Merge branch 'main' into lambertw

Author: cwhanse

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

@@ -96,7 +96,7 @@ jobs:
         shell: bash -l {0}  # necessary for conda env to be active
         env:
           # copy GitHub Secrets into environment variables for the tests to access
-          NREL_API_KEY: ${{ secrets.NRELAPIKEY }}
+          NLR_API_KEY: ${{ secrets.NRELAPIKEY }}
           SOLARANYWHERE_API_KEY: ${{ secrets.SOLARANYWHERE_API_KEY }}
           BSRN_FTP_USERNAME: ${{ secrets.BSRN_FTP_USERNAME }}
           BSRN_FTP_PASSWORD: ${{ secrets.BSRN_FTP_PASSWORD }}

.gitignore

@@ -99,3 +99,8 @@ coverage.xml
 env
 results
 
+
+# Gas Town runtime
+.beads/
+.claude/
+.runtime/

docs/examples/bifacial/plot_irradiance_nonuniformity_loss.py

@@ -47,7 +47,7 @@
 # described in Figure 1 (A), [1]_. We will cover this case for educational
 # purposes, although it can be achieved with the packages
 # `solarfactors <https://github.com/pvlib/solarfactors/>`_ and
-# `bifacial_radiance <https://github.com/NREL/bifacial_radiance>`_.
+# `bifacial_radiance <https://github.com/NatLabRockies/bifacial_radiance>`_.
 #
 # Here we set and plot the global irradiance level of each cell.
 

docs/examples/irradiance-decomposition/plot_diffuse_fraction.py

@@ -52,7 +52,7 @@
 # ----
 #
 # DISC :py:func:`~pvlib.irradiance.disc` is an empirical correlation developed
-# at SERI (now NREL) in 1987. The direct normal irradiance (DNI) is related to
+# at SERI (now NLR) in 1987. The direct normal irradiance (DNI) is related to
 # clearness index (kt) by two polynomials split at kt = 0.6, then combined with
 # an exponential relation with airmass.
 
@@ -216,5 +216,5 @@
 # correlations, which include additional variables such as airmass. These
 # methods seem to reduce DNI spikes over 1000 [W/m^2].
 #
-# .. _TMY3: https://www.nrel.gov/docs/fy08osti/43156.pdf
-# .. _NSRDB: https://www.nrel.gov/docs/fy12osti/54824.pdf
+# .. _TMY3: https://doi.org/10.2172/928611
+# .. _NSRDB: https://doi.org/10.2172/1054832

docs/examples/spectrum/average_photon_energy.py

@@ -34,7 +34,7 @@
 from scipy.integrate import trapezoid
 from pvlib import spectrum, solarposition, irradiance, atmosphere
 
-lat, lon = 39.742, -105.18  # NREL SRRL location
+lat, lon = 39.742, -105.18  # SRRL location
 surface_tilt = 25
 surface_azimuth = 180  # south-facing system
 pressure = 81190  # at 1828 metres AMSL, roughly
@@ -194,5 +194,5 @@
 #        for the solar spectral influence on photovoltaic device performance."
 #        Energy 286 :doi:`10.1016/j.energy.2023.129461`
 # .. [4] Bird Simple Spectral Model: spectrl2_2.c
-#        https://www.nrel.gov/grid/solar-resource/spectral.html
-#        (Last accessed: 18/09/2024)
+#        https://www.nlr.gov/grid/solar-resource/spectral
+#        (Last accessed: 03/03/2026)

docs/sphinx/source/reference/iotools.rst

@@ -179,7 +179,7 @@ A solar radiation network in the USA, run by NOAA.
 MIDC
 ^^^^
 
-A solar radiation network in the USA, run by NREL.
+A solar radiation network in the USA, run by NLR (known as NREL prior to December 2025).
 
 .. autosummary::
    :toctree: generated/

docs/sphinx/source/user_guide/extras/faq.rst

@@ -126,7 +126,7 @@ The CEC table doesn't include my module or inverter, what should I do?
 ----------------------------------------------------------------------
 
 The CEC tables for module and inverter parameters included in pvlib are periodically
-copied from `SAM <https://github.com/NREL/SAM/tree/develop/deploy/libraries>`_,
+copied from `SAM <https://github.com/NatLabRockies/SAM/tree/develop/deploy/libraries>`_,
 so you can check the tables there for more up-to-date tables.
 
 For modules, if even the SAM files don't include the module you're looking for

docs/sphinx/source/user_guide/getting_started/installation.rst

@@ -242,15 +242,15 @@ pvlib-python is distributed with several validated, high-precision, and
 high-performance solar position calculators. We strongly recommend using
 the built-in solar position calculators.
 
-pvlib-python also includes unsupported wrappers for the official NREL
-SPA algorithm. NREL's license does not allow redistribution of the
+pvlib-python also includes unsupported wrappers for the official NLR
+implementation of NREL SPA. NLR's license does not allow redistribution of the
 source code, so you must jump through some hoops to use it with pvlib.
 You will need a C compiler to use this code.
 
 To install the NREL SPA algorithm for use with pvlib:
 
 #. Download the pvlib repository (as described in :ref:`obtainsource`)
-#. Download the `SPA files from NREL <http://www.nrel.gov/midc/spa/>`_
+#. Download the `SPA files from NLR <http://www.nlr.gov/midc/spa/>`_
 #. Copy the SPA files into ``pvlib-python/pvlib/spa_c_files``
 #. From the ``pvlib-python`` directory, run ``pip uninstall pvlib``
    followed by ``pip install .``

docs/sphinx/source/whatsnew/v0.15.1.rst

@@ -20,12 +20,22 @@ Bug fixes
 * Fix a division-by-zero condition in
   :py:func:`pvlib.transformer.simple_efficiency` when ``load_loss = 0``.
   (:issue:`2645`, :pull:`2646`)
+* Update URLs in :py:mod:`pvlib.iotools.psm4` to use nlr.gov instead of
+  nrel.gov. (:issue:`2701`, :pull:`2705`)
+* Fix a bug in :py:func:`pvlib.iotools.era5._m_to_cm` where meters were
+  incorrectly divided by 100 instead of multiplied, causing precipitations to be
+  underestimated by a factor of 10,000 when ``map_variables=True``.
+  (:issue:`2724`, :pull:`2725`)
+
 
 Enhancements
 ~~~~~~~~~~~~
 * Use ``k`` and ``cap_adjustment`` from :py:func:`pvlib.pvsystem.Array.module_parameters`
   in :py:func:`pvlib.pvsystem.PVSystem.pvwatts_dc`
   (:issue:`2714`, :pull:`2715`)
+* Include `ross` and `faiman_rad` in the allowed models within 
+  :py:meth:`pvlib.pvsystem.PVSystem.get_cell_temperature` (:issue:`2625`, :pull:`2631`)  
+* Accelerate the internals of :py:func:`~pvlib.solarpostion.ephemeris`. (:pull:`2626`)
 
 Documentation
 ~~~~~~~~~~~~~
@@ -67,6 +77,10 @@ Requirements
 
 Maintenance
 ~~~~~~~~~~~
+* Update all NREL references to NLR (National Laboratory of the Rockies)
+  following the laboratory rename and domain migration from ``nrel.gov``
+  to ``nlr.gov``. Rename ``NREL_API_KEY`` environment variable to
+  ``NLR_API_KEY``. (:issue:`2701`, :pull:`2705`)
 
 
 Contributors
@@ -77,6 +91,10 @@ Contributors
 * Cliff Hansen (:ghuser:`cwhanse`)
 * Anton Driesse (:ghuser:`adriesse`)
 * Kevin Anderson (:ghuser:`kandersolar`)
+* Jason Curtis (:ghuser:`jason-curtis`)
 * Rohan Saxena (:ghuser:`r0hansaxena`)
 * Marco Fumagalli (:ghuser:`fuma900`)
+* Jean-Baptiste Pasquier (:ghuser:`pasquierjb`)
+* Rodrigo Amaro e Silva (:ghuser:`ramaroesilva`)
+* James Fulton (:ghuser:`dfulu`)
 

docs/tutorials/tmy_to_power.ipynb

@@ -70,11 +70,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "pvlib comes with a couple of TMY files, and we'll use one of them for simplicity. You could also load a file from disk, or specify a url. See this NREL website for a list of TMY files:\n",
-    "\n",
-    "http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/by_state_and_city.html"
-   ]
+   "source": "pvlib comes with a couple of TMY files, and we'll use one of them for simplicity. You could also load a file from disk, or specify a url. See this NLR website for a list of TMY files:\n\nhttps://nsrdb.nlr.gov/data-sets/archives"
   },
   {
    "cell_type": "code",
@@ -1515,13 +1511,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "Next, we will assume that the SAPM model is representative of the real world performance so that we can use scipy's optimization routine to derive simulated PVUSA coefficients. You will need to install scipy to run these functions.\n",
-    "\n",
-    "Here's one PVUSA reference:\n",
-    "\n",
-    "http://www.nrel.gov/docs/fy09osti/45376.pdf\n"
-   ]
+   "source": "Next, we will assume that the SAPM model is representative of the real world performance so that we can use scipy's optimization routine to derive simulated PVUSA coefficients. You will need to install scipy to run these functions.\n\nHere's one PVUSA reference:\n\nhttps://www.osti.gov/biblio/951223\n"
   },
   {
    "cell_type": "code",