{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5.0+105.g065f6f3.dirty\n"
]
}
],
"source": [
"import os\n",
"import folium\n",
"\n",
"print(folium.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"\n",
"def sample_data(shape=(73, 145)):\n",
" nlats, nlons = shape\n",
" lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)\n",
" lons = np.linspace(0, 2 * np.pi, nlons)\n",
" lons, lats = np.meshgrid(lons, lats)\n",
" wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)\n",
" mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)\n",
"\n",
" lats = np.rad2deg(lats)\n",
" lons = np.rad2deg(lons)\n",
" data = wave + mean\n",
"\n",
" return lons, lats, data\n",
"\n",
"\n",
"lon, lat, data = sample_data(shape=(73, 145))\n",
"lon -= 180"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib\n",
"\n",
"cm = matplotlib.cm.get_cmap('cubehelix')\n",
"\n",
"normed_data = (data - data.min()) / (data.max() - data.min())\n",
"colored_data = cm(normed_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bad"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n",
"\n",
"folium.raster_layers.ImageOverlay(\n",
" image=colored_data,\n",
" bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n",
" opacity=0.25\n",
").add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'GeodedeticImageOverlay_0.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Good"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n",
"\n",
"folium.raster_layers.ImageOverlay(\n",
" image=colored_data,\n",
" bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n",
" mercator_project=True,\n",
" opacity=0.25\n",
").add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'GeodedeticImageOverlay_1.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Same as above but with cartopy"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import cartopy.crs as ccrs\n",
"from cartopy.img_transform import warp_array\n",
"\n",
"source_extent = [lon.min(), lon.max(), lat.min(), lat.max()]\n",
"\n",
"new_data = warp_array(colored_data,\n",
" target_proj=ccrs.GOOGLE_MERCATOR,\n",
" source_proj=ccrs.PlateCarree(),\n",
" target_res=data.shape,\n",
" source_extent=source_extent,\n",
" target_extent=None,\n",
" mask_extrapolated=False)\n",
"\n",
"\n",
"m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n",
"\n",
"folium.raster_layers.ImageOverlay(\n",
" image=new_data[0],\n",
" bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n",
" opacity=0.25\n",
").add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'GeodedeticImageOverlay_2.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TODO: Try [rasterio](https://github.com/mapbox/rasterio/blob/ca75cf0a842943c1b3da4522e6ea3500215130fd/docs/reproject.rst). Rasterio can warp images and arrays."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Compare to original"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import IFrame\n",
"\n",
"url = 'http://scitools.org.uk/cartopy/docs/latest/gallery/waves.html'\n",
"IFrame(url, width=900, height=750)"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}