climind.readers package

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.

Submodules

climind.readers.generic_reader module

climind.readers.generic_reader.get_last_modified_time(file: Path) → Optional[str]

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)

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) → Optional[str]

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) → Union[TimeSeriesMonthly, TimeSeriesAnnual, GridMonthly]

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.reader_aviso module

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

climind.readers.reader_aviso_ftp module

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

climind.readers.reader_badc_csv module

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

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

climind.readers.reader_berkeley module

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

climind.readers.reader_berkeley.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly

climind.readers.reader_berkeley.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly

climind.readers.reader_berkeley.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly

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

climind.readers.reader_berkeley_ts module

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

climind.readers.reader_berkeley_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly

climind.readers.reader_berkeley_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly

climind.readers.reader_berkeley_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly

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

climind.readers.reader_cheng module

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

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

climind.readers.reader_clsat module

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

climind.readers.reader_clsat.read_monthly_grid(filename: List[Path], metadata) → GridMonthly

climind.readers.reader_cmems_ph module

climind.readers.reader_cmems_ph.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual

climind.readers.reader_cmems_ph.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)

climind.readers.reader_era5 module

climind.readers.reader_era5.find_latest(out_dir: Path, filename_with_wildcards: str) → Path

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]

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

climind.readers.reader_era5.read_grid(filename: str)

climind.readers.reader_era5.read_monthly_1x1_grid(filename, metadata) → GridMonthly

climind.readers.reader_era5.read_monthly_5x5_grid(filename, metadata) → GridMonthly

climind.readers.reader_era5.read_monthly_grid(filename: str, metadata) → GridMonthly

climind.readers.reader_era5.read_monthly_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesMonthly

climind.readers.reader_era5.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)

climind.readers.reader_gcos module

climind.readers.reader_gcos.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual

climind.readers.reader_gcos.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)

climind.readers.reader_gistemp_ts module

climind.readers.reader_gistemp_ts.build_transfer(xx: int, yy: int)

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

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

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

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

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

climind.readers.reader_gml module

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

climind.readers.reader_grace module

climind.readers.reader_grace.find_latest(out_dir: Path, filename_with_wildcards: str) → Path

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

climind.readers.reader_grace.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata, **kwargs) → TimeSeriesMonthly

climind.readers.reader_grace.read_ts(out_dir: Path, metadata: CombinedMetadata, **kwargs)

climind.readers.reader_gsfc module

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

climind.readers.reader_hadcrut_ts module

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

climind.readers.reader_hadcrut_ts.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly

climind.readers.reader_hadcrut_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly

climind.readers.reader_hadcrut_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly

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

climind.readers.reader_hadsst_ts module

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

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

climind.readers.reader_imbie module

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

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

climind.readers.reader_imbie_2021_antarctica module

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

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

climind.readers.reader_imbie_antarctica module

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

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

climind.readers.reader_ipcc_ts module

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

climind.readers.reader_ishii module

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

climind.readers.reader_jra55 module

climind.readers.reader_jra55.read_annual_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesAnnual

climind.readers.reader_jra55.read_grid(filename: List[Path])

climind.readers.reader_jra55.read_monthly_1x1_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly

climind.readers.reader_jra55.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly

climind.readers.reader_jra55.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs) → GridMonthly

climind.readers.reader_jra55.read_monthly_ts(filename: List[Path], metadata: CombinedMetadata) → TimeSeriesMonthly

climind.readers.reader_kennaook module

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

climind.readers.reader_levitus module

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

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

climind.readers.reader_mauna_loa module

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

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

climind.readers.reader_mhw_ts module

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

climind.readers.reader_noaa_interim_ts module

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

climind.readers.reader_noaa_interim_ts.read_monthly_1x1_grid(filename: Path, metadata: CombinedMetadata, **kwargs) → GridMonthly

climind.readers.reader_noaa_interim_ts.read_monthly_5x5_grid(filename: List[Path], metadata: CombinedMetadata, **kwargs)

climind.readers.reader_noaa_interim_ts.read_monthly_grid(filename: List[Path], metadata: CombinedMetadata) → GridMonthly

climind.readers.reader_noaa_interim_ts.read_one_month(filehandle)

climind.readers.reader_noaa_ts module

climind.readers.reader_noaa_ts.find_latest(out_dir: Path, filename_with_wildcards: str) → str

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

climind.readers.reader_noaa_ts.read_monthly_ts(filename: str, metadata: CombinedMetadata) → TimeSeriesMonthly

climind.readers.reader_noaa_ts.read_ts(out_dir: Path, metadata: CombinedMetadata)

climind.readers.reader_noaaglobaltemp module

climind.readers.reader_noaaglobaltemp.find_latest(out_dir: Path, filename_with_wildcards: str) → Path

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.reader_noaaglobaltemp.get_latest_filename_and_url(filename: Path, url: str) → Tuple[str, str]

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_noaaglobaltemp.read_annual_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesAnnual

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

climind.readers.reader_noaaglobaltemp.read_monthly_grid(filename: Path, metadata: CombinedMetadata) → GridMonthly

climind.readers.reader_noaaglobaltemp.read_monthly_ts(filename: Path, metadata: CombinedMetadata) → TimeSeriesMonthly

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)

climind.readers.reader_nsidc module

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

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

climind.readers.reader_osisaf module

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

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

climind.readers.reader_promice module

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

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

climind.readers.reader_standard_grid module

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

climind.readers.reader_test module

climind.readers.reader_test.read_annual_ts(filename, metadata, **kwargs)

climind.readers.reader_test.read_monthly_1x1_grid(filename, metadata, **kwargs)

climind.readers.reader_test.read_monthly_grid(filename, metadata, **kwargs)

climind.readers.reader_test.read_monthly_ts(filename, metadata, **kwargs)

climind.readers.reader_wdcgg_rate_ts module

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

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

climind.readers.reader_wdcgg_ts module

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

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

climind.readers.reader_wdcgg_ts_update module

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

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

climind.readers.reader_wgms module

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