Merge branch 'main' of https://github.com/pvlib/pvlib-python into golden_mpp

Author: cwhanse

.github/workflows/pytest-remote-data.yml

@@ -100,6 +100,9 @@ jobs:
           SOLARANYWHERE_API_KEY: ${{ secrets.SOLARANYWHERE_API_KEY }}
           BSRN_FTP_USERNAME: ${{ secrets.BSRN_FTP_USERNAME }}
           BSRN_FTP_PASSWORD: ${{ secrets.BSRN_FTP_PASSWORD }}
+          ECMWF_API_KEY: ${{ secrets.ECMWF_API_KEY }}
+          EARTHDATA_USERNAME: ${{ secrets.EARTHDATA_USERNAME }}
+          EARTHDATA_PASSWORD: ${{ secrets.EARTHDATA_PASSWORD }}
         run: pytest tests/iotools --cov=./ --cov-report=xml --remote-data
 
       - name: Upload coverage to Codecov

docs/examples/irradiance-decomposition/plot_diffuse_fraction.py

@@ -44,7 +44,7 @@
 # %%
 # pvlib Decomposition Functions
 # -----------------------------
-# Methods for separating DHI into diffuse and direct components include:
+# Methods for separating GHI into diffuse and direct components include:
 # `DISC`_, `DIRINT`_, `Erbs`_, and `Boland`_.
 
 # %%
@@ -112,7 +112,7 @@
 # ----------------
 # In the plots below we compare the four decomposition models to the TMY3 file
 # for Greensboro, North Carolina. We also compare the clearness index, kt, with
-# GHI normalized by a reference irradiance, E0 = 1000 [W/m^2], to highlight
+# GHI normalized by a reference irradiance, E0 = 1000 [Wm⁻²], to highlight
 # spikes caused when cosine of zenith approaches zero, particularly at sunset.
 #
 # First we combine the dataframes for the decomposition models and the TMY3

docs/examples/shading/plot_simple_irradiance_adjustment_for_horizon_shading.py

@@ -14,7 +14,7 @@
 # After location information and a date range is established, solar position
 # data is calculated using :py:func:`pvlib.solarposition.get_solarposition`.
 # Horizon data is assigned, and interpolated to the solar azimuth time
-# series data. Finally, in times when solar elevation is greater than the
+# series data. Finally, in times when solar elevation is less than the
 # interpolated horizon elevation angle, DNI is set to 0.
 
 import numpy as np

docs/examples/spectrum/average_photon_energy.py

@@ -11,8 +11,8 @@
 # This example demonstrates how to use the
 # :py:func:`~pvlib.spectrum.average_photon_energy` function to calculate the
 # Average Photon Energy (APE, :math:`\overline{E_\gamma}`) of spectral
-# irradiance distributions. This example uses spectral irradiance simulated
-# using :py:func:`~pvlib.spectrum.spectrl2`, but the same method is
+# irradiance distributions. This example uses clearsky spectral irradiance
+# simulated using :py:func:`~pvlib.spectrum.spectrl2`, but the same method is
 # applicable to spectral irradiance from any source.
 # More information on the SPECTRL2 model can be found in [1]_.
 # The APE parameter is a useful indicator of the overall shape of the solar
@@ -35,18 +35,19 @@
 from pvlib import spectrum, solarposition, irradiance, atmosphere
 
 lat, lon = 39.742, -105.18  # NREL SRRL location
-tilt = 25
-azimuth = 180  # south-facing system
+surface_tilt = 25
+surface_azimuth = 180  # south-facing system
 pressure = 81190  # at 1828 metres AMSL, roughly
-water_vapor_content = 0.5  # cm
-tau500 = 0.1
+precipitable_water = 0.5  # cm
+aerosol_turbidity_500nm = 0.1
 ozone = 0.31  # atm-cm
 albedo = 0.2
 
 times = pd.date_range('2023-01-01 08:00', freq='h', periods=9,
                       tz='America/Denver')
 solpos = solarposition.get_solarposition(times, lat, lon)
-aoi = irradiance.aoi(tilt, azimuth, solpos.apparent_zenith, solpos.azimuth)
+aoi = irradiance.aoi(surface_tilt, surface_azimuth,
+                     solpos.apparent_zenith, solpos.azimuth)
 
 relative_airmass = atmosphere.get_relative_airmass(solpos.apparent_zenith,
                                                    model='kastenyoung1989')
@@ -64,13 +65,13 @@
 spectra_components = spectrum.spectrl2(
     apparent_zenith=solpos.apparent_zenith,
     aoi=aoi,
-    surface_tilt=tilt,
+    surface_tilt=surface_tilt,
     ground_albedo=albedo,
     surface_pressure=pressure,
     relative_airmass=relative_airmass,
-    precipitable_water=water_vapor_content,
+    precipitable_water=precipitable_water,
     ozone=ozone,
-    aerosol_turbidity_500nm=tau500,
+    aerosol_turbidity_500nm=aerosol_turbidity_500nm,
 )
 
 # %%

tests/iotools/test_meteonorm.py

@@ -72,9 +72,9 @@ def expected_meteonorm_data():
         [0.0, 0.0],
         [0.0, 0.0],
         [2.5, 2.68],
-        [77.5, 77.47],
-        [165.0, 164.98],
-        [210.75, 210.74],
+        [77.5, 77.48],
+        [165.0, 164.99],
+        [210.75, 210.75],
         [221.0, 220.99],
     ]
     index = pd.date_range('2023-01-01 00:30', periods=12, freq='1h', tz='UTC')
@@ -207,7 +207,7 @@ def test_get_meteonorm_custom_horizon(demo_api_key, demo_url):
 @pytest.mark.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
 def test_get_meteonorm_forecast_HTTPError(demo_api_key, demo_url):
     with pytest.raises(
-            HTTPError, match="unknown parameter: not_a_real_parameter"):
+            HTTPError, match='invalid parameter "not_a_real_parameter"'):
         _ = pvlib.iotools.get_meteonorm_forecast_basic(
             latitude=50, longitude=10,
             start=pd.Timestamp.now(tz='UTC'),
@@ -265,9 +265,9 @@ def expected_meteonorm_tmy_data():
         [0.],
         [0.],
         [0.],
-        [9.06],
-        [8.43],
-        [86.63],
+        [9.07],
+        [8.44],
+        [86.64],
         [110.44],
     ]
     index = pd.date_range(

tests/test_modelchain.py

@@ -139,7 +139,8 @@ def cec_dc_adr_ac_system(sam_data, cec_module_cs5p_220m,
                       module=module_parameters['Name'],
                       module_parameters=module_parameters,
                       temperature_model_parameters=temp_model_params,
-                      inverter_parameters=inverter)
+                      inverter_parameters=inverter,
+                      modules_per_string=14)
     return system