Smooth Contourf plot completely filled

Question

I have the data with (X,Y,Z) values. I tried to make a density plot with Z values for intensity. However the plot I get is not smooth and and has polytope i.e not completely filled.

The following is the code with the Data

but I want to obtain smooth and completely filled plot

import numpy as np
from scipy.interpolate import griddata
import matplotlib.pyplot as plt
import xlrd
location = "~/Desktop/Data.xlsx"
data = xlrd.open_workbook(location)
sheet = data.sheet_by_index(0)
sample=2000

x=np.array(sheet.col_values(0))[0:sample]
y=np.array(sheet.col_values(1))[0:sample]
z=np.hamming(9000)[0:sample]
print z
def plot_contour(x,y,z,resolution = 500,contour_method='cubic'):
    resolution = str(resolution)+'j'
    X,Y = np.mgrid[min(x):max(x):complex(resolution),   min(y):max(y):complex(resolution)]
    points = [[a,b] for a,b in zip(x,y)]
    Z = griddata(points, z, (X, Y), method=contour_method)
    return X,Y,Z

X,Y,Z = plot_contour(x,y,z,resolution = 500,contour_method='linear')

plt.style.context("seaborn-deep")

plt.contourf(X,Y,Z)
plt.colorbar()
plt.show()

This is the output:

This is what I want to achieve using contourplotf:

Answer 1

plt.contourf() is not the main problem here, it's just working with the data it has. The problem is the linear interpolation in scipy.interpolate.griddata().

I recommend not using griddata, but instead using one of the following methods:

• scipy.interpolate.Rbf() — this is what you were using before (see my previous answer).
• verde — an awesome gridding package.
• sklearn.gaussian_process — or some other prediction model.

All of these methods will fill in the grid. If you plot the result with plt.imshow() you'll get the type of plot you show in your question — that is not a plt.contourf() plot.

Here's a demo notebook showing all of these approaches (including griddata).