diff --git a/docs/contributing/contributing.md b/docs/contributing/contributing.md
index 2f9007e..3018a69 100644
--- a/docs/contributing/contributing.md
+++ b/docs/contributing/contributing.md
@@ -98,25 +98,6 @@ More information about pre-commit hooks can be found [here](https://pre-commit.c
 
 Install Black:
 
-We use ruff to enforce the code style and code formatting. You can run it with:
-
-```bash
-pipenv run ruff check .
-pipenv run ruff format .
-```
-
-To ensure that the code is formatted correctly, we use a pre-commit hook that runs Ruff before every commit.
-Run the following once to enable hooks in your local repo:
-
-```bash
-pipenv run pre-commit install
-# optional: run on all files
-pipenv run pre-commit run --all-files
-```
-
-Hence, you will need to make sure that the code is formatted correctly before committing your changes; otherwise, the commit will fail.
-More information about pre-commit hooks can be found [here](https://pre-commit.com/).
-
 ```bash
 pipenv install black
 ```
diff --git a/pages/lib/charts_data_explorer.py b/pages/lib/charts_data_explorer.py
index c747763..4101271 100644
--- a/pages/lib/charts_data_explorer.py
+++ b/pages/lib/charts_data_explorer.py
@@ -1,9 +1,7 @@
-from math import ceil, floor
-
 import numpy as np
-import math
 import plotly.express as px
 import plotly.graph_objects as go
+from pages.lib.utils import get_max_min_value
 from pages.lib.global_scheme import template, mapping_dictionary, month_lst
 from pages.lib.global_column_names import ColNames
 
@@ -43,8 +41,7 @@ def custom_heatmap(df, global_local, var, time_filter_info, data_filter_info, si
         range_z = var_range
     else:
         # Set maximum and minimum according to data
-        data_max = 5 * ceil(df[var].max() / 5)
-        data_min = 5 * floor(df[var].min() / 5)
+        data_max, data_min = get_max_min_value(df[var])
         range_z = [data_min, data_max]
 
     title = var_name + " (" + var_unit + ")"
@@ -121,8 +118,7 @@ def three_var_graph(
 
     if global_local != "global":
         # Set maximum and minimum according to data
-        data_max = 5 * math.ceil(df[var].max() / 5)
-        data_min = 5 * math.floor(df[var].min() / 5)
+        data_max, data_min = get_max_min_value(df[var])
         var_range = [data_min, data_max]
 
     color_scale = var_color
diff --git a/pages/lib/charts_sun.py b/pages/lib/charts_sun.py
index c6449dd..9bd9747 100644
--- a/pages/lib/charts_sun.py
+++ b/pages/lib/charts_sun.py
@@ -1,11 +1,12 @@
 from datetime import timedelta
-from math import ceil, cos, floor, radians
+from math import cos, radians
 
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 
 from config import UnitSystem
+from pages.lib.utils import get_max_min_value
 from pages.lib.global_scheme import (
     template,
     mapping_dictionary,
@@ -135,8 +136,7 @@ def polar_graph(df, meta, global_local, var, si_ip):
             range_z = var_range
         else:
             # Set maximum and minimum according to data
-            data_max = 5 * ceil(solpos[var].max() / 5)
-            data_min = 5 * floor(solpos[var].min() / 5)
+            data_max, data_min = get_max_min_value(solpos[var])
             range_z = [data_min, data_max]
 
     tz = "UTC"
@@ -348,8 +348,7 @@ def custom_cartesian_solar(df, meta, global_local, var, si_ip):
             range_z = var_range
         else:
             # Set maximum and minimum according to data
-            data_max = 5 * ceil(df[var].max() / 5)
-            data_min = 5 * floor(df[var].min() / 5)
+            data_max, data_min = get_max_min_value(df[var])
             range_z = [data_min, data_max]
 
     if var == "None":
diff --git a/pages/lib/extract_df.py b/pages/lib/extract_df.py
index bda5e38..81cdda5 100644
--- a/pages/lib/extract_df.py
+++ b/pages/lib/extract_df.py
@@ -77,6 +77,66 @@ def get_location_info(lst, file_name):
     return location_info
 
 
+# ==== Unified UTCI computation and binning ====
+UTCI_BINS = [-999, -40, -27, -13, 0, 9, 26, 32, 38, 46, 999]
+UTCI_LABELS = [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
+
+
+def utci_calc(
+    df: pd.DataFrame,
+    t_air_col: str,
+    t_rad_col: str,
+    wind_col: str,
+    rh_col: str = ColNames.RH,
+) -> pd.Series:
+    """Call utci() using values from df columns."""
+    return utci(df[t_air_col], df[t_rad_col], df[wind_col], df[rh_col])
+
+
+def add_utci_variants(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Generate the four UTCI variants:
+      - noSun_Wind   : DBT + DBT + wind_speed_utci
+      - noSun_noWind : DBT + DBT + wind_speed_utci_0
+      - Sun_Wind     : DBT + MRT + wind_speed_utci
+      - Sun_noWind   : DBT + MRT + wind_speed_utci_0
+    """
+    recipes = {
+        ColNames.UTCI_NO_SUN_WIND: (
+            ColNames.DBT,
+            ColNames.DBT,
+            ColNames.WIND_SPEED_UTCI,
+        ),
+        ColNames.UTCI_NO_SUN_NO_WIND: (
+            ColNames.DBT,
+            ColNames.DBT,
+            ColNames.WIND_SPEED_UTCI_0,
+        ),
+        ColNames.UTCI_SUN_WIND: (ColNames.DBT, ColNames.MRT, ColNames.WIND_SPEED_UTCI),
+        ColNames.UTCI_SUN_NO_WIND: (
+            ColNames.DBT,
+            ColNames.MRT,
+            ColNames.WIND_SPEED_UTCI_0,
+        ),
+    }
+    for out_col, (t_air, t_rad, wind) in recipes.items():
+        df[out_col] = utci_calc(df, t_air, t_rad, wind)
+    return df
+
+
+def add_utci_categories(df: pd.DataFrame) -> pd.DataFrame:
+    """Bin the four UTCI columns into categories."""
+    mapping = {
+        ColNames.UTCI_NO_SUN_WIND: ColNames.UTCI_NOSUN_WIND_CATEGORIES,
+        ColNames.UTCI_NO_SUN_NO_WIND: ColNames.UTCI_NOSUN_NOWIND_CATEGORIES,
+        ColNames.UTCI_SUN_WIND: ColNames.UTCI_SUN_WIND_CATEGORIES,
+        ColNames.UTCI_SUN_NO_WIND: ColNames.UTCI_SUN_NOWIND_CATEGORIES,
+    }
+    for src_col, dst_col in mapping.items():
+        df[dst_col] = pd.cut(x=df[src_col], bins=UTCI_BINS, labels=UTCI_LABELS)
+    return df
+
+
 @code_timer
 def create_df(lst, file_name):
     """Extract and clean the data. Return a pandas data from a url."""
@@ -241,14 +301,14 @@ def create_df(lst, file_name):
 
     # Add in UTCI
     sol_altitude = epw_df[ColNames.ELEVATION].mask(epw_df[ColNames.ELEVATION] <= 0, 0)
-    sharp = [45] * 8760
+    sharp = expand_to_hours(45)
     sol_radiation_dir = epw_df[ColNames.DIR_NOR_RAD]
-    sol_transmittance = [1] * 8760  # CHECK VALUE
-    f_svv = [1] * 8760  # CHECK VALUE
-    f_bes = [1] * 8760  # CHECK VALUE
-    asw = [0.7] * 8760  # CHECK VALUE
-    posture = ["standing"] * 8760
-    floor_reflectance = [0.6] * 8760  # EXPOSE AS A VARIABLE?
+    sol_transmittance = expand_to_hours(1)  # CHECK VALUE
+    f_svv = expand_to_hours(1)  # CHECK VALUE
+    f_bes = expand_to_hours(1)  # CHECK VALUE
+    asw = expand_to_hours(0.7)  # CHECK VALUE
+    posture = expand_to_hours("standing")
+    floor_reflectance = expand_to_hours(0.6)  # EXPOSE AS A VARIABLE?
 
     mrt = np.vectorize(solar_gain)(
         sol_altitude,
@@ -280,45 +340,10 @@ def create_df(lst, file_name):
     epw_df[ColNames.WIND_SPEED_UTCI_0] = epw_df[ColNames.WIND_SPEED_UTCI].mask(
         epw_df[ColNames.WIND_SPEED_UTCI] >= 0, 0.5
     )
-    epw_df[ColNames.UTCI_NO_SUN_WIND] = utci(
-        epw_df[ColNames.DBT],
-        epw_df[ColNames.DBT],
-        epw_df[ColNames.WIND_SPEED_UTCI],
-        epw_df[ColNames.RH],
-    )
-    epw_df[ColNames.UTCI_NO_SUN_NO_WIND] = utci(
-        epw_df[ColNames.DBT],
-        epw_df[ColNames.DBT],
-        epw_df[ColNames.WIND_SPEED_UTCI_0],
-        epw_df[ColNames.RH],
-    )
-    epw_df[ColNames.UTCI_SUN_WIND] = utci(
-        epw_df[ColNames.DBT],
-        epw_df[ColNames.MRT],
-        epw_df[ColNames.WIND_SPEED_UTCI],
-        epw_df[ColNames.RH],
-    )
-    epw_df[ColNames.UTCI_SUN_NO_WIND] = utci(
-        epw_df[ColNames.DBT],
-        epw_df[ColNames.MRT],
-        epw_df[ColNames.WIND_SPEED_UTCI_0],
-        epw_df[ColNames.RH],
-    )
 
-    utci_bins = [-999, -40, -27, -13, 0, 9, 26, 32, 38, 46, 999]
-    utci_labels = [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
-    epw_df[ColNames.UTCI_NOSUN_WIND_CATEGORIES] = pd.cut(
-        x=epw_df[ColNames.UTCI_NO_SUN_WIND], bins=utci_bins, labels=utci_labels
-    )
-    epw_df[ColNames.UTCI_NOSUN_NOWIND_CATEGORIES] = pd.cut(
-        x=epw_df[ColNames.UTCI_NO_SUN_NO_WIND], bins=utci_bins, labels=utci_labels
-    )
-    epw_df[ColNames.UTCI_SUN_WIND_CATEGORIES] = pd.cut(
-        x=epw_df[ColNames.UTCI_SUN_WIND], bins=utci_bins, labels=utci_labels
-    )
-    epw_df[ColNames.UTCI_SUN_NOWIND_CATEGORIES] = pd.cut(
-        x=epw_df[ColNames.UTCI_SUN_NO_WIND], bins=utci_bins, labels=utci_labels
-    )
+    epw_df = add_utci_variants(epw_df)
+
+    epw_df = add_utci_categories(epw_df)
 
     # Add psy values
     ta_rh = np.vectorize(psy.psy_ta_rh)(epw_df[ColNames.DBT], epw_df[ColNames.RH])
@@ -407,11 +432,11 @@ def enthalpy(df, name):
 
 
 def convert_data(df, mapping_json):
-    df[ColNames.ADAPTIVE_COMFORT] = df[ColNames.ADAPTIVE_COMFORT] * 1.8 + 32
-    df[ColNames.ADAPTIVE_CMF_80_LOW] = df[ColNames.ADAPTIVE_CMF_80_LOW] * 1.8 + 32
-    df[ColNames.ADAPTIVE_CMF_80_UP] = df[ColNames.ADAPTIVE_CMF_80_UP] * 1.8 + 32
-    df[ColNames.ADAPTIVE_CMF_90_LOW] = df[ColNames.ADAPTIVE_CMF_90_LOW] * 1.8 + 32
-    df[ColNames.ADAPTIVE_CMF_90_UP] = df[ColNames.ADAPTIVE_CMF_90_UP] * 1.8 + 32
+    convert_t_to_f(df, ColNames.ADAPTIVE_COMFORT)
+    convert_t_to_f(df, ColNames.ADAPTIVE_CMF_80_LOW)
+    convert_t_to_f(df, ColNames.ADAPTIVE_CMF_80_UP)
+    convert_t_to_f(df, ColNames.ADAPTIVE_CMF_90_LOW)
+    convert_t_to_f(df, ColNames.ADAPTIVE_CMF_90_UP)
 
     mapping_dict = json.loads(mapping_json)
     for key in json.loads(mapping_json):
@@ -423,6 +448,32 @@ def convert_data(df, mapping_json):
     return json.dumps(mapping_dict)
 
 
+def convert_t_to_f(df: pd.DataFrame, name: str):
+    """Convert temperature from Celsius to Fahrenheit in-place for a given column.
+
+    Args:
+        df: The DataFrame containing the temperature column.
+        name: Column name to convert.
+
+    Returns:
+        None. The DataFrame is modified in-place.
+    """
+    df[name] = df[name] * 1.8 + 32
+
+
+def expand_to_hours(value: any, hours: int = 8760) -> list[any]:
+    """Return a list with the input value repeated for a given number of hours.
+
+    Args:
+        value: The value to repeat.
+        hours: Number of repetitions. Defaults to 8760 (hours in a year).
+
+    Returns:
+        A list containing the value repeated `hours` times.
+    """
+    return [value] * hours
+
+
 if __name__ == "__main__":
     # fmt: off
     test_url = "https://www.energyplus.net/weather-download/europe_wmo_region_6/ITA//ITA_Bologna-Borgo.Panigale.161400_IGDG/all"
diff --git a/pages/lib/template_graphs.py b/pages/lib/template_graphs.py
index c57fd22..f8e3641 100644
--- a/pages/lib/template_graphs.py
+++ b/pages/lib/template_graphs.py
@@ -1,11 +1,10 @@
-from math import ceil, floor
-
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
 from config import UnitSystem
+from pages.lib.utils import get_max_min_value
 from pages.lib.global_scheme import mapping_dictionary
 import dash_bootstrap_components as dbc
 from .global_scheme import month_lst, template, tight_margins
@@ -25,8 +24,7 @@ def violin(df, var, global_local, si_ip):
     data_night = df.loc[mask_night, var]
 
     if global_local != "global":
-        data_max = 5 * ceil(df[var].max() / 5)
-        data_min = 5 * floor(df[var].min() / 5)
+        data_max, data_min = get_max_min_value(df[var])
         var_range = [data_min, data_max]
 
     fig = go.Figure()
@@ -95,8 +93,7 @@ def yearly_profile(df, var, global_local, si_ip):
         range_y = var_range
     else:
         # Set maximum and minimum according to data
-        data_max = 5 * ceil(df[var].max() / 5)
-        data_min = 5 * floor(df[var].min() / 5)
+        data_max, data_min = get_max_min_value(df[var])
         range_y = [data_min, data_max]
 
     var_single_color = var_color[len(var_color) // 2]
@@ -255,8 +252,7 @@ def daily_profile(df, var, global_local, si_ip):
         range_y = var_range
     else:
         # Set maximum and minimum according to data
-        data_max = 5 * ceil(df[var].max() / 5)
-        data_min = 5 * floor(df[var].min() / 5)
+        data_max, data_min = get_max_min_value(df[var])
         range_y = [data_min, data_max]
 
     var_single_color = var_color[len(var_color) // 2]
@@ -371,8 +367,7 @@ def heatmap_with_filter(
         range_z = var_range
     else:
         # Set maximum and minimum according to data
-        data_max = 5 * ceil(df[var].max() / 5)
-        data_min = 5 * floor(df[var].min() / 5)
+        data_max, data_min = get_max_min_value(df[var])
         range_z = [data_min, data_max]
     fig = go.Figure(
         data=go.Heatmap(
@@ -430,8 +425,7 @@ def heatmap(df, var, global_local, si_ip):
         range_z = var_range
     else:
         # Set maximum and minimum according to data
-        data_max = 5 * ceil(df[var].max() / 5)
-        data_min = 5 * floor(df[var].min() / 5)
+        data_max, data_min = get_max_min_value(df[var])
         range_z = [data_min, data_max]
     fig = go.Figure(
         data=go.Heatmap(
@@ -844,29 +838,55 @@ def barchart(df, var, time_filter_info, data_filter_info, normalize, si_ip):
     return fig
 
 
+def time_filtering(
+    df: pd.DataFrame, start_time: int, end_time: int, time_col: str, target_col: str
+) -> pd.DataFrame:
+    """Mask values in the target column based on the given time range.
+
+    Args:
+        df: Input dataframe.
+        start_time: Start of the time range.
+        end_time: End of the time range.
+        time_col: Column name representing time (e.g., hour or month).
+        target_col: Column name to apply the mask on.
+
+    Returns:
+        A modified DataFrame with masked values outside the given time range.
+    """
+    if start_time <= end_time:
+        mask = (df[time_col] < start_time) | (df[time_col] > end_time)
+    else:
+        mask = (df[time_col] >= end_time) & (df[time_col] <= start_time)
+    df.loc[mask, target_col] = None
+    return df
+
+
 def filter_df_by_month_and_hour(
     df, time_filter, month, hour, invert_month, invert_hour, var
 ):
+    """Apply month and hour filtering to the DataFrame based on user selections.
+
+    Args:
+        df: Input DataFrame.
+        time_filter: Whether to apply the time filter.
+        month: Selected month range.
+        hour: Selected hour range.
+        invert_month: Whether to invert the month range.
+        invert_hour: Whether to invert the hour range.
+        var: Target variable column name.
+
+    Returns:
+        Filtered DataFrame with appropriate masking applied.
+    """
     start_month, end_month, start_hour, end_hour = determine_month_and_hour_filter(
         month, hour, invert_month, invert_hour
     )
 
     if time_filter:
-        if start_month <= end_month:
-            mask = (df[ColNames.MONTH] < start_month) | (df[ColNames.MONTH] > end_month)
-            df.loc[mask, var] = None
-        else:
-            mask = (df[ColNames.MONTH] >= end_month) & (
-                df[ColNames.MONTH] <= start_month
-            )
-            df.loc[mask, var] = None
-
-        if start_hour <= end_hour:
-            mask = (df[ColNames.HOUR] <= start_hour) | (df[ColNames.HOUR] > end_hour)
-            df.loc[mask, var] = None
-        else:
-            mask = (df[ColNames.HOUR] > end_hour) & (df[ColNames.HOUR] <= start_hour)
-            df.loc[mask, var] = None
+        # Month filter
+        time_filtering(df, start_month, end_month, ColNames.MONTH, var)
+        # Hour filter
+        time_filtering(df, start_hour, end_hour, ColNames.HOUR, var)
 
     return df
 
diff --git a/pages/lib/utils.py b/pages/lib/utils.py
index 2455b14..539ca23 100644
--- a/pages/lib/utils.py
+++ b/pages/lib/utils.py
@@ -1,6 +1,7 @@
 import copy
 import functools
 import time
+import math
 
 import dash_bootstrap_components as dbc
 import pandas as pd
@@ -289,3 +290,18 @@ def dropdown(options=None, **kwargs):
         clearable=False,
         **kwargs,
     )
+
+
+def get_max_min_value(series: pd.Series, base: int = 5) -> tuple[int, int]:
+    """Calculate rounded-up max and rounded-down min values based on a base step.
+
+    Args:
+        series: Pandas Series of numeric values.
+        base: The rounding base. Default is 5.
+
+    Returns:
+        Tuple of (max_value, min_value) adjusted to nearest base step.
+    """
+    data_max = base * math.ceil(series.max() / base)
+    data_min = base * math.floor(series.min() / base)
+    return data_max, data_min
diff --git a/pages/natural_ventilation.py b/pages/natural_ventilation.py
index 1f5f7ce..ee002e2 100644
--- a/pages/natural_ventilation.py
+++ b/pages/natural_ventilation.py
@@ -1,5 +1,3 @@
-import math
-
 import dash
 from dash import dcc, html
 import dash_bootstrap_components as dbc
@@ -17,6 +15,7 @@
     container_row_center_full,
     container_col_center_one_of_three,
 )
+from pages.lib.utils import get_max_min_value
 from pages.lib.template_graphs import filter_df_by_month_and_hour
 from pages.lib.global_column_names import ColNames
 from pages.lib.global_element_ids import ElementIds
@@ -373,8 +372,7 @@ def nv_heatmap(
     if global_local == "global":
         range_z = var_range
     else:
-        data_max = 5 * math.ceil(df[var].max() / 5)
-        data_min = 5 * math.floor(df[var].min() / 5)
+        data_max, data_min = get_max_min_value(df[var])
         range_z = [data_min, data_max]
 
     title = (
diff --git a/pages/psy-chart.py b/pages/psy-chart.py
index fc49e8f..7cf0dfb 100644
--- a/pages/psy-chart.py
+++ b/pages/psy-chart.py
@@ -1,5 +1,3 @@
-from math import ceil, floor
-
 import dash
 from dash import dcc, html
 import dash_bootstrap_components as dbc
@@ -12,6 +10,8 @@
 from pythermalcomfort import psychrometrics as psy
 
 from config import PageUrls, DocLinks, PageInfo, UnitSystem
+from pages.lib.utils import get_max_min_value
+from pages.lib.extract_df import convert_t_to_f
 from pages.lib.global_element_ids import ElementIds
 from pages.lib.global_column_names import ColNames
 from pages.lib.global_id_buttons import IdButtons
@@ -321,8 +321,7 @@ def update_psych_chart(
 
     else:
         # Set maximum and minimum according to data
-        data_max = 5 * ceil(df[ColNames.DBT].max() / 5)
-        data_min = 5 * floor(df[ColNames.DBT].min() / 5)
+        data_max, data_min = get_max_min_value(df[ColNames.DBT])
         var_range_x = [data_min, data_max]
 
         data_max = round(df[ColNames.HR].max(), 4)
@@ -358,7 +357,7 @@ def update_psych_chart(
 
         if si_ip == UnitSystem.IP:
             for j in range(len(dbt_list)):
-                dbt_list_convert[j] = dbt_list_convert[j] * 1.8 + 32
+                convert_t_to_f(dbt_list_convert, j)
 
         fig.add_trace(
             go.Scatter(