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docs/patch_level_cloud_cover.ipynb

@@ -160,7 +160,7 @@
     "epsg = items_L2A[0].properties[\"proj:epsg\"]\n",
     "\n",
     "# Convert point from lon/lat to UTM projection\n",
-    "poidf = gpd.GeoDataFrame(crs=\"OGC:CRS84\", \n",
+    "poidf = gpd.GeoDataFrame(crs=\"OGC:CRS84\",\n",
     "                         geometry=[area_of_interest.centroid]).to_crs(epsg)\n",
     "geom = poidf.iloc[0].geometry\n",
     "\n",
@@ -217,13 +217,13 @@
     "    # Write tile to output dir, whilst dropping the SCL band in the process\n",
     "    for tile in stack_L2A.sel(band=[\"red\", \"green\", \"blue\"]):\n",
     "        date = str(tile.time.values)[:10]\n",
-    "    \n",
+    "\n",
     "        name = \"{dir}/claytile_{date}.tif\".format(\n",
     "            dir=outdir,\n",
     "            date=date.replace(\"-\", \"\"),\n",
     "        )\n",
     "        tile.rio.to_raster(name, compress=\"deflate\")\n",
-    "    \n",
+    "\n",
     "        with rasterio.open(name, \"r+\") as rst:\n",
     "            rst.update_tags(date=date)"
    ]
@@ -282,14 +282,14 @@
     "transform = ds_chunked_L2A.attrs['transform']\n",
     "crs = ds_chunked_L2A.attrs['crs']\n",
     "\n",
-    "for x in range((ds_chunked_L2A.sizes['x'] // chunk_size['x'])): # + 1):\n",
-    "    for y in range((ds_chunked_L2A.sizes['y'] // chunk_size['y'])): # + 1):\n",
+    "for x in range(ds_chunked_L2A.sizes['x'] // chunk_size['x']): # + 1):\n",
+    "    for y in range(ds_chunked_L2A.sizes['y'] // chunk_size['y']): # + 1):\n",
     "        # Compute chunk coordinates\n",
     "        x_start = x * chunk_size['x']\n",
     "        y_start = y * chunk_size['y']\n",
     "        x_end = min(x_start + chunk_size['x'], ds_chunked_L2A.sizes['x'])\n",
     "        y_end = min(y_start + chunk_size['y'], ds_chunked_L2A.sizes['y'])\n",
-    "        \n",
+    "\n",
     "        # Compute chunk geospatial bounds\n",
     "        lon_start, lat_start = transform * (x_start, y_start)\n",
     "        lon_end, lat_end = transform * (x_end, y_end)\n",
@@ -303,10 +303,10 @@
     "\n",
     "        # Extract the subset of the SCL band\n",
     "        subset_scl = ds_chunked_L2A.sel(band=\"scl\")[:, y_start:y_end, x_start:x_end]\n",
-    "        \n",
+    "\n",
     "        # Count the cloud pixels in the subset\n",
     "        cloud_pct = count_cloud_pixels(subset_scl, SCL_CLOUD_LABELS)\n",
-    "        \n",
+    "\n",
     "        # Store the cloud percent for this chunk\n",
     "        cloud_pcts[(x, y)] = int(100 * (cloud_pct / 1024))\n",
     "\n",
@@ -414,7 +414,7 @@
    "outputs": [],
    "source": [
     "# remove date and lat/lon and reshape to disaggregated patches\n",
-    "embeddings_patch = embeddings[0][:, :-2, :].reshape([1,16,16,768]) "
+    "embeddings_patch = embeddings[0][:, :-2, :].reshape([1,16,16,768])"
    ]
   },
   {
@@ -490,20 +490,20 @@
     "        date = batch[\"date\"]\n",
     "        data = {\n",
     "            \"source_url\": batch[\"source_url\"][0],\n",
-    "            \"date\": pd.to_datetime(arg=date, \n",
+    "            \"date\": pd.to_datetime(arg=date,\n",
     "                                   format=\"%Y-%m-%d\").astype(dtype=\"date32[day][pyarrow]\"),\n",
     "            \"embeddings\": [numpy.ascontiguousarray(embeddings_output_patch)],\n",
     "            \"cloud_cover\": cloud_pct_[0][1]\n",
     "        }\n",
-    "        \n",
+    "\n",
     "        # Define the bounding box as a Polygon (xmin, ymin, xmax, ymax)\n",
-    "        # The box_ list is encoded as \n",
+    "        # The box_ list is encoded as\n",
     "        # [bottom left x, bottom left y, top right x, top right y]\n",
     "        box_emb = shapely.geometry.box(box_[0], box_[1], box_[2], box_[3])\n",
-    "        \n",
+    "\n",
     "        # Create the GeoDataFrame\n",
     "        gdf = gpd.GeoDataFrame(data, geometry=[box_emb], crs=f\"EPSG:{epsg}\")\n",
-    "        \n",
+    "\n",
     "        # Reproject to WGS84 (lon/lat coordinates)\n",
     "        gdf = gdf.to_crs(epsg=4326)\n",
     "\n",
@@ -554,7 +554,7 @@
     "    gdf[\"idx\"] = '_'.join(emb.split(\"/\")[-1].split(\"_\")[2:]).replace('.gpq', '')\n",
     "    gdf[\"box\"] = [box(*geom.bounds) for geom in gdf.geometry]\n",
     "    gdfs.append(gdf)\n",
-    "    \n",
+    "\n",
     "    for _,row in gdf.iterrows():\n",
     "        data.append({\n",
     "            \"vector\": row[\"embeddings\"],\n",
@@ -612,7 +612,7 @@
     "epsg = items_L2A[0].properties[\"proj:epsg\"]\n",
     "\n",
     "# Convert point from lon/lat to UTM projection\n",
-    "box_embedding = gpd.GeoDataFrame(crs=\"OGC:CRS84\", \n",
+    "box_embedding = gpd.GeoDataFrame(crs=\"OGC:CRS84\",\n",
     "                                 geometry=[area_of_interest_embedding]).to_crs(epsg)\n",
     "geom_embedding = box_embedding.iloc[0].geometry\n",
     "\n",
@@ -823,23 +823,23 @@
     "        for row in range(0, height, window_size[1]):\n",
     "            # Define the window\n",
     "            window = ((row, row + window_size[1]), (col, col + window_size[0]))\n",
-    "            \n",
+    "\n",
     "            # Read the data within the window\n",
     "            data = chip.read(window=window)\n",
-    "            \n",
+    "\n",
     "            # Get the index of the window\n",
     "            index = (col // window_size[0], row // window_size[1])\n",
-    "            \n",
+    "\n",
     "            # Process the window data here\n",
     "            # For example, print the index and the shape of the window data\n",
     "            #print(\"Index:\", index)\n",
     "            #print(\"Window Shape:\", data.shape)\n",
     "\n",
     "            idxs_windows['idx'].append('_'.join(map(str, index)))\n",
     "            idxs_windows['window'].append(data)\n",
-    "            \n",
+    "\n",
     "    #print(idxs_windows)\n",
-    "    \n",
+    "\n",
     "    for ax, (_, row) in zip(axs.flatten(), df.iterrows()):\n",
     "        idx = row[\"idx\"]\n",
     "        # Find the corresponding window based on the idx\n",
@@ -908,13 +908,13 @@
     "# Make geodataframe of the search results\n",
     "# cloudy\n",
     "result_cloudy_boxes = [\n",
-    "    Polygon([(bbox[0], bbox[1]), (bbox[2], bbox[1]), (bbox[2], bbox[3]), (bbox[0], bbox[3])]) \n",
+    "    Polygon([(bbox[0], bbox[1]), (bbox[2], bbox[1]), (bbox[2], bbox[3]), (bbox[0], bbox[3])])\n",
     "    for bbox in result_cloudy['box']]\n",
     "result_cloudy_gdf = gpd.GeoDataFrame(result_cloudy, geometry=result_cloudy_boxes)\n",
     "result_cloudy_gdf.crs = \"EPSG:4326\"\n",
     "# non-cloudy\n",
     "result_non_cloudy_boxes = [\n",
-    "    Polygon([(bbox[0], bbox[1]), (bbox[2], bbox[1]), (bbox[2], bbox[3]), (bbox[0], bbox[3])]) \n",
+    "    Polygon([(bbox[0], bbox[1]), (bbox[2], bbox[1]), (bbox[2], bbox[3]), (bbox[0], bbox[3])])\n",
     "    for bbox in result_non_cloudy['box']]\n",
     "result_non_cloudy_gdf = gpd.GeoDataFrame(\n",
     "    result_non_cloudy, geometry=result_non_cloudy_boxes)\n",