some formating and add missing dependency for gps

app.py

@@ -5,12 +5,12 @@
 import streamlit as st
 
 import analysis_tab
+import contacts_tab
 import datafactory
 import docs
 import map_tab
 import traj_tab
 import ui
-import contacts_tab
 
 # Basic configuration for logging
 logging.basicConfig(
@@ -40,6 +40,6 @@
 
     if selected == "Analysis":
         analysis_tab.run_tab3()
-    
+
     if selected == "Contacts":
         contacts_tab.call_main()

contacts_tab.py

@@ -1,101 +1,114 @@
 """ Map of the gps trajectories coupled with the contacts locations. """
 
+from typing import Tuple
+
 import folium
+import matplotlib.colors as mcolors
 import numpy as np
 import pandas as pd
-import streamlit as st
 import plotly.express as px
 import plotly.graph_objects as go
-import matplotlib.colors as mcolors
-
-from typing import Tuple
+import streamlit as st
 from matplotlib import colormaps
-from streamlit_folium import st_folium
 from plotly.graph_objects import Figure
+from streamlit_folium import st_folium
 
-def load_data(gps_path: str, contacts_path: str) -> tuple:
+
+def load_data(
+    gps_path: str, contacts_path: str
+) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
     """
     Load GPS tracks and contact data from pickled files.
-    
+
     Args:
         gps_path (str): The path to the GPS data directory.
         contacts_path (str): The path to the contacts data directory.
-    
+
     Returns:
         tuple: A tuple containing all GPS tracks and contact GPS merged data.
     """
-    all_gps_tracks = pd.read_pickle(gps_path + 'all_gps_tracks.pickle')
-    contact_gps_merged = pd.read_pickle(contacts_path + 'contacts_gps_merged.pickle')
-    contacts_data = pd.read_pickle(contacts_path + 'contacts_data.pickle')
+    all_gps_tracks = pd.read_pickle(gps_path + "all_gps_tracks.pickle")
+    contact_gps_merged = pd.read_pickle(contacts_path + "contacts_gps_merged.pickle")
+    contacts_data = pd.read_pickle(contacts_path + "contacts_data.pickle")
     return all_gps_tracks, contact_gps_merged, contacts_data
 
+
 def initialize_map(all_gps_tracks: pd.DataFrame) -> folium.Map:
     """
     Initialize the map centered on the middle point of Ludovic-Gardre1's first track.
-    
+
     Args:
         all_gps_tracks (pd.DataFrame): DataFrame containing all GPS tracks.
-    
+
     Returns:
         folium.Map: A folium map object.
     """
-    first_track_df = all_gps_tracks[all_gps_tracks['name_subj'] == 'Ludovic-Gardre1']
-    map_center = first_track_df.iloc[len(first_track_df) // 2][['latitude', 'longitude']].tolist()
+    first_track_df = all_gps_tracks[all_gps_tracks["name_subj"] == "Ludovic-Gardre1"]
+    map_center = first_track_df.iloc[len(first_track_df) // 2][
+        ["latitude", "longitude"]
+    ].tolist()
     return folium.Map(location=map_center, zoom_start=17.5)
 
+
 def add_tile_layer(map_object: folium.Map) -> None:
     """
     Add a Google Satellite tile layer to enhance the map visualization.
-    
+
     Args:
         map_object (folium.Map): The folium map object to which the tile layer will be added.
     """
     google_satellite = folium.TileLayer(
-        tiles='http://www.google.cn/maps/vt?lyrs=s@189&gl=cn&x={x}&y={y}&z={z}',
-        attr='Google',
-        name='Google Satellite',
+        tiles="http://www.google.cn/maps/vt?lyrs=s@189&gl=cn&x={x}&y={y}&z={z}",
+        attr="Google",
+        name="Google Satellite",
         overlay=True,
         control=True,
-        opacity=1.
+        opacity=1.0,
     )
     google_satellite.add_to(map_object)
 
+
 def plot_gps_tracks(map_object: folium.Map, all_gps_tracks: pd.DataFrame) -> None:
     """
     Plot each GPS track on the map with a unique color.
-    
+
     Args:
         map_object (folium.Map): The folium map object where tracks will be plotted.
         all_gps_tracks (pd.DataFrame): DataFrame containing all GPS tracks.
     """
-    unique_tracks = all_gps_tracks['name_subj'].unique()
-    viridis = colormaps.get_cmap('viridis')
+    unique_tracks = all_gps_tracks["name_subj"].unique()
+    viridis = colormaps.get_cmap("viridis")
     for track_index, name_subj in enumerate(unique_tracks):
-        track_df = all_gps_tracks[all_gps_tracks['name_subj'] == name_subj]
-        track_points = track_df[['latitude', 'longitude']].values.tolist()
+        track_df = all_gps_tracks[all_gps_tracks["name_subj"] == name_subj]
+        track_points = track_df[["latitude", "longitude"]].values.tolist()
         rgba_color = viridis(track_index / len(unique_tracks))
         hex_color = mcolors.to_hex(rgba_color)
-        folium.PolyLine(track_points, color=hex_color, weight=2.5, opacity=1, popup=name_subj).add_to(map_object)
+        folium.PolyLine(
+            track_points, color=hex_color, weight=2.5, opacity=1, popup=name_subj
+        ).add_to(map_object)
+
 
-def add_contact_markers(map_object: folium.Map, contact_gps_merged: pd.DataFrame, path_icon: str) -> None:
+def add_contact_markers(
+    map_object: folium.Map, contact_gps_merged: pd.DataFrame, path_icon: str
+) -> None:
     """
     Add markers for each contact point on the map.
-    
+
     Args:
         map_object (folium.Map): The folium map object where markers will be added.
         contact_gps_merged (pd.DataFrame): DataFrame containing contact GPS merged data.
     """
     for index, row in contact_gps_merged.iterrows():
         icon_person = folium.features.CustomIcon(
-            icon_image=path_icon + '/contact_icon.png',
-            icon_size=(30, 30)
+            icon_image=path_icon + "/contact_icon.png", icon_size=(30, 30)
         )
         folium.Marker(
-            location=[row['latitude'], row['longitude']],
+            location=[row["latitude"], row["longitude"]],
             icon=icon_person,
-            popup=row['name_subj'],
+            popup=row["name_subj"],
         ).add_to(map_object)
 
+
 def plot_histogram(df: pd.DataFrame) -> Figure:
     """
     Creates an interactive bar chart using Plotly to visualize the total number of collisions.
@@ -107,40 +120,49 @@ def plot_histogram(df: pd.DataFrame) -> Figure:
         Figure: The Plotly figure object for the histogram.
     """
     # Slider for selecting the number of bins
-    bins = st.slider('Select number of bins:', min_value=5, max_value=11, value=10, step=3)
-    fig = px.histogram(df['Total-number-of-collisions'], x='Total-number-of-collisions', nbins=bins)
-    print(df['Total-number-of-collisions'])
+    bins = st.slider(
+        "Select number of bins:", min_value=5, max_value=11, value=10, step=3
+    )
+    fig = px.histogram(
+        df["Total-number-of-collisions"], x="Total-number-of-collisions", nbins=bins
+    )
+    print(df["Total-number-of-collisions"])
     fig.update_layout(
-        xaxis_title='Number of contacts along the path',
-        yaxis_title='Number of people')  # Set the range for the log scale
-    
+        xaxis_title="Number of contacts along the path", yaxis_title="Number of people"
+    )  # Set the range for the log scale
+
     return fig
 
 
-def plot_cumulative_contacts(df):
-    """ To plot cumulative contacts as a function of time using Plotly """
+def plot_cumulative_contacts(df: pd.DataFrame) -> Figure:
+    """To plot cumulative contacts as a function of time using Plotly"""
     # Initialize an empty figure
     fig = go.Figure()
-    
+
     # Loop through the DataFrame and plot each person's contact times
     for index, row in df.iterrows():
         times = row.dropna().values  # Get the 'Détail' times for the person
         if len(times) > 0:
-            values = np.cumsum(np.concatenate(([0], np.ones(len(times), dtype='int'))))
-            edges = np.concatenate((times, [df['Duration'].iloc[index].total_seconds()]))
+            values = np.cumsum(np.concatenate(([0], np.ones(len(times), dtype="int"))))  # type: ignore
+            edges = np.concatenate(
+                (times, [df["Duration"].iloc[index].total_seconds()])
+            )
             # Add a trace for each person
-            fig.add_trace(go.Scatter(x=edges, y=values, mode='lines+markers', name=row['Name']))
-
+            fig.add_trace(
+                go.Scatter(x=edges, y=values, mode="lines+markers", name=row["Name"])
+            )
+
     # Update layout of the figure
     fig.update_layout(
         title="Cumulative Contacts as a Function of Time",
         xaxis_title="Time [microseconds]",
         yaxis_title="Cumulative Number of Contacts",
-        drawstyle='steps-pre'
+        drawstyle="steps-pre",
     )
-    
+
     return fig
 
+
 def main() -> None:
     """
     Main function to orchestrate the map creation process and integrate with Streamlit.
@@ -169,4 +191,4 @@ def main() -> None:
 
 
 def call_main() -> None:
-    main()
+    main()

datafactory.py

@@ -9,7 +9,7 @@
 from typing import List, Union
 
 import pedpy
-import requests  # type:ignore
+import requests  # type: ignore
 import streamlit as st
 
 
@@ -24,6 +24,8 @@ class DirectionInfo:
 
 @dataclass
 class Direction:
+    """Data for measurement line."""
+
     info: DirectionInfo
     line: pedpy.MeasurementLine
 

map_tab.py

@@ -6,8 +6,9 @@
 
 import folium
 import streamlit as st
-from streamlit_folium import st_folium
 from folium.features import CustomIcon
+from streamlit_folium import st_folium
+
 
 @dataclass
 class Camera:
@@ -63,7 +64,9 @@ def load_cameras_from_json(file_path: str) -> Dict[str, Camera]:
             name = info["name"]
             field = info["field"]
             logo = info["logo"]
-            cameras[key] = Camera(location=location, url=url, name=name, field=field, logo=logo)
+            cameras[key] = Camera(
+                location=location, url=url, name=name, field=field, logo=logo
+            )
 
         except KeyError as e:
             # Handle missing keys in the data
@@ -153,10 +156,10 @@ def create_map(
         polygon.add_to(layer)
 
     for (key, camera), layer in zip(cameras.items(), camera_layers):
-        icon = CustomIcon(camera.logo, icon_size=(110, 110) )
+        icon = CustomIcon(camera.logo, icon_size=(110, 110))
         coords = camera.location
         tooltip = f"{key}: {camera.name}"
-        folium.Marker(location=coords, tooltip=tooltip,icon=icon).add_to(layer)
+        folium.Marker(location=coords, tooltip=tooltip, icon=icon).add_to(layer)
         vision_fields[key].add_to(layer)
 
     # folium.FitOverlays().add_to(m)

requirements.txt

@@ -8,3 +8,4 @@ kaleido
 matplotlib
 streamlit_drawable_canvas==0.8.0
 streamlit-option-menu
+gpxpy

ui.py

@@ -42,7 +42,13 @@ def init_sidebar() -> Any:
     return option_menu(
         "Multi-agent modelling of dense crowd dynamics: Predict & Understand",
         ["About", "Map", "Trajectories", "Analysis", "Contacts"],
-        icons=["info-square", "pin-map", "people", "bar-chart-line", "exclamation-triangle"],
+        icons=[
+            "info-square",
+            "pin-map",
+            "people",
+            "bar-chart-line",
+            "exclamation-triangle",
+        ],
         menu_icon="cast",
         default_index=0,
         orientation="horizontal",