Source code for qutip.rhs_generate

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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