xarray loses hours of the day

Chasing resample output starts at the beginning of the day when input data data did not.ipynb

{
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    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "import datetime as dt\nimport xarray as xr\n",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
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      "cell_type": "code",
      "source": "infile = \"E:\\\\data\\\\Matanzas\\\\WellTest2017\\\\Signature\\\\python2\\\\1108sigall.nc\"\n",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
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      "cell_type": "code",
      "source": "# first lets see what kind of time we have for the main axis\n# if xarray - MUST set decode_time=False to see the coordinate time variable units\nds = xr.open_dataset(infile, decode_times=False)\ntimeunits = ds['time'].attrs['units']\nprint(ds['time'][0:5])\nds.close()\nvars2omit = {'EPIC_time','EPIC_time2'}\nds = xr.open_dataset(infile, drop_variables=vars2omit)\nprint(ds['time'][0:5])\nds.close()",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": "<xarray.DataArray 'time' (time: 5)>\narray([ 0.    ,  0.2503,  0.5003,  0.7503,  1.0001])\nCoordinates:\n  * time     (time) float64 0.0 0.2503 0.5003 0.7503 1.0\nAttributes:\n    units:    seconds since 2017-9-25 15:0:0.063300 0:00\n<xarray.DataArray 'time' (time: 5)>\narray(['2017-09-25T00:00:00.063300000', '2017-09-25T00:00:00.313600000',\n       '2017-09-25T00:00:00.563599999', '2017-09-25T00:00:00.813600000',\n       '2017-09-25T00:00:01.063400000'], dtype='datetime64[ns]')\nCoordinates:\n  * time     (time) datetime64[ns] 2017-09-25T00:00:00.063300 ...\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "vars2omit = {'EPIC_time','EPIC_time2'}\nds = xr.open_dataset(infile,decode_times=True,drop_variables=vars2omit,mask_and_scale=False)\n\nds_1h = ds.resample('H','time',how='mean',keep_attrs=True)\n",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(ds_1h['time'])",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": "<xarray.DataArray 'time' (time: 75)>\narray(['2017-09-25T00:00:00.000000000', '2017-09-25T01:00:00.000000000',\n       '2017-09-25T02:00:00.000000000', '2017-09-25T03:00:00.000000000',\n       '2017-09-25T04:00:00.000000000', '2017-09-25T05:00:00.000000000',\n       '2017-09-25T06:00:00.000000000', '2017-09-25T07:00:00.000000000',\n       '2017-09-25T08:00:00.000000000', '2017-09-25T09:00:00.000000000',\n       '2017-09-25T10:00:00.000000000', '2017-09-25T11:00:00.000000000',\n       '2017-09-25T12:00:00.000000000', '2017-09-25T13:00:00.000000000',\n       '2017-09-25T14:00:00.000000000', '2017-09-25T15:00:00.000000000',\n       '2017-09-25T16:00:00.000000000', '2017-09-25T17:00:00.000000000',\n       '2017-09-25T18:00:00.000000000', '2017-09-25T19:00:00.000000000',\n       '2017-09-25T20:00:00.000000000', '2017-09-25T21:00:00.000000000',\n       '2017-09-25T22:00:00.000000000', '2017-09-25T23:00:00.000000000',\n       '2017-09-26T00:00:00.000000000', '2017-09-26T01:00:00.000000000',\n       '2017-09-26T02:00:00.000000000', '2017-09-26T03:00:00.000000000',\n       '2017-09-26T04:00:00.000000000', '2017-09-26T05:00:00.000000000',\n       '2017-09-26T06:00:00.000000000', '2017-09-26T07:00:00.000000000',\n       '2017-09-26T08:00:00.000000000', '2017-09-26T09:00:00.000000000',\n       '2017-09-26T10:00:00.000000000', '2017-09-26T11:00:00.000000000',\n       '2017-09-26T12:00:00.000000000', '2017-09-26T13:00:00.000000000',\n       '2017-09-26T14:00:00.000000000', '2017-09-26T15:00:00.000000000',\n       '2017-09-26T16:00:00.000000000', '2017-09-26T17:00:00.000000000',\n       '2017-09-26T18:00:00.000000000', '2017-09-26T19:00:00.000000000',\n       '2017-09-26T20:00:00.000000000', '2017-09-26T21:00:00.000000000',\n       '2017-09-26T22:00:00.000000000', '2017-09-26T23:00:00.000000000',\n       '2017-09-27T00:00:00.000000000', '2017-09-27T01:00:00.000000000',\n       '2017-09-27T02:00:00.000000000', '2017-09-27T03:00:00.000000000',\n       '2017-09-27T04:00:00.000000000', '2017-09-27T05:00:00.000000000',\n       '2017-09-27T06:00:00.000000000', '2017-09-27T07:00:00.000000000',\n       '2017-09-27T08:00:00.000000000', '2017-09-27T09:00:00.000000000',\n       '2017-09-27T10:00:00.000000000', '2017-09-27T11:00:00.000000000',\n       '2017-09-27T12:00:00.000000000', '2017-09-27T13:00:00.000000000',\n       '2017-09-27T14:00:00.000000000', '2017-09-27T15:00:00.000000000',\n       '2017-09-27T16:00:00.000000000', '2017-09-27T17:00:00.000000000',\n       '2017-09-27T18:00:00.000000000', '2017-09-27T19:00:00.000000000',\n       '2017-09-27T20:00:00.000000000', '2017-09-27T21:00:00.000000000',\n       '2017-09-27T22:00:00.000000000', '2017-09-27T23:00:00.000000000',\n       '2017-09-28T00:00:00.000000000', '2017-09-28T01:00:00.000000000',\n       '2017-09-28T02:00:00.000000000'], dtype='datetime64[ns]')\nCoordinates:\n  * time     (time) datetime64[ns] 2017-09-25 2017-09-25T01:00:00 ...\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "ds.close()",
      "execution_count": 6,
      "outputs": []
    },
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        "description": "xarray loses hours of the day",
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