# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
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__all__ = ['rhs_generate', 'rhs_clear']
import os
import numpy as np
from types import FunctionType, BuiltinFunctionType
from functools import partial
from qutip.cy.codegen import Codegen
from qutip.solver import Options, config
from qutip.qobj import Qobj
from qutip.superoperator import spre, spost
from qutip.interpolate import Cubic_Spline
[docs]def rhs_clear():
"""
Resets the string-format time-dependent Hamiltonian parameters.
Parameters
----------
Returns
-------
Nothing, just clears data from internal config module.
"""
# time-dependent (TD) function stuff
config.tdfunc = None # Placeholder for TD RHS function.
config.colspmv = None # Placeholder for TD col-spmv function.
config.colexpect = None # Placeholder for TD col_expect function.
config.string = None # Holds string of variables to be passed to solver
config.tdname = None # Name of td .pyx file (used in parallel mc code)
[docs]def rhs_generate(H, c_ops, args={}, options=Options(), name=None,
cleanup=True):
"""
Generates the Cython functions needed for solving the dynamics of a
given system using the mesolve function inside a parfor loop.
Parameters
----------
H : qobj
System Hamiltonian.
c_ops : list
``list`` of collapse operators.
args : dict
Arguments for time-dependent Hamiltonian and collapse operator terms.
options : Options
Instance of ODE solver options.
name: str
Name of generated RHS
cleanup: bool
Whether the generated cython file should be automatically removed or
not.
Notes
-----
Using this function with any solver other than the mesolve function
will result in an error.
"""
config.reset()
config.options = options
if name:
config.tdname = name
else:
config.tdname = "rhs" + str(os.getpid()) + str(config.cgen_num)
Lconst = 0
Ldata = []
Linds = []
Lptrs = []
Lcoeff = []
Lobj = []
# loop over all hamiltonian terms, convert to superoperator form and
# add the data of sparse matrix represenation to
msg = "Incorrect specification of time-dependence: "
for h_spec in H:
if isinstance(h_spec, Qobj):
h = h_spec
if not isinstance(h, Qobj):
raise TypeError(msg + "expected Qobj")
if h.isoper:
Lconst += -1j * (spre(h) - spost(h))
elif h.issuper:
Lconst += h
else:
raise TypeError(msg + "expected operator or superoperator")
elif isinstance(h_spec, list):
h = h_spec[0]
h_coeff = h_spec[1]
if not isinstance(h, Qobj):
raise TypeError(msg + "expected Qobj")
if h.isoper:
L = -1j * (spre(h) - spost(h))
elif h.issuper:
L = h
else:
raise TypeError(msg + "expected operator or superoperator")
Ldata.append(L.data.data)
Linds.append(L.data.indices)
Lptrs.append(L.data.indptr)
if isinstance(h_coeff, Cubic_Spline):
Lobj.append(h_coeff.coeffs)
Lcoeff.append(h_coeff)
else:
raise TypeError(msg + "expected string format")
# loop over all collapse operators
for c_spec in c_ops:
if isinstance(c_spec, Qobj):
c = c_spec
if not isinstance(c, Qobj):
raise TypeError(msg + "expected Qobj")
if c.isoper:
cdc = c.dag() * c
Lconst += spre(c) * spost(c.dag()) - 0.5 * spre(cdc) \
- 0.5 * spost(cdc)
elif c.issuper:
Lconst += c
else:
raise TypeError(msg + "expected operator or superoperator")
elif isinstance(c_spec, list):
c = c_spec[0]
c_coeff = c_spec[1]
if not isinstance(c, Qobj):
raise TypeError(msg + "expected Qobj")
if c.isoper:
cdc = c.dag() * c
L = spre(c) * spost(c.dag()) - 0.5 * spre(cdc) \
- 0.5 * spost(cdc)
c_coeff = "(" + c_coeff + ")**2"
elif c.issuper:
L = c
else:
raise TypeError(msg + "expected operator or superoperator")
Ldata.append(L.data.data)
Linds.append(L.data.indices)
Lptrs.append(L.data.indptr)
Lcoeff.append(c_coeff)
else:
raise TypeError(msg + "expected string format")
# add the constant part of the lagrangian
if Lconst != 0:
Ldata.append(Lconst.data.data)
Linds.append(Lconst.data.indices)
Lptrs.append(Lconst.data.indptr)
Lcoeff.append("1.0")
# the total number of liouvillian terms (hamiltonian terms + collapse
# operators)
n_L_terms = len(Ldata)
cgen = Codegen(h_terms=n_L_terms, h_tdterms=Lcoeff, args=args,
config=config)
cgen.generate(config.tdname + ".pyx")
code = compile('from ' + config.tdname +
' import cy_td_ode_rhs', '<string>', 'exec')
exec(code, globals())
config.tdfunc = cy_td_ode_rhs
if cleanup:
try:
os.remove(config.tdname + ".pyx")
except:
pass
def _td_format_check(H, c_ops, solver='me'):
"""
Checks on time-dependent format.
"""
h_const = []
h_func = []
h_str = []
h_obj = []
# check H for incorrect format
if isinstance(H, Qobj):
pass
elif isinstance(H, (FunctionType, BuiltinFunctionType, partial)):
pass # n_func += 1
elif isinstance(H, list):
for k, H_k in enumerate(H):
if isinstance(H_k, Qobj):
h_const.append(k)
elif isinstance(H_k, list):
if len(H_k) != 2 or not isinstance(H_k[0], Qobj):
raise TypeError("Incorrect hamiltonian specification")
else:
if isinstance(H_k[1], (FunctionType,
BuiltinFunctionType, partial)):
h_func.append(k)
elif isinstance(H_k[1], str):
h_str.append(k)
elif isinstance(H_k[1], Cubic_Spline):
h_obj.append(k)
elif isinstance(H_k[1], np.ndarray):
h_str.append(k)
else:
raise TypeError("Incorrect hamiltonian specification")
else:
raise TypeError("Incorrect hamiltonian specification")
# the the whole thing again for c_ops
c_const = []
c_func = []
c_str = []
c_obj = []
if isinstance(c_ops, list):
for k in range(len(c_ops)):
if isinstance(c_ops[k], Qobj):
c_const.append(k)
elif isinstance(c_ops[k], list):
if len(c_ops[k]) != 2:
raise TypeError(
"Incorrect collapse operator specification.")
else:
if isinstance(c_ops[k][1], (FunctionType,
BuiltinFunctionType, partial)):
c_func.append(k)
elif isinstance(c_ops[k][1], str):
c_str.append(k)
elif isinstance(c_ops[k][1], Cubic_Spline):
c_obj.append(k)
elif isinstance(c_ops[k][1], np.ndarray):
c_str.append(k)
elif isinstance(c_ops[k][1], tuple):
c_str.append(k)
else:
raise TypeError(
"Incorrect collapse operator specification")
else:
raise TypeError("Incorrect collapse operator specification")
#
# if n_str == 0 and n_func == 0:
# # no time-dependence at all
#
if ((len(h_str) > 0 and len(h_func) > 0) or
(len(c_str) > 0 and len(c_func) > 0)):
raise TypeError(
"Cannot mix string and function type time-dependence formats")
# check to see if Cython is installed and version is high enough.
if len(h_str) > 0 or len(c_str) > 0:
try:
import Cython
except:
raise Exception(
"Unable to load Cython. Use Python function format.")
else:
if Cython.__version__ < '0.21':
raise Exception("Cython version (%s) is too old. Upgrade to " +
"version 0.21+" % Cython.__version__)
# If only time-dependence is in Objects, then prefer string based format
if (len(h_func) + len(c_func) + len(h_str) + len(c_str)) == 0:
h_str += h_obj #Does nothing if not objects
c_str += c_obj
else:
# Combine Hamiltonian objects
if len(h_func) > 0:
h_func += h_obj
elif len(h_str) > 0:
h_str += h_obj
#Combine collapse objects
if len(c_func) > 0:
c_func += c_obj
elif len(c_str) > 0:
c_str += c_obj
if solver == 'me':
return (len(h_const + c_const),
len(h_func) + len(c_func),
len(h_str) + len(c_str))
elif solver == 'mc':
# H C_ops #
# -- ----- --
# NO NO 00
# NO STR 01
# NO FUNC 02
#
# STR NO 10
# STR STR 11
#
# FUNC NO 20
#
# FUNC FUNC 22
if isinstance(H, FunctionType):
time_type = 3
# Time-indepdent problems
elif ((len(h_func) == len(h_str) == 0) and
(len(c_func) == len(c_str) == 0)):
time_type = 0
# constant Hamiltonian, time-dependent collapse operators
elif len(h_func) == len(h_str) == 0:
if len(c_str) > 0:
time_type = 1
elif len(c_func) > 0:
time_type = 2
else:
raise Exception("Error determining time-dependence.")
# list style Hamiltonian
elif len(h_str) > 0:
if len(c_func) == len(c_str) == 0:
time_type = 10
elif len(c_str) > 0:
time_type = 11
else:
raise Exception("Error determining time-dependence.")
# Python function style Hamiltonian
elif len(h_func) > 0:
if len(c_func) == len(c_str) == 0:
time_type = 20
elif len(c_func) > 0:
time_type = 22
else:
raise Exception("Error determining time-dependence.")
return time_type, [h_const, h_func, h_str], [c_const, c_func, c_str]
def _td_wrap_array_str(H, c_ops, args, times):
"""
Wrap numpy-array based time-dependence in the string-based time dependence
format
"""
n = 0
H_new = []
c_ops_new = []
args_new = {}
if not isinstance(H, list):
H_new = H
else:
for Hk in H:
if isinstance(Hk, list) and isinstance(Hk[1], np.ndarray):
H_op, H_td = Hk
td_array_name = "_td_array_%d" % n
H_td_str = '(0 if (t > %f) else %s[int(round(%d * (t/%f)))])' %\
(times[-1], td_array_name, len(times) - 1, times[-1])
args_new[td_array_name] = H_td
H_new.append([H_op, H_td_str])
n += 1
else:
H_new.append(Hk)
if not isinstance(c_ops, list):
c_ops_new = c_ops
else:
for ck in c_ops:
if isinstance(ck, list) and isinstance(ck[1], np.ndarray):
c_op, c_td = ck
td_array_name = "_td_array_%d" % n
c_td_str = '(0 if (t > %f) else %s[int(round(%d * (t/%f)))])' %\
(times[-1], td_array_name, len(times) - 1, times[-1])
args_new[td_array_name] = c_td
c_ops_new.append([c_op, c_td_str])
n += 1
else:
c_ops_new.append(ck)
if not args_new:
args_new = args
elif isinstance(args, dict):
args_new.update(args)
else:
raise ValueError("Time-dependent array format requires args to " +
"be a dictionary")
return H_new, c_ops_new, args_new