def add_muscle(self, muscle):
"""Add the muscle model to the system.
Parameters
----------
muscle: <Muscle>
Instance of muscle model
Example:
--------
>>> from muscle import Muscle
>>> from system_parameters import MuscleParameters
>>> muscle = Muscle(MuscleParameters()) #: Default muscle
>>> isometric_system = IsometricMuscleSystem()
>>> isometric_system.add_muscle(muscle)
"""
if self.muscle is not None:
pylog.warning(
'You have already added the muscle model to the system.')
return
else:
if muscle.__class__ is not Muscle:
pylog.error('Trying to set of type {} to muscle'.format(
muscle.__class__))
raise TypeError()
else:
pylog.info('Added new muscle model to the system')
self.muscle = muscle
def exercise_example(timestep):
"""Exercise example"""
# Parameters
parameter_set = [
SimulationParameters(
duration=10, # Simulation duration in [s]
timestep=timestep, # Simulation timestep in [s]
spawn_position=[0, 0, 0.1], # Robot position in [m]
spawn_orientation=[0, 0, 0], # Orientation in Euler angles [rad]
drive=3, # An example of parameter part of the grid search
amplitude_gradient=None,
#amplitudes=[1, 2, 3], # Just an example
phase_lag=2 * np.pi / 10, # or np.zeros(n_joints) for example
# Another example
frequency=1,
# ...
) for drive in np.linspace(1, 2, 2)
# for amplitudes in ...
# for ...
]
# Grid search
for simulation_i, sim_parameters in enumerate(parameter_set):
filename = './logs/example/simulation_{}.{}'
sim, data = simulation(
sim_parameters=sim_parameters, # Simulation parameters, see above
arena='water', # Can also be 'ground' or 'amphibious'
# fast=True, # For fast mode (not real-time)
# headless=True, # For headless mode (No GUI, could be faster)
# record=True, # Record video, see below for saving
# video_distance=1.5, # Set distance of camera to robot
# video_yaw=0, # Set camera yaw for recording
# video_pitch=-45, # Set camera pitch for recording
)
# Log robot data
data.to_file(filename.format(simulation_i, 'h5'), sim.iteration)
# Log simulation parameters
with open(filename.format(simulation_i, 'pickle'), 'wb') as param_file:
pickle.dump(sim_parameters, param_file)
# Save video
if sim.options.record:
if 'ffmpeg' in manimation.writers.avail:
sim.interface.video.save(
filename='salamandra_robotica_simulation.mp4',
iteration=sim.iteration,
writer='ffmpeg',
)
elif 'html' in manimation.writers.avail:
# FFmpeg might not be installed, use html instead
sim.interface.video.save(
filename='salamandra_robotica_simulation.html',
iteration=sim.iteration,
writer='html',
)
else:
pylog.error('No known writers, maybe you can use: {}'.format(
manimation.writers.avail))
def exercise_8f(timestep, phase_lag_vector, experience):
"""Exercise 8f"""
# Parameters
parameter_set = [
SimulationParameters(
duration=10, # Simulation duration in [s]
timestep=timestep, # Simulation timestep in [s]
spawn_position=[0, 0, 0.1], # Robot position in [m]
spawn_orientation=[0, 0, 0], # Orientation in Euler angles [rad]
drive_mlr=2, # drive so the robot can walk
amplitude_limb=0.5,
phase_lag=phase_lag,
phase_limb_body=np.pi / 2,
exercise_8f=True
# ...
) for phase_lag in phase_lag_vector
]
# Grid search
for simulation_i, sim_parameters in enumerate(parameter_set):
filename = './logs/{}/simulation_{}.{}'
sim, data = simulation(
sim_parameters=sim_parameters, # Simulation parameters, see above
arena='ground', # Can also be 'ground' or 'amphibious'
#fast=True, # For fast mode (not real-time)
#headless=True, # For headless mode (No GUI, could be faster)
# record=True, # Record video, see below for saving
# video_distance=1.5, # Set distance of camera to robot
# video_yaw=0, # Set camera yaw for recording
# video_pitch=-45, # Set camera pitch for recording
)
# Log robot data
data.to_file(filename.format(experience, simulation_i, 'h5'),
sim.iteration)
# Log simulation parameters
with open(filename.format(experience, simulation_i, 'pickle'),
'wb') as param_file:
pickle.dump(sim_parameters, param_file)
# Save video
if sim.options.record:
if 'ffmpeg' in manimation.writers.avail:
sim.interface.video.save(
filename='salamandra_robotica_simulation.mp4',
iteration=sim.iteration,
writer='ffmpeg',
)
elif 'html' in manimation.writers.avail:
# FFmpeg might not be installed, use html instead
sim.interface.video.save(
filename='salamandra_robotica_simulation.html',
iteration=sim.iteration,
writer='html',
)
else:
pylog.error('No known writers, maybe you can use: {}'.format(
manimation.writers.avail))
def exercise_8g(timestep):
"""Exercise 8g"""
# Parameters
parameter_set = [
SimulationParameters(
duration=30,
timestep=timestep,
spawn_position=[-0.8, 0, 0.1], # Robot position in [m]
spawn_orientation=[0, 0,
np.pi], # Orientation in Euler angles [rad]
amplitude_gradient=True,
drive=1.5,
amplitudes=[0.4, 1],
frequency=1,
nominal_radius=0.3,
phase_lag=1.95)
]
# Grid search
for simulation_i, sim_parameters in enumerate(parameter_set):
filename = './logs/exercise_8g/simulation_{}.{}'
sim, data = simulation(
sim_parameters=sim_parameters, # Simulation parameters, see above
arena='amphibious', # Can also be 'ground' or 'amphibious'
fast=True, # For fast mode (not real-time)
# headless=True, # For headless mode (No GUI, could be faster)
# record=True, # Record video, see below for saving
# video_distance=1.5, # Set distance of camera to robot
# video_yaw=0, # Set camera yaw for recording
# video_pitch=-45, # Set camera pitch for recording
)
# Log robot data
data.to_file(filename.format(simulation_i, 'h5'), sim.iteration)
# Log simulation parameters
with open(filename.format(simulation_i, 'pickle'), 'wb') as param_file:
pickle.dump(sim_parameters, param_file)
# Save video
if sim.options.record:
if 'ffmpeg' in manimation.writers.avail:
sim.interface.video.save(
filename='salamandra_robotica_simulation.mp4',
iteration=sim.iteration,
writer='ffmpeg',
)
elif 'html' in manimation.writers.avail:
# FFmpeg might not be installed, use html instead
sim.interface.video.save(
filename='salamandra_robotica_simulation.html',
iteration=sim.iteration,
writer='html',
)
else:
pylog.error('No known writers, maybe you can use: {}'.format(
manimation.writers.avail))
def load_config_file(self):
"""Load the animal configuration file"""
try:
stream = open(os.path.realpath(self.muscle_joint_config_path), 'r')
self.muscle_joint_config = yaml.load(stream)
biolog.info('Successfully loaded the file : {}'.format(
os.path.split(self.muscle_joint_config_path)[-1]))
return
except ValueError:
biolog.error('Unable to read the file {}'.format(
self.muscle_joint_config_path))
raise ValueError()
def val(self, data):
"""
Set the value of the parameter
Parameters
----------
data : <float>
Value of the parameter
"""
try:
self._p_list[self.idx] = data
except IndexError:
biolog.error('Unable to find index {} in list {}'.format(
self.idx, self._p_list))
raise IndexError()
""" Analyse system symbolically (corrections) """
import numpy as np
import farms_pylog as pylog
from ex3_pendulum import PendulumParameters, pendulum_system, pendulum_equation
USE_SYMPY = False
try:
import sympy as sp
USE_SYMPY = True
except ImportError as err:
pylog.error(err)
USE_SYMPY = False
def exercise_example(timestep):
"""Exercise example"""
# Parameters
cv_body = [0.2, 0.3]
cv_limb = [0.2, 0.]
cr_body = [0.056, 0.196]
cr_limb = [0.131, 0.131]
parameter_set = [
SimulationParameters(
freqs=1,
duration=10, # Simulation duration in [s]
timestep=timestep, # Simulation timestep in [s]
spawn_position=[-1.3, 0, 0.1], # Robot position in [m]
spawn_orientation=[0, 0, 0], # Orientation in Euler angles [rad]
drive=4, # An example of parameter part of the grid search
amplitudes=[0.3, 0.3],
turn=0,
pattern='walk',
phase_lag=0.2 * np.pi,
absolute_amplitude=False,
phase_body_limb=4 * np.pi / 9,
#transition=['walk','swim'],
#amplitude=1.,
# ...
) for drive in np.linspace(1, 1, 1)
# for amplitudes in ...
# for ...
]
# Grid search
for simulation_i, sim_parameters in enumerate(parameter_set):
filename = './logs/example/simulation_{}.{}'
sim, data = simulation(
sim_parameters=sim_parameters, # Simulation parameters, see above
arena='amphibious', # Can also be 'ground' or 'amphibious'
fast=True, # For fast mode (not real-time)
# headless=True, # For headless mode (No GUI, could be faster)
#record=True, # Record video, see below for saving
# video_distance=1.5, # Set distance of camera to robot
# video_yaw=0, # Set camera yaw for recording
# video_pitch=-45, # Set camera pitch for recording
)
# Log robot data
data.to_file(filename.format(simulation_i, 'h5'), sim.iteration)
# Log simulation parameters
with open(filename.format(simulation_i, 'pickle'), 'wb') as param_file:
pickle.dump(sim_parameters, param_file)
# Save video
if sim.options.record:
if 'ffmpeg' in manimation.writers.avail:
sim.interface.video.save(
filename='video_transition_from_walking_to_swimming.mp4',
iteration=sim.iteration,
writer='ffmpeg',
)
elif 'html' in manimation.writers.avail:
# FFmpeg might not be installed, use html instead
sim.interface.video.save(
filename='transition_from_walking_to_swimming.html',
iteration=sim.iteration,
writer='html',
)
else:
pylog.error('No known writers, maybe you can use: {}'.format(
manimation.writers.avail))
pylog.info('Element wise subtraction of numpy arrays b-a :\n{}'.format(b - a))
pylog.info("""Have a look at Array broadcasting to understand how numpy
compares two arrays for math operations""")
# Create a random numpy array using random method
A = np.ones((2, 3))
pylog.info('Numpy array A of size (2,3) using ones method :\n{}'.format(A))
# Create a random numpy array using random method
B = np.ones((3, 2))
pylog.info('Numpy array B of size (3,2) using ones method :\n{}'.format(B))
pylog.info('Matrix multiplication A*B using numpy dot method :\n{}'.format(
np.dot(A, B)))
pylog.info('Matrix multiplication B*A using numpy dot method :\n{}'.format(
np.dot(B, A)))
try:
pylog.info("""Matrix multiplication transpose(A)*B using numpy dot method:
{}""".format(np.dot(A.T, B)))
except ValueError:
pylog.error(
'ValueError: shapes (3,2) and (3,2) not aligned: 2 (dim 1) != 3 (dim 0)'
)
pylog.info('Have a look at numpy matrices method!')
pylog.warning(' Numpy matrices are not very often used!')
digits = (0, 1, 'two') # Create a tuple directly
digits = tuple([0, 1, 'two']) # Create a tuple from list
pylog.warning('A trailing comma is required to indicate its a tuple')
zero = (0, )
# examine a tuple
digits[2] # returns 'two'
len(digits) # returns 3
digits.count(0) # counts the number of instances of that value (1)
digits.index(1) # returns the index of the first instance of that value (1)
# elements of a tuple cannot be modified
try:
digits[2] = 2 # throws an error
except BaseException:
pylog.error('Tuples cannot be edited once initialized')
# concatenate tuples
digits = digits + (3, 4)
# create a single tuple with elements repeated (also works with lists)
(3, 4) * 2 # returns (3, 4, 3, 4)
# sort a list of tuples
tens = [(20, 60), (10, 40), (20, 30)]
sorted(tens) # sorts by first element in tuple, then second element
# returns [(10, 40), (20, 30), (20, 60)]
# tuple unpacking
bart = ('male', 10, 'simpson') # create a tuple
(sex, age, surname) = bart # assign three values at once
