{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.0 Basic Variable Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import (absolute_import, division, print_function, unicode_literals)\n",
    "\n",
    "from wrf import getvar\n",
    "from netCDF4 import Dataset as nc\n",
    "ncfile = nc(\"/Users/ladwig/Documents/wrf_files/wrfout_d01_2016-02-25_18_00_00\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = getvar(ncfile, \"P\")\n",
    "print(p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.0.1 DataArray attributes:  'dims', 'coords', 'attrs'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"dims: \", p.dims)\n",
    "print(\"coords: \", p.coords) \n",
    "print(\"attrs: \", p.attrs)\n",
    "del p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.0.2 Removing implicit 'squeeze' behavior to preserve single sized dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_nosqueeze = getvar(ncfile, \"P\", timeidx=0, squeeze=False)\n",
    "print (p_nosqueeze)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.0.3 Single element metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print (p_nosqueeze[0,0,100,200])\n",
    "del p_nosqueeze"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.0.4 Disabling/Enabling xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from wrf import disable_xarray, enable_xarray\n",
    "\n",
    "# Disable xarray completely\n",
    "disable_xarray()\n",
    "p_no_meta = getvar(ncfile, \"P\")\n",
    "print(type(p_no_meta))\n",
    "print (p_no_meta)\n",
    "del p_no_meta\n",
    "enable_xarray()\n",
    "\n",
    "# Disable on extraction\n",
    "p_no_meta = getvar(ncfile, \"P\", meta=False)\n",
    "print(\"\\n\")\n",
    "print(type(p_no_meta))\n",
    "print(p_no_meta)\n",
    "del p_no_meta\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.0 Sequences of Input Files "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.0.1 Combining via the 'cat' method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wrflist = [ncfile, ncfile, ncfile]\n",
    "p_cat = getvar(wrflist, \"P\", method=\"cat\")\n",
    "print(p_cat)\n",
    "del p_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.0.2 Combining via the 'join' method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_join = getvar(wrflist, \"P\", method=\"join\")\n",
    "print(p_join)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the Time dimension was replaced with the file dimension, due to the 'squeezing' of the Time dimension.\n",
    "\n",
    "\n",
    "To maintain the Time dimension, set squeeze to False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_join = getvar(wrflist, \"P\", timeidx=0, method=\"join\", squeeze=False)\n",
    "print(p_join)\n",
    "del p_join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.0.3 Dictionary Sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wrf_dict = {\"label1\" : [ncfile, ncfile],\n",
    "           \"label2\" : [ncfile, ncfile]}\n",
    "p_dict = getvar(wrf_dict, \"P\")\n",
    "print(p_dict)\n",
    "del p_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.0.4 Generator Sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def gen_seq():\n",
    "    wrfseq = [ncfile, ncfile, ncfile]\n",
    "    for wrf in wrfseq:\n",
    "        yield wrf\n",
    "        \n",
    "p_gen = getvar(gen_seq(), \"P\", method=\"join\")\n",
    "print(p_gen)\n",
    "del p_gen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.0.5 Custom Iterable Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class FileGen(object):\n",
    "    def __init__(self, ncfile, count=3):\n",
    "        self._total = count\n",
    "        self._i = 0\n",
    "        self.ncfile = [ncfile]*count\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return self\n",
    "    \n",
    "    def next(self):\n",
    "        if self._i >= self._total:\n",
    "            raise StopIteration\n",
    "        else:\n",
    "            val = self.ncfile[self._i]\n",
    "            self._i += 1\n",
    "            return val\n",
    "    \n",
    "    # Python 3\n",
    "    def __next__(self):\n",
    "        return self.next()\n",
    "\n",
    "obj_gen = FileGen(ncfile, 3)\n",
    "\n",
    "p_obj_gen = getvar(gen_seq(), \"P\", method=\"join\", squeeze=False)\n",
    "print(p_obj_gen)\n",
    "\n",
    "del p_obj_gen\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.0 WRF Variable Computational Routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wrf_vars = [\"avo\", \"eth\", \"cape_2d\", \"cape_3d\", \"ctt\", \"dbz\", \"mdbz\", \n",
    "            \"geopt\", \"helicity\", \"lat\", \"lon\", \"omg\", \"p\", \"pressure\", \n",
    "            \"pvo\", \"pw\", \"rh2\", \"rh\", \"slp\", \"ter\", \"td2\", \"td\", \"tc\",\n",
    "            \"theta\", \"tk\", \"tv\", \"twb\", \"updraft_helicity\", \"ua\", \"va\", \n",
    "            \"wa\", \"uvmet10\", \"uvmet\", \"z\", \"ctt\"]\n",
    "\n",
    "vard = {varname: getvar(ncfile, varname, method=\"join\", squeeze=False) for varname in wrf_vars}\n",
    "for varname in wrf_vars:\n",
    "    print(vard[varname])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note all of the NaNs in the above routines which produce missing values (e.g. cape_2d).  xarray always converts all masked_array missing values to NaN in order to work with pandas.  To get back the original missing values in a numpy masked_array, you need to use the 'npvalues' method from wrf.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from wrf import npvalues\n",
    "masked_ndarray = npvalues(vard[\"cape_2d\"])\n",
    "print(masked_ndarray)\n",
    "del masked_ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "for key in vard.keys():\n",
    "    del vard[key]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Interpolation Routines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 Horizontal Level Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 500 MB Heights\n",
    "from wrf import getvar, interplevel\n",
    "\n",
    "z = getvar(ncfile, \"z\")\n",
    "p = getvar(ncfile, \"pressure\")\n",
    "ht_500mb = interplevel(z, p, 500)\n",
    "\n",
    "print(ht_500mb)\n",
    "del ht_500mb, z, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 Vertical Cross Section Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Pressure using pivot and angle\n",
    "from wrf import getvar, vertcross\n",
    "\n",
    "z = getvar(ncfile, \"z\")\n",
    "p = getvar(ncfile, \"pressure\")\n",
    "pivot_point = (z.shape[-2] / 2, z.shape[-1] / 2) \n",
    "angle = 90.0\n",
    "\n",
    "p_vert = vertcross(p, z, pivot_point=pivot_point, angle=angle)\n",
    "print(p_vert)\n",
    "del p_vert\n",
    "\n",
    "# Pressure using start_point and end_point\n",
    "start_point = (z.shape[-2]/2, 0)\n",
    "end_point = (z.shape[-2]/2, -1)\n",
    "\n",
    "p_vert = vertcross(p, z, start_point=start_point, end_point=end_point)\n",
    "print(p_vert)\n",
    "del p_vert, p, z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.3 Interpolate 2D Variable to a Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# T2 using pivot and angle\n",
    "from wrf import interpline, getvar\n",
    "\n",
    "t2 = getvar(ncfile, \"T2\")\n",
    "pivot_point = (t2.shape[-2] / 2, t2.shape[-1] / 2) \n",
    "angle = 90.0\n",
    "\n",
    "t2_line = interpline(t2, pivot_point=pivot_point, angle=angle)\n",
    "print(t2_line)\n",
    "\n",
    "del t2_line\n",
    "\n",
    "# T2 using start_point and end_point\n",
    "start_point = (t2.shape[-2]/2, 0)\n",
    "end_point = (t2.shape[-2]/2, -1)\n",
    "\n",
    "t2_line = interpline(t2, start_point=start_point, end_point=end_point)\n",
    "print(t2_line)\n",
    "\n",
    "del t2_line, t2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.4 Vertical Coordinate Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from wrf import vinterp, getvar\n",
    "\n",
    "# Interpolate tk to theta levels         \n",
    "tk = getvar(ncfile, \"tk\")            \n",
    "interp_levels = [200, 300, 500, 1000]\n",
    "\n",
    "interp_field = vinterp(ncfile, \n",
    "                field=tk, \n",
    "                vert_coord=\"theta\", \n",
    "                interp_levels=interp_levels, \n",
    "                extrapolate=True, \n",
    "                field_type=\"tk\", \n",
    "                log_p=True)\n",
    "\n",
    "print(interp_field)\n",
    "del interp_field\n",
    "\n",
    "# Interpolate tk to theta-e levels         \n",
    "           \n",
    "interp_levels = [200, 300, 500, 1000]\n",
    "\n",
    "interp_field = vinterp(ncfile, \n",
    "                field=tk, \n",
    "                vert_coord=\"eth\", \n",
    "                interp_levels=interp_levels, \n",
    "                extrapolate=True, \n",
    "                field_type=\"tk\", \n",
    "                log_p=True)\n",
    "\n",
    "print(interp_field)\n",
    "del interp_field\n",
    "\n",
    "# Interpolate tk to geopotential height (MSL) levels         \n",
    "           \n",
    "interp_levels = [30, 60, 90]\n",
    "\n",
    "interp_field = vinterp(ncfile, \n",
    "                field=tk, \n",
    "                vert_coord=\"ght_msl\", \n",
    "                interp_levels=interp_levels, \n",
    "                extrapolate=True, \n",
    "                field_type=\"tk\", \n",
    "                log_p=True)\n",
    "\n",
    "print(interp_field)\n",
    "del interp_field\n",
    "\n",
    "# Interpolate tk to geopotential height (MSL) levels         \n",
    "           \n",
    "interp_levels = [30, 60, 90]\n",
    "\n",
    "interp_field = vinterp(ncfile, \n",
    "                field=tk, \n",
    "                vert_coord=\"ght_agl\", \n",
    "                interp_levels=interp_levels, \n",
    "                extrapolate=True, \n",
    "                field_type=\"tk\", \n",
    "                log_p=True)\n",
    "\n",
    "print(interp_field)\n",
    "del interp_field\n",
    "\n",
    "# Interpolate tk to pressure levels\n",
    "interp_levels = [850, 500]\n",
    "            \n",
    "interp_field = vinterp(ncfile, \n",
    "                field=tk, \n",
    "                vert_coord=\"pressure\", \n",
    "                interp_levels=interp_levels, \n",
    "                extrapolate=True, \n",
    "                field_type=\"tk\", \n",
    "                log_p=True)\n",
    "\n",
    "print(interp_field)\n",
    "del interp_field, tk\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Lat/Lon to X/Y Routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from wrf.latlon import get_ll, get_ij # These names are going to change\n",
    "\n",
    "a = get_ll(ncfile, [400,105], [200,205])\n",
    "b = get_ij(ncfile, 45.5, -110.8)\n",
    "\n",
    "# Note: Lists/Dictionaries of files will add a new dimension ('domain') only if the domain is moving\n",
    "c = get_ll([ncfile, ncfile, ncfile], [400,105], [200,205])\n",
    "d = get_ll({\"label1\" : [ncfile, ncfile],\n",
    "           \"label2\" : [ncfile, ncfile]},\n",
    "          [400,105], [200,205])\n",
    "\n",
    "print(a)\n",
    "print(\"\\n\")\n",
    "print(b)\n",
    "print(\"\\n\")\n",
    "print(c)\n",
    "print(\"\\n\")\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 4.0 Plotting with Cartopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# SLP\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.cm import get_cmap\n",
    "import cartopy.crs as crs\n",
    "from cartopy.feature import NaturalEarthFeature\n",
    "\n",
    "from wrf import npvalues, getvar\n",
    "\n",
    "slp = getvar(ncfile, \"slp\")\n",
    "lons = slp.coords[\"XLONG\"]\n",
    "lats = slp.coords[\"XLAT\"]\n",
    "\n",
    "wrf_proj = slp.attrs[\"projection\"]\n",
    "cart_proj = wrf_proj.cartopy()\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = plt.axes([0.1,0.1,0.8,0.8], projection=cart_proj)\n",
    "\n",
    "states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',\n",
    "                             name='admin_1_states_provinces_shp')\n",
    "ax.add_feature(states, linewidth=.5)\n",
    "ax.coastlines('50m', linewidth=0.8)\n",
    "\n",
    "# Can only get this to work if I manually transform the lat/lon points to projected space.\n",
    "xform_coords = cart_proj.transform_points(crs.PlateCarree(), npvalues(lons), npvalues(lats))\n",
    "x = xform_coords[:,:,0]\n",
    "y = xform_coords[:,:,1]\n",
    "\n",
    "plt.contour(x, y, npvalues(slp), 20, cmap=get_cmap(\"gist_ncar\"))\n",
    "plt.colorbar(ax=ax, shrink=.7)\n",
    "\n",
    "ax.set_xlim(wrf_proj.cartopy_xlim())\n",
    "ax.set_ylim(wrf_proj.cartopy_ylim())\n",
    "ax.gridlines()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 500 MB Heights and Winds\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.cm import get_cmap\n",
    "import cartopy.crs as crs\n",
    "from cartopy.feature import NaturalEarthFeature\n",
    "\n",
    "from wrf import getvar, interplevel, npvalues\n",
    "\n",
    "\n",
    "p = getvar(ncfile, \"pressure\")\n",
    "z = getvar(ncfile, \"z\", units=\"dm\")\n",
    "ua = getvar(ncfile, \"ua\", units=\"kts\")\n",
    "va = getvar(ncfile, \"va\", units=\"kts\")\n",
    "\n",
    "ht_500 = interplevel(z, p, 500)\n",
    "u_500 = interplevel(ua, p, 500)\n",
    "v_500 = interplevel(va, p, 500)\n",
    "\n",
    "lons = ht_500.coords[\"XLONG\"]\n",
    "lats = ht_500.coords[\"XLAT\"]\n",
    "\n",
    "wrf_proj = slp.attrs[\"projection\"]\n",
    "cart_proj = wrf_proj.cartopy()\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = plt.axes([0.1,0.1,0.8,0.8], projection=cart_proj)\n",
    "\n",
    "states = NaturalEarthFeature(category='cultural', scale='50m', facecolor='none',\n",
    "                             name='admin_1_states_provinces_shp')\n",
    "ax.add_feature(states, linewidth=0.5)\n",
    "ax.coastlines('50m', linewidth=0.8)\n",
    "\n",
    "# Can only get this to work if I manually transform the lat/lon points to projected space.\n",
    "xform_coords = cart_proj.transform_points(crs.PlateCarree(), npvalues(lons), npvalues(lats))\n",
    "x = xform_coords[:,:,0]\n",
    "y = xform_coords[:,:,1]\n",
    "\n",
    "plt.contour(x, y, npvalues(ht_500), 20, cmap=get_cmap(\"plasma\"))\n",
    "plt.barbs(x[::50,::50], y[::50,::50], npvalues(u_500[::50, ::50]), npvalues(v_500[::50, ::50]))\n",
    "plt.colorbar(ax=ax, shrink=.7)\n",
    "\n",
    "ax.set_xlim(wrf_proj.cartopy_xlim())\n",
    "ax.set_ylim(wrf_proj.cartopy_ylim())\n",
    "ax.gridlines()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Cross-section of pressure using xarray's builtin plotting\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.cm import get_cmap\n",
    "\n",
    "from wrf import getvar, vertcross, npvalues\n",
    "\n",
    "p = getvar(ncfile, \"pressure\")\n",
    "z = getvar(ncfile, \"z\", units=\"dm\")\n",
    "\n",
    "pivot_point = (z.shape[-2] / 2, z.shape[-1] / 2) \n",
    "angle = 90.0\n",
    "\n",
    "p_vert = vertcross(p, z, pivot_point=pivot_point, angle=angle)\n",
    "\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "ax = plt.axes([0.1,0.1,0.8,0.8])\n",
    "\n",
    "p_vert.plot.contour(ax=ax, levels=[0 + 50*n for n in xrange(20)], cmap=get_cmap(\"viridis\"))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Multi-time Moving Domain Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from wrf import getvar\n",
    "from netCDF4 import Dataset as nc\n",
    "\n",
    "dir = \"/Users/ladwig/Documents/wrf_files/wrf_vortex_multi\"\n",
    "ncfilenames = [os.path.join(dir, x) for x in os.listdir(dir) if x.find(\"_d02_\") > 0]\n",
    "ncfiles = [nc(x) for x in ncfilenames]\n",
    "#print (ncfiles[0].variables[\"XLONG\"][0,0,-1], ncfiles[0].variables[\"XLONG\"][-1,0,-1])\n",
    "#print (ncfiles[1].variables[\"XLONG\"][0,0,-1], ncfiles[1].variables[\"XLONG\"][-1,0,-1])\n",
    "#print (ncfiles[-1].variables[\"XLONG\"][0,0,-1], ncfiles[-1].variables[\"XLONG\"][-1,0,-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = getvar(ncfiles, \"P\", timeidx=3, method=\"join\", meta=True, squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print (p)\n",
    "#print (p.attrs[\"projection\"].shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print (p.attrs[\"projection\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ncfiles[2].variables[\"XTIME\"][:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = getvar(ncfiles, \"P\", timeidx=None, method=\"cat\", meta=True, squeeze=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print (p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print (type(p.coords[\"Time\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas\n",
    "print (type(p.coords[\"Time\"].values.astype(datetime.datetime)))\n",
    "print (repr(datetime.datetime.utcfromtimestamp(p.coords[\"Time\"][0].values.astype(int) * 1E-9)))\n",
    "print (pandas.to_datetime(p.coords[\"Time\"].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wrf_vars = [\"avo\", \"eth\", \"cape_2d\", \"cape_3d\", \"ctt\", \"dbz\", \"mdbz\", \n",
    "            \"geopt\", \"helicity\", \"lat\", \"lon\", \"omg\", \"p\", \"pressure\", \n",
    "            \"pvo\", \"pw\", \"rh2\", \"rh\", \"slp\", \"ter\", \"td2\", \"td\", \"tc\",\n",
    "            \"theta\", \"tk\", \"tv\", \"twb\", \"updraft_helicity\", \"ua\", \"va\", \n",
    "            \"wa\", \"uvmet10\", \"uvmet\", \"z\", \"ctt\"]\n",
    "\n",
    "vard = {varname: getvar(ncfiles, varname, method=\"join\", squeeze=False) for varname in wrf_vars}\n",
    "for varname in wrf_vars:\n",
    "    print(vard[varname])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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