Initial commit

resultats/vidéos/accordéon clip.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import time
+from colour import Color
+
+t0 = -10
+tf = 100
+dt = 0.5
+t = t0
+
+nbv = 30
+
+def rainbow_gradient(num_colors):
+    colors = []
+    base_color = Color("violet")
+    gradient = list(base_color.range_to(Color("red"), num_colors))
+    for color in gradient:
+        hex_code = color.hex_l
+        colors.append(hex_code)
+    return colors
+colors = rainbow_gradient(nbv)
+
+def status(distances):
+    num_colors = len(distances)
+    colors = []
+    base_color = Color("green")
+    target_color = Color("red")
+    luminance_start = base_color.get_luminance()
+    luminance_end = target_color.get_luminance()
+    for i in range(num_colors):
+        moydist = distances[i]
+        t = i / (num_colors - 1)
+        adjusted_luminance = luminance_start + (luminance_end - luminance_start) * (1 - t) * (moydist - 1) / 18
+        color = Color(rgb=(base_color.rgb[0] * (1 - t) + target_color.rgb[0] * t,
+                           base_color.rgb[1] * (1 - t) + target_color.rgb[1] * t,
+                           base_color.rgb[2] * (1 - t) + target_color.rgb[2] * t))
+        color.set_luminance(adjusted_luminance)
+        hex_code = color.hex_l
+        colors.append(hex_code)
+    return colors
+
+
+U = 1.25 # vitesse m.s-¹
+Wm = 0.3 # distance minimale entre la voiture et celle qui la précède m
+Ws = 0.9 # m
+
+def phi(ww): # prend en entrée la distance entre les deux véhicules
+    PHI = (U*(1 - np.exp(- (ww-Wm)/Ws)))
+    return (ww >= Wm)* PHI  # retourne la vitesse du véhicule
+
+y = np.linspace(1, 1, nbv)
+xxbase = np.linspace(0, 1, nbv)
+
+def distances(fposition):
+    # print('fposition', fposition)
+    dist = np.diff(fposition)
+    inter = fposition[0]+20-fposition[-1]
+    newdist = np.insert(dist, len(dist), inter)
+    return newdist
+
+def position(fposition, newv):
+    newp = fposition + newv * dt
+    return newp
+
+xxold = xxbase.copy()
+while t<0:
+    plt.figure(1,figsize=[16,9])
+    plt.clf()
+
+    plt.draw()
+    plt.pause(1 )
+    print(t)
+    t += 2*dt
+    
+while(t<tf):
+    plt.figure(1,figsize=[16,9])
+    plt.clf()
+
+    nb = 360
+
+    r=np.linspace(1,1,nb)
+    theta=np.linspace(0,2*np.pi,nb)
+
+    # plt.polar(theta, r)
+    # plt.scatter(1*np.pi, 0.5)
+
+    dst = distances(xxold)
+    statusc = status(dst)
+    print(statusc)
+
+    vt = phi(dst)
+    # print('vitesses : ', vt)
+
+    xx = position(xxold, vt)
+    # print('position : ', xx)
+
+    plt.scatter(xx/10*np.pi, y, c=colors)
+
+    for i in range(len(xx)):
+        plt.plot(xx[i]/10*np.pi, y[i], color=statusc[i])
+
+    plt.title('Vitesse maximale : ' + str(U) + 'm.s-¹\ndistance minimale entre deux voitures : ' + str(Wm) + 'm\nnombre de voitures : ' + str(nbv))
+    plt.draw()
+    plt.pause(0.00001)
+    t += dt
+    xxold = xx.copy()