tempord 0.1.2

Temporal orders and causal vector for physiological data analysis

tempord

tempord is a Python package for computing temporal order between pairs of time series signals based on paper:

M. Młyńczak, "Temporal orders and causal vector for physiological data analysis," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 750-753, doi: 10.1109/EMBC44109.2020.9176842.

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📦 Installation

You can install tempord from PyPI or clone the repository for development.

Option 1: Install from PyPI

pip install tempord

Option 2: Clone and install from source

1. Clone the repository

   git clone [e-mail]:mrosol/tempord.git
   cd tempord
   

2. Create a virtual environment (recommended)

   python -m venv .venv
   source .venv/bin/activate   # macOS / Linux
   # .\.venv\Scripts\activate  # Windows
   

3. Install required packages

   pip install -r requirements.txt
   

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⚙️ Usage

Import the main function from the tempord module and call it with a pandas.DataFrame containing your signals as columns.

from tempord import tempord
import pandas as pd

# example DataFrame with two signals
df = pd.DataFrame({'s1': signal1, 's2': signal2})

results = tempord(
    df,
    method="LM",                # or "TD"
    thr=0.7,                      # threshold (>=0 to apply)
    scaling=1,                    # 0=no scaling, 1=min-max, 2=z-score
    sig_length=10,                # window length in seconds
    max_shift_seconds=(-1, 1),    # backward/forward range in seconds
    fs=25,                        # sampling frequency (Hz)
    make_figure=True              # generate a matplotlib Figure
)

# `results` is a dict keyed by signal‑pair tuples (e.g. ('s1','s2')).
# Each value contains:
# - "Tempord": DataFrame of parameter values for each point/shift
# - "Max": DataFrame with the shift giving the maximum parameter per point
# - "Fig": matplotlib figure object (or None)

Plotting and post‑processing

The module provides helper functions used internally:

• get_causal_vector – extracts the shift of maximum parameter values.
• make_plot – draws a heatmap of the parameter matrix and overlays the maxima curve.

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📌 Notes

• Input signals should be numeric and of equal length. Windowing and shifting that fall outside the signal boundaries are skipped quietly.

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Contact

mail: [e-mail]