Test all the combinations of dB and estimate when plotting the PSD of raw object.

mne_test_raw_psd.py

from __future__ import print_function, division
import numpy as np
import matplotlib.pyplot as plt
from itertools import product

import mne

import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)

def scipy_ex():
    """Data from scipy.signals.welch doc example.

    We change the sampling frequency, frequency and amplitude to make it
    EEG-like.

    https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.welch.html
    """
    fs = 100
    tmax = 60
    amp = 1e-6 * np.sqrt(2)
    freq = 10.
    noise_power = 0.0000001e-6 * fs / 2
    time = np.arange(tmax * fs) / fs
    x = amp * np.sin(2*np.pi*freq*time)
    x += np.random.normal(scale=np.sqrt(noise_power), size=time.shape)

    return fs, time, x

if __name__ == '__main__':
    fs, t, x = scipy_ex()

    ch_types = ['eeg']
    ch_names = ['ch1']

    info = mne.create_info(ch_names=ch_names, sfreq=fs, ch_types=ch_types)
    raw = mne.io.RawArray(np.atleast_2d(x), info)

    plt.close('all')

    # | dB    | estimate    | plot | units           |
    # |-------+-------------+------+-----------------|
    # | True  | 'power'     | PSD  | u**2/Hz (dB)    |
    # | False | 'amplitude' | ASD  | u/sqrt{Hz}      |
    # | True  | 'auto'      | PSD  | u**2/Hz (dB)    |
    # | False | 'power'     | PSD  | u**2/Hz         |
    # | True  | 'amplitude' | ASD  | u/sqrt{Hz} (dB) |
    # | False | 'auto'      | ASD  | u/sqrt{Hz}      |

    for dB, estimate in product((True, False), ('power', 'amplitude', 'auto')):
        msg = 'db: %s, estimate: %s' % (dB, estimate)
        print(msg)
        fig = raw.plot_psd(n_fft=1024, n_overlap=512, dB=dB, estimate=estimate)
        fig.gca().set_title(msg)