从信号的ftf寻找局部最大值

Question

I'm trying to find a peak of an fft of a signal to be used for a further analysis of the signal. I'm using a SpanSelect of data and doing an fft, represented as a frequency spectrum. I really wanted to have the plot be interactive and the user click a point to be further analyzed, but I don't see a way to do that so would like a way to find local frequency peaks. The frequency spectrum may look like this:

So I would want a way to return the frequency that has a peak at 38 hz for example. Is there a way to do this?

Answer 1

use argrelextrema for finding local maxima:

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() 

Answer 2

You can do something like that using matplotlib widgets, for example check out the lasso method of selecting points.

You can then use the selected point in any form of analysis you need.

EDIT: Combined lasso and SpanSelect widget from matplotlib examples

#!/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')