climind.readers package

Submodules

climind.readers.generic_reader module

climind.readers.generic_reader.get_last_modified_time(file: Path) → str | None[source]

Get the update time of file if it exists, else None

Parameters:

file (Path) – File path of the file for which the last modified time is required

Returns:

string containing last updated time for the file or None if it does not exist

Return type:

Optional[str]

climind.readers.generic_reader.get_module(package_name: str, script_name: str)[source]

Get the module from the package name and the script name

Parameters:

  • package_name (str) – String containing the package path as a dot separated string

  • script_name (str) – Name of the script to import

Returns:

Returns module specified by the package name and script name

Return type:

module

climind.readers.generic_reader.get_reader_script_name(metadata: CombinedMetadata, **kwargs) → str | None[source]

Get the name of the reader function for the provided metadata combination

Parameters:

  • metadata (CombinedMetadata) – contains the metadata required to chose the reader script

  • kwargs – list of keyword arguments

Returns:

Returns the name of the reader function that will read that combination of metadata, or None

Return type:

Optional[str]

climind.readers.generic_reader.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs) → TimeSeriesMonthly | TimeSeriesAnnual | GridMonthly[source]

Generic reader for the data sets. This works out which reader is needed, imports and runs it. If a particular reader is not available (e.g. because the data is only a timeseries and not a grid) then it raises a not implemented error.

Parameters:

  • out_dir (Path) – Path of the directory in which the data are to be found

  • metadata (CombinedMetadata) – Metadata describing the required dataset

  • kwargs (dict) – Optional arguments as required for particular data sets

Returns:

Returns a TimeSeries or Grid of some kind

Return type:

Union[TimeSeriesMonthly, TimeSeriesAnnual, GridMonthly]

climind.readers.generic_reader_utils module

climind.readers.generic_reader_utils.find_latest(out_dir: Path, filename_with_wildcards: str) → Path[source]

Find the most recent file that matches

Parameters:

  • filename_with_wildcards (str) – Filename including wildcards

  • out_dir (Path) – Path of data directory

Returns:

Path of latest file that matches the filename with wildcards in the directory

Return type:

Path

climind.readers.generic_reader_utils.get_latest_filename_and_url(filename: Path, url: str) → Tuple[str, str][source]

Get the filename and url from a filled filename Path and URL with placeholders

Parameters:

  • filename (Path) – Path of filename

  • url (str) – URL to be replaced

Returns:

The filename and the url with placeholders replaced

Return type:

Tuple[str, str]

climind.readers.reader_aviso module

climind.readers.reader_aviso.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]

climind.readers.reader_aviso_ftp module

climind.readers.reader_aviso_ftp.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_aviso_ftp.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]

climind.readers.reader_aviso_ll module

climind.readers.reader_aviso_ll.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]

climind.readers.reader_badc_csv module

climind.readers.reader_badc_csv.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_badc_csv.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_berkeley module

climind.readers.reader_berkeley.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_berkeley.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_berkeley.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_berkeley.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]

Although Berkeley Earth is 1x1 already, the time dimension is extremely non-standard. In order to get consistency with the other data sets regridded to 1x1, the data is copied into a consistent xarray Dataset.

Parameters:

  • filename (str) – Filename of the netcdf grid

  • metadata (CombinedMetadata) – CombinedMetadata object holding the dataset metadata.

Return type:

GridMonthly

climind.readers.reader_berkeley.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_berkeley_hires module

climind.readers.reader_berkeley_hires.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_berkeley_hires.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_berkeley_ts module

climind.readers.reader_berkeley_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_berkeley_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_berkeley_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_berkeley_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_berkeley_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_blair_berkeley module

climind.readers.reader_blair_berkeley.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_calvert module

climind.readers.reader_calvert.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_calvert.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_calvert.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_calvert.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_calvert.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_cds_sea_level module

climind.readers.reader_cds_sea_level.read_grid(filename: Path)[source]
climind.readers.reader_cds_sea_level.read_monthly_grid(filename, metadata)[source]
climind.readers.reader_cds_sea_level.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_cheng module

climind.readers.reader_cheng.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_cheng.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_cheng_update module

climind.readers.reader_cheng_update.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]

climind.readers.reader_cherry module

climind.readers.reader_cherry.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]

climind.readers.reader_climate_reanalyzer module

climind.readers.reader_climate_reanalyzer.read_irregular_ts(filenames: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_climtrace_ts module

climind.readers.reader_climtrace_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]

climind.readers.reader_clsat module

climind.readers.reader_clsat.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_clsat.read_monthly_grid(filename: List[Path], metadata) → GridMonthly[source]

climind.readers.reader_cmems_ph module

climind.readers.reader_cmems_ph.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_cmems_ph.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_cmems_sealevel module

climind.readers.reader_cmems_sealevel.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]

climind.readers.reader_cmems_trend module

climind.readers.reader_cmems_trend.gread_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]
climind.readers.reader_cmems_trend.read_grid(filename: List[Path])[source]
climind.readers.reader_cmems_trend.read_monthly_grid(filename, metadata)[source]

climind.readers.reader_cmst module

climind.readers.reader_cmst.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_cmst.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_cmst.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_cmst.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_cmst.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_cobe_stemp_ts module

climind.readers.reader_cobe_stemp_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_collab_ts module

climind.readers.reader_collab_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_colorado_sealevel module

climind.readers.reader_colorado_sealevel.convert_partial_year(number)[source]
climind.readers.reader_colorado_sealevel.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]

climind.readers.reader_cowtan_and_way_ts module

climind.readers.reader_cowtan_and_way_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_cowtan_and_way_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_cpc module

climind.readers.reader_cpc.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_csiro_sealevel module

climind.readers.reader_csiro_sealevel.convert_partial_year(number)[source]
climind.readers.reader_csiro_sealevel.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]

climind.readers.reader_dcent_ts module

climind.readers.reader_dcent_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_dcent_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_dcent_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_dcent_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_dcent_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_era5 module

climind.readers.reader_era5.find_latest(out_dir: Path, filename_with_wildcards: str) → Path[source]

Find the most recent file that matches

Parameters:

  • filename_with_wildcards (str) – Filename including wildcards

  • out_dir (Path) – Path of data directory

climind.readers.reader_era5.get_latest_filename_and_url(filename: Path, url: str) → Tuple[str, str][source]

Get the filename and url from a filled filename Path and URL with placeholders

Parameters:

  • filename (Path) – Path of filename

  • url (str) – URL to be replaced

Returns:

  • Tuple[str, str] – The filename and the url with placeholders replaced

  • ——-

climind.readers.reader_era5.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_era5.read_grid(filename: str)[source]
climind.readers.reader_era5.read_irregular_ts(filenames: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]
climind.readers.reader_era5.read_monthly_1x1_grid(filename, metadata) → GridMonthly[source]
climind.readers.reader_era5.read_monthly_5x5_grid(filename, metadata) → GridMonthly[source]
climind.readers.reader_era5.read_monthly_grid(filename: str, metadata) → GridMonthly[source]
climind.readers.reader_era5.read_monthly_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesMonthly[source]
climind.readers.reader_era5.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_era5_ensemble module

climind.readers.reader_era5_ensemble.find_latest(out_dir: Path, filename_with_wildcards: str) → Path[source]

Find the most recent file that matches

Parameters:

  • filename_with_wildcards (str) – Filename including wildcards

  • out_dir (Path) – Path of data directory

climind.readers.reader_era5_ensemble.get_latest_filename_and_url(filename: Path, url: str) → Tuple[str, str][source]

Get the filename and url from a filled filename Path and URL with placeholders

Parameters:

  • filename (Path) – Path of filename

  • url (str) – URL to be replaced

Returns:

  • Tuple[str, str] – The filename and the url with placeholders replaced

  • ——-

climind.readers.reader_era5_ensemble.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_era5_ensemble.read_grid(filename: str)[source]
climind.readers.reader_era5_ensemble.read_irregular_ts(filenames: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]
climind.readers.reader_era5_ensemble.read_monthly_1x1_grid(filename, metadata) → GridMonthly[source]
climind.readers.reader_era5_ensemble.read_monthly_5x5_grid(filename, metadata) → GridMonthly[source]
climind.readers.reader_era5_ensemble.read_monthly_grid(filename: str, metadata) → GridMonthly[source]
climind.readers.reader_era5_ensemble.read_monthly_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesMonthly[source]
climind.readers.reader_era5_ensemble.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_gcos module

climind.readers.reader_gcos.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_gcos.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_gcos_temp module

climind.readers.reader_gcos_temp.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_gcos_temp.read_annual_ts_2024(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_gcos_temp.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_getquocs_ts module

climind.readers.reader_getquocs_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_getquocs_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_getquocs_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_getquocs_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]

climind.readers.reader_giss_aod module

climind.readers.reader_giss_aod.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_gistemp_ts module

climind.readers.reader_gistemp_ts.build_transfer(xx: int, yy: int)[source]

Build the transfer matrix for this 5x5 grid cell

Parameters:

  • xx (int) – Longitudinal index of grid cell in range 0, 71

  • yy (int) – Latitudinal index of grid cell in range 0, 35

climind.readers.reader_gistemp_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_gistemp_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]

Convert 2x2 grid to 1x1 grid by copying 2x2 value into all 4 1x1 grid cells it contains

Parameters:

  • filename

  • metadata

climind.readers.reader_gistemp_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_gistemp_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_gistemp_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_gml module

climind.readers.reader_gml.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_gpcc module

climind.readers.reader_gpcc.read_monthly_1x1_grid(filename, metadata) → GridMonthly[source]
climind.readers.reader_gpcc.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_gpcc_quantile module

climind.readers.reader_gpcc_quantile.read_monthly_1x1_grid(filename, metadata) → GridMonthly[source]
climind.readers.reader_gpcc_quantile.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_grace module

climind.readers.reader_grace.find_latest(out_dir: Path, filename_with_wildcards: str) → Path[source]
climind.readers.reader_grace.read_annual_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesAnnual[source]
climind.readers.reader_grace.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesMonthly[source]
climind.readers.reader_grace.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_gsfc module

climind.readers.reader_gsfc.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_hadcrut4_ts module

climind.readers.reader_hadcrut4_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_hadcrut4_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_hadcrut4_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_hadcrut4_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_hadcrut4_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_hadcrut_ts module

climind.readers.reader_hadcrut_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_hadcrut_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_hadcrut_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_hadcrut_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_hadcrut_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_hadsst_ts module

climind.readers.reader_hadsst_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_hadsst_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_imbie module

climind.readers.reader_imbie.read_annual_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesAnnual[source]
climind.readers.reader_imbie.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesMonthly[source]

climind.readers.reader_imbie_2021_antarctica module

climind.readers.reader_imbie_2021_antarctica.read_annual_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesAnnual[source]
climind.readers.reader_imbie_2021_antarctica.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesMonthly[source]

climind.readers.reader_imbie_antarctica module

climind.readers.reader_imbie_antarctica.read_annual_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesAnnual[source]
climind.readers.reader_imbie_antarctica.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesMonthly[source]

climind.readers.reader_ipcc_ts module

climind.readers.reader_ipcc_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]

climind.readers.reader_ishii module

climind.readers.reader_ishii.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]

climind.readers.reader_jaxa module

climind.readers.reader_jaxa.read_irregular_ts(filenames: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]
climind.readers.reader_jaxa.read_monthly_ts(filenames: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_jra3q module

climind.readers.reader_jra3q.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_jra3q.read_grid(filename: List[Path])[source]
climind.readers.reader_jra3q.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_jra3q.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_jra3q.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_jra3q.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_jra55 module

climind.readers.reader_jra55.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_jra55.read_grid(filename: List[Path])[source]
climind.readers.reader_jra55.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_jra55.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_jra55.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_jra55.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_kadow_ts module

climind.readers.reader_kadow_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_kadow_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_kadow_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_kadow_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_kadow_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]
climind.readers.reader_kadow_ts.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_kennaook module

climind.readers.reader_kennaook.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_kennaook.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_knmi_gmt module

climind.readers.reader_knmi_gmt.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_knmi_gmt.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_law_dome module

climind.readers.reader_law_dome.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_law_dome.read_chunk(f, search_string)[source]

climind.readers.reader_levitus module

climind.readers.reader_levitus.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_levitus.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_mauna_loa module

climind.readers.reader_mauna_loa.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_mauna_loa.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_mcculloch module

climind.readers.reader_mcculloch.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]

climind.readers.reader_mhw_ts module

climind.readers.reader_mhw_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]

climind.readers.reader_nasa_sealevel module

climind.readers.reader_nasa_sealevel.convert_partial_year(number)[source]
climind.readers.reader_nasa_sealevel.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]
climind.readers.reader_nasa_sealevel.read_new_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]

climind.readers.reader_noaa_index module

climind.readers.reader_noaa_index.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_noaa_index.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_noaa_interim_ts module

climind.readers.reader_noaa_interim_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_noaa_interim_ts.read_monthly_1x1_grid(filename: Path, metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_noaa_interim_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs)[source]
climind.readers.reader_noaa_interim_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_noaa_interim_ts.read_one_month(filehandle)[source]

climind.readers.reader_noaa_sealevel module

climind.readers.reader_noaa_sealevel.convert_partial_year(number)[source]
climind.readers.reader_noaa_sealevel.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]

climind.readers.reader_noaa_ts module

climind.readers.reader_noaa_ts.find_latest(out_dir: Path, filename_with_wildcards: str) → str[source]

Find the most recent file that matches

Parameters:

  • filename_with_wildcards (str) – Filename including wildcards

  • out_dir (Path) – Path of data directory

climind.readers.reader_noaa_ts.read_annual_ts(filename: str, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_noaa_ts.read_monthly_ts(filename: str, metadata: CombinedMetadata) → TimeSeriesMonthly[source]
climind.readers.reader_noaa_ts.read_ts(out_dir: Path, metadata: CombinedMetadata)[source]

climind.readers.reader_noaaglobaltemp module

climind.readers.reader_noaaglobaltemp.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]

Read in annual file

Parameters:

  • filename (Path) – Filename for annual file

  • metadata (dict) – Dictionary containing metadata

Return type:

ts.TimeSeriesAnnual

climind.readers.reader_noaaglobaltemp.read_monthly_1x1_grid(filename: Path, metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_noaaglobaltemp.read_monthly_grid(filename: Path, metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_noaaglobaltemp.read_monthly_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesMonthly[source]

Read in monthly file

Parameters:

  • filename (Path) – Path of monthly file

  • metadata (dict) – Dictionary containing metadata

Return type:

ts.TimeSeriesMonthly

climind.readers.reader_noaaglobaltemp.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_nsidc module

climind.readers.reader_nsidc.read_irregular_ts(filenames: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]
climind.readers.reader_nsidc.read_monthly_ts(filenames: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_osisaf module

climind.readers.reader_osisaf.read_irregular_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesIrregular[source]
climind.readers.reader_osisaf.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_osmc module

climind.readers.reader_osmc.read_irregular_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_osmc.read_monthly_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_osmc.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)[source]

climind.readers.reader_ozone_watch module

climind.readers.reader_ozone_watch.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]

climind.readers.reader_promice module

climind.readers.reader_promice.read_annual_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesAnnual[source]
climind.readers.reader_promice.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesMonthly[source]

climind.readers.reader_psl module

climind.readers.reader_psl.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_psl.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

The PSL monthly format has three main sections. The first line has the start and end years, then there is a data section with each row being a year and 13 columns year and 12 months of data. Finally, there’s a metadata section at the end. The first line of the metadata gives the missing data indicator.

Parameters:

  • filename (List[Path]) – List of paths for the filenames

  • metadata (CombinedMetadata) – Metadata object

Returns:

Monthly time series read from the file

Return type:

ts.TimeSeriesMonthly

climind.readers.reader_roni module

climind.readers.reader_roni.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_rss module

climind.readers.reader_rss.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_rss.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_rutgers module

climind.readers.reader_rutgers.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_rutgers.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_standard_grid module

climind.readers.reader_standard_grid.read_annual_grid(filename: List[Path], metadata: CombinedMetadata) → GridAnnual[source]

climind.readers.reader_star module

climind.readers.reader_star.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_star.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_test module

climind.readers.reader_test.read_annual_ts(filename, metadata, **kwargs)[source]
climind.readers.reader_test.read_monthly_1x1_grid(filename, metadata, **kwargs)[source]
climind.readers.reader_test.read_monthly_grid(filename, metadata, **kwargs)[source]
climind.readers.reader_test.read_monthly_ts(filename, metadata, **kwargs)[source]

climind.readers.reader_uah module

climind.readers.reader_uah.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_uah.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_vaccaro_ts module

climind.readers.reader_vaccaro_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_vaccaro_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_vaccaro_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly[source]
climind.readers.reader_vaccaro_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly[source]
climind.readers.reader_vaccaro_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_velicogna module

climind.readers.reader_velicogna.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_wdcgg_rate_ts module

climind.readers.reader_wdcgg_rate_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_wdcgg_rate_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_wdcgg_ts module

climind.readers.reader_wdcgg_ts.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_wdcgg_ts.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_wdcgg_ts_update module

climind.readers.reader_wdcgg_ts_update.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual[source]
climind.readers.reader_wdcgg_ts_update.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly[source]

climind.readers.reader_wgms module

climind.readers.reader_wgms.read_annual_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesAnnual[source]

Module contents

The readers package contains all the scripts needed to read the data sets. Because of the diversity of data formats, there is roughly one reader per dataset.

Most readers will import the generic_reader script which handles the selection of the appropriate reaader routines based on the metadata provided.

Individual reader scripts generally have one or more of the following functions:

  • read_monthly_ts

  • read_annual_ts

  • read_monthly_grid

  • read_monthly_5x5_grid

  • read_monthly_1x1_grid

Each of these function must take a list of filenames and a CombinedMetadata object as inputs. These are used to read the data and create an appropriate dataset.