load#

geowombat.load(image_list, time_names, band_names, chunks=512, nodata=65535, in_range=None, out_range=None, data_slice=None, num_workers=1, src=None, scheduler='ray')[source]#

Loads data into memory using xarray.open_mfdataset() and ray. This function does not check data alignments and CRSs. It assumes each image in image_list has the same y and x dimensions and that the coordinates align.

The load function cannot be used if dataclasses was pip installed.

Parameters:

  • image_list (list) – The list of image file paths.

  • time_names (list) – The list of image datetime objects.

  • band_names (list) – The list of bands to open.

  • chunks (Optional[int]) – The dask chunk size.

  • nodata (Optional[float | int]) – The ‘no data’ value.

  • in_range (Optional[tuple]) – The input (min, max) range. If not given, defaults to (0, 10000).

  • out_range (Optional[tuple]) – The output (min, max) range. If not given, defaults to (0, 1).

  • data_slice (Optional[tuple]) – The slice object to read, given as (time, bands, rows, columns).

  • num_workers (Optional[int]) – The number of threads.

  • scheduler (Optional[str]) – The distributed scheduler. Currently not implemented.

Returns:

Datetime list, array of (time x bands x rows x columns)

Return type:

list, numpy.ndarray

Example

>>> import datetime
>>> import geowombat as gw
>>>
>>> image_names = ['LT05_L1TP_227082_19990311_20161220_01_T1.nc',
>>>                'LT05_L1TP_227081_19990311_20161220_01_T1.nc',
>>>                'LT05_L1TP_227082_19990327_20161220_01_T1.nc']
>>>
>>> image_dates = [datetime.datetime(1999, 3, 11, 0, 0),
>>>                datetime.datetime(1999, 3, 11, 0, 0),
>>>                datetime.datetime(1999, 3, 27, 0, 0)]
>>>
>>> data_slice = (slice(0, None), slice(0, None), slice(0, 64), slice(0, 64))
>>>
>>> # Load data into memory
>>> dates, y = gw.load(image_names,
>>>                    image_dates,
>>>                    ['red', 'nir'],
>>>                    chunks=512,
>>>                    nodata=65535,
>>>                    data_slice=data_slice,
>>>                    num_workers=4)