Substitute occurrences of pvlib.__file__ or similar where appropiate

Author: echedey-ls

docs/examples/adr-pvarray/plot_simulate_system.py

@@ -12,12 +12,11 @@
 Author: Anton Driesse
 """
 
-import os
 import pandas as pd
 import matplotlib.pyplot as plt
 
 import pvlib
-from pvlib import iotools, location
+from pvlib import iotools, location, tools
 from pvlib.irradiance import get_total_irradiance
 from pvlib.pvarray import pvefficiency_adr
 
@@ -26,10 +25,9 @@
 # Read a TMY3 file containing weather data and select needed columns
 #
 
-PVLIB_DIR = pvlib.__path__[0]
-DATA_FILE = os.path.join(PVLIB_DIR, 'data', '723170TYA.CSV')
+tmy3_filepath = tools.get_test_dataset_path('723170TYA.CSV')
 
-tmy, metadata = iotools.read_tmy3(DATA_FILE, coerce_year=1990,
+tmy, metadata = iotools.read_tmy3(tmy3_filepath, coerce_year=1990,
                                   map_variables=True)
 
 df = pd.DataFrame({'ghi': tmy['ghi'], 'dhi': tmy['dhi'], 'dni': tmy['dni'],

docs/examples/irradiance-decomposition/plot_diffuse_fraction.py

@@ -14,20 +14,18 @@
 # GHI into the diffuse and direct components. The separate components are
 # needed to estimate the total irradiance on a tilted surface.
 
-import pathlib
 from matplotlib import pyplot as plt
 import pandas as pd
 from pvlib.iotools import read_tmy3
 from pvlib.solarposition import get_solarposition
-from pvlib import irradiance
-import pvlib
+from pvlib import irradiance, tools
 
 # For this example we use the Greensboro, North Carolina, TMY3 file which is
 # in the pvlib data directory. TMY3 are made from the median months from years
 # of data measured from 1990 to 2010. Therefore we change the timestamps to a
 # common year, 1990.
-DATA_DIR = pathlib.Path(pvlib.__file__).parent / 'data'
-greensboro, metadata = read_tmy3(DATA_DIR / '723170TYA.CSV', coerce_year=1990,
+tmy3_filepath = tools.get_test_dataset_path('723170TYA.CSV')
+greensboro, metadata = read_tmy3(tmy3_filepath, coerce_year=1990,
                                  map_variables=True)
 
 # Many of the diffuse fraction estimation methods require the "true" zenith, so

docs/examples/irradiance-transposition/plot_seasonal_tilt.py

@@ -12,11 +12,9 @@
 # to use a custom Mount class to use the Seasonal Tilt strategy
 # with :py:class:`~pvlib.modelchain.ModelChain`.
 
-import pvlib
-from pvlib import pvsystem, location, modelchain, iotools
+from pvlib import pvsystem, location, modelchain, iotools, tools
 from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
 import pandas as pd
-import pathlib
 import matplotlib.pyplot as plt
 from dataclasses import dataclass
 
@@ -43,8 +41,8 @@ def get_orientation(self, solar_zenith, solar_azimuth):
 # First let's grab some weather data and make sure our mount produces tilts
 # like we expect:
 
-DATA_DIR = pathlib.Path(pvlib.__file__).parent / 'data'
-tmy, metadata = iotools.read_tmy3(DATA_DIR / '723170TYA.CSV', coerce_year=1990,
+tmy3_filepath = tools.get_test_dataset_path('723170TYA.CSV')
+tmy, metadata = iotools.read_tmy3(tmy3_filepath, coerce_year=1990,
                                   map_variables=True)
 # shift from TMY3 right-labeled index to left-labeled index:
 tmy.index = tmy.index - pd.Timedelta(hours=1)

docs/examples/irradiance-transposition/plot_transposition_gain.py

@@ -19,21 +19,19 @@
 # insolation is calculated for each strategy to show how orientation affects
 # seasonal irradiance collection.
 
-import pvlib
 from pvlib import location
 from pvlib import irradiance
 from pvlib import tracking
+from pvlib import tools
 from pvlib.iotools import read_tmy3
 import pandas as pd
 from matplotlib import pyplot as plt
-import pathlib
 
-# get full path to the data directory
-DATA_DIR = pathlib.Path(pvlib.__file__).parent / 'data'
+# get full path to the example file
+tmy3_filepath = tools.get_test_dataset_path('723170TYA.CSV')
 
 # get TMY3 dataset
-tmy, metadata = read_tmy3(DATA_DIR / '723170TYA.CSV', coerce_year=1990,
-                          map_variables=True)
+tmy, metadata = read_tmy3(tmy3_filepath, coerce_year=1990, map_variables=True)
 # TMY3 datasets are right-labeled (AKA "end of interval") which means the last
 # interval of Dec 31, 23:00 to Jan 1 00:00 is labeled Jan 1 00:00. When rolling
 # up hourly irradiance to monthly insolation, a spurious January value is

docs/examples/soiling/plot_fig3A_hsu_soiling_example.py

@@ -21,18 +21,18 @@
 # PM2.5 and PM10 data come from the EPA. First, let's read in the
 # weather data and run the HSU soiling model:
 
-import pathlib
 from matplotlib import pyplot as plt
 from pvlib import soiling
 import pvlib
 import pandas as pd
 
-# get full path to the data directory
-DATA_DIR = pathlib.Path(pvlib.__file__).parent / 'data'
+# get full path to the example file
+soiling_hsu_filepath = \
+    pvlib.tools.get_test_dataset_path('soiling_hsu_example_inputs.csv')
 
 # read rainfall, PM2.5, and PM10 data from file
-imperial_county = pd.read_csv(DATA_DIR / 'soiling_hsu_example_inputs.csv',
-                              index_col=0, parse_dates=True)
+imperial_county = pd.read_csv(soiling_hsu_filepath, index_col=0,
+                              parse_dates=True)
 rainfall = imperial_county['rain']
 depo_veloc = {'2_5': 0.0009, '10': 0.004}  # default values from [1] (m/s)
 rain_accum_period = pd.Timedelta('1h')     # default

docs/sphinx/source/user_guide/clearsky.rst

@@ -30,8 +30,6 @@ We'll need these imports for the examples below.
 
 .. ipython::
 
-    In [1]: import os
-
     In [1]: import itertools
 
     In [1]: import matplotlib.pyplot as plt
@@ -40,7 +38,7 @@ We'll need these imports for the examples below.
 
     In [1]: import pvlib
 
-    In [1]: from pvlib import clearsky, atmosphere, solarposition
+    In [1]: from pvlib import clearsky, atmosphere, solarposition, tools
 
     In [1]: from pvlib.location import Location
 
@@ -130,13 +128,9 @@ the year. You could run it in a loop to create plots for all months.
 
     In [1]: import calendar
 
-    In [1]: import os
-
     In [1]: import h5py
 
-    In [1]: pvlib_path = os.path.dirname(os.path.abspath(pvlib.clearsky.__file__))
-
-    In [1]: filepath = os.path.join(pvlib_path, 'data', 'LinkeTurbidities.h5')
+    In [1]: filepath = tools.get_test_dataset_path('LinkeTurbidities.h5')
 
     In [1]: def plot_turbidity_map(month, vmin=1, vmax=100):
        ...:     plt.figure();
@@ -210,13 +204,11 @@ wavelengths [Bir80]_, and is implemented in
 
 .. ipython::
 
-    In [1]: pvlib_data = os.path.join(os.path.dirname(pvlib.__file__), 'data')
-
     In [1]: mbars = 100  # conversion factor from mbars to Pa
 
-    In [1]: tmy_file = os.path.join(pvlib_data, '703165TY.csv')  # TMY file
+    In [1]: tmy_file_path = tools.get_test_dataset_path('703165TY.csv')  # TMY file
 
-    In [1]: tmy_data, tmy_header = read_tmy3(tmy_file, coerce_year=1999, map_variables=True)
+    In [1]: tmy_data, tmy_header = read_tmy3(tmy_file_path, coerce_year=1999, map_variables=True)
 
     In [1]: tl_historic = clearsky.lookup_linke_turbidity(time=tmy_data.index,
        ...:     latitude=tmy_header['latitude'], longitude=tmy_header['longitude'])

pvlib/clearsky.py

@@ -3,7 +3,6 @@
 to calculate clear sky GHI, DNI, and DHI.
 """
 
-import os
 from collections import OrderedDict
 import calendar
 
@@ -14,7 +13,7 @@
 import h5py
 
 from pvlib import atmosphere, tools
-from pvlib.tools import _degrees_to_index
+from pvlib.tools import _degrees_to_index, get_test_dataset_path
 
 
 def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
@@ -189,8 +188,7 @@ def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
     # 1st column: 179.9583 W, 2nd column: 179.875 W
 
     if filepath is None:
-        pvlib_path = os.path.dirname(os.path.abspath(__file__))
-        filepath = os.path.join(pvlib_path, 'data', 'LinkeTurbidities.h5')
+        filepath = get_test_dataset_path('LinkeTurbidities.h5')
 
     latitude_index = _degrees_to_index(latitude, coordinate='latitude')
     longitude_index = _degrees_to_index(longitude, coordinate='longitude')

pvlib/location.py

@@ -4,15 +4,15 @@
 
 # Will Holmgren, University of Arizona, 2014-2016.
 
-import os
 import datetime
 
 import pandas as pd
 import pytz
 import h5py
 
 from pvlib import solarposition, clearsky, atmosphere, irradiance
-from pvlib.tools import _degrees_to_index
+from pvlib.tools import _degrees_to_index, get_test_dataset_path
+
 
 class Location:
     """
@@ -426,8 +426,7 @@ def lookup_altitude(latitude, longitude):
 
     """
 
-    pvlib_path = os.path.dirname(os.path.abspath(__file__))
-    filepath = os.path.join(pvlib_path, 'data', 'Altitude.h5')
+    filepath = get_test_dataset_path('Altitude.h5')
 
     latitude_index = _degrees_to_index(latitude, coordinate='latitude')
     longitude_index = _degrees_to_index(longitude, coordinate='longitude')

pvlib/pvsystem.py

@@ -7,7 +7,6 @@
 import functools
 import io
 import itertools
-import os
 from urllib.request import urlopen
 import numpy as np
 from scipy import constants
@@ -20,7 +19,7 @@
 
 from pvlib import (atmosphere, iam, inverter, irradiance,
                    singlediode as _singlediode, temperature)
-from pvlib.tools import _build_kwargs, _build_args
+from pvlib.tools import _build_kwargs, _build_args, get_test_dataset_path
 
 
 # a dict of required parameter names for each DC power model
@@ -2371,24 +2370,19 @@ def retrieve_sam(name=None, path=None):
 
     if name is not None:
         name = name.lower()
-        data_path = os.path.join(
-            os.path.dirname(os.path.abspath(__file__)), 'data')
         if name == 'cecmod':
-            csvdata = os.path.join(
-                data_path, 'sam-library-cec-modules-2019-03-05.csv')
+            csvdata = 'sam-library-cec-modules-2019-03-05.csv'
         elif name == 'sandiamod':
-            csvdata = os.path.join(
-                data_path, 'sam-library-sandia-modules-2015-6-30.csv')
+            csvdata = 'sam-library-sandia-modules-2015-6-30.csv'
         elif name == 'adrinverter':
-            csvdata = os.path.join(
-                data_path, 'adr-library-cec-inverters-2019-03-05.csv')
+            csvdata = 'adr-library-cec-inverters-2019-03-05.csv'
         elif name in ['cecinverter', 'sandiainverter']:
             # Allowing either, to provide for old code,
             # while aligning with current expectations
-            csvdata = os.path.join(
-                data_path, 'sam-library-cec-inverters-2019-03-05.csv')
+            csvdata = 'sam-library-cec-inverters-2019-03-05.csv'
         else:
             raise ValueError(f'invalid name {name}')
+        csvdata = get_test_dataset_path(csvdata)
     elif path is not None:
         if path.startswith('http'):
             response = urlopen(path)

pvlib/spectrum/mismatch.py

@@ -6,7 +6,6 @@
 import numpy as np
 import pandas as pd
 from scipy.interpolate import interp1d
-import os
 
 
 def get_example_spectral_response(wavelength=None):
@@ -117,10 +116,9 @@ def get_am15g(wavelength=None):
     '''
     # Contributed by Anton Driesse (@adriesse), PV Performance Labs. Aug. 2022
 
-    pvlib_path = pvlib.__path__[0]
-    filepath = os.path.join(pvlib_path, 'data', 'astm_g173_am15g.csv')
+    am15g_file = pvlib.tools.get_test_dataset_path('astm_g173_am15g.csv')
 
-    am15g = pd.read_csv(filepath, index_col=0).squeeze()
+    am15g = pd.read_csv(am15g_file, index_col=0).squeeze()
 
     if wavelength is not None:
         interpolator = interp1d(am15g.index, am15g,