1 clim_recal.utils.xarray

clim_recal.utils.xarray

1.1 Functions

Name	Description
apply_geo_func	Apply a Callable to netcdf_source file and export via to_netcdf.
cftime360_to_date	Convert a Datetime360Day into a date.
cftime_range_gen	Convert a banded time index a banded standard (Gregorian).
check_xarray_path_and_var_name	Check and return a T_Dataset instances and included variable name.
convert_xr_calendar	Convert cpm 360 day time series to a standard 365/366 day time series.
converted_output_path	source_path in export_folder via new_path_name_func.
cpm_check_converted	Check if cpm_xr_time_series is likely already reprojected.
cpm_reproject_with_standard_calendar	Convert raw cpm_xr_time_series to an 365/366 days and 27700 coords.
cpm_xarray_to_standard_calendar	Convert a CPM nc file of 360 day calendars to standard calendar.
crop_xarray	Crop xr_time_series with crop_path shapefile.
data_path_to_date_range	Extract date range as tuple or str from path.
date_seq_to_str	Return a str joining stard print format dates from datetime_seq.
ensure_xr_dataset	Return xr_time_series as a xarray.Dataset instance.
execute_configs	Run all converter configurations
file_name_to_start_end_dates	Return dates of file name with date_format-date_format structure.
gdal_warp_wrapper	Execute the gdalwrap function within python.
generate_360_to_standard	Return array_to_expand 360 days expanded to 365 or 366 days.
get_cpm_for_coord_alignment	Check if cpm_for_coord_alignment is a Dataset, process if a Path.
hads_resample_and_reproject	Resample HADs xarray time series to 2.2km.
interpolate_xr_ts_nans	Interpolate nan values in a Dataset time series.
path_print_progress	Extract relevant path info to print progress.
plot_xarray	Plot da with **kwargs to path.
progress_wrapper	Iterate over instance with or without a progress bar.
region_crop_file_name	Generate a file name for a regional crop.
xr_reproject_crs	Reproject source_xr to target_xr coordinate structure.

1.1.1 apply_geo_func

clim_recal.utils.xarray.apply_geo_func(source_path, func, export_folder, new_path_name_func=None, to_netcdf=True, to_raster=False, export_path_as_output_path_kwarg=False, return_results=False, **kwargs)

Apply a Callable to netcdf_source file and export via to_netcdf.

1.1.1.1 Parameters

Name	Type	Description	Default
source_path	PathLike	netcdf file to apply func to.	required
func	ReprojectFuncType	Callable to modify netcdf.	required
export_folder	PathLike	Where to save results.	required
new_path_name_func	Callable[[Path], Path] | None	Callabe to generate new path to save to.	None
to_netcdf	bool	Whether to call to_netcdf() method on results Dataset.	True
to_raster	bool	Whether to call rio.to_raster() on results Dataset.	False
export_path_as_output_path_kwarg	bool	Whether to add output_path = export_path to kwargs passed to func. Meant for cases calling gdal_warp_wrapper.	False
return_results	bool	Whether to return results, which would be a Dataset or GDALDataset (the latter if gdal_warp_wrapper is used).	False
**kwargs		Other parameters passed to func call.	{}

1.1.1.2 Returns

Type	Description
Either a Path to generated file or converted xarray object.

1.1.2 cftime360_to_date

clim_recal.utils.xarray.cftime360_to_date(cf_360)

Convert a Datetime360Day into a date.

1.1.2.1 Examples

>>> cftime360_to_date(Datetime360Day(1980, 1, 1))
datetime.date(1980, 1, 1)

1.1.3 cftime_range_gen

clim_recal.utils.xarray.cftime_range_gen(time_data_array, **kwargs)

Convert a banded time index a banded standard (Gregorian).

1.1.4 check_xarray_path_and_var_name

clim_recal.utils.xarray.check_xarray_path_and_var_name(xr_time_series, variable_name, ignore_warnings=True)

Check and return a T_Dataset instances and included variable name.

1.1.5 convert_xr_calendar

clim_recal.utils.xarray.convert_xr_calendar(xr_time_series, align_on=DEFAULT_CALENDAR_ALIGN, calendar=CFCalendarSTANDARD, use_cftime=False, missing_value=np.nan, interpolate_na=False, ensure_output_type_is_dataset=False, interpolate_method=DEFAULT_INTERPOLATION_METHOD, keep_crs=True, keep_attrs=True, limit=1, engine=NETCDF4_XARRAY_ENGINE, check_cftime_cols=None, cftime_range_gen_kwargs=None)

Convert cpm 360 day time series to a standard 365/366 day time series.

1.1.5.1 Notes

Short time examples (like 2 skipped out of 8 days) raises: ValueError("date_range_like was unable to generate a range as the source frequency was not inferable.")

1.1.5.2 Parameters

Name	Type	Description	Default
xr_time_series	T_DataArray | T_Dataset | PathLike	A DataArray or Dataset to convert to calendar time.	required
align_on	ConvertCalendarAlignOptions	Whether and how to align calendar types.	DEFAULT_CALENDAR_ALIGN
calendar	CFCalendar	Type of calendar to convert xr_time_series to.	CFCalendarSTANDARD
use_cftime	bool	Whether to enforce cftime vs datetime64 time format.	False
missing_value	Any | None	Missing value to populate missing date interpolations with.	np.nan
keep_crs	bool	Reapply initial Coordinate Reference System (CRS) after time projection.	True
interpolate_na	bool	Whether to apply temporal interpolation for missing values.	False
interpolate_method	InterpOptions	Which InterpOptions method to apply if interpolate_na is True.	DEFAULT_INTERPOLATION_METHOD
keep_attrs	bool	Whether to keep all attributes on after interpolate_na	True
limit	int	Limit of number of continuous missing day values allowed in interpolate_na.	1
engine	XArrayEngineType	Which XArrayEngineType to use in parsing files and operations.	NETCDF4_XARRAY_ENGINE
extrapolate_fill_value		If True, then pass fill_value=extrapolate. See: * https://docs.xarray.dev/en/stable/generated/xarray.Dataset.interpolate_na.html * https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d	required
check_cftime_cols	tuple[str] | None	Columns to check cftime format on	None
cftime_range_gen_kwargs	dict[str, Any] | None	Any kwargs to pass to cftime_range_gen	None
**kwargs		Any additional parameters to pass to interpolate_na.	required

1.1.5.3 Raises

Type	Description
ValueError	Likely from xarray calling date_range_like.

1.1.5.4 Returns

Type	Description
T_DataArrayOrSet	Converted xr_time_series to specified calendar with optional interpolation.

1.1.5.5 Notes

Certain values may fail to interpolate in cases of 360 -> 365/366 (Gregorian) calendar. Examples include projecting CPM data, which is able to fill in measurement values (e.g. tasmax) but the year and month_number variables have nan values

1.1.5.6 Examples

2 Note a new doctest needs to be written to deal

3 with default year vs date parameters

>>> xr_360_to_365_datetime64: T_Dataset = convert_xr_calendar(
...     xarray_spatial_4_years_360_day, align_on="date")
>>> xr_360_to_365_datetime64.sel(
...     time=slice("1981-01-30", "1981-02-01"),
...     space="Glasgow").day_360
<xarray.DataArray 'day_360' (time: 3)>...
Coordinates:
  * time     (time) datetime64[ns] ...1981-01-30 1981-01-31 1981-02-01
    space    <U10 ...'Glasgow'
>>> xr_360_to_365_datetime64_interp: T_Dataset = convert_xr_calendar(
...     xarray_spatial_4_years_360_day, interpolate_na=True)
>>> xr_360_to_365_datetime64_interp.sel(
...     time=slice("1981-01-30", "1981-02-01"),
...     space="Glasgow").day_360
<xarray.DataArray 'day_360' (time: 3)>...
array([0.23789282, 0.5356328 , 0.311945  ])
Coordinates:
  * time     (time) datetime64[ns] ...1981-01-30 1981-01-31 1981-02-01
    space    <U10 ...'Glasgow'
>>> convert_xr_calendar(xarray_spatial_6_days_2_skipped)
Traceback (most recent call last):
   ...
ValueError: `date_range_like` was unable to generate a range as the source frequency was not inferable.

3.0.1 converted_output_path

clim_recal.utils.xarray.converted_output_path(source_path, export_folder, new_path_name_func=None, **kwargs)

source_path in export_folder via new_path_name_func.

3.0.1.1 Parameters

Name	Type	Description	Default
source_path	PathLike | None	Original path to extract file name via Path(source_path).name	required
export_folder	PathLike	Folder to save new path in.	required
new_path_name_func	Callable[…, Path] | None	Function to convert old file name to new file name.	None
**kwargs		Additional parameters passed to new_path_name_func	{}

3.0.1.2 Returns

Type	Description
Converted path of export_folder / then either
source_path or results of new_path_name_func(source_path).

3.0.2 cpm_check_converted

clim_recal.utils.xarray.cpm_check_converted(cpm_xr_time_series)

Check if cpm_xr_time_series is likely already reprojected.

3.0.2.1 Parameters

Name	Type	Description	Default
cpm_xr_time_series	T_Dataset | PathLike	Dataset instance or Path to check.	required

3.0.2.2 Returns

Type	Description
True if all of the methics are True, else False

3.0.3 cpm_reproject_with_standard_calendar

clim_recal.utils.xarray.cpm_reproject_with_standard_calendar(cpm_xr_time_series, variable_name=None, close_temp_paths=True, force=False)

Convert raw cpm_xr_time_series to an 365/366 days and 27700 coords.

3.0.3.1 Notes

Currently makes UTM coordinate structure

3.0.3.2 Parameters

Name	Type	Description	Default
cpm_xr_time_series	T_Dataset | PathLike	Dataset (or path to load as Dataset) expected to be in raw UKCPM format, with 360 day years and a rotated coordinate system.	required
variable_name	str | None	Name of variable used, usually a measure of climate change like tasmax and tasmin.	None

3.0.3.3 Returns

Type	Description
Final xarray Dataset after spatial and temporal changes.

3.0.3.4 Examples

>>> tasmax_cpm_1980_raw = getfixture('tasmax_cpm_1980_raw')
>>> if not tasmax_cpm_1980_raw:
...     pytest.skip(mount_or_cache_doctest_skip_message)
>>> tasmax_cpm_1980_365_day: T_Dataset = cpm_reproject_with_standard_calendar(
...     cpm_xr_time_series=tasmax_cpm_1980_raw,
...     variable_name="tasmax")
Warp:      ...nc ...100%...
Translate: ...tif ...100%...
>>> tasmax_cpm_1980_365_day
<xarray.Dataset>
Dimensions:              (time: 365, x: 493, y: 607)
Coordinates:
  * time                 (time) datetime64[ns]...
  * x                    (x) float64...
  * y                    (y) float64...
    transverse_mercator  |S1...
    spatial_ref          int64...
Data variables:
    tasmax               (time, y, x) float32...
Attributes: (12/18)
    ...
>>> tasmax_cpm_1980_365_day.dims
Frozen...({'time': 365, 'x': 493, 'y': 607})

3.0.4 cpm_xarray_to_standard_calendar

clim_recal.utils.xarray.cpm_xarray_to_standard_calendar(cpm_xr_time_series, include_bnds_index=False)

Convert a CPM nc file of 360 day calendars to standard calendar.

3.0.4.1 Parameters

Name	Type	Description	Default
cpm_xr_time_series	T_Dataset | PathLike	A raw xarray of the form provided by CPM.	required
include_bnds_index	bool	Whether to fix bnds indexing in returned Dataset.	False

3.0.4.2 Returns

Type	Description
Dataset calendar converted to standard (Gregorian).

3.0.5 crop_xarray

clim_recal.utils.xarray.crop_xarray(xr_time_series, crop_box, **kwargs)

Crop xr_time_series with crop_path shapefile.

3.0.5.1 Parameters

Name	Type	Description	Default
xr_time_series	T_Dataset | PathLike	Dataset or path to netcdf file to load and crop.	required
crop_box	BoundingBoxCoords	Instance of BoundingBoxCoords with coords.	required
kwargs		Any additional parameters to pass to clip	{}

3.0.5.2 Returns

Type	Description
T_Dataset	Spatially cropped xr_time_series Dataset with final_crs spatial coords.

3.0.5.3 Examples

>>> from clim_recal.utils.data import GlasgowCoordsEPSG27700
>>> from numpy.testing import assert_allclose
>>> tasmax_cpm_1980_raw = getfixture('tasmax_cpm_1980_raw')
>>> if not tasmax_cpm_1980_raw:
...     pytest.skip(mount_or_cache_doctest_skip_message)
>>> tasmax_cpm_1980_365_day: T_Dataset = cpm_reproject_with_standard_calendar(
...     cpm_xr_time_series=tasmax_cpm_1980_raw,
...     variable_name="tasmax")
>>> cropped = crop_xarray(
...     tasmax_cpm_1980_365_day,
...     crop_box=GlasgowCoordsEPSG27700)
>>> assert_allclose(cropped.rio.bounds(),
...                 GlasgowCoordsEPSG27700.as_rioxarray_tuple(),
...                 rtol=.01)
>>> tasmax_cpm_1980_365_day.sizes
Frozen({'x': 529, 'y': 653, 'time': 365})
>>> cropped.sizes
Frozen({'x': 10, 'y': 8, 'time': 365})

3.0.6 data_path_to_date_range

clim_recal.utils.xarray.data_path_to_date_range(path, return_type='string')

Extract date range as tuple or str from path.

3.0.7 date_seq_to_str

clim_recal.utils.xarray.date_seq_to_str(datetime_seq, join_str=' ')

Return a str joining stard print format dates from datetime_seq.

3.0.8 ensure_xr_dataset

clim_recal.utils.xarray.ensure_xr_dataset(xr_time_series, default_name='to_convert')

Return xr_time_series as a xarray.Dataset instance.

3.0.8.1 Parameters

Name	Type	Description	Default
xr_time_series	T_DataArrayOrSet	Instance to check and if necessary to convert to Dataset.	required
default_name		Name to give returned Dataset if xr_time_series.name is empty.	'to_convert'

3.0.8.2 Returns

Type	Description
T_Dataset	Converted (or original) Dataset.

3.0.8.3 Examples

>>> ensure_xr_dataset(xarray_spatial_4_days)
<xarray.Dataset>...
Dimensions:      (time: 5, space: 3)
Coordinates:
  * time         (time) datetime64[ns] ...1980-11-30 1980-12-01 ... 1980-12-04
  * space        (space) <U10 ...'Glasgow' 'Manchester' 'London'
Data variables:
    xa_template  (time, space) float64 ...0.5488 0.7152 ... 0.9256 0.07104

3.0.9 execute_configs

clim_recal.utils.xarray.execute_configs(instance, configs_method='yield_configs', multiprocess=False, cpus=None, return_instances=False, return_path=True, description_func=None, **kwargs)

Run all converter configurations

3.0.9.1 Parameters

Name	Type	Description	Default
multiprocess	bool	If True run parameters in resample_configs with multiprocess_execute.	False
configs_method	str	Method name to yield model parameters.	'yield_configs'
cpus	int | None	Number of cpus to pass to multiprocess_execute.	None
return_converters		Return instances of generated classe (e.g. HADsConvert or CPMConvert), or return the results of each execute call.	required
return_path	bool	Return Path to results object if True, else resampled Dataset.	True
**kwargs		Parameters to path to sampler execute calls.	{}

3.0.10 file_name_to_start_end_dates

clim_recal.utils.xarray.file_name_to_start_end_dates(path, date_format=CLI_DATE_FORMAT_STR)

Return dates of file name with date_format-date_format structure.

3.0.10.1 Parameters

Name	Type	Description	Default
path	PathLike	Path to file	required
date_format	str	Format of date for strptime	CLI_DATE_FORMAT_STR

3.0.10.2 Examples

The examples below are meant to demonstrate usage, and the significance of when the last date is included or not by default.

>>> from .core import date_range_generator
>>> tif_365_path: Path = (Path('some') /
...     'folder' /
...     'pr_rcp85_land-cpm_uk_2.2km_06_day_20761201-20771130.tif')
>>> start_date, end_date = file_name_to_start_end_dates(tif_365_path)
>>> start_date
datetime.datetime(2076, 12, 1, 0, 0)
>>> end_date
datetime.datetime(2077, 11, 30, 0, 0)
>>> dates: tuple[date, ...] =  tuple(
...     date_range_generator(start_date=start_date,
...                          end_date=end_date,
...                          inclusive=True))
>>> dates[:3]
(datetime.datetime(2076, 12, 1, 0, 0),
 datetime.datetime(2076, 12, 2, 0, 0),
 datetime.datetime(2076, 12, 3, 0, 0))
>>> len(dates)
365
>>> tif_366_path: Path = (Path('some') /
...     'folder' /
...     'pr_rcp85_land-cpm_uk_2.2km_06_day_20791201-20801130.tif')
>>> from pandas import date_range
>>> dates = date_range(*file_name_to_start_end_dates(tif_366_path))
>>> len(dates)
366

3.0.11 gdal_warp_wrapper

clim_recal.utils.xarray.gdal_warp_wrapper(input_path, output_path, output_crs=BRITISH_NATIONAL_GRID_EPSG, output_x_resolution=None, output_y_resolution=None, copy_metadata=True, return_path=True, format=GDALNetCDFFormatStr, multithread=True, warp_dict_options=DEFAULT_WARP_DICT_OPTIONS, use_tqdm_progress_bar=True, tqdm_file_name_chars=TQDM_FILE_NAME_PRINT_CHARS_INDEX, resampling_method=None, supress_warnings=True, **kwargs)

Execute the gdalwrap function within python.

This is following a script in the bash/ folder that uses this programme:

f=$1 # The first argument is the file to reproject
fn=${f/Raw/Reprojected_infill} # Replace Raw with Reprojected_infill in the filename
folder=`dirname $fn` # Get the folder name
mkdir -p $folder # Create the folder if it doesn't exist
gdalwarp -t_srs 'EPSG:27700' -tr 2200 2200 -r near -overwrite $f "${fn%.nc}.tif" # Reproject the file

3.0.11.1 Parameters

Name	Type	Description	Default
input_path	PathLike	Path with CPRUK files to resample. srcDSOrSrcDSTab in Warp.	required
output_path	PathLike	Path to save resampled input_path file(s) to destNameOrDestDS in Warp.	required
output_crs	str	Coordinate system to convert input_path file(s) to. dstSRS in WarpOptions.	BRITISH_NATIONAL_GRID_EPSG
format	GDALFormatsType | str | None	Format to convert input_path to in output_path.	GDALNetCDFFormatStr
output_x_resolution	int | None	Resolution of x cordinates to convert input_path file(s) to. xRes in WarpOptions.	None
output_y_resolution	int | None	Resolution of y cordinates to convert input_path file(s) to. yRes in WarpOptions.	None
copy_metadata	bool	Whether to copy metadata when possible.	True
return_path	bool	Return the resulting path if True, else the new GDALDataset.	True
format	GDALFormatsType | str | None	Format to write new file to.	GDALNetCDFFormatStr
multithread	bool	Whether to use multithread to speed up calculations.	True
kwargs		Any additional parameters to pass to WarpOption.	{}

3.0.12 generate_360_to_standard

clim_recal.utils.xarray.generate_360_to_standard(array_to_expand)

Return array_to_expand 360 days expanded to 365 or 366 days.

This may be dropped if cpm_reproject_with_standard_calendar is successful.

3.0.13 get_cpm_for_coord_alignment

clim_recal.utils.xarray.get_cpm_for_coord_alignment(cpm_for_coord_alignment, skip_reproject=False, cpm_regex=CPM_REGEX)

Check if cpm_for_coord_alignment is a Dataset, process if a Path.

3.0.13.1 Parameters

Name	Type	Description	Default
cpm_for_coord_alignment	PathLike | T_Dataset | None	Either a Path or a file or folder with a cpm file to align to or a xarray.Dataset. If a folder, the first file matching cpm_regex will be used. It will then be processed via cpm_reproject_with_standard_calendar for comparability and use alongside cpm files.	required
skip_reproject	bool	Whether to skip calling cpm_reproject_with_standard_calendar.	False
cpm_regex	str	A regular expression to filter suitable files if cpm_for_coord_alignment is a folder Path.	CPM_REGEX

3.0.13.2 Returns

Type	Description
An xarray.Dataset coordinate structure to align HADs coordinates.

3.0.14 hads_resample_and_reproject

clim_recal.utils.xarray.hads_resample_and_reproject(hads_xr_time_series, variable_name, cpm_to_match, cpm_to_match_func=cpm_reproject_with_standard_calendar, x_dim_name=HADS_RAW_X_COLUMN_NAME, y_dim_name=HADS_RAW_Y_COLUMN_NAME)

Resample HADs xarray time series to 2.2km.

3.0.15 interpolate_xr_ts_nans

clim_recal.utils.xarray.interpolate_xr_ts_nans(xr_ts, original_xr_ts=None, check_cftime_cols=None, interpolate_method=DEFAULT_INTERPOLATION_METHOD, keep_crs=True, keep_attrs=True, limit=1, cftime_range_gen_kwargs=None, **kwargs)

Interpolate nan values in a Dataset time series.

3.0.15.1 Notes

For details and details of keep_attrs, limit and **kwargs parameters see: https://docs.xarray.dev/en/stable/generated/xarray.DataArray.interpolate_na.html

3.0.15.2 Parameters

Name	Type	Description	Default
xr_ts	T_Dataset	Dataset to interpolate via interpolate_na. Requires a time coordinate.	required
original_xr_ts	T_Dataset | None	A Dataset to compare the conversion process with. If not provided, set to the original xr_ts as a reference.	None
check_cftime_cols	tuple[str] | None	tuple of column names in a cftime format to check.	None
interpolate_method	InterpOptions	Which of the xarray interpolation methods to use.	DEFAULT_INTERPOLATION_METHOD
keep_crs	bool	Whether to ensure the original crs is kept via rio.write_crs.	True
keep_attrs	bool	Passed to keep_attrs in interpolate_na. See Notes.	True
limit	int	How many nan are allowed either side of data point to interpolate. See Notes.	1
cftime_range_gen_kwargs	dict[str, Any] | None	Any cftime_range_gen arguments to use with check_cftime_cols calls.	None

3.0.15.3 Returns

Type	Description
Dataset where xr_ts nan values are iterpolated with respect to the time coordinate.

3.0.16 path_print_progress

clim_recal.utils.xarray.path_print_progress(path, data_type=None)

Extract relevant path info to print progress.

3.0.17 plot_xarray

clim_recal.utils.xarray.plot_xarray(da, path, time_stamp=False, **kwargs)

Plot da with **kwargs to path.

3.0.17.1 Parameters

Name	Type	Description	Default
da	T_DataArrayOrSet	xarray objec to plot.	required
path	PathLike	File to write plot to.	required
time_stamp	bool	Whather to add a datetime str of time of writing in file name.	False
kwargs		Additional parameters to pass to plot.	{}

3.0.17.2 Examples

>>> example_path: Path = (
...     getfixture('tmp_path') / 'test-path/example.png')
>>> image_path: Path = plot_xarray(
...     xarray_spatial_4_days, example_path)
>>> example_path == image_path
True
>>> example_time_stamped: Path = (
...      example_path.parent / 'example-stamped.png')
>>> timed_image_path: Path = plot_xarray(
...     xarray_spatial_4_days, example_time_stamped,
...     time_stamp=True)
>>> example_time_stamped != timed_image_path
True
>>> print(timed_image_path)
/.../test-path/example-stamped_...-...-..._...png

3.0.18 progress_wrapper

clim_recal.utils.xarray.progress_wrapper(instance, method_name, start=None, stop=None, step=1, description='', override_export_path=None, source_to_index=None, return_path=True, write_results=True, progress_bar=True, progress_bar_refresh_per_sec=1, description_func=None, **kwargs)

Iterate over instance with or without a progress bar.

3.0.18.1 Parameters

Name	Type	Description	Default
instance	Sequence	An instance of a class with method_name for iterating calls.	required
method_name	str	Method name to call on instance to iterate calculations.	required
start	int | None	Index to start iterating from.	None
stop	int | None	Index to end interating at.	None
step	int	Hops of iterating between start and stop of instance.	1
description	str	What to print in front of progress bar if progress_bar is True.	''
override_export_path	Path | None	Export Path to write to instead of self.output_path.	None
source_to_index	Sequence | None	Sequence of paths to iterate over instaed of self	None
return_path	bool	Whether to return Path of export. If not, result objects are returned.	True
write_results	bool	Whether to write results to disk. Required if return_path is True.	True
progress_bar	bool	Whether to print progress bar or skip	True
progress_bar_refresh_per_sec	int	How many progress_bar refreshes per second if progress_bar is used.	1
description_func	Callable[[PathLike, Any], str] | None	Function to return description.	None
**kwargs		Additional parameters to pass to method_name.	{}

3.0.19 region_crop_file_name

clim_recal.utils.xarray.region_crop_file_name(file_name, crop_region)

Generate a file name for a regional crop.

3.0.19.1 Parameters

Name	Type	Description	Default
file_name	PathLike	File name to add crop_region name to.	required
crop_region	str | RegionOptions | None	Region name to include in cropped file name.	required

3.0.19.2 Examples

>>> region_crop_file_name(
...    'tasmax.nc',
...    'Glasgow')
'crop_Glasgow_tasmax.nc'
>>> region_crop_file_name(
...    'tasmax_hadukgrid_uk_2_2km_day_19800601-19800630.nc',
...    'Glasgow')
'crop_Glasgow_tasmax_hads_19800601-19800630.nc'
>>> region_crop_file_name(
...     'tasmax_rcp85_land-cpm_uk_2.2km_05_day_std_year_19861201-19871130.nc',
...     'Glasgow')
'crop_Glasgow_tasmax_cpm_05_19861201-19871130.nc'

3.0.20 xr_reproject_crs

clim_recal.utils.xarray.xr_reproject_crs(xr_time_series, x_dim_name=CPM_RAW_X_COLUMN_NAME, y_dim_name=CPM_RAW_Y_COLUMN_NAME, time_dim_name=TIME_COLUMN_NAME, variable_name=None, final_crs=BRITISH_NATIONAL_GRID_EPSG, match_xr_time_series=None, match_xr_time_series_load_func=None, match_xr_time_series_load_kwargs=None, resampling_method=DEFAULT_RESAMPLING_METHOD, nodata=np.nan, **kwargs)

Reproject source_xr to target_xr coordinate structure.

3.0.20.1 Parameters

Name	Type	Description	Default
xr_time_series	T_Dataset | PathLike	Dataset or PathLike to load and reproject.	required
x_dim_name	str	str name of x spatial dimension in xr_time_series. Default matches CPM UK projections.	CPM_RAW_X_COLUMN_NAME
y_dim_name	str	str name of y spatial dimension in xr_time_series. Default matches CPM UK projections.	CPM_RAW_Y_COLUMN_NAME
time_dim_name	str	str name of time dimension in xr_time_series.	TIME_COLUMN_NAME
variable_name	str | None	Name of datset to apply projection to within xr_time_series. Inferred if None assuming only one data_var attribute.	None
final_crs	str	Coordinate system str to project xr_time_series to.	BRITISH_NATIONAL_GRID_EPSG
resampling_method	Resampling	rasterio resampling method to apply.	DEFAULT_RESAMPLING_METHOD

3.0.20.2 Examples

>>> tasmax_hads_1980_raw = getfixture('tasmax_hads_1980_raw')
>>> if not tasmax_hads_1980_raw:
...     pytest.skip(mount_or_cache_doctest_skip_message)
>>> tasmax_hads_1980_raw.dims
FrozenMappingWarningOnValuesAccess({'time': 31,
                                    'projection_y_coordinate': 1450,
                                    'projection_x_coordinate': 900,
                                    'bnds': 2})
>>> tasmax_hads_2_2km: T_Dataset = xr_reproject_crs(
...     tasmax_hads_1980_raw,
...     variable_name="tasmax",
...     x_dim_name=HADS_RAW_X_COLUMN_NAME,
...     y_dim_name=HADS_RAW_Y_COLUMN_NAME,
...     resolution=(CPM_RESOLUTION_METERS,
...                 CPM_RESOLUTION_METERS),)
>>> tasmax_hads_2_2km.dims
FrozenMappingWarningOnValuesAccess({'x': 410,
                                    'y': 660,
                                    'time': 31})