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  "cells": [
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      "cell_type": "markdown",
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      "source": [
        "\n# Spectrogram\n\nPlotting a spectrogram using `~.Axes.specgram`.\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 = 0.0005\nt = np.arange(0.0, 20.5, dt)\ns1 = np.sin(2 * np.pi * 100 * t)\ns2 = 2 * np.sin(2 * np.pi * 400 * t)\n\n# create a transient \"chirp\"\ns2[t <= 10] = s2[12 <= t] = 0\n\n# add some noise into the mix\nnse = 0.01 * np.random.random(size=len(t))\n\nx = s1 + s2 + nse  # the signal\nNFFT = 1024  # the length of the windowing segments\nFs = 1/dt  # the sampling frequency\n\nfig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\nax1.plot(t, x)\nax1.set_ylabel('Signal')\n\nPxx, freqs, bins, im = ax2.specgram(x, NFFT=NFFT, Fs=Fs)\n# The `specgram` method returns 4 objects. They are:\n# - Pxx: the periodogram\n# - freqs: the frequency vector\n# - bins: the centers of the time bins\n# - im: the .image.AxesImage instance representing the data in the plot\nax2.set_xlabel('Time (s)')\nax2.set_ylabel('Frequency (Hz)')\nax2.set_xlim(0, 20)\n\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ".. admonition:: References\n\n   The use of the following functions, methods, classes and modules is shown\n   in this example:\n\n   - `matplotlib.axes.Axes.specgram` / `matplotlib.pyplot.specgram`\n\n"
      ]
    }
  ],
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