python_code stringlengths 0 869k |
|---|
from .vec_env import VecEnvWrapper
import numpy as np
from gym import spaces
class VecFrameStack(VecEnvWrapper):
def __init__(self, venv, nstack):
self.venv = venv
self.nstack = nstack
wos = venv.observation_space # wrapped ob space
low = np.repeat(wos.low, self.nstack, axis=-1)
... |
import contextlib
import os
from abc import ABC, abstractmethod
from baselines.common.tile_images import tile_images
class AlreadySteppingError(Exception):
"""
Raised when an asynchronous step is running while
step_async() is called again.
"""
def __init__(self):
msg = 'already running an... |
import multiprocessing as mp
import numpy as np
from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
def worker(remote, parent_remote, env_fn_wrappers):
def step_env(env, action):
ob, reward, done, info = env.step(action)
if done:
ob = env.reset()
return ob, rew... |
"""
Helpers for dealing with vectorized environments.
"""
from collections import OrderedDict
import gym
import numpy as np
def copy_obs_dict(obs):
"""
Deep-copy an observation dict.
"""
return {k: np.copy(v) for k, v in obs.items()}
def dict_to_obs(obs_dict):
"""
Convert an observation di... |
import os
from baselines import logger
from baselines.common.vec_env import VecEnvWrapper
from gym.wrappers.monitoring import video_recorder
class VecVideoRecorder(VecEnvWrapper):
"""
Wrap VecEnv to record rendered image as mp4 video.
"""
def __init__(self, venv, directory, record_video_trigger, vide... |
from .vec_env import VecEnvObservationWrapper
class VecExtractDictObs(VecEnvObservationWrapper):
def __init__(self, venv, key):
self.key = key
super().__init__(venv=venv,
observation_space=venv.observation_space.spaces[self.key])
def process(self, obs):
return obs[self.key]... |
from .vec_env import AlreadySteppingError, NotSteppingError, VecEnv, VecEnvWrapper, VecEnvObservationWrapper, CloudpickleWrapper
from .dummy_vec_env import DummyVecEnv
from .shmem_vec_env import ShmemVecEnv
from .subproc_vec_env import SubprocVecEnv
from .vec_frame_stack import VecFrameStack
from .vec_monitor import Ve... |
from . import VecEnvWrapper
from baselines.bench.monitor import ResultsWriter
import numpy as np
import time
from collections import deque
class VecMonitor(VecEnvWrapper):
def __init__(self, venv, filename=None, keep_buf=0, info_keywords=()):
VecEnvWrapper.__init__(self, venv)
self.eprets = None
... |
from . import VecEnvWrapper
import numpy as np
class VecNormalize(VecEnvWrapper):
"""
A vectorized wrapper that normalizes the observations
and returns from an environment.
"""
def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8, use_tf=False):
Vec... |
import numpy as np
from .vec_env import VecEnv
from .util import copy_obs_dict, dict_to_obs, obs_space_info
class DummyVecEnv(VecEnv):
"""
VecEnv that does runs multiple environments sequentially, that is,
the step and reset commands are send to one environment at a time.
Useful when debugging and when... |
import numpy as np
from baselines.common.runners import AbstractEnvRunner
class Runner(AbstractEnvRunner):
"""
We use this object to make a mini batch of experiences
__init__:
- Initialize the runner
run():
- Make a mini batch
"""
def __init__(self, *, env, model, nsteps, gamma, lam):
... |
import os
import time
import numpy as np
import os.path as osp
from baselines import logger
from collections import deque
from baselines.common import explained_variance, set_global_seeds
from baselines.common.policies import build_policy
try:
from mpi4py import MPI
except ImportError:
MPI = None
from baselines... |
import tensorflow as tf
import numpy as np
from baselines.ppo2.model import Model
class MicrobatchedModel(Model):
"""
Model that does training one microbatch at a time - when gradient computation
on the entire minibatch causes some overflow
"""
def __init__(self, *, policy, ob_space, ac_space, nbat... |
import gym
import tensorflow as tf
import numpy as np
from functools import partial
from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
from baselines.common.tf_util import make_session
from baselines.ppo2.ppo2 import learn
from baselines.ppo2.microbatched_model import MicrobatchedModel
def test_microbatc... |
import tensorflow as tf
import functools
from baselines.common.tf_util import get_session, save_variables, load_variables
from baselines.common.tf_util import initialize
try:
from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
from mpi4py import MPI
from baselines.common.mpi_util import sync_... |
def mujoco():
return dict(
nsteps=2048,
nminibatches=32,
lam=0.95,
gamma=0.99,
noptepochs=10,
log_interval=1,
ent_coef=0.0,
lr=lambda f: 3e-4 * f,
cliprange=0.2,
value_network='copy'
)
def atari():
return dict(
nsteps=1... |
import numpy as np
def make_sample_her_transitions(replay_strategy, replay_k, reward_fun):
"""Creates a sample function that can be used for HER experience replay.
Args:
replay_strategy (in ['future', 'none']): the HER replay strategy; if set to 'none',
regular DDPG experience replay is u... |
import tensorflow as tf
from baselines.her.util import store_args, nn
class ActorCritic:
@store_args
def __init__(self, inputs_tf, dimo, dimg, dimu, max_u, o_stats, g_stats, hidden, layers,
**kwargs):
"""The actor-critic network and related training code.
Args:
in... |
import os
import subprocess
import sys
import importlib
import inspect
import functools
import tensorflow as tf
import numpy as np
from baselines.common import tf_util as U
def store_args(method):
"""Stores provided method args as instance attributes.
"""
argspec = inspect.getfullargspec(method)
def... |
from collections import deque
import numpy as np
import pickle
from baselines.her.util import convert_episode_to_batch_major, store_args
class RolloutWorker:
@store_args
def __init__(self, venv, policy, dims, logger, T, rollout_batch_size=1,
exploit=False, use_target_net=False, compute_Q=F... |
from collections import OrderedDict
import numpy as np
import tensorflow as tf
from tensorflow.contrib.staging import StagingArea
from baselines import logger
from baselines.her.util import (
import_function, store_args, flatten_grads, transitions_in_episode_batch, convert_episode_to_batch_major)
from baselines.h... |
import threading
import numpy as np
class ReplayBuffer:
def __init__(self, buffer_shapes, size_in_transitions, T, sample_transitions):
"""Creates a replay buffer.
Args:
buffer_shapes (dict of ints): the shape for all buffers that are used in the replay
buffer
... |
import os
import click
import numpy as np
import json
from mpi4py import MPI
from baselines import logger
from baselines.common import set_global_seeds, tf_util
from baselines.common.mpi_moments import mpi_moments
import baselines.her.experiment.config as config
from baselines.her.rollout import RolloutWorker
def mp... |
import threading
import numpy as np
from mpi4py import MPI
import tensorflow as tf
from baselines.her.util import reshape_for_broadcasting
class Normalizer:
def __init__(self, size, eps=1e-2, default_clip_range=np.inf, sess=None):
"""A normalizer that ensures that observations are approximately distribu... |
# DEPRECATED, use baselines.common.plot_util instead
import os
import matplotlib.pyplot as plt
import numpy as np
import json
import seaborn as sns; sns.set()
import glob2
import argparse
def smooth_reward_curve(x, y):
halfwidth = int(np.ceil(len(x) / 60)) # Halfwidth of our smoothing convolution
k = halfwi... |
import os
import numpy as np
import gym
from baselines import logger
from baselines.her.ddpg import DDPG
from baselines.her.her_sampler import make_sample_her_transitions
from baselines.bench.monitor import Monitor
DEFAULT_ENV_PARAMS = {
'FetchReach-v1': {
'n_cycles': 10,
},
}
DEFAULT_PARAMS = {
... |
# DEPRECATED, use --play flag to baselines.run instead
import click
import numpy as np
import pickle
from baselines import logger
from baselines.common import set_global_seeds
import baselines.her.experiment.config as config
from baselines.her.rollout import RolloutWorker
@click.command()
@click.argument('policy_fil... |
import gym
import numpy as np
"""Data generation for the case of a single block pick and place in Fetch Env"""
actions = []
observations = []
infos = []
def main():
env = gym.make('FetchPickAndPlace-v1')
numItr = 100
initStateSpace = "random"
env.reset()
print("Reset!")
while len(actions) < ... |
import tensorflow as tf
import tensorflow.contrib.layers as layers
def build_q_func(network, hiddens=[256], dueling=True, layer_norm=False, **network_kwargs):
if isinstance(network, str):
from baselines.common.models import get_network_builder
network = get_network_builder(network)(**network_kwarg... |
from baselines.deepq import models # noqa
from baselines.deepq.build_graph import build_act, build_train # noqa
from baselines.deepq.deepq import learn, load_act # noqa
from baselines.deepq.replay_buffer import ReplayBuffer, PrioritizedReplayBuffer # noqa
def wrap_atari_dqn(env):
from baselines.common.atari_wr... |
"""Deep Q learning graph
The functions in this file can are used to create the following functions:
======= act ========
Function to chose an action given an observation
Parameters
----------
observation: object
Observation that can be feed into the output of make_obs_ph
stochastic: bool... |
from baselines.common.input import observation_input
from baselines.common.tf_util import adjust_shape
# ================================================================
# Placeholders
# ================================================================
class TfInput(object):
def __init__(self, name="(unnamed)"):
... |
import os
import tempfile
import tensorflow as tf
import zipfile
import cloudpickle
import numpy as np
import baselines.common.tf_util as U
from baselines.common.tf_util import load_variables, save_variables
from baselines import logger
from baselines.common.schedules import LinearSchedule
from baselines.common impor... |
import numpy as np
import random
from baselines.common.segment_tree import SumSegmentTree, MinSegmentTree
class ReplayBuffer(object):
def __init__(self, size):
"""Create Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. W... |
def atari():
return dict(
network='conv_only',
lr=1e-4,
buffer_size=10000,
exploration_fraction=0.1,
exploration_final_eps=0.01,
train_freq=4,
learning_starts=10000,
target_network_update_freq=1000,
gamma=0.99,
prioritized_replay=True,
... |
import gym
import itertools
import numpy as np
import tensorflow as tf
import tensorflow.contrib.layers as layers
import baselines.common.tf_util as U
from baselines import logger
from baselines import deepq
from baselines.deepq.replay_buffer import ReplayBuffer
from baselines.deepq.utils import ObservationInput
from... |
import gym
from baselines import deepq
def main():
env = gym.make("PongNoFrameskip-v4")
env = deepq.wrap_atari_dqn(env)
model = deepq.learn(
env,
"conv_only",
convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[256],
dueling=True,
total_timesteps=0
)
... |
import gym
from baselines import deepq
def callback(lcl, _glb):
# stop training if reward exceeds 199
is_solved = lcl['t'] > 100 and sum(lcl['episode_rewards'][-101:-1]) / 100 >= 199
return is_solved
def main():
env = gym.make("CartPole-v0")
act = deepq.learn(
env,
network='mlp'... |
import gym
from baselines import deepq
def main():
env = gym.make("CartPole-v0")
act = deepq.learn(env, network='mlp', total_timesteps=0, load_path="cartpole_model.pkl")
while True:
obs, done = env.reset(), False
episode_rew = 0
while not done:
env.render()
... |
import gym
from baselines import deepq
from baselines.common import models
def main():
env = gym.make("MountainCar-v0")
# Enabling layer_norm here is import for parameter space noise!
act = deepq.learn(
env,
network=models.mlp(num_hidden=64, num_layers=1),
lr=1e-3,
total_t... |
from baselines import deepq
from baselines import bench
from baselines import logger
from baselines.common.atari_wrappers import make_atari
def main():
logger.configure()
env = make_atari('PongNoFrameskip-v4')
env = bench.Monitor(env, logger.get_dir())
env = deepq.wrap_atari_dqn(env)
model = deep... |
import gym
from baselines import deepq
from baselines.common import models
def main():
env = gym.make("MountainCar-v0")
act = deepq.learn(
env,
network=models.mlp(num_layers=1, num_hidden=64),
total_timesteps=0,
load_path='mountaincar_model.pkl'
)
while True:
... |
import tensorflow as tf
def gmatmul(a, b, transpose_a=False, transpose_b=False, reduce_dim=None):
assert reduce_dim is not None
# weird batch matmul
if len(a.get_shape()) == 2 and len(b.get_shape()) > 2:
# reshape reduce_dim to the left most dim in b
b_shape = b.get_shape()
if redu... |
import os.path as osp
import time
import functools
import tensorflow as tf
from baselines import logger
from baselines.common import set_global_seeds, explained_variance
from baselines.common.policies import build_policy
from baselines.common.tf_util import get_session, save_variables, load_variables
from baselines.a... |
import tensorflow as tf
def dense(x, size, name, weight_init=None, bias_init=0, weight_loss_dict=None, reuse=None):
with tf.variable_scope(name, reuse=reuse):
assert (len(tf.get_variable_scope().name.split('/')) == 2)
w = tf.get_variable("w", [x.get_shape()[1], size], initializer=weight_init)
... |
def mujoco():
return dict(
nsteps=2500,
value_network='copy'
)
|
import tensorflow as tf
import numpy as np
import re
# flake8: noqa F403, F405
from baselines.acktr.kfac_utils import *
from functools import reduce
KFAC_OPS = ['MatMul', 'Conv2D', 'BiasAdd']
KFAC_DEBUG = False
class KfacOptimizer():
# note that KfacOptimizer will be truly synchronous (and thus deterministic) ... |
import numpy as np
from baselines.a2c.utils import discount_with_dones
from baselines.common.runners import AbstractEnvRunner
class Runner(AbstractEnvRunner):
"""
We use this class to generate batches of experiences
__init__:
- Initialize the runner
run():
- Make a mini batch of experiences
... |
import os
import numpy as np
import tensorflow as tf
from collections import deque
def sample(logits):
noise = tf.random_uniform(tf.shape(logits))
return tf.argmax(logits - tf.log(-tf.log(noise)), 1)
def cat_entropy(logits):
a0 = logits - tf.reduce_max(logits, 1, keepdims=True)
ea0 = tf.exp(a0)
z0... |
import time
import functools
import tensorflow as tf
from baselines import logger
from baselines.common import set_global_seeds, explained_variance
from baselines.common import tf_util
from baselines.common.policies import build_policy
from baselines.a2c.utils import Scheduler, find_trainable_variables
from baselin... |
from baselines.common import explained_variance, zipsame, dataset
from baselines import logger
import baselines.common.tf_util as U
import tensorflow as tf, numpy as np
import time
from baselines.common import colorize
from collections import deque
from baselines.common import set_global_seeds
from baselines.common.mpi... |
from baselines.common.models import mlp, cnn_small
def atari():
return dict(
network = cnn_small(),
timesteps_per_batch=512,
max_kl=0.001,
cg_iters=10,
cg_damping=1e-3,
gamma=0.98,
lam=1.0,
vf_iters=3,
vf_stepsize=1e-4,
entcoeff=0.00,... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Setup script for pybox2d.
For installation instructions, see INSTALL.
Basic install steps:
python setup.py build
If that worked, then:
python setup.py install
"""
import os
import sys
from glob import glob
__author__='Ken Lauer'
__license__='zlib'
__date__="$Date... |
import os
for platform in ['win32', 'win-amd64']:
versions=['2.5', '2.6', '2.7', '3.0', '3.1', '3.2']
def get_path(platform, version):
if platform=='win32':
version_path=''
else:
version_path='-x64'
return r'c:\python%s%s\python.exe'%(version.replace('.', ''), v... |
from all_classes import *
print("""
/*
* pybox2d -- http://pybox2d.googlecode.com
*
* Copyright (c) 2010 Ken Lauer / sirkne at gmail dot com
*
* This software is provided 'as-is', without any express or implied
* warranty. In no event will the authors be held liable for any damages
* arising from the use of this sof... |
classname = "b2DebugDraw"
gets = "GetFlags SetFlags ClearFlags AppendFlags".split(" ")
sets = "".split(" ")
kwargs = True
# remove duplicates
gets = list(set(gets))
sets = list(set(sets))
renames = ["%%rename(__%s) %s::%s;" % (s, classname, s) for s in gets+sets
if s not in ('GetAnchorA', 'GetAnch... |
from __future__ import print_function
# add "b2ContactPoint", as it's custom
# First, define some classes not seen by swig
ignore = ["b2ContactConstraint", "b2PolygonContact", "b2PolygonAndCircleContact", "b2CircleContact", "b2JointType", "b2BodyType", "b2ContactSolver", "b2PointState", "b2WorldQueryWrapper", "b2Worl... |
from __future__ import print_function
import glob
import re
import os
from all_classes import *
files = glob.glob('../Box2D/Box2D_*.i')
classes = {}
ignore_files = [
'Box2D_kwargs.i',
'Box2D_dir.i',
]
def find_extended(path):
f = open(path, "r")
fn = os.path.split(path)[1]
for li... |
from all_classes import *
print("""
/*
* pybox2d -- http://pybox2d.googlecode.com
*
* Copyright (c) 2010 Ken Lauer / sirkne at gmail dot com
*
* This software is provided 'as-is', without any express or implied
* warranty. In no event will the authors be held liable for any damages
* arising from the use of this sof... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
import itertools
import sys
class testJoints (unittest.TestCase):
world = None
dbody1 = None
dbody2 = None
sbody1 = None
sbody2 = None
def _fail(self, s):
# This little function would save us from an AssertionError on garbag... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
from Box2D import *
import Box2D
class cl (b2ContactListener):
pass
class test_body (unittest.TestCase):
def setUp(self):
pass
def test_world(self):
world = b2World(gravity=(0,-10), doSleep=True)
world = b2World((0,-10)... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
from Box2D import *
class cl (b2ContactListener):
pass
class test_kwargs (unittest.TestCase):
def setUp(self):
pass
def test_kwargs(self):
self.cont_list=cl()
world = b2World(gravity=(0,-10), doSleep=True, contactListen... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
from Box2D import b2Vec2, b2Vec3, b2Mat22, b2Mat33
class testMatrix (unittest.TestCase):
def checkAlmostEqual(self, v1, v2, msg):
for a, b in zip(v1, v2):
self.assertAlmostEqual(a, b, places=3,
msg="%s, a=%f b=%f ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
import Box2D
import sys
class testWorld (unittest.TestCase):
def setUp(self):
pass
def test_world(self):
try:
world = Box2D.b2World(Box2D.b2Vec2(0.0, -10.0), True)
world = Box2D.b2World((0.0, -10.0), True)
... |
#!/usr/bin/env python
import unittest
from Box2D import (b2Vec2, b2Vec3)
class testVector (unittest.TestCase):
def test_vec2_zero(self):
v2 = b2Vec2()
self.assertAlmostEqual(v2.x, 0.0)
self.assertAlmostEqual(v2.y, 0.0)
def test_vec2(self):
x, y = 1.0, 2.0
v = b2Vec2(x... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
from Box2D import b2Color
class testColor (unittest.TestCase):
def checkAlmostEqual(self, v1, v2, msg, places=3):
for i, (a, b) in enumerate(zip(v1, v2)):
self.assertAlmostEqual(a, b, places=places,
msg="(index %... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
import sys
class testBasic (unittest.TestCase):
# def setUp(self):
# pass
def test_import(self):
try:
import Box2D
except ImportError:
self.fail("Unable to import Box2D library (%s)" % sys.exc_info()[1]... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
from Box2D import *
from math import cos, sin
import sys
class ContactListener(b2ContactListener):
pass
class testPolyshape (unittest.TestCase):
def setUp(self):
pass
def dotest(self, world, v):
body = world.CreateDynamicBody(p... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import unittest
import Box2D as b2
import sys
class testEdgeChain (unittest.TestCase):
def setUp(self):
pass
def test_create_edge_chain(self):
world = b2.b2World()
ground = world.CreateBody(position=(0, 20))
try:
groun... |
raise NotImplementedError('This should be auto-generated by swig')
|
raise NotImplementedError
|
# pybox2d -- http://pybox2d.googlecode.com
#
# Copyright (c) 2010 Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# arising from the use of this software.
# Permission is granted to anyone... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This is a simple example of building and running a simulation using Box2D. Here
we create a large ground box and a small dynamic box.
** NOTE **
There is no graphical output for this simple example, only text.
"""
from Box2D import (b2PolygonShape, b2World)
world = ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version Copyright (c) 2010 kne / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any d... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
A small self contained example showing how to use OpencvDrawFuncs
to integrate pybox2d into an opencv mainloop
In short:
One static body:
+ One fixture: big polygon to represent the ground
Two dynamic bodies:
+ One fixture: a polygon
+ One fixture: a circle... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version Copyright (c) 2010 kne / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any d... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version Copyright (c) 2010 kne / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any d... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version Copyright (c) 2010 kne / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any d... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version Copyright (c) 2010 kne / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any d... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version Copyright (c) 2010 kne / sirkne at gmail dot com
#
# Implemented using the pybox2d SWIG interface for Box2D (pybox2d.googlecode.com)
#
# This software is provided 'as-is', without any e... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# C++ version Copyright (c) 2006-2007 Erin Catto http://www.box2d.org
# Python version by Ken Lauer / sirkne at gmail dot com
#
# This software is provided 'as-is', without any express or implied
# warranty. In no event will the authors be held liable for any damages
# a... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Based on Chris Campbell's tutorial from iforce2d.net:
http://www.iforce2d.net/b2dtut/top-down-car
"""
from .framework import (Framework, Keys, main)
import math
class TDGroundArea(object):
"""
An area on the ground that the car can run over
"""
def _... |
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