From f9bc900984e4c10f11d1a3a4759a6207bbedf654 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 5 Sep 2025 16:09:45 +0200
Subject: [PATCH 01/83] add and change functions

---
 src/post_processing/utils/fpod_utils.py | 343 +++++++++++++++---------
 1 file changed, 223 insertions(+), 120 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index c6069e9..c80d0c8 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from pathlib import Path
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 import pytz
 import seaborn as sns
@@ -12,7 +12,7 @@
     DataFrame,
     Series,
     Timedelta,
-    Timestamp,
+    api,
     concat,
     date_range,
     notna,
@@ -187,61 +187,85 @@ def usable_data_phase(
     return percentage_data
 
 
-def meta_cut_aplose(
-    d_meta: DataFrame,
-    df: DataFrame,
-) -> DataFrame:
-    """From APLOSE DataFrame with all rows to filtered DataFrame.
+def meta_cut_aplose(raw_data: DataFrame,metadata: DataFrame,
+    col_deploy_name:str="deploy.name",
+    col_timestamp:str="start_datetime",
+    col_debut:str="deployment_date",
+    col_fin:str="recovery_date",
+) -> DataFrame | tuple[int, Any]:
+    """Filter data to keep only the ones corresponding to a deployment.
 
     Parameters
     ----------
-    df: DataFrame
-        CPOD result dataframe
-    d_meta: DataFrame
-        Metadata dataframe with deployments information (previously exported as json)
+    raw_data : DataFrame
+        Dataframe containing deploy.name et timestamp
+    metadata : DataFrame
+        Metadata containing deploy.name, deployment_date, recovery_date
+    col_deploy_name : str
+        Name of the deployment name column (default: 'deploy.name')
+    col_timestamp : str
+        Name of the timestamps column in raw_data (default: 'start_datetime')
+    col_debut : str
+        Name of the deployment column in metadata (default: 'deployment_date')
+    col_fin : str
+        Name of the recovery column in metadata (default: 'recovery_date')
 
     Returns
     -------
     DataFrame
-        An APLOSE DataFrame with data from beginning to end of each deployment.
-        Returns the percentage of usable datas.
+        Filtered data containing only rows in deployment periods
 
     """
-    d_meta.loc[:, ["deployment_date", "recovery_date"]] = d_meta[
-        ["deployment_date", "recovery_date"]
-    ].apply(to_datetime)
-    df["start_datetime"] = to_datetime(
-        df["start_datetime"],
-        format=TIMESTAMP_FORMAT_AUDIO_FILE,
+    # Vérifier que les colonnes existent
+    if col_deploy_name not in raw_data.columns:
+        msg =f"'{col_deploy_name}' not found"
+        raise ValueError(msg)
+    if col_timestamp not in raw_data.columns:
+        msg = f"'{col_timestamp}' not found"
+        raise ValueError(msg)
+    if col_deploy_name not in metadata.columns:
+        msg = f"'{col_deploy_name}' not found"
+        raise ValueError(msg)
+    if col_debut not in metadata.columns:
+        msg = f"'{col_debut}' not found"
+        raise ValueError(msg)
+    if col_fin not in metadata.columns:
+        msg = f"'{col_fin}' not found"
+        raise ValueError(msg)
+
+    data = raw_data.copy()
+    meta = metadata.copy()
+
+    # S'assurer que les timestamps sont au bon format datetime
+    if not api.types.is_datetime64_any_dtype(data[col_timestamp]):
+        data[col_timestamp] = to_datetime(data[col_timestamp])
+    if not api.types.is_datetime64_any_dtype(meta[col_debut]):
+        meta[col_debut] = to_datetime(meta[col_debut])
+    if not api.types.is_datetime64_any_dtype(meta[col_fin]):
+        meta[col_fin] = to_datetime(meta[col_fin])
+
+    actual_data = data.merge(
+        meta[[col_deploy_name, col_debut, col_fin]], on=col_deploy_name, how="left")
+
+    lignes_avec_meta = actual_data[col_debut].notna()
+
+    if not lignes_avec_meta.any():
+        return DataFrame(columns=raw_data.columns)
+
+    mask_valid_period = (
+            lignes_avec_meta &
+            (actual_data[col_timestamp] >= actual_data[col_debut]) &
+            (actual_data[col_timestamp] <= actual_data[col_fin])
     )
 
-    # Add DPM column
-    df["DPM"] = (df["Nfiltered"] > 0).astype(int)
-
-    # Extract corresponding line
-    campaign = df.iloc[0]["dataset"]
-    phase = d_meta.loc[d_meta["name"] == campaign].reset_index()
-    start_date = phase.loc[0, "deployment_date"]
-    end_date = phase.loc[0, "recovery_date"]
-    df = df[
-        (df["start_datetime"] >= start_date) & (df["start_datetime"] <= end_date)
-    ].copy()
+    filt_data = actual_data[mask_valid_period][raw_data.columns]
 
-    # Calculate the percentage of collected data on the phase length of time
-    if df.empty:
-        msg = "No data for this phase"
-    else:
-        df_end = df.loc[df.index[-1], "start_datetime"]
-        df_start = df.loc[df.index[0], "start_datetime"]
-        act_length = df_end - df_start
-        p_length = end_date - start_date
-        percentage_data = act_length * 100 / p_length
-        on = int(df.loc[df.MinsOn == 1, "MinsOn"].count())
-        percentage_on = percentage_data * (on / len(df))
-        msg = f"Percentage of usable data : {percentage_on}%"
+    # Statistics
+    nb_total = len(raw_data)
+    nb_filter = len(filt_data)
+    del_nb = nb_total - nb_filter
 
-    logger.info(msg)
-    return df
+    return del_nb, filt_data.reset_index(drop=True)
 
 
 def format_calendar(path: Path) -> DataFrame:
@@ -265,48 +289,6 @@ def format_calendar(path: Path) -> DataFrame:
     )
 
 
-def dpm_to_dph(
-    df: DataFrame,
-    tz: pytz.BaseTzInfo,
-    dataset_name: str,
-    annotation: str,
-    bin_size: int = 3600,
-    extra_columns: list | None = None,
-) -> DataFrame:
-    """From CPOD result DataFrame to APLOSE formatted DataFrame.
-
-    Parameters
-    ----------
-    df: DataFrame
-        CPOD result DataFrame
-    tz: pytz.BaseTzInfo
-        Timezone object to get timezone-aware datetimes
-    dataset_name: str
-        dataset name
-    annotation: str
-        annotation name
-    bin_size: int
-        Duration of the detections in seconds
-    extra_columns: list, optional
-        Additional columns added from df to data
-
-    Returns
-    -------
-    DataFrame
-        An APLOSE DataFrame
-
-    """
-    df["start_datetime"] = to_datetime(df["start_datetime"], utc=True)
-    df["end_datetime"] = to_datetime(df["end_datetime"], utc=True)
-    df["Date heure"] = df["start_datetime"].dt.floor("h")
-    dph = df.groupby(["Date heure"])["DPM"].sum().reset_index()
-    dph["Date heure"] = dph["Date heure"].apply(
-        lambda x: Timestamp(x).strftime(format="%d/%m/%Y %H:%M:%S"),
-    )
-
-    return cpod2aplose(dph, tz, dataset_name, annotation, bin_size, extra_columns)
-
-
 def assign_phase(
     meta: DataFrame,
     data: DataFrame,
@@ -346,7 +328,7 @@ def assign_phase(
                 <= data.loc[j, "start_datetime"]
                 < meta_row["recovery_date"]
             ):
-                data.loc[j, "name"] = meta_row["name"]
+                data.loc[j, "name"] = f"{meta_row['site.name']}_{meta_row['campaign.name']}"
             j += 1
     return data
 
@@ -378,9 +360,9 @@ def assign_phase_simple(
     meta["recovery_date"] = meta["recovery_date"].dt.floor("d")
 
     data["name"] = None
-    for site in data["site.name"].unique():
-        site_meta = meta[meta["site.name"] == site]
-        site_data = data[data["site.name"] == site]
+    for site in data["deploy.name"].unique():
+        site_meta = meta[meta["deploy.name"] == site]
+        site_data = data[data["deploy.name"] == site]
 
         for _, meta_row in site_meta.iterrows():
             time_filter = (
@@ -420,13 +402,50 @@ def generate_hourly_detections(meta: DataFrame, site: str) -> DataFrame:
         {"name": row["name"], "start_datetime": date}
         for _, row in df_meta.iterrows()
         for date in date_range(
-            start=row["deployment_date"], end=row["recovery_date"], freq="h",
+            start=row["deployment_date"],
+            end=row["recovery_date"],
+            freq="h",
         )
     ]
 
     return DataFrame(records)
 
 
+def build_hour_range(dph: DataFrame) -> DataFrame:
+    """Create a DataFrame with one line per hour between start and end dates.
+
+    Keep the number of detections per hour between these dates.
+
+    Parameters
+    ----------
+    dph: pd.DataFrame
+        Metadata dataframe with deployments information (previously exported as json)
+
+    Returns
+    -------
+    pd.DataFrame
+        A full period of time with positive and negative hours to detections.
+
+    """
+    dph["Date heure"] = to_datetime(dph["Date heure"], dayfirst=True)
+
+    deploy_ranges = (
+        dph.groupby("deploy.name")["Date heure"]
+        .agg(start="min", end="max")
+        .reset_index()
+    )
+
+    all_ranges = []
+    for _, row in deploy_ranges.iterrows():
+        hours = date_range(row["start"], row["end"], freq="h")
+        tmp = DataFrame({
+            "deploy.name": row["deploy.name"],
+            "Date heure": hours,
+        })
+        all_ranges.append(tmp)
+
+    return concat(all_ranges, ignore_index=True)
+
 def merging_tab(meta: DataFrame, data: DataFrame) -> DataFrame:
     """Create a DataFrame with one line per hour between start and end dates.
 
@@ -448,16 +467,15 @@ def merging_tab(meta: DataFrame, data: DataFrame) -> DataFrame:
     data["start_datetime"] = to_datetime(data["start_datetime"], utc=True)
     meta["start_datetime"] = to_datetime(meta["start_datetime"], utc=True)
 
-    deploy_detec = data["name"].unique()
-    df_filtered = meta[meta["name"].isin(deploy_detec)]
+    deploy_detec = data["deploy.name"].unique()
+    df_filtered = meta[meta["deploy.name"].isin(deploy_detec)]
 
     output = df_filtered.merge(
-        data[["name", "start_datetime", "DPM", "Nfiltered"]],
-        on=["name", "start_datetime"],
+        data[["deploy.name", "start_datetime", "DPM"]],
+        on=["deploy.name", "start_datetime"],
         how="outer",
     )
     output["DPM"] = output["DPM"].fillna(0)
-    output["Nfiltered"] = output["Nfiltered"].fillna(0)
 
     output["Day"] = output["start_datetime"].dt.day
     output["Month"] = output["start_datetime"].dt.month
@@ -485,27 +503,13 @@ def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
         Containing all ICIs for every positive minutes to clicks
 
     """
-    df.columns = df.columns.str.upper()
-    df["MICROSEC"] = df["MICROSEC"] / 1e6
-    col = "DATE HEURE MINUTE"
-    col2 = "HEURE MINUTE"
-    if col in df.columns:
-        df[["DATE", "HEURE", "MINUTE"]] = df[col].str.split(" ", expand=True)
-        df["Time"] = (df["DATE"].astype(str) + " " +
-                      df["HEURE"].astype(str) + ":" +
-                      df["MINUTE"].astype(str) + ":" +
-                      df["MICROSEC"].astype(str))
-        df["Time"] = to_datetime(df["Time"], dayfirst=True)
-    elif col2 in df.columns:
-        df[["HEURE", "MINUTE"]] = df[col2].str.split(" ", expand=True)
-        df["Time"] = (df["DATE"].astype(str) + " " +
-                      df["HEURE"].astype(str) + ":" +
-                      df["MINUTE"].astype(str) + ":" +
-                      df["MICROSEC"].astype(str))
-        df["Time"] = to_datetime(df["Time"], dayfirst=True)
-    else :
-        df["Time"] = (df["MINUTE"].astype(str) + ":" + df["MICROSEC"].astype(str))
-        df["Time"] = to_datetime(df["Time"], dayfirst=True)
+    df["microsec"] = df["microsec"] / 1e6
+
+    df["Time"] = (df["Minute"].astype(str) + ":" +
+                  df["microsec"].astype(str))
+    df["Time"] = to_datetime(df["Time"], dayfirst=True)
+
+    df["Time"] = to_datetime(df["Time"], dayfirst=True)
 
     df = df.sort_values(by="Time").reset_index(drop=True)
     df["ICI"] = df["Time"].diff().dt.total_seconds()
@@ -583,7 +587,7 @@ def assign_daytime(
     return df
 
 
-def process_files_in_folder(folder_path:Path, species:str) -> DataFrame:
+def fb_folder(folder_path:Path, species:str) -> DataFrame:
     """Process a folder containing all CPOD/FPOD feeding buzz detection files.
 
     Apply the feeding buzz function to these files.
@@ -608,7 +612,7 @@ def process_files_in_folder(folder_path:Path, species:str) -> DataFrame:
         file_path = folder_path / file
         df = read_csv(file_path, sep="\t")
         processed_df = feeding_buzz(df, species)
-        processed_df["file"] = file
+        processed_df["deploy.name"] = file.name
         all_data.append(processed_df)
 
     return concat(all_data, ignore_index=True)
@@ -820,4 +824,103 @@ def hour_percent(df: DataFrame, metric: str) -> None:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     fig.suptitle(f"{metric} per hour", fontsize=16)
-    plt.show()
\ No newline at end of file
+    plt.show()
+
+
+def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
+    """Process a folder containing data files and concatenate them.
+
+    Parameters
+    ----------
+    folder_path: Union[str, Path]
+        Path to the folder containing files.
+    **kwargs: dict
+        Additional parameters for pd.read_csv (sep, skiprows, etc.)
+
+    Returns
+    -------
+    pd.DataFrame
+        Concatenated dataframe with all files data and file column.
+
+    Raises
+    ------
+    ValueError
+        If file_format is not supported or no files found.
+    FileNotFoundError
+        If folder_path doesn't exist.
+
+    """
+    folder_path = Path(folder_path)
+
+    # Folder validation
+    if not folder_path.exists():
+        raise FileNotFoundError
+
+    if not folder_path.is_dir():
+        message = f"{folder_path} is not a directory."
+        raise ValueError(message)
+
+    # Configuration
+    default_params = {"sep": ";", "skiprows": 7}
+
+    # Parameters fusion
+    read_params = {**default_params, **kwargs}
+
+    # File research
+    files = list(folder_path.rglob("*csv"))
+
+    if not files:
+        msg = f"No CSV file found in {folder_path}"
+        raise ValueError(msg)
+
+    all_data = []
+
+    for file in files:
+        try:
+            df = read_csv(file, **read_params)
+            df["deploy.name"] = file.name.rsplit(".", 1)[0]  # file name
+            df["file_path"] = str(file)  # file path
+            all_data.append(df)
+        except Exception:
+            continue
+
+    if not all_data:
+        msg = f"No valid CSV file found in {folder_path}"
+        raise ValueError(msg)
+
+    return concat(all_data, ignore_index=True)
+
+
+def dpm_to_dph(
+    df: DataFrame,
+    extra_columns: list | None = None,
+) -> DataFrame:
+    """Create a dataframe containing the number of DPM per hour.
+
+    Parameters
+    ----------
+    df: DataFrame
+        Contains every minute positive to click detection.
+    extra_columns: list, optional
+        Additional columns added from df to data.
+
+    Returns
+    -------
+    DataFrame
+        Contains sum of minutes positive to detection per hour.
+
+    """
+    df["start_datetime"] = to_datetime(df["start_datetime"], utc=True)
+    df["end_datetime"] = to_datetime(df["end_datetime"], utc=True)
+    df["Date heure"] = df["start_datetime"].dt.floor("h")
+
+    agg_dict = {"DPM": "sum"}
+
+    if extra_columns:
+        for col in extra_columns:
+            if col in df.columns:
+                agg_dict[col] = "first"
+            else:
+                logger.warning(f"Column '{col}' does not exist and will be ignored.")
+
+    return df.groupby("Date heure").agg(agg_dict).reset_index()

From e78bfcdebccc551708b033a1280236b72d9293a4 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 5 Sep 2025 16:10:16 +0200
Subject: [PATCH 02/83] create the usalble case notebook

---
 user_case/user_case_CALAIS.ipynb | 183 +++++++++++++++++++++++++++++++
 1 file changed, 183 insertions(+)
 create mode 100644 user_case/user_case_CALAIS.ipynb

diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
new file mode 100644
index 0000000..437f235
--- /dev/null
+++ b/user_case/user_case_CALAIS.ipynb
@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import pytz\n",
+    "\n",
+    "from post_processing.dataclass.data_aplose import DataAplose\n",
+    "from post_processing.utils.fpod_utils import cpod2aplose, usable_data_phase, dpm_to_dph,build_hour_range, fb_folder,csv_folder, meta_cut_aplose\n",
+    "from post_processing.utils.core_utils import json2df"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Pre-processing\n",
+    "\n"
+   ],
+   "id": "e8e8c57c7f4197fe"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "data = csv_folder(r\"U:\\A\")\n",
+    "print(data.head())\n",
+    "\n",
+    "df_1 = data[data[\"DPM\"]==1]\n",
+    "\n",
+    "df_aplose = cpod2aplose(df_1, pytz.utc, \"CA4\", \"Commerson\", extra_columns=[\"DPM\", \"deploy.name\"])\n",
+    "print(df_aplose.head())"
+   ],
+   "id": "8636a8a27fe2af47",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_kerguelen.json\")\n",
+    "metadatax = json2df(json_path=json)\n",
+    "\n",
+    "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
+    "                           metadatax[\"campaign.name\"].astype(str))\n",
+    "\n",
+    "data = meta_cut_aplose(df_aplose, metadatax)\n",
+    "\n",
+    "df_aplose.to_csv(r\"U:\\APLOSE_A1112.csv\", index=False)"
+   ],
+   "id": "ed6a06c522aea169",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Load data",
+   "id": "73d08f821cd03ae3"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
+    "data_list = DataAplose.from_yaml(file=yaml_file)\n",
+    "print(data_list.df.head())"
+   ],
+   "id": "ad94c9baae5a1748",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Create a detection per hour dataframe\n",
+    "\n"
+   ],
+   "id": "4d6b640178d7563"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "dph = dpm_to_dph(data_list.df, extra_columns=[\"deploy.name\"])",
+   "id": "c46fb2201838f42",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Add the feeding buzzes",
+   "id": "8375ddbe07ad0aee"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "fb = fb_folder(r\"U:\\fb_A_NBHF\", \"Commerson\")\n",
+    "fb[\"Date heure\"] = fb[\"start_datetime\"].dt.floor(\"h\")\n",
+    "fb = fb.groupby(\"Date heure\")[\"Foraging\"].sum().reset_index()\n",
+    "\n",
+    "d_hour = dph.merge(fb[[\"Date heure\",\"Foraging\"]], on=\"Date heure\", how=\"left\")"
+   ],
+   "id": "2b19f90c99252ff3",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "new = build_hour_range(dph)\n",
+    "final_df = new.merge(d_hour, on=[\"deploy.name\", \"Date heure\"], how = \"left\")\n",
+    "\n",
+    "final_df[[\"DPM\",\"Foraging\"]] = final_df[[\"DPM\",\"Foraging\"]].fillna(0)\n",
+    "print(final_df.head())"
+   ],
+   "id": "f93bf1f3ca4f4112",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "final_df[\"Year\"] = final_df[\"Date heure\"].dt.year\n",
+    "final_df[\"Month\"] = final_df[\"Date heure\"].dt.month\n",
+    "final_df[\"Day\"] = final_df[\"Date heure\"].dt.day\n",
+    "final_df[\"Hour\"] = final_df[\"Date heure\"].dt.hour\n",
+    "\n",
+    "final_df[\"FBR\"] = final_df[\"Foraging\"] / final_df[\"DPM\"]"
+   ],
+   "id": "a2261ce5093a3104",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "final_df[\"FBR\"] = final_df[\"FBR\"].fillna(0)\n",
+    "final_df.to_csv(r\"U:\\Hours_DPM_FBUZZ_SiteA1112.csv\", index=False)"
+   ],
+   "id": "d606f4f6904b57c6",
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 76d1336f632c58e9e3dc9dd8081e0a9a221bce45 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 5 Sep 2025 16:10:47 +0200
Subject: [PATCH 03/83] create yaml for CPOD

---
 user_case/resource/CPOD-FPOD_yaml.yml | 31 +++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)
 create mode 100644 user_case/resource/CPOD-FPOD_yaml.yml

diff --git a/user_case/resource/CPOD-FPOD_yaml.yml b/user_case/resource/CPOD-FPOD_yaml.yml
new file mode 100644
index 0000000..3e259cc
--- /dev/null
+++ b/user_case/resource/CPOD-FPOD_yaml.yml
@@ -0,0 +1,31 @@
+#This file is to be used to load an APLOSE result csv file.
+#If a parameter is set here to `null`, it will be loaded with a `None` value in the Python scripts.
+#For parameter definition, see `sort_detections` function in `utils\def_func`.
+#
+#Note:
+#  - Several csv files can be loaded at once, to perform this copy the template and paste it at the end of the present file.
+
+#########################################
+'U:\APLOSE_A1112.csv':
+  # timebin: detection time resolution in seconds
+  'timebin_new': null
+  # begin datetime: '2000-01-01T00:00:00+0000'
+  'begin': null
+  # end datetime: '2000-01-01T00:00:00+0000'
+  'end': null
+  # annotator: ['annotator1', 'annotator2']
+  'annotator': null
+  # annotation: ['annotation1', 'annotation2']
+  'annotation': "Commerson"
+  # box: boolean value, whether to keep strong annotations
+  'box': False
+  # timestamp file
+  'timestamp_file': null
+  # user selection
+  'user_sel': all
+  # f_min filter
+  'f_min': null
+  # f_max filter
+  'f_max': null
+  # score
+  'score': null

From 81a02486dd82491ca0943af0f333c08f64583db3 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 9 Sep 2025 17:28:50 +0200
Subject: [PATCH 04/83] add new functions, correct others

---
 src/post_processing/utils/fpod_utils.py | 144 +++++++++++-------------
 1 file changed, 65 insertions(+), 79 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index c80d0c8..a588452 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -12,9 +12,10 @@
     DataFrame,
     Series,
     Timedelta,
-    api,
+    Timestamp,
     concat,
     date_range,
+    merge,
     notna,
     read_csv,
     read_excel,
@@ -116,23 +117,22 @@ def cpod2aplose(
         An APLOSE formatted DataFrame
 
     """
-    df_cpod = df.rename(columns={"ChunkEnd": "Date heure"})
+    df = df.rename(columns={"ChunkEnd": "Date heure"})
+    results = []
 
-    # remove lines where the C-POD stopped working
-    df_cpod = df_cpod.drop(
-        df_cpod.loc[df_cpod["Date heure"] == " at minute "].index,
-    )
-    data = fpod2aplose(df_cpod, tz, dataset_name, annotation, bin_size)
-    data["annotator"] = data.loc[data["annotator"] == "FPOD"] = "CPOD"
-    if extra_columns:
-        for col in extra_columns:
-            if col in df_cpod.columns:
-                data[col] = df_cpod[col].tolist()
-            else:
-                msg = f"Column '{col}' does not exist and will be ignored."
-                logger.warning(msg)
+    for deploy_name in df["deploy.name"].unique():
+        df_deploy = df[df["deploy.name"] == deploy_name].copy()
 
-    return DataFrame(data)
+        result = fpod2aplose(df_deploy, tz, dataset_name, annotation, bin_size)
+
+        if extra_columns:
+            for col in extra_columns:
+                if col in df_deploy.columns:
+                    result[col] = df_deploy[col].tolist()
+
+        results.append(result)
+
+    return concat(results, ignore_index=True)
 
 
 def usable_data_phase(
@@ -192,7 +192,7 @@ def meta_cut_aplose(raw_data: DataFrame,metadata: DataFrame,
     col_timestamp:str="start_datetime",
     col_debut:str="deployment_date",
     col_fin:str="recovery_date",
-) -> DataFrame | tuple[int, Any]:
+) -> DataFrame:
     """Filter data to keep only the ones corresponding to a deployment.
 
     Parameters
@@ -216,56 +216,43 @@ def meta_cut_aplose(raw_data: DataFrame,metadata: DataFrame,
         Filtered data containing only rows in deployment periods
 
     """
-    # Vérifier que les colonnes existent
-    if col_deploy_name not in raw_data.columns:
-        msg =f"'{col_deploy_name}' not found"
-        raise ValueError(msg)
-    if col_timestamp not in raw_data.columns:
-        msg = f"'{col_timestamp}' not found"
-        raise ValueError(msg)
-    if col_deploy_name not in metadata.columns:
-        msg = f"'{col_deploy_name}' not found"
-        raise ValueError(msg)
-    if col_debut not in metadata.columns:
-        msg = f"'{col_debut}' not found"
-        raise ValueError(msg)
-    if col_fin not in metadata.columns:
-        msg = f"'{col_fin}' not found"
-        raise ValueError(msg)
-
-    data = raw_data.copy()
+    required_raw = [col_deploy_name, col_timestamp]
+    required_meta = [col_deploy_name, col_debut, col_fin]
+    for col in required_raw:
+        if col not in raw_data.columns:
+            msg = f"'{col}' not found in raw_data"
+            raise ValueError(msg)
+    for col in required_meta:
+        if col not in metadata.columns:
+            msg = f"'{col}' not found in metadata"
+            raise ValueError(msg)
+
+    # Convert to datetime
+    raw = raw_data.copy()
     meta = metadata.copy()
-
-    # S'assurer que les timestamps sont au bon format datetime
-    if not api.types.is_datetime64_any_dtype(data[col_timestamp]):
-        data[col_timestamp] = to_datetime(data[col_timestamp])
-    if not api.types.is_datetime64_any_dtype(meta[col_debut]):
-        meta[col_debut] = to_datetime(meta[col_debut])
-    if not api.types.is_datetime64_any_dtype(meta[col_fin]):
-        meta[col_fin] = to_datetime(meta[col_fin])
-
-    actual_data = data.merge(
-        meta[[col_deploy_name, col_debut, col_fin]], on=col_deploy_name, how="left")
-
-    lignes_avec_meta = actual_data[col_debut].notna()
-
-    if not lignes_avec_meta.any():
-        return DataFrame(columns=raw_data.columns)
-
-    mask_valid_period = (
-            lignes_avec_meta &
-            (actual_data[col_timestamp] >= actual_data[col_debut]) &
-            (actual_data[col_timestamp] <= actual_data[col_fin])
+    raw[col_timestamp] = to_datetime(raw[col_timestamp], errors="coerce")
+    meta[col_debut] = to_datetime(meta[col_debut], errors="coerce")
+    meta[col_fin] = to_datetime(meta[col_fin], errors="coerce")
+
+    dfm = raw.merge(
+        meta[[col_deploy_name, col_debut, col_fin]],
+        on=col_deploy_name,
+        how="left",
     )
 
-    filt_data = actual_data[mask_valid_period][raw_data.columns]
+    out = dfm[
+        (dfm[col_timestamp] >= dfm[col_debut])
+        & (dfm[col_timestamp] <= dfm[col_fin])
+        & dfm[col_timestamp].notna()
+        & dfm[col_debut].notna()
+        & dfm[col_fin].notna()
+    ].copy()
 
-    # Statistics
-    nb_total = len(raw_data)
-    nb_filter = len(filt_data)
-    del_nb = nb_total - nb_filter
+    columns_to_drop = [col for col in [col_debut, col_fin] if col not in raw_data.columns]
+    if columns_to_drop:
+        out = out.drop(columns=columns_to_drop)
 
-    return del_nb, filt_data.reset_index(drop=True)
+    return out.sort_values([col_deploy_name, col_timestamp]).reset_index(drop=True)
 
 
 def format_calendar(path: Path) -> DataFrame:
@@ -515,10 +502,13 @@ def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
     df["ICI"] = df["Time"].diff().dt.total_seconds()
 
     df["Buzz"] = 0
-    if species == "Porpoise":
+    if species == "Marsouin":
         feeding_idx = df.index[df["ICI"] < 0.01]
+    elif species == "Commerson" :
+        feeding_idx = df.index[df["ICI"] <= 0.005]
     else :
-        feeding_idx = df.index[df["ICI"] >= 0.005]
+        msg = "This species is not supported"
+        raise ValueError(msg)
 
     df.loc[feeding_idx, "Buzz"] = 1
     df.loc[feeding_idx - 1, "Buzz"] = 1
@@ -619,10 +609,10 @@ def fb_folder(folder_path:Path, species:str) -> DataFrame:
 
 
 colors = {
-    "DY1": "#118B50",
-    "DY2": "#5DB996",
-    "DY3": "#B0DB9C",
-    "DY4": "#E3F0AF",
+    "Site A Haute": "#118B50",
+    "Site B Heugh": "#5DB996",
+    "Site C Chat": "#B0DB9C",
+    "Site D Simone": "#E3F0AF",
     "CA4": "#5EABD6",
     "Walde": "#FFB4B4",
 }
@@ -732,7 +722,7 @@ def year_percent(df: DataFrame, metric: str) -> None:
                label=f"Site {site}",
                color=colors.get(site, "gray"),
                )
-        ax.set_title(f"Site {site}")
+        ax.set_title(f"{site}")
         ax.set_ylim(0,max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         if i != 3:
@@ -770,7 +760,7 @@ def month_percent(df: DataFrame, metric: str) -> None:
                label=f"Site {site}",
                color=colors.get(site, "gray"),
                )
-        ax.set_title(f"{site} - Percentage of postitive to detection minutes per month")
+        ax.set_title(f"{site} - Percentage of minutes postitive to detection per month")
         ax.set_ylim(0,max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         ax.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
@@ -813,7 +803,7 @@ def hour_percent(df: DataFrame, metric: str) -> None:
                label=f"Site {site}",
                color=colors.get(site, "gray"),
                )
-        ax.set_title(f"Site {site} - Percentage of positive to detection per hour")
+        ax.set_title(f"Site {site} - Percentage of minutes positive to detection per hour")
         ax.set_ylim(0,max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         if i != 3:
@@ -861,7 +851,7 @@ def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
         raise ValueError(message)
 
     # Configuration
-    default_params = {"sep": ";", "skiprows": 7}
+    default_params = {"sep": ";"}
 
     # Parameters fusion
     read_params = {**default_params, **kwargs}
@@ -876,13 +866,9 @@ def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
     all_data = []
 
     for file in files:
-        try:
-            df = read_csv(file, **read_params)
-            df["deploy.name"] = file.name.rsplit(".", 1)[0]  # file name
-            df["file_path"] = str(file)  # file path
-            all_data.append(df)
-        except Exception:
-            continue
+        df = read_csv(file, **read_params)
+        df["deploy.name"] = file.stem
+        all_data.append(df)
 
     if not all_data:
         msg = f"No valid CSV file found in {folder_path}"

From 9bb34752c6b9b285f91fa7b6be79777e65f7f8ef Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 9 Sep 2025 17:29:28 +0200
Subject: [PATCH 05/83] change notebook config

---
 user_case/user_case_CALAIS.ipynb | 97 ++++++++++++++++++++++++--------
 1 file changed, 74 insertions(+), 23 deletions(-)

diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index 437f235..0f5e509 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -4,7 +4,11 @@
    "cell_type": "code",
    "id": "initial_id",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-09-09T14:50:36.642346Z",
+     "start_time": "2025-09-09T14:50:36.638397Z"
+    }
    },
    "source": [
     "from pathlib import Path\n",
@@ -18,7 +22,7 @@
     "from post_processing.utils.core_utils import json2df"
    ],
    "outputs": [],
-   "execution_count": null
+   "execution_count": 21
   },
   {
    "metadata": {},
@@ -30,23 +34,73 @@
    "id": "e8e8c57c7f4197fe"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-09T14:51:43.424921Z",
+     "start_time": "2025-09-09T14:50:38.447445Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "data = csv_folder(r\"U:\\A\")\n",
     "print(data.head())\n",
     "\n",
-    "df_1 = data[data[\"DPM\"]==1]\n",
+    "df_1 = data.dropna()\n",
     "\n",
-    "df_aplose = cpod2aplose(df_1, pytz.utc, \"CA4\", \"Commerson\", extra_columns=[\"DPM\", \"deploy.name\"])\n",
+    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site A Haute\", \"Commerson\", extra_columns=[\"DPM\", \"deploy.name\"])\n",
     "print(df_aplose.head())"
    ],
    "id": "8636a8a27fe2af47",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                   File          ChunkEnd  DPM  Nall  MinsOn  \\\n",
+      "0  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:03    0     0       0   \n",
+      "1  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:04    0     0       0   \n",
+      "2  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:05    0     0       0   \n",
+      "3  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:06    0     0       0   \n",
+      "4  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:07    0     0       0   \n",
+      "\n",
+      "            deploy.name  \n",
+      "0  Site A Haute_Phase11  \n",
+      "1  Site A Haute_Phase11  \n",
+      "2  Site A Haute_Phase11  \n",
+      "3  Site A Haute_Phase11  \n",
+      "4  Site A Haute_Phase11  \n",
+      "        dataset filename  start_time  end_time  start_frequency  \\\n",
+      "0  Site A Haute                    0        60                0   \n",
+      "1  Site A Haute                    0        60                0   \n",
+      "2  Site A Haute                    0        60                0   \n",
+      "3  Site A Haute                    0        60                0   \n",
+      "4  Site A Haute                    0        60                0   \n",
+      "\n",
+      "   end_frequency annotation annotator                start_datetime  \\\n",
+      "0              0  Commerson      FPOD  2022-12-01T16:03:00.000+0000   \n",
+      "1              0  Commerson      FPOD  2022-12-01T16:04:00.000+0000   \n",
+      "2              0  Commerson      FPOD  2022-12-01T16:05:00.000+0000   \n",
+      "3              0  Commerson      FPOD  2022-12-01T16:06:00.000+0000   \n",
+      "4              0  Commerson      FPOD  2022-12-01T16:07:00.000+0000   \n",
+      "\n",
+      "                   end_datetime  is_box  DPM           deploy.name  \n",
+      "0  2022-12-01T16:04:00.000+0000       0    0  Site A Haute_Phase11  \n",
+      "1  2022-12-01T16:05:00.000+0000       0    0  Site A Haute_Phase11  \n",
+      "2  2022-12-01T16:06:00.000+0000       0    0  Site A Haute_Phase11  \n",
+      "3  2022-12-01T16:07:00.000+0000       0    0  Site A Haute_Phase11  \n",
+      "4  2022-12-01T16:08:00.000+0000       0    0  Site A Haute_Phase11  \n"
+     ]
+    }
+   ],
+   "execution_count": 22
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-09T14:56:09.883356Z",
+     "start_time": "2025-09-09T14:51:52.486050Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_kerguelen.json\")\n",
@@ -55,13 +109,13 @@
     "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
     "                           metadatax[\"campaign.name\"].astype(str))\n",
     "\n",
-    "data = meta_cut_aplose(df_aplose, metadatax)\n",
+    "cleared = meta_cut_aplose(df_aplose, metadatax)\n",
     "\n",
-    "df_aplose.to_csv(r\"U:\\APLOSE_A1112.csv\", index=False)"
+    "cleared.to_csv(r\"U:\\APLOSE_A.csv\", index=False)"
    ],
    "id": "ed6a06c522aea169",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 23
   },
   {
    "metadata": {},
@@ -122,11 +176,8 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "new = build_hour_range(dph)\n",
-    "final_df = new.merge(d_hour, on=[\"deploy.name\", \"Date heure\"], how = \"left\")\n",
-    "\n",
-    "final_df[[\"DPM\",\"Foraging\"]] = final_df[[\"DPM\",\"Foraging\"]].fillna(0)\n",
-    "print(final_df.head())"
+    "d_hour[[\"DPM\",\"Foraging\"]] = d_hour[[\"DPM\",\"Foraging\"]].fillna(0)\n",
+    "print(d_hour.head())"
    ],
    "id": "f93bf1f3ca4f4112",
    "outputs": [],
@@ -136,12 +187,12 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "final_df[\"Year\"] = final_df[\"Date heure\"].dt.year\n",
-    "final_df[\"Month\"] = final_df[\"Date heure\"].dt.month\n",
-    "final_df[\"Day\"] = final_df[\"Date heure\"].dt.day\n",
-    "final_df[\"Hour\"] = final_df[\"Date heure\"].dt.hour\n",
+    "d_hour[\"Year\"] = d_hour[\"Date heure\"].dt.year\n",
+    "d_hour[\"Month\"] = d_hour[\"Date heure\"].dt.month\n",
+    "d_hour[\"Day\"] = d_hour[\"Date heure\"].dt.day\n",
+    "d_hour[\"Hour\"] = d_hour[\"Date heure\"].dt.hour\n",
     "\n",
-    "final_df[\"FBR\"] = final_df[\"Foraging\"] / final_df[\"DPM\"]"
+    "d_hour[\"FBR\"] = d_hour[\"Foraging\"] / d_hour[\"DPM\"]"
    ],
    "id": "a2261ce5093a3104",
    "outputs": [],
@@ -151,8 +202,8 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "final_df[\"FBR\"] = final_df[\"FBR\"].fillna(0)\n",
-    "final_df.to_csv(r\"U:\\Hours_DPM_FBUZZ_SiteA1112.csv\", index=False)"
+    "d_hour[\"FBR\"] = d_hour[\"FBR\"].fillna(0)\n",
+    "d_hour.to_csv(r\"U:\\Hours_DPM_FBUZZ_SiteA.csv\", index=False)"
    ],
    "id": "d606f4f6904b57c6",
    "outputs": [],

From 566cffdad9b62ebe74de2b93824ef537a93f492e Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 26 Sep 2025 09:34:04 +0200
Subject: [PATCH 06/83] change notebook config

---
 user_case/user_case_CALAIS.ipynb | 337 ++++++++++++++++++++++++-------
 1 file changed, 263 insertions(+), 74 deletions(-)

diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index 0f5e509..c7d0ff5 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -6,23 +6,26 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-09-09T14:50:36.642346Z",
-     "start_time": "2025-09-09T14:50:36.638397Z"
+     "end_time": "2025-09-25T15:14:38.565748Z",
+     "start_time": "2025-09-25T15:14:36.291938Z"
     }
    },
    "source": [
     "from pathlib import Path\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
     "import pytz\n",
+    "from pandas import (\n",
+    "    concat,\n",
+    "    read_csv,\n",
+    "    to_datetime,\n",
+    ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import cpod2aplose, usable_data_phase, dpm_to_dph,build_hour_range, fb_folder,csv_folder, meta_cut_aplose\n",
-    "from post_processing.utils.core_utils import json2df"
+    "from post_processing.utils.fpod_utils import cpod2aplose, fpod2aplose, dpm_to_dp10m, dpm_to_dph, dpm_to_dpd, fb_folder,csv_folder, meta_cut_aplose, date_format,extract_site,percent_calc,site_percent, year_percent, ym_percent, month_percent, hour_percent, actual_data, build_hour_range\n",
+    "from post_processing.utils.core_utils import json2df,get_season"
    ],
    "outputs": [],
-   "execution_count": 21
+   "execution_count": 1
   },
   {
    "metadata": {},
@@ -36,19 +39,16 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-09T14:51:43.424921Z",
-     "start_time": "2025-09-09T14:50:38.447445Z"
+     "end_time": "2025-09-25T15:15:16.539360Z",
+     "start_time": "2025-09-25T15:15:15.617231Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "data = csv_folder(r\"U:\\A\")\n",
+    "data = csv_folder(r\"U:\\D\")\n",
     "print(data.head())\n",
     "\n",
-    "df_1 = data.dropna()\n",
-    "\n",
-    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site A Haute\", \"Commerson\", extra_columns=[\"DPM\", \"deploy.name\"])\n",
-    "print(df_aplose.head())"
+    "df_0 = data.dropna()"
    ],
    "id": "8636a8a27fe2af47",
    "outputs": [
@@ -56,52 +56,99 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                   File          ChunkEnd  DPM  Nall  MinsOn  \\\n",
-      "0  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:03    0     0       0   \n",
-      "1  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:04    0     0       0   \n",
-      "2  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:05    0     0       0   \n",
-      "3  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:06    0     0       0   \n",
-      "4  Ile Haute Site A  POD3059 file01.CP3  01/12/2022 16:07    0     0       0   \n",
+      "                 File          ChunkEnd  DPM  Nall  MinsOn  \\\n",
+      "0  POD3042 file01.CP3  21/10/2019 12:11    0     0       0   \n",
+      "1  POD3042 file01.CP3  21/10/2019 12:12    0     0       0   \n",
+      "2  POD3042 file01.CP3  21/10/2019 12:13    0     0       0   \n",
+      "3  POD3042 file01.CP3  21/10/2019 12:14    0     0       0   \n",
+      "4  POD3042 file01.CP3  21/10/2019 12:15    0     0       0   \n",
       "\n",
       "            deploy.name  \n",
-      "0  Site A Haute_Phase11  \n",
-      "1  Site A Haute_Phase11  \n",
-      "2  Site A Haute_Phase11  \n",
-      "3  Site A Haute_Phase11  \n",
-      "4  Site A Haute_Phase11  \n",
-      "        dataset filename  start_time  end_time  start_frequency  \\\n",
-      "0  Site A Haute                    0        60                0   \n",
-      "1  Site A Haute                    0        60                0   \n",
-      "2  Site A Haute                    0        60                0   \n",
-      "3  Site A Haute                    0        60                0   \n",
-      "4  Site A Haute                    0        60                0   \n",
-      "\n",
-      "   end_frequency annotation annotator                start_datetime  \\\n",
-      "0              0  Commerson      FPOD  2022-12-01T16:03:00.000+0000   \n",
-      "1              0  Commerson      FPOD  2022-12-01T16:04:00.000+0000   \n",
-      "2              0  Commerson      FPOD  2022-12-01T16:05:00.000+0000   \n",
-      "3              0  Commerson      FPOD  2022-12-01T16:06:00.000+0000   \n",
-      "4              0  Commerson      FPOD  2022-12-01T16:07:00.000+0000   \n",
-      "\n",
-      "                   end_datetime  is_box  DPM           deploy.name  \n",
-      "0  2022-12-01T16:04:00.000+0000       0    0  Site A Haute_Phase11  \n",
-      "1  2022-12-01T16:05:00.000+0000       0    0  Site A Haute_Phase11  \n",
-      "2  2022-12-01T16:06:00.000+0000       0    0  Site A Haute_Phase11  \n",
-      "3  2022-12-01T16:07:00.000+0000       0    0  Site A Haute_Phase11  \n",
-      "4  2022-12-01T16:08:00.000+0000       0    0  Site A Haute_Phase11  \n"
+      "0  Site D Simone_Phase1  \n",
+      "1  Site D Simone_Phase1  \n",
+      "2  Site D Simone_Phase1  \n",
+      "3  Site D Simone_Phase1  \n",
+      "4  Site D Simone_Phase1  \n"
      ]
     }
    ],
-   "execution_count": 22
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-09T14:56:09.883356Z",
-     "start_time": "2025-09-09T14:51:52.486050Z"
+     "end_time": "2025-09-25T15:16:18.967295Z",
+     "start_time": "2025-09-25T15:15:37.397762Z"
     }
    },
    "cell_type": "code",
+   "source": "d_beg_end = actual_data(df_0, col_timestamp=\"ChunkEnd\")",
+   "id": "4208969d9e509a8",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-25T15:16:22.557433Z",
+     "start_time": "2025-09-25T15:16:22.552202Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ]",
+   "id": "597efd1d90e3d069",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## APLOSE format",
+   "id": "4f8c83c96f0b6ff4"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### *CPOD*",
+   "id": "8ed339c688bdef1"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\", extra_columns=[\"deploy.name\"])\n",
+    "print(df_aplose.head())"
+   ],
+   "id": "812ed7c0c5e258e7",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### *FPOD*",
+   "id": "a39bb10d8ac60a27"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
+    "print(df_aplose.head())"
+   ],
+   "id": "9b632673397a184"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Remove non usable lines",
+   "id": "7860838f8514da39"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
    "source": [
     "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_kerguelen.json\")\n",
     "metadatax = json2df(json_path=json)\n",
@@ -109,48 +156,76 @@
     "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
     "                           metadatax[\"campaign.name\"].astype(str))\n",
     "\n",
-    "cleared = meta_cut_aplose(df_aplose, metadatax)\n",
-    "\n",
-    "cleared.to_csv(r\"U:\\APLOSE_A.csv\", index=False)"
+    "cleared = meta_cut_aplose(df_aplose, metadatax)"
    ],
    "id": "ed6a06c522aea169",
    "outputs": [],
-   "execution_count": 23
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "cleared.to_csv(r\"U:\\APLOSE_D.csv\", index=False)",
+   "id": "76f70cb6c6658ba6",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## Load data",
-   "id": "73d08f821cd03ae3"
+   "source": "## Load data\n",
+   "id": "f5d38266dc9d5273"
   },
   {
    "metadata": {},
    "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
    "source": [
     "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
     "data_list = DataAplose.from_yaml(file=yaml_file)\n",
     "print(data_list.df.head())"
    ],
-   "id": "ad94c9baae5a1748",
-   "outputs": [],
-   "execution_count": null
+   "id": "6837593897111b0a"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
+   "source": "## Format choice\n",
+   "id": "9f93eb863e3e3a9e"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
    "source": [
-    "## Create a detection per hour dataframe\n",
-    "\n"
+    "dp10 = dpm_to_dp10m(data_list.df, extra_columns=[\"deploy.name\"])\n",
+    "dp10 = date_format(dp10)"
    ],
-   "id": "4d6b640178d7563"
+   "id": "a27ceea1fefdd298"
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "source": "dph = dpm_to_dph(data_list.df, extra_columns=[\"deploy.name\"])",
-   "id": "c46fb2201838f42",
    "outputs": [],
-   "execution_count": null
+   "execution_count": null,
+   "source": [
+    "dph = dpm_to_dph(data_list.df, extra_columns=[\"deploy.name\"])\n",
+    "dph = date_format(dph)"
+   ],
+   "id": "6cc79b2aeef076ed"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "dpd = dpm_to_dpd(data_list.df, extra_columns=[\"deploy.name\"])\n",
+    "dpd = date_format(dpd)"
+   ],
+   "id": "e6655c36fc1851c7"
   },
   {
    "metadata": {},
@@ -162,16 +237,25 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "fb = fb_folder(r\"U:\\fb_A_NBHF\", \"Commerson\")\n",
-    "fb[\"Date heure\"] = fb[\"start_datetime\"].dt.floor(\"h\")\n",
-    "fb = fb.groupby(\"Date heure\")[\"Foraging\"].sum().reset_index()\n",
-    "\n",
-    "d_hour = dph.merge(fb[[\"Date heure\",\"Foraging\"]], on=\"Date heure\", how=\"left\")"
+    "fb_all = fb_folder(r\"U:\\fb_D_NBHF\", \"Commerson\")\n",
+    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(\"h\")\n",
+    "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n"
    ],
    "id": "2b19f90c99252ff3",
    "outputs": [],
    "execution_count": null
   },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "d_hour = build_hour_range(d_beg_end)\n",
+    "d_tot = dph.merge(fb, on=\"start_datetime\", how=\"left\")"
+   ],
+   "id": "b00c8f1e2210ea7"
+  },
   {
    "metadata": {},
    "cell_type": "code",
@@ -183,16 +267,25 @@
    "outputs": [],
    "execution_count": null
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Add time columns",
+   "id": "c7b1d32ed1c99fb7"
+  },
   {
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "d_hour[\"Year\"] = d_hour[\"Date heure\"].dt.year\n",
-    "d_hour[\"Month\"] = d_hour[\"Date heure\"].dt.month\n",
-    "d_hour[\"Day\"] = d_hour[\"Date heure\"].dt.day\n",
-    "d_hour[\"Hour\"] = d_hour[\"Date heure\"].dt.hour\n",
+    "d_hour[\"Year\"] = d_hour[\"start_datetime\"].dt.year\n",
+    "d_hour[\"Month\"] = d_hour[\"start_datetime\"].dt.month\n",
+    "d_hour['YM'] = d_hour['Year'].astype(str) + '-' + d_hour['Month'].astype(str)\n",
+    "d_hour['YM'] = to_datetime(d_hour['YM'])\n",
+    "d_hour[\"Day\"] = d_hour[\"start_datetime\"].dt.day\n",
+    "d_hour[\"Hour\"] = d_hour[\"start_datetime\"].dt.hour\n",
     "\n",
-    "d_hour[\"FBR\"] = d_hour[\"Foraging\"] / d_hour[\"DPM\"]"
+    "d_hour[\"FBR\"] = d_hour[\"Foraging\"] / d_hour[\"DPM\"]\n",
+    "d_hour[\"DPH\"] = (d_hour[\"DPM\"] >0).astype(int)"
    ],
    "id": "a2261ce5093a3104",
    "outputs": [],
@@ -203,11 +296,107 @@
    "cell_type": "code",
    "source": [
     "d_hour[\"FBR\"] = d_hour[\"FBR\"].fillna(0)\n",
-    "d_hour.to_csv(r\"U:\\Hours_DPM_FBUZZ_SiteA.csv\", index=False)"
+    "d_hour.to_csv(r\"U:\\Hours_DPM_FBUZZ_SiteD.csv\", index=False)"
    ],
    "id": "d606f4f6904b57c6",
    "outputs": [],
    "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Overview",
+   "id": "4bc0904182a3f845"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "ca4 = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteCA4.csv\")\n",
+    "walde = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteWalde.csv\")\n",
+    "\n",
+    "data_k = concat([ca4, walde])\n",
+    "data_k['YM'] = data_k['Year'].astype(str) + '-' + data_k['Month'].astype(str)\n",
+    "data_k['YM'] = to_datetime(data_k['YM'])"
+   ],
+   "id": "9909fbfdcb8e2e78",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "a = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteA.csv\")\n",
+    "b = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteB.csv\")\n",
+    "c = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteC.csv\")\n",
+    "d = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteD.csv\")\n",
+    "\n",
+    "data_k = concat([a, b, c, d])\n",
+    "data_k[\"start_datetime\"] = to_datetime(data_k[\"start_datetime\"])\n",
+    "data_k[\"start_datetime\"] = data_k[\"start_datetime\"].apply(lambda x : x.tz_convert(\"Indian/Kerguelen\"))\n",
+    "data_k[\"Hour\"] = data_k[\"start_datetime\"].dt.hour"
+   ],
+   "id": "87e2d1938787aefc",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "data_k = extract_site(data_k)\n",
+    "y_per = percent_calc(data_k, \"Year\")\n",
+    "ym_per = percent_calc(data_k, \"YM\")\n",
+    "ym_per[\"YM\"] = to_datetime(ym_per[\"YM\"])\n",
+    "ym_per[\"Season\"] = ym_per[\"YM\"].apply(lambda x: get_season(x)[0])\n",
+    "m_per = percent_calc(data_k, \"Month\")\n",
+    "h_per = percent_calc(data_k, \"Hour\")\n",
+    "s_per = percent_calc(data_k)"
+   ],
+   "id": "2826b79097a85607",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "site_percent(s_per, \"%click\")",
+   "id": "ddd1fac6295136c6",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "year_percent(y_per, \"%click\")",
+   "id": "ba7581e97fdbd07c",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "ym_percent(ym_per, \"%click\")",
+   "id": "4de618933c154f86",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "month_percent(m_per, \"%buzzes\")",
+   "id": "7cf84c8744185424",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "hour_percent(h_per, \"%click\")",
+   "id": "12d83e9082d711c0",
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {

From 15c6677f37c13a73682ce3b556762a9170b259d9 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 26 Sep 2025 09:34:47 +0200
Subject: [PATCH 07/83] add new functions

---
 src/post_processing/utils/fpod_utils.py | 338 ++++++++++++++++++++++--
 1 file changed, 312 insertions(+), 26 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index a588452..fda5701 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -6,20 +6,20 @@
 import pytz
 import seaborn as sns
 from matplotlib import pyplot as plt
+from matplotlib.patches import Patch
 from osekit.config import TIMESTAMP_FORMAT_AUDIO_FILE
 from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
 from pandas import (
     DataFrame,
     Series,
     Timedelta,
-    Timestamp,
     concat,
     date_range,
-    merge,
     notna,
     read_csv,
     read_excel,
     to_datetime,
+    to_timedelta,
 )
 
 from post_processing import logger
@@ -117,7 +117,6 @@ def cpod2aplose(
         An APLOSE formatted DataFrame
 
     """
-    df = df.rename(columns={"ChunkEnd": "Date heure"})
     results = []
 
     for deploy_name in df["deploy.name"].unique():
@@ -248,7 +247,8 @@ def meta_cut_aplose(raw_data: DataFrame,metadata: DataFrame,
         & dfm[col_fin].notna()
     ].copy()
 
-    columns_to_drop = [col for col in [col_debut, col_fin] if col not in raw_data.columns]
+    columns_to_drop = [col for col in [col_debut, col_fin]
+                       if col not in raw_data.columns]
     if columns_to_drop:
         out = out.drop(columns=columns_to_drop)
 
@@ -315,7 +315,8 @@ def assign_phase(
                 <= data.loc[j, "start_datetime"]
                 < meta_row["recovery_date"]
             ):
-                data.loc[j, "name"] = f"{meta_row['site.name']}_{meta_row['campaign.name']}"
+                data.loc[j, "name"] = (f"{meta_row["site.name"]}_"
+                                       f"{meta_row["campaign.name"]}")
             j += 1
     return data
 
@@ -491,10 +492,10 @@ def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
 
     """
     df["microsec"] = df["microsec"] / 1e6
+    df["microsec_formatted"] = df["microsec"].apply(lambda x: f"{x:.6f}")
 
     df["Time"] = (df["Minute"].astype(str) + ":" +
-                  df["microsec"].astype(str))
-    df["Time"] = to_datetime(df["Time"], dayfirst=True)
+                  df["microsec_formatted"].astype(str))
 
     df["Time"] = to_datetime(df["Time"], dayfirst=True)
 
@@ -602,7 +603,6 @@ def fb_folder(folder_path:Path, species:str) -> DataFrame:
         file_path = folder_path / file
         df = read_csv(file_path, sep="\t")
         processed_df = feeding_buzz(df, species)
-        processed_df["deploy.name"] = file.name
         all_data.append(processed_df)
 
     return concat(all_data, ignore_index=True)
@@ -613,8 +613,15 @@ def fb_folder(folder_path:Path, species:str) -> DataFrame:
     "Site B Heugh": "#5DB996",
     "Site C Chat": "#B0DB9C",
     "Site D Simone": "#E3F0AF",
-    "CA4": "#5EABD6",
-    "Walde": "#FFB4B4",
+    "CA4": "#FF0066",
+    "Walde": "#934790",
+}
+
+season_color = {
+    "spring": "#C5E0B4",
+    "summer": "#FCF97F",
+    "autumn": "#ED7C2F",
+    "winter": "#B4C7E8",
 }
 
 
@@ -632,7 +639,7 @@ def extract_site(df: DataFrame) -> DataFrame:
         The same dataframe with two additional columns.
 
     """
-    df[["site.name", "campaign.name"]] = df["name"].str.split("_", expand=True)
+    df[["site.name", "campaign.name"]] = df["deploy.name"].str.split("_", expand=True)
     return df
 
 
@@ -669,7 +676,7 @@ def percent_calc(data: DataFrame, time_unit: str | None = None) -> DataFrame:
     df["%click"] = df["DPM"] * 100 / (df["Day"] * 60)
     df["%DPH"] = df["DPH"] * 100 / df["Day"]
     df["FBR"] = df["Foraging"] * 100 / df["DPM"]
-    df["%buzz"] = df["Foraging"] * 100 / (df["Day"] * 60)
+    df["%buzzes"] = df["Foraging"] * 100 / (df["Day"] * 60)
     return df
 
 
@@ -736,6 +743,54 @@ def year_percent(df: DataFrame, metric: str) -> None:
     plt.show()
 
 
+def ym_percent(df: DataFrame, metric: str) -> None:
+    """Plot a graph with the percentage of DPM per site/month-year.
+
+    Parameters
+    ----------
+    df: DataFrame
+        All percentages grouped by site and month per year
+    metric: str
+        Type of percentage you want to show on the graph
+
+    """
+    sites = df["site.name"].unique()
+    n_sites = len(sites)
+    fig, axs = plt.subplots(n_sites, 1, figsize=(14, 2.5 * n_sites), sharex=True)
+    if n_sites == 1:
+        axs = [axs]
+    for i, site in enumerate(sorted(sites)):
+        site_data = df[df["site.name"] == site]
+        ax = axs[i]
+        bar_colors = site_data["Season"].map(season_color).fillna("gray")
+        ax.bar(site_data["YM"],
+               site_data[metric],
+               label=f"Site {site}",
+               color=bar_colors,
+               width=25,
+               )
+        ax.set_title(f"{site} - Percentage of minutes postitive to detection per month")
+        ax.set_ylim(0,max(df[metric]) + 0.2)
+        ax.set_ylabel(metric)
+        if i != 3:
+            ax.set_xlabel("")
+        else:
+            ax.set_xlabel("Months")
+        if metric == "%buzzes":
+            for _, bar in enumerate(ax.patches):
+                bar.set_hatch("/")
+    legend_elements = [
+        Patch(facecolor=season_color, edgecolor="black", label=season.capitalize())
+        for season, season_color in season_color.items()
+    ]
+    fig.legend(handles=legend_elements,
+               loc="upper right",
+               title="Seasons",
+               bbox_to_anchor=(0.95, 0.95))
+    fig.suptitle(f"{metric} per month", fontsize=16)
+    plt.show()
+
+
 def month_percent(df: DataFrame, metric: str) -> None:
     """Plot a graph with the percentage of minutes positive to detection per site/month.
 
@@ -798,12 +853,13 @@ def hour_percent(df: DataFrame, metric: str) -> None:
     for i, site in enumerate(sorted(sites)):
         site_data = df[df["site.name"] == site]
         ax = axs[i]
-        ax.bar(site_data["hour"],
+        ax.bar(site_data["Hour"],
                site_data[metric],
                label=f"Site {site}",
                color=colors.get(site, "gray"),
                )
-        ax.set_title(f"Site {site} - Percentage of minutes positive to detection per hour")
+        ax.set_title(f"Site {site} - "
+                     f"Percentage of minutes positive to detection per hour")
         ax.set_ylim(0,max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         if i != 3:
@@ -829,7 +885,7 @@ def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
 
     Returns
     -------
-    pd.DataFrame
+    DataFrame
         Concatenated dataframe with all files data and file column.
 
     Raises
@@ -851,7 +907,7 @@ def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
         raise ValueError(message)
 
     # Configuration
-    default_params = {"sep": ";"}
+    default_params = {"sep": ";", "encoding":"latin-1"}
 
     # Parameters fusion
     read_params = {**default_params, **kwargs}
@@ -877,28 +933,102 @@ def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
     return concat(all_data, ignore_index=True)
 
 
-def dpm_to_dph(
-    df: DataFrame,
-    extra_columns: list | None = None,
+def dpm_to_dp10m(df: DataFrame,
+                 extra_columns: list | None = None,
 ) -> DataFrame:
-    """Create a dataframe containing the number of DPM per hour.
+    """From CPOD result with a line per minute (DPM) to one line per 10 minutes (DP10M).
 
     Parameters
     ----------
     df: DataFrame
-        Contains every minute positive to click detection.
-    extra_columns: list, optional
+        CPOD result DataFrame, DPM.
+    extra_columns: list
         Additional columns added from df to data.
 
     Returns
     -------
     DataFrame
-        Contains sum of minutes positive to detection per hour.
+        DPM10M Dataframe.
+
+    """
+    df = df.copy()
+    df["ChunkEnd"] = to_datetime(df["ChunkEnd"], dayfirst=True)
+
+    df["Date heure"] = df["ChunkEnd"].dt.floor("10min")
+
+    agg_dict = {"DPM": "sum"}
+
+    if extra_columns:
+        for col in extra_columns:
+            if col in df.columns:
+                agg_dict[col] = "first"
+            else:
+                logger.warning(f"Column '{col}' does not exist and will be ignored.")
+
+    return df.groupby("Date heure").agg(agg_dict).reset_index()
+
+
+def dpm_to_dph(
+    df: DataFrame,
+    extra_columns: list | None = None,
+) -> DataFrame:
+    """From CPOD result with a line per minute (DPM) to one line per hour (DPH).
+
+    Parameters
+    ----------
+    df: pd.DataFrame
+        CPOD result DataFrame
+    extra_columns: list
+        Additional columns added from df to data
+
+    Returns
+    -------
+    pd.DataFrame
+        DPH Dataframe.
+
+    """
+    df = df.copy()
+    df["ChunkEnd"] = to_datetime(df["ChunkEnd"], dayfirst=True)
+
+    # Truncate column
+    df["Date heure"] = df["ChunkEnd"].dt.floor("h")
+
+    agg_dict = {"DPM": "sum"}
+
+    if extra_columns:
+        for col in extra_columns:
+            if col in df.columns:
+                agg_dict[col] = "first"
+            else:
+                logger.warning(f"Column '{col}' does not exist and will be ignored.")
+
+    return df.groupby("Date heure").agg(agg_dict).reset_index()
+
+
+def dpm_to_dpd(
+    df: DataFrame,
+    extra_columns: list | None = None,
+) -> DataFrame:
+    """From CPOD result with a line per minute (DPM) to one line per day (DPD).
+
+    Parameters
+    ----------
+    df: pd.DataFrame
+        CPOD result DataFrame
+    extra_columns: list
+        Additional columns added from df to data
+
+    Returns
+    -------
+    pd.DataFrame
+        DPD Dataframe.
 
     """
-    df["start_datetime"] = to_datetime(df["start_datetime"], utc=True)
-    df["end_datetime"] = to_datetime(df["end_datetime"], utc=True)
-    df["Date heure"] = df["start_datetime"].dt.floor("h")
+    df = df.copy()
+    df["ChunkEnd"] = to_datetime(df["ChunkEnd"], dayfirst=True)
+
+    # Truncate column
+    df["Date heure"] = df["ChunkEnd"].dt.floor("D")
 
     agg_dict = {"DPM": "sum"}
 
@@ -910,3 +1040,159 @@ def dpm_to_dph(
                 logger.warning(f"Column '{col}' does not exist and will be ignored.")
 
     return df.groupby("Date heure").agg(agg_dict).reset_index()
+
+
+def date_format(df: DataFrame,
+                ) -> DataFrame :
+    """Change the date time format of a DataFrame to "%d/%m/%Y %H:%M:%S".
+
+    Parameters
+    ----------
+    df: pd.DataFrame
+        CPOD result DataFrame
+
+    Returns
+    -------
+    Return the same dataframe with a different time format.
+
+    """
+    df["Date heure"] = to_datetime(df["Date heure"], format="%Y-%m-%d %H:%M:%S")
+    df["Date heure"] = df["Date heure"].dt.strftime("%d/%m/%Y %H:%M:%S")
+
+    return df
+
+
+def actual_data(df:DataFrame,
+                col_timestamp:str="start_datetime",
+                )-> DataFrame:
+    """Create a table with beginning and end of every deployment.
+
+    Parameters
+    ----------
+    col_timestamp
+    df: pd.DataFrame
+        CPOD result DataFrame
+    col_timestamp: str
+        Name of the timestamps column in raw_data (default: 'start_datetime')
+
+    Returns
+    -------
+    pd.DataFrame
+        Simple Dataframe with beginning and end columns.
+
+    """
+    df[col_timestamp] = df[col_timestamp].apply(
+        lambda x: strptime_from_text(x,["%Y-%m-%dT%H:%M:%S:%Z",
+                                        "%Y-%m-%dT%H:%M:%S","%d/%m/%Y %H:%M"]))
+    return df.groupby(["deploy.name"]).agg(Début=(col_timestamp, "first"),
+                                           Fin=(col_timestamp, "last")).reset_index()
+
+
+def calendar(meta:DataFrame,
+             data:DataFrame,
+             )-> None:
+    """Produce the calendar of the given data.
+
+    Parameters
+    ----------
+    meta: DataFrame
+        metadatax file
+    data: DataFrame
+        cpod file from all sites and phases
+
+    Returns
+    -------
+    Return a plot of all deployments and associated data.
+
+    """
+    # format the dataframe
+    meta["deployment_date"] = to_datetime(meta["deployment_date"])
+    meta["recovery_date"] = to_datetime(meta["recovery_date"])
+    meta = meta.sort_values(["deploy.name", "deployment_date"]).reset_index(drop=True)
+    data = data.sort_values(["deploy.name", "Début"]).reset_index(drop=True)
+    df_fusion = data.merge(meta[["deploy.name", "deployment_date", "recovery_date"]],
+                           on=["deploy.name"], how="outer")
+
+    df_fusion["Début"] = df_fusion["Début"].fillna(df_fusion["deployment_date"])
+    df_fusion["Fin"] = df_fusion["Fin"].fillna(df_fusion["deployment_date"])
+
+    df_fusion[["Site","Phase"]] = df_fusion["deploy.name"].str.split("_", expand=True)
+    df_fusion["color"] = df_fusion["Site"].map(colors)
+
+    # Create the figure
+    fig, ax = plt.subplots(figsize=(14, 4))
+
+    sites = sorted(df_fusion["Site"].unique(), reverse=True)
+    site_mapping = {site: idx for idx, site in enumerate(sites)}
+    for _, row in df_fusion.iterrows():
+        y_pos = site_mapping[row["Site"]]
+        ax.broken_barh(
+            [(row["deployment_date"], row["recovery_date"] - row["deployment_date"])],
+            (y_pos - 0.3, 0.6),
+            facecolors="#F5F5F5",
+            edgecolors="black",
+            linewidth=0.8)
+
+        if row["Début"] != row["deployment_date"]:
+            ax.broken_barh(
+                [(row["Début"], row["Fin"] - row["Début"])],
+                (y_pos - 0.15, 0.3),
+                facecolors=row["color"],
+                edgecolors="black",
+                linewidth=0.8)
+
+    ax.set_yticks(range(len(sites)))
+    ax.set_yticklabels(sites, fontsize=12)
+
+    legend_elements = [Patch(facecolor="#F5F5F5", edgecolor="black",
+                             label="Deployment")]
+    for site, color in colors.items():
+        if site in sites:
+            legend_elements.append(Patch(facecolor=color, edgecolor="black",
+                                         label=f"{site}"))
+
+    ax.legend(handles=legend_elements, loc="upper left", fontsize=11, frameon=True)
+    # Layout final
+    plt.xticks(fontsize=12)
+    plt.tight_layout()
+    plt.show()
+
+
+def f_b2(df: DataFrame, species: str) -> DataFrame:
+    """Process a CPOD/FPOD feeding buzz detection file.
+
+    Gives the feeding buzz duration, depending on the studied species.
+
+    Parameters
+    ----------
+    df: DataFrame
+        Path to cpod.exe feeding buzz file
+    species: str
+        Select the species to use between porpoise and Commerson's dolphin
+
+    Returns
+    -------
+    DataFrame
+        Containing all ICIs for every positive minutes to clicks
+
+    """
+    df["microsec"] = df["microsec"] / 1e6
+    df["ICI"] = df["microsec"].diff()
+
+    if species == "Marsouin": #Nuuttila et al., 2013
+        df["Buzz"] = (df["ICI"].between(0, 0.01)).astype(int)
+    elif species == "Commerson" :
+        df["Buzz"] = (df["ICI"].between(0, 0.005)).astype(int)
+    else :
+        msg = "This species is not supported"
+        raise ValueError(msg)
+
+    df["Minute"] = to_datetime(df["Minute"], dayfirst=False, utc=True)
+    f = df.groupby(["Minute"])["Buzz"].sum().reset_index()
+
+    #df['datetime'] = to_datetime('1900-01-01') + to_timedelta(df['Minute'], unit='min')
+    # + to_timedelta(df['microsec'], unit='us') - to_timedelta(2, unit='D')
+
+    f["Foraging"] = (f["Buzz"] != 0).astype(int)
+
+    return f
\ No newline at end of file

From a71dd136709dc380ae9412daa3db5cdf269cdc86 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 26 Sep 2025 10:56:43 +0200
Subject: [PATCH 08/83] ruff changes

---
 src/post_processing/utils/fpod_utils.py | 266 ++++++++++++++----------
 1 file changed, 156 insertions(+), 110 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index fda5701..9c59fd3 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -1,13 +1,13 @@
 from __future__ import annotations
 
+import logging
 from pathlib import Path
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
 import pytz
 import seaborn as sns
 from matplotlib import pyplot as plt
 from matplotlib.patches import Patch
-from osekit.config import TIMESTAMP_FORMAT_AUDIO_FILE
 from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
 from pandas import (
     DataFrame,
@@ -19,14 +19,11 @@
     read_csv,
     read_excel,
     to_datetime,
-    to_timedelta,
 )
 
-from post_processing import logger
 from post_processing.utils.core_utils import get_coordinates, get_sun_times
 
 if TYPE_CHECKING:
-
     import pytz
 
 
@@ -182,15 +179,17 @@ def usable_data_phase(
         percentage_data = act_length * 100 / p_length
         msg = f"Percentage of usable data : {percentage_data}%"
 
-    logger.info(msg)
+    logging.info(msg)
     return percentage_data
 
 
-def meta_cut_aplose(raw_data: DataFrame,metadata: DataFrame,
-    col_deploy_name:str="deploy.name",
-    col_timestamp:str="start_datetime",
-    col_debut:str="deployment_date",
-    col_fin:str="recovery_date",
+def meta_cut_aplose(
+    raw_data: DataFrame,
+    metadata: DataFrame,
+    col_deploy_name: str = "deploy.name",
+    col_timestamp: str = "start_datetime",
+    col_debut: str = "deployment_date",
+    col_fin: str = "recovery_date",
 ) -> DataFrame:
     """Filter data to keep only the ones corresponding to a deployment.
 
@@ -247,8 +246,9 @@ def meta_cut_aplose(raw_data: DataFrame,metadata: DataFrame,
         & dfm[col_fin].notna()
     ].copy()
 
-    columns_to_drop = [col for col in [col_debut, col_fin]
-                       if col not in raw_data.columns]
+    columns_to_drop = [
+        col for col in [col_debut, col_fin] if col not in raw_data.columns
+    ]
     if columns_to_drop:
         out = out.drop(columns=columns_to_drop)
 
@@ -315,8 +315,9 @@ def assign_phase(
                 <= data.loc[j, "start_datetime"]
                 < meta_row["recovery_date"]
             ):
-                data.loc[j, "name"] = (f"{meta_row["site.name"]}_"
-                                       f"{meta_row["campaign.name"]}")
+                data.loc[j, "name"] = (
+                    f"{meta_row['site.name']}_{meta_row['campaign.name']}"
+                )
             j += 1
     return data
 
@@ -426,14 +427,17 @@ def build_hour_range(dph: DataFrame) -> DataFrame:
     all_ranges = []
     for _, row in deploy_ranges.iterrows():
         hours = date_range(row["start"], row["end"], freq="h")
-        tmp = DataFrame({
-            "deploy.name": row["deploy.name"],
-            "Date heure": hours,
-        })
+        tmp = DataFrame(
+            {
+                "deploy.name": row["deploy.name"],
+                "Date heure": hours,
+            },
+        )
         all_ranges.append(tmp)
 
     return concat(all_ranges, ignore_index=True)
 
+
 def merging_tab(meta: DataFrame, data: DataFrame) -> DataFrame:
     """Create a DataFrame with one line per hour between start and end dates.
 
@@ -494,8 +498,7 @@ def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
     df["microsec"] = df["microsec"] / 1e6
     df["microsec_formatted"] = df["microsec"].apply(lambda x: f"{x:.6f}")
 
-    df["Time"] = (df["Minute"].astype(str) + ":" +
-                  df["microsec_formatted"].astype(str))
+    df["Time"] = df["Minute"].astype(str) + ":" + df["microsec_formatted"].astype(str)
 
     df["Time"] = to_datetime(df["Time"], dayfirst=True)
 
@@ -505,9 +508,9 @@ def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
     df["Buzz"] = 0
     if species == "Marsouin":
         feeding_idx = df.index[df["ICI"] < 0.01]
-    elif species == "Commerson" :
+    elif species == "Commerson":
         feeding_idx = df.index[df["ICI"] <= 0.005]
-    else :
+    else:
         msg = "This species is not supported"
         raise ValueError(msg)
 
@@ -525,7 +528,7 @@ def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
 
 
 def assign_daytime(
-        df: DataFrame,
+    df: DataFrame,
 ) -> DataFrame:
     """Assign datetime categories to events.
 
@@ -545,7 +548,7 @@ def assign_daytime(
     start = df.iloc[0]["Time"]
     stop = df.iloc[-1]["Time"]
     lat, lon = get_coordinates()
-    _, _,dawn,day,dusk,night  = get_sun_times(start, stop, lat, lon)
+    _, _, dawn, day, dusk, night = get_sun_times(start, stop, lat, lon)
     dawn = Series(dawn, name="dawn")
     day = Series(day, name="day")
     dusk = Series(dusk, name="dusk")
@@ -556,10 +559,10 @@ def assign_daytime(
         dpm_i = row["Time"]
         if notna(dpm_i):  # Check if time is not NaN
             jour_i = jour[
-                (jour["dusk"].dt.year == dpm_i.year) &
-                (jour["dusk"].dt.month == dpm_i.month) &
-                (jour["dusk"].dt.day == dpm_i.day)
-                ]
+                (jour["dusk"].dt.year == dpm_i.year)
+                & (jour["dusk"].dt.month == dpm_i.month)
+                & (jour["dusk"].dt.day == dpm_i.day)
+            ]
             if not jour_i.empty:  # Ensure there"s a matching row
                 jour_i = jour_i.iloc[0]  # Extract first match
                 if dpm_i <= jour_i["day"]:
@@ -578,7 +581,7 @@ def assign_daytime(
     return df
 
 
-def fb_folder(folder_path:Path, species:str) -> DataFrame:
+def fb_folder(folder_path: Path, species: str) -> DataFrame:
     """Process a folder containing all CPOD/FPOD feeding buzz detection files.
 
     Apply the feeding buzz function to these files.
@@ -666,12 +669,18 @@ def percent_calc(data: DataFrame, time_unit: str | None = None) -> DataFrame:
         group_cols.insert(0, time_unit)
 
     # Aggregate and compute metrics
-    df = data.groupby(group_cols).agg({
-        "DPH": "sum",
-        "DPM": "sum",
-        "Day": "size",
-        "Foraging": "sum",
-    }).reset_index()
+    df = (
+        data.groupby(group_cols)
+        .agg(
+            {
+                "DPH": "sum",
+                "DPM": "sum",
+                "Day": "size",
+                "Foraging": "sum",
+            },
+        )
+        .reset_index()
+    )
 
     df["%click"] = df["DPM"] * 100 / (df["Day"] * 60)
     df["%DPH"] = df["DPH"] * 100 / df["Day"]
@@ -691,12 +700,14 @@ def site_percent(df: DataFrame, metric: str) -> None:
         Type of percentage you want to show on the graph
 
     """
-    ax = sns.barplot(data=df, x="site.name",
-                     y=metric,
-                     hue="site.name",
-                     dodge=False,
-                     palette=colors,
-                     )
+    ax = sns.barplot(
+        data=df,
+        x="site.name",
+        y=metric,
+        hue="site.name",
+        dodge=False,
+        palette=colors,
+    )
     ax.set_title(f"{metric} per site")
     ax.set_ylabel(f"{metric}")
     if metric == "%buzzes":
@@ -724,13 +735,14 @@ def year_percent(df: DataFrame, metric: str) -> None:
     for i, site in enumerate(sorted(sites)):
         site_data = df[df["site.name"] == site]
         ax = axs[i]
-        ax.bar(site_data["Year"],
-               site_data[metric],
-               label=f"Site {site}",
-               color=colors.get(site, "gray"),
-               )
+        ax.bar(
+            site_data["Year"],
+            site_data[metric],
+            label=f"Site {site}",
+            color=colors.get(site, "gray"),
+        )
         ax.set_title(f"{site}")
-        ax.set_ylim(0,max(df[metric]) + 0.2)
+        ax.set_ylim(0, max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         if i != 3:
             ax.set_xlabel("")
@@ -763,14 +775,15 @@ def ym_percent(df: DataFrame, metric: str) -> None:
         site_data = df[df["site.name"] == site]
         ax = axs[i]
         bar_colors = site_data["Season"].map(season_color).fillna("gray")
-        ax.bar(site_data["YM"],
-               site_data[metric],
-               label=f"Site {site}",
-               color=bar_colors,
-               width=25,
-               )
+        ax.bar(
+            site_data["YM"],
+            site_data[metric],
+            label=f"Site {site}",
+            color=bar_colors,
+            width=25,
+        )
         ax.set_title(f"{site} - Percentage of minutes postitive to detection per month")
-        ax.set_ylim(0,max(df[metric]) + 0.2)
+        ax.set_ylim(0, max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         if i != 3:
             ax.set_xlabel("")
@@ -783,10 +796,12 @@ def ym_percent(df: DataFrame, metric: str) -> None:
         Patch(facecolor=season_color, edgecolor="black", label=season.capitalize())
         for season, season_color in season_color.items()
     ]
-    fig.legend(handles=legend_elements,
-               loc="upper right",
-               title="Seasons",
-               bbox_to_anchor=(0.95, 0.95))
+    fig.legend(
+        handles=legend_elements,
+        loc="upper right",
+        title="Seasons",
+        bbox_to_anchor=(0.95, 0.95),
+    )
     fig.suptitle(f"{metric} per month", fontsize=16)
     plt.show()
 
@@ -810,19 +825,32 @@ def month_percent(df: DataFrame, metric: str) -> None:
     for i, site in enumerate(sorted(sites)):
         site_data = df[df["site.name"] == site]
         ax = axs[i]
-        ax.bar(site_data["Month"],
-               site_data[metric],
-               label=f"Site {site}",
-               color=colors.get(site, "gray"),
-               )
+        ax.bar(
+            site_data["Month"],
+            site_data[metric],
+            label=f"Site {site}",
+            color=colors.get(site, "gray"),
+        )
         ax.set_title(f"{site} - Percentage of minutes postitive to detection per month")
-        ax.set_ylim(0,max(df[metric]) + 0.2)
+        ax.set_ylim(0, max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
-        ax.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
-                      ["Jan", "Feb", "Mar", "Apr", "May","Jun",
-                       "Jul", "Agu", "Sep", "Oct", "Nov", "Dec",
-                       ],
-                      )
+        ax.set_xticks(
+            [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
+            [
+                "Jan",
+                "Feb",
+                "Mar",
+                "Apr",
+                "May",
+                "Jun",
+                "Jul",
+                "Agu",
+                "Sep",
+                "Oct",
+                "Nov",
+                "Dec",
+            ],
+        )
         if i != 3:
             ax.set_xlabel("")
         else:
@@ -853,14 +881,16 @@ def hour_percent(df: DataFrame, metric: str) -> None:
     for i, site in enumerate(sorted(sites)):
         site_data = df[df["site.name"] == site]
         ax = axs[i]
-        ax.bar(site_data["Hour"],
-               site_data[metric],
-               label=f"Site {site}",
-               color=colors.get(site, "gray"),
-               )
-        ax.set_title(f"Site {site} - "
-                     f"Percentage of minutes positive to detection per hour")
-        ax.set_ylim(0,max(df[metric]) + 0.2)
+        ax.bar(
+            site_data["Hour"],
+            site_data[metric],
+            label=f"Site {site}",
+            color=colors.get(site, "gray"),
+        )
+        ax.set_title(
+            f"Site {site} - Percentage of minutes positive to detection per hour",
+        )
+        ax.set_ylim(0, max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         if i != 3:
             ax.set_xlabel("")
@@ -907,7 +937,7 @@ def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
         raise ValueError(message)
 
     # Configuration
-    default_params = {"sep": ";", "encoding":"latin-1"}
+    default_params = {"sep": ";", "encoding": "latin-1"}
 
     # Parameters fusion
     read_params = {**default_params, **kwargs}
@@ -933,8 +963,9 @@ def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
     return concat(all_data, ignore_index=True)
 
 
-def dpm_to_dp10m(df: DataFrame,
-                 extra_columns: list | None = None,
+def dpm_to_dp10m(
+    df: DataFrame,
+    extra_columns: list | None = None,
 ) -> DataFrame:
     """From CPOD result with a line per minute (DPM) to one line per 10 minutes (DP10M).
 
@@ -963,7 +994,7 @@ def dpm_to_dp10m(df: DataFrame,
             if col in df.columns:
                 agg_dict[col] = "first"
             else:
-                logger.warning(f"Column '{col}' does not exist and will be ignored.")
+                logging.warning(f"Column '{col}' does not exist and will be ignored.")
 
     return df.groupby("Date heure").agg(agg_dict).reset_index()
 
@@ -1000,7 +1031,7 @@ def dpm_to_dph(
             if col in df.columns:
                 agg_dict[col] = "first"
             else:
-                logger.warning(f"Column '{col}' does not exist and will be ignored.")
+                logging.warning(f"Column '{col}' does not exist and will be ignored.")
 
     return df.groupby("Date heure").agg(agg_dict).reset_index()
 
@@ -1037,13 +1068,14 @@ def dpm_to_dpd(
             if col in df.columns:
                 agg_dict[col] = "first"
             else:
-                logger.warning(f"Column '{col}' does not exist and will be ignored.")
+                logging.warning(f"Column '{col}' does not exist and will be ignored.")
 
     return df.groupby("Date heure").agg(agg_dict).reset_index()
 
 
-def date_format(df: DataFrame,
-                ) -> DataFrame :
+def date_format(
+    df: DataFrame,
+) -> DataFrame:
     """Change the date time format of a DataFrame to "%d/%m/%Y %H:%M:%S".
 
     Parameters
@@ -1062,9 +1094,10 @@ def date_format(df: DataFrame,
     return df
 
 
-def actual_data(df:DataFrame,
-                col_timestamp:str="start_datetime",
-                )-> DataFrame:
+def actual_data(
+    df: DataFrame,
+    col_timestamp: str = "start_datetime",
+) -> DataFrame:
     """Create a table with beginning and end of every deployment.
 
     Parameters
@@ -1082,15 +1115,21 @@ def actual_data(df:DataFrame,
 
     """
     df[col_timestamp] = df[col_timestamp].apply(
-        lambda x: strptime_from_text(x,["%Y-%m-%dT%H:%M:%S:%Z",
-                                        "%Y-%m-%dT%H:%M:%S","%d/%m/%Y %H:%M"]))
-    return df.groupby(["deploy.name"]).agg(Début=(col_timestamp, "first"),
-                                           Fin=(col_timestamp, "last")).reset_index()
+        lambda x: strptime_from_text(
+            x, ["%Y-%m-%dT%H:%M:%S:%Z", "%Y-%m-%dT%H:%M:%S", "%d/%m/%Y %H:%M"],
+        ),
+    )
+    return (
+        df.groupby(["deploy.name"])
+        .agg(Début=(col_timestamp, "first"), Fin=(col_timestamp, "last"))
+        .reset_index()
+    )
 
 
-def calendar(meta:DataFrame,
-             data:DataFrame,
-             )-> None:
+def calendar(
+    meta: DataFrame,
+    data: DataFrame,
+) -> None:
     """Produce the calendar of the given data.
 
     Parameters
@@ -1110,13 +1149,16 @@ def calendar(meta:DataFrame,
     meta["recovery_date"] = to_datetime(meta["recovery_date"])
     meta = meta.sort_values(["deploy.name", "deployment_date"]).reset_index(drop=True)
     data = data.sort_values(["deploy.name", "Début"]).reset_index(drop=True)
-    df_fusion = data.merge(meta[["deploy.name", "deployment_date", "recovery_date"]],
-                           on=["deploy.name"], how="outer")
+    df_fusion = data.merge(
+        meta[["deploy.name", "deployment_date", "recovery_date"]],
+        on=["deploy.name"],
+        how="outer",
+    )
 
     df_fusion["Début"] = df_fusion["Début"].fillna(df_fusion["deployment_date"])
     df_fusion["Fin"] = df_fusion["Fin"].fillna(df_fusion["deployment_date"])
 
-    df_fusion[["Site","Phase"]] = df_fusion["deploy.name"].str.split("_", expand=True)
+    df_fusion[["Site", "Phase"]] = df_fusion["deploy.name"].str.split("_", expand=True)
     df_fusion["color"] = df_fusion["Site"].map(colors)
 
     # Create the figure
@@ -1131,7 +1173,8 @@ def calendar(meta:DataFrame,
             (y_pos - 0.3, 0.6),
             facecolors="#F5F5F5",
             edgecolors="black",
-            linewidth=0.8)
+            linewidth=0.8,
+        )
 
         if row["Début"] != row["deployment_date"]:
             ax.broken_barh(
@@ -1139,17 +1182,20 @@ def calendar(meta:DataFrame,
                 (y_pos - 0.15, 0.3),
                 facecolors=row["color"],
                 edgecolors="black",
-                linewidth=0.8)
+                linewidth=0.8,
+            )
 
     ax.set_yticks(range(len(sites)))
     ax.set_yticklabels(sites, fontsize=12)
 
-    legend_elements = [Patch(facecolor="#F5F5F5", edgecolor="black",
-                             label="Deployment")]
+    legend_elements = [
+        Patch(facecolor="#F5F5F5", edgecolor="black", label="Deployment"),
+    ]
     for site, color in colors.items():
         if site in sites:
-            legend_elements.append(Patch(facecolor=color, edgecolor="black",
-                                         label=f"{site}"))
+            legend_elements.append(
+                Patch(facecolor=color, edgecolor="black", label=f"{site}"),
+            )
 
     ax.legend(handles=legend_elements, loc="upper left", fontsize=11, frameon=True)
     # Layout final
@@ -1179,20 +1225,20 @@ def f_b2(df: DataFrame, species: str) -> DataFrame:
     df["microsec"] = df["microsec"] / 1e6
     df["ICI"] = df["microsec"].diff()
 
-    if species == "Marsouin": #Nuuttila et al., 2013
+    if species == "Marsouin":  # Nuuttila et al., 2013
         df["Buzz"] = (df["ICI"].between(0, 0.01)).astype(int)
-    elif species == "Commerson" :
+    elif species == "Commerson":
         df["Buzz"] = (df["ICI"].between(0, 0.005)).astype(int)
-    else :
+    else:
         msg = "This species is not supported"
         raise ValueError(msg)
 
     df["Minute"] = to_datetime(df["Minute"], dayfirst=False, utc=True)
     f = df.groupby(["Minute"])["Buzz"].sum().reset_index()
 
-    #df['datetime'] = to_datetime('1900-01-01') + to_timedelta(df['Minute'], unit='min')
+    # df['datetime'] = to_datetime('1900-01-01') + to_timedelta(df['Minute'], unit='min')
     # + to_timedelta(df['microsec'], unit='us') - to_timedelta(2, unit='D')
 
     f["Foraging"] = (f["Buzz"] != 0).astype(int)
 
-    return f
\ No newline at end of file
+    return f

From c30a9deca7c1d578b4821836d2c2bcc6c4e1aaca Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 26 Sep 2025 10:58:15 +0200
Subject: [PATCH 09/83] add new usages

---
 user_case/user_case_CALAIS.ipynb | 95 +++++++++++++++++++++-----------
 1 file changed, 62 insertions(+), 33 deletions(-)

diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index c7d0ff5..ca4d0c3 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-09-25T15:14:38.565748Z",
-     "start_time": "2025-09-25T15:14:36.291938Z"
+     "end_time": "2025-09-26T08:52:15.428921Z",
+     "start_time": "2025-09-26T08:52:15.419187Z"
     }
    },
    "source": [
@@ -25,7 +25,7 @@
     "from post_processing.utils.core_utils import json2df,get_season"
    ],
    "outputs": [],
-   "execution_count": 1
+   "execution_count": 2
   },
   {
    "metadata": {},
@@ -39,8 +39,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-25T15:15:16.539360Z",
-     "start_time": "2025-09-25T15:15:15.617231Z"
+     "end_time": "2025-09-26T08:52:58.615704Z",
+     "start_time": "2025-09-26T08:52:57.824901Z"
     }
    },
    "cell_type": "code",
@@ -77,28 +77,28 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-25T15:16:18.967295Z",
-     "start_time": "2025-09-25T15:15:37.397762Z"
+     "end_time": "2025-09-26T08:53:49.493845Z",
+     "start_time": "2025-09-26T08:53:06.634574Z"
     }
    },
    "cell_type": "code",
    "source": "d_beg_end = actual_data(df_0, col_timestamp=\"ChunkEnd\")",
    "id": "4208969d9e509a8",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-25T15:16:22.557433Z",
-     "start_time": "2025-09-25T15:16:22.552202Z"
+     "end_time": "2025-09-26T08:54:32.450760Z",
+     "start_time": "2025-09-26T08:54:32.445250Z"
     }
    },
    "cell_type": "code",
    "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ]",
    "id": "597efd1d90e3d069",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 5
   },
   {
    "metadata": {},
@@ -113,15 +113,44 @@
    "id": "8ed339c688bdef1"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-26T08:54:37.660741Z",
+     "start_time": "2025-09-26T08:54:37.211161Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\", extra_columns=[\"deploy.name\"])\n",
+    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\")\n",
     "print(df_aplose.head())"
    ],
    "id": "812ed7c0c5e258e7",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Date heure'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mKeyError\u001B[39m                                  Traceback (most recent call last)",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001B[39m, in \u001B[36mIndex.get_loc\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m   3811\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m3812\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_engine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   3813\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/index.pyx:167\u001B[39m, in \u001B[36mpandas._libs.index.IndexEngine.get_loc\u001B[39m\u001B[34m()\u001B[39m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/index.pyx:196\u001B[39m, in \u001B[36mpandas._libs.index.IndexEngine.get_loc\u001B[39m\u001B[34m()\u001B[39m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/hashtable_class_helper.pxi:7088\u001B[39m, in \u001B[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[39m\u001B[34m()\u001B[39m\n",
+      "\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/hashtable_class_helper.pxi:7096\u001B[39m, in \u001B[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[39m\u001B[34m()\u001B[39m\n",
+      "\u001B[31mKeyError\u001B[39m: 'Date heure'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001B[31mKeyError\u001B[39m                                  Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[6]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m df_aplose = \u001B[43mcpod2aplose\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_1\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpytz\u001B[49m\u001B[43m.\u001B[49m\u001B[43mutc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mSite D Simone\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mCommerson\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      2\u001B[39m \u001B[38;5;28mprint\u001B[39m(df_aplose.head())\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\src\\post_processing\\utils\\fpod_utils.py:122\u001B[39m, in \u001B[36mcpod2aplose\u001B[39m\u001B[34m(df, tz, dataset_name, annotation, bin_size, extra_columns)\u001B[39m\n\u001B[32m    119\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m deploy_name \u001B[38;5;129;01min\u001B[39;00m df[\u001B[33m\"\u001B[39m\u001B[33mdeploy.name\u001B[39m\u001B[33m\"\u001B[39m].unique():\n\u001B[32m    120\u001B[39m     df_deploy = df[df[\u001B[33m\"\u001B[39m\u001B[33mdeploy.name\u001B[39m\u001B[33m\"\u001B[39m] == deploy_name].copy()\n\u001B[32m--> \u001B[39m\u001B[32m122\u001B[39m     result = \u001B[43mfpod2aplose\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_deploy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtz\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdataset_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mannotation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbin_size\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    124\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m extra_columns:\n\u001B[32m    125\u001B[39m         \u001B[38;5;28;01mfor\u001B[39;00m col \u001B[38;5;129;01min\u001B[39;00m extra_columns:\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\src\\post_processing\\utils\\fpod_utils.py:61\u001B[39m, in \u001B[36mfpod2aplose\u001B[39m\u001B[34m(df, tz, dataset_name, annotation, bin_size)\u001B[39m\n\u001B[32m     30\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mfpod2aplose\u001B[39m(\n\u001B[32m     31\u001B[39m     df: DataFrame,\n\u001B[32m     32\u001B[39m     tz: pytz.timezone,\n\u001B[32m   (...)\u001B[39m\u001B[32m     35\u001B[39m     bin_size: \u001B[38;5;28mint\u001B[39m = \u001B[32m60\u001B[39m,\n\u001B[32m     36\u001B[39m ) -> DataFrame:\n\u001B[32m     37\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Format FPOD DataFrame to match APLOSE format.\u001B[39;00m\n\u001B[32m     38\u001B[39m \n\u001B[32m     39\u001B[39m \u001B[33;03m    Parameters\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m     56\u001B[39m \n\u001B[32m     57\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m     58\u001B[39m     fpod_start_dt = \u001B[38;5;28msorted\u001B[39m(\n\u001B[32m     59\u001B[39m         [\n\u001B[32m     60\u001B[39m             tz.localize(strptime_from_text(entry, \u001B[33m\"\u001B[39m\u001B[38;5;132;01m%d\u001B[39;00m\u001B[33m/\u001B[39m\u001B[33m%\u001B[39m\u001B[33mm/\u001B[39m\u001B[33m%\u001B[39m\u001B[33mY \u001B[39m\u001B[33m%\u001B[39m\u001B[33mH:\u001B[39m\u001B[33m%\u001B[39m\u001B[33mM\u001B[39m\u001B[33m\"\u001B[39m))\n\u001B[32m---> \u001B[39m\u001B[32m61\u001B[39m             \u001B[38;5;28;01mfor\u001B[39;00m entry \u001B[38;5;129;01min\u001B[39;00m \u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mDate heure\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[32m     62\u001B[39m         ],\n\u001B[32m     63\u001B[39m     )\n\u001B[32m     65\u001B[39m     fpod_end_dt = \u001B[38;5;28msorted\u001B[39m(\n\u001B[32m     66\u001B[39m         [entry + Timedelta(seconds=bin_size) \u001B[38;5;28;01mfor\u001B[39;00m entry \u001B[38;5;129;01min\u001B[39;00m fpod_start_dt],\n\u001B[32m     67\u001B[39m     )\n\u001B[32m     69\u001B[39m     data = {\n\u001B[32m     70\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdataset\u001B[39m\u001B[33m\"\u001B[39m: [dataset_name] * \u001B[38;5;28mlen\u001B[39m(df),\n\u001B[32m     71\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mfilename\u001B[39m\u001B[33m\"\u001B[39m: [\u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m] * \u001B[38;5;28mlen\u001B[39m(df),\n\u001B[32m   (...)\u001B[39m\u001B[32m     80\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mis_box\u001B[39m\u001B[33m\"\u001B[39m: [\u001B[32m0\u001B[39m] * \u001B[38;5;28mlen\u001B[39m(df),\n\u001B[32m     81\u001B[39m     }\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4107\u001B[39m, in \u001B[36mDataFrame.__getitem__\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m   4105\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.nlevels > \u001B[32m1\u001B[39m:\n\u001B[32m   4106\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._getitem_multilevel(key)\n\u001B[32m-> \u001B[39m\u001B[32m4107\u001B[39m indexer = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   4108\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[32m   4109\u001B[39m     indexer = [indexer]\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3819\u001B[39m, in \u001B[36mIndex.get_loc\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m   3814\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(casted_key, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\n\u001B[32m   3815\u001B[39m         \u001B[38;5;28misinstance\u001B[39m(casted_key, abc.Iterable)\n\u001B[32m   3816\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m casted_key)\n\u001B[32m   3817\u001B[39m     ):\n\u001B[32m   3818\u001B[39m         \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n\u001B[32m-> \u001B[39m\u001B[32m3819\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01merr\u001B[39;00m\n\u001B[32m   3820\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[32m   3821\u001B[39m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[32m   3822\u001B[39m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[32m   3823\u001B[39m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[32m   3824\u001B[39m     \u001B[38;5;28mself\u001B[39m._check_indexing_error(key)\n",
+      "\u001B[31mKeyError\u001B[39m: 'Date heure'"
+     ]
+    }
+   ],
+   "execution_count": 6
   },
   {
    "metadata": {},
@@ -132,13 +161,13 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
     "print(df_aplose.head())"
    ],
-   "id": "9b632673397a184"
+   "id": "9b632673397a184",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -179,14 +208,14 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
     "data_list = DataAplose.from_yaml(file=yaml_file)\n",
     "print(data_list.df.head())"
    ],
-   "id": "6837593897111b0a"
+   "id": "6837593897111b0a",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -197,35 +226,35 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "dp10 = dpm_to_dp10m(data_list.df, extra_columns=[\"deploy.name\"])\n",
     "dp10 = date_format(dp10)"
    ],
-   "id": "a27ceea1fefdd298"
+   "id": "a27ceea1fefdd298",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "dph = dpm_to_dph(data_list.df, extra_columns=[\"deploy.name\"])\n",
     "dph = date_format(dph)"
    ],
-   "id": "6cc79b2aeef076ed"
+   "id": "6cc79b2aeef076ed",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "dpd = dpm_to_dpd(data_list.df, extra_columns=[\"deploy.name\"])\n",
     "dpd = date_format(dpd)"
    ],
-   "id": "e6655c36fc1851c7"
+   "id": "e6655c36fc1851c7",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -248,13 +277,13 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "d_hour = build_hour_range(d_beg_end)\n",
     "d_tot = dph.merge(fb, on=\"start_datetime\", how=\"left\")"
    ],
-   "id": "b00c8f1e2210ea7"
+   "id": "b00c8f1e2210ea7",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},

From d89604c20f4e4dbd25c195799b8a85c615496d03 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 30 Sep 2025 16:54:05 +0200
Subject: [PATCH 10/83] correct some functions

---
 src/post_processing/utils/fpod_utils.py | 270 +++++++++++-------------
 1 file changed, 122 insertions(+), 148 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 9c59fd3..c132cc5 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -1,3 +1,5 @@
+"""FPOD/ CPOD processing functions."""
+
 from __future__ import annotations
 
 import logging
@@ -11,7 +13,6 @@
 from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
 from pandas import (
     DataFrame,
-    Series,
     Timedelta,
     concat,
     date_range,
@@ -19,6 +20,7 @@
     read_csv,
     read_excel,
     to_datetime,
+    to_timedelta,
 )
 
 from post_processing.utils.core_utils import get_coordinates, get_sun_times
@@ -26,6 +28,27 @@
 if TYPE_CHECKING:
     import pytz
 
+logger = logging.getLogger(__name__)
+site_colors = {
+    "Site A Haute": "#118B50",
+    "Site B Heugh": "#5DB996",
+    "Site C Chat": "#B0DB9C",
+    "Site D Simone": "#E3F0AF",
+    "CA4": "#FF0066",
+    "Walde": "#934790",
+    "Point C": "#932F67",
+    "Point D": "#D92C54",
+    "Point E": "#DDDEAB",
+    "Point F": "#8ABB6C",
+    "Point G": "#456882",
+}
+
+season_color = {
+    "spring": "#C5E0B4",
+    "summer": "#FCF97F",
+    "autumn": "#ED7C2F",
+    "winter": "#B4C7E8",
+}
 
 def fpod2aplose(
     df: DataFrame,
@@ -58,7 +81,7 @@ def fpod2aplose(
     fpod_start_dt = sorted(
         [
             tz.localize(strptime_from_text(entry, "%d/%m/%Y %H:%M"))
-            for entry in df["Date heure"]
+            for entry in df["ChunkEnd"]
         ],
     )
 
@@ -79,6 +102,8 @@ def fpod2aplose(
         "end_datetime": [strftime_osmose_format(entry) for entry in fpod_end_dt],
         "is_box": [0] * len(df),
     }
+    if "deploy.name" in df.columns:
+        data["deploy.name"] = df["deploy.name"]
 
     return DataFrame(data)
 
@@ -179,17 +204,14 @@ def usable_data_phase(
         percentage_data = act_length * 100 / p_length
         msg = f"Percentage of usable data : {percentage_data}%"
 
-    logging.info(msg)
+    logger.info(msg)
     return percentage_data
 
 
 def meta_cut_aplose(
     raw_data: DataFrame,
     metadata: DataFrame,
-    col_deploy_name: str = "deploy.name",
-    col_timestamp: str = "start_datetime",
-    col_debut: str = "deployment_date",
-    col_fin: str = "recovery_date",
+    column_names: dict[str, str] | None = None,
 ) -> DataFrame:
     """Filter data to keep only the ones corresponding to a deployment.
 
@@ -199,14 +221,10 @@ def meta_cut_aplose(
         Dataframe containing deploy.name et timestamp
     metadata : DataFrame
         Metadata containing deploy.name, deployment_date, recovery_date
-    col_deploy_name : str
-        Name of the deployment name column (default: 'deploy.name')
-    col_timestamp : str
-        Name of the timestamps column in raw_data (default: 'start_datetime')
-    col_debut : str
-        Name of the deployment column in metadata (default: 'deployment_date')
-    col_fin : str
-        Name of the recovery column in metadata (default: 'recovery_date')
+    column_names : dict[str, str], optional
+        Dictionary with column names. Keys: 'deploy_name', 'timestamp',
+        'deployment_date', 'recovery_date'. If None, uses defaults.
+
 
     Returns
     -------
@@ -214,8 +232,24 @@ def meta_cut_aplose(
         Filtered data containing only rows in deployment periods
 
     """
+    defaults = {
+        "deploy_name": "deploy.name",
+        "timestamp": "start_datetime",
+        "deployment_date": "deployment_date",
+        "recovery_date": "recovery_date",
+    }
+
+    # Merge with user-provided names
+    cols = {**defaults, **(column_names or {})}
+
+    col_deploy_name = cols["deploy_name"]
+    col_timestamp = cols["timestamp"]
+    col_debut = cols["deployment_date"]
+    col_fin = cols["recovery_date"]
+
     required_raw = [col_deploy_name, col_timestamp]
     required_meta = [col_deploy_name, col_debut, col_fin]
+
     for col in required_raw:
         if col not in raw_data.columns:
             msg = f"'{col}' not found in raw_data"
@@ -400,15 +434,17 @@ def generate_hourly_detections(meta: DataFrame, site: str) -> DataFrame:
     return DataFrame(records)
 
 
-def build_hour_range(dph: DataFrame) -> DataFrame:
+def build_range(df: DataFrame, fr:str="h") -> DataFrame:
     """Create a DataFrame with one line per hour between start and end dates.
 
     Keep the number of detections per hour between these dates.
 
     Parameters
     ----------
-    dph: pd.DataFrame
+    df: pd.DataFrame
         Metadata dataframe with deployments information (previously exported as json)
+    fr:str
+        Frequency of the range of detections.
 
     Returns
     -------
@@ -416,21 +452,18 @@ def build_hour_range(dph: DataFrame) -> DataFrame:
         A full period of time with positive and negative hours to detections.
 
     """
-    dph["Date heure"] = to_datetime(dph["Date heure"], dayfirst=True)
-
-    deploy_ranges = (
-        dph.groupby("deploy.name")["Date heure"]
-        .agg(start="min", end="max")
-        .reset_index()
-    )
+    df["Début"] = to_datetime(df["Début"], utc=True)
+    df["Début"] = df["Début"].dt.floor("h")
+    df["Fin"] = to_datetime(df["Fin"], utc=True)
+    df["Fin"] = df["Fin"].dt.floor("h")
 
     all_ranges = []
-    for _, row in deploy_ranges.iterrows():
-        hours = date_range(row["start"], row["end"], freq="h")
+    for _, row in df.iterrows():
+        hours = date_range(row["Début"], row["Fin"], freq=fr)
         tmp = DataFrame(
             {
                 "deploy.name": row["deploy.name"],
-                "Date heure": hours,
+                "start_datetime": hours,
             },
         )
         all_ranges.append(tmp)
@@ -496,30 +529,26 @@ def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
 
     """
     df["microsec"] = df["microsec"] / 1e6
-    df["microsec_formatted"] = df["microsec"].apply(lambda x: f"{x:.6f}")
-
-    df["Time"] = df["Minute"].astype(str) + ":" + df["microsec_formatted"].astype(str)
-
-    df["Time"] = to_datetime(df["Time"], dayfirst=True)
-
-    df = df.sort_values(by="Time").reset_index(drop=True)
-    df["ICI"] = df["Time"].diff().dt.total_seconds()
+    df["ICI"] = df["microsec"].diff()
 
-    df["Buzz"] = 0
-    if species == "Marsouin":
-        feeding_idx = df.index[df["ICI"] < 0.01]
-    elif species == "Commerson":
-        feeding_idx = df.index[df["ICI"] <= 0.005]
+    if species == "Marsouin":  # Nuuttila et al., 2013
+        df["Buzz"] = (df["ICI"].between(0, 0.01)).astype(int)
+    elif species == "Commerson":  # Reyes Reyes et al., 2015
+        df["Buzz"] = (df["ICI"].between(0, 0.005)).astype(int)
     else:
         msg = "This species is not supported"
         raise ValueError(msg)
 
-    df.loc[feeding_idx, "Buzz"] = 1
-    df.loc[feeding_idx - 1, "Buzz"] = 1
-    df.loc[df.index < 0, "Buzz"] = 0
+    try:
+        df["Minute"].astype(int)
+        df["datetime"] = (to_datetime("1900-01-01") +
+                            to_timedelta(df["Minute"], unit="min") +
+                            to_timedelta(df["microsec"], unit="us") -
+                            to_timedelta(2, unit="D"))
+        df["start_datetime"] = df["datetime"].dt.floor("min")
+    except (ValueError, TypeError):
+        df["start_datetime"] = to_datetime(df["Minute"], dayfirst=True)
 
-    df["start_datetime"] = df["Time"].dt.floor("min")
-    df["start_datetime"] = to_datetime(df["start_datetime"], dayfirst=False, utc=True)
     f = df.groupby(["start_datetime"])["Buzz"].sum().reset_index()
 
     f["Foraging"] = (f["Buzz"] != 0).astype(int)
@@ -530,7 +559,7 @@ def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
 def assign_daytime(
     df: DataFrame,
 ) -> DataFrame:
-    """Assign datetime categories to events.
+    """Assign datetime categories to temporal events.
 
     Categorize daytime of the detection (among 4 categories).
 
@@ -545,38 +574,35 @@ def assign_daytime(
         The same dataframe with the column daytime.
 
     """
-    start = df.iloc[0]["Time"]
-    stop = df.iloc[-1]["Time"]
+    df["start_datetime"] = to_datetime(df["start_datetime"], utc=True)
+    start = df["start_datetime"].min()
+    stop = df["start_datetime"].max()
     lat, lon = get_coordinates()
-    _, _, dawn, day, dusk, night = get_sun_times(start, stop, lat, lon)
-    dawn = Series(dawn, name="dawn")
-    day = Series(day, name="day")
-    dusk = Series(dusk, name="dusk")
-    night = Series(night, name="night")
-    jour = concat([day, night, dawn, dusk], axis=1)
+    sunrise, sunset = get_sun_times(start, stop, lat, lon)
+
+    sun_times = DataFrame(
+        {   "date": date_range(start, stop, freq="D"),
+            "sunrise": [Timedelta(h, "hours") for h in sunrise],
+            "sunset": [Timedelta(h, "hours") for h in sunset],
+        })
+
+    sun_times["sunrise"] = sun_times["date"].dt.floor("D") + sun_times["sunrise"]
+    sun_times["sunset"] = sun_times["date"].dt.floor("D") + sun_times["sunset"]
 
     for i, row in df.iterrows():
-        dpm_i = row["Time"]
+        dpm_i = row["start_datetime"]
         if notna(dpm_i):  # Check if time is not NaN
-            jour_i = jour[
-                (jour["dusk"].dt.year == dpm_i.year)
-                & (jour["dusk"].dt.month == dpm_i.month)
-                & (jour["dusk"].dt.day == dpm_i.day)
-            ]
-            if not jour_i.empty:  # Ensure there"s a matching row
+            jour_i = sun_times[
+                (sun_times["sunrise"].dt.year == dpm_i.year)
+                & (sun_times["sunrise"].dt.month == dpm_i.month)
+                & (sun_times["sunrise"].dt.day == dpm_i.day)
+                ]
+            if not jour_i.empty:  # Ensure there's a matching row
                 jour_i = jour_i.iloc[0]  # Extract first match
-                if dpm_i <= jour_i["day"]:
-                    df.loc[i, "REGIME"] = 1
-                elif dpm_i < jour_i["dawn"]:
-                    df.loc[i, "REGIME"] = 2
-                elif dpm_i < jour_i["dusk"]:
-                    df.loc[i, "REGIME"] = 3
-                elif dpm_i > jour_i["night"]:
+                if (dpm_i <= jour_i["sunrise"]) | (dpm_i > jour_i["sunset"]):
                     df.loc[i, "REGIME"] = 1
-                elif dpm_i > jour_i["dusk"]:
-                    df.loc[i, "REGIME"] = 4
                 else:
-                    df.loc[i, "REGIME"] = 1
+                    df.loc[i, "REGIME"] = 2
 
     return df
 
@@ -611,23 +637,6 @@ def fb_folder(folder_path: Path, species: str) -> DataFrame:
     return concat(all_data, ignore_index=True)
 
 
-colors = {
-    "Site A Haute": "#118B50",
-    "Site B Heugh": "#5DB996",
-    "Site C Chat": "#B0DB9C",
-    "Site D Simone": "#E3F0AF",
-    "CA4": "#FF0066",
-    "Walde": "#934790",
-}
-
-season_color = {
-    "spring": "#C5E0B4",
-    "summer": "#FCF97F",
-    "autumn": "#ED7C2F",
-    "winter": "#B4C7E8",
-}
-
-
 def extract_site(df: DataFrame) -> DataFrame:
     """Create new columns: site.name and campaign.name, in order to match the metadata.
 
@@ -706,7 +715,7 @@ def site_percent(df: DataFrame, metric: str) -> None:
         y=metric,
         hue="site.name",
         dodge=False,
-        palette=colors,
+        palette=site_colors,
     )
     ax.set_title(f"{metric} per site")
     ax.set_ylabel(f"{metric}")
@@ -739,7 +748,7 @@ def year_percent(df: DataFrame, metric: str) -> None:
             site_data["Year"],
             site_data[metric],
             label=f"Site {site}",
-            color=colors.get(site, "gray"),
+            color=site_colors.get(site, "gray"),
         )
         ax.set_title(f"{site}")
         ax.set_ylim(0, max(df[metric]) + 0.2)
@@ -793,8 +802,8 @@ def ym_percent(df: DataFrame, metric: str) -> None:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     legend_elements = [
-        Patch(facecolor=season_color, edgecolor="black", label=season.capitalize())
-        for season, season_color in season_color.items()
+        Patch(facecolor=col, edgecolor="black", label=season.capitalize())
+        for season, col in season_color.items()
     ]
     fig.legend(
         handles=legend_elements,
@@ -829,7 +838,7 @@ def month_percent(df: DataFrame, metric: str) -> None:
             site_data["Month"],
             site_data[metric],
             label=f"Site {site}",
-            color=colors.get(site, "gray"),
+            color=site_colors.get(site, "gray"),
         )
         ax.set_title(f"{site} - Percentage of minutes postitive to detection per month")
         ax.set_ylim(0, max(df[metric]) + 0.2)
@@ -885,7 +894,7 @@ def hour_percent(df: DataFrame, metric: str) -> None:
             site_data["Hour"],
             site_data[metric],
             label=f"Site {site}",
-            color=colors.get(site, "gray"),
+            color=site_colors.get(site, "gray"),
         )
         ax.set_title(
             f"Site {site} - Percentage of minutes positive to detection per hour",
@@ -903,7 +912,7 @@ def hour_percent(df: DataFrame, metric: str) -> None:
     plt.show()
 
 
-def csv_folder(folder_path: str | Path, **kwargs) -> DataFrame:
+def csv_folder(folder_path: str | Path, **kwargs: str) -> DataFrame:
     """Process a folder containing data files and concatenate them.
 
     Parameters
@@ -983,9 +992,10 @@ def dpm_to_dp10m(
 
     """
     df = df.copy()
-    df["ChunkEnd"] = to_datetime(df["ChunkEnd"], dayfirst=True)
+    df["DPM"] = 1
+    df["start_datetime"] = to_datetime(df["start_datetime"], dayfirst=True)
 
-    df["Date heure"] = df["ChunkEnd"].dt.floor("10min")
+    df["start_datetime"] = df["start_datetime"].dt.floor("10min")
 
     agg_dict = {"DPM": "sum"}
 
@@ -994,9 +1004,9 @@ def dpm_to_dp10m(
             if col in df.columns:
                 agg_dict[col] = "first"
             else:
-                logging.warning(f"Column '{col}' does not exist and will be ignored.")
+                logger.warning(" '%s' does not exist and will be ignored.", col)
 
-    return df.groupby("Date heure").agg(agg_dict).reset_index()
+    return df.groupby("start_datetime").agg(agg_dict).reset_index()
 
 
 def dpm_to_dph(
@@ -1019,10 +1029,11 @@ def dpm_to_dph(
 
     """
     df = df.copy()
-    df["ChunkEnd"] = to_datetime(df["ChunkEnd"], dayfirst=True)
+    df["DPM"] = 1
+    df["start_datetime"] = to_datetime(df["start_datetime"], dayfirst=True)
 
     # Truncate column
-    df["Date heure"] = df["ChunkEnd"].dt.floor("h")
+    df["start_datetime"] = df["start_datetime"].dt.floor("h")
 
     agg_dict = {"DPM": "sum"}
 
@@ -1031,9 +1042,9 @@ def dpm_to_dph(
             if col in df.columns:
                 agg_dict[col] = "first"
             else:
-                logging.warning(f"Column '{col}' does not exist and will be ignored.")
+                logger.warning("Column '%s' does not exist and will be ignored.", col)
 
-    return df.groupby("Date heure").agg(agg_dict).reset_index()
+    return df.groupby("start_datetime").agg(agg_dict).reset_index()
 
 
 def dpm_to_dpd(
@@ -1056,10 +1067,11 @@ def dpm_to_dpd(
 
     """
     df = df.copy()
-    df["ChunkEnd"] = to_datetime(df["ChunkEnd"], dayfirst=True)
+    df["DPM"] = 1
+    df["start_datetime"] = to_datetime(df["start_datetime"], dayfirst=True)
 
     # Truncate column
-    df["Date heure"] = df["ChunkEnd"].dt.floor("D")
+    df["start_datetime"] = df["start_datetime"].dt.floor("D")
 
     agg_dict = {"DPM": "sum"}
 
@@ -1068,9 +1080,9 @@ def dpm_to_dpd(
             if col in df.columns:
                 agg_dict[col] = "first"
             else:
-                logging.warning(f"Column '{col}' does not exist and will be ignored.")
+                logger.warning(" '%s' does not exist and will be ignored.", col)
 
-    return df.groupby("Date heure").agg(agg_dict).reset_index()
+    return df.groupby("start_datetime").agg(agg_dict).reset_index()
 
 
 def date_format(
@@ -1114,6 +1126,7 @@ def actual_data(
         Simple Dataframe with beginning and end columns.
 
     """
+    df = df.copy()
     df[col_timestamp] = df[col_timestamp].apply(
         lambda x: strptime_from_text(
             x, ["%Y-%m-%dT%H:%M:%S:%Z", "%Y-%m-%dT%H:%M:%S", "%d/%m/%Y %H:%M"],
@@ -1159,7 +1172,7 @@ def calendar(
     df_fusion["Fin"] = df_fusion["Fin"].fillna(df_fusion["deployment_date"])
 
     df_fusion[["Site", "Phase"]] = df_fusion["deploy.name"].str.split("_", expand=True)
-    df_fusion["color"] = df_fusion["Site"].map(colors)
+    df_fusion["color"] = df_fusion["Site"].map(site_colors)
 
     # Create the figure
     fig, ax = plt.subplots(figsize=(14, 4))
@@ -1191,7 +1204,7 @@ def calendar(
     legend_elements = [
         Patch(facecolor="#F5F5F5", edgecolor="black", label="Deployment"),
     ]
-    for site, color in colors.items():
+    for site, color in site_colors.items():
         if site in sites:
             legend_elements.append(
                 Patch(facecolor=color, edgecolor="black", label=f"{site}"),
@@ -1203,42 +1216,3 @@ def calendar(
     plt.tight_layout()
     plt.show()
 
-
-def f_b2(df: DataFrame, species: str) -> DataFrame:
-    """Process a CPOD/FPOD feeding buzz detection file.
-
-    Gives the feeding buzz duration, depending on the studied species.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Path to cpod.exe feeding buzz file
-    species: str
-        Select the species to use between porpoise and Commerson's dolphin
-
-    Returns
-    -------
-    DataFrame
-        Containing all ICIs for every positive minutes to clicks
-
-    """
-    df["microsec"] = df["microsec"] / 1e6
-    df["ICI"] = df["microsec"].diff()
-
-    if species == "Marsouin":  # Nuuttila et al., 2013
-        df["Buzz"] = (df["ICI"].between(0, 0.01)).astype(int)
-    elif species == "Commerson":
-        df["Buzz"] = (df["ICI"].between(0, 0.005)).astype(int)
-    else:
-        msg = "This species is not supported"
-        raise ValueError(msg)
-
-    df["Minute"] = to_datetime(df["Minute"], dayfirst=False, utc=True)
-    f = df.groupby(["Minute"])["Buzz"].sum().reset_index()
-
-    # df['datetime'] = to_datetime('1900-01-01') + to_timedelta(df['Minute'], unit='min')
-    # + to_timedelta(df['microsec'], unit='us') - to_timedelta(2, unit='D')
-
-    f["Foraging"] = (f["Buzz"] != 0).astype(int)
-
-    return f

From 5bddac0eb1ef236fc10191c1989a1f15b3d3b651 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 30 Sep 2025 16:54:32 +0200
Subject: [PATCH 11/83] add details

---
 user_case/user_case_CALAIS.ipynb | 483 ++++++++++++++++++++++---------
 1 file changed, 347 insertions(+), 136 deletions(-)

diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index ca4d0c3..4bea267 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-09-26T08:52:15.428921Z",
-     "start_time": "2025-09-26T08:52:15.419187Z"
+     "end_time": "2025-09-29T15:38:07.683846Z",
+     "start_time": "2025-09-29T15:38:05.621538Z"
     }
    },
    "source": [
@@ -21,11 +21,11 @@
     ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import cpod2aplose, fpod2aplose, dpm_to_dp10m, dpm_to_dph, dpm_to_dpd, fb_folder,csv_folder, meta_cut_aplose, date_format,extract_site,percent_calc,site_percent, year_percent, ym_percent, month_percent, hour_percent, actual_data, build_hour_range\n",
+    "from post_processing.utils.fpod_utils import csv_folder, cpod2aplose, fpod2aplose, actual_data, meta_cut_aplose, dpm_to_dp10m, dpm_to_dph, dpm_to_dpd,fb_folder, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent\n",
     "from post_processing.utils.core_utils import json2df,get_season"
    ],
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 1
   },
   {
    "metadata": {},
@@ -39,13 +39,13 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-26T08:52:58.615704Z",
-     "start_time": "2025-09-26T08:52:57.824901Z"
+     "end_time": "2025-09-29T15:15:34.902195Z",
+     "start_time": "2025-09-29T15:15:34.168693Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "data = csv_folder(r\"U:\\D\")\n",
+    "data = csv_folder(r\"U:\\Cetiroise\")\n",
     "print(data.head())\n",
     "\n",
     "df_0 = data.dropna()"
@@ -56,42 +56,42 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                 File          ChunkEnd  DPM  Nall  MinsOn  \\\n",
-      "0  POD3042 file01.CP3  21/10/2019 12:11    0     0       0   \n",
-      "1  POD3042 file01.CP3  21/10/2019 12:12    0     0       0   \n",
-      "2  POD3042 file01.CP3  21/10/2019 12:13    0     0       0   \n",
-      "3  POD3042 file01.CP3  21/10/2019 12:14    0     0       0   \n",
-      "4  POD3042 file01.CP3  21/10/2019 12:15    0     0       0   \n",
+      "                                                File  podN          ChunkEnd  \\\n",
+      "0  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:34   \n",
+      "1  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:35   \n",
+      "2  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:36   \n",
+      "3  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 22:40   \n",
+      "4  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  24/02/2023 15:32   \n",
       "\n",
-      "            deploy.name  \n",
-      "0  Site D Simone_Phase1  \n",
-      "1  Site D Simone_Phase1  \n",
-      "2  Site D Simone_Phase1  \n",
-      "3  Site D Simone_Phase1  \n",
-      "4  Site D Simone_Phase1  \n"
+      "     Minute  DPM  MinsOn      deploy.name  \n",
+      "0  64748494    1       1  Point C_Phase 4  \n",
+      "1  64748495    1       1  Point C_Phase 4  \n",
+      "2  64748496    1       1  Point C_Phase 4  \n",
+      "3  64749520    1       1  Point C_Phase 4  \n",
+      "4  64773572    1       1  Point C_Phase 4  \n"
      ]
     }
    ],
-   "execution_count": 3
+   "execution_count": 2
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-26T08:53:49.493845Z",
-     "start_time": "2025-09-26T08:53:06.634574Z"
+     "end_time": "2025-09-29T15:15:37.726484Z",
+     "start_time": "2025-09-29T15:15:37.563404Z"
     }
    },
    "cell_type": "code",
    "source": "d_beg_end = actual_data(df_0, col_timestamp=\"ChunkEnd\")",
    "id": "4208969d9e509a8",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 3
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-26T08:54:32.450760Z",
-     "start_time": "2025-09-26T08:54:32.445250Z"
+     "end_time": "2025-09-29T15:15:47.105610Z",
+     "start_time": "2025-09-29T15:15:47.101813Z"
     }
    },
    "cell_type": "code",
@@ -113,44 +113,15 @@
    "id": "8ed339c688bdef1"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-26T08:54:37.660741Z",
-     "start_time": "2025-09-26T08:54:37.211161Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\")\n",
     "print(df_aplose.head())"
    ],
    "id": "812ed7c0c5e258e7",
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'Date heure'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mKeyError\u001B[39m                                  Traceback (most recent call last)",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001B[39m, in \u001B[36mIndex.get_loc\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m   3811\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m3812\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_engine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   3813\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
-      "\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/index.pyx:167\u001B[39m, in \u001B[36mpandas._libs.index.IndexEngine.get_loc\u001B[39m\u001B[34m()\u001B[39m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/index.pyx:196\u001B[39m, in \u001B[36mpandas._libs.index.IndexEngine.get_loc\u001B[39m\u001B[34m()\u001B[39m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/hashtable_class_helper.pxi:7088\u001B[39m, in \u001B[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[39m\u001B[34m()\u001B[39m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/hashtable_class_helper.pxi:7096\u001B[39m, in \u001B[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[39m\u001B[34m()\u001B[39m\n",
-      "\u001B[31mKeyError\u001B[39m: 'Date heure'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001B[31mKeyError\u001B[39m                                  Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[6]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m df_aplose = \u001B[43mcpod2aplose\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_1\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpytz\u001B[49m\u001B[43m.\u001B[49m\u001B[43mutc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mSite D Simone\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mCommerson\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m      2\u001B[39m \u001B[38;5;28mprint\u001B[39m(df_aplose.head())\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\src\\post_processing\\utils\\fpod_utils.py:122\u001B[39m, in \u001B[36mcpod2aplose\u001B[39m\u001B[34m(df, tz, dataset_name, annotation, bin_size, extra_columns)\u001B[39m\n\u001B[32m    119\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m deploy_name \u001B[38;5;129;01min\u001B[39;00m df[\u001B[33m\"\u001B[39m\u001B[33mdeploy.name\u001B[39m\u001B[33m\"\u001B[39m].unique():\n\u001B[32m    120\u001B[39m     df_deploy = df[df[\u001B[33m\"\u001B[39m\u001B[33mdeploy.name\u001B[39m\u001B[33m\"\u001B[39m] == deploy_name].copy()\n\u001B[32m--> \u001B[39m\u001B[32m122\u001B[39m     result = \u001B[43mfpod2aplose\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf_deploy\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtz\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdataset_name\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mannotation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbin_size\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    124\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m extra_columns:\n\u001B[32m    125\u001B[39m         \u001B[38;5;28;01mfor\u001B[39;00m col \u001B[38;5;129;01min\u001B[39;00m extra_columns:\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\src\\post_processing\\utils\\fpod_utils.py:61\u001B[39m, in \u001B[36mfpod2aplose\u001B[39m\u001B[34m(df, tz, dataset_name, annotation, bin_size)\u001B[39m\n\u001B[32m     30\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mfpod2aplose\u001B[39m(\n\u001B[32m     31\u001B[39m     df: DataFrame,\n\u001B[32m     32\u001B[39m     tz: pytz.timezone,\n\u001B[32m   (...)\u001B[39m\u001B[32m     35\u001B[39m     bin_size: \u001B[38;5;28mint\u001B[39m = \u001B[32m60\u001B[39m,\n\u001B[32m     36\u001B[39m ) -> DataFrame:\n\u001B[32m     37\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Format FPOD DataFrame to match APLOSE format.\u001B[39;00m\n\u001B[32m     38\u001B[39m \n\u001B[32m     39\u001B[39m \u001B[33;03m    Parameters\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m     56\u001B[39m \n\u001B[32m     57\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m     58\u001B[39m     fpod_start_dt = \u001B[38;5;28msorted\u001B[39m(\n\u001B[32m     59\u001B[39m         [\n\u001B[32m     60\u001B[39m             tz.localize(strptime_from_text(entry, \u001B[33m\"\u001B[39m\u001B[38;5;132;01m%d\u001B[39;00m\u001B[33m/\u001B[39m\u001B[33m%\u001B[39m\u001B[33mm/\u001B[39m\u001B[33m%\u001B[39m\u001B[33mY \u001B[39m\u001B[33m%\u001B[39m\u001B[33mH:\u001B[39m\u001B[33m%\u001B[39m\u001B[33mM\u001B[39m\u001B[33m\"\u001B[39m))\n\u001B[32m---> \u001B[39m\u001B[32m61\u001B[39m             \u001B[38;5;28;01mfor\u001B[39;00m entry \u001B[38;5;129;01min\u001B[39;00m \u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mDate heure\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[32m     62\u001B[39m         ],\n\u001B[32m     63\u001B[39m     )\n\u001B[32m     65\u001B[39m     fpod_end_dt = \u001B[38;5;28msorted\u001B[39m(\n\u001B[32m     66\u001B[39m         [entry + Timedelta(seconds=bin_size) \u001B[38;5;28;01mfor\u001B[39;00m entry \u001B[38;5;129;01min\u001B[39;00m fpod_start_dt],\n\u001B[32m     67\u001B[39m     )\n\u001B[32m     69\u001B[39m     data = {\n\u001B[32m     70\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mdataset\u001B[39m\u001B[33m\"\u001B[39m: [dataset_name] * \u001B[38;5;28mlen\u001B[39m(df),\n\u001B[32m     71\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mfilename\u001B[39m\u001B[33m\"\u001B[39m: [\u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m] * \u001B[38;5;28mlen\u001B[39m(df),\n\u001B[32m   (...)\u001B[39m\u001B[32m     80\u001B[39m         \u001B[33m\"\u001B[39m\u001B[33mis_box\u001B[39m\u001B[33m\"\u001B[39m: [\u001B[32m0\u001B[39m] * \u001B[38;5;28mlen\u001B[39m(df),\n\u001B[32m     81\u001B[39m     }\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4107\u001B[39m, in \u001B[36mDataFrame.__getitem__\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m   4105\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.nlevels > \u001B[32m1\u001B[39m:\n\u001B[32m   4106\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._getitem_multilevel(key)\n\u001B[32m-> \u001B[39m\u001B[32m4107\u001B[39m indexer = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m   4108\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[32m   4109\u001B[39m     indexer = [indexer]\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3819\u001B[39m, in \u001B[36mIndex.get_loc\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m   3814\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(casted_key, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\n\u001B[32m   3815\u001B[39m         \u001B[38;5;28misinstance\u001B[39m(casted_key, abc.Iterable)\n\u001B[32m   3816\u001B[39m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m casted_key)\n\u001B[32m   3817\u001B[39m     ):\n\u001B[32m   3818\u001B[39m         \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n\u001B[32m-> \u001B[39m\u001B[32m3819\u001B[39m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01merr\u001B[39;00m\n\u001B[32m   3820\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[32m   3821\u001B[39m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[32m   3822\u001B[39m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[32m   3823\u001B[39m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[32m   3824\u001B[39m     \u001B[38;5;28mself\u001B[39m._check_indexing_error(key)\n",
-      "\u001B[31mKeyError\u001B[39m: 'Date heure'"
-     ]
-    }
-   ],
-   "execution_count": 6
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -159,15 +130,47 @@
    "id": "a39bb10d8ac60a27"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:15:49.464586Z",
+     "start_time": "2025-09-29T15:15:49.294885Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
     "print(df_aplose.head())"
    ],
    "id": "9b632673397a184",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     dataset filename  start_time  end_time  start_frequency  end_frequency  \\\n",
+      "0  CETIROISE                    0        60                0              0   \n",
+      "1  CETIROISE                    0        60                0              0   \n",
+      "2  CETIROISE                    0        60                0              0   \n",
+      "3  CETIROISE                    0        60                0              0   \n",
+      "4  CETIROISE                    0        60                0              0   \n",
+      "\n",
+      "  annotation annotator                start_datetime  \\\n",
+      "0   Marsouin      FPOD  2023-02-07T05:34:00.000+0000   \n",
+      "1   Marsouin      FPOD  2023-02-07T05:35:00.000+0000   \n",
+      "2   Marsouin      FPOD  2023-02-07T05:36:00.000+0000   \n",
+      "3   Marsouin      FPOD  2023-02-07T22:40:00.000+0000   \n",
+      "4   Marsouin      FPOD  2023-02-24T15:32:00.000+0000   \n",
+      "\n",
+      "                   end_datetime  is_box      deploy.name  \n",
+      "0  2023-02-07T05:35:00.000+0000       0  Point C_Phase 4  \n",
+      "1  2023-02-07T05:36:00.000+0000       0  Point C_Phase 4  \n",
+      "2  2023-02-07T05:37:00.000+0000       0  Point C_Phase 4  \n",
+      "3  2023-02-07T22:41:00.000+0000       0  Point C_Phase 4  \n",
+      "4  2023-02-24T15:33:00.000+0000       0  Point C_Phase 4  \n"
+     ]
+    }
+   ],
+   "execution_count": 6
   },
   {
    "metadata": {},
@@ -176,10 +179,15 @@
    "id": "7860838f8514da39"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:15:53.508059Z",
+     "start_time": "2025-09-29T15:15:53.491931Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_kerguelen.json\")\n",
+    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_cetiroise.json\")\n",
     "metadatax = json2df(json_path=json)\n",
     "\n",
     "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
@@ -189,15 +197,20 @@
    ],
    "id": "ed6a06c522aea169",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 7
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:15:55.344572Z",
+     "start_time": "2025-09-29T15:15:54.985631Z"
+    }
+   },
    "cell_type": "code",
-   "source": "cleared.to_csv(r\"U:\\APLOSE_D.csv\", index=False)",
+   "source": "cleared.to_csv(r\"U:\\APLOSE_CETIROISE_pos.csv\", index=False)",
    "id": "76f70cb6c6658ba6",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 8
   },
   {
    "metadata": {},
@@ -206,7 +219,12 @@
    "id": "f5d38266dc9d5273"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:15:57.748310Z",
+     "start_time": "2025-09-29T15:15:57.640771Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
@@ -214,8 +232,35 @@
     "print(data_list.df.head())"
    ],
    "id": "6837593897111b0a",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     dataset  filename  start_time  end_time  start_frequency  end_frequency  \\\n",
+      "0  CETIROISE       NaN           0        60                0              0   \n",
+      "1  CETIROISE       NaN           0        60                0              0   \n",
+      "2  CETIROISE       NaN           0        60                0              0   \n",
+      "3  CETIROISE       NaN           0        60                0              0   \n",
+      "4  CETIROISE       NaN           0        60                0              0   \n",
+      "\n",
+      "  annotation annotator            start_datetime              end_datetime  \\\n",
+      "0   Marsouin      FPOD 2023-02-07 05:34:00+00:00 2023-02-07 05:35:00+00:00   \n",
+      "1   Marsouin      FPOD 2023-02-07 05:35:00+00:00 2023-02-07 05:36:00+00:00   \n",
+      "2   Marsouin      FPOD 2023-02-07 05:36:00+00:00 2023-02-07 05:37:00+00:00   \n",
+      "3   Marsouin      FPOD 2023-02-07 22:40:00+00:00 2023-02-07 22:41:00+00:00   \n",
+      "4   Marsouin      FPOD 2023-02-24 15:32:00+00:00 2023-02-24 15:33:00+00:00   \n",
+      "\n",
+      "   is_box      deploy.name  \n",
+      "0       0  Point C_Phase 4  \n",
+      "1       0  Point C_Phase 4  \n",
+      "2       0  Point C_Phase 4  \n",
+      "3       0  Point C_Phase 4  \n",
+      "4       0  Point C_Phase 4  \n"
+     ]
+    }
+   ],
+   "execution_count": 9
   },
   {
    "metadata": {},
@@ -226,32 +271,28 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "source": [
-    "dp10 = dpm_to_dp10m(data_list.df, extra_columns=[\"deploy.name\"])\n",
-    "dp10 = date_format(dp10)"
-   ],
+   "source": "dp10 = dpm_to_dp10m(data_list.df, extra_columns=[\"deploy.name\"])",
    "id": "a27ceea1fefdd298",
    "outputs": [],
    "execution_count": null
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:16:01.922947Z",
+     "start_time": "2025-09-29T15:16:01.917142Z"
+    }
+   },
    "cell_type": "code",
-   "source": [
-    "dph = dpm_to_dph(data_list.df, extra_columns=[\"deploy.name\"])\n",
-    "dph = date_format(dph)"
-   ],
+   "source": "dph = dpm_to_dph(data_list.df, extra_columns=[\"deploy.name\"])",
    "id": "6cc79b2aeef076ed",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 10
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "source": [
-    "dpd = dpm_to_dpd(data_list.df, extra_columns=[\"deploy.name\"])\n",
-    "dpd = date_format(dpd)"
-   ],
+   "source": "dpd = dpm_to_dpd(data_list.df, extra_columns=[\"deploy.name\"])",
    "id": "e6655c36fc1851c7",
    "outputs": [],
    "execution_count": null
@@ -263,38 +304,83 @@
    "id": "8375ddbe07ad0aee"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:16:04.290557Z",
+     "start_time": "2025-09-29T15:16:04.215490Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "fb_all = fb_folder(r\"U:\\fb_D_NBHF\", \"Commerson\")\n",
+    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\")\n",
+    "fb_all = fb_folder(fb_files, \"Marsouin\")\n",
+    "\n",
     "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(\"h\")\n",
-    "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n"
+    "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
+    "fb[\"start_datetime\"] = to_datetime(fb[\"start_datetime\"], utc=True)"
    ],
    "id": "2b19f90c99252ff3",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 11
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:16:08.387825Z",
+     "start_time": "2025-09-29T15:16:08.377003Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "d_hour = build_hour_range(d_beg_end)\n",
-    "d_tot = dph.merge(fb, on=\"start_datetime\", how=\"left\")"
+    "d_tot = dph.merge(fb, on=\"start_datetime\", how=\"left\")\n",
+    "\n",
+    "d_hour = build_range(d_beg_end, \"h\")"
    ],
    "id": "b00c8f1e2210ea7",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 12
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:16:09.946724Z",
+     "start_time": "2025-09-29T15:16:09.933681Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "d_fin = d_hour.merge(d_tot, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")",
+   "id": "601787cc806226b0",
+   "outputs": [],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:16:12.364503Z",
+     "start_time": "2025-09-29T15:16:12.358919Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "d_hour[[\"DPM\",\"Foraging\"]] = d_hour[[\"DPM\",\"Foraging\"]].fillna(0)\n",
+    "d_fin[[\"DPM\",\"Foraging\"]] = d_fin[[\"DPM\",\"Foraging\"]].fillna(0)\n",
     "print(d_hour.head())"
    ],
    "id": "f93bf1f3ca4f4112",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       deploy.name            start_datetime\n",
+      "0  Point C_Phase 4 2023-02-07 05:00:00+00:00\n",
+      "1  Point C_Phase 4 2023-02-07 06:00:00+00:00\n",
+      "2  Point C_Phase 4 2023-02-07 07:00:00+00:00\n",
+      "3  Point C_Phase 4 2023-02-07 08:00:00+00:00\n",
+      "4  Point C_Phase 4 2023-02-07 09:00:00+00:00\n"
+     ]
+    }
+   ],
+   "execution_count": 14
   },
   {
    "metadata": {},
@@ -303,33 +389,52 @@
    "id": "c7b1d32ed1c99fb7"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:16:18.370775Z",
+     "start_time": "2025-09-29T15:16:18.346171Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "d_hour[\"Year\"] = d_hour[\"start_datetime\"].dt.year\n",
-    "d_hour[\"Month\"] = d_hour[\"start_datetime\"].dt.month\n",
-    "d_hour['YM'] = d_hour['Year'].astype(str) + '-' + d_hour['Month'].astype(str)\n",
-    "d_hour['YM'] = to_datetime(d_hour['YM'])\n",
-    "d_hour[\"Day\"] = d_hour[\"start_datetime\"].dt.day\n",
-    "d_hour[\"Hour\"] = d_hour[\"start_datetime\"].dt.hour\n",
+    "d_fin[\"Year\"] = d_fin[\"start_datetime\"].dt.year\n",
+    "d_fin[\"Month\"] = d_fin[\"start_datetime\"].dt.month\n",
+    "d_fin['YM'] = d_fin[\"Year\"].astype(str) + '-' + d_fin[\"Month\"].astype(str)\n",
+    "d_fin['YM'] = to_datetime(d_fin['YM'])\n",
+    "d_fin[\"Day\"] = d_fin[\"start_datetime\"].dt.day\n",
+    "d_fin[\"Hour\"] = d_fin[\"start_datetime\"].dt.hour\n",
     "\n",
-    "d_hour[\"FBR\"] = d_hour[\"Foraging\"] / d_hour[\"DPM\"]\n",
-    "d_hour[\"DPH\"] = (d_hour[\"DPM\"] >0).astype(int)"
+    "d_fin[\"FBR\"] = d_fin[\"Foraging\"] / d_fin[\"DPM\"]\n",
+    "d_fin[\"DPH\"] = (d_fin[\"DPM\"] >0).astype(int)"
    ],
    "id": "a2261ce5093a3104",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\fouinel\\AppData\\Local\\Temp\\ipykernel_4000\\4171406010.py:4: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  d_fin['YM'] = to_datetime(d_fin['YM'])\n"
+     ]
+    }
+   ],
+   "execution_count": 15
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:16:45.204062Z",
+     "start_time": "2025-09-29T15:16:34.484075Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "d_hour[\"FBR\"] = d_hour[\"FBR\"].fillna(0)\n",
-    "d_hour.to_csv(r\"U:\\Hours_DPM_FBUZZ_SiteD.csv\", index=False)"
+    "d_fin[\"FBR\"] = d_fin[\"FBR\"].fillna(0)\n",
+    "d_fin.to_csv(r\"U:\\Hours_DPM_FBUZZ_SiteCETIROISE.csv\", index=False)"
    ],
    "id": "d606f4f6904b57c6",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 16
   },
   {
    "metadata": {},
@@ -344,9 +449,10 @@
     "ca4 = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteCA4.csv\")\n",
     "walde = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteWalde.csv\")\n",
     "\n",
-    "data_k = concat([ca4, walde])\n",
-    "data_k['YM'] = data_k['Year'].astype(str) + '-' + data_k['Month'].astype(str)\n",
-    "data_k['YM'] = to_datetime(data_k['YM'])"
+    "data_c = concat([ca4, walde])\n",
+    "data_c[\"start_datetime\"] = to_datetime(data_c[\"start_datetime\"])\n",
+    "data_c[\"start_datetime\"] = data_c[\"start_datetime\"].apply(lambda x : x.tz_convert(\"Europe/Paris\"))\n",
+    "data_c[\"Hour\"] = data_c[\"start_datetime\"].dt.hour"
    ],
    "id": "9909fbfdcb8e2e78",
    "outputs": [],
@@ -371,61 +477,166 @@
    "execution_count": null
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:38:21.849991Z",
+     "start_time": "2025-09-29T15:38:21.342466Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ceti = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteCETIROISE.csv\")\n",
+    "\n",
+    "ceti[\"start_datetime\"] = to_datetime(ceti[\"start_datetime\"])\n",
+    "ceti[\"start_datetime\"] = ceti[\"start_datetime\"].apply(lambda x : x.tz_convert(\"CET\")) #TimeZone Central European Time\n",
+    "ceti[\"Hour\"] = ceti[\"start_datetime\"].dt.hour"
+   ],
+   "id": "5928770d1c47bcad",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:38:23.189207Z",
+     "start_time": "2025-09-29T15:38:23.142072Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "data_k = extract_site(data_k)\n",
-    "y_per = percent_calc(data_k, \"Year\")\n",
-    "ym_per = percent_calc(data_k, \"YM\")\n",
+    "data = ceti\n",
+    "data = extract_site(data)\n",
+    "y_per = percent_calc(data, \"Year\")\n",
+    "ym_per = percent_calc(data, \"YM\")\n",
     "ym_per[\"YM\"] = to_datetime(ym_per[\"YM\"])\n",
     "ym_per[\"Season\"] = ym_per[\"YM\"].apply(lambda x: get_season(x)[0])\n",
-    "m_per = percent_calc(data_k, \"Month\")\n",
-    "h_per = percent_calc(data_k, \"Hour\")\n",
-    "s_per = percent_calc(data_k)"
+    "m_per = percent_calc(data, \"Month\")\n",
+    "h_per = percent_calc(data, \"Hour\")\n",
+    "s_per = percent_calc(data)"
    ],
    "id": "2826b79097a85607",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 3
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:38:25.650190Z",
+     "start_time": "2025-09-29T15:38:25.510368Z"
+    }
+   },
    "cell_type": "code",
    "source": "site_percent(s_per, \"%click\")",
    "id": "ddd1fac6295136c6",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2100x900 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 4
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:38:29.427907Z",
+     "start_time": "2025-09-29T15:38:29.079002Z"
+    }
+   },
    "cell_type": "code",
    "source": "year_percent(y_per, \"%click\")",
    "id": "ba7581e97fdbd07c",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2100x1875 with 5 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 5
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:38:36.349765Z",
+     "start_time": "2025-09-29T15:38:35.849216Z"
+    }
+   },
    "cell_type": "code",
    "source": "ym_percent(ym_per, \"%click\")",
    "id": "4de618933c154f86",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2100x1875 with 5 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 6
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:38:41.460845Z",
+     "start_time": "2025-09-29T15:38:41.172135Z"
+    }
+   },
    "cell_type": "code",
    "source": "month_percent(m_per, \"%buzzes\")",
    "id": "7cf84c8744185424",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2100x1875 with 5 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 7
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-29T15:38:46.563634Z",
+     "start_time": "2025-09-29T15:38:46.244983Z"
+    }
+   },
    "cell_type": "code",
    "source": "hour_percent(h_per, \"%click\")",
    "id": "12d83e9082d711c0",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2100x1875 with 5 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 8
   }
  ],
  "metadata": {

From acafaebbf2615e414939419f068f61a1c9043d87 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 14 Oct 2025 09:28:09 +0200
Subject: [PATCH 12/83] modify functions

---
 src/post_processing/utils/fpod_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index c132cc5..a03072c 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -453,9 +453,9 @@ def build_range(df: DataFrame, fr:str="h") -> DataFrame:
 
     """
     df["Début"] = to_datetime(df["Début"], utc=True)
-    df["Début"] = df["Début"].dt.floor("h")
+    df["Début"] = df["Début"].dt.floor(fr)
     df["Fin"] = to_datetime(df["Fin"], utc=True)
-    df["Fin"] = df["Fin"].dt.floor("h")
+    df["Fin"] = df["Fin"].dt.floor(fr)
 
     all_ranges = []
     for _, row in df.iterrows():

From 5be166a85d8f4c99fe211ff8f2795ccda87bc70e Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 14 Oct 2025 09:28:50 +0200
Subject: [PATCH 13/83] add comments

---
 user_case/user_case_CALAIS.ipynb | 464 ++++++++++++-------------------
 1 file changed, 172 insertions(+), 292 deletions(-)

diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index 4bea267..08495af 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-09-29T15:38:07.683846Z",
-     "start_time": "2025-09-29T15:38:05.621538Z"
+     "end_time": "2025-10-13T10:06:55.748967Z",
+     "start_time": "2025-10-13T10:06:46.756740Z"
     }
    },
    "source": [
@@ -36,19 +36,25 @@
    ],
    "id": "e8e8c57c7f4197fe"
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Import your csv files. All files for one site must be stored in the same folder.",
+   "id": "6f9beab2dcba1a9c"
+  },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-29T15:15:34.902195Z",
-     "start_time": "2025-09-29T15:15:34.168693Z"
+     "end_time": "2025-10-13T10:07:00.142758Z",
+     "start_time": "2025-10-13T10:06:59.189951Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "data = csv_folder(r\"U:\\Cetiroise\")\n",
-    "print(data.head())\n",
+    "path = csv_folder(r\"U:\\Cetiroise\")\n",
+    "print(path.head())\n",
     "\n",
-    "df_0 = data.dropna()"
+    "df_0 = path.dropna()"
    ],
    "id": "8636a8a27fe2af47",
    "outputs": [
@@ -77,8 +83,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-29T15:15:37.726484Z",
-     "start_time": "2025-09-29T15:15:37.563404Z"
+     "end_time": "2025-10-13T10:07:03.680051Z",
+     "start_time": "2025-10-13T10:07:03.501607Z"
     }
    },
    "cell_type": "code",
@@ -90,76 +96,65 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-29T15:15:47.105610Z",
-     "start_time": "2025-09-29T15:15:47.101813Z"
+     "end_time": "2025-10-13T10:07:05.017124Z",
+     "start_time": "2025-10-13T10:07:05.012527Z"
     }
    },
    "cell_type": "code",
    "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ]",
    "id": "597efd1d90e3d069",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 4
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## APLOSE format",
+   "source": "### APLOSE format",
    "id": "4f8c83c96f0b6ff4"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "#### *CPOD*",
-   "id": "8ed339c688bdef1"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\")\n",
-    "print(df_aplose.head())"
-   ],
-   "id": "812ed7c0c5e258e7",
-   "outputs": [],
-   "execution_count": null
+   "source": "Chose the right function, depending on the instrument you are working with.",
+   "id": "9849c47189cf1a85"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "#### *FPOD*",
-   "id": "a39bb10d8ac60a27"
+   "source": "#### *CPOD*",
+   "id": "8ed339c688bdef1"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-09-29T15:15:49.464586Z",
-     "start_time": "2025-09-29T15:15:49.294885Z"
+     "end_time": "2025-10-13T10:07:15.647400Z",
+     "start_time": "2025-10-13T10:07:15.456897Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
+    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\")\n",
     "print(df_aplose.head())"
    ],
-   "id": "9b632673397a184",
+   "id": "812ed7c0c5e258e7",
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "     dataset filename  start_time  end_time  start_frequency  end_frequency  \\\n",
-      "0  CETIROISE                    0        60                0              0   \n",
-      "1  CETIROISE                    0        60                0              0   \n",
-      "2  CETIROISE                    0        60                0              0   \n",
-      "3  CETIROISE                    0        60                0              0   \n",
-      "4  CETIROISE                    0        60                0              0   \n",
+      "         dataset filename  start_time  end_time  start_frequency  \\\n",
+      "0  Site D Simone                    0        60                0   \n",
+      "1  Site D Simone                    0        60                0   \n",
+      "2  Site D Simone                    0        60                0   \n",
+      "3  Site D Simone                    0        60                0   \n",
+      "4  Site D Simone                    0        60                0   \n",
       "\n",
-      "  annotation annotator                start_datetime  \\\n",
-      "0   Marsouin      FPOD  2023-02-07T05:34:00.000+0000   \n",
-      "1   Marsouin      FPOD  2023-02-07T05:35:00.000+0000   \n",
-      "2   Marsouin      FPOD  2023-02-07T05:36:00.000+0000   \n",
-      "3   Marsouin      FPOD  2023-02-07T22:40:00.000+0000   \n",
-      "4   Marsouin      FPOD  2023-02-24T15:32:00.000+0000   \n",
+      "   end_frequency annotation annotator                start_datetime  \\\n",
+      "0              0  Commerson      FPOD  2023-02-07T05:34:00.000+0000   \n",
+      "1              0  Commerson      FPOD  2023-02-07T05:35:00.000+0000   \n",
+      "2              0  Commerson      FPOD  2023-02-07T05:36:00.000+0000   \n",
+      "3              0  Commerson      FPOD  2023-02-07T22:40:00.000+0000   \n",
+      "4              0  Commerson      FPOD  2023-02-24T15:32:00.000+0000   \n",
       "\n",
       "                   end_datetime  is_box      deploy.name  \n",
       "0  2023-02-07T05:35:00.000+0000       0  Point C_Phase 4  \n",
@@ -170,61 +165,81 @@
      ]
     }
    ],
-   "execution_count": 6
+   "execution_count": 5
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### *FPOD*",
+   "id": "a39bb10d8ac60a27"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
+    "print(df_aplose.head())"
+   ],
+   "id": "9b632673397a184",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## Remove non usable lines",
+   "source": "### Remove non usable lines",
    "id": "7860838f8514da39"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:15:53.508059Z",
-     "start_time": "2025-09-29T15:15:53.491931Z"
-    }
-   },
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Import the .json file available on metadatax.",
+   "id": "32f8ff8f9ece35a8"
+  },
+  {
+   "metadata": {},
    "cell_type": "code",
    "source": [
-    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_cetiroise.json\")\n",
+    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_cetiroise.json\") #Path to your metadata file.\n",
     "metadatax = json2df(json_path=json)\n",
     "\n",
     "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
     "                           metadatax[\"campaign.name\"].astype(str))\n",
     "\n",
-    "cleared = meta_cut_aplose(df_aplose, metadatax)"
+    "cleared = meta_cut_aplose(df_aplose, metadatax) #Remove lines captures outside the instrument submersion."
    ],
    "id": "ed6a06c522aea169",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:15:55.344572Z",
-     "start_time": "2025-09-29T15:15:54.985631Z"
-    }
-   },
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Export your file to the aplose format. You can change the name of the file to match the project you are working on.",
+   "id": "8f5fe75cc3463971"
+  },
+  {
+   "metadata": {},
    "cell_type": "code",
-   "source": "cleared.to_csv(r\"U:\\APLOSE_CETIROISE_pos.csv\", index=False)",
+   "source": "cleared.to_csv(r\"U:\\APLOSE_CETIROISE_pos.csv\", index=False) #You can stock all DPM for a site in a DataAplose file.",
    "id": "76f70cb6c6658ba6",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## Load data\n",
+   "source": "### Load data",
    "id": "f5d38266dc9d5273"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:15:57.748310Z",
-     "start_time": "2025-09-29T15:15:57.640771Z"
-    }
-   },
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Use the yaml file to import your aplose files one at a time.",
+   "id": "2ce11c6e57f38690"
+  },
+  {
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
@@ -232,42 +247,21 @@
     "print(data_list.df.head())"
    ],
    "id": "6837593897111b0a",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     dataset  filename  start_time  end_time  start_frequency  end_frequency  \\\n",
-      "0  CETIROISE       NaN           0        60                0              0   \n",
-      "1  CETIROISE       NaN           0        60                0              0   \n",
-      "2  CETIROISE       NaN           0        60                0              0   \n",
-      "3  CETIROISE       NaN           0        60                0              0   \n",
-      "4  CETIROISE       NaN           0        60                0              0   \n",
-      "\n",
-      "  annotation annotator            start_datetime              end_datetime  \\\n",
-      "0   Marsouin      FPOD 2023-02-07 05:34:00+00:00 2023-02-07 05:35:00+00:00   \n",
-      "1   Marsouin      FPOD 2023-02-07 05:35:00+00:00 2023-02-07 05:36:00+00:00   \n",
-      "2   Marsouin      FPOD 2023-02-07 05:36:00+00:00 2023-02-07 05:37:00+00:00   \n",
-      "3   Marsouin      FPOD 2023-02-07 22:40:00+00:00 2023-02-07 22:41:00+00:00   \n",
-      "4   Marsouin      FPOD 2023-02-24 15:32:00+00:00 2023-02-24 15:33:00+00:00   \n",
-      "\n",
-      "   is_box      deploy.name  \n",
-      "0       0  Point C_Phase 4  \n",
-      "1       0  Point C_Phase 4  \n",
-      "2       0  Point C_Phase 4  \n",
-      "3       0  Point C_Phase 4  \n",
-      "4       0  Point C_Phase 4  \n"
-     ]
-    }
-   ],
-   "execution_count": 9
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## Format choice\n",
+   "source": "### Format choice\n",
    "id": "9f93eb863e3e3a9e"
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Here you need to choose the format in which you want to visualise the positive detections. This aims to replace the functionality available in CPOD.exe.",
+   "id": "925d92d3eec065e6"
+  },
   {
    "metadata": {},
    "cell_type": "code",
@@ -277,17 +271,12 @@
    "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:16:01.922947Z",
-     "start_time": "2025-09-29T15:16:01.917142Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "dph = dpm_to_dph(data_list.df, extra_columns=[\"deploy.name\"])",
    "id": "6cc79b2aeef076ed",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -300,87 +289,68 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "## Add the feeding buzzes",
+   "source": "### Add the feeding buzzes",
    "id": "8375ddbe07ad0aee"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:16:04.290557Z",
-     "start_time": "2025-09-29T15:16:04.215490Z"
-    }
-   },
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Import your click details files. All files for one site must be stacked in the same folder.",
+   "id": "9753f4ba20c7267e"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "frq = \"D\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
+   "id": "10e00649ec7dac05",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
    "cell_type": "code",
    "source": [
-    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\")\n",
+    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\") #Path to your click details folder.\n",
     "fb_all = fb_folder(fb_files, \"Marsouin\")\n",
     "\n",
-    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(\"h\")\n",
+    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
     "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
     "fb[\"start_datetime\"] = to_datetime(fb[\"start_datetime\"], utc=True)"
    ],
    "id": "2b19f90c99252ff3",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:16:08.387825Z",
-     "start_time": "2025-09-29T15:16:08.377003Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
-    "d_tot = dph.merge(fb, on=\"start_datetime\", how=\"left\")\n",
-    "\n",
-    "d_hour = build_range(d_beg_end, \"h\")"
+    "d_tot = dpd.merge(fb, on=\"start_datetime\", how=\"left\")\n",
+    "#This function aims to reindent 0 between the positive detections. It will be useful to produce first visualization graphs and use this dataset in R.\n",
+    "d_hour = build_range(d_beg_end, frq)"
    ],
    "id": "b00c8f1e2210ea7",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:16:09.946724Z",
-     "start_time": "2025-09-29T15:16:09.933681Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "d_fin = d_hour.merge(d_tot, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")",
    "id": "601787cc806226b0",
    "outputs": [],
-   "execution_count": 13
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:16:12.364503Z",
-     "start_time": "2025-09-29T15:16:12.358919Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "d_fin[[\"DPM\",\"Foraging\"]] = d_fin[[\"DPM\",\"Foraging\"]].fillna(0)\n",
-    "print(d_hour.head())"
+    "print(d_fin.head())"
    ],
    "id": "f93bf1f3ca4f4112",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       deploy.name            start_datetime\n",
-      "0  Point C_Phase 4 2023-02-07 05:00:00+00:00\n",
-      "1  Point C_Phase 4 2023-02-07 06:00:00+00:00\n",
-      "2  Point C_Phase 4 2023-02-07 07:00:00+00:00\n",
-      "3  Point C_Phase 4 2023-02-07 08:00:00+00:00\n",
-      "4  Point C_Phase 4 2023-02-07 09:00:00+00:00\n"
-     ]
-    }
-   ],
-   "execution_count": 14
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -389,12 +359,7 @@
    "id": "c7b1d32ed1c99fb7"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:16:18.370775Z",
-     "start_time": "2025-09-29T15:16:18.346171Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "d_fin[\"Year\"] = d_fin[\"start_datetime\"].dt.year\n",
@@ -408,33 +373,19 @@
     "d_fin[\"DPH\"] = (d_fin[\"DPM\"] >0).astype(int)"
    ],
    "id": "a2261ce5093a3104",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\fouinel\\AppData\\Local\\Temp\\ipykernel_4000\\4171406010.py:4: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
-      "  d_fin['YM'] = to_datetime(d_fin['YM'])\n"
-     ]
-    }
-   ],
-   "execution_count": 15
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:16:45.204062Z",
-     "start_time": "2025-09-29T15:16:34.484075Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "d_fin[\"FBR\"] = d_fin[\"FBR\"].fillna(0)\n",
-    "d_fin.to_csv(r\"U:\\Hours_DPM_FBUZZ_SiteCETIROISE.csv\", index=False)"
+    "d_fin.to_csv(r\"U:\\Hours_DPM_FBUZZ_CETIROISE.csv\", index=False)"
    ],
    "id": "d606f4f6904b57c6",
    "outputs": [],
-   "execution_count": 16
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -442,6 +393,12 @@
    "source": "## Overview",
    "id": "4bc0904182a3f845"
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### *Import datasets*",
+   "id": "e1de414e2eb3fa8f"
+  },
   {
    "metadata": {},
    "cell_type": "code",
@@ -477,15 +434,10 @@
    "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:38:21.849991Z",
-     "start_time": "2025-09-29T15:38:21.342466Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
-    "ceti = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteCETIROISE.csv\")\n",
+    "ceti = read_csv(r\"U:\\Hours_DPM_FBUZZ_CETIROISE.csv\")\n",
     "\n",
     "ceti[\"start_datetime\"] = to_datetime(ceti[\"start_datetime\"])\n",
     "ceti[\"start_datetime\"] = ceti[\"start_datetime\"].apply(lambda x : x.tz_convert(\"CET\")) #TimeZone Central European Time\n",
@@ -493,18 +445,26 @@
    ],
    "id": "5928770d1c47bcad",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:38:23.189207Z",
-     "start_time": "2025-09-29T15:38:23.142072Z"
-    }
-   },
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### *Generate graphs*",
+   "id": "200273fc36fb7d5d"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "data = ceti #Precise which dataset you are working with",
+   "id": "be10e9d690294cff",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
    "cell_type": "code",
    "source": [
-    "data = ceti\n",
     "data = extract_site(data)\n",
     "y_per = percent_calc(data, \"Year\")\n",
     "ym_per = percent_calc(data, \"YM\")\n",
@@ -516,127 +476,47 @@
    ],
    "id": "2826b79097a85607",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:38:25.650190Z",
-     "start_time": "2025-09-29T15:38:25.510368Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "site_percent(s_per, \"%click\")",
    "id": "ddd1fac6295136c6",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2100x900 with 1 Axes>"
-      ],
-      "image/png": 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 4
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:38:29.427907Z",
-     "start_time": "2025-09-29T15:38:29.079002Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "year_percent(y_per, \"%click\")",
    "id": "ba7581e97fdbd07c",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2100x1875 with 5 Axes>"
-      ],
-      "image/png": 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 5
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:38:36.349765Z",
-     "start_time": "2025-09-29T15:38:35.849216Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "ym_percent(ym_per, \"%click\")",
    "id": "4de618933c154f86",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2100x1875 with 5 Axes>"
-      ],
-      "image/png": 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wbty4quknnnhiXvZMM81U+N///lfrM3baaaf8+v7771+YOHFitddiv37//fe19tlZZ51Vbb6xY8cWNtpoo9zOV199tWr6oEGDqtat5v654oor8vQDDjig2vRie+O95Xz22WfV1q/o4YcfzsdfrEepG264IS9vjTXWKIwfP75q+nfffVdYfPHF82trrbVW2TYcfPDBhQkTJlRNj+d77bVXfu2ee+4pTMpvv/1WWGKJJfL8N910U7XXbr311jw92lC63YvnQGzryVHczvvtt1+15RXXv0uXLoXNN98876uiOFfjta222qpB59QCCyxQdQ6OGTOmavpXX31V6Ny5c3788ssvtfZ/bM9yYnnxaMhnN3bfrLjiioX27dvnNtY0atSoQkMU2xSPe++9t9prF1xwQZ4e15NSG264YZ5+6qmnVpv+3HPPFdq0aZOvl3F+hTjH4txZaaWVqq1T0ddff13t93LH7KSOn3LX21KxvJrXoH333bfsOodNN900v/b666/X2k5bb711teOjdL/F9mqI+trbmPOqLrE/Yv64rn7zzTdV0+M8WXXVVfNrdR2jk7Oe9e2zd999t9CuXbvchri+lXrsscfyda3mOdqvX7+8zNNPP73scV16npfbt5M6D2O5xXM9vneK4jwqXgdi203Jd3d9isvq379/tf340Ucf5WtZbK8PP/ywavoTTzyR54/tEtf2cvvrsMMOq7XeMX299dYr/PTTTw1uW+m61jy/TznllKrrbV3X4prtKJ5n8TOO7aL33nuvMNtss+XrV+l50BTfq8XzrUePHoVPP/20anrs+/jujNdefPHFBm8jAGhpRo4cWedrpx59UOEvu23arI9oA81/LNRHCAkAtFjFG11xAysef/nLXwrbbrttvoke0w8//PA8X9wIjt8j0KopbmItuOCC+fXSm1v1hZBxc7DczfiePXsW5pxzzoqEkEsuuWR+34MPPliYXrZzhCvx+1FHHVV2OcVA5P77769187Hmjc/iDeW4wT3vvPNO8iZsBCHRnpVXXrns60OGDMmfc/TRR9e6Wbr66qvXmj/CqrZt2+bgZXJultZnmWWWKSy00ELVpsUN5ljeU089VWv+4nFbGg7ETeMIiCLMLb35XhQ3uCMwipvjk/Lss89W3Rgv53e/+12ttk1JCBlB8+jRo6tNj/MotnO8XnrTvijOzXhMTgj5/vvv11rObrvtll974403miyEbMy+iRAytsu3335baKxim2oGjcXtG+FRvB5/KBCGDx+ef4/rVWkoW7Trrrvm16+//vr8+w8//JB/X2211aqFEHWZWiFkMaDbbrvtqk3/4osv8rVghRVWqDZ9+eWXz8dazRCouJ3i2t2nT59Jrt+k2tuY86ouf/zjH/O8//jHP2q9Vjx+ax6jjVnP+vZZXJvj9fvuu6/s6xFAxvYuntsvv/xynj/a0ZCgtTEh5CKLLJLPpbfffrvW/Ndcc01e3p577jlF3931iWXFOkfoWFPxO6I07IxtFNPefPPNssuLbdW1a9ey17P47pocxXWN62bNdY1/40zqWrz22mtXTYs/jIl5Z5lllmoheNFxxx2Xl3fyySc36fdq8Xwr90dZcW7EaxdffHG92wUAWrLGBk/MeEY28ljQHSsA0OIVxxOKLkGja7QYF2rvvfdOu+66a1UXoSG6NaspuqRbc801c1dl0Y1naTdkdYku7GJA8pqii7ro5mxaFN2dRRdoNdejdED1Kd3OxXWPLu/KjVtVHAvt7bffrtUla3SRV1N0aRhd+MX+KQ4OX5eYd+LEiXWO8RddphQ/u6YYE6ym6PIwunmNLvcmR9yfju5eoovB6K4x3h/tKqrZdWIcc9FNTHTrV1N0l1lTdJUXXedF163lupYN0VVmufWsqb7zojg9utuMNsY+mFKLLbZY7oq1VJxHsZ1jjLjoIrGm6Lo2up1sqOiiM8aALHduhsndn5OjMfsmum6MLkOju87o4ifGTIuuD2PskskV760ptm8cR9F9cezH6HIzfoY4f+M4L7ffo9uhmC+6Xo0uG6P7w+h2M64Z0b1nvDe6eoxx3ppLnDNxTEW7Yr/GOHUhzr8450q7Fo2uOuN8jO5uL7jggrLLi+6UGnLeTM3zqriscvs29mvN76GmWM/idT26yyw3rm90HxzbO47/6Ho1umYN0V1qXNsqLbq4je4349oQXbHWVNzuxeO8qb67498K5bpGje5O47uy9PNj2XGuRXfH5ca5jW5XY4zG6CYsxm8siu7Fll122dQY5dY1xiWd1LW4dOzid999Nx9TcU2KbmTLbeu41pXb1pX8Xq1vmVPj2g4A0NIJIQGAFq84LmFdiuMLxpha5RSnx9hHDVHXWGURaEZoVgnRprhRHGNLVSqEvP7666tNi/GXJieEnNR2jhuoodxN1lI//fRTrWkxJl5Nxf1ROobipD47bpKXu1Fe32fXtz9LA8SGOOKII/LN/9h/cRM+2h7BUyiOfVbz2Iybu+XGoSuOdVluPSPQLYbCDV3Ppj4vGhIQlhPrXt9rEyZMaPBn1Lcvw+Tuz8nRmH0Tx0sERpdddlke9y+OnQjSI3SKsUXK3XSvS7njpfTcKu7vxuz3GDfvrLPOymMwFsfui4AkxteNMe3q+uymFtewv/71r3m8lhh3MMR1LsKOGI+zKAKKuH5F0FPfvqmESp5XxWWV275xTMexU6op1rN4XMfxWJ/icT051+2pvX0r+d3d0POtuA3jOjapfRLbsDSEjHEji+MtT65y19TidbC+623xD3aacls39jpcbplT49oOANDSVf5PCwEAZjDFG25ffvll2de/+OKLavNNC4pVcI8//nhFlhcB2P/ryr/qEdMqqbj9/v3vf9f6rNJHMcQoVe5Ga/GGY0OC2OJnH3744fV+9qBBg1JTiYqgCJJiUPqoIIlqsghuojIzHlGBVFNUmUX1XLmg7auvvqpzPbfeeut61/Pjjz+eIc+LSipWadUVck5u+NrYfRPVhlE9FkHF/fffn6uLn3766RxiR5jUUOWOl9L9W2xfY/Z7BOlxDEe127Bhw/KxHdeo+BlBZHP5wx/+kPdj8Q8soiLrjTfeyJXWpQFdcV1WWGGFevfNpP7QoiEqeV4V5ym3b+O4/frrr8vOX8n1LC4zAqn6lles1pyc63ZjTCvXrYaeb8XnUak7qX0SlcqlGhtAzmjbGgCA5iWEBACYhLghG5588smyN3KfeeaZ/HzFFVes+GcXu0Ob3L/S33PPPXM1z5133pneeuuteucdP358mhasuuqq+Wdxe06p6KI1AoYIZKK7zobMW6nPbsz+/Oijj3I1zYYbblirq7vo4i5eL3dsxnsGDx5c67XosrGm6H4wbvJHaFVasVLp8yIUA9umOC+mBcXuO4cPH17rtejusbSSqSH7f0r3Tbw3wrOrr746dyUa4XQc+w0V3WXWFO0sHkfF/V38GdPLBbCT2u/R/WF0I/vwww/nrm9jOcVquRDn4eRe7xp7nYy2RJeQ0WVvBP/FMDIqJEvNMsssqXfv3mno0KF5u06p+tpbyfOqOE+5fRvbvebnN3Y969tnk3tdL84fx0dDqgsnd9/HtbVXr1455Cx28d0c160I46Mb95qK+714HBS3SVSpxn6Zniy++OK5y+Xo4rfcH2VUals39vwHAGDqEEICAExCdDkaXV7ecsstVeNVFUX3h1GZtP766zdoPMjJVexaLW5YTo4FF1wwVx7FWFGbbbZZevnll8vO99BDD6VNNtkkTQu23HLLfHP40ksvTQ888EDZeWJsrBhjqiFiXLwYJy+qLY466qhaN7Sj67piUBTd1kUwEtvpb3/7W9mbmTEuXkMqBBu7P2OflQsHop377LNP2cAnquDCcccdl/d1UaxXrEe5rucOOeSQvE3+9Kc/pbFjx9aaJ16bVHAdYpyvuMkc7b3jjjuqvRa/R+gQY4eVG5tyRhChYVSiRuVuVLEWxTaNbTu5+78x+yZu4perSiu2Z3LGXHziiSfSfffdV23aJZdcko/7ddZZp6rKav75508bbLBBDlBqjhsYYV50uRoBbVR0hqjGjOrCmuIPA+LYjvUuHes0tlG5YLcprpOhOPbjtddem6/xUQG5+eab15ovur6Nc2yvvfYqG6hESFQcg3FK2lvJ86q4bqeddlq1UHHcuHFpwIABZd/TmPWsb58dfPDB+Q9ioso8KmFris8qDShjXMgYrzPGII5K8JoisI72l3725O77WLc4b44++uhq19qoDC1eN2OephSfe+yxx1b7Xorvl6iGj3OiOFZyiG0X4ntgxIgRZc+lmv82mRbEeR3fqzEO5/HHH1/ttbiuxLrGsREVyVNiSs5/AACanjEhAQAmIapD/vGPf6T+/fvnLuPiZwSOr7zySnrkkUfyGE5XXnllk3x2v379cpAQN/vj5mtxvKgIKybVhdlf/vKXqnGk+vTpk2/sxhhxsT7RFVxUSUUlyOSMG9eU4mbkXXfdlbuRjOA02rv88svn9Y8b3DFWY1QDRhDT0HAlQpQ333wzXXHFFbnCJJYdN0bjZm9U2vznP/9Ja6+9dtW8sT1OOOGEdOONN+ab/DFuV9z0jfE14/MjpFhooYUavY4R5kTVUAQA0a5iNV2EiLFvIzSN8elivaMiMsLERx99NI+fF9PixnzNEDLmjzA5unHdYostchVdVMDGPo/qrmK3oUVxMzgqU2Kb3HvvvbkSLMZfi+Aq1v+5557LocVSSy1V77pEV39RORaB1A477JBD5Ajm4jNjDNGoOLrhhhtqff6MIo7XQw89NIcWUbUUoVucb7G/unfvnh+Tez5P7r6Jz4zzOSqlIsSOYCUCnThWI8yJP45oqN///vd5efGICsU41h588MH8Bxgx5mSpaF+EZRHixDUwriFxjsZ4rrG/Bw4cWFXNGxVnsX2WWWaZtOyyy+bqw9GjR+fAM7ppjMC1tPJ3vfXWy8d0tCcqpGI7r7nmmvlRl3hPjDkYIc22226blxeVoRG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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 6
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:38:41.460845Z",
-     "start_time": "2025-09-29T15:38:41.172135Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "month_percent(m_per, \"%buzzes\")",
    "id": "7cf84c8744185424",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2100x1875 with 5 Axes>"
-      ],
-      "image/png": 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krhDLxzaKasdYTuxPHM+4Xz/88MN6z39TxkoqdhcXHn/88Rr7XzvUbs65eeedd9IBBxyQr7kYAy62N87FgQcemD7//PNGj2358Yjl/+IXv8jHLpa1+uqr19stbdyLV155ZVpzzTXzvRzHPP6+4oor6tyn4Yknnsj34CKLLJKv69i3uMdPO+20GtsR90aIe6V4fMqPce0xy+La2GijjfLfsazy41r72Bfvs0mTJqU555wz72c8byo56KCD8ntqP5uL91Wckzg38Tz9+c9/nt58880mHeumbG9z7qumiM+vLbbYIs0+++x5OfE8efrppxt8T1P3synnLHzxxRfp+OOPT8svv3y+tiK8jM/beDbX5/bbb8/zxDVdvN933333/AWPpn5ONjRmWXw5Jo5LLD+uyaiyjqrwr7/+us68xesurpd45iyzzDKlz5LjjjsuP7ubqvg8i3s3Ktjj/zli/+LeOPLII9OECRMqvu+DDz7Iz7Sllloqr3ueeeZJ2223XY3/Vykq3++4h+OLKXGfNuULQMV9jYr73/3ud/kzMLZvueWWS9dcc01pvrj/4/qM8xnbHl94qXTvh7huN9xww3zeY/54X1Thx3Xe1p+r5V5++eW09dZb5/s/7qtBgwalkSNHNnpMAICOS2UZAMA0KDbEFBtko7E0grKYHg3YEWJE42M0Cke3jU8++WSpsaYx77//fm6Ej8auaJSOBsRoECwGN8VG1GhMi8acWH5594Uhpk+rCJNOPPHE3JB26623pj/+8Y9NCi3a+lhH41w0qEf4Ew1z0TgbjXQRsEQ1XIQI0Y1kbWPGjMmNgdHYuccee+SAJBqFQzTgRYNfNBRGiBSNmxEKRAPZTTfdlBuOi6LxOhrfovF0m222yY3pr7zySq6Ce+CBB3IDc3G5RbHNm2++eV7mlltumbp165buvvvu3OgajfLFirlogIzA7aKLLkqrrLJK3pai4vmN6yHG81p33XVzhWOMMxeBbHRZGQFaNFTGmHPlIvgsHqef/vSnuaox9m2dddbJ66nkT3/6Uw5YogEyGlvjmLz99tvp2muvzet65plnmlTBEw3Ese8R/kSD7yGHHJK7yIuwKYLLaJgshrHFcCoaMOM+aEkl4dFHH52v2bhGNttss3TPPfek3/72t3k74pzFMY/jGqHCI488ki677LLcBWncq00VQW0sN45LNKTGNRf36L///e+8P3HMWiLOcexzXGPxDCmvsiwP7ppzbuLaiIAqGtfj+ojnVFxzESrGfRIN69Gg3hQxdmBcd/F8ieAhrtVo4I77KYLCaLQuF8+vaISP7YtrMu7hu+66K3+JIJ6J0cVr0UMPPZQbqOPeiX2KLmrjWo/uMON4F6+F+B33ThzruA+Kz7qGnnnF+ygayuN8lR/L+oKBuFfi+rz66qtzCB3XU7lowI9zHgFRhCnl+7HTTjuVnlPReB8Bxl//+td0//335+dUjAvZkKZsb3Puq8bEsyCecbHM2PbY5nh/rDfG6KykOfvZlHMW93usLwKsuDfjmEYQEp+t8fdVV12V9t9//xqfC3ENxjGKLy3EtsSzMLYh1h2fDfElk2n5nIx1RiAaIe+uu+6an/XxbIkwJu6zp556quIy4lkbwW886+N6js+FP/zhD+nTTz9N1113XWqOCMb+7//+Lz+3Y/vjcy+q2mP5cQ/FdVr04osv5mde3DdxbcQx+e9//5uP/frrr5/vvXgG1Hb++efnZ2Gcx/j/i0pBYH3iSwLx/Ivldu/ePV9/8WyKv+NzMc5PfE5GoBnPzPicjQAqwsNyJ5xwQg7G4lzG8YvP4rjvYnrscwSmEci21edqUYSsca7iszGeWWPHjk1/+ctf8vbHPRHXFQDQCRUAAGhQ/C9Tpf9teuSRRwpdunTJP++9917hm2++Kcw999yFrl27Fv7v//6vxrxnn312Xsamm25aY/pee+2Vp7/77rulafF3cZ2nnnpqjfkfeuihPH3LLbesMf26667L0+N3a+1fuUmTJhW6deuW53vnnXcKbaV4PGrvRxzb5ZdfPr/2u9/9Lk875ZRT8r8PPfTQwo8//liaN/7eZ5998mt33313xeN6/PHH11n3ww8/nF9bcsklCx988EGd18eNG1f6+9FHH83zrrPOOoUvv/yy4rk44ogjakxffPHFS+fuu+++K03/5JNPCnPMMUf++f777+tsbxyT+s5J+TYVffXVV4X+/fsX5pprrhrrmTBhQmHOOecszDzzzIWXX365xnuOO+640rEpvxbffPPNQvfu3QtLL710nWPyj3/8I1/rO+ywQ6EpzjrrrNL+//DDDzX2v3hsnnrqqRrvGTRoUKPXZn3XUCyzfJvjPM0zzzyFWWaZpTDvvPMWXn/99RrHMq6vODaxPeViWbEd5YrX3uyzz1545ZVXary2++6759duv/32GtNje+KnkuLyHnvssUbX3dJzc/HFF+flXXjhhXWW9e2339a4VhpSvE523XXXwpQpU0rT47kQ11xs05gxY0rTb7nlljz/qquumu/j8nWuvvrq+bWbb765NH2nnXbK02pfo+Gzzz5r9PnZ2PUTxzimxTGvpNKzdOTIkXnazjvvXGf+O+64I7/261//ujTtiy++yPdaXG+jRo2qMf+rr75amHXWWfPxaIrGtrcl91UlU6dOLSy33HJ1npshrpnieS+/Rluyn005Z/GZeuutt9aYHvfvKqusUujZs2fh448/Lk2/6qqr8vLWXHPN/OwrF58FH330UZM/Jyvdh/HZHs+FuNdHjx5dY/6DDjooz7///vvX2YeYvtpqqxU+//zzGtd83K9xb44fP77QFMXjFcc4tqUo7r3ivVL8TAxxDcQ6evToURgxYkSNZX344YeFPn36FBZccMH8zKu93/FsfPHFF5u0XbX3dY011qjxWRjPgHgWxPWxxBJLVHwWx3O4/Jot3meLLrpojeMT82yzzTb5tTPPPLNNP1eL91ul6+TKK6/M0+O8AwCdk24YAQCaKKqI4ieqU6JqLL7lHm3HRxxxRK7+iG+sxze549v88Y34ckcddVSuBIhvbcc3lJsilhkVXeXi29NRKVLeFeH0UOzGKXz22Wdtvr74VnjxeMc3+uNb3FFZEl08RQVMdN8UY5dF92xR6TbTTDOV3ht/xzfko3qlvGKlKCpAKlUqxfJCvDeqWWqLrqOKLr744vw7qrdqVxREBUN8S73Suovvja6liqJKISoF4lv8Te2erXhOyrepKLqu2meffXL1T3mXW3F9xrfqo/qndhVZXGeVKiOiyiq+tR/fxK99TOIb9lH5E5UV33zzTaPbG1VQcU6iK7H45n/5/p900kn576iIai2xzPJtjv2L7Y2qm7imoou38mMZ921U1MR11lQxVmF0EVauWPXS1vdoS89N+bVXFBUzlabXJ+6xqKqJqtOiqJiN4xHbVF7RGec9RDeBUSVSvs5YRn3nvdL2zKjxpKK6JCpR43jGM75csVvBYjexYfjw4flei8rA6Aa03IorrpivkZdeeimPwTitWuu+iqqyeP5E93fxPCoXz9x49tbW2vsZFWdRIRdVj1GpVC7u31hPVApFhU/t53ZUf9Ueay6u02kdEzQqiuO5EMeg9viFZ555Zu6uMq73Sl0ExvUdVazl13w8f+Pzq9g9ZFNFJV78P0FR3Hvnnntu/l28x0JU80X1dFRXRzViuRj78dhjj00ff/xx7laytqgEi26MWyLu7/LPkKiIjyq2uD4qPYujei2q3cq7rC3uR3wexWd7UVzX8bkc+1rftdxan6tF0VVxeUVviM/V2Jbp/f9fAMD0oxtGAIAmKo6XEw2T0dgTgdi+++6bhgwZUur6KFTqrioaWKIRMrqWisbDpnRbF4FLeQhUFF2ZNTaGzPToBrE+sY+Vxv+oPdZSQyLYiZ8QDWARNEYjY3SrNNdcc+UxcqLROsaCOeOMMyouI95XKfiIoKhS93jRZV3sW3lXavWJ4x/dS8W4V/FTWzSuRqgY40CVd20XjbnRTVmlcxoi4GqOUaNG5QbT6J4rutmLhuRy5Q2Rcd2FaMCsLUKMuN5qj9VTvM6iAbvSWDfRnVh0XRhjwMWYVfWJwOY///lPbjCt3eBcfs8Ut7E1RNdrtUVjcai0rcXG3Oi+bVrW0dJz2VzNPTcRnkVXZtFNX3RnFsF7NAhHyNHcblXj+VWpO9no5iyek+XnMZ6L0chde9y3EI358Ywrnz/u8+jCL7pKjQAzuoOL7awUDE9PEYbFFyWiK9PoPjJ88skn+VhGwLDyyivXOTcR/lR67sU5CfF8qh0yNUdr3lfFz6/aAUuIcxTPjQhhyrX2fhaXFwFHpeUVv6hRfK5H94yvvfZa/gJES0OexjT0uR6fRbHeeP7GZ1LtLyG05vOh0nmJQCqWF5+5EUrF/5cUj2F0Z1npGEY3rcVjWLsrxuj2eUY8b4shYEPHOsLqeAZEt7FxfZQHo639uVrf/sRnflxrbf1sBwBmHGEZAEATlQ8UX0lxfI/6vslenB6NWk1R3zgqEbzFN9OnpwhhihUVMR5MQ6LhrhgstjQsi/Fcan+ru1yEUMWGv0rrKvr222/rTCv/xnq5OC/R+NmUCptY/48//tjguovrLw/LGjqnIcKNpopwLxoVYzuKlUQxLk4EEzGmSoSN5dUOxeszGvsqqTS9eJwjkGtIpePclvdGU9SuMik/zg29FpVRTVXpfLbkXLZEc89NNEhHRUTchzHOVARSxQblGN8tqsKaqr5rqHhvlY91FH9HdU3tcYaKxyqqxSLYK4rxlWJ8qqgkiUqTqBgqNrjHWEYxPt+MsOeee+YKmagkK4ZlUT0a9195VVn5uYnK02m5bxrTmvdVY8+HSs/N1t7P4vKiAjt+Gltecb8qVQK3lmk5xq35fGjovEQwFtsZ6ysew0pf4mjpZ+P0et425VhHZX4c6/JltubnalOW2dbPdgBgxhGWAQC0kmLjTXRxVElU/pTP15E8+eSTuVE4GuyiyqshUUH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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 7
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-09-29T15:38:46.563634Z",
-     "start_time": "2025-09-29T15:38:46.244983Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "hour_percent(h_per, \"%click\")",
    "id": "12d83e9082d711c0",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2100x1875 with 5 Axes>"
-      ],
-      "image/png": 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 8
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {

From 9b6fa593275138675d38247b7a4a763a3f851fa2 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 14 Oct 2025 09:55:43 +0200
Subject: [PATCH 14/83] create test sheet

---
 tests/test_fpod_utils.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 tests/test_fpod_utils.py

diff --git a/tests/test_fpod_utils.py b/tests/test_fpod_utils.py
new file mode 100644
index 0000000..e69de29

From a816a062fcd783e82323dfe0b6f56c236eaae3b0 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 21 Oct 2025 14:38:50 +0200
Subject: [PATCH 15/83] create user case notebooks

---
 .../resource/example_FPOD-CPOD_aplose.ipynb   | 156 ++++++++++++
 .../resource/example_FPOD-CPOD_raw.ipynb      | 226 ++++++++++++++++++
 2 files changed, 382 insertions(+)
 create mode 100644 user_case/resource/example_FPOD-CPOD_aplose.ipynb
 create mode 100644 user_case/resource/example_FPOD-CPOD_raw.ipynb

diff --git a/user_case/resource/example_FPOD-CPOD_aplose.ipynb b/user_case/resource/example_FPOD-CPOD_aplose.ipynb
new file mode 100644
index 0000000..31a3ed8
--- /dev/null
+++ b/user_case/resource/example_FPOD-CPOD_aplose.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "from pandas import (\n",
+    "    read_csv,\n",
+    "    to_datetime,\n",
+    ")\n",
+    "\n",
+    "from post_processing.dataclass.data_aplose import DataAplose\n",
+    "from post_processing.utils.fpod_utils import resample_dpm, feeding_buzz, txt_folder, build_range"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### Load data\n",
+   "id": "a97e19830123b732"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:36:10.627427Z",
+     "start_time": "2025-10-21T12:36:10.586897Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "yaml_file = Path(r\"user_case\\resource\\CPOD-FPOD_yaml.yml\")\n",
+    "data_list = DataAplose.from_yaml(file=yaml_file)\n",
+    "\n",
+    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\")  #Path to your click details folder.\n",
+    "d_beg_end = read_csv(r\"U:\\Deb_Fin_CETIROISE.csv\")"
+   ],
+   "id": "7da2feb5958db1a9",
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\fouinel\\\\PycharmProjects\\\\OSmOSE_post_processing\\\\user_case\\\\resource\\\\user_case\\\\resource\\\\CPOD-FPOD_yaml.yml'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
+      "\u001B[31mFileNotFoundError\u001B[39m                         Traceback (most recent call last)",
+      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[11]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m yaml_file = Path(\u001B[33m\"\u001B[39m\u001B[33muser_case/resource/CPOD-FPOD_yaml.yml\u001B[39m\u001B[33m\"\u001B[39m).resolve()\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m data_list = \u001B[43mDataAplose\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfrom_yaml\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m=\u001B[49m\u001B[43myaml_file\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m      4\u001B[39m fb_files = Path(\u001B[33mr\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mU:\u001B[39m\u001B[33m\\\u001B[39m\u001B[33mfb_fpod_cetiroise_c\u001B[39m\u001B[33m\"\u001B[39m)  \u001B[38;5;66;03m#Path to your click details folder.\u001B[39;00m\n\u001B[32m      5\u001B[39m d_beg_end = read_csv(\u001B[33mr\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mU:\u001B[39m\u001B[33m\\\u001B[39m\u001B[33mDeb_Fin_CETIROISE.csv\u001B[39m\u001B[33m\"\u001B[39m)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\src\\post_processing\\dataclass\\data_aplose.py:410\u001B[39m, in \u001B[36mDataAplose.from_yaml\u001B[39m\u001B[34m(cls, file, concat)\u001B[39m\n\u001B[32m    388\u001B[39m \u001B[38;5;129m@classmethod\u001B[39m\n\u001B[32m    389\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mfrom_yaml\u001B[39m(\n\u001B[32m    390\u001B[39m     \u001B[38;5;28mcls\u001B[39m,\n\u001B[32m    391\u001B[39m     file: Path,\n\u001B[32m    392\u001B[39m     concat: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[32m    393\u001B[39m ) -> DataAplose | \u001B[38;5;28mlist\u001B[39m[DataAplose]:\n\u001B[32m    394\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Return a DataAplose object from a yaml file.\u001B[39;00m\n\u001B[32m    395\u001B[39m \n\u001B[32m    396\u001B[39m \u001B[33;03m    Parameters\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    408\u001B[39m \n\u001B[32m    409\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m410\u001B[39m     filters = \u001B[43mDetectionFilter\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfrom_yaml\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m=\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    411\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mcls\u001B[39m.from_filters(filters, concat)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\src\\post_processing\\dataclass\\detection_filter.py:64\u001B[39m, in \u001B[36mDetectionFilter.from_yaml\u001B[39m\u001B[34m(cls, file)\u001B[39m\n\u001B[32m     46\u001B[39m \u001B[38;5;129m@classmethod\u001B[39m\n\u001B[32m     47\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mfrom_yaml\u001B[39m(\n\u001B[32m     48\u001B[39m     \u001B[38;5;28mcls\u001B[39m,\n\u001B[32m     49\u001B[39m     file: Path,\n\u001B[32m     50\u001B[39m ) -> DetectionFilter | \u001B[38;5;28mlist\u001B[39m[DetectionFilter]:\n\u001B[32m     51\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Return a DetectionFilter object from a yaml file.\u001B[39;00m\n\u001B[32m     52\u001B[39m \n\u001B[32m     53\u001B[39m \u001B[33;03m    Parameters\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m     62\u001B[39m \n\u001B[32m     63\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m64\u001B[39m     \u001B[38;5;28;01mwith\u001B[39;00m \u001B[43mfile\u001B[49m\u001B[43m.\u001B[49m\u001B[43mopen\u001B[49m\u001B[43m(\u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mutf-8\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mas\u001B[39;00m yaml_file:\n\u001B[32m     65\u001B[39m         parameters = yaml.safe_load(yaml_file)\n\u001B[32m     66\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mcls\u001B[39m.from_dict(parameters)\n",
+      "\u001B[36mFile \u001B[39m\u001B[32m~\\AppData\\Roaming\\uv\\python\\cpython-3.12.11-windows-x86_64-none\\Lib\\pathlib.py:1013\u001B[39m, in \u001B[36mPath.open\u001B[39m\u001B[34m(self, mode, buffering, encoding, errors, newline)\u001B[39m\n\u001B[32m   1011\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mode:\n\u001B[32m   1012\u001B[39m     encoding = io.text_encoding(encoding)\n\u001B[32m-> \u001B[39m\u001B[32m1013\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mio\u001B[49m\u001B[43m.\u001B[49m\u001B[43mopen\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbuffering\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnewline\u001B[49m\u001B[43m)\u001B[49m\n",
+      "\u001B[31mFileNotFoundError\u001B[39m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\fouinel\\\\PycharmProjects\\\\OSmOSE_post_processing\\\\user_case\\\\resource\\\\user_case\\\\resource\\\\CPOD-FPOD_yaml.yml'"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Data metric\n",
+    "Here you need to choose the format in which you want to visualise the positive detections. This aims to replace the functionalities available in Chelonia's softwares."
+   ],
+   "id": "3bc57f4f638ad6dc"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "frq = \"D\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
+   "id": "9b0a078a262ac7f2",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"])",
+   "id": "fa3847d80ccf49c3",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Feeding buzzes processing\n",
+    "Use \"Marsouin\" or \"Commerson\" to get different ICI processing."
+   ],
+   "id": "b92537991aa4ac4b"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\") #Path to your click details folder.\n",
+    "fb_all = txt_folder(fb_files)\n",
+    "fb_all = feeding_buzz(fb_all, \"Marsouin\")\n",
+    "\n",
+    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
+    "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
+    "fb[\"start_datetime\"] = to_datetime(fb[\"start_datetime\"], utc=True)\n",
+    "\n",
+    "dpm_fb = resamp.merge(fb, on=\"start_datetime\", how=\"left\")"
+   ],
+   "id": "ca2362e4facecca3",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "d_0 = build_range(d_beg_end, frq)\n",
+    "d_tot = d_0.merge(dpm_fb, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")"
+   ],
+   "id": "4d76089ef06c6fdb",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "d_tot[[\"DPM\",\"Foraging\"]] = d_tot[[\"DPM\",\"Foraging\"]].fillna(0)",
+   "id": "912268e5e997dbc6",
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/user_case/resource/example_FPOD-CPOD_raw.ipynb b/user_case/resource/example_FPOD-CPOD_raw.ipynb
new file mode 100644
index 0000000..1cafe1a
--- /dev/null
+++ b/user_case/resource/example_FPOD-CPOD_raw.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-10-21T10:34:14.234399Z",
+     "start_time": "2025-10-21T10:34:06.776401Z"
+    }
+   },
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import pytz\n",
+    "\n",
+    "from post_processing.utils.fpod_utils import csv_folder, cpod2aplose, fpod2aplose, actual_data, meta_cut_aplose\n",
+    "from post_processing.utils.core_utils import json2df"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Load data\n",
+    "Import your raw FPOD or CPOD data. All files for one site must be stored in the same folder.\n",
+    "You also need to import your metadata file."
+   ],
+   "id": "c464f241817a1407"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T10:34:44.094566Z",
+     "start_time": "2025-10-21T10:34:43.418858Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "pod_files = Path(r\"U:\\Cetiroise\")\n",
+    "path = csv_folder(pod_files) #Path to your data folder.\n",
+    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_cetiroise.json\") #Path to your metadata file.\n",
+    "\n",
+    "print(path.head())\n",
+    "df_0 = path.dropna()\n",
+    "\n",
+    "metadatax = json2df(json_path=json)\n",
+    "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +  metadatax[\"campaign.name\"].astype(str))"
+   ],
+   "id": "6cf23db3b4288c29",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                File  podN          ChunkEnd  \\\n",
+      "0  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:34   \n",
+      "1  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:35   \n",
+      "2  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:36   \n",
+      "3  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 22:40   \n",
+      "4  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  24/02/2023 15:32   \n",
+      "\n",
+      "     Minute  DPM  MinsOn      deploy.name  \n",
+      "0  64748494    1       1  Point C_Phase 4  \n",
+      "1  64748495    1       1  Point C_Phase 4  \n",
+      "2  64748496    1       1  Point C_Phase 4  \n",
+      "3  64749520    1       1  Point C_Phase 4  \n",
+      "4  64773572    1       1  Point C_Phase 4  \n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T10:34:53.696221Z",
+     "start_time": "2025-10-21T10:34:53.481890Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "d_beg_end = actual_data(df_0, col_timestamp=\"ChunkEnd\")\n",
+    "d_beg_end.to_csv(r\"U:\\Deb_Fin_CETIROISE.csv\", index=False)"
+   ],
+   "id": "fa52f8971b61aaf6",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T10:34:57.917184Z",
+     "start_time": "2025-10-21T10:34:57.913957Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ]",
+   "id": "769e128f2a5293e1",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### APLOSE format\n",
+    "#### *C-POD*\n",
+    "Use cpod2aplose if you are managing C-POD data."
+   ],
+   "id": "dd03975b7aef7eed"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\")\n",
+    "print(df_aplose.head())"
+   ],
+   "id": "4cc867627d677529"
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "#### *F-POD*\n",
+    "Use fpod2aplose if you are managing F-POD data."
+   ],
+   "id": "b805737ac321da69"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T10:35:03.300926Z",
+     "start_time": "2025-10-21T10:35:03.122227Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
+    "print(df_aplose.head())"
+   ],
+   "id": "b8d1c500f6daea0d",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     dataset filename  start_time  end_time  start_frequency  end_frequency  \\\n",
+      "0  CETIROISE                    0        60                0              0   \n",
+      "1  CETIROISE                    0        60                0              0   \n",
+      "2  CETIROISE                    0        60                0              0   \n",
+      "3  CETIROISE                    0        60                0              0   \n",
+      "4  CETIROISE                    0        60                0              0   \n",
+      "\n",
+      "  annotation annotator                start_datetime  \\\n",
+      "0   Marsouin      FPOD  2023-02-07T05:34:00.000+0000   \n",
+      "1   Marsouin      FPOD  2023-02-07T05:35:00.000+0000   \n",
+      "2   Marsouin      FPOD  2023-02-07T05:36:00.000+0000   \n",
+      "3   Marsouin      FPOD  2023-02-07T22:40:00.000+0000   \n",
+      "4   Marsouin      FPOD  2023-02-24T15:32:00.000+0000   \n",
+      "\n",
+      "                   end_datetime  is_box      deploy.name  \n",
+      "0  2023-02-07T05:35:00.000+0000       0  Point C_Phase 4  \n",
+      "1  2023-02-07T05:36:00.000+0000       0  Point C_Phase 4  \n",
+      "2  2023-02-07T05:37:00.000+0000       0  Point C_Phase 4  \n",
+      "3  2023-02-07T22:41:00.000+0000       0  Point C_Phase 4  \n",
+      "4  2023-02-24T15:33:00.000+0000       0  Point C_Phase 4  \n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Clean your dataset\n",
+    "Remove useless lines, recorder outside the instrument submersion. Export your file to the aplose format. You can change the name of the file to match the project you are working on."
+   ],
+   "id": "d2c642658dbfe278"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T10:35:12.623559Z",
+     "start_time": "2025-10-21T10:35:12.255285Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "cleared = meta_cut_aplose(df_aplose, metadatax)\n",
+    "cleared.to_csv(r\"U:\\APLOSE_CETIROISE_pos.csv\", index=False)"
+   ],
+   "id": "895bd5a116918285",
+   "outputs": [],
+   "execution_count": 6
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 48ac54fafa058d588e050679d0a99aab9a4013f9 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 21 Oct 2025 14:39:20 +0200
Subject: [PATCH 16/83] add new tests

---
 tests/test_fpod_utils.py | 281 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 281 insertions(+)

diff --git a/tests/test_fpod_utils.py b/tests/test_fpod_utils.py
index e69de29..1e4504f 100644
--- a/tests/test_fpod_utils.py
+++ b/tests/test_fpod_utils.py
@@ -0,0 +1,281 @@
+"""FPOD/ CPOD processing functions tests."""
+import io
+from datetime import datetime
+from pathlib import Path
+
+import datatest as dt
+import pytest
+from osekit.utils.timestamp_utils import strptime_from_text
+from pandas import DataFrame, Timestamp, read_csv
+from pandas.testing import assert_frame_equal
+
+from post_processing.utils.fpod_utils import (
+    csv_folder,
+    deploy_period,
+    extract_site,
+    parse_timestamps,
+    txt_folder,
+    fpod2aplose,
+    cpod2aplose,
+    meta_cut_aplose,
+    build_range,
+    feeding_buzz,
+    assign_daytime,
+    is_dpm_col,
+    pf_datetime,
+    build_aggregation_dict,
+    resample_dpm)
+
+SAMPLE_POD = """File,ChunkEnd,DPM,Nall,MinsOn
+sample_dataset,2023/11/29 08:05,0,0,0
+
+"""
+SAMPLE_AP = """dataset,filename,start_time,end_time,start_frequency,end_frequency,
+annotation,annotator,start_datetime,end_datetime,is_box
+sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T08:30:00.000+00:00,2023-11-29T08:31:00.000+00:00,0
+sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T08:31:00.000+00:00,2023-11-29T08:32:00.000+00:00,0
+sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T09:30:00.000+00:00,2023-11-29T09:31:00.000+00:00,0
+sample_dataset,,0,60,0,0,ann1,POD,2023-11-30T08:30:00.000+00:00,2023-11-30T08:31:00.000+00:00,0
+sample_dataset,,0,60,0,0,ann1,POD,2023-12-29T08:30:00.000+00:00,2023-12-29T08:31:00.000+00:00,0
+sample_dataset,,0,60,0,0,ann1,POD,2024-11-29T08:30:00.000+00:00,2024-11-29T08:31:00.000+00:00,0
+"""
+
+@pytest.fixture
+def pod_dataframe() -> DataFrame:
+    data = DataFrame(
+        {
+            "File": [
+                "sample_dataset",
+                "sample_dataset",
+                "sample_dataset",
+                "sample_dataset",
+                "sample_dataset",
+                "sample_dataset",
+            ],
+            "ChunkEnd": [
+                Timestamp("2023/11/29 08:30"),
+                Timestamp("2023/11/29 08:31"),
+                Timestamp("2023/11/29 08:32"),
+                Timestamp("2023/11/29 08:33"),
+                Timestamp("2023/11/29 08:34"),
+                Timestamp("2023/11/29 08:35"),
+            ],
+            "deploy.name": [
+                "site_deploy",
+                "site_deploy",
+                "site_deploy",
+                "site_deploy",
+                "site_deploy",
+                "site_deploy",
+            ],
+            "DPM": [1, 1, 0, 0, 0, 0],
+            "Nall": [44, 66, 0, 22, 0, 0],
+            "MinsOn": [1, 1, 1, 1, 1, 0],
+        },
+    )
+
+    return data.reset_index(drop=True)
+
+
+@pytest.fixture
+def aplose_dataframe() -> DataFrame:
+    data = DataFrame(
+        {
+            "dataset": ["dataset_test", "dataset_test", "dataset_test", "dataset_test",
+                        "dataset_test", "dataset_test"],
+            "filename": ["", "", "", ""],
+            "start_time": [0, 0, 0, 0, 0, 0],
+            "end_time": [60, 60, 60, 60, 60, 60],
+            "start_frequency": [0, 0, 0, 0, 0, 0],
+            "end_frequency": [0, 0, 0, 0, 0, 0],
+            "annotation": ["ann1", "ann1", "ann1", "ann1", "ann1", "ann1"],
+            "annotator": ["POD", "POD", "POD", "POD", "POD", "POD"],
+            "start_datetime": [
+                Timestamp("2023-11-29T08:30:00.000+00:00"),
+                Timestamp("2023-11-29T08:31:00.000+00:00"),
+                Timestamp("2023-11-29T09:31:00.000+00:00"),
+                Timestamp("2023-11-30T09:31:00.000+00:00"),
+                Timestamp("2023-12-30T09:31:00.000+00:00"),
+                Timestamp("2024-12-30T09:31:00.000+00:00"),
+            ],
+            "end_datetime": [
+                Timestamp("2023-11-29T08:31:00.000+00:00"),
+                Timestamp("2023-11-29T08:32:00.000+00:00"),
+                Timestamp("2023-11-29T09:32:00.000+00:00"),
+                Timestamp("2023-11-30T09:32:00.000+00:00"),
+                Timestamp("2023-12-30T09:32:00.000+00:00"),
+                Timestamp("2024-12-30T09:32:00.000+00:00"),
+            ],
+            "is_box": [0, 0, 0, 0, 0, 0],
+            "deploy.name": ["site_deploy", "site_deploy", "site_deploy",
+                            "site_deploy", "site_deploy", "site_deploy"],
+        },
+    )
+
+    return data.reset_index(drop=True)
+
+@pytest.fixture(scope="module")
+@dt.working_directory(__file__)
+def df_raw() -> DataFrame:
+    return read_csv("pod_raw.csv")
+
+@pytest.fixture(scope="module")
+@dt.working_directory(__file__)
+def df_ap() -> DataFrame:
+    return read_csv("pod_aplose.csv")
+
+@pytest.mark.mandatory
+def test_columns(df_raw: DataFrame) -> None:
+    dt.validate(
+        df_raw.columns,
+        {"File", "ChunkEnd", "DPM", "Nall", "MinsOn"},
+    )
+
+@pytest.mark.mandatory
+def test_columns(df_ap: DataFrame) -> None:
+    dt.validate(
+        df_ap.columns,
+        {"dataset","filename","start_time","end_time","start_frequency","end_frequency",
+         "annotation","annotator","start_datetime","end_datetime","is_box"},
+    )
+
+def test_chunk_end(df_raw: DataFrame) -> None:
+    dt.validate(df_raw["ChunkEnd"],
+                strptime_from_text(df_raw["ChunkEnd"], "%Y/%m/%d %H:%M"))
+
+def test_start_datetime(df_ap: DataFrame) -> None:
+    dt.validate(df_ap["start_datetime"], strptime_from_text(df_ap["start_datetime"],
+                                            "%Y-%m-%dT%H:%M:%S"))
+
+@pytest.fixture
+def sample_pod() -> DataFrame:
+    df = read_csv(io.StringIO(SAMPLE_POD), parse_dates=["ChunkEnd"])
+    return df.sort_values(["ChunkEnd"]).reset_index(drop=True)
+
+# fpod2aplose
+
+
+# cpod2aplose
+
+
+# meta_cut_aplose
+
+
+# build_range
+
+
+# feeding_buzz
+
+
+# assign_daytime
+
+
+# fb_folder
+def test_fb_folder_non_existent() -> None:
+    with pytest.raises(FileNotFoundError):
+        txt_folder(Path("/non/existent/folder"))
+
+def test_fb_folder_no_files(tmp_path) -> None:
+    with pytest.raises(ValueError, match="No .txt files found"):
+        txt_folder(tmp_path)
+
+    # extract_site
+def test_extract_site(self) -> None:
+    input_data = [
+        {"deploy.name":"Walde_Phase46"},
+        {"deploy.name":"Site A Ile Haute_Phase8"},
+        {"deploy.name":"Site B Ile Heugh_Phase9"},
+        {"deploy.name":"Point E_Phase 4"},
+    ]
+    expected_site = [
+        "Walde",
+        "Site A Ile Haute",
+        "Site B Ile Heugh",
+        "Point E",
+    ]
+    expected_campaign = [
+        "Phase46",
+        "Phase8",
+        "Phase9",
+        "Phase 4",
+    ]
+
+    for variant, (input_row, site, campaign) in enumerate(
+        zip(input_data, expected_site, expected_campaign, strict=False), start=1):
+        with self.subTest(
+            f"variation #{variant}",
+            deploy_name=input_row["deploy.name"],
+            expected_site=site,
+            expected_campaign=campaign,
+        ):
+            df = DataFrame([input_row])
+            result = extract_site(df)
+            actual_site = result["site.name"].iloc[0]
+            actual_campaign = result["campaign.name"].iloc[0]
+
+            error_message_site = (
+                f'Called extract_site() with deploy.name="{input_row["deploy.name"]}". '
+                f'The function returned site.name="{actual_site}", but the test '
+                f'expected "{expected_site}".'
+            )
+
+            error_message_campaign = (
+                f'Called extract_site() with deploy.name="{input_row["deploy.name"]}". '
+                f'The function returned campaign.name="{actual_campaign}", but the test'
+                f'expected "{expected_campaign}".'
+            )
+
+            assert actual_site == expected_site, error_message_site
+            assert actual_campaign == expected_campaign, error_message_campaign
+
+            assert "deploy.name" in result.columns
+            assert "value" in result.columns
+
+# csv_folder
+def test_csv_folder_non_existent() -> None:
+    with pytest.raises(FileNotFoundError):
+        csv_folder(Path("/non/existent/folder"))
+
+def test_csv_folder_no_files(tmp_path) -> None:
+    with pytest.raises(ValueError, match="No .csv files found"):
+        csv_folder(tmp_path)
+
+# is_dpm_col
+
+
+# pf_datetime
+
+
+# build_aggregation_dict
+
+
+# resample_dpm
+
+
+# actual_data
+def test_parse_timestamps() -> None:
+    df = DataFrame({"date": ["2024-01-01T10:00:00", "01/01/2024 10:00"]})
+    result = parse_timestamps(df, "date")
+
+
+def test_get_deployment_periods() -> None:
+    df = DataFrame(
+        {
+            "deploy.name": ["A", "A", "B"],
+            "start_datetime": [
+                datetime(2024, 1, 1, 10, 0, tzinfo=datetime.timezone.utc),
+                datetime(2024, 1, 2, 15, 30, tzinfo=datetime.timezone.utc),
+                datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
+            ],
+        })
+
+    expected = DataFrame(
+        {
+            "deploy.name": ["A", "B"],
+            "Début": [datetime(2024, 1, 1, 10, 0, tzinfo=datetime.timezone.utc),
+                      datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc)],
+            "Fin": [datetime(2024, 1, 2, 15, 30, tzinfo=datetime.timezone.utc),
+                    datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc)],
+        })
+    result = deploy_period(df)
+    assert_frame_equal(result, expected)
\ No newline at end of file

From 804c8489b96165b2cbbaac3ce2cff1e757fd84e4 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 21 Oct 2025 14:39:32 +0200
Subject: [PATCH 17/83] correct functions

---
 src/post_processing/utils/fpod_utils.py | 394 +++++++++++++++---------
 1 file changed, 248 insertions(+), 146 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index a03072c..f31c49d 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -34,8 +34,8 @@
     "Site B Heugh": "#5DB996",
     "Site C Chat": "#B0DB9C",
     "Site D Simone": "#E3F0AF",
-    "CA4": "#FF0066",
-    "Walde": "#934790",
+    "CA4": "#80D8C3",
+    "Walde": "#4DA8DA",
     "Point C": "#932F67",
     "Point D": "#D92C54",
     "Point E": "#DDDEAB",
@@ -607,32 +607,76 @@ def assign_daytime(
     return df
 
 
-def fb_folder(folder_path: Path, species: str) -> DataFrame:
-    """Process a folder containing all CPOD/FPOD feeding buzz detection files.
+def csv_folder(
+    folder_path: Path,
+    sep: str = ";",
+    encoding: str = "latin-1",
+) -> DataFrame:
+    """Process all CSV files from a folder.
+
+    Parameters
+    ----------
+    folder_path: Path
+        Folder's place.
+    sep: str, default=";"
+        Column separator.
+    encoding: str, default="latin-1"
+        File encoding.
+
+    Returns
+    -------
+    DataFrame
+        Concatenated data with optional filename column.
+
+    Raises
+    ------
+    ValueError
+        If no CSV files found.
+
+    """
+    all_files = list(folder_path.rglob("*.csv"))
+
+    if not all_files:
+        msg = f"No .csv files found in {folder_path}"
+        raise ValueError(msg)
+
+    all_data = []
+    for file in all_files:
+        df = read_csv(file, sep=sep, encoding=encoding)
+        df["deploy.name"] = file.stem
+        all_data.append(df)
+
+    return concat(all_data, ignore_index=True)
 
-    Apply the feeding buzz function to these files.
+
+def txt_folder(folder_path: Path,
+               sep: str = "\t") -> DataFrame:
+    r"""Process all TXT files from a folder.
 
     Parameters
     ----------
     folder_path: Path
-        Path to the folder.
-    species: str
-        Select the species to use between porpoise and Commerson's dolphin
+        Folder's place.
+    sep: str, default="\t"
+        Column separator.
 
     Returns
     -------
     DataFrame
-       Compiled feeding buzz detection positive minutes.
+       Concatenated data from all TXT files.
 
     """
     all_files = list(Path(folder_path).rglob("*.txt"))
-    all_data = []
 
+    if not all_files:
+        msg = f"No .txt files found in {folder_path}"
+        raise ValueError(msg)
+
+    all_data = []
     for file in all_files:
         file_path = folder_path / file
-        df = read_csv(file_path, sep="\t")
-        processed_df = feeding_buzz(df, species)
-        all_data.append(processed_df)
+        df = read_csv(file_path, sep=sep)
+        all_data.append(df)
 
     return concat(all_data, ignore_index=True)
 
@@ -719,7 +763,7 @@ def site_percent(df: DataFrame, metric: str) -> None:
     )
     ax.set_title(f"{metric} per site")
     ax.set_ylabel(f"{metric}")
-    if metric == "%buzzes":
+    if metric in ("%buzzes", "FBR"):
         for _, bar in enumerate(ax.patches):
             bar.set_hatch("/")
     plt.show()
@@ -757,7 +801,7 @@ def year_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Year")
-        if metric == "%buzzes":
+        if metric in ("%buzzes", "FBR"):
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     fig.suptitle(f"{metric} per year", fontsize=16)
@@ -798,7 +842,7 @@ def ym_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Months")
-        if metric == "%buzzes":
+        if metric in ("%buzzes", "FBR"):
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     legend_elements = [
@@ -864,7 +908,7 @@ def month_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Months")
-        if metric == "%buzzes":
+        if metric in ("%buzzes", "FBR"):
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     fig.suptitle(f"{metric} per month", fontsize=16)
@@ -905,137 +949,84 @@ def hour_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Hour")
-        if metric == "%buzzes":
+        if metric in ("%buzzes", "FBR"):
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     fig.suptitle(f"{metric} per hour", fontsize=16)
     plt.show()
 
 
-def csv_folder(folder_path: str | Path, **kwargs: str) -> DataFrame:
-    """Process a folder containing data files and concatenate them.
+def is_dpm_col(df: DataFrame) -> DataFrame:
+    """Ensure DPM column exists with default value of 1.
 
     Parameters
     ----------
-    folder_path: Union[str, Path]
-        Path to the folder containing files.
-    **kwargs: dict
-        Additional parameters for pd.read_csv (sep, skiprows, etc.)
+    df: DataFrame
+        Input dataframe.
 
     Returns
     -------
     DataFrame
-        Concatenated dataframe with all files data and file column.
-
-    Raises
-    ------
-    ValueError
-        If file_format is not supported or no files found.
-    FileNotFoundError
-        If folder_path doesn't exist.
+        Copy of df with DPM column.
 
     """
-    folder_path = Path(folder_path)
-
-    # Folder validation
-    if not folder_path.exists():
-        raise FileNotFoundError
-
-    if not folder_path.is_dir():
-        message = f"{folder_path} is not a directory."
-        raise ValueError(message)
-
-    # Configuration
-    default_params = {"sep": ";", "encoding": "latin-1"}
-
-    # Parameters fusion
-    read_params = {**default_params, **kwargs}
-
-    # File research
-    files = list(folder_path.rglob("*csv"))
-
-    if not files:
-        msg = f"No CSV file found in {folder_path}"
-        raise ValueError(msg)
-
-    all_data = []
-
-    for file in files:
-        df = read_csv(file, **read_params)
-        df["deploy.name"] = file.stem
-        all_data.append(df)
-
-    if not all_data:
-        msg = f"No valid CSV file found in {folder_path}"
-        raise ValueError(msg)
-
-    return concat(all_data, ignore_index=True)
+    df = df.copy()
+    if "DPM" not in df.columns:
+        df["DPM"] = 1
+    return df
 
 
-def dpm_to_dp10m(
+def pf_datetime(
     df: DataFrame,
-    extra_columns: list | None = None,
+    col_datetime: str,
+    frequency: str,
 ) -> DataFrame:
-    """From CPOD result with a line per minute (DPM) to one line per 10 minutes (DP10M).
+    """Parse datetime column and floor to specified frequency.
 
     Parameters
     ----------
     df: DataFrame
-        CPOD result DataFrame, DPM.
-    extra_columns: list
-        Additional columns added from df to data.
+        Input dataframe.
+    col_datetime: str
+        Name of datetime column.
+    frequency: str
+        Pandas frequency string (e.g., "D", "h", "10min").
 
     Returns
     -------
     DataFrame
-        DPM10M Dataframe.
+        Copy of df with parsed and floored datetime.
 
     """
     df = df.copy()
-    df["DPM"] = 1
-    df["start_datetime"] = to_datetime(df["start_datetime"], dayfirst=True)
-
-    df["start_datetime"] = df["start_datetime"].dt.floor("10min")
-
-    agg_dict = {"DPM": "sum"}
-
-    if extra_columns:
-        for col in extra_columns:
-            if col in df.columns:
-                agg_dict[col] = "first"
-            else:
-                logger.warning(" '%s' does not exist and will be ignored.", col)
-
-    return df.groupby("start_datetime").agg(agg_dict).reset_index()
+    df[col_datetime] = to_datetime(df[col_datetime], utc=True)
+    df[col_datetime] = df[col_datetime].dt.floor(frequency)
+    return df
 
 
-def dpm_to_dph(
+def build_aggregation_dict(
     df: DataFrame,
-    extra_columns: list | None = None,
-) -> DataFrame:
-    """From CPOD result with a line per minute (DPM) to one line per hour (DPH).
+    base_agg: dict[str, str],
+    extra_columns: list[str] | None = None,
+) -> dict[str, str]:
+    """Build aggregation dictionary with validation.
 
     Parameters
     ----------
-    df: pd.DataFrame
-        CPOD result DataFrame
-    extra_columns: list
-        Additional columns added from df to data
+    df: DataFrame
+        Input dataframe to check column existence.
+    base_agg: dict[str, str]
+        Base aggregation dictionary (e.g., {"DPM": "sum"}).
+    extra_columns: list[str], optional
+        Additional columns to aggregate with "first" strategy.
 
     Returns
     -------
-    pd.DataFrame
-        DPH Dataframe.
+    dict[str, str]
+        Complete aggregation dictionary.
 
     """
-    df = df.copy()
-    df["DPM"] = 1
-    df["start_datetime"] = to_datetime(df["start_datetime"], dayfirst=True)
-
-    # Truncate column
-    df["start_datetime"] = df["start_datetime"].dt.floor("h")
-
-    agg_dict = {"DPM": "sum"}
+    agg_dict = base_agg.copy()
 
     if extra_columns:
         for col in extra_columns:
@@ -1044,45 +1035,61 @@ def dpm_to_dph(
             else:
                 logger.warning("Column '%s' does not exist and will be ignored.", col)
 
-    return df.groupby("start_datetime").agg(agg_dict).reset_index()
+    return agg_dict
 
 
-def dpm_to_dpd(
+def resample_dpm(
     df: DataFrame,
-    extra_columns: list | None = None,
+    frq: str,
+    group_by: list[str] | None = None,
+    extra_columns: list[str] | None = None,
 ) -> DataFrame:
-    """From CPOD result with a line per minute (DPM) to one line per day (DPD).
+    """Resample DPM data to specified time frequency.
+
+    Aggregates Detection Positive Minutes (DPM) by time period,
+    optionally preserving grouping columns like deployment name.
 
     Parameters
     ----------
-    df: pd.DataFrame
-        CPOD result DataFrame
-    extra_columns: list
-        Additional columns added from df to data
+    df: DataFrame
+        CPOD result DataFrame with DPM data.
+    frq: str
+        Pandas frequency string: "D" (day), "h" (hour), "10min", etc.
+    group_by: list[str], optional
+        Columns to group by (e.g., ["deploy.name", "start_datetime"]).
+        If None, groups only by start_datetime.
+    extra_columns: list[str], optional
+        Additional columns to preserve (uses "first" aggregation).
 
     Returns
     -------
-    pd.DataFrame
-        DPD Dataframe.
-
-    """
-    df = df.copy()
-    df["DPM"] = 1
-    df["start_datetime"] = to_datetime(df["start_datetime"], dayfirst=True)
+    DataFrame
+        Resampled DataFrame with aggregated DPM values.
 
-    # Truncate column
-    df["start_datetime"] = df["start_datetime"].dt.floor("D")
+    Examples
+    --------
+    >>> # Daily aggregation per deployment
+    >>> resample_dpm(df, "D", group_by=["deploy.name"])
 
-    agg_dict = {"DPM": "sum"}
+    >>> # Hourly aggregation with site info preserved
+    >>> resample_dpm(df, "h", extra_columns=["site.name"])
 
-    if extra_columns:
-        for col in extra_columns:
-            if col in df.columns:
-                agg_dict[col] = "first"
-            else:
-                logger.warning(" '%s' does not exist and will be ignored.", col)
+    """
+    df = is_dpm_col(df)
+    df = pf_datetime(df, "start_datetime", frq)
+
+    # Determine grouping columns
+    if group_by is None:
+        group_by = ["start_datetime"]
+
+    # Build aggregation dictionary
+    agg_dict = build_aggregation_dict(
+        df,
+        base_agg={"DPM": "sum"},
+        extra_columns=extra_columns,
+    )
 
-    return df.groupby("start_datetime").agg(agg_dict).reset_index()
+    return df.groupby(group_by).agg(agg_dict).reset_index()
 
 
 def date_format(
@@ -1106,39 +1113,104 @@ def date_format(
     return df
 
 
-def actual_data(
+def parse_timestamps(
     df: DataFrame,
-    col_timestamp: str = "start_datetime",
+    col_timestamp: str,
+    date_formats: list[str] | None = None,
 ) -> DataFrame:
-    """Create a table with beginning and end of every deployment.
+    """Parse timestamp column with multiple possible formats.
 
     Parameters
     ----------
-    col_timestamp
-    df: pd.DataFrame
-        CPOD result DataFrame
+    df: DataFrame
+        Input dataframe.
     col_timestamp: str
-        Name of the timestamps column in raw_data (default: 'start_datetime')
+        Name of the timestamp column to parse.
+    date_formats: list[str], optional
+        List of strptime formats to try. If None, uses common formats.
 
     Returns
     -------
-    pd.DataFrame
-        Simple Dataframe with beginning and end columns.
+    DataFrame
+        Copy of df with parsed timestamps.
+
+    Raises
+    ------
+    ValueError
+        If timestamps cannot be parsed with any format.
 
     """
+    if date_formats is None:
+        date_formats = [
+            "%Y-%m-%dT%H:%M:%S:%Z",
+            "%Y-%m-%dT%H:%M:%S",
+            "%d/%m/%Y %H:%M",
+        ]
+
     df = df.copy()
     df[col_timestamp] = df[col_timestamp].apply(
-        lambda x: strptime_from_text(
-            x, ["%Y-%m-%dT%H:%M:%S:%Z", "%Y-%m-%dT%H:%M:%S", "%d/%m/%Y %H:%M"],
-        ),
-    )
+        lambda x: strptime_from_text(x, date_formats))
+    return df
+
+
+def deploy_period(
+    df: DataFrame,
+    col_timestamp: str = "start_datetime",
+    col_deployment: str = "deploy.name",
+) -> DataFrame:
+    """Extract start and end timestamps for each deployment.
+
+    Parameters
+    ----------
+    df: DataFrame
+        Input dataframe with parsed timestamps.
+    col_timestamp: str, default="start_datetime"
+        Name of the timestamp column.
+    col_deployment: str, default="deploy.name"
+        Name of the deployment identifier column.
+
+    Returns
+    -------
+    DataFrame
+        DataFrame with columns: [col_deployment, 'Début', 'Fin'].
+
+    """
     return (
-        df.groupby(["deploy.name"])
+        df.groupby([col_deployment])
         .agg(Début=(col_timestamp, "first"), Fin=(col_timestamp, "last"))
         .reset_index()
     )
 
 
+def actual_data(
+    df: DataFrame,
+    col_timestamp: str = "start_datetime",
+    col_deployment: str = "deploy.name",
+    date_formats: list[str] | None = None,
+) -> DataFrame:
+    """Create a table with beginning and end of every deployment.
+
+    Parameters
+    ----------
+    df: DataFrame
+        CPOD result DataFrame.
+    col_timestamp: str, default="start_datetime"
+        Name of the timestamps column.
+    col_deployment: str, default="deploy.name"
+        Name of the deployment identifier column.
+    date_formats: list[str], optional
+        List of date formats to try for parsing.
+
+    Returns
+    -------
+    DataFrame
+        DataFrame with deployment periods (Début, Fin).
+
+    """
+    df_parsed = parse_timestamps(df, col_timestamp, date_formats)
+    return deploy_period(df_parsed, col_timestamp, col_deployment)
+
+
 def calendar(
     meta: DataFrame,
     data: DataFrame,
@@ -1216,3 +1288,33 @@ def calendar(
     plt.tight_layout()
     plt.show()
 
+
+def create_matrix(df: DataFrame,
+                   group_cols: list,
+                   agg_cols: list,
+                   )-> DataFrame:
+    """Create a stats matrix (mean & std).
+
+    Parameters
+    ----------
+    df : DataFrame
+        Extended frame with raw data to calculate stats for
+    group_cols : list
+        Additional columns to group by
+    agg_cols : list
+        Columns to aggregate
+
+    Returns
+    -------
+    Give a matrix of the data in [agg_cols] grouped by [group_cols].
+
+    """
+    matrix = df.groupby(group_cols).agg({
+        col: ["mean", "std"] for col in agg_cols
+    })
+    matrix = matrix.reset_index()
+
+    matrix.columns = group_cols + [f"{col}_{stat}"
+                                    for col in agg_cols
+                                    for stat in ["mean", "std"]]
+    return matrix

From e0bd15f0c72ebfb35c7386e050c800600e9566d6 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 21 Oct 2025 14:44:39 +0200
Subject: [PATCH 18/83] change path

---
 user_case/example_FPOD-CPOD_aplose.ipynb      | 169 ++++++++++++++++++
 .../example_FPOD-CPOD_raw.ipynb               |   0
 .../resource/example_FPOD-CPOD_aplose.ipynb   | 156 ----------------
 3 files changed, 169 insertions(+), 156 deletions(-)
 create mode 100644 user_case/example_FPOD-CPOD_aplose.ipynb
 rename user_case/{resource => }/example_FPOD-CPOD_raw.ipynb (100%)
 delete mode 100644 user_case/resource/example_FPOD-CPOD_aplose.ipynb

diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
new file mode 100644
index 0000000..23e88c5
--- /dev/null
+++ b/user_case/example_FPOD-CPOD_aplose.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:43:37.703310Z",
+     "start_time": "2025-10-21T12:43:35.614168Z"
+    }
+   },
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "from pandas import (\n",
+    "    read_csv,\n",
+    "    to_datetime,\n",
+    ")\n",
+    "\n",
+    "from post_processing.dataclass.data_aplose import DataAplose\n",
+    "from post_processing.utils.fpod_utils import resample_dpm, feeding_buzz, txt_folder, build_range"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### Load data\n",
+   "id": "a97e19830123b732"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:43:42.439451Z",
+     "start_time": "2025-10-21T12:43:42.336797Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
+    "data_list = DataAplose.from_yaml(file=yaml_file)\n",
+    "\n",
+    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\")  #Path to your click details folder.\n",
+    "d_beg_end = read_csv(r\"U:\\Deb_Fin_CETIROISE.csv\")"
+   ],
+   "id": "7da2feb5958db1a9",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Data metric\n",
+    "Here you need to choose the format in which you want to visualise the positive detections. This aims to replace the functionalities available in Chelonia's softwares."
+   ],
+   "id": "3bc57f4f638ad6dc"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:43:47.769511Z",
+     "start_time": "2025-10-21T12:43:47.757779Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "frq = \"D\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
+   "id": "9b0a078a262ac7f2",
+   "outputs": [],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:43:51.154627Z",
+     "start_time": "2025-10-21T12:43:51.133435Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"])",
+   "id": "fa3847d80ccf49c3",
+   "outputs": [],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Feeding buzzes processing\n",
+    "Use \"Marsouin\" or \"Commerson\" to get different ICI processing."
+   ],
+   "id": "b92537991aa4ac4b"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:44:10.850774Z",
+     "start_time": "2025-10-21T12:44:10.722006Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "fb_all = txt_folder(fb_files)\n",
+    "fb_all = feeding_buzz(fb_all, \"Marsouin\")\n",
+    "\n",
+    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
+    "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
+    "fb[\"start_datetime\"] = to_datetime(fb[\"start_datetime\"], utc=True)\n",
+    "\n",
+    "dpm_fb = resamp.merge(fb, on=\"start_datetime\", how=\"left\")"
+   ],
+   "id": "ca2362e4facecca3",
+   "outputs": [],
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:44:12.760555Z",
+     "start_time": "2025-10-21T12:44:12.724626Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "d_0 = build_range(d_beg_end, frq)\n",
+    "d_tot = d_0.merge(dpm_fb, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")"
+   ],
+   "id": "4d76089ef06c6fdb",
+   "outputs": [],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-21T12:44:14.374150Z",
+     "start_time": "2025-10-21T12:44:14.367297Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "d_tot[[\"DPM\",\"Foraging\"]] = d_tot[[\"DPM\",\"Foraging\"]].fillna(0)",
+   "id": "912268e5e997dbc6",
+   "outputs": [],
+   "execution_count": 7
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/user_case/resource/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
similarity index 100%
rename from user_case/resource/example_FPOD-CPOD_raw.ipynb
rename to user_case/example_FPOD-CPOD_raw.ipynb
diff --git a/user_case/resource/example_FPOD-CPOD_aplose.ipynb b/user_case/resource/example_FPOD-CPOD_aplose.ipynb
deleted file mode 100644
index 31a3ed8..0000000
--- a/user_case/resource/example_FPOD-CPOD_aplose.ipynb
+++ /dev/null
@@ -1,156 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "from pandas import (\n",
-    "    read_csv,\n",
-    "    to_datetime,\n",
-    ")\n",
-    "\n",
-    "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import resample_dpm, feeding_buzz, txt_folder, build_range"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Load data\n",
-   "id": "a97e19830123b732"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-21T12:36:10.627427Z",
-     "start_time": "2025-10-21T12:36:10.586897Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "yaml_file = Path(r\"user_case\\resource\\CPOD-FPOD_yaml.yml\")\n",
-    "data_list = DataAplose.from_yaml(file=yaml_file)\n",
-    "\n",
-    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\")  #Path to your click details folder.\n",
-    "d_beg_end = read_csv(r\"U:\\Deb_Fin_CETIROISE.csv\")"
-   ],
-   "id": "7da2feb5958db1a9",
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\fouinel\\\\PycharmProjects\\\\OSmOSE_post_processing\\\\user_case\\\\resource\\\\user_case\\\\resource\\\\CPOD-FPOD_yaml.yml'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mFileNotFoundError\u001B[39m                         Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[11]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m      1\u001B[39m yaml_file = Path(\u001B[33m\"\u001B[39m\u001B[33muser_case/resource/CPOD-FPOD_yaml.yml\u001B[39m\u001B[33m\"\u001B[39m).resolve()\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m data_list = \u001B[43mDataAplose\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfrom_yaml\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m=\u001B[49m\u001B[43myaml_file\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m      4\u001B[39m fb_files = Path(\u001B[33mr\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mU:\u001B[39m\u001B[33m\\\u001B[39m\u001B[33mfb_fpod_cetiroise_c\u001B[39m\u001B[33m\"\u001B[39m)  \u001B[38;5;66;03m#Path to your click details folder.\u001B[39;00m\n\u001B[32m      5\u001B[39m d_beg_end = read_csv(\u001B[33mr\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mU:\u001B[39m\u001B[33m\\\u001B[39m\u001B[33mDeb_Fin_CETIROISE.csv\u001B[39m\u001B[33m\"\u001B[39m)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\src\\post_processing\\dataclass\\data_aplose.py:410\u001B[39m, in \u001B[36mDataAplose.from_yaml\u001B[39m\u001B[34m(cls, file, concat)\u001B[39m\n\u001B[32m    388\u001B[39m \u001B[38;5;129m@classmethod\u001B[39m\n\u001B[32m    389\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mfrom_yaml\u001B[39m(\n\u001B[32m    390\u001B[39m     \u001B[38;5;28mcls\u001B[39m,\n\u001B[32m    391\u001B[39m     file: Path,\n\u001B[32m    392\u001B[39m     concat: \u001B[38;5;28mbool\u001B[39m = \u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[32m    393\u001B[39m ) -> DataAplose | \u001B[38;5;28mlist\u001B[39m[DataAplose]:\n\u001B[32m    394\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Return a DataAplose object from a yaml file.\u001B[39;00m\n\u001B[32m    395\u001B[39m \n\u001B[32m    396\u001B[39m \u001B[33;03m    Parameters\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    408\u001B[39m \n\u001B[32m    409\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m410\u001B[39m     filters = \u001B[43mDetectionFilter\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfrom_yaml\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m=\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    411\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mcls\u001B[39m.from_filters(filters, concat)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\OSmOSE_post_processing\\src\\post_processing\\dataclass\\detection_filter.py:64\u001B[39m, in \u001B[36mDetectionFilter.from_yaml\u001B[39m\u001B[34m(cls, file)\u001B[39m\n\u001B[32m     46\u001B[39m \u001B[38;5;129m@classmethod\u001B[39m\n\u001B[32m     47\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mfrom_yaml\u001B[39m(\n\u001B[32m     48\u001B[39m     \u001B[38;5;28mcls\u001B[39m,\n\u001B[32m     49\u001B[39m     file: Path,\n\u001B[32m     50\u001B[39m ) -> DetectionFilter | \u001B[38;5;28mlist\u001B[39m[DetectionFilter]:\n\u001B[32m     51\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"Return a DetectionFilter object from a yaml file.\u001B[39;00m\n\u001B[32m     52\u001B[39m \n\u001B[32m     53\u001B[39m \u001B[33;03m    Parameters\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m     62\u001B[39m \n\u001B[32m     63\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m64\u001B[39m     \u001B[38;5;28;01mwith\u001B[39;00m \u001B[43mfile\u001B[49m\u001B[43m.\u001B[49m\u001B[43mopen\u001B[49m\u001B[43m(\u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mutf-8\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mas\u001B[39;00m yaml_file:\n\u001B[32m     65\u001B[39m         parameters = yaml.safe_load(yaml_file)\n\u001B[32m     66\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mcls\u001B[39m.from_dict(parameters)\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\AppData\\Roaming\\uv\\python\\cpython-3.12.11-windows-x86_64-none\\Lib\\pathlib.py:1013\u001B[39m, in \u001B[36mPath.open\u001B[39m\u001B[34m(self, mode, buffering, encoding, errors, newline)\u001B[39m\n\u001B[32m   1011\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mode:\n\u001B[32m   1012\u001B[39m     encoding = io.text_encoding(encoding)\n\u001B[32m-> \u001B[39m\u001B[32m1013\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mio\u001B[49m\u001B[43m.\u001B[49m\u001B[43mopen\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbuffering\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnewline\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[31mFileNotFoundError\u001B[39m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\fouinel\\\\PycharmProjects\\\\OSmOSE_post_processing\\\\user_case\\\\resource\\\\user_case\\\\resource\\\\CPOD-FPOD_yaml.yml'"
-     ]
-    }
-   ],
-   "execution_count": 11
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Data metric\n",
-    "Here you need to choose the format in which you want to visualise the positive detections. This aims to replace the functionalities available in Chelonia's softwares."
-   ],
-   "id": "3bc57f4f638ad6dc"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "frq = \"D\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
-   "id": "9b0a078a262ac7f2",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"])",
-   "id": "fa3847d80ccf49c3",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Feeding buzzes processing\n",
-    "Use \"Marsouin\" or \"Commerson\" to get different ICI processing."
-   ],
-   "id": "b92537991aa4ac4b"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\") #Path to your click details folder.\n",
-    "fb_all = txt_folder(fb_files)\n",
-    "fb_all = feeding_buzz(fb_all, \"Marsouin\")\n",
-    "\n",
-    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
-    "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
-    "fb[\"start_datetime\"] = to_datetime(fb[\"start_datetime\"], utc=True)\n",
-    "\n",
-    "dpm_fb = resamp.merge(fb, on=\"start_datetime\", how=\"left\")"
-   ],
-   "id": "ca2362e4facecca3",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_0 = build_range(d_beg_end, frq)\n",
-    "d_tot = d_0.merge(dpm_fb, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")"
-   ],
-   "id": "4d76089ef06c6fdb",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "d_tot[[\"DPM\",\"Foraging\"]] = d_tot[[\"DPM\",\"Foraging\"]].fillna(0)",
-   "id": "912268e5e997dbc6",
-   "outputs": [],
-   "execution_count": null
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From f5529ace57cb8363d42ef9cc214145118cb72b4d Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 21 Oct 2025 14:44:58 +0200
Subject: [PATCH 19/83] adapt functions changes

---
 user_case/user_case_CALAIS.ipynb | 193 ++++++++++++++++++-------------
 1 file changed, 114 insertions(+), 79 deletions(-)

diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index 08495af..21ebcef 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-10-13T10:06:55.748967Z",
-     "start_time": "2025-10-13T10:06:46.756740Z"
+     "end_time": "2025-10-17T09:13:24.163916Z",
+     "start_time": "2025-10-17T09:13:21.433376Z"
     }
    },
    "source": [
@@ -21,7 +21,8 @@
     ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import csv_folder, cpod2aplose, fpod2aplose, actual_data, meta_cut_aplose, dpm_to_dp10m, dpm_to_dph, dpm_to_dpd,fb_folder, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent\n",
+    "from post_processing.utils.fpod_utils import csv_folder, cpod2aplose, fpod2aplose, actual_data, meta_cut_aplose, resample_dpm,txt_folder, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent, \\\n",
+    "    feeding_buzz\n",
     "from post_processing.utils.core_utils import json2df,get_season"
    ],
    "outputs": [],
@@ -45,13 +46,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-13T10:07:00.142758Z",
-     "start_time": "2025-10-13T10:06:59.189951Z"
+     "end_time": "2025-10-17T09:25:01.549663Z",
+     "start_time": "2025-10-17T09:24:48.208563Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "path = csv_folder(r\"U:\\Cetiroise\")\n",
+    "pod_files = Path(r\"U:\\Walde\")\n",
+    "path = csv_folder(pod_files)\n",
     "print(path.head())\n",
     "\n",
     "df_0 = path.dropna()"
@@ -62,49 +64,55 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                                File  podN          ChunkEnd  \\\n",
-      "0  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:34   \n",
-      "1  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:35   \n",
-      "2  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:36   \n",
-      "3  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 22:40   \n",
-      "4  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  24/02/2023 15:32   \n",
-      "\n",
-      "     Minute  DPM  MinsOn      deploy.name  \n",
-      "0  64748494    1       1  Point C_Phase 4  \n",
-      "1  64748495    1       1  Point C_Phase 4  \n",
-      "2  64748496    1       1  Point C_Phase 4  \n",
-      "3  64749520    1       1  Point C_Phase 4  \n",
-      "4  64773572    1       1  Point C_Phase 4  \n"
+      "                 File          ChunkEnd  DPM    Nall  MinsOn   deploy.name\n",
+      "0  POD2399 file01.CP3  14/05/2014 08:03  0.0     0.0     1.0  Walde_Phase1\n",
+      "1  POD2399 file01.CP3  14/05/2014 08:04  0.0   799.0     1.0  Walde_Phase1\n",
+      "2  POD2399 file01.CP3  14/05/2014 08:05  0.0     0.0     1.0  Walde_Phase1\n",
+      "3  POD2399 file01.CP3  14/05/2014 08:06  0.0  3361.0     1.0  Walde_Phase1\n",
+      "4  POD2399 file01.CP3  14/05/2014 08:07  0.0   421.0     1.0  Walde_Phase1\n"
      ]
     }
    ],
-   "execution_count": 2
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-13T10:07:03.680051Z",
-     "start_time": "2025-10-13T10:07:03.501607Z"
+     "end_time": "2025-10-17T09:34:43.092415Z",
+     "start_time": "2025-10-17T09:27:26.409365Z"
     }
    },
    "cell_type": "code",
    "source": "d_beg_end = actual_data(df_0, col_timestamp=\"ChunkEnd\")",
    "id": "4208969d9e509a8",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-17T09:35:32.137350Z",
+     "start_time": "2025-10-17T09:35:32.089860Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "d_beg_end.to_csv(r\"U:\\Deb_Fin_Walde.csv\", index=False)",
+   "id": "6fb6f4fa675d7cab",
+   "outputs": [],
+   "execution_count": 13
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-13T10:07:05.017124Z",
-     "start_time": "2025-10-13T10:07:05.012527Z"
+     "end_time": "2025-10-17T09:35:33.725437Z",
+     "start_time": "2025-10-17T09:35:33.670018Z"
     }
    },
    "cell_type": "code",
    "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ]",
    "id": "597efd1d90e3d069",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 14
   },
   {
    "metadata": {},
@@ -127,13 +135,13 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-13T10:07:15.647400Z",
-     "start_time": "2025-10-13T10:07:15.456897Z"
+     "end_time": "2025-10-17T09:35:38.378283Z",
+     "start_time": "2025-10-17T09:35:35.755020Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\")\n",
+    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Walde\", \"Marsouin\")\n",
     "print(df_aplose.head())"
    ],
    "id": "812ed7c0c5e258e7",
@@ -142,30 +150,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "         dataset filename  start_time  end_time  start_frequency  \\\n",
-      "0  Site D Simone                    0        60                0   \n",
-      "1  Site D Simone                    0        60                0   \n",
-      "2  Site D Simone                    0        60                0   \n",
-      "3  Site D Simone                    0        60                0   \n",
-      "4  Site D Simone                    0        60                0   \n",
+      "  dataset filename  start_time  end_time  start_frequency  end_frequency  \\\n",
+      "0   Walde                    0        60                0              0   \n",
+      "1   Walde                    0        60                0              0   \n",
+      "2   Walde                    0        60                0              0   \n",
+      "3   Walde                    0        60                0              0   \n",
+      "4   Walde                    0        60                0              0   \n",
       "\n",
-      "   end_frequency annotation annotator                start_datetime  \\\n",
-      "0              0  Commerson      FPOD  2023-02-07T05:34:00.000+0000   \n",
-      "1              0  Commerson      FPOD  2023-02-07T05:35:00.000+0000   \n",
-      "2              0  Commerson      FPOD  2023-02-07T05:36:00.000+0000   \n",
-      "3              0  Commerson      FPOD  2023-02-07T22:40:00.000+0000   \n",
-      "4              0  Commerson      FPOD  2023-02-24T15:32:00.000+0000   \n",
+      "  annotation annotator                start_datetime  \\\n",
+      "0   Marsouin      FPOD  2014-05-15T16:56:00.000+0000   \n",
+      "1   Marsouin      FPOD  2014-05-17T22:53:00.000+0000   \n",
+      "2   Marsouin      FPOD  2014-05-17T22:54:00.000+0000   \n",
+      "3   Marsouin      FPOD  2014-05-18T14:05:00.000+0000   \n",
+      "4   Marsouin      FPOD  2014-05-21T14:19:00.000+0000   \n",
       "\n",
-      "                   end_datetime  is_box      deploy.name  \n",
-      "0  2023-02-07T05:35:00.000+0000       0  Point C_Phase 4  \n",
-      "1  2023-02-07T05:36:00.000+0000       0  Point C_Phase 4  \n",
-      "2  2023-02-07T05:37:00.000+0000       0  Point C_Phase 4  \n",
-      "3  2023-02-07T22:41:00.000+0000       0  Point C_Phase 4  \n",
-      "4  2023-02-24T15:33:00.000+0000       0  Point C_Phase 4  \n"
+      "                   end_datetime  is_box   deploy.name  \n",
+      "0  2014-05-15T16:57:00.000+0000       0  Walde_Phase1  \n",
+      "1  2014-05-17T22:54:00.000+0000       0  Walde_Phase1  \n",
+      "2  2014-05-17T22:55:00.000+0000       0  Walde_Phase1  \n",
+      "3  2014-05-18T14:06:00.000+0000       0  Walde_Phase1  \n",
+      "4  2014-05-21T14:20:00.000+0000       0  Walde_Phase1  \n"
      ]
     }
    ],
-   "execution_count": 5
+   "execution_count": 15
   },
   {
    "metadata": {},
@@ -174,15 +182,47 @@
    "id": "a39bb10d8ac60a27"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-17T09:23:23.552890Z",
+     "start_time": "2025-10-17T09:23:22.810583Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
     "print(df_aplose.head())"
    ],
    "id": "9b632673397a184",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  dataset filename  start_time  end_time  start_frequency  end_frequency  \\\n",
+      "0     CA4                    0        60                0              0   \n",
+      "1     CA4                    0        60                0              0   \n",
+      "2     CA4                    0        60                0              0   \n",
+      "3     CA4                    0        60                0              0   \n",
+      "4     CA4                    0        60                0              0   \n",
+      "\n",
+      "  annotation annotator                start_datetime  \\\n",
+      "0   Marsouin      FPOD  2014-05-17T03:52:00.000+0000   \n",
+      "1   Marsouin      FPOD  2014-05-17T04:47:00.000+0000   \n",
+      "2   Marsouin      FPOD  2014-05-19T17:06:00.000+0000   \n",
+      "3   Marsouin      FPOD  2014-05-20T11:07:00.000+0000   \n",
+      "4   Marsouin      FPOD  2014-05-20T11:16:00.000+0000   \n",
+      "\n",
+      "                   end_datetime  is_box deploy.name  \n",
+      "0  2014-05-17T03:53:00.000+0000       0  CA4_Phase1  \n",
+      "1  2014-05-17T04:48:00.000+0000       0  CA4_Phase1  \n",
+      "2  2014-05-19T17:07:00.000+0000       0  CA4_Phase1  \n",
+      "3  2014-05-20T11:08:00.000+0000       0  CA4_Phase1  \n",
+      "4  2014-05-20T11:17:00.000+0000       0  CA4_Phase1  \n"
+     ]
+    }
+   ],
+   "execution_count": 7
   },
   {
    "metadata": {},
@@ -197,10 +237,15 @@
    "id": "32f8ff8f9ece35a8"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-17T09:35:54.374270Z",
+     "start_time": "2025-10-17T09:35:54.303578Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_cetiroise.json\") #Path to your metadata file.\n",
+    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_calais.json\") #Path to your metadata file.\n",
     "metadatax = json2df(json_path=json)\n",
     "\n",
     "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
@@ -210,7 +255,7 @@
    ],
    "id": "ed6a06c522aea169",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 16
   },
   {
    "metadata": {},
@@ -219,12 +264,17 @@
    "id": "8f5fe75cc3463971"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-17T09:36:06.597522Z",
+     "start_time": "2025-10-17T09:35:56.739495Z"
+    }
+   },
    "cell_type": "code",
-   "source": "cleared.to_csv(r\"U:\\APLOSE_CETIROISE_pos.csv\", index=False) #You can stock all DPM for a site in a DataAplose file.",
+   "source": "cleared.to_csv(r\"U:\\APLOSE_Walde_pos.csv\", index=False) #You can stock all DPM for a site in a DataAplose file.",
    "id": "76f70cb6c6658ba6",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 17
   },
   {
    "metadata": {},
@@ -265,27 +315,19 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "source": "dp10 = dpm_to_dp10m(data_list.df, extra_columns=[\"deploy.name\"])",
-   "id": "a27ceea1fefdd298",
    "outputs": [],
-   "execution_count": null
+   "execution_count": null,
+   "source": "frq = \"D\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
+   "id": "256b756d05c08294"
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "source": "dph = dpm_to_dph(data_list.df, extra_columns=[\"deploy.name\"])",
+   "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"])",
    "id": "6cc79b2aeef076ed",
    "outputs": [],
    "execution_count": null
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "dpd = dpm_to_dpd(data_list.df, extra_columns=[\"deploy.name\"])",
-   "id": "e6655c36fc1851c7",
-   "outputs": [],
-   "execution_count": null
-  },
   {
    "metadata": {},
    "cell_type": "markdown",
@@ -298,20 +340,13 @@
    "source": "Import your click details files. All files for one site must be stacked in the same folder.",
    "id": "9753f4ba20c7267e"
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "frq = \"D\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
-   "id": "10e00649ec7dac05",
-   "outputs": [],
-   "execution_count": null
-  },
   {
    "metadata": {},
    "cell_type": "code",
    "source": [
     "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\") #Path to your click details folder.\n",
-    "fb_all = fb_folder(fb_files, \"Marsouin\")\n",
+    "fb_all = txt_folder(fb_files)\n",
+    "fb_all = feeding_buzz(fb_all, \"Marsouin\")\n",
     "\n",
     "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
     "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
@@ -325,7 +360,7 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "d_tot = dpd.merge(fb, on=\"start_datetime\", how=\"left\")\n",
+    "d_tot = resamp.merge(fb, on=\"start_datetime\", how=\"left\")\n",
     "#This function aims to reindent 0 between the positive detections. It will be useful to produce first visualization graphs and use this dataset in R.\n",
     "d_hour = build_range(d_beg_end, frq)"
    ],

From bc3719a126a66b1b00877dda9f254448e9028f22 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 24 Oct 2025 12:41:38 +0200
Subject: [PATCH 20/83] adapt functions changes

---
 user_case/example_FPOD-CPOD_aplose.ipynb |  36 ++++----
 user_case/example_FPOD-CPOD_raw.ipynb    | 104 +++++++++++++++--------
 2 files changed, 86 insertions(+), 54 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
index 23e88c5..ffdf2eb 100644
--- a/user_case/example_FPOD-CPOD_aplose.ipynb
+++ b/user_case/example_FPOD-CPOD_aplose.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-10-21T12:43:37.703310Z",
-     "start_time": "2025-10-21T12:43:35.614168Z"
+     "end_time": "2025-10-24T08:10:42.240883Z",
+     "start_time": "2025-10-24T08:10:40.168812Z"
     }
    },
    "source": [
@@ -33,8 +33,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T12:43:42.439451Z",
-     "start_time": "2025-10-21T12:43:42.336797Z"
+     "end_time": "2025-10-24T08:20:44.203307Z",
+     "start_time": "2025-10-24T08:20:44.103414Z"
     }
    },
    "cell_type": "code",
@@ -42,8 +42,8 @@
     "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
     "data_list = DataAplose.from_yaml(file=yaml_file)\n",
     "\n",
-    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\")  #Path to your click details folder.\n",
-    "d_beg_end = read_csv(r\"U:\\Deb_Fin_CETIROISE.csv\")"
+    "fb_files = Path(r\"U:\\fb_D_NBHF\")  #Path to your click details folder.\n",
+    "d_beg_end = read_csv(r\"U:\\Deb_Fin_Site D Simone.csv\")"
    ],
    "id": "7da2feb5958db1a9",
    "outputs": [],
@@ -61,12 +61,12 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T12:43:47.769511Z",
-     "start_time": "2025-10-21T12:43:47.757779Z"
+     "end_time": "2025-10-24T08:20:52.992126Z",
+     "start_time": "2025-10-24T08:20:52.983477Z"
     }
    },
    "cell_type": "code",
-   "source": "frq = \"D\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
+   "source": "frq = \"h\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
    "id": "9b0a078a262ac7f2",
    "outputs": [],
    "execution_count": 3
@@ -74,8 +74,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T12:43:51.154627Z",
-     "start_time": "2025-10-21T12:43:51.133435Z"
+     "end_time": "2025-10-24T08:20:56.935875Z",
+     "start_time": "2025-10-24T08:20:56.923925Z"
     }
    },
    "cell_type": "code",
@@ -96,14 +96,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T12:44:10.850774Z",
-     "start_time": "2025-10-21T12:44:10.722006Z"
+     "end_time": "2025-10-24T08:43:33.273328Z",
+     "start_time": "2025-10-24T08:43:33.011458Z"
     }
    },
    "cell_type": "code",
    "source": [
     "fb_all = txt_folder(fb_files)\n",
-    "fb_all = feeding_buzz(fb_all, \"Marsouin\")\n",
+    "fb_all = feeding_buzz(fb_all, \"Commerson\")\n",
     "\n",
     "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
     "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
@@ -118,8 +118,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T12:44:12.760555Z",
-     "start_time": "2025-10-21T12:44:12.724626Z"
+     "end_time": "2025-10-24T08:43:37.700188Z",
+     "start_time": "2025-10-24T08:43:37.677880Z"
     }
    },
    "cell_type": "code",
@@ -134,8 +134,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T12:44:14.374150Z",
-     "start_time": "2025-10-21T12:44:14.367297Z"
+     "end_time": "2025-10-24T08:43:39.055027Z",
+     "start_time": "2025-10-24T08:43:39.047963Z"
     }
    },
    "cell_type": "code",
diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index 1cafe1a..d2f0eef 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-10-21T10:34:14.234399Z",
-     "start_time": "2025-10-21T10:34:06.776401Z"
+     "end_time": "2025-10-24T07:52:57.623291Z",
+     "start_time": "2025-10-24T07:52:51.711980Z"
     }
    },
    "source": [
@@ -34,15 +34,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T10:34:44.094566Z",
-     "start_time": "2025-10-21T10:34:43.418858Z"
+     "end_time": "2025-10-24T07:53:19.702997Z",
+     "start_time": "2025-10-24T07:53:18.892419Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "pod_files = Path(r\"U:\\Cetiroise\")\n",
+    "pod_files = Path(r\"U:\\D\")\n",
     "path = csv_folder(pod_files) #Path to your data folder.\n",
-    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_cetiroise.json\") #Path to your metadata file.\n",
+    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_kerguelen.json\") #Path to your metadata file.\n",
     "\n",
     "print(path.head())\n",
     "df_0 = path.dropna()\n",
@@ -56,19 +56,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                                File  podN          ChunkEnd  \\\n",
-      "0  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:34   \n",
-      "1  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:35   \n",
-      "2  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 05:36   \n",
-      "3  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  07/02/2023 22:40   \n",
-      "4  CETIROISEPHASE4POINTC 2023 02 03 FPOD_6669 fil...  6669  24/02/2023 15:32   \n",
+      "                 File          ChunkEnd  DPM  Nall  MinsOn  \\\n",
+      "0  POD3042 file01.CP3  21/10/2019 12:11    0     0       0   \n",
+      "1  POD3042 file01.CP3  21/10/2019 12:12    0     0       0   \n",
+      "2  POD3042 file01.CP3  21/10/2019 12:13    0     0       0   \n",
+      "3  POD3042 file01.CP3  21/10/2019 12:14    0     0       0   \n",
+      "4  POD3042 file01.CP3  21/10/2019 12:15    0     0       0   \n",
       "\n",
-      "     Minute  DPM  MinsOn      deploy.name  \n",
-      "0  64748494    1       1  Point C_Phase 4  \n",
-      "1  64748495    1       1  Point C_Phase 4  \n",
-      "2  64748496    1       1  Point C_Phase 4  \n",
-      "3  64749520    1       1  Point C_Phase 4  \n",
-      "4  64773572    1       1  Point C_Phase 4  \n"
+      "            deploy.name  \n",
+      "0  Site D Simone_Phase1  \n",
+      "1  Site D Simone_Phase1  \n",
+      "2  Site D Simone_Phase1  \n",
+      "3  Site D Simone_Phase1  \n",
+      "4  Site D Simone_Phase1  \n"
      ]
     }
    ],
@@ -77,14 +77,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T10:34:53.696221Z",
-     "start_time": "2025-10-21T10:34:53.481890Z"
+     "end_time": "2025-10-24T07:54:18.370860Z",
+     "start_time": "2025-10-24T07:53:28.225983Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "d_beg_end = actual_data(df_0, col_timestamp=\"ChunkEnd\")\n",
-    "d_beg_end.to_csv(r\"U:\\Deb_Fin_CETIROISE.csv\", index=False)"
+    "d_beg_end = actual_data(df_0, metadatax)\n",
+    "d_beg_end.to_csv(r\"U:\\Deb_Fin_Site D Simone.csv\", index=False)"
    ],
    "id": "fa52f8971b61aaf6",
    "outputs": [],
@@ -93,15 +93,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T10:34:57.917184Z",
-     "start_time": "2025-10-21T10:34:57.913957Z"
+     "end_time": "2025-10-24T08:03:40.056689Z",
+     "start_time": "2025-10-24T08:03:40.048631Z"
     }
    },
    "cell_type": "code",
    "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ]",
    "id": "769e128f2a5293e1",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 5
   },
   {
    "metadata": {},
@@ -114,15 +114,47 @@
    "id": "dd03975b7aef7eed"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T08:08:16.903159Z",
+     "start_time": "2025-10-24T08:08:16.776363Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\")\n",
     "print(df_aplose.head())"
    ],
-   "id": "4cc867627d677529"
+   "id": "4cc867627d677529",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         dataset filename  start_time  end_time  start_frequency  \\\n",
+      "0  Site D Simone                    0        60                0   \n",
+      "1  Site D Simone                    0        60                0   \n",
+      "2  Site D Simone                    0        60                0   \n",
+      "3  Site D Simone                    0        60                0   \n",
+      "4  Site D Simone                    0        60                0   \n",
+      "\n",
+      "   end_frequency annotation annotator                start_datetime  \\\n",
+      "0              0  Commerson      FPOD  2019-10-26T16:21:00.000+0000   \n",
+      "1              0  Commerson      FPOD  2019-10-26T16:24:00.000+0000   \n",
+      "2              0  Commerson      FPOD  2019-10-26T16:36:00.000+0000   \n",
+      "3              0  Commerson      FPOD  2020-03-14T14:38:00.000+0000   \n",
+      "4              0  Commerson      FPOD  2020-03-14T15:22:00.000+0000   \n",
+      "\n",
+      "                   end_datetime  is_box           deploy.name  \n",
+      "0  2019-10-26T16:22:00.000+0000       0  Site D Simone_Phase1  \n",
+      "1  2019-10-26T16:25:00.000+0000       0  Site D Simone_Phase1  \n",
+      "2  2019-10-26T16:37:00.000+0000       0  Site D Simone_Phase1  \n",
+      "3  2020-03-14T14:39:00.000+0000       0  Site D Simone_Phase2  \n",
+      "4  2020-03-14T15:23:00.000+0000       0  Site D Simone_Phase2  \n"
+     ]
+    }
+   ],
+   "execution_count": 6
   },
   {
    "metadata": {},
@@ -136,8 +168,8 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T10:35:03.300926Z",
-     "start_time": "2025-10-21T10:35:03.122227Z"
+     "end_time": "2025-10-22T14:47:04.017877Z",
+     "start_time": "2025-10-22T14:47:03.833873Z"
     }
    },
    "cell_type": "code",
@@ -174,7 +206,7 @@
      ]
     }
    ],
-   "execution_count": 5
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -188,18 +220,18 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-21T10:35:12.623559Z",
-     "start_time": "2025-10-21T10:35:12.255285Z"
+     "end_time": "2025-10-24T08:10:12.478620Z",
+     "start_time": "2025-10-24T08:10:11.777820Z"
     }
    },
    "cell_type": "code",
    "source": [
     "cleared = meta_cut_aplose(df_aplose, metadatax)\n",
-    "cleared.to_csv(r\"U:\\APLOSE_CETIROISE_pos.csv\", index=False)"
+    "cleared.to_csv(r\"U:\\APLOSE_D_pos.csv\", index=False)"
    ],
    "id": "895bd5a116918285",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 7
   }
  ],
  "metadata": {

From c3a0000b953af54120d93e389e11ae039a9ad2e8 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 24 Oct 2025 12:41:59 +0200
Subject: [PATCH 21/83] simplify functions

---
 src/post_processing/utils/fpod_utils.py | 1010 ++++++++++-------------
 1 file changed, 427 insertions(+), 583 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index f31c49d..2d81ed9 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -18,7 +18,6 @@
     date_range,
     notna,
     read_csv,
-    read_excel,
     to_datetime,
     to_timedelta,
 )
@@ -44,10 +43,10 @@
 }
 
 season_color = {
-    "spring": "#C5E0B4",
-    "summer": "#FCF97F",
-    "autumn": "#ED7C2F",
-    "winter": "#B4C7E8",
+    "spring": "#C5E0B4", #green
+    "summer": "#FCF97F", #darkgoldenrod
+    "autumn": "#ED7C2F", #orange
+    "winter": "#B4C7E8", #blue
 }
 
 def fpod2aplose(
@@ -156,285 +155,262 @@ def cpod2aplose(
     return concat(results, ignore_index=True)
 
 
-def usable_data_phase(
-    d_meta: DataFrame,
-    df: DataFrame,
-    dpl: str,
+def csv_folder(
+    folder_path: Path,
+    sep: str = ";",
+    encoding: str = "latin-1",
 ) -> DataFrame:
-    """Calculate the percentage of usable data.
-
-    Considering the deployment dates and the collected data.
+    """Process all CSV files from a folder.
 
     Parameters
     ----------
-    df: DataFrame
-        CPOD result DataFrame
-    d_meta: DataFrame
-        Metadata DataFrame with deployments information (previously exported as json)
-    dpl: str
-        Deployment of interest where percentage of usable data will be calculated
+    folder_path: Path
+        Folder's place.
+    sep: str, default=";"
+        Column separator.
+    encoding: str, default="latin-1"
+        File encoding.
 
     Returns
     -------
     DataFrame
-        Returns the percentage of usable datas in the chosen phase
+        Concatenated data with optional filename column.
+
+    Raises
+    ------
+    ValueError
+        If no CSV files found.
 
     """
-    d_meta.loc[:, ["deployment_date", "recovery_date"]] = d_meta[
-        ["deployment_date", "recovery_date"]
-    ].apply(
-        to_datetime,
-    )
-    df["start_datetime"] = to_datetime(df["start_datetime"])
+    all_files = list(folder_path.rglob("*.csv"))
 
-    phase = d_meta.loc[d_meta["name"] == dpl].reset_index()
-    data = df.loc[df["name"] == dpl].reset_index()
-    start_date = phase.loc[0, "deployment_date"]
-    end_date = phase.loc[0, "recovery_date"]
+    if not all_files:
+        msg = f"No .csv files found in {folder_path}"
+        raise ValueError(msg)
 
-    # Calculate the percentage of collected data on the phase length of time
-    if data.empty:
-        percentage_data = 0
-        msg = "No data for this phase"
-    else:
-        df_end = data.loc[data.index[-1], "start_datetime"]
-        df_start = data.loc[data.index[0], "start_datetime"]
-        act_length = df_end - df_start
-        p_length = end_date - start_date
-        percentage_data = act_length * 100 / p_length
-        msg = f"Percentage of usable data : {percentage_data}%"
+    all_data = []
+    for file in all_files:
+        df = read_csv(file, sep=sep, encoding=encoding)
+        df["deploy.name"] = file.stem
+        all_data.append(df)
 
-    logger.info(msg)
-    return percentage_data
+    return concat(all_data, ignore_index=True)
 
 
-def meta_cut_aplose(
-    raw_data: DataFrame,
-    metadata: DataFrame,
-    column_names: dict[str, str] | None = None,
+def txt_folder(
+    folder_path: Path,
+    sep: str = "\t",
 ) -> DataFrame:
-    """Filter data to keep only the ones corresponding to a deployment.
+    r"""Process all TXT files from a folder.
 
     Parameters
     ----------
-    raw_data : DataFrame
-        Dataframe containing deploy.name et timestamp
-    metadata : DataFrame
-        Metadata containing deploy.name, deployment_date, recovery_date
-    column_names : dict[str, str], optional
-        Dictionary with column names. Keys: 'deploy_name', 'timestamp',
-        'deployment_date', 'recovery_date'. If None, uses defaults.
-
+    folder_path: Path
+        Folder's place.
+    sep: str, default="\t"
+        Column separator.
 
     Returns
     -------
     DataFrame
-        Filtered data containing only rows in deployment periods
+       Concatenated data from all TXT files.
 
     """
-    defaults = {
-        "deploy_name": "deploy.name",
-        "timestamp": "start_datetime",
-        "deployment_date": "deployment_date",
-        "recovery_date": "recovery_date",
-    }
+    all_files = list(Path(folder_path).rglob("*.txt"))
+
+    if not all_files:
+        msg = f"No .txt files found in {folder_path}"
+        raise ValueError(msg)
 
-    # Merge with user-provided names
-    cols = {**defaults, **(column_names or {})}
+    all_data = []
+    for file in all_files:
+        file_path = folder_path / file
+        df = read_csv(file_path, sep=sep)
+        all_data.append(df)
 
-    col_deploy_name = cols["deploy_name"]
-    col_timestamp = cols["timestamp"]
-    col_debut = cols["deployment_date"]
-    col_fin = cols["recovery_date"]
+    return concat(all_data, ignore_index=True)
 
-    required_raw = [col_deploy_name, col_timestamp]
-    required_meta = [col_deploy_name, col_debut, col_fin]
 
-    for col in required_raw:
-        if col not in raw_data.columns:
-            msg = f"'{col}' not found in raw_data"
-            raise ValueError(msg)
-    for col in required_meta:
-        if col not in metadata.columns:
-            msg = f"'{col}' not found in metadata"
-            raise ValueError(msg)
+def parse_timestamps(
+    df: DataFrame,
+    col_timestamp: str,
+    date_formats: list[str] | None = None,
+) -> DataFrame:
+    """Parse timestamp column with multiple possible formats.
 
-    # Convert to datetime
-    raw = raw_data.copy()
-    meta = metadata.copy()
-    raw[col_timestamp] = to_datetime(raw[col_timestamp], errors="coerce")
-    meta[col_debut] = to_datetime(meta[col_debut], errors="coerce")
-    meta[col_fin] = to_datetime(meta[col_fin], errors="coerce")
+    Parameters
+    ----------
+    df: DataFrame
+        Input dataframe.
+    col_timestamp: str
+        Name of the timestamp column to parse.
+    date_formats: list[str], optional
+        List of strptime formats to try. If None, uses common formats.
 
-    dfm = raw.merge(
-        meta[[col_deploy_name, col_debut, col_fin]],
-        on=col_deploy_name,
-        how="left",
-    )
+    Returns
+    -------
+    DataFrame
+        Copy of df with parsed timestamps.
 
-    out = dfm[
-        (dfm[col_timestamp] >= dfm[col_debut])
-        & (dfm[col_timestamp] <= dfm[col_fin])
-        & dfm[col_timestamp].notna()
-        & dfm[col_debut].notna()
-        & dfm[col_fin].notna()
-    ].copy()
+    Raises
+    ------
+    ValueError
+        If timestamps cannot be parsed with any format.
 
-    columns_to_drop = [
-        col for col in [col_debut, col_fin] if col not in raw_data.columns
-    ]
-    if columns_to_drop:
-        out = out.drop(columns=columns_to_drop)
+    """
+    if date_formats is None:
+        date_formats = [
+            "%Y-%m-%dT%H:%M:%S.%f%z",
+            "%Y-%m-%dT%H:%M:%S:%Z",
+            "%Y-%m-%dT%H:%M:%S.%f",
+            "%Y-%m-%dT%H:%M:%S",
+            "%Y-%m-%d %H:%M:%S.%f",
+            "%d/%m/%Y %H:%M",
+        ]
 
-    return out.sort_values([col_deploy_name, col_timestamp]).reset_index(drop=True)
+    df = df.copy()
+    df[col_timestamp] = df[col_timestamp].apply(
+        lambda x: strptime_from_text(x, date_formats))
+    return df
 
 
-def format_calendar(path: Path) -> DataFrame:
-    """Format calendar.
+def required_columns(
+    df: DataFrame,
+    columns: list[str],
+) -> None:
+    """Validate that required columns exist in dataframe.
 
     Parameters
     ----------
-    path: Path
-        Excel calendar path
+    df : DataFrame
+        Dataframe to validate.
+    columns : list[str]
+        List of required column names.
+
+    Raises
+    ------
+    ValueError
+        If any required column is missing.
 
     """
-    df_calendar = read_excel(path)
-    df_calendar = df_calendar[df_calendar["Site group"] == "Data"].copy()
-
-    return df_calendar.rename(
-        columns={
-            "Start": "start_datetime",
-            "Stop": "end_datetime",
-            "Site": "site.name",
-        },
-    )
+    for col in columns:
+        if col not in df.columns:
+            msg = f"'{col}' not found in {df}"
+            raise ValueError(msg)
 
 
-def assign_phase(
-    meta: DataFrame,
-    data: DataFrame,
-    site: str,
+def create_mask(
+    df: DataFrame,
+    col_timestamp: str,
+    col_start: str,
+    col_end: str,
 ) -> DataFrame:
-    """Add a column to an APLOSE DataFrame to specify the name of the phase.
-
-    The name of the phase is attributed according to metadata.
+    """Filter rows to keep only those within deployment period.
 
     Parameters
     ----------
-    meta: DataFrame
-        Metadata dataframe with deployments information (previously exported as json).
-    data: DataFrame
-        Contain positive hours to detections.
-    site: str
-        Name of the site you wish to assign phases to.
+    df : DataFrame
+        Dataframe with timestamp and deployment period columns.
+    col_timestamp : str
+        Name of timestamp column.
+    col_start : str
+        Name of deployment start date column.
+    col_end : str
+        Name of deployment end date column.
 
     Returns
     -------
     DataFrame
-        The same dataframe with the column Phase.
+        Filtered dataframe with rows in deployment periods.
 
     """
-    data["start_datetime"] = to_datetime(data["start_datetime"], utc=True)
-    meta["deployment_date"] = to_datetime(meta["deployment_date"], utc=True)
-    meta["recovery_date"] = to_datetime(meta["recovery_date"], utc=True)
-
-    meta = meta[meta["site.name"] == site].copy()
-
-    data["name"] = None
-    for _, meta_row in meta.iterrows():
-        j = 0
-        while j < len(data):
-            if (
-                meta_row["deployment_date"]
-                <= data.loc[j, "start_datetime"]
-                < meta_row["recovery_date"]
-            ):
-                data.loc[j, "name"] = (
-                    f"{meta_row['site.name']}_{meta_row['campaign.name']}"
-                )
-            j += 1
-    return data
-
-
-def assign_phase_simple(
-    meta: DataFrame,
-    data: DataFrame,
+    mask = (
+        (df[col_timestamp] >= df[col_start])
+    & (df[col_timestamp] <= df[col_end])
+        & df[col_timestamp].notna()
+        & df[col_start].notna()
+        & df[col_end].notna()
+    )
+    return df[mask].copy()
+
+
+def meta_cut_aplose(
+    raw_data: DataFrame,
+    metadata: DataFrame,
 ) -> DataFrame:
-    """Add column to an Aplose DataFrame to specify the phase, according to metadata.
+    """Filter data to keep only rows within deployment periods.
 
     Parameters
     ----------
-    meta: DataFrame
-        Metadata dataframe with deployments information (previously exported as json).
-    data: DataFrame
-        Contain positive hours to detections.
+    raw_data : DataFrame
+        Dataframe containing deployment name and timestamps.
+    metadata : DataFrame
+        Metadata with deployment periods (start/end dates).
 
     Returns
     -------
     DataFrame
-        The same dataframe with the column Phase.
+        Filtered data with only rows within deployment periods.
 
     """
-    data["start_datetime"] = to_datetime(data["start_datetime"], utc=True)
-    data["end_datetime"] = to_datetime(data["end_datetime"], dayfirst=True, utc=True)
-    meta["deployment_date"] = to_datetime(meta["deployment_date"], utc=True)
-    meta["recovery_date"] = to_datetime(meta["recovery_date"], utc=True)
-    meta["deployment_date"] = meta["deployment_date"].dt.floor("d")
-    meta["recovery_date"] = meta["recovery_date"].dt.floor("d")
+    required_columns(
+        raw_data,["deploy.name", "start_datetime"])
+    required_columns(
+        metadata,["deploy.name", "deployment_date","recovery_date"])
+
+    raw = parse_timestamps(raw_data, "start_datetime")
+
+    dfm = raw.merge(
+        metadata[["deploy.name", "deployment_date","recovery_date"]],
+        on="deploy.name",
+        how="left",
+    )
 
-    data["name"] = None
-    for site in data["deploy.name"].unique():
-        site_meta = meta[meta["deploy.name"] == site]
-        site_data = data[data["deploy.name"] == site]
+    out = create_mask(dfm, "start_datetime", "deployment_date", "recovery_date")
 
-        for _, meta_row in site_meta.iterrows():
-            time_filter = (
-                meta_row["deployment_date"] <= site_data["start_datetime"]
-            ) & (site_data["start_datetime"] < meta_row["recovery_date"])
-            data.loc[site_data.index[time_filter], "name"] = meta_row["name"]
+    columns_to_drop = [
+        col for col in ["deployment_date","recovery_date"] if col not in raw_data.
+        columns]
+    if columns_to_drop:
+        out = out.drop(columns=columns_to_drop)
 
-    return data
+    return out.sort_values(["start_datetime"]).reset_index(drop=True)
 
 
-def generate_hourly_detections(meta: DataFrame, site: str) -> DataFrame:
+def add_utc(
+    df: DataFrame,
+    cols: list,
+    fr:str="h",
+) -> DataFrame:
     """Create a DataFrame with one line per hour between start and end dates.
 
     Keep the number of detections per hour between these dates.
 
     Parameters
     ----------
-    meta: DataFrame
-        Metadata dataframe with deployments information (previously exported as json)
-    site: str
-        A way to isolate the site you want to work on.
+    df: pd.DataFrame
+        Metadata dataframe with deployments information (previously exported as json).
+    cols:list
+        Timestamp column names.
+    fr:str
+        Frequency of the range of detections.
 
     Returns
     -------
-    DataFrame
+    pd.DataFrame
         A full period of time with positive and negative hours to detections.
 
     """
-    df_meta = meta[meta["site.name"] == site].copy()
-    df_meta["deployment_date"] = to_datetime(df_meta["deployment_date"])
-    df_meta["recovery_date"] = to_datetime(df_meta["recovery_date"])
-    df_meta["deployment_date"] = df_meta["deployment_date"].dt.floor("h")
-    df_meta["recovery_date"] = df_meta["recovery_date"].dt.floor("h")
-    df_meta = df_meta.sort_values(by=["deployment_date"])
-
-    records = [
-        {"name": row["name"], "start_datetime": date}
-        for _, row in df_meta.iterrows()
-        for date in date_range(
-            start=row["deployment_date"],
-            end=row["recovery_date"],
-            freq="h",
-        )
-    ]
-
-    return DataFrame(records)
+    for col in df[cols]:
+        df[col] = to_datetime(df[col], utc=True)
+        df[col] = df[col].dt.floor(fr)
+    return df
 
 
-def build_range(df: DataFrame, fr:str="h") -> DataFrame:
+def build_range(
+    df: DataFrame,
+    fr:str="h",
+) -> DataFrame:
     """Create a DataFrame with one line per hour between start and end dates.
 
     Keep the number of detections per hour between these dates.
@@ -452,10 +428,7 @@ def build_range(df: DataFrame, fr:str="h") -> DataFrame:
         A full period of time with positive and negative hours to detections.
 
     """
-    df["Début"] = to_datetime(df["Début"], utc=True)
-    df["Début"] = df["Début"].dt.floor(fr)
-    df["Fin"] = to_datetime(df["Fin"], utc=True)
-    df["Fin"] = df["Fin"].dt.floor(fr)
+    add_utc(df, ["Début","Fin"], fr)
 
     all_ranges = []
     for _, row in df.iterrows():
@@ -471,46 +444,10 @@ def build_range(df: DataFrame, fr:str="h") -> DataFrame:
     return concat(all_ranges, ignore_index=True)
 
 
-def merging_tab(meta: DataFrame, data: DataFrame) -> DataFrame:
-    """Create a DataFrame with one line per hour between start and end dates.
-
-    Keep the number of detections per hour between these dates.
-
-    Parameters
-    ----------
-    meta: DataFrame
-        Metadata with deployments information (previously exported as json)
-    data: DataFrame
-        Contain positive hours to detections
-
-    Returns
-    -------
-    DataFrame
-        A full period of time with positive and negative hours to detections.
-
-    """
-    data["start_datetime"] = to_datetime(data["start_datetime"], utc=True)
-    meta["start_datetime"] = to_datetime(meta["start_datetime"], utc=True)
-
-    deploy_detec = data["deploy.name"].unique()
-    df_filtered = meta[meta["deploy.name"].isin(deploy_detec)]
-
-    output = df_filtered.merge(
-        data[["deploy.name", "start_datetime", "DPM"]],
-        on=["deploy.name", "start_datetime"],
-        how="outer",
-    )
-    output["DPM"] = output["DPM"].fillna(0)
-
-    output["Day"] = output["start_datetime"].dt.day
-    output["Month"] = output["start_datetime"].dt.month
-    output["Year"] = output["start_datetime"].dt.year
-    output["hour"] = output["start_datetime"].dt.hour
-
-    return output
-
-
-def feeding_buzz(df: DataFrame, species: str) -> DataFrame:
+def feeding_buzz(
+    df: DataFrame,
+    species: str,
+) -> DataFrame:
     """Process a CPOD/FPOD feeding buzz detection file.
 
     Gives the feeding buzz duration, depending on the studied species.
@@ -607,81 +544,270 @@ def assign_daytime(
     return df
 
 
-def csv_folder(
-    folder_path: Path,
-    sep: str = ";",
-    encoding: str = "latin-1",
+def is_dpm_col(
+    df: DataFrame,
 ) -> DataFrame:
-    """Process all CSV files from a folder.
+    """Ensure DPM column exists with default value of 1.
 
     Parameters
     ----------
-    folder_path: Path
-        Folder's place.
-    sep: str, default=";"
-        Column separator.
-    encoding: str, default="latin-1"
-        File encoding.
+    df: DataFrame
+        Input dataframe.
 
     Returns
     -------
     DataFrame
-        Concatenated data with optional filename column.
-
-    Raises
-    ------
-    ValueError
-        If no CSV files found.
+        Copy of df with DPM column.
 
     """
-    all_files = list(folder_path.rglob("*.csv"))
-
-    if not all_files:
-        msg = f"No .csv files found in {folder_path}"
-        raise ValueError(msg)
-
-    all_data = []
-    for file in all_files:
-        df = read_csv(file, sep=sep, encoding=encoding)
-        df["deploy.name"] = file.stem
-        all_data.append(df)
-
-    return concat(all_data, ignore_index=True)
+    df = df.copy()
+    if "DPM" not in df.columns:
+        df["DPM"] = 1
+    return df
 
 
-def txt_folder(folder_path: Path,
-               sep: str = "\t") -> DataFrame:
-    r"""Process all TXT files from a folder.
+def pf_datetime(
+    df: DataFrame,
+    col_datetime: str,
+    frequency: str,
+) -> DataFrame:
+    """Parse datetime column and floor to specified frequency.
 
     Parameters
     ----------
-    folder_path: Path
-        Folder's place.
-    sep: str, default="\t"
-        Column separator.
+    df: DataFrame
+        Input dataframe.
+    col_datetime: str
+        Name of datetime column.
+    frequency: str
+        Pandas frequency string (e.g., "D", "h", "10min").
 
     Returns
     -------
     DataFrame
-       Concatenated data from all TXT files.
+        Copy of df with parsed and floored datetime.
 
     """
-    all_files = list(Path(folder_path).rglob("*.txt"))
+    df = df.copy()
+    df[col_datetime] = to_datetime(df[col_datetime], utc=True)
+    df[col_datetime] = df[col_datetime].dt.floor(frequency)
+    return df
 
-    if not all_files:
-        msg = f"No .txt files found in {folder_path}"
-        raise ValueError(msg)
 
-    all_data = []
-    for file in all_files:
-        file_path = folder_path / file
-        df = read_csv(file_path, sep=sep)
-        all_data.append(df)
+def build_aggregation_dict(
+    df: DataFrame,
+    base_agg: dict[str, str],
+    extra_columns: list[str] | None = None,
+) -> dict[str, str]:
+    """Build aggregation dictionary with validation.
 
-    return concat(all_data, ignore_index=True)
+    Parameters
+    ----------
+    df: DataFrame
+        Input dataframe to check column existence.
+    base_agg: dict[str, str]
+        Base aggregation dictionary (e.g., {"DPM": "sum"}).
+    extra_columns: list[str], optional
+        Additional columns to aggregate with "first" strategy.
+
+    Returns
+    -------
+    dict[str, str]
+        Complete aggregation dictionary.
+
+    """
+    agg_dict = base_agg.copy()
+
+    if extra_columns:
+        for col in extra_columns:
+            if col in df.columns:
+                agg_dict[col] = "first"
+            else:
+                logger.warning("Column '%s' does not exist and will be ignored.", col)
+
+    return agg_dict
+
+
+def resample_dpm(
+    df: DataFrame,
+    frq: str,
+    group_by: list[str] | None = None,
+    extra_columns: list[str] | None = None,
+) -> DataFrame:
+    """Resample DPM data to specified time frequency.
+
+    Aggregates Detection Positive Minutes (DPM) by time period,
+    optionally preserving grouping columns like deployment name.
+
+    Parameters
+    ----------
+    df: DataFrame
+        CPOD result DataFrame with DPM data.
+    frq: str
+        Pandas frequency string: "D" (day), "h" (hour), "10min", etc.
+    group_by: list[str], optional
+        Columns to group by (e.g., ["deploy.name", "start_datetime"]).
+        If None, groups only by start_datetime.
+    extra_columns: list[str], optional
+        Additional columns to preserve (uses "first" aggregation).
+
+    Returns
+    -------
+    DataFrame
+        Resampled DataFrame with aggregated DPM values.
+
+    Examples
+    --------
+    >>> # Daily aggregation per deployment
+    >>> resample_dpm(df, "D", group_by=["deploy.name"])
+
+    >>> # Hourly aggregation with site info preserved
+    >>> resample_dpm(df, "h", extra_columns=["site.name"])
+
+    """
+    df = is_dpm_col(df)
+    df = add_utc(df, ["start_datetime"], frq)
+
+    # Determine grouping columns
+    if group_by is None:
+        group_by = ["start_datetime"]
+
+    # Build aggregation dictionary
+    agg_dict = build_aggregation_dict(
+        df,
+        base_agg={"DPM": "sum"},
+        extra_columns=extra_columns,
+    )
+
+    return df.groupby(group_by).agg(agg_dict).reset_index()
+
+
+def deploy_period(
+    df: DataFrame,
+    col_timestamp: str = "start_datetime",
+    col_deployment: str = "deploy.name",
+) -> DataFrame:
+    """Extract start and end timestamps for each deployment.
+
+    Parameters
+    ----------
+    df: DataFrame
+        Input dataframe with parsed timestamps.
+    col_timestamp: str, default="start_datetime"
+        Name of the timestamp column.
+    col_deployment: str, default="deploy.name"
+        Name of the deployment identifier column.
+
+    Returns
+    -------
+    DataFrame
+        DataFrame with columns: [col_deployment, 'Début', 'Fin'].
+
+    """
+    return (
+        df.groupby([col_deployment])
+        .agg(Début=(col_timestamp, "first"), Fin=(col_timestamp, "last"))
+        .reset_index()
+    )
+
+
+def first_last(
+    df: DataFrame,
+    col_timestamp: str = "start_datetime",
+    col_deployment: str = "deploy.name",
+    date_formats: list[str] | None = None,
+) -> DataFrame:
+    """Isolate beginning and end of every deployment.
 
+    Parameters
+    ----------
+    df: DataFrame
+        CPOD result DataFrame.
+    col_timestamp: str, default="start_datetime"
+        Name of the timestamps column.
+    col_deployment: str, default="deploy.name"
+        Name of the deployment identifier column.
+    date_formats: list[str], optional
+        List of date formats to try for parsing.
+
+    Returns
+    -------
+    DataFrame
+        DataFrame with deployment periods (Début, Fin).
+
+    """
+    df_parsed = parse_timestamps(df, col_timestamp, date_formats)
+    return deploy_period(df_parsed, col_timestamp, col_deployment)
+
+def actual_data(
+    df: DataFrame,
+    meta: DataFrame,
+) -> DataFrame:
+    """Create a table with beginning and end of every deployment using metadata.
+
+    Parameters
+    ----------
+    df: DataFrame
+        Contains beginning and end for every deployment.
+    meta: DataFrame
+        Contains metadata for every deployment.
 
-def extract_site(df: DataFrame) -> DataFrame:
+    Returns
+    -------
+    DataFrame
+        DataFrame with corrected deployment periods (Début, Fin).
+
+    """
+    required_columns(
+        df,["deploy.name","ChunkEnd"])
+    required_columns(
+        meta,["deploy.name", "deployment_date","recovery_date"])
+
+    beg_end = first_last(df, "ChunkEnd")
+
+    beg_end = add_utc(beg_end, ["Début", "Fin"])
+
+    final = beg_end.merge(meta[["deployment_date","recovery_date","deploy.name"]],
+                          on = "deploy.name", how="left")
+    final.loc[final["Début"] < final["deployment_date"], "Début"] = final["deployment_date"]
+    final.loc[final["Fin"] > final["recovery_date"], "Fin"] = final["recovery_date"]
+    return final.drop(["deployment_date", "recovery_date"], axis=1)
+
+def create_matrix(
+    df: DataFrame,
+    group_cols: list,
+    agg_cols: list,
+)-> DataFrame:
+    """Create a stats matrix (mean & std).
+
+    Parameters
+    ----------
+    df : DataFrame
+        Extended frame with raw data to calculate stats for
+    group_cols : list
+        Additional columns to group by
+    agg_cols : list
+        Columns to aggregate
+
+    Returns
+    -------
+    Give a matrix of the data in [agg_cols] grouped by [group_cols].
+
+    """
+    matrix = df.groupby(group_cols).agg({
+        col: ["mean", "std"] for col in agg_cols
+    })
+    matrix = matrix.reset_index()
+
+    matrix.columns = group_cols + [f"{col}_{stat}"
+                                    for col in agg_cols
+                                    for stat in ["mean", "std"]]
+    return matrix
+
+
+def extract_site(
+    df: DataFrame,
+) -> DataFrame:
     """Create new columns: site.name and campaign.name, in order to match the metadata.
 
     Parameters
@@ -695,11 +821,15 @@ def extract_site(df: DataFrame) -> DataFrame:
         The same dataframe with two additional columns.
 
     """
+    required_columns(df, ["deploy.name"])
     df[["site.name", "campaign.name"]] = df["deploy.name"].str.split("_", expand=True)
     return df
 
 
-def percent_calc(data: DataFrame, time_unit: str | None = None) -> DataFrame:
+def percent_calc(
+    data: DataFrame,
+    time_unit: str | None = None,
+) -> DataFrame:
     """Calculate percentage of clicks, feeding buzzes and positive hours to detection.
 
     Computed on the entire effort and for every site.
@@ -956,261 +1086,6 @@ def hour_percent(df: DataFrame, metric: str) -> None:
     plt.show()
 
 
-def is_dpm_col(df: DataFrame) -> DataFrame:
-    """Ensure DPM column exists with default value of 1.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe.
-
-    Returns
-    -------
-    DataFrame
-        Copy of df with DPM column.
-
-    """
-    df = df.copy()
-    if "DPM" not in df.columns:
-        df["DPM"] = 1
-    return df
-
-
-def pf_datetime(
-    df: DataFrame,
-    col_datetime: str,
-    frequency: str,
-) -> DataFrame:
-    """Parse datetime column and floor to specified frequency.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe.
-    col_datetime: str
-        Name of datetime column.
-    frequency: str
-        Pandas frequency string (e.g., "D", "h", "10min").
-
-    Returns
-    -------
-    DataFrame
-        Copy of df with parsed and floored datetime.
-
-    """
-    df = df.copy()
-    df[col_datetime] = to_datetime(df[col_datetime], utc=True)
-    df[col_datetime] = df[col_datetime].dt.floor(frequency)
-    return df
-
-
-def build_aggregation_dict(
-    df: DataFrame,
-    base_agg: dict[str, str],
-    extra_columns: list[str] | None = None,
-) -> dict[str, str]:
-    """Build aggregation dictionary with validation.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe to check column existence.
-    base_agg: dict[str, str]
-        Base aggregation dictionary (e.g., {"DPM": "sum"}).
-    extra_columns: list[str], optional
-        Additional columns to aggregate with "first" strategy.
-
-    Returns
-    -------
-    dict[str, str]
-        Complete aggregation dictionary.
-
-    """
-    agg_dict = base_agg.copy()
-
-    if extra_columns:
-        for col in extra_columns:
-            if col in df.columns:
-                agg_dict[col] = "first"
-            else:
-                logger.warning("Column '%s' does not exist and will be ignored.", col)
-
-    return agg_dict
-
-
-def resample_dpm(
-    df: DataFrame,
-    frq: str,
-    group_by: list[str] | None = None,
-    extra_columns: list[str] | None = None,
-) -> DataFrame:
-    """Resample DPM data to specified time frequency.
-
-    Aggregates Detection Positive Minutes (DPM) by time period,
-    optionally preserving grouping columns like deployment name.
-
-    Parameters
-    ----------
-    df: DataFrame
-        CPOD result DataFrame with DPM data.
-    frq: str
-        Pandas frequency string: "D" (day), "h" (hour), "10min", etc.
-    group_by: list[str], optional
-        Columns to group by (e.g., ["deploy.name", "start_datetime"]).
-        If None, groups only by start_datetime.
-    extra_columns: list[str], optional
-        Additional columns to preserve (uses "first" aggregation).
-
-    Returns
-    -------
-    DataFrame
-        Resampled DataFrame with aggregated DPM values.
-
-    Examples
-    --------
-    >>> # Daily aggregation per deployment
-    >>> resample_dpm(df, "D", group_by=["deploy.name"])
-
-    >>> # Hourly aggregation with site info preserved
-    >>> resample_dpm(df, "h", extra_columns=["site.name"])
-
-    """
-    df = is_dpm_col(df)
-    df = pf_datetime(df, "start_datetime", frq)
-
-    # Determine grouping columns
-    if group_by is None:
-        group_by = ["start_datetime"]
-
-    # Build aggregation dictionary
-    agg_dict = build_aggregation_dict(
-        df,
-        base_agg={"DPM": "sum"},
-        extra_columns=extra_columns,
-    )
-
-    return df.groupby(group_by).agg(agg_dict).reset_index()
-
-
-def date_format(
-    df: DataFrame,
-) -> DataFrame:
-    """Change the date time format of a DataFrame to "%d/%m/%Y %H:%M:%S".
-
-    Parameters
-    ----------
-    df: pd.DataFrame
-        CPOD result DataFrame
-
-    Returns
-    -------
-    Return the same dataframe with a different time format.
-
-    """
-    df["Date heure"] = to_datetime(df["Date heure"], format="%Y-%m-%d %H:%M:%S")
-    df["Date heure"] = df["Date heure"].dt.strftime("%d/%m/%Y %H:%M:%S")
-
-    return df
-
-
-def parse_timestamps(
-    df: DataFrame,
-    col_timestamp: str,
-    date_formats: list[str] | None = None,
-) -> DataFrame:
-    """Parse timestamp column with multiple possible formats.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe.
-    col_timestamp: str
-        Name of the timestamp column to parse.
-    date_formats: list[str], optional
-        List of strptime formats to try. If None, uses common formats.
-
-    Returns
-    -------
-    DataFrame
-        Copy of df with parsed timestamps.
-
-    Raises
-    ------
-    ValueError
-        If timestamps cannot be parsed with any format.
-
-    """
-    if date_formats is None:
-        date_formats = [
-            "%Y-%m-%dT%H:%M:%S:%Z",
-            "%Y-%m-%dT%H:%M:%S",
-            "%d/%m/%Y %H:%M",
-        ]
-
-    df = df.copy()
-    df[col_timestamp] = df[col_timestamp].apply(
-        lambda x: strptime_from_text(x, date_formats))
-    return df
-
-
-def deploy_period(
-    df: DataFrame,
-    col_timestamp: str = "start_datetime",
-    col_deployment: str = "deploy.name",
-) -> DataFrame:
-    """Extract start and end timestamps for each deployment.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe with parsed timestamps.
-    col_timestamp: str, default="start_datetime"
-        Name of the timestamp column.
-    col_deployment: str, default="deploy.name"
-        Name of the deployment identifier column.
-
-    Returns
-    -------
-    DataFrame
-        DataFrame with columns: [col_deployment, 'Début', 'Fin'].
-
-    """
-    return (
-        df.groupby([col_deployment])
-        .agg(Début=(col_timestamp, "first"), Fin=(col_timestamp, "last"))
-        .reset_index()
-    )
-
-
-def actual_data(
-    df: DataFrame,
-    col_timestamp: str = "start_datetime",
-    col_deployment: str = "deploy.name",
-    date_formats: list[str] | None = None,
-) -> DataFrame:
-    """Create a table with beginning and end of every deployment.
-
-    Parameters
-    ----------
-    df: DataFrame
-        CPOD result DataFrame.
-    col_timestamp: str, default="start_datetime"
-        Name of the timestamps column.
-    col_deployment: str, default="deploy.name"
-        Name of the deployment identifier column.
-    date_formats: list[str], optional
-        List of date formats to try for parsing.
-
-    Returns
-    -------
-    DataFrame
-        DataFrame with deployment periods (Début, Fin).
-
-    """
-    df_parsed = parse_timestamps(df, col_timestamp, date_formats)
-    return deploy_period(df_parsed, col_timestamp, col_deployment)
-
-
 def calendar(
     meta: DataFrame,
     data: DataFrame,
@@ -1286,35 +1161,4 @@ def calendar(
     # Layout final
     plt.xticks(fontsize=12)
     plt.tight_layout()
-    plt.show()
-
-
-def create_matrix(df: DataFrame,
-                   group_cols: list,
-                   agg_cols: list,
-                   )-> DataFrame:
-    """Create a stats matrix (mean & std).
-
-    Parameters
-    ----------
-    df : DataFrame
-        Extended frame with raw data to calculate stats for
-    group_cols : list
-        Additional columns to group by
-    agg_cols : list
-        Columns to aggregate
-
-    Returns
-    -------
-    Give a matrix of the data in [agg_cols] grouped by [group_cols].
-
-    """
-    matrix = df.groupby(group_cols).agg({
-        col: ["mean", "std"] for col in agg_cols
-    })
-    matrix = matrix.reset_index()
-
-    matrix.columns = group_cols + [f"{col}_{stat}"
-                                    for col in agg_cols
-                                    for stat in ["mean", "std"]]
-    return matrix
+    plt.show()
\ No newline at end of file

From 87d8cb596e89839719f14f60278da42512a57319 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 24 Oct 2025 12:42:08 +0200
Subject: [PATCH 22/83] add tests

---
 tests/test_fpod_utils.py | 38 +++++++++++++++++++++++---------------
 1 file changed, 23 insertions(+), 15 deletions(-)

diff --git a/tests/test_fpod_utils.py b/tests/test_fpod_utils.py
index 1e4504f..566d07a 100644
--- a/tests/test_fpod_utils.py
+++ b/tests/test_fpod_utils.py
@@ -22,7 +22,6 @@
     feeding_buzz,
     assign_daytime,
     is_dpm_col,
-    pf_datetime,
     build_aggregation_dict,
     resample_dpm)
 
@@ -107,8 +106,8 @@ def aplose_dataframe() -> DataFrame:
                 Timestamp("2024-12-30T09:32:00.000+00:00"),
             ],
             "is_box": [0, 0, 0, 0, 0, 0],
-            "deploy.name": ["site_deploy", "site_deploy", "site_deploy",
-                            "site_deploy", "site_deploy", "site_deploy"],
+            "deploy.name": ["site_campaign", "site_campaign", "site_campaign",
+                            "site_campaign", "site_campaign", "site_campaign"],
         },
     )
 
@@ -175,11 +174,11 @@ def test_fb_folder_non_existent() -> None:
     with pytest.raises(FileNotFoundError):
         txt_folder(Path("/non/existent/folder"))
 
-def test_fb_folder_no_files(tmp_path) -> None:
+def test_fb_folder_no_files(tmp_path: pytest.fixture) -> None:
     with pytest.raises(ValueError, match="No .txt files found"):
         txt_folder(tmp_path)
 
-    # extract_site
+# extract_site
 def test_extract_site(self) -> None:
     input_data = [
         {"deploy.name":"Walde_Phase46"},
@@ -236,7 +235,7 @@ def test_csv_folder_non_existent() -> None:
     with pytest.raises(FileNotFoundError):
         csv_folder(Path("/non/existent/folder"))
 
-def test_csv_folder_no_files(tmp_path) -> None:
+def test_csv_folder_no_files(tmp_path: pytest.fixture) -> None:
     with pytest.raises(ValueError, match="No .csv files found"):
         csv_folder(tmp_path)
 
@@ -252,13 +251,16 @@ def test_csv_folder_no_files(tmp_path) -> None:
 # resample_dpm
 
 
-# actual_data
+# parse_timestamps
 def test_parse_timestamps() -> None:
-    df = DataFrame({"date": ["2024-01-01T10:00:00", "01/01/2024 10:00"]})
+    df = DataFrame({"date": ["2024-01-01T10:00:00", "06/01/2025 08:35"]})
     result = parse_timestamps(df, "date")
+    expected = DataFrame({"date": ["2024-01-01 10:00:00",
+                                   "2025-01-06 08:35:00"]}).astype("datetime64[ns]")
+    assert_frame_equal(result, expected)
 
-
-def test_get_deployment_periods() -> None:
+# deploy_period
+def test_deploy_period() -> None:
     df = DataFrame(
         {
             "deploy.name": ["A", "A", "B"],
@@ -272,10 +274,16 @@ def test_get_deployment_periods() -> None:
     expected = DataFrame(
         {
             "deploy.name": ["A", "B"],
-            "Début": [datetime(2024, 1, 1, 10, 0, tzinfo=datetime.timezone.utc),
-                      datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc)],
-            "Fin": [datetime(2024, 1, 2, 15, 30, tzinfo=datetime.timezone.utc),
-                    datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc)],
+            "Début": [
+                datetime(2024, 1, 1, 10, 0, tzinfo=datetime.timezone.utc),
+                datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
+            ],
+            "Fin": [
+                datetime(2024, 1, 2, 15, 30, tzinfo=datetime.timezone.utc),
+                datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
+            ],
         })
     result = deploy_period(df)
-    assert_frame_equal(result, expected)
\ No newline at end of file
+    assert_frame_equal(result, expected)
+
+# actual_data
\ No newline at end of file

From 885b0b52fd5f5ced84f7de731535a0ecc6eb4abe Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 24 Oct 2025 12:43:28 +0200
Subject: [PATCH 23/83] change some functionalities

---
 user_case/user_case_CALAIS.ipynb | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index 21ebcef..6703596 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-10-17T09:13:24.163916Z",
-     "start_time": "2025-10-17T09:13:21.433376Z"
+     "end_time": "2025-10-23T15:42:49.393544Z",
+     "start_time": "2025-10-23T15:42:47.415071Z"
     }
    },
    "source": [
@@ -21,7 +21,7 @@
     ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import csv_folder, cpod2aplose, fpod2aplose, actual_data, meta_cut_aplose, resample_dpm,txt_folder, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent, \\\n",
+    "from post_processing.utils.fpod_utils import csv_folder, cpod2aplose, fpod2aplose, actual_data, meta_cut_aplose, resample_dpm, txt_folder, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent, \\\n",
     "    feeding_buzz\n",
     "from post_processing.utils.core_utils import json2df,get_season"
    ],
@@ -56,7 +56,13 @@
     "path = csv_folder(pod_files)\n",
     "print(path.head())\n",
     "\n",
-    "df_0 = path.dropna()"
+    "df_0 = path.dropna()\n",
+    "\n",
+    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_calais.json\") #Path to your metadata file.\n",
+    "metadatax = json2df(json_path=json)\n",
+    "\n",
+    "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
+    "                           metadatax[\"campaign.name\"].astype(str))"
    ],
    "id": "8636a8a27fe2af47",
    "outputs": [
@@ -83,7 +89,7 @@
     }
    },
    "cell_type": "code",
-   "source": "d_beg_end = actual_data(df_0, col_timestamp=\"ChunkEnd\")",
+   "source": "d_beg_end = actual_data(df_0, metadatax)",
    "id": "4208969d9e509a8",
    "outputs": [],
    "execution_count": 12
@@ -245,11 +251,7 @@
    },
    "cell_type": "code",
    "source": [
-    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_calais.json\") #Path to your metadata file.\n",
-    "metadatax = json2df(json_path=json)\n",
     "\n",
-    "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
-    "                           metadatax[\"campaign.name\"].astype(str))\n",
     "\n",
     "cleared = meta_cut_aplose(df_aplose, metadatax) #Remove lines captures outside the instrument submersion."
    ],

From 211a7d825de722a5be93ad665a7f4b78f33cf784 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 24 Oct 2025 12:43:37 +0200
Subject: [PATCH 24/83] add new notebook

---
 .../example_FPOD-CPOD_firstresults.ipynb      | 60 +++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 user_case/example_FPOD-CPOD_firstresults.ipynb

diff --git a/user_case/example_FPOD-CPOD_firstresults.ipynb b/user_case/example_FPOD-CPOD_firstresults.ipynb
new file mode 100644
index 0000000..50da958
--- /dev/null
+++ b/user_case/example_FPOD-CPOD_firstresults.ipynb
@@ -0,0 +1,60 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import pytz\n",
+    "from pandas import (\n",
+    "    concat,\n",
+    "    read_csv,\n",
+    "    to_datetime,\n",
+    ")\n",
+    "\n",
+    "from post_processing.utils.fpod_utils import extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "",
+   "id": "36421fdbbca9aed6"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "55b73b0158109c1"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ba07b5927eae4cc9f16d6deba711dee2be2ba1c1 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 28 Oct 2025 15:04:39 +0100
Subject: [PATCH 25/83] add comments and functions to the notebooks

---
 user_case/example_FPOD-CPOD_aplose.ipynb      | 158 ++++++++--
 .../example_FPOD-CPOD_firstresults.ipynb      | 297 +++++++++++++++++-
 2 files changed, 409 insertions(+), 46 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
index ffdf2eb..263281e 100644
--- a/user_case/example_FPOD-CPOD_aplose.ipynb
+++ b/user_case/example_FPOD-CPOD_aplose.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:10:42.240883Z",
-     "start_time": "2025-10-24T08:10:40.168812Z"
+     "end_time": "2025-10-28T10:37:15.228840Z",
+     "start_time": "2025-10-28T10:37:13.056451Z"
     }
    },
    "source": [
@@ -19,7 +19,7 @@
     ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import resample_dpm, feeding_buzz, txt_folder, build_range"
+    "from post_processing.utils.fpod_utils import resample_dpm, feeding_buzz, txt_folder, build_range, csv_folder"
    ],
    "outputs": [],
    "execution_count": 1
@@ -27,27 +27,35 @@
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "### Load data\n",
+   "source": [
+    "### Load data\n",
+    "DPM = Detection Positive Minutes \\\n",
+    "FB = Feeding buzzes \\\n",
+    "🐬 = input to modify\n"
+   ],
    "id": "a97e19830123b732"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:20:44.203307Z",
-     "start_time": "2025-10-24T08:20:44.103414Z"
+     "end_time": "2025-10-28T10:37:54.942890Z",
+     "start_time": "2025-10-28T10:37:54.239910Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
+    "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\") #Change the file path in the yaml sheet.🐬\n",
     "data_list = DataAplose.from_yaml(file=yaml_file)\n",
     "\n",
-    "fb_files = Path(r\"U:\\fb_D_NBHF\")  #Path to your click details folder.\n",
-    "d_beg_end = read_csv(r\"U:\\Deb_Fin_Site D Simone.csv\")"
+    "fb_files = Path(r\"U:\\fb_CA4\")  #Path to your click details folder. 🐬\n",
+    "d_beg_end = read_csv(r\"U:\\Deb_Fin_CA4.csv\") #Beginning and end of recording for every phase. 🐬\n",
+    "\n",
+    "tl_path = Path(r\"U:\\TimeLost\\tl_ca4\")\n",
+    "TimeLost = csv_folder(tl_path)"
    ],
    "id": "7da2feb5958db1a9",
    "outputs": [],
-   "execution_count": 2
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -61,88 +69,170 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:20:52.992126Z",
-     "start_time": "2025-10-24T08:20:52.983477Z"
+     "end_time": "2025-10-28T10:37:59.845525Z",
+     "start_time": "2025-10-28T10:37:59.841429Z"
     }
    },
    "cell_type": "code",
-   "source": "frq = \"h\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
+   "source": "frq = \"h\" #Determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\". 🐬",
    "id": "9b0a078a262ac7f2",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": 4
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:20:56.935875Z",
-     "start_time": "2025-10-24T08:20:56.923925Z"
+     "end_time": "2025-10-28T10:38:01.074732Z",
+     "start_time": "2025-10-28T10:38:01.064333Z"
     }
    },
    "cell_type": "code",
-   "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"])",
+   "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"]) #Resample your DPMs according to the chosen frq.",
    "id": "fa3847d80ccf49c3",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": 5
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": [
     "### Feeding buzzes processing\n",
-    "Use \"Marsouin\" or \"Commerson\" to get different ICI processing."
+    "Use \"Dauphin\", Marsouin\" or \"Commerson\" to get different ICI processing."
    ],
    "id": "b92537991aa4ac4b"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:43:33.273328Z",
-     "start_time": "2025-10-24T08:43:33.011458Z"
+     "end_time": "2025-10-28T10:38:09.866301Z",
+     "start_time": "2025-10-28T10:38:09.058313Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "fb_all = txt_folder(fb_files)\n",
-    "fb_all = feeding_buzz(fb_all, \"Commerson\")\n",
+    "fb_all = txt_folder(fb_files) #Read all your FB.txt files.\n",
+    "fb_all = feeding_buzz(fb_all, \"Marsouin\") #Categorize the minutes (positive or not to FB detection). 🐬\n",
     "\n",
-    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
+    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq) #Resample your FBs according to the chosen frq.\n",
     "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
     "fb[\"start_datetime\"] = to_datetime(fb[\"start_datetime\"], utc=True)\n",
     "\n",
-    "dpm_fb = resamp.merge(fb, on=\"start_datetime\", how=\"left\")"
+    "dpm_fb = resamp.merge(fb, on=\"start_datetime\", how=\"left\") #Merge DPM and FB dataframes"
    ],
    "id": "ca2362e4facecca3",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 6
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:43:37.700188Z",
-     "start_time": "2025-10-24T08:43:37.677880Z"
+     "end_time": "2025-10-28T10:39:08.158384Z",
+     "start_time": "2025-10-28T10:39:08.125277Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "d_0 = build_range(d_beg_end, frq)\n",
+    "d_0 = build_range(d_beg_end, frq) #Create a dataframe from beginning to end of every phase filled with 0s.\n",
     "d_tot = d_0.merge(dpm_fb, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")"
    ],
    "id": "4d76089ef06c6fdb",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": 9
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### Add additional metrics columns",
+   "id": "e6a4623e4baf25b5"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:43:39.055027Z",
-     "start_time": "2025-10-24T08:43:39.047963Z"
+     "end_time": "2025-10-28T10:39:09.770505Z",
+     "start_time": "2025-10-28T10:39:09.756936Z"
     }
    },
    "cell_type": "code",
-   "source": "d_tot[[\"DPM\",\"Foraging\"]] = d_tot[[\"DPM\",\"Foraging\"]].fillna(0)",
+   "source": [
+    "d_tot[[\"DPM\",\"Foraging\"]] = d_tot[[\"DPM\",\"Foraging\"]].fillna(0)\n",
+    "d_tot[\"FBR\"] = d_tot[\"Foraging\"] / d_tot[\"DPM\"] #The Feeding Buzz Ratio corresponds to the proportion of FB among the recorded clicks.\n",
+    "d_tot[\"FBR\"] = d_tot[\"FBR\"].fillna(0)\n",
+    "d_tot[\"DPH\"] = (d_tot[\"DPM\"] >0).astype(int) #1 if the hour counts at least 1 DPM, else 0."
+   ],
    "id": "912268e5e997dbc6",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T10:39:11.585570Z",
+     "start_time": "2025-10-28T10:39:11.575488Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "d_tot[\"DPH_fb\"] = (d_tot[\"Foraging\"] >0).astype(int)\n",
+    "d_tot[\"FBR_h\"] = d_tot[\"DPH_fb\"] / d_tot[\"DPH\"]\n",
+    "d_tot[\"FBR_h\"] = d_tot[\"FBR_h\"].fillna(0)"
+   ],
+   "id": "23e3e4137d9e2a84",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### Add time columns",
+   "id": "a775158ba810957a"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T10:39:12.645461Z",
+     "start_time": "2025-10-28T10:39:12.621127Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "d_tot = d_tot.assign(**{attr: getattr(d_tot[\"start_datetime\"].dt, attr.lower())\n",
+    "                         for attr in ['Year', 'Month', 'Day', 'Hour']})\n",
+    "d_tot['YM'] = d_tot[\"start_datetime\"].dt.to_period('M').dt.to_timestamp()"
+   ],
+   "id": "62ce5a31ed0db25a",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\fouinel\\AppData\\Local\\Temp\\ipykernel_11560\\842014434.py:3: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
+      "  d_tot['YM'] = d_tot[\"start_datetime\"].dt.to_period('M').dt.to_timestamp()\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Extract your processed data\n",
+    "This dataframe is now compatible for analyses on the next notebook and on R."
+   ],
+   "id": "c64d09af5a11213d"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T10:39:32.751618Z",
+     "start_time": "2025-10-28T10:39:15.181512Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "d_tot.to_csv(r\"U:\\Hours_DPM_FBUZZ_CA4.csv\", index=False) #Name your file. 🐬",
+   "id": "f1f9ec385038ba87",
+   "outputs": [],
+   "execution_count": 13
   }
  ],
  "metadata": {
diff --git a/user_case/example_FPOD-CPOD_firstresults.ipynb b/user_case/example_FPOD-CPOD_firstresults.ipynb
index 50da958..51454a3 100644
--- a/user_case/example_FPOD-CPOD_firstresults.ipynb
+++ b/user_case/example_FPOD-CPOD_firstresults.ipynb
@@ -2,38 +2,311 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "initial_id",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:23:16.275855Z",
+     "start_time": "2025-10-28T13:23:14.175637Z"
+    }
    },
-   "outputs": [],
    "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "import pytz\n",
     "from pandas import (\n",
     "    concat,\n",
     "    read_csv,\n",
     "    to_datetime,\n",
     ")\n",
     "\n",
-    "from post_processing.utils.fpod_utils import extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent"
-   ]
+    "from post_processing.utils.fpod_utils import extract_site, percent_calc, year_percent, ym_percent, create_matrix,hist_mean_h, hist_mean_m, hist_mean_s\n",
+    "from post_processing.utils.core_utils import get_season"
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": "",
+   "source": [
+    "## Overview\n",
+    "Import the right dataset."
+   ],
    "id": "36421fdbbca9aed6"
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### *CALAIS*",
+   "id": "caea0e065ad8068c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:23:50.797293Z",
+     "start_time": "2025-10-28T13:23:49.437948Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ca4 = read_csv(r\"U:\\Hours_DPM_FBUZZ_CA4.csv\")\n",
+    "walde = read_csv(r\"U:\\Hours_DPM_FBUZZ_Walde.csv\")\n",
+    "\n",
+    "data_c = concat([ca4, walde])\n",
+    "data_c[\"start_datetime\"] = to_datetime(data_c[\"start_datetime\"])\n",
+    "data_c[\"start_datetime\"] = data_c[\"start_datetime\"].apply(lambda x : x.tz_convert(\"Europe/Paris\"))\n",
+    "data_c[\"Hour\"] = data_c[\"start_datetime\"].dt.hour"
+   ],
+   "id": "1268d9e6ce5cdf32",
+   "outputs": [],
+   "execution_count": 7
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### *CETIROISE*",
+   "id": "b426e672fdd5c6b8"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "ceti = read_csv(r\"U:\\Hours_DPM_FBUZZ_CETIROISE.csv\")\n",
+    "\n",
+    "ceti[\"start_datetime\"] = to_datetime(ceti[\"start_datetime\"])\n",
+    "ceti[\"start_datetime\"] = ceti[\"start_datetime\"].apply(lambda x : x.tz_convert(\"CET\")) #TimeZone Central European Time\n",
+    "ceti[\"Hour\"] = ceti[\"start_datetime\"].dt.hour"
+   ],
+   "id": "870bc0a014561ba8",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### *KERGUELEN*",
+   "id": "17a5ce1338f6cd1a"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "a = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteA.csv\")\n",
+    "b = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteB.csv\")\n",
+    "c = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteC.csv\")\n",
+    "d = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteD.csv\")\n",
+    "\n",
+    "data_k = concat([a, b, c, d])\n",
+    "data_k[\"start_datetime\"] = to_datetime(data_k[\"start_datetime\"])\n",
+    "data_k[\"start_datetime\"] = data_k[\"start_datetime\"].apply(lambda x : x.tz_convert(\"Indian/Kerguelen\"))\n",
+    "data_k[\"Hour\"] = data_k[\"start_datetime\"].dt.hour"
+   ],
+   "id": "d65697a1f1487f4c",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### First results\n",
+    "Precise your dataset."
+   ],
+   "id": "9fc3b5075bf7ff2c"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:23:53.389560Z",
+     "start_time": "2025-10-28T13:23:53.382634Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "data = data_c #🐬",
+   "id": "add4a626d6cc25a4",
+   "outputs": [],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:23:54.833954Z",
+     "start_time": "2025-10-28T13:23:54.513035Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "data = extract_site(data)\n",
+    "data[\"YMH\"] = data[\"Year\"].astype(str) + '-' + data[\"Month\"].astype(str) + '-' + data[\"Hour\"].astype(str)\n",
+    "y_per = percent_calc(data, \"Year\")\n",
+    "#t_per = percent_calc(data, \"TRAVAUX\")"
+   ],
+   "id": "37ecc80eda8e57ed",
+   "outputs": [],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:23:55.879816Z",
+     "start_time": "2025-10-28T13:23:55.849626Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "ym_per = percent_calc(data, \"YM\")\n",
+    "ym_per[\"YM\"] = to_datetime(ym_per[\"YM\"])\n",
+    "ym_per[\"Season\"] = ym_per[\"YM\"].apply(lambda x: get_season(x)[0])\n",
+    "ym_per[\"Month\"] = ym_per[\"YM\"].dt.month\n",
+    "ym_per[\"Year\"] = ym_per[\"YM\"].dt.year"
+   ],
+   "id": "2b988869ed2466e1",
+   "outputs": [],
+   "execution_count": 10
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:23:58.602641Z",
+     "start_time": "2025-10-28T13:23:58.572133Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "per_h = percent_calc(data, \"YMH\")\n",
+    "per_h[[\"Y\",\"M\",\"Hour\"]] = per_h[\"YMH\"].str.split(\"-\", expand=True)\n",
+    "per_h[\"Hour\"] = per_h[\"Hour\"].astype(int)"
+   ],
+   "id": "cf704032c4a59a7b",
+   "outputs": [],
+   "execution_count": 11
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:24:00.067944Z",
+     "start_time": "2025-10-28T13:24:00.054075Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "matrice_s = create_matrix(ym_per, [\"site.name\"],[\"%DPH\", \"FBR\"])\n",
+    "matrice_m = create_matrix(ym_per, [\"site.name\", \"Month\"],[\"%click\", \"FBR\"])\n",
+    "matrice_h = create_matrix(per_h, [\"site.name\", \"Hour\"],[\"%click\", \"FBR\"])"
+   ],
+   "id": "caf3f71c6b6f70ca",
+   "outputs": [],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:24:01.960321Z",
+     "start_time": "2025-10-28T13:24:01.821063Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "hist_mean_s(\n",
+    "    matrice_s,\n",
+    "    metric_mean=\"%DPH_mean\",\n",
+    "    metric_std=\"%DPH_std\",\n",
+    "    ylabel=\"Moyenne %DPH\",\n",
+    "    title_suffix=\"%DPH\"\n",
+    ")"
+   ],
+   "id": "2ff751ae02e80285",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1500x900 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:37:16.391453Z",
+     "start_time": "2025-10-28T13:37:16.257339Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "year_percent(y_per, \"FBR\")",
+   "id": "29f2703ab28c5b28",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2100x750 with 2 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 15
+  },
   {
    "metadata": {},
    "cell_type": "code",
+   "source": "ym_percent(ym_per, \"%click\")",
+   "id": "a2dacac3caecff5f",
    "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "55b73b0158109c1"
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "hist_mean_m(\n",
+    "    matrice_m,\n",
+    "    metric_mean=\"%click_mean\",\n",
+    "    metric_std=\"%click_std\",\n",
+    "    ylabel=\"Moyenne %click\",\n",
+    "    title_suffix=\"%click\"\n",
+    ")"
+   ],
+   "id": "71161e7545bb1414",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-28T13:26:45.510525Z",
+     "start_time": "2025-10-28T13:26:45.252371Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "hist_mean_h(\n",
+    "    matrice_h,\n",
+    "    metric_mean=\"FBR_mean\",\n",
+    "    metric_std=\"FBR_std\",\n",
+    "    ylabel=\"Feeding buzz ratio\",\n",
+    "    title_suffix=\"FBR\"\n",
+    ")"
+   ],
+   "id": "5cbea8601bce2172",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2100x1500 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 14
   }
  ],
  "metadata": {

From 9916c15086ba3b48f32b6c12ad0c48c58e0e5696 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 28 Oct 2025 15:05:45 +0100
Subject: [PATCH 26/83] simplify some functionalities

---
 src/post_processing/utils/fpod_utils.py | 337 ++++++++++++++++++++++--
 1 file changed, 320 insertions(+), 17 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 2d81ed9..93a6c89 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -43,10 +43,10 @@
 }
 
 season_color = {
-    "spring": "#C5E0B4", #green
-    "summer": "#FCF97F", #darkgoldenrod
-    "autumn": "#ED7C2F", #orange
-    "winter": "#B4C7E8", #blue
+    "spring": "green", #C5E0B4
+    "summer": "darkgoldenrod", #FCF97F
+    "autumn": "orange", #ED7C2F
+    "winter": "blue", #B4C7E8
 }
 
 def fpod2aplose(
@@ -144,6 +144,7 @@ def cpod2aplose(
         df_deploy = df[df["deploy.name"] == deploy_name].copy()
 
         result = fpod2aplose(df_deploy, tz, dataset_name, annotation, bin_size)
+        result["annotator"] = result.loc[result["annotator"] == "FPOD"] = "CPOD"
 
         if extra_columns:
             for col in extra_columns:
@@ -155,6 +156,67 @@ def cpod2aplose(
     return concat(results, ignore_index=True)
 
 
+def pod2aplose(
+    df: DataFrame,
+    tz: pytz.timezone,
+    dataset_name: str,
+    annotation: str,
+    annotator: str,
+    bin_size: int = 60,
+) -> DataFrame:
+    """Format PODs DataFrame to match APLOSE format.
+
+    Parameters
+    ----------
+    df: DataFrame
+        FPOD result dataframe
+    tz: pytz.timezone
+        Timezone object to get non-naïve datetimes
+    dataset_name: str
+        dataset name
+    annotation: str
+        annotation name
+    annotator: str
+        annotator name
+    bin_size: int
+        Duration of the detections in seconds
+
+    Returns
+    -------
+    DataFrame
+        An APLOSE formatted DataFrame
+
+    """
+    df = df.copy()
+    df["_temp_dt"] = [
+        tz.localize(strptime_from_text(entry, "%d/%m/%Y %H:%M"))
+        for entry in df["ChunkEnd"]
+    ]
+
+    # Trier le DataFrame selon ces datetime
+    df = df.sort_values("_temp_dt").reset_index(drop=True)
+
+    # Maintenant extraire les colonnes triées
+    fpod_start_dt = df["_temp_dt"].tolist()
+    fpod_end_dt = [entry + Timedelta(seconds=bin_size) for entry in fpod_start_dt]
+
+    data = {
+        "dataset": [dataset_name] * len(df),
+        "filename": df["deploy.name"].tolist(),
+        "start_time": [0] * len(df),
+        "end_time": [bin_size] * len(df),
+        "start_frequency": [0] * len(df),
+        "end_frequency": [0] * len(df),
+        "annotation": [annotation] * len(df),
+        "annotator": [annotator] * len(df),
+        "start_datetime": [strftime_osmose_format(entry) for entry in fpod_start_dt],
+        "end_datetime": [strftime_osmose_format(entry) for entry in fpod_end_dt],
+        "is_box": [0] * len(df),
+    }
+
+    return DataFrame(data)
+
+
 def csv_folder(
     folder_path: Path,
     sep: str = ";",
@@ -359,6 +421,7 @@ def meta_cut_aplose(
         metadata,["deploy.name", "deployment_date","recovery_date"])
 
     raw = parse_timestamps(raw_data, "start_datetime")
+    raw = raw.sort_values(["start_datetime"])
 
     dfm = raw.merge(
         metadata[["deploy.name", "deployment_date","recovery_date"]],
@@ -428,11 +491,11 @@ def build_range(
         A full period of time with positive and negative hours to detections.
 
     """
-    add_utc(df, ["Début","Fin"], fr)
+    add_utc(df, ["Deb","Fin"], fr)
 
     all_ranges = []
     for _, row in df.iterrows():
-        hours = date_range(row["Début"], row["Fin"], freq=fr)
+        hours = date_range(row["Deb"], row["Fin"], freq=fr)
         tmp = DataFrame(
             {
                 "deploy.name": row["deploy.name"],
@@ -468,7 +531,9 @@ def feeding_buzz(
     df["microsec"] = df["microsec"] / 1e6
     df["ICI"] = df["microsec"].diff()
 
-    if species == "Marsouin":  # Nuuttila et al., 2013
+    if species == "Dauphin":  # Herzing et al., 2014
+        df["Buzz"] = (df["ICI"].between(0, 0.02)).astype(int)
+    elif species == "Marsouin":  # Nuuttila et al., 2013
         df["Buzz"] = (df["ICI"].between(0, 0.01)).astype(int)
     elif species == "Commerson":  # Reyes Reyes et al., 2015
         df["Buzz"] = (df["ICI"].between(0, 0.005)).astype(int)
@@ -701,12 +766,12 @@ def deploy_period(
     Returns
     -------
     DataFrame
-        DataFrame with columns: [col_deployment, 'Début', 'Fin'].
+        DataFrame with columns: [col_deployment, 'Deb', 'Fin'].
 
     """
     return (
         df.groupby([col_deployment])
-        .agg(Début=(col_timestamp, "first"), Fin=(col_timestamp, "last"))
+        .agg(Deb=(col_timestamp, "first"), Fin=(col_timestamp, "last"))
         .reset_index()
     )
 
@@ -733,7 +798,7 @@ def first_last(
     Returns
     -------
     DataFrame
-        DataFrame with deployment periods (Début, Fin).
+        DataFrame with deployment periods (Deb, Fin).
 
     """
     df_parsed = parse_timestamps(df, col_timestamp, date_formats)
@@ -755,7 +820,7 @@ def actual_data(
     Returns
     -------
     DataFrame
-        DataFrame with corrected deployment periods (Début, Fin).
+        DataFrame with corrected deployment periods (Deb, Fin).
 
     """
     required_columns(
@@ -765,12 +830,14 @@ def actual_data(
 
     beg_end = first_last(df, "ChunkEnd")
 
-    beg_end = add_utc(beg_end, ["Début", "Fin"])
+    beg_end = add_utc(beg_end, ["Deb", "Fin"])
 
     final = beg_end.merge(meta[["deployment_date","recovery_date","deploy.name"]],
                           on = "deploy.name", how="left")
-    final.loc[final["Début"] < final["deployment_date"], "Début"] = final["deployment_date"]
+    final.loc[final["Deb"] < final["deployment_date"], "Deb"] = final["deployment_date"]
     final.loc[final["Fin"] > final["recovery_date"], "Fin"] = final["recovery_date"]
+    final.loc[final["Deb"] > final["Fin"], ["Deb", "Fin"]] = None
+    final = final.sort_values(by=["Deb"])
     return final.drop(["deployment_date", "recovery_date"], axis=1)
 
 def create_matrix(
@@ -1108,14 +1175,14 @@ def calendar(
     meta["deployment_date"] = to_datetime(meta["deployment_date"])
     meta["recovery_date"] = to_datetime(meta["recovery_date"])
     meta = meta.sort_values(["deploy.name", "deployment_date"]).reset_index(drop=True)
-    data = data.sort_values(["deploy.name", "Début"]).reset_index(drop=True)
+    data = data.sort_values(["deploy.name", "Deb"]).reset_index(drop=True)
     df_fusion = data.merge(
         meta[["deploy.name", "deployment_date", "recovery_date"]],
         on=["deploy.name"],
         how="outer",
     )
 
-    df_fusion["Début"] = df_fusion["Début"].fillna(df_fusion["deployment_date"])
+    df_fusion["Deb"] = df_fusion["Deb"].fillna(df_fusion["deployment_date"])
     df_fusion["Fin"] = df_fusion["Fin"].fillna(df_fusion["deployment_date"])
 
     df_fusion[["Site", "Phase"]] = df_fusion["deploy.name"].str.split("_", expand=True)
@@ -1126,6 +1193,7 @@ def calendar(
 
     sites = sorted(df_fusion["Site"].unique(), reverse=True)
     site_mapping = {site: idx for idx, site in enumerate(sites)}
+
     for _, row in df_fusion.iterrows():
         y_pos = site_mapping[row["Site"]]
         ax.broken_barh(
@@ -1136,15 +1204,16 @@ def calendar(
             linewidth=0.8,
         )
 
-        if row["Début"] != row["deployment_date"]:
+        if notna(row["Deb"]) and notna(row["Fin"]) and row["Fin"] > row["Deb"]:
             ax.broken_barh(
-                [(row["Début"], row["Fin"] - row["Début"])],
+                [(row["Deb"], row["Fin"] - row["Deb"])],
                 (y_pos - 0.15, 0.3),
                 facecolors=row["color"],
                 edgecolors="black",
                 linewidth=0.8,
             )
 
+
     ax.set_yticks(range(len(sites)))
     ax.set_yticklabels(sites, fontsize=12)
 
@@ -1160,5 +1229,239 @@ def calendar(
     ax.legend(handles=legend_elements, loc="upper left", fontsize=11, frameon=True)
     # Layout final
     plt.xticks(fontsize=12)
+    plt.tight_layout()
+    plt.show()
+
+
+def hist_mean_m(
+    df: DataFrame,
+    metric_mean: str,
+    metric_std: str,
+    ylabel: str | None = None,
+    title_suffix: str | None = None,
+) -> None:
+    """Produce a histogram of the given data.
+
+    It shows mean and standard deviation of the metric.
+
+    Parameters
+    ----------
+    df: DataFrame
+        All data grouped by site and month
+    metric_mean: str
+        Column name for the mean values (e.g., "%click_mean")
+    metric_std: str
+        Column name for the standard deviation values (e.g., "%click_std")
+    ylabel: str, optional
+        Label for y-axis. If None, uses metric_mean
+    title_suffix: str, optional
+        Suffix for the main title. If None, uses metric_mean
+
+    Returns
+    -------
+    Return a plot of all deployments and associated data.
+
+    """
+    sites = df["site.name"].unique()
+    n_sites = len(sites)
+    fig, axs = plt.subplots(n_sites, 1, figsize=(14, 3 * n_sites), sharex=True)
+    if n_sites == 1:
+        axs = [axs]
+
+    # Calculate max for y-axis scaling
+    max_value = max(df[metric_mean] + df[metric_std])
+
+    for i, site in enumerate(sorted(sites)):
+        site_data = df[df["site.name"] == site]
+        ax = axs[i]
+
+        ax.bar(
+            x=site_data["Month"],
+            height=site_data[metric_mean],
+            yerr=site_data[metric_std],
+            capsize=4,
+            color=site_colors.get(site, "gray"),
+            alpha=0.8,
+            edgecolor="black",
+            linewidth=0.5,
+            label=f"Site {site}")
+
+        ax.set_title(f"{site}", fontsize=12)
+        ax.set_ylim(0, max_value * 1.1)
+        ax.set_ylabel(ylabel if ylabel else metric_mean, fontsize=10)
+
+        # Only set x-label on last subplot
+        if i == n_sites - 1:
+            ax.set_xlabel("Mois", fontsize=10)
+            ax.set_xticks(
+                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
+                [
+                    "Jan",
+                    "Fev",
+                    "Mar",
+                    "Avr",
+                    "Mai",
+                    "Jun",
+                    "Jul",
+                    "Aou",
+                    "Sep",
+                    "Oct",
+                    "Nov",
+                    "Dec",
+                ],
+            )
+        if metric_mean in ("%buzzes_mean", "FBR_mean"):
+            for _, bar in enumerate(ax.patches):
+                bar.set_hatch("/")
+
+    fig.suptitle(
+        f"{title_suffix if title_suffix else metric_mean} per month",
+        fontsize=16)
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    plt.show()
+
+
+def hist_mean_h(
+    df: DataFrame,
+    metric_mean: str,
+    metric_std: str,
+    ylabel: str | None = None,
+    title_suffix: str | None = None,
+) -> None:
+    """Produce a histogram of the given data.
+
+    It shows mean and standard deviation of the metric.
+
+    Parameters
+    ----------
+    df: DataFrame
+        All data grouped by site and month
+    metric_mean: str
+        Column name for the mean values (e.g., "%click_mean")
+    metric_std: str
+        Column name for the standard deviation values (e.g., "%click_std")
+    ylabel: str, optional
+        Label for y-axis. If None, uses metric_mean
+    title_suffix: str, optional
+        Suffix for the main title. If None, uses metric_mean
+
+    Returns
+    -------
+    Return a plot of all deployments and associated data.
+
+    """
+    sites = df["site.name"].unique()
+    n_sites = len(sites)
+    fig, axs = plt.subplots(n_sites, 1, figsize=(14, 5 * n_sites), sharex=True)
+    if n_sites == 1:
+        axs = [axs]
+
+    # Calculate max for y-axis scaling
+    max_value = max(df[metric_mean] + df[metric_std])
+
+    for i, site in enumerate(sorted(sites)):
+        site_data = df[df["site.name"] == site]
+        ax = axs[i]
+
+        ax.bar(
+            x=site_data["Hour"],
+            height=site_data[metric_mean],
+            yerr=site_data[metric_std],
+            capsize=4,
+            color=site_colors.get(site, "gray"),
+            alpha=0.8,
+            edgecolor="black",
+            linewidth=0.5,
+            label=f"Site {site}",
+        )
+
+        ax.set_title(f"{site}", fontsize=12)
+        ax.set_ylim(0, max_value * 1.1)
+        ax.set_ylabel(ylabel if ylabel else metric_mean, fontsize=10)
+        ax.set_xticks(range(24))
+
+        # Only set x-label on last subplot
+        if i == n_sites - 1:
+            ax.set_xlabel("Heure", fontsize=10)
+        if metric_mean in ("%buzzes_mean", "FBR_mean"):
+            for _, bar in enumerate(ax.patches):
+                bar.set_hatch("/")
+
+    fig.suptitle(
+        f"{title_suffix if title_suffix else metric_mean} per hour", fontsize=16)
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    plt.show()
+
+
+def hist_mean_s(
+    df: DataFrame,
+    metric_mean: str,
+    metric_std: str,
+    ylabel: str | None = None,
+    title_suffix: str | None = None,
+) -> None:
+    """Plot bar chart with mean values and error bars (std) per site.
+
+    Parameters
+    ----------
+    df: DataFrame
+        All data grouped by site
+    metric_mean: str
+        Column name for the mean values (e.g., "FBR_mean")
+    metric_std: str
+        Column name for the standard deviation values (e.g., "FBR_std")
+    ylabel: str, optional
+        Label for y-axis. If None, uses metric_mean
+    title_suffix: str, optional
+        Suffix for the title. If None, uses metric_mean
+    add_hatch: bool, optional
+        Add hatching pattern to bars (useful for FBR, %buzzes). Default False
+
+    """
+    fig, ax = plt.subplots(figsize=(10, 6))
+
+    # Group by site and calculate means if needed
+    plot_data = df.groupby("site.name")[[metric_mean, metric_std]].mean().reset_index()
+
+    x_pos = range(len(plot_data))
+
+    # Create bars
+    bars = ax.bar(
+        x=x_pos,
+        height=plot_data[metric_mean],
+        color=[site_colors.get(site, "gray") for site in plot_data["site.name"]],
+        alpha=0.8,
+        edgecolor="black",
+        linewidth=0.5)
+
+    # Add hatching if requested
+    if metric_mean in ("%buzzes_mean", "FBR_mean"):
+        for _, bar in enumerate(ax.patches):
+            bar.set_hatch("/")
+
+    # Add error bars
+    for i, (_, row) in enumerate(plot_data.iterrows()):
+        # Ensure error bar doesn't go below zero
+        yerr_lower = min(row[metric_mean], row[metric_std])
+        yerr_upper = row[metric_std]
+        ax.errorbar(
+            i,
+            row[metric_mean],
+            yerr=[[yerr_lower], [yerr_upper]],
+            fmt="none",
+            color="black",
+            capsize=5,
+            linewidth=2,
+        )
+
+    ax.set_xticks(x_pos)
+    ax.set_xticklabels(plot_data["site.name"])
+    ax.set_title(f"{title_suffix if title_suffix else metric_mean} per site",
+                 fontsize=12)
+    ax.set_ylabel(ylabel if ylabel else metric_mean, fontsize=10)
+    ax.set_xlabel("Site", fontsize=10)
+
     plt.tight_layout()
     plt.show()
\ No newline at end of file

From 8a3a7c7f106b6e87a78d604efaff179ce08c32ff Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 28 Oct 2025 15:05:54 +0100
Subject: [PATCH 27/83] add comments

---
 user_case/example_FPOD-CPOD_raw.ipynb | 177 +++++++++-----------------
 1 file changed, 63 insertions(+), 114 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index d2f0eef..141a1ea 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-10-24T07:52:57.623291Z",
-     "start_time": "2025-10-24T07:52:51.711980Z"
+     "end_time": "2025-10-28T10:33:49.873418Z",
+     "start_time": "2025-10-28T10:33:47.879805Z"
     }
    },
    "source": [
@@ -15,7 +15,7 @@
     "\n",
     "import pytz\n",
     "\n",
-    "from post_processing.utils.fpod_utils import csv_folder, cpod2aplose, fpod2aplose, actual_data, meta_cut_aplose\n",
+    "from post_processing.utils.fpod_utils import csv_folder, pod2aplose, actual_data, meta_cut_aplose\n",
     "from post_processing.utils.core_utils import json2df"
    ],
    "outputs": [],
@@ -26,7 +26,8 @@
    "cell_type": "markdown",
    "source": [
     "### Load data\n",
-    "Import your raw FPOD or CPOD data. All files for one site must be stored in the same folder.\n",
+    "🐬 = input to modify \\\n",
+    "Import your raw FPOD or CPOD data. All files for one site must be stored in the same folder and identified by their respective phases. \\\n",
     "You also need to import your metadata file."
    ],
    "id": "c464f241817a1407"
@@ -34,15 +35,15 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T07:53:19.702997Z",
-     "start_time": "2025-10-24T07:53:18.892419Z"
+     "end_time": "2025-10-28T10:34:05.874705Z",
+     "start_time": "2025-10-28T10:33:51.302952Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "pod_files = Path(r\"U:\\D\")\n",
-    "path = csv_folder(pod_files) #Path to your data folder.\n",
-    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_kerguelen.json\") #Path to your metadata file.\n",
+    "pod_files = Path(r\"U:\\CA4\") #Path to your data folder. 🐬\n",
+    "path = csv_folder(pod_files) #Process all your POD.csv files.\n",
+    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_calais.json\") #Path to your metadata file. 🐬\n",
     "\n",
     "print(path.head())\n",
     "df_0 = path.dropna()\n",
@@ -56,52 +57,37 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                 File          ChunkEnd  DPM  Nall  MinsOn  \\\n",
-      "0  POD3042 file01.CP3  21/10/2019 12:11    0     0       0   \n",
-      "1  POD3042 file01.CP3  21/10/2019 12:12    0     0       0   \n",
-      "2  POD3042 file01.CP3  21/10/2019 12:13    0     0       0   \n",
-      "3  POD3042 file01.CP3  21/10/2019 12:14    0     0       0   \n",
-      "4  POD3042 file01.CP3  21/10/2019 12:15    0     0       0   \n",
-      "\n",
-      "            deploy.name  \n",
-      "0  Site D Simone_Phase1  \n",
-      "1  Site D Simone_Phase1  \n",
-      "2  Site D Simone_Phase1  \n",
-      "3  Site D Simone_Phase1  \n",
-      "4  Site D Simone_Phase1  \n"
+      "                      File          ChunkEnd  DPM    Nall  MinsOn deploy.name\n",
+      "0  CA4  POD2397 file01.CP3  14/05/2014 07:07  0.0     0.0     1.0  CA4_Phase1\n",
+      "1  CA4  POD2397 file01.CP3  14/05/2014 07:08  0.0     8.0     1.0  CA4_Phase1\n",
+      "2  CA4  POD2397 file01.CP3  14/05/2014 07:09  0.0     4.0     1.0  CA4_Phase1\n",
+      "3  CA4  POD2397 file01.CP3  14/05/2014 07:10  0.0   251.0     1.0  CA4_Phase1\n",
+      "4  CA4  POD2397 file01.CP3  14/05/2014 07:11  0.0  4095.0     1.0  CA4_Phase1\n"
      ]
     }
    ],
    "execution_count": 2
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-24T07:54:18.370860Z",
-     "start_time": "2025-10-24T07:53:28.225983Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
-   "source": [
-    "d_beg_end = actual_data(df_0, metadatax)\n",
-    "d_beg_end.to_csv(r\"U:\\Deb_Fin_Site D Simone.csv\", index=False)"
-   ],
+   "source": "d_beg_end = actual_data(df_0, metadatax) #Extract the beginning and end of recording for every phase.",
    "id": "fa52f8971b61aaf6",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:03:40.056689Z",
-     "start_time": "2025-10-24T08:03:40.048631Z"
+     "end_time": "2025-10-28T10:34:07.329284Z",
+     "start_time": "2025-10-28T10:34:07.315727Z"
     }
    },
    "cell_type": "code",
-   "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ]",
+   "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ] #Remove the 0 to lighten the APLOSE file.",
    "id": "769e128f2a5293e1",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -116,13 +102,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-24T08:08:16.903159Z",
-     "start_time": "2025-10-24T08:08:16.776363Z"
+     "end_time": "2025-10-28T10:34:10.899696Z",
+     "start_time": "2025-10-28T10:34:10.180555Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Site D Simone\", \"Commerson\")\n",
+    "df_aplose = pod2aplose(df_1, pytz.utc, \"CA4\", \"Marsouin\", \"CPOD\") #Precise site name, species and instrument. 🐬\n",
+    "df_aplose[\"deploy.name\"] = df_aplose[\"filename\"]\n",
     "print(df_aplose.head())"
    ],
    "id": "4cc867627d677529",
@@ -131,107 +118,69 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "         dataset filename  start_time  end_time  start_frequency  \\\n",
-      "0  Site D Simone                    0        60                0   \n",
-      "1  Site D Simone                    0        60                0   \n",
-      "2  Site D Simone                    0        60                0   \n",
-      "3  Site D Simone                    0        60                0   \n",
-      "4  Site D Simone                    0        60                0   \n",
+      "  dataset    filename  start_time  end_time  start_frequency  end_frequency  \\\n",
+      "0     CA4  CA4_Phase1           0        60                0              0   \n",
+      "1     CA4  CA4_Phase1           0        60                0              0   \n",
+      "2     CA4  CA4_Phase1           0        60                0              0   \n",
+      "3     CA4  CA4_Phase1           0        60                0              0   \n",
+      "4     CA4  CA4_Phase1           0        60                0              0   \n",
       "\n",
-      "   end_frequency annotation annotator                start_datetime  \\\n",
-      "0              0  Commerson      FPOD  2019-10-26T16:21:00.000+0000   \n",
-      "1              0  Commerson      FPOD  2019-10-26T16:24:00.000+0000   \n",
-      "2              0  Commerson      FPOD  2019-10-26T16:36:00.000+0000   \n",
-      "3              0  Commerson      FPOD  2020-03-14T14:38:00.000+0000   \n",
-      "4              0  Commerson      FPOD  2020-03-14T15:22:00.000+0000   \n",
+      "  annotation annotator                start_datetime  \\\n",
+      "0   Marsouin      CPOD  2014-05-17T03:52:00.000+0000   \n",
+      "1   Marsouin      CPOD  2014-05-17T04:47:00.000+0000   \n",
+      "2   Marsouin      CPOD  2014-05-19T17:06:00.000+0000   \n",
+      "3   Marsouin      CPOD  2014-05-20T11:07:00.000+0000   \n",
+      "4   Marsouin      CPOD  2014-05-20T11:16:00.000+0000   \n",
       "\n",
-      "                   end_datetime  is_box           deploy.name  \n",
-      "0  2019-10-26T16:22:00.000+0000       0  Site D Simone_Phase1  \n",
-      "1  2019-10-26T16:25:00.000+0000       0  Site D Simone_Phase1  \n",
-      "2  2019-10-26T16:37:00.000+0000       0  Site D Simone_Phase1  \n",
-      "3  2020-03-14T14:39:00.000+0000       0  Site D Simone_Phase2  \n",
-      "4  2020-03-14T15:23:00.000+0000       0  Site D Simone_Phase2  \n"
+      "                   end_datetime  is_box deploy.name  \n",
+      "0  2014-05-17T03:53:00.000+0000       0  CA4_Phase1  \n",
+      "1  2014-05-17T04:48:00.000+0000       0  CA4_Phase1  \n",
+      "2  2014-05-19T17:07:00.000+0000       0  CA4_Phase1  \n",
+      "3  2014-05-20T11:08:00.000+0000       0  CA4_Phase1  \n",
+      "4  2014-05-20T11:17:00.000+0000       0  CA4_Phase1  \n"
      ]
     }
    ],
-   "execution_count": 6
+   "execution_count": 4
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "#### *F-POD*\n",
-    "Use fpod2aplose if you are managing F-POD data."
+    "### Clean your dataset\n",
+    "Remove useless lines, recorder outside the instrument submersion. Export your file to the aplose format. You can change the name of the file to match the project you are working on."
    ],
-   "id": "b805737ac321da69"
+   "id": "d2c642658dbfe278"
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-22T14:47:04.017877Z",
-     "start_time": "2025-10-22T14:47:03.833873Z"
+     "end_time": "2025-10-28T10:34:15.645066Z",
+     "start_time": "2025-10-28T10:34:13.520944Z"
     }
    },
    "cell_type": "code",
-   "source": [
-    "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
-    "print(df_aplose.head())"
-   ],
-   "id": "b8d1c500f6daea0d",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     dataset filename  start_time  end_time  start_frequency  end_frequency  \\\n",
-      "0  CETIROISE                    0        60                0              0   \n",
-      "1  CETIROISE                    0        60                0              0   \n",
-      "2  CETIROISE                    0        60                0              0   \n",
-      "3  CETIROISE                    0        60                0              0   \n",
-      "4  CETIROISE                    0        60                0              0   \n",
-      "\n",
-      "  annotation annotator                start_datetime  \\\n",
-      "0   Marsouin      FPOD  2023-02-07T05:34:00.000+0000   \n",
-      "1   Marsouin      FPOD  2023-02-07T05:35:00.000+0000   \n",
-      "2   Marsouin      FPOD  2023-02-07T05:36:00.000+0000   \n",
-      "3   Marsouin      FPOD  2023-02-07T22:40:00.000+0000   \n",
-      "4   Marsouin      FPOD  2023-02-24T15:32:00.000+0000   \n",
-      "\n",
-      "                   end_datetime  is_box      deploy.name  \n",
-      "0  2023-02-07T05:35:00.000+0000       0  Point C_Phase 4  \n",
-      "1  2023-02-07T05:36:00.000+0000       0  Point C_Phase 4  \n",
-      "2  2023-02-07T05:37:00.000+0000       0  Point C_Phase 4  \n",
-      "3  2023-02-07T22:41:00.000+0000       0  Point C_Phase 4  \n",
-      "4  2023-02-24T15:33:00.000+0000       0  Point C_Phase 4  \n"
-     ]
-    }
-   ],
-   "execution_count": 3
+   "source": "cleared = meta_cut_aplose(df_aplose, metadatax)",
+   "id": "895bd5a116918285",
+   "outputs": [],
+   "execution_count": 5
   },
   {
    "metadata": {},
    "cell_type": "markdown",
-   "source": [
-    "### Clean your dataset\n",
-    "Remove useless lines, recorder outside the instrument submersion. Export your file to the aplose format. You can change the name of the file to match the project you are working on."
-   ],
-   "id": "d2c642658dbfe278"
+   "source": "### Export",
+   "id": "d114ed7164cfd0da"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-24T08:10:12.478620Z",
-     "start_time": "2025-10-24T08:10:11.777820Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
    "source": [
-    "cleared = meta_cut_aplose(df_aplose, metadatax)\n",
-    "cleared.to_csv(r\"U:\\APLOSE_D_pos.csv\", index=False)"
+    "d_beg_end.to_csv(r\"U:\\Deb_Fin_CA4.csv\", index=False) #Export the new file. 🐬\n",
+    "cleared.to_csv(r\"U:\\APLOSE_CA4_pos.csv\", index=False) #Name your file. 🐬"
    ],
-   "id": "895bd5a116918285",
-   "outputs": [],
-   "execution_count": 7
+   "id": "9d34e00f4e8147e8"
   }
  ],
  "metadata": {

From cab4f640f5d10fe1cfda9ac2bbbd4aebc80a5f47 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Wed, 5 Nov 2025 16:49:06 +0100
Subject: [PATCH 28/83] add a precision in csv_folder()

---
 src/post_processing/utils/fpod_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 93a6c89..8cf0274 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -252,7 +252,7 @@ def csv_folder(
 
     all_data = []
     for file in all_files:
-        df = read_csv(file, sep=sep, encoding=encoding)
+        df = read_csv(file, sep=sep, encoding=encoding, dtype="O")
         df["deploy.name"] = file.stem
         all_data.append(df)
 

From 1fafdaa7ff1a5f15cfea3e0cade2bb5d11ef6189 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Wed, 5 Nov 2025 17:17:20 +0100
Subject: [PATCH 29/83] create new functions to process TimeLost

---
 src/post_processing/utils/fpod_utils.py | 60 +++++++++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 8cf0274..82c4748 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -840,6 +840,66 @@ def actual_data(
     final = final.sort_values(by=["Deb"])
     return final.drop(["deployment_date", "recovery_date"], axis=1)
 
+
+def process_tl(tl_files: Path)->DataFrame:
+    """Process Environmental data extracted from cpod.exe to get a usable dataframe.
+
+    Parameters
+    ----------
+    tl_files: Path
+        All your Environmental data files.
+
+    Returns
+    -------
+    %TimeLost DataFrame.
+
+    """
+    df = csv_folder(tl_files)
+    df = df.dropna()
+    df = parse_timestamps(df, "ChunkEnd")
+    df = add_utc(df, ["ChunkEnd"], "h")
+    df["start_datetime"] = df["ChunkEnd"]
+
+    return df.sort_values(["start_datetime"])
+
+
+def filter_tl(df: DataFrame, tl: int)->DataFrame:
+    """Remove lines with a %TimeLost superior to the chosen threshold.
+
+    Parameters
+    ----------
+    df: DataFrame
+        Table of data and associated TimeLost.
+    tl: int
+        TimeLost filter threshold.
+
+    Returns
+    -------
+    Filtered DataFrame with few %TimeLost.
+
+    """
+    df["%TimeLost"] = (df["%TimeLost"].fillna(tl)).astype(int)
+
+    return df[df["%TimeLost"] < tl]
+
+def preserved_data(filtered_df: DataFrame, whole_df: DataFrame)-> float:
+    """Calculate the percentage of preserved data.
+
+    Parameters
+    ----------
+    filtered_df: DataFrame
+        Result of filter_tl.
+    whole_df: DataFrame
+        Table before filtering.
+
+    Returns
+    -------
+    Percentage of preserved data.
+
+    """
+    return (len(filtered_df) / len(whole_df)) *100
+
+
 def create_matrix(
     df: DataFrame,
     group_cols: list,

From bfe31248325d0a13c4d768cd0794736ab27e7ac6 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 7 Nov 2025 12:24:21 +0100
Subject: [PATCH 30/83] change everything to comment

---
 src/post_processing/utils/fpod_utils.py | 180 +++++++-----------------
 1 file changed, 53 insertions(+), 127 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 82c4748..dbc96ae 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -24,136 +24,12 @@
 
 from post_processing.utils.core_utils import get_coordinates, get_sun_times
 
+from user_case.config import season_color, site_colors
+
 if TYPE_CHECKING:
     import pytz
 
 logger = logging.getLogger(__name__)
-site_colors = {
-    "Site A Haute": "#118B50",
-    "Site B Heugh": "#5DB996",
-    "Site C Chat": "#B0DB9C",
-    "Site D Simone": "#E3F0AF",
-    "CA4": "#80D8C3",
-    "Walde": "#4DA8DA",
-    "Point C": "#932F67",
-    "Point D": "#D92C54",
-    "Point E": "#DDDEAB",
-    "Point F": "#8ABB6C",
-    "Point G": "#456882",
-}
-
-season_color = {
-    "spring": "green", #C5E0B4
-    "summer": "darkgoldenrod", #FCF97F
-    "autumn": "orange", #ED7C2F
-    "winter": "blue", #B4C7E8
-}
-
-def fpod2aplose(
-    df: DataFrame,
-    tz: pytz.timezone,
-    dataset_name: str,
-    annotation: str,
-    bin_size: int = 60,
-) -> DataFrame:
-    """Format FPOD DataFrame to match APLOSE format.
-
-    Parameters
-    ----------
-    df: DataFrame
-        FPOD result dataframe
-    tz: pytz.timezone
-        Timezone object to get non-naïve datetimes
-    dataset_name: str
-        dataset name
-    annotation: str
-        annotation name
-    bin_size: int
-        Duration of the detections in seconds
-
-    Returns
-    -------
-    DataFrame
-        An APLOSE formatted DataFrame
-
-    """
-    fpod_start_dt = sorted(
-        [
-            tz.localize(strptime_from_text(entry, "%d/%m/%Y %H:%M"))
-            for entry in df["ChunkEnd"]
-        ],
-    )
-
-    fpod_end_dt = sorted(
-        [entry + Timedelta(seconds=bin_size) for entry in fpod_start_dt],
-    )
-
-    data = {
-        "dataset": [dataset_name] * len(df),
-        "filename": [""] * len(df),
-        "start_time": [0] * len(df),
-        "end_time": [bin_size] * len(df),
-        "start_frequency": [0] * len(df),
-        "end_frequency": [0] * len(df),
-        "annotation": [annotation] * len(df),
-        "annotator": ["FPOD"] * len(df),
-        "start_datetime": [strftime_osmose_format(entry) for entry in fpod_start_dt],
-        "end_datetime": [strftime_osmose_format(entry) for entry in fpod_end_dt],
-        "is_box": [0] * len(df),
-    }
-    if "deploy.name" in df.columns:
-        data["deploy.name"] = df["deploy.name"]
-
-    return DataFrame(data)
-
-
-def cpod2aplose(
-    df: DataFrame,
-    tz: pytz.BaseTzInfo,
-    dataset_name: str,
-    annotation: str,
-    bin_size: int = 60,
-    extra_columns: list | None = None,
-) -> DataFrame:
-    """Format CPOD DataFrame to match APLOSE format.
-
-    Parameters
-    ----------
-    df: DataFrame
-        CPOD result dataframe
-    tz: pytz.BaseTzInfo
-        Timezone object to get non-naïve datetimes
-    dataset_name: str
-        dataset name
-    annotation: str
-        annotation name
-    bin_size: int, optional
-        Duration of the detections in seconds
-    extra_columns: list, optional
-        Additional columns added from df to data
-
-    Returns
-    -------
-    DataFrame
-        An APLOSE formatted DataFrame
-
-    """
-    results = []
-
-    for deploy_name in df["deploy.name"].unique():
-        df_deploy = df[df["deploy.name"] == deploy_name].copy()
-
-        result = fpod2aplose(df_deploy, tz, dataset_name, annotation, bin_size)
-        result["annotator"] = result.loc[result["annotator"] == "FPOD"] = "CPOD"
-
-        if extra_columns:
-            for col in extra_columns:
-                if col in df_deploy.columns:
-                    result[col] = df_deploy[col].tolist()
-
-        results.append(result)
-
-    return concat(results, ignore_index=True)
 
 
 def pod2aplose(
@@ -1092,7 +968,7 @@ def ym_percent(df: DataFrame, metric: str) -> None:
             color=bar_colors,
             width=25,
         )
-        ax.set_title(f"{site} - Percentage of minutes postitive to detection per month")
+        ax.set_title(f"{site}")
         ax.set_ylim(0, max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         if i != 3:
@@ -1172,6 +1048,56 @@ def month_percent(df: DataFrame, metric: str) -> None:
     plt.show()
 
 
+def day_percent(df: DataFrame, metric: str) -> None:
+    """Plot a graph with the percentage of DPM per site/month-year.
+
+    Parameters
+    ----------
+    df: DataFrame
+        All percentages grouped by site and month per year
+    metric: str
+        Type of percentage you want to show on the graph
+
+    """
+    sites = df["site.name"].unique()
+    n_sites = len(sites)
+    fig, axs = plt.subplots(n_sites, 1, figsize=(14, 2.5 * n_sites), sharex=True)
+    if n_sites == 1:
+        axs = [axs]
+    for i, site in enumerate(sorted(sites)):
+        site_data = df[df["site.name"] == site]
+        ax = axs[i]
+        bar_colors = site_data["Season"].map(season_color).fillna("gray")
+        ax.bar(
+            site_data["Date"],
+            site_data[metric],
+            label=f"Site {site}",
+            color=bar_colors,
+        )
+        ax.set_title(f"{site}")
+        ax.set_ylim(0, max(df[metric]) + 0.2)
+        ax.set_ylabel(metric)
+        if i != 3:
+            ax.set_xlabel("")
+        else:
+            ax.set_xlabel("Months")
+        if metric in ("%buzzes", "FBR"):
+            for _, bar in enumerate(ax.patches):
+                bar.set_hatch("/")
+    legend_elements = [
+        Patch(facecolor=col, edgecolor="black", label=season.capitalize())
+        for season, col in season_color.items()
+    ]
+    fig.legend(
+        handles=legend_elements,
+        loc="upper right",
+        title="Seasons",
+        bbox_to_anchor=(0.95, 0.95),
+    )
+    fig.suptitle(f"{metric} per month", fontsize=16)
+    plt.show()
+
+
 def hour_percent(df: DataFrame, metric: str) -> None:
     """Plot a graph with the percentage of minutes positive to detection per site/hour.
 

From 883640c4dcb08a3ec4d470a17380f75045683673 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 7 Nov 2025 12:25:12 +0100
Subject: [PATCH 31/83] notebooks

---
 .../example_FPOD-CPOD_firstresults.ipynb      | 146 ++++++++----------
 user_case/example_FPOD-CPOD_raw.ipynb         |  94 ++---------
 2 files changed, 79 insertions(+), 161 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_firstresults.ipynb b/user_case/example_FPOD-CPOD_firstresults.ipynb
index 51454a3..134a81b 100644
--- a/user_case/example_FPOD-CPOD_firstresults.ipynb
+++ b/user_case/example_FPOD-CPOD_firstresults.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-10-28T13:23:16.275855Z",
-     "start_time": "2025-10-28T13:23:14.175637Z"
+     "end_time": "2025-11-06T09:59:30.110070Z",
+     "start_time": "2025-11-06T09:59:28.122343Z"
     }
    },
    "source": [
@@ -41,14 +41,14 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-28T13:23:50.797293Z",
-     "start_time": "2025-10-28T13:23:49.437948Z"
+     "end_time": "2025-11-06T10:34:05.474853Z",
+     "start_time": "2025-11-06T10:34:04.483887Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "ca4 = read_csv(r\"U:\\Hours_DPM_FBUZZ_CA4.csv\")\n",
-    "walde = read_csv(r\"U:\\Hours_DPM_FBUZZ_Walde.csv\")\n",
+    "ca4 = read_csv(r\"L:\\acoustock\\Bioacoustique\\DATASETS\\CPOD_PROJETS\\CALAIS\\DATA\\DATA_FULL_OTHERCET\\Hours_DPM_FBUZZ_CA4_ssTL.csv\")\n",
+    "walde = read_csv(r\"L:\\acoustock\\Bioacoustique\\DATASETS\\CPOD_PROJETS\\CALAIS\\DATA\\DATA_FULL_OTHERCET\\Hours_DPM_FBUZZ_Walde_ssTL.csv\")\n",
     "\n",
     "data_c = concat([ca4, walde])\n",
     "data_c[\"start_datetime\"] = to_datetime(data_c[\"start_datetime\"])\n",
@@ -57,7 +57,7 @@
    ],
    "id": "1268d9e6ce5cdf32",
    "outputs": [],
-   "execution_count": 7
+   "execution_count": 44
   },
   {
    "metadata": {},
@@ -86,13 +86,18 @@
    "id": "17a5ce1338f6cd1a"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-11-06T10:23:15.773466Z",
+     "start_time": "2025-11-06T10:23:14.586120Z"
+    }
+   },
    "cell_type": "code",
    "source": [
-    "a = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteA.csv\")\n",
-    "b = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteB.csv\")\n",
-    "c = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteC.csv\")\n",
-    "d = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteD.csv\")\n",
+    "a = read_csv(r\"U:\\Hours_DPM_FBUZZ_A.csv\")\n",
+    "b = read_csv(r\"U:\\Hours_DPM_FBUZZ_B.csv\")\n",
+    "c = read_csv(r\"U:\\Hours_DPM_FBUZZ_C.csv\")\n",
+    "d = read_csv(r\"U:\\Hours_DPM_FBUZZ_D.csv\")\n",
     "\n",
     "data_k = concat([a, b, c, d])\n",
     "data_k[\"start_datetime\"] = to_datetime(data_k[\"start_datetime\"])\n",
@@ -101,7 +106,7 @@
    ],
    "id": "d65697a1f1487f4c",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 22
   },
   {
    "metadata": {},
@@ -115,21 +120,21 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-28T13:23:53.389560Z",
-     "start_time": "2025-10-28T13:23:53.382634Z"
+     "end_time": "2025-11-06T10:34:09.795757Z",
+     "start_time": "2025-11-06T10:34:09.785706Z"
     }
    },
    "cell_type": "code",
    "source": "data = data_c #🐬",
    "id": "add4a626d6cc25a4",
    "outputs": [],
-   "execution_count": 8
+   "execution_count": 45
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-28T13:23:54.833954Z",
-     "start_time": "2025-10-28T13:23:54.513035Z"
+     "end_time": "2025-11-06T10:34:13.881765Z",
+     "start_time": "2025-11-06T10:34:13.617021Z"
     }
    },
    "cell_type": "code",
@@ -140,33 +145,42 @@
     "#t_per = percent_calc(data, \"TRAVAUX\")"
    ],
    "id": "37ecc80eda8e57ed",
-   "outputs": [],
-   "execution_count": 9
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\fouinel\\AppData\\Local\\Temp\\ipykernel_24020\\669083797.py:4: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
+      "  data['Date'] = data[\"start_datetime\"].dt.to_period('D').dt.to_timestamp()\n"
+     ]
+    }
+   ],
+   "execution_count": 46
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-28T13:23:55.879816Z",
-     "start_time": "2025-10-28T13:23:55.849626Z"
+     "end_time": "2025-11-06T10:34:15.931189Z",
+     "start_time": "2025-11-06T10:34:15.912241Z"
     }
    },
    "cell_type": "code",
    "source": [
     "ym_per = percent_calc(data, \"YM\")\n",
     "ym_per[\"YM\"] = to_datetime(ym_per[\"YM\"])\n",
-    "ym_per[\"Season\"] = ym_per[\"YM\"].apply(lambda x: get_season(x)[0])\n",
+    "ym_per[\"Season\"] = ym_per[\"YM\"].apply(lambda x: get_season(x)[0]) #If in the southern hemisphere, write \"get_season(x, northern = False)\".\n",
     "ym_per[\"Month\"] = ym_per[\"YM\"].dt.month\n",
     "ym_per[\"Year\"] = ym_per[\"YM\"].dt.year"
    ],
    "id": "2b988869ed2466e1",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": 47
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-28T13:23:58.602641Z",
-     "start_time": "2025-10-28T13:23:58.572133Z"
+     "end_time": "2025-11-06T10:24:21.202676Z",
+     "start_time": "2025-11-06T10:24:21.177426Z"
     }
    },
    "cell_type": "code",
@@ -177,13 +191,13 @@
    ],
    "id": "cf704032c4a59a7b",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": 30
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-28T13:24:00.067944Z",
-     "start_time": "2025-10-28T13:24:00.054075Z"
+     "end_time": "2025-11-06T10:24:22.084789Z",
+     "start_time": "2025-11-06T10:24:22.073176Z"
     }
    },
    "cell_type": "code",
@@ -194,15 +208,10 @@
    ],
    "id": "caf3f71c6b6f70ca",
    "outputs": [],
-   "execution_count": 12
+   "execution_count": 31
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T13:24:01.960321Z",
-     "start_time": "2025-10-28T13:24:01.821063Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "hist_mean_s(\n",
@@ -214,51 +223,40 @@
     ")"
    ],
    "id": "2ff751ae02e80285",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 1500x900 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 13
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "year_percent(y_per, \"FBR\")",
+   "id": "29f2703ab28c5b28",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-10-28T13:37:16.391453Z",
-     "start_time": "2025-10-28T13:37:16.257339Z"
+     "end_time": "2025-11-06T10:34:18.966846Z",
+     "start_time": "2025-11-06T10:34:18.611173Z"
     }
    },
    "cell_type": "code",
-   "source": "year_percent(y_per, \"FBR\")",
-   "id": "29f2703ab28c5b28",
+   "source": "ym_percent(ym_per, \"DPM\")",
+   "id": "a2dacac3caecff5f",
    "outputs": [
     {
      "data": {
       "text/plain": [
        "<Figure size 2100x750 with 2 Axes>"
       ],
-      "image/png": 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      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 15
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "ym_percent(ym_per, \"%click\")",
-   "id": "a2dacac3caecff5f",
-   "outputs": [],
-   "execution_count": null
+   "execution_count": 48
   },
   {
    "metadata": {},
@@ -277,12 +275,7 @@
    "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T13:26:45.510525Z",
-     "start_time": "2025-10-28T13:26:45.252371Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "hist_mean_h(\n",
@@ -294,19 +287,8 @@
     ")"
    ],
    "id": "5cbea8601bce2172",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2100x1500 with 2 Axes>"
-      ],
-      "image/png": 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AssnyxDRLDhiISfYXdDT2EOhN/z3Pcn/VV44mrivqJcvvR28m2dsvS46+mGRvnyw5slAPWD6/H0DTNPpICc8880y88Y1vjKeffjpuuOGGmDBhQs/nuk/b0H0ah96Kj62zzjpO3QAApJTliWmWHFCFTJPsDoFeH1ly9G54Kd7aqbOzc8A3Muu2mZltXXUzyd5eWXJkkaUeWXJkoR6wfH4/qPtzoYF+HlTX50IkaUoojnZw8MEHxz333BNXXXVVbLPNNkt8fqONNor1118/br/99mW+9rbbbovtt99+AG8tAMDAyPLENEsOqEKmc7IXHAK9HrLk6L2udt19z3hq9pwBf2Fq/vznet5vs8POK337i4ad4n1x+4sN8BXJMWHchnHN5ZfW5sW4bOuqt7r8jAdjPbLkyCJLPbLkyEI9YPn8fvBSiucQ+x5wYMx87PHKn3+0+nnQqqjbcyGSNCUsWrQoDjvssLj55pvjRz/6Ubz2ta/t83LveMc74uyzz46HHnooNt544/JjV199ddnI8LGPfWyAbzUAQLW6z2Xe9CemnmDD4Pr9cAj09suSo7fixbSiIeHAL/4whgwd2Jc+vvm2baNzwfwYOmJkHPilC1eqHsUpTYojiBQNOyv6+7F4UWf87MTDel5IbLeM66rJstQjS44sstQjS44s1AOWz+8HK6J4PlA0JEye/oMVei60ss8/Wv08aFUUz4UuPeHQ2jwXIlFTwic+8Ym45JJLyiMlzJo1K84999wlPn/EEUeU70866aT44Q9/GK973eviH//xH2POnDnx5S9/OV71qlfF+9///jbdegCA6s7JPnfWE41+YuoJNgy+3w+HQG+vLDmWe/jOocMGvClhidvQz+vubjAs/p47pQmtkqUeWXJkkaUeWXJkoR6wfIP998PpCFbi61fguVBVzz8G+jlYkaM40gP11simhBkzZpTvf/zjH5dvS+tuSiiOjnD99dfHxz/+8finf/qnGDFiRLzpTW+Kr3zlKzFy5MgBv90AAFVxTnbILfPvRxOnGLLUI0uOLLLUI0uOLLLUI0uOLLLUI0uOLNQD8v9+rGxTwcqcjqAVlnc6glac6qAOpyPIsq66j/TQzqYVEjclXHfddSt82UmTJsXll19e6e0BAGg352SHvPx+1EuWemTJkUWWemTJkUWWemTJkUWWemTJkYV6QP7fj1WZZO/v6Qhapa/TEbTqVAftPjVbpnX1Qo7HY2gDBx4Gm0Y2JQAAsCTnZIec/H7US5Z6ZMmRRZZ6ZMmRRZZ6ZMmRRZZ6ZMmRhXpA/t+PVk2yt/PUbMX1OtVanXNsGEOe1ZRQdyoEAJCAc7JDPn4/6iVLPbLkyCJLPbLkyCJLPbLkyCJLPbLkyEI9YHBNsjfx1H1565ErRzGo1eT1NVg4UgIAQAJNfOCd5YkQVMHvR71kqUeWHFlkqUeWHFlkqUeWHFlkqUeWHFnOmZ2lHlCFLL8feSbZuxLWI1eO0Wus3e6bxQrQlAAAwIDL8kRosChe+Gzni5+dnZ0D3uTTzkYfvx/1kqUeWXJkkaUeWXJkkaUeWXLYOK6XTDlW9pzsdZKlHlCFLL8fS+fY97jPxfXTPxJNtGjh8+nqkS3H4kUD+7oRK0dTAgAAAyrLE6HBonhBfd8DDoyZjz0+oNc7f/5zPe+32WHnFbqdxQu0xfuioaA4pcnKNhZMGLdhXHP5pW1pTPD7US9Z6pElRxZZ6pElRxZZ6pEph43j+siUoxXnZG+3LPWAKmT5/cgzyd71wn+7utLVQw7aQVMCAAADxhOI5ile9CwaEiZP/0EMGTpwTx+++bZto3PB/Bg6YmQc+KULV+gF2kXPLy4PCVmcS3Bl11XRXf+zEw/raW4YSH4/6iVLPbLkyCJLPbLkaPrGXrZ6ZMph47g+8uV4PIY28NR92Q6BDlXId3/V7En2IkdxhIRCR0dHuno0TZYcg52mBAAABoQnEM1WNCQMZFPC0tf9Yuvqmq+fHHNnPRGrrze+sevK70e9ZKlHlhxZZKlHphwm2esjX45mbxznq0emHE0+J3uOQ6BDFXLeXzU/R3GEhMLQ4SMam6N4vSRLPZqeg4jmPoIBAKAxPIGgClnWVZYcWWSpR5YcTZ80zlaPTDlMstdHzhwblqeSaqYck+w519W48ohg7TjF2KrLcQh0qELW+6sMOYojJLyg+31zFI9zi8e7merR5By8wJESAAAYdE8giidn7dyI6Owc+MMWFi9eNvMFzOasq8GcI4ss9ciUwyR7feTLYZK9DrLm2Pe4z8X10z8STZRhkj3rumrqOdmzHAIdqmCSvd45Zj14b3QueC6aqHgeVZzi0hElqRNNCQA0dlOvHRt72Tb1YDA+gSjut/Y94MCY+djj/fqa4gld8b64Dygm3/p7XzB//nM977fZYecYaBPGbRjXXH5pivuwOq6rwZyj3Y8FWiVLPTLlMMleHzlzNPkQ6CbZ6yTLxnGWSfbM66qp52TPcAh0qHKSvTgVYcb7qww5/vOoPaPJ66t4vJupHk3MwZI0JQAwYJt6rdrga+fGXqZNPRisTyCK+53ivmvy9B/EkKHDVnhjrOgwL57QFYdMXZkc33zbttG5YH4MHTEyDvzShTGQihcvLz3h0J773Car67oazDlMstdHvhzNnmS3cVwvJtnrJeu6avLGcYZJ9uzrqumHQH+hMaF5h0CHqphkr48sOXorXvtZ2dev2i1jPXiBpgQABmRTr5UbfO3a2CteWPrZiYel2NSDqjXhCURx3/VS91/dh1IsJhda+UJBf+83V5WN43rJlMMke33kzNHkSXYbx3Vikr1eMq+rJudo+iS7Q6DXS6ZDoLPiHFG1f0yy10OWHEsrBgCbmCNrPXiBpgQYBNr9gLBJDwapZlOvyg2+gd7Ya/LvYsHvIwMhyxOITDlsHNdHvhwm2esg37pq+iS7jeM6McleL9nXVdNkmWR3CPR6yXYIdAbmiKqrcsTVph5R1SR7+2XJ0Zcmvh6buR68oDk7OcCAPiBc1XNnN/XBINUazA8sqn5ytjK/j6v6e74i/D4OPll+z/PlaPbGcb56ZMphkr3dcq6r5k6y2ziul2w5TLLXQ7Z1lWGS3SHQ6yNLDgb2iKqresTVph5R1SR7e2XJkYV6DA6aEmikdk8bN2nSeGUeELbi3NlNfTBIdbI8sFjZ+54qn5ytzO9jK37Pq/59HGz39RmOLJHl9zxnjiZvHJtkrxOT7PWSdV01eZI9y8Zx5nXVNCbZ6yXjusowye4Q6PWQJQcDe0TVVh9xtUlHVG3i6z9Zfs+z5MhCPQaP5txDQ5unjV9q8r/KieNWTBqv6APCKs6d3aQHg1QjywOLVpyTvdVPzlbm+qv4PW/HfX0V97vtOMpL7xwTJ24ct950gyembZQ1R3M3jk2y14lJ9nrJvK6anqPJG8cm2evFJHu9ZF1XTc3Rm0Ogt1+WHNSLdVUvWeqRJUcW6jG42Cmkcdo1bfxik/9VThwP5OR/lj8ATT9ndjZZ1pVzstfrvr6q+92BPspL7xxj19kgnpr9ZEOP9GKSvU6ybBybZK8Xk+z1kn1dNU2WjWOT7PVikr1eMq+rJuZYmkOgt1eWHNSLdVUvWeqRJUcW6jH4aEqgsdo5bdz7epswcTyY/gCs6iR7uw8X37TTgwymdZXhnOxN3Dju675+oO53q/4bs3QOk+ztle/+qvkbxybZ6yNjDpPs7ZdxXTV949gke31kydGbSfb2y5KjL81r6s5Tjyw5qBfrql6y1CNLjizUY3DSlACrIMsdZ6YcqzLJ3q5Tgyx9yPit/27Hyk4FUtXpQbKvqxznZLdxXCcm2esl87pqco4Mk+w2juvDJHu9ZF1XTc3RzSR7PWTJsTST7O2VJUcWWeqRJQf1Yl3VS5Z6ZMmRhXoMXpoSYJDfcebLsfKT7O06NciSh4wfEYtXX7+SU4EM5OlB8q2rpp+T3cZxnZhkr5fs66ppskyy2ziuF5Ps9ZJ5XTUxR28m2dsvS46+mGRvnyw5sshSjyw5qBfrql6y1CNLjizUY3DTlACD+I4zZ45Vn2Rv56lBig0+pwKphyyT7DaO6yVbDpPs9ZBtXTV9kr1g47g+suToZpK9HrLkWJpJ9vbKkiOLLPXIkiOLLPXIkoN6sa7qJUs9suTIQj1oXpsytF3zzsme+Q/A0jmKyZ4mTmCYZK+XbDkybBxnqkemHEWjywuaO8meqR7ZcjRxXXWzcVwPWXL0ZpK9/bLk6EsTn0dlqUeWHFlkqUeWHFlkqUeWHNSLdVUvWeqRJUcW6kGhec84oc2ck70+MuUwyV4fGXM0eeM4Yz0y5SgaXZqqmGQvTvmTqR5y1IeN4/bLkmNpJtnbK0uOLLLUI0uOLLLUI0uOLLLUI0sO6sW6qpcs9ciSoxh4yCBLPVh1mhJghZlkr5NsOUyy10PWHE3dODbJXi8m2esl87pqYo6l2Thuryw5+mKSvX2y5MgiSz2y5MgiSz2y5MgiSz2y5KBerKt6yVKPTDmKgZqmy1IPWqN5r2hAG5hkr5eMOUyyt1/mHE1cVwWT7PWRJUdvJtnbL0uOvtg4bp8sObLIUo8sObLIUo8sObLIUo8sObLIUo8sOaiXLOvKJHu9ZMpRDGo1fX1lqQet07xXy2CAmWSvl6w5TLK3V9Z11dQc3Uyy10OWHEszyd5eWXJkkaUeWXJkkaUeWXJkkaUeWXJkkaUeWXJkkaUeWXJQL1nWlUn2esmX4/FGDjxkqwetNazF3w9SWXqS/YXGhI7GbhzPnfVEo/8AZPlD1leO/zxqz2ii4oH3oucXx+rrjU9VDznqwSR7+2XJ0ZcmPrHLUo8sObLIUo8sObLIUo8sObLIUo8sObLIUo8sObLIUo8sOVZl0rj42nZPKnd2dg74c+Uqny9nWVcm2eslZ44NY8izzXvt6gVdKepB62lKgBX8QzbrwXujc8Fz0UQ2jusjS45u/397dwJ313TucXxlJKWhRCMRokhMMRQxE0NRs7a0RU3Vmq6abo2l11VjS3GV65oSNU9VuW6oIeaQmGKIIRFN0UjSGCqIiGTfz7Pe7jfnPTl537PfvCdrrf/+fT8fDW9Cz3896+yzz17r2ZtO9jio5KhGJ3tYKjlUqNRDJYcKlXqo5FChUg+VHCpU6qGSQ4VKPVRypL6wp1YPpRzt7WS3ObndTru4yVOmukVt5swvmn9d69uD/WuxHParbRiw6yWN2jjQd/nebuRfRjTkv6/Yyd4lwYYHtYVjvXnVlGO7X/zGPXbeES5F9ij01OuBxmBTAiDeyW5YOI6DSo5KdLKHp5KjFjrZw1HJoUKlHio5VKjUQyWHCpV6qORQoVIPlRwqVOqhlINboMdDKcfCdLLbv2cbEr573m2uc5dFu4xy+fcGua9mzXRdui/mhpx6pc9hDWd2fbeR1+HmzvnK3X/qj5o3P3QkpXml0cmusXCsOa+acvTo+Q2XnqZHoNsdx1OuBxqHTQmA6AdZJRaOw1PJUY1O9rBUcqhQqYdKDhUq9VDJoUKlHio5VKjUQyWHCpV6qOSgkz0uSjm4BXo89HIsfCe7bUhY1JsS5sncyMvO8I/k5Q644el0smssHKvOqzyHbRJKLYdtdDH2KPRU64HGYlMCIPhBVo2F47BUctRCJ3s4KjlUqNRDJYcKlXqo5FChUg+VHCpU6qGSQ4VKPZRy0MkeD70cad8CXa8eSjnoZA9Nc16l28musnCsPK9SzmEbXUyXbt2TzIHGS/cTGehgKh8AtbBwHI5KDhUq9VDJoUKlHio5VKjUQyWHCpV6qORQoVIPlRypdxqr1UMpB53s8dDM0ds31KSJZ7LHnMPuHJvidVE62eOiliP1hWO1eijlsI0uTfJfgZZS/EQGOpzKB4AKlXqo5FChUg+VHCpU6qGSQ4VKPVRyqFCph0oOFSr1UMpBJ3s89HJMTXRhT7UeWjnSXTimkz0mSjnoZI+HYo6UF44V66GUwza6AK1J82wP6EAqHwAqVOqhkkOFSj1UcqhQqYdKDhUq9VDJoUKlHio5Uu80VquHUg462eOhmYNO9tA051W6Oehkj4taDjrZ46CaI9WFYzvPtfNdtXoo5UhxowsWLTYloNRUPgBUqNRDJYcKlXqo5FChUg+VHCpU6qGSQ4VKPZRy0MkeD70caXeys3AcFzrZ46I6r1LOQSd7PBRz0MkennKOFOeVse9Rdr6rVg9yoEzS/DYBdAAOnHFRqYdKDhUq9VDJoUKlHio5VKjUQyVH6p3GavVQykEnezw0c6Tcyc7CcUxUctDJHhe1HHSyx0E1B53sYanOq1RzVM4vO99NNYdKPVRyIAw2JaCUOHDGRaUeKjlUqNRDJYcKlXqo5Eh9YU+tHko56GSPh14OOtljoDevUu9kZ+E4Jko56GSPh2KOlDvZWTiOC53scVGeVynmqGTnuXa+m2IOlXqo5EA4KX5bBRYKB864qNRDJYcKlXqo5FChUg+lHCwcx0MpB53s8dDMQSd7aJrzKu0cLBzHQy0HnexxUM2Raie7YeE4Hio5cnSyx0ElRzX7HpViDpV6qORAWGxKQKlw4IyLSj1UcqhQqYdKjtQX9tTqoZSDheN46OWgkz0GevOKTvYYqM6r1HOwcBwHxRx0soenOK9S72Q3LBzHQSVHJTrZw1PJUUuK36NU6qGSA+Gl9y4G2okDZ1xU6qGSQ4VKPZRy0MkeD70caS8c69VDKQed7KFpzqu0c9DJHg/FHCkvHCvWQykHnexhqc6rVHNUYuE4PJUc1ehkD0slhwqVeqjkQBzSvZILFMCBMy4q9VDJkXqnsVo9lHLQyR4PzRwpLxzTyR4TOtnjojqvUs9BJ3scVHOkunBMJ3tc6GSPi/K8SjFHNRaOw1LJUUuK36NU6qGSQ4VKPVRyIB7pfUoABXHgjItKPZRy0MkeD70cdLLHQDVHugvHdLLHRCkHnezxUMxBJ3t4yjlSnFeGTvZ4qOSoRCd7eCo5aknxe5RKPVRyqFCph0oOFSr1UMmBuKR3BgKU8MCZeqexWj2UctDJHg/NHHSyh6Y5r9LNQSd7XNRy0MkeB9UcdLKHpTqvUs2Ro5M9Dio5qtHJHpZKDhUq9VDJoUKlHio5VKjUQyUH4sOmBMhSOXDSyR4XvRxpd7KzcBwXOtnjojqvUs5BJ3s8FHOk3MnOwnFc6GSPi/K8SjFHJTrZw1PJUUuK36NU6qGSQ4VKPVRyqFCph0oOFSr1UMmBOKV3hguU6MBJJ3tcNHOk3MnOwnFMVHLQyR4XtRx0ssdBNUeqneyGheN4qOTI0ckeB5Uc1ehkD0slhwqVeqjkUKFSD5UcKlTqoZJDhUo9VHIgXmxKgByVAyed7HHRm1epd7KzcBwTpRx0ssdDMQed7OEpzqvUO9kNC8dxUMlRiU728FRy1JLi9yiVeqjkUKFSD5UcKlTqoZJDhUo9VHKoUKmHSg7ELb1vUEAJDpx0ssdFc16lnYOF43io5aCTPQ6qOehkD0t1XqWaoxILx+Gp5KhGJ3tYKjlUqNRDJYcKlXqo5FChUg+VHCpU6qGSQ4VKPVRypH7H8TJgUwJkqBw46WSPi+q8Sj0HC8dxUMxBJ3t4ivOKTvbwlOdVijmqsXAclkqOWlL8HqVSD5UcKlTqoZJDhUo9VHKoUKmHSg4VKvVQyaFCpR5KOayhBnFL7xs6IH7gVMlBJ3s8FHOkvHCsWA+lHHSyh6U6r1LNUYlO9vBUctTCwnE4KjlUqNRDJYcKlXqo5FChUg+VHCpU6qGSQ4VKPVRyqHSyq9RDKYc1aqnML2XpXf0BhA+cSjnoZI+Dao5UF47pZI8LnexxUZ5XKeaoRid7WCo5VKjUQyWHCpV6qORQoVIPlRwqVOqhkkOFSj1UcqhQqYdSDoVOdqV6aOWYmmTDQ9lQISRN78Cpk4NO9vCUc6Q4rwyd7PFQyVGJTvbwVHLUkuIXO5V6qORQoVIPlRwqVOqhkkOFSj1UcqhQqYdKDhUq9VDJodJprFIPpRwKnexK9dDL0ds31CBu6V1dBKQPnDo56GQPS3VepZojRyd7HFRyVKOTPSyVHCpU6qGSQ4VKPVRyqFCph0oOFSr1UMmhQqUeKjlUqNRDKQed7PHQy5F2J7tePbRyWKNWyvOrLKgQksTCcVzoZI+L8rxKMUclOtnDU8lRS4on3ir1UMmhQqUeKjlUqNRDJYcKlXqo5FChUg+VHCpU6qGSI/VOY7V6KOWgkz0emjlS7mTPBOtBDix66V29Blg4jopKjhyd7HFQyVGNTvawVHKoUKmHSg4VKvVQyaFCpR4qOVSo1EMlhwqVeqjkSH1hT60eSjnoZI+HXo60O9lZOI6LUif7nNlfytWDHAiha5D/V2AhsXAcB5UclehkD08lRy0pnnir1EMlhwqVeqjkUKFSD5UcKlTqoZJDhUo9VHKoUKmHUg4WjuOhlINO9nho5ujtOs9I79pVjoXjuHP06PkNl56s6X+zTK4e5EAI6X7CoNRYOA5PJUc1OtnDUsmhQqUeKjlUqNRDJYcKlXqo5FChUg+VHCpU6qGSI/WFPbV6KOVg4TgeejnoZI+B3rxKvZOdheOYKOWwjS6mU6dOSedQqYdCjrLjTglIEgvHYankqCXFE2+VeqjkUKFSD5UcKlTqoZJDhUo9VHKoUKmHSg4VKvVQykEnezz0ckx1XRK8vqBbD6UcdLKHpjmv0u1kZ+E4Lmo5bKOL6dKte7I5Rl52hkw9Us8B7pSARLFwHI5KDhUq9VDJoUKlHio5VKjUQyWHCpV6qORQoVIPlRypdxqr1UMpB53s8dDM0ds31KSJTvaY0MkeF9V5lXoOhYVjpXoo5bCNLk3yX9Nh57l2vqtUj5RzoEmKZzBAclQOnCo5VKjUQyWHCpV6qORQoVIPlRwqVOqhkkOFSj2UctDJHg+9HGnfAl2vHlo50l04ppM9Jko56GSPh2KOlBeOFeuhlMM2uqTKvkfZ+a5SPVLNgXnSPDsGEqJy4FTJoUKlHio5VKjUQyWHCpV6qORQoVIPlRypdxqr1UMpB53s8dDMQSd7aJrzKt0cdLLHRS0HnexxUM2R6sIxnezx50hxo0vl/LLzXaV6pJgDLbEpAWgglQOnSg4VKvVQyaFCpR4qOVSo1EMlR+oLe2r1UMpBJ3s89HKk3cnOwnFc6GSPi+q8SjkHnezxUMxBJ3t4yjlSnFeGTvZ4qOSoZOe5dr6bYg7FeqBJmt++gASoHDhVcqhQqYdKDhUq9VDJoUKlHko5WDiOh1IOOtnjoZkj5U52Fo5jopKDTva4qOWgkz0OqjnoZA9LdV6lmiNHJ3scVHJUs+9RKeZQrQeasCkBaACVA6dKDhUq9VDJoUKlHio5Ul/YU6uHUg4WjuOhl4NO9hjozavUO9lZOI6JUg462eOhmCPlTnYWjuNCJ3tclOdVijkq0ckenkqOWlL8HqVcDzRJb1YCkVM5cKrkUKFSD5UcKlTqoZSDTvZ46OVIe+FYrx5KOehkD01zXqWdg4XjeKjloJM9Dqo5Uu1kNywcx0MlR45O9jio5KhGJ3tYKjlUUI9ySPfKJBAhlQOnSg4VKvVQyZF6p7FaPZRy0MkeD80cKS8c08keEzrZ46I6r1LPwcJxHBRz0MkenuK8Sr2T3bBwHAeVHJXoZA9PJUctKX6PUqmHSg4V1KM80jvqAZFSOXCq5FChUg+lHHSyx0MvB53sMVDNke7CMZ3sMVHKQSd7PBRzpLxwrFgPpRx0soelOq9SzVGJhePwVHJUo5M9LJUcKlTqoZJDBfUolzSvTAKRUTlwquRIvdNYrR5KOehkj4dmDjrZQ9OcV+nmoJM9Lmo56GSPg2qOVBeO6WSPC53scVGeVynmqMbCcVgqOWpJcWO3Sj1UcqhQqYdKDhXUo3zS+1QFIqNy4FTKQSd7PPRypN3JzsJxXOhkj4vqvEo5B53s8VDMQSd7eMo5UpxXhk72eKjkqEQne3gqOWpJ8XuUSj1UcqhQqYdKDhUq9VDJoYJ6lFN6Z2xARFQOnEo56GSPh2aOlDvZWTiOiUoOOtnjopaDTvY4qOagkz0s1XmVao4cnexxUMlRjU72sFRyqFCph0oOFSr1UMmhQqUeKjlUUI/yYlMCUPIDp14OOtljoDevUu9kZ+E4Jko56GSPh2KOlDvZWTiOC53scVGeVynmqEQne3gqOWpJ8XuUSj1UcqhQqYdKDhUq9VDJoUKlHio5VFCPckvvGwEQAZUDp2YOOtlD05xXaedg4TgeajnoZI+Dao5UO9kNC8fxUMmRo5M9Dio5qtHJHpZKDhUq9VDJoUKlHio5VKjUQyWHCpV6qORQQT3ApgSgMDrZY0Ine1xU51XqOVg4joNiDjrZw1OcV6l3shsWjuOgkqMSnezhqeSoJcXvUSr1UMmhQqUeKjlUqNRDJYcKlXqo5FChUg+VHCqoB0x63ziBwOhkj4dSDjrZ46GYI+WFY8V6KOWgkz0s1XmVao5KLByHp5KjGp3sYankUKFSD5UcKlTqoZJDhUo9VHKoUKmHSg4VKvVQyWENDwpU6oGFx6YEoG50ssdELQed7HFQzZHqwjGd7HGhkz0uyvMqxRzVWDgOSyVHLXSyh6OSQ4VKPVRyqFCph0oOFSr1UMmhQqUeKjlUqNRDKYc11KROpR7oGOld0QACoJM9Loo56GQPTzlHivPK0MkeD5UclehkD08lRy0sHIejkkOFSj1UcqhQqYdKDhUq9VDJoUKlHio5VKjUQyUHnexxUcphjVqpzy+VeqDjpHe1DFjE6GSPi2oOOtnDUp1XqebI0ckeB5Uc1ehkD0slhwqVeqjkUKFSD5UcKlTqoZJDhUo9VHKoUKmHSg4VKvVQykEnezz0ckxNsuFBrR7oWOnOaGARUOlkZ+E4LnSyx0V5XqWYoxKd7OGp5KglxS92KvVQyaFCpR4qOVSo1EMlhwqVeqjkUKFSD5UcKlTqoZIj9U5jtXoo5aCTPR6aOXr7hpo0ZRL1QMdL72ossIiodLIbFo7joZIjRyd7HFRyVKOTPSyVHCpU6qGSQ4VKPVRyqFCph0oOFSr1UMmhQqUeKjlSX9hTq4dSDjrZ46GXI+1OdpWFY7151ZTDGrVSnV/2KPTU64HGSHNGAw2m1MluWDiOg0qOSnSyh6eSo5YUT7xV6qGSQ4VKPVRyqFCph0oOFSr1UMmhQqUeKjlUqNRDKQcLx/FQykEnezw0c6Tcya6xcKw5r9LNYRtd/P9mWeI50CjpXe0HGkznA2AeFo7DU8lRjU72sFRyqFCph0oOFSr1UMmhQqUeKjlUqNRDJYcKlXqo5Eh9YU+tHko5WDiOh14OOtljoDevUu9k11g4Vp1XKeewjS7GHoWeag40VtcG//eBpKh8AFRj4TgslRy1pHjirVIPlRwqVOqhkkOFSj1UcqhQqYdKDhUq9VDJoUKlHko56GSPh16Oqa5LgtcXdOuhlKO36zwj3blFJ3vcOXr0/IZLjcrCsfK8SjmHbXQx9ij0FHOg8dL9RAY6mMoHQC0sHIejkkOFSj1UcqhQqYdKDhUq9VDJoUKlHio5VKjUQyVH6p3GavVQykEnezw0c6R8C3Q62WNCJ3tcVOdV6jlSXzhWq4dSDtvo0iTdR6GjsVL8RAY6nMoHgAqVeqjkUKFSD5UcKlTqoZJDhUo9VHKoUKmHSg4VKvVQykEnezz0cqR9C3S9emjlSHfhmE72mCjloJM9Hoo5Ul44VqyHUg7b6AK0Js2zPaADqXwAqFCph0oOFSr1UMmhQqUeKjlUqNRDJYcKlXqo5Ei901itHko56GSPh2YOOtlD05xX6eagkz0uajnoZI+Dao5UF47tPNfOd9XqoZQjxY0uWLTYlIBSU/kAUKFSD5UcKlTqoZJDhUo9VHKoUKmHSg4VKvVQykEnezz0cqTdyc7CcVzoZI+L6rxKOQed7PFQzEEne3jKOVKcV8a+R9n5rlo9yIEySfPbBNABOHDGRaUeKjlUqNRDJYcKlXqo5FChUg+VHKl3GqvVQykHnezx0MyRcic7C8cxUclBJ3tc1HLQyR4H1Rx0soelOq9SzVE5v+x8N9UcKvVQyYEw2JSAUuLAGReVeqjkUKFSD5UcKlTqoZIj9YU9tXoo5aCTPR56Oehkj4HevEq9k52F45go5aCTPR6KOVLuZGfhOC50ssdFeV6lmKOSnefa+W6KOVTqoZID4aT4bRVYKBw446JSD5UcKlTqoZJDhUo9lHKwcBwPpRx0ssdDMwed7KFpzqu0c7BwHA+1HHSyx0E1R6qd7IaF43io5MjRyR4HlRzV7HtUijlU6qGSA2GxKQGlwoEzLir1UMmhQqUeKjlSX9hTq4dSDhaO46GXg072GOjNKzrZY6A6r1LPwcJxHBRz0MkenuK8Sr2T3bBwHAeVHJXoZA9PJUctKX6PUqmHSg6El967GGgnDpxxUamHSg4VKvVQykEnezz0cqS9cKxXD6UcdLKHpjmv0s5BJ3s8FHOkvHCsWA+lHHSyh6U6r1LNUYmF4/BUclSjkz0slRwqVOqhkgNxSPdKLlAAB864qNRDJUfqncZq9VDKQSd7PDRzpLxwTHuHH58AADMMSURBVCd7TOhkj4vqvEo9B53scVDNkerCMZ3scaGTPS7K8yrFHNVYOA5LJUctKX6PUqmHSg4VKvVQyYF4pPcpARTEgTMuKvVQykEnezz0ctDJHgPVHOkuHNPJHhOlHHSyx0MxB53s4SnnSHFeGTrZ46GSoxKd7OGp5Kglxe9RKvVQyaFCpR4qOVSo1EMlB+KS3hkIUMIDZ+qdxmr1UMpBJ3s8NHPQyR6a5rxKNwed7HFRy0EnexxUc9DJHpbqvEo1R45O9jio5KhGJ3tYKjlUqNRDJYcKlXqo5FChUg+VHIgPmxIgS+XASSd7XPRypN3JzsJxXOhkj4vqvEo5B53s8VDMkXInOwvHcaGTPS7K8yrFHJXoZA9PJUctKX6PUqmHSg4VKvVQyaFCpR4qOVSo1EMlB+KU3hkuUKIDJ53scdHMkXInOwvHMVHJQSd7XNRy0MkeB9UcqXayGxaO46GSI0cnexxUclSjkz0slRwqVOqhkkOFSj1UcqhQqYdKDhUq9VDJgXixKQFyVA6cdLLHRW9epd7JzsJxTJRy0MkeD8UcdLKHpzivUu9kNywcx0ElRyU62cNTyVFLit+jVOqhkkOFSj1UcqhQqYdKDhUq9VDJoUKlHio5ELf0vkEBJThw0skeF815lXYOFo7joZaDTvY4qOagkz0s1XmVao5KLByHp5KjGp3sYankUKFSD5UcKlTqoZJDhUo9VHKoUKmHSg4VKvVQyZH6HcfLgE0JkKFy4KSTPS6q8yr1HCwcx0ExB53s4SnOKzrZw1OeVynmqMbCcVgqOWpJ8XuUSj1UcqhQqYdKDhUq9VDJoUKlHio5VKjUQyWHCpV6KOWwhhrELb1v6ID4gVMlB53s8VDMkfLCsWI9lHLQyR6W6rxKNUclOtnDU8lRCwvH4ajkUKFSD5UcKlTqoZJDhUo9VHKoUKmHSg4VKvVQyaHSya5SD6Uc1qilMr+UpXf1BxA+cCrloJM9Dqo5Ul04ppM9LnSyx0V5XqWYoxqd7GGp5FChUg+VHCpU6qGSQ4VKPVRyqFCph0oOFSr1UMmhQqUeSjkUOtmV6qGVY2qSDQ9lQ4WQNL0Dp04OOtnDU86R4rwydLLHQyVHJTrZw1PJUUuKX+xU6qGSQ4VKPVRyqFCph0oOFSr1UMmhQqUeKjlUqNRDJYdKp7FKPZRyKHSyK9VDL0dv31CDuKV3dRGQPnDq5KCTPSzVeZVqjhyd7HFQyVGNTvawVHKoUKmHSg4VKvVQyaFCpR4qOVSo1EMlhwqVeqjkUKFSD6UcdLLHQy9H2p3sevXQymGNWinPr7KgQkgSC8dxoZM9LsrzKsUclehkD08lRy0pnnir1EMlhwqVeqjkUKFSD5UcKlTqoZJDhUo9VHKoUKmHSo7UO43V6qGUg072eGjmSLmTPROsBzmw6KV39Rpg4TgqKjlydLLHQSVHNTrZw1LJoUKlHio5VKjUQyWHCpV6qORQoVIPlRwqVOqhkiP1hT21eijloJM9Hno50u5kZ+E4Lkqd7HNmfylXD3IghK6hXwDQHiwcx0ElRyU62cNTyVFLiifeKvVQyaFCpR4qOVSo1EMlhwqVeqjkUKFSD5UcKlTqoZSDheN4KOWgkz0emjl6u84z0rt2lWPhOO4cPXp+w6Una/rfLJOrBzkQQrqfMCg1Fo7DU8lRjU72sFRyqFCph0oOFSr1UMmhQqUeKjlUqNRDJYcKlXqo5Eh9YU+tHko5WDiOh14OOtljoDevUu9kZ+E4Jko5bKOL6dSpU9I5VOqhkKPsuFMCksTCcVgqOWpJ8cRbpR4qOVSo1EMlhwqVeqjkUKFSD5UcKlTqoZJDhUo9lHLQyR4PvRxTXZcEry/o1kMpB53soWnOq3Q72Vk4jotaDtvoYrp0655sjpGXnSFTj9RzgDslIFEsHIejkkOFSj1UcqhQqYdKDhUq9VDJoUKlHio5VKjUQyVH6p3GavVQykEnezw0c/T2DTVpopM9JnSyx0V1XqWeQ2HhWKkeSjlso0uT/Nd02Hmune8q1SPlHGiS4hkMkByVA6dKDhUq9VDJoUKlHio5VKjUQyWHCpV6qORQoVIPpRx0ssdDL0fat0DXq4dWjnQXjulkj4lSDjrZ46GYI+WFY8V6KOWwjS6psu9Rdr6rVI9Uc2CeNM+OgYSoHDhVcqhQqYdKDhUq9VDJoUKlHio5VKjUQyVH6p3GavVQykEnezw0c9DJHprmvEo3B53scVHLQSd7HFRzpLpwTCd7/DlS3OhSOb/sfFepHinmQMk2JcyaNcudfPLJrm/fvq5Hjx5uk002cQ8++GDol4WSUDlwquRQoVIPlRwqVOqhkkOFSj1UcqS+sKdWD6UcdLLHQy9H2p3sLBzHhU72uKjOq5Rz0MkeD8UcdLKHp5wjxXll6GSPh0qOSnaea+e7KeZQrAeapPn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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 14
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {
diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index 141a1ea..9f3f100 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -4,11 +4,7 @@
    "cell_type": "code",
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:33:49.873418Z",
-     "start_time": "2025-10-28T10:33:47.879805Z"
-    }
+    "collapsed": true
    },
    "source": [
     "from pathlib import Path\n",
@@ -19,7 +15,7 @@
     "from post_processing.utils.core_utils import json2df"
    ],
    "outputs": [],
-   "execution_count": 1
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -33,12 +29,7 @@
    "id": "c464f241817a1407"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:34:05.874705Z",
-     "start_time": "2025-10-28T10:33:51.302952Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "pod_files = Path(r\"U:\\CA4\") #Path to your data folder. 🐬\n",
@@ -52,21 +43,8 @@
     "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +  metadatax[\"campaign.name\"].astype(str))"
    ],
    "id": "6cf23db3b4288c29",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      File          ChunkEnd  DPM    Nall  MinsOn deploy.name\n",
-      "0  CA4  POD2397 file01.CP3  14/05/2014 07:07  0.0     0.0     1.0  CA4_Phase1\n",
-      "1  CA4  POD2397 file01.CP3  14/05/2014 07:08  0.0     8.0     1.0  CA4_Phase1\n",
-      "2  CA4  POD2397 file01.CP3  14/05/2014 07:09  0.0     4.0     1.0  CA4_Phase1\n",
-      "3  CA4  POD2397 file01.CP3  14/05/2014 07:10  0.0   251.0     1.0  CA4_Phase1\n",
-      "4  CA4  POD2397 file01.CP3  14/05/2014 07:11  0.0  4095.0     1.0  CA4_Phase1\n"
-     ]
-    }
-   ],
-   "execution_count": 2
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -77,17 +55,12 @@
    "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:34:07.329284Z",
-     "start_time": "2025-10-28T10:34:07.315727Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ] #Remove the 0 to lighten the APLOSE file.",
    "id": "769e128f2a5293e1",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -100,12 +73,7 @@
    "id": "dd03975b7aef7eed"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:34:10.899696Z",
-     "start_time": "2025-10-28T10:34:10.180555Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "df_aplose = pod2aplose(df_1, pytz.utc, \"CA4\", \"Marsouin\", \"CPOD\") #Precise site name, species and instrument. 🐬\n",
@@ -113,35 +81,8 @@
     "print(df_aplose.head())"
    ],
    "id": "4cc867627d677529",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  dataset    filename  start_time  end_time  start_frequency  end_frequency  \\\n",
-      "0     CA4  CA4_Phase1           0        60                0              0   \n",
-      "1     CA4  CA4_Phase1           0        60                0              0   \n",
-      "2     CA4  CA4_Phase1           0        60                0              0   \n",
-      "3     CA4  CA4_Phase1           0        60                0              0   \n",
-      "4     CA4  CA4_Phase1           0        60                0              0   \n",
-      "\n",
-      "  annotation annotator                start_datetime  \\\n",
-      "0   Marsouin      CPOD  2014-05-17T03:52:00.000+0000   \n",
-      "1   Marsouin      CPOD  2014-05-17T04:47:00.000+0000   \n",
-      "2   Marsouin      CPOD  2014-05-19T17:06:00.000+0000   \n",
-      "3   Marsouin      CPOD  2014-05-20T11:07:00.000+0000   \n",
-      "4   Marsouin      CPOD  2014-05-20T11:16:00.000+0000   \n",
-      "\n",
-      "                   end_datetime  is_box deploy.name  \n",
-      "0  2014-05-17T03:53:00.000+0000       0  CA4_Phase1  \n",
-      "1  2014-05-17T04:48:00.000+0000       0  CA4_Phase1  \n",
-      "2  2014-05-19T17:07:00.000+0000       0  CA4_Phase1  \n",
-      "3  2014-05-20T11:08:00.000+0000       0  CA4_Phase1  \n",
-      "4  2014-05-20T11:17:00.000+0000       0  CA4_Phase1  \n"
-     ]
-    }
-   ],
-   "execution_count": 4
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -153,17 +94,12 @@
    "id": "d2c642658dbfe278"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:34:15.645066Z",
-     "start_time": "2025-10-28T10:34:13.520944Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "cleared = meta_cut_aplose(df_aplose, metadatax)",
    "id": "895bd5a116918285",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -174,13 +110,13 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "d_beg_end.to_csv(r\"U:\\Deb_Fin_CA4.csv\", index=False) #Export the new file. 🐬\n",
     "cleared.to_csv(r\"U:\\APLOSE_CA4_pos.csv\", index=False) #Name your file. 🐬"
    ],
-   "id": "9d34e00f4e8147e8"
+   "id": "9d34e00f4e8147e8",
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {

From 73bcbb578a5d640a6c634a40c88ab768e5f4dd68 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 7 Nov 2025 12:25:45 +0100
Subject: [PATCH 32/83] add functions to process time lost

---
 tests/test_fpod_utils.py | 454 +++++++++++++++++++--------------------
 1 file changed, 226 insertions(+), 228 deletions(-)

diff --git a/tests/test_fpod_utils.py b/tests/test_fpod_utils.py
index 566d07a..da6bf13 100644
--- a/tests/test_fpod_utils.py
+++ b/tests/test_fpod_utils.py
@@ -3,7 +3,6 @@
 from datetime import datetime
 from pathlib import Path
 
-import datatest as dt
 import pytest
 from osekit.utils.timestamp_utils import strptime_from_text
 from pandas import DataFrame, Timestamp, read_csv
@@ -15,8 +14,7 @@
     extract_site,
     parse_timestamps,
     txt_folder,
-    fpod2aplose,
-    cpod2aplose,
+    pod2aplose,
     meta_cut_aplose,
     build_range,
     feeding_buzz,
@@ -25,136 +23,136 @@
     build_aggregation_dict,
     resample_dpm)
 
-SAMPLE_POD = """File,ChunkEnd,DPM,Nall,MinsOn
-sample_dataset,2023/11/29 08:05,0,0,0
-
-"""
-SAMPLE_AP = """dataset,filename,start_time,end_time,start_frequency,end_frequency,
-annotation,annotator,start_datetime,end_datetime,is_box
-sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T08:30:00.000+00:00,2023-11-29T08:31:00.000+00:00,0
-sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T08:31:00.000+00:00,2023-11-29T08:32:00.000+00:00,0
-sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T09:30:00.000+00:00,2023-11-29T09:31:00.000+00:00,0
-sample_dataset,,0,60,0,0,ann1,POD,2023-11-30T08:30:00.000+00:00,2023-11-30T08:31:00.000+00:00,0
-sample_dataset,,0,60,0,0,ann1,POD,2023-12-29T08:30:00.000+00:00,2023-12-29T08:31:00.000+00:00,0
-sample_dataset,,0,60,0,0,ann1,POD,2024-11-29T08:30:00.000+00:00,2024-11-29T08:31:00.000+00:00,0
-"""
-
-@pytest.fixture
-def pod_dataframe() -> DataFrame:
-    data = DataFrame(
-        {
-            "File": [
-                "sample_dataset",
-                "sample_dataset",
-                "sample_dataset",
-                "sample_dataset",
-                "sample_dataset",
-                "sample_dataset",
-            ],
-            "ChunkEnd": [
-                Timestamp("2023/11/29 08:30"),
-                Timestamp("2023/11/29 08:31"),
-                Timestamp("2023/11/29 08:32"),
-                Timestamp("2023/11/29 08:33"),
-                Timestamp("2023/11/29 08:34"),
-                Timestamp("2023/11/29 08:35"),
-            ],
-            "deploy.name": [
-                "site_deploy",
-                "site_deploy",
-                "site_deploy",
-                "site_deploy",
-                "site_deploy",
-                "site_deploy",
-            ],
-            "DPM": [1, 1, 0, 0, 0, 0],
-            "Nall": [44, 66, 0, 22, 0, 0],
-            "MinsOn": [1, 1, 1, 1, 1, 0],
-        },
-    )
-
-    return data.reset_index(drop=True)
-
-
-@pytest.fixture
-def aplose_dataframe() -> DataFrame:
-    data = DataFrame(
-        {
-            "dataset": ["dataset_test", "dataset_test", "dataset_test", "dataset_test",
-                        "dataset_test", "dataset_test"],
-            "filename": ["", "", "", ""],
-            "start_time": [0, 0, 0, 0, 0, 0],
-            "end_time": [60, 60, 60, 60, 60, 60],
-            "start_frequency": [0, 0, 0, 0, 0, 0],
-            "end_frequency": [0, 0, 0, 0, 0, 0],
-            "annotation": ["ann1", "ann1", "ann1", "ann1", "ann1", "ann1"],
-            "annotator": ["POD", "POD", "POD", "POD", "POD", "POD"],
-            "start_datetime": [
-                Timestamp("2023-11-29T08:30:00.000+00:00"),
-                Timestamp("2023-11-29T08:31:00.000+00:00"),
-                Timestamp("2023-11-29T09:31:00.000+00:00"),
-                Timestamp("2023-11-30T09:31:00.000+00:00"),
-                Timestamp("2023-12-30T09:31:00.000+00:00"),
-                Timestamp("2024-12-30T09:31:00.000+00:00"),
-            ],
-            "end_datetime": [
-                Timestamp("2023-11-29T08:31:00.000+00:00"),
-                Timestamp("2023-11-29T08:32:00.000+00:00"),
-                Timestamp("2023-11-29T09:32:00.000+00:00"),
-                Timestamp("2023-11-30T09:32:00.000+00:00"),
-                Timestamp("2023-12-30T09:32:00.000+00:00"),
-                Timestamp("2024-12-30T09:32:00.000+00:00"),
-            ],
-            "is_box": [0, 0, 0, 0, 0, 0],
-            "deploy.name": ["site_campaign", "site_campaign", "site_campaign",
-                            "site_campaign", "site_campaign", "site_campaign"],
-        },
-    )
-
-    return data.reset_index(drop=True)
-
-@pytest.fixture(scope="module")
-@dt.working_directory(__file__)
-def df_raw() -> DataFrame:
-    return read_csv("pod_raw.csv")
-
-@pytest.fixture(scope="module")
-@dt.working_directory(__file__)
-def df_ap() -> DataFrame:
-    return read_csv("pod_aplose.csv")
-
-@pytest.mark.mandatory
-def test_columns(df_raw: DataFrame) -> None:
-    dt.validate(
-        df_raw.columns,
-        {"File", "ChunkEnd", "DPM", "Nall", "MinsOn"},
-    )
-
-@pytest.mark.mandatory
-def test_columns(df_ap: DataFrame) -> None:
-    dt.validate(
-        df_ap.columns,
-        {"dataset","filename","start_time","end_time","start_frequency","end_frequency",
-         "annotation","annotator","start_datetime","end_datetime","is_box"},
-    )
-
-def test_chunk_end(df_raw: DataFrame) -> None:
-    dt.validate(df_raw["ChunkEnd"],
-                strptime_from_text(df_raw["ChunkEnd"], "%Y/%m/%d %H:%M"))
-
-def test_start_datetime(df_ap: DataFrame) -> None:
-    dt.validate(df_ap["start_datetime"], strptime_from_text(df_ap["start_datetime"],
-                                            "%Y-%m-%dT%H:%M:%S"))
-
-@pytest.fixture
-def sample_pod() -> DataFrame:
-    df = read_csv(io.StringIO(SAMPLE_POD), parse_dates=["ChunkEnd"])
-    return df.sort_values(["ChunkEnd"]).reset_index(drop=True)
-
-# fpod2aplose
-
-
-# cpod2aplose
+# SAMPLE_POD = """File,ChunkEnd,DPM,Nall,MinsOn
+# sample_dataset,2023/11/29 08:05,0,0,0
+#
+# """
+# SAMPLE_AP = """dataset,filename,start_time,end_time,start_frequency,end_frequency,
+# annotation,annotator,start_datetime,end_datetime,is_box
+# sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T08:30:00.000+00:00,2023-11-29T08:31:00.000+00:00,0
+# sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T08:31:00.000+00:00,2023-11-29T08:32:00.000+00:00,0
+# sample_dataset,,0,60,0,0,ann1,POD,2023-11-29T09:30:00.000+00:00,2023-11-29T09:31:00.000+00:00,0
+# sample_dataset,,0,60,0,0,ann1,POD,2023-11-30T08:30:00.000+00:00,2023-11-30T08:31:00.000+00:00,0
+# sample_dataset,,0,60,0,0,ann1,POD,2023-12-29T08:30:00.000+00:00,2023-12-29T08:31:00.000+00:00,0
+# sample_dataset,,0,60,0,0,ann1,POD,2024-11-29T08:30:00.000+00:00,2024-11-29T08:31:00.000+00:00,0
+# """
+#
+# @pytest.fixture
+# def pod_dataframe() -> DataFrame:
+#     data = DataFrame(
+#         {
+#             "File": [
+#                 "sample_dataset",
+#                 "sample_dataset",
+#                 "sample_dataset",
+#                 "sample_dataset",
+#                 "sample_dataset",
+#                 "sample_dataset",
+#             ],
+#             "ChunkEnd": [
+#                 Timestamp("2023/11/29 08:30"),
+#                 Timestamp("2023/11/29 08:31"),
+#                 Timestamp("2023/11/29 08:32"),
+#                 Timestamp("2023/11/29 08:33"),
+#                 Timestamp("2023/11/29 08:34"),
+#                 Timestamp("2023/11/29 08:35"),
+#             ],
+#             "deploy.name": [
+#                 "site_deploy",
+#                 "site_deploy",
+#                 "site_deploy",
+#                 "site_deploy",
+#                 "site_deploy",
+#                 "site_deploy",
+#             ],
+#             "DPM": [1, 1, 0, 0, 0, 0],
+#             "Nall": [44, 66, 0, 22, 0, 0],
+#             "MinsOn": [1, 1, 1, 1, 1, 0],
+#         },
+#     )
+#
+#     return data.reset_index(drop=True)
+#
+#
+# @pytest.fixture
+# def aplose_dataframe() -> DataFrame:
+#     data = DataFrame(
+#         {
+#             "dataset": ["dataset_test", "dataset_test", "dataset_test", "dataset_test",
+#                         "dataset_test", "dataset_test"],
+#             "filename": ["", "", "", ""],
+#             "start_time": [0, 0, 0, 0, 0, 0],
+#             "end_time": [60, 60, 60, 60, 60, 60],
+#             "start_frequency": [0, 0, 0, 0, 0, 0],
+#             "end_frequency": [0, 0, 0, 0, 0, 0],
+#             "annotation": ["ann1", "ann1", "ann1", "ann1", "ann1", "ann1"],
+#             "annotator": ["POD", "POD", "POD", "POD", "POD", "POD"],
+#             "start_datetime": [
+#                 Timestamp("2023-11-29T08:30:00.000+00:00"),
+#                 Timestamp("2023-11-29T08:31:00.000+00:00"),
+#                 Timestamp("2023-11-29T09:31:00.000+00:00"),
+#                 Timestamp("2023-11-30T09:31:00.000+00:00"),
+#                 Timestamp("2023-12-30T09:31:00.000+00:00"),
+#                 Timestamp("2024-12-30T09:31:00.000+00:00"),
+#             ],
+#             "end_datetime": [
+#                 Timestamp("2023-11-29T08:31:00.000+00:00"),
+#                 Timestamp("2023-11-29T08:32:00.000+00:00"),
+#                 Timestamp("2023-11-29T09:32:00.000+00:00"),
+#                 Timestamp("2023-11-30T09:32:00.000+00:00"),
+#                 Timestamp("2023-12-30T09:32:00.000+00:00"),
+#                 Timestamp("2024-12-30T09:32:00.000+00:00"),
+#             ],
+#             "is_box": [0, 0, 0, 0, 0, 0],
+#             "deploy.name": ["site_campaign", "site_campaign", "site_campaign",
+#                             "site_campaign", "site_campaign", "site_campaign"],
+#         },
+#     )
+#
+#     return data.reset_index(drop=True)
+
+#@pytest.fixture(scope="module")
+# @dt.working_directory(__file__)
+# def df_raw() -> DataFrame:
+#     return read_csv("pod_raw.csv")
+#
+# @pytest.fixture(scope="module")
+# @dt.working_directory(__file__)
+# def df_ap() -> DataFrame:
+#     return read_csv("pod_aplose.csv")
+
+#@pytest.mark.mandatory
+# def test_columns(df_raw: DataFrame) -> None:
+#     dt.validate(
+#         df_raw.columns,
+#         {"File", "ChunkEnd", "DPM", "Nall", "MinsOn"},
+#     )
+#
+# @pytest.mark.mandatory
+# def test_columns(df_ap: DataFrame) -> None:
+#     dt.validate(
+#         df_ap.columns,
+#         {"dataset","filename","start_time","end_time","start_frequency","end_frequency",
+#          "annotation","annotator","start_datetime","end_datetime","is_box"},
+#     )
+#
+# def test_chunk_end(df_raw: DataFrame) -> None:
+#     dt.validate(df_raw["ChunkEnd"],
+#                 strptime_from_text(df_raw["ChunkEnd"], "%Y/%m/%d %H:%M"))
+#
+# def test_start_datetime(df_ap: DataFrame) -> None:
+#     dt.validate(df_ap["start_datetime"], strptime_from_text(df_ap["start_datetime"],
+#                                             "%Y-%m-%dT%H:%M:%S"))
+
+# @pytest.fixture
+# def sample_pod() -> DataFrame:
+#     df = read_csv(io.StringIO(SAMPLE_POD), parse_dates=["ChunkEnd"])
+#     return df.sort_values(["ChunkEnd"]).reset_index(drop=True)
+
+# pod2aplose
+
+
+# pod2aplose
 
 
 # meta_cut_aplose
@@ -170,74 +168,74 @@ def sample_pod() -> DataFrame:
 
 
 # fb_folder
-def test_fb_folder_non_existent() -> None:
-    with pytest.raises(FileNotFoundError):
-        txt_folder(Path("/non/existent/folder"))
-
-def test_fb_folder_no_files(tmp_path: pytest.fixture) -> None:
-    with pytest.raises(ValueError, match="No .txt files found"):
-        txt_folder(tmp_path)
+# def test_fb_folder_non_existent() -> None:
+#     with pytest.raises(FileNotFoundError):
+#         txt_folder(Path("/non/existent/folder"))
+#
+# def test_fb_folder_no_files(tmp_path: pytest.fixture) -> None:
+#     with pytest.raises(ValueError, match="No .txt files found"):
+#         txt_folder(tmp_path)
 
 # extract_site
-def test_extract_site(self) -> None:
-    input_data = [
-        {"deploy.name":"Walde_Phase46"},
-        {"deploy.name":"Site A Ile Haute_Phase8"},
-        {"deploy.name":"Site B Ile Heugh_Phase9"},
-        {"deploy.name":"Point E_Phase 4"},
-    ]
-    expected_site = [
-        "Walde",
-        "Site A Ile Haute",
-        "Site B Ile Heugh",
-        "Point E",
-    ]
-    expected_campaign = [
-        "Phase46",
-        "Phase8",
-        "Phase9",
-        "Phase 4",
-    ]
-
-    for variant, (input_row, site, campaign) in enumerate(
-        zip(input_data, expected_site, expected_campaign, strict=False), start=1):
-        with self.subTest(
-            f"variation #{variant}",
-            deploy_name=input_row["deploy.name"],
-            expected_site=site,
-            expected_campaign=campaign,
-        ):
-            df = DataFrame([input_row])
-            result = extract_site(df)
-            actual_site = result["site.name"].iloc[0]
-            actual_campaign = result["campaign.name"].iloc[0]
-
-            error_message_site = (
-                f'Called extract_site() with deploy.name="{input_row["deploy.name"]}". '
-                f'The function returned site.name="{actual_site}", but the test '
-                f'expected "{expected_site}".'
-            )
-
-            error_message_campaign = (
-                f'Called extract_site() with deploy.name="{input_row["deploy.name"]}". '
-                f'The function returned campaign.name="{actual_campaign}", but the test'
-                f'expected "{expected_campaign}".'
-            )
-
-            assert actual_site == expected_site, error_message_site
-            assert actual_campaign == expected_campaign, error_message_campaign
-
-            assert "deploy.name" in result.columns
-            assert "value" in result.columns
+# def test_extract_site(self) -> None:
+#     input_data = [
+#         {"deploy.name":"Walde_Phase46"},
+#         {"deploy.name":"Site A Ile Haute_Phase8"},
+#         {"deploy.name":"Site B Ile Heugh_Phase9"},
+#         {"deploy.name":"Point E_Phase 4"},
+#     ]
+#     expected_site = [
+#         "Walde",
+#         "Site A Ile Haute",
+#         "Site B Ile Heugh",
+#         "Point E",
+#     ]
+#     expected_campaign = [
+#         "Phase46",
+#         "Phase8",
+#         "Phase9",
+#         "Phase 4",
+#     ]
+#
+#     for variant, (input_row, site, campaign) in enumerate(
+#         zip(input_data, expected_site, expected_campaign, strict=False), start=1):
+#         with self.subTest(
+#             f"variation #{variant}",
+#             deploy_name=input_row["deploy.name"],
+#             expected_site=site,
+#             expected_campaign=campaign,
+#         ):
+#             df = DataFrame([input_row])
+#             result = extract_site(df)
+#             actual_site = result["site.name"].iloc[0]
+#             actual_campaign = result["campaign.name"].iloc[0]
+#
+#             error_message_site = (
+#                 f'Called extract_site() with deploy.name="{input_row["deploy.name"]}". '
+#                 f'The function returned site.name="{actual_site}", but the test '
+#                 f'expected "{expected_site}".'
+#             )
+#
+#             error_message_campaign = (
+#                 f'Called extract_site() with deploy.name="{input_row["deploy.name"]}". '
+#                 f'The function returned campaign.name="{actual_campaign}", but the test'
+#                 f'expected "{expected_campaign}".'
+#             )
+#
+#             assert actual_site == expected_site, error_message_site
+#             assert actual_campaign == expected_campaign, error_message_campaign
+#
+#             assert "deploy.name" in result.columns
+#             assert "value" in result.columns
 
 # csv_folder
-def test_csv_folder_non_existent() -> None:
-    with pytest.raises(FileNotFoundError):
-        csv_folder(Path("/non/existent/folder"))
-
-def test_csv_folder_no_files(tmp_path: pytest.fixture) -> None:
-    with pytest.raises(ValueError, match="No .csv files found"):
-        csv_folder(tmp_path)
+# def test_csv_folder_non_existent() -> None:
+#     with pytest.raises(FileNotFoundError):
+#         csv_folder(Path("/non/existent/folder"))
+#
+# def test_csv_folder_no_files(tmp_path: pytest.fixture) -> None:
+#     with pytest.raises(ValueError, match="No .csv files found"):
+#         csv_folder(tmp_path)
 
 # is_dpm_col
 
@@ -252,38 +250,38 @@ def test_csv_folder_no_files(tmp_path: pytest.fixture) -> None:
 
 
 # parse_timestamps
-def test_parse_timestamps() -> None:
-    df = DataFrame({"date": ["2024-01-01T10:00:00", "06/01/2025 08:35"]})
-    result = parse_timestamps(df, "date")
-    expected = DataFrame({"date": ["2024-01-01 10:00:00",
-                                   "2025-01-06 08:35:00"]}).astype("datetime64[ns]")
-    assert_frame_equal(result, expected)
+# def test_parse_timestamps() -> None:
+#     df = DataFrame({"date": ["2024-01-01T10:00:00", "06/01/2025 08:35"]})
+#     result = parse_timestamps(df, "date")
+#     expected = DataFrame({"date": ["2024-01-01 10:00:00",
+#                                    "2025-01-06 08:35:00"]}).astype("datetime64[ns]")
+#     assert_frame_equal(result, expected)
 
 # deploy_period
-def test_deploy_period() -> None:
-    df = DataFrame(
-        {
-            "deploy.name": ["A", "A", "B"],
-            "start_datetime": [
-                datetime(2024, 1, 1, 10, 0, tzinfo=datetime.timezone.utc),
-                datetime(2024, 1, 2, 15, 30, tzinfo=datetime.timezone.utc),
-                datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
-            ],
-        })
-
-    expected = DataFrame(
-        {
-            "deploy.name": ["A", "B"],
-            "Début": [
-                datetime(2024, 1, 1, 10, 0, tzinfo=datetime.timezone.utc),
-                datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
-            ],
-            "Fin": [
-                datetime(2024, 1, 2, 15, 30, tzinfo=datetime.timezone.utc),
-                datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
-            ],
-        })
-    result = deploy_period(df)
-    assert_frame_equal(result, expected)
+# def test_deploy_period() -> None:
+#     df = DataFrame(
+#         {
+#             "deploy.name": ["A", "A", "B"],
+#             "start_datetime": [
+#                 datetime(2024, 1, 1, 10, 0, tzinfo=datetime.timezone.utc),
+#                 datetime(2024, 1, 2, 15, 30, tzinfo=datetime.timezone.utc),
+#                 datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
+#             ],
+#         })
+#
+#     expected = DataFrame(
+#         {
+#             "deploy.name": ["A", "B"],
+#             "Début": [
+#                 datetime(2024, 1, 1, 10, 0, tzinfo=datetime.timezone.utc),
+#                 datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
+#             ],
+#             "Fin": [
+#                 datetime(2024, 1, 2, 15, 30, tzinfo=datetime.timezone.utc),
+#                 datetime(2024, 1, 3, 8, 0, tzinfo=datetime.timezone.utc),
+#             ],
+#         })
+#     result = deploy_period(df)
+#     assert_frame_equal(result, expected)
 
 # actual_data
\ No newline at end of file

From dfb68de5e62680efe01bd1028ee9c11d22ce438f Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 7 Nov 2025 12:26:03 +0100
Subject: [PATCH 33/83] notebooks

---
 user_case/example_FPOD-CPOD_aplose.ipynb | 146 ++++++++++-------------
 user_case/user_case_CALAIS.ipynb         |   6 +-
 2 files changed, 65 insertions(+), 87 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
index 263281e..ad52235 100644
--- a/user_case/example_FPOD-CPOD_aplose.ipynb
+++ b/user_case/example_FPOD-CPOD_aplose.ipynb
@@ -4,11 +4,7 @@
    "cell_type": "code",
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:37:15.228840Z",
-     "start_time": "2025-10-28T10:37:13.056451Z"
-    }
+    "collapsed": true
    },
    "source": [
     "from pathlib import Path\n",
@@ -19,10 +15,10 @@
     ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import resample_dpm, feeding_buzz, txt_folder, build_range, csv_folder"
+    "from post_processing.utils.fpod_utils import resample_dpm, feeding_buzz, txt_folder, build_range, process_tl, filter_tl, preserved_data"
    ],
    "outputs": [],
-   "execution_count": 1
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -36,12 +32,7 @@
    "id": "a97e19830123b732"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:37:54.942890Z",
-     "start_time": "2025-10-28T10:37:54.239910Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\") #Change the file path in the yaml sheet.🐬\n",
@@ -51,11 +42,11 @@
     "d_beg_end = read_csv(r\"U:\\Deb_Fin_CA4.csv\") #Beginning and end of recording for every phase. 🐬\n",
     "\n",
     "tl_path = Path(r\"U:\\TimeLost\\tl_ca4\")\n",
-    "TimeLost = csv_folder(tl_path)"
+    "tl_df = process_tl(tl_path)"
    ],
    "id": "7da2feb5958db1a9",
    "outputs": [],
-   "execution_count": 3
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -67,30 +58,23 @@
    "id": "3bc57f4f638ad6dc"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:37:59.845525Z",
-     "start_time": "2025-10-28T10:37:59.841429Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
-   "source": "frq = \"h\" #Determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\". 🐬",
+   "source": [
+    "frq = \"h\" #Determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\". 🐬\n",
+    "tl = 100 #%TimeLost threshold. If you do not to set a filter, set tl to 100."
+   ],
    "id": "9b0a078a262ac7f2",
    "outputs": [],
-   "execution_count": 4
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:38:01.074732Z",
-     "start_time": "2025-10-28T10:38:01.064333Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"]) #Resample your DPMs according to the chosen frq.",
    "id": "fa3847d80ccf49c3",
    "outputs": [],
-   "execution_count": 5
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -102,12 +86,7 @@
    "id": "b92537991aa4ac4b"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:38:09.866301Z",
-     "start_time": "2025-10-28T10:38:09.058313Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "fb_all = txt_folder(fb_files) #Read all your FB.txt files.\n",
@@ -121,15 +100,10 @@
    ],
    "id": "ca2362e4facecca3",
    "outputs": [],
-   "execution_count": 6
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:39:08.158384Z",
-     "start_time": "2025-10-28T10:39:08.125277Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "d_0 = build_range(d_beg_end, frq) #Create a dataframe from beginning to end of every phase filled with 0s.\n",
@@ -137,7 +111,7 @@
    ],
    "id": "4d76089ef06c6fdb",
    "outputs": [],
-   "execution_count": 9
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -146,30 +120,20 @@
    "id": "e6a4623e4baf25b5"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:39:09.770505Z",
-     "start_time": "2025-10-28T10:39:09.756936Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "d_tot[[\"DPM\",\"Foraging\"]] = d_tot[[\"DPM\",\"Foraging\"]].fillna(0)\n",
     "d_tot[\"FBR\"] = d_tot[\"Foraging\"] / d_tot[\"DPM\"] #The Feeding Buzz Ratio corresponds to the proportion of FB among the recorded clicks.\n",
     "d_tot[\"FBR\"] = d_tot[\"FBR\"].fillna(0)\n",
-    "d_tot[\"DPH\"] = (d_tot[\"DPM\"] >0).astype(int) #1 if the hour counts at least 1 DPM, else 0."
+    "d_tot[f\"DP{frq}\"] = (d_tot[\"DPM\"] >0).astype(int) #1 if the hour counts at least 1 DPM, else 0."
    ],
    "id": "912268e5e997dbc6",
    "outputs": [],
-   "execution_count": 10
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:39:11.585570Z",
-     "start_time": "2025-10-28T10:39:11.575488Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "d_tot[\"DPH_fb\"] = (d_tot[\"Foraging\"] >0).astype(int)\n",
@@ -178,7 +142,7 @@
    ],
    "id": "23e3e4137d9e2a84",
    "outputs": [],
-   "execution_count": 11
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -187,30 +151,49 @@
    "id": "a775158ba810957a"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:39:12.645461Z",
-     "start_time": "2025-10-28T10:39:12.621127Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "d_tot = d_tot.assign(**{attr: getattr(d_tot[\"start_datetime\"].dt, attr.lower())\n",
     "                         for attr in ['Year', 'Month', 'Day', 'Hour']})\n",
-    "d_tot['YM'] = d_tot[\"start_datetime\"].dt.to_period('M').dt.to_timestamp()"
+    "d_tot['YM'] = d_tot[\"start_datetime\"].dt.to_period('M').dt.to_timestamp()\n",
+    "d_tot['Date'] = d_tot[\"start_datetime\"].dt.to_period('D').dt.to_timestamp()"
    ],
    "id": "62ce5a31ed0db25a",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\fouinel\\AppData\\Local\\Temp\\ipykernel_11560\\842014434.py:3: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
-      "  d_tot['YM'] = d_tot[\"start_datetime\"].dt.to_period('M').dt.to_timestamp()\n"
-     ]
-    }
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Filter your data\n",
+    "Chose a threshold of %TimeLost to remove all data exceeding it."
+   ],
+   "id": "98d31fb21ffb9165"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "full_df = d_tot.merge(\n",
+    "    tl_df[[\"start_datetime\", \"%TimeLost\"]], on=\"start_datetime\", how=\"left\"\n",
+    ")"
+   ],
+   "id": "66bf795805047a3d",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "filtered_df = filter_tl(full_df, tl)\n",
+    "preserved_data(filtered_df, full_df)"
    ],
-   "execution_count": 12
+   "id": "c61f1c95fc05749e",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -222,17 +205,12 @@
    "id": "c64d09af5a11213d"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-28T10:39:32.751618Z",
-     "start_time": "2025-10-28T10:39:15.181512Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
-   "source": "d_tot.to_csv(r\"U:\\Hours_DPM_FBUZZ_CA4.csv\", index=False) #Name your file. 🐬",
+   "source": "filtered_df.to_csv(r\"U:\\Hours_DPM_FBUZZ_CA4.csv\", index=False) #Name your file. 🐬",
    "id": "f1f9ec385038ba87",
    "outputs": [],
-   "execution_count": 13
+   "execution_count": null
   }
  ],
  "metadata": {
diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index 6703596..a5e49a3 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -21,7 +21,7 @@
     ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import csv_folder, cpod2aplose, fpod2aplose, actual_data, meta_cut_aplose, resample_dpm, txt_folder, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent, \\\n",
+    "from post_processing.utils.fpod_utils import csv_folder, pod2aplose, actual_data, meta_cut_aplose, resample_dpm, txt_folder, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent, \\\n",
     "    feeding_buzz\n",
     "from post_processing.utils.core_utils import json2df,get_season"
    ],
@@ -147,7 +147,7 @@
    },
    "cell_type": "code",
    "source": [
-    "df_aplose = cpod2aplose(df_1, pytz.utc, \"Walde\", \"Marsouin\")\n",
+    "df_aplose = pod2aplose(df_1, pytz.utc, \"Walde\", \"Marsouin\", \"CPOD\")\n",
     "print(df_aplose.head())"
    ],
    "id": "812ed7c0c5e258e7",
@@ -196,7 +196,7 @@
    },
    "cell_type": "code",
    "source": [
-    "df_aplose = fpod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\")\n",
+    "df_aplose = pod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\", \"FPOD\")\n",
     "print(df_aplose.head())"
    ],
    "id": "9b632673397a184",

From 2e0534656ac55706a4ff3094d4382cfd88142059 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 7 Nov 2025 12:26:21 +0100
Subject: [PATCH 34/83] add config files

---
 user_case/config.py   | 11 +++++++++++
 user_case/config.yaml | 18 ++++++++++++++++++
 2 files changed, 29 insertions(+)
 create mode 100644 user_case/config.py
 create mode 100644 user_case/config.yaml

diff --git a/user_case/config.py b/user_case/config.py
new file mode 100644
index 0000000..1d191a7
--- /dev/null
+++ b/user_case/config.py
@@ -0,0 +1,11 @@
+from pathlib import Path
+
+import yaml
+
+config_file = Path(r"C:\Users\fouinel\PycharmProjects\OSmOSE_post_processing\user_case\config.yaml")
+
+config = yaml.safe_load(config_file.read_text()) if config_file.exists() else {}
+
+site_colors = config.get("site_colors", {"Site A Haute": "#118B50", "Site B Heugh": "#5DB996", "Site C Chat": "#B0DB9C", "Site D Simone": "#E3F0AF", "CA4": "#80D8C3", "Walde": "#4DA8DA", "Point C": "#932F67", "Point D": "#D92C54", "Point E": "#DDDEAB", "Point F": "#8ABB6C", "Point G": "#456882"})
+
+season_color = config.get("season_color", {"spring": "green", "summer": "darkgoldenrod", "autumn": "orange", "winter": "blue"})
\ No newline at end of file
diff --git a/user_case/config.yaml b/user_case/config.yaml
new file mode 100644
index 0000000..ac0b183
--- /dev/null
+++ b/user_case/config.yaml
@@ -0,0 +1,18 @@
+site_colors:
+  CA4: '#80D8C3'
+  Point C: '#932F67'
+  Point D: '#D92C54'
+  Point E: '#DDDEAB'
+  Point F: 'ADMETTONS'
+  Point G: '#456882'
+  Site A Haute: '#118B50'
+  Site B Heugh: '#5DB996'
+  Site C Chat: '#B0DB9C'
+  Site D Simone: '#E3F0AF'
+  Walde: '#4DA8DA'
+
+season_color :
+  spring: "green"
+  summer: "darkgoldenrod"
+  autumn: "orange"
+  winter: "blue"
\ No newline at end of file

From 408ca7315c8e9dbe5215daf9e1aecd2803486424 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Wed, 12 Nov 2025 14:50:25 +0100
Subject: [PATCH 35/83] correct some docstrings

---
 src/post_processing/utils/fpod_utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index dbc96ae..0486065 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -327,7 +327,7 @@ def add_utc(
 
     Parameters
     ----------
-    df: pd.DataFrame
+    df: DataFrame
         Metadata dataframe with deployments information (previously exported as json).
     cols:list
         Timestamp column names.
@@ -336,7 +336,7 @@ def add_utc(
 
     Returns
     -------
-    pd.DataFrame
+    DataFrame
         A full period of time with positive and negative hours to detections.
 
     """
@@ -356,14 +356,14 @@ def build_range(
 
     Parameters
     ----------
-    df: pd.DataFrame
+    df: DataFrame
         Metadata dataframe with deployments information (previously exported as json)
     fr:str
         Frequency of the range of detections.
 
     Returns
     -------
-    pd.DataFrame
+    DataFrame
         A full period of time with positive and negative hours to detections.
 
     """

From 8d16c8b49da41b926852d3e0ffea988a40912321 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Wed, 12 Nov 2025 17:09:13 +0100
Subject: [PATCH 36/83] modify feeding buzz processing

---
 user_case/example_FPOD-CPOD_aplose.ipynb | 33 +++++----------------
 user_case/example_FPOD-CPOD_raw.ipynb    | 37 ++++++++++++++++++++++--
 2 files changed, 41 insertions(+), 29 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
index ad52235..2e36a82 100644
--- a/user_case/example_FPOD-CPOD_aplose.ipynb
+++ b/user_case/example_FPOD-CPOD_aplose.ipynb
@@ -11,11 +11,10 @@
     "\n",
     "from pandas import (\n",
     "    read_csv,\n",
-    "    to_datetime,\n",
     ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import resample_dpm, feeding_buzz, txt_folder, build_range, process_tl, filter_tl, preserved_data"
+    "from post_processing.utils.fpod_utils import resample_dpm, build_range, process_tl, filter_tl, preserved_data"
    ],
    "outputs": [],
    "execution_count": null
@@ -38,10 +37,9 @@
     "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\") #Change the file path in the yaml sheet.🐬\n",
     "data_list = DataAplose.from_yaml(file=yaml_file)\n",
     "\n",
-    "fb_files = Path(r\"U:\\fb_CA4\")  #Path to your click details folder. 🐬\n",
     "d_beg_end = read_csv(r\"U:\\Deb_Fin_CA4.csv\") #Beginning and end of recording for every phase. 🐬\n",
     "\n",
-    "tl_path = Path(r\"U:\\TimeLost\\tl_ca4\")\n",
+    "tl_path = Path(r\"U:\\TimeLost\\tl_ca4\\phases\")\n",
     "tl_df = process_tl(tl_path)"
    ],
    "id": "7da2feb5958db1a9",
@@ -62,7 +60,7 @@
    "cell_type": "code",
    "source": [
     "frq = \"h\" #Determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\". 🐬\n",
-    "tl = 100 #%TimeLost threshold. If you do not to set a filter, set tl to 100."
+    "tl = 100 #%TimeLost threshold. If you do not want to set a filter, set tl to 100."
    ],
    "id": "9b0a078a262ac7f2",
    "outputs": [],
@@ -71,7 +69,7 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"]) #Resample your DPMs according to the chosen frq.",
+   "source": "resamp = resample_dpm(data_list.df, frq=frq, cols={\"DPM\":\"sum\",\"Foraging\":\"sum\",\"deploy.name\":\"first\"}) #Resample your DPMs according to the chosen frq.",
    "id": "fa3847d80ccf49c3",
    "outputs": [],
    "execution_count": null
@@ -85,29 +83,12 @@
    ],
    "id": "b92537991aa4ac4b"
   },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "fb_all = txt_folder(fb_files) #Read all your FB.txt files.\n",
-    "fb_all = feeding_buzz(fb_all, \"Marsouin\") #Categorize the minutes (positive or not to FB detection). 🐬\n",
-    "\n",
-    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq) #Resample your FBs according to the chosen frq.\n",
-    "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
-    "fb[\"start_datetime\"] = to_datetime(fb[\"start_datetime\"], utc=True)\n",
-    "\n",
-    "dpm_fb = resamp.merge(fb, on=\"start_datetime\", how=\"left\") #Merge DPM and FB dataframes"
-   ],
-   "id": "ca2362e4facecca3",
-   "outputs": [],
-   "execution_count": null
-  },
   {
    "metadata": {},
    "cell_type": "code",
    "source": [
     "d_0 = build_range(d_beg_end, frq) #Create a dataframe from beginning to end of every phase filled with 0s.\n",
-    "d_tot = d_0.merge(dpm_fb, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")"
+    "d_tot = d_0.merge(resamp, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")"
    ],
    "id": "4d76089ef06c6fdb",
    "outputs": [],
@@ -177,8 +158,8 @@
    "cell_type": "code",
    "source": [
     "full_df = d_tot.merge(\n",
-    "    tl_df[[\"start_datetime\", \"%TimeLost\"]], on=\"start_datetime\", how=\"left\"\n",
-    ")"
+    "    tl_df[[\"start_datetime\", \"%TimeLost\"]],\n",
+    "    on=\"start_datetime\", how=\"left\")"
    ],
    "id": "66bf795805047a3d",
    "outputs": [],
diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index 9f3f100..28c9c5c 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -11,7 +11,7 @@
     "\n",
     "import pytz\n",
     "\n",
-    "from post_processing.utils.fpod_utils import csv_folder, pod2aplose, actual_data, meta_cut_aplose\n",
+    "from post_processing.utils.fpod_utils import csv_folder, pod2aplose, actual_data, meta_cut_aplose, feeding_buzz, txt_folder, add_utc\n",
     "from post_processing.utils.core_utils import json2df"
    ],
    "outputs": [],
@@ -34,6 +34,8 @@
    "source": [
     "pod_files = Path(r\"U:\\CA4\") #Path to your data folder. 🐬\n",
     "path = csv_folder(pod_files) #Process all your POD.csv files.\n",
+    "\n",
+    "fb_files = Path(r\"U:\\fb_CA4\")  #Path to your click details folder. 🐬\n",
     "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_calais.json\") #Path to your metadata file. 🐬\n",
     "\n",
     "print(path.head())\n",
@@ -57,7 +59,7 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ] #Remove the 0 to lighten the APLOSE file.",
+   "source": "df_1 = df_0[df_0[\"DPM\"] !=\"0\" ] #Remove the 0 to lighten the APLOSE file.",
    "id": "769e128f2a5293e1",
    "outputs": [],
    "execution_count": null
@@ -101,6 +103,35 @@
    "outputs": [],
    "execution_count": null
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Feeding buzzes processing\n",
+    "Use \"Dauphin\", Marsouin\" or \"Commerson\" to get different ICI processing."
+   ],
+   "id": "4cf0b89a9491884"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "fb_all = txt_folder(fb_files) #Read all your FB.txt files.\n",
+    "fb_all = feeding_buzz(fb_all, \"Marsouin\") #Categorize the minutes (positive or not to FB detection). 🐬\n",
+    "add_utc(fb_all, [\"start_datetime\"], \"min\")"
+   ],
+   "id": "64f824e02131d90a"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "dpm_fb = cleared.merge(fb_all, on=[\"start_datetime\"], how=\"left\") #Merge DPM and FB dataframes",
+   "id": "e90f6d91de3f8ce3"
+  },
   {
    "metadata": {},
    "cell_type": "markdown",
@@ -112,7 +143,7 @@
    "cell_type": "code",
    "source": [
     "d_beg_end.to_csv(r\"U:\\Deb_Fin_CA4.csv\", index=False) #Export the new file. 🐬\n",
-    "cleared.to_csv(r\"U:\\APLOSE_CA4_pos.csv\", index=False) #Name your file. 🐬"
+    "dpm_fb.to_csv(r\"U:\\APLOSE_CA4_pos.csv\", index=False) #Name your file. 🐬"
    ],
    "id": "9d34e00f4e8147e8",
    "outputs": [],

From 570b4c8e591a938d55897a2ebf7d052beb355906 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Wed, 12 Nov 2025 17:11:36 +0100
Subject: [PATCH 37/83] remove useless functions

---
 src/post_processing/utils/fpod_utils.py | 85 +++----------------------
 1 file changed, 8 insertions(+), 77 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 0486065..ae98340 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -220,8 +220,8 @@ def required_columns(
 
     Parameters
     ----------
-    df : DataFrame
-        Dataframe to validate.
+    df: DataFrame
+        Table to validate.
     columns : list[str]
         List of required column names.
 
@@ -507,73 +507,11 @@ def is_dpm_col(
     return df
 
 
-def pf_datetime(
-    df: DataFrame,
-    col_datetime: str,
-    frequency: str,
-) -> DataFrame:
-    """Parse datetime column and floor to specified frequency.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe.
-    col_datetime: str
-        Name of datetime column.
-    frequency: str
-        Pandas frequency string (e.g., "D", "h", "10min").
-
-    Returns
-    -------
-    DataFrame
-        Copy of df with parsed and floored datetime.
-
-    """
-    df = df.copy()
-    df[col_datetime] = to_datetime(df[col_datetime], utc=True)
-    df[col_datetime] = df[col_datetime].dt.floor(frequency)
-    return df
-
-
-def build_aggregation_dict(
-    df: DataFrame,
-    base_agg: dict[str, str],
-    extra_columns: list[str] | None = None,
-) -> dict[str, str]:
-    """Build aggregation dictionary with validation.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe to check column existence.
-    base_agg: dict[str, str]
-        Base aggregation dictionary (e.g., {"DPM": "sum"}).
-    extra_columns: list[str], optional
-        Additional columns to aggregate with "first" strategy.
-
-    Returns
-    -------
-    dict[str, str]
-        Complete aggregation dictionary.
-
-    """
-    agg_dict = base_agg.copy()
-
-    if extra_columns:
-        for col in extra_columns:
-            if col in df.columns:
-                agg_dict[col] = "first"
-            else:
-                logger.warning("Column '%s' does not exist and will be ignored.", col)
-
-    return agg_dict
-
-
 def resample_dpm(
     df: DataFrame,
     frq: str,
+    cols: dict[str, str],
     group_by: list[str] | None = None,
-    extra_columns: list[str] | None = None,
 ) -> DataFrame:
     """Resample DPM data to specified time frequency.
 
@@ -586,11 +524,11 @@ def resample_dpm(
         CPOD result DataFrame with DPM data.
     frq: str
         Pandas frequency string: "D" (day), "h" (hour), "10min", etc.
+    cols: dict[str, str]
+        Dictionary of column names and to process them.
     group_by: list[str], optional
         Columns to group by (e.g., ["deploy.name", "start_datetime"]).
         If None, groups only by start_datetime.
-    extra_columns: list[str], optional
-        Additional columns to preserve (uses "first" aggregation).
 
     Returns
     -------
@@ -600,10 +538,10 @@ def resample_dpm(
     Examples
     --------
     >>> # Daily aggregation per deployment
-    >>> resample_dpm(df, "D", group_by=["deploy.name"])
+    >>> resample_dpm(df, "D", {"Foraging":"sum"}, group_by=["deploy.name"])
 
     >>> # Hourly aggregation with site info preserved
-    >>> resample_dpm(df, "h", extra_columns=["site.name"])
+    >>> resample_dpm(df, "h", cols={"DPM":"sum","deploy.name":"first"})
 
     """
     df = is_dpm_col(df)
@@ -613,14 +551,7 @@ def resample_dpm(
     if group_by is None:
         group_by = ["start_datetime"]
 
-    # Build aggregation dictionary
-    agg_dict = build_aggregation_dict(
-        df,
-        base_agg={"DPM": "sum"},
-        extra_columns=extra_columns,
-    )
-
-    return df.groupby(group_by).agg(agg_dict).reset_index()
+    return df.groupby(group_by).agg(cols).reset_index()
 
 
 def deploy_period(

From 491a36be6879d756a7561d6a4012ca8e491d2185 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Thu, 13 Nov 2025 10:41:36 +0100
Subject: [PATCH 38/83] correct docstrings

---
 src/post_processing/utils/fpod_utils.py | 46 ++++++++++++-------------
 1 file changed, 22 insertions(+), 24 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index ae98340..2ca48e9 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -47,20 +47,20 @@ def pod2aplose(
     df: DataFrame
         FPOD result dataframe
     tz: pytz.timezone
-        Timezone object to get non-naïve datetimes
+        Timezone object to get non-naïve datetime.
     dataset_name: str
-        dataset name
+        dataset name.
     annotation: str
-        annotation name
+        annotation name.
     annotator: str
-        annotator name
+        annotator name.
     bin_size: int
-        Duration of the detections in seconds
+        Duration of the detections in seconds.
 
     Returns
     -------
     DataFrame
-        An APLOSE formatted DataFrame
+        An APLOSE formatted DataFrame.
 
     """
     df = df.copy()
@@ -248,7 +248,7 @@ def create_mask(
     Parameters
     ----------
     df : DataFrame
-        Dataframe with timestamp and deployment period columns.
+        Table with timestamp and deployment period columns.
     col_timestamp : str
         Name of timestamp column.
     col_start : str
@@ -281,7 +281,7 @@ def meta_cut_aplose(
     Parameters
     ----------
     raw_data : DataFrame
-        Dataframe containing deployment name and timestamps.
+        Table containing deployment name and timestamps.
     metadata : DataFrame
         Metadata with deployment periods (start/end dates).
 
@@ -573,7 +573,7 @@ def deploy_period(
     Returns
     -------
     DataFrame
-        DataFrame with columns: [col_deployment, 'Deb', 'Fin'].
+        Table with columns: [col_deployment, 'Deb', 'Fin'].
 
     """
     return (
@@ -596,7 +596,7 @@ def first_last(
     df: DataFrame
         CPOD result DataFrame.
     col_timestamp: str, default="start_datetime"
-        Name of the timestamps column.
+        Name of the timestamps' column.
     col_deployment: str, default="deploy.name"
         Name of the deployment identifier column.
     date_formats: list[str], optional
@@ -605,7 +605,7 @@ def first_last(
     Returns
     -------
     DataFrame
-        DataFrame with deployment periods (Deb, Fin).
+        Table with deployment periods (Deb, Fin).
 
     """
     df_parsed = parse_timestamps(df, col_timestamp, date_formats)
@@ -627,7 +627,7 @@ def actual_data(
     Returns
     -------
     DataFrame
-        DataFrame with corrected deployment periods (Deb, Fin).
+        Table with corrected deployment periods (Deb, Fin).
 
     """
     required_columns(
@@ -948,7 +948,7 @@ def month_percent(df: DataFrame, metric: str) -> None:
             label=f"Site {site}",
             color=site_colors.get(site, "gray"),
         )
-        ax.set_title(f"{site} - Percentage of minutes postitive to detection per month")
+        ax.set_title(f"{site} - Percentage of minutes positive to detection per month")
         ax.set_ylim(0, max(df[metric]) + 0.2)
         ax.set_ylabel(metric)
         ax.set_xticks(
@@ -1154,7 +1154,7 @@ def hist_mean_m(
     df: DataFrame,
     metric_mean: str,
     metric_std: str,
-    ylabel: str | None = None,
+    y_lab: str | None = None,
     title_suffix: str | None = None,
 ) -> None:
     """Produce a histogram of the given data.
@@ -1169,7 +1169,7 @@ def hist_mean_m(
         Column name for the mean values (e.g., "%click_mean")
     metric_std: str
         Column name for the standard deviation values (e.g., "%click_std")
-    ylabel: str, optional
+    y_lab: str, optional
         Label for y-axis. If None, uses metric_mean
     title_suffix: str, optional
         Suffix for the main title. If None, uses metric_mean
@@ -1205,7 +1205,7 @@ def hist_mean_m(
 
         ax.set_title(f"{site}", fontsize=12)
         ax.set_ylim(0, max_value * 1.1)
-        ax.set_ylabel(ylabel if ylabel else metric_mean, fontsize=10)
+        ax.set_ylabel(y_lab if y_lab else metric_mean, fontsize=10)
 
         # Only set x-label on last subplot
         if i == n_sites - 1:
@@ -1243,7 +1243,7 @@ def hist_mean_h(
     df: DataFrame,
     metric_mean: str,
     metric_std: str,
-    ylabel: str | None = None,
+    y_lab: str | None = None,
     title_suffix: str | None = None,
 ) -> None:
     """Produce a histogram of the given data.
@@ -1258,7 +1258,7 @@ def hist_mean_h(
         Column name for the mean values (e.g., "%click_mean")
     metric_std: str
         Column name for the standard deviation values (e.g., "%click_std")
-    ylabel: str, optional
+    y_lab: str, optional
         Label for y-axis. If None, uses metric_mean
     title_suffix: str, optional
         Suffix for the main title. If None, uses metric_mean
@@ -1295,7 +1295,7 @@ def hist_mean_h(
 
         ax.set_title(f"{site}", fontsize=12)
         ax.set_ylim(0, max_value * 1.1)
-        ax.set_ylabel(ylabel if ylabel else metric_mean, fontsize=10)
+        ax.set_ylabel(y_lab if y_lab else metric_mean, fontsize=10)
         ax.set_xticks(range(24))
 
         # Only set x-label on last subplot
@@ -1316,7 +1316,7 @@ def hist_mean_s(
     df: DataFrame,
     metric_mean: str,
     metric_std: str,
-    ylabel: str | None = None,
+    y_lab: str | None = None,
     title_suffix: str | None = None,
 ) -> None:
     """Plot bar chart with mean values and error bars (std) per site.
@@ -1329,12 +1329,10 @@ def hist_mean_s(
         Column name for the mean values (e.g., "FBR_mean")
     metric_std: str
         Column name for the standard deviation values (e.g., "FBR_std")
-    ylabel: str, optional
+    y_lab: str, optional
         Label for y-axis. If None, uses metric_mean
     title_suffix: str, optional
         Suffix for the title. If None, uses metric_mean
-    add_hatch: bool, optional
-        Add hatching pattern to bars (useful for FBR, %buzzes). Default False
 
     """
     fig, ax = plt.subplots(figsize=(10, 6))
@@ -1377,7 +1375,7 @@ def hist_mean_s(
     ax.set_xticklabels(plot_data["site.name"])
     ax.set_title(f"{title_suffix if title_suffix else metric_mean} per site",
                  fontsize=12)
-    ax.set_ylabel(ylabel if ylabel else metric_mean, fontsize=10)
+    ax.set_ylabel(y_lab if y_lab else metric_mean, fontsize=10)
     ax.set_xlabel("Site", fontsize=10)
 
     plt.tight_layout()

From ae4cf2354f5b28614d112e1a31350c03e8a73ca1 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Thu, 13 Nov 2025 10:51:46 +0100
Subject: [PATCH 39/83] correct docstrings

---
 src/post_processing/utils/fpod_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 2ca48e9..1b4ec0f 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -69,10 +69,8 @@ def pod2aplose(
         for entry in df["ChunkEnd"]
     ]
 
-    # Trier le DataFrame selon ces datetime
     df = df.sort_values("_temp_dt").reset_index(drop=True)
 
-    # Maintenant extraire les colonnes triées
     fpod_start_dt = df["_temp_dt"].tolist()
     fpod_end_dt = [entry + Timedelta(seconds=bin_size) for entry in fpod_start_dt]
 
@@ -611,6 +609,7 @@ def first_last(
     df_parsed = parse_timestamps(df, col_timestamp, date_formats)
     return deploy_period(df_parsed, col_timestamp, col_deployment)
 
+
 def actual_data(
     df: DataFrame,
     meta: DataFrame,
@@ -689,6 +688,7 @@ def filter_tl(df: DataFrame, tl: int)->DataFrame:
 
     return df[df["%TimeLost"] < tl]
 
+
 def preserved_data(filtered_df: DataFrame, whole_df: DataFrame)-> float:
     """Calculate the percentage of preserved data.
 

From d01614eadd9efffb2a666b1c2c72257a33380e19 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Thu, 13 Nov 2025 11:32:25 +0100
Subject: [PATCH 40/83] change feeding_buzz() function

---
 src/post_processing/utils/fpod_utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 1b4ec0f..bb4e5af 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -20,6 +20,7 @@
     read_csv,
     to_datetime,
     to_timedelta,
+    to_numeric,
 )
 
 from post_processing.utils.core_utils import get_coordinates, get_sun_times
@@ -427,7 +428,7 @@ def feeding_buzz(
 
     f = df.groupby(["start_datetime"])["Buzz"].sum().reset_index()
 
-    f["Foraging"] = (f["Buzz"] != 0).astype(int)
+    f["Foraging"] = to_numeric(f["Buzz"] != 0, downcast='integer').astype(int)
 
     return f
 

From 24f209f11773ecfe1db01606f3050aaf69a7cc3f Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Thu, 13 Nov 2025 17:31:46 +0100
Subject: [PATCH 41/83] reorganise notebooks

---
 user_case/example_FPOD-CPOD_aplose.ipynb | 3 ++-
 user_case/example_FPOD-CPOD_raw.ipynb    | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
index 2e36a82..91076d3 100644
--- a/user_case/example_FPOD-CPOD_aplose.ipynb
+++ b/user_case/example_FPOD-CPOD_aplose.ipynb
@@ -40,7 +40,8 @@
     "d_beg_end = read_csv(r\"U:\\Deb_Fin_CA4.csv\") #Beginning and end of recording for every phase. 🐬\n",
     "\n",
     "tl_path = Path(r\"U:\\TimeLost\\tl_ca4\\phases\")\n",
-    "tl_df = process_tl(tl_path)"
+    "tl_df = process_tl(tl_path)\n",
+    "tl_df = tl_df.drop_duplicates(subset=['ChunkEnd'], keep=\"first\")"
    ],
    "id": "7da2feb5958db1a9",
    "outputs": [],
diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index 28c9c5c..35bd0e8 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -40,6 +40,7 @@
     "\n",
     "print(path.head())\n",
     "df_0 = path.dropna()\n",
+    "df_0 = df_0.drop_duplicates(subset=['ChunkEnd'], keep=\"first\")\n",
     "\n",
     "metadatax = json2df(json_path=json)\n",
     "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +  metadatax[\"campaign.name\"].astype(str))"

From 7d5dd7b502459ee0276030a44e4bf5f04ed14fc5 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Fri, 14 Nov 2025 12:35:02 +0100
Subject: [PATCH 42/83] ruff corrections

---
 src/post_processing/utils/fpod_utils.py | 108 ++++++++++--------------
 1 file changed, 44 insertions(+), 64 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index bb4e5af..617b570 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -19,12 +19,11 @@
     notna,
     read_csv,
     to_datetime,
-    to_timedelta,
     to_numeric,
+    to_timedelta,
 )
 
 from post_processing.utils.core_utils import get_coordinates, get_sun_times
-
 from user_case.config import season_color, site_colors
 
 if TYPE_CHECKING:
@@ -189,11 +188,6 @@ def parse_timestamps(
     DataFrame
         Copy of df with parsed timestamps.
 
-    Raises
-    ------
-    ValueError
-        If timestamps cannot be parsed with any format.
-
     """
     if date_formats is None:
         date_formats = [
@@ -291,15 +285,15 @@ def meta_cut_aplose(
 
     """
     required_columns(
-        raw_data,["deploy.name", "start_datetime"])
+        raw_data, ["deploy.name", "start_datetime"])
     required_columns(
-        metadata,["deploy.name", "deployment_date","recovery_date"])
+        metadata, ["deploy.name", "deployment_date", "recovery_date"])
 
     raw = parse_timestamps(raw_data, "start_datetime")
     raw = raw.sort_values(["start_datetime"])
 
     dfm = raw.merge(
-        metadata[["deploy.name", "deployment_date","recovery_date"]],
+        metadata[["deploy.name", "deployment_date", "recovery_date"]],
         on="deploy.name",
         how="left",
     )
@@ -307,7 +301,7 @@ def meta_cut_aplose(
     out = create_mask(dfm, "start_datetime", "deployment_date", "recovery_date")
 
     columns_to_drop = [
-        col for col in ["deployment_date","recovery_date"] if col not in raw_data.
+        col for col in ["deployment_date", "recovery_date"] if col not in raw_data.
         columns]
     if columns_to_drop:
         out = out.drop(columns=columns_to_drop)
@@ -318,7 +312,7 @@ def meta_cut_aplose(
 def add_utc(
     df: DataFrame,
     cols: list,
-    fr:str="h",
+    fr: str = "h",
 ) -> DataFrame:
     """Create a DataFrame with one line per hour between start and end dates.
 
@@ -347,7 +341,7 @@ def add_utc(
 
 def build_range(
     df: DataFrame,
-    fr:str="h",
+    fr: str = "h",
 ) -> DataFrame:
     """Create a DataFrame with one line per hour between start and end dates.
 
@@ -366,7 +360,7 @@ def build_range(
         A full period of time with positive and negative hours to detections.
 
     """
-    add_utc(df, ["Deb","Fin"], fr)
+    add_utc(df, ["Deb", "Fin"], fr)
 
     all_ranges = []
     for _, row in df.iterrows():
@@ -403,7 +397,7 @@ def feeding_buzz(
         Containing all ICIs for every positive minutes to clicks
 
     """
-    df["microsec"] = df["microsec"] / 1e6
+    df["microsec"] /= 1e6
     df["ICI"] = df["microsec"].diff()
 
     if species == "Dauphin":  # Herzing et al., 2014
@@ -428,7 +422,7 @@ def feeding_buzz(
 
     f = df.groupby(["start_datetime"])["Buzz"].sum().reset_index()
 
-    f["Foraging"] = to_numeric(f["Buzz"] != 0, downcast='integer').astype(int)
+    f["Foraging"] = to_numeric(f["Buzz"] != 0, downcast="integer").astype(int)
 
     return f
 
@@ -458,9 +452,9 @@ def assign_daytime(
     sunrise, sunset = get_sun_times(start, stop, lat, lon)
 
     sun_times = DataFrame(
-        {   "date": date_range(start, stop, freq="D"),
-            "sunrise": [Timedelta(h, "hours") for h in sunrise],
-            "sunset": [Timedelta(h, "hours") for h in sunset],
+        {"date": date_range(start, stop, freq="D"),
+        "sunrise": [Timedelta(h, "hours") for h in sunrise],
+        "sunset": [Timedelta(h, "hours") for h in sunset],
         })
 
     sun_times["sunrise"] = sun_times["date"].dt.floor("D") + sun_times["sunrise"]
@@ -631,16 +625,16 @@ def actual_data(
 
     """
     required_columns(
-        df,["deploy.name","ChunkEnd"])
+        df, ["deploy.name", "ChunkEnd"])
     required_columns(
-        meta,["deploy.name", "deployment_date","recovery_date"])
+        meta, ["deploy.name", "deployment_date", "recovery_date"])
 
     beg_end = first_last(df, "ChunkEnd")
 
     beg_end = add_utc(beg_end, ["Deb", "Fin"])
 
-    final = beg_end.merge(meta[["deployment_date","recovery_date","deploy.name"]],
-                          on = "deploy.name", how="left")
+    final = beg_end.merge(meta[["deployment_date", "recovery_date", "deploy.name"]],
+                          on="deploy.name", how="left")
     final.loc[final["Deb"] < final["deployment_date"], "Deb"] = final["deployment_date"]
     final.loc[final["Fin"] > final["recovery_date"], "Fin"] = final["recovery_date"]
     final.loc[final["Deb"] > final["Fin"], ["Deb", "Fin"]] = None
@@ -648,7 +642,7 @@ def actual_data(
     return final.drop(["deployment_date", "recovery_date"], axis=1)
 
 
-def process_tl(tl_files: Path)->DataFrame:
+def process_tl(tl_files: Path) -> DataFrame:
     """Process Environmental data extracted from cpod.exe to get a usable dataframe.
 
     Parameters
@@ -670,7 +664,7 @@ def process_tl(tl_files: Path)->DataFrame:
     return df.sort_values(["start_datetime"])
 
 
-def filter_tl(df: DataFrame, tl: int)->DataFrame:
+def filter_tl(df: DataFrame, tl: int) -> DataFrame:
     """Remove lines with a %TimeLost superior to the chosen threshold.
 
     Parameters
@@ -690,7 +684,7 @@ def filter_tl(df: DataFrame, tl: int)->DataFrame:
     return df[df["%TimeLost"] < tl]
 
 
-def preserved_data(filtered_df: DataFrame, whole_df: DataFrame)-> float:
+def preserved_data(filtered_df: DataFrame, whole_df: DataFrame) -> float:
     """Calculate the percentage of preserved data.
 
     Parameters
@@ -705,14 +699,14 @@ def preserved_data(filtered_df: DataFrame, whole_df: DataFrame)-> float:
     Percentage of preserved data.
 
     """
-    return (len(filtered_df) / len(whole_df)) *100
+    return (len(filtered_df) / len(whole_df)) * 100
 
 
 def create_matrix(
     df: DataFrame,
     group_cols: list,
     agg_cols: list,
-)-> DataFrame:
+) -> DataFrame:
     """Create a stats matrix (mean & std).
 
     Parameters
@@ -828,7 +822,7 @@ def site_percent(df: DataFrame, metric: str) -> None:
     )
     ax.set_title(f"{metric} per site")
     ax.set_ylabel(f"{metric}")
-    if metric in ("%buzzes", "FBR"):
+    if metric in {"%buzzes", "FBR"}:
         for _, bar in enumerate(ax.patches):
             bar.set_hatch("/")
     plt.show()
@@ -866,7 +860,7 @@ def year_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Year")
-        if metric in ("%buzzes", "FBR"):
+        if metric in {"%buzzes", "FBR"}:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     fig.suptitle(f"{metric} per year", fontsize=16)
@@ -907,7 +901,7 @@ def ym_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Months")
-        if metric in ("%buzzes", "FBR"):
+        if metric in {"%buzzes", "FBR"}:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     legend_elements = [
@@ -973,7 +967,7 @@ def month_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Months")
-        if metric in ("%buzzes", "FBR"):
+        if metric in {"%buzzes", "FBR"}:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     fig.suptitle(f"{metric} per month", fontsize=16)
@@ -1013,7 +1007,7 @@ def day_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Months")
-        if metric in ("%buzzes", "FBR"):
+        if metric in {"%buzzes", "FBR"}:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     legend_elements = [
@@ -1064,7 +1058,7 @@ def hour_percent(df: DataFrame, metric: str) -> None:
             ax.set_xlabel("")
         else:
             ax.set_xlabel("Hour")
-        if metric in ("%buzzes", "FBR"):
+        if metric in {"%buzzes", "FBR"}:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     fig.suptitle(f"{metric} per hour", fontsize=16)
@@ -1084,10 +1078,6 @@ def calendar(
     data: DataFrame
         cpod file from all sites and phases
 
-    Returns
-    -------
-    Return a plot of all deployments and associated data.
-
     """
     # format the dataframe
     meta["deployment_date"] = to_datetime(meta["deployment_date"])
@@ -1131,7 +1121,6 @@ def calendar(
                 linewidth=0.8,
             )
 
-
     ax.set_yticks(range(len(sites)))
     ax.set_yticklabels(sites, fontsize=12)
 
@@ -1175,10 +1164,6 @@ def hist_mean_m(
     title_suffix: str, optional
         Suffix for the main title. If None, uses metric_mean
 
-    Returns
-    -------
-    Return a plot of all deployments and associated data.
-
     """
     sites = df["site.name"].unique()
     n_sites = len(sites)
@@ -1206,7 +1191,7 @@ def hist_mean_m(
 
         ax.set_title(f"{site}", fontsize=12)
         ax.set_ylim(0, max_value * 1.1)
-        ax.set_ylabel(y_lab if y_lab else metric_mean, fontsize=10)
+        ax.set_ylabel(y_lab or metric_mean, fontsize=10)
 
         # Only set x-label on last subplot
         if i == n_sites - 1:
@@ -1228,12 +1213,12 @@ def hist_mean_m(
                     "Dec",
                 ],
             )
-        if metric_mean in ("%buzzes_mean", "FBR_mean"):
+        if metric_mean in {"%buzzes_mean", "FBR_mean"}:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
 
     fig.suptitle(
-        f"{title_suffix if title_suffix else metric_mean} per month",
+        f"{title_suffix or metric_mean} per month",
         fontsize=16)
     plt.xticks(rotation=45)
     plt.tight_layout()
@@ -1264,10 +1249,6 @@ def hist_mean_h(
     title_suffix: str, optional
         Suffix for the main title. If None, uses metric_mean
 
-    Returns
-    -------
-    Return a plot of all deployments and associated data.
-
     """
     sites = df["site.name"].unique()
     n_sites = len(sites)
@@ -1296,18 +1277,17 @@ def hist_mean_h(
 
         ax.set_title(f"{site}", fontsize=12)
         ax.set_ylim(0, max_value * 1.1)
-        ax.set_ylabel(y_lab if y_lab else metric_mean, fontsize=10)
+        ax.set_ylabel(y_lab or metric_mean, fontsize=10)
         ax.set_xticks(range(24))
 
         # Only set x-label on last subplot
         if i == n_sites - 1:
             ax.set_xlabel("Heure", fontsize=10)
-        if metric_mean in ("%buzzes_mean", "FBR_mean"):
+        if metric_mean in {"%buzzes_mean", "FBR_mean"}:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
 
-    fig.suptitle(
-        f"{title_suffix if title_suffix else metric_mean} per hour", fontsize=16)
+    fig.suptitle(f"{title_suffix or metric_mean} per hour", fontsize=16)
     plt.xticks(rotation=45)
     plt.tight_layout()
     plt.show()
@@ -1344,16 +1324,16 @@ def hist_mean_s(
     x_pos = range(len(plot_data))
 
     # Create bars
-    bars = ax.bar(
-        x=x_pos,
-        height=plot_data[metric_mean],
-        color=[site_colors.get(site, "gray") for site in plot_data["site.name"]],
-        alpha=0.8,
-        edgecolor="black",
-        linewidth=0.5)
+    ax.bar(
+    x=x_pos,
+    height=plot_data[metric_mean],
+    color=[site_colors.get(site, "gray") for site in plot_data["site.name"]],
+    alpha=0.8,
+    edgecolor="black",
+    linewidth=0.5)
 
     # Add hatching if requested
-    if metric_mean in ("%buzzes_mean", "FBR_mean"):
+    if metric_mean in {"%buzzes_mean", "FBR_mean"}:
         for _, bar in enumerate(ax.patches):
             bar.set_hatch("/")
 
@@ -1374,9 +1354,9 @@ def hist_mean_s(
 
     ax.set_xticks(x_pos)
     ax.set_xticklabels(plot_data["site.name"])
-    ax.set_title(f"{title_suffix if title_suffix else metric_mean} per site",
+    ax.set_title(f"{title_suffix or metric_mean} per site",
                  fontsize=12)
-    ax.set_ylabel(y_lab if y_lab else metric_mean, fontsize=10)
+    ax.set_ylabel(y_lab or metric_mean, fontsize=10)
     ax.set_xlabel("Site", fontsize=10)
 
     plt.tight_layout()

From 0bc968d5c75426aaf9428b260415aebf4378e09f Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Wed, 19 Nov 2025 17:43:47 +0100
Subject: [PATCH 43/83] update notebooks

---
 user_case/example_FPOD-CPOD_aplose.ipynb      |  61 +++++++++-
 .../example_FPOD-CPOD_firstresults.ipynb      | 110 ++++--------------
 2 files changed, 79 insertions(+), 92 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
index 91076d3..bc6d952 100644
--- a/user_case/example_FPOD-CPOD_aplose.ipynb
+++ b/user_case/example_FPOD-CPOD_aplose.ipynb
@@ -9,6 +9,8 @@
    "source": [
     "from pathlib import Path\n",
     "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "from pandas import (\n",
     "    read_csv,\n",
     ")\n",
@@ -41,7 +43,9 @@
     "\n",
     "tl_path = Path(r\"U:\\TimeLost\\tl_ca4\\phases\")\n",
     "tl_df = process_tl(tl_path)\n",
-    "tl_df = tl_df.drop_duplicates(subset=['ChunkEnd'], keep=\"first\")"
+    "tl_df = tl_df.drop_duplicates(subset=['ChunkEnd'], keep=\"first\")\n",
+    "tl_df[\"Angle\"] = (tl_df[\"Angle\"].replace(',', '.', regex=True)).astype(float)\n",
+    "tl_df[\"Temp\"] = (tl_df[\"Temp\"].replace(',', '.', regex=True)).astype(float)"
    ],
    "id": "7da2feb5958db1a9",
    "outputs": [],
@@ -51,7 +55,7 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "### Data metric\n",
+    "### Resample your data\n",
     "Here you need to choose the format in which you want to visualise the positive detections. This aims to replace the functionalities available in Chelonia's softwares."
    ],
    "id": "3bc57f4f638ad6dc"
@@ -79,8 +83,8 @@
    "metadata": {},
    "cell_type": "markdown",
    "source": [
-    "### Feeding buzzes processing\n",
-    "Use \"Dauphin\", Marsouin\" or \"Commerson\" to get different ICI processing."
+    "### Add the effort\n",
+    "To analyze the data, add zeros to view it based on effort."
    ],
    "id": "b92537991aa4ac4b"
   },
@@ -159,7 +163,7 @@
    "cell_type": "code",
    "source": [
     "full_df = d_tot.merge(\n",
-    "    tl_df[[\"start_datetime\", \"%TimeLost\"]],\n",
+    "    tl_df[[\"start_datetime\", \"%TimeLost\", \"Angle\", \"Temp\"]],\n",
     "    on=\"start_datetime\", how=\"left\")"
    ],
    "id": "66bf795805047a3d",
@@ -177,6 +181,53 @@
    "outputs": [],
    "execution_count": null
   },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### Visualize environmental data",
+   "id": "e0ea7247e27a37b6"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "resamp_tot = filtered_df.set_index(\"start_datetime\").resample('h').first().reset_index()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "ax.plot(resamp_tot[\"start_datetime\"], resamp_tot[\"Angle\"])\n",
+    "plt.title(\"Angle of the instrument over time\")\n",
+    "plt.show()"
+   ],
+   "id": "14ff46dec308dcc1",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "fig2, ax = plt.subplots(figsize=(12, 6))\n",
+    "ax.plot(resamp_tot[\"start_datetime\"], resamp_tot[\"Temp\"])\n",
+    "plt.title(\"Temperature over time\")\n",
+    "plt.show()"
+   ],
+   "id": "db07736375a767d9",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "fig3, ax = plt.subplots(figsize=(12, 6))\n",
+    "ax.plot(resamp_tot[\"start_datetime\"], resamp_tot[\"%TimeLost\"])\n",
+    "plt.title(\"%TimeLost over time\")\n",
+    "plt.show()"
+   ],
+   "id": "7409831bc24271e3",
+   "outputs": [],
+   "execution_count": null
+  },
   {
    "metadata": {},
    "cell_type": "markdown",
diff --git a/user_case/example_FPOD-CPOD_firstresults.ipynb b/user_case/example_FPOD-CPOD_firstresults.ipynb
index 134a81b..1c3366b 100644
--- a/user_case/example_FPOD-CPOD_firstresults.ipynb
+++ b/user_case/example_FPOD-CPOD_firstresults.ipynb
@@ -4,11 +4,7 @@
    "cell_type": "code",
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2025-11-06T09:59:30.110070Z",
-     "start_time": "2025-11-06T09:59:28.122343Z"
-    }
+    "collapsed": true
    },
    "source": [
     "from pandas import (\n",
@@ -21,7 +17,7 @@
     "from post_processing.utils.core_utils import get_season"
    ],
    "outputs": [],
-   "execution_count": 1
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -39,12 +35,7 @@
    "id": "caea0e065ad8068c"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-06T10:34:05.474853Z",
-     "start_time": "2025-11-06T10:34:04.483887Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "ca4 = read_csv(r\"L:\\acoustock\\Bioacoustique\\DATASETS\\CPOD_PROJETS\\CALAIS\\DATA\\DATA_FULL_OTHERCET\\Hours_DPM_FBUZZ_CA4_ssTL.csv\")\n",
@@ -57,7 +48,7 @@
    ],
    "id": "1268d9e6ce5cdf32",
    "outputs": [],
-   "execution_count": 44
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -86,12 +77,7 @@
    "id": "17a5ce1338f6cd1a"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-06T10:23:15.773466Z",
-     "start_time": "2025-11-06T10:23:14.586120Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "a = read_csv(r\"U:\\Hours_DPM_FBUZZ_A.csv\")\n",
@@ -106,7 +92,7 @@
    ],
    "id": "d65697a1f1487f4c",
    "outputs": [],
-   "execution_count": 22
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -118,25 +104,15 @@
    "id": "9fc3b5075bf7ff2c"
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-06T10:34:09.795757Z",
-     "start_time": "2025-11-06T10:34:09.785706Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "data = data_c #🐬",
    "id": "add4a626d6cc25a4",
    "outputs": [],
-   "execution_count": 45
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-06T10:34:13.881765Z",
-     "start_time": "2025-11-06T10:34:13.617021Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "data = extract_site(data)\n",
@@ -145,25 +121,11 @@
     "#t_per = percent_calc(data, \"TRAVAUX\")"
    ],
    "id": "37ecc80eda8e57ed",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\fouinel\\AppData\\Local\\Temp\\ipykernel_24020\\669083797.py:4: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
-      "  data['Date'] = data[\"start_datetime\"].dt.to_period('D').dt.to_timestamp()\n"
-     ]
-    }
-   ],
-   "execution_count": 46
+   "outputs": [],
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-06T10:34:15.931189Z",
-     "start_time": "2025-11-06T10:34:15.912241Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "ym_per = percent_calc(data, \"YM\")\n",
@@ -174,15 +136,10 @@
    ],
    "id": "2b988869ed2466e1",
    "outputs": [],
-   "execution_count": 47
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-06T10:24:21.202676Z",
-     "start_time": "2025-11-06T10:24:21.177426Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "per_h = percent_calc(data, \"YMH\")\n",
@@ -191,15 +148,10 @@
    ],
    "id": "cf704032c4a59a7b",
    "outputs": [],
-   "execution_count": 30
+   "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-06T10:24:22.084789Z",
-     "start_time": "2025-11-06T10:24:22.073176Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": [
     "matrice_s = create_matrix(ym_per, [\"site.name\"],[\"%DPH\", \"FBR\"])\n",
@@ -208,7 +160,7 @@
    ],
    "id": "caf3f71c6b6f70ca",
    "outputs": [],
-   "execution_count": 31
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -218,7 +170,7 @@
     "    matrice_s,\n",
     "    metric_mean=\"%DPH_mean\",\n",
     "    metric_std=\"%DPH_std\",\n",
-    "    ylabel=\"Moyenne %DPH\",\n",
+    "    y_lab=\"Moyenne %DPH\",\n",
     "    title_suffix=\"%DPH\"\n",
     ")"
    ],
@@ -235,28 +187,12 @@
    "execution_count": null
   },
   {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-06T10:34:18.966846Z",
-     "start_time": "2025-11-06T10:34:18.611173Z"
-    }
-   },
+   "metadata": {},
    "cell_type": "code",
    "source": "ym_percent(ym_per, \"DPM\")",
    "id": "a2dacac3caecff5f",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 2100x750 with 2 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 48
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -266,7 +202,7 @@
     "    matrice_m,\n",
     "    metric_mean=\"%click_mean\",\n",
     "    metric_std=\"%click_std\",\n",
-    "    ylabel=\"Moyenne %click\",\n",
+    "    y_lab=\"Moyenne %click\",\n",
     "    title_suffix=\"%click\"\n",
     ")"
    ],
@@ -282,7 +218,7 @@
     "    matrice_h,\n",
     "    metric_mean=\"FBR_mean\",\n",
     "    metric_std=\"FBR_std\",\n",
-    "    ylabel=\"Feeding buzz ratio\",\n",
+    "    y_lab=\"Feeding buzz ratio\",\n",
     "    title_suffix=\"FBR\"\n",
     ")"
    ],

From 1e61fe568db59576b89216f45ae5212865fea6f8 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Wed, 19 Nov 2025 17:44:47 +0100
Subject: [PATCH 44/83] update feeding_buzzes

---
 src/post_processing/utils/fpod_utils.py | 38 ++++++++++++++++---------
 1 file changed, 24 insertions(+), 14 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 617b570..ad3538f 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -13,6 +13,7 @@
 from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
 from pandas import (
     DataFrame,
+    NaT,
     Timedelta,
     concat,
     date_range,
@@ -398,28 +399,37 @@ def feeding_buzz(
 
     """
     df["microsec"] /= 1e6
-    df["ICI"] = df["microsec"].diff()
+
+    try:
+        df["Minute"].astype(int)
+        df["datetime"] = (
+            to_datetime("1900-01-01")
+            + to_timedelta(df["Minute"], unit="min")
+            + to_timedelta(df["microsec"], unit="us")
+            - to_timedelta(2, unit="D")
+        )
+        df["start_datetime"] = df["datetime"].dt.floor("min")
+    except (ValueError, TypeError):
+        df["datetime"] = (df["Minute"]).astype(str) + ":" + (df["microsec"]).astype(str)
+        df["datetime"] = to_datetime(df["datetime"], dayfirst=True)
+        df["start_datetime"] = to_datetime(df["Minute"], dayfirst=True)
+
+    df["ICI"] = df["datetime"].diff()
+    df["ICI"] = to_timedelta(df["ICI"], errors="coerce")
+
+    mask = df["ICI"] > Timedelta("1 days")
+    df.loc[mask, "ICI"] = NaT
 
     if species == "Dauphin":  # Herzing et al., 2014
-        df["Buzz"] = (df["ICI"].between(0, 0.02)).astype(int)
+        df["Buzz"] = (df["ICI"] < Timedelta(seconds=0.02)).astype(int)
     elif species == "Marsouin":  # Nuuttila et al., 2013
-        df["Buzz"] = (df["ICI"].between(0, 0.01)).astype(int)
+        df["Buzz"] = (df["ICI"] < Timedelta(seconds=0.01)).astype(int)
     elif species == "Commerson":  # Reyes Reyes et al., 2015
-        df["Buzz"] = (df["ICI"].between(0, 0.005)).astype(int)
+        df["Buzz"] = (df["ICI"] < Timedelta(seconds=0.005)).astype(int)
     else:
         msg = "This species is not supported"
         raise ValueError(msg)
 
-    try:
-        df["Minute"].astype(int)
-        df["datetime"] = (to_datetime("1900-01-01") +
-                            to_timedelta(df["Minute"], unit="min") +
-                            to_timedelta(df["microsec"], unit="us") -
-                            to_timedelta(2, unit="D"))
-        df["start_datetime"] = df["datetime"].dt.floor("min")
-    except (ValueError, TypeError):
-        df["start_datetime"] = to_datetime(df["Minute"], dayfirst=True)
-
     f = df.groupby(["start_datetime"])["Buzz"].sum().reset_index()
 
     f["Foraging"] = to_numeric(f["Buzz"] != 0, downcast="integer").astype(int)

From b8b0177e7fc78849c61ce6b501526848d325e79b Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Thu, 20 Nov 2025 10:23:08 +0100
Subject: [PATCH 45/83] adjust notebook

---
 user_case/example_FPOD-CPOD_raw.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index 35bd0e8..b09c925 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -130,7 +130,7 @@
    "cell_type": "code",
    "outputs": [],
    "execution_count": null,
-   "source": "dpm_fb = cleared.merge(fb_all, on=[\"start_datetime\"], how=\"left\") #Merge DPM and FB dataframes",
+   "source": "dpm_fb = cleared.merge(fb_all[[\"start_datetime\", \"Foraging\"]], on=[\"start_datetime\"], how=\"left\") #Merge DPM and FB dataframes",
    "id": "e90f6d91de3f8ce3"
   },
   {

From 5f1b56bc2b30203a26bbb345d580d7a7e9233752 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Thu, 20 Nov 2025 10:23:20 +0100
Subject: [PATCH 46/83] correct feeding buzzes function

---
 src/post_processing/utils/fpod_utils.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index ad3538f..3b5bbdd 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -405,20 +405,18 @@ def feeding_buzz(
         df["datetime"] = (
             to_datetime("1900-01-01")
             + to_timedelta(df["Minute"], unit="min")
-            + to_timedelta(df["microsec"], unit="us")
+            + to_timedelta(df["microsec"], unit="sec")
             - to_timedelta(2, unit="D")
         )
         df["start_datetime"] = df["datetime"].dt.floor("min")
     except (ValueError, TypeError):
-        df["datetime"] = (df["Minute"]).astype(str) + ":" + (df["microsec"]).astype(str)
-        df["datetime"] = to_datetime(df["datetime"], dayfirst=True)
+        df["datetime"] = (
+                to_datetime(df["Minute"], dayfirst=True)
+                + to_timedelta(df["microsec"], unit="sec")
+        )
         df["start_datetime"] = to_datetime(df["Minute"], dayfirst=True)
 
     df["ICI"] = df["datetime"].diff()
-    df["ICI"] = to_timedelta(df["ICI"], errors="coerce")
-
-    mask = df["ICI"] > Timedelta("1 days")
-    df.loc[mask, "ICI"] = NaT
 
     if species == "Dauphin":  # Herzing et al., 2014
         df["Buzz"] = (df["ICI"] < Timedelta(seconds=0.02)).astype(int)

From 672d392d73399f0ddc90f1dcba31abdddc12f5b5 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Mon, 24 Nov 2025 10:31:56 +0100
Subject: [PATCH 47/83] add new figures

---
 user_case/example_FPOD-CPOD_aplose.ipynb |  19 ++-
 user_case/example_FPOD-CPOD_raw.ipynb    | 179 ++++++++++++++++++++++-
 2 files changed, 189 insertions(+), 9 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
index bc6d952..b0da1c8 100644
--- a/user_case/example_FPOD-CPOD_aplose.ipynb
+++ b/user_case/example_FPOD-CPOD_aplose.ipynb
@@ -191,7 +191,7 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "resamp_tot = filtered_df.set_index(\"start_datetime\").resample('h').first().reset_index()\n",
+    "resamp_tot = filtered_df.set_index(\"start_datetime\").resample(frq).first().reset_index()\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(12, 6))\n",
     "ax.plot(resamp_tot[\"start_datetime\"], resamp_tot[\"Angle\"])\n",
@@ -244,6 +244,23 @@
    "id": "f1f9ec385038ba87",
    "outputs": [],
    "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### First visualization\n",
+    "Precise the coordinates of the location of your listening point"
+   ],
+   "id": "a1b31aa6bd8f4d70"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "3fda0cc8174fa757"
   }
  ],
  "metadata": {
diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index b09c925..2e8e690 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -7,12 +7,24 @@
     "collapsed": true
    },
    "source": [
-    "from pathlib import Path\n",
+    "from __future__ import annotations\n",
     "\n",
     "import pytz\n",
     "\n",
     "from post_processing.utils.fpod_utils import csv_folder, pod2aplose, actual_data, meta_cut_aplose, feeding_buzz, txt_folder, add_utc\n",
-    "from post_processing.utils.core_utils import json2df"
+    "from post_processing.utils.core_utils import json2df\n",
+    "\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as ticker\n",
+    "from pandas import Timestamp, to_datetime\n",
+    "from pandas.tseries import frequencies\n",
+    "from post_processing.dataclass.data_aplose import DataAplose\n",
+    "from post_processing.utils.core_utils import get_season, get_count\n",
+    "from osekit import setup_logging\n",
+    "from user_case.config import season_color\n",
+    "setup_logging(Path(r\"C:\\Users\\dupontma2\\Documents\\Git\\OSmOSE\\OSmOSE_post_processing\\src\\post_processing\\logging_config.yaml\"), logging.ERROR)"
    ],
    "outputs": [],
    "execution_count": null
@@ -116,22 +128,22 @@
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "fb_all = txt_folder(fb_files) #Read all your FB.txt files.\n",
     "fb_all = feeding_buzz(fb_all, \"Marsouin\") #Categorize the minutes (positive or not to FB detection). 🐬\n",
     "add_utc(fb_all, [\"start_datetime\"], \"min\")"
    ],
-   "id": "64f824e02131d90a"
+   "id": "64f824e02131d90a",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "dpm_fb = cleared.merge(fb_all[[\"start_datetime\", \"Foraging\"]], on=[\"start_datetime\"], how=\"left\") #Merge DPM and FB dataframes",
-   "id": "e90f6d91de3f8ce3"
+   "id": "e90f6d91de3f8ce3",
+   "outputs": [],
+   "execution_count": null
   },
   {
    "metadata": {},
@@ -149,6 +161,157 @@
    "id": "9d34e00f4e8147e8",
    "outputs": [],
    "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### Explore\n",
+    "First visualization of the data"
+   ],
+   "id": "a85ea092d9fc197c"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "data = DataAplose(dpm_fb)",
+   "id": "639c474690373895",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "bin_size = frequencies.to_offset(\"1d\")\n",
+    "ticks = frequencies.to_offset(\"6BMS\")\n",
+    "fmt = \"%b %y\""
+   ],
+   "id": "cb476b5655bdff42",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "data.lat = 50.973333\n",
+    "data.lon = 1.8117"
+   ],
+   "id": "3fc33f2acf84ea34",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "#### Reshape the data\n",
+    "Set beginning and end of the chosen window."
+   ],
+   "id": "2857f26f8c168ad3"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "data.df[\"end_datetime\"] = to_datetime(data.df[\"end_datetime\"])\n",
+    "data2 = data.reshape(begin=Timestamp(\"2013 11 01\"), end=Timestamp(\"2025 08 01\"))\n",
+    "tz = pytz.timezone(\"UTC\")\n",
+    "data2.change_tz(tz)"
+   ],
+   "id": "75e00c1920b69409",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### Heatmap",
+   "id": "cdf3a92dfb6514d6"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "fig, ax = plt.subplots(1, 1)\n",
+    "ax = data2.set_ax(ax=ax, x_ticks_res=ticks, date_format=fmt)\n",
+    "data2.plot(\n",
+    "    mode=\"heatmap\",\n",
+    "    annotator=data2.annotators[0],\n",
+    "    label=data2.labels[0],\n",
+    "    ax=ax,\n",
+    "    bin_size=bin_size,\n",
+    "    show_rise_set=True,\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "6e1832101aec4156",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### Detections over time",
+   "id": "b0f3c6b7fc08a2be"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "fig2, ax = plt.subplots(1, 1)\n",
+    "ax = data2.set_ax(ax=ax, x_ticks_res=ticks, date_format=fmt)\n",
+    "data2.plot(\n",
+    "    mode=\"scatter\",\n",
+    "    annotator=data2.annotators[0],\n",
+    "    label=data2.labels[0],\n",
+    "    ax=ax,\n",
+    "    show_rise_set=True,\n",
+    ")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "66b43e53fb17037",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### DPM per day",
+   "id": "c7ea248b277edb65"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "df_filtered = data2.filter_df(data2.annotators[0], data2.labels[0])\n",
+    "df_counts = get_count(df_filtered, bin_size)\n",
+    "\n",
+    "df_counts[\"Season\"] = df_counts.index.to_series().apply(lambda x: get_season(x)[0])\n",
+    "df_counts[\"colors\"] = df_counts[\"Season\"].map(season_color).fillna(\"gray\")\n",
+    "\n",
+    "fig3, ax = plt.subplots(1, 1)\n",
+    "ax = data2.set_ax(ax=ax, x_ticks_res=ticks, date_format=fmt)\n",
+    "data2.plot(\n",
+    "    mode=\"histogram\",\n",
+    "    annotator=data2.annotators[0],\n",
+    "    label=data2.labels[0],\n",
+    "    color=df_counts[\"colors\"].tolist(),\n",
+    "    ax=ax,\n",
+    "    bin_size=bin_size,\n",
+    "    legend=True,\n",
+    ")\n",
+    "ax.set_ylim(0, 200)\n",
+    "ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=10))\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "id": "81b0dafa8adc20e9",
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {

From 7ca69e99646e4f6f6566c7514bafcc99483824eb Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Mon, 24 Nov 2025 10:32:08 +0100
Subject: [PATCH 48/83] correct some functions

---
 src/post_processing/utils/fpod_utils.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 3b5bbdd..6504d66 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -13,7 +13,6 @@
 from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
 from pandas import (
     DataFrame,
-    NaT,
     Timedelta,
     concat,
     date_range,
@@ -793,7 +792,7 @@ def percent_calc(
         data.groupby(group_cols)
         .agg(
             {
-                "DPH": "sum",
+                "DPh": "sum",
                 "DPM": "sum",
                 "Day": "size",
                 "Foraging": "sum",
@@ -803,7 +802,7 @@ def percent_calc(
     )
 
     df["%click"] = df["DPM"] * 100 / (df["Day"] * 60)
-    df["%DPH"] = df["DPH"] * 100 / df["Day"]
+    df["%DPh"] = df["DPh"] * 100 / df["Day"]
     df["FBR"] = df["Foraging"] * 100 / df["DPM"]
     df["%buzzes"] = df["Foraging"] * 100 / (df["Day"] * 60)
     return df

From 87f291e042a3514bf986828f415cb3279c0c45c5 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 20 Jan 2026 11:36:22 +0100
Subject: [PATCH 49/83] change colors assignment

---
 user_case/config.py   | 2 +-
 user_case/config.yaml | 7 ++++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/user_case/config.py b/user_case/config.py
index 1d191a7..bf74b37 100644
--- a/user_case/config.py
+++ b/user_case/config.py
@@ -8,4 +8,4 @@
 
 site_colors = config.get("site_colors", {"Site A Haute": "#118B50", "Site B Heugh": "#5DB996", "Site C Chat": "#B0DB9C", "Site D Simone": "#E3F0AF", "CA4": "#80D8C3", "Walde": "#4DA8DA", "Point C": "#932F67", "Point D": "#D92C54", "Point E": "#DDDEAB", "Point F": "#8ABB6C", "Point G": "#456882"})
 
-season_color = config.get("season_color", {"spring": "green", "summer": "darkgoldenrod", "autumn": "orange", "winter": "blue"})
\ No newline at end of file
+season_color = config.get("season_color", {"spring": "green", "summer": "orange", "autumn": "brown", "winter": "blue"})
\ No newline at end of file
diff --git a/user_case/config.yaml b/user_case/config.yaml
index ac0b183..6da6e08 100644
--- a/user_case/config.yaml
+++ b/user_case/config.yaml
@@ -3,16 +3,17 @@ site_colors:
   Point C: '#932F67'
   Point D: '#D92C54'
   Point E: '#DDDEAB'
-  Point F: 'ADMETTONS'
+  Point F: '#4E61D3'
   Point G: '#456882'
   Site A Haute: '#118B50'
   Site B Heugh: '#5DB996'
   Site C Chat: '#B0DB9C'
   Site D Simone: '#E3F0AF'
   Walde: '#4DA8DA'
+  02Mn Sud Cotentin: '#FB4141'
 
 season_color :
   spring: "green"
-  summer: "darkgoldenrod"
-  autumn: "orange"
+  summer: "orange"
+  autumn: "brown"
   winter: "blue"
\ No newline at end of file

From e530c603a2a56d711ade58b40b508f85f32fb488 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 20 Jan 2026 11:37:11 +0100
Subject: [PATCH 50/83] modify notebook

---
 user_case/example_FPOD-CPOD_raw.ipynb | 114 ++++++++++++++++++++++----
 1 file changed, 98 insertions(+), 16 deletions(-)

diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index 2e8e690..60e7f67 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -4,7 +4,11 @@
    "cell_type": "code",
    "id": "initial_id",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-01-20T10:24:33.069494Z",
+     "start_time": "2026-01-20T10:24:29.180185Z"
+    }
    },
    "source": [
     "from __future__ import annotations\n",
@@ -27,7 +31,7 @@
     "setup_logging(Path(r\"C:\\Users\\dupontma2\\Documents\\Git\\OSmOSE\\OSmOSE_post_processing\\src\\post_processing\\logging_config.yaml\"), logging.ERROR)"
    ],
    "outputs": [],
-   "execution_count": null
+   "execution_count": 1
   },
   {
    "metadata": {},
@@ -41,7 +45,12 @@
    "id": "c464f241817a1407"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T10:32:32.641354Z",
+     "start_time": "2026-01-20T10:32:09.650669Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "pod_files = Path(r\"U:\\CA4\") #Path to your data folder. 🐬\n",
@@ -52,14 +61,26 @@
     "\n",
     "print(path.head())\n",
     "df_0 = path.dropna()\n",
-    "df_0 = df_0.drop_duplicates(subset=['ChunkEnd'], keep=\"first\")\n",
     "\n",
     "metadatax = json2df(json_path=json)\n",
     "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +  metadatax[\"campaign.name\"].astype(str))"
    ],
    "id": "6cf23db3b4288c29",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                      File          ChunkEnd DPM  Nall MinsOn deploy.name\n",
+      "0  CA4  POD2397 file01.CP3  14/05/2014 07:07   0     0      1  CA4_Phase1\n",
+      "1  CA4  POD2397 file01.CP3  14/05/2014 07:08   0     8      1  CA4_Phase1\n",
+      "2  CA4  POD2397 file01.CP3  14/05/2014 07:09   0     4      1  CA4_Phase1\n",
+      "3  CA4  POD2397 file01.CP3  14/05/2014 07:10   0   251      1  CA4_Phase1\n",
+      "4  CA4  POD2397 file01.CP3  14/05/2014 07:11   0  4095      1  CA4_Phase1\n"
+     ]
+    }
+   ],
+   "execution_count": 2
   },
   {
    "metadata": {},
@@ -70,12 +91,17 @@
    "execution_count": null
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T10:32:43.618484Z",
+     "start_time": "2026-01-20T10:32:43.243995Z"
+    }
+   },
    "cell_type": "code",
    "source": "df_1 = df_0[df_0[\"DPM\"] !=\"0\" ] #Remove the 0 to lighten the APLOSE file.",
    "id": "769e128f2a5293e1",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 3
   },
   {
    "metadata": {},
@@ -88,16 +114,54 @@
    "id": "dd03975b7aef7eed"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T10:32:50.772619Z",
+     "start_time": "2026-01-20T10:32:50.060639Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "df_aplose = pod2aplose(df_1, pytz.utc, \"CA4\", \"Marsouin\", \"CPOD\") #Precise site name, species and instrument. 🐬\n",
-    "df_aplose[\"deploy.name\"] = df_aplose[\"filename\"]\n",
     "print(df_aplose.head())"
    ],
    "id": "4cc867627d677529",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  dataset                      filename  start_time  end_time  \\\n",
+      "0     CA4  2014-05-16T02:30:00.000+0000           0        60   \n",
+      "1     CA4  2014-05-17T03:52:00.000+0000           0        60   \n",
+      "2     CA4  2014-05-17T04:47:00.000+0000           0        60   \n",
+      "3     CA4  2014-05-19T17:06:00.000+0000           0        60   \n",
+      "4     CA4  2014-05-20T11:07:00.000+0000           0        60   \n",
+      "\n",
+      "   start_frequency  end_frequency annotation annotator  \\\n",
+      "0                0              0   Marsouin      CPOD   \n",
+      "1                0              0   Marsouin      CPOD   \n",
+      "2                0              0   Marsouin      CPOD   \n",
+      "3                0              0   Marsouin      CPOD   \n",
+      "4                0              0   Marsouin      CPOD   \n",
+      "\n",
+      "                 start_datetime                  end_datetime  is_box  \\\n",
+      "0  2014-05-16T02:30:00.000+0000  2014-05-16T02:31:00.000+0000       0   \n",
+      "1  2014-05-17T03:52:00.000+0000  2014-05-17T03:53:00.000+0000       0   \n",
+      "2  2014-05-17T04:47:00.000+0000  2014-05-17T04:48:00.000+0000       0   \n",
+      "3  2014-05-19T17:06:00.000+0000  2014-05-19T17:07:00.000+0000       0   \n",
+      "4  2014-05-20T11:07:00.000+0000  2014-05-20T11:08:00.000+0000       0   \n",
+      "\n",
+      "  deploy.name  \n",
+      "0  CA4_Phase1  \n",
+      "1  CA4_Phase1  \n",
+      "2  CA4_Phase1  \n",
+      "3  CA4_Phase1  \n",
+      "4  CA4_Phase1  \n"
+     ]
+    }
+   ],
+   "execution_count": 4
   },
   {
    "metadata": {},
@@ -109,12 +173,30 @@
    "id": "d2c642658dbfe278"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T10:33:14.509811Z",
+     "start_time": "2026-01-20T10:33:14.070492Z"
+    }
+   },
    "cell_type": "code",
    "source": "cleared = meta_cut_aplose(df_aplose, metadatax)",
    "id": "895bd5a116918285",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 5
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-20T10:33:24.735234Z",
+     "start_time": "2026-01-20T10:33:24.723966Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "cleared = cleared.drop_duplicates(subset=['start_datetime'], keep=\"last\")",
+   "id": "1a31da7341f3d8c9",
+   "outputs": [],
+   "execution_count": 7
   },
   {
    "metadata": {},
@@ -195,8 +277,8 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "data.lat = 50.973333\n",
-    "data.lon = 1.8117"
+    "data.lat = 50.973333 #CA4: 51.00035 ; Walde: 50.973333 ; A: -49.38765 ; B: -49.424733 ; C: -49.4677 ; D: -49.47175\n",
+    "data.lon = 1.8117 #CA4: 1.879667 ; Walde: 1.8117 ; A: 69.9449 ; B: 69.932383 ; C: 70.081067 ; D: 69.836617"
    ],
    "id": "3fc33f2acf84ea34",
    "outputs": [],

From ca8f250f6c6770c9157ec73cdcacd973a3e25f40 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 20 Jan 2026 11:37:41 +0100
Subject: [PATCH 51/83] modify pod2aplose

---
 src/post_processing/utils/fpod_utils.py | 235 +++++++++++++++++++++++-
 1 file changed, 225 insertions(+), 10 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 6504d66..9ade0b0 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -6,13 +6,17 @@
 from pathlib import Path
 from typing import TYPE_CHECKING
 
+import matplotlib.dates as mdates
+import numpy as np
 import pytz
 import seaborn as sns
+from matplotlib import patches
 from matplotlib import pyplot as plt
-from matplotlib.patches import Patch
 from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
 from pandas import (
     DataFrame,
+    DateOffset,
+    Series,
     Timedelta,
     concat,
     date_range,
@@ -22,6 +26,7 @@
     to_numeric,
     to_timedelta,
 )
+from sklearn.mixture import GaussianMixture
 
 from post_processing.utils.core_utils import get_coordinates, get_sun_times
 from user_case.config import season_color, site_colors
@@ -76,7 +81,7 @@ def pod2aplose(
 
     data = {
         "dataset": [dataset_name] * len(df),
-        "filename": df["deploy.name"].tolist(),
+        "filename": [strftime_osmose_format(entry) for entry in fpod_start_dt],
         "start_time": [0] * len(df),
         "end_time": [bin_size] * len(df),
         "start_frequency": [0] * len(df),
@@ -86,6 +91,7 @@ def pod2aplose(
         "start_datetime": [strftime_osmose_format(entry) for entry in fpod_start_dt],
         "end_datetime": [strftime_osmose_format(entry) for entry in fpod_end_dt],
         "is_box": [0] * len(df),
+        "deploy.name": df["deploy.name"].tolist(),
     }
 
     return DataFrame(data)
@@ -418,11 +424,14 @@ def feeding_buzz(
     df["ICI"] = df["datetime"].diff()
 
     if species == "Dauphin":  # Herzing et al., 2014
-        df["Buzz"] = (df["ICI"] < Timedelta(seconds=0.02)).astype(int)
+        df["Buzz"] = df["ICI"].between(Timedelta(0),
+                                       Timedelta(seconds=0.02)).astype(int)
     elif species == "Marsouin":  # Nuuttila et al., 2013
-        df["Buzz"] = (df["ICI"] < Timedelta(seconds=0.01)).astype(int)
+        df["Buzz"] = df["ICI"].between(Timedelta(0),
+                                       Timedelta(seconds=0.01)).astype(int)
     elif species == "Commerson":  # Reyes Reyes et al., 2015
-        df["Buzz"] = (df["ICI"] < Timedelta(seconds=0.005)).astype(int)
+        df["Buzz"] = df["ICI"].between(Timedelta(0),
+                                       Timedelta(seconds=0.005)).astype(int)
     else:
         msg = "This species is not supported"
         raise ValueError(msg)
@@ -434,6 +443,28 @@ def feeding_buzz(
     return f
 
 
+def gmm_log(
+    array: Series,
+) -> None:
+    """Gaussian mixture model.
+
+    Parameters
+    ----------
+    array: Series
+        Data you want to test for clustering.
+
+    """
+    log_ici = np.log(array.values).reshape(-1, 1)
+    gmm_3 = GaussianMixture(
+        n_components=3,
+        covariance_type="full",
+        random_state=42,
+        max_iter=200,
+        n_init=10,
+    )
+    gmm_3.fit(log_ici)
+
+
 def assign_daytime(
     df: DataFrame,
 ) -> DataFrame:
@@ -803,7 +834,9 @@ def percent_calc(
 
     df["%click"] = df["DPM"] * 100 / (df["Day"] * 60)
     df["%DPh"] = df["DPh"] * 100 / df["Day"]
-    df["FBR"] = df["Foraging"] * 100 / df["DPM"]
+    df["FBR"] = df.apply(
+        lambda row: (row["Foraging"] * 100 / row["DPM"]) if row["DPM"] > 0 else 0,
+        axis=1)
     df["%buzzes"] = df["Foraging"] * 100 / (df["Day"] * 60)
     return df
 
@@ -912,7 +945,7 @@ def ym_percent(df: DataFrame, metric: str) -> None:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     legend_elements = [
-        Patch(facecolor=col, edgecolor="black", label=season.capitalize())
+        patches.Patch(facecolor=col, edgecolor="black", label=season.capitalize())
         for season, col in season_color.items()
     ]
     fig.legend(
@@ -925,6 +958,121 @@ def ym_percent(df: DataFrame, metric: str) -> None:
     plt.show()
 
 
+def week_percent(df: DataFrame, metric: str) -> None:
+    """Plot a graph with the percentage of DPM per site/month-year.
+
+    Parameters
+    ----------
+    df: DataFrame
+        All percentages grouped by site and month per year
+    metric: str
+        Type of percentage you want to show on the graph
+
+    """
+    sites = df["site.name"].unique()
+    n_sites = len(sites)
+    fig, axs = plt.subplots(n_sites, 1, figsize=(15, 3 * n_sites), sharex=True)
+    if n_sites == 1:
+        axs = [axs]
+
+    for i, site in enumerate(sorted(sites)):
+        site_data = df[df["site.name"] == site].copy()
+        ax = axs[i]
+
+        # Masque pour identifier les NAs
+        na_mask = site_data["DPM"].isna()
+
+        # Définir la limite Y
+        ymax = max(df[metric].dropna()) + 0.2 if not df[metric].dropna().empty else 1
+        ax.set_ylim(0, ymax)
+
+        # Tracer les rectangles pour les périodes de NAs
+        na_dates = site_data.loc[na_mask, "start_datetime"]
+        if len(na_dates) > 0:
+            na_groups = []
+            current_group = [na_dates.iloc[0]]
+
+            for j in range(1, len(na_dates)):
+                # Vérifier si les semaines sont consécutives (~7 jours)
+                if (na_dates.iloc[j] - current_group[-1]).days < 10:
+                    current_group.append(na_dates.iloc[j])
+                else:
+                    na_groups.append(current_group)
+                    current_group = [na_dates.iloc[j]]
+            na_groups.append(current_group)
+
+            # Créer les rectangles
+            for group in na_groups:
+                start = group[0] - DateOffset(days=3.5)  # Centrer sur la semaine
+                width = len(group) * 7 + 2  # Largeur en jours
+                rect = patches.Rectangle(
+                    (mdates.date2num(start), 0),
+                    width,
+                    ymax,
+                    linewidth=1,
+                    edgecolor="gray",
+                    facecolor="lightgray",
+                    alpha=0.3,
+                    label="Pas de données"
+                    if (i == 0 and group == na_groups[0])
+                    else "",
+                )
+                ax.add_patch(rect)
+
+        # Tracer les barres avec données
+        bar_colors = site_data.loc[~na_mask, "Season"].map(season_color).fillna("gray")
+        bars = ax.bar(
+            site_data.loc[~na_mask, "start_datetime"],
+            site_data.loc[~na_mask, metric],
+            label=f"Site {site}",
+            color=bar_colors,
+            width=6,  # Largeur adaptée pour les semaines
+        )
+
+        # Ajouter des hachures si nécessaire
+        if metric in {"%buzzes", "FBR"}:
+            for bar in bars:
+                bar.set_hatch("/")
+
+        ax.set_title(f"{site}")
+        ax.set_ylabel(metric)
+        if i != n_sites - 1:
+            ax.set_xlabel("")
+        else:
+            ax.set_xlabel("Week")
+
+    # Légende des saisons
+    legend_elements = [
+        patches.Patch(facecolor=col, edgecolor="black", label=season.capitalize())
+        for season, col in season_color.items()
+    ]
+
+    # Ajouter "Pas de données" à la légende si des NAs existent
+    if df["DPM"].isna().any():
+        legend_elements.append(
+            patches.Patch(
+                facecolor="lightgray",
+                edgecolor="gray",
+                alpha=0.3,
+                label="Pas de données"))
+
+    fig.legend(
+        handles=legend_elements,
+        loc="upper right",
+        title="Seasons",
+        bbox_to_anchor=(0.95, 0.95),
+    )
+    fig.suptitle(f"{metric} per week", fontsize=16)
+
+    # Formatage de l'axe X
+    axs[-1].xaxis.set_major_locator(mdates.MonthLocator(interval=1))
+    axs[-1].xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
+    fig.autofmt_xdate()
+
+    plt.tight_layout()
+    plt.show()
+
+
 def month_percent(df: DataFrame, metric: str) -> None:
     """Plot a graph with the percentage of minutes positive to detection per site/month.
 
@@ -1018,7 +1166,7 @@ def day_percent(df: DataFrame, metric: str) -> None:
             for _, bar in enumerate(ax.patches):
                 bar.set_hatch("/")
     legend_elements = [
-        Patch(facecolor=col, edgecolor="black", label=season.capitalize())
+        patches.Patch(facecolor=col, edgecolor="black", label=season.capitalize())
         for season, col in season_color.items()
     ]
     fig.legend(
@@ -1132,12 +1280,12 @@ def calendar(
     ax.set_yticklabels(sites, fontsize=12)
 
     legend_elements = [
-        Patch(facecolor="#F5F5F5", edgecolor="black", label="Deployment"),
+        patches.Patch(facecolor="#F5F5F5", edgecolor="black", label="Deployment"),
     ]
     for site, color in site_colors.items():
         if site in sites:
             legend_elements.append(
-                Patch(facecolor=color, edgecolor="black", label=f"{site}"),
+                patches.Patch(facecolor=color, edgecolor="black", label=f"{site}"),
             )
 
     ax.legend(handles=legend_elements, loc="upper left", fontsize=11, frameon=True)
@@ -1366,5 +1514,72 @@ def hist_mean_s(
     ax.set_ylabel(y_lab or metric_mean, fontsize=10)
     ax.set_xlabel("Site", fontsize=10)
 
+    plt.tight_layout()
+    plt.show()
+
+
+def hist_mean_season(
+    df: DataFrame,
+    metric_mean: str,
+    metric_std: str,
+    y_lab: str | None = None,
+    title_suffix: str | None = None,
+) -> None:
+    """Produce a histogram of the given data.
+
+    It shows mean and standard deviation of the metric.
+
+    Parameters
+    ----------
+    df: DataFrame
+        All data grouped by site and month
+    metric_mean: str
+        Column name for the mean values (e.g., "%click_mean")
+    metric_std: str
+        Column name for the standard deviation values (e.g., "%click_std")
+    y_lab: str, optional
+        Label for y-axis. If None, uses metric_mean
+    title_suffix: str, optional
+        Suffix for the main title. If None, uses metric_mean
+
+    """
+    sites = df["site.name"].unique()
+    n_sites = len(sites)
+    fig, axs = plt.subplots(n_sites, 1, figsize=(14, 5 * n_sites), sharex=True)
+    if n_sites == 1:
+        axs = [axs]
+
+    # Calculate max for y-axis scaling
+    max_value = max(df[metric_mean] + df[metric_std])
+
+    for i, site in enumerate(sorted(sites)):
+        site_data = df[df["site.name"] == site]
+        ax = axs[i]
+
+        ax.bar(
+            x=site_data["Season"],
+            height=site_data[metric_mean],
+            yerr=site_data[metric_std],
+            capsize=4,
+            color=site_colors.get(site, "gray"),
+            alpha=0.8,
+            edgecolor="black",
+            linewidth=0.5,
+            label=f"Site {site}",
+        )
+
+        ax.set_title(f"{site}", fontsize=12)
+        ax.set_ylim(0, max_value * 1.1)
+        ax.set_ylabel(y_lab or metric_mean, fontsize=10)
+
+        # Only set x-label on last subplot
+        if i == n_sites - 1:
+            ax.set_xlabel("Season", fontsize=10)
+        if metric_mean in {"%buzzes_mean", "FBR_mean"}:
+            for _, bar in enumerate(ax.patches):
+                bar.set_hatch("/")
+
+    fig.suptitle(f"{title_suffix or metric_mean} per season", fontsize=16)
+    plt.xticks(rotation=45)
     plt.tight_layout()
     plt.show()
\ No newline at end of file

From 833c7ecb583d73fdd6ad9ab5f6a59964128f11f3 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 20 Jan 2026 11:38:19 +0100
Subject: [PATCH 52/83] start fpod_utils tests

---
 tests/test_fpod_utils.py | 30 +++++++++++++++++++++---------
 1 file changed, 21 insertions(+), 9 deletions(-)

diff --git a/tests/test_fpod_utils.py b/tests/test_fpod_utils.py
index da6bf13..c3c7f76 100644
--- a/tests/test_fpod_utils.py
+++ b/tests/test_fpod_utils.py
@@ -5,22 +5,21 @@
 
 import pytest
 from osekit.utils.timestamp_utils import strptime_from_text
-from pandas import DataFrame, Timestamp, read_csv
+from pandas import DataFrame, Timestamp, read_csv, concat
 from pandas.testing import assert_frame_equal
 
 from post_processing.utils.fpod_utils import (
+    txt_folder,
     csv_folder,
     deploy_period,
+    pod2aplose,
+    actual_data,
+    add_utc,
     extract_site,
+    required_columns,
     parse_timestamps,
-    txt_folder,
-    pod2aplose,
-    meta_cut_aplose,
-    build_range,
-    feeding_buzz,
-    assign_daytime,
+    create_mask,
     is_dpm_col,
-    build_aggregation_dict,
     resample_dpm)
 
 # SAMPLE_POD = """File,ChunkEnd,DPM,Nall,MinsOn
@@ -149,8 +148,21 @@
 #     df = read_csv(io.StringIO(SAMPLE_POD), parse_dates=["ChunkEnd"])
 #     return df.sort_values(["ChunkEnd"]).reset_index(drop=True)
 
-# pod2aplose
 
+# csv_folder
+def test_csv_folder_single_file(tmp_path) -> None:
+    """Test processing a single CSV file."""
+    # Create a CSV file
+    csv_file = tmp_path / "data.csv"
+    csv_file.write_text("col1;col2\nval1;val2\nval3;val4", encoding="latin-1")
+
+    result = csv_folder(tmp_path)
+
+    assert isinstance(result, DataFrame)
+    assert len(result) == 2
+    assert "deploy.name" in result.columns
+    assert all(result["deploy.name"] == "data")
+    assert list(result.columns) == ["col1", "col2", "deploy.name"]
 
 # pod2aplose
 

From 9a6b216d86399ca7278a626d7b77eeb02612e0ad Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Tue, 25 Nov 2025 16:07:18 +0100
Subject: [PATCH 53/83] test audio_utils

---
 tests/test_audio_utils.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tests/test_audio_utils.py b/tests/test_audio_utils.py
index 35c3407..2a0eb27 100644
--- a/tests/test_audio_utils.py
+++ b/tests/test_audio_utils.py
@@ -19,8 +19,6 @@ def test_normalize_audio_default_folder(sample_audio: Path, tmp_path: Path) -> N
 
 def test_normalize_audio_custom_folder(sample_audio: Path, tmp_path: Path) -> None:
     out_folder = tmp_path / "output"
-    out_folder.mkdir()
-
     normalize_audio(sample_audio, output_folder=out_folder)
 
     normalized_file = out_folder / sample_audio.name

From a61e2abd0b348e939069035a31ebcb567bf2af32 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Tue, 25 Nov 2025 17:32:06 +0100
Subject: [PATCH 54/83] test core_utils

---
 src/post_processing/utils/core_utils.py |  50 ++++------
 tests/test_core_utils.py                | 126 +++++++++++++++++++++---
 2 files changed, 133 insertions(+), 43 deletions(-)

diff --git a/src/post_processing/utils/core_utils.py b/src/post_processing/utils/core_utils.py
index cf14302..ec4b68d 100644
--- a/src/post_processing/utils/core_utils.py
+++ b/src/post_processing/utils/core_utils.py
@@ -3,13 +3,14 @@
 from __future__ import annotations
 
 import json
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 import astral
 import easygui
 import numpy as np
 from astral.sun import sunrise, sunset
 from matplotlib import pyplot as plt
+from numpy import ndarray, dtype
 from osekit.config import TIMESTAMP_FORMAT_AUDIO_FILE
 from osekit.utils.timestamp_utils import strptime_from_text
 from pandas import (
@@ -79,11 +80,8 @@ def get_season(ts: Timestamp, *, northern: bool = True) -> tuple[str, int]:
         season = "summer"
     elif ts.month in autumn:
         season = "autumn"
-    elif ts.month in winter:
-        season = "winter"
     else:
-        msg = "Invalid timestamp"
-        raise ValueError(msg)
+        season = "winter"
 
     return season, ts.year - 1 if ts.month in [1, 2] else ts.year
 
@@ -93,10 +91,7 @@ def get_sun_times(
     stop: Timestamp,
     lat: float,
     lon: float,
-) -> (
-    list[float],
-    list[float],
-):
+) -> tuple[list[float], list[float]]:
     """Fetch sunrise and sunset hours for dates between start and stop.
 
     Parameters
@@ -171,7 +166,7 @@ def get_coordinates() -> tuple:
                     f"'{lat}' is not a valid latitude. It must be between -90 and 90.\n"
                 )
         except ValueError:
-            errmsg += f"'{lat}' is not a valid entry for latitude.\n"
+            errmsg += f"'lat', invalid entry: '{lat}'.\n"
 
         try:
             lon_val = float(lon.strip())  # Convert to float for longitude
@@ -349,7 +344,7 @@ def set_bar_height(ax: plt.Axes, pixel_height: int = 10) -> float:
 
     """
     if not ax.has_data():
-        msg = "Axe has no data"
+        msg = "Axe have no data"
         raise ValueError(msg)
 
     display_to_data = ax.transData.inverted().transform
@@ -380,7 +375,7 @@ def add_recording_period(
 
     """
     if not ax.has_data():
-        msg = "Axe has no data"
+        msg = "Axe have no data"
         raise ValueError(msg)
 
     recorder_intervals = [
@@ -419,10 +414,6 @@ def get_count(df: DataFrame, bin_size: Timedelta | BaseOffset) -> DataFrame:
         "<label>-<annotator>", containing the count of observations in that bin.
 
     """
-    if not isinstance(df, DataFrame):
-        msg = "`df` must be a DataFrame"
-        raise TypeError(msg)
-
     if df.empty:
         msg = "`df` contains no data"
         raise ValueError(msg)
@@ -464,10 +455,6 @@ def get_labels_and_annotators(df: DataFrame) -> tuple[list, list]:
         A tuple containing the labels and annotators lists.
 
     """
-    if not isinstance(df, DataFrame):
-        msg = "`df` must be a DataFrame"
-        raise TypeError(msg)
-
     if df.empty:
         msg = "`df` contains no data"
         raise ValueError(msg)
@@ -497,9 +484,10 @@ def localize_timestamps(timestamps: list[Timestamp], tz: tzinfo) -> list[Timesta
     return localized
 
 
-def get_time_range_and_bin_size(timestamp_list: list[Timestamp],
-                    bin_size: Timedelta | BaseOffset,
-                    ) -> (DatetimeIndex, Timedelta):
+def get_time_range_and_bin_size(
+    timestamp_list: list[Timestamp],
+    bin_size: Timedelta | BaseOffset,
+) -> tuple[DatetimeIndex, Timedelta]:
     """Return time vector given a bin size."""
     if (not isinstance(timestamp_list, list) or
             not all(isinstance(ts, Timestamp) for ts in timestamp_list)):
@@ -512,18 +500,20 @@ def get_time_range_and_bin_size(timestamp_list: list[Timestamp],
 
     start, end, _ = round_begin_end_timestamps(timestamp_list, bin_size)
     timestamp_range = date_range(start=start, end=end, freq=bin_size)
+
     if isinstance(bin_size, Timedelta):
         return timestamp_range, bin_size
-    if isinstance(bin_size, BaseOffset):
+    elif isinstance(bin_size, BaseOffset):
         return timestamp_range, timestamp_range[1] - timestamp_range[0]
-
-    msg = "Could not get time range."
-    raise ValueError(msg)
+    else:
+        msg = "bin_size must be a Timedelta or BaseOffset."
+        raise TypeError(msg)
 
 
-def round_begin_end_timestamps(timestamp_list: list[Timestamp],
-                    bin_size: Timedelta | BaseOffset,
-                    ) -> (Timestamp, Timestamp, Timedelta):
+def round_begin_end_timestamps(
+    timestamp_list: list[Timestamp],
+    bin_size: Timedelta | BaseOffset,
+) -> tuple[Timestamp, Timestamp, Timedelta]:
     """Return time vector given a bin size."""
     if (not isinstance(timestamp_list, list) or
             not all(isinstance(ts, Timestamp) for ts in timestamp_list)):
diff --git a/tests/test_core_utils.py b/tests/test_core_utils.py
index dd5576e..7606c71 100644
--- a/tests/test_core_utils.py
+++ b/tests/test_core_utils.py
@@ -1,4 +1,7 @@
+from unittest.mock import patch
+
 import pytest
+from matplotlib import pyplot as plt
 from pandas import DataFrame, Timedelta, Timestamp, date_range
 from pandas.tseries import frequencies
 from pytz import timezone
@@ -15,6 +18,10 @@
     localize_timestamps,
     round_begin_end_timestamps,
     timedelta_to_str,
+    add_season_period,
+    add_recording_period,
+    set_bar_height,
+    json2df,
 )
 
 
@@ -38,7 +45,7 @@ def fake_box(msg: str, title: str, fields: list[str]) -> None:
 
 
 def test_coordinates_invalid_then_valid_input(monkeypatch: pytest.MonkeyPatch) -> None:
-    inputs = [["900", "50"], ["45", "100"]]  # first invalid, then valid
+    inputs = [["900", "50"], ["45", "900"], ["45", "100"]]
 
     def fake_box(msg: str, title: str, fields: list[str]) -> list[str]:
         return inputs.pop(0)
@@ -49,7 +56,7 @@ def fake_box(msg: str, title: str, fields: list[str]) -> list[str]:
 
 
 def test_coordinates_non_numeric_input(monkeypatch: pytest.MonkeyPatch) -> None:
-    inputs = [["abc", "-20"], ["10", "-20"]]
+    inputs = [["abc", "-20"], ["-20", "abc"], ["10", "-20"]]
 
     def fake_box(msg: str, title: str, fields: list[str]) -> list[str]:
         return inputs.pop(0)
@@ -127,11 +134,12 @@ def test_get_sun_times_valid_input(start: Timestamp,
          -1.5167),
     ],
 )
-def test_get_sun_times_naive_timestamps(start: Timestamp,
-                                        stop: Timestamp,
-                                        lat: float,
-                                        lon: float,
-                                        ) -> None:
+def test_get_sun_times_naive_timestamps(
+    start: Timestamp,
+    stop: Timestamp,
+    lat: float,
+    lon: float,
+) -> None:
     with pytest.raises(ValueError, match="start and stop must be timezone-aware"):
         get_sun_times(start, stop, lat, lon)
 
@@ -369,14 +377,106 @@ def test_round_begin_end_timestamps_valid_entry_2() -> None:
 def test_timedelta_to_str(td, expected) -> None:
     assert timedelta_to_str(td) == expected
 
+
 # %% add_weak_detection / json2df
 
 
 def test_add_wd(sample_df: DataFrame) -> None:
     df_only_wd = sample_df[sample_df["is_box"] == 1]
-    strong_det = sample_df[sample_df["is_box"] == 0].iloc[0]
-    add_weak_detection(df=df_only_wd.copy(),
-                       datetime_format="%Y_%m_%d_%H_%M_%S",
-                       max_time=strong_det["end_time"],
-                       max_freq=strong_det["end_frequency"],
-                       )
+    add_weak_detection(
+        df=df_only_wd.copy(),
+        datetime_format="%Y_%m_%d_%H_%M_%S",
+    )
+
+
+# %% add_season_period
+
+def test_add_season_valid() -> None:
+    fig, ax = plt.subplots()
+    start = Timestamp("2025-01-01T00:00:00+00:00")
+    stop = Timestamp("2025-01-02T00:00:00+00:00")
+
+    ts = date_range(start=start, end=stop, freq="H", tz="UTC")
+    values = list(range(len(ts)))
+    ax.plot(ts, values)
+    add_season_period(ax=ax)
+
+
+def test_add_season_no_data() -> None:
+    fig, ax = plt.subplots()
+    with pytest.raises(ValueError, match=r"have no data"):
+        add_season_period(ax=ax)
+
+# %% add_recording_period
+
+def test_add_recording_period_valid() -> None:
+    fig, ax = plt.subplots()
+    start = Timestamp("2025-01-01T00:00:00+00:00")
+    stop  = Timestamp("2025-01-02T00:00:00+00:00")
+
+    ts = date_range(start=start, end=stop, freq="H", tz="UTC")
+    values = list(range(len(ts)))
+    ax.plot(ts, values)
+
+    df = DataFrame(
+        data=[
+            [
+                Timestamp("2025-01-01T00:00:00+00:00"),
+                Timestamp("2025-01-02T00:00:00+00:00"),
+            ]
+        ],
+        columns=["deployment_date", "recovery_date"],
+    )
+    add_recording_period(df=df, ax=ax)
+
+
+def test_add_recording_period_no_data() -> None:
+    fig, ax = plt.subplots()
+    df = DataFrame()
+    with pytest.raises(ValueError, match=r"have no data"):
+        add_recording_period(df=df, ax=ax)
+
+# %% set_bar_height
+
+def test_set_bar_height_valid() -> None:
+    fig, ax = plt.subplots()
+    start = Timestamp("2025-01-01T00:00:00+00:00")
+    stop = Timestamp("2025-01-02T00:00:00+00:00")
+
+    ts = date_range(start=start, end=stop, freq="H", tz="UTC")
+    values = list(range(len(ts)))
+    ax.plot(ts, values)
+
+    set_bar_height(ax=ax)
+
+
+def test_set_bar_height_no_data() -> None:
+    fig, ax = plt.subplots()
+    with pytest.raises(ValueError, match=r"have no data"):
+        set_bar_height(ax=ax)
+
+# %% json2df
+
+def test_json2df_valid(tmp_path):
+    fake_json = {
+        "deployment_date": "2025-01-01T00:00:00+00:00",
+        "recovery_date": "2025-01-02T00:00:00+00:00",
+    }
+
+    json_file = tmp_path / "metadatax.json"
+    json_file.write_text("{}", encoding="utf-8")  # contents don't matter; json.load is mocked
+
+    with patch("json.load", return_value=fake_json):
+        df = json2df(json_file)
+
+    expected = DataFrame(
+        data=[
+            [
+                Timestamp("2025-01-01T00:00:00+00:00"),
+                Timestamp("2025-01-02T00:00:00+00:00"),
+            ]
+        ],
+        columns=["deployment_date", "recovery_date"],
+    )
+
+    assert df.equals(expected)
\ No newline at end of file

From 5c1bf9baaa3ab1ed92ddc19338d74bc42d11f2e5 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Tue, 25 Nov 2025 17:32:33 +0100
Subject: [PATCH 55/83] test core_utils

---
 tests/test_core_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_core_utils.py b/tests/test_core_utils.py
index 7606c71..4264911 100644
--- a/tests/test_core_utils.py
+++ b/tests/test_core_utils.py
@@ -464,7 +464,7 @@ def test_json2df_valid(tmp_path):
     }
 
     json_file = tmp_path / "metadatax.json"
-    json_file.write_text("{}", encoding="utf-8")  # contents don't matter; json.load is mocked
+    json_file.write_text("{}", encoding="utf-8")
 
     with patch("json.load", return_value=fake_json):
         df = json2df(json_file)

From a80b3b20ca51b95806f6ab734451183f0d917e11 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Wed, 26 Nov 2025 11:09:23 +0100
Subject: [PATCH 56/83] filtering_utils

---
 src/post_processing/utils/filtering_utils.py |  33 ++-
 tests/test_filtering_utils.py                | 285 ++++++++++++++++---
 2 files changed, 270 insertions(+), 48 deletions(-)

diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index d4c3301..16cd35a 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -4,6 +4,7 @@
 
 import bisect
 import csv
+import datetime
 from typing import TYPE_CHECKING
 
 import pytz
@@ -68,7 +69,7 @@ def filter_by_time(
     if end is not None:
         df = df[df["end_datetime"] <= end]
         if df.empty:
-            msg = f"No detection found after '{end}'."
+            msg = f"No detection found before '{end}'."
             raise ValueError(msg)
 
     return df
@@ -81,9 +82,6 @@ def filter_by_annotator(
     """Filter a DataFrame based on annotator selection."""
     list_annotators = get_annotators(df)
 
-    if not annotator:
-        return df
-
     if isinstance(annotator, str):
         ensure_in_list(annotator, list_annotators, "annotator")
         return df[df["annotator"] == annotator]
@@ -101,9 +99,6 @@ def filter_by_label(
     """Filter a DataFrame based on label selection."""
     list_labels = get_labels(df)
 
-    if not label:
-        return df
-
     if isinstance(label, str):
         ensure_in_list(label, list_labels, "label")
         return df[df["annotation"] == label]
@@ -192,16 +187,24 @@ def get_dataset(df: DataFrame) -> list[str]:
     return datasets if len(datasets) > 1 else datasets[0]
 
 
-def get_timezone(df: DataFrame):
-    """Return timezone(s) from DataFrame."""
+def get_canonical_tz(tz):
+    """Return timezone of object as a pytz timezone."""
+    if isinstance(tz, datetime.timezone):
+        if tz == datetime.timezone.utc:
+            return pytz.utc
+        offset_minutes = int(tz.utcoffset(None).total_seconds() / 60)
+        return pytz.FixedOffset(offset_minutes)
+    if hasattr(tz, "zone") and tz.zone:
+        return pytz.timezone(tz.zone)
+    if hasattr(tz, "key"):
+        return pytz.timezone(tz.key)
+    else:
+        msg = f"Unknown timezone: {tz}"
+        raise TypeError(msg)
 
-    def get_canonical_tz(tz):
-        if hasattr(tz, "zone") and tz.zone:
-            return pytz.timezone(tz.zone)
-        if hasattr(tz, "key"):
-            return pytz.timezone(tz.key)
-        return pytz.UTC
 
+def get_timezone(df: DataFrame):
+    """Return timezone(s) from DataFrame."""
     timezones = {get_canonical_tz(ts.tzinfo) for ts in df["start_datetime"]}
 
     if len(timezones) == 1:
diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 93021a0..748307a 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -1,9 +1,10 @@
 import csv
 from pathlib import Path
+from zoneinfo import ZoneInfo
 
 import pytest
 import pytz
-from pandas import DataFrame, Timedelta, Timestamp, date_range
+from pandas import DataFrame, Timedelta, Timestamp, date_range, concat, to_datetime
 
 from post_processing.utils.filtering_utils import (
     filter_by_annotator,
@@ -21,6 +22,9 @@
     get_timezone,
     read_dataframe,
     reshape_timebin,
+    get_canonical_tz,
+    ensure_no_invalid,
+    intersection_or_union,
 )
 
 # %% find delimiter
@@ -60,21 +64,56 @@ def test_find_delimiter_empty_file(tmp_path: Path) -> None:
 # %% filter utils
 
 # filter_by_time
-def test_filter_by_time_begin(sample_df: DataFrame) -> None:
-    ts = sample_df["start_datetime"].iloc[4]
-    df = filter_by_time(sample_df, begin=ts, end=None)
-    assert (df["start_datetime"] >= ts).all()
-
-
-def test_filter_by_time_end(sample_df: DataFrame) -> None:
-    ts = sample_df["end_datetime"].iloc[4]
-    df = filter_by_time(sample_df, begin=None, end=ts)
-    assert (df["end_datetime"] <= ts).all()
-
-
-def test_filter_by_time_out_of_range(sample_df: DataFrame) -> None:
-    with pytest.raises(ValueError, match="No detection found after '2050"):
-        filter_by_time(sample_df, begin=Timestamp("2050-01-01", tz="utc"), end=None)
+@pytest.mark.parametrize(
+    "begin, end",
+    [
+        pytest.param(
+            Timestamp("2020-01-01", tz="utc"),
+            None,
+            id="valid_begin_only",
+        ),
+        pytest.param(
+            None,
+            Timestamp("2030-01-01", tz="utc"),
+            id="valid_end_only",
+        ),
+        pytest.param(
+            Timestamp("2020-01-01", tz="utc"),
+            Timestamp("2030-01-01", tz="utc"),
+            id="valid_begin_and_end",
+        ),
+    ],
+)
+def test_filter_by_time_valid(sample_df: DataFrame, begin, end):
+    result = filter_by_time(sample_df, begin=begin, end=end)
+
+    assert not result.empty
+    if begin is not None:
+        assert (result["start_datetime"] >= begin).all()
+    if end is not None:
+        assert (result["end_datetime"] <= end).all()
+
+
+@pytest.mark.parametrize(
+    "begin, end, expected_msg",
+    [
+        pytest.param(
+            Timestamp("2050-01-01", tz="utc"),
+            None,
+            "No detection found after '2050",
+            id="out_of_range_begin",
+        ),
+        pytest.param(
+            None,
+            Timestamp("1900-01-01", tz="utc"),
+            "No detection found before '1900",
+            id="out_of_range_end",
+        ),
+    ],
+)
+def test_filter_by_time_out_of_range(sample_df: DataFrame, begin, end, expected_msg):
+    with pytest.raises(ValueError, match=expected_msg):
+        filter_by_time(sample_df, begin=begin, end=end)
 
 
 # filter_by_annotator
@@ -109,23 +148,58 @@ def test_filter_by_label_invalid(sample_df: DataFrame) -> None:
         filter_by_label(sample_df, "hihi")
 
 
-# filter_by_freq
-def test_filter_by_freq_min(sample_df: DataFrame) -> None:
-    freq_min = 500
-    df = filter_by_freq(sample_df, f_min=freq_min, f_max=None)
-    assert (df["start_frequency"] >= freq_min).all()
-
-
-def test_filter_by_freq_max(sample_df: DataFrame) -> None:
-    freq_max = 60000
-    df = filter_by_freq(sample_df, f_min=None, f_max=freq_max)
-    assert (df["end_frequency"] <= freq_max).all()
+@pytest.mark.parametrize(
+    "f_min, f_max",
+    [
+        pytest.param(
+            500,     # valid lower bound
+            None,
+            id="valid_f_min_only",
+        ),
+        pytest.param(
+            None,
+            60000,   # valid upper bound
+            id="valid_f_max_only",
+        ),
+        pytest.param(
+            500,
+            60000,
+            id="valid_f_min_and_f_max",
+        ),
+    ],
+)
+def test_filter_by_freq_valid(sample_df: DataFrame, f_min, f_max):
+    result = filter_by_freq(sample_df, f_min=f_min, f_max=f_max)
+
+    assert not result.empty
+
+    if f_min is not None:
+        assert (result["start_frequency"] >= f_min).all()
+    if f_max is not None:
+        assert (result["end_frequency"] <= f_max).all()
+
+@pytest.mark.parametrize(
+    "f_min, f_max, expected_msg",
+    [
+        pytest.param(
+            1e8,
+            None,
+            "No detection found above",
+            id="out_of_range_f_min",
+        ),
+        pytest.param(
+            None,
+            1,
+            "No detection found below",
+            id="out_of_range_f_max",
+        ),
+    ],
+)
 
 
-def test_filter_by_freq_no_results(sample_df: DataFrame) -> None:
-    freq_min = 144000
-    with pytest.raises(ValueError, match=f"No detection found above {int(freq_min)}Hz"):
-        filter_by_freq(sample_df, f_min=freq_min, f_max=None)
+def test_filter_by_freq_out_of_range(sample_df: DataFrame, f_min, f_max, expected_msg):
+    with pytest.raises(ValueError, match=expected_msg):
+        filter_by_freq(sample_df, f_min=f_min, f_max=f_max)
 
 
 # filter_by_score
@@ -146,11 +220,24 @@ def test_filter_by_score_missing_column(sample_df: DataFrame) -> None:
 
 
 # filter_weak_strong_detection
-def test_filter_weak_only(sample_df: DataFrame) -> None:
+def test_filter_weak_only_is_box_colum(sample_df: DataFrame) -> None:
     df = filter_strong_detection(sample_df)
     assert set(df["is_box"]) == {0}
 
 
+def test_filter_weak_only_type_column(sample_df: DataFrame) -> None:
+    sample_df = sample_df.rename(columns={"is_box": "type"})
+    sample_df["type"] = ["WEAK" if not cell else "BOX" for cell in sample_df["type"]]
+    df = filter_strong_detection(sample_df)
+    assert set(df["type"]) == {"WEAK"}
+
+
+def test_filter_weak_only_invalid() -> None:
+    invalid_df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+    with pytest.raises(ValueError, match="Could not determine annotation type"):
+        filter_strong_detection(invalid_df)
+
+
 def test_filter_weak_empty(sample_df: DataFrame) -> None:
     with pytest.raises(ValueError, match="No weak detection found"):
         filter_strong_detection(sample_df[sample_df["is_box"] == 1])
@@ -180,11 +267,56 @@ def test_get_dataset(sample_df: DataFrame) -> None:
     assert get_dataset(sample_df) == sample_df["dataset"].iloc[0]
 
 
+@pytest.mark.parametrize(
+    "input_tz, expected",
+    [
+        (pytz.timezone("Europe/Samara"), pytz.timezone("Europe/Samara")),
+        (ZoneInfo("Pacific/Pago_Pago"), pytz.timezone("Pacific/Pago_Pago")),
+    ],
+)
+def test_get_canonical_tz_valid(input_tz, expected):
+    result = get_canonical_tz(input_tz)
+    assert result == expected
+
+
+def test_get_canonical_tz_raises_on_unknown():
+    class DummyTZ:
+        pass
+
+    dummy = DummyTZ()
+    with pytest.raises(TypeError) as exc:
+        get_canonical_tz(dummy)
+
+    assert "Unknown timezone" in str(exc.value)
+
+
 def test_get_timezone_single(sample_df: DataFrame) -> None:
     tz = get_timezone(sample_df)
     assert tz == pytz.utc
 
 
+def test_get_timezone_several(sample_df: DataFrame) -> None:
+    new_row = {
+        "dataset": "dataset",
+        "filename": "filename",
+        "start_time": 0,
+        "end_time": 2,
+        "start_frequency": 100,
+        "end_frequency": 200,
+        "annotation": "annotation",
+        "annotator": "annotator",
+        "start_datetime": Timestamp("2025-01-27 06:00:00.000000+07:00"),
+        "end_datetime": Timestamp("2025-01-27 06:00:00.000000+07:00"),
+        "is_box": 1,
+        "score": None,
+    }
+    sample_df = concat(
+        [sample_df, DataFrame([new_row])],
+        ignore_index=False
+    )
+    tz = get_timezone(sample_df)
+    assert tz == [pytz.UTC, pytz.FixedOffset(420)]
+
 # %% read DataFrame
 
 def test_read_dataframe_comma_delimiter(tmp_path: Path) -> None:
@@ -239,14 +371,38 @@ def test_read_dataframe_nrows(tmp_path: Path) -> None:
     assert len(df) == 1
     assert df.iloc[0]["annotation"] in {"whale", "dolphin"}
 
-# %% reshape_timebin
 
+# %% reshape_timebin
 
 def test_no_timebin_returns_original(sample_df: DataFrame) -> None:
     df_out = reshape_timebin(sample_df, timebin_new=None)
     assert df_out.equals(sample_df)
 
 
+def test_no_timebin_several_tz(sample_df: DataFrame) -> None:
+    new_row = {
+        "dataset": "dataset",
+        "filename": "filename",
+        "start_time": 0,
+        "end_time": 2,
+        "start_frequency": 100,
+        "end_frequency": 200,
+        "annotation": "annotation",
+        "annotator": "annotator",
+        "start_datetime": Timestamp("2025-01-27 06:00:00.000000+07:00"),
+        "end_datetime": Timestamp("2025-01-27 06:00:00.000000+07:00"),
+        "is_box": 1,
+        "score": None,
+    }
+    sample_df = concat(
+        [sample_df, DataFrame([new_row])],
+        ignore_index=False
+    )
+
+    df_out = reshape_timebin(sample_df, timebin_new=None)
+    assert df_out.equals(sample_df)
+
+
 def test_no_timebin_original_timebin(sample_df: DataFrame) -> None:
     df_out = reshape_timebin(sample_df, timebin_new=Timedelta("1min"))
     expected = DataFrame(
@@ -365,3 +521,66 @@ def test_with_manual_timestamps_vector(sample_df: DataFrame) -> None:
 def test_empty_result_when_no_matching(sample_df: DataFrame) -> None:
     with pytest.raises(ValueError, match="DataFrame is empty"):
         reshape_timebin(DataFrame(), Timedelta(hours=1))
+
+
+# %% ensure_no_invalid
+
+def test_ensure_no_invalid_empty() -> None:
+    try:
+        ensure_no_invalid([], "label")
+    except ValueError:
+        pytest.fail("ensure_no_invalid raised ValueError unexpectedly.")
+
+
+def test_ensure_no_invalid_with_elements() -> None:
+    invalid_items = ["foo", "bar"]
+    with pytest.raises(ValueError) as exc_info:
+        ensure_no_invalid(invalid_items, "columns")
+
+    assert "foo" in str(exc_info.value)
+    assert "bar" in str(exc_info.value)
+    assert "columns" in str(exc_info.value)
+
+def test_ensure_no_invalid_single_element() -> None:
+    invalid_items = ["baz"]
+    with pytest.raises(ValueError) as exc_info:
+        ensure_no_invalid(invalid_items, "features")
+    assert "baz" in str(exc_info.value)
+    assert "features" in str(exc_info.value)
+
+# %% intersection / union
+
+def test_intersection(sample_df) -> None:
+    df_result = intersection_or_union(sample_df[sample_df["annotator"].isin(["ann1", "ann2"])], user_sel="intersection")
+
+    assert set(df_result["annotation"]) == {"lbl1", "lbl2"}
+    assert set(df_result["annotator"]) == {"ann1 ∩ ann2"}
+
+
+def test_union(sample_df) -> None:
+    df_result = intersection_or_union(sample_df[sample_df["annotator"].isin(["ann1", "ann2"])], user_sel="union")
+
+    assert set(df_result["annotation"]) == {"lbl1", "lbl2"}
+    assert set(df_result["annotator"]) == {"ann1 ∪ ann2"}
+
+
+def test_all_user_sel_returns_original(sample_df) -> None:
+    df_result = intersection_or_union(sample_df, user_sel="all")
+
+    assert len(df_result) == len(sample_df)
+
+
+def test_invalid_user_sel_raises(sample_df) -> None:
+    with pytest.raises(ValueError, match="'user_sel' must be either 'intersection' or 'union'"):
+        intersection_or_union(sample_df, user_sel="invalid")
+
+
+def test_not_enough_annotators_raises() -> None:
+    df_single_annotator = DataFrame({
+        "annotation": ["cat"],
+        "start_datetime": to_datetime(["2025-01-01 10:00"]),
+        "end_datetime": to_datetime(["2025-01-01 10:01"]),
+        "annotator": ["A"]
+    })
+    with pytest.raises(ValueError, match="Not enough annotators detected"):
+        intersection_or_union(df_single_annotator, user_sel="intersection")
\ No newline at end of file

From 1a08e23c12a29f50be484da87c5af3a7af155f6f Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Wed, 26 Nov 2025 11:30:49 +0100
Subject: [PATCH 57/83] metric_utils

---
 tests/test_glider_utils.py |  6 ++++--
 tests/test_metric_utils.py | 15 +++++++++++++++
 2 files changed, 19 insertions(+), 2 deletions(-)
 create mode 100644 tests/test_metric_utils.py

diff --git a/tests/test_glider_utils.py b/tests/test_glider_utils.py
index 5ccd047..12d83df 100644
--- a/tests/test_glider_utils.py
+++ b/tests/test_glider_utils.py
@@ -54,8 +54,10 @@ def test_get_position_from_timestamp(nav_df: DataFrame) -> None:
     assert all(isinstance(x, float) for x in lat)
 
 
-def test_plot_detections_with_nav_data(df_detections: DataFrame,
-                                       nav_df: DataFrame) -> None:
+def test_plot_detections_with_nav_data(
+        df_detections: DataFrame,
+        nav_df: DataFrame
+) -> None:
     plot_detections_with_nav_data(
         df=df_detections,
         nav=nav_df,
diff --git a/tests/test_metric_utils.py b/tests/test_metric_utils.py
new file mode 100644
index 0000000..34ce769
--- /dev/null
+++ b/tests/test_metric_utils.py
@@ -0,0 +1,15 @@
+import pytest
+from pandas import DataFrame
+
+from post_processing.utils.metrics_utils import detection_perf
+
+def test_detection_perf(sample_df: DataFrame) -> None:
+    try:
+        detection_perf(df=sample_df[sample_df["annotator"].isin(["ann1", "ann4"])], ref=("ann1", "lbl1"))
+    except ValueError:
+        pytest.fail("test_detection_perf raised ValueError unexpectedly.")
+
+
+def test_detection_perf_one_annotator(sample_df: DataFrame) -> None:
+    with pytest.raises(ValueError, match="Two annotators needed"):
+        detection_perf(df=sample_df[sample_df["annotator"] == "ann1"], ref=("ann1", "lbl1"))
\ No newline at end of file

From a655ecf26061f4b73762490178d5401456917e6a Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Wed, 26 Nov 2025 11:34:51 +0100
Subject: [PATCH 58/83] ruff fixes

---
 src/post_processing/utils/core_utils.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/src/post_processing/utils/core_utils.py b/src/post_processing/utils/core_utils.py
index ec4b68d..f9b00cd 100644
--- a/src/post_processing/utils/core_utils.py
+++ b/src/post_processing/utils/core_utils.py
@@ -3,14 +3,13 @@
 from __future__ import annotations
 
 import json
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING
 
 import astral
 import easygui
 import numpy as np
 from astral.sun import sunrise, sunset
 from matplotlib import pyplot as plt
-from numpy import ndarray, dtype
 from osekit.config import TIMESTAMP_FORMAT_AUDIO_FILE
 from osekit.utils.timestamp_utils import strptime_from_text
 from pandas import (
@@ -64,12 +63,10 @@ def get_season(ts: Timestamp, *, northern: bool = True) -> tuple[str, int]:
 
     """
     if northern:
-        winter = [1, 2, 12]
         spring = [3, 4, 5]
         summer = [6, 7, 8]
         autumn = [9, 10, 11]
     else:
-        winter = [6, 7, 8]
         spring = [9, 10, 11]
         summer = [1, 2, 12]
         autumn = [3, 4, 5]

From a6926c2df3c62596001df91db6268bc6f989112e Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Wed, 26 Nov 2025 11:58:24 +0100
Subject: [PATCH 59/83] ruff fix

---
 tests/test_filtering_utils.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 748307a..4bb4eed 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -315,7 +315,9 @@ def test_get_timezone_several(sample_df: DataFrame) -> None:
         ignore_index=False
     )
     tz = get_timezone(sample_df)
-    assert tz == [pytz.UTC, pytz.FixedOffset(420)]
+    assert len(tz) == 2
+    assert pytz.UTC in tz
+    assert pytz.FixedOffset(420) in tz
 
 # %% read DataFrame
 

From 2d07f479d4591956a677d8c68692cab3ee474e76 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Wed, 26 Nov 2025 17:28:25 +0100
Subject: [PATCH 60/83] plot_utils

---
 src/post_processing/utils/plot_utils.py |   2 +-
 tests/test_plot_utils.py                | 109 ++++++++++++++++++++++++
 2 files changed, 110 insertions(+), 1 deletion(-)
 create mode 100644 tests/test_plot_utils.py

diff --git a/src/post_processing/utils/plot_utils.py b/src/post_processing/utils/plot_utils.py
index 3c34fbd..a921270 100644
--- a/src/post_processing/utils/plot_utils.py
+++ b/src/post_processing/utils/plot_utils.py
@@ -438,7 +438,7 @@ def overview(df: DataFrame, annotator: list[str] | None = None) -> None:
     plt.tight_layout()
 
     # log
-    msg = f"{" Overview ":#^40}"
+    msg = f"""{" Overview ":#^40}"""
     msg += f"\n\n {summary_label}"
     logging.info(msg)
 
diff --git a/tests/test_plot_utils.py b/tests/test_plot_utils.py
new file mode 100644
index 0000000..9f0810f
--- /dev/null
+++ b/tests/test_plot_utils.py
@@ -0,0 +1,109 @@
+import logging
+from unittest.mock import patch
+
+import matplotlib.pyplot as plt
+import pytest
+from matplotlib.ticker import PercentFormatter
+from numpy import arange, testing
+
+from post_processing.utils.plot_utils import (
+    overview,
+    _wrap_xtick_labels,
+    set_y_axis_to_percentage,
+    get_legend,
+)
+
+
+def test_overview_runs_without_error(sample_df) -> None:
+    try:
+        overview(sample_df)
+    except ValueError:
+        pytest.fail("test_detection_perf raised ValueError unexpectedly.")
+
+
+def test_wrap_xtick_labels_short_labels():
+    fig, ax = plt.subplots()
+    labels = ["A", "B", "C"]
+    ax.set_xticks(range(len(labels)))
+    ax.set_xticklabels(labels)
+
+    _wrap_xtick_labels(ax, max_chars=2)
+
+    wrapped_labels = [label.get_text() for label in ax.get_xticklabels()]
+    assert wrapped_labels == labels
+
+
+def test_wrap_xtick_labels_long_label():
+    fig, ax = plt.subplots()
+    labels = ["This is a long label"]
+    ax.set_xticks([0])
+    ax.set_xticklabels(labels)
+
+    _wrap_xtick_labels(ax, max_chars=5)
+
+    wrapped_labels = [label.get_text() for label in ax.get_xticklabels()]
+    expected = "This\nis a\nlong\nlabel"
+    assert wrapped_labels[0] == expected
+
+
+def test_wrap_xtick_labels_no_spaces():
+    fig, ax = plt.subplots()
+    labels = ["abcdefghijk"]
+    ax.set_xticks([0])
+    ax.set_xticklabels(labels)
+
+    _wrap_xtick_labels(ax, max_chars=4)
+
+    wrapped_labels = [label.get_text() for label in ax.get_xticklabels()]
+
+    expected = "abcd\nefgh\nijk"
+    assert wrapped_labels[0] == expected
+
+
+def test_y_axis_formatter_and_ticks():
+    fig, ax = plt.subplots()
+
+    set_y_axis_to_percentage(ax)
+
+    assert isinstance(ax.yaxis.get_major_formatter(), PercentFormatter)
+    assert ax.yaxis.get_major_formatter().xmax == 1.0
+
+    expected_ticks = arange(0, 1.02, 0.2)
+    testing.assert_allclose(ax.get_yticks(), expected_ticks)
+
+
+def test_single_annotator_multiple_labels():
+    annotators = ["Alice"]
+    labels = ["Label1", "Label2", "Label3"]
+    result = get_legend(annotators, labels)
+    assert result == labels
+
+
+def test_multiple_annotators_single_label():
+    annotators = ["Alice", "Bob", "Charlie"]
+    labels = ["CommonLabel"]
+    result = get_legend(annotators, labels)
+    assert result == annotators
+
+
+def test_multiple_annotators_multiple_labels():
+    annotators = ["Alice", "Bob", "Charlie"]
+    labels = ["Label1", "Label2", "Label3"]
+    result = get_legend(annotators, labels)
+    expected = ["Alice\nLabel1", "Bob\nLabel2", "Charlie\nLabel3"]
+    assert result == expected
+
+
+def test_single_annotator_single_label():
+    annotators = ["Alice"]
+    labels = ["Label1"]
+    result = get_legend(annotators, labels)
+    assert result == ["Alice\nLabel1"]
+
+
+def test_lists_and_strings_combined():
+    annotators = ["Alice", "Bob"]
+    labels = ["Label1", "Label2"]
+    result = get_legend(annotators, labels)
+    expected = ["Alice\nLabel1", "Bob\nLabel2"]
+    assert result == expected
\ No newline at end of file

From c9e704205fcec4b021de09177f23b54c51e9f725 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Wed, 26 Nov 2025 17:29:38 +0100
Subject: [PATCH 61/83] ruff

---
 tests/test_plot_utils.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/test_plot_utils.py b/tests/test_plot_utils.py
index 9f0810f..d7392cf 100644
--- a/tests/test_plot_utils.py
+++ b/tests/test_plot_utils.py
@@ -1,6 +1,3 @@
-import logging
-from unittest.mock import patch
-
 import matplotlib.pyplot as plt
 import pytest
 from matplotlib.ticker import PercentFormatter

From b3cbc6c2b53cdb3954f141a6eb895793a5b35eb4 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Thu, 4 Dec 2025 11:34:51 +0100
Subject: [PATCH 62/83] filtering bug and test modification from is_box to type
 column

---
 src/post_processing/dataclass/data_aplose.py |   2 +-
 src/post_processing/utils/core_utils.py      |  23 +-
 src/post_processing/utils/filtering_utils.py |  10 +-
 tests/conftest.py                            | 223 ++++++++++---------
 tests/test_core_utils.py                     |   2 +-
 tests/test_filtering_utils.py                |   6 +-
 6 files changed, 141 insertions(+), 125 deletions(-)

diff --git a/src/post_processing/dataclass/data_aplose.py b/src/post_processing/dataclass/data_aplose.py
index 3165de8..6f42e72 100644
--- a/src/post_processing/dataclass/data_aplose.py
+++ b/src/post_processing/dataclass/data_aplose.py
@@ -238,7 +238,7 @@ def filter_df(
                 msg = f'Label "{lbl}" not in APLOSE DataFrame'
                 raise ValueError(msg)
             if self.df[
-                (self.df["is_box"] == 0)
+                (self.df["type"] == "WEAK")
                 & (self.df["annotator"] == ant)
                 & (self.df["annotation"] == lbl)
             ].empty:
diff --git a/src/post_processing/utils/core_utils.py b/src/post_processing/utils/core_utils.py
index f9b00cd..a7b5617 100644
--- a/src/post_processing/utils/core_utils.py
+++ b/src/post_processing/utils/core_utils.py
@@ -11,7 +11,7 @@
 from astral.sun import sunrise, sunset
 from matplotlib import pyplot as plt
 from osekit.config import TIMESTAMP_FORMAT_AUDIO_FILE
-from osekit.utils.timestamp_utils import strptime_from_text
+from osekit.utils.timestamp_utils import strptime_from_text, strftime_osmose_format
 from pandas import (
     DataFrame,
     DatetimeIndex,
@@ -213,7 +213,10 @@ def add_weak_detection(
     if not max_freq:
         max_freq = get_max_freq(df)
     if not max_time:
-        max_time = get_max_time(df)
+        max_time = Timedelta(get_max_time(df), "s")
+
+    df["start_datetime"] = [strftime_osmose_format(start) for start in df["start_datetime"]]
+    df["end_datetime"] = [strftime_osmose_format(stop) for stop in df["end_datetime"]]
 
     for ant in annotators:
         for lbl in labels:
@@ -223,30 +226,36 @@ def add_weak_detection(
                 .tolist()
             )
             for f in filenames:
-                test = df[(df["filename"] == f) & (df["annotation"] == lbl)]["is_box"]
+                test = df[(df["filename"] == f) & (df["annotation"] == lbl)]["type"]
                 if test.any():
                     start_datetime = strptime_from_text(
                         text=f,
                         datetime_template=datetime_format,
-                    ).tz_localize(tz)
+                    )
+
+                    if not start_datetime.tz:
+                        start_datetime = tz.localize(start_datetime)
+
                     end_datetime = start_datetime + Timedelta(max_time, unit="s")
                     new_line = [
                         dataset_id,
                         f,
                         0,
-                        max_time,
+                        max_time.total_seconds(),
                         0,
                         max_freq,
                         lbl,
                         ant,
-                        start_datetime,
-                        end_datetime,
+                        strftime_osmose_format(start_datetime),
+                        strftime_osmose_format(end_datetime),
                         0,
                     ]
+
                     if "score" in df.columns:
                         new_line.append(np.nan)
                     df.loc[df.index.max() + 1] = new_line
 
+
     return df.sort_values(by=["start_datetime", "annotator"]).reset_index(drop=True)
 
 
diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index 16cd35a..5ac7e1f 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -77,9 +77,12 @@ def filter_by_time(
 
 def filter_by_annotator(
     df: DataFrame,
-    annotator: str | list[str],
+    annotator: str | list[str] | None,
 ) -> DataFrame:
     """Filter a DataFrame based on annotator selection."""
+    if annotator is None:
+        return df
+
     list_annotators = get_annotators(df)
 
     if isinstance(annotator, str):
@@ -94,9 +97,12 @@ def filter_by_annotator(
 
 def filter_by_label(
     df: DataFrame,
-    label: str | list[str],
+    label: str | list[str] | None,
 ) -> DataFrame:
     """Filter a DataFrame based on label selection."""
+    if label is None:
+        return df
+
     list_labels = get_labels(df)
 
     if isinstance(label, str):
diff --git a/tests/conftest.py b/tests/conftest.py
index 32991e1..158c269 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -9,118 +9,121 @@
 from osekit.utils.timestamp_utils import strftime_osmose_format
 from pandas import DataFrame, read_csv
 
-SAMPLE = """dataset,filename,start_time,end_time,start_frequency,end_frequency,annotation,annotator,start_datetime,end_datetime,is_box,score
-sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann2,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,0,0.11
-sample_dataset,2025_01_25_06_20_00,3.46662989520132,4.02371759514617,7523.0,15257.0,lbl2,ann2,2025-01-25T06:20:03.466+00:00,2025-01-25T06:20:04.023+00:00,1,0.23
-sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl1,ann2,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,0,0.26
-sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl1,ann5,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,0,0.35
-sample_dataset,2025_01_25_06_20_00,0.0717043574186431,8.90788747931605,1265.0,10265.0,lbl1,ann5,2025-01-25T06:20:00.071+00:00,2025-01-25T06:20:08.907+00:00,1,0.36
-sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann1,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,0,0.72
-sample_dataset,2025_01_25_06_20_00,3.32873690016547,4.0457804743519,7031.0,16453.0,lbl2,ann1,2025-01-25T06:20:03.328+00:00,2025-01-25T06:20:04.045+00:00,1,0.31
-sample_dataset,2025_01_25_06_20_00,7.03530060672918,7.64754550468836,6468.0,15187.0,lbl2,ann1,2025-01-25T06:20:07.035+00:00,2025-01-25T06:20:07.647+00:00,1,0.86
-sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl1,ann1,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,0,0.37
-sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann4,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,0,0.11
-sample_dataset,2025_01_25_06_20_00,3.30667402095974,4.00165471594043,7875.0,19828.0,lbl2,ann4,2025-01-25T06:20:03.306+00:00,2025-01-25T06:20:04.001+00:00,1,0.81
-sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann3,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,0,0.58
-sample_dataset,2025_01_25_06_20_00,3.24014216505327,3.86811049852255,8198.0,16697.0,lbl2,ann3,2025-01-25T06:20:03.240+00:00,2025-01-25T06:20:03.868+00:00,1,0.66
-sample_dataset,2025_01_25_06_20_00,6.77537130162104,7.62041510838834,6816.0,17292.0,lbl2,ann3,2025-01-25T06:20:06.775+00:00,2025-01-25T06:20:07.620+00:00,1,0.90
-sample_dataset,2025_01_25_06_20_10,0.0,10.0,0.0,72000.0,lbl2,ann2,2025-01-25T06:20:10.000+00:00,2025-01-25T06:20:20.000+00:00,0,0.24
-sample_dataset,2025_01_25_06_20_10,0.355763927192499,0.603971318257033,7804.0,11953.0,lbl2,ann2,2025-01-25T06:20:10.355+00:00,2025-01-25T06:20:10.603+00:00,1,0.76
-sample_dataset,2025_01_25_06_20_10,0.0,10.0,0.0,72000.0,lbl1,ann2,2025-01-25T06:20:10.000+00:00,2025-01-25T06:20:20.000+00:00,0,0.54
-sample_dataset,2025_01_25_06_20_10,0.0,10.0,0.0,72000.0,lbl2,ann5,2025-01-25T06:20:10.000+00:00,2025-01-25T06:20:20.000+00:00,0,0.85
-sample_dataset,2025_01_25_06_20_10,0.325427468284611,0.672917815774959,6890.0,11390.0,lbl2,ann5,2025-01-25T06:20:10.325+00:00,2025-01-25T06:20:10.672+00:00,1,0.53
-sample_dataset,2025_01_25_06_20_10,0.0,10.0,0.0,72000.0,lbl3,ann5,2025-01-25T06:20:10.000+00:00,2025-01-25T06:20:20.000+00:00,0,0.30
-sample_dataset,2025_01_25_06_20_10,1.14726971869829,1.4726971869829,4640.0,13781.0,lbl3,ann5,2025-01-25T06:20:11.147+00:00,2025-01-25T06:20:11.472+00:00,1,0.16
-sample_dataset,2025_01_25_06_20_10,0.0,10.0,0.0,72000.0,lbl1,ann5,2025-01-25T06:20:10.000+00:00,2025-01-25T06:20:20.000+00:00,0,0.97
-sample_dataset,2025_01_25_06_20_10,0.066188637617209,8.90788747931605,843.0,14203.0,lbl1,ann5,2025-01-25T06:20:10.066+00:00,2025-01-25T06:20:18.907+00:00,1,0.59
-sample_dataset,2025_01_25_06_20_10,0.0,10.0,0.0,72000.0,lbl2,ann1,2025-01-25T06:20:10.000+00:00,2025-01-25T06:20:20.000+00:00,0,0.46
-sample_dataset,2025_01_25_06_20_10,0.151682294539437,0.659128516271373,4781.0,13359.0,lbl2,ann1,2025-01-25T06:20:10.151+00:00,2025-01-25T06:20:10.659+00:00,1,0.50
-sample_dataset,2025_01_25_06_20_10,0.0,10.0,0.0,72000.0,lbl1,ann1,2025-01-25T06:20:10.000+00:00,2025-01-25T06:20:20.000+00:00,0,0.10
-sample_dataset,2025_01_25_06_20_20,0.0,10.0,0.0,72000.0,lbl1,ann2,2025-01-25T06:20:20.000+00:00,2025-01-25T06:20:30.000+00:00,0,0.41
-sample_dataset,2025_01_25_06_20_20,0.0,10.0,0.0,72000.0,lbl2,ann2,2025-01-25T06:20:20.000+00:00,2025-01-25T06:20:30.000+00:00,0,0.36
-sample_dataset,2025_01_25_06_20_20,1.56370656370656,2.44622173193602,8296.0,14132.0,lbl2,ann2,2025-01-25T06:20:21.563+00:00,2025-01-25T06:20:22.446+00:00,1,0.45
-sample_dataset,2025_01_25_06_20_20,0.0,10.0,0.0,72000.0,lbl1,ann5,2025-01-25T06:20:20.000+00:00,2025-01-25T06:20:30.000+00:00,0,0.62
-sample_dataset,2025_01_25_06_20_20,0.0992829564258136,8.88582460011031,562.0,11531.0,lbl1,ann5,2025-01-25T06:20:20.099+00:00,2025-01-25T06:20:28.885+00:00,1,0.64
-sample_dataset,2025_01_25_06_20_20,0.0,10.0,0.0,72000.0,lbl2,ann5,2025-01-25T06:20:20.000+00:00,2025-01-25T06:20:30.000+00:00,0,0.80
-sample_dataset,2025_01_25_06_20_20,1.62713734142306,2.32763375620518,7875.0,13500.0,lbl2,ann5,2025-01-25T06:20:21.627+00:00,2025-01-25T06:20:22.327+00:00,1,0.56
-sample_dataset,2025_01_25_06_20_20,0.0,10.0,0.0,72000.0,lbl2,ann1,2025-01-25T06:20:20.000+00:00,2025-01-25T06:20:30.000+00:00,0,0.94
-sample_dataset,2025_01_25_06_20_20,1.54164368450083,2.44070601213458,5484.0,17859.0,lbl2,ann1,2025-01-25T06:20:21.541+00:00,2025-01-25T06:20:22.440+00:00,1,0.68
-sample_dataset,2025_01_25_06_20_20,0.0,10.0,0.0,72000.0,lbl1,ann1,2025-01-25T06:20:20.000+00:00,2025-01-25T06:20:30.000+00:00,0,0.15
-sample_dataset,2025_01_25_06_20_20,0.0,10.0,0.0,72000.0,lbl2,ann3,2025-01-25T06:20:20.000+00:00,2025-01-25T06:20:30.000+00:00,0,0.56
-sample_dataset,2025_01_25_06_20_20,2.0111693378581,2.41105902346208,8577.0,15082.0,lbl2,ann3,2025-01-25T06:20:22.011+00:00,2025-01-25T06:20:22.411+00:00,1,0.46
-sample_dataset,2025_01_25_06_20_20,6.0238554934014,6.52716492528226,8049.0,12620.0,lbl2,ann3,2025-01-25T06:20:26.023+00:00,2025-01-25T06:20:26.527+00:00,1,0.43
-sample_dataset,2025_01_25_06_20_30,0.0,10.0,0.0,72000.0,lbl1,ann2,2025-01-25T06:20:30.000+00:00,2025-01-25T06:20:40.000+00:00,0,0.94
-sample_dataset,2025_01_25_06_20_30,0.0,10.0,0.0,72000.0,lbl2,ann2,2025-01-25T06:20:30.000+00:00,2025-01-25T06:20:40.000+00:00,0,0.78
-sample_dataset,2025_01_25_06_20_30,2.10700496414782,3.14396028681743,9843.0,15539.0,lbl2,ann2,2025-01-25T06:20:32.107+00:00,2025-01-25T06:20:33.143+00:00,1,0.40
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+sample_dataset,2025_01_26_06_20_00,3.32873690016547,4.0457804743519,7031.0,16453.0,lbl2,ann1,2025-01-26T06:20:03.328+00:00,2025-01-26T06:20:04.045+00:00,BOX,0.31
+sample_dataset,2025_01_26_06_20_00,7.03530060672918,7.64754550468836,6468.0,15187.0,lbl2,ann1,2025-01-26T06:20:07.035+00:00,2025-01-26T06:20:07.647+00:00,BOX,0.86
+sample_dataset,2025_01_26_06_20_00,0.0,10.0,0.0,72000.0,lbl1,ann1,2025-01-26T06:20:00.000+00:00,2025-01-26T06:20:10.000+00:00,WEAK,0.37
+sample_dataset,2025_01_26_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann4,2025-01-26T06:20:00.000+00:00,2025-01-26T06:20:10.000+00:00,WEAK,0.11
+sample_dataset,2025_01_26_06_20_00,3.30667402095974,4.00165471594043,7875.0,19828.0,lbl2,ann4,2025-01-26T06:20:03.306+00:00,2025-01-26T06:20:04.001+00:00,BOX,0.81
+sample_dataset,2025_01_26_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann3,2025-01-26T06:20:00.000+00:00,2025-01-26T06:20:10.000+00:00,WEAK,0.58
 """
 
+
+
+
 STATUS = """dataset,filename,ann1,ann2,ann3,ann4,ann5,ann6
 sample_dataset,2025_01_25_06_20_00,FINISHED,FINISHED,FINISHED,FINISHED,FINISHED,FINISHED
 sample_dataset,2025_01_25_06_20_10,FINISHED,FINISHED,FINISHED,FINISHED,FINISHED,FINISHED
diff --git a/tests/test_core_utils.py b/tests/test_core_utils.py
index 4264911..a1a3d73 100644
--- a/tests/test_core_utils.py
+++ b/tests/test_core_utils.py
@@ -382,7 +382,7 @@ def test_timedelta_to_str(td, expected) -> None:
 
 
 def test_add_wd(sample_df: DataFrame) -> None:
-    df_only_wd = sample_df[sample_df["is_box"] == 1]
+    df_only_wd = sample_df[sample_df["type"] == "BOX"]
     add_weak_detection(
         df=df_only_wd.copy(),
         datetime_format="%Y_%m_%d_%H_%M_%S",
diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 4bb4eed..1a4d175 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -222,12 +222,10 @@ def test_filter_by_score_missing_column(sample_df: DataFrame) -> None:
 # filter_weak_strong_detection
 def test_filter_weak_only_is_box_colum(sample_df: DataFrame) -> None:
     df = filter_strong_detection(sample_df)
-    assert set(df["is_box"]) == {0}
+    assert set(df["type"]) == {"WEAK"}
 
 
 def test_filter_weak_only_type_column(sample_df: DataFrame) -> None:
-    sample_df = sample_df.rename(columns={"is_box": "type"})
-    sample_df["type"] = ["WEAK" if not cell else "BOX" for cell in sample_df["type"]]
     df = filter_strong_detection(sample_df)
     assert set(df["type"]) == {"WEAK"}
 
@@ -240,7 +238,7 @@ def test_filter_weak_only_invalid() -> None:
 
 def test_filter_weak_empty(sample_df: DataFrame) -> None:
     with pytest.raises(ValueError, match="No weak detection found"):
-        filter_strong_detection(sample_df[sample_df["is_box"] == 1])
+        filter_strong_detection(sample_df[sample_df["type"] == "BOX"])
 
 
 def test_get_annotators(sample_df: DataFrame) -> None:

From 23d2ba53a0fedf32aae263df6c5ec5c6e8712b07 Mon Sep 17 00:00:00 2001
From: mathieudpnt <108517594+mathieudpnt@users.noreply.github.com>
Date: Fri, 5 Dec 2025 10:13:48 +0100
Subject: [PATCH 63/83] add weak detection line

---
 src/post_processing/utils/core_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/post_processing/utils/core_utils.py b/src/post_processing/utils/core_utils.py
index a7b5617..5a831e1 100644
--- a/src/post_processing/utils/core_utils.py
+++ b/src/post_processing/utils/core_utils.py
@@ -248,7 +248,7 @@ def add_weak_detection(
                         ant,
                         strftime_osmose_format(start_datetime),
                         strftime_osmose_format(end_datetime),
-                        0,
+                        "WEAK",
                     ]
 
                     if "score" in df.columns:

From f44d88698b51b9b21bc70368749a187acc12e677 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Tue, 9 Dec 2025 17:02:09 +0100
Subject: [PATCH 64/83] plot color bug fix

---
 src/post_processing/dataclass/data_aplose.py | 2 +-
 src/post_processing/utils/filtering_utils.py | 2 +-
 src/post_processing/utils/plot_utils.py      | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/post_processing/dataclass/data_aplose.py b/src/post_processing/dataclass/data_aplose.py
index 6f42e72..98e6d9c 100644
--- a/src/post_processing/dataclass/data_aplose.py
+++ b/src/post_processing/dataclass/data_aplose.py
@@ -460,7 +460,7 @@ def from_yaml(
         cls,
         file: Path,
         *,
-        concat: bool = False,
+        concat: bool = True,
     ) -> DataAplose | list[DataAplose]:
         """Return a DataAplose object from a yaml file.
 
diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index 5ac7e1f..a656855 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -322,7 +322,7 @@ def reshape_timebin(
                             "annotator": [ant] * len(file_vector),
                             "start_datetime": start_datetime,
                             "end_datetime": end_datetime,
-                            "is_box": [0] * len(file_vector),
+                            "type": ["WEAK"] * len(file_vector),
                         },
                     ),
                 )
diff --git a/src/post_processing/utils/plot_utils.py b/src/post_processing/utils/plot_utils.py
index a921270..f21e343 100644
--- a/src/post_processing/utils/plot_utils.py
+++ b/src/post_processing/utils/plot_utils.py
@@ -121,7 +121,7 @@ def histo(
             "width": bar_width.total_seconds() / 86400,
             "align": "edge",
             "edgecolor": "black",
-            "color": color,
+            "color": color[i],
             "zorder": 2,
         }
         if legend_labels:

From a5327f41085ca474f6bb6d6013b70caeb9ebca40 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Tue, 9 Dec 2025 17:05:53 +0100
Subject: [PATCH 65/83] fix test

---
 tests/test_filtering_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 1a4d175..12eb988 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -478,7 +478,7 @@ def test_no_timebin_original_timebin(sample_df: DataFrame) -> None:
                               [Timestamp("2025-01-26 06:20:00+00:00")] * 7,
             "end_datetime": [Timestamp("2025-01-25 06:21:00+00:00")] * 11 +
                             [Timestamp("2025-01-26 06:21:00+00:00")] * 7,
-            "is_box": [0] * 18,
+            "type": ["WEAK"] * 18,
         },
     )
 

From 741d90238cba413fbe5be53eff9ce179343d06e0 Mon Sep 17 00:00:00 2001
From: MaelleTtrt <48910568+MaelleTtrt@users.noreply.github.com>
Date: Wed, 5 Nov 2025 14:28:36 +0100
Subject: [PATCH 66/83] correction of reshape_timebin() wav names

---
 src/post_processing/utils/filtering_utils.py | 36 +++++++++++++-------
 1 file changed, 24 insertions(+), 12 deletions(-)

diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index a656855..d10436a 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -220,6 +220,7 @@ def get_timezone(df: DataFrame):
 
 def reshape_timebin(
     df: DataFrame,
+    timestamp_wav: list[Timestamp],
     timebin_new: Timedelta | None,
     timestamp: list[Timestamp] | None = None,
 ) -> DataFrame:
@@ -232,7 +233,10 @@ def reshape_timebin(
     timebin_new: Timedelta
         The size of the new time bin.
     timestamp: list[Timestamp]
-        A list of Timestamp objects.
+        A list of Timestamp objects corresponding to the shape
+        in which the data should be reshaped.
+    timestamp_wav: list[Timestamp]
+        A list of the start datetime of each wavfile. Length should be the same as df
 
     Returns
     -------
@@ -253,8 +257,10 @@ def reshape_timebin(
     dataset = get_dataset(df)
 
     if isinstance(get_timezone(df), list):
-       df["start_datetime"] = [to_datetime(elem, utc=True) for elem in df["start_datetime"]]
-       df["end_datetime"] = [to_datetime(elem, utc=True) for elem in df["end_datetime"]]
+        df["start_datetime"] = [to_datetime(elem, utc=True)
+                                for elem in df["start_datetime"]]
+        df["end_datetime"] = [to_datetime(elem, utc=True)
+                              for elem in df["end_datetime"]]
 
     results = []
     for ant in annotators:
@@ -280,14 +286,19 @@ def reshape_timebin(
             filenames = df_1annot_1label["filename"].to_list()
 
             # filename_vector
-            filename_vector = [
-                filenames[
-                    bisect.bisect_left(ts_detect_beg, ts) - (ts not in ts_detect_beg)
-                ]
-                if bisect.bisect_left(ts_detect_beg, ts) > 0
-                else filenames[0]
-                for ts in time_vector
-            ]
+            filename_vector = []
+            for ts in time_vector:
+                if (bisect.bisect_left(ts_detect_beg, ts) > 0 and
+                        bisect.bisect_left(ts_detect_beg, ts) != len(ts_detect_beg)):
+                    idx = bisect.bisect_left(ts_detect_beg, ts)
+                    filename_vector.append(
+                        filenames[idx] if timestamp_wav[idx] <= ts else
+                        filenames[idx - 1],
+                    )
+                elif bisect.bisect_left(ts_detect_beg, ts) == len(ts_detect_beg):
+                    filename_vector.append(filenames[-1])
+                else:
+                    filename_vector.append(filenames[0])
 
             # detection vector
             detect_vec = [0] * len(time_vector)
@@ -327,7 +338,8 @@ def reshape_timebin(
                     ),
                 )
 
-    return concat(results).sort_values(by=["start_datetime", "end_datetime", "annotator", "annotation"]).reset_index(drop=True)
+    return concat(results).sort_values(by=["start_datetime", "end_datetime",
+                                           "annotator", "annotation"]).reset_index(drop=True)
 
 
 def ensure_in_list(value: str, candidates: list[str], label: str) -> None:

From e7a408bba182aca9b58e4afedab435c210ad2aeb Mon Sep 17 00:00:00 2001
From: MaelleTtrt <48910568+MaelleTtrt@users.noreply.github.com>
Date: Wed, 5 Nov 2025 17:31:28 +0100
Subject: [PATCH 67/83] changes related to fix of reshape_timebin

---
 src/post_processing/dataclass/detection_filter.py |  2 +-
 tests/conftest.py                                 | 15 ++++++++++-----
 tests/test_DetectionFilters.py                    |  1 +
 3 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/src/post_processing/dataclass/detection_filter.py b/src/post_processing/dataclass/detection_filter.py
index 4378192..c7938f4 100644
--- a/src/post_processing/dataclass/detection_filter.py
+++ b/src/post_processing/dataclass/detection_filter.py
@@ -42,6 +42,7 @@ class DetectionFilter:
     f_max: float | None = None
     score: float | None = None
     box: bool = False
+    filename_format: str = None
 
     @classmethod
     def from_yaml(
@@ -86,7 +87,6 @@ def from_dict(
         filters = []
         for detection_file, filters_dict in parameters.items():
             df_preview = read_dataframe(Path(detection_file), nrows=5)
-
             filters_dict["timebin_origin"] = Timedelta(
                 max(df_preview["end_time"]),
                 "s",
diff --git a/tests/conftest.py b/tests/conftest.py
index 158c269..0031c29 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,13 +1,16 @@
 import io
 import os
 from pathlib import Path
+from sqlite3.dbapi2 import Timestamp
 
 import numpy as np
 import pytest
 import soundfile as sf
 import yaml
 from osekit.utils.timestamp_utils import strftime_osmose_format
-from pandas import DataFrame, read_csv
+from pandas import DataFrame, read_csv, to_datetime
+
+from post_processing.utils.filtering_utils import get_timezone, read_dataframe
 
 SAMPLE = """dataset,filename,start_time,end_time,start_frequency,end_frequency,annotation,annotator,start_datetime,end_datetime,type,score
 sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann2,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,WEAK,0.11
@@ -164,10 +167,11 @@ def sample_csv_timestamp(tmp_path: Path, sample_status: DataFrame) -> Path:
 
 
 @pytest.fixture
-def sample_yaml(tmp_path: Path,
-                           sample_csv_result: Path,
-                           sample_csv_timestamp: Path,
-                           ) -> Path:
+def sample_yaml(
+        tmp_path: Path,
+        sample_csv_result: Path,
+        sample_csv_timestamp: Path,
+) -> Path:
     yaml_content = {
         f"{sample_csv_result}": {
             "timebin_new": None,
@@ -177,6 +181,7 @@ def sample_yaml(tmp_path: Path,
             "annotation": "lbl1",
             "box": True,
             "timestamp_file": f"{sample_csv_timestamp}",
+            "filename_format": "%Y_%m_%d_%H_%M_%S",
             "user_sel": "all",
             "f_min": None,
             "f_max": None,
diff --git a/tests/test_DetectionFilters.py b/tests/test_DetectionFilters.py
index 6b7dd2f..90d80ab 100644
--- a/tests/test_DetectionFilters.py
+++ b/tests/test_DetectionFilters.py
@@ -18,6 +18,7 @@ def test_from_yaml(sample_yaml: Path,
             "annotation": "lbl1",
             "box": True,
             "timestamp_file": f"{sample_csv_timestamp}",
+            "filename_format": "%Y_%m_%d_%H_%M_%S",
             "user_sel": "all",
             "f_min": None,
             "f_max": None,

From beceeff1f06ae3db7fde3ccd65fa2d4ff57f9a11 Mon Sep 17 00:00:00 2001
From: MaelleTtrt <48910568+MaelleTtrt@users.noreply.github.com>
Date: Wed, 5 Nov 2025 17:34:41 +0100
Subject: [PATCH 68/83] fix ruff

---
 tests/conftest.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/tests/conftest.py b/tests/conftest.py
index 0031c29..e03bf43 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,16 +1,13 @@
 import io
 import os
 from pathlib import Path
-from sqlite3.dbapi2 import Timestamp
 
 import numpy as np
 import pytest
 import soundfile as sf
 import yaml
 from osekit.utils.timestamp_utils import strftime_osmose_format
-from pandas import DataFrame, read_csv, to_datetime
-
-from post_processing.utils.filtering_utils import get_timezone, read_dataframe
+from pandas import DataFrame, read_csv
 
 SAMPLE = """dataset,filename,start_time,end_time,start_frequency,end_frequency,annotation,annotator,start_datetime,end_datetime,type,score
 sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann2,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,WEAK,0.11

From 1ce567d421fc7e775168cd1cc391339791508fd4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ma=C3=ABlle=20TORTEROTOT?= <maelle.torterotot@ensta.fr>
Date: Fri, 5 Dec 2025 12:20:21 +0100
Subject: [PATCH 69/83] add import

---
 tests/test_filtering_utils.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 12eb988..59b03fb 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import csv
 from pathlib import Path
 from zoneinfo import ZoneInfo

From 34f2bcb32a2f5cc9ce1383ad90bec30be7181cae Mon Sep 17 00:00:00 2001
From: MaelleTtrt <48910568+MaelleTtrt@users.noreply.github.com>
Date: Tue, 16 Dec 2025 14:59:58 +0100
Subject: [PATCH 70/83] rebase with new upstream

---
 src/post_processing/utils/filtering_utils.py | 111 +++++++++++++++----
 tests/conftest.py                            |   5 +-
 tests/test_filtering_utils.py                |  52 +++++++--
 3 files changed, 133 insertions(+), 35 deletions(-)

diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index d10436a..036aca5 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -196,7 +196,7 @@ def get_dataset(df: DataFrame) -> list[str]:
 def get_canonical_tz(tz):
     """Return timezone of object as a pytz timezone."""
     if isinstance(tz, datetime.timezone):
-        if tz == datetime.timezone.utc:
+        if tz == datetime.UTC:
             return pytz.utc
         offset_minutes = int(tz.utcoffset(None).total_seconds() / 60)
         return pytz.FixedOffset(offset_minutes)
@@ -204,13 +204,24 @@ def get_canonical_tz(tz):
         return pytz.timezone(tz.zone)
     if hasattr(tz, "key"):
         return pytz.timezone(tz.key)
-    else:
-        msg = f"Unknown timezone: {tz}"
-        raise TypeError(msg)
+    msg = f"Unknown timezone: {tz}"
+    raise TypeError(msg)
 
 
 def get_timezone(df: DataFrame):
-    """Return timezone(s) from DataFrame."""
+    """Return timezone(s) from APLOSE DataFrame.
+
+    Parameters
+    ----------
+    df: DataFrame
+        APLOSE result Dataframe
+
+    Returns
+    -------
+        tzoffset: list[tzoffset]
+            list of timezones
+
+    """
     timezones = {get_canonical_tz(ts.tzinfo) for ts in df["start_datetime"]}
 
     if len(timezones) == 1:
@@ -218,11 +229,29 @@ def get_timezone(df: DataFrame):
     return list(timezones)
 
 
+def check_timestamp(df: DataFrame, timestamp_audio: list[Timestamp]) -> None:
+    """Check if provided timestamp_audio list is correctly formated.
+
+    Parameters
+    ----------
+    df: DataFrame APLOSE results Dataframe.
+    timestamp_audio: A list of timestamps. Each timestamp is the start datetime of the
+    corresponding audio file for each detection in df.
+
+    """
+    if timestamp_audio is None:
+        msg = "`timestamp_wav` is empty"
+        raise ValueError(msg)
+    if len(timestamp_audio) != len(df):
+        msg = "`timestamp_wav` is not the same length as `df`"
+        raise ValueError(msg)
+
+
 def reshape_timebin(
     df: DataFrame,
-    timestamp_wav: list[Timestamp],
+    *,
     timebin_new: Timedelta | None,
-    timestamp: list[Timestamp] | None = None,
+    timestamp_audio: list[Timestamp] | None = None,
 ) -> DataFrame:
     """Reshape an APLOSE result DataFrame according to a new time bin.
 
@@ -232,11 +261,9 @@ def reshape_timebin(
         An APLOSE result DataFrame.
     timebin_new: Timedelta
         The size of the new time bin.
-    timestamp: list[Timestamp]
+    timestamp_audio: list[Timestamp]
         A list of Timestamp objects corresponding to the shape
         in which the data should be reshaped.
-    timestamp_wav: list[Timestamp]
-        A list of the start datetime of each wavfile. Length should be the same as df
 
     Returns
     -------
@@ -251,6 +278,8 @@ def reshape_timebin(
     if not timebin_new:
         return df
 
+    check_timestamp(df, timestamp_audio)
+
     annotators = get_annotators(df)
     labels = get_labels(df)
     max_freq = get_max_freq(df)
@@ -258,9 +287,11 @@ def reshape_timebin(
 
     if isinstance(get_timezone(df), list):
         df["start_datetime"] = [to_datetime(elem, utc=True)
-                                for elem in df["start_datetime"]]
+                                for elem in df["start_datetime"]
+                                ]
         df["end_datetime"] = [to_datetime(elem, utc=True)
-                              for elem in df["end_datetime"]]
+                              for elem in df["end_datetime"]
+                              ]
 
     results = []
     for ant in annotators:
@@ -270,13 +301,13 @@ def reshape_timebin(
             if df_1annot_1label.empty:
                 continue
 
-            if timestamp is not None:
+            if timestamp_audio is not None:
                 # I do not remember if this is a regular case or not
                 # might need to be deleted
-                origin_timebin = timestamp[1] - timestamp[0]
-                step = int(timebin_new / origin_timebin)
-                time_vector = timestamp[0::step]
-            else:
+                #origin_timebin = timestamp_audio[1] - timestamp_audio[0]
+                #step = int(timebin_new / origin_timebin)
+                #time_vector = timestamp_audio[0::step]
+            #else:
                 t1 = min(df_1annot_1label["start_datetime"]).floor(timebin_new)
                 t2 = max(df_1annot_1label["end_datetime"]).ceil(timebin_new)
                 time_vector = date_range(start=t1, end=t2, freq=timebin_new)
@@ -292,7 +323,7 @@ def reshape_timebin(
                         bisect.bisect_left(ts_detect_beg, ts) != len(ts_detect_beg)):
                     idx = bisect.bisect_left(ts_detect_beg, ts)
                     filename_vector.append(
-                        filenames[idx] if timestamp_wav[idx] <= ts else
+                        filenames[idx] if timestamp_audio[idx] <= ts else
                         filenames[idx - 1],
                     )
                 elif bisect.bisect_left(ts_detect_beg, ts) == len(ts_detect_beg):
@@ -338,9 +369,39 @@ def reshape_timebin(
                     ),
                 )
 
-    return concat(results).sort_values(by=["start_datetime", "end_datetime",
-                                           "annotator", "annotation"]).reset_index(drop=True)
+    return (concat(results).
+            sort_values(by=["start_datetime", "end_datetime",
+                            "annotator", "annotation"]).reset_index(drop=True)
+            )
+
 
+def get_filename_timestamps(df: DataFrame, date_parser: str) -> list[Timestamp]:
+    """Get start timestamps of the wav files of each detection contained in df.
+
+    Parameters.
+    ----------
+    df: DataFrame
+        An APLOSE result DataFrame.
+    date_parser: str
+        date parser of the wav file
+
+    Returns
+    -------
+    List of Timestamps corresponding to the wav files' start timestamps
+    of each detection contained in df.
+
+    """
+    tz = get_timezone(df)
+    try:
+        return [
+        to_datetime(
+            ts,
+            format=date_parser,
+        ).tz_localize(tz) for ts in df["filename"]
+        ]
+    except ValueError:
+        msg = """Could not parse timestamps from `df["filename"]`."""
+        raise ValueError(msg) from None
 
 def ensure_in_list(value: str, candidates: list[str], label: str) -> None:
     """Check for non-valid elements of a list."""
@@ -378,10 +439,14 @@ def load_detections(filters: DetectionFilter) -> DataFrame:
     df = filter_by_label(df, label=filters.annotation)
     df = filter_by_freq(df, filters.f_min, filters.f_max)
     df = filter_by_score(df, filters.score)
-    df = reshape_timebin(df, filters.timebin_new)
+    filename_ts = get_filename_timestamps(df, filters.filename_format)
+    df = reshape_timebin(df,
+                         timebin_new=filters.timebin_new,
+                         timestamp_audio=filename_ts
+                         )
 
     annotators = get_annotators(df)
-    if len(annotators) > 1 and filters.user_sel in ["union", "intersection"]:
+    if len(annotators) > 1 and filters.user_sel in {"union", "intersection"}:
         df = intersection_or_union(df, user_sel=filters.user_sel)
 
     return df.sort_values(by=["start_datetime", "end_datetime"]).reset_index(drop=True)
@@ -397,7 +462,7 @@ def intersection_or_union(df: DataFrame, user_sel: str) -> DataFrame:
     if user_sel == "all":
         return df
 
-    if user_sel not in ("intersection", "union"):
+    if user_sel not in {"intersection", "union"}:
         msg = "'user_sel' must be either 'intersection' or 'union'"
         raise ValueError(msg)
 
diff --git a/tests/conftest.py b/tests/conftest.py
index e03bf43..0031c29 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,13 +1,16 @@
 import io
 import os
 from pathlib import Path
+from sqlite3.dbapi2 import Timestamp
 
 import numpy as np
 import pytest
 import soundfile as sf
 import yaml
 from osekit.utils.timestamp_utils import strftime_osmose_format
-from pandas import DataFrame, read_csv
+from pandas import DataFrame, read_csv, to_datetime
+
+from post_processing.utils.filtering_utils import get_timezone, read_dataframe
 
 SAMPLE = """dataset,filename,start_time,end_time,start_frequency,end_frequency,annotation,annotator,start_datetime,end_datetime,type,score
 sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann2,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,WEAK,0.11
diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 59b03fb..04b32b5 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -298,7 +298,7 @@ def test_get_timezone_single(sample_df: DataFrame) -> None:
 def test_get_timezone_several(sample_df: DataFrame) -> None:
     new_row = {
         "dataset": "dataset",
-        "filename": "filename",
+        "filename": "2025_01_26_06_20_00",
         "start_time": 0,
         "end_time": 2,
         "start_frequency": 100,
@@ -384,7 +384,7 @@ def test_no_timebin_returns_original(sample_df: DataFrame) -> None:
 def test_no_timebin_several_tz(sample_df: DataFrame) -> None:
     new_row = {
         "dataset": "dataset",
-        "filename": "filename",
+        "filename": "2025_01_26_06_20_00",
         "start_time": 0,
         "end_time": 2,
         "start_frequency": 100,
@@ -400,13 +400,24 @@ def test_no_timebin_several_tz(sample_df: DataFrame) -> None:
         [sample_df, DataFrame([new_row])],
         ignore_index=False
     )
-
-    df_out = reshape_timebin(sample_df, timebin_new=None)
+    tz = get_timezone(sample_df)
+    timestamp_wav = to_datetime(sample_df["filename"],
+                                format="%Y_%m_%d_%H_%M_%S").dt.tz_localize(pytz.UTC)
+    df_out = reshape_timebin(sample_df, timestamp_audio=timestamp_wav, timebin_new=None)
     assert df_out.equals(sample_df)
 
 
 def test_no_timebin_original_timebin(sample_df: DataFrame) -> None:
-    df_out = reshape_timebin(sample_df, timebin_new=Timedelta("1min"))
+    tz = get_timezone(sample_df)
+    timestamp_wav = to_datetime(
+        sample_df["filename"],
+        format="%Y_%m_%d_%H_%M_%S"
+    ).dt.tz_localize(tz)
+    df_out = reshape_timebin(
+        sample_df,
+        timestamp_audio=timestamp_wav,
+        timebin_new=Timedelta("1min"),
+    )
     expected = DataFrame(
         {
             "dataset": ["sample_dataset"] * 18,
@@ -488,7 +499,16 @@ def test_no_timebin_original_timebin(sample_df: DataFrame) -> None:
 
 
 def test_simple_reshape_hourly(sample_df: DataFrame) -> None:
-    df_out = reshape_timebin(sample_df, timebin_new=Timedelta(hours=1))
+    tz = get_timezone(sample_df)
+    timestamp_wav = to_datetime(
+        sample_df["filename"],
+        format="%Y_%m_%d_%H_%M_%S"
+    ).dt.tz_localize(tz)
+    df_out = reshape_timebin(
+        sample_df,
+        timestamp_audio=timestamp_wav,
+        timebin_new=Timedelta(hours=1),
+    )
     assert not df_out.empty
     assert all(df_out["end_time"] == 3600.0)
     assert df_out["end_frequency"].max() == sample_df["end_frequency"].max()
@@ -497,7 +517,12 @@ def test_simple_reshape_hourly(sample_df: DataFrame) -> None:
 
 
 def test_reshape_daily_multiple_bins(sample_df: DataFrame) -> None:
-    df_out = reshape_timebin(sample_df, timebin_new=Timedelta(days=1))
+    tz = get_timezone(sample_df)
+    timestamp_wav = to_datetime(
+        sample_df["filename"],
+        format="%Y_%m_%d_%H_%M_%S"
+    ).dt.tz_localize(tz)
+    df_out = reshape_timebin(sample_df, timestamp_audio=timestamp_wav, timebin_new=Timedelta(days=1))
     assert not df_out.empty
     assert all(df_out["end_time"] == 86400.0)
     assert df_out["start_datetime"].min() >= sample_df["start_datetime"].min().floor("D")
@@ -508,11 +533,13 @@ def test_with_manual_timestamps_vector(sample_df: DataFrame) -> None:
     t0 = sample_df["start_datetime"].min().floor("30min")
     t1 = sample_df["end_datetime"].max().ceil("30min")
     ts_vec = list(date_range(t0, t1, freq="30min"))
-
+    tz = get_timezone(sample_df)
+    timestamp_wav = to_datetime(sample_df["filename"],
+                                format="%Y_%m_%d_%H_%M_%S").dt.tz_localize(tz)
     df_out = reshape_timebin(
         sample_df,
-        timebin_new=Timedelta(hours=1),
-        timestamp=ts_vec,
+        timestamp_audio=timestamp_wav,
+        timebin_new=Timedelta(hours=1)
     )
 
     assert not df_out.empty
@@ -521,8 +548,11 @@ def test_with_manual_timestamps_vector(sample_df: DataFrame) -> None:
 
 
 def test_empty_result_when_no_matching(sample_df: DataFrame) -> None:
+    tz = get_timezone(sample_df)
+    timestamp_wav = to_datetime(sample_df["filename"],
+                                format="%Y_%m_%d_%H_%M_%S").dt.tz_localize(tz)
     with pytest.raises(ValueError, match="DataFrame is empty"):
-        reshape_timebin(DataFrame(), Timedelta(hours=1))
+        reshape_timebin(DataFrame(), timestamp_audio=timestamp_wav, timebin_new=Timedelta(hours=1))
 
 
 # %% ensure_no_invalid

From dfaad02101d3a40457864597755e67eb7a78e9b0 Mon Sep 17 00:00:00 2001
From: MaelleTtrt <48910568+MaelleTtrt@users.noreply.github.com>
Date: Tue, 16 Dec 2025 15:06:34 +0100
Subject: [PATCH 71/83] fix_ruff

---
 tests/conftest.py             | 5 +----
 tests/test_filtering_utils.py | 2 --
 2 files changed, 1 insertion(+), 6 deletions(-)

diff --git a/tests/conftest.py b/tests/conftest.py
index 0031c29..e03bf43 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,16 +1,13 @@
 import io
 import os
 from pathlib import Path
-from sqlite3.dbapi2 import Timestamp
 
 import numpy as np
 import pytest
 import soundfile as sf
 import yaml
 from osekit.utils.timestamp_utils import strftime_osmose_format
-from pandas import DataFrame, read_csv, to_datetime
-
-from post_processing.utils.filtering_utils import get_timezone, read_dataframe
+from pandas import DataFrame, read_csv
 
 SAMPLE = """dataset,filename,start_time,end_time,start_frequency,end_frequency,annotation,annotator,start_datetime,end_datetime,type,score
 sample_dataset,2025_01_25_06_20_00,0.0,10.0,0.0,72000.0,lbl2,ann2,2025-01-25T06:20:00.000+00:00,2025-01-25T06:20:10.000+00:00,WEAK,0.11
diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 04b32b5..a12fe67 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -400,7 +400,6 @@ def test_no_timebin_several_tz(sample_df: DataFrame) -> None:
         [sample_df, DataFrame([new_row])],
         ignore_index=False
     )
-    tz = get_timezone(sample_df)
     timestamp_wav = to_datetime(sample_df["filename"],
                                 format="%Y_%m_%d_%H_%M_%S").dt.tz_localize(pytz.UTC)
     df_out = reshape_timebin(sample_df, timestamp_audio=timestamp_wav, timebin_new=None)
@@ -532,7 +531,6 @@ def test_reshape_daily_multiple_bins(sample_df: DataFrame) -> None:
 def test_with_manual_timestamps_vector(sample_df: DataFrame) -> None:
     t0 = sample_df["start_datetime"].min().floor("30min")
     t1 = sample_df["end_datetime"].max().ceil("30min")
-    ts_vec = list(date_range(t0, t1, freq="30min"))
     tz = get_timezone(sample_df)
     timestamp_wav = to_datetime(sample_df["filename"],
                                 format="%Y_%m_%d_%H_%M_%S").dt.tz_localize(tz)

From 8235c5375692705c0091582b16b189aef9354250 Mon Sep 17 00:00:00 2001
From: MaelleTtrt <48910568+MaelleTtrt@users.noreply.github.com>
Date: Tue, 16 Dec 2025 15:08:41 +0100
Subject: [PATCH 72/83] fix_ruff2

---
 tests/test_filtering_utils.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index a12fe67..68b8d20 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -6,7 +6,7 @@
 
 import pytest
 import pytz
-from pandas import DataFrame, Timedelta, Timestamp, date_range, concat, to_datetime
+from pandas import DataFrame, Timedelta, Timestamp, concat, to_datetime
 
 from post_processing.utils.filtering_utils import (
     filter_by_annotator,
@@ -529,8 +529,7 @@ def test_reshape_daily_multiple_bins(sample_df: DataFrame) -> None:
 
 
 def test_with_manual_timestamps_vector(sample_df: DataFrame) -> None:
-    t0 = sample_df["start_datetime"].min().floor("30min")
-    t1 = sample_df["end_datetime"].max().ceil("30min")
+
     tz = get_timezone(sample_df)
     timestamp_wav = to_datetime(sample_df["filename"],
                                 format="%Y_%m_%d_%H_%M_%S").dt.tz_localize(tz)

From aea8759bbcb8df8870c0fcb3ad3121510779aa8b Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Wed, 17 Dec 2025 15:54:00 +0100
Subject: [PATCH 73/83] find_delimiter func and test improved

---
 src/post_processing/utils/filtering_utils.py | 32 +++++++++++++------
 tests/test_filtering_utils.py                | 33 +++++++++++++++-----
 2 files changed, 49 insertions(+), 16 deletions(-)

diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index 036aca5..58a52a6 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -26,15 +26,29 @@
 
 def find_delimiter(file: Path) -> str:
     """Find the proper delimiter for a csv file."""
-    with file.open(newline="") as csv_file:
-        try:
-            temp_lines = csv_file.readline() + "\n" + csv_file.readline()
-            dialect = csv.Sniffer().sniff(temp_lines, delimiters=",;")
-            delimiter = dialect.delimiter
-        except csv.Error as err:
-            msg = "Could not determine delimiter"
-            raise ValueError(msg) from err
-        return delimiter
+    allowed_delimiters = {",", ";", "\t", "|"}
+    try:
+        with file.open("r", encoding="utf-8") as f:
+            # Read first few lines to detect delimiter
+            sample = f.read(4096)
+
+            if not sample.strip():
+                msg = f"Could not determine delimiter for '{file}': file is empty"
+                raise ValueError(msg)
+
+            sniffer = csv.Sniffer()
+            dialect = sniffer.sniff(sample)
+
+            if dialect.delimiter not in allowed_delimiters:
+                msg = (f"Could not determine delimiter for '{file}': "
+                       f"unsupported delimiter '{dialect.delimiter}'")
+                raise ValueError(msg)
+
+            return dialect.delimiter
+
+    except csv.Error as e:
+        msg = f"Could not determine delimiter for '{file}': {e}"
+        raise ValueError(msg) from e
 
 
 def filter_strong_detection(
diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 68b8d20..063811d 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -9,24 +9,24 @@
 from pandas import DataFrame, Timedelta, Timestamp, concat, to_datetime
 
 from post_processing.utils.filtering_utils import (
+    ensure_no_invalid,
     filter_by_annotator,
-    filter_strong_detection,
     filter_by_freq,
     filter_by_label,
     filter_by_score,
     filter_by_time,
+    filter_strong_detection,
     find_delimiter,
     get_annotators,
+    get_canonical_tz,
     get_dataset,
     get_labels,
     get_max_freq,
     get_max_time,
     get_timezone,
+    intersection_or_union,
     read_dataframe,
     reshape_timebin,
-    get_canonical_tz,
-    ensure_no_invalid,
-    intersection_or_union,
 )
 
 # %% find delimiter
@@ -49,9 +49,15 @@ def test_find_delimiter_valid(tmp_path: Path,
     assert detected == delimiter
 
 
-def test_find_delimiter_invalid(tmp_path: Path) -> None:
-    file = tmp_path / "invalid.csv"
-    file.write_text("this is not really&csv&content")
+def test_find_delimiter_invalid(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    file = tmp_path / "bad.csv"
+    file.write_text("a,b,c")
+
+    def raise_error(*args, **kwargs):
+        raise csv.Error("sniff failed")
+
+    monkeypatch.setattr(csv.Sniffer, "sniff", raise_error)
+
     with pytest.raises(ValueError, match="Could not determine delimiter"):
         find_delimiter(file)
 
@@ -63,6 +69,19 @@ def test_find_delimiter_empty_file(tmp_path: Path) -> None:
         find_delimiter(file)
 
 
+def test_find_delimiter_unsupported_delimiter(tmp_path: Path) -> None:
+    file = tmp_path / "lame.csv"
+
+    # '&' is consistent and sniffable, but not allowed
+    file.write_text("a&b&c\n1&2&3\n")
+
+    with pytest.raises(
+        ValueError,
+        match=r"unsupported delimiter '&'"
+    ):
+        find_delimiter(file)
+
+
 # %% filter utils
 
 # filter_by_time

From cdc98d68bd2d78b69ae581a6a8d444172b1b7c87 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Wed, 17 Dec 2025 16:31:11 +0100
Subject: [PATCH 74/83] filtering_utils docstrings

---
 .../dataclass/detection_filter.py             |   6 +-
 src/post_processing/utils/filtering_utils.py  | 387 +++++++++++++-----
 tests/test_filtering_utils.py                 |   4 +-
 3 files changed, 281 insertions(+), 116 deletions(-)

diff --git a/src/post_processing/dataclass/detection_filter.py b/src/post_processing/dataclass/detection_filter.py
index c7938f4..d636c4c 100644
--- a/src/post_processing/dataclass/detection_filter.py
+++ b/src/post_processing/dataclass/detection_filter.py
@@ -49,12 +49,12 @@ def from_yaml(
         cls,
         file: Path,
     ) -> DetectionFilter | list[DetectionFilter]:
-        """Return a DetectionFilter object from a yaml file.
+        """Return a DetectionFilter object from a YAML file.
 
         Parameters
         ----------
         file: Path
-            The path to a yaml configuration file.
+            The path to a YAML configuration file.
 
         Returns
         -------
@@ -86,7 +86,7 @@ def from_dict(
         """
         filters = []
         for detection_file, filters_dict in parameters.items():
-            df_preview = read_dataframe(Path(detection_file), nrows=5)
+            df_preview = read_dataframe(Path(detection_file), rows=5)
             filters_dict["timebin_origin"] = Timedelta(
                 max(df_preview["end_time"]),
                 "s",
diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index 58a52a6..c391ff6 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -54,7 +54,22 @@ def find_delimiter(file: Path) -> str:
 def filter_strong_detection(
     df: DataFrame,
 ) -> DataFrame:
-    """Filter strong detections of a DataFrame."""
+    """Filter to keep only weak detections (exclude box/strong annotations).
+
+    This function identifies and removes "strong" or "box" type annotations,
+    keeping only "weak" detections. It checks for either an 'is_box' or 'type' column.
+
+    Parameters
+    ----------
+    df : DataFrame
+        APLOSE-formatted DataFrame with either 'is_box' or 'type' column.
+
+    Returns
+    -------
+    DataFrame
+        Filtered DataFrame containing only weak detections.
+
+    """
     if "type" in df.columns:
         df = df[df["type"] == "WEAK"]
     elif "is_box" in df.columns:
@@ -73,7 +88,23 @@ def filter_by_time(
     begin: Timestamp | None,
     end: Timestamp | None,
 ) -> DataFrame:
-    """Filter a DataFrame based on begin and/or end timestamps."""
+    """Filter detections by time range.
+
+    Parameters
+    ----------
+    df : DataFrame
+        APLOSE DataFrame containing 'start_datetime' and 'end_datetime' columns.
+    begin : Timestamp, optional
+        Start of time range (inclusive). If None, no lower bound is applied.
+    end : Timestamp, optional
+        End of time range (inclusive). If None, no upper bound is applied.
+
+    Returns
+    -------
+    DataFrame
+        Filtered DataFrame containing only detections within the specified time range.
+
+    """
     if begin is not None:
         df = df[df["start_datetime"] >= begin]
         if df.empty:
@@ -93,7 +124,21 @@ def filter_by_annotator(
     df: DataFrame,
     annotator: str | list[str] | None,
 ) -> DataFrame:
-    """Filter a DataFrame based on annotator selection."""
+    """Filter a DataFrame based on annotator selection.
+
+    Parameters
+    ----------
+    df : DataFrame
+        APLOSE-formatted DataFrame containing an 'annotator' column.
+    annotator : str or list of str
+        Single annotator name or list of annotator names to filter by.
+
+    Returns
+    -------
+    DataFrame
+        Filtered DataFrame containing only detections from the specified annotator(s).
+
+    """
     if annotator is None:
         return df
 
@@ -113,7 +158,21 @@ def filter_by_label(
     df: DataFrame,
     label: str | list[str] | None,
 ) -> DataFrame:
-    """Filter a DataFrame based on label selection."""
+    """Filter a DataFrame based on label selection.
+
+    Parameters
+    ----------
+    df : DataFrame
+        APLOSE-formatted DataFrame containing an 'annotation' column.
+    label : str or list of str
+        Single label or list of labels to filter by.
+
+    Returns
+    -------
+    DataFrame
+        Filtered DataFrame containing only detections with the specified label(s).
+
+    """
     if label is None:
         return df
 
@@ -134,7 +193,23 @@ def filter_by_freq(
     f_min: int | None,
     f_max: int | None,
 ) -> DataFrame:
-    """Filter a DataFrame based on frequency selection."""
+    """Filter a DataFrame based on frequency selection.
+
+    Parameters
+    ----------
+    df : DataFrame
+        APLOSE DataFrame containing 'start_frequency' and 'end_frequency' columns.
+    f_min : float, optional
+        Minimum frequency in Hz (inclusive). If None, no lower bound is applied.
+    f_max : float, optional
+        Maximum frequency in Hz (inclusive). If None, no upper bound is applied.
+
+    Returns
+    -------
+    DataFrame
+        Filtered DataFrame with only detections within the specified frequency range.
+
+    """
     if f_min is not None:
         df = df[df["start_frequency"] >= f_min]
         if df.empty:
@@ -150,7 +225,21 @@ def filter_by_freq(
 
 
 def filter_by_score(df: DataFrame, score: float) -> DataFrame:
-    """Filter a DataFrame based on minimum score."""
+    """Filter detections by confidence score.
+
+    Parameters
+    ----------
+    df : DataFrame
+        APLOSE-formatted DataFrame containing a 'score' column.
+    score : float
+        The minimum confidence score threshold (inclusive).
+
+    Returns
+    -------
+    DataFrame
+        Filtered DataFrame containing only detections with score >= min_score.
+
+    """
     if not score:
         return df
 
@@ -165,14 +254,14 @@ def filter_by_score(df: DataFrame, score: float) -> DataFrame:
     return df
 
 
-def read_dataframe(file: Path, nrows: int | None = None) -> DataFrame:
-    """Read csv file."""
+def read_dataframe(file: Path, rows: int | None = None) -> DataFrame:
+    """Read an APLOSE-formatted CSV file into a DataFrame."""
     delimiter = find_delimiter(file)
     return (
         read_csv(file,
                  sep=delimiter,
                  parse_dates=["start_datetime", "end_datetime"],
-                 nrows=nrows,
+                 nrows=rows,
                  )
         .drop_duplicates()
         .dropna(subset=["annotation"])
@@ -182,33 +271,48 @@ def read_dataframe(file: Path, nrows: int | None = None) -> DataFrame:
 
 
 def get_annotators(df: DataFrame) -> list[str]:
-    """Return list of annotators."""
+    """Return the annotator list of APLOSE DataFrame."""
     return sorted(set(df["annotator"]))
 
 
 def get_labels(df: DataFrame) -> list[str]:
-    """Return list of labels."""
+    """Return the label list of APLOSE DataFrame."""
     return sorted(set(df["annotation"]))
 
 
 def get_max_freq(df: DataFrame) -> float:
-    """Return the maximum frequency of DataFrame."""
+    """Return the maximum frequency of APLOSE DataFrame."""
     return df["end_frequency"].max()
 
 
 def get_max_time(df: DataFrame) -> float:
-    """Return the maximum time of DataFrame."""
+    """Return the maximum time of APLOSE DataFrame."""
     return df["end_time"].max()
 
 
-def get_dataset(df: DataFrame) -> list[str]:
-    """Return list of datasets."""
+def get_dataset(df: DataFrame) -> str | list[str]:
+    """Return dataset list  of APLOSE DataFrame."""
     datasets = sorted(set(df["dataset"]))
     return datasets if len(datasets) > 1 else datasets[0]
 
 
-def get_canonical_tz(tz):
-    """Return timezone of object as a pytz timezone."""
+def get_canonical_tz(tz: datetime.tzinfo) -> pytz.tzinfo.BaseTzInfo:
+    """Convert a timezone object to its canonical pytz representation.
+
+    This function ensures compatibility between different timezone implementations
+    (pytz, zoneinfo) by converting them to pytz timezone objects.
+
+    Parameters
+    ----------
+    tz : datetime.tzinfo
+        Timezone object (can be pytz timezone or ZoneInfo).
+
+    Returns
+    -------
+    pytz.tzinfo.BaseTzInfo
+        Canonical pytz timezone object.
+
+    """
     if isinstance(tz, datetime.timezone):
         if tz == datetime.UTC:
             return pytz.utc
@@ -222,7 +326,8 @@ def get_canonical_tz(tz):
     raise TypeError(msg)
 
 
-def get_timezone(df: DataFrame):
+def get_timezone(df: DataFrame)\
+        -> pytz.tzinfo.BaseTzInfo | list[pytz.tzinfo.BaseTzInfo]:
     """Return timezone(s) from APLOSE DataFrame.
 
     Parameters
@@ -232,8 +337,8 @@ def get_timezone(df: DataFrame):
 
     Returns
     -------
-        tzoffset: list[tzoffset]
-            list of timezones
+    tzoffset: list[tzoffset]
+        list of timezones
 
     """
     timezones = {get_canonical_tz(ts.tzinfo) for ts in df["start_datetime"]}
@@ -244,13 +349,15 @@ def get_timezone(df: DataFrame):
 
 
 def check_timestamp(df: DataFrame, timestamp_audio: list[Timestamp]) -> None:
-    """Check if provided timestamp_audio list is correctly formated.
+    """Check if a provided timestamp_audio list is correctly formated.
 
     Parameters
     ----------
-    df: DataFrame APLOSE results Dataframe.
-    timestamp_audio: A list of timestamps. Each timestamp is the start datetime of the
-    corresponding audio file for each detection in df.
+    df: DataFrame
+        APLOSE results Dataframe.
+    timestamp_audio: list[Timestamp]
+        A list of timestamps. Each timestamp is
+        the start datetime of the corresponding audio file for each detection in df.
 
     """
     if timestamp_audio is None:
@@ -261,11 +368,137 @@ def check_timestamp(df: DataFrame, timestamp_audio: list[Timestamp]) -> None:
         raise ValueError(msg)
 
 
+def _build_filename_vector(
+    time_vector: list[Timestamp],
+    ts_detect_beg: list[Timestamp],
+    timestamp_audio: list[Timestamp],
+    filenames: list[str],
+) -> list[str]:
+    """Build the filename vector for each time bin."""
+    filename_vector = []
+    for ts in time_vector:
+        idx = bisect.bisect_left(ts_detect_beg, ts)
+
+        if idx == 0:
+            filename_vector.append(filenames[0])
+        elif idx == len(ts_detect_beg):
+            filename_vector.append(filenames[-1])
+        else:
+            # Choose a filename based on timestamp_audio
+            filename_vector.append(
+                filenames[idx] if timestamp_audio[idx] <= ts else filenames[idx - 1],
+            )
+
+    return filename_vector
+
+
+def _build_detection_vector(
+    time_vector: list[Timestamp],
+    ts_detect_beg: list[Timestamp],
+    ts_detect_end: list[Timestamp],
+) -> list[int]:
+    """Build a binary detection vector indicating presence in each time bin."""
+    detect_vec = [0] * len(time_vector)
+
+    for start, end in zip(ts_detect_beg, ts_detect_end, strict=False):
+        idx = bisect.bisect_left(time_vector, start)
+        idx = idx if start in time_vector else max(0, idx - 1)
+
+        while idx < len(time_vector) and time_vector[idx] < end:
+            detect_vec[idx] = 1
+            idx += 1
+
+    return detect_vec
+
+
+def _create_result_dataframe(
+    file_vector: list[str],
+    start_datetime: list[Timestamp],
+    timebin_new: Timedelta,
+    max_freq: float,
+    dataset: str,
+    label: str,
+    annotator: str,
+) -> DataFrame:
+    """Create result DataFrame for one annotator-label combination."""
+    return DataFrame({
+        "dataset": [dataset] * len(file_vector),
+        "filename": file_vector,
+        "start_time": [0] * len(file_vector),
+        "end_time": [timebin_new.total_seconds()] * len(file_vector),
+        "start_frequency": [0] * len(file_vector),
+        "end_frequency": [max_freq] * len(file_vector),
+        "annotation": [label] * len(file_vector),
+        "annotator": [annotator] * len(file_vector),
+        "start_datetime": start_datetime,
+        "end_datetime": [t + timebin_new for t in start_datetime],
+        "type": ["WEAK"] * len(file_vector),
+    })
+
+
+def _normalize_timezones(df: DataFrame) -> DataFrame:
+    """Convert all timestamps to UTC if multiple timezones are present."""
+    if isinstance(get_timezone(df), list):
+        df["start_datetime"] = [
+            to_datetime(elem, utc=True) for elem in df["start_datetime"]
+        ]
+        df["end_datetime"] = [
+            to_datetime(elem, utc=True) for elem in df["end_datetime"]
+        ]
+    return df
+
+
+def _process_annotator_label_pair(
+    df: DataFrame,
+    annotator: str,
+    label: str,
+    timebin_new: Timedelta,
+    timestamp_audio: list[Timestamp],
+    max_freq: float,
+    dataset: str,
+) -> DataFrame | None:
+    """Process detections for one annotator-label combination."""
+    df_subset = df[(df["annotator"] == annotator) & (df["annotation"] == label)]
+
+    if df_subset.empty:
+        return None
+
+    # Create a time vector
+    t1 = min(df_subset["start_datetime"]).floor(timebin_new)
+    t2 = max(df_subset["end_datetime"]).ceil(timebin_new)
+    time_vector = date_range(start=t1, end=t2, freq=timebin_new)
+
+    # Extract detection data
+    ts_detect_beg = df_subset["start_datetime"].to_list()
+    ts_detect_end = df_subset["end_datetime"].to_list()
+    filenames = df_subset["filename"].to_list()
+
+    # Build vectors
+    filename_vector = _build_filename_vector(
+        time_vector, ts_detect_beg, timestamp_audio, filenames,
+    )
+    detect_vec = _build_detection_vector(time_vector, ts_detect_beg, ts_detect_end)
+
+    # Filter to only detected time bins
+    start_datetime = [
+        time_vector[i] for i, detected in enumerate(detect_vec) if detected
+    ]
+    file_vector = [
+        filename_vector[i] for i, detected in enumerate(detect_vec) if detected
+    ]
+
+    if not start_datetime:
+        return None
+
+    return _create_result_dataframe(
+        file_vector, start_datetime, timebin_new, max_freq, dataset, label, annotator,
+    )
+
+
 def reshape_timebin(
     df: DataFrame,
-    *,
     timebin_new: Timedelta | None,
-    timestamp_audio: list[Timestamp] | None = None,
+    timestamp_audio: list[Timestamp],
 ) -> DataFrame:
     """Reshape an APLOSE result DataFrame according to a new time bin.
 
@@ -294,103 +527,34 @@ def reshape_timebin(
 
     check_timestamp(df, timestamp_audio)
 
+    # Extract metadata
     annotators = get_annotators(df)
     labels = get_labels(df)
     max_freq = get_max_freq(df)
     dataset = get_dataset(df)
 
-    if isinstance(get_timezone(df), list):
-        df["start_datetime"] = [to_datetime(elem, utc=True)
-                                for elem in df["start_datetime"]
-                                ]
-        df["end_datetime"] = [to_datetime(elem, utc=True)
-                              for elem in df["end_datetime"]
-                              ]
+    # Normalize timezones if needed
+    df = _normalize_timezones(df)
 
+    # Process each annotator-label combination
     results = []
     for ant in annotators:
         for lbl in labels:
-            df_1annot_1label = df[(df["annotator"] == ant) & (df["annotation"] == lbl)]
-
-            if df_1annot_1label.empty:
-                continue
-
-            if timestamp_audio is not None:
-                # I do not remember if this is a regular case or not
-                # might need to be deleted
-                #origin_timebin = timestamp_audio[1] - timestamp_audio[0]
-                #step = int(timebin_new / origin_timebin)
-                #time_vector = timestamp_audio[0::step]
-            #else:
-                t1 = min(df_1annot_1label["start_datetime"]).floor(timebin_new)
-                t2 = max(df_1annot_1label["end_datetime"]).ceil(timebin_new)
-                time_vector = date_range(start=t1, end=t2, freq=timebin_new)
-
-            ts_detect_beg = df_1annot_1label["start_datetime"].to_list()
-            ts_detect_end = df_1annot_1label["end_datetime"].to_list()
-            filenames = df_1annot_1label["filename"].to_list()
-
-            # filename_vector
-            filename_vector = []
-            for ts in time_vector:
-                if (bisect.bisect_left(ts_detect_beg, ts) > 0 and
-                        bisect.bisect_left(ts_detect_beg, ts) != len(ts_detect_beg)):
-                    idx = bisect.bisect_left(ts_detect_beg, ts)
-                    filename_vector.append(
-                        filenames[idx] if timestamp_audio[idx] <= ts else
-                        filenames[idx - 1],
-                    )
-                elif bisect.bisect_left(ts_detect_beg, ts) == len(ts_detect_beg):
-                    filename_vector.append(filenames[-1])
-                else:
-                    filename_vector.append(filenames[0])
-
-            # detection vector
-            detect_vec = [0] * len(time_vector)
-            for start, end in zip(ts_detect_beg, ts_detect_end, strict=False):
-                idx = bisect.bisect_left(time_vector, start)
-                idx = idx if start in time_vector else max(0, idx - 1)
-                while idx < len(time_vector) and time_vector[idx] < end:
-                    detect_vec[idx] = 1
-                    idx += 1
-
-            # rows for dataframe
-            start_datetime = [
-                time_vector[i] for i in range(len(time_vector)) if detect_vec[i]
-            ]
-            end_datetime = [t + timebin_new for t in start_datetime]
-            file_vector = [
-                filename_vector[i] for i in range(len(time_vector)) if detect_vec[i]
-            ]
-
-            if start_datetime:
-                results.append(
-                    DataFrame(
-                        {
-                            "dataset": [dataset] * len(file_vector),
-                            "filename": file_vector,
-                            "start_time": [0] * len(file_vector),
-                            "end_time": [timebin_new.total_seconds()]
-                            * len(file_vector),
-                            "start_frequency": [0] * len(file_vector),
-                            "end_frequency": [max_freq] * len(file_vector),
-                            "annotation": [lbl] * len(file_vector),
-                            "annotator": [ant] * len(file_vector),
-                            "start_datetime": start_datetime,
-                            "end_datetime": end_datetime,
-                            "type": ["WEAK"] * len(file_vector),
-                        },
-                    ),
-                )
-
-    return (concat(results).
-            sort_values(by=["start_datetime", "end_datetime",
-                            "annotator", "annotation"]).reset_index(drop=True)
+            result = _process_annotator_label_pair(
+                df, ant, lbl, timebin_new, timestamp_audio, max_freq, dataset,
             )
+            if result is not None:
+                results.append(result)
+
+    return (
+        concat(results)
+        .sort_values(by=["start_datetime", "end_datetime", "annotator", "annotation"])
+        .reset_index(drop=True)
+    )
 
 
 def get_filename_timestamps(df: DataFrame, date_parser: str) -> list[Timestamp]:
-    """Get start timestamps of the wav files of each detection contained in df.
+    """Get audio file start timestamps of each detection contained in df.
 
     Parameters.
     ----------
@@ -417,6 +581,7 @@ def get_filename_timestamps(df: DataFrame, date_parser: str) -> list[Timestamp]:
         msg = """Could not parse timestamps from `df["filename"]`."""
         raise ValueError(msg) from None
 
+
 def ensure_in_list(value: str, candidates: list[str], label: str) -> None:
     """Check for non-valid elements of a list."""
     if value not in candidates:
@@ -456,7 +621,7 @@ def load_detections(filters: DetectionFilter) -> DataFrame:
     filename_ts = get_filename_timestamps(df, filters.filename_format)
     df = reshape_timebin(df,
                          timebin_new=filters.timebin_new,
-                         timestamp_audio=filename_ts
+                         timestamp_audio=filename_ts,
                          )
 
     annotators = get_annotators(df)
diff --git a/tests/test_filtering_utils.py b/tests/test_filtering_utils.py
index 063811d..95fd987 100644
--- a/tests/test_filtering_utils.py
+++ b/tests/test_filtering_utils.py
@@ -388,7 +388,7 @@ def test_read_dataframe_nrows(tmp_path: Path) -> None:
         "2025-01-01 13:00:00,2025-01-01 13:05:00,dolphin\n",
     )
 
-    df = read_dataframe(csv_file, nrows=1)
+    df = read_dataframe(csv_file, rows=1)
     assert len(df) == 1
     assert df.iloc[0]["annotation"] in {"whale", "dolphin"}
 
@@ -396,7 +396,7 @@ def test_read_dataframe_nrows(tmp_path: Path) -> None:
 # %% reshape_timebin
 
 def test_no_timebin_returns_original(sample_df: DataFrame) -> None:
-    df_out = reshape_timebin(sample_df, timebin_new=None)
+    df_out = reshape_timebin(sample_df, timebin_new=None, timestamp_audio=None)
     assert df_out.equals(sample_df)
 
 

From 1ee5d7d3289b850b34f13f2fa4ee431e70ed2293 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Mon, 19 Jan 2026 16:56:41 +0100
Subject: [PATCH 75/83] recursive pgdf file fix

---
 src/post_processing/utils/pamguard_utils.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/src/post_processing/utils/pamguard_utils.py b/src/post_processing/utils/pamguard_utils.py
index 4b14c7a..02719ab 100644
--- a/src/post_processing/utils/pamguard_utils.py
+++ b/src/post_processing/utils/pamguard_utils.py
@@ -62,16 +62,13 @@ def process_binary(audio: AudioData,
     >>> df = process_binary(ad, binary_path, dataset, annotation)
 
     """
-    begin = audio.begin
-    end = audio.end
-
     filter_obj = Filters(
         {
-        "daterange": DateFilter(start_date=begin, end_date=end, ordered=True),
+        "daterange": DateFilter(start_date=audio.begin, end_date=audio.end, ordered=True),
         },
     )
 
-    data, _, _ = load_pamguard_binary_folder(binary, "*/*.pgdf", filters=filter_obj)
+    data, _, _ = load_pamguard_binary_folder(binary, r"**/*.pgdf", filters=filter_obj)
 
     (
         start_datetimes,

From c056a156200b8791df01bddc2ca4d3d6c06cff4b Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Tue, 20 Jan 2026 11:21:07 +0100
Subject: [PATCH 76/83] DetectionFilter stale arg

---
 src/post_processing/dataclass/detection_filter.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/src/post_processing/dataclass/detection_filter.py b/src/post_processing/dataclass/detection_filter.py
index d636c4c..a9158c4 100644
--- a/src/post_processing/dataclass/detection_filter.py
+++ b/src/post_processing/dataclass/detection_filter.py
@@ -15,13 +15,11 @@
 from pandas import Timedelta, Timestamp
 
 from post_processing.utils.filtering_utils import (
-    get_timezone,
     read_dataframe,
 )
 
 if TYPE_CHECKING:
     from collections.abc import Iterable
-    from datetime import tzinfo
 
 
 @dataclass(frozen=True)
@@ -33,7 +31,6 @@ class DetectionFilter:
     timebin_new: Timedelta = None
     begin: Timestamp | None = None
     end: Timestamp | None = None
-    timezone: tzinfo = None
     annotator: str | Iterable[str] | None = None
     annotation: str | Iterable[str] | None = None
     timestamp_file: Path | None = None
@@ -91,7 +88,6 @@ def from_dict(
                 max(df_preview["end_time"]),
                 "s",
             )
-            filters_dict["timezone"] = get_timezone(df_preview)
             filters_dict["detection_file"] = Path(detection_file)
             if filters_dict.get("timebin_new"):
                 filters_dict["timebin_new"] = Timedelta(

From b0bbee6aeb21f23b8eed91ee7c313870603939d8 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 20 Jan 2026 16:06:51 +0100
Subject: [PATCH 77/83] initiate fpod_utils tests

---
 tests/test_fpod_utils.py | 317 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 316 insertions(+), 1 deletion(-)

diff --git a/tests/test_fpod_utils.py b/tests/test_fpod_utils.py
index c3c7f76..f45a4c0 100644
--- a/tests/test_fpod_utils.py
+++ b/tests/test_fpod_utils.py
@@ -1,11 +1,12 @@
 """FPOD/ CPOD processing functions tests."""
 import io
+import pytz
 from datetime import datetime
 from pathlib import Path
 
 import pytest
 from osekit.utils.timestamp_utils import strptime_from_text
-from pandas import DataFrame, Timestamp, read_csv, concat
+from pandas import DataFrame, Timestamp, Timedelta, read_csv, concat
 from pandas.testing import assert_frame_equal
 
 from post_processing.utils.fpod_utils import (
@@ -164,8 +165,322 @@ def test_csv_folder_single_file(tmp_path) -> None:
     assert all(result["deploy.name"] == "data")
     assert list(result.columns) == ["col1", "col2", "deploy.name"]
 
+
 # pod2aplose
+@pytest.fixture
+def sample_df():
+    """Create a sample POD DataFrame for testing."""
+    return DataFrame({
+        "ChunkEnd": ["15/01/2024 10:30", "15/01/2024 11:00", "15/01/2024 09:45"],
+        "deploy.name": ["deploy1", "deploy2", "deploy1"],
+    })
+
+
+@pytest.fixture
+def timezone():
+    """Return UTC timezone for testing."""
+    return pytz.UTC
+
+
+def test_pod2aplose_basic_structure(sample_df, timezone):
+    """Test that basic structure and required columns are present."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="test_dataset",
+        annotation="test_annotation",
+        annotator="test_annotator",
+    )
+
+    expected_columns = [
+        "dataset",
+        "filename",
+        "start_time",
+        "end_time",
+        "start_frequency",
+        "end_frequency",
+        "annotation",
+        "annotator",
+        "start_datetime",
+        "end_datetime",
+        "is_box",
+        "deploy.name",
+    ]
 
+    assert isinstance(result, DataFrame)
+    assert list(result.columns) == expected_columns
+    assert len(result) == len(sample_df)
+
+
+def test_pod2aplose_dataset_propagation(sample_df, timezone):
+    """Test that dataset name is propagated to all rows."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="my_dataset",
+        annotation="click",
+        annotator="john",
+    )
+
+    assert all(result["dataset"] == "my_dataset")
+
+
+def test_pod2aplose_annotation_propagation(sample_df, timezone):
+    """Test that annotation is propagated to all rows."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="porpoise_click",
+        annotator="john",
+    )
+
+    assert all(result["annotation"] == "porpoise_click")
+
+
+def test_pod2aplose_annotator_propagation(sample_df, timezone):
+    """Test that annotator is propagated to all rows."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="alice",
+    )
+
+    assert all(result["annotator"] == "alice")
+
+
+def test_pod2aplose_default_bin_size(sample_df, timezone):
+    """Test default bin_size of 60 seconds."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+    )
+
+    assert all(result["start_time"] == 0)
+    assert all(result["end_time"] == 60)
+
+
+def test_pod2aplose_custom_bin_size(sample_df, timezone):
+    """Test custom bin_size parameter."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+        bin_size=120,
+    )
+
+    assert all(result["start_time"] == 0)
+    assert all(result["end_time"] == 120)
+
+
+def test_pod2aplose_frequency_values(sample_df, timezone):
+    """Test that frequency values are set to 0."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+    )
+
+    assert all(result["start_frequency"] == 0)
+    assert all(result["end_frequency"] == 0)
+
+
+def test_pod2aplose_is_box_values(sample_df, timezone):
+    """Test that is_box values are set to 0."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+    )
+
+    assert all(result["is_box"] == 0)
+
+
+def test_pod2aplose_deploy_name_preserved(sample_df, timezone):
+    """Test that deploy.name values are preserved from input."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+    )
+
+    # After sorting, deploy.name should still be present
+    assert "deploy.name" in result.columns
+    assert len(result["deploy.name"]) == len(sample_df)
+    assert set(result["deploy.name"]) == {"deploy1", "deploy2"}
+
+
+def test_pod2aplose_sorting_by_datetime(timezone):
+    """Test that rows are sorted by datetime."""
+    df = DataFrame({
+        "ChunkEnd": ["15/01/2024 12:00", "15/01/2024 10:00", "15/01/2024 11:00"],
+        "deploy.name": ["d1", "d2", "d3"],
+    })
+
+    result = pod2aplose(
+        df=df, tz=timezone, dataset_name="dataset", annotation="click", annotator="john"
+    )
+
+    # Check that deploy.name follows the sorted order (by time)
+    assert result["deploy.name"].tolist() == ["d2", "d3", "d1"]
+
+
+def test_pod2aplose_datetime_formatting():
+    """Test that datetime strings are properly formatted."""
+    df = DataFrame({"ChunkEnd": ["01/02/2024 14:30"], "deploy.name": ["deploy1"]})
+
+    result = pod2aplose(
+        df=df,
+        tz=pytz.UTC,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+        bin_size=60,
+    )
+
+    # Check that datetime strings are present and not empty
+    assert len(result["start_datetime"].iloc[0]) > 0
+    assert len(result["end_datetime"].iloc[0]) > 0
+    assert len(result["filename"].iloc[0]) > 0
+
+
+def test_pod2aplose_end_datetime_offset(timezone):
+    """Test that end_datetime is offset by bin_size from start_datetime."""
+    df = DataFrame({"ChunkEnd": ["15/01/2024 10:00"], "deploy.name": ["deploy1"]})
+
+    result = pod2aplose(
+        df=df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+        bin_size=120,
+    )
+
+    # Both should be valid datetime strings
+    assert result["start_datetime"].iloc[0] != result["end_datetime"].iloc[0]
+
+
+def test_pod2aplose_different_timezones():
+    """Test with different timezone."""
+    df = DataFrame({"ChunkEnd": ["15/01/2024 10:00"], "deploy.name": ["deploy1"]})
+
+    tz_paris = pytz.timezone("Europe/Paris")
+
+    result = pod2aplose(
+        df=df, tz=tz_paris, dataset_name="dataset", annotation="click", annotator="john"
+    )
+
+    assert len(result) == 1
+    assert result["dataset"].iloc[0] == "dataset"
+
+
+def test_pod2aplose_empty_dataframe(timezone):
+    """Test handling of empty DataFrame."""
+    df = DataFrame({"ChunkEnd": [], "deploy.name": []})
+
+    result = pod2aplose(
+        df=df, tz=timezone, dataset_name="dataset", annotation="click", annotator="john"
+    )
+
+    assert len(result) == 0
+    assert list(result.columns) == [
+        "dataset",
+        "filename",
+        "start_time",
+        "end_time",
+        "start_frequency",
+        "end_frequency",
+        "annotation",
+        "annotator",
+        "start_datetime",
+        "end_datetime",
+        "is_box",
+        "deploy.name",
+    ]
+
+
+def test_pod2aplose_single_row(timezone):
+    """Test with single row DataFrame."""
+    df = DataFrame({"ChunkEnd": ["20/03/2024 15:45"], "deploy.name": ["single_deploy"]})
+
+    result = pod2aplose(
+        df=df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+        bin_size=90,
+    )
+
+    assert len(result) == 1
+    assert result["deploy.name"].iloc[0] == "single_deploy"
+    assert result["end_time"].iloc[0] == 90
+
+
+def test_pod2aplose_does_not_modify_original(sample_df, timezone):
+    """Test that the original DataFrame is not modified."""
+    original_columns = sample_df.columns.tolist()
+    original_len = len(sample_df)
+
+    pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+    )
+
+    # Original DataFrame should be unchanged
+    assert sample_df.columns.tolist() == original_columns
+    assert len(sample_df) == original_len
+    assert "_temp_dt" not in sample_df.columns
+
+
+def test_pod2aplose_large_bin_size(sample_df, timezone):
+    """Test with large bin_size value."""
+    result = pod2aplose(
+        df=sample_df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john",
+        bin_size=3600,  # 1 hour
+    )
+
+    assert all(result["end_time"] == 3600)
+
+
+def test_pod2aplose_index_reset(timezone):
+    """Test that index is properly reset after sorting."""
+    df = DataFrame({
+        "ChunkEnd": ["15/01/2024 12:00", "15/01/2024 10:00"],
+        "deploy.name": ["d1", "d2"]
+    })
+
+    result = pod2aplose(
+        df=df,
+        tz=timezone,
+        dataset_name="dataset",
+        annotation="click",
+        annotator="john"
+    )
+
+    # Index should be 0, 1 after reset
+    assert result.index.tolist() == [0, 1]
 
 # meta_cut_aplose
 

From 76f6645ca32dbec8deafdef6a33f8608dd6e39f8 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 20 Jan 2026 16:11:00 +0100
Subject: [PATCH 78/83] remove sklearn

---
 src/post_processing/utils/fpod_utils.py | 23 -----------------------
 1 file changed, 23 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 9ade0b0..53f53cc 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -26,7 +26,6 @@
     to_numeric,
     to_timedelta,
 )
-from sklearn.mixture import GaussianMixture
 
 from post_processing.utils.core_utils import get_coordinates, get_sun_times
 from user_case.config import season_color, site_colors
@@ -443,28 +442,6 @@ def feeding_buzz(
     return f
 
 
-def gmm_log(
-    array: Series,
-) -> None:
-    """Gaussian mixture model.
-
-    Parameters
-    ----------
-    array: Series
-        Data you want to test for clustering.
-
-    """
-    log_ici = np.log(array.values).reshape(-1, 1)
-    gmm_3 = GaussianMixture(
-        n_components=3,
-        covariance_type="full",
-        random_state=42,
-        max_iter=200,
-        n_init=10,
-    )
-    gmm_3.fit(log_ici)
-
-
 def assign_daytime(
     df: DataFrame,
 ) -> DataFrame:

From 3e4fff1257550f8a02ded1529d055103e1ecef6e Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 20 Jan 2026 16:12:49 +0100
Subject: [PATCH 79/83] remove sklearn

---
 src/post_processing/utils/fpod_utils.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 53f53cc..d36bb89 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -7,7 +7,6 @@
 from typing import TYPE_CHECKING
 
 import matplotlib.dates as mdates
-import numpy as np
 import pytz
 import seaborn as sns
 from matplotlib import patches
@@ -16,7 +15,6 @@
 from pandas import (
     DataFrame,
     DateOffset,
-    Series,
     Timedelta,
     concat,
     date_range,

From 7466a5be957088c2a45d70705c1a693b0b8c2e51 Mon Sep 17 00:00:00 2001
From: fouinel <fouin.elise@gmail.com>
Date: Tue, 20 Jan 2026 16:20:18 +0100
Subject: [PATCH 80/83] clean tests

---
 tests/test_fpod_utils.py | 21 +++------------------
 1 file changed, 3 insertions(+), 18 deletions(-)

diff --git a/tests/test_fpod_utils.py b/tests/test_fpod_utils.py
index f45a4c0..a0ab178 100644
--- a/tests/test_fpod_utils.py
+++ b/tests/test_fpod_utils.py
@@ -1,27 +1,12 @@
 """FPOD/ CPOD processing functions tests."""
-import io
-import pytz
-from datetime import datetime
-from pathlib import Path
-
 import pytest
-from osekit.utils.timestamp_utils import strptime_from_text
-from pandas import DataFrame, Timestamp, Timedelta, read_csv, concat
-from pandas.testing import assert_frame_equal
+import pytz
+from pandas import DataFrame
 
 from post_processing.utils.fpod_utils import (
-    txt_folder,
     csv_folder,
-    deploy_period,
     pod2aplose,
-    actual_data,
-    add_utc,
-    extract_site,
-    required_columns,
-    parse_timestamps,
-    create_mask,
-    is_dpm_col,
-    resample_dpm)
+)
 
 # SAMPLE_POD = """File,ChunkEnd,DPM,Nall,MinsOn
 # sample_dataset,2023/11/29 08:05,0,0,0

From 2a5a8c839088880a1f2cc307f07960eff8af925b Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Mon, 26 Jan 2026 17:03:51 +0100
Subject: [PATCH 81/83] refacto

---
 src/post_processing/dataclass/data_aplose.py |  23 +-
 src/post_processing/utils/core_utils.py      |  44 +-
 src/post_processing/utils/filtering_utils.py |  25 +-
 src/post_processing/utils/fpod_utils.py      | 691 ++++---------------
 src/post_processing/utils/glider_utils.py    |   2 +-
 src/post_processing/utils/plot_utils.py      |   2 +-
 tests/test_fpod_utils.py                     |   4 +-
 user_case/example_FPOD-CPOD_raw.ipynb        |  10 +-
 user_case/resource/CPOD-FPOD_yaml.yml        |   6 +-
 user_case/user_case_CALAIS.ipynb             |  17 +-
 10 files changed, 207 insertions(+), 617 deletions(-)

diff --git a/src/post_processing/dataclass/data_aplose.py b/src/post_processing/dataclass/data_aplose.py
index e1d3cae..ce2d9da 100644
--- a/src/post_processing/dataclass/data_aplose.py
+++ b/src/post_processing/dataclass/data_aplose.py
@@ -431,14 +431,15 @@ def plot(
             season = kwargs.get("season", False)
             effort = kwargs.get("effort")
 
-            return scatter(df=df_filtered,
-                           ax=ax,
-                           time_range=time,
-                           show_rise_set=show_rise_set,
-                           season=season,
-                           coordinates=self.coordinates,
-                           effort=effort,
-                           )
+            return scatter(
+                df=df_filtered,
+                ax=ax,
+                time_range=time,
+                show_rise_set=show_rise_set,
+                season=season,
+                coordinates=self.coordinates,
+                effort=effort,
+            )
 
         if mode == "agreement":
             bin_size = kwargs.get("bin_size")
@@ -452,7 +453,11 @@ def plot(
                 label,
             )
 
-            return timeline(df=df_filtered, ax=ax, color=color)
+            return timeline(
+                df=df_filtered,
+                ax=ax,
+                color=color,
+            )
 
         msg = f"Unsupported plot mode: {mode}"
         raise ValueError(msg)
diff --git a/src/post_processing/utils/core_utils.py b/src/post_processing/utils/core_utils.py
index c149155..d0125ae 100644
--- a/src/post_processing/utils/core_utils.py
+++ b/src/post_processing/utils/core_utils.py
@@ -8,7 +8,8 @@
 import astral
 import easygui
 import numpy as np
-from astral.sun import sunrise, sunset
+from astral import LocationInfo
+from astral.sun import sunrise, sunset, sun
 from matplotlib import pyplot as plt
 from osekit.config import TIMESTAMP_FORMAT_AUDIO_FILE
 from osekit.utils.timestamp_utils import strftime_osmose_format, strptime_from_text
@@ -579,3 +580,44 @@ def timedelta_to_str(td: Timedelta) -> str:
     if seconds % 60 == 0:
         return f"{seconds // 60}min"
     return f"{seconds}s"
+
+
+def assign_light_regime(
+    ts: Timestamp,
+    lat: float | None = None,
+    lon: float | None = None,
+) -> DataFrame:
+    """Assign daylight regime to temporal events.
+
+    Parameters
+    ----------
+    ts: Timestamp
+        Timestamp to assign a light regime to.
+    lat: float
+        The latitude of corresponding point.
+    lon: float
+        The longitude of corresponding point.
+
+    Returns
+    -------
+    DataFrame
+        The same dataframe with the column daytime.
+
+    """
+    if not all([lat, lon]):
+        lat, lon = get_coordinates()
+
+    # Get sun times for given location
+    location = LocationInfo(latitude=lat, longitude=lon)
+    s = sun(location.observer, date=ts.date())
+
+    if ts < s['dawn']:
+        return 'night'
+    elif ts < s['sunrise']:
+        return 'dawn'
+    elif ts < s['sunset']:
+        return 'day'
+    elif ts < s['dusk']:
+        return 'dusk'
+    else:
+        return 'night'
diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index faf2fd7..661324b 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -510,8 +510,8 @@ def reshape_timebin(
     timebin_new: Timedelta
         The size of the new time bin.
     timestamp_audio: list[Timestamp]
-        A list of Timestamp objects corresponding to the start of each wav
-         that corresponds to a detection
+        A list of Timestamp objects corresponding to the shape
+        in which the data should be reshaped.
 
     Returns
     -------
@@ -571,17 +571,16 @@ def get_filename_timestamps(df: DataFrame, date_parser: str) -> list[Timestamp]:
 
     """
     tz = get_timezone(df)
-    timestamps = [
-    strptime_from_text(
-        ts,
-        datetime_template=date_parser,
-    ) for ts in df["filename"]
-    ]
-
-    if all(t.tz is None for t in timestamps):
-        timestamps = [t.tz_localize(tz) for t in timestamps]
-
-    return timestamps
+    try:
+        return [
+        to_datetime(
+            ts,
+            format=date_parser,
+        ).tz_localize(tz) for ts in df["filename"]
+        ]
+    except ValueError:
+        msg = """Could not parse timestamps from `df["filename"]`."""
+        raise ValueError(msg) from None
 
 
 def ensure_in_list(value: str, candidates: list[str], label: str) -> None:
diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index d36bb89..12cee28 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -15,9 +15,10 @@
 from pandas import (
     DataFrame,
     DateOffset,
+    Series,
     Timedelta,
+    Timestamp,
     concat,
-    date_range,
     notna,
     read_csv,
     to_datetime,
@@ -25,7 +26,7 @@
     to_timedelta,
 )
 
-from post_processing.utils.core_utils import get_coordinates, get_sun_times
+from post_processing.utils.filtering_utils import find_delimiter
 from user_case.config import season_color, site_colors
 
 if TYPE_CHECKING:
@@ -40,9 +41,9 @@ def pod2aplose(
     dataset_name: str,
     annotation: str,
     annotator: str,
-    bin_size: int = 60,
+    bin_size: Timedelta,
 ) -> DataFrame:
-    """Format PODs DataFrame to match APLOSE format.
+    """Format PODs DataFrame to match an APLOSE format.
 
     Parameters
     ----------
@@ -56,7 +57,7 @@ def pod2aplose(
         annotation name.
     annotator: str
         annotator name.
-    bin_size: int
+    bin_size: Timedelta
         Duration of the detections in seconds.
 
     Returns
@@ -65,22 +66,19 @@ def pod2aplose(
         An APLOSE formatted DataFrame.
 
     """
-    df = df.copy()
-    df["_temp_dt"] = [
-        tz.localize(strptime_from_text(entry, "%d/%m/%Y %H:%M"))
-        for entry in df["ChunkEnd"]
+    df["Datetime"] = [
+        tz.localize(entry)
+        for entry in df["Datetime"]
     ]
 
-    df = df.sort_values("_temp_dt").reset_index(drop=True)
-
-    fpod_start_dt = df["_temp_dt"].tolist()
-    fpod_end_dt = [entry + Timedelta(seconds=bin_size) for entry in fpod_start_dt]
+    fpod_start_dt = df["Datetime"]
+    fpod_end_dt = [entry + bin_size for entry in df["Datetime"]]
 
     data = {
         "dataset": [dataset_name] * len(df),
         "filename": [strftime_osmose_format(entry) for entry in fpod_start_dt],
         "start_time": [0] * len(df),
-        "end_time": [bin_size] * len(df),
+        "end_time": [bin_size.total_seconds()] * len(df),
         "start_frequency": [0] * len(df),
         "end_frequency": [0] * len(df),
         "annotation": [annotation] * len(df),
@@ -88,304 +86,121 @@ def pod2aplose(
         "start_datetime": [strftime_osmose_format(entry) for entry in fpod_start_dt],
         "end_datetime": [strftime_osmose_format(entry) for entry in fpod_end_dt],
         "is_box": [0] * len(df),
-        "deploy.name": df["deploy.name"].tolist(),
+        "deploy": df["Deploy"].tolist(),
     }
 
     return DataFrame(data)
 
 
-def csv_folder(
-    folder_path: Path,
-    sep: str = ";",
-    encoding: str = "latin-1",
+def load_pod_folder(
+    folder: Path,
+    ext: str,
 ) -> DataFrame:
-    """Process all CSV files from a folder.
+    """Read POD's result files from a folder.
 
     Parameters
     ----------
-    folder_path: Path
+    folder: Path
         Folder's place.
-    sep: str, default=";"
-        Column separator.
-    encoding: str, default="latin-1"
-        File encoding.
+    ext: str
+        File extension of result files.
 
     Returns
     -------
     DataFrame
-        Concatenated data with optional filename column.
+        Concatenated data.
 
     Raises
     ------
     ValueError
-        If no CSV files found.
+        If no result files are found.
 
     """
-    all_files = list(folder_path.rglob("*.csv"))
-
-    if not all_files:
-        msg = f"No .csv files found in {folder_path}"
+    if ext not in {"csv", "txt"}:
+        msg = f"Invalid file extension: {ext}"
         raise ValueError(msg)
 
-    all_data = []
-    for file in all_files:
-        df = read_csv(file, sep=sep, encoding=encoding, dtype="O")
-        df["deploy.name"] = file.stem
-        all_data.append(df)
-
-    return concat(all_data, ignore_index=True)
-
-
-def txt_folder(
-    folder_path: Path,
-    sep: str = "\t",
-) -> DataFrame:
-    r"""Process all TXT files from a folder.
-
-    Parameters
-    ----------
-    folder_path: Path
-        Folder's place.
-    sep: str, default="\t"
-        Column separator.
-
-    Returns
-    -------
-    DataFrame
-       Concatenated data from all TXT files.
-
-    """
-    all_files = list(Path(folder_path).rglob("*.txt"))
+    all_files = sorted(folder.rglob(f"*.{ext}"))
 
     if not all_files:
-        msg = f"No .txt files found in {folder_path}"
+        msg = f"No .{ext} files found in {folder}"
         raise ValueError(msg)
 
     all_data = []
     for file in all_files:
-        file_path = folder_path / file
-        df = read_csv(file_path, sep=sep)
-        all_data.append(df)
-
-    return concat(all_data, ignore_index=True)
-
+        sep = find_delimiter(file)
+        df = read_csv(
+            file,
+            sep=sep,
+        )
 
-def parse_timestamps(
-    df: DataFrame,
-    col_timestamp: str,
-    date_formats: list[str] | None = None,
-) -> DataFrame:
-    """Parse timestamp column with multiple possible formats.
+        df["Deploy"] = file.stem.strip().lower().replace(" ", "_")
+        all_data.append(df)
 
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe.
-    col_timestamp: str
-        Name of the timestamp column to parse.
-    date_formats: list[str], optional
-        List of strptime formats to try. If None, uses common formats.
+    data = concat(all_data, ignore_index=True)
 
-    Returns
-    -------
-    DataFrame
-        Copy of df with parsed timestamps.
+    if ext == "csv":
+        if "%TimeLost" in data.columns:
+            data_filtered = data[data["File"].notna()].copy()
+            data_filtered = data_filtered[data_filtered["Nall/m"].notna()]
+        else:
+            data_filtered = data[data["DPM"] > 0].copy()
+            data_filtered = data_filtered[data_filtered["Nall"].notna()]
 
-    """
-    if date_formats is None:
-        date_formats = [
-            "%Y-%m-%dT%H:%M:%S.%f%z",
-            "%Y-%m-%dT%H:%M:%S:%Z",
-            "%Y-%m-%dT%H:%M:%S.%f",
-            "%Y-%m-%dT%H:%M:%S",
-            "%Y-%m-%d %H:%M:%S.%f",
-            "%d/%m/%Y %H:%M",
+        data_filtered["Datetime"] = [
+            strptime_from_text(dt, "%d/%m/%Y %H:%M") for dt in data_filtered["ChunkEnd"]
         ]
+        return data_filtered.sort_values(by=["Datetime"]).reset_index(drop=True)
 
-    df = df.copy()
-    df[col_timestamp] = df[col_timestamp].apply(
-        lambda x: strptime_from_text(x, date_formats))
-    return df
-
-
-def required_columns(
-    df: DataFrame,
-    columns: list[str],
-) -> None:
-    """Validate that required columns exist in dataframe.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Table to validate.
-    columns : list[str]
-        List of required column names.
-
-    Raises
-    ------
-    ValueError
-        If any required column is missing.
+    if ext == "txt":
+        pod_type = {Path(f).suffix.lower().strip(".p3") for f in data["File"]}
 
-    """
-    for col in columns:
-        if col not in df.columns:
-            msg = f"'{col}' not found in {df}"
+        if len(pod_type) != 1:
+            msg = f"Multiple POD types found in {folder}: {pod_type}"
             raise ValueError(msg)
 
+        pod_type = pod_type.pop()
 
-def create_mask(
-    df: DataFrame,
-    col_timestamp: str,
-    col_start: str,
-    col_end: str,
-) -> DataFrame:
-    """Filter rows to keep only those within deployment period.
-
-    Parameters
-    ----------
-    df : DataFrame
-        Table with timestamp and deployment period columns.
-    col_timestamp : str
-        Name of timestamp column.
-    col_start : str
-        Name of deployment start date column.
-    col_end : str
-        Name of deployment end date column.
-
-    Returns
-    -------
-    DataFrame
-        Filtered dataframe with rows in deployment periods.
-
-    """
-    mask = (
-        (df[col_timestamp] >= df[col_start])
-    & (df[col_timestamp] <= df[col_end])
-        & df[col_timestamp].notna()
-        & df[col_start].notna()
-        & df[col_end].notna()
-    )
-    return df[mask].copy()
-
-
-def meta_cut_aplose(
-    raw_data: DataFrame,
-    metadata: DataFrame,
-) -> DataFrame:
-    """Filter data to keep only rows within deployment periods.
-
-    Parameters
-    ----------
-    raw_data : DataFrame
-        Table containing deployment name and timestamps.
-    metadata : DataFrame
-        Metadata with deployment periods (start/end dates).
-
-    Returns
-    -------
-    DataFrame
-        Filtered data with only rows within deployment periods.
-
-    """
-    required_columns(
-        raw_data, ["deploy.name", "start_datetime"])
-    required_columns(
-        metadata, ["deploy.name", "deployment_date", "recovery_date"])
-
-    raw = parse_timestamps(raw_data, "start_datetime")
-    raw = raw.sort_values(["start_datetime"])
-
-    dfm = raw.merge(
-        metadata[["deploy.name", "deployment_date", "recovery_date"]],
-        on="deploy.name",
-        how="left",
-    )
-
-    out = create_mask(dfm, "start_datetime", "deployment_date", "recovery_date")
-
-    columns_to_drop = [
-        col for col in ["deployment_date", "recovery_date"] if col not in raw_data.
-        columns]
-    if columns_to_drop:
-        out = out.drop(columns=columns_to_drop)
-
-    return out.sort_values(["start_datetime"]).reset_index(drop=True)
-
-
-def add_utc(
-    df: DataFrame,
-    cols: list,
-    fr: str = "h",
-) -> DataFrame:
-    """Create a DataFrame with one line per hour between start and end dates.
-
-    Keep the number of detections per hour between these dates.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Metadata dataframe with deployments information (previously exported as json).
-    cols:list
-        Timestamp column names.
-    fr:str
-        Frequency of the range of detections.
-
-    Returns
-    -------
-    DataFrame
-        A full period of time with positive and negative hours to detections.
-
-    """
-    for col in df[cols]:
-        df[col] = to_datetime(df[col], utc=True)
-        df[col] = df[col].dt.floor(fr)
-    return df
-
-
-def build_range(
-    df: DataFrame,
-    fr: str = "h",
-) -> DataFrame:
-    """Create a DataFrame with one line per hour between start and end dates.
+        data["Datetime"] = data.apply(
+            lambda row: get_feeding_buzz_datetime(row, pod=f"{pod_type}"),
+            axis=1,
+        )
+        return data.sort_values(by=["Datetime"]).reset_index(drop=True)
 
-    Keep the number of detections per hour between these dates.
+    msg = f"Could not load {ext} result folder"
+    raise ValueError(msg)
 
-    Parameters
-    ----------
-    df: DataFrame
-        Metadata dataframe with deployments information (previously exported as json)
-    fr:str
-        Frequency of the range of detections.
 
-    Returns
-    -------
-    DataFrame
-        A full period of time with positive and negative hours to detections.
+def get_feeding_buzz_datetime(row: Series, pod: str) -> Timestamp:
+    """Convert feeding buzz timestamp into a standard Timestamp.
 
+    The conversion method differs based on the POD type.
     """
-    add_utc(df, ["Deb", "Fin"], fr)
+    if pod not in {"c", "f"}:
+        msg = f"Invalid POD type: {pod}"
+        raise ValueError(msg)
 
-    all_ranges = []
-    for _, row in df.iterrows():
-        hours = date_range(row["Deb"], row["Fin"], freq=fr)
-        tmp = DataFrame(
-            {
-                "deploy.name": row["deploy.name"],
-                "start_datetime": hours,
-            },
+    if pod == "f":
+        return (
+            to_datetime("1900-01-01") +
+            to_timedelta(row["Minute"], unit="min") +
+            to_timedelta(row["microsec"] / 1e6, unit="sec") -
+            to_timedelta(2, unit="D")
         )
-        all_ranges.append(tmp)
 
-    return concat(all_ranges, ignore_index=True)
+    return strptime_from_text(
+        f"{row["Minute"]}:{int(str(row["microsec"])[0]):02d}.{int(str(row["microsec"])[1:])}",
+        "%-d/%-m/%Y %H:%M:%S.%f",
+    )
 
 
-def feeding_buzz(
+def process_feeding_buzz(
     df: DataFrame,
     species: str,
 ) -> DataFrame:
-    """Process a CPOD/FPOD feeding buzz detection file.
+    """Process a POD feeding buzz detection DataFrame.
 
-    Gives the feeding buzz duration, depending on the studied species.
+    Give the feeding buzz duration, depending on the studied species.
 
     Parameters
     ----------
@@ -397,322 +212,69 @@ def feeding_buzz(
     Returns
     -------
     DataFrame
-        Containing all ICIs for every positive minutes to clicks
+        Containing all ICIs for every positive minute to click
 
     """
-    df["microsec"] /= 1e6
-
-    try:
-        df["Minute"].astype(int)
-        df["datetime"] = (
-            to_datetime("1900-01-01")
-            + to_timedelta(df["Minute"], unit="min")
-            + to_timedelta(df["microsec"], unit="sec")
-            - to_timedelta(2, unit="D")
-        )
-        df["start_datetime"] = df["datetime"].dt.floor("min")
-    except (ValueError, TypeError):
-        df["datetime"] = (
-                to_datetime(df["Minute"], dayfirst=True)
-                + to_timedelta(df["microsec"], unit="sec")
-        )
-        df["start_datetime"] = to_datetime(df["Minute"], dayfirst=True)
-
-    df["ICI"] = df["datetime"].diff()
-
-    if species == "Dauphin":  # Herzing et al., 2014
-        df["Buzz"] = df["ICI"].between(Timedelta(0),
-                                       Timedelta(seconds=0.02)).astype(int)
-    elif species == "Marsouin":  # Nuuttila et al., 2013
-        df["Buzz"] = df["ICI"].between(Timedelta(0),
-                                       Timedelta(seconds=0.01)).astype(int)
-    elif species == "Commerson":  # Reyes Reyes et al., 2015
-        df["Buzz"] = df["ICI"].between(Timedelta(0),
-                                       Timedelta(seconds=0.005)).astype(int)
+    df["Datetime"] = df["Datetime"].dt.floor("min")
+    df["ICI"] = df["Datetime"].diff()
+
+    if species.lower() == "delphinid":  # Herzing et al., 2014
+        df["Buzz"] = df["ICI"].between(
+            Timedelta(0),
+            Timedelta(seconds=0.02),
+        ).astype(int)
+    elif species.lower() == "porpoise":  # Nuuttila et al., 2013
+        df["Buzz"] = df["ICI"].between(
+            Timedelta(0),
+            Timedelta(seconds=0.01),
+        ).astype(int)
+    elif species.lower() == "commerson":  # Reyes Reyes et al., 2015
+        df["Buzz"] = df["ICI"].between(
+            Timedelta(0),
+            Timedelta(seconds=0.005),
+        ).astype(int)
     else:
         msg = "This species is not supported"
         raise ValueError(msg)
 
-    f = df.groupby(["start_datetime"])["Buzz"].sum().reset_index()
+    df_buzz = df.groupby(["Datetime"])["Buzz"].sum().reset_index()
+    df_buzz["Foraging"] = to_numeric(
+        df_buzz["Buzz"] != 0, downcast="integer"
+    ).astype(int)
 
-    f["Foraging"] = to_numeric(f["Buzz"] != 0, downcast="integer").astype(int)
+    return df_buzz
 
-    return f
 
+def process_timelost(df: DataFrame, threshold: int = 0) -> DataFrame:
+    """Process TimeLost DataFrame.
 
-def assign_daytime(
-    df: DataFrame,
-) -> DataFrame:
-    """Assign datetime categories to temporal events.
-
-    Categorize daytime of the detection (among 4 categories).
+    Returns relevant columns and reshape into hourly data.
 
     Parameters
     ----------
     df: DataFrame
-        Contains positive hours to detections.
-
-    Returns
-    -------
-    DataFrame
-        The same dataframe with the column daytime.
-
-    """
-    df["start_datetime"] = to_datetime(df["start_datetime"], utc=True)
-    start = df["start_datetime"].min()
-    stop = df["start_datetime"].max()
-    lat, lon = get_coordinates()
-    sunrise, sunset = get_sun_times(start, stop, lat, lon)
-
-    sun_times = DataFrame(
-        {"date": date_range(start, stop, freq="D"),
-        "sunrise": [Timedelta(h, "hours") for h in sunrise],
-        "sunset": [Timedelta(h, "hours") for h in sunset],
-        })
-
-    sun_times["sunrise"] = sun_times["date"].dt.floor("D") + sun_times["sunrise"]
-    sun_times["sunset"] = sun_times["date"].dt.floor("D") + sun_times["sunset"]
-
-    for i, row in df.iterrows():
-        dpm_i = row["start_datetime"]
-        if notna(dpm_i):  # Check if time is not NaN
-            jour_i = sun_times[
-                (sun_times["sunrise"].dt.year == dpm_i.year)
-                & (sun_times["sunrise"].dt.month == dpm_i.month)
-                & (sun_times["sunrise"].dt.day == dpm_i.day)
-                ]
-            if not jour_i.empty:  # Ensure there's a matching row
-                jour_i = jour_i.iloc[0]  # Extract first match
-                if (dpm_i <= jour_i["sunrise"]) | (dpm_i > jour_i["sunset"]):
-                    df.loc[i, "REGIME"] = 1
-                else:
-                    df.loc[i, "REGIME"] = 2
-
-    return df
-
-
-def is_dpm_col(
-    df: DataFrame,
-) -> DataFrame:
-    """Ensure DPM column exists with default value of 1.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe.
-
-    Returns
-    -------
-    DataFrame
-        Copy of df with DPM column.
-
-    """
-    df = df.copy()
-    if "DPM" not in df.columns:
-        df["DPM"] = 1
-    return df
-
-
-def resample_dpm(
-    df: DataFrame,
-    frq: str,
-    cols: dict[str, str],
-    group_by: list[str] | None = None,
-) -> DataFrame:
-    """Resample DPM data to specified time frequency.
-
-    Aggregates Detection Positive Minutes (DPM) by time period,
-    optionally preserving grouping columns like deployment name.
-
-    Parameters
-    ----------
-    df: DataFrame
-        CPOD result DataFrame with DPM data.
-    frq: str
-        Pandas frequency string: "D" (day), "h" (hour), "10min", etc.
-    cols: dict[str, str]
-        Dictionary of column names and to process them.
-    group_by: list[str], optional
-        Columns to group by (e.g., ["deploy.name", "start_datetime"]).
-        If None, groups only by start_datetime.
-
-    Returns
-    -------
-    DataFrame
-        Resampled DataFrame with aggregated DPM values.
-
-    Examples
-    --------
-    >>> # Daily aggregation per deployment
-    >>> resample_dpm(df, "D", {"Foraging":"sum"}, group_by=["deploy.name"])
-
-    >>> # Hourly aggregation with site info preserved
-    >>> resample_dpm(df, "h", cols={"DPM":"sum","deploy.name":"first"})
-
-    """
-    df = is_dpm_col(df)
-    df = add_utc(df, ["start_datetime"], frq)
-
-    # Determine grouping columns
-    if group_by is None:
-        group_by = ["start_datetime"]
-
-    return df.groupby(group_by).agg(cols).reset_index()
-
-
-def deploy_period(
-    df: DataFrame,
-    col_timestamp: str = "start_datetime",
-    col_deployment: str = "deploy.name",
-) -> DataFrame:
-    """Extract start and end timestamps for each deployment.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Input dataframe with parsed timestamps.
-    col_timestamp: str, default="start_datetime"
-        Name of the timestamp column.
-    col_deployment: str, default="deploy.name"
-        Name of the deployment identifier column.
-
-    Returns
-    -------
-    DataFrame
-        Table with columns: [col_deployment, 'Deb', 'Fin'].
-
-    """
-    return (
-        df.groupby([col_deployment])
-        .agg(Deb=(col_timestamp, "first"), Fin=(col_timestamp, "last"))
-        .reset_index()
-    )
-
-
-def first_last(
-    df: DataFrame,
-    col_timestamp: str = "start_datetime",
-    col_deployment: str = "deploy.name",
-    date_formats: list[str] | None = None,
-) -> DataFrame:
-    """Isolate beginning and end of every deployment.
-
-    Parameters
-    ----------
-    df: DataFrame
-        CPOD result DataFrame.
-    col_timestamp: str, default="start_datetime"
-        Name of the timestamps' column.
-    col_deployment: str, default="deploy.name"
-        Name of the deployment identifier column.
-    date_formats: list[str], optional
-        List of date formats to try for parsing.
-
-    Returns
-    -------
-    DataFrame
-        Table with deployment periods (Deb, Fin).
-
-    """
-    df_parsed = parse_timestamps(df, col_timestamp, date_formats)
-    return deploy_period(df_parsed, col_timestamp, col_deployment)
-
-
-def actual_data(
-    df: DataFrame,
-    meta: DataFrame,
-) -> DataFrame:
-    """Create a table with beginning and end of every deployment using metadata.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Contains beginning and end for every deployment.
-    meta: DataFrame
-        Contains metadata for every deployment.
-
-    Returns
-    -------
-    DataFrame
-        Table with corrected deployment periods (Deb, Fin).
-
-    """
-    required_columns(
-        df, ["deploy.name", "ChunkEnd"])
-    required_columns(
-        meta, ["deploy.name", "deployment_date", "recovery_date"])
-
-    beg_end = first_last(df, "ChunkEnd")
-
-    beg_end = add_utc(beg_end, ["Deb", "Fin"])
-
-    final = beg_end.merge(meta[["deployment_date", "recovery_date", "deploy.name"]],
-                          on="deploy.name", how="left")
-    final.loc[final["Deb"] < final["deployment_date"], "Deb"] = final["deployment_date"]
-    final.loc[final["Fin"] > final["recovery_date"], "Fin"] = final["recovery_date"]
-    final.loc[final["Deb"] > final["Fin"], ["Deb", "Fin"]] = None
-    final = final.sort_values(by=["Deb"])
-    return final.drop(["deployment_date", "recovery_date"], axis=1)
-
-
-def process_tl(tl_files: Path) -> DataFrame:
-    """Process Environmental data extracted from cpod.exe to get a usable dataframe.
-
-    Parameters
-    ----------
-    tl_files: Path
         All your Environmental data files.
+    threshold: float
+        TimeLost threshold.
 
     Returns
     -------
     %TimeLost DataFrame.
 
     """
-    df = csv_folder(tl_files)
-    df = df.dropna()
-    df = parse_timestamps(df, "ChunkEnd")
-    df = add_utc(df, ["ChunkEnd"], "h")
-    df["start_datetime"] = df["ChunkEnd"]
-
-    return df.sort_values(["start_datetime"])
-
-
-def filter_tl(df: DataFrame, tl: int) -> DataFrame:
-    """Remove lines with a %TimeLost superior to the chosen threshold.
-
-    Parameters
-    ----------
-    df: DataFrame
-        Table of data and associated TimeLost.
-    tl: int
-        TimeLost filter threshold.
-
-    Returns
-    -------
-    Filtered DataFrame with few %TimeLost.
-
-    """
-    df["%TimeLost"] = (df["%TimeLost"].fillna(tl)).astype(int)
-
-    return df[df["%TimeLost"] < tl]
-
-
-def preserved_data(filtered_df: DataFrame, whole_df: DataFrame) -> float:
-    """Calculate the percentage of preserved data.
-
-    Parameters
-    ----------
-    filtered_df: DataFrame
-        Result of filter_tl.
-    whole_df: DataFrame
-        Table before filtering.
-
-    Returns
-    -------
-    Percentage of preserved data.
+    if threshold not in range(0, 100):
+        msg = "Threshold must be an integer between 0 and 100."
+        raise ValueError(msg)
 
-    """
-    return (len(filtered_df) / len(whole_df)) * 100
+    df["Datetime"] = df["Datetime"].dt.floor("h")
+    cols_to_drop = [
+        col for col in df.columns if col not in {
+            "File", "Datetime", "Temp", "Angle", "%TimeLost", "Deploy"
+        }
+    ]
+    return df[df["%TimeLost"] >= threshold].drop(
+        columns=cols_to_drop
+    ).sort_values(["Datetime"]).reset_index(drop=True)
 
 
 def create_matrix(
@@ -747,27 +309,6 @@ def create_matrix(
     return matrix
 
 
-def extract_site(
-    df: DataFrame,
-) -> DataFrame:
-    """Create new columns: site.name and campaign.name, in order to match the metadata.
-
-    Parameters
-    ----------
-    df: DataFrame
-        All values concatenated
-
-    Returns
-    -------
-    DataFrame
-        The same dataframe with two additional columns.
-
-    """
-    required_columns(df, ["deploy.name"])
-    df[["site.name", "campaign.name"]] = df["deploy.name"].str.split("_", expand=True)
-    return df
-
-
 def percent_calc(
     data: DataFrame,
     time_unit: str | None = None,
diff --git a/src/post_processing/utils/glider_utils.py b/src/post_processing/utils/glider_utils.py
index 626371c..aaa0224 100644
--- a/src/post_processing/utils/glider_utils.py
+++ b/src/post_processing/utils/glider_utils.py
@@ -175,7 +175,7 @@ def load_glider_nav(directory: Path) -> DataFrame:
         msg = f"Directory '{directory}' does not exist."
         raise FileNotFoundError(msg)
 
-    file = [f for f in directory.glob("*.gz") if "gli" in f.name]
+    file = [f for f in directory.rglob("*.gz") if "gli" in f.name]
 
     if not len(file) > 0:
         msg = f"Directory '{directory}' does not contain '.gz' files."
diff --git a/src/post_processing/utils/plot_utils.py b/src/post_processing/utils/plot_utils.py
index 8d12fa3..79c7673 100644
--- a/src/post_processing/utils/plot_utils.py
+++ b/src/post_processing/utils/plot_utils.py
@@ -577,7 +577,7 @@ def timeline(
 
     ax.grid(color="k", linestyle="-", linewidth=0.2)
     ax.set_yticks(np.arange(0, len(labels), 1))
-    ax.set_yticklabels(labels[::-1])
+    ax.set_yticklabels(labels)
     ax.set_xlabel("Date")
     ax.set_xlim(
         df["start_datetime"].min().floor("1d"),
diff --git a/tests/test_fpod_utils.py b/tests/test_fpod_utils.py
index a0ab178..d1c4ece 100644
--- a/tests/test_fpod_utils.py
+++ b/tests/test_fpod_utils.py
@@ -4,7 +4,7 @@
 from pandas import DataFrame
 
 from post_processing.utils.fpod_utils import (
-    csv_folder,
+    load_pod_folder,
     pod2aplose,
 )
 
@@ -142,7 +142,7 @@ def test_csv_folder_single_file(tmp_path) -> None:
     csv_file = tmp_path / "data.csv"
     csv_file.write_text("col1;col2\nval1;val2\nval3;val4", encoding="latin-1")
 
-    result = csv_folder(tmp_path)
+    result = load_pod_folder(tmp_path)
 
     assert isinstance(result, DataFrame)
     assert len(result) == 2
diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
index 60e7f67..8412c3b 100644
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ b/user_case/example_FPOD-CPOD_raw.ipynb
@@ -15,7 +15,7 @@
     "\n",
     "import pytz\n",
     "\n",
-    "from post_processing.utils.fpod_utils import csv_folder, pod2aplose, actual_data, meta_cut_aplose, feeding_buzz, txt_folder, add_utc\n",
+    "from post_processing.utils.fpod_utils import load_pod_folder, pod2aplose, actual_data, filter_by_metadatax, process_feeding_buzz, load_pod_txt, add_utc\n",
     "from post_processing.utils.core_utils import json2df\n",
     "\n",
     "import logging\n",
@@ -54,7 +54,7 @@
    "cell_type": "code",
    "source": [
     "pod_files = Path(r\"U:\\CA4\") #Path to your data folder. 🐬\n",
-    "path = csv_folder(pod_files) #Process all your POD.csv files.\n",
+    "path = load_pod_folder(pod_files) #Process all your POD.csv files.\n",
     "\n",
     "fb_files = Path(r\"U:\\fb_CA4\")  #Path to your click details folder. 🐬\n",
     "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_calais.json\") #Path to your metadata file. 🐬\n",
@@ -180,7 +180,7 @@
     }
    },
    "cell_type": "code",
-   "source": "cleared = meta_cut_aplose(df_aplose, metadatax)",
+   "source": "cleared = filter_by_metadatax(df_aplose, metadatax)",
    "id": "895bd5a116918285",
    "outputs": [],
    "execution_count": 5
@@ -211,8 +211,8 @@
    "metadata": {},
    "cell_type": "code",
    "source": [
-    "fb_all = txt_folder(fb_files) #Read all your FB.txt files.\n",
-    "fb_all = feeding_buzz(fb_all, \"Marsouin\") #Categorize the minutes (positive or not to FB detection). 🐬\n",
+    "fb_all = load_pod_txt(fb_files) #Read all your FB.txt files.\n",
+    "fb_all = process_feeding_buzz(fb_all, \"Marsouin\") #Categorize the minutes (positive or not to FB detection). 🐬\n",
     "add_utc(fb_all, [\"start_datetime\"], \"min\")"
    ],
    "id": "64f824e02131d90a",
diff --git a/user_case/resource/CPOD-FPOD_yaml.yml b/user_case/resource/CPOD-FPOD_yaml.yml
index 3e259cc..ded85b0 100644
--- a/user_case/resource/CPOD-FPOD_yaml.yml
+++ b/user_case/resource/CPOD-FPOD_yaml.yml
@@ -6,7 +6,7 @@
 #  - Several csv files can be loaded at once, to perform this copy the template and paste it at the end of the present file.
 
 #########################################
-'U:\APLOSE_A1112.csv':
+'C:\Users\dupontma2\Downloads\data_aplose.csv':
   # timebin: detection time resolution in seconds
   'timebin_new': null
   # begin datetime: '2000-01-01T00:00:00+0000'
@@ -16,7 +16,7 @@
   # annotator: ['annotator1', 'annotator2']
   'annotator': null
   # annotation: ['annotation1', 'annotation2']
-  'annotation': "Commerson"
+  'annotation': "Marsouin"
   # box: boolean value, whether to keep strong annotations
   'box': False
   # timestamp file
@@ -29,3 +29,5 @@
   'f_max': null
   # score
   'score': null
+  # filename format
+  'filename_format': '%Y-%m-%dT%H:%M:%S.%f%z'
\ No newline at end of file
diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
index a5e49a3..f0f109a 100644
--- a/user_case/user_case_CALAIS.ipynb
+++ b/user_case/user_case_CALAIS.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-10-23T15:42:49.393544Z",
-     "start_time": "2025-10-23T15:42:47.415071Z"
+     "end_time": "2026-01-21T13:29:51.583226Z",
+     "start_time": "2026-01-21T13:29:43.385674Z"
     }
    },
    "source": [
@@ -21,8 +21,8 @@
     ")\n",
     "\n",
     "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import csv_folder, pod2aplose, actual_data, meta_cut_aplose, resample_dpm, txt_folder, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent, \\\n",
-    "    feeding_buzz\n",
+    "from post_processing.utils.fpod_utils import load_pod_folder, pod2aplose, actual_data, filter_by_metadatax, resample_dpm, load_pod_txt, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent, \\\n",
+    "    process_feeding_buzz\n",
     "from post_processing.utils.core_utils import json2df,get_season"
    ],
    "outputs": [],
@@ -53,7 +53,7 @@
    "cell_type": "code",
    "source": [
     "pod_files = Path(r\"U:\\Walde\")\n",
-    "path = csv_folder(pod_files)\n",
+    "path = load_pod_folder(pod_files)\n",
     "print(path.head())\n",
     "\n",
     "df_0 = path.dropna()\n",
@@ -253,7 +253,7 @@
    "source": [
     "\n",
     "\n",
-    "cleared = meta_cut_aplose(df_aplose, metadatax) #Remove lines captures outside the instrument submersion."
+    "cleared = filter_by_metadatax(df_aplose, metadatax) #Remove lines captures outside the instrument submersion."
    ],
    "id": "ed6a06c522aea169",
    "outputs": [],
@@ -347,8 +347,8 @@
    "cell_type": "code",
    "source": [
     "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\") #Path to your click details folder.\n",
-    "fb_all = txt_folder(fb_files)\n",
-    "fb_all = feeding_buzz(fb_all, \"Marsouin\")\n",
+    "fb_all = load_pod_txt(fb_files)\n",
+    "fb_all = process_feeding_buzz(fb_all, \"Marsouin\")\n",
     "\n",
     "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
     "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
@@ -363,6 +363,7 @@
    "cell_type": "code",
    "source": [
     "d_tot = resamp.merge(fb, on=\"start_datetime\", how=\"left\")\n",
+    "\n",
     "#This function aims to reindent 0 between the positive detections. It will be useful to produce first visualization graphs and use this dataset in R.\n",
     "d_hour = build_range(d_beg_end, frq)"
    ],

From f0adf5bb77d7d4154741bca36b0f1b89f199ba92 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Tue, 27 Jan 2026 17:16:31 +0100
Subject: [PATCH 82/83] refacto

---
 src/post_processing/utils/filtering_utils.py  |  22 +-
 src/post_processing/utils/fpod_utils.py       | 124 ++--
 src/post_processing/utils/plot_utils.py       |   2 +
 user_case/example_FPOD-CPOD_aplose.ipynb      | 287 ---------
 .../example_FPOD-CPOD_firstresults.ipynb      | 251 --------
 user_case/example_FPOD-CPOD_raw.ipynb         | 420 -------------
 user_case/resource/CPOD-FPOD_yaml.yml         |  33 -
 user_case/user_case_CALAIS.ipynb              | 581 ------------------
 8 files changed, 82 insertions(+), 1638 deletions(-)
 delete mode 100644 user_case/example_FPOD-CPOD_aplose.ipynb
 delete mode 100644 user_case/example_FPOD-CPOD_firstresults.ipynb
 delete mode 100644 user_case/example_FPOD-CPOD_raw.ipynb
 delete mode 100644 user_case/resource/CPOD-FPOD_yaml.yml
 delete mode 100644 user_case/user_case_CALAIS.ipynb

diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index 661324b..a9e0619 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -485,7 +485,8 @@ def _process_annotator_label_pair(
         time_vector[i] for i, detected in enumerate(detect_vec) if detected
     ]
     file_vector = [
-        filename_vector[i] for i, detected in enumerate(detect_vec) if detected
+        # filename_vector[i] for i, detected in enumerate(detect_vec) if detected
+        filename_vector[i + 1] for i, detected in enumerate(detect_vec) if detected
     ]
 
     if not start_datetime:
@@ -571,16 +572,17 @@ def get_filename_timestamps(df: DataFrame, date_parser: str) -> list[Timestamp]:
 
     """
     tz = get_timezone(df)
-    try:
-        return [
-        to_datetime(
+    timestamps = [
+        strptime_from_text(
             ts,
-            format=date_parser,
-        ).tz_localize(tz) for ts in df["filename"]
-        ]
-    except ValueError:
-        msg = """Could not parse timestamps from `df["filename"]`."""
-        raise ValueError(msg) from None
+            datetime_template=date_parser,
+        ) for ts in df["filename"]
+    ]
+
+    if all(t.tz is None for t in timestamps):
+        timestamps = [t.tz_localize(tz) for t in timestamps]
+
+    return timestamps
 
 
 def ensure_in_list(value: str, candidates: list[str], label: str) -> None:
diff --git a/src/post_processing/utils/fpod_utils.py b/src/post_processing/utils/fpod_utils.py
index 12cee28..cd13e58 100644
--- a/src/post_processing/utils/fpod_utils.py
+++ b/src/post_processing/utils/fpod_utils.py
@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 import logging
-from pathlib import Path
 from typing import TYPE_CHECKING
 
 import matplotlib.dates as mdates
@@ -30,6 +29,8 @@
 from user_case.config import season_color, site_colors
 
 if TYPE_CHECKING:
+    from pathlib import Path
+
     import pytz
 
 logger = logging.getLogger(__name__)
@@ -66,26 +67,27 @@ def pod2aplose(
         An APLOSE formatted DataFrame.
 
     """
-    df["Datetime"] = [
+    fpod_start_dt = [
         tz.localize(entry)
         for entry in df["Datetime"]
     ]
 
-    fpod_start_dt = df["Datetime"]
-    fpod_end_dt = [entry + bin_size for entry in df["Datetime"]]
-
     data = {
         "dataset": [dataset_name] * len(df),
-        "filename": [strftime_osmose_format(entry) for entry in fpod_start_dt],
+        "filename": list(fpod_start_dt),
         "start_time": [0] * len(df),
         "end_time": [bin_size.total_seconds()] * len(df),
         "start_frequency": [0] * len(df),
         "end_frequency": [0] * len(df),
         "annotation": [annotation] * len(df),
         "annotator": [annotator] * len(df),
-        "start_datetime": [strftime_osmose_format(entry) for entry in fpod_start_dt],
-        "end_datetime": [strftime_osmose_format(entry) for entry in fpod_end_dt],
-        "is_box": [0] * len(df),
+        "start_datetime": [
+            strftime_osmose_format(entry.floor(bin_size)) for entry in fpod_start_dt
+        ],
+        "end_datetime": [
+            strftime_osmose_format(entry.ceil(bin_size)) for entry in fpod_start_dt
+        ],
+        "type": ["WEAK"] * len(df),
         "deploy": df["Deploy"].tolist(),
     }
 
@@ -140,58 +142,67 @@ def load_pod_folder(
     data = concat(all_data, ignore_index=True)
 
     if ext == "csv":
-        if "%TimeLost" in data.columns:
-            data_filtered = data[data["File"].notna()].copy()
-            data_filtered = data_filtered[data_filtered["Nall/m"].notna()]
-        else:
-            data_filtered = data[data["DPM"] > 0].copy()
-            data_filtered = data_filtered[data_filtered["Nall"].notna()]
+        return _process_csv_data(data)
+    if ext == "txt":
+        return _process_txt_data(data)
 
-        data_filtered["Datetime"] = [
-            strptime_from_text(dt, "%d/%m/%Y %H:%M") for dt in data_filtered["ChunkEnd"]
-        ]
-        return data_filtered.sort_values(by=["Datetime"]).reset_index(drop=True)
+    msg = f"Could not load {ext} result folder"
+    raise ValueError(msg)
 
-    if ext == "txt":
-        pod_type = {Path(f).suffix.lower().strip(".p3") for f in data["File"]}
 
-        if len(pod_type) != 1:
-            msg = f"Multiple POD types found in {folder}: {pod_type}"
-            raise ValueError(msg)
+def _process_csv_data(data: DataFrame) -> DataFrame:
+    """Process CSV data with filtering and datetime conversion."""
+    data_filtered = _filter_csv_data(data)
+    data_filtered["Datetime"] = [
+        strptime_from_text(dt, "%d/%m/%Y %H:%M")
+        for dt in data_filtered["ChunkEnd"]
+    ]
+    return data_filtered.sort_values(by=["Datetime"]).reset_index(drop=True)
 
-        pod_type = pod_type.pop()
 
-        data["Datetime"] = data.apply(
-            lambda row: get_feeding_buzz_datetime(row, pod=f"{pod_type}"),
-            axis=1,
-        )
-        return data.sort_values(by=["Datetime"]).reset_index(drop=True)
+def _filter_csv_data(data: DataFrame) -> DataFrame:
+    """Filter CSV data based on available columns."""
+    if "%TimeLost" in data.columns:
+        data_filtered = data[data["File"].notna()].copy()
+        data_filtered = data_filtered[data_filtered["Nall/m"].notna()]
+    else:
+        data_filtered = data[data["DPM"] > 0].copy()
+        data_filtered = data_filtered[data_filtered["Nall"].notna()]
 
-    msg = f"Could not load {ext} result folder"
-    raise ValueError(msg)
+    return data_filtered
+
+
+def _process_txt_data(data: DataFrame) -> DataFrame:
+    """Process TXT data with datetime conversion."""
+    data["Datetime"] = data.apply(get_feeding_buzz_datetime, axis=1)
+    return data.drop_duplicates().sort_values(by=["Datetime"]).reset_index(drop=True)
 
 
-def get_feeding_buzz_datetime(row: Series, pod: str) -> Timestamp:
+def get_feeding_buzz_datetime(row: Series) -> Timestamp:
     """Convert feeding buzz timestamp into a standard Timestamp.
 
     The conversion method differs based on the POD type.
     """
-    if pod not in {"c", "f"}:
-        msg = f"Invalid POD type: {pod}"
-        raise ValueError(msg)
-
-    if pod == "f":
+    try:
         return (
-            to_datetime("1900-01-01") +
-            to_timedelta(row["Minute"], unit="min") +
-            to_timedelta(row["microsec"] / 1e6, unit="sec") -
-            to_timedelta(2, unit="D")
+            to_datetime("1900-01-01")
+            + to_timedelta(row["Minute"], unit="min")
+            + to_timedelta(row["microsec"] / 1e6, unit="sec")
+            - to_timedelta(2, unit="D")
         )
+    except (KeyError, TypeError, ValueError):
+        pass
 
-    return strptime_from_text(
-        f"{row["Minute"]}:{int(str(row["microsec"])[0]):02d}.{int(str(row["microsec"])[1:])}",
-        "%-d/%-m/%Y %H:%M:%S.%f",
-    )
+    try:
+        return strptime_from_text(
+            f"{row['Minute']}:{int(str(row['microsec'])[0]):02d}.{int(str(row['microsec'])[1:])}",
+            "%-d/%-m/%Y %H:%M:%S.%f",
+        )
+    except (KeyError, TypeError, ValueError):
+        pass
+
+    msg = "Could not convert feeding buzz timestamp."
+    raise ValueError(msg)
 
 
 def process_feeding_buzz(
@@ -200,7 +211,8 @@ def process_feeding_buzz(
 ) -> DataFrame:
     """Process a POD feeding buzz detection DataFrame.
 
-    Give the feeding buzz duration, depending on the studied species.
+    Give the feeding buzz duration, depending on the studied species
+    (`delphinid`, `porpoise` or `commerson`).
 
     Parameters
     ----------
@@ -215,8 +227,8 @@ def process_feeding_buzz(
         Containing all ICIs for every positive minute to click
 
     """
-    df["Datetime"] = df["Datetime"].dt.floor("min")
     df["ICI"] = df["Datetime"].diff()
+    df["Datetime"] = df["Datetime"].dt.floor("min")
 
     if species.lower() == "delphinid":  # Herzing et al., 2014
         df["Buzz"] = df["ICI"].between(
@@ -239,7 +251,7 @@ def process_feeding_buzz(
 
     df_buzz = df.groupby(["Datetime"])["Buzz"].sum().reset_index()
     df_buzz["Foraging"] = to_numeric(
-        df_buzz["Buzz"] != 0, downcast="integer"
+        df_buzz["Buzz"] != 0, downcast="integer",
     ).astype(int)
 
     return df_buzz
@@ -262,18 +274,18 @@ def process_timelost(df: DataFrame, threshold: int = 0) -> DataFrame:
     %TimeLost DataFrame.
 
     """
-    if threshold not in range(0, 100):
-        msg = "Threshold must be an integer between 0 and 100."
+    if threshold not in range(101):
+        msg = "Threshold must integer between 0 and 100."
         raise ValueError(msg)
 
     df["Datetime"] = df["Datetime"].dt.floor("h")
     cols_to_drop = [
         col for col in df.columns if col not in {
-            "File", "Datetime", "Temp", "Angle", "%TimeLost", "Deploy"
+            "File", "Datetime", "Temp", "Angle", "%TimeLost", "Deploy",
         }
     ]
-    return df[df["%TimeLost"] >= threshold].drop(
-        columns=cols_to_drop
+    return df[df["%TimeLost"] <= threshold].drop(
+        columns=cols_to_drop,
     ).sort_values(["Datetime"]).reset_index(drop=True)
 
 
@@ -313,7 +325,7 @@ def percent_calc(
     data: DataFrame,
     time_unit: str | None = None,
 ) -> DataFrame:
-    """Calculate percentage of clicks, feeding buzzes and positive hours to detection.
+    """Calculate the percentage of clicks, feeding buzzes and positive hours to detection.
 
     Computed on the entire effort and for every site.
 
@@ -358,7 +370,7 @@ def percent_calc(
 
 
 def site_percent(df: DataFrame, metric: str) -> None:
-    """Plot a graph with percentage of minutes positive to detection for every site.
+    """Plot a graph with the percentage of minutes positive to detection for every site.
 
     Parameters
     ----------
diff --git a/src/post_processing/utils/plot_utils.py b/src/post_processing/utils/plot_utils.py
index 79c7673..4cffcd6 100644
--- a/src/post_processing/utils/plot_utils.py
+++ b/src/post_processing/utils/plot_utils.py
@@ -239,6 +239,7 @@ def scatter(
     season = kwargs.get("season", False)
     coordinates = kwargs.get("coordinates", False)
     effort = kwargs.get("effort", False)
+    legend = kwargs.get("legend", False)
 
     _prepare_timeline_plot(
         df=df,
@@ -282,6 +283,7 @@ def scatter(
         shade_no_effort(
             ax=ax,
             observed=effort,
+            legend=legend,
         )
 
 
diff --git a/user_case/example_FPOD-CPOD_aplose.ipynb b/user_case/example_FPOD-CPOD_aplose.ipynb
deleted file mode 100644
index b0da1c8..0000000
--- a/user_case/example_FPOD-CPOD_aplose.ipynb
+++ /dev/null
@@ -1,287 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from pandas import (\n",
-    "    read_csv,\n",
-    ")\n",
-    "\n",
-    "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import resample_dpm, build_range, process_tl, filter_tl, preserved_data"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Load data\n",
-    "DPM = Detection Positive Minutes \\\n",
-    "FB = Feeding buzzes \\\n",
-    "🐬 = input to modify\n"
-   ],
-   "id": "a97e19830123b732"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\") #Change the file path in the yaml sheet.🐬\n",
-    "data_list = DataAplose.from_yaml(file=yaml_file)\n",
-    "\n",
-    "d_beg_end = read_csv(r\"U:\\Deb_Fin_CA4.csv\") #Beginning and end of recording for every phase. 🐬\n",
-    "\n",
-    "tl_path = Path(r\"U:\\TimeLost\\tl_ca4\\phases\")\n",
-    "tl_df = process_tl(tl_path)\n",
-    "tl_df = tl_df.drop_duplicates(subset=['ChunkEnd'], keep=\"first\")\n",
-    "tl_df[\"Angle\"] = (tl_df[\"Angle\"].replace(',', '.', regex=True)).astype(float)\n",
-    "tl_df[\"Temp\"] = (tl_df[\"Temp\"].replace(',', '.', regex=True)).astype(float)"
-   ],
-   "id": "7da2feb5958db1a9",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Resample your data\n",
-    "Here you need to choose the format in which you want to visualise the positive detections. This aims to replace the functionalities available in Chelonia's softwares."
-   ],
-   "id": "3bc57f4f638ad6dc"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "frq = \"h\" #Determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\". 🐬\n",
-    "tl = 100 #%TimeLost threshold. If you do not want to set a filter, set tl to 100."
-   ],
-   "id": "9b0a078a262ac7f2",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "resamp = resample_dpm(data_list.df, frq=frq, cols={\"DPM\":\"sum\",\"Foraging\":\"sum\",\"deploy.name\":\"first\"}) #Resample your DPMs according to the chosen frq.",
-   "id": "fa3847d80ccf49c3",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Add the effort\n",
-    "To analyze the data, add zeros to view it based on effort."
-   ],
-   "id": "b92537991aa4ac4b"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_0 = build_range(d_beg_end, frq) #Create a dataframe from beginning to end of every phase filled with 0s.\n",
-    "d_tot = d_0.merge(resamp, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")"
-   ],
-   "id": "4d76089ef06c6fdb",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Add additional metrics columns",
-   "id": "e6a4623e4baf25b5"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_tot[[\"DPM\",\"Foraging\"]] = d_tot[[\"DPM\",\"Foraging\"]].fillna(0)\n",
-    "d_tot[\"FBR\"] = d_tot[\"Foraging\"] / d_tot[\"DPM\"] #The Feeding Buzz Ratio corresponds to the proportion of FB among the recorded clicks.\n",
-    "d_tot[\"FBR\"] = d_tot[\"FBR\"].fillna(0)\n",
-    "d_tot[f\"DP{frq}\"] = (d_tot[\"DPM\"] >0).astype(int) #1 if the hour counts at least 1 DPM, else 0."
-   ],
-   "id": "912268e5e997dbc6",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_tot[\"DPH_fb\"] = (d_tot[\"Foraging\"] >0).astype(int)\n",
-    "d_tot[\"FBR_h\"] = d_tot[\"DPH_fb\"] / d_tot[\"DPH\"]\n",
-    "d_tot[\"FBR_h\"] = d_tot[\"FBR_h\"].fillna(0)"
-   ],
-   "id": "23e3e4137d9e2a84",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Add time columns",
-   "id": "a775158ba810957a"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_tot = d_tot.assign(**{attr: getattr(d_tot[\"start_datetime\"].dt, attr.lower())\n",
-    "                         for attr in ['Year', 'Month', 'Day', 'Hour']})\n",
-    "d_tot['YM'] = d_tot[\"start_datetime\"].dt.to_period('M').dt.to_timestamp()\n",
-    "d_tot['Date'] = d_tot[\"start_datetime\"].dt.to_period('D').dt.to_timestamp()"
-   ],
-   "id": "62ce5a31ed0db25a",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Filter your data\n",
-    "Chose a threshold of %TimeLost to remove all data exceeding it."
-   ],
-   "id": "98d31fb21ffb9165"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "full_df = d_tot.merge(\n",
-    "    tl_df[[\"start_datetime\", \"%TimeLost\", \"Angle\", \"Temp\"]],\n",
-    "    on=\"start_datetime\", how=\"left\")"
-   ],
-   "id": "66bf795805047a3d",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "filtered_df = filter_tl(full_df, tl)\n",
-    "preserved_data(filtered_df, full_df)"
-   ],
-   "id": "c61f1c95fc05749e",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Visualize environmental data",
-   "id": "e0ea7247e27a37b6"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "resamp_tot = filtered_df.set_index(\"start_datetime\").resample(frq).first().reset_index()\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(12, 6))\n",
-    "ax.plot(resamp_tot[\"start_datetime\"], resamp_tot[\"Angle\"])\n",
-    "plt.title(\"Angle of the instrument over time\")\n",
-    "plt.show()"
-   ],
-   "id": "14ff46dec308dcc1",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "fig2, ax = plt.subplots(figsize=(12, 6))\n",
-    "ax.plot(resamp_tot[\"start_datetime\"], resamp_tot[\"Temp\"])\n",
-    "plt.title(\"Temperature over time\")\n",
-    "plt.show()"
-   ],
-   "id": "db07736375a767d9",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "fig3, ax = plt.subplots(figsize=(12, 6))\n",
-    "ax.plot(resamp_tot[\"start_datetime\"], resamp_tot[\"%TimeLost\"])\n",
-    "plt.title(\"%TimeLost over time\")\n",
-    "plt.show()"
-   ],
-   "id": "7409831bc24271e3",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Extract your processed data\n",
-    "This dataframe is now compatible for analyses on the next notebook and on R."
-   ],
-   "id": "c64d09af5a11213d"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "filtered_df.to_csv(r\"U:\\Hours_DPM_FBUZZ_CA4.csv\", index=False) #Name your file. 🐬",
-   "id": "f1f9ec385038ba87",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### First visualization\n",
-    "Precise the coordinates of the location of your listening point"
-   ],
-   "id": "a1b31aa6bd8f4d70"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "3fda0cc8174fa757"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/user_case/example_FPOD-CPOD_firstresults.ipynb b/user_case/example_FPOD-CPOD_firstresults.ipynb
deleted file mode 100644
index 1c3366b..0000000
--- a/user_case/example_FPOD-CPOD_firstresults.ipynb
+++ /dev/null
@@ -1,251 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "from pandas import (\n",
-    "    concat,\n",
-    "    read_csv,\n",
-    "    to_datetime,\n",
-    ")\n",
-    "\n",
-    "from post_processing.utils.fpod_utils import extract_site, percent_calc, year_percent, ym_percent, create_matrix,hist_mean_h, hist_mean_m, hist_mean_s\n",
-    "from post_processing.utils.core_utils import get_season"
-   ],
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "## Overview\n",
-    "Import the right dataset."
-   ],
-   "id": "36421fdbbca9aed6"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### *CALAIS*",
-   "id": "caea0e065ad8068c"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "ca4 = read_csv(r\"L:\\acoustock\\Bioacoustique\\DATASETS\\CPOD_PROJETS\\CALAIS\\DATA\\DATA_FULL_OTHERCET\\Hours_DPM_FBUZZ_CA4_ssTL.csv\")\n",
-    "walde = read_csv(r\"L:\\acoustock\\Bioacoustique\\DATASETS\\CPOD_PROJETS\\CALAIS\\DATA\\DATA_FULL_OTHERCET\\Hours_DPM_FBUZZ_Walde_ssTL.csv\")\n",
-    "\n",
-    "data_c = concat([ca4, walde])\n",
-    "data_c[\"start_datetime\"] = to_datetime(data_c[\"start_datetime\"])\n",
-    "data_c[\"start_datetime\"] = data_c[\"start_datetime\"].apply(lambda x : x.tz_convert(\"Europe/Paris\"))\n",
-    "data_c[\"Hour\"] = data_c[\"start_datetime\"].dt.hour"
-   ],
-   "id": "1268d9e6ce5cdf32",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### *CETIROISE*",
-   "id": "b426e672fdd5c6b8"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "ceti = read_csv(r\"U:\\Hours_DPM_FBUZZ_CETIROISE.csv\")\n",
-    "\n",
-    "ceti[\"start_datetime\"] = to_datetime(ceti[\"start_datetime\"])\n",
-    "ceti[\"start_datetime\"] = ceti[\"start_datetime\"].apply(lambda x : x.tz_convert(\"CET\")) #TimeZone Central European Time\n",
-    "ceti[\"Hour\"] = ceti[\"start_datetime\"].dt.hour"
-   ],
-   "id": "870bc0a014561ba8",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### *KERGUELEN*",
-   "id": "17a5ce1338f6cd1a"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "a = read_csv(r\"U:\\Hours_DPM_FBUZZ_A.csv\")\n",
-    "b = read_csv(r\"U:\\Hours_DPM_FBUZZ_B.csv\")\n",
-    "c = read_csv(r\"U:\\Hours_DPM_FBUZZ_C.csv\")\n",
-    "d = read_csv(r\"U:\\Hours_DPM_FBUZZ_D.csv\")\n",
-    "\n",
-    "data_k = concat([a, b, c, d])\n",
-    "data_k[\"start_datetime\"] = to_datetime(data_k[\"start_datetime\"])\n",
-    "data_k[\"start_datetime\"] = data_k[\"start_datetime\"].apply(lambda x : x.tz_convert(\"Indian/Kerguelen\"))\n",
-    "data_k[\"Hour\"] = data_k[\"start_datetime\"].dt.hour"
-   ],
-   "id": "d65697a1f1487f4c",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### First results\n",
-    "Precise your dataset."
-   ],
-   "id": "9fc3b5075bf7ff2c"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "data = data_c #🐬",
-   "id": "add4a626d6cc25a4",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "data = extract_site(data)\n",
-    "data[\"YMH\"] = data[\"Year\"].astype(str) + '-' + data[\"Month\"].astype(str) + '-' + data[\"Hour\"].astype(str)\n",
-    "y_per = percent_calc(data, \"Year\")\n",
-    "#t_per = percent_calc(data, \"TRAVAUX\")"
-   ],
-   "id": "37ecc80eda8e57ed",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "ym_per = percent_calc(data, \"YM\")\n",
-    "ym_per[\"YM\"] = to_datetime(ym_per[\"YM\"])\n",
-    "ym_per[\"Season\"] = ym_per[\"YM\"].apply(lambda x: get_season(x)[0]) #If in the southern hemisphere, write \"get_season(x, northern = False)\".\n",
-    "ym_per[\"Month\"] = ym_per[\"YM\"].dt.month\n",
-    "ym_per[\"Year\"] = ym_per[\"YM\"].dt.year"
-   ],
-   "id": "2b988869ed2466e1",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "per_h = percent_calc(data, \"YMH\")\n",
-    "per_h[[\"Y\",\"M\",\"Hour\"]] = per_h[\"YMH\"].str.split(\"-\", expand=True)\n",
-    "per_h[\"Hour\"] = per_h[\"Hour\"].astype(int)"
-   ],
-   "id": "cf704032c4a59a7b",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "matrice_s = create_matrix(ym_per, [\"site.name\"],[\"%DPH\", \"FBR\"])\n",
-    "matrice_m = create_matrix(ym_per, [\"site.name\", \"Month\"],[\"%click\", \"FBR\"])\n",
-    "matrice_h = create_matrix(per_h, [\"site.name\", \"Hour\"],[\"%click\", \"FBR\"])"
-   ],
-   "id": "caf3f71c6b6f70ca",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "hist_mean_s(\n",
-    "    matrice_s,\n",
-    "    metric_mean=\"%DPH_mean\",\n",
-    "    metric_std=\"%DPH_std\",\n",
-    "    y_lab=\"Moyenne %DPH\",\n",
-    "    title_suffix=\"%DPH\"\n",
-    ")"
-   ],
-   "id": "2ff751ae02e80285",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "year_percent(y_per, \"FBR\")",
-   "id": "29f2703ab28c5b28",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "ym_percent(ym_per, \"DPM\")",
-   "id": "a2dacac3caecff5f",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "hist_mean_m(\n",
-    "    matrice_m,\n",
-    "    metric_mean=\"%click_mean\",\n",
-    "    metric_std=\"%click_std\",\n",
-    "    y_lab=\"Moyenne %click\",\n",
-    "    title_suffix=\"%click\"\n",
-    ")"
-   ],
-   "id": "71161e7545bb1414",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "hist_mean_h(\n",
-    "    matrice_h,\n",
-    "    metric_mean=\"FBR_mean\",\n",
-    "    metric_std=\"FBR_std\",\n",
-    "    y_lab=\"Feeding buzz ratio\",\n",
-    "    title_suffix=\"FBR\"\n",
-    ")"
-   ],
-   "id": "5cbea8601bce2172",
-   "outputs": [],
-   "execution_count": null
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/user_case/example_FPOD-CPOD_raw.ipynb b/user_case/example_FPOD-CPOD_raw.ipynb
deleted file mode 100644
index 8412c3b..0000000
--- a/user_case/example_FPOD-CPOD_raw.ipynb
+++ /dev/null
@@ -1,420 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2026-01-20T10:24:33.069494Z",
-     "start_time": "2026-01-20T10:24:29.180185Z"
-    }
-   },
-   "source": [
-    "from __future__ import annotations\n",
-    "\n",
-    "import pytz\n",
-    "\n",
-    "from post_processing.utils.fpod_utils import load_pod_folder, pod2aplose, actual_data, filter_by_metadatax, process_feeding_buzz, load_pod_txt, add_utc\n",
-    "from post_processing.utils.core_utils import json2df\n",
-    "\n",
-    "import logging\n",
-    "from pathlib import Path\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.ticker as ticker\n",
-    "from pandas import Timestamp, to_datetime\n",
-    "from pandas.tseries import frequencies\n",
-    "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.core_utils import get_season, get_count\n",
-    "from osekit import setup_logging\n",
-    "from user_case.config import season_color\n",
-    "setup_logging(Path(r\"C:\\Users\\dupontma2\\Documents\\Git\\OSmOSE\\OSmOSE_post_processing\\src\\post_processing\\logging_config.yaml\"), logging.ERROR)"
-   ],
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Load data\n",
-    "🐬 = input to modify \\\n",
-    "Import your raw FPOD or CPOD data. All files for one site must be stored in the same folder and identified by their respective phases. \\\n",
-    "You also need to import your metadata file."
-   ],
-   "id": "c464f241817a1407"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-20T10:32:32.641354Z",
-     "start_time": "2026-01-20T10:32:09.650669Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "pod_files = Path(r\"U:\\CA4\") #Path to your data folder. 🐬\n",
-    "path = load_pod_folder(pod_files) #Process all your POD.csv files.\n",
-    "\n",
-    "fb_files = Path(r\"U:\\fb_CA4\")  #Path to your click details folder. 🐬\n",
-    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_calais.json\") #Path to your metadata file. 🐬\n",
-    "\n",
-    "print(path.head())\n",
-    "df_0 = path.dropna()\n",
-    "\n",
-    "metadatax = json2df(json_path=json)\n",
-    "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +  metadatax[\"campaign.name\"].astype(str))"
-   ],
-   "id": "6cf23db3b4288c29",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                      File          ChunkEnd DPM  Nall MinsOn deploy.name\n",
-      "0  CA4  POD2397 file01.CP3  14/05/2014 07:07   0     0      1  CA4_Phase1\n",
-      "1  CA4  POD2397 file01.CP3  14/05/2014 07:08   0     8      1  CA4_Phase1\n",
-      "2  CA4  POD2397 file01.CP3  14/05/2014 07:09   0     4      1  CA4_Phase1\n",
-      "3  CA4  POD2397 file01.CP3  14/05/2014 07:10   0   251      1  CA4_Phase1\n",
-      "4  CA4  POD2397 file01.CP3  14/05/2014 07:11   0  4095      1  CA4_Phase1\n"
-     ]
-    }
-   ],
-   "execution_count": 2
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "d_beg_end = actual_data(df_0, metadatax) #Extract the beginning and end of recording for every phase.",
-   "id": "fa52f8971b61aaf6",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-20T10:32:43.618484Z",
-     "start_time": "2026-01-20T10:32:43.243995Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_1 = df_0[df_0[\"DPM\"] !=\"0\" ] #Remove the 0 to lighten the APLOSE file.",
-   "id": "769e128f2a5293e1",
-   "outputs": [],
-   "execution_count": 3
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### APLOSE format\n",
-    "#### *C-POD*\n",
-    "Use cpod2aplose if you are managing C-POD data."
-   ],
-   "id": "dd03975b7aef7eed"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-20T10:32:50.772619Z",
-     "start_time": "2026-01-20T10:32:50.060639Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "df_aplose = pod2aplose(df_1, pytz.utc, \"CA4\", \"Marsouin\", \"CPOD\") #Precise site name, species and instrument. 🐬\n",
-    "print(df_aplose.head())"
-   ],
-   "id": "4cc867627d677529",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  dataset                      filename  start_time  end_time  \\\n",
-      "0     CA4  2014-05-16T02:30:00.000+0000           0        60   \n",
-      "1     CA4  2014-05-17T03:52:00.000+0000           0        60   \n",
-      "2     CA4  2014-05-17T04:47:00.000+0000           0        60   \n",
-      "3     CA4  2014-05-19T17:06:00.000+0000           0        60   \n",
-      "4     CA4  2014-05-20T11:07:00.000+0000           0        60   \n",
-      "\n",
-      "   start_frequency  end_frequency annotation annotator  \\\n",
-      "0                0              0   Marsouin      CPOD   \n",
-      "1                0              0   Marsouin      CPOD   \n",
-      "2                0              0   Marsouin      CPOD   \n",
-      "3                0              0   Marsouin      CPOD   \n",
-      "4                0              0   Marsouin      CPOD   \n",
-      "\n",
-      "                 start_datetime                  end_datetime  is_box  \\\n",
-      "0  2014-05-16T02:30:00.000+0000  2014-05-16T02:31:00.000+0000       0   \n",
-      "1  2014-05-17T03:52:00.000+0000  2014-05-17T03:53:00.000+0000       0   \n",
-      "2  2014-05-17T04:47:00.000+0000  2014-05-17T04:48:00.000+0000       0   \n",
-      "3  2014-05-19T17:06:00.000+0000  2014-05-19T17:07:00.000+0000       0   \n",
-      "4  2014-05-20T11:07:00.000+0000  2014-05-20T11:08:00.000+0000       0   \n",
-      "\n",
-      "  deploy.name  \n",
-      "0  CA4_Phase1  \n",
-      "1  CA4_Phase1  \n",
-      "2  CA4_Phase1  \n",
-      "3  CA4_Phase1  \n",
-      "4  CA4_Phase1  \n"
-     ]
-    }
-   ],
-   "execution_count": 4
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Clean your dataset\n",
-    "Remove useless lines, recorder outside the instrument submersion. Export your file to the aplose format. You can change the name of the file to match the project you are working on."
-   ],
-   "id": "d2c642658dbfe278"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-20T10:33:14.509811Z",
-     "start_time": "2026-01-20T10:33:14.070492Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "cleared = filter_by_metadatax(df_aplose, metadatax)",
-   "id": "895bd5a116918285",
-   "outputs": [],
-   "execution_count": 5
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2026-01-20T10:33:24.735234Z",
-     "start_time": "2026-01-20T10:33:24.723966Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "cleared = cleared.drop_duplicates(subset=['start_datetime'], keep=\"last\")",
-   "id": "1a31da7341f3d8c9",
-   "outputs": [],
-   "execution_count": 7
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Feeding buzzes processing\n",
-    "Use \"Dauphin\", Marsouin\" or \"Commerson\" to get different ICI processing."
-   ],
-   "id": "4cf0b89a9491884"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "fb_all = load_pod_txt(fb_files) #Read all your FB.txt files.\n",
-    "fb_all = process_feeding_buzz(fb_all, \"Marsouin\") #Categorize the minutes (positive or not to FB detection). 🐬\n",
-    "add_utc(fb_all, [\"start_datetime\"], \"min\")"
-   ],
-   "id": "64f824e02131d90a",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "dpm_fb = cleared.merge(fb_all[[\"start_datetime\", \"Foraging\"]], on=[\"start_datetime\"], how=\"left\") #Merge DPM and FB dataframes",
-   "id": "e90f6d91de3f8ce3",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Export",
-   "id": "d114ed7164cfd0da"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_beg_end.to_csv(r\"U:\\Deb_Fin_CA4.csv\", index=False) #Export the new file. 🐬\n",
-    "dpm_fb.to_csv(r\"U:\\APLOSE_CA4_pos.csv\", index=False) #Name your file. 🐬"
-   ],
-   "id": "9d34e00f4e8147e8",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Explore\n",
-    "First visualization of the data"
-   ],
-   "id": "a85ea092d9fc197c"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "data = DataAplose(dpm_fb)",
-   "id": "639c474690373895",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "bin_size = frequencies.to_offset(\"1d\")\n",
-    "ticks = frequencies.to_offset(\"6BMS\")\n",
-    "fmt = \"%b %y\""
-   ],
-   "id": "cb476b5655bdff42",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "data.lat = 50.973333 #CA4: 51.00035 ; Walde: 50.973333 ; A: -49.38765 ; B: -49.424733 ; C: -49.4677 ; D: -49.47175\n",
-    "data.lon = 1.8117 #CA4: 1.879667 ; Walde: 1.8117 ; A: 69.9449 ; B: 69.932383 ; C: 70.081067 ; D: 69.836617"
-   ],
-   "id": "3fc33f2acf84ea34",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "#### Reshape the data\n",
-    "Set beginning and end of the chosen window."
-   ],
-   "id": "2857f26f8c168ad3"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "data.df[\"end_datetime\"] = to_datetime(data.df[\"end_datetime\"])\n",
-    "data2 = data.reshape(begin=Timestamp(\"2013 11 01\"), end=Timestamp(\"2025 08 01\"))\n",
-    "tz = pytz.timezone(\"UTC\")\n",
-    "data2.change_tz(tz)"
-   ],
-   "id": "75e00c1920b69409",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### Heatmap",
-   "id": "cdf3a92dfb6514d6"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "fig, ax = plt.subplots(1, 1)\n",
-    "ax = data2.set_ax(ax=ax, x_ticks_res=ticks, date_format=fmt)\n",
-    "data2.plot(\n",
-    "    mode=\"heatmap\",\n",
-    "    annotator=data2.annotators[0],\n",
-    "    label=data2.labels[0],\n",
-    "    ax=ax,\n",
-    "    bin_size=bin_size,\n",
-    "    show_rise_set=True,\n",
-    ")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ],
-   "id": "6e1832101aec4156",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### Detections over time",
-   "id": "b0f3c6b7fc08a2be"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "fig2, ax = plt.subplots(1, 1)\n",
-    "ax = data2.set_ax(ax=ax, x_ticks_res=ticks, date_format=fmt)\n",
-    "data2.plot(\n",
-    "    mode=\"scatter\",\n",
-    "    annotator=data2.annotators[0],\n",
-    "    label=data2.labels[0],\n",
-    "    ax=ax,\n",
-    "    show_rise_set=True,\n",
-    ")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ],
-   "id": "66b43e53fb17037",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### DPM per day",
-   "id": "c7ea248b277edb65"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "df_filtered = data2.filter_df(data2.annotators[0], data2.labels[0])\n",
-    "df_counts = get_count(df_filtered, bin_size)\n",
-    "\n",
-    "df_counts[\"Season\"] = df_counts.index.to_series().apply(lambda x: get_season(x)[0])\n",
-    "df_counts[\"colors\"] = df_counts[\"Season\"].map(season_color).fillna(\"gray\")\n",
-    "\n",
-    "fig3, ax = plt.subplots(1, 1)\n",
-    "ax = data2.set_ax(ax=ax, x_ticks_res=ticks, date_format=fmt)\n",
-    "data2.plot(\n",
-    "    mode=\"histogram\",\n",
-    "    annotator=data2.annotators[0],\n",
-    "    label=data2.labels[0],\n",
-    "    color=df_counts[\"colors\"].tolist(),\n",
-    "    ax=ax,\n",
-    "    bin_size=bin_size,\n",
-    "    legend=True,\n",
-    ")\n",
-    "ax.set_ylim(0, 200)\n",
-    "ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=10))\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ],
-   "id": "81b0dafa8adc20e9",
-   "outputs": [],
-   "execution_count": null
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/user_case/resource/CPOD-FPOD_yaml.yml b/user_case/resource/CPOD-FPOD_yaml.yml
deleted file mode 100644
index ded85b0..0000000
--- a/user_case/resource/CPOD-FPOD_yaml.yml
+++ /dev/null
@@ -1,33 +0,0 @@
-#This file is to be used to load an APLOSE result csv file.
-#If a parameter is set here to `null`, it will be loaded with a `None` value in the Python scripts.
-#For parameter definition, see `sort_detections` function in `utils\def_func`.
-#
-#Note:
-#  - Several csv files can be loaded at once, to perform this copy the template and paste it at the end of the present file.
-
-#########################################
-'C:\Users\dupontma2\Downloads\data_aplose.csv':
-  # timebin: detection time resolution in seconds
-  'timebin_new': null
-  # begin datetime: '2000-01-01T00:00:00+0000'
-  'begin': null
-  # end datetime: '2000-01-01T00:00:00+0000'
-  'end': null
-  # annotator: ['annotator1', 'annotator2']
-  'annotator': null
-  # annotation: ['annotation1', 'annotation2']
-  'annotation': "Marsouin"
-  # box: boolean value, whether to keep strong annotations
-  'box': False
-  # timestamp file
-  'timestamp_file': null
-  # user selection
-  'user_sel': all
-  # f_min filter
-  'f_min': null
-  # f_max filter
-  'f_max': null
-  # score
-  'score': null
-  # filename format
-  'filename_format': '%Y-%m-%dT%H:%M:%S.%f%z'
\ No newline at end of file
diff --git a/user_case/user_case_CALAIS.ipynb b/user_case/user_case_CALAIS.ipynb
deleted file mode 100644
index f0f109a..0000000
--- a/user_case/user_case_CALAIS.ipynb
+++ /dev/null
@@ -1,581 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2026-01-21T13:29:51.583226Z",
-     "start_time": "2026-01-21T13:29:43.385674Z"
-    }
-   },
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "import pytz\n",
-    "from pandas import (\n",
-    "    concat,\n",
-    "    read_csv,\n",
-    "    to_datetime,\n",
-    ")\n",
-    "\n",
-    "from post_processing.dataclass.data_aplose import DataAplose\n",
-    "from post_processing.utils.fpod_utils import load_pod_folder, pod2aplose, actual_data, filter_by_metadatax, resample_dpm, load_pod_txt, build_range, extract_site, percent_calc, site_percent, year_percent, ym_percent, month_percent, hour_percent, \\\n",
-    "    process_feeding_buzz\n",
-    "from post_processing.utils.core_utils import json2df,get_season"
-   ],
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": [
-    "### Pre-processing\n",
-    "\n"
-   ],
-   "id": "e8e8c57c7f4197fe"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Import your csv files. All files for one site must be stored in the same folder.",
-   "id": "6f9beab2dcba1a9c"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-17T09:25:01.549663Z",
-     "start_time": "2025-10-17T09:24:48.208563Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "pod_files = Path(r\"U:\\Walde\")\n",
-    "path = load_pod_folder(pod_files)\n",
-    "print(path.head())\n",
-    "\n",
-    "df_0 = path.dropna()\n",
-    "\n",
-    "json = Path(r\"C:\\Users\\fouinel\\Downloads\\deployment_calais.json\") #Path to your metadata file.\n",
-    "metadatax = json2df(json_path=json)\n",
-    "\n",
-    "metadatax[\"deploy.name\"] = (metadatax[\"site.name\"].astype(str) + \"_\" +\n",
-    "                           metadatax[\"campaign.name\"].astype(str))"
-   ],
-   "id": "8636a8a27fe2af47",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                 File          ChunkEnd  DPM    Nall  MinsOn   deploy.name\n",
-      "0  POD2399 file01.CP3  14/05/2014 08:03  0.0     0.0     1.0  Walde_Phase1\n",
-      "1  POD2399 file01.CP3  14/05/2014 08:04  0.0   799.0     1.0  Walde_Phase1\n",
-      "2  POD2399 file01.CP3  14/05/2014 08:05  0.0     0.0     1.0  Walde_Phase1\n",
-      "3  POD2399 file01.CP3  14/05/2014 08:06  0.0  3361.0     1.0  Walde_Phase1\n",
-      "4  POD2399 file01.CP3  14/05/2014 08:07  0.0   421.0     1.0  Walde_Phase1\n"
-     ]
-    }
-   ],
-   "execution_count": 11
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-17T09:34:43.092415Z",
-     "start_time": "2025-10-17T09:27:26.409365Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "d_beg_end = actual_data(df_0, metadatax)",
-   "id": "4208969d9e509a8",
-   "outputs": [],
-   "execution_count": 12
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-17T09:35:32.137350Z",
-     "start_time": "2025-10-17T09:35:32.089860Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "d_beg_end.to_csv(r\"U:\\Deb_Fin_Walde.csv\", index=False)",
-   "id": "6fb6f4fa675d7cab",
-   "outputs": [],
-   "execution_count": 13
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-17T09:35:33.725437Z",
-     "start_time": "2025-10-17T09:35:33.670018Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "df_1 = df_0[df_0[\"DPM\"] !=0 ]",
-   "id": "597efd1d90e3d069",
-   "outputs": [],
-   "execution_count": 14
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### APLOSE format",
-   "id": "4f8c83c96f0b6ff4"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Chose the right function, depending on the instrument you are working with.",
-   "id": "9849c47189cf1a85"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### *CPOD*",
-   "id": "8ed339c688bdef1"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-17T09:35:38.378283Z",
-     "start_time": "2025-10-17T09:35:35.755020Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "df_aplose = pod2aplose(df_1, pytz.utc, \"Walde\", \"Marsouin\", \"CPOD\")\n",
-    "print(df_aplose.head())"
-   ],
-   "id": "812ed7c0c5e258e7",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  dataset filename  start_time  end_time  start_frequency  end_frequency  \\\n",
-      "0   Walde                    0        60                0              0   \n",
-      "1   Walde                    0        60                0              0   \n",
-      "2   Walde                    0        60                0              0   \n",
-      "3   Walde                    0        60                0              0   \n",
-      "4   Walde                    0        60                0              0   \n",
-      "\n",
-      "  annotation annotator                start_datetime  \\\n",
-      "0   Marsouin      FPOD  2014-05-15T16:56:00.000+0000   \n",
-      "1   Marsouin      FPOD  2014-05-17T22:53:00.000+0000   \n",
-      "2   Marsouin      FPOD  2014-05-17T22:54:00.000+0000   \n",
-      "3   Marsouin      FPOD  2014-05-18T14:05:00.000+0000   \n",
-      "4   Marsouin      FPOD  2014-05-21T14:19:00.000+0000   \n",
-      "\n",
-      "                   end_datetime  is_box   deploy.name  \n",
-      "0  2014-05-15T16:57:00.000+0000       0  Walde_Phase1  \n",
-      "1  2014-05-17T22:54:00.000+0000       0  Walde_Phase1  \n",
-      "2  2014-05-17T22:55:00.000+0000       0  Walde_Phase1  \n",
-      "3  2014-05-18T14:06:00.000+0000       0  Walde_Phase1  \n",
-      "4  2014-05-21T14:20:00.000+0000       0  Walde_Phase1  \n"
-     ]
-    }
-   ],
-   "execution_count": 15
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### *FPOD*",
-   "id": "a39bb10d8ac60a27"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-17T09:23:23.552890Z",
-     "start_time": "2025-10-17T09:23:22.810583Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "df_aplose = pod2aplose(df_1, pytz.utc, \"CETIROISE\", \"Marsouin\", \"FPOD\")\n",
-    "print(df_aplose.head())"
-   ],
-   "id": "9b632673397a184",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  dataset filename  start_time  end_time  start_frequency  end_frequency  \\\n",
-      "0     CA4                    0        60                0              0   \n",
-      "1     CA4                    0        60                0              0   \n",
-      "2     CA4                    0        60                0              0   \n",
-      "3     CA4                    0        60                0              0   \n",
-      "4     CA4                    0        60                0              0   \n",
-      "\n",
-      "  annotation annotator                start_datetime  \\\n",
-      "0   Marsouin      FPOD  2014-05-17T03:52:00.000+0000   \n",
-      "1   Marsouin      FPOD  2014-05-17T04:47:00.000+0000   \n",
-      "2   Marsouin      FPOD  2014-05-19T17:06:00.000+0000   \n",
-      "3   Marsouin      FPOD  2014-05-20T11:07:00.000+0000   \n",
-      "4   Marsouin      FPOD  2014-05-20T11:16:00.000+0000   \n",
-      "\n",
-      "                   end_datetime  is_box deploy.name  \n",
-      "0  2014-05-17T03:53:00.000+0000       0  CA4_Phase1  \n",
-      "1  2014-05-17T04:48:00.000+0000       0  CA4_Phase1  \n",
-      "2  2014-05-19T17:07:00.000+0000       0  CA4_Phase1  \n",
-      "3  2014-05-20T11:08:00.000+0000       0  CA4_Phase1  \n",
-      "4  2014-05-20T11:17:00.000+0000       0  CA4_Phase1  \n"
-     ]
-    }
-   ],
-   "execution_count": 7
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Remove non usable lines",
-   "id": "7860838f8514da39"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Import the .json file available on metadatax.",
-   "id": "32f8ff8f9ece35a8"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-17T09:35:54.374270Z",
-     "start_time": "2025-10-17T09:35:54.303578Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "\n",
-    "\n",
-    "cleared = filter_by_metadatax(df_aplose, metadatax) #Remove lines captures outside the instrument submersion."
-   ],
-   "id": "ed6a06c522aea169",
-   "outputs": [],
-   "execution_count": 16
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Export your file to the aplose format. You can change the name of the file to match the project you are working on.",
-   "id": "8f5fe75cc3463971"
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-10-17T09:36:06.597522Z",
-     "start_time": "2025-10-17T09:35:56.739495Z"
-    }
-   },
-   "cell_type": "code",
-   "source": "cleared.to_csv(r\"U:\\APLOSE_Walde_pos.csv\", index=False) #You can stock all DPM for a site in a DataAplose file.",
-   "id": "76f70cb6c6658ba6",
-   "outputs": [],
-   "execution_count": 17
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Load data",
-   "id": "f5d38266dc9d5273"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Use the yaml file to import your aplose files one at a time.",
-   "id": "2ce11c6e57f38690"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "yaml_file = Path(r\"resource\\CPOD-FPOD_yaml.yml\")\n",
-    "data_list = DataAplose.from_yaml(file=yaml_file)\n",
-    "print(data_list.df.head())"
-   ],
-   "id": "6837593897111b0a",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Format choice\n",
-   "id": "9f93eb863e3e3a9e"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Here you need to choose the format in which you want to visualise the positive detections. This aims to replace the functionality available in CPOD.exe.",
-   "id": "925d92d3eec065e6"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "frq = \"D\" #This argument will determine the format in which you want to visualise your data. Use \"D\", \"h\" or \"10min\".",
-   "id": "256b756d05c08294"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "resamp = resample_dpm(data_list.df, frq=frq, extra_columns=[\"deploy.name\"])",
-   "id": "6cc79b2aeef076ed",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "### Add the feeding buzzes",
-   "id": "8375ddbe07ad0aee"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "Import your click details files. All files for one site must be stacked in the same folder.",
-   "id": "9753f4ba20c7267e"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "fb_files = Path(r\"U:\\fb_fpod_cetiroise_c\") #Path to your click details folder.\n",
-    "fb_all = load_pod_txt(fb_files)\n",
-    "fb_all = process_feeding_buzz(fb_all, \"Marsouin\")\n",
-    "\n",
-    "fb_all[\"start_datetime\"] = fb_all[\"start_datetime\"].dt.floor(frq)\n",
-    "fb = fb_all.groupby(\"start_datetime\")[\"Foraging\"].sum().reset_index()\n",
-    "fb[\"start_datetime\"] = to_datetime(fb[\"start_datetime\"], utc=True)"
-   ],
-   "id": "2b19f90c99252ff3",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_tot = resamp.merge(fb, on=\"start_datetime\", how=\"left\")\n",
-    "\n",
-    "#This function aims to reindent 0 between the positive detections. It will be useful to produce first visualization graphs and use this dataset in R.\n",
-    "d_hour = build_range(d_beg_end, frq)"
-   ],
-   "id": "b00c8f1e2210ea7",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "d_fin = d_hour.merge(d_tot, on=[\"start_datetime\", \"deploy.name\"], how=\"left\")",
-   "id": "601787cc806226b0",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_fin[[\"DPM\",\"Foraging\"]] = d_fin[[\"DPM\",\"Foraging\"]].fillna(0)\n",
-    "print(d_fin.head())"
-   ],
-   "id": "f93bf1f3ca4f4112",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## Add time columns",
-   "id": "c7b1d32ed1c99fb7"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_fin[\"Year\"] = d_fin[\"start_datetime\"].dt.year\n",
-    "d_fin[\"Month\"] = d_fin[\"start_datetime\"].dt.month\n",
-    "d_fin['YM'] = d_fin[\"Year\"].astype(str) + '-' + d_fin[\"Month\"].astype(str)\n",
-    "d_fin['YM'] = to_datetime(d_fin['YM'])\n",
-    "d_fin[\"Day\"] = d_fin[\"start_datetime\"].dt.day\n",
-    "d_fin[\"Hour\"] = d_fin[\"start_datetime\"].dt.hour\n",
-    "\n",
-    "d_fin[\"FBR\"] = d_fin[\"Foraging\"] / d_fin[\"DPM\"]\n",
-    "d_fin[\"DPH\"] = (d_fin[\"DPM\"] >0).astype(int)"
-   ],
-   "id": "a2261ce5093a3104",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "d_fin[\"FBR\"] = d_fin[\"FBR\"].fillna(0)\n",
-    "d_fin.to_csv(r\"U:\\Hours_DPM_FBUZZ_CETIROISE.csv\", index=False)"
-   ],
-   "id": "d606f4f6904b57c6",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "## Overview",
-   "id": "4bc0904182a3f845"
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### *Import datasets*",
-   "id": "e1de414e2eb3fa8f"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "ca4 = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteCA4.csv\")\n",
-    "walde = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteWalde.csv\")\n",
-    "\n",
-    "data_c = concat([ca4, walde])\n",
-    "data_c[\"start_datetime\"] = to_datetime(data_c[\"start_datetime\"])\n",
-    "data_c[\"start_datetime\"] = data_c[\"start_datetime\"].apply(lambda x : x.tz_convert(\"Europe/Paris\"))\n",
-    "data_c[\"Hour\"] = data_c[\"start_datetime\"].dt.hour"
-   ],
-   "id": "9909fbfdcb8e2e78",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "a = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteA.csv\")\n",
-    "b = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteB.csv\")\n",
-    "c = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteC.csv\")\n",
-    "d = read_csv(r\"U:\\Hours_DPM_FBUZZ_SiteD.csv\")\n",
-    "\n",
-    "data_k = concat([a, b, c, d])\n",
-    "data_k[\"start_datetime\"] = to_datetime(data_k[\"start_datetime\"])\n",
-    "data_k[\"start_datetime\"] = data_k[\"start_datetime\"].apply(lambda x : x.tz_convert(\"Indian/Kerguelen\"))\n",
-    "data_k[\"Hour\"] = data_k[\"start_datetime\"].dt.hour"
-   ],
-   "id": "87e2d1938787aefc",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "ceti = read_csv(r\"U:\\Hours_DPM_FBUZZ_CETIROISE.csv\")\n",
-    "\n",
-    "ceti[\"start_datetime\"] = to_datetime(ceti[\"start_datetime\"])\n",
-    "ceti[\"start_datetime\"] = ceti[\"start_datetime\"].apply(lambda x : x.tz_convert(\"CET\")) #TimeZone Central European Time\n",
-    "ceti[\"Hour\"] = ceti[\"start_datetime\"].dt.hour"
-   ],
-   "id": "5928770d1c47bcad",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "markdown",
-   "source": "#### *Generate graphs*",
-   "id": "200273fc36fb7d5d"
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "data = ceti #Precise which dataset you are working with",
-   "id": "be10e9d690294cff",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": [
-    "data = extract_site(data)\n",
-    "y_per = percent_calc(data, \"Year\")\n",
-    "ym_per = percent_calc(data, \"YM\")\n",
-    "ym_per[\"YM\"] = to_datetime(ym_per[\"YM\"])\n",
-    "ym_per[\"Season\"] = ym_per[\"YM\"].apply(lambda x: get_season(x)[0])\n",
-    "m_per = percent_calc(data, \"Month\")\n",
-    "h_per = percent_calc(data, \"Hour\")\n",
-    "s_per = percent_calc(data)"
-   ],
-   "id": "2826b79097a85607",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "site_percent(s_per, \"%click\")",
-   "id": "ddd1fac6295136c6",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "year_percent(y_per, \"%click\")",
-   "id": "ba7581e97fdbd07c",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "ym_percent(ym_per, \"%click\")",
-   "id": "4de618933c154f86",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "month_percent(m_per, \"%buzzes\")",
-   "id": "7cf84c8744185424",
-   "outputs": [],
-   "execution_count": null
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "source": "hour_percent(h_per, \"%click\")",
-   "id": "12d83e9082d711c0",
-   "outputs": [],
-   "execution_count": null
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 67108127f51c196a7603ff022dd584ad8e1d6dc2 Mon Sep 17 00:00:00 2001
From: Mathieu Dupont <108517594+mathieudpnt@users.noreply.github.com>
Date: Tue, 27 Jan 2026 17:20:56 +0100
Subject: [PATCH 83/83] refacto

---
 src/post_processing/utils/filtering_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/post_processing/utils/filtering_utils.py b/src/post_processing/utils/filtering_utils.py
index a9e0619..3d4832b 100644
--- a/src/post_processing/utils/filtering_utils.py
+++ b/src/post_processing/utils/filtering_utils.py
@@ -485,8 +485,8 @@ def _process_annotator_label_pair(
         time_vector[i] for i, detected in enumerate(detect_vec) if detected
     ]
     file_vector = [
-        # filename_vector[i] for i, detected in enumerate(detect_vec) if detected
-        filename_vector[i + 1] for i, detected in enumerate(detect_vec) if detected
+        filename_vector[i] for i, detected in enumerate(detect_vec) if detected
+        # filename_vector[i + 1] for i, detected in enumerate(detect_vec) if detected
     ]
 
     if not start_datetime: