gewitterblitz/subset_nldn_flashes.ipynb Secret

## subset_nldn_flashes.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Date started:** April 24, 2021\\\n",
    "**Author:** Milind Sharma\\\n",
    "**env:** vortexse_analysis (Macbook)\n",
    "\n",
    "**Goal:** Extract all NLDN flashes within lassos created using lmatools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# conda environments:\n",
      "#\n",
      "                         /Users/ms/.julia/conda/3\n",
      "base                     /Users/ms/anaconda3\n",
      "betterbib                /Users/ms/anaconda3/envs/betterbib\n",
      "cmac_env                 /Users/ms/anaconda3/envs/cmac_env\n",
      "dedalus                  /Users/ms/anaconda3/envs/dedalus\n",
      "glue                     /Users/ms/anaconda3/envs/glue\n",
      "jmatlab                  /Users/ms/anaconda3/envs/jmatlab\n",
      "lmasimulation            /Users/ms/anaconda3/envs/lmasimulation\n",
      "napari                   /Users/ms/anaconda3/envs/napari\n",
      "pyvista                  /Users/ms/anaconda3/envs/pyvista\n",
      "segmentation             /Users/ms/anaconda3/envs/segmentation\n",
      "vortexse_analysis     *  /Users/ms/anaconda3/envs/vortexse_analysis\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda info -e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from spatialpandas.geometry import PolygonArray\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas\n",
    "from spatialpandas import GeoSeries, GeoDataFrame\n",
    "from datetime import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# set rcparams\n",
    "plt.rcParams[\"figure.figsize\"] = [20, 12]\n",
    "plt.rcParams[\"font.size\"] = 15\n",
    "plt.rcParams[\"xtick.labelsize\"] = 18\n",
    "plt.rcParams[\"ytick.labelsize\"] = 18\n",
    "plt.rcParams[\"axes.labelsize\"] = 20\n",
    "plt.rcParams[\"axes.titlesize\"] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = Path('../../data/20160331_IOP3/NLDN').resolve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nldn_file = 'NLDN_20160331230000_20160401040000.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "                     time      lat      lon  current type  \\\n",
       "10419 2016-04-01 00:27:01  33.8893 -87.6599     -9.4    G   \n",
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       "10455 2016-04-01 00:27:13  34.0341 -87.9844     -9.6    G   \n",
       "...                   ...      ...      ...      ...  ...   \n",
       "25251 2016-04-01 02:47:12  34.7662 -86.4725     33.7    G   \n",
       "25252 2016-04-01 02:47:12  34.7436 -86.4607     36.7    G   \n",
       "25255 2016-04-01 02:47:32  35.4031 -87.5657     17.0    G   \n",
       "25258 2016-04-01 02:47:32  35.3661 -87.4825     -7.3    G   \n",
       "25269 2016-04-01 02:48:20  33.9570 -86.5588    -13.6    G   \n",
       "\n",
       "                         geometry  \n",
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       "\n",
       "[2268 rows x 6 columns]"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a geodataframe for NLDN flash data (CG + IC)\n",
    "df = pd.read_csv(data_path.joinpath(nldn_file),delim_whitespace=True,header=None,\n",
    "            names=['date','time','lat','lon','current','type'])\n",
    "\n",
    "df['datetime'] = df['date'] + ' ' + df['time']\n",
    "df.drop(['date','time'], axis=1, inplace=True)\n",
    "\n",
    "reorder_cols = ['datetime','lat','lon','current','type']\n",
    "df = df[reorder_cols]\n",
    "\n",
    "# rename datetime to just time to be consistent with lma dataframe conventions\n",
    "df.columns = ['time','lat','lon','current','type']\n",
    "df.time = np.array(df.time.values, dtype='datetime64[s]')\n",
    "\n",
    "df_cg_nldn = df[(df['time'] > datetime(2016,4,1,0,27,0)) & (df['time'] < datetime(2016,4,1,2,49,0)) \n",
    "             & (df['type'] == 'G')]\n",
    "\n",
    "df_ic_nldn = df[(df['time'] > datetime(2016,4,1,0,27,0)) & (df['time'] < datetime(2016,4,1,2,49,0)) \n",
    "             & (df['type'] == 'C')]\n",
    "\n",
    "nldn_cg_gdf = geopandas.GeoDataFrame(df_cg_nldn, geometry=geopandas.points_from_xy(df_cg_nldn.lon, df_cg_nldn.lat))\n",
    "nldn_ic_gdf = geopandas.GeoDataFrame(df_ic_nldn, geometry=geopandas.points_from_xy(df_ic_nldn.lon, df_ic_nldn.lat))\n",
    "\n",
    "nldn_cg_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "from lmatools.lasso.cell_lasso_util import read_poly_log_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract polygon vertices from lasso log file\n",
    "polylog = '../data/interim/DemoNCGridLassoLog_without_radar_guidance.txt'\n",
    "\n",
    "polys, t_edges_polys = read_poly_log_file(polylog)\n",
    "\n",
    "all_poly_lons = np.hstack([np.asarray(p)[:,0].flatten() for p in polys])\n",
    "all_poly_lats = np.hstack([np.asarray(p)[:,1].flatten() for p in polys])\n",
    "lonmin, lonmax = all_poly_lons.min(), all_poly_lons.max() \n",
    "latmin, latmax = all_poly_lats.min(), all_poly_lats.max() \n",
    "\n",
    "t_start, t_end = min(t_edges_polys), max(t_edges_polys)\n",
    "dt = timedelta(minutes=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flatten lon/lat pairs in each polygon and append all polygons to a mega list\n",
    "polygons = []\n",
    "_ = [polygons.append([np.asarray(p).flatten()]) for p in polys]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
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       "                                                                                       Polygon([[-86.57912082706129, 34.6125603747674, -86.50023051803126, 34.60587076834463, -86.4297927421116, 34.67611163578361, -86.39880012070694, 34.729628487165705, -86.384712565523, 34.79317974818193, -86.50023051803126, 34.83331738671849, -86.62138349261309, 34.87011022204368, -86.67773371334883, 34.77645573212503, -86.64674109194418, 34.6292843908243, -86.57912082706129, 34.6125603747674]]),\n",
       "                                                                                                                                                                   Polygon([[-86.51713558425199, 34.6125603747674, -86.61574847053953, 34.71624927432019, -86.6016609153556, 34.876799828466424, -86.35371994411835, 34.89352384452334, -86.19312181502151, 34.843351796352636, -86.173399237764, 34.71624927432019, -86.41007016485409, 34.55569872017393, -86.51713558425199, 34.6125603747674]]),\n",
       "                                          Polygon([[-86.39598260967016, 34.92697187663714, -86.50586554010484, 34.90690305736885, -86.62138349261309, 34.82328297708436, -86.63547104779703, 34.76307651927951, -86.48050794077376, 34.62928439082429, -86.39880012070694, 34.622594784401535, -86.27201212405156, 34.67276683257223, -86.15931168258007, 34.80655896102744, -86.26074207990439, 34.94369589269404, -86.33117985582406, 34.95373030232818, -86.39598260967016, 34.92697187663714]]),\n",
       "                                                                                                                        Polygon([[-86.46360287455303, 34.96041990875095, -86.65801113609132, 34.92697187663714, -86.65237611401776, 34.77645573212503, -86.57630331602452, 34.679456438995004, -86.42415772003804, 34.60587076834463, -86.38189505448622, 34.61590517797878, -86.30018723441941, 34.78983494497055, -86.35371994411835, 34.93031667984851, -86.46360287455303, 34.96041990875095]])]\n",
       "Length: 126, dtype: polygon[float64]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a polygon array data structure from retrieved polygon coordinates \n",
    "# needed for manipulation using spatialpandas\n",
    "polyarr = PolygonArray(polygons)\n",
    "polyarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'spatialpandas.geodataframe.GeoDataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# create spatialpandas geodataframe for NLDN data\n",
    "# so that we can use spatial interesection functionality\n",
    "\n",
    "geo_cg_nldn = GeoDataFrame(nldn_cg_gdf)\n",
    "geo_ic_nldn = GeoDataFrame(nldn_ic_gdf)\n",
    "\n",
    "print(type(geo_cg_nldn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'spatialpandas.geodataframe.GeoDataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# create spatialpandas geodataframe for lasso polygons\n",
    "geo_poly = GeoDataFrame(pd.DataFrame(polyarr,columns=['geometry']))\n",
    "geo_poly['t_start'] = t_edges_polys[:-1]\n",
    "print(type(geo_poly))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geometry    Polygon([[-87.59624231134131, 34.1375983187513...\n",
       "t_start                                   2016-04-01 00:27:00\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_poly.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>current</th>\n",
       "      <th>type</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10848</th>\n",
       "      <td>2016-04-01 00:30:43</td>\n",
       "      <td>34.1944</td>\n",
       "      <td>-87.4986</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>G</td>\n",
       "      <td>Point([-87.4986, 34.1944])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11511</th>\n",
       "      <td>2016-04-01 00:36:44</td>\n",
       "      <td>34.1828</td>\n",
       "      <td>-87.4669</td>\n",
       "      <td>6.0</td>\n",
       "      <td>G</td>\n",
       "      <td>Point([-87.4669, 34.1828])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11512</th>\n",
       "      <td>2016-04-01 00:36:44</td>\n",
       "      <td>34.2083</td>\n",
       "      <td>-87.4834</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>G</td>\n",
       "      <td>Point([-87.4834, 34.2083])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11678</th>\n",
       "      <td>2016-04-01 00:37:48</td>\n",
       "      <td>34.2071</td>\n",
       "      <td>-87.4842</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>G</td>\n",
       "      <td>Point([-87.4842, 34.2071])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11779</th>\n",
       "      <td>2016-04-01 00:38:29</td>\n",
       "      <td>34.2075</td>\n",
       "      <td>-87.4522</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>G</td>\n",
       "      <td>Point([-87.4522, 34.2075])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>25028</th>\n",
       "      <td>2016-04-01 02:40:26</td>\n",
       "      <td>34.7489</td>\n",
       "      <td>-86.4073</td>\n",
       "      <td>19.4</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>25158</th>\n",
       "      <td>2016-04-01 02:43:44</td>\n",
       "      <td>34.7309</td>\n",
       "      <td>-86.4700</td>\n",
       "      <td>57.8</td>\n",
       "      <td>G</td>\n",
       "      <td>Point([-86.47, 34.7309])</td>\n",
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       "    <tr>\n",
       "      <th>25247</th>\n",
       "      <td>2016-04-01 02:47:12</td>\n",
       "      <td>34.7105</td>\n",
       "      <td>-86.4691</td>\n",
       "      <td>-16.9</td>\n",
       "      <td>G</td>\n",
       "      <td>Point([-86.4691, 34.7105])</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25251</th>\n",
       "      <td>2016-04-01 02:47:12</td>\n",
       "      <td>34.7662</td>\n",
       "      <td>-86.4725</td>\n",
       "      <td>33.7</td>\n",
       "      <td>G</td>\n",
       "      <td>Point([-86.4725, 34.7662])</td>\n",
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       "    <tr>\n",
       "      <th>25252</th>\n",
       "      <td>2016-04-01 02:47:12</td>\n",
       "      <td>34.7436</td>\n",
       "      <td>-86.4607</td>\n",
       "      <td>36.7</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time      lat      lon  current type  \\\n",
       "10848 2016-04-01 00:30:43  34.1944 -87.4986     -4.5    G   \n",
       "11511 2016-04-01 00:36:44  34.1828 -87.4669      6.0    G   \n",
       "11512 2016-04-01 00:36:44  34.2083 -87.4834     -9.4    G   \n",
       "11678 2016-04-01 00:37:48  34.2071 -87.4842     -5.3    G   \n",
       "11779 2016-04-01 00:38:29  34.2075 -87.4522    -21.0    G   \n",
       "...                   ...      ...      ...      ...  ...   \n",
       "25028 2016-04-01 02:40:26  34.7489 -86.4073     19.4    G   \n",
       "25158 2016-04-01 02:43:44  34.7309 -86.4700     57.8    G   \n",
       "25247 2016-04-01 02:47:12  34.7105 -86.4691    -16.9    G   \n",
       "25251 2016-04-01 02:47:12  34.7662 -86.4725     33.7    G   \n",
       "25252 2016-04-01 02:47:12  34.7436 -86.4607     36.7    G   \n",
       "\n",
       "                         geometry  \n",
       "10848  Point([-87.4986, 34.1944])  \n",
       "11511  Point([-87.4669, 34.1828])  \n",
       "11512  Point([-87.4834, 34.2083])  \n",
       "11678  Point([-87.4842, 34.2071])  \n",
       "11779  Point([-87.4522, 34.2075])  \n",
       "...                           ...  \n",
       "25028  Point([-86.4073, 34.7489])  \n",
       "25158    Point([-86.47, 34.7309])  \n",
       "25247  Point([-86.4691, 34.7105])  \n",
       "25251  Point([-86.4725, 34.7662])  \n",
       "25252  Point([-86.4607, 34.7436])  \n",
       "\n",
       "[133 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract all CGs within lassos created using lmatools for storm of interest\n",
    "\n",
    "final_cg_nldn = pd.DataFrame()\n",
    "final_ic_nldn = pd.DataFrame()\n",
    "\n",
    "# find and append intersection of each polygon with available NLDN IC + CG flash data\n",
    "final_cg_nldn = final_cg_nldn.append([geo_cg_nldn[(geo_cg_nldn.geometry.intersects(geo_poly.iloc[i]['geometry'])) \n",
    "            & (geo_cg_nldn.time >= geo_poly.iloc[i]['t_start'])\n",
    "            & (geo_cg_nldn.time < (geo_poly.iloc[i]['t_start'] + timedelta(seconds=60)))] for i in range(len(geo_poly))])\n",
    "final_ic_nldn = final_ic_nldn.append([geo_ic_nldn[(geo_ic_nldn.geometry.intersects(geo_poly.iloc[i]['geometry'])) \n",
    "            & (geo_ic_nldn.time >= geo_poly.iloc[i]['t_start'])\n",
    "            & (geo_ic_nldn.time < (geo_poly.iloc[i]['t_start'] + timedelta(seconds=60)))] for i in range(len(geo_poly))])\n",
    "\n",
    "final_cg_nldn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(20,10))\n",
    "final_cg_nldn.plot('time','current',ax=ax,c='b',label='CG current')\n",
    "final_ic_nldn.plot('time','current',ax=ax,c='r',label='IC current')\n",
    "\n",
    "ax.xaxis.set_major_locator(mdates.MinuteLocator(byminute=[0, 10, 20, 30, 40, 50]))\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%H%M\"))\n",
    "ax.tick_params(axis='x',labelrotation=0)\n",
    "plt.ylabel('current (kA)')\n",
    "plt.xlabel('Time (UTC)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vortexse",
   "language": "python",
   "name": "vortexse_analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}