From 1633cd60164cd7883a5480c19d476ceb05f16d7a Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Tue, 30 May 2023 09:41:48 -0400 Subject: [PATCH 1/9] add a bit of content --- docs/sphinx/source/user_guide/index.rst | 1 + .../sphinx/source/user_guide/weather_data.rst | 63 +++++++++++++++++++ 2 files changed, 64 insertions(+) create mode 100644 docs/sphinx/source/user_guide/weather_data.rst diff --git a/docs/sphinx/source/user_guide/index.rst b/docs/sphinx/source/user_guide/index.rst index 0db1bfd000..5e28940d35 100644 --- a/docs/sphinx/source/user_guide/index.rst +++ b/docs/sphinx/source/user_guide/index.rst @@ -15,6 +15,7 @@ User Guide clearsky bifacial forecasts + weather_data comparison_pvlib_matlab variables_style_rules singlediode diff --git a/docs/sphinx/source/user_guide/weather_data.rst b/docs/sphinx/source/user_guide/weather_data.rst new file mode 100644 index 0000000000..ad087b14f8 --- /dev/null +++ b/docs/sphinx/source/user_guide/weather_data.rst @@ -0,0 +1,63 @@ +.. _weatherdata: + +Importing weather data +====================== + +Many PV modeling applications require irradiance and meteorological datasets +as the inputs to a PV system model. These weather datasets are available +from many sources and in many formats. The :py:mod:`pvlib.iotools` module +contains functions to retrieve and import these datasets in a form +that is convenient to use with the rest of pvlib. + + +Usage +----- + +Although there are some exceptions, the :py:mod:`pvlib.iotools` functions +provide a uniform interface for reading data files in many common formats. +Specifically, :py:mod:`pvlib.iotools` functions usually return two objects: +a :py:class:`pandas.DataFrame` of the actual dataset and, plus a metadata +dictionary. Most :py:mod:`pvlib.iotools` functions also have +a ``map_variables`` parameter to automatically translate +the column names used in the data file (which vary widely across datasets) +into standard pvlib names (see :ref:`variables_style_rules`). Typical usage +looks like this: + +.. code-block:: python + + df, metadata = pvlib.iotools.function(..., map_variables=True) + + +Most :py:mod:`pvlib.iotools` functions work with time series datasets. +In that case, the returned ``df`` DataFrame has a datetime index, localized +to the appropriate time zone where possible. The metadata dictionary +varies based on the function/dataset being used. + +For the full list of available :py:mod:`pvlib.iotools` functions, see +:ref:`iotools`. + + +File readers +------------ + +Some weather data file formats have internal structure that requires +more than just a call to :py:func:`pandas.read_csv`. pvlib provides +functions for reading files in many of these formats. Functions that +read and parse files in a particular format have names that begin with ``read_``: +:py:func:`~pvlib.iotools.read_tmy3`, :py:func:`~pvlib.iotools.read_epw`, and so on. + + +Online APIs +----------- + +Several :py:mod:`pvlib.iotools` functions access the internet to fetch data from +external web APIs. For example, :py:func:`~pvlib.iotools.get_pvgis_hourly` +downloads data from PVGIS's webservers and returns it as a python variable. +Functions that retrieve data from the internet have names that begin with +``get_``: :py:func:`~pvlib.iotools.get_bsrn`, :py:func:`~pvlib.iotools.get_psm3`, +:py:func:`~pvlib.iotools.get_pvgis_tmy`, and so on. + +Some of these data providers require registration. In those cases, your +access credentials must be passed as parameters to the function. See the +individual function documentation pages for details. + From aee7b7dbacf3b90d979dc27c1433f0b45279f43a Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Thu, 9 Nov 2023 08:55:04 -0500 Subject: [PATCH 2/9] Apply suggestions from code review Co-authored-by: Adam R. Jensen <39184289+AdamRJensen@users.noreply.github.com> --- docs/sphinx/source/user_guide/weather_data.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/sphinx/source/user_guide/weather_data.rst b/docs/sphinx/source/user_guide/weather_data.rst index ad087b14f8..874ec59d85 100644 --- a/docs/sphinx/source/user_guide/weather_data.rst +++ b/docs/sphinx/source/user_guide/weather_data.rst @@ -1,13 +1,13 @@ .. _weatherdata: -Importing weather data -====================== +Weather data +========= -Many PV modeling applications require irradiance and meteorological datasets -as the inputs to a PV system model. These weather datasets are available +Simulating the performance of a PV system requires irradiance and meteorological data +as the inputs to a PV system model. Weather datasets are available from many sources and in many formats. The :py:mod:`pvlib.iotools` module -contains functions to retrieve and import these datasets in a form -that is convenient to use with the rest of pvlib. +contains functions to easily retrieve and import such datasets in a standardized +form that is convenient to use with the rest of pvlib. Usage From 6f5e243cf76b1b8bf3fe82ad9cab78ced968affe Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Thu, 9 Nov 2023 09:59:00 -0500 Subject: [PATCH 3/9] revisions --- .../sphinx/source/user_guide/weather_data.rst | 101 +++++++++++++----- 1 file changed, 75 insertions(+), 26 deletions(-) diff --git a/docs/sphinx/source/user_guide/weather_data.rst b/docs/sphinx/source/user_guide/weather_data.rst index 874ec59d85..06a309401c 100644 --- a/docs/sphinx/source/user_guide/weather_data.rst +++ b/docs/sphinx/source/user_guide/weather_data.rst @@ -1,57 +1,90 @@ .. _weatherdata: Weather data -========= +============ Simulating the performance of a PV system requires irradiance and meteorological data as the inputs to a PV system model. Weather datasets are available from many sources and in many formats. The :py:mod:`pvlib.iotools` module contains functions to easily retrieve and import such datasets in a standardized -form that is convenient to use with the rest of pvlib. +form that is convenient to use with the rest of pvlib. For a complete list +of functions related to retrieving and importing weather data, see :ref:`iotools`. -Usage ------ +Types of weather data sources +----------------------------- -Although there are some exceptions, the :py:mod:`pvlib.iotools` functions -provide a uniform interface for reading data files in many common formats. +Ground station measurements +*************************** + +From in-situ monitoring equipment. If properly maintained and quality-controlled, +these are the highest quality source of weather information. However, the coverage +depends on a weather station having been set up in advance for the location and +times of interest. There are datasets such as BSRN and SURFRAD which make their +measurement data publicly available. + + +Numerical Weather Prediction (NWP) +********************************** + +These are mathematical simulations of weather systems. The data quality is much +lower than that of measurements, owing in part to coarser spatial and temporal +resolution, as well as many models not being optimised for solar irradiance for +PV applications. On the plus side, these models typically have worldwide coverage, +with some regional models (e.g. HRRR) sacrifice global coverage for somewhat higher +spatial and temporal resolution. Various forecast (e.g. GFS, ECMWF, ICON) and +reanalysis sources (ERA5, MERRA2) exist. + + +Satellite Data +************** + +These sources process satellite imagery (typically from geostationary satellites) +to identify and classify clouds, and combine this with solar irradiance models to +produce irradiance estimates. The quality is generally much higher than NWP, but +still not as good as a well-maintained weather station. They have high spatial +and temporal resolution corresponding to the source satellite imagery, and are +generally optimised to estimate solar irradiance for PV applications. Free sources +such as PVGIS are available, and commerical sources such as SolarAnywhere, +Solcast and Solargis provide paid options though often have free trials. + + +:py:mod:`pvlib.iotools` usage +----------------------------- + +With some exceptions, the :py:mod:`pvlib.iotools` functions +provide a uniform interface for accessing data across many formats. Specifically, :py:mod:`pvlib.iotools` functions usually return two objects: -a :py:class:`pandas.DataFrame` of the actual dataset and, plus a metadata +a :py:class:`pandas.DataFrame` of the actual dataset, plus a metadata dictionary. Most :py:mod:`pvlib.iotools` functions also have a ``map_variables`` parameter to automatically translate -the column names used in the data file (which vary widely across datasets) +the column names used in the data file (which vary widely from dataset to dataset) into standard pvlib names (see :ref:`variables_style_rules`). Typical usage looks like this: .. code-block:: python - df, metadata = pvlib.iotools.function(..., map_variables=True) + # reading a local data file: + df, metadata = pvlib.iotools.read_XYZ(filepath, map_variables=True, ...) + + # retrieving data from an online service + df, metadata = pvlib.iotools.get_XYZ(location, date_range, map_variables=True, ...) Most :py:mod:`pvlib.iotools` functions work with time series datasets. In that case, the returned ``df`` DataFrame has a datetime index, localized -to the appropriate time zone where possible. The metadata dictionary -varies based on the function/dataset being used. - -For the full list of available :py:mod:`pvlib.iotools` functions, see -:ref:`iotools`. - - -File readers ------------- +to the appropriate time zone where possible. Make sure to understand each +dataset's timestamping convention (e.g. center versus end of interval), as +pvlib will use these timestamps for solar position calculations. -Some weather data file formats have internal structure that requires -more than just a call to :py:func:`pandas.read_csv`. pvlib provides -functions for reading files in many of these formats. Functions that -read and parse files in a particular format have names that begin with ``read_``: -:py:func:`~pvlib.iotools.read_tmy3`, :py:func:`~pvlib.iotools.read_epw`, and so on. +The content of the metadata dictionary varies for each function/dataset. -Online APIs ------------ +Data retrieval +************** Several :py:mod:`pvlib.iotools` functions access the internet to fetch data from -external web APIs. For example, :py:func:`~pvlib.iotools.get_pvgis_hourly` +online web APIs. For example, :py:func:`~pvlib.iotools.get_pvgis_hourly` downloads data from PVGIS's webservers and returns it as a python variable. Functions that retrieve data from the internet have names that begin with ``get_``: :py:func:`~pvlib.iotools.get_bsrn`, :py:func:`~pvlib.iotools.get_psm3`, @@ -61,3 +94,19 @@ Some of these data providers require registration. In those cases, your access credentials must be passed as parameters to the function. See the individual function documentation pages for details. + +Reading local files +******************* + +:py:mod:`pvlib.iotools` also provides functions for parsing data files +stored locally on your computer. +Functions that read and parse files in a particular format have names +that begin with ``read_``: :py:func:`~pvlib.iotools.read_tmy3`, +:py:func:`~pvlib.iotools.read_epw`, and so on. + + +References +---------- +.. [1] Jensen et al. "pvlib iotools—Open-source Python functions for seamless + access to solar irradiance data". Solar Energy, 2023. + :doi:`10.1016/j.solener.2023.112092`. From c2ba0e6095539faa0a9729ee5460f8c2f32bde4d Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Fri, 10 Nov 2023 11:15:32 -0500 Subject: [PATCH 4/9] further improvements --- .../sphinx/source/user_guide/weather_data.rst | 141 ++++++++++++------ 1 file changed, 94 insertions(+), 47 deletions(-) diff --git a/docs/sphinx/source/user_guide/weather_data.rst b/docs/sphinx/source/user_guide/weather_data.rst index 06a309401c..956b124e73 100644 --- a/docs/sphinx/source/user_guide/weather_data.rst +++ b/docs/sphinx/source/user_guide/weather_data.rst @@ -7,46 +7,70 @@ Simulating the performance of a PV system requires irradiance and meteorological as the inputs to a PV system model. Weather datasets are available from many sources and in many formats. The :py:mod:`pvlib.iotools` module contains functions to easily retrieve and import such datasets in a standardized -form that is convenient to use with the rest of pvlib. For a complete list -of functions related to retrieving and importing weather data, see :ref:`iotools`. +form that is convenient to use with the rest of pvlib. +The primary focus of :py:mod:`pvlib.iotools` is time series solar resource +data like the datasets from PVGIS and the NSRDB, but it also provides +functionality for other types of data useful for certain aspects of PV modeling +(e.g. precipitation data from :py:func:`~pvlib.iotools.get_acis_prism` +for soiling modeling, and horizon profiles from :py:func:`~pvlib.iotools.get_pvgis_horizon` +for horizon shade modeling). -Types of weather data sources ------------------------------ - -Ground station measurements -*************************** - -From in-situ monitoring equipment. If properly maintained and quality-controlled, -these are the highest quality source of weather information. However, the coverage -depends on a weather station having been set up in advance for the location and -times of interest. There are datasets such as BSRN and SURFRAD which make their -measurement data publicly available. - - -Numerical Weather Prediction (NWP) -********************************** +For a complete list of functions related to retrieving and importing weather +data, see :ref:`iotools`. -These are mathematical simulations of weather systems. The data quality is much -lower than that of measurements, owing in part to coarser spatial and temporal -resolution, as well as many models not being optimised for solar irradiance for -PV applications. On the plus side, these models typically have worldwide coverage, -with some regional models (e.g. HRRR) sacrifice global coverage for somewhat higher -spatial and temporal resolution. Various forecast (e.g. GFS, ECMWF, ICON) and -reanalysis sources (ERA5, MERRA2) exist. +Types of weather data sources +----------------------------- -Satellite Data -************** - -These sources process satellite imagery (typically from geostationary satellites) -to identify and classify clouds, and combine this with solar irradiance models to -produce irradiance estimates. The quality is generally much higher than NWP, but -still not as good as a well-maintained weather station. They have high spatial -and temporal resolution corresponding to the source satellite imagery, and are -generally optimised to estimate solar irradiance for PV applications. Free sources -such as PVGIS are available, and commerical sources such as SolarAnywhere, -Solcast and Solargis provide paid options though often have free trials. +Weather data can be grouped into a few fundamental categories. Which +type is most useful depends on the application. Here we provide a high-level +overview of different types of weather data, and when you might want to use +them. + +1. **Ground station measurements**: + From in-situ monitoring equipment. If properly maintained and + quality-controlled, these are the highest quality + source of weather information. However, the coverage depends on + a weather station having been set up in advance for the location and + times of interest. Some ground station networks like the BSRN and SURFRAD + make their measurement data publicly available. + + Data from public ground station measurement networks are useful if you + want accurate, high-resolution data but have flexibility around the + specific measurement location. + +2. **Satellite data**: + These sources process satellite imagery (typically from geostationary + satellites) to identify and classify clouds, and combine this with solar + irradiance models to produce irradiance estimates. The quality is + generally much higher than NWP, but still not as good as a well-maintained + weather station. They have high spatial and temporal resolution + corresponding to the source satellite imagery, and are generally + optimised to estimate solar irradiance for PV applications. Free sources + such as PVGIS are available, and commerical sources such as SolarAnywhere, + Solcast and Solargis provide paid options though often have free trials. + + Satellite data is useful when suitable ground station measurements are + not available for the location and/or times of interest. + +3. **Numerical Weather Prediction (NWP)**: + These are mathematical simulations of weather systems. + The data quality is much lower than that of measurements and + satellite data, owing in part to coarser spatial and temporal + resolution, as well as many models not being optimised for solar + irradiance for PV applications. On the plus side, these models typically + have worldwide coverage, with some regional models (e.g. HRRR) sacrifice + global coverage for somewhat higher spatial and temporal resolution. + Various forecast (e.g. GFS, ECMWF, ICON) and reanalysis sources (ERA5, + MERRA2) exist. + + NWP datasets are primarily useful for parts of the world not covered + by satellite-based datasets (e.g. the poles) or if extremely long time + ranges are needed. + +For a more detailed comparison of the weather datasets available through +pvlib, see [1]_. :py:mod:`pvlib.iotools` usage @@ -59,17 +83,18 @@ a :py:class:`pandas.DataFrame` of the actual dataset, plus a metadata dictionary. Most :py:mod:`pvlib.iotools` functions also have a ``map_variables`` parameter to automatically translate the column names used in the data file (which vary widely from dataset to dataset) -into standard pvlib names (see :ref:`variables_style_rules`). Typical usage -looks like this: +into standard pvlib names (see :ref:`variables_style_rules`). + +Typical usage looks something like this: .. code-block:: python - # reading a local data file: - df, metadata = pvlib.iotools.read_XYZ(filepath, map_variables=True, ...) - - # retrieving data from an online service - df, metadata = pvlib.iotools.get_XYZ(location, date_range, map_variables=True, ...) + # get_pvgis_tmy returns two additional values besides df and metadata + df, _, _, metadata = pvlib.iotools.get_pvgis_tmy(latitude, longitude, map_variables=True) +This code will fetch a Typical Meteorological Year (TMY) dataset from PVGIS, +returning a :py:class:`pandas.DataFrame` containing the hourly weather data +and a python dict with information about the dataset. Most :py:mod:`pvlib.iotools` functions work with time series datasets. In that case, the returned ``df`` DataFrame has a datetime index, localized @@ -86,9 +111,25 @@ Data retrieval Several :py:mod:`pvlib.iotools` functions access the internet to fetch data from online web APIs. For example, :py:func:`~pvlib.iotools.get_pvgis_hourly` downloads data from PVGIS's webservers and returns it as a python variable. -Functions that retrieve data from the internet have names that begin with -``get_``: :py:func:`~pvlib.iotools.get_bsrn`, :py:func:`~pvlib.iotools.get_psm3`, -:py:func:`~pvlib.iotools.get_pvgis_tmy`, and so on. +Functions that retrieve data from the internet are named ``get_``, followed +by the name of the data source: :py:func:`~pvlib.iotools.get_bsrn`, +:py:func:`~pvlib.iotools.get_psm3`, :py:func:`~pvlib.iotools.get_pvgis_tmy`, +and so on. + +For satellite/reanalysis datasets, the location is specified by latitude and +longitude in decimal degrees: + +.. code-block:: python + + lat, lon = 33.75, -84.39 # Atlanta, Georgia, United States + df, metadata = pvlib.iotools.get_psm3(lat, lon, map_variables=True, ...) + + +For ground station networks, the location identifier is the station ID: + +.. code-block:: python + + df, metadata = pvlib.iotools.get_bsrn(station='cab', start='2020-01-01', end='2020-01-31', ...) Some of these data providers require registration. In those cases, your access credentials must be passed as parameters to the function. See the @@ -100,10 +141,16 @@ Reading local files :py:mod:`pvlib.iotools` also provides functions for parsing data files stored locally on your computer. -Functions that read and parse files in a particular format have names -that begin with ``read_``: :py:func:`~pvlib.iotools.read_tmy3`, +Functions that read and parse local data files are named ``read_``, followed by +the name of the file format they parse: :py:func:`~pvlib.iotools.read_tmy3`, :py:func:`~pvlib.iotools.read_epw`, and so on. +For example, here is how to read a file in the TMY3 file format: + +.. code-block:: python + + df, metadata = pvlib.iotools.read_tmy3(r"C:\path\to\file.csv", map_variables=True) + References ---------- From 37adc7d698a3e5cb721ad53aec2e146ba547c582 Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Tue, 21 Nov 2023 14:31:29 -0500 Subject: [PATCH 5/9] Apply suggestions from code review Co-authored-by: Adam R. Jensen <39184289+AdamRJensen@users.noreply.github.com> --- .../sphinx/source/user_guide/weather_data.rst | 28 ++++++++++--------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/docs/sphinx/source/user_guide/weather_data.rst b/docs/sphinx/source/user_guide/weather_data.rst index 956b124e73..04fb3eb598 100644 --- a/docs/sphinx/source/user_guide/weather_data.rst +++ b/docs/sphinx/source/user_guide/weather_data.rst @@ -10,9 +10,9 @@ contains functions to easily retrieve and import such datasets in a standardized form that is convenient to use with the rest of pvlib. The primary focus of :py:mod:`pvlib.iotools` is time series solar resource -data like the datasets from PVGIS and the NSRDB, but it also provides -functionality for other types of data useful for certain aspects of PV modeling -(e.g. precipitation data from :py:func:`~pvlib.iotools.get_acis_prism` +data like the irradiance datasets from PVGIS and the NSRDB, but it also provides +functionality for other types of data useful for certain aspects of PV modeling. +For example, precipitation data is available from :py:func:`~pvlib.iotools.get_acis_prism` for soiling modeling, and horizon profiles from :py:func:`~pvlib.iotools.get_pvgis_horizon` for horizon shade modeling). @@ -34,7 +34,8 @@ them. source of weather information. However, the coverage depends on a weather station having been set up in advance for the location and times of interest. Some ground station networks like the BSRN and SURFRAD - make their measurement data publicly available. + make their measurement data publicly available. An global overview of ground + stations is available at https://SolarStations.org. Data from public ground station measurement networks are useful if you want accurate, high-resolution data but have flexibility around the @@ -43,15 +44,16 @@ them. 2. **Satellite data**: These sources process satellite imagery (typically from geostationary satellites) to identify and classify clouds, and combine this with solar - irradiance models to produce irradiance estimates. The quality is - generally much higher than NWP, but still not as good as a well-maintained + irradiance models and aerosol data to produce irradiance estimates. The + quality is generally much higher than NWP, but still not as good as a well-maintained weather station. They have high spatial and temporal resolution corresponding to the source satellite imagery, and are generally optimised to estimate solar irradiance for PV applications. Free sources - such as PVGIS are available, and commerical sources such as SolarAnywhere, - Solcast and Solargis provide paid options though often have free trials. + such as PVGIS, NSRDB, and CAMS are available, and commerical sources such + as SolarAnywhere, Solcast, and Solargis provide paid options though often + have free trials. - Satellite data is useful when suitable ground station measurements are + Satellite data is useful when suitable ground measurements are not available for the location and/or times of interest. 3. **Numerical Weather Prediction (NWP)**: @@ -66,14 +68,14 @@ them. MERRA2) exist. NWP datasets are primarily useful for parts of the world not covered - by satellite-based datasets (e.g. the poles) or if extremely long time + by satellite-based datasets (e.g. polar regions) or if extremely long time ranges are needed. For a more detailed comparison of the weather datasets available through pvlib, see [1]_. -:py:mod:`pvlib.iotools` usage +Usage ----------------------------- With some exceptions, the :py:mod:`pvlib.iotools` functions @@ -121,8 +123,8 @@ longitude in decimal degrees: .. code-block:: python - lat, lon = 33.75, -84.39 # Atlanta, Georgia, United States - df, metadata = pvlib.iotools.get_psm3(lat, lon, map_variables=True, ...) + latitude, longitude = 33.75, -84.39 # Atlanta, Georgia, United States + df, metadata = pvlib.iotools.get_psm3(latitude, longitude, map_variables=True, ...) For ground station networks, the location identifier is the station ID: From b15cd4c401b552fac8777004e2023857c380a394 Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Tue, 21 Nov 2023 14:34:43 -0500 Subject: [PATCH 6/9] make :py:mod:`pvlib.iotools` link to API reference --- docs/sphinx/source/reference/iotools.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/sphinx/source/reference/iotools.rst b/docs/sphinx/source/reference/iotools.rst index 39bd4f2ad3..b41a30b203 100644 --- a/docs/sphinx/source/reference/iotools.rst +++ b/docs/sphinx/source/reference/iotools.rst @@ -1,3 +1,6 @@ + +.. py:module:: pvlib.iotools + .. currentmodule:: pvlib .. _iotools: From f744ed7a7868bac6761b6e4f8521964f45fb8543 Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Tue, 21 Nov 2023 14:35:24 -0500 Subject: [PATCH 7/9] move mention of iotools paper --- docs/sphinx/source/user_guide/weather_data.rst | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/sphinx/source/user_guide/weather_data.rst b/docs/sphinx/source/user_guide/weather_data.rst index 04fb3eb598..482f2c62ae 100644 --- a/docs/sphinx/source/user_guide/weather_data.rst +++ b/docs/sphinx/source/user_guide/weather_data.rst @@ -17,7 +17,8 @@ for soiling modeling, and horizon profiles from :py:func:`~pvlib.iotools.get_pvg for horizon shade modeling). For a complete list of functions related to retrieving and importing weather -data, see :ref:`iotools`. +data, see :ref:`iotools`, and for a detailed comparison of the weather datasets +available through pvlib, see [1]_. Types of weather data sources @@ -71,9 +72,6 @@ them. by satellite-based datasets (e.g. polar regions) or if extremely long time ranges are needed. -For a more detailed comparison of the weather datasets available through -pvlib, see [1]_. - Usage ----------------------------- From 400f205e831e7a6bff19d74dd6df8841f7b05d71 Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Tue, 21 Nov 2023 14:37:46 -0500 Subject: [PATCH 8/9] cleanup --- docs/sphinx/source/user_guide/weather_data.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/sphinx/source/user_guide/weather_data.rst b/docs/sphinx/source/user_guide/weather_data.rst index 482f2c62ae..06e54d2296 100644 --- a/docs/sphinx/source/user_guide/weather_data.rst +++ b/docs/sphinx/source/user_guide/weather_data.rst @@ -13,8 +13,8 @@ The primary focus of :py:mod:`pvlib.iotools` is time series solar resource data like the irradiance datasets from PVGIS and the NSRDB, but it also provides functionality for other types of data useful for certain aspects of PV modeling. For example, precipitation data is available from :py:func:`~pvlib.iotools.get_acis_prism` -for soiling modeling, and horizon profiles from :py:func:`~pvlib.iotools.get_pvgis_horizon` -for horizon shade modeling). +for soiling modeling and horizon profiles can be retrieved from +:py:func:`~pvlib.iotools.get_pvgis_horizon` for horizon shade modeling. For a complete list of functions related to retrieving and importing weather data, see :ref:`iotools`, and for a detailed comparison of the weather datasets @@ -74,7 +74,7 @@ them. Usage ------------------------------ +----- With some exceptions, the :py:mod:`pvlib.iotools` functions provide a uniform interface for accessing data across many formats. From 7257ca810351599951d4bde44c22c5d070d5a7da Mon Sep 17 00:00:00 2001 From: Kevin Anderson Date: Tue, 21 Nov 2023 14:44:49 -0500 Subject: [PATCH 9/9] whatsnew --- docs/sphinx/source/whatsnew/v0.10.3.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/sphinx/source/whatsnew/v0.10.3.rst b/docs/sphinx/source/whatsnew/v0.10.3.rst index b7eece4108..295c1e138f 100644 --- a/docs/sphinx/source/whatsnew/v0.10.3.rst +++ b/docs/sphinx/source/whatsnew/v0.10.3.rst @@ -24,6 +24,7 @@ Testing Documentation ~~~~~~~~~~~~~ +* Create :ref:`weatherdata` User's Guide page. (:pull:`1754`) * Fixed a plotting issue in the IV curve gallery example (:pull:`1895`) Contributors @@ -33,3 +34,6 @@ Contributors * Will Hobbs (:ghuser:`williamhobbs`) * Anton Driesse (:ghuser:`adriesse`) * :ghuser:`matsuobasho` +* Harry Jack (:ghuser:`harry-solcast`) +* Adam R. Jensen (:ghuser:`AdamRJensen`) +* Kevin Anderson (:ghuser:`kandersolar`)