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,nfolds=num_runs,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
# nfolds refers to the K to use for KFold cross-validation. We recommend setting this to the number of runs you have for 1-run-left-out CV.
# 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)

Release files for inverted-encoding 0.2.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for inverted-encoding 0.2.3
File	Size	Uploaded
inverted_encoding-0.2.3.tar.gz	155.8 kB	Jul 16, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for inverted-encoding 0.2.3
File	Interpreter	ABI	Platform	Reset
inverted_encoding-0.2.3-py3-none-any.whl 26.9 kB Jul 16, 2022	Python 3	none	any	Details

Total release size: 182.7 kB

Release files / inverted_encoding-0.2.3.tar.gz

Download URL	inverted_encoding-0.2.3.tar.gz
Size	155.8 kB
Tags	Source
SHA-256 checksum How to use checksums	`49ffbfc02782556afedd9db2404907cffbd16be13306d1be89ccf2c2bfdb3916`
BLAKE2b-256 checksum How to use checksums	`ec77ab4005b23ea1318936ee821b885c74dfdc9967f6e883b60d9dbeae11dfd5`
Upload date	Jul 16, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.12`

Release files / inverted_encoding-0.2.3-py3-none-any.whl

Download URL	inverted_encoding-0.2.3-py3-none-any.whl
Size	26.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`b1dc075404f9c412043771a743986ed9a076f29c54e7038c25a946899563d038`
BLAKE2b-256 checksum How to use checksums	`98086e671b28a6817f6a67481a93a2f6b1d3bf7e3ca8a72dab9d56334eb2cb5f`
Upload date	Jul 16, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.12`