From f0580a988cd855c22a24665fba42ae63ef8400fd Mon Sep 17 00:00:00 2001
From: alexandre <alexandre@micouleau.info>
Date: Mon, 28 Oct 2019 11:52:03 +0100
Subject: [PATCH] blacked all

---
 Confirmation_PP_FR8039550.pdf | Bin 0 -> 30053 bytes
 brain.py                      |  49 ++++++++-----
 car.py                        | 133 +++++++++++++++++++++-------------
 genetics.py                   |  51 +++++--------
 main.py                       |  32 ++++----
 maps.py                       |  58 ++++++++-------
 params.py                     |  26 +++----
 trigo.py                      |  42 ++++++-----
 8 files changed, 213 insertions(+), 178 deletions(-)
 create mode 100644 Confirmation_PP_FR8039550.pdf

diff --git a/Confirmation_PP_FR8039550.pdf b/Confirmation_PP_FR8039550.pdf
new file mode 100644
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diff --git a/brain.py b/brain.py
index 4b98296..0cc48d5 100644
--- a/brain.py
+++ b/brain.py
@@ -1,37 +1,50 @@
 import numpy as np
 import random
 
-def mat_mult(A,B):
-    return [[sum([A[i][m]*B[m][j] for m in range(len(A[0]))]) for j in range(len(B[0]))] for i in range(len(A))]
+
+def mat_mult(A, B):
+    return [
+        [sum([A[i][m] * B[m][j] for m in range(len(A[0]))]) for j in range(len(B[0]))]
+        for i in range(len(A))
+    ]
+
 
 class Neural_Network(object):
     # inspired from https://enlight.nyc/projects/neural-network/
     def __init__(self, W1=None, W2=None):
-        #parameters
+        # parameters
         self.inputSize = 3
         self.outputSize = 2
         self.hiddenSize = 3
-        self.fitness = 0 
+        self.fitness = 0
 
-        #weights
+        # weights
         if W1 is not None:
-            self.W1=W1
-        else :
-            self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # weights from input to hidden layer
-        
+            self.W1 = W1
+        else:
+            self.W1 = np.random.randn(
+                self.inputSize, self.hiddenSize
+            )  # weights from input to hidden layer
+
         if W2 is not None:
-            self.W2=W2
-        else :
-            self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # weights from hidden to output layer
+            self.W2 = W2
+        else:
+            self.W2 = np.random.randn(
+                self.hiddenSize, self.outputSize
+            )  # weights from hidden to output layer
         # self.w1 = [[random.random() for i in range(self.hiddenSize)] for i in range(self.inputSize)]
         # self.w2 = [[random.random() for i in range(self.outputSize)] for i in range(self.hiddenSize)]
 
     def predict(self, X):
-        #forward propagation through our network
-        self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
-        self.z2 = self.sigmoid(self.z) # activation function
-        self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights
-        o = self.sigmoid(self.z3) # final activation function
+        # forward propagation through our network
+        self.z = np.dot(
+            X, self.W1
+        )  # dot product of X (input) and first set of 3x2 weights
+        self.z2 = self.sigmoid(self.z)  # activation function
+        self.z3 = np.dot(
+            self.z2, self.W2
+        )  # dot product of hidden layer (z2) and second set of 3x1 weights
+        o = self.sigmoid(self.z3)  # final activation function
         # self.z = mat_mult(X, self.w1) # dot product of X (input) and first set of 3x2 weights
         # self.z2 = self.sigmoid(self.z) # activation function
         # self.z3 = mat_mult(self.z2, self.w2) # dot product of hidden layer (z2) and second set of 3x1 weights
@@ -40,4 +53,4 @@ class Neural_Network(object):
 
     def sigmoid(self, s):
         # activation function
-        return 1/(1+np.exp(-s)) -0.5
\ No newline at end of file
+        return 1 / (1 + np.exp(-s)) - 0.5
diff --git a/car.py b/car.py
index 866098d..bb342b3 100644
--- a/car.py
+++ b/car.py
@@ -4,10 +4,20 @@ import random
 import pygame
 
 from brain import Neural_Network
-from params import GY, CAR_MAX_SPEED, CAR_MAX_FITNESS, CAR_SIZE, CAR_STEERING_FACTOR, VISION_LENGTH, VISION_SPAN, THROTTLE_POWER, screen
+from params import (
+    GY,
+    CAR_MAX_SPEED,
+    CAR_MAX_FITNESS,
+    CAR_SIZE,
+    CAR_STEERING_FACTOR,
+    VISION_LENGTH,
+    VISION_SPAN,
+    THROTTLE_POWER,
+    screen,
+)
 from trigo import angle_to_vector, get_line_feats, segments_intersection, distance
 
-IMG = pygame.image.load("car20.png")#.convert()
+IMG = pygame.image.load("car20.png")  # .convert()
 
 
 class Car(pygame.sprite.Sprite):
@@ -22,28 +32,28 @@ class Car(pygame.sprite.Sprite):
         self.image = self.original_image
 
         self.rect = self.image.get_rect()
-        self.vision_length = VISION_LENGTH # line liength
-        self.vision_span = VISION_SPAN # degrees
+        self.vision_length = VISION_LENGTH  # line liength
+        self.vision_span = VISION_SPAN  # degrees
         self.draw_sensors = False
 
-        # lets add 3 sensors as a start 
+        # lets add 3 sensors as a start
         # 1 straight ahead
         # 2 left 15°
         # 3 right 15 °
-        # we will give each of them a max lenght to 
-        # and we will eventually detect any line crossing the sensor and 
+        # we will give each of them a max lenght to
+        # and we will eventually detect any line crossing the sensor and
         # retain the min value as a distance to collision input
         self.center_sensor = None
         self.left_sensor = None
         self.right_sensor = None
         self.sensors = [self.left_sensor, self.center_sensor, self.right_sensor]
-        self.probes = [self.vision_length] *3 
-        
-        if brain :
+        self.probes = [self.vision_length] * 3
+
+        if brain:
             self.brain = brain
-        else :
+        else:
             self.brain = Neural_Network()
-        
+
         self.reset_car_pos()
         self.update_sensors()
         self.probe_brain()
@@ -52,9 +62,9 @@ class Car(pygame.sprite.Sprite):
 
     def reset_car_pos(self):
         self.rect.center = (
-                75 - int(random.random()*20) - 10, 
-                GY -50 - int(random.random()*20)-10
-                )
+            75 - int(random.random() * 20) - 10,
+            GY - 50 - int(random.random() * 20) - 10,
+        )
         self.speed = 1
         self.heading = random.random() * 20
         self.heading_change = random.random() * 30
@@ -62,53 +72,71 @@ class Car(pygame.sprite.Sprite):
     def update_sensors(self):
         center = self.rect.center
         vc = angle_to_vector(self.heading)
-        self.center_sensor = [center, (int(self.vision_length * vc[0] + center[0]), int(-self.vision_length * vc[1] + center[1]))]
-        
-        vl = angle_to_vector(self.heading+self.vision_span)
-        self.left_sensor = [center, (int(self.vision_length * vl[0] + center[0]), int(-self.vision_length * vl[1] + center[1]))]
-        
-        vr = angle_to_vector(self.heading-self.vision_span)
-        self.right_sensor = [center, (int(self.vision_length * vr[0] + center[0]), int(-self.vision_length * vr[1] + center[1]))]
+        self.center_sensor = [
+            center,
+            (
+                int(self.vision_length * vc[0] + center[0]),
+                int(-self.vision_length * vc[1] + center[1]),
+            ),
+        ]
 
+        vl = angle_to_vector(self.heading + self.vision_span)
+        self.left_sensor = [
+            center,
+            (
+                int(self.vision_length * vl[0] + center[0]),
+                int(-self.vision_length * vl[1] + center[1]),
+            ),
+        ]
+
+        vr = angle_to_vector(self.heading - self.vision_span)
+        self.right_sensor = [
+            center,
+            (
+                int(self.vision_length * vr[0] + center[0]),
+                int(-self.vision_length * vr[1] + center[1]),
+            ),
+        ]
 
     def update_position(self):
         vec = angle_to_vector(self.heading)
         old_center = self.rect.center
-        self.rect.center = (self.speed * vec[0] / 2 + old_center[0], -self.speed * vec[1] / 2 + old_center[1])
+        self.rect.center = (
+            self.speed * vec[0] / 2 + old_center[0],
+            -self.speed * vec[1] / 2 + old_center[1],
+        )
         self.update_sensors()
         self.distance_run += int(distance(old_center, self.rect.center))
         self.brain.fitness = int(math.sqrt(self.distance_run))
 
-
-    
     def probe_lines_proximity(self, lines):
         # print(self.center_sensor, lines[0])
-        self.probes = [self.vision_length*2] *3 
-        for idx,sensor in enumerate([self.left_sensor, self.center_sensor, self.right_sensor]) :
-            for line in lines :
+        self.probes = [self.vision_length * 2] * 3
+        for idx, sensor in enumerate(
+            [self.left_sensor, self.center_sensor, self.right_sensor]
+        ):
+            for line in lines:
                 ip = segments_intersection(sensor, line)
                 # print(ip)
-                if ip :
-                    if self.draw_sensors :
-                        pygame.draw.circle(screen, (125,125,255), ip, 4, 2)
-                    dist = int(distance(ip,self.rect.center))
+                if ip:
+                    if self.draw_sensors:
+                        pygame.draw.circle(screen, (125, 125, 255), ip, 4, 2)
+                    dist = int(distance(ip, self.rect.center))
                     self.probes[idx] = min(dist, self.probes[idx])
-                    if dist < 1.2 * self.speed or self.speed < 0.01 :
+                    if dist < 1.2 * self.speed or self.speed < 0.01:
                         self.run = False
                         self.speed = 0
                         # print(f'Car {id(self)} crashed')
                         return
 
                 # else :
-                #     self.probes[idx] = self.vision_length * 2 
+                #     self.probes[idx] = self.vision_length * 2
         # print(self.probes)
 
-
     def probe_brain(self):
         res = self.brain.predict(np.array(self.probes))
         self.heading_change = res[0] * 15
-        self.throttle = res[1] * 10 
-
+        self.throttle = res[1] * 10
 
     def update(self):
         # rotate
@@ -117,9 +145,9 @@ class Car(pygame.sprite.Sprite):
         self.rect = self.image.get_rect()
         self.rect.center = old_center
         self.update_position()
-        if self.speed < 0.01 or self.brain.fitness > CAR_MAX_FITNESS :
+        if self.speed < 0.01 or self.brain.fitness > CAR_MAX_FITNESS:
             self.run = False
-            print(f'Car {id(self)} crashed')
+            print(f"Car {id(self)} crashed")
         # print(
         #     'id', id(self),
         #     'Speed', self.speed,
@@ -128,27 +156,30 @@ class Car(pygame.sprite.Sprite):
         #     'heading change', self.heading_change,
         # )
 
-        if self.speed : 
-            self.heading += self.heading_change * CAR_STEERING_FACTOR / self.speed 
+        if self.speed:
+            self.heading += self.heading_change * CAR_STEERING_FACTOR / self.speed
             self.heading = self.heading % 360
 
-            self.speed += self.throttle #THROTTLE_POWER
+            self.speed += self.throttle  # THROTTLE_POWER
             # if self.throttle :
-                # self.speed += self.throttle #THROTTLE_POWER
+            # self.speed += self.throttle #THROTTLE_POWER
             # else :
-                # self.speed -= self.throttle #THROTTLE_POWER
+            # self.speed -= self.throttle #THROTTLE_POWER
 
             self.speed = max(0, self.speed)
             self.speed = min(self.speed, CAR_MAX_SPEED)
 
         super().update()
 
-
     def show_features(self):
         if self.draw_sensors:
-            pygame.draw.line(screen, (255,0,0), self.center_sensor[0], self.center_sensor[1])
-            pygame.draw.line(screen, (0,255,0), self.left_sensor[0], self.left_sensor[1])
-            pygame.draw.line(screen, (0,0,255), self.right_sensor[0], self.right_sensor[1])
-            pygame.draw.circle(screen, (125,255,125), self.rect.center, 4, 2)
-
-
+            pygame.draw.line(
+                screen, (255, 0, 0), self.center_sensor[0], self.center_sensor[1]
+            )
+            pygame.draw.line(
+                screen, (0, 255, 0), self.left_sensor[0], self.left_sensor[1]
+            )
+            pygame.draw.line(
+                screen, (0, 0, 255), self.right_sensor[0], self.right_sensor[1]
+            )
+            pygame.draw.circle(screen, (125, 255, 125), self.rect.center, 4, 2)
diff --git a/genetics.py b/genetics.py
index 7fd2754..3cfdcea 100644
--- a/genetics.py
+++ b/genetics.py
@@ -3,67 +3,60 @@ import random
 from brain import Neural_Network
 from params import MUTATION_RATE, SELECTION_ALG, KWAY_TOURNAMENT_PLAYERS
 
+
 def kway_selection(brains, exclude=None):
     tourn_pool = []
     best_play = None
-    if exclude :
+    if exclude:
         brains = [x for x in brains if x != exclude]
     for x in range(KWAY_TOURNAMENT_PLAYERS):
         new_play = random.choice(brains)
-        while new_play in tourn_pool :
+        while new_play in tourn_pool:
             new_play = random.choice(brains)
-        if not best_play or best_play.fitness < new_play.fitness :
+        if not best_play or best_play.fitness < new_play.fitness:
             best_play = new_play
     return best_play
 
+
 def genetic_selection(brains):
     parents_pool = []
-    half_pop = int(len(brains)/2)
+    half_pop = int(len(brains) / 2)
 
     if SELECTION_ALG == "kway":
-        for x in range(half_pop) :
+        for x in range(half_pop):
             p1 = kway_selection(brains)
             p2 = kway_selection(brains, exclude=p1)
-            parents_pool.append([
-                p1, 
-                p2
-            ])
+            parents_pool.append([p1, p2])
 
-
-
-    elif SELECTION_ALG == "roulette" :
+    elif SELECTION_ALG == "roulette":
         # does not seem very optimized... TBR
         # constitute a list where every brain is represented
         # proportionnally to its relative fitness
         wheel = []
-        for b in brains :
+        for b in brains:
             wheel += [b] * b.fitness
 
-        
         tot_fitness = len(wheel)
 
-        # selection of pool/2 pair of parents to reproduce  
+        # selection of pool/2 pair of parents to reproduce
         for _ in range(half_pop):
-            idx1 = round(random.random()*tot_fitness - 1)
-            idx2 = round(random.random()*tot_fitness - 1)
-            parents_pool.append([
-                wheel[idx1], 
-                wheel[idx2]
-                ])
+            idx1 = round(random.random() * tot_fitness - 1)
+            idx2 = round(random.random() * tot_fitness - 1)
+            parents_pool.append([wheel[idx1], wheel[idx2]])
     return parents_pool
-    
+
 
 def cross_mutate_genes(p1_gene, p2_gene):
     child = []
     p1_gene = list(p1_gene)
     p2_gene = list(p1_gene)
-    for idx,x in enumerate(p2_gene):
-        if random.random() > 0.5 :
+    for idx, x in enumerate(p2_gene):
+        if random.random() > 0.5:
             choice = p1_gene[idx]
-        else :
+        else:
             choice = p2_gene[idx]
         # Mutation
-        if random.random() < MUTATION_RATE :
+        if random.random() < MUTATION_RATE:
             choice[random.randint(0, len(choice) - 1)] = random.random()
             print("Mutation !")
         child.append(choice)
@@ -73,7 +66,7 @@ def cross_mutate_genes(p1_gene, p2_gene):
 def genetic_reproduction(parents_pool):
     # every pair of parents will produce a mixed child
     new_pop = []
-    for [p1,p2] in parents_pool:
+    for [p1, p2] in parents_pool:
         W1_kid = cross_mutate_genes(p1.W1, p2.W1)
         W2_kid = cross_mutate_genes(p1.W2, p2.W2)
         c_brain1 = Neural_Network(W1=W1_kid, W2=W2_kid)
@@ -81,7 +74,3 @@ def genetic_reproduction(parents_pool):
         new_pop.append(c_brain1)
         new_pop.append(c_brain2)
     return new_pop
-
-
-
-
diff --git a/main.py b/main.py
index 4d0f403..218e69e 100755
--- a/main.py
+++ b/main.py
@@ -10,15 +10,13 @@ from maps import map1
 from params import CELL_COLOR, screen
 
 
-
-#https://medium.com/intel-student-ambassadors/demystifying-genetic-algorithms-to-enhance-neural-networks-cde902384b6e
+# https://medium.com/intel-student-ambassadors/demystifying-genetic-algorithms-to-enhance-neural-networks-cde902384b6e
 clock = pygame.time.Clock()
 
 
 map_lines = map1
 
 
-
 all_cars = pygame.sprite.Group()
 
 for x in range(100):
@@ -28,47 +26,47 @@ for x in range(100):
 
 def run_round(all_cars):
     running_cars = True
-    while running_cars :
+    while running_cars:
         running_cars = False
         screen.fill(CELL_COLOR)
         all_cars.draw(screen)
-        for c in all_cars :
+        for c in all_cars:
             c.show_features()
-            if c.run :
+            if c.run:
                 running_cars = True
                 c.probe_lines_proximity(map_lines)
                 c.probe_brain()
                 c.update()
 
-        for line in map_lines :
-            pygame.draw.line(screen, (255,255,255), line[0], line[1])
+        for line in map_lines:
+            pygame.draw.line(screen, (255, 255, 255), line[0], line[1])
 
         pygame.display.flip()
         clock.tick(48)
 
     # for c in all_cars :
     #     print(f"Car {id(c)} Fitness : {c.brain.fitness})")
-    
-    print('Collecting brains')
+
+    print("Collecting brains")
     brains = [c.brain for c in all_cars]
-    print(f"Max fitness = {max([b.fitness for b in brains])}" )
-    print(f"Avg fitness = {sum([b.fitness for b in brains])/len(brains)}" )
-    print('selecting')
+    print(f"Max fitness = {max([b.fitness for b in brains])}")
+    print(f"Avg fitness = {sum([b.fitness for b in brains])/len(brains)}")
+    print("selecting")
     parents_pool = genetic_selection(brains)
     # import ipdb; ipdb.set_trace()
     print("breeding")
     new_brains = genetic_reproduction(parents_pool)
-    print(f'building {len(new_brains)} cars with new brains')
+    print(f"building {len(new_brains)} cars with new brains")
     all_cars.empty()
-    for b in new_brains :
+    for b in new_brains:
         all_cars.add(Car(brain=b))
     print("Waiting before new run")
-    for x in range(1) :
+    for x in range(1):
         time.sleep(0.5)
         pygame.display.flip()
 
 
-while True :
+while True:
     run_round(all_cars)
     pygame.display.flip()
     clock.tick(24)
diff --git a/maps.py b/maps.py
index 0b03be3..b3875f6 100644
--- a/maps.py
+++ b/maps.py
@@ -1,40 +1,42 @@
 from params import GX, GY
 
-def generate_map_1() :
+
+def generate_map_1():
     path = [
-        (25,                int(GY-25)), 
-        (int(GX/2),         int(GY-25)),
-        (int(GX/2 + 75),    int(GY-150)),
-        (int(GX/2 + 150),   int(GY-150)),
-        (int(GX -75),       int(GY/2)),
-        (int(GX - 100),         int(GY/2 - 75)),
-        (int(GX - 100),         int(GY/2 - 150)),
-        (int(GX -50),       int( GY/4 )),
-        (int(3*GX/4 - 50),  int(50)),
-        (int(50),           int(50)),
-        (int(100),          int(GY/2)),
-        (25,                int(GY-25)), 
+        (25, int(GY - 25)),
+        (int(GX / 2), int(GY - 25)),
+        (int(GX / 2 + 75), int(GY - 150)),
+        (int(GX / 2 + 150), int(GY - 150)),
+        (int(GX - 75), int(GY / 2)),
+        (int(GX - 100), int(GY / 2 - 75)),
+        (int(GX - 100), int(GY / 2 - 150)),
+        (int(GX - 50), int(GY / 4)),
+        (int(3 * GX / 4 - 50), int(50)),
+        (int(50), int(50)),
+        (int(100), int(GY / 2)),
+        (25, int(GY - 25)),
     ]
 
     path2 = [
-        (100,               int(GY-85)), 
-        (int(GX/2 - 50 ),   int(GY-85)),
-        (int(GX/2 + 50),    int(GY-210)),
-        (int(GX/2 + 110),   int(GY-210)),
-        (int(GX - 170),     int(GY/2 + 30)),
-        (int(GX - 200 ),   int(GY/2 - 20)),
-        (int(GX - 200),    int(GY/2 - 200)),
-        (int(GX -170),      int( GY/4 -20)),
-        (int(3*GX/4 - 100), int(120)),
-        (int(120),          int(120)),
-        (int(175),          int(GY/2)),
-        (100,               int(GY-85)), 
+        (100, int(GY - 85)),
+        (int(GX / 2 - 50), int(GY - 85)),
+        (int(GX / 2 + 50), int(GY - 210)),
+        (int(GX / 2 + 110), int(GY - 210)),
+        (int(GX - 170), int(GY / 2 + 30)),
+        (int(GX - 200), int(GY / 2 - 20)),
+        (int(GX - 200), int(GY / 2 - 200)),
+        (int(GX - 170), int(GY / 4 - 20)),
+        (int(3 * GX / 4 - 100), int(120)),
+        (int(120), int(120)),
+        (int(175), int(GY / 2)),
+        (100, int(GY - 85)),
     ]
 
-    lines = [[path[i], path[i+1]] for i in range(len(path)-1)]
-    lines2 = [[path2[i], path2[i+1]] for i in range(len(path2)-1)]
+    lines = [[path[i], path[i + 1]] for i in range(len(path) - 1)]
+    lines2 = [[path2[i], path2[i + 1]] for i in range(len(path2) - 1)]
 
     lines = lines + lines2
     return lines
 
-map1 = generate_map_1()
\ No newline at end of file
+
+map1 = generate_map_1()
diff --git a/params.py b/params.py
index d8ff346..fff0245 100644
--- a/params.py
+++ b/params.py
@@ -1,21 +1,21 @@
 import pygame
 from pygame.locals import HWSURFACE, DOUBLEBUF
 
-FLAGS =  HWSURFACE | DOUBLEBUF #| FULLSCREEN
+FLAGS = HWSURFACE | DOUBLEBUF  # | FULLSCREEN
 
-GX                      = 1000
-GY                      = 1000
-CELL_COLOR              = (80,80,80)
-CAR_SIZE                = 20
-CAR_MAX_SPEED           = 100
-CAR_MAX_FITNESS         = 100
-CAR_STEERING_FACTOR     = 10
-VISION_LENGTH           = 60
-VISION_SPAN             = 35 # degrees
-THROTTLE_POWER          = 3
+GX = 1000
+GY = 1000
+CELL_COLOR = (80, 80, 80)
+CAR_SIZE = 20
+CAR_MAX_SPEED = 100
+CAR_MAX_FITNESS = 100
+CAR_STEERING_FACTOR = 10
+VISION_LENGTH = 60
+VISION_SPAN = 35  # degrees
+THROTTLE_POWER = 3
 
-MUTATION_RATE           = 0.01
-SELECTION_ALG           = "kway" # roulette
+MUTATION_RATE = 0.01
+SELECTION_ALG = "kway"  # roulette
 KWAY_TOURNAMENT_PLAYERS = 3
 
 pygame.init()
diff --git a/trigo.py b/trigo.py
index 3821fcb..5ca48de 100644
--- a/trigo.py
+++ b/trigo.py
@@ -1,45 +1,47 @@
 #!/usr/bin/env python
 import math
 
+
 def angle_to_vector(angle):
-    angle=angle*math.pi/180
+    angle = angle * math.pi / 180
     return [math.cos(angle), math.sin(angle)]
 
 
 def get_line_feats(point1, point2):
-    x1,y1 = point1
-    x2,y2 = point2
+    x1, y1 = point1
+    x2, y2 = point2
 
     # if x1 == x2 :
     #     x1=x1+1
-    a = (y1-y2)/(x1-x2)
+    a = (y1 - y2) / (x1 - x2)
     b = y2 - a * x2
-    return a,b
+    return a, b
 
 
 def segments_intersection(line1, line2):
-    p1,p2 = line1
-    p3,p4 = line2
-    if p1[0] == p2[0] :
+    p1, p2 = line1
+    p3, p4 = line2
+    if p1[0] == p2[0]:
         p1 = (p1[0] + 1, p1[1])
-    if p3[0] == p4[0] :
+    if p3[0] == p4[0]:
         p3 = (p3[0] + 1, p3[1])
 
+    a1, b1 = get_line_feats(p1, p2)
+    a2, b2 = get_line_feats(p3, p4)
 
-    a1,b1 = get_line_feats(p1,p2)
-    a2,b2 = get_line_feats(p3,p4)
+    if a1 == a2:
+        return None  # parrallel lines
 
-    if a1==a2 :
-        return None # parrallel lines
-    
-    x = (b2-b1)/(a1-a2)
+    x = (b2 - b1) / (a1 - a2)
 
-    if min(p1[0], p2[0]) <= x <= max (p1[0], p2[0]) and min(p3[0], p4[0]) <= x <= max (p3[0], p4[0]) :
+    if min(p1[0], p2[0]) <= x <= max(p1[0], p2[0]) and min(p3[0], p4[0]) <= x <= max(
+        p3[0], p4[0]
+    ):
         y = a1 * x + b1
-        return x,y
-    else : 
-        return None # intersect is outside segments
+        return x, y
+    else:
+        return None  # intersect is outside segments
 
 
 def distance(point1, point2):
-    return math.hypot(point1[0] - point2[0], point1[1] - point2[1])
\ No newline at end of file
+    return math.hypot(point1[0] - point2[0], point1[1] - point2[1])