format ruff files

PedestrianDynamics · Sep 30, 2024 · 05d9ab2 · 05d9ab2
1 parent 05d4ae2
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Showing 2 changed files with 138 additions and 44 deletions.
diff --git a/src/tabs/LargeView_analysis.py b/src/tabs/LargeView_analysis.py
@@ -112,7 +112,9 @@ def gaussian_kernel_scalar(r: float, r_c: float, xi: float) -> float:
 
 
 @njit
-def get_r(i_line: int, j_column: int, r_cg: float, x_min: float, y_min: float) -> Tuple[float, float]:
+def get_r(
+    i_line: int, j_column: int, r_cg: float, x_min: float, y_min: float
+) -> Tuple[float, float]:
     """
     Calculate the coordinates (x, y) of a point in a grid based on its indices and grid parameters.
 
@@ -152,7 +154,9 @@ def get_cell(r: tuple, x_min: float, y_min, r_cg: float) -> Tuple[int, int]:
 # ensuring that the output signal has no phase shift relative to the input.
 # The padlen parameter is set to int(1 / delta_t) + 1 to determine the number of samples for edge padding,
 # which helps in reducing boundary effects in the filtering process.
-def butter_lowpass_filter(pd_series: pd.Series, delta_t: float, order: int, cutoff: float) -> np.ndarray:
+def butter_lowpass_filter(
+    pd_series: pd.Series, delta_t: float, order: int, cutoff: float
+) -> np.ndarray:
     """
     Apply a Butterworth lowpass filter to a pandas Series.
 
@@ -168,7 +172,9 @@ def butter_lowpass_filter(pd_series: pd.Series, delta_t: float, order: int, cuto
     nyquist_freq = 0.5 / delta_t  # Nyquist Frequency
     normal_cutoff = cutoff / nyquist_freq  # Normalized cutoff frequency
     # Get the filter coefficients
-    b, a = butter(order, normal_cutoff, btype="low", analog=False)  # Generate the filter coefficients
+    b, a = butter(
+        order, normal_cutoff, btype="low", analog=False
+    )  # Generate the filter coefficients
     return filtfilt(b, a, pd_series, padlen=int(1 / delta_t) + 1)  # Apply the filter.
 
 
@@ -198,12 +204,17 @@ def create_save_folder(pa: Parameters) -> Path:
         Path: The path of the created save folder.
 
     """
-    save_folder = pa.FOLDER_SAVE / f"{pa.SELECTED_NAME}_start={pa.START_TIME:.1f}_dur={pa.DURATION:.1f}_dt={pa.DELTA_T:.1f}_xi={pa.XI:.1f}_rcg={pa.R_CG:.1f}"
+    save_folder = (
+        pa.FOLDER_SAVE
+        / f"{pa.SELECTED_NAME}_start={pa.START_TIME:.1f}_dur={pa.DURATION:.1f}_dt={pa.DELTA_T:.1f}_xi={pa.XI:.1f}_rcg={pa.R_CG:.1f}"
+    )
     save_folder.mkdir(parents=True, exist_ok=True)
     return save_folder
 
 
-def process_trajectories(all_datas: pd.DataFrame, pa: Parameters) -> Dict[str, pd.DataFrame]:
+def process_trajectories(
+    all_datas: pd.DataFrame, pa: Parameters
+) -> Dict[str, pd.DataFrame]:
     """
     Process trajectories and return a dictionary of processed dataframes.
 
@@ -249,8 +260,12 @@ def process_trajectories(all_datas: pd.DataFrame, pa: Parameters) -> Dict[str, p
 
         try:
             # Apply Butterworth low-pass filter to x and y trajectory data
-            X_bw = butter_lowpass_filter(traj_data["x_m"].values, pa.DELTA_T, 2, pa.CUTOFF)
-            Y_bw = butter_lowpass_filter(traj_data["y_m"].values, pa.DELTA_T, 2, pa.CUTOFF)
+            X_bw = butter_lowpass_filter(
+                traj_data["x_m"].values, pa.DELTA_T, 2, pa.CUTOFF
+            )
+            Y_bw = butter_lowpass_filter(
+                traj_data["y_m"].values, pa.DELTA_T, 2, pa.CUTOFF
+            )
         except ValueError as e:
             # Log the exception with the trajectory number
             logging.error("Filtering failed for trajectory %s: %s", traj_nb, e)
@@ -269,7 +284,9 @@ def process_trajectories(all_datas: pd.DataFrame, pa: Parameters) -> Dict[str, p
 
         # Compute the alpha values for interpolation
         alpha = np.maximum(
-            np.exp(np.clip(-4.0 * pa.CUTOFF * (t_s_values - starting_time_traj), -700, 700)),
+            np.exp(
+                np.clip(-4.0 * pa.CUTOFF * (t_s_values - starting_time_traj), -700, 700)
+            ),
             np.exp(np.clip(-4.0 * pa.CUTOFF * (end_time - t_s_values), -700, 700)),
         )
 
@@ -278,15 +295,20 @@ def process_trajectories(all_datas: pd.DataFrame, pa: Parameters) -> Dict[str, p
         traj_data.loc[:, "y_m"] = (1.0 - alpha) * Y_bw + alpha * traj_data["y_m"].values
 
         # Filter the data to the desired time interval
-        traj_data = traj_data[(traj_data["t_s"] >= pa.START_TIME) & (traj_data["t_s"] < pa.START_TIME + pa.DURATION + 2.0 * pa.DT)]
+        traj_data = traj_data[
+            (traj_data["t_s"] >= pa.START_TIME)
+            & (traj_data["t_s"] < pa.START_TIME + pa.DURATION + 2.0 * pa.DT)
+        ]
 
         # If the trajectory is empty, skip it
         if traj_data.empty:
             list_files_skipped.append(traj_nb)
             continue
 
         # Compute the cumulative time
-        pa.CUM_TIME += traj_data["t_s"].max(skipna=True) - traj_data["t_s"].min(skipna=True)
+        pa.CUM_TIME += traj_data["t_s"].max(skipna=True) - traj_data["t_s"].min(
+            skipna=True
+        )
 
         # Update min and max coordinates
         pa.X_MIN = min(pa.X_MIN, traj_data["x_m"].min(skipna=True))
@@ -403,7 +425,10 @@ def compute_fields(
     nb_traj = len(all_trajs)
     # Iterate over all trajectories
     for i_traj, traj in tqdm(enumerate(all_trajs.values()), desc="Processing grid"):
-        traj = traj.loc[(traj["t_s"] >= pa.START_TIME) & (traj["t_s"] < pa.START_TIME + pa.DURATION + 2.0 * pa.DT)]
+        traj = traj.loc[
+            (traj["t_s"] >= pa.START_TIME)
+            & (traj["t_s"] < pa.START_TIME + pa.DURATION + 2.0 * pa.DT)
+        ]
         if traj.shape[0] == 0:
             continue
         # Iterate over all rows in the trajectory (ie all time steps)
@@ -413,14 +438,18 @@ def compute_fields(
             # Iterate over the cells in the grid
             for i in range(i_rel - pa.DELTA, i_rel + pa.DELTA + 1):
                 for j in range(j_rel - pa.DELTA, j_rel + pa.DELTA + 1):
-                    if i < 0 or i >= pa.NB_CG_X or j < 0 or j >= pa.NB_CG_Y:  # Check if pedestrian is outside the grid
+                    if (
+                        i < 0 or i >= pa.NB_CG_X or j < 0 or j >= pa.NB_CG_Y
+                    ):  # Check if pedestrian is outside the grid
                         continue
 
                     # Compute the position of the grid cell
                     pos_grid_cell = get_r(i, j, pa.R_CG, pa.X_MIN, pa.Y_MIN)
 
                     # Compute the Gaussian kernel
-                    phi_r = gaussian_kernel_scalar(calculate_distance(pos_grid_cell, pos_ped), pa.R_C, pa.XI)
+                    phi_r = gaussian_kernel_scalar(
+                        calculate_distance(pos_grid_cell, pos_ped), pa.R_C, pa.XI
+                    )
 
                     # Update the fields
                     df_observables["X"][i, j] = pos_grid_cell[0]
@@ -473,7 +502,12 @@ def normalize_density(density: np.ndarray, pa: Parameters) -> np.ndarray:
     return density
 
 
-def update_figure(df_observables: Dict[str, np.ndarray], pa: Parameters, plot_density: bool, zsmooth=False) -> go.Figure:
+def update_figure(
+    df_observables: Dict[str, np.ndarray],
+    pa: Parameters,
+    plot_density: bool,
+    zsmooth=False,
+) -> go.Figure:
     """
     Update the figure with the given observables and parameters.
 
@@ -487,14 +521,25 @@ def update_figure(df_observables: Dict[str, np.ndarray], pa: Parameters, plot_de
 
     """
     # Create the X and Y axes
-    X_axis = np.linspace(pa.X_MIN, pa.X_MIN + len(df_observables["rho"][0]) * pa.R_CG, len(df_observables["rho"][0]))
-    Y_axis = np.linspace(pa.Y_MIN, pa.Y_MIN + len(df_observables["rho"]) * pa.R_CG, len(df_observables["rho"]))
+    X_axis = np.linspace(
+        pa.X_MIN,
+        pa.X_MIN + len(df_observables["rho"][0]) * pa.R_CG,
+        len(df_observables["rho"][0]),
+    )
+    Y_axis = np.linspace(
+        pa.Y_MIN,
+        pa.Y_MIN + len(df_observables["rho"]) * pa.R_CG,
+        len(df_observables["rho"]),
+    )
 
     # Create the heatmap
     if plot_density:
         hovertext = np.array(
             [
-                [f"x: {X_axis[i]:.2f} m<br>y: {Y_axis[j]:.2f} m<br>Density: {df_observables['rho'][j,i]:.2f} ped/m²" for i in range(df_observables["rho"].shape[1])]
+                [
+                    f"x: {X_axis[i]:.2f} m<br>y: {Y_axis[j]:.2f} m<br>Density: {df_observables['rho'][j,i]:.2f} ped/m²"
+                    for i in range(df_observables["rho"].shape[1])
+                ]
                 for j in range(df_observables["rho"].shape[0])
             ]
         )
@@ -516,7 +561,10 @@ def update_figure(df_observables: Dict[str, np.ndarray], pa: Parameters, plot_de
     else:
         hovertext = np.array(
             [
-                [f"x: {X_axis[i]:.2f} m<br>y: {Y_axis[j]:.2f} m<br>Var_v: {(df_observables['var_vs'][j,i]):.2f} m²/s²" for i in range(df_observables["var_vs"].shape[1])]
+                [
+                    f"x: {X_axis[i]:.2f} m<br>y: {Y_axis[j]:.2f} m<br>Var_v: {(df_observables['var_vs'][j,i]):.2f} m²/s²"
+                    for i in range(df_observables["var_vs"].shape[1])
+                ]
                 for j in range(df_observables["var_vs"].shape[0])
             ]
         )
@@ -554,7 +602,9 @@ def update_figure(df_observables: Dict[str, np.ndarray], pa: Parameters, plot_de
     arrow_velocity = 1  # Velocity represented by the annotation arrow (1 m/s)
     arrow_start_x = 8.0
     arrow_start_y = 3.0
-    arrow_end_x = arrow_start_x + (arrow_velocity * pa.QUIVER_SCALE)  # Adjusted to represent 1 m/s
+    arrow_end_x = arrow_start_x + (
+        arrow_velocity * pa.QUIVER_SCALE
+    )  # Adjusted to represent 1 m/s
     arrow_end_y = arrow_start_y
 
     # Add arrow annotation
@@ -576,12 +626,25 @@ def update_figure(df_observables: Dict[str, np.ndarray], pa: Parameters, plot_de
     )
 
     # Add text annotation for the arrow
-    fig.add_annotation(x=9.5, y=3.8, text="<b>1 m/s</b>", showarrow=False, font={"color": "#009999", "size": 25})
+    fig.add_annotation(
+        x=9.5,
+        y=3.8,
+        text="<b>1 m/s</b>",
+        showarrow=False,
+        font={"color": "#009999", "size": 25},
+    )
 
     # Correct layout update with proper domain
     fig.update_layout(
         font={"size": 20},
-        title={"text": ("Velocity field for density heatmap" if plot_density else "Velocity field for variance velocity heatmap"), "font_size": 20},
+        title={
+            "text": (
+                "Velocity field for density heatmap"
+                if plot_density
+                else "Velocity field for variance velocity heatmap"
+            ),
+            "font_size": 20,
+        },
         xaxis={
             "title": {"text": "x [m]", "font_size": 20},
             "scaleanchor": "y",
@@ -605,7 +668,7 @@ def update_figure(df_observables: Dict[str, np.ndarray], pa: Parameters, plot_de
 
 def main(selected_file: str):
     """
-    Main function for the CCTV_analysis module.
+    Run main function for the CCTV_analysis module.
 
     Args:
         selected_file (str): The path to the selected file.
@@ -628,7 +691,9 @@ def main(selected_file: str):
     path = Path(__file__)
 
     pa = Parameters(
-        FOLDER_TRAJ=Path(path.parent.parent.parent.absolute() / "data" / "trajectories"),
+        FOLDER_TRAJ=Path(
+            path.parent.parent.parent.absolute() / "data" / "trajectories"
+        ),
         FOLDER_SAVE=Path(path.parent.parent.parent.absolute() / "data" / "pickle"),
         SELECTED_NAME=Path(selected_file).stem,
         START_TIME=0.0,
@@ -648,10 +713,16 @@ def main(selected_file: str):
         QUIVER_SCALE=3.0,
     )
     pa.R_CG = slider_value_R_CG
-    pa.DELTA = int(ceil(pa.R_C / pa.R_CG)) + 1  # Number of cells to consider around the cell containing the point
+    pa.DELTA = (
+        int(ceil(pa.R_C / pa.R_CG)) + 1
+    )  # Number of cells to consider around the cell containing the point
     folder_save = create_save_folder(pa)
 
-    if not Path(folder_save / "traj_data.pkl").exists() or not Path(folder_save / "parameters.pkl").exists() or not Path(folder_save / "dictionnary_observables.pkl").exists():
+    if (
+        not Path(folder_save / "traj_data.pkl").exists()
+        or not Path(folder_save / "parameters.pkl").exists()
+        or not Path(folder_save / "dictionnary_observables.pkl").exists()
+    ):
         # PROGRESS BAR
         title_text = st.text("Progress Bar")
         my_progress_bar = st.progress(0)
@@ -680,7 +751,9 @@ def main(selected_file: str):
     st.session_state["zsmooth"] = False
 
     # Button to toggle interpolation
-    toggle_interpolation = st.sidebar.checkbox("Interpolation", value=st.session_state["zsmooth"])
+    toggle_interpolation = st.sidebar.checkbox(
+        "Interpolation", value=st.session_state["zsmooth"]
+    )
 
     # Update session state based on checkbox
     st.session_state["zsmooth"] = "best" if toggle_interpolation else False
@@ -690,16 +763,28 @@ def main(selected_file: str):
     with col1:
         fig = update_figure(dict_observables, params, True, st.session_state["zsmooth"])
         st.plotly_chart(fig)
-        figname = pa.SELECTED_NAME + f"_velocity_field_for_density_heatmap_zsmooth{st.session_state['zsmooth']}.pdf"
+        figname = (
+            pa.SELECTED_NAME
+            + f"_velocity_field_for_density_heatmap_zsmooth{st.session_state['zsmooth']}.pdf"
+        )
         img_bytes = fig.to_image(format="pdf")
-        st.sidebar.download_button(label="Download Density Heatmap", data=img_bytes, file_name=figname)
+        st.sidebar.download_button(
+            label="Download Density Heatmap", data=img_bytes, file_name=figname
+        )
         plt.close()
     with col2:
-        fig = update_figure(dict_observables, params, False, st.session_state["zsmooth"])
+        fig = update_figure(
+            dict_observables, params, False, st.session_state["zsmooth"]
+        )
         st.plotly_chart(fig)
-        figname = pa.SELECTED_NAME + f"_velocity_field_for_density_heatmap_zsmooth{st.session_state['zsmooth']}.pdf"
+        figname = (
+            pa.SELECTED_NAME
+            + f"_velocity_field_for_density_heatmap_zsmooth{st.session_state['zsmooth']}.pdf"
+        )
         img_bytes = fig.to_image(format="pdf")
-        st.sidebar.download_button(label="Download Variance Heatmap", data=img_bytes, file_name=figname)
+        st.sidebar.download_button(
+            label="Download Variance Heatmap", data=img_bytes, file_name=figname
+        )
         plt.close()
 
 

diff --git a/src/tabs/survey_tab.py b/src/tabs/survey_tab.py
@@ -38,10 +38,17 @@ def histogram_survey(df_survey: pd.DataFrame, remove_outlier: bool) -> Figure:
 
     # Add histogram traces for each category
     fig.add_trace(go.Histogram(x=values_children, name="Children", marker_color="pink"))
-    fig.add_trace(go.Histogram(x=values_both, name="Adults and children", marker_color="blue"))
+    fig.add_trace(
+        go.Histogram(x=values_both, name="Adults and children", marker_color="blue")
+    )
 
     # Create a DataFrame for custom data in hover templates
-    data = pd.DataFrame({"Categories": ["Adults and children"] * len(values_both) + ["Children"] * len(values_children)})
+    data = pd.DataFrame(
+        {
+            "Categories": ["Adults and children"] * len(values_both)
+            + ["Children"] * len(values_children)
+        }
+    )
 
     # Update hover template with custom data
     fig.update_traces(
@@ -61,16 +68,16 @@ def histogram_survey(df_survey: pd.DataFrame, remove_outlier: bool) -> Figure:
         title={"text": "Distribution of Group Sizes", "font_size": 28},
         width=1000,
         height=700,
-        xaxis=dict(
-            title={"text": "Group Size", "font_size": 30, "font_color": "black"},
-            tickfont_size=30,
-            tickfont_color="black",
-        ),
-        yaxis=dict(
-            title={"text": "Counts", "font_size": 30, "font_color": "black"},
-            tickfont_size=30,
-            tickfont_color="black",
-        ),
+        xaxis={
+            "title": {"text": "Group Size", "font_size": 30, "font_color": "black"},
+            "tickfont_size": 30,
+            "tickfont_color": "black",
+        },
+        yaxis={
+            "title": {"text": "Counts", "font_size": 30, "font_color": "black"},
+            "tickfont_size": 30,
+            "tickfont_color": "black",
+        },
         legend={"font_size": 30, "font_color": "black"},
     )
 
@@ -92,7 +99,9 @@ def main() -> None:
     5. Provides a sidebar button to download the histogram as a PDF file.
     """
     path = Path(__file__).resolve()
-    survey_path = path.parent.parent.parent.absolute() / "data" / "surveys" / "survey_results.csv"
+    survey_path = (
+        path.parent.parent.parent.absolute() / "data" / "surveys" / "survey_results.csv"
+    )
     path_pickle = path.parent.parent.parent.absolute() / "data" / "pickle"
 
     # Check if survey.pkl exists, if not create it, else load it