New directory structure of the app

app.py

@@ -2,38 +2,37 @@
 
 import streamlit as st
 
-import analysis_tab
-import contacts_tab
-import datafactory
-import docs
-import map_tab
-import traj_tab
-import ui
-from log_config import setup_logging
+from src.classes.datafactory import init_session_state
+from src.docs import docs
+from src.helpers.log_config import setup_logging
+from src.tabs.analysis_tab import run_tab3
+from src.tabs.contacts_tab import run_tab_contact
+from src.tabs.map_tab import run_tab_map
+from src.tabs.traj_tab import run_tab2
+from src.ui.ui import init_app_looks, init_sidebar, setup_app
 
 setup_logging()
 if __name__ == "__main__":
-    ui.setup_app()
-    selected = ui.init_sidebar()
-    ui.init_app_looks()
-    datafactory.init_session_state()
+    setup_app()
+    selected_tab = init_sidebar()
+    init_app_looks()
+    init_session_state()
 
-    if selected == "About":
+    if selected_tab == "About":
         docs.about()
 
-    if selected == "Map":
-        map_tab.call_main()
+    if selected_tab == "Map":
+        run_tab_map()
 
-    if selected == "Trajectories":
+    if selected_tab == "Trajectories":
         msg = st.empty()
-        filename = str(
-            st.selectbox(":open_file_folder: **Select a file**", st.session_state.files)
-        )
-        st.session_state.selected_file = filename
-        traj_tab.run_tab2(filename, msg)
+        file_name_to_path = {path.split('/')[-1]: path for path in st.session_state.files}
+        filename = str(st.selectbox(":open_file_folder: **Select a file**", file_name_to_path))
+        st.session_state.selected_file = file_name_to_path[filename]
+        run_tab2(file_name_to_path[filename], msg)
 
-    if selected == "Analysis":
-        analysis_tab.run_tab3()
+    if selected_tab == "Analysis":
+        run_tab3()
 
-    if selected == "Contacts":
-        contacts_tab.call_main()
+    if selected_tab == "Contacts":
+        run_tab_contact()

cameras.json → data/assets/cameras.json

@@ -4,62 +4,62 @@
         "field" : [[45.767808, 4.833684], [45.767708, 4.833739], [45.767719, 4.833813], [45.767808, 4.833684]], 
         "url": "https://youtu.be/EVgS2sGqrMk?si=nnApDBU_RpagUwbC",
         "name": "restaurant l'Etage",
-        "logo": "./logo_cameras/cam_letage.png"
+        "logo": "cam_letage.png"
     },
     "camera2": {
         "location": [45.767807, 4.833659],
         "field" : [[45.767807, 4.833659], [45.767708, 4.833739], [45.767700, 4.833664], [45.767807, 4.833659]], 
         "url": "https://youtu.be/L__Rpl45gqU?si=s9OxC8m3pRiFpMQ6",
         "name": "airbnb terreaux 1",
-        "logo": "./logo_cameras/cam_airbnb_terreaux1.png"
+        "logo": "cam_airbnb_terreaux1.png"
     },
     "camera3": {
         "location": [45.767803, 4.833640],
         "field" : [[45.767803, 4.833640], [45.767700, 4.833664], [45.767686, 4.833547], [45.767803, 4.833640]], 
         "url": "https://youtu.be/EVgS2sGqrMk?si=nnApDBU_RpagUwbC",
         "name": "airbnb terreaux 2",
-        "logo": "./logo_cameras/cam_airbnb_terreaux2.png"
+        "logo": "cam_airbnb_terreaux2.png"
     },
     "camera4": {
         "location": [45.767562, 4.834459],
         "field" : [[45.767562, 4.834459], [45.767090, 4.832775], [45.767700, 4.832677], [45.767562, 4.834459]], 
         "url": "https://youtu.be/L__Rpl45gqU?si=s9OxC8m3pRiFpMQ6",
         "name": "bell tower of the city hall" ,
-        "logo": "./logo_cameras/cam_bell_tower.png"
+        "logo": "cam_bell_tower.png"
     },
      "camera5": {
         "location": [45.767353, 4.834343], 
         "field" : [[45.767353, 4.834343], [45.767275, 4.834433], [45.767372, 4.834200], [45.767353, 4.834343]], 
         "url": "https://youtu.be/L__Rpl45gqU?si=s9OxC8m3pRiFpMQ6",
         "name": "city hall corner",
-        "logo": "./logo_cameras/cam_city_hall_corner.png"
+        "logo": "cam_city_hall_corner.png"
     },
      "camera6": {
         "location": [45.767026, 4.832412],
          "field" : [[45.767026, 4.832412], [45.767124, 4.832372], [45.767136, 4.832460], [45.767026, 4.832412]], 
         "url": "https://youtu.be/L__Rpl45gqU?si=s9OxC8m3pRiFpMQ6",
         "name": "airbnb street Constantine 1",
-        "logo": "./logo_cameras/cam_airbnb_constantine1.png"
+        "logo": "cam_airbnb_constantine1.png"
     },
      "camera7": {
         "location": [45.767021, 4.832376],
         "field" : [[45.767021, 4.832376],  [45.767124, 4.832372], [45.767115, 4.832296], [45.767021, 4.832376]], 
         "url": "https://youtu.be/L__Rpl45gqU?si=s9OxC8m3pRiFpMQ6",
         "name": "airbnb street Constantine 2",
-        "logo": "./logo_cameras/cam_airbnb_constantine2.png"
+        "logo": "cam_airbnb_constantine2.png"
     },
     "camera8": {
         "location": [45.761136, 4.826233], 
         "field" : [[45.761136, 4.826233], [45.761089, 4.826463], [45.761231, 4.826482], [45.761136, 4.826233]], 
         "url": "https://youtu.be/L__Rpl45gqU?si=s9OxC8m3pRiFpMQ6", 
         "name": "Saint Jean 1",
-        "logo": "./logo_cameras/cam_saint_jean1.png"
+        "logo": "cam_saint_jean1.png"
     },
     "camera9": {
         "location": [45.761070, 4.826219],
         "field" : [[45.761070, 4.826219], [45.761089, 4.826463], [45.760959, 4.826404], [45.761070, 4.826219]], 
         "url": "https://youtu.be/L__Rpl45gqU?si=s9OxC8m3pRiFpMQ6", 
         "name": "Saint Jean 2",
-        "logo": "./logo_cameras/cam_saint_jean2.png"
+        "logo": "cam_saint_jean2.png"
     }
 }

datafactory.py → src/classes/datafactory.py

@@ -1,6 +1,7 @@
 """Unsorted datastructure for the app."""
 
 import glob
+import logging
 import os
 import shutil
 import zipfile
@@ -89,8 +90,10 @@ def init_state_bg_image() -> None:
 
 def init_session_state() -> None:
     """Init session_state throughout the app."""
+    path = Path(__file__)
+    trajectories_directory = path.parent.parent.parent.absolute() / "data" / "trajectories"
+    logging.info(f"{trajectories_directory = }")
     init_state_bg_image()
-
     # Initialize a list of DirectionInfo objects using the provided dictionaries
     if "direction_infos" not in st.session_state:
         st.session_state.direction_infos = [
@@ -115,7 +118,7 @@ def init_session_state() -> None:
     if not hasattr(st.session_state, "trajectory_data"):
         st.session_state.trajectoryData = pedpy.TrajectoryData
 
-    dataconfig = DataConfig(Path("AppData"))
+    dataconfig = DataConfig(trajectories_directory)
     st.session_state.files = dataconfig.files
 
 
@@ -134,21 +137,15 @@ def unzip_files(zip_path: Union[str, Path], destination: Union[str, Path]) -> No
             # Extract only if file (ignores directories)
             if not member.is_dir():
                 # Build target filename path
-                target_path = os.path.join(
-                    destination, os.path.basename(member.filename)
-                )
+                target_path = os.path.join(destination, os.path.basename(member.filename))
                 # Ensure target directory exists (e.g., if not extracting directories)
                 os.makedirs(os.path.dirname(target_path), exist_ok=True)
                 # Extract file
-                with zip_ref.open(member, "r") as source, open(
-                    target_path, "wb"
-                ) as target:
+                with zip_ref.open(member, "r") as source, open(target_path, "wb") as target:
                     shutil.copyfileobj(source, target)
 
 
-def download_and_unzip_files(
-    url: str, destination: Union[str, Path], unzip_destination: Union[str, Path]
-) -> None:
+def download_and_unzip_files(url: str, destination: Union[str, Path], unzip_destination: Union[str, Path]) -> None:
     """
     Download a ZIP file from a specified URL.
 

docs.py → src/docs/docs.py

@@ -1,10 +1,11 @@
 """Documentation texts for the app."""
 
+from pathlib import Path
 from typing import List
 
 import streamlit as st
 
-from datafactory import Direction
+from ..classes.datafactory import Direction
 
 
 def flow(directions: List[Direction]) -> None:
@@ -63,15 +64,18 @@ def density_speed() -> None:
 
 def about() -> None:
     """Write About text."""
+    path = Path(__file__)
+    ROOT_DIR = path.parent.parent.parent.absolute()
+    img_path_1 = ROOT_DIR / "data" / "images" / "fcaym-FdL22.png"
+    img_path_2 = ROOT_DIR / "data" / "images" / "fbppj-FestivalOfLights2-min.png"
     text = """
 
-
     ## Overview
     The [MADRAS-project](https://www.madras-crowds.eu/) is a collaborative cooperation funded by [ANR](https://anr.fr) :flag-fr: and [DFG](htpps://dfg.de) :flag-de:, aims to develop innovative agent-based models to predict and understand dense crowd dynamics and to apply these models in a large-scale case study.
     This app offers a visualisation of data collection of the festival of lights in 2022, a distinguished open-air event that draws nearly two million visitors over four days.
     """
     st.markdown(text)
-    st.image("images/fcaym-FdL22.png", caption="Festival of Lights in Lyon 2022.")
+    st.image(str(img_path_1), caption="Festival of Lights in Lyon 2022.")
 
     text2 = """
     This app is part of the MADRAS project, which focuses on collecting and analyzing videos of crowded scenes during the festival. The primary goal is to extract valuable pedestrian dynamics measurements to enhance our understanding of crowd behaviors during such large-scale events.
@@ -91,6 +95,6 @@ def about() -> None:
     """
     st.markdown(text3)
     st.image(
-        "images/fbppj-FestivalOfLights2-min.png",
+        str(img_path_2),
         caption="Emplacement of cameras for the video recording during the Festival of Lights 2022.",
     )

utilities.py → src/helpers/utilities.py

@@ -10,7 +10,7 @@
 import streamlit as st
 from shapely import Polygon
 
-from datafactory import Direction, DirectionInfo
+from ..classes.datafactory import Direction, DirectionInfo
 
 
 def is_running_locally() -> bool:
@@ -61,9 +61,7 @@ def get_id_by_name(direction_name: str) -> int:
     return -1
 
 
-def get_measurement_lines(
-    trajectory_data: pd.DataFrame, distance_to_bounding: float
-) -> List[Direction]:
+def get_measurement_lines(trajectory_data: pd.DataFrame, distance_to_bounding: float) -> List[Direction]:
     """Create 4 measurement lines inside the walkable_area."""
     min_x = trajectory_data.data["x"].min() + distance_to_bounding
     max_x = trajectory_data.data["x"].max() - distance_to_bounding
@@ -72,15 +70,11 @@ def get_measurement_lines(
 
     return [
         Direction(
-            info=DirectionInfo(
-                id=get_id_by_name("East"), name="East", color=get_color_by_name("East")
-            ),
+            info=DirectionInfo(id=get_id_by_name("East"), name="East", color=get_color_by_name("East")),
             line=pedpy.MeasurementLine([[min_x, min_y], [min_x, max_y]]),
         ),
         Direction(
-            info=DirectionInfo(
-                id=get_id_by_name("West"), name="West", color=get_color_by_name("West")
-            ),
+            info=DirectionInfo(id=get_id_by_name("West"), name="West", color=get_color_by_name("West")),
             line=pedpy.MeasurementLine([[max_x, min_y], [max_x, max_y]]),
         ),
         Direction(
@@ -118,9 +112,7 @@ def setup_walkable_area(trajectory_data: pd.DataFrame) -> pedpy.WalkableArea:
     return pedpy.WalkableArea(rectangle_polygon)
 
 
-def setup_measurement_area(
-    min_x: float, max_x: float, min_y: float, max_y: float
-) -> pedpy.MeasurementArea:
+def setup_measurement_area(min_x: float, max_x: float, min_y: float, max_y: float) -> pedpy.MeasurementArea:
     """Create measurement_area from trajectories."""
     rectangle_coords = [
         [min_x, min_y],

anim.py → src/plotting/anim.py

@@ -8,7 +8,6 @@
 code here. Use it at your own peril.
 """
 
-
 from typing import Any, Dict, List, Tuple
 
 import matplotlib.pyplot as plt
@@ -21,7 +20,8 @@
 from plotly.graph_objs.layout import Shape
 from shapely import Polygon
 
-import datafactory
+from ..classes.datafactory import (decrement_frame_start,
+                                   increment_frame_start, reset_frame_start)
 
 DUMMY_SPEED = -1000
 
@@ -40,9 +40,7 @@ def _get_line_color(disk_color: str) -> str:
     return "black" if brightness > 127 else "white"
 
 
-def _create_orientation_line(
-    row: pd.DataFrame, line_length: float = 0.2, color: str = "black"
-) -> Shape:
+def _create_orientation_line(row: pd.DataFrame, line_length: float = 0.2, color: str = "black") -> Shape:
     """Create orientation Shape object."""
     end_x = row["x"] + line_length * 0
     end_y = row["y"] + line_length * 0
@@ -113,9 +111,7 @@ def _get_colormap(frame_data: pd.DataFrame, max_speed: float) -> List[Scatter]:
     return [scatter_trace]
 
 
-def _get_shapes_for_frame(
-    frame_data: pd.DataFrame, min_speed: float, max_speed: float
-) -> Tuple[Shape, Scatter, Shape]:
+def _get_shapes_for_frame(frame_data: pd.DataFrame, min_speed: float, max_speed: float) -> Tuple[Shape, Scatter, Shape]:
     """Construct circles as Shapes for agents, Hover and Directions."""
 
     def create_shape(row: pd.DataFrame) -> Shape:
@@ -210,9 +206,7 @@ def _create_fig(
     fig = go.Figure(
         data=geometry_traces + initial_scatter_trace + initial_hover_trace,
         frames=frames,
-        layout=go.Layout(
-            shapes=initial_shapes + initial_arrows, title=title, title_x=0.5
-        ),
+        layout=go.Layout(shapes=initial_shapes + initial_arrows, title=title, title_x=0.5),
     )
     fig.update_layout(
         updatemenus=[_get_animation_controls()],
@@ -275,9 +269,7 @@ def _get_slider_controls(steps: List[Dict[str, Any]]) -> Dict[str, Any]:
     }
 
 
-def _get_processed_frame_data(
-    data_df: pd.DataFrame, frame_num: int, max_agents: int
-) -> Tuple[pd.DataFrame, int]:
+def _get_processed_frame_data(data_df: pd.DataFrame, frame_num: int, max_agents: int) -> Tuple[pd.DataFrame, int]:
     """Process frame data and ensure it matches the maximum agent count."""
     frame_data = data_df[data_df["frame"] == frame_num]
     agent_count = len(frame_data)
@@ -317,18 +309,18 @@ def animate(
         p1.text("Backward")
         decrement = st.button(":arrow_backward:")
         if decrement:
-            datafactory.decrement_frame_start(int(page_size))
+            decrement_frame_start(int(page_size))
     with col2:
         p2.text("Forward")
         increment = st.button(":arrow_forward:")
         if increment:
-            datafactory.increment_frame_start(int(page_size))
+            increment_frame_start(int(page_size))
 
     with col3:
         p3.text("Reset")
         reset = st.button(":leftwards_arrow_with_hook:")
         if reset:
-            datafactory.reset_frame_start(fr0)
+            reset_frame_start(fr0)
 
     every_nth_frame = st.sidebar.number_input(
         "fps",
@@ -350,9 +342,7 @@ def animate(
     # Calculate page_end
     frame_end = st.session_state.start_frame + page_size
     frame_start = st.session_state.start_frame
-    data_df = data_df0[
-        (data_df0["frame"] >= frame_start) & (data_df0["frame"] <= frame_end)
-    ]
+    data_df = data_df0[(data_df0["frame"] >= frame_start) & (data_df0["frame"] <= frame_end)]
 
     min_speed = data_df["speed"].min()
     max_speed = data_df["speed"].max()
@@ -371,12 +361,8 @@ def animate(
     ) = _get_shapes_for_frame(initial_frame_data, min_speed, max_speed)
     color_map_trace = _get_colormap(initial_frame_data, max_speed)
     for frame_num in selected_frames:
-        frame_data, agent_count = _get_processed_frame_data(
-            data_df, frame_num, max_agents
-        )
-        shapes, hover_traces, arrows = _get_shapes_for_frame(
-            frame_data, min_speed, max_speed
-        )
+        frame_data, agent_count = _get_processed_frame_data(data_df, frame_num, max_agents)
+        shapes, hover_traces, arrows = _get_shapes_for_frame(frame_data, min_speed, max_speed)
         # title = f"<b>{title_note + '  |  ' if title_note else ''}N: {agent_count}</b>"
         title = f"<b>{title_note + '  |  ' if title_note else ''}Number of Agents: {initial_agent_count}. Frame: {frame_num}</b>"
         frame_name = str(int(frame_num))