uniform weather pre-save format

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

docs/source/_autosummary/pvdeg.utilities.linear_normalize.rst

@@ -0,0 +1,6 @@
+pvdeg.utilities.linear\_normalize
+=================================
+
+.. currentmodule:: pvdeg.utilities
+
+.. autofunction:: linear_normalize

docs/source/_autosummary/pvdeg.utilities.merge_sparse.rst

@@ -0,0 +1,6 @@
+pvdeg.utilities.merge\_sparse
+=============================
+
+.. currentmodule:: pvdeg.utilities
+
+.. autofunction:: merge_sparse

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

docs/source/_autosummary/pvdeg.weather.weather_distributed.rst

@@ -0,0 +1,6 @@
+pvdeg.weather.weather\_distributed
+==================================
+
+.. currentmodule:: pvdeg.weather
+
+.. autofunction:: weather_distributed

pvdeg/weather.py

@@ -1233,4 +1233,69 @@ def weather_distributed(
 
         weather_ds[dict(gid=i)] = weather_ds_collection[i]
 
-    return weather_ds, meta_df, gids_failed
+    return weather_ds, meta_df, gids_failed
+
+
+
+# def _nsrdb_to_uniform(weather_df: pd.DataFrame, meta: dict) -> tuple[pd.DataFrame, dict]:
+
+#     map_weather(weather_df=weather_df)
+#     map_meta(meta)
+
+#     # check if weather is localized, convert to GMT (like pvgis)
+#     # check if time index is on the hour or 30 minutes
+#         # weather_df.index - pd.Timedelta("30m")
+
+#     # NSRDB datavars
+#     # Year  Month  Day  Hour  Minute  dew_point  dhi
+#     # dni  ghi  albedo  pressure  temp_air
+#     # wind_direction  wind_speed  relative_humidity
+
+#     weather_dropables = ['Year',  'Month',  'Day',  'Hour',  'Minute',  'dew_point']
+#     meta_dropables = [...]
+
+#     # NSRDB meta
+#     # {'Source': 'NSRDB',
+#     #  'Location ID': '145809',
+#     #  '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': 39.73,
+#     #  'longitude': -105.18,
+#     #  'altitude': 1820,
+#     #  'tz': -7,
+#     #  'wind_height': 2}
+#     ...
+
+# def _pvgis_to_uniform(weather_df: pd.DataFrame, meta: dict) -> tuple[pd.DataFrame, dict]:
+
+#     map_weather(weather_df=weather_df)
+#     map_meta(meta)
+
+#     # drop meaningless variables
+
+#     # pvgis datavars
+#     # temp_air  relative_humidity   ghi  dni   dhi
+#     # IR(h)  wind_speed  wind_direction  pressure
+
+#     weather_dropables = ["IR(h)"]
+#     meta_dropables = ['irradiance_time_offset', ...]
+
+#     # pvgis meta
+#     # {'latitude': 24.7136,
+#     #   'longitude': 46.6753,
+#     #   'irradiance_time_offset': -0.1955,
+#     #   'altitude': 646.0,
+#     #   'wind_height': 10,
+#     #   'Source': 'PVGIS'})
+#     ...