Visualize Power Spectral Density (PSD)

The goal of this example is to calculate and plot the Power Spectral Density (PSD) for a given channel of the raw data.

By default, this script will download the data to be plotted from AWS S3. If you prefer to use your own data, you can start the closed loop simulation in one terminal:

make run-closed-loop

And then record the stream in another terminal:

recorder --session "test" --lsl "NDS-RawData" --recording-time 10

Make sure to change the variable:

LOCAL_DATA = True

and replace the variable with the path to your data:

RAW_DATA_PATH = "the_path_to_your_recorded_raw_data.npz"

Environment setup

LOCAL_DATA = False

Set data source

Retrieve the data from AWS S3 or define the path to your local file.

from urllib.parse import urljoin

import pooch

DOWNLOAD_BASE_URL = "https://neural-data-simulator.s3.amazonaws.com/sample_data/v1/"

if not LOCAL_DATA:
    RAW_DATA_PATH = pooch.retrieve(
        url=urljoin(DOWNLOAD_BASE_URL, "example_NDS-RawData.npz"),
        known_hash="md5:887d88387674d8a7d27726e11663eee4",
    )
else:
    RAW_DATA_PATH = "the_path_to_your_recorded_raw_data.npz"

Load data

Load the data to be plotted.

import numpy as np

raw_data_file = np.load(RAW_DATA_PATH)
raw_data = raw_data_file["data"] / 4
raw_data_timestamps = raw_data_file["timestamps"] - raw_data_file["timestamps"][0]


CHANNEL = 20
SAMPLING_RATE = 30000

Plot data

import matplotlib.pyplot as plt
from scipy import signal

(f, S) = signal.welch(raw_data[:, CHANNEL], SAMPLING_RATE, nperseg=2**18)

plt.semilogy(f, S)
plt.xlim([0, 1000])
plt.xlabel("frequency [Hz]")
plt.ylabel("PSD [V**2/Hz]")
plt.show()