Muse-Analysis-Library (old test version, do not use!)

This is a library built for analaysis of a specific set of signal data. To use the code, read through the documentation to learn more. This is simply an early library and will be updated much more later on.

Import

import muse_an_libr
#overview
#Base validation 
from muse_an_libr import Validate

#Random Data Generator
from muse_an_libr import RandomData

#Filters
from muse_an_libr import Filter

#For Visualization Purposes
from muse_an_libr import Visualize

Base

The base module consists of the class Validate and this is primarily used for validation and extracting specific data from the data.

#muse_an_libr.Validate is the class from the base module

#muse_an_libr.Validate.Valid() Checks if the format is proper. False or error means that the format would not be correct for the requirements of other projects.
print(muse_an_libr.Validate.Valid([[1,[1,1]]])) #returns False
print(muse_an_libr.Validate.Valid([[1,[1,1,1,1,1]]])) #returns True

For data extraction from a valid dataset, you can use: muse_an_libr.Validate.TimeStamp to get the timestamps muse_an_libr.Validate.Values to get the values muse_an_libr.Validate.ValuesSpecific to get specific values from a specific channel

Random Data Generator

We can generate random data to work with using RandomData.

x = muse_an_libr.RandomData.sample_muse(3)
print(x)
muse_an_libr.Visualize.plot_rand(x)

y = muse_an_libr.RandomData.sig_gen(10, 200, 100, 10, 15)
print(y)
muse_an_libr.Visualize.plot_rand(y)

Filters

The filters are a bit more complex. The filters include fft, low pass and high pass as of now.

FFT

Fast fourier transform is an algorithm that computes Fourier transforms. To use:

z = muse_an_libr.Filter.fft(muse_an_libr.RandomData.sig_gen(3, 2, 80, 50, 3), 3)

Low Pass

The low pass filter passes frequencies that are lower than a certain cutoff frequency and it attenuates signals that have a higher frequency than desired. The current method takes from the fft to get the cuttoff.

cutoff = muse_an_libr.Filter.amp_calc(z[1])
peak_freq = freq_calc(z[0], z[2], 3)
filt_data = muse_an_libr.Filter.butter_lowpass_filter(y, peak_freq[0], 50, 2)

High Pass

The high pass filter passes frequencies that are higher than a certain cutoff frequency and it attenuates signals that have a lower frequency than desired.

high = muse_an_libr.Filter.butter_highpass_filter(muse_an_libr.RandomData.sig_gen(5, 6, 80, 1, 1), peak_freq[0], 50)

Visualize

The visualization functions will be more robust going onwards but as of now, there is only a simple plot function designed initially for the random data generated.

y = muse_an_libr.RandomData.sig_gen(10, 200, 100, 10, 15)
muse_an_libr.Visualize.plot_rand(y)