oil_chunk_example.ipynb

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     ]
    }
   ],
   "source": [
    "!pip install zarr xarray xarray[io]\n",
    "!pip install numpy\n",
    "!pip install opencv-python\n",
    "!pip install matplotlib\n",
    "!pip install google-cloud-storage\n",
    "!pip install scipy\n",
    "#!pip install dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b9885a69-9748-4b3f-be89-1833e518e1c1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import gcsfs\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "abae8ad1-a0a5-48a9-a4ec-ba757aa52d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfrom dask.distributed import Client\\nfrom dask_gateway import GatewayCluster\\n\\ncluster = GatewayCluster()\\nclient = Client(cluster)\\ncluster.scale(2)\\n'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "from dask.distributed import Client\n",
    "from dask_gateway import GatewayCluster\n",
    "\n",
    "cluster = GatewayCluster()\n",
    "client = Client(cluster)\n",
    "cluster.scale(2)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0ec49e1c-d190-4799-9895-bb76ebd78ed3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from google.cloud import storage\n",
    "\n",
    "def list_bucket_contents(bucket_name, folder_path=None):\n",
    "    \"\"\"Lists the contents of a folder within a Google Cloud Storage bucket.\"\"\"\n",
    "    storage_client = storage.Client()\n",
    "    bucket = storage_client.bucket(bucket_name)\n",
    "    blobs = bucket.list_blobs(prefix=folder_path)\n",
    "\n",
    "    for blob in blobs:\n",
    "        print(blob.name)\n",
    "\n",
    "#list_bucket_contents(\"ohw23-oil\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cea87b60-27e6-442e-9a28-43b290bded63",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fs = gcsfs.GCSFileSystem()\n",
    "mapper = fs.get_mapper(\"gs://ohw23-oil/CSKS2.zarr\")\n",
    "data_array = xr.open_zarr(mapper,consolidated=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c1434899-1769-4964-b6e4-e574daa4530a",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (band: 1, y: 8316, x: 11199)\n",
       "Coordinates:\n",
       "  * band         (band) int32 1\n",
       "  * x            (x) float64 4.023e+05 4.023e+05 ... 5.702e+05 5.702e+05\n",
       "  * y            (y) float64 7.834e+06 7.834e+06 ... 7.709e+06 7.709e+06\n",
       "Data variables:\n",
       "    band_data    (band, y, x) float32 dask.array&lt;chunksize=(1, 520, 1400), meta=np.ndarray&gt;\n",
       "    spatial_ref  int32 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c6d4afa8-1bcd-4dd9-bea8-073fe26e6394' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c6d4afa8-1bcd-4dd9-bea8-073fe26e6394' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 8316</li><li><span class='xr-has-index'>x</span>: 11199</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f69fbc63-f702-4ce9-8ef5-2e069977d2d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f69fbc63-f702-4ce9-8ef5-2e069977d2d9' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-e080e01d-e0cd-49ab-9224-bc84cc8a809e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e080e01d-e0cd-49ab-9224-bc84cc8a809e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85b21667-fe4a-4487-b559-a9bb4e4a3347' class='xr-var-data-in' type='checkbox'><label for='data-85b21667-fe4a-4487-b559-a9bb4e4a3347' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.023e+05 4.023e+05 ... 5.702e+05</div><input id='attrs-3c426662-ebbb-44a8-9ed8-6c8c6272be9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c426662-ebbb-44a8-9ed8-6c8c6272be9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afacda0d-2979-4de0-9a83-37cb1117dc05' class='xr-var-data-in' type='checkbox'><label for='data-afacda0d-2979-4de0-9a83-37cb1117dc05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([402262.5, 402277.5, 402292.5, ..., 570202.5, 570217.5, 570232.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.834e+06 7.834e+06 ... 7.709e+06</div><input id='attrs-f0ce9ecd-e6cd-4015-b72c-64a159ee6fbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0ce9ecd-e6cd-4015-b72c-64a159ee6fbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40a6832c-de80-4e3d-a2d4-c5f19dff1b45' class='xr-var-data-in' type='checkbox'><label for='data-40a6832c-de80-4e3d-a2d4-c5f19dff1b45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([7834042.5, 7834027.5, 7834012.5, ..., 7709347.5, 7709332.5, 7709317.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9260550-5703-486d-a852-a7ec30fb5a40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a9260550-5703-486d-a852-a7ec30fb5a40' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band_data</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 520, 1400), meta=np.ndarray&gt;</div><input id='attrs-d62397de-4679-4cfc-a4dd-8a833eaa3497' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d62397de-4679-4cfc-a4dd-8a833eaa3497' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9366f08c-98a9-48d3-95a6-e75ba730c25f' class='xr-var-data-in' type='checkbox'><label for='data-9366f08c-98a9-48d3-95a6-e75ba730c25f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 355.27 MiB </td>\n",
       "                        <td> 2.78 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 8316, 11199) </td>\n",
       "                        <td> (1, 520, 1400) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 128 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"84.948598\" y=\"124.056554\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >11199</text>\n",
       "  <text x=\"164.948598\" y=\"59.502576\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,59.502576)\">8316</text>\n",
       "  <text x=\"7.474299\" y=\"116.582255\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,116.582255)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9e57106-0044-4f48-bdab-6ba0025153e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9e57106-0044-4f48-bdab-6ba0025153e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c8f11d0-ad1e-40a5-98af-74adcb64b57d' class='xr-var-data-in' type='checkbox'><label for='data-6c8f11d0-ad1e-40a5-98af-74adcb64b57d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GeoTransform :</span></dt><dd>402255.0 15.0 0.0 7834050.0 0.0 -15.0</dd></dl></div><div class='xr-var-data'><pre>array(0, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d05ac84a-27ad-48ad-83d7-14f66e1ffd91' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d05ac84a-27ad-48ad-83d7-14f66e1ffd91' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (band: 1, y: 8316, x: 11199)\n",
       "Coordinates:\n",
       "  * band         (band) int32 1\n",
       "  * x            (x) float64 4.023e+05 4.023e+05 ... 5.702e+05 5.702e+05\n",
       "  * y            (y) float64 7.834e+06 7.834e+06 ... 7.709e+06 7.709e+06\n",
       "Data variables:\n",
       "    band_data    (band, y, x) float32 dask.array<chunksize=(1, 520, 1400), meta=np.ndarray>\n",
       "    spatial_ref  int32 ..."
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1c5f4e31-6112-4cfa-9ee7-14e1cdcc11aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Example of setting a threshold on the data before doing histogram binning\n",
    "threshold = 2000\n",
    "ds = data_array.where(data_array < threshold)\n",
    "ds = ds.fillna(threshold-1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a6281404-3e4c-4650-b613-f0ec08913177",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.00000000e+00  2.21333333e+02  4.43666667e+02  6.66000000e+02\n",
      "  8.88333333e+02  1.11066667e+03  1.33300000e+03  1.55533333e+03\n",
      "  1.77766667e+03  2.00000000e+03]\n"
     ]
    }
   ],
   "source": [
    "#Setup the histogram bins.  In this case, a bin size of size 10\n",
    "bins = np.linspace(-1, threshold, 10)\n",
    "print(bins)\n",
    "\n",
    "#Function to process each chunk.  Example of histogram (not sure if this is the best/fastest way to do it)\n",
    "def process_chunk(chunk):\n",
    "  chunk = chunk.to_array()[0, :]\n",
    "  #print(np.unique(chunk.to_numpy()))\n",
    "  #chunk = gaussian_filter(chunk, sigma=5)  \n",
    "  result = chunk.groupby_bins(chunk, bins).apply(xr.DataArray.count).fillna(0)\n",
    "  result = result.fillna(0)\n",
    "  return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "09dce2bb-a1cc-4dd5-8313-b8e053a4b226",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8316, 11199)\n"
     ]
    }
   ],
   "source": [
    "def get_otsu_threshold(hist):\n",
    "    hist = np.divide(hist.ravel(), hist.max())\n",
    " \n",
    "    # Calculate centers of bins\n",
    "    bin_mids = (bins[:-1] + bins[1:]) / 2.\n",
    "\n",
    "    # Iterate over all thresholds (indices) and get the probabilities w1(t), w2(t)\n",
    "    weight1 = np.cumsum(hist)\n",
    "    weight2 = np.cumsum(hist[::-1])[::-1]\n",
    "\n",
    "    # Get the class means mu0(t)\n",
    "    mean1 = np.cumsum(hist * bin_mids) / weight1\n",
    "    # Get the class means mu1(t)\n",
    "    mean2 = (np.cumsum((hist * bin_mids)[::-1]) / weight2[::-1])[::-1]\n",
    "\n",
    "    inter_class_variance = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:]) ** 2\n",
    "\n",
    "    # Maximize the inter_class_variance function val\n",
    "    index_of_max_val = np.argmax(inter_class_variance)\n",
    "\n",
    "    threshold = bin_mids[:-1][index_of_max_val]\n",
    "    return threshold\n",
    "\n",
    "from scipy.ndimage import gaussian_filter\n",
    "mask = np.zeros((8316, 11199), dtype=np.uint8) #Hack: hard coding dimensions\n",
    "print(mask.shape)\n",
    "#This will create N levels of chunk size (like a pyramid).  \n",
    "def pyramid(data_array, levels=[500, 250, 100]):\n",
    "  res = []  \n",
    "  for chunksize in levels:\n",
    "    data = data_array.chunk({'y':chunksize,'x':chunksize, 'band':1}).chunks\n",
    "    ix = data['x']\n",
    "    iy = data['y']\n",
    "\n",
    "    idx = [sum(ix[:i]) for i in range(len(ix)+1)]\n",
    "    idy = [sum(iy[:i]) for i in range(len(iy)+1)]\n",
    "\n",
    "    for i in range(0, len(idx)-1, 1):\n",
    "      tgt_x = xr.DataArray(np.arange(idx[i], idx[i+1]), dims=\"x\")\n",
    "      for j in range(0, len(idy)-1, 1):\n",
    "        tgt_y = xr.DataArray(np.arange(idy[j], idy[j+1]), dims=\"y\")\n",
    "        chunk = data_array.isel(y=tgt_y, x=tgt_x, band=0)\n",
    "        #if (chunk.sum()==0): continue\n",
    "        histogram = process_chunk(chunk)\n",
    "        print(f\"{idx[i]}, {idy[j]} histogram: {histogram.to_numpy()}\")\n",
    "        #upper left corner of the chunk\n",
    "        #size of the chunk (i.e. level)\n",
    "        #histogram as a np array (I'm just more familiary with numpy)\n",
    "        histogram = histogram.to_numpy()\n",
    "        res.append({'x' : idx[i], 'y' : idy[j], 'chunksize' : chunksize, 'histogram' : histogram})\n",
    "        if sum(histogram[1:]) == 0: continue\n",
    "        th = get_otsu_threshold(histogram) #adaptively determine between threshold to get a binary mask\n",
    "        print(f\"Otsu threshold: {th}\")\n",
    "        chunk = chunk.to_array().to_numpy()[0,:] #assuming band_data is at index 0\n",
    "        chunk = gaussian_filter(chunk, sigma=5)  #smo\n",
    "\n",
    "        #chunk = ((chunk < th) & (chunk > 0)).astype(np.uint8)\n",
    "        chunk = (chunk < th).astype(np.uint8)\n",
    "        h, w = chunk.shape[-2:]\n",
    "        x, y = idx[i], idy[j]\n",
    "        mask[y:y+h, x:x+w] = chunk\n",
    "        plt.imshow(mask)\n",
    "        plt.show()\n",
    "        ####\n",
    "        # Do something with the result of the process_chunk function\n",
    "        # \n",
    "        ####\n",
    "  return res\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ef3d8d-999f-4e97-88f3-274c6a14186a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, 0 histogram: [250000.      0.      0.      0.      0.      0.      0.      0.      0.]\n",
      "0, 500 histogram: [250000.      0.      0.      0.      0.      0.      0.      0.      0.]\n",
      "0, 1000 histogram: [250000.      0.      0.      0.      0.      0.      0.      0.      0.]\n",
      "0, 1500 histogram: [250000.      0.      0.      0.      0.      0.      0.      0.      0.]\n",
      "0, 2000 histogram: [250000.      0.      0.      0.      0.      0.      0.      0.      0.]\n",
      "0, 2500 histogram: [250000.      0.      0.      0.      0.      0.      0.      0.      0.]\n",
      "0, 3000 histogram: [250000.      0.      0.      0.      0.      0.      0.      0.      0.]\n",
      "0, 3500 histogram: [248133     13     70    170    277    318    329    248    442]\n",
      "Otsu threshold: 777.1666666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, 4000 histogram: [212981    282   1730   4065   6181   7113   6148   4712   6788]\n",
      "Otsu threshold: 554.8333333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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EAAAsFHeJSU5O1j/+4z9qwoQJqqys1Ec+8hH9x3/8h/x+vyT1Wy1pb293V2f8fr96enoUiUTOmzl+/Hi/7z1x4kS/VZ6/5vV63aem3t8AAMDINej3xBhj1N3drby8PPn9ftXW1rrHenp6VFdXp0mTJkmSCgsLlZSUFJNpa2vT/v373UxxcbGi0aiamprczO7duxWNRt0MAABAYjzhr371q7rtttuUm5urzs5OVVdX66WXXlJNTY08Ho9CoZAqKiqUn5+v/Px8VVRUKDU1VeXl5ZIkx3E0d+5cLV26VFlZWcrMzNSyZcs0fvx4TZ06VZI0duxYzZw5U/PmzdOGDRskSfPnz1dpaelFP5kEAABGvrhKzPHjxxUMBtXW1ibHcXTTTTeppqZG06ZNkyQtX75cXV1dWrhwoSKRiIqKirRjxw6lp6e751i3bp0SExM1a9YsdXV1acqUKdq0aZMSEhLczNatW7VkyRL3KaaysjJVVVUNxXgBAMAIMej3xFypeE8MAAD2uSzviQEAABhOlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArJQ43BdwqXkSE+XxDO0wzenTQ3o+AAAQvxFfYpL+J0tJaclDes7uyeEhPR8AAIjfiC8xz/2/ncpIH9rfms3QR4f0fAAAIH7cEwMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRITp4M97w73JQAAAA2yxFRWVsrj8SgUCrn7jDFatWqVAoGAUlJSVFJSogMHDsR8rru7W4sXL1Z2drbS0tJUVlamY8eOxWQikYiCwaAcx5HjOAoGgzp58uRgLndI3LX7/uG+BAAAoEGUmD179ujJJ5/UTTfdFLN/zZo1Wrt2raqqqrRnzx75/X5NmzZNnZ2dbiYUCmnbtm2qrq7Wrl27dOrUKZWWlqqvr8/NlJeXq6WlRTU1NaqpqVFLS4uCweBAL3fI+LdcPdyXAAAANMASc+rUKd17773auHGjRo0a5e43xuiJJ57QI488os985jMqKCjQM888o3fffVfPPvusJCkajeqpp57St7/9bU2dOlU333yztmzZon379mnnzp2SpIMHD6qmpkb/9V//peLiYhUXF2vjxo36yU9+otdee20Ihj1wKTUtw/r9AADgPQMqMQ888IBuv/12TZ06NWZ/a2urwuGwpk+f7u7zer2aPHmy6uvrJUnNzc3q7e2NyQQCARUUFLiZhoYGOY6joqIiNzNx4kQ5juNmztbd3a2Ojo6Y7VIwvT2X5LwAACA+ifF+oLq6Wnv37tWePXv6HQuHw5Ikn88Xs9/n8+nw4cNuJjk5OWYF5/3M+58Ph8PKycnpd/6cnBw3c7bKykp94xvfiHc4cfnNn89c0vMDAICLF9dKzNGjR/Xggw9qy5Ytuvrqv31viMfjifnZGNNv39nOzpwrf77zrFy5UtFo1N2OHj163u8biC/8Zu6QnxMAAAxMXCWmublZ7e3tKiwsVGJiohITE1VXV6fvfOc7SkxMdFdgzl4taW9vd4/5/X719PQoEomcN3P8+PF+33/ixIl+qzzv83q9ysjIiNmG2j9+9/SQnxMAAAxMXCVmypQp2rdvn1paWtxtwoQJuvfee9XS0qIbbrhBfr9ftbW17md6enpUV1enSZMmSZIKCwuVlJQUk2lra9P+/fvdTHFxsaLRqJqamtzM7t27FY1G3cywePnV4ftuAAAQI657YtLT01VQUBCzLy0tTVlZWe7+UCikiooK5efnKz8/XxUVFUpNTVV5ebkkyXEczZ07V0uXLlVWVpYyMzO1bNkyjR8/3r1ReOzYsZo5c6bmzZunDRs2SJLmz5+v0tJSjRkzZtCDHrAzfRfOAACAyyLuG3svZPny5erq6tLChQsViURUVFSkHTt2KD093c2sW7dOiYmJmjVrlrq6ujRlyhRt2rRJCQkJbmbr1q1asmSJ+xRTWVmZqqqqhvpyL1pr76lh+24AANCfxxhjhvsiLoWOjg45jqPI6zcoI33wf13hrjdmqGty//t0AADA0DltevWSXlA0Gr3g/a387aSLdKLqQ8N9CQAA4K9QYi5Sxvb9w30JAADgr1BiLtKZd94Z7ksAAAB/hRJzEd49w58aAADgSkOJuQjL2/55uC8BAACchRJzEeqfumW4LwEAAJyFEnMR/D/6/XBfAgAAOAsl5iL0nTgx3JcAAADOQokBAABWosRcQHXnqOG+BAAAcA6UmAv46s5Zw30JAADgHCgxFzBmY+dwXwIAADgHSswFnPntweG+BAAAcA6UmAsZmX/kGwAA6yUO9wVcahNfvlsJqd4Bfbav7yrlij/8CADAlWjEl5hryg8p0ZM03JcBAACGGL9OAgAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaKq8SsWrVKHo8nZvP7/e5xY4xWrVqlQCCglJQUlZSU6MCBAzHn6O7u1uLFi5Wdna20tDSVlZXp2LFjMZlIJKJgMCjHceQ4joLBoE6ePDnwUQIAgBEn7pWYD3/4w2pra3O3ffv2ucfWrFmjtWvXqqqqSnv27JHf79e0adPU2dnpZkKhkLZt26bq6mrt2rVLp06dUmlpqfr6+txMeXm5WlpaVFNTo5qaGrW0tCgYDA5yqAAAYCRJjPsDiYkxqy/vM8boiSee0COPPKLPfOYzkqRnnnlGPp9Pzz77rBYsWKBoNKqnnnpKmzdv1tSpUyVJW7ZsUW5urnbu3KkZM2bo4MGDqqmpUWNjo4qKiiRJGzduVHFxsV577TWNGTNmMOMFAAAjRNwrMW+88YYCgYDy8vL0uc99Tn/4wx8kSa2trQqHw5o+fbqb9Xq9mjx5surr6yVJzc3N6u3tjckEAgEVFBS4mYaGBjmO4xYYSZo4caIcx3Ez59Ld3a2Ojo6YDQAAjFxxlZiioiL94Ac/0M9+9jNt3LhR4XBYkyZN0ttvv61wOCxJ8vl8MZ/x+XzusXA4rOTkZI0aNeq8mZycnH7fnZOT42bOpbKy0r2HxnEc5ebmxjM0AABgmbhKzG233abPfvazGj9+vKZOnar//d//lfTer43e5/F4Yj5jjOm372xnZ86Vv9B5Vq5cqWg06m5Hjx69qDEBAAA7DeoR67S0NI0fP15vvPGGe5/M2asl7e3t7uqM3+9XT0+PIpHIeTPHjx/v910nTpzot8rz17xerzIyMmI2AAAwcg2qxHR3d+vgwYO65pprlJeXJ7/fr9raWvd4T0+P6urqNGnSJElSYWGhkpKSYjJtbW3av3+/mykuLlY0GlVTU5Ob2b17t6LRqJsBAACI6+mkZcuW6Y477tB1112n9vZ2ffOb31RHR4fmzJkjj8ejUCikiooK5efnKz8/XxUVFUpNTVV5ebkkyXEczZ07V0uXLlVWVpYyMzO1bNky99dTkjR27FjNnDlT8+bN04YNGyRJ8+fPV2lpKU8mAQAAV1wl5tixY7rnnnv0xz/+UaNHj9bEiRPV2Nio66+/XpK0fPlydXV1aeHChYpEIioqKtKOHTuUnp7unmPdunVKTEzUrFmz1NXVpSlTpmjTpk1KSEhwM1u3btWSJUvcp5jKyspUVVU1FOMFAAAjhMcYY4b7Ii6Fjo4OOY6jEt2pRE/ScF8OAAC4CKdNr17SC4pGoxe8v5W/nQQAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVoq7xLz55pv6/Oc/r6ysLKWmpuqjH/2ompub3ePGGK1atUqBQEApKSkqKSnRgQMHYs7R3d2txYsXKzs7W2lpaSorK9OxY8diMpFIRMFgUI7jyHEcBYNBnTx5cmCjBAAAI05cJSYSiejjH/+4kpKS9NOf/lSvvvqqvv3tb+sf/uEf3MyaNWu0du1aVVVVac+ePfL7/Zo2bZo6OzvdTCgU0rZt21RdXa1du3bp1KlTKi0tVV9fn5spLy9XS0uLampqVFNTo5aWFgWDwcGPGAAAjAgeY4y52PDDDz+s3/zmN/r1r399zuPGGAUCAYVCIa1YsULSe6suPp9Pq1ev1oIFCxSNRjV69Ght3rxZs2fPliS99dZbys3N1fbt2zVjxgwdPHhQ48aNU2Njo4qKiiRJjY2NKi4u1qFDhzRmzJgLXmtHR4ccx1GJ7lSiJ+lihwgAAIbRadOrl/SCotGoMjIyzpuNayXmxRdf1IQJE3T33XcrJydHN998szZu3Ogeb21tVTgc1vTp0919Xq9XkydPVn19vSSpublZvb29MZlAIKCCggI309DQIMdx3AIjSRMnTpTjOG7mbN3d3ero6IjZAADAyBVXifnDH/6g9evXKz8/Xz/72c90//33a8mSJfrBD34gSQqHw5Ikn88X8zmfz+ceC4fDSk5O1qhRo86bycnJ6ff9OTk5buZslZWV7v0zjuMoNzc3nqEBAADLxFVizpw5o1tuuUUVFRW6+eabtWDBAs2bN0/r16+PyXk8npifjTH99p3t7My58uc7z8qVKxWNRt3t6NGjFzssAABgobhKzDXXXKNx48bF7Bs7dqyOHDkiSfL7/ZLUb7Wkvb3dXZ3x+/3q6elRJBI5b+b48eP9vv/EiRP9Vnne5/V6lZGREbMBAICRK64S8/GPf1yvvfZazL7XX39d119/vSQpLy9Pfr9ftbW17vGenh7V1dVp0qRJkqTCwkIlJSXFZNra2rR//343U1xcrGg0qqamJjeze/duRaNRNwMAAP6+JcYT/vKXv6xJkyapoqJCs2bNUlNTk5588kk9+eSTkt77FVAoFFJFRYXy8/OVn5+viooKpaamqry8XJLkOI7mzp2rpUuXKisrS5mZmVq2bJnGjx+vqVOnSnpvdWfmzJmaN2+eNmzYIEmaP3++SktLL+rJJAAAMPLFVWI+9rGPadu2bVq5cqUeffRR5eXl6YknntC9997rZpYvX66uri4tXLhQkUhERUVF2rFjh9LT093MunXrlJiYqFmzZqmrq0tTpkzRpk2blJCQ4Ga2bt2qJUuWuE8xlZWVqaqqarDjBQAAI0Rc74mxCe+JAQDAPpfsPTEAAABXCkoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGCluErMhz70IXk8nn7bAw88IEkyxmjVqlUKBAJKSUlRSUmJDhw4EHOO7u5uLV68WNnZ2UpLS1NZWZmOHTsWk4lEIgoGg3IcR47jKBgM6uTJk4MbKQAAGFHiKjF79uxRW1ubu9XW1kqS7r77bknSmjVrtHbtWlVVVWnPnj3y+/2aNm2aOjs73XOEQiFt27ZN1dXV2rVrl06dOqXS0lL19fW5mfLycrW0tKimpkY1NTVqaWlRMBgcivECAIARwmOMMQP9cCgU0k9+8hO98cYbkqRAIKBQKKQVK1ZIem/VxefzafXq1VqwYIGi0ahGjx6tzZs3a/bs2ZKkt956S7m5udq+fbtmzJihgwcPaty4cWpsbFRRUZEkqbGxUcXFxTp06JDGjBlzUdfW0dEhx3FUojuV6Eka6BABAMBldNr06iW9oGg0qoyMjPNmB3xPTE9Pj7Zs2aL77rtPHo9Hra2tCofDmj59upvxer2aPHmy6uvrJUnNzc3q7e2NyQQCARUUFLiZhoYGOY7jFhhJmjhxohzHcTPn0t3drY6OjpgNAACMXAMuMT/+8Y918uRJffGLX5QkhcNhSZLP54vJ+Xw+91g4HFZycrJGjRp13kxOTk6/78vJyXEz51JZWeneQ+M4jnJzcwc6NAAAYIEBl5innnpKt912mwKBQMx+j8cT87Mxpt++s52dOVf+QudZuXKlotGoux09evRihgEAACw1oBJz+PBh7dy5U//yL//i7vP7/ZLUb7Wkvb3dXZ3x+/3q6elRJBI5b+b48eP9vvPEiRP9Vnn+mtfrVUZGRswGAABGrgGVmKefflo5OTm6/fbb3X15eXny+/3uE0vSe/fN1NXVadKkSZKkwsJCJSUlxWTa2tq0f/9+N1NcXKxoNKqmpiY3s3v3bkWjUTcDAACQGO8Hzpw5o6efflpz5sxRYuJfPu7xeBQKhVRRUaH8/Hzl5+eroqJCqampKi8vlyQ5jqO5c+dq6dKlysrKUmZmppYtW6bx48dr6tSpkqSxY8dq5syZmjdvnjZs2CBJmj9/vkpLSy/6ySQAADDyxV1idu7cqSNHjui+++7rd2z58uXq6urSwoULFYlEVFRUpB07dig9Pd3NrFu3TomJiZo1a5a6uro0ZcoUbdq0SQkJCW5m69atWrJkifsUU1lZmaqqqgYyPgAAMEIN6j0xVzLeEwMAgH0uy3tiAAAAhhMlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwEiUGAABYiRIDAACsRIkBAABWosQAAAArUWIAAICVKDEAAMBKlBgAAGAlSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlSgxAADASpQYAABgJUoMAACwUlwl5vTp0/ra176mvLw8paSk6IYbbtCjjz6qM2fOuBljjFatWqVAIKCUlBSVlJTowIEDMefp7u7W4sWLlZ2drbS0NJWVlenYsWMxmUgkomAwKMdx5DiOgsGgTp48OfCRAgCAESWuErN69Wp9//vfV1VVlQ4ePKg1a9boW9/6lr773e+6mTVr1mjt2rWqqqrSnj175Pf7NW3aNHV2drqZUCikbdu2qbq6Wrt27dKpU6dUWlqqvr4+N1NeXq6WlhbV1NSopqZGLS0tCgaDQzBkAAAwEniMMeZiw6WlpfL5fHrqqafcfZ/97GeVmpqqzZs3yxijQCCgUCikFStWSHpv1cXn82n16tVasGCBotGoRo8erc2bN2v27NmSpLfeeku5ubnavn27ZsyYoYMHD2rcuHFqbGxUUVGRJKmxsVHFxcU6dOiQxowZc8Fr7ejokOM4KtGdSvQkxTUpAABgeJw2vXpJLygajSojI+O82bhWYm699Vb9/Oc/1+uvvy5J+u1vf6tdu3bp05/+tCSptbVV4XBY06dPdz/j9Xo1efJk1dfXS5Kam5vV29sbkwkEAiooKHAzDQ0NchzHLTCSNHHiRDmO42bO1t3drY6OjpgNAACMXInxhFesWKFoNKobb7xRCQkJ6uvr02OPPaZ77rlHkhQOhyVJPp8v5nM+n0+HDx92M8nJyRo1alS/zPufD4fDysnJ6ff9OTk5buZslZWV+sY3vhHPcAAAgMXiWol57rnntGXLFj377LPau3evnnnmGf37v/+7nnnmmZicx+OJ+dkY02/f2c7OnCt/vvOsXLlS0WjU3Y4ePXqxwwIAABaKayXmK1/5ih5++GF97nOfkySNHz9ehw8fVmVlpebMmSO/3y/pvZWUa665xv1ce3u7uzrj9/vV09OjSCQSsxrT3t6uSZMmuZnjx4/3+/4TJ070W+V5n9frldfrjWc4AADAYnGtxLz77ru66qrYjyQkJLiPWOfl5cnv96u2ttY93tPTo7q6OregFBYWKikpKSbT1tam/fv3u5ni4mJFo1E1NTW5md27dysajboZAADw9y2ulZg77rhDjz32mK677jp9+MMf1iuvvKK1a9fqvvvuk/Ter4BCoZAqKiqUn5+v/Px8VVRUKDU1VeXl5ZIkx3E0d+5cLV26VFlZWcrMzNSyZcs0fvx4TZ06VZI0duxYzZw5U/PmzdOGDRskSfPnz1dpaelFPZkEAABGvrhKzHe/+13967/+qxYuXKj29nYFAgEtWLBA//Zv/+Zmli9frq6uLi1cuFCRSERFRUXasWOH0tPT3cy6deuUmJioWbNmqaurS1OmTNGmTZuUkJDgZrZu3aolS5a4TzGVlZWpqqpqsOMFAAAjRFzvibEJ74kBAMA+l+w9MQAAAFcKSgwAALASJQYAAFiJEgMAAKxEiQEAAFaixAAAACvF9Z4Ym7z/5Php9Uoj8iFyAABGntPqlfSX/x8/nxFbYt5++21J0i5tH+YrAQAA8ers7JTjOOfNjNgSk5mZKUk6cuTIBScBA9PR0aHc3FwdPXr0gi8kQvyY30uPOb70mONLayTOrzFGnZ2dCgQCF8yO2BLz/h+qdBxnxPzDXqkyMjKY40uI+b30mONLjzm+tEba/F7s4gM39gIAACtRYgAAgJVGbInxer36+te/Lq/XO9yXMmIxx5cW83vpMceXHnN8af29z++I/SvWAABgZBuxKzEAAGBko8QAAAArUWIAAICVKDEAAMBKlBgAAGClEVtivve97ykvL09XX321CgsL9etf/3q4L+mKU1lZqY997GNKT09XTk6O7rrrLr322msxGWOMVq1apUAgoJSUFJWUlOjAgQMxme7ubi1evFjZ2dlKS0tTWVmZjh07FpOJRCIKBoNyHEeO4ygYDOrkyZOXeohXlMrKSnk8HoVCIXcf8zt4b775pj7/+c8rKytLqamp+uhHP6rm5mb3OHM8OKdPn9bXvvY15eXlKSUlRTfccIMeffRRnTlzxs0wx/H51a9+pTvuuEOBQEAej0c//vGPY45fzvk8cuSI7rjjDqWlpSk7O1tLlixRT0/PpRj2pWFGoOrqapOUlGQ2btxoXn31VfPggw+atLQ0c/jw4eG+tCvKjBkzzNNPP232799vWlpazO23326uu+46c+rUKTfz+OOPm/T0dPOjH/3I7Nu3z8yePdtcc801pqOjw83cf//95oMf/KCpra01e/fuNZ/85CfNRz7yEXP69Gk3M3PmTFNQUGDq6+tNfX29KSgoMKWlpZd1vMOpqanJfOhDHzI33XSTefDBB939zO/g/OlPfzLXX3+9+eIXv2h2795tWltbzc6dO83vf/97N8McD843v/lNk5WVZX7yk5+Y1tZW89///d/mAx/4gHniiSfcDHMcn+3bt5tHHnnE/OhHPzKSzLZt22KOX675PH36tCkoKDCf/OQnzd69e01tba0JBAJm0aJFl3wOhsqILDH/9E//ZO6///6YfTfeeKN5+OGHh+mK7NDe3m4kmbq6OmOMMWfOnDF+v988/vjjbubPf/6zcRzHfP/73zfGGHPy5EmTlJRkqqur3cybb75prrrqKlNTU2OMMebVV181kkxjY6ObaWhoMJLMoUOHLsfQhlVnZ6fJz883tbW1ZvLkyW6JYX4Hb8WKFebWW2/9m8eZ48G7/fbbzX333Rez7zOf+Yz5/Oc/b4xhjgfr7BJzOedz+/bt5qqrrjJvvvmmm/nhD39ovF6viUajl2S8Q23E/Tqpp6dHzc3Nmj59esz+6dOnq76+fpiuyg7RaFTSX/4CeGtrq8LhcMxcer1eTZ482Z3L5uZm9fb2xmQCgYAKCgrcTENDgxzHUVFRkZuZOHGiHMf5u/g3eeCBB3T77bdr6tSpMfuZ38F78cUXNWHCBN19993KycnRzTffrI0bN7rHmePBu/XWW/Xzn/9cr7/+uiTpt7/9rXbt2qVPf/rTkpjjoXY557OhoUEFBQUxfy16xowZ6u7ujvmV7JVsxP0V6z/+8Y/q6+uTz+eL2e/z+RQOh4fpqq58xhg99NBDuvXWW1VQUCBJ7nyday4PHz7sZpKTkzVq1Kh+mfc/Hw6HlZOT0+87c3JyRvy/SXV1tfbu3as9e/b0O8b8Dt4f/vAHrV+/Xg899JC++tWvqqmpSUuWLJHX69UXvvAF5ngIrFixQtFoVDfeeKMSEhLU19enxx57TPfcc48k/jseapdzPsPhcL/vGTVqlJKTk62Z8xFXYt7n8XhifjbG9NuHv1i0aJF+97vfadeuXf2ODWQuz86cKz/S/02OHj2qBx98UDt27NDVV1/9N3PM78CdOXNGEyZMUEVFhSTp5ptv1oEDB7R+/Xp94QtfcHPM8cA999xz2rJli5599ll9+MMfVktLi0KhkAKBgObMmePmmOOhdbnm0/Y5H3G/TsrOzlZCQkK/Ftne3t6vceI9ixcv1osvvqhf/vKXuvbaa939fr9fks47l36/Xz09PYpEIufNHD9+vN/3njhxYkT/mzQ3N6u9vV2FhYVKTExUYmKi6urq9J3vfEeJiYnu2Jnfgbvmmms0bty4mH1jx47VkSNHJPHf8FD4yle+oocfflif+9znNH78eAWDQX35y19WZWWlJOZ4qF3O+fT7/f2+JxKJqLe315o5H3ElJjk5WYWFhaqtrY3ZX1tbq0mTJg3TVV2ZjDFatGiRnn/+ef3iF79QXl5ezPG8vDz5/f6Yuezp6VFdXZ07l4WFhUpKSorJtLW1af/+/W6muLhY0WhUTU1Nbmb37t2KRqMj+t9kypQp2rdvn1paWtxtwoQJuvfee9XS0qIbbriB+R2kj3/84/1eC/D666/r+uuvl8R/w0Ph3Xff1VVXxf5fRUJCgvuINXM8tC7nfBYXF2v//v1qa2tzMzt27JDX61VhYeElHeeQucw3El8W7z9i/dRTT5lXX33VhEIhk5aWZv7v//5vuC/tivKlL33JOI5jXnrpJdPW1uZu7777rpt5/PHHjeM45vnnnzf79u0z99xzzzkf9bv22mvNzp07zd69e82nPvWpcz7qd9NNN5mGhgbT0NBgxo8fPyIfnbyQv346yRjmd7CamppMYmKieeyxx8wbb7xhtm7dalJTU82WLVvcDHM8OHPmzDEf/OAH3Uesn3/+eZOdnW2WL1/uZpjj+HR2dppXXnnFvPLKK0aSWbt2rXnllVfc14Bcrvl8/xHrKVOmmL1795qdO3eaa6+9lkesrwT/+Z//aa6//nqTnJxsbrnlFvexYfyFpHNuTz/9tJs5c+aM+frXv278fr/xer3mE5/4hNm3b1/Mebq6usyiRYtMZmamSUlJMaWlpebIkSMxmbffftvce++9Jj093aSnp5t7773XRCKRyzDKK8vZJYb5Hbz/+Z//MQUFBcbr9Zobb7zRPPnkkzHHmePB6ejoMA8++KC57rrrzNVXX21uuOEG88gjj5ju7m43wxzH55e//OU5/7d3zpw5xpjLO5+HDx82t99+u0lJSTGZmZlm0aJF5s9//vOlHP6Q8hhjzPCsAQEAAAzciLsnBgAA/H2gxAAAACtRYgAAgJUoMQAAwEqUGAAAYCVKDAAAsBIlBgAAWIkSAwAArESJAQAAVqLEAAAAK1FiAACAlf4/38vY5gayqasAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, 4500 histogram: [165611    859   4723  10875  15838  16784  13541   9613  12156]\n",
      "Otsu threshold: 554.8333333333334\n"
     ]
    }
   ],
   "source": [
    "#This will be slow :(\n",
    "histogram_dict = pyramid(ds, levels=[500])\n",
    "plt.imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e1285ac-bbe9-45bc-b2f4-c07f19233545",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1821dd8-1e6b-4f64-9e6d-c984bed07c98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}