Data populated ratio

docs/source/coreapi_usage.rst

@@ -294,7 +294,7 @@ field:
 .. code-block:: python
 
     # Filtering the ads data to remove data without audio (e.g. between files)
-    ads.data = [ad for ad in ads.data if not ad.is_empty]
+    ads.remove_empty_data(threshold=0.)
 
     # Resampling/Exporting only the first audio data
     ad = ads.data[0]

docs/source/example_multiple_spectrograms_core.ipynb

@@ -106,11 +106,19 @@
    "source": [
     "print(f\"{' BEFORE FILTERING ':#^60}\")\n",
     "print(\n",
-    "    f\"{'Nb of Empty data:':<30}{str(len([ad for ad in audio_dataset.data if ad.is_empty])):>30}\\n\"\n",
+    "    f\"{'Nb of Empty data:':<30}{sum(1 for ad in audio_dataset.data if ad.is_empty):>30}\\n\"\n",
     ")\n",
     "\n",
-    "# Remove the empty data by using the default AudioDataset constructor:\n",
-    "audio_dataset = AudioDataset([ad for ad in audio_dataset.data if not ad.is_empty])"
+    "# Remove the empty data:\n",
+    "removed_data = audio_dataset.remove_empty_data(threshold=0.0)\n",
+    "\n",
+    "# We can take a look at which data has been removed:\n",
+    "print(f\"{' REMOVED DATA ':#^60}\")\n",
+    "print(f\"{'Begin':<20}{'Duration':^20}{'Fill rate':>20}\")\n",
+    "for data in removed_data:\n",
+    "    print(\n",
+    "        f\"{data.begin.strftime('%H:%M:%S'):<20}{str(data.duration):^20}{str(data.populated_ratio) + ' %':>20}\"\n",
+    "    )"
    ]
   },
   {

docs/source/example_multiple_spectrograms_public.ipynb

@@ -2,13 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "dc7ebca70b3b5da",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-13T11:25:15.629114Z",
-     "start_time": "2025-11-13T11:25:15.535616Z"
-    },
     "tags": [
      "remove-cell"
     ]
@@ -50,50 +46,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "bb002105fc9632e8",
    "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-11-13T11:25:21.374824Z",
-     "start_time": "2025-11-13T11:25:18.067636Z"
-    },
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\t2025-11-13 12:25:20,650\n",
-      "Building the dataset...\n",
-      "\n",
-      "\t2025-11-13 12:25:20,652\n",
-      "Analyzing original audio files...\n",
-      "\n",
-      "\t2025-11-13 12:25:20,680\n",
-      "Organizing dataset folder...\n",
-      "\n"
-     ]
-    },
-    {
-     "ename": "PermissionError",
-     "evalue": "[WinError 32] Le processus ne peut pas accéder au fichier car ce fichier est utilisé par un autre processus: '_static\\\\sample_audio\\\\sample_220925_223530.wav' -> '_static\\\\sample_audio\\\\data\\\\audio\\\\original\\\\sample_220925_223530.wav'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
-      "\u001B[31mPermissionError\u001B[39m                           Traceback (most recent call last)",
-      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 14\u001B[39m\n\u001B[32m      6\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mosekit\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mcore_api\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01minstrument\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m Instrument\n\u001B[32m      8\u001B[39m dataset = Dataset(\n\u001B[32m      9\u001B[39m     folder=audio_folder,\n\u001B[32m     10\u001B[39m     strptime_format=\u001B[33m\"\u001B[39m\u001B[33m%\u001B[39m\u001B[33my\u001B[39m\u001B[33m%\u001B[39m\u001B[33mm\u001B[39m\u001B[38;5;132;01m%d\u001B[39;00m\u001B[33m_\u001B[39m\u001B[33m%\u001B[39m\u001B[33mH\u001B[39m\u001B[33m%\u001B[39m\u001B[33mM\u001B[39m\u001B[33m%\u001B[39m\u001B[33mS\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m     11\u001B[39m     instrument=Instrument(end_to_end_db=\u001B[32m150.0\u001B[39m),\n\u001B[32m     12\u001B[39m )\n\u001B[32m---> \u001B[39m\u001B[32m14\u001B[39m \u001B[43mdataset\u001B[49m\u001B[43m.\u001B[49m\u001B[43mbuild\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\Documents\\GitHub\\OSEkit\\src\\osekit\\public_api\\dataset.py:156\u001B[39m, in \u001B[36mDataset.build\u001B[39m\u001B[34m(self)\u001B[39m\n\u001B[32m    144\u001B[39m \u001B[38;5;28mself\u001B[39m.logger.info(\u001B[33m\"\u001B[39m\u001B[33mOrganizing dataset folder...\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m    145\u001B[39m move_tree(\n\u001B[32m    146\u001B[39m     source=\u001B[38;5;28mself\u001B[39m.folder,\n\u001B[32m    147\u001B[39m     destination=\u001B[38;5;28mself\u001B[39m.folder / \u001B[33m\"\u001B[39m\u001B[33mother\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m   (...)\u001B[39m\u001B[32m    154\u001B[39m     | {\u001B[38;5;28mself\u001B[39m.folder / \u001B[33m\"\u001B[39m\u001B[33mlog\u001B[39m\u001B[33m\"\u001B[39m},\n\u001B[32m    155\u001B[39m )\n\u001B[32m--> \u001B[39m\u001B[32m156\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_sort_dataset\u001B[49m\u001B[43m(\u001B[49m\u001B[43mads\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    157\u001B[39m ads.write_json(ads.folder)\n\u001B[32m    158\u001B[39m \u001B[38;5;28mself\u001B[39m.write_json()\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\Documents\\GitHub\\OSEkit\\src\\osekit\\public_api\\dataset.py:513\u001B[39m, in \u001B[36mDataset._sort_dataset\u001B[39m\u001B[34m(self, dataset)\u001B[39m\n\u001B[32m    511\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_sort_dataset\u001B[39m(\u001B[38;5;28mself\u001B[39m, dataset: \u001B[38;5;28mtype\u001B[39m[DatasetChild]) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m    512\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mtype\u001B[39m(dataset) \u001B[38;5;129;01mis\u001B[39;00m AudioDataset:\n\u001B[32m--> \u001B[39m\u001B[32m513\u001B[39m         \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_sort_audio_dataset\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdataset\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    514\u001B[39m         \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[32m    515\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mtype\u001B[39m(dataset) \u001B[38;5;129;01mis\u001B[39;00m SpectroDataset | LTASDataset:\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\Documents\\GitHub\\OSEkit\\src\\osekit\\public_api\\dataset.py:520\u001B[39m, in \u001B[36mDataset._sort_audio_dataset\u001B[39m\u001B[34m(self, dataset)\u001B[39m\n\u001B[32m    519\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34m_sort_audio_dataset\u001B[39m(\u001B[38;5;28mself\u001B[39m, dataset: AudioDataset) -> \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m520\u001B[39m     \u001B[43mdataset\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmove_files\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_get_audio_dataset_subpath\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdataset\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\Documents\\GitHub\\OSEkit\\src\\osekit\\core_api\\base_dataset.py:152\u001B[39m, in \u001B[36mBaseDataset.move_files\u001B[39m\u001B[34m(self, folder)\u001B[39m\n\u001B[32m    143\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Move the dataset files to the destination folder.\u001B[39;00m\n\u001B[32m    144\u001B[39m \n\u001B[32m    145\u001B[39m \u001B[33;03mParameters\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    149\u001B[39m \n\u001B[32m    150\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    151\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m file \u001B[38;5;129;01min\u001B[39;00m tqdm(\u001B[38;5;28mself\u001B[39m.files, disable=os.environ.get(\u001B[33m\"\u001B[39m\u001B[33mDISABLE_TQDM\u001B[39m\u001B[33m\"\u001B[39m, \u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m)):\n\u001B[32m--> \u001B[39m\u001B[32m152\u001B[39m     \u001B[43mfile\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmove\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfolder\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    153\u001B[39m \u001B[38;5;28mself\u001B[39m._folder = folder\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\Documents\\GitHub\\OSEkit\\src\\osekit\\core_api\\audio_file.py:128\u001B[39m, in \u001B[36mAudioFile.move\u001B[39m\u001B[34m(self, folder)\u001B[39m\n\u001B[32m    119\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"Move the file to the target folder.\u001B[39;00m\n\u001B[32m    120\u001B[39m \n\u001B[32m    121\u001B[39m \u001B[33;03mParameters\u001B[39;00m\n\u001B[32m   (...)\u001B[39m\u001B[32m    125\u001B[39m \n\u001B[32m    126\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    127\u001B[39m afm.close()\n\u001B[32m--> \u001B[39m\u001B[32m128\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmove\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfolder\u001B[49m\u001B[43m)\u001B[49m\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\Documents\\GitHub\\OSEkit\\src\\osekit\\core_api\\base_file.py:171\u001B[39m, in \u001B[36mBaseFile.move\u001B[39m\u001B[34m(self, folder)\u001B[39m\n\u001B[32m    169\u001B[39m destination_path = folder / \u001B[38;5;28mself\u001B[39m.path.name\n\u001B[32m    170\u001B[39m folder.mkdir(exist_ok=\u001B[38;5;28;01mTrue\u001B[39;00m, parents=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m--> \u001B[39m\u001B[32m171\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mpath\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrename\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdestination_path\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    172\u001B[39m \u001B[38;5;28mself\u001B[39m.path = destination_path\n",
-      "\u001B[36mFile \u001B[39m\u001B[32m~\\AppData\\Roaming\\uv\\python\\cpython-3.13.3-windows-x86_64-none\\Lib\\pathlib\\_local.py:767\u001B[39m, in \u001B[36mPath.rename\u001B[39m\u001B[34m(self, target)\u001B[39m\n\u001B[32m    757\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mrename\u001B[39m(\u001B[38;5;28mself\u001B[39m, target):\n\u001B[32m    758\u001B[39m \u001B[38;5;250m    \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m    759\u001B[39m \u001B[33;03m    Rename this path to the target path.\u001B[39;00m\n\u001B[32m    760\u001B[39m \n\u001B[32m   (...)\u001B[39m\u001B[32m    765\u001B[39m \u001B[33;03m    Returns the new Path instance pointing to the target path.\u001B[39;00m\n\u001B[32m    766\u001B[39m \u001B[33;03m    \"\"\"\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m767\u001B[39m     \u001B[43mos\u001B[49m\u001B[43m.\u001B[49m\u001B[43mrename\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtarget\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m    768\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m.with_segments(target)\n",
-      "\u001B[31mPermissionError\u001B[39m: [WinError 32] Le processus ne peut pas accéder au fichier car ce fichier est utilisé par un autre processus: '_static\\\\sample_audio\\\\sample_220925_223530.wav' -> '_static\\\\sample_audio\\\\data\\\\audio\\\\original\\\\sample_220925_223530.wav'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -225,7 +183,15 @@
     "audio_dataset = dataset.get_analysis_audiodataset(analysis=analysis)\n",
     "\n",
     "# Filter the returned AudioDataset\n",
-    "audio_dataset.data = [ad for ad in audio_dataset.data if not ad.is_empty]"
+    "removed_data = audio_dataset.remove_empty_data(threshold=0.0)\n",
+    "\n",
+    "# We can take a look at which data has been removed:\n",
+    "print(f\"{' REMOVED DATA ':#^60}\")\n",
+    "print(f\"{'Begin':<20}{'Duration':^20}{'Fill rate':>20}\")\n",
+    "for data in removed_data:\n",
+    "    print(\n",
+    "        f\"{data.begin.strftime('%H:%M:%S'):<20}{str(data.duration):^20}{str(data.populated_ratio) + ' %':>20}\"\n",
+    "    )"
    ]
   },
   {

docs/source/example_reshaping_multiple_files_core.ipynb

@@ -105,8 +105,16 @@
     "    f\"{'Nb of Empty data:':<30}{str(len([ad for ad in audio_dataset.data if ad.is_empty])):>30}\\n\"\n",
     ")\n",
     "\n",
-    "# Remove the empty data by using the default AudioDataset constructor:\n",
-    "audio_dataset = AudioDataset([ad for ad in audio_dataset.data if not ad.is_empty])"
+    "# Remove the empty data:\n",
+    "removed_data = audio_dataset.remove_empty_data(threshold=0.0)\n",
+    "\n",
+    "# We can take a look at which data has been removed:\n",
+    "print(f\"{' REMOVED DATA ':#^60}\")\n",
+    "print(f\"{'Begin':<20}{'Duration':^20}{'Fill rate':>20}\")\n",
+    "for data in removed_data:\n",
+    "    print(\n",
+    "        f\"{data.begin.strftime('%H:%M:%S'):<20}{str(data.duration):^20}{str(data.populated_ratio) + ' %':>20}\"\n",
+    "    )"
    ]
   },
   {

docs/source/example_reshaping_multiple_files_public.ipynb

@@ -150,7 +150,15 @@
     "audio_dataset = dataset.get_analysis_audiodataset(analysis=analysis)\n",
     "\n",
     "# Filter the returned AudioDataset\n",
-    "audio_dataset.data = [ad for ad in audio_dataset.data if not ad.is_empty]"
+    "removed_data = audio_dataset.remove_empty_data(threshold=0.0)\n",
+    "\n",
+    "# We can take a look at which data has been removed:\n",
+    "print(f\"{' REMOVED DATA ':#^60}\")\n",
+    "print(f\"{'Begin':<20}{'Duration':^20}{'Fill rate':>20}\")\n",
+    "for data in removed_data:\n",
+    "    print(\n",
+    "        f\"{data.begin.strftime('%H:%M:%S'):<20}{str(data.duration):^20}{str(data.populated_ratio) + ' %':>20}\"\n",
+    "    )"
    ]
   },
   {

docs/source/publicapi_usage.rst

@@ -186,7 +186,7 @@ The returned ``AudioDataset`` can be edited at will and passed as a parameter la
     ads = dataset.get_analysis_audiodataset(analysis=analysis)
 
     # Filtering out the AudioData that are not linked to any audio file:
-    ads.data = [ad for ad in ads.data if not ad.is_empty]
+    ads.remove_empty_data(threshold=0.)
 
 The returned ``SpectroDataset`` can be used e.g. to plot sample spectrograms prior to the analysis:
 

src/osekit/core_api/base_data.py

@@ -12,7 +12,7 @@
 from typing import Self, TypeVar
 
 import numpy as np
-from pandas import Timestamp, date_range
+from pandas import Timedelta, Timestamp, date_range
 
 from osekit.config import (
     DPDEFAULT,
@@ -129,6 +129,21 @@ def end(self, value: Timestamp) -> None:
         for item in self.items:
             item.end = min(item.end, value)
 
+    @property
+    def populated_duration(self) -> Timedelta:
+        """Total duration of the non-empty parts of the data."""
+        return Timedelta(
+            sum(
+                (item.duration for item in self.items if not item.is_empty),
+                start=Timedelta(0),
+            ),
+        )
+
+    @property
+    def populated_ratio(self) -> float:
+        """Percentage of the non-empty parts of the data."""
+        return self.populated_duration / self.duration
+
     def get_value(self) -> np.ndarray:
         """Get the concatenated values from all Items."""
         return np.concatenate([item.get_value() for item in self.items])

src/osekit/core_api/base_dataset.py

@@ -170,6 +170,33 @@ def data_duration(self) -> Timedelta:
         ]
         return max(set(data_durations), key=data_durations.count)
 
+    def remove_empty_data(self, threshold: float = 0.0) -> list[TData]:
+        """Remove data that has less than ``threshold`` % of non-empty duration.
+
+        Parameters
+        ----------
+        threshold: float
+            Threshold percentage of emptiness duration under which the
+            data should be removed.
+            Must be in the ``[0.,1.]`` interval.
+
+        Returns
+        -------
+        list[TData]:
+            The removed Data objects.
+
+        """
+        if not 0.0 <= threshold <= 1.0:
+            msg = f"Threshold should be between 0 and 1. Got {threshold}"
+            raise ValueError(msg)
+
+        kept, removed = [], []
+        for data in self.data:
+            (kept if data.populated_ratio > threshold else removed).append(data)
+
+        self.data = kept
+        return removed
+
     def write(
         self,
         folder: Path,

src/osekit/core_api/spectro_data.py

@@ -18,6 +18,7 @@
 import numpy as np
 import pandas as pd
 from matplotlib.dates import date2num
+from pandas import Timedelta
 from scipy.signal import ShortTimeFFT, welch
 
 from osekit.config import (
@@ -248,6 +249,19 @@ def v_lim(self, v_lim: tuple[float, float] | None) -> None:
             v_lim = (-120.0, 0.0) if self.db_type == "FS" else (0.0, 170.0)
         self._v_lim = v_lim
 
+    @property
+    def populated_duration(self) -> Timedelta:
+        """Override BaseData.populated_duration.
+
+        If the SpectroData has no associated file, it will return the
+        populated duration of the associated AudioData.
+        """
+        if self.files:
+            return super().populated_duration
+        if not self.audio_data:
+            return Timedelta(0)
+        return self.audio_data.populated_duration
+
     def get_value(self) -> np.ndarray:
         """Return the Sx matrix of the spectrogram.
 

tests/helpers/dummy.py

@@ -0,0 +1,106 @@
+import typing
+from pathlib import Path
+from typing import Self
+
+import numpy as np
+from pandas import Timestamp
+
+from osekit.core_api.base_data import BaseData, TFile
+from osekit.core_api.base_dataset import BaseDataset, TData
+from osekit.core_api.base_file import BaseFile
+from osekit.core_api.base_item import BaseItem
+
+
+class DummyFile(BaseFile):
+    supported_extensions: typing.ClassVar = [""]
+
+    def read(self, start: Timestamp, stop: Timestamp) -> np.ndarray: ...
+
+
+class DummyItem(BaseItem[DummyFile]): ...
+
+
+class DummyData(BaseData[DummyItem, DummyFile]):
+    item_cls = DummyItem
+
+    def write(self, folder: Path, *, link: bool = False) -> None: ...
+
+    def link(self, folder: Path) -> None: ...
+
+    def _make_split_data(
+        self,
+        files: list[DummyFile],
+        begin: Timestamp,
+        end: Timestamp,
+        **kwargs,  # noqa: ANN003
+    ) -> Self:
+        return DummyData.from_files(files=files, begin=begin, end=end, **kwargs)
+
+    @classmethod
+    def _make_file(cls, path: Path, begin: Timestamp) -> DummyFile:
+        return DummyFile(path=path, begin=begin)
+
+    @classmethod
+    def _make_item(
+        cls,
+        file: TFile | None = None,
+        begin: Timestamp | None = None,
+        end: Timestamp | None = None,
+    ) -> DummyItem:
+        return DummyItem(file=file, begin=begin, end=end)
+
+    @classmethod
+    def _from_base_dict(
+        cls,
+        dictionary: dict,
+        files: list[TFile],
+        begin: Timestamp,
+        end: Timestamp,
+        **kwargs,  # noqa: ANN003
+    ) -> Self:
+        return cls.from_files(
+            files=files,
+            begin=begin,
+            end=end,
+        )
+
+    @classmethod
+    def from_files(
+        cls,
+        files: list[DummyFile],
+        begin: Timestamp | None = None,
+        end: Timestamp | None = None,
+        name: str | None = None,
+        **kwargs,  # noqa: ANN003
+    ) -> Self:
+        return super().from_files(
+            files=files,
+            begin=begin,
+            end=end,
+            name=name,
+            **kwargs,
+        )
+
+
+class DummyDataset(BaseDataset[DummyData, DummyFile]):
+    @classmethod
+    def _data_from_dict(cls, dictionary: dict) -> list[TData]:
+        return [DummyData.from_dict(data) for data in dictionary.values()]
+
+    @classmethod
+    def _data_from_files(
+        cls,
+        files: list[DummyFile],
+        begin: Timestamp | None = None,
+        end: Timestamp | None = None,
+        name: str | None = None,
+        **kwargs,
+    ) -> TData:
+        return DummyData.from_files(
+            files=files,
+            begin=begin,
+            end=end,
+            name=name,
+        )
+
+    file_cls = DummyFile