File:Varianz.gif

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Varianz.gif(300 × 300 pixels, file size: 135 KB, MIME type: image/gif, looped, 48 frames, 14 s)

Captions

Captions

Normal distributions with different covariances

Summary[edit]

Description
English: Animated scatter diagram with two normal distributions with different covariances from -1 to +1.
Deutsch: Animiertes Streudiagramm mit zwei Normalverteilungen mit verschiedenen Kovarianzen von -1 bis +1.
Date
Source Own work
Author Physikinger
GIF development
InfoField
 
This plot was created with Matplotlib.
Source code
InfoField

Python code

# This source code is public domain

import numpy
import matplotlib.pyplot as plt
import imageio

sigma = 5
N = 500
nFrames = 50
numpy.random.seed(0)
d1 = numpy.random.randn(N) * sigma
d2 = numpy.random.randn(N) * sigma

images = []
duration = []
blend = 0.998
for c in numpy.linspace(-1,1,nFrames//2).tolist() + numpy.linspace(-1,1,nFrames//2).tolist()[::-1][1:-1]:
    d1 = d1 * blend + numpy.sqrt(1-blend**2) * numpy.random.randn(N) * sigma
    d2 = d2 * blend + numpy.sqrt(1-blend**2) * numpy.random.randn(N) * sigma

    zero = abs(c) < 1E-4
    if c == -1: duration.append(2)
    elif zero: duration.append(1.0)
    elif c == 1: duration.append(2)
    else: duration.append(0.175)
    cMat = numpy.array([[1,c],[c,1]]) / numpy.sqrt(1+c**2)
    x,y = cMat @ [d1, d2]
    fig = plt.figure(figsize=(3.0,3.0), dpi=100)
    plt.plot(x,y,'.')
    plt.text(0.25, 0.89, ('cov=%'+('' if zero else '+')+'i')%(c*sigma**2), transform=plt.gca().transAxes, fontsize=20)
    plt.text(0.32, 0.03, '$\sigma_x$=%0.1f'%sigma, transform=plt.gca().transAxes, fontsize=20)
    plt.text(0.03, 0.35, '$\sigma_y$=%0.1f'%sigma, transform=plt.gca().transAxes, rotation=90, fontsize=20)
    plt.xlabel('x', labelpad=1)
    plt.ylabel('y', labelpad=0)
    plt.xlim(-20,20)
    plt.ylim(-20,20)
    fig.subplots_adjust(
        top=0.95,
        bottom=0.14,
        left=0.18,
        right=0.95,
        hspace=0.2,
        wspace=0.2
    )
    plt.xlim(-20,20)
    plt.ylim(-20,20)
    fig.canvas.draw()
    s, (width, height) = fig.canvas.print_to_buffer()
    images.append(numpy.array(list(s), numpy.uint8).reshape((height, width, 4)))
    fig.clf()
    plt.close('all')

# Save GIF animation
fileOut = 'Varianz.gif'
imageio.mimsave(fileOut, images, duration=duration)

# Optimize GIF size
# from pygifsicle import optimize
# optimize(fileOut, colors=6)

Licensing[edit]

I, the copyright holder of this work, hereby publish it under the following license:
Creative Commons CC-Zero This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication.
The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

File history

Click on a date/time to view the file as it appeared at that time.

Date/TimeThumbnailDimensionsUserComment
current00:05, 10 March 2022Thumbnail for version as of 00:05, 10 March 2022300 × 300 (135 KB)Physikinger (talk | contribs)Slower
23:59, 9 March 2022Thumbnail for version as of 23:59, 9 March 2022300 × 300 (135 KB)Physikinger (talk | contribs)More random
23:43, 9 March 2022Thumbnail for version as of 23:43, 9 March 2022300 × 300 (134 KB)Physikinger (talk | contribs)Correct variance
23:23, 9 March 2022Thumbnail for version as of 23:23, 9 March 2022300 × 300 (133 KB)Physikinger (talk | contribs)Uploaded own work with UploadWizard

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