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deeplook/covidohedron.ipynb

Last active April 3, 2020 09:43
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A sperical view to the COVID-19 distribution.
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covidohedron.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Covidohedron"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from math import pi"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import geojson\n",
"import ipyvolume as ipv\n",
"from ipyvolume.datasets import UrlCached\n",
"from PIL import Image\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Helpers"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def radius_sphere(volume):\n",
" return (volume / pi / 4 * 3) ** (1/3)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def sphere(x, y, z, radius, num=30, color=None, texture=None, wireframe=False):\n",
" \"\"\"Create a sphere mesh with origin at x, y, z and radius.\n",
" \"\"\"\n",
" assert num > 0\n",
" u = np.linspace(0, 1, num)\n",
" v = np.linspace(0, 1, num)\n",
" u, v = np.meshgrid(u, v)\n",
" phi = u * 2 * np.pi\n",
" theta = v * np.pi\n",
" x = x + radius * np.cos(phi) * np.sin(theta)\n",
" y = y + radius * np.cos(theta)\n",
" z = z + radius * np.sin(phi) * np.sin(theta)\n",
"\n",
" kwargs = dict(color=color or \"blue\", texture=texture, wireframe=wireframe)\n",
" return ipv.plot_mesh(x, y, z, u=0.75-u, v=1-v, **kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"pi_div_180 = np.pi / 180\n",
"\n",
"def lonlat2xyz(lon, lat, radius=1, unit='deg'):\n",
" \"Convert lat/lon pair to Cartesian x/y/z triple.\"\n",
"\n",
" if unit == 'deg':\n",
" lat = lat * pi_div_180\n",
" lon = lon * pi_div_180\n",
" cos_lat = np.cos(lat)\n",
" x = radius * cos_lat * np.sin(lon)\n",
" y = radius * np.sin(lat)\n",
" z = radius * cos_lat * np.cos(lon)\n",
" return (x, y, z)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data processing"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"base = (\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/\"\n",
" \"master/csse_covid_19_data/csse_covid_19_time_series/\")\n",
"df_confirmed = pd.read_csv(base + \"time_series_covid19_confirmed_global.csv\")\n",
"df_confirmed[\"Province/State\"].fillna(\"\", inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df = df_confirmed\n",
"provinces = df[\"Province/State\"]\n",
"countries = df[\"Country/Region\"]\n",
"locations = list(zip(df.Lat, df.Long))\n",
"day_cols = [col for col in df.columns if col.count(\"/\") == 2]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data range: 1/22/20 – 4/2/20.\n"
]
}
],
"source": [
"print(f\"Data range: {day_cols[0]} – {day_cols[-1]}.\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Province/State</th>\n",
" <th>Country/Region</th>\n",
" <th>Lat</th>\n",
" <th>Long</th>\n",
" <th>1/22/20</th>\n",
" <th>1/23/20</th>\n",
" <th>1/24/20</th>\n",
" <th>1/25/20</th>\n",
" <th>1/26/20</th>\n",
" <th>1/27/20</th>\n",
" <th>...</th>\n",
" <th>3/24/20</th>\n",
" <th>3/25/20</th>\n",
" <th>3/26/20</th>\n",
" <th>3/27/20</th>\n",
" <th>3/28/20</th>\n",
" <th>3/29/20</th>\n",
" <th>3/30/20</th>\n",
" <th>3/31/20</th>\n",
" <th>4/1/20</th>\n",
" <th>4/2/20</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td></td>\n",
" <td>Afghanistan</td>\n",
" <td>33.0000</td>\n",
" <td>65.0000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>74</td>\n",
" <td>84</td>\n",
" <td>94</td>\n",
" <td>110</td>\n",
" <td>110</td>\n",
" <td>120</td>\n",
" <td>170</td>\n",
" <td>174</td>\n",
" <td>237</td>\n",
" <td>273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td></td>\n",
" <td>Albania</td>\n",
" <td>41.1533</td>\n",
" <td>20.1683</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>123</td>\n",
" <td>146</td>\n",
" <td>174</td>\n",
" <td>186</td>\n",
" <td>197</td>\n",
" <td>212</td>\n",
" <td>223</td>\n",
" <td>243</td>\n",
" <td>259</td>\n",
" <td>277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td></td>\n",
" <td>Algeria</td>\n",
" <td>28.0339</td>\n",
" <td>1.6596</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>264</td>\n",
" <td>302</td>\n",
" <td>367</td>\n",
" <td>409</td>\n",
" <td>454</td>\n",
" <td>511</td>\n",
" <td>584</td>\n",
" <td>716</td>\n",
" <td>847</td>\n",
" <td>986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td></td>\n",
" <td>Andorra</td>\n",
" <td>42.5063</td>\n",
" <td>1.5218</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>164</td>\n",
" <td>188</td>\n",
" <td>224</td>\n",
" <td>267</td>\n",
" <td>308</td>\n",
" <td>334</td>\n",
" <td>370</td>\n",
" <td>376</td>\n",
" <td>390</td>\n",
" <td>428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td></td>\n",
" <td>Angola</td>\n",
" <td>-11.2027</td>\n",
" <td>17.8739</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 76 columns</p>\n",
"</div>"
],
"text/plain": [
" Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 \\\n",
"0 Afghanistan 33.0000 65.0000 0 0 0 \n",
"1 Albania 41.1533 20.1683 0 0 0 \n",
"2 Algeria 28.0339 1.6596 0 0 0 \n",
"3 Andorra 42.5063 1.5218 0 0 0 \n",
"4 Angola -11.2027 17.8739 0 0 0 \n",
"\n",
" 1/25/20 1/26/20 1/27/20 ... 3/24/20 3/25/20 3/26/20 3/27/20 \\\n",
"0 0 0 0 ... 74 84 94 110 \n",
"1 0 0 0 ... 123 146 174 186 \n",
"2 0 0 0 ... 264 302 367 409 \n",
"3 0 0 0 ... 164 188 224 267 \n",
"4 0 0 0 ... 3 3 4 4 \n",
"\n",
" 3/28/20 3/29/20 3/30/20 3/31/20 4/1/20 4/2/20 \n",
"0 110 120 170 174 237 273 \n",
"1 197 212 223 243 259 277 \n",
"2 454 511 584 716 847 986 \n",
"3 308 334 370 376 390 428 \n",
"4 5 7 7 7 8 8 \n",
"\n",
"[5 rows x 76 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Map texture"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"url = \"https://www.evl.uic.edu/pape/data/Earth/1024/BigEarth.jpg\"\n",
"image_file = UrlCached(url)\n",
"map_texture = Image.open(image_file.fetch())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Borders"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"url = \"https://raw.githubusercontent.com/johan/world.geo.json/master/countries.geo.json\"\n",
"borders_file = UrlCached(url)\n",
"data = geojson.load(open(borders_file.fetch()))\n",
"lon, lat = np.array(list(geojson.utils.coords(data))).T\n",
"z = [lonlat2xyz(xi, yi) for (xi, yi) in zip(lon, lat)]\n",
"borders_xyz = np.array(z).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## COVID-19 data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"lon_lat_conf = list(zip(df.Long, df.Lat, df[\"4/1/20\"]))\n",
"xyz = [lonlat2xyz(lon, lat) for lon, lat, conf in lon_lat_conf]\n",
"c19_size = [radius_sphere(conf) for lon, lat, conf in lon_lat_conf]\n",
"c19_xyz = np.array(xyz).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Final plot"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "42a493e7d8294c80871f65f5096f69cb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Figure(camera=PerspectiveCamera(fov=46.0, position=(0.0, 0.0, 2.0), quaternion=(0.0, 0.0, 0.0, …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = ipv.figure()\n",
"s = sphere(0, 0, 0, radius=1, num=24, color=\"white\", texture=map_texture)\n",
"borders = ipv.scatter(*borders_xyz, size=1, color='limegreen', marker='sphere')\n",
"c19 = ipv.scatter(*c19_xyz, size=c19_size, color=\"red\", marker=\"sphere\")\n",
"# ipv.view(azimuth=0, elevation=90) # lon/lat ??\n",
"ipv.show()"
]
}
],
"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
}
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