Introduction to Computational Analysis

 Pay Notebook Creator: Roy Hyunjin Han 0 Set Container: Numerical CPU with TINY Memory for 10 Minutes 0 Total 0
In [ ]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.ndimage
import scipy.spatial

Make 50 random points with X and Y coordinates from 0 to 256.

In [ ]:
point_count = 50
points = np.random.rand(point_count, 2) * 256
points[:3]

Look at the last three points.

In [ ]:
# Type your solution here and press CTRL-ENTER

Make a scatter plot of the 50 random points.

In [ ]:
values = np.random.rand(point_count) * 300
plt.scatter(points[:, 0], points[:, 1], c=values, s=values, cmap=plt.cm.Greens)
plt.axis('tight');

Set points with an area greater than 50 to a different color using the Paired colormap.

In [ ]:
# Type your solution here and press CTRL-ENTER

Paint a heatmap of the 50 random points.

In [ ]:
def get_heatmap(points):
image = np.zeros((256, 256))
for x, y in points:
image[int(y), int(x)] += 1
return scipy.ndimage.gaussian_filter(image, (10, 10))

plt.imshow(get_heatmap(points), origin='lower');

Overlay a scatter plot on the heatmap.

In [ ]:
# Type your solution here and press CTRL-ENTER

Pick a random point.

In [ ]:
import random
random_point = random.choice(points)
random_point

Get the five points closest to the random point.

In [ ]:
import scipy.spatial
kd_tree = scipy.spatial.KDTree(points)
neighbor_distances, neighbor_indices = kd_tree.query(random_point, k=5)
points[neighbor_indices]

Generate fifty heatmaps where the first heatmap contains a random point and the next heatmap adds a point that is close to the random point.

In [ ]:
target_path = '/tmp/points%03d.png'
kd_tree = scipy.spatial.KDTree(points)
image = get_heatmap([random_point])
plt.imsave(target_path % 1, image, origin='lower')
for point_count in range(2, len(points) + 1):
nearest_points = points[kd_tree.query(random_point, k=point_count - 1)]
image = get_heatmap(np.vstack([random_point, nearest_points]))
plt.imsave(target_path % point_count, image, origin='lower')
print(target_path % point_count)

Combine the fifty heatmaps into an animated GIF.

In [ ]:
import subprocess
subprocess.call('sudo dnf -y install GraphicsMagick'.split())
In [ ]:
subprocess.call('gm convert -delay 1 /tmp/points*.png /tmp/points.gif'.split())
In [ ]:
from IPython.display import Image
Image('/tmp/points.gif')