In [1]:

    

import matplotlib.colors as clr
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
% matplotlib inline


    

In [2]:

    

np.random.seed(0)
n_samples = 100000
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)
X, _ = noisy_moons
with open("/tmp/moons.txt", "wb") as fw:
    for i in range(n_samples):
        fw.write("{} {} {}\n".format(i, X[i][0] * 100, X[i][1] * 100))


    

In [3]:

    

moons = np.genfromtxt('/tmp/moons.txt', delimiter=' ', names=['id', 'x', 'y'])
plt.figure(1)
plt.scatter(moons['x'], moons['y'], color='r')
plt.show(block=False)


    

In [4]:

    

parts = np.genfromtxt('/tmp/parts.csv', delimiter=',', names=['id', 'x', 'y', 'c'])
colors = ['black', 'red', 'green', 'blue', 'purple']
plt.figure(2)
plt.scatter(parts['x'], parts['y'], c=parts['c'], cmap=clr.ListedColormap(colors), lw=0)
plt.show(block=False)