{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Plotting the coherence of two signals\n\nAn example showing how to plot the coherence of two signals using `~.Axes.cohere`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\ndt = 0.01\nt = np.arange(0, 30, dt)\nnse1 = np.random.randn(len(t))                 # white noise 1\nnse2 = np.random.randn(len(t))                 # white noise 2\n\n# Two signals with a coherent part at 10 Hz and a random part\ns1 = np.sin(2 * np.pi * 10 * t) + nse1\ns2 = np.sin(2 * np.pi * 10 * t) + nse2\n\nfig, axs = plt.subplots(2, 1, layout='constrained')\naxs[0].plot(t, s1, t, s2)\naxs[0].set_xlim(0, 2)\naxs[0].set_xlabel('Time (s)')\naxs[0].set_ylabel('s1 and s2')\naxs[0].grid(True)\n\ncxy, f = axs[1].cohere(s1, s2, NFFT=256, Fs=1. / dt)\naxs[1].set_ylabel('Coherence')\n\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ".. tags::\n\n   domain: signal-processing\n   plot-type: line\n   level: beginner\n\n"
      ]
    }
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