Delete test files

DRAF_udp_send.py

-# -*- coding: utf-8 -*-
-"""
-Created on Tue May  9 13:31:50 2023
-
-@author: Jessica Dreyer, Sebastian Böttger
-"""
-#%% imports
-import socket
-import random
-from datetime import datetime
-
-#%% constants
-HOST = "127.0.0.1"
-PORT = 50007
-
-#%% script
-# create socket connection
-with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-    s.connect((HOST,PORT))
-    time_last_send = datetime.utcnow()
-    
-    while (1):
-        # buid send string
-        w = random.random()
-        v = random.random() + 144
-        v_str = str(format((round(v, 5)), '.5f')).zfill(3)
-        w_str = format((round(w, 5)), '.5f')
-        if (w >= 0):
-            w_str = "+" + w_str
-        time_delta = datetime.utcnow() - time_last_send
-        t_str = format((round(time_delta.total_seconds(), 6)), '.6f')
-        stringSend = "w=" + w_str + "v=" + v_str +"t=" + t_str
-        
-        # send string
-        s.sendall(stringSend.encode())
-        time_last_send = datetime.utcnow()
\ No newline at end of file

TEST_grafic.py

-# -*- coding: utf-8 -*-
-"""
-Created on Tue Jun  6 08:42:32 2023
-
-@author: Basti-Uni
-"""
-
-#%% imports
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-from matplotlib.patches import Circle
-import matplotlib.image as img
-
-# not impoertend imports
-import time
-import random
-
-#%% classes
-
-
-background_url = './Bilder/Strecke_02.jpg'
-background = img.imread(background_url)
-#background = background.resize((1500, 800))
-fig = plt.figure()
-ax = fig.add_subplot(111)
-live_image = ax.imshow(background)
-ax.set_xlim(0,4032)
-ax.set_ylim(3024,0)
-plt.show(block=False)
-
-#%%
-for x in range(10):
-    ax.plot([(x*100)+550, 500], [800, 2000], color='red', linewidth=10)
-    fig.canvas.draw_idle()
-    fig.canvas.flush_events()
-    time.sleep(1)
-    
-#%%
-import matplotlib.pyplot as plt
-import numpy as np
-from matplotlib.animation import FuncAnimation
-
-# Bild laden
-image_path = './Bilder/Strecke_02.jpg'
-image = plt.imread(image_path)
-
-# Start- und Endposition der Linie
-start_x, start_y = 100, 100
-end_x, end_y = 400, 400
-
-# Anzahl der Schritte
-num_steps = 100
-
-# Zwischenwerte für die Linienposition berechnen
-x_values = np.linspace(start_x, end_x, num_steps)
-y_values = np.linspace(start_y, end_y, num_steps)
-
-# Funktion zum Zeichnen des Bildes und der Linie
-def draw_image(frame):
-    plt.imshow(image)
-    plt.plot([x_values[frame], y_values[frame]], [x_values[frame+1], y_values[frame+1]], color='red', linewidth=2)
-    plt.axis('off')
-
-# Funktion zum Aktualisieren der Linienposition
-def update_line(frame):
-    # Bild anzeigen und Linie zeichnen
-    plt.cla()  # Vorherigen Plot löschen
-    draw_image(frame)
-
-# Animation erstellen
-fig = plt.figure()
-animation = FuncAnimation(fig, update_line, frames=num_steps-1, repeat=True, interval=50)  # 50 Millisekunden Verzögerung zwischen den Schritten
-
-# Bild anzeigen
-plt.show()
-
-
-

TEST_lokalisierung.py

-# -*- coding: utf-8 -*-
-"""
-Created on Tue May 30 19:26:11 2023
-
-@author: Basti-Uni
-"""
-
-#%% imports
-import numpy as np
-import pandas as pd
-
-#%% Constans
-S = 0 # Value for a straight part of the track
-R = 1 # Value for a right turn
-L = 2 # Value for a left turn
-
-
-#%% Erstellen der Strecke
-track = []
-track.append(["S1", S, [L,S,L,R,S]])
-track.append(["S2", L, [S,L,S,L,R]])
-track.append(["S3", S, [L,S,L,S,L]])
-track.append(["S4", L, [S,L,S,L,S]])
-track.append(["S5", S, [L,S,L,S,L]])
-track.append(["S6", R, [S,L,S,L,S]])
-track.append(["S7", S, [R,S,L,S,L]])
-track.append(["S8", L, [S,R,S,L,S]])
-track.append(["S9", S, [L,S,R,S,L]])
-track.append(["S10", R, [S,L,S,R,S]])
-track.append(["S11", L, [R,S,L,S,R]])
-track.append(["S12", S, [L,R,S,L,S]])
-track.append(["S13", L, [S,L,R,S,L]])
-
-# DataFrame erstellen
-sections = pd.DataFrame(track, columns=['s_name', 'type', 'previous']);
-current_section_type = S
-last_sections_type = [L,S,L,S,L]
-last_known_section = sections.iloc[3]
-
-
-#%% Allgorithmus:
-possible_sections = sections[sections.type == current_section_type]
-previous_count = 0
-while(len(possible_sections) > 1 and previous_count < 5):
-    if(last_sections_type[previous_count] == None):
-        previous_count = 5
-    else:
-        previous_types = pd.DataFrame(possible_sections.previous.array)
-        possible_sections = possible_sections[(previous_types[previous_count] == last_sections_type[previous_count]).array]
-        previous_count += 1
-
-if(len(possible_sections) > 1):
-    print("More than one part of the track is possible for localization.")
-    if (last_known_section is not None):
-        
-        # Calculate distance from last known_section to all possible sections
-        index_last_known_section = sections[sections.s_name == last_known_section.s_name].index[0]
-        print("Index of Last Section: ", index_last_known_section)
-        
-        ### Änderungsvorschlag Anfang
-        help_sections = sections.copy()
-        help_sections.index += sections.shape[0]
-        help_sections = sections.copy().append(help_sections)
-        
-        filtert_section = pd.DataFrame()
-        for possible_section_s_name in np.array(possible_sections.s_name):
-            filtert_section = filtert_section.append(help_sections[help_sections.s_name == possible_section_s_name])
-
-        
-        indexs = abs(filtert_section.index - index_last_known_section)
-        print("Distance from indexs: ", indexs)
-        
-        # Get index of entry with the smallest distance
-        min_index = np.argmin(indexs)
-
-        # Check if there is more than one entry with the same distance like min_index
-        all_min_indices = np.where(indexs == indexs[min_index])[0]
-        
-        # If there is more than on index, localization failed
-        if(len(all_min_indices) > 1):
-            min_index = all_min_indices[1]
-            print("No localization possible")
-        print(possible_sections)
-        s_name_possible_section = filtert_section.iloc[min_index].s_name
-        possible_sections = possible_sections[possible_sections.s_name == s_name_possible_section]
-        print ("Am nahliegenste Section:", possible_sections)
-    else:
-        print("No localization possible - More than one part of the track can fit")
-        print(possible_sections)
-        
-elif(len(possible_sections) < 1):
-     print("No possible section was found. No localization possible.")
-
-if(len(possible_sections) == 1):
-    #print(possible_sections)
-    last_known_section = possible_sections.iloc[0]
-
-
-
-
-
-
-