From 9076e5e65f79d7771556707d891af616ada015d1 Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Thu, 11 Jun 2015 15:27:44 -0400 Subject: [PATCH 01/11] initial commit --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f63bc20..500c038 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Traffic simulation - + ## Description Analyze the behavior of drivers on a new road to determine the optimal speed limits. From 16ceba33831cc508169454d0e7755bc0d8ac5093 Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Thu, 11 Jun 2015 21:50:13 -0400 Subject: [PATCH 02/11] added baseball practice files as this was prework for traffic sim --- .DS_Store | Bin 0 -> 6148 bytes baseball3.py | 26 +++++++++++++++++++++++ baseball4.py | 58 +++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 84 insertions(+) create mode 100644 .DS_Store create mode 100644 baseball3.py create mode 100644 baseball4.py diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..cf86e8925a3083ceff7f4604f43f013d50c95fc2 GIT binary patch literal 6148 zcmeHKOHKko5PgN1Ky<;sw>nVtq9{l(-2=mF@^6KONZnUJ-Py}KwW{oc-hna zfB5_PzfSTiSHKn6DFviGxE}O5rL?y;CZ~ICpx@BdG_Q)dqOf6GF>`e*KBAkkKWKxP UC#)i}hvpvwCW8l8;7=9!22{OE!2kdN literal 0 HcmV?d00001 diff --git a/baseball3.py b/baseball3.py new file mode 100644 index 0000000..e0c1257 --- /dev/null +++ b/baseball3.py @@ -0,0 +1,26 @@ +class Team: + def __init__(self, name, city, rank): + self.name = name + self.city = city + self.rank = rank + + def __str__(self): + return "{} {}".format(self.city, self.name) + + +class Game: + def __init__(self, team_1, team_2): + self.home_team = team_1 + self.visiting_team = team_2 + + def __str__(self): + return "Welcome to a game between the {}d {} and the {}d {}".format(self.home_team.rank, self.home_team, self.visiting_team.rank, self.visiting_team) + +team_1 = Team("Astros", "Houston", "3rd place") +print(team_1) +team_2 = Team("Yankees", "New York", "5th place") +print(team_2) + +game = Game(team_1, team_2) + +print(game) \ No newline at end of file diff --git a/baseball4.py b/baseball4.py new file mode 100644 index 0000000..0f26fdd --- /dev/null +++ b/baseball4.py @@ -0,0 +1,58 @@ +import random + +class Team: + def __init__(self, name, city, rank, stadium): + self.name = name + self.city = city + self.rank = rank + self.stadium = stadium + + def __str__(self): + return "{} {}".format(self.city, self.name) + +class Game: + def __init__(self, team_1, team_2): + self.home_team = team_1 + self.visiting_team = team_2 + + def __str__(self): + return "WELCOME to {}, the home of the {}d {}. Tonight they will be playing against the {}d {}.".format\ + (self.home_team.stadium, self.home_team.rank, self.home_team, self.visiting_team.rank, self.visiting_team) + +'''class TeamData: #this isn't how I should do this I think. I should do sub-classes + def __init__(self): + self.random_team_1 = random.rand + + team_info = [["Cubs", "Chicago", "last place", "Wrigley Field"], + ["Royals", "Kansas City", "first place", "The 'K', Kaufman Stadium"], + ["Cardinals", "St. Louis", "fifth place", "Busch Stadium"], + ["Astros", "Houston", "forth place", "Minute Maid Park"], + ["Padres", "San Diego", "second place", "Petco Park"], + ["Yankees", "New York", "third place", "Yankee Stadium"] + ]''' + +# ok I see an alert here mentioning that I'm missing a Super... but it still works so I'm curious +class Cubs(Team): #not sure about this + def __init__(self): + self.name = "Cubs" + self.city = "Chicago" + self.rank = "last place" + self.stadium = "Wrigley Field" + + + + +# standard team entry +team_1 = Team("Astros", "Houston", "3rd place", "Minute Maid Park") +print(team_1) + +# attempting toward auto entry for use with random choice for team +# t1 = ["Cubs", "Chicago", "last place", "Wrigley Field"] +# team_2 = Team(t1) + +# attempting a subclass to create a team... SUCCESS! +team_2 = Cubs() +print(team_2) + +game = Game(team_1, team_2) +print(game) \ No newline at end of file From 43b6928e180cdbcc66fb6ce32d456889f0f7d0ae Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Thu, 11 Jun 2015 23:33:59 -0400 Subject: [PATCH 03/11] added cars.py with Cars class --- .idea/.name | 1 + .idea/inspectionProfiles/Project_Default.xml | 15 + .../inspectionProfiles/profiles_settings.xml | 7 + .idea/misc.xml | 14 + .idea/modules.xml | 8 + .idea/traffic-simulation.iml | 8 + .idea/vcs.xml | 6 + .idea/workspace.xml | 404 ++++++++++++++++++ cars.py | 50 +++ 9 files changed, 513 insertions(+) create mode 100644 .idea/.name create mode 100644 .idea/inspectionProfiles/Project_Default.xml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/traffic-simulation.iml create mode 100644 .idea/vcs.xml create mode 100644 .idea/workspace.xml create mode 100644 cars.py diff --git a/.idea/.name b/.idea/.name new file mode 100644 index 0000000..9b1c5e1 --- /dev/null +++ b/.idea/.name @@ -0,0 +1 @@ +traffic-simulation \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..e76d54e --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,15 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..3b31283 --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,7 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..da36f54 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..25ff64a --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/traffic-simulation.iml b/.idea/traffic-simulation.iml new file mode 100644 index 0000000..d0876a7 --- /dev/null +++ b/.idea/traffic-simulation.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 0000000..52b9726 --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,404 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + true + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1434074563454 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/cars.py b/cars.py new file mode 100644 index 0000000..598bcd0 --- /dev/null +++ b/cars.py @@ -0,0 +1,50 @@ +import random + +class Car: + def __init__(self, make, model, color): + self.color = color + self.make = make + self. model = model + self.speed = 0 + self.moving = False + self.safe_driver = True + self.accelerate = False + + def __str__(self): + return "{} {} {}".format(self.color, self.make, self.model) + + +car_1 = Car("Hummer", "H1", "red") +car_2 = Car("Jeep", "Wrangler", "black") +car_3 = Car("VW", "Jetta", "white") +car_4 = Car("Jeep", "Cherokee", "green") +car_5 = Car("Isuzu", "Rodeo", "red") +car_6 = Car("Mitsubishi", "Lancer", "Aqua") +car_7 = Car("Ford", "Festiva", "baby blue") +car_8 = Car("Hyundai", "Elantra", "dark blue") +car_9 = Car("Lincoln", "Towncar", "light blue") +car_10 = Car("Ford", "Mustang", "red") +car_11 = Car("Chevy", "Impala", "tan") +car_12 = Car("Ford", "Fairmont", "green") +car_13 = Car("Dodge", "minivan", "plum") +car_14 = Car("Chevy", "pickup", "green") +car_15 = Car("Ford", "Taurus", "green") +car_16 = Car("Ford", "Escrort", "red") +car_17 = Car("Icon", "CJ", "grey") +car_18 = Car("Mack", "dump truck", "white") +car_19 = Car("Dodge", "Ram", "silver") +car_20 = Car("Chevy", "dually", "gold") +# need 10 more cars but it's a good start + +# random just for fun and may use somehow for homework +x = random.randint(0,19) + +car_list = [car_1, car_2, car_3, car_4, car_5, car_6, car_7, car_8, car_9, car_10, car_11, car_12, car_13, car_14, + car_15, car_16, car_17, car_18, car_19, car_20] +print(car_list[x]) + + + + + + From 2514a9177852194052b547318c5872d60f18ab26 Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Sat, 13 Jun 2015 00:11:24 -0400 Subject: [PATCH 04/11] 30 cars built, decelerate works, accelerate doesn't yet --- .idea/workspace.xml | 124 ++++++++++++++++++++++++++++++++++++-------- cars.py | 114 ++++++++++++++++++++++++++++------------ 2 files changed, 182 insertions(+), 56 deletions(-) diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 52b9726..e62d092 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -1,7 +1,9 @@ - + + + @@ -14,7 +16,8 @@ - + + + @@ -81,6 +95,7 @@ + @@ -101,7 +116,6 @@ - @@ -110,7 +124,7 @@ - + @@ -131,6 +145,24 @@ + + + + + + + true + @@ -313,12 +351,14 @@ - + + - + + @@ -336,21 +376,21 @@ - + - + - + - - - - - - - - + + + + + + + + @@ -376,11 +416,18 @@ + + + + + + + + - @@ -388,14 +435,45 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + diff --git a/cars.py b/cars.py index 598bcd0..68e1f6e 100644 --- a/cars.py +++ b/cars.py @@ -1,50 +1,98 @@ import random +class Road: + def __init__(self): + self.length = 1000 + + def __str__(self): + return self.length + + class Car: - def __init__(self, make, model, color): + def __init__(self, make, model, color, location, following_who): self.color = color self.make = make - self. model = model - self.speed = 0 - self.moving = False + self.model = model + self.size = 15 + # speed in m/s + self.speed = 10 + self.max_speed = 33 self.safe_driver = True - self.accelerate = False + self.min_distance = self.speed + 15 + self.location = location + self.following_who = following_who def __str__(self): - return "{} {} {}".format(self.color, self.make, self.model) - - -car_1 = Car("Hummer", "H1", "red") -car_2 = Car("Jeep", "Wrangler", "black") -car_3 = Car("VW", "Jetta", "white") -car_4 = Car("Jeep", "Cherokee", "green") -car_5 = Car("Isuzu", "Rodeo", "red") -car_6 = Car("Mitsubishi", "Lancer", "Aqua") -car_7 = Car("Ford", "Festiva", "baby blue") -car_8 = Car("Hyundai", "Elantra", "dark blue") -car_9 = Car("Lincoln", "Towncar", "light blue") -car_10 = Car("Ford", "Mustang", "red") -car_11 = Car("Chevy", "Impala", "tan") -car_12 = Car("Ford", "Fairmont", "green") -car_13 = Car("Dodge", "minivan", "plum") -car_14 = Car("Chevy", "pickup", "green") -car_15 = Car("Ford", "Taurus", "green") -car_16 = Car("Ford", "Escrort", "red") -car_17 = Car("Icon", "CJ", "grey") -car_18 = Car("Mack", "dump truck", "white") -car_19 = Car("Dodge", "Ram", "silver") -car_20 = Car("Chevy", "dually", "gold") -# need 10 more cars but it's a good start + return "{} {} {}".format(self.color, self.make, self.model, self.location, self.following_who) + + def decelerate(self): + distraction = random.randint(0, 9) + if distraction == 0: + # speed in m/s + if self.speed <= 0: + self.speed = 0 + else: + self.speed -= 2 + else: + return + + def accelerate(self): + if self.following_who.location >= self.min_distance: + self.speed += 2 + else: + return + + + + + +car_1 = Car("Hummer", "H1", "red", 580, "car_30") +car_2 = Car("Jeep", "Wrangler", "black", 560, "car_1") +car_3 = Car("VW", "Jetta", "white", 540, "car_2") +car_4 = Car("Jeep", "Cherokee", "green", 520, "car_3") +car_5 = Car("Isuzu", "Rodeo", "red", 500, "car_4") +car_6 = Car("Mitsubishi", "Lancer", "Aqua", 480, "car_5") +car_7 = Car("Ford", "Festiva", "baby blue", 460, "car_6") +car_8 = Car("Hyundai", "Elantra", "dark blue", 440, "car_7") +car_9 = Car("Lincoln", "Towncar", "light blue", 420, "car_8") +car_10 = Car("Ford", "Mustang", "red", 400, "car_9") +car_11 = Car("Chevy", "Impala", "tan", 380, "car_10") +car_12 = Car("Ford", "Fairmont", "green", 360, "car_11") +car_13 = Car("Dodge", "minivan", "plum", 340, "car_12") +car_14 = Car("Chevy", "pickup", "green", 320, "car_13") +car_15 = Car("Ford", "Taurus", "green", 300, "car_14") +car_16 = Car("Ford", "Escrort", "red", 280, "car_15") +car_17 = Car("Icon", "CJ", "grey", 260, "car_16") +car_18 = Car("Mack", "dump truck", "white", 240, "car_17") +car_19 = Car("Dodge", "Ram", "silver", 220, "car_18") +car_20 = Car("Chevy", "dually", "gold", 200, "car_19") +car_21 = Car("Mazda", "mini truck", "rusty, brown", 180, "car_20") +car_22 = Car("Chevy", "Chevette", "tan", 160, "car_21") +car_23 = Car("Datsun", "B210", "red", 140, "car_22") +car_24 = Car("Ford", "pickup", "black", 120, "car_23") +car_25 = Car("AMC", "Pacer", "white", 100, "car_24") +car_26 = Car("Jeep", "CJ7", "red", 80, "car_25") +car_27 = Car("Honda", "Accord", "white", 60, "car_26") +car_28 = Car("Chevy", "Nova", "bronze", 40, "car_27") +car_29 = Car("Wayne Ind.", "Batmobile", "black", 20, "car_28") +car_30 = Car("home-made", "motorized lawnchair", "ducktaped", 0, "car_29") -# random just for fun and may use somehow for homework -x = random.randint(0,19) car_list = [car_1, car_2, car_3, car_4, car_5, car_6, car_7, car_8, car_9, car_10, car_11, car_12, car_13, car_14, - car_15, car_16, car_17, car_18, car_19, car_20] -print(car_list[x]) + car_15, car_16, car_17, car_18, car_19, car_20, car_21, car_22, car_23, car_24, car_25, car_26, car_27, + car_28, car_29, car_30] + +# random just for fun and may use somehow for homework +x = random.randint(0, 29) +print(car_list[x]) +print(car_1.speed) +car_1.decelerate() +print(car_1.speed) +#car_1.accelerate() +print(car_1.speed) \ No newline at end of file From add1a130b1c701ccb98bdc602a8410fc010bf8ae Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Sat, 13 Jun 2015 18:09:21 -0400 Subject: [PATCH 05/11] making progress --- .idea/workspace.xml | 113 ++++++++++++++++++++++++++--------- cars-only 2 cars practice.py | 86 ++++++++++++++++++++++++++ cars.py | 2 +- 3 files changed, 172 insertions(+), 29 deletions(-) create mode 100644 cars-only 2 cars practice.py diff --git a/.idea/workspace.xml b/.idea/workspace.xml index e62d092..73f2917 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -3,6 +3,7 @@ + @@ -16,8 +17,9 @@ - + + - - + + - - - + + + + + + + @@ -57,6 +63,7 @@ @@ -95,7 +102,7 @@ - + @@ -116,7 +123,7 @@ - + @@ -126,7 +133,12 @@ - + + + + + + + + - + + - - - + + + + @@ -384,20 +416,20 @@ - + - + - + - + @@ -416,6 +448,22 @@ + + + + + + + + + + + + + + + + @@ -442,7 +490,6 @@ - @@ -450,31 +497,41 @@ - - + + + + + + + + - + - + - - + + - + - - - + + + + + + + diff --git a/cars-only 2 cars practice.py b/cars-only 2 cars practice.py new file mode 100644 index 0000000..b75b394 --- /dev/null +++ b/cars-only 2 cars practice.py @@ -0,0 +1,86 @@ +import random + +class Road: + def __init__(self): + self.length = 1000 + + def __str__(self): + return self.length + + +class Car: + def __init__(self, make, model, color, location, following_who): + self.color = color + self.make = make + self.model = model + self.size = 15 + # speed in m/s + self.speed = 10 + self.max_speed = 33 + self.safe_driver = True + self.min_distance = int(self.speed + 15) + self.location = location + self.following_who = following_who + + def __str__(self): + return "{} {} {}".format(self.color, self.make, self.model, self.location, self.following_who) + + def accelerate(self): + if self.speed < self.max_speed: + self.speed += 2 + return + else: + self.speed = self.following_who.speed + return + + def decelerate(self): + distraction = random.randint(0, 9) + if distraction == 0: + self.speed -= 2 + if self.speed < 0: + self.speed = 0 + else: + return + else: + return + + def simulation(self): + for _ in range (5000): + car_list = [car_1, car_2] + prev_car_location = 1000 + for car in car_list: + if prev_car_location > minimum_distance: + accelerate(self, car) + decelerate(self, car) + self.location += self.speed + else: + decelerate(self, car) + self.location += self.speed + simulation(self) + + + + + + + +car_1 = Car("Hummer", "H1", "red", 25, "car_2") +car_2 = Car("Jeep", "Wrangler", "black", 5, "car_1") + +print(car_1) +print("spd", car_1.speed) +print("loc", car_1.location) + +print(car_2) +print("spd", car_2.speed) +print("loc", car_2.location) + + + +print(car_1) +print("spd", car_1.speed) +print("loc", car_1.location) + +print(car_2) +print("spd", car_2.speed) +print("loc", car_2.location) diff --git a/cars.py b/cars.py index 68e1f6e..de16102 100644 --- a/cars.py +++ b/cars.py @@ -93,6 +93,6 @@ def accelerate(self): print(car_1.speed) -#car_1.accelerate() +# car_1.accelerate() print(car_1.speed) \ No newline at end of file From 0f622d8d054393c28ddc4a509491a12c1852468c Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Sun, 14 Jun 2015 17:42:32 -0400 Subject: [PATCH 06/11] working simulation. cannot plot a scatter yet --- .DS_Store | Bin 6148 -> 8196 bytes .idea/workspace.xml | 111 +++++------ .../traffic_sim-checkpoint.ipynb | 186 ++++++++++++++++++ cars-only 2 cars practice.py | 86 -------- cars.py => original bloated cars.py | 0 traffic_sim.ipynb | 186 ++++++++++++++++++ traffic_sim.py | 81 ++++++++ 7 files changed, 507 insertions(+), 143 deletions(-) create mode 100644 .ipynb_checkpoints/traffic_sim-checkpoint.ipynb delete mode 100644 cars-only 2 cars practice.py rename cars.py => original bloated cars.py (100%) create mode 100644 traffic_sim.ipynb create mode 100644 traffic_sim.py diff --git a/.DS_Store b/.DS_Store index cf86e8925a3083ceff7f4604f43f013d50c95fc2..3d90124fb48177a0332b154edc2dc85a6172e727 100644 GIT binary patch literal 8196 zcmeHM%T60H6g>tOsX#5=rMp?OiXf#Ac2g88vFL_=z>qLNQ$mu4&_Gx78GKYfpnnt2 zy|&Q2qS7p?qF!6}89YAM*T>^=?f}5;U#B}j6F`GSaC4niO_KXkDrqIOydXOAffICD zzTgx|rMESh0;Yf|U<#N5rog|T0MBemjRohvziYKAU<&+~3ds8*ViAl3W*+s`!Ae5_ z;u@PZ@tSdfa-x87z|5mkY12m!E>K+vF`TFKek9$Aalp)@r^9(VTo~Df2*v5>VX-dvZ7!o%9By9(%~9`p8Yym}^oq8|X3GzhbN}IADH_ zmRPCsutkQO&lm7^Yk5D}JI`xkgbDgMXBp~<5l0@dwTmgeNcqN)?++9DRO5#`qP;to zmEgxS9N_>T(7`8S3ztZXo-1Q3H{(s(KE*d&(*wD>WEpa78E=XkOiTPtX;BLelj29V;6gz`30Y#J-jXJxRj4$ zd}SWd$1$zmlzd!9$1B>o;McTm<~C*}i{)8AU%1xQJt=!j_QGn|3%smJa@0mFf^opi zqclmop29>s|3wf>roa6fgz;Q~|fv`P|tjOHcJ0ww&@2 s%Q1^2>6dv_3ai2o0sj5)4?|u@>?)i%VCIo6O!gr_Ww6Q=_^S%s14&*0h5!Hn delta 135 zcmZp1XfcprU|?W$DortDU=RQ@Ie-{MGjUEV6q~50D9Q?w2a6>!Br@axp)rHr#=_-{ zjI5KL1d1oK3NG4M_KamQI|qj#Gf*uM2yg=lSCAPS3%@f@=9h5Pz6a)YO diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 73f2917..2d8690e 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -2,8 +2,8 @@ - - + + @@ -19,7 +19,8 @@ - + + - - + + - - + + - + - + + + + + - - - - - - - - - - @@ -64,6 +59,7 @@ @@ -138,7 +134,7 @@ - + + + - + + - - - - + + + + + @@ -408,7 +424,7 @@ - + @@ -416,10 +432,10 @@ - + - + @@ -448,14 +464,6 @@ - - - - - - - - @@ -464,14 +472,6 @@ - - - - - - - - @@ -490,6 +490,7 @@ + @@ -507,14 +508,6 @@ - - - - - - - - @@ -523,14 +516,18 @@ - + - - + + - + - + + + + + diff --git a/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb b/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb new file mode 100644 index 0000000..6c2ca1e --- /dev/null +++ b/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb @@ -0,0 +1,186 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import random\n", + "import math\n", + "import statistics\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "\n", + "#import traffic_sim.py" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "import random\n", + "\n", + "class Road:\n", + " def __init__(self):\n", + " self.length = 1000\n", + "\n", + " def __str__(self):\n", + " return self.length\n", + "\n", + "\n", + "class Car:\n", + " def __init__(self, location, following_who=None):\n", + " self.speed = 0\n", + " self.max_speed = 33\n", + " self.min_distance = int(self.speed + 15)\n", + " self.location = location\n", + " self.following_who = following_who\n", + "\n", + " @property\n", + " # wha? So we use this to call without passing variables right?\n", + " def __str__(self):\n", + " return \"{}\".format(self.location)\n", + "\n", + " def accelerate(self):\n", + " if self.speed < self.max_speed:\n", + " self.speed += 2\n", + " return\n", + " else:\n", + " self.speed = self.following_who.speed\n", + " return\n", + "\n", + " def decelerate(self):\n", + " distraction = random.randint(0, 9)\n", + " if distraction == 0:\n", + " self.speed -= 2\n", + " if self.speed < 0:\n", + " self.speed = 0\n", + "\n", + " def simulate(self):\n", + " if self.following_who.location > self.min_distance:\n", + " self.accelerate()\n", + " self.decelerate()\n", + " self.location += self.speed\n", + " if self.location >= road.length:\n", + " self.location -= road.length\n", + " else:\n", + " self.decelerate()\n", + " self.location += self.speed\n", + " if self.location >= road.length:\n", + " self.location -= road.length\n", + "\n", + "\n", + "road = Road()\n", + "car_list = []\n", + "location = 980\n", + "car_in_front = None\n", + "traffic = []\n", + "traffic_log = []\n", + "print(traffic_log)\n", + "\n", + "for _ in range(30):\n", + " # this creates the car instances\n", + " car_to_spawn = Car(location, car_in_front)\n", + " car_list.append(car_to_spawn)\n", + " # this next line allows the first car to spawn without knowing who it is following by setting var above to none\n", + " # Each iteration allows the first car to be assigned the last car no matter how many are put on the road\n", + " car_list[0].following_who = car_list[-1]\n", + " location -= 32\n", + " car_in_front = car_to_spawn\n", + "\n", + "for _ in range(120):\n", + " for car in car_list:\n", + " car.simulate()\n", + " [traffic.append(car.location)]\n", + " [traffic_log.append(traffic)]\n", + " traffic = []\n", + "# print(traffic_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must be the same size", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraffic_log\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/traffic-simulation/.direnv/python-3.4.3/lib/python3.4/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, hold, **kwargs)\u001b[0m\n\u001b[1;32m 3198\u001b[0m ret = ax.scatter(x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,\n\u001b[1;32m 3199\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3200\u001b[0;31m linewidths=linewidths, verts=verts, **kwargs)\n\u001b[0m\u001b[1;32m 3201\u001b[0m \u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3202\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/traffic-simulation/.direnv/python-3.4.3/lib/python3.4/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, **kwargs)\u001b[0m\n\u001b[1;32m 3589\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3591\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3593\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# This doesn't have to match x, y in size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = traffic_log\n", + "y = 60\n", + "plt.scatter(x, y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/cars-only 2 cars practice.py b/cars-only 2 cars practice.py deleted file mode 100644 index b75b394..0000000 --- a/cars-only 2 cars practice.py +++ /dev/null @@ -1,86 +0,0 @@ -import random - -class Road: - def __init__(self): - self.length = 1000 - - def __str__(self): - return self.length - - -class Car: - def __init__(self, make, model, color, location, following_who): - self.color = color - self.make = make - self.model = model - self.size = 15 - # speed in m/s - self.speed = 10 - self.max_speed = 33 - self.safe_driver = True - self.min_distance = int(self.speed + 15) - self.location = location - self.following_who = following_who - - def __str__(self): - return "{} {} {}".format(self.color, self.make, self.model, self.location, self.following_who) - - def accelerate(self): - if self.speed < self.max_speed: - self.speed += 2 - return - else: - self.speed = self.following_who.speed - return - - def decelerate(self): - distraction = random.randint(0, 9) - if distraction == 0: - self.speed -= 2 - if self.speed < 0: - self.speed = 0 - else: - return - else: - return - - def simulation(self): - for _ in range (5000): - car_list = [car_1, car_2] - prev_car_location = 1000 - for car in car_list: - if prev_car_location > minimum_distance: - accelerate(self, car) - decelerate(self, car) - self.location += self.speed - else: - decelerate(self, car) - self.location += self.speed - simulation(self) - - - - - - - -car_1 = Car("Hummer", "H1", "red", 25, "car_2") -car_2 = Car("Jeep", "Wrangler", "black", 5, "car_1") - -print(car_1) -print("spd", car_1.speed) -print("loc", car_1.location) - -print(car_2) -print("spd", car_2.speed) -print("loc", car_2.location) - - - -print(car_1) -print("spd", car_1.speed) -print("loc", car_1.location) - -print(car_2) -print("spd", car_2.speed) -print("loc", car_2.location) diff --git a/cars.py b/original bloated cars.py similarity index 100% rename from cars.py rename to original bloated cars.py diff --git a/traffic_sim.ipynb b/traffic_sim.ipynb new file mode 100644 index 0000000..6c2ca1e --- /dev/null +++ b/traffic_sim.ipynb @@ -0,0 +1,186 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import random\n", + "import math\n", + "import statistics\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "\n", + "#import traffic_sim.py" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "import random\n", + "\n", + "class Road:\n", + " def __init__(self):\n", + " self.length = 1000\n", + "\n", + " def __str__(self):\n", + " return self.length\n", + "\n", + "\n", + "class Car:\n", + " def __init__(self, location, following_who=None):\n", + " self.speed = 0\n", + " self.max_speed = 33\n", + " self.min_distance = int(self.speed + 15)\n", + " self.location = location\n", + " self.following_who = following_who\n", + "\n", + " @property\n", + " # wha? So we use this to call without passing variables right?\n", + " def __str__(self):\n", + " return \"{}\".format(self.location)\n", + "\n", + " def accelerate(self):\n", + " if self.speed < self.max_speed:\n", + " self.speed += 2\n", + " return\n", + " else:\n", + " self.speed = self.following_who.speed\n", + " return\n", + "\n", + " def decelerate(self):\n", + " distraction = random.randint(0, 9)\n", + " if distraction == 0:\n", + " self.speed -= 2\n", + " if self.speed < 0:\n", + " self.speed = 0\n", + "\n", + " def simulate(self):\n", + " if self.following_who.location > self.min_distance:\n", + " self.accelerate()\n", + " self.decelerate()\n", + " self.location += self.speed\n", + " if self.location >= road.length:\n", + " self.location -= road.length\n", + " else:\n", + " self.decelerate()\n", + " self.location += self.speed\n", + " if self.location >= road.length:\n", + " self.location -= road.length\n", + "\n", + "\n", + "road = Road()\n", + "car_list = []\n", + "location = 980\n", + "car_in_front = None\n", + "traffic = []\n", + "traffic_log = []\n", + "print(traffic_log)\n", + "\n", + "for _ in range(30):\n", + " # this creates the car instances\n", + " car_to_spawn = Car(location, car_in_front)\n", + " car_list.append(car_to_spawn)\n", + " # this next line allows the first car to spawn without knowing who it is following by setting var above to none\n", + " # Each iteration allows the first car to be assigned the last car no matter how many are put on the road\n", + " car_list[0].following_who = car_list[-1]\n", + " location -= 32\n", + " car_in_front = car_to_spawn\n", + "\n", + "for _ in range(120):\n", + " for car in car_list:\n", + " car.simulate()\n", + " [traffic.append(car.location)]\n", + " [traffic_log.append(traffic)]\n", + " traffic = []\n", + "# print(traffic_log)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "x and y must be the same size", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraffic_log\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/traffic-simulation/.direnv/python-3.4.3/lib/python3.4/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, hold, **kwargs)\u001b[0m\n\u001b[1;32m 3198\u001b[0m ret = ax.scatter(x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,\n\u001b[1;32m 3199\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3200\u001b[0;31m linewidths=linewidths, verts=verts, **kwargs)\n\u001b[0m\u001b[1;32m 3201\u001b[0m \u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3202\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/traffic-simulation/.direnv/python-3.4.3/lib/python3.4/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, **kwargs)\u001b[0m\n\u001b[1;32m 3589\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3591\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3593\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# This doesn't have to match x, y in size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = traffic_log\n", + "y = 60\n", + "plt.scatter(x, y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/traffic_sim.py b/traffic_sim.py new file mode 100644 index 0000000..93e5e28 --- /dev/null +++ b/traffic_sim.py @@ -0,0 +1,81 @@ +import random + +class Road: + def __init__(self): + self.length = 1000 + + def __str__(self): + return self.length + + +class Car: + def __init__(self, location, following_who=None): + self.speed = 0 + self.max_speed = 33 + self.min_distance = int(self.speed + 15) + self.location = location + self.following_who = following_who + + @property + # wha? So we use this to call without passing variables right? + def __str__(self): + return "{}".format(self.location) + + def accelerate(self): + if self.speed < self.max_speed: + self.speed += 2 + return + else: + self.speed = self.following_who.speed + return + + def decelerate(self): + distraction = random.randint(0, 9) + if distraction == 0: + self.speed -= 2 + if self.speed < 0: + self.speed = 0 + + def simulate(self): + if self.following_who.location > self.min_distance: + self.accelerate() + self.decelerate() + self.location += self.speed + if self.location >= road.length: + self.location -= road.length + else: + self.decelerate() + self.location += self.speed + if self.location >= road.length: + self.location -= road.length + + +road = Road() +car_list = [] +location = 600 +car_in_front = None +traffic = [] +traffic_log = [] +print(traffic_log) + +for _ in range(30): + # this creates the car instances + car_to_spawn = Car(location, car_in_front) + car_list.append(car_to_spawn) + # this next line allows the first car to spawn without knowing who it is following by setting var above to none + # Each iteration allows the first car to be assigned the last car no matter how many are put on the road + car_list[0].following_who = car_list[-1] + location -= 20 + car_in_front = car_to_spawn + +print("DEBUG: first run") +for _ in range(60): + print(_, " RUN") + for car in car_list: + car.simulate() + [traffic.append(car.location)] + [traffic_log.append(traffic)] + traffic = [] +print(len(traffic_log[0])) +print(len(traffic_log)) +print(traffic_log) \ No newline at end of file From 70e5fd80f97d6d84354da439301d946f90e2c4bf Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Mon, 15 Jun 2015 00:15:11 -0400 Subject: [PATCH 07/11] working scatter plot and calculated optimal speed --- .idea/workspace.xml | 42 +++++++----- .../traffic_sim-checkpoint.ipynb | 64 +++++++++++-------- traffic_sim.ipynb | 64 +++++++++++-------- traffic_sim.py | 6 ++ 4 files changed, 109 insertions(+), 67 deletions(-) diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 2d8690e..ff1270c 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -2,8 +2,8 @@ - - + + @@ -19,7 +19,7 @@ - + @@ -34,21 +34,28 @@ - - + + - - - + + + + + + + + + + @@ -425,7 +432,7 @@ - + @@ -435,7 +442,7 @@ - + @@ -516,18 +523,23 @@ + + + + + + + + - - + + - - - diff --git a/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb b/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb index 6c2ca1e..e8cb92e 100644 --- a/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb +++ b/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 88, + "execution_count": 141, "metadata": { "collapsed": false }, @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 142, "metadata": { "collapsed": false }, @@ -93,6 +93,9 @@ "traffic = []\n", "traffic_log = []\n", "print(traffic_log)\n", + "in_range = []\n", + "itteration_log = []\n", + "speed_log = []\n", "\n", "for _ in range(30):\n", " # this creates the car instances\n", @@ -104,41 +107,31 @@ " location -= 32\n", " car_in_front = car_to_spawn\n", "\n", - "for _ in range(120):\n", + "for _ in range(60):\n", " for car in car_list:\n", " car.simulate()\n", + " [speed_log.append(car.speed)]\n", " [traffic.append(car.location)]\n", + " [in_range.append(_)]\n", " [traffic_log.append(traffic)]\n", - " traffic = []\n", - "# print(traffic_log)" + " #for use with scatter plot\n", + " itteration_log.append(in_range)\n", + " \n" ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 153, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must be the same size", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraffic_log\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/traffic-simulation/.direnv/python-3.4.3/lib/python3.4/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, hold, **kwargs)\u001b[0m\n\u001b[1;32m 3198\u001b[0m ret = ax.scatter(x, y, s=s, c=c, marker=marker, cmap=cmap, norm=norm,\n\u001b[1;32m 3199\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3200\u001b[0;31m linewidths=linewidths, verts=verts, **kwargs)\n\u001b[0m\u001b[1;32m 3201\u001b[0m \u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3202\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/traffic-simulation/.direnv/python-3.4.3/lib/python3.4/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, **kwargs)\u001b[0m\n\u001b[1;32m 3589\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3591\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3593\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# This doesn't have to match x, y in size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" - ] - }, { "data": { - "image/png": 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BSks1tKsT26+OwsmR+GtUusfu58XrS33oJgDnMuy6A/qBUNYBbGAehjETsD2w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\n38A3J5oEZRlqK3+tSqq9ihSE9Bl5FBARkVKKpHN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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -147,19 +140,38 @@ ], "source": [ "x = traffic_log\n", - "y = 60\n", + "y = itteration_log\n", + "\n", "plt.scatter(x, y)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "35.81240245791174\n" + ] + } + ], + "source": [ + "average_speed = (sum(speed_log) / len(speed_log))\n", + "variation = []\n", + "for _ in speed_log:\n", + " variation.append((_ - average_speed)**2)\n", + "variation = (sum(variation) / len(variation))\n", + "standard_dev = variation**(.5)\n", + "optimum_speed = average_speed + standard_dev\n", + "\n", + "print(optimum_speed)\n" + ] } ], "metadata": { diff --git a/traffic_sim.ipynb b/traffic_sim.ipynb index 6c2ca1e..e8cb92e 100644 --- a/traffic_sim.ipynb +++ b/traffic_sim.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 88, + "execution_count": 141, "metadata": { "collapsed": false }, @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 142, "metadata": { "collapsed": false }, @@ -93,6 +93,9 @@ "traffic = []\n", "traffic_log = []\n", "print(traffic_log)\n", + "in_range = []\n", + "itteration_log = []\n", + "speed_log = []\n", "\n", "for _ in range(30):\n", " # this creates the car instances\n", @@ -104,41 +107,31 @@ " location -= 32\n", " car_in_front = car_to_spawn\n", "\n", - "for _ in range(120):\n", + "for _ in range(60):\n", " for car in car_list:\n", " car.simulate()\n", + " [speed_log.append(car.speed)]\n", " [traffic.append(car.location)]\n", + " [in_range.append(_)]\n", " [traffic_log.append(traffic)]\n", - " traffic = []\n", - "# print(traffic_log)" + " #for use with scatter plot\n", + " itteration_log.append(in_range)\n", + " \n" ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 153, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must be the same size", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraffic_log\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m 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\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3200\u001b[0;31m linewidths=linewidths, verts=verts, **kwargs)\n\u001b[0m\u001b[1;32m 3201\u001b[0m \u001b[0mdraw_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3202\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Users/jeffreyhacker/Documents/Python/Iron_Yard/homework/traffic-simulation/.direnv/python-3.4.3/lib/python3.4/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, **kwargs)\u001b[0m\n\u001b[1;32m 3589\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3590\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3591\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3593\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# This doesn't have to match x, y in size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: x and y must be the same size" - ] - }, { "data": { - "image/png": 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BSks1tKsT26+OwsmR+GtUusfu58XrS33oJgDnMuy6A/qBUNYBbGAehjETsD2w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\nfTlQUFBQUNAOXcJA/6ZS6isADgD4t0qpzwAAET0N4JMAngb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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -147,19 +140,38 @@ ], "source": [ "x = traffic_log\n", - "y = 60\n", + "y = itteration_log\n", + "\n", "plt.scatter(x, y)\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "35.81240245791174\n" + ] + } + ], + "source": [ + "average_speed = (sum(speed_log) / len(speed_log))\n", + "variation = []\n", + "for _ in speed_log:\n", + " variation.append((_ - average_speed)**2)\n", + "variation = (sum(variation) / len(variation))\n", + "standard_dev = variation**(.5)\n", + "optimum_speed = average_speed + standard_dev\n", + "\n", + "print(optimum_speed)\n" + ] } ], "metadata": { diff --git a/traffic_sim.py b/traffic_sim.py index 93e5e28..5d59266 100644 --- a/traffic_sim.py +++ b/traffic_sim.py @@ -56,6 +56,8 @@ def simulate(self): car_in_front = None traffic = [] traffic_log = [] +in_range = [] +itteration_log = [] print(traffic_log) for _ in range(30): @@ -74,8 +76,12 @@ def simulate(self): for car in car_list: car.simulate() [traffic.append(car.location)] + [in_range.append(_)] [traffic_log.append(traffic)] + # itteration log is for using with a scatter plot in homework + itteration_log.append(in_range) traffic = [] + in_range = [] print(len(traffic_log[0])) print(len(traffic_log)) print(traffic_log) \ No newline at end of file From d05b3b021742e71886d14f77a46074c763197164 Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Mon, 15 Jun 2015 15:58:05 -0400 Subject: [PATCH 08/11] updated plots --- .idea/workspace.xml | 15 +++++++-------- traffic_sim.ipynb | 37 ++++++++++++++++++++++--------------- 2 files changed, 29 insertions(+), 23 deletions(-) diff --git a/.idea/workspace.xml b/.idea/workspace.xml index ff1270c..de3bb01 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -3,7 +3,6 @@ - @@ -34,8 +33,8 @@ - - + + @@ -431,8 +430,8 @@ - - + + @@ -442,7 +441,7 @@ - + @@ -533,8 +532,8 @@ - - + + diff --git a/traffic_sim.ipynb b/traffic_sim.ipynb index e8cb92e..2fffc2b 100644 --- a/traffic_sim.ipynb +++ b/traffic_sim.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 141, + "execution_count": 175, "metadata": { "collapsed": false }, @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 224, "metadata": { "collapsed": false }, @@ -34,8 +34,6 @@ } ], "source": [ - "import random\n", - "\n", "class Road:\n", " def __init__(self):\n", " self.length = 1000\n", @@ -46,9 +44,9 @@ "\n", "class Car:\n", " def __init__(self, location, following_who=None):\n", - " self.speed = 0\n", + " self.speed = 30\n", " self.max_speed = 33\n", - " self.min_distance = int(self.speed + 15)\n", + " self.min_distance = int(self.speed + 5)\n", " self.location = location\n", " self.following_who = following_who\n", "\n", @@ -60,10 +58,10 @@ " def accelerate(self):\n", " if self.speed < self.max_speed:\n", " self.speed += 2\n", - " return\n", + " # return\n", " else:\n", " self.speed = self.following_who.speed\n", - " return\n", + " # return\n", "\n", " def decelerate(self):\n", " distraction = random.randint(0, 9)\n", @@ -121,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 228, "metadata": { "collapsed": false, "scrolled": true @@ -129,9 +127,9 @@ "outputs": [ { "data": { - "image/png": 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BSks1tKsT26+OwsmR+GtUusfu58XrS33oJgDnMuy6A/qBUNYBbGAehjETsD2w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\nfTlQUFBQUNAOXcJA/6ZS6isADgD4t0qpzwAAET0N4JMAngb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j7zXvOuXz/jL951v3zA6wv52a0+771QEyO8Xodby3ofcLF3auW0ae/0Pdq0VM29u/LHfh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\nACKib7/Mx3sBAAAAYDGafgA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -141,14 +139,14 @@ "source": [ "x = traffic_log\n", "y = itteration_log\n", - "\n", - "plt.scatter(x, y)\n", + "plt.rcParams['figure.figsize'] = 22, 12\n", + "plt.scatter(x, y, marker = \"_\", c=['b', 'r', 'g'])\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 215, "metadata": { "collapsed": false }, @@ -157,7 +155,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "35.81240245791174\n" + "34.59764396783137\n" ] } ], @@ -172,6 +170,15 @@ "\n", "print(optimum_speed)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { From ac898dc21646a2e34d600acb00681efe07a7bace Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Mon, 20 Jul 2015 22:56:57 -0400 Subject: [PATCH 09/11] uncommitted changes --- .DS_Store | Bin 8196 -> 6148 bytes .idea/workspace.xml | 44 +++++++++++++++++++++++++++++++------------- traffic_sim.ipynb | 14 +++++++------- 3 files changed, 38 insertions(+), 20 deletions(-) diff --git a/.DS_Store b/.DS_Store index 3d90124fb48177a0332b154edc2dc85a6172e727..a8680dd3608e37fa595e33f132a055aa80707c8b 100644 GIT binary patch delta 108 zcmZp1XfcprU|?W$DortDU=RQ@Ie-{Mvv5r;6q~50$jG@dU^g=(=Vl&(yNr{s3I}a$ uIK#M@or6P=8K?>f1h|2OD@en}!tczJ`DHvo+8LN2MuALc*c{I@hZz7V?GmK` delta 373 zcmZoMXmOBWU|?W$DortDU;r^WfEYvza8E20o2aMA$iFdQH}hr%jz7$c**Q2SHn1@A zZ{}gS%g87>c?GMI0fPlYIfDU%IYT8wGD99il4nkSa#Buy637T3J_9tI<3AVxc?=9t zP4Nsy48;s73}rwqi40Z5XcAzE2ijx;)RM+v%3#c3G+B*B9_ENd21}qrvKf+qv>CCE zC}79}!hE241wf4z#5tlEs5F})1L$P~pdZRXj+p$CHE6Rs&vRxj32vYlT|t4jS&-v9 Y^JIPz&&mEg92|_0aAnvW&ohS^0H3&8hyVZp diff --git a/.idea/workspace.xml b/.idea/workspace.xml index de3bb01..68195de 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -2,7 +2,7 @@ - + @@ -33,7 +33,7 @@ - + @@ -129,7 +129,7 @@ - + @@ -430,7 +430,7 @@ - + @@ -438,20 +438,20 @@ - - + - - + + - + + @@ -470,7 +470,20 @@ - + + + + + + + + + + + + + + @@ -478,6 +491,13 @@ + + + + + + + @@ -496,7 +516,6 @@ - @@ -518,7 +537,6 @@ - @@ -532,7 +550,7 @@ - + diff --git a/traffic_sim.ipynb b/traffic_sim.ipynb index 2fffc2b..a84ed10 100644 --- a/traffic_sim.ipynb +++ b/traffic_sim.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 175, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -45,7 +45,7 @@ "class Car:\n", " def __init__(self, location, following_who=None):\n", " self.speed = 30\n", - " self.max_speed = 33\n", + " self.max_speed = 60\n", " self.min_distance = int(self.speed + 5)\n", " self.location = location\n", " self.following_who = following_who\n", @@ -105,7 +105,7 @@ " location -= 32\n", " car_in_front = car_to_spawn\n", "\n", - "for _ in range(60):\n", + "for _ in range(120):\n", " for car in car_list:\n", " car.simulate()\n", " [speed_log.append(car.speed)]\n", @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 10, "metadata": { "collapsed": false, "scrolled": true @@ -127,9 +127,9 @@ "outputs": [ { "data": { - "image/png": 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Wc7X3AODS5E1W6/W67vNI7ccPpDGOTo/1frNMbOpUxdz3OkHrvA49p5PO3wit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\nFUn+IclNSU4k+VKSt3T3wyvf4/JeAAA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Ek5zztw9+zqQfAAAAANxoatJv8YZN+gEAAADArS3ny9Y2/eByFeyhrDGnebK4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\nbRGr3nfL/ZGqx7VgmuGwgbe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"text/plain": [ - "" + "" ] }, "metadata": {}, From 5cce4a776b2e44071e9dc5ba5c8e0fb1ace313fa Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Wed, 22 Jul 2015 21:49:14 -0400 Subject: [PATCH 10/11] cleanup --- baseball3.py | 26 ----------------------- baseball4.py | 58 ---------------------------------------------------- 2 files changed, 84 deletions(-) delete mode 100644 baseball3.py delete mode 100644 baseball4.py diff --git a/baseball3.py b/baseball3.py deleted file mode 100644 index e0c1257..0000000 --- a/baseball3.py +++ /dev/null @@ -1,26 +0,0 @@ -class Team: - def __init__(self, name, city, rank): - self.name = name - self.city = city - self.rank = rank - - def __str__(self): - return "{} {}".format(self.city, self.name) - - -class Game: - def __init__(self, team_1, team_2): - self.home_team = team_1 - self.visiting_team = team_2 - - def __str__(self): - return "Welcome to a game between the {}d {} and the {}d {}".format(self.home_team.rank, self.home_team, self.visiting_team.rank, self.visiting_team) - -team_1 = Team("Astros", "Houston", "3rd place") -print(team_1) -team_2 = Team("Yankees", "New York", "5th place") -print(team_2) - -game = Game(team_1, team_2) - -print(game) \ No newline at end of file diff --git a/baseball4.py b/baseball4.py deleted file mode 100644 index 0f26fdd..0000000 --- a/baseball4.py +++ /dev/null @@ -1,58 +0,0 @@ -import random - -class Team: - def __init__(self, name, city, rank, stadium): - self.name = name - self.city = city - self.rank = rank - self.stadium = stadium - - def __str__(self): - return "{} {}".format(self.city, self.name) - -class Game: - def __init__(self, team_1, team_2): - self.home_team = team_1 - self.visiting_team = team_2 - - def __str__(self): - return "WELCOME to {}, the home of the {}d {}. Tonight they will be playing against the {}d {}.".format\ - (self.home_team.stadium, self.home_team.rank, self.home_team, self.visiting_team.rank, self.visiting_team) - -'''class TeamData: #this isn't how I should do this I think. I should do sub-classes - def __init__(self): - self.random_team_1 = random.rand - - team_info = [["Cubs", "Chicago", "last place", "Wrigley Field"], - ["Royals", "Kansas City", "first place", "The 'K', Kaufman Stadium"], - ["Cardinals", "St. Louis", "fifth place", "Busch Stadium"], - ["Astros", "Houston", "forth place", "Minute Maid Park"], - ["Padres", "San Diego", "second place", "Petco Park"], - ["Yankees", "New York", "third place", "Yankee Stadium"] - ]''' - -# ok I see an alert here mentioning that I'm missing a Super... but it still works so I'm curious -class Cubs(Team): #not sure about this - def __init__(self): - self.name = "Cubs" - self.city = "Chicago" - self.rank = "last place" - self.stadium = "Wrigley Field" - - - - -# standard team entry -team_1 = Team("Astros", "Houston", "3rd place", "Minute Maid Park") -print(team_1) - -# attempting toward auto entry for use with random choice for team -# t1 = ["Cubs", "Chicago", "last place", "Wrigley Field"] -# team_2 = Team(t1) - -# attempting a subclass to create a team... SUCCESS! -team_2 = Cubs() -print(team_2) - -game = Game(team_1, team_2) -print(game) \ No newline at end of file From b8fa32b4f1a2957befc89c0cd0382bbabaa1b056 Mon Sep 17 00:00:00 2001 From: Jeff Hacker Date: Tue, 18 Aug 2015 16:29:08 -0400 Subject: [PATCH 11/11] Readme --- .gitignore | 1 + .../traffic_sim-checkpoint.ipynb | 43 ++++---- README.md | 4 +- original bloated cars.py | 98 ------------------- traffic_sim.ipynb | 16 +-- 5 files changed, 37 insertions(+), 125 deletions(-) delete mode 100644 original bloated cars.py diff --git a/.gitignore b/.gitignore index 574ba10..606512b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ .direnv/ __pycache__/ +.envrc diff --git a/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb b/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb index e8cb92e..6459344 100644 --- a/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb +++ b/.ipynb_checkpoints/traffic_sim-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 141, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -12,15 +12,15 @@ "import math\n", "import statistics\n", "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", "import numpy as np\n", + "%matplotlib inline\n", "\n", "#import traffic_sim.py" ] }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -34,8 +34,6 @@ } ], "source": [ - "import random\n", - "\n", "class Road:\n", " def __init__(self):\n", " self.length = 1000\n", @@ -46,9 +44,9 @@ "\n", "class Car:\n", " def __init__(self, location, following_who=None):\n", - " self.speed = 0\n", - " self.max_speed = 33\n", - " self.min_distance = int(self.speed + 15)\n", + " self.speed = 30\n", + " self.max_speed = 60\n", + " self.min_distance = int(self.speed + 5)\n", " self.location = location\n", " self.following_who = following_who\n", "\n", @@ -60,10 +58,10 @@ " def accelerate(self):\n", " if self.speed < self.max_speed:\n", " self.speed += 2\n", - " return\n", + " # return\n", " else:\n", " self.speed = self.following_who.speed\n", - " return\n", + " # return\n", "\n", " def decelerate(self):\n", " distraction = random.randint(0, 9)\n", @@ -107,7 +105,7 @@ " location -= 32\n", " car_in_front = car_to_spawn\n", "\n", - "for _ in range(60):\n", + "for _ in range(120):\n", " for car in car_list:\n", " car.simulate()\n", " [speed_log.append(car.speed)]\n", @@ -121,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true @@ -129,9 +127,9 @@ "outputs": [ { "data": { - "image/png": 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BSks1tKsT26+OwsmR+GtUusfu58XrS33oJgDnMuy6A/qBUNYBbGAehjETsD2w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\nfTlQUFBQUNAOXcJA/6ZS6isADgD4t0qpzwAAET0N4JMAngb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AZzT9IKOWPbyLfXmQTrsaW+nhNex9LdGJXGvszqUX1tpmO3g3D9J3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\nbv8uItlrn7gez92NzDh/Tn7NZ9YQm2M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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -141,14 +139,14 @@ "source": [ "x = traffic_log\n", "y = itteration_log\n", - "\n", - "plt.scatter(x, y)\n", + "plt.rcParams['figure.figsize'] = 22, 12\n", + "plt.scatter(x, y, marker = \"_\", c=['b', 'r', 'g'])\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -157,7 +155,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "35.81240245791174\n" + "62.216772722464235\n" ] } ], @@ -172,6 +170,15 @@ "\n", "print(optimum_speed)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/README.md b/README.md index 500c038..58fb3a0 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,6 @@ -# Traffic simulation +# Run traffic_sim.ipynb using Ipython Notebook + +## Traffic simulation ## Description diff --git a/original bloated cars.py b/original bloated cars.py deleted file mode 100644 index de16102..0000000 --- a/original bloated cars.py +++ /dev/null @@ -1,98 +0,0 @@ -import random - -class Road: - def __init__(self): - self.length = 1000 - - def __str__(self): - return self.length - - -class Car: - def __init__(self, make, model, color, location, following_who): - self.color = color - self.make = make - self.model = model - self.size = 15 - # speed in m/s - self.speed = 10 - self.max_speed = 33 - self.safe_driver = True - self.min_distance = self.speed + 15 - self.location = location - self.following_who = following_who - - def __str__(self): - return "{} {} {}".format(self.color, self.make, self.model, self.location, self.following_who) - - def decelerate(self): - distraction = random.randint(0, 9) - if distraction == 0: - # speed in m/s - if self.speed <= 0: - self.speed = 0 - else: - self.speed -= 2 - else: - return - - def accelerate(self): - if self.following_who.location >= self.min_distance: - self.speed += 2 - else: - return - - - - - -car_1 = Car("Hummer", "H1", "red", 580, "car_30") -car_2 = Car("Jeep", "Wrangler", "black", 560, "car_1") -car_3 = Car("VW", "Jetta", "white", 540, "car_2") -car_4 = Car("Jeep", "Cherokee", "green", 520, "car_3") -car_5 = Car("Isuzu", "Rodeo", "red", 500, "car_4") -car_6 = Car("Mitsubishi", "Lancer", "Aqua", 480, "car_5") -car_7 = Car("Ford", "Festiva", "baby blue", 460, "car_6") -car_8 = Car("Hyundai", "Elantra", "dark blue", 440, "car_7") -car_9 = Car("Lincoln", "Towncar", "light blue", 420, "car_8") -car_10 = Car("Ford", "Mustang", "red", 400, "car_9") -car_11 = Car("Chevy", "Impala", "tan", 380, "car_10") -car_12 = Car("Ford", "Fairmont", "green", 360, "car_11") -car_13 = Car("Dodge", "minivan", "plum", 340, "car_12") -car_14 = Car("Chevy", "pickup", "green", 320, "car_13") -car_15 = Car("Ford", "Taurus", "green", 300, "car_14") -car_16 = Car("Ford", "Escrort", "red", 280, "car_15") -car_17 = Car("Icon", "CJ", "grey", 260, "car_16") -car_18 = Car("Mack", "dump truck", "white", 240, "car_17") -car_19 = Car("Dodge", "Ram", "silver", 220, "car_18") -car_20 = Car("Chevy", "dually", "gold", 200, "car_19") -car_21 = Car("Mazda", "mini truck", "rusty, brown", 180, "car_20") -car_22 = Car("Chevy", "Chevette", "tan", 160, "car_21") -car_23 = Car("Datsun", "B210", "red", 140, "car_22") -car_24 = Car("Ford", "pickup", "black", 120, "car_23") -car_25 = Car("AMC", "Pacer", "white", 100, "car_24") -car_26 = Car("Jeep", "CJ7", "red", 80, "car_25") -car_27 = Car("Honda", "Accord", "white", 60, "car_26") -car_28 = Car("Chevy", "Nova", "bronze", 40, "car_27") -car_29 = Car("Wayne Ind.", "Batmobile", "black", 20, "car_28") -car_30 = Car("home-made", "motorized lawnchair", "ducktaped", 0, "car_29") - - -car_list = [car_1, car_2, car_3, car_4, car_5, car_6, car_7, car_8, car_9, car_10, car_11, car_12, car_13, car_14, - car_15, car_16, car_17, car_18, car_19, car_20, car_21, car_22, car_23, car_24, car_25, car_26, car_27, - car_28, car_29, car_30] - -# random just for fun and may use somehow for homework -x = random.randint(0, 29) - -print(car_list[x]) - -print(car_1.speed) - -car_1.decelerate() - -print(car_1.speed) - -# car_1.accelerate() - -print(car_1.speed) \ No newline at end of file diff --git a/traffic_sim.ipynb b/traffic_sim.ipynb index a84ed10..6459344 100644 --- a/traffic_sim.ipynb +++ b/traffic_sim.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -12,15 +12,15 @@ "import math\n", "import statistics\n", "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", "import numpy as np\n", + "%matplotlib inline\n", "\n", "#import traffic_sim.py" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true @@ -127,9 +127,9 @@ "outputs": [ { "data": { - "image/png": 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Ek5zztw9+zqQfAAAAANxoatJv8YZN+gEAAADArS3ny9Y2/eByFeyhrDGnebK4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\nbRGr3nfL/ZGqx7VgmuGwgbe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AZzT9IKOWPbyLfXmQTrsaW+nhNex9LdGJXGvszqUX1tpmO3g3D9J3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\nbv8uItlrn7gez92NzDh/Tn7NZ9YQm2M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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -155,7 +155,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "34.59764396783137\n" + "62.216772722464235\n" ] } ],