Power Spectral Density Plots of rBCBG firing rates

class analyseur.rbcbg.visual.powspec.VizPSD

Bases: object

View Power Spectral Density (PSD) Class.

Plot in axis	View
plot_global_in_ax()	overall frequency content
plot_tv_in_axis()	temporal evolution of frequencies

Use Case:

1. Setup

from analyseur.rbcbg.loader import LoadRates
from analyseur.rbcbg.visual.powspec import VizPSD

loadFR = LoadRates("GPiSNr_model_9_percent_0.csv")
t_sec, rates_Hz = loadFR.get_rates()

2. Power Spectral Density for the entire signal

binsz = 0.001

fig, ax = plt.subplots(figsize=(6, 10))

ax = VizPSD.plot_global_in_ax(ax, rates_Hz, binsz, nucleus="GPiSNr")

plt.show()

3. Time-varying Power Spectrum

fig, ax = plt.subplots(figsize=(6, 10))

fig, ax = VizPSD.plot_tv_in_axis(fig, ax, rates_Hz, nucleus="GPiSNr")

plt.show()

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classmethod plot_global_in_ax(ax, mu_rate_arr, binsz, nucleus=None, resolution=None, method=None, withbands=False, offset_title=None)

PSD (log scale)
│
│   ▲
│  ▲ ▲
│ ▲   ▲
│▲     ▲
│       ▲
│        ▲
│         ▲
│          ▲
│           ▲
│            ▲____
│                 \______
│                        \________
│                                 \______
└──────────────────────────────────────────→ f (Hz)
strong low-freq power     →     high-freq attenuation

Given a matplotlib.pyplot.axis this draws the average (or single/global) power spectrum of the entire signal tells us the overall frequency content using compute_for_rate().

Parameters:

• ax – object matplotlib.pyplot.axis

• mu_rate_array – array returned using get_rates()

• binsz – integer or float

[OPTIONAL]

Parameters:

• nucleus – string; name of the nucleus

• method – “welch” or “fft” or “fft-mag”

• resolution – ~ 9.76 Hz = sampling_rate/1024 [default]

• withbands – True or False [default]

• offset_title – x-axis offset; scalar or None [default]

Returns:

ax with respective plotting

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classmethod plot_tv_in_axis(fig, ax, mu_rate_arr, nucleus=None, resolution=None, offset_title=None)

Frequency (Hz)
│100 ─────────────────────────────────────────────
│ 80 ─────────────────────────────────────────────
│ 60 ────────────────:::::::::────────────────────
│ 40 ───────────:::::::::::::::───────────:::::::::
│ 20 ───────::::::::::::::::::::::::::::::: :::::::
│ 10 ─────:::::::::::::::::::::::::::::::::::::::::
│  5 ────::::::::::::::::::::::::::::::::::::::::::
│  0 ─────────────────────────────────────────────
│
└──────────────────────────────────────────────────→ Time (s)
0        2        4        6        8       10

Legend:
":"  → higher power
"."  → lower power
" "  → minimal / background power

Given a matplotlib.pyplot.figure and its matplotlib.pyplot.axis this draws the time-varying power spectrum telling us how frequencies evolve using compute_spectrogram().

Parameters:

• fig – object matplotlib.pyplot.figure

• ax – object matplotlib.pyplot.axis

• mu_rate_array – array returned using get_rates()

• binsz – integer or float

[OPTIONAL]

Parameters:

• nucleus – string; name of the nucleus

• resolution – ~ 9.76 Hz = sampling_rate/1024 [default]

• offset_title – x-axis offset; scalar or None [default]

Returns:

fig and ax with respective plotting

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