用 plt.plot 颜色重叠线（Matplotlib）

Question

How can I configure plt.plot such that overlapped lines will have darker colors?
For example, I would like to use plt.plot to display the samples in such a way that the density that can be seen in the upper plot will be clear in the lower plot.
From the lower plot it's hard to understand where most of the samples are located

Here is the code I used in order to generate the example:

import numpy as np
import matplotlib.pyplot as plt

time = 100
n_samples = 7000
x = np.linspace(0, time, n_samples)
r1 = np.random.normal(0, 1, x.size)
r2 = np.random.uniform(-6, 6, x.size)
data = np.dstack((r1, r2)).flatten()

fig, axs = plt.subplots(2, 1, figsize=(9, 6))
axs[0].scatter(np.arange(len(data)), data, alpha=0.1)
axs[1].plot(np.arange(len(data)), data, alpha=0.2)
plt.show()

Answer 1

I found this code:

import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
 
data = np.random.normal(10,3,100) # Generate Data
density = gaussian_kde(data)
 
x_vals = np.linspace(0,20,200) # Specifying the limits of our data
density.covariance_factor = lambda : .5 #Smoothing parameter
 
density._compute_covariance()
plt.plot(x_vals,density(x_vals))
plt.show()

From: https://www.askpython.com/python/examples/density-plots-in-python

Answer 2

Instead of drawing one large curve, you could create each line segment separately and then draw these. That way, the overlapping segments will be blended via the transparency.

import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np

time = 100
n_samples = 7000
x = np.linspace(0, time, n_samples)
r1 = np.random.normal(0, 1, x.size)
r2 = np.random.uniform(-6, 6, x.size)
data = np.dstack((r1, r2)).flatten()

fig, axs = plt.subplots(2, 1, figsize=(9, 6))
axs[0].scatter(np.arange(len(data)), data, alpha=0.1)
axs[0].margins(x=0)

xs = np.arange(len(data))
ys = data
segments = np.c_[xs[:-1], ys[:-1], xs[1:], ys[1:]].reshape(-1, 2, 2)
axs[1].add_collection(LineCollection(segments, alpha=0.05))
axs[1].autoscale()
axs[1].margins(x=0)
plt.show()