Sehara_et_al_2021_RealtimeDLC_DataRepository/05_PerformanceProfiling/spike2-profiling/README.md

Profiling of timings during real-time behavioral sessions

Using CED Power1401 and Spike2, we recorded the timings of each frame capture and the timings of trigger output generation.

1. Analyzed data

The latency-data.h5 HDF5 file contains infomation on each video.

1-1. Code

The code to generate this dataset is found in 01_data-extraction.ipynb, and the panels generated during the procedures are in the figures subdirectory.

1-2. Entries in latency-data.h5

There are three subgroups, frame_intervals, on_latency and off_latency, corresponding to the qualities analyzed. All the subgroups have the same structure.

One video comprises a list of values, and forms one dataset entry under each subgroup, numbered as 001, 002, etc.

Attributes of the entry contains the information of the video, such as:

• subject: name of the animal.
• session: name of the behavioral session.
• run: the index corresponding to the run of Spike2 recording.
• epoch: the index corresponding to the run of video recording during the Spike2 recording session.
• has_trigger: whether or not this session involved real-time trigger-output generation.

2. Summary data

Several types of summary data are found here:

• 02_summary.ipynb is the Jupyter notebook used to summarize the data in latency-data.h5.
• latency_summary_26sessions.json contains the distribution of values during each video acquisition.
• latency_stats.tsv is the distribution of each statistics across sessions.
• summary.tsv must be identical to what we used as the table in the paper.