Apply mask_timeless to eo-patch

This is probably something simple to answer: I am working with eo-learn and trying to apply an AOI mask (defined in mask_timeless) and set masked areas to NaN across all images in a temporal stack within an eo-patch.

I am using the code below from How To: Land-Use-Land-Cover Prediction for Slovenia — eo-learn 0.9.2 documentation

ndvi_cleana~aoi_mask] = np.nan

where ndvi_clean is the multitemporal NDVI from the eo-patch and aoi_mask is a single band mask.

Issue is how I apply the single band mask across the temporal stack. Appreciate any guidance on this as I am still learning python and numpy.

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You should broadcast the mask shape to the data shape.

broadcasted_mask = np.broadcast_to(array=aoi_mask, shape=ndvi_clean.shape)

ndvi_clean[~broadcasted_mask] = np.nan

A full example:



import numpy as np

import matplotlib.pyplot as plt



n_timestamps = 10

size = 20



# made up data

ndvi_clean = np.random.random(size=(n_timestamps,size,size))



# made up mask

aoi_mask = np.zeros((size,size))

aoi_mask[5:15,5:15] = 1

aoi_mask = aoi_mask > 0.5



# new mask is broadcasted (in shape) to the array we want to mask

broadcasted_mask = np.broadcast_to(array=aoi_mask, shape=ndvi_clean.shape)



# test that first slice of broadcasted_mask equals to aoi_mask

np.testing.assert_equal(broadcasted_mask[0,...], aoi_mask)



# work on a copy for illustration

aoi_ndvi = ndvi_clean.copy()



# mask the ndvi array

aoi_ndvi[~broadcasted_mask] = 0



fig, ax = plt.subplots(ncols=2, figsize=(11,5))

ax[0].imshow(ndvi_clean[0][...])

ax[1].imshow(aoi_ndvi[0][...])