In [1]:

import os
import folium

print(folium.__version__)

0.5.0+105.g065f6f3.dirty

In [2]:

import numpy as np


def sample_data(shape=(73, 145)):
    nlats, nlons = shape
    lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)
    lons = np.linspace(0, 2 * np.pi, nlons)
    lons, lats = np.meshgrid(lons, lats)
    wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)
    mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)

    lats = np.rad2deg(lats)
    lons = np.rad2deg(lons)
    data = wave + mean

    return lons, lats, data


lon, lat, data = sample_data(shape=(73, 145))
lon -= 180

In [3]:

%matplotlib inline

import matplotlib

cm = matplotlib.cm.get_cmap('cubehelix')

normed_data = (data - data.min()) / (data.max() - data.min())
colored_data = cm(normed_data)

Bad¶

In [4]:

m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)

folium.raster_layers.ImageOverlay(
    image=colored_data,
    bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],
    opacity=0.25
).add_to(m)

m.save(os.path.join('results', 'GeodedeticImageOverlay_0.html'))

m

Out[4]:

Good¶

In [5]:

m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)

folium.raster_layers.ImageOverlay(
    image=colored_data,
    bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],
    mercator_project=True,
    opacity=0.25
).add_to(m)

m.save(os.path.join('results', 'GeodedeticImageOverlay_1.html'))

m

Out[5]:

Same as above but with cartopy¶

In [6]:

import cartopy.crs as ccrs
from cartopy.img_transform import warp_array

source_extent = [lon.min(), lon.max(), lat.min(), lat.max()]

new_data = warp_array(colored_data,
                      target_proj=ccrs.GOOGLE_MERCATOR,
                      source_proj=ccrs.PlateCarree(),
                      target_res=data.shape,
                      source_extent=source_extent,
                      target_extent=None,
                      mask_extrapolated=False)


m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)

folium.raster_layers.ImageOverlay(
    image=new_data[0],
    bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],
    opacity=0.25
).add_to(m)

m.save(os.path.join('results', 'GeodedeticImageOverlay_2.html'))

m

Out[6]:

TODO: Try rasterio. Rasterio can warp images and arrays.

Compare to original¶

In [7]:

from IPython.display import IFrame

url = 'http://scitools.org.uk/cartopy/docs/latest/gallery/waves.html'
IFrame(url, width=900, height=750)

Out[7]: