JOSS-review: comparison to other tools

benchmarks/benchmarks/location.py

@@ -4,7 +4,7 @@
 
 import pandas as pd
 import pvlib
-from pkg_resources import parse_version
+from packaging.version import Version
 
 
 def set_solar_position(obj):
@@ -37,7 +37,7 @@ def time_location_get_clearsky(self):
 class Location_0_6_1:
 
     def setup(self):
-        if parse_version(pvlib.__version__) < parse_version('0.6.1'):
+        if Version(pvlib.__version__) < Version('0.6.1'):
             raise NotImplementedError
 
         set_solar_position(self)

benchmarks/benchmarks/solarposition.py

@@ -7,10 +7,10 @@
 import pvlib
 from pvlib import solarposition
 
-from pkg_resources import parse_version
+from packaging.version import Version
 
 
-if parse_version(pvlib.__version__) >= parse_version('0.6.1'):
+if Version(pvlib.__version__) >= Version('0.6.1'):
     sun_rise_set_transit_spa = solarposition.sun_rise_set_transit_spa
 else:
     sun_rise_set_transit_spa = solarposition.get_sun_rise_set_transit

benchmarks/benchmarks/solarposition_numba.py

@@ -7,7 +7,7 @@
 Try to keep relevant sections in sync with benchmarks/solarposition.py
 """
 
-from pkg_resources import parse_version
+from packaging.version import Version
 import pandas as pd
 
 import os
@@ -18,7 +18,7 @@
 from pvlib import solarposition  # NOQA: E402
 
 
-if parse_version(pvlib.__version__) >= parse_version('0.6.1'):
+if Version(pvlib.__version__) >= Version('0.6.1'):
     sun_rise_set_transit_spa = solarposition.sun_rise_set_transit_spa
 else:
     sun_rise_set_transit_spa = solarposition.get_sun_rise_set_transit

benchmarks/benchmarks/temperature.py

@@ -4,7 +4,7 @@
 
 import pandas as pd
 import pvlib
-from pkg_resources import parse_version
+from packaging.version import Version
 from functools import partial
 
 
@@ -20,7 +20,7 @@ class SAPM:
 
     def setup(self):
         set_weather_data(self)
-        if parse_version(pvlib.__version__) >= parse_version('0.7.0'):
+        if Version(pvlib.__version__) >= Version('0.7.0'):
             kwargs = pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']
             kwargs = kwargs['open_rack_glass_glass']
             self.sapm_cell_wrapper = partial(pvlib.temperature.sapm_cell,
@@ -41,7 +41,7 @@ def time_sapm_cell(self):
 class Fuentes:
 
     def setup(self):
-        if parse_version(pvlib.__version__) < parse_version('0.8.0'):
+        if Version(pvlib.__version__) < Version('0.8.0'):
             raise NotImplementedError
 
         set_weather_data(self)

docs/examples/irradiance-decomposition/plot_diffuse_fraction.py

@@ -203,7 +203,7 @@
 # `NSRDB`_ user manuals.
 #
 # The Erbs and Boland models are correlations only based on the clearness index
-# kt, which is the ratio of GHI to the the horizontal component of the
+# kt, which is the ratio of GHI to the horizontal component of the
 # extra-terrestrial irradiance. At low sun elevation (zenith near 90 degrees),
 # especially near sunset, kt can explode because the denominator
 # (extra-terrestrial irradiance) approaches zero. In pvlib this behavior is

docs/examples/iv-modeling/plot_singlediode.py

@@ -118,7 +118,6 @@
 plt.xlabel('Module voltage [V]')
 plt.ylabel('Module current [A]')
 plt.title(parameters['Name'])
-plt.show()
 plt.gcf().set_tight_layout(True)
 
 
@@ -136,6 +135,7 @@ def draw_arrow(ax, label, x0, y0, rotation, size, direction):
 ax = plt.gca()
 draw_arrow(ax, 'Irradiance', 20, 2.5, 90, 15, 'r')
 draw_arrow(ax, 'Temperature', 35, 1, 0, 15, 'l')
+plt.show()
 
 print(pd.DataFrame({
     'i_sc': curve_info['i_sc'],

docs/sphinx/source/reference/irradiance/transposition.rst

@@ -10,6 +10,7 @@ Transposition models
    irradiance.get_sky_diffuse
    irradiance.isotropic
    irradiance.perez
+   irradiance.perez_driesse
    irradiance.haydavies
    irradiance.klucher
    irradiance.reindl

docs/sphinx/source/whatsnew/v0.10.3.rst

@@ -7,20 +7,29 @@ v0.10.3 (Anticipated December, 2023)
 
 Enhancements
 ~~~~~~~~~~~~
-
+* Added the continuous Perez-Driesse transposition model.
+  :py:func:`pvlib.irradiance.perez_driesse` (:issue:`1841`, :pull:`1876`)
+* :py:func:`pvlib.bifacial.infinite_sheds.get_irradiance` and
+  :py:func:`pvlib.bifacial.infinite_sheds.get_irradiance_poa` now include
+  shaded fraction in returned variables. (:pull:`1871`)
 
 Bug fixes
 ~~~~~~~~~
 
 
 Testing
 ~~~~~~~
+* Replace use of deprecated ``pkg_resources``. (:issue:`1881`, :pull:`1882`)
 
 
 Documentation
 ~~~~~~~~~~~~~
-
+* Fixed a plotting issue in the IV curve gallery example (:pull:`1895`)
 
 Contributors
 ~~~~~~~~~~~~
-
+* Arjan Keeman (:ghuser:`akeeman`)
+* Miguel Sánchez de León Peque (:ghuser:`Peque`)
+* Will Hobbs (:ghuser:`williamhobbs`)
+* Anton Driesse (:ghuser:`adriesse`)
+* :ghuser:`matsuobasho`

paper/paper.bib

@@ -66,12 +66,83 @@ @article{pvlibjoss2018
 
 @inproceedings{Holmgren2018,
 	Author = {William F. Holmgren and Clifford W. Hansen and Joshua S. Stein and Mark A. Mikofski},
-	Booktitle = {IEEE 45th Photovoltaic Specialists Conference},
+	Booktitle = {45th IEEE Photovoltaic Specialists Conference},
 	Title = {Review of open source tools for PV modeling},
 	Year = {2018},
 	doi = {10.5281/zenodo.1401378}
 }
 
+@inproceedings{Andrews2014,
+  doi = {10.1109/pvsc.2014.6925501},
+  url = {https://doi.org/10.1109/pvsc.2014.6925501},
+  year = {2014},
+  month = jun,
+  publisher = {{IEEE}},
+  author = {Robert W. Andrews and Joshua S. Stein and Clifford Hansen and Daniel Riley},
+  title = {Introduction to the open source {PV} {LIB} for python Photovoltaic system modelling package},
+  booktitle = {40th IEEE Photovoltaic Specialists Conference}
+}
+
+@inproceedings{Mermoud1994,
+  title = {{PVSYST}: a user-friendly software for {PV}-systems simulation},
+  booktitle = {Twelfth European Photovoltaic Solar Energy Conference},
+  year = {1994},
+  author = {André Mermoud}
+}
+
+@inproceedings{Mikofski2018,
+  title = {Accurate Performance Predictions of Large PV Systems with Shading using Submodule Mismatch Calculation},
+  url = {http://dx.doi.org/10.1109/PVSC.2018.8547323},
+  DOI = {10.1109/pvsc.2018.8547323},
+  booktitle = {7th World Conference on Photovoltaic Energy Conversion (WCPEC)},
+  publisher = {IEEE},
+  author = {Mikofski,  Mark A. and Lynn,  Matthew and Byrne,  James and Hamer,  Mike and Neubert,  Anja and Newmiller,  Jeff},
+  year = {2018},
+  month = jun 
+}
+
+@inproceedings{Passow2017,
+  title = {PlantPredict: Solar Performance Modeling Made Simple},
+  url = {http://dx.doi.org/10.1109/PVSC.2017.8366450},
+  DOI = {10.1109/pvsc.2017.8366450},
+  booktitle = {44th IEEE Photovoltaic Specialists Conference (PVSC)},
+  publisher = {IEEE},
+  author = {Passow,  Kendra and Ngan,  Lauren and Rich,  Geoffrey and Lee,  Mitch and Kaplan,  Stephen},
+  year = {2017},
+  month = jun 
+}
+
+@techreport{Gilman2018,
+  title = {SAM Photovoltaic Model Technical Reference 2016 Update},
+  url = {http://dx.doi.org/10.2172/1429291},
+  DOI = {10.2172/1429291},
+  institution = {Office of Scientific and Technical Information (OSTI)},
+  author = {Gilman,  Paul and DiOrio,  Nicholas A. and Freeman,  Janine M. and Janzou,  Steven and Dobos,  Aron and Ryberg,  David},
+  year = {2018},
+  month = mar 
+}
+
+@misc{pysam,
+  doi = {10.11578/DC.20190903.1},
+  url = {https://www.osti.gov/doecode/biblio/29023},
+  author = {Gilman,  Paul and Janzou,  Steven and Guittet,  Darice and Freeman,  Janine and DiOrio,  Nicholas and Blair,  Nathan and Boyd,  Matthew and Neises,  Ty and Wagner,  Michael},
+  language = {en},
+  title = {PySAM (Python Wrapper for System Advisor Model "SAM") [SWR-19-57]},
+  publisher = {National Renewable Energy Laboratory (NREL),  Golden,  CO (United States)},
+  year = {2019}
+}
+
+@inproceedings{Pai2016,
+  title = {Introducing CASSYS: An open-source software for simulation of grid-connected photovoltaic systems},
+  url = {http://dx.doi.org/10.1109/PVSC.2016.7749839},
+  DOI = {10.1109/pvsc.2016.7749839},
+  booktitle = {43rd IEEE Photovoltaic Specialists Conference},
+  publisher = {IEEE},
+  author = {Pai,  Abhijeet and Thevenard,  Didier},
+  year = {2016},
+  month = jun 
+}
+
 @techreport{Augspurger2023,
   author = {Tobias Augspurger and Eirini Malliaraki and Josh Hopkins and Dan Brown},
   title = {The Open Source Sustainability Ecosystem},

paper/paper.md

@@ -73,26 +73,41 @@ of the global solar energy industry demands correspondingly more
 capable models.  Per the United States Department of Energy,
 "the importance of accurate modeling is hard to overstate" [@seto2022].
 
-Compared with other PV modeling tools, pvlib python stands out in several
+Compared with other modern PV system modeling tools, pvlib python stands out in several
 key aspects.  One is its toolbox design, providing the user a
 level of flexibility and customization beyond that of other tools.  Rather than organizing
-the user interface around pre-built modeling workflows, pvlib python
-makes the individual "building blocks" of PV performance models accessible to
+the user interface around pre-built modeling workflows as in other PV system
+modeling tools (e.g. SAM [@Gilman2018], PVsyst [@Mermoud1994], SolarFarmer [@Mikofski2018],
+PlantPredict [@Passow2017], and CASSYS [@Pai2016], to name a few software tools with
+comparable breadth of PV system modeling capability), pvlib python
+makes the individual "building blocks" of PV system performance models accessible to
 the user.  This allows the user to assemble their own model workflows, including
 the ability of incorporating custom modeling steps.  This flexibility
-is essential for applications in both academia and industry.
+is essential for applications in both academia and industry.  To our knowledge,
+the only other PV system modeling software with such a toolbox design is the
+original MATLAB version of pvlib [@Andrews2014].  pvlib python began as
+a translation of that code base and has since surpassed it in terms of
+capability, community uptake, and development attention.
 
 Another key aspect of pvlib python is that it is used via
 a general-purpose programming language (Python), which
 allows pvlib python functions to be combined with capabilities in other Python packages,
 such as database query, data manipulation, numerical optimization,
-plotting, and reporting packages.
+plotting, and reporting packages.  In contrast, most other PV system modeling
+tools are used via some form of GUI.  Some of these other tools are also accessible
+from Python via web APIs or wrapper libraries (e.g. [@pysam]), but these
+"black box" interfaces offer only limited ability to combine the PV models
+with functionality from other Python packages.
 
 A final key aspect of pvlib python is its open peer review approach and
 foundation on published scientific research, allowing it to be developed by
 a decentralized and diverse community of PV researchers and practitioners
 without compromising its focus on transparent and reliable model
-implementations.
+implementations.  This is in contrast to the inherent opaqueness of closed-source
+commercial software, which prevents users from inspecting the source code
+to ensure a model implementation's validity or traceability to a reference.
+It is also in contrast to other open-source PV projects, where code review
+and contributions typically come from a single institution.
 
 These key aspects, along with sustained contributions from a passionate and
 committed community, have led to pvlib python's widespread adoption across the PV

pvlib/bifacial/infinite_sheds.py

@@ -276,6 +276,8 @@ def get_irradiance_poa(surface_tilt, surface_azimuth, solar_zenith,
       [W/m^2]
     - ``poa_ground_diffuse`` : total ground-reflected diffuse irradiance on the
       plane of array. [W/m^2]
+    - ``shaded_fraction`` : fraction of row slant height from the bottom that
+      is shaded from direct irradiance by adjacent rows. [unitless]
 
     References
     ----------
@@ -369,7 +371,7 @@ def get_irradiance_poa(surface_tilt, surface_azimuth, solar_zenith,
     output = {
         'poa_global': poa_global, 'poa_direct': poa_direct,
         'poa_diffuse': poa_diffuse, 'poa_ground_diffuse': poa_gnd_pv,
-        'poa_sky_diffuse': poa_sky_pv}
+        'poa_sky_diffuse': poa_sky_pv, 'shaded_fraction': f_x}
     if isinstance(poa_global, pd.Series):
         output = pd.DataFrame(output)
     return output
@@ -502,6 +504,9 @@ def get_irradiance(surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
       cells from the front surface. [W/m^2]
     - ``poa_front_ground_diffuse`` : ground-reflected diffuse irradiance
       reaching the module cells from the front surface. [W/m^2]
+    - ``shaded_fraction_front`` : fraction of row slant height from the bottom
+      that is shaded from direct irradiance on the front surface by adjacent
+      rows. [unitless]
     - ``poa_back_direct`` : direct irradiance reaching the module cells from
       the back surface. [W/m^2]
     - ``poa_back_diffuse`` : total diffuse irradiance reaching the module
@@ -510,6 +515,9 @@ def get_irradiance(surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
       cells from the back surface. [W/m^2]
     - ``poa_back_ground_diffuse`` : ground-reflected diffuse irradiance
       reaching the module cells from the back surface. [W/m^2]
+    - ``shaded_fraction_back`` : fraction of row slant height from the bottom
+      that is shaded from direct irradiance on the back surface by adjacent
+      rows. [unitless]
 
     References
     ----------
@@ -545,13 +553,15 @@ def get_irradiance(surface_tilt, surface_azimuth, solar_zenith, solar_azimuth,
         'poa_diffuse': 'poa_front_diffuse',
         'poa_sky_diffuse': 'poa_front_sky_diffuse',
         'poa_ground_diffuse': 'poa_front_ground_diffuse',
+        'shaded_fraction': 'shaded_fraction_front',
     }
     colmap_back = {
         'poa_global': 'poa_back',
         'poa_direct': 'poa_back_direct',
         'poa_diffuse': 'poa_back_diffuse',
         'poa_sky_diffuse': 'poa_back_sky_diffuse',
         'poa_ground_diffuse': 'poa_back_ground_diffuse',
+        'shaded_fraction': 'shaded_fraction_back',
     }
 
     if isinstance(ghi, pd.Series):

pvlib/iam.py

@@ -840,7 +840,7 @@ def schlick(aoi):
 
 
 def schlick_diffuse(surface_tilt):
-    """
+    r"""
     Determine the incidence angle modifiers (IAM) for diffuse sky and
     ground-reflected irradiance on a tilted surface using the Schlick
     incident angle model.