fishtoolbox

Contains a set of modules to analyze and visualize data from block1 and block2 of the experiment from 2021 September.

Dependencies

This repository is based on python and therefore requires conda and python-pip for installations. The following repositories are project-dependencies that have to been build inside the underlying environment: - fishproviz

• motionmapperpy fork

Data Flow

Using the HPC cluster

Installation

1. Environment installations using Conda, including the python environment and c++ dependencies bash conda env create -n toolbox --file environment.yml conda activate toolbox
2. Python package installations using python-pip bash # conda environment should be activated python -m venv .venv # python virtual environment creation source .venv/bin/activate pip install -r requirements.txt
3. Fishproviz project-dependencies installation bash # working directory should be equal to <path/to/fishtoolbox> # conda environment should be activated # python venv should be activated cd .. git clone git@github.com:lukastaerk/Fish-Tracking-Visualization.git cd Fish-Tracking-Visualization python setup.py install
4. Motionmapper project-dependencies installation bash # working directory should be equal to <path/to/fishtoolbox> # conda environment should be activated # python venv should be activated cd .. git clone git@github.com:lukastaerk/motionmapperpy.git cd motionmapperpy python setup.py install

HPC Usage

1. Start on the GPU sbatch scripts/hpc-python.sh
2. NOTEBOOK
3. conda activate rapids-22.04
4. Type ifconfig and get the inet entry for eth0, i.e. the IP address of the node
5. srun --pty --partition=ex_scioi_gpu --gres=gpu:1 --time=0-02:00 bash -i to start a new shell with a GPU
6. ssh -L localhost:5000:localhost:5000 user.name@[IP address] on your local machine
7. jupyter-lab --no-browser --port=5000

Start

• set the BLOCK variable to BLOCK1 or BLOCK2 in config.py
• set the projectPath variable to the path of a new folder in config.py this is where the data will be stored
• setup fishprovis with the correct paths and area configurations.
• export the preprocessed data with python3 -m data_factory.processing
• repeat for the other block

Program Parts

Parameters

• parameters = set_parameters() to get the parameters that are used throughout the fishtoolbox

Data Factory

Processing

• load_trajectory_data_concat load the x y coordinates, projections (the three features), time index, area
• load_zVals_concat load the umap data
• load_clusters_concat load cluster labels for individuals and day paramerter.kmeans = 5 to specify the clustering that you want to load.

Plasticity

There are three ways in this module to compute plasticity. - compute_cluster_entropy computes the cluster entropy for each individual and day. Using the watershed regions or kmeans clusters, by providing the function to load the corresponding clusters. - compute_coefficient_of_variation computes the coefficient of variation for each individual and day.

Table Export

Records function to export averaged step length to a csv file and melted them into a long format table for statistical analysis (Repeatability).

Repeatability

From means of features (step, angle, wall distance), e.g. batches of 60 data frames. Produce a long table, recording block number, id.

Sampling

The research question is how many samples are needed to get a good estimate of the repeatability. Provided a table with means of a feature (step length) over a number of consecutive data frames, we can sample from this table a number of minutes for a number of days. Further we look at the effect when sampling the time of the day only once for all days versus sampling the time of the day for each day.

Poltting

Caterpillar Plots

• ethnogramofclusters

TODOs:

• check the new area files, see if there are significant updates for any of them, what is the difference, do we need an refined getareafunction(fishkey,day) ?