fromzidv_ERROR_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why the hell do nans appear when from_zidv() method is used? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import G5NR_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29fec02b1458459b857ceb4e33dcb211",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<h3>ipython_IDV Control Panel</h3>'), HBox(children=(HTML(value='<b>Resources:</b>'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%reload_ext ipython_IDV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grab a G5NR case study using from_zidv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/Users/bem/Jupyter/G5NR_Interactive_notebooks/'\n",
    "zbundlefile = path+'data/ZIDV_cases/skedot_40.1_prec_119.7_lat_28.6_lon_148.0_time_200610131230.zidv'\n",
    "xrdata_3d = xr.from_zidv(zbundlefile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'u' (lat: 176)>\n",
      "array([ 13.246612,  13.465362,  13.590362,  13.699737,  14.121612,  14.652862,\n",
      "        14.840362,  14.840362,  15.090362,  15.559112,  15.996612,  16.309113,\n",
      "        16.434113,  16.465363,  16.559113,  16.652863,  16.809113,  17.059113,\n",
      "        17.465363,  17.934113,  18.340363,        nan,  18.652863,  18.902863,\n",
      "        19.246613,  19.652863,  20.121613,  20.715363,  21.371613,  21.902863,\n",
      "        22.465363,  23.215363,  23.902863,  24.434113,  25.027863,  25.871613,\n",
      "        26.902863,  28.090363,  29.340363,  30.496613,  31.277863,  31.965363,\n",
      "        32.715363,  33.715363,  34.652863,  35.215363,  36.027863,  37.215363,\n",
      "        38.402863,  39.527863,  40.777863,  42.527863,  44.652863,  46.840363,\n",
      "        49.027863,  51.215363,  51.902863,  49.215363,  42.402863,  32.152863,\n",
      "        21.027863,  14.090362,  11.840362,  10.215362,   8.105987,   5.879424,\n",
      "         3.434112,   0.574737,  -2.526826,  -6.737763, -12.644013, -20.034637,\n",
      "       -29.409637, -40.472137, -50.284637, -56.034637, -58.784637, -59.409637,\n",
      "       -58.659637, -56.722137, -53.909637, -50.722137, -47.222137, -43.909637,\n",
      "       -40.972137, -38.597137, -36.972137, -35.847137,        nan, -34.847137,\n",
      "       -33.722137, -32.690887, -31.815887, -30.972137, -30.159637, -29.347137,\n",
      "       -28.534637, -27.847137, -27.409637, -27.097137, -26.534637, -25.753387,\n",
      "       -24.909637, -24.065887, -23.503387, -23.190887, -22.878387, -22.284637,\n",
      "       -21.784637, -21.347137, -20.940887, -20.659637, -20.409637, -20.128387,\n",
      "       -19.753387, -19.284637, -18.878387, -18.503387, -18.190887, -18.128387,\n",
      "       -18.003387, -17.659637, -17.440887, -17.253387, -17.019012, -16.769012,\n",
      "       -16.425262, -16.019012, -15.675263, -15.440888, -15.222138, -15.003388,\n",
      "       -14.784638, -14.597138, -14.440888, -14.253388, -14.034638, -13.847138,\n",
      "       -13.722138, -13.581513, -13.394013, -13.128388, -12.784638, -12.347138,\n",
      "       -11.862763, -11.487763, -11.222138, -10.987763, -10.847138, -10.737763,\n",
      "       -10.581513, -10.315888, -10.019013,  -9.815888,        nan,  -9.612763,\n",
      "        -9.315888,  -9.206513,  -9.347138,  -9.347138,  -9.120576,  -8.854951,\n",
      "        -8.651826,  -8.683076,  -8.722138,  -8.612763,  -8.440888,  -8.300263,\n",
      "        -8.190888,  -8.003388,  -7.808076,  -7.636201,  -7.503388,  -7.456513,\n",
      "        -7.534638,  -7.714326], dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float64 23.25 23.31 23.38 23.44 23.5 23.56 23.62 23.69 ...\n",
      "    lon      float64 148.4\n",
      "    time     datetime64[ns] 2006-10-13T11:30:00\n",
      "    lev      float64 970.0\n",
      "Attributes:\n",
      "    _CoordinateAxes:  time lev lat lon \n",
      "    units:            m s-1\n",
      "    long_name:        eastward_wind \n"
     ]
    }
   ],
   "source": [
    "# Why the $&#(@#* are there NANs here? \n",
    "print( xrdata_3d.u[0,70,:,128] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'u' (lat: 173)>\n",
      "array([ 13.152862,  13.340362,  13.527862,  13.684112,  13.965362,  14.449737,\n",
      "        14.809112,  14.809112,  14.871612,  15.152862,  15.590362,  16.027863,\n",
      "        16.371613,  16.434113,  16.465363,  16.590363,  16.746613,  16.934113,\n",
      "        17.215363,  17.590363,  18.059113,  18.496613,  18.809113,  19.121613,\n",
      "        19.465363,  19.934113,  20.465363,  21.027863,  21.652863,  22.309113,\n",
      "        23.027863,  23.746613,  24.402863,  24.996613,  25.715363,  26.715363,\n",
      "        27.902863,  29.246613,  30.559113,  31.465363,  31.840363,  32.152863,\n",
      "        32.652863,  33.340363,  33.840363,  34.340363,  35.215363,  36.277863,\n",
      "        37.402863,  38.527863,  39.840363,  41.652863,  43.902863,  46.027863,\n",
      "        47.965363,  49.027863,  46.965363,  40.777863,  31.590363,  21.965363,\n",
      "        15.590362,  13.090362,  11.043487,   8.355987,   5.090362,   1.555206,\n",
      "        -2.36081 ,  -6.409638, -11.300263, -17.753387, -25.784637, -35.347137,\n",
      "       -44.597137, -51.284637, -55.597137, -58.097137, -58.909637, -58.472137,\n",
      "       -56.847137, -54.347137, -51.097137, -47.847137, -44.847137, -42.097137,\n",
      "       -39.847137, -38.159637, -36.909637, -35.784637, -34.659637, -33.472137,\n",
      "       -32.472137, -31.628387, -30.722137, -29.847137, -29.065887, -28.253387,\n",
      "       -27.628387, -27.315887, -26.815887, -26.065887, -25.315887, -24.472137,\n",
      "       -23.784637, -23.409637, -23.034637, -22.503387, -21.972137, -21.534637,\n",
      "       -21.159637, -20.847137, -20.628387, -20.409637, -20.097137, -19.659637,\n",
      "       -19.128387, -18.722137, -18.347137, -18.097137, -17.940887, -17.722137,\n",
      "       -17.503387, -17.315887, -17.065887, -16.831512, -16.519012, -16.112762,\n",
      "       -15.753388, -15.503388, -15.284638, -15.065888, -14.831513, -14.628388,\n",
      "       -14.456513, -14.253388, -14.034638, -13.815888, -13.628388, -13.472138,\n",
      "       -13.300263, -13.097138, -12.862763, -12.519013, -12.050263, -11.644013,\n",
      "       -11.362763, -11.159638, -11.019013, -10.909638, -10.737763, -10.487763,\n",
      "       -10.159638,  -9.847138,  -9.612763,  -9.394013,  -9.237763,  -9.300263,\n",
      "        -9.347138,  -9.183076,  -8.995576,  -8.761201,  -8.729951,  -8.839326,\n",
      "        -8.784638,  -8.612763,  -8.417451,  -8.269013,  -8.081513,  -7.870576,\n",
      "        -7.683076,  -7.534638,  -7.448701,  -7.456513,  -7.581513],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "    time     datetime64[ns] 2006-10-13T11:30:00\n",
      "    lev      float64 970.0\n",
      "  * lat      (lat) float64 23.25 23.31 23.38 23.44 23.5 23.56 23.62 23.69 ...\n",
      "    lon      float64 148.5\n",
      "Attributes:\n",
      "    _CoordinateAxes:  time lev lat lon \n",
      "    units:            m s-1\n",
      "    long_name:        eastward_wind \n"
     ]
    }
   ],
   "source": [
    "# open the file that was just created by the unzip part of from_zidv()\n",
    "xrdata_3d_fromunzip = xr.open_dataset('data_0_3D7km30minuteInst')\n",
    "print( xrdata_3d_fromunzip.u[0,70,:,128] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10656ae10>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "#contourf(uvortex, 50); colorbar()\n",
    "contourf(xrdata_3d.u[0,70,:,:], 50); colorbar() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1c24926a58>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "contourf(xrdata_3d_fromunzip.u[0,70,:,:], 50); colorbar() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}