CSIROBoeingPhase5-Vietnam/01.train_ODC_CNN.ipynb

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     "text": [
      "CPU times: user 8.65 s, sys: 6.03 s, total: 14.7 s\n",
      "Wall time: 8.59 s\n"
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   ],
   "source": [
    "%%time\n",
    "%matplotlib inline\n",
    "\n",
    "import importlib\n",
    "import new_import_ODC  \n",
    "\n",
    "importlib.reload(new_import_ODC)\n",
    "\n",
    "from new_import_ODC import *"
   ]
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  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting new cluster.\n",
      "CPU times: user 595 ms, sys: 46.3 ms, total: 641 ms\n",
      "Wall time: 3min 55s\n"
     ]
    },
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     "data": {
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       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-885db99f-9114-11ef-806a-ba496ec8b996</p>\n",
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       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
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       "                    <strong>Dashboard: </strong> <a href=\"https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.127ae1e5b0de4184b0adf8062163f7df/status\" target=\"_blank\">https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.127ae1e5b0de4184b0adf8062163f7df/status</a>\n",
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       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
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       "  <h3>GatewayCluster</h3>\n",
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       "    <li><b>Name: </b>easihub.127ae1e5b0de4184b0adf8062163f7df\n",
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       "  </ul>\n",
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       "\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tls://10.0.61.9:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Cấu hình Daskgateway\n",
    "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n",
    "# Khai báo 1 Datacube là dc\n",
    "dc = datacube.Datacube()\n",
    "\n",
    "# Cấu hình truy cập dịch vụ S3\n",
    "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
    "\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbed4c80-bbf8-4ea8-aa45-2460b2ba04c7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## cấu hình thời gian lấy ảnh và tọa độ\n",
    "date_range = (\"2022-09-01\", \"2023-10-01\")\n",
    "longtitude_range = (105.5, 106.4)\n",
    "latitude_range = (9.2, 10.0)\n",
    "\n",
    "coordinates = (longtitude_range, latitude_range)"
   ]
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   "execution_count": 4,
   "id": "6b90c49b-0665-4478-a23b-d111ef88eb79",
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    "tags": []
   },
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "Most common native CRS: EPSG:32648\n",
      "No datasets require offset correction\n",
      "The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n"
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    },
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       "<h4>Dataset size: 111.21 GB</h4>"
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       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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; Size: 119GB\n",
       "Dimensions:      (time: 151, y: 8874, x: 9902)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "    spatial_ref  int32 4B 32648\n",
       "Data variables:\n",
       "    red          (time, y, x) float32 53GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
       "    nir          (time, y, x) float32 53GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
       "    scl          (time, y, x) uint8 13GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    crs:           EPSG:32648\n",
       "    grid_mapping:  spatial_ref</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-2b9a7dbc-b71d-4b5b-8dcc-e3747f0c2645' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b9a7dbc-b71d-4b5b-8dcc-e3747f0c2645' 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'>time</span>: 151</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-74794f3a-490f-433f-bfe2-3422cef7ceb1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74794f3a-490f-433f-bfe2-3422cef7ceb1' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-09-02T03:35:23.960000 ... 2...</div><input id='attrs-4331605a-5c58-4648-87f0-4749d52a0eb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4331605a-5c58-4648-87f0-4749d52a0eb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72830bee-e5c7-4894-86b6-af2af49f9129' class='xr-var-data-in' type='checkbox'><label for='data-72830bee-e5c7-4894-86b6-af2af49f9129' 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>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-02T03:35:23.960000000&#x27;, &#x27;2022-09-04T03:25:18.037000000&#x27;,\n",
       "       &#x27;2022-09-07T03:35:13.646000000&#x27;, &#x27;2022-09-09T03:25:27.617000000&#x27;,\n",
       "       &#x27;2022-09-12T03:35:22.247000000&#x27;, &#x27;2022-09-14T03:25:18.529000000&#x27;,\n",
       "       &#x27;2022-09-17T03:35:13.648000000&#x27;, &#x27;2022-09-19T03:25:24.773000000&#x27;,\n",
       "       &#x27;2022-09-22T03:35:18.931000000&#x27;, &#x27;2022-09-24T03:25:17.431000000&#x27;,\n",
       "       &#x27;2022-09-27T03:35:12.140000000&#x27;, &#x27;2022-09-29T03:25:23.829000000&#x27;,\n",
       "       &#x27;2022-10-02T03:35:19.048000000&#x27;, &#x27;2022-10-07T03:35:09.133000000&#x27;,\n",
       "       &#x27;2022-10-09T03:25:22.746000000&#x27;, &#x27;2022-10-12T03:35:17.151000000&#x27;,\n",
       "       &#x27;2022-10-14T03:25:15.533000000&#x27;, &#x27;2022-10-17T03:35:11.148000000&#x27;,\n",
       "       &#x27;2022-10-19T03:25:18.808000000&#x27;, &#x27;2022-10-22T03:35:13.746000000&#x27;,\n",
       "       &#x27;2022-10-24T03:25:15.878000000&#x27;, &#x27;2022-10-27T03:35:10.923000000&#x27;,\n",
       "       &#x27;2022-10-29T03:25:20.127000000&#x27;, &#x27;2022-11-01T03:35:15.818000000&#x27;,\n",
       "       &#x27;2022-11-03T03:25:14.204000000&#x27;, &#x27;2022-11-06T03:35:10.096000000&#x27;,\n",
       "       &#x27;2022-11-11T03:35:15.334000000&#x27;, &#x27;2022-11-13T03:25:16.056000000&#x27;,\n",
       "       &#x27;2022-11-16T03:35:11.542000000&#x27;, &#x27;2022-11-18T03:25:18.155000000&#x27;,\n",
       "       &#x27;2022-11-21T03:35:12.396000000&#x27;, &#x27;2022-11-23T03:25:16.067000000&#x27;,\n",
       "       &#x27;2022-11-26T03:35:11.063000000&#x27;, &#x27;2022-11-28T03:25:16.297000000&#x27;,\n",
       "       &#x27;2022-12-01T03:35:12.009000000&#x27;, &#x27;2022-12-03T03:25:14.366000000&#x27;,\n",
       "       &#x27;2022-12-06T03:35:10.293000000&#x27;, &#x27;2022-12-08T03:25:17.072000000&#x27;,\n",
       "       &#x27;2022-12-11T03:35:12.184000000&#x27;, &#x27;2022-12-16T03:35:10.130000000&#x27;,\n",
       "       &#x27;2022-12-18T03:25:15.584000000&#x27;, &#x27;2022-12-21T03:35:09.988000000&#x27;,\n",
       "       &#x27;2022-12-23T03:25:15.763000000&#x27;, &#x27;2022-12-26T03:35:11.128000000&#x27;,\n",
       "       &#x27;2022-12-28T03:25:15.151000000&#x27;, &#x27;2022-12-31T03:35:10.753000000&#x27;,\n",
       "       &#x27;2023-01-02T03:25:15.152000000&#x27;, &#x27;2023-01-05T03:35:09.844000000&#x27;,\n",
       "       &#x27;2023-01-07T03:25:15.343000000&#x27;, &#x27;2023-01-10T03:35:10.149000000&#x27;,\n",
       "       &#x27;2023-01-12T03:25:12.066000000&#x27;, &#x27;2023-01-15T03:35:08.140000000&#x27;,\n",
       "       &#x27;2023-01-17T03:25:12.454000000&#x27;, &#x27;2023-01-20T03:35:06.593000000&#x27;,\n",
       "       &#x27;2023-01-22T03:25:14.446000000&#x27;, &#x27;2023-01-25T03:35:09.824000000&#x27;,\n",
       "       &#x27;2023-01-27T03:25:13.987000000&#x27;, &#x27;2023-01-30T03:35:09.963000000&#x27;,\n",
       "       &#x27;2023-02-01T03:25:13.595000000&#x27;, &#x27;2023-02-04T03:35:09.008000000&#x27;,\n",
       "       &#x27;2023-02-06T03:25:15.471000000&#x27;, &#x27;2023-02-09T03:35:10.646000000&#x27;,\n",
       "       &#x27;2023-02-11T03:25:15.100000000&#x27;, &#x27;2023-02-14T03:35:10.240000000&#x27;,\n",
       "       &#x27;2023-02-16T03:25:28.099000000&#x27;, &#x27;2023-02-24T03:35:12.118000000&#x27;,\n",
       "       &#x27;2023-02-26T03:25:14.146000000&#x27;, &#x27;2023-03-01T03:35:09.182000000&#x27;,\n",
       "       &#x27;2023-03-03T03:25:17.841000000&#x27;, &#x27;2023-03-06T03:35:14.717000000&#x27;,\n",
       "       &#x27;2023-03-08T03:25:12.032000000&#x27;, &#x27;2023-03-11T03:35:07.230000000&#x27;,\n",
       "       &#x27;2023-03-13T03:25:20.837000000&#x27;, &#x27;2023-03-16T03:35:15.940000000&#x27;,\n",
       "       &#x27;2023-03-18T03:25:14.467000000&#x27;, &#x27;2023-03-21T03:35:10.279000000&#x27;,\n",
       "       &#x27;2023-03-23T03:25:18.795000000&#x27;, &#x27;2023-03-26T03:35:14.570000000&#x27;,\n",
       "       &#x27;2023-03-28T03:25:14.536000000&#x27;, &#x27;2023-03-31T03:35:10.140000000&#x27;,\n",
       "       &#x27;2023-04-02T03:25:19.945000000&#x27;, &#x27;2023-04-05T03:35:14.925000000&#x27;,\n",
       "       &#x27;2023-04-07T03:25:16.518000000&#x27;, &#x27;2023-04-10T03:35:12.000000000&#x27;,\n",
       "       &#x27;2023-04-15T03:35:12.743000000&#x27;, &#x27;2023-04-20T03:35:10.328000000&#x27;,\n",
       "       &#x27;2023-04-22T03:25:18.572000000&#x27;, &#x27;2023-04-25T03:35:13.748000000&#x27;,\n",
       "       &#x27;2023-04-27T03:25:16.567000000&#x27;, &#x27;2023-04-30T03:35:12.151000000&#x27;,\n",
       "       &#x27;2023-05-02T03:25:18.866000000&#x27;, &#x27;2023-05-05T03:35:14.433000000&#x27;,\n",
       "       &#x27;2023-05-07T03:25:16.583000000&#x27;, &#x27;2023-05-10T03:35:11.284000000&#x27;,\n",
       "       &#x27;2023-05-12T03:25:19.200000000&#x27;, &#x27;2023-05-15T03:35:15.683000000&#x27;,\n",
       "       &#x27;2023-05-17T03:25:17.911000000&#x27;, &#x27;2023-05-20T03:35:14.193000000&#x27;,\n",
       "       &#x27;2023-05-22T03:25:36.378000000&#x27;, &#x27;2023-05-25T03:35:16.657000000&#x27;,\n",
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       "    <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> 12.36 GiB </td>\n",
       "                        <td> 4.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (151, 8874, 9902) </td>\n",
       "                        <td> (1, 2048, 2048) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3775 chunks in 1 graph layer </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a1fe7261-b87b-4c78-be9a-a9dfd53772be' class='xr-section-summary-in' type='checkbox'  ><label for='section-a1fe7261-b87b-4c78-be9a-a9dfd53772be' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5cec1b01-6921-45cf-ad71-0891dbccf326' class='xr-index-data-in' type='checkbox'/><label for='index-5cec1b01-6921-45cf-ad71-0891dbccf326' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-09-02 03:35:23.960000&#x27;, &#x27;2022-09-04 03:25:18.037000&#x27;,\n",
       "               &#x27;2022-09-07 03:35:13.646000&#x27;, &#x27;2022-09-09 03:25:27.617000&#x27;,\n",
       "               &#x27;2022-09-12 03:35:22.247000&#x27;, &#x27;2022-09-14 03:25:18.529000&#x27;,\n",
       "               &#x27;2022-09-17 03:35:13.648000&#x27;, &#x27;2022-09-19 03:25:24.773000&#x27;,\n",
       "               &#x27;2022-09-22 03:35:18.931000&#x27;, &#x27;2022-09-24 03:25:17.431000&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-09-07 03:35:17.134000&#x27;, &#x27;2023-09-09 03:25:20.648000&#x27;,\n",
       "               &#x27;2023-09-12 03:35:15.326000&#x27;, &#x27;2023-09-14 03:25:35.755000&#x27;,\n",
       "               &#x27;2023-09-17 03:35:15.269000&#x27;, &#x27;2023-09-19 03:25:24.089000&#x27;,\n",
       "               &#x27;2023-09-22 03:35:15.439000&#x27;, &#x27;2023-09-24 03:25:18.524000&#x27;,\n",
       "               &#x27;2023-09-27 03:35:14.112000&#x27;, &#x27;2023-09-29 03:25:19.046000&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=151, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61fa7351-6e92-4e7f-9cad-19c2135eb661' class='xr-index-data-in' type='checkbox'/><label for='index-61fa7351-6e92-4e7f-9cad-19c2135eb661' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
       "              1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
       "              ...\n",
       "              1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
       "              1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-baaaebf5-f875-41ce-b29a-c772fd69fef5' class='xr-index-data-in' type='checkbox'/><label for='index-baaaebf5-f875-41ce-b29a-c772fd69fef5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0,\n",
       "              554855.0, 554865.0, 554875.0, 554885.0,\n",
       "              ...\n",
       "              653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0,\n",
       "              653775.0, 653785.0, 653795.0, 653805.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-838b98ae-67e4-4e08-b25f-e494d45e32db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-838b98ae-67e4-4e08-b25f-e494d45e32db' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 119GB\n",
       "Dimensions:      (time: 151, y: 8874, x: 9902)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "    spatial_ref  int32 4B 32648\n",
       "Data variables:\n",
       "    red          (time, y, x) float32 53GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
       "    nir          (time, y, x) float32 53GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
       "    scl          (time, y, x) uint8 13GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
       "Attributes:\n",
       "    crs:           EPSG:32648\n",
       "    grid_mapping:  spatial_ref"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## truy vấn ảnh vệ tinh sen2\n",
    "data = load_data(dc, date_range, longtitude_range, latitude_range)\n",
    "notebook_utils.heading(notebook_utils.xarray_object_size(data))\n",
    "display(data)"
   ]
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   "id": "2f6938b6-82e2-4916-bc1d-719c169e25e4",
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       "      <td>{'0': 'no data', '1': 'saturated or defective'...</td>\n",
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       "                        bits  \\\n",
       "qa  [0, 1, 2, 3, 4, 5, 6, 7]   \n",
       "\n",
       "                                               values  \\\n",
       "qa  {'0': 'no data', '1': 'saturated or defective'...   \n",
       "\n",
       "                     description  \n",
       "qa  Sen2Cor Scene Classification  "
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      "text/plain": [
       "{'0': 'no data',\n",
       " '1': 'saturated or defective',\n",
       " '2': 'dark area pixels',\n",
       " '3': 'cloud shadows',\n",
       " '4': 'vegetation',\n",
       " '5': 'bare soils',\n",
       " '6': 'water',\n",
       " '7': 'unclassified',\n",
       " '8': 'cloud medium probability',\n",
       " '9': 'cloud high probability',\n",
       " '10': 'thin cirrus',\n",
       " '11': 'snow or ice'}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.21 s, sys: 20.7 ms, total: 1.23 s\n",
      "Wall time: 1.23 s\n"
     ]
    },
    {
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       "VBox()"
      ]
     },
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   ],
   "source": [
    "%%time\n",
    "# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\n",
    "result = mask_clean(data)\n",
    "progress(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "435f9f78-a9a4-4226-86ca-d4bec42d454e",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;NDVI&#x27; (time: 151, y: 8874, x: 9902)&gt; Size: 53GB\n",
       "dask.array&lt;truediv, shape=(151, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "    spatial_ref  int32 4B 32648</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'NDVI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 151</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-895c727b-5c3a-4746-9fa8-3bd8937b5e89' class='xr-array-in' type='checkbox' checked><label for='section-895c727b-5c3a-4746-9fa8-3bd8937b5e89' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 49.43 GiB </td>\n",
       "                        <td> 16.00 MiB </td>\n",
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       "       &#x27;2023-09-19T03:25:24.089000000&#x27;, &#x27;2023-09-22T03:35:15.439000000&#x27;,\n",
       "       &#x27;2023-09-24T03:25:18.524000000&#x27;, &#x27;2023-09-27T03:35:14.112000000&#x27;,\n",
       "       &#x27;2023-09-29T03:25:19.046000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</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'>1.106e+06 1.106e+06 ... 1.017e+06</div><input id='attrs-2ffefe14-1b39-4003-96dd-d837587566e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ffefe14-1b39-4003-96dd-d837587566e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fad1a5b-c891-471b-8012-3a6ebaef8844' class='xr-var-data-in' type='checkbox'><label for='data-9fad1a5b-c891-471b-8012-3a6ebaef8844' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([1105735., 1105725., 1105715., ..., 1017025., 1017015., 1017005.])</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'>5.548e+05 5.548e+05 ... 6.538e+05</div><input id='attrs-33663653-d7fd-452f-a77d-a672c207cd4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33663653-d7fd-452f-a77d-a672c207cd4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-098b25fe-3b4d-420c-a481-d65e1ec38a07' class='xr-var-data-in' type='checkbox'><label for='data-098b25fe-3b4d-420c-a481-d65e1ec38a07' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([554795., 554805., 554815., ..., 653785., 653795., 653805.])</pre></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'>32648</div><input id='attrs-58879100-ef7d-45be-844b-e34e98631fdc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58879100-ef7d-45be-844b-e34e98631fdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8784e40-4592-42ec-a956-8e5a4418ca64' class='xr-var-data-in' type='checkbox'><label for='data-b8784e40-4592-42ec-a956-8e5a4418ca64' 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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 48N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32648&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr
       "               &#x27;2022-09-07 03:35:13.646000&#x27;, &#x27;2022-09-09 03:25:27.617000&#x27;,\n",
       "               &#x27;2022-09-12 03:35:22.247000&#x27;, &#x27;2022-09-14 03:25:18.529000&#x27;,\n",
       "               &#x27;2022-09-17 03:35:13.648000&#x27;, &#x27;2022-09-19 03:25:24.773000&#x27;,\n",
       "               &#x27;2022-09-22 03:35:18.931000&#x27;, &#x27;2022-09-24 03:25:17.431000&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-09-07 03:35:17.134000&#x27;, &#x27;2023-09-09 03:25:20.648000&#x27;,\n",
       "               &#x27;2023-09-12 03:35:15.326000&#x27;, &#x27;2023-09-14 03:25:35.755000&#x27;,\n",
       "               &#x27;2023-09-17 03:35:15.269000&#x27;, &#x27;2023-09-19 03:25:24.089000&#x27;,\n",
       "               &#x27;2023-09-22 03:35:15.439000&#x27;, &#x27;2023-09-24 03:25:18.524000&#x27;,\n",
       "               &#x27;2023-09-27 03:35:14.112000&#x27;, &#x27;2023-09-29 03:25:19.046000&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=151, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5895aa23-af55-4edb-a3cb-5c47714e0ddc' class='xr-index-data-in' type='checkbox'/><label for='index-5895aa23-af55-4edb-a3cb-5c47714e0ddc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
       "              1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
       "              ...\n",
       "              1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
       "              1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-99f3886a-42a9-4eec-b9ff-150240356aa9' class='xr-index-data-in' type='checkbox'/><label for='index-99f3886a-42a9-4eec-b9ff-150240356aa9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0,\n",
       "              554855.0, 554865.0, 554875.0, 554885.0,\n",
       "              ...\n",
       "              653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0,\n",
       "              653775.0, 653785.0, 653795.0, 653805.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-396afcf6-ebe5-4809-8493-637f3291f110' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-396afcf6-ebe5-4809-8493-637f3291f110' 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.DataArray 'NDVI' (time: 151, y: 8874, x: 9902)> Size: 53GB\n",
       "dask.array<truediv, shape=(151, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "    spatial_ref  int32 4B 32648"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tiến hành tính toán NDVI\n",
    "ds1 = calculate_indices(result, index=\"NDVI\", satellite_mission=\"s2\")\n",
    "ndvi = ds1[\"NDVI\"]\n",
    "display(ndvi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84992d28-8e3f-468e-be08-ded511f2c662",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f975db3b2b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Hiển thị ảnh NDVI chưa điền các giá trị mây (chưa fill nan)\n",
    "plt.imshow(ndvi.isel(time=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72318b60-532a-4f08-a5f7-94762d08a42c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f975e1fce20>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Thiết lập giá trị trung bình mùa vụ để xử lý các điểm ảnh bị mây dựa vào sự thay đổi theo mùa\n",
    "time_split = [\n",
    "    slice(\"2022-09-01\", \"2023-01-01\"),\n",
    "    slice(\"2023-01-01\", \"2023-05-01\"),\n",
    "    slice(\"2023-05-01\", \"2023-07-01\"),\n",
    "    slice(\"2023-07-01\", \"2023-10-01\"),\n",
    "]\n",
    "\n",
    "# Điền mây ở các vị trí mang giá trị nan (fill nan)\n",
    "fill_nan_ndvi = fill_nan(ndvi, time_split)\n",
    "\n",
    "# In kết quả ảnh NDVI đã điền mây (đã fill nan)\n",
    "plt.imshow(fill_nan_ndvi.isel(time=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "375b1cfb-37f5-49fe-8061-ea32eb47f9f6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:6: FutureWarning: 'M' is deprecated and will be removed in a future version. Please use 'ME' instead of 'M'.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.19 s, sys: 3.26 s, total: 10.5 s\n",
      "Wall time: 4min 16s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "## tính ndvi theo tháng\n",
    "average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n",
    "progress(average_ndvi)\n",
    "\n",
    "# compute average_ndvi\n",
    "average_ndvi = average_ndvi.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "187cc640-aef9-476b-91fc-b63f4d3ff2e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h4>Dataset size: 21.60 GB</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  color: var(--xr-font-color2);\n",
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       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
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       ".xr-section-item input {\n",
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       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
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       "\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 23GB\n",
       "Dimensions:      (time: 33, y: 8874, x: 9902)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n",
       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "    spatial_ref  int32 4B 32648\n",
       "Data variables:\n",
       "    vv           (time, y, x) float32 12GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
       "    vh           (time, y, x) float32 12GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    crs:           EPSG:32648\n",
       "    grid_mapping:  spatial_ref</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-4706b96a-27b6-43d5-a1bd-343eef391ee2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4706b96a-27b6-43d5-a1bd-343eef391ee2' 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'>time</span>: 33</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1974bd77-4c2c-419c-a275-3dac4fb8ab3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1974bd77-4c2c-419c-a275-3dac4fb8ab3e' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-09-06T22:46:14.500000 ... 2...</div><input id='attrs-78c8147a-1fb5-4065-a549-f41ff162e825' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78c8147a-1fb5-4065-a549-f41ff162e825' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7f86062-a307-460b-b309-1531e5e7eb59' class='xr-var-data-in' type='checkbox'><label for='data-a7f86062-a307-460b-b309-1531e5e7eb59' 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>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-06T22:46:14.500000000&#x27;, &#x27;2022-09-18T22:46:13.500000000&#x27;,\n",
       "       &#x27;2022-09-30T22:46:14.500000000&#x27;, &#x27;2022-10-12T22:46:14.500000000&#x27;,\n",
       "       &#x27;2022-10-24T22:46:14.500000000&#x27;, &#x27;2022-11-05T22:46:14.500000000&#x27;,\n",
       "       &#x27;2022-11-17T22:46:13.500000000&#x27;, &#x27;2022-11-29T22:46:13.500000000&#x27;,\n",
       "       &#x27;2022-12-11T22:46:13.500000000&#x27;, &#x27;2022-12-23T22:46:12.500000000&#x27;,\n",
       "       &#x27;2023-01-04T22:46:11.500000000&#x27;, &#x27;2023-01-16T22:46:10.500000000&#x27;,\n",
       "       &#x27;2023-01-28T22:46:11.500000000&#x27;, &#x27;2023-02-09T22:46:10.500000000&#x27;,\n",
       "       &#x27;2023-02-21T22:46:09.500000000&#x27;, &#x27;2023-03-05T22:46:10.500000000&#x27;,\n",
       "       &#x27;2023-03-17T22:46:10.500000000&#x27;, &#x27;2023-03-29T22:46:10.500000000&#x27;,\n",
       "       &#x27;2023-04-10T22:46:11.500000000&#x27;, &#x27;2023-04-22T22:46:11.500000000&#x27;,\n",
       "       &#x27;2023-05-04T22:46:11.500000000&#x27;, &#x27;2023-05-17T11:11:32.500000000&#x27;,\n",
       "       &#x27;2023-05-29T11:11:32.500000000&#x27;, &#x27;2023-06-10T11:11:28.500000000&#x27;,\n",
       "       &#x27;2023-06-21T22:46:13.500000000&#x27;, &#x27;2023-07-04T11:11:34.500000000&#x27;,\n",
       "       &#x27;2023-07-16T11:11:35.500000000&#x27;, &#x27;2023-07-28T11:11:36.500000000&#x27;,\n",
       "       &#x27;2023-08-08T22:46:16.500000000&#x27;, &#x27;2023-08-20T22:46:17.500000000&#x27;,\n",
       "       &#x27;2023-09-01T22:46:18.500000000&#x27;, &#x27;2023-09-13T22:46:18.500000000&#x27;,\n",
       "       &#x27;2023-09-25T22:46:19.500000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</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'>1.106e+06 1.106e+06 ... 1.017e+06</div><input id='attrs-d9c86852-e567-459e-9b4f-caeb5b9ffde8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9c86852-e567-459e-9b4f-caeb5b9ffde8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc35ee8d-12c7-47e3-8db2-383e99b1c387' class='xr-var-data-in' type='checkbox'><label for='data-fc35ee8d-12c7-47e3-8db2-383e99b1c387' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([1105735., 1105725., 1105715., ..., 1017025., 1017015., 1017005.])</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'>5.548e+05 5.548e+05 ... 6.538e+05</div><input id='attrs-e8580485-540b-49dc-aba9-9bdf96c44a76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8580485-540b-49dc-aba9-9bdf96c44a76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5712ee21-8757-4f27-b5f1-d49903ec15f0' class='xr-var-data-in' type='checkbox'><label for='data-5712ee21-8757-4f27-b5f1-d49903ec15f0' 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>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([554795., 554805., 554815., ..., 653785., 653795., 653805.])</pre></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'>32648</div><input id='attrs-8ccd0122-7cfd-43da-8870-657101e3265b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ccd0122-7cfd-43da-8870-657101e3265b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-490c3888-f685-441a-9908-30ecb94a7b0a' class='xr-var-data-in' type='checkbox'><label for='data-490c3888-f685-441a-9908-30ecb94a7b0a' 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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 48N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32648&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr
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       "                <tbody>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vh</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;</div><input id='attrs-a77a8e54-d17f-4afa-8087-4b8c3b990ce1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a77a8e54-d17f-4afa-8087-4b8c3b990ce1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f399674-038a-423a-bd1d-18420727a409' class='xr-var-data-in' type='checkbox'><label for='data-9f399674-038a-423a-bd1d-18420727a409' 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>units :</span></dt><dd>intensity</dd><dt><span>nodata :</span></dt><dd>nan</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd><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> 10.80 GiB </td>\n",
       "                        <td> 16.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (33, 8874, 9902) </td>\n",
       "                        <td> (1, 2048, 2048) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 825 chunks in 1 graph layer </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",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d32d405-25d9-4ecf-a716-a8485d39b045' class='xr-section-summary-in' type='checkbox'  ><label for='section-2d32d405-25d9-4ecf-a716-a8485d39b045' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0ab920ef-93da-4d1a-ba62-91bf35efa86a' class='xr-index-data-in' type='checkbox'/><label for='index-0ab920ef-93da-4d1a-ba62-91bf35efa86a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-09-06 22:46:14.500000&#x27;, &#x27;2022-09-18 22:46:13.500000&#x27;,\n",
       "               &#x27;2022-09-30 22:46:14.500000&#x27;, &#x27;2022-10-12 22:46:14.500000&#x27;,\n",
       "               &#x27;2022-10-24 22:46:14.500000&#x27;, &#x27;2022-11-05 22:46:14.500000&#x27;,\n",
       "               &#x27;2022-11-17 22:46:13.500000&#x27;, &#x27;2022-11-29 22:46:13.500000&#x27;,\n",
       "               &#x27;2022-12-11 22:46:13.500000&#x27;, &#x27;2022-12-23 22:46:12.500000&#x27;,\n",
       "               &#x27;2023-01-04 22:46:11.500000&#x27;, &#x27;2023-01-16 22:46:10.500000&#x27;,\n",
       "               &#x27;2023-01-28 22:46:11.500000&#x27;, &#x27;2023-02-09 22:46:10.500000&#x27;,\n",
       "               &#x27;2023-02-21 22:46:09.500000&#x27;, &#x27;2023-03-05 22:46:10.500000&#x27;,\n",
       "               &#x27;2023-03-17 22:46:10.500000&#x27;, &#x27;2023-03-29 22:46:10.500000&#x27;,\n",
       "               &#x27;2023-04-10 22:46:11.500000&#x27;, &#x27;2023-04-22 22:46:11.500000&#x27;,\n",
       "               &#x27;2023-05-04 22:46:11.500000&#x27;, &#x27;2023-05-17 11:11:32.500000&#x27;,\n",
       "               &#x27;2023-05-29 11:11:32.500000&#x27;, &#x27;2023-06-10 11:11:28.500000&#x27;,\n",
       "               &#x27;2023-06-21 22:46:13.500000&#x27;, &#x27;2023-07-04 11:11:34.500000&#x27;,\n",
       "               &#x27;2023-07-16 11:11:35.500000&#x27;, &#x27;2023-07-28 11:11:36.500000&#x27;,\n",
       "               &#x27;2023-08-08 22:46:16.500000&#x27;, &#x27;2023-08-20 22:46:17.500000&#x27;,\n",
       "               &#x27;2023-09-01 22:46:18.500000&#x27;, &#x27;2023-09-13 22:46:18.500000&#x27;,\n",
       "               &#x27;2023-09-25 22:46:19.500000&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1e62476-abdc-4278-bfbb-2c248523948f' class='xr-index-data-in' type='checkbox'/><label for='index-a1e62476-abdc-4278-bfbb-2c248523948f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
       "              1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
       "              ...\n",
       "              1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
       "              1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c7c1b6cd-d5bf-4ae1-a0eb-1ba5886a318d' class='xr-index-data-in' type='checkbox'/><label for='index-c7c1b6cd-d5bf-4ae1-a0eb-1ba5886a318d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0,\n",
       "              554855.0, 554865.0, 554875.0, 554885.0,\n",
       "              ...\n",
       "              653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0,\n",
       "              653775.0, 653785.0, 653795.0, 653805.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a4c7b2c-40d9-42c5-9c8d-52e053b31f94' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0a4c7b2c-40d9-42c5-9c8d-52e053b31f94' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 23GB\n",
       "Dimensions:      (time: 33, y: 8874, x: 9902)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n",
       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
       "    spatial_ref  int32 4B 32648\n",
       "Data variables:\n",
       "    vv           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
       "    vh           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
       "Attributes:\n",
       "    crs:           EPSG:32648\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:6: FutureWarning: 'M' is deprecated and will be removed in a future version. Please use 'ME' instead of 'M'.\n",
      "<string>:6: FutureWarning: 'M' is deprecated and will be removed in a future version. Please use 'ME' instead of 'M'.\n"
     ]
    }
   ],
   "source": [
    "#Load dữ liệu ảnh Sentinel 1\n",
    "dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n",
    "average_vv = calculate_average(dsvv, time_pattern='1M')\n",
    "average_vh = calculate_average(dsvh, time_pattern='1M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)\n",
    "train_path = \"train/ST_training data_updated_1130points_new.shp\"  # đường dẫn shp file train\n",
    "\n",
    "## load dữ liệu điểm huấn luyện mô hình (train file)\n",
    "train = load_train_data(train_path)\n",
    "train.head()\n",
    "\n",
    "# cấu hình nhãn dữ liệu \n",
    "label_mapping = {\n",
    "    \"Lua tom\": \"0\",\n",
    "    \"Lua\": \"1\",\n",
    "    \"CHN\": \"2\",\n",
    "    \"CLN\": \"3\",\n",
    "    \"TS\": \"4\",\n",
    "    \"Song\": \"5\",\n",
    "    \"Dat xay dung\": \"6\",\n",
    "    \"Rung\": \"7\",\n",
    "}\n",
    "\n",
    "# xây dựng tập dữ liệu (dataset) chứa dữ liệu VH, VV, NDVI\n",
    "datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n",
    "\n",
    "# chia tập dữ liệu thành các phần theo tỉ lệ 80(80-20)-20 tương ứng với tập train, validate, test\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n",
    "    train, label_mapping, datasets\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e955884-d4af-422d-a8e6-d436199540e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'classifier__criterion': 'entropy', 'classifier__max_depth': 15, 'classifier__n_estimators': 300}\n",
      "Accuracy: 89.0 %\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Import CNN model\n",
    "from cnn_model import CNNTrainer, reshape_for_cnn\n",
    "import torch\n",
    "\n",
    "# Reshape data for CNN (n_samples, 39) -> (n_samples, 3, 13)\n",
    "print(\"🔄 Reshaping data for CNN...\")\n",
    "X_train_cnn = reshape_for_cnn(X_train.values)\n",
    "X_val_cnn = reshape_for_cnn(X_val.values)\n",
    "X_test_cnn = reshape_for_cnn(X_test.values)\n",
    "\n",
    "print(f\"   Train shape: {X_train_cnn.shape}\")\n",
    "print(f\"   Val shape: {X_val_cnn.shape}\")\n",
    "print(f\"   Test shape: {X_test_cnn.shape}\\n\")\n",
    "\n",
    "# Normalize data (important for neural networks)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train_flat = X_train_cnn.reshape(X_train_cnn.shape[0], -1)\n",
    "X_val_flat = X_val_cnn.reshape(X_val_cnn.shape[0], -1)\n",
    "X_test_flat = X_test_cnn.reshape(X_test_cnn.shape[0], -1)\n",
    "\n",
    "scaler.fit(X_train_flat)\n",
    "X_train_flat = scaler.transform(X_train_flat)\n",
    "X_val_flat = scaler.transform(X_val_flat)\n",
    "X_test_flat = scaler.transform(X_test_flat)\n",
    "\n",
    "X_train_cnn = X_train_flat.reshape(X_train_cnn.shape)\n",
    "X_val_cnn = X_val_flat.reshape(X_val_cnn.shape)\n",
    "X_test_cnn = X_test_flat.reshape(X_test_cnn.shape)\n",
    "\n",
    "print(\"✅ Data normalized\\n\")\n",
    "\n",
    "# Initialize trainer\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"🚀 Using device: {device}\\n\")\n",
    "\n",
    "trainer = CNNTrainer(num_classes=8, learning_rate=0.001, device=device)\n",
    "\n",
    "# Train model\n",
    "trainer.fit(\n",
    "    X_train_cnn, y_train.values,\n",
    "    X_val_cnn, y_val.values,\n",
    "    epochs=50,\n",
    "    batch_size=32,\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "# Plot training history\n",
    "trainer.plot_history()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2a1e42c-cf1b-4d82-a6af-b06e3496918f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for test data 90.0 %\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Evaluate on test data\n",
    "print(\"📊 Evaluating CNN model on test set...\\n\")\n",
    "results = trainer.evaluate(X_test_cnn, y_test.values)\n",
    "\n",
    "# Plot confusion matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class_names = list(label_mapping.keys())\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=results['confusion_matrix'], \n",
    "                               display_labels=class_names)\n",
    "disp.plot(figsize=(10, 8), cmap='Blues')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1a14379-ed6e-4897-9ca4-2669743fab40",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Lưu mô hình huấn luyện\n",
    "import json\n",
    "\n",
    "# Save CNN model\n",
    "model_path = \"model_cnn_pytorch.pth\"\n",
    "trainer.save(model_path)\n",
    "\n",
    "# Save training info\n",
    "info = {\n",
    "    \"model_type\": \"PyTorch 1D CNN\",\n",
    "    \"num_classes\": 8,\n",
    "    \"classes\": list(label_mapping.keys()),\n",
    "    \"input_shape\": [3, 13],  # 3 channels, 13 timesteps\n",
    "    \"accuracy\": float(results['accuracy']),\n",
    "    \"precision\": float(results['precision']),\n",
    "    \"recall\": float(results['recall']),\n",
    "    \"f1_score\": float(results['f1']),\n",
    "}\n",
    "\n",
    "with open(\"model_info.json\", \"w\") as f:\n",
    "    json.dump(info, f, indent=2)\n",
    "\n",
    "print(f\"✅ Model saved to {model_path}\")\n",
    "print(f\"✅ Model info saved to model_info.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33dd516d-9824-499e-96b9-5cd9224c194c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# đóng client, cluster\n",
    "client.close()\n",
    "cluster.close()"
   ]
  }
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