diff --git a/src/scripts/proximity.py b/src/scripts/proximity.py
index 4352630..ad16dc9 100644
--- a/src/scripts/proximity.py
+++ b/src/scripts/proximity.py
@@ -7,15 +7,16 @@
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Dict, List, Tuple
-
+from datetime import datetime
 import numpy as np
 import pandas as pd
 import pedpy
 from joblib import Parallel, delayed
 from tqdm import tqdm
+from enum import Enum
 
 parent_dir = str(Path(__file__).resolve().parent.parent)
-print(parent_dir)
+print("parent dir", parent_dir)
 if parent_dir not in sys.path:
     print(parent_dir)
     sys.path.append(parent_dir)
@@ -34,12 +35,23 @@
 # print(f"distances {len(path_distances)}")
 
 
+class Method(Enum):
+    """Method to calculate distances. vect and merge are Euc."""
+
+    VECT = "vect"
+    ARC = "arc"
+    MERGE = "merge"
+
+
 @dataclass
 class InitData:
+    """Class to hold data."""
+
     countries: List[str]
     files: Dict[str, List[str]]
     result_csv: Path
     fps: int
+    method: str
 
 
 def load_file(file: str) -> pedpy.TrajectoryData:
@@ -74,7 +86,6 @@ def filter_frames(data: pd.DataFrame, nagents: int) -> pd.DataFrame:
     With this method with basically skip all these frames and keep
     only these with <nagents> agents
     """
-
     # Group data by frame and count the unique IDs in each frame
     frame_counts = data.groupby("frame")["id"].nunique()
 
@@ -314,7 +325,7 @@ def init_gender_code(filename: str) -> str:
     return name
 
 
-def calculate_proximity_analysis(country: str, filename: str, data: pd.DataFrame, fps: int = 25) -> List[Dict[str, Any]]:
+def calculate_proximity_analysis(country: str, filename: str, data: pd.DataFrame, fps: int = 25, method: Method = Method.VECT) -> List[Dict[str, Any]]:
     """
     Perform proximity analysis on given data of agents.
 
@@ -337,25 +348,24 @@ def calculate_proximity_analysis(country: str, filename: str, data: pd.DataFrame
         'gender_of_next_neighbor', 'gender_of_prev_neighbor', 'distance_to_next_neighbor',
         and 'distance_to_prev_neighbor' columns.
     """
-
     pp = Path(filename).parts
     country_short = Path(pp[-2]).name
     if country_short != "pal":
         # print(f"WARNINNG in calculate_proximity_analysis(): method={method} is hard-coded")
         # merge and vect are basically the same, just testing which implementation is faster
         # arc uses a different calculation of distances based on arc.
-        method = "arc"
-        if method == "merge":
+        if method == method.MERGE:
             processed_data = compute_distance_merge(data)
-        elif method == "vect":
+        elif method == method.VECT:
             nagents = extract_first_number(filename)
             cleaned_data = filter_frames(data, nagents)
             processed_data = calculate_circular_distance_and_gender_vect(cleaned_data)
-        else:  # arc
+        elif method == method.ARC:
             nagents = extract_first_number(filename)
             cleaned_data = filter_frames(data, nagents)
             processed_data = calculate_circular_distance_and_gender_arc(cleaned_data)
-        fps = 25
+        else:
+            print(f"Method {method} is not recognised.")
     else:
         processed_data = calculate_circular_distance_and_gender_pal(data)
         fps = 1
@@ -363,7 +373,7 @@ def calculate_proximity_analysis(country: str, filename: str, data: pd.DataFrame
     proximity_analysis_res = []
     if processed_data.empty:
         print("========")
-        print(filename)
+        print("Processed_data empty", filename)
         print(processed_data)
         print("========")
     frames_to_include = set(range(0, processed_data["frame"].max(), fps))
@@ -420,11 +430,11 @@ def unpack_and_process(args: Any) -> List[Dict[str, Any]]:
     return calculate_proximity_analysis(*args)
 
 
-def prepare_data(country: str, selected_file: str, fps: int) -> Tuple[str, str, pd.DataFrame, int]:
+def prepare_data(country: str, selected_file: str, fps: int, method: Method) -> Tuple[str, str, pd.DataFrame, int]:
     """Load file and make pedpy.datatrajectory."""
     trajectory_data = load_file(selected_file)
     data = trajectory_data.data
-    return country, selected_file, data, fps
+    return country, selected_file, data, fps, method
 
 
 def calculate_with_joblib(init_data: InitData) -> pd.DataFrame:
@@ -435,7 +445,7 @@ def calculate_with_joblib(init_data: InitData) -> pd.DataFrame:
         print(f"prepare tasks: {country} with {key}")
 
         for filename in init_data.files[key]:
-            tasks.append(prepare_data(country, filename, init_data.fps))
+            tasks.append(prepare_data(country, filename, init_data.fps, init_data.method))
 
     # Define a function to be executed in parallel
     def process_task(task: List[Any]) -> List[Dict[str, Any]]:
@@ -456,13 +466,15 @@ def process_task(task: List[Any]) -> List[Dict[str, Any]]:
 
 def init() -> InitData:
     """
-    Initializes the application by setting up the countries, file paths, result CSV path, and FPS (frames per second).
+    Initialize the application by setting up the countries, file paths, result CSV path, and FPS (frames per second).
 
     Returns:
         InitData: A data class containing initialized data including countries, files dictionary, result CSV path, and FPS.
     """
     print("Enter Init")
-    result_csv = Path(f"{parent_dir}/app_data/proximity_analysis_results_arc.csv")
+    method = Method.VECT
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    result_csv = Path(f"{parent_dir}/app_data/proximity_analysis_results_{method}_{timestamp}.csv")
     result_csv.parent.mkdir(parents=True, exist_ok=True)
     print(f"Created file: {result_csv}")
     fps = 25  # For distance calculations, calculate every fps-frame
@@ -478,7 +490,7 @@ def init() -> InitData:
         key = Path(country).name
         files[key] = [str(path) for path in Path(country).glob("*.csv")]
 
-    return InitData(countries=countries, files=files, result_csv=result_csv, fps=fps)
+    return InitData(countries=countries, files=files, result_csv=result_csv, fps=fps, method=method)
 
 
 if __name__ == "__main__":