data_preprocessing/sample_quadruplets/sample_for_counties.py [246:315]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def _sample_neighbor(img, a_lat, a_lon, neighborhood_radius, tile_radius, timestep, size_even): if neighborhood_radius is None: return _sample_distant_diff(img, tile_radius, timestep, size_even) _, _, img_h, img_w = img.shape while True: n_lat, n_lon = a_lat, a_lon while n_lat == a_lat and n_lon == a_lon: n_lat = np.random.randint(max(a_lat - neighborhood_radius, tile_radius), min(a_lat + neighborhood_radius, img_h - tile_radius)) n_lon = np.random.randint(max(a_lon - neighborhood_radius, tile_radius), min(a_lon + neighborhood_radius, img_w - tile_radius)) lat0, lat1, lon0, lon1 = _get_lat_lon_range(n_lat, n_lon, tile_radius, size_even) tile = img[:, timestep, lat0:lat1+1, lon0:lon1+1] if ma.count_masked(tile) == 0: break return tile, n_lat, n_lon def _sample_distant_same(img, a_lat, a_lon, neighborhood_radius, distant_radius, tile_radius, timestep, size_even): if neighborhood_radius is None: return _sample_distant_diff(img, tile_radius, timestep, size_even) _, _, img_h, img_w = img.shape while True: d_lat, d_lon = a_lat, a_lon if distant_radius is None: while (d_lat >= a_lat - neighborhood_radius) and (d_lat <= a_lat + neighborhood_radius): d_lat = np.random.randint(tile_radius, img_h - tile_radius) while (d_lon >= a_lon - neighborhood_radius) and (d_lon <= a_lon + neighborhood_radius): d_lon = np.random.randint(tile_radius, img_w - tile_radius) else: while ((d_lat >= a_lat - neighborhood_radius) and (d_lat <= a_lat + neighborhood_radius)) \ or d_lat >= a_lat + distant_radius \ or d_lat <= a_lat - distant_radius: d_lat = np.random.randint(tile_radius, img_h - tile_radius) while ((d_lon >= a_lon - neighborhood_radius) and (d_lon <= a_lon + neighborhood_radius))\ or d_lon >= a_lon + distant_radius \ or d_lon <= a_lon - distant_radius: d_lon = np.random.randint(tile_radius, img_w - tile_radius) lat0, lat1, lon0, lon1 = _get_lat_lon_range(d_lat, d_lon, tile_radius, size_even) tile = img[:, timestep, lat0:lat1 + 1, lon0:lon1 + 1] if ma.count_masked(tile) == 0: break return tile, d_lat, d_lon def _sample_distant_diff(img, tile_radius, timestep, size_even): _, _, img_h, img_w = img.shape while True: d_lat = np.random.randint(tile_radius, img_h - tile_radius) d_lon = np.random.randint(tile_radius, img_w - tile_radius) lat0, lat1, lon0, lon1 = _get_lat_lon_range(d_lat, d_lon, tile_radius, size_even) tile = img[:, timestep, lat0:lat1 + 1, lon0:lon1 + 1] if ma.count_masked(tile) == 0: break return tile, d_lat, d_lon def plot_sampled_centers(lats, lons, img_shape, out_dir, name): c, t, h, w = img_shape plt.scatter(lons, lats, s=5) plt.axis([0, w, 0, h]) plt.savefig('{}/{}.jpg'.format(out_dir, name)) plt.close() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - data_preprocessing/sample_quadruplets/sample_for_pretrained.py [177:246]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def _sample_neighbor(img, a_lat, a_lon, neighborhood_radius, tile_radius, timestep, size_even): if neighborhood_radius is None: return _sample_distant_diff(img, tile_radius, timestep, size_even) _, _, img_h, img_w = img.shape while True: n_lat, n_lon = a_lat, a_lon while n_lat == a_lat and n_lon == a_lon: n_lat = np.random.randint(max(a_lat - neighborhood_radius, tile_radius), min(a_lat + neighborhood_radius, img_h - tile_radius)) n_lon = np.random.randint(max(a_lon - neighborhood_radius, tile_radius), min(a_lon + neighborhood_radius, img_w - tile_radius)) lat0, lat1, lon0, lon1 = _get_lat_lon_range(n_lat, n_lon, tile_radius, size_even) tile = img[:, timestep, lat0:lat1+1, lon0:lon1+1] if ma.count_masked(tile) == 0: break return tile, n_lat, n_lon def _sample_distant_same(img, a_lat, a_lon, neighborhood_radius, distant_radius, tile_radius, timestep, size_even): if neighborhood_radius is None: return _sample_distant_diff(img, tile_radius, timestep, size_even) _, _, img_h, img_w = img.shape while True: d_lat, d_lon = a_lat, a_lon if distant_radius is None: while (d_lat >= a_lat - neighborhood_radius) and (d_lat <= a_lat + neighborhood_radius): d_lat = np.random.randint(tile_radius, img_h - tile_radius) while (d_lon >= a_lon - neighborhood_radius) and (d_lon <= a_lon + neighborhood_radius): d_lon = np.random.randint(tile_radius, img_w - tile_radius) else: while ((d_lat >= a_lat - neighborhood_radius) and (d_lat <= a_lat + neighborhood_radius)) \ or d_lat >= a_lat + distant_radius \ or d_lat <= a_lat - distant_radius: d_lat = np.random.randint(tile_radius, img_h - tile_radius) while ((d_lon >= a_lon - neighborhood_radius) and (d_lon <= a_lon + neighborhood_radius))\ or d_lon >= a_lon + distant_radius \ or d_lon <= a_lon - distant_radius: d_lon = np.random.randint(tile_radius, img_w - tile_radius) lat0, lat1, lon0, lon1 = _get_lat_lon_range(d_lat, d_lon, tile_radius, size_even) tile = img[:, timestep, lat0:lat1 + 1, lon0:lon1 + 1] if ma.count_masked(tile) == 0: break return tile, d_lat, d_lon def _sample_distant_diff(img, tile_radius, timestep, size_even): _, _, img_h, img_w = img.shape while True: d_lat = np.random.randint(tile_radius, img_h - tile_radius) d_lon = np.random.randint(tile_radius, img_w - tile_radius) lat0, lat1, lon0, lon1 = _get_lat_lon_range(d_lat, d_lon, tile_radius, size_even) tile = img[:, timestep, lat0:lat1 + 1, lon0:lon1 + 1] if ma.count_masked(tile) == 0: break return tile, d_lat, d_lon def plot_sampled_centers(lats, lons, img_shape, out_dir, name): c, t, h, w = img_shape plt.scatter(lons, lats, s=5) plt.axis([0, w, 0, h]) plt.savefig('{}/{}.jpg'.format(out_dir, name)) plt.close() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -