I have two lists, x and y, that I wish to plot together in a scatter plot.
The lists contain too many data points. I would like a graph with much less points. I cannot crop or trim these lists, I need to randomly subsamble a set number of points from both of these lists. What would be the best way to approach this?
You could subsample the lists using
idx = np.random.choice(np.arange(len(x)), num_samples)
plt.scatter(x[idx], y[idx])
However, this leaves the result a bit up to random luck. We can do better by making a heatmap. plt.hexbin makes this particularly easy:
plt.hexbin(x, y)
Here is an example, comparing the two methods:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
np.random.seed(2015)
N = 10**5
val1 = np.random.normal(loc=10, scale=2,size=N)
val2 = np.random.normal(loc=0, scale=1, size=N)
fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True)
cmap = plt.get_cmap('jet')
norm = mcolors.LogNorm()
num_samples = 10**4
idx = np.random.choice(np.arange(len(val1)), num_samples)
ax[0].scatter(val1[idx], val2[idx])
ax[0].set_title('subsample')
im = ax[1].hexbin(val1, val2, gridsize=50, cmap=cmap, norm=norm)
ax[1].set_title('hexbin heatmap')
plt.tight_layout()
fig.colorbar(im, ax=ax.ravel().tolist())
plt.show()
You can pick randomly from x and y using a random index mask
import numpy as np
import matplotlib.pyplot as plt
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
# Pick random 10 samples, 2 means two choices from [0, 1] for the mask
subsample = np.random.choice(2, 10).astype(bool)
plt.scatter(x[subsample], y[subsample])
plt.show()
Alternatively you can use hist2d to plot a 2D histogram, which uses densities instead of data points
plt.hist2d(x, y) # No need to subsample
You can use random.sample():
max_points = len(x)
# Assuming you only want 50 points.
random_indexes = random.sample(range(max_points), 50)
new_x = [x[i] for i in random_indexes]
new_y = [y[i] for i in random_indexes]
