Desktop/indexing_exercise.ipynb

indexing_exercise.ipynb

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      "source": "import xarray as xr\nimport numpy as np\nfrom xarrayutils.utils import extract_surf",
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      "source": "a = np.repeat(np.repeat(np.array([0 ,21 ,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)\nda[{'x':1,'y':0,'z':3}] = 20\nprint(da)\nprint(db)",
      "execution_count": 2,
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        {
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          "text": "<xarray.DataArray (x: 3, y: 2, z: 4)>\narray([[[ 0, 21, 30, 40],\n        [ 0, 21, 30, 40]],\n\n       [[ 0, 21, 30, 20],\n        [ 0, 21, 30, 40]],\n\n       [[ 0, 21, 30, 40],\n        [ 0, 21, 30, 40]]])\nCoordinates:\n  * x        (x) int64 0 1 2\n  * y        (y) int64 0 1\n  * z        (z) int64 0 1 2 3\n<xarray.DataArray (x: 3, y: 2, z: 4)>\narray([[[200, 300, 400, 500],\n        [200, 300, 400, 500]],\n\n       [[200, 300, 400, 500],\n        [200, 300, 400, 500]],\n\n       [[200, 300, 400, 500],\n        [200, 300, 400, 500]]])\nCoordinates:\n  * x        (x) int64 0 1 2\n  * y        (y) int64 0 1\n  * z        (z) int64 0 1 2 3\n",
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      "source": "# This illustrates a problem with noisy data or values that are found multiple times \n# in the watercolumn. Since one profile has the exact value of 20 at the bottom, 500 is \n# extracted from db. I will have to work on that at a later time. For 'well-behaved' data \n# (e.g. long averages this should work)\nsurf = extract_surf(da, db, 20, 'z')\nsurf",
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          "execution_count": 3,
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            "text/plain": "<xarray.DataArray (x: 3, y: 2)>\narray([[ 300.,  300.],\n       [ 500.,  300.],\n       [ 300.,  300.]])\nCoordinates:\n  * x        (x) int64 0 1 2\n  * y        (y) int64 0 1"
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