Merge branch 'main' into cross-axis-whatever-name-consistent-name

Author: echedey-ls

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

@@ -56,7 +56,7 @@ jobs:
     strategy:
       fail-fast: false  # don't cancel other matrix jobs when one fails
       matrix:
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
         suffix: ['']  # the alternative to "-min"
         include:
           - python-version: "3.10"
@@ -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

.github/workflows/pytest.yml

@@ -12,7 +12,7 @@ jobs:
       fail-fast: false  # don't cancel other matrix jobs when one fails
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
         environment-type: [conda, bare]
         suffix: ['']  # placeholder as an alternative to "-min"
         include:

.github/workflows/top-ranked-issues.yml

@@ -31,4 +31,4 @@ jobs:
 
       - name: Run update_top_ranking_issues.py
         run: |
-          python ./scripts/update_top_ranking_issues.py
+          python .github/workflows/update_top_ranking_issues.py

scripts/update_top_ranking_issues.py …b/workflows/update_top_ranking_issues.pyscripts/update_top_ranking_issues.py renamed to .github/workflows/update_top_ranking_issues.py scripts/update_top_ranking_issues.py renamed to .github/workflows/update_top_ranking_issues.py

@@ -0,0 +1,28 @@
+name: test_env
+channels:
+    - defaults
+    - conda-forge
+dependencies:
+    - coveralls
+    - cython
+    - ephem
+    - h5py
+    # - numba  # not available for py 3.14 as of 2025-11-03
+    - numpy >= 1.21.2
+    - pandas >= 1.3.3
+    - pip
+    - pytest
+    - pytest-cov
+    - pytest-mock
+    - requests-mock
+    - pytest-timeout
+    - pytest-rerunfailures
+    - conda-forge::pytest-remotedata  # version in default channel is old
+    - python=3.14
+    - pytz
+    - requests
+    - scipy >= 1.7.2
+    - statsmodels
+    - pip:
+        # - nrel-pysam>=2.0  # not available for py 3.14 as of 2025-11-03
+        - solarfactors

ci/requirements-py3.14.yml

@@ -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/irradiance-decomposition/plot_diffuse_fraction.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/shading/plot_simple_irradiance_adjustment_for_horizon_shading.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,
 )
 
 # %%

docs/examples/spectrum/average_photon_energy.py

@@ -143,10 +143,12 @@
 
 keys = ['ghi', 'dni', 'dhi', 'temp_air', 'wind_speed',
         'albedo', 'precipitable_water']
-psm3, psm3_metadata = pvlib.iotools.get_psm3(latitude, longitude, api_key,
-                                             email, interval=5, names=2019,
-                                             map_variables=True, leap_day=True,
-                                             attributes=keys)
+psm3, psm3_metadata = pvlib.iotools.get_nsrdb_psm4_conus(latitude, longitude,
+                                                         api_key, email,
+                                                         year=2019, interval=5,
+                                                         parameters=keys,
+                                                         map_variables=True,
+                                                         leap_day=True)
 
 # %%
 # Pre-generate some model inputs

docs/examples/system-models/plot_oedi_9068.py

@@ -81,9 +81,6 @@ Satellite-derived irradiance and weather data for the Americas.
    iotools.get_nsrdb_psm4_conus
    iotools.get_nsrdb_psm4_full_disc
    iotools.read_nsrdb_psm4
-   iotools.get_psm3
-   iotools.read_psm3
-   iotools.parse_psm3
 
 
 Commercial datasets
@@ -237,6 +234,17 @@ lower quality.
    iotools.read_crn
 
 
+ECMWF ERA5
+^^^^^^^^^^
+
+A global reanalysis dataset providing weather and solar resource data.
+
+.. autosummary::
+   :toctree: generated/
+
+   iotools.get_era5
+
+
 Generic data file readers
 -------------------------