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

.. only:: html

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

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

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

.. _sphx_glr_auto_gallery_solids_plot_raman_response_silica.py:


Raman: fused silica's response
==============================

Fused silica's Raman models (:func:`optmat.solid.raman_response.silica_raman_response`).

**Left:** the normalised responses :math:`h_R(t)`: Hollenbeck & Cantrell's 13 modes
(the default), Lin & Agrawal's two-part form and Blow & Wood's single oscillator.
Hollenbeck & Cantrell's has the measured response's phase reversal near 160 fs and
recurrence near 500 fs (their Fig. 7); the other two only decay.

**Centre:** the copolarised Stokes gain for a 526 nm pump, each model with its own
:math:`f_R` and optmat's long-pulse :math:`n_2` (the Kerr fit), against Stolen &
Ippen's measured gain of fused quartz at 330 cm\ :sup:`-1` (TC-693). The Kerr fit
takes that gain as data, so the default's :math:`f_R = 0.219` is set by it (TC-696).

**Right:** Lin & Agrawal's isotropic part :math:`a` and anisotropic part :math:`b` of
the gain. The anisotropic part, from the Boson peak, carries the low-frequency
shoulder; a probe polarised across the pump sees only :math:`b/2` of the gain (their
Eq. (1)).

.. GENERATED FROM PYTHON SOURCE LINES 28-84



.. image-sg:: /auto_gallery/solids/images/sphx_glr_plot_raman_response_silica_001.svg
   :alt: lin_2006
   :srcset: /auto_gallery/solids/images/sphx_glr_plot_raman_response_silica_001.svg
   :class: sphx-glr-single-img





.. code-block:: Python

    import math
    import warnings

    import matplotlib.pyplot as plt
    import numpy as np
    from _helpers import COLOURS

    from optmat.constants import C
    from optmat.datasets import datasets
    from optmat.exceptions import ModelRangeWarning
    from optmat.solid.raman_response import silica_raman_response, silica_raman_responses

    warnings.simplefilter("ignore", ModelRangeWarning)
    WAVENUMBER = 2 * math.pi * C * 100
    fig, axes = plt.subplots(1, 3, figsize=(14, 4.6), layout="constrained")

    t = np.linspace(0.0, 1e-12, 4001)
    shift = np.linspace(1.0, 1300.0, 2600)
    for colour, (name, model) in zip(
        COLOURS, silica_raman_responses().items(), strict=False
    ):
        axes[0].plot(t * 1e15, model.response(t) * 1e-15, color=colour, label=name)
        gain = model.gain(526e-9, shift * WAVENUMBER) * 100 * 1e11
        axes[1].plot(
            shift, gain, color=colour, label=f"{name}, f$_R$ = {model.raman_fraction:.3f}"
        )
    axes[0].set_xlabel("delay (fs)")
    axes[0].set_ylabel("h$_R$ (1/fs)")
    axes[0].legend()

    (stolen,) = (
        d
        for d in datasets("SiO2", quantity="raman_gain")
        if d.key == "stolen_1973/gain_amplifier"
    )
    axes[1].errorbar(
        [float(str(stolen.parameters["shift_cm1"]))],
        stolen.scaled_values() * 1e11,
        yerr=stolen.standard_uncertainty() * 1e11,
        fmt="o",
        color="#0b0b0b",
        label="Stolen & Ippen 1973",
    )
    axes[1].set_xlabel("Stokes shift (cm$^{-1}$)")
    axes[1].set_ylabel("gain at 526 nm (10$^{-11}$ cm/W)")
    axes[1].legend(fontsize=8)

    lin = silica_raman_response("lin_2006")
    for colour, component in zip(COLOURS, ("parallel", "a", "b"), strict=False):
        part = np.imag(lin.spectrum(shift * WAVENUMBER, component))
        axes[2].plot(shift, part, color=colour, label=component)
    axes[2].set_xlabel("Stokes shift (cm$^{-1}$)")
    axes[2].set_ylabel("Im H (normalised response)")
    axes[2].set_title("lin_2006")
    axes[2].legend()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.580 seconds)


.. _sphx_glr_download_auto_gallery_solids_plot_raman_response_silica.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_raman_response_silica.zip <plot_raman_response_silica.zip>`