Custom Parameter (Strange Attractor Inside!)ΒΆ
Download: example_05_custom_parameter.py
Here you can see an example of a custom parameter and how to reload results and use them for analysis. We will simulate the Lorenz Attractor and integrate with a simple Euler method. We will explore three different initial conditions.
__author__ = 'Robert Meyer'
import numpy as np
import inspect
import os # For path names being viable under Windows and Linux
from pypet import Environment, Parameter, ArrayParameter, Trajectory
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Here we will see how we can write our own custom parameters and how we can use
# it with a trajectory.
# Now we want to do a more sophisticated simulations, we will integrate a differential equation
# with an Euler scheme
# Let's first define our job to do
def euler_scheme(traj, diff_func):
"""Simulation function for Euler integration.
:param traj:
Container for parameters and results
:param diff_func:
The differential equation we want to integrate
"""
steps = traj.steps
initial_conditions = traj.initial_conditions
dimension = len(initial_conditions)
# This array will collect the results
result_array = np.zeros((steps,dimension))
# Get the function parameters stored into `traj` as a dictionary
# with the (short) names as keys :
func_params_dict = traj.func_params.f_to_dict(short_names=True, fast_access=True)
# Take initial conditions as first result
result_array[0] = initial_conditions
# Now we compute the Euler Scheme steps-1 times
for idx in range(1,steps):
result_array[idx] = diff_func(result_array[idx-1], **func_params_dict) * traj.dt + \
result_array[idx-1]
# Note the **func_params_dict unzips the dictionary, it's the reverse of **kwargs in function
# definitions!
#Finally we want to keep the results
traj.f_add_result('euler_evolution', data=result_array, comment='Our time series data!')
# Ok, now we want to make our own (derived) parameter that stores source code of python functions.
# We do NOT want a parameter that stores an executable function. This would complicate
# the problem a lot. If you have something like that in mind, you might wanna take a look
# at the marshal (http://docs.python.org/2/library/marshal) module
# or dill (https://pypi.python.org/pypi/dill) package.
# Our intention here is to define a parameter that we later on use as a derived parameter
# to simply keep track of the source code we use ('git' would be, of course, the better solution
# but this is just an illustrative example)
class FunctionParameter(Parameter):
# We need to override the `f_set` function and simply extract the the source code if our
# item is callable and store this instead.
def f_set(self, data):
if callable(data):
data = inspect.getsource(data)
return super(FunctionParameter, self).f_set(data)
# For more complicate parameters you might consider implementing:
# `f_supports` (we do not need it since we convert the data to stuff the parameter already
# supports, and that is strings!)
#
# and
# the private functions
#
# `_values_of_same_type` (to tell whether data is similar, i.e. of two data items agree in their
# type, this is important to only allow exploration within the same dimension.
# For instance, a parameter that stores integers, should only explore integers etc.)
#
# and
#
# `_equal_values` (to tell if two data items are equal. This is important for merging if you
# want to erase duplicate parameter points. The trajectory needs to know when a
# parameter space point was visited before.)
#
# and
#
# `_store` (to be able to turn complex data into basic types understood by the storage service)
#
# and
#
# `_load` (to be able to recover your complex data form the basic types understood by the storage
# service)
#
# But for now we will rely on the parent functions and hope for the best!
# Ok now let's follow the ideas in the final section of the cookbook and let's
# have a part in our simulation that only defines the parameters.
def add_parameters(traj):
"""Adds all necessary parameters to the `traj` container"""
traj.f_add_parameter('steps', 10000, comment='Number of time steps to simulate')
traj.f_add_parameter('dt', 0.01, comment='Step size')
# Here we want to add the initial conditions as an array parameter. We will simulate
# a 3-D differential equation, the Lorenz attractor.
traj.f_add_parameter(ArrayParameter,'initial_conditions', np.array([0.0,0.0,0.0]),
comment = 'Our initial conditions, as default we will start from'
' origin!')
# We will group all parameters of the Lorenz differential equation into the group 'func_params'
traj.f_add_parameter('func_params.sigma', 10.0)
traj.f_add_parameter('func_params.beta', 8.0/3.0)
traj.f_add_parameter('func_params.rho', 28.0)
#For the fun of it we will annotate the group
traj.func_params.v_annotations.info='This group contains as default the original values chosen ' \
'by Edward Lorenz in 1963. Check it out on wikipedia ' \
'(https://en.wikipedia.org/wiki/Lorenz_attractor)!'
# We need to define the lorenz function, we will assume that the value array is 3 dimensional,
# First dimension contains the x-component, second y-component, and third the z-component
def diff_lorenz(value_array, sigma, beta, rho):
"""The Lorenz attractor differential equation
:param value_array: 3d array containing the x,y, and z component values.
:param sigma: Constant attractor parameter
:param beta: FConstant attractor parameter
:param rho: Constant attractor parameter
:return: 3d array of the Lorenz system evaluated at `value_array`
"""
diff_array = np.zeros(3)
diff_array[0] = sigma * (value_array[1]-value_array[0])
diff_array[1] = value_array[0] * (rho - value_array[2]) - value_array[1]
diff_array[2] = value_array[0] * value_array[1] - beta * value_array[2]
return diff_array
# And here goes our main function
def main():
filename = os.path.join('hdf5', 'example_05.hdf5')
env = Environment(trajectory='Example_05_Euler_Integration',
filename=filename,
file_title='Example_05_Euler_Integration',
overwrite_file=True,
comment='Go for Euler!')
traj = env.trajectory
trajectory_name = traj.v_name
# 1st a) phase parameter addition
add_parameters(traj)
# 1st b) phase preparation
# We will add the differential equation (well, its source code only) as a derived parameter
traj.f_add_derived_parameter(FunctionParameter,'diff_eq', diff_lorenz,
comment='Source code of our equation!')
# We want to explore some initial conditions
traj.f_explore({'initial_conditions' : [
np.array([0.01,0.01,0.01]),
np.array([2.02,0.02,0.02]),
np.array([42.0,4.2,0.42])
]})
# 3 different conditions are enough for an illustrative example
# 2nd phase let's run the experiment
# We pass `euler_scheme` as our top-level simulation function and
# the Lorenz equation 'diff_lorenz' as an additional argument
env.run(euler_scheme, diff_lorenz)
# We don't have a 3rd phase of post-processing here
# 4th phase analysis.
# I would recommend to do post-processing completely independent from the simulation,
# but for simplicity let's do it here.
# Let's assume that we start all over again and load the entire trajectory new.
# Yet, there is an error within this approach, do you spot it?
del traj
traj = Trajectory(filename=filename)
# We will only fully load parameters and derived parameters.
# Results will be loaded manually later on.
try:
# However, this will fail because our trajectory does not know how to
# build the FunctionParameter. You have seen this coming, right?
traj.f_load(name=trajectory_name, load_parameters=2, load_derived_parameters=2,
load_results=1)
except ImportError as e:
print('That did\'nt work, I am sorry: %s ' % str(e))
# Ok, let's try again but this time with adding our parameter to the imports
traj = Trajectory(filename=filename,
dynamically_imported_classes=FunctionParameter)
# Now it works:
traj.f_load(name=trajectory_name, load_parameters=2, load_derived_parameters=2,
load_results=1)
#For the fun of it, let's print the source code
print('\n ---------- The source code of your function ---------- \n %s' % traj.diff_eq)
# Let's get the exploration array:
initial_conditions_exploration_array = traj.f_get('initial_conditions').f_get_range()
# Now let's plot our simulated equations for the different initial conditions:
# We will iterate through the run names
for idx, run_name in enumerate(traj.f_get_run_names()):
#Get the result of run idx from the trajectory
euler_result = traj.results.f_get(run_name).euler_evolution
# Now we manually need to load the result. Actually the results are not so large and we
# could load them all at once. But for demonstration we do as if they were huge:
traj.f_load_item(euler_result)
euler_data = euler_result.data
#Plot fancy 3d plot
fig = plt.figure(idx)
ax = fig.gca(projection='3d')
x = euler_data[:,0]
y = euler_data[:,1]
z = euler_data[:,2]
ax.plot(x, y, z, label='Initial Conditions: %s' % str(initial_conditions_exploration_array[idx]))
plt.legend()
plt.show()
# Now we free the data again (because we assume its huuuuuuge):
del euler_data
euler_result.f_empty()
# You have to click through the images to stop the example_05 module!
# Finally disable logging and close all log-files
env.disable_logging()
if __name__ == '__main__':
main()