TDT Python Examples
Start with the Introduction to Python and the TDT package example workbook.
This gives an overview of the differences between Matlab and Python and a starting point for reading TDT data directly into Python.
Import stream and epoc data into Python using read_block
Plot the average waveform around the epoc event
Good for Evoked Potential detection
Import streaming EEG data into Python using read_block
Filter around behavioral events that were timestamped by the user using the Run-time Notes feature in Synapse, using epoc_filter
Plot each occurrence in a subplot organized by Note type
Good for sleep scoring and behavioral discrimination
Raster Peristimulus Time Histogram (PSTH) Example
Import snippet and epoc data into Python using read_block
Generate peristimulus raster and histogram plots over all trials
Good for stim-response experiments, such as optogenetic or electrical stimulation
Import continuous data into Python using read_block
Plot a single channel of data with various filtering schemes
Good for first-pass visualization of streamed data
TDT Community Examples
We think that cultivating a strong and open community of TDT users is essential for maximizing research efficiency. The cross-pollination of ideas and methods across labs, especially those with similar research interests and using the same equipment, makes the science more rigorous overall. As such, we would like YOU to consider contributing to the TDT community with an example analysis and workbook, like the ones at the top of this page.
You can submit your examples to TDT by emailing support@tdt.com. Include your name, lab information, research interests, Python script, and a link to the block of data used in the analysis. Accepted submissions will be featured on this webpage, along with a description of the analysis and a bio of the submitting researcher. We hope that this exposure will help our users learn more about what great research others in the TDT community are doing!
Please note that, as with all our example workbooks, these workbooks represent one way of processing data. Moreover, the goal is really to show users how they might implement certain techniques on their data. These examples should not represent complete pipelines for your data analysis, and critical thought about which processing techniques and statistical analyses are most appropriate for your data set should be taken.
Fiber Photometry Epoch Averaging Example
This example goes through fiber photometry analysis using techniques
such as data smoothing, bleach detrending, and z-score analysis.
The epoch averaging was done using epoc_filter.
Author Contributions:
TDT, David Root, and the Morales Lab contributed to the writing and/or conceptualization of the code.
The signal processing pipeline was inspired by the workflow developed by David Barker et al. (2017) for the Morales Lab.
The data used in the example were provided by David Root.
Author Information:
David H. Root
Assistant Professor
Department of Psychology & Neuroscience
University of Colorado, Boulder
Lab Website: https://www.root-lab.org
david.root@colorado.edu
About the authors:
The Root lab and Morales lab investigate the neurobiology of reward, aversion, addiction, and depression.
TDT edits all user submissions in coordination with the contributing author(s) prior to publishing.
This example looks at fiber photometry data in the VTA where subjects are provided sucrose water after a fasting period. Lick events are captured as TTL pulses. Objective is to combine many consecutive licking events into a single event based on time difference and lick count thresholds. New lick bout events can then be used for clear peri-event filtering.