diff --git a/disdrodb/l0/l0_reader.py b/disdrodb/l0/l0_reader.py
index eafd5ab6..78327337 100644
--- a/disdrodb/l0/l0_reader.py
+++ b/disdrodb/l0/l0_reader.py
@@ -258,7 +258,7 @@ def _check_reader_arguments(reader):
     import inspect
 
     signature = inspect.signature(reader)
-    reader_arguments = sorted(list(signature.parameters.keys()))
+    reader_arguments = sorted(signature.parameters.keys())
     expected_arguments = sorted(_get_expected_reader_arguments())
     if reader_arguments != expected_arguments:
         raise ValueError(f"The reader must be defined with the following arguments: {expected_arguments}")
diff --git a/disdrodb/tests/test_l0/test_io.py b/disdrodb/tests/test_l0/test_io.py
index 2fbee384..d3b546d5 100644
--- a/disdrodb/tests/test_l0/test_io.py
+++ b/disdrodb/tests/test_l0/test_io.py
@@ -159,7 +159,7 @@ def test__read_l0a(tmp_path):
 
 
 def test_read_l0a_dataframe(tmp_path):
-    filepaths = list()
+    filepaths = []
 
     for i in [0, 1]:
         # create dummy dataframe
diff --git a/disdrodb/tests/test_l0/test_l0b_processing.py b/disdrodb/tests/test_l0/test_l0b_processing.py
index f381bb54..cd5b20aa 100644
--- a/disdrodb/tests/test_l0/test_l0b_processing.py
+++ b/disdrodb/tests/test_l0/test_l0b_processing.py
@@ -134,7 +134,7 @@ def test_create_l0b_from_l0a(create_test_config_files):
         "diameter_bin_center",
     ]
     assert set(ds.variables) == set(expected_variables)
-    assert set(ds.dims) == set(["diameter_bin_center", "time", "velocity_bin_center", "crs"])
+    assert set(ds.dims) == {"diameter_bin_center", "time", "velocity_bin_center", "crs"}
 
     # Check that the geolocation coordinates have been properly set
     assert np.allclose(ds.latitude.values, df.latitude.values)
diff --git a/pyproject.toml b/pyproject.toml
index 0a3faafb..d03ab33b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -173,7 +173,7 @@ select = [
     # flake8-slots
     "SLOT",
     # flake8-comprehensions
-    # "C4",
+    "C4",
     # Ruff custom rules
     # "RUF"
 
diff --git a/tutorials/reader_preparation.ipynb b/tutorials/reader_preparation.ipynb
index 880a97e3..0e9a1761 100644
--- a/tutorials/reader_preparation.ipynb
+++ b/tutorials/reader_preparation.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "7fb27b941602401d91542211134fc71a",
    "metadata": {},
    "source": [
     "# DISDRODB reader preparation tutorial"
@@ -9,6 +10,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "acae54e37e7d407bbb7b55eff062a284",
    "metadata": {},
    "source": [
     "This notebook aims to guide you through creating the reader for the raw files logged by a disdrometer device. \n",
@@ -20,6 +22,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
    "metadata": {},
    "source": [
     "In this notebook, we uses a lightweight dataset for illustratory purposes. You may use it and readapt it for exploring your own dataset, when preparing a new reader. \n",
@@ -35,6 +38,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8dd0d8092fe74a7c96281538738b07e2",
    "metadata": {},
    "source": [
     "For this tutorial, we have prepared some sample data in the folder [data/DISDRODB](https://github.com/ltelab/disdrodb/tree/main/data/DISDRODB) of the [disdrodb](https://github.com/ltelab/disdrodb/) repository. In this tutorial, this `data/DISDRODB` directory will act as toy DISDRODB base directory.\n",
@@ -65,6 +69,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "72eea5119410473aa328ad9291626812",
    "metadata": {},
    "source": [
     "## Step 1: Read and analyse the data"
@@ -72,6 +77,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8edb47106e1a46a883d545849b8ab81b",
    "metadata": {},
    "source": [
     "The goal of Step 1 is to define the specifications to read the raw data into a dataframe and ensure that the dataframe columns match the DISDRODB standards.\n",
@@ -80,6 +86,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "10185d26023b46108eb7d9f57d49d2b3",
    "metadata": {},
    "source": [
     "--------------------------------------------------------------------\n",
@@ -89,6 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "8763a12b2bbd4a93a75aff182afb95dc",
    "metadata": {},
    "outputs": [
     {
@@ -110,56 +118,54 @@
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "7623eae2785240b9bd12b16a66d81610",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import logging\n",
     "import pandas as pd\n",
     "\n",
+    "from disdrodb.api.checks import check_sensor_name\n",
+    "\n",
     "# Directory\n",
     "from disdrodb.api.create_directories import create_l0_directory_structure\n",
+    "from disdrodb.api.info import infer_path_info_dict\n",
     "\n",
-    "# Tools to develop the reader\n",
-    "from disdrodb.l0.template_tools import (\n",
-    "    check_column_names,\n",
-    "    infer_column_names,\n",
-    "    print_df_first_n_rows,\n",
-    "    print_df_random_n_rows,\n",
-    "    print_df_column_names,\n",
-    "    print_valid_l0_column_names,\n",
-    "    get_df_columns_unique_values_dict,\n",
-    "    print_df_columns_unique_values,\n",
-    "    print_df_summary_stats,\n",
-    ")\n",
+    "# Standards\n",
+    "from disdrodb.api.path import define_campaign_dir\n",
+    "from disdrodb.l0.check_standards import check_l0a_column_names\n",
     "\n",
     "# L0A processing\n",
     "from disdrodb.l0.io import get_raw_filepaths\n",
     "from disdrodb.l0.l0a_processing import (\n",
     "    read_raw_file,\n",
     "    read_raw_files,\n",
-    "    cast_column_dtypes,\n",
-    "    write_l0a,\n",
     ")\n",
     "\n",
     "# L0B processing\n",
     "from disdrodb.l0.l0b_processing import (\n",
-    "    retrieve_l0b_arrays,\n",
     "    create_l0b_from_l0a,\n",
-    "    set_encodings,\n",
     ")\n",
     "\n",
-    "# Metadata\n",
-    "from disdrodb.metadata import read_station_metadata\n",
+    "# Tools to develop the reader\n",
+    "from disdrodb.l0.template_tools import (\n",
+    "    check_column_names,\n",
+    "    get_df_columns_unique_values_dict,\n",
+    "    infer_column_names,\n",
+    "    print_df_column_names,\n",
+    "    print_df_columns_unique_values,\n",
+    "    print_df_first_n_rows,\n",
+    "    print_df_random_n_rows,\n",
+    "    print_df_summary_stats,\n",
+    "    print_valid_l0_column_names,\n",
+    ")\n",
     "\n",
-    "# Standards\n",
-    "from disdrodb.api.path import define_campaign_dir\n",
-    "from disdrodb.api.info import infer_path_info_dict\n",
-    "from disdrodb.api.checks import check_sensor_name\n",
-    "from disdrodb.l0.check_standards import check_l0a_column_names\n"
+    "# Metadata\n",
+    "from disdrodb.metadata import read_station_metadata\n"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "7cdc8c89c7104fffa095e18ddfef8986",
    "metadata": {},
    "source": [
     "**1. Define paths and running parameters**\n",
@@ -174,6 +180,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "b118ea5561624da68c537baed56e602f",
    "metadata": {},
    "outputs": [
     {
@@ -189,15 +196,15 @@
     "base_dir = os.path.join(root_path, \"data\", \"DISDRODB\")\n",
     "data_source = \"DATA_SOURCE\"\n",
     "campaign_name = \"CAMPAIGN_NAME\"\n",
-    "raw_dir = define_campaign_dir(base_dir=base_dir,   \n",
+    "raw_dir = define_campaign_dir(base_dir=base_dir,\n",
     "                                    product=\"RAW\",\n",
     "                                    data_source=data_source,\n",
-    "                                    campaign_name=campaign_name,  \n",
+    "                                    campaign_name=campaign_name,\n",
     ")\n",
-    "processed_dir = define_campaign_dir(base_dir=base_dir,   \n",
+    "processed_dir = define_campaign_dir(base_dir=base_dir,\n",
     "                                    product=\"L0A\",\n",
     "                                    data_source=data_source,\n",
-    "                                    campaign_name=campaign_name,  \n",
+    "                                    campaign_name=campaign_name,\n",
     ")\n",
     "assert os.path.exists(raw_dir), \"Raw directory does not exist\"\n",
     "print(f\"raw_dir: {raw_dir}\")\n",
@@ -206,6 +213,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "938c804e27f84196a10c8828c723f798",
    "metadata": {},
    "source": [
     "Then we define the reader execution parameters. When the new reader will be created, these parameters will be become the reader function arguments. Please have a look [at the documentation](https://disdrodb.readthedocs.io/en/latest/readers.html#runing-a-reader) to get a full description. "
@@ -214,6 +222,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "504fb2a444614c0babb325280ed9130a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,6 +235,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "59bbdb311c014d738909a11f9e486628",
    "metadata": {},
    "source": [
     "**2. Selection of the station**\n",
@@ -236,6 +246,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "b43b363d81ae4b689946ece5c682cd59",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,6 +255,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8a65eabff63a45729fe45fb5ade58bdc",
    "metadata": {},
    "source": [
     "**3. Initialization**\n",
@@ -254,6 +266,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "c3933fab20d04ec698c2621248eb3be0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,6 +283,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4dd4641cc4064e0191573fe9c69df29b",
    "metadata": {},
    "source": [
     "Please, be sure to run the cell above only one time. If it is run many times, the log file blocks the folder creation.  "
@@ -277,6 +291,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8309879909854d7188b41380fd92a7c3",
    "metadata": {},
    "source": [
     "**4. Get the list of file to process**\n",
@@ -289,6 +304,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "3ed186c9a28b402fb0bc4494df01f08d",
    "metadata": {},
    "outputs": [
     {
@@ -316,6 +332,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "cb1e1581032b452c9409d6c6813c49d1",
    "metadata": {},
    "source": [
     "🚨 The `glob_pattern` variable definition will be transferred into your reader function at the end of this notebook.\n",
@@ -325,6 +342,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "379cbbc1e968416e875cc15c1202d7eb",
    "metadata": {},
    "source": [
     "**5. Retrieve metadata from YAML files**\n",
@@ -337,11 +355,12 @@
   {
    "cell_type": "code",
    "execution_count": 12,
+   "id": "277c27b1587741f2af2001be3712ef0d",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Retrieve metadata\n",
-    "attrs = read_station_metadata(station_name=station_name, \n",
+    "attrs = read_station_metadata(station_name=station_name,\n",
     "                              product=\"RAW\",\n",
     "                              **infer_path_info_dict(raw_dir))\n",
     "# Retrieve sensor name\n",
@@ -351,6 +370,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "db7b79bc585a40fcaf58bf750017e135",
    "metadata": {},
    "source": [
     "**5. Load the one file into a dataframe**\n",
@@ -361,6 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
+   "id": "916684f9a58a4a2aa5f864670399430d",
    "metadata": {},
    "outputs": [
     {
@@ -805,6 +826,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
+   "id": "1671c31a24314836a5b85d7ef7fbf015",
    "metadata": {},
    "outputs": [
     {
@@ -825,6 +847,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "33b0902fd34d4ace834912fa1002cf8e",
    "metadata": {},
    "source": [
     "Here we expect the `df_raw` to have: \n",
@@ -836,6 +859,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f6fa52606d8c4a75a9b52967216f8f3f",
    "metadata": {},
    "source": [
     "Depending on the schema of your data, this `reader_kwargs` dictionary may be fairly different from the one above. \n",
@@ -845,6 +869,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f5a1fa73e5044315a093ec459c9be902",
    "metadata": {},
    "source": [
     "**6. Data exploration**\n",
@@ -860,6 +885,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "cdf66aed5cc84ca1b48e60bad68798a8",
    "metadata": {},
    "source": [
     "We print the content first 3 rows :\n",
@@ -869,6 +895,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
+   "id": "28d3efd5258a48a79c179ea5c6759f01",
    "metadata": {},
    "outputs": [
     {
@@ -939,6 +966,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
+   "id": "3f9bc0b9dd2c44919cc8dcca39b469f8",
    "metadata": {},
    "outputs": [
     {
@@ -1103,6 +1131,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0e382214b5f147d187d36a2058b9c724",
    "metadata": {},
    "source": [
     "We print the content of n rows picked randomly : "
@@ -1111,6 +1140,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
+   "id": "5b09d5ef5b5e4bb6ab9b829b10b6a29f",
    "metadata": {},
    "outputs": [
     {
@@ -1191,6 +1221,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a50416e276a0479cbe66534ed1713a40",
    "metadata": {},
    "source": [
     "Get the number of column :"
@@ -1199,6 +1230,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
+   "id": "46a27a456b804aa2a380d5edf15a5daf",
    "metadata": {},
    "outputs": [
     {
@@ -1218,6 +1250,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1944c39560714e6e80c856f20744a8e5",
    "metadata": {},
    "source": [
     "Look at unique values for a single column :"
@@ -1226,6 +1259,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
+   "id": "d6ca27006b894b04b6fc8b79396e2797",
    "metadata": {},
    "outputs": [
     {
@@ -1243,6 +1277,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f61877af4e7f4313ad8234302950b331",
    "metadata": {},
    "source": [
     "Look at unique values for a few columns :\n",
@@ -1253,6 +1288,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
+   "id": "84d5ab97d17b4c38ab41a2b065bbd0c0",
    "metadata": {},
    "outputs": [
     {
@@ -1272,6 +1308,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "35ffc1ce1c7b4df9ace1bc936b8b1dc2",
    "metadata": {},
    "source": [
     "Get the unique values as dictionary"
@@ -1280,6 +1317,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
+   "id": "76127f4a2f6a44fba749ea7800e59d51",
    "metadata": {},
    "outputs": [
     {
@@ -1360,6 +1398,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "903197826d2e44dfa0208e8f97c69327",
    "metadata": {},
    "source": [
     "**7. Columns name**\n",
@@ -1369,6 +1408,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "015066fb96f841e5be1e03a9eaadc3b6",
    "metadata": {},
    "source": [
     "The function `infer_column_names()` tries to guess the column names based on the type of sensor and the sensor specifications described within the [raw_data_format.yml config file](https://disdrodb.readthedocs.io/en/latest/sensor_configs.html#sensor-logged-variables) file."
@@ -1377,6 +1417,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
+   "id": "81ff116bae5b45f6b6dae177083008cf",
    "metadata": {},
    "outputs": [
     {
@@ -1419,6 +1460,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9075f00cfa8d463f84130041b1e44ca7",
    "metadata": {},
    "source": [
     "This can help us to define later the `column_names` list.\n",
@@ -1429,6 +1471,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
+   "id": "15abde8c5d2e435093904b13db685a53",
    "metadata": {},
    "outputs": [
     {
@@ -1445,6 +1488,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5e20a2a0e21149b5b06860e930401eb5",
    "metadata": {},
    "source": [
     "It's time now to define our current column names : \n",
@@ -1458,6 +1502,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
+   "id": "72c31777baf4441b988909d29205560c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1491,6 +1536,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5734001bcbac423990a4356310d8df13",
    "metadata": {},
    "source": [
     "> 🚨 The `column_names` list will be transferred  to the reader function at the end of this notebook. "
@@ -1498,6 +1544,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "27531e93873647d9a5bf1112f2051a59",
    "metadata": {},
    "source": [
     "Check the validity of your definition "
@@ -1506,6 +1553,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
+   "id": "f3041e9ffdb2416ea2009d3a6a4c5716",
    "metadata": {},
    "outputs": [
     {
@@ -1525,6 +1573,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "94ae71b6e24e4355a139fb9fe2e09b64",
    "metadata": {},
    "source": [
     "Ok, fair enough.\n",
@@ -1535,6 +1584,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9141936c6c8a4c478a75aea4ff665469",
    "metadata": {},
    "source": [
     "**8. Read the dataframe with correct columns name**\n",
@@ -1545,6 +1595,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
+   "id": "bd7c096f4dcf400fbdceb075ef31fca3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1553,6 +1604,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b427a666a1b549ef9b573d6f946bfc3b",
    "metadata": {},
    "source": [
     "And print the dataframe column names : "
@@ -1561,6 +1613,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
+   "id": "0310869696a145bf841235dd6c036af8",
    "metadata": {},
    "outputs": [
     {
@@ -1600,6 +1653,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "91f166d9f0ce4939b04b8e9245f75c27",
    "metadata": {},
    "source": [
     "**9. Perform further tests and analysis to check the correctness of `column_names`**"
@@ -1607,6 +1661,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7c10029e1707434ab3fe295caea7d13f",
    "metadata": {},
    "source": [
     "You can for example check some statistics for a specific column."
@@ -1615,6 +1670,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
+   "id": "f68f0888c55a4478ace3eac39384dff4",
    "metadata": {},
    "outputs": [
     {
@@ -1640,6 +1696,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f94f5a17fa734d288763e7d9a3758173",
    "metadata": {},
    "source": [
     "**10. Final columns formatting**"
@@ -1648,6 +1705,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
+   "id": "421b839107204e409e0496d3d944026c",
    "metadata": {},
    "outputs": [
     {
@@ -1677,6 +1735,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
+   "id": "e981b37c798e44f684168605b9db02c6",
    "metadata": {},
    "outputs": [
     {
@@ -1696,6 +1755,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "40c1800d1c114147a536b8aa907907c7",
    "metadata": {},
    "source": [
     "Now, it's time to remove all the columns that does not match the DISDRODB standard."
@@ -1704,6 +1764,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
+   "id": "9082857fd66e4025bba99b1a80c5d976",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1712,6 +1773,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "71b4158a9f3d49279f06ec9197b84529",
    "metadata": {},
    "source": [
     "It's also time to define the column `time` which is requested by the DISDRODB standard."
@@ -1720,6 +1782,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
+   "id": "6038e703eb6b4df2ba5a71336b77ea4e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1729,6 +1792,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "82ca0f16c5be4617b5535c40faa36c79",
    "metadata": {},
    "source": [
     "Now let's check that the column names, after custom processing, conform with the DISDRODB standards:"
@@ -1737,6 +1801,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
+   "id": "04aa693c20bd460494e518b8cb84ef11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1745,6 +1810,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c8acbee542ae4f9e87d350f4747cb88d",
    "metadata": {},
    "source": [
     "Finally, check if the dataframe looks as desired:"
@@ -1753,6 +1819,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
+   "id": "e0a51dd6baab431990c6adcc83aa7fba",
    "metadata": {},
    "outputs": [
     {
@@ -1787,6 +1854,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
+   "id": "d84866adce69467fa81441dc6c47fd8a",
    "metadata": {},
    "outputs": [
     {
@@ -1853,6 +1921,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
+   "id": "deb6c23654f54f5b91a38c31054c5587",
    "metadata": {},
    "outputs": [
     {
@@ -1870,6 +1939,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9768d501ade64311a8ed6e9eb433fa9b",
    "metadata": {},
    "source": [
     "**11. Define the dataframe sanitizer function**\n",
@@ -1884,6 +1954,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
+   "id": "061962c182f9482c8b1341b2a6f73cd5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1913,6 +1984,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8edfd84aa00d4424a71141df99c55e72",
    "metadata": {},
    "source": [
     "> 🚨 The `df_sanitizer_fun()` function will be transfered to the reader function at the end of this notebook. "
@@ -1920,6 +1992,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8c55fba33aa346cf939c9cd632996673",
    "metadata": {},
    "source": [
     "**12. Now let's try calling the reader function as it will be called in the DISDRODB L0 reader**\n",
@@ -1929,6 +2002,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ab13b4b6e2874167b7f2debb11dcd1d9",
    "metadata": {},
    "source": [
     "Here we combine all raw files in a single dataframe. \n",
@@ -1946,6 +2020,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
+   "id": "47d658866ccb404681cf21a4419000da",
    "metadata": {},
    "outputs": [
     {
@@ -2362,6 +2437,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5d973992396c4b758db40b17e80dc1fc",
    "metadata": {},
    "source": [
     "Here we derive the corresponding `xr.Dataset` object "
@@ -2370,6 +2446,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
+   "id": "1c9cbe9d9a794dbd8aaa1fa72bce99a9",
    "metadata": {},
    "outputs": [
     {
@@ -2439,6 +2516,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b434e0d67d894725aa1f6d707a143313",
    "metadata": {},
    "source": [
     "which can be saved as DISDRODB L0B netCDF by running the following code:"
@@ -2447,6 +2525,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
+   "id": "594a68b434714bab9e1df903f88edcdd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2456,6 +2535,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4d59fedcae41437686ad1a71a1884d48",
    "metadata": {},
    "source": [
     "## Step 2 : Create the reader"
@@ -2463,6 +2543,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "74a36f4709894f8e822f9c383b5c8674",
    "metadata": {},
    "source": [
     "Now we have all the parameters required to define a DISDRODB reader.\n",
@@ -2472,6 +2553,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "13e578e05204494e9a74b868b4a9c0da",
    "metadata": {},
    "source": [
     "\n",
@@ -2492,6 +2574,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "472c087bdaf047c4bd2daf3102168774",
    "metadata": {},
    "source": [
     "2. **Update the** `columns_names` **list**\n",
@@ -2536,6 +2619,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c11e5ea01d13400882806a9c4ab303ae",
    "metadata": {},
    "source": [
     "3. **Update the** `reader_kwargs` **"
@@ -2543,6 +2627,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a0755c77c2af48ad9c17a637f99c96f7",
    "metadata": {},
    "source": [
     "dictionary**\n",
@@ -2594,6 +2679,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c01db64ab1c14c5599cb15ec16f03e31",
    "metadata": {},
    "source": [
     "4. **Update the** `df_sanitizer_fun()` **function**\n",
@@ -2637,6 +2723,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f9ba92a60e8d4a7c9e00b1317491f415",
    "metadata": {},
    "source": [
     "You arrived at the end of the tutorial. Well done 👋👋👋 \n",
@@ -2651,6 +2738,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8b9c4a8a72cf4b1f8d918f50ea71471d",
    "metadata": {},
    "source": []
   }