```
# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the QuTiP: Quantum Toolbox in Python nor the names
# of its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
###############################################################################
from __future__ import print_function
__all__ = ['Options', 'Odeoptions', 'Odedata']
import os
import warnings
from qutip import __version__
[docs]class Options():
"""
Class of options for evolution solvers such as :func:`qutip.mesolve` and
:func:`qutip.mcsolve`. Options can be specified either as arguments to the
constructor::
opts = Options(order=10, ...)
or by changing the class attributes after creation::
opts = Options()
opts.order = 10
Returns options class to be used as options in evolution solvers.
Attributes
----------
atol : float {1e-8}
Absolute tolerance.
rtol : float {1e-6}
Relative tolerance.
method : str {'adams','bdf'}
Integration method.
order : int {12}
Order of integrator (<=12 'adams', <=5 'bdf')
nsteps : int {2500}
Max. number of internal steps/call.
first_step : float {0}
Size of initial step (0 = automatic).
min_step : float {0}
Minimum step size (0 = automatic).
max_step : float {0}
Maximum step size (0 = automatic)
tidy : bool {True,False}
Tidyup Hamiltonian and initial state by removing small terms.
num_cpus : int
Number of cpus used by mcsolver (default = # of cpus).
norm_tol : float
Tolerance used when finding wavefunction norm in mcsolve.
norm_steps : int
Max. number of steps used to find wavefunction norm to within norm_tol
in mcsolve.
average_states : bool {False}
Average states values over trajectories in stochastic solvers.
average_expect : bool {True}
Average expectation values over trajectories for stochastic solvers.
mc_corr_eps : float {1e-10}
Arbitrarily small value for eliminating any divide-by-zero errors in
correlation calculations when using mcsolve.
ntraj : int {500}
Number of trajectories in stochastic solvers.
rhs_reuse : bool {False,True}
Reuse Hamiltonian data.
rhs_with_state : bool {False,True}
Whether or not to include the state in the Hamiltonian function
callback signature.
rhs_filename : str
Name for compiled Cython file.
seeds : ndarray
Array containing random number seeds for mcsolver.
store_final_state : bool {False, True}
Whether or not to store the final state of the evolution in the
result class.
store_states : bool {False, True}
Whether or not to store the state vectors or density matrices in the
result class, even if expectation values operators are given. If no
expectation are provided, then states are stored by default and this
option has no effect.
"""
def __init__(self, atol=1e-8, rtol=1e-6, method='adams', order=12,
nsteps=1000, first_step=0, max_step=0, min_step=0,
average_expect=True, average_states=False, tidy=True,
num_cpus=0, norm_tol=1e-3, norm_steps=5, rhs_reuse=False,
rhs_filename=None, ntraj=500, gui=False, rhs_with_state=False,
store_final_state=False, store_states=False, seeds=None,
steady_state_average=False):
# Absolute tolerance (default = 1e-8)
self.atol = atol
# Relative tolerance (default = 1e-6)
self.rtol = rtol
# Integration method (default = 'adams', for stiff 'bdf')
self.method = method
# Max. number of internal steps/call
self.nsteps = nsteps
# Size of initial step (0 = determined by solver)
self.first_step = first_step
# Minimal step size (0 = determined by solver)
self.min_step = min_step
# Max step size (0 = determined by solver)
self.max_step = max_step
# Maximum order used by integrator (<=12 for 'adams', <=5 for 'bdf')
self.order = order
# Average expectation values over trajectories (default = True)
self.average_states = average_states
# average expectation values
self.average_expect = average_expect
# Number of trajectories (default = 500)
self.ntraj = ntraj
# Holds seeds for rand num gen
self.seeds = seeds
# tidyup Hamiltonian before calculation (default = True)
self.tidy = tidy
# include the state in the function callback signature
self.rhs_with_state = rhs_with_state
# Use preexisting RHS function for time-dependent solvers
self.rhs_reuse = rhs_reuse
# Use filename for preexisting RHS function (will default to last
# compiled function if None & rhs_exists=True)
self.rhs_filename = rhs_filename
# small value in mc solver for computing correlations
self.mc_corr_eps = 1e-10
# Number of processors to use (mcsolve only)
if num_cpus:
self.num_cpus = num_cpus
else:
self.num_cpus = 0
# Tolerance for wavefunction norm (mcsolve only)
self.norm_tol = norm_tol
# Max. number of steps taken to find wavefunction norm to within
# norm_tol (mcsolve only)
self.norm_steps = norm_steps
# store final state?
self.store_final_state = store_final_state
# store states even if expectation operators are given?
self.store_states = store_states
# average mcsolver density matricies assuming steady state evolution
self.steady_state_average = steady_state_average
def __str__(self):
if self.seeds is None:
seed_length = 0
else:
seed_length = len(self.seeds)
s = ""
s += "Options:\n"
s += "-----------\n"
s += "atol: " + str(self.atol) + "\n"
s += "rtol: " + str(self.rtol) + "\n"
s += "method: " + str(self.method) + "\n"
s += "order: " + str(self.order) + "\n"
s += "nsteps: " + str(self.nsteps) + "\n"
s += "first_step: " + str(self.first_step) + "\n"
s += "min_step: " + str(self.min_step) + "\n"
s += "max_step: " + str(self.max_step) + "\n"
s += "tidy: " + str(self.tidy) + "\n"
s += "num_cpus: " + str(self.num_cpus) + "\n"
s += "norm_tol: " + str(self.norm_tol) + "\n"
s += "norm_steps: " + str(self.norm_steps) + "\n"
s += "rhs_filename: " + str(self.rhs_filename) + "\n"
s += "rhs_reuse: " + str(self.rhs_reuse) + "\n"
s += "seeds: " + str(seed_length) + "\n"
s += "rhs_with_state: " + str(self.rhs_with_state) + "\n"
s += "average_expect: " + str(self.average_expect) + "\n"
s += "average_states: " + str(self.average_states) + "\n"
s += "ntraj: " + str(self.ntraj) + "\n"
s += "store_states: " + str(self.store_states) + "\n"
s += "store_final_state: " + str(self.store_final_state) + "\n"
return s
[docs]class Result():
"""Class for storing simulation results from any of the dynamics solvers.
Attributes
----------
solver : str
Which solver was used [e.g., 'mesolve', 'mcsolve', 'brmesolve', ...]
times : list/array
Times at which simulation data was collected.
expect : list/array
Expectation values (if requested) for simulation.
states : array
State of the simulation (density matrix or ket) evaluated at ``times``.
num_expect : int
Number of expectation value operators in simulation.
num_collapse : int
Number of collapse operators in simualation.
ntraj : int/list
Number of trajectories (for stochastic solvers). A list indicates
that averaging of expectation values was done over a subset of total
number of trajectories.
col_times : list
Times at which state collpase occurred. Only for Monte Carlo solver.
col_which : list
Which collapse operator was responsible for each collapse in
``col_times``. Only for Monte Carlo solver.
"""
def __init__(self):
self.solver = None
self.times = None
self.states = []
self.expect = []
self.num_expect = 0
self.num_collapse = 0
self.ntraj = None
self.seeds = None
self.col_times = None
self.col_which = None
def __str__(self):
s = "Result object "
if self.solver:
s += "with " + self.solver + " data.\n"
else:
s += "missing solver information.\n"
s += "-" * (len(s) - 1) + "\n"
if self.states is not None and len(self.states) > 0:
s += "states = True\n"
elif self.expect is not None and len(self.expect) > 0:
s += "expect = True\nnum_expect = " + str(self.num_expect) + ", "
else:
s += "states = True, expect = True\n" + \
"num_expect = " + str(self.num_expect) + ", "
s += "num_collapse = " + str(self.num_collapse)
if self.solver == 'mcsolve':
s += ", ntraj = " + str(self.ntraj)
return s
def __repr__(self):
return self.__str__()
def __getstate__(self):
# defines what happens when Qobj object gets pickled
self.__dict__.update({'qutip_version': __version__[:5]})
return self.__dict__
def __setstate__(self, state):
# defines what happens when loading a pickled Qobj
if 'qutip_version' in state.keys():
del state['qutip_version']
(self.__dict__).update(state)
class SolverConfiguration():
def __init__(self):
self.cgen_num = 0
self.reset()
def reset(self):
# General stuff
self.tlist = None # evaluations times
self.ntraj = None # number / list of trajectories
self.options = None # options for solvers
self.norm_tol = None # tolerance for wavefunction norm
self.norm_steps = None # max. number of steps to take in finding
# Initial state stuff
self.psi0 = None # initial state
self.psi0_dims = None # initial state dims
self.psi0_shape = None # initial state shape
# flags for setting time-dependence, collapse ops, and number of times
# codegen has been run
self.cflag = 0 # Flag signaling collapse operators
self.tflag = 0 # Flag signaling time-dependent problem
# time-dependent (TD) function stuff
self.tdfunc = None # Placeholder for TD RHS function.
self.tdname = None # Name of td .pyx file
self.colspmv = None # Placeholder for TD col-spmv function.
self.colexpect = None # Placeholder for TD col_expect function.
self.string = None # Holds string of variables passed to td solver
self.soft_reset()
def soft_reset(self):
# Hamiltonian stuff
self.h_td_inds = [] # indicies of time-dependent Hamiltonian operators
self.h_data = None # List of sparse matrix data
self.h_ind = None # List of sparse matrix indices
self.h_ptr = None # List of sparse matrix ptrs
# Expectation operator stuff
self.e_num = 0 # number of expect ops
self.e_ops_data = [] # expect op data
self.e_ops_ind = [] # expect op indices
self.e_ops_ptr = [] # expect op indptrs
self.e_ops_isherm = [] # expect op isherm
# Collapse operator stuff
self.c_num = 0 # number of collapse ops
self.c_const_inds = [] # indicies of constant collapse operators
self.c_td_inds = [] # indicies of time-dependent collapse operators
self.c_ops_data = [] # collapse op data
self.c_ops_ind = [] # collapse op indices
self.c_ops_ptr = [] # collapse op indptrs
self.c_args = [] # store args for time-dependent collapse func.
# Norm collapse operator stuff
self.n_ops_data = [] # norm collapse op data
self.n_ops_ind = [] # norm collapse op indices
self.n_ops_ptr = [] # norm collapse op indptrs
# holds executable strings for time-dependent collapse evaluation
self.col_expect_code = None
self.col_spmv_code = None
# hold stuff for function list based time dependence
self.h_td_inds = []
self.h_td_data = []
self.h_td_ind = []
self.h_td_ptr = []
self.h_funcs = None
self.h_func_args = None
self.c_funcs = None
self.c_func_args = None
#
# create a global instance of the SolverConfiguration class
#
config = SolverConfiguration()
# for backwards compatibility
Odeoptions = Options
Odedata = Result
```

© Copyright 2011 and later, P.D. Nation, J.R. Johansson.

Last updated on Dec 31, 2014.

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