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√(noham)² 2023-07-18 16:20:23 +02:00
commit 004f3ab862
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.gitattributes vendored Normal file
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# Auto detect text files and perform LF normalization
* text=auto

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modelv.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
import time
t0 = 0
tf = 20
# tf=3
dt = 0.5
t = t0
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(0, 0, 11)
xxbase = np.linspace(0, 1, 11)
def position(fposition, newv):
newp = fposition + newv * dt
return newp
def vitesses(fposition):
dist = np.diff(fposition)
vitesses = phi(dist)
newv = np.insert(vitesses, 10, 1.25)
return newv
xxold = xxbase.copy()
while(t<tf):
plt.figure(1,figsize=[16,9])
plt.clf()
plt.xlim([-1,20])
plt.ylim([-0.5, 1.5])
vt = vitesses(xxold)
xx = position(xxold, vt)
color = ['#ff0000', '#ff5300', '#ffa500', '#ffd200', '#ffff00', '#80c000', '#008000', '#004080', '#0000ff', '#2600c1', '#4b0082']
plt.scatter(xx, vt)
print(xx)
plt.scatter(xx, y, c=color)
plt.plot([0,20],[1.25, 1.25], color='k', linestyle='-', linewidth=1)
plt.xlabel('distance w en m')
plt.ylabel('vitesse en m.s-¹')
plt.title('Evolution de la vitesse des voitures\nvitesse du leader : ' + str(U) + 'm.s-¹\ndistance minimale entre deux voitures : ' + str(Wm) + 'm')
plt.draw()
# plt.savefig(str(t)+'.png')
plt.pause(0.00001)
t += dt
xxold = xx.copy()

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polaired.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
import time
from colour import Color
t0 = 0
tf = 100
dt = 0.5
t = t0
nbv = 20
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<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()

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polaired_results.txt Normal file
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0.9661094913648753 0.9808613246864439 0.9962575865691292 1.011831233309401 1.0271255870876104 1.0415641521151926 1.054306614673088 1.064089112571537 1.0690995921820843 1.067056393118527 1.0558144284664905 1.0347589004509956 1.0064500907015415 0.9767472454720263 0.9522385163027778 0.9369432100941424 0.9313055390927758 0.933533681919279 0.9412925948025972 0.952614705019478
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1.0177384608378546 1.0311977534253245 1.0433635037569218 1.053258589537279 1.0595214428902793 1.0604216601390632 1.0541763170518124 1.0397682162802795 1.0181346153149704 0.992839124201744 0.9690856567196136 0.9513628926431466 0.9416861541620705 0.9396714918460845 0.9437037285056036 0.9519880903858393 0.9630480325777526 0.9758149001593779 0.9895420973244455 1.0036772722405374
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1.0334108942196067 1.0445490920474683 1.0531074381291319 1.057687181806287 1.0566258966354596 1.0484230406836375 1.0326157484401506 1.010797218328399 0.9868399029079598 0.9655642753104061 0.950611332913951 0.9432664703731248 0.9428488813176656 0.9477898926760631 0.9564718399507441 0.9675890835747083 0.9801914555697948 0.9935973207532101 1.0072765997286979 1.020736434633534
1.0368138855005142 1.0471354125371022 1.0544813618165279 1.0573694181844573 1.0541571652005644 1.043601749462141 1.0258203553516836 1.0031434733634086 0.9798699623975509 0.9605661254651565 0.9481256690945301 0.9431245386392391 0.944524037894837 0.9507111577186933 0.9601716120563282 0.9717282033053039 0.984531317158968 0.9979595596204547 1.0115046277247757 1.0246603675077637
1.0399568856570696 1.0493504673819274 1.0553472703065623 1.0564061342032858 1.0509675081602552 1.0381432159137773 1.0187006913310057 0.9956475464334318 0.9735038230343207 0.9563904855361613 0.9464307060729453 0.943599800740472 0.9466163159068302 0.9538826863636487 0.964000886556633 0.9759134942328842 0.9888574576815472 1.0022584337926048 1.0156184578411285 1.028407732853509
1.0428088132452658 1.0511556143839087 1.0556647601276836 1.0547714411201596 1.0470735630343952 1.032132876263688 1.0114038803460943 0.9884603832153545 0.9678269536831081 0.9530365737319428 0.9454706175143883 0.9446227325932739 0.9490663870795757 0.9572615532540141 0.967930592564656 0.9801248682198036 0.9931541758336095 1.0064788073797786 1.0196002637315473 1.0319551426777451
1.0453363838368475 1.0525113486804472 1.0553967112206166 1.0524505613696817 1.0425119551793074 1.0256772785690629 1.0040831057394755 0.9817154042249499 0.9628960018719042 0.9504826633020471 0.9451830713796809 0.9461267184849618 0.951820697433682 0.96080993235433 0.9719350634897239 0.9843440198345519 0.9974062481085184 1.010604972247819 1.0234307666681417 1.0352770960042506
1.0475044156711206 1.0533783464893887 1.0545114180420967 1.0494426256845273 1.0373405128893438 1.0189005596429368 0.9968900751527698 0.9755234103571411 0.9587402989685074 0.9486909472374307 0.9455028774787095 0.9480493329267219 0.954831327995592 0.9644945102850144 0.9759914596145478 0.9885539722841798 1.0015985818622823 1.0146203730114252 1.027089031360461 1.0383459230457959
1.0492763272636552 1.0537188167661782 1.0529849894103762 1.0457632104481362 1.031638203537078 1.0119393701628852 0.9899676897481839 0.9699695854340575 0.9553648797920076 0.9476122258035602 0.9463648080760265 0.9503330581502993 0.9580556603735602 0.9682858937489129 0.9800792370310063 0.9927386665827669 1.0057158978718945 1.0185073563997378 1.0305523019854235 1.041131821414254

106
resultats/vidéos/accordéon clip.py Normal file
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@ -0,0 +1,106 @@
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()

15
test/color.py Normal file
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@ -0,0 +1,15 @@
from colour import Color
def rainbow_gradient(num_colors):
colors = []
base_color = Color("red")
gradient = list(base_color.range_to(Color("violet"), num_colors))
for color in gradient:
hex_code = color.hex_l
colors.append(hex_code)
return colors
num_colors = 10
gradient = rainbow_gradient(num_colors)
print(gradient)

45
test/model.py Normal file
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@ -0,0 +1,45 @@
import numpy as np
import matplotlib.pyplot as plt
import time
t0 = 0
tf = 20
# tf=3
dt = 1
t = t0
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
ww = np.linspace(0, 10, 200)
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(0, 0, 11)
xxbase = np.linspace(0, 1, 11)
def position(fposition):
dist = np.diff(fposition)
vitesses = phi(dist)
newv = np.insert(vitesses, 10, 1.25)
newp = fposition + newv * dt
return newp
xxold = xxbase.copy()
while(t<tf):
plt.figure(1,figsize=[16,9])
plt.clf()
plt.xlim([-1,10])
xx = position(xxold)
color = ['#ff0000', '#ff5300', '#ffa500', '#ffd200', '#ffff00', '#80c000', '#008000', '#004080', '#0000ff', '#2600c1', '#4b0082']
plt.scatter(xx, y, c=color)
plt.draw()
# plt.savefig(str(t)+'.png')
plt.pause(0.1)
t += dt
xxold = xx.copy()

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test/phi(ww).png Normal file
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test/phi(ww).py Normal file
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import numpy as np
import matplotlib.pyplot as plt
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
ww = np.linspace(0, 10, 200)
def phi(ww):
PHI = (U*(1 - np.exp(- (ww-Wm)/Ws)))
return (ww >= Wm)* PHI
plt.figure(figsize=[16,9])
plt.xlabel('distance w en m')
plt.ylabel('vitesse en m.s-¹')
plt.plot(ww, phi(ww))
plt.savefig('phi(ww).png', dpi=300)
plt.show()

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test/polaire.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
import time
t0 = 0
tf = 100
dt = 0.5
t = t0
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, 11)
xxbase = np.linspace(0, 1, 11)
def position(fposition, newv):
newp = fposition + newv * dt
return newp
def vitesses(fposition):
print('fposition', fposition)
dist = np.diff(fposition)
print('distance : ', dist)
vitesses = phi(dist)
newv = np.insert(vitesses, 10, 1.25)
return newv
xxold = xxbase.copy()
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)
vt = vitesses(xxold)
print('vitesses : ', vt)
xx = position(xxold, vt)
print('position : ', xx)
color = ['#ff0000', '#ff5300', '#ffa500', '#ffd200', '#ffff00', '#80c000', '#008000', '#004080', '#0000ff', '#2600c1', '#4b0082']
plt.scatter(xx/10*np.pi, y, c=color)
plt.draw()
plt.pause(0.00001)
t += dt
xxold = xx.copy()

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test/status.py Normal file
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from colour import Color
def rainbow_gradient(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) # Interpolation paramètre t
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
distances = [0.05263158, 0.05263158, 0.05263158, 0.05263158, 0.05263158,
0.05263158, 0.05263158, 0.05263158, 0.05263158, 0.05263158,
0.05263158, 0.05263158, 0.05263158, 0.05263158, 0.05263158,
0.05263158, 0.05263158, 0.05263158, 19.0]
gradient = rainbow_gradient(distances)
print(gradient)