yescarpenter 0.1.4

A library providing frequently used functions in data analysis for YESlab members and other researchers

yescarpenter

This library provides some frequent used functions for YESlab members and other researchers, including data processing and analyses

Installation

From PyPI

Once the package is uploaded to PyPI, users can install it using pip:

pip install yescarpenter

Functions

perform_pca

This function leverages scikit-learn along with other popular data science libraries.

Usage:

import pandas as pd
from yescarpenter import perform_pca

# Create a sample DataFrame
data = pd.DataFrame({
    'feature1': [1, 2, 3, 4],
    'feature2': [2, 3, 4, 5],
    'feature3': [3, 4, 5, 6]
})

# Perform PCA with 2 components
loadings, explained_variance, components = perform_pca(data, n_components=2)

print("Loadings:\n", loadings)
print("Explained Variance:\n", explained_variance)
print("Components:\n", components)

create_scree_plot

This function creates a scree plot to visualize the explained variance of each principal component.

scree_plot(explained_variance, n_components)

pc_plot

Create a plot to visualize the PCA loadings.

pc_plot(loadings, df)

construct_RDM

For IS-RSA. Construct the Representational Dissimilarity Matrix(RDM) from the data.

Usage:

construct_RDM(data, n_target, method = "cityblock")

do_rsa

Calculate the Spearman correlation between two RDMs(lower triangle) and do permutation

Usage:

do_RSA(rdm1, rdm2, n_perm=1000)