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

.. only:: html

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

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

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

.. _sphx_glr_gallery_ramped_noise.py:


Generate noise linearly ramped in dB
====================================

This demonstrates how to generate a bandlimited noise that is ramped linearly
at a fixed rate in dB per second.

.. GENERATED FROM PYTHON SOURCE LINES 8-19

.. code-block:: Python

    from matplotlib import pyplot as plt
    import numpy as np
    from numpy.lib.stride_tricks import sliding_window_view

    from scipy.signal import stft

    from psiaudio.calibration import FlatCalibration
    from psiaudio.stim import EnvelopeFactory, BandlimitedFIRNoiseFactory

    from psiaudio import util








.. GENERATED FROM PYTHON SOURCE LINES 20-25

Define some basic parameters. The noise will be ramped from 0 dB SPL to
`max_level` at the given `ramp_rate`. Sweep duration is defined by the max
noise level and ramp rate.

The calibration is set such that 1 Vrms = 1 Pa.

.. GENERATED FROM PYTHON SOURCE LINES 25-35

.. code-block:: Python

    fs = 100e3
    calibration = FlatCalibration.from_spl(94)

    min_level = 10
    max_level = 80   # max level for testing
    ramp_rate = 17.5 # dB/s

    sweep_duration = (max_level - min_level) / ramp_rate * 2









.. GENERATED FROM PYTHON SOURCE LINES 36-40

Assemble the factories that will generate the noise. The bartlett envelope is
a triangular envelope. Since the envelope operates on the waveform, which is
in units of Vrms, we need to apply an exponential transform to achieve the
equivalent scaling for the dB domain.

.. GENERATED FROM PYTHON SOURCE LINES 40-98

.. code-block:: Python

    noise = BandlimitedFIRNoiseFactory(
        fs=fs,
        fl=4e3,
        fh=45.2e3,
        level=max_level,
        calibration=calibration,
    )


    stim = EnvelopeFactory(
        envelope='bartlett',
        fs=fs,
        duration=sweep_duration,
        rise_time=None,
        transform=lambda x: util.dbi((max_level-min_level) * (x - 1)),
        input_factory=noise,
    )


    figure, axes = plt.subplots(1, 3, figsize=(9, 3), constrained_layout=True,
                                sharex=True)

    s = stim.get_samples_remaining()
    t = np.arange(len(s)) / fs
    axes[0].plot(t, s)
    axes[0].set_xlabel('Time (s)')
    axes[0].set_ylabel('Amplitude (V)')

    window_size = int(0.05 * fs)
    window_step = window_size // 10
    sv = sliding_window_view(s, window_size)
    s_rms = util.rms(sv[::window_step])
    s_spl = util.patodb(s_rms)
    t_window = np.arange(len(s_spl)) * window_step / fs
    axes[1].plot(t_window, s_spl, label='Actual RMS')

    i = np.argmax(s_spl)
    peak_time = t_window[i]
    peak = s_spl[i]
    expected = np.abs(t_window - peak_time) * -ramp_rate + peak
    axes[1].plot(t_window, expected, color='seagreen', ls='-', lw=5, alpha=0.25, label='Expected RMS')
    axes[1].set_xlabel('Time (s)')
    axes[1].set_ylabel('Level (dB SPL)')
    axes[1].legend()

    f, t, Zxx = stft(s, fs, scaling='psd')
    spectrum = util.patodb(np.abs(Zxx))
    extent = (t.min(), t.max(), f.min(), f.max())
    im = axes[2].imshow(spectrum, aspect='auto', extent=extent, origin='lower', vmin=-20)
    figure.colorbar(im, ax=axes[2])
    axes[2].set_xlabel('Time (s)')
    axes[2].set_ylabel('Frequency (Hz)')

    axes[0].set_title('Waveform')
    axes[1].set_title('Instantaneous level')
    axes[2].set_title('Spectral Density')

    plt.show()



.. image-sg:: /gallery/images/sphx_glr_ramped_noise_001.png
   :alt: Waveform, Instantaneous level, Spectral Density
   :srcset: /gallery/images/sphx_glr_ramped_noise_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.760 seconds)


.. _sphx_glr_download_gallery_ramped_noise.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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