DataAplose simplification

.gitignore

@@ -1,5 +1,5 @@
-/src/post_processing/utils/matlab_utils/pgmatlab/
+/src/disclose/utils/matlab/pgmatlab/
 __pycache__/
 /.idea/
 /dist/
-/.venv/
+/.venv/

docs/source/conf.py

@@ -6,9 +6,9 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = "OSmOSE_post_processing"
-copyright = "2025, Mathieu Dupont"
-author = "Mathieu Dupont"
+project = "disclOSE"
+copyright = "OSmOSE"
+author = "OSmOSE"
 release = "0.0.1"
 
 # -- General configuration ---------------------------------------------------
@@ -26,7 +26,7 @@
 
 html_theme = "sphinx_book_theme"
 html_logo = "../logo/osekit_small.png"
-html_title = "post_processing"
+html_title = "disclose"
 
 html_theme_options = {
     "repository_url": "https://github.com/mathieudpnt/OSmOSE_post_processing",

docs/source/example_APOCADO.ipynb

@@ -4,7 +4,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "6ea41f4ba78fb69f",
-   "metadata": {},
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
+    }
+   },
    "outputs": [],
    "source": [
     "from __future__ import annotations\n",
@@ -15,14 +19,16 @@
     "from pandas import Timedelta\n",
     "from pandas.tseries import frequencies\n",
     "\n",
-    "from post_processing.dataclass.data_aplose import DataAplose"
+    "from disclose.dataclass.data_aplose import DataAplose"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "4b10aaa427d1da6e",
    "metadata": {},
-   "source": "### Load data"
+   "source": [
+    "### Load data"
+   ]
   },
   {
    "cell_type": "code",
@@ -31,8 +37,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "yaml_file = Path(r\"resource/APOCADO_yaml.yml\")\n",
-    "data = DataAplose.from_yaml(file=yaml_file)\n",
+    "config = {\n",
+    "    \"detection_file\": Path(r\"../../user_case/resource/APOCADO_results.csv\"),\n",
+    "    \"timebin_new\": Timedelta(\"60s\"),\n",
+    "    \"filename_format\": \"%Y-%m-%dT%H-%M-%S\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "923a565737b4a05a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = DataAplose.from_dict(config)\n",
     "data.lat = 47\n",
     "data.lon = -5"
    ]
@@ -41,7 +60,9 @@
    "cell_type": "markdown",
    "id": "e17cf669b1d31019",
    "metadata": {},
-   "source": "### Overview"
+   "source": [
+    "### Overview"
+   ]
   },
   {
    "cell_type": "code",
@@ -59,7 +80,9 @@
    "cell_type": "markdown",
    "id": "29bb29560f971722",
    "metadata": {},
-   "source": "### Single barplot"
+   "source": [
+    "### Single barplot"
+   ]
   },
   {
    "cell_type": "code",
@@ -91,7 +114,9 @@
    "cell_type": "markdown",
    "id": "2dd2f7611b2d5d73",
    "metadata": {},
-   "source": "### Multi-barplot"
+   "source": [
+    "### Multi-barplot"
+   ]
   },
   {
    "cell_type": "code",
@@ -130,7 +155,9 @@
    "cell_type": "markdown",
    "id": "5d82f65b7966fa04",
    "metadata": {},
-   "source": "### Detection performances"
+   "source": [
+    "### Detection performances"
+   ]
   },
   {
    "cell_type": "code",
@@ -149,7 +176,9 @@
    "cell_type": "markdown",
    "id": "fdca3535add2d488",
    "metadata": {},
-   "source": "### Agreement"
+   "source": [
+    "### Agreement"
+   ]
   },
   {
    "cell_type": "code",
@@ -194,6 +223,18 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,

docs/source/example_OHAGEODAMS.ipynb

@@ -4,18 +4,17 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "initial_id",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "from osekit.utils.timestamp_utils import strptime_from_text\n",
+    "from osekit.utils.timestamp import strptime_from_text\n",
     "from pandas import Timedelta, Timestamp, read_csv\n",
     "\n",
-    "from post_processing.utils.glider_utils import (\n",
+    "from disclose.utils.filtering import read_dataframe\n",
+    "from disclose.utils.glider import (\n",
     "    compute_acoustic_diversity,\n",
     "    export_gpx,\n",
     "    load_glider_nav,\n",
@@ -27,7 +26,9 @@
    "cell_type": "markdown",
    "id": "429edfb71ec618a",
    "metadata": {},
-   "source": "### Load data"
+   "source": [
+    "### Load data"
+   ]
   },
   {
    "cell_type": "code",
@@ -36,17 +37,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "input_dir = Path(r\"resource\\OHAGEODAMS_nav\")\n",
+    "input_dir = Path(r\"../../user_case/resource/OHAGEODAMS_nav\")\n",
     "nav = load_glider_nav(input_dir)\n",
     "nav = nav[nav[\"Timestamp\"] > Timestamp(\"2024-02-21\", tz=\"UTC\")]\n",
-    "df = read_csv(Path(r\"resource\\OHAGEODAMS_results.csv\"), parse_dates=[\"start_datetime\", \"end_datetime\"]).dropna(subset=[\"annotation\"]).reset_index(drop=True)"
+    "df = read_dataframe(Path(r\"../../user_case/resource/OHAGEODAMS_results.csv\"))"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "303cd23d5b4d12dd",
    "metadata": {},
-   "source": "### Plot detection with nav data"
+   "source": [
+    "### Plot detection with nav data"
+   ]
   },
   {
    "cell_type": "code",
@@ -58,7 +61,12 @@
     "ticks = Timedelta(\"1d\")\n",
     "\n",
     "# plot specific labels\n",
-    "plot_detections_with_nav_data(df=df[df[\"annotation\"].isin([\"Sperm_Whale\", \"Delphinids clics\"])], nav=nav, criterion=\"Depth\", ticks=ticks)\n",
+    "plot_detections_with_nav_data(\n",
+    "    df=df[df[\"annotation\"].isin([\"Sperm_Whale\", \"Delphinids clics\"])],\n",
+    "    nav=nav,\n",
+    "    criterion=\"Depth\",\n",
+    "    ticks=ticks,\n",
+    ")\n",
     "plt.show()\n",
     "\n",
     "# plot all labels\n",
@@ -70,7 +78,9 @@
    "cell_type": "markdown",
    "id": "798518fc43a69afc",
    "metadata": {},
-   "source": "### Compute acoustic diversity"
+   "source": [
+    "### Compute acoustic diversity"
+   ]
   },
   {
    "cell_type": "code",
@@ -79,19 +89,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "task_status_filenames = read_csv(r\"resource\\OHAGEODAMS_status.csv\")[\"filename\"]\n",
-    "time_vector = [strptime_from_text(f, \"%Y_%m_%d_%H_%M_%S\").tz_localize(\"UTC\") for f in task_status_filenames]\n",
+    "task_status_filenames = read_csv(\n",
+    "    Path(r\"../../user_case/resource/OHAGEODAMS_results.csv\")\n",
+    ")[\"filename\"]\n",
+    "time_vector = [\n",
+    "    strptime_from_text(f, \"%Y_%m_%d_%H_%M_%S\").tz_localize(\"UTC\")\n",
+    "    for f in task_status_filenames\n",
+    "]\n",
     "\n",
     "df_acoustic_div = compute_acoustic_diversity(df=df, nav=nav, time_vector=time_vector)\n",
-    "\n",
-    "# df_acoustic_div.to_csv(Path(r'\\output\\path\\acoustic_diversity.csv'), index=False)"
+    "df_acoustic_div.to_csv(\n",
+    "    Path(r\"../../user_case/output/acoustic_diversity.csv\"), index=False\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "aa6d95c921329545",
    "metadata": {},
-   "source": "### Export navigation data to GPX"
+   "source": [
+    "### Export navigation data to GPX"
+   ]
   },
   {
    "cell_type": "code",
@@ -100,27 +118,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "export_gpx(nav=nav, output_dir=Path(r\"\\output\\path\"), output_file=\"test_trace\")"
+    "export_gpx(\n",
+    "    nav=nav, output_dir=Path(r\"../../user_case/output\"), output_file=\"test_trace\"\n",
+    ")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,