Glacier Area Evolution.ipynb

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              "            Temperature  Precipitation  GlacierRunoff  MassBalance  \\\n",
              "Date                                                                 \n",
              "1980-01-01   -18.532047       4.787212           0.00     4.787212   \n",
              "1980-02-01   -18.935651      10.279112           0.00    10.279112   \n",
              "1980-03-01   -15.375955      49.322324           0.00    49.322324   \n",
              "1980-04-01   -11.978969      28.852930           0.00    28.852930   \n",
              "1980-05-01    -7.747039      30.570363           0.26    30.310363   \n",
              "\n",
              "            CumulativeMassBalance  \n",
              "Date                               \n",
              "1980-01-01               4.787212  \n",
              "1980-02-01              15.066324  \n",
              "1980-03-01              64.388648  \n",
              "1980-04-01              93.241578  \n",
              "1980-05-01             123.551941  "
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            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# prompt: data.head() add date column as index column\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Read the CSV file\n",
        "data = pd.read_csv('hist_gl_runoff.csv')\n",
        "\n",
        "# Assume a density factor for converting precipitation to water equivalent (simplified as 1)\n",
        "density_factor = 1\n",
        "\n",
        "# Calculate daily mass balance\n",
        "data['MassBalance'] = density_factor * data['Precipitation'] - data['GlacierRunoff']\n",
        "\n",
        "# Calculate cumulative mass balance over the period\n",
        "data['CumulativeMassBalance'] = data['MassBalance'].cumsum()\n",
        "\n",
        "# Set the date column as the index\n",
        "data = data.set_index('Date')\n",
        "\n",
        "data.head()\n"
      ]
    },
    {
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        "id": "JRrEPiaDFXgd",
        "outputId": "90b4db9f-538f-413c-b166-07bbf10a2a14"
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          "data": {
            "text/plain": [
              "            Temperature  Precipitation  GlacierRunoff  MassBalance  \\\n",
              "Date                                                                 \n",
              "1980-01-01   -18.532047       4.787212           0.00     4.787212   \n",
              "1980-02-01   -18.935651      10.279112           0.00    10.279112   \n",
              "1980-03-01   -15.375955      49.322324           0.00    49.322324   \n",
              "1980-04-01   -11.978969      28.852930           0.00    28.852930   \n",
              "1980-05-01    -7.747039      30.570363           0.26    30.310363   \n",
              "\n",
              "            CumulativeMassBalance   GlacierArea  \n",
              "Date                                             \n",
              "1980-01-01               4.787212  3.478449e+09  \n",
              "1980-02-01              15.066324  3.478949e+09  \n",
              "1980-03-01              64.388648  3.481349e+09  \n",
              "1980-04-01              93.241578  3.482753e+09  \n",
              "1980-05-01             123.551941  3.484228e+09  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 372,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 372,\n        \"samples\": [\n          \"2007-04-01\",\n          \"1982-10-01\",\n          \"1981-04-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.11979636317221,\n        \"min\": -22.35451525,\n        \"max\": 9.19054358,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          -12.43584912,\n          -2.5540888,\n          -12.37787157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precipitation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.83933220522113,\n        \"min\": 0.004824146,\n        \"max\": 225.6234934,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          40.39585537,\n          15.88472761,\n          64.37644853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierRunoff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36.36515900060593,\n        \"min\": 0.0,\n        \"max\": 184.84,\n        \"num_unique_values\": 184,\n        \"samples\": [\n          129.96,\n          0.43,\n          53.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.64430778917925,\n        \"min\": -170.92963005000001,\n        \"max\": 225.5834934,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          40.39585537,\n          4.284727610000001,\n          64.37644853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CumulativeMassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 473.0152870321969,\n        \"min\": 4.787211547,\n        \"max\": 1797.3024959820002,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          1420.8116470100001,\n          203.55822170099998,\n          190.79690972299997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23069035.697647933,\n        \"min\": 3478449265.2033424,\n        \"max\": 3565884865.9416323,\n        \"num_unique_values\": 372,\n        \"samples\": [\n          3547481714.093804,\n          3488121927.0910397,\n          3487500758.583351\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Initial conditions (synthetic values)\n",
        "initial_volume = 4.32e8  # Initial glacier volume in cubic meters\n",
        "initial_area = 4.27e9  # Initial glacier area in square meters\n",
        "density_of_ice = 900  # Density of ice in kg/m^3 (from water equivalent to ice equivalent)\n",
        "\n",
        "# Volume-area scaling parameters (example values, these should be determined empirically for real-world cases)\n",
        "c = 0.035\n",
        "gamma = 1.2735\n",
        "#gamma = 0.923\n",
        "# Calculate volume change from cumulative mass balance\n",
        "# Convert cumulative mass balance to volume change (assuming glacier area remains constant initially)\n",
        "volume_change = data['CumulativeMassBalance'] * initial_area * 1e-3 / density_of_ice  # in cubic meters\n",
        "\n",
        "# Calculate total volume at each time step\n",
        "total_volume = initial_volume + volume_change\n",
        "\n",
        "# Calculate glacier area using volume-area scaling\n",
        "data['GlacierArea'] = c * total_volume**gamma\n",
        "#data.to_csv('hist_gl_area.csv', index=False)\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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    {
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        {
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          "data": {
            "text/plain": [
              "      Temperature  Precipitation  GlacierRunoff  MassBalance  \\\n",
              "Date                                                           \n",
              "1980   -89.488576     258.381799         173.75    84.631799   \n",
              "1981   -99.997542     334.874241         143.24   191.634241   \n",
              "1982  -103.334055     186.906133         196.29    -9.383867   \n",
              "1983  -100.133579     166.038786         221.08   -55.041214   \n",
              "1984   -92.183970     238.465005         299.56   -61.094995   \n",
              "\n",
              "      CumulativeMassBalance   GlacierArea  \n",
              "Date                                       \n",
              "1980            1076.156530  4.179096e+10  \n",
              "1981            2938.973912  4.188163e+10  \n",
              "1982            3598.844754  4.191375e+10  \n",
              "1983            3721.712311  4.191974e+10  \n",
              "1984            2953.014407  4.188231e+10  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_annual",
              "summary": "{\n  \"name\": \"data_annual\",\n  \"rows\": 31,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 1980,\n        \"max\": 2010,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          2007,\n          1995,\n          2003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.69960279834714,\n        \"min\": -113.46064573999999,\n        \"max\": -79.589567555,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          -103.01586060300001,\n          -101.462514365,\n          -100.09971321\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precipitation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.0505541017576,\n        \"min\": 105.00535726999999,\n        \"max\": 467.11663653,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          223.854091658,\n          267.608046288,\n          366.154739727\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierRunoff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 74.13532652035445,\n        \"min\": 99.76,\n        \"max\": 353.74,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          157.43,\n          190.32000000000002,\n          120.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131.88984537244173,\n        \"min\": -170.94464273,\n        \"max\": 288.81663653,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          66.424091658,\n          77.28804628799999,\n          246.01473972699998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CumulativeMassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5682.838767262232,\n        \"min\": 1076.156529594,\n        \"max\": 19834.220793649,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          16872.794455433002,\n          10066.109472631999,\n          14118.359898193\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 277152046.6452929,\n        \"min\": 41790957521.69233,\n        \"max\": 42705866237.11706,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          42561139059.138596,\n          42228901400.883575,\n          42426628885.58777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Extract the year from the index (assuming 'Date' is in YYYY-MM-DD format)\n",
        "data_annual = data.groupby(data.index.str[:4].astype(int)).sum()\n",
        "#data_annual.to_csv('hist_gl_area.csv', index=False)\n",
        "data_annual.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IgPo109u1s-R"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
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          "height": 564
        },
        "id": "AgfiMB_u52vn",
        "outputId": "0b6026d0-94b6-422c-aa4d-2905127700b5"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# prompt: show standard deviation in green color\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "# ... (your preceding code here)\n",
        "\n",
        "# Calculate one standard deviation of glacier area\n",
        "std_dev = np.std(data_annual['GlacierArea'])\n",
        "\n",
        "# Plot glacier area over time with one standard deviation\n",
        "data_annual.plot(y='GlacierArea', figsize=(10, 6))\n",
        "plt.fill_between(data_annual.index, data_annual['GlacierArea'] - std_dev, data_annual['GlacierArea'] + std_dev, alpha=0.2, color='green')\n",
        "plt.title('Glacier Area over Time with Standard Deviation')\n",
        "plt.ylabel('Glacier Area (m^2)')\n",
        "plt.xlabel('Date')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kg_wcLl21lKy"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "4jqI7_08_ikN",
        "outputId": "8a7d2fb4-ae4b-40d4-b2c7-7094f333804a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a5e40f69-937d-495d-a8d5-9a817382d0b0\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a5e40f69-937d-495d-a8d5-9a817382d0b0\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-bf0d25bda47d>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Read the CSV file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m     67\u001b[0m   \"\"\"\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m   \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m   \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m   \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m   \u001b[0;31m# First result is always an indication that the file picker has completed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m   result = _output.eval_js(\n\u001b[0m\u001b[1;32m    157\u001b[0m       'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[1;32m    158\u001b[0m           \u001b[0minput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     if (\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# prompt: data.head() add date column as index column\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Read the CSV file\n",
        "ssp2 = pd.read_csv('ssp2_gl_runoff.csv')\n",
        "\n",
        "# Assume a density factor for converting precipitation to water equivalent (simplified as 1)\n",
        "density_factor = 1\n",
        "\n",
        "# Calculate daily mass balance\n",
        "ssp2['MassBalance'] = density_factor * ssp2['Precipitation'] - ssp2['GlacierRunoff']\n",
        "\n",
        "# Calculate cumulative mass balance over the period\n",
        "ssp2['CumulativeMassBalance'] = ssp2['MassBalance'].cumsum()\n",
        "\n",
        "# Set the date column as the index\n",
        "ssp2 = ssp2.set_index('Date')\n",
        "\n",
        "ssp2.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "jBclU7WOARd2",
        "outputId": "7daa9f56-8e3a-4303-bda4-4d692b90e887"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Temperature  Precipitation  GlacierRunoff  MassBalance  \\\n",
              "Date                                                                 \n",
              "2030-01-01   -24.537485      21.523235            0.0    21.523235   \n",
              "2030-02-01   -24.011502      16.675009            0.0    16.675009   \n",
              "2030-03-01   -21.826804      30.314503            0.0    30.314503   \n",
              "2030-04-01   -16.756203      47.350606            0.0    47.350606   \n",
              "2030-05-01   -12.152137      49.029923            0.0    49.029923   \n",
              "\n",
              "            CumulativeMassBalance   GlacierArea  \n",
              "Date                                             \n",
              "2030-01-01              21.523235  3.479263e+09  \n",
              "2030-02-01              38.198243  3.480075e+09  \n",
              "2030-03-01              68.512746  3.481550e+09  \n",
              "2030-04-01             115.863352  3.483854e+09  \n",
              "2030-05-01             164.893275  3.486240e+09  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2773a6a-9c05-47c2-850c-245ab19205f7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>GlacierRunoff</th>\n",
              "      <th>MassBalance</th>\n",
              "      <th>CumulativeMassBalance</th>\n",
              "      <th>GlacierArea</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2030-01-01</th>\n",
              "      <td>-24.537485</td>\n",
              "      <td>21.523235</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.523235</td>\n",
              "      <td>21.523235</td>\n",
              "      <td>3.479263e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2030-02-01</th>\n",
              "      <td>-24.011502</td>\n",
              "      <td>16.675009</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.675009</td>\n",
              "      <td>38.198243</td>\n",
              "      <td>3.480075e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2030-03-01</th>\n",
              "      <td>-21.826804</td>\n",
              "      <td>30.314503</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.314503</td>\n",
              "      <td>68.512746</td>\n",
              "      <td>3.481550e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2030-04-01</th>\n",
              "      <td>-16.756203</td>\n",
              "      <td>47.350606</td>\n",
              "      <td>0.0</td>\n",
              "      <td>47.350606</td>\n",
              "      <td>115.863352</td>\n",
              "      <td>3.483854e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2030-05-01</th>\n",
              "      <td>-12.152137</td>\n",
              "      <td>49.029923</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49.029923</td>\n",
              "      <td>164.893275</td>\n",
              "      <td>3.486240e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2773a6a-9c05-47c2-850c-245ab19205f7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ssp2",
              "summary": "{\n  \"name\": \"ssp2\",\n  \"rows\": 840,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 840,\n        \"samples\": [\n          \"2087-12-01\",\n          \"2098-01-01\",\n          \"2032-07-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.90870513716815,\n        \"min\": -24.80828857,\n        \"max\": 7.252517995,\n        \"num_unique_values\": 814,\n        \"samples\": [\n          4.773951516,\n          -22.6167058,\n          -15.94599752\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precipitation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.159940359020394,\n        \"min\": 7.00191586,\n        \"max\": 55.42224776,\n        \"num_unique_values\": 804,\n        \"samples\": [\n          20.95265092,\n          14.36870644,\n          22.3079273\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierRunoff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57.85138058230606,\n        \"min\": 0.0,\n        \"max\": 179.91,\n        \"num_unique_values\": 404,\n        \"samples\": [\n          57.45,\n          0.13,\n          4.16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.79554267189458,\n        \"min\": -166.21970172,\n        \"max\": 55.38224776,\n        \"num_unique_values\": 804,\n        \"samples\": [\n          20.95265092,\n          -69.59129356,\n          22.3079273\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CumulativeMassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3271.57024358884,\n        \"min\": -11075.382941012997,\n        \"max\": 203.06488473000002,\n        \"num_unique_values\": 840,\n        \"samples\": [\n          -8299.275222657,\n          -10547.614161422996,\n          80.80942402\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156719727.58051306,\n        \"min\": 2948671671.300314,\n        \"max\": 3488097913.0222287,\n        \"num_unique_values\": 840,\n        \"samples\": [\n          3079628490.1785517,\n          2973473689.1280565,\n          3482148002.4727464\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Initial conditions (synthetic values)\n",
        "initial_volume = 4.32e8  # Initial glacier volume in cubic meters\n",
        "initial_area = 4.27e9  # Initial glacier area in square meters\n",
        "density_of_ice = 900  # Density of ice in kg/m^3 (from water equivalent to ice equivalent)\n",
        "\n",
        "# Volume-area scaling parameters (example values, these should be determined empirically for real-world cases)\n",
        "c = 0.035\n",
        "gamma = 1.2735\n",
        "#gamma = 0.923\n",
        "# Calculate volume change from cumulative mass balance\n",
        "# Convert cumulative mass balance to volume change (assuming glacier area remains constant initially)\n",
        "volume_change = ssp2['CumulativeMassBalance'] * initial_area * 1e-3 / density_of_ice  # in cubic meters\n",
        "\n",
        "# Calculate total volume at each time step\n",
        "total_volume = initial_volume + volume_change\n",
        "\n",
        "# Calculate glacier area using volume-area scaling\n",
        "ssp2['GlacierArea'] = c * total_volume**gamma\n",
        "#data.to_csv('hist_gl_area.csv', index=False)\n",
        "ssp2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "VI6knSHJmsf_",
        "outputId": "60369837-a2e3-404a-df94-a6d094f02483"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Temperature  Precipitation  GlacierRunoff  MassBalance  \\\n",
              "Date                                                           \n",
              "2030  -125.954883     318.012013         357.13   -39.117987   \n",
              "2031  -122.456040     317.348774         358.40   -41.051226   \n",
              "2032  -118.542978     303.808556         366.03   -62.221444   \n",
              "2033  -119.129385     301.540000         372.84   -71.300000   \n",
              "2034  -120.847561     311.449184         359.52   -48.070816   \n",
              "\n",
              "      CumulativeMassBalance   GlacierArea  \n",
              "Date                                       \n",
              "2030             668.407776  4.177112e+10  \n",
              "2031             174.168587  4.174708e+10  \n",
              "2032            -457.555033  4.171635e+10  \n",
              "2033           -1252.178148  4.167770e+10  \n",
              "2034           -1980.020515  4.164231e+10  "
            ],
            "text/html": [
              "\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b5ddaaf6-5426-4d7c-8516-9ed72ca14257 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ssp2_annual",
              "summary": "{\n  \"name\": \"ssp2_annual\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 2030,\n        \"max\": 2099,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          2052,\n          2030,\n          2079\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.599553181731055,\n        \"min\": -125.95488289800001,\n        \"max\": -89.353860836,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          -112.174638802,\n          -125.95488289800001,\n          -101.859234468\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precipitation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.072265357112267,\n        \"min\": 284.57295517,\n        \"max\": 335.67462840999997,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          313.557201371,\n          305.190798858,\n          311.44918443\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierRunoff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56.90459372291648,\n        \"min\": 357.13,\n        \"max\": 557.15,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          483.76,\n          429.56,\n          359.52\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57.390426446236575,\n        \"min\": -272.57704483,\n        \"max\": -39.11798702,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          -170.202798629,\n          -124.36920114200001,\n          -48.07081556999998\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CumulativeMassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39483.997828613676,\n        \"min\": -129904.93377809598,\n        \"max\": 668.4077755900003,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          -24005.310936813992,\n          668.4077755900003,\n          -78508.74964879997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1891426593.3168828,\n        \"min\": 35524986551.251785,\n        \"max\": 41771123243.20489,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          40574349370.206566,\n          41771123243.20489,\n          37957592098.463165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "# Extract the year from the index (assuming 'Date' is in YYYY-MM-DD format)\n",
        "ssp2_annual = ssp2.groupby(ssp2.index.str[:4].astype(int)).sum()\n",
        "#ssp2_annual.to_csv('ssp2_gl_area.csv', index=False)\n",
        "ssp2_annual.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "VoCG0VVDArzV",
        "outputId": "cf242fbf-2b63-4184-f2db-829cb04d3d73"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# prompt: show standard deviation in green color\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "# ... (your preceding code here)\n",
        "\n",
        "# Calculate one standard deviation of glacier area\n",
        "std_dev = np.std(ssp2_annual['GlacierArea'])\n",
        "\n",
        "# Plot glacier area over time with one standard deviation\n",
        "ssp2_annual.plot(y='GlacierArea', figsize=(10, 6))\n",
        "plt.fill_between(ssp2_annual.index, ssp2_annual['GlacierArea'] - std_dev, ssp2_annual['GlacierArea'] + std_dev, alpha=0.15, color='green')\n",
        "plt.title('Glacier Area over Time with Standard Deviation')\n",
        "plt.ylabel('Glacier Area (m^2)')\n",
        "plt.xlabel('Date')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "lNO-3ImTBEpd",
        "outputId": "f224e648-11b6-4eec-c381-b9cd49806a99"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-bd098f03-5da7-412e-8e4c-d199054d8174\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-bd098f03-5da7-412e-8e4c-d199054d8174\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-edee3323ff1e>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Read the CSV file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m()\u001b[0m\n\u001b[1;32m     67\u001b[0m   \"\"\"\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m   \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m   \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m   \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m   \u001b[0;31m# First result is always an indication that the file picker has completed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m   result = _output.eval_js(\n\u001b[0m\u001b[1;32m    157\u001b[0m       'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[1;32m    158\u001b[0m           \u001b[0minput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     if (\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# prompt: data.head() add date column as index column\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "# Read the CSV file\n",
        "ssp5 = pd.read_csv('ssp5_gl_runoff.csv')\n",
        "\n",
        "# Assume a density factor for converting precipitation to water equivalent (simplified as 1)\n",
        "density_factor = 1\n",
        "\n",
        "# Calculate daily mass balance\n",
        "ssp5['MassBalance'] = density_factor * ssp5['Precipitation'] - ssp5['GlacierRunoff']\n",
        "\n",
        "# Calculate cumulative mass balance over the period\n",
        "ssp5['CumulativeMassBalance'] = ssp5['MassBalance'].cumsum()\n",
        "\n",
        "# Set the date column as the index\n",
        "ssp5 = ssp5.set_index('Date')\n",
        "\n",
        "ssp5.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "I9SJkhD-BrxG"
      },
      "outputs": [],
      "source": [
        "# Initial conditions (synthetic values)\n",
        "initial_volume = 4.32e8  # Initial glacier volume in cubic meters\n",
        "initial_area = 4.27e9  # Initial glacier area in square meters\n",
        "density_of_ice = 900  # Density of ice in kg/m^3 (from water equivalent to ice equivalent)\n",
        "\n",
        "# Volume-area scaling parameters (example values, these should be determined empirically for real-world cases)\n",
        "c = 0.035\n",
        "gamma = 1.2735\n",
        "#gamma = 0.923\n",
        "# Calculate volume change from cumulative mass balance\n",
        "# Convert cumulative mass balance to volume change (assuming glacier area remains constant initially)\n",
        "volume_change = ssp5['CumulativeMassBalance'] * initial_area * 1e-3 / density_of_ice  # in cubic meters\n",
        "\n",
        "# Calculate total volume at each time step\n",
        "total_volume = initial_volume + volume_change\n",
        "\n",
        "# Calculate glacier area using volume-area scaling\n",
        "ssp5['GlacierArea'] = c * total_volume**gamma\n",
        "#data.to_csv('hist_gl_area.csv', index=False)\n",
        "#volume_change.to_csv('ssp5_volume_CHANGE.csv', index=False)\n",
        "#total_volume.head(50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "2oE1JM1Cjxii",
        "outputId": "961f839f-c708-45fa-d31b-c8ee66109031"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Temperature  Precipitation  GlacierRunoff  MassBalance  \\\n",
              "Date                                                           \n",
              "2030  -106.949567     280.330164         446.11  -165.779836   \n",
              "2031  -105.218826     318.946036         431.66  -112.713964   \n",
              "2032  -103.305580     298.915276         456.91  -157.994724   \n",
              "2033  -103.279461     289.970932         440.83  -150.859068   \n",
              "2034  -102.247059     299.828084         448.05  -148.221916   \n",
              "\n",
              "      CumulativeMassBalance   GlacierArea  \n",
              "Date                                       \n",
              "2030            -153.938992  4.173112e+10  \n",
              "2031           -1720.755725  4.165492e+10  \n",
              "2032           -3287.864962  4.157873e+10  \n",
              "2033           -5238.813154  4.148392e+10  \n",
              "2034           -6933.757254  4.140160e+10  "
            ],
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-38abc43e-1deb-4482-9a81-bfd30dd0a457 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ssp5_annual",
              "summary": "{\n  \"name\": \"ssp5_annual\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 2030,\n        \"max\": 2099,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          2052,\n          2030,\n          2079\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Temperature\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.45085711062058,\n        \"min\": -106.949566682,\n        \"max\": -43.695300096,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          -90.677476476,\n          -106.949566682,\n          -70.85166583200001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precipitation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.21004552300639,\n        \"min\": 278.72774599,\n        \"max\": 364.15875816,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          326.61064567,\n          278.72774599,\n          299.82808382\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierRunoff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 137.57697513256082,\n        \"min\": 431.65999999999997,\n        \"max\": 878.2,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          635.05,\n          540.81,\n          448.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 127.43379674015219,\n        \"min\": -546.57043794,\n        \"max\": -112.71396413999999,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          -308.43935433,\n          -262.08225401,\n          -148.22191618\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CumulativeMassBalance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78398.24439477373,\n        \"min\": -265605.9789783131,\n        \"max\": -153.93899178999987,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          -48296.69536435998,\n          -153.93899178999987,\n          -150695.19593423413\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GlacierArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3690875615.177186,\n        \"min\": 29275588825.11039,\n        \"max\": 41731122581.59658,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          39403479328.49857,\n          41731122581.59658,\n          34550946000.40943\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# Extract the year from the index (assuming 'Date' is in YYYY-MM-DD format)\n",
        "ssp5_annual = ssp5.groupby(ssp5.index.str[:4].astype(int)).sum()\n",
        "#ssp5_annual.to_csv('ssp5_gl_area.csv', index=False)\n",
        "ssp5_annual.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "IhhZpSPBB1TW",
        "outputId": "f708b210-c49c-41de-80fd-cd0c3d4abb2c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# prompt: show standard deviation in green color\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "# ... (your preceding code here)\n",
        "\n",
        "# Calculate one standard deviation of glacier area\n",
        "std_dev = np.std(ssp5_annual['GlacierArea'])\n",
        "\n",
        "# Plot glacier area over time with one standard deviation\n",
        "ssp5_annual.plot(y='GlacierArea', figsize=(10, 6))\n",
        "plt.fill_between(ssp5_annual.index, ssp5_annual['GlacierArea'] - std_dev, ssp5_annual['GlacierArea'] + std_dev, alpha=0.15, color='green')\n",
        "plt.title('Glacier Area over Time with Standard Deviation')\n",
        "plt.ylabel('Glacier Area (m^2)')\n",
        "plt.xlabel('Date')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ancG5lLQkuZA"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OLnT8CgMi0V4",
        "outputId": "2b108aff-3a45-461f-bf2a-d4c5c8836bbc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 3 Axes>"
            ],
            "image/png": "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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# prompt: combine Historical, ssp2 and ssp5 in one plot\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "fig, axes = plt.subplots(1, 3, figsize=(16, 6))\n",
        "\n",
        "# Plot data\n",
        "data_annual.plot(y='GlacierArea', ax=axes[0], title='Historical Glacier Area')\n",
        "axes[0].fill_between(data_annual.index, data_annual['GlacierArea'] - std_dev, data_annual['GlacierArea'] + std_dev, alpha=0.2)\n",
        "axes[0].set_ylabel('Glacier Area (m^2)')\n",
        "axes[0].set_xlabel('Date')\n",
        "\n",
        "# Plot ssp2\n",
        "ssp2_annual.plot(y='GlacierArea', ax=axes[1], title='SSP2 Glacier Area')\n",
        "axes[1].fill_between(ssp2_annual.index, ssp2_annual['GlacierArea'] - std_dev, ssp2_annual['GlacierArea'] + std_dev, alpha=0.15, color='green')\n",
        "axes[1].set_ylabel('Glacier Area (m^2)')\n",
        "axes[1].set_xlabel('Date')\n",
        "\n",
        "# Plot ssp5\n",
        "ssp5_annual.plot(y='GlacierArea', ax=axes[2], title='SSP5 Glacier Area')\n",
        "axes[2].fill_between(ssp5_annual.index, ssp5_annual['GlacierArea'] - std_dev, ssp5_annual['GlacierArea'] + std_dev, alpha=0.15, color='green')\n",
        "axes[2].set_ylabel('Glacier Area (m^2)')\n",
        "axes[2].set_xlabel('Date')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Combine data into a single DataFrame\n",
        "combined_data = pd.concat([data_annual['GlacierArea'], ssp2_annual['GlacierArea'], ssp5_annual['GlacierArea']], axis=1)\n",
        "\n",
        "# Plot combined data\n",
        "combined_data.plot(figsize=(10, 6), title='Glacier Area Simulation')\n",
        "plt.ylabel('Glacier Area (m^2)')\n",
        "plt.xlabel('Date')\n",
        "plt.legend(['Historical', 'SSP2', 'SSP5'])\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "GewMwXo9Cx4t",
        "outputId": "7720ed6f-a84e-4fa6-b981-6adc1850928a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "\n",
        "# Combine data into a single DataFrame\n",
        "combined_data = pd.concat([data_annual['GlacierArea'], ssp2_annual['GlacierArea'], ssp5_annual['GlacierArea']], axis=1)\n",
        "\n",
        "# Plot combined data\n",
        "combined_data.plot(figsize=(10, 6), title='Combined Glacier Area')\n",
        "plt.ylabel('Glacier Area (m^2)')\n",
        "plt.xlabel('Date')\n",
        "plt.legend(['Historical', 'SSP2', 'SSP5'])\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "hiqKNTXvg1CI",
        "outputId": "244f7913-fc97-4c10-e0f2-16abe26c2601"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# prompt: increase font of x and y axis in above plot\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Plot combined data\n",
        "combined_data.plot(ax=ax, title='')\n",
        "ax.set_ylabel('Glacier Area (m^2)', fontsize=16)\n",
        "ax.set_xlabel('Date', fontsize=16)\n",
        "ax.legend(['Historical', 'SSP2', 'SSP5'])\n",
        "\n",
        "# Calculate standard deviation for each scenario\n",
        "std_dev_hist = np.std(data_annual['GlacierArea'])\n",
        "std_dev_ssp2 = np.std(ssp2_annual['GlacierArea'])\n",
        "std_dev_ssp5 = np.std(ssp5_annual['GlacierArea'])\n",
        "\n",
        "# Fill standard deviation envelop for each scenario\n",
        "ax.fill_between(data_annual.index, data_annual['GlacierArea'] - std_dev_hist, data_annual['GlacierArea'] + std_dev_hist, alpha=0.2)\n",
        "ax.fill_between(ssp2_annual.index, ssp2_annual['GlacierArea'] - std_dev_ssp2, ssp2_annual['GlacierArea'] + std_dev_ssp2, alpha=0.15, color='green')\n",
        "ax.fill_between(ssp5_annual.index, ssp5_annual['GlacierArea'] - std_dev_ssp5, ssp5_annual['GlacierArea'] + std_dev_ssp5, alpha=0.15, color='orange')\n",
        "\n",
        "# Increase font size of tick labels\n",
        "ax.tick_params(axis='both', which='major', labelsize=16)\n",
        "\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}