diff --git a/comptages/core/report.py b/comptages/core/report.py
index e1b7940..8091bb5 100644
--- a/comptages/core/report.py
+++ b/comptages/core/report.py
@@ -1,11 +1,9 @@
 import os
 from datetime import date, datetime, timedelta
-from typing import Generator
+from typing import Generator, Optional
+from qgis.core import Qgis, QgsMessageLog
 
-from datetime import timedelta, datetime
-from typing import Optional
 from openpyxl import load_workbook, Workbook
-from qgis.core import Qgis, QgsMessageLog
 
 from comptages.core import statistics
 from comptages.datamodel import models
@@ -24,6 +22,7 @@ def prepare_reports(
     callback_progress=simple_print_callback,
     sections_days: Optional[dict[str, list[date]]] = None,
 ):
+    print(f"{datetime.now()}: _prepare_reports: begin, folder: {file_path}")
     current_dir = os.path.dirname(os.path.abspath(__file__))
 
     if template == "default":
@@ -48,6 +47,7 @@ def prepare_reports(
         )
     elif template == "yearly_bike":
         pass
+    print(f"{datetime.now()}: _prepare_reports: ended, folder: {file_path}")
 
 
 def _prepare_default_reports(
@@ -58,6 +58,7 @@ def _prepare_default_reports(
     sections_days: Optional[dict[str, list[date]]] = None,
 ):
     """Write default reports to disk (1 per section in count, per week)"""
+    print(f"{datetime.now()}: _prepare_default_reports: begin, count: {count}")
     # We do by section and not by count because of special cases.
     sections = models.Section.objects.filter(
         lane__id_installation__count=count
@@ -95,6 +96,8 @@ def _prepare_default_reports(
 
             workbook.save(filename=output)
 
+    print(f"{datetime.now()}: _prepare_default_reports: ended, count: {count}")
+
 
 def _prepare_yearly_report(
     file_path: str,
@@ -105,6 +108,9 @@ def _prepare_yearly_report(
     sections_days: Optional[dict[str, list[date]]] = None,
 ):
     """Write default reports to disk (1 per section included in the count)"""
+    print(
+        f"{datetime.now()}: _prepare_yearly_report: begin, sections_ids: {sections_ids}"
+    )
     # Get first count to be used as example
     count_qs = models.Count.objects.filter(
         id_installation__lane__id_section=sections_ids[0],
@@ -113,6 +119,7 @@ def _prepare_yearly_report(
     )
     if not count_qs.exists():
         info_str = f"{datetime.now()}: Aucun comptage trouvé pour cette section {sections_ids[0]} et cette année {year}"
+        print(info_str)
         QgsMessageLog.logMessage(info_str, "Comptages", Qgis.Warning)
 
         return
@@ -137,6 +144,10 @@ def _prepare_yearly_report(
         output = os.path.join(file_path, f"{section.id}_{year}_r.xlsx")
         workbook.save(filename=output)
 
+    print(
+        f"{datetime.now()}: _prepare_yearly_report: ended, sections_ids: {sections_ids}"
+    )
+
 
 def _mondays_of_count(count: models.Count) -> Generator[date, None, None]:
     """Generator that return the Mondays of the count"""
@@ -247,7 +258,12 @@ def _data_count_yearly(
         ws["B14"] = lanes[1].direction_desc
 
 
-def _data_day(count: models.Count, section: models.Section, monday, workbook: Workbook):
+def _data_day(
+    count: models.Count,
+    section: models.Section,
+    monday,
+    workbook: Workbook,
+):
     ws = workbook["Data_day"]
 
     # Monthly coefficients
@@ -285,6 +301,7 @@ def _data_day(count: models.Count, section: models.Section, monday, workbook: Wo
             section,
             start=monday + timedelta(days=i),
             end=monday + timedelta(days=i + 1),
+            exclude_trash=True,
         )
 
         for row in df.itertuples():
@@ -300,6 +317,7 @@ def _data_day(count: models.Count, section: models.Section, monday, workbook: Wo
             start=monday + timedelta(days=i),
             end=monday + timedelta(days=i + 1),
             direction=1,
+            exclude_trash=True,
         )
 
         for row in df.itertuples():
@@ -315,6 +333,7 @@ def _data_day(count: models.Count, section: models.Section, monday, workbook: Wo
             start=monday + timedelta(days=i),
             end=monday + timedelta(days=i + 1),
             direction=1,
+            exclude_trash=True,
         )
         ws.cell(row=row_offset, column=col_offset + i, value=light.get(True, 0))
         ws.cell(row=row_offset + 1, column=col_offset + i, value=light.get(False, 0))
@@ -330,6 +349,7 @@ def _data_day(count: models.Count, section: models.Section, monday, workbook: Wo
                 start=monday + timedelta(days=i),
                 end=monday + timedelta(days=i + 1),
                 direction=2,
+                exclude_trash=True,
             )
 
             for row in df.itertuples():
@@ -345,6 +365,7 @@ def _data_day(count: models.Count, section: models.Section, monday, workbook: Wo
                 start=monday + timedelta(days=i),
                 end=monday + timedelta(days=i + 1),
                 direction=2,
+                exclude_trash=True,
             )
             ws.cell(row=row_offset, column=col_offset + i, value=light.get(True, 0))
             ws.cell(
@@ -353,15 +374,21 @@ def _data_day(count: models.Count, section: models.Section, monday, workbook: Wo
 
 
 def _data_day_yearly(
-    count: models.Count, section: models.Section, year: int, workbook: Workbook
+    count: models.Count,
+    section: models.Section,
+    year: int,
+    workbook: Workbook,
 ):
     ws = workbook["Data_day"]
 
     # Total (section)
     row_offset = 69
     col_offset = 2
-
-    df = statistics.get_time_data_yearly(year, section)
+    df = statistics.get_time_data_yearly(
+        year,
+        section,
+        exclude_trash=True,
+    )
 
     if df is None:
         print(
@@ -378,7 +405,10 @@ def _data_day_yearly(
     row_offset = 95
     col_offset = 2
     df = statistics.get_light_numbers_yearly(
-        section, start=datetime(year, 1, 1), end=datetime(year + 1, 1, 1)
+        section,
+        start=datetime(year, 1, 1),
+        end=datetime(year + 1, 1, 1),
+        exclude_trash=True,
     )
 
     for i in range(7):
@@ -396,10 +426,17 @@ def _data_day_yearly(
     # Direction 1
     row_offset = 5
     col_offset = 2
-
-    df = statistics.get_time_data_yearly(year, section, direction=1)
+    df = statistics.get_time_data_yearly(
+        year,
+        section,
+        direction=1,
+        exclude_trash=True,
+    )
 
     if df is None:
+        print(
+            f"{datetime.now()}:_data_day_yearly - Pas de données pour cette section:{section}, cette direction:1 et cette année:{year} /!\\/!\\/!\\"
+        )
         return
 
     for i in range(7):
@@ -411,7 +448,11 @@ def _data_day_yearly(
     row_offset = 31
     col_offset = 2
     df = statistics.get_light_numbers_yearly(
-        section, start=datetime(year, 1, 1), end=datetime(year + 1, 1, 1), direction=1
+        section,
+        start=datetime(year, 1, 1),
+        end=datetime(year + 1, 1, 1),
+        direction=1,
+        exclude_trash=True,
     )
 
     for i in range(7):
@@ -430,8 +471,12 @@ def _data_day_yearly(
     if len(section.lane_set.all()) == 2:
         row_offset = 37
         col_offset = 2
-
-        df = statistics.get_time_data_yearly(year, section, direction=2)
+        df = statistics.get_time_data_yearly(
+            year,
+            section,
+            direction=2,
+            exclude_trash=True,
+        )
 
         for i in range(7):
             day_df = df[df["date"] == i]
@@ -446,6 +491,7 @@ def _data_day_yearly(
             start=datetime(year, 1, 1),
             end=datetime(year + 1, 1, 1),
             direction=2,
+            exclude_trash=True,
         )
 
         for i in range(7):
@@ -469,28 +515,42 @@ def _data_month_yearly(
     end = datetime(year + 1, 1, 1)
 
     # Section
-    df = statistics.get_month_data(section, start, end)
-
     row_offset = 14
     col_offset = 2
+    df = statistics.get_month_data(
+        section,
+        start,
+        end,
+        exclude_trash=True,
+    )
 
     for col in df.itertuples():
         ws.cell(row=row_offset, column=col_offset + col.Index, value=col.tm)
 
     # Direction 1
-    df = statistics.get_month_data(section, start, end, direction=1)
-
     row_offset = 4
     col_offset = 2
+    df = statistics.get_month_data(
+        section,
+        start,
+        end,
+        direction=1,
+        exclude_trash=True,
+    )
 
     for col in df.itertuples():
         ws.cell(row=row_offset, column=col_offset + col.Index, value=col.tm)
 
     # Direction 2
-    df = statistics.get_month_data(section, start, end, direction=2)
-
     row_offset = 9
     col_offset = 2
+    df = statistics.get_month_data(
+        section,
+        start,
+        end,
+        direction=2,
+        exclude_trash=True,
+    )
 
     for col in df.itertuples():
         ws.cell(row=row_offset, column=col_offset + col.Index, value=col.tm)
@@ -573,8 +633,8 @@ def _data_speed(
             end=monday + timedelta(days=7),
             speed_low=range_[0],
             speed_high=range_[1],
+            exclude_trash=True,
         )
-
         for row in res:
             ws.cell(row=row_offset + row[0], column=col_offset + i, value=row[1])
 
@@ -590,6 +650,7 @@ def _data_speed(
                 start=monday,
                 end=monday + timedelta(days=7),
                 v=v,
+                exclude_trash=True,
             )
             for row in df.itertuples():
                 ws.cell(
@@ -599,9 +660,13 @@ def _data_speed(
         # Average speed direction 1
         row_offset = 5
         col_offset = 19
-
         df = statistics.get_average_speed_by_hour(
-            count, section, direction=1, start=monday, end=monday + timedelta(days=7)
+            count,
+            section,
+            direction=1,
+            start=monday,
+            end=monday + timedelta(days=7),
+            exclude_trash=True,
         )
         for row in df.itertuples():
             ws.cell(row=row_offset + row.Index, column=col_offset, value=row.speed)
@@ -619,8 +684,8 @@ def _data_speed(
                 end=monday + timedelta(days=7),
                 speed_low=range_[0],
                 speed_high=range_[1],
+                exclude_trash=True,
             )
-
             for row in res:
                 ws.cell(row=row_offset + row[0], column=col_offset + i, value=row[1])
 
@@ -636,6 +701,7 @@ def _data_speed(
                     start=monday,
                     end=monday + timedelta(days=7),
                     v=v,
+                    exclude_trash=True,
                 )
                 for row in df.itertuples():
                     ws.cell(
@@ -647,9 +713,13 @@ def _data_speed(
         # Average speed direction 2
         row_offset = 33
         col_offset = 19
-
         df = statistics.get_average_speed_by_hour(
-            count, section, direction=2, start=monday, end=monday + timedelta(days=7)
+            count,
+            section,
+            direction=2,
+            start=monday,
+            end=monday + timedelta(days=7),
+            exclude_trash=True,
         )
         for row in df.itertuples():
             ws.cell(row=row_offset + row.Index, column=col_offset, value=row.speed)
@@ -708,8 +778,8 @@ def _data_speed_yearly(
             end=end,
             speed_low=range_[0],
             speed_high=range_[1],
+            exclude_trash=True,
         )
-
         for row in res:
             ws.cell(row=row_offset + row[0], column=col_offset + i, value=row[1])
 
@@ -719,7 +789,13 @@ def _data_speed_yearly(
         col_offset = 16
         for i, v in enumerate(characteristic_speeds):
             df = statistics.get_characteristic_speed_by_hour(
-                None, section, direction=1, start=start, end=end, v=v
+                None,
+                section,
+                direction=1,
+                start=start,
+                end=end,
+                v=v,
+                exclude_trash=True,
             )
             for row in df.itertuples():
                 ws.cell(
@@ -729,13 +805,13 @@ def _data_speed_yearly(
         # Average speed direction 1
         row_offset = 5
         col_offset = 19
-
         df = statistics.get_average_speed_by_hour(
-            count,
+            None,
             section,
             direction=1,
             start=start,
             end=end,
+            exclude_trash=True,
         )
         for row in df.itertuples():
             ws.cell(row=row_offset + row.Index, column=col_offset, value=row.speed)
@@ -746,13 +822,14 @@ def _data_speed_yearly(
         col_offset = 2
         for i, range_ in enumerate(speed_ranges):
             res = statistics.get_speed_data_by_hour(
-                count,
+                None,
                 section,
                 direction=2,
                 start=start,
                 end=end,
                 speed_low=range_[0],
                 speed_high=range_[1],
+                exclude_trash=True,
             )
 
             for row in res:
@@ -764,7 +841,13 @@ def _data_speed_yearly(
             col_offset = 16
             for i, v in enumerate(characteristic_speeds):
                 df = statistics.get_characteristic_speed_by_hour(
-                    count, section, direction=2, start=start, end=end, v=v
+                    None,
+                    section,
+                    direction=2,
+                    start=start,
+                    end=end,
+                    v=v,
+                    exclude_trash=True,
                 )
                 for row in df.itertuples():
                     ws.cell(
@@ -776,13 +859,13 @@ def _data_speed_yearly(
             # Average speed direction 2
             row_offset = 33
             col_offset = 19
-
             df = statistics.get_average_speed_by_hour(
-                count,
+                None,
                 section,
                 direction=2,
                 start=start,
                 end=end,
+                exclude_trash=True,
             )
             for row in df.itertuples():
                 ws.cell(row=row_offset + row.Index, column=col_offset, value=row.speed)
diff --git a/comptages/core/statistics.py b/comptages/core/statistics.py
index 2c19377..d20f4d7 100644
--- a/comptages/core/statistics.py
+++ b/comptages/core/statistics.py
@@ -1,9 +1,9 @@
 from typing import Any
-from pandas import DataFrame, cut
 from functools import reduce
 from datetime import timedelta, datetime
-from pytz import timezone
 
+from pandas import DataFrame, cut
+from pytz import timezone
 from django.db.models import F, CharField, Value, Q, Sum, QuerySet
 from django.db.models.functions import ExtractHour, Trunc, Concat
 
@@ -11,8 +11,6 @@
 from comptages.core import utils
 from comptages.datamodel import models
 
-TZ = timezone("Europe/Zurich")
-
 
 def get_time_data(
     count,
@@ -57,16 +55,22 @@ def get_time_data(
         .annotate(thm=Sum("times"))
         .values("import_status", "date", "hour", "thm")
     )
+    print(f"statistics.py : get_time_data - qs.query={str(qs.query)}")
 
     df = DataFrame.from_records(qs)
     if not df.empty:
         df["date"] = df["date"].dt.strftime("%a %d.%m.%Y")
         df["import_status"].replace({0: "Existant", 1: "Nouveau"}, inplace=True)
+
     return df
 
 
 def get_time_data_yearly(
-    year, section: models.Section, lane=None, direction=None
+    year,
+    section: models.Section,
+    lane=None,
+    direction=None,
+    exclude_trash=False,
 ) -> DataFrame:
     """Vehicles by hour and day of the week"""
     start = datetime(year, 1, 1)
@@ -82,6 +86,9 @@ def get_time_data_yearly(
         timestamp__lt=end,
     )
 
+    if exclude_trash:
+        qs = qs.exclude(id_category__trash=True)
+
     if lane is not None:
         qs = qs.filter(id_lane=lane)
 
@@ -107,6 +114,7 @@ def get_time_data_yearly(
         print(
             f"statistics.py : get_time_data_yearly - Nothing found !!! for Year: {year}. Section: {section}. Lane: {lane}. Direction: {direction}. !!!)"
         )
+    print(f"statistics.py : get_time_data_yearly - qsa.query={str(qs.query)}")
 
     df = DataFrame.from_records(qs)
     if not df.empty:
@@ -162,7 +170,7 @@ def get_day_data(
         .annotate(tj=Sum("times"))
         .values("date", "tj", "import_status")
     )
-    print(f"statistics.py : get_day_data - qs.query=", str(qs.query))
+    print(f"statistics.py : get_day_data - qs.query={str(qs.query)}")
 
     df = DataFrame.from_records(qs)
     mean = 0
@@ -290,6 +298,7 @@ def get_light_numbers(
     direction=None,
     start=None,
     end=None,
+    exclude_trash=False,
 ) -> dict:
     if not start:
         start = count.start_process_date
@@ -305,6 +314,9 @@ def get_light_numbers(
         timestamp__lt=end,
     )
 
+    if exclude_trash:
+        qs = qs.exclude(id_category__trash=True)
+
     if lane is not None:
         qs = qs.filter(id_lane=lane)
 
@@ -325,7 +337,12 @@ def get_light_numbers(
 
 
 def get_light_numbers_yearly(
-    section: models.Section, lane=None, direction=None, start=None, end=None
+    section: models.Section,
+    lane=None,
+    direction=None,
+    start=None,
+    end=None,
+    exclude_trash=False,
 ) -> DataFrame:
     qs = models.CountDetail.objects.filter(
         id_lane__id_section=section,
@@ -334,6 +351,9 @@ def get_light_numbers_yearly(
         timestamp__lt=end,
     )
 
+    if exclude_trash:
+        qs = qs.exclude(id_category__trash=True)
+
     if lane is not None:
         qs = qs.filter(id_lane=lane)
 
@@ -359,6 +379,7 @@ def get_speed_data_by_hour(
     end=None,
     speed_low=0,
     speed_high=15,
+    exclude_trash=False,
 ) -> "ValuesQuerySet[models.CountDetail, Any]":
     if not start:
         start = count.start_process_date
@@ -374,6 +395,9 @@ def get_speed_data_by_hour(
         timestamp__lt=end,
     )
 
+    if exclude_trash:
+        qs = qs.exclude(id_category__trash=True)
+
     if count is not None:
         qs = qs.filter(id_count=count)
 
@@ -403,6 +427,7 @@ def get_characteristic_speed_by_hour(
     start=None,
     end=None,
     v=0.15,
+    exclude_trash=False,
 ) -> DataFrame:
     if not start:
         start = count.start_process_date
@@ -417,6 +442,9 @@ def get_characteristic_speed_by_hour(
         timestamp__lt=end,
     )
 
+    if exclude_trash:
+        qs = qs.exclude(id_category__trash=True)
+
     if count is not None:
         qs = qs.filter(id_count=count)
 
@@ -450,6 +478,7 @@ def get_average_speed_by_hour(
     start=None,
     end=None,
     v=0.15,
+    exclude_trash=False,
 ) -> DataFrame:
     if not start:
         start = count.start_process_date
@@ -464,6 +493,9 @@ def get_average_speed_by_hour(
         timestamp__lt=end,
     )
 
+    if exclude_trash:
+        qs = qs.exclude(id_category__trash=True)
+
     if count is not None:
         qs = qs.filter(id_count=count)
 
@@ -541,7 +573,13 @@ def get_special_periods(first_day, last_day) -> QuerySet[models.SpecialPeriod]:
     return qs
 
 
-def get_month_data(section: models.Section, start, end, direction=None) -> DataFrame:
+def get_month_data(
+    section: models.Section,
+    start,
+    end,
+    direction=None,
+    exclude_trash=False,
+) -> DataFrame:
     qs = models.CountDetail.objects.filter(
         id_lane__id_section=section, timestamp__gte=start, timestamp__lt=end
     )
@@ -554,6 +592,9 @@ def get_month_data(section: models.Section, start, end, direction=None) -> DataF
         .values("month", "tm", "import_status")
     )
 
+    if exclude_trash:
+        qs = qs.exclude(id_category__trash=True)
+
     if direction is not None:
         qs = qs.filter(id_lane__direction=direction)
 
@@ -569,8 +610,9 @@ def get_valid_days(year: int, section: models.Section) -> int:
     where a day is deemed valid just in case there are at least 14 1-hour blocks
     between 6pm and 4pm with at least 1 vehicle.
     """
-    start = TZ.localize(datetime(year, 1, 1))
-    end = TZ.localize(datetime(year + 1, 1, 1))
+    tz = timezone("Europe/Zurich")
+    start = tz.localize(datetime(year, 1, 1))
+    end = tz.localize(datetime(year + 1, 1, 1))
     iterator = (
         models.CountDetail.objects.filter(
             id_lane__id_section=section,
@@ -584,6 +626,7 @@ def get_valid_days(year: int, section: models.Section) -> int:
         .order_by("date")
         .values("date", "hour", "tj")
     )
+    print(f"statistics.py : get_valid_days - iterator.query={str(iterator.query)}")
 
     def count_valid_blocks(acc: dict, item: dict) -> dict[str, int]:
         date = item["date"]
diff --git a/comptages/report/template_yearly.xlsx b/comptages/report/template_yearly.xlsx
index 3aea05e..8de2ff9 100644
Binary files a/comptages/report/template_yearly.xlsx and b/comptages/report/template_yearly.xlsx differ
diff --git a/comptages/report/yearly_report_bike.py b/comptages/report/yearly_report_bike.py
index 9eea11b..0206b4b 100644
--- a/comptages/report/yearly_report_bike.py
+++ b/comptages/report/yearly_report_bike.py
@@ -5,24 +5,24 @@
 from decimal import Decimal
 from qgis.core import Qgis, QgsMessageLog
 
-from django.db.models import Sum, F, Avg  # , Count, ExpressionWrapper
+from openpyxl import load_workbook
+from django.db.models import Sum, F, Avg
 from django.db.models.functions import (
     ExtractHour,
     ExtractIsoWeekDay,
     ExtractMonth,
     TruncDate,
 )
-from openpyxl import load_workbook
 
-from comptages.core import definitions, utils  # , statistics
+from comptages.core import definitions, utils
 from comptages.datamodel.models import (
     CountDetail,
     Section,
     Lane,
     ClassCategory,
     Category,
+    Count as modelCount,
 )
-from comptages.datamodel.models import Count as modelCount
 
 
 class YearlyReportBike:
@@ -33,7 +33,6 @@ def __init__(self, path_to_output_dir, year, section_id, classtxt):
         self.year = year
         self.section_id = section_id
         self.classtxt = classtxt
-        # Assuming seasons to run from 21 <month> to 20 <month n + 3> -> month20 = (date - timedelta(days=20)).month
         self.seasons = {
             "printemps": [3, 4, 5],
             "été": [6, 7, 8],
@@ -78,6 +77,9 @@ def tjms_by_weekday_category(
             .annotate(tjm=Sum("times"))
             .values("weekday", "id_category__code", "tjm")
         )
+        print(
+            f"yearly_report_bike.py: tjms_by_weekday_category - results.query:{str(results.query)}"
+        )
         return results
 
     def tjms_by_weekday_hour(self) -> "ValuesQuerySet[CountDetail, dict[str, Any]]":
@@ -98,11 +100,17 @@ def tjms_by_weekday_hour(self) -> "ValuesQuerySet[CountDetail, dict[str, Any]]":
             .values("date")
             .annotate(tj=Sum("times"))
             .values("date", "tj")
-            .annotate(weekday=ExtractIsoWeekDay("date"), hour=ExtractHour("timestamp"))
+            .annotate(
+                weekday=ExtractIsoWeekDay("date"),
+                hour=ExtractHour("timestamp"),
+            )
             .values("weekday", "hour")
             .annotate(tjm=Avg("tj"))
             .values("weekday", "hour", "tjm")
         )
+        print(
+            f"yearly_report_bike.py : tjms_by_weekday_hour - results.query:{str(results.query)}"
+        )
         return results
 
     def total_runs_by_hour_and_direction(
@@ -127,6 +135,9 @@ def total_runs_by_hour_and_direction(
             )
             .values("runs", "hour", "direction", "section")
         )
+        print(
+            f"yearly_report_bike.py : total_runs_by_hour_and_direction - results.query:{str(results.query)}"
+        )
 
         def partition(acc: dict, val: dict) -> dict:
             hour = val["hour"]
@@ -159,6 +170,9 @@ def total_runs_by_hour_one_direction(self, direction: int) -> dict[int, Any]:
             .annotate(day=ExtractIsoWeekDay("timestamp"))
             .order_by("day")
         )
+        print(
+            f"yearly_report_bike.py : total_runs_by_hour_one_direction - results.query:{str(results.query)}"
+        )
 
         def reducer(acc: dict, val: dict) -> dict:
             day = val["day"]
@@ -192,6 +206,9 @@ def total_runs_by_hour_weekday_one_direction(
             .annotate(day=ExtractIsoWeekDay("timestamp"))
             .order_by("day")
         )
+        print(
+            f"yearly_report_bike.py : total_runs_by_hour_weekday_one_direction - results.query:{str(results.query)}"
+        )
         return results
 
     def tjms_by_weekday_and_month(
@@ -218,6 +235,9 @@ def tjms_by_weekday_and_month(
             .annotate(month=ExtractMonth("timestamp"))
             .values("week_day", "month", "daily_runs")
         )
+        print(
+            f"yearly_report_bike.py : tjms_by_weekday_and_month - results.query:{str(results.query)}"
+        )
 
         # FIXME
         # Aggregation via `values()` into `annotate()` all the way to the end result would be more performant.
@@ -291,6 +311,7 @@ def nb_weekday_by_month(self) -> "ValuesQuerySet[CountDetail, dict[str, Any]]":
                 month=ExtractMonth("timestamp"), week_day=ExtractIsoWeekDay("timestamp")
             )
             .values("date", "month", "week_day")
+            # .order_by("date")
         )
         print(
             f"yearly_report_bike.py : nb_weekday_by_month - results.query:{str(results.query)}"
@@ -325,6 +346,10 @@ def total_runs_by_day(self) -> "ValuesQuerySet[CountDetail, dict[str, Any]]":
             .values("date")
             .annotate(daily_runs=Sum("times"))
             .values("date", "daily_runs")
+            # .order_by("date")
+        )
+        print(
+            f"yearly_report_bike.py : total_runs_by_day - results.query:{str(results.query)}"
         )
 
         return results
@@ -364,6 +389,7 @@ def tjms_total_runs_by_day_of_week(self) -> dict[str, Any]:
                     builder[item["week_day"]]["runs"]
                     / builder[item["week_day"]]["days"]
                 )
+
         return builder
 
     def total_runs_by_class(self) -> dict[str, Any]:
@@ -380,6 +406,9 @@ def total_runs_by_class(self) -> dict[str, Any]:
             .annotate(runs=Sum("times"), code=F("id_category__code"))
             .values("day", "runs", "code")
         )
+        print(
+            f"yearly_report_bike.py : total_runs_by_class - results.query:{str(results.query)}"
+        )
 
         def reducer(acc: dict, i: dict):
             code = i["code"]
@@ -407,6 +436,9 @@ def tjms_by_direction_bike(
         )
         assert qs.exists()
         results = qs.aggregate(res=Sum("times"))["res"]
+        print(
+            f"yearly_report_bike.py : tjms_by_direction_bike - results.query:{str(results.query)}"
+        )
         # TODO: avoid the division?
         return results / 365
 
@@ -417,6 +449,8 @@ def total(self, categories=[1]) -> float:
             import_status=definitions.IMPORT_STATUS_DEFINITIVE,
         )
         results = qs.aggregate(res=Sum("times"))["res"]
+        print(f"yearly_report_bike.py : total - results.query:{str(results.query)}")
+
         return results
 
     def max_day(self, categories=[1]) -> tuple[str, Any]:
@@ -431,6 +465,7 @@ def max_day(self, categories=[1]) -> tuple[str, Any]:
             .annotate(total=Sum("times"))
             .order_by("-total")
         )
+        print(f"yearly_report_bike.py : max_day - qs.query:{str(qs.query)}")
 
         return qs[0]["total"], qs[0]["date"]
 
@@ -446,6 +481,7 @@ def max_month(self, categories=[1]) -> tuple[str, Any]:
             .annotate(total=Sum("times"))
             .order_by("-total")
         )
+        print(f"yearly_report_bike.py : max_month - qs.query:{str(qs.query)}")
 
         return qs[0]["total"], qs[0]["month"]
 
@@ -461,6 +497,7 @@ def min_month(self, categories=[1]) -> tuple[str, Any]:
             .annotate(total=Sum("times"))
             .order_by("total")
         )
+        print(f"yearly_report_bike.py : min_month - qs.query:{str(qs.query)}")
 
         return qs[0]["total"], qs[0]["month"]
 
@@ -477,12 +514,19 @@ def count_details_by_various_criteria(
             .exclude(id_category__name__in=categories_name_to_exclude)
             .values_list("id_category", flat=True)
         )
+        print(
+            f"yearly_report_bike.py : count_details_by_various_criteria - categories_ids.query:{str(categories_ids.query)}"
+        )
+
         # Base QuerySet
         base_qs = CountDetail.objects.filter(
             id_count=count.id,
             id_category__in=categories_ids,
             timestamp__year=self.year,
         )
+        print(
+            f"yearly_report_bike.py : count_details_by_various_criteria - base_qs.query:{str(base_qs.query)}"
+        )
 
         # Specialized QuerySets
         total_runs_in_year = (
@@ -490,15 +534,21 @@ def count_details_by_various_criteria(
             .values("category_name")
             .annotate(value=Sum("times"))
         )
+        print(
+            f"yearly_report_bike.py : count_details_by_various_criteria - total_runs_in_year.query:{str(total_runs_in_year.query)}"
+        )
 
         busy_date = (
             base_qs.annotate(
-                date=TruncDate("timestamp"), category_name=F("id_category__name")
+                date=TruncDate("timestamp"),
             )
-            .values("date", "category_name")
+            .values("date")
             .annotate(value=Sum("times"))
             .order_by("-value")
         )
+        print(
+            f"yearly_report_bike.py : count_details_by_various_criteria - busy_date.query:{str(busy_date.query)}"
+        )
 
         busiest_date = busy_date.first()
         least_busy_date = busy_date.last()
@@ -513,6 +563,9 @@ def count_details_by_various_criteria(
             .values("date", "category_name")
             .annotate(value=Sum("times"))
         )
+        print(
+            f"yearly_report_bike.py : count_details_by_various_criteria - busiest_date_row.query:{str(busiest_date_row.query)}"
+        )
 
         least_busy_date_row = (
             base_qs.annotate(
@@ -522,6 +575,9 @@ def count_details_by_various_criteria(
             .values("date", "category_name")
             .annotate(value=Sum("times"))
         )
+        print(
+            f"yearly_report_bike.py : count_details_by_various_criteria - least_busy_date_row.query:{str(least_busy_date_row.query)}"
+        )
 
         busy_month = (
             base_qs.annotate(month=ExtractMonth("timestamp"))
@@ -529,6 +585,9 @@ def count_details_by_various_criteria(
             .annotate(value=Sum("times"))
             .order_by("-value")
         )
+        print(
+            f"yearly_report_bike.py : count_details_by_various_criteria - busy_month.query:{str(busy_month.query)}"
+        )
 
         busiest_month = busy_month.first()
         least_busy_month = busy_month.last()
@@ -563,8 +622,12 @@ def count_details_by_various_criteria(
             .annotate(value=Sum("times"))
             .order_by("-value")
         )
+
         total_runs_busiest_hour_weekday = busiest_hour.exclude(week_day__gt=5)
         total_runs_busiest_hour_weekend = busiest_hour.exclude(week_day__lt=6)
+        print(
+            f"yearly_report_bike.py : count_details_by_various_criteria - busiest_weekend_hour.query:{str(total_runs_busiest_hour_weekend.query)}"
+        )
 
         busiest_weekday = total_runs_busiest_hour_weekday.first()
         busiest_weekend = total_runs_busiest_hour_weekend.first()
@@ -592,6 +655,7 @@ def count_details_by_various_criteria(
 
     def count_details_by_season(self, count_id) -> dict[int, Any]:
         """Break down count details by season x section x class"""
+        # Assuming seasons to run from 21 <month> to 20 <month n + 3> -> month20 = (date - timedelta(days=20)).month
         # Preparing to filter out categories that don't reference the class picked out by `class_name`
         class_name = self.classtxt
         # Excluding irrelevant
@@ -616,6 +680,9 @@ def count_details_by_season(self, count_id) -> dict[int, Any]:
             .annotate(value=Sum("times"))
             .values("date", "category_name", "value")
         )
+        print(
+            f"yearly_report_bike.py : count_details_by_season - count_details.query:{str(count_details.query)}"
+        )
 
         # Preparing to collect data
         def reducer(acc: dict, detail) -> dict:
@@ -703,9 +770,14 @@ def get_category_data_by_dow(
             .values("week_day", "value")
             .values_list("week_day", "value")
         )
+        print(
+            f"yearly_report_bike.py : get_category_data_by_dow - qs.query={str(qs.query)}"
+        )
+
         return qs
 
     def run(self):
+        print(f"{datetime.now()}: YRB_run - begin... ({self.path_to_output_dir})")
         current_dir = path.dirname(path.abspath(__file__))
         template = path.join(current_dir, "template_yearly_bike.xlsx")
         workbook = load_workbook(filename=template)
@@ -731,7 +803,7 @@ def render_section_dist(value: Union[str, Decimal, None]) -> str:
         ws[
             "B3"
         ] = f"""
-            Poste de comptage : {section.id}  
+            Poste de comptage : {section.id}
             Axe : {section.owner}:{section.road}{section.way}
             PR {section.start_pr} + {section_start_dist} m à PR {section.end_pr} + {section_end_dist} m
         """
@@ -1000,5 +1072,6 @@ def render_section_dist(value: Union[str, Decimal, None]) -> str:
         output = path.join(
             self.path_to_output_dir, "{}_{}_r.xlsx".format(self.section_id, self.year)
         )
+
         workbook.save(filename=output)
         print(f"{datetime.now()}: YRB_run - end: Saved report to {output}")