Skip to main content

Implementation of inverted encoding model as described in Scotti, Chen, & Golomb

Project description

Inverted Encoding

Python package for easy implementation of inverted encoding modeling as described in Scotti, Chen, & Golomb (in-prep).

Contact: scottibrain@gmail.com (Paul Scotti)


Installation

Run the following to install:

pip install inverted-encoding

Usage

from inverted_encoding import IEM, permutation, circ_diff
import numpy as np

predictions, confidences, aligned_at_prediction_recons, aligned_at_zero_recons = IEM(trialbyvoxel,features,stim_max=180,is_circular=True)
# use "help(IEM)" for more information, below is a summary:
# trialbyvoxel: your matrix of brain activations, does not necessarily have to be voxels
# features: array of your stimulus features (must be integers within range defined by stim_max)
# stim_max=180 means that your stimulus space ranges 0-179° degrees
# is_circular=True for a circular stimulus space, False for non-circular stimulus space
# predictions: array of predicted stimulus for each trial
# confidences: array of goodness of fit values for each trial
# aligned_at_prediction_recons: trial-by-trial reconstructions (matrix of num_trials x stim_max) such that
# when plotted ideally each reconstruction is centered at the original trial stimulus
# aligned_at_zero_recons: trial-by-trial reconstructions aligned at zero, such that when
# plotted ideally each reconstruction is centered at zero on the x axis (e.g., plt.plot(aligned_at_zero_recons[trial,:]))

## Compute mean absolute error (MAE) by doing the following, then compare to null distribution:
if is_circular: # if your stimulus space is circular, need to compute circular differences
    mae = np.mean(np.abs(circ_diff(predictions,features,stim_max))) 
else:
    mae = np.mean(np.abs(predictions-features)) 
null_mae_distribution = permutation(features,stim_max=180,num_perm=1000,is_circular=True)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

inverted_encoding-0.1.0.tar.gz (109.2 kB view hashes)

Uploaded Source

Built Distribution

inverted_encoding-0.1.0-py2-none-any.whl (26.7 kB view hashes)

Uploaded Python 2

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page