我正在尝试找到信号fft的峰值,用于进一步分析信号。我正在使用SpanSelect数据并执行fft,表示为频谱。我真的希望让绘图是交互式的,并且用户点击要进一步分析的点,但是我没有看到这样做的方法,因此想要找到局部频率峰值的方法。频谱可能如下所示:
所以我想要一种方法来返回具有38赫兹峰值的频率。有没有办法做到这一点?
答案 0 :(得分:2)
使用argrelextrema
查找本地最大值:
import numpy as np
from scipy.signal import argrelextrema
from matplotlib.pyplot import *
np.random.seed()
x = np.random.random(50)
m = argrelextrema(x, np.greater) #array of indexes of the locals maxima
y = [x[m] for i in m]
plot(x)
plot(m, y, 'rs')
show()
答案 1 :(得分:1)
您可以使用matplotlib小部件执行类似的操作,例如check out选择点的套索方法。
然后,您可以在所需的任何形式的分析中使用所选点。
编辑:来自matplotlib示例的组合套索和SpanSelect小部件
#!/usr/bin/env python
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import SpanSelector, LassoSelector
from matplotlib.path import Path
import matplotlib.pyplot as plt
try:
raw_input
except NameError:
# Python 3
raw_input = input
class SelectFromCollection(object):
"""Select indices from a matplotlib collection using `LassoSelector`.
Selected indices are saved in the `ind` attribute. This tool highlights
selected points by fading them out (i.e., reducing their alpha values).
If your collection has alpha < 1, this tool will permanently alter them.
Note that this tool selects collection objects based on their *origins*
(i.e., `offsets`).
Parameters
----------
ax : :class:`~matplotlib.axes.Axes`
Axes to interact with.
collection : :class:`matplotlib.collections.Collection` subclass
Collection you want to select from.
alpha_other : 0 <= float <= 1
To highlight a selection, this tool sets all selected points to an
alpha value of 1 and non-selected points to `alpha_other`.
"""
def __init__(self, ax, collection, alpha_other=0.3):
self.canvas = ax.figure.canvas
self.collection = collection
self.alpha_other = alpha_other
self.xys = collection.get_offsets()
self.Npts = len(self.xys)
# Ensure that we have separate colors for each object
self.fc = collection.get_facecolors()
if len(self.fc) == 0:
raise ValueError('Collection must have a facecolor')
elif len(self.fc) == 1:
self.fc = np.tile(self.fc, self.Npts).reshape(self.Npts, -1)
self.lasso = LassoSelector(ax, onselect=self.onselect)
self.ind = []
def onselect(self, verts):
path = Path(verts)
self.ind = np.nonzero([path.contains_point(xy) for xy in self.xys])[0]
self.fc[:, -1] = self.alpha_other
self.fc[self.ind, -1] = 1
self.collection.set_facecolors(self.fc)
self.canvas.draw_idle()
def disconnect(self):
self.lasso.disconnect_events()
self.fc[:, -1] = 1
self.collection.set_facecolors(self.fc)
self.canvas.draw_idle()
def onselect(xmin, xmax):
indmin, indmax = np.searchsorted(x, (xmin, xmax))
indmax = min(len(x)-1, indmax)
thisx = x[indmin:indmax]
thisy = y[indmin:indmax]
line2.set_data(thisx, thisy)
ax2.set_xlim(thisx[0], thisx[-1])
ax2.set_ylim(thisy.min(), thisy.max())
fig.canvas.draw()
if __name__ == '__main__':
plt.ion()
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(211, axisbg='#FFFFCC')
x = np.arange(0.0, 5.0, 0.01)
y = np.sin(2*np.pi*x) + 0.5*np.random.randn(len(x))
ax.plot(x, y, '-')
ax.set_ylim(-2,2)
ax.set_title('Press left mouse button and drag to test')
ax2 = fig.add_subplot(212, axisbg='#FFFFCC')
line2, = ax2.plot(x, y, '-')
pts = ax2.scatter(x, y)
# set useblit True on gtkagg for enhanced performance
span = SpanSelector(ax, onselect, 'horizontal', useblit=True,
rectprops=dict(alpha=0.5, facecolor='red') )
selector = SelectFromCollection(ax2, pts)
plt.draw()
raw_input('Press any key to accept selected points')
print("Selected points:")
print(selector.xys[selector.ind])
selector.disconnect()
# Block end of script so you can check that the lasso is disconnected.
raw_input('Press any key to quit')