TimeSeriesConcatv1.ipynb

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      "name": "TimeSeriesConcatv1.ipynb",
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      "name": "python"
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ericslevenson/arctic-surface-water/blob/main/TimeSeriesConcatv1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Setup"
      ],
      "metadata": {
        "id": "tNzPQoA2nHGF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        "colab": {
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        },
        "id": "dDlDoTK8lz1s",
        "outputId": "bd939317-ac16-42fb-cd3a-b45fa7cd6d4c"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Google Drive setup\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import csv\n",
        "from pathlib import Path\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import xarray as xr \n",
        "from datetime import date\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "WY8HTHp3l4cz"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "folder = Path('/content/drive/MyDrive/tooliktimeseries')"
      ],
      "metadata": {
        "id": "PI9yfWnGl6Hq"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Create Dataset"
      ],
      "metadata": {
        "id": "7feAs0GynOrU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get a list of dataframes from folder\n",
        "data = [] # list of dataframes\n",
        "for i in os.listdir(folder):\n",
        "  dfpath = Path(folder, i) # define path\n",
        "  df = pd.read_csv(dfpath) # read into csv\n",
        "  # **CREATE NEW COLUMNS WITH X AND Y COORDINATE**\n",
        "  df['x'] = df['centroid'].map(lambda centroid: float(centroid.strip('][').split(',')[0])) # x coordinate\n",
        "  df['y'] = df['centroid'].map(lambda centroid: float(centroid.strip('][').split(',')[1])) # y coordinate\n",
        "  # ** ADD DATE AS COLUMN**\n",
        "  df['date'] = date.fromisoformat(i.strip('Toolik.csv')[1:].replace('_', '-'))\n",
        "  df['date']= pd.to_datetime(df['date']) # convert to proper datetime...could be optimized\n",
        "  data.append(df)"
      ],
      "metadata": {
        "id": "54xBHBgJl79S"
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      "execution_count": 4,
      "outputs": []
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        "dslist =[]\n",
        "for i in data:\n",
        "  dfs = xr.Dataset(\n",
        "    {\n",
        "        'date': (['date'], [i['date'].iloc[0]]),\n",
        "        'id': (['id'], i['id']),\n",
        "        'waterPixels': (['id'], i['waterPixels']),\n",
        "        'clearPixels': (['id'], i['clearPixels']),\n",
        "        'allPixels': (['id'], i['allPixels'])},\n",
        "    coords={\n",
        "        'x': (['id'], i['x']),\n",
        "        'y': (['id'], i['y'])}\n",
        "        )\n",
        "  dslist.append(dfs)"
      ],
      "metadata": {
        "id": "D369uT0sl96S"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = xr.concat(dslist, dim=\"date\")"
      ],
      "metadata": {
        "id": "xODRSvLcl_tS"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "View the dataset"
      ],
      "metadata": {
        "id": "tJJ5VnrinlrN"
      }
    },
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      "cell_type": "code",
      "source": [
        "ds"
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        "outputId": "ebc1220a-d0db-41d3-f41b-739b88ed7f32"
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      "execution_count": 7,
      "outputs": [
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              "    waterPixels  (date, id) float64 0.0 0.0 0.0 0.0 ... 2.047e+03 3.405e+03 58.0\n",
              "    clearPixels  (date, id) float64 87.73 82.53 84.42 ... 5.473e+03 9.72e+03\n",
              "    allPixels    (date, id) int64 94 87 88 91 92 92 ... 0 2827 2936 5523 9829</pre><div class='xr-wrap' hidden><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-985dea9c-cad3-4cdf-a405-430ae512e4ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-985dea9c-cad3-4cdf-a405-430ae512e4ad' 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'>date</span>: 5</li><li><span class='xr-has-index'>id</span>: 17827</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-872b2958-df78-4ccc-8e3e-f7c686003d24' class='xr-section-summary-in' type='checkbox'  checked><label for='section-872b2958-df78-4ccc-8e3e-f7c686003d24' 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'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-07 ... 2020-07-08</div><input id='attrs-14044a52-52db-4549-a2b7-036401e3f450' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14044a52-52db-4549-a2b7-036401e3f450' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67b7459a-9262-4bda-b00b-2c43496121c9' class='xr-var-data-in' type='checkbox'><label for='data-67b7459a-9262-4bda-b00b-2c43496121c9' 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([&#x27;2020-07-07T00:00:00.000000000&#x27;, &#x27;2020-07-05T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-07-02T00:00:00.000000000&#x27;, &#x27;2020-07-03T00:00:00.000000000&#x27;,\n",
              "       &#x27;2020-07-08T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;00000000000000000010&#x27; ... &#x27;0000...</div><input id='attrs-e61f3dbb-780a-4e73-91ca-08e792711f49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e61f3dbb-780a-4e73-91ca-08e792711f49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed57bffc-2732-4d5d-b441-0df562a01604' class='xr-var-data-in' type='checkbox'><label for='data-ed57bffc-2732-4d5d-b441-0df562a01604' 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([&#x27;00000000000000000010&#x27;, &#x27;000000000000000002a7&#x27;, &#x27;00000000000000000ee1&#x27;,\n",
              "       ..., &#x27;000000000000000022c6&#x27;, &#x27;0000000000000000081d&#x27;,\n",
              "       &#x27;0000000000000000116e&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-150.1 -150.2 ... -150.4 -150.9</div><input id='attrs-9c7d1c67-5920-4133-a07f-7c86fa7f8a7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c7d1c67-5920-4133-a07f-7c86fa7f8a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7324131f-f678-46ff-9172-b9d4285999e1' class='xr-var-data-in' type='checkbox'><label for='data-7324131f-f678-46ff-9172-b9d4285999e1' 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([-150.14145364, -150.2223391 , -150.77552826, ..., -148.62221876,\n",
              "       -150.40444354, -150.91501186])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>68.66 69.75 69.53 ... 69.37 69.69</div><input id='attrs-3302cecf-bf0b-47d6-a4b0-5463723d6da1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3302cecf-bf0b-47d6-a4b0-5463723d6da1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7af14030-5338-4df1-b553-fa808e1f7aaf' class='xr-var-data-in' type='checkbox'><label for='data-7af14030-5338-4df1-b553-fa808e1f7aaf' 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([68.65998934, 69.75134202, 69.52852502, ..., 69.48081959,\n",
              "       69.37491042, 69.69133156])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7cd81241-c8f5-4e1d-81b1-2dbcebf9d3f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7cd81241-c8f5-4e1d-81b1-2dbcebf9d3f5' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>waterPixels</span></div><div class='xr-var-dims'>(date, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 3.405e+03 58.0</div><input id='attrs-daff3e38-f275-4b05-a0b2-60453e7f44c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-daff3e38-f275-4b05-a0b2-60453e7f44c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c01a8a5a-0b54-467c-add0-35d1688e4167' class='xr-var-data-in' type='checkbox'><label for='data-c01a8a5a-0b54-467c-add0-35d1688e4167' 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., ..., 2885., 3407.,   50.],\n",
              "       [   0.,    0.,    0., ..., 2782., 3238.,    0.],\n",
              "       [   0.,    0.,    0., ..., 2849., 3374.,  105.],\n",
              "       [   0.,    0.,    0., ...,  863., 3374.,   57.],\n",
              "       [   0.,    0.,    0., ..., 2047., 3405.,   58.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clearPixels</span></div><div class='xr-var-dims'>(date, id)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>87.73 82.53 ... 5.473e+03 9.72e+03</div><input id='attrs-e533595e-56f0-443d-986f-6075c8a9c60b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e533595e-56f0-443d-986f-6075c8a9c60b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9b884e4-c230-4b07-a1ea-ae01e8b34034' class='xr-var-data-in' type='checkbox'><label for='data-e9b884e4-c230-4b07-a1ea-ae01e8b34034' 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([[  87.72941176,   82.52941176,   84.41568627, ..., 4387.98039216,\n",
              "        5472.74509804, 9720.48235294],\n",
              "       [   0.        ,   82.52941176,    0.        , ..., 4387.98039216,\n",
              "        5472.74509804, 9720.48235294],\n",
              "       [  87.72941176,   82.52941176,   84.41568627, ..., 4387.98039216,\n",
              "        5472.74509804, 9720.48235294],\n",
              "       [  87.72941176,   82.52941176,   84.41568627, ..., 4387.98039216,\n",
              "        5472.74509804, 9720.48235294],\n",
              "       [  87.72941176,   82.52941176,   84.41568627, ..., 4387.98039216,\n",
              "        5472.74509804, 9720.48235294]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>allPixels</span></div><div class='xr-var-dims'>(date, id)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>94 87 88 91 ... 2827 2936 5523 9829</div><input id='attrs-f004d86f-cddf-4474-95e9-4ddac539af25' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f004d86f-cddf-4474-95e9-4ddac539af25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d50fcbb-6552-494a-928e-7228aa3f1ad1' class='xr-var-data-in' type='checkbox'><label for='data-7d50fcbb-6552-494a-928e-7228aa3f1ad1' 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([[  94,   87,   88, ..., 4421, 5523, 9829],\n",
              "       [  94,   87,   88, ..., 4421, 5523, 9829],\n",
              "       [  94,   87,   88, ..., 4421, 5523, 9829],\n",
              "       [  94,   87,   88, ..., 1345, 5523, 9829],\n",
              "       [  94,   87,   88, ..., 2936, 5523, 9829]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8edeeb94-ebbf-492f-9834-142b578dd429' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8edeeb94-ebbf-492f-9834-142b578dd429' 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 an example waterody (this one is random)"
      ],
      "metadata": {
        "id": "zU5e9H9FnrN1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "examples = ds.sel(id='0000000000000000081d') \n",
        "# Sort by date\n",
        "examples = examples.sortby('date')\n",
        "# Plot time series\n",
        "examples['waterPixels'].plot(x = 'date')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "CrMYZMSAmCAr",
        "outputId": "b3dc5619-6c58-4249-efe9-33694ff37997"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f95920bcc90>]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}