Python中的 Matplotlib.axes.Axes.cohere()

Matplotlib是Python中的一个库，它是 NumPy 库的数值数学扩展。 Axes 类包含大部分图形元素：Axis、Tick、Line2D、Text、Polygon 等，并设置坐标系。 Axes 的实例通过回调属性支持回调。

matplotlib.axes.Axes.cohere()函数

matplotlib 库的 axes 模块中的Axes.cohere()函数用于绘制 x 和 y 之间的连贯性。相干性是归一化的交叉谱密度。

Syntax: Axes.cohere(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=, window=, noverlap=0, pad_to=None, sides=’default’, scale_by_freq=None, *, data=None, **kwargs)

Parameters: This method accept the following parameters that are described below:

x, y: These parameter are the sequence of data.
Fs : This parameter is a scalar. Its default value is 2.
window: This parameter take a data segment as an argument and return the windowed version of the segment. Its default value is window_hanning()
sides: This parameter specifies which sides of the spectrum to return. This can have following values : ‘default’, ‘onesided’ and ‘twosided’.
pad_to : This parameter contains the integer value to which the data segment is padded.
Fc: This parameter is also contains the integer value to offsets the x extents of the plot to reflect the frequency range. Its default value is 0
NFFT : This parameter contains the number of data points used in each block for the FFT.
detrend : This parameter contains the function applied to each segment before fft-ing, designed to remove the mean or linear trend {‘none’, ‘mean’, ‘linear’}.
scale_by_freq : This parameter is allows for integration over the returned frequency values.
noverlap : This parameter is the number of points of overlap between blocks.
Fc : This parameter is the center frequency of x.

Returns: This returns the following:

Cxy:This returns the coherence vector..
freqs :This returns the frequencies for the elements in Cxy.

The resultant is (Cxy, freqs)

编程需要懂一点英语

下面的示例说明了 matplotlib.axes 中的 matplotlib.axes.Axes.cohere()函数：

示例 1：

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
  
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t))
nse2 = np.random.randn(len(t))
  
s1 = 1.5 * np.sin(2 * np.pi * 10 * t) + nse1
s2 = np.cos(np.pi * t) + nse2
  
fig, ax1 = plt.subplots()
ax1.cohere(s1, s2**2, 128, 1./dt)
ax1.set_xlabel('time')
ax1.set_ylabel('coherence')
  
ax1.set_title('matplotlib.axes.Axes.cohere() Example')
plt.show()

输出：

示例 2：

# Implementation of matplotlib function
import numpy as np
import matplotlib.pyplot as plt
  
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t))
nse2 = np.random.randn(len(t))
r = np.exp(-t / 0.05)
  
cnse1 = np.convolve(nse1, r, mode ='same')*dt
cnse2 = np.convolve(nse2, r, mode ='same')*dt
  
s1 = 1.5 * np.sin(2 * np.pi * 10 * t) + cnse1
s2 = np.cos(np.pi * t) + cnse2 + np.sin(2 * np.pi * 10 * t)
  
fig, [ax1, ax2] = plt.subplots(2, 1)
ax1.plot(t, s1, t, s2)
ax1.set_xlim(0, 5)
ax1.set_xlabel('time')
ax1.set_ylabel('s1 and s2')
ax1.grid(True)
  
ax2.cohere(s1, s2, 256, 1./dt)
ax2.set_ylabel('coherence')
  
ax1.set_title('matplotlib.axes.Axes.cohere() Example')
plt.show()

输出：