.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/generate_SC_PSD.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_generate_SC_PSD.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_generate_SC_PSD.py:


Generate a PSD from SpinCore Data
=================================
Here, data containing the noise signal acquired on the SpinCore is converted to
a power spectral density and convolved to display a smooth spectra illustrating
the noise power.

.. GENERATED FROM PYTHON SOURCE LINES 8-57



.. image-sg:: /auto_examples/images/sphx_glr_generate_SC_PSD_001.png
   :alt: PSD acquired on SpinCore
   :srcset: /auto_examples/images/sphx_glr_generate_SC_PSD_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    1: PSD acquired on SpinCore |||ks







|

.. code-block:: Python

    from numpy import r_
    import pyspecdata as psd
    from pyspecProcScripts import lookup_table
    from pylab import diff, sqrt

    lambda_G = 4e3  # Width for Gaussian convolution
    dg_per_V = 583e6  # Calibration coefficient to convert the
    #                  intrinsic SC units to V at the input of
    #                  the receiver. Note this value will change
    #                  with different SW
    filename = "230822_BNC_RX_magon_200kHz.h5"
    with psd.figlist_var() as fl:
        # Load data and apply preprocessing
        s = psd.find_file(
            filename,
            exp_type="ODNP_NMR_comp/Echoes",
            expno="signal",
            postproc="spincore_general",
            lookup=lookup_table,
        )
        s.rename("nScans", "capture")  # To be more consistent
        #                               with the oscilloscope
        #                               data, rename the nScans
        #                               dimension
        s /= dg_per_V  # Convert the intrinsic units of the SC
        #               to $V_{p}$
        s.set_units("t", "s")
        # Calculate $t_{acq}$
        acq_time = diff(s.getaxis("t")[r_[0, -1]])[0]
        s /= sqrt(
            2
        )  # Instantaneous Vₚ√s / √Hz to
        #    Vᵣₘₛ√s / √Hz 
        # {{{ equation 21
        s = abs(s) ** 2  # Take mod squared to convert to
        #                   energy $\frac{V_{rms}^{2} \cdot s}{Hz}$
        s.mean("capture")  # Average captures
        s /= acq_time  # Convert to Power $\frac{V_{rms}^2}{Hz} = W$
        s /= 50  # Divide by impedance $\rightarrow$ W/Hz
        # }}}
        # Plot unconvolved PSD on a semilog plot
        s.name(r"$S(\nu)$").set_units("W/Hz")
        fl.next("PSD acquired on SpinCore")
        fl.plot(s, color="blue", alpha=0.1, plottype="semilogy")
        # Convolve using the $\lambda_{G}$ specified above
        s.convolve("t", lambda_G, enforce_causality=False)
        # Plot the convolved PSD on the semilog plot with the
        # unconvolved
        fl.plot(s, color="blue", alpha=0.5, plottype="semilogy")


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.631 seconds)


.. _sphx_glr_download_auto_examples_generate_SC_PSD.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: generate_SC_PSD.ipynb <generate_SC_PSD.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: generate_SC_PSD.py <generate_SC_PSD.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_