# Libraries
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
# Create data: 200 points
data = np.random.multivariate_normal([0, 0], [[1, 0.5], [0.5, 3]], 200)
x, y = data.T
# Create a figure with 6 plot areas
fig, axes = plt.subplots(ncols=7, nrows=1, figsize=(25, 5))
# Everything starts with a Scatterplot
axes[0].set_title('Scatterplot')
axes[0].plot(x, y, 'ko')
# As you can see there is a lot of overlapping here!
# Everything starts with a Scatterplot
axes[1].set_title('Scatterplot')
axes[1].scatter(x, y, color='green', edgecolor='black')
# Thus we can cut the plotting window in several hexbins
nbins = 20
axes[2].set_title('Hexbin')
axes[2].hexbin(x, y, gridsize=nbins, cmap=plt.cm.BuGn_r)
# 2D Histogram
axes[3].set_title('2D Histogram')
axes[3].hist2d(x, y, bins=nbins, cmap=plt.cm.BuGn_r)
# Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents
k = gaussian_kde(data.T)
xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
# plot a density
axes[4].set_title('Calculate Gaussian KDE')
axes[4].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='auto', cmap=plt.cm.BuGn_r)
# add shading
axes[5].set_title('2D Density with shading')
axes[5].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=plt.cm.BuGn_r)
# contour
axes[6].set_title('Contour')
axes[6].pcolormesh(xi, yi, zi.reshape(xi.shape), shading='gouraud', cmap=plt.cm.BuGn_r)
axes[6].contour(xi, yi, zi.reshape(xi.shape) )
plt.show()RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4