{
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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Imag\n\nThis example shows how to use the :py:class:`pylops.basicoperators.Imag`\noperator.\nThis operator returns the imaginary part of the data as a real value in\nforward mode, and the real part of the model as an imaginary value in\nadjoint mode (with zero real part).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as pltgs\n\nimport pylops\n\nplt.close('all')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's define a Imag operator $\\mathbf{\\Im}$ to extract the imaginary\ncomponent of the input.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "M = 5\nx = np.arange(M) + 1j * np.arange(M)[::-1]\nRop = pylops.basicoperators.Imag(M, dtype='complex128')\n\ny = Rop*x\nxadj = Rop.H*y\n\n_, axs = plt.subplots(1, 3, figsize=(10, 4))\naxs[0].plot(np.real(x), lw=2, label='Real')\naxs[0].plot(np.imag(x), lw=2, label='Imag')\naxs[0].legend()\naxs[0].set_title('Input')\naxs[1].plot(np.real(y), lw=2, label='Real')\naxs[1].plot(np.imag(y), lw=2, label='Imag')\naxs[1].legend()\naxs[1].set_title('Forward of Input')\naxs[2].plot(np.real(xadj), lw=2, label='Real')\naxs[2].plot(np.imag(xadj), lw=2, label='Imag')\naxs[2].legend()\naxs[2].set_title('Adjoint of Forward')"
      ]
    }
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