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codegen.py
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codegen.py
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# 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.
###############################################################################
import numpy as np
class Codegen():
"""
Class for generating cython code files at runtime.
"""
def __init__(self, h_terms=None, h_tdterms=None, h_td_inds=None,
args=None, c_terms=None, c_tdterms=[], c_td_inds=None,
type='me', odeconfig=None):
import sys
import os
sys.path.append(os.getcwd())
# Hamiltonian time-depdendent pieces
self.type = type
if isinstance(h_terms, int):
h_terms = range(h_terms)
self.h_terms = h_terms # number of H pieces
self.h_tdterms = h_tdterms # list of time-dependent strings
self.h_td_inds = h_td_inds # indicies of time-dependnt terms
self.args = args # args for strings
# Collapse operator time-depdendent pieces
self.c_terms = c_terms # number of C pieces
self.c_tdterms = c_tdterms # list of time-dependent strings
self.c_td_inds = c_td_inds # indicies of time-dependent terms
# Code generator properties
self.code = [] # strings to be written to file
self.level = 0 # indent level
self.odeconfig = odeconfig
def write(self, string):
"""write lines of code to self.code"""
self.code.append(" " * self.level + string + "\n")
def file(self, filename):
"""open file called filename for writing"""
self.file = open(filename, "w")
def generate(self, filename="rhs.pyx"):
"""generate the file"""
for line in cython_preamble():
self.write(line)
# write function for Hamiltonian terms (there is always at least one
# term)
for line in cython_checks() + self.ODE_func_header():
self.write(line)
self.indent()
for line in self.func_vars():
self.write(line)
for line in self.func_for():
self.write(line)
self.write(self.func_end())
self.dedent()
# generate collapse operator functions if any c_terms
if any(self.c_tdterms):
for line in (cython_checks() + self.col_spmv_header() +
cython_col_spmv()):
self.write(line)
self.indent()
for line in self.func_which():
self.write(line)
self.write(self.func_end())
self.dedent()
for line in (cython_checks() + self.col_expect_header() +
cython_col_expect(self.args)):
self.write(line)
self.indent()
for line in self.func_which_expect():
self.write(line)
self.write(self.func_end_real())
self.dedent()
self.file(filename)
self.file.writelines(self.code)
self.file.close()
self.odeconfig.cgen_num += 1
def indent(self):
"""increase indention level by one"""
self.level += 1
def dedent(self):
"""decrease indention level by one"""
if self.level == 0:
raise SyntaxError("Error in code generator")
self.level -= 1
def _get_arg_str(self, args):
if len(args) == 0:
return ''
ret = ''
for name, value in self.args.items():
if isinstance(value, np.ndarray):
ret += ", np.ndarray[np.%s_t, ndim=1] %s" % \
(value.dtype.name, name)
else:
kind = type(value).__name__
ret += ", np." + kind + "_t " + name
return ret
def ODE_func_header(self):
"""Creates function header for time-dependent ODE RHS."""
func_name = "def cy_td_ode_rhs("
# strings for time and vector variables
input_vars = "double t, np.ndarray[CTYPE_t, ndim=1] vec"
for k in self.h_terms:
input_vars += (", np.ndarray[CTYPE_t, ndim=1] data" + str(k) +
", np.ndarray[int, ndim=1] idx" + str(k) +
", np.ndarray[int, ndim=1] ptr" + str(k))
if any(self.c_tdterms):
for k in range(len(self.h_terms),
len(self.h_terms) + len(self.c_tdterms)):
input_vars += (", np.ndarray[CTYPE_t, ndim=1] data" + str(k) +
", np.ndarray[int, ndim=1] idx" + str(k) +
", np.ndarray[int, ndim=1] ptr" + str(k))
input_vars += self._get_arg_str(self.args)
func_end = "):"
return [func_name + input_vars + func_end]
def col_spmv_header(self):
"""
Creates function header for time-dependent
collapse operator terms.
"""
func_name = "def col_spmv("
input_vars = ("int which, double t, np.ndarray[CTYPE_t, ndim=1] " +
"data, np.ndarray[int] idx,np.ndarray[int] " +
"ptr,np.ndarray[CTYPE_t, ndim=1] vec")
input_vars += self._get_arg_str(self.args)
func_end = "):"
return [func_name + input_vars + func_end]
def col_expect_header(self):
"""
Creates function header for time-dependent
collapse expectation values.
"""
func_name = "def col_expect("
input_vars = ("int which, double t, np.ndarray[CTYPE_t, ndim=1] " +
"data, np.ndarray[int] idx,np.ndarray[int] " +
"ptr,np.ndarray[CTYPE_t, ndim=1] vec")
input_vars += self._get_arg_str(self.args)
func_end = "):"
return [func_name + input_vars + func_end]
def func_vars(self):
"""Writes the variables and their types & spmv parts"""
func_vars = ["", 'cdef Py_ssize_t row', 'cdef int num_rows = len(vec)',
'cdef np.ndarray[CTYPE_t, ndim=1] ' +
'out = np.zeros((num_rows),dtype=np.complex)']
func_vars.append(" ")
tdterms = self.h_tdterms
hinds = 0
for ht in self.h_terms:
hstr = str(ht)
if self.type == 'mc':
str_out = ("cdef np.ndarray[CTYPE_t, ndim=1] Hvec" + hstr +
" = " + "spmv_csr(data" + hstr + "," +
"idx" + hstr + "," + "ptr" + hstr +
"," + "vec" + ")")
if ht in self.h_td_inds:
str_out += " * " + tdterms[hinds]
hinds += 1
func_vars.append(str_out)
else:
if self.h_tdterms[ht] == "1.0":
str_out = "spmvpy(data%s, idx%s, ptr%s, vec, 1.0, out)" % (
ht, ht, ht)
else:
str_out = "spmvpy(data%s, idx%s, ptr%s, vec, %s, out)" % (
ht, ht, ht, self.h_tdterms[ht])
func_vars.append(str_out)
if len(self.c_tdterms) > 0:
# add a spacer line between Hamiltonian components and collapse
# components.
func_vars.append(" ")
terms = len(self.c_tdterms)
tdterms = self.c_tdterms
cinds = 0
for ct in range(terms):
cstr = str(ct + hinds + 1)
str_out = ("cdef np.ndarray[CTYPE_t, ndim=1] Cvec" + str(ct) +
" = " + "spmv_csr(data" + cstr + "," +
"idx" + cstr + "," +
"ptr" + cstr + "," + "vec" + ")")
if ct in range(len(self.c_td_inds)):
str_out += " * np.abs(" + tdterms[ct] + ")**2"
cinds += 1
func_vars.append(str_out)
return func_vars
def func_for(self):
"""Writes function for-loop"""
func_terms = []
if self.type == 'mc':
func_terms.append("for row in range(num_rows):")
sum_string = " out[row] = Hvec0[row]"
for ht in range(1, len(self.h_terms)):
sum_string += " + Hvec" + str(ht) + "[row]"
if any(self.c_tdterms):
for ct in range(len(self.c_tdterms)):
sum_string += " + Cvec" + str(ct) + "[row]"
func_terms.append(sum_string)
return func_terms
def func_which(self):
"""Writes 'else-if' statements forcollapse operator eval function"""
out_string = []
ind = 0
for k in self.c_td_inds:
out_string.append("if which == " + str(k) + ":")
out_string.append(" out *= " + self.c_tdterms[ind])
ind += 1
return out_string
def func_which_expect(self):
"""Writes 'else-if' statements for collapse expect function
"""
out_string = []
ind = 0
for k in self.c_td_inds:
out_string.append("if which == " + str(k) + ":")
out_string.append(" out *= np.conj(" +
self.c_tdterms[ind] + ")")
ind += 1
return out_string
def func_end(self):
return "return out"
def func_end_real(self):
return "return np.float64(np.real(out))"
def cython_preamble():
"""
Returns list of code segments for Cython preamble.
"""
return ["""\
# This file is generated automatically by QuTiP.
# (C) Paul D. Nation & J. R. Johansson
from numpy import *
cimport libc.math as cmath
import numpy as np
cimport numpy as np
cimport cython
from qutip.cy.spmatfuncs import spmv_csr, spmvpy
ctypedef np.complex128_t CTYPE_t
ctypedef np.float64_t DTYPE_t
"""]
def cython_checks():
"""
List of strings that turn off Cython checks.
"""
return ["""
@cython.boundscheck(False)
@cython.wraparound(False)
"""]
def cython_col_spmv():
"""
Writes col_SPMV vars.
"""
return ["""\
cdef Py_ssize_t row
cdef int jj, row_start, row_end
cdef int num_rows = len(vec)
cdef CTYPE_t dot
cdef np.ndarray[CTYPE_t, ndim=1] out = np.zeros(num_rows, dtype=np.complex)
for row in range(num_rows):
dot = 0.0
row_start = ptr[row]
row_end = ptr[row+1]
for jj in range(row_start,row_end):
dot = dot + data[jj] * vec[idx[jj]]
out[row] = dot
"""]
def cython_col_expect(args):
"""
Writes col_expect vars.
"""
return ["""\
cdef Py_ssize_t row
cdef int num_rows=len(vec)
cdef CTYPE_t out = 0.0
cdef np.ndarray[CTYPE_t, ndim=1] vec_ct = vec.conj()
cdef np.ndarray[CTYPE_t, ndim=1] dot = col_spmv(which, t, data, idx, ptr,
vec%s)
for row in range(num_rows):
out += vec_ct[row] * dot[row]
""" % "".join(["," + str(td_const[0])
for td_const in args.items()]) if args else ""]