Contours with map overlay on irregular grid in python

Question

Here is my data:

Lon   Lat     Z   Z2  pos
32.6  -13.6   41   9  CHIP
27.1  -16.9   43  12  CHOM
32.7  -10.2   46  14  ISOK
24.2  -13.6   33  13  KABO
28.5           


        

Answer 1

To start with, let's ignore the map-based part of things, and just treat your lat, long coordinates as a cartesian coordinate system.

import numpy as np
import pandas as pd
from matplotlib.mlab import griddata
import matplotlib.pyplot as plt

#-- Read the data.
# I'm going to use `pandas` to read in and work with your data, mostly due to
# the text site names. Using pandas is optional, however.
data = pd.read_csv('your_data.txt', delim_whitespace=True)

#-- Now let's grid your data.
# First we'll make a regular grid to interpolate onto. This is equivalent to
# your call to `mgrid`, but it's broken down a bit to make it easier to
# understand. The "30j" in mgrid refers to 30 rows or columns.
numcols, numrows = 30, 30
xi = np.linspace(data.Lon.min(), data.Lon.max(), numcols)
yi = np.linspace(data.Lat.min(), data.Lat.max(), numrows)
xi, yi = np.meshgrid(xi, yi)

#-- Interpolate at the points in xi, yi
# "griddata" expects "raw" numpy arrays, so we'll pass in
# data.x.values instead of just the pandas series data.x
x, y, z = data.Lon.values, data.Lat.values, data.Z.values
zi = griddata(x, y, z, xi, yi)

#-- Display the results
fig, ax = plt.subplots()
im = ax.contourf(xi, yi, zi)
ax.scatter(data.Lon, data.Lat, c=data.Z, s=100,
           vmin=zi.min(), vmax=zi.max())
fig.colorbar(im)

plt.show()

The "blocky" boundary is due to the coarse (30x30) resolution of the grid. griddata uses a triangulation method, so nothing outside of the convex hull of your data points is interpolated. To see this more clearly, bump up numcols and numrows to, say, 300x300:

You could also use several other interpolation methods (particularly if you want to extend the interpolation beyond the convex hull of the data).