Practice Assignment: Understanding Distributions Through Sampling

第三周Optional的pratice，做不做都可以，不做也可以直接去看看其他做了人的代码，学习下。
难度不大不小，主要是读懂题，然后考animation。
有个问题折腾了我很久，希望有知道的小伙伴解答。a = animation.FuncAnimation(fig, update, interval=100)这里，貌似只能用a = ...，其他的变量名，比如simulation b等都不行，都会造成动画效果不停止，一直运行下去，尝试了很久，还是找不到原因 :(
欢迎评论区提出建议~

** This assignment is optional, and I encourage you to share your solutions with me and your peers in the discussion forums! **

To complete this assignment, create a code cell that:

Creates a number of subplots using the pyplot subplots or matplotlib gridspec functionality.
Creates an animation, pulling between 100 and 1000 samples from each of the random variables (x1, x2, x3, x4) for each plot and plotting this as we did in the lecture on animation.
Bonus: Go above and beyond and “wow” your classmates (and me!) by looking into matplotlib widgets and adding a widget which allows for parameterization of the distributions behind the sampling animations.

Tips:

Before you start, think about the different ways you can create this visualization to be as interesting and effective as possible.
Take a look at the histograms below to get an idea of what the random variables look like, as well as their positioning with respect to one another. This is just a guide, so be creative in how you lay things out!
Try to keep the length of your animation reasonable (roughly between 10 and 30 seconds).

import matplotlib.pyplot as plt
import numpy as np

%matplotlib notebook

# generate 4 random variables from the random, gamma, exponential, and uniform distributions
x1 = np.random.normal(-2.5, 1, 10000)
x2 = np.random.gamma(2, 1.5, 10000)
x3 = np.random.exponential(2, 10000)+7
x4 = np.random.uniform(14,20, 10000)

# plot the histograms
plt.figure(figsize=(9,3))
plt.hist(x1, normed=True, bins=20, alpha=0.5)
plt.hist(x2, normed=True, bins=20, alpha=0.5)
plt.hist(x3, normed=True, bins=20, alpha=0.5)
plt.hist(x4, normed=True, bins=20, alpha=0.5);
plt.axis([-7,21,0,0.6])

plt.text(x1.mean()-1.5, 0.5, 'x1\nNormal')
plt.text(x2.mean()-1.5, 0.5, 'x2\nGamma')
plt.text(x3.mean()-1.5, 0.5, 'x3\nExponential')
plt.text(x4.mean()-1.5, 0.5, 'x4\nUniform')

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.animation as animation
%matplotlib notebook



# generate 4 random variables from the random, gamma, exponential, and uniform distributions
x1 = np.random.normal(-2.5, 1, 10000)
x2 = np.random.gamma(2, 1.5, 10000)
x3 = np.random.exponential(2, 10000)+7
x4 = np.random.uniform(14,20, 10000)
x = [x1, x2, x3, x4]

# generate 4 subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharey = True)
ax = [ax1, ax2, ax3, ax4]

# generate 4 axises(xmin, xmax, ymin, ymax) for each graph 
axis1 = [-7.5, 2.5, 0, 0.6]
axis2 = [0, 10, 0, 0.6]
axis3 = [7, 17, 0, 0.6]
axis4 = [14, 20, 0, 0.6]
axis = [axis1, axis2, axis3, axis4]

# generate 4 bins for each graph 
bins1 = np.arange(-7.5, 2.5, 0.2)
bins2 = np.arange(0, 10, 0.2)
bins3 = np.arange(7, 17, 0.2)
bins4 = np.arange(12, 22, 0.2)
bins = [bins1, bins2, bins3, bins4]

# annotation positions
anno_x = [-1, 6.5, 13.5, 18]

# generate titles
titles = ["Normal", "Gamma", "Exponential", "Uniform"]


# create the function that will do the plotting, where curr is the current frame
def update(curr):

    # check if animation is at the last frame, and if so, stop the animation
    if curr == n: 
        a.event_source.stop()
        
    # plot the histograms
    for i in range(len(ax)):
        ax[i].cla()
        ax[i].hist(x[i][:100*curr], normed = True, bins = bins[i])
        ax[i].axis(axis[i])
        ax[i].set_title(titles[i])
        ax[i].set_ylabel('Probability')
        ax[i].set_xlabel('Value')
        ax[i].annotate('n = {}'.format(100*curr), [anno_x[i], 0.5])
    plt.tight_layout()
    
n=100
# only variable name is a is available why?
a = animation.FuncAnimation(fig, update, interval=100)

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.animation as animation
%matplotlib notebook



# generate 4 random variables from the random, gamma, exponential, and uniform distributions
x1 = np.random.normal(-2.5, 1, 10000)
x2 = np.random.gamma(2, 1.5, 10000)
x3 = np.random.exponential(2, 10000)+7
x4 = np.random.uniform(14,20, 10000)
x = [x1, x2, x3, x4]

# generate 4 subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharey = True)
ax = [ax1, ax2, ax3, ax4]

# generate 4 axises(xmin, xmax, ymin, ymax) for each graph 
axis1 = [-7.5, 2.5, 0, 0.6]
axis2 = [0, 10, 0, 0.6]
axis3 = [7, 17, 0, 0.6]
axis4 = [14, 20, 0, 0.6]
axis = [axis1, axis2, axis3, axis4]

# generate 4 bins for each graph 
bins1 = np.arange(-7.5, 2.5, 0.2)
bins2 = np.arange(0, 10, 0.2)
bins3 = np.arange(7, 17, 0.2)
bins4 = np.arange(12, 22, 0.2)
bins = [bins1, bins2, bins3, bins4]

# annotation positions
anno_x = [-1, 6.5, 13.5, 18]

# generate titles
titles = ["Normal", "Gamma", "Exponential", "Uniform"]

# create the function that will do the plotting, where curr is the current frame
def update(curr):
    # check if animation is at the last frame, and if so, stop the animation
    if curr == n: 
        a.event_source.stop()
        
    # plot the histograms
    for i in range(len(ax)):
        ax[i].cla()
        ax[i].hist(x[i][:1000*curr], normed = True, bins = bins[i])
        ax[i].axis(axis[i])
        ax[i].set_title(titles[i])
        ax[i].set_ylabel('Probability')
        ax[i].set_xlabel('Value')
        ax[i].annotate('n = {}'.format(1000*curr), [anno_x[i], 0.5])
    plt.tight_layout()
    
n=10
a = animation.FuncAnimation(fig, update, interval=100)

猫咪的小鱼干

Coursera | Applied Plotting, Charting & Data Representation in Python（University of Michigan）| W3 Practice Assignment

Practice Assignment: Understanding Distributions Through Sampling