngUG1bJTKwWpS74ZSjr8uU+LiNiJS0SoeDJAk1qYgBuqGwt0Jo6KLDkI/UD/HSOvwuQkaTjqfAndL\n6Ray6zhG1F9EEEPB7l1j+OP/8zKEJmOQIWH+1/P43O0PYXF5ZQWJbduQTS6Ytm1N9+JUiQAxlMLI\nZgwhgy/AoyPJtoe7OjildGYHpXSxmAZTkSFBwq8pgKFGhb8WjqM2NcUnWFtiUUR4T0kscMPRzppY\nVfE5CprgX1lKR8IIqKXD8VlG7r3ejmM0nq4f7sqPT44RQQwP0UgIf/Tu/dh9+VZeWpfX8ZU7DuGX\nT5yu/sx8XWjT2Gi0V6dKBIShFEbliomwzRd/ajxei+pus29Aqc4x6ixAISTSU6oxk0POcqYMlQE2\ngO2buejcLJwj2XR3eCTRmEqJi9BIh8LIEQ3OcYJCSSdhtJp6xyjnqmMkhFGdY+SU0pFjRBDDx++8\neg+u+4OLYagyQgx46HvP4WvfeAK2bePEKd6bzFQZktRhaBBBCIZSGBXKJiJCGCl1wqjdvgG1rseo\nk6nvadFHMzeTb/t3g8iLRxYBAJYqQRXlWBPjMdgQpTq59uYaEJ1hCHcu1mHsqeMi6AFz+epL6ajH\niBOqc4yyBXccI8YYFkUp3Xiq1mPklNKRY0QQw8k520dx4/95GcJTcUiQsPjiIj77Tw/i8GG+dlCj\ndG0gumdIhZGBiMWFkRyLQ++wx0gKRyBpGpiug1XaX7RvFrWwxRaiKIcBJ7o8VDe1WpZlWCI2+vQZ\nEpB+YIrS0mSis9hTJzXM+VwFhfrwBRowytHC7jtGhbKJimEhoimI1j3P5BgRBBGJqHjXuy7HuVdu\ngwVALhqY//U8ACBWF9ZCEJ0ynMKoUIHGTDBIkMNhGE5cd5sN1ZIkrXCN2mXPOWP8OBWr5SFmQWZB\nzCFIjq5ckMtCsM6Qs+YLtsHfi+l0ZzeZSIQ7TVaAhBFjDKUyzTFajVN+7PQY2R0456tZyNT6i+rL\nYsgxIgjC4dUHduONN1wCI1RbxjpptgTRDUMpjEqZHADACIUhyXLHpXRAd0NeN29MwgRfVJyezrX9\n+0GjmOHlM1NTK+M2Q8IeX1ykmU++YHFhNJLuzDFySvAsIzjCyDBt2IxBhgRZaW8QdJBxHKOIqsBm\nDMVy9+WTi2v0FwFAhRwjgiDq2LF1BH/yvpchujkJMyTjsks39/qUiAAwlHeYUjaHFABL4zfeWvhC\n+8KomyGvsiwDYQWoWHj+xQVs3TLcMZNW2YAMYNu2lc9DPBlGbqmMbIaGvPqBLDb9O033SYhSSNsK\njgu6IpGO+ouqOINuo6oMmBayBR2JDkM7HOaFMBpbJYx0GvBKEMQqNE3FO99xGSYnk5ibow1monta\n3vb88Y9/jOuuuw6vec1r8NWvfrWrB/385z+Pt73tbXjLW96Cu+++G8ePH8cf/MEf4IYbbsBHP/rR\nro7dCnqWl2SxMF/4OcJI7UAYdTPkFQDiYld++nS2o98PCsWiDtVmYGA4Z+foin9LieeolKdZRl5T\nLOpQANhgSMS1pj+/FvGE+D0rOEmCxfoZRlRGV8VxjMLCQXOjz6jmGK0s5aw6RiSMCIIgCI9oKIwW\nFxdXfP21r30N3/72t/H9738fd911V8cP+PDDD+PRRx/FV7/6Vdx5552Ynp7Grbfeiptvvhlf+cpX\nYNs2Dh482PHxW8HIC2EUWSmM2o3rBrob8goAkyKOOrMw3GVih48tQYIEU5YRXvU6TEzwWUZGwOKf\n+xFncJ4tSdzR7IBUQixo7eAIo7JeC14IUSlXFeeaqYmAlKwLs4wWqol0aztGFHxBEARBeEXDlc/H\nPvYx/PM//zNKJb5Q2rhxIz72sY/h4x//OMbHxzt+wPvvvx/nnnsu3vve9+I973kPDhw4gKeffhr7\n9u0DAFx99dV48MEHOz5+Kxh5LkLkKG/U63TAK1DnGHU4y2jHDu6OGC5F3Q4qJ07y509ZI25zw4YE\nAIAFqGelX1la5rv1TO58FkRS7PTLwdFFKFIp3Zo410zVEUaF7h0jJ3xhdSkdOUYEQRCE1zS8w9x2\n2204dOgQbrrpJhw4cAAf/vCH8dBDD8EwDHzwgx/s+AGXlpZw+vRp3HHHHThx4gTe8573rEhki8fj\nyOW8rRO1izz9TI5xYdRpXDfQvWO0d/cY7geDajMUijrisc7Klwad+Vnu4iVGzm74d4a8qozBNO3q\njCPCfTKijEnq4jlOxjTY4EEFhmEFQkhQIt3aOO6NIkSwm6V0E+m1hRE5RgRBEIRXrHuHueKKK3DF\nFVfgO9/5Dt773vfirW99K6655pquHnBkZAS7d++GqqrYtWsXwuEwZmZmqv9eKBSQSqWaHmd0NAZV\n7WyBIuv8xhsfH8HkZLJa8jM5leRft0Fk52acBoBivu3fdbBUGarJMD1TwFX713fjOn2Mfqcgyme2\nbhtd8280JUBlEsoVG7s29TakIqivAQCYJv8saJFQV3+nDQkyADWkYnIy4dLZrcTP10E9vFh1jBLJ\ncKDfA+0wtYFfqx3HyLC7e10M00KmoEOWJezZOV5N/2OMVUvptmwdpVTAVdD7sffQa9B76DUg3KCh\nMDp48CBuv/12aJqG97///bj99ttx11134U/+5E/wx3/8x7jssss6esDLLrsMd955J975zndiZmYG\npVIJV155JR5++GHs378f9913H6688sqmx1la6rwnxypwx8hWNczN5VAUu5yFQqXtVBPD5k9heWGx\n40SUUEwDy1bw5JPT2LNrtOHPBTl1Rc/rCAGYmIit+TcyVQYMG888dwaJeO92jIP8GgDA/Bx37uSQ\n3NXfaUsAGHD02AIkuF9T5/frMLtQqAojxlig3wOtMjmZRKHINzQsMftqdqHQ1XMzI67rowkNi4uF\n6vf1ignGADUkr/g+Efxr0iBAr0Hv8eM1IOE1HDRcYX7mM5/Bl7/8ZRSLRfzpn/4pvvnNb+Kd73wn\n3vzmN+Pzn/98x8LowIED+PnPf47rr78ejDH89V//NbZs2YKPfOQjMAwDu3fvxrXXXtvxH9QKcoX3\nTWkpvpNdDV9oc8ArAChJvmNqZbNgtg2pg4b1kYkYlrKVajnZsGGYFhTLBiBh966xNX9GDauAoWNu\nfrhDKrymXOK9bl33cSgSYDLk88HonStVqJRuLZxrJhPR7NkuS+kWM2vPMKKoboIgCMIPGt5l3jcx\n+QAAIABJREFU4vE47rnnHlQqlRVhC6lUCh/4wAe6etC1fv/OO+/s6pjt4JTSRUXJXjcDXuVQCHI8\nDrtQgFXIQ002LwNczZataSwdXkJJDDgdNo4eX+b9KBIaRkRHExrKeR3LXTiFRHPKIvkv2uUsGknh\nc23y+WC8p1fMMeogvTKoOKl0jmPUbSqdk0g3Rv1FBEEQRA9oaG/cfvvtCIVCGB0dxac//Wk/z8lT\nbJshZPCbbyS90jHqdMFTHfLacQADF56Sbq0IohgWjh9fBgDI6+wGJ0TSWSFHs4y8xKjwhW0s0V0I\niCzCG4ouxDf3AzyVjvfRkGNUQw3JkCTAsmxIAHJdptLVZhitEkbkGBEEQRA+0PAuMzY2hne84x1+\nnosvFCsmIja/eavxBGybJ50B/CbfCUoqDZw+DTOTQXjrtrZ/f8NUAib4i3HiZBY7to90dB6DyswM\nrwuOrRroWM/oWAzzWEAlIAvtfsXSbSgAUsnGr0UrqCEFDAaKpWAI2TKV0q2JJEkIaSr0igkVEooV\nE6ZlQ+0wHGG+gTDSnajucHdOJkEQBEGsx9BF+xTLRlUYKbFYnVukQJI6m91SdYw6nGUkSRIksRP6\nwuGFjo4xyGQXxdwSMch1LaZEspldoSGvXmKb/POQSp0dm94OqihLLQdkKG99KZ1GwmgFTp9RSswg\ny3WxeeE4RmfNMCLHiCAIgvCBoRNGhbKJiMVL6eR4DIYuate7WOw4s4zMDkvpACAu5vdMn852fIxB\nRRflN5s3N+7P2rKJp8HIVoCmhvYj4vkdWWOeVDs4i+VKQIRssWKRY9QAp+8nGeFuTjezjJweo/FV\n7rFOPUYEQRCEDzQVRu9617v8OA/fKJYMhG2+o6lEYzAMvkOudrHYUdLdC6PJjdwRyS6WOj7GIGLb\nNmRRytgokQ4ARkcisAAoAJZFchXhPs6gzrGRaFfH0cTOvlEOhjAq6xS+0AhnUykhxHCnyXSMscaO\nUZkcI4IgCMJ7mgqjcrmM6elpP87FFwrZPGQwmEoIkqrWorq7EUZJ7mZY+c4z9Hft5KLAdGFy/CBx\n8nQOCgATwPhYrOHPybIMW+Gljqemh89V84NCUYcMwAYQj3XXyxER7oGz8TDoFFek0pFjVE9IuDhx\nUT6ZK3RWSpcrGjBMG/GIiugqZ8hxHskxIgiCILyk6V1maWkJv/3bv43x8XGEw2EwxiBJEn74wx/6\ncX6uU85w8WJpfEeym6huByUu+l8KnQ8e3HPOGH4ChpAtIZevIJnorvl9UDh2fIn/TwszpGRNAUom\nZmfzuPD8KY/PbPhYEG6lLXEh2g1O3LcT4zzo1M8xoh6jlTjPRzjUnWO00MAtAurCF8gxIgiCIDyk\n6V3mC1/4gh/n4RuVHB+iaod5qZDeZVQ3UBNGVhfCKKypsFQZssnw3AsL2HfJ5o6PNUhMT3OhGmkh\nBU2LhmCVTCwOWbmhXzglikzpLISknljMEUaD7xiZlg3dsCmuuwGOixMRSXQdC6MGw10BcowIgiAI\nf2i6Lbxlyxb88pe/xNe//nWMjY3hkUcewZYtW/w4N08w8lwYIbzKMepisSPHeZqaVch3dW6acImO\nOy7KEJBZ5ANbR8aa97TEk3y2Ti5DwsgLMln+vEpq95ksCTEHiQUgLKMsrhEkjNbGeT40mT8/nZbS\nNZphBFCPEUEQBOEPTVdAn/rUp/DTn/4U//mf/wnLsnD33Xfj4x//uB/n5glmXrg6Ud7P4oYwUoQw\n6qaUDgBGx/k5Lcx2d5xBoiwGtm7a1DiRziE9ysVTqcshksTa5MRr0U0QiUPCKQUNwMDiknArqMdo\nbZxSOlWMO+i8lI4n0o2lz3aPdYrrJgiCIHygqTC6//778clPfhLhcBiJRAJf/OIXcd999/lxbp5g\nFblDocQcYcRvuG4II6tQAGOd75Bv2cbT7UrZIUpdE8J0187Rpj86Mc6fZzMgSWf9RjHPF7RupK6l\nhDCSBl8XoVQxIQGQAMiyBKXD4aVBxSlvc66gncZ1L7TgGFEpHUEQBOElTe/wThO2M/xU1/WuG7N7\niV3kboyS4ItsNxwjSVUhhSOAbcMudy5qzt0zAQCQdRtWAHbamzE7X4AKwAKwaUOi6c9vdH4mIA39\n/UapxBe04agLwkj0jMlAV5sF/UB98EI3g6CDSkgEpzjPUbbDUrr1hBE5RgRBEIQfNFU41157Lf7s\nz/4MmUwGX/rSl3DDDTfgDW94gx/n5g1CuGgJvsh2I64bqC+n67zPaGoiDlPiO6/Hji93dT6DwOGj\niwAAOyS3JLY3b0yCgUFhDLpOrpHbVEr8OY1Gta6PFQkrsMAgASgWO1so9wulikVldOugOQ6jxTcs\nckW9IzHcaIYRY6wqjLQW0isJgiAIolOabr/deOON+NnPfobNmzdjenoa73vf+/CqV73Kj3PzBLnC\nG8y1pBBGRveOEcCFkbm4AKtQQGhisuPjSBEVKJl48fAiztnZeOBpEJg+zRPpwvHWFuKapsKSJKgM\nmD6Tx47tI16e3tBhiMVnLNG9MJIkCbYkQWFAJldGvMXXuB8p0QyjdXHEimnY0FQZummjYliItFGS\nqRsWckUDiiwhver9p1csMMaf+0GuViAIgiD6n6Z3rve+971405vehJtuugmaNriLGwdF58IokuLC\nyI24bqA+ma674ITEaBSlUg5nhmCI6eI8f65SLSTSObCQDOg2pmdzJIxcxjQsqKiVwXULkySAMeTy\ngx2WUVxVSkesxHGMDN1CMqZhIVtGtmi0JYxqM4zCkFeVKlIZHUEQBOEXTbff3vrWt+LgwYO45ppr\n8OEPfxiHDh3y47w8wbJtqAZPPoqkeQqaGwNeAfeS6aZEH012CGb1lEQK1YYNyZZ/JyQWR/Pzw5Pc\n5xfM5KVQqTV6PDpCzEPK5yruHK9HlPWaY6S5EEwRNJweI103kYrz+VW5NpMjF8W1gIIXCIIgiF7S\n9E5z4MABHDhwAOVyGT/5yU/wiU98AktLS/jxj3/sx/m5SrFsImLzG3Ytlc6tUjpnyGt3s4x27RrD\nsUenYQ14X0Yr2KJEaceO1p2faFxDKasjszxEyX0+IYmZQyNpd4SRrMiAYaPQYUpZv1CsL6XrcgMl\niDhiUa9YSKb5dTXX5vVroUF/ET+ucIxIGBEEQRAe09Kd5oUXXsB3v/td/OAHP8CmTZvwjne8w+vz\n8oRi2UTEEsIo7qTSdR/XDdSV0uW7E0Z7zhnDjwCEGLCcKbu2SO03srkyQgywAezY2rowSqYjKE3n\nURxwF6LfsG0bsuiXH2+jtHE95JAMlIFihyll/UKpYlEp3To4PUaGbiIV4+XW7c4yWsi0ENVNpXQE\nQRCExzS907zxjW+Eoih405vehH/913/F1NSUH+flCYWyibBwjORVA167TqUTKXfdltKFVAWWKkM2\nbTz3wjyuuGxrV8frVw4fXQIAWIoEVW29oXpsLIZZAJUhcNT8pFA0IIML1XjMnV5CNaTABlAuDfZr\ntSJ8gVLRziJU5xiNx/j/tzvLyEmkG19jI6hCjhFBEAThE03vNJ/61Kdw3nnnIZ/Pwx7w2TrFslEr\npYu7N8eo/njdhi8AQDipwV4q4+SJ5cAKoxMnMwCAkOhJaJWpqQSeBWCL141wh0XR02a5OKInFFZQ\nAVAe8IG8JSqlWxdZlqCGZJiGjWSYf57bnWVUH76wGr1M4QsEQRCEPzS900SjUVx//fU4ceIEbNvG\nli1bcNttt2HXrl1+nJ+rFPIljDILtiRDEgl7bsZ1A4BV7F4YjU3EMb9UxsJscAMGFub435Ycaa9s\na8tGHtQgW4yXf1F8ryssi4WpE5jgBpqmooJaj8igwge88uelW2c5qGhhFaahIxbin8d2HaP1hrs6\njhGV0hEEQRBe03RVecstt+CP/uiPcOjQITzyyCO48cYb8Vd/9Vd+nJvrlLN8bo6lRSBJEhhjrjlG\nctydUjoA2LqN99yUA9xHUxA9BRNTibZ+b2QkCgt8CO4SBTC4RlYsTGXVvYV/RCxk9QF391bOMaLF\n+Vo4gjEqHLV2eoxsxqqpdGuGL5SplI4gCILwh6bCaGlpCddee2316+uuuw7Ly8uenpRXlLM8GMHW\n+M3XNG0wBiiq3LXzUCul6y58AQDO3TvOj2nYMM3BLl9shFXii53tW9Nt/64tXI1T0zlXz2mYyQoR\nrmjuOXCRKC+rMgdeGFH4QjMcwRhWhDBqo5QuW9Bh2QyJaAjhNUoVK2V+LCqlIwiCILym6SpI0zQ8\n9dRT1a+ffPJJRKPupFb5jZ7jooVF3I3qBtztMRofjcGQ+Itz+Nhi18frN0plA6ptg4HhnJ1jbf++\nLBrg5+a6F6EEpyiGsIbC7fV8rUc0xo9lGYMujOodIxJGa+Ek02li06KdUrpqIl2DBM5qKR05RgRB\nEITHNL3T/MVf/AXe9773YWRkBIwxZDIZ3HbbbX6cm+uYjmgRwq4a1e1CQ7Ucqw14ZYxBkrrr1ZCj\nIaBo4PCRJZy7e6Lr8+snjhxdggQJhixVy63aIRzTYBZNLC4WPTi74aQkkuMiUfcWn/E47+Nj1uC6\nnowxlHQTiugxImG0Ns4sI1VyhJEBmzHILVwH1+svAih8gSAIgvCPpneaSy65BPfeey+OHj1aDV9I\nJNrrC+kXrAJfSK8e7upGQ7WsaZA0DUzXwXQdUvjsdKV2SI5GUCwamJnOdn1u/cZxkUindLgIjyc1\nZOaLyGeD24PlN86sGMflcYOaMGKuHdNvyroFxsSCn1H4QiOcGHPLsBALqyhWTBTLJhLR5u+nWn/R\n2tdMcowIgiAIv2haSve9730Pb37zm7F3715Eo1G8/vWvx8GDB/04N9dhJe4YKcLdcZrC3ZpN4maf\n0YZNKQBAfil4AQPzs/z5iXc4vHZEJNmVCu0lXxGNMcTiMxZ3Z4YRAKQSfKEr2YMtjIDaDhI5Rmvj\nOEa6biEp3kPZFj+f5BgRBEEQ/UJTYfTZz34WX/ziFwEA27dvxz333IN//Md/9PzEvICV+KyWUGLV\nDCOXZpPUl9N1yzm7RgEA1oAPx1yL3DJ/HcYn4x39/sQE/z1zwOfj9BOW+Cwkk905nfWkhAMgM16S\nNogUhWBUJKeUjhbna+H0GBkVEynhOrbaZ1TtMWogjKoDXkkYEQRBEB7TVBgZhoGJiVqPy/j4+MAu\ncqQKX5BrSV4KWAtfcOeG62YAwzk7xmADCDFgYSlYvTSGSKza1kEiHQBsFrOMpIAm9vUCJp7LdIPF\naSck4xpsMEiozQsbNEpiUS6Lax45RmvjlLnpuoVUTDhGxdY2dRazjcMXGGPQK+5epwmCIAiiEU3v\nNJdddhluvvlmvPGNbwQAfP/738cll1zi+Yl5gaLzG3A4xRfWbqbSAYAiZhlZ+e5L6VRVhhWSIRs2\nfv38Aq7aH+v6mP2AadpQRM/JOTtHOzrGhg0JMDCoTEK5bHYU4ECsxCl3Gx1xTxipigwLEmQAhbwB\nbWzwXidHGEliL4h6jNbGuYbqFbPjUro1ZxhV+4sUyLJ7w4cJgiAIYi1aGvB64YUX4mtf+xruvvtu\nXHDBBfjIRz7ix7m5imnZCBm8yTeSXukYubXYkRPuOUYAEBFlTSdPDObcqLU4dnIZMgBDAlLJzhbh\nIVWBKUqbTp0JXjiF39i2XXVExkbcjeJnYi2byQ1mr1ypYkIC+H8Sn3lGnM1Kx6j1UrqybqJQNqEq\ncvX36qnQcFeCIAjCR5rebTRNw7vf/W68+93v9uN8PKNYMRGx+Y1ajTvCiN90VZcdI9uF8AUAGJuM\nY26xhMV5d4RWP3DsGBd5cpcLHSmkALqFmZk8dncwC4mokS/okCHBAhCLuRe+AABMlgCLITegQRml\nirliuGu3MfxBxdlcMiomkuI9lGuhlG5BJNKNp8JrPrdVx4hcYYIgCMIHhmb7s1g2EbH4TViOirhu\nw13HyM0eIwDYtn0EAFDJDeaici1mZ3IAgGiDaN5WCYmo74WFYPVf9YLFJd57Z3uw5peEw1LID+Z7\nuFSx6oa70uK8EVXHqGIh5ZTSteAYLa5TRgeQY0QQBEH4y9AIo0LJqDpGcpwLI93tVDqXhdF5e3jo\nhWLaMMzBbF5fTWaRL8LHJjpLpHOIJvjiKyMS7ojOWRapYFDcvxzIjjAKgGNE/UWNqfYY6XWpdC28\n5tVEugbR/Y4wIseIIAiC8IOGd5vTp0+v+4ubN292/WS8pFA2ERbCSImuHPDqXviCe3HdADCSjsCQ\neDLdi0eWsHnTiCvH7SWVgo4QgE2bk10dJ5WOongqh0KOhrx2SyYjnNSQ+8JIFcKoNKCx86WKWecY\nkTBqRL1jlGgjla7pDKMKOUYEQRCEfzS829xwww2QJAmVSgULCwvYtm0bZFnG8ePHsW3bNtx7771+\nnmfXFIsVjNn8Ri3HVgsjt+K6RSqdSz1GAKDEQkDBwOEji3jFy3a5dtxeYNs2ZIPHQp/TZV/Q2FgM\nZwAYJZpl1C25PF+cqi45p/WomgITQJmEUaCpzjHS25tjVCulW7u0lhwjgiAIwk8a3m1+9KMfAQBu\nuukmvP3tb8e+ffsAAE888QS+8IUv+HN2LlLMFTAGwAyFIcl8F9srx8itUjoASI5GUSgYmD2Tc+2Y\nvWJ6Jg8FgAlgqstSuqmpOJ4GYOvBKDHsJUUxVyrkweIzFFZhojakc9AorgpfINamFtdtIRZRIUnc\npTctG+o6JZpOKd1Es+Gu5BgRBEEQPtC0dubFF1+siiIAuOiii3DkyBFPT8oL9CwXFrZWuwG7Htft\nlNIV3RNGGzelAAD5pcHvpTlydIn/jwvP95ZNvBRPsRlsmwa9doPj5oSj7i8+w0JsGQMqjMq6RY5R\nCyiKDFmWYNsMzGYtJ9M5qXRjDXqMdHKMCIIgCB9pKow2btyIz3zmM3j++efx3HPP4ZOf/CR27tzp\nw6m5SyXHy9tYpDanxUmlc33Aq4uO0e7dvOSMlQdzYVnP9DSfORRJdh8JnUpGYIG/gecpma4rKkIY\nRaPuRnUDQCTCy6qMAXX2iitK6Whx3ghJkurK6VqbZWTbDEuiR3As2aCUjhwjgiAIwkeaCqNPfvKT\nyGazuPnmm/GBD3wApmni1ltv9ePcXMXMC7EigheA2i62W8JI0jRIqgqm67B1d1K4dm4fgQVAZcD0\nzGCX0y2LRLr0WKzJT7aGrfJ86dMBKDPsJUaFi5Z4wn1hFBUulGkMpqtHqXSt4whHvWJVHaP1IruX\n8xXYjCEd1xBS135uHccoTI4RQRAE4QNN7zbpdBp/+Zd/6ce5eIrj4sh1wkh3ucdIkiTI8TisTAZ2\nsQBZ636hqcgybE2Gott46qkZXPzSDS6caW8o5ypQAWza1F0inYOiqYBpYHY2OANwe4FlWFABpBrs\n2neDMzDWHtC4+XLFRBJcgFMp3fo4jpFeMauzjHKFxqV0C01mGAF1jpFwHgmCIAjCS5oKo/PPP/+s\nieSTk5O47777PDspL7BLvNzKCUgA3C+lc45vZTKwCgWoI6OuHDOaisCcL+LI0YWBFkYQzsSO7e48\nL+G4BqNoYHmJSum6gZnczUk36PPohng8JB6DuX5sPyhWLDgh+W7NOwsqmnCMDN1CUpTSrecY1aK6\nGwvyaiodldIRBEEQPtD0bvPss89W/98wDBw8eBCPPfaYpyflCUIYhcRwV8uyYVsMsixBcXGwZbXP\nKO9eZPf4ZBwz80XMDXDJ2MJiESoAC8DWLmcYOSRSGpbmCshnaZZRN0g2Fy2jI9EmP9k+yYQQWwMY\nkGGYNkzLhuI4RmESRuuxwjFqoZRuUXxuGw13dY4FUCkdQRAE4Q9tKYJQKITXve51eOihh7w6H8+Q\nKnx3Ukty4eI0g6sh5SxHrBtkDyK7t2/ne9blzOAKgBePLAIAbFWGLLsjREdGucgttzBIklgb27ah\nMC6MxrwQRknuHEiDp4tQEotyTWycaBS+sC4hZ8irbrVWSpdpoZSOHCOCIAjCR5rebb71rW9V/58x\nhueffx6h0ODVe0sV3vgfTnG3ohrV7fIusOMY2S4OeT1v7wQevvfXUEwLZ+by2DiZcO3YfnH6NE+k\n0+LuNfhPTsRxBIAZgMS+XpHN6ZAgwQIQ8WBXPpUIg4FBhsQH/Lokiv2gpPP3lcj4oFK6JmhazTFK\nJps7RrVSurWFkW3bnl2nCYIgCGItmq6EDh06tOLr0dFR3HbbbZ6dkBeYlg3N5G5LeJVj5PZix5Mh\nr4kwWEyDXDRw331H8Na3vNS1Y/vF4jx/PlJj7vWxbNrIRa5kDqAd0Scsiv4s2z3TdAXRSIinKgIo\nl0zEXBTGXuM4RqokAWAUvtAEx9Xhcd3OHKP1SunWF0Z6xRFFqquuPkEQBEE0oqkwuvXWW2EYBo4c\nOQLLsrB3716o6mCVNRTKJiI2v0E7wkXX3Y3qdvCilA4A9l64AS8+chJnREnaoFHMVqAA2LDBnf4i\nANgwFYcNBhUSCkUd8djgLLr7hSVRzgQX++zqkSUJtujRyebKgyWMhBNJA15bY4VjFG8+4LXqGDUa\n7kr9RQRBEITPNF0NPfnkk3jta1+LD33oQ/jzP/9zHDhwAI8//rgf5+YaxbKBsLVSGBkuR3U7OMe3\nXRZGr3j5DlgAQibD08/NuXpsP7DFItPpl3IDRZZhyXzRfUoMjyXaIycGbMoh70rcmOw8ljuzvfyi\nKBwLWQTqUTnX+lR7jCq1Aa+NSumKZROligUtJCPeQPg4/UU03JUgCILwi6Z3nL/927/Fbbfdhosv\nvhgA8Nhjj+FjH/sYvvnNb3p+cm5R7xg5c4y8E0Yilc7FHiOAlyTFpuKozBbw8EPHcMF5k64e30ty\n+QpCDLDBsNNFYQQAUkgBKhZmZws4d/eEq8ceBvJCGKleuiGKDNg2cvnBCg8pC1dZEuEU1GO0PlXH\nSDcRDikIqTJ0w0ZFtxBe9f6qL6NrVCZXDV4gx4ggCILwiabbxMVisSqKAOCSSy5BpTJYC5xi2agr\npVspjNxOmvKqlA4ArnrFLgBA7kwe5gD11Rw+ugQAsBS54YT7TglF+c70wgLNMuqEQoF/LkIe7srL\nokyvOGDpgUVRygURZx6iVLp1qfYYVSxIkrSuazTfpL8IoFI6giAIwn+aCqN0Oo2DBw9Wvz548CBG\nRtzd9feaQtFARJTSyTEhjMRwV7d3yr0qpQOA37pyBwwZUBnw0M9PuH58rzh5MgMAUGPupxnGEryX\nIbNccv3Yw0C5xMVKJOqhMBJleoV1GvH7ESd8AaKUTvWw3DAIhOocIwBIrjPLyHGMWonqplI6giAI\nwi+a3un/5m/+BnfccQeuuOIK7N+/H5/73Ofw0Y9+1I9zc41ivggFNmxFhRziN+uaY9T/qXQOsixj\ndEsKAPDko9OuH98r5ud4WWFyxL1EOoe0mL1Tyg/WortfqJT44jPqYSiCKkrQSgPmGJUr1orgBUpG\nW5/6VDoA684yqkV1hxsej0rpCIIgCL9pesfZtWsXvvGNb6BYLMK2bSQS7szQWVhYwFve8hZ88Ytf\nhKIo+NCHPgRZlrF3717ccsstrjyGQyXLF+aWVluYexXXLVd7jNwXRgDw8pfvxL1ffQJmpoxcvoJk\novHCol/IL5chA5iYcn/+0th4FKcB6KXBWnT3C6ZuQQKQiHv3PlI1BQZqC91BoVgxq8LI7Q2UIFKf\nSgcAyXVK6RazvBy7USJd/XHIMSIIgiD8ouEd5w//8A/X3SH98pe/3PGDmqaJW265BZEIvyneeuut\nuPnmm7Fv3z7ccsstOHjwIF7zmtd0fPzVVHJcGLFwtPo9z+K6IxFAUcAqZTDThORytPk5O8dgajJU\n3cZP7z+CN1x7vqvH9wKrZEIGsG1r2vVjb5hK4EkATJRGEu1hGRZUAMmkd8IoHFZhoLbQHRRKFbNq\nqVNUd3OqqXSOY7TOLKOFTPMeI3KMCIIgCL9peMd53/ve59mDfuITn8Dv//7v44477gBjDE8//TT2\n7dsHALj66qvx3//9364KIzMvEuKiNWHkVSqdJElQYnFYuSysQh5q2v1+rK17JnDm6VkcfXYeuNb1\nw7tKRTeh2jYYgHN2jLp+/C2beGmhYjNYtg1Fpj6QdmAixCO9TklTt4QjKvKoLZgHhVKdY0TBC805\n2zESPUbrlNKt12NEjhFBEAThNw1Xkfv378d5552HPXv2YP/+/di/fz8AVL/ulHvuuQfj4+N4+ctf\nDiZicG27lrAWj8eRy+U6Pv5amEWeWCZH49XveSWMAG/7jADglVfvgg0GuWzg5OmMJ4/hFoePLkGC\nBFOWEPNgAGs8psEEIEPC7Jw3z3eQkSz+GRwdjTb5yc6JiORAc9CEkU6OUTs4z5Fp2LBthlScv+6r\nHSPTsrGcr0ACMLqOU0mOEUEQBOE3De84Tz/9NG688Ub83d/9Ha6++moAwAMPPID3v//9+Jd/+Rec\nf35nJVz33HMPJEnCAw88gOeeew4f/OAHsbS0VP33QqGAVCrV9DijozGorUY/l3liWXQkicnJJABA\nAi8TnJisfc8tpkdS0M9MIxViSLl87Elxvko6Apap4OGHT+LSP97q6mO4yc8ePA4ACMU015/nKiEZ\nMGzk8gYueolHj1GHZ3+Hz1imDQUMgIRz90wh4tECdGI8jlMAmMVcfe68fh10k1Udo0QiHJjX3U1W\nPyfhiIpK2UQ6FcW2TdwtLxv2ip+bWSyCMd5ftGlj4/JaW8Skb9iQoue+CfT89B56DXoPvQaEGzRc\nCX3iE5/Apz/9aVxxxRXV7910003Yt28fPv7xj+NLX/pSRw/4la98pfr/73jHO/DRj34Uf//3f49H\nHnkEl19+Oe677z5ceeWVTY+ztNT63JpSJguAD3edm+NuVEEMmyyV9Or33MIWIQ8Lp+ZQmXRPtExO\nJqvnuveCKfz6wRM4+fw8ZmYykPu0hOzY0QUAQCSpuf48OyhhFTB0HD6ygHN3j3nyGA62t4ZQAAAg\nAElEQVT1r8Ggs7hcggQJFoBcrgSXjdoqTquiZZiuPXd+vA75ol4VRgwsMK+7W6z1GqghBZWyidOn\nlmEb3PGZXy6t+Lnnj/ONsJHE+teE6jW67P41OkgE6Zo0qNBr0Hv8eA1IeA0HDVfT2Wx2hShyeMUr\nXrHC4XGDD37wg/iHf/gHvO1tb4Npmrj2WvcaZ3TDAitxxyg2UntTe1tK5yTT5V0/tsNvXbUDJoCQ\nxfDkM3OePU635JZEk/VEvMlPdk5YpF9llmiWUTssidlPtseaOiFmTTFRtjcoFCl8oW20cG2WUTWu\ne1UpXTWRbp3+IqCulI56jAiCIAifaHjHMU0Ttm2f5UTYtg3DcCcauT7Z7s4773TlmKuZz5Srw13V\neF2PkeGdMJI9HPLqENZUxCbj0OcK+Pmh47jowg2ePVY3GEUdIQBbPEikc0ikwlicLSAnFlxEaywv\nc9EKxVtl5Agj2IMjjGyboaKvnGNENEcTIRVGxcLEGB+mnSsasBmDLKzD+WzzRDoAqJT5fSZMPUYE\nQRCETzRcEV1++eX4p3/6p7O+f/vtt+MlL3mJpyflJvOZEiI2F0ZyrCaMvIrrBrwPX3C4bD8v0yvM\nFqp/Tz9hmjYUky+Gd+/0rsRtdJQvwCprxAITjcmKBaqseiuMUmLWlsxQDVzpd8ri8xQWG0OUStca\n9Y6RqsiIhVXYjKFYN8NqsYVEOsuyYRo2JIlEKUEQBOEfDe/2N998M2688Ub8x3/8B1760pdWY7XH\nxsbw2c9+1s9z7Iq55TIiNncS5Fis+n2vBrwC/gmjiy7cgPt+8GuELIYHDp3Aq16xy9PHa5fjpzOQ\nAZgSMLLOIMdumZqK40UA1oDNyek1+bxwUj1e9CfiGiwwKJBg6NZAlEYVxXtJU2TAtmnAa4s4AlKv\n8OtrMq6hWDGRK+pIiHRCJ6q7leGuWlhdd54eQRAEQbhJwxVKIpHAXXfdhYceegjPPPMMZFnG29/+\n9uq8oUFhbrmESYsLI0ew2DaDafCIcG9K6XiPke1hjxEAyLKMie1pZI4s49lfTfedMDp2bJn/T9jb\nReXGjbx3TDYHw43oF4oFLoy0iLevj6bKsAAoAIpFYyCEUVks7EMyX5STa9Ea9Y4RACRjIcwsAtmC\njk3j/PrbynDX6gwjKqMjCIIgfGTdu44kSbjqqqtw1VVX+XU+rjOfKWOPyZ0bdYyXc5miv0gNyZ7s\nRvrlGAHAb718F7575FHYWR3LyyWMjHg3j6ZdZs7whJhY0ju3CACmxmOwwRfe2VwZKY8fLyiUSryH\nw5kz5BWSJIFJEsD46zPi4cwkt3AcI5WEUVvU9xgBQCrmBDDw9xpjrC58oYUZRgMgogmCIIjg0J8Z\nzy6ysJhHwiwBkoTQKBdGThmd5lEJkZ/CaPvWNMywAhnAT+8/6vnjtUNmkUeqj07Emvxkd8iyDEss\nYE+f8dalCxK6WHxG4+4P3l0NE69PLj8YfWAlRxiJr0kYtYbzPOm6I4y46M6K/r9C2UTFsBANK4hF\nGgtycowIgiCIXhBoYcQYQ3lhATIY5FQakirq3z2M6gbqS+m8F0YAsPO8SQDA8efnfXm8VtHFInjT\npuYDe7tF0vhbeWaGhFGrGGLxmfBBGEki+S5fGCxh5FwhvNpECRpOKZ3z3koKxygrXvdWghcAcowI\ngiCI3hBoYVQomwgX+HBXbXKy+n3Tw6huoN4x8meR/spX7IQNQK1YOHrc3RlTnWLbNiTRx7Vr16jn\nj6eJcrDFxdYH/w47tnh9ksnGJU1u4STfFQZFGInNE0m0rZFj1BqOkKk6RvGVpXSt9BcBQIUcI4Ig\nCKIHBFoYzWdKSIn+otD4ePX7TpmGZ45RNApIEuxSCcz0PiktlYxAEfX69z9w1PPHa4WZuQIUABZ4\nD5DXxMXiPpuhIa+twiwujNIeJgY6KCF+qSkV3ZmB5jWOYySJeHESRq1RLaWr1MIXgFop3UKLjpFT\n5hkmx4ggCILwkWALo+Uy0iZ3bULjE9XvGx6X0kmyXB3yapX8cTAuvGQTAGDxRBa2bfvymOtx+Mgi\nAIBpyllDgr0gJRb3pQHpYekHJIsv+kd9CENQxWetXB4sYeQMpSVh1BpnOUZO+EK1lK558AIAlEvk\nGBEEQRD+E2hhNJcpIW1wYaTWOUaG4YQveLfYqUaD+9RndOX+bTAlIGQzPPrEGV8ecz2mp3kiXTjh\nff8KAExM8OfbKNEso1awbBsK+KJ/zAfHyOnRqZQH4/VxhBET4tGLeWdBxLmmVnuMRCldVjiF89nW\nSunyOf5zcR/KPAmCIAjCIdjCaLlcV0p3tmOkerjY8TOZDgBCqoLEBh768Mufn/TlMddjeYE7Zekx\nf6KZN4i/nQnRS6zPcqYMCRIs+BMsUJ1vUxmM16cqjMgxaouzHSNeSpcrthe+kM9wZynR5OcIgiAI\nwk0CLYzml0tIr9Fj5HVcNwDIMb5Q9yuAAQD2X7kdAFCZL6Lc4535Uo4vbDZu9D6RDgA2iyGvKmMw\nzd6XEvY7i0u8F8v26QrgzEoy9UFxjKxqIl1IUzyZdxZEVvcYxaMhSBIPwjEtu9pjNNHEpcyLn0v6\n4GYSBEEQhEOghdHccgkpwxnuWhe+4HGPEeB/KR0AXHj+FAxVggLgZw8e8+1x10Q8xzt3jvjycNFI\nCKYESJBwZjbny2MOMhmRDgbFn0tA1BFGxmCI1mLFrF4cyS1qnapjJJxBWZKqkd1LuQoyeR2yJCG9\nTomtbdvIi42VBJXSEQRBED4SWGFkM4bKwgIU2JATScjh2g3W6/AFwP9SOocNO3g09q+f7F2f0eJy\nCSrjiXRbN/vjGAEAE4v8M7P+PueDSEbsyMs+9c7EREmVbQ5GKV25YtYcI+ovahnnmmroJphI9HPK\n6Y6d4RsWo0kNyjqBLMW8DsaAWFyDogb2FkUQBEH0IYG96yznKohXRCLdxMSKfzN0b+O6AdRS6XwW\nRldfvQsMDCjoWOjRTB8nkc5W5XUXQG6jigSr+XkSRs0oiPQ+v9wQZ4isbTJfHq9bivXCiByjllEU\nGaoqg7GaO+g4RkeFMGoWvJATyXWJNLlFBEEQhL8EVhjNZ8przjAC/HKMeI+R7WOPEQBs2pCEHQ1B\nhoSf3HfE18d2OHVKDNWNh3x93LDYmc4s0SyjZhRFM7zmUxxyIilKp/ogSr4VSnWldF6mVwaRkBO0\nITagnCGvR8/w60KzFMScKPNMUvACQRAE4TOBFUZzy7Wo7vpEOqA+fCF4pXQAcM5vTAEATr244Ptj\nA8DCHP+bUz7Mx6nH6UfIix1nojFlEZ8cjvojjJIJ/tpIA6CLGGOrwhdolk47OKE2Tp9RUvSXHZ1u\nzTFyghcSTWYdEQRBEITbBFYYzWfK1UQ6dZVj5Ef4Qq9K6QDglb+1ExaAkGHjRz877PvjF8XCZnJD\n0tfHHRmLAQDKRRry2gxnnlAs5s+cqWRcgw0GGYDV56mBumnDZgwhiV8eqZSuPZxodqdk2ZllVBRJ\ndc2FEUV1EwRBEL0hsMJobrmElLm2Y2QajjDybie4F6l0DvGYhslzxgAAv3rgOI6czPj6+LZYdO/Y\nnvb1cSfGuTCyBmRWTi8xxeZAPOHPrnwsEoLzqlTKhi+P2SnODKOIyiO6SRi1R2iVY+SELzg0m2GU\ny1IpHUEQBNEbAiuM5pdLSBtnD3cF6hwjLwe8JsQco7y/PUYO17/lJUBEhQbgG19/HPmSP4vRQlGH\nyhgYGHZu8yeq22GTmGUk9bkj0Q/YojE+5VO5kqrIsMGFRq7Q346eI4zCIhGNeozaQ9NWOkapVa7k\neJP3XM0xolI6giAIwl8CK4y4Y7R2KZ2f4Qu9KKUDeDrU7/7eRWAAkrqNf/nqo7B8aHx/8cgSJEgw\nFdnTAbprMTURBwODilq4ALE2TIjHER935Zm42mT7vAesJJwOTSQqqiSM2mL1LCOnlM6hmWNEw10J\ngiCIXhFIYWSYNiqZLDRmQo5GocRiK/9d7GQ6tfBeIIvHtEtFsB4lcW3clMJFV2wDAEgzBXzjhy94\n/pgnTy4DANSov4l0AKCqMkyJuxLTM71x6gYFyeax2aN+BmSIOVP5Qr8LIxHnL/P3EjlG7XFWKl1d\nKV08oiIabrxhUimb0CsW1JCMsE+JiQRBEAThEEhhtJAtIyUS6dRVZXSMsapjpHpYSifJMhdHjMEu\n9maeEAC87JW7kBqPIQwJv/7FKTz0tLeDX+dFIl1ipDe7vZJ4TWdmSRg1wjRtKODljqM+vk6ywoVG\nsTAYPUaq5PQY0QK9Hc5KpasrpWvZLUpFIInnnyAIgiD8IpDCaH65VE2kWz3c1TJtMAYoigRF8fbP\n72Vkt4Msy7judy+EJEuYhIS7v/NsdQK9F+SX+cJmYjLu2WOsR0jETy8s9E6M9jtLmRIkSLAhIaT6\n54bI4rEKfV7mWKwKI/41hS+0x+pUuoimICT6tZoPd6WoboIgCKJ3BFIYzWXKSDnBC2P+R3U7yD3u\nM3IYnYjjiqt3AQC22gy33/04sh4tTk0R8rB1q7/BCw4R0c+QydCQ10YsLfPnxpb93ZFXNX65KfkU\nBNIpZSGMnIsjCaP2WO0YSZJULadrNaqb+osIgiCIXhBIYcQdI6eUbqUw8iOq26Ea2V3sfVnXxfu3\nYWpTEhokxHMGPvetJ2Fa7vY+6boJ1bLBwLB716irx24VJ+K3mOtvV6KXZDJ8V75qifiE85lzZij1\nK45jJPE2LOoxapPVPUZArZxuLL2+E5TLOI4RCSOCIAjCfwIpjObqhNFZUd0V/xyjfiilc5BlCb/9\nht+AovCSuunjy/j6j9wNYzhybJkn0kkS4j4NDl3N2BgPE9D73JXoJU4qnOJjGR1QWzD3uzByUumc\ngApyjNpjtWMEAGnh5E6k1w/7oKhugiAIopcEUxjVl9Kt6jEyDD9L6fpHGAHA6HgMV7zyHADATkj4\n8S9O4v4npl07/vETPJFOjvauWX1S9DbZOg15bUQ+z9001cNUxrWIhHk5ldHnr40TvgASRh1R7TGq\n1ATw61+2E6++bCsu2j3e6NcArAxfIAiCIAi/CaQwqg9fOHuGkYjq9tExsvtEGAHAS/dtxcatKWiQ\nsB0Svnzvczh8OuvKsWdFEly8h4uazZtSAADZYrB7FJPe7zgznrR1YpO9ICIEs9nvwkhcI2xRakqp\ndO1RnWNU9zrv2ZLG2685F+EmSaAUvkAQBEH0ksAJo2LZhFkoImLrkDQNSiK54t/9iOp2UGJO+ELv\ne4wcZFnCq647H6oqYwISEpaNf/73XyGT7362TG6JN/WPT/QmkQ4ARlJhWAAUAMt9Pki0V5SLvMww\nEvN31lRMlFdaZn8LVscxsi3uGFGPUXs4Dpteaa9k0rJsFERvYDxJwoggCILwn8AJo/lMCSmzlki3\nehaGI4z8WOz0Wymdw8hYraRut6wgm6vgn10IY9ALfFGzeWu663PsFFmWYYt5OdMexpIPMs6CNeZz\nH1gszoWYPSjCSJynH5soQWItx6gVCjm+kRFPap6PUiAIgiCItQjc3WduuVxLpFvVXwT4G9etJLhj\nZOf7xzFyeOm+Ldi0NQ3ZZtirKnjhZAb/dvD5jo9n2TYUk++w79nZm0Q6B1m8trM05HVNTNEUn0j4\nK4wSogGfuZyG6DalilW9MKohGbLPseaDjrPp1G4vWTWqm/qLCIIgiB4ROGE0nynVghfGz270NXT/\n47r7zTEC+GyRV73+fKghGUmTYVyW8ZNHT+GJFxc6Ot7Jk1nIAEwJGBlZP3nKa7QodyaWlmiW0VpY\nJv8MpHzu40gm+OM5aW/9SrFswNk2oeCF9lFUGZLESyatNkRwrb+IhBFBEATRG4InjOoco9VR3UC9\nMBquuO61SI9GceUBXlK3R5GhArj34eMdHevocZ5Ihz5YSMaEE+IstIhVCGcv7fMQzaowYgBj/SmO\nShUT2aIBTbhEGgUvtI0kSdVyunZcI4rqJgiCIHpN4ITRXKaEtLF2Ih3grzCS46KUrk+FEQC85De3\nYPP2EdiGjV2yjGeOLeH4TPu9OWemebJdtA8WNWnhWJXyNOR1LWTh2Iz67OzFoyGYYJDQfmO+X8ws\nFRG2dGwRT02I+os6QusggMEZ7pr0WbATBEEQhEPwhNFyCSnHMRpbyzHyMa47FgPAU+lYn0ZHS5KE\nV113HtSQjBEbSAO49+ETbR8ns1gEwGcl9RpnyKvR54NEe4FhWlAAMDCMNhm26TaRsALHPyj16QDe\n6YUi3nb6v/CKF38EgErpOiUUPnvIazPyFNVNEARB9JhACSPGGBYy5doMox6HL0iqCjkSARiDXe7f\nsq7USBSX/9ZOAMAWSHj46Rks5dqLuq6IRLpNYo5QL5ma4k4d6/N5Ob1gaZm/Dy1IUFV/P/6yJIGJ\nlMhsrj/dvJnpJWyqLNSGu/o8BDcoOENedb31zQkKXyAIgiB6TaCEUbagw9Z1xK0yoChQ02fHRpuG\nf8IIqEV293M5HQBc+JtbEI2FEIeEBGP44S9Otvy7tm1D0rkjtqvHiXQAsGUjn12lMAazz6Oh/cYJ\npGA9SlpzHjfvwtwsL8if4O97S+YBHlRK1xlOb5bRomPEGKPwBYIgCKLnBEoYzS2X62YYjUGSz/7z\nqo6RTwseJe4Mee1vYRQKKbj0yu0AuGv0k1+eRLnF3d7Z+QIUABaADZO9G+7qEItpMAHIkDA739/P\nu98si8Wn5LNb5CApjjDqT8fIPHMaQE0YScVsL09nYGnXMaqUTZiGDS2sIByhwAuCIAiiNwRLGGVK\nSBtihtEaiXSAv3HdQH0yXf/P1Lng0s2IxrlrpOkWHvjVmZZ+7/BRnkhnazLkNcRoL2Bi4T/dQZBE\nkMmKciU51JvXSRavS7HYf8LIthmUpTkAgBURTu+ZU708pYHFub622mPkBC+QW0QQBEH0kv5YxbrE\n/HKp5hg1FUZUSvf/2XvvMDnKKw/3rerqHCcHaYIySEJISOScZWPW4LTGBnZtbG+wjcPd9XVkYZNZ\ne732OnDXxtgYYxsbBzBggoQiIBEkIZCE8oxmRqMZTeqezqnq/lHdPSNp8vR0dUvf+zx6HuiprjrT\n33R3/b5zzu+czPCsUT0SL7x2BHUCM2eOdeq76lZnYQeGjoUps2Pd2xsxOJLiIpwpYVMMMhUwZTK1\nkUjxmS/0DcYoj+si37JoKQBq91FSfr+RYZUkk80YDfUXDRkvDMT8RWvrLhAIBILTk9NKGPUMM14Y\nabgrFF4YFfsso5NZsrweh9OCE4lkIM6OA73jPmegTxcfvnLjHemy2By6SPMPCGE0nGymxqhyJSUj\njGKx4hNGx/oiVCQC+v/4ygFQ0gkCL20yMKrSZLI9Rif3F/3hwFN8/ZX/5LWu7TMToEAgEAgEI3Ba\nCaNe//BSutGEkb6DWThhlO0xKv5SOtBvXFdcPJQ1ev61I+M+JxrUb2pq6twzGttkcHp0YRQeLM4m\nf6OIR/W/f5vDbMj1s5mEeBFaqXcfD+BLhtCQUM36DbpJTRHYvLFo7faLlayb30TnGA236t567A3W\ntW8G4KD/8MwEKBAIBALBCJxWwkg3X8jMMBqhlC6dVkmnNSSJglkVyyWWMQJYvLwulzXqPTrIoc7A\n2E/I7Ao3N/oKEN3E8JXp2atYHku20qk0/V19eTufESQygsThMKbs0WLL9p4UnzDyt3Ugo5HylpNM\n6SVcVpedVF8fkd27DI6utMhmjBITtMzPltLFzGF+s++PucePhY/nPziBQCAQCEbhtBFGqbRKf3B4\nKd2pwmi4VbckFcauOJsxUkskYwSgKCbOu2TIoe6FV9tGPdYfiKFooAINs0+1RzeKysyg2VQeb8C3\nPPAwu//u79i78fW8nbPQZEtJXS5jhmjabOYT4igm4p26I52pujZXAuY5exEA/o3rDYurFLFMMmOU\nNV9Yc3wtKTXF8qpzAOiKHBd9RgKBQCAoGKeNMOoPxpHUNK5UFCQJpezUeTpZh6RCTrMvtR6jLGef\nW4fdacGBxMF9PfT6oyMed6ilH4C0ImEqEkc6gNrMkFcpj3OMTC37kdHofe4veTtnoVEzmwMej0HC\nyK5nEtLJ4itNk3r17IRj9qxcya1n2TlgMhHe+SbJ/n4jwyspLNZMj9EEBXC2x2hA6mWut5mPLbkN\nu2InmooymCidTSWBQCAQlDbFcyc7TXr8UdypCDIaiq8MSTm1uTyZLKxVN5RmKR3oWaNVlzQBeq/R\nmjfaRzzu6FG9zM5sUGnWaNTUuNDQUDSI5aGfRVVV3GG9jK6yp5WeI53TPqchpHVB4vUaY4vszPyd\npFPFlTGKxJK4MuvraW7IfVbYyjy4VpwHmsagMGGYMNnNp4m40qVTKtFwEg0Vp9vKJ5begSIr1Dqq\nAeiOdM9orAKBQCAQZDlthJFuvKCLj9GNFwo73BWGl9KVljACPWtkc5hxILHzzU4iIwiMvh799/KU\nFdf8EbNiIpUpl+zsmv6Qzp4jnVhU/feX0dj/5+emfc5Co6oqciZRU+6zGxKDM2PprqWKqzzqWP+Q\nI52tflauN8ZiMeG78moAAps3oaWLS9AVK5NxpdtwYCsAKUucTy27E69VN3GpderCSPQZCQQCgaBQ\nnD7CKBDDO4bxAhTeqhtKa8DryZgUmfMvawagKqWx8c1Th11mXd+qqovHkS6LlBHA3cenL0p79rcA\nEDPpN/aOPW+QShaf5fRYdPeEMQFpoMxnjJB1uTKZxSJzeevqCVGe1AW0pa7uhM8K+6KzMFfXkBro\nJ/z2W0aGWTJMdI7R4UAra/a9DIDP56TJ05D7WVYYdQlhJBAIBIICcdoIo54ThrsWh1U3gOzUTQDS\n4XBJNhGfvawOi13PGm19tY30STe0amYeTWMROdJlUWz6Ovf1TV8YBY/oBhTh+ecSsHpxJiPseeGl\naZ+3kBw4qJeKaRYTskH9YJ6M6YOkUlTvh/62ThRNJenwINvsJwgjSZbxXnElAIFNGwyMsnTIlisn\nxsgY+eMBHnz7l5jiuliurzpxQytbStcVEcJIIBAIBIXhNBJGsWEzjEbOGA0vjykUstmCZLFAOo0W\nL72ZOiZF5sIrmgHwRlO8/s7QTUokkkBRNTQ05jQVnzCyZ8q2AhnHq+mQOnYMAEdTA6nlFwIwuHnj\ntM9bSDoz/WB2g/qLAFxOCyoaEpDKozHGdIl0ZLKhVTVomnZKdtlz6WVIikL47bdI9pW2ZXshyL5u\nyUR6RAGcTCf5yduPMJgIUivXA+D2nPh3WeusAaA7LHqMBAKBQFAYThth1BuIDrPqHqfHqIDCCIYP\neS29PiPQs0aKTcGOxMZNLbkbncNHBpCQSJlkrAU0tJgorsyNViQ4fUGqDOiCsGrhPBa/90ZSkkxV\nbyvdhzumfe5C4e+NAFBV4zIsBodVIZtDKKZZRurxLkDvL8ra+iuKnMusKW4PrvNWgqYRKDFBbASy\nLJ0gjoajaRq/3f8ERwbbKbP6WGQ7Gxh6v2Ypt/kwy2YCiSCR5MiumAKBQCAQ5JPTQhjFEimCkWTR\nCiO5hPuMAEwmmQsvbwbAHIixv80PQHuHnoFQ7GajQhuTsnLdYCA+zSGv6XQaT1i3am48dyHe6gr6\n6hchAQeeKh0ThkQ4AUCTgdk9syLnhFE4E4/RpFUVa6AHAG9zQy6zrJz0OeG94ioAAi8JE4aJkM3M\nnyyANx59hS3HXscsm/m7ZX9DPJyZrXWShbwsydQ4qgBRTicQCASCwlBwYZRKpfjSl77ERz/6UT70\noQ+xbt062tra+MhHPsLtt9/OfffdN+lz9gZioGm4U1lXuvHMFwqb3cgaMJSiM12WJcvrka0m7Eis\nWXcQgJ5uXeg5vcbMxBmPqsqMI+A0h4n2tHZi1lKEFTtl1eUAVF93LQDOd7aRTBTHDf5YRGNJlLSK\nhsbCeSNvHBQCSZLQZN0tcDBYHK9brz9GeVwX+Y6G2bnPiZNLbu2LzsJcW0va7yf81psFj7PUMGdm\nGSWGvf/2DxziDweeAuD2sz5Ag3tWboaRe4QST2HAIBAIBIJCUnBh9Oc//5mysjJ+9atf8dOf/pR/\n+7d/45vf/CZf/OIXefTRR1FVlbVr107qnD3+KK50FJOmYnJ7kC0jz9Qxwq4bSnfI63BMJplVlzYD\nkOgO0dUXJuTXb2gqq4wrzRqLWXW6U56cnl6T//EDuiNdxD0kuBdceh5+WxnOVJQ9z2+e1vkLwYFD\n/bmyR4fRM6cywigcLo6eu2N94ZxV98mOdMORJAlfJmvk37ihkCGWJCdnjPpjAzy061FUTeW6xitZ\nVbsCTdMIZZwtXe5TN1hqHXqfUZeYZSQQCASCAlBwYfSud72Lz33uc4BeomQymdizZw+rVq0C4Ior\nrmDLli2TOmfvCcYLo++GZ4c2Zq1kC0WpDnk9meUrZ4FFzxo9u+YAqaheotbQUHzGC6BbUquACfBP\nw4AhlHGkUytrco/Jsoy64iIAgi8Vf89Ja6teCmh2GF/2KCn6x04oVBwZo562LqxakqTFjuL2jJlZ\n9lyimzBEdu8i2dtT6FBLCksmY5RMpEmkE/zkrV8QSoY5u3wh7533LgCikSTplIrVpuSOH042Y9Qt\nMkYCgUAgKAAFF0Z2ux2Hw0EoFOJzn/scX/jCF05wLXI6nQSDwUmdsycQHTbDaAxhlLHrVgqeMcoO\neS3NHqMsJpPM8gv1OSOBVj+mtO4qNre5zMiwRkWWZdJydsjr5P6mhpN1pLPOmnXC41kThuq+No4d\napt6oAUgW/borXAYHInudAgQmWbvV74ItbcDkK7Qb8ITY9j6m1wuXCvP100YNhW/IDYS87CM0dOH\nX6A91EmlrZyPLfkIspQRx5kyupP7i7KIIa8CgUAgKCSGWIkdO3aMz3zmM9x+++3cdNNNfPvb3879\nLBwO4/F4xj1HWZkDRdG/eIPRFJ6kno3xNNRTVTXasFH9JrmyyjXGMfknXlPBAAYcUCwAACAASURB\nVGBRE9O+biHjHombbl7Cjq1tWJMqIJGSYU6zcT0r42GyKhBNEo4kp/zaWf36TVnDskXA0BpUVbl5\nu3ExVUd20fbsGpbd9/n8BD0DxIJxZGD+girD/4YsNjNaKImmadOKJV+/h9qjl2m5GhuoqnLT3aEP\nenW7rSNew3rLTbz96haCW15i0V13ICvF58hYKMZaA49XNz+xWszs6dsLwGcu/huaq4cyr73H9A2L\nilE+k8vK7Zhek+mPDeAts2JRDC4DLVKMfk8LxBoUA2INBPmg4N/ovb293HXXXdxzzz1cdJFeinT2\n2Wfz+uuvc/7557Np06bc42MxMBDJ/XfH8SBLM8YLSYeHnp6RswPhkF7LHo0mRj1mJohp+ssc6h2Y\n1nWrqtwFjXs0Fi2vZ//ruk21ZFWKIqbRUGwm1GiSjnb/lOJMp9O4Mo507tl6xmj4eaquvQZ+tgvr\nrtfp7OjDbC2+GzdVVVGjKWSgvs5l+HrJJok0EPBHpxxLXt8LGWFkra2jpydIX6+eXVM1bcRraJWz\nsNTXk+jspHXtJtwrz89PHCXGeGugZoZBH+vp51j4OGZZoUyrOuE5R9t1h0vLGJ8jlfZKuiPH2dXW\nQoO7Po+/welBsXwvnMmINTCeQqyBEF5nBgUvpfvxj3/M4OAgDzzwAHfccQd33nknn//85/n+97/P\nhz/8YVKpFKtXr57w+TRN03uMsqV05WOV0hlt113aPUZZrrpqLqlMiZpjlBKYYsHh0oVK1vlqshxv\nPYpZSxM2O3CVe0/5+fyLluO3l+FIxdj17IbphDpjdHYFUYAUUF9r/Ad7tpckHjPe8joUTeKNDAC6\nVTeMb9IiSRLeK64GILBhw8wHWaJk17lnMGN1756NIp+4F5czXhjD2XKoz0gYMAgEAoFgZil4xuhr\nX/saX/va1055/Je//OWUzheMJokn0/hyM4xGtuoGYdedL0wmmeWXNbPj5VYuXjXb6HDGxOO1E2of\nJDJFa+je/S1YOdGRbjiyLKOedwm8/AyRlzfBLTdMI9qZ4cAh/cYUqyk3sNRIrDaFKEM9f0bS1Reh\nIqk70mV7yHKfE2OYtHguvoTeP/yOyDu7SXR3Y6mpGfXYM5XsBlRfMAA+mOttPuWYnFW351Sr7iy1\njmp2AsfELCOBQCAQzDDG3yVNk16/PsPIk5thVHwZo6z5wumSMQK4/JIm7v7nK1l+Tp3RoYxJRcZs\nIBmbWqN/MOdIVzvqMUvfewNJyUTVQAed+1qndJ2Z5NjRzIyeEebEGIE9MxA4Nc35Uvmgq6MHZzpG\nymRGKdNnVCVyc4xG30AxOZ24z78AgMBmYcIwEln3z0BEz+bP8Taecsx45gsgZhkJBAKBoHCUvDDq\n8Uexq3EUNYXscGByjO66lbPrNqyUrrRd6UqR6mr9tdeSU7sJT3fpjnS22bNGPcZV7qWv4WwADj/9\n3JSuM5ME+vR+vKqa4pg3lRVG6ZRqcCQwmBG+ybIqJEkvD02O4Uo3HO+Vejnd4Mub0VLGZ7+KjWxm\nPhzVxc8cb9MpxwQzpXRjZoyywkhkjAQCgUAww5S8MOoNRHMzjMay6tY0LZcxKrxd9+lVSldK1Nfq\nDocmVSOtTv5G3Dyg34z55p56U3fCdW64HgD3/jdJRItjcGmWZFjPljU3lxsciU52wKxaBMIo1tkJ\ngKl6KCM40UHQtrnzsMyaTToYJLR928wFWaJkM0ZSSqbSVo7HcmJ/WzKZJhZJIstSrhdwJGocujDq\nifSSVo3PMgoEAoHg9KXkhVGPP4Y3V0Y3fn+RYpaRM8YBhUKyWJAUBS2ZRE0Ux1DLMwWX00IKkJHo\n6YuMe/xw0qk07kxjft2iuWMeO/eCcxhwVGBPx9j17Pqphpt3IpEEiqqiobFgXnEII1fmJlhLGy+M\npF69od8xLCM40ZJbSZLwXXkVAP5NG2YkvlImW4oop5URs0U54wWPNZetGwmryUK5rYy0lqY32jcz\nwQoEAoFAwGkgjE7MGI0hjJIT2wWeCSRJQj4N+4xKBU3Rb7q6JjnktbulHbOWJmR24iobe7aWLMuw\n6hIAoq9snlqgM8D+Q31ISKRMMnab2ehwAPC4M/0kqjb2gTNMKq3iCOo32uXDMoK5HqMxzBeyuC+6\nBMliIbr3HeKZ7JNAJ/v6jS6Msv1F4/e+1WayRsKAQSAQCAQzSckLox5/NGe8MFYpnVHGC1mGyulE\nn1GhMWV2rnt6JidKe/e3ABDxTGyA7ZKbrycpKVT5j9LxzqHJBTlDHDmSmRPjLJ75Si6nGQ0NWYO0\ngVmjHn+U8kTGmGL2kLviZNwrTQ4Hnot1Qdz/zFMzEGXpkrXrNqWVEY0Xhhzpxrf8FwYMAoFAICgE\nJS2MVFWjfzCem2E0EUe6sZymZhLTaTbLqJSwOvRMycBAdFLPC7W1A6CN4Ug3HFeZh/7GxQC0PP38\npK41U/R06+8NX+XopiSFxmm3kO0USRroTNd1bABvKkxaMmGuqso9njNfmGB2ufzd7wGTieBrW0kc\nE1mjLHFJFz4mVWGW81T3ylAgW0o3gYyREEYCgUAgKAAlLYz6gzHSqkZZWu8dmcgMI8WgjJHsypTS\nhUTGqNA4MzvS2Z6GiZJzpJs18VlN9at1EwbPwZ3Eo1MbKptPogE9hvpZpw6nNQqbxUTWwy0WnZqN\nej7ob9WFb8JTjmQa+lyYbHbZXFGJ99LLQdPoe/rP+Q+0ROmIHgVATpuRpVO/anKldGMMd81S69Dn\nRHVHxJBXgUAgEMwcJS2Mev36F6snOZmMkdGldCJjVGi8mfk9sfDkjC+yjnRl4zjSDWfOyiX0O6uw\np+PsetpYEwZVVZEyf/cL5k+sHLAQKCYZDb3vKxQyzowk0t4BgFY5NJw1GkkQDiUwmSQckyg/LL/p\n5kzW6FXRa5ThSKgNVUojadKI1uwTserOMjxjpGrGm3YIBAKB4PSkpIVRjz+KNZ3Akk4gWSyYXO5R\nj01McDbJTCFK6YyjslJ/7VPxic+aSafSeKIZR7qzxnakG44sy8jn6z0n8a2bJhFl/mnvHMQEpICa\nKqehsZyMZtKFUTBknLW5erwLAHt9fe6xY+16z1HNLC8mZeIfj+aKCryXXQGaRv/TT+Y30BKlZfAI\nqkl/zyVGKJmcjPmC0+zAbXaRUJMMxAL5DVQgEAgEggylLYwCsWHGC5VjWr7mXOkME0ZZVzpRSldo\najKDTaVJzM3pOtSGoqmEzE6cvtEF90gsufl6ErJCZeAY7bsOTOq5+eTgId1xTbIpumteESFlLPND\nk8zi5QtN07D4ewDwzhkyBjiaMauY1eib9DnLb3oPkqIQfP014keP5ifQEiWtpjky2EE6I4yyfVtZ\nNE07wa77ZMJ7dnP4y/9E969+SbK/HxCDXgUCgUAw8xTX3dIk6Q1EJ2S8AJCMG2fXDSBnS+kiImNU\naOqqXWhomDSNeGJiWaMhR7rR+9ZGw+l1MdC8FIDWZ4wzYTjWOajH4xt/R77QyGb9oycSMUYYBSNJ\nfDFdBPmaG3KPd7brj9VPQRiZyyvwXK5njfqeOrOzRh2hTpJqEjnjEJ+In5gxioQTqKqGzW4e8TO5\n/+k/k+rtJbD+RVq/+iW6H32EhrRumd8dFn1GAoFAIJgZSlsY+WPDZhiNI4yyDdUTmE0yE4hSOuOw\nWBTSkoSERFfXxDJ2OUe6qok50p3M7NU3AOA99Bax8OTc8PLFYJ9+3apalyHXH4tsmVokbIz5QlfP\nIGXJIBpgqdUd06KRBP09YUyKTE392HOrRqP8XXrWKLTtdeJHO/IYcWnREmgDwGrVlVHipDLWYMYU\nxD2C8UKyt4fo/n1IZjOuVeejpdMENqxj6YNrufq1IP1dR2Y4eoFAIBCcqZS0MDpxhtHYO/vZUjrj\n7LrFgFcj0TIZimPHJzbkVe3OONLNnrgj3XDmnLeYPlc1NjXB20+umdI5pksqoouOOc3lhlx/LLLu\nkLGYMcKo51AbMhoxpw/ZopssdLbp2aLaWZ5J9RcNx1xejufyK/Ws0Z+fyFu8pUbLoC5enDY9W3ly\nj9FQGd2p2czBrVv0n61YSf3ff5qm+/4d9wUXgqay7GCUpT9dR/cjD5Ps7ZnJX0EgEAgEZyAlK4wS\nyTSBcAJfRhgp4wij7Bez4aV0osfIEMyZYZN9fZEJHW/x630M5XObp3xNy6VXAmDa/DzR4MSumy+C\noTiKqqKiMX9u8Qmj7AZFPDZxQ4x8EmzTsznp8urcY1lhNJUyuuGUvzubNXqDeHv7tM5VqrQEdGHk\nyXzuJU/KGA0ZL5yYMdI0jcEtL+vPzQzOtdbPou5T/0DF17/C3iYraBqBTRto+dqX6frFz0j2CIEk\nEAgEgvxQssKoN1OKUa5mZxiNXUqXmuRsknwjSumMxZaxXg74xy9rSyWTuKP6TXLdWXOmfM3lt66m\n31mFKxlm+8OPTfk8U2H/wT4kJNImGatBWdKxsGSE6sklVoUilckIZsvoAI62Td14YTjmsjK8V1wF\nQN8Z6FAXiA/SFxvAZrLideiZ8pMzRsHAyFbdsZbDJLu7MXk8OBYvOeFnFY0L2XhFDb+8qRz7BeeD\nqjK4eRMtX/8yXQ8/RKJHmDIIBAKBYHqUsDDSb3CHZhiNlzESdt1nMtmd6XBw/Gb/roO6I13Q4sLh\nmXp/jkkxUf7BDwPge+tljrcWbr5NW+Ym3+Iaf3imEdjsujBKjmDjXAhM/XqWwdWol0pGwgkGeiMo\nikx13dT6i4ZT/u6bkMzmTNaobdrnKyWy2aImT8OoAng0q+5ctujCi08YugsgSRK1zmr8HoXYB1fT\n/G/fxH3xJbpAemkzrV/7Mv71L87I7yQQCASCM4OSFUY9/hiKmsKajILJhOL1jnn8ZKfZ5xvJagOT\nCS0eR00a01dxJuMrcwAQj4z/2vcdbAUg6qma9nUXXraS7rpFmLU0+372yLTPN1F6j+sbBmWVjoJd\nczLY7HpTfipZ+GGdyZSKK6RbmVfMawbgWMaNrnb25OYXjYbiK8N75VUA9P35zMoaHc70F831Ng0J\no1F6jIabL2ipFMHXXgXAc8mlI5671qGXPh4LH8dSW0vdXZ+i+d+/iefiS0FVOf7Yr89o0wuBQCAQ\nTI8SFkbDjBfKK5DGmdNiuDCSJEwOYdltFNWZAafqBEq3putIdzILP34HSclETede9r+yPS/nHI9Y\nZkd+VsPYGwZG4XDopY1qsvAZo+6+EOVJ3crcMXsWMDS/aLr9RcMpX53JGu3YRqztzHFSyzrSzfE2\nYcl83p7cYxQcIWMUfnsnajiMZdZsrA2NjMRIs4wsNbXU3vVJvXwxnabrZz9FSxlToikQCASC0qZk\nhVFvYMiqe7wZRmC8MIJh5XQhIYwKTV1myKuc1sY9NutIZ8/cNE+XmjmzGThHbyTv+91vSKdnVgyk\nVRUpoWdiFs6f/BymQuBy6hkjdQLrkW+6W45i1tLErE5MDj2jli/jheEoPh/eq64BOGMc6lJqirag\nnrGZ42nMmZ4Mn2OUiKeIx1KYTBJ2hzn3+OArrwCjZ4tgSBh1h0/tJ6r60F+jVFQQP9JK/3N/mf4v\nIxAIBIIzjtIVRv4o3gladYPxdt0Asitr2S2c6QpNZYUDFTABg8HYmMdaBvT+k7JMmVU+WPmx2wiZ\nnVSEetj++MzetB1p82MCUhJUVzpn9FpTxZXtfUoXvpRu8IieEUz69FLJSCjOQF8ExSxTXefO67XK\nV78LyWIh/OYOYkda83ruYqQ92ElKTVHjqMZhduQyRolhg5VDwSGrbkmSAEiHQoTeehMkCc+FF416\n/lpHDXBixiiLbLNT+7d3AdD31JNnXG+XQCAQCKZPyQqjnkAUTyoz3LVyfGGU3bEshoyRKgwYCo4s\ny6Rl/Sasc4whr8lEAk/Gka5+0dy8Xd/udqBd8x4AlA1/IRyYOXF86HA/AJKt+NzosrjdeimdpOkW\nzYUk1qmbYMg1eqlkZ3sAgNpZXkym/H4kKl4fviuvBs6MrFHLsP4iGO4+OJQxGsmqO/j6a5BO41i8\nBMVXNur5K+xlKLKCPx4gmjrVYdJx9mI9SydK6gQCgUAwBUpWGEXjacrSulW3Uj52KZ2maaSSxSOM\nhDOdMUgW/c+9p2d0UdJ9sB0TKkGLG7s7v8YFy993A73uGpypKDt+9uu8nns4Xcf0/hmX79ThmcWC\ny24hjYZE4Z3ppN4uAJyZ4b05m+6m/JXRDads9bv1rNHON4m1tszINYqFrCPdHK/eI2SxZnqMhmWM\nclbd3qG/z5NnF42GLMnUOPRMX1d45PlFVR/4EEplJfH2Nvr/8vRUfg2BQCDIMRDz873t/2d0GIIC\nUbLCCKBCm9gMo3RaRVU1ZFnK+47wZJCdopTOSCw2vZ+hv3/0WUa9B/Qb16g3/705JpOJ6ts+CkD5\nnq0cOzQzpT6Dmd+vuja/ZWH5xG5VyMqhQs4y0jQN+6DuSFc2T89qdB4ZAPLbXzQcxevFd/WZ0Wt0\nOCuMPPpra7aMlTHShVGiq4vY4UNIVhuuFSvHvUbWmW6kcjoA2WYbKql75qkzyvhCIBDkn+eOrOOA\n/7DRYQgKREkLI08iU0o3To9RMRgvgCilMxq7a/whr+F2vf+EPDnSncz8C5bRNXsJiqZy8Ge/nJFr\npDOW5HPmlM/I+fOB1WLKCaNotHD29f5QnLK4niHyNTcQDsXx90dRzDJVMygky27MZI3e2kms5fT8\ngh2I+fHHA9gVW84kYShjNFwYZYe76qV0g1v1bJF75Spk6/hzt8YyYMjiOOtsfNdcp5fUPfSgKKkT\nCARTIpQI8+qxbUaHISggJSuMZC2NNR4CSUIpG70mHYpPGIlSOmPwZEp3ouHRh7xmHelsmTKrmWDx\nXXeQlBRqug/wzsbX83ruwGAMswYqMH9u8QojWZLQMo33wQkM3c0XXUe6sKlJEooVxePNudHVzc5/\nf9FwFI9Hv1Hn9M0atQzqGdBmTyOypL+WWbObxAmldEMZI01VGdy6BRi/jC5LrTNrwNA95nGV7/8g\n5qpqEkc76Hv6zJolJRAI8sPmo1tJqkkWVywyOhRBgShZYeRORZAApawMSRm7ybxYhJEshJGhlJfr\nPUOJMTIUVr/et1CeKbOaCSobavGvuByAwT88RjqVvx6bfQd6AUgrMmbF2L/3cTFlhFE4XrBLDhzW\nb95jnkokSZoRm+7RKLtxNZLVSvjtt4gePjTj1ys0hwOtgD6/KItilpEkfZCvquoOhNlSOrfXSvTg\nAVK9vSjl5dgXnTWh6wwf8joWstVKzcfuAkmi/y/PEGttneRvJBAIzmSSaoqNR/WM9rUNVxgcjaBQ\nlKww8iYnYdWdMN6qG8CU6TESpXTGUF2tv/5aYmSL6GQ8gTvqRwPqzsqfI91IrPybDxG0uCiL9LHt\nN3/O23nb2/UbfWvG9a2YkTIZmnCocBmjcIc+Y0er0rMOQ8YLY2ed84Hi9uC7+lrg9MwaZQe7zvUM\nCSNJknIbUslEGlXVcnbdTreVwVcypgsXXTLukO4sVY5KJCT6ov0k02OXYToWLsJ37fWgqnT97EHU\nZOHKNgUCQWnzRtcOgokQs1x1LCqbb3Q4ggJRusIoY9U9niMdQKJIMkZDpXTCfMEI6mt1YWRStdzu\n9XC6DhzBhEbQ6sbuzK8j3cnYnHbkG28BwPrS84T6A3k5b1+PLrrLinR+0XBkRf/4iUQKd7OqHdcd\n6Wz19YSDcQL9UcwWE5WZAcAzTfmN70Ky2ojsepvooYMFuWYhSKaTtAePIiHR7G044WfDLbsjoTia\nBg6nBVlNE9qml5K6L5pYGR2AWVaoclSgoXE82jvu8ZW3vh9zTQ2JzqP0PyVK6gQCwfhomsa69s2A\nni3KzlwTnP6UrDCqlvQG+vEc6aAIS+kiImNkBB63jTT6H33vCM50vQd1R7qYp6og8Zx78zX0euux\np2PseCg/9t2xTGP77IaZLw2bLopZfz8W0nzBEtBvpH3NjblsUe0M9xcNx+R247tGzxoNPPdsQa5Z\nCNpDR0lraeqcNdgV+wk/y37uJuIpgoPZ4a5Wwm/uQI1GsTbPwVpfP6nr5Qa9hsfuMwK9pK72Y5/Q\nS+qefea0Nb8QCAT5Y2//ATrDXXgtblbWnGt0OIICUrLC6Ko5+pevMoHhrtkZGmZzcWSMRCmdcaiZ\nvpZjXcFTfhZp18usqJ4ZR7qTkWWZuo/ejgZU7nuNo3und8OWSqnIST0TtmjB+BsGRpO9YY7HCiOM\n4sk0nohuzV05vznXXzSrAP1Fwym79nokRSH05nYSx8fukykVDp80v2g4uYxRIn2CVfdEZxeNRNaZ\nbrw+oyz2+Qsou/5G0DS6fvZT1GThyjcFAkHp8WL7JgCunH0pily8w9IF+adkhZHq129wJtRjlB3u\najU4Y2SzgyShRqPCPtYg5MxNWk/vqeI060hnn91wys9mijnnLaa7eRkmNA7/Ynr23S1tA5iApAQV\nZTNbCpgPsjfM8Vhh3gtdR3txpaMkZQVbVWVBjReGo/h8uC+4CDQN/9rnC3rtmaLlpPlFw7EMzxhl\nHOmcNonw7l1gMuG+4MJJX2+8WUYjUXHL+zDX1pI41knfk6dfj5dAIMgPnaEu3unfj0U2c9msi4wO\nR1BgSlYYpXr1kphJldIZnDGSZDlnwJCORAyN5UzF6tCHvPr7T339C+FINxJL77qTuGympqeF3Wtf\nnvJ5DrfomwWy3Zyv0GYUq10XRsNn3MwkvQdbAYi4ygmHEgQG9P6iqtrC9BcNp+z6GwEIvLS55F0q\nNU3LCaO53hGEkXVonbMzjMz9naCqOJeeg+L2TPqaE5lldDKyxZIrqRt4/tnTqsdLIBDkj2xv0UV1\n5+M0F/8moyC/lKwwSg70A5MzX7AY3GMEQ31GqjBgMARnxq0t2+uQJRGN44kFUJGoWzSnoDGV11US\nWnUVAOEnfk8yMbUyn+5jgwC4y2z5Cm1Gsdl0AVcoYRRq00sl1fLqoflFDV7kCbqh5RNrQwOOxUvQ\nEgkCG9cX/Pr5pD/mJ5AI4lQcVDtO7c8b3mOULaXj8DsAeC6+dErXrMlc53ikh7Q68b8f+7z5lN34\nLr2k7uc/RZ3ie00gEJyeBOJBXu/ajoTE1Q2XGR2OwABKVhiRTmPyeJAt49sSpzI3XkoRCCMx5NVY\nvF69Ny0WOfGGqOtAK3LGkc7mtI/01Bll5R0fIGD14IsNsO1XU3POCmYMJWrqJr8DbwT2TMYonRrZ\nPj3fpDKlkua6uiGb7gKX0Q2n7AY9azSwbm1Jl9a2ZOYXNXsbR3RuGu5Kl92QMHW1IDscOM+dWlOz\nTbFRZvWR0tL0xfon9dyK996Cpb6eZFcXfX/6w5SuLxAITk82H32FlJZmWeViqh3jt2oITj9KVxgx\nsf4iGJ4xMr6BTggjY6mo0NPiyZP6WvoOtQIQ8xbGke5kLHYr1pveB4Bjyxq6j/ZM+hxqxt1t7pyZ\nn8mTD5wuKwBqHgfcjoXcr7+m7sYGw/qLhuNYcg6W+nrSfj/B1181LI7pcnhQn180Un8RDOsxSgxl\njOzJMO5VFyCbpz5va7IGDFlkc6akTpYZWPsC0QMHphyDQCA4fUikE2w6ugWAaxrFQNczlZIWRsoE\n+ougeOy6YXgpnRBGRlCbnVeTPDFLEc440kkFcqQbiXPedSV9lU3Y1AQ7HvwlqqZN+Ln9/giKBiow\nt6l85oLMI06nXkqnpSb+e04VTdNwhfoAsNfNZtAfw2It3PyikZAkKddrNPDC82iTWO9iYqz+IhjK\nGIUH4yTiaWQthaLGp1xGlyVrwDCZPqMstjlzKV/97qGSunh8/CcJBILTmle7thNORmhyNzDP22x0\nOAKDMD6FMkW21d+IOVaO+ddvjnts33G9n6cYhFHOfEH0GBlCXa0bDQ1Fg2QqjVnR/ya0bn3wp6Oh\ncI50JyNJEos+8bccv/9fae54m81rt3Pl9Ssn9Nz9B/Sb/rRZRlFKY7/D49J7oSR14oIgEIrz+42H\nuGjZLJY0eCf8vP6+IJ5kiDQSobSeqaqb7TOkv2g47osupvePfyDe3kZ07zs4zl5saDyTJZFO0BHq\nREKiyTPyeyfrBtqfcYK0JUNYqqqwzZ/eJPlsxmgyznTDKb/5vYR2vkniaAe9f/oD1R/+yLTiEQgE\npYuqqazLWHRf03j5CWXBKf8AvX/4PVVf/qJR4QkKSGncQY2A31FHT8xKZ5t/3H9ZO2BvWeF7R05G\nlNIZi9WikJYkJCSOdQ2JU1vAGEe6kymfP4fU8ouQ0Yg//Qe6+yf2d9LerpeG2dzWmQwvrzgdCioa\nEhPrM/KH4nzrNzt4+e0uvvOrbazb3jHha3UdbEUCwg4fxzr0GVZGltFlkc2WoYGva0rPuvvIYAeq\nplLvqsWmjPy3ly1hHhJGYTwXXzrtSfK1zuyQ16kJI9lspvbjekmd/8U1RPbvm1Y8AoGgdNndt5fj\nkV7KrD5WVJ2Te1xNJuj80Q9yc9cEpz8lmzFacfQ5Km99P/Z5E9t1dLqt+MqNt12UhTAyHE2RIanS\n1R2kcbaXRDSO2yBHupE4+28+wt7d22kOd/LsI89y5+fejzzOTWR/j/73VF7lLESIecFpM5NG352J\nx1M4lNH7TfyhON/69Q66+iOUua0MBOM8+sJ+NA2uXTl73GsNtrRTASR9VUPGC03GCyMA71VX0/+X\npwm/tZN4ZyfW+nqjQ5owLYPZwa6jbyhYMhmjVKZ81ZYK4b7o+mlfe2iWUTeapk1JaNmamim/6Wb6\nn3qS7p//lKZ7/x3ZWjqbCwKBID+82KZni65quBSTnKkk0TSO//IRYi2HJ9y6ISh9SjZjVB7tomFh\nLbOayib0rxhEEQxljESPkXEomZ6H3j59ltGx/VlHOg9Wu/FW1ya3m4q/uhWAs/Zt5MVXW8d9Tjyo\nu+w1FEEWZKI4bApZ24V4LDnqcQPBOP+VEUVzyxQ+79jH3fOjoGn8as1+8HkBmAAAIABJREFU1r7R\nPu614p1HAUhWNRAM6P1FFdXG9RcNR3F7cv02/rUvGBzN5GgJ6MYLc0cxXoBTTW9cXhuW6uppX9tl\nceIyO4mnE/jjgSmfp+Kmm7E2NJDs6aH3D7+bdlwCgaC0aAt2cMB/GJvJyqX1F+Qe969/kcFXXkKy\nWKj/9N0GRigoJCUrjGS7HXPV9L9cC43oMTIeW6bpP+DX7a37DrUAEPMVjzVnzfXXoVZUU54M0vrk\nM3SNMJA2SzKVxpQpRVs0r3R2tcyKiWwBXSQ8sjAaCMb51q+3090fobHKwR3BV4m+vAnHc49zNzuw\nphP8eu0B1rw+jjjq7QYg6akDoK7BhyxPr5Qrn5RdfwMAg1teJhUcNDiaiTF8sOtYGaOTezvL5+ev\nXLUmmzWaYjkdgKQo1HzsE2Ay4V/3IpG97+QrPIFAUAKsa9MHul5SfwF2RW+5iOx9h57Hfg1Azd9+\nHFujsWX2gsJRssKo6b7SLHkQpXTG4/LoWaFwUHeiiuQc6eoMi+lkJEWh4Y7bAbiwbye/+tM21FFM\nCg61DCADSQl8PuP76CaDlhEnwdCprmD9gzH+69fb6R6I0ljt4lPOVuJ7dyM7ncg2G45Du/hs3/PU\nxPr4zYsHeOG1tlGvYw/q5hRRkxswdn7RSFjq6nEuOxctmSSwoTQGvnaHewkmQ7jMTqrsowvyrCtd\nlspli/IWQ900DRiy2BqbqHjPX+nnevgh1Fhs2rEJBILiZyDmZ9vxnUhIXDVbH+ia7Ovj2P89AKpK\n2ep347ngIoOjFBSSkhVG5vLS2RkfjiilM56seIhH9CyFdlwf/OloGL9XpZA4ly7DuuQcbGqSht2b\nWDNKyVhLiz7g0uQwFzK8vCCZ9I+gUPjEgbv9gzG+9esdHB+I0ljj4tMLU4TWPgeyTP0/fIbl3/02\n1oZGlMEB/rbzOVb63+GxFw/w/AjiKBqN4YkF0ICBgF68VwzGCydTdsNqAPzrXkRNJsY52nj29x4G\nYM4og12zKPKJxhre6vy99lkDhmPh7mmfq/xdN2FtbCLV20vP70VJnUBwJrCx4xVUTWVF9TlU2MtQ\n43E6f/R90qEgjiVLqXzfB4wOUVBgSlYYlSqilM54qqszWbu47lZo8/cCUDHPeOOFk6m77SNossy5\ngwd46YXXOdZ3qqDu7tJd1txF4Lo4WaSMtXh4mDDKZoqO+6M01bj53GUV+H/9MABVf30bjrPOxl5f\nT8NXv4736muR1DTX977OrV0beWLNbp579URx1HWwHRMafY4aQsEEVpti6Pyi0bAvOgtrQyPp4CDB\nrVuMDmdc9vfpwmiup3nEn6vJBAMvrqHjvq+d8Lgzj86JtXkopcsiKYruUmcyEdiwjvCe3dM+p0Ag\nKF5iqTgvdW4F4NrGK9A0je5HHibedgRzVTV1n/x7JINHOggKj1jxAiM7HCBJqJEImjq+RbEg/9Rm\nmu6llEY8GsMdH9Qd6RY2GxvYCFhq6yi79nok4Mqu13jo6T2nlNSFBvReqdo6jwERTg/FrH8ERaN6\n9q4voIuiHn+Mplo3n3/PPAZ++gBaIoHnksvwXXNd7rmy2ULNR++g7u8/jWy3syjcxsfan2Hzs1t4\n9tUjueP6D+n/3efThW9dg3faVtEzgSRJuazRwJriH/h6oFfvzZvjbTzhcTWRYGDtGlq+8iV6fvMr\nNP8AJk3P1DndFkym/H3tZGcZdU+zlC6LdXYDFTe/Vz/nwz8jHY3m5bwCgaD42HLsdaKpGHO9zTR7\nGvGveZ7gq1uQrFbqP3M3JlfxbaAJZh4hjAqMJMvIdt0hT42M3lAvmDmqqpyoaChAy86DuiOdzYvF\nXpw9axU3/xWyy0VjrBvzgV2nlItpmTld8+aVGxHetDCZ9cb8oD9Gjz+SE0XNtW7+nw+ew+AvHiTV\n14dtzlyq77hzREHjXnU+jffch7V5Dr5UiNs7nqP1j0/xly2tAESP6o50YZfeQ1aMZXRZ3OdfgMnn\nI9HZSWT320aHMyqxVJzWQAeyJNOYGeyqJhIMrHmelq/8Mz2P/Yq034+1oZG6f/wsVpeezcz29+UL\nn9WL1WQhlAwTSuSnPLn8XTdhbWom1d9H7+OP5eWcAoGguFA1lfXtLwF6tii8Zzc9j/8WgNqPfxLr\nrOIqrRcUDiGMDGBoyKsopzMCkyyTzjT9t+7KONJ5q4wMaUxMDieVt7wPgKt7t/HUxgMczQzL7OkL\no2iQBpqL+IZ/NCx2vS+q94ifX//kddKBGHNqXfzTh5cTeeqPRPe+g8njoe4fP4tsHn3OkaWqmob/\n96v4rrsBExrX9r2B9puf8uyGPaSPd6EBYSlrvFBWiF9tSkiKQtm1+oyfgeeLd+BrW7AdTdOY7arD\nnNIYeOE5Wr78T/T89jekAwGsjU3Uf/puGu+5D/d5KzFnZhm5PfndfJAkiVpHZtBrnrJGkslE7cc/\niaQoBDZtJLx7V17OKxAIioedPbvpi/VTaa/gbK2SYz9+ADSN8vfcjHvlKqPDExiIEEYGIJzpjEfK\nZCoCnT36/9fUGhnOuHgvvxLLrNn4UiFW9O3iZ8/sIa2qHDig90dpZhlTCdZCu2pdtKISR8OiasxH\nZl5C4+BzWxlYuwZMJur/4bOYy8YXM7LZTPWHP0L9p+9GtdpYEOmg8rEf4uxuI6a4SKRlrDaFiuri\nHoLrveIqJKuVyDu7ibePP6PJCA4H2lBSGhfsS9Dy5X+m53ePkR4cxNrUTP1nPkfjN+7FteK8XIYv\nO8so3xkjGCqny4cBQxbrrFlUvFefJdb98M9Ii+y+QHBasa5dH+h6Tc2FHPvRD1HDYZzLzs3NEBSc\nuZTendRpgHCmMx6zLePgFtFteZ0NDQZGMz6SyUT1hz8CwCX+XfS0d/Pcq210dOiDLW153okvFE6b\nmR7gbTRCXitOt5VAf5SX96bZ2vheIjfcgXXe/Emd07XiPOb967+TrG3Am4rgSYUZsOvCt77RV5T9\nRcMxOZ14L70cgIE1zxkczalEI0Fia9fzsSd7mbVhN+ngINbmOdTf/Xkav/4vuJavOOU1tuQyRjMg\njDIGDN15MGAYTtkNq7HNmUtqoJ+e3/0mr+cWCATGoGka27p3cjhwBLvJxrzn3iZxtANzbS21n/g7\nYbYgEMLICIQznfHYXbowyhZnVcxvNiyWieI4ezGuFSsxqymu7NvOE5tb6OzUh4FWVJdmk2hVxjp9\nbr2Hz3z8fP76trNYHHoTWzJExOJjywH47UOvs39396hznEbCXFHJ4nv/hcjKKwA47tINAoq5v2g4\nvutuAEli8NWtpPx+o8MBIBIKsPXX32f/P3+OxVvbccQ1lKZG6u/+Ao1fuwfXsuWjik63VxdE5VX5\nz9bV5GmW0clIJhM1H/sEkqIw+NJmQm/tzOv5BQJB4dA0jd19+/jOth/xs92/AuDWNh+R7duQbTZm\nffpuTA6HwVEKigFl/EME+UaU0hmPx2sncjSIJCuki9SRbiQqP/TXhN/eyTnBw2z3nkVKrcKKRGOJ\n3PCfzAWLqylzW5lb78EsaXQ8+P9R17WfpvkRIjfcyfZXO/D3RXjxqXd44+VWVl3SxPzF1RM6t6Qo\nLP+Hj9O64yIim/ohmiq6wa6jYamuxrXiPELbt+Fft9bQWRqRkJ+3nvwFjld2Uh7XnTT7a5w0fPiD\n1C+9ckIZuEuumc+ic2qpm+3Ne3y5Ia95zhgBWOvrqbj1/fQ+/lu6H/k59vv+I5fxFwgExY8uiPby\nl9a1HBnUS5NdZifvic+latM6AGo/8XdY6uqNDFNQRBSNMNI0jXvvvZd9+/ZhsVj4j//4DxqKvLxp\nqsxkKV0inaQlcIQD/sPE03Hm+eYw3zcHl1l8mQ+nrNxOFxBXXARtPszW0Rv7iwlLVTW+626g+4UX\nuTjazhFrFUiwcH6JDjyWZc5q0vuHuh99hOiB/Zh8Pmb/wz+ieH0sOree/bu62b7lCIH+KC8+vZc3\nXj7Cyoub8FbYqap1j2v/XNbcTPT549jsyoxkLGaKsutX68Jo43rKb7oZ2VrYcslwcIC3n3xEF0SJ\nIUHkfc/NXHDhDdTUeOnpCU7oXFabQn3DzIjSCls5imRiIO4nlophU/Jbrld2/Y2Etm8jduggxx78\nP3zXXId9wUJM9tKbGyYwjkQ6weHMd/NAbHJZYKvJwhxvEwt8cymzlcbmjtFomsauvnd4tuVFjgQz\ngkhx8C7Hcpb47QSefAJV06h47624lq8wOFpBMVE0wmjt2rUkEgkee+wxdu7cyTe/+U0eeOABo8Oa\nEUx5zBjF04mcEDowcIgjg+2kMjNDANa1bwag3lnLgrK5LPDNY75vDm5LaZZe5YvqKhfvADHFSdxX\nvI50WSLhBMfa/XS2+ekcaKJ/7m2AXgubNEl43Pnv3Sgk/k0bCGxYh6Qo1P/j3She/cvfZJI5+9w6\nFi6t4cDubra9coTAQJR1f9kL6HOQ6mZ7qWvwMavRR1XdqULpaJt+E1LXUPz9RcOxzZ+Pbe5cYocP\nM/jKS/iuvrYg1w0H+3nriV/g3PL2kCCqdemC6ILrkYusBt8km6h2VNEZ7qI70kOTJ78bapIsU/ux\nT3DkX+8hsuttIrveBknC2tSMY9Ei7AvPwr5gASZH6YhuwcwTTyc4HGjlwMBhDvgPc2SwnfSw7+bJ\nsumoPvS50l7BAt9c/V/ZXMptxeuyaQRZQfSXljW0DXZQEUhzQZ/EuYNuXB19qMEnGMgc61qxkvKb\nbjY0XkHxUTTCaNu2bVx+ud5wfO6557Jr1+lrkTqdHqNYKj4khPyHODLYccKHrYREg6ueBWXzsJms\nHPS3cHjwCJ3hLjrDXWzseAWAOmdN5oN1Hgt8c884oVRfq1s3xxQHkq/4HOki4YQugjJiaKD3RFcs\nkwye0DF86gCL/vb9BkWZH6IHD3D8V78EoPr2v8E+d+4px5hMMmct0wVSy/4++rpDHNrfg78vQnvL\nAO0t+ledosjUzvZS3+ijvsFLdZ2HzowwmtVUWjutkiRRdv1qjv34AQbWvID3yqtntDE4HOznrT89\njGvrLioygqiv1kXZzX/FBedfV3SCaDg1zmo6w110hY/nXRgBWGprafzaPQRf3Upk315irS3EM/8G\nnn9OF0oNjTgWnYV90Vl6RkmU3J1RxFJxXQj5D3Ng4DBHgu2o2tAQdwmJBvcsFvjmUu+sndQmTTAR\n4qD/MAf9rfRG++iN9rHl2OuAnjHNiqQFvrlU2Etvnl0+0DSNnd27eHnH0ygtHZxzPMnq40ns8ewa\n9KECJq8Px6JFOM5ajPviS4TZguAUJK1Ixqt//etf58Ybb8yJo2uuuYa1a9eO+mX8vU0/L2R4ecV7\nqJuFT7xO3G1nsLlyQs/RgFg6RjAeQmP4kkm4zA48FjdeqwePxY1JNp3wXFXTCCVCDCaCBBKDBBOh\nEz6wAeyKHZfZiVxCO+rTZVf4clTZgsXZi1Y5MWFoMsmk0+r4B06VNKT9CmropD0Lk4apLIlSmcRU\nmcTkTbLksZdwdgcYbKwg7i3dplHfoeOYI3G6VzTTds3SCT3HZjcTiyZRYxLpPjOpXjPpPjNq8NTX\nDQ1QJZxXD2DyTH3H1hBUlWUPrcc6GKV/QS3prJtivkmreA4ew5oVRPVuyt/zVyxYde2on8FVVe4J\nl9LNNM8cfoG/tK6l0T2L2a5ZM349OZnCdXQAd0cf7vY+nF1+5GHmIBoQqfIQqfaAPLOfqTP+mSQY\nk3G/m60evBb3iN/Nk76WphFORvTv8vggg4ngKVkoq8mK2+LCJJ1ZN/yp0CC+o4PYEyfe0pp8vtyG\nhWPhWZhraqZcOVBV5c5HqIIip2iE0f3338/y5ctZvXo1AFdddRUbNmwwNiiBQCAQCAQCgUBwRlA0\nWwrnnXceGzduBODNN99k4cKFBkckEAgEAoFAIBAIzhSKJmM03JUO4Jvf/CZz5swxOCqBQCAQCAQC\ngUBwJlA0wkggEAgEAoFAIBAIjKJoSukEAoFAIBAIBAKBwCiEMBIIBAKBQCAQCARnPEIYCQQCgUAg\nEAgEgjMeIYwEAoFAIBAIBALBGY8QRkVGMBgkFAoZHcYZjViD4kCsg/GINTCe7u5u1qxZg6qKIa5G\nIdagOBDrICgEpnvvvfdeo4MQ6PzkJz/hhz/8IQMDAzQ1NeF0Oo0O6YxDrEFxINbBeMQaGM9PfvIT\nHnzwQeLxOIqi0NDQgCRJRod1RiHWoDgQ6yAoFCJjVCRs3bqVjo4OHnroIZqbm8Ub3gDEGhQHYh2M\nR6yB8cTjcY4fP86DDz7I5ZdfzsDAANFo1OiwzijEGhQHYh0EhURkjAykv78fu90OwKOPPorP52P3\n7t2sX7+e1157DZvNxuzZs5FloV9nCrEGxYFYB+MRa2A8R48epbW1lZqaGvbs2cPjjz+OqqqsX7+e\n3t5etmzZgslkoqmpyehQT1vEGhQHYh0ERiGEkUEcPXqU//3f/8Vms9HY2IiiKDzxxBMsXryYr371\nqwQCAfbs2YPP56OmpsbocE9LxBoUB2IdjEesQXHw8MMPs3HjRq677jrq6up46aWX2L9/Pw888ADn\nn38+gUCAvXv3smrVKkwmk9HhnpaINSgOxDoIjEJs/RWYbNPghg0b2LFjB6+99hqhUIilS5eSSCTY\nu3cvALfccgvt7e2YzWYjwz0tEWtQHIh1MB6xBsXD9u3bWbduHZFIhMcffxyA973vfWzdupVQKITL\n5cJsNmOz2TCbzWiaZnDEpx9iDYoDsQ4CIxEZowKxd+9eLBYLNpsNgI0bN3LeeeehqioDAwOcc845\nNDY28uijj3LuuefS29vLxo0bueyyy6isrDQ4+tMDsQbFgVgH4xFrYDxr165l3759yLJMeXk5fr+f\n8vJy3v3ud/PMM8+wYsUKlixZQktLC88//zyRSIQnn3ySBQsWsHz5ctH3lQfEGhQHYh0ExYQQRjNM\nMBjkvvvu409/+hNvvfUWLS0trFy5knnz5rFkyRJ6enrYs2cPc+bMYfHixUiSxMsvv8wf//hHPvWp\nT7Fy5Uqjf4WSR6xBcSDWwXjEGhhPKpXixz/+MX/+85+prq7me9/7HhdddBELFixg0aJFWK1WWltb\n2bdvHxdccAFXXXUVNpuNnTt38qEPfYibb77Z6F+h5BFrUByIdRAUJZpgRtm8ebP2xS9+UdM0TWtr\na9NuvfVWbc+ePbmfHzx4UPvBD36g/fznP889lkgkCh3maY1Yg+JArIPxiDUwjmQyqWmapoXDYe2T\nn/ykNjAwoGmapv3whz/U/vu//1vr6OjQNE3T0um0tn37du3uu+/Wtm3bNuK50ul0YYI+zRBrUByI\ndRAUMyJjNAM8++yzbNmyhVmzZpFOp3njjTe44IILq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('GFS 0.25 deg')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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temp_airwind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-07-27 09:00:00-07:0029.5500180.997246593.672839649.421406164.8527469.00.05.06.0
2016-07-27 12:00:00-07:0029.7500001.371787721.296120250.902795478.77498340.00.038.06.0
2016-07-27 15:00:00-07:0032.5500181.530555278.61595315.616103265.995968100.00.0100.088.0
2016-07-27 18:00:00-07:0047.6499943.052212108.50347031.01275799.89782177.00.057.068.0
2016-07-27 21:00:00-07:0049.8500064.9486260.0000000.0000000.00000066.00.00.066.0
2016-07-28 00:00:00-07:0044.5500184.6669260.0000000.0000000.00000044.00.00.044.0
2016-07-28 03:00:00-07:0033.5500182.5146170.0000000.0000000.00000033.01.00.033.0
2016-07-28 06:00:00-07:0031.2500002.96082830.598827113.61874722.99796628.00.00.027.0
2016-07-28 09:00:00-07:0029.1499941.416616551.159407515.370371211.52955819.00.00.019.0
2016-07-28 12:00:00-07:0027.5500182.625052695.028812217.769141484.73938444.00.00.044.0
2016-07-28 15:00:00-07:0036.8500061.325632438.11071381.856367372.06037169.00.00.069.0
2016-07-28 18:00:00-07:0050.8500061.566269166.000965255.35185595.65990035.00.00.035.0
2016-07-28 21:00:00-07:0054.3500061.8836670.0000000.0000000.0000000.00.00.00.0
2016-07-29 00:00:00-07:0045.3500060.7559100.0000000.0000000.00000024.00.024.00.0
2016-07-29 03:00:00-07:0033.8500060.8163330.0000000.0000000.00000014.00.014.00.0
2016-07-29 06:00:00-07:0031.7500004.01400034.451534189.99300622.1464517.00.07.00.0
2016-07-29 09:00:00-07:0029.6499943.881907622.989782763.902754120.5880701.00.00.01.0
2016-07-29 12:00:00-07:0028.0500183.598013826.776057434.105737408.00340123.00.00.023.0
2016-07-29 15:00:00-07:0037.4500123.013254406.25350660.719977357.33683675.00.00.075.0
2016-07-29 18:00:00-07:0050.2500001.642011117.18063957.612326101.43138269.00.00.069.0
2016-07-29 21:00:00-07:0058.6499940.4110960.0000000.0000000.0000002.00.00.03.0
2016-07-30 00:00:00-07:0047.5500180.3041380.0000000.0000000.0000006.00.05.01.0
2016-07-30 03:00:00-07:0035.2500002.3048860.0000000.0000000.0000002.00.02.00.0
2016-07-30 06:00:00-07:0034.9500124.41439728.470389106.21347121.81937528.00.027.016.0
2016-07-30 09:00:00-07:0031.2500003.230015226.98474813.280513218.26822598.00.017.097.0
2016-07-30 12:00:00-07:0028.7500003.515921440.72145736.952525405.11137984.00.09.084.0
2016-07-30 15:00:00-07:0034.8500062.670749415.62773167.079282361.67889673.00.00.073.0
2016-07-30 18:00:00-07:0047.3500061.818378100.79485919.79086395.42789280.00.00.080.0
2016-07-30 21:00:00-07:0050.9500123.1218740.0000000.0000000.00000082.00.00.082.0
2016-07-31 00:00:00-07:0043.7500005.7780010.0000000.0000000.00000077.02.02.076.0
2016-07-31 03:00:00-07:0035.0500187.6385670.0000000.0000000.00000030.020.04.02.0
2016-07-31 06:00:00-07:0030.5500185.86586719.5884790.00000019.58847964.025.015.036.0
2016-07-31 09:00:00-07:0029.0500183.377129218.23497811.054507210.994422100.00.013.0100.0
2016-07-31 12:00:00-07:0028.0500184.253293339.27861818.330290321.633356100.00.07.0100.0
2016-07-31 15:00:00-07:0031.3500064.744945276.17949015.411908263.806246100.00.01.0100.0
2016-07-31 18:00:00-07:0041.7500003.82781975.2962370.00000075.29623798.00.00.098.0
2016-07-31 21:00:00-07:0046.2500004.6669900.0000000.0000000.00000095.00.00.095.0
2016-08-01 00:00:00-07:0042.0500182.8076500.0000000.0000000.00000090.00.00.090.0
2016-08-01 03:00:00-07:0033.1499942.4387090.0000000.0000000.00000099.00.00.099.0
2016-08-01 06:00:00-07:0030.7500003.35715711.5173380.00000011.51733899.00.00.099.0
2016-08-01 09:00:00-07:0029.0500182.067293229.73446214.266428220.40963797.00.00.097.0
2016-08-01 12:00:00-07:0027.8500061.895521351.36625019.695637332.42788698.00.00.098.0
2016-08-01 15:00:00-07:0034.1499942.507369280.63165616.211345267.64060599.00.00.099.0
2016-08-01 18:00:00-07:0045.2500002.47588475.6705780.00000075.67057897.00.00.097.0
2016-08-01 21:00:00-07:0049.1499943.1189260.0000000.0000000.00000091.00.00.091.0
2016-08-02 00:00:00-07:0042.7500003.6643150.0000000.0000000.00000091.00.00.091.0
2016-08-02 03:00:00-07:0033.7500003.8910410.0000000.0000000.00000098.01.00.098.0
2016-08-02 06:00:00-07:0031.6499944.35839411.6939640.00000011.69396496.01.00.096.0
2016-08-02 09:00:00-07:0029.6499942.584956289.51636041.058552262.73635282.00.00.082.0
2016-08-02 12:00:00-07:0028.2500002.030665401.09844927.615695374.57528990.00.00.090.0
2016-08-02 15:00:00-07:0034.8500061.974361295.24337519.055234280.00279296.00.00.096.0
2016-08-02 18:00:00-07:0046.4500122.54031595.68601214.73249491.79438781.00.00.081.0
2016-08-02 21:00:00-07:0049.6499943.8154030.0000000.0000000.00000065.00.00.065.0
2016-08-03 00:00:00-07:0043.4500124.7801360.0000000.0000000.00000062.00.00.062.0
2016-08-03 03:00:00-07:0034.1499944.0300130.0000000.0000000.00000092.02.01.091.0
2016-08-03 06:00:00-07:0031.9500122.98452711.4468480.00000011.44684895.011.06.094.0
\n", + "
" + ], + "text/plain": [ + " temp_air wind_speed ghi dni \\\n", + "2016-07-27 09:00:00-07:00 29.550018 0.997246 593.672839 649.421406 \n", + "2016-07-27 12:00:00-07:00 29.750000 1.371787 721.296120 250.902795 \n", + "2016-07-27 15:00:00-07:00 32.550018 1.530555 278.615953 15.616103 \n", + "2016-07-27 18:00:00-07:00 47.649994 3.052212 108.503470 31.012757 \n", + "2016-07-27 21:00:00-07:00 49.850006 4.948626 0.000000 0.000000 \n", + "2016-07-28 00:00:00-07:00 44.550018 4.666926 0.000000 0.000000 \n", + "2016-07-28 03:00:00-07:00 33.550018 2.514617 0.000000 0.000000 \n", + "2016-07-28 06:00:00-07:00 31.250000 2.960828 30.598827 113.618747 \n", + "2016-07-28 09:00:00-07:00 29.149994 1.416616 551.159407 515.370371 \n", + "2016-07-28 12:00:00-07:00 27.550018 2.625052 695.028812 217.769141 \n", + "2016-07-28 15:00:00-07:00 36.850006 1.325632 438.110713 81.856367 \n", + "2016-07-28 18:00:00-07:00 50.850006 1.566269 166.000965 255.351855 \n", + "2016-07-28 21:00:00-07:00 54.350006 1.883667 0.000000 0.000000 \n", + "2016-07-29 00:00:00-07:00 45.350006 0.755910 0.000000 0.000000 \n", + "2016-07-29 03:00:00-07:00 33.850006 0.816333 0.000000 0.000000 \n", + "2016-07-29 06:00:00-07:00 31.750000 4.014000 34.451534 189.993006 \n", + "2016-07-29 09:00:00-07:00 29.649994 3.881907 622.989782 763.902754 \n", + "2016-07-29 12:00:00-07:00 28.050018 3.598013 826.776057 434.105737 \n", + "2016-07-29 15:00:00-07:00 37.450012 3.013254 406.253506 60.719977 \n", + "2016-07-29 18:00:00-07:00 50.250000 1.642011 117.180639 57.612326 \n", + "2016-07-29 21:00:00-07:00 58.649994 0.411096 0.000000 0.000000 \n", + "2016-07-30 00:00:00-07:00 47.550018 0.304138 0.000000 0.000000 \n", + "2016-07-30 03:00:00-07:00 35.250000 2.304886 0.000000 0.000000 \n", + "2016-07-30 06:00:00-07:00 34.950012 4.414397 28.470389 106.213471 \n", + "2016-07-30 09:00:00-07:00 31.250000 3.230015 226.984748 13.280513 \n", + "2016-07-30 12:00:00-07:00 28.750000 3.515921 440.721457 36.952525 \n", + "2016-07-30 15:00:00-07:00 34.850006 2.670749 415.627731 67.079282 \n", + "2016-07-30 18:00:00-07:00 47.350006 1.818378 100.794859 19.790863 \n", + "2016-07-30 21:00:00-07:00 50.950012 3.121874 0.000000 0.000000 \n", + "2016-07-31 00:00:00-07:00 43.750000 5.778001 0.000000 0.000000 \n", + "2016-07-31 03:00:00-07:00 35.050018 7.638567 0.000000 0.000000 \n", + "2016-07-31 06:00:00-07:00 30.550018 5.865867 19.588479 0.000000 \n", + "2016-07-31 09:00:00-07:00 29.050018 3.377129 218.234978 11.054507 \n", + "2016-07-31 12:00:00-07:00 28.050018 4.253293 339.278618 18.330290 \n", + "2016-07-31 15:00:00-07:00 31.350006 4.744945 276.179490 15.411908 \n", + "2016-07-31 18:00:00-07:00 41.750000 3.827819 75.296237 0.000000 \n", + "2016-07-31 21:00:00-07:00 46.250000 4.666990 0.000000 0.000000 \n", + "2016-08-01 00:00:00-07:00 42.050018 2.807650 0.000000 0.000000 \n", + "2016-08-01 03:00:00-07:00 33.149994 2.438709 0.000000 0.000000 \n", + "2016-08-01 06:00:00-07:00 30.750000 3.357157 11.517338 0.000000 \n", + "2016-08-01 09:00:00-07:00 29.050018 2.067293 229.734462 14.266428 \n", + "2016-08-01 12:00:00-07:00 27.850006 1.895521 351.366250 19.695637 \n", + "2016-08-01 15:00:00-07:00 34.149994 2.507369 280.631656 16.211345 \n", + "2016-08-01 18:00:00-07:00 45.250000 2.475884 75.670578 0.000000 \n", + "2016-08-01 21:00:00-07:00 49.149994 3.118926 0.000000 0.000000 \n", + "2016-08-02 00:00:00-07:00 42.750000 3.664315 0.000000 0.000000 \n", + "2016-08-02 03:00:00-07:00 33.750000 3.891041 0.000000 0.000000 \n", + "2016-08-02 06:00:00-07:00 31.649994 4.358394 11.693964 0.000000 \n", + "2016-08-02 09:00:00-07:00 29.649994 2.584956 289.516360 41.058552 \n", + "2016-08-02 12:00:00-07:00 28.250000 2.030665 401.098449 27.615695 \n", + "2016-08-02 15:00:00-07:00 34.850006 1.974361 295.243375 19.055234 \n", + "2016-08-02 18:00:00-07:00 46.450012 2.540315 95.686012 14.732494 \n", + "2016-08-02 21:00:00-07:00 49.649994 3.815403 0.000000 0.000000 \n", + "2016-08-03 00:00:00-07:00 43.450012 4.780136 0.000000 0.000000 \n", + "2016-08-03 03:00:00-07:00 34.149994 4.030013 0.000000 0.000000 \n", + "2016-08-03 06:00:00-07:00 31.950012 2.984527 11.446848 0.000000 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-07-27 09:00:00-07:00 164.852746 9.0 0.0 5.0 \n", + "2016-07-27 12:00:00-07:00 478.774983 40.0 0.0 38.0 \n", + "2016-07-27 15:00:00-07:00 265.995968 100.0 0.0 100.0 \n", + "2016-07-27 18:00:00-07:00 99.897821 77.0 0.0 57.0 \n", + "2016-07-27 21:00:00-07:00 0.000000 66.0 0.0 0.0 \n", + "2016-07-28 00:00:00-07:00 0.000000 44.0 0.0 0.0 \n", + "2016-07-28 03:00:00-07:00 0.000000 33.0 1.0 0.0 \n", + "2016-07-28 06:00:00-07:00 22.997966 28.0 0.0 0.0 \n", + "2016-07-28 09:00:00-07:00 211.529558 19.0 0.0 0.0 \n", + "2016-07-28 12:00:00-07:00 484.739384 44.0 0.0 0.0 \n", + "2016-07-28 15:00:00-07:00 372.060371 69.0 0.0 0.0 \n", + "2016-07-28 18:00:00-07:00 95.659900 35.0 0.0 0.0 \n", + "2016-07-28 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-29 00:00:00-07:00 0.000000 24.0 0.0 24.0 \n", + "2016-07-29 03:00:00-07:00 0.000000 14.0 0.0 14.0 \n", + "2016-07-29 06:00:00-07:00 22.146451 7.0 0.0 7.0 \n", + "2016-07-29 09:00:00-07:00 120.588070 1.0 0.0 0.0 \n", + "2016-07-29 12:00:00-07:00 408.003401 23.0 0.0 0.0 \n", + "2016-07-29 15:00:00-07:00 357.336836 75.0 0.0 0.0 \n", + "2016-07-29 18:00:00-07:00 101.431382 69.0 0.0 0.0 \n", + "2016-07-29 21:00:00-07:00 0.000000 2.0 0.0 0.0 \n", + "2016-07-30 00:00:00-07:00 0.000000 6.0 0.0 5.0 \n", + "2016-07-30 03:00:00-07:00 0.000000 2.0 0.0 2.0 \n", + "2016-07-30 06:00:00-07:00 21.819375 28.0 0.0 27.0 \n", + "2016-07-30 09:00:00-07:00 218.268225 98.0 0.0 17.0 \n", + "2016-07-30 12:00:00-07:00 405.111379 84.0 0.0 9.0 \n", + "2016-07-30 15:00:00-07:00 361.678896 73.0 0.0 0.0 \n", + "2016-07-30 18:00:00-07:00 95.427892 80.0 0.0 0.0 \n", + "2016-07-30 21:00:00-07:00 0.000000 82.0 0.0 0.0 \n", + "2016-07-31 00:00:00-07:00 0.000000 77.0 2.0 2.0 \n", + "2016-07-31 03:00:00-07:00 0.000000 30.0 20.0 4.0 \n", + "2016-07-31 06:00:00-07:00 19.588479 64.0 25.0 15.0 \n", + "2016-07-31 09:00:00-07:00 210.994422 100.0 0.0 13.0 \n", + "2016-07-31 12:00:00-07:00 321.633356 100.0 0.0 7.0 \n", + "2016-07-31 15:00:00-07:00 263.806246 100.0 0.0 1.0 \n", + "2016-07-31 18:00:00-07:00 75.296237 98.0 0.0 0.0 \n", + "2016-07-31 21:00:00-07:00 0.000000 95.0 0.0 0.0 \n", + "2016-08-01 00:00:00-07:00 0.000000 90.0 0.0 0.0 \n", + "2016-08-01 03:00:00-07:00 0.000000 99.0 0.0 0.0 \n", + "2016-08-01 06:00:00-07:00 11.517338 99.0 0.0 0.0 \n", + "2016-08-01 09:00:00-07:00 220.409637 97.0 0.0 0.0 \n", + "2016-08-01 12:00:00-07:00 332.427886 98.0 0.0 0.0 \n", + "2016-08-01 15:00:00-07:00 267.640605 99.0 0.0 0.0 \n", + "2016-08-01 18:00:00-07:00 75.670578 97.0 0.0 0.0 \n", + "2016-08-01 21:00:00-07:00 0.000000 91.0 0.0 0.0 \n", + "2016-08-02 00:00:00-07:00 0.000000 91.0 0.0 0.0 \n", + "2016-08-02 03:00:00-07:00 0.000000 98.0 1.0 0.0 \n", + "2016-08-02 06:00:00-07:00 11.693964 96.0 1.0 0.0 \n", + "2016-08-02 09:00:00-07:00 262.736352 82.0 0.0 0.0 \n", + "2016-08-02 12:00:00-07:00 374.575289 90.0 0.0 0.0 \n", + "2016-08-02 15:00:00-07:00 280.002792 96.0 0.0 0.0 \n", + "2016-08-02 18:00:00-07:00 91.794387 81.0 0.0 0.0 \n", + "2016-08-02 21:00:00-07:00 0.000000 65.0 0.0 0.0 \n", + "2016-08-03 00:00:00-07:00 0.000000 62.0 0.0 0.0 \n", + "2016-08-03 03:00:00-07:00 0.000000 92.0 2.0 1.0 \n", + "2016-08-03 06:00:00-07:00 11.446848 95.0 11.0 6.0 \n", + "\n", + " high_clouds \n", + "2016-07-27 09:00:00-07:00 6.0 \n", + "2016-07-27 12:00:00-07:00 6.0 \n", + "2016-07-27 15:00:00-07:00 88.0 \n", + "2016-07-27 18:00:00-07:00 68.0 \n", + "2016-07-27 21:00:00-07:00 66.0 \n", + "2016-07-28 00:00:00-07:00 44.0 \n", + "2016-07-28 03:00:00-07:00 33.0 \n", + "2016-07-28 06:00:00-07:00 27.0 \n", + "2016-07-28 09:00:00-07:00 19.0 \n", + "2016-07-28 12:00:00-07:00 44.0 \n", + "2016-07-28 15:00:00-07:00 69.0 \n", + "2016-07-28 18:00:00-07:00 35.0 \n", + "2016-07-28 21:00:00-07:00 0.0 \n", + "2016-07-29 00:00:00-07:00 0.0 \n", + "2016-07-29 03:00:00-07:00 0.0 \n", + "2016-07-29 06:00:00-07:00 0.0 \n", + "2016-07-29 09:00:00-07:00 1.0 \n", + "2016-07-29 12:00:00-07:00 23.0 \n", + "2016-07-29 15:00:00-07:00 75.0 \n", + "2016-07-29 18:00:00-07:00 69.0 \n", + "2016-07-29 21:00:00-07:00 3.0 \n", + "2016-07-30 00:00:00-07:00 1.0 \n", + "2016-07-30 03:00:00-07:00 0.0 \n", + "2016-07-30 06:00:00-07:00 16.0 \n", + "2016-07-30 09:00:00-07:00 97.0 \n", + "2016-07-30 12:00:00-07:00 84.0 \n", + "2016-07-30 15:00:00-07:00 73.0 \n", + "2016-07-30 18:00:00-07:00 80.0 \n", + "2016-07-30 21:00:00-07:00 82.0 \n", + "2016-07-31 00:00:00-07:00 76.0 \n", + "2016-07-31 03:00:00-07:00 2.0 \n", + "2016-07-31 06:00:00-07:00 36.0 \n", + "2016-07-31 09:00:00-07:00 100.0 \n", + "2016-07-31 12:00:00-07:00 100.0 \n", + "2016-07-31 15:00:00-07:00 100.0 \n", + "2016-07-31 18:00:00-07:00 98.0 \n", + "2016-07-31 21:00:00-07:00 95.0 \n", + "2016-08-01 00:00:00-07:00 90.0 \n", + "2016-08-01 03:00:00-07:00 99.0 \n", + "2016-08-01 06:00:00-07:00 99.0 \n", + "2016-08-01 09:00:00-07:00 97.0 \n", + "2016-08-01 12:00:00-07:00 98.0 \n", + "2016-08-01 15:00:00-07:00 99.0 \n", + "2016-08-01 18:00:00-07:00 97.0 \n", + "2016-08-01 21:00:00-07:00 91.0 \n", + "2016-08-02 00:00:00-07:00 91.0 \n", + "2016-08-02 03:00:00-07:00 98.0 \n", + "2016-08-02 06:00:00-07:00 96.0 \n", + "2016-08-02 09:00:00-07:00 82.0 \n", + "2016-08-02 12:00:00-07:00 90.0 \n", + "2016-08-02 15:00:00-07:00 96.0 \n", + "2016-08-02 18:00:00-07:00 81.0 \n", + "2016-08-02 21:00:00-07:00 65.0 \n", + "2016-08-03 00:00:00-07:00 62.0 \n", + "2016-08-03 03:00:00-07:00 91.0 \n", + "2016-08-03 06:00:00-07:00 94.0 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## NAM" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fm = NAM()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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YGFlMVzqnliIcTVJbQIEp6Epbu7EoalcTWDyF+X6XEjabjeXLlxd6GMOCWJ4o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\nEEJkPEmMhBBCCCGEEBnv/wH66Vxyg9HZHQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('NAM')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0do6c3NPY8dqO3rHGLgCAz2Mf8Zrl+rounefHXw+34ZU/v4tVK2bl7OvqEd+zIwv0DiAY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\nSKCqqgp/f3+cnGx7y8+WqqGhgddee434+Hiys7Px9PTE39/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temp_airwind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-07-27 09:00:00-07:0029.0489812.065518220.69623111.387513213.176931100.00.0100.0100.0
2016-07-27 12:00:00-07:0027.8854061.924674341.15356018.433557323.335794100.00.0100.0100.0
2016-07-27 15:00:00-07:0033.9208070.736886278.61595315.616103265.995968100.00.0100.0100.0
2016-07-27 18:00:00-07:0045.4557191.57346076.0284550.00000076.028455100.00.0100.0100.0
2016-07-27 21:00:00-07:0048.8163152.7905560.0000000.0000000.0000000.00.00.00.0
2016-07-28 00:00:00-07:0043.6661682.5578730.0000000.0000000.0000000.00.00.00.0
2016-07-28 03:00:00-07:0033.4427801.2990190.0000000.0000000.00000086.00.086.00.0
2016-07-28 06:00:00-07:0031.7897034.69626213.0924080.00000013.092408100.00.0100.02.0
2016-07-28 09:00:00-07:0028.9378971.318510579.770598607.401084179.49237012.00.010.02.0
2016-07-28 12:00:00-07:0025.3170471.569927973.429728752.724436246.5591030.00.00.00.0
2016-07-28 15:00:00-07:0035.6830140.743108794.398445779.384573165.5088540.00.00.00.0
2016-07-28 18:00:00-07:0047.3163151.569387214.888003569.23936258.0812200.00.00.00.0
2016-07-28 21:00:00-07:0050.1549381.8400500.0000000.0000000.0000000.00.00.00.0
2016-07-29 00:00:00-07:0043.5245672.3233400.0000000.0000000.00000086.02.086.00.0
2016-07-29 03:00:00-07:0035.5453193.3370800.0000000.0000000.00000082.00.076.06.0
2016-07-29 06:00:00-07:0030.2709054.57774636.093803217.40410822.0134130.00.00.00.0
2016-07-29 09:00:00-07:0027.3072811.002360627.065713777.980844115.4051560.00.00.00.0
2016-07-29 12:00:00-07:0025.2618710.942670972.105901751.801227246.8591020.00.00.00.0
2016-07-29 15:00:00-07:0035.8094791.184753792.689787778.307094165.6771700.00.00.00.0
2016-07-29 18:00:00-07:0046.7933651.876681212.476241564.33308558.2067020.00.00.00.0
2016-07-29 21:00:00-07:0049.6529852.7107550.0000000.0000000.0000000.00.00.00.0
2016-07-30 00:00:00-07:0043.7994693.8753780.0000000.0000000.0000000.00.00.00.0
2016-07-30 03:00:00-07:0034.1896064.8533670.0000000.0000000.000000100.00.00.0100.0
2016-07-30 06:00:00-07:0029.7945868.13393519.4211210.00000019.42112168.00.026.054.0
2016-07-30 09:00:00-07:0027.3077701.480417251.37154421.289887237.39815492.00.084.034.0
2016-07-30 12:00:00-07:0026.5487371.117122339.76323518.356744322.073372100.02.0100.0100.0
2016-07-30 15:00:00-07:0031.4156802.503182276.82154115.465265264.383527100.08.0100.072.0
2016-07-30 18:00:00-07:0042.9432681.972205204.529580519.83364963.5589794.04.00.02.0
2016-07-30 21:00:00-07:0046.6835022.4842820.0000000.0000000.0000000.00.00.00.0
2016-07-31 00:00:00-07:0041.4059143.4003380.0000000.0000000.0000002.02.00.00.0
2016-07-31 03:00:00-07:0031.8175353.5842350.0000000.0000000.00000018.00.00.018.0
2016-07-31 06:00:00-07:0029.4393623.34247311.7396700.00000011.739670100.00.0100.0100.0
\n", + "
" + ], + "text/plain": [ + " temp_air wind_speed ghi dni \\\n", + "2016-07-27 09:00:00-07:00 29.048981 2.065518 220.696231 11.387513 \n", + "2016-07-27 12:00:00-07:00 27.885406 1.924674 341.153560 18.433557 \n", + "2016-07-27 15:00:00-07:00 33.920807 0.736886 278.615953 15.616103 \n", + "2016-07-27 18:00:00-07:00 45.455719 1.573460 76.028455 0.000000 \n", + "2016-07-27 21:00:00-07:00 48.816315 2.790556 0.000000 0.000000 \n", + "2016-07-28 00:00:00-07:00 43.666168 2.557873 0.000000 0.000000 \n", + "2016-07-28 03:00:00-07:00 33.442780 1.299019 0.000000 0.000000 \n", + "2016-07-28 06:00:00-07:00 31.789703 4.696262 13.092408 0.000000 \n", + "2016-07-28 09:00:00-07:00 28.937897 1.318510 579.770598 607.401084 \n", + "2016-07-28 12:00:00-07:00 25.317047 1.569927 973.429728 752.724436 \n", + "2016-07-28 15:00:00-07:00 35.683014 0.743108 794.398445 779.384573 \n", + "2016-07-28 18:00:00-07:00 47.316315 1.569387 214.888003 569.239362 \n", + "2016-07-28 21:00:00-07:00 50.154938 1.840050 0.000000 0.000000 \n", + "2016-07-29 00:00:00-07:00 43.524567 2.323340 0.000000 0.000000 \n", + "2016-07-29 03:00:00-07:00 35.545319 3.337080 0.000000 0.000000 \n", + "2016-07-29 06:00:00-07:00 30.270905 4.577746 36.093803 217.404108 \n", + "2016-07-29 09:00:00-07:00 27.307281 1.002360 627.065713 777.980844 \n", + "2016-07-29 12:00:00-07:00 25.261871 0.942670 972.105901 751.801227 \n", + "2016-07-29 15:00:00-07:00 35.809479 1.184753 792.689787 778.307094 \n", + "2016-07-29 18:00:00-07:00 46.793365 1.876681 212.476241 564.333085 \n", + "2016-07-29 21:00:00-07:00 49.652985 2.710755 0.000000 0.000000 \n", + "2016-07-30 00:00:00-07:00 43.799469 3.875378 0.000000 0.000000 \n", + "2016-07-30 03:00:00-07:00 34.189606 4.853367 0.000000 0.000000 \n", + "2016-07-30 06:00:00-07:00 29.794586 8.133935 19.421121 0.000000 \n", + "2016-07-30 09:00:00-07:00 27.307770 1.480417 251.371544 21.289887 \n", + "2016-07-30 12:00:00-07:00 26.548737 1.117122 339.763235 18.356744 \n", + "2016-07-30 15:00:00-07:00 31.415680 2.503182 276.821541 15.465265 \n", + "2016-07-30 18:00:00-07:00 42.943268 1.972205 204.529580 519.833649 \n", + "2016-07-30 21:00:00-07:00 46.683502 2.484282 0.000000 0.000000 \n", + "2016-07-31 00:00:00-07:00 41.405914 3.400338 0.000000 0.000000 \n", + "2016-07-31 03:00:00-07:00 31.817535 3.584235 0.000000 0.000000 \n", + "2016-07-31 06:00:00-07:00 29.439362 3.342473 11.739670 0.000000 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-07-27 09:00:00-07:00 213.176931 100.0 0.0 100.0 \n", + "2016-07-27 12:00:00-07:00 323.335794 100.0 0.0 100.0 \n", + "2016-07-27 15:00:00-07:00 265.995968 100.0 0.0 100.0 \n", + "2016-07-27 18:00:00-07:00 76.028455 100.0 0.0 100.0 \n", + "2016-07-27 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-28 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-28 03:00:00-07:00 0.000000 86.0 0.0 86.0 \n", + "2016-07-28 06:00:00-07:00 13.092408 100.0 0.0 100.0 \n", + "2016-07-28 09:00:00-07:00 179.492370 12.0 0.0 10.0 \n", + "2016-07-28 12:00:00-07:00 246.559103 0.0 0.0 0.0 \n", + "2016-07-28 15:00:00-07:00 165.508854 0.0 0.0 0.0 \n", + "2016-07-28 18:00:00-07:00 58.081220 0.0 0.0 0.0 \n", + "2016-07-28 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-29 00:00:00-07:00 0.000000 86.0 2.0 86.0 \n", + "2016-07-29 03:00:00-07:00 0.000000 82.0 0.0 76.0 \n", + "2016-07-29 06:00:00-07:00 22.013413 0.0 0.0 0.0 \n", + "2016-07-29 09:00:00-07:00 115.405156 0.0 0.0 0.0 \n", + "2016-07-29 12:00:00-07:00 246.859102 0.0 0.0 0.0 \n", + "2016-07-29 15:00:00-07:00 165.677170 0.0 0.0 0.0 \n", + "2016-07-29 18:00:00-07:00 58.206702 0.0 0.0 0.0 \n", + "2016-07-29 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-30 00:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-30 03:00:00-07:00 0.000000 100.0 0.0 0.0 \n", + "2016-07-30 06:00:00-07:00 19.421121 68.0 0.0 26.0 \n", + "2016-07-30 09:00:00-07:00 237.398154 92.0 0.0 84.0 \n", + "2016-07-30 12:00:00-07:00 322.073372 100.0 2.0 100.0 \n", + "2016-07-30 15:00:00-07:00 264.383527 100.0 8.0 100.0 \n", + "2016-07-30 18:00:00-07:00 63.558979 4.0 4.0 0.0 \n", + "2016-07-30 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-31 00:00:00-07:00 0.000000 2.0 2.0 0.0 \n", + "2016-07-31 03:00:00-07:00 0.000000 18.0 0.0 0.0 \n", + "2016-07-31 06:00:00-07:00 11.739670 100.0 0.0 100.0 \n", + "\n", + " high_clouds \n", + "2016-07-27 09:00:00-07:00 100.0 \n", + "2016-07-27 12:00:00-07:00 100.0 \n", + "2016-07-27 15:00:00-07:00 100.0 \n", + "2016-07-27 18:00:00-07:00 100.0 \n", + "2016-07-27 21:00:00-07:00 0.0 \n", + "2016-07-28 00:00:00-07:00 0.0 \n", + "2016-07-28 03:00:00-07:00 0.0 \n", + "2016-07-28 06:00:00-07:00 2.0 \n", + "2016-07-28 09:00:00-07:00 2.0 \n", + "2016-07-28 12:00:00-07:00 0.0 \n", + "2016-07-28 15:00:00-07:00 0.0 \n", + "2016-07-28 18:00:00-07:00 0.0 \n", + "2016-07-28 21:00:00-07:00 0.0 \n", + "2016-07-29 00:00:00-07:00 0.0 \n", + "2016-07-29 03:00:00-07:00 6.0 \n", + "2016-07-29 06:00:00-07:00 0.0 \n", + "2016-07-29 09:00:00-07:00 0.0 \n", + "2016-07-29 12:00:00-07:00 0.0 \n", + "2016-07-29 15:00:00-07:00 0.0 \n", + "2016-07-29 18:00:00-07:00 0.0 \n", + "2016-07-29 21:00:00-07:00 0.0 \n", + "2016-07-30 00:00:00-07:00 0.0 \n", + "2016-07-30 03:00:00-07:00 100.0 \n", + "2016-07-30 06:00:00-07:00 54.0 \n", + "2016-07-30 09:00:00-07:00 34.0 \n", + "2016-07-30 12:00:00-07:00 100.0 \n", + "2016-07-30 15:00:00-07:00 72.0 \n", + "2016-07-30 18:00:00-07:00 2.0 \n", + "2016-07-30 21:00:00-07:00 0.0 \n", + "2016-07-31 00:00:00-07:00 0.0 \n", + "2016-07-31 03:00:00-07:00 18.0 \n", + "2016-07-31 06:00:00-07:00 100.0 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## NDFD" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fm = NDFD()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "total_cloud_cover = data['total_clouds']\n", + "temp = data['temp_air']\n", + "wind = data['wind_speed']" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 100)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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KipLdbldSUpJOnjypLl26aMqUKd7MCljG58SvCu3+kGzZ2crq1lPZPR/1egbXgMHyX/mZ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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "total_cloud_cover.plot(color='r', linewidth=2)\n", + "plt.ylabel('Total cloud cover' + ' (%s)' % fm.units['total_clouds'])\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('NDFD')\n", + "plt.ylim(0,100)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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3NW3alKZNm1oeSkREpMSZJu71HxA5eQKunUkAeC65lJzHpuFp2drmcCJS2gUs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\naxYAHTt2pGPHjgCMGTOGpKQkTp06xf79+wkICCA4OJicnBx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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temp.plot(color='r', linewidth=2)\n", + "plt.ylabel('Temperature' + ' (%s)' % fm.units['temp_air'])\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')') " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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iic8wcL+3HG92Fu5VHxy+yeGgqHNXAkOGEb6oqcUBRSqPo57K6na7cf9mr/UL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\nM2cOZ599Ns2bN2fcuHHcfvvtRCIRate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2016-07-27 22:00:00-07:0040.0500186.40.0000000.0000000.00000035.0
\n", + "
" + ], + "text/plain": [ + " temp_air wind_speed ghi dni \\\n", + "2016-07-27 07:00:00-07:00 30.050018 2.0 143.988387 143.163497 \n", + "2016-07-27 08:00:00-07:00 28.750000 1.6 285.328219 177.281221 \n", + "2016-07-27 09:00:00-07:00 27.649994 1.6 417.431165 177.522093 \n", + "2016-07-27 10:00:00-07:00 27.350006 1.6 526.891322 170.772024 \n", + "2016-07-27 11:00:00-07:00 27.750000 1.6 497.912275 75.299590 \n", + "2016-07-27 12:00:00-07:00 27.750000 3.2 721.296120 250.902795 \n", + "2016-07-27 13:00:00-07:00 27.149994 3.2 721.328643 250.899698 \n", + "2016-07-27 14:00:00-07:00 28.950012 2.8 676.149456 255.156679 \n", + "2016-07-27 15:00:00-07:00 31.350006 2.8 589.073746 262.865948 \n", + "2016-07-27 16:00:00-07:00 33.550018 3.2 466.677299 270.995730 \n", + "2016-07-27 17:00:00-07:00 34.850006 3.6 318.902919 269.042086 \n", + "2016-07-27 18:00:00-07:00 36.050018 4.0 167.805647 257.806962 \n", + "2016-07-27 19:00:00-07:00 37.950012 4.8 29.679606 91.101791 \n", + "2016-07-27 20:00:00-07:00 39.649994 5.2 0.000000 0.000000 \n", + "2016-07-27 21:00:00-07:00 39.649994 5.6 0.000000 0.000000 \n", + "2016-07-27 22:00:00-07:00 40.050018 6.4 0.000000 0.000000 \n", + "\n", + " dhi total_clouds \n", + "2016-07-27 07:00:00-07:00 104.222554 52.0 \n", + "2016-07-27 08:00:00-07:00 200.331021 52.0 \n", + "2016-07-27 09:00:00-07:00 300.211375 52.0 \n", + "2016-07-27 10:00:00-07:00 388.904615 52.0 \n", + "2016-07-27 11:00:00-07:00 429.202244 70.0 \n", + "2016-07-27 12:00:00-07:00 478.774983 40.0 \n", + "2016-07-27 13:00:00-07:00 478.800746 40.0 \n", + "2016-07-27 14:00:00-07:00 443.295855 40.0 \n", + "2016-07-27 15:00:00-07:00 376.641482 40.0 \n", + "2016-07-27 16:00:00-07:00 287.715326 40.0 \n", + "2016-07-27 17:00:00-07:00 189.925817 40.0 \n", + "2016-07-27 18:00:00-07:00 96.267467 35.0 \n", + "2016-07-27 19:00:00-07:00 23.446353 35.0 \n", + "2016-07-27 20:00:00-07:00 0.000000 35.0 \n", + "2016-07-27 21:00:00-07:00 0.000000 35.0 \n", + "2016-07-27 22:00:00-07:00 0.000000 35.0 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## RAP" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fm = RAP(resolution=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "cloud_vars = ['total_clouds', 'high_clouds', 'mid_clouds', 'low_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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8V199NR6Ph1GjRvnfl3fu3JkuXboA0KtXL1JSUo441t13382DDz7ISy+9RFRU\nFE8++SS//vorAFdddRUPPPAAkydPpq6ujj/84Q8kJSUxZcoUHn74YTp37kx6uve9+8CBAzn11FO5\n4oorSE1N9d932LBh3HfffURGRmIymfjrX//aorkHPDHq2bOnPwOtqKjAbDbz888/M3r0aABOO+00\nfvjhhxYlRvPWfc+iwgUYzG4i7RlMG3cLyTExTb7OaDQS406nkmxWZ29RYiQiYcHfeCFp/7rqQLXq\nBoi1xGDAgMdsp7i8BofThcVsavP7iohI67rsssv8/77hhhsOuv7MM8/4/300bb27d+/O22+/3eix\nJ554wv/vv//97we95vTTT+f0008/6PHbbruN2267rdFjnTt3bnGVqKGAJ0bR0dHs2bOH888/n9LS\nUl555RV/Gc13vaKiotnjv/z9AjbULsVghGRnP+476wZsFstRv757TDc22rPZXrqr2TGIiATSgY0X\nAIoD1KobwGQ0EWONpsJeicdiJ7+0li4p6kwnItIROBwOfve73x3U5rtXr16tukUmEAKeGL399tuc\neuqp3HHHHeTl5TFlyhQcDof/elVVFXFxcU2Ok5gYhbnBJ5IOl4t7P/o3e9wbMBhgSOTJPHDxNf6N\nY0fr5L5D2bjxB0pcOaSmxh7Ta8NdR5tvQx117h113tC+5l5c5T17bUCvZP+8ypxlAPTp1JXUlMZz\nbYu5J0clUGGvxGCto9blCdnvb6jG1dY66rxBc++oOvLcA81isTBr1qxgh9EqAp4YxcfHYzZ7bxsb\nG4vT6WTw4MGsXLmSE044gSVLlhy2VV9DJQ3WsZfX1PC3716lyroXj9vAqQnnc/XoMyiqX15ytFJT\nYxmY1A2P24jLWsGvmXtIi2vbNrehIjU1loKC5lfqwllHnXtHnTe0v7nv3udNgqItRv+88ioKATDW\nRjSaa1vNPcrkrRAZLHVs21VMn/Smly8HWnv7uR+tjjpv0Nw199YdU9q/gCdG119/PdOnT+eaa67B\n6XRy1113MWTIEP/hT3369PH3Jj8ae4oLeXLFazhtJeC0cGWPqzhzwPBmxxdptWJzJGO3FfDj7k38\ndmjTSZqISLC43R5yi70tsjPq9xjVOGupclZjMVqIswYmQUmw+lp21+osIxERCUsBT4yioqL45z//\nedDjzSnBrcvewesbZ+Kx1WCwR3Hr0P/juC49Whxjhq0LWRSwuWAHv0WJkYiErsKyGpwuN4mxNiJt\n3l/p+zuf12DJAAAgAElEQVTSJR605rutNDrkVS27RUQkDIXtAa/fbFrLx3s+BIsTS10S95x0C50T\nWqf70sDU3mQVrCOvbm+rjCci0lZ8jRc6NWi8UFR/uGsgOtL5xDc8y6hIh7yKiEj4CfgBr61l/t45\nYHISa+/Oo2f8qdWSIoCTegwCoNZSRK3D3mrjioi0Nn9ilLS/C5z/cNcAdKTz8Z1lZLTWUVJRR53D\nFbB7i4iItIawTYwMRg9dGcrfzr2N2IjIVh07LS4eoz0Wg9HNqt3bW3VsEZHW5DvDqGGrbv9SugBW\njOLq9xiZI7xdRgtKVDUSEZHwEraJ0UXpE5h25hTMxrY5RDDJ1AmA9bnb2mR8EZHWkFN8qKV0vopR\nAJfS2eqX0lnrANSAQUREwk7YJkYXDhnTpuP3ie8JQHZlVpveR0SkJXL9e4waLKWr8e0xCtxSurj6\nPUYuQy3gIV8VIxERCTNhmxi1tdHdBgBQYczD7XYHORoRkYNVVNuprHEQYTWREGMFwOPxUFhfMUoJ\nYMXIbDQTY4kGgwcsdlWMREQk7CgxOoyB6V3AaQWznS356k4nIqGnYUc6X1vuKkc1dpedCFMEkebW\n3X/ZlDh/Z7pa8opVMRIRkfCixOgwjEYjMe50AFZnbwlyNCIiB/M3XjhUR7rIwJ1h5OPrTGew1Kli\nJCIiYUeJ0RF0j+kGQGbpruAGIiJyCL6KUeeU/Y0XCmsCv4zOJ76+M53JZqe00k6dXS27RUQkfCgx\nOoKhGf0AKHLlBDkSEZGD5dZ3pDt0xSjwiVFcfWe6mDhvQqSqkYiIhBMlRkcwpns/PG4jbmsF+eVl\nwQ5HRKSRfYXepXSdDnWGURArRrYoJ4A604mISFhRYnQEkVYrNof3zcWK3ZuDHI2IyH52h4uislqM\nBgNpifubLBTVBr5Vt49vj5HZZgdUMRIRkfCixKgJ6bYuAGwqzAxyJCIi++WV1OAB0hIjMZv2/yoP\nxuGuPr5DXt3mWm+M6kwnIiJhRIlREwal9AEgt1Ytu0UkdPg60jVcRuf2uCmuP9w1KSLwFaO4+qV0\ndryVIlWMREQknCgxasLYnoMAqLUUUeuwBzkaEREvX0e6jAaJUbm9AqfHRYwlmgizLeAxxdefY1Tj\nqgI85GmPkYiIhBElRk1Ij4vHaI/FYHSzKmt7sMMREQEaVIwadKTzteoORkc6AIvJQpQ5EjduzDYn\n5VV2auqcQYlFRETkWCkxOgpJxgwA1udsC3IkIiJeufUVo04NzjAq9jVeCMIyOp+4+gYMScner9WZ\nTkREwoUSo6PQO6EnANmVWcENREQEcHs8/jOMOiWFRqtun4T6fUbx8R5A+4xERCR8KDE6CqO7DQSg\nwpiH2+0OcjQi0tEVl9Vid7qJj7YSFWHxP14YxMNdfXyHvEbH+g55VcVIRETCgxKjozAovQs4rWC2\nsyVf3elEJLhyfNWiBo0XYH/FKCWIFSPfIa+WSG+zmvxiVYxERCQ8KDE6CkajkRh3OgCrs7cEORoR\n6eh8Hek6JUc3ejyYh7v6+CpGBksdoIqRiIiEDyVGR6l7TDcAMkt3BTcQEenwcus70jVs1e1yuyit\nK8OAgcQgNl/wVYxcxvpDXrXHSEREwoQSo6M0NKMfAEWunCBHIiId3b6ig5fSldSV4fa4ibfFYTGa\ngxUa8fVd6ardlVjMRiqqHVTXqmW3iIiEPiVGR2lM93543Ebc1gryy8uCHY6IdGC+ilHnBkvp9nek\nC161CCCu/pDXcnsFaYmRgKpGIiISHpQYHaVIqxWbw7uhecXuzUGORkQ6qsoaB+XVDmwWEwmxNv/j\nRSHQkQ72V4zK68qVGImISFhRYnQM0m1dANhUmBnkSESko/KdX5SRFIXRYPA/HgpnGAHYTFYiTBE4\nPS6SE7x/YvKL1YBBRERCnxKjYzAopQ8AubVq2S0iwZFT6F1Gd1Crbn9HuuAmRgDx9Z3pYuK9576p\nM52IiIQDJUbHYGzPQQDUWoqoddiDHI2IdESHPcOoNjT2GMH+znSR0d6mC/laSiciImFAidExSI+L\nx2iPxWB0szpre7DDEZEOKPdwZxiFyFI62H+Wkcnm/QBJFSMREQkHSoyOUZIxA4D1OduCHImIdET7\nDnGGkd3loMxegdFgJDEiPlih+fkqRg5DNTaLicoaB1W1jiBHJSIicmRKjI5R74SeAGRVZgc3EBHp\ncBxONwWlNRgMkJ64PzEqrt9flGRLwGgI/q91f2e6hi271YBBRERCXPD/goaZ0V0HAFBhzMXtdgc5\nGhHpSPJLqvF4IDUhEot5/6/vUGq8ABBff5ZRmb2CdLXsFhGRMKHE6BgNyugKTiuY7WzNV3c6EQmc\nHN/+oqQDGi+EyOGuPnH1FaOyunLS6itbecVKjEREJLQpMTpGRqORGHcaAKuytwQ5GhHpSHKKfK26\nD2i8ECKHu/r4KkbldeX+ilG+GjCIiEiIU2LUDN2iuwGQWboruIGISIfia9WdcWCr7hDqSAf79xiV\nNdxjpKV0IiIS4pQYNcOwjP4AFLtyghyJiHQkvqV0nUO8YhRhjsBqsuJwO4iPMwDe5gsejyfIkYmI\niByeEqNmGNOjHx63EZe1goKKsmCHIyIdgMfj8Z9hdFDFyNd8IUQqRrB/OZ3HXIvNaqK6zklVrTPI\nUYmIiByeEqNmiLRasTm8b0CW79oc5GhC074tu6irqQ12GCLtRklFHXUOF7FRFmIiLf7Ha521VDmq\nsRjNxFljghhhY/tbdlfu70ynBgwiIhLClBg1U7qtCwCbCjODHEno+XXRD1Q++TArH30y2KGItBuH\n7UjnO8MoIgmDwRDwuA7Hd8hrmb3cf+aS9hmJiEgoU2LUTANTegOQW6uW3Q253W7KF3wMQHp+Jr98\ntTTIEYm0D/6OdCmN9xcV+hovRIZGq26fOFv9WUZ15aQn6ZBXEREJfUqMmmlsj0EA1FqKqHXYgxxN\n6Ni46EeSK/PxbbGuWTAPh13fH5GW8nWkO7hi5E2MUkJofxHsrxiV2ytUMRIRkbCgxKiZMuITMNpj\nMRjdrM7aHuxwQoLb7abii08BKDz+TMoi4omvLWXNrPlBjkwk/OUUeitGGQd0pCuuqW+8ECId6Xzi\nGxzyuj8xUsVIRERClxKjFkgyZgCwPmdbkCMJDZsXryC5Io8aUwQjr72ciIuvACB6xSJK84qCHJ1I\nePNXjA7oSFdYXzFKigixpXT1XenK7OX+s4zyS6rVsltEREKWEqMW6J3QE4CsyuzgBhIC3G43pZ97\nq0VVI08hKi6GIeeOIz+5Bza3nQ1vzApyhCLhq7rWSVmlHYvZSHJ8RKNrvsNdQ20pXYKvK11dBbFR\nFiJtJmrqXFRUO4IcmYiIyKEpMWqB0V0HAFBhyMPtdgc5muDa8t0qUspzqDHZGH71ZQAYjUZ6Xn8d\nbgyk7viZXes2BTlKkfCUW18tykiKwtig85zH4wm5w1194hp0pTMYDKRpn5GIiIQ4JUYtMCijKzit\nYKlja/6+YIcTVMWffQJA5fBTiI7ff5ZK18F9KOg/GiMe9r77bodPIEWaw9+R7oBldFWOaupcdiJM\nEUSZI4MR2mFFmiOwGM3UuezUOmsbnGWkfUYiIhKalBi1gNFoJMadBsCq7C1BjiZ4tixZRWrZPmqN\nVkZMvuyg6yNuuoYak43U0r2s/2xxECIUCW++M4wyDtORLjkyMaTOMAIwGAwNqkbqTCciIqFPiVEL\ndYvuBkBm6a7gBhJEhZ96q0UVw04mOiH2oOuxSQnUnXIuAK4vP6aupjag8YmEO1/FqPMBZxj5DndN\nDrH9RT7x/n1G+88yyldnOhERCVFKjFpoWEZ/AIpdOUGOJDi2LltDauke6oxWhh+iWuRz/NW/oSQq\niVh7BWvenhvACEXCX8M9Rg0Vhejhrj7x/s50qhiJiEjoU2LUQmN69MPjNuKyllNQURbscAKu4NOP\nASgfOpaYpPjDPs9ssRB32UQA4tYuoXBPXkDiEwl3Tpeb/JIaDEB60qFbdYdqxSiu4VlGSfvPMlLL\nbhERCUVKjFoo0mrF5vC+KVm+a3OQowmsbT+sI7U4mzqjhWHXXN7k8wedcSJ56X2xepxseuOdAEQo\nEv4KSmtwuT0kx0dgs5gaXfO36g6xjnQ+CQ0608VEWoiOMFNnd1FeZQ9yZCIiIgdTYtQK0m1dANhU\nmBnkSAIr75P5AJQNOZHYpISjek3f312H02AkPftXtq/4uS3DE2kXfI0XOiVHH3StKOQrRt6ldOV1\nFQANWnZrn5GIiIQeJUatYGBKbwBya/cGOZLA2b7iZ9KKdmM3mhl2zRVH/bpOfbpTPHgsAAWz38fl\ncrVViCLtwuFadbs9boprSwFIigjVPUb7l9IB/gYMecXaZyQiIqHnqBOjxYsXc+GFF3L22Wcze/bs\nFt301VdfZdKkSVxxxRXMmzePrKwsJk+ezLXXXssjjzzSorGDYWyPgQDUWoqodXSMJSK58717i0oH\nnUBcyrG9KRv5u8lUmSNJrshj3Udft0V4Iu2Gv1X3AYlRub0Cp9tJjCWaCLMtGKE1ydeVrsxeXzFK\nqE+MVDESEZEQdNjEqLi4uNHXc+bM4ZNPPuG///0v7733XrNvuHLlSn766Sdmz57NrFmzyMnJ4Ykn\nnmDq1Km8W38A6MKFC5s9fjBkxCditMdiMLpZk9X+l9NlrtpAWuFO7AYzQ4+hWuQTHR+Da/xFABj/\nt4Dq8srWDlGk3fAvpTuoI11ot+qGBkvp7L6KkTrTiYhI6DpsYvToo4/y4osvUlPj/WQvIyODRx99\nlL///e8kJyc3+4bLli2jf//+/P73v+e2225j/PjxbNy4kdGjRwNw2mmn8eOPPzZ7/GBJNGYA8HPO\n1iBH0vZyPvLuLSoZOIb4tOb9Xxg54QKKYtKIdlSz9q05rRmeSLvh8XjILa5fSnfQGUah3aobINoc\nhdlgosZZi91l39+yu1gVIxERCT3mw1149tlnWbFiBXfccQfjx4/nvvvuY/ny5TgcDu69995m37Ck\npIR9+/bx73//m+zsbG677Tbcbrf/enR0NBUVFc0eP1j6xPekqGobWVXZwQ6lTe1cu5G0gh04DGaO\na0a1yMdkMpEyaTKe1/9J0i/fk7fjPNJ7d23FSEXCX1mVnZo6F9ERZmIjLY2u+c8wCuGKkcFgIM4W\nR3FtCWV1FaQneZfW5ZdW4/F4MBgMQY5QRERkv8MmRgAnnngiJ554Ip999hm///3vueqqqzjnnHNa\ndMOEhAT69OmD2WymV69e2Gw28vL2n2lTVVVFXFxck+MkJkZhNpuafN6xSk2NbdbrzjxuBCtXfEOF\nIY/k5GiMxvDra3E0c/9+/nxSgfIhJzB+aK+W3e+SU/n4629IzfqV7TPf5biXHmvReC2KpZk/93DX\nUecN4TH3faW1AHTPiCMtrfHvxaqd3iWoPVI7HfNcAjn35OgEimtLMEY56ZmaRGyUlYpqOyabheT4\nyIDF4RMOP/e20FHnDZp7R9WR5y7Nd9jEaOHChbz00ktYrVbuvPNOXnrpJd577z1uvfVWbr75ZkaN\nGtWsG44aNYpZs2Zxww03kJeXR01NDWPHjmXlypWccMIJLFmyhLFjxzY5TkkbrFFPTY2loKB51aou\nkcngtIK5ju83bmVgepdWjq5tHc3cd63bRGrONhwGEwOuurTZ36uG+t9wLfmPPkDq3s0sm/c/Bpw2\npsVjHquW/NzDWUedN4TP3DfvKAQgOc52ULx7S7wfKNmcUcc0l0DPPcroXQK4Oz+PZNJJS4igotrO\npu0FDOge2GWA4fJzb20ddd6guWvurTumtH+HTYz+9a9/8c4771BdXc2f/vQnPvzwQ2644QYuv/xy\nXn311WYnRuPHj2f16tVceeWVeDweHn74Ybp06cL999+Pw+GgT58+nH/++c2eULAYjUZi3GlUsofV\nWZvDLjE6GtkffEQ6UNxvFEM6p7XKmKndO7F1+DhS131LyYezcZ18PKY2qASKhCNf44XORzrDKEQP\nd/WJt3rfTPhadqclRpG5r5y8kpqAJ0YiIiJHctjEKDo6mo8++oi6urpGzRbi4uK46667WnTTQ71+\n1qxZLRozFHSL7sYmxx62l+4KdiitLmv9FtLztuE0GBk0ufl7iw7l+P+7io13ryapuog1cxZwwjWX\ntur4IuEqt/4MowNbdbvcLkrqyjBgCNkzjHz8Lbt1lpGIiIS4w26Eeemll7BYLCQmJvL0008HMqaw\nNTSjHwDFrpwgR9L6dn8wD4CivseT0jW9VceOjI7CeO5vALAu+ZLKkvJWHV8kXO3zteo+IDEqqSvD\n7XETb4vDYjziVtGgi6s/5LW8/iwjf2c6nWUkIiIh5rCJUVJSEtdddx1XX301MTExgYwpbJ3Qox8e\ntxGXtZyCyvbz5j77l22k5WzFaTAy8OrWrRb5DP/N2RTGdybKVctPbzT/nCyR9qKmzklJRR1mk4HU\nA5oUFPuW0YV4tQgg3tZ4KZ2/YqSzjEREJMSEX+u0EBZptWFzeNf7r9i1KcjRtJ5dc+dhAIp6jyC1\ne6c2uYfRaKTTNdfiAVI2r2Tv5h1tch+RcOFLHNKTojAaG7e1Lqw/3DUphFt1+8TXV4zK6g95TUvw\nVozyS2pwezxBi0tERORASoxaWbrN23RhY0H7eGO/Z1Mmafs248LIgKuvbNN79Tp+MPk9h2HCw86Z\n4b/nTKQlfI0XOiVFHXTN13ghJYQPd/Xx7TEqr/MupYuKMBMbZcHhdFNaURfM0ERERBppMjH63e9+\nF4g42o2BKb0ByK3dE+RIWsfOOR9iAAp7DyOtZ+c2v9+QG6+jzmghrWAnv3zzfZvfTyRU5fgbLxyi\nI10YHO7qE22JwmgwUuWsxuFyAA32GakBg4iIhJAmE6Pa2lpyctpfM4G2MrbHQABqLUXUOuxBjqZl\n9m7ZSeqezbgw0L+Nq0U+SZ1SqBxzBgDVn3yAwx7e30OR5trfqvvwFaNQb9UNYDQYiatv2b2/AYNv\nn5EaMIiISOhoMjEqKSnhzDPPZNy4cZx11lmceeaZnHXWWYGILSxlxCditMdgMLpZk5UZ7HBaZMfs\nDzHiobDnUNJ7dQ3YfUdfdyVltjgSaktZ8+7HAbuvSCjJ9XekO1TFyLvHKBwqRtBwn5E3MUpL8nWm\nC++KkdvtDnYIIiLSiprs8/r6668HIo52JdHYiSK28XPOVk7pMyjY4TTLvm27SM3eiBsD/a6eENB7\nW2xWIi6+Aua9RdTyhZRdfBbxaclNv1CknXC53Q2aLzTuSOdwOSizl2M0GEmo378T6uJssVDRoDNd\nfcUoP4wrRt9s/JmP98ylh2UIU0+fiNmog6lFRMJdkxWjLl26sHbtWubOnUtSUhKrVq2iS5cugYgt\nbPWJ7wlAVlV2cANpgcz/zMOIh4Lux5HRp1vA7z/kvFPJT+5OhNvO+jfeDfj9RYKppKIOp8tDfIyV\nCGvjz6+Ka73VokRbAqYweTPuP+TV7kuMwvsso/IqO59sXApmB7s963jgm1eottcGOywREWmhJhOj\np556iu+++46vv/4al8vFvHnz+Pvf/x6I2MLWqK79AaggLyyXWuRkZpGa9QtuDPQN0N6iAxmNRnpc\ndx1uDKRmriNr/ZagxCESDEVl3jfZB55fBFBYnxiFw/4in3jfHqP6znRpDSpG4day2+PxMPPLzbii\nCrxfu42UW3Zz36J/kVtWEuToRESkJZpMjJYtW8aTTz6JzWYjJiaGt956iyVLlgQitrA1uFM3cFrB\nUsfWgvBrXLH9P969RQXdBtOpX4+gxdFtSF8K+o3CiIfsd2aFZZIp0hyF9YlRcnzEQdd8HelSwuBw\nV58DzzKKtJmJj7bidLkpLg+vSsuyDTmsy8rCGFFDpCmSG/vdBI5I7LYi/vbjc2zMCd+VAiIiHV2T\niZHR6H2KweA9YNBut/sfk0MzGo3EuNMAWJ21OcjRHJu8HXtI2bUBNwZ6TwpOtaih4TdeS43JRmrp\nHjZ8/m2wwxEJCF/FKOUQiVFxGFaM4mzeipFvjxGEZ2e6gtIa/rNwG8a4IgAGJPVlVI++3DP6D5jr\nEvBYq3hxwyt8t21DkCMVEZHmaDLDOf/88/nzn/9MWVkZb7/9Ntdeey0XX3xxIGILa92ivftyMkt3\nBTeQY7R19oeY8FDQdSBdBvQKdjjEpSRQe/I5ADi+nE9dTXh9uizSHEeqGBXWt+pOCqeKke+Q1/qu\ndLC/M11+mJxl5HZ7eOOzjdTaXaR0qQRgYFJfAHokp/LI6X8iyt4ZzA7m7H6PuWu1skJEJNw0mRjd\ncsstXHnllZx33nnk5ORw++23c+uttwYitrA2NKMfAPmOfcz53zY27SrG6QrtpWD5u/aRsmM9HqDX\nxOBXi3xGTf4tpZGJxNVVsPa9+cEOR6TNFdUvL0uJO8JSujCqGPmX0oVxxeirVVls3VNGXLQFZ/3+\nogGJ/fzXE6Kieezs/0eaayAGo5vvSj/j+aXztQRYRCSMNJkY/f73v6eqqoo77riDadOmccYZZwQi\nrrA3pkc/8Bjw2Cr4atUunpy9jtv/tZQXPtrAkp/3UVJRF+wQD7LlPx9iwk1+54F0HdQn2OH4mS0W\nLGddAIB768YgRyPS9grLvMnCIfcY+Q53DZMzjABirTEYMFDpqMLpdgINOtOFQcUoO7+S+Ut2AHDJ\nWUlUO6tJikgkNbLxMQJWs4UHzrqBwdaTAdjs+JFHF72F3ekIdMgiItIMTSZGV111FQsXLuScc87h\nvvvuY8WKFYGIK+xFWW2kR6dgMMC4MbF0SY2mzu5i7dYC3v7vZu588XseenMl877LZGt2Ka4gf6qY\ns2MPyTvW4QF6hlC1yKfryOMAiCnL1yew0q653R6Ky70fnCQfUDGqddZS5ajGYjQTV9/pLRwYDUbi\nrDEAVNi9y9DSwqRi5HC6eW3BRpwuD+NHdMYd7a0WDUzs699725DRaOT/jbuU8YmX4HEbyTdtYfrC\nFyipqgp06CIicoyaPOB1/PjxjB8/ntraWr799ltmzJhBSUkJixcvDkR8Ya1zTCfyqgsYMsjC784c\nRVFZLRt2FLE+s4iNu4vJzq8kO7+Sz3/cTZTNzHG9kxjaO5mhvZOJi7YGNNZV/36XZI+bvE4DOHVI\n34De+2gkd8tgn8lGpKuOouxcUnt0DnZIIm2itLIOl9tDXLQVq6XxOUVF9Y0XkiKSDvmmPJTF2eIo\ns1dQZi8nMSLBXzEqKK3B5XZjCtGmPh8v28GegkrSEiK56sy+vPbrUgAGJPU74usmjDyV9G1JzNn5\nH2qsOTy09J/cOeYWeiSnBiJsERFphiYTI4Dt27fz+eef8+WXX9KpUyeuu+66to6rXegcnc5PwL6q\nXMC7LGb8yC6MH9kFh9PN1uxS1mcWsX5HEXnF1azclM/KTfkA9OoU602S+iTTKyMOo7Ht3gQV7skj\nfssaAHpMuLzN7tMSRqORyvg0IouzyfllixIjabcKj9CRzre/KDmMGi/4xFtjyWb/PiOb1URCjJXS\nSjvF5XWkJhx8ZlOwbc0u5cvlWRgMcNMlgzGZPGSW7QRgQGLTHyCd1m8IabG38uK6N3BZy/jH6uf5\nv4FTGN3jyEmViIgER5OJ0SWXXILJZOI3v/kNM2fOJC0tLRBxtQudozOA/YlRQxazkSG9khjSK4mr\n6Ud+STUbdhSzPrOIzVkl7MypYGdOBZ9+v4uYSAtDeycxtE8yx/VKJibS0qpxbnr/Q9I9bvIy+nHq\nsAGtOnZrcqd3heJsKnbsBLTXTdonX6vuA5fRwf6KUTi16vbxdaYrq9vfmS49MYrSSjt5JdUhlxjV\n1Dl5/bONeICLxvagb5d4thRvx+F20jWmM7H1SwObMjCjK/ef/Gf+/v2/sdsKeXPrm+RXXs6FQ8a0\n7QREROSYNZkYPfXUUwwYMIDKykrt7ThGnWO8iVFOZV6Tz01LjOKsUVGcNaordQ4XW7JKvNWkzCIK\ny2r58dc81q7P5qqcRSQ6Kpoc71ikurz7GbpdGZrVIp/oXj1g04+4c/YGOxSRNlNY3j4rRnFWX8vu\nBp3pkiLZkl1KXnENxwX/dIBGZi/aRmFZLd3TY/jtOG9wm0u2Ad7zi45Felw8j531R/62+E3KLLv4\nLPdDciuK+d3Y81o9bp/Mgly+zVzH1tKtVFPGhD6Xc1q/IW12PxGR9qDJxCgyMpIrr7yS7Oxs3G43\nXbp04dlnn6VXrxD7KxaCUiKTsRjNlNSVUu2oIcpydJ+I2iwmhvVJYVifFDweD7nF1azPLML15cd0\nqS1ok1gLehzHKSMGtcnYrSV9cH+qvoDokoMrcCLtRVF9R7pDJUa+M4zCs2LkO+S1ccUIIK8ktDrT\n/bStgKXrczCbjNx88WDMJu/+py3F2wEYmHjsS+GirBH89Zz/j2e+m8Nu1rGmehEF/yvizvETMRtN\nTQ/QhDqHg2U7NrJq7y/ss+/EZa1PQOu3q87dMZehne8mMfroKl0iIh1Rk4nRQw89xE033cT5558P\nwBdffMGDDz7IrFmz2jy4cGc0GMmITie7Yi85VXn0Seh5zGMYDAY6JUeT7Khg196fwWAg6Q9TMaV3\narU4DQYDJw3uTlFRaHdNyujXg00GMzGOKsryi4hPS276RSJhpugIh7sW1y+lSwmjVt0+/rOMGlSM\n0uoTo/wQ6kxXXm1n5n83A3Dl6b3pkupNJKod1WRV7MFsMNEnoXkfDJqNJu45YzJvLU9mVdX/yDKs\n48FvSrn/jN8RZT34592UPcWFLNr+E5tLtlBu3Acmbyt0rOBxmYhzdWZA4gB+LlmDw1rCs9//h7+e\ne3OzYhcR6QiaTIxKSkr8SRHAhRdeyMsvv9ymQbUnnaMzyK7Yy76q3GYlRj4Fc/8DLhdx404jZfjQ\n1guwnjFEO0I1ZDKZKI9NJaU8h70bthJ/1knBDkmk1RX6E6PGFWaPx+NfSpcUGX5L6Xx7jMrrGi+l\ngwXFZhsAACAASURBVNA5y8jj8TDzv5spr3YwsHsCZ4/p5r+2pSQTDx56xffAZmpZ19D/G3se6b8m\n8dm++ZRZdnHfouf4yyn/H+lx8Ud8ndPl4oedm1mRvYE9tTtw2kq9F+q3nRrtsXS29mR0pyGc2vc4\nIizeOHdXDGfGimcpMm/jo59/4PLhJ7cofhGR9qrJxMhqtfLrr78yZIh3bfIvv/xCZGRobZINZf59\nRodowHC0qn7ZQNX6nzFGRJBy2RWtFVpYcqV1gvIcyrbvACVG0s64PR6KfHuMDmi+UOWsptZVR4TJ\nRrQ5KhjhtYjv3KUy+/6ldGn1DRcKy2pDomX39xty+WlbIZE2EzdeNBhjg5bovv1FA5to0320Lhwy\nhrSYBN7aPAu7rZC//fBPfj/idwzK6NboeXnlZfxv61p+KdpMqWEPmOsPi7WBx20k2plB//j+nN57\nBP3TD92tc3Tvvgxbfwob6payKO8LTigdSNeE8Ks6ioi0tSYTo+nTp3P77beTkJCAx+OhrKyMZ599\nNhCxtQudfJ3pKpuXGHmcTgrm/AeApIt/gzn+yJ8otncR3XvC9rU492YHOxSRVldeZcfp8hATacFm\nPeAMo5r9+4vC7Qwj8CZGBgxU2CtxuV2YjCasFhOJsTZKKuooLKv17zkKhsLSGt5fuBWAyWf3P2gp\n45bi+sYLzdhfdDije/QjNeZ2nl71Ki5rGS+s/zdXVUzEZrayPGsDu6ozsVuLMBjwV4UM9ijSzT05\nPmMIp/cdSkzE0S3Bu+mkC7n3q63U2vJ4bvks/n7u7WGxUkBEJJCaTIxGjBjBV199xa5du/zNF2Ji\ngr95M3vG40QNGkz00GHYevTEEKK/4DtHpwPelt0ej+eY39CUfrsYe87/z96dR8d11/f/f947u0aa\n0b5Ltmx5jbd4S5zE2fcNAgSyF0jLt7RQIHDa0ny/cCgtgdKWhh+EtaXFCU0gJCE7ieOEJE6cOIt3\nW7It2bK176OZkWa79/fHaEbyIsuS7qx6P87xObY0937e401638/n8/q0YSktI/+KqxJRYkYpXlxP\naAvY+ySAQWSfM55hFIvqzsD9RQAm1USuxclQyMtQyEu+LfqQp6zAQf9QgM6+4ZQ1Rpqm88vn9jMS\njLBmYQkXLCs/4fO9w310D/fiMNupzasydOw5RSV8a+OX+ec3fs6wtZ3fHhu3f9cGaAqOUBnz8xZw\ncd1KlpRXT6uhMasmvrDuLv71wwfxWVv59fbNfPq8q417I0IIkQUm/d/1+eef52Mf+xgLFizA4XBw\nww03sHnz5mTUdkbDBxvpffopWv75H2n66pfo+M9fMPTuO0R86RUgkG9z4zDb8YX8eILeKV0bGRqi\n9+knASj55G2oFmPPL8pE1efUE0HFNTKA3zO1308h0l3PaCLd6YIXxmaMMm9/UYxrNJnOMz6ZrjD1\nyXQvbT9G47EBXE4r91y76JQHWA390TS6hQX1mAxIkDtZgdPJd678AqWRxQAoIQelkUVcVXwLD1z4\nDf712vv4/IU3cU5l7YxmeeqKy7iwIPqA7V3PqzR0ytEHQggx3qQzRj/5yU/41a9+BUBtbS1PPPEE\nn/3sZ7nyyisTXtyZVH7xy/h278K3eyfh3l48b2/F8/ZWUBQc9QtwLl+Bc/kKrNU1KV12oigKlc5y\nDg8eoc3XHo+sPRs9Tz+J5veTs/QcnCtXJbDKzGGxWRl0FlLo6+H4nkYWXrA61SUJYZjeLJ4xgmgy\nXSvtJyTTxWaJuvpSk0x3vMvLE68fBuAz1y0mL+fUYIUDo8voFhdM7fyiqbCaLXzzqs/SMdhPaZ47\nYcvcblt9Cbtf3ofHcpSffvAI37/6q5hNxjd7QgiRiSZtjEKhEMXFxfFfFxUVoet6Qos6G7krV5G7\nchW6rhNsbxttknYxfLAx/qPniccx5eePNkkrcS5dimpPfnBERW60MWr3drCkcOFZXRM4fozB114F\nVaXkU3dk5J6CRAkVVYCvh/7GJpDGSGSRscbo1P+nMvlw15hYMt3g+GS6gtFkuoHkzxiFwhq/eHYf\n4YjOJasqWVlffMprNF2LzxgtMih44UzK3Yn981VVlS9vuIt/fPtfCdp6+MlbT/PFjbckdEwhhMgU\nkzZGa9as4b777uOmm24C4IUXXmDVqvSZvVAUBVtlFbbKKgqvuY7I8DD+fXvjjVJkYADPG6/jeeN1\nMJlwLFgYb5SsFRVJaTgqYwEMvs6zer2u63Q9+hvQdfIvuxxblbFr2jOdpaYWWnYTOHY01aUIYah4\nVLfrdDNGmXu4a4z7dMl0hambMfrDm80c6/JSmu/gU5effjaozduBN+SjwJZPqePUxikTlbncXFd1\nEy90Pc7+wDbeO7qMtXMS3/QJIUS6O6sDXjdt2sRjjz2G2Wxm7dq13HHHHcmobVpMDgd5a9aSt2Zt\ndDbp+LGx2aRDBxk+sJ/hA/vp+d1jmIuL40vuchYtAc5+mdtUjDVGZxcY4NvxAcMH9qM6nRTdLE/y\nTla4aD5sBWtPe6pLEcJQ8ajuk5bSabqWFUvpXKeZMSrNt6MQbQrDEQ2zKTlBOo3HBnjhnaMoCvz5\njUuxW0//5TAW072osD6rZu5vXLae91/eQ5fpAL/e/xjnVPwtDuvMzmcSQohMd1bnGN17773ce++9\nyajHUIqiYKupxVZTS+H1NxLxesdmk/bsItzTw+CrWxh8dQuK2Yzn0ktwf+J2FPOkvy1TUpEbTaZr\n93Wi6RqqMvEXfi0Uovu3jwJQ9JFbMKVBAmC6qV62kGOA29dHKBDEYpMv5iLz6boeX0p3cviCJzhE\nWAuTa3FiN9tSUZ4h4oe8jttjZDGbKHTZ6fWM0DM4Qnlh4pPphgNhfvnsPnQdbtgwh/rqiY9BGNtf\nlH0zKl+56Hb+4U//QsTq4T/e/C1fv/yuVJckhBAplZ4Z1wliys0lb/15lN/7F8z7twepvf8bFN38\nUex189AjEbo2v0LXbzYZvocq1+LEbc0jGAnSN/rUdyIDm18i1N2NtbKK/EsuM7SObJHjysVjd2NC\n4/jeQ6kuRwhDDPlDBMMaTrsZh+3EhzOx/zcKM3h/EYxbSjculQ6grHB0n1FfcvYZPbblID2DI9SW\n5vKRi+omfF1IC3NooBmIzhhlG5fDwafqb0XX4Zi+iy0NO1NdkhBCpNSsaozGU1QVe908im7+KLX3\nf4Oav78f1Wpl8PU/0f/i84aPdzYHvYYHBuh99hkASj51O4okBU1opDD6+9nTII2RyA49E8wWAfQM\nZ/7+IgCXNTZjdFJjVBCL7E78PqMdB3t4fWc7ZpPCn9+09IxL95oHjxLSQlQ6y3FZE7PUOtU21i+l\nTl2NosATzU/S75NjEIQQs9eEXxHa2trO+CPbOObXs+DLfwNAz+9/x9D2dw29f2Xu5PuMep78PXpg\nBOeqc3Ges8zQ8bONqaoGgJGjEsAgskPsDKPTJ9JFZ4yKM3h/EYw7xyg4hKZr8Y/Hk+kSfJbRoDfA\nf7+wH4CPXTyf6pIzL1VuiC2jS0IaXSr9zUUfxxzIR7f6+cHW/011OUIIkTITbqa56667UBSFQCBA\nb28vNTU1qKpKS0sLNTU1/PGPf0xmnUlRfOEGem/9FD2/e4yO//w55oICHPXGfEGsnGTGaKS5Cc/W\nN8BkouTW2wwZM5vl18+H7ZsxdWVfky5mp1jwwpkT6TJ7KZ1FNeO05OAL+fGGfPFZmNL4WUaJa4x0\nXefHj+/E4w+xqCafq9fXTHrNgdGY7mxvjGwWC59Zfgc/P/BTes0HeWrnW3x05QWpLksIIZJuwhmj\nLVu28Morr7Bu3To2bdrESy+9xIsvvsijjz7KokWLklljUhVcfS3uSy5DD4dp/dGDBDvPLmJ7MrEZ\no/bTRHbH47mBgiuvxlpWZsiY2axqefQ8KNdQN5FwJMXVCDFzPWc63DV+hlFmzxhB9JBXOHGfUXyP\nUQKX0r21p4O3d7djt5q498YlqJMkzPlDwxz1HMOkmJjvnngfUrZYVT2X5Y4LAXi583naBvpSXJEQ\nQiTfpHuMDh8+zNq1a+O/XrFiBc3NzQktKpUURaH0jrvIWbYCzeul9Yf/TsQ78zXX5c4yFBQ6/F2E\ntfAJnxt69x1GDh/ClOei8MabZzzWbOAuLcJrcWLVw3QckuV0IvNNlEgHjEV1Z/geIyA+SzQYGIx/\nrCTfgaJEZ81CYW2iS6ettdvLIy83AnDnVQtPu1zxZI0Dh9HRqXPXZnQS4FT8xYbrsQfKwBzkwW0P\no2nG/1kIIUQ6m7QxKi8v58EHH+TgwYM0NDTw/e9/n7lz5yahtNRRTCYq//Lz2GpqCXV20vbjH6KF\ngjO6p81kpchRiKZrdPl74h/XAgF6Hv8tAMUf+zgmx+RfsEWUPz86s9a572CKKxFi5nonmDGKaBH6\nAwMAFNryk16X0cYiu8dmjMwmlSKXHV0f22tllP1H+vjOwx8wEoywYXkFFywrP6vrGrI4pnsiZtXE\nX629EyJmvNbjbNr+SqpLEkKIpJq0Mfr+97+Px+Phvvvu42tf+xrhcJgHHnggGbWllGp3UPk3X8Fc\nUMDwwUY6f/Vf6DN8ena6g177XnyecH8ftto5uC7cOKP7zzZKZTUA/iNHUluIEDOk6/qES+kGAoNo\nuobb6sJisqSiPEO5bacupQMoGz2/qLPPuMZo6+52/v23OxkOhFmzsISv3rnmrA9pHTvYdfY0RgDz\nS8q5oOBKAN7xbKGxU/ZxCiFmj0kbI7fbzf/7f/+PZ555hmeeeYavf/3r5M6SQ0ctBQVU/c1XUGx2\nht7dRu8fnpzR/Sqdowe9jgYwhHp749HgpbffiaLO2vT0acmbF133r3S0prgSIWbGOxwiEIrgsJnI\nsZ/Y/IwFL2T+MjoYt5Ru3CGvYGwyna7rPPVGE//53H4ims4162v4/C3LsFnO7giEvpF+uvw92E12\n5uRVz7ieTHP76ktxhWpRTBF++sEjhCOyj1MIMTtM+p344sWLWbJkyQk/Lr744mTUlhZsNbVUfv6v\nQFXpe+4ZBt98fdr3igUwtI7OGPU8/hh6KETeuvU4Fiw0pN7ZpPycaAhI7mCnrIUXGW0ske7UpbQ9\no1Hd2RC8AOOW0gVOboyMOcsoHNH45bP7eXrrERQF7rp6IZ+6fMGkYQvjNfRF0+gWFszHpM6+8+RU\nVeXLG+6GkI2ArZufvvV0qksSQoikmDCuO+bAgQPxn4dCITZv3syOHTsSWlS6cS5bQemd99C16b/p\n3PQ/mAsKp3XOUOyQ13ZvB/7GBoa2v4tisVD8iU8ZXfKsUFxbTrvJhiMSoPdYByVzKlNdkhDT0jMw\ncSJd3+iMUXGGR3XHxFPpTj7kNZZMN4PIbt9IiB8/sZsDLQNYLSp/+ZFlrKovnvJ9xpbR1U+7lkxX\n5nJzXdVNvND1OPsC23i/ZQVrauenuiwhhEioKa3dslgsXHfddWzbti1R9aSt/EsupeDa6yESof2n\nPybQenzK9yjLKcGkmOgZ7qXr0UcAKLj2eixFRUaXOyuoqorXVQpA+97GFFcjxPTFZ4xO0xjFZowK\ns2bGKJZKd/oZo65pLqXrGRjmO5ve50DLAG6nlb+/c/W0miJd1+MzRrMpeOF0bly2ntLIIhRV53/2\nPspwcGYhREIIke4mnTF66qmn4j/XdZ2DBw9isWT+BuDpKP7YJwj1dON9bzutD/47tf/wDcz5Z58S\nZVJNlOWUkL+rmWBLN+aCQgqvvT6BFWc/vbwK+o8x1NQMXJrqcoSYljOeYZRlM0Yu61gqna7r8TCE\nIrcdVVHo8wQIhSNYzGe/hK253cODj+/C4wtSVezky7euPG2TeTbafB0Mhbzk29yU5ZRM6x7Z5MsX\n3cb9f/pXIrZBHnzzd/z95XemuiQhhEiYSWeM3nnnnfiPd999F4Af/OAHCS8sHSmqSvln/wL7/HrC\nfX20/vAHaCMjU7pHjaWYC3dGz0Uq/sStqLbZcT5GouTUzQVAa5v6DJ4Q6SJ+hpEruw93BbCaLDjM\nDiJ6BF9obHbIbFIpdtvRga4p7DP6sLGb7z3yAR5fkKVzC/j6XWum3RQBHBiN6V5UUH/WCXbZzO1w\n8qn6W9F1aNF38mrjrlSXJIQQCTPpjNEDDzxAKBSiubmZSCTCggULMJsnvSxrqVYrlV/4G45959sE\nWo7S/oufUvnXf3PWiXKLP+wkZ0THV1VE3vrzE1xt9itdsgD/8+Ds75j8xUKkqfiMUf6J39CHIiEG\ngx5URSXf5k5FaQnhtuYxHB5mMOgh1+qMf7y00EHXwDCd/cNUlUyefvrye8d4dPNBdOCi5RXcc+0i\nzKaZpXvG9hctnmUx3WeysX4p246dyxH9Q37f9ASrquZT4HROfqEQQmSYSb+C7Nmzh2uuuYa///u/\n5+tf/zqXXnopO3fuTEZtacuc56LqS19FdTrx7dxB96OPoOv6pNcFOztwv7sfgF0XVMnTSANULJhL\nSDGTG/Lh6elPdTlCTJmu6/R6ojMkxe4TU+n6Rg92LbDlZ1U6mit+ltFEyXRn3mekaTq/2dzI/442\nRbdsrOMz1y+ecVMU1sIc6m8CojNGYswXL/o45kA+utXPf2z9TarLEUKIhJj0q8g//dM/8YMf/IAn\nnniCp556ih/96Ed8+9vfTkZtac1aXh6dKTKbGdjyCgObX5r0mu7fPYYS0dg3z84Bpy8JVWY/k9nE\nYF50g/XxXQ0prkaIqfMHwgwHItgsJpz2E2fjx5bRZcf+opgJk+liZxmd4ZDXQDDCj5/czeb3jmM2\nKfzFTUu56cI6Qx40NQ+2ENRCVDjL4rHiIspusfKZ5Xegayo95oM8tevtVJckhBCGm7Qx8vv9rFy5\nMv7rVatWEQgEElpUpshZuIiyz9wLQPdvH8X74fsTvta3dw++HR+i2GxsP7cAT3AIb1CaIyNESqIx\n3YOHDqe4EiGmrndc8MLJ39xn2+GuMbFkulPOMio8czLdoC/Iv/zvB3x4sAen3cxXP7WKDeeUG1ZX\ngyyjO6NV1XNZbr8AgJc7nqNtUGbphRDZZdLGyO12s3nz5vivN2/eTP4Uktiyneu8DRR99GOg67T/\n4mcMNzWd8ho9EqH7sejSg6IbbsI9+o18u0/2xRjBPmcuAOFpRKgLkWqx/UWnCwzozbLDXWPc1tHI\n7uDJS+lGZ4xOE77Q1uPjn3/9Hs3tQxS77fzD3WtYVGvsTNoBieme1F9ccAO2QCmYgzy2Y/PkFwgh\nRAaZtDH6x3/8R372s59x3nnnsX79en7605/yrW99Kxm1ZYzCG27CddFG9GCQtv/vPwj1dJ/w+YE/\nvUqwrQ1LSQn5V11N1ehBr22+zlSUm3WKF0f3Ath721NciRBTd6bGqCc+Y5RlS+nie4xOXEpX5LZj\nUhX6hwIEQpH4x/cf7ec7m96nZ3CEugoX//eetVQUGbv5fzg8zNGhY6iKSn1+naH3ziZm1cRVtZcC\ncHh4L2EtcuYLhBAig0zaGNXV1fG73/2OV199lS1btvD4448zb968GQ/c29vLpZdeSnNzMy0tLdxx\nxx3cddddGdl0KYpC2V1/Rs6Sc4gMeWh98AdE/NFlchGvl96nngSg5JO3oVqsVOSONkZe+UbeCFVL\n5hNBwTUygN/jTXU5QkxJ7xnOMOobnTEqzrKldGNnGZ04Y2RSVYrzo7NG3aOzRlt3t/Pvj+3AHwiz\nemEJf3vHubicVsNrOtjfhKZr1LlqsZunH/c9G1y1+FyUkAPd6ufVhtkdxiSEyC4T5m7ffffdZ9zM\n+utf/3rag4bDYb75zW9it0e/+DzwwAPcd999rF27lm9+85ts3ryZK6+8ctr3TwXFbKbi83/Nse/+\nM8G2Vtoe+hHVX/4qvU8/ieb34Vi8BOeq1QBUyoyRoawOG4M5hRT6e2ndc4gFF6xKdUlCnLWewdMn\n0sHYHqPCbAtfGN1jdHIqHUSX03X2+ens9/NBYzdPvdkMwNXravjkZfWoamLSPGMx3Ytkf9GkzCYT\ndbalNGnv89qxd7lqyepUlySEEIaYsDH64he/mLBBv/e973H77bfzs5/9DF3X2bdvH2vXrgXg4osv\n5q233sq4xgjAlJND1Ze+Qst3vs3wgf20/fiH+PbuAUWh9LY74o1mxWhj1O7rOOHkdzF9weIKaOml\n7+AhkMZIZJBez+kPdx0JB/CGfJhVM67RPTnZwjUule7k/wOjkd29/GbzQfqHAigK3HHlQq5YU53Q\nmmR/0dTcvGQj/7H3ffrVI/R6PRTlSoqfECLzTbiUbv369SxatIj6+nrWr1/P+vXrAeK/nq4nnniC\noqIiLrzwwvjZP5qmxT/vdDoZGhqa6PK0ZykqpuqLX0GxWvHt3gWahvuSy7BV18Rf47LmkmtxMhwe\nYSAwmMJqs4e1Zg4AgZajKa5EiKmZaCldPJHOXoCqzOx8nnRjN9uwm2yEtTDD4RODFsoKozNn/UMB\nrBaVL35sRcKbov6RATr9XdhMVua6aia/QLCgrBJ7oAxF1Xhy95upLkcIIQwx4YzRvn37+NznPsd3\nvvMdLr74YgC2bt3KV7/6VX7xi1+wePHiaQ34xBNPoCgKW7dupaGhgb/7u7+jv38s8tPn8+FyTf7k\nqaAgB7PZ+AMPS0oMeDJbspycr93HgQe+h9npZNG9d2NxnXjfOQVV7O1qxGceZGFJenwhNuS9p0jt\n6qV4tz6HrbdjWu8jk9/7TMzW9w3p8d79IyF8I2GsFhPz5hSeMHNyNBhNuKxwlRheazq894IcN+1D\nXZicGiXusXpWLirj4ZcaKciz8Y17z6e+xtgU1NO9973NewA4p2wR5WXZmbqaiD/zi2rPZ3PnH9gz\nuIuSkk8Zfn+jpMPf91SR9y7E1EzYGH3ve9/j3/7t3zjvvPPiH/vKV77C2rVr+e53v8t///d/T2vA\nhx9+OP7ze+65h29961v8y7/8C9u3b2fdunW8/vrrnH/++ZPep3+Sk9Gno6Qkj+5ug2ar5i2m9v5v\noNodDAQUOOm+xdZioJH9rc1Um+cYM+YMGPreU8A9dw5DgMvbS9vxXiy2s9+cnenvfbpm6/uG9Hnv\nx7uiYSFFLhs9PScGhzR1tgGQZ3IbWmu6vPdcUy7QRXNHO7ZgbvzjhTlmvnbbKqpLcnHZTUl579uP\nRhujec66tPi9MVqi/syvmr+Gza3PE7L28eL7O1hTO9/wMWYqXf6+p4K8d2PfuzRas8OE6zM8Hs8J\nTVHMxo0bT5jhMcLf/d3f8cMf/pDbbruNcDjMtddea+j9U8U+tw5r+ekPHxzbZyQBDEZwunPx2NyY\n0GjdJwe9isxwxjOMxi2ly0Zjkd0nBjAoisLSuYUJSZ47HV3X5WDXacq12ylTo8clvHBQltMJITLf\nhDNG4XAYTdNQ1RN7J03TCIVChgw+Ptlu06ZNhtwzU1RJZLfhRgrLcbcP0t1wiLnnLkl1OUJMKp5I\n5zrD4a5ZFtUdEwuU8ART+0S73deJJziE25pHeU5pSmvJRNfMv5BfN++nPdLIcDCIw5qchlYIIRJh\nwhmjdevW8aMf/eiUjz/00EMsW7YsoUXNBhXOMgDa/V1oujbJq8XZMI0GXIwcOZLaQoQ4S/FEOpkx\nSpnxMd2SEDp16+bUYwq6wRzi2b3vpLocIYSYkQlnjO677z4+97nP8cwzz7B8+fJ4rHZhYSE/+clP\nklljVnKYHRTY8ukPDNA93EtZTkmqS8p47vp5sB1MXW2pLkWIs9ITT6Q78QwjXdfpHR5tjLJ0xsgd\nj+xObWPU0De6jE5iuqdFVVWW5q1kd+B13ul8j1vZmOqShBBi2iZsjHJzc3nkkUfYtm0b+/fvR1VV\n7rzzzvh5Q2LmKnPL6Q8M0ObtkMbIAFXLFtEFuLzdRCIRTCbjUwuFMNJEe4y8IR8jkQB2kx2nOScV\npSXc2CGvqVtKF9EiNA5E0/8WFdanrI5Md8vyjex69w38lnaaezqpKy5LdUlCCDEtZzwcQ1EUNmzY\nwGc/+1k+/elPS1NksMrRAIY2X0eKK8kO+WVFeC1OrFqYjkMtqS5HiElNdIZR6+jew8rcsqxd3uVK\ngxmjZk8LwUiQ8pxS8m3ulNWR6cpcbtyRWhQF/rD3jVSXI4QQ05ZdpwZmmPg+I680Rkbx50c3T3ft\nO5jiSoQ4s0Awgnc4hNmknpLAdtwbXQ5alVuZitKSIrbHyBPwxA/7TrbYMrpFkkY3YxdXR1NsDw3v\nIaxFUlyNEEJMjzRGKVQZS6aTyG7DKJXRAAZfc3OKKxHizHpiwQsuG+pJs0Jtow9LqnIrkl5XsthN\nNqyqhaAWYiQSSEkNB/oPAbBEGqMZu2rxuSghB7rVz6uNu1NdjhBCTIs0RilUnlOKgkL3cA+hiDER\n6LNd7rw6AJSO1hRXIsSZ9caiuk+TSDc2Y5S9jZGiKLjGzRol23B4hCOeFlRFpT5/XtLHzzZmk4m5\n1qUA/KllW4qrEUKI6ZHGKIUsJgulOcVoukaHvzvV5WSF8qXRJ7+5g11oWvJj0L19g2z7r9/i93iT\nPrbILBMFL4S1MB2+LmBsH2K2SmUy3aGBJjRdY66rBof51OZUTN3NS6OJdH3qEXq9qU0bFEKI6ZDG\nKMUqRr/xaZcABkOUzKlkxGTFERmh93jylyh++NB/UvjW83z48O+TPrbILL3xxujEqO5OfzcRPUKx\nowi72ZaK0pImlcl0B2L7iySm2zALyyqxB8pQVI0n92xNdTlCCDFl0hilWHyfkQQwGEJVVYZc0QCG\njr2NSR3b7/FS0LIPgMixo0kdW2SenkkS6aqzeBldTCpnjGL7ixbL/iJDrS1ZDcDu/h0prkQIIaZO\nGqMUk8hu4+nl1QB4Djclddy9z7+KVQsD4BzoREtR0pbIDPGldK7TN0bZvL8oJpZMN5jkPUYDgUE6\nfJ1YTVbmumqSOna2u2nZBoiYCdv6eb/lcKrLEUKIKZHGKMUqRyO7ZcbIODlz5wKgtR1P6rihmkOJ\nHQAAIABJREFU7W/Ff54X8tFxrCup44vM0us584zRbGiMXNboUjpPMLlL6Rr6orNFC/LnYVYnPOdc\nTEOu3U4Z0cNyXzj4ZoqrEUKIqZHGKMWKHUWYVTP9gQGGwyOpLicrlC6JLo3JGUjeHqPWA00UD7YT\nVM0M5hYDcHx3cpfyicwRDEXw+IKYVIX83BP3Ec2GM4xiUjVj1BBbRldQn9RxZ4ur6y8EoD3SyHAw\nmOJqhBDi7EljlGIm1URFTnRPjAQwGKNi4VxCipm8oBdPT39Sxmx+YTMAfTVLoSYaGe45lNylfCJz\nxGaLCl02VHXsDCNPcIihoBe7yUaRvSBV5SVNbMYomXuMdF2PBy8sLlyYtHFnk/VzFmAKuMEc4rm9\n76S6HCGEOGvSGKWBCglgMJTJbMKTWwRA6+6GhI8XDoVwNnwIQMUVl+Oqj56JorcndymfyBy98eCF\nExPpYsvoKnMrUE469DUb5cfPMUreUrpOfxeDQQ951lwqRpcyC2OpqspS10oAtnW+l+JqhBDi7Elj\nlAbGAhiSHy+drcKlVQAMHEz85t/9m9/CGR5m0J7P/PNXUHFO9Cl07mAngVAk4eOLzDPRGUazKZEO\nwGF2YFbNjEQCjIQDSRnzQF9sGd2CWdF8psotyzeiawp+SzvNPfK1TQiRGaQxSgNjkd3tKa4ke9jn\nzAEg3Jr4WZuBN16PjrViHaqqkjunFk1RKQx5ONrSk/DxReaJBy9MkEhXOUsaI0VRcMcDGJKznO5A\nf3Tv3yKJ6U6oMpcbd6QWRYE/7H0j1eUIIcRZkcYoDYyP7NYl4tkQRYvmA2DrTWyz2dfWRUlXExoK\ni268CgDVYmHYVYwCtO6RAAZxKpkxGjMWwJD45XRhLcLB/ujePwleSLyN1esBODS8h7Ams+dCiPQn\njVEayLe5sZvs+EJ+hkLeVJeTFaqXLiCCgntkgOEhf8LGaXhuMyo63aXzKKwsjX9crYyepTTU1Jyw\nsUXm6hkcBk6M6g5rYTp8XSgoVIw+LJkNXKOHvCZjxuhw3xFGIgHKckoosOcnfLzZ7urFq1FCDnSr\nn9cad6e6HCGEmJQ0RmlAURQqc+U8IyNZHTY8OYUoJC42W9M0TDvfBcB90cYTPueuj85Y0SEBDOJU\nvaeZMer0dxPRIxQ7CrGbbRNdmnXctlgyXeJnjHZ3HgBgUYEso0sGs8nEXOtSAF5r2ZbiaoQQYnLS\nGKWJ8cvphDECxdHlSH0HDyXk/off3UX+yAB+s4MlV15wwudKlkSX6eR7e+gfSs6mcpEZQmGNAW8Q\nVVEoyBtrgI4PzZ7zi8ZzW5N3llGsMVpcKMvokuWmpRcB0KceodcrKyKEEOlNGqM0EYvsbpcZI8NY\na2oBCLa0JOT+HS9vAWBo4SosVusJn3PU1qIDJYEBmo71JWR8kZn6RoMXCvJsmNSx/4JbfdH9RVW5\ns2cZHYArSXuMRsIBGnuaUFBYWDA/oWOJMYvKqrAFSlFUjSf3SAiDECK9SWOUJiSy23iFC6Lf/Fh6\njA9g8Hu8FBzbB0DddVee8nnV7iCYV4gJjfYDiY8MF5mjJ5ZId3LwwlCsMZptM0bJSaU7NNBERNeY\n66rBYXZMfoEwzNqSNQDs6d+R4kqEEOLMpDFKE+OX0mm6luJqskPVsuh5Qi5/L6FA0NB773v+Naxa\nmB5XBdVLTv/02VRVA4BXAhjEOGOHu57UGPlmXyIdjE+lS2xjdKD/ICAx3alw87INEDETsvXzQYs8\nKBJCpC9pjNJErtWJy5pHMBKkb2Qg1eVkBWd+HoM2F2Zdo3WfsV+Mg9u3AmBZt2HC1+SPzlgpnW1o\nmsSwi6hYIt344AVPcIihoBe7yU6hvSBVpaVEfI9RgsMXGuIHu8r+omTLtdspI/r7/sLBrSmuRggh\nJiaNURqJzRq1SwCDYUYKo0/fuxuMC2BobWimeLCdoGJm6fWXT/g6d/08AIqHe2jt8Rk2vshsp0uk\nG1tGV46iKCmpK1WclhxMionh8DDBSCghYwwGhmjzdWAzWZnrnpOQMcSZXTU/GlDTFmlgJGTsDL4Q\nQhhFGqM0UiGR3YYzVUXPExo5etSwezY//zIAfbVLcLpzJ3ydrTYa/lAW6Odwa79h44vMFjvctdg1\nrjHyzc79RRA9rsCV4H1G2zs/AGBJST0W1ZyQMcSZnTd3IaagC8whnt3zTqrLEUKI05LGKI1UOqOz\nGxLZbZzYrI2pq9WQ+4VDIZwNHwJQfsVlZ3ytOc9F2OnCqodpbzSuMROZrXc0fKEofywA4PjQ7Eyk\ni3EnMJluKOjlxSOvAHDtgksNv784O6qqsjRvJQDvdL6f4mqEEOL0pDFKI3LIq/Eqly0CwDXUTSQS\nmfH99m9+C2d4mEF7PvXnr5r09ebRAAZ/85EZjy0yXzii0T8UQFGgcNwZRq3e2XmGUUwsmW4wATNG\nzzb9keHwCEsLF3FuxTLD7y/O3keXXYSuKfgsbTT3SAKrECL9SGOURspzoo1Rp7+biDbzb+IFFJQX\n47PkYNXCdBya+XlGA2++DkB4+TpUdfJ/PvkLojNWlp52hgPhGY8vMlvfUABdh/xcG2ZT9O9PWAvT\n4e9CQaFyls4Yxc4y8hg8Y3R8qI2tbe+iKiofX3DjrNu/lW7K3QW4I7UoCvxhn5xpJIRIP9IYpRG7\n2UaRvZCIHqHT353qcrKGLz/acHbtOzij+/S191Dc2YSGwqKbrjqra3LmzgWgLNBHc3ti44hF+jtd\nVHeHrwtN1yhxFGEzWSe6NKuNJdMZ929E13UeP/g0OjqXVF1AubPMsHuL6buoaj0Ah/x7CMsDQCFE\nmpHGKM3EnhhLMp2BKqIBDL4jR2Z0m4ZnX8KETnfpPAorS8/qGlttNAGrLNBHU+vgjMYXmS8W1T2+\nMWr1xvYXza7zi8Zz20aX0hl4ltGO7j0cHGjCacnh+rpTD2EWqXHNktUQsqNb/bzWuDvV5QghxAmk\nMUozYwe9yvpro+TV1UV/0nF82vfQNA3Tzu0AuC+86KyvMxcWodkc5GgB2prapj2+yA6njeqWxmhc\nKp0xS+lCkRBPHnoWgBvrriHHkmPIfcXMmU0m5lqXAvDaUUmnE0KkF2mM0kzl6HKPdglgMEz5OQsB\nyB3oQtO0ad3j8Lu7yB/px292sOSqC8/6OkVRsNZEY7v9R5rRdTnodTYbW0o3lkgnjRG4bW7AuBmj\nV469Qe9IP5XOci6sXG/IPYVxbl66EYA+UzO9Xm+KqxFCiDHSGKWZytFvjlplKZ1hSuZWMmKykhMZ\noff49GbiOjZvAWBowSos1qntA8mdF52xyhvqjp9hI2anHpkxOq34UjoD9hgNBAb549Hov9dPLLgZ\nk2qa8T2FsRaVVWELlKKoGk/teTPV5QghRJw0RmmmNKcYVVHpHe4jEJHTwY2gqipDruieoI69jVO+\n3u/xUtCyD4C666e+V8E+Z9w+ozYJYJjNTj7cdTAwxFDIi8Nsp9BekMrSUirX4kRVVHwhPyFtZumN\nTx9+kWAkyMqSZSwqrDeoQmG0NSWrAdjdvyPFlQghxBhpjNKMWTVTllOCjk6H7DMyjFZWBYDncPOU\nr933/GtYtTA9rgqql8yf8vW2GmmMBES06BlGAIWjjVHb6GxRpbNiVkdJq4o6ts9oBpHdzYMtvNPx\nPmbFxMfqbzCqPJEANy/bABEzIVsfHx5rSnU5QggBSGOUluIBDLLPyDCx2Gyt/diUrw1ufwsAy9oN\n0xrbWl6ObrHiDvs4flT+TGer/qEAmq7jzrViMUf/6z0eP9h19i6jixkLYJjewwNN13j84NMAXF57\nMcWOIsNqE8bLszsoJTqj90KjLKcTQqQHaYzSUCyyu032GRmmdMkCAHL6pzYL19rQTPFgG0HFzNIb\nLp/W2IqqYquKRoYHjh0jHJleAITIbKc7wyi2v6haGqNx+4ymN2P0XucOjnhacFnzuGbOZUaWJhLk\nqvkXANAaaWQkJEvHhRCpJ41RGqpwxs4ykqV0RqlcVEdIMZEX9OLp6T/r65qffxmA/polON250x7f\nUTcXgOLhHo51SQrTbNRzpkS6PGmMXLFDXqeRTDcSDvDUoecB+Mj867Cb7ZNcIdLB+XMXYgq6wBzk\n2T3vprocIYSQxigdyVI645nMJjy5xQC07m44q2vCoRDOhujG4LIrZvYE2j5un9FhOeh1VoqfYTS6\nvyikhenwd6GgxB+GzGZuW7Qx8kyjMXq55TUGgx7m5NWwvny10aWJBFFVlSV5KwF4p/O9FFcjhBDS\nGKWlIkcBVtXCYNCDL+RPdTlZI1xaCcDAobPb6Lvvlbdxhv0M2t3Ub1g1o7FttdHGqDzQR1O7BDDM\nRj2eE5fSdfi60HSNkpwibKapRcBnI7d1ekvpeof72NzyJwA+sfBmVEW+rGWSW5ZdhK4p+CxtHO3t\nTnU5QohZTr6CpCFVUeNPkGXWyDix5iR8/OwCGAbfeB2A0PJ1qOrM/qlYq6pAVSkMeWg51juje4nM\ndPIeo1giXZVTltHB2IzRVM8yevLw84S1MOvKzmWee04iShMJVO4uwB2pQVHgqb2vp7ocIcQsJ41R\nmqrILQOgXQIYDFO0KJqAZOttn/S1fe09FHceRkNh0Y1Xz3hs1WLBWlmJAiidbQz5ZaPxbNMzOAyM\nHe46lkhXmbKa0ol7GnuMDvYf5sOuXVhVCx+Zf12iShMJdmHVegAO+vcQ1iIprkYIMZtJY5SmYvuM\nWqUxMkz1OfVoKLhHBhgeOvMSxYZnX8aETk9JHUVVpYaMb6+dC0T3GTXLcrpZRdN0+jzRM4yK4mcY\nRf9tV0vwAgAu29TOMdJ0jd+NxnNfPecyCuz5CatNJNa1S9ZAyI5u9bH5gBz4KoRIHWmM0lQssrtd\nltIZxuawM5hTiAIc39s44es0TcO0M5qQlHfRRuPGH13KVxaUg15nmwFvgIim43JasVpM6LoenzGq\nlKV0AORZclFQ8IZ8RM5i1uDttu20etspsOVzRe0lSahQJIrZZGKhIxrC8NLRLWiaHGkghEgNaYzS\nVDyZzteJruspriZ7BIui34T2NR6e8DVN7+4mf6Qfv9nO0qsuNGxsW20tMJpMJ43RrNLrOTGRzhMc\nwhvy4TDbKZSZDgBMqok8ay46Op5JAhj8oWGebnoRgFvqb8BqsiSjRJFA96y5BsIWArZutjTuSnU5\nQohZShqjNOWy5uE05zAcHp7yZmQxMUtNtDkJthyd8DXtm18BwLtgFRarcWlhttGxSwIDHD3ejyYN\n76zRc1LwQuz8okpnBYqipKyudBNLppusMXrhyGa8IR/z3XWsLl2RjNJEghU4c6m3RdM/n2/enOJq\nhBCzlTRGaUpRlHgAQ6sspzNMwYJ5AFi6Tx/AMDzkp6BlHwBzr7vK0LFNDgeWsjJMaOR4e+nskyj2\n2SLWGBWd1BjJ/qITuWyTBzB0+rp47fhWFBRuXXizNJZZ5J4110HETMDWxZaGnakuRwgxC0ljlMZi\new8kmc441csXAeDy9xIKnpoMt/f5LVi1MD2ucqqXzjd8fNu4g15ln9HscXJUd6wxqsqVxmi8eDLd\nGWbJnzj0LJqusaFiHTV5VckqTSRBUW4u8yzRvUbPNcmskRAi+aQxSmOVozNGcpaRcZz5eQzaXJh1\njdZ9p+4zCr77FgCWtRckZHz7uH1G0hjNHr2jUd3SGJ2ZezSZbnCCZLq9vQ3s6T2A3WTnpvnXJLM0\nkST3rI3OGo3YOvnTwd2pLkcIMctIY5TG4oe8yoyRoUYKo7+vPQdObIxaG5opHmwjpJhZesPlCRk7\nnkwX6OVw22BCxhDpJ76UzmUnpIXp8HehoMRDVkSUa3TGyHOaGaOIFuH3B58B4Lq6K3CN7kcS2aUk\n10WdOTpr9Mzhl1NcjRBitpHGKI3Fvmnq8HWi6RJfahRTVQ0Aw0ePnPDx5uejX4R7axbjdOcmZOxY\nY1Qa6Od4p5dASA4zzHaartMbO8PIbafD14Wma5TmFGM1GRfukQ3ONGP0euvbdPq7KHUUc2m1cWmR\nIv3cs+Za9IiZYWsHbxzal+pyhBCzSNIbo3A4zN/+7d9y55138slPfpItW7bQ0tLCHXfcwV133cW3\nvvWtZJeUtnIsDvJtbkJamJ7h3lSXkzVc86MBDKau1vjHwqEQzobowYLlVyRmtgjA7HJhLijApodx\nBz0c7Ti7wyxF5vL4goQjGrkOC3armdbY+UWyjO4Ubtvp9xh5gz6ea44+uPjYghsxq+ak1yaSp9Tl\nZq55OQBPH3opxdUIIWaTpDdGTz/9NAUFBTzyyCP88pe/5Nvf/jYPPPAA9913Hw8//DCaprF5s2y6\njBl/npEwRuWy0QCGoW4ikeiMzb5X3sYZ9jNod1O/YVVCx4/FdpcFemWf0SwwYSKdNEaniIUveE5K\npXu2+SWGw8MsKVzIsqIlqShNJNk9q69Fj5jwW9t4q+lAqssRQswSSW+MrrvuOr70pS8BEIlEMJlM\n7Nu3j7Vr1wJw8cUX8/bbbye7rLRVEQ9gOH28tJi6wopifJYcrFqYzkPHABh843UAQsvWoaqJ/Wcx\nts+ojybZZ5T10imRLhQME4mk77LcPGt0Casn6I0vH271tvNm6zZUReXjC26SeO5ZotxdQK1pGQBP\nNf4xxdUIIWaLpDdGDoeDnJwcvF4vX/rSl/jKV76CPu6gS6fTydCQLC+KqRqN7JYZI2P58qMNZ+f+\nRnqOd1HceRgNhUU3XZ3wsWONUXmgj6Z2mTHKdj2jiXRFLju6rqesMRro87PpoW088vNtSR13Ksyq\nmVyLEx2doaAXXdd5vPFpdHQ2Vm2gwlmW6hJFEt2z5nr0iAmftZV3mhtTXY4QYhZIyULt9vZ2vvCF\nL3DXXXdxww038P3vfz/+OZ/Ph8vlmvQeBQU5mM0mw2srKUmvpKOlpnmwH7pGuhJeW7q990Sy1MyB\n7maCx4/x3mPPkodOd9l8Nq4y/uyik+WtWko7UB7sp29wBNVqpsjtSPi4pzOb/sxPlqz37g9GZz7m\nVuVjydPxhnw4LQ4WVtckbfZDi2g8/ZsdBEbCHDnUi6JBcVl6/tkX5eTjHfSh5kQ44m+iceAwuVYn\nf7b2FnJtzhnff7b+nc/E911SkkfdzhUcCX/IUwf/yI3r10z7PrOVvHchpibpjVFPTw/33nsv3/jG\nNzj//PMBWLJkCdu3b2fdunW8/vrr8Y+fSX+/3/DaSkry6O5Or9kqWyQXBYX2oS7aOvuxJGjTcTq+\n90Sy19TABxA6dhTFH33fuRdclJTfA12xo+Y4cfh95EX8bN/dzppFJQkf92Sz7c98vGS+92Od0VlB\nu0lh59GDQDSKv6fHm5TxAd7feoTWloH4r9/Z2sz6jXVJG38qckzR5udQ+3GeOvQcADfMvYphj8Yw\nM/szm61/5zP5fd++4hq+895OPOZjPL/9A9bNXTCl6zP5vc+UvHdj37s0WrND0pfS/exnP8Pj8fDQ\nQw9x9913c8899/DlL3+ZH/7wh9x2222Ew2GuvfbaZJeVtqwmCyU5RWi6RqevK9XlZI2ypQsBKOo9\nRv5wP36znaVXJScCWFEUbCcc9Cr7jLJZ77jwhVgiXVVuZdLG7+4Y4r2tRwFYdV40qv7Q/q4TljCn\nk1gy3bPNL9Ez0kels5wLK89LcVUiVarzC6lWlgLw+wOy10gIkVhJnzG6//77uf/++0/5+KZNm5Jd\nSsaodJbT5e+hzddBdV7yvqHKZqV1VXSpVuxaEADvglVYrMk7U8ZeO4fhA/tHGyPZZ5StdF0fa4xc\ndlq7Y/uLknOwazgU4ZVn9qNpOsvXVHHeJXU07u1ksG+Ynk4vJeXp9wQ0lkzXMbqv8uMLbsKkGr9s\nWmSOu869nu9+sI8hawvvtxxmTW3ilzwLIWYnOeA1A1TEIru9HSmuJHuoqsqQqzT+6znXXpnU8cfP\nGB3pGCKipW9SmJi+IX+IYFjDaTeTYzePi+pOzgOObX9qor/XT35RDuddOg9VVVm6Ijr2of3pOQPt\nso01ayuKz2Fx4dSWTonsU1tYTCXRmPbH97+Y0lrCkQiBUCilNQghEkcaowxQOfp0ud0njZGRtLIq\nAPpc5dScU5/UsWPJdBWhfgKhCK3dvqSOL5KjZ9xsUUgL0+nvRkFJSrra8SP97H6vFVVVuOLGxVgs\n0VmXZeeONUbpuJwuf3TGyKyYuKX+hhRXI9LF3edej64pDJqPsuNYU0pq6Pf5+NpL3+e+Lf9Ix2B/\nSmoQQiSWNEYZQA55TYw5115Bn7OEqjtvT/rY1vIKFKuVvKAXe2REYruzVK9nbH9Rh68TTdcozSnG\nakrsss3ASIgtz0UPxVxzwRxKK8aSPmvmFpLrsuH1BOhoTb+/dwsL5lPnmsPHF9xMaU5xqssRaWJO\nUQnlLEZR4Lf7kj9rFAyH+M4bvyBk6wNLgMd2bkl6DUKIxJPGKAOUOIowKyb6RvoZDo+kupysUbt8\nIec/+H1WX3tR0sdWVBVbdXQjfFmgX/YZZanYGUbFbgfHk3h+0ZsvH8I3FKC0Io/VF9Se8DlFVZi/\nOLqM9NC+9HvYkmPJ4Wtr/5qLqzekuhSRZu5eFZ01GjAfYdfxI0kbV9M0vvvqJvzWNnQt+m1T4/BO\nWVInRBaSxigDmFQTZc7oNzLtMmuUNWLL6coCvdIYZamecYl0bfHGKLH7iw4f6KJxbydms8oVNy1B\nVU/9b37B0tLR13ajyf42kSHqisso0xehKPBYEmeNfvLWM3SaDqBrKrfW3okazAPLCE/tfitpNQgh\nkkMaowxR6Yw+ZW6XAIasEQtgKA/2097jYzgQTnFFwmixRLpit33cjFHiEul83gB/erERgA2XzSe/\nMOe0rysuy8Vd4GDYH6L16MBpXyNEOrprdNao39TE3raWhI/3+Idvsi+4FYBLCq/jsoXLWemOHjS7\nrXNbwscXQiSXNEYZojI3ulm7TQIYsoZ9dMaoOjyADjTLPqOsE2uMCvNs8TOMEpVIp+s6rz3fQGAk\nTE1dAeesnngcRVGoXxpbTpee6XRCnM78knJK9QUoCjy654WEjvXGoX1s6X0WgEXm8/nU6ksAuHXV\nJRAxE7T18nbTgYTWIIRILmmMMkSlRHZnHWtVFZhM5A33Y9FCHJbldFlF13V6RsMXrM4QvpAfh9lB\nvs2dkPH27WijpakPm93MpdcvRlGUM75+wZJoY9TU2E0kLMvpROa4c0V01qjX1MT+jmMJGaOhs5VH\nm36DomoURxbyhYs+Gv+c2+Gk2hSND3/u0GsJGV8IkRrSGGWI+FlGMmOUNVSLFWtFJQpQEhigWRqj\nrOIbCRMIRnDYTPQFo7My1bkVkzYs0zHQ5+etLYcBuPiaheTm2Sa9pqDYSVGpk2AgQktTn+E1CZEo\nC8oqKdbqURSd/91l/F6jLs8gP/rwP8EcxBEs5x8u/bNT9up94pwr0HXoU5s53tdjeA1CiNSQxihD\nFNrzsZtseEM+hoLeVJcjDGKP7TMK9NLUNpiW58qI6Ykl0hW57PEHGolIpNM0jS3PHiAc0qhfWkr9\nktLJLxq1YGl0iW66HvYqxETuWHEduq7QYzpEQ2erYfcdDgb57tafo1m9mIIuvn7R57BZLKe8bkFZ\nJXmhGhRV57Fdrxg2vhAitaQxyhCKoozNGslyuqwRS6arigzg8YfiKWYi840FLzhoTWBU94fbjtHZ\n5sGZZ+XiqxdM6dr5i0sAOHKoh1AwYnhtQiTK4vJqiiLzUBSd3+wyZq+Rpmn882v/RcDWDSEbX1n7\nFxTl5k74+qvmbgSgKbib4WDQkBqEEKkljVEGkQCG7BNrjKoj0WQwie3OHuOjuhN1hlF3xxDvvXkE\ngMuuX4zNfuqT7TNx5Tsoq3IRDmkcOSTLgURmuX359eg6dCsHOdjZNuP7/eD139FvbkKPmLhn4d3U\nFZed8fWXL1yBKeAGc5DHd74x4/GFEKknjdEoXdcT8sNIschumTHKHraa6FK6PG8vqq5xuG0wxRUJ\no8Qao/w8M13+bhTGZn2NEA5HeOXZ/WiazrLVVdTUFU7rPrGld5JOJzLN0ooaCiPzUFSdR2Y4a7Rp\n+2aatPfRdbiu/KOcV7dw0mtUVWVN4XoA3ut5V84EEyILmFNdQDrQNI0//GYHHceNf1o/Z34h131i\nuSEbrmMzRk2eo0S0CCbVNON7itQyORxYSssIdXVSHByguS0/1SUJg8SW0plyfGh+jbKcUqymqc3o\nnMk7f2qmv8dPfqGD8y+bN+371C8u4a1XDtHS1EdgJDTlWSchUum2c67joQM/pktp5HB3B/NLpv7w\n4eX9H/D24MsoKqxyXMJNy88762s/vnIj777+GmFbP28c3sslC5ZPeXwhRPqQGSPg4L6uhDRFAEcP\n9xmW+FSbV0OeJZcOXyePNjwpG/WzROyg17JAH0c7vYQkOjkrxGaMwtboMslqA5fRHT/Sz67tx1EU\nuOKmJVgs039IkpNro7I2H03TaWqQ5XQisyyrmkNBuC46a7Tj+Slfv+P4EZ46/jiKqlOlL+dzF9ww\npetz7XbmWJYB8GLT61MeXwiRXmb9jJGm6bz/1lEAPnLbKirnGvfEfsc7x3j71cO8t/UItfMKZzxr\nZDfb+D8rPs2DH/6Ut9rfpdhRyDVzLzeoWpEq9to5eN/bzjzFw+6IxrEuL/MqXakuS8xQryeaSufR\negGoNKgxCoyEePX56KGSay6cS2nFzP+u1C8tpfXoAIf2d7FkpfEBEUIk0q3nXMfPGx6iQ2mguadz\n0r1BMcf7evjl3l+BJUxesIa/vfqOaY3/qRVX8r0dOxg0t0xpfCFE+pn1M0aH9ncx2DeMK9/O8tVV\nht77nHMrsedY6Gob4lhzvyH3rHPX8umlt6Og8HTTi7zXucOQ+4rUic0YVYZjAQyyzyjT+UdCDAci\n2Cwmukc6AeNmjN58+RBeT4DSijxWb6g15J7zFpagqgqtR/vx+yRdS2SWVdVzcYfnoKgBlCs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UuXWrsRl6PxIu4x2bEpjbqaZmKvCryoleM1GruLet6upFYraWpooay4jqbG1s6hK91FLpMzyD2K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\nmD9/Ph4eHiQmJpKfn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('RAP')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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temp_airwind_speedghidnidhitotal_cloudslow_cloudsmid_cloudshigh_clouds
2016-07-27 07:00:00-07:0025.9107972.757028107.23002327.94315299.46838878.00.050.078.0
2016-07-27 08:00:00-07:0026.0738831.539441195.67826331.685784180.48656484.014.048.084.0
2016-07-27 09:00:00-07:0027.0345762.107574409.233831164.283273300.75578054.014.040.054.0
2016-07-27 10:00:00-07:0026.7735901.335156526.891322170.772024388.90461552.00.052.014.0
2016-07-27 11:00:00-07:0026.5235901.463499604.803516164.680864454.53407552.00.052.06.0
2016-07-27 12:00:00-07:0026.1146551.602453556.56765588.828571470.70649066.00.066.00.0
2016-07-27 13:00:00-07:0026.6647031.682302632.624638148.998629488.59766454.08.054.00.0
2016-07-27 14:00:00-07:0027.1114812.301865806.810775486.540430362.79852318.06.018.00.0
2016-07-27 15:00:00-07:0029.3480532.814540609.770902299.440687367.78116036.08.036.012.0
2016-07-27 16:00:00-07:0031.2967831.871677614.248207722.774692136.9374204.00.00.04.0
2016-07-27 17:00:00-07:0033.6495671.497542430.949886735.45401878.3778030.00.00.00.0
2016-07-27 18:00:00-07:0039.2682192.356843217.224161574.02187557.9403320.00.00.00.0
2016-07-27 19:00:00-07:0047.5968322.99469738.420206227.81686222.8328060.00.00.00.0
2016-07-27 20:00:00-07:0050.1051333.8589230.0000000.0000000.0000000.00.00.00.0
2016-07-27 21:00:00-07:0050.7257394.1505200.0000000.0000000.0000000.00.00.00.0
2016-07-27 22:00:00-07:0049.3487854.5291860.0000000.0000000.00000056.010.00.038.0
2016-07-27 23:00:00-07:0046.2272033.3760600.0000000.0000000.000000100.020.04.092.0
2016-07-28 00:00:00-07:0030.8607485.3038710.0000000.0000000.00000088.00.04.088.0
2016-07-28 01:00:00-07:0030.7345284.1837630.0000000.0000000.00000040.00.00.018.0
2016-07-28 02:00:00-07:0029.9745181.8173680.0000000.0000000.00000046.00.00.030.0
2016-07-28 03:00:00-07:0028.8556212.0983460.0000000.0000000.00000056.00.00.056.0
2016-07-28 04:00:00-07:0028.2538152.2465640.0000000.0000000.00000050.00.00.050.0
2016-07-28 05:00:00-07:0027.8912662.0138620.0000000.0000000.00000052.00.00.052.0
2016-07-28 06:00:00-07:0027.9991761.83335127.19480263.18631422.96776842.00.00.042.0
2016-07-28 07:00:00-07:0026.9842832.792719181.767920344.28556586.76596824.00.00.024.0
2016-07-28 08:00:00-07:0026.8773501.740562401.131780603.481897112.72906210.00.00.010.0
2016-07-28 09:00:00-07:0027.3602601.053696555.246730529.261416206.46266218.00.00.018.0
2016-07-28 10:00:00-07:0027.0609441.228584784.062655746.253192181.9127382.00.00.02.0
2016-07-28 11:00:00-07:0025.7352601.553703912.237618764.545024215.3585490.00.00.00.0
2016-07-28 12:00:00-07:0025.5587461.259921973.429728752.724436246.5591030.00.00.00.0
2016-07-28 13:00:00-07:0025.6446841.470278973.446199752.720904246.5680210.00.00.00.0
2016-07-28 14:00:00-07:0028.7574770.992960912.271953764.539141215.3749940.00.00.00.0
2016-07-28 15:00:00-07:0032.6268621.784865794.398445779.384573165.5088540.00.00.00.0
\n", + "
" + ], + "text/plain": [ + " temp_air wind_speed ghi dni \\\n", + "2016-07-27 07:00:00-07:00 25.910797 2.757028 107.230023 27.943152 \n", + "2016-07-27 08:00:00-07:00 26.073883 1.539441 195.678263 31.685784 \n", + "2016-07-27 09:00:00-07:00 27.034576 2.107574 409.233831 164.283273 \n", + "2016-07-27 10:00:00-07:00 26.773590 1.335156 526.891322 170.772024 \n", + "2016-07-27 11:00:00-07:00 26.523590 1.463499 604.803516 164.680864 \n", + "2016-07-27 12:00:00-07:00 26.114655 1.602453 556.567655 88.828571 \n", + "2016-07-27 13:00:00-07:00 26.664703 1.682302 632.624638 148.998629 \n", + "2016-07-27 14:00:00-07:00 27.111481 2.301865 806.810775 486.540430 \n", + "2016-07-27 15:00:00-07:00 29.348053 2.814540 609.770902 299.440687 \n", + "2016-07-27 16:00:00-07:00 31.296783 1.871677 614.248207 722.774692 \n", + "2016-07-27 17:00:00-07:00 33.649567 1.497542 430.949886 735.454018 \n", + "2016-07-27 18:00:00-07:00 39.268219 2.356843 217.224161 574.021875 \n", + "2016-07-27 19:00:00-07:00 47.596832 2.994697 38.420206 227.816862 \n", + "2016-07-27 20:00:00-07:00 50.105133 3.858923 0.000000 0.000000 \n", + "2016-07-27 21:00:00-07:00 50.725739 4.150520 0.000000 0.000000 \n", + "2016-07-27 22:00:00-07:00 49.348785 4.529186 0.000000 0.000000 \n", + "2016-07-27 23:00:00-07:00 46.227203 3.376060 0.000000 0.000000 \n", + "2016-07-28 00:00:00-07:00 30.860748 5.303871 0.000000 0.000000 \n", + "2016-07-28 01:00:00-07:00 30.734528 4.183763 0.000000 0.000000 \n", + "2016-07-28 02:00:00-07:00 29.974518 1.817368 0.000000 0.000000 \n", + "2016-07-28 03:00:00-07:00 28.855621 2.098346 0.000000 0.000000 \n", + "2016-07-28 04:00:00-07:00 28.253815 2.246564 0.000000 0.000000 \n", + "2016-07-28 05:00:00-07:00 27.891266 2.013862 0.000000 0.000000 \n", + "2016-07-28 06:00:00-07:00 27.999176 1.833351 27.194802 63.186314 \n", + "2016-07-28 07:00:00-07:00 26.984283 2.792719 181.767920 344.285565 \n", + "2016-07-28 08:00:00-07:00 26.877350 1.740562 401.131780 603.481897 \n", + "2016-07-28 09:00:00-07:00 27.360260 1.053696 555.246730 529.261416 \n", + "2016-07-28 10:00:00-07:00 27.060944 1.228584 784.062655 746.253192 \n", + "2016-07-28 11:00:00-07:00 25.735260 1.553703 912.237618 764.545024 \n", + "2016-07-28 12:00:00-07:00 25.558746 1.259921 973.429728 752.724436 \n", + "2016-07-28 13:00:00-07:00 25.644684 1.470278 973.446199 752.720904 \n", + "2016-07-28 14:00:00-07:00 28.757477 0.992960 912.271953 764.539141 \n", + "2016-07-28 15:00:00-07:00 32.626862 1.784865 794.398445 779.384573 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-07-27 07:00:00-07:00 99.468388 78.0 0.0 50.0 \n", + "2016-07-27 08:00:00-07:00 180.486564 84.0 14.0 48.0 \n", + "2016-07-27 09:00:00-07:00 300.755780 54.0 14.0 40.0 \n", + "2016-07-27 10:00:00-07:00 388.904615 52.0 0.0 52.0 \n", + "2016-07-27 11:00:00-07:00 454.534075 52.0 0.0 52.0 \n", + "2016-07-27 12:00:00-07:00 470.706490 66.0 0.0 66.0 \n", + "2016-07-27 13:00:00-07:00 488.597664 54.0 8.0 54.0 \n", + "2016-07-27 14:00:00-07:00 362.798523 18.0 6.0 18.0 \n", + "2016-07-27 15:00:00-07:00 367.781160 36.0 8.0 36.0 \n", + "2016-07-27 16:00:00-07:00 136.937420 4.0 0.0 0.0 \n", + "2016-07-27 17:00:00-07:00 78.377803 0.0 0.0 0.0 \n", + "2016-07-27 18:00:00-07:00 57.940332 0.0 0.0 0.0 \n", + "2016-07-27 19:00:00-07:00 22.832806 0.0 0.0 0.0 \n", + "2016-07-27 20:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-27 21:00:00-07:00 0.000000 0.0 0.0 0.0 \n", + "2016-07-27 22:00:00-07:00 0.000000 56.0 10.0 0.0 \n", + "2016-07-27 23:00:00-07:00 0.000000 100.0 20.0 4.0 \n", + "2016-07-28 00:00:00-07:00 0.000000 88.0 0.0 4.0 \n", + "2016-07-28 01:00:00-07:00 0.000000 40.0 0.0 0.0 \n", + "2016-07-28 02:00:00-07:00 0.000000 46.0 0.0 0.0 \n", + "2016-07-28 03:00:00-07:00 0.000000 56.0 0.0 0.0 \n", + "2016-07-28 04:00:00-07:00 0.000000 50.0 0.0 0.0 \n", + "2016-07-28 05:00:00-07:00 0.000000 52.0 0.0 0.0 \n", + "2016-07-28 06:00:00-07:00 22.967768 42.0 0.0 0.0 \n", + "2016-07-28 07:00:00-07:00 86.765968 24.0 0.0 0.0 \n", + "2016-07-28 08:00:00-07:00 112.729062 10.0 0.0 0.0 \n", + "2016-07-28 09:00:00-07:00 206.462662 18.0 0.0 0.0 \n", + "2016-07-28 10:00:00-07:00 181.912738 2.0 0.0 0.0 \n", + "2016-07-28 11:00:00-07:00 215.358549 0.0 0.0 0.0 \n", + "2016-07-28 12:00:00-07:00 246.559103 0.0 0.0 0.0 \n", + "2016-07-28 13:00:00-07:00 246.568021 0.0 0.0 0.0 \n", + "2016-07-28 14:00:00-07:00 215.374994 0.0 0.0 0.0 \n", + "2016-07-28 15:00:00-07:00 165.508854 0.0 0.0 0.0 \n", + "\n", + " high_clouds \n", + "2016-07-27 07:00:00-07:00 78.0 \n", + "2016-07-27 08:00:00-07:00 84.0 \n", + "2016-07-27 09:00:00-07:00 54.0 \n", + "2016-07-27 10:00:00-07:00 14.0 \n", + "2016-07-27 11:00:00-07:00 6.0 \n", + "2016-07-27 12:00:00-07:00 0.0 \n", + "2016-07-27 13:00:00-07:00 0.0 \n", + "2016-07-27 14:00:00-07:00 0.0 \n", + "2016-07-27 15:00:00-07:00 12.0 \n", + "2016-07-27 16:00:00-07:00 4.0 \n", + "2016-07-27 17:00:00-07:00 0.0 \n", + "2016-07-27 18:00:00-07:00 0.0 \n", + "2016-07-27 19:00:00-07:00 0.0 \n", + "2016-07-27 20:00:00-07:00 0.0 \n", + "2016-07-27 21:00:00-07:00 0.0 \n", + "2016-07-27 22:00:00-07:00 38.0 \n", + "2016-07-27 23:00:00-07:00 92.0 \n", + "2016-07-28 00:00:00-07:00 88.0 \n", + "2016-07-28 01:00:00-07:00 18.0 \n", + "2016-07-28 02:00:00-07:00 30.0 \n", + "2016-07-28 03:00:00-07:00 56.0 \n", + "2016-07-28 04:00:00-07:00 50.0 \n", + "2016-07-28 05:00:00-07:00 52.0 \n", + "2016-07-28 06:00:00-07:00 42.0 \n", + "2016-07-28 07:00:00-07:00 24.0 \n", + "2016-07-28 08:00:00-07:00 10.0 \n", + "2016-07-28 09:00:00-07:00 18.0 \n", + "2016-07-28 10:00:00-07:00 2.0 \n", + "2016-07-28 11:00:00-07:00 0.0 \n", + "2016-07-28 12:00:00-07:00 0.0 \n", + "2016-07-28 13:00:00-07:00 0.0 \n", + "2016-07-28 14:00:00-07:00 0.0 \n", + "2016-07-28 15:00:00-07:00 0.0 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## HRRR" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fm = HRRR()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cloud_vars = ['total_clouds', 'high_clouds', 'mid_clouds', 'low_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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88sl2/e5eT4wSExMbMtDKykpMJhO7d+8mNTUVgEsvvZSNGzdKYiSEEOe53BIt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\no5HHH3+cBQsWcPDgQbRaLeXl5Xg8nompQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time (' + str(data.index.tz) + ')')\n", + "plt.title('HRRR')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2016-07-28 12:00:00-07:00-1.3298952.542055650.737721167.600006488.89426951.0000.0000.50051.000
2016-07-28 13:00:00-07:000.5351562.597037629.393766146.573966487.85203854.3750.0000.50054.375
\n", + "
" + ], + "text/plain": [ + " temp_air wind_speed ghi dni \\\n", + "2016-07-27 07:00:00-07:00 -8.788116 3.683798 111.471370 37.599771 \n", + "2016-07-27 08:00:00-07:00 -8.513641 2.235231 238.752264 84.727200 \n", + "2016-07-27 09:00:00-07:00 -6.579926 1.586132 492.743765 330.711074 \n", + "2016-07-27 10:00:00-07:00 -6.916046 0.887758 449.936938 90.649614 \n", + "2016-07-27 11:00:00-07:00 -4.685425 1.578304 761.428845 420.144848 \n", + "2016-07-27 12:00:00-07:00 -6.816193 1.174678 525.681075 70.693705 \n", + "2016-07-27 13:00:00-07:00 -7.108551 1.560096 620.744725 138.037424 \n", + "2016-07-27 14:00:00-07:00 -9.462189 1.620842 809.037931 489.789234 \n", + "2016-07-27 15:00:00-07:00 -5.107452 2.782871 673.155994 451.408810 \n", + "2016-07-27 16:00:00-07:00 -2.124542 1.865137 622.959017 754.123038 \n", + "2016-07-27 17:00:00-07:00 0.754883 1.796860 427.098229 719.050038 \n", + "2016-07-27 18:00:00-07:00 4.102264 2.246583 217.224161 574.021875 \n", + "2016-07-27 19:00:00-07:00 8.565674 2.841491 38.420206 227.816862 \n", + "2016-07-27 20:00:00-07:00 9.335693 3.910746 0.000000 0.000000 \n", + "2016-07-27 21:00:00-07:00 11.624084 4.460042 0.000000 0.000000 \n", + "2016-07-27 22:00:00-07:00 14.837433 5.001012 0.000000 0.000000 \n", + "2016-07-27 23:00:00-07:00 14.662415 5.200049 0.000000 0.000000 \n", + "2016-07-28 00:00:00-07:00 18.115845 4.136905 0.000000 0.000000 \n", + "2016-07-28 01:00:00-07:00 1.913818 11.852090 0.000000 0.000000 \n", + "2016-07-28 02:00:00-07:00 12.629303 9.709138 0.000000 0.000000 \n", + "2016-07-28 03:00:00-07:00 14.147736 2.727995 0.000000 0.000000 \n", + "2016-07-28 04:00:00-07:00 5.271881 9.792562 0.000000 0.000000 \n", + "2016-07-28 05:00:00-07:00 -0.759430 6.252571 0.000000 0.000000 \n", + "2016-07-28 06:00:00-07:00 -4.579681 3.104227 24.003528 20.095883 \n", + "2016-07-28 07:00:00-07:00 2.053741 1.961879 132.072478 101.153444 \n", + "2016-07-28 08:00:00-07:00 -5.461853 2.519691 308.758856 242.220677 \n", + "2016-07-28 09:00:00-07:00 -0.750641 3.682376 431.094366 202.764092 \n", + "2016-07-28 10:00:00-07:00 -2.924988 2.174952 404.542958 59.282226 \n", + "2016-07-28 11:00:00-07:00 -0.212585 3.385307 559.429680 120.980946 \n", + "2016-07-28 12:00:00-07:00 -1.329895 2.542055 650.737721 167.600006 \n", + "2016-07-28 13:00:00-07:00 0.535156 2.597037 629.393766 146.573966 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-07-27 07:00:00-07:00 101.027463 75.000 0.000 18.875 \n", + "2016-07-27 08:00:00-07:00 198.129943 68.625 15.750 28.875 \n", + "2016-07-27 09:00:00-07:00 274.371607 33.625 15.750 33.625 \n", + "2016-07-27 10:00:00-07:00 376.690504 66.875 0.250 66.875 \n", + "2016-07-27 11:00:00-07:00 378.051384 25.625 0.000 25.625 \n", + "2016-07-27 12:00:00-07:00 457.348963 70.875 4.000 70.875 \n", + "2016-07-27 13:00:00-07:00 487.313213 55.875 8.750 55.875 \n", + "2016-07-27 14:00:00-07:00 362.060851 17.625 0.000 17.625 \n", + "2016-07-27 15:00:00-07:00 308.354862 23.750 7.375 23.750 \n", + "2016-07-27 16:00:00-07:00 124.946200 1.875 0.250 0.000 \n", + "2016-07-27 17:00:00-07:00 82.390113 1.375 0.375 1.375 \n", + "2016-07-27 18:00:00-07:00 57.940332 0.000 0.000 0.000 \n", + "2016-07-27 19:00:00-07:00 22.832806 0.000 0.000 0.000 \n", + "2016-07-27 20:00:00-07:00 0.000000 0.000 0.000 0.000 \n", + "2016-07-27 21:00:00-07:00 0.000000 0.000 0.000 0.000 \n", + "2016-07-27 22:00:00-07:00 0.000000 11.125 0.125 0.000 \n", + "2016-07-27 23:00:00-07:00 0.000000 34.125 3.750 4.250 \n", + "2016-07-28 00:00:00-07:00 0.000000 81.375 4.125 6.125 \n", + "2016-07-28 01:00:00-07:00 0.000000 98.875 0.000 7.875 \n", + "2016-07-28 02:00:00-07:00 0.000000 83.125 2.250 35.000 \n", + "2016-07-28 03:00:00-07:00 0.000000 33.875 0.500 22.750 \n", + "2016-07-28 04:00:00-07:00 0.000000 63.750 4.500 40.750 \n", + "2016-07-28 05:00:00-07:00 0.000000 59.375 2.500 27.625 \n", + "2016-07-28 06:00:00-07:00 22.659155 55.125 0.000 20.625 \n", + "2016-07-28 07:00:00-07:00 104.160258 59.500 0.250 0.875 \n", + "2016-07-28 08:00:00-07:00 193.002109 43.125 0.000 4.625 \n", + "2016-07-28 09:00:00-07:00 297.472525 48.375 0.000 0.000 \n", + "2016-07-28 10:00:00-07:00 356.708272 75.500 0.000 0.000 \n", + "2016-07-28 11:00:00-07:00 449.156137 59.500 0.250 0.625 \n", + "2016-07-28 12:00:00-07:00 488.894269 51.000 0.000 0.500 \n", + "2016-07-28 13:00:00-07:00 487.852038 54.375 0.000 0.500 \n", + "\n", + " high_clouds \n", + "2016-07-27 07:00:00-07:00 75.000 \n", + "2016-07-27 08:00:00-07:00 68.625 \n", + "2016-07-27 09:00:00-07:00 31.625 \n", + "2016-07-27 10:00:00-07:00 0.000 \n", + "2016-07-27 11:00:00-07:00 0.000 \n", + "2016-07-27 12:00:00-07:00 0.000 \n", + "2016-07-27 13:00:00-07:00 0.000 \n", + "2016-07-27 14:00:00-07:00 0.000 \n", + "2016-07-27 15:00:00-07:00 0.000 \n", + "2016-07-27 16:00:00-07:00 1.875 \n", + "2016-07-27 17:00:00-07:00 0.000 \n", + "2016-07-27 18:00:00-07:00 0.000 \n", + "2016-07-27 19:00:00-07:00 0.000 \n", + "2016-07-27 20:00:00-07:00 0.000 \n", + "2016-07-27 21:00:00-07:00 0.000 \n", + "2016-07-27 22:00:00-07:00 0.000 \n", + "2016-07-27 23:00:00-07:00 8.250 \n", + "2016-07-28 00:00:00-07:00 60.000 \n", + "2016-07-28 01:00:00-07:00 98.875 \n", + "2016-07-28 02:00:00-07:00 83.125 \n", + "2016-07-28 03:00:00-07:00 33.875 \n", + "2016-07-28 04:00:00-07:00 63.750 \n", + "2016-07-28 05:00:00-07:00 59.375 \n", + "2016-07-28 06:00:00-07:00 55.125 \n", + "2016-07-28 07:00:00-07:00 59.500 \n", + "2016-07-28 08:00:00-07:00 43.125 \n", + "2016-07-28 09:00:00-07:00 48.375 \n", + "2016-07-28 10:00:00-07:00 75.500 \n", + "2016-07-28 11:00:00-07:00 59.500 \n", + "2016-07-28 12:00:00-07:00 51.000 \n", + "2016-07-28 13:00:00-07:00 54.375 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## HRRR (ESRL)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/holmgren/git_repos/pvlib-python/pvlib/forecast.py:742: UserWarning: HRRR_ESRL is an experimental model and is not always available.\n", + " warnings.warn('HRRR_ESRL is an experimental model and is not always available.')\n" + ] + }, + { + "ename": "HTTPError", + "evalue": "Error accessing http://thredds-jumbo.unidata.ucar.edu/thredds/ncss/grib/HRRR/CONUS_3km/surface/Best/dataset.xml: 404 Not Found", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHRRR_ESRL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/holmgren/git_repos/pvlib-python/pvlib/forecast.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, set_type)\u001b[0m\n\u001b[1;32m 766\u001b[0m 'high_clouds',]\n\u001b[1;32m 767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 768\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHRRR_ESRL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprocess_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcloud_cover\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'total_clouds'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/holmgren/git_repos/pvlib-python/pvlib/forecast.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model_type, model_name, set_type)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 139\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/holmgren/git_repos/pvlib-python/pvlib/forecast.py\u001b[0m in \u001b[0;36mset_dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccess_url\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m/Users/holmgren/miniconda3/envs/pvlib35fx/lib/python3.5/site-packages/siphon/http_util.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, url)\u001b[0m\n\u001b[1;32m 351\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_http_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/holmgren/miniconda3/envs/pvlib35fx/lib/python3.5/site-packages/siphon/ncss.py\u001b[0m in \u001b[0;36m_get_metadata\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;31m# Need to use .content here to avoid decode problems\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mmeta_xml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dataset.xml'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0mroot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mET\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromstring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeta_xml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNCSSDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/holmgren/miniconda3/envs/pvlib35fx/lib/python3.5/site-packages/siphon/http_util.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self, path, query)\u001b[0m\n\u001b[1;32m 420\u001b[0m '''\n\u001b[1;32m 421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/holmgren/miniconda3/envs/pvlib35fx/lib/python3.5/site-packages/siphon/http_util.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, path, params)\u001b[0m\n\u001b[1;32m 457\u001b[0m raise requests.HTTPError('Error accessing %s: %d %s' % (resp.request.url,\n\u001b[1;32m 458\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m text))\n\u001b[0m\u001b[1;32m 460\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mHTTPError\u001b[0m: Error accessing http://thredds-jumbo.unidata.ucar.edu/thredds/ncss/grib/HRRR/CONUS_3km/surface/Best/dataset.xml: 404 Not Found" + ] + } + ], + "source": [ + "fm = HRRR_ESRL()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'HRRR' object has no attribute 'get_query_data'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# retrieve data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_query_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlatitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlongitude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'HRRR' object has no attribute 'get_query_data'" + ] + } + ], + "source": [ + "# retrieve data\n", + "data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "cloud_vars = ['total_clouds','high_clouds','mid_clouds','low_clouds']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "for varname in cloud_vars:\n", + " data[varname].plot(ls='-', linewidth=2)\n", + "plt.ylabel('Cloud cover' + ' %')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')\n", + "plt.title('HRRR_ESRL')\n", + "plt.legend(bbox_to_anchor=(1.18,1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data['ghi'].plot(linewidth=2, ls='-')\n", + "plt.ylabel('GHI W/m**2')\n", + "plt.xlabel('Forecast Time ('+str(data.index.tz)+')')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick power calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ModelChain for: PVSystem with tilt:32.2 and azimuth: 180 with Module: None and Inverter: None orientation_startegy: south_at_latitude_tilt clearsky_model: ineichen transposition_model: haydavies solar_position_method: nrel_numpy airmass_model: kastenyoung1989" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pvlib.pvsystem import PVSystem, retrieve_sam\n", + "from pvlib.modelchain import ModelChain\n", + "\n", + "sandia_modules = retrieve_sam('SandiaMod')\n", + "sapm_inverters = retrieve_sam('cecinverter')\n", + "module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']\n", + "inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']\n", + "\n", + "system = PVSystem(module_parameters=module,\n", + " inverter_parameters=inverter)\n", + "\n", + "# fx is a common abbreviation for forecast\n", + "fx_model = GFS()\n", + "fx_data = fx_model.get_processed_data(latitude, longitude, start, end)\n", + "\n", + "# use a ModelChain object to calculate modeling intermediates\n", + "mc = ModelChain(system, fx_model.location,\n", + " orientation_strategy='south_at_latitude_tilt')\n", + "\n", + "# extract relevant data for model chain\n", + "irradiance = fx_data[['ghi', 'dni', 'dhi']]\n", + "weather = fx_data[['wind_speed', 'temp_air']]\n", + "mc.run_model(fx_data.index, irradiance=irradiance, weather=weather)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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fvsAf/vAyfr+fNWt+w2mnncE3vjGP/fv3cccdK1m16uHeXYwj5GyQXNccJpE0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\nWQlH4iQNDXoZaDsq60kkDWa2lFpAa32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4TJKukIuOoGv15+XZWVZ+8D3mex4hXnYxdDDM\nO56xR7bncWLk82sSLlrzXmjTpk2bRuK+KJJnsjeYzy/yQPQ4+swsGWcH/qBzNWEhoNrFnBkqICsi\ny95BCrflJa9O3StU9nfx9yET1Xzk2qQ9LR4nYdjFU+8O8pHXw+d3YEgOcgtxXKqMS5VJtqh6Vmso\nFR32e7Me8W81akWyIdpraZ+8bJ/rc2nKFeOWVAvLMFj82p9RED1c9T2I0yXzzPNH8HgdSIJJUfHb\npypVJblV74U2bdq0aSTuiyL55/OvAvDO8IOkVwroooOuvpsd48FqkZzVswyMhCnIPtKpMpVYbPV3\nvM62krxVrs/XhojY/652PnIPAhY9/btTJIc67EIwWW1YCvvVllXPctXmOEuyC6O6Nu5Vr21gvxaF\nduPetqn5kU+syUdO/tOLFKenuXTweQwT3v2hw7i9qn2q4hIoKD7KscXVqXttu0WbNm3a1J+GL5LL\nRoU3Ym8TVAMMZRXSDludqfmR4abdIlVOM3TQ/vOEu/8WNXlVSW4XyZsyvZhDlkT6O21V0c5Hjlb9\nyPKuXCPYYxcQmaTdYBn2OSmWDYoteLxfU5IN0S5M62u3cAM3x2O37Rbb58LECpIocHTQ3lBWFhdZ\nfuHbzHY/RlJ3ceh4JwePdq7+fiDgwBQVMnPx1aSX5XZWcps2bdrUnYYvkt9aOkfJKPNkz2NUpqbu\n8CMDBG4pku3iK+HpvyUKrj11b+vEU0U6gk4k0X57LEzGsQSJvl3wI9cIRu1CMJOvZiWvJly0noKW\nL9qFqCbYhWk97Raeqt2iaBSRJaGdk7xN0vkK07EshweCOB0ylmkS+4uvkhF8XPedwON18PSzh255\nTLCj+vkVy+BxyjhksWVPVdq0adOmkWj4Ivml+VcREHiy5zSlqUkyzg5EUSDadVNtk0UZr+IhXc7g\n9qp0dPtIubrJXLqGpdtf8l6XA2gXyZtRKGkUyjrRgN1wpC0nSGi2utg3fG/5yGsJhKoxcIaCWanc\nHCjSggpa7T1ZsWxVvb5Ksn3t2kCRdpG8PcYmb021SP/4R+SuXWd8+FksC5758NE70mFC1VOVdKqI\nIAiE/M623aJNmzZtGoCGLpJj+SWup6c4Ehol4gpTmJomq4aJdHqRZemW3w2qAVLlNJZlMTQawRJE\nElKY4sR1ALwu2yaQa4+m3pBE2i5So0H72LdYzUe2/cj3lo+8Fq9fRcSkoPjR4nFCq139rVcc1Irk\nkllEFiRcsmuTR+wdNbtFvpLH5VTadottcmFyGYATI2G0RJz4t/4nk12nyZpOTjzcy+CB8B2PCUar\ntqaCnc4T9qnkihoVzdi/hTcRxbLOj9+eo9z+96sr04vZlp6i2ghousFyuvWEp92koYvklxZeA+DJ\n3tPo2QyprIElSHT23nkcHVT9VEyNol5atVwsewZWo+DaSvLWiKeqRXLALlozly6TcXYQiThxqLvj\nR4bqqHAVitUYuFW7RQsqyfmShiBATs/hdXjrNpIaQBLtIr2tJG8fTTc5d32ZoNdBf4eH2H//Gkkh\nwLT3KP6gkyffe3DdxwXC1Y2Jpd4yXKddYOyMfzwzw3//x8t8458u13spLUsmX+H//vrr/L9/+Qbl\nSnuzUi/+x79c5T9+6WWuVROr2myfhi2SDdPgzMIbeGQ3p6InKE9Nka4OEela07RXo5ZwkSqn6ej2\n4XYrJDz95KpFskuVkEShXSRvwnLa9sV2VO0Wdj6yuCv5yLfj9yvo1Ri4Vh5NnStquJ0y2TqPpK7h\nVdzktDwep4xumG1Fc4ucn1gmX9J5x/Eusi/9nMz4ZcYH3o8gwPt+4RiKQ1r3cS63gixUT1WWYmsS\nLlpvw3ivWJbFmTE71ejn5xd5ZWyxzitqTV67tIRuWMRTJb71k+v1Xk5LUtEMzozFMEyLP//BOJre\n/hzfCQ1bJF9YHier5Tjd/TCKpNh+ZLXatLeuknyzSBYEgcHRCJrkJL6Yw8hmEQQBj0sh2y6SNyS+\nxm6hrawQL9tf2LvpR65Ra95LLqZWB4okW7BhKV/U8LgFNFNrkCLZS07L43LaRV3bcrE1Xr5gF2RP\nDLiIf/OvuNb1BAVL5aF3DGxoVbJj4KCo+KjEYmsSLlpvw3ivTC1mWUoVOdwfQFUkvv7iZeKpYr2X\n1XKcGY8hYJ9I/vD1G1yZTdV7SS3HuevLlCoGXpfCwnKB7/xsst5Lui9p2CL5pVo2cu/jAKtNe4pD\nJFg9nlxLYLVIzgAwPLomCm58DLCn7uXbRfKGJKpfKNGAy85Hdtt+5O6+3fMj11htWFopoCoSXpfS\ncp5ky7LIl3RcbrsQ9ddxJHUNr8ONaZmoqp080rZcbE6+pHH2eoK+iBv5xb9jiRBz3lHCHR5OPzWy\n6eP9ARVTlEnNxVebWNsJF9unpiI/944hPvPsYYplgy+/cBHdMOu8stZhOV3i2o00RwaD/Pt/cwKA\nr/5gvO0R32fOjNv3wv/xiQfoCDr5xzMzTMxn6ryq+4+GLJJT5TQXly8z5Bugz9uDZVlkp29QcATo\n7PGv69msZSWny7b3pn84hChWo+Cqeckel0K+pGOY7Q/Mu5FIl3A6JDxOmeylK2TVKNGIuqt+5BrB\nTvs1y+btD8+wT2UlU8ayrF2/VqNSqhgYpoWjWiQ3gpJci4GTVHtNbSV5c16vHi9/0LNM6twFLvW+\nB1EUeP8vHEOSN/+YDa2JgQu1sPXoXjBNi1fHY3hUicNqgSdPdPHE8S6uz2f4+7aKtm+8Wi3OnjgU\n4mC3l2cfH2ApWeTbP5mo88pah2JZ5+y1ZXoibkb7Avza88ewLHuzount+mc7NGSR/MrCG1hYPNl7\nGgA9mSRZtmOTunrv9CPDrXYLAMUh0zsYIqdGWBm/hmVZ+KpZye1Ru+tjWRaJdIlowIUgCMxNVP3I\no52bP3gH1GLgcpqEpeuEfCplzWipoqx2sqE4q0pyAxTJvmoMnOyoFsntqXub8vLFGIJl0vvWD7nc\n9U7KODj99PAtUZUbEeqrDtdJFdtK8g65MpsilavwEeUGN/6v3yX5/e/y2Q8dIRpw8oOXpxmfTtZ7\niS3BmbEYTnR6/+o/M/ef/4iPv2uYrrCbf35tlqs32raL/eDNK3F0w+R5/RqT//E/cMCt8d5H+phP\n5Hnh5+0N43ZoyCL55flXcYgKj3U9BNy0WsCtk/bWcnPq3s3jhJrlIqb5qCzM46kNFGnHwK1LtqhR\n1gw6gk70VIpE2f6y7hu+M7ZqN7glBm450ZLNe7WR1KJSAeo7SKSGpxoDJ8j2mtp2i41JpIpcmU3x\ntDfDguYn5hmmq8/PQ+8Y2PJzrA7XKZi4VBm3KpNsoftgNzgzHgPLYnj+Ahk1QuKF72BcvsD//rET\niKLAn373ItlCpd7LbGoWlvPMLOV4nzNBOVckN36J3Ivf59c/fBSAr35/vN0IvA+cGY8hWiZdl8+g\nLS+z+Kf/jX/79BDRgJN/eGWGqcW27WKrNGSRnCit8HDng7hku2gqb9K0B+CSXSiismq3ABiq+ZI9\n/RQuXsDXHk29IYnV+DcXxSs385G7+9bfmNwrdgycZcfALcVaMgZu9b2o2AVRI9gtvNU1WLUiuYWU\n/Z3wStUH+3D2GlcjjyFJAu/7yFFEcesfr6vDdSwVs1Qi7Ffbo6m3gW6YvH5piVEpw41ykNcGPspE\n9FEW//RLDEhFPv70CKlchT//waWWsnPtNzVP+KHkdV4e+iXeGvow8e++QF9+kQ+eHiCWLPLtn7Zt\nF3tJplBhbDLJE45lJpQRfjbyv7AymyD3/Rf43PNHMS2Lr35/vO3T3yINWSTDzYY9gOKErSR7vA48\nXnXd3xcEgaDqv0VJ9gddBIMqK64eMhcutkdTb0IiXWvac5K+dIWsGqEjoqI4dt+PXMPvq8bAzccJ\nVweKtFI+bG0ktSXZf2d/AyjJtYEipmivqW1PujuWZfHyxUUCVoncUp6y4uXIgz3rNhdvhNOloNRi\n4OJLhP1OShWjreJvkQuTK+RLOk+bs8wFbNVyKnCSBamL+f/6x3zoVCdHB4O8fS3Bv741V+fVNie1\n+L1OM8tKVkKTnKSUKNcjj7D4lS/xbx7ppDPk4p9eneXajXZu717xxuU4pmXxcH6SmeBxKpKLC/0f\nIPHiiwzl53nmoV5uxPN876Wpei/1vqAhi+ROd5SDgWHAvvFSczEqsouuTRTNoBogq+XQzZtfLMNH\nOjFFhfnZNN7qNNh2kbw+a6ftLUzEsQSRvj3yI9cIhO0mseRC8qaS3EJezNp7URfsDUpDKMlVT7Iu\n2EVysV2o3ZWZWI6F5QIfVBaY840CcOKh3m0/z9oYuFJsse1L3iavjsWQTR3HUpyCI0BPvx/FITHe\n826WEyWW/uKr/G+/cByvS+Gvf3iNG0u5ei+56ZiOZYkli7xPnGPefwgB8AWczARPslj2kPzGn/Pr\nz1dtFz9o2y72ijNjMbx6gXyygi45cboUspKfqx2Ps/hnX+aXHu0g7Ff5/svTzMSy9V5uw9OQRfKH\nhz+4mmChLS2RMu1C6m5+5Bq15r10+eYLP3TQtlzEHd34l28A7SL5bqzGv0k6S0V7R9E/sjd+5Bqh\n3loMXL4lu/prjXsVioiCuOoHrifearqFhv1+KJTb98vdeOnCIlgWPUsTLLv76ez2bLlZ73b8ARVL\nkEjfSLTkvbBTyprBW1cTPGYtMO8cAuD00yO8/xeOYVgi5wc/xMrbF+Bn/8Kvf/gYumHypRcutou0\nXebMmN28Go4vkHF2MHAgxPOfOIkki4z1PsPyxat0XHmd9z/Wz+JKO7d3L1jJlLg6m+I94gLz1U37\nRz91ikiHhzn/EebNMKlv/Dmfe+4whmnxZ23bxaY0ZJF8uvvh1f+/tWlv46Po1SK5cvMop6vPj0MR\nWPYMoM5cA9qNe3ejNkjEvTRDytWNKFh09e5+PvJaQt3282dzOiFvC3qSq417JbOAV/EgCvW/Jb0O\nu0gum9Uiua0kr4thmpwZj3HUjLNIJwgCJx7derPe7QQ77M+3ZCxNpAVPVXbK2WsJyprBQ8VZlrzD\n+HwKvYNBRg5HefzpYYqoXOj/AEvf/jaHygu875E+5hJ5/uZH1+q99KbBtCxeHV/imLbIgmKfpBx/\nqI9Ip5enPjCKhsyF3vex9Lff5KMjMp1BFy++OsP19rjkXeXV8SUsy2IoNUvS3UtPr49ol5cPfvwE\nsiJyqftpElem6b36Ou8+1cPsUo4fvDxd72U3NPX/Rt4Eu0iOIrB5kRyoJlwkSzdvPEkSGTgQoaR4\nKUzZXrS2krw+iXQJr0uhdO0aGTVCNKzedZTubuEP2g1L2YqELILf3VoDRWpKcsHIN4TVAsApqUiC\nRNEoAG1P8t0Yn0qSyVd4lz7DvP8wDkVg9OjOx7eH++1Tr3SqtOrPb6UN4045MxYjoGXJFSRMUeb4\nI/2rJ5GPvHOIA0c6SCpRrnY8zsKX/xu/eDJAf4eHf31rjjcux+u8+ubg6myKZLbME/oNFn0HcLkk\nhkbtU8Jjp3oYPd5J2hHhWuBBEl/9Er/2/uE1ub1tRX+3ODMeY7CSYAk76OD4I30AhCJu3vPcEXQk\nLvS+n9i3/46PH5AJ+VS++9IUs2370V1p+CK5ODlJRo0Qirg2bSC7abe4dXc6fMh+wywVHHj0YrtI\nXgfTslhOF4kGnMxPr4Ag0n9ob/3IAF6/ExGTouxFTyYJ+Z0ks60zUCRX1EE0qJiVhmjag2rqiOIm\nrxdwKGI73eIuvHxxEbdexEwVqcgujpzqRVZ2vqkMRu3XP1sw1yS9tM6GcScUShrnJ5Z5pz7DXOAw\nAnD0ge7VPxcEgfd95AiRDg83/EeYkfuIf/m/8u+fP4Qii3ztH8bbG5Fd4Mz4Em69iJHT0CWVo6d6\nV9NdBEHgPR86TCDkYib0AAtZhcBPvsf7H+1vj0veRWIrBaYXszxlzrDgH8WhCBw4cnPTfvhEF0cf\n7CarBLkaepTk177Mv3vvMIbZTrvYiIYuki3DYGUhiSkqdG5hLPJ6WckAAwfCCFgkPP2MFBfaRfI6\npHMVdMMiGnCylLc3I33DkT2/rigKeBzVrv6lGGGfiqabLfMa5UsakqOWkdwYSjLYMXC5Sh63Krcb\n99ahVNF540qcJ/QZ5nyHADjx8PYb9tYSDFdj4EyVgGoroe0CbmPeuBzH0A0GcnFyaoShgyHctyUg\nKQ6Z5z5xEqdL5krnk8QSZaTvfZNPvW+UfEnnT787hmm2xqZ8L6jF7z1amWG+ei8cO3XrveBQZZ79\n+AkkSWCs5z0svfY2zzlj7XHJu8iZsRiKqeHMFqjIbg4/0HPHpv2pDx4i3OFhLnCUGwU3HT/7Lu86\n2cV0LMs/npmp08obm4YukisL86SkIHD3SXtruX3qXg2X20FHh4u0s4ODlTjZFinAtkMt/q3HoZFy\nRBAwN00T2S38XgVdUsnOL7XcQJFcUcPttY8bG6lI9igeSkYJl1NqK8nr8NaVBJWKweH8vO396/MR\ninju6TlVp4JDNCgoflhJtJz1aCecGY8xXFhgyWEfKx9/pH/d3/MHXTz78RMgCFzo+wDxN89zKn6B\nRw53cHk2xfdfntq/RTcZY1NJcoUKx4qLpFzd9Pb7VnO/1xLt8vLUBw+hCQoXet5L4q//kl97PNK2\nXewClmVxZjzGicIMC54DwJ0bFQBFkXj2Y8eRZdufHHvzIh/1xAl6Hbzw80nm4m3bxe00dJFcWjtE\nZBM/MtgZswLCHUoywPCxHhBEPJZAKltqKwe3URsk0qllyDuC+Bwmyj0cHW+HQDVTNrmw0nIxcPmi\nhtNtfzk0it0CwFdNuHC67azeVrG/bJWXLy4yWIwRF7sAOPnYzhv21uJzQUnxUlqMEW4x69F2SefK\njE8neUKfZdE3gtslMbBBGk/fUIinPnCIiuDgXN8HiX3rb/lfD1iEfCp//7Optpq5Q86MxegpJ1gW\naz7Y9TcqsMafrEa57j2B63v/g/ef6qqOS57apxU3H7NLdhTlw9oCCXcf0ajrrik7oaiHd3/oMLog\nc6H3vST/51/z706H0Q2Lr/5gHMNs2y7W0thF8qSdbCFJAuGOzVUaSZTwObx3eJLh5ojqjNpJIJfg\nRnvHdAvxqpLsKmQwRAfBgGPfrh3qCQGQTuTXNCw1v4JmmhaFko7itE82fEpjKckADqeOaVmUKm2V\np0Y6V+bi1ApPatPM+0dxqiIjh6O78tyBgBNLEEnNxQn7nWi62T75uguvXVpC1UvIZTBEB8ce7kcU\nhQ0fc+KRXo6d6iGrBBnvfBcrX/syn3tnJ6Zl8bNz8/u08uahohm8eTXO6dIUCz7bB7vRvbDWnzwd\neoC5hMH7Vt4iGnDyg1emmVxob1R2wpmxGOFKmoLpBUHkxCab9iMPdHP0gW6yjjBX/A8S/Me/4p1H\no0wuZHnx1dl9WvX9QUMXybnpGXKOIB3dvi2PeA2qflKVzB3qS7jDg1uFZXcfw4V5Ls+m9mLJ9y01\nJdnI5AEIde6fqhmsFsmZnEaohYYoFMo6FiCpdhHUSEpyLQZOVm2rRbFtuVjlzFgMVS/hLBvokpOj\nD/UhSbvzURrsqPZVxDI3B4q0fcnrcmY8xsncJAu+UcDi2KmeTR8jCAJPP3uI7n4/Me8Ik/IIvu9+\nA6dgMLnQHqywXc5dX7bHqGsVNNnFkQd7keWNTyBtf/Lxqj/53cR/+hK/NmpgWfD1Fy/v08qbBzt+\nL8ZD+Qnm/YeQRBg9tnnT/VMfPEQo6uZG8DizKwLPZ98m4HHwnZ9OslSdmdCmgYtkU9NILBVAELfk\nR64RUAPopk5eL9zyc0EQGBqNoksqPXqRKzPtInktNU9yIWM3kUUGdh5ltV1qI3xzZZGQz1awky2g\nJNfi30Rldxr39Oydm8OdUhsoIjmqEXXt5r1VXr4Y48Hc5K417K0lVI2BS6WKLefP3w7xVJHrN9Kc\n1OKkXZ30DwbwBZxbeqwkiXzoF0/i8alcjz7CfMLg4+nXubGUbftit8mZsRhHcjMsukcAOL7FaZPR\nLh/v+sAhNMHBhe5nkL7/TR7ukplazLZM0/ZuMTGXYSVdZFDLUVJ8jB7vxKFunAQGoDgknv34CWRZ\nZLz7aRI/f5Vf7i+hGybnry/vw8rvDxq2SK7cmCXjsP1lndsokkOrMXB3HtuMHLeVBkn2cG060fb6\nrSGRLhHwOsgWbD9SuHtvh4isxet3ImBSkDz4zDICtETDUu3LwJLtv+u9FMmVhXkm/sNvs/ztb+3K\n2mpFsqDYa8yX2l9cAHOJPNOLGU5Wlki7uugf9K9mfe8GoY5aDJy1JgaurSTfzqvjMbrKK2RkezN/\n/JHtecLdHkd1GpzExZ5n6EguciQzwUysbcPbKoWSztnryzxYXmDZ3Udnp2tLtsgaxx/qYfRYJ2ln\nJ9fUwzx17YcIlsnUYttysR3OjMU4UJgn7hoE4PjDfVt+bDjq4elnD6ELChd6niH0r9/Bp+fb/vw1\nNGyRvN2mvRqBuyRcAPQNBpEEixV3H8HkPPOJ/O4s9j7HME1WMmV6PBI50wlYBCP7Nx5ZFAW8Xiht\nuQAAIABJREFUih0DZy0nCHgdLVEY1IpkQywhIKwWpjsh++YbYBgk//lFtGTynte2uhbJLuDbCRc2\nr1xcpL+0RFKxs3h3q2GvRi0VIG86CDntj+dW2DBulzNjMU7lrrPgO4jqEBg+tP24yo5uH888fwRd\nkDnX8z4OFhbantht8NbVON5iCl3ygyBw/NHBbT1eEATe89xNf3Ixa/DO5Hkm2wXaljFMk9cuxThV\nmCHuHSQYULZ18g5w9MEejpzsIqNGueY+xseWfs7UfPukvUbjFsnVpj2nKm35GA3WZiXfWSTLikRX\nVCGvhhgorbR9yVWSmTKmZdEvFcg7ArglY9+SLWr4vLIdAze3uNrVbza50l9TZzWKeBQ3krjzf/P8\nubMAWJrGyvdeuOe11TzJpmhbQdp2C9v798rFRR7KTbDgO4jLKTI0urtZ4g5VRhV1CoqfgGZ7ZFth\nw7gd5uI5FmNpuqyqJ/zUzj3hh0908dA7Big4AghquO1L3gZnxmI8kL3OvG8UWYLRY9u36N3iT+5+\nN6cyU0zOt0dVb5VL0yn0TAaHoGIJEscfG1ydNrkdnn72MKGIm9ngCVTBgXf2Svszv0rDFsnpqTlK\nipfOvsC2XvTVrOTS+jfawBHbchGyDK60i2QA4mn7Sziq56jIbgLe/X9b1BS05MIKYZ+KYVpk85V9\nX8d+kivaH0Jlq3BPVgsjl6M0cR3ngYMoXd2kf/YTKvGle1pbTUk2RPu90f7AtEfv5pIZgogYkoPj\nj/RvuaF4O/hcAiXZgyOTRBSEtif5Ns6MxzicnyHmqfpg79ET/siTQ4BFUY0Qm24nXGyFTKHC+OQy\nw0aBsuLl0PGuTSfi3o1Vf7Lo4FrnO0hPTretkFvkzFiME9kJFvyHEAWLwye6dvQ8ikPigx8/jiTB\nWOe76K0k27aXKg1ZJJulEstp+0t5u0cHd5u6V2NgtNr1qXi4PtX2JQMkqp2satFudgxF720owk5Y\nGwMXqsXANfkxc66ogWBSscr47iHZIn/xPFgW3oceJvqxXwTDYPmF79zT2jyKbbfRrGqR3LZb8PLF\nGCeyk8z7DiFgbblJabsEAioIIun5BEGfoyWSXraKZVmcGYtxvGAPcenucq82/u4U1SkTcFpk1CjK\n4mx7Q7gFXr+0xFB+nmV31Qf7yNZ9sOtx/KEeoj5IunoIJBdJNvln/26g6SZvXF7ikJak4AgyMhrB\n5d55dGukw8vpdw6iSypeQW5bj6o0ZJFcmpkmXfMjb7NIXvUkV9ZXkiOdXhTRJOXqxrt8g1iyHXVS\nU5LNgq3chvvuHsi/V4R67WtmMpWWaVjKlzQExf4y8N+Dkpw/dxYLmFWH4ciDqAMDZF95mfLc3I6f\nUxZlXLKTslVNPWnxwkHTDV4bj3GsHCfrjDI4HMTr37oNbDvUYuCSi2nCPiepbKU9/KjK5EKWSjwO\nij2J9cTp7flg70ZXtwdTlOnWC0y3FbRNOTMW42RumoRngHBQoaP73uIrBUGgbzgMgkCHWWk3jm2B\n8xPLBDOL5Jz26fjxR++9P2L4aFWJlj1M3rj33pZmoCGL5PLUJBnn9pv2AFyyE1VyrJtuAXaTWHeH\nSknx0V9Ocnmm/UZYrsa/FYv2F/F+xr/VCFZH+mbLws3oqyZXE/JFDe4x/s0yTfIXzpPqPMpLryX4\n0fcvE/7YL4Flsfz3f3dP6/MoHopmtUgut3a6xdlrt34h7VZxth7hfvuzL50qEfarmJZFKtfc98JW\nOTMW42TmOgv+URQZDhzZnc+qviO2EuoFJtoK2oYsp0vcmI7hkl1Ygsjx00M78sHeTt8R+2RGbauY\nW+LMWIyT2Uli3hG8bpG+oeA9P2cw7MYhGmScHeQmJndhlfc/DVkkFyftZAu/T8HpUrb9+KAaWLdx\nr8bAMftmDFtmu3kPW0kWMcnrtqcsXAe7hdev2jFwopuQZKuWzZ6VnCtqCNX4N7+yMyWmNHEdM59n\npfsEAPMzKZa9AzgPjpJ78w1KUzv/oPMpHop6AbBaXkl++eIiD2YmWPSN4HWJG44/vleCnbaSnC2a\nLbNh3AqmafHa+AJDRpGK7ObwyW7kXWow7hm2GzAtycN0W0HbkFcvVX2wvkNI9+CDvZ3ufvsUuOII\nMDe9uCvP2ayUKjoXrywQRcQUZY4/urOGvdsRBIGOoExJ8eFPLbVtLzRokbwyE0OXVDoHQjt6fEAN\nkNcKaMb66tfAQduXbCperk/GW96XnEgVGVR18rIfh6DvaGNyr4iiiEc2KCg+/JVqV3+TezHzxTUj\nqXeoJOfPn8MCYoYfxSEhCPDKjycIf/yXAEjcQ26yR/FgWAZIeksXybmixqUrC4RFCVNUOH56cNPx\nx/dCrYk1ZzgIu+0isNmtR1vh8myKYHyGFY+t4p94pH/XntsfdKLWFLR72Fi2AmcuLnKwnKLo8DNy\nOILq3J3vC9Wp4Fft10CbnmxbjDbgrasJDqSmiPkOIGBx9MHNp01ulZ4R+3QmbOptRZ8GLZKX8/ay\nttu0V6PWvJeurP8Ch6JuVMkk5erBk7hBIt26X0CabpLKVRgQCxQVL4Hdm4uwbfweCV1ywspyS3T1\n54oaDpddfO64SD53lqy7i2LZ4uCRDo480E0yUWC2EsJ97ASFixcoXNnZqNdawoXLbbR0495rl5Y4\nkrGVMwGLY6f2pmGvhuKQcIo6RcVPxLSbaZv9XtgKZ8ZinMjNsuzuIxpSiHTe24TKtQiCQEdYpqx4\n8K3ESbftLeuysJynMjNNwV21HW0zG3kzuro9GKJCtJBlYbk9x+BuvDoW43BxiawzysCgD091hP1u\n0FtNAHOISrtIpkGL5J36kWusxsDdxZcsCAI9XU4qspuBcorLLTyiermqUIXNEggiwdDeNCNthUDY\nrtBTCyst0dWfK2mrSrJ/B+kWWjJJeXaG1MApAIYPRTj91DCyLPLaTycJfPQXAVj+9rd2dFpSy0pW\nXUZLK8kvX1zkcClBXg0xcjCI27PzDvKtUouB8xbtz6ZWV5J1w+TixRncshMEkeOPj+z6NXoP2Apa\nyNLbecl3wfbBTrHkGcbvEekZ2N3JrH2H7Q2oT2h7w+9Grqhx4/IUpmpbvk6cHt7V5+/q8SNgUVTD\nzE/svPm7WWjQIrkDUYBo186UguAGU/dqDB6zGzUCWFyebV0PWi3+zVGxi7Vwz72b/3dKsMu+djqR\na/quft0wKVcMRMfOG/cK588BsOToRpJF+ofDeP1OHjzdTz5X4UpCwfPQwxSvXqFw4fy2n7+mJKsu\no2Ub95ZSRbLXJii6bHXl5OPD+3JdOwZOwEzahUKre5IvTKwwnLjKou8Qsmhx6Hjnrl+j95BdoCmi\no62grYNlWbx+YY6IIGCKEscf252GvbX0HrAFMiQ3U3OtK15txBuXlziWvk7MdxCXAwYP7m5/hKxI\nhNwmWTWCPj3Z9EO9NqMhi+SsGiYcdSHLO2vK2GjqXo3+0Zov2cfE5L0NXrifWY1/q1STLYZ2/8tn\nq4T77A/IWgycaVmkm3SgSL46khp550Vy/vw5irKXdFGkfyiE4rDvl4feMYjTpfDWKzN4nvsYCAKJ\nb38LyzS39fy1IllWdYplo2k3LBtx5uIiD2YnWPKO4PeI9A7uzyYy2GmfLBRWcsiS2PJK8pmxRUYq\naUqKl4NHojjUnQ2u2IiOHh8iJkVHiIXJtoJ2OzOxHKEbl1nyHUTA4sgu+mBr+IOuVW946mrbG74e\nZy4u0Gtq6JKDow/vzUCj7l4/liASLuWJrRR2/fnvJ3b0r6vrOr/zO7/Dr/zKr/DLv/zL/OhHP2Jm\nZoZPf/rTfOYzn+H3fu/3Vn/3m9/8Jp/4xCf41Kc+xY9//OMtPb8lSHT176xpD24qyXeLgQO7UcMt\nGyRd3bhisy37JZSoxr+VdLvACnfuzAe+GwQ77EIxW4JwbaBIk74uuWqRbEol3LILWdzel76paeTH\nLrLSfRywrRY1VKfMY+8aQqsYXJjU8D3+Dsoz0+TefGNb16jZLSSHvdZipfUsF6+du0FIlDFFiROP\nD++6cnY3bo+Ba2UluVwxmDt/eVXNP3F6aE+uI8sSIbdFTg2jTU22fEP37ZwZi3G4GCenhhka8u+J\n7UgQBDpCtjdcWpyjohm7fo37mWS2jHZpnBXvMADHH763IS53o++o/bwehJbPrN5RkfzCCy8QCoX4\ny7/8S77yla/w+7//+/zBH/wBX/jCF/jGN76BaZr8y7/8C4lEgq9//ev8zd/8DV/5ylf4oz/6IzRt\na8e22x0ispZAVUlObqAkC4JAT48bXXLSV0m3bBRcIlXCaZQpiB4kDLz+3WsA2C61GLg8Tqr25KYt\nDvJVj68ulHY0ba907SpWucRy8CAAQ6ORW/78+MO9+INOLr41j/LMR0AUWf7O321LTa4pyaKi3bLm\nViGZLROeGWPRN4oo7G4H+WYEu+yNfrZoEfapZPIVNH17JwHNwtvXEhxJTRD3DhL0ijvuVdkK3X22\nghYo5lZP2dqAaVmMvX0VU7U/Z068Y/c94TV6R+wNYtDUmVnK7dl17kdeu7TE0fw8aVcXvV1O/MG9\n6bSvRSKairflIxF3VCQ///zzfP7znwfAMAwkSWJsbIzHHnsMgHe/+9289NJLnDt3jkcffRRZlvF6\nvQwPD3P58tY67bt6d/5B6Hf4EAWR9AZFMsDgcXtCjR+hZZv3EukiHXqGghLA7zD2TSlbj1oMXFHx\nE9HtzuamVpIFE43Sjqbt5c6dRRMdJMoqnb0+PN5bNzeSJPLEMwcwTYs3L2YIPPU0lcUFMi+/tOVr\neKpFslW1hBRbrEieXMhwqLxC0RHgwGhoX6MRA8FaDJxCxGtfN9nkjax34/yVBcKChCXsvZp/U0ET\nmWxxBW0t04tZBhcuE/ON4Fahf3jnJ72b0Vt9DVRRab8Gt3H+/DSqYn9fHN/DjYrHq+KWdNLODlJX\nr+/Zde4HdlQku1wu3G43uVyOz3/+8/z2b//2LUdTHo+HXC5HPp/H57tZ7LrdbrLZzbuGHapEMOze\nydIAEAURv8N313SLGv0H7R2r4fAzMdWavuREusSAVMEUJQKBve/a3wyfW0STnLjy9ganWaOv8kXt\nHv3IZ1kJDGNZMDwaXfd3DhzpoLPHx/VLcfTHP4Agyyx/9ztY+taKXV/VbmGJ9mtQKLVW897M5AIl\nVzcAD+zhF9J6yIqES9QoKn66RNsS1az3wmYYF8+z5DuIKFgcfmBv1fyeoZsK2tSNlT291v3ExI0U\nPZaGISocf6R/T3PCO7v9VW94mLnJ+T27zv2Gbpi4r51j0XcAh2Rx4PDeTsbtjDrQJSdSbKFlT7EA\ndtz9sLCwwG/+5m/ymc98ho985CP84R/+4eqf5fN5/H4/Xq+XXC53x883o3cgSOc9emOjnhCTqVki\nUQ+isP5eoKPDh181SZldqPNTSOr7Vydc1YOOjr07RlyPYlknW9AIiyYxoGcwvO9ruJ2OLh8LmQJK\nqQgo5Cv6vq5p364liQjVkdSdge39uxcXFtEWF0kffxIq8Mjjg3d9/PO/+AB/8Scv8fa5JE8/9xyL\n3/sexltn6Pnwc5tex7K8SIKIJdvFsawq+/LvU+/3YI3C9Uky3kECToOTD/Xt+ylL0CuxkPHQU92k\naAjNeS9sQDpXJpSKsxwZ4NjhEAODezfpEIAOHx5ZtxOWJqfo6Hh8b6+32XIa4DUAyF6/Tt47DFi8\n6/1HVwfe7BVRHyxZIcSJCTo6PrCn19oKjfA6XL+Roq9SZM7v4vRjfXT37G783u0cOjXI1D9NETAM\ncprJ4T2+3mbU6zXYUZGcSCT4jd/4DX73d3+XJ554AoBjx47x2muvcfr0aX7yk5/wxBNP8MADD/Cf\n/tN/olKpUC6XmZiY4NChQ5s+/8CBMPH4veVUeiQvhmkwNR/bUKnr7nFzZapEn5bl5bdv8Pix3Rmx\nuV06Onz3/HfeLjfi9gZG0i1wgKczuO9ruB130AMUWFlMI4kdLMTz+7am/XwNFhM5BMUufhRD3dZ1\nkz9+CRORRSOAP+gEibs+3u13MDwaYeraMvHnH0dQ/5npv/4m4oOPIaqb+889ioeSYXc3Lyxl9/zf\npx73wXqYlkVlLoblC9Dbo5JI7L830utTIGOgJ5KAl+m5FPGh/UnXaJTX4ey1BKJiCyajDw/sy5o6\nIw4mYybi7FUWY2mkPUgP2AqN8hoAZK5OkXeO0BMWqOj6nq+rs9vHUjaPI51mcmYFbx2mwNZolNfh\nzfOzFF12fXLggd49X1OgLwJMIUsqb15cIOTa/USZrbLXr8FGBfiO7v4vfelLZDIZ/uRP/oTPfvaz\n/Oqv/iq/9Vu/xR//8R/zqU99Cl3Xee6554hGo3z2s5/l05/+NJ/73Of4whe+gMOx+ZH+yUfuvWNz\nKzFwcNOX7ENsuea9RMr2OBqG/TaI9EU2+vV9ITxwMwYu5FOb1oeZL+oIO7Rb5M+fJenqQjNsq8Vm\nCucTzxxAEODVVxcJvP9ZjHSa1L/+cEvX8ioeKqZ93N9KA0WWkkVc1ci73sN700G+GcEO+zPMyNXs\nFs15L2zEzNUbFNQIIsa+xe/1HrQLEb9usJBo7fgrsE8c5YK9oR85uj8iUt8xe+S4B4GpxbYvGSA+\ndpW0qwuPVCEc9ez59SIdXmQM8mqE+es39vx6jcqOtgZf/OIX+eIXv3jHz7/+9a/f8bNPfvKTfPKT\nn9zJZe6JtQNFBnx3/5LrO9gBXEVzBJiciAFH9meBDUAiXUS0TIqCC8Ey9/wIbSvUCoNc0SLc5+Tq\nbArdMJGlhoz03jH5orZqt/ApWy+SzXKZ4uVLJAfeA9wa/XY3QlEPx071MPb2AkunHsHp/iEr//B9\nAu9+Bsm9sfffq3iYtxZBMFtqNPXkXApk+37oPVif7PBwfxTeTlLK23aXZk162YjM5avk1Q6ibn1P\n8mDXo/dIL7y0gCypTCxk6N/F8df3I9OLWRTRFrf2a8O4mq4ge5maXeHkSP0FnHpTmp7HUIbo7Nif\n+0AUBSI+iGUDyBMTwJP7ct1Go7kqjzVsNpq6htvjIKDqpJ2dyHOTZAvNObxiPRLpEkEtS0Hx45F1\npAYoRH0BOwYuZ6lEPBIWkMo1X3GQK2pQtVtsJwKuMD6Gqessqb04VJnu/q35xB57ahhZEXnjzAK+\nD34YM58n+c8vbvq4WlYystZS6RbzlybIqVFUQcNbpz6FUHX6Za4ETofUckqyaVmYcTt+qndg/yaB\nhqsKWkENt7SCVmNmcp6iI4RoGUQ6917BBHC5HXgkjYyzg8Tl1k5XAChrBlLBvv9rJx37QU/1vnNk\nsi11kriW+ldFe0TNbrFZDBxAb58PU5Tp1fJcaSHLRTxVJKrl0CWVgLcx3gqiKOKWdIqKn27B/lBo\nxq7+fElDUW2FcDsRcPnzZ8k5whR1iaHR8JY3Nh6vyqnHByjkK8z4jyL5/CT/6UWMTdJmalnJglwh\n30KjqQuT01RkN9HgzqZ+7gb+oAssy46B8zma8j7YiNhKAdW0rUR9VVvcfiCKAhGvRcERIH9tYt+u\n26isXLpG3hEk6DL3Tc0H6OpwYkgOjBvzLT/YZSaWRa6p+Ud69+26tfvOJUhMt6jtpTEqoz0gsEUl\nGWDwAXuCk1eQWioveTldImLZynkosj8KwVbwV2PgAmW7gFtpQl9yrqghqTVP8taUZMuyyJ8/RyJ0\nALh79NvdeOjxAVxuhbdfn8f97EexyiVWXvyHDR+ztkhuFSVBN0yElN2o1ztUv2NeSRJxSxpF2U+f\nUqFQ1im10NTD69PLGIoXLIueoT1OtbiN7qqCJifTaHprT33TFxIgCPT07W+6QO+orZi6K1rLbRBv\nZ3pigaIaQrIMwtH9s/90D4TBstAVP5OziX27biPRtEXyWk/yZvSNRACLiiPA5MTiHq+scYinS/gl\nW6mJ9O3vl9BG+IP28bZcsAeKJJvsA9KyLHJFHUGp4JRUHNLWOrcrczfQV1ZYCR5EFAUGD2zvNXOo\nMo89NYyumVxhANHrJfPKSxtO4fNU7RaCorWMJ3kunsch2O0afcf2T8FcD59LoCK76DRqw3Wa617Y\niNjYVTLOKF5ZQ3Xub7pB//FBAJyCxEysdae+ZQoVZM3+fOg72r+v1+6rKqaSpDI5v/n3eDOTHLtC\nwREk6DL2NKP6dhyqjN+hkVUjrFxqTdtL0xbJquTAJTu3VCSrToWQ07CzMWcnybfA0IR8SaNY1pGw\nj5OjQ/WJvluPYDUj28xX7RZN1rBU0U10w8SSy9tKtsifP0dJcpMyXPQNBXGo2++7PXaqh0DYxfi5\nRazjpzFSKUpTU3f9/ZqSrLr0lvEkT03HqSh+BMuko6++2aCB6obRo7VewkVxahZTVOjq3H9PePeg\nraAZipfJ2eV9v36jMDWfBslu7u0Z2dvhFbcTinqQ0cmrEWZb3BteWbRV3J6+e5sfsRO6Ot2Yoow2\nt7Dv124EmrZIBttysRW7BUDfYABLkOjVi1ydbf5dayJVAstCw87KDXXWPyy9Rrjf/jBe7epvssIg\nX9QAC0Mob6tpL3/uLAmvrXBt12pRQ5JEnnjPASwLLssH7ed9+827/n6tSFZUoyU2jwDx8avk1DB+\nh4Ys18+TDBCs3pdS0bbmNNuG8W6UNQMxb/+d+w/v7ZS99XCoMj6lQlaNkrh0bd+v3yjMjU+Sc0Zw\nUsHt3TxXfTcRBIGoD0qKj2wLN+/lSxpSxfZk1+Nkq++Iff85KzrJFvn8WUtTF8lBh5+iXqRibJ5Y\nUfMlewSJy7PJvV5a3Umki7iNEkXFh0uooDjqWwysJdRtq3f5Eiiy2HSFQa42klqwtty0Z+TzFK9f\nYyV6GICh0Z17ZUcOR+nu8zO7pJP29pLbQpEsqa1jtyjNLmIJEt3d9ffp1zaMetn2xTbbhvFuTC9k\nkCRbQe49Up+c6u5OF6YoU5lpTQUNIH11Ek1yEqlTA2tv1YsurKQwzdZs3puaT6/GUXbvs5oP0F+9\n/0TJZa+lxWjuInkbvuTeoQgCJmU1yOT15vclx1MlolqGsuzBX/945FvwBZwIlknedBDxKiSbrDC4\nJSN5i0py4eIFdEtkWQwR7fTiC+z8CFoQBJ58r60iT/c9QWV+nsri+u/5WgScqFSoaLZNpJkpVwzE\nqmrbe2j/FczbCfWEAChV9/mt4kmevjJNXo2goNUtv732+svFcss0ra7FsiyMpN083VennOK+E7Zy\n6kBiYTlflzXUm9nxCbJqFCdl3J7Nh7HtNl6/ioMKOTXC7JWpfb9+vWnuItm59YQLxSERcZtk1AjC\nzHWKTa6aJdJFIob9hRsM7u8x2mbUYuAKso9epUKmoKHpzVOc5Ur66kjqrXqS8+fPsezuw7SELQ0Q\n2Yzu/gCdPT4Shp+K5LyrmuxRajnJdpXW7MXC9GIGsaZgHt6/qKW7YW8YLQqmimCZTZn0sh7JsWuU\nFQ8RH5tOlNwr+o7aCpokOZlaaD0FbSVTRjJtBbnv+FBd1tDVH0awTDTFz0SLesPTV6bQJZVosD5j\noQVBoCMgUpHdZK9O1mUN9aS5i+Qtjqau0TcYBEGkWy9xba65PxQT6RJe0z7CDffsX1D/VvG5BTTZ\nRadlNyw103jq3BoleSt2C8s0yV84x3LIVn+HD+3Mj3w7o8c6sYAl7zC5t99a93cUUcYpOTHFapHc\n5JvH6auzFNQwCpV7Uut3i1oMXEHx0S1XWG4RJVmL21GcfcP1i+DzB104qJB3Rpi5OlO3ddSLyZll\nNMWPYBl09NangVVRJHyKRk6NEL94tS5rqDfGSlXNP7A7n/s7oXfEvra1nMVssczqJi+S7Rs7vcXm\nvcEHhwFwCXLT5yUn0iWcYjXZYrhxki1qBAK2uu3TakVy8xQHtt1i69P2SlNTaNkcCVcfHp9KtGt3\ncjIPHrX9bfGOY5SuX0NPr78x9CpuDNHepDS7kpy8dJ2y7CHsrZ+CeTs+l4AmOemXSiQzpaYfrJDM\n3lQw+0/WR8EE+/UPewXKsof0pdYbKrJw8Qp5NYRP0eo6jbWnx40liBSn5+u2hnqRzJYRrZqaP1i3\ndQycsO9DBYnYSqFu66gHTV0kB7apJHcPhhAxKathJq837w1pWRaJdBEk29/USBnJNQIddvEoV82Y\nzeTFXG3cY2tKcv78WdLOTjRLYvhQZNeKN6/fSXd/gBX8lEUn+bNvr/97Di8aJcCi0ORT97Qlu2m3\nb5+HV2xEoGqHipgaFd0k3+QblYmp+KqC2dUXquta+kfs94GeaO6TxfXIT81jCSJdne66rqPWOCYU\nKlS01hrsMjWzjKb4EC2DaE/94iijvQFEy6DsCDA13VpDRZq6SA5uY+oegCxLRD0mOTUMUxOUK815\nQ2YKGma5QlnyoKDhcu9/M8Bm3NHV30R2i3xpjZKsbK4k58+fI+Gzd/I7jX67G6PHOgCharlY35fs\nVdxYmCAaTa0kZwsVxOpfr79OHsz1qOWGO43mjES8nfnzl8mpIfyKhiTX9yuq/8QwAJIhkM41z0Z9\nM0zLwszZ77P+I/VtYK15wwXZxXQsW9e17DcLF6+Qc4TwypW6qvmSJOJ3aOQdIWIXLtdtHfWgqYtk\nr+JBEiTSW1SSAfqHbeWgUy9xrUnjThLpIiEtS1Hx4Xc05kYgWO3qL1ds1bSZlOR8ddoebN64p6dT\nlKYmSQQOoDgk2ze/ixw80oEgwFLkCIWxi5ilOwuwWvOeoDT3aOqpuRSG4kOwTLoGG0dJrm0YBc22\nWTTTvbAeual5uzekp/4RfJ39IQTLQFP8TM6u1Hs5+0ZspYAg2CcYvfs8ae92PF4VlbKdrjA+Vde1\n7DfZSfte6Oqqr5oP0NvnA0GgMNv86V9raeoiWRRE/A7flpVkgKFTwwC4RIUrTepLTqRKdOgFEEQC\n/v0d97pV/EEXgmVSMB1gWU2lntUa9xyiglPeOFkkf/48eSVAAScDI+FdV9bcXpWegSCUeSHRAAAg\nAElEQVQpMUgRlfzF83f8Ti0GDrnS1I17c+evkFPDeKQystJAueG99oaxYtivfTOdqtyOYZpQHSI0\neLy+I8EBJFnEJ1fIqyHmz7eOgjZ59YbdwGpV8Pnr38AaCYjokpOV8dYZKmJZFmb1Xh84Wp+s8LUM\nHLc3S0JBa/oo0LU0dZEMtuUiXclgWlt7UTv6gkgYFNUIU03qS06kiwRMu9gJd+5OE9huY3f16xRk\nL2Gh3FQDRfIlDdFR2VLTXv78WRIeu2FjZBei39bj0PFOAGLeYXJv3Wm5qA0UEeTmVpLT129gCRKd\n0foXBWup5YaXLBUsi+Um2jDezo2lHJZk5yL3Hal/BB9Ad7cHSxDJTjTn98F6xC9eoyK7CbrNhmhg\nHTxof0ZVWsgbHk8VoabmH6mvmg/Qe8i+Hy3JzUwL2V5aoEj2Y1om2UpuS78vSSKdPouCI4A1OYGm\nN6Yd4V5IpEvUyoDIwP5P8NkqPidosotBudRU6RbZYgXk8qZNe5auUxi7yHLwAIIAgwf3pkgeORy1\nLRfBUfLnzmLptxbCXsVep6BoFJp0NLVlWRhpu2t7oA5jkDdCFEU81Rg4n14g2cR2i+mxSXJqBCel\nhumVGKoqaEau+ZNFahTn7UzivqH6Nk7WGHhgGADBlOzG5xZg8uocBTWMwyo3RByl06XgokjOGWV2\nrHUU/RYokrc+da9G3wG7GIloJSbmt27VuF9IpIpIom2ziAx313k1d8cfsL8ko2jkihrlJuhstiyL\nQqUIgrWpkly8dpVixSIlB+npD+B07Y01xuV20D8cIiOHyFUkilev3PLnXsX2wwlNbLdYyZQRBPv9\n1tcAx/y343ML6JJKRM81lfXodhJj1zEkB5FA49hdVsdii06Wks0ff6UbJmbZPnmtRX/Vm3CXH8nS\nKTuCTE0t1Xs5+0Ls/FUqsougu3E2ZpGgjCEqJMZbZ6hI8xfJ25i6V2P4wREAVEltyrzkeKqILruR\nLAN/sMFmUq8h2GF39bsNuzBrBjW5WDaw5FpG8saNSfnzZ1l2DwDCrg0QuRujx+zjzCXfnZYLb03x\nbmK7xdTEAkVHCMWq4A/Vv0nmdgJVJalb0pvKenQ75bgtZgwe6qzzSm7i9qo4rRJ5NcL05eYfKnIj\nlsWUPWCZdA03xkmjKAr4FY2iI8Dc2dbwhhcWbDW/d7Ax1HyAwUO2qFZJtO0WTUPQYRda20m4iPYG\nUNBtX/LVG3u1tLpgmhaV5RWKih+vXGkIv9ndCPXZir5QHUndDAparrT1QSL58+eI16Lf9rhIHjkc\nRRQFYv6D5N5+85Zj5ZqSLDm0ps3oXTh7mbLiIeA0GvKeqMXA+bFIZsuYZuOoS7tFoaRhmfbo3YGT\nI3Veza0EfbaSHzvf/FPfZsauk3eE8QgllAZqYO3usz8vM1MLdV7J3mOYJmbJ/t4bfKAx1HyAwart\nBVOi2KSnirfT9EVyYJtZyWBPWuoKQEnxYk5NNlUnZypXJlzJYooyAU9jv/yhPrswNGpd/U3gxcyv\nGUm9UfyblohTXIiRdPcSiroJhPZW8VedCgMHwuSUIOmsQXlmevXPao17kkNvWrtFfi4OQO9A441o\nBwgP2PeCgoBhWqTzlTqvaPeZnIpTdgSRLI1w1+ZNrfvJ4EFbUS0sNt/J4u0kxicxRYlwqLGSj0Ye\ntItFPa81vTd8fimHIXvsOMqhxlDzAYIdXmSzQskRahnbS2NXSbvATjzJAH3VD8WQVmFqoXmOFhLp\nEiHD/oINRuqfQ7oR/pCr2tVvx8AtrOTrvaR7Jl/UoKok+zdQknNvvsmKqxcDcdcHiNyNmuXi9pQL\nl+xCFERkVSORKpJvsuY907QwilXVpo5jkDeidqpiVMc1N2MM3I23xikpPvwOreHU/KGHbGXb1AQ7\npq6JKS/bTe5DhxurX6VntAcsC0N0s5wq1ns5e8r0xWvk1RBuodRQcZSCIOBXdUqKl9m3xuu9nH2h\n6Yvk2mjq9DaUZIChUzVfsoPLs8ldX1e9iKeKuLF34Y04jnotkiTiEjWKkpeAqPHGpfh9ryDk1irJ\nyvpKsmVZpH/2k5tT9vYo+u12hkcjSJJAzHeA7Ntvrf5cEAQ8ihvFqWOYFm9cju/LevaL+UQOU3aD\nZdJ9oKvey1kXn9+JaBlUBDsG7ups80VhpadjAPT0+eu8kjuJdAeRTI2y4mduoXnV5LJmYBl2UTZw\ncri+i7kNhyrjpkhejTB1obnTFeLj01hC46n5AD39tvCYmWqNoSJNXyQ7JAWP7N62khzu9OFAI69G\nmb48u0er23+W0yUUwf4QjI7UJ+rKsizy2ta6xH1OqMguTndILKWKTC3e36p+rqghyHaRfLcIuNLE\ndcrzcyz7h3G5Fbp696docKgygwcjFBwBVpZyVOI3j9N8ihdTtNf96nhsX9azX8yMTdz0YDoaR7VZ\niyAIqzFwAbPIKxeb6wvKsiz06hCRA9WBTo2EIAh45QpFR4DpN5tXQZueXabsCCCbFQLRxsvQDwVl\nTFFiocmL5NKy/T3XaGo+wMhDwwBo+fs/bWorNH2RDLaavB1PMtgfit0hgYrsRpuaahpfcjxdxJLs\n4QSh7sC+Xz9XyfOl81/j//zp7/HW0p3T3W7H77djuUaqMTivXLy/C7R8Sd+0cS/9s5+QcXZQtmSG\nRiP7evRcGyyy5B0m/9ZNNdmjuCkZRUZ6vYxPJ0nn7n9/eI2lMduDGQrI9V7KhvhcAobo4KGIwMxS\njrnE/W8/qrGULGBItgeze7SxcqprdHTb9rTlq3N1XsneMfv2ZUqKD18DWl4Aho7Y743i0v0tlmyE\nphuYVTV/8IHGamAF6DnYjWCZ6JKbZKa5bS/QIkVyUA1QMkqU9O35+AZG7aPXkFbhwuTKXixt30nF\n05QUH26hjCTt78t/aeUq/8+r/x/nE+NYWPzt1RcoGxs3IAWraoZaqeBxyrx6KXZfd/bX7BaSIOGS\n7wyIN0slsq++ykLHSQAOHNnfpo3BgxFkWSTmGyG7xpdci4F76EgAy4LXm8hyUYrbG+h6qDYvL7zO\nf3n7K1v6bAoE7ffLiNsuXppJTZ48X/NgFhsqUWEtB0/Z9qdSuvn84DWS1amCnV2NpyIDjDx8AADd\nkO7r74GNmL6xQlmx1Xx/A/YNybJUtb2EmTjb/GkvLVIk28fV21WTh6o3pCI6+enZ5hhJaiwtoUtO\n/PsYj6ybOt+59gP+y9tfIavl+djB53l26L2kymlenPrRho8N9dp+3EyqyGNHO0nnKlye/f/Ze+/4\nuMor//99y/QZ9S6rWLJcZMvd2AbTTTMQDAQ21M03CdmSsLupP7JZCNlsEjZlISE9IYFAEggk9GqM\ne7flJssqltVt9VGZPnPv/f0x0rjJxrLKzMj3/XrpNSPNzJ2j+cxz73nOc55z4jcncGjjnl22Dxup\nGdi9E39Q47g5n8RkC/lFE5s3bjBIFJak4TUk0NHcjTIQjtgMVbgomWpBAHZMkpSLYEhFGYrazJvY\nqI0v5OPV2rc43FPDuw1rP/b5Q2XgTMEQJqPEjsr2uM/RH6LtYH04BzMp9nIwh8idMQU0jZBgJhCc\nnFVegoM1uKeW5UfZkuFJSLZhUH14jMm0NE/O6gpNe6vwG+w4DLFbojU52YAmiLRVHI22KePOReEk\nJ15ghYuEZCsWIRBpwxjvS8whRcXgCjs9SUkT0+ayw9PJj/f8gjVN60m1pPDVRV/g+oKrubHwWpJN\nSaxt2kCHp+usr0/JD0dSB9wKS2eFI/s7KuM3gjbgCyAYAiSYho/U9G3aSGviTFRNoGxRblROktNm\nhT/zdlshrgP7gBNOsmgIMiM/iSMtfXRPgohac+uJqE1i6sRGzza1bscd8iAgsK55M+3uc1/0h8rA\nDfT7WTQ9na4+H0daJ8cGvqEczMJZOVG25OyYzAYsmge3MYWGw40f/4I4w+0LEhLMoGnkzJwSbXPO\nisMUIihbqN81OXPDe+rCdaDTs2Izmg9QMDM8Tt2dkzftZYiLwklOHnSSR1rhQhAEcnOshCQTsz3t\nbKmIX+cMoGfAj10NJ9unZI3vZjBN09h+fDff3/UTmgZaWJq1iG8s+XcKEsItf02Skdun3UxIU/hb\n7ZtnPU5Cig00DVdQZnpeEkl2I3uqOwmG4jNH3OXzIogqCaYz85H9x47hqTtKa+psjCaJGWXR2bSR\nV5SCwSDSYS9koDyccjHkJLuCbi4pDU9WdlbFfzS5ofxwVKI2ASXI2uaNmCUz9878JIqm8Ertm+eM\nDCdPGZwwelSWzQ5rEO85+jAUzQ/ngxfMK4qyNecmwS6gijKN5TUf/+Q4o766BY8xGYvmwWSO3Yh+\nRk74et5TH9/X47MxFM0viqEmIqdTvGgaAEHFgDpJVrPOxkXhJCdG0i1GHnWZf20ZACZDAtvKG+J6\nebOr14tJCEueVjh+Dpg35OUPh/7M84f/iojIp0vv4cHSf8B8Wg7uwoy5TE8qpqL7MBVdw0cFJEnE\nKgbwiDY0n5dLZmXi9oWoqO8eN/vHE3cwXIN0uBrJ/Zs30mEvxI+RmXOzMZqis5FMliWmzkjHZ7Bz\nvK4d1e+PdN1zBdwsnpGBJArsqIx/B80ZidpMbPOKrcd3MhBwccWU5SzPXszM5BIqe6qp6D57dMzu\nMCFpCi7FyMz8JBJsRnZVdcT9puKGhja8pmSMqhdHUuy1BD+ZnKLwRKXv2OQpCzpE874aVNFAgi02\nl/iHmL4k7KD5XJMv5cXrD8VFNN/mMGNSPHiMybS1TJ79KcNxUTjJSRfQdW+I9JxE0qwhnNYcMlpr\nqYnjfNiuPh+CGK4WkVqQMS7vcbSvke/vfIo9HfuZmlDANy75D5ZkLRj2uYIgcNf02xAFkVdq3yCo\nDn/Ss5s0ArIFz7E2lpYOpVzEp4PmVsIVCU7vtqeFQvRt20Jz6hwEAcoW5UbDvAiRxiLmKXgqD0U2\n7rmDbuwWA7OnptDU7uJ4d3xXWPAPRm2K505c1CakhljTuB6DaOCavMsHx8EnwuOg5g2CyvDNWsJl\n4AJ4ZTvaQD+XzMrA5Q3G/abixj1VBCUzCebYd/ZnLJ0BQMAf247khTDQGv4e5RZOTF32CyVraiai\nGiIg2fH7J1djo6M18RHNB7CZFUKSibodldE2ZVy5qJzkvguIJAPMWxGeuWZqQlxv4OtyugnKdkyq\nb8wHoKqpvFv/IU+W/5IeXy83FV7Llxb+M2mWc288y7FncUXucjq93axr2jTscxIdYVt7mjoozHKQ\nmWxhX20XvkB8RRIUVcWvhUvmnB5Jdu3fS0/QTL8hhcJpaSQkTeDOymGYUpiM0SjSPphyYRtMtxgI\nhp3iofzwnYfjd/OM1x9CEcygqRMatdnZVk6vv48VuUsjk6UsWyZXTbmMLl8Pa5uHHwcwVAbOQN/R\nFpbPDq8GxXuVC+dge9usGG0JfjJJaQ4Mig+vIRFXnyva5owpQX94klK0sDjKlpwbSRKxCl48xiTq\n9k6utJfmvTWoohzz0XyAjNzweO2qj8+A1flyUTjJNoMVWZQvKJIMMG1+PhYxSK89n47yg3jitC3v\nQGsbfoMNu3FsncsOTxdPlf+at+o/IMHo4N8X/BO3FN2AJJ5fKaebp16P3WDj3ca1w6bEDBW172lq\nRxAElpZmEgip7Ks9+4a/WMRzSo3kUyPJfZs20ZxYCsDcJdFfZpMkkaIZGQRkK81VLdilcKqMe9BJ\nnl+ShkEW2Xk4fiss1Fc1RaI2E5XaoqgK7zeuQxYkVuZfecpjq6auxGGw837DWpy+4VesknPCk87m\nTbtOmTB6/fE1YTyZoCe8T2L6YJ5jLCMIAlY5QEC2UjOJImjOfi9ByY6kBkmbEtuRZICEwSoozfsn\nV3WF/tZwGmFOQWx3wwWYsaQEAN8kbypyUTjJgiCQaEy4oJxkAFEUKZ2diiIaKPQPxO1Sf6A9PACT\nE8Yminzc3c6zh17kv7f/kLq+euanl/Gfl3yJkuSRbb6xGizcVnwTASXAq0fePuPxtOnhpfDjtcdQ\nXK5IysX2ONPh5JbUJ3fbC/Z001N9lE57IWkZdrLzJr7Jy3CUzA6nXLTJWUiN4dxdVyDsJFtMMvOK\nUzne7aG5Iz4jakNRm0T7xJ0GyzsO0OXtZlnOksgK1xAWeXAcqMFhxwFA8SXTAajpNhNoaWbZ7CwC\nIZW9tfGZF9jn8hGQ7IhqiPTCia0JfqEkp4dXVY5Pok6sR/bW4DUmYhW9MVt27GTyZ4TT0fonWXWF\noC8ccCiOgwljdlFmuFW7aCc4SUsiwkXiJEO4VnJ/YABFvbBZT9nVcxA1Bb8li9074nOJRxgIOzgp\nGaOrbNE8cIzfHXye7+74P3a1l5Nty+Szc+7nc3Pux2a4sI03y7IXU+DIY3f7Pmqdp0YHphSnY5Y1\nWmzTOPb2e2Sn2sjPtHOovgeXN36i+m7v8N32+rdspiVhBpogULY4OmXfhiMnPwmzUaDDVoBn/wHM\nkglX8EQO8iVDJfnitGby0OarnKlpE/J+qqbyXuNHiILIdflXDfucpdmLKEjIY0/HfmqdZ7bezclL\nIjtdpseaQ/WrH8Z9lYva8mq8xkRsohdRjI/LUdH8cD1td5cHVY39POrz4fihcEm75GRTlC05P2Yu\nnQ6ahj8U23m7I2Fowhgv0XxBELDgwWtMoK68OtrmjBvxcVYaA5JMiWhoDAQvLOplsRqZmi3jMzhI\naa6jqT2+ZrCBoII8WDYtNe/CnIL6vkZ+uf8PPLHrKfZ2HiTPkcvny/6Rb1zyHyzMmDsq504URO6a\nfhsAL9e+fspkxmCUWHxFEYpoYN+BbkL9/SwrzUJRNXZXxU9OrMsXRJDDkeShdAtNVenespXWxBlY\nrDLTSsdnQ+WFIIoiRbMyCcoWmiuaSDDa6fR2RaLJc4tTMRsldlZ2xGXKRdAftrlkgqI2BzoP0eZu\nZ0nmgrPm6ouCyN2RcfDGsJP6ZTeGK+5UdNtIHOhianYChxp66HOfu3tlLHK8ogGAlJT4cM4Aps0r\nRFb9OE3Z7Hl9Q7TNGRM83eHrYkEM16k+GYvNhEl14zYm0XjwSLTNGRPq9tbgMyZgE+Ijmg+QmBQe\ntw174jNweD5cNE7yaMrADbHw+rkAmA0ONpfH11Jbd78PSQzPutOKz/9EqGkatc46nt77W3605+dU\ndB+mKLGQL8z7LF9f/DDz0mcjCmPzNZqamM+y7MW0uo6z+diOUx4rXZSH3aTRap9G8xvvcclg9YV4\nSn0Jd9sLICBilcMb8zxVh2kJJBESjcxekIssx1ZL3pLBzWHHlGSuMczErwR4q/4DAIwGiQUl6XT3\n+6g7dmH5/tGit9+LX05AVgMkZyeP+/tpmsZ7jR8hIHBDwdXnfG5hQj7Ls5cMOw4AsnITmZJhoM+S\nSdVrH7GsNBNNg11xGNF3d4UnXFNnRz8P/3yRJJE5i7JRRZmj5c0EA/E3OTkZTdMIDtapLl4Q+8v8\nQ6Rn2VBFA3te2TgpIvrHBieMSXESzQeYvixc7aWn3c9Az+RobHQ6F42THCkD57twIdNykkm3+Om3\nZNGzYw+BYPwkrHf1+QhJFmQ1gC3x41MiNE2jsruaJ8t/yVN7f02Vs5aZySX8x4J/4ssL/4XS1Bnj\nMtu9rfgmzJKZt46+H4lYQvjCtHTlDDRBYl+1mwTVy/QpidQ099LTHx+d38I5yX7MoiUysejbtJHm\npFmIAsxeEHtRnKwpiViM0GEvYHqrSpY1g82t22kZCFd5ideSfLV7wq1fbaJvQqI2lT3VNA+0siCj\njEzbx68WfKL4xmHHwRDLbg5P2Ct67CxIDCAI8Zejr2oaAdUImkbRwvhxzgCWXV+GWfPQY8tn47PD\n54/HC+3tTnyGJEyKG4sjulV1RsLKey9DVv10mvLZ/Ub8R/TdcRbNB5ixoBCH7KXPksWG374RbXPG\nhYvPSb7AChdDzB8sB5caUtlTEz+bZbqPdeE3OLAI/o91Co701vOD3U/z8/3PUNfXwJzUWXx10Rd4\neMFDlCQXj6tTkWB0cHPRdXhCXt44+t4pj5XMySbZBm22qdS9+j5LZ2ehET9lyNy+8MY9mxxOtVBc\nLhqrjuExJjFtdiZWe+xFEERRoLg0k5BkoulAE58s+QQaGq/UvoGmaZQWJmO3GNhV1YGqxk/KRVtl\nEwDJqePfnl3TNN5rWAvAjYXXntdrTh4Hb542DgDSMx0UZBsYMKfR+N5WSgtTOHqsn3anZ0xtH09a\n69vxGpOwqG5MFmO0zRkRgiBw2ao5ABw7pjDQHb/184/sOExIMmI3xk/QB8BiMUYi+nXlx/B74yNY\nMhyaphEIDUbz42jCKAgCN9y7HEFT6AimcHRvVbRNGnMuQid5dEsCRQuKsOCn35rLgQ3lY2HahOBs\nOIYmiNgt53Zwu7w9/HL/72kaaGF+ehmPLPl3/mXe/2Nq4sQ1W7gy91KybJlsPbaTpv6WyN8FQeDS\nm2YDsL9BZUG6GO78FifLzH1eL4KkRCpb9G/fRrM9vFw1d3HsLjeXlIUjGy1eG9NIpSytlNreo+zt\nPIgsiSyekU6/O0B1U/x0IXP3hKOzhaXj/7nX9h7laF8jZWml5Nqzz/t1Q+Ngy7GdNA+0nvH4spvn\ng6ZxyGnn0ozwBGVHHG3gq91ZiSrK2C3xM7k6menz8kk0eHCZ01j/+3eibc4F03k0XGc7LXtiu06O\nBcuuL8OKh15rLuviOJLZ2dGLNw6j+QDpOUlMyZLwyzb2vLp9UqS+nMxF5CQP5SSPLpIsigKzS1NQ\nRRlLa1vcRG58HWEHJiX17KkWiqrwXOVf8Cl+7p95Fw+VPUCeY+I7v0mixN0lt6Gh8dea11G1E4Mu\nrziNzCSBbksOLe+uZ/bUFBrbBmjriX0d+nzhzZ6JJgeaptG6eRfdtilkZdsnvC3ySMjMScBq1Oi0\n59NXXs6d025FFiT+XvsWASUQd1UuNE0joBhBUydkmf9EFPmaEb1OEiXuGozc/7Xm9TM2R6ak2SjK\nNeI2JSMeOIRRFtlWGT91q3uawnXOswtifyf/2bj+vksRNIVOXyIt1Y3RNueC8PaHc6qnDVbtiCcE\nQeDqTy4ETaWt10xb/ZmTyXigdsdhFMmIzRCfpdRuvP9yDKqXbks+2/66NtrmjCkXjZM8tHHvQrvu\nnUzZynmIWoiQOZOtO2pHfbyJQHGHS49lFpy9ssV7jR9xtK+RhRlzWZa9eKJMG5YZKdNYkF5GfX8j\nu9r2Rv4uCAKX3TofgP2tBpZlhTe6xUNO7FBllWRLAv6Geo76wxUO5i2buCj9hSAIAtNmZaCIRo7u\nOUKaJYVr8q/A6e9lTeN6puclkWQ3sqe6k5AS+1GE48e68RqTME/AMn99XyPVziPMTC6hMCF/xK+f\nmVLC/PQ5HO1rYFf73jMeX3brAgRNparPwfJMaO/x0NAWH5V3/J6wMz9jcPNPPJKWlURWiobfYGPH\nS/GXF6uoKgHBgqiGyJmZF21zLoj8aVlkJATxGhPY+sJH0TbnguisG4zmx3Cw5FzIBolFKwrRBJH6\nql7cffFxDjofLhonWRZl7AYbvYHRO8lmq5Gp6QJ+gw3n9v0ocbC8oIXCaRYZxcNHho/2NfBu/Yck\nm5K4Z8YdMVGC5o6SWzCIBl6textv6ES+WWZuIvkZEv3mNMT9+zHKItvjIILmCYWd5ESzg86Nmznu\nKMZuESksmZg6vaNh+oKwg9c8YKbn7Te5oeBqEo0O1jStx+nvZcnMTNy+EBX1PVG29OOp2z64zG8a\n/+/Lew3hi/ZIo8gnc8e0WzCIMq8deRtf6NS8y8RkK9PyTHiMieS1hfOs46FmstfjxysnYFB8pObE\nfnexc3HDA5cjK3665GwqNp45kYllGqtb8BoSsWjumKusMxJu+vRVyIqfTimLAx/uirY5I8YzEI7m\nFy8YWSOuWGLBFbNwiG4GzOl89Lv43sx6MheNkwzhvORef/+YOFOLbgpHMw1YOBjj3a68/hCKaELQ\nFBKzz7wgeUM+nj30FwD+sfRTWC+wIchYk2JO5oaCqxkIuHi3/sNTHrv0E+EI2uEeO8sGI2hN7bHd\n+c2rhlNCEjBTfbgHVTRQtrQQUYz+hOTjSMu0k5RkosMxlYoP9xEo38dtxasIqiFerXs7UuViZxyk\nXHQ2hDd6pk9JGtf3aR5opaL7MMWJhUxLuvCLX6olhevyr6IvMBBxuk9m6a2LEDWFRl8yubjZebg9\n5jdRVu+qIihbsE5QdZHxxGI1MW1GAqpooGJtZVzlZB7dUwOCgMMW366A1WZiRmkyqmjg0KZalFD8\nbEJUNY0A4Wh+XunIV5tiiesfuBRRDdHuTaTh4JnNkOKR+B4ZIyTJlEBACeBTRr8LNjU3lVSDG5c5\nnUPvbxsD68aPzq4BAgY7ZsUzrEP2UvVrdPucXF9w9YhbSo83K/OvJNWcwrqWzRxztUX+npxmPxFB\nOz4YQatsO9thYgK/5gXAfriZJts0JEFj1vzz38gVTQRB4Po7yjAaRQ5nrqDiL+8yx+1gakI+ezsO\nEDR3kJ5kZm9NF/4YL43oc4Xz/mYsHt985PcjUeRrR+0IXldwNSnmZD5q3kS759RJuSPRzPQCMz6D\ngyWhbvrcAQ43xvYmypbBDm9JafG1SelsXHnHMsyKC6cll61/jZ8lf2dr+HuSNUFdJ8eTy1cvwaK6\n6LXksO4P8RPJbKxpwWtIwKK5kKT4dskyspPJzRQIyhZ2/m1bXE0Yz0Z8KzJCEseoDNwQiy4vAUDr\ndNPr8o/JMceD4zVNKKIBs3ym87KrbS+72sspSMjj5qnXRcG6c2OQDHyy5FZUTeWn+35D82B9XoBl\nnxiMoAVSydP62Xm4AzVGUy6CIRVVDE/OnHva8RvszJiViskcP21VUzPs3Hz3PCRZ5GD6FVT+5i98\nMvUKAP525E0Wz0zHH1Q4UNcdZUvPTkhR8Ik2JCVAVvH4TVDa3O3s66wg35HLrJcXgSAAACAASURB\nVJTpoz6eUTJwx7RbUDSFv9W+ecbjl9yyCElT6CKDlEA/2w/F9oRxoDs8YSyaWxhdQ8YIURRYeGUx\nAPXVvQR8sXs9OBn/YLxo5tKZ0TVkDBAEgRW3loGm0nJcw9ke+6lfAEd3h6P5duvkcMdueOBKjIqb\nblMOO17dGG1zRs3kUOU8SRqDrnsnU7SkBLPqwWXNYutH+8fkmONBT2N4CdzukE/5e7e3hxerX8Uo\nGfl06T1IYmzmpM1Nn81dJbcxEHDxVPmvqHWGl3HsCWZKp9nwyzYWBPtwDvipbY7NeqXhGsl+kvpD\nNPrDUZt5K+KnHuYQWVMSueHOuSCJ7E1aTt+zb7MiZT6truMYM8Pl+mJ5E2X9wXr8BgdWPOO6zP9+\n4zo0tDGJIg8xP30OM5Kncai7iteOvHNK1RdbgpmZhWb8spVFQSd7ajpjutmRXzMhqqG4zsE8nXkr\nZuGgH5cpjbXPxH5JOL8/iFd2YAx5SM4a/66TE8G0sgLSzB68xkTW/eH9aJtzXjhbw858VmH8R/MB\nDEaZBZcVgCBSW9GNz+ONtkmj4iJzksc2kiwIArNmJKIJEt3lNTG7cWyok09yVmLkb4qq8Gzli/gU\nH3eX3EaGNbYH6FV5l/H/Su8hqAb52f5n2NdZAcCSWxZh0IJ0Sllk+Xpi1kFzDbaknlFroc+SSW6q\nSFJKbOR+j5T8ohRW3jYbRTSwy7iAsg87sAgmtnRuIDvDwIG6bjy+2CxlVL83PMFyJMof88wLp8vb\nze72feTYsihLKx2z4wqCwL0z7yTDksaapvX8Yv/vcQdPlD5c8onFyFoItzkLi8vJviNdY/beY8nx\n+uPhzWKqC4MxflZSzoer77oEQVM43mPA2Rbbkcya3dWEJDMWMT6i3ufLTZ+5JryRUk2jZmdltM35\nWHyDPuSs5fEfzR9i4VVzSBD6cZtSWfPr+El9GY6L00keRWvq01mwajGSGsQvp1JVc3zMjjuWBL1h\nhyVn2ol2lx80ruNoXwMLYqDc2/myOGsB/zL3/yEKIr87+Dxbju3AbDEwd04yIclEmeJiV1VHTJYh\nc3uDSJIPS394SXb+VbOibNHoKJ6ZwRU3TCcomdnpLeGOyiTcQTcJRfWEFJW9MbqZta8tPPZzpo1f\nqsUHjetRNZUbCq6OtB8fK9IsqXxt8cPMTp3J4Z4a/nfXT2l1hc87FpuJ0iILQcnCPL8zZqtcVG+v\nAkHAZovvDXvDkVucRZrFi99gZ91za6JtzjlprgjnhSemjH/XyYnEnmhl6lQLimhg97sHYjovNhAY\niua7Sc6K7yovp3PdfZchqkHaPHaaDsdnDXG4WJ3kMSgDN4TJYiInIUhQtnDwrdjcwBdUJdBUcmeE\n62Ae7WvknYYPSTIlcm+MlHs7X2alTuffF3weq8HCn6v+xnsNa5l/w3zM+Om3TCHZ2c6hGCxD5vKG\nKG4L0G0txGEIkjdt7CP3quJH0yZuiX32glyWrsjDb7BR2z2Ny+osNKuHECwDMdtYxBuUQNPGLWrj\n9PWy/fhuMixpLMycNy7vYTVY+Oe5n+amwmvp9vXwo90/Y0/7PgAWfWIJBi2A35pDa1V9eAUjxuhq\nDo/PzML0KFsyPtzw6avDkUwlhaMHj0TbnLPS3xXuOpk3a+IbRo03195zOZZQP32mLDa9uC7a5pyV\n6kkazQfImJJKbjqEJDNbX45N3+h8GL81xwlAUxVCwT4kyYoof/xsOGkMG4qczPLbFtP8wiG8LgG3\nN4BtnBsUnC915VXsfXs3XjkLk+LFaDZGyr1pmsanY6jc20goTMjnKwv/laf3/Y43j75Pf8DFggV5\nbNvbz3TVz47KduaNgxM6Gvq9XrKP59JrEJm7MHNUExMl6Cbo6yTo6xq8Df+oofBFT5QsiLINyWBD\nkm3D3hdlG5JsR5RG911duKIY74CPA/vBfnwehcattJTUUHnQQb8nQII1NsYCQFPlUbxyAuaQC3ui\nbUyP7Q35qOutZ0PrVhRN4fpxiCKfjCiI3FJ0A1Mcufyx8kV+f+jPNA208omiG5k9zcq+uhCzXO3s\nrurgqgWx4QQdO9LM9le20KskgQSll41dKgqEOym2eTqo6qmleaAVh9FOqjmZFHMyqZYUUszJmEb5\nfT8fHEk28nIk6ttldr62h6Ky2Nx74FeMCKIy5pv2PEEP1c46apxHAIF0ayoZljTSLamkWlKQxfF3\nOwRBYMnKGWxcd4z6Iy4W97uwJdjH/X1HSnNFE2AlIXlso/mKqtA00EJVTy1d3h5SLclhDaxhHSbq\nun/Dp6/mhf99F6chk+2vb2HZbZdNyPuOJTHtJGuaihLsJ+TvJRQI/yiB3sjvSnAot1jAaMnC5CjE\nbC/EZM9HlExnHM8iWzCIBnp8vQTVEIYxGqypeRkksZVeUwpbX9/KdZ+6akyOe6FUbNhH5YZDOOUM\nVMMUJDVAyZyw0/hyzet0+3oGy70VR9XO0ZBpy+Cri7/Az/b9jg0tWxjInod9bwoD1hx69x/Gf9NM\nTIbY2YjY296MW5yGpPqZteL8LkpK0HWSE9xF0NdB0NeFGjqzBbdkTMLsKEbTFNSQGyXkJuT/+JxU\nyZiEJaEYs6MYs6MQURr5yfrSG0vxDeyh5ijkNFzCgG0nrqQ29lR3cnWUHTRFUdj91hYa9jXTJ6ej\nSkZsptFvJPErAY72NlDtPEJNbx3NA62RjXQ5tiwuyVo46vc4H+anzyFr8cP8+uBzfNi0gZaBY9x/\n011U/nQHAWsWB3ZWR91Jrtt/hPK3duEkFUXKQhSCFGSESMkYfZ3q/sAAVT21kZ++wLn3m9gNtrDT\nbE4mxZJMqjmF1EEnOtOaPmYTm5UPXM3zT7yDU85gz/u7WHTDkjE57ljR190fLjsW6sdoHt3EIaSG\nqO9rospZy+GeGpr6W9AYfn+OgECKOYl0Sxpp1lTSLakR5y3NnIJBGrsc9dnLZlCx8TA9QhJrfvc+\nq79855gde6wY6HKDaCVv1pRRH6vL28Phnhqqemqpdh7BGzr7ec4mWyMOc7oldfB+GunWVOyGsQsg\nGAwyc5fns3OXk+r9nSy43o/JcqZvFsvEpJPcXvt82BkO9AHD5xNJhgRM9nwkQxJKoBe/p4WA9zgD\nHdsAAaM1B7OjENOQ0ywaEASBZHMira7j/Mf6/8RhsJNkTiTZlESSKZFkU+Lg74kkmZJIMiWc96At\nWz6VTdudtFdHZ5lZVVX2vLODI3sa6DNmohlzMSheCrJlrrr7KswWI7vb97GjbQ/5jincMvX6Eb+H\npqmoIS9KyIUaDDtiSsiNGnKjKn4QRARBRBCkwfvSKfcRpMjjgmhANiYjm1MRxQs7MSaZEvnSwn/h\nVwf+QHnXfhYXzoSGIqaEQuyv7eSS0qwLOu5YoYQUarYd4Oiuarx9CkFLLgmONgxncd41NYTP1Yiv\nvw7fQB1B35l5vbIpBZNtCgZz+uBPGrIpbdiIcFgvD0rQFdFJCbpPuj+A392Kq2sPrq49gIDJNgVz\nQjFmRxFGaw7CeTgNgiBw9ScX4XtuC03tmcw4tABP2SG2V06LmpPc29nD1pc20N2t4DKlgSkXWfGR\nmxLiuvtG/t0PKEHq+xqp6a2jxllHY38zymBqiyiIFDjymJ5czPTkYooTC89ZKUZTQyiKFzXkQQ15\nAQ3ZlIpkcFzQCkOWLZOvL36YZw+9SEX3YZ488EuumbqIw41GElpa6OrzkpY48fWID205SMVHh+iV\n0lGlbGTFT26yn2vuuRx7woVFsgJKkLreeg47w87AUD42hB3gRRnzmJkynaLEAjwhD91eJz0+J92+\nodsejrnbaBpoOePYdoONOWmzmJtWysyU6aOKOsuyxKyFWew94ObQjibmr1yIJEV/0h4KhqjcWkHN\n9lo0IR2LaeT5upqm0e7pjDhktb11+JVwxzhREClKLGBmSgkzU6ZjEGU6vd10errCt94uOj3dVDlr\nwVl7ynEFBDKs6cxOncHs1JkUJ00ddSBr5T9exd9/t4MOn4OGQw0Uzi4c1fHGimAgwOFtlXgUE4Kg\nMHPpyFuze0Neapx1HO6ppaqnhk7vidKbKeZkFmaUMTNlOjm2LHp8zpM+/7AWzQOtNPQ3nXFcm8HK\njORplKbOpDRlBomm0bXKXnTtPKp3vkqfMZkPfvcutz68elTHG0u8bi+HNuznpn88e/lbQRvnkgya\npvH4449TXV2N0Wjku9/9Lnl55+4Rv+eDryHKNmRjErIpOXw7+COZkpANiQinXYRUNYjf1YTf1YBv\noIGA5xgMzWYFEZM1F5OjkOOKyJ6+Frr9A/T6+3H6w1Hls2E32Eg1pzDFkUOeI5d8Ry45tqwznGdV\nVXnue2/jl2xcdUMBMxeOLEqbnu6gs3Pk/c5DoRBb/7aJpqouBkzhHD9zyEXh9GRW3L4UgyF8kun2\nOvn+ridRNJVvLPl3Mqyn5gNqmoYS7BuMWHahBPrCTnDQHYlMhiOYY/91kYyJGExpYYfPnBq5L8q2\n83IaAkqAZyr+REXXYZbsWIpXTMVqd/OPX7x5RHZcqAYn09/Vy74PdtFxtANvyITLlAKDjqasDZB9\nk5Fb5t8QeX7Q34Ov/wje/jr8rgY0NZxDKggyJkchRktWxCEezYTibGiaSsDdgnfgKL7+ulPGjSiZ\nMTuKIk6zbEw857EUReWNX35Em0sm0V/Pljwf/3PXZ0h2nH/kYLQaVG49SMX6Svq1RIKDUXGb2s/0\nBXlccsM8RPHjnX5FVej0dtHqauOYu4263nrq+5sIDZ4nBASKHLnMSMynKCGXKdY0ZFQ0NYCq+FEV\nL2rIG7lVQh5UxRd2ihVvROPTEUQjBlMqsjn8/Q9PgFIxmFIQzsNZUDWVd+o/5N2GDzGpRmbvuIyQ\nYGDKvGRuvnnRCD7F0emw54NdVG+vp8+QBoKIMeQmd4qFq/9hBaYRpKJpmkZfoJ92dyeNA81U9dRS\n19cQ0cEgysxIzGdmYiHFCTmkmxJgUANNDQxOyiUEUT4xSRdlNES8ip/egJvegIte/wBt3h4OOo/Q\nHwhXApJFmZnJ0yhLK2VO2qzIfpaR8sL//J0BOYWS7CArz3EhHo6xOB8BNFTUc3hLJb0dbjyCjYB8\nIlI4d6aJy1YvP+frA0qQDk8nx9xtg05ZzSllVLOtqcxOKqIkYQr59iwMqGiKH1UN59kKghzRQBBl\nEGRCmkZvwEWPv59uXx+d/l46vD0c7W/FPzg+jJKRGcnTmJ06k9mpM0gxX1iZunef+YCGTiOJwQ7u\nffTuEb9+LHQIhUIc3n6Yhr1HGXD68Yp2AnJ4omgLOnnw0dvP+XpN0+j199Hm6eBoXyNVPTU09DdH\nVq8skonSpKnMSCqg2JFDosGCpvrRFD+aGhr8/GUQpYgeINIf9NIT6Kfb10+Xr5dOn5MmVxvdJ+mb\n58iNaFCYkH9Bqy3tTZ28/sJeBE3jpn+Yw5RpIwuejNVYcPW6OLh+P8ePtOFxq3jkRBTJyGM/vvWs\nrxl3J3nNmjV89NFHfP/732f//v38+te/5he/+MU5X9Pe3jNqZ0BV/PhdTfhcDfhdjQQ8xznFwRMk\nJMmCKFvRJBMhQSagiXg0Dbeq0BcK4Az56A54aPf14VZDBLTwEURBJMeWRZ4jN+I459qz2fDceo50\nGEkVurj7//vkiOwd6ZfA7/Oz4S/rON7sw2MML1vaQr1MX5TH0hsXnuJcqprKU+W/pq6vnvtm3MHi\nlCJCvi6C/q6IUxzyd5/94i2ZkeTBnNbB3NYT9+1IshVBMoOmhjeOnXaraSpoyin3VTVAyN8Tfm9f\nF0rozJbSgmQ64TwPRtok2YooWwfza62RCKqiKvyp6hWa99SS0roCq7+LO752Kw7r+DpoqqpSu6eG\nmm2H6XcG8MjJhIYiUJqKVfOQmmVFLhV50/0OD866k7m2ZLyD0eKQ/8QmQ4M5PZz2kFCM2V5wXo7R\nWKOEvPgH6vEO1OHrrzsppQlkcxoma25Yh8iPPXIrCBIBf4i/P70GZ8iCI1SD7Zrl3H5p2Xm//4Vo\n4Pf52fzyRo7X9zFgDKcVyYqfZFuA5bcuIbd4+BWFoQtPa38zXa4WnO42XL5OQoF+LALYRAGbIGAW\nBeySAYsoYQQELcRIJ4uCaBz8vlqQZMtg3nj4Bw1C/m6Cvm6C/i44Y/OlEA4WmFLDzrMpNTz2pPBY\nCB/XHBnz+zsP8cfKF5l6MAXJtQC7/zgPfOueEdk7Uh0URWHb61upr2jHNaiBOdhPwfRkrrjzUmT5\n7FFURVXo8nbT4Wql291Kv6cDr99JKNCPEQWbIGAVBUyCgE0yYBUlDGiDOowlAppkwasJdId8dAd9\nuFQNl6piNaeSm1hMSdpschKnIp7n2KzZd4SP3m3CGPLwqf+4Bqvj/CPoF+oY9LT1sH/tXjqbenCH\nTPgMCZHHZMWPVfCQmu2g7IrZ5J7UUMcVcNPmbqNzoJVez3Fcvi4C/l4ExXtiLAgCFlHEJhkwCQKS\npnC21d4LQ0CVTHg1gZ6gn56QH7eq4dJUDIZEshMLmZo8k6kpM5Hl8zu3h0IKL3z/LbyGRObPMrL8\ntktHZNGF6KAoCtU7qzlaXkd/jw+PYCcon1jNkRUfVsFLSpadJTcuJC0nFYCgGqLD3UmH+xg9rmMM\neDvw+XsIBgewoJ4yFobOSfJ4jAXRiF+Q6VNCdAU8DKgqblUjJBhItU8hL7mE6WlzcJhTznv1661f\nvUNzr5UkpZN7vnnXiMy50LHg7HBSseEg7fUdeHwCXjkR9aSxawq5sBiC/NuPHjzrMcbdSX7iiSeY\nO3cuq1atAuCKK65g48Zzd2EZixnD6aghHz53I/6BBoL+nsGlTg+K4kFTzn9nqYKIX9Pwqgo+TcOv\naQQ0Db8GBiwYGrNQQiJG05lO37kQBOH86ywrEApZUCQjgqBhwoUpW8KcYUFAA7TwrQYCGq6AC4+v\nixyjGRsKp1/gBUE+JYIrm9OQTcknHOAJcNZUxRd2EnxdhPxdEYch5O8B7ewnYUGQBzehWRFlG61e\nJ66aEJ5QCga1j5HMtUakAaAFQdVMKJIJQdQQBQ2JAAbZj5woYU43IkogaBr9gT5CgX4KDEaEwYuK\nIBoxO4rCOcEJxcjG0edojiWaphHyd+Prr8M7UIff1XjWiRQQnrwYHAiCjYbqPtwBG/j7kUznH3kY\nsQYhAUWxoEgGBEHDqHkwJCpY822IkgBoCNrQmABNUwiF3BDyIGtBLAKYPvYkLyBIJkTJhCiGb4XT\n7w/+Hr5vRpQtg05s2CE+feXrrP+PpqIEBld0Biex4Qlt97C56CfbGHa6rUiSlYAgUe1swtCYhi9k\nwST1j6iW0Yh00EDxGwkNOgFGzY0xTcOaZ2Pokz1xXgI0DUX1owRdCIoPEyGsgoD8sTqI4c9ZMp/4\nzCUTgmiO3A/rYUYUDWhDk3U1NDhBD6Gpg7eaAifd19Tg4MqZCzXoQjuH06FpEBJkNNGAJohogoQm\niKhIg7+LaEiog4/1H+zHoyRhUAYQR5DeMPKxAGrIQEiywOBnKaIga14ks4Ix3YQp1YQghh/T1BCB\n4ACE3EhqAKugYRUExI/RQRCNp+ognvS5R3QwI4iDTmzksw+ddjvM3xVfOD0sOHBODQCCiGiiKfL5\nn6lB+PNXBRFvm5f+NhtiKIBs9p33ZwojHQsCakBAESwoQvicBCBpQWTBh2RVMWaYMSaZEATQ0AgF\nPSjBAUTVh0lTsIkCxo8bC4IYPs+Ip3/uQ9//k3WQB7/jJ33Wkc89/HdO0kFVAoPpeC5U5dz7N0Ia\nKKIJRDnyvT+hxYmxoSGiauDcH8CPDbPYCyPIPhr5tVlAVQyEJHNkLICGQfMhSQEkh4gp04LBFvZt\nrlvxr2c91rh7Py6XC4fjRE6LLMuoqnrOZc/09NHlwAyPA0gH4qMm8MXHkD6jYzbANaM+jE6EBGAq\nsHJErypbMS7GXCQkAvmjPsrEbB3U+Vguj7YBOjoxwpXRNmDkjHudZLvdjtvtjvz+cQ6yjo6Ojo6O\njo6OTrQZd2914cKFbNiwAYB9+/Yxffr08X5LHR0dHR0dHR0dnVExodUtAL7//e8zderU8XxLHR0d\nHR0dHR0dnVEx7k6yjo6Ojo6Ojo6OTryhJwfr6Ojo6Ojo6OjonIbuJOvo6Ojo6Ojo6Oichu4k6+jo\n6Ojo6Ojo6JyG7iRPIAMDA7hcI2syojO26BpEH12D2EDXIfq0t7ezZs0aVHUsu9bpjARdg+gTyxpI\njz/++OPRNuJi4De/+Q0/+9nPcDqdFBQUYLPZom3SRYeuQfTRNYgNdB2iz29+8xt++9vf4vf7kWWZ\nvLy8827xqzM26BpEn1jXQI8kTwDbt2+npaWFZ555hsLCwpj6Alws6BpEH12D2EDXIfr4/X46Ojr4\n7W9/y+WXX47T6cTrPXcLYJ2xRdcg+sSDBnokeZzo6enBYrEA8MILL5CUlMShQ4dYt24dO3fuxGw2\nM2XKFL374DiiaxB9dA1iA12H6NPa2kpDQwOZmZlUVlby8ssvo6oq69ato6uri23btiFJEgUFBdE2\nddKiaxB94k0D3UkeB1pbW/nJT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lL2EwGFi2bBkPPfRQ1gakqiq7j/lY1VqJ3Srd1grVjsMefvCbToxGhT+7cwNr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\nIRbxVBFsFapSeXMandXocIoFxnyynfhezORSGsmbRkLa3D1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9vxLN5OGrMxLuhvnmnJJX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\nO1UBBQDEh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u3euXfFx2ciN3S4iIZEN1AbHbbkYqLfJNfgVOXphGWhTzOp1uPqNBhwpnmebH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\n67+U2wUkrwHxwYMHEY/H8fjjj+Pzn/887rvvvnw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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mc.ac.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/tutorials/forecast_to_power.ipynb b/docs/tutorials/forecast_to_power.ipynb new file mode 100644 index 0000000000..c26524e568 --- /dev/null +++ b/docs/tutorials/forecast_to_power.ipynb @@ -0,0 +1,1088 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Forecast to Power Tutorial\n", + "\n", + "This tutorial will walk through the process of going from Unidata forecast model data to AC power using the SAPM.\n", + "\n", + "Table of contents:\n", + "1. [Setup](#Setup)\n", + "2. [Load Forecast data](#Load-Forecast-data)\n", + "2. [Calculate modeling intermediates](#Calculate-modeling-intermediates)\n", + "2. [DC power using SAPM](#DC-power-using-SAPM)\n", + "2. [AC power using SAPM](#AC-power-using-SAPM)\n", + "\n", + "This tutorial has been tested against the following package versions:\n", + "* Python 3.5.2\n", + "* IPython 5.0.0\n", + "* pandas 0.18.0\n", + "* matplotlib 1.5.1\n", + "* netcdf4 1.2.1\n", + "* siphon 0.4.0\n", + "\n", + "It should work with other Python and Pandas versions. It requires pvlib >= 0.3.0 and IPython >= 3.0.\n", + "\n", + "Authors:\n", + "* Derek Groenendyk (@moonraker), University of Arizona, November 2015\n", + "* Will Holmgren (@wholmgren), University of Arizona, November 2015, January 2016, April 2016, July 2016" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are just your standard interactive scientific python imports that you'll get very used to using." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/holmgren/git_repos/pvlib-python/pvlib/forecast.py:22: UserWarning: The forecast module algorithms and features are highly experimental. The API may change, the functionality may be consolidated into an io module, or the module may be separated into its own package.\n", + " 'module, or the module may be separated into its own package.')\n" + ] + } + ], + "source": [ + "# built-in python modules\n", + "import datetime\n", + "import inspect\n", + "import os\n", + "\n", + "# scientific python add-ons\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# plotting stuff\n", + "# first line makes the plots appear in the notebook\n", + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "# seaborn makes your plots look better\n", + "try:\n", + " import seaborn as sns\n", + " sns.set(rc={\"figure.figsize\": (12, 6)})\n", + " sns.set_color_codes()\n", + "except ImportError:\n", + " print('We suggest you install seaborn using conda or pip and rerun this cell')\n", + "\n", + "# finally, we import the pvlib library\n", + "from pvlib import solarposition,irradiance,atmosphere,pvsystem\n", + "from pvlib.forecast import GFS, NAM, NDFD, RAP, HRRR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Forecast data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "pvlib forecast module only includes several models. To see the full list of forecast models visit the Unidata website:\n", + "\n", + "http://www.unidata.ucar.edu/data/#tds" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Choose a location.\n", + "# Tucson, AZ\n", + "latitude = 32.2\n", + "longitude = -110.9\n", + "tz = 'US/Mountain'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define some PV system parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "surface_tilt = 30\n", + "surface_azimuth = 180 # pvlib uses 0=North, 90=East, 180=South, 270=West convention\n", + "albedo = 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "start = pd.Timestamp(datetime.date.today(), tz=tz) # today's date\n", + "end = start + pd.Timedelta(days=7) # 7 days from today" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define forecast model\n", + "fm = GFS()\n", + "#fm = NAM()\n", + "#fm = NDFD()\n", + "#fm = RAP()\n", + "#fm = HRRR()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Retrieve data\n", + "forecast_data = fm.get_processed_data(latitude, longitude, start, end)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the downloaded version of the forecast data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " temp_air wind_speed ghi dni \\\n", + "2016-07-27 06:00:00-06:00 30.149994 1.916377 0.000000 0.000000 \n", + "2016-07-27 09:00:00-06:00 28.450012 0.990202 374.978175 468.494996 \n", + "2016-07-27 12:00:00-06:00 27.450012 3.230511 765.140377 427.065650 \n", + "2016-07-27 15:00:00-06:00 32.350006 1.501533 325.739565 18.348873 \n", + "2016-07-27 18:00:00-06:00 46.050018 4.222464 240.470023 87.506983 \n", + "\n", + " dhi total_clouds low_clouds mid_clouds \\\n", + "2016-07-27 06:00:00-06:00 0.000000 7.0 0.0 3.0 \n", + "2016-07-27 09:00:00-06:00 150.358998 20.0 0.0 7.0 \n", + "2016-07-27 12:00:00-06:00 375.447762 25.0 0.0 6.0 \n", + "2016-07-27 15:00:00-06:00 308.994555 99.0 0.0 77.0 \n", + "2016-07-27 18:00:00-06:00 198.519719 68.0 0.0 44.0 \n", + "\n", + " high_clouds \n", + "2016-07-27 06:00:00-06:00 5.0 \n", + "2016-07-27 09:00:00-06:00 17.0 \n", + "2016-07-27 12:00:00-06:00 22.0 \n", + "2016-07-27 15:00:00-06:00 96.0 \n", + "2016-07-27 18:00:00-06:00 64.0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecast_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a ``pandas DataFrame`` object. It has a lot of great properties that are beyond the scope of our tutorials." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JMgeA/+Eqr8Biiu87NVvhCxhd3+1aBxqHPu1U+E5AbimIhkXEovN1X8IhESfK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4l/eno6/Pz8EBkZCYFA4NhlobKG8RvckaGsrAx1dXWOnaoFCxYgMjISb775JgBAp9Oho6MDYrH4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\nIT7h/wOoskLCYmbHrwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "forecast_data['temp_air'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the GHI data. Most pvlib forecast models derive this data from the weather models' cloud clover data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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gs9kwNDSEYDCo9LoMT/T5rq3/FqqtCNN5dIU8mq6LSC2JdB4SgIBH7S4ozIATEZG5NRSA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\nKFQ78g8dOoQbb7wRALB06VIcP378oj9TDBYUt90Ik0GL070RvP3Xi+8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ghi = forecast_data['ghi']\n", + "ghi.plot()\n", + "plt.ylabel('Irradiance ($W/m^{-2}$)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate modeling intermediates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we can calculate power for all the forecast times, we will need to calculate:\n", + "* solar position \n", + "* extra terrestrial radiation\n", + "* airmass\n", + "* angle of incidence\n", + "* POA sky and ground diffuse radiation\n", + "* cell and module temperatures\n", + "\n", + "The approach here follows that of the pvlib tmy_to_power notebook. You will find more details regarding this approach and the values being calculated in that notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solar position" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate the solar position for all times in the forecast data. \n", + "\n", + "The default solar position algorithm is based on Reda and Andreas (2004). Our implementation is pretty fast, but you can make it even faster if you install [``numba``](http://numba.pydata.org/#installing) and use add ``method='nrel_numba'`` to the function call below." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# retrieve time and location parameters\n", + "time = forecast_data.index\n", + "a_point = fm.location" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "solpos = a_point.get_solarposition(time)\n", + "#solpos.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The funny looking jump in the azimuth is just due to the coarse time sampling in the TMY file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### DNI ET\n", + "\n", + "Calculate extra terrestrial radiation. This is needed for many plane of array diffuse irradiance models." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dni_extra = irradiance.extraradiation(fm.time)\n", + "\n", + "#dni_extra.plot()\n", + "#plt.ylabel('Extra terrestrial radiation ($W/m^{-2}$)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Airmass\n", + "\n", + "Calculate airmass. Lots of model options here, see the ``atmosphere`` module tutorial for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "airmass = atmosphere.relativeairmass(solpos['apparent_zenith'])\n", + "\n", + "#airmass.plot()\n", + "#plt.ylabel('Airmass')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The funny appearance is due to aliasing and setting invalid numbers equal to ``NaN``. Replot just a day or two and you'll see that the numbers are right." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### POA sky diffuse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the Hay Davies model to calculate the plane of array diffuse sky radiation. See the ``irradiance`` module tutorial for comparisons of different models." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "poa_sky_diffuse = irradiance.haydavies(surface_tilt, surface_azimuth,\n", + " forecast_data['dhi'], forecast_data['dni'], dni_extra,\n", + " solpos['apparent_zenith'], solpos['azimuth'])\n", + "#poa_sky_diffuse.plot()\n", + "#plt.ylabel('Irradiance ($W/m^{-2}$)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### POA ground diffuse\n", + "\n", + "Calculate ground diffuse. We specified the albedo above. You could have also provided a string to the ``surface_type`` keyword argument." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "poa_ground_diffuse = irradiance.grounddiffuse(surface_tilt, ghi, albedo=albedo)\n", + "\n", + "#poa_ground_diffuse.plot()\n", + "#plt.ylabel('Irradiance ($W/m^{-2}$)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### AOI\n", + "\n", + "Calculate AOI" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "aoi = irradiance.aoi(surface_tilt, surface_azimuth, solpos['apparent_zenith'], solpos['azimuth'])\n", + "\n", + "#aoi.plot()\n", + "#plt.ylabel('Angle of incidence (deg)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that AOI has values greater than 90 deg. This is ok." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### POA total\n", + "\n", + "Calculate POA irradiance" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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goKrWhcrK5GTXO8NqNffIdaeKnrL/J07Vh/4nEIzbejWSCIfbj/LyeoiiEJdr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\n8/Hxj3+cdevWkZ+fTzQavWBMupz79PLww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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "poa_irrad = irradiance.globalinplane(aoi, forecast_data['dni'], poa_sky_diffuse, poa_ground_diffuse)\n", + "\n", + "poa_irrad.plot()\n", + "plt.ylabel('Irradiance ($W/m^{-2}$)')\n", + "plt.title('POA Irradiance')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cell and module temperature\n", + "\n", + "Calculate pv cell and module temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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GeDAGQLnLQiqdYjgyQp29BlXJ31tCS2kjAAeGJWmei0w9c2l1BA3tLUNNTrbW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gAAAg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\nRbMQQgghhBCtkKJZC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temperature = forecast_data['temp_air']\n", + "wnd_spd = forecast_data['wind_speed']\n", + "pvtemps = pvsystem.sapm_celltemp(poa_irrad['poa_global'], wnd_spd, temperature)\n", + "\n", + "pvtemps.plot()\n", + "plt.ylabel('Temperature (C)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DC power using SAPM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get module data from the web." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sandia_modules = pvsystem.retrieve_sam('SandiaMod')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose a particular module" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Vintage 2009\n", + "Area 1.701\n", + "Material c-Si\n", + "Cells_in_Series 96\n", + "Parallel_Strings 1\n", + "Isco 5.09115\n", + "Voco 59.2608\n", + "Impo 4.54629\n", + "Vmpo 48.3156\n", + "Aisc 0.000397\n", + "Aimp 0.000181\n", + "C0 1.01284\n", + "C1 -0.0128398\n", + "Bvoco -0.21696\n", + "Mbvoc 0\n", + "Bvmpo -0.235488\n", + "Mbvmp 0\n", + "N 1.4032\n", + "C2 0.279317\n", + "C3 -7.24463\n", + "A0 0.928385\n", + "A1 0.068093\n", + "A2 -0.0157738\n", + "A3 0.0016606\n", + "A4 -6.93e-05\n", + "B0 1\n", + "B1 -0.002438\n", + "B2 0.0003103\n", + "B3 -1.246e-05\n", + "B4 2.11e-07\n", + "B5 -1.36e-09\n", + "DTC 3\n", + "FD 1\n", + "A -3.40641\n", + "B -0.0842075\n", + "C4 0.996446\n", + "C5 0.003554\n", + "IXO 4.97599\n", + "IXXO 3.18803\n", + "C6 1.15535\n", + "C7 -0.155353\n", + "Notes Source: Sandia National Laboratories Updated 9...\n", + "Name: Canadian_Solar_CS5P_220M___2009_, dtype: object" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sandia_module = sandia_modules.Canadian_Solar_CS5P_220M___2009_\n", + "sandia_module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the SAPM using the parameters we calculated above." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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kvT89WlyXVChovka304qpWEb3vWjk/VfTwYHpcwGH+sdwbk+LgauZZpb9b0Tc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\nhMCichviiRROEdL0Jja6EZ4RBTLLC8gIpHmeR32rD0adCk47WUOIJmJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "effective_irradiance = pvsystem.sapm_effective_irradiance(poa_irrad.poa_direct, poa_irrad.poa_diffuse, \n", + " airmass, aoi, sandia_module)\n", + "\n", + "sapm_out = pvsystem.sapm(effective_irradiance, pvtemps['temp_cell'], sandia_module)\n", + "#print(sapm_out.head())\n", + "\n", + "sapm_out[['p_mp']].plot()\n", + "plt.ylabel('DC Power (W)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## AC power using SAPM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the inverter database from the web" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sapm_inverters = pvsystem.retrieve_sam('sandiainverter')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Choose a particular inverter" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Vac 208.000000\n", + "Paco 250.000000\n", + "Pdco 259.522050\n", + "Vdco 40.242603\n", + "Pso 1.771614\n", + "C0 -0.000025\n", + "C1 -0.000090\n", + "C2 0.000669\n", + "C3 -0.018900\n", + "Pnt 0.020000\n", + "Vdcmax 65.000000\n", + "Idcmax 10.000000\n", + "Mppt_low 20.000000\n", + "Mppt_high 50.000000\n", + "Name: ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_, dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sapm_inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']\n", + "sapm_inverter" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 200.0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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B66+/XsvzqkY0GorV4ACU7HSUgTQZUCu3GgKARZLgc9sZSBMRkWmsGUgPDAzg61//+jm/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\nTbgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p_ac = pvsystem.snlinverter(sapm_out.v_mp, sapm_out.p_mp, sapm_inverter)\n", + "\n", + "p_ac.plot()\n", + "plt.ylabel('AC Power (W)')\n", + "plt.ylim(0, None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot just a few days." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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qiAsKCnD+/HksXboUx48fR2lpKYqKivCrX/0K4+PjcDgcaGpqQm5u7m2fp79/1KvQvmI0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\n45J5870yXnvnGA+m9bK6pNt2ru46V+oarS7DMlb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p_ac[start:start+pd.Timedelta(days=2)].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some statistics on the AC power" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 57.000000\n", + "mean 29.373181\n", + "std 40.204146\n", + "min -0.020000\n", + "25% -0.020000\n", + "50% -0.020000\n", + "75% 54.245105\n", + "max 161.882005\n", + "dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_ac.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "<3 * Hours>" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_ac.index.freq" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5022.81403518355" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# integrate power to find energy yield over the forecast period\n", + "p_ac.sum() * 3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/pvlib/__init__.py b/pvlib/__init__.py index 6311f9b7be..d8b0657822 100644 --- a/pvlib/__init__.py +++ b/pvlib/__init__.py @@ -4,6 +4,7 @@ from pvlib import tools from pvlib import atmosphere from pvlib import clearsky +# from pvlib import forecast from pvlib import irradiance from pvlib import location from pvlib import solarposition diff --git a/pvlib/forecast.py b/pvlib/forecast.py new file mode 100644 index 0000000000..ba5c9fd8a7 --- /dev/null +++ b/pvlib/forecast.py @@ -0,0 +1,1112 @@ +''' +The 'forecast' module contains class definitions for +retreiving forecasted data from UNIDATA Thredd servers. +''' +import datetime +from netCDF4 import num2date +import numpy as np +import pandas as pd +from requests.exceptions import HTTPError +from xml.etree.ElementTree import ParseError + +from pvlib.location import Location +from pvlib.irradiance import liujordan, extraradiation, disc, dirint +from siphon.catalog import TDSCatalog +from siphon.ncss import NCSS + +import warnings + +warnings.warn( + 'The forecast module algorithms and features are highly experimental. ' + + 'The API may change, the functionality may be consolidated into an io ' + + 'module, or the module may be separated into its own package.') + + +class ForecastModel(object): + """ + An object for querying and holding forecast model information for + use within the pvlib library. + + Simplifies use of siphon library on a THREDDS server. + + Parameters + ---------- + model_type: string + UNIDATA category in which the model is located. + model_name: string + Name of the UNIDATA forecast model. + set_type: string + Model dataset type. + + Attributes + ---------- + access_url: string + URL specifying the dataset from data will be retrieved. + base_tds_url : string + The top level server address + catalog_url : string + The url path of the catalog to parse. + data: pd.DataFrame + Data returned from the query. + data_format: string + Format of the forecast data being requested from UNIDATA. + dataset: Dataset + Object containing information used to access forecast data. + dataframe_variables: list + Model variables that are present in the data. + datasets_list: list + List of all available datasets. + fm_models: Dataset + TDSCatalog object containing all available + forecast models from UNIDATA. + fm_models_list: list + List of all available forecast models from UNIDATA. + latitude: list + A list of floats containing latitude values. + location: Location + A pvlib Location object containing geographic quantities. + longitude: list + A list of floats containing longitude values. + lbox: boolean + Indicates the use of a location bounding box. + ncss: NCSS object + NCSS + model_name: string + Name of the UNIDATA forecast model. + model: Dataset + A dictionary of Dataset object, whose keys are the name of the + dataset's name. + model_url: string + The url path of the dataset to parse. + modelvariables: list + Common variable names that correspond to queryvariables. + query: NCSS query object + NCSS object used to complete the forecast data retrival. + queryvariables: list + Variables that are used to query the THREDDS Data Server. + time: DatetimeIndex + Time range. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + vert_level: float or integer + Vertical altitude for query data. + """ + + access_url_key = 'NetcdfSubset' + catalog_url = 'http://thredds.ucar.edu/thredds/catalog.xml' + base_tds_url = catalog_url.split('/thredds/')[0] + data_format = 'netcdf' + vert_level = 100000 + + units = { + 'temp_air': 'C', + 'wind_speed': 'm/s', + 'ghi': 'W/m^2', + 'ghi_raw': 'W/m^2', + 'dni': 'W/m^2', + 'dhi': 'W/m^2', + 'total_clouds': '%', + 'low_clouds': '%', + 'mid_clouds': '%', + 'high_clouds': '%'} + + def __init__(self, model_type, model_name, set_type): + self.model_type = model_type + self.model_name = model_name + self.set_type = set_type + self.catalog = TDSCatalog(self.catalog_url) + self.fm_models = TDSCatalog(self.catalog.catalog_refs[model_type].href) + self.fm_models_list = sorted(list(self.fm_models.catalog_refs.keys())) + + try: + model_url = self.fm_models.catalog_refs[model_name].href + except ParseError: + raise ParseError(self.model_name + ' model may be unavailable.') + + try: + self.model = TDSCatalog(model_url) + except HTTPError: + try: + self.model = TDSCatalog(model_url) + except HTTPError: + raise HTTPError(self.model_name + ' model may be unavailable.') + + self.datasets_list = list(self.model.datasets.keys()) + self.set_dataset() + + def __repr__(self): + return '{}, {}'.format(self.model_name, self.set_type) + + def set_dataset(self): + ''' + Retrieves the designated dataset, creates NCSS object, and + creates a NCSS query object. + ''' + + keys = list(self.model.datasets.keys()) + labels = [item.split()[0].lower() for item in keys] + if self.set_type == 'best': + self.dataset = self.model.datasets[keys[labels.index('best')]] + elif self.set_type == 'latest': + self.dataset = self.model.datasets[keys[labels.index('latest')]] + elif self.set_type == 'full': + self.dataset = self.model.datasets[keys[labels.index('full')]] + + self.access_url = self.dataset.access_urls[self.access_url_key] + self.ncss = NCSS(self.access_url) + self.query = self.ncss.query() + + def set_query_latlon(self): + ''' + Sets the NCSS query location latitude and longitude. + ''' + + if (isinstance(self.longitude, list) and + isinstance(self.latitude, list)): + self.lbox = True + # west, east, south, north + self.query.lonlat_box(self.latitude[0], self.latitude[1], + self.longitude[0], self.longitude[1]) + else: + self.lbox = False + self.query.lonlat_point(self.longitude, self.latitude) + + def set_location(self, time, latitude, longitude): + ''' + Sets the location for the query. + + Parameters + ---------- + time: datetime or DatetimeIndex + Time range of the query. + ''' + if isinstance(time, datetime.datetime): + tzinfo = time.tzinfo + else: + tzinfo = time.tz + + if tzinfo is None: + self.location = Location(latitude, longitude) + else: + self.location = Location(latitude, longitude, tz=tzinfo) + + def get_data(self, latitude, longitude, start, end, + vert_level=None, query_variables=None, + close_netcdf_data=True): + """ + Submits a query to the UNIDATA servers using Siphon NCSS and + converts the netcdf data to a pandas DataFrame. + + Parameters + ---------- + latitude: float + The latitude value. + longitude: float + The longitude value. + start: datetime or timestamp + The start time. + end: datetime or timestamp + The end time. + vert_level: None, float or integer + Vertical altitude of interest. + variables: None or list + If None, uses self.variables. + close_netcdf_data: bool + Controls if the temporary netcdf data file should be closed. + Set to False to access the raw data. + + Returns + ------- + forecast_data : DataFrame + column names are the weather model's variable names. + """ + if vert_level is not None: + self.vert_level = vert_level + + if query_variables is None: + self.query_variables = list(self.variables.values()) + else: + self.query_variables = query_variables + + self.latitude = latitude + self.longitude = longitude + self.set_query_latlon() # modifies self.query + self.set_location(start, latitude, longitude) + + self.start = start + self.end = end + self.query.time_range(self.start, self.end) + + self.query.vertical_level(self.vert_level) + self.query.variables(*self.query_variables) + self.query.accept(self.data_format) + + self.netcdf_data = self.ncss.get_data(self.query) + + # might be better to go to xarray here so that we can handle + # higher dimensional data for more advanced applications + self.data = self._netcdf2pandas(self.netcdf_data, self.query_variables) + + if close_netcdf_data: + self.netcdf_data.close() + + return self.data + + def process_data(self, data, **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. Most forecast models implement + their own version of this method which also call this one. + + Parameters + ---------- + data: DataFrame + Raw forecast data + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + data = self.rename(data) + return data + + def get_processed_data(self, *args, **kwargs): + """ + Get and process forecast data. + + Parameters + ---------- + *args: positional arguments + Passed to get_data + **kwargs: keyword arguments + Passed to get_data and process_data + + Returns + ------- + data: DataFrame + Processed forecast data + """ + return self.process_data(self.get_data(*args, **kwargs), **kwargs) + + def rename(self, data, variables=None): + """ + Renames the columns according the variable mapping. + + Parameters + ---------- + data: DataFrame + variables: None or dict + If None, uses self.variables + + Returns + ------- + data: DataFrame + Renamed data. + """ + if variables is None: + variables = self.variables + return data.rename(columns={y: x for x, y in variables.items()}) + + def _netcdf2pandas(self, netcdf_data, query_variables): + """ + Transforms data from netcdf to pandas DataFrame. + + Parameters + ---------- + data: netcdf + Data returned from UNIDATA NCSS query. + query_variables: list + The variables requested. + + Returns + ------- + pd.DataFrame + """ + # set self.time + try: + time_var = 'time' + self.set_time(netcdf_data.variables[time_var]) + except KeyError: + # which model does this dumb thing? + time_var = 'time1' + self.set_time(netcdf_data.variables[time_var]) + + data_dict = {key: data[:].squeeze() for key, data in + netcdf_data.variables.items() if key in query_variables} + + return pd.DataFrame(data_dict, index=self.time) + + def set_time(self, time): + ''' + Converts time data into a pandas date object. + + Parameters + ---------- + time: netcdf + Contains time information. + + Returns + ------- + pandas.DatetimeIndex + ''' + times = num2date(time[:].squeeze(), time.units) + self.time = pd.DatetimeIndex(pd.Series(times), tz=self.location.tz) + + def cloud_cover_to_ghi_linear(self, cloud_cover, ghi_clear, offset=35, + **kwargs): + """ + Convert cloud cover to GHI using a linear relationship. + + 0% cloud cover returns ghi_clear. + + 100% cloud cover returns offset*ghi_clear. + + Parameters + ---------- + cloud_cover: numeric + Cloud cover in %. + ghi_clear: numeric + GHI under clear sky conditions. + offset: numeric + Determines the minimum GHI. + kwargs + Not used. + + Returns + ------- + ghi: numeric + Estimated GHI. + + References + ---------- + Larson et. al. "Day-ahead forecasting of solar power output from + photovoltaic plants in the American Southwest" Renewable Energy + 91, 11-20 (2016). + """ + + offset = offset / 100. + cloud_cover = cloud_cover / 100. + ghi = (offset + (1 - offset) * (1 - cloud_cover)) * ghi_clear + return ghi + + def cloud_cover_to_irradiance_clearsky_scaling(self, cloud_cover, + method='linear', + **kwargs): + """ + Estimates irradiance from cloud cover in the following steps: + + 1. Determine clear sky GHI using Ineichen model and + climatological turbidity. + 2. Estimate cloudy sky GHI using a function of + cloud_cover e.g. + :py:meth:`~ForecastModel.cloud_cover_to_ghi_linear` + 3. Estimate cloudy sky DNI using the DISC model. + 4. Calculate DHI from DNI and DHI. + + Parameters + ---------- + cloud_cover : Series + Cloud cover in %. + method : str + Method for converting cloud cover to GHI. + 'linear' is currently the only option. + **kwargs + Passed to the method that does the conversion + + Returns + ------- + irrads : DataFrame + Estimated GHI, DNI, and DHI. + """ + solpos = self.location.get_solarposition(cloud_cover.index) + cs = self.location.get_clearsky(cloud_cover.index, model='ineichen', + solar_position=solpos) + + method = method.lower() + if method == 'linear': + ghi = self.cloud_cover_to_ghi_linear(cloud_cover, cs['ghi'], + **kwargs) + else: + raise ValueError('invalid method argument') + + dni = disc(ghi, solpos['zenith'], cloud_cover.index)['dni'] + dhi = ghi - dni * np.cos(np.radians(solpos['zenith'])) + + irrads = pd.DataFrame({'ghi': ghi, 'dni': dni, 'dhi': dhi}).fillna(0) + return irrads + + def cloud_cover_to_transmittance_linear(self, cloud_cover, offset=0.75, + **kwargs): + """ + Convert cloud cover to atmospheric transmittance using a linear + model. + + 0% cloud cover returns offset. + + 100% cloud cover returns 0. + + Parameters + ---------- + cloud_cover : numeric + Cloud cover in %. + offset : numeric + Determines the maximum transmittance. + kwargs + Not used. + + Returns + ------- + ghi : numeric + Estimated GHI. + """ + transmittance = ((100.0 - cloud_cover) / 100.0) * 0.75 + + return transmittance + + def cloud_cover_to_irradiance_liujordan(self, cloud_cover, **kwargs): + """ + Estimates irradiance from cloud cover in the following steps: + + 1. Determine transmittance using a function of cloud cover e.g. + :py:meth:`~ForecastModel.cloud_cover_to_transmittance_linear` + 2. Calculate GHI, DNI, DHI using the + :py:func:`pvlib.irradiance.liujordan` model + + Parameters + ---------- + cloud_cover : Series + + Returns + ------- + irradiance : DataFrame + Columns include ghi, dni, dhi + """ + # in principle, get_solarposition could use the forecast + # pressure, temp, etc., but the cloud cover forecast is not + # accurate enough to justify using these minor corrections + solar_position = self.location.get_solarposition(cloud_cover.index) + dni_extra = extraradiation(cloud_cover.index) + airmass = self.location.get_airmass(cloud_cover.index) + + transmittance = self.cloud_cover_to_transmittance_linear(cloud_cover, + **kwargs) + + irrads = liujordan(solar_position['apparent_zenith'], + transmittance, airmass['airmass_absolute'], + dni_extra=dni_extra) + irrads = irrads.fillna(0) + + return irrads + + def cloud_cover_to_irradiance(self, cloud_cover, how='clearsky_scaling', + **kwargs): + """ + Convert cloud cover to irradiance. A wrapper method. + + Parameters + ---------- + cloud_cover : Series + how : str + Selects the method for conversion. Can be one of + clearsky_scaling or liujordan. + **kwargs + Passed to the selected method. + + Returns + ------- + irradiance : DataFrame + Columns include ghi, dni, dhi + """ + + how = how.lower() + if how == 'clearsky_scaling': + irrads = self.cloud_cover_to_irradiance_clearsky_scaling( + cloud_cover, **kwargs) + elif how == 'liujordan': + irrads = self.cloud_cover_to_irradiance_liujordan( + cloud_cover, **kwargs) + else: + raise ValueError('invalid how argument') + + return irrads + + def kelvin_to_celsius(self, temperature): + """ + Converts Kelvin to celsius. + + Parameters + ---------- + temperature: numeric + + Returns + ------- + temperature: numeric + """ + return temperature - 273.15 + + def isobaric_to_ambient_temperature(self, data): + """ + Calculates temperature from isobaric temperature. + + Parameters + ---------- + data: DataFrame + Must contain columns pressure, temperature_iso, + temperature_dew_iso. Input temperature in K. + + Returns + ------- + temperature : Series + Temperature in K + """ + + P = data['pressure'] / 100.0 + Tiso = data['temperature_iso'] + Td = data['temperature_dew_iso'] - 273.15 + + # saturation water vapor pressure + e = 6.11 * 10**((7.5 * Td) / (Td + 273.3)) + + # saturation water vapor mixing ratio + w = 0.622 * (e / (P - e)) + + T = Tiso - ((2.501 * 10.**6) / 1005.7) * w + + return T + + def uv_to_speed(self, data): + """ + Computes wind speed from wind components. + + Parameters + ---------- + data : DataFrame + Must contain the columns 'wind_speed_u' and 'wind_speed_v'. + + Returns + ------- + wind_speed : Series + """ + wind_speed = np.sqrt(data['wind_speed_u']**2 + data['wind_speed_v']**2) + + return wind_speed + + def gust_to_speed(self, data, scaling=1/1.4): + """ + Computes standard wind speed from gust. + Very approximate and location dependent. + + Parameters + ---------- + data : DataFrame + Must contain the column 'wind_speed_gust'. + + Returns + ------- + wind_speed : Series + """ + wind_speed = data['wind_speed_gust'] * scaling + + return wind_speed + + +class GFS(ForecastModel): + """ + Subclass of the ForecastModel class representing GFS + forecast model. + + Model data corresponds to 0.25 degree resolution forecasts. + + Parameters + ---------- + resolution: string + Resolution of the model, either 'half' or 'quarter' degree. + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + _resolutions = ['Half', 'Quarter'] + + def __init__(self, resolution='half', set_type='best'): + model_type = 'Forecast Model Data' + + resolution = resolution.title() + if resolution not in self._resolutions: + raise ValueError('resolution must in {}'.format(self._resolutions)) + + model = 'GFS {} Degree Forecast'.format(resolution) + + self.variables = { + 'temp_air': 'Temperature_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'wind_speed_u': 'u-component_of_wind_isobaric', + 'wind_speed_v': 'v-component_of_wind_isobaric', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere_Mixed_intervals_Average', + 'low_clouds': 'Total_cloud_cover_low_cloud_Mixed_intervals_Average', + 'mid_clouds': 'Total_cloud_cover_middle_cloud_Mixed_intervals_Average', + 'high_clouds': 'Total_cloud_cover_high_cloud_Mixed_intervals_Average', + 'boundary_clouds': 'Total_cloud_cover_boundary_layer_cloud_Mixed_intervals_Average', + 'convect_clouds': 'Total_cloud_cover_convective_cloud', + 'ghi_raw': 'Downward_Short-Wave_Radiation_Flux_surface_Mixed_intervals_Average', } + + self.output_variables = [ + 'temp_air', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds',] + + super(GFS, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + data = super(GFS, self).process_data(data, **kwargs) + data['temp_air'] = self.kelvin_to_celsius(data['temp_air']) + data['wind_speed'] = self.uv_to_speed(data) + irrads = self.cloud_cover_to_irradiance(data[cloud_cover], **kwargs) + data = data.join(irrads, how='outer') + return data.ix[:, self.output_variables] + + +class HRRR_ESRL(ForecastModel): + """ + Subclass of the ForecastModel class representing + NOAA/GSD/ESRL's HRRR forecast model. + This is not an operational product. + + Model data corresponds to NOAA/GSD/ESRL HRRR CONUS 3km resolution + surface forecasts. + + Parameters + ---------- + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + def __init__(self, set_type='best'): + import warnings + warnings.warn('HRRR_ESRL is an experimental model and is not always available.') + + model_type = 'Forecast Model Data' + model = 'GSD HRRR CONUS 3km surface' + + self.variables = { + 'temp_air': 'Temperature_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere', + 'low_clouds': 'Low_cloud_cover_UnknownLevelType-214', + 'mid_clouds': 'Medium_cloud_cover_UnknownLevelType-224', + 'high_clouds': 'High_cloud_cover_UnknownLevelType-234', + 'ghi_raw': 'Downward_short-wave_radiation_flux_surface', } + + self.output_variables = [ + 'temp_air', + 'wind_speed' + 'ghi_raw', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds',] + + super(HRRR_ESRL, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + data = super(HRRR_ESRL, self).process_data(data, **kwargs) + data['temp_air'] = self.kelvin_to_celsius(data['temp_air']) + data['wind_speed'] = self.gust_to_speed(data) + irrads = self.cloud_cover_to_irradiance(data[cloud_cover], **kwargs) + data = data.join(irrads, how='outer') + return data.ix[:, self.output_variables] + + +class NAM(ForecastModel): + """ + Subclass of the ForecastModel class representing NAM + forecast model. + + Model data corresponds to NAM CONUS 12km resolution forecasts + from CONDUIT. + + Parameters + ---------- + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + def __init__(self, set_type='best'): + model_type = 'Forecast Model Data' + model = 'NAM CONUS 12km from CONDUIT' + + self.variables = { + 'temp_air': 'Temperature_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere_single_layer', + 'low_clouds': 'Low_cloud_cover_low_cloud', + 'mid_clouds': 'Medium_cloud_cover_middle_cloud', + 'high_clouds': 'High_cloud_cover_high_cloud', + 'ghi_raw': 'Downward_Short-Wave_Radiation_Flux_surface', } + + self.output_variables = [ + 'temp_air', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds',] + + super(NAM, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + data = super(NAM, self).process_data(data, **kwargs) + data['temp_air'] = self.kelvin_to_celsius(data['temp_air']) + data['wind_speed'] = self.gust_to_speed(data) + irrads = self.cloud_cover_to_irradiance(data[cloud_cover], **kwargs) + data = data.join(irrads, how='outer') + return data.ix[:, self.output_variables] + + +class HRRR(ForecastModel): + """ + Subclass of the ForecastModel class representing HRRR + forecast model. + + Model data corresponds to NCEP HRRR CONUS 2.5km resolution + forecasts. + + Parameters + ---------- + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + def __init__(self, set_type='best'): + model_type = 'Forecast Model Data' + model = 'NCEP HRRR CONUS 2.5km' + + self.variables = { + 'temperature_dew_iso': 'Dewpoint_temperature_isobaric', + 'temperature_iso': 'Temperature_isobaric', + 'pressure': 'Pressure_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere', + 'low_clouds': 'Low_cloud_cover_low_cloud', + 'mid_clouds': 'Medium_cloud_cover_middle_cloud', + 'high_clouds': 'High_cloud_cover_high_cloud', + 'condensation_height': 'Geopotential_height_adiabatic_condensation_lifted'} + + self.output_variables = [ + 'temp_air', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds', ] + + super(HRRR, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + data = super(HRRR, self).process_data(data, **kwargs) + data['temp_air'] = self.isobaric_to_ambient_temperature(data) + data['temp_air'] = self.kelvin_to_celsius(data['temp_air']) + data['wind_speed'] = self.gust_to_speed(data) + irrads = self.cloud_cover_to_irradiance(data[cloud_cover], **kwargs) + data = data.join(irrads, how='outer') + return data.ix[:, self.output_variables] + + +class NDFD(ForecastModel): + """ + Subclass of the ForecastModel class representing NDFD forecast + model. + + Model data corresponds to NWS CONUS CONDUIT forecasts. + + Parameters + ---------- + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + def __init__(self, set_type='best'): + model_type = 'Forecast Products and Analyses' + model = 'National Weather Service CONUS Forecast Grids (CONDUIT)' + self.variables = { + 'temp_air': 'Temperature_surface', + 'wind_speed': 'Wind_speed_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_surface', } + self.output_variables = [ + 'temp_air', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', ] + super(NDFD, self).__init__(model_type, model, set_type) + + def process_data(self, data, **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + cloud_cover = 'total_clouds' + data = super(NDFD, self).process_data(data, **kwargs) + data['temp_air'] = self.kelvin_to_celsius(data['temp_air']) + irrads = self.cloud_cover_to_irradiance(data[cloud_cover], **kwargs) + data = data.join(irrads, how='outer') + return data.ix[:, self.output_variables] + + +class RAP(ForecastModel): + """ + Subclass of the ForecastModel class representing RAP forecast model. + + Model data corresponds to Rapid Refresh CONUS 20km resolution + forecasts. + + Parameters + ---------- + resolution: string or int + The model resolution, either '20' or '40' (km) + set_type: string + Type of model to pull data from. + + Attributes + ---------- + dataframe_variables: list + Common variables present in the final set of data. + model: string + Name of the UNIDATA forecast model. + model_type: string + UNIDATA category in which the model is located. + variables: dict + Defines the variables to obtain from the weather + model and how they should be renamed to common variable names. + units: dict + Dictionary containing the units of the standard variables + and the model specific variables. + """ + + _resolutions = ['20', '40'] + + def __init__(self, resolution='20', set_type='best'): + + resolution = str(resolution) + if resolution not in self._resolutions: + raise ValueError('resolution must in {}'.format(self._resolutions)) + + model_type = 'Forecast Model Data' + model = 'Rapid Refresh CONUS {}km'.format(resolution) + self.variables = { + 'temp_air': 'Temperature_surface', + 'wind_speed_gust': 'Wind_speed_gust_surface', + 'total_clouds': 'Total_cloud_cover_entire_atmosphere_single_layer', + 'low_clouds': 'Low_cloud_cover_low_cloud', + 'mid_clouds': 'Medium_cloud_cover_middle_cloud', + 'high_clouds': 'High_cloud_cover_high_cloud', } + self.output_variables = [ + 'temp_air', + 'wind_speed', + 'ghi', + 'dni', + 'dhi', + 'total_clouds', + 'low_clouds', + 'mid_clouds', + 'high_clouds', ] + super(RAP, self).__init__(model_type, model, set_type) + + def process_data(self, data, cloud_cover='total_clouds', **kwargs): + """ + Defines the steps needed to convert raw forecast data + into processed forecast data. + + Parameters + ---------- + data: DataFrame + Raw forecast data + cloud_cover: str + The type of cloud cover used to infer the irradiance. + + Returns + ------- + data: DataFrame + Processed forecast data. + """ + + data = super(RAP, self).process_data(data, **kwargs) + data['temp_air'] = self.kelvin_to_celsius(data['temp_air']) + data['wind_speed'] = self.gust_to_speed(data) + irrads = self.cloud_cover_to_irradiance(data[cloud_cover], **kwargs) + data = data.join(irrads, how='outer') + return data.ix[:, self.output_variables] diff --git a/pvlib/irradiance.py b/pvlib/irradiance.py index 0e4bc27e41..5dbf561f91 100644 --- a/pvlib/irradiance.py +++ b/pvlib/irradiance.py @@ -1386,6 +1386,65 @@ def erbs(ghi, zenith, doy): return data +def liujordan(zenith, transmittance, airmass, pressure=101325., + dni_extra=1367.0): + ''' + Determine DNI, DHI, GHI from extraterrestrial flux, transmittance, + and optical air mass number. + + Liu and Jordan, 1960, developed a simplified direct radiation model. + DHI is from an empirical equation for diffuse radiation from Liu and + Jordan, 1960. + + Parameters + ---------- + zenith: pd.Series + True (not refraction-corrected) zenith angles in decimal + degrees. If Z is a vector it must be of the same size as all + other vector inputs. Z must be >=0 and <=180. + + transmittance: float + Atmospheric transmittance between 0 and 1. + + pressure: float + Air pressure + + dni_extra: float + Direct irradiance incident at the top of the atmosphere. + + Returns + ------- + irradiance: DataFrame + Modeled direct normal irradiance, direct horizontal irradiance, + and global horizontal irradiance in W/m^2 + + References + ---------- + [1] Campbell, G. S., J. M. Norman (1998) An Introduction to + Environmental Biophysics. 2nd Ed. New York: Springer. + + [2] Liu, B. Y., R. C. Jordan, (1960). "The interrelationship and + characteristic distribution of direct, diffuse, and total solar + radiation". Solar Energy 4:1-19 + ''' + + tao = transmittance + + dni = dni_extra*tao**airmass + dhi = 0.3 * (1.0 - tao**airmass) * dni_extra * np.cos(np.radians(zenith)) + ghi = dhi + dni * np.cos(np.radians(zenith)) + + irrads = OrderedDict() + irrads['ghi'] = ghi + irrads['dni'] = dni + irrads['dhi'] = dhi + + if isinstance(ghi, pd.Series): + irrads = pd.DataFrame(irrads) + + return irrads + + def _get_perez_coefficients(perezmodel): ''' Find coefficients for the Perez model diff --git a/pvlib/test/conftest.py b/pvlib/test/conftest.py index a128f17091..1a2788f595 100644 --- a/pvlib/test/conftest.py +++ b/pvlib/test/conftest.py @@ -69,3 +69,12 @@ def has_numba(): return True requires_numba = pytest.mark.skipif(not has_numba(), reason="requires numba") + +try: + import siphon + has_siphon = True +except ImportError: + has_siphon = False + +requires_siphon = pytest.mark.skipif(not has_siphon, + reason='requires siphon') diff --git a/pvlib/test/test_forecast.py b/pvlib/test/test_forecast.py new file mode 100644 index 0000000000..eeffbe12a8 --- /dev/null +++ b/pvlib/test/test_forecast.py @@ -0,0 +1,138 @@ +from datetime import datetime, timedelta +import inspect +from math import isnan +from pytz import timezone + +import numpy as np +import pandas as pd + +import pytest +from numpy.testing import assert_allclose + +from conftest import requires_siphon, has_siphon + +pytestmark = pytest.mark.skipif(not has_siphon, reason='requires siphon') + +from pvlib.location import Location + +if has_siphon: + import requests + from requests.exceptions import HTTPError + from xml.etree.ElementTree import ParseError + + from pvlib.forecast import GFS, HRRR_ESRL, HRRR, NAM, NDFD, RAP + + # setup times and location to be tested. Tucson, AZ + _latitude = 32.2 + _longitude = -110.9 + _tz = 'US/Arizona' + _start = pd.Timestamp.now(tz=_tz) + _end = _start + pd.Timedelta(days=1) + _modelclasses = [ + GFS, NAM, HRRR, RAP, NDFD, + pytest.mark.xfail(HRRR_ESRL, reason="HRRR_ESRL is unreliable")] + _working_models = [] + _variables = ['temp_air', 'wind_speed', 'total_clouds', 'low_clouds', + 'mid_clouds', 'high_clouds', 'dni', 'dhi', 'ghi',] + _nonnan_variables = ['temp_air', 'wind_speed', 'total_clouds', 'dni', + 'dhi', 'ghi',] +else: + _modelclasses = [] + + +# make a model object for each model class +# get the data for that model and store it in an +# attribute for further testing +@requires_siphon +@pytest.fixture(scope='module', params=_modelclasses) +def model(request): + amodel = request.param() + amodel.raw_data = \ + amodel.get_data(_latitude, _longitude, _start, _end) + return amodel + + +@requires_siphon +def test_process_data(model): + for how in ['liujordan', 'clearsky_scaling']: + data = model.process_data(model.raw_data, how=how) + for variable in _nonnan_variables: + assert not data[variable].isnull().values.any() + + +@requires_siphon +def test_vert_level(): + amodel = RAP() + vert_level = 5000 + data = amodel.get_processed_data(_latitude, _longitude, _start, _end, + vert_level=vert_level) + +@requires_siphon +def test_datetime(): + amodel = RAP() + start = datetime.now() + end = start + timedelta(days=1) + data = amodel.get_processed_data(_latitude, _longitude , start, end) + + +@requires_siphon +def test_queryvariables(): + amodel = GFS() + old_variables = amodel.variables + new_variables = ['u-component_of_wind_height_above_ground'] + data = amodel.get_data(_latitude, _longitude, _start, _end, + query_variables=new_variables) + data['u-component_of_wind_height_above_ground'] + + +@requires_siphon +def test_latest(): + GFS(set_type='latest') + + +@requires_siphon +def test_full(): + GFS(set_type='full') + + +@requires_siphon +def test_temp_convert(): + amodel = GFS() + data = pd.DataFrame({'temp_air': [273.15]}) + data['temp_air'] = amodel.kelvin_to_celsius(data['temp_air']) + + assert_allclose(data['temp_air'].values, 0.0) + + +# @requires_siphon +# def test_bounding_box(): +# amodel = GFS() +# latitude = [31.2,32.2] +# longitude = [-111.9,-110.9] +# new_variables = {'temperature':'Temperature_surface'} +# data = amodel.get_query_data(latitude, longitude, _start, _end, +# variables=new_variables) + + +@requires_siphon +def test_set_location(): + amodel = GFS() + latitude, longitude = 32.2, -110.9 + time = datetime.now(timezone('UTC')) + amodel.set_location(time, latitude, longitude) + + +def test_cloud_cover_to_transmittance_linear(): + amodel = GFS() + assert_allclose(amodel.cloud_cover_to_transmittance_linear(0), 0.75) + assert_allclose(amodel.cloud_cover_to_transmittance_linear(100), 0.0) + + +def test_cloud_cover_to_ghi_linear(): + amodel = GFS() + ghi_clear = 1000 + offset = 25 + out = amodel.cloud_cover_to_ghi_linear(0, ghi_clear, offset=offset) + assert_allclose(out, 1000) + out = amodel.cloud_cover_to_ghi_linear(100, ghi_clear, offset=offset) + assert_allclose(out, 250) diff --git a/pvlib/test/test_irradiance.py b/pvlib/test/test_irradiance.py index ee0a13a87f..e499497c51 100644 --- a/pvlib/test/test_irradiance.py +++ b/pvlib/test/test_irradiance.py @@ -161,6 +161,18 @@ def test_perez_arrays(): assert_allclose(out, expected, atol=1e-2) + +def test_liujordan(): + expected = pd.DataFrame(np. + array([[863.859736967, 653.123094076, 220.65905025]]), + columns=['ghi', 'dni', 'dhi'], + index=[0]) + out = irradiance.liujordan( + pd.Series([10]), pd.Series([0.5]), pd.Series([1.1]), + pressure=93000., dni_extra=1400) + assert_frame_equal(out, expected) + + # klutcher (misspelling) will be removed in 0.3 def test_total_irrad(): models = ['isotropic', 'klutcher', 'klucher',