move the script that generates the specializations for Evaluation to bin/
This commit is contained in:
Andreas Lauser
parent
cba7ad9530
commit
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214
bin/genEvalSpecializations.py
Executable file
214
bin/genEvalSpecializations.py
Executable file
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#! /usr/bin/python
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import os
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import sys
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import jinja2
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fileNames = []
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specializationTemplate = \
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"""// -*- mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
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// vi: set et ts=4 sw=4 sts=4:
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/*
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This file is part of the Open Porous Media project (OPM).
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OPM is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 2 of the License, or
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(at your option) any later version.
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OPM is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with OPM. If not, see <http://www.gnu.org/licenses/>.
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Consult the COPYING file in the top-level source directory of this
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module for the precise wording of the license and the list of
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copyright holders.
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*/
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/*!
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* \\file
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*
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* \\brief This file specializes the dense-AD Evaluation class for {{ numDerivs }} derivatives.
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*
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* \\attention THIS FILE GETS AUTOMATICALLY GENERATED BY THE "{{ scriptName }}"
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* SCRIPT. DO NOT EDIT IT MANUALLY!
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*/
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#ifndef OPM_DENSEAD_EVALUATION{{numDerivs}}_HPP
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#define OPM_DENSEAD_EVALUATION{{numDerivs}}_HPP
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namespace Opm {
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namespace DenseAd {
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template <class ValueT>
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struct EvaluationOps<ValueT, {{numDerivs}}>
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{
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private:
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typedef Evaluation<ValueT, {{numDerivs}} > Eval;
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public:
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typedef std::array<ValueT, {{numDerivs + 1}} > DataVector;
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static inline void assign(Eval& a, const Eval& b)
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{
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{% for i in range(0, numDerivs+1) %}
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a.data_[{{i}}] = b.data_[{{i}}];{% endfor %}
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}
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static inline void assignNegative(Eval& a, const Eval& b)
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{
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{% for i in range(0, numDerivs+1) %}
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a.data_[{{i}}] = - b.data_[{{i}}];{% endfor %}
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}
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static inline void clearDerivatives(Eval& a)
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{
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{% for i in range(1, numDerivs+1) %}
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a.data_[{{i}}] = 0.0;{% endfor %}
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}
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static inline Eval& addEq(Eval& a, const Eval& b)
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{
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{% for i in range(0, numDerivs+1) %}
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a.data_[{{i}}] += b.data_[{{i}}];{% endfor %}
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return a;
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}
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static inline Eval& subEq(Eval& a, const Eval& b)
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{
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{% for i in range(0, numDerivs+1) %}
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a.data_[{{i}}] -= b.data_[{{i}}];{% endfor %}
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return a;
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}
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static inline Eval& mulEq(Eval& a, const Eval& b)
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{
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// while the values are multiplied, the derivatives follow the product rule,
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// i.e., (u*v)' = (v'u + u'v).
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const ValueT u = a.value();
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const ValueT v = b.value();
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// value
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a.data_[0] *= v ;
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// derivatives{% for i in range(1, numDerivs+1) %}
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a.data_[{{i}}] = a.data_[{{i}}]*v + b.data_[{{i}}] * u;{% endfor %}
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return a;
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}
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template <class RhsType>
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static inline Eval& scalarMulEq(Eval& a, const RhsType& other)
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{
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{% for i in range(0, numDerivs+1) %}
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a.data_[{{i}}] *= other;{% endfor %}
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return a;
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}
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static inline Eval& divEq(Eval& a, const Eval& b)
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{
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// values are divided, derivatives follow the rule for division, i.e., (u/v)' = (v'u - u'v)/v^2.
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const ValueT v_vv = 1.0 / b.value();
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const ValueT u_vv = a.value() * v_vv * v_vv;
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// value
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a.data_[0] *= v_vv;
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// derivatives{% for i in range(1, numDerivs+1) %}
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a.data_[{{i}}] = a.data_[{{i}}]*v_vv - b.data_[{{i}}]*u_vv;{% endfor %}
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return a;
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}
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template <class RhsType>
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static inline Eval& scalarDivEq(Eval& a, const RhsType& other)
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{
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{% for i in range(0, numDerivs+1) %}
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a.data_[{{i}}] /= other;{% endfor %}
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return a;
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}
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template <class RhsValueType>
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static inline Eval divide(const RhsValueType& a, const Eval& b)
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{
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Eval result;
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result.setValue( a/b.value() );
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const ValueT df_dg = - result.value()/b.value();
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{% for i in range(1, numDerivs+1) %}
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result.data_[{{i}}] = df_dg*b.data_[{{i}}];{% endfor %}
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return result;
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}
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};
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} } // namespace DenseAd, Opm
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#endif // OPM_DENSEAD_EVALUATION{{numDerivs}}_HPP
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"""
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includeSpecializationsTemplate = \
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"""// -*- mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*-
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// vi: set et ts=4 sw=4 sts=4:
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/*
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This file is part of the Open Porous Media project (OPM).
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OPM is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 2 of the License, or
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(at your option) any later version.
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OPM is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with OPM. If not, see <http://www.gnu.org/licenses/>.
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Consult the COPYING file in the top-level source directory of this
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module for the precise wording of the license and the list of
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copyright holders.
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*/
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/*!
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* \\file
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*
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* \\brief This file includes all specializations for the dense-AD Evaluation class.
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*
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* \\attention THIS FILE GETS AUTOMATICALLY GENERATED BY THE "{{ scriptName }}"
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* SCRIPT. DO NOT EDIT IT MANUALLY!
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*/
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#ifndef OPM_DENSEAD_EVALUATION_SPECIALIZATIONS_HPP
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#define OPM_DENSEAD_EVALUATION_SPECIALIZATIONS_HPP
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{% for fileName in fileNames %}#include <{{ fileName }}>
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{% endfor %}
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#endif // OPM_DENSEAD_EVALUATION_SPECIALIZATIONS_HPP
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"""
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for numDerivs in range(1, 12 + 1):
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print "Generating specialization for %d derivatives"%numDerivs
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fileName = "opm/material/densead/Evaluation%d.hpp"%numDerivs
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fileNames.append(fileName)
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template = jinja2.Template(specializationTemplate)
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fileContents = template.render(numDerivs=numDerivs, scriptName=os.path.basename(sys.argv[0]))
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f = open(fileName, "w")
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f.write(fileContents)
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f.close()
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template = jinja2.Template(includeSpecializationsTemplate)
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fileContents = template.render(fileNames=fileNames, scriptName=os.path.basename(sys.argv[0]))
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f = open("opm/material/densead/EvaluationSpecializations.hpp", "w")
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f.write(fileContents)
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f.close()
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