diff --git a/.gitignore b/.gitignore index a8967708..05839f7d 100644 --- a/.gitignore +++ b/.gitignore @@ -47,6 +47,9 @@ coverage.xml .hypothesis/ .pytest_cache/ +# pvdeg scenario output files generated during testing +tests/pvd_* + # Translations *.mo *.pot diff --git a/README.md b/README.md index 4df399a7..0837bdc5 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ - + diff --git a/docs/source/_autosummary/pvdeg.geospatial.can_auto_template.rst b/docs/source/_autosummary/pvdeg.geospatial.can_auto_template.rst new file mode 100644 index 00000000..dfce6002 --- /dev/null +++ b/docs/source/_autosummary/pvdeg.geospatial.can_auto_template.rst @@ -0,0 +1,6 @@ +pvdeg.geospatial.can\_auto\_template +==================================== + +.. currentmodule:: pvdeg.geospatial + +.. autofunction:: can_auto_template \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.geospatial.plot_sparse_analysis_land.rst b/docs/source/_autosummary/pvdeg.geospatial.plot_sparse_analysis_land.rst new file mode 100644 index 00000000..16e8a987 --- /dev/null +++ b/docs/source/_autosummary/pvdeg.geospatial.plot_sparse_analysis_land.rst @@ -0,0 +1,6 @@ +pvdeg.geospatial.plot\_sparse\_analysis\_land +============================================= + +.. currentmodule:: pvdeg.geospatial + +.. autofunction:: plot_sparse_analysis_land \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.geospatial.rst b/docs/source/_autosummary/pvdeg.geospatial.rst index 4524da2d..ac667b85 100644 --- a/docs/source/_autosummary/pvdeg.geospatial.rst +++ b/docs/source/_autosummary/pvdeg.geospatial.rst @@ -24,6 +24,7 @@ pvdeg.geospatial pvdeg.geospatial.auto_template pvdeg.geospatial.calc_block pvdeg.geospatial.calc_gid + pvdeg.geospatial.can_auto_template pvdeg.geospatial.elevation_stochastic_downselect pvdeg.geospatial.feature_downselect pvdeg.geospatial.identify_mountains_radii @@ -87,6 +88,13 @@ pvdeg.geospatial .. minigallery:: pvdeg.geospatial.calc_gid :add-heading: + .. autofunction:: can_auto_template + + .. _sphx_glr_backref_pvdeg.geospatial.can_auto_template: + + .. minigallery:: pvdeg.geospatial.can_auto_template + :add-heading: + .. autofunction:: elevation_stochastic_downselect .. _sphx_glr_backref_pvdeg.geospatial.elevation_stochastic_downselect: diff --git a/docs/source/_autosummary/pvdeg.utilities.add_time_columns_tmy.rst b/docs/source/_autosummary/pvdeg.utilities.add_time_columns_tmy.rst new file mode 100644 index 00000000..2c0ff9b8 --- /dev/null +++ b/docs/source/_autosummary/pvdeg.utilities.add_time_columns_tmy.rst @@ -0,0 +1,6 @@ +pvdeg.utilities.add\_time\_columns\_tmy +======================================= + +.. currentmodule:: pvdeg.utilities + +.. autofunction:: add_time_columns_tmy \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.utilities.display_json.rst b/docs/source/_autosummary/pvdeg.utilities.display_json.rst new file mode 100644 index 00000000..a59150c1 --- /dev/null +++ b/docs/source/_autosummary/pvdeg.utilities.display_json.rst @@ -0,0 +1,6 @@ +pvdeg.utilities.display\_json +============================= + +.. currentmodule:: pvdeg.utilities + +.. autofunction:: display_json \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.utilities.linear_normalize.rst b/docs/source/_autosummary/pvdeg.utilities.linear_normalize.rst new file mode 100644 index 00000000..8fae071b --- /dev/null +++ b/docs/source/_autosummary/pvdeg.utilities.linear_normalize.rst @@ -0,0 +1,6 @@ +pvdeg.utilities.linear\_normalize +================================= + +.. currentmodule:: pvdeg.utilities + +.. autofunction:: linear_normalize \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.utilities.merge_sparse.rst b/docs/source/_autosummary/pvdeg.utilities.merge_sparse.rst new file mode 100644 index 00000000..9da49b57 --- /dev/null +++ b/docs/source/_autosummary/pvdeg.utilities.merge_sparse.rst @@ -0,0 +1,6 @@ +pvdeg.utilities.merge\_sparse +============================= + +.. currentmodule:: pvdeg.utilities + +.. autofunction:: merge_sparse \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.utilities.read_material.rst b/docs/source/_autosummary/pvdeg.utilities.read_material.rst new file mode 100644 index 00000000..875bb77f --- /dev/null +++ b/docs/source/_autosummary/pvdeg.utilities.read_material.rst @@ -0,0 +1,6 @@ +pvdeg.utilities.read\_material +============================== + +.. currentmodule:: pvdeg.utilities + +.. autofunction:: read_material \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.utilities.rst b/docs/source/_autosummary/pvdeg.utilities.rst index fe0b3402..f744c610 100644 --- a/docs/source/_autosummary/pvdeg.utilities.rst +++ b/docs/source/_autosummary/pvdeg.utilities.rst @@ -19,19 +19,25 @@ pvdeg.utilities :nosignatures: + pvdeg.utilities.add_time_columns_tmy pvdeg.utilities.compare_templates pvdeg.utilities.convert_tmy + pvdeg.utilities.display_json pvdeg.utilities.fix_metadata pvdeg.utilities.geospatial_from_csv pvdeg.utilities.get_kinetics pvdeg.utilities.get_state_bbox pvdeg.utilities.gid_downsampling + pvdeg.utilities.linear_normalize + pvdeg.utilities.merge_sparse pvdeg.utilities.meta_as_dict pvdeg.utilities.new_id pvdeg.utilities.nrel_kestrel_check pvdeg.utilities.quantile_df + pvdeg.utilities.read_material pvdeg.utilities.remove_scenario_filetrees pvdeg.utilities.restore_gids + pvdeg.utilities.search_json pvdeg.utilities.strip_normalize_tmy pvdeg.utilities.tilt_azimuth_scan pvdeg.utilities.ts_gid_df @@ -51,6 +57,13 @@ pvdeg.utilities + .. autofunction:: add_time_columns_tmy + + .. _sphx_glr_backref_pvdeg.utilities.add_time_columns_tmy: + + .. minigallery:: pvdeg.utilities.add_time_columns_tmy + :add-heading: + .. autofunction:: compare_templates .. _sphx_glr_backref_pvdeg.utilities.compare_templates: @@ -65,6 +78,13 @@ pvdeg.utilities .. minigallery:: pvdeg.utilities.convert_tmy :add-heading: + .. autofunction:: display_json + + .. _sphx_glr_backref_pvdeg.utilities.display_json: + + .. minigallery:: pvdeg.utilities.display_json + :add-heading: + .. autofunction:: fix_metadata .. _sphx_glr_backref_pvdeg.utilities.fix_metadata: @@ -100,6 +120,20 @@ pvdeg.utilities .. minigallery:: pvdeg.utilities.gid_downsampling :add-heading: + .. autofunction:: linear_normalize + + .. _sphx_glr_backref_pvdeg.utilities.linear_normalize: + + .. minigallery:: pvdeg.utilities.linear_normalize + :add-heading: + + .. autofunction:: merge_sparse + + .. _sphx_glr_backref_pvdeg.utilities.merge_sparse: + + .. minigallery:: pvdeg.utilities.merge_sparse + :add-heading: + .. autofunction:: meta_as_dict .. _sphx_glr_backref_pvdeg.utilities.meta_as_dict: @@ -128,6 +162,13 @@ pvdeg.utilities .. minigallery:: pvdeg.utilities.quantile_df :add-heading: + .. autofunction:: read_material + + .. _sphx_glr_backref_pvdeg.utilities.read_material: + + .. minigallery:: pvdeg.utilities.read_material + :add-heading: + .. autofunction:: remove_scenario_filetrees .. _sphx_glr_backref_pvdeg.utilities.remove_scenario_filetrees: @@ -142,6 +183,13 @@ pvdeg.utilities .. minigallery:: pvdeg.utilities.restore_gids :add-heading: + .. autofunction:: search_json + + .. _sphx_glr_backref_pvdeg.utilities.search_json: + + .. minigallery:: pvdeg.utilities.search_json + :add-heading: + .. autofunction:: strip_normalize_tmy .. _sphx_glr_backref_pvdeg.utilities.strip_normalize_tmy: diff --git a/docs/source/_autosummary/pvdeg.utilities.search_json.rst b/docs/source/_autosummary/pvdeg.utilities.search_json.rst new file mode 100644 index 00000000..11f7324b --- /dev/null +++ b/docs/source/_autosummary/pvdeg.utilities.search_json.rst @@ -0,0 +1,6 @@ +pvdeg.utilities.search\_json +============================ + +.. currentmodule:: pvdeg.utilities + +.. autofunction:: search_json \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.weather.empty_weather_ds.rst b/docs/source/_autosummary/pvdeg.weather.empty_weather_ds.rst new file mode 100644 index 00000000..d926b23f --- /dev/null +++ b/docs/source/_autosummary/pvdeg.weather.empty_weather_ds.rst @@ -0,0 +1,6 @@ +pvdeg.weather.empty\_weather\_ds +================================ + +.. currentmodule:: pvdeg.weather + +.. autofunction:: empty_weather_ds \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.weather.emtpy_weather_ds.rst b/docs/source/_autosummary/pvdeg.weather.emtpy_weather_ds.rst new file mode 100644 index 00000000..00180317 --- /dev/null +++ b/docs/source/_autosummary/pvdeg.weather.emtpy_weather_ds.rst @@ -0,0 +1,6 @@ +pvdeg.weather.emtpy\_weather\_ds +================================ + +.. currentmodule:: pvdeg.weather + +.. autofunction:: emtpy_weather_ds \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.weather.roll_tmy.rst b/docs/source/_autosummary/pvdeg.weather.roll_tmy.rst new file mode 100644 index 00000000..f23e7c16 --- /dev/null +++ b/docs/source/_autosummary/pvdeg.weather.roll_tmy.rst @@ -0,0 +1,6 @@ +pvdeg.weather.roll\_tmy +======================= + +.. currentmodule:: pvdeg.weather + +.. autofunction:: roll_tmy \ No newline at end of file diff --git a/docs/source/_autosummary/pvdeg.weather.rst b/docs/source/_autosummary/pvdeg.weather.rst index 1dd72fc9..3b05dcf6 100644 --- a/docs/source/_autosummary/pvdeg.weather.rst +++ b/docs/source/_autosummary/pvdeg.weather.rst @@ -20,6 +20,7 @@ pvdeg.weather pvdeg.weather.csv_read + pvdeg.weather.empty_weather_ds pvdeg.weather.get pvdeg.weather.get_NSRDB pvdeg.weather.get_NSRDB_fnames @@ -32,6 +33,8 @@ pvdeg.weather pvdeg.weather.read pvdeg.weather.read_h5 pvdeg.weather.repeat_annual_time_series + pvdeg.weather.roll_tmy + pvdeg.weather.weather_distributed pvdeg.weather.write @@ -55,6 +58,13 @@ pvdeg.weather .. minigallery:: pvdeg.weather.csv_read :add-heading: + .. autofunction:: empty_weather_ds + + .. _sphx_glr_backref_pvdeg.weather.empty_weather_ds: + + .. minigallery:: pvdeg.weather.empty_weather_ds + :add-heading: + .. autofunction:: get .. _sphx_glr_backref_pvdeg.weather.get: @@ -139,6 +149,20 @@ pvdeg.weather .. minigallery:: pvdeg.weather.repeat_annual_time_series :add-heading: + .. autofunction:: roll_tmy + + .. _sphx_glr_backref_pvdeg.weather.roll_tmy: + + .. minigallery:: pvdeg.weather.roll_tmy + :add-heading: + + .. autofunction:: weather_distributed + + .. _sphx_glr_backref_pvdeg.weather.weather_distributed: + + .. minigallery:: pvdeg.weather.weather_distributed + :add-heading: + .. autofunction:: write .. _sphx_glr_backref_pvdeg.weather.write: diff --git a/docs/source/_autosummary/pvdeg.weather.weather_distributed.rst b/docs/source/_autosummary/pvdeg.weather.weather_distributed.rst new file mode 100644 index 00000000..9cfde67b --- /dev/null +++ b/docs/source/_autosummary/pvdeg.weather.weather_distributed.rst @@ -0,0 +1,6 @@ +pvdeg.weather.weather\_distributed +================================== + +.. currentmodule:: pvdeg.weather + +.. autofunction:: weather_distributed \ No newline at end of file diff --git a/docs/source/_static/PVDeg-Flow.svg b/docs/source/_static/PVDeg-Flow.svg new file mode 100644 index 00000000..935de386 --- /dev/null +++ b/docs/source/_static/PVDeg-Flow.svg @@ -0,0 +1,10 @@ + + + + + + + + NSRDBPVGISResultsSatellite Data \ No newline at end of file diff --git a/docs/source/_static/logo-vectors/PVdeg-Logo-Black.svg b/docs/source/_static/logo-vectors/PVdeg-Logo-Black.svg new file mode 100644 index 00000000..60087393 --- /dev/null +++ b/docs/source/_static/logo-vectors/PVdeg-Logo-Black.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/source/_static/logo-vectors/PVdeg-Logo-Color.svg b/docs/source/_static/logo-vectors/PVdeg-Logo-Color.svg new file mode 100644 index 00000000..d082f0c3 --- /dev/null +++ b/docs/source/_static/logo-vectors/PVdeg-Logo-Color.svg @@ -0,0 +1,56 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-Color.svg b/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-Color.svg new file mode 100644 index 00000000..cb6121a9 --- /dev/null +++ b/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-Color.svg @@ -0,0 +1,56 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-black.svg b/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-black.svg new file mode 100644 index 00000000..ab3b6480 --- /dev/null +++ b/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-black.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-white.svg b/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-white.svg new file mode 100644 index 00000000..22d61d60 --- /dev/null +++ b/docs/source/_static/logo-vectors/PVdeg-Logo-Horiz-white.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/source/_static/logo-vectors/PVdeg-Logo-White.svg b/docs/source/_static/logo-vectors/PVdeg-Logo-White.svg new file mode 100644 index 00000000..cc795b30 --- /dev/null +++ b/docs/source/_static/logo-vectors/PVdeg-Logo-White.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/source/_static/pvdeg.ico b/docs/source/_static/pvdeg.ico new file mode 100644 index 00000000..f36d3275 Binary files /dev/null and b/docs/source/_static/pvdeg.ico differ diff --git a/docs/source/conf.py b/docs/source/conf.py index 680216c2..f07206d5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -147,12 +147,12 @@ # The name of an image file (relative to this directory) to place at the top # of the sidebar. -html_logo = "../../tutorials_and_tools/pvdeg_logo.png" # logo should work at this path +html_logo = "./_static/logo-vectors/PVdeg-Logo-Horiz-Color.svg" # logo should work at this path # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. -# html_favicon = None +html_favicon = "./_static/pvdeg.ico" html_static_path = ["_static"] diff --git a/docs/source/index.rst b/docs/source/index.rst index f9786b8d..5eef84de 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,9 +3,10 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -.. image:: ../../tutorials_and_tools/pvdeg_logo.png - :width: 500 +.. .. image:: ../../tutorials_and_tools/pvdeg_logo.png +.. :width: 500 +.. image:: ./_static/logo-vectors/PVdeg-Logo-Horiz-Color.svg Welcome to pvdeg! @@ -25,35 +26,44 @@ relative humidity in the outside, front and back encapsulant and backsheet, spectral degradation, and solder fatigue. More functions for standards and other degradation profiles are in the works. -The source code for pvdeg is hosted on `github -`_. +The source code for pvdeg is hosted on `github `_. Please see the :ref:`installation` page for installation help. See :ref:`tutorials` to learn how to use and experiment with various functionalities -Please see the :ref:`installation` page for installation help. + +.. image:: ./_static/PVDeg-Flow.svg + :alt: PVDeg-Flow diagram. + How the Model Works =================== Coupled with pvlib for module performan and weather/irradiance calculations, -the PVDegradation Tool estimates degradations, and accelerated factors on +PVDegradation Tool estimates degradations, and accelerated factors on user-defined parameters. The `Data Library` is under development as part of the PVDegradationTool project, compiling literature parameters and functions. The PVDegradationTool simulatineously reads tens of terabytes of time-series solar data from state-of-art resource data set National Solar Radiation Database -(NSRDB), publicly avialable no the cloud, enabling the execution of pvdeg +(NSRDB), publicly available on the cloud, enabling the execution of pvdeg beyond the confines of NREL's high-performance computing capabilities. - Citing PVDegradation Tools ========================== If you use this calculator in a published work, please cite: +.. code-block:: + + Holsapple, Derek, Ayala Pelaez, Silvana, Kempe, Michael. "PV Degradation Tools", NREL Github 2020, Software Record SWR-20-71. + Please also cite the DOI corresponding to the specific version that you used. -DOIs are listed at Zenodo.org +DOIs are listed at Zenodo.org. `linked here `_ + +.. code-block:: + + Martin Springer, Tobin Ford, Matt, MDKempe, Silvana Ovaitt, AidanWesley, joe karas, Mark Campanelli, Derek M Holsapple, and Kevin Anderson. “NREL/PVDegradationTools: 0.4.2.” Zenodo, 2024. doi:10.5281/zenodo.13760911. .. toctree:: diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst index ec01477b..60713e1e 100644 --- a/docs/source/tutorials/index.rst +++ b/docs/source/tutorials/index.rst @@ -50,3 +50,20 @@ You can also run the tutorial locally in a virtual environment, i.e., `venv` or 1. Use the file explorer in Jupyter lab to browse to `tutorials` and start the first Tutorial. +NREL HPC (Kestrel) +------------------ + +Running notebooks on Kestrel is documented on the `NREL HPC Docs `_. + +**NOTE**: To run jupyter notebooks on Kestrel you must add a custom iPykernel. This section is borrowed from the NREL HPC docs. + + + A kernel is what allows Jupyter to use your customized conda environment inside Jupyter, in a notebook. Use ipykernel to build your kernel. Inside your custom conda environment, run: + + ``python -m ipykernel install --user --name=myjupyter`` + + If you already have a Jupyter server running, restart it to load the new kernel. + + The new kernel will appear in the drop-down as an option to open a new notebook. + + You can have multiple kernels, allowing you to load different conda environments for your different projects into Notebooks. diff --git a/docs/source/user_guide/geospatial-details/ds_res-gid-shape.svg b/docs/source/user_guide/geospatial-details/ds_res-gid-shape.svg new file mode 100644 index 00000000..61bf52fe --- /dev/null +++ b/docs/source/user_guide/geospatial-details/ds_res-gid-shape.svg @@ -0,0 +1,10 @@ + + + + + + + + LatitudeLongitudeds_res \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/ds_res-gid-time-shape.svg b/docs/source/user_guide/geospatial-details/ds_res-gid-time-shape.svg new file mode 100644 index 00000000..a6e6f75a --- /dev/null +++ b/docs/source/user_guide/geospatial-details/ds_res-gid-time-shape.svg @@ -0,0 +1,10 @@ + + + + + + + + LatitudeLongitudeTimeds_res \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/geospatial-analysis-horizontal.svg b/docs/source/user_guide/geospatial-details/geospatial-analysis-horizontal.svg new file mode 100644 index 00000000..5ad8e5c5 --- /dev/null +++ b/docs/source/user_guide/geospatial-details/geospatial-analysis-horizontal.svg @@ -0,0 +1,10 @@ + + + + + + + + LatitudeLongitudeTimeTimeLocation IDgeospatial.analysisweather_dsLocation IDmeta_dfds_res \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/geospatial-analysis-vertical.svg b/docs/source/user_guide/geospatial-details/geospatial-analysis-vertical.svg new file mode 100644 index 00000000..8f7f82df --- /dev/null +++ b/docs/source/user_guide/geospatial-details/geospatial-analysis-vertical.svg @@ -0,0 +1,10 @@ + + + + + + + + LatitudeLongitudeTimeTimeLocation IDgeospatial.analysisweather_dsLocation IDmeta_df \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/geospatial-analysis.svg b/docs/source/user_guide/geospatial-details/geospatial-analysis.svg new file mode 100644 index 00000000..c46a4f80 --- /dev/null +++ b/docs/source/user_guide/geospatial-details/geospatial-analysis.svg @@ -0,0 +1,10 @@ + + + + + + + + LatitudeLongitudeTimeTimeLocation IDgeospatial.analysis \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/meta_df.svg b/docs/source/user_guide/geospatial-details/meta_df.svg new file mode 100644 index 00000000..3616b0f6 --- /dev/null +++ b/docs/source/user_guide/geospatial-details/meta_df.svg @@ -0,0 +1,10 @@ + + + + + + + + Location IDmeta_dfsingle location meta_dict \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/multi-dimensional-outputs.svg b/docs/source/user_guide/geospatial-details/multi-dimensional-outputs.svg new file mode 100644 index 00000000..a03a2138 --- /dev/null +++ b/docs/source/user_guide/geospatial-details/multi-dimensional-outputs.svg @@ -0,0 +1,10 @@ + + + + + + + + LatitudeLongitudeTimeds_resLatitudeLongitudeds_resTimeLocation IDweather_dsLocation IDmeta_dfgeospatial.analysisoutput is template/function dependent \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/weatherCube.svg b/docs/source/user_guide/geospatial-details/weatherCube.svg new file mode 100644 index 00000000..31954545 --- /dev/null +++ b/docs/source/user_guide/geospatial-details/weatherCube.svg @@ -0,0 +1,10 @@ + + + + + + + + LatitudeLongitudeTime \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/weatherSheet.svg b/docs/source/user_guide/geospatial-details/weatherSheet.svg new file mode 100644 index 00000000..a494e827 --- /dev/null +++ b/docs/source/user_guide/geospatial-details/weatherSheet.svg @@ -0,0 +1,10 @@ + + + + + + + + Location IDTime \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-details/weather_ds.svg b/docs/source/user_guide/geospatial-details/weather_ds.svg new file mode 100644 index 00000000..952b465f --- /dev/null +++ b/docs/source/user_guide/geospatial-details/weather_ds.svg @@ -0,0 +1,10 @@ + + + + + + + + TimeLocation IDweather_dssingle location weather_df \ No newline at end of file diff --git a/docs/source/user_guide/geospatial-templates.rst b/docs/source/user_guide/geospatial-templates.rst deleted file mode 100644 index c19dead2..00000000 --- a/docs/source/user_guide/geospatial-templates.rst +++ /dev/null @@ -1,40 +0,0 @@ -.. _geospatial-templates: - -Geospatial Templates -=================== -Using 3 dimensional labeled arrays (`Xarray`) we are able to run calculations using meteorological data across many points at once. This process has been parallelized using `dask` and `xarray`. Both of these packages can be run locally or on cloud HPC environments. - -This presents a new issue, our models produce outputs in many different shapes and sizes. We can have single numerical results, multiple numeric results or a timeseries of numeric results at each location. To parallelize this process, we cannot wait until runtime to know what shape to store the outputs in. This is where the need for `templates` arises. - -Previously, ``pvdeg.geospatial`` provided minimal templates and forced users to create their own for each function they wanted to use in a geospatial calculation. - -Auto-templating: allows users to skip creating templates for most functions within pvdeg by using ``pvdeg.geospatial.autotemplate`` to generate templates on the spot, instead of figuring out the output shape. For any given function within the source code decorated with `geospatial_result_type`, we can use `pvdeg.geospatial.autotemplate` - - -Example --------- - -Here we are providing a function to autotemplate along with an ``Xarray.Dataset`` of weather data. Combining these two will give us enough information to produce an output template. - -Autotemplate approach to creating a template - -.. code-block:: Python - - edge_seal_template = pvdeg.geospatial.auto_template( - func=pvdeg.design.edge_seal_width, - ds_gids=geo_weather - ) - -Manual Approach to Creating the Sample Template - -.. code-block:: Python - - shapes = { - "width" : ("gid",) # one return value at each datapoint, only dependent on datapoint, not time - } - - template = pvdeg.geospatial.output_template( - ds_gids=geo_weather, # xarray dataset - shapes=shapes, # output shapes - ) - diff --git a/docs/source/user_guide/geospatial.rst b/docs/source/user_guide/geospatial.rst new file mode 100644 index 00000000..afaaba69 --- /dev/null +++ b/docs/source/user_guide/geospatial.rst @@ -0,0 +1,305 @@ +.. _geospatial: + +Geospatial 🌍 +========== + +Geospatial data is time based data that maps to a location on Earth. PVDeg supports single site and geospatial analyses using meteorological and solar radiation data, +such as Typical meteorological year (TMY) data. This can be used to extrapolate the perfomance of PV systems over many years beacause it is statistically representative of +weather conditions given a typical year. PVDeg collects an arbitrary amount of location specific meteorological and solar radiation data to run geospatial analyses. + +These datasets are multidimensional, with time and location as coordinates. These data come from :ref:`NSRDB` and :ref:`PVGIS` and can commonly be expressed in two ways. + +- three dimensions as a cube with coordinates ``time``, ``latitude``, and ``longitude``. +- two dimensions as a sheet with coordinates ``time``, and ``location id`` (often represented as gid). Gid is a geospatial id, these are problematic and largely meaningless, see :ref:`GIDS`. + +.. image:: geospatial-details/weatherCube.svg + :width: 65% + :alt: multidimensional Meterological Solar Radiation data represented with dimensions time, latitude and longitude. + +.. image:: geospatial-details/weatherSheet.svg + :width: 50% + :alt: multidimensional Meterological Solar Radiation data represented with dimensions time, location id + +*The orange 3d shape and 2d band represent a single location's data, in the corresponding representation. This can be weather and solar radiation or other calculated results.* + + +Geospatial Analysis +------------------- + +To develop some intuition about what ``geospatial.analysis`` is doing lets examine the docstring. It says "applies a function to each gid of a weather dataset". +This is a very simple message but it is not clear how this works at a cursory look. This is a powerful paradigm. + +The most consequential part of the function is the mapping from the inputs to the output. The input and outputs are multi-dimensional and have a different number of dimensions. + +*The specific function applied is not relevant at this point, it does change the data-variable results in the multi-dimensional output but this is a different aspect of the analysis function.* +This is explained in `Geospatial Templates`_ + +.. autofunction:: pvdeg.geospatial.analysis + +Multi-dimensional inputs +^^^^^^^^^^^^^^^^^^^^^^^^ +- ``weather_ds`` is an ``xarray.Dataset`` with coordinates/dimensions of ``time`` and ``gid``. +- ``meta_df`` is a ``pandas.DataFrame`` consisting of a row of data, extracting a single row yeilds a dictionary with metadata attributes for the specific location. + +Looking at ``weather_ds``, we generally want to get one of the tall rectangles shown in the figure. To do this we only need to index by ``gid``. +This will get a "slice" that contains all of the weather data for that location for the length of the dataset (usually 1 year). +This slice is roughly equivalent to the weather ``pandas.DataFrame`` taken by ``pvdeg`` functions called ``weather_df``. + +.. image:: geospatial-details/weather_ds.svg + :width: 50% + :alt: + +Looking at ``meta_df``, we want one of the wide rectangles shown in the figure. The dataframe is indexed by ``gid`` so we only need to index by a single ``gid``. +This will get a "row" that contains the gid's meta data, such as latitude, longitude, time zone, and elevation/altitude. +This can be unpacked to the standard python dictionary (``dict``) taken by ``pvdeg`` functions called ``meta_dict``. + +.. image:: geospatial-details/meta_df.svg + :width: 50% + :alt: + +In this context, gids serve purely as indexes, gid a in ``weather_ds`` coresponds to index a in ``meta_df``. No other information can be reliabily derived from gids. + +Multi-dimensional output +^^^^^^^^^^^^^^^^^^^^^^^^ + +- ``ds_res`` is a ``xarray.Dataset`` with coordinates/dimensions of ``time``, ``latitude``, and ``longitude`` or simply ``latitude`` and ``longitude`` as shown below. + +Notice, ``ds_res`` is a multi-dimensional result similar to the inputs but it's shape can vary. The two standard appearances of ``ds_res`` are shown stacked on the right side of the figure below. + +The shape ``ds_res`` takes is determined by the provided function and template, ``func`` and ``template`` respectively. Oftentimes, ``template`` is not required because ``pvdeg`` +can automatically generate simple templates for commonly used builtin functions. + +When a function returns a timeseries result then the result will look like the cube version of ``ds_res`` with a time axis shown below. +If the function returns single numeric results such as averages of a timeseries value then there is no need for a ``time`` axis. So the result will look like the plane version of ``ds_res`` shown below. +For more on this see `Geospatial Templates`_. + + +.. image:: geospatial-details/multi-dimensional-outputs.svg + :width: 100% + :alt: + + +.. _GeospatialTemplates: +Geospatial Templates +--------------------- +Using multi-dimensional labeled arrays (``Xarray``) we are able to run calculations using meteorological data across many points at once. This process has been parallelized using `dask` and `xarray`. Both of these packages can be run locally or on cloud HPC environments. + +This presents a new issue, our models produce outputs in many different shapes and sizes. We can have single numerical results, multiple numeric results or a timeseries of numeric results at each location. To parallelize this process, we cannot wait until runtime to know what shape to store the outputs in. This is where the need for `templates` arises. + +Previously, ``pvdeg.geospatial`` provided minimal templates and forced users to create their own for each function they wanted to use in a geospatial calculation. This is still an option via ``geospatial.output_template``. +But many pvdeg functions do not require a template for geospatial analysis. + +Auto-templating: allows users to skip creating templates for most ``pvdeg`` functions. +It is integrated into ``geospatial.analysis``. If a function is defined with the ``@decorators.geospatial_quick_shape`` decorator in the source code, we can call ``geospatial.analysis`` without providing a template. +The function responsible for this is called ``geospatial.auto_template`` and is exposed publicly to create templates outside of ``geospatial.analysis``. + +If a function cannot be auto-templated, both ``geospatial.analysis`` and ``geospatial.auto_template`` will raise the following error. + +.. code-block:: Python + + " cannot be autotemplated. create a template manually" + +Auto-templating Example +----------------------- + +The code below shows how to use auto-templating on a function implicitly. We simply call ``geospatial.analysis`` on a function that can be auto-templated and ``geospatial.analysis`` does the work for us. +Note: *we do not need to provide a template to "analysis" if the function can be auto-templated* + +.. code-block:: Python + + geo_res = pvdeg.geospatial.analysis( + weather_ds = geo_weather, + meta_df = geo_meta, + func = pvdeg.design.edge_seal_width, + ) + +The code below shows the auto-templating process as the section above but this time we will explicitly call ``geospatial.auto_template`` and pass the generated template to ``geospatial.analysis``. +The approach above is more direct and thus preferable. + +.. code-block:: Python + + edge_seal_template = pvdeg.geospatial.auto_template( + func=pvdeg.design.edge_seal_width, + ds_gids=geo_weather + ) + + geo_res = pvdeg.geospatial.analysis( + weather_ds = geo_weather, + meta_df = geo_meta, + func = pvdeg.design.edge_seal_width, + template = edge_seal_template, + ) + + + +Manual Templating Example I +--------------------- + +Creating manual templates is one of the most complicated parts of ``pvdeg``. We will use ``geospatial.output_template`` to tell ``pvdeg`` how to go from the multi-dimensional inputs to a multi-dimensional output. +We have do to this because the dimensions are chaning. Refer to the sketch in `Multi-dimensional output`_. + +Lets examine some functions, comprehensive examples are the best way to illustrate this process. + +We will start by creating templates for functions that support auto-templating. If you run the code below or use the auto-templating approches shown above, the result will be identical. + +A simple function that has auto-templating is ``pvdeg.standards.standoff``. The docstring is shown below. + +.. autofunction:: pvdeg.standards.standoff + +We can see that this will return single numeric outputs for various aspects of standoff height calculation for each location. We want the output to rely only on the input location. +This is identified with an index, ``gid``. Since we have single numeric outputs, we do not want a time dimension. Borrowing from above, a simple sketch of the analysis output should look like the following. + +.. image:: geospatial-details/ds_res-gid-shape.svg + :width: 100% + :alt: + +The crux of this process is defining the shapes dictionary. As presented above, we only care about the ``gid`` axis on the input so the process for creating a template and running the analysis with it will be as follows. +The keys in the dictionary are named after the return values of the desired function. See the docstring for evidence of this. The values is a tuple of the dimensions that we map to in the output. + +.. code-block:: Python + + shapes = { + "x": ("gid",), + "T98_inf": ("gid",), + "T98_0": ("gid",), + } + +*Note: the tuples appear with a comma after the string, such that ("gid",) NOT ("gid"). This is because python will interpret the string as an group of characters to unpack if we do not +enforce the tuple requirement. Adding a comma forces python to interpret the parenthesis as a tuple* + +Next, we will create a template using this shapes dictionary and the ``weather_ds``. The parameters may be misleadly named as ``ds_gids`` but this is the same as ``weather_ds`` in ``geospatial.analysis``. + +``geo_weather`` and ``geo_meta`` are placeholders for the geospatial weather and metadata that we would generally have in this scenario. It is not possible to generate an output template without providing the +geospatial weather data beacause the function needs to know how many entries it needs to make along the ``gid`` axis in this case. + +.. code-block:: Python + + standoff_template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, # geospatial xarray dataset + shapes=shapes, # output shapes defined above + ) + + geo_res = pvdeg.geospatial.analysis( + weather_ds = geo_weather, # geospatial xarray dataset + meta_df = geo_meta, # geospatial metadata dataframe + func = pvdeg.standards.standoff, + template = standoff_template # template created in this example + ) + + +Manual Templating Example II +----------------------------- +Another function we can look at that supports auto-templating is ``pvdeg.humidity.module``. This calculates module humidity parameters over a timeseries. This is where we diverge from the previous example. +Inspect the docstring below and look at the return types, notice this will be a timeseries result. + +.. autofunction:: pvdeg.humidity.module + +Now we will define the shapes dictionary, the output will be a mapping from the input dimensions of ``gid`` and ``time`` so both of these will appear in our ``shapes`` value tuples. +Thus our output will have a time axis and show look like the ``ds_res`` as a cube with the time axis as shown below. + +.. image:: geospatial-details/ds_res-gid-time-shape.svg + :width: 100% + :alt: + +This is an oversimplification but each column in the cube represets a ``pandas.DataFrame`` result with columns represeting each return value and a ``pd.DatetimeIndex``. The columns will be named as follows. + +- "RH_surface_outside" +- "RH_front_encap" +- "RH_back_encap" +- "RH_backsheet" + +The docstring does not give us that much useful information about the results so we can run it on a single location and get the column names or dict keys then these will become our shape names. +This is not ideal but simply running at a single site before a geospatial calculation can yield useful context. ``geospatial.analysis`` error messages are oftentimes clear. +This is a result of dask, lazy-computing and confusing higher dimensional datasets. + +.. code-block:: Python + + shapes = { + "RH_surface_outside": ("gid", "time"), + "RH_front_encap": ("gid", "time"), + "RH_back_encap": ("gid", "time"), + "RH_backsheet": ("gid", "time"), + } + +This shapes dictionary is valid, so we can pass it to ``geospatial.output_template`` as in the above example and run the analysis. + +.. code-block:: Python + + module_humidity_template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, # geospatial xarray dataset + shapes=shapes, # output shapes defined above + ) + + geo_res = pvdeg.geospatial.analysis( + weather_ds = geo_weather, # geospatial xarray dataset + meta_df = geo_meta, # geospatial metadata dataframe + func = pvdeg.module.humidity, + template = module_humidity_template # template created in this example + ) + + +Manual Templating Example III +----------------------------- +Last, lets look at another example. This one will be abridged as it covers the same topic as `Manual Templating Example II`_. + +This time consider ``pvdeg.letid.calc_letid_outdoors``. Lets inspect the docstring to see what the return values look like. + +.. autofunction:: pvdeg.letid.calc_letid_outdoors + +Once again we can see that the output shapes are obscured. It just says we are returning a ``pandas.DataFrame`` called timesteps. This is not helpful. +We will have to run the function at a single location to see what the column names are. + +Assuming we ran ``pvdeg.letid.calc_letid_outdoors`` at a single site we would see that the DataFrame columns are named as follows. + +- "Temperature" +- "Injection" +- "NA" +- "NB" +- "NC" +- "tau" +- "Jsc" +- "Voc" +- "Isc" +- "FF" +- "Pmp" +- "Pmp_norm" + +.. image:: geospatial-details/ds_res-gid-time-shape.svg + :width: 100% + :alt: + +Because we know the function returns a ``pandas.DataFrame`` with a time index, all of the columns will have entries at each timestep. This means that we need to include, the ``time`` dimension in our output. +The shapes dictionary will look like the following. For visual assistance, refer to the cube shaped ``ds_res`` sketch. + +.. code-block:: Python + + shapes = { + "Temperature": ("gid", "time"), + "Injection": ("gid", "time"), + "NA": ("gid", "time"), + "NB": ("gid", "time"), + "NC": ("gid", "time"), + "tau": ("gid", "time"), + "Jsc": ("gid", "time"), + "Voc": ("gid", "time"), + "Isc": ("gid", "time"), + "FF": ("gid", "time"), + "Pmp": ("gid", "time"), + "Pmp_norm": ("gid", "time"), + } + +Now we have defined shapes, as above we can simply pass it to ``geospatial.output_template`` and use the generated template in our analysis. + +.. code-block:: Python + + letid_template = pvdeg.geospatial.output_template( + ds_gids=geo_weather, # geospatial xarray dataset + shapes=shapes, # output shapes defined above + ) + + geo_res = pvdeg.geospatial.analysis( + weather_ds = geo_weather, # geospatial xarray dataset + meta_df = geo_meta, # geospatial metadata dataframe + func = pvdeg.letid.calc_letid_outdoors + template = letid_template # template created in this example + ) \ No newline at end of file diff --git a/docs/source/user_guide/index.rst b/docs/source/user_guide/index.rst index d4a4357b..195542d7 100644 --- a/docs/source/user_guide/index.rst +++ b/docs/source/user_guide/index.rst @@ -8,9 +8,12 @@ User Guide installation package_overview + meteorological-data NSRDB_API_Key montecarlo - geospatial-templates + geospatial + pysam + materials pv-variables-terms contributing diff --git a/docs/source/user_guide/installation.rst b/docs/source/user_guide/installation.rst index dcb90740..31ef24cb 100644 --- a/docs/source/user_guide/installation.rst +++ b/docs/source/user_guide/installation.rst @@ -5,13 +5,63 @@ Installation pvdeg releases may be installed using the pip and conda tools. -Install with: +Base Install: +--------------------- +To get PVDeg the simplest way use: .. code:: pip install pvdeg -For developer installation, download the repository, navigate to the folder location and install as: +Optional Install: +---------------- +PVDeg provides optional installs for testing, and documenation. We can specify these using `[]` syntax. + + +.. list-table:: Extra Installs in PVDeg + :widths: 30 70 + :header-rows: 1 + + * - **Extra Install Name** + - **Explanation** + * - ``docs`` + - Documentation dependencies + * - ``test`` + - Testing dependencies + * - ``books`` + - Jupyter utilities + * - ``sam`` + - NREL System Advisor Model dependencies + * - ``all`` + - all of the above names combined + +To download packages required for testing, run: + +.. code:: + + pip install pvdeg[test] + +To download all optional packages, run: + +.. code:: + + pip install pvdeg[all] + + +Developer Installation +---------------------- + + +For developer installation, use the following steps. + + +1. clone the reposityory using + +.. code:: + + git clone https://github.com/NREL/PVDegradationTools.git + +2. Navigate the the root of the repository such that you are in ``path/to/PVDegradationTools``, then run the following command. .. code:: diff --git a/docs/source/user_guide/materials.rst b/docs/source/user_guide/materials.rst new file mode 100644 index 00000000..c81fa0e8 --- /dev/null +++ b/docs/source/user_guide/materials.rst @@ -0,0 +1,107 @@ +.. materials:: + +Materials Storage and Access +============================ + +PVDeg contains a library of material parameters suitable for estimating the durability of materials and components. + + +These material parameters and other relevant information sit in a directiory at ``PVDegradationTools/pvdeg/data``. + +This location can be quickly accessed through a special variable as shown below. + +.. code-block:: Python + + import pvdeg + + file_path = os.path.join(pvdeg.DATA_DIR, ) + +.. code-block:: Python + + from pvdeg import DATA_DIR + + file_path = os.path.join(DATA_DIR, ) + +File Organization +------------------------------------ +There are many files in this directory. We will generally be interested in one of the following files. + + +- `AApermeation.json `_ (acetic acid permeation parameters) +- `H2Opermeation.json `_ (water permeation parameters) +- `O2permeation.json `_ (oxygen permeation parameters) +- kinetic_parameters.json (letid/bolid parameters) +- DegradationDatabase.json (degredation models) + +Material Parameters +------------------------------------ +Each of the material permeation parameters files above is a json indexed by arbitrary names. +These are not a mapping of material names or aliases and are not consistent across the three files below. + +- `AApermeation.json `_ (acetic acid permeation parameters) +- `H2Opermeation.json `_ (water permeation parameters) +- `O2permeation.json `_ (oxygen permeation parameters) + +Accessing Material Parameters +----------------------------- + +PVDeg provides convenience methods/functions to access material parameters. ``pvdeg.utilities.read_material`` is the simplest way to access material parameters. We will also show a sample use. + +.. autofunction:: pvdeg.utilities.read_material + +.. code-block:: Python + + material_dict = pvdeg.utilities.read_material( + pvdeg_file = "AApermeation", + key = "AA001", + ) + +.. code-block:: Python + + material_dict = pvdeg.utilities.read_material( + pvdeg_file = "H2Opermeation", + key = "W003", + ) + + +The result of both of these functions will be a dictionary that looks like the following. The keys may vary depending on the structure of the json but this is the general idea. + +.. code-block:: Python + + { + "name": string, + "alias": string, + "contributor": string, + "source": string, + "Fickian": bool, + "Ead": numeric, + "Do": numeric, + "Eas": numeric, + "So": numeric, + "Eap": numeric, + "Po": numeric + } + +There are also convenience functions to view and search jsons in jupyter notebooks called ``pvdeg.utilities.display_json`` and ``pvdeg.utilities.search_json``. + + +.. _AApermeation: + +AApermeation +~~~~~~~~~~~~ +.. literalinclude:: ../../../pvdeg/data/AApermeation.json + :language: json + +.. _H2Opermeation: + +H2Opermeation +~~~~~~~~~~~~ +.. literalinclude:: ../../../pvdeg/data/H2Opermeation.json + :language: json + +.. _O2permeation: + +O2permeation +~~~~~~~~~~~~ +.. literalinclude:: ../../../pvdeg/data/O2permeation.json + :language: json \ No newline at end of file diff --git a/docs/source/user_guide/meteorological-data-details/data_flow_chart.png b/docs/source/user_guide/meteorological-data-details/data_flow_chart.png new file mode 100644 index 00000000..7b19bd4e Binary files /dev/null and b/docs/source/user_guide/meteorological-data-details/data_flow_chart.png differ diff --git a/docs/source/user_guide/meteorological-data-details/nsrdb_global_coverage.jpg b/docs/source/user_guide/meteorological-data-details/nsrdb_global_coverage.jpg new file mode 100644 index 00000000..dee1cc68 Binary files /dev/null and b/docs/source/user_guide/meteorological-data-details/nsrdb_global_coverage.jpg differ diff --git a/docs/source/user_guide/meteorological-data-details/parallel-download.svg b/docs/source/user_guide/meteorological-data-details/parallel-download.svg new file mode 100644 index 00000000..d575ae8c --- /dev/null +++ b/docs/source/user_guide/meteorological-data-details/parallel-download.svg @@ -0,0 +1,2 @@ +ABCDParallelt1t2t3 \ No newline at end of file diff --git a/docs/source/user_guide/meteorological-data-details/sequential-download.svg b/docs/source/user_guide/meteorological-data-details/sequential-download.svg new file mode 100644 index 00000000..67049bb7 --- /dev/null +++ b/docs/source/user_guide/meteorological-data-details/sequential-download.svg @@ -0,0 +1,2 @@ +Sequentialt1t2t3 \ No newline at end of file diff --git a/docs/source/user_guide/meteorological-data.rst b/docs/source/user_guide/meteorological-data.rst new file mode 100644 index 00000000..05376706 --- /dev/null +++ b/docs/source/user_guide/meteorological-data.rst @@ -0,0 +1,100 @@ +.. _meteorological-data: + +Meterological Data +================== + +PVDeg seeks to automate the tedious parts of degradation analysis by providing simple tools to work with weather data. +``pvdeg.weather.get`` seeks to unify this functionality into a simple function. + +The PVDeg tutorials and examples use two datasets, `NSRDB`_ and `PVGIS`_. These are serially complete data including meteorological data and solar radiation (irradiance) measurements. +The methodology for these datasets varies but both are gridded geospatial datasets with similar attributes. + +.. _NSRDB: +NSRDB +------ +The NSRDB is produced by NREL and combines multiple datasets but we are most concerned with `Physical Solar Model 3 (PSM3) `_. This data was generated using satellite data from multiple channels to derive cloud +and aerosol properties, then fed into a radiative transfer model. Learn more about the NSRDB `here `_. + +The NSRDB is free to use but requires an api-key and email. See :ref:`NSRDB_API_Key` for more information. +For our purposes, the api is limited to 1000 requests per day, although you can request a batch download via email with a singificantly higher rate limit (not recommended for PVDeg). + +Flowchart showing the dataflow from satellite to solar radiation measurement. + +.. image:: meteorological-data-details/data_flow_chart.png + :alt: dataflow from satellite to solar radiation measurement, image missing + +``_ + +NSRDB data are seperated by satellite/model source. Each dataset is shown below, much of the PVDeg project uses the *Americas* data. + +.. image:: meteorological-data-details/nsrdb_global_coverage.jpg + :alt: NSRDB data sources, image missing + +``_ + + +.. _PVGIS: +PVGIS +------ +`PVGIS`_ is the European counterpart of the `NSRDB`_. The data was sourced similarly. With PVGIS we are most concerned with a `typical meteorological year `_. +PVDeg uses utilities built in `pvlib `_ to access the data. + +PVGIS is free to use and does NOT require an api-key. It has a rate limit of 30 requests per second and covers a much larger range of longitudes and latitudes. + +The PVDeg tutorials and examples use two datasets, `NSRDB`_ and `PVGIS`_. These are serially complete data including meteorological data and solar radiation (irradiance) measurements. +The methodology for these datasets varies but both are gridded geospatial datasets with similar attributes. + +PVGIS data are seperated by satellite/model source. Visit the links below for more information about the datasets. + +- `PVGIS 5.2 `_ +- `PVGIS 5.3 `_ + +.. _GIDS: +Issues with Gids +---------------- + +"Gids", plural or "gid" singular refer to a geospatial id. This is where the simplicity ends because gids are largely meaningless. + +When using ``pvdeg.weather.get`` to grab PVGIS data as follows. We will get a gid back but it will always be the same because PVGIS gids are meaningless. The gids created during this process only serve as indexes. + +.. code-block:: Python + + weather_df, meta_df = pvdeg.weather.get( + database="PVGIS", + id = (, ), + ) + + +When using the NSRDB PSM3 dataset, gids are unique only to their satellite. Because of this, gids can only be treated as unique if we can guarantee only one satellite source is being utilized. +This is possible but causes headaches. + +.. code-block:: Python + + weather_df, meta_df = pvdeg.weather.get( + database="PSM3", + id = (, ), + email = , + api_key = , + ) + +Takeaway: gids are not unique or necessarily meaningful, be careful when using them. Duplicate gids can exist in geospatial data and will be loaded using Xarray without raising an error. + + + +.. _PARALLEL_DOWNLOADS: +Accelerated Downloads +---------------------- + +``PVDeg`` provides tools for the accelerated downloading of meteorological data outside of HPC environments. This is particularly useul for ``PVGIS`` which allows us to download up to +30 locations per second of tmy data. Due to the single-threaded nature of Python, we are traditionally limited to 1 request at a time. Often, ``PVGIS`` takes 2-3 seconds per location. +This cumulates in massive download times for large datasets. We can use ``Dask`` for parallelization to greatly accellerate this process and approach the 30 requests per second rate limit. + +.. + .. image:: meteorological-data-letails/parallel-download.svg + :alt: parallel downloading visualization + +We can see that abstract potential speedup offered by parallelization. It is easy to utilize this funtionality as provided by ``pvdeg.weather.weather_distributed``. + +.. autofunction:: pvdeg.weather.weather_distributed + + diff --git a/docs/source/user_guide/package_overview.rst b/docs/source/user_guide/package_overview.rst index 47c331e2..79662c01 100644 --- a/docs/source/user_guide/package_overview.rst +++ b/docs/source/user_guide/package_overview.rst @@ -29,11 +29,27 @@ produced via simulation (for example: results from `bifacial_radiance * Van 't Hoff environment characterization * Arrhenius degradation acceleration factor * Arrhenius environment characterization - * Solder Fatigue * Spectral Degradation + * LETID outdoor, lab + * Solder Fatigue 3. Standards * Ideal Installation Distance + * Effective Operating Temperatures + +4. Monte Carlo + + * Uncorrelated Monte Carlo simulation + * Multi-variate Correlated Monte Carlo simulation + +5. Geospatial + + * Geospatial analysis framework + * High Performance Computing Tools + +6. Weather Utilities + + * Parallelized downloads of NSRDB, and PVGIS For an in depth look at each class and function, please refer to API \ No newline at end of file diff --git a/docs/source/user_guide/pysam.rst b/docs/source/user_guide/pysam.rst new file mode 100644 index 00000000..cfbe2677 --- /dev/null +++ b/docs/source/user_guide/pysam.rst @@ -0,0 +1,10 @@ +.. _pysam: + +PySAM Implementation +==================== + +PVDeg provides a convient wrapper for ``NREL-PySAM`` which is a Python wrapper for `NREL's System Advisor Model (SAM) `_. PySAM has a steep learning curve so we seek to provide a simple implementation that allows users to run geospatial analyses with SAM. + +This work was produced to support `Innovative Solar Practices Integrated with Rural Economies and Ecosystems (InSPIRE) `_. + +**Details coming soon** \ No newline at end of file diff --git a/docs/source/whatsnew/index.rst b/docs/source/whatsnew/index.rst index 78b35369..cc683d36 100644 --- a/docs/source/whatsnew/index.rst +++ b/docs/source/whatsnew/index.rst @@ -4,7 +4,9 @@ What's New ========== PVDegradationTools (pvdeg) change log: - +.. include:: releases/v0.5.0.rst +.. include:: releases/v0.4.3.rst +.. include:: releases/v0.4.2.rst .. include:: releases/v0.4.1.rst .. include:: releases/v0.4.0.rst .. include:: releases/v0.3.3.rst diff --git a/docs/source/whatsnew/releases/v0.4.0.rst b/docs/source/whatsnew/releases/v0.4.0.rst index e6855b63..443781e6 100644 --- a/docs/source/whatsnew/releases/v0.4.0.rst +++ b/docs/source/whatsnew/releases/v0.4.0.rst @@ -2,51 +2,68 @@ v0.4.0 (2024-07-29) ======================= Enhancements ---------- +---------------- + Scenarios -* Unified Cell and Module Temperature function. See: ``temperature.temperature`` -* Added support for pvlib temperature models. ``see pvlib docs `` -* Overhauled scenario class for pv system analysis. Scenario object for single point analysis and GeospatialScenario object for geospatial analysis. See: ``scenario.Scenario`` and ``scenario.GeospatialScenario`` -* Created Scenario tutorials to showcase new scenario functionality. -* Added a number of geospatial downselection functions. Find coordinates near mountains, rivers, coastlines and lakes from NSRDB data. See: ``geospatial.identify_mountains_weights``, ``geospatial.identify_mountains_radii``, ``geospatial.feature_downselect`` -* Added geospatial bounding box support. Clip unwanted data with rectangular bounding boxes. See: ``geospatial.apply_bounding_box`` -* Added stochastic non-uniform density downselection function to preferentially select for mountains (higher point density with changes in elevation, lower density when flatter.) See: ``geospatial.elevation_stochastic_downselection`` -* Updated non-uniform downselection with thresholding and non-linear normalization (support for logarithmic and exponential normalization) See: ``geospatial.identify_mountains_weights`` -* Added Scenario and GeospatialScenario methods for quick plotting and downselection. See: ``GeospatialScenario.plot_coords``, ``GeospatialScenario.plot_meta_classification``, ``GeospatialScenario.plot_USA``, ``Scenario.extract``, ``Scenario.plot``, ``GeospatialScenario.classify_mountain_weights``, ``GeospatialScenario.classify_mountain_radii``, ``GeospatialScenario.downselect_elevation_stochastic``. +- Unified Cell and Module Temperature function. See: + - ``temperature.temperature`` +- Added support for pvlib temperature models. See pvlib docs: + - `https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html`_ +- Overhauled scenario class for pv system analysis. Scenario object for single point analysis and GeospatialScenario object for geospatial analysis. See: ``scenario.Scenario`` and ``scenario.GeospatialScenario`` +- Created Scenario tutorials to showcase new scenario functionality. +- Added a number of geospatial downselection functions. Find coordinates near mountains, rivers, coastlines and lakes from NSRDB data. See: + - ``geospatial.identify_mountains_weights`` + - ``geospatial.identify_mountains_radii`` + - ``geospatial.feature_downselect`` +* Added geospatial bounding box support. Clip unwanted data with rectangular bounding boxes. See: + - ``geospatial.apply_bounding_box`` +* Added stochastic non-uniform density downselection function to preferentially select for mountains (higher point density with changes in elevation, lower density when flatter.) See: + - ``geospatial.elevation_stochastic_downselection`` +* Updated non-uniform downselection with thresholding and non-linear normalization (support for logarithmic and exponential normalization) See: + - ``geospatial.identify_mountains_weights`` +* Added Scenario and GeospatialScenario methods for quick plotting and downselection. See: + - ``GeospatialScenario.plot_coords`` + - ``GeospatialScenario.plot_meta_classification`` + - ``GeospatialScenario.plot_USA`` + - ``Scenario.extract`` + - ``Scenario.plot`` + - ``GeospatialScenario.classify_mountain_weights`` + - ``GeospatialScenario.classify_mountain_radii`` + - ``GeospatialScenario.downselect_elevation_stochastic`` * Added a convenience method ``GepspatialScenario.geospatial_data`` to quickly pull the geospatial weather and metadata from a scenario. Matches the API for ``pvdeg.weather.get``. Geospatial Improvements -* Autotemplating system for geospatial analysis using `pvdeg.geospatial.autotemplate`. -* New module `pvdeg.decorators` that contains `pvdeg` specific decorator functions. -* Implemented `geospatial_result_type` decorator to update functions and preform runtime introspection to determine if a function is autotemplate-able. -* `Geospatial Templates.ipynb` notebook to showcase new and old templating functionality for users. +* Autotemplating system for geospatial analysis using ``pvdeg.geospatial.autotemplate``. +* New module ``pvdeg.decorators`` that contains ``pvdeg`` specific decorator functions. +* Implemented ``geospatial_result_type`` decorator to update functions and preform runtime introspection to determine if a function is autotemplate-able. +* ``Geospatial Templates.ipynb`` notebook to showcase new and old templating functionality for users. * Implemented testing for geospatial analysis. * Added chunked and unchunked testing. Symbolic Evaluation * symbolic equation solver for simple models. -* notebook tutorial `Custom-Functions-Nopython.ipynb` +* notebook tutorial ``Custom-Functions-Nopython.ipynb`` IEC-63126 Tool -* Added `GeospatialScenario` to standoff tool for regional analyses. +* Added ``GeospatialScenario`` to standoff tool for regional analyses. * Increased plotting functionality within tool. Bug Fixes --------- -* Added type hinting to many `pvdeg` functions -* Fixed broken keywords in many `pvdeg.standards` function calls -* Replaced deprecated numba `jit(nopython=True)` calls with `njit` -* Fix incorrect keyword arguments in `pvdeg.standards.T98_estimate` +* Added type hinting to many ``pvdeg`` functions +* Fixed broken keywords in many ``pvdeg.standards`` function calls +* Replaced deprecated numba ``jit(nopython=True)`` calls with ``njit`` +* Fix incorrect keyword arguments in ``pvdeg.standards.T98_estimate`` * Fixed ``pvdeg.temperature.module`` and ``pvdeg.temperature.cell`` docstring return type. Correct return type ``pd.Series`` * Fixed broken Geospatial Analysis, when using chunked (dask) xarrays for weather data Dependencies ------------ -* `sympy` package required for `pvdeg.symbolic` functions and notebook. Not added to dependency list. +* ``sympy`` package required for ``pvdeg.symbolic`` functions and notebook. Not added to dependency list. * Restrict the following dependencies to fix unit testing * ``numpy==1.26.4`` * ``pvlib==0.10.3`` diff --git a/docs/source/whatsnew/releases/v0.4.2.rst b/docs/source/whatsnew/releases/v0.4.2.rst index 597e5510..3b3fa6cb 100644 --- a/docs/source/whatsnew/releases/v0.4.2.rst +++ b/docs/source/whatsnew/releases/v0.4.2.rst @@ -4,13 +4,13 @@ v0.4.2 (2024-09-13) Bug Fixes --------- -* Remove duplicate gid's from `pvdeg.geospatial.elevation_stochastic_downselection` +* Remove duplicate gid's from ``pvdeg.geospatial.elevation_stochastic_downselection`` Tests ----- -* Added a test for Xmin in `test_standards.py` and removed dependency on pvgis. +* Added a test for Xmin in ``test_standards.py`` and removed dependency on pvgis. Contributors ~~~~~~~~~~~~ * Martin Springer (:ghuser:`martin-springer`) -* Aidan Wesley (:ghuser:`AidanWesley``) +* Aidan Wesley (:ghuser:`AidanWesley``) \ No newline at end of file diff --git a/docs/source/whatsnew/releases/v0.4.3.rst b/docs/source/whatsnew/releases/v0.4.3.rst new file mode 100644 index 00000000..a5e3eb6d --- /dev/null +++ b/docs/source/whatsnew/releases/v0.4.3.rst @@ -0,0 +1,16 @@ +v0.4.3 (2024-11-1) +======================= + +Enhancements +------------- +``pvdeg.geospatial.analysis`` implements autotemplating. No need to specify a template for common ``pvdeg`` functions during analysis. Manually creating and providing templates is still an option. Docstrings updated with examples. + +Suite of utility functions to facilitate accessing material parameter json files. + +* ``pvdeg.utilities.read_material`` creates a public api to replace the private ``pvdeg.untility._read_material`` function (to be deprecated soon) +* ``pvdeg.utilities.search_json`` to search jsons and identify keys for desired materials. +* ``pvdeg.utilities.display_json`` to view 2 level jsons in Jupyter Notebooks using HTML. + +Contributors +-------------- +* Tobin Ford (:ghuser:`tobin-ford`) \ No newline at end of file diff --git a/docs/source/whatsnew/releases/v0.5.0.rst b/docs/source/whatsnew/releases/v0.5.0.rst new file mode 100644 index 00000000..d346a1d2 --- /dev/null +++ b/docs/source/whatsnew/releases/v0.5.0.rst @@ -0,0 +1,56 @@ +v0.5.0 (2025-2-7) +=================== + +Enhancements +-------------- +- New Logo!🎉🎉🎉 +- Created utilities for parallelized downloads of ``PVGIS`` and ``NSRDB`` so users can download geospatial data for at home simulations. This will interface nicely with the soon to be released ``GeoGridStore``, allowing users to grow stores of geospatial data overtime as their needs grow for at-home, large-scale analyses. +- Documenation overhaul. Significant ``User Guide`` improvements. Added geospatial information with visual aids, added meteorological data page and materials access page. +- Suite of utility functions to facilitate accessing material parameter json files. +- Moved ``GeospatialScenario`` class definition to ``geospatialscenario.py`` +- Remove kdtree as required argument from ``GeospatialScenario`` downselection methods, kdtree objects are now generated on the fly when required and saved to scenario object for future use. +- ``decorators.geospatial_quick_shape`` arguments modified. ``numeric_or_timeseries``, now takes a string ``('numeric', 'timeseries')`` to determine type of outputs rather than a ``Bool`` or ``Int`` + - previously, ``0`` or ``False`` represented a numeric/scalar result, now this is represented by ``'numeric'`` + - previosuly, ``1`` or ``True`` represented a timeseries result, now this is represented by ``'timeseries'`` +- ``decorators.py`` namespace changed to default ``pvdeg`` namespace. now this can be directly accessed via ``pvdeg.decorators``. This reduces the need for an extra import. + - ``geospatial_quick_shape`` decorator namespace changed to defualt pvdeg namespace + +Previously, + +.. code-block:: Python + + import pvdeg + from pvdeg.decorator import geospatial_quick_shape + + @geospatial_quick_shape(0, ...) + def myfunc(...): + .... + +Now, either of the following options work. + +.. code-block:: Python + + import pvdeg + + # now takes string instead of integer or boolean value + @pvdeg.decorators.geospatial_quick_shape('numeric', ...) + def myfunc(...): + .... + +.. code-block:: Python + + # this is the style used in the PVDeg package implementations + import pvdeg.decorators + + @decorators.geospatial_quick_shape('numeric', ...) + def myfunc(...): + .... + +Bug Fixes +------------- +- fixed an issue where pvdeg.geospatial.elevation_stochastic_downselect would return metadata row indicies which were downselected instead of named gids. + + +Contributors +----------- +- Tobin Ford (:ghuser:`tobin-ford`) diff --git a/pvdeg/__init__.py b/pvdeg/__init__.py index 66135977..2f987dd3 100644 --- a/pvdeg/__init__.py +++ b/pvdeg/__init__.py @@ -5,15 +5,19 @@ # from . import cli from . import collection +from . import decorators from . import degradation from . import design from . import fatigue from . import geospatial +#from .geospatialscenario import GeospatialScenario from . import humidity from . import letid from . import montecarlo -from . import scenario +from . import pysam +from .scenario import Scenario, GeospatialScenario from . import spectral +from . import store from . import symbolic from . import standards from . import temperature diff --git a/pvdeg/config.py b/pvdeg/config.py index d40706a8..14afbf37 100644 --- a/pvdeg/config.py +++ b/pvdeg/config.py @@ -11,6 +11,9 @@ DATA_DIR = PVDEG_DIR / "data" TEST_DIR = PVDEG_DIR.parent / "tests" TEST_DATA_DIR = PVDEG_DIR.parent / "tests" / "data" + +# downloader target directory +METOROLOGICAL_DOWNLOAD_PATH = Path.home() / "PVDeg-Meteorological" # DATA_LIBRARY = PVDEG_DIR.parent / "DataLibrary" # if not os.path.isdir(DATA_LIBRARY): diff --git a/pvdeg/data/O2permeation.json b/pvdeg/data/O2permeation.json index 9ecc24de..18eedd38 100644 --- a/pvdeg/data/O2permeation.json +++ b/pvdeg/data/O2permeation.json @@ -36,10 +36,10 @@ "Fickian": true, "Ead": 29.43112031, "Do": 0.129061678, - "Eas": 32.3137806, - "So": 87.81142774, - "Eap": 61.7449009, - "Po": 97917899126 + "Eas": 16.6314948252219, + "So": 0.136034525059804, + "Eap": 49.1083457348515, + "Po": 528718258.338532 }, "OX004": { "name": "AAA polyamide backsheet", diff --git a/pvdeg/decorators.py b/pvdeg/decorators.py index b5927399..c0567a1b 100644 --- a/pvdeg/decorators.py +++ b/pvdeg/decorators.py @@ -3,21 +3,24 @@ Private API, should only be used in PVDeg implemenation files. """ +import functools +import inspect +import warnings -def geospatial_quick_shape(numeric_or_timeseries: bool, shape_names: list[str]) -> None: +def geospatial_quick_shape(numeric_or_timeseries: str, shape_names: list[str]) -> None: """ Add an attribute to the functions that can be run with geospatial analysis. Strict typing is not enough for this purpose so we can view this attribute at runtime to create a template for the function. For single numeric results, includes tabular numeric data - >>> value = False (0) + >>> value = 'numeric' Example if a function returns a dataframe with 1 row of numerics (not timeseries) `pvdeg.standards.standoff` does this. For timeseries results - >>> value = True (1) + >>> value = 'timeseries' Example, `pvdeg.temperature.temperature` @@ -32,7 +35,9 @@ def geospatial_quick_shape(numeric_or_timeseries: bool, shape_names: list[str]) >>> func.shape_names = ["T98", "x_eff"] # function attribute names * Note: we cannot autotemplate functions with ambiguous return types that depend on runtime input, - the function will need strictly return a timeseries or numeric but not one or the other. + the function will need to strictly return a timeseries or numeric. + + * Note: this is accessed through the ``decorators.geospatial_quick_shape`` namespace Parameters: ----------- @@ -57,3 +62,79 @@ def decorator(func): return func return decorator + +# Taken from: https://stackoverflow.com/questions/2536307/decorators-in-the-python-standard-lib-deprecated-specifically +# A future Python version (after 3.13) will include the warnings.deprecated decorator +def deprecated(reason): + """ + This is a decorator which can be used to mark functions + as deprecated. It will result in a warning being emitted + when the function is used. + """ + + string_types = (type(b''), type(u'')) + + if isinstance(reason, string_types): + + # The @deprecated is used with a 'reason'. + # + # .. code-block:: python + # + # @deprecated("please, use another function") + # def old_function(x, y): + # pass + + def decorator(func1): + + if inspect.isclass(func1): + fmt1 = "Call to deprecated class {name} ({reason})." + else: + fmt1 = "Call to deprecated function {name} ({reason})." + + @functools.wraps(func1) + def new_func1(*args, **kwargs): + warnings.simplefilter('always', DeprecationWarning) + warnings.warn( + fmt1.format(name=func1.__name__, reason=reason), + category=DeprecationWarning, + stacklevel=2 + ) + warnings.simplefilter('default', DeprecationWarning) + return func1(*args, **kwargs) + + return new_func1 + + return decorator + + elif inspect.isclass(reason) or inspect.isfunction(reason): + + # The @deprecated is used without any 'reason'. + # + # .. code-block:: python + # + # @deprecated + # def old_function(x, y): + # pass + + func2 = reason + + if inspect.isclass(func2): + fmt2 = "Call to deprecated class {name}." + else: + fmt2 = "Call to deprecated function {name}." + + @functools.wraps(func2) + def new_func2(*args, **kwargs): + warnings.simplefilter('always', DeprecationWarning) + warnings.warn( + fmt2.format(name=func2.__name__), + category=DeprecationWarning, + stacklevel=2 + ) + warnings.simplefilter('default', DeprecationWarning) + return func2(*args, **kwargs) + + return new_func2 + + else: + raise TypeError(repr(type(reason))) \ No newline at end of file diff --git a/pvdeg/degradation.py b/pvdeg/degradation.py index 7c9b85c5..0e8c3274 100644 --- a/pvdeg/degradation.py +++ b/pvdeg/degradation.py @@ -9,11 +9,12 @@ from concurrent.futures import ProcessPoolExecutor, as_completed from typing import Union -from . import temperature -from . import spectral -from . import weather - -from pvdeg.decorators import geospatial_quick_shape +from . import ( + temperature, + spectral, + weather, + decorators, +) # TODO: Clean up all those functions and add gaps functionality @@ -236,7 +237,7 @@ def _to_eq_vantHoff(temp, Tf=1.41): return Toeq -@geospatial_quick_shape(0, ["Iwa"]) +@decorators.geospatial_quick_shape('numeric', ["Iwa"]) def IwaVantHoff( weather_df, meta, diff --git a/pvdeg/design.py b/pvdeg/design.py index 25c5b076..6061f391 100644 --- a/pvdeg/design.py +++ b/pvdeg/design.py @@ -1,7 +1,10 @@ """Collection of functions for PV module design considertations.""" -from . import humidity -from pvdeg.decorators import geospatial_quick_shape +from . import ( + humidity, + decorators +) + import pandas as pd @@ -42,7 +45,7 @@ def edge_seal_ingress_rate(avg_psat): return k -@geospatial_quick_shape(0, ["width"]) +@decorators.geospatial_quick_shape('numeric', ["width"]) def edge_seal_width( weather_df: pd.DataFrame, meta: dict, diff --git a/pvdeg/diffusion.py b/pvdeg/diffusion.py index c299def4..e40f26df 100644 --- a/pvdeg/diffusion.py +++ b/pvdeg/diffusion.py @@ -8,25 +8,24 @@ from pvdeg import DATA_DIR from numba import jit import numpy as np +from typing import Callable def esdiffusion( temperature, - edge_seal=None, - encapsulant=None, + edge_seal="OX005", + encapsulant="OX003", edge_seal_width=1.5, encapsulant_width=10, seal_nodes=20, encapsulant_nodes=50, press = 0.209, repeat = 1, - Dos=None, - Eads=None, - Sos=None, - Eass=None, - Doe=None, - Eade=None, - Soe=None, - Ease=None, + Dos=None, Eads=None, Sos=None,Eass=None, Doe=None, Eade=None, Soe=None, Ease=None, + react_func = None, + deg_func = None, + deg = None, + perm = None, + printout = True, **kwarg ): @@ -58,40 +57,68 @@ def esdiffusion( This is the partial pressure of oxygen. repeat : integer, optional This is the number of times to do the calculation for the whole dataset. E.g. repeat the 1-y data for 10 years. + react_func : string, optional + This is the name of the function that will be calculating the consumption of oxygen. + deg_func :string, optional + This is the name of the function that will be calculating the degradation. + printout : Boolean + This allows you to suppress printing messages during code execution by setting it to false. + deg : Numpy Array + One can send in an array with predefined degradation data already in it if desired. + I.e. you can have some pre degradation or areas that require more degradation. + perm : Numpy Array + One can send in an array with the permeant already in it if desired. kwargs : dict, optional - If es or enc are left at 'None' then the use parameters, Dos, Eads, Sos, Eass, Doe, Eade, Soe, Ease in units of + If edge_seal or encapsulant are set at 'None' then you can enter your own parameters for, Dos, Eads, Sos, Eass, Doe, Eade, Soe, Ease in units of [cm²/s], [g/cm³], or [kJ/mol] for diffusivity, solubility, or activation energy respectively. If specific parameters are provided, - then the JSON ones can be overridden. + then the JSON ones will be overridden. + Should also contain any key word arguments that need to be passed to the function calculating consumption of the permeant or degradation. Returns ------- ingress_data : pandas.DataFrame - This will give the concentration profile as a function of temperature along with degradation parameters in futur iterations.. + This will give the concentration profile as a function of time. + If there is a degradation function called, this data will also be inclueded on a node by node basis under a third index. """ with open(os.path.join(DATA_DIR, "O2permeation.json")) as user_file: O2 = json.load(user_file) user_file.close() - # O2 - if edge_seal == None: - esp = O2.get("OX005") # This is the number for the edge seal in the json file - else: - esp = O2.get(edge_seal) + with open(os.path.join(DATA_DIR, "H2Opermeation.json")) as user_file: + H2O = json.load(user_file) + user_file.close() - if encapsulant == None: - encp = O2.get( - "OX003" - ) # This is the number for the encapsulant in the json file + if edge_seal[0:2]=="OX": + esp = O2.get(edge_seal) + if printout: + print("Oxygen ingress parameters loaded for the edge seal.") else: - encp = O2.get(encapsulant) + if edge_seal[0:1]=="W": + esp = H2O.get(edge_seal) + if printout: + print("Water ingress parameters loaded for the edge seal.") + else: + print("Edge seal material not found") - try: - print("The edge seal is", esp.get("name"), ".") - print("The encapsulant is", encp.get("name"), ".") - except: - print("") + if encapsulant[0:2]=="OX": + encp = O2.get(encapsulant) + if printout: + print("Oxygen ingress parameters loaded for the encapsulant.") + else: + if encapsulant[0:1]=="W": + encp = H2O.get(encapsulant) + if printout: + print("Water ingress parameters loaded for the eencapsulant.") + else: + print("Encapsulant material not found") + if printout: + try: + print("The edge seal is", esp.get("name"), ".") + print("The encapsulant is", encp.get("name"), ".") + except: + print("Unknown material selected.") - # These are the edge seal oxygen permeation parameters + # These are the edge seal oxygen or water permeation parameters if Dos == None: Dos = esp.get("Do") if Eads == None: @@ -136,12 +163,14 @@ def esdiffusion( perm_mid = np.array( np.zeros((seal_nodes + encapsulant_nodes + 3)), dtype=np.float64 - ) # This is the profile at a transition point between output points. - perm = np.array( - np.zeros( - (len(temperature) * repeat - repeat + 1, seal_nodes + encapsulant_nodes + 3), dtype=np.float64 - ) - ) # It adds in two nodes for the interface concentration for both materials and one for the hour column. + ) # This is the profile at a transition point between output points. + if perm == None: + perm = np.array( + np.zeros( + (len(temperature) * repeat - repeat + 1, seal_nodes + encapsulant_nodes + 3), dtype=np.float64 + ) + ) # It adds in two nodes for the interface concentration for both materials and one for the hour column. + temperature = pd.DataFrame( temperature, columns=["module_temperature", "time", "time_step"] ) # This adds the number of time steps to be used as a subdivision between data points. [s] @@ -163,7 +192,8 @@ def esdiffusion( time_step[row] = np.trunc(fos / f_max) + 1 else: time_step[row] = np.trunc(foe / f_max) + 1 - + if deg_func != None and deg == None: # Sets up an array to do the degradation calculation. + deg=perm perm[0][1] = Sos * np.exp(-Eass / met_data[0][0]) perm_mid = perm[0] for rp_num in range(repeat): @@ -187,10 +217,9 @@ def esdiffusion( ) # Cs edge seal/Ce encapsulant r1 = so * np.exp(-eas / (met_data[row][0] + dtemp * mid_point)) - r2 = ( - dod * np.exp(-ead / (met_data[row][0] + dtemp * mid_point)) - * r1 * encapsulant_width / edge_seal_width - ) # Ds/De*Cs/Ce*We/Ws + r2 = dod * np.exp(-ead / (met_data[row][0] + dtemp * mid_point) + )* r1 * encapsulant_width / edge_seal_width + # Ds/De*Cs/Ce*We/Ws # Calculates the edge seal nodes. Adjusted to not calculate ends and to have the first node be temperature. for node in range(2, seal_nodes): perm[row + 1 + rp_row][node] = perm_mid[node] + fos * ( @@ -201,36 +230,54 @@ def esdiffusion( perm[row + 1 + rp_row][node] = perm_mid[node] + foe * ( perm_mid[node - 1] + perm_mid[node + 1] - 2 * perm_mid[node] ) - # Calculates the center encapsulant node. Accounts for temperature and two interfade nodes. + # Calculates the center encapsulant node. Accounts for temperature and two interface nodes. perm[row + 1 + rp_row][encapsulant_nodes + seal_nodes + 2] = perm_mid[ - encapsulant_nodes + seal_nodes + 2 - ] + 2 * foe * (perm_mid[encapsulant_nodes + seal_nodes + 1] - perm_mid[encapsulant_nodes + seal_nodes + 2]) + encapsulant_nodes + seal_nodes + 2] + 2 * foe * ( + perm_mid[encapsulant_nodes + seal_nodes + 1] - + perm_mid[encapsulant_nodes + seal_nodes + 2]) + # Calculated edge seal node adjacent to the first encapsulant node. Node numbers shifted. perm[row + 1 + rp_row][seal_nodes] = perm_mid[seal_nodes] + fos * ( perm_mid[seal_nodes - 1] + perm_mid[seal_nodes + 3] * r1 * 2 / (1 + r2) - - perm_mid[seal_nodes] * (1 + 2 / (1 + r2)) - ) + - perm_mid[seal_nodes] * (1 + 2 / (1 + r2))) + # Calculated encapsulant node adjacent to the last edge seal node. Node numbers shifted. perm[row + 1 + rp_row][seal_nodes + 3] = perm_mid[seal_nodes + 3] + foe * ( perm_mid[seal_nodes] / r1 * 2 / (1 + 1 / r2) + perm_mid[seal_nodes + 4] - - perm_mid[seal_nodes + 3] * (1 + 2 / (1 + 1 / r2)) - ) + - perm_mid[seal_nodes + 3] * (1 + 2 / (1 + 1 / r2))) + # sets the concentration at the edge seal to air interface. perm[row + 1 + rp_row][1] = Sos * np.exp( -Eass / (met_data[row + 1][0] + dtemp * mid_point) ) + + + # Runs the degradation calculation. + if deg_func != None: + print('oops') + # Runs the reaction with permeant function. + if react_func != None: + print('oops') + perm_mid = perm[row + 1 + rp_row] - # calculate edge seal at interface to encapsulant. - perm[row + 1 + rp_row][seal_nodes + 1] = ( - perm_mid[seal_nodes + 3] / r2 * r1 + perm_mid[seal_nodes] - ) / (1 / r2 + 1) - # calculate encapsulant at interface to the edge seal. - perm[row + 1 + rp_row][seal_nodes + 2] = perm[row + 1 + rp_row][seal_nodes + 1] / r1 + # Calculate edge seal at interface to encapsulant. + # Blocked out code did weird things and was based on equal fluxes. Actually using a simple averaging. This looks better and is not used in the diffusion calculations. + #perm[row + 1 + rp_row][seal_nodes + 1] = (perm_mid[seal_nodes + 3]*r1 + # + perm_mid[seal_nodes]*r2) / (1+r2) + perm[row + 1 + rp_row][seal_nodes + 1] = perm_mid[seal_nodes ]+(perm_mid[seal_nodes]-perm_mid[seal_nodes-1])/2 + + # Calculate encapsulant at interface to the edge seal. + #perm[row + 1 + rp_row][seal_nodes + 2] = perm[row + 1 + rp_row][seal_nodes + 1] / r1 + perm[row + 1 + rp_row][seal_nodes + 2] = perm_mid[seal_nodes + 3]+(perm_mid[seal_nodes + 4]-perm_mid[seal_nodes+3])/2 + + # Puts in the time for the first column. perm[row + 1 + rp_row][0] = rp_time + met_data[row + 1][1] + + # Because it is cycling around, it needs to start with the last temperature. met_data[0][0] = met_data[met_data.shape[0] - 1][0] diff --git a/pvdeg/fatigue.py b/pvdeg/fatigue.py index ccf66433..4db36043 100644 --- a/pvdeg/fatigue.py +++ b/pvdeg/fatigue.py @@ -1,9 +1,11 @@ import numpy as np import pandas as pd from scipy.constants import convert_temperature -from pvdeg import temperature -from pvdeg.decorators import geospatial_quick_shape +from pvdeg import ( + temperature, + decorators +) def _avg_daily_temp_change(time_range, temp_cell): """ @@ -98,7 +100,7 @@ def _times_over_reversal_number(temp_cell, reversal_temp): return num_changes_temp_hist -@geospatial_quick_shape(0, ["damage"]) +@decorators.geospatial_quick_shape('numeric', ["damage"]) def solder_fatigue( weather_df: pd.DataFrame, meta: dict, diff --git a/pvdeg/geospatial.py b/pvdeg/geospatial.py index d54877e5..8a4b3186 100644 --- a/pvdeg/geospatial.py +++ b/pvdeg/geospatial.py @@ -2,11 +2,13 @@ Collection of classes and functions for geospatial analysis. """ -from . import ( +from pvdeg import ( standards, humidity, letid, utilities, + pysam, + decorators, ) import xarray as xr @@ -89,8 +91,42 @@ def start_dask(hpc=None): return client +# rename this? +# and combine into a single function with _df_from_arbitrary, this ds_from_arbitray isnt really doing anything anymore +# we only want ds_from_arbitrary and then convert to ds, but if the input is a dataset already then we dont want to anything +# def _ds_from_arbitrary(res, func): +# """ +# Convert an arbitrary return type to xarray.Dataset. +# """ + +# ######## STRUCTURAL ######### +# # functions can just return xr.Dataset to take advantage of geospatial +# # this should not be required to implement a new geospatial function + +# if isinstance(res, xr.Dataset): +# return res + + +# # if isinstance(res, pysam.inspirePysamReturn): +# # return pysam._handle_pysam_return(res) +# # add more conditionals if we have special cases +# # or add general case for mixed return dimensions: HARD + +# # handles collections with elements of same shapes +# df = _df_from_arbitrary(res=res, func=func) +# ds = xr.Dataset.from_dataframe(df) + +# if not df.index.name: +# ds = ds.isel(index=0, drop=True) + +# return ds + def _df_from_arbitrary(res, func): + """ + Convert an arbitrary return type to dataframe. + Results must be of similar shape currently. Either all numerics or all timeseries. + """ numerics = (int, float, np.number) arrays = (np.ndarray, pd.Series) @@ -103,12 +139,13 @@ def _df_from_arbitrary(res, func): elif isinstance(res, tuple) and all(isinstance(item, numerics) for item in res): return pd.DataFrame([res]) elif isinstance(res, tuple) and all(isinstance(item, arrays) for item in res): + # add check for same size, raise value error otherwise return pd.concat( res, axis=1 ) # they must all be the same length here or this will error out else: raise NotImplementedError( - f"function return type: {type(res)} not available for geospatial analysis yet." + f"function return type: {type(res)} not available for geospatial analysis yet. This could be result of mismatched coordinates of outputs. EX. tuple(dataframe, int)." ) @@ -138,19 +175,26 @@ def calc_gid(ds_gid, meta_gid, func, **kwargs): if type(meta_gid["latitude"]) == dict: meta_gid = utilities.fix_metadata(meta_gid) - df_weather = ( - ds_gid.to_dataframe() - ) # set time index here? is there any reason the weather shouldn't always have only pd.datetime index? @ martin? + # set time index here? is there any reason the weather shouldn't always have only pd.datetime index? @ martin? + df_weather = ds_gid.to_dataframe() if isinstance( df_weather.index, pd.MultiIndex ): # check for multiindex and convert to just time index, don't know what was causing this df_weather = df_weather.reset_index().set_index("time") res = func(weather_df=df_weather, meta=meta_gid, **kwargs) - df_res = _df_from_arbitrary(res, func) # convert all return types to dataframe - ds_res = xr.Dataset.from_dataframe(df_res) + # res is the type returned by func + # can be float, tuple, list, dataframe, dataset, etc. + # need to convert it to a dataset + + if isinstance(res, xr.Dataset): + return res + + # handles collections with elements of same shapes + df = _df_from_arbitrary(res=res, func=func) + ds_res = xr.Dataset.from_dataframe(df) - if not df_res.index.name: + if not df.index.name: ds_res = ds_res.isel(index=0, drop=True) return ds_res @@ -190,7 +234,12 @@ def calc_block(weather_ds_block, future_meta_df, func, func_kwargs): def analysis(weather_ds, meta_df, func, template=None, **func_kwargs): """ - Applies a function to each gid of a weather dataset. + Applies a function to each gid of a weather dataset. `analysis` will attempt to create a template using `geospatial.auto_template`. + If this process fails you will have to provide a geospatial template to the template argument. + + ValueError: cannot be autotemplated. create a template manually with `geospatial.output_template` + + Parameters ---------- @@ -212,8 +261,10 @@ def analysis(weather_ds, meta_df, func, template=None, **func_kwargs): """ if template is None: - param = template_parameters(func) - template = output_template(weather_ds, **param) + template = auto_template( + func=func, + ds_gids=weather_ds + ) # future_meta_df = client.scatter(meta_df) kwargs = {"func": func, "future_meta_df": meta_df, "func_kwargs": func_kwargs} @@ -241,7 +292,11 @@ def analysis(weather_ds, meta_df, func, template=None, **func_kwargs): def output_template( - ds_gids, shapes, attrs=dict(), global_attrs=dict(), add_dims=dict() + ds_gids: xr.Dataset, + shapes: dict, + attrs=dict(), + global_attrs=dict(), + add_dims=dict() ): """ Generates a xarray template for output data. Output variables and @@ -249,6 +304,31 @@ def output_template( The dimension length are derived from the input data. Additonal output dimensions can be defined with the add_dims argument. + Examples + -------- + Providing the shapes dictionary can be confusing. Here is what the `shapes` dictionary should look like for `pvdeg.standards.standoff`. + Refer to the docstring, the function will have one result per location so the only dimension for each return value is "gid", a geospatial ID number. + + .. code-block:: python + shapes = { + "x": ("gid",), + "T98_inf": ("gid",), + "T98_0": ("gid",), + } + + **Note: The dimensions are stored in a tuple, this this why all of the parenthesis have commas after the single string, otherwise python will interpret the value as a string.** + + This is what the shapes dictinoary should look like for `pvdeg.humidity.module`. Refering to the docstring, + we can see that the function will return a timeseries result for each location. This means we need dimensions of "gid" and "time". + + .. code-block:: python + shapes = { + "RH_surface_outside": ("gid", "time"), + "RH_front_encap": ("gid", "time"), + "RH_back_encap": ("gid", "time"), + "RH_backsheet": ("gid", "time"), + } + Parameters ---------- ds_gids : xarray.Dataset @@ -269,6 +349,17 @@ def output_template( dims = set([d for dim in shapes.values() for d in dim]) dims_size = dict(ds_gids.sizes) | add_dims + # update the coordinates with the dims which include add_dims + coords = {} + for dim in dims: + if dim in ds_gids: + coords[dim] = ds_gids[dim] + elif dim in add_dims: + coords[dim] = np.arange(dims_size[dim]) # placeholder array (edge case) + else: + raise ValueError(f"dim: {dim} not in ds_gids or add_dims") + + output_template = xr.Dataset( data_vars={ var: ( @@ -278,21 +369,28 @@ def output_template( ) # this will produce a dask array with 1 chunk of the same size as the input for var, dim in shapes.items() }, - coords={dim: ds_gids[dim] for dim in dims}, + coords=coords, attrs=global_attrs, - ) + ) - if ds_gids.chunks: # chunk to match input + # we can only chunk dimensions existing in the input + # added dimensions will fail if chunked under this scheme + # because they do not exist in ds gids + if ds_gids.chunks: output_template = output_template.chunk( - {dim: ds_gids.chunks[dim] for dim in dims} + { + dim: ds_gids.chunks[dim] + for dim in dims + if dim in ds_gids + } ) return output_template -# we should be able to get rid of this with the new autotemplating function and decorator -# this is helpful for users so we should move it to a section in the documenation, -# discuss with group +# This has been replaced with pvdeg.geospatial.auto_templates inside of pvdeg.geospatial.analysis. +# it is here for completeness. it can be removed. +@decorators.deprecated(reason="use geospatial.auto_template or create a template with geospatial.output_template") def template_parameters(func): """ Output parameters for xarray template. @@ -334,6 +432,33 @@ def template_parameters(func): add_dims = {} + elif func == standards.vertical_POA: + # res = {"annual_gh": x, "annual_energy": annual_energy, "lcoe_nom": lcoe_nom} + + shapes = { + "annual_gh": ("gid",), + "annual_energy": ("gid",), + "lcoe_nom": ("gid",), + + } + + attrs = { + "annual_gh": {"long_name": "SAM Annual GHI", "units": "Wh/m2/yr"}, + "annual_energy": { + "long_name": "Annual AC energy", + "units": "kWh", + }, + "lcoe_nom": { + "long_name": "LCOE Levelized cost of energy nominal", + "units": "cents/kWh", + }, + } + + global_attrs = { + "long_name": "Vertical dataset", + } + add_dims = {} + elif func == humidity.module: shapes = { "RH_surface_outside": ("gid", "time"), @@ -410,6 +535,28 @@ def zero_template( return res +def can_auto_template(func) -> None: + """ + Check if we can use `geospatial.auto_template on a given function. + + Raise an error if the function was not declared with the `@geospatial_quick_shape` decorator. + No error raised if we can run `geospatial.auto_template` on provided function, `func`. + + Parameters + ---------- + func: callable + function to create template from. + + Returns + ------- + None + """ + if not (hasattr(func, "numeric_or_timeseries") and hasattr(func, "shape_names")): + raise ValueError( + f"{func.__name__} cannot be autotemplated. create a template manually" + ) + + def auto_template(func: Callable, ds_gids: xr.Dataset) -> xr.Dataset: """ @@ -417,11 +564,10 @@ def auto_template(func: Callable, ds_gids: xr.Dataset) -> xr.Dataset: Only works on functions that have the `numeric_or_timeseries` and `shape_names` attributes. These attributes are assigned at function definition with the `@geospatial_quick_shape` decorator. - Otherwise you will have to create your own template. - Don't worry, this is easy. See the Geospatial Templates Notebook - for more information. + Otherwise you will have to create your own template using `geospatial.output_template`. + See the Geospatial Templates Notebook for more information. - examples: + Examples --------- the function returns a numeric value @@ -430,22 +576,34 @@ def auto_template(func: Callable, ds_gids: xr.Dataset) -> xr.Dataset: the function returns a timeseries result >>> pvdeg.module.humidity - counter example: + Counter example: ---------------- the function could either return a single numeric or a series based on changed in the input. Because it does not have a known result shape we cannot determine the attributes required for autotemplating ahead of time. + + Parameters + ---------- + func: callable + function to create template from. This will raise an error if the function was not declared with the `@geospatial_quick_shape` decorator. + ds_gids : xarray.Dataset + Dataset containing the gids and their associated dimensions. (geospatial weather dataset) + Dataset should already be chunked. + + Returns + ------- + output_template : xarray.Dataset + Template for output data. """ - if not (hasattr(func, "numeric_or_timeseries") and hasattr(func, "shape_names")): - raise ValueError( - f"{func.__name__} cannot be autotemplated. create a template manually" - ) + can_auto_template(func=func) - if func.numeric_or_timeseries == 0: + if func.numeric_or_timeseries == 'numeric': shapes = {datavar: ("gid",) for datavar in func.shape_names} - elif func.numeric_or_timeseries == 1: + elif func.numeric_or_timeseries == 'timeseries': shapes = {datavar: ("gid", "time") for datavar in func.shape_names} + else: + raise ValueError(f"{func.__name__} 'numeric_or_timseries' attribute invalid. is {func.numeric_or_timeseries} should be 'numeric' or 'timeseries'") template = output_template(ds_gids=ds_gids, shapes=shapes) # zeros_template? @@ -546,7 +704,7 @@ def meta_KDtree(meta_df, leaf_size=40, fp=None): Create a sklearn.neighbors.KDTree for fast geospatial lookup operations. Requires Scikit Learn library. Not included in pvdeg depency list. - Parameters: + Parameters ----------- meta_df: pd.DataFrame Dataframe of metadata as generated by pvdeg.weather.get for geospatial @@ -557,7 +715,7 @@ def meta_KDtree(meta_df, leaf_size=40, fp=None): If none, no file saved. must be ``.pkl`` file extension. Open saved pkl file with joblib (sklearn dependency). - Returns: + Returns -------- kdtree: sklearn.neighbors.KDTree kdtree containing latitude-longitude pairs for quick lookups @@ -898,7 +1056,7 @@ def elevation_stochastic_downselect( Options : `'mean'`, `'sum'`, `'median'` normalization : str, (default = 'linear') function to apply when normalizing weights. Logarithmic uses log_e/ln - options : `'linear'`, `'logarithmic'`, '`exponential'` + options : `'linear'`, `'log'`, '`exp'`, `'invert-linear'` Returns: -------- @@ -923,11 +1081,13 @@ def elevation_stochastic_downselect( a=len(coords), p=normalized_weights / np.sum(normalized_weights), size=m ) - return np.unique(selected_indicies) + return meta_df.index.values[np.unique(selected_indicies)] + #return meta_df.iloc[np.unique(selected_indicies)].index.values + #return np.unique(selected_indicies) def interpolate_analysis( - result: xr.Dataset, data_var: str, method="nearest" + result: xr.Dataset, data_var: str, method="nearest", resolution=100j, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ Interpolate sparse spatial result data against DataArray coordinates. @@ -935,6 +1095,9 @@ def interpolate_analysis( Parameters: ----------- + resolution: complex + Change the amount the input is interpolated. + For more interpolation set higher (200j is more than 100j) Result: ------- @@ -951,8 +1114,8 @@ def interpolate_analysis( ) # probably a nicer way to do this grid_lat, grid_lon = np.mgrid[ - df["latitude"].min() : df["latitude"].max() : 100j, - df["longitude"].min() : df["longitude"].max() : 100j, + df["latitude"].min() : df["latitude"].max() : resolution, + df["longitude"].min() : df["longitude"].max() : resolution, ] grid_z = griddata(data[:, 0:2], data[:, 2], xi=(grid_lat, grid_lon), method=method) @@ -960,14 +1123,29 @@ def interpolate_analysis( return grid_z, grid_lat, grid_lon -def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") -> None: +# api could be updated to match that of plot_USA +def plot_sparse_analysis( + result: xr.Dataset, + data_var: str, + method="nearest", + resolution=100j, + cmap='viridis', + ax=None + +) -> None: grid_values, lat, lon = interpolate_analysis( - result=result, data_var=data_var, method=method + result=result, data_var=data_var, method=method, resolution=resolution ) - fig = plt.figure() - ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False) - ax.patch.set_visible(False) + if ax is None: + fig = plt.figure() + ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False) + ax.patch.set_visible(False) + + show = True + else: + fig = None + show = False extent = [lon.min(), lon.max(), lat.min(), lat.max()] ax.set_extent(extent) @@ -975,9 +1153,9 @@ def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") -> grid_values, extent=extent, origin="lower", - cmap="viridis", + cmap=cmap, transform=ccrs.PlateCarree(), - ) # should this be trnsposed + ) shapename = "admin_1_states_provinces_lakes" states_shp = shpreader.natural_earth( @@ -991,10 +1169,14 @@ def plot_sparse_analysis(result: xr.Dataset, data_var: str, method="nearest") -> edgecolor="gray", ) - cbar = plt.colorbar(img, ax=ax, orientation="vertical", fraction=0.02, pad=0.04) - cbar.set_label("Value") + if fig is not None: + cbar = plt.colorbar(img, ax=ax, orientation="vertical", fraction=0.02, pad=0.04) + cbar.set_label("Value") + plt.title("Interpolated Heatmap") + plt.xlabel("Longitude") + plt.ylabel("Latitude") + + if show and fig is not None: + plt.show() - plt.title("Interpolated Heatmap") - plt.xlabel("Longitude") - plt.ylabel("Latitude") - plt.show() + return fig, ax diff --git a/pvdeg/geospatialscenario.py b/pvdeg/geospatialscenario.py new file mode 100644 index 00000000..90a1d60a --- /dev/null +++ b/pvdeg/geospatialscenario.py @@ -0,0 +1 @@ +... \ No newline at end of file diff --git a/pvdeg/humidity.py b/pvdeg/humidity.py index 2ef26905..8bf099f1 100644 --- a/pvdeg/humidity.py +++ b/pvdeg/humidity.py @@ -10,14 +10,13 @@ from pathlib import Path from concurrent.futures import ProcessPoolExecutor, as_completed -from . import ( +from pvdeg import ( temperature, spectral, - weather + weather, + decorators ) -from pvdeg.decorators import geospatial_quick_shape - def _ambient(weather_df): """ @@ -654,7 +653,7 @@ def backsheet( return backsheet -@geospatial_quick_shape(1, ["RH_surface_outside", "RH_front_encap", "RH_back_encap", "RH_backsheet"]) +@decorators.geospatial_quick_shape('timeseries', ["RH_surface_outside", "RH_front_encap", "RH_back_encap", "RH_backsheet"]) def module( weather_df, meta, diff --git a/pvdeg/letid.py b/pvdeg/letid.py index 9cc1d938..d3b19b07 100644 --- a/pvdeg/letid.py +++ b/pvdeg/letid.py @@ -10,8 +10,13 @@ import pvlib -from pvdeg import collection, utilities, standards, DATA_DIR -from pvdeg.decorators import geospatial_quick_shape +from pvdeg import ( + collection, + utilities, + standards, + decorators, + DATA_DIR, +) def tau_now(tau_0, tau_deg, n_b): @@ -872,8 +877,8 @@ def calc_injection_outdoors(results): return injection -@geospatial_quick_shape( - 1, +@decorators.geospatial_quick_shape( + 'timeseries', [ "Temperature", "Injection", diff --git a/pvdeg/pysam.py b/pvdeg/pysam.py new file mode 100644 index 00000000..5d80833f --- /dev/null +++ b/pvdeg/pysam.py @@ -0,0 +1,555 @@ +""" +Pysam Integration for pvdeg, supports single site and geospatial calculations. +Produced to support Inspire Agrivoltaics: https://openei.org/wiki/InSPIRE +""" + +import dask.dataframe as dd +import dask.array as da +import pandas as pd +import xarray as xr +import numpy as np +import pickle +import json +import sys +import os + + + +from pvdeg import ( + weather, + utilities, + decorators, + DATA_DIR +) + +@decorators.deprecated("unverified") +def vertical_POA( + weather_df, + meta, + jsonfolder='/projects/pvsoiling/pvdeg/analysis/northern_lat/jsons', + samjsonname='vertical', + weather_kwarg=None, +): + """ + Run a SAM + + Parameters + ---------- + weather_df : pd.DataFrame + Weather data for a single location. + meta : pd.DataFrame + Meta data for a single location. + weather_kwarg : dict + other variables needed to access a particular weather dataset. + jsonfolder : string + Location and base name for the json files + + Returns + ------- + annual_gh : float [Wh/m2/y] + Annual GHI + annual_energy : float [kWh] + Annual AC energy + lcoa_nom : float [cents/kWh] + LCOE Levelized cost of energy nominal + """ + + try: + import PySAM + import PySAM.Pvsamv1 as pv1 + import PySAM.Pvwattsv8 as pv8 + import PySAM.Grid as Grid + import PySAM.Utilityrate5 as UtilityRate + import PySAM.Cashloan as Cashloan + except ModuleNotFoundError: + print("pysam not found. run `pip install pvdeg[sam]` to install the NREL-PySAM dependency") + return + + + parameters = ["temp_air", "wind_speed", "dhi", "ghi", "dni"] + + if isinstance(weather_df, dd.DataFrame): + weather_df = weather_df[parameters].compute() + weather_df.set_index("time", inplace=True) + elif isinstance(weather_df, pd.DataFrame): + weather_df = weather_df[parameters] + elif weather_df is None: + weather_df, meta = weather.get(**weather_kwarg) + + + file_names = ["pvsamv1", "grid", "utilityrate5", "cashloan"] + pv4 = PV.new() # also tried PVWattsSingleOwner + grid4 = Grid.from_existing(pv4) + ur4 = UtilityRate.from_existing(pv4) + so4 = Cashloan.from_existing(grid4, 'FlatPlatePVCommercial') + + # LOAD Values + for count, module in enumerate([pv4, grid4, ur4, so4]): + filetitle= samjsonname + '_' + file_names[count] + ".json" + with open(os.path.join(jsonfolder,filetitle), 'r') as file: + data = json.load(file) + for k, v in data.items(): + if k == 'number_inputs': + continue + try: + #if sys.version.split(' ')[0] == '3.11.7': + # Bypassing this as it's not working with my pandas. the printouts + # like " dc_adjust_periods [[0, 0, 0]]" + # means it is going to the except. !!!! + + + # Check needed for python 3.10.7 and perhaps other releases above 3.10.4. + # This prevents the failure "UnicodeDecodeError: 'utf-8' codec can't decode byte... + # This bug will be fixed on a newer version of pysam (currently not working on 5.1.0) + if 'adjust_' in k: # This check is needed for Python 3.10.7 and some others. Not needed for 3.7.4 + k = k.split('adjust_')[1] + module.value(k, v) + except AttributeError: + # there is an error is setting the value for ppa_escalation + print(module, k, v) + + pv4.unassign('solar_resource_file') + + if 'tz' not in meta: + meta['tz'] = '+0' + + if "albedo" not in weather_df.columns: + print("using placeholder albedo of 0.2 for all timesteps") + weather_df.loc[:,'albedo'] = 0.2 + + data = {'dn':list(weather_df.dni), + 'df':list(weather_df.dhi), + 'gh':list(weather_df.ghi), + 'tdry':list(weather_df.temp_air), + 'wspd':list(weather_df.wind_speed), + 'lat':meta['latitude'], + 'lon':meta['longitude'], + 'tz':meta['tz'], + 'elev':meta['altitude'], + 'year':list(weather_df.index.year), + 'month':list(weather_df.index.month), + 'day':list(weather_df.index.day), + 'hour':list(weather_df.index.hour), + 'minute':list(weather_df.index.minute), + 'alb':list(weather_df.albedo)} + + pv4.value('solar_resource_data', data) + + pv4.execute() + grid4.execute() + ur4.execute() + so4.execute() + + # SAVE RESULTS| + results = pv4.Outputs.export() + economicresults = so4.Outputs.export() + + annual_gh = results['annual_gh'] + annual_energy = results['annual_ac_gross'] + lcoe_nom = economicresults['lcoe_nom'] + + res = {"annual_gh": annual_gh, "annual_energy": annual_energy, "lcoe_nom": lcoe_nom} + df_res = pd.DataFrame.from_dict(res, orient="index").T + + return df_res + +# TODO: add grid_default, cashloan_default, utilityrate_defaults for expanded pysam simulation capabilities +def pysam( + weather_df: pd.DataFrame, + meta: dict, + pv_model: str, + pv_model_default: str = None, + config_files: dict[str: str] = None, + results: list[str] = None, +) -> dict: + """ + Run pySam simulation. Only works with pysam weather. + + Parameters + ----------- + weather_df: pd.DataFrame + DataFrame of weather data. As returned by ``pvdeg.weather.get`` + meta: dict + Dictionary of metadata for the weather data. As returned by ``pvdeg.weather.get`` + pv_model: str + choose pySam photovoltaic system model. + Some models are less thorough and run faster. + pvwatts8 is ~50x faster than pysamv1 but only calculates 46 parameters while pysamv1 calculates 195. + + options: ``pvwatts8``, ``pysamv1``, etc. + + pv_model_default: str + pysam config for pv model. [Pysam Modules](https://nrel-pysam.readthedocs.io/en/main/ssc-modules.html) + + On the docs some modules have availabile defaults listed. + + For example: + [Pvwattsv8](https://nrel-pysam.readthedocs.io/en/main/modules/Pvwattsv8.html) + - "FuelCellCommercial" + - "FuelCellSingleOwner" + - "GenericPVWattsWindFuelCellBatteryHybridHostDeveloper" + - "GenericPVWattsWindFuelCellBatteryHybridSingleOwner" + - "PVWattsBatteryCommercial" + - "PVWattsBatteryHostDeveloper" + - "PVWattsBatteryResidential" + - "PVWattsBatteryThirdParty" + - "PVWattsWindBatteryHybridHostDeveloper" + - "PVWattsWindBatteryHybridSingleOwner" + - "PVWattsWindFuelCellBatteryHybridHostDeveloper" + - "PVWattsWindFuelCellBatteryHybridSingleOwner" + - "PVWattsAllEquityPartnershipFlip" + - "PVWattsCommercial" + - "PVWattsCommunitySolar" + - "PVWattsHostDeveloper" + - "PVWattsLCOECalculator" + - "PVWattsLeveragedPartnershipFlip" + - "PVWattsMerchantPlant" + - "PVWattsNone" + - "PVWattsResidential" + - "PVWattsSaleLeaseback" + - "PVWattsSingleOwner" + - "PVWattsThirdParty" + + [Pvsamv1](https://nrel-pysam.readthedocs.io/en/main/modules/Pvsamv1.html) + - "FlatPlatePVAllEquityPartnershipFlip" + - "FlatPlatePVCommercial" + - "FlatPlatePVHostDeveloper" + - "FlatPlatePVLCOECalculator" + - "FlatPlatePVLeveragedPartnershipFlip" + - "FlatPlatePVMerchantPlant" + - "FlatPlatePVNone" + - "FlatPlatePVResidential" + - "FlatPlatePVSaleLeaseback" + - "FlatPlatePVSingleOwner" + - "FlatPlatePVThirdParty" + - "PVBatteryAllEquityPartnershipFlip" + - "PVBatteryCommercial" + - "PVBatteryHostDeveloper" + - "PVBatteryLeveragedPartnershipFlip" + - "PVBatteryMerchantPlant" + - "PVBatteryResidential" + - "PVBatterySaleLeaseback" + - "PVBatterySingleOwner" + - "PVBatteryThirdParty" + - "PhotovoltaicWindBatteryHybridHostDeveloper" + - "PhotovoltaicWindBatteryHybridSingleOwner" + + grid_default: str + + pysam default config for grid model. [Grid Defaults](https://nrel-pysam.readthedocs.io/en/main/modules/Grid.html) + + cashloan_default: str + + pysam default config for cashloan model. [Cashloan Defaults](https://nrel-pysam.readthedocs.io/en/main/modules/Cashloan.html) + - "FlatPlatePVCommercial" + - "FlatPlatePVResidential" + - "PVBatteryCommercial" + - "PVBatteryResidential" + - "PVWattsBatteryCommercial" + - "PVWattsBatteryResidential" + - "PVWattsCommercial" + - "PVWattsResidential" + + utiltityrate_default: str + + pysam default config for utilityrate5 model. [Utilityrate5 Defaults](https://nrel-pysam.readthedocs.io/en/main/modules/Utilityrate5.html()) + + config_files: dict + SAM configuration files. A dictionary containing a mapping to filepaths. + + Keys must be `'pv', 'grid', 'utilityrate', 'cashloan'`. Each key should contain a value as a string representing the file path to a SAM config file. Cannot deal with the entire SAM config json. + + ``` + files = { + 'pv' : 'example/path/1/pv-file.json' + 'grid' : 'example/path/1/grid-file.json' + 'utilityrate' : 'example/path/1/utilityrate-file.json' + 'cashloan' : 'example/path/1/cashloan-file.json' + } + ``` + + results: list[str] + list of strings corresponding to pysam outputs to return. + Pysam models such as `Pvwatts8` and `Pvsamv1` return hundreds of results. + So we can chose to take only the specified results while throwing away the others. + + To grab only 'annual_energy' and 'ac' from the model results. + + >>> results = ['annual_energy', 'ac'] + + This may cause some undesired behavior with geospatial calculations if the lengths of the results within the list are different. + + Returns + ------- + pysam_res: dict + dictionary of outputs. Keys are result name and value is the corresponding result. + If `results` is not specified, the dictionary will contain every calculation from the model. + """ + + try: + import PySAM + import PySAM.Pvsamv1 as pv1 + import PySAM.Pvwattsv8 as pv8 + import PySAM.Grid as Grid + import PySAM.Utilityrate5 as UtilityRate + import PySAM.Cashloan as Cashloan + except ModuleNotFoundError: + print("pysam not found. run `pip install pvdeg[sam]` to install the NREL-PySAM dependency") + return + + sr = solar_resource_dict(weather_df=weather_df, meta=meta) + + # https://nrel-pysam.readthedocs.io/en/main/modules/Pvwattsv8.html + # https://nrel-pysam.readthedocs.io/en/main/modules/Pvsamv1.html + model_map = { + "pvwatts8" : pv8, + "pysamv1" : pv1, + } + + model_module = model_map[pv_model] + if pv_model_default: + pysam_model = model_module.default(pv_model_default) + elif pv_model_default is None: + pysam_model = model_module.new() + + with open( config_files['pv'], 'r') as f: + pv_inputs = json.load( f ) + + # these break the model when being loaded using InSpire doubleday configs + # this is NREL-PySAM version dependent, these are problematic on 5.1.0 + bad_parameters = { + 'adjust_constant', + 'adjust_en_timeindex', + 'adjust_en_periods', + 'adjust_timeindex', + 'adjust_periods', + 'dc_adjust_constant', + 'dc_adjust_en_timeindex', + 'dc_adjust_en_periods', + 'dc_adjust_timeindex', + 'dc_adjust_periods' + } + + for k, v in pv_inputs.items(): + if k not in ({'number_inputs', 'solar_resource_file'} | bad_parameters): + pysam_model.value(k, v) + + + + pysam_model.unassign('solar_resource_file') # unassign file + + + # Duplicate Columns in the dataframe seem to cause this issue + # Error (-4) converting nested tuple 0 into row in matrix. + pysam_model.SolarResource.solar_resource_data = sr + pysam_model.execute() + outputs = pysam_model.Outputs.export() + + if not results: + return outputs + + pysam_res = {key: outputs[key] for key in results} + return pysam_res + +# class inspirePysamReturn(): +# """simple struct to facilitate handling weirdly shaped pysam simulation return values""" + +# # removes __dict__ atribute and breaks pickle +# # __slots__ = ("annual_poa", "ground_irradiance", "timeseries_index") + +# def __init__(self, annual_poa, ground_irradiance, timeseries_index, annual_energy, poa_front, poa_rear, subarray1_poa_front, subarray1_poa_rear): +# self.annual_energy = annual_energy +# self.annual_poa = annual_poa +# self.ground_irradiance = ground_irradiance +# self.timeseries_index = timeseries_index +# self.poa_front = poa_front +# self.poa_rear = poa_rear +# self.subarray1_poa_front = subarray1_poa_front +# self.subarray1_poa_rear = subarray1_poa_rear + +# def _handle_pysam_return(pysam_res : inspirePysamReturn) -> xr.Dataset: +def _handle_pysam_return(pysam_res_dict : dict, weather_df: pd.DataFrame) -> xr.Dataset: + """Handle a pysam return object and transform it to an xarray""" + + ground_irradiance = pysam_res_dict["subarray1_ground_rear_spatial"] + + annual_poa = pysam_res_dict["annual_poa_front"] + annual_energy = pysam_res_dict["annual_energy"] + + poa_front = pysam_res_dict["poa_front"][:8760] # 25 * 8760 entries, all pairs of 8760 entries are identical + poa_rear = pysam_res_dict["poa_rear"][:8760] # same for the following + subarray1_poa_front = pysam_res_dict["subarray1_poa_front"][:8760] + subarray1_poa_rear = pysam_res_dict["subarray1_poa_rear"][:8760] + + timeseries_index = weather_df.index + + # redo this using numba? + distances = ground_irradiance[0][1:] + ground_irradiance_values = da.from_array([row[1:] for row in ground_irradiance[1:]]) + + single_location_ds = xr.Dataset( + data_vars={ + # scalars + "annual_poa" : annual_poa, + "annual_energy" : annual_energy, + + # simple timeseries + # which poa do we want to use, we can elimiate one of the pairs to save a lot of memory + "poa_front" : (("time", ), da.array(poa_front)), + "poa_rear" : (("time", ), da.array(poa_rear)), + "subarray1_poa_front" : (("time", ), da.array(subarray1_poa_front)), + "subarray1_poa_rear" : (("time", ), da.array(subarray1_poa_rear)), + + # spatio-temporal + "ground_irradiance" : (("time", "distance"), ground_irradiance_values), + }, + coords={ + "time" : timeseries_index, + # "distance" : distances, + # would be convient to define distances after being calculated + # by pysam but we need to know ahead of time to create the template + "distance" : np.arange(10), # convient way to match the distances in the template + } + ) + + return single_location_ds + + +INSPIRE_NSRDB_ATTRIBUTES = [ + "air_temperature", + "wind_speed", + "wind_direction", + "dhi", + "ghi", + "dni", + "relative_humidity", + "surface_albedo", +] + +def inspire_ground_irradiance(weather_df, meta, config_files): + """ + Get ground irradiance array and annual poa irradiance for a given point using pvsamv1 + + Parameters + ---------- + weather_df : pd.DataFrame + weather dataframe + meta : dict + meta data + config_files : dict[str] + see pvdeg.pysam.pysam + # config_files={'pv' : }, + + Returns + -------- + result : inspirePysamReturn + returns an custom class object so we can unpack it later. + """ + + if (not isinstance(weather_df, pd.DataFrame) or not isinstance(meta, dict)): + raise ValueError(f""" + weather_df must be pandas DataFrame, meta must be dict. + weather_df type : {type(weather_df)} + meta type : {type(meta)} + """) + + # force localize utc from tmy to local time by moving rows + weather_df = weather.roll_tmy(weather_df, meta) + + outputs = pysam( + weather_df = weather_df, + meta = meta, + pv_model = "pysamv1", + config_files=config_files, + ) + + ds_result = _handle_pysam_return(pysam_res_dict=outputs, weather_df=weather_df) + + return ds_result + +def solar_resource_dict(weather_df, meta): + """ + Create a solar resource dict mapping from weather and metadata. + + Works on PVGIS and appears to work on NSRDB (NOT PSM3). + """ + + # weather_df = weather_df.reset_index(drop=True) # Probably dont need to do this + weather_df = utilities.add_time_columns_tmy(weather_df) # only supports hourly data + + # enforce tmy scheme + times = pd.date_range(start="2001-01-01", periods=8760, freq="1h") + + # all options + # lat,lon,tz,elev,year,month,hour,minute,gh,dn,df,poa,tdry,twet,tdew,rhum,pres,snow,alb,aod,wspd,wdir + sr = { + 'lat': meta['latitude'], + 'lon': meta['longitude'], + 'tz': meta['tz'] if 'tz' in meta.keys() else 0, + 'elev': meta['altitude'], + 'year': list(times.year), #list(weather_df['Year']), + 'month': list(times.month), + 'day': list(times.day), + 'hour': list(times.hour), + 'minute': list(times.minute), + 'gh': list(weather_df['ghi']), + 'dn': list(weather_df['dni']), + 'df': list(weather_df['dhi']), + 'wspd': list(weather_df['wind_speed']), + 'tdry': list(weather_df['temp_air']), + 'alb' : list(weather_df['albedo']) if 'albedo' in weather_df.columns.values else [0.2] * len(weather_df) + } + + # if we have wind direction then add it + if 'wind_direction' in weather_df.columns.values: + sr['wdir'] = list(weather_df['wind_direction']) + + return sr + +def sample_inspire_result(weather_df, meta): # throw weather, meta away + """ + returns a sample inspire_ground_irradiance xarray. Dataset for geospatial testing. + Weather_df and meta exist to provide a homogenous arugment structure for geospatial calculations but are not used. + + Parameters + ---------- + weather: pd.Dataframe + weather dataframe, is thrown away + meta: dict + metadata dictionary, is thrown away + + Returns + ------- + inspire_ground_irradiance: xr.Dataset + returns an xarray dataset of the same shape generated by inpspire_ground_irradiance() + """ + + return xr.Dataset( + data_vars={ + 'poa_rear': (('time',), np.zeros((8760,))), + 'ground_irradiance': (('time', 'distance'), np.zeros((8760, 10))), + 'annual_energy': ((), 0), + 'annual_poa': ((), 0), + 'subarray1_poa_front': (('time',), np.zeros((8760,))), + 'subarray1_poa_rear': (('time',), np.zeros((8760,))), + 'poa_front': (('time',), np.zeros((8760,))), + }, + coords={ + 'time':pd.date_range(start="2001-01-01 00:30:00", periods=8760, freq='h'), + 'distance':np.arange(10), + } + ) + + +def ground_irradiance_monthly(inspire_res_ds : xr.Dataset) -> xr.Dataset: + """ + Many rows are not populated because the model only calculates ground irradiance when certain measurements are met. + Drop the rows and calculate the monthly average irradiance at each distance. + """ + + nonzero_mask = (inspire_res_ds["ground_irradiance"] != 0).any(dim="distance") + filtered_data = inspire_res_ds["ground_irradiance"].where(nonzero_mask, drop=True) + + monthly_avg_ground_irradiance = filtered_data.groupby(filtered_data.time.dt.month).mean() + return monthly_avg_ground_irradiance + diff --git a/pvdeg/scenario.py b/pvdeg/scenario.py index 1e4d1e7b..5629a115 100644 --- a/pvdeg/scenario.py +++ b/pvdeg/scenario.py @@ -1,8 +1,13 @@ -"""Class to define an analysis scenario.""" +""" +Scenario objects and methods for accelerated analysis +""" import pvdeg from pvdeg import utilities +import matplotlib +import matplotlib.figure +import matplotlib.pyplot as plt from datetime import date from datetime import datetime as dt import os @@ -13,7 +18,6 @@ import pandas as pd import xarray as xr import numpy as np -import matplotlib.pyplot as plt from collections import OrderedDict from copy import deepcopy from typing import List, Union, Optional, Tuple, Callable @@ -22,10 +26,7 @@ from IPython.display import display, HTML import cartopy.crs as ccrs import cartopy.feature as cfeature - -### premade scenario with locations of interest. Ask Mike? -# TODO: geospatial reset weather and addLocation from gids. - +from dask.distributed import Client class Scenario: """ @@ -115,7 +116,7 @@ def compare_ordereddict_values(od1, od2): and self.path == other.path and np.array_equal(self.gids, other.gids) and self.modules == other.modules - and compare_ordereddict_values(self.pipeline, other.pipeline) + and compare_ordereddict_values(self.pipeline, other.pipeline) # keys are random and self.file == other.file and self.results == other.results and ( @@ -237,6 +238,7 @@ def addModule( module_name: str = None, racking: str = "open_rack_glass_polymer", material: str = "OX003", + material_file: str = "O2permeation", temperature_model: str = "sapm", model_kwarg: dict = {}, irradiance_kwarg: dict = {}, @@ -246,23 +248,27 @@ def addModule( Add a module to the Scenario. Multiple modules can be added. Each module will be tested in the given scenario. - Parameters: + Parameters ----------- - module_name : (str) + module_name : str unique name for the module. adding multiple modules of the same name will replace the existing entry. - racking : (str) + racking : str temperature model racking type as per PVLIB (see pvlib.temperature). Allowed entries: 'open_rack_glass_glass', 'open_rack_glass_polymer', 'close_mount_glass_glass', 'insulated_back_glass_polymer' - material : (str) + material : str Key of the material desired. For a complete list, see pvdeg/data/O2permeation.json or pvdeg/data/H2Opermedation.json or pvdeg/data/AApermeation.json. To add a custom material, see pvdeg.addMaterial (ex: EVA, Tedlar) - temp_model : (str) + material_file : str + Material file used to access parameters from. + Use material json file in `pvdeg/data`. Options: + >>> "AApermeation", "H2Opermeation", "O2permeation" + temp_model : str select pvlib temperature models. See ``pvdeg.temperature.temperature`` for more. Options : ``'sapm', 'pvsyst', 'faiman', 'faiman_rad', 'fuentes', 'ross'`` - model_kwarg : (dict), optional + model_kwarg : dict, (optional) provide a dictionary of temperature model coefficents to be used instead of pvlib defaults. Some models will require additional arguments such as ``ross`` which requires nominal operating cell @@ -270,14 +276,14 @@ def addModule( should be provided. Pvlib temp models: https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/temperature.html - irradiance_kwarg : (dict), optional + irradiance_kwarg : dict, (optional) provide keyword arguments for poa irradiance calculations. Options : ``sol_position``, ``tilt``, ``azimuth``, ``sky_model`` see_added : (bool), optional """ try: - mat_params = utilities._read_material(name=material) + mat_params = utilities.read_material(pvdeg_file=material_file, key=material) except KeyError: print("Material Not Found - No module added to scenario.") print("If you need to add a custom material, use .add_material()") @@ -303,9 +309,9 @@ def addModule( if see_added: print(f'Module "{module_name}" added.') - # test this? + # add testing def add_material( - self, name, alias, Ead, Eas, So, Do=None, Eap=None, Po=None, fickian=True + self, name, alias, Ead, Eas, So, Do=None, Eap=None, Po=None, fickian=True, fname="O2permeation.json", ): """ add a new material type to main list @@ -320,6 +326,7 @@ def add_material( Eap=Eap, Po=Po, fickian=fickian, + fname=fname, ) print("Material has been added.") print("To add the material as a module in your current scene, run .addModule()") @@ -792,7 +799,7 @@ def plot( start_time: Optional[dt] = None, end_time: Optional[dt] = None, title: str = "", - ) -> None: + ) -> tuple: """ Plot scenario results along an axis using `Scenario.extract` @@ -844,7 +851,8 @@ def plot( Returns: ------- - None + fig, ax: tuple + matplotlib figure and axis objects See Also: --------- @@ -866,6 +874,8 @@ def plot( ax.set_title(f"{self.name} : {title}") plt.show() + return fig, ax + def _ipython_display_(self): file_url = "no file provided" if self.path: @@ -873,32 +883,33 @@ def _ipython_display_(self): html_content = f"""
-

{self.name}: Scenario Analysis

-

Path: {self.path}

-

GIDs: {self.gids}

-

Email: {self.email}

-

API Key: {self.api_key}

+

self.name: {self.name}

+

self.path: {self.path}

+

self.gids: {self.gids}

+

self.email: {self.email}

+

self.api_key: {self.api_key}

-

Results

+

self.results

{self.format_results() if self.results else None}
-

Pipeline

+

self.pipeline

{self.format_pipeline()}
-

Modules

+

self.modules

{self.format_modules()}
-

Weather Data

+

self.weather_data

{self.format_weather()}
-

Meta Data

+

self.meta_data

{self.meta_data}
+

All attributes can be accessed by the names shown above.

\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geospatial_standoff_scenario" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the job in the pipeline\n", + "\n", + "Currently ``scenario`` only supports one geospatial analysis at a time. We cannot have two geospatial jobs at the same time." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dashboard: http://127.0.0.1:8787/status\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" ] } @@ -769,164 +1945,29 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.Dataset> Size: 6kB\n",
-       "Dimensions:    (latitude: 14, longitude: 17)\n",
+       "
<xarray.Dataset> Size: 164B\n",
+       "Dimensions:    (latitude: 2, longitude: 3)\n",
        "Coordinates:\n",
-       "  * latitude   (latitude) float32 56B 39.37 39.41 39.45 ... 39.81 39.85 39.89\n",
-       "  * longitude  (longitude) float32 68B -106.4 -106.4 -106.3 ... -105.8 -105.8\n",
+       "  * latitude   (latitude) float32 8B 39.61 39.85\n",
+       "  * longitude  (longitude) float32 12B -106.4 -106.2 -105.9\n",
        "Data variables:\n",
-       "    T98_inf    (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
-       "    x          (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
-       "    T98_0      (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n",
-       "Attributes:\n",
-       "    long_name:  Standoff dataset
"
+       "    T98_0      (latitude, longitude) float64 48B nan nan 72.15 75.22 75.25 nan\n",
+       "    x          (latitude, longitude) float64 48B nan nan 0.5751 1.432 1.421 nan\n",
+       "    T98_inf    (latitude, longitude) float64 48B nan nan 46.77 48.81 48.51 nan
" ], "text/plain": [ - " Size: 6kB\n", - "Dimensions: (latitude: 14, longitude: 17)\n", + " Size: 164B\n", + "Dimensions: (latitude: 2, longitude: 3)\n", "Coordinates:\n", - " * latitude (latitude) float32 56B 39.37 39.41 39.45 ... 39.81 39.85 39.89\n", - " * longitude (longitude) float32 68B -106.4 -106.4 -106.3 ... -105.8 -105.8\n", + " * latitude (latitude) float32 8B 39.61 39.85\n", + " * longitude (longitude) float32 12B -106.4 -106.2 -105.9\n", "Data variables:\n", - " T98_inf (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n", - " x (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n", - " T98_0 (latitude, longitude) float64 2kB nan nan nan nan ... nan nan nan\n", - "Attributes:\n", - " long_name: Standoff dataset" + " T98_0 (latitude, longitude) float64 48B nan nan 72.15 75.22 75.25 nan\n", + " x (latitude, longitude) float64 48B nan nan 0.5751 1.432 1.421 nan\n", + " T98_inf (latitude, longitude) float64 48B nan nan 46.77 48.81 48.51 nan" ] }, "execution_count": 7, @@ -949,7 +1990,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -960,11 +2001,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Limited Geospatial Functionality\n", - "Only the following functions are currently supported for pvdeg geospatial analysis\n", + "## Example Geospatial Functionality\n", + "Many functions are supported for geospatial analysis, here are a few.\n", "- ``pvdeg.standards.standoff`` \n", "- ``pvdeg.humidity.module`` \n", - "- ``pvdeg.letid.calc_letid_outdoors``" + "- ``pvdeg.letid.calc_letid_outdoors``\n", + "\n", + "See the Geospatial Templates tutorial for an example on this." ] }, { @@ -976,9 +2019,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/tford/.conda-envs/pvdeg_env/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"phony_dim_1\" starting at index 500. This could degrade performance. Instead, consider rechunking after loading.\n", + "/home/tford/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/dataset.py:274: UserWarning: The specified chunks separate the stored chunks along dimension \"phony_dim_1\" starting at index 500. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", - "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1155: UserWarning: Gids Added - [474405 474406 474407 474408 474409 474410 474411 474412 474413 474414\n", + "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1248: UserWarning: Gids Added - [474405 474406 474407 474408 474409 474410 474411 474412 474413 474414\n", " 474415 474416 474417 474418 474419 474420 474421 474422 474423 475420\n", " 475421 475422 475423 475424 475425 475426 475427 475428 475429 475430\n", " 475431 475432 475433 475434 475435 475436 475437 475438 476435 476436\n", @@ -993,14 +2036,22 @@ " 481539 481540 481542 481543 481544 482557 482558 482559 482560 482561\n", " 482562 482564 482565]\n", " warnings.warn(message, UserWarning)\n", - "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1183: UserWarning: module added to pipeline as \n", - " {'geospatial_job': }\n", + "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:1635: UserWarning: module added to scenario with arguments {} using template: Size: 75MB\n", + "Dimensions: (gid: 133, time: 17520)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n", + " * gid (gid) int64 1kB 474405 474406 474407 ... 482564 482565\n", + "Data variables:\n", + " RH_surface_outside (gid, time) float64 19MB dask.array\n", + " RH_front_encap (gid, time) float64 19MB dask.array\n", + " RH_back_encap (gid, time) float64 19MB dask.array\n", + " RH_backsheet (gid, time) float64 19MB dask.array\n", " warnings.warn(message, UserWarning)\n" ] } ], "source": [ - "geospatial_humidity_scenario = pvdeg.scenario.Geospatial_Scenario(\n", + "geospatial_humidity_scenario = pvdeg.GeospatialScenario(\n", " name = 'humidity scenario',\n", " geospatial = True\n", ")\n", @@ -1022,420 +2073,144 @@ "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/tford/.conda-envs/pvdeg_env/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 37307 instead\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Dashboard: http://127.0.0.1:37307/status\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "Dashboard: http://127.0.0.1:8787/status\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", + "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n" + "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n" ] } ], @@ -1821,176 +2596,176 @@ " * longitude (longitude) float32 36B -105.4 -105.3 ... -105.1 -105.1\n", " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n", "Data variables:\n", + " RH_backsheet (time, latitude, longitude) float64 24MB 76.96 ... 51.74\n", " RH_back_encap (time, latitude, longitude) float64 24MB 76.96 ... 44.67\n", " RH_front_encap (time, latitude, longitude) float64 24MB 42.21 ... 31.69\n", - " RH_backsheet (time, latitude, longitude) float64 24MB 76.96 ... 51.74\n", - " RH_surface_outside (time, latitude, longitude) float64 24MB 76.96 ... 58.81
    • " ], "text/plain": [ " Size: 96MB\n", @@ -2021,9 +2796,9 @@ " * longitude (longitude) float32 36B -105.4 -105.3 ... -105.1 -105.1\n", " * time (time) datetime64[ns] 140kB 2022-01-01 ... 2022-12-31...\n", "Data variables:\n", + " RH_backsheet (time, latitude, longitude) float64 24MB 76.96 ... 51.74\n", " RH_back_encap (time, latitude, longitude) float64 24MB 76.96 ... 44.67\n", " RH_front_encap (time, latitude, longitude) float64 24MB 42.21 ... 31.69\n", - " RH_backsheet (time, latitude, longitude) float64 24MB 76.96 ... 51.74\n", " RH_surface_outside (time, latitude, longitude) float64 24MB 76.96 ... 58.81" ] }, @@ -2035,13 +2810,20 @@ "source": [ "geospatial_humidity_scenario.results" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "pvdeg_env", + "display_name": "rpp", "language": "python", - "name": "python3" + "name": "rpp" }, "language_info": { "codemirror_mode": { @@ -2057,5 +2839,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/tutorials_and_tools/tutorials_and_tools/Scenario - Non-uniform Mountain Prefferential Downselect.ipynb b/tutorials_and_tools/tutorials_and_tools/Scenario - Non-uniform Mountain Prefferential Downselect.ipynb index 1dcf4a84..77430160 100644 --- a/tutorials_and_tools/tutorials_and_tools/Scenario - Non-uniform Mountain Prefferential Downselect.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Scenario - Non-uniform Mountain Prefferential Downselect.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -33,7 +33,7 @@ } ], "source": [ - "dynamic_points = pvdeg.scenario.GeospatialScenario(\n", + "dynamic_points = pvdeg.GeospatialScenario(\n", " name='dynamic-selection'\n", ")\n", "\n", @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -66,6 +66,17 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
    ,\n", + " )" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -76,18 +87,9 @@ ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Downselecting\n", - "\n", - "Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately." - ] - }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -123,21 +125,21 @@ " \n", "
    \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -145,36 +147,36 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -189,96 +191,96 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", "
    40739338.529999-108.260002220328290141.889999-114.0199971704-7United StatesColoradoMontroseUtahBox Elder2
    29574641.889999-113.379997253728290341.810001-114.0199972093-7United StatesUtah2
    46255237.410000-105.860001229728290541.730000-114.0199971880-7United StatesColoradoAlamosaUtahBox Elder2
    41631337.889999-107.860001306128290741.650002-114.0199971903-7United StatesColoradoSan MiguelUtahBox Elder2
    37741237.250000-109.620003135228290941.570000-114.0199971936-7United StatesUtahSan JuanBox Elder2
    ...
    36854538.209999-110.019997152755978737.330002-102.0999981139-7United StatesUtahWayneColoradoBaca2
    31060541.810001-112.660004149655978937.250000-102.0999981141-7United StatesUtahBox ElderColoradoBaca2
    34365140.529999-111.139999239455979137.169998-102.0999981149-7United StatesUtahWasatchColoradoBaca2
    45684238.849998-106.099998268255979337.090000-102.0999981096-7United StatesColoradoChaffeeBaca2
    38984139.889999-109.059998192155979537.009998-102.0999981122-7United StatesUtahUintahColoradoBaca2
    \n", - "

    795 rows × 8 columns

    \n", + "

    7956 rows × 8 columns

    \n", "" ], "text/plain": [ " latitude longitude altitude tz country state \\\n", - "407393 38.529999 -108.260002 2203 -7 United States Colorado \n", - "295746 41.889999 -113.379997 2537 -7 United States Utah \n", - "462552 37.410000 -105.860001 2297 -7 United States Colorado \n", - "416313 37.889999 -107.860001 3061 -7 United States Colorado \n", - "377412 37.250000 -109.620003 1352 -7 United States Utah \n", + "282901 41.889999 -114.019997 1704 -7 United States Utah \n", + "282903 41.810001 -114.019997 2093 -7 United States Utah \n", + "282905 41.730000 -114.019997 1880 -7 United States Utah \n", + "282907 41.650002 -114.019997 1903 -7 United States Utah \n", + "282909 41.570000 -114.019997 1936 -7 United States Utah \n", "... ... ... ... .. ... ... \n", - "368545 38.209999 -110.019997 1527 -7 United States Utah \n", - "310605 41.810001 -112.660004 1496 -7 United States Utah \n", - "343651 40.529999 -111.139999 2394 -7 United States Utah \n", - "456842 38.849998 -106.099998 2682 -7 United States Colorado \n", - "389841 39.889999 -109.059998 1921 -7 United States Utah \n", - "\n", - " county wind_height \n", - "407393 Montrose 2 \n", - "295746 Box Elder 2 \n", - "462552 Alamosa 2 \n", - "416313 San Miguel 2 \n", - "377412 San Juan 2 \n", - "... ... ... \n", - "368545 Wayne 2 \n", - "310605 Box Elder 2 \n", - "343651 Wasatch 2 \n", - "456842 Chaffee 2 \n", - "389841 Uintah 2 \n", - "\n", - "[795 rows x 8 columns]" + "559787 37.330002 -102.099998 1139 -7 United States Colorado \n", + "559789 37.250000 -102.099998 1141 -7 United States Colorado \n", + "559791 37.169998 -102.099998 1149 -7 United States Colorado \n", + "559793 37.090000 -102.099998 1096 -7 United States Colorado \n", + "559795 37.009998 -102.099998 1122 -7 United States Colorado \n", + "\n", + " county wind_height \n", + "282901 Box Elder 2 \n", + "282903 Box Elder 2 \n", + "282905 Box Elder 2 \n", + "282907 Box Elder 2 \n", + "282909 Box Elder 2 \n", + "... ... ... \n", + "559787 Baca 2 \n", + "559789 Baca 2 \n", + "559791 Baca 2 \n", + "559793 Baca 2 \n", + "559795 Baca 2 \n", + "\n", + "[7956 rows x 8 columns]" ] }, - "execution_count": 32, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -287,34 +289,72 @@ "dynamic_points.meta_data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Downselecting\n", + "\n", + "Using weighted random choices based on elevation we will create a sparse grid from the full metadata for fast calculations. This requires sklearn to be installed but this is not in the `pvdeg` dependency list to you will have to install it seperately.\n", + "\n", + "### Normalization\n", + "\n", + "At each metadata point in our dataset we will calculate a weight based on its changes in elevation compared to its neighbors. The higher the weight, the greater the change in elevation from a point's immediate neighbors. The downselection methods and functions use these weights to randomly select a subset of the datapoints, prefferentially selecting those with higher weights. \n", + "\n", + "We have some control over which points get selected because all points' weights must be normalized (mapped from 0 to 1) before downselecting. We can apply a function such as $e^x$ or $\\log x$ to the weights during normalization. This could help change the distribution of weights that are chosen. This could remove points from the mountains and add them to areas with fewer changes in elevation, or vice versa.\n", + "\n", + "*Note: `pvdeg`'s downselection functions use `numpy.random`, the random seed is not fixed so the result will change between runs.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Providing a KdTree\n", + "\n", + "As shown below the lines to create a kdtree are commented out. \n" + ] + }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "west_tree = pvdeg.geospatial.meta_KDtree(meta_df=dynamic_points.meta_data)\n", + "# west_tree = pvdeg.geospatial.meta_KDtree(meta_df=dynamic_points.meta_data)\n", "\n", "dynamic_points.downselect_elevation_stochastic(\n", - " kdtree=west_tree,\n", + " #kdtree=west_tree,\n", " downselect_prop=0.1,\n", + " normalization=\"linear\",\n", ")" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
    ,\n", + " )" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -332,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -354,1478 +394,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 41.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 41.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.1 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.3 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.2 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.6 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.0 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.4 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 38.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 40.9 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 37.5 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - }, - { - "ename": "ValueError", - "evalue": "conflicting sizes for dimension 'gid': length 795 on 'gid' and length 723 on {'gid': 'x'}", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[28], line 9\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# geospatial analysis now\u001b[39;00m\n\u001b[1;32m 3\u001b[0m geo \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m: pvdeg\u001b[38;5;241m.\u001b[39mstandards\u001b[38;5;241m.\u001b[39mstandoff,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweather_ds\u001b[39m\u001b[38;5;124m\"\u001b[39m: sub_weather,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmeta_df\u001b[39m\u001b[38;5;124m\"\u001b[39m: dynamic_points\u001b[38;5;241m.\u001b[39mmeta_data,\n\u001b[1;32m 7\u001b[0m }\n\u001b[0;32m----> 9\u001b[0m analysis_result \u001b[38;5;241m=\u001b[39m \u001b[43mpvdeg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeospatial\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalysis\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mgeo\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/dev/PVDegradationTools/pvdeg/geospatial.py:206\u001b[0m, in \u001b[0;36manalysis\u001b[0;34m(weather_ds, meta_df, func, template, **func_kwargs)\u001b[0m\n\u001b[1;32m 202\u001b[0m mindex_obj \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mMultiIndex\u001b[38;5;241m.\u001b[39mfrom_arrays(\n\u001b[1;32m 203\u001b[0m [meta_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlatitude\u001b[39m\u001b[38;5;124m\"\u001b[39m], meta_df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlongitude\u001b[39m\u001b[38;5;124m\"\u001b[39m]], names\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlatitude\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlongitude\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 204\u001b[0m )\n\u001b[1;32m 205\u001b[0m mindex_coords \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mCoordinates\u001b[38;5;241m.\u001b[39mfrom_pandas_multiindex(mindex_obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 206\u001b[0m stacked \u001b[38;5;241m=\u001b[39m \u001b[43mstacked\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massign_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmindex_coords\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 208\u001b[0m stacked \u001b[38;5;241m=\u001b[39m stacked\u001b[38;5;241m.\u001b[39mdrop_duplicates(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgid\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 209\u001b[0m res \u001b[38;5;241m=\u001b[39m stacked\u001b[38;5;241m.\u001b[39munstack(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgid\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;66;03m# , sparse=True\u001b[39;00m\n", - "File \u001b[0;32m~/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/common.py:644\u001b[0m, in \u001b[0;36mDataWithCoords.assign_coords\u001b[0;34m(self, coords, **coords_kwargs)\u001b[0m\n\u001b[1;32m 641\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 642\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_calc_assign_results(coords_combined)\n\u001b[0;32m--> 644\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 645\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n", - "File \u001b[0;32m~/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/coordinates.py:566\u001b[0m, in \u001b[0;36mCoordinates.update\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[38;5;66;03m# special case for PandasMultiIndex: updating only its dimension coordinate\u001b[39;00m\n\u001b[1;32m 561\u001b[0m \u001b[38;5;66;03m# is still allowed but depreciated.\u001b[39;00m\n\u001b[1;32m 562\u001b[0m \u001b[38;5;66;03m# It is the only case where we need to actually drop coordinates here (multi-index levels)\u001b[39;00m\n\u001b[1;32m 563\u001b[0m \u001b[38;5;66;03m# TODO: remove when removing PandasMultiIndex's dimension coordinate.\u001b[39;00m\n\u001b[1;32m 564\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_drop_coords(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_names \u001b[38;5;241m-\u001b[39m coords_to_align\u001b[38;5;241m.\u001b[39m_names)\n\u001b[0;32m--> 566\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexes\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/coordinates.py:751\u001b[0m, in \u001b[0;36mDatasetCoordinates._update_coords\u001b[0;34m(self, coords, indexes)\u001b[0m\n\u001b[1;32m 748\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(coords)\n\u001b[1;32m 750\u001b[0m \u001b[38;5;66;03m# check for inconsistent state *before* modifying anything in-place\u001b[39;00m\n\u001b[0;32m--> 751\u001b[0m dims \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_dimensions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 752\u001b[0m new_coord_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m(coords)\n\u001b[1;32m 753\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dim, size \u001b[38;5;129;01min\u001b[39;00m dims\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[0;32m~/.conda-envs/rpp/lib/python3.10/site-packages/xarray/core/variable.py:3008\u001b[0m, in \u001b[0;36mcalculate_dimensions\u001b[0;34m(variables)\u001b[0m\n\u001b[1;32m 3006\u001b[0m last_used[dim] \u001b[38;5;241m=\u001b[39m k\n\u001b[1;32m 3007\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m dims[dim] \u001b[38;5;241m!=\u001b[39m size:\n\u001b[0;32m-> 3008\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 3009\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconflicting sizes for dimension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3010\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlength \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msize\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m and length \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims[dim]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlast_used\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 3011\u001b[0m )\n\u001b[1;32m 3012\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dims\n", - "\u001b[0;31mValueError\u001b[0m: conflicting sizes for dimension 'gid': length 795 on 'gid' and length 723 on {'gid': 'x'}" - ] - } - ], + "outputs": [], "source": [ "# geospatial analysis now\n", "\n", @@ -1849,483 +420,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    <xarray.Dataset> Size: 61kB\n",
-       "Dimensions:    (latitude: 42, longitude: 60)\n",
-       "Coordinates:\n",
-       "  * latitude   (latitude) float32 168B 37.01 37.09 37.17 ... 41.49 41.65 41.89\n",
-       "  * longitude  (longitude) float32 240B -113.9 -113.6 -113.4 ... -102.7 -102.6\n",
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    " - ], - "text/plain": [ - " Size: 61kB\n", - "Dimensions: (latitude: 42, longitude: 60)\n", - "Coordinates:\n", - " * latitude (latitude) float32 168B 37.01 37.09 37.17 ... 41.49 41.65 41.89\n", - " * longitude (longitude) float32 240B -113.9 -113.6 -113.4 ... -102.7 -102.6\n", - "Data variables:\n", - " x (latitude, longitude) float64 20kB nan nan nan ... nan nan nan\n", - " T98_0 (latitude, longitude) float64 20kB nan nan nan ... nan nan nan\n", - " T98_inf (latitude, longitude) float64 20kB nan nan nan ... nan nan nan" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "analysis_result" ] @@ -2341,31 +438,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
    ,\n", - " )" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pvdeg.geospatial.plot_USA(analysis_result['x'])" ] @@ -2381,20 +456,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pvdeg.geospatial.plot_sparse_analysis(analysis_result, data_var='x', method='linear')" ] diff --git a/tutorials_and_tools/tutorials_and_tools/Scenario - Single Location.ipynb b/tutorials_and_tools/tutorials_and_tools/Scenario - Single Location.ipynb index 65cf9f31..64de7660 100644 --- a/tutorials_and_tools/tutorials_and_tools/Scenario - Single Location.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Scenario - Single Location.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -44,8 +44,8 @@ "output_type": "stream", "text": [ "Working on a Linux 4.18.0-477.10.1.el8_8.x86_64\n", - "Python version 3.10.9 | packaged by conda-forge | (main, Feb 2 2023, 20:20:04) [GCC 11.3.0]\n", - "pvdeg version 0.3.1.dev220+gaf1083c.d20240608\n" + "Python version 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0]\n", + "pvdeg version 0.3.1.dev223+g03f3e6a.d20240708\n" ] } ], @@ -73,11 +73,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "simple_scenario = pvdeg.scenario.Scenario(\n", + "simple_scenario = pvdeg.Scenario(\n", " name=\"Point Minimum Standoff\",\n", " email='user@mail.com',\n", " api_key='DEMO_KEY'\n", @@ -96,21 +96,21 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Column \"relative_humidity\" not found in DataFrame. Calculating...\n" + " \r" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:212: UserWarning: Gids Added - [1060699]\n", + "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:230: UserWarning: Gids Added - [1060699]\n", " warnings.warn(message, UserWarning)\n" ] } @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -412,7 +412,7 @@ "[8760 rows x 15 columns]" ] }, - "execution_count": 20, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -443,15 +443,15 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:375: UserWarning: standoff added to pipeline as \n", - " {'job': , 'params': {}}\n", + "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:396: UserWarning: standoff added to pipeline as \n", + " {'job': , 'params': {}}\n", " warnings.warn(message, UserWarning)\n" ] } @@ -474,15 +474,15 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:375: UserWarning: solder_fatigue added to pipeline as \n", - " {'job': , 'params': {'wind_factor': 0.33}}\n", + "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:396: UserWarning: solder_fatigue added to pipeline as \n", + " {'job': , 'params': {'wind_factor': 0.33}}\n", " warnings.warn(message, UserWarning)\n" ] } @@ -511,15 +511,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:375: UserWarning: standoff added to pipeline as \n", - " {'job': , 'params': {'weather_df': Year Month Day Hour Minute dni dhi ghi \\\n", + "/home/tford/dev/PVDegradationTools/pvdeg/scenario.py:396: UserWarning: standoff added to pipeline as \n", + " {'job': , 'params': {'weather_df': Year Month Day Hour Minute dni dhi ghi \\\n", "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", @@ -577,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -586,34 +586,8 @@ "text": [ "Name : Point Minimum Standoff\n", "Pipeline : \n", - " BLHZH.job WCBOQ.job WCBOQ.params.wind_factor PUKRE.job PUKRE.params.weather_df PUKRE.params.meta.Source PUKRE.params.meta.Location ID PUKRE.params.meta.City PUKRE.params.meta.State PUKRE.params.meta.Country PUKRE.params.meta.Clearsky DHI Units PUKRE.params.meta.Clearsky DNI Units PUKRE.params.meta.Clearsky GHI Units PUKRE.params.meta.Dew Point Units PUKRE.params.meta.DHI Units PUKRE.params.meta.DNI Units PUKRE.params.meta.GHI Units PUKRE.params.meta.Solar Zenith Angle Units PUKRE.params.meta.Temperature Units PUKRE.params.meta.Pressure Units PUKRE.params.meta.Relative Humidity Units PUKRE.params.meta.Precipitable Water Units PUKRE.params.meta.Wind Direction Units PUKRE.params.meta.Wind Speed Units PUKRE.params.meta.Cloud Type -15 PUKRE.params.meta.Cloud Type 0 PUKRE.params.meta.Cloud Type 1 PUKRE.params.meta.Cloud Type 2 PUKRE.params.meta.Cloud Type 3 PUKRE.params.meta.Cloud Type 4 PUKRE.params.meta.Cloud Type 5 PUKRE.params.meta.Cloud Type 6 PUKRE.params.meta.Cloud Type 7 PUKRE.params.meta.Cloud Type 8 PUKRE.params.meta.Cloud Type 9 PUKRE.params.meta.Cloud Type 10 PUKRE.params.meta.Cloud Type 11 PUKRE.params.meta.Cloud Type 12 PUKRE.params.meta.Fill Flag 0 PUKRE.params.meta.Fill Flag 1 PUKRE.params.meta.Fill Flag 2 PUKRE.params.meta.Fill Flag 3 PUKRE.params.meta.Fill Flag 4 PUKRE.params.meta.Fill Flag 5 PUKRE.params.meta.Surface Albedo Units PUKRE.params.meta.Version PUKRE.params.meta.latitude PUKRE.params.meta.longitude PUKRE.params.meta.altitude PUKRE.params.meta.tz MUDMB.job FOYEQ.job FOYEQ.params.wind_factor UJIKB.job UJIKB.params.weather_df UJIKB.params.meta.Source UJIKB.params.meta.Location ID UJIKB.params.meta.City UJIKB.params.meta.State UJIKB.params.meta.Country UJIKB.params.meta.Clearsky DHI Units UJIKB.params.meta.Clearsky DNI Units UJIKB.params.meta.Clearsky GHI Units UJIKB.params.meta.Dew Point Units UJIKB.params.meta.DHI Units UJIKB.params.meta.DNI Units UJIKB.params.meta.GHI Units UJIKB.params.meta.Solar Zenith Angle Units UJIKB.params.meta.Temperature Units UJIKB.params.meta.Pressure Units UJIKB.params.meta.Relative Humidity Units UJIKB.params.meta.Precipitable Water Units UJIKB.params.meta.Wind Direction Units UJIKB.params.meta.Wind Speed Units UJIKB.params.meta.Cloud Type -15 UJIKB.params.meta.Cloud Type 0 UJIKB.params.meta.Cloud Type 1 UJIKB.params.meta.Cloud Type 2 UJIKB.params.meta.Cloud Type 3 UJIKB.params.meta.Cloud Type 4 UJIKB.params.meta.Cloud Type 5 UJIKB.params.meta.Cloud Type 6 UJIKB.params.meta.Cloud Type 7 UJIKB.params.meta.Cloud Type 8 UJIKB.params.meta.Cloud Type 9 UJIKB.params.meta.Cloud Type 10 UJIKB.params.meta.Cloud Type 11 UJIKB.params.meta.Cloud Type 12 UJIKB.params.meta.Fill Flag 0 UJIKB.params.meta.Fill Flag 1 UJIKB.params.meta.Fill Flag 2 UJIKB.params.meta.Fill Flag 3 UJIKB.params.meta.Fill Flag 4 UJIKB.params.meta.Fill Flag 5 UJIKB.params.meta.Surface Albedo Units UJIKB.params.meta.Version UJIKB.params.meta.latitude UJIKB.params.meta.longitude UJIKB.params.meta.altitude UJIKB.params.meta.tz\n", - "0 0.33 Year Month Day Hour Minute dni dhi ghi \\\n", - "1999-01-01 00:30:00-07:00 1999 1 1 0 30 0.0 0.0 0.0 \n", - "1999-01-01 01:30:00-07:00 1999 1 1 1 30 0.0 0.0 0.0 \n", - "1999-01-01 02:30:00-07:00 1999 1 1 2 30 0.0 0.0 0.0 \n", - "1999-01-01 03:30:00-07:00 1999 1 1 3 30 0.0 0.0 0.0 \n", - "1999-01-01 04:30:00-07:00 1999 1 1 4 30 0.0 0.0 0.0 \n", - "... ... ... ... ... ... ... ... ... \n", - "1999-12-31 19:30:00-07:00 1999 12 31 19 30 0.0 0.0 0.0 \n", - "1999-12-31 20:30:00-07:00 1999 12 31 20 30 0.0 0.0 0.0 \n", - "1999-12-31 21:30:00-07:00 1999 12 31 21 30 0.0 0.0 0.0 \n", - "1999-12-31 22:30:00-07:00 1999 12 31 22 30 0.0 0.0 0.0 \n", - "1999-12-31 23:30:00-07:00 1999 12 31 23 30 0.0 0.0 0.0 \n", - "\n", - " temp_air dew_point wind_speed relative_humidity \n", - "1999-01-01 00:30:00-07:00 0.0 -5.0 1.8 79.39 \n", - "1999-01-01 01:30:00-07:00 0.0 -4.0 1.7 80.84 \n", - "1999-01-01 02:30:00-07:00 0.0 -4.0 1.5 82.98 \n", - "1999-01-01 03:30:00-07:00 0.0 -4.0 1.3 85.01 \n", - "1999-01-01 04:30:00-07:00 0.0 -4.0 1.3 85.81 \n", - 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"execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -728,57 +702,13 @@ "
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    \n", @@ -826,7 +756,7 @@ " \n", "
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    \n", @@ -1098,25 +1028,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", - "The array tilt angle was not provided, therefore the latitude tilt of 39.7 was used.\n", - "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n", "The array azimuth was not provided, therefore an azimuth of 180.0 was used.\n", "The array tilt angle was not provided, therefore the latitude tilt of 25.8 was used.\n", @@ -1140,183 +1058,25 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "BLHZH x T98_0 T98_inf\n", + "KWRFI x T98_0 T98_inf\n", "0 1.20347 74...\n", - "WCBOQ WCBOQ\n", + "MRFGY MRFGY\n", "0 4.562892\n", - "PUKRE x T98_0 T98_inf\n", - "0 2.008636 ...\n", - "MUDMB x T98_0 T98_inf\n", - "0 1.20347 74...\n", - "FOYEQ FOYEQ\n", - "0 4.562892\n", - "UJIKB x T98_0 T98_inf\n", + "CXKHX x T98_0 T98_inf\n", "0 2.008636 ...\n", "dtype: object\n", "We can't see out data in here so we need to do another step\n", "\n", - "this is the list of all available frames in results : Index(['BLHZH', 'WCBOQ', 'PUKRE', 'MUDMB', 'FOYEQ', 'UJIKB'], dtype='object')\n", + "this is the list of all available frames in results : Index(['KWRFI', 'MRFGY', 'CXKHX'], dtype='object')\n", "\n", - "BLHZH\n" - ] - }, - { - "data": { - "text/html": [ - "
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    " ], "text/plain": [ - " FOYEQ\n", + " MRFGY\n", "0 4.562892" ] }, @@ -1416,7 +1176,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "UJIKB\n" + "CXKHX\n" ] }, { @@ -1489,7 +1249,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1499,9 +1259,9 @@ ], "metadata": { "kernelspec": { - "display_name": "rd-tools", + "display_name": "rpp", "language": "python", - "name": "python3" + "name": "rpp" }, "language_info": { "codemirror_mode": { @@ -1513,9 +1273,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.10.14" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/tutorials_and_tools/tutorials_and_tools/Scenario - Temperatrure.ipynb b/tutorials_and_tools/tutorials_and_tools/Scenario - Temperatrure.ipynb index 71737e52..bb89f7cc 100644 --- a/tutorials_and_tools/tutorials_and_tools/Scenario - Temperatrure.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Scenario - Temperatrure.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -37,51 +37,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Adding Modules and Pipeline Jobs (Run Functions on Scenario Object)" + "## Adding Modules and Pipeline Jobs (Run Functions on Scenario Object)\n", + "\n", + "Material: `OX003` corresponds to a set of EVA material parameters from the default file `O2Permeation.json` in the `pvdeg/data` directory. Look in these files to see available options." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Column \"relative_humidity\" not found in DataFrame. Calculating...\n", - "WARNING - Module already found by name \"sapm_1\"\n", - "Module will be replaced with new instance.\n", - "Module \"sapm_1\" added.\n", - "Module \"pvsyst_1\" added.\n", - "Module \"sapm_2\" added.\n", - "Module \"sapm_3\" added.\n", - "Module \"pvsyst_2\" added.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\tford\\dev\\PVDegradationTools\\pvdeg\\scenario.py:236: UserWarning: Gids Added - [1060699]\n", - " warnings.warn(message, UserWarning)\n", - "C:\\Users\\tford\\dev\\PVDegradationTools\\pvdeg\\scenario.py:399: UserWarning: temperature added to pipeline as \n", - " {'job': , 'params': {'cell_or_mod': 'cell'}}\n", - " warnings.warn(message, UserWarning)\n", - "C:\\Users\\tford\\dev\\PVDegradationTools\\pvdeg\\scenario.py:399: UserWarning: vantHoff_deg added to pipeline as \n", - " {'job': , 'params': {'I_chamber': 1000, 'temp_chamber': 25}}\n", - " warnings.warn(message, UserWarning)\n", - "C:\\Users\\tford\\dev\\PVDegradationTools\\pvdeg\\scenario.py:399: UserWarning: vantHoff_deg added to pipeline as \n", - " {'job': , 'params': {'I_chamber': 1000, 'temp_chamber': 30}}\n", - " warnings.warn(message, UserWarning)\n", - "C:\\Users\\tford\\dev\\PVDegradationTools\\pvdeg\\scenario.py:399: UserWarning: IwaVantHoff added to pipeline as \n", - " {'job': , 'params': {}}\n", - " warnings.warn(message, UserWarning)\n" - ] - } - ], + "outputs": [], "source": [ - "scene_temp = pvdeg.scenario.Scenario(\n", + "scene_temp = pvdeg.Scenario(\n", " name= 'temperature and degradation',\n", " api_key='DEMO_KEY',\n", " email='user@mail.com',\n", @@ -101,7 +68,7 @@ "scene_temp.addModule(\n", " module_name='sapm_1',\n", " racking='open_rack_glass_polymer',\n", - " material='EVA',\n", + " material='OX003',\n", " temperature_model='sapm',\n", " irradiance_kwarg={'azimuth' : 120, 'tilt' : 30}, \n", " model_kwarg={'irrad_ref' : 1100}, \n", @@ -111,7 +78,7 @@ "scene_temp.addModule(\n", " module_name='pvsyst_1',\n", " racking='freestanding',\n", - " material='EVA',\n", + " material='OX003',\n", " temperature_model='pvsyst',\n", " irradiance_kwarg={'azimuth' : 180, 'tilt' : 0}, \n", " model_kwarg={'module_efficiency' : 0.15}, \n", @@ -120,7 +87,7 @@ "scene_temp.addModule(\n", " module_name='sapm_2',\n", " racking='open_rack_glass_polymer',\n", - " material='EVA',\n", + " material='OX003',\n", " temperature_model='sapm',\n", " irradiance_kwarg={'azimuth' : 120, 'tilt' : 30}, \n", " model_kwarg={'irrad_ref' : 1000}, \n", @@ -129,7 +96,7 @@ "scene_temp.addModule(\n", " module_name='sapm_3',\n", " racking='open_rack_glass_polymer',\n", - " material='EVA',\n", + " material='OX003',\n", " temperature_model='sapm',\n", " irradiance_kwarg={'azimuth' : 180, 'tilt' : 0}, \n", " model_kwarg={'irrad_ref' : 1000}, \n", @@ -139,7 +106,7 @@ "scene_temp.addModule(\n", " module_name='pvsyst_2',\n", " racking='freestanding',\n", - " material='EVA',\n", + " material='OX003',\n", " temperature_model='pvsyst',\n", " irradiance_kwarg={'azimuth' : 180, 'tilt' : 0}, \n", " model_kwarg={'module_efficiency' : 0.2}, \n", @@ -179,1273 +146,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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    2005-01-01 02:30:00-05:0021.000000
    2005-01-01 03:30:00-05:0021.000000
    2005-01-01 04:30:00-05:0021.000000
    2005-01-01 05:30:00-05:0021.000000
    2005-01-01 06:30:00-05:0021.000000
    2005-01-01 07:30:00-05:0022.035591
    2005-01-01 08:30:00-05:0028.016994
    2005-01-01 09:30:00-05:0032.514264

    ...
    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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    1999-12-31 14:30:00-05:0037.285424
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    1999-12-31 16:30:00-05:0026.971577
    1999-12-31 17:30:00-05:0022.391783
    1999-12-31 18:30:00-05:0022.000000
    1999-12-31 19:30:00-05:0022.000000
    1999-12-31 20:30:00-05:0022.000000
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    1999-12-31 23:30:00-05:0022.000000
    \n", - "
    \n", - " \n", - "
    \n", - "
    \n", - " ► \n", - " JUBNK\n", - "
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    \n", - " 1.8691839343880285\n", - "
    \n", - " \n", - "
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    \n", - " ► \n", - " GLDTX\n", - "
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    \n", - "
    \n", - " 2.2195329460518884\n", - "
    \n", - " \n", - "
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    \n", - " ► \n", - " DJFXB\n", - "
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    \n", - " 176.59902314734833\n", - "
    \n", - "
    \n", - "
    \n", - "

    \n", - " ► \n", - " pvsyst_2\n", - "

    \n", - "
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    \n", - " ► \n", - " HKXVD\n", - "
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    2005-01-01 00:30:00-05:0022.000000
    2005-01-01 01:30:00-05:0021.000000
    2005-01-01 02:30:00-05:0021.000000
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    2005-01-01 04:30:00-05:0021.000000
    2005-01-01 05:30:00-05:0021.000000
    2005-01-01 06:30:00-05:0021.000000
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    2005-01-01 08:30:00-05:0027.663053
    2005-01-01 09:30:00-05:0031.895778

    ...
    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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    1999-12-31 14:30:00-05:0036.445105
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    1999-12-31 17:30:00-05:0022.368737
    1999-12-31 18:30:00-05:0022.000000
    1999-12-31 19:30:00-05:0022.000000
    1999-12-31 20:30:00-05:0022.000000
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    1999-12-31 22:30:00-05:0022.000000
    1999-12-31 23:30:00-05:0022.000000
    \n", - "
    \n", - " \n", - "
    \n", - "
    \n", - " ► \n", - " JUBNK\n", - "
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    \n", - "
    \n", - " 1.9322091218798985\n", - "
    \n", - " \n", - "
    \n", - "
    \n", - " ► \n", - " GLDTX\n", - "
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    \n", - "
    \n", - " 2.2943712096897046\n", - "
    \n", - " \n", - "
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    \n", - " 170.98696553432933\n", - "
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    \n", - "
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    \n", - " ► \n", - " sapm_1\n", - "

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    0
    2005-01-01 00:30:00-05:0022.000000
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    ...
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    1999-12-31 14:30:00-05:0033.588274
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    \n", - "
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    \n", - " ► \n", - " sapm_2\n", - "

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    2005-01-01 00:30:00-05:0022.000000
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    2005-01-01 09:30:00-05:0034.331368

    ...
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    0
    1999-12-31 14:30:00-05:0033.711677
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    1999-12-31 18:30:00-05:0022.000000
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    \n", - "
    \n", - " \n", - "
    \n", - "
    \n", - " ► \n", - " JUBNK\n", - "
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    \n", - "
    \n", - " 2.068044987190104\n", - "
    \n", - " \n", - "
    \n", - "
    \n", - " ► \n", - " GLDTX\n", - "
    \n", - "
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    \n", - " 2.4556673629279246\n", - "
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    \n", - " 160.0713321390226\n", - "
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    \n", - "
    \n", - "

    \n", - " ► \n", - " sapm_3\n", - "

    \n", - "
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    0
    2005-01-01 00:30:00-05:0022.000000
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    2005-01-01 07:30:00-05:0021.746611
    2005-01-01 08:30:00-05:0026.245927
    2005-01-01 09:30:00-05:0029.262679

    ...
    \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    0
    1999-12-31 14:30:00-05:0035.820095
    1999-12-31 15:30:00-05:0030.634275
    1999-12-31 16:30:00-05:0026.220871
    1999-12-31 17:30:00-05:0022.325367
    1999-12-31 18:30:00-05:0022.000000
    1999-12-31 19:30:00-05:0022.000000
    1999-12-31 20:30:00-05:0022.000000
    1999-12-31 21:30:00-05:0022.000000
    1999-12-31 22:30:00-05:0022.000000
    1999-12-31 23:30:00-05:0022.000000
    \n", - "
    \n", - " \n", - "
    \n", - "
    \n", - " ► \n", - " JUBNK\n", - "
    \n", - "
    \n", - "
    \n", - " 2.0696368225191195\n", - "
    \n", - " \n", - "
    \n", - "
    \n", - " ► \n", - " GLDTX\n", - "
    \n", - "
    \n", - "
    \n", - " 2.4575575626522204\n", - "
    \n", - " \n", - "
    \n", - "
    \n", - " ► \n", - " DJFXB\n", - "
    \n", - "
    \n", - "
    \n", - " 160.09303513122694\n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "

    Pipeline

    \n", - "
    \n", - "
    \n", - "

    \n", - " ► \n", - " temperature, #HKXVD\n", - "

    \n", - "
    \n", - "
    \n", - "

    Job: temperature

    \n", - "

    Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"cell_or_mod\": \"cell\"\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " vantHoff_deg, #JUBNK\n", - "

    \n", - "
    \n", - "
    \n", - "

    Job: vantHoff_deg

    \n", - "

    Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"I_chamber\": 1000,\n",
-       "  \"temp_chamber\": 25\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " vantHoff_deg, #GLDTX\n", - "

    \n", - "
    \n", - "
    \n", - "

    Job: vantHoff_deg

    \n", - "

    Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"I_chamber\": 1000,\n",
-       "  \"temp_chamber\": 30\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " IwaVantHoff, #DJFXB\n", - "

    \n", - "
    \n", - "
    \n", - "

    Job: IwaVantHoff

    \n", - "

    Parameters:

    \n", - "
    \n", - " 
    {}
    \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "
    \n", - "

    Modules

    \n", - "
    \n", - "
    \n", - "

    \n", - " ► \n", - " sapm_1\n", - "

    \n", - "
    \n", - "
    \n", - "

    Racking: open_rack_glass_polymer

    \n", - "

    Temperature Model: sapm

    \n", - "

    Material Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
-       "  \"Eap\": 61.74,\n",
-       "  \"Po\": 97900000000.0\n",
-       "}
    \n", - "
    \n", - "

    Model Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"irrad_ref\": 1100\n",
-       "}
    \n", - "
    \n", - "

    Irradiance Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"azimuth\": 120,\n",
-       "  \"tilt\": 30\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " pvsyst_1\n", - "

    \n", - "
    \n", - "
    \n", - "

    Racking: freestanding

    \n", - "

    Temperature Model: pvsyst

    \n", - "

    Material Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
-       "  \"Eap\": 61.74,\n",
-       "  \"Po\": 97900000000.0\n",
-       "}
    \n", - "
    \n", - "

    Model Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"module_efficiency\": 0.15\n",
-       "}
    \n", - "
    \n", - "

    Irradiance Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"azimuth\": 180,\n",
-       "  \"tilt\": 0\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " sapm_2\n", - "

    \n", - "
    \n", - "
    \n", - "

    Racking: open_rack_glass_polymer

    \n", - "

    Temperature Model: sapm

    \n", - "

    Material Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
-       "  \"Eap\": 61.74,\n",
-       "  \"Po\": 97900000000.0\n",
-       "}
    \n", - "
    \n", - "

    Model Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"irrad_ref\": 1000\n",
-       "}
    \n", - "
    \n", - "

    Irradiance Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"azimuth\": 120,\n",
-       "  \"tilt\": 30\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " sapm_3\n", - "

    \n", - "
    \n", - "
    \n", - "

    Racking: open_rack_glass_polymer

    \n", - "

    Temperature Model: sapm

    \n", - "

    Material Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
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-       "  \"Po\": 97900000000.0\n",
-       "}
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    Model Arguments:

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    {\n",
-       "  \"irrad_ref\": 1000\n",
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    Irradiance Arguments:

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    {\n",
-       "  \"azimuth\": 180,\n",
-       "  \"tilt\": 0\n",
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    Racking: freestanding

    \n", - "

    Temperature Model: pvsyst

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    Material Parameters:

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    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
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    Model Arguments:

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-       "  \"module_efficiency\": 0.2\n",
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    Irradiance Arguments:

    \n", - "
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    {\n",
-       "  \"azimuth\": 180,\n",
-       "  \"tilt\": 0\n",
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    Weather Data

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    420051143016.00.00.00.00.11020.021.089.05.173.11466
    ................................................
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21.000000 \n", - "1970-01-01 03:30:00-05:00 21.000000 \n", - "1970-01-01 04:30:00-05:00 21.000000 \n", - "1970-01-01 05:30:00-05:00 21.000000 \n", - "1970-01-01 06:30:00-05:00 21.000000 \n", - "1970-01-01 07:30:00-05:00 21.974674 \n", - "1970-01-01 08:30:00-05:00 27.663053 \n", - "1970-01-01 09:30:00-05:00 31.895778 \n", - "1970-01-01 10:30:00-05:00 34.391573 \n", - "1970-01-01 11:30:00-05:00 33.354222 \n", - "1970-01-01 12:30:00-05:00 40.263699 \n", - "1970-01-01 13:30:00-05:00 36.143473 \n", - "1970-01-01 14:30:00-05:00 34.584193 \n", - "1970-01-01 15:30:00-05:00 32.569263 \n", - "1970-01-01 16:30:00-05:00 27.757752 \n", - "1970-01-01 17:30:00-05:00 22.397241 \n", - "1970-01-01 18:30:00-05:00 22.000000 \n", - "1970-01-01 19:30:00-05:00 22.000000 \n", - "1970-01-01 20:30:00-05:00 22.000000 \n", - "1970-01-01 21:30:00-05:00 22.000000 \n", - "1970-01-01 22:30:00-05:00 22.000000 \n", - "1970-01-01 23:30:00-05:00 21.000000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import datetime\n", "t0=datetime.datetime(1970,1,1,0,0) \n", @@ -1771,20 +191,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "scene_temp.plot(('function', 'HKXVD'),tmy=True,start_time=t0,end_time=tf,title='single day cell temperature')" ] @@ -1798,551 +207,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "credentials not in json file using arguments\n", - "Column \"relative_humidity\" not found in DataFrame. Calculating...\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
    \n", - "

    temperature and degradation: Scenario Analysis

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    Path: c:\\Users\\tford\\dev\\PVDegradationTools\\tutorials_and_tools\\tutorials_and_tools\\pvd_job_temperature and degradation

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    GIDs: [1060699]

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    Email: user@mail.com

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    API Key: DEMO_KEY

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    \n", - "

    Results

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    \n", - "
    \n", - "

    Pipeline

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    \n", - "
    \n", - "

    \n", - " ► \n", - " temperature, #HKXVD\n", - "

    \n", - "
    \n", - "
    \n", - "

    Job: temperature

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    Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"cell_or_mod\": \"cell\"\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " vantHoff_deg, #JUBNK\n", - "

    \n", - "
    \n", - "
    \n", - "

    Job: vantHoff_deg

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    Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"I_chamber\": 1000,\n",
-       "  \"temp_chamber\": 25\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " vantHoff_deg, #GLDTX\n", - "

    \n", - "
    \n", - "
    \n", - "

    Job: vantHoff_deg

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    Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"I_chamber\": 1000,\n",
-       "  \"temp_chamber\": 30\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " IwaVantHoff, #DJFXB\n", - "

    \n", - "
    \n", - "
    \n", - "

    Job: IwaVantHoff

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    Parameters:

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    {}
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    \n", - "
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    \n", - "
    \n", - "
    \n", - "

    Modules

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    \n", - "
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    \n", - " ► \n", - " sapm_1\n", - "

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    \n", - "
    \n", - "

    Racking: open_rack_glass_polymer

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    Temperature Model: sapm

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    Material Parameters:

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    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
-       "  \"Eap\": 61.74,\n",
-       "  \"Po\": 97900000000.0\n",
-       "}
    \n", - "
    \n", - "

    Model Arguments:

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    \n", - " 
    {\n",
-       "  \"irrad_ref\": 1100\n",
-       "}
    \n", - "
    \n", - "

    Irradiance Arguments:

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    \n", - " 
    {\n",
-       "  \"azimuth\": 120,\n",
-       "  \"tilt\": 30\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " pvsyst_1\n", - "

    \n", - "
    \n", - "
    \n", - "

    Racking: freestanding

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    Temperature Model: pvsyst

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    Material Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
-       "  \"Eap\": 61.74,\n",
-       "  \"Po\": 97900000000.0\n",
-       "}
    \n", - "
    \n", - "

    Model Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"module_efficiency\": 0.15\n",
-       "}
    \n", - "
    \n", - "

    Irradiance Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"azimuth\": 180,\n",
-       "  \"tilt\": 0\n",
-       "}
    \n", - "
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    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " sapm_2\n", - "

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    \n", - "
    \n", - "

    Racking: open_rack_glass_polymer

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    Temperature Model: sapm

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    Material Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
-       "  \"Eap\": 61.74,\n",
-       "  \"Po\": 97900000000.0\n",
-       "}
    \n", - "
    \n", - "

    Model Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"irrad_ref\": 1000\n",
-       "}
    \n", - "
    \n", - "

    Irradiance Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"azimuth\": 120,\n",
-       "  \"tilt\": 30\n",
-       "}
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    \n", - "

    \n", - " ► \n", - " sapm_3\n", - "

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    \n", - "
    \n", - "

    Racking: open_rack_glass_polymer

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    Temperature Model: sapm

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    Material Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
-       "  \"Eap\": 61.74,\n",
-       "  \"Po\": 97900000000.0\n",
-       "}
    \n", - "
    \n", - "

    Model Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"irrad_ref\": 1000\n",
-       "}
    \n", - "
    \n", - "

    Irradiance Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"azimuth\": 180,\n",
-       "  \"tilt\": 0\n",
-       "}
    \n", - "
    \n", - "
    \n", - " \n", - "
    \n", - "

    \n", - " ► \n", - " pvsyst_2\n", - "

    \n", - "
    \n", - "
    \n", - "

    Racking: freestanding

    \n", - "

    Temperature Model: pvsyst

    \n", - "

    Material Parameters:

    \n", - "
    \n", - " 
    {\n",
-       "  \"alias\": \"EVA\",\n",
-       "  \"Fickian\": true,\n",
-       "  \"Ead\": 29.43,\n",
-       "  \"Do\": 0.13,\n",
-       "  \"Eas\": 32.31,\n",
-       "  \"So\": 87.8,\n",
-       "  \"Eap\": 61.74,\n",
-       "  \"Po\": 97900000000.0\n",
-       "}
    \n", - "
    \n", - "

    Model Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"module_efficiency\": 0.2\n",
-       "}
    \n", - "
    \n", - "

    Irradiance Arguments:

    \n", - "
    \n", - " 
    {\n",
-       "  \"azimuth\": 180,\n",
-       "  \"tilt\": 0\n",
-       "}
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    Weather Data

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    \n", - " ► \n", - " Weather Data\n", - "

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    ................................................
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    Meta Data

    \n", - " {'Source': 'NSRDB', 'Location ID': '1060699', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 25.77, 'longitude': -80.18, 'altitude': 0, 'tz': -5, 'wind_height': 2}\n", - "
    \n", - "
    \n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pathlib import Path\n", "\n", @@ -2361,9 +228,9 @@ ], "metadata": { "kernelspec": { - "display_name": "rd-tools", + "display_name": "rpp", "language": "python", - "name": "python3" + "name": "rpp" }, "language_info": { "codemirror_mode": { @@ -2379,5 +246,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb index 4e229ecc..d6304444 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Tools - Edge Seal Oxygen Ingress.ipynb @@ -25,29 +25,26 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# if running on google colab, uncomment the next line and execute this cell to install the dependencies \n", - "# and prevent \"ModuleNotFoundError\" in later cells:\n", - "!pip install pvdeg==0.4.2" + "# if running on google colab, uncomment the next line and execute this cell to install the dependencies and prevent \"ModuleNotFoundError\" in later cells:\n", + "# !pip install pvdeg==0.4.2" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", - "from pathlib import Path\n", "import pvdeg\n", "import pandas as pd\n", "from pvdeg import DATA_DIR\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import seaborn as sns\n", "import json" ] }, @@ -80,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -100,14 +97,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'latitude': 33.4152, 'longitude': -111.8315, 'altitude': 381.0, 'wind_height': 10, 'Source': 'PVGIS'}\n" + "{'Source': 'NSRDB', 'Location ID': '77855', 'City': '-', 'State': '-', 'Country': '-', 'Dew Point Units': 'c', 'DHI Units': 'w/m2', 'DNI Units': 'w/m2', 'GHI Units': 'w/m2', 'Temperature Units': 'c', 'Pressure Units': 'mbar', 'Wind Direction Units': 'Degrees', 'Wind Speed Units': 'm/s', 'Surface Albedo Units': 'N/A', 'Version': '3.2.0', 'latitude': 33.41, 'longitude': -111.82, 'altitude': 381, 'tz': -7, 'wind_height': 2}\n" ] } ], @@ -125,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -166,9 +163,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['OX000', 'OX001', 'OX002', 'OX003', 'OX004', 'OX005', 'OX006', 'OX007', 'OX008', 'OX009', 'OX010', 'OX011', 'OX012', 'OX013']\n", + "['W000', 'W001', 'W002', 'W003', 'W004', 'W005', 'W006', 'W007', 'W008', 'W009', 'W010', 'W011', 'W012', 'W013', 'W014', 'W015', 'W016', 'W017', 'W018', 'W019', 'W020', 'W021', 'W022', 'W023', 'W024', 'W025', 'W026', 'W027', 'W028', 'W029', 'W030', 'W031', 'W032', 'W033', 'W034', 'W035', 'W036', 'W037', 'W038', 'W039', 'W040']\n" + ] + } + ], "source": [ "es = 'OX005' #This is the number for the edge seal in the json file\n", "enc = 'OX003' #This is the number for the encapsulant in the json file\n", @@ -176,7 +182,9 @@ "encw = 10 #This is the encapsulant width in [cm]\n", "sn = 20 #This is the number of edge seal nodes to use\n", "en = 50 #This is the number of encapsulant nodes to use\n", - "pressure = 0.2109 * (1 - 0.0065 * meta.get('altitude') / 288.15) ** 5.25588" + "pressure = 0.2109 * (1 - 0.0065 * meta.get('altitude') / 288.15) ** 5.25588\n", + "print(pvdeg.utilities._read_material(name=None, fname=\"O2permeation.json\"))\n", + "print(pvdeg.utilities._read_material(name=None, fname=\"H2Opermeation.json\"))" ] }, { @@ -185,20 +193,20 @@ "source": [ "## 3. Run the Calculations\n", "\n", - "This runs the calculations for diffusion using a simple 1-D finite difference calculation. The first set of calculations is just for diffusion, then the next two include some consumption of oxygen. In typical PV applications, it is common for oxygen ingress distance to be limited by its consumption rate in the encapsulant." + "This runs the calculations for diffusion using a simple 1-D finite difference calculation. The first set of calculations is just for diffusion, then the next two (when written) will include some consumption of oxygen. In typical PV applications, it is common for oxygen ingress distance to be limited by its consumption rate in the encapsulant." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The edge seal is None .\n", - "The encapsulant is None .\n" + "The edge seal is Helioseal_101_dry .\n", + "The encapsulant is EVA .\n" ] } ], @@ -218,12 +226,34 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[WinError 183] Cannot create a file when that file already exists: 'c:\\\\Users\\\\mkempe\\\\Documents\\\\GitHub\\\\PVDegradationTools\\\\TEMP\\\\results'\n" + ] + } + ], + "source": [ + "#This sets up an a variable with the output folder information.\n", + "output_folder = os.path.join(os.path.dirname(os.path.dirname(os.getcwd())), 'TEMP', 'results')\n", + "try:\n", + " os.makedirs(output_folder)\n", + "except OSError as error:\n", + " print(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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    " ] @@ -234,82 +264,93 @@ ], "source": [ "n_lines=10\n", - "for index in range(1,n_lines):\n", - " plt.plot(oxygen_profile.iloc[int(np.trunc(index*(len(oxygen_profile)-1)/n_lines))], label=np.round(index*((len(oxygen_profile)-1)/n_lines)/8760,3))\n", + "times=oxygen_profile.index.tolist()\n", + "for index in range(n_lines):\n", + " plt.plot(oxygen_profile.iloc[int(np.trunc((index+1)*(len(oxygen_profile)-1)/n_lines))], \n", + " label=np.round(times[int(np.trunc((index+1)*((len(oxygen_profile)-1)/n_lines)))]/365.25/24,2)) \n", "plt.legend(title='Time [year]')\n", "plt.ylabel('Oxygen Concentration [g/cm³]')\n", "plt.xlabel('Distance From Edge [cm]')\n", "plt.ticklabel_format(axis='y', style='plain')\n", + "\n", + "plt.savefig(os.path.join(output_folder, 'Edge_Seal_O2_ingress.png'), bbox_inches='tight') #Creates an image file of the standoff plot\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Save data outputs.\n", + "\n", + "This cell contains a number of pre-scripted commands for exporting and saving data. The code to save plots is located after the plot creation. First check that the output folder exists." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OX001=Tedlar\n", + "OX002=ST504\n", + "OX003=EVA\n", + "OX004=AAA polyamide backsheet\n", + "OX005=Helioseal_101_dry\n", + "OX006=Helioseal_101_wet\n", + "OX007=LPO3_dry\n", + "OX008=LPO3_wet\n", + "OX009=Nagase_dry\n", + "OX010=Nagase_wet\n", + "OX011=Oppanol_B_200\n", + "OX012=EVA\n", + "OX013=polyolefin elastomer (POE)\n" + ] + } + ], + "source": [ + "fpath = os.path.join(DATA_DIR, \"O2permeation.json\")\n", + "with open(fpath) as f:\n", + " data = json.load(f)\n", + "f.close()\n", + "\n", + "material_list = ''\n", + "for key in data:\n", + " if 'name' in data[key].keys():\n", + " material_list = material_list + key + \"=\" + data[key]['name'] + '\\n'\n", + "material_list = material_list[0:len(material_list)-1]\n", + "print(material_list)\n" + ] + }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 0.000000 0.076923 0.153846 0.230769 0.307692 \\\n", - "0.0 0.000048 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "1.0 0.000048 4.025938e-07 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "2.0 0.000048 7.914468e-07 3.295784e-09 0.000000e+00 0.000000e+00 \n", - "3.0 0.000048 1.165328e-06 9.571569e-09 2.635343e-11 0.000000e+00 \n", - "4.0 0.000049 1.525385e-06 1.852325e-08 1.006938e-10 2.058149e-13 \n", - "... ... ... ... ... ... \n", - "17514.0 0.000042 4.267309e-05 4.035455e-05 3.816304e-05 3.614315e-05 \n", - "17515.0 0.000043 4.255340e-05 4.035980e-05 3.817013e-05 3.614761e-05 \n", - "17516.0 0.000043 4.247354e-05 4.035994e-05 3.817617e-05 3.615158e-05 \n", - "17517.0 0.000045 4.244049e-05 4.035799e-05 3.818062e-05 3.615468e-05 \n", - "17518.0 0.000047 4.244476e-05 4.035617e-05 3.818351e-05 3.615683e-05 \n", - "\n", - " 0.384615 0.461538 0.538462 0.615385 0.692308 ... \\\n", - "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 ... \n", - "1.0 0.000000 0.000000 0.000000 0.000000 0.000000 ... \n", - "2.0 0.000000 0.000000 0.000000 0.000000 0.000000 ... \n", - "3.0 0.000000 0.000000 0.000000 0.000000 0.000000 ... \n", - "4.0 0.000000 0.000000 0.000000 0.000000 0.000000 ... \n", - "... ... ... ... ... ... ... \n", - "17514.0 0.000034 0.000032 0.000031 0.000029 0.000027 ... \n", - "17515.0 0.000034 0.000032 0.000031 0.000029 0.000027 ... \n", - "17516.0 0.000034 0.000032 0.000031 0.000029 0.000027 ... \n", - "17517.0 0.000034 0.000032 0.000031 0.000029 0.000027 ... \n", - "17518.0 0.000034 0.000032 0.000031 0.000029 0.000027 ... \n", - "\n", - " 9.681818 9.883838 10.085859 10.287879 10.489899 10.691919 \\\n", - "0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "2.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "3.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "4.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... ... ... \n", - "17514.0 0.000009 0.000009 0.000009 0.000009 0.000009 0.000009 \n", - "17515.0 0.000009 0.000009 0.000009 0.000009 0.000009 0.000009 \n", - "17516.0 0.000009 0.000009 0.000009 0.000009 0.000009 0.000009 \n", - "17517.0 0.000009 0.000009 0.000009 0.000009 0.000009 0.000009 \n", - "17518.0 0.000009 0.000009 0.000009 0.000009 0.000009 0.000009 \n", - "\n", - " 10.893939 11.095960 11.297980 11.500000 \n", - "0.0 0.000000 0.000000 0.000000 0.000000 \n", - "1.0 0.000000 0.000000 0.000000 0.000000 \n", - "2.0 0.000000 0.000000 0.000000 0.000000 \n", - "3.0 0.000000 0.000000 0.000000 0.000000 \n", - "4.0 0.000000 0.000000 0.000000 0.000000 \n", - "... ... ... ... ... \n", - "17514.0 0.000009 0.000009 0.000009 0.000009 \n", - "17515.0 0.000009 0.000009 0.000009 0.000009 \n", - "17516.0 0.000009 0.000009 0.000009 0.000009 \n", - "17517.0 0.000009 0.000009 0.000009 0.000009 \n", - "17518.0 0.000009 0.000009 0.000009 0.000009 \n", - "\n", - "[17519 rows x 72 columns]\n" + "Your results will be stored in c:\\Users\\mkempe\\Documents\\GitHub\\PVDegradationTools\\TEMP\\results\n", + "The folder must already exist or the file will not be created\n" ] } ], "source": [ - "print(oxygen_profile)" + "print (\"Your results will be stored in %s\" % output_folder)\n", + "print ('The folder must already exist or the file will not be created')\n", + "\n", + "#Writes the meterological data to an *.csv file.\n", + "pvdeg.weather.write(data_df=weather_df, metadata=meta, savefile=os.path.join(output_folder, 'WeatherFile.csv')) \n", + "\n", + "#Writes a file with the edge seal oxygen profile calculations.\n", + "pd.DataFrame(oxygen_profile).to_csv(os.path.join(output_folder, 'ES_Oxygen_profile.csv')) \n", + "\n", + "#Writes a file with temperature data used in the model calculations.\n", + "pd.DataFrame(temperature).to_csv(os.path.join(output_folder, 'ES_Temperature_profile.csv')) \n" ] } ], diff --git a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb index ebaec28a..ce38cad6 100644 --- a/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb +++ b/tutorials_and_tools/tutorials_and_tools/Tools - Module Standoff for IEC TS 63126.ipynb @@ -578,7 +578,7 @@ "metadata": {}, "outputs": [], "source": [ - "geospatial_standoff_scenario = pvdeg.scenario.Geospatial_Scenario(\n", + "geospatial_standoff_scenario = pvdeg.Geospatial_Scenario(\n", " name='standoff geospatial',\n", " geospatial = True,\n", ") " diff --git a/tutorials_and_tools/tutorials_and_tools/load_nsrdb_distributed.ipynb b/tutorials_and_tools/tutorials_and_tools/load_nsrdb_distributed.ipynb new file mode 100644 index 00000000..2a3639b6 --- /dev/null +++ b/tutorials_and_tools/tutorials_and_tools/load_nsrdb_distributed.ipynb @@ -0,0 +1,1890 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import LocalCluster, Client\n", + "from dotenv import load_dotenv\n", + "import pvdeg\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Setting Up\n", + "\n", + "As in [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb) we need to get ready to make our parallelized API calls. The notebook linked here goes through the process in more detail but we need to import our API key and email. This cell will not work for you unless you replace the `api_key` and `email` with your personal NSRDB api keys. [REQUEST A KEY](https://developer.nrel.gov/signup/).\n", + "\n", + "We also need to initalize a dask client. `pvdeg.weather.weather_distributed` will not work without it. It will fail silently and not populate and of the results in the resulting `weather_ds` called `geo_weather` in the example below. It is hard to recognize that this has occured so be careful. Make sure to initialize a dask client first. Visiting the link takes you to a daskboard that shows what dask is doing." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "http://127.0.0.1:8787/status\n" + ] + } + ], + "source": [ + "load_dotenv()\n", + "\n", + "### REPLACE WITH YOUR API KEY AND EMAIL ###\n", + "api_key = os.getenv(\"api_key\")\n", + "email = os.getenv(\"email\")\n", + "###########################################\n", + "\n", + "workers = 4\n", + "\n", + "cluster = LocalCluster(\n", + " n_workers=workers,\n", + " processes=True, \n", + " )\n", + "\n", + "client = Client(cluster)\n", + "\n", + "print(\"Daskboard link\")\n", + "print(client.dashboard_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requesting Weather\n", + "\n", + "As with the other script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). We will create a list of tuple (latitude, longitude) pairs and call the function on all of them at once. failed will represent a list of failed gids, unique location ID's that correspond to points in space on the NSRDB. These are different than on PVGIS where they are arbitrary indexes that do NOT correspond to a spatial location on earth.\n", + "\n", + "We will request \"PSM3\" data from the Physical Solar Model that represents a typical meteorological year (TMY) from the NSRDB. We will have to supply the api key and email from above here. Refer to the linked script to see this in further detail. The only difference between the scripts, lies in the NSRDB/PSM3 data requiring Api keys." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "coords = [\n", + " (25.783388, -80.189029),\n", + " (24.783388, -80.189029),\n", + "]\n", + "\n", + "geo_weather, geo_meta, failed = pvdeg.weather.weather_distributed(database=\"PSM3\", coords=coords, api_key=api_key, email=email)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Viewing Results\n", + "\n", + "Same as in the other tutorial, our results are stored in an xarray dataset with a dask backend so you will have to use `.compute()` on the dataset to inspect the individual values of the dask arrays. \n", + "\n", + "Click on the `Data variables` dropdown to expand the dataset viewer." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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We can index into the first entry at the 0th index by using isel (index-select). This will grab the data from the first gid. Then we pick the air temperature attribute. This can be replaced with bracket notation so `.temp_air` becomes `[\"temp_air\"]. \n", + "\n", + "This selects a single array from the dataset that is labeled as \"temp_air\". This array will be a dask array so the values will be stored out of memory, we would have to load it using `.compute()` to directly inspect it but when plotting with matplotlib it will load the array for us." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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G9sboR9/XnJ4ZxvfLw7qq04Zfn+wR0KFy9/zh5efgnksGBR7Pe3EjXvksuuAqnvivkX9vPoK5Sz5zODfKqhbMxA9e3ISXPzuouk9hlhdrfzwNAPDb93fiTx/tVtxvRJ9svHXvhZbkk6yh5/7Nmg+KCTuO1Esen2ntsCXdmvpWzcHHO1tDf9GH4w881KgFHgCwsboWU4eE1pZYLZrAA4Bq4AEAK748IQk+/iP7Re13xfBi5KSnwOMR0NjagdaOrtqKtJQkZKeloLWjE01tnahrbkdruw+pyZ6QX+cAMGVwLyzbeVxX/m+YUAYA6JObDp/YVdsliiKO1regqa0TqUkeJHkENLd3Ij0lCR5BQIfPh9YOH7YfrsfJxraQY84+r6t2LTVZgEcQIAhAbnoqPtxRgy+ONoTsL2/umFCRj7X71GujLh1SiE92n0Bbh7ZhMuP65mH9fmPnWRRFvLEpfMB4rKH7uv/sgHo6Ww/Vqz5HsY/BB8Uku+rrPDq6J9jdkyFR29z/fPNY3a95cV01Hnx5c+DxX28Zh8uGFeEvK/bgl29/oekYfQsyMH/WKN1p++04Uo8Zf/xEsq00Lx2//cZoxf1vndwP437xYch2+Xl/8TuVgZo6uZ9+ZRi+dUEFth2uw8zHVwIAfvO1UVj0nypsPxJ6cx/aOxt/mzMO5/7PB5rek1yCXpJkADucUkyy7carI6Kws9wVBM6zoIvsPCqtVWK1JD2RLACPCQvO+NMMTjtcPgQAHp35DKb3knRz52OyFoMPikl23XftKhz1d70SYmI2Tj3kI3osWovvbFpnt+sJLqPMj957utruegJvf5rBgUykoCaaoMcniro+p1ha0I/MxeCDYpKVNR/BN3U9haOdq70KAqu49RBkJ9L/2cm3W0nvTV1tfz3n3V+LERz4RKrZSIriM+E1SVox+KCYZOWv/uBDm1H1rYXeYEow8BoKZecPb73XkqBSOutpbvOnKa35CDMfDaJftVhXwM6qj4TF4INikpX33U6DNR/R0N3owpoPXUJPY9eHF0X3Bt309vkwo9klSSH4iFSzoTefwXTX4BlOiWIdgw+KSVZ2tgwu3HWNdomyrdyO1yQq+alxotlFb1KqNSUG+lQEBxSR3nM0tX16Z+JlxUfiYvBBMcnKG6901VC7Opzq21+AwNEuOoQEH2f/dXPNhzwIUFpZVmuawYdK8ghhb/rRfCZ6r0nGHomLwQfFJCt/9PucaHYx0OHUmSXGYpN81FLg83Zxh1O12hojfT6CA59IwYXdNXiUmBh8UEyyssOppM+HZalIGetwak1e4lFozYd/ng/7RBt8+K9LPU0b3aNdhJBtSqL9XukPoln3kagYfFBMsvLGKwY1u7h2tIsg8FemCew6v11p6d1f+gJfoNlFf5ryeT6smr9Gb/DC0CNxMfigmGTlEvNGm12imufDyGsYexjW3eHUvjSV+nyESz9kYrTAsvbaT7w/yJDM82Hhe9bd54PRR8Ji8EExycqaD2mzi00dTrWt+SXBmg/tQiYZ8293MA+RyGs+Os9e9EZOu101PEZm6qXExIXlyNX2HD+D59cewNOr9ku2WzraJejY/1xdhV/NHhW4cXy6+wTe3HwERdlezKnsh7weqaak+beVe3H/tHMC7fHrq9RXKQWAxZ9W4eDpZlPSdot1Vafx329sw8bqWgwvyZasfhottVucnc0uuuf5kO3+w5c2ISstGS+uV1+uXo28n4fa29a68q2ai3+zDO2dkb+bL6w7gI3VdfhwR01U6QV7d+tRtHZ04oKBPfH8umpcM6YP+uSmm3Z8MheDD3K1+1/YiM0H60KfsPBHf0NLR+DvF9cfxJUje2PK4K7l62/825rAc6cb2/Do1SNMSfNPH+3GlMGFGNs3DwBwU1A6aswsuM3SJzcdh2qNB0WLP60CAGysrjUnQ2dV9OwhedyvIAMAkJWmvQg8f0BBVHlITQqtaL56dB/V/eU1Je9sPRqyT3F2GgBgfL88rKvqWp6+Z2YqTpxpAwDkZaQAALzJ3WnnpqeiKDsNQOj3au+JxrNpd9ewpCQJmgIKADjT2hF5JwA/enmLpv06faKmoK21oxN3PbMBAHD5sCK8v70G66tOYdFtEzSlQ/Zj8EGuphh4wL61XQCg6kQjMDh0vw+215gWfABAY1DB3RrlL1C5XllefGVUbzS0dKChpR3vbdMeuJTkpGFYSU5IsJPkEQJNAQDwx+vPxXnlefjrJ3vR3ikGfkXnZaTgbyv3KR77mnNL0NTWife3R87PDy47BxcP7qU538FG9MnB768bjZNn2tAry4tRpbkAgEuGdgV8G/afDnlNZf8C9C3IQENLBwqzvbhn6kBDafulJnuw+Lbx2LD/NM60diA3PRW3X1hh+HjfnTIAXxlVAgD43TfOxfPrDqC9U8S1Y/rgaH0LDp1uxqT+XQFTWkoSFt06HscbWjGiTzZ++pVhKM/PwFMq5+Vfd1Vi5a6TyEhNQlpqEh55bavk+ZsmlkMQgKa2Tuw/2aT4+ZlBa/DRERQc+a+lj3cetyRPZA4GHxSTrJ3h1Njroq3B77QwoPrFNSMwfXhx4HG/h/6t+bV/mzMew0qyccn/Lgv8MgaAX88ehYdf2YK2zq4g4+pzu37F/49CQLb5UB3W7pM2Jf3sqmG4bXLXzfezA6cx64lPVfMwtm8e7r10kOY8K7l2TGnINm9yEm6b3E/x5vncnZOiSk/JlMGFgVq0aNxxYQUevGJI4HFZfgYemN79eGjv7JDXTB1SKNn/h5cPVg0+xvbNx9i++QC6mjOCzbvsHHxPdi4m/vJD1NSb10zmp/VHBvs/xR52OKWYZNfCcuGYPUeBle8pmr4N/l+eWvsNKIm0a6T1Rtz62Tgl0sq02o6hcT9ZUtGs/aKX1tPOOW9iD4MPikmWzvPh0Myhnea2tEhEc7vonitCvl37UZX21bN6sJVnJPZCD3MCJq3HkAfZSi+zKjbU/F1k8BFzGHxQjLJrbRd1oYVwdDcEK6uOtf7KVeK/+chvVvqWTg/dFvxuI+XPylr1WJxl04zKB63BR0jNh42fl9YfGWx2iT0MPigmuaHmw+wy2MqmhWhusEkKU3TrPabyr+Xg1YOdq/mwc3E5s5gRAGh93/Jzo3SurIpH2OcjfjH4oJhk7WgXbfuZPQGZ25td5DcYPTftSMFFxH4ELg3MnGJGnjUfQ37eXdjng6FH7GHwQTHJyh86ase2smYCsLjZJYqbldprow2+pH0+IuwbVUrhxWTNh42ZDq35sC1pzd851nzEHgYfFJMsrflQudV1ytp6zP7BbOV7iiavar909RwzUvDj5IiTmBztYmOWnRztorV5lbFH7GHwQTHJysJGrcCTb5cXwdHew9z6602tf4Ge7Cp3ONXR54PDXSTsbPqQ13DZ2UyltebDpV8dCoPBB8UkK4fDqhV48uDA7ELYyj4f0RTOavc5PedA6RDBeYr0a9rK8x2bNR92NrtIH3O0C5mBwQfFJK3DYY1QK8ZCgg+T07W2Kck4tV/ZerKrOM9H0N+R7mdW3ltiss+HjQGAlnk+rMI+H/GLwQfFJDvXdulOU7ZBCPvQcLpWdGyN5pj+wEF+0xEBzW860sRUEWs+rJznIwbbXewMAORp2flpaT3tjD1iD4MPiklWljVqBZm8w2nI66JM19/sEikdI6Kq+VBrdtFV4ivVfHCeD6PsbXZx7gPS+iODwUfs4cJyCa65rRN//88+jCrNwYWDjK0YCgBr953C7mNncOPEcsn2HUfq8fHOY5hT2Q89vMl4e8sRvLP1KAp6pCIrLRk3TixH75z0kOO9tfkw3g+z8urh2mZ0+kQs/rQKEyvy4RNFvLnpMNJTk3H9+DKU5HYfs6mtA3/7ZB8Onm5CXXM7JlYU4LbJ/VT7bMjLsf9+cztOnGlDfUu7ZPve443YdrgOw0tyUH2qCccboltY6/Glu3D9+DL89RPlxb6c4m92aYtipd1IN3gnb3CxOM+HvUNtbUsqhFIc/u/NRyBCxKDCLDy39gBaOzrxymeHFF+/sboW55blGkr71c8PIiXJE1g5OJLdx87gtc8PocMnoqmtAwKAKUMKMdWEhQTjEYOPBPevDdX4zXs7AQBVC2YaPs43/m8VAKB/rx6BZbwB4O5nNqDqZBM6OkXccWF/fPfZzySv23m0AX+5ZZxk28kzrbhnyedh09tVcwYvrq/Gz9/aDgDomZmKE2faAHQFPH8NOuav3vkCT6/aH3j83rYajO2bh9EqhZJPocT7fx/vVtz3O//cgJU/ugQzH/8kbH61OFrfgne3HcWv3v0i6mMBwJDiLHxxtAEA0CvTK3luZJ8cbDlUBwA4f0ABPt1zEgAwoSI/ZPXZ1KSuCtIva85ItvfK9OLGCeVY/GkVLh9WFDYvWWkpIdvK8jICf6enJoV9/QUDC8I+H42stNBiMFthm5tkp5ubv74FGdh/sglTB4f+AJGfu9yM1JB9rhheLPmOyRX0SMXJxjbd+ZLXrjW2dmDukq4ypHdOGo7UtYR9/TUL/2OoXDve0Ir7X9gEAJg6uBA9vJE/72m/Wx6y7elV+6MqV+OZu79hZLmDtc2mHu/AqSZJ8FF1sgkAsGbfSdxS2Tdk//e3h9Zu1DW3h2yTy0pLxvbD9YHH/sADAD6QHXON7GYKdBViavTU4B483fX51beoH0+Pg6ebFLcneQRcNrQIbZ0+fPTFMU3HeuKm83CothlH61owvES6xPqfbhiDF9dXIy0lCd8YV4ath+rQ4RNROaAAr288hIaWDtTUt2DqkEKkpYQGBr+8diQm9S/A8D456FuQgStH9g6bl/svG4SXPzsYePw/Vw/HFSOKA48zvcn42y3jsHrvSZw404p2n4h+BRlISfKgINOLr48t1fSejRheko1fzR6JL442IC8jFWkpHszU+GvXam9/70K8u/UI6ls6cLyhFYdqm/GVUb1xxfDwn7dWr373fBw41YTzB/TE6xsPYeao0OOeU5SJ33xtFFbvPYVRpTm4ZEjoL/n7LzsHpXkZ2H+qETX1rRjQKxMdnT7sO9GI7PQU3Hp+P+RmpOAvK/bi2TUHwubphglleG5tNYDQ5pTWoNq3SIFHNILLh7YOH3p4w+xMhjD4INto7cugZS89HU6Vdu0M83o39pz/8zfH4rKztQszH/8E24ICLyVDirPQv1cm+vfKVHy+X88eePCKIYHHxTlpgb9vqeyn+BpvsidQ+Pub13LSU3Db5IqI+S/Ny0BWWjIazgZpSmlMG1aEaRFqUKwgCAKuG18eeUcHDCvJxjBZ4GimMeV5GFOeBwD49oX9FfcRBAFfH1eGr48rUz1ObkYq7rhI+fXBHrt2ZMTgY/6sUXhj42E0tnU69l0MTtWN5UE8YIfTBGdnT38zZysURe1zPyjtFzYvDpY1au89uN1dS3u/FeUli2Cyi78fjvw6tnqJA6V0ou3/bVeeYw2DD7KFKOr5BRF5Pz1fZ6Vklfp1GDm22dTSDu4TqaVzpiWTcrEMJZv4L3F5mWHXJRicbrTfJStGr8UDBh9krjDfMzOHzflEMapam3B5cbKaVS3t4BEZWmo+rCjvrJxllCiY/wqXX3F2fTeDvz/RJsnYQxmDjwRn1yhDQTC3z4ee+6DSruHy4mQtqXqzixD0t02ZkWHtMdnFP7w7pMnCpmtQUvMRdfDBL44SBh9krjA3RjO/g/qaXfT1+XC02UWt5kPyt5Y+HyzwKHb5g23599Suqzr4x0m0wQODD2UMPshcYb5nmms+NDa7aO9wqvz6cMd2G0k/Dw01H+xwSrEs0Owiu+js+m4GJxN98BFlZuIUgw8KsPrXsuY+H1o6nOqq+tCZF1c2u3T/raXVxYq3wNoUsosQqPmQdTi16RIM/qEUbZrscKqMwQcFmPLFDnNnNLvDqVZ6+3w4WfOhmnJw8KGp5sP898AilOziURntYl+HU/OCDwbtyhh8JLjg+5gpX+ywo100HkLLPB/aDmUoDbd3ONXU58OsDAUfk2Uo2cR/uYfO82FP+sFlYbTlIms+lOkKPubPn4/x48cjKysLhYWFuOaaa7Bz507JPlOmTIEgCJL/77rrLlMzTdaw+jui9Uuo6cuuZ7SLwvHCjnbRfmjTqb13vQuvMVCgWOZRmWTMLpKhtiYei7rpCj6WL1+OuXPnYvXq1fjggw/Q3t6Oyy+/HI2NjZL97rjjDhw5ciTw/69//WtTM03WsHoeBzc1u7i1w6mWScY0Nbu4sJEk9taOJaf4rxXHml1MHO3CZhdlutZ2effddyWPFy9ejMLCQmzYsAEXXXRRYHtGRgaKi4vlLycXCr6RWf0d8WlckT3cuit+evKqOMNpmAM4Wlao1nx0/62tz4dJ+SFyQGB6ddl22zqcmtjnQ0t5loiiWliurq5rSe78/HzJ9meffRbPPPMMiouLcdVVV+GRRx5BRkaG0iHQ2tqK1tbWwOP6+vALZlltY3UtXv3sIEpy0zHn/H6BFT23HKzDW1sOQ4CAr43tg4GFWaanvWbvSew53hhYtMsq72w5gs8OnEZ7p4jFn1YFtv9n9wnsOX4Gt02uQEqS9kqx59cqLxT1zpYjQcfuWq00kmP1Lfjf93ZG3O/dbUdxxfDIAe7WQ3U4cCp0pdj573yBr4wqwQvrqjGhIh8j+uSg6kQj/rZyL15YVx3xuMG+99znuvYP5/GPdituF/T2+WB5R3Fg/ts7MHNUb0wdXIjn1h7A9iP67w91Te3IyUiRbNt97Axe2lCN9JQkdPpEXDKkEGPK83C0rgX/XF2Fd7ceDez7xLLdeGTmMDy/rhqHa5vR3N6JrLRkiCLQ4fOhtd0Xtp7RaLPLyl0n8PbWI0gSBGSkJuHGieXoW9ADALDvRCOeWb0fDS3tSEtJgkcQcLqpDaV56bj1/Ar0ynL/MryGgw+fz4f77rsPkydPxogRIwLbb7zxRvTt2xclJSXYvHkzfvSjH2Hnzp145ZVXFI8zf/58PProo0azYbprFv4n8PeppjY8PGMoAODb/1iHmvqum+fK3cfx1r0Xmp72dX9ZDQDo36uHZFl6M7W0d+LuZz9TfO72p9cDAHrnpOOq0dqWFF9fdQoPvbIl8Nib4lFN57bF6yIeb8E7X2Dl7hOa0n5329GI+9zxj/WK22ub2nHDX1dj88GuALpqwUw8+PJmrN13SlPawd7YdFjzvhed0wsrvjyuO43stO6vav9ePSJ+RpdZsDrsZcOK8MH2GozoY2yV1alDCvH6xsMYbuEqreR+4b4Ds87rAwA4VNsMAFiz7xTW7DsFYJvh9B7/aBce+cowybZpv1suefynj3ajasFM3PDX1dh3QtqN4JXPDuHdrUfR1NZpKP3apjb0yU3X/bpvPrVG8nhjdS1e+E4lAODBf23CuqrTiq870dCGX31tlP6M2sxw8DF37lxs3boVK1eulGy/8847A3+PHDkSvXv3xqWXXoo9e/ZgwIABIcd5+OGHMW/evMDj+vp6lJWpL91spw1BJ9cfeADA1kPW1s4cONVkWfDR1hm57UOppkDN/pPSfXukdl1S/qXXI5FPFb4mzM2/JCcN3pSkkMIhnCN1LarP+QMPPyOBhxaXDCnE4OIsZHqT8bWxpfj4i2OSgC2Sn8wcKqlp+/6lg1BT34K2Dh8uGdoVZEzol4/jDa1YtvMYemV5cb0FS8T/79dH4/WNh3DlyN6GXv+TmcMwtHc2pmuosaL4tWDWSPzo5c34ZFdoAP3jK4fqPp432YPzyvOwYf9pJHkEnFOUiXOKsvDShoMAumpg5cGHGrWyxWjgYabgslEt8ACADQfUn3MTQ8HHPffcg7feegsrVqxAaWlp2H0nTpwIANi9e7di8OH1euH1urOKKB5rrkUNMYGeDlLyPf19KcKtGhssWda8E64/w6NXj8Cpxlb86GXtN243+Put4yWPpw4plDyeWJGPg6ebA7/25L42VvodK8j04v9uHhey3+DiLFwwqGeUuVWXk56CWyr7GX59rywv7ro4tAygxFKSm467Lh4QEnx4kz0oyNR/L3juzkk4rzxPsu1wbXMg+BDsWsBKhd1NoLHSwVVX8CGKIu699168+uqrWLZsGSoqKiK+ZuPGjQCA3r2N/Vpykhun2Y6W2bOHyi90/yOtn528WIhUTkSzkq1byN+BKIZfqTYe3jNRMKXvudHiVunbEfx9cjj2sF2s3LV0BR9z587FkiVL8PrrryMrKwtHj3a1uefk5CA9PR179uzBkiVLcOWVV6KgoACbN2/G/fffj4suugijRrm/DUouDmMP0yfwCu2N3rXFaA/viDfaOChI5L/ERIhhV6oVOBUgJQCjw8OV5sDROzTdSrbfR2LkvqUr+HjyyScBdE0kFmzRokW49dZbkZqaig8//BB/+MMf0NjYiLKyMsyePRs/+clPTMuwnWLkHOpidW2O//CGf8VEKCj0TrblRvJAQxTDv6/Yf8dEkZlZZiS5qJywe86dWLlv6W52CaesrAzLly8Pu09MicOqDy3vKJpF2/xdPbTOZiovIyLdhN1TpBgXWvMBeMI1u7ioICUyhULxYLS0VSozgrc5XYwbST+afhux0ueDFbphxMYp1Edbs4ueDqfKMxAarWEJd5sV4XwVqhnkcYZPjNDsYm12iGynVDoYvWkqlQnBwbzT92IjySfClOwMPsJw+qK1gtVRcaDDqcbZTEMkQLNLSM1HhGaXeHjPRMGUiiGjJZNSP7FwwbzdjJS50SxGFyu3LQYfYcTnaBcN+0QxdbloYc2HgPio+ZC/B1EUwzc3xcF7JorEaHHrUbiLJUlqPpwtx43VfETT7GL4pbZi8BFGrJxEPbRc1NGMdvEf3/BolzB32ng5HfJAQ0T4obZE8cbMTpgR+3yYlpIxRoKfqIIPx9+xNgw+woiNU6iPpmtazyRjITUf/n+1zvMhyB6HFw9NEMqjXdT3j4O3TCRh5g87pa9HrHc4jabPh9PvVysGH2E4XV1nBU2xh67jyTucdv2rYRZ3RRFHu8TBjVgecIkQw492YZdTijNmlqxKtaXBXyenawKMpB5Vn48YuW0x+EgwWqc9N3z8aPt8JMIMp/LRLr5IHU4tzhCRzcz8Yac4z4ebRrvYPNQ2VjD4CCMBzr+iaDqcIlDzYWyej0ji4Uas1OcjfLNLHLxpoiBWF61CjPf5iK7mw+l3rI3hVW3jwfbD9XhxfTVSkgQ0t3eiuU3aVrCzpgEPvbwZHTYPuo62dqKlvRNPf1qFCwf1wjDZ8uVarsv/9/FutLR3Ij01CUfqWpCW4oFPBJpaO3DHRf0xvCQHWw7W4Z2tR7DoP1WS19Y1t6OhpR1/+HCX5vw+v/YAMtOSMbo0F18cbQi7r9b78MOvbEG7jraf2xat1bxvtHSPdrE4P0TxTOu9+AcvbrImfdnjTdW1eGF9NTK9yWjr8OFMaweuGl2CTG8y3th4CCKAapWVxV9cV421VeFX3z4cZiVvN0no4OPKxz+JuM/z66ptyElXwOB3prUjqmM9t/YA5r/zBZ5dcwArHpwqeU5rc8jfVu5T3P7ZgVqseHAqvv2Pdaipbw15vr6lHUvWHMCHO2o0pdPU1hlYXj41KXxFXGl+OvafVP5Syj239oCm/fw+3nlc1/5a5WWkhGyTBxpjyvPweZhlsFnxQfGmNDc98PeEinys3XcK04YWhnmFuixv+NtY75w0Tcd5+bODhtKPRF7kXr3wPyH7/GvDQWR5k9EQoex/8OXNZmbNUQkdfBhVmpceeSedgn+l98mN7vhr9nZFxgcUoudo63D8x1QKPAAgKy0Zh04rLw0fSZtKTcVPZg5FcU4ahhRn44DG4CMaHiF8b/PvXTIQe0404t+bj4Q9zt1TBmDmyNDVnOVNLD+8/Bzc/JR6zQubXSjeDCrKwuM3jEFRlhcluel4a/MRXDumj+7jfP/SQSjMVg4uRpfmYNPBOkysyI82u5p4kz1o7Qgtw7R2eI0UeGiVlRYbt3X2+TAgKy3012y0zGzZCXexWz1xmhUtVN++sD++MqoEgD034qfmjMfo0hzF50rz0jHv8sF44PLBYY8xvl8efnTFEIzoE3qc4Pdw18UDUJDpRd+CjOgyTRRjvjq6BBP7F6AsPwN3TxmAYo01FMHunjJA9blJ/QuiyZ4us87rgz9/c2zg8f+7cQwGFWZ2PYiNLhi2Y/BhgCUdeswMPsIcy+q+SD5RtDRAsKPDabjs+z+/SG9RaxDmDxTDDbUlImVhv4dnn7Pj3i9AkOTFI3Q/ZuyhjMGHS5g5Fj38kSxe28Xib5odLRCCIET8lCJNdqY3QI2HydOI7BZu6L3/OTsGf3gEaY2mYHP6sYjBhwFWNF3YNaDG6nSsHuZlxzwf4SohtL4/vZ9zEmMPIt20zAxsxyRjgiDNiyCp+bA5+oiRYIfBhwGWtLqYeFBnm12srZ2wo4JASy1EpF00f8yi9jSJSErLEHV7aj4ESV4kM6zGSDBgNwYfBlhxLYkqf0d/NPkz1je7WFk7YUeHUwHqc2v4P72I+dDb7MI+H0S6hfsa2hnPC4K01Ouq+Tjb7GJfNs4mbneCxjD4MMCaZhdR8W8jwr3cZ3DNFa26Opxad3w7vlfh+nyIgZqK8MfQ3uFU2/GIKFS4HwH+mgg7ZvwUZH0+PEHNMLbPOBojNS0MPoywuOoj2ms13Mutr/kQLQ0Q7GieCNvn4+znF6l2R+vn7C+Ykhh9EJkq0OxiU3rBX2GOdomMwYcBVnc4jb7mI0yzi9UdThH7fT60NO1EHGqrs4aJE4kRmSxQ82F9UqIo+w4LQT9Q7I4+YqQoYfBhgDUVH/ZcoXbM82ElO75X4Ue7aMuH3k8hicEHkan83yiry6QuYpiaDza7KGHwYYA1o13MO76TzS4++S8Ak9nS4TRMGoFPL2J/U63NLl3/stWFyFx2NnvIaz48QlDwY3E/u1jF4MOAmO5wavk8H9bWTtjT7KL+XHewYG5GONqFyFx2TvIlyqYYECB0N/tYn3xMYvBhQEzXfFj8TbT++JYeHkCkwMLf4TQ8vfnkPB9E5ur+SllfaIgQQ+b56J5nhOGHkthY/s4kR+ta8NTKvTha3wpvsvG461BtM9bsPYmJOhcu6uj0YfGnVRjfLx+jy3Kx5/gZ/HvzEdw4sVyy3/+8tR1vbj4Mb7IH3xhXhkuHFqGuqR1P/WcfTjW24quj+2BCmJUaj9W3KG5v6/Dhz8v36Mqzku8997nqc/+3Ym/Ux3ealkqISM0/Wmuv/HtxtAuRudQmGVu6o8aS9NRnOPXnQ8Q/V++3JG09vjhaj2U7j+PW8/shLSXJsXwkVPDx2Ns78Oamw6Yc67q/rEbVgpm6XvPm5sP4xb93IL9HKj575DLMe2EjNh2sQ/WpJtxzycDAfnXN7Vi28zgA4L1tNahaMBMP/GsT3t/e9aV5fm01dv/yStV02lWWpv9wRw3e2xb9F+8Nkz5DLUaX5Uoe56RHt6JwWX46qk81h90nKy0Fp5vaAo/zMlJwuqkdAHDV6LOr60ZIZ+ao3mGf9y+/PfDsypfZMbIMNpEdrhtXhhfWV0d1jMDNX1KrLOL2p9dHdVwlvbK8ktXOs9KSQ4KfnTUN+Onr20xPW84bIaCY++xn2HO8EUmCgDsu6m95ftQkVIm3as/JqF4/77Jz8LsPvjT8+s0H6wAApxq7bmybzj5e+sUxzJ06UPV1oihi+ZfHA487InTcULtBnzzTqiu/Zrj1/H7o4U3C2n2ncLqpHbuPndH0uuLsNMwYWYw5lf0k24f2zsKs8/rglc8Oac7Dxef0wrllueiZmYpZ55XijU2H8eya/dh6qD5k3999YzQqevZARkr3V+Oft0/E+9uOIicjFV8bWwpAvZnk/mnnIDcjJbCfmufvnIRN1bWYdV4fAMA3J/WFxyPg1+/ulOz38t3na36fRPHioRlDFIOPvgUZuGhQL3T4xMB3R033DKPd5aXWFpD+vXpg1pg+6PQBv/+wu8zvmZmKGSN6o7a5Hacb25CVlowRfXJw44Ry5PVIxe+vG422Dh+Gl2QH1Y52JdrY2qEt8SDThxfhwKlm7DgiLatGl+ViU3Vt4PHs80qx78QZfHagFhmp4YOPPccbAQD/2XOCwUesuHRoYVTBh9rEVKIohq2m19tkqLq7Rf0KfnzlUNQ2t2Hhx9Imne9OGYAHrxgi2fZfr27BkjUHIh7zJ18Ziq+MKgnZLggCvn1Bf0nwcemQQnz13BJ8//mNisd6+lsTJI9vmFCOkX1y8JU/rQzZd9Z5XUFDrywvdtY0AABG9MnBiD45sowo5/v70wapvSWJMeV5GFOeF3icm5GK704ZGBJ8jO2bJ38pUdzL65GKtBQPWtqltbi/vHYkJg/sqekYijUfGtP/76uG46JzeuEZWTPJQzOGhv1hce2Y7ufkNR96y/Frx/TB7687F8caWjDhsaWB7UN7Z+OZ2ydg5H+/DwC46+IBeGjGEHx+4DSufeJT7U2+DndFSagOp9Hee6Nds0QtfZ8YaXisOazqVSAIyp+NUu2A1jyEXSpb9pSoklY4kfpYRDoc+4cS2U/P1y4w2iVom9Ybs//7HVLW6Lhjy/t8GB1pKJ8DyCObyj0w67LOSdWc7gabWMFHlK/3WPRpiaIYYXisOZeJVTdMQRAUO2kqpac1SAgXG8iPIRpYTyba0SWMPYjsp2eeH+U+H9pe6y8fQsoazamHDvXVO+rFv788D4KgXD7qXUvG6VE4CRV8RCvqmg+V7SLsmRLdqtVmPQIUIw2lgkJr2aFntUqfaKTmI1L64Y/HobFE1lIqr/R87bp7XAT1+dAYPgiyf4MOoDsD/jT11nx0Lzopr/kQlD8bhZqesMdns4t9om52sep+E7HZRd9VopZNy2o+ED4Sl++r/ajanhFV0gp79AgfRqTDMfYgspbSd0xXs0sUNR+CSs2HHqF9Pozd7eU17sHDeLsO7N/e9a/2Yf6s+bBNtL/8rZqKoavmI8zzJnU4tep+6fEIKv07tNWGKB5TR81H15daZ81HxJqN8K+3qhaJiNTpmQm4u9lD/2gXfzKh/cuM9/nQe6tXm01ZUNgWvJ/WGhbWfNgo+l+r1nQ4jTTaxSxW9vlQOrTWfiBqx1Sj9FHpDQwj/aKJWDPC2IPIdoZqPoK2aW52OftieTmgp5j2lzH+4EdvGa82AWFXh9PQ/ZVqesIen8GHfaLucBp1s43yAcyu+bD7vihA+ReJ0jatNQZhV5aVPzbQ5yNS52HGFkTOUvoOGllY0liHU+m/gdfrSFepb5oe/qBFfhyPoFzTLA92IrFntV91iRV8RDvCwaKfu6IY/kLQ2zantrdVi8oFLx8diRn7yT8qEeaPdol0PHY4JbKfrg6nCgu7aS0C1fp86LlfRzvaxU/eRBxptIvepR2cklDBR7Tk59un826u9r2JdLGYFTRYFeh2LaKkHokH01p2hKshkQdjPp8V83yw2YXISYqj5XS8XmnoqdYAQHWeD0N9PsSzaWt+6dnXdQnt8yHI5vmQp6czAYcw+NBBfhHorrZS6/OBSM0uoU+GC3zUvqBW9W6OFIlLtmlsu7K65iPiJGKRXq8vOSIygZ4fGYHRJkHbtP6Q86djRm23v7zSfb/wdziVlZlq5bg/r1p/FHO0i43MHmprWjNGxGaXUEba66xqdgkZ+hXYrrBNxzHVhAQfov5CItJol8gznDL8ILKbkWYXI+0uguzfwMv1NLvIZhzVH3sov0DtOEKE5+Wsuh9olVDBh9n0BgCqa7tEiEG7bq7SbZ1G2lAsandRm/RG8VeK1j4fYZ6Tf1pG5vmI2OcjQkatGnZNRF0UO5zqmSlI1uwh/zscc2Y4lb5G92gXld1DO9x3bQl0ONV8fNZ82OJ4QysOng6/lHok8uqvTpXQsbapDX/48Ev89PWt2HV2cTK5/36je2nl9k4RP3hxo2q6z609ELLA0r82HERtUxv+vHwPjtSpv6/gPFp1qakVB8pttlpHu+i4uxsa7cKaDyJX01ibGunlogi0tHfiz8v34KGXt2h7rcpoFz3kk35ZXdPgLwPPaFw997MDtRbmJrKECT7qW9qjen2WNxk9ZEsV7zvRqLjvHz7chT98uAv/WLUfl/1+heI+iz+tkjz2L3OsZME7X4Rs+/GrWzHn72ux4J0vcP1fVkueSw/K57qqU4G/9XaQ1crj6fpyy2WlhS6afG5ZLoCuVWPDUXqtX89M6Wsn9c9X/ZUwUr4a7Vne5NBLP79HauDviRX5ALqW8NYqJSn6gKQ4Oy0kD0TUJTstRfvOQc0ef/poFxa88wXe3XZUVzpZsvQqCnpoT97/hxjyhyZl+cplT15GquRxn9x0AEBaaneZ1twWWh67jXoJH2ci/TIeUpyFKYML0dHpw+mmdrz82cHAczdOLMf148uQKzvpbZ0++WEAAG9uOqy43ewfy5sO1gEA9p9skmzP8nZ/YRqDomCrAm+PIGBkqfQmf/6AAnxlVO+QfS8fVoT5s0ZiVGkOZj4euqQ9ANwzdSBGlSoHDQBQFHSDBoC5lwzEIZVarcdvGKO4PS0lCYtuHY91VafgEQS0d/pw1eiSwPPXTyiHxyNgUv8C1XwsuWMi/rXhILK8yRAEQfJ6oxbdNh5vbzmCZI8Hs87rE/XxiOLFbZP7oVzHjwF/cesTRWzYf1rz6/5w3bmBdCb1z8fPrxmBAycbMaBXJiYPVC8PQtIPNIOE1nxcO6YPXv38UODx3VMGAACeXLYHQNePo7svHhB4/sXvVOK37+/EoKJM3HFhfwDAC3dOwpZDdbhufDkAID/o/tTW4ZP8CA1W0CMVJxvbcPmwIs3vxQoJE3xE6mD4068Mw/kDewYe+4OPK4YX45fXjuw+jkcINGWo1SSoJWVXRX1w22Jws4uV1X7pKdIL/Y4L+yMjNfTy8ngE3DChPOyxbpxYrrlZo6JnD3iTkxSHzg7tnY2Knuq/VKYOKcTUIYWKz6WlJOGWyn5h0z5/QE+cP6Bn2H30Gto7G0N7Z5t6TKJYJP9GzxpTqu/1Qf1N9fQVuWZMd9CfnOTBzZP66ko3kP7Zf+WjXSZW5OO3Xx8dCD5uPb8ffnTFEADdwcfdUwYgL6gmdkJFPl74TqXk+BP7F2Bi0I+j4B/Y4fqX5GSk4GRjG26/oMLQ+zJLwjS7RLyXqQ6DFVV3U7+ZO9sfIDhfwX9b1cFIcT4PE9pKdb1G4TN3ukMVEZlH93D6oEm+nOiiFbK2i3/orCBI+pwplVNGiq7g9xi2c6vKEF67JUzwEWlSKbVmGfk5DN5NrcOpas2Hbec6uLbD+huw8jBb42/WyOyh7P9JFN+Mz+Xj1I8Q6VBbf1kcOnGZSakpTDymJJAPk9I1KmGCj8hDK5XJT2LwL2y1X9ZOD8OU1nxYH4gorzNg/HhGXqp0elnxQRQ/9K4k3T3DqdM1H9KCKJop27WmqWXeKKd/sCVO8BFpITGNNR/B179as4val8SuZdhFlT4fVk6vHrrN+Hs1UmvCtVaI4ou8HDDc7GJWhnRS6/OhZcp2o3nuXlxOfZ/u59jsYotINyf1X+rqfT7UJvpyutlF2s8j6G/LUlSaz8M4I7UmijUfTi9eQESGKa3mqu8AXf841fdL3ufD5/Nvt67mI7i2R42/XHS6hj5hgg+j02mH6/OhVrXl9G9w9dEuVjW7hG6Lps+Hkdcqdzg1nAUichmDsYfu0S5mCQRLZwsiMbBdup+ZxZT/fYYr69WCILslTPAReQl1bSci+CJWH2rrdPjRLfgitK7ZRQh5z9FE1WZF5Iw9iOKH3nIheG0VN4x28ZfFTvf5COxrXrKGJE7wEanPh8r28L2GVY7lonk+pMGHNbdjQQg9tt01H0o41JYodkXbmBtc8+GE4KG+Xf+qjTIxL4fa+nwo9z2xW+IEHwZrPkJvqt1/6x1qaxdf0MSrav0/zGT6aBfT+nwQUbwwOtTWsR8hsvT92ZDfa3xKE2UbzLOspUdRYLQLO5zaw2iH0zCDXVQvatWTalNUEtzR0paF5QSlnun2zvNBRPEltClXZ82HZKit/WWKvObFXxSH9vkwv+Yj7FDbQBBkWrKGJE7wEbHZRdtQ2+CL2L3NLt1/iypNMGZSXL3W5nk+FLHqgyhu6C0XuofaOjXaRdoEojrU1sw+H/5jhtlHLR92S5zgI8InrXaBhqv5UBtq6/gv96BsxeI8H2Z9fow9iOKH8WYXZzpXygOB7tEusg6nCq81WnbpmmSMzS72iDTUVvONOegwB083hTy940g99p1oDNle29SGv6zYqzER/dbuO4Xth+vxo39txtqqU4HtpxrbAAB1ze3449JdlqTtij4fxpMjIheSf6f1N7t07f/pnpPOjnYRRdS3tOMPH3wJwNrRLi0dXR1Ith2uD2w7Vt+C33/wJXYfa5Ck5/Rv5IQJPiJ90NnpKYrbJ1bkSx4HLxO/qbo2ZP+7n9mgeJwfvLgJze2d4TMRhW/+bQ2ufPwTvLC+WrL9TGtXmr99f6fqay8YqG9lVvly91lpySiWLXOflab8eQbLzVDeJ1lD5HL+gK7VHK86ez4yvKEr6E7qr335ayJyl9NNbVG9PrgUWbP3lOp+F5/TK6p0tKT/+sbDOHn2h2BWWldZlX3232El3atY+7cZXdm67Wzw8b3nPg9s++XbO/DHpbtw73Mbz25RHvJrt9ASO04JgoAl356INzcfRkZqMgQAbZ0+5PdIRb+CHiFLry9/YApW7j6Br48tk2x/cPoQPLe26wbfQ+GGV3UytDYEAJZ/edycN6KirVOpy3T3hR4u/dsm98O0oYW4YFAvrK86hYde2aK6b4/UJDx92wQs/eIYth6qw5DiLIzskwNBEPDH68/FO1uO4vLhRWGXsvd75vaJeH97DdJSPGhp9+HkmVZMH16M5KTIMfGvvzYKS3ccw9fGdi2znelNxqLbxmN3zRlUn25Cr0wvbjK4FDYROS+/hxcnzrQGHutdhTX43qr2w++nXxmGGyaU41+fHcSZlg5UDjDvB0twn4/G1o7A9u9dOggA8Oy3J2Fd1SncMKE88Nyz356ETQdrMX14sWn5WHa27N9xpKs2xOeSmo+ECT4A4PyBPXG+xl/5fQt6oG9B6A00r0cqfjJzKH7x7x2qk4y5QVG2FzX1rYH2PbVhwQCQnpKEWydXAAAGFmaGDT5uv6ACeT1S8bWxpYEbv9/V5/bB1ef20ZzHEX1yMKJPTuQdFZTmZWDO+f0k26YOLsTUwYWGjkdE7lKcIw0+jHY4DWfWeX2QnpqEmy34oeJP3SeKgT4YXx9bipLcdADAyNIcjJTVIittM5v6fCP2SphmFzN1D2dyOCNhJAWi7q5Mhgs+9FyFbn7PRBQ/5MGD0Q6netIwVdAMp072s5AnGehwyj4fscdf+6c22sUN5MO8wgUfer6AXKyNiOwgb2XR3eHUtJ2MCZ7h1P8j0Ol+FoD6ZGd2Y/BhgL/t0c3Tdyf584jINR96rkEXv2UiiifyiQuje7nhfYzqXttFDOpn4Xzw4WOzS+zyR69hmzIcFgg+/DUfYaIGPdG4e98xEcWTkFJJ990y8gusvAEH5vkQHW52CZnVTGW7zRh8GBALfT78VZY+Lc0urPkgIpeRl0tGp1cPx8pmkOBDd69oa1lymnVPMuYsBh8G+EeCunm0i7zZJVxe9VyEbm5qIqL4IQ8M9N4stQQWVv749wR1+ndXnw935IXBhwFCoObDvTdiT3eDIwCgw6yajyjyRESkVdQznGrax/qaD1F0T20D4J55Phh8GOAfxtrp4jtxIOo++zh8oKSjz4eLAy4iih/ym6MlQ20tvQF3l8Hdi7k5H364ZcQigw8D/CvkuvlGnCQbkcM+H0QUS0Lm+dBZb+D0fT645sNf/DrR1BEyz0cs1nzMnz8f48ePR1ZWFgoLC3HNNddg507pmiEtLS2YO3cuCgoKkJmZidmzZ6OmpsbUTDstFka7+IcD+7NoVlZd/JaJKI6E1Hzo/KmsJVixdKjt2X9FiK654QPBk4zFUJ+P5cuXY+7cuVi9ejU++OADtLe34/LLL0djY/cqrvfffz/efPNNvPTSS1i+fDkOHz6MWbNmmZ5xJ3lioM9HUlDUbSa3VNkRUXwLCT50HyDyLnaMdpFOMmZZctoFamGczYautV3effddyePFixejsLAQGzZswEUXXYS6ujo89dRTWLJkCS655BIAwKJFizB06FCsXr0akyZNMi/nDgoEH8prublCd5+PyMGCngDFxfEWEcWR0OnVrehwah0hqM+Hv9h0pNlFlmT3JGMxvKptXV0dACA/v2vZ+Q0bNqC9vR3Tpk0L7DNkyBCUl5dj1apVisFHa2srWlu7Fw+qr6+PJku28A+1XVt1CmdaO/Ds6v043dSOqYPVl2YON9rECv4+Hx/uqMHPrhoedl83910hosQUOs+H3tdraXaxvubj8aW7gjZalpyiI3XNOHGmLfD4oy9qAvcip5uADHc49fl8uO+++zB58mSMGDECAHD06FGkpqYiNzdXsm9RURGOHj2qeJz58+cjJycn8H9ZWZnifm6SnZYS+PvGv67G/He+wJ+X78F1f1kd1XGz0sxbZLi2qR0AUH2qOaRvysSKfMnj3IwUyePBRVmSx4VZXmSfzdvQ3tLniIisUNlfury97g6nQX8nqUQuVjY9KB3a7pqPG2T3pG8tXh/42+kWIMPBx9y5c7F161Y8//zzUWXg4YcfRl1dXeD/6urqqI5nh4lBX4rNB+vC7puTnqK4/cJBPdHn7NLKQNeX4zsX9Q88nnfZOSGvmXfZOeiRmqQpjw0t7YG/faKIy4cVBR7/dc44/PprozB36gD87hujMbBQGlA88c3zcNfFA3DftEH43qWDsOi28Xj225Pw6FeH49oxpZrSJyKKxncuHoBpQ7vLrWiG2hZnp4U8/8ztEy2u+Qg9ttX9LObPGgmg+75TdbJJdV+nO5wa+ql9zz334K233sKKFStQWtp9MyouLkZbWxtqa2sltR81NTUoLi5WPJbX64XX6zWSDcckeQQMLMzE7mNnIu6bnR76Ed82uR9+dtVw7D7WgGm/WwEAmDt1IHpldX8OUwcX4ncffCl53fXjy/CXFXs15dEju8r9lR/zZ41EdloKvjFOvYZpQK9MPDRjSMj2kaU5mtImIopWarIH35xUjg93dI2WNPteecGgnuYeUAOr+1kMLu76Ial03wnJSyw1u4iiiHvuuQevvvoqPvroI1RUVEieHzt2LFJSUrB06dLAtp07d+LAgQOorKw0J8cuofW8KXWn6L4AhaBt0gtT6cLQE6kGVzMG97Z2uqqNiEir4DJP743b6a5sSsW11TUfegZDOH0v0FXzMXfuXCxZsgSvv/46srKyAv04cnJykJ6ejpycHNx+++2YN28e8vPzkZ2djXvvvReVlZVxM9LFT2vbnWLwIfiPIT1epEPqiVSTgnYWg8a8OD2fPxGREdHcuJ3oVK8YLFlc/uo5ekw1uzz55JMAgClTpki2L1q0CLfeeisA4Pe//z08Hg9mz56N1tZWTJ8+HU888YQpmXWTaM5boN5DkNZ0yB/L6QkcPLKaj+7pffXllYjIDfTeLIPDDScmR3Si5qN7bpHIbzim5vnQ8obS0tKwcOFCLFy40HCm4onSZ+a/QILPvRDyOLrOSvJ9u2fYY/RBRLFBXkbq4fQUAk6MdgmeW0Trvk7h2i4GaW52UdjmDwA8spoOjweSxyGv03GxBB87uObD6WiXiMiIaO7bTszMrFyG25Omptm3Y6nDKXXT+kVQmtO/u9kl+HhC5A6nOs6WpMNp0NoC7PNBRLEommYXJypBlMpa+ShEswVP6a51X6cw+DBIe82HwigTf7OLJPiQPla8cKMZ7QL2+SCi2BJVeSUq/mkf+/ub6mx2cRaDD4P013x0v8AfRMiHkUkfK6SpI3/BgYpPFANDr9jng4gSjRM1H0rN5Fb3s9BT8+F0LTiDD4M0z/OhsH/oLB9naz5kj+UM13yAfT6IiOzk9tEuTv8OZfBhkNYaBKW+Ft3zfEhrOqTBhULUbHSeDzE4CGL0QUSxIZryStrJ1Il5PkJZXdvQvZp5ZE7fCxh8GKT9GvK3uwS9Fv5ml+5t8knGlGc41Z6/4JEzCJrhlDUfRJRoHGl2caTPRxfWfMQxzc0ugZqPoNcqzfMhb3ZROJauScbkM5xyng8iSiCiwx1OFft8WD3Ph7/ZRce+TmHwYZCeeT62H65HS3v3ZPvdQ22lx1DqlGokTSB0tAtnOCWiWBPV3B5Bd+BTjW3RZ0YnJ/p8+O8uPg1TurLZJUZpH+0i4q5nNii+ODWp++Nv6/RFbHbRc+FmpXVPXtvV4dR/DEYfRBQbSnLTDb+2d26a6nPnFGUaPq5WSiVtVlqKpWl6dNR8JDncBq9renXqprnDKYADp5qkrz37b3pqUmBbR6co64AqPf6z354YMc0eqUlobOvE/dPOwYkzrd15EIMXltOUbSIix1X07IEnbjoPPTO9ul87vCQnZNs3J5WjNC8D04YWmZG98GTl9cg+OZg5srfFSWqLPp7+1gTHgw/WfBikt8+H5LWB0S7S/cINtZ08sGfEtPzBy5UjiyWvF9HdAYkVH0QUS64c2RsTKvINvXbK4F6SxwU9vLjr4gEYWGh/zce3L6yQ/OC0Ms3A6EaV8v5CDfcTqzH4MEhPs0vIa89eIsGRp08Uow4M1Pp1SPt8MPogosQgL+3sLP6cKGrla7uoZcENtwEGHwZFt7Cc/1/pRGBKc4Ho0d3HSPpiyWgX/YclIopJ8nLazk6W8rTs6G8XmOcjwuhGN/wIZfBhkOZzpxB9KDa1ydpdjFwcwTUfkleL7HBKRIlHXtzZ2c1BvpKunUWvP2039/Fj8GGQ1ghaueYj9LUipAGDkYtGrXYjuM8Hgw8iShzy6QzsS1ne4m5HrYt8bRenh9OGw+DDoGj6fCjxieFHu2g9RlfeZM0uYnA1nO7DEhHFJPmPOCebG+yohRBCml2sT9MoBh8G6RlqG/pahf1E6XYjF02nyhTqIkROMkZECcdN5Z0deeme58P95T2DD4O0RrGKQ20VajV8YuhCc8rHU69JCa5qk3RmDVpYjs0uRJQoQjqc2trvQsqOWhf/vUVpQVO3YfBhkPb+pgpDbZVqPiBKjxnFNRMy1BaRh14REcUbeVnoZB8IO1KWzzHm5vKewYdBmptdFGs+lHaUPqH2JTESPYti91Bbj5u7PxMRmUhejtpaESAr/O2p+egSC/M6MfgwSHOzi8I25ZoP6RclmmtGaZIxUaU/CBFRvAqt+bCPvOy3pewNGe3iXgw+DDMefSj2+fBJZziNpq1OabSL2gRkRETxKnTlcIcyYlPaIfcNFxf3DD4M0jzUVnOfD9k++rOk+lrx7H8Aaz6IKHHIizs7O2A6Ms+HJH2RHU7jkdabeHuntnk+Qi5Uk5tdqk81A3B372ciIjM5+WPLiRlOg2t6/uvVLahrbrc+UYMYfBgUTRQbHABkeZMBAEN6ZyE3IyWw3ZuchLF98wAAg4uyAtsvHx55KWh53oK/Ak4vo0xEZJfTTdKbr50dMIuy02xPOziF59ZWW55eNJKdzkCs8kQI226p7It/rNqv+FzwNfjsHROxsboW15zbBylJAn7ztVEoyExFemoSfveN0Xhj42HMGNk7sP+PrxyKVz47FDbt0JqP7vBD/oUgIopXBT1SJY/t/On1tbGl+OXbO9DS7gNgTy1MLNVss+bDoEg1HzdP6hvmtd1Glebilsp+SE32QBAEfH1cGS4Z0lW70begB+69dBAGFmYG9i/I9GJmUDAS6fhAcGdT9vkgosTRO1de+2Bf2hmpyfjh5YO707Yj9Imh8p3Bh1ERTnK4Kraoq98ivVz2fGdQ9MFmFyJKFCHzfNicvmTWalv6fFifhlkYfBgUqXor3D3e+thDukdw8OHmSWeIiMzk9MJywenbEnxYn4RpGHwYFOkkh6thiPYCifQFkj/Nmg8iSkROz/MRXN7a0ezCPh8JINI5DnsRRHmBRIofBFkSnUEdThl7EFGicHrOLYHNLqoYfBgUsdnFypqPSM/L8hZc8xFLkTERUTRCZ/y0t/wLrvmwo+x1cuE8vRh8GBTpFFtZwxCp2UWeNoMPIkpE8rLQ7ppf2/t8xFDxzuDDoMgBgPrz2uY8DZN2xOfVaz7Y54OIEkVInw+bawaC7wMseqUYfBgUKcK0NAKNYqgtvwBElCgcbnWR/Qhlh9NgDD4MijjaxcKLIFL0HjLa5WyHU0HgUFsiShzym7HdpZ9ktAubXSQYfBgUeZ6PMM/LV5HTKWKti+xxp88/vW8MXZlERFGSl3h2F4HB6dnT4TR2MPgwKKqhtlGKONQ2ZLRL179W1sYQEblNaM2Hc6Nd7Eg5lmq2GXwYFLH2IcwnG32H0wjNLrLH/pqPGLouiYiiFlLmOdjnw47yN5b69DH4MKijM3wIEa6WIcpWF02dXYMDlIOnm7vyFEtXJhFRlEJHu9hLOtrFhmaXGPqFyeDDoJc2HAz7fLgLrSAzVfU5LWqb2sM+L0DAqNKcwON/nc1rU1tnVOkSEcUSp9d2yU5PDvydlZYcZk9rpSS5Lyhh8GGBZ789UbV24tIhhbh8WHFUx+/hDX8RCwLw1dElIdunDu4VVbpERLFE/iPQ7srfiRUF+Pk1I/D760ajb0EPexMP8tg1Ix1LWw2DDwtMHthT9bnbJlcgNTm6j70w2xtxH49HQGleOgDAd7adZ1y//KjSJSKKJU7P85HkEXDzpL64dkypvQnLFOekYcaI6H70mo3Bh0XULnIzLv7Ia7tI//XPMRZDzYFERFFzeoZTt+ia48npXEgx+LCI2kVuxvmPPM+HIPnXX/ORqF88IkpMTs/z4RbC2f/chMGHRVQvchPOf6Re0/KaD5E1H0SUgDixYhfWfCQQ9dgj+itA69Iu/n+7az6IiBKH06Nd3EKA+947gw+LqJ1oU/p8RKz5ECT/+oLWdiEiShQhHU6dyYbzBPe9dwYfFrGw1UXD1O7S/c5OcOq6Nj8iIiuFdDhN0CJQgOC6987gwyJqJ9pjwkDzyKva+jucdmHNBxElIqfXdnELQXBf/xcGHxZRbXYx5dj68tAdfLjr4iMishJHu3TxCO4Luxh82MyMi19r5Ul3zYf0MRFRIvDI7nCJuryVIMB1NwAGH7YzodlFYwTTPdSWzS5ElHjc1tTglK7Yw12fBYMPm9n5XeieZMz/mIgocUW7onis4jwfZEoAoDWa755enX0+iCjxyMvKBI094L56DwYftjOjGlDvIXw+NrsQUeKRl7e+BK36YM1HHBnRJ9vQ6zpNuPgbWto17eev6RDZ7EJECUhe3uZlpDqUE2cJAM4rz3M6GxIMPgyaf+2owN+3X1CBZI3dqP21ENEo6OEN/P3RDy5W3c+fo04u7kJECWhgr8zA33OnDsCk/gUO5sY5giDg6+PKMH/WSHw47yKnswOAwYdh6andH92t5/dDv549NL3OjEq/9NSkwN99C9TTDenzYULaRESxIjW5u9S7eVI/JCXoWFsBQJJHwA0TyjGwMMvp7ABg8GGYk0O4BJW/Q/YLBB/Sx0REiaG70EvQuAOAO8t+Bh8GBQcfek6sGf2dtKbt798cmOeDdR9ElEA8Wn+pxTk3lv0MPgwKrr7TUwsimhF9BCUXbvgsaz6IKJEFl82JPOGYG986gw+DBEH5b1vS1rkf+3wQUaJj8OEuDD4MCq75sLtKS/NkYfKhti68AImI7JDIxR+bXeKIvN+F1lNrxmgXrR2n5Pu58QIkIrIDaz7chcGHQZLgQ8frTOnyoXNVW/UNRETxS9I8nsB3OwYfcUTei9rOk6t9bRfpfi68/oiILBP8Yy+Ryz831noz+DBIMlmNjtoM0caljeSXGxeWI6JExWYXd9EdfKxYsQJXXXUVSkpKIAgCXnvtNcnzt956KwRBkPx/xRVXmJVf1wi+keuaMd2UZhd9q9r6JfIkO0SUeILLwIQOPpzOgALdwUdjYyNGjx6NhQsXqu5zxRVX4MiRI4H/n3vuuagy6UaCpOJDtLVaS/tQW1mzixuvQCIiGyRy+efG956s9wUzZszAjBkzwu7j9XpRXFxsOFOxIPhciqK9zSmaI3iOdiEiIheW/Zb0+Vi2bBkKCwsxePBg3H333Th58qTqvq2traivr5f8HwuCmz5sbnUxPNrFjdEvEZEdErn8c+N7Nz34uOKKK/CPf/wDS5cuxa9+9SssX74cM2bMQGdnp+L+8+fPR05OTuD/srIys7NkidSk7o8u05usuVahd05a1Gn3LcjQtJ8bLzgiIrvkpKcE/k5K4ALRjf1ddDe7RHL99dcH/h45ciRGjRqFAQMGYNmyZbj00ktD9n/44Ycxb968wOP6+vqYCEBSkz345+0T0N7pk1zg4fzphjHo3ysz6rSHl+TgD9ediz556WH3C+3z4b4LkIjIKrkZqXhqzjh4k5OQnJS4gzvdWPKbHnzI9e/fHz179sTu3bsVgw+v1wuv12t1Nixx4aBegb+13NdnjuxtWtrXjOkTcR95ntx4ARIRWenSoUVOZ8FxbvzdaXkoePDgQZw8eRK9e5t3441Vti9AJw8+XHgBEhGRtdw42EB3zceZM2ewe/fuwON9+/Zh48aNyM/PR35+Ph599FHMnj0bxcXF2LNnDx588EEMHDgQ06dPNzXjscjuZo+QZhcXXoBERGQtN/7w1B18rF+/HlOnTg089vfXmDNnDp588kls3rwZTz/9NGpra1FSUoLLL78cP//5z2O2aSWWseaDiIjcSHfwMWXKFIhhVkd77733osoQWYexBxFR4nHjD8/E7f6bAEIWlnPhBUhERNZy40hHBh9xLHQtF/ddgEREZC03lvwMPuIYZzglIiI3lv0MPkzixmqtkGYXh/JBRETOceMMpww+4lhozYf7LkAiIrKWG0t+Bh8JxI0XIBERWcyFhT+DD5O48NyGrKDLig8iosTjxgkmGXwkEDe2+xERkbXcWPQz+IhjIZPBufACJCIia7mx6GfwYRK3RJaFWerT2Lski0REZJELB/UEAKQmdd/e01KSnMqOKt3Tq5MxL91VaUs6i2+boPocR7sQEcW3P90wBm9sOoyZI3vj/e016JXpRQ+v+2717stRnBrfL9+WdIaVZAf+DulwaksOiIjIKbkZqbilsh8A4IYJ5c5mJgw2uyQQVnwQEZEbMPhIIG4cbkVERImHwYdJ3FirEDLYxYV5JCKixMPgI4Ew9iAiIjdg8JFIGH0QEZELMPgwSSz0p4iFPBIRUfxj8JFA2OeDiIjcgMGHSWLhxh4DWSQiogTA4MMk8pElbhC6qi3DDyIich6DjwTC2IOIiNyAwYdJYuHGHgNZJCKiBMDgI46JsragWAiQiIgo/jH4SCiMPoiIyHkMPkwyfXgxAGBMea7taVf2LwAATBncS7K9rrld8pg1H0RE5AbJTmcgXnxrcgWy0pIx5ZxC29P+zddH4c1NR3D1uSWS7UOLs7H5YF3gsYfRBxERuQCDD5Okpybhlsp+km0pSQLaO60fg1ual4G7pwwI2V6U7ZU8ZuhBRERuwGYXCzk+nbmspoMVH0RE5AYMPqzkspu948EQERERGHxYyuPwvV6ePms+iIjIDRh8WMjpmgan0yciIlLC4MNCTtd8yGs6WPNBRERuwODDQk4v5CZPnTUhRETkBgw+LOR0TQNrPoiIyI0YfFjI6Xu9vOaFwQcREbkBgw8LOd7sIq/5cDwcIiIiYvBhKadrGuTBhtP5ISIiAhh8WMpt93q35YeIiBITgw8LOb2QGzucEhGRGzH4sJDTN/vQ5Bl9EBGR8xh8WGj22FIAwMXn9HIk/aG9syWPnQ6GiIiIACDZ6QzEs3umDkTv7DRcPrzYkfQvHNQTuRkpqG1qB8B6DyIicgfWfFgoKy0Ft06uQEluuiPpC4KA71w0QPKYiIjIaQw+4lxwvMHQg4iI3IDBR5wLXtzO6dE3REREAIOPuBcccDD2ICIiN2DwQURERLZi8BHnWPNBRERuw+AjzgX3+eBoFyIicgMGH3EuOOBg6EFERG7A4CPOSWs+nMsHERGRH4OPOCet+WD0QUREzmPwEecE1nwQEZHLMPiIcx72+SAiIpdh8BHngvt8MPogIiI3YPAR54L7ebDPBxERuQGDj3jHPh9EROQyDD7iHFtdiIjIbRh8xDnJUFtWfRARkQsw+EggDD2IiMgNGHzEOUmzC6MPIiJyAQYfCYSjXYiIyA0YfMQ5yQynPNtEROQCvB3FOUnw4Vw2iIiIAhh8JBCOdiEiIjdg8JFAGHoQEZEbMPiIc5Lp1Rl9EBGRCzD4SCAc7UJERG6gO/hYsWIFrrrqKpSUlEAQBLz22muS50VRxE9/+lP07t0b6enpmDZtGnbt2mVWfkkngWu7EBGRy+gOPhobGzF69GgsXLhQ8flf//rXePzxx/HnP/8Za9asQY8ePTB9+nS0tLREnVkiIiKKfcl6XzBjxgzMmDFD8TlRFPGHP/wBP/nJT3D11VcDAP7xj3+gqKgIr732Gq6//vrocktRYc0HERG5gal9Pvbt24ejR49i2rRpgW05OTmYOHEiVq1apfia1tZW1NfXS/4n80gWlmOfDyIicgFTg4+jR48CAIqKiiTbi4qKAs/JzZ8/Hzk5OYH/y8rKzMxSwivM8gb+TvIw+CAiIuc5Ptrl4YcfRl1dXeD/6upqp7MUVyZW5OOX147EP2+fwOCDiIhcQXefj3CKi4sBADU1Nejdu3dge01NDc4991zF13i9Xni9XsXnKHqCIODGieVOZ4OIiCjA1JqPiooKFBcXY+nSpYFt9fX1WLNmDSorK81MioiIiGKU7pqPM2fOYPfu3YHH+/btw8aNG5Gfn4/y8nLcd999+MUvfoFBgwahoqICjzzyCEpKSnDNNdeYmW8iIiKKUbqDj/Xr12Pq1KmBx/PmzQMAzJkzB4sXL8aDDz6IxsZG3HnnnaitrcUFF1yAd999F2lpaeblmoiIiGKWIIqi6HQmgtXX1yMnJwd1dXXIzs52OjtERESkgZ77t+OjXYiIiCixMPggIiIiWzH4ICIiIlsx+CAiIiJbMfggIiIiWzH4ICIiIlsx+CAiIiJbMfggIiIiWzH4ICIiIluZuqqtGfwTrtbX1zucEyIiItLKf9/WMnG664KPhoYGAEBZWZnDOSEiIiK9GhoakJOTE3Yf163t4vP5cPjwYWRlZUEQBFOPXV9fj7KyMlRXV3PdGJfhuXEvnhv34rlxr0Q8N6IooqGhASUlJfB4wvfqcF3Nh8fjQWlpqaVpZGdnJ8zFEGt4btyL58a9eG7cK9HOTaQaDz92OCUiIiJbMfggIiIiWyVU8OH1evGzn/0MXq/X6ayQDM+Ne/HcuBfPjXvx3ITnug6nREREFN8SquaDiIiInMfgg4iIiGzF4IOIiIhsxeCDiIiIbJUwwcfChQvRr18/pKWlYeLEiVi7dq3TWYor8+fPx/jx45GVlYXCwkJcc8012Llzp2SflpYWzJ07FwUFBcjMzMTs2bNRU1Mj2efAgQOYOXMmMjIyUFhYiAceeAAdHR2SfZYtW4bzzjsPXq8XAwcOxOLFi61+e3FlwYIFEAQB9913X2Abz41zDh06hG9+85soKChAeno6Ro4cifXr1weeF0URP/3pT9G7d2+kp6dj2rRp2LVrl+QYp06dwk033YTs7Gzk5ubi9ttvx5kzZyT7bN68GRdeeCHS0tJQVlaGX//617a8v1jV2dmJRx55BBUVFUhPT8eAAQPw85//XLJuCc9NFMQE8Pzzz4upqani3//+d3Hbtm3iHXfcIebm5oo1NTVOZy1uTJ8+XVy0aJG4detWcePGjeKVV14plpeXi2fOnAnsc9ddd4llZWXi0qVLxfXr14uTJk0Szz///MDzHR0d4ogRI8Rp06aJn3/+ufj222+LPXv2FB9++OHAPnv37hUzMjLEefPmidu3bxf/9Kc/iUlJSeK7775r6/uNVWvXrhX79esnjho1Svz+978f2M5z44xTp06Jffv2FW+99VZxzZo14t69e8X33ntP3L17d2CfBQsWiDk5OeJrr70mbtq0SfzqV78qVlRUiM3NzYF9rrjiCnH06NHi6tWrxU8++UQcOHCgeMMNNwSer6urE4uKisSbbrpJ3Lp1q/jcc8+J6enp4v/93//Z+n5jyWOPPSYWFBSIb731lrhv3z7xpZdeEjMzM8U//vGPgX14boxLiOBjwoQJ4ty5cwOPOzs7xZKSEnH+/PkO5iq+HTt2TAQgLl++XBRFUaytrRVTUlLEl156KbDPjh07RADiqlWrRFEUxbffflv0eDzi0aNHA/s8+eSTYnZ2ttja2iqKoig++OCD4vDhwyVpXXfddeL06dOtfksxr6GhQRw0aJD4wQcfiBdffHEg+OC5cc6PfvQj8YILLlB93ufzicXFxeJvfvObwLba2lrR6/WKzz33nCiKorh9+3YRgLhu3brAPu+8844oCIJ46NAhURRF8YknnhDz8vIC58qf9uDBg81+S3Fj5syZ4re+9S3JtlmzZok33XSTKIo8N9GK+2aXtrY2bNiwAdOmTQts83g8mDZtGlatWuVgzuJbXV0dACA/Px8AsGHDBrS3t0vOw5AhQ1BeXh44D6tWrcLIkSNRVFQU2Gf69Omor6/Htm3bAvsEH8O/D89lZHPnzsXMmTNDPj+eG+e88cYbGDduHL7+9a+jsLAQY8aMwV//+tfA8/v27cPRo0cln2tOTg4mTpwoOTe5ubkYN25cYJ9p06bB4/FgzZo1gX0uuugipKamBvaZPn06du7cidOnT1v9NmPS+eefj6VLl+LLL78EAGzatAkrV67EjBkzAPDcRMt1C8uZ7cSJE+js7JQUmgBQVFSEL774wqFcxTefz4f77rsPkydPxogRIwAAR48eRWpqKnJzcyX7FhUV4ejRo4F9lM6T/7lw+9TX16O5uRnp6elWvKWY9/zzz+Ozzz7DunXrQp7juXHO3r178eSTT2LevHn4r//6L6xbtw7f+973kJqaijlz5gQ+W6XPNfhzLywslDyfnJyM/Px8yT4VFRUhx/A/l5eXZ8n7i2UPPfQQ6uvrMWTIECQlJaGzsxOPPfYYbrrpJgDguYlS3AcfZL+5c+di69atWLlypdNZIQDV1dX4/ve/jw8++ABpaWlOZ4eC+Hw+jBs3Dr/85S8BAGPGjMHWrVvx5z//GXPmzHE4d4ntxRdfxLPPPoslS5Zg+PDh2LhxI+677z6UlJTw3Jgg7ptdevbsiaSkpJCe+zU1NSguLnYoV/HrnnvuwVtvvYWPP/4YpaWlge3FxcVoa2tDbW2tZP/g81BcXKx4nvzPhdsnOzubv6xVbNiwAceOHcN5552H5ORkJCcnY/ny5Xj88ceRnJyMoqIinhuH9O7dG8OGDZNsGzp0KA4cOACg+7MNV34VFxfj2LFjkuc7Ojpw6tQpXeePpB544AE89NBDuP766zFy5EjcfPPNuP/++zF//nwAPDfRivvgIzU1FWPHjsXSpUsD23w+H5YuXYrKykoHcxZfRFHEPffcg1dffRUfffRRSDXi2LFjkZKSIjkPO3fuxIEDBwLnobKyElu2bJF8WT/44ANkZ2cHCujKykrJMfz78Fyqu/TSS7FlyxZs3Lgx8P+4ceNw0003Bf7muXHG5MmTQ4akf/nll+jbty8AoKKiAsXFxZLPtb6+HmvWrJGcm9raWmzYsCGwz0cffQSfz4eJEycG9lmxYgXa29sD+3zwwQcYPHhw3FbrR6upqQkej/QWmZSUBJ/PB4DnJmpO93i1w/PPPy96vV5x8eLF4vbt28U777xTzM3NlfTcp+jcfffdYk5Ojrhs2TLxyJEjgf+bmpoC+9x1111ieXm5+NFHH4nr168XKysrxcrKysDz/uGcl19+ubhx40bx3XffFXv16qU4nPOBBx4Qd+zYIS5cuJDDOQ0IHu0iijw3Tlm7dq2YnJwsPvbYY+KuXbvEZ599VszIyBCfeeaZwD4LFiwQc3Nzxddff13cvHmzePXVVysO5xwzZoy4Zs0aceXKleKgQYMkwzlra2vFoqIi8eabbxa3bt0qPv/882JGRkbcD+eMxpw5c8Q+ffoEhtq+8sorYs+ePcUHH3wwsA/PjXEJEXyIoij+6U9/EsvLy8XU1FRxwoQJ4urVq53OUlwBoPj/okWLAvs0NzeL3/3ud8W8vDwxIyNDvPbaa8UjR45IjlNVVSXOmDFDTE9PF3v27Cn+4Ac/ENvb2yX7fPzxx+K5554rpqamiv3795ekQdrIgw+eG+e8+eab4ogRI0Sv1ysOGTJE/Mtf/iJ53ufziY888ohYVFQker1e8dJLLxV37twp2efkyZPiDTfcIGZmZorZ2dnibbfdJjY0NEj22bRpk3jBBReIXq9X7NOnj7hgwQLL31ssq6+vF7///e+L5eXlYlpamti/f3/xxz/+sWRILM+NcYIoBk3XRkRERGSxuO/zQURERO7C4IOIiIhsxeCDiIiIbMXgg4iIiGzF4IOIiIhsxeCDiIiIbMXgg4iIiGzF4IOIiIhsxeCDiIiIbMXgg4iIiGzF4IOIiIhsxeCDiIiIbPX/AZKh+tS7u+ORAAAAAElFTkSuQmCC", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(geo_weather.isel(gid=0).temp_air)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Next Steps\n", + "\n", + "Now we have data ready to use for geospatial calculations. This is shown in the other distributed script [load_pvgis_distributed.ipynb](./load_pvgis_distributed.ipynb). You can also see how to do this in [Geospatial Templates.ipynb](../tutorials_and_tools/tutorials_and_tools/Geospatial%20Templates.ipynb)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "deg", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials_and_tools/tutorials_and_tools/load_pvgis_distributed.ipynb b/tutorials_and_tools/tutorials_and_tools/load_pvgis_distributed.ipynb new file mode 100644 index 00000000..99080b58 --- /dev/null +++ b/tutorials_and_tools/tutorials_and_tools/load_pvgis_distributed.ipynb @@ -0,0 +1,4589 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pvdeg\n", + "from global_land_mask import globe\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from mpl_toolkits.basemap import Basemap\n", + "import pandas as pd\n", + "import xarray as xr\n", + "import dask.array as da\n", + "import dask.dataframe as dd\n", + "from dask.distributed import LocalCluster, Client, Lock\n", + "import dask.delayed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Coordinates List\n", + "\n", + "Lets Generate a Grid of Latitude and Longitude Coordinates over the UK." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# decrease the arange step size for more fine resolution\n", + "# increase the arange step size for increased granularity\n", + "lon_UK = np.arange(-10.5, 1.76, 1)\n", + "lat_UK = np.arange(49.95, 60, 2)\n", + "lon_grid_UK, lat_grid_UK = np.meshgrid(lon_UK,lat_UK)\n", + "land_UK = globe.is_land(lat_grid_UK, lon_grid_UK)\n", + "\n", + "lon_land_UK = lon_grid_UK[land_UK]\n", + "lat_land_UK = lat_grid_UK[land_UK]\n", + "\n", + "lon_Scan = np.arange(-10.5, 31.6, 0.3)\n", + "lat_Scan = np.arange(60, 71.2, 0.3)\n", + "lon_grid_Scan, lat_grid_Scan = np.meshgrid(lon_Scan,lat_Scan)\n", + "land_Scan = globe.is_land(lat_grid_Scan, lon_grid_Scan)\n", + "\n", + "lon_land_Scan = lon_grid_Scan[land_Scan]\n", + "lat_land_Scan = lat_grid_Scan[land_Scan]\n", + "\n", + "coords = list(zip(lat_land_UK, lon_land_UK)) # easiest way to make a list of the right shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dask\n", + "\n", + "We are using dask to parallelize the weather API calls. We need to start a dask client as shown below. This can also be done with `pvdeg.geospatial.start_dask`. \n", + "\n", + "Click on the link to open a localhost dashboard for the dask client. This will allow us to see what happens when we run functions using dask." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "http://127.0.0.1:8787/status\n" + ] + } + ], + "source": [ + "workers = 4\n", + "\n", + "cluster = LocalCluster(\n", + " n_workers=workers,\n", + " processes=True, \n", + " )\n", + "\n", + "client = Client(cluster)\n", + "\n", + "print(client.dashboard_link)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Requesting Weather\n", + "\n", + "Now that we have our coordinates, and dask client initialized we can make our parallel api calls using `pvdeg.weather.weather_distributed`. \n", + "\n", + "We want data from PVGIS so we will use that as the database. For more information about the databases look at the `pvdeg.weather.get` docstring. We will pass in the coordinates list which is a list of tuple pairs for the latitude and longiutudes to request weather at.\n", + "\n", + "`weather_ds` is the collected weather dataset from the API calls to PVGIS. \n", + "`meta_df` is the collected meta dataframe from the API calls to PVGIS. \n", + "`failed_gids` will be a list of the failed indexes from the input coordinates list that we could not get weather from using PVGIS. These may have failed randomly so it is worth trying again." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "weather_ds, meta_df, failed_gids = pvdeg.weather.weather_distributed(database=\"PVGIS\", coords=coords)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Viewing Result\n", + "\n", + "The result is stored in an xarray dataset with a dask array backend. This allows us to parallelize the computation/api requests but makes it a little harder to view the data. We can inspect the dataset using the following but we will not be able to inspect any values.\n", + "\n", + "```\n", + "weather_ds\n", + "```\n", + "\n", + "To load the values from the dask arrays we need to use `.compute()` as follows.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\tford\\AppData\\Local\\miniconda3\\envs\\deg\\lib\\site-packages\\distributed\\client.py:3362: UserWarning: Sending large graph of size 13.27 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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To do this `PVDeg` will save to a folder called `PVDeg-Meteorological` your user home directory. For me this is located at `C:\\Users\\tford\\PVDeg-Meteorological`. This directory will contain a `zarr` store, this is a popular format for storing multi-dimensional array data, not dissimilar to `h5` files. It was chosen over `h5` because `zarr` stores arrays in chunked compressed files that make access very easy without opening an entire file like `h5`. This is an oversimplification of the design process but we felt `zarr` was a better fit.\n", + "\n", + "## Store\n", + "\n", + "We can use `pvdeg.store.store` to save geospatial data to our living dataset in the common form provided by `pvdeg`. The data is stored in various groups and subfolders but they will be arranged based on the *source* and *periodicity*.\n", + "\n", + "For example: \n", + " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\" \n", + " - 30 minute PVGIS to a group called \"PVGIS-30min\" \n", + " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\" " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Zarr\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\tford\\AppData\\Local\\miniconda3\\envs\\deg\\lib\\site-packages\\distributed\\client.py:3362: UserWarning: Sending large graph of size 13.27 MiB.\n", + "This may cause some slowdown.\n", + "Consider loading the data with Dask directly\n", + " or using futures or delayed objects to embed the data into the graph without repetition.\n", + "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dataset saved to zarr store at C:\\Users\\tford\\PVDeg-Meteorological\n" + ] + } + ], + "source": [ + "pvdeg.store.store(weather_ds, meta_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load\n", + "\n", + "`PVDeg` makes use of `dask` to handle larger than memory datasets. Trandionally, this was only useful in our HPC environment but as your local database grows overtime, it will eventually surpass the limits of your computer's volatile memory. Additionally, `dask` allows us to parallelize geospatial calculations via `pvdeg.geospatial.analysis`. This ability can be utilized on local machines or HPC clusters alike. \n", + "\n", + "`PVDeg` implements the ability to access your local living database via `pvdeg.store.get`. This method takes a string called `group`. Groups are created automatically in your store when you save data using `pvdeg.store.store`. As described in the `pvdeg.store.store` docstring and the *Store* section above, NSRDB will follow a similar scheme but it not implemented yet. \n", + " - Hourly PVGIS data will be saved to a group called \"PVGIS-1hr\" \n", + " - 30 minute PVGIS to a group called \"PVGIS-30min\" \n", + " - 15 minute PVGIS will be saved to a group called \"PVGIS-15min\" " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load PVGIS-1hr Data\n", + "\n", + "The example below shows us loading the hourly tmy data from PVGIS that we gathered and saved to our zarr store in the above cells. This gets us the form of a weather xarray.Dataset (`geo_weather` in this example) and a metadata dataframe (`geo_meta` in this example).\n", + "\n", + "These can be treated like any other geospatial data shown in the `pvdeg` tutorials and tools or documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "geo_weather, geo_meta = pvdeg.store.get(group=\"PVGIS-1hr\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspecting the Results\n", + "\n", + "explain *.compute() and dask here*" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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    " + ], + "text/plain": [ + " latitude longitude altitude Source wind_height\n", + "gid \n", + "0 49.95 1.5 176.0 PVGIS 10\n", + "1 51.95 -9.5 330.0 PVGIS 10\n", + "2 51.95 -8.5 137.0 PVGIS 10\n", + "3 51.95 -4.5 184.0 PVGIS 10\n", + "4 51.95 -3.5 160.0 PVGIS 10\n", + "5 51.95 -2.5 86.0 PVGIS 10\n", + "6 51.95 -1.5 217.0 PVGIS 10\n", + "7 51.95 -0.5 94.0 PVGIS 10\n", + "8 51.95 0.5 64.0 PVGIS 10\n", + "9 53.95 -9.5 119.0 PVGIS 10\n", + "10 53.95 -8.5 129.0 PVGIS 10\n", + "11 53.95 -7.5 97.0 PVGIS 10\n", + "12 53.95 -6.5 33.0 PVGIS 10\n", + "13 53.95 -2.5 136.0 PVGIS 10\n", + "14 53.95 -1.5 115.0 PVGIS 10\n", + "15 53.95 -0.5 33.0 PVGIS 10\n", + "16 55.95 -5.5 205.0 PVGIS 10\n", + "17 55.95 -4.5 248.0 PVGIS 10\n", + "18 55.95 -3.5 122.0 PVGIS 10\n", + "19 55.95 -2.5 150.0 PVGIS 10\n", + "20 57.95 -6.5 137.0 PVGIS 10\n", + "21 57.95 -4.5 59.0 PVGIS 10" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geo_meta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Geospatial Calculations from Locally Stored Data\n", + "\n", + "As shown above we can load from our `zarr` store and treat it like any other geospatial data in `pvdeg`.\n", + "\n", + "For demonstration we can run the analysis below to estimate effective standoff height and operating temperatures for the provided data. It may look like the `geo_res` contains empty results but that is because we did not have input data for all of the points in the input grid (think of this as a 2D plane formed between the latitude and longitude axes). Clicking on the stack of three circles in the bottom cell will expand the datavariable (like an attribute of the multidimensional array structure) and show the results.\n", + "\n", + "Additionally, we can interpolate and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "func=pvdeg.standards.standoff\n", + "\n", + "template = pvdeg.geospatial.auto_template(\n", + " func=func,\n", + " ds_gids=geo_weather\n", + ")\n", + "\n", + "geo_res = pvdeg.geospatial.analysis(\n", + " weather_ds=geo_weather,\n", + " meta_df=geo_meta,\n", + " func=func,\n", + " template=template\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    <xarray.Dataset> Size: 2kB\n",
+       "Dimensions:    (latitude: 5, longitude: 12)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 40B 49.95 51.95 53.95 55.95 57.95\n",
+       "  * longitude  (longitude) float64 96B -9.5 -8.5 -7.5 -6.5 ... -1.5 -0.5 0.5 1.5\n",
+       "Data variables:\n",
+       "    T98_0      (latitude, longitude) float64 480B nan nan nan ... nan nan nan\n",
+       "    x          (latitude, longitude) float64 480B nan nan nan ... nan nan nan\n",
+       "    T98_inf    (latitude, longitude) float64 480B nan nan nan ... nan nan nan
    " + ], + "text/plain": [ + " Size: 2kB\n", + "Dimensions: (latitude: 5, longitude: 12)\n", + "Coordinates:\n", + " * latitude (latitude) float64 40B 49.95 51.95 53.95 55.95 57.95\n", + " * longitude (longitude) float64 96B -9.5 -8.5 -7.5 -6.5 ... -1.5 -0.5 0.5 1.5\n", + "Data variables:\n", + " T98_0 (latitude, longitude) float64 480B nan nan nan ... nan nan nan\n", + " x (latitude, longitude) float64 480B nan nan nan ... nan nan nan\n", + " T98_inf (latitude, longitude) float64 480B nan nan nan ... nan nan nan" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geo_res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot lacks information on the area and does not include some political boundary lines. For more information on plotting look at the `Scenario - Non-uniform Mountain Downselect.ipynb` tutorial in the tutorials and tools folder." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
    , )" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pvdeg.geospatial.plot_sparse_analysis_land(geo_res, data_var='T98_0', method=\"nearest\", resolution=10j)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Growing Our Living Store\n", + "\n", + "What if we want to download more points from Europe? We can keep our old download in the store and shelve it to look at northern Europe.\n", + "\n", + "We will start by generating a range of points that cover Europe." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "lon_EU = np.arange(-25.0, 51.0, 1) # Adjusted for EU longitudes\n", + "lat_EU = np.arange(34.0, 73.0, 2) # Adjusted for EU latitudes\n", + "\n", + "# Create meshgrid for EU\n", + "lon_grid_EU, lat_grid_EU = np.meshgrid(lon_EU, lat_EU)\n", + "\n", + "# Check land coverage in the EU\n", + "land_EU = globe.is_land(lat_grid_EU, lon_grid_EU)\n", + "\n", + "# Extract land coordinates in the EU\n", + "lon_land_EU = lon_grid_EU[land_EU]\n", + "lat_land_EU = lat_grid_EU[land_EU]\n", + "\n", + "# Define the Scan grid ranges\n", + "lon_Scan = np.arange(-10.5, 31.6, 0.3)\n", + "lat_Scan = np.arange(60, 71.2, 0.3)\n", + "lon_grid_Scan, lat_grid_Scan = np.meshgrid(lon_Scan, lat_Scan)\n", + "land_Scan = globe.is_land(lat_grid_Scan, lon_grid_Scan)\n", + "\n", + "lon_land_Scan = lon_grid_Scan[land_Scan]\n", + "lat_land_Scan = lat_grid_Scan[land_Scan]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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r16///xvo13kTd955px5++OH4zzk5OQm0BwAAMo3r8NGvXz8VFBSccnlOTs5plwMAgDOb63s+du7cqaKiIo0ZM0azZs1SS0tLp+UrV67U0KFDVVpaqrq6On3yySen3V40GlUkEuk0AQCAzOXqysfUqVO1YsUKjR8/Xnv37tWiRYt05ZVX6u2339bgwYN12223KRQKqaioSE1NTVqwYIGam5v13HPPnXKbixcv1qJFixIeCAAASA8Jfc7HoUOHFAqF9Oijj2rOnDldlm/cuFHl5eXatWuXxo4d2+02otGootFo/OdIJKLi4mI+5wMAgDTi5nM+XN/z8XlDhgzReeedp127dnW7fOrUqZJ02vCRnZ2t7OzsRNoAAABpJKHP+Thy5Ih2796twsLCbpdv375dkk65HAAAnHlcXfm4//77ddNNNykUCqmtrU319fXq27evbr31Vu3evVurVq3SjTfeqPz8fDU1NammpkZXXXWVJk6cmKr+AQBAmnEVPvbs2aNbb71VH3/8sYYNG6YrrrhCmzdv1rBhw9Te3q7169dr6dKlOnr0qIqLi1VVVaUHH3wwVb0DAIA0xBfLAQCAhPHFcgAAwLcIH0i+hgappCQ2T0U91fzWj1eStR+82k6m1v3YE/XMqNtkfMZxHCPJOI7jdSvorVDIGCk2T0U91fzWj1eStR+82k6m1v3YE/XMqCfIzfmb8IHkC4djb+pwODX1VPNbP15J1n7wajuZWvdjT9Qzo54gN+dvbjgFAAAJ44ZTAADgW4QPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPJF9Dg1RSEpsnUvfqeVNdTxa/jYu6nbofe6KeGXWbjM84jmMkGcdxvG4FvRUKGSPF5onUvXreVNeTxW/jom6n7seeqGdGPUFuzt+EDyRfOBx7U4fDidW9et5U15PFb+Oibqfux56oZ0Y9QW7O3wFjjPHuuktXkUhEubm5chxHwWDQ63YAAEAPuDl/c88HAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAq1yFj4ULFyoQCHSaJkyYEF/e3t6u6upq5efna9CgQaqqqtL+/fuT3jQAAEhfrq98XHjhhdq7d298+tOf/hRfVlNTo+eff15r1qzRpk2b1NbWpptvvjmpDQMAgPTWz/UD+vVTQUFBl7rjOHr66ae1atUqXXvttZKk5cuX6/zzz9fmzZt16aWXJt4tAABIe66vfOzcuVNFRUUaM2aMZs2apZaWFknS1q1bdfz4cVVUVMTXnTBhgkaNGqXGxsZTbi8ajSoSiXSagIzW0CCVlMTm1Km7rfuxJ+qZUbfJuPDCCy+YX//61+Yvf/mLeemll0xZWZkZNWqUiUQiZuXKlSYrK6vLY6ZMmWIeeOCBU26zvr7eSOoyOY7jpjUgfYRCxkixOXXqbut+7Il6ZtQT5DhOj8/frsLHFx08eNAEg0Hz1FNP9Tp8tLe3G8dx4lNrayvhA5ktHI4d9OEwderu637siXpm1BPkJnwEjDEmkSsnU6ZMUUVFha677jqVl5fr4MGDGjJkSHx5KBTSvHnzVFNT06PtRSIR5ebmynEcBYPBRFoDAACWuDl/J/Q5H0eOHNHu3btVWFioSZMmqX///tqwYUN8eXNzs1paWlRWVpbI0wAAgAzi6q9d7r//ft10000KhUJqa2tTfX29+vbtq1tvvVW5ubmaM2eO7rvvPp199tkKBoO6++67VVZWxl+6AACAOFfhY8+ePbr11lv18ccfa9iwYbriiiu0efNmDRs2TJL02GOPqU+fPqqqqlI0GlVlZaUef/zxlDQOAADSU8L3fCQb93wAAJB+rN3zAQAA4BbhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+EDma2iQSkpic+rU073ux56oZ0bdJuMzjuMYScZxHK9bQaYIhYyRYnPq1NO97seeqGdGPUFuzt+ED2S+cDh2kIXD1Kmnf92PPVHPjHqC3Jy/+VZbAACQML7VFgAA+BbhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhA+iphgappCQ2p07dq7rXz50MXm2f+unrNhmfcRzHSDKO43jdCtBZKGSMFJtTp+5V3evnTgavtk/99PUEuTl/Ez6AngqHYwdrOEydund1r587GbzaPvXT1xPk5vwdMMYY7667dBWJRJSbmyvHcRQMBr1uBwAA9ICb8zf3fAAAAKsIHwAAwKqEwseSJUsUCAQ0b968eO3qq69WIBDoNN11112J9gkAADJEv94+cMuWLVq2bJkmTpzYZdmdd96phx9+OP5zTk5Ob58GAABkmF5d+Thy5IhmzZqlJ598Unl5eV2W5+TkqKCgID5x4ygAADipV+Gjurpa06dPV0VFRbfLV65cqaFDh6q0tFR1dXX65JNPTrmtaDSqSCTSaQIAAJnL9T+7rF69Wm+99Za2bNnS7fLbbrtNoVBIRUVFampq0oIFC9Tc3Kznnnuu2/UXL16sRYsWuW0DAACkKVef89Ha2qrJkyfr5Zdfjt/rcfXVV+tLX/qSli5d2u1jNm7cqPLycu3atUtjx47tsjwajSoajcZ/jkQiKi4u5nM+AABII24+58PVlY+tW7fqwIED+spXvhKvdXR06NVXX9UvfvELRaNR9e3bt9Njpk6dKkmnDB/Z2dnKzs520wYAAEhjrsJHeXm5duzY0an27W9/WxMmTNCCBQu6BA9J2r59uySpsLCw910CAICM4Sp8DB48WKWlpZ1qZ511lvLz81VaWqrdu3dr1apVuvHGG5Wfn6+mpibV1NToqquu6vZPcgEAwJmn15/z0Z2srCytX79eS5cu1dGjR1VcXKyqqio9+OCDyXwaAACQxvhiOQAAkDC+WA4AAPgW4QPoqYYGqaQkNj8T6m6levvJet50ryd7W/Dfa5wux3wijM84jmMkGcdxvG4F6CwUMkaKzc+Eulup3n6ynjfd68neFvz3GqfLMf8Fbs7fhA+gp8Lh2MEaDp8ZdbdSvf1kPW+615O9LfjvNU6XY/4L3Jy/ueEUAAAkjBtOAQCAbxE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPeK+hQSopic2pd617JVn9ZOp+8PL9kO779FTSvc90r9tkfMZxHCPJOI7jdSuwJRQyRorNqXeteyVZ/WTqfvDy/ZDu+/RU0r3PdK8nyM35mysf8F5trRQKxebUu9a9kqx+MnU/ePl+SPd9eirp3me61y0KGGOMZ8/ejUgkotzcXDmOo2Aw6HU7AACgB9ycv7nyAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsSCh9LlixRIBDQvHnz4rX29nZVV1crPz9fgwYNUlVVlfbv359onwAAIEP0Onxs2bJFy5Yt08SJEzvVa2pq9Pzzz2vNmjXatGmT2tradPPNNyfcKAAAyAy9Ch9HjhzRrFmz9OSTTyovLy9edxxHTz/9tB599FFde+21mjRpkpYvX67XX39dmzdvTlrTAAAgffUqfFRXV2v69OmqqKjoVN+6dauOHz/eqT5hwgSNGjVKjY2N3W4rGo0qEol0mnCGaWiQSkpi80yqJ0uqt++353XLq/2frPdDMvv32zFAPb3qNhmXnn32WVNaWmo+/fRTY4wxX/3qV829995rjDFm5cqVJisrq8tjpkyZYh544IFut1dfX28kdZkcx3HbGtJVKGSMFJtnUj1ZUr19vz2vW17t/2S9H5LZv9+OAerpVU+Q4zg9Pn+7uvLR2tqqe++9VytXrtSAAQOSEn7q6urkOE58am1tTcp2kUZqa6VQKDbPpHqypHr7fntet7za/8l6PySzf78dA9TTq25RwBhjerryb3/7W339619X375947WOjg4FAgH16dNH69atU0VFhQ4ePKghQ4bE1wmFQpo3b55qamr+7nNEIhHl5ubKcRwFg0F3owEAAJ5wc/7u52bD5eXl2rFjR6fat7/9bU2YMEELFixQcXGx+vfvrw0bNqiqqkqS1NzcrJaWFpWVlbkcBgAAyESuwsfgwYNVWlraqXbWWWcpPz8/Xp8zZ47uu+8+nX322QoGg7r77rtVVlamSy+9NHldAwCAtOUqfPTEY489pj59+qiqqkrRaFSVlZV6/PHHk/00AAAgTbm658MG7vkAACD9uDl/890uAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAe81NEglJbF5OtbRO5n6uvixf78dM9T9WbfJ+IzjOEaScRzH61ZgSyhkjBSbp2MdvZOpr4sf+/fbMUPdn/UEuTl/Ez7gvXA4dhCEw+lZR+9k6uvix/79dsxQ92c9QW7O33yrLQAASBjfagsAAHyL8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifACp0tAglZTE5mdCHUgVv73XM7Vuk/EZx3GMJOM4jtetAIkJhYyRYvMzoQ6kit/e65laT5Cb8zdXPoBUqa2VQqHY/EyoA6nit/d6ptYtChhjjGfP3o1IJKLc3Fw5jqNgMOh1OwAAoAfcnL+58gEAAKxyFT7C4bAmTpyoYDCoYDCosrIyvfjii/HlV199tQKBQKfprrvuSnrTAAAgffVzs/LIkSO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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(loaded_weather.sel(gid=0).dhi)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "wet, met = pvdeg.store.get(\"PVGIS-1hr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset> Size: 15MB\n",
+       "Dimensions:            (gid: 23, time: 8760)\n",
+       "Coordinates:\n",
+       "  * gid                (gid) int32 92B 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22\n",
+       "  * time               (time) datetime64[ns] 70kB 2022-01-01 ... 2022-12-31T2...\n",
+       "Data variables:\n",
+       "    IR(h)              (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>\n",
+       "    dhi                (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>\n",
+       "    dni                (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>\n",
+       "    ghi                (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>\n",
+       "    pressure           (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>\n",
+       "    relative_humidity  (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>\n",
+       "    temp_air           (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>\n",
+       "    wind_direction     (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>\n",
+       "    wind_speed         (gid, time) float64 2MB dask.array<chunksize=(22, 8760), meta=np.ndarray>
    " + ], + "text/plain": [ + " Size: 15MB\n", + "Dimensions: (gid: 23, time: 8760)\n", + "Coordinates:\n", + " * gid (gid) int32 92B 0 1 2 3 4 5 6 7 ... 16 17 18 19 20 21 22\n", + " * time (time) datetime64[ns] 70kB 2022-01-01 ... 2022-12-31T2...\n", + "Data variables:\n", + " IR(h) (gid, time) float64 2MB dask.array\n", + " dhi (gid, time) float64 2MB dask.array\n", + " dni (gid, time) float64 2MB dask.array\n", + " ghi (gid, time) float64 2MB dask.array\n", + " pressure (gid, time) float64 2MB dask.array\n", + " relative_humidity (gid, time) float64 2MB dask.array\n", + " temp_air (gid, time) float64 2MB dask.array\n", + " wind_direction (gid, time) float64 2MB dask.array\n", + " wind_speed (gid, time) float64 2MB dask.array" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wet" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    latitudelongitudealtitudeSourcewind_height
    gid
    049.951.5176.0PVGIS10
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    1655.95-5.5205.0PVGIS10
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    1955.95-2.5150.0PVGIS10
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    2234.0-6.0277.0PVGIS10
    \n", + "
    " + ], + "text/plain": [ + " latitude longitude altitude Source wind_height\n", + "gid \n", + "0 49.95 1.5 176.0 PVGIS 10\n", + "1 51.95 -9.5 330.0 PVGIS 10\n", + "2 51.95 -8.5 137.0 PVGIS 10\n", + "3 51.95 -4.5 184.0 PVGIS 10\n", + "4 51.95 -3.5 160.0 PVGIS 10\n", + "5 51.95 -2.5 86.0 PVGIS 10\n", + "6 51.95 -1.5 217.0 PVGIS 10\n", + "7 51.95 -0.5 94.0 PVGIS 10\n", + "8 51.95 0.5 64.0 PVGIS 10\n", + "9 53.95 -9.5 119.0 PVGIS 10\n", + "10 53.95 -8.5 129.0 PVGIS 10\n", + "11 53.95 -7.5 97.0 PVGIS 10\n", + "12 53.95 -6.5 33.0 PVGIS 10\n", + "13 53.95 -2.5 136.0 PVGIS 10\n", + "14 53.95 -1.5 115.0 PVGIS 10\n", + "15 53.95 -0.5 33.0 PVGIS 10\n", + "16 55.95 -5.5 205.0 PVGIS 10\n", + "17 55.95 -4.5 248.0 PVGIS 10\n", + "18 55.95 -3.5 122.0 PVGIS 10\n", + "19 55.95 -2.5 150.0 PVGIS 10\n", + "20 57.95 -6.5 137.0 PVGIS 10\n", + "21 57.95 -4.5 59.0 PVGIS 10\n", + "22 34.0 -6.0 277.0 PVGIS 10" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loaded_meta" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "func=pvdeg.standards.standoff\n", + "\n", + "template = pvdeg.geospatial.auto_template(\n", + " func=func,\n", + " ds_gids=loaded_weather\n", + ")\n", + "\n", + "loaded_geo_res = pvdeg.geospatial.analysis(\n", + " weather_ds=loaded_weather,\n", + " meta_df=loaded_meta,\n", + " func=func,\n", + " template=template\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
    , )" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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