{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.3.0.dev\n"
]
}
],
"source": [
"import os\n",
"import folium\n",
"\n",
"print(folium.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"import cartopy.feature as cfeature\n",
"\n",
"LAND = cfeature.NaturalEarthFeature('physical', 'land', '50m',\n",
" edgecolor='face',\n",
" facecolor=cfeature.COLORS['land'])\n",
"\n",
"\n",
"def make_map(bbox, projection=ccrs.Mercator()):\n",
" fig, ax = plt.subplots(figsize=(8, 6),\n",
" subplot_kw=dict(projection=projection))\n",
" ax.set_extent(bbox)\n",
" ax.add_feature(LAND, facecolor='0.25')\n",
" ax.coastlines(resolution='50m')\n",
" return fig, ax"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy.ma as ma\n",
"from scipy.io import loadmat\n",
"\n",
"fname = os.path.join('data', 'mercator_temperature.mat')\n",
"merc = loadmat(fname, squeeze_me=True)\n",
"\n",
"x, y = merc['x'], merc['y']\n",
"bbox = [x.min(), x.max(), y.min(), y.max()]\n",
"\n",
"level = 10 # 10 meters temperature.\n",
"data = ma.masked_invalid(merc['temp'][level, ...])\n",
"\n",
"fig, ax = make_map(bbox=bbox)\n",
"cs = ax.pcolormesh(x, y, data, transform=ccrs.PlateCarree())\n",
"fig.savefig('GS.png', transparent=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from PIL import Image, ImageChops\n",
"\n",
"\n",
"def trim(img):\n",
" border = Image.new(img.mode, img.size, img.getpixel((0, 0)))\n",
" diff = ImageChops.difference(img, border)\n",
" diff = ImageChops.add(diff, diff, 2.0, -100)\n",
" bbox = diff.getbbox()\n",
" if bbox:\n",
" img = img.crop(bbox)\n",
" return np.array(img)\n",
"\n",
"img = Image.open(\"GS.png\")\n",
"img = trim(img)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from folium import plugins\n",
"\n",
"\n",
"m = folium.Map(location=[y.mean(), x.mean()], zoom_start=5)\n",
"\n",
"plugins.ImageOverlay(img,\n",
" [[y.min(), x.min()], [y.max(), x.max()]],\n",
" opacity=0.5).add_to(m)\n",
"\n",
"\n",
"m.save(os.path.join('results', 'GulfStreamImageOverlay_0.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bypass the figure creation entirely:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/filipe/miniconda3/envs/FOLIUM/lib/python3.5/site-packages/numpy/ma/core.py:852: RuntimeWarning: invalid value encountered in greater_equal\n",
" return umath.absolute(a) * self.tolerance >= umath.absolute(b)\n"
]
}
],
"source": [
"def colorize(array, cmap='rainbow'):\n",
" normed_data = (array - array.min()) / (array.max() - array.min())\n",
" cm = plt.cm.get_cmap(cmap)\n",
" return cm(normed_data)\n",
"\n",
"colored_data = colorize(data, cmap='rainbow')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[y.mean(), x.mean()], zoom_start=5)\n",
"\n",
"plugins.ImageOverlay(np.flipud(colored_data),\n",
" [[y.min(), x.min()], [y.max(), x.max()]],\n",
" opacity=0.5).add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'GulfStreamImageOverlay_1.html'))\n",
"\n",
"m"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}