scipy.signal.find_peaks（峰值检测）

Posted 2021-06-02 月疯

函数用法：

scipy.signal.find_peaks(x, height=None, threshold=None, distance=None, prominence=None, width=None, wlen=None, rel_height=0.5, plateau_size=None)

解释：输入

x: 带有峰值的信号序列

height: 低于指定height的信号都不考虑

threshold: 其与相邻样本的垂直距离

distance: 相邻峰之间的最小水平距离, 先移除较小的峰，直到所有剩余峰的条件都满足为止。

prominence: 个人理解是突起程度，详见peak_prominences

width: 波峰的宽度，详见peak_widths

plateau_size: 保证峰对应的平顶数目大于给定值

 返回值：

peaks: x对应的峰值的索引

properties:

height--> ‘peak_heights’

threshold-->‘left_thresholds’, ‘right_thresholds’

prominence-->‘prominences’, ‘right_bases’, ‘left_bases’

width-->‘width_heights’, ‘left_ips’, ‘right_ips’

plateau_size-->‘plateau_sizes’, left_edges’, ‘right_edges’

小列子01：

import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks
import numpy as np
x = electrocardiogram()[2000:4000]
peaks, _ = find_peaks(x, height=0)
plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.plot(np.zeros_like(x), "--", color="gray")
plt.show()

小列子02：

import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks
import numpy as np
#选择大于0的
# x = electrocardiogram()[2000:4000]
# peaks, _ = find_peaks(x, height=0)
# plt.plot(x)
# plt.plot(peaks, x[peaks], "x")
# plt.plot(np.zeros_like(x), "--", color="gray")
# plt.show()

#小于0一下的
x = electrocardiogram()[2000:4000]
border = np.sin(np.linspace(0, 3 * np.pi, x.size))
peaks, _ = find_peaks(x, height=(-border, border))
plt.plot(x)
plt.plot(-border, "--", color="gray")
plt.plot(border, ":", color="gray")
plt.plot(peaks, x[peaks], "x")
plt.show()

小列子03：

import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks
import numpy as np

#我们可以通过要求至少150个样本的距离来轻松选择心电图(ECG)中QRS络合物的位置
x = electrocardiogram()[2000:4000]
peaks, _ = find_peaks(x, distance=150)
plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.show()

小列子04：

import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks
import numpy as np

#通过将允许的突出限制为0.6
x = electrocardiogram()[2000:4000]
peaks, properties = find_peaks(x, prominence=(None, 0.6))
plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.show()

小列子05：

import matplotlib.pyplot as plt
from scipy.misc import electrocardiogram
from scipy.signal import find_peaks
import numpy as np

#要仅选择非典型心跳，我们将两个条件结合起来：最小突出1和至少20个样本的宽度。
x = electrocardiogram()[17000:18000]
peaks, properties = find_peaks(x, prominence=1, width=20)

plt.plot(x)
plt.plot(peaks, x[peaks], "x")
plt.vlines(x=peaks, ymin=x[peaks] - properties["prominences"],
           ymax = x[peaks], color = "C1")
plt.hlines(y=properties["width_heights"], xmin=properties["left_ips"],
          xmax=properties["right_ips"], color = "C1")
plt.show()