From aaf0df7d9d1be94f6c3a62b575e96cdac1deeb86 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 26 Jul 2024 14:13:37 +0200
Subject: [PATCH 01/22] Add trajectory loader for Vadere trajectory
 (postvis.traj) files

---
 pedpy/io/trajectory_loader.py | 243 +++++++++++++++++++++++++++++++++-
 1 file changed, 241 insertions(+), 2 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index b3d4fafa..f09572b0 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -1,5 +1,5 @@
 """Load trajectories to the internal trajectory data format."""
-
+import logging
 import math
 import pathlib
 import sqlite3
@@ -7,13 +7,16 @@
 from typing import Any, Optional, Tuple
 
 import h5py  # type: ignore
+import numpy as np
 import pandas as pd
 import shapely
 
-from pedpy.column_identifier import FRAME_COL, ID_COL, X_COL, Y_COL
+from pedpy.column_identifier import FRAME_COL, ID_COL, X_COL, Y_COL, TIME_COL
 from pedpy.data.geometry import WalkableArea
 from pedpy.data.trajectory_data import TrajectoryData
 
+_log = logging.getLogger(__name__)
+
 
 class LoadTrajectoryError(Exception):
     """Class reflecting errors when loading trajectories with PedPy."""
@@ -634,3 +637,239 @@ def _load_trajectory_data_from_viswalk(
         return data
     except pd.errors.ParserError as exc:
         raise LoadTrajectoryError(common_error_message) from exc
+
+
+def load_trajectory_from_vadere(
+    *,
+    trajectory_file: pathlib.Path,
+    frame_rate: float = 24,
+) -> TrajectoryData:
+    """Loads trajectory data from Vadere-traj file as :class:`~trajectory_data.TrajectoryData`.
+
+    This function reads a traj file containing trajectory data from Vadere simulations and
+    converts it into a :class:`~trajectory_data.TrajectoryData` object which can be used for
+    further analysis and processing in the *PedPy* framework.
+
+    Args:
+        trajectory_file: The full path of the trajectory file containing the Vadere
+            trajectory data. The expected format is a traj file with space character as delimiter,
+            and it should contain the following columns: pedestrianId, simTime (in sec),
+            startX (in m), startY (in m). Additional columns (e.g. endTime, endX, endY, targetId)
+            will be ignored.
+        frame_rate: Frame rate in frames per second.
+
+    Returns:
+        TrajectoryData: :class:`~trajectory_data.TrajectoryData` representation of the file data
+
+    Raises:
+        LoadTrajectoryError: If the provided path does not exist or is not a file.
+    """
+
+    _validate_is_file(trajectory_file)
+
+    traj_dataframe = _load_trajectory_data_from_vadere(
+        trajectory_file=trajectory_file
+    )
+
+    traj_dataframe = _event_driven_traj_to_const_frame_rate(
+        traj_dataframe=traj_dataframe,
+        frame_rate=frame_rate
+    )
+
+    return TrajectoryData(
+        data=traj_dataframe[[ID_COL, FRAME_COL, X_COL, Y_COL]],
+        frame_rate=frame_rate,
+    )
+
+
+def _load_trajectory_data_from_vadere(
+    *, trajectory_file: pathlib.Path
+) -> pd.DataFrame:
+    """Parse the trajectory file for trajectory data.
+
+    Args:
+        trajectory_file (pathlib.Path): The full path of the trajectory file containing the Vadere
+            trajectory data. The expected format is a traj file with space character as delimiter,
+            and it should contain the following columns: pedestrianId, simTime (in sec),
+            startX (in m), startY (in m). Additional columns (e.g. endTime, endX, endY, targetId)
+            will be ignored.
+
+    Returns:
+        The trajectory data as :class:`DataFrame`, the coordinates are in meter (m).
+    """
+
+    VADERE_KEY_ID = "pedestrianId"
+    VADERE_KEY_TIME = "simTime"
+    VADERE_KEY_X = "startX"
+    VADERE_KEY_Y = "startY"
+    columns_to_keep = [VADERE_KEY_ID, VADERE_KEY_TIME, VADERE_KEY_X, VADERE_KEY_Y]
+    name_mapping = {
+        VADERE_KEY_ID: ID_COL,
+        VADERE_KEY_TIME: TIME_COL,
+        VADERE_KEY_X: X_COL,
+        VADERE_KEY_Y: Y_COL,
+    }
+
+    common_error_message = (
+        "The given trajectory file seems to be incorrect or empty. "
+        "It should contain the following columns, which should be "
+        f"uniquely identifiably by: {', '.join(columns_to_keep)}. "
+        f"Columns should be separated by a space character. "
+        "Comment lines may start with '#' and will be ignored. "
+        f"Please check your trajectory file: {trajectory_file}."
+    )
+    try:
+        vadere_cols = list(pd.read_csv(trajectory_file, comment="#", delimiter=" ", nrows=1).columns)
+        use_vadere_cols = list()
+        non_unique_cols = list()
+        missing_cols = list()
+        rename_mapping = dict()
+
+        for col in columns_to_keep:
+            matching = [vc for vc in vadere_cols if col in vc]
+            if len(matching) == 1:
+                use_vadere_cols += matching
+                rename_mapping[matching[0]] = name_mapping[col]
+            elif len(matching) > 1:
+                non_unique_cols += [col]
+            elif len(matching) == 0:
+                missing_cols += [col]
+
+        if non_unique_cols:
+            raise LoadTrajectoryError(
+                f"{common_error_message}"
+                f"Non-unique columns: {', '.join(non_unique_cols)}"
+            )
+
+        if missing_cols:
+            raise LoadTrajectoryError(
+                f"{common_error_message}"
+                f"Missing column: {', '.join(missing_cols)}."
+            )
+
+        data = pd.read_csv(
+            trajectory_file,
+            delimiter=" ",
+            usecols=use_vadere_cols,
+            comment="#",
+            dtype={
+                VADERE_KEY_ID: "int64",
+                VADERE_KEY_TIME: "float64",
+                VADERE_KEY_X: "float64",
+                VADERE_KEY_Y: "float64",
+            },
+            encoding="utf-8-sig",
+        )
+
+        data.rename(columns=rename_mapping, inplace=True)
+
+        if data.empty:
+            raise LoadTrajectoryError(common_error_message)
+
+        return data
+    except pd.errors.ParserError as exc:
+        raise LoadTrajectoryError(common_error_message) from exc
+
+
+def _event_driven_traj_to_const_frame_rate(
+        traj_dataframe, frame_rate):
+    """Interpolate trajectory data linearly for non-equidistant time steps.
+
+    Args:
+        traj_dataframe: trajectory data as :class:`DataFrame`
+        frame_rate: Frame rate in frames per second.
+
+    Returns:
+        The trajectory data as :class:`DataFrame` with positions x and y being
+        linearly interpolated for frames between two recorded time steps.
+    """
+
+    frame_duration = 1 / frame_rate
+
+    _calc_deviation_vadere_pedpy_traj_transform(traj_dataframe, frame_rate)
+
+    traj_dataframe.set_index(TIME_COL, inplace=True)
+    traj_by_ped = traj_dataframe.groupby(ID_COL)
+    traj_dataframe_interpolated = pd.DataFrame()
+    for ped_id, traj in traj_by_ped:
+
+        t = traj.index
+        t_start = traj.index.values.min()
+        t_stop = traj.index.values.max()
+
+        # Round t_start up / t_stop down to nearest multiple of
+        # frame_duration to avoid extrapolation of trajectories to
+        # times before / after fist / last pedestrian step.
+        t_start_ = math.ceil(t_start * frame_rate) / frame_rate
+        t_stop_ = math.floor(t_stop * frame_rate) / frame_rate
+
+        if t_start == t_stop:
+            _log.warning(
+                f"Trajectory of pedestrian {str(ped_id)} is too short "
+                f"(in time) to be captured by the chosen frame rate. "
+                f"Therefore, this trajectory will be ignored."
+            )
+        else:
+            equidist_time_steps = np.arange(
+                start=t_start_,
+                stop=t_stop_ + 1 / frame_rate,
+                step=1 / frame_rate,
+            )
+            r = pd.Index(equidist_time_steps, name=t.name)
+            traj = traj.reindex(t.union(r)).interpolate(method='index').loc[r]
+
+            traj[ID_COL] = traj[ID_COL].astype(int)
+
+            traj_dataframe_interpolated = pd.concat([traj_dataframe_interpolated, traj])
+
+    traj_dataframe_interpolated.reset_index(inplace=True)
+
+    traj_dataframe_interpolated[FRAME_COL] = (traj_dataframe_interpolated[TIME_COL] * frame_rate) \
+        .round(decimals=0) \
+        .astype(int)
+    traj_dataframe_interpolated.drop(labels=TIME_COL, axis="columns", inplace=True)
+
+    traj_dataframe_interpolated.sort_values(
+        by=[FRAME_COL, ID_COL],
+        ignore_index=True,
+        inplace=True
+    )
+    return traj_dataframe_interpolated
+
+
+def _calc_deviation_vadere_pedpy_traj_transform(traj_dataframe, frame_rate):
+    """Calculates the maximum deviation between event-based vadere trajectories
+    and their interpolated version with fixed frames.
+
+    Max difference occurs when first/last step of a trajectory happens just
+    after/before the last/next frame.
+    Example for an agent that moves with s = 2.2 m/s (equivalent to typical
+    cut-off in Vadere):
+        First frame at t_f1, second frame at t_f2 = t_f1 + 1 / frame_rate
+        First step at t_s1 = t_f1 + t_offset
+        Distance walked between t_s1 and t_f2 will not be captured:
+        x_s1f2 =  s * (1 / frame_rate - t_offset)
+        with t_offset --> 0s: x_s1f2 = s / frame_rate
+
+    Args:
+        traj_dataframe: trajectory data as :class:`DataFrame`
+        frame_rate: Frame rate in frames per second.
+    """
+    ACCEPT_DIFF = 0.01  # threshold difference in meter (m), otherwise log warning
+
+    traj_groups = traj_dataframe.groupby(ID_COL)
+    max_speed = 0
+    for _, traj in traj_groups:
+        diff = traj.diff().dropna()
+        dx_dt = (np.sqrt(diff[[X_COL, Y_COL]].pow(2).sum(axis=1))).divide(diff[TIME_COL])
+        max_speed = max([max_speed, round(max(dx_dt), 2)])
+
+    max_diff = round(max_speed / frame_rate, 2)
+    if max_diff > ACCEPT_DIFF:
+        _log.warning(
+            f"For fastest step with approx. {str(max_speed)} "
+            f"m/s, interpolated trajectory could deviate up "
+            f"to {str(max_diff)} m from Vadere trajectory. "
+            f"If smaller deviation required, choose higher "
+            f"frame rate."
+        )

From ac65d62fb6ceec0acd6f7b095aa4e180cf27dba9 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Tue, 6 Aug 2024 11:07:03 +0200
Subject: [PATCH 02/22] Refactor trajectory_loader for Vadere trajectories

---
 pedpy/io/trajectory_loader.py | 118 ++++++++++++++++++----------------
 1 file changed, 62 insertions(+), 56 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index f09572b0..d961b397 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -60,10 +60,10 @@ def _validate_is_file(file: pathlib.Path) -> None:
 
 
 def load_trajectory(
-    *,
-    trajectory_file: pathlib.Path,
-    default_frame_rate: Optional[float] = None,
-    default_unit: Optional[TrajectoryUnit] = None,
+        *,
+        trajectory_file: pathlib.Path,
+        default_frame_rate: Optional[float] = None,
+        default_unit: Optional[TrajectoryUnit] = None,
 ) -> TrajectoryData:
     """Loads the trajectory file in the internal :class:`~trajectory_data.TrajectoryData` format.
 
@@ -89,10 +89,10 @@ def load_trajectory(
 
 
 def load_trajectory_from_txt(
-    *,
-    trajectory_file: pathlib.Path,
-    default_frame_rate: Optional[float] = None,
-    default_unit: Optional[TrajectoryUnit] = None,
+        *,
+        trajectory_file: pathlib.Path,
+        default_frame_rate: Optional[float] = None,
+        default_unit: Optional[TrajectoryUnit] = None,
 ) -> TrajectoryData:
     """Loads the trajectory file in the internal :class:`~trajectory_data.TrajectoryData` format.
 
@@ -125,7 +125,7 @@ def load_trajectory_from_txt(
 
 
 def _load_trajectory_data_from_txt(
-    *, trajectory_file: pathlib.Path, unit: TrajectoryUnit
+        *, trajectory_file: pathlib.Path, unit: TrajectoryUnit
 ) -> pd.DataFrame:
     """Parse the trajectory file for trajectory data.
 
@@ -179,10 +179,10 @@ def _load_trajectory_data_from_txt(
 
 
 def _load_trajectory_meta_data_from_txt(  # pylint: disable=too-many-branches
-    *,
-    trajectory_file: pathlib.Path,
-    default_frame_rate: Optional[float],
-    default_unit: Optional[TrajectoryUnit],
+        *,
+        trajectory_file: pathlib.Path,
+        default_frame_rate: Optional[float],
+        default_unit: Optional[TrajectoryUnit],
 ) -> Tuple[float, TrajectoryUnit]:
     """Extract the trajectory metadata from file, use defaults if none found.
 
@@ -281,7 +281,7 @@ def _load_trajectory_meta_data_from_txt(  # pylint: disable=too-many-branches
 
 
 def load_trajectory_from_jupedsim_sqlite(
-    trajectory_file: pathlib.Path,
+        trajectory_file: pathlib.Path,
 ) -> TrajectoryData:
     """Loads data from the sqlite file as :class:`~trajectory_data.TrajectoryData`.
 
@@ -333,7 +333,7 @@ def load_trajectory_from_jupedsim_sqlite(
 
 
 def load_walkable_area_from_jupedsim_sqlite(
-    trajectory_file: pathlib.Path,
+        trajectory_file: pathlib.Path,
 ) -> WalkableArea:
     """Loads the walkable area from the sqlite file as :class:`~geometry.WalkableArea`.
 
@@ -375,7 +375,7 @@ def _get_jupedsim_sqlite_version(connection: sqlite3.Connection) -> int:
 
 
 def _load_walkable_area_from_jupedsim_sqlite_v1(
-    con: sqlite3.Connection,
+        con: sqlite3.Connection,
 ) -> WalkableArea:
     try:
         walkable_query_result = (
@@ -397,7 +397,7 @@ def _load_walkable_area_from_jupedsim_sqlite_v1(
 
 
 def _load_walkable_area_from_jupedsim_sqlite_v2(
-    con: sqlite3.Connection,
+        con: sqlite3.Connection,
 ) -> WalkableArea:
     try:
         res = con.cursor().execute("SELECT wkt FROM geometry")
@@ -418,7 +418,7 @@ def _load_walkable_area_from_jupedsim_sqlite_v2(
 
 
 def load_trajectory_from_ped_data_archive_hdf5(
-    trajectory_file: pathlib.Path,
+        trajectory_file: pathlib.Path,
 ) -> TrajectoryData:
     """Loads data from the hdf5 file as :class:`~trajectory_data.TrajectoryData`.
 
@@ -476,7 +476,7 @@ def load_trajectory_from_ped_data_archive_hdf5(
 
 
 def load_walkable_area_from_ped_data_archive_hdf5(
-    trajectory_file: pathlib.Path,
+        trajectory_file: pathlib.Path,
 ) -> WalkableArea:
     """Loads the walkable area from the hdf5 file as :class:`~geometry.WalkableArea`.
 
@@ -509,8 +509,8 @@ def load_walkable_area_from_ped_data_archive_hdf5(
 
 
 def load_trajectory_from_viswalk(
-    *,
-    trajectory_file: pathlib.Path,
+        *,
+        trajectory_file: pathlib.Path,
 ) -> TrajectoryData:
     """Loads data from Viswalk-csv file as :class:`~trajectory_data.TrajectoryData`.
 
@@ -555,7 +555,7 @@ def load_trajectory_from_viswalk(
 
 
 def _calculate_frames_and_fps(
-    traj_dataframe: pd.DataFrame,
+        traj_dataframe: pd.DataFrame,
 ) -> Tuple[pd.Series, int]:
     """Calculates fps and frames based on the time column of the dataframe."""
     mean_diff = traj_dataframe.groupby(ID_COL)["time"].diff().dropna().mean()
@@ -572,7 +572,7 @@ def _calculate_frames_and_fps(
 
 
 def _load_trajectory_data_from_viswalk(
-    *, trajectory_file: pathlib.Path
+        *, trajectory_file: pathlib.Path
 ) -> pd.DataFrame:
     """Parse the trajectory file for trajectory data.
 
@@ -640,9 +640,9 @@ def _load_trajectory_data_from_viswalk(
 
 
 def load_trajectory_from_vadere(
-    *,
-    trajectory_file: pathlib.Path,
-    frame_rate: float = 24,
+        *,
+        trajectory_file: pathlib.Path,
+        frame_rate: float = 24.0,
 ) -> TrajectoryData:
     """Loads trajectory data from Vadere-traj file as :class:`~trajectory_data.TrajectoryData`.
 
@@ -683,7 +683,7 @@ def load_trajectory_from_vadere(
 
 
 def _load_trajectory_data_from_vadere(
-    *, trajectory_file: pathlib.Path
+        *, trajectory_file: pathlib.Path
 ) -> pd.DataFrame:
     """Parse the trajectory file for trajectory data.
 
@@ -698,6 +698,7 @@ def _load_trajectory_data_from_vadere(
         The trajectory data as :class:`DataFrame`, the coordinates are in meter (m).
     """
 
+    VADERE_COMMENT = "#"    # Comment identifier in Vadere trajectory files
     VADERE_KEY_ID = "pedestrianId"
     VADERE_KEY_TIME = "simTime"
     VADERE_KEY_X = "startX"
@@ -715,11 +716,14 @@ def _load_trajectory_data_from_vadere(
         "It should contain the following columns, which should be "
         f"uniquely identifiably by: {', '.join(columns_to_keep)}. "
         f"Columns should be separated by a space character. "
-        "Comment lines may start with '#' and will be ignored. "
+        f"Comment lines may start with '{VADERE_COMMENT}' and will be ignored. "
         f"Please check your trajectory file: {trajectory_file}."
     )
     try:
-        vadere_cols = list(pd.read_csv(trajectory_file, comment="#", delimiter=" ", nrows=1).columns)
+        vadere_cols = list(
+            pd.read_csv(trajectory_file, comment=VADERE_COMMENT, delimiter=" ", nrows=1)
+            .columns
+        )
         use_vadere_cols = list()
         non_unique_cols = list()
         missing_cols = list()
@@ -772,7 +776,9 @@ def _load_trajectory_data_from_vadere(
 
 
 def _event_driven_traj_to_const_frame_rate(
-        traj_dataframe, frame_rate):
+        traj_dataframe: pd.DataFrame,
+        frame_rate: float,
+) -> pd.DataFrame:
     """Interpolate trajectory data linearly for non-equidistant time steps.
 
     Args:
@@ -780,13 +786,11 @@ def _event_driven_traj_to_const_frame_rate(
         frame_rate: Frame rate in frames per second.
 
     Returns:
-        The trajectory data as :class:`DataFrame` with positions x and y being
-        linearly interpolated for frames between two recorded time steps.
+        The trajectory data as :class:`DataFrame` with positions x and y being linearly interpolated
+        for frames between two recorded time steps.
     """
 
-    frame_duration = 1 / frame_rate
-
-    _calc_deviation_vadere_pedpy_traj_transform(traj_dataframe, frame_rate)
+    _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(traj_dataframe, frame_rate)
 
     traj_dataframe.set_index(TIME_COL, inplace=True)
     traj_by_ped = traj_dataframe.groupby(ID_COL)
@@ -797,16 +801,15 @@ def _event_driven_traj_to_const_frame_rate(
         t_start = traj.index.values.min()
         t_stop = traj.index.values.max()
 
-        # Round t_start up / t_stop down to nearest multiple of
-        # frame_duration to avoid extrapolation of trajectories to
-        # times before / after fist / last pedestrian step.
+        # Round t_start up (t_stop down) to nearest multiple of frame period (= 1/frame_rate) to
+        # avoid extrapolation of trajectories to times before first (after last) pedestrian step.
         t_start_ = math.ceil(t_start * frame_rate) / frame_rate
         t_stop_ = math.floor(t_stop * frame_rate) / frame_rate
 
         if t_start == t_stop:
             _log.warning(
-                f"Trajectory of pedestrian {str(ped_id)} is too short "
-                f"(in time) to be captured by the chosen frame rate. "
+                f"Trajectory of pedestrian {str(ped_id)} is too short in time "
+                f"to be captured by the chosen frame rate of {str(frame_rate)}. "
                 f"Therefore, this trajectory will be ignored."
             )
         else:
@@ -837,14 +840,16 @@ def _event_driven_traj_to_const_frame_rate(
     return traj_dataframe_interpolated
 
 
-def _calc_deviation_vadere_pedpy_traj_transform(traj_dataframe, frame_rate):
-    """Calculates the maximum deviation between event-based vadere trajectories
-    and their interpolated version with fixed frames.
+def _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(
+        traj_dataframe: pd.DataFrame,
+        frame_rate: float,
+        deviation_threshold: float = 0.1,
+) -> None:
+    """Validates whether the maximum deviation between event-based vadere trajectories and their
+    interpolated version with fixed frames is below given threshold.
 
-    Max difference occurs when first/last step of a trajectory happens just
-    after/before the last/next frame.
-    Example for an agent that moves with s = 2.2 m/s (equivalent to typical
-    cut-off in Vadere):
+    Max difference occurs when first (last) step of a trajectory happens just after (before) the
+    last (next) frame. Example for an agent that moves with a certain speed, s:
         First frame at t_f1, second frame at t_f2 = t_f1 + 1 / frame_rate
         First step at t_s1 = t_f1 + t_offset
         Distance walked between t_s1 and t_f2 will not be captured:
@@ -854,22 +859,23 @@ def _calc_deviation_vadere_pedpy_traj_transform(traj_dataframe, frame_rate):
     Args:
         traj_dataframe: trajectory data as :class:`DataFrame`
         frame_rate: Frame rate in frames per second.
+        deviation_threshold: acceptable max. difference in meter (m), otherwise log warning
     """
-    ACCEPT_DIFF = 0.01  # threshold difference in meter (m), otherwise log warning
 
     traj_groups = traj_dataframe.groupby(ID_COL)
-    max_speed = 0
+
+    max_speed = 0  # max pedestrian speed that actually reads from the traj file
     for _, traj in traj_groups:
         diff = traj.diff().dropna()
         dx_dt = (np.sqrt(diff[[X_COL, Y_COL]].pow(2).sum(axis=1))).divide(diff[TIME_COL])
         max_speed = max([max_speed, round(max(dx_dt), 2)])
 
-    max_diff = round(max_speed / frame_rate, 2)
-    if max_diff > ACCEPT_DIFF:
+    max_deviation = round(max_speed / frame_rate, 2)
+    if max_deviation > deviation_threshold:
         _log.warning(
-            f"For fastest step with approx. {str(max_speed)} "
-            f"m/s, interpolated trajectory could deviate up "
-            f"to {str(max_diff)} m from Vadere trajectory. "
-            f"If smaller deviation required, choose higher "
-            f"frame rate."
+            f"The interpolated trajectory potentially deviates up to "
+            f"{str(max_deviation)} m from the original trajectory, at least "
+            f"for the fastest pedestrian with max. speed of {str(max_speed)} m/s. "
+            f"If smaller deviations are required, choose a higher frame rate. "
+            f"The current frame rate is {str(frame_rate)} fps."
         )

From bfe1d56f2293d9d5fd5b4b11fb4384868a8a6123 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Tue, 6 Aug 2024 11:35:52 +0200
Subject: [PATCH 03/22] Refactor using scripts/format.sh

---
 pedpy/io/trajectory_loader.py | 64 +++++++++++++++++++++--------------
 1 file changed, 38 insertions(+), 26 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index d961b397..a14d38c6 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -640,9 +640,9 @@ def _load_trajectory_data_from_viswalk(
 
 
 def load_trajectory_from_vadere(
-        *,
-        trajectory_file: pathlib.Path,
-        frame_rate: float = 24.0,
+    *,
+    trajectory_file: pathlib.Path,
+    frame_rate: float = 24.0,
 ) -> TrajectoryData:
     """Loads trajectory data from Vadere-traj file as :class:`~trajectory_data.TrajectoryData`.
 
@@ -672,8 +672,7 @@ def load_trajectory_from_vadere(
     )
 
     traj_dataframe = _event_driven_traj_to_const_frame_rate(
-        traj_dataframe=traj_dataframe,
-        frame_rate=frame_rate
+        traj_dataframe=traj_dataframe, frame_rate=frame_rate
     )
 
     return TrajectoryData(
@@ -683,7 +682,7 @@ def load_trajectory_from_vadere(
 
 
 def _load_trajectory_data_from_vadere(
-        *, trajectory_file: pathlib.Path
+    *, trajectory_file: pathlib.Path
 ) -> pd.DataFrame:
     """Parse the trajectory file for trajectory data.
 
@@ -698,12 +697,17 @@ def _load_trajectory_data_from_vadere(
         The trajectory data as :class:`DataFrame`, the coordinates are in meter (m).
     """
 
-    VADERE_COMMENT = "#"    # Comment identifier in Vadere trajectory files
+    VADERE_COMMENT = "#"  # Comment identifier in Vadere trajectory files
     VADERE_KEY_ID = "pedestrianId"
     VADERE_KEY_TIME = "simTime"
     VADERE_KEY_X = "startX"
     VADERE_KEY_Y = "startY"
-    columns_to_keep = [VADERE_KEY_ID, VADERE_KEY_TIME, VADERE_KEY_X, VADERE_KEY_Y]
+    columns_to_keep = [
+        VADERE_KEY_ID,
+        VADERE_KEY_TIME,
+        VADERE_KEY_X,
+        VADERE_KEY_Y,
+    ]
     name_mapping = {
         VADERE_KEY_ID: ID_COL,
         VADERE_KEY_TIME: TIME_COL,
@@ -721,8 +725,9 @@ def _load_trajectory_data_from_vadere(
     )
     try:
         vadere_cols = list(
-            pd.read_csv(trajectory_file, comment=VADERE_COMMENT, delimiter=" ", nrows=1)
-            .columns
+            pd.read_csv(
+                trajectory_file, comment=VADERE_COMMENT, delimiter=" ", nrows=1
+            ).columns
         )
         use_vadere_cols = list()
         non_unique_cols = list()
@@ -776,8 +781,8 @@ def _load_trajectory_data_from_vadere(
 
 
 def _event_driven_traj_to_const_frame_rate(
-        traj_dataframe: pd.DataFrame,
-        frame_rate: float,
+    traj_dataframe: pd.DataFrame,
+    frame_rate: float,
 ) -> pd.DataFrame:
     """Interpolate trajectory data linearly for non-equidistant time steps.
 
@@ -790,13 +795,14 @@ def _event_driven_traj_to_const_frame_rate(
         for frames between two recorded time steps.
     """
 
-    _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(traj_dataframe, frame_rate)
+    _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(
+        traj_dataframe, frame_rate
+    )
 
     traj_dataframe.set_index(TIME_COL, inplace=True)
     traj_by_ped = traj_dataframe.groupby(ID_COL)
     traj_dataframe_interpolated = pd.DataFrame()
     for ped_id, traj in traj_by_ped:
-
         t = traj.index
         t_start = traj.index.values.min()
         t_stop = traj.index.values.max()
@@ -819,31 +825,35 @@ def _event_driven_traj_to_const_frame_rate(
                 step=1 / frame_rate,
             )
             r = pd.Index(equidist_time_steps, name=t.name)
-            traj = traj.reindex(t.union(r)).interpolate(method='index').loc[r]
+            traj = traj.reindex(t.union(r)).interpolate(method="index").loc[r]
 
             traj[ID_COL] = traj[ID_COL].astype(int)
 
-            traj_dataframe_interpolated = pd.concat([traj_dataframe_interpolated, traj])
+            traj_dataframe_interpolated = pd.concat(
+                [traj_dataframe_interpolated, traj]
+            )
 
     traj_dataframe_interpolated.reset_index(inplace=True)
 
-    traj_dataframe_interpolated[FRAME_COL] = (traj_dataframe_interpolated[TIME_COL] * frame_rate) \
-        .round(decimals=0) \
+    traj_dataframe_interpolated[FRAME_COL] = (
+        (traj_dataframe_interpolated[TIME_COL] * frame_rate)
+        .round(decimals=0)
         .astype(int)
-    traj_dataframe_interpolated.drop(labels=TIME_COL, axis="columns", inplace=True)
+    )
+    traj_dataframe_interpolated.drop(
+        labels=TIME_COL, axis="columns", inplace=True
+    )
 
     traj_dataframe_interpolated.sort_values(
-        by=[FRAME_COL, ID_COL],
-        ignore_index=True,
-        inplace=True
+        by=[FRAME_COL, ID_COL], ignore_index=True, inplace=True
     )
     return traj_dataframe_interpolated
 
 
 def _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(
-        traj_dataframe: pd.DataFrame,
-        frame_rate: float,
-        deviation_threshold: float = 0.1,
+    traj_dataframe: pd.DataFrame,
+    frame_rate: float,
+    deviation_threshold: float = 0.1,
 ) -> None:
     """Validates whether the maximum deviation between event-based vadere trajectories and their
     interpolated version with fixed frames is below given threshold.
@@ -867,7 +877,9 @@ def _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(
     max_speed = 0  # max pedestrian speed that actually reads from the traj file
     for _, traj in traj_groups:
         diff = traj.diff().dropna()
-        dx_dt = (np.sqrt(diff[[X_COL, Y_COL]].pow(2).sum(axis=1))).divide(diff[TIME_COL])
+        dx_dt = (np.sqrt(diff[[X_COL, Y_COL]].pow(2).sum(axis=1))).divide(
+            diff[TIME_COL]
+        )
         max_speed = max([max_speed, round(max(dx_dt), 2)])
 
     max_deviation = round(max_speed / frame_rate, 2)

From d739abb575cbd83b06b2456450ade2959f266d85 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 9 Aug 2024 11:35:35 +0200
Subject: [PATCH 04/22] Add test_load_trajectory_from_vadere_no_data

---
 tests/unit_tests/io/test_trajectory_loader.py | 46 +++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/tests/unit_tests/io/test_trajectory_loader.py b/tests/unit_tests/io/test_trajectory_loader.py
index 254b878e..a0b91b99 100644
--- a/tests/unit_tests/io/test_trajectory_loader.py
+++ b/tests/unit_tests/io/test_trajectory_loader.py
@@ -361,6 +361,29 @@ def write_viswalk_csv_file(
     data.to_csv(file, sep=";", index=False, mode="a", encoding="utf-8-sig")
 
 
+def write_vadere_csv_file(*,
+    data: Optional[pd.DataFrame] = None,
+    file: pathlib.Path,
+    frame_rate: float = 0,
+):
+    data = data.rename(
+        columns={
+            ID_COL: "pedestrianId",
+            FRAME_COL: "simTime",
+            X_COL: "startX-PID1",   # "-PID1" stands for processor id 1 and is used in Vadere
+                                    # outputs as extension of the generic column name startX
+            Y_COL: "startY-PID1",   # "-PID1" see comment above
+        }
+    )
+    data["simTime"] = data["simTime"] / frame_rate
+
+    vadere_traj_header = "#IDXCOL=2,DATACOL=2,SEP=' '\n"
+    with open(file, "w", encoding="utf-8-sig") as writer:
+        writer.write(vadere_traj_header)
+
+    data.to_csv(file, sep=" ", index=False, mode="a", encoding="utf-8-sig")
+
+
 def write_header_viswalk(file, data):
     column_description = {
         "$PEDESTRIAN:NO": "No, Number (Unique pedestrian number)",
@@ -1665,6 +1688,29 @@ def test_load_trajectory_from_viswalk_no_data(
     )
 
 
+def test_load_trajectory_from_vadere_no_data(
+    tmp_path: pathlib.Path,
+):
+    data_empty = pd.DataFrame(
+        columns=[ID_COL, FRAME_COL, X_COL, Y_COL],
+    )
+    trajectory_vadere = pathlib.Path(tmp_path / "postvis.traj")
+
+    written_data = get_data_frame_to_write(data_empty, TrajectoryUnit.METER)
+    write_vadere_csv_file(
+        file=trajectory_vadere,
+        data=written_data,
+    )
+
+    with pytest.raises(LoadTrajectoryError) as error_info:
+        load_trajectory_from_viswalk(
+            trajectory_file=trajectory_vadere,
+        )
+    assert "The given trajectory file seems to be incorrect or empty." in str(
+        error_info.value
+    )
+
+
 def test_load_trajectory_from_viswalk_frame_rate_zero(
     tmp_path: pathlib.Path,
 ):

From 5dc5505eb8c1588a76319ed9754c4f3de2735f0a Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 9 Aug 2024 11:47:39 +0200
Subject: [PATCH 05/22] Add test_load_trajectory_from_vadere_reference_file
 including reference file (created with Vadere 3.0, d79a4bfd)

---
 .../io/test-data/vadere_postvis.traj          | 495 ++++++++++++++++++
 tests/unit_tests/io/test_trajectory_loader.py |   9 +-
 2 files changed, 503 insertions(+), 1 deletion(-)
 create mode 100644 tests/unit_tests/io/test-data/vadere_postvis.traj

diff --git a/tests/unit_tests/io/test-data/vadere_postvis.traj b/tests/unit_tests/io/test-data/vadere_postvis.traj
new file mode 100644
index 00000000..bd832234
--- /dev/null
+++ b/tests/unit_tests/io/test-data/vadere_postvis.traj
@@ -0,0 +1,495 @@
+#IDXCOL=2,DATACOL=6,SEP=' '
+pedestrianId simTime endTime-PID1 startX-PID1 startY-PID1 endX-PID1 endY-PID1 targetId-PID2
+1 0.4 1.0683293133551417 2.556629741868144 7.096775246629484 2.4880996733806358 6.383746164613437 8
+2 0.4 1.1228955894556543 4.033198711877434 7.273571859422713 3.830065282451616 6.633436593130925 8
+3 0.4 0.9589899632551299 2.874990568393157 5.901051547816886 3.1418946843907762 5.1003391998240275 8
+4 0.4 0.9405647359179485 6.682443997985858 7.3875115817824675 6.682443997985858 6.565625494640466 8
+5 0.4 0.9705423320788561 6.161786542547321 7.257709027784086 5.778875227619709 6.5734459847095845 8
+6 0.4 0.9592050617723284 1.6156340783490035 7.974670220649376 1.9922150218237316 7.344783545885745 8
+7 0.4 0.9678513601082895 5.22444338821864 7.837887328680241 5.625461412600841 7.43686930429804 8
+8 0.4 1.0842289219391668 5.993488722286024 8.765665584066898 5.5266550411028925 8.29696956845795 8
+9 0.4 0.9944644208309537 1.2741014208098662 6.771428322484198 1.1626365562665977 7.040235366567192 8
+10 0.4 0.8649438339114517 3.5602674366800784 8.56930176610384 4.050339758303757 7.8466163397077064 8
+11 0.4 0.9340539716916719 2.256571265092003 5.30579029842332 2.2565712650920036 4.4893377038751305 8
+12 0.4 1.0589748434132438 2.8658748773759415 8.07554961073426 3.5065592801279593 7.656547759392701 8
+13 0.4 0.9532688802055487 5.076082742391011 6.435542259943723 5.413391046443367 5.631711257541063 8
+14 0.4 0.9605232222733574 2.8552316367139827 8.496707407724088 2.5747670034043693 8.272951750435013 8
+15 0.4 0.9806293136779161 1.4494421471783905 6.290887327331154 1.4578234277992967 5.542398245881677 8
+16 0.4 1.0496971486381375 5.030906623599764 5.763888154413127 4.57660265824458 5.208679340719964 8
+17 0.4 0.9743152122973685 4.4104519512736315 6.428887201364168 3.879018159151501 5.888100717434825 8
+18 0.4 1.0176206330509814 6.692035519636171 8.797641445755035 6.692035519636172 7.876807532235983 8
+19 0.4 0.9385115346207752 2.984772824696212 7.25309094051753 3.0705721243650608 6.522200654363935 8
+20 0.4 0.9629521396050456 4.564515259361686 7.505863901535077 4.845317387303051 6.707777171945942 8
+10 0.8649438339114517 1.3298876678229035 4.050339758303757 7.8466163397077064 4.517720521290628 7.462398627487666 8
+11 0.9340539716916719 1.4681079433833437 2.2565712650920036 4.4893377038751305 2.97096728532167 4.18316798091956 8
+19 0.9385115346207752 1.4770230692415502 3.0705721243650608 6.522200654363935 2.9350416689619188 5.754076021545039 8
+4 0.9405647359179485 1.4811294718358972 6.682443997985858 6.565625494640466 6.571674240140841 5.78479004399073 8
+13 0.9532688802055487 1.5065377604110974 5.413391046443367 5.631711257541063 5.413391046443367 4.759976680730929 8
+3 0.9589899632551299 1.5179799265102596 3.1418946843907762 5.1003391998240275 3.633934917263397 4.407765751087824 8
+6 0.9592050617723284 1.518410123544657 1.9922150218237316 7.344783545885745 2.6792695859571634 7.045405064297001 8
+14 0.9605232222733574 1.5210464445467147 2.5747670034043693 8.272951750435013 2.122243275564501 7.641703349368925 8
+20 0.9629521396050456 1.5259042792100912 4.845317387303051 6.707777171945942 5.0649887331873416 5.793532144956444 8
+7 0.9678513601082895 1.535702720216579 5.625461412600841 7.43686930429804 5.08006060774587 6.89746556680659 8
+5 0.9705423320788561 1.541084664157712 5.778875227619709 6.5734459847095845 5.778875227619709 5.789330567684024 8
+17 0.9743152122973685 1.5486304245947369 3.879018159151501 5.888100717434825 3.8069809759124906 5.191326363185752 8
+15 0.9806293136779161 1.5612586273558322 1.4578234277992967 5.542398245881677 1.839253976688929 4.8904204011195915 8
+9 0.9944644208309537 1.5889288416619074 1.1626365562665977 7.040235366567192 1.5109395366028688 6.4301158546845345 8
+18 1.0176206330509814 1.6352412661019629 6.692035519636172 7.876807532235983 6.404274921661466 7.013525738311872 8
+16 1.0496971486381375 1.699394297276275 4.57660265824458 5.208679340719964 4.576602658244581 4.477912181883949 8
+12 1.0589748434132438 1.7179496868264876 3.5065592801279593 7.656547759392701 3.5065592801279593 7.656547759392701 8
+1 1.0683293133551417 1.7366586267102833 2.4880996733806358 6.383746164613437 2.2552178879918254 5.862093002994459 8
+8 1.0842289219391668 1.7684578438783336 5.5266550411028925 8.29696956845795 5.635107838453344 7.589205782993118 8
+2 1.1228955894556543 1.8457911789113086 3.830065282451616 6.633436593130925 4.060287175395098 5.967929058473601 8
+10 1.3298876678229035 1.7948315017343552 4.517720521290628 7.462398627487666 4.085924469911161 6.703453783668246 8
+11 1.4681079433833437 2.002161915075016 2.97096728532167 4.18316798091956 2.658612393595044 4.190582537897392 8
+19 1.4770230692415502 2.015534603862325 2.9350416689619188 5.754076021545039 2.9350416689619188 4.974086306301981 8
+4 1.4811294718358972 2.021694207753846 6.571674240140841 5.78479004399073 6.571674240140841 4.962903956848729 8
+13 1.5065377604110974 2.059806640616646 5.413391046443367 4.759976680730929 5.325751373304048 4.1493824020410335 8
+3 1.5179799265102596 2.0769698897653894 3.633934917263397 4.407765751087824 3.954007311104009 3.7123325107892757 8
+6 1.518410123544657 2.0776151853169855 2.6792695859571634 7.045405064297001 3.151239951135087 6.406485287146242 8
+14 1.5210464445467147 2.081569666820072 2.122243275564501 7.641703349368925 2.365851029929011 7.123864737828244 8
+20 1.5259042792100912 2.0888564188151366 5.0649887331873416 5.793532144956444 5.0471377432163935 5.127681059427964 8
+7 1.535702720216579 2.1035540803248685 5.08006060774587 6.89746556680659 5.069115274480355 6.095504206641824 8
+5 1.541084664157712 2.111626996236568 5.778875227619709 5.789330567684024 5.778875227619709 5.005215150658463 8
+17 1.5486304245947369 2.1229456368921054 3.8069809759124906 5.191326363185752 3.763464673854365 4.459106477823001 8
+15 1.5612586273558322 2.1418879410337484 1.839253976688929 4.8904204011195915 1.839253976688929 4.135063284460948 8
+9 1.5889288416619074 2.183393262492861 1.5109395366028688 6.4301158546845345 1.5109395366028688 5.669693322228172 8
+18 1.6352412661019629 2.2528618991529443 6.404274921661466 7.013525738311872 6.058962204091823 6.207796063982699 8
+16 1.699394297276275 2.3490914459144125 4.576602658244581 4.477912181883949 4.678653803744837 4.127069305641034 8
+12 1.7179496868264876 2.3769245302397315 3.5065592801279593 7.656547759392701 3.5902433379560446 7.3757980290234055 8
+1 1.7366586267102833 2.404987940065425 2.2552178879918254 5.862093002994459 2.255217887991825 5.086759488758865 8
+8 1.7684578438783336 2.4526867658175004 5.635107838453344 7.589205782993118 5.267968674501011 7.210331635976659 8
+10 1.7948315017343552 2.259775335645807 4.085924469911161 6.703453783668246 4.406989142677327 6.683477664536015 8
+2 1.8457911789113086 2.568686768366963 4.060287175395098 5.967929058473601 4.060287175395099 5.221544172042813 8
+11 2.002161915075016 2.536215886766688 2.658612393595044 4.190582537897392 2.9847772995540507 4.026881893486731 8
+19 2.015534603862325 2.5540461384831 2.9350416689619188 4.974086306301981 3.1727233557121113 5.283284308809791 8
+4 2.021694207753846 2.5622589436717944 6.571674240140841 4.962903956848729 6.408582297219672 4.1573620532882805 8
+13 2.059806640616646 2.6130755208221945 5.325751373304048 4.1493824020410335 5.325751373304048 4.1493824020410335 8
+3 2.0769698897653894 2.6359598530205193 3.954007311104009 3.7123325107892757 3.95067100965404 2.8949386555465653 8
+6 2.0776151853169855 2.636820247089314 3.151239951135087 6.406485287146242 3.389089140237964 6.08841078321164 8
+14 2.081569666820072 2.6420928890934294 2.365851029929011 7.123864737828244 2.3658510299290114 6.3471715756936815 8
+20 2.0888564188151366 2.651808558420182 5.0471377432163935 5.127681059427964 4.694538129051324 5.0101478547062746 8
+7 2.1035540803248685 2.671405440433158 5.069115274480355 6.095504206641824 5.122767646365526 5.741045350901901 8
+5 2.111626996236568 2.682169328315424 5.778875227619709 5.005215150658463 5.778875227619709 5.005215150658463 8
+17 2.1229456368921054 2.697260849189474 3.763464673854365 4.459106477823001 3.9416680670073707 3.8460836131485987 8
+15 2.1418879410337484 2.7225172547116645 1.839253976688929 4.135063284460948 2.2148061557824454 4.175083956617047 8
+9 2.183393262492861 2.7778576833238144 1.5109395366028688 5.669693322228172 1.3867640417054752 4.97821575111104 8
+18 2.2528618991529443 2.870482532203926 6.058962204091823 6.207796063982699 6.4625169333052845 5.458862517552782 8
+10 2.259775335645807 2.724719169557259 4.406989142677327 6.683477664536015 4.323881752957871 6.254870107135584 8
+16 2.3490914459144125 2.99878859455255 4.678653803744837 4.127069305641034 4.499398343505813 4.0526474033842845 8
+12 2.3769245302397315 3.0358993736529754 3.5902433379560446 7.3757980290234055 3.734539751425399 7.120843174790568 8
+1 2.404987940065425 3.0733172534205666 2.255217887991825 5.086759488758865 2.524133363349633 4.961381327492846 8
+8 2.4526867658175004 3.1369156877566673 5.267968674501011 7.210331635976659 5.563799881388341 6.558276820565998 8
+11 2.536215886766688 3.07026985845836 2.9847772995540507 4.026881893486731 3.3816045845383673 4.122677591083716 8
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+4 23.644283644471763 24.18484838038971 4.003140288698718 2.1482482162601912 4.713331130797646 1.8795448467924567 8
+18 23.869584055937302 24.487204688988285 5.774471686877664 1.410309474731846 6.695305600396717 1.410309474731846 8
+4 24.18484838038971 24.725413116307656 4.713331130797646 1.8795448467924567 5.520469662831764 1.7513758412459786 8
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diff --git a/tests/unit_tests/io/test_trajectory_loader.py b/tests/unit_tests/io/test_trajectory_loader.py
index a0b91b99..201018f9 100644
--- a/tests/unit_tests/io/test_trajectory_loader.py
+++ b/tests/unit_tests/io/test_trajectory_loader.py
@@ -27,7 +27,7 @@
     load_trajectory_from_txt,
     load_trajectory_from_viswalk,
     load_walkable_area_from_jupedsim_sqlite,
-    load_walkable_area_from_ped_data_archive_hdf5,
+    load_walkable_area_from_ped_data_archive_hdf5, load_trajectory_from_vadere,
 )
 
 
@@ -1688,6 +1688,13 @@ def test_load_trajectory_from_viswalk_no_data(
     )
 
 
+def test_load_trajectory_from_vadere_reference_file():
+    traj_txt = pathlib.Path(__file__).parent / pathlib.Path(
+        "test-data/vadere_postvis.traj"
+    )
+    load_trajectory_from_vadere(trajectory_file=traj_txt)
+
+
 def test_load_trajectory_from_vadere_no_data(
     tmp_path: pathlib.Path,
 ):

From 0bea69e3b4108acf728c0da76d4e1474ff2448d2 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 9 Aug 2024 14:31:41 +0200
Subject: [PATCH 06/22] Fix unstable floating point operations in
 trajectory_loader

---
 pedpy/io/trajectory_loader.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index a14d38c6..6edd9df5 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -809,8 +809,9 @@ def _event_driven_traj_to_const_frame_rate(
 
         # Round t_start up (t_stop down) to nearest multiple of frame period (= 1/frame_rate) to
         # avoid extrapolation of trajectories to times before first (after last) pedestrian step.
-        t_start_ = math.ceil(t_start * frame_rate) / frame_rate
-        t_stop_ = math.floor(t_stop * frame_rate) / frame_rate
+        precision = 14
+        t_start_ = math.ceil(np.round(t_start * frame_rate, precision)) / frame_rate
+        t_stop_ = math.floor(np.round(t_stop * frame_rate, precision)) / frame_rate
 
         if t_start == t_stop:
             _log.warning(
@@ -819,11 +820,17 @@ def _event_driven_traj_to_const_frame_rate(
                 f"Therefore, this trajectory will be ignored."
             )
         else:
-            equidist_time_steps = np.arange(
+            equidist_time_steps = np.linspace(
                 start=t_start_,
-                stop=t_stop_ + 1 / frame_rate,
-                step=1 / frame_rate,
+                stop=t_stop_,
+                num=int(np.round((t_stop_ - t_start_) * frame_rate, 0)) + 1,
+                endpoint=True
             )
+            # np.arange(
+            #    start=t_start_,
+            #    stop=math.ceil(t_stop_ * 10**precision) / 10**precision,
+            #    step=1 / frame_rate,
+            #)
             r = pd.Index(equidist_time_steps, name=t.name)
             traj = traj.reindex(t.union(r)).interpolate(method="index").loc[r]
 

From 491f7f259893660217513714a03de18ba8f556b9 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 9 Aug 2024 14:31:58 +0200
Subject: [PATCH 07/22] Add test_load_trajectory_from_vadere_success

---
 tests/unit_tests/io/test_trajectory_loader.py | 46 +++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/tests/unit_tests/io/test_trajectory_loader.py b/tests/unit_tests/io/test_trajectory_loader.py
index 201018f9..2a8068e1 100644
--- a/tests/unit_tests/io/test_trajectory_loader.py
+++ b/tests/unit_tests/io/test_trajectory_loader.py
@@ -1815,3 +1815,49 @@ def test_load_trajectory_from_viswalk_non_file(tmp_path):
         load_trajectory_from_viswalk(trajectory_file=tmp_path)
 
     assert "is not a file" in str(error_info.value)
+
+
+@pytest.mark.parametrize(
+    "data, expected_frame_rate",
+    [
+        (
+            np.array([[0, 0, 5, 1], [0, 1, -5, -1]]),
+            7.0,
+        ),
+        (
+            np.array([[0, 0, 5, 1], [0, 1, -5, -1]]),
+            15.0,
+        ),
+        (
+            np.array([[0, 0, 5, 1], [0, 1, -5, -1]]),
+            50.0,
+        ),
+    ],
+)
+def test_load_trajectory_from_vadere_success(
+    tmp_path: pathlib.Path,
+    data: List[npt.NDArray[np.float64]],
+    expected_frame_rate: float,
+):
+    trajectory_vadere = pathlib.Path(tmp_path / "postvis.traj")
+
+    expected_data = pd.DataFrame(
+        data=data,
+        columns=[ID_COL, FRAME_COL, X_COL, Y_COL],
+    )
+    written_data = get_data_frame_to_write(expected_data, TrajectoryUnit.METER)
+    write_vadere_csv_file(
+        file=trajectory_vadere,
+        frame_rate=expected_frame_rate,
+        data=written_data,
+    )
+    expected_data = prepare_data_frame(expected_data)
+
+    traj_data_from_file = load_trajectory_from_vadere(
+        trajectory_file=trajectory_vadere,
+        frame_rate=expected_frame_rate,
+    )
+
+    assert (traj_data_from_file.data[[ID_COL, FRAME_COL, X_COL, Y_COL]].to_numpy()
+            == expected_data.to_numpy()).all()
+    assert traj_data_from_file.frame_rate == expected_frame_rate

From e5f64002e311743cb2ad65da8d52639f73d9b918 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 9 Aug 2024 14:35:19 +0200
Subject: [PATCH 08/22] Group vadere test functions

---
 tests/unit_tests/io/test_trajectory_loader.py | 60 +++++++++----------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/tests/unit_tests/io/test_trajectory_loader.py b/tests/unit_tests/io/test_trajectory_loader.py
index 2a8068e1..55acfdf0 100644
--- a/tests/unit_tests/io/test_trajectory_loader.py
+++ b/tests/unit_tests/io/test_trajectory_loader.py
@@ -1688,36 +1688,6 @@ def test_load_trajectory_from_viswalk_no_data(
     )
 
 
-def test_load_trajectory_from_vadere_reference_file():
-    traj_txt = pathlib.Path(__file__).parent / pathlib.Path(
-        "test-data/vadere_postvis.traj"
-    )
-    load_trajectory_from_vadere(trajectory_file=traj_txt)
-
-
-def test_load_trajectory_from_vadere_no_data(
-    tmp_path: pathlib.Path,
-):
-    data_empty = pd.DataFrame(
-        columns=[ID_COL, FRAME_COL, X_COL, Y_COL],
-    )
-    trajectory_vadere = pathlib.Path(tmp_path / "postvis.traj")
-
-    written_data = get_data_frame_to_write(data_empty, TrajectoryUnit.METER)
-    write_vadere_csv_file(
-        file=trajectory_vadere,
-        data=written_data,
-    )
-
-    with pytest.raises(LoadTrajectoryError) as error_info:
-        load_trajectory_from_viswalk(
-            trajectory_file=trajectory_vadere,
-        )
-    assert "The given trajectory file seems to be incorrect or empty." in str(
-        error_info.value
-    )
-
-
 def test_load_trajectory_from_viswalk_frame_rate_zero(
     tmp_path: pathlib.Path,
 ):
@@ -1817,6 +1787,36 @@ def test_load_trajectory_from_viswalk_non_file(tmp_path):
     assert "is not a file" in str(error_info.value)
 
 
+def test_load_trajectory_from_vadere_reference_file():
+    traj_txt = pathlib.Path(__file__).parent / pathlib.Path(
+        "test-data/vadere_postvis.traj"
+    )
+    load_trajectory_from_vadere(trajectory_file=traj_txt)
+
+
+def test_load_trajectory_from_vadere_no_data(
+    tmp_path: pathlib.Path,
+):
+    data_empty = pd.DataFrame(
+        columns=[ID_COL, FRAME_COL, X_COL, Y_COL],
+    )
+    trajectory_vadere = pathlib.Path(tmp_path / "postvis.traj")
+
+    written_data = get_data_frame_to_write(data_empty, TrajectoryUnit.METER)
+    write_vadere_csv_file(
+        file=trajectory_vadere,
+        data=written_data,
+    )
+
+    with pytest.raises(LoadTrajectoryError) as error_info:
+        load_trajectory_from_viswalk(
+            trajectory_file=trajectory_vadere,
+        )
+    assert "The given trajectory file seems to be incorrect or empty." in str(
+        error_info.value
+    )
+
+
 @pytest.mark.parametrize(
     "data, expected_frame_rate",
     [

From 2b26626044c6336b7d4021fb596484e6f78fe5e4 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 9 Aug 2024 15:22:43 +0200
Subject: [PATCH 09/22] Add tests for missing and non-unique columns in vadere
 trajectory files

---
 pedpy/io/trajectory_loader.py                 | 16 +++--
 tests/unit_tests/io/test_trajectory_loader.py | 58 ++++++++++++++++++-
 2 files changed, 66 insertions(+), 8 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index 6edd9df5..1b2dd539 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -730,7 +730,7 @@ def _load_trajectory_data_from_vadere(
             ).columns
         )
         use_vadere_cols = list()
-        non_unique_cols = list()
+        non_unique_cols = dict()
         missing_cols = list()
         rename_mapping = dict()
 
@@ -740,20 +740,24 @@ def _load_trajectory_data_from_vadere(
                 use_vadere_cols += matching
                 rename_mapping[matching[0]] = name_mapping[col]
             elif len(matching) > 1:
-                non_unique_cols += [col]
+                non_unique_cols[col] = matching
             elif len(matching) == 0:
                 missing_cols += [col]
 
         if non_unique_cols:
             raise LoadTrajectoryError(
-                f"{common_error_message}"
-                f"Non-unique columns: {', '.join(non_unique_cols)}"
+                f"{common_error_message} " +
+                ". ".join(
+                    ["The identifier '{0}' is non-unique. "
+                     "It is contained in the columns: {1}".format(k, ", ".join(v))
+                     for k, v in non_unique_cols.items()]) +
+                "."
             )
 
         if missing_cols:
             raise LoadTrajectoryError(
-                f"{common_error_message}"
-                f"Missing column: {', '.join(missing_cols)}."
+                f"{common_error_message} "
+                f"Missing columns: {', '.join(missing_cols)}."
             )
 
         data = pd.read_csv(
diff --git a/tests/unit_tests/io/test_trajectory_loader.py b/tests/unit_tests/io/test_trajectory_loader.py
index 55acfdf0..378285cd 100644
--- a/tests/unit_tests/io/test_trajectory_loader.py
+++ b/tests/unit_tests/io/test_trajectory_loader.py
@@ -21,13 +21,13 @@
     _load_trajectory_data_from_txt,
     _load_trajectory_meta_data_from_txt,
     _validate_is_file,
-    load_trajectory,
     load_trajectory_from_jupedsim_sqlite,
     load_trajectory_from_ped_data_archive_hdf5,
     load_trajectory_from_txt,
+    load_trajectory_from_vadere,
     load_trajectory_from_viswalk,
     load_walkable_area_from_jupedsim_sqlite,
-    load_walkable_area_from_ped_data_archive_hdf5, load_trajectory_from_vadere,
+    load_walkable_area_from_ped_data_archive_hdf5,
 )
 
 
@@ -1861,3 +1861,57 @@ def test_load_trajectory_from_vadere_success(
     assert (traj_data_from_file.data[[ID_COL, FRAME_COL, X_COL, Y_COL]].to_numpy()
             == expected_data.to_numpy()).all()
     assert traj_data_from_file.frame_rate == expected_frame_rate
+
+
+def test_load_trajectory_from_vadere_columns_missing(
+    tmp_path: pathlib.Path,
+):
+    trajectory_vadere = pathlib.Path(tmp_path / "postvis.traj")
+
+    data_with_missing_column = pd.DataFrame(
+        data=np.array([[0, 0, 5, 1], [0, 1, -5, -1]]),
+        columns=[ID_COL, FRAME_COL, X_COL, "FOO!"],
+    )
+
+    written_data = get_data_frame_to_write(
+        data_with_missing_column, TrajectoryUnit.METER
+    )
+    write_vadere_csv_file(
+        file=trajectory_vadere,
+        data=written_data,
+    )
+
+    with pytest.raises(LoadTrajectoryError) as error_info:
+        load_trajectory_from_vadere(
+            trajectory_file=trajectory_vadere,
+        )
+    assert "The given trajectory file seems to be incorrect or empty." in str(
+        error_info.value
+    )
+
+
+def test_load_trajectory_from_vadere_columns_non_unique(
+    tmp_path: pathlib.Path,
+):
+    trajectory_vadere = pathlib.Path(tmp_path / "postvis.traj")
+
+    data_with_missing_column = pd.DataFrame(
+        data=np.array([[0, 0, 5, 5, 1], [0, 1, -5, -5, -1]]),
+        columns=[ID_COL, FRAME_COL, X_COL, "startX-PID2", Y_COL],
+    )
+
+    written_data = get_data_frame_to_write(
+        data_with_missing_column, TrajectoryUnit.METER
+    )
+    write_vadere_csv_file(
+        file=trajectory_vadere,
+        data=written_data,
+    )
+
+    with pytest.raises(LoadTrajectoryError) as error_info:
+        load_trajectory_from_vadere(
+            trajectory_file=trajectory_vadere,
+        )
+    assert "The given trajectory file seems to be incorrect or empty." in str(
+        error_info.value
+    )
\ No newline at end of file

From a3723ecf5c613cb5419529b68f95bdb77a81eda4 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 9 Aug 2024 15:34:20 +0200
Subject: [PATCH 10/22] Refactor using scripts/format.sh

---
 pedpy/io/trajectory_loader.py                 | 77 +++++++++++--------
 tests/unit_tests/io/test_trajectory_loader.py | 17 ++--
 2 files changed, 53 insertions(+), 41 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index 1b2dd539..f3772529 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -11,7 +11,7 @@
 import pandas as pd
 import shapely
 
-from pedpy.column_identifier import FRAME_COL, ID_COL, X_COL, Y_COL, TIME_COL
+from pedpy.column_identifier import FRAME_COL, ID_COL, TIME_COL, X_COL, Y_COL
 from pedpy.data.geometry import WalkableArea
 from pedpy.data.trajectory_data import TrajectoryData
 
@@ -60,10 +60,10 @@ def _validate_is_file(file: pathlib.Path) -> None:
 
 
 def load_trajectory(
-        *,
-        trajectory_file: pathlib.Path,
-        default_frame_rate: Optional[float] = None,
-        default_unit: Optional[TrajectoryUnit] = None,
+    *,
+    trajectory_file: pathlib.Path,
+    default_frame_rate: Optional[float] = None,
+    default_unit: Optional[TrajectoryUnit] = None,
 ) -> TrajectoryData:
     """Loads the trajectory file in the internal :class:`~trajectory_data.TrajectoryData` format.
 
@@ -89,10 +89,10 @@ def load_trajectory(
 
 
 def load_trajectory_from_txt(
-        *,
-        trajectory_file: pathlib.Path,
-        default_frame_rate: Optional[float] = None,
-        default_unit: Optional[TrajectoryUnit] = None,
+    *,
+    trajectory_file: pathlib.Path,
+    default_frame_rate: Optional[float] = None,
+    default_unit: Optional[TrajectoryUnit] = None,
 ) -> TrajectoryData:
     """Loads the trajectory file in the internal :class:`~trajectory_data.TrajectoryData` format.
 
@@ -125,7 +125,7 @@ def load_trajectory_from_txt(
 
 
 def _load_trajectory_data_from_txt(
-        *, trajectory_file: pathlib.Path, unit: TrajectoryUnit
+    *, trajectory_file: pathlib.Path, unit: TrajectoryUnit
 ) -> pd.DataFrame:
     """Parse the trajectory file for trajectory data.
 
@@ -179,10 +179,10 @@ def _load_trajectory_data_from_txt(
 
 
 def _load_trajectory_meta_data_from_txt(  # pylint: disable=too-many-branches
-        *,
-        trajectory_file: pathlib.Path,
-        default_frame_rate: Optional[float],
-        default_unit: Optional[TrajectoryUnit],
+    *,
+    trajectory_file: pathlib.Path,
+    default_frame_rate: Optional[float],
+    default_unit: Optional[TrajectoryUnit],
 ) -> Tuple[float, TrajectoryUnit]:
     """Extract the trajectory metadata from file, use defaults if none found.
 
@@ -281,7 +281,7 @@ def _load_trajectory_meta_data_from_txt(  # pylint: disable=too-many-branches
 
 
 def load_trajectory_from_jupedsim_sqlite(
-        trajectory_file: pathlib.Path,
+    trajectory_file: pathlib.Path,
 ) -> TrajectoryData:
     """Loads data from the sqlite file as :class:`~trajectory_data.TrajectoryData`.
 
@@ -333,7 +333,7 @@ def load_trajectory_from_jupedsim_sqlite(
 
 
 def load_walkable_area_from_jupedsim_sqlite(
-        trajectory_file: pathlib.Path,
+    trajectory_file: pathlib.Path,
 ) -> WalkableArea:
     """Loads the walkable area from the sqlite file as :class:`~geometry.WalkableArea`.
 
@@ -375,7 +375,7 @@ def _get_jupedsim_sqlite_version(connection: sqlite3.Connection) -> int:
 
 
 def _load_walkable_area_from_jupedsim_sqlite_v1(
-        con: sqlite3.Connection,
+    con: sqlite3.Connection,
 ) -> WalkableArea:
     try:
         walkable_query_result = (
@@ -397,7 +397,7 @@ def _load_walkable_area_from_jupedsim_sqlite_v1(
 
 
 def _load_walkable_area_from_jupedsim_sqlite_v2(
-        con: sqlite3.Connection,
+    con: sqlite3.Connection,
 ) -> WalkableArea:
     try:
         res = con.cursor().execute("SELECT wkt FROM geometry")
@@ -418,7 +418,7 @@ def _load_walkable_area_from_jupedsim_sqlite_v2(
 
 
 def load_trajectory_from_ped_data_archive_hdf5(
-        trajectory_file: pathlib.Path,
+    trajectory_file: pathlib.Path,
 ) -> TrajectoryData:
     """Loads data from the hdf5 file as :class:`~trajectory_data.TrajectoryData`.
 
@@ -476,7 +476,7 @@ def load_trajectory_from_ped_data_archive_hdf5(
 
 
 def load_walkable_area_from_ped_data_archive_hdf5(
-        trajectory_file: pathlib.Path,
+    trajectory_file: pathlib.Path,
 ) -> WalkableArea:
     """Loads the walkable area from the hdf5 file as :class:`~geometry.WalkableArea`.
 
@@ -509,8 +509,8 @@ def load_walkable_area_from_ped_data_archive_hdf5(
 
 
 def load_trajectory_from_viswalk(
-        *,
-        trajectory_file: pathlib.Path,
+    *,
+    trajectory_file: pathlib.Path,
 ) -> TrajectoryData:
     """Loads data from Viswalk-csv file as :class:`~trajectory_data.TrajectoryData`.
 
@@ -555,7 +555,7 @@ def load_trajectory_from_viswalk(
 
 
 def _calculate_frames_and_fps(
-        traj_dataframe: pd.DataFrame,
+    traj_dataframe: pd.DataFrame,
 ) -> Tuple[pd.Series, int]:
     """Calculates fps and frames based on the time column of the dataframe."""
     mean_diff = traj_dataframe.groupby(ID_COL)["time"].diff().dropna().mean()
@@ -572,7 +572,7 @@ def _calculate_frames_and_fps(
 
 
 def _load_trajectory_data_from_viswalk(
-        *, trajectory_file: pathlib.Path
+    *, trajectory_file: pathlib.Path
 ) -> pd.DataFrame:
     """Parse the trajectory file for trajectory data.
 
@@ -746,12 +746,17 @@ def _load_trajectory_data_from_vadere(
 
         if non_unique_cols:
             raise LoadTrajectoryError(
-                f"{common_error_message} " +
-                ". ".join(
-                    ["The identifier '{0}' is non-unique. "
-                     "It is contained in the columns: {1}".format(k, ", ".join(v))
-                     for k, v in non_unique_cols.items()]) +
-                "."
+                f"{common_error_message} "
+                + ". ".join(
+                    [
+                        "The identifier '{0}' is non-unique. "
+                        "It is contained in the columns: {1}".format(
+                            k, ", ".join(v)
+                        )
+                        for k, v in non_unique_cols.items()
+                    ]
+                )
+                + "."
             )
 
         if missing_cols:
@@ -814,8 +819,12 @@ def _event_driven_traj_to_const_frame_rate(
         # Round t_start up (t_stop down) to nearest multiple of frame period (= 1/frame_rate) to
         # avoid extrapolation of trajectories to times before first (after last) pedestrian step.
         precision = 14
-        t_start_ = math.ceil(np.round(t_start * frame_rate, precision)) / frame_rate
-        t_stop_ = math.floor(np.round(t_stop * frame_rate, precision)) / frame_rate
+        t_start_ = (
+            math.ceil(np.round(t_start * frame_rate, precision)) / frame_rate
+        )
+        t_stop_ = (
+            math.floor(np.round(t_stop * frame_rate, precision)) / frame_rate
+        )
 
         if t_start == t_stop:
             _log.warning(
@@ -828,13 +837,13 @@ def _event_driven_traj_to_const_frame_rate(
                 start=t_start_,
                 stop=t_stop_,
                 num=int(np.round((t_stop_ - t_start_) * frame_rate, 0)) + 1,
-                endpoint=True
+                endpoint=True,
             )
             # np.arange(
             #    start=t_start_,
             #    stop=math.ceil(t_stop_ * 10**precision) / 10**precision,
             #    step=1 / frame_rate,
-            #)
+            # )
             r = pd.Index(equidist_time_steps, name=t.name)
             traj = traj.reindex(t.union(r)).interpolate(method="index").loc[r]
 
diff --git a/tests/unit_tests/io/test_trajectory_loader.py b/tests/unit_tests/io/test_trajectory_loader.py
index 378285cd..15a3fd46 100644
--- a/tests/unit_tests/io/test_trajectory_loader.py
+++ b/tests/unit_tests/io/test_trajectory_loader.py
@@ -361,7 +361,8 @@ def write_viswalk_csv_file(
     data.to_csv(file, sep=";", index=False, mode="a", encoding="utf-8-sig")
 
 
-def write_vadere_csv_file(*,
+def write_vadere_csv_file(
+    *,
     data: Optional[pd.DataFrame] = None,
     file: pathlib.Path,
     frame_rate: float = 0,
@@ -370,9 +371,9 @@ def write_vadere_csv_file(*,
         columns={
             ID_COL: "pedestrianId",
             FRAME_COL: "simTime",
-            X_COL: "startX-PID1",   # "-PID1" stands for processor id 1 and is used in Vadere
-                                    # outputs as extension of the generic column name startX
-            Y_COL: "startY-PID1",   # "-PID1" see comment above
+            X_COL: "startX-PID1",  # "-PID1" stands for processor id 1 and is used in Vadere
+            # outputs as extension of the generic column name startX
+            Y_COL: "startY-PID1",  # "-PID1" see comment above
         }
     )
     data["simTime"] = data["simTime"] / frame_rate
@@ -1858,8 +1859,10 @@ def test_load_trajectory_from_vadere_success(
         frame_rate=expected_frame_rate,
     )
 
-    assert (traj_data_from_file.data[[ID_COL, FRAME_COL, X_COL, Y_COL]].to_numpy()
-            == expected_data.to_numpy()).all()
+    assert (
+        traj_data_from_file.data[[ID_COL, FRAME_COL, X_COL, Y_COL]].to_numpy()
+        == expected_data.to_numpy()
+    ).all()
     assert traj_data_from_file.frame_rate == expected_frame_rate
 
 
@@ -1914,4 +1917,4 @@ def test_load_trajectory_from_vadere_columns_non_unique(
         )
     assert "The given trajectory file seems to be incorrect or empty." in str(
         error_info.value
-    )
\ No newline at end of file
+    )

From f0bfcdbddfb9244c108d8d8a6323e3d9d7dc0299 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 9 Aug 2024 15:40:47 +0200
Subject: [PATCH 11/22] Refactoring

Removed commented code
---
 pedpy/io/trajectory_loader.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index f3772529..305c3ea0 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -839,11 +839,7 @@ def _event_driven_traj_to_const_frame_rate(
                 num=int(np.round((t_stop_ - t_start_) * frame_rate, 0)) + 1,
                 endpoint=True,
             )
-            # np.arange(
-            #    start=t_start_,
-            #    stop=math.ceil(t_stop_ * 10**precision) / 10**precision,
-            #    step=1 / frame_rate,
-            # )
+
             r = pd.Index(equidist_time_steps, name=t.name)
             traj = traj.reindex(t.union(r)).interpolate(method="index").loc[r]
 

From 7b1558e858e62fbb830d01e2e5d00c68cdb74ff3 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Mon, 12 Aug 2024 12:04:19 +0200
Subject: [PATCH 12/22] Add load_trajectory_from_vadere to __init__.py

---
 pedpy/__init__.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pedpy/__init__.py b/pedpy/__init__.py
index 6c421ef5..41c1cc23 100644
--- a/pedpy/__init__.py
+++ b/pedpy/__init__.py
@@ -20,6 +20,7 @@
     load_trajectory_from_ped_data_archive_hdf5,
     load_trajectory_from_txt,
     load_trajectory_from_viswalk,
+    load_trajectory_from_vadere,
     load_walkable_area_from_jupedsim_sqlite,
     load_walkable_area_from_ped_data_archive_hdf5,
 )
@@ -97,6 +98,7 @@
     "load_trajectory_from_ped_data_archive_hdf5",
     "load_trajectory_from_txt",
     "load_trajectory_from_viswalk",
+    "load_trajectory_from_vadere",
     "load_walkable_area_from_jupedsim_sqlite",
     "load_walkable_area_from_ped_data_archive_hdf5",
     "compute_classic_density",

From cbad152bc624cfffa7e2f5eb77f64e3898568488 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Mon, 12 Aug 2024 12:05:48 +0200
Subject: [PATCH 13/22] Add Vadere example to jupyter notebook

* Add scenario and trajectory file

* Update user_guide.ipynb
---
 .../vadere/bottleneck/bottleneck.scenario     | 268 ++++++++++
 .../demo-data/vadere/bottleneck/postvis.traj  | 495 ++++++++++++++++++
 notebooks/user_guide.ipynb                    | 152 +++++-
 3 files changed, 894 insertions(+), 21 deletions(-)
 create mode 100644 notebooks/demo-data/vadere/bottleneck/bottleneck.scenario
 create mode 100644 notebooks/demo-data/vadere/bottleneck/postvis.traj

diff --git a/notebooks/demo-data/vadere/bottleneck/bottleneck.scenario b/notebooks/demo-data/vadere/bottleneck/bottleneck.scenario
new file mode 100644
index 00000000..bfc5a3b2
--- /dev/null
+++ b/notebooks/demo-data/vadere/bottleneck/bottleneck.scenario
@@ -0,0 +1,268 @@
+{
+  "name" : "bottleneck",
+  "description" : "",
+  "release" : "3.0",
+  "commithash" : "d79a4bfd2394cfa6fc92372d2ebd1b117ab9a2da",
+  "processWriters" : {
+    "files" : [ {
+      "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.EventtimePedestrianIdOutputFile",
+      "filename" : "postvis.traj",
+      "processors" : [ 1, 2 ]
+    } ],
+    "processors" : [ {
+      "type" : "org.vadere.simulator.projects.dataprocessing.processor.FootStepProcessor",
+      "id" : 1
+    }, {
+      "type" : "org.vadere.simulator.projects.dataprocessing.processor.FootStepTargetIDProcessor",
+      "id" : 2
+    } ],
+    "isTimestamped" : false,
+    "isWriteMetaData" : true
+  },
+  "scenario" : {
+    "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel",
+    "attributesModel" : {
+      "org.vadere.state.attributes.models.AttributesOSM" : {
+        "stepCircleResolution" : 4,
+        "numberOfCircles" : 1,
+        "optimizationType" : "NELDER_MEAD",
+        "varyStepDirection" : true,
+        "movementType" : "ARBITRARY",
+        "stepLengthIntercept" : 0.4625,
+        "stepLengthSlopeSpeed" : 0.2345,
+        "stepLengthSD" : 0.036,
+        "movementThreshold" : 0.0,
+        "minStepLength" : 0.1,
+        "minimumStepLength" : true,
+        "maxStepDuration" : 1.7976931348623157E308,
+        "dynamicStepLength" : true,
+        "updateType" : "EVENT_DRIVEN",
+        "seeSmallWalls" : false,
+        "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid",
+        "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell",
+        "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell",
+        "submodels" : [ ]
+      },
+      "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : {
+        "pedPotentialIntimateSpaceWidth" : 0.45,
+        "pedPotentialPersonalSpaceWidth" : 1.2,
+        "pedPotentialHeight" : 50.0,
+        "obstPotentialWidth" : 0.8,
+        "obstPotentialHeight" : 6.0,
+        "intimateSpaceFactor" : 1.2,
+        "personalSpacePower" : 1,
+        "intimateSpacePower" : 1
+      },
+      "org.vadere.state.attributes.models.AttributesFloorField" : {
+        "createMethod" : "HIGH_ACCURACY_FAST_MARCHING",
+        "potentialFieldResolution" : 0.1,
+        "obstacleGridPenalty" : 0.1,
+        "targetAttractionStrength" : 1.0,
+        "cacheType" : "NO_CACHE",
+        "cacheDir" : "",
+        "timeCostAttributes" : {
+          "standardDeviation" : 0.7,
+          "type" : "UNIT",
+          "obstacleDensityWeight" : 3.5,
+          "pedestrianSameTargetDensityWeight" : 3.5,
+          "pedestrianOtherTargetDensityWeight" : 3.5,
+          "pedestrianWeight" : 3.5,
+          "queueWidthLoading" : 1.0,
+          "pedestrianDynamicWeight" : 6.0,
+          "loadingType" : "CONSTANT",
+          "width" : 0.2,
+          "height" : 1.0
+        }
+      }
+    },
+    "attributesSimulation" : {
+      "finishTime" : 50.0,
+      "simTimeStepLength" : 0.4,
+      "realTimeSimTimeRatio" : 0.1,
+      "writeSimulationData" : true,
+      "visualizationEnabled" : true,
+      "printFPS" : false,
+      "digitsPerCoordinate" : 2,
+      "useFixedSeed" : true,
+      "fixedSeed" : 98957596352036733,
+      "simulationSeed" : 98957596352036733
+    },
+    "attributesPsychology" : {
+      "usePsychologyLayer" : false,
+      "psychologyLayer" : {
+        "perception" : "SimplePerceptionModel",
+        "cognition" : "SimpleCognitionModel",
+        "attributesModel" : {
+          "org.vadere.state.attributes.models.psychology.perception.AttributesSimplePerceptionModel" : {
+            "priority" : {
+              "1" : "InformationStimulus",
+              "2" : "ChangeTargetScripted",
+              "3" : "ChangeTarget",
+              "4" : "Threat",
+              "5" : "Wait",
+              "6" : "WaitInArea",
+              "7" : "DistanceRecommendation"
+            }
+          },
+          "org.vadere.state.attributes.models.psychology.cognition.AttributesSimpleCognitionModel" : { }
+        }
+      }
+    },
+    "topography" : {
+      "attributes" : {
+        "bounds" : {
+          "x" : 0.0,
+          "y" : 0.0,
+          "width" : 8.0,
+          "height" : 10.0
+        },
+        "boundingBoxWidth" : 0.5,
+        "bounded" : true,
+        "referenceCoordinateSystem" : null
+      },
+      "obstacles" : [ {
+        "id" : 5,
+        "shape" : {
+          "type" : "POLYGON",
+          "points" : [ {
+            "x" : 0.5,
+            "y" : 3.5
+          }, {
+            "x" : 0.5,
+            "y" : 3.0
+          }, {
+            "x" : 3.0,
+            "y" : 3.0
+          }, {
+            "x" : 3.0,
+            "y" : 2.5
+          }, {
+            "x" : 3.5,
+            "y" : 2.5
+          }, {
+            "x" : 3.5,
+            "y" : 3.0
+          }, {
+            "x" : 3.5,
+            "y" : 3.5
+          }, {
+            "x" : 3.0,
+            "y" : 3.5
+          } ]
+        },
+        "visible" : true
+      }, {
+        "id" : 6,
+        "shape" : {
+          "type" : "POLYGON",
+          "points" : [ {
+            "x" : 7.5,
+            "y" : 3.5
+          }, {
+            "x" : 5.0,
+            "y" : 3.5
+          }, {
+            "x" : 4.5,
+            "y" : 3.5
+          }, {
+            "x" : 4.5,
+            "y" : 3.0
+          }, {
+            "x" : 4.5,
+            "y" : 2.5
+          }, {
+            "x" : 5.0,
+            "y" : 2.5
+          }, {
+            "x" : 5.0,
+            "y" : 3.0
+          }, {
+            "x" : 7.5,
+            "y" : 3.0
+          } ]
+        },
+        "visible" : true
+      } ],
+      "measurementAreas" : [ ],
+      "stairs" : [ ],
+      "targets" : [ {
+        "id" : 8,
+        "shape" : {
+          "x" : 6.4,
+          "y" : 1.0,
+          "width" : 0.6,
+          "height" : 1.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "absorber" : {
+          "enabled" : true,
+          "deletionDistance" : 0.1
+        },
+        "waiter" : {
+          "enabled" : false,
+          "distribution" : null,
+          "individualWaiting" : true
+        },
+        "leavingSpeed" : -1.0,
+        "parallelEvents" : 0
+      } ],
+      "targetChangers" : [ ],
+      "absorbingAreas" : [ ],
+      "aerosolClouds" : [ ],
+      "droplets" : [ ],
+      "sources" : [ {
+        "id" : 1,
+        "shape" : {
+          "x" : 1.0,
+          "y" : 5.0,
+          "width" : 6.0,
+          "height" : 4.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "targetIds" : [ 8 ],
+        "spawner" : {
+          "type" : "org.vadere.state.attributes.spawner.AttributesRegularSpawner",
+          "constraintsElementsMax" : -1,
+          "constraintsTimeStart" : 0.0,
+          "constraintsTimeEnd" : 0.0,
+          "eventPositionRandom" : true,
+          "eventPositionGridCA" : false,
+          "eventPositionFreeSpace" : true,
+          "eventElementCount" : 20,
+          "eventElement" : null,
+          "distribution" : {
+            "type" : "org.vadere.state.attributes.distributions.AttributesConstantDistribution",
+            "updateFrequency" : 1.0
+          }
+        },
+        "groupSizeDistribution" : [ 1.0 ]
+      } ],
+      "dynamicElements" : [ ],
+      "attributesPedestrian" : {
+        "shape" : {
+          "x" : 0.0,
+          "y" : 0.0,
+          "width" : 1.0,
+          "height" : 1.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "radius" : 0.2,
+        "densityDependentSpeed" : false,
+        "speedDistributionMean" : 1.34,
+        "speedDistributionStandardDeviation" : 0.26,
+        "minimumSpeed" : 0.5,
+        "maximumSpeed" : 2.2,
+        "acceleration" : 2.0,
+        "footstepHistorySize" : 4,
+        "searchRadius" : 1.0,
+        "walkingDirectionSameIfAngleLessOrEqual" : 45.0,
+        "walkingDirectionCalculation" : "BY_TARGET_CENTER"
+      },
+      "teleporter" : null
+    },
+    "stimulusInfos" : [ ]
+  }
+}
diff --git a/notebooks/demo-data/vadere/bottleneck/postvis.traj b/notebooks/demo-data/vadere/bottleneck/postvis.traj
new file mode 100644
index 00000000..bd832234
--- /dev/null
+++ b/notebooks/demo-data/vadere/bottleneck/postvis.traj
@@ -0,0 +1,495 @@
+#IDXCOL=2,DATACOL=6,SEP=' '
+pedestrianId simTime endTime-PID1 startX-PID1 startY-PID1 endX-PID1 endY-PID1 targetId-PID2
+1 0.4 1.0683293133551417 2.556629741868144 7.096775246629484 2.4880996733806358 6.383746164613437 8
+2 0.4 1.1228955894556543 4.033198711877434 7.273571859422713 3.830065282451616 6.633436593130925 8
+3 0.4 0.9589899632551299 2.874990568393157 5.901051547816886 3.1418946843907762 5.1003391998240275 8
+4 0.4 0.9405647359179485 6.682443997985858 7.3875115817824675 6.682443997985858 6.565625494640466 8
+5 0.4 0.9705423320788561 6.161786542547321 7.257709027784086 5.778875227619709 6.5734459847095845 8
+6 0.4 0.9592050617723284 1.6156340783490035 7.974670220649376 1.9922150218237316 7.344783545885745 8
+7 0.4 0.9678513601082895 5.22444338821864 7.837887328680241 5.625461412600841 7.43686930429804 8
+8 0.4 1.0842289219391668 5.993488722286024 8.765665584066898 5.5266550411028925 8.29696956845795 8
+9 0.4 0.9944644208309537 1.2741014208098662 6.771428322484198 1.1626365562665977 7.040235366567192 8
+10 0.4 0.8649438339114517 3.5602674366800784 8.56930176610384 4.050339758303757 7.8466163397077064 8
+11 0.4 0.9340539716916719 2.256571265092003 5.30579029842332 2.2565712650920036 4.4893377038751305 8
+12 0.4 1.0589748434132438 2.8658748773759415 8.07554961073426 3.5065592801279593 7.656547759392701 8
+13 0.4 0.9532688802055487 5.076082742391011 6.435542259943723 5.413391046443367 5.631711257541063 8
+14 0.4 0.9605232222733574 2.8552316367139827 8.496707407724088 2.5747670034043693 8.272951750435013 8
+15 0.4 0.9806293136779161 1.4494421471783905 6.290887327331154 1.4578234277992967 5.542398245881677 8
+16 0.4 1.0496971486381375 5.030906623599764 5.763888154413127 4.57660265824458 5.208679340719964 8
+17 0.4 0.9743152122973685 4.4104519512736315 6.428887201364168 3.879018159151501 5.888100717434825 8
+18 0.4 1.0176206330509814 6.692035519636171 8.797641445755035 6.692035519636172 7.876807532235983 8
+19 0.4 0.9385115346207752 2.984772824696212 7.25309094051753 3.0705721243650608 6.522200654363935 8
+20 0.4 0.9629521396050456 4.564515259361686 7.505863901535077 4.845317387303051 6.707777171945942 8
+10 0.8649438339114517 1.3298876678229035 4.050339758303757 7.8466163397077064 4.517720521290628 7.462398627487666 8
+11 0.9340539716916719 1.4681079433833437 2.2565712650920036 4.4893377038751305 2.97096728532167 4.18316798091956 8
+19 0.9385115346207752 1.4770230692415502 3.0705721243650608 6.522200654363935 2.9350416689619188 5.754076021545039 8
+4 0.9405647359179485 1.4811294718358972 6.682443997985858 6.565625494640466 6.571674240140841 5.78479004399073 8
+13 0.9532688802055487 1.5065377604110974 5.413391046443367 5.631711257541063 5.413391046443367 4.759976680730929 8
+3 0.9589899632551299 1.5179799265102596 3.1418946843907762 5.1003391998240275 3.633934917263397 4.407765751087824 8
+6 0.9592050617723284 1.518410123544657 1.9922150218237316 7.344783545885745 2.6792695859571634 7.045405064297001 8
+14 0.9605232222733574 1.5210464445467147 2.5747670034043693 8.272951750435013 2.122243275564501 7.641703349368925 8
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+7 0.9678513601082895 1.535702720216579 5.625461412600841 7.43686930429804 5.08006060774587 6.89746556680659 8
+5 0.9705423320788561 1.541084664157712 5.778875227619709 6.5734459847095845 5.778875227619709 5.789330567684024 8
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+18 1.6352412661019629 2.2528618991529443 6.404274921661466 7.013525738311872 6.058962204091823 6.207796063982699 8
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+17 2.1229456368921054 2.697260849189474 3.763464673854365 4.459106477823001 3.9416680670073707 3.8460836131485987 8
+15 2.1418879410337484 2.7225172547116645 1.839253976688929 4.135063284460948 2.2148061557824454 4.175083956617047 8
+9 2.183393262492861 2.7778576833238144 1.5109395366028688 5.669693322228172 1.3867640417054752 4.97821575111104 8
+18 2.2528618991529443 2.870482532203926 6.058962204091823 6.207796063982699 6.4625169333052845 5.458862517552782 8
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diff --git a/notebooks/user_guide.ipynb b/notebooks/user_guide.ipynb
index 0417735f..4311eb06 100644
--- a/notebooks/user_guide.ipynb
+++ b/notebooks/user_guide.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -153,7 +153,18 @@
      "hide-input"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from pedpy import plot_walkable_area\n",
     "import matplotlib.pyplot as plt\n",
@@ -184,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -211,13 +222,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from pedpy import plot_measurement_setup\n",
     "import matplotlib.pyplot as plt\n",
@@ -265,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -375,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -385,7 +407,18 @@
      "hide-input"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from pedpy import plot_trajectories\n",
     "import matplotlib.pyplot as plt\n",
@@ -413,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -439,13 +472,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from pedpy import plot_trajectories\n",
@@ -476,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,13 +537,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from pedpy import plot_trajectories\n",
@@ -508,6 +563,61 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading from Vadere trajectory files\n",
+    "\n",
+    "It is also possible to load trajectory files from [Vadere](https://www.vadere.org/) directly into *PedPy*. \n",
+    "The expected format is a CSV file with space character as delimiter, and it should contain at least the following columns: `pedestrianId`, `simTime`, `startX`, `startY`.\n",
+    "Comment lines may start with a `#` and will be ignored.\n",
+    "\n",
+    "\n",
+    "To make the data usable for *PedPy* use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pedpy import (\n",
+    "    TrajectoryData,\n",
+    "    load_trajectory_from_vadere,\n",
+    ")\n",
+    "import pathlib\n",
+    "\n",
+    "vadere_file = pathlib.Path(\"demo-data/vadere/bottleneck/postvis.traj\")\n",
+    "\n",
+    "traj_vadere = load_trajectory_from_vadere(trajectory_file=vadere_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from pedpy import plot_trajectories\n",
+    "\n",
+    "plot_trajectories(traj=traj_vadere).set_aspect(\"equal\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2905,9 +3015,9 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-   "tags": [
-    "hide-input"
-   ]
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [],
    "source": [
@@ -4428,7 +4538,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,

From dade2839f097fdd9f63deb54e609e2d7a20f0f55 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Mon, 12 Aug 2024 16:44:38 +0200
Subject: [PATCH 14/22] Add draft for load_walkable_area_from_vadere_scenario

---
 pedpy/__init__.py             |   2 +
 pedpy/io/trajectory_loader.py | 152 ++++++++++++++++++++++++++++++++++
 2 files changed, 154 insertions(+)

diff --git a/pedpy/__init__.py b/pedpy/__init__.py
index 41c1cc23..9d843b04 100644
--- a/pedpy/__init__.py
+++ b/pedpy/__init__.py
@@ -23,6 +23,7 @@
     load_trajectory_from_vadere,
     load_walkable_area_from_jupedsim_sqlite,
     load_walkable_area_from_ped_data_archive_hdf5,
+    load_walkable_area_from_vadere_scenario,
 )
 from .methods.acceleration_calculator import (
     AccelerationCalculation,
@@ -101,6 +102,7 @@
     "load_trajectory_from_vadere",
     "load_walkable_area_from_jupedsim_sqlite",
     "load_walkable_area_from_ped_data_archive_hdf5",
+    "load_walkable_area_from_vadere_scenario",
     "compute_classic_density",
     "compute_passing_density",
     "compute_voronoi_density",
diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index 305c3ea0..ff079d80 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -1,4 +1,5 @@
 """Load trajectories to the internal trajectory data format."""
+import json
 import logging
 import math
 import pathlib
@@ -907,3 +908,154 @@ def _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(
             f"If smaller deviations are required, choose a higher frame rate. "
             f"The current frame rate is {str(frame_rate)} fps."
         )
+
+
+def load_walkable_area_from_vadere_scenario(
+        vadere_scenario_file: pathlib.Path,
+        bounding_box: bool = False,
+        margin: float = 1e-9,
+) -> WalkableArea:
+    """Loads the walkable area from the Vadere scenario file as :class:`~geometry.WalkableArea`.
+
+    .. note::
+        Obstacles in the scenario files are not allowed to overlap with other obstacles or the
+        bounding box. Merge overlapping obstacles in Vadere before loading the scenario into PedPy.
+
+    Args:
+        vadere_scenario_file: Vadere scenario file (json format)
+        bounding_box: Indicates whether the bounding box should be included as obstacle or not.
+                      Note that the bounding box obstacle will not be an exact representation
+                      of the original bounding box but slightly tighter.
+        margin: If bounding_box == False, increases the walkable area to prevent the topography
+                bound to coincide with obstacles that originally coincide with the topography bound.
+                If bounding_box == True, narrows down the outer and inner side of the bounding box
+                by the value of margin to prevent the bounding box to coincide with the topography
+                bound or obstacles that originally coincide with the bounding box.
+
+    Returns:
+        WalkableArea: :class:`~geometry.WalkableArea` used in the simulation
+    """
+    _validate_is_file(vadere_scenario_file)
+
+    with open(vadere_scenario_file, 'r') as f:
+        data = json.load(f)
+        topography = data["scenario"]["topography"]
+        scenario_attributes = topography["attributes"]
+
+        obstacles = topography["obstacles"]
+        obstacles_ = list()
+        for obstacle in obstacles:
+            obstacles_ += [_vadere_shape_to_point_list(obstacle["shape"])]
+
+        # bound
+        complete_area = scenario_attributes["bounds"]
+        bounding_box_with = scenario_attributes["boundingBoxWidth"]
+
+        if bounding_box:
+            bounding_box = _vadere_bounding_box_to_point_list(
+                scenario_attributes["bounds"],
+                box_with=bounding_box_with,
+                margin=margin,
+            )
+            obstacles_ += [bounding_box]
+        else:
+            complete_area["x"] = complete_area["x"] - margin + bounding_box_with
+            complete_area["y"] = complete_area["y"] - margin + bounding_box_with
+            complete_area["width"] = complete_area["width"] + 2 * (margin - bounding_box_with)
+            complete_area["height"] = complete_area["height"] + 2 * (margin - bounding_box_with)
+
+        complete_area["type"] = "RECTANGLE"
+        complete_area_points = _vadere_shape_to_point_list(complete_area)
+
+    return WalkableArea(polygon=complete_area_points, obstacles=obstacles_)
+
+
+def _vadere_shape_to_point_list(shape):
+    """Transforms dictionary describing a rectangle or polygon into a list of points (polygon).
+
+    Args:
+        shape: Dict containing the shape as RECTANGLE or POLYGON
+               * 'shape' RECTANGLE requires key value pairs for 'x', 'y', 'width', 'height'
+               * 'shape' POLYGON requires key value pair for 'points': [{'x': ..., 'y': ...},
+               {'x': ..., 'y': ...}, ...]
+
+    Returns:
+        list
+
+    """
+    _supported_types = ["RECTANGLE", "POLYGON"]
+
+    shape_type = shape["type"]
+    if shape_type not in _supported_types:
+        raise LoadTrajectoryError(
+            f"The given Vadere scenario contains an unsupported obstacle shape '{shape_type}'. "
+        )
+
+    if shape_type == "RECTANGLE":
+        # lower left corner (x1, y1)
+        x1 = shape["x"]
+        y1 = shape["y"]
+
+        # upper right corner (x2, y2)
+        x2 = x1 + shape["width"]
+        y2 = y1 + shape["height"]
+
+        points = [
+            shapely.Point(x1, y1),
+            shapely.Point(x2, y1),
+            shapely.Point(x2, y2),
+            shapely.Point(x1, y2),
+        ]
+
+    elif shape_type == "POLYGON":
+        points = [shapely.Point(p["x"], p["y"]) for p in shape["points"]]
+
+    return points
+
+
+def _vadere_bounding_box_to_point_list(
+        bounds: dict,
+        box_with: float,
+        margin: float,
+) -> list:
+    """
+    Creates a polygon (list of shapely.Point), which describes the bounding box of Vadere scenarios
+    from topography attributes.
+
+    Args:
+        bounds: dict containing a description of the outer border of the bounding box with keys
+                'x', 'y', 'width', 'height'. Keys 'x' and 'y' refer to the lower left corner of the
+                bounding box. Units are in m.
+        box_with: width of the bounding box (in m)
+        margin: prevents the resulting bounding box to coincide with the topography bound and to
+                produce overlapping areas within the bounding box
+
+    Returns:
+        list
+    """
+    # lower left corner (x1, y1)
+    x1_outer = bounds["x"] + margin
+    y1_outer = bounds["y"] + margin
+    x1_inner = bounds["x"] + box_with - margin
+    y1_inner = bounds["y"] + box_with - margin
+
+    # upper right corner (x2, y2)
+    x2_outer = bounds["x"] + bounds["width"] - margin
+    y2_outer = bounds["y"] + bounds["height"] - margin
+    x2_inner = bounds["x"] + bounds["width"] - box_with + margin
+    y2_inner = bounds["y"] + bounds["height"] - box_with + margin
+
+    points = [
+        shapely.Point(x1_outer, y1_outer),
+        shapely.Point(x2_outer, y1_outer),
+        shapely.Point(x2_outer, y2_outer),
+        shapely.Point(x1_outer, y2_outer),
+        shapely.Point(x1_outer, y1_inner + margin),
+        shapely.Point(x1_inner, y1_inner + margin),
+        shapely.Point(x1_inner, y2_inner),
+        shapely.Point(x2_inner, y2_inner),
+        shapely.Point(x2_inner, y1_inner),
+        shapely.Point(x1_outer, y1_inner),
+    ]
+
+    return points

From 533f721796986d6bdcd7461071e031d61cbe0137 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Mon, 12 Aug 2024 16:45:39 +0200
Subject: [PATCH 15/22] Update vadere scenario and trajectory files

---
 .../bottleneck/vadere_bottleneck.scenario     | 268 ++++++++
 .../vadere/bottleneck/vadere_postvis.traj     | 472 ++++++++++++++
 .../io/test-data/vadere_bottleneck.scenario   | 268 ++++++++
 .../io/test-data/vadere_postvis.traj          | 603 +++++++++---------
 4 files changed, 1298 insertions(+), 313 deletions(-)
 create mode 100644 notebooks/demo-data/vadere/bottleneck/vadere_bottleneck.scenario
 create mode 100644 notebooks/demo-data/vadere/bottleneck/vadere_postvis.traj
 create mode 100644 tests/unit_tests/io/test-data/vadere_bottleneck.scenario

diff --git a/notebooks/demo-data/vadere/bottleneck/vadere_bottleneck.scenario b/notebooks/demo-data/vadere/bottleneck/vadere_bottleneck.scenario
new file mode 100644
index 00000000..258a861d
--- /dev/null
+++ b/notebooks/demo-data/vadere/bottleneck/vadere_bottleneck.scenario
@@ -0,0 +1,268 @@
+{
+  "name" : "vadere_bottleneck",
+  "description" : "",
+  "release" : "3.0",
+  "commithash" : "d79a4bfd2394cfa6fc92372d2ebd1b117ab9a2da",
+  "processWriters" : {
+    "files" : [ {
+      "type" : "org.vadere.simulator.projects.dataprocessing.outputfile.EventtimePedestrianIdOutputFile",
+      "filename" : "vadere_postvis.traj",
+      "processors" : [ 1, 2 ]
+    } ],
+    "processors" : [ {
+      "type" : "org.vadere.simulator.projects.dataprocessing.processor.FootStepProcessor",
+      "id" : 1
+    }, {
+      "type" : "org.vadere.simulator.projects.dataprocessing.processor.FootStepTargetIDProcessor",
+      "id" : 2
+    } ],
+    "isTimestamped" : false,
+    "isWriteMetaData" : true
+  },
+  "scenario" : {
+    "mainModel" : "org.vadere.simulator.models.osm.OptimalStepsModel",
+    "attributesModel" : {
+      "org.vadere.state.attributes.models.AttributesOSM" : {
+        "stepCircleResolution" : 4,
+        "numberOfCircles" : 1,
+        "optimizationType" : "NELDER_MEAD",
+        "varyStepDirection" : true,
+        "movementType" : "ARBITRARY",
+        "stepLengthIntercept" : 0.4625,
+        "stepLengthSlopeSpeed" : 0.2345,
+        "stepLengthSD" : 0.036,
+        "movementThreshold" : 0.0,
+        "minStepLength" : 0.1,
+        "minimumStepLength" : true,
+        "maxStepDuration" : 1.7976931348623157E308,
+        "dynamicStepLength" : true,
+        "updateType" : "EVENT_DRIVEN",
+        "seeSmallWalls" : true,
+        "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid",
+        "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell",
+        "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell",
+        "submodels" : [ ]
+      },
+      "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : {
+        "pedPotentialIntimateSpaceWidth" : 0.45,
+        "pedPotentialPersonalSpaceWidth" : 1.2,
+        "pedPotentialHeight" : 50.0,
+        "obstPotentialWidth" : 0.8,
+        "obstPotentialHeight" : 6.0,
+        "intimateSpaceFactor" : 1.2,
+        "personalSpacePower" : 1,
+        "intimateSpacePower" : 1
+      },
+      "org.vadere.state.attributes.models.AttributesFloorField" : {
+        "createMethod" : "HIGH_ACCURACY_FAST_MARCHING",
+        "potentialFieldResolution" : 0.1,
+        "obstacleGridPenalty" : 0.1,
+        "targetAttractionStrength" : 1.0,
+        "cacheType" : "NO_CACHE",
+        "cacheDir" : "",
+        "timeCostAttributes" : {
+          "standardDeviation" : 0.7,
+          "type" : "UNIT",
+          "obstacleDensityWeight" : 3.5,
+          "pedestrianSameTargetDensityWeight" : 3.5,
+          "pedestrianOtherTargetDensityWeight" : 3.5,
+          "pedestrianWeight" : 3.5,
+          "queueWidthLoading" : 1.0,
+          "pedestrianDynamicWeight" : 6.0,
+          "loadingType" : "CONSTANT",
+          "width" : 0.2,
+          "height" : 1.0
+        }
+      }
+    },
+    "attributesSimulation" : {
+      "finishTime" : 50.0,
+      "simTimeStepLength" : 0.4,
+      "realTimeSimTimeRatio" : 0.1,
+      "writeSimulationData" : true,
+      "visualizationEnabled" : true,
+      "printFPS" : false,
+      "digitsPerCoordinate" : 2,
+      "useFixedSeed" : true,
+      "fixedSeed" : 98957596352036733,
+      "simulationSeed" : 98957596352036733
+    },
+    "attributesPsychology" : {
+      "usePsychologyLayer" : false,
+      "psychologyLayer" : {
+        "perception" : "SimplePerceptionModel",
+        "cognition" : "SimpleCognitionModel",
+        "attributesModel" : {
+          "org.vadere.state.attributes.models.psychology.perception.AttributesSimplePerceptionModel" : {
+            "priority" : {
+              "1" : "InformationStimulus",
+              "2" : "ChangeTargetScripted",
+              "3" : "ChangeTarget",
+              "4" : "Threat",
+              "5" : "Wait",
+              "6" : "WaitInArea",
+              "7" : "DistanceRecommendation"
+            }
+          },
+          "org.vadere.state.attributes.models.psychology.cognition.AttributesSimpleCognitionModel" : { }
+        }
+      }
+    },
+    "topography" : {
+      "attributes" : {
+        "bounds" : {
+          "x" : 0.0,
+          "y" : 0.0,
+          "width" : 8.0,
+          "height" : 10.0
+        },
+        "boundingBoxWidth" : 0.5,
+        "bounded" : true,
+        "referenceCoordinateSystem" : null
+      },
+      "obstacles" : [ {
+        "id" : 5,
+        "shape" : {
+          "type" : "POLYGON",
+          "points" : [ {
+            "x" : 0.5,
+            "y" : 3.5
+          }, {
+            "x" : 0.5,
+            "y" : 3.0
+          }, {
+            "x" : 3.0,
+            "y" : 3.0
+          }, {
+            "x" : 3.0,
+            "y" : 2.5
+          }, {
+            "x" : 3.5,
+            "y" : 2.5
+          }, {
+            "x" : 3.5,
+            "y" : 3.0
+          }, {
+            "x" : 3.5,
+            "y" : 3.5
+          }, {
+            "x" : 3.0,
+            "y" : 3.5
+          } ]
+        },
+        "visible" : true
+      }, {
+        "id" : 6,
+        "shape" : {
+          "type" : "POLYGON",
+          "points" : [ {
+            "x" : 7.5,
+            "y" : 3.5
+          }, {
+            "x" : 5.0,
+            "y" : 3.5
+          }, {
+            "x" : 4.5,
+            "y" : 3.5
+          }, {
+            "x" : 4.5,
+            "y" : 3.0
+          }, {
+            "x" : 4.5,
+            "y" : 2.5
+          }, {
+            "x" : 5.0,
+            "y" : 2.5
+          }, {
+            "x" : 5.0,
+            "y" : 3.0
+          }, {
+            "x" : 7.5,
+            "y" : 3.0
+          } ]
+        },
+        "visible" : true
+      } ],
+      "measurementAreas" : [ ],
+      "stairs" : [ ],
+      "targets" : [ {
+        "id" : 8,
+        "shape" : {
+          "x" : 6.4,
+          "y" : 1.0,
+          "width" : 0.6,
+          "height" : 1.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "absorber" : {
+          "enabled" : true,
+          "deletionDistance" : 0.1
+        },
+        "waiter" : {
+          "enabled" : false,
+          "distribution" : null,
+          "individualWaiting" : true
+        },
+        "leavingSpeed" : -1.0,
+        "parallelEvents" : 0
+      } ],
+      "targetChangers" : [ ],
+      "absorbingAreas" : [ ],
+      "aerosolClouds" : [ ],
+      "droplets" : [ ],
+      "sources" : [ {
+        "id" : 1,
+        "shape" : {
+          "x" : 1.0,
+          "y" : 5.0,
+          "width" : 6.0,
+          "height" : 4.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "targetIds" : [ 8 ],
+        "spawner" : {
+          "type" : "org.vadere.state.attributes.spawner.AttributesRegularSpawner",
+          "constraintsElementsMax" : -1,
+          "constraintsTimeStart" : 0.0,
+          "constraintsTimeEnd" : 0.0,
+          "eventPositionRandom" : true,
+          "eventPositionGridCA" : false,
+          "eventPositionFreeSpace" : true,
+          "eventElementCount" : 20,
+          "eventElement" : null,
+          "distribution" : {
+            "type" : "org.vadere.state.attributes.distributions.AttributesConstantDistribution",
+            "updateFrequency" : 1.0
+          }
+        },
+        "groupSizeDistribution" : [ 1.0 ]
+      } ],
+      "dynamicElements" : [ ],
+      "attributesPedestrian" : {
+        "shape" : {
+          "x" : 0.0,
+          "y" : 0.0,
+          "width" : 1.0,
+          "height" : 1.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "radius" : 0.2,
+        "densityDependentSpeed" : false,
+        "speedDistributionMean" : 1.34,
+        "speedDistributionStandardDeviation" : 0.26,
+        "minimumSpeed" : 0.5,
+        "maximumSpeed" : 2.2,
+        "acceleration" : 2.0,
+        "footstepHistorySize" : 4,
+        "searchRadius" : 1.0,
+        "walkingDirectionSameIfAngleLessOrEqual" : 45.0,
+        "walkingDirectionCalculation" : "BY_TARGET_CENTER"
+      },
+      "teleporter" : null
+    },
+    "stimulusInfos" : [ ]
+  }
+}
diff --git a/notebooks/demo-data/vadere/bottleneck/vadere_postvis.traj b/notebooks/demo-data/vadere/bottleneck/vadere_postvis.traj
new file mode 100644
index 00000000..48a5c58e
--- /dev/null
+++ b/notebooks/demo-data/vadere/bottleneck/vadere_postvis.traj
@@ -0,0 +1,472 @@
+#IDXCOL=2,DATACOL=6,SEP=' '
+pedestrianId simTime endTime-PID1 startX-PID1 startY-PID1 endX-PID1 endY-PID1 targetId-PID2
+1 0.4 1.0683293133551417 2.556629741868144 7.096775246629484 2.4880996733806358 6.383746164613437 8
+2 0.4 1.1228955894556543 4.033198711877434 7.273571859422713 3.830065282451616 6.633436593130925 8
+3 0.4 0.9589899632551299 2.874990568393157 5.901051547816886 3.1418946843907762 5.1003391998240275 8
+4 0.4 0.9405647359179485 6.682443997985858 7.3875115817824675 6.682443997985858 6.565625494640466 8
+5 0.4 0.9705423320788561 6.161786542547321 7.257709027784086 5.778875227619709 6.5734459847095845 8
+6 0.4 0.9592050617723284 1.6156340783490035 7.974670220649376 1.9922150218237316 7.344783545885745 8
+7 0.4 0.9678513601082895 5.22444338821864 7.837887328680241 5.625461412600841 7.43686930429804 8
+8 0.4 1.0842289219391668 5.993488722286024 8.765665584066898 5.5266550411028925 8.29696956845795 8
+9 0.4 0.9944644208309537 1.2741014208098662 6.771428322484198 1.1626365562665977 7.040235366567192 8
+10 0.4 0.8649438339114517 3.5602674366800784 8.56930176610384 4.050339758303757 7.8466163397077064 8
+11 0.4 0.9340539716916719 2.256571265092003 5.30579029842332 2.2565712650920036 4.4893377038751305 8
+12 0.4 1.0589748434132438 2.8658748773759415 8.07554961073426 3.5065592801279593 7.656547759392701 8
+13 0.4 0.9532688802055487 5.076082742391011 6.435542259943723 5.413391046443367 5.631711257541063 8
+14 0.4 0.9605232222733574 2.8552316367139827 8.496707407724088 2.5747670034043693 8.272951750435013 8
+15 0.4 0.9806293136779161 1.4494421471783905 6.290887327331154 1.4578234277992967 5.542398245881677 8
+16 0.4 1.0496971486381375 5.030906623599764 5.763888154413127 4.57660265824458 5.208679340719964 8
+17 0.4 0.9743152122973685 4.4104519512736315 6.428887201364168 3.879018159151501 5.888100717434825 8
+18 0.4 1.0176206330509814 6.692035519636171 8.797641445755035 6.692035519636172 7.876807532235983 8
+19 0.4 0.9385115346207752 2.984772824696212 7.25309094051753 3.0705721243650608 6.522200654363935 8
+20 0.4 0.9629521396050456 4.564515259361686 7.505863901535077 4.845317387303051 6.707777171945942 8
+10 0.8649438339114517 1.3298876678229035 4.050339758303757 7.8466163397077064 4.517720521290628 7.462398627487666 8
+11 0.9340539716916719 1.4681079433833437 2.2565712650920036 4.4893377038751305 2.97096728532167 4.18316798091956 8
+19 0.9385115346207752 1.4770230692415502 3.0705721243650608 6.522200654363935 2.9350416689619188 5.754076021545039 8
+4 0.9405647359179485 1.4811294718358972 6.682443997985858 6.565625494640466 6.571674240140841 5.78479004399073 8
+13 0.9532688802055487 1.5065377604110974 5.413391046443367 5.631711257541063 5.413391046443367 4.759976680730929 8
+3 0.9589899632551299 1.5179799265102596 3.1418946843907762 5.1003391998240275 3.633934917263397 4.407765751087824 8
+6 0.9592050617723284 1.518410123544657 1.9922150218237316 7.344783545885745 2.6792695859571634 7.045405064297001 8
+14 0.9605232222733574 1.5210464445467147 2.5747670034043693 8.272951750435013 2.122243275564501 7.641703349368925 8
+20 0.9629521396050456 1.5259042792100912 4.845317387303051 6.707777171945942 5.0649887331873416 5.793532144956444 8
+7 0.9678513601082895 1.535702720216579 5.625461412600841 7.43686930429804 5.08006060774587 6.89746556680659 8
+5 0.9705423320788561 1.541084664157712 5.778875227619709 6.5734459847095845 5.778875227619709 5.789330567684024 8
+17 0.9743152122973685 1.5486304245947369 3.879018159151501 5.888100717434825 3.8069809759124906 5.191326363185752 8
+15 0.9806293136779161 1.5612586273558322 1.4578234277992967 5.542398245881677 1.839253976688929 4.8904204011195915 8
+9 0.9944644208309537 1.5889288416619074 1.1626365562665977 7.040235366567192 1.5109395366028688 6.4301158546845345 8
+18 1.0176206330509814 1.6352412661019629 6.692035519636172 7.876807532235983 6.404274921661466 7.013525738311872 8
+16 1.0496971486381375 1.699394297276275 4.57660265824458 5.208679340719964 4.576602658244581 4.477912181883949 8
+12 1.0589748434132438 1.7179496868264876 3.5065592801279593 7.656547759392701 3.5065592801279593 7.656547759392701 8
+1 1.0683293133551417 1.7366586267102833 2.4880996733806358 6.383746164613437 2.2552178879918254 5.862093002994459 8
+8 1.0842289219391668 1.7684578438783336 5.5266550411028925 8.29696956845795 5.635107838453344 7.589205782993118 8
+2 1.1228955894556543 1.8457911789113086 3.830065282451616 6.633436593130925 4.060287175395098 5.967929058473601 8
+10 1.3298876678229035 1.7948315017343552 4.517720521290628 7.462398627487666 4.085924469911161 6.703453783668246 8
+11 1.4681079433833437 2.002161915075016 2.97096728532167 4.18316798091956 2.658612393595044 4.190582537897392 8
+19 1.4770230692415502 2.015534603862325 2.9350416689619188 5.754076021545039 2.9350416689619188 4.974086306301981 8
+4 1.4811294718358972 2.021694207753846 6.571674240140841 5.78479004399073 6.571674240140841 4.962903956848729 8
+13 1.5065377604110974 2.059806640616646 5.413391046443367 4.759976680730929 5.325751373304048 4.1493824020410335 8
+3 1.5179799265102596 2.0769698897653894 3.633934917263397 4.407765751087824 3.954007311104009 3.7123325107892757 8
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diff --git a/tests/unit_tests/io/test-data/vadere_bottleneck.scenario b/tests/unit_tests/io/test-data/vadere_bottleneck.scenario
new file mode 100644
index 00000000..258a861d
--- /dev/null
+++ b/tests/unit_tests/io/test-data/vadere_bottleneck.scenario
@@ -0,0 +1,268 @@
+{
+  "name" : "vadere_bottleneck",
+  "description" : "",
+  "release" : "3.0",
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+        "movementType" : "ARBITRARY",
+        "stepLengthIntercept" : 0.4625,
+        "stepLengthSlopeSpeed" : 0.2345,
+        "stepLengthSD" : 0.036,
+        "movementThreshold" : 0.0,
+        "minStepLength" : 0.1,
+        "minimumStepLength" : true,
+        "maxStepDuration" : 1.7976931348623157E308,
+        "dynamicStepLength" : true,
+        "updateType" : "EVENT_DRIVEN",
+        "seeSmallWalls" : true,
+        "targetPotentialModel" : "org.vadere.simulator.models.potential.fields.PotentialFieldTargetGrid",
+        "pedestrianPotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldPedestrianCompactSoftshell",
+        "obstaclePotentialModel" : "org.vadere.simulator.models.potential.PotentialFieldObstacleCompactSoftshell",
+        "submodels" : [ ]
+      },
+      "org.vadere.state.attributes.models.AttributesPotentialCompactSoftshell" : {
+        "pedPotentialIntimateSpaceWidth" : 0.45,
+        "pedPotentialPersonalSpaceWidth" : 1.2,
+        "pedPotentialHeight" : 50.0,
+        "obstPotentialWidth" : 0.8,
+        "obstPotentialHeight" : 6.0,
+        "intimateSpaceFactor" : 1.2,
+        "personalSpacePower" : 1,
+        "intimateSpacePower" : 1
+      },
+      "org.vadere.state.attributes.models.AttributesFloorField" : {
+        "createMethod" : "HIGH_ACCURACY_FAST_MARCHING",
+        "potentialFieldResolution" : 0.1,
+        "obstacleGridPenalty" : 0.1,
+        "targetAttractionStrength" : 1.0,
+        "cacheType" : "NO_CACHE",
+        "cacheDir" : "",
+        "timeCostAttributes" : {
+          "standardDeviation" : 0.7,
+          "type" : "UNIT",
+          "obstacleDensityWeight" : 3.5,
+          "pedestrianSameTargetDensityWeight" : 3.5,
+          "pedestrianOtherTargetDensityWeight" : 3.5,
+          "pedestrianWeight" : 3.5,
+          "queueWidthLoading" : 1.0,
+          "pedestrianDynamicWeight" : 6.0,
+          "loadingType" : "CONSTANT",
+          "width" : 0.2,
+          "height" : 1.0
+        }
+      }
+    },
+    "attributesSimulation" : {
+      "finishTime" : 50.0,
+      "simTimeStepLength" : 0.4,
+      "realTimeSimTimeRatio" : 0.1,
+      "writeSimulationData" : true,
+      "visualizationEnabled" : true,
+      "printFPS" : false,
+      "digitsPerCoordinate" : 2,
+      "useFixedSeed" : true,
+      "fixedSeed" : 98957596352036733,
+      "simulationSeed" : 98957596352036733
+    },
+    "attributesPsychology" : {
+      "usePsychologyLayer" : false,
+      "psychologyLayer" : {
+        "perception" : "SimplePerceptionModel",
+        "cognition" : "SimpleCognitionModel",
+        "attributesModel" : {
+          "org.vadere.state.attributes.models.psychology.perception.AttributesSimplePerceptionModel" : {
+            "priority" : {
+              "1" : "InformationStimulus",
+              "2" : "ChangeTargetScripted",
+              "3" : "ChangeTarget",
+              "4" : "Threat",
+              "5" : "Wait",
+              "6" : "WaitInArea",
+              "7" : "DistanceRecommendation"
+            }
+          },
+          "org.vadere.state.attributes.models.psychology.cognition.AttributesSimpleCognitionModel" : { }
+        }
+      }
+    },
+    "topography" : {
+      "attributes" : {
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+          "x" : 0.0,
+          "y" : 0.0,
+          "width" : 8.0,
+          "height" : 10.0
+        },
+        "boundingBoxWidth" : 0.5,
+        "bounded" : true,
+        "referenceCoordinateSystem" : null
+      },
+      "obstacles" : [ {
+        "id" : 5,
+        "shape" : {
+          "type" : "POLYGON",
+          "points" : [ {
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+            "y" : 3.5
+          }, {
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+            "y" : 3.0
+          }, {
+            "x" : 3.0,
+            "y" : 3.0
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+            "x" : 3.0,
+            "y" : 2.5
+          }, {
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+            "y" : 2.5
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+            "y" : 3.0
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+            "y" : 3.5
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+            "y" : 3.5
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+        },
+        "visible" : true
+      }, {
+        "id" : 6,
+        "shape" : {
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+          "points" : [ {
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+            "y" : 3.5
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+            "y" : 3.5
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+            "y" : 3.5
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+            "y" : 3.0
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+            "y" : 2.5
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+            "y" : 3.0
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+            "y" : 3.0
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+        },
+        "visible" : true
+      } ],
+      "measurementAreas" : [ ],
+      "stairs" : [ ],
+      "targets" : [ {
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+        "shape" : {
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+          "y" : 1.0,
+          "width" : 0.6,
+          "height" : 1.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "absorber" : {
+          "enabled" : true,
+          "deletionDistance" : 0.1
+        },
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+          "enabled" : false,
+          "distribution" : null,
+          "individualWaiting" : true
+        },
+        "leavingSpeed" : -1.0,
+        "parallelEvents" : 0
+      } ],
+      "targetChangers" : [ ],
+      "absorbingAreas" : [ ],
+      "aerosolClouds" : [ ],
+      "droplets" : [ ],
+      "sources" : [ {
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+        "shape" : {
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+          "y" : 5.0,
+          "width" : 6.0,
+          "height" : 4.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "targetIds" : [ 8 ],
+        "spawner" : {
+          "type" : "org.vadere.state.attributes.spawner.AttributesRegularSpawner",
+          "constraintsElementsMax" : -1,
+          "constraintsTimeStart" : 0.0,
+          "constraintsTimeEnd" : 0.0,
+          "eventPositionRandom" : true,
+          "eventPositionGridCA" : false,
+          "eventPositionFreeSpace" : true,
+          "eventElementCount" : 20,
+          "eventElement" : null,
+          "distribution" : {
+            "type" : "org.vadere.state.attributes.distributions.AttributesConstantDistribution",
+            "updateFrequency" : 1.0
+          }
+        },
+        "groupSizeDistribution" : [ 1.0 ]
+      } ],
+      "dynamicElements" : [ ],
+      "attributesPedestrian" : {
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+          "y" : 0.0,
+          "width" : 1.0,
+          "height" : 1.0,
+          "type" : "RECTANGLE"
+        },
+        "visible" : true,
+        "radius" : 0.2,
+        "densityDependentSpeed" : false,
+        "speedDistributionMean" : 1.34,
+        "speedDistributionStandardDeviation" : 0.26,
+        "minimumSpeed" : 0.5,
+        "maximumSpeed" : 2.2,
+        "acceleration" : 2.0,
+        "footstepHistorySize" : 4,
+        "searchRadius" : 1.0,
+        "walkingDirectionSameIfAngleLessOrEqual" : 45.0,
+        "walkingDirectionCalculation" : "BY_TARGET_CENTER"
+      },
+      "teleporter" : null
+    },
+    "stimulusInfos" : [ ]
+  }
+}
diff --git a/tests/unit_tests/io/test-data/vadere_postvis.traj b/tests/unit_tests/io/test-data/vadere_postvis.traj
index bd832234..48a5c58e 100644
--- a/tests/unit_tests/io/test-data/vadere_postvis.traj
+++ b/tests/unit_tests/io/test-data/vadere_postvis.traj
@@ -160,336 +160,313 @@ pedestrianId simTime endTime-PID1 startX-PID1 startY-PID1 endX-PID1 endY-PID1 ta
 18 4.723344431356869 5.34096506440785 5.817988045893175 4.941587670495053 6.108998180597757 5.079543540009666 8
 4 4.7245178873435885 5.265082623261537 6.408582297219672 4.1573620532882805 6.408582297219672 4.1573620532882805 8
 2 4.737373536733926 5.46026912618958 4.084779303663541 4.95878667366747 4.084779303663541 4.95878667366747 8
-13 4.826151041644389 5.379419921849937 4.720644842072647 3.7201693254799197 4.263817890547763 2.98545599835664 8
+13 4.826151041644389 5.379419921849937 4.720644842072647 3.7201693254799197 4.720644842072647 3.7201693254799197 8
 6 4.873640494178628 5.432845555950956 3.3042243780702374 5.159747694791196 3.3042243780702374 5.159747694791196 8
 14 4.884185778186859 5.444709000460216 2.010412395144811 5.318152745449369 2.163219389200398 5.063213003845942 8
-20 4.903617116840365 5.4665692564454105 4.39276784241817 4.241357495750232 4.701292504812606 3.9915994357166404 8
-7 4.9428108808663165 5.5106622409746056 4.965514372901494 4.906406944636389 4.837726051578626 4.702600252907104 8
-16 4.947880040466963 5.597577189105101 4.055555666932992 3.4522100744449977 3.951008591217292 3.2886831698686785 8
+20 4.903617116840365 5.4665692564454105 4.39276784241817 4.241357495750232 4.39276784241817 4.241357495750232 8
+7 4.9428108808663165 5.5106622409746056 4.965514372901494 4.906406944636389 5.169321064630779 4.7786186233135215 8
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 5 4.964338656630849 5.5348809887097055 5.4532429862509195 4.137536829186787 5.4532429862509195 4.137536829186787 8
 17 4.994521698378947 5.568836910676316 5.031468028438167 1.7640939533806073 5.789671099264161 1.7640939533806073 8
 12 5.0128239038927065 5.67179874730595 2.8518798836188792 6.018510181520128 2.8518798836188792 6.018510181520128 8
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From 241168e66feb1f8ffac3cac3f2ce537ad21e9635 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Mon, 12 Aug 2024 16:48:04 +0200
Subject: [PATCH 16/22] Add walkable area for Vadere to user guide jupyter
 notebook

---
 notebooks/user_guide.ipynb | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/notebooks/user_guide.ipynb b/notebooks/user_guide.ipynb
index 4311eb06..18b64f3c 100644
--- a/notebooks/user_guide.ipynb
+++ b/notebooks/user_guide.ipynb
@@ -579,19 +579,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "from pedpy import (\n",
     "    TrajectoryData,\n",
     "    load_trajectory_from_vadere,\n",
+    "    load_walkable_area_from_vadere_scenario,\n",
     ")\n",
     "import pathlib\n",
     "\n",
-    "vadere_file = pathlib.Path(\"demo-data/vadere/bottleneck/postvis.traj\")\n",
+    "vadere_traj_file = pathlib.Path(\"demo-data/vadere/bottleneck/vadere_postvis.traj\")\n",
+    "vadere_scenario_file = pathlib.Path(\"demo-data/vadere/bottleneck/vadere_bottleneck.scenario\")\n",
     "\n",
-    "traj_vadere = load_trajectory_from_vadere(trajectory_file=vadere_file)"
+    "traj_vadere = load_trajectory_from_vadere(trajectory_file=vadere_traj_file)\n",
+    "vadere_walkable_area = load_walkable_area_from_vadere_scenario(vadere_scenario_file=vadere_scenario_file, bounding_box=False)"
    ]
   },
   {
@@ -601,7 +604,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -614,7 +617,7 @@
     "import matplotlib.pyplot as plt\n",
     "from pedpy import plot_trajectories\n",
     "\n",
-    "plot_trajectories(traj=traj_vadere).set_aspect(\"equal\")\n",
+    "plot_trajectories(traj=traj_vadere, walkable_area=vadere_walkable_area).set_aspect(\"equal\")\n",
     "plt.show()"
    ]
   },

From 420289e9aae3ff6583e3a3f10994cb6ffb0c5322 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Tue, 13 Aug 2024 11:23:48 +0200
Subject: [PATCH 17/22] Simplify the way how the Vadere bounding box is loaded
 to PedPy

---
 notebooks/user_guide.ipynb    |   6 +-
 pedpy/io/trajectory_loader.py | 110 ++++++++++------------------------
 2 files changed, 36 insertions(+), 80 deletions(-)

diff --git a/notebooks/user_guide.ipynb b/notebooks/user_guide.ipynb
index 18b64f3c..441ceaaf 100644
--- a/notebooks/user_guide.ipynb
+++ b/notebooks/user_guide.ipynb
@@ -579,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -594,12 +594,12 @@
     "vadere_scenario_file = pathlib.Path(\"demo-data/vadere/bottleneck/vadere_bottleneck.scenario\")\n",
     "\n",
     "traj_vadere = load_trajectory_from_vadere(trajectory_file=vadere_traj_file)\n",
-    "vadere_walkable_area = load_walkable_area_from_vadere_scenario(vadere_scenario_file=vadere_scenario_file, bounding_box=False)"
+    "vadere_walkable_area = load_walkable_area_from_vadere_scenario(vadere_scenario_file=vadere_scenario_file, margin=1e-9)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index ff079d80..a197d408 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -912,8 +912,7 @@ def _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(
 
 def load_walkable_area_from_vadere_scenario(
         vadere_scenario_file: pathlib.Path,
-        bounding_box: bool = False,
-        margin: float = 1e-9,
+        margin: float = 0,
 ) -> WalkableArea:
     """Loads the walkable area from the Vadere scenario file as :class:`~geometry.WalkableArea`.
 
@@ -923,14 +922,13 @@ def load_walkable_area_from_vadere_scenario(
 
     Args:
         vadere_scenario_file: Vadere scenario file (json format)
-        bounding_box: Indicates whether the bounding box should be included as obstacle or not.
-                      Note that the bounding box obstacle will not be an exact representation
-                      of the original bounding box but slightly tighter.
-        margin: If bounding_box == False, increases the walkable area to prevent the topography
-                bound to coincide with obstacles that originally coincide with the topography bound.
-                If bounding_box == True, narrows down the outer and inner side of the bounding box
-                by the value of margin to prevent the bounding box to coincide with the topography
-                bound or obstacles that originally coincide with the bounding box.
+        margin: Increases the walkable area by the value of margin to avoid that the topography
+                bound touches obstacles because shapely Polygons used in PedPy do not allow this.
+                By default (margin = .0), the bound of the walkable area in PedPy coincides with the
+                inner bound of the bounding box (obstacle) in Vadere. PedPy cannot process the case
+                where obstacles touch the bounding box defined in Vadere. To avoid errors, either
+                increase the value of margin (e.g. to 1e-9) or make sure that the obstacles in
+                Vadere do not touch the bounding box.
 
     Returns:
         WalkableArea: :class:`~geometry.WalkableArea` used in the simulation
@@ -942,30 +940,36 @@ def load_walkable_area_from_vadere_scenario(
         topography = data["scenario"]["topography"]
         scenario_attributes = topography["attributes"]
 
-        obstacles = topography["obstacles"]
-        obstacles_ = list()
-        for obstacle in obstacles:
-            obstacles_ += [_vadere_shape_to_point_list(obstacle["shape"])]
-
         # bound
         complete_area = scenario_attributes["bounds"]
         bounding_box_with = scenario_attributes["boundingBoxWidth"]
-
-        if bounding_box:
-            bounding_box = _vadere_bounding_box_to_point_list(
-                scenario_attributes["bounds"],
-                box_with=bounding_box_with,
-                margin=margin,
-            )
-            obstacles_ += [bounding_box]
-        else:
-            complete_area["x"] = complete_area["x"] - margin + bounding_box_with
-            complete_area["y"] = complete_area["y"] - margin + bounding_box_with
-            complete_area["width"] = complete_area["width"] + 2 * (margin - bounding_box_with)
-            complete_area["height"] = complete_area["height"] + 2 * (margin - bounding_box_with)
-
+        complete_area["x"] = complete_area["x"] + bounding_box_with - margin
+        complete_area["y"] = complete_area["y"] + bounding_box_with - margin
+        complete_area["width"] = complete_area["width"] - 2 * (bounding_box_with - margin)
+        complete_area["height"] = complete_area["height"] - 2 * (bounding_box_with - margin)
         complete_area["type"] = "RECTANGLE"
         complete_area_points = _vadere_shape_to_point_list(complete_area)
+        area_poly = shapely.Polygon(complete_area_points)
+
+        # obstacles
+        obstacles = topography["obstacles"]
+        obstacles_ = list()
+        error_obst_ids = list()
+        for obstacle in obstacles:
+            obst_points = _vadere_shape_to_point_list(obstacle["shape"])
+            if area_poly.contains_properly(shapely.Polygon(obst_points)):
+                obstacles_ += [obst_points]
+            else:
+                error_obst_ids += [str(obstacle["id"])]
+
+        if error_obst_ids:
+            error_obst_ids = {", ".join(error_obst_ids)}
+            raise LoadTrajectoryError(
+                f"Cannot convert obstacles with IDs {error_obst_ids} because they touch the bound "
+                f"of the walkable area (inner bound of the bounding box in Vadere). Increase "
+                f"the walkable area or adapt the scenario file to make sure that obstacles have "
+                f"no common points with the bounding box."
+            )
 
     return WalkableArea(polygon=complete_area_points, obstacles=obstacles_)
 
@@ -1011,51 +1015,3 @@ def _vadere_shape_to_point_list(shape):
         points = [shapely.Point(p["x"], p["y"]) for p in shape["points"]]
 
     return points
-
-
-def _vadere_bounding_box_to_point_list(
-        bounds: dict,
-        box_with: float,
-        margin: float,
-) -> list:
-    """
-    Creates a polygon (list of shapely.Point), which describes the bounding box of Vadere scenarios
-    from topography attributes.
-
-    Args:
-        bounds: dict containing a description of the outer border of the bounding box with keys
-                'x', 'y', 'width', 'height'. Keys 'x' and 'y' refer to the lower left corner of the
-                bounding box. Units are in m.
-        box_with: width of the bounding box (in m)
-        margin: prevents the resulting bounding box to coincide with the topography bound and to
-                produce overlapping areas within the bounding box
-
-    Returns:
-        list
-    """
-    # lower left corner (x1, y1)
-    x1_outer = bounds["x"] + margin
-    y1_outer = bounds["y"] + margin
-    x1_inner = bounds["x"] + box_with - margin
-    y1_inner = bounds["y"] + box_with - margin
-
-    # upper right corner (x2, y2)
-    x2_outer = bounds["x"] + bounds["width"] - margin
-    y2_outer = bounds["y"] + bounds["height"] - margin
-    x2_inner = bounds["x"] + bounds["width"] - box_with + margin
-    y2_inner = bounds["y"] + bounds["height"] - box_with + margin
-
-    points = [
-        shapely.Point(x1_outer, y1_outer),
-        shapely.Point(x2_outer, y1_outer),
-        shapely.Point(x2_outer, y2_outer),
-        shapely.Point(x1_outer, y2_outer),
-        shapely.Point(x1_outer, y1_inner + margin),
-        shapely.Point(x1_inner, y1_inner + margin),
-        shapely.Point(x1_inner, y2_inner),
-        shapely.Point(x2_inner, y2_inner),
-        shapely.Point(x2_inner, y1_inner),
-        shapely.Point(x1_outer, y1_inner),
-    ]
-
-    return points

From 2b84da75ccc2c1af6eab1992c060c4b75381fdd1 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Tue, 13 Aug 2024 14:44:36 +0200
Subject: [PATCH 18/22] Update jupyter notebook

---
 notebooks/user_guide.ipynb | 108 ++++++++-----------------------------
 1 file changed, 21 insertions(+), 87 deletions(-)

diff --git a/notebooks/user_guide.ipynb b/notebooks/user_guide.ipynb
index 441ceaaf..b7f6ec31 100644
--- a/notebooks/user_guide.ipynb
+++ b/notebooks/user_guide.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -153,18 +153,7 @@
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pedpy import plot_walkable_area\n",
     "import matplotlib.pyplot as plt\n",
@@ -195,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -222,24 +211,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUgAAAGwCAYAAADL1fxHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAZ+klEQVR4nO3de3BUhdnH8d8mJEvAJEBIArzEEO5yV8JFGDC01EvFysz7OrRFBoEyVgOKcXwBHWGoI5HKQEag4GUE6giidSzWvqWTMuVW5CJQCipIitiYAAkgWQiwwey+f3RIS+UJbMjuydn9fmZ2hj17zp6HDHzn7NnsWU8wGAwKAPAdcU4PAABNFYEEAAOBBAADgQQAA4EEAAOBBAADgQQAQzOnB4ikQCCg8vJyJScny+PxOD0OAAcEg0GdO3dOHTp0UFxc/ceIMRXI8vJyZWVlOT0GgCagtLRUHTt2rHedmApkcnKypH/+YFJSUhyeBoATfD6fsrKy6npQn5gK5JWX1SkpKQQSiHE3cpqNN2kAwEAgAcBAIAHAQCABwEAgAcBAIAHAQCABwEAgAcBAIAHAQCABwOCaQNbW1ur5559XTk6OkpKS1KVLF73wwgviSxkBhItrPou9YMECLV++XKtXr1bv3r31ySefaNKkSUpNTdUTTzzh9HgAopBrArl9+3Y9+OCDuv/++yVJnTp10tq1a7Vr1y6HJwMQrVwTyGHDhum1117TF198oe7du2v//v3atm2bFi1aZG7j9/vl9/vr7vt8vpD2+c1Zn6ovXGzwzAAip2WLJLVu1bhX6XJNIGfNmiWfz6eePXsqPj5etbW1evHFFzV+/Hhzm8LCQs2bN69B+/vmrE+/XPKmai5/29CRAURQYkIz/e/0yY0aSdcE8t1339Xbb7+tNWvWqHfv3vrrX/+qGTNmqEOHDpo4ceI1t5k9e7YKCgrq7l+5UOaNqL5wUTWXv9VP//uHykxPa5S/A4DwOFl5Wmve/z9VX7gYm4F85plnNGvWLP34xz+WJPXt21dfffWVCgsLzUB6vV55vd6b2m9mepo6dsi8qecA4E6u+TWfCxcufOcLduLj4xUIBByaCEC0c80R5AMPPKAXX3xRt956q3r37q19+/Zp0aJFmjx5stOjAYhSrgnkkiVL9Pzzz+vxxx9XRUWFOnTooEcffVRz5sxxejQAUco1gUxOTlZRUZGKioqcHgVAjHDNOUgAiDQCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQCGZk4PEK0u+WtUU3O53nWSb2khj8cToYkAZ507X61g0H48MTFBzb2JkRvoBhDIMDhffUGv//o9tUjymusEagO6rWc35Q0fFMHJAGds27lX+w98rmbN4s11qi/49bMJ/6OU5JYRnKx+BDIMqi9cVOvUW5Tbr4e5zuXL3+rQl+URnApwzvETpzTk9tvqPULce/CIzp2vblKB5BykU3hpjRjjxtNJBBIADAQSAAwEEgAMBBIADAQSAAwEEgAMBBIADAQSAAwEEgAMBBIADAQSAAwEEgAMBBIADAQSAAwEEgAMBBIADAQSAAwEEgAMrgpkWVmZHn74YaWlpSkpKUl9+/bVJ5984vRYAKKUa76065tvvtHw4cM1atQo/eEPf1B6erqOHDmi1q1bOz0agCjlmkAuWLBAWVlZWrlyZd2ynJycerfx+/3y+/11930+X9jmAxB9XPMS+8MPP1Rubq4eeughZWRk6Pbbb9frr79e7zaFhYVKTU2tu2VlZUVoWgDRwDWBPHr0qJYvX65u3brpj3/8ox577DE98cQTWr16tbnN7NmzVVVVVXcrLS2N4MQA3M41L7EDgYByc3M1f/58SdLtt9+ugwcPasWKFZo4ceI1t/F6vfJ6vZEcE0AUcc0RZPv27dWrV6+rlt122236xz/+4dBEAKKdawI5fPhwHT58+KplX3zxhbKzsx2aCEC0c00gn3rqKe3YsUPz589XSUmJ1qxZo9dee035+flOjwYgSrkmkIMGDdIHH3ygtWvXqk+fPnrhhRdUVFSk8ePHOz0agCjlmjdpJGnMmDEaM2aM02MAiBGuOYIEgEgjkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgcG0gX3rpJXk8Hs2YMcPpUQBEKVcGcvfu3Xr11VfVr18/p0cBEMVcF8jz589r/Pjxev3119W6det61/X7/fL5fFfdAOBGuS6Q+fn5uv/++zV69OjrrltYWKjU1NS6W1ZWVgQmBBAtXBXId955R3v37lVhYeENrT979mxVVVXV3UpLS8M8IYBo0szpAW5UaWmpnnzySRUXF6t58+Y3tI3X65XX6w3zZACilWsCuWfPHlVUVOiOO+6oW1ZbW6stW7Zo6dKl8vv9io+Pd3BCANHGNYH8/ve/rwMHDly1bNKkSerZs6dmzpxJHAE0OtcEMjk5WX369LlqWcuWLZWWlvad5QDQGFz1Jg0ARJJrjiCvZdOmTU6PACCKcQQJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQCGkD+Lffr0ac2ZM0d//vOfVVFRoUAgcNXjZ86cabThAMBJIQdywoQJKikp0ZQpU5SZmSmPxxOOuQDAcSEHcuvWrdq2bZv69+8fjnkAoMkI+Rxkz549dfHixXDMAgBNSsiB/NWvfqXnnntOmzdv1unTp/neaQBRK+SX2K1atZLP59P3vve9q5YHg0F5PB7V1tY22nAA4KSQAzl+/HglJCRozZo1vEkDIKqFHMiDBw9q37596tGjRzjmAYAmI+RzkLm5uSotLQ3HLADQpIR8BDl9+nQ9+eSTeuaZZ9S3b18lJCRc9Xi/fv0abTgAcFLIgRw3bpwkafLkyXXLPB4Pb9IAiDohB/LLL78MxxwA0OSEHMjs7OxwzAEATQ5X8wEAA4EEAAOBBADDDQfy6NGj4ZwDAJqcGw5kv3791KdPHz377LPauXNnOGcCgCbhhgN56tQpFRYWqqKiQg8++KDat2+vqVOn6ne/+50uXboUzhkBwBE3HMjmzZvrgQce0BtvvKHjx4/r/fffV1pammbOnKm2bdtq7NixevPNN1VZWRnOeQEgYhr0Jo3H49GwYcP00ksv6bPPPtO+ffs0YsQIrVq1Sh07dtSyZcsae04AiLiQf1H8Wrp166ann35aTz/9tE6fPs0XdwGICo0SyH+XlpamtLS0xn5aAIg4fg8SAAwEEgAMBBIADCEHcuLEidqyZUs4ZgGAJiXkQFZVVWn06NHq1q2b5s+fr7KysnDMBQCOCzmQv/3tb1VWVqbHHntM69atU6dOnXTffffpN7/5jS5fvhyOGQHAEQ06B5menq6CggLt379fO3fuVNeuXTVhwgR16NBBTz31lI4cOdLYcwJAxN3UmzTHjx9XcXGxiouLFR8frx/+8Ic6cOCAevXqpcWLFzfWjADgiJADefnyZb3//vsaM2aMsrOz9d5772nGjBkqLy/X6tWr9ac//UnvvvuufvGLX4RjXgCImJA/SdO+fXsFAgH95Cc/0a5duzRgwIDvrDNq1Ci1atWqEcYDAOeEHMjFixfroYceUvPmzc11WrVqxbcfAnC9kAM5YcKEcMwBAE0On6QBAAOBBAADgQQAA4EEAAOBBAADgQQAA4EEAAOBBAADgQQAA4EEAINrAllYWKhBgwYpOTlZGRkZGjt2rA4fPuz0WACimGsCuXnzZuXn52vHjh0qLi7W5cuXdffdd6u6utrp0QBEqZAvVuGUDRs2XHV/1apVysjI0J49ezRy5EiHpgIQzVwTyP9UVVUlSWrTpo25jt/vl9/vr7vv8/nCPheA6OGal9j/LhAIaMaMGRo+fLj69OljrldYWKjU1NS6W1ZWVgSnBOB2rgxkfn6+Dh48qHfeeafe9WbPnq2qqqq6W2lpaYQmBBANXPcSe9q0afroo4+0ZcsWdezYsd51vV6vvF5vhCYDEG1cE8hgMKjp06frgw8+0KZNm5STk+P0SACinGsCmZ+frzVr1mj9+vVKTk7WiRMnJEmpqalKSkpyeDoA0cg15yCXL1+uqqoq5eXlqX379nW3devWOT0agCjlmiPIYDDo9AgAYoxrjiABINIIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGFxzPUjgPz235Ziq/LUR21+qN14vjuwUsf3BeQQSrlXlr9WZS986PQaiGIFEVEhJjA/bc/tqIneUiqaFQML1UhLj9b9D6v8K4Jvxy51fE8kYxZs0AGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABg4Hux4Xq+mlr9cufXYX1+xCYCiahAxBAOBBKuleqNj+r9wXkEEq714shOTo+AKMebNABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgcF0gly1bpk6dOql58+YaMmSIdu3a5fRIAKKUqwK5bt06FRQUaO7cudq7d6/69++ve+65RxUVFU6PBiAKuSqQixYt0tSpUzVp0iT16tVLK1asUIsWLfTmm286PRqAKOSaq/nU1NRoz549mj17dt2yuLg4jR49Wh9//PE1t/H7/fL7/XX3fT5f2Oe8UXEe6fiJChWt+LXTo7jWhUt+nUjvpkBGVlj3E1dZpoyTh5XkTVR8nCes+4pWgUBAXbMznB4jZK4J5KlTp1RbW6vMzMyrlmdmZurQoUPX3KawsFDz5s2LxHghi4+P190jc50ew9UCwaC27vyb9pQFdCntv8Kyj+ZnynW7v0x3fX+o4ohjzHFNIBti9uzZKigoqLvv8/mUlRXeow1ETpzHoxFD+kk7/6bDvrhGP5KMqyxTN3+Z7hranzjGKNcEsm3btoqPj9fJkyevWn7y5Em1a9fumtt4vV55vd5IjAeHxHk8Gjmknzy7DmhoZqbu6Hdbozzv/k8Pa+tX/9Bdd/aXx0McY5Vr3qRJTEzUwIEDtXHjxrplgUBAGzdu1J133ungZHCax+PRiMF9tWP3Pu392+c3/Xz7Pz2srR/v1l1DiGOsc80RpCQVFBRo4sSJys3N1eDBg1VUVKTq6mpNmjTJ6dHgsCuR3LRjj9q2aaVbO7Zv0PN8ffykNm/bqVHDbieOcFcgx40bp8rKSs2ZM0cnTpzQgAEDtGHDhu+8cYPY5PF49F+ZbeU7V93g5zh//oLaZ7YljpDkskBK0rRp0zRt2jSnxwAQA1xzDhIAIo1AAoDBdS+x3SAuLk4XL9XozNlznMtqgBZJXnkTE5weo0Fqai6r+qL/+iviPwR14aJf8fFN65iNQIZBelprDR7YX6fOnJUUdHocV/H5zst/6YIG9O7q9CgNcujvpYpPSFSr1BSnR3GdgQP6qF1GW6fHuAqBDJOB/Xs5PYIrlZad0OZtO5we46YMvqOvcm4Nz0cfEVlN63gWAJoQAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAIZmTg8A/LvExASd9Z3XV1+fbND2p8/6lJiYcFP7P3PW1+D9f1N1Tt6b2D+aFgKJJiUzPU13f2+EzvrONWj7Tp06qXuX7Abvv3N2R424c5DOX7jYoO27du2iDu0yGrx/NC0EEk3OzQSuMfTq0cXR/aPp4BwkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGFwRyGPHjmnKlCnKyclRUlKSunTporlz56qmpsbp0QBEsWZOD3AjDh06pEAgoFdffVVdu3bVwYMHNXXqVFVXV2vhwoVOjwcgSrkikPfee6/uvffeuvudO3fW4cOHtXz5cgIJIGxcEchrqaqqUps2bepdx+/3y+/31933+Xwh7+dk5emQtwEQWeH6f+rKQJaUlGjJkiXXPXosLCzUvHnzGrSPli2SlJjQTGve/78GbQ8gshITmqlli6RGfU5PMBgMNuozhmDWrFlasGBBvet8/vnn6tmzZ939srIy3XXXXcrLy9Mbb7xR77bXOoLMyspSVVWVUlJSrjvfN2d9qr5w8brrAXBeyxZJat3q+v+vfT6fUlNTb6gDjgaysrJSp0/Xf2jcuXNnJSYmSpLKy8uVl5enoUOHatWqVYqLC+1N+FB+MACiUygdcPQldnp6utLT029o3bKyMo0aNUoDBw7UypUrQ44jAITKFecgy8rKlJeXp+zsbC1cuFCVlZV1j7Vr187ByQBEM1cEsri4WCUlJSopKVHHjh2veszBMwQAopwrXqc+8sgjCgaD17wBQLi4IpAA4AQCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABld81LCxXPnkTUMunAsgOlz5/38jn8SLqUCeO3dOkpSVleXwJACcdu7cOaWmpta7jqPXg4y0QCCg8vJyJScny+PxOD1OxFy5UHBpaWnMXweTn8W/xOrPIhgM6ty5c+rQocN1L5sYU0eQcXFx37kaUCxJSUmJqf8I9eFn8S+x+LO43pHjFbxJAwAGAgkABgIZA7xer+bOnSuv1+v0KI7jZ/Ev/CyuL6bepAGAUHAECQAGAgkABgIJAAYCCQAGAhlDjh07pilTpignJ0dJSUnq0qWL5s6dq5qaGqdHi5hly5apU6dOat68uYYMGaJdu3Y5PVLEFRYWatCgQUpOTlZGRobGjh2rw4cPOz1Wk0QgY8ihQ4cUCAT06quv6tNPP9XixYu1YsUKPfvss06PFhHr1q1TQUGB5s6dq71796p///665557VFFR4fRoEbV582bl5+drx44dKi4u1uXLl3X33Xerurra6dGaHH7NJ8a9/PLLWr58uY4ePer0KGE3ZMgQDRo0SEuXLpX0z8/mZ2Vlafr06Zo1a5bD0zmnsrJSGRkZ2rx5s0aOHOn0OE0KR5AxrqqqSm3atHF6jLCrqanRnj17NHr06LplcXFxGj16tD7++GMHJ3NeVVWVJMXEv4NQEcgYVlJSoiVLlujRRx91epSwO3XqlGpra5WZmXnV8szMTJ04ccKhqZwXCAQ0Y8YMDR8+XH369HF6nCaHQEaBWbNmyePx1Hs7dOjQVduUlZXp3nvv1UMPPaSpU6c6NDmclp+fr4MHD+qdd95xepQmKaYudxatnn76aT3yyCP1rtO5c+e6P5eXl2vUqFEaNmyYXnvttTBP1zS0bdtW8fHxOnny5FXLT548qXbt2jk0lbOmTZumjz76SFu2bInpywDWh0BGgfT0dKWnp9/QumVlZRo1apQGDhyolStXXveCodEiMTFRAwcO1MaNGzV27FhJ/3x5uXHjRk2bNs3Z4SIsGAxq+vTp+uCDD7Rp0ybl5OQ4PVKTRSBjSFlZmfLy8pSdna2FCxeqsrKy7rFYOIoqKCjQxIkTlZubq8GDB6uoqEjV1dWaNGmS06NFVH5+vtasWaP169crOTm57hxsamqqkpKSHJ6uaeHXfGLIqlWrzBjEyj+DpUuX6uWXX9aJEyc0YMAAvfLKKxoyZIjTY0WU9XUjK1euvO6pmlhDIAHAEBsnoACgAQgkABgIJAAYCCQAGAgkABgIJAAYCCQAGAgkABgIJAAYCCRiwsWLF9WyZUuVlJQ4PQpchEAiJhQXFys7O1tdu3Z1ehS4CIGEa1RWVqpdu3aaP39+3bLt27crMTFRGzdurHfb9evX60c/+tE1Hzt27Jg8Ho/effddjRgxQklJSRo0aJC++OIL7d69W7m5ubrlllt03333XXUFJMSAIOAiv//974MJCQnB3bt3B30+X7Bz587Bp556qt5tamtrgxkZGcHt27df8/Evv/wyKCnYs2fP4IYNG4KfffZZcOjQocGBAwcG8/Lygtu2bQvu3bs32LVr1+DPf/7zcPy10EQRSLjO448/HuzevXvwpz/9abBv377BS5cu1bv+X/7yl2BGRkawtrb2mo9fCeQbb7xRt2zt2rVBScGNGzfWLSssLAz26NGjcf4ScAVeYsN1Fi5cqG+//Vbvvfee3n77bXm93nrXX79+vcaMGXPdq6f369ev7s9Xvtyrb9++Vy2Lte/QjnUEEq7z97//XeXl5QoEAjp27Nh11//www/N84//LiEhoe7PVy4q+5/LAoFA6APDtfjKBbhKTU2NHn74YY0bN049evTQz372Mx04cEAZGRnXXP/IkSP66quv9IMf/CDCkyIacAQJV3nuuedUVVWlV155RTNnzlT37t01efJkc/3169dr9OjRatGiRQSnRLQgkHCNTZs2qaioSG+99ZZSUlIUFxent956S1u3btXy5cuvuU19v94DXA/fSYOoderUKbVv315ff/113ZsuQCg4gkTUOnPmjBYtWkQc0WAcQQKAgSNIADAQSAAwEEgAMBBIADAQSAAwEEgAMBBIADAQSAAwEEgAMPw/77FV5QnxBvsAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pedpy import plot_measurement_setup\n",
     "import matplotlib.pyplot as plt\n",
@@ -287,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -397,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -407,18 +385,7 @@
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pedpy import plot_trajectories\n",
     "import matplotlib.pyplot as plt\n",
@@ -446,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -472,24 +439,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from pedpy import plot_trajectories\n",
@@ -520,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,24 +493,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "tags": [
      "hide-input"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from pedpy import plot_trajectories\n",
@@ -579,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -599,20 +544,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "from pedpy import plot_trajectories\n",
@@ -4541,7 +4475,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

From 2d4a08cc2c6d6bd6cc88497f2be1b6a9e9d9e610 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 16 Aug 2024 11:37:48 +0200
Subject: [PATCH 19/22] Add decimal places as input variable

This allows us to explicitly handle floating point errors that occur when transforming walkable areas and shapely polygons
---
 pedpy/io/trajectory_loader.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index a197d408..0e76b8f6 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -913,6 +913,7 @@ def _validate_is_deviation_vadere_pedpy_traj_transform_below_threshold(
 def load_walkable_area_from_vadere_scenario(
         vadere_scenario_file: pathlib.Path,
         margin: float = 0,
+        decimals: int = 6,
 ) -> WalkableArea:
     """Loads the walkable area from the Vadere scenario file as :class:`~geometry.WalkableArea`.
 
@@ -927,14 +928,18 @@ def load_walkable_area_from_vadere_scenario(
                 By default (margin = .0), the bound of the walkable area in PedPy coincides with the
                 inner bound of the bounding box (obstacle) in Vadere. PedPy cannot process the case
                 where obstacles touch the bounding box defined in Vadere. To avoid errors, either
-                increase the value of margin (e.g. to 1e-9) or make sure that the obstacles in
+                increase the value of margin (e.g. to 1e-3) or make sure that the obstacles in
                 Vadere do not touch the bounding box.
+        decimals: Integer defining the decimals of the coordinates of the walkable area
 
     Returns:
         WalkableArea: :class:`~geometry.WalkableArea` used in the simulation
     """
     _validate_is_file(vadere_scenario_file)
 
+    if margin is not 0 and margin < 10 ** -decimals:
+        raise LoadTrajectoryError("Margin should be greater than 10 ** (-decimals).")
+
     with open(vadere_scenario_file, 'r') as f:
         data = json.load(f)
         topography = data["scenario"]["topography"]
@@ -948,7 +953,7 @@ def load_walkable_area_from_vadere_scenario(
         complete_area["width"] = complete_area["width"] - 2 * (bounding_box_with - margin)
         complete_area["height"] = complete_area["height"] - 2 * (bounding_box_with - margin)
         complete_area["type"] = "RECTANGLE"
-        complete_area_points = _vadere_shape_to_point_list(complete_area)
+        complete_area_points = _vadere_shape_to_point_list(complete_area, decimals=decimals)
         area_poly = shapely.Polygon(complete_area_points)
 
         # obstacles
@@ -956,7 +961,7 @@ def load_walkable_area_from_vadere_scenario(
         obstacles_ = list()
         error_obst_ids = list()
         for obstacle in obstacles:
-            obst_points = _vadere_shape_to_point_list(obstacle["shape"])
+            obst_points = _vadere_shape_to_point_list(obstacle["shape"], decimals=decimals)
             if area_poly.contains_properly(shapely.Polygon(obst_points)):
                 obstacles_ += [obst_points]
             else:
@@ -974,7 +979,7 @@ def load_walkable_area_from_vadere_scenario(
     return WalkableArea(polygon=complete_area_points, obstacles=obstacles_)
 
 
-def _vadere_shape_to_point_list(shape):
+def _vadere_shape_to_point_list(shape: dict, decimals: int):
     """Transforms dictionary describing a rectangle or polygon into a list of points (polygon).
 
     Args:
@@ -983,6 +988,8 @@ def _vadere_shape_to_point_list(shape):
                * 'shape' POLYGON requires key value pair for 'points': [{'x': ..., 'y': ...},
                {'x': ..., 'y': ...}, ...]
 
+        decimals: Integer defining the decimals of the returned coordinates
+
     Returns:
         list
 
@@ -1014,4 +1021,6 @@ def _vadere_shape_to_point_list(shape):
     elif shape_type == "POLYGON":
         points = [shapely.Point(p["x"], p["y"]) for p in shape["points"]]
 
+    # handle floating point errors
+    points = [shapely.Point(np.round(p.x, decimals), np.round(p.y, decimals)) for p in points]
     return points

From c0abee1487e12bb179a9991df2d8950c892df3a5 Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Fri, 16 Aug 2024 15:32:30 +0200
Subject: [PATCH 20/22] Add test_load_walkable_area_from_vadere_scenario

---
 tests/unit_tests/io/test_trajectory_loader.py | 175 ++++++++++++++++++
 1 file changed, 175 insertions(+)

diff --git a/tests/unit_tests/io/test_trajectory_loader.py b/tests/unit_tests/io/test_trajectory_loader.py
index 15a3fd46..391d83dd 100644
--- a/tests/unit_tests/io/test_trajectory_loader.py
+++ b/tests/unit_tests/io/test_trajectory_loader.py
@@ -1,3 +1,5 @@
+import json
+import math
 import pathlib
 import re
 import sqlite3
@@ -28,6 +30,7 @@
     load_trajectory_from_viswalk,
     load_walkable_area_from_jupedsim_sqlite,
     load_walkable_area_from_ped_data_archive_hdf5,
+    load_walkable_area_from_vadere_scenario,
 )
 
 
@@ -385,6 +388,75 @@ def write_vadere_csv_file(
     data.to_csv(file, sep=" ", index=False, mode="a", encoding="utf-8-sig")
 
 
+def is_rectangular(poly: shapely.Polygon):
+    return math.isclose(
+            a=poly.area,
+            b=poly.minimum_rotated_rectangle.area,
+            abs_tol=0,
+        )
+
+
+def write_vadere_scenario_file(
+        file,
+        complete_area,
+        obstacles,
+        bounding_box_width,
+):
+    obstacles_ = list()
+
+    for obstacle in obstacles:
+        obst_coords = list(obstacle.coords)
+
+        if is_rectangular(shapely.Polygon(obstacle)):
+            obstacles_ += [
+                {
+                    "shape": {
+                        "type": "RECTANGLE",
+                        "x": obst_coords[0][0],
+                        "y": obst_coords[0][1],
+                        "width": abs(obst_coords[0][0] - obst_coords[2][0]),
+                        "height": abs(obst_coords[0][1] - obst_coords[2][1]),
+                    }
+                }
+            ]
+        else:  # is polygon
+            obstacles_ += [
+                {
+                    "shape": {
+                        "type": "POLYGON",
+                        "points": [{"x": p[0], "y": p[1]} for p in obst_coords]
+                    }
+                }
+            ]
+
+    if is_rectangular(shapely.Polygon(complete_area)):
+        scenario = {
+            "name": "vadere_test",
+            "release": str(),
+            "scenario": {
+                "topography": {
+                    "attributes": {
+                        "bounds": {
+                            "x": complete_area.bounds[0],
+                            "y": complete_area.bounds[1],
+                            "width": abs(complete_area.bounds[2] - complete_area.bounds[0]),
+                            "height": abs(complete_area.bounds[3] - complete_area.bounds[1]),
+                        },
+                        "boundingBoxWidth": bounding_box_width,
+                    },
+                    "obstacles": obstacles_,
+                }
+            }
+        }
+    else:
+        raise RuntimeError("Internal Error: Trying to write non-rectangular shape as Vadere "
+                           "scenario bound.")
+
+    # Convert and write JSON object to file
+    with open(file, "w") as f:
+        json.dump(scenario, f, indent=2)
+
+
 def write_header_viswalk(file, data):
     column_description = {
         "$PEDESTRIAN:NO": "No, Number (Unique pedestrian number)",
@@ -1918,3 +1990,106 @@ def test_load_trajectory_from_vadere_columns_non_unique(
     assert "The given trajectory file seems to be incorrect or empty." in str(
         error_info.value
     )
+
+
+@pytest.mark.parametrize(
+    "area_poly, margin, bounding_box",
+    [
+        (
+            shapely.Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]),
+            0,
+            0,
+        ),
+        (
+            shapely.Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]),
+            1e-6,
+            0,
+        ),
+        (
+            shapely.Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]),
+            0,
+            0.5,
+        ),
+        (
+            shapely.Polygon([(0, 0), (10, 0), (10, 10), (0, 10)]),
+            1e-6,
+            0.5,
+        ),
+        (
+            shapely.Polygon(
+                [(-10, -10), (-10, 10), (10, 10), (10, -10)],
+                [[(0, 0), (1, 1), (2, 0)], [(-2, -2), (-3, -3), (-4, -2)]],
+            ),
+            0,
+            0,
+        ),
+        (
+            shapely.Polygon(
+                [(-10, -10), (-10, 10), (10, 10), (10, -10)],
+                [[(0, 0), (1, 1), (2, 0)], [(-2, -2), (-3, -3), (-4, -2)]],
+            ),
+            1e-6,
+            0,
+        ),
+        (
+            shapely.Polygon(
+                [(-10, -10), (-10, 10), (10, 10), (10, -10)],
+                [[(0, 0), (1, 1), (2, 0)], [(-2, -2), (-3, -3), (-4, -2)]],
+            ),
+            0,
+            0.5,
+        ),
+        (
+            shapely.Polygon(
+                [(-10, -10), (-10, 10), (10, 10), (10, -10)],
+                [[(0, 0), (1, 0), (1, 1), (0, 1)]],  # rectangular hole
+            ),
+            1e-6,
+            0.5,
+        ),
+    ],
+)
+def test_load_walkable_area_from_vadere_scenario_success(
+        tmp_path: pathlib.Path,
+        area_poly: shapely.Polygon,
+        margin: bool,
+        bounding_box: float,
+):
+    file_path = pathlib.Path(tmp_path / "vadere_test.scenario")
+    decimals = 6
+
+    complete_area = area_poly.exterior
+    if len(area_poly.interiors) > 0:
+        obstacles = area_poly.interiors
+    else:
+        obstacles = []
+
+    write_vadere_scenario_file(
+        file=file_path,
+        complete_area=complete_area,
+        obstacles=obstacles,
+        bounding_box_width=bounding_box,
+    )
+    walkable_area_from_file = load_walkable_area_from_vadere_scenario(
+        file_path,
+        margin=margin,
+        decimals=decimals,
+    )
+
+    # convert test input to expected WalkableArea
+    # 1) shrink expected walkable area to area without bounding box
+    expected_shell = shapely.Polygon(area_poly.exterior)
+    expected_shell = expected_shell.buffer(
+        distance=-bounding_box + margin,
+        single_sided=True,
+        join_style="mitre",
+    )
+    # convert polygon shell to points to use it as input to WalkableArea
+    expected_shell_points = list(expected_shell.exterior.coords)
+    # handle floating point errors
+    expected_shell_points = np.round(expected_shell_points, decimals)
+    # 2) treat holes separately to avoid buffering of holes
+    expected_holes = [list(p.coords) for p in area_poly.interiors]
+    expected_walkable_area = WalkableArea(expected_shell_points, obstacles=expected_holes)
+
+    assert expected_walkable_area.polygon.equals(walkable_area_from_file.polygon)

From dbb924df3026bfbcfd447b133b2787078987559b Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Thu, 26 Sep 2024 17:15:12 +0200
Subject: [PATCH 21/22] Refactor error messages and syntax

---
 pedpy/io/trajectory_loader.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/pedpy/io/trajectory_loader.py b/pedpy/io/trajectory_loader.py
index b95252c7..da0ee087 100644
--- a/pedpy/io/trajectory_loader.py
+++ b/pedpy/io/trajectory_loader.py
@@ -954,8 +954,8 @@ def load_walkable_area_from_vadere_scenario(
     """
     _validate_is_file(vadere_scenario_file)
 
-    if margin is not 0 and margin < 10 ** -decimals:
-        raise LoadTrajectoryError("Margin should be greater than 10 ** (-decimals).")
+    if margin != 0 and margin < 10 ** -decimals:
+        raise LoadTrajectoryError(f"Margin should be greater than 10 ** (-decimals).")
 
     with open(vadere_scenario_file, 'r') as f:
         data = json.load(f)
@@ -988,9 +988,9 @@ def load_walkable_area_from_vadere_scenario(
             error_obst_ids = {", ".join(error_obst_ids)}
             raise LoadTrajectoryError(
                 f"Cannot convert obstacles with IDs {error_obst_ids} because they touch the bound "
-                f"of the walkable area (inner bound of the bounding box in Vadere). Increase "
-                f"the walkable area or adapt the scenario file to make sure that obstacles have "
-                f"no common points with the bounding box."
+                f"of the walkable area (inner bound of the bounding box in Vadere). Increase the "
+                f"walkable area by adjusting 'margin' or adapt the scenario file to make sure that "
+                f"obstacles have no common points with the bounding box."
             )
 
     return WalkableArea(polygon=complete_area_points, obstacles=obstacles_)

From 40800a90f21cbe11fa1ce6ced03d4c3905a9d59e Mon Sep 17 00:00:00 2001
From: Simon Rahn <simon.rahn@hm.edu>
Date: Thu, 26 Sep 2024 17:28:19 +0200
Subject: [PATCH 22/22] Correct input argument that leads to error

---
 notebooks/user_guide.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/notebooks/user_guide.ipynb b/notebooks/user_guide.ipynb
index d049e335..40df346c 100644
--- a/notebooks/user_guide.ipynb
+++ b/notebooks/user_guide.ipynb
@@ -547,7 +547,7 @@
     "vadere_scenario_file = pathlib.Path(\"demo-data/vadere/bottleneck/vadere_bottleneck.scenario\")\n",
     "\n",
     "traj_vadere = load_trajectory_from_vadere(trajectory_file=vadere_traj_file)\n",
-    "vadere_walkable_area = load_walkable_area_from_vadere_scenario(vadere_scenario_file=vadere_scenario_file, margin=1e-9)"
+    "vadere_walkable_area = load_walkable_area_from_vadere_scenario(vadere_scenario_file=vadere_scenario_file, margin=1e-3)"
    ]
   },
   {