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Generalize for exploiting a Dask cluster and multiple projections #6

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9d47e46
Add median() for a Dask-backed array
luigidifraia Nov 2, 2019
fb7b149
Configurable coordinates for geomedian calculation
luigidifraia Nov 2, 2019
3073596
Adjust function documentation to match parameters
luigidifraia Nov 3, 2019
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43 changes: 40 additions & 3 deletions data_cube_utilities/dc_mosaic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
# License for the specific language governing permissions and limitations
# under the License.

import dask.array
import numpy as np
import xarray as xr
from collections import OrderedDict
Expand Down Expand Up @@ -164,6 +165,38 @@ def create_mean_mosaic(dataset_in, clean_mask=None, no_data=-9999, dtype=None, *
dataset_out = restore_or_convert_dtypes(dtype, band_list, dataset_in_dtypes, dataset_out, no_data)
return dataset_out

# Based on a gist by Andrew Hicks: https://gist.github.com/andrewdhicks/d89849997453cdfad6fa568816ca7160

def median(array, dim, keep_attrs=False, skipna=False, **kwargs):
""" Runs a median on an dask-backed xarray.

This function does not scale!
It will rechunk along the given dimension, so make sure
your other chunk sizes are small enough that it
will fit into memory.

:param array: An xarray.DataArray or xarray.Dataset wrapping one or more dask arrays
:type array: xarray.DataArray or xarray.Dataset
:param dim: The name of the dim in array to calculate the median
:type dim: str
"""
if type(array) is xr.Dataset:
return array.apply(median, dim=dim, keep_attrs=keep_attrs, skipna=skipna, **kwargs)

if not hasattr(array.data, 'dask'):
return array.median(dim, keep_attrs=keep_attrs, skipna=skipna, **kwargs)

array = array.chunk({dim:-1})
axis = array.dims.index(dim)
median_func = np.nanmedian if skipna else np.median
blocks = dask.array.map_blocks(median_func, array.data, dtype=array.dtype, drop_axis=axis, axis=axis, **kwargs)

new_coords={k: v for k, v in array.coords.items() if k != dim and dim not in v.dims}
new_dims = tuple(d for d in array.dims if d != dim)
new_attrs = array.attrs if keep_attrs else None

return xr.DataArray(blocks, coords=new_coords, dims=new_dims, attrs=new_attrs)


def create_median_mosaic(dataset_in, clean_mask=None, no_data=-9999, dtype=None, **kwargs):
"""
Expand Down Expand Up @@ -205,7 +238,7 @@ def create_median_mosaic(dataset_in, clean_mask=None, no_data=-9999, dtype=None,

# Mask out clouds and Landsat 7 scan lines.
dataset_in = dataset_in.where((dataset_in != no_data) & (clean_mask))
dataset_out = dataset_in.median(dim='time', skipna=True, keep_attrs=False)
dataset_out = median(dataset_in, dim='time', skipna=True, keep_attrs=False)

# Handle datatype conversions.
dataset_out = restore_or_convert_dtypes(dtype, band_list, dataset_in_dtypes, dataset_out, no_data)
Expand Down Expand Up @@ -478,6 +511,8 @@ def create_hdmedians_multiple_band_mosaic(dataset_in,
dtype=None,
intermediate_product=None,
operation="median",
x_coord='longitude',
y_coord='latitude',
**kwargs):
"""
Calculates the geomedian or geomedoid using a multi-band processing method.
Expand All @@ -497,6 +532,8 @@ def create_hdmedians_multiple_band_mosaic(dataset_in,
A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g.
np.int16, np.float32) to convert the data to.
operation: str in ['median', 'medoid']
x_coord, y_coord: str
Names of DataArrays in `dataset_in` to use as x and y coordinates.

Returns
-------
Expand Down Expand Up @@ -546,8 +583,8 @@ def create_hdmedians_multiple_band_mosaic(dataset_in,
for index, value in enumerate(band_list)
}
dataset_out = xr.Dataset(output_dict,
coords={'latitude': dataset_in['latitude'],
'longitude': dataset_in['longitude']},
coords={y_coord: dataset_in[y_coord],
x_coord: dataset_in[x_coord]},
attrs=dataset_in.attrs)
dataset_out = restore_or_convert_dtypes(dtype, band_list, dataset_in_dtypes, dataset_out, no_data)
return dataset_out
Expand Down