Desktop/indexing_exercise.ipynb

indexing_exercise.ipynb

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      "source": "import xarray as xr\nimport numpy as np",
      "execution_count": 2,
      "outputs": []
    },
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      "metadata": {
        "trusted": true
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      "cell_type": "code",
      "source": "a = np.repeat(np.repeat(np.array([0 ,20 ,30, 40])[np.newaxis,np.newaxis,:],2, axis=1),3, axis=0)\nb = np.repeat(np.repeat(np.array([200 ,300 ,400 , 500])[np.newaxis,np.newaxis,:],2, axis=1),3, axis=0)\n\ncoords = {'x':range(3), 'y':range(2), 'z':range(4)}\ndims =  ['x', 'y', 'z']\nda = xr.DataArray(a,coords=coords,dims=dims)\ndb = xr.DataArray(b,coords=coords,dims=dims)",
      "execution_count": 3,
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    },
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      "cell_type": "code",
      "source": "# find the dimensional ('z') index at every position\nz_ind = abs(da-20).argmin('z')\nprint(z_ind)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": "<xarray.DataArray (x: 3, y: 2)>\narray([[1, 1],\n       [1, 1],\n       [1, 1]])\nCoordinates:\n  * x        (x) int64 0 1 2\n  * y        (y) int64 0 1\n",
          "name": "stdout"
        }
      ]
    },
    {
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      "cell_type": "code",
      "source": "# Select the depth slice\ndb.isel_points(z_ind)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "<xarray.DataArray (y: 2, z: 4, x: 3)>\narray([[[200, 200, 200],\n        [300, 300, 300],\n        [400, 400, 400],\n        [500, 500, 500]],\n\n       [[200, 200, 200],\n        [300, 300, 300],\n        [400, 400, 400],\n        [500, 500, 500]]])\nCoordinates:\n  * x        (x) int64 0 1 2\n  * y        (y) int64 0 1\n  * z        (z) int64 0 1 2 3\n    None     (x, y) int64 1 1 1 1 1 1"
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