bradyrx/tornados.ipynb

## tornados.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from xhistogram.xarray import histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lats = np.linspace(0, 60, 61)\n",
    "lons = np.linspace(145, 175, 31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_lats = []\n",
    "df_lons = []\n",
    "for lat in lats:\n",
    "    for lon in lons:\n",
    "        df_lats.append(lat)\n",
    "        df_lons.append(lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rileybrady/miniconda3/envs/analysis/lib/python3.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: This function is deprecated. Please call randint(0, 1 + 1) instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame([df_lats, df_lons]).T\n",
    "df.columns = ['lat', 'lon']\n",
    "df['tornados'] = np.random.random_integers(low=0, high=1, size=1891)\n",
    "# Simulate a lat/lon list of places where there's tornados\n",
    "df = df[df['tornados']==1]\n",
    "del df['tornados']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lon_locs = df['lon'].to_xarray().rename({'index': 'lon'})\n",
    "lat_locs = df['lat'].to_xarray().rename({'index': 'lat'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xbins = np.linspace(0.5, 359.5, 360)\n",
    "ybins = np.linspace(-89.5, 89.5, 180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = histogram(lon_locs, lat_locs, bins=[xbins, ybins])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7feaa85538d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import xarray as xr\n",
	"import numpy as np\n",
	"import pandas as pd\n",
	"from xhistogram.xarray import histogram"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"lats = np.linspace(0, 60, 61)\n",
	"lons = np.linspace(145, 175, 31)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"df_lats = []\n",
	"df_lons = []\n",
	"for lat in lats:\n",
	" for lon in lons:\n",
	" df_lats.append(lat)\n",
	" df_lons.append(lon)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/Users/rileybrady/miniconda3/envs/analysis/lib/python3.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: This function is deprecated. Please call randint(0, 1 + 1) instead\n",
	" This is separate from the ipykernel package so we can avoid doing imports until\n"
	]
	}
	],
	"source": [
	"df = pd.DataFrame([df_lats, df_lons]).T\n",
	"df.columns = ['lat', 'lon']\n",
	"df['tornados'] = np.random.random_integers(low=0, high=1, size=1891)\n",
	"# Simulate a lat/lon list of places where there's tornados\n",
	"df = df[df['tornados']==1]\n",
	"del df['tornados']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"outputs": [],
	"source": [
	"lon_locs = df['lon'].to_xarray().rename({'index': 'lon'})\n",
	"lat_locs = df['lat'].to_xarray().rename({'index': 'lat'})"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [],
	"source": [
	"xbins = np.linspace(0.5, 359.5, 360)\n",
	"ybins = np.linspace(-89.5, 89.5, 180)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {},
	"outputs": [],
	"source": [
	"test = histogram(lon_locs, lat_locs, bins=[xbins, ybins])"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.collections.QuadMesh at 0x7feaa85538d0>"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 2 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"test.plot()"
	]
	}
	],
	"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": "3.7.6"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 4
	}