icenet.data.sic package

icenet.data.sic package#

Submodules#

icenet.data.sic.mask module#

class icenet.data.sic.mask.Masks(*args, polarhole_dates: object = (datetime.date(1987, 6, 1), datetime.date(2005, 10, 1), datetime.date(2015, 12, 1)), polarhole_radii: object = (28, 11, 3), data_shape: object = (432, 432), dtype: object = <class 'numpy.float32'>, longitudes=None, latitudes=None, **kwargs)[source]#

Bases: Generator

Masking of regions to include/omit in dataset.

TODO: Add example usage.

LAND_MASK_FILENAME = 'land_mask.npy'#
POLARHOLE_DATES = (datetime.date(1987, 6, 1), datetime.date(2005, 10, 1), datetime.date(2015, 12, 1))#
POLARHOLE_RADII = (28, 11, 3)#
generate(year: int = 2000, save_land_mask: bool = True, save_polarhole_masks: bool = True, remove_temp_files: bool = False)[source]#

Generate a set of data masks.

Parameters:

  • year (optional) – Which year to use for generate masks from. Defaults to 2000.

  • save_land_mask (optional) – Whether to output land mask. Defaults to True.

  • save_polarhole_masks (optional) – Whether to output polar hole masks. Defaults to True.

  • remove_temp_files (optional) – Whether to remove temporary directory. Defaults to False.

get_active_cell_da(src_da: object) → object[source]#
Generate an xarray.DataArray object containing the active cell masks

for each timestamp in a given source DataArray.

Parameters:

src_da – Source xarray.DataArray object containing time, xc, yc coordinates.

Returns:

An xarray.DataArray containing active cell masks for each time

in source DataArray.

get_active_cell_mask(month: object) → object[source]#

Loads an active grid cell mask from numpy file.

Also, checks if a mask file exists for input month, and raises an error if it does not.

Parameters:

month – Month index representing the month for which the mask file is being checked.

Returns:

Active cell mask boolean(s) for corresponding month and pre-defined self._region.

Raises:

RuntimeError – If the mask file for the input month does not exist.

get_blank_mask() → object[source]#

Returns an empty mask.

Returns:

A numpy array of flags set to false for pre-defined self._region

of shape self._shape (the data_shape instance initialisation value).

get_land_mask(land_mask_filename: str = 'land_mask.npy') → object[source]#
Generate an xarray.DataArray object containing the active cell masks

for each timestamp in a given source DataArray.

Parameters:

land_mask_filename (optional) – Land mask output filename. Defaults to Masks.LAND_MASK_FILENAME.

Returns:

An numpy array of land mask flag(s) for corresponding month and

pre-defined self._region.

get_polarhole_mask(date: object) → object[source]#

Get mask of polar hole region.

get_region_data(data)[source]#

Get either a lat/lon region or a pixel bounded region via slicing.

If setting region via lat/lon, coordinates must be passed by calling self.set_region_by_lonlat method first.

init_params()[source]#

Initialises the parameters of the Masks class.

This method will create a masks.params file if it does not exist. And, stores the polar_radii and polar_dates instance variables into it. If it already exists, it will read and store the values to the instance variables

reset_region()[source]#

Resets the mask region and logs a message indicating that the whole mask will be returned.

set_region_by_lonlat(xc, yc, lon, lat, region)[source]#

Sets the region based on longitude and latitude bounds by converting them into index slices based on the provided xarray DataArray.

Alternative to __getitem__ if not using slicing.

Parameters:

  • xc

  • yc

  • lon

  • lat

  • region – lat/lon region bounds to get masks for, [lon_min, lat_min, lon_max, lat_max]

  • src_da – An xarray.DataArray that contains longitude and latitude coordinates.

icenet.data.sic.mask.main()[source]#

Entry point of Masks class - used to create executable that calls it.

icenet.data.sic.osisaf module#

class icenet.data.sic.osisaf.DaskWrapper(dask_port: int = 8888, dask_timeouts: int = 60, dask_tmp_dir: object = '/tmp', workers: int = 8)[source]#

Bases: object

Parameters:

  • dask_port

  • dask_timeouts

  • dask_tmp_dir

  • workers

dask_process(*args, method: callable, **kwargs)[source]#
Parameters:

method

class icenet.data.sic.osisaf.SICDownloader(*args, additional_invalid_dates: object = (), chunk_size: int = 10, dates: object = (), delete_tempfiles: bool = True, download: bool = True, dtype: object = <class 'numpy.float32'>, parallel_opens: bool = True, **kwargs)[source]#

Bases: Downloader

Downloads OSI-SAF SIC data from 1979-present using OpenDAP.

The dataset comprises OSI-450 (1979-2015) and OSI-430-b (2016-ownards) Monthly averages are-computed on the server-side. This script can take about an hour to run.

The query URLs were obtained from the following sites:
Parameters:

  • additional_invalid_dates

  • chunk_size

  • dates

  • delete_tempfiles

  • download

  • dtype

download()[source]#
missing_dates()[source]#
Returns:

zero_dates()[source]#

Write out any dates that have zero file size on the ftp server to csv

icenet.data.sic.osisaf.main()[source]#

Module contents#