{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Cross spectral density (CSD)\n\nPlot the cross spectral density (CSD) of two signals using `~.Axes.csd`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfig, (ax1, ax2) = plt.subplots(2, 1, layout='constrained')\n\ndt = 0.01\nt = np.arange(0, 30, dt)\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n\nnse1 = np.random.randn(len(t))                 # white noise 1\nnse2 = np.random.randn(len(t))                 # white noise 2\nr = np.exp(-t / 0.05)\n\ncnse1 = np.convolve(nse1, r, mode='same') * dt   # colored noise 1\ncnse2 = np.convolve(nse2, r, mode='same') * dt   # colored noise 2\n\n# two signals with a coherent part and a random part\ns1 = 0.01 * np.sin(2 * np.pi * 10 * t) + cnse1\ns2 = 0.01 * np.sin(2 * np.pi * 10 * t) + cnse2\n\nax1.plot(t, s1, t, s2)\nax1.set_xlim(0, 5)\nax1.set_xlabel('Time (s)')\nax1.set_ylabel('s1 and s2')\nax1.grid(True)\n\ncxy, f = ax2.csd(s1, s2, NFFT=256, Fs=1. / dt)\nax2.set_ylabel('CSD (dB)')\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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      "file_extension": ".py",
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