grid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
      "## library for working with weather radar data. Py-ART is partly\n",
      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
      "## Science user facility.\n",
      "##\n",
      "## If you use this software to prepare a publication, please cite:\n",
      "##\n",
      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.animation import FuncAnimation\n",
    "from IPython.display import HTML, Image, display\n",
    "import tempfile\n",
    "import os\n",
    "import shutil\n",
    "from copy import deepcopy\n",
    "import cartopy.crs as ccrs\n",
    "import shapely\n",
    "import pyart\n",
    "from tint.data_utils import get_nexrad_keys, read_nexrad_key\n",
    "from tint import Cell_tracks, animate\n",
    "from tint.visualization import embed_mp4_as_gif\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "from lmatools.io.LMA_h5_file import LMAh5File\n",
    "from lmatools.coordinateSystems import RadarCoordinateSystem, GeographicSystem, TangentPlaneCartesianSystem, MapProjection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### - dualpol\n",
    "\n",
    "def radar_coords_to_cart(rng, az, ele, debug=False):\n",
    "    \"\"\"\n",
    "    TJL - taken from old Py-ART version\n",
    "    Calculate Cartesian coordinate from radar coordinates\n",
    "    Parameters\n",
    "    ----------\n",
    "    rng : array\n",
    "        Distances to the center of the radar gates (bins) in kilometers.\n",
    "    az : array\n",
    "        Azimuth angle of the radar in degrees.\n",
    "    ele : array\n",
    "        Elevation angle of the radar in degrees.\n",
    "    Returns\n",
    "    -------\n",
    "    x, y, z : array\n",
    "        Cartesian coordinates in meters from the radar.\n",
    "    Notes\n",
    "    -----\n",
    "    The calculation for Cartesian coordinate is adapted from equations\n",
    "    2.28(b) and 2.28(c) of Doviak and Zrnic [1]_ assuming a\n",
    "    standard atmosphere (4/3 Earth's radius model).\n",
    "    .. math::\n",
    "        z = \\\\sqrt{r^2+R^2+r*R*sin(\\\\theta_e)} - R\n",
    "        s = R * arcsin(\\\\frac{r*cos(\\\\theta_e)}{R+z})\n",
    "        x = s * sin(\\\\theta_a)\n",
    "        y = s * cos(\\\\theta_a)\n",
    "    Where r is the distance from the radar to the center of the gate,\n",
    "    :math:`\\\\theta_a` is the azimuth angle, :math:`\\\\theta_e` is the\n",
    "    elevation angle, s is the arc length, and R is the effective radius\n",
    "    of the earth, taken to be 4/3 the mean radius of earth (6371 km).\n",
    "    References\n",
    "    ----------\n",
    "    .. [1] Doviak and Zrnic, Doppler Radar and Weather Observations, Second\n",
    "        Edition, 1993, p. 21.\n",
    "    \"\"\"\n",
    "    theta_e = ele * np.pi / 180.0       # elevation angle in radians.\n",
    "    theta_a = az * np.pi / 180.0        # azimuth angle in radians.\n",
    "    R = 6371.0 * 1000.0 * 4.0 / 3.0     # effective radius of earth in meters.\n",
    "    r = rng * 1000.0                    # distances to gates in meters.\n",
    "\n",
    "    z = (r ** 2 + R ** 2 + 2.0 * r * R * np.sin(theta_e)) ** 0.5 - R\n",
    "    s = R * np.arcsin(r * np.cos(theta_e) / (R + z))  # arc length in m.\n",
    "    x = s * np.sin(theta_a)\n",
    "    y = s * np.cos(theta_a)\n",
    "    return x, y, z\n",
    "\n",
    "####\n",
    "\n",
    "\"\"\"\n",
    "This is the main source code for DualPol.\n",
    "Key classes:\n",
    "- DualPolRetrieval\n",
    "- HidColors\n",
    "- _FhcVars\n",
    "Key functions:\n",
    "- get_xyz_from_radar\n",
    "- check_kwargs\n",
    "\"\"\"\n",
    "\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import warnings\n",
    "import time as tt\n",
    "import pyart\n",
    "import matplotlib.colors as colors\n",
    "# from .common import radar_coords_to_cart\n",
    "# from skewt import SkewT\n",
    "from csu_radartools import (csu_fhc, csu_liquid_ice_mass, csu_blended_rain,\n",
    "                            csu_dsd, csu_kdp, csu_misc)\n",
    "\n",
    "VERSION = '1.0.2'\n",
    "RNG_MULT = 1000.0\n",
    "DEFAULT_WEIGHTS = csu_fhc.DEFAULT_WEIGHTS\n",
    "BAD = -32768\n",
    "DEFAULT_SDP = 12.0\n",
    "DEFAULT_DZ_RANGE = csu_misc.DEFAULT_DZ_RANGE\n",
    "DEFAULT_DZ_THRESH = 38.0\n",
    "DEFAULT_KD_THRESH = 0.3\n",
    "DEFAULT_DR_THRESH = csu_misc.DEFAULT_DR_THRESH\n",
    "\n",
    "#####################################\n",
    "\n",
    "DEFAULT_KW = {'dz': 'DZ', 'dr': 'DR', 'dp': None, 'rh': 'RH',\n",
    "              'kd': None, 'ld': None, 'sounding': None,\n",
    "              'verbose': False, 'thresh_sdp': DEFAULT_SDP, 'fhc_T_factor': 1,\n",
    "              'fhc_weights': DEFAULT_WEIGHTS, 'name_fhc': 'FH', 'band': 'S',\n",
    "              'fhc_method': 'hybrid', 'kdp_method': 'CSU', 'bad': BAD,\n",
    "              'use_temp': True, 'ice_flag': False, 'dsd_flag': True,\n",
    "              'fhc_flag': True, 'rain_method': 'hidro', 'precip_flag': True,\n",
    "              'liquid_ice_flag': True, 'winter': False, 'gs': 150.0,\n",
    "              'qc_flag': False, 'kdp_window': 3.0, 'std_gate': 11,\n",
    "              'dz_range': DEFAULT_DZ_RANGE, 'name_sdp': 'SDP_CSU',\n",
    "              'thresh_dr': DEFAULT_DR_THRESH, 'speckle': 4,\n",
    "              'thresh_dz': DEFAULT_DZ_THRESH, 'thresh_kd': DEFAULT_KD_THRESH,\n",
    "              'r_kdp_a': 40.5, 'r_kdp_b': 0.85, 'r_z_a': 300.0,\n",
    "              'r_z_b': 1.4, 'r_z_zdr_a': 6.7e-3, 'r_z_zdr_b': 0.927,\n",
    "              'r_z_zdr_c': -0.343, 'r_kdp_zdr_a': 90.8, 'r_kdp_zdr_b': 0.93,\n",
    "              'r_kdp_zdr_c': -0.169, 'thresh_zdr': 0.5}\n",
    "\n",
    "kwargs = np.copy(DEFAULT_KW)\n",
    "\n",
    "#####################################\n",
    "\n",
    "\n",
    "class DualPolRetrieval(object):\n",
    "\n",
    "    \"\"\"\n",
    "    Class that wraps all the dual-polarization retrievals powered by\n",
    "    CSU_RadarTools.\n",
    "    Brief overview of DualPolRetrieval structure\n",
    "    --------------------------------------------\n",
    "    Main attributes of interest is radar, which is the original Py-ART radar\n",
    "    object provided to DualPolRetrieval. DualPolRetrieval.radar contains\n",
    "    new fields based on what the user wanted DualPolRetrieval to do.\n",
    "    New fields that can be in DualPolRetrieval.radar.fields:\n",
    "    'FH' (or whatever user provided in name_fhc kwarg) = HID\n",
    "    'FI' = Ice Fraction\n",
    "    'ZDP' = Difference Reflectivity\n",
    "    'KDP_CSU' = KDP as calculated by CSU_RadarTools\n",
    "    'FDP_CSU' = Filtered differential phase\n",
    "    'SDP_CSU' = Standard deviation of differential phase\n",
    "    'MI' = Mass of ice\n",
    "    'MW' = Mass of liquid water\n",
    "    'rain' = Rainfall rate\n",
    "    'method' = Rainfall method used\n",
    "    'D0' = Median Volume Diameter\n",
    "    'NW' = Normalized Intercept Parameter\n",
    "    'MU' = Mu in Gamma DSD model\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, radar, **kwargs):\n",
    "        \"\"\"\n",
    "        Arguments\n",
    "        ---------\n",
    "        radar = Py-ART radar object\n",
    "        Keywords\n",
    "        --------\n",
    "        dz = String name of reflectivity field\n",
    "        dr = String name of differential reflectivity field\n",
    "        kd = String name of specific differential phase field, if not provided\n",
    "             it will be calculated using csu_radartools\n",
    "        rh = String name of correlation coefficient field\n",
    "        ld = String name of linear depolarization ratio field\n",
    "        dp = String name of differential phase field\n",
    "        sounding = Name of UWYO sounding file or 2xN array where:\n",
    "                   sounding['z'] = Heights (m MSL), must be montonic\n",
    "                   sounding['T'] = Temperatures (C)\n",
    "        winter = Flag to note whether to use wintertime retrievals\n",
    "        band = Radar frequency band letter ('C' or 'S' supported)\n",
    "        verbose = Set to True to get text feedback\n",
    "        thresh_sdp = Threshold on standard deviation of differential phase to\n",
    "                     use on KDP calculation (if done)\n",
    "        fhc_T_factor = Extra weighting on T to be used in HID calculations\n",
    "        fhc_weights = Weights for variables in HID. Dictionary form, like so:\n",
    "            {'DZ': 1.5, 'DR': 0.8, 'KD': 1.0, 'RH': 0.8, 'LD': 0.5, 'T': 0.4}\n",
    "        name_fhc = Name to give HID field once calculated\n",
    "        fhc_method = 'hybrid' or 'linear' methods; hybrid preferred\n",
    "        kdp_method = 'CSU' currently supported\n",
    "        bad = Value to provide bad data\n",
    "        use_temp = Set to False to not consider T in HID calculations\n",
    "        rain_method = Method to use to estimate rainfall. If not 'hidro', then\n",
    "                      will use blended rainfall algorithm based on ZDP & ice\n",
    "                      fraction. If 'hidro', then uses CSU_HIDRO approach.\n",
    "        ice_flag = Set to True to return ice fraction and ZDP from CSU blended\n",
    "                   rainfall algorithm and store them as radar object fields.\n",
    "                   Only used if rain_method is not 'hidro'.\n",
    "        dsd_flag = Set to False to not calculate DSD parameters\n",
    "        fhc_flag = Set to False to not calculate HID\n",
    "        precip_flag = Set to False to not calculate rainfall\n",
    "        liquid_ice_flag = Set to False to not calculate liquid/ice mass\n",
    "        gs = Gate spacing of the radar (meters). Only used if KDP is calculated\n",
    "             using CSU_RadarTools.\n",
    "        kdp_window = Window length (in km) used as basis for PHIDP filtering.\n",
    "                     Only used if KDP is calculated using CSU_RadarTools.\n",
    "        name_sdp = Name of field holding (or that will hold) the SDP data.\n",
    "        qc_flag = Set to true to filter the data for insects, high SDP\n",
    "                  (set by thresh_sdp keyword), and speckles. Will permanently\n",
    "                  change the reflectivity field's mask, and by extension affect\n",
    "                  all retrieved fields' masks.\n",
    "        dz_range = Used by the insect filter. A list of 2-element tuples.\n",
    "                   Within each DZ range represented by a tuple, the ZDR\n",
    "                   threshold in dr_thresh (see below) will be applied.\n",
    "        thresh_dr = List of thresholds on ZDR to be applied within a given\n",
    "                    element of dz_range (see above).\n",
    "        speckle = Number of contiguous gates or less for an element to be\n",
    "                  considered a speckle.\n",
    "        std_gate = Number of gates for standard deviation of phase calculation.\n",
    "                   Must be odd or csu_kdp will set it to the default value.\n",
    "        thresh_dz = Value of Z that determines logical breakpoints in blended\n",
    "                    rainfall calculations.\n",
    "        thresh_kd = Value of Kdp that determines logical breakpoints in blended\n",
    "                    rainfall calculations.\n",
    "        thresh_zdr = Value of Zdr that determines logical breaks in blended\n",
    "                     rainfall calculations.\n",
    "        r_z_a, r_z_b => Z = a * R**b\n",
    "        r_kdp_a, r_kdp_b => R = a * kdp**b\n",
    "        r_kdp_zdr_a, r_kdp_zdr_b, r_kdp_zdr_c => R = a*kdp**b*10.0**(c*zdr)\n",
    "        r_z_zdr_a,r_z_zdr_b,r_z_zdr_c => R = a*linearize(z)**b*10.0**(c*zdr)\n",
    "        \"\"\"\n",
    "        # Set radar fields\n",
    "        kwargs = check_kwargs(kwargs, DEFAULT_KW)\n",
    "        self.verbose = kwargs['verbose']\n",
    "        flag = self.do_radar_check(radar)\n",
    "        if not flag:\n",
    "            return\n",
    "        self._populate_attributes(kwargs)\n",
    "        flag = self.do_name_check()\n",
    "        if not flag:\n",
    "            return\n",
    "\n",
    "        # Get sounding info\n",
    "        self.T_flag = kwargs['use_temp']\n",
    "        self.T_factor = kwargs['fhc_T_factor']\n",
    "        self.get_sounding(kwargs['sounding'])\n",
    "        self.winter_flag = kwargs['winter']\n",
    "\n",
    "        # Do QC\n",
    "        if kwargs['qc_flag']:\n",
    "            if self.verbose:\n",
    "                print('Performing QC')\n",
    "            self.dz_range = kwargs['dz_range']\n",
    "            self.dr_thresh = kwargs['thresh_dr']\n",
    "            self.speckle = kwargs['speckle']\n",
    "            self.do_qc()\n",
    "\n",
    "        # Do FHC\n",
    "        self.band = kwargs['band']\n",
    "        self.name_fhc = kwargs['name_fhc']\n",
    "        if kwargs['fhc_flag']:\n",
    "            if self.verbose:\n",
    "                print('Performing FHC')\n",
    "            self.fhc_weights = kwargs['fhc_weights']\n",
    "            self.fhc_method = kwargs['fhc_method']\n",
    "            self.get_hid()\n",
    "\n",
    "        # Other precip retrievals\n",
    "        if kwargs['precip_flag']:\n",
    "            if self.verbose:\n",
    "                print('Performing precip rate calculations')\n",
    "            for key in ['r_kdp_a', 'r_kdp_b', 'r_z_a', 'r_z_b',\n",
    "                        'r_z_zdr_a', 'r_z_zdr_b', 'r_z_zdr_c',\n",
    "                        'r_kdp_zdr_a', 'r_kdp_zdr_b', 'r_kdp_zdr_c']:\n",
    "                setattr(self, key, kwargs[key])\n",
    "            self.get_precip_rate(\n",
    "                ice_flag=kwargs['ice_flag'], rain_method=kwargs['rain_method'],\n",
    "                thresh_dz=kwargs['thresh_dz'], thresh_kd=kwargs['thresh_kd'],\n",
    "                thresh_zdr=kwargs['thresh_zdr'])\n",
    "        if kwargs['dsd_flag']:\n",
    "            if self.verbose:\n",
    "                print('Performing DSD calculations')\n",
    "            self.get_dsd()\n",
    "        if kwargs['liquid_ice_flag']:\n",
    "            if self.verbose:\n",
    "                print('Performing mass calculations')\n",
    "            self.get_liquid_and_frozen_mass()\n",
    "\n",
    "    def do_radar_check(self, radar):\n",
    "        \"\"\"\n",
    "        Checks to see if radar variable is a file or a Py-ART radar object.\n",
    "        \"\"\"\n",
    "        if isinstance(radar, str):\n",
    "            try:\n",
    "                self.radar = pyart.io.read(radar)\n",
    "            except:\n",
    "                warnings.warn('Bad file name provided, try again')\n",
    "                return False\n",
    "        else:\n",
    "            self.radar = radar\n",
    "        # Checking for actual radar object\n",
    "        try:\n",
    "            junk = self.radar.latitude['data']\n",
    "        except:\n",
    "            warnings.warn('Need a real Py-ART radar object, try again')\n",
    "            return False\n",
    "        return True  # Actual radar object provided by user\n",
    "\n",
    "    def do_name_check(self):\n",
    "        \"\"\"\n",
    "        Simple name checking to ensure the file actually contains the\n",
    "        right polarimetric variables.\n",
    "        \"\"\"\n",
    "        wstr = ' field not in radar object, check variable names'\n",
    "        if self.name_dz in self.radar.fields:\n",
    "            if self.name_dr in self.radar.fields:\n",
    "                if self.name_rh in self.radar.fields:\n",
    "                    if self.name_ld is not None:\n",
    "                        if self.name_ld not in self.radar.fields:\n",
    "                            if self.verbose:\n",
    "                                print('Not finding LDR field, not using')\n",
    "                            self.name_ld = None\n",
    "                    else:\n",
    "                        if self.verbose:\n",
    "                            print('Not provided LDR field, not using')\n",
    "                    if self.name_kd is not None:\n",
    "                        if self.name_kd not in self.radar.fields:\n",
    "                            if self.verbose:\n",
    "                                print('Not finding KDP field, calculating')\n",
    "                            kdp_flag = self.calculate_kdp()\n",
    "                        else:\n",
    "                            kdp_flag = True\n",
    "                    else:\n",
    "                        if self.verbose:\n",
    "                            print('Not provided KDP field, calculating')\n",
    "                        kdp_flag = self.calculate_kdp()\n",
    "                    return kdp_flag  # All required variables present?\n",
    "                else:\n",
    "                    warnings.warn(self.name_rh+wstr)\n",
    "                    return False\n",
    "            else:\n",
    "                warnings.warn(self.name_dr+wstr)\n",
    "                return False\n",
    "        else:\n",
    "            warnings.warn(self.name_dz+wstr)\n",
    "            return False\n",
    "\n",
    "    def calculate_kdp(self):\n",
    "        \"\"\"\n",
    "        Wrapper method for calculating KDP.\n",
    "        \"\"\"\n",
    "        wstr = 'Missing differential phase and KDP fields, failing ...'\n",
    "        if self.name_dp is not None:\n",
    "            if self.name_dp in self.radar.fields:\n",
    "                if self.kdp_method.upper() == 'CSU':\n",
    "                    kdp = self.call_csu_kdp()\n",
    "                self.name_kd = 'KDP_' + self.kdp_method\n",
    "                self.add_field_to_radar_object(\n",
    "                    kdp, standard_name='KDP',\n",
    "                    field_name=self.name_kd, units='deg km-1',\n",
    "                    long_name='Specific Differential Phase')\n",
    "            else:\n",
    "                warnings.warn(wstr)\n",
    "                return False\n",
    "        else:\n",
    "            warnings.warn(wstr)\n",
    "            return False\n",
    "        return True\n",
    "\n",
    "    def call_csu_kdp(self):\n",
    "        \"\"\"\n",
    "        Calls the csu_radartools.csu_kdp module to obtain KDP, FDP, and SDP.\n",
    "        \"\"\"\n",
    "        if self.verbose:\n",
    "            print('Calculating KDP via CSU method')\n",
    "        dp = self.extract_unmasked_data(self.name_dp)\n",
    "        dz = self.extract_unmasked_data(self.name_dz)\n",
    "        kdp = np.zeros_like(dp) + self.bad\n",
    "        fdp = kdp * 1.0\n",
    "        sdp = kdp * 1.0\n",
    "        rng = self.radar.range['data'] / RNG_MULT\n",
    "        az = self.radar.azimuth['data']\n",
    "        rng2d, az2d = np.meshgrid(rng, az)\n",
    "#        print('debug calling csu_kdp')\n",
    "        bt = tt.time()\n",
    "        kdp, fdp, sdp = \\\n",
    "            csu_kdp.calc_kdp_bringi(dp=dp, dz=dz, rng=rng2d, gs=self.gs,\n",
    "                                    thsd=self.thresh_sdp, bad=self.bad,\n",
    "                                    window=self.kdp_window,\n",
    "                                    std_gate=self.std_gate)\n",
    "        print(tt.time()-bt, 'seconds to run csu_kdp')\n",
    "#        print('debug dualpol kdp', np.shape(kdp))\n",
    "        self.name_fdp = 'FDP_' + self.kdp_method\n",
    "        self.add_field_to_radar_object(\n",
    "            fdp, units='deg', standard_name='Filtered Differential Phase',\n",
    "            field_name=self.name_fdp,\n",
    "            long_name='Filtered Differential Phase')\n",
    "        self.add_field_to_radar_object(\n",
    "            sdp, units='deg', standard_name='Std Dev Differential Phase',\n",
    "            field_name=self.name_sdp,\n",
    "            long_name='Standard Deviation of Differential Phase')\n",
    "        return kdp\n",
    "\n",
    "    def extract_unmasked_data(self, field, bad=None):\n",
    "        \"\"\"Extracts an unmasked field from the radar object.\"\"\"\n",
    "        var = self.radar.fields[field]['data']\n",
    "        if hasattr(var, 'mask'):\n",
    "            if bad is None:\n",
    "                bad = self.bad\n",
    "            var = var.filled(fill_value=bad)\n",
    "        return var\n",
    "\n",
    "    def get_sounding(self, sounding):\n",
    "        \"\"\"\n",
    "        Ingests the sounding (either a skewt - i.e., UWYO - formatted file\n",
    "        or a properly formatted dict).\n",
    "        \"\"\"\n",
    "        if sounding is None:\n",
    "            print('No sounding provided')\n",
    "            self.T_flag = False\n",
    "        else:\n",
    "            if isinstance(sounding, str):\n",
    "                # Removing exception handling so if SkewT is messing up\n",
    "                # the end user will understand better how to fix it.\n",
    "                if True:\n",
    "#                     snd = SkewT.Sounding(sounding)\n",
    "                    # Test for new version of skewt package\n",
    "                    if hasattr(snd, 'soundingdata'):\n",
    "                        self.snd_T = snd.soundingdata['temp']\n",
    "                        self.snd_z = snd.soundingdata['hght']\n",
    "                    else:\n",
    "                        self.snd_T = snd.data['temp']\n",
    "                        self.snd_z = snd.data['hght']\n",
    "                else:\n",
    "                    print('Sounding read fail')\n",
    "                    self.T_flag = False\n",
    "            else:\n",
    "                try:\n",
    "                    self.snd_T = sounding['T']\n",
    "                    self.snd_z = sounding['z']\n",
    "                except:\n",
    "                    print('Sounding in wrong data format')\n",
    "                    self.T_flag = False\n",
    "        self.interpolate_sounding_to_radar()\n",
    "\n",
    "    def do_qc(self):\n",
    "        if self.name_sdp not in self.radar.fields:\n",
    "            print('Cannot do QC, no SDP field identified')\n",
    "            return\n",
    "        if self.verbose:\n",
    "            print('Masking insects and high SDP,', end=' ')\n",
    "        dz = self.extract_unmasked_data(self.name_dz)\n",
    "        dr = self.extract_unmasked_data(self.name_dr)\n",
    "        sdp = self.extract_unmasked_data(self.name_sdp)\n",
    "        dp = self.extract_unmasked_data(self.name_dp)\n",
    "        dz_mask = dz == self.bad\n",
    "        insect_mask = csu_misc.insect_filter(\n",
    "            dz, dr, dz_range=self.dz_range, dr_thresh=self.dr_thresh,\n",
    "            bad=self.bad, mask=dz_mask)\n",
    "        sdp_mask = csu_misc.differential_phase_filter(\n",
    "            sdp, thresh_sdp=self.thresh_sdp)\n",
    "        dp_cond = dp != self.bad\n",
    "        sdp_mask = np.logical_and(sdp_mask, dp_cond)\n",
    "        new_mask = np.logical_or(insect_mask, sdp_mask)\n",
    "        dz_qc = 1.0 * dz\n",
    "        dz_qc[new_mask] = self.bad\n",
    "        if self.verbose:\n",
    "            print('Despeckling')\n",
    "        mask_ds = csu_misc.despeckle(dz_qc, bad=self.bad, ngates=self.speckle)\n",
    "        final_mask = np.logical_or(new_mask, mask_ds)\n",
    "        setattr(self.radar.fields[self.name_dz]['data'], 'mask', final_mask)\n",
    "\n",
    "    def get_hid(self):\n",
    "        \"\"\"Calculate hydrometeror ID, add to radar object.\"\"\"\n",
    "        _fhv = _FhcVars(self)\n",
    "        if not self.winter_flag:\n",
    "            bt = tt.time()\n",
    "            # Cut down on processing missing/bad data\n",
    "            good = _fhv.dz != self.bad\n",
    "            if self.name_ld is not None:\n",
    "                _fhv.ld = _fhv.ld[good]\n",
    "            if _fhv.tt is not None:\n",
    "                _fhv.tt = _fhv.tt[good]\n",
    "            # Only run FHC if the scope is not completely blank\n",
    "            if np.size(_fhv.dz[good]) > 0:\n",
    "                scores = csu_fhc.csu_fhc_summer(\n",
    "                    dz=_fhv.dz[good], zdr=_fhv.dr[good], rho=_fhv.rh[good],\n",
    "                    kdp=_fhv.kd[good], ldr=_fhv.ld, use_temp=self.T_flag,\n",
    "                    band=self.band, method=self.fhc_method, T=_fhv.tt,\n",
    "                    verbose=self.verbose, temp_factor=self.T_factor,\n",
    "                    weights=self.fhc_weights)\n",
    "                # Create and populate the FHC field\n",
    "                fh = np.zeros_like(_fhv.dz) + self.bad\n",
    "                fh[good] = np.argmax(scores, axis=0) + 1\n",
    "                fh = np.reshape(fh, _fhv.shp)\n",
    "                self.add_field_to_radar_object(fh, field_name=self.name_fhc)\n",
    "                print(tt.time()-bt, 'seconds to do FHC')\n",
    "            else:\n",
    "                print('No good data for FHC, not performed')\n",
    "        else:\n",
    "            print('Winter HID not enabled yet, sorry!')\n",
    "\n",
    "    def get_precip_rate(\n",
    "            self, ice_flag=False, rain_method='hidro', thresh_zdr=0.5,\n",
    "            thresh_dz=DEFAULT_DZ_THRESH, thresh_kd=DEFAULT_KD_THRESH):\n",
    "        \"\"\"Calculate rain rate, add to radar object.\"\"\"\n",
    "        dz = self.radar.fields[self.name_dz]['data']\n",
    "        dr = self.radar.fields[self.name_dr]['data']\n",
    "        kd = self.radar.fields[self.name_kd]['data']\n",
    "        if not self.winter_flag:\n",
    "            if rain_method == 'hidro':\n",
    "                fhc = self.radar.fields[self.name_fhc]['data']\n",
    "                rain, method = csu_blended_rain.csu_hidro_rain(\n",
    "                    dz=dz, zdr=dr, kdp=kd, fhc=fhc, band=self.band,\n",
    "                    thresh_dz=thresh_dz, thresh_kdp=thresh_kd,\n",
    "                    thresh_zdr=thresh_zdr, r_z_a=self.r_z_a, r_z_b=self.r_z_b,\n",
    "                    r_kdp_a=self.r_kdp_a, r_kdp_b=self.r_kdp_b,\n",
    "                    r_z_zdr_a=self.r_z_zdr_a, r_z_zdr_b=self.r_z_zdr_b,\n",
    "                    r_z_zdr_c=self.r_z_zdr_c, r_kdp_zdr_a=self.r_kdp_zdr_a,\n",
    "                    r_kdp_zdr_b=self.r_kdp_zdr_b, r_kdp_zdr_c=self.r_kdp_zdr_c)\n",
    "            else:\n",
    "                if not ice_flag:\n",
    "                    rain, method = csu_blended_rain.calc_blended_rain(\n",
    "                        dz=dz, zdr=dr, kdp=kd, band=self.band,\n",
    "                        thresh_dz=thresh_dz, thresh_kdp=thresh_kd,\n",
    "                        thresh_zdr=thresh_zdr, r_z_a=self.r_z_a,\n",
    "                        r_z_b=self.r_z_b, r_kdp_a=self.r_kdp_a,\n",
    "                        r_kdp_b=self.r_kdp_b, r_z_zdr_a=self.r_z_zdr_a,\n",
    "                        r_z_zdr_b=self.r_z_zdr_b, r_z_zdr_c=self.r_z_zdr_c,\n",
    "                        r_kdp_zdr_a=self.r_kdp_zdr_a,\n",
    "                        r_kdp_zdr_b=self.r_kdp_zdr_b,\n",
    "                        r_kdp_zdr_c=self.r_kdp_zdr_c)\n",
    "                else:\n",
    "                    rain, method, zdp, fi = csu_blended_rain.calc_blended_rain(\n",
    "                        dz=dz, zdr=dr, kdp=kd, ice_flag=ice_flag,\n",
    "                        band=self.band, thresh_dz=thresh_dz,\n",
    "                        thresh_kdp=thresh_kd, thresh_zdr=thresh_zdr,\n",
    "                        r_z_a=self.r_z_a, r_z_b=self.r_z_b,\n",
    "                        r_kdp_a=self.r_kdp_a, r_kdp_b=self.r_kdp_b,\n",
    "                        r_z_zdr_a=self.r_z_zdr_a,\n",
    "                        r_z_zdr_b=self.r_z_zdr_b, r_z_zdr_c=self.r_z_zdr_c,\n",
    "                        r_kdp_zdr_a=self.r_kdp_zdr_a,\n",
    "                        r_kdp_zdr_b=self.r_kdp_zdr_b,\n",
    "                        r_kdp_zdr_c=self.r_kdp_zdr_c)\n",
    "                    self.add_field_to_radar_object(\n",
    "                        zdp, field_name='ZDP', units='dB',\n",
    "                        long_name='Difference Reflectivity',\n",
    "                        standard_name='Difference Reflectivity')\n",
    "                    self.add_field_to_radar_object(\n",
    "                        fi, field_name='FI', units='',\n",
    "                        long_name='Ice Fraction', standard_name='Ice Fraction')\n",
    "        else:\n",
    "            print('Winter precip not enabled yet, sorry!')\n",
    "            return\n",
    "        self.add_field_to_radar_object(rain, field_name='rain', units='mm h-1',\n",
    "                                       long_name='Rainfall Rate',\n",
    "                                       standard_name='Rainfall Rate')\n",
    "        self.add_field_to_radar_object(method, field_name='method', units='',\n",
    "                                       long_name='Rainfall Method',\n",
    "                                       standard_name='Rainfall Method')\n",
    "\n",
    "    def get_dsd(self):\n",
    "        \"\"\"Calculate DSD information, add to radar object.\"\"\"\n",
    "        dz = self.radar.fields[self.name_dz]['data']\n",
    "        dr = self.radar.fields[self.name_dr]['data']\n",
    "        kd = self.radar.fields[self.name_kd]['data']\n",
    "        d0, Nw, mu = csu_dsd.calc_dsd(dz=dz, zdr=dr, kdp=kd, band=self.band,\n",
    "                                      method='2009')\n",
    "        self.add_field_to_radar_object(d0, field_name='D0', units='mm',\n",
    "                                       long_name='Median Volume Diameter',\n",
    "                                       standard_name='Median Volume Diameter')\n",
    "        self.add_field_to_radar_object(\n",
    "            Nw, field_name='NW', units='mm-1 m-3',\n",
    "            long_name='Normalized Intercept Parameter',\n",
    "            standard_name='Normalized Intercept Parameter')\n",
    "        self.add_field_to_radar_object(mu, field_name='MU', units=' ',\n",
    "                                       long_name='Mu', standard_name='Mu')\n",
    "\n",
    "    def get_liquid_and_frozen_mass(self):\n",
    "        \"\"\"Calculate liquid/ice mass, add to radar object.\"\"\"\n",
    "        mw, mi = csu_liquid_ice_mass.calc_liquid_ice_mass(\n",
    "                         self.radar.fields[self.name_dz]['data'],\n",
    "                         self.radar.fields[self.name_dr]['data'],\n",
    "                         self.radar_z/1000.0, T=self.radar_T)\n",
    "        self.add_field_to_radar_object(mw, field_name='MW', units='g m-3',\n",
    "                                       long_name='Liquid Water Mass',\n",
    "                                       standard_name='Liquid Water Mass')\n",
    "        self.add_field_to_radar_object(mi, field_name='MI', units='g m-3',\n",
    "                                       long_name='Ice Water Mass',\n",
    "                                       standard_name='Ice Water Mass')\n",
    "\n",
    "    def add_field_to_radar_object(self, field, field_name='FH',\n",
    "                                  units='unitless', long_name='Hydrometeor ID',\n",
    "                                  standard_name='Hydrometeor ID'):\n",
    "        \"\"\"\n",
    "        Adds a newly created field to the Py-ART radar object.\n",
    "        \"\"\"\n",
    "        masked_field = np.ma.asanyarray(field)\n",
    "        masked_field.mask = masked_field == self.bad\n",
    "        if hasattr(self.radar.fields[self.name_dz]['data'], 'mask'):\n",
    "            masked_field.mask = np.logical_or(\n",
    "                masked_field.mask,\n",
    "                self.radar.fields[self.name_dz]['data'].mask)\n",
    "            try:\n",
    "                fill_value = self.radar.fields[self.name_dz]['_FillValue']\n",
    "            except KeyError:\n",
    "                fill_value = self.bad\n",
    "        field_dict = {'data': masked_field,\n",
    "                      'units': units,\n",
    "                      'long_name': long_name,\n",
    "                      'standard_name': standard_name,\n",
    "                      '_FillValue': fill_value}\n",
    "        self.radar.add_field(field_name, field_dict, replace_existing=True)\n",
    "\n",
    "    def interpolate_sounding_to_radar(self):\n",
    "        \"\"\"Takes sounding data and interpolates it to every radar gate.\"\"\"\n",
    "        xx, yy, self.radar_z = get_xyz_from_radar(self.radar)\n",
    "        self.radar_T = None\n",
    "        self.check_sounding_for_montonic()\n",
    "        if self.T_flag:\n",
    "            shape = np.shape(self.radar_z)\n",
    "            rad_z1d = self.radar_z.ravel()\n",
    "            rad_T1d = np.interp(rad_z1d, self.snd_z, self.snd_T)\n",
    "            if self.verbose:\n",
    "                print('Trying to get radar_T')\n",
    "            self.radar_T = np.reshape(rad_T1d, shape)\n",
    "            self.add_field_to_radar_object(\n",
    "                self.radar_T, field_name='temperature', units='deg C',\n",
    "                long_name='Temperature', standard_name='Temperature')\n",
    "\n",
    "    def check_sounding_for_montonic(self):\n",
    "        \"\"\"\n",
    "        So sounding interpolation doesn't fail, force the sounding to behave\n",
    "        monotonically so that z always increases. This eliminates data from\n",
    "        descending balloons.\n",
    "        \"\"\"\n",
    "        dummy_z = []\n",
    "        dummy_T = []\n",
    "        if hasattr(self, 'snd_T'):\n",
    "            if not self.snd_T.mask[0]:  # May cause issue for some soundings\n",
    "                dummy_z.append(self.snd_z[0])\n",
    "                dummy_T.append(self.snd_T[0])\n",
    "            for i, height in enumerate(self.snd_z):\n",
    "                if i > 0:\n",
    "                    if self.snd_z[i] > self.snd_z[i-1] and not\\\n",
    "                       self.snd_T.mask[i]:\n",
    "                        dummy_z.append(self.snd_z[i])\n",
    "                        dummy_T.append(self.snd_T[i])\n",
    "            self.snd_z = np.array(dummy_z)\n",
    "            self.snd_T = np.array(dummy_T)\n",
    "\n",
    "    def _populate_attributes(self, kwargs):\n",
    "        \"\"\"\n",
    "        This internal method declutters self.__init__()\n",
    "        \"\"\"\n",
    "        self.name_dz = kwargs['dz']\n",
    "        self.name_dr = kwargs['dr']\n",
    "        self.name_kd = kwargs['kd']\n",
    "        self.name_rh = kwargs['rh']\n",
    "        self.name_ld = kwargs['ld']\n",
    "        self.name_dp = kwargs['dp']\n",
    "        self.kdp_method = kwargs['kdp_method']\n",
    "        self.bad = kwargs['bad']\n",
    "        self.thresh_sdp = kwargs['thresh_sdp']\n",
    "        self.gs = kwargs['gs']\n",
    "        self.name_sdp = kwargs['name_sdp']\n",
    "        self.kdp_window = kwargs['kdp_window']\n",
    "        self.std_gate = kwargs['std_gate']\n",
    "\n",
    "################################\n",
    "\n",
    "\n",
    "class _FhcVars(object):\n",
    "\n",
    "    \"\"\"\n",
    "    Internal class for parsing and prepping the radar variables for FHC.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, dpret):\n",
    "        \"\"\"\n",
    "        dpret = DualPolRetrieval object\n",
    "        \"\"\"\n",
    "        self.grab_vars(dpret)\n",
    "\n",
    "    def grab_vars(self, dpret):\n",
    "        \"\"\"\n",
    "        Added try statements in case fields are not masked arrays\n",
    "        \"\"\"\n",
    "        try:\n",
    "            self.dz = dpret.radar.fields[dpret.name_dz]['data'].filled(\n",
    "                fill_value=dpret.bad)\n",
    "        except AttributeError:\n",
    "            self.dz = dpret.radar.fields[dpret.name_dz]['data']\n",
    "        self.shp = np.shape(self.dz)\n",
    "        self.dz = self.dz.flatten()\n",
    "        try:\n",
    "            self.dr = dpret.radar.fields[dpret.name_dr]['data'].filled(\n",
    "                fill_value=dpret.bad).flatten()\n",
    "        except AttributeError:\n",
    "            self.dr = dpret.radar.fields[dpret.name_dr]['data'].flatten()\n",
    "        try:\n",
    "            self.kd = dpret.radar.fields[dpret.name_kd]['data'].filled(\n",
    "                fill_value=dpret.bad).flatten()\n",
    "        except:\n",
    "            self.kd = dpret.radar.fields[dpret.name_kd]['data'].flatten()\n",
    "        try:\n",
    "            self.rh = dpret.radar.fields[dpret.name_rh]['data'].filled(\n",
    "                fill_value=dpret.bad).flatten()\n",
    "        except:\n",
    "            self.rh = dpret.radar.fields[dpret.name_rh]['data'].flatten()\n",
    "        # LDR is optional\n",
    "        if dpret.name_ld is not None:\n",
    "            try:\n",
    "                self.ld = dpret.radar.fields[dpret.name_ld]['data'].filled(\n",
    "                    fill_value=dpret.bad).flatten()\n",
    "            except AttributeError:\n",
    "                self.ld = dpret.radar.fields[dpret.name_ld]['data'].flatten()\n",
    "        else:\n",
    "            self.ld = None\n",
    "        # Temperature is optional\n",
    "        if dpret.radar_T is not None:\n",
    "            self.tt = dpret.radar_T.flatten()\n",
    "        else:\n",
    "            self.tt = None\n",
    "\n",
    "################################\n",
    "\n",
    "\n",
    "class HidColors(object):\n",
    "\n",
    "    \"\"\"\n",
    "    Class to help with colormaps/bars when plotting\n",
    "    hydrometeor ID and rainfall method data with Py-ART.\n",
    "    Sample interface\n",
    "    ----------------\n",
    "    radar = pyart.io.read(filename)\n",
    "    retrieve = dualpol.DualPolRetrieval(radar, **kwargs)\n",
    "    hidcolor = dualpol.HidColors()\n",
    "    display = pyart.graph.RadarDisplay(retrieve.radar)\n",
    "    display.plot_ppi('FH', vmin=0, vmax=10, cmap=hidcolor.cmaphid)\n",
    "    display.cbs[0] = hidcolor.adjust_fhc_colorbar_for_pyart(display.cbs[0])\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, winter=False):\n",
    "        if not winter:\n",
    "            self.hid_colors = ['White', 'LightBlue', 'MediumBlue',\n",
    "                               'DarkOrange', 'LightPink', 'Cyan', 'DarkGray',\n",
    "                               'Lime', 'Yellow', 'Red', 'Fuchsia']\n",
    "            self.cmapmeth = colors.ListedColormap(self.hid_colors[0:6])\n",
    "        self.cmaphid = colors.ListedColormap(self.hid_colors)\n",
    "\n",
    "    def adjust_fhc_colorbar_for_pyart(self, cb):\n",
    "        \"\"\"Mods to make a hydrometeor ID colorbar\"\"\"\n",
    "        cb.set_ticks(np.arange(1.4, 10, 0.9))\n",
    "        cb.ax.set_yticklabels(['Drizzle', 'Rain', 'Crystal', 'Aggregate',\n",
    "                               'Wet Snow', 'Vert Ice', 'LD Graup',\n",
    "                               'HD Graup', 'Hail', 'Big Drop'])\n",
    "        cb.ax.set_ylabel('')\n",
    "        cb.ax.tick_params(length=0)\n",
    "        return cb\n",
    "\n",
    "    def adjust_meth_colorbar_for_pyart(self, cb):\n",
    "        \"\"\"Mods to make a rainfall method colorbar\"\"\"\n",
    "        cb.set_ticks(np.arange(1.25, 5, 0.833))\n",
    "        cb.ax.set_yticklabels(['R(Kdp, Zdr)', 'R(Kdp)', 'R(Z, Zdr)', 'R(Z)',\n",
    "                               'R(Zrain)'])\n",
    "        cb.ax.set_ylabel('')\n",
    "        cb.ax.tick_params(length=0)\n",
    "        return cb\n",
    "\n",
    "################################\n",
    "\n",
    "\n",
    "def get_xyz_from_radar(radar):\n",
    "    \"\"\"Input radar object, return z from radar (km, 2D)\"\"\"\n",
    "    azimuth_1D = radar.azimuth['data']\n",
    "    elevation_1D = radar.elevation['data']\n",
    "    srange_1D = radar.range['data']\n",
    "    sr_2d, az_2d = np.meshgrid(srange_1D, azimuth_1D)\n",
    "    el_2d = np.meshgrid(srange_1D, elevation_1D)[1]\n",
    "    xx, yy, zz = radar_coords_to_cart(sr_2d/RNG_MULT, az_2d, el_2d)\n",
    "    # Using median value for now to avoid error with broadcasting diff shapes\n",
    "    return xx, yy, zz + np.median(radar.altitude['data'])\n",
    "\n",
    "\n",
    "def check_kwargs(kwargs, default_kw):\n",
    "    \"\"\"\n",
    "    Check user-provided kwargs against defaults, and if some defaults aren't\n",
    "    provided by user make sure they are provided to the function regardless.\n",
    "    \"\"\"\n",
    "    for key in default_kw:\n",
    "        if key not in kwargs:\n",
    "            kwargs[key] = default_kw[key]\n",
    "    return kwargs\n",
    "\n",
    "#####################################\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from boto.s3.connection import S3Connection\n",
    "from datetime import datetime, timedelta, date, time\n",
    "def get_nexrad_keys(site, start=None, end=None):\n",
    "    \"\"\"\n",
    "    Get generator of pyart radar objects for all nexrad data between two\n",
    "    datetimes from Amazon S3.\n",
    "    ----------\n",
    "    site : string\n",
    "        site code e.g. 'khgx'\n",
    "    start : string\n",
    "        datetime e.g. '20180101_000000'\n",
    "    end : string\n",
    "        same format as start\n",
    "\n",
    "    \"\"\"\n",
    "    fmt = '%Y%m%d_%H%M%S'\n",
    "\n",
    "    if start is None:\n",
    "        start = datetime.utcnow() - timedelta(hours=1)\n",
    "    else:\n",
    "        start = datetime.strptime(start, fmt)\n",
    "    if end is None:\n",
    "        end = datetime.utcnow()\n",
    "    else:\n",
    "        end = datetime.strptime(end, fmt)\n",
    "    if end < start:\n",
    "        print('end datetime precedes start datetime')\n",
    "        return\n",
    "\n",
    "    site = site.upper()\n",
    "\n",
    "    dates = []\n",
    "    day_i = start\n",
    "    while day_i < end:\n",
    "        dates.append(day_i)\n",
    "        day_i += timedelta(days=1)\n",
    "\n",
    "    date_keys = [datetime.strftime(date, '%Y/%m/%d/' + site) for date in dates]\n",
    "\n",
    "    conn = S3Connection(anon=True)\n",
    "    bucket = conn.get_bucket('noaa-nexrad-level2')\n",
    "\n",
    "    keys = [key for date_key in date_keys\n",
    "            for key in list(bucket.list(date_key))\n",
    "            if '.tar' not in str(key)]\n",
    "#\n",
    "#\n",
    "    nkeys = []\n",
    "    for i in np.arange(len(keys)):\n",
    "        if str(keys[i])[-5:-1] != '_MDM':\n",
    "            nkeys.append(keys[i])\n",
    "\n",
    "    keys = nkeys\n",
    "#\n",
    "#\n",
    "    if len(keys) == 0:\n",
    "        print('Found 0 files.')\n",
    "        return\n",
    "\n",
    "    # Key ealier for keys before 'V06'\n",
    "    if '.gz>' in str(keys[0]):\n",
    "        key_fmt = site + '%Y%m%d_%H%M%S_V06.gz>'\n",
    "        key_fmt_earlier = site + '%Y%m%d_%H%M%S.gz>'\n",
    "    else:\n",
    "        key_fmt = site + '%Y%m%d_%H%M%S_V06>'\n",
    "        key_fmt_earlier = site + '%Y%m%d_%H%M%S>'\n",
    "\n",
    "    key_dts = []\n",
    "    for key in keys:\n",
    "        try:\n",
    "            key_dts.append(datetime.strptime(str(key).split('/')[-1], key_fmt))\n",
    "        except ValueError:\n",
    "            key_dts.append(\n",
    "                datetime.strptime(str(key).split('/')[-1], key_fmt_earlier))\n",
    "    key_dts = zip(keys, key_dts)\n",
    "    keys = [key for key, dt in key_dts if dt > start and dt < end]\n",
    "    print('Found', len(keys), 'keys.')\n",
    "    return keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 22 keys.\n"
     ]
    }
   ],
   "source": [
    "keys = get_nexrad_keys('khgx', start='20170708_203000', end='20170708_223000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_203136_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_203723_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_204311_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_204858_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_205445_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_210033_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_210620_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_211207_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_211728_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_212250_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_212744_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_213238_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_213733_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_214227_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_214721_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_215216_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_215711_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_220258_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_220845_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_221432_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_222020_V06>,\n",
       " <Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_222607_V06>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'KHGX20170708_203136_V06'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "1.5875797271728516 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 383153)\n",
      "mu max:  1.0\n",
      "0.8266470432281494 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_203136_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "1.0196659564971924 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 404133)\n",
      "mu max:  1.0\n",
      "0.9595770835876465 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_203723_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.7447998523712158 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 403093)\n",
      "mu max:  1.0\n",
      "0.6196379661560059 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_204311_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.7847974300384521 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 392261)\n",
      "mu max:  1.0\n",
      "0.6272869110107422 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_204858_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.7935435771942139 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 396594)\n",
      "mu max:  1.0\n",
      "0.6396801471710205 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_205445_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.757033109664917 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 392534)\n",
      "mu max:  1.0\n",
      "0.6657953262329102 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_210033_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.7687444686889648 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 374598)\n",
      "mu max:  1.0\n",
      "0.6418628692626953 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_210620_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.9159092903137207 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 373560)\n",
      "mu max:  1.0\n",
      "0.6228575706481934 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_211207_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.8525974750518799 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 373674)\n",
      "mu max:  1.0\n",
      "0.9570982456207275 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_211728_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "1.0163941383361816 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 367106)\n",
      "mu max:  1.0\n",
      "0.7142055034637451 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_212250_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.896019697189331 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 365719)\n",
      "mu max:  1.0\n",
      "0.7281322479248047 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_212744_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.8020339012145996 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 365348)\n",
      "mu max:  1.0\n",
      "0.6756608486175537 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_213238_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.7527730464935303 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 365604)\n",
      "mu max:  1.0\n",
      "0.6746537685394287 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_213733_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.7821760177612305 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 366237)\n",
      "mu max:  1.0\n",
      "0.6978216171264648 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_214227_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.8157563209533691 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 365490)\n",
      "mu max:  1.0\n",
      "0.6755242347717285 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_214721_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.8370256423950195 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 367632)\n",
      "mu max:  1.0\n",
      "0.7222158908843994 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_215216_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.9448049068450928 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 372035)\n",
      "mu max:  1.0\n",
      "0.7029867172241211 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_215711_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.9476394653320312 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 373314)\n",
      "mu max:  1.0\n",
      "0.7211439609527588 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_220258_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.9467668533325195 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 376832)\n",
      "mu max:  1.0\n",
      "0.761307954788208 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_220845_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.8907654285430908 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 379796)\n",
      "mu max:  1.0\n",
      "0.7087235450744629 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_221432_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.9081778526306152 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 383639)\n",
      "mu max:  1.0\n",
      "0.739389181137085 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_222020_V06>\n",
      "Not provided LDR field, not using\n",
      "Not provided KDP field, calculating\n",
      "Calculating KDP via CSU method\n",
      "0.9698553085327148 seconds to run csu_kdp\n",
      "No sounding provided\n",
      "Performing QC\n",
      "Masking insects and high SDP, Despeckling\n",
      "Performing FHC\n",
      "USING VARIABLES:  {'DZ': 1, 'DR': 1, 'KD': 1, 'RH': 1, 'LD': 0, 'T': 0}\n",
      "-->get_mbf_sets_summer   10 Category Summer S-band HID\n",
      "-->get_mbf_sets_summer  No Temperature used in HID\n",
      "Not using T in FHC\n",
      "Using hybrid HID method. Pol vars weighted, Z and T (if used) are multiplied\n",
      "weight_sum:  2.6\n",
      "(10, 388438)\n",
      "mu max:  1.0\n",
      "0.7766125202178955 seconds to do FHC\n",
      "Performing precip rate calculations\n",
      "Performing DSD calculations\n",
      "Performing mass calculations\n",
      "<Key: noaa-nexrad-level2,2017/07/08/KHGX/KHGX20170708_222607_V06>\n"
     ]
    }
   ],
   "source": [
    "for key in keys:\n",
    "    fname = os.path.split(str(key))[1][:-1]\n",
    "    if os.path.isfile(fname) = True :\n",
    "      pass\n",
    "    if os.path.isfile(fname) = False :\n",
    "      radar = read_nexrad_key(key)\n",
    "      zc = deepcopy(radar.fields['reflectivity']['data'])    \n",
    "      radar.add_field_like('reflectivity', 'ZC', zc, replace_existing=True)\n",
    "      retrieve = DualPolRetrieval(radar, dz='ZC', dr='differential_reflectivity',dp='differential_phase', \n",
    "                                  rh='cross_correlation_ratio', \n",
    "                                  fhc_T_factor=2, kdp_window=5,ice_flag=True, \n",
    "                                  rain_method='hidro', qc_flag=True, gs=250.0, \n",
    "                                  verbose=True,thresh_sdp=20.0, speckle=3)\n",
    "      pyart.io.write_cfradial(fname + '_proc.nc', radar)\n",
    "      print(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "radar = read_nexrad_key(keys[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "projection = ccrs.PlateCarree()\n",
    "# fig, ax = plt.subplots(figsize=(12,10), dpi=100)\n",
    "fig = plt.figure(figsize=(5,5), dpi=100)\n",
    "\n",
    "d = date(int(radar.time['units'][14:18]),int(radar.time['units'][19:21]),int(radar.time['units'][22:24]))\n",
    "t = time(int(radar.time['units'][25:27]),int(radar.time['units'][28:30]),int(radar.time['units'][31:33]))\n",
    "tt = datetime.combine(d,t)\n",
    "elevations = radar.fixed_angle['data']\n",
    "\n",
    "ax = fig.add_subplot(1,1,1, projection = projection)\n",
    "ax.coastlines(resolution='10m')\n",
    "ax.gridlines(color='gray', linestyle='--', draw_labels=False)\n",
    "\n",
    "zoom = 3\n",
    "ext = [ radar.longitude['data'][0] - zoom, radar.longitude['data'][0] + zoom, \n",
    "       radar.latitude['data'][0] - zoom, radar.latitude['data'][0] + zoom]\n",
    "ax.set_extent(ext)\n",
    "\n",
    "display = pyart.graph.RadarMapDisplay(radar)\n",
    "display.plot_ppi_map('reflectivity',sweep=0, vmin=0, vmax=65, ax=ax, fig=fig,mask_outside=False, \n",
    "                     cmap='jet',colorbar_flag=False, embelish=False, alpha = 0.9)\n",
    "\n",
    "\n",
    "td = fig.text(0.465, 0.147, tt.strftime(\"%m/%d/%y %H:%M:%S\"),fontsize = 16,)\n",
    "td.set_bbox(dict(facecolor='white', alpha=0.7))\n",
    "\n",
    "# plt.setp( ax.get_xticklabels(), visible=False)\n",
    "ax.set_title('')\n",
    "\n",
    "# ax.text(-0.10, 0.55, 'Latitude (degree)', va='bottom', ha='center', rotation='vertical',rotation_mode='anchor',\n",
    "#     transform=ax.transAxes, fontsize = 14)\n",
    "# ax.text(0.5, -0.12, 'Longitude (degree)', va='bottom', ha='center',rotation='horizontal', rotation_mode='anchor',\n",
    "#     transform=ax.transAxes, fontsize = 14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_grid(radar):\n",
    "    \"\"\" Returns grid object from radar object. \"\"\"\n",
    "    grid = pyart.map.grid_from_radars(\n",
    "        radar, grid_shape=(31, 501, 501),\n",
    "        grid_limits=((0, 15000), (-250000,250000), (-250000, 250000)),\n",
    "        fields=['reflectivity', 'differential_reflectivity', 'KDP_CSU', 'D0', 'NW', 'MU', 'MW', 'MI'],\n",
    "        gridding_algo='map_gates_to_grid',\n",
    "        h_factor=0., nb=0.6, bsp=1., min_radius=200.)\n",
    "    return grid\n",
    "\n",
    "# def get_grid(radar):\n",
    "#     \"\"\" Returns grid object from radar object. \"\"\"\n",
    "#     grid = pyart.map.grid_from_radars(\n",
    "#         radar, grid_shape=(31, 401, 401),\n",
    "#         grid_limits=((0, 15000), (-200000,200000), (-200000, 200000)),\n",
    "#         fields=['reflectivity'], gridding_algo='map_gates_to_grid',\n",
    "#         h_factor=0., nb=0.6, bsp=1., min_radius=200.)\n",
    "#     return grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['differential_phase', 'reflectivity', 'spectrum_width', 'velocity', 'differential_reflectivity', 'cross_correlation_ratio', 'ZC', 'FDP_CSU', 'SDP_CSU', 'KDP_CSU', 'FH', 'rain', 'method', 'D0', 'NW', 'MU', 'MW', 'MI'])\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "arq = sorted(glob.glob('/home/jessica/tracer/KHGX*.nc'))\n",
    "radar = pyart.io.read_cfradial(arq[0])\n",
    "fname = arq[0]\n",
    "print(radar.fields.keys())\n",
    "\n",
    "# d1 = date(int(fname[48:52]),int(fname[52:54]),int(fname[54:56]))\n",
    "# t1 = time(int(fname[57:59]),int(fname[59:61]),int(fname[61:63]))\n",
    "# tt1 = datetime.combine(d1,t1)\n",
    "# tt1.strftime(\"%d/%m/%y %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving grid 0\n",
      "saving grid 1\n",
      "saving grid 2\n",
      "saving grid 3\n",
      "saving grid 4\n",
      "saving grid 5\n",
      "saving grid 6\n",
      "saving grid 7\n",
      "saving grid 8\n",
      "saving grid 9\n",
      "saving grid 10\n",
      "saving grid 11\n",
      "saving grid 12\n",
      "saving grid 13\n",
      "saving grid 14\n",
      "saving grid 15\n",
      "saving grid 16\n",
      "saving grid 17\n",
      "saving grid 18\n",
      "saving grid 19\n",
      "saving grid 20\n",
      "saving grid 21\n"
     ]
    }
   ],
   "source": [
    "filenames = []\n",
    "for num, key in enumerate(arq):\n",
    "    print('saving grid', num)\n",
    "    radar = pyart.io.read_cfradial(key)\n",
    "    grid = get_grid(radar)\n",
    "    name = os.path.join('Tgrid_' + str(num).zfill(3) + '.nc')\n",
    "    filenames.append(name)\n",
    "    pyart.io.write_grid(name, grid)\n",
    "    del radar, grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}