Python API

The usage of Python API is demonstrated in an example Notebook

netcdf_to_dataframe

This function queries a local or remote (via OpenDAP) NetCDF file and transform it into pandas DataFrame.

libinsitu.common.netcdf_to_dataframe(ncfile: Union[Dataset, str], start_time: Optional[Union[datetime, datetime64]] = None, end_time: Optional[Union[datetime, datetime64]] = None, rel_start_time=None, rel_end_time=None, drop_duplicates=True, skip_na=False, skip_qc=False, vars=None, user=None, password=None, chunked=False, chunk_size=5000, steps=1, rename_cols=False)

Load NETCDF in-situ file (or part of it) into a panda Dataframe, with time as index.

Parameters

  • ncfile – NetCDF Dataset or filename, or OpenDAP URL

  • rename_cols – If True (default) rename solar irradiance columns as per convention (GHI, BNI, DHI)

  • drop_duplicates – If true (default), duplicate rows are droppped

  • skip_qc – If True, skip lines with bad QC (at least one failing)

  • skip_na – If True, drop rows containing only nan values

  • start_time – Start time (first record by default) : Datetime or datetime64

  • end_time – End time (last record by default) : Datetile or datetime64

  • rel_end_time – End time, relative to actual start time : relativedelta

  • rel_start_time – Start time, relatie to actual end time : relativedelta

  • vars – List of columns names to convert (all by default)

  • user – Optional login for OpenDAP URL

  • password – Optional password OpenDAP URL

  • chunked – If True, does not load the whole file in memory at once : returns an iterator on Dataframe chunks.

  • chunk_size – Size of chunks for chunked data

  • steps – Downsampling (1 by default)

Returns

Pandas Dataframe, or iterator on Dataframes is chunk is activated

visual_qc

This function applies QC checks on an irradiance Dataframe and output visual graphs.

libinsitu.qc.qc_utils.visual_qc(df, latitude=None, longitude=None, elevation=None, station_id=None, station_name=None, with_horizons=False, with_mc_clear=False, show_flag=ShowFlag.SHOW, engine='matplotlib')

Generates matplotlib graphs for visual QC

Parameters

  • df – Dataframe of input irradiance. It should have a time index and 3 columns : GHI, DHI, BNI). This dataframe can typically be obtained with netcdf_to_dataframe(… rename_cols=True)

  • latitude – Latitude of the station. Can also be passed as meta data (.attrs) of the Dataframe

  • longitude – Longitude of the station. Can also be passed as meta data (.attrs) of the Dataframe

  • elevation – elevation of the station. Can also be passed as meta data (.attrs) of the Dataframe

  • station_id – Id of the station (optional). Can also be passed as meta data (.attrs) of the Dataframe

  • station_id – Name of the station (optional). Can also be passed as meta data (.attrs) of the Dataframe

  • with_horizons – True to compute horizons (requires network)

  • with_mc_clear – True to compute mc_clear from SODA (requires SODA credentials and network access)