firstsom.ipynb

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        "<a href=\"https://colab.research.google.com/github/mariajmolina/UMDAOSC650/blob/main/firstsom.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Self-Organizing Maps (SOMs)\n",
        "\n",
        "Here we will be training a 3x3 SOM using simulated storm data (used previously for our CNN notebook). Following SOM training, we will create composite maps to visualize the grouping of our storm dat across the SOM lattice."
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        "id": "5wZr88Vag1uF"
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      "source": [
        "import warnings\n",
        "from itertools import product\n",
        "import numpy as np\n",
        "import xarray as xr\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patheffects as path_effects"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install minisom"
      ],
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        "colab": {
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        },
        "id": "N0A_ADcd1eZJ",
        "outputId": "84785ff8-2057-4619-e892-a2ed2977257f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: minisom in /usr/local/lib/python3.10/dist-packages (2.3.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from minisom import MiniSom, asymptotic_decay"
      ],
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        "id": "RNkqmB3_1qpL"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "! wget https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc"
      ],
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        "outputId": "d5ba5412-807b-4d44-dbc8-603034279ec9"
      },
      "execution_count": 4,
      "outputs": [
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          "text": [
            "--2024-03-26 04:13:01--  https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc\n",
            "Resolving zenodo.org (zenodo.org)... 188.184.98.238, 188.185.79.172, 188.184.103.159, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.184.98.238|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 109400556 (104M) [application/octet-stream]\n",
            "Saving to: ‘current_SP3hourly_20110427.nc.2’\n",
            "\n",
            "current_SP3hourly_2 100%[===================>] 104.33M  25.2MB/s    in 5.0s    \n",
            "\n",
            "2024-03-26 04:13:07 (21.0 MB/s) - ‘current_SP3hourly_20110427.nc.2’ saved [109400556/109400556]\n",
            "\n"
          ]
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      "source": [
        "def z_score(da):\n",
        "    \"\"\"\n",
        "    Compute z-score for training data.\n",
        "    Normalizing data for ML model training.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - np.mean(da)) / np.std(da)"
      ],
      "metadata": {
        "id": "-8D_1cdV2cDD"
      },
      "execution_count": 5,
      "outputs": []
    },
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      "source": [
        "dsfull = xr.open_dataset('current_SP3hourly_20110427.nc')"
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        "id": "wlGZUQB92F7i"
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      "execution_count": 6,
      "outputs": []
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              "    col_indices  (starttime, y, x) int64 ...\n",
              "    lats         (starttime, y, x) float32 ...\n",
              "    lons         (starttime, y, x) float32 ...</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-999edb4a-c578-4874-95a6-1d098280f406' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-999edb4a-c578-4874-95a6-1d098280f406' 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'>starttime</span>: 2224</li><li><span>y</span>: 32</li><li><span>x</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d36884bd-b69c-4151-8e10-ddf2175283aa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d36884bd-b69c-4151-8e10-ddf2175283aa' 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'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-7c1b2ea3-28fb-4549-9644-09ef4a960061' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c1b2ea3-28fb-4549-9644-09ef4a960061' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e056f66b-357a-45c7-a0f6-d8cda7c68f21' class='xr-var-data-in' type='checkbox'><label for='data-e056f66b-357a-45c7-a0f6-d8cda7c68f21' 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([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-173fc077-850f-4e5a-80c5-1e1ddff1586b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-173fc077-850f-4e5a-80c5-1e1ddff1586b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dab0044d-bb8c-48a4-84c2-58d5f7fa67a8' class='xr-var-data-in' type='checkbox'><label for='data-dab0044d-bb8c-48a4-84c2-58d5f7fa67a8' 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>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-af1d05e7-63ab-4ff9-84ec-d822eccac4e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af1d05e7-63ab-4ff9-84ec-d822eccac4e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbd3a0b3-f478-4184-bcf3-96a782bc7a9d' class='xr-var-data-in' type='checkbox'><label for='data-dbd3a0b3-f478-4184-bcf3-96a782bc7a9d' 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>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84d1574f-1d10-470c-95b5-f29b4aa54963' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84d1574f-1d10-470c-95b5-f29b4aa54963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5810cb6-9441-432b-8505-920bce88828b' class='xr-var-data-in' type='checkbox'><label for='data-f5810cb6-9441-432b-8505-920bce88828b' 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>[2224 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e401b742-2e76-4cdd-9182-7b1c4c3a8eaa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e401b742-2e76-4cdd-9182-7b1c4c3a8eaa' class='xr-section-summary' >Data variables: <span>(9)</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>uh25_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f98381e3-0fd2-4710-9bd9-8e6791fb8b0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f98381e3-0fd2-4710-9bd9-8e6791fb8b0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ede73ddc-1061-4788-836c-7d55c2f1e463' class='xr-var-data-in' type='checkbox'><label for='data-ede73ddc-1061-4788-836c-7d55c2f1e463' 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>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uh03_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-09e53bc1-e3c0-4a2f-8bed-55a6450a0252' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09e53bc1-e3c0-4a2f-8bed-55a6450a0252' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae5d0283-4ebb-4d91-8b60-49fe8e962417' class='xr-var-data-in' type='checkbox'><label for='data-ae5d0283-4ebb-4d91-8b60-49fe8e962417' 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>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctt_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-580155e1-9401-4673-ac39-037a99c3d359' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-580155e1-9401-4673-ac39-037a99c3d359' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4627628b-513a-42ad-bd2d-47ca0017b349' class='xr-var-data-in' type='checkbox'><label for='data-4627628b-513a-42ad-bd2d-47ca0017b349' 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>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dbz_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3f26f576-ceed-4d4e-9a36-46af7c87f41c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f26f576-ceed-4d4e-9a36-46af7c87f41c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6499271e-d292-4edb-a724-7a5b6023915f' class='xr-var-data-in' type='checkbox'><label for='data-6499271e-d292-4edb-a724-7a5b6023915f' 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>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8e76cbf4-814f-4457-8a06-41ef319ddf78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e76cbf4-814f-4457-8a06-41ef319ddf78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da228fad-9eba-4b48-b30f-4904eca4d7f6' class='xr-var-data-in' type='checkbox'><label for='data-da228fad-9eba-4b48-b30f-4904eca4d7f6' 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>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>row_indices</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9d8994e-3534-4fba-93bb-8482447fa512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9d8994e-3534-4fba-93bb-8482447fa512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6a64151-cc39-4e94-a638-46d8f80d30f5' class='xr-var-data-in' type='checkbox'><label for='data-c6a64151-cc39-4e94-a638-46d8f80d30f5' 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>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>col_indices</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b2f837a2-62b8-42d7-a5b4-cebe5f1d56ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2f837a2-62b8-42d7-a5b4-cebe5f1d56ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56c99bb6-7c7b-4bca-b142-ff4b429a283d' class='xr-var-data-in' type='checkbox'><label for='data-56c99bb6-7c7b-4bca-b142-ff4b429a283d' 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>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lats</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d2a1c5e-e2b1-42ae-8eb4-0e6d1b589958' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d2a1c5e-e2b1-42ae-8eb4-0e6d1b589958' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0360a81-9666-4a72-b63c-f9e853aad0a8' class='xr-var-data-in' type='checkbox'><label for='data-d0360a81-9666-4a72-b63c-f9e853aad0a8' 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>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lons</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8e03274e-8d45-4ebc-9df7-33daf6ab7b56' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e03274e-8d45-4ebc-9df7-33daf6ab7b56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76e46a39-78cb-48d5-958f-8b2d28f31423' class='xr-var-data-in' type='checkbox'><label for='data-76e46a39-78cb-48d5-958f-8b2d28f31423' 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>[2277376 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-384c700b-0ce5-472e-a090-95856d174802' class='xr-section-summary-in' type='checkbox'  ><label for='section-384c700b-0ce5-472e-a090-95856d174802' class='xr-section-summary' >Indexes: <span>(1)</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>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cb667a3c-22cc-45d1-a2a6-2b253db46375' class='xr-index-data-in' type='checkbox'/><label for='index-cb667a3c-22cc-45d1-a2a6-2b253db46375' 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([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "              92640.0, 92640.0, 92640.0,\n",
              "              ...\n",
              "              92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "              92661.0, 92661.0, 92661.0],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa3af3fd-68ee-4111-a891-f83a605b819d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aa3af3fd-68ee-4111-a891-f83a605b819d' 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>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Select one variable and fit 3x3 SOM."
      ],
      "metadata": {
        "id": "nKUeUFnH23VU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# we will use dbz/simulated radar reflectivity\n",
        "dsfull['dbz_grid']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "BMRS-2k72tVY",
        "outputId": "916c3140-90d5-4f1d-f97e-a0a4207667cc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<xarray.DataArray 'dbz_grid' (starttime: 2224, y: 32, x: 32)>\n",
              "[2277376 values with dtype=float32]\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "Dimensions without coordinates: y, x"
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              "  --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.DataArray &#x27;dbz_grid&#x27; (starttime: 2224, y: 32, x: 32)&gt;\n",
              "[2277376 values with dtype=float32]\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "Dimensions without coordinates: y, x</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'>'dbz_grid'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>starttime</span>: 2224</li><li><span>y</span>: 32</li><li><span>x</span>: 32</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a1058635-63d2-40e3-ba9d-a703c9f6e756' class='xr-array-in' type='checkbox' checked><label for='section-a1058635-63d2-40e3-ba9d-a703c9f6e756' 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>...</span></div><div class='xr-array-data'><pre>[2277376 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-7596221f-e72c-4335-8525-9fdff75fc00c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7596221f-e72c-4335-8525-9fdff75fc00c' 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'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-2363e080-f32d-4896-89bf-d5cb3f0b982f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2363e080-f32d-4896-89bf-d5cb3f0b982f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65572131-1e8c-4d9e-abf3-a7d6ca958f32' class='xr-var-data-in' type='checkbox'><label for='data-65572131-1e8c-4d9e-abf3-a7d6ca958f32' 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([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7713a503-27f1-4b3c-b63c-2d345722e2fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7713a503-27f1-4b3c-b63c-2d345722e2fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2195d44e-1b7e-4df6-8281-8ec3ba517b3b' class='xr-var-data-in' type='checkbox'><label for='data-2195d44e-1b7e-4df6-8281-8ec3ba517b3b' 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>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5d55bb6d-44d3-4b36-ac7e-ccc1d01df140' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d55bb6d-44d3-4b36-ac7e-ccc1d01df140' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fe9eb90-a140-4b3a-9513-c34634528a98' class='xr-var-data-in' type='checkbox'><label for='data-3fe9eb90-a140-4b3a-9513-c34634528a98' 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>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8804513f-3f0e-440a-bffc-1897a1e5a2cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8804513f-3f0e-440a-bffc-1897a1e5a2cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b16db1d-9431-4fc0-8001-a6a8b87d873d' class='xr-var-data-in' type='checkbox'><label for='data-3b16db1d-9431-4fc0-8001-a6a8b87d873d' 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>[2224 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5892abc-9fd2-42ab-bb9f-5c36bd2416e2' class='xr-section-summary-in' type='checkbox'  ><label for='section-a5892abc-9fd2-42ab-bb9f-5c36bd2416e2' class='xr-section-summary' >Indexes: <span>(1)</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>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7014c2b-6db1-464d-ba01-8b4fd635831f' class='xr-index-data-in' type='checkbox'/><label for='index-f7014c2b-6db1-464d-ba01-8b4fd635831f' 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([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "              92640.0, 92640.0, 92640.0,\n",
              "              ...\n",
              "              92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "              92661.0, 92661.0, 92661.0],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-13cc9046-dcb8-4465-9f42-a384f508b2ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13cc9046-dcb8-4465-9f42-a384f508b2ab' 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>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# each grid cell (x, y) will be feature for each sample\n",
        "# let's reshape accordingly (samples, features)\n",
        "ds = dsfull['dbz_grid'].stack(features=('y','x'))"
      ],
      "metadata": {
        "id": "E5vA7QST3E90"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "VUKqNS8F3Y0K",
        "outputId": "775b5a7e-14b2-45cf-bf77-437a4b019012"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.DataArray 'dbz_grid' (starttime: 2224, features: 1024)>\n",
              "array([[-35.      , -35.      , -35.      , ...,  13.949479,  12.911252,\n",
              "         10.474219],\n",
              "       [-35.      , -35.      , -35.      , ...,  15.252525,   9.551773,\n",
              "        -12.005707],\n",
              "       [-35.      , -35.      , -35.      , ..., -35.      , -35.      ,\n",
              "        -35.      ],\n",
              "       ...,\n",
              "       [-29.150763, -33.773006, -35.      , ..., -12.28549 , -21.292902,\n",
              "        -30.720982],\n",
              "       [ 24.57368 ,  13.765106,  14.084679, ...,  29.180717,  28.653072,\n",
              "         28.988672],\n",
              "       [ 13.142024,  16.35466 ,  17.247124, ...,  25.714262,  23.523518,\n",
              "         26.171114]], dtype=float32)\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "  * features   (features) object MultiIndex\n",
              "  * y          (features) int64 0 0 0 0 0 0 0 0 0 ... 31 31 31 31 31 31 31 31 31\n",
              "  * x          (features) int64 0 1 2 3 4 5 6 7 8 ... 23 24 25 26 27 28 29 30 31"
            ],
            "text/html": [
              "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
              "<defs>\n",
              "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
              "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
              "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
              "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
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              "\n",
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              "\n",
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              "\n",
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              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
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              "  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.DataArray &#x27;dbz_grid&#x27; (starttime: 2224, features: 1024)&gt;\n",
              "array([[-35.      , -35.      , -35.      , ...,  13.949479,  12.911252,\n",
              "         10.474219],\n",
              "       [-35.      , -35.      , -35.      , ...,  15.252525,   9.551773,\n",
              "        -12.005707],\n",
              "       [-35.      , -35.      , -35.      , ..., -35.      , -35.      ,\n",
              "        -35.      ],\n",
              "       ...,\n",
              "       [-29.150763, -33.773006, -35.      , ..., -12.28549 , -21.292902,\n",
              "        -30.720982],\n",
              "       [ 24.57368 ,  13.765106,  14.084679, ...,  29.180717,  28.653072,\n",
              "         28.988672],\n",
              "       [ 13.142024,  16.35466 ,  17.247124, ...,  25.714262,  23.523518,\n",
              "         26.171114]], dtype=float32)\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "  * features   (features) object MultiIndex\n",
              "  * y          (features) int64 0 0 0 0 0 0 0 0 0 ... 31 31 31 31 31 31 31 31 31\n",
              "  * x          (features) int64 0 1 2 3 4 5 6 7 8 ... 23 24 25 26 27 28 29 30 31</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'>'dbz_grid'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>starttime</span>: 2224</li><li><span class='xr-has-index'>features</span>: 1024</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a6a4c64d-ec51-443a-9c8d-63af81d790b2' class='xr-array-in' type='checkbox' checked><label for='section-a6a4c64d-ec51-443a-9c8d-63af81d790b2' 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>-35.0 -35.0 -35.0 -35.0 -35.0 -35.0 ... 26.44 29.09 25.71 23.52 26.17</span></div><div class='xr-array-data'><pre>array([[-35.      , -35.      , -35.      , ...,  13.949479,  12.911252,\n",
              "         10.474219],\n",
              "       [-35.      , -35.      , -35.      , ...,  15.252525,   9.551773,\n",
              "        -12.005707],\n",
              "       [-35.      , -35.      , -35.      , ..., -35.      , -35.      ,\n",
              "        -35.      ],\n",
              "       ...,\n",
              "       [-29.150763, -33.773006, -35.      , ..., -12.28549 , -21.292902,\n",
              "        -30.720982],\n",
              "       [ 24.57368 ,  13.765106,  14.084679, ...,  29.180717,  28.653072,\n",
              "         28.988672],\n",
              "       [ 13.142024,  16.35466 ,  17.247124, ...,  25.714262,  23.523518,\n",
              "         26.171114]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-8a76f3af-d1b3-4e0d-b51c-2d39ebabdcb7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8a76f3af-d1b3-4e0d-b51c-2d39ebabdcb7' class='xr-section-summary' >Coordinates: <span>(7)</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'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-5dba303c-d376-4088-8041-74ca65001652' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5dba303c-d376-4088-8041-74ca65001652' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83a39678-6df8-408a-93d2-14c2c6c584b8' class='xr-var-data-in' type='checkbox'><label for='data-83a39678-6df8-408a-93d2-14c2c6c584b8' 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([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a6de730-41f5-402b-95c0-a9a4d2c0c32b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a6de730-41f5-402b-95c0-a9a4d2c0c32b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca59e6c9-3458-4035-8483-4789b8b27c0d' class='xr-var-data-in' type='checkbox'><label for='data-ca59e6c9-3458-4035-8483-4789b8b27c0d' 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>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a837c6bd-b3e1-4201-85a6-7c2f72f33dad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a837c6bd-b3e1-4201-85a6-7c2f72f33dad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57908ce8-82ee-44d5-bce4-6a09278b16d6' class='xr-var-data-in' type='checkbox'><label for='data-57908ce8-82ee-44d5-bce4-6a09278b16d6' 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>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-785f04db-5912-4631-87db-2527dafeb318' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-785f04db-5912-4631-87db-2527dafeb318' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fc6c9b5-49be-435b-a41a-e155b2ec51e8' class='xr-var-data-in' type='checkbox'><label for='data-9fc6c9b5-49be-435b-a41a-e155b2ec51e8' 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>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>features</span></div><div class='xr-var-dims'>(features)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-b9e5fb7d-fa90-412f-bbba-460e0f52cccb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9e5fb7d-fa90-412f-bbba-460e0f52cccb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b311353-47d9-4caa-a9e9-e64b72eeb3ec' class='xr-var-data-in' type='checkbox'><label for='data-1b311353-47d9-4caa-a9e9-e64b72eeb3ec' 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([(0, 0), (0, 1), (0, 2), ..., (31, 29), (31, 30), (31, 31)], dtype=object)</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'>(features)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 ... 31 31 31 31 31 31</div><input id='attrs-fefaff17-1e59-4155-9a2b-35dd603157f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fefaff17-1e59-4155-9a2b-35dd603157f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5142872-3379-4045-9768-d65012b6f73c' class='xr-var-data-in' type='checkbox'><label for='data-c5142872-3379-4045-9768-d65012b6f73c' 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([ 0,  0,  0, ..., 31, 31, 31])</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'>(features)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 26 27 28 29 30 31</div><input id='attrs-263359d0-54c6-4640-bf0e-e3d1d1b1768b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-263359d0-54c6-4640-bf0e-e3d1d1b1768b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be3cdca6-1076-449f-839e-cb876b19de95' class='xr-var-data-in' type='checkbox'><label for='data-be3cdca6-1076-449f-839e-cb876b19de95' 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([ 0,  1,  2, ..., 29, 30, 31])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0dc17be-d9d5-400b-80c7-65eec5aefd4e' class='xr-section-summary-in' type='checkbox'  ><label for='section-e0dc17be-d9d5-400b-80c7-65eec5aefd4e' class='xr-section-summary' >Indexes: <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-index-name'><div>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0a741b04-8925-456a-a5ef-9973c95ea390' class='xr-index-data-in' type='checkbox'/><label for='index-0a741b04-8925-456a-a5ef-9973c95ea390' 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([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "              92640.0, 92640.0, 92640.0,\n",
              "              ...\n",
              "              92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "              92661.0, 92661.0, 92661.0],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>features<br>y<br>x</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-168ffa3d-e4c2-4ae6-b3cf-d3fa59caf240' class='xr-index-data-in' type='checkbox'/><label for='index-168ffa3d-e4c2-4ae6-b3cf-d3fa59caf240' 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(MultiIndex([( 0,  0),\n",
              "            ( 0,  1),\n",
              "            ( 0,  2),\n",
              "            ( 0,  3),\n",
              "            ( 0,  4),\n",
              "            ( 0,  5),\n",
              "            ( 0,  6),\n",
              "            ( 0,  7),\n",
              "            ( 0,  8),\n",
              "            ( 0,  9),\n",
              "            ...\n",
              "            (31, 22),\n",
              "            (31, 23),\n",
              "            (31, 24),\n",
              "            (31, 25),\n",
              "            (31, 26),\n",
              "            (31, 27),\n",
              "            (31, 28),\n",
              "            (31, 29),\n",
              "            (31, 30),\n",
              "            (31, 31)],\n",
              "           name=&#x27;features&#x27;, length=1024))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a98c7344-2e72-478c-8584-9f287f5df72b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a98c7344-2e72-478c-8584-9f287f5df72b' 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>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalization of our training data (note each variable should be individually processed!)\n",
        "ds_scaled = z_score(ds)\n",
        "# convert to numpy array for use with minisom\n",
        "ds_scaled = ds_scaled.values"
      ],
      "metadata": {
        "id": "XqJw-RHb2ZKK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SOMs time!"
      ],
      "metadata": {
        "id": "PJwBOPnT3xd6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# SOM hyperparameters\n",
        "\n",
        "# set som lattice dimensions (i.e., number of nodes)\n",
        "som_grid_rows = 3\n",
        "som_grid_columns = 3\n",
        "\n",
        "# number of weights per node\n",
        "input_length = ds_scaled.shape[1]\n",
        "\n",
        "# Spread of the neighborhood function, needs to be adequate to the dimensions\n",
        "# of the map. (rows/columns minus 1 usually)\n",
        "sigma = 2.0\n",
        "\n",
        "# initial learning rate (at the iteration t we have learning_rate(t) =\n",
        "# learning_rate / (1 + t/T) where T is #num_iteration/2)\n",
        "learning_rate = 0.5\n",
        "\n",
        "# Function that reduces learning_rate and sigma at each iteration\n",
        "# the default function is (asymptotic_decay):\n",
        "# learning_rate / (1+t/(max_iterarations/2))\n",
        "decay_function = asymptotic_decay\n",
        "\n",
        "# Function that weights the neighborhood of a position in the map.\n",
        "# Possible values: 'gaussian', 'mexican_hat', 'bubble', 'triangle',\n",
        "# which takes in sigma.\n",
        "neighborhood_function = 'gaussian'\n",
        "\n",
        "# Topology of the map; Possible values: 'rectangular', 'hexagonal'\n",
        "topology = 'rectangular'\n",
        "\n",
        "# Distance used to activate the map; Possible values: 'euclidean', 'cosine', 'manhattan', 'chebyshev'\n",
        "activation_distance = 'euclidean'\n",
        "\n",
        "# Random seed to use for reproducibility. Using 1.\n",
        "random_seed = 1"
      ],
      "metadata": {
        "id": "5OPL9Ao-2fnP"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# lets create our som object with preset hyperparameters\n",
        "som = MiniSom(\n",
        "            som_grid_rows,\n",
        "            som_grid_columns,\n",
        "            input_length,\n",
        "            sigma,\n",
        "            learning_rate,\n",
        "            decay_function,\n",
        "            neighborhood_function,\n",
        "            topology,\n",
        "            activation_distance,\n",
        "            random_seed)"
      ],
      "metadata": {
        "id": "ZkTjIwhm3kFT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# some training hyperparameters\n",
        "epochs = 10000\n",
        "random_order = True\n",
        "verbose = True"
      ],
      "metadata": {
        "id": "byk6fXtV4iL4"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# let's initialize the weights\n",
        "som.random_weights_init(ds_scaled)\n",
        "# som.pca_weights_init(ds_scaled)"
      ],
      "metadata": {
        "id": "pXJprDHG4nJH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# just vieweing the random weights\n",
        "som.get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adcYBTv84wqz",
        "outputId": "62caaa38-41a4-4583-e2d1-bfdd4687e500"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[-1.03932369, -0.52485353, -0.45033053, ...,  1.44800484,\n",
              "          1.15314555,  0.67560792],\n",
              "        [-1.13367176, -1.07694936, -0.99383473, ..., -0.53624862,\n",
              "         -0.68146396, -0.8066346 ],\n",
              "        [-0.56518501, -0.67954421,  0.48663226, ..., -0.83972389,\n",
              "         -0.71767533, -0.5213986 ]],\n",
              "\n",
              "       [[ 0.14352404, -0.00613375,  0.1140321 , ...,  1.00546145,\n",
              "          1.19885612,  1.45407248],\n",
              "        [ 1.06086552,  1.05668283,  1.11776388, ...,  0.76450711,\n",
              "          0.80701393,  0.81377196],\n",
              "        [ 0.42516214,  0.57606477,  0.67520875, ..., -0.4708569 ,\n",
              "         -1.11932063, -0.92772609]],\n",
              "\n",
              "       [[ 1.0284909 ,  1.10503435,  1.01927316, ...,  0.3609637 ,\n",
              "          0.12877603,  0.0352688 ],\n",
              "        [-2.53033018, -2.53033018, -2.53033018, ..., -0.74737114,\n",
              "          0.13885419, -0.23701039],\n",
              "        [-0.74855947, -0.71945316, -0.72111547, ..., -1.73292708,\n",
              "         -1.68697429, -1.63191032]]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now train!\n",
        "som.train(\n",
        "    ds_scaled,\n",
        "    epochs,\n",
        "    random_order,\n",
        "    verbose\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z3SvFUIz4z4b",
        "outputId": "b0206527-2a21-4fa2-e821-beca20b604fa"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " [ 10000 / 10000 ] 100% - 0:00:00 left \n",
            " quantization error: 21.499034895366535\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "frequencies = som.activation_response(ds_scaled)\n",
        "plt.pcolormesh(frequencies, cmap='Blues')\n",
        "plt.colorbar()\n",
        "plt.title(\"data frequency (2d histogram) across SOM lattice\", fontsize=12)\n",
        "plt.ylim(som_grid_rows, 0)  # flip the y axis to be the same as composite map axes later\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "Hzd99q3E48iE",
        "outputId": "e899c106-96f8-4f40-ae48-d3afa299c63c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# grabbing indices from SOM\n",
        "\n",
        "# create an empty dictionary using the rows and columns of SOM\n",
        "keys = [i for i in product(range(som_grid_rows), range(som_grid_columns))]\n",
        "winmap = {key: [] for key in keys}\n",
        "\n",
        "# grab the indices for the data within the SOM lattice\n",
        "for i, x in enumerate(ds_scaled):\n",
        "    winmap[som.winner(x)].append(i)"
      ],
      "metadata": {
        "id": "FhaLYkZr5J-g"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "som_keys = list(winmap.keys())\n",
        "print(f\"Number of SOM nodes: {len(som_keys)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LL1HjHfE5bUq",
        "outputId": "c06713ad-491c-40ae-e7fc-425fc638f74a"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of SOM nodes: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "D6EIWWFz518c",
        "outputId": "96735fff-3164-4b85-8e93-24723d1a42e6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<xarray.DataArray 'dbz_grid' (starttime: 2224, features: 1024)>\n",
              "array([[-35.      , -35.      , -35.      , ...,  13.949479,  12.911252,\n",
              "         10.474219],\n",
              "       [-35.      , -35.      , -35.      , ...,  15.252525,   9.551773,\n",
              "        -12.005707],\n",
              "       [-35.      , -35.      , -35.      , ..., -35.      , -35.      ,\n",
              "        -35.      ],\n",
              "       ...,\n",
              "       [-29.150763, -33.773006, -35.      , ..., -12.28549 , -21.292902,\n",
              "        -30.720982],\n",
              "       [ 24.57368 ,  13.765106,  14.084679, ...,  29.180717,  28.653072,\n",
              "         28.988672],\n",
              "       [ 13.142024,  16.35466 ,  17.247124, ...,  25.714262,  23.523518,\n",
              "         26.171114]], dtype=float32)\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "  * features   (features) object MultiIndex\n",
              "  * y          (features) int64 0 0 0 0 0 0 0 0 0 ... 31 31 31 31 31 31 31 31 31\n",
              "  * x          (features) int64 0 1 2 3 4 5 6 7 8 ... 23 24 25 26 27 28 29 30 31"
            ],
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              ".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.DataArray &#x27;dbz_grid&#x27; (starttime: 2224, features: 1024)&gt;\n",
              "array([[-35.      , -35.      , -35.      , ...,  13.949479,  12.911252,\n",
              "         10.474219],\n",
              "       [-35.      , -35.      , -35.      , ...,  15.252525,   9.551773,\n",
              "        -12.005707],\n",
              "       [-35.      , -35.      , -35.      , ..., -35.      , -35.      ,\n",
              "        -35.      ],\n",
              "       ...,\n",
              "       [-29.150763, -33.773006, -35.      , ..., -12.28549 , -21.292902,\n",
              "        -30.720982],\n",
              "       [ 24.57368 ,  13.765106,  14.084679, ...,  29.180717,  28.653072,\n",
              "         28.988672],\n",
              "       [ 13.142024,  16.35466 ,  17.247124, ...,  25.714262,  23.523518,\n",
              "         26.171114]], dtype=float32)\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "  * features   (features) object MultiIndex\n",
              "  * y          (features) int64 0 0 0 0 0 0 0 0 0 ... 31 31 31 31 31 31 31 31 31\n",
              "  * x          (features) int64 0 1 2 3 4 5 6 7 8 ... 23 24 25 26 27 28 29 30 31</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'>'dbz_grid'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>starttime</span>: 2224</li><li><span class='xr-has-index'>features</span>: 1024</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a446af92-b054-4097-9a85-684111fcadda' class='xr-array-in' type='checkbox' checked><label for='section-a446af92-b054-4097-9a85-684111fcadda' 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>-35.0 -35.0 -35.0 -35.0 -35.0 -35.0 ... 26.44 29.09 25.71 23.52 26.17</span></div><div class='xr-array-data'><pre>array([[-35.      , -35.      , -35.      , ...,  13.949479,  12.911252,\n",
              "         10.474219],\n",
              "       [-35.      , -35.      , -35.      , ...,  15.252525,   9.551773,\n",
              "        -12.005707],\n",
              "       [-35.      , -35.      , -35.      , ..., -35.      , -35.      ,\n",
              "        -35.      ],\n",
              "       ...,\n",
              "       [-29.150763, -33.773006, -35.      , ..., -12.28549 , -21.292902,\n",
              "        -30.720982],\n",
              "       [ 24.57368 ,  13.765106,  14.084679, ...,  29.180717,  28.653072,\n",
              "         28.988672],\n",
              "       [ 13.142024,  16.35466 ,  17.247124, ...,  25.714262,  23.523518,\n",
              "         26.171114]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-972baf53-91f8-4d4c-a088-e178d78743b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-972baf53-91f8-4d4c-a088-e178d78743b5' class='xr-section-summary' >Coordinates: <span>(7)</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'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-c93e59aa-aaa1-4a7b-b4e0-3f4bbbbf1892' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c93e59aa-aaa1-4a7b-b4e0-3f4bbbbf1892' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac0bf48d-c079-4b14-8681-a29802e7f0e0' class='xr-var-data-in' type='checkbox'><label for='data-ac0bf48d-c079-4b14-8681-a29802e7f0e0' 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([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-336eac8b-12d6-461a-b3c8-5196369c7a66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-336eac8b-12d6-461a-b3c8-5196369c7a66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02e7b11f-85cc-4cd7-8aa5-1d4aca4391b3' class='xr-var-data-in' type='checkbox'><label for='data-02e7b11f-85cc-4cd7-8aa5-1d4aca4391b3' 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>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3b8b4f7d-d623-4f2f-9d83-707a01180d1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b8b4f7d-d623-4f2f-9d83-707a01180d1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c98538c-2412-434c-a71e-20171807ea44' class='xr-var-data-in' type='checkbox'><label for='data-4c98538c-2412-434c-a71e-20171807ea44' 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>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d82a917d-6256-434b-9e40-f3519f5d8b26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d82a917d-6256-434b-9e40-f3519f5d8b26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ddde098-fd0f-415b-a256-bd6e93d87568' class='xr-var-data-in' type='checkbox'><label for='data-6ddde098-fd0f-415b-a256-bd6e93d87568' 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>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>features</span></div><div class='xr-var-dims'>(features)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-ecf5735a-c6ee-41b8-8330-26f2846cb234' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecf5735a-c6ee-41b8-8330-26f2846cb234' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a46f5359-2080-4e65-b00c-49a2b689a8f5' class='xr-var-data-in' type='checkbox'><label for='data-a46f5359-2080-4e65-b00c-49a2b689a8f5' 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([(0, 0), (0, 1), (0, 2), ..., (31, 29), (31, 30), (31, 31)], dtype=object)</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'>(features)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 ... 31 31 31 31 31 31</div><input id='attrs-08f17025-8ad1-4972-87c4-20ccf53762a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08f17025-8ad1-4972-87c4-20ccf53762a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db0badff-4700-41f7-b43f-ea903f043343' class='xr-var-data-in' type='checkbox'><label for='data-db0badff-4700-41f7-b43f-ea903f043343' 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([ 0,  0,  0, ..., 31, 31, 31])</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'>(features)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 26 27 28 29 30 31</div><input id='attrs-f1e8a899-5c48-42be-8292-45a7a5e2f1a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1e8a899-5c48-42be-8292-45a7a5e2f1a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b284fa0-44d6-4e38-87ba-4c2a3c1a0032' class='xr-var-data-in' type='checkbox'><label for='data-5b284fa0-44d6-4e38-87ba-4c2a3c1a0032' 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([ 0,  1,  2, ..., 29, 30, 31])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7248a646-85b7-406f-8662-1c2af46c4b0f' class='xr-section-summary-in' type='checkbox'  ><label for='section-7248a646-85b7-406f-8662-1c2af46c4b0f' class='xr-section-summary' >Indexes: <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-index-name'><div>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0b963adc-08c7-479c-9f33-9da4c726be2e' class='xr-index-data-in' type='checkbox'/><label for='index-0b963adc-08c7-479c-9f33-9da4c726be2e' 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([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "              92640.0, 92640.0, 92640.0,\n",
              "              ...\n",
              "              92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "              92661.0, 92661.0, 92661.0],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>features<br>y<br>x</div></div><div class='xr-index-preview'>PandasMultiIndex</div><div></div><input id='index-4775d28d-aafa-43bc-9275-27ae96d55b4b' class='xr-index-data-in' type='checkbox'/><label for='index-4775d28d-aafa-43bc-9275-27ae96d55b4b' 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(MultiIndex([( 0,  0),\n",
              "            ( 0,  1),\n",
              "            ( 0,  2),\n",
              "            ( 0,  3),\n",
              "            ( 0,  4),\n",
              "            ( 0,  5),\n",
              "            ( 0,  6),\n",
              "            ( 0,  7),\n",
              "            ( 0,  8),\n",
              "            ( 0,  9),\n",
              "            ...\n",
              "            (31, 22),\n",
              "            (31, 23),\n",
              "            (31, 24),\n",
              "            (31, 25),\n",
              "            (31, 26),\n",
              "            (31, 27),\n",
              "            (31, 28),\n",
              "            (31, 29),\n",
              "            (31, 30),\n",
              "            (31, 31)],\n",
              "           name=&#x27;features&#x27;, length=1024))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7cd3e96-ae7e-491f-90ba-65ad440175af' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e7cd3e96-ae7e-491f-90ba-65ad440175af' 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>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view all SOM composite maps\n",
        "# (this cell takes a few minutes to run, especially if SOM lattice is large)\n",
        "\n",
        "# grab values for grid\n",
        "lats = ds.unstack('features').y\n",
        "lons = ds.unstack('features').x\n",
        "\n",
        "# create a figure using the same dimensions as SOM\n",
        "fig, axs = plt.subplots(som_grid_rows, som_grid_columns)\n",
        "\n",
        "# (1) loop through the SOM neurons,\n",
        "# (2) grab data samples using SOM indices,\n",
        "# (3) compute the mean for each SOM neuron, plot each\n",
        "for map_num in range(len(som_keys)):\n",
        "\n",
        "    # the data\n",
        "    temp_data = ds.unstack('features')[\n",
        "        np.array(winmap[som_keys[map_num]])].mean(dim=\"starttime\", skipna=True).values\n",
        "\n",
        "    # plot\n",
        "    cs = axs[som_keys[map_num][0], som_keys[map_num][1]].pcolormesh(\n",
        "        lons, lats, temp_data, cmap=\"seismic\", vmin=-40, vmax=40)\n",
        "\n",
        "    # title\n",
        "    axs[som_keys[map_num][0], som_keys[map_num][1]].set_title(\n",
        "        f\"Sample size: {frequencies.flatten()[map_num]}\", fontsize=12)\n",
        "\n",
        "    # add plot stuff\n",
        "    axs[som_keys[map_num][0], som_keys[map_num][1]].margins(x=0, y=0)\n",
        "\n",
        "# plot the figure containing all plots\n",
        "plt.colorbar(cs)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "I0bz2iWb5uwK",
        "outputId": "7a58afe3-7c1d-454b-bd32-752e2801d7b1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view all SOM composite maps\n",
        "# (this cell takes a few minutes to run, especially if SOM lattice is large)\n",
        "\n",
        "# grab values for grid\n",
        "lats = ds.unstack('features').y\n",
        "lons = ds.unstack('features').x\n",
        "\n",
        "# create a figure using the same dimensions as SOM\n",
        "fig, axs = plt.subplots(som_grid_rows, som_grid_columns)\n",
        "\n",
        "# (1) loop through the SOM neurons,\n",
        "# (2) grab data samples using SOM indices,\n",
        "# (3) compute the sum of another variable at each SOM neuron, plot each\n",
        "for map_num in range(len(som_keys)):\n",
        "\n",
        "    # the data\n",
        "    temp_data = dsfull['mask'][\n",
        "        np.array(winmap[som_keys[map_num]])].sum(dim=\"starttime\", skipna=True).values\n",
        "\n",
        "    # plot\n",
        "    cs = axs[som_keys[map_num][0], som_keys[map_num][1]].pcolormesh(\n",
        "        lons, lats, temp_data, cmap=\"magma\", vmin=0, vmax=200)\n",
        "\n",
        "    # title\n",
        "    axs[som_keys[map_num][0], som_keys[map_num][1]].set_title(\n",
        "        f\"Sample size: {frequencies.flatten()[map_num]}\", fontsize=12)\n",
        "\n",
        "    # add plot stuff\n",
        "    axs[som_keys[map_num][0], som_keys[map_num][1]].margins(x=0, y=0)\n",
        "\n",
        "# plot the figure containing all plots\n",
        "plt.colorbar(cs)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "yq6jH-ZD8GfW",
        "outputId": "2c323403-0608-470e-f1f9-5100793d2608"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2HO_3evG9bjB"
      },
      "execution_count": 24,
      "outputs": []
    }
  ]
}