1. Reproduce this figure

Here's the data and some code to get you started.



In [39]:

    
import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.cos(x), np.cos(x + 1), np.cos(x + 2)
names = ['Signal 1', 'Signal 2', 'Signal 3']

Solution



In [19]:

    
fig, axes = plt.subplots(nrows=3, ncols=1)

for f, name, ax in zip(y, names, axes):
    ax.plot(x, f, color='k')
    ax.set(xticks=[], yticks=[], title=name, xlim=(0, 10))

plt.tight_layout()
plt.show()

2. Reproduce this figure

Here is some code to start with:



In [20]:

    
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)

# Generate data...
y_raw = np.random.randn(1000).cumsum() + 15
x_raw = np.linspace(0, 24, y_raw.size)

# Get averages of every 100 samples...
x_pos = x_raw.reshape(-1, 100).min(axis=1)
y_avg = y_raw.reshape(-1, 100).mean(axis=1)
y_err = y_raw.reshape(-1, 100).ptp(axis=1)

bar_width = x_pos[1] - x_pos[0]

# Make a made up future prediction with a fake confidence
x_pred = np.linspace(0, 30)
y_max_pred = y_avg[0] + y_err[0] + 2.3 * x_pred
y_min_pred = y_avg[0] - y_err[0] + 1.2 * x_pred

# Just so you don't have to guess at the colors...
barcolor, linecolor, fillcolor = 'wheat', 'salmon', 'lightblue'

# Now you're on your own!

Solution



In [92]:

    
fig, axis = plt.subplots()

axis.set(xlim=(0, 30), xlabel='Minutes since class began', ylabel='Snarkiness (snark units)')

axis.plot(x_raw, y_raw, color=linecolor)
axis.bar(x_pos, y_avg, width=bar_width, edgecolor='grey', color=barcolor, yerr=y_err, ecolor='grey', capsize=3, align='edge')

axis.fill_between(x_pred, y_min_pred, y_max_pred, color=fillcolor)

plt.show()



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]:



In [ ]: