I want to make my life easier. Less copying code snippets from example scripts.
Below is an example workflow that would make QC'ing solar radiation data even easier!
ds = solarpy.SolarDataset.from_t16("filename.csv", freq='1min', map_variables=True)
ds.calculate_solarposition(method="nrel-numpy")
ds.calculate_clearsky(method='cams', email='redacted@gmail.com')
ds.calculate_horizon()
ds.calculate_quality_flags()
# clean slicing that returns a new SolarDataset rather than a raw DataFrame slice, preserving meta and freq
ds.select(start, end)
fig, axes = ds.plot_shading_heatmap(parameter='ghi')
fig, axes = ds.multiplot()
Solar position and other derived variables could also be lazily computed, i.e., only computed when an action is executed that requires it.
I want to make my life easier. Less copying code snippets from example scripts.
Below is an example workflow that would make QC'ing solar radiation data even easier!
Solar position and other derived variables could also be lazily computed, i.e., only computed when an action is executed that requires it.