CardioWave: A tool for waveform analysis

Parameters we can calculate

Common waveform parameters include peak count, average peak amplitude, etc. For more details please check Supporting Parameters

Usage

Prepare a CSV table like this format:

compound	concentration	well	plate	..others..	time	signal
CP1	0.1	A1	P1	...	0	1000
CP1	0.1	A1	P1	...	0.33	1001
...	...	...	...	...	...	...
CP2	0.1	A2	P1	...	0	1000
...	...	...	...	...	...	...

The order of the rows and columns do not need to be fixed but the column names must be exactly the same to the required (e.g. lowercase). The following columns are compursory: 'plate', 'compound', 'concentration', 'well', 'time', 'signal'. Optional columns include 'cpid' (compound ID) and 'vendor'. Other columns will not be used.

import pandas as pd
from cdwave import data
from cdwave import derive

# Load and convert
df = pd.read_csv('data.csv')
loader = data.StandardCSVLoader(data=df)
dataset = loader.transfer()

# Calculate parameters
# The calculated parameters will be included in the dataset
derive.calc_parameters_for_waveforms(dataset)

# Save and load dataset
dataset.save('data.pickle.gz')
dataset = Data.loaddata('data.pickle.gz')

# Export parameters
parameter_df = dataset.get_parameter_df()
parameter_df.to_csv(os.path.join(data_path, 'parameters.csv'))

GUI has been moved to CarioWaveGUI

# Run by python package
python -m CardioWaveGUI
# Or run by command
python CardioWaveGUI/gui.py

Installation

Requirements

Basic requirements for core functions (deriving parameters from waveforms)

numpy>=1.16
scipy>=1.2
tqdm>=4.32
pandas>=0.24
statsmodels>=0.10.2
scikit-learn>=0.21

For GUI support and parameter analysis

matplotlib>=3.1
pyqt5>=5.9

All the packages required are included in the lastest Anaconda envrionment.

Make documents

0. Matplotlib is required to render pictures
1. Generate source code of documents

sphinx-apidoc -o docs/source -f cdwave

2. Build

sphinx-build docs docs/_build