dewan_h5 1.2.5

Python package to load and parse H5 files in the Dewan Lab.

Dewan Lab H5 Library

---

This library serves to load, parse, and serve the contents of H5 files from the lab's Arduino- and Voyeur-based behavior setups.

Installation

1. Using PyPi: Simply run pip install dewan_h5 and the latest version will be pulled from PyPi
2. Local Installation
   1. Clone the repository using git clone https://github.com/OlfactoryBehaviorLab/dewan_h5
   2. Navigate into the dewan_h5 directory
   3. Execute pip install . or pip install -e . if you want the repository to be editable

Usage

Info: The DewanH5 class is intended to be used within a Python context manager.
Usage outside a context manager is not supported

Example:

from dewan_h5 import DewanH5

file_path = 'path/to/h5/file/file.h5'

with DewanH5(file_path) as dh5:
    # Do things here
    # When done, the file is automatically closed when the context is exited
    print(dh5)  # Print summary of contents of file

# Context is now exited

Class Constructor

---

DewanH5(file_path, trim_trials=True, suppress_errors=False)

• file_path : None, Path, or str Path to the H5 file
  • None: If None is explicitly supplied, a file selector will open
  • Path: pathlib.Path representing the location of the H5 file
  • str: string containing raw path to H5 file; internally converted to a pathlib.Path
• trim_trials : bool Indicates whether below trials are trimmed from the raw dataset
  • When set to true the following are trimmed:
    • The first 10 Go Trials
    • If three Go trials are missed in a row, everything after (and including) the third missed trial are dropped
• suppress_errors : bool Determines whether certain errors are suppressed and handled silently or allowed to propagate to the context manager

Public Parameters

---

Constructor Parameters

• file_path : pathlib.Path System-agnostic path to the H5 file
• file_name : str Filename with suffix
• suppress_errors : bool Determines whether errors are suppressed and handled silently or allowed to propagate to the context manager
• trim_trial : bool Normally, certain trials are trimmed from the raw dataset. This indicates whether those trials were trimmed or left in the dataset

General Parameters

• date : str Date the H5 file was created
• time : str Time the H5 file was created
• mouse : int ID number of animal ran in experiment
• rig : str Rig name experiment was ran in

Odor Information

• odors : list[str] List of all odors present in H5 File
  • This includes blank
• concentrations : list[str] List of all concentrations present in H5 file

Performance Values

All performance values are calculated after trimming (if applicable)

• total_trials : int Total number of trials in experiment
• go_performance : float Percentage of correct go trials vs total number of go trials
• nogo_performance : float Percentage of correct nogo trials vs total number of nogo trials
• total_performance : float Percentage of correct go and nogo trials vs total number of trials
  • (correct go trials + correct nogo trials) / total number of trials
• three_missed : bool Indicates whether the animal missed three go trials in a row
• last_good_trial : int If three_missed is true, this is the last trial included in the data
• did_cheat : bool Indicates whether cheating was detected
  • See description of cheating check below
• cheat_check_tirals : list[int] List of cheating check trial indices

Data Containers

Note 1: If trimming the dataset, the below containers are also trimmed
Note 2: All the timestamps are offset by the FV on time. Negative values represent events before the FV turned on

• trial_parameters : pd.DataFrame Pandas DataFrame containing the parameters recorded for each trial
  • See list of important parameters below
• sniff : dict[int, pd.Series] Dictionary where each key is an int representing a trial number; each Pandas Series contains timestamped samples representing data recorded from the sniff sensor
• lick1 : dict[int, list] Dictionary where each key is an int representing a trial number; each list contains timestamps for licks of lick sensor 1
• lick2: dict[int, list] Dictionary where each key is an int representing a trial number; each list contains timestamps for licks of lick sensor 2

H5 File Structure

---

• Values:
  • N -> number of trials
  • n -> references any arbitrary trial
  • x -> arbitrary number
• File Structure
  • /: Contains N number of Groups for each trial with an additional group containing the trial data matrix
  • Trials: Matrix containing n rows with columns for multiple parameters captured for each trial
  • Trial000n [type: group] (one group per trial): Holds samples for each trial
    • Events [type: dataset] (x number of tuples): (timestamp, number of sniff samples)
    • lick1 [type: dataset] (x number of arrays): each array contains a variable number of lick timestamps for the 'left' lick tube
    • lick2 [type: dataset] (x number of arrays): each array contains a variable number of lick timestamps for the 'right' lick tube
    • sniff [type: dataset] (len(Events) number of arrays): each array contains samples recorded from the sniff sensor

Pertinent Trial Parameters:

Matrix located at /Trials that contains parameters for each trial

Each entry in the following format Common Name [column_name, data type]

Note: the column names are strings, but I have left the quotations off for clarity

• Trial Type [Trialtype, int]: Value which encodes the trial type
  • 1: Go (no licking)
  • 2: NoGo (licking)
• Response [_result, int]: Value that represents the response of the animal
  • 1: Correct "Go" Response >>> The stimulus was a 'Go' stimulus and the animal correctly withheld licking
  • 2: Correct "NoGo" Response >>> The stimulus was a 'NoGo' stimulus and the animal correctly licked
  • 3: False Alarm / Incorrect "NoGo" Response >>> The stimulus was a "NoGo" stimulus and the animal incorrectly withheld licking
  • 4: Unused
  • 5: Missed "Go" Response >>> The stimulus was a 'Go' stimulus and the animal incorrectly licked

Note: Some trials are designated "cheating checks" to test whether the animal is utilizing a non-odor cue to get extra water rewards. A cheating check is identified by a trial type of 2 with an odor of blank.
- A response of 2 indicates CHEATING
- A response of 3 indicates NO CHEATING

• Odor Name [Odor, str]: Name of odorant presented during the trial
• Odor Concentration [Odorconc, str]: Concentration of odorant presented during the trial
• Odor Vial Number [Odorvial, int]: Olfactometer vial used to deliver the odorant during the trial
• Inter Trial Interval (ITI) [iti, str]: Time in milliseconds between current trial and next trial
• Mouse ID [mouse, int]: ID number of animal ran in the experiment
• Rig Name [rig, str]: Name of behavioral chamber

License

This project is licensed under GNU General Public License v3.0. You can find the included copy of the license here. For more information about the license and its terms, please visit SPDX