Contour plot data (lat,lon,value) within boundaries and export GeoJSON

Question

I\'m trying to interpolate data within boundaries and plot contour lines (polygons) based on Latitude, longitude, value data from csv files.

Results I want print as geoj

Answer 1

Almost I got what I excepted. Based on mgc answer. I change griddata as you suggest to scipy.interpolate.griddata and interpolation method to nearest. Now its works like I want.

Only last thing that I need is to limit this interpolation to polygon (boundaries) also from geoJson.

The other problem is to export to geojson polygons WITH colors as attributes.

import numpy as np
import matplotlib.pyplot as plt
#from matplotlib.mlab import griddata
from scipy.interpolate import griddata

from geopandas import GeoDataFrame
from shapely.geometry import Polygon, MultiPolygon

def collec_to_gdf(collec_poly):
    """Transform a `matplotlib.contour.QuadContourSet` to a GeoDataFrame"""
    polygons = []
    for i, polygon in enumerate(collec_poly.collections):
        mpoly = []
        for path in polygon.get_paths():
            try:
                path.should_simplify = False
                poly = path.to_polygons()
                # Each polygon should contain an exterior ring + maybe hole(s):
                exterior, holes = [], []
                if len(poly) > 0 and len(poly[0]) > 3:
                    # The first of the list is the exterior ring :
                    exterior = poly[0]
                    # Other(s) are hole(s):
                    if len(poly) > 1:
                        holes = [h for h in poly[1:] if len(h) > 3]
                mpoly.append(Polygon(exterior, holes))
            except:
                print('Warning: Geometry error when making polygon #{}'
                      .format(i))
        if len(mpoly) > 1:
            mpoly = MultiPolygon(mpoly)
            polygons.append(mpoly)
        elif len(mpoly) == 1:
            polygons.append(mpoly[0])
    return GeoDataFrame(geometry=polygons, crs={'init': 'epsg:4326'})

data = np.genfromtxt('temp2.csv', delimiter=',')
x = data[..., 1]
y = data[..., 2]
z = data[..., 4]
xi = np.linspace(x.min(), x.max(), num=100)
yi = np.linspace(y.min(), y.max(), num=100)

#zi = griddata(x, y, z, xi, yi, interp='nn') # You could also take a look to scipy.interpolate.griddata
zi = griddata((x, y), z, (xi[None,:], yi[:,None]), method='nearest')

nb_class = [5,10,15,20,25,30,35,40,45,50] # Set the number of class for contour creation
# The class can also be defined by their limits like [0, 122, 333]
collec_poly = plt.contourf(
    xi, yi, zi, nb_class, vmax=abs(zi).max(), vmin=-abs(zi).max())

gdf = collec_to_gdf(collec_poly)
#gdf.to_json()
print gdf.to_json()

plt.plot(x,y)

plt.show()