data-access/nexustiles/backends/nexusproto/backend.py [316:400]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def mask_tiles_to_bbox(self, min_lat, max_lat, min_lon, max_lon, tiles): for tile in tiles: tile.latitudes = ma.masked_outside(tile.latitudes, min_lat, max_lat) tile.longitudes = ma.masked_outside(tile.longitudes, min_lon, max_lon) # Or together the masks of the individual arrays to create the new mask data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \ | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \ | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :] # If this is multi-var, need to mask each variable separately. if tile.is_multi: # Combine space/time mask with existing mask on data data_mask = reduce(np.logical_or, [tile.data[0].mask, data_mask]) num_vars = len(tile.data) multi_data_mask = np.repeat(data_mask[np.newaxis, ...], num_vars, axis=0) tile.data = ma.masked_where(multi_data_mask, tile.data) else: tile.data = ma.masked_where(data_mask, tile.data) tiles[:] = [tile for tile in tiles if not tile.data.mask.all()] return tiles def mask_tiles_to_bbox_and_time(self, min_lat, max_lat, min_lon, max_lon, start_time, end_time, tiles): for tile in tiles: tile.times = ma.masked_outside(tile.times, start_time, end_time) tile.latitudes = ma.masked_outside(tile.latitudes, min_lat, max_lat) tile.longitudes = ma.masked_outside(tile.longitudes, min_lon, max_lon) # Or together the masks of the individual arrays to create the new mask data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \ | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \ | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :] tile.data = ma.masked_where(data_mask, tile.data) tiles[:] = [tile for tile in tiles if not tile.data.mask.all()] return tiles def mask_tiles_to_polygon(self, bounding_polygon, tiles): min_lon, min_lat, max_lon, max_lat = bounding_polygon.bounds return self.mask_tiles_to_bbox(min_lat, max_lat, min_lon, max_lon, tiles) def mask_tiles_to_polygon_and_time(self, bounding_polygon, start_time, end_time, tiles): min_lon, min_lat, max_lon, max_lat = bounding_polygon.bounds return self.mask_tiles_to_bbox_and_time(min_lat, max_lat, min_lon, max_lon, start_time, end_time, tiles) def mask_tiles_to_time_range(self, start_time, end_time, tiles): """ Masks data in tiles to specified time range. :param start_time: The start time to search for tiles :param end_time: The end time to search for tiles :param tiles: List of tiles :return: A list tiles with data masked to specified time range """ if 0 <= start_time <= end_time: for tile in tiles: tile.times = ma.masked_outside(tile.times, start_time, end_time) # Or together the masks of the individual arrays to create the new mask data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \ | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \ | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :] # If this is multi-var, need to mask each variable separately. if tile.is_multi: # Combine space/time mask with existing mask on data data_mask = reduce(np.logical_or, [tile.data[0].mask, data_mask]) num_vars = len(tile.data) multi_data_mask = np.repeat(data_mask[np.newaxis, ...], num_vars, axis=0) tile.data = ma.masked_where(multi_data_mask, tile.data) else: tile.data = ma.masked_where(data_mask, tile.data) tiles[:] = [tile for tile in tiles if not tile.data.mask.all()] return tiles - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - data-access/nexustiles/nexustiles.py [666:749]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def mask_tiles_to_bbox(self, min_lat, max_lat, min_lon, max_lon, tiles): for tile in tiles: tile.latitudes = ma.masked_outside(tile.latitudes, min_lat, max_lat) tile.longitudes = ma.masked_outside(tile.longitudes, min_lon, max_lon) # Or together the masks of the individual arrays to create the new mask data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \ | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \ | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :] # If this is multi-var, need to mask each variable separately. if tile.is_multi: # Combine space/time mask with existing mask on data data_mask = reduce(np.logical_or, [tile.data[0].mask, data_mask]) num_vars = len(tile.data) multi_data_mask = np.repeat(data_mask[np.newaxis, ...], num_vars, axis=0) tile.data = ma.masked_where(multi_data_mask, tile.data) else: tile.data = ma.masked_where(data_mask, tile.data) tiles[:] = [tile for tile in tiles if not tile.data.mask.all()] return tiles def mask_tiles_to_bbox_and_time(self, min_lat, max_lat, min_lon, max_lon, start_time, end_time, tiles): for tile in tiles: tile.times = ma.masked_outside(tile.times, start_time, end_time) tile.latitudes = ma.masked_outside(tile.latitudes, min_lat, max_lat) tile.longitudes = ma.masked_outside(tile.longitudes, min_lon, max_lon) # Or together the masks of the individual arrays to create the new mask data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \ | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \ | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :] tile.data = ma.masked_where(data_mask, tile.data) tiles[:] = [tile for tile in tiles if not tile.data.mask.all()] return tiles def mask_tiles_to_polygon(self, bounding_polygon, tiles): min_lon, min_lat, max_lon, max_lat = bounding_polygon.bounds return self.mask_tiles_to_bbox(min_lat, max_lat, min_lon, max_lon, tiles) def mask_tiles_to_polygon_and_time(self, bounding_polygon, start_time, end_time, tiles): min_lon, min_lat, max_lon, max_lat = bounding_polygon.bounds return self.mask_tiles_to_bbox_and_time(min_lat, max_lat, min_lon, max_lon, start_time, end_time, tiles) def mask_tiles_to_time_range(self, start_time, end_time, tiles): """ Masks data in tiles to specified time range. :param start_time: The start time to search for tiles :param end_time: The end time to search for tiles :param tiles: List of tiles :return: A list tiles with data masked to specified time range """ if 0 <= start_time <= end_time: for tile in tiles: tile.times = ma.masked_outside(tile.times, start_time, end_time) # Or together the masks of the individual arrays to create the new mask data_mask = ma.getmaskarray(tile.times)[:, np.newaxis, np.newaxis] \ | ma.getmaskarray(tile.latitudes)[np.newaxis, :, np.newaxis] \ | ma.getmaskarray(tile.longitudes)[np.newaxis, np.newaxis, :] # If this is multi-var, need to mask each variable separately. if tile.is_multi: # Combine space/time mask with existing mask on data data_mask = reduce(np.logical_or, [tile.data[0].mask, data_mask]) num_vars = len(tile.data) multi_data_mask = np.repeat(data_mask[np.newaxis, ...], num_vars, axis=0) tile.data = ma.masked_where(multi_data_mask, tile.data) else: tile.data = ma.masked_where(data_mask, tile.data) tiles[:] = [tile for tile in tiles if not tile.data.mask.all()] return tiles - 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