numpy/scipy:经过一段时间后,让一个系列向另一个系列汇聚

一般来说,你会在某个范围内应用"缓动功能".

例如,请考虑下图:

在这里,我们有两个原始数据集.我们将减去两者,将差值乘以第三行中显示的缓动函数,然后将结果添加回第一条曲线.这将导致一个新系列,即灰色区域左侧的原始数据,灰色区域内两者的混合,以及灰色区域右侧的另一条曲线的数据.

举个例子:

import numpy as np
import matplotlib.pyplot as plt

# Generate some interesting random data
np.random.seed(1)
series1 = np.random.normal(0, 1, 1000).cumsum() + 20
series2 = np.random.normal(0, 1, 1000).cumsum()
# Our x-coordinates
index = np.arange(series1.size)

# Boundaries of the gray "easing region"
i0, i1 = 300, 700    

# In this case, I've chosen a sinusoidal easing function...
x = np.pi * (index - i0) / (i1 - i0)
easing = 0.5 * np.cos(x) + 0.5

# To the left of the gray region, easing should be 1 (all series2)
easing[index < i0] = 1

# To the right, it should be 0 (all series1)
easing[index >= i1] = 0

# Now let's calculate the new series that will slowly approach the first
# We'll operate on the difference and then add series1 back in 
diff = series2 - series1
series3 = easing * diff + series1

另外,如果你对上面的情节感到好奇,这里是它的生成方式:

fig, axes = plt.subplots(nrows=4, sharex=True)

axes[0].plot(series1, color='lightblue', lw=2)
axes[0].plot(series2, color='salmon', lw=1.5)
axes[0].set(ylabel='Original Series')

axes[1].plot(diff, color='gray')
axes[1].set(ylabel='Difference')

axes[2].plot(easing, color='black', lw=2)
axes[2].margins(y=0.1)
axes[2].set(ylabel='Easing')

axes[3].plot(series1, color='lightblue', lw=2)
axes[3].plot(series3, color='salmon', ls='--', lw=2, dashes=(12,20))
axes[3].set(ylabel='Modified Series')

for ax in axes:
    ax.locator_params(axis='y', nbins=4)
for ax in axes[-2:]:
    ax.axvspan(i0, i1, color='0.8', alpha=0.5)

plt.show()