shadyquant
😎
This python package allows you to quantile and plot lines where you have multiple samples, typically for visualizing uncertainty. Your data should be shaped (N, T), where N is the number of samples, T is the dimension of your lines.
Install
pip install shadyquant
Usage
Consider you have 100 lines that you want to compute confidence intervals (quantiles) on:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,2,1000)
y = np.sin(x)
plt.plot(x,y)
I'll make 100 examples of this plot, each scaled by a random number.
w = np.random.normal(size=100)**2
traj = y + y * w[:, None]
plt.plot(traj.T, color='C0')
plt.show()
You can use shadyquant to plot a 95% confidence interval (default):
import sq
sq.traj_quantile(traj)
You can also do weighted quantiling, if you have weights attached to each line
sq.traj_quantile(traj, weights=w)
You may want to do fancy shading, which just plots a series of quantiles as polygons with transparency. The quantiles overlap, which gives a nice blending. The outer edges of the polygons still correspond to the 95% confidence interval.
sq.traj_quantile(traj, fancy_shading=True)
Here are some further options you can modify:
plt.figure(figsize=(8, 3))
ax = plt.gca()
sq.traj_quantile(
traj,
ax=ax,
fancy_shading=True,
lower_q_bound=1 / 3,
upper_q_bound=2 / 3,
levels=100,
color="red",
alpha=0.01,
)
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