** 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).

```
In [9]:
```import matplotlib.pyplot as plt
import matplotlib.animation as animation
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)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True, sharey=True)
n = 10000
def update(curr):
if curr == n:
a.event_source.stop()
else:
ax1.cla()
ax1.hist(x1[:curr], normed=True, bins=10, alpha=0.5)
ax2.cla()
ax2.hist(x2[:curr], normed=True, bins=10, alpha=0.5)
ax3.cla()
ax3.hist(x3[:curr], normed=True, bins=10, alpha=0.5)
ax4.cla()
ax4.hist(x4[:curr], normed=True, bins=10, alpha=0.5)
plt.axis([-7,21,0,0.6])
a = animation.FuncAnimation(fig, update, interval=10)

```
```

```
In [ ]:
```