semvec/pypi-packages huggingface dataset:
scodakit 0.0.1
ScodaKit: A scientific Python-based command line toolkit for S-coda seismic wave analysis and scattering parameters estimation
Requires Python: >=3.9
Keywords: geophysics, seismology, S-coda waves, mean free path, coda attenuation factor, earthquake, seismic wave scattering

# ScodaKit: A scientific Python-based command line toolkit for S-coda seismic wave analysis and scattering parameters estimation

**ScodaKit** is an open-source command-line pipeline for estimating the **scattering parameters of S-coda waves** from seismic waveform data. It combines waveform downloading, manual seismic phase picking, catalog metadata merging, coda window extraction, energy decay analysis and visualization — all within a modular and extensible architecture.

> Designed for researchers, seismologists, and geophysicists working on seismic attenuation, crustal scattering, or seismic coda-based energy models.

---

## 🛠️ Features

This command-line pipeline allows you to:

- 📡 Download waveform data from FDSN-compliant data centers (e.g., NOA, IRIS)
- ⌛ Manual picking of **P and S seismic wave arrivals** using interactive seismic waveform viewers
- 🗃️ Merge waveform arrival data with seismic event metadata
- 🌍 Generate interactive or GIS-compatible **maps of events and stations**
- 🧠 Extract **S-coda waveforms** from the seismic traces using signal to noise ratio (SNR) method
- 📉 Analyze S-coda energy decay to estimate **mean free path (ℓ)** and **Coda attenuation factor (B)** using the Single Isotropic Scattering Model
- 📊 Visualize full waveforms, spectrograms and coda windows 
- 🧪 Modular stages via CLI for reproducible research

---

For more information please visit https://github.com/marioskaragiorgas/ScodaKit

**Copyright:** Marios Karagiorgas <karagiorgasmarios@gmail.com>

**license:** https://www.apache.org/licenses/LICENSE-2.0
dinkum-bio 0.6.1
directed interaction network kit for modeling, focused on GRNs
Requires Python: >=3.11

# dinkum

Directed Interaction NetworKs are fair dinkum!

## What is dinkum?

`dinkum` is a piece of software for simple modeling of gene regulatory
networks, initially based on the GeNeTool software described in
[Faure, Peter, and Davidson, 2013](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3667423/). It
supports simple definition of genes, tissues, ligands/receptors, and
regulatory influences, and is intended to run in Jupyter
Notebooks. It's primarily intended for teaching purposes, and was
developed for the 2024
[Gene Regulatory Networks for Development](https://www.mbl.edu/education/advanced-research-training-courses/course-offerings/gene-regulatory-networks-development)
course at the Marine Biological Laboratory.

To get started with dinkum, see [notebooks/0-getting-started.ipynb](notebooks/0-getting-started.ipynb). Here is a full list of example Jupyter notebooks:

* [0-getting-started.ipynb](notebooks/0-getting-started.ipynb)
* [1-positive-feedback.ipynb](notebooks/1-positive-feedback.ipynb)
* [2-simple-oscillation.ipynb](notebooks/2-simple-oscillation.ipynb)
* [4-double-negative-gate.ipynb](notebooks/4-double-negative-gate.ipynb)
* [5-intermediate-custom-logic.ipynb](notebooks/5-intermediate-custom-logic.ipynb)
* [6-decay-example.ipynb](notebooks/6-decay-example.ipynb)
* [6-multi-level-activation.ipynb](notebooks/6-multi-level-activation.ipynb)
* [7-fit-functions.ipynb](notebooks/7-fit-functions.ipynb)
* [9-advanced-examples.ipynb](notebooks/9-advanced-examples.ipynb)

## Why 'dinkum'?

Dinkum is a backronym constructed from "directed interaction
networks". It's also named in honor of one of the course directors,
who is Australian; it turns out that dinkum is one of the few
Australian-specific slang words that is not rude.

## Installing dinkum

dinkum is available on the Python Package Index, PyPI, as `dinkum-bio`.
It requires Python 3.11 or later.

To install:
```
pip install dinkum-bio
```

## Developing dinkum

Dinkum is developed on github under
[dinkum-bio/dinkum](https://github.com/dinkum-bio/dinkum/). It is
released under the GNU Affero General Public License v3 open source
license.

You can run the tests with `make test`.

CTB 10/2024