03_hist_1D.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D-histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We look here at the overall distribution and the distribution of values in hail or non-hail of those variables not explored during the EDA in the proposal, namely the texture of the variables"
   ]
  },
  {
   "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 glob\n",
    "import os\n",
    "import pyart\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from copy import deepcopy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/exts-ml2/lib/python3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "# suppress anoying iypthon warnings. Not ideal since we suppress also potentially relevant warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Auxiliary functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to read original dataset\n",
    "# data is stored as (nz, ny, nx), we return (nx, ny)\n",
    "def read_nc(fname):\n",
    "    sat_grid = pyart.io.read_grid(fname)\n",
    "    for field_name in sat_grid.fields.keys():\n",
    "        data = np.transpose(np.squeeze(sat_grid.fields[field_name]['data']))\n",
    "    return data        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_1d_hist(field, vmin, vmax, step):\n",
    "    # get bins\n",
    "    bin_edges = np.linspace(vmin-step/2., vmax+step/2., num=int((vmax-vmin)/step)+2)\n",
    "    step_aux = bin_edges[1]-bin_edges[0]\n",
    "    bin_centers = bin_edges[:-1]+step_aux/2.\n",
    "    \n",
    "    # data off limits is put at the bins in the extrems\n",
    "    field[field < bin_centers[0]] = bin_centers[0]\n",
    "    field[field > bin_centers[-1]] = bin_centers[-1]\n",
    "    \n",
    "    return np.histogram(field, bins=bin_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_1d_hist(fbasepath, feature, vmin, vmax, step, target=None, target_filter=None):\n",
    "    flist = glob.glob(fbasepath+'*/NETCDF/'+feature+'/'+'*.nc')\n",
    "    if len(flist) == 0:\n",
    "        return None\n",
    "    flist.sort()\n",
    "    \n",
    "    hist_1d = None\n",
    "    for fname in flist:\n",
    "        # Get time step\n",
    "        bfile = os.path.basename(fname)\n",
    "        dt_str = bfile[0:14]\n",
    "        print(dt_str, end=\"\\r\", flush=True)\n",
    "        \n",
    "        # get target file name    \n",
    "        if target is not None:\n",
    "            tflist = glob.glob(fbasepath+'*/NETCDF/'+target+'/'+dt_str+'*.nc')\n",
    "            if len(tflist) == 0:\n",
    "                continue\n",
    "            \n",
    "        # read data\n",
    "        data = read_nc(fname).flatten()\n",
    "        \n",
    "        if target_filter is not None:\n",
    "            tdata = read_nc(tflist[0]).flatten()\n",
    "            tdata[tdata == 1] = 0\n",
    "            tdata[tdata == 2] = 1\n",
    "            data = data[tdata == target_filter]\n",
    "            \n",
    "        if hist_1d is None:\n",
    "            hist_1d, bin_edges = compute_1d_hist(data, vmin, vmax, step)\n",
    "        else:    \n",
    "            hist_1d_aux, bin_edges = compute_1d_hist(data, vmin, vmax, step)\n",
    "            hist_1d += hist_1d_aux\n",
    "    \n",
    "    return hist_1d, bin_edges        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_1d_hist(hist_1d, bin_edges, feature, target_filter=None):\n",
    "    fig = plt.figure(figsize=(8, 5))\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    ax.bar(bin_edges[:-1], hist_1d, align='edge', width=bin_edges[1]-bin_edges[0])\n",
    "    \n",
    "    ax.set_xlabel(feature)\n",
    "    ax.set_ylabel('Number of pixels')\n",
    "    if target_filter is None:\n",
    "        ax.set_title('All data')\n",
    "    elif target_filter == 0:\n",
    "        ax.set_title('No POH90')\n",
    "    else:\n",
    "        ax.set_title('POH90')\n",
    "        \n",
    "    fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_1d_hist(hist_1d, bin_edges, feature, target_filter=None):\n",
    "    if target_filter is None:\n",
    "        lab = 'all_data'\n",
    "    elif target_filter == 0:\n",
    "        lab = 'No_POH90'\n",
    "    else:\n",
    "        lab = 'POH90'\n",
    "    \n",
    "    # Save data into a .npz file\n",
    "    np.savez(\n",
    "        '/data/ml_course/05_Capstone_project/hist_'+lab+'_'+feature+'.npz',\n",
    "        hist=hist_1d, bin_edges=bin_edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HRV_norm texture histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fbasepath = '/data/pyrad_products/MSG_ML/'\n",
    "feature = 'HRV_norm_text'\n",
    "target = 'POH90'\n",
    "\n",
    "vmin = 0.\n",
    "vmax = 100.\n",
    "step = 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=None, target_filter=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No hail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=target, target_filter=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=target, target_filter=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no large differences between hail and non-hail pixels but pixels containing hail tend to have a larger texture value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IR_108 texture histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fbasepath = '/data/pyrad_products/MSG_ML/'\n",
    "feature = 'IR_108_text'\n",
    "target = 'POH90'\n",
    "\n",
    "vmin = 0.\n",
    "vmax = 40.\n",
    "step = 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=None, target_filter=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No hail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=target, target_filter=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=target, target_filter=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minor differences can be observed between hail and no hail pixels. Much more POH90 have texture values close to 0 than non-hail pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Channel differences texture histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fbasepath = '/data/pyrad_products/MSG_ML/'\n",
    "feature = 'WV_062-IR_108_text'\n",
    "target = 'POH90'\n",
    "\n",
    "vmin = 0.\n",
    "vmax = 30.\n",
    "step = 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## All data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=None, target_filter=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Akvw0cCFgwJEkSQvSMGNwNo+Fm9YNwOaO6pEkSZqzSXtwkhzVvr0myUXAR2nG4LwEuGIb1KYhzWUG9I1rDp/HSiRJWhimukT1woH33wUObt9vAR7VWUWSJElzNGnAqapXbctCJEmS5sswd1HtBbwOWDnYvqqO6K4sSZKk2RvmLqrzgb+jeXrxvZ1WI0mSNA+GCTj/r6r+uvNKJEmS5skwAefUJCcDnwHuHltZVV/prCpJkqQ5GCbgPA14GfBL3H+JqtplSZKkBWeYgPMi4IlV9eOZ7jzJauBUYBlwRlWtmaDNIcB7gB2A26rq4PFtJEmSZmKYgPM1YBdm+PTiJMuA04AXAJuAK5JcUFXXDrTZBXgfsLqqbkry2Jl8hyRJ0kSGCTiPA76R5AoeOAZnutvEDwI2VNUNAEnOAY4Erh1o81Lg41V1U7tPp4CQJElzNkzAOXmW+14B3DywvAn42XFtngTskOTzwE7AqVX1ofE7SnIccBzAnnvuOctyJEnSUjFtwKmqS2a570y0uwm+/xnA84AdgS8lubyqvjmuhrXAWoBVq1aN34ckSdIDDPMk4x9wfzB5CM1g4B9W1c7TfHQTsMfA8u7ALRO0ua2qfgj8MMmlwP7AN5EkSZql7aZrUFU7VdXO7euhwK8D7x1i31cAeyfZK8lDgKOBC8a1+WfgF5Jsn+RhNJewrpvZT5AkSXqgYcbgPEBVnZ/kpCHabU1yAnAxzW3iZ1bVNUmOb7efXlXXJfk0sJ7mGTtnVNXVM61JkiRp0DCXqI4aWNwOWMWDx9JMqKouAi4at+70ccvvBN45zP4kSZKGMUwPzgsH3m8FNtLc7i1JkrQgDXMX1au2RSGSJEnzZdKAk+StU3yuquqUDuqRJEmas6l6cH44wbqHA68GHgMYcCRJ0oI0acCpqnePvU+yE/AG4FXAOcC7J/ucJEnSqE05BifJo4HfBY4FPggcWFX/uS0KkyRJmq2pxuC8EziKZoqEp1XVXdusKkmSpDmY6knGvwc8HngLcEuSO9vXD5LcuW3KkyRJmrmpxuBMO42DJEnSQmSIkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvWPAkSRJvbP9qAvQaK086cI5fX7jmsPnqRJJkuaPPTiSJKl3Og04SVYnuT7JhiQnTdHumUnuSfLiLuuRJElLQ2cBJ8ky4DTgUGAf4Jgk+0zS7h3AxV3VIkmSlpYue3AOAjZU1Q1V9WPgHODICdq9DvgYsLnDWiRJ0hLSZcBZAdw8sLypXXefJCuAFwGnT7WjJMclWZdk3ZYtW+a9UEmS1C9dBpxMsK7GLb8HOLGq7plqR1W1tqpWVdWq5cuXz1d9kiSpp7q8TXwTsMfA8u7ALeParALOSQKwK3BYkq1VdX6HdUmSpJ7rMuBcAeydZC/g28DRwEsHG1TVXmPvk5wFfNJwI0mS5qqzgFNVW5OcQHN31DLgzKq6Jsnx7fYpx91IkiTNVqdPMq6qi4CLxq2bMNhU1Su7rEWSJC0dPslYkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1jgFHkiT1zvajLkCL28qTLpz1ZzeuOXweK5Ek6X724EiSpN4x4EiSpN4x4EiSpN4x4EiSpN4x4EiSpN4x4EiSpN4x4EiSpN4x4EiSpN4x4EiSpN4x4EiSpN7pNOAkWZ3k+iQbkpw0wfZjk6xvX5cl2b/LeiRJ0tLQWcBJsgw4DTgU2Ac4Jsk+45rdCBxcVfsBpwBru6pHkiQtHV324BwEbKiqG6rqx8A5wJGDDarqsqr6z3bxcmD3DuuRJElLRJcBZwVw88DypnbdZF4NfGqiDUmOS7IuybotW7bMY4mSJKmPugw4mWBdTdgw+UWagHPiRNuram1VraqqVcuXL5/HEiVJUh9t3+G+NwF7DCzvDtwyvlGS/YAzgEOr6nsd1iNJkpaILntwrgD2TrJXkocARwMXDDZIsifwceBlVfXNDmuRJElLSGc9OFW1NckJwMXAMuDMqromyfHt9tOBtwKPAd6XBGBrVa3qqiZJkrQ0pGrCYTEL1qpVq2rdunWd7X/lSRd2tm/Nn41rDh91CZKkBSDJlRN1jvgkY0mS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DsGHEmS1DudTbYpdWkuc4Y5j5Uk9Z89OJIkqXcMOJIkqXcMOJIkqXcMOJIkqXcMOJIkqXcMOJIkqXcMOJIkqXd8Do6WHJ+hI0n9Zw+OJEnqHQOOJEnqHQOOJEnqHQOOJEnqHQcZSzPgAGVJWhzswZEkSb1jwJEkSb3jJSppG5nL5S3wEpckzYQ9OJIkqXfswZEWCQc4S9LwOg04SVYDpwLLgDOqas247Wm3Hwb8CHhlVX2ly5qkpchwJGmp6SzgJFkGnAa8ANgEXJHkgqq6dqDZocDe7etngfe3/0paIAxHkhajLntwDgI2VNUNAEnOAY4EBgPOkcCHqqqAy5PskmS3qrq1w7okbSNzHVg9KgYzafHrMuCsAG4eWN7Eg3tnJmqzAnhAwElyHHBcu3hXkuvnt9Sh7QrcNqLvXow8XjPj8ZqZzo5X3tHFXkfO82tmPF7DG/WxesJEK7sMOJlgXc2iDVW1Flg7H0XNRZJ1VbVq1HUsFh6vmfF4zYzHa2Y8XjPj8RreQj1WXd4mvgnYY2B5d+CWWbSRJEmakS4DzhXA3kn2SvIQ4GjggnFtLgBensazgDscfyNJkuaqs0tUVbU1yQnAxTS3iZ9ZVdckOb7dfjpwEc0t4htobhN/VVf1zJORXyZbZDxeM+PxmhmP18x4vGbG4zW8BXms0tzAJEmS1B9O1SBJknrHgCNJknrHgDOkJKuTXJ9kQ5KTRl3PQpdkY5KvJ7kqybpR17PQJDkzyeYkVw+se3SSzyb59/bfR42yxoVkkuP1tiTfbs+xq5IcNsoaF4okeyT5XJLrklyT5A3tes+vCUxxvDy/JpDkoUm+nORr7fH643b9gju/HIMzhHbaiW8yMO0EcMy4aSc0IMlGYFVV+aCsCSR5LnAXzZO8923X/QVwe1WtaUP0o6rqxFHWuVBMcrzeBtxVVe8aZW0LTZLdgN2q6itJdgKuBH4NeCWeXw8yxfH6DTy/HqSdQ/LhVXVXkh2A/wu8ATiKBXZ+2YMznPumnaiqHwNj005Is1JVlwK3j1t9JPDB9v0Haf7IikmPlyZQVbeOTVpcVT8ArqN5Qrzn1wSmOF6aQDXuahd3aF/FAjy/DDjDmWxKCU2ugM8kubKdakPTe9zYc6Dafx874noWgxOSrG8vYY28S3yhSbISeDrwb3h+TWvc8QLPrwklWZbkKmAz8NmqWpDnlwFnOENNKaEHeE5VHUgzY/xr20sM0nx6P/DTwAE089e9e6TVLDBJHgF8DHhjVd056noWugmOl+fXJKrqnqo6gGb2gYOS7DvikiZkwBmOU0rMUFXd0v67Gfgnmst8mtp32/EAY+MCNo+4ngWtqr7b/qG9F/gAnmP3acdGfAz4cFV9vF3t+TWJiY6X59f0qur7wOeB1SzA88uAM5xhpp1QK8nD28F6JHk48MvA1VN/SjTn1Cva968A/nmEtSx4Y39MWy/Ccwy4bxDo3wHXVdVfDmzy/JrAZMfL82tiSZYn2aV9vyPwfOAbLMDzy7uohtTeIvge7p924s9GW9HCleSJNL020EwH8hGP1wMlORs4BNgV+C5wMnA+8FFgT+Am4CVV5cBaJj1eh9BcPihgI/DbzmUHSX4e+ALwdeDedvUf0Ywr8fwaZ4rjdQyeXw+SZD+aQcTLaDpJPlpVf5LkMSyw88uAI0mSesdLVJIkqXcMOJIkqXcMOJIkqXcMOJIkqXcMOJIkqXcMOJIkqXcMONISkuSvkrxxYPniJGcMLL87SSV58rjPvSfJm6bY7x8m2ZDk+iS/MrD+IUnWJvlmkm8k+fV2/e8mubad5+dfkjxhkv2+Lcnvt+/PSnJjkquSfC3J86b5rSe0NVWSXQfWJ8lft9vWJzlwYNvvJLkmydVJzk7y0Cn2/8okj5+qhmnq+6PZflbS9Aw40tJyGfBsgCTb0Tw476kD258NfJrmad0MtHsx8L8n2mGSfdr2T6V5ZPv7kixrN78Z2FxVTwL2AS5p138VWFVV+wHnAX8xZP1/0M6B80bg9GnafpHmKav/MW79ocDe7es4mjmHSLICeH1b1740DzI7msm9Eph1wKF5mJykjhhwpKXli7QBhyaQXA38IMmjkvwU8BTgRB74H/tzgY1VNT4ojDkSOKeq7q6qG4EN3D9vz/8A3g5QVfdW1W3t+89V1Y/aNpfTzO82E18CVkzVoKq+WlUbJ6n3Q9W4HNhl4LH82wM7JtkeeBiTzDmX5MXAKuDDbY/SjkmekeSSJFe2PWO7JXlk26v15PZzZyf5rSRr2u+5KsmHZ/jbJQ3BgCMtIe0kqFuT7EkTdL5E8wj/n6P5D3t9Va0H7k2yf/uxo4Gzp9jtCuDmgeVNwIqx+WqAU5J8Jcm5SR43wedfDXxqhj9lNc3UFrMxYb1V9W3gXTSPmb8VuKOqPjPRDqrqPGAdcGzbo7QV+BvgxVX1DOBM4M+q6g7gBOCsJEcDj6qqD1TVScB/VdUBVXXsLH+HpCkYcKSlZ6wXZyzgfGlg+bK2zdnA0W1PxpHAuVPsLxOsK5rekN2BL1bVge33vOsBH0x+kyZYvXPI2t+Z5AbgH4E/H/IzQ9Wb5FE0v3UvmktPD2/rG8aTgX2Bzya5CngLba9UVX2WZp6j04DXzLJmSTNkwJGWnrFxOE+juUR1OU0PzrNpwg80Aec3aMawrK+qzVPsbxOwx8Dy7jSXdr4H/Ij7J149Fxgc0Pt8mjE6R1TV3e26P2sv21w1yXf9AfDfaQLEB4f4rTOp9/nAjVW1pap+Anyc+y/nTSfANW2PzAFV9bSq+mW4bwzTU4D/Ah49y5olzZABR1p6vgj8KnB7Vd3Tzvi7C03I+RJAVX2LJqCsYerLUwAX0PT2/FSSvWgG7365mpl8P0Ez6zfA84BrAZI8HfhbmnBzX3iqqjePhYTJvqyq7gVOBbYbvGNrBi4AXt7eTfUsmktRt9JcmnpWkoclSVvvdVPs5wfATu3764HlSX6u/X07JBkbvP077X6OAc5MskO7/icD7yXNMwOOtPR8nebuqcvHrbtjbBBw62zgZ7i/B2ZCVXUN8FGa8PJp4LVVdU+7+UTgbUnWAy8Dfq9d/07gEcC5bY/NBTP5AW14+lNgqlvXX59kE00PzfqB2+EvAm6gGQz9AeB/tvv8N5o7ur5Cczy2A9ZOUcZZwOltb9MymjvN3pHka8BVwLOTPInmstTvVdUXgEtpep9o973eQcZSN9L8nZAkSeoPe3AkSVLvbD/qAiQtDu14l3eMW31jVb1oFPWMSfJPNHc+DTqxqi6ep/2fBjxn3OpTq+rv52P/krrhJSpJktQ7XqKSJEm9Y8CRJEm9Y8CRJEm9Y8CRJEm98/8BfCt4H+qp2fAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No hail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=target, target_filter=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20200731173000\r"
     ]
    }
   ],
   "source": [
    "hist_1d, bin_edges = add_1d_hist(\n",
    "    fbasepath, feature, vmin, vmax, step, target=target, target_filter=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_1d_hist(hist_1d, bin_edges, feature, target_filter=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_1d_hist(hist_1d, bin_edges, feature, target_filter=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, a significantly higher amount of pixels have values close to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall there are subtle differences in the distribution of the texture values between pixels containing hail and those not containing hail. Hence texture variables may have a minor but significant role in the classification."
   ]
  },
  {
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}