Trying to find out why downloads are failing for band maths

true-color-image/02b Timeseries.ipynb

@@ -0,0 +1,209 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from multiprocessing import Pool\n",
+    "import geopandas as gpd\n",
+    "from sentinel_helpers import scihub_band_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_path = Path('input/tempelhofer_feld/')\n",
+    "input_files = list(base_path.glob('*.zip'))\n",
+    "area_of_interest = gpd.read_file(base_path / 'tempelhofer_feld.geojson')\n",
+    "ndvi_path = base_path / 'ndvi'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio as r\n",
+    "import rasterio.mask\n",
+    "import rasterio.plot as rplt\n",
+    "import numpy as np\n",
+    "from sentinel_helpers import scihub_normalize_range\n",
+    "from zipfile import BadZipFile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_ndvi(raster_file):\n",
+    "    global area_of_interest\n",
+    "    \n",
+    "    try:\n",
+    "        b04_path, b08_path = scihub_band_paths(raster_file, ['B04', 'B08'], '10m')\n",
+    "    except BadZipFile:\n",
+    "        print(f'Problem reading {raster_file}, skipping it')\n",
+    "        return\n",
+    "\n",
+    "    with r.open(b04_path, 'r') as b04, r.open(b08_path, 'r') as b08:\n",
+    "        # we want to only write the bare minimum data necessary to disk\n",
+    "        out_meta = b04.meta.copy()\n",
+    "\n",
+    "        # we reproject the geojson file we fetched above and convert it so that rasterio\n",
+    "        # can use it as a mask\n",
+    "        mask = area_of_interest.to_crs(out_meta['crs']).iloc[0].geometry\n",
+    "        miny, minx, maxy, maxx = mask.bounds\n",
+    "\n",
+    "        # update the dimensions and save as geotiff, not jp2\n",
+    "        out_meta.update({\n",
+    "            'width': maxx - minx,\n",
+    "            'height': maxy - miny,\n",
+    "            'driver': 'GTiff',\n",
+    "            'dtype': 'float32'\n",
+    "        })    \n",
+    "        out_name = Path(b04_path).name.replace('B04', 'NDVI').replace('.jp2', '.tif')\n",
+    "\n",
+    "        ! mkdir -p {ndvi_path}\n",
+    "        with r.open(ndvi_path / out_name, 'w+', **out_meta) as dst:\n",
+    "            # we take only the part out of our source raster that we actually need\n",
+    "            # crop=True clips off the borders\n",
+    "            b04, transform_b04 = rasterio.mask.mask(b04, shapes=[mask], crop=True)\n",
+    "            b08, _ = rasterio.mask.mask(b08, shapes=[mask], crop=True) # we ignore the returned transform because it's identical to the previous one\n",
+    "\n",
+    "            b04 = scihub_normalize_range(b04).astype('f4') # <- f4 = float32\n",
+    "            b08 = scihub_normalize_range(b08).astype('f4')\n",
+    "\n",
+    "            # uncomment the following line to see if your masked shape looks correct\n",
+    "            #rplt.show(b04, transform=transform_b04)\n",
+    "            #rplt.show(b08, transform=transform_b04)\n",
+    "\n",
+    "            # we want to be able to ignore divide by zero errors so the formula is nicer to write\n",
+    "            np.seterr(divide='ignore', invalid='ignore')\n",
+    "            ndvi = (b08 - b04) / (b08 + b04)\n",
+    "\n",
+    "            # uncomment the following line to see if we calculated the index correctly\n",
+    "            # rplt.show(ndvi, transform=transform_b04)\n",
+    "\n",
+    "            dst.write(ndvi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d6bb63ba04f94679ab26c02235782b3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=40.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Problem reading input/tempelhofer_feld/S2A_MSIL2A_20190603T101031_N0212_R022_T33UUU_20190603T114652.zip, skipping it\n",
+      "Problem reading input/tempelhofer_feld/S2A_MSIL2A_20190404T101031_N0211_R022_T32UQD_20190404T174806.zip, skipping it\n",
+      "Problem reading input/tempelhofer_feld/S2A_MSIL2A_20190216T102111_N0211_R065_T33UUU_20190216T130428.zip, skipping it\n",
+      "Problem reading input/tempelhofer_feld/S2B_MSIL2A_20190419T101029_N0211_R022_T33UUU_20190419T132322.zip, skipping it\n",
+      "Problem reading input/tempelhofer_feld/S2A_MSIL2A_20190407T102021_N0211_R065_T33UUU_20190407T134109.zip, skipping it\n",
+      "Problem reading input/tempelhofer_feld/S2B_MSIL2A_20190512T102029_N0212_R065_T33UUU_20190512T134103.zip, skipping it\n",
+      "Problem reading input/tempelhofer_feld/S2A_MSIL2A_20190613T101031_N0212_R022_T33UUU_20190614T125329.zip, skipping itProblem reading input/tempelhofer_feld/S2A_MSIL2A_20190424T101031_N0211_R022_T32UQD_20190424T162325.zip, skipping it\n",
+      "\n",
+      "Problem reading input/tempelhofer_feld/S2A_MSIL2A_20190822T101031_N0213_R022_T32UQD_20190822T143621.zip, skipping it\n",
+      "Problem reading input/tempelhofer_feld/S2A_MSIL2A_20190623T101031_N0212_R022_T33UUU_20190623T132509.zip, skipping it\n",
+      "\n",
+      "CPU times: user 179 ms, sys: 154 ms, total: 333 ms\n",
+      "Wall time: 23.7 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "pool = Pool()\n",
+    "\n",
+    "for _ in tqdm(pool.imap_unordered(calculate_ndvi, input_files), total=len(input_files)):\n",
+    "    # this loop is only here so we can get the progress bar\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many files could we process?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "31\n"
+     ]
+    }
+   ],
+   "source": [
+    "! ls -l {ndvi_path} | wc -l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}