yescarpenter 0.2.7

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

Users can install it using pip:

pip install yescarpenter

Functions

perform_pca

This function leverages scikit-learn to perform a tailored PCA analysis (e.g., with rotation to maximize variance)

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")

• data: The input data for RDM construction.
• method: cityblock, or spearman

do_rsa

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

Usage:

do_RSA(rdm1, rdm2, n_perm=1000)

permutation_histogram

Plot the histogram of null distribution, with the observed value and p-value marked.

Usage:

permutation_histogram(r, perm_r)

• r: The observed value.
• perm_r: The null distribution, which consists of the iterated surrogated values

maximal_permutation_test

This fuction is used to address multiple comparison, which provides an alternative of Bonferroni correction.

Usage:

[perm_r, perm_p, observed_r] = maximal_permutation_test(data, iv_single, iv_multiplecomp, nperm)

• data: For IS-RSA, each row is a subject, while each column is a variable.
  For example, if you have 20 subjects and 5 variables, the shape of data is (20, 5).
  • iv_single: the independent variable that will be shuffled and compare across iv_multiplecomp
  • iv_multiplecomp: the independent variable that are inter-related and elicit the multiple comparison problem
  • n_perm: number of permutation

align_data

This function is used to align different sources of data, such as visual and semantic embeddings, and behavioral rating data.

Usage:

aligned_cong_fec, aligned_vgg, aligned_sem = align_data(
    {'data': cong_fec['Vote share percentage'].values, 'order': cong_fec['Image_name'].values},
    {'data': response, 'order': vgglist['image_name'].values},
    {'data': sememb, 'order': sem_imgname}
)

print(aligned_cong_fec.shape, aligned_vgg.shape, aligned_sem.shape)

• You can put as many data as you want. Each input has to be a dictionary with two keys: 'data' and 'order'.