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‎.github/workflows/github_ci.yml‎

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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/checkout@v5
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- name: Install uv
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uses: astral-sh/setup-uv@v5
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uses: astral-sh/setup-uv@v7
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with:
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version: "0.7.8"
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enable-cache: true
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- name: "Set up Python"
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uses: actions/setup-python@v5
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uses: actions/setup-python@v6
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with:
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python-version-file: "pyproject.toml"
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python-version: '3.13'
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- name: Install the project
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run: uv sync --locked --all-extras --dev

‎docs/source/conf.py‎

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# -- Project information -----------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
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project = 'OSmOSE_post_processing'
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copyright = '2025, Mathieu Dupont'
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author = 'Mathieu Dupont'
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release = '0.0.1'
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project = "OSmOSE_post_processing"
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copyright = "2025, Mathieu Dupont"
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author = "Mathieu Dupont"
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release = "0.0.1"
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# -- General configuration ---------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

‎docs/source/example_APOCADO.ipynb‎

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{
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"cells": [
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "6ea41f4ba78fb69f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import annotations\n",
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"\n",
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"from pathlib import Path\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from pandas import Timedelta\n",
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"from pandas.tseries import frequencies\n",
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"\n",
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"from post_processing.dataclass.data_aplose import DataAplose"
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],
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"id": "6ea41f4ba78fb69f",
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Load data",
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"id": "4b10aaa427d1da6e"
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"id": "4b10aaa427d1da6e",
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"metadata": {},
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"source": "### Load data"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "c19ddde8bf965ee8",
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"metadata": {},
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"outputs": [],
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"source": [
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"yaml_file = Path(r\"resource/APOCADO_yaml.yml\")\n",
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"data = DataAplose.from_yaml(file=yaml_file)\n",
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"data.lat = 47\n",
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"data.lon = -5"
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],
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"id": "c19ddde8bf965ee8",
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Overview",
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"id": "e17cf669b1d31019"
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"id": "e17cf669b1d31019",
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"metadata": {},
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"source": "### Overview"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "cf59962e0e23eb96",
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"metadata": {},
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"outputs": [],
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"source": [
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"data.overview()\n",
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"plt.tight_layout()\n",
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"plt.show()"
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],
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"id": "cf59962e0e23eb96",
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Single barplot",
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"id": "29bb29560f971722"
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"id": "29bb29560f971722",
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"metadata": {},
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"source": "### Single barplot"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "8f7c3085e3f2550e",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1, 1)\n",
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"\n",
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")\n",
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"plt.tight_layout()\n",
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"plt.show()"
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],
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"id": "8f7c3085e3f2550e",
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Multi-barplot",
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"id": "2dd2f7611b2d5d73"
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"id": "2dd2f7611b2d5d73",
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"metadata": {},
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"source": "### Multi-barplot"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "fd3b245c3fd9bce1",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, axs = plt.subplots(3, 1)\n",
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"\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()"
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],
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"id": "fd3b245c3fd9bce1",
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Detection performances",
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"id": "5d82f65b7966fa04"
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"id": "5d82f65b7966fa04",
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"metadata": {},
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"source": "### Detection performances"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "dd6b3d9e2e9284ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"data.detection_perf(\n",
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" annotators=[\"mdupon\", \"bcolon\"],\n",
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" labels=\"Odontocete whistle\",\n",
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")"
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],
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"id": "dd6b3d9e2e9284ae",
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Agreement",
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"id": "fdca3535add2d488"
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"id": "fdca3535add2d488",
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"metadata": {},
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"source": "### Agreement"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "1995f0abbfa15e93",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, (ax1, ax2) = plt.subplots(1, 2, gridspec_kw={\"width_ratios\": [4, 1]})\n",
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"\n",
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")\n",
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"plt.tight_layout()\n",
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"plt.show()"
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],
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"id": "1995f0abbfa15e93",
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"outputs": [],
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"execution_count": null
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"display_name": "Python 3 (ipykernel)"
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"name": "python3"
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}
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},
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"nbformat": 4,

‎docs/source/example_OHAGEODAMS.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "initial_id",
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"\n",
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"from pandas import Timedelta, Timestamp, read_csv\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from osekit.utils.timestamp_utils import strptime_from_text\n",
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"from pandas import Timedelta, Timestamp, read_csv\n",
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"\n",
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"from post_processing.utils.glider_utils import load_glider_nav, plot_detections_with_nav_data, \\\n",
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" compute_acoustic_diversity, export_gpx\n",
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"import matplotlib.pyplot as plt"
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],
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"outputs": [],
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"execution_count": null
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"from post_processing.utils.glider_utils import (\n",
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" compute_acoustic_diversity,\n",
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" export_gpx,\n",
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" load_glider_nav,\n",
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" plot_detections_with_nav_data,\n",
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")"
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Load data",
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"id": "429edfb71ec618a"
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"id": "429edfb71ec618a",
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"metadata": {},
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"source": "### Load data"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "3014915fe657e321",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_dir = Path(r\"resource\\OHAGEODAMS_nav\")\n",
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"nav = load_glider_nav(input_dir)\n",
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"nav = nav[nav['Timestamp'] > Timestamp('2024-02-21', tz='UTC')]\n",
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"df = read_csv(Path(r\"resource\\OHAGEODAMS_results.csv\"), parse_dates=['start_datetime', 'end_datetime']).dropna(subset=['annotation']).reset_index(drop=True)"
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],
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"id": "3014915fe657e321",
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"outputs": [],
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"execution_count": null
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"nav = nav[nav[\"Timestamp\"] > Timestamp(\"2024-02-21\", tz=\"UTC\")]\n",
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"df = read_csv(Path(r\"resource\\OHAGEODAMS_results.csv\"), parse_dates=[\"start_datetime\", \"end_datetime\"]).dropna(subset=[\"annotation\"]).reset_index(drop=True)"
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Plot detection with nav data",
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"id": "303cd23d5b4d12dd"
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"id": "303cd23d5b4d12dd",
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"metadata": {},
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"source": "### Plot detection with nav data"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "7b478cccaf1c70eb",
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"metadata": {},
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"outputs": [],
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"source": [
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"ticks = Timedelta('1d')\n",
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"ticks = Timedelta(\"1d\")\n",
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"\n",
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"# plot specific labels\n",
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"plot_detections_with_nav_data(df=df[df['annotation'].isin([\"Sperm_Whale\", \"Delphinids clics\"])], nav=nav, criterion='Depth', ticks=ticks)\n",
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"plot_detections_with_nav_data(df=df[df[\"annotation\"].isin([\"Sperm_Whale\", \"Delphinids clics\"])], nav=nav, criterion=\"Depth\", ticks=ticks)\n",
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"plt.show()\n",
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"\n",
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"# plot all labels\n",
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"plot_detections_with_nav_data(df=df, nav=nav, criterion='Depth', ticks=ticks)\n",
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"plot_detections_with_nav_data(df=df, nav=nav, criterion=\"Depth\", ticks=ticks)\n",
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"plt.show()"
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],
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"id": "7b478cccaf1c70eb",
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Compute acoustic diversity",
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"id": "798518fc43a69afc"
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"id": "798518fc43a69afc",
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"metadata": {},
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"source": "### Compute acoustic diversity"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"id": "395af53656d318b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"task_status_filenames = read_csv(r\"resource\\OHAGEODAMS_status.csv\")['filename']\n",
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"time_vector = [strptime_from_text(f, \"%Y_%m_%d_%H_%M_%S\").tz_localize('UTC') for f in task_status_filenames]\n",
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"task_status_filenames = read_csv(r\"resource\\OHAGEODAMS_status.csv\")[\"filename\"]\n",
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"time_vector = [strptime_from_text(f, \"%Y_%m_%d_%H_%M_%S\").tz_localize(\"UTC\") for f in task_status_filenames]\n",
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"\n",
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"df_acoustic_div = compute_acoustic_diversity(df=df, nav=nav, time_vector=time_vector)\n",
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"\n",
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"# df_acoustic_div.to_csv(Path(r'\\output\\path\\acoustic_diversity.csv'), index=False)"
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],
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"id": "395af53656d318b4",
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "### Export navigation data to GPX",
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"id": "aa6d95c921329545"
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"id": "aa6d95c921329545",
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"metadata": {},
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"source": "### Export navigation data to GPX"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"source": "export_gpx(nav=nav, output_dir=Path(r\"\\output\\path\"), output_file=\"test_trace\")",
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"execution_count": null,
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"id": "54f691859e36b232",
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"metadata": {},
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"outputs": [],
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"execution_count": null
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"source": [
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"export_gpx(nav=nav, output_dir=Path(r\"\\output\\path\"), output_file=\"test_trace\")"
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]
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}
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],
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"metadata": {

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