1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
|
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import multiscale_graphcorr
def mgc_plot(x, y, mgc_dict):
"""Plot sim and MGC-plot"""
plt.figure(figsize=(8, 8))
ax = plt.gca()
# local correlation map
mgc_map = mgc_dict["mgc_map"]
# draw heatmap
ax.set_title("Local Correlation Map", fontsize=20)
im = ax.imshow(mgc_map, cmap='YlGnBu')
# colorbar
cbar = ax.figure.colorbar(im, ax=ax)
cbar.ax.set_ylabel("", rotation=-90, va="bottom")
ax.invert_yaxis()
# Turn spines off and create white grid.
for _, spine in ax.spines.items():
spine.set_visible(False)
# optimal scale
opt_scale = mgc_dict["opt_scale"]
ax.scatter(opt_scale[0], opt_scale[1],
marker='X', s=200, color='red')
# other formatting
ax.tick_params(bottom="off", left="off")
ax.set_xlabel('#Neighbors for X', fontsize=15)
ax.set_ylabel('#Neighbors for Y', fontsize=15)
ax.tick_params(axis="x", labelsize=15)
ax.tick_params(axis="y", labelsize=15)
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
rng = np.random.default_rng()
unif = np.array(rng.uniform(0, 5, size=100))
x = unif * np.cos(np.pi * unif)
y = unif * np.sin(np.pi * unif) + 0.4 * rng.random(x.size)
_, _, mgc_dict = multiscale_graphcorr(x, y, random_state=rng)
mgc_plot(x, y, mgc_dict)
|
