-
-#ifndef _CAMPL_CAMPL_H
-#define _CAMPL_CAMPL_H
-
-PyObject * cAmpl_generate_ampl_repn(PyObject * self, PyObject * args);
-PyObject * internal_generate_ampl_repn(PyObject * context, PyObject * exp);
-
-PyObject * recursive_generate_ampl_repn(PyObject * context, PyObject * exp);
-PyObject * new_ampl_representation();
-PyObject * get_pyomo_module();
-
-#endif
diff --git a/pyomo/repn/plugins/ampl/cAmpl/doxygen/HACKING.doxygen b/pyomo/repn/plugins/ampl/cAmpl/doxygen/HACKING.doxygen
deleted file mode 100644
index efd34872778..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/doxygen/HACKING.doxygen
+++ /dev/null
@@ -1,223 +0,0 @@
-/*! @page Developers Developing for cAmpl
-
-This document serves as a brief introduction to the cAmpl module and its
-development process. It seeks to provide pointers for future developers
-working on the C NL writer implementation.
-
-Functionality
-
-At present, the cAmpl module implements precisely one Python function:
-generate_ampl_repn, defined in pyomo.core/io/ampl/ampl.py. The
-cAmpl implementation is a clone of the Python version, not the other
-way around; any changes in the Python version should be reflected in
-cAmpl, but changes in cAmpl that alter functionality must also appear
-in ampl.py.
-
-The pyomo.data.cute package contains a number of unit tests for the NL
-writer. These are the standard by which NL writer implementations are
-measured; ampl.py is the reference implementation for these tests, and
-if any tests are failing with the pure Python implementation, there is a
-problem beyond cAmpl's scope.
-
-Structure
-
-The primary definition of the cAmpl module exists in the cAmpl.c file;
-this file defines the master cAmpl_generate_ampl_repn function, as well
-as a few helper functions for recursion and package imports.
-
-The util.c file implements some debugging utility functions for developer
-use.
-
-The remainder of the files appear in the handlers subdirectory, which
-breaks down the generate_ampl_repn Python function into manageable C
-chunks to be developed individually. The reference Python function is
-roughly subdivided by a series of if checks on the type of its argument
-exp. This functional type hierarchy is as follows:
-
-
- - Variable
-
- Expression
-
- - Sum expression
-
- Product expression
-
- Power expression
-
- Intrinsic function expression
-
- - Fixed value
-
-
-Each of the top-level types receives their own C "handler;" in addition,
-the "expression" handler is further subdivided into four smaller handlers,
-contained in the handlers/expression subdirectory.
-
-Handlers
-
-A C expression handler in the context of cAmpl has the following signature:
-
-int _handle_XYZ(PyObject * context, PyObject * exp, PyObject ** ampl_repn);
-
-Each handler is responsible for populating ampl_repn with the appropriate
-contents, then returning an integer code that represents success, failure,
-or error. The context variable holds information about the calling frame
-and parent instance of the currently executing method; exp is the Python
-object being parsed into an AMPL representation.
-
-Handlers may execute each other, as is the case in the expression handler,
-or they may recurse back to the C implementation of generate_ampl_repn,
-in which case it is recommended to use the recursive_generate_ampl_repn
-function provided in cAmpl.c.
-
-Conventions
-
-Being effectively a straight port of Python code into C, the cAmpl project
-has its own unique coding conventions that are recommended for future
-developers.
-
-Variables
-
-Variables are named the same as their Python counterparts, if they exist
-in the local scope:
-
-
- one = 1.0 # Python
- PyObject * one = Py_BuildValue("f", 1.0); // C
-
-
-Attributes of variables are named as their full expression, with delimiters
-replaced by underscores:
-
-
- myobj.myattr # Python
- PyObject * myobj.myattr = PyObject_GetAttrString(myobj, "myattr"); // C
-
-
-This may sometimes require a double-underscore, when the attribute itself
-begins with an underscore:
-
-
- myobj._secret # Python
- PyObject * myobj__secret = PyObject_GetAttrString(myobj, "_secret"); // C
-
-
-A similar convention is followed for items pulled out of a dictionary:
-
-
- mydict["key"] # Python
- PyObject * mydict_key = PyDict_GetItemString(mydict, "key"); // C
-
-
-Packages, modules, and classes are named with Hungarian three-character types
-appended to their variable name (PKG, MOD, and CLS, respectively):
-
-
- import pyomo.core # Python
- PyObject * pyomo.core_MOD = PyImport_ImportModule("pyomo.core"); // C
-
-
-Python objects declared in C with no direct counterpart in the reference
-Python function are prefixed with an underscore:
-
-
- a = b * c * d # Python
- PyObject * _tmp = PyNumber_Multiply(b, c); // C
- PyObject * a = PyNumber_Multiply(_tmp, d); // C
-
-
-Memory use
-
-Being a C implementation, cAmpl must concern itself with the Python
-reference
-count of its objects. Generally, cAmpl decrements the reference
-count of a local object as soon as possible in a code block:
-
-
- PyObject * _tmp = PyNumber_Multiply(b, c);
- PyObject * a = PyNumber_Multiply(_tmp, d);
- Py_DECREF(_tmp);
-
-
-In addition, temporary objects (such as that shown above) and C-local result
-objects are generally set to NULL immediately after use, to avoid potential
-access of a garbage-collected object:
-
-
- _tmp = NULL;
-
-
-In certain cases, however, it is desirable to retain an object reference for
-multiple statements or code blocks. In this case, the call to Py_DECREF is
-deferred until immediately after the last access of the object in question:
-
-
- PyObject * myobj_mydict = PyObject_GetAttrString(myobj, "mydict");
- PyDict_DelItemString(myobj_mydict, "a"); // Do not decref yet
- PyDict_DelItemString(myobj_mydict, "b");
- Py_DECREF(myobj_mydict);
-
-
-This technique is occasionally extended to retain a single object reference
-through a loop that accesses the object repeatedly, or to keep an object
-reference outside multiple sequential local scope blocks that access it.
-
-Comments
-
-Comments in cAmpl tend to follow one of a few basic templates throughout the
-module. Most frequently, since cAmpl is a direct translation of its Python
-counterpart, a comment will have a single line of Python code that represents
-the operation performed by the following block of C:
-
-
- // myobj.mydict["a"] = None
- PyObject * myobj_mydict = PyObject_GetAttrString(myobj, "mydict");
- PyDict_SetItemString(myobj_mydict, "a", Py_None);
- Py_DECREF(myobj_mydict);
-
-
-C comments may also include comments from the corresponding Python code in
-the appropriate place within the C translation. These comments match those
-existing in the Python implementation as closely as possible, and generally
-immediately precede a Python code listing comment:
-
-
- // clear the object
- // myobj = None
- Py_INCREF(Py_None);
- myobj = Py_None;
-
-
-Comments in cAmpl sometimes refer directly to a C implementation detail,
-rather than any information or code drawn from the Python reference function.
-In that case, the comment is preceded with the string "C NOTE:"
-
-
- // C NOTE: use plain int counter rather than Python integer
- // mycounter = 0
- int mycounter = 0;
-
-
-Comments in cAmpl can also indicate that the code is not a direct translation,
-but instead applies some optimization strategy or C-specific cleanup with the
-comments // opt and // cleanup, respectively:
-
-
- // opt
- PyObject * myobj = NULL;
-
- // some loop that sets myobj repeatedly...
-
- // cleanup
- Py_DECREF(myobj);
-
-
-Finally, certain functions receive Javadoc-style, Doxygen-compatible comments
-above their definitions in the cAmpl .c files. These are not cAmpl-specific;
-for more information, see the
-Doxygen documentation.
-
-
- /**
- Frobnicate.
- */
- void frob();
-
- */
diff --git a/pyomo/repn/plugins/ampl/cAmpl/doxygen/README.doxygen b/pyomo/repn/plugins/ampl/cAmpl/doxygen/README.doxygen
deleted file mode 100644
index 6930330a19f..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/doxygen/README.doxygen
+++ /dev/null
@@ -1,41 +0,0 @@
-/*!
- * @mainpage
- *
- * This project contains the C implementation of parts of the Pyomo NL writer.
- * Implemented as a Python module, the C implementation (colloquially named
- * cAmpl) aims to improve performance in this processor-bound part of Pyomo.
- *
- * Building
- *
- * A basic distutils setup script is included; to build, simply run:
- *
- * $ python setup.py build
- *
- * After building, Pyomo will attempt to autodetect the appropriate cAmpl
- * module and load it in place of the pure Python implementation used by
- * default. To do so, however, it must recognize a module named cAmpl in
- * the pyomo.core/io/ampl subdirectory. On Linux and other UNIX-based
- * systems, the module can be registered by running (in the appropriate
- * ampl directory):
- *
- * $ ln -s cAmpl/build/lib.<ARCH>/cAmpl.so .
- *
- * Substitute ARCH for your architecture; for example, this could
- * be linux-x86_64-2.6 for a 64-bit Linux platform on Python 2.6.
- *
- * If done properly, the cAmpl.so file will be linked inside the
- * ampl directory, allowing Pyomo to load the C-based functions it
- * provides.
- *
- * Windows
- *
- * cAmpl is completely untested on Windows-based platforms, so YMMV. However,
- * in theory it is possible to perform similar steps as above to achieve
- * the performance boosts of the cAmpl module. You must have Visual Studio or
- * a similar development platform installed. Run the setup.py script as
- * above, then copy the generated .dll file to the appropriate
- * ampl folder.
- *
- * Cygwin users should attempt the UNIX-based system instructions, and will
- * need gcc and/or mingw32 for their installations.
- */
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers.h b/pyomo/repn/plugins/ampl/cAmpl/handlers.h
deleted file mode 100644
index 4b5fe2963cc..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers.h
+++ /dev/null
@@ -1,4 +0,0 @@
-#include "util.h"
-#include "handlers/variable.h"
-#include "handlers/expression.h"
-#include "handlers/fixedval.h"
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression.c b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression.c
deleted file mode 100644
index 57a7f8288be..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression.c
+++ /dev/null
@@ -1,65 +0,0 @@
-#include "expression.h"
-
-int _handle_expression(PyObject * context, PyObject * exp, PyObject ** ampl_repn) {
- // exp_type = type(exp)
- PyObject * exp_type = PyObject_Type(exp);
-
- // Get various expression types
- PyObject * pyomo_MOD = PyImport_ImportModule("pyomo.core");
- PyObject * base_MOD = PyObject_GetAttrString(pyomo_MOD, "base");
- PyObject * expr_MOD = PyObject_GetAttrString(base_MOD, "expr");
- Py_XDECREF(pyomo_MOD);
- Py_XDECREF(base_MOD);
-
- // if expr_type is expr._SumExpression:
- PyObject * _SumExpression_CLS = PyObject_GetAttrString(expr_MOD, "_SumExpression");
- if(exp_type == _SumExpression_CLS) {
- Py_XDECREF(_SumExpression_CLS);
- Py_XDECREF(expr_MOD);
- return _handle_sumexp(context, exp, ampl_repn);
- }
- Py_XDECREF(_SumExpression_CLS);
-
- // elif exp_type is expr._ProductExpression:
- PyObject * _ProductExpression_CLS = PyObject_GetAttrString(expr_MOD, "_ProductExpression");
- if(exp_type == _ProductExpression_CLS) {
- Py_XDECREF(_ProductExpression_CLS);
- Py_XDECREF(expr_MOD);
- return _handle_prodexp(context, exp, ampl_repn);
- }
- Py_XDECREF(_ProductExpression_CLS);
-
- // elif exp_type is expr._PowExpression:
- PyObject * _PowExpression_CLS = PyObject_GetAttrString(expr_MOD, "_PowExpression");
- if(exp_type == _PowExpression_CLS) {
- Py_XDECREF(_PowExpression_CLS);
- Py_XDECREF(expr_MOD);
- return _handle_powexp(context, exp, ampl_repn);
- }
- Py_XDECREF(_PowExpression_CLS);
-
- // elif exp_type is expr._IntrinsicFunctionExpression:
- PyObject * _IntrinsicFunctionExpression_CLS = PyObject_GetAttrString(expr_MOD, "_IntrinsicFunctionExpression");
- if(exp_type == _IntrinsicFunctionExpression_CLS) {
- Py_XDECREF(_IntrinsicFunctionExpression_CLS);
- Py_XDECREF(expr_MOD);
- return _handle_ifexp(context, exp, ampl_repn);
- }
- Py_XDECREF(_IntrinsicFunctionExpression_CLS);
-
- // else:
- // raise ValueError, "Unsupported expression type: "+str(exp)
- PyObject * error_msg = PyString_FromString("Unsupported expression type: ");
- PyObject * exp_str = PyObject_Str(exp);
- if(exp_str == NULL) {
- exp_str = PyString_FromString("");
- }
- PyString_ConcatAndDel(&error_msg, exp_str);
- PyErr_SetObject(PyExc_ValueError, error_msg);
-
- // Free types
- Py_XDECREF(expr_MOD);
-
- // Raise the error
- return ERROR;
-}
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression.h b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression.h
deleted file mode 100644
index baf1d501932..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression.h
+++ /dev/null
@@ -1,10 +0,0 @@
-#include
-#include "../util.h"
-#include "../cAmpl.h"
-
-#include "expression/sumexp.h"
-#include "expression/prodexp.h"
-#include "expression/powexp.h"
-#include "expression/ifexp.h"
-
-int _handle_expression(PyObject * context, PyObject * exp, PyObject ** ampl_repn);
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/ifexp.c b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/ifexp.c
deleted file mode 100644
index 6912584133d..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/ifexp.c
+++ /dev/null
@@ -1,39 +0,0 @@
-#include "ifexp.h"
-#include "../../cAmpl.h"
-
-int _handle_ifexp(PyObject * context, PyObject * exp, PyObject ** ampl_repn) {
- // assert(len(exp._args) == 1)
- PyObject * exp__args = PyObject_GetAttrString(exp, "_args");
- Py_ssize_t len_exp__args = PySequence_Length(exp__args);
- if(len_exp__args != 1) {
- PyErr_SetString(PyExc_AssertionError, "");
- return ERROR;
- }
-
- // child_repn = generate_ampl_repn(exp._args[0])
- PyObject * exp__args_0 = PySequence_GetItem(exp__args, 0);
- PyObject * child_repn = recursive_generate_ampl_repn(context, exp__args_0);
- if(child_repn == NULL) return ERROR;
- Py_DECREF(exp__args_0);
- Py_DECREF(exp__args);
-
- // ampl_repn._nonlinear_expr = exp
- PyObject_SetAttrString(*ampl_repn, "_nonlinear_expr", exp);
-
- // ampl_repn._nonlinear_vars = child_repn._nonlinear_vars
- PyObject * child_repn__nlv = PyObject_GetAttrString(child_repn, "_nonlinear_vars");
- PyObject_SetAttrString(*ampl_repn, "_nonlinear_vars", child_repn__nlv);
- Py_DECREF(child_repn__nlv);
-
- // ampl_repn._nonlinear_vars.update(child_repn._linear_terms_var)
- PyObject * ampl_repn__nlv = PyObject_GetAttrString(*ampl_repn, "_nonlinear_vars");
- PyObject * child_repn__ltv = PyObject_GetAttrString(child_repn, "_linear_terms_var");
- PyObject_CallMethod(ampl_repn__nlv, "update", "(O)", child_repn__ltv);
- Py_DECREF(child_repn__ltv);
- Py_DECREF(ampl_repn__nlv);
-
- Py_DECREF(child_repn);
-
- assert((*ampl_repn)->ob_refcnt == 1);
- return TRUE;
-}
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/ifexp.h b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/ifexp.h
deleted file mode 100644
index 4803e2cd6e7..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/ifexp.h
+++ /dev/null
@@ -1,4 +0,0 @@
-#include
-#include "../../util.h"
-
-int _handle_ifexp(PyObject * context, PyObject * exp, PyObject ** ampl_repn);
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/powexp.c b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/powexp.c
deleted file mode 100644
index 88eba90c3da..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/powexp.c
+++ /dev/null
@@ -1,105 +0,0 @@
-#include "powexp.h"
-
-int _handle_powexp(PyObject * context, PyObject * exp, PyObject ** ampl_repn) {
- // result object
- PyObject * _result = NULL;
-
- // assert(len(exp._args) == 2)
- PyObject * exp__args = PyObject_GetAttrString(exp, "_args");
- Py_ssize_t len_exp__args = PySequence_Length(exp__args);
- if(len_exp__args != 2) {
- PyErr_SetString(PyExc_AssertionError, "");
- return ERROR;
- }
-
- // base_repn = generate_ampl_repn(exp._args[0])
- PyObject * exp__args_0 = PySequence_GetItem(exp__args, 0);
- PyObject * base_repn = recursive_generate_ampl_repn(context, exp__args_0);
- if(base_repn == NULL) return ERROR;
- Py_DECREF(exp__args_0);
-
- // exponent_repn = generate_ampl_repn(exp._args[1])
- PyObject * exp__args_1 = PySequence_GetItem(exp__args, 1);
- PyObject * exponent_repn = recursive_generate_ampl_repn(context, exp__args_1);
- if(exponent_repn == NULL) return ERROR;
- Py_DECREF(exp__args_1);
-
- Py_DECREF(exp__args);
-
- // if base_repn.is_constant() and exponent_repn.is_constant():
-
- // opt
- PyObject * br_ic = PyObject_CallMethod(base_repn, "is_constant", "()");
- PyObject * er_ic = PyObject_CallMethod(exponent_repn, "is_constant", "()");
- PyObject * br__constant = PyObject_GetAttrString(base_repn, "_constant");
- PyObject * er__constant = PyObject_GetAttrString(exponent_repn, "_constant");
-
- if(PyObject_IsTrue(br_ic) && PyObject_IsTrue(er_ic)) {
- // ampl_repn._constant = base_repn._constant**exponent_repn._constant
- _result = PyNumber_Power(br__constant, er__constant, Py_None);
- PyObject_SetAttrString(*ampl_repn, "_constant", _result);
- Py_DECREF(_result); _result = NULL;
-
- // elif exponent_repn.is_constant() and exponent_repn._constant == 1.0:
- } else if(PyObject_IsTrue(er_ic) && PyFloat_AS_DOUBLE(er__constant) == 1.0) {
- // ampl_repn = base_repn
- Py_DECREF(*ampl_repn);
- *ampl_repn = base_repn;
- Py_INCREF(*ampl_repn);
-
- // elif exponent_repn.is_constant() and exponent_repn._constant == 0.0:
- } else if(PyObject_IsTrue(er_ic) && PyFloat_AS_DOUBLE(er__constant) == 0.0) {
- // ampl_repn._constant = 1.0
- PyObject * _one = Py_BuildValue("f", 1.0);
- PyObject_SetAttrString(*ampl_repn, "_constant", _one);
- Py_DECREF(_one);
-
- // else:
- } else {
- // instead, let's just return the expression we are given and only
- // use the ampl_repn for the vars
- // ampl_repn._nonlinear_expr = exp
- PyObject_SetAttrString(*ampl_repn, "_nonlinear_expr", exp);
-
- // ampl_repn._nonlinear_vars = base_repn._nonlinear_vars
- PyObject * base_repn__nlv = PyObject_GetAttrString(base_repn, "_nonlinear_vars");
- PyObject_SetAttrString(*ampl_repn, "_nonlinear_vars", base_repn__nlv);
- Py_DECREF(base_repn__nlv);
-
- // opt
- PyObject * ampl_repn__nlv = PyObject_GetAttrString(*ampl_repn, "_nonlinear_vars");
- PyObject * _update = PyString_FromString("update");
-
- // ampl_repn._nonlinear_vars.update(exponent_repn._nonlinear_vars)
- PyObject * exponent_repn__nlv = PyObject_GetAttrString(exponent_repn, "_nonlinear_vars");
- PyObject_CallMethodObjArgs(ampl_repn__nlv, _update, exponent_repn__nlv, NULL);
- Py_DECREF(exponent_repn__nlv);
-
- // ampl_repn._nonlinear_vars.update(base_repn._linear_terms_var)
- PyObject * base_repn__ltv = PyObject_GetAttrString(base_repn, "_linear_terms_var");
- PyObject_CallMethodObjArgs(ampl_repn__nlv, _update, base_repn__ltv, NULL);
- Py_DECREF(base_repn__ltv);
-
- // ampl_repn._nonlinear_vars.update(exponent_repn._linear_terms_var)
- PyObject * exponent_repn__ltv = PyObject_GetAttrString(exponent_repn, "_linear_terms_var");
- PyObject_CallMethodObjArgs(ampl_repn__nlv, _update, exponent_repn__ltv, NULL);
- Py_DECREF(exponent_repn__ltv);
-
- // cleanup
- Py_DECREF(_update);
- Py_DECREF(ampl_repn__nlv);
- }
-
- // cleanup
- Py_DECREF(br_ic);
- Py_DECREF(er_ic);
- Py_DECREF(br__constant);
- Py_DECREF(er__constant);
-
- // cleanup
- Py_DECREF(base_repn);
- Py_DECREF(exponent_repn);
-
- assert((*ampl_repn)->ob_refcnt == 1);
- return TRUE;
-}
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/powexp.h b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/powexp.h
deleted file mode 100644
index 7f7126e43a7..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/powexp.h
+++ /dev/null
@@ -1,5 +0,0 @@
-#include
-#include "../../util.h"
-#include "../../cAmpl.h"
-
-int _handle_powexp(PyObject * context, PyObject * exp, PyObject ** ampl_repn);
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/prodexp.c b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/prodexp.c
deleted file mode 100644
index 440859a597c..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/prodexp.c
+++ /dev/null
@@ -1,389 +0,0 @@
-#include "prodexp.h"
-#include "../../cAmpl.h"
-
-// C NOTE: this function is potentially dangerous, since in some cases it
-// uses the PyFloat_AS_DOUBLE function instead of a safer downcast
-// or a PyObject_RichCompareBool call. In testing, this saved up
-// to 90% of the function call time, but it has implicit limitations
-// when the represented PyFloat object is outside the range of a C
-// double. Users experiencing crashes should use the pure Python
-// implementation instead.
-
-int _handle_prodexp(PyObject * context, PyObject * exp, PyObject ** ampl_repn) {
- // tmp
- PyObject * _result = NULL;
- PyObject * _result2 = NULL;
- Py_ssize_t i;
-
- // denom=1.0
- PyObject * denom = Py_BuildValue("f", 1.0);
-
- // for e in exp._denominator:
- PyObject * exp__denominator = PyObject_GetAttrString(exp, "_denominator");
- Py_ssize_t len_exp__denominator = PySequence_Length(exp__denominator);
- for(i = 0; i < len_exp__denominator; i++) {
- PyObject * e = PySequence_GetItem(exp__denominator, i);
-
- // if e.fixed_value():
- PyObject * e_fixed_value = PyObject_CallMethod(e, "fixed_value", NULL);
- if(PyObject_IsTrue(e_fixed_value) == TRUE) {
- // denom *= e.value
- PyObject * e_value = PyObject_GetAttrString(e, "value");
- _result = PyNumber_Multiply(denom, e_value);
- Py_DECREF(denom);
- denom = _result;
- _result = NULL;
- Py_DECREF(e_value);
-
- // elif e.is_constant():
- } else {
- PyObject * e_is_constant = PyObject_CallMethod(e, "is_constant", "()");
- if(PyObject_IsTrue(e_is_constant) == TRUE) {
- // denom *= e()
- PyObject * e_ = PyObject_CallObject(e, NULL);
- _result = PyNumber_Multiply(denom, e_);
- Py_DECREF(denom);
- denom = _result;
- _result = NULL;
- Py_DECREF(e_);
-
- // else:
- } else {
- // ampl_repn._nonlinear_expr = exp
- PyObject_SetAttrString(*ampl_repn, "_nonlinear_expr", exp);
-
- // break
- Py_DECREF(e_is_constant);
- Py_DECREF(e_fixed_value);
- Py_DECREF(e);
- break;
- }
- Py_DECREF(e_is_constant);
- }
- Py_DECREF(e_fixed_value);
-
- // if denom == 0.0:
- if(PyFloat_AS_DOUBLE(denom) == 0.0) {
- // print "Divide-by-zero error - offending sub-expression:"
- PySys_WriteStdout("Divide-by-zero error - offending sub-expression:\n");
-
- // e.pprint()
- PyObject_CallMethod(e, "pprint", NULL);
-
- // raise ZeroDivisionError
- PyErr_SetString(PyExc_ZeroDivisionError, "");
- Py_DECREF(e);
- return ERROR;
- }
-
- Py_DECREF(e);
- }
- Py_DECREF(exp__denominator);
-
- // if not ampl_repn._nonlinear_expr is None:
- PyObject * ampl_repn__nle = PyObject_GetAttrString(*ampl_repn, "_nonlinear_expr");
- if(ampl_repn__nle != Py_None) {
- // opt
- PyObject * _update = PyString_FromString("update");
-
- // we have a nonlinear expression ... build up all the vars
- // for e in exp._denominator:
- PyObject * exp__denominator = PyObject_GetAttrString(exp, "_denominator");
- Py_ssize_t len_exp__denominator = PySequence_Length(exp__denominator);
- for(i = 0; i < len_exp__denominator; i++) {
- PyObject * e = PySequence_GetItem(exp__denominator, i);
-
- // arg_repn = generate_ampl_repn(e)
- PyObject * arg_repn = recursive_generate_ampl_repn(context, e);
- Py_DECREF(e);
- if(arg_repn == NULL) return ERROR;
-
- // opt
- PyObject * ampl_repn__nlv = PyObject_GetAttrString(*ampl_repn, "_nonlinear_vars");
-
- // ampl_repn._nonlinear_vars.update(arg_repn._linear_terms_var)
- PyObject * arg_repn__ltv = PyObject_GetAttrString(arg_repn, "_linear_terms_var");
- PyObject_CallMethodObjArgs(ampl_repn__nlv, _update, arg_repn__ltv, NULL);
- Py_DECREF(arg_repn__ltv);
-
- // ampl_repn._nonlinear_vars.update(arg_repn._nonlinear_vars)
- PyObject * arg_repn__nlv = PyObject_GetAttrString(arg_repn, "_nonlinear_vars");
- PyObject_CallMethodObjArgs(ampl_repn__nlv, _update, arg_repn__nlv, NULL);
- Py_DECREF(arg_repn__nlv);
-
- Py_DECREF(ampl_repn__nlv);
- Py_DECREF(arg_repn);
- }
- Py_DECREF(exp__denominator);
-
- // for e in exp._numerator;
- PyObject * exp__numerator = PyObject_GetAttrString(exp, "_numerator");
- Py_ssize_t len_exp__numerator = PySequence_Length(exp__numerator);
- for(i = 0; i < len_exp__numerator; i++) {
- PyObject * e = PySequence_GetItem(exp__numerator, i);
-
- // arg_repn = generate_ampl_repn(e)
- PyObject * arg_repn = recursive_generate_ampl_repn(context, e);
- if(arg_repn == NULL) return ERROR;
- Py_DECREF(e);
-
- // opt
- PyObject * ampl_repn__nlv = PyObject_GetAttrString(*ampl_repn, "_nonlinear_vars");
-
- // ampl_repn._nonlinear_vars.update(arg_repn._linear_terms_var)
- PyObject * arg_repn__ltv = PyObject_GetAttrString(arg_repn, "_linear_terms_var");
- PyObject_CallMethodObjArgs(ampl_repn__nlv, _update, arg_repn__ltv, NULL);
- Py_DECREF(arg_repn__ltv);
-
- // ampl_repn._nonlinear_vars.update(arg_repn._nonlinear_vars)
- PyObject * arg_repn__nlv = PyObject_GetAttrString(arg_repn, "_nonlinear_vars");
- PyObject_CallMethodObjArgs(ampl_repn__nlv, _update, arg_repn__nlv, NULL);
- Py_DECREF(arg_repn__nlv);
-
- Py_DECREF(ampl_repn__nlv);
- Py_DECREF(arg_repn);
- }
- Py_DECREF(exp__numerator);
-
- Py_DECREF(_update);
- return TRUE;
- }
- Py_DECREF(ampl_repn__nle);
-
- // OK, the denominator is a constant
- // build up the ampl_repns for the numerator
- // C NOTE: we break from Python objects a bit here for the counters
- // n_linear_args = 0
- int n_linear_args = 0;
-
- // n_nonlinear_args = 0
- int n_nonlinear_args = 0;
-
- // arg_repns = list();
- PyObject * arg_repns = Py_BuildValue("[]");
-
- // for i in xrange(len(exp._numerator)):
- // C NOTE: don't actually care about the xrange
- PyObject * exp__numerator = PyObject_GetAttrString(exp, "_numerator");
- Py_ssize_t len_exp__numerator = PySequence_Length(exp__numerator);
- for(i = 0; i < len_exp__numerator; i++) {
- // e = exp._numerator[i]
- PyObject * e = PySequence_GetItem(exp__numerator, i);
-
- // e_repn = generate_ampl_repn(e)
- PyObject * e_repn = recursive_generate_ampl_repn(context, e);
- Py_DECREF(e);
- if(e_repn == NULL) return ERROR;
-
- // arg_repns.append(e_repn)
- PyList_Append(arg_repns, e_repn);
-
- // check if the expression is not nonlinear else it is nonlinear
- // if not e_repn._nonlinear_expr is None:
- PyObject * e_repn__nle = PyObject_GetAttrString(e_repn, "_nonlinear_expr");
- if(e_repn__nle != Py_None) {
- // n_nonlinear_args += 1
- n_nonlinear_args += 1;
-
- // check whether the expression is constant or else it is linear
- // elif not ((len(e_repn._linear_terms_var) == 0) and (e_repn._nonlinear_expr is None)):
- } else {
- PyObject * e_repn__ltv = PyObject_GetAttrString(e_repn, "_linear_terms_var");
- Py_ssize_t len_e_repn__ltv = PyDict_Size(e_repn__ltv);
- if(!(len_e_repn__ltv == 0 && e_repn__nle == Py_None)) {
- // n_linear_args += 1
- n_linear_args += 1;
- }
- Py_DECREF(e_repn__ltv);
- }
- Py_DECREF(e_repn__nle);
- Py_DECREF(e_repn);
- }
- Py_DECREF(exp__numerator);
-
- // is_nonlinear = False;
- // C NOTE: more trickery to avoid extraneous PyObjects
- int is_nonlinear = FALSE;
-
- // if n_linear_args > 1 or n_nonlinear_args > 0:
- if(n_linear_args > 1 || n_nonlinear_args > 0) {
- is_nonlinear = TRUE;
- }
-
- // if is_nonlinear is True:
- if(is_nonlinear == TRUE) {
- // do like AMPL and simply return the expression
- // without extracting the potentially linear part
- // ampl_repn = ampl_representation()
- *ampl_repn = new_ampl_representation();
-
- // ampl_repn._nonlinear_expr = exp
- PyObject_SetAttrString(*ampl_repn, "_nonlinear_expr", exp);
-
- // for repn in arg_repns:
- Py_ssize_t len_arg_repns = PySequence_Length(arg_repns);
- for(i = 0; i < len_arg_repns; i++) {
- PyObject * repn = PySequence_GetItem(arg_repns, i);
-
- // opt
- PyObject * ampl_repn__nlv = PyObject_GetAttrString(*ampl_repn, "_nonlinear_vars");
-
- // ampl_repn._nonlinear_vars.update(repn._linear_terms_var)
- PyObject * repn__ltv = PyObject_GetAttrString(repn, "_linear_terms_var");
- PyObject_CallMethod(ampl_repn__nlv, "update", "(O)", repn__ltv);
- Py_DECREF(repn__ltv);
-
- // ampl_repn._nonlinear_vars.update(repn._nonlinear_vars)
- PyObject * repn__nlv = PyObject_GetAttrString(repn, "_nonlinear_vars");
- PyObject_CallMethod(ampl_repn__nlv, "update", "(O)", repn__nlv);
- Py_DECREF(repn__nlv);
-
- // cleanup
- Py_DECREF(ampl_repn__nlv);
- Py_DECREF(repn);
- }
-
- // return ampl_repn
- Py_DECREF(arg_repns);
- return TRUE;
-
- // else:
- } else {
- // is linear or constant
- // ampl_repn = current_repn = arg_repns[0]
- PyObject * current_repn = PySequence_GetItem(arg_repns, 0);
- Py_DECREF(*ampl_repn); // C NOTE: decref current repn so we don't leak on next line
- *ampl_repn = current_repn;
-
- // for i in xrange(1, len(arg_repns)):
- // C NOTE: don't care about the xrange
- Py_ssize_t len_arg_repns = PySequence_Length(arg_repns);
- for(i = 1; i < len_arg_repns; i++) {
- // e_repn = arg_repns[i]
- PyObject * e_repn = PySequence_GetItem(arg_repns, i);
-
- // ampl_repn = ampl_representation()
- Py_DECREF(*ampl_repn);
- *ampl_repn = new_ampl_representation();
-
- // const_c * const_e
- // ampl_repn._constant = current_repn._constant * e_repn._constant
- PyObject * current_repn__constant = PyObject_GetAttrString(current_repn, "_constant");
- PyObject * e_repn__constant = PyObject_GetAttrString(e_repn, "_constant");
- _result = PyNumber_Multiply(current_repn__constant, e_repn__constant);
- PyObject_SetAttrString(*ampl_repn, "_constant", _result);
- Py_DECREF(_result); _result = NULL;
-
- // const_e * L_c
- // if e_repn._constant != 0.0:
- if(PyFloat_AS_DOUBLE(e_repn__constant) != 0.0) {
- // opt
- PyObject * current_repn__ltv = PyObject_GetAttrString(current_repn, "_linear_terms_var");
- PyObject * current_repn__ltc = PyObject_GetAttrString(current_repn, "_linear_terms_coef");
- PyObject * ampl_repn__ltv = PyObject_GetAttrString(*ampl_repn, "_linear_terms_var");
- PyObject * ampl_repn__ltc = PyObject_GetAttrString(*ampl_repn, "_linear_terms_coef");
-
- // for(var_name, var) in current_repn._linear_terms_var.iteritems():
- PyObject * var_name, * var;
- i = 0;
- while(PyDict_Next(current_repn__ltv, &i, &var_name, &var)) {
- // ampl_repn._linear_terms_coef[var_name] = current_repn._linear_terms_coef[var_name] * e_repn._constant
- PyObject * cr__ltc_vn = PyDict_GetItem(current_repn__ltc, var_name);
- _result = PyNumber_Multiply(cr__ltc_vn, e_repn__constant);
- Py_DECREF(cr__ltc_vn);
- PyDict_SetItem(ampl_repn__ltc, var_name, _result);
- Py_DECREF(_result); _result = NULL;
-
- // ampl_repn._linear_terms_var[var_name] = var
- PyDict_SetItem(ampl_repn__ltv, var_name, var);
- }
-
- Py_DECREF(current_repn__ltv);
- Py_DECREF(current_repn__ltc);
- Py_DECREF(ampl_repn__ltv);
- Py_DECREF(ampl_repn__ltc);
- }
-
- // const_c * L_e
- // if current_repn._constant != 0.0:
- if(PyFloat_AS_DOUBLE(current_repn__constant) != 0) {
- // opt
- PyObject * e_repn__ltv = PyObject_GetAttrString(e_repn, "_linear_terms_var");
- PyObject * e_repn__ltc = PyObject_GetAttrString(e_repn, "_linear_terms_coef");
- PyObject * ampl_repn__ltv = PyObject_GetAttrString(*ampl_repn, "_linear_terms_var");
- PyObject * ampl_repn__ltc = PyObject_GetAttrString(*ampl_repn, "_linear_terms_coef");
-
- // for (e_var_name,e_var) in e_repn._linear_terms_var.iteritems():
- PyObject * e_var_name, * e_var;
- i = 0;
- while(PyDict_Next(e_repn__ltv, &i, &e_var_name, &e_var)) {
- // opt
- PyObject * er__ltc_evn = PyDict_GetItem(e_repn__ltc, e_var_name);
- _result = PyNumber_Multiply(current_repn__constant, er__ltc_evn);
- Py_DECREF(er__ltc_evn);
-
- // if e_var_name in ampl_repn._linear_terms_var:
- if(PyMapping_HasKey(ampl_repn__ltv, e_var_name) == TRUE) {
- // ampl_repn._linear_terms_coef[e_var_name] += current_repn._constant * e_repn._linear_terms_coef[e_var_name]
- PyObject * _tmp = PyDict_GetItem(ampl_repn__ltc, e_var_name);
- _result2 = PyNumber_Add(_tmp, _result);
- PyDict_SetItem(ampl_repn__ltc, e_var_name, _result2);
- Py_DECREF(_tmp);
- Py_DECREF(_result2); _result2 = NULL;
-
- // else:
- } else {
- // ampl_repn._linear_terms_coef[e_var_name] = current_repn._constant * e_repn._linear_terms_coef[e_var_name]
- PyDict_SetItem(ampl_repn__ltc, e_var_name, _result);
- }
- Py_DECREF(_result); _result = NULL;
- }
-
- // cleanup
- Py_DECREF(e_repn__ltv);
- Py_DECREF(e_repn__ltc);
- Py_DECREF(ampl_repn__ltv);
- Py_DECREF(ampl_repn__ltc);
- }
-
- Py_DECREF(e_repn__constant);
- Py_DECREF(current_repn__constant);
- Py_DECREF(e_repn);
-
- // current_repn = ampl_repn
- Py_DECREF(current_repn);
- current_repn = *ampl_repn;
- }
- }
-
- // now deal with the product expression's coefficient that needs
- // to be applied to all parts of the ampl_repn
- // opt
- PyObject * exp_coef = PyObject_GetAttrString(exp, "coef");
- PyObject * quotient = PyNumber_Divide(exp_coef, denom);
- Py_DECREF(exp_coef);
-
- // ampl_repn._constant *= exp._coef/denom
- PyObject * ampl_repn__constant = PyObject_GetAttrString(*ampl_repn, "_constant");
- _result = PyNumber_Multiply(ampl_repn__constant, quotient);
- Py_DECREF(ampl_repn__constant);
- PyObject_SetAttrString(*ampl_repn, "_constant", _result);
- Py_DECREF(_result); _result = NULL;
-
- // for var_name in ampl_repn._linear_terms_coef:
- PyObject * ampl_repn__ltc = PyObject_GetAttrString(*ampl_repn, "_linear_terms_coef");
- PyObject * var_name, * _var;
- i = 0;
- // C NOTE: TODO this is scary. is this mutation allowed?
- while(PyDict_Next(ampl_repn__ltc, &i, &var_name, &_var)) {
- _result = PyNumber_Multiply(_var, quotient);
- PyDict_SetItem(ampl_repn__ltc, var_name, _result);
- Py_DECREF(_result); _result = NULL;
- }
- Py_DECREF(ampl_repn__ltc);
- Py_DECREF(quotient);
-
- // return ampl_repn
- assert((*ampl_repn)->ob_refcnt == 1);
- return TRUE;
-}
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/prodexp.h b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/prodexp.h
deleted file mode 100644
index beae3d54002..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/prodexp.h
+++ /dev/null
@@ -1,4 +0,0 @@
-#include
-#include "../../util.h"
-
-int _handle_prodexp(PyObject * context, PyObject * exp, PyObject ** ampl_repn);
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/sumexp.c b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/sumexp.c
deleted file mode 100644
index 84c4b2f731d..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/sumexp.c
+++ /dev/null
@@ -1,136 +0,0 @@
-#include "sumexp.h"
-#include "../../cAmpl.h"
-
-int _handle_sumexp(PyObject * context, PyObject * exp, PyObject ** ampl_repn) {
- // Objects usable for temp calculations
- PyObject * _result, * _result2;
-
- // ampl_repn._constant = exp._const
- PyObject * exp__const = PyObject_GetAttrString(exp, "_const");
- PyObject_SetAttrString(*ampl_repn, "_constant", exp__const);
- Py_DECREF(exp__const);
-
- // ampl_repn._nonlinear_expr = None
- PyObject_SetAttrString(*ampl_repn, "_nonlinear_expr", Py_None);
-
- // for i in xrange(len(exp._args)):
- PyObject * exp__args = PyObject_GetAttrString(exp, "_args");
- Py_ssize_t len_exp__args = PySequence_Length(exp__args);
- Py_ssize_t i;
- PyObject * exp__coef = PyObject_GetAttrString(exp, "_coef"); // opt
- for(i = 0; i < len_exp__args; i++) {
- // exp_coef = exp._coef[i]
- PyObject * exp_coef = PySequence_GetItem(exp__coef, i);
-
- // child_repn = generate_ampl_repn(exp._args[i])
- PyObject * exp__args_i = PySequence_GetItem(exp__args, i);
- PyObject * child_repn = recursive_generate_ampl_repn(context, exp__args_i);
- if(child_repn == NULL) {
- Py_DECREF(exp__args_i);
- Py_DECREF(child_repn);
- Py_DECREF(exp_coef);
- Py_DECREF(exp__coef);
- Py_DECREF(exp__args);
- return ERROR;
- }
- Py_DECREF(exp__args_i);
-
- // adjust the constant
- // ampl_repn._constant += exp_coef * child_repn._constant
- PyObject * child_repn__constant = PyObject_GetAttrString(child_repn, "_constant");
- _result = PyNumber_Multiply(exp_coef, child_repn__constant);
- PyObject * ampl_repn__constant = PyObject_GetAttrString(*ampl_repn, "_constant");
- _result2 = PyNumber_Add(ampl_repn__constant, _result);
- PyObject_SetAttrString(*ampl_repn, "_constant", _result2);
- Py_DECREF(_result2); _result2 = NULL;
- Py_DECREF(ampl_repn__constant);
- Py_DECREF(_result); _result = NULL;
- Py_DECREF(child_repn__constant);
-
- // adjust the linear terms
- // for (var_name,var) in child_repn._linear_terms_var.iteritems():
- PyObject * child_repn__ltv = PyObject_GetAttrString(child_repn, "_linear_terms_var");
- PyObject * child_repn__ltc = PyObject_GetAttrString(child_repn, "_linear_terms_coef");
- PyObject * var_name, * var;
- Py_ssize_t pos = 0;
- while(PyDict_Next(child_repn__ltv, &pos, &var_name, &var)) {
- // if var_name in ampl_repn._linear_terms_var:
- PyObject * ampl_repn__ltv = PyObject_GetAttrString(*ampl_repn, "_linear_terms_var");
- PyObject * ampl_repn__ltc = PyObject_GetAttrString(*ampl_repn, "_linear_terms_coef");
-
- // opt
- PyObject * child_repn__ltc_vn = PyDict_GetItem(child_repn__ltc, var_name);
- _result = PyNumber_Multiply(exp_coef, child_repn__ltc_vn);
-
- if(PyMapping_HasKey(ampl_repn__ltv, var_name)) {
- // ampl_repn._linear_terms_coef[var_name] += exp_coef * child_repn._linear_terms_coef[var_name]
- PyObject * ampl_repn__ltc_vn = PyDict_GetItem(ampl_repn__ltc, var_name);
- _result2 = PyNumber_Add(ampl_repn__ltc_vn, _result);
-
- PyDict_SetItem(ampl_repn__ltc, var_name, _result2);
-
- Py_DECREF(ampl_repn__ltc_vn);
- Py_DECREF(_result2); _result2 = NULL;
- // else:
- } else {
- // ampl_repn._linear_terms_var[var_name] = var
- PyDict_SetItem(ampl_repn__ltv, var_name, var);
-
- // ampl_repn._linear_terms_coef[var_name] = exp_coef*child_repn._linear_terms_coef[var_name]
- PyDict_SetItem(ampl_repn__ltc, var_name, _result);
- }
-
- Py_DECREF(child_repn__ltc_vn);
- Py_DECREF(_result); _result = NULL;
-
- Py_DECREF(ampl_repn__ltv);
- Py_DECREF(ampl_repn__ltc);
- }
-
- Py_DECREF(child_repn__ltv);
- Py_DECREF(child_repn__ltc);
-
- // adjust the nonlinear terms
- // if not child_repn._nonlinear_expr is None:
- PyObject * child_repn__nle = PyObject_GetAttrString(child_repn, "_nonlinear_expr");
- if(child_repn__nle != Py_None) {
- // if ampl_repn._nonlinear_expr is None:
- PyObject * ampl_repn__nle = PyObject_GetAttrString(*ampl_repn, "_nonlinear_expr");
-
- // opt
- _result = Py_BuildValue("(OO)", exp_coef, child_repn__nle);
-
- if(ampl_repn__nle == Py_None) {
- // ampl_repn._nonlinear_expr = [(exp_coef, child_repn._nonlinear_expr)]
- _result2 = Py_BuildValue("[O]", _result);
- PyObject_SetAttrString(*ampl_repn, "_nonlinear_expr", _result2);
- Py_DECREF(_result2); _result2 = NULL;
- // else:
- } else {
- // ampl_repn._nonlinear_expr.append((exp_coef, child_repn._nonlinear_expr))
- PyList_Append(ampl_repn__nle, _result);
- }
- Py_DECREF(_result); _result = NULL;
- Py_DECREF(ampl_repn__nle);
- }
- Py_DECREF(child_repn__nle);
-
- // adjust the nonlinear vars
- // ampl_repn._nonlinear_vars.update(child_repn._nonlinear_vars)
- PyObject * ampl_repn__nlv = PyObject_GetAttrString(*ampl_repn, "_nonlinear_vars");
- PyObject * child_repn__nlv = PyObject_GetAttrString(child_repn, "_nonlinear_vars");
- PyObject_CallMethod(ampl_repn__nlv, "update", "(O)", child_repn__nlv);
- Py_DECREF(child_repn__nlv);
- Py_DECREF(ampl_repn__nlv);
-
- // cleanup
- Py_DECREF(exp_coef);
- Py_DECREF(child_repn);
- }
-
- Py_DECREF(exp__coef);
- Py_DECREF(exp__args);
-
- assert((*ampl_repn)->ob_refcnt == 1);
- return TRUE;
-}
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/sumexp.h b/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/sumexp.h
deleted file mode 100644
index 209f44a9a4e..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/expression/sumexp.h
+++ /dev/null
@@ -1,4 +0,0 @@
-#include
-#include "../../util.h"
-
-int _handle_sumexp(PyObject * context, PyObject * exp, PyObject ** ampl_repn);
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/fixedval.c b/pyomo/repn/plugins/ampl/cAmpl/handlers/fixedval.c
deleted file mode 100644
index e785b2a5365..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/fixedval.c
+++ /dev/null
@@ -1,10 +0,0 @@
-#include "fixedval.h"
-
-int _handle_fixedval(PyObject * context, PyObject * exp, PyObject ** ampl_repn) {
- // ampl_repn._constant = exp.value
- PyObject * exp_value = PyObject_GetAttrString(exp, "value");
- PyObject_SetAttrString(*ampl_repn, "_constant", exp_value);
- Py_DECREF(exp_value);
-
- return TRUE;
-}
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/fixedval.h b/pyomo/repn/plugins/ampl/cAmpl/handlers/fixedval.h
deleted file mode 100644
index f11b809f1cc..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/fixedval.h
+++ /dev/null
@@ -1,4 +0,0 @@
-#include
-#include "../util.h"
-
-int _handle_fixedval(PyObject * context, PyObject * exp, PyObject ** ampl_repn);
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/variable.c b/pyomo/repn/plugins/ampl/cAmpl/handlers/variable.c
deleted file mode 100644
index e8cca0c5ce5..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/variable.c
+++ /dev/null
@@ -1,33 +0,0 @@
-#include "variable.h"
-
-int _handle_variable(PyObject * context, PyObject * exp, PyObject ** ampl_repn) {
- // if exp.fixed:
- PyObject * exp_fixed = PyObject_GetAttrString(exp, "fixed");
- if(exp_fixed != NULL && PyObject_IsTrue(exp_fixed)) {
- // ampl_repn._constant = exp.value
- PyObject * exp_value = PyObject_GetAttrString(exp, "value");
- PyObject_SetAttrString(*ampl_repn, "_constant", exp_value);
-
- // return ampl_repn
- Py_DECREF(exp_value);
- Py_DECREF(exp_fixed);
- return TRUE;
- }
-
- PyObject * exp_name = PyObject_GetAttrString(exp, "name");
-
- // ampl_repn._linear_terms_coef[exp.name] = 1.0
- PyObject * _linear_terms_coef = PyObject_GetAttrString(*ampl_repn, "_linear_terms_coef");
- PyObject * one = Py_BuildValue("f", 1.0);
- PyDict_SetItem(_linear_terms_coef, exp_name, one);
- Py_DECREF(_linear_terms_coef);
-
- // ampl_repn._linear_terms_var[exp.name] = exp
- PyObject * _linear_terms_var = PyObject_GetAttrString(*ampl_repn, "_linear_terms_var");
- PyDict_SetItem(_linear_terms_var, exp_name, exp);
- Py_DECREF(_linear_terms_var);
-
- // Should return generated ampl_repn
- Py_DECREF(exp_name);
- return TRUE;
-}
diff --git a/pyomo/repn/plugins/ampl/cAmpl/handlers/variable.h b/pyomo/repn/plugins/ampl/cAmpl/handlers/variable.h
deleted file mode 100644
index 8a3e53699e1..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/handlers/variable.h
+++ /dev/null
@@ -1,4 +0,0 @@
-#include
-#include "../util.h"
-
-int _handle_variable(PyObject * context, PyObject * exp, PyObject ** ampl_repn);
diff --git a/pyomo/repn/plugins/ampl/cAmpl/setup.py b/pyomo/repn/plugins/ampl/cAmpl/setup.py
deleted file mode 100644
index f6a6c319217..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/setup.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from distutils.core import setup, Extension
-
-import os
-import fnmatch
-
-sourceFiles = []
-for root, dirnames, filenames in os.walk('.'):
- for filename in fnmatch.filter(filenames, "*.c"):
- sourceFiles.append(os.path.join(root, filename))
-
-# Enable for debug only
-eca = ['-Werror', '-O3']
-cAmpl = Extension('cAmpl', sources = sourceFiles, extra_compile_args = eca)
-
-setup( name = 'cAmpl',
- version = '0.1',
- description = 'C AMPL representation generator',
- ext_modules = [cAmpl] )
diff --git a/pyomo/repn/plugins/ampl/cAmpl/util.c b/pyomo/repn/plugins/ampl/cAmpl/util.c
deleted file mode 100644
index 0340656958a..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/util.c
+++ /dev/null
@@ -1,173 +0,0 @@
-#include
-#include "util.h"
-
-/**
- * Print the current Python environment within which the calling C code
- * is executing, including:
- *
- * - The list of builtins available in the current scope
- * - The list of global variables available
- * - The list of local variables defined in the current scope
- *
- * Flushes standard output before returning.
- */
-void dump_env() {
- printf("Builtins: ");
- PyObject_Print(PyEval_GetBuiltins(), stdout, Py_PRINT_RAW);
- printf("\n\n");
-
- printf("Globals: ");
- PyObject_Print(PyEval_GetGlobals(), stdout, Py_PRINT_RAW);
- printf("\n\n");
-
- printf("Locals: ");
- PyObject_Print(PyEval_GetLocals(), stdout, Py_PRINT_RAW);
- printf("\n\n");
- fflush(stdout);
-}
-
-/**
- * Print the type of the Python object arg. Properly handles
- * NULL arguments (so any PyObject * pointer may be
- * passed), and if the argument is an instance of a class, will also
- * print the class itself.
- *
- * Flushes standard output before returning.
- *
- * @param arg The Python object whose type to print.
- */
-void dump_type(PyObject * arg) {
- if(arg == NULL) {
- printf("null\n");
- return;
- }
-
- PyObject * arg_type = PyObject_Type(arg);
- PyObject_Print(arg_type, stdout, 0);
- if(PyInstance_Check(arg)) {
- printf(": ");
- PyObject * arg_cls = PyObject_GetAttrString(arg, "__class__");
- PyObject_Print(arg_cls, stdout, 0);
- Py_DECREF(arg_cls);
- }
- printf("\n");
- Py_DECREF(arg_type);
- fflush(stdout);
-}
-
-/**
- * Print the list of attributes currently defined on the Python object
- * arg. Equivalent to calling print(dir(arg)) in pure
- * Python.
- *
- * Flushes standard output before returning.
- *
- * @param arg The Python object whose attributes to print.
- */
-void dump_dir(PyObject * arg) {
- if(arg == NULL) {
- printf("null\n");
- return;
- }
- PyObject * dir_arg = PyObject_Dir(arg);
- PyObject_Print(dir_arg, stdout, 0); printf("\n\n");
- Py_XDECREF(dir_arg);
- fflush(stdout);
-}
-
-/**
- * Print the current reference count of the Python object arg.
- * Properly handles NULL arguments (so any PyObject *
- * pointer may be passed).
- *
- * Flushes standard output before returning.
- *
- * @param arg The Python object whose reference count to print.
- * @see dump_refcnt_l()
- * @see dump_refcnt_lw()
- */
-void dump_refcnt(PyObject * arg) {
- if(arg == NULL) {
- printf("Null object has no refcount\n");
- } else {
- printf("Object has refcount %ld\n", Py_SAFE_DOWNCAST(arg->ob_refcnt, Py_ssize_t, long));
- }
- fflush(stdout);
-}
-
-/**
- * Prints the reference count of the Python object arg as named
- * by the string label. Useful for debugging when distinguishing
- * between reference counts on multiple objects.
- *
- * Flushes standard output before returning.
- *
- * @param arg The Python object whose reference count to print.
- * @param label The name of the Python object arg.
- * @see dump_refcnt()
- * @see dump_refcnt_lw()
- */
-void dump_refcnt_l(PyObject * arg, const char * label) {
- if(arg == NULL) {
- printf("Null %s object has no refcount\n", label);
- } else {
- printf("%s object has refcount %ld\n", label, Py_SAFE_DOWNCAST(arg->ob_refcnt, Py_ssize_t, long));
- }
- fflush(stdout);
-}
-
-/**
- * Prints the reference count of the Python object arg as named
- * by the string label. Takes an additional "temporal" label
- * when, which specifies (as a human-readable string) the point in
- * program execution when the reference count is printed. Useful for debugging
- * when distinguishing reference counts on the same object across function
- * calls or other code blocks.
- *
- * Flushes standard output before returning.
- *
- * @param arg The Python object whose reference count to print.
- * @param label The name of the Python object arg.
- * @param when The label of the time within program execution when the
- * reference count is printed.
- * @see dump_refcnt()
- * @see dump_refcnt_l()
- */
-void dump_refcnt_lw(PyObject * arg, const char * label, const char * when) {
- if(arg == NULL) {
- printf("%s, null %s object has no refcount\n", when, label);
- } else {
- printf("%s, %s object has refcount %ld\n", when, label, Py_SAFE_DOWNCAST(arg->ob_refcnt, Py_ssize_t, long));
- }
- fflush(stdout);
-}
-
-/**
- * Print the given message. Flushes standard output before returning.
- * Useful as a faster-to-code alternative to printf() and
- * fflush().
- *
- * @param msg The message to print
- * @see dump_msg_va()
- */
-void dump_msg(const char * msg) {
- printf(msg);
- fflush(stdout);
-}
-
-/**
- * Print the given message, formatted with the given arguments in standard
- * printf() style. Flushes standard output before returning. Useful
- * as a faster-to-code alternative to printf() and fflush().
- *
- * @param fmt The format string to print
- * @param ... Additional arguments to apply to the format string
- * @see dump_msg()
- */
-void dump_msg_va(const char * fmt, ...) {
- va_list args;
- va_start(args, fmt);
- vprintf(fmt, args);
- va_end(args);
- fflush(stdout);
-}
diff --git a/pyomo/repn/plugins/ampl/cAmpl/util.h b/pyomo/repn/plugins/ampl/cAmpl/util.h
deleted file mode 100644
index 6a5b096f80e..00000000000
--- a/pyomo/repn/plugins/ampl/cAmpl/util.h
+++ /dev/null
@@ -1,19 +0,0 @@
-#ifndef _CAMPL_UTIL_H
-#define _CAMPL_UTIL_H
-
-void dump_env();
-void dump_type(PyObject * arg);
-void dump_dir(PyObject * arg);
-
-void dump_refcnt(PyObject * arg);
-void dump_refcnt_l(PyObject * arg, const char * label);
-void dump_refcnt_lw(PyObject * arg, const char * label, const char * when);
-
-void dump_msg(const char * msg);
-void dump_msg_va(const char * fmt, ...);
-
-#define ERROR (-1)
-#define FALSE (0)
-#define TRUE (1)
-
-#endif
diff --git a/pyomo/repn/plugins/baron_writer.py b/pyomo/repn/plugins/baron_writer.py
index a2067b2ac05..f74fabd1f3d 100644
--- a/pyomo/repn/plugins/baron_writer.py
+++ b/pyomo/repn/plugins/baron_writer.py
@@ -14,10 +14,15 @@
import logging
import itertools
+from six import iteritems, StringIO, iterkeys
+from six.moves import xrange
+from pyutilib.math import isclose
import pyomo.util.plugin
from pyomo.opt import ProblemFormat
from pyomo.opt.base import AbstractProblemWriter
+from pyomo.core.expr.numvalue import is_fixed, value, as_numeric, native_numeric_types, native_types
+from pyomo.core.expr import current as EXPR
from pyomo.core.base import (SortComponents,
SymbolMap,
AlphaNumericTextLabeler,
@@ -25,20 +30,100 @@
BooleanSet, Constraint,
IntegerSet, Objective,
Var, Param)
-from pyomo.core.base.numvalue import is_fixed, value, as_numeric
from pyomo.core.base.set_types import *
#CLH: EXPORT suffixes "constraint_types" and "branching_priorities"
# pass their respective information to the .bar file
import pyomo.core.base.suffix
-from pyomo.repn import LinearCanonicalRepn
import pyomo.core.kernel.component_suffix
from pyomo.core.kernel.component_block import IBlockStorage
+logger = logging.getLogger('pyomo.core')
+
+
+#
+# A visitor pattern that creates a string for an expression
+# that is compatible with the BARON syntax.
+#
+class ToBaronVisitor(EXPR.ExpressionValueVisitor):
+
+ def __init__(self, variables, smap):
+ super(ToBaronVisitor, self).__init__()
+ self.variables = variables
+ self.smap = smap
+
+ def visit(self, node, values):
+ """ Visit nodes that have been expanded """
+ tmp = []
+ for i,val in enumerate(values):
+ arg = node._args_[i]
+
+ if arg is None:
+ tmp.append('Undefined')
+ elif arg.__class__ in native_numeric_types:
+ tmp.append(val)
+ elif arg.__class__ in native_types:
+ tmp.append("'{0}'".format(val))
+ elif arg.is_variable_type():
+ tmp.append(val)
+ elif arg.is_expression_type() and node._precedence() < arg._precedence():
+ tmp.append("({0})".format(val))
+ else:
+ tmp.append(val)
+
+ if node.__class__ is EXPR.LinearExpression:
+ for v in node.linear_vars:
+ self.variables.add(id(v))
+
+ if node.__class__ is EXPR.ProductExpression:
+ return "{0} * {1}".format(tmp[0], tmp[1])
+ elif node.__class__ is EXPR.MonomialTermExpression:
+ if tmp[0] == '-1':
+ # It seems dumb to construct a temporary NegationExpression object
+ # Should we copy the logic from that function here?
+ return EXPR.NegationExpression._to_string(EXPR.NegationExpression((None,)), [tmp[1]], None, self.smap, True)
+ else:
+ return "{0} * {1}".format(tmp[0], tmp[1])
+ elif node.__class__ is EXPR.PowExpression:
+ return "{0} ^ {1}".format(tmp[0], tmp[1])
+ else:
+ return node._to_string(tmp, None, self.smap, True)
+
+ def visiting_potential_leaf(self, node):
+ """
+ Visiting a potential leaf.
+
+ Return True if the node is not expanded.
+ """
+ #print("ISLEAF")
+ #print(node.__class__)
+ if node is None:
+ return True, None
+
+ if node.__class__ in native_types:
+ return True, str(node)
+
+ if node.is_variable_type():
+ if node.fixed:
+ return True, str(value(node))
+ self.variables.add(id(node))
+ label = self.smap.getSymbol(node)
+ return True, label
+
+ if not node.is_expression_type():
+ return True, str(value(node))
+
+ return False, None
+
+
+def expression_to_string(expr, variables, labeler=None, smap=None):
+ if labeler is not None:
+ if smap is None:
+ smap = SymbolMap()
+ smap.default_labeler = labeler
+ visitor = ToBaronVisitor(variables, smap)
+ return visitor.dfs_postorder_stack(expr)
-from six import iteritems, StringIO, iterkeys
-from six.moves import xrange
-logger = logging.getLogger('pyomo.core')
# TODO: The to_string function is handy, but the fact that
# it calls .name under the hood for all components
@@ -91,11 +176,7 @@ def _write_equations_section(self,
all_blocks_list,
active_components_data_var,
symbol_map,
- labeler,
- create_symbol_func,
- create_symbols_func,
- alias_symbol_func,
- object_symbol_dictionary,
+ c_labeler,
output_fixed_variable_bounds,
skip_trivial_constraints,
sorter):
@@ -109,14 +190,8 @@ def _skip_trivial(constraint_data):
if (repn.variables is None) or \
(len(repn.variables) == 0):
return True
- elif isinstance(constraint_data, LinearCanonicalRepn):
- repn = constraint_data
- if (repn.variables is None) or \
- (len(repn.variables) == 0):
- return True
- else:
- if constraint_data.body.polynomial_degree() == 0:
- return True
+ elif constraint_data.body.polynomial_degree() == 0:
+ return True
return False
#
@@ -161,20 +236,6 @@ def _skip_trivial(constraint_data):
non_standard_eqns = r_o_eqns + c_eqns + l_eqns
- # GAH 1/5/15: Substituting all non-alphanumeric characters for underscore
- # in labeler so this manual update should no longer be needed
- #
- # If the text labeler is used, correct the labels to be
- # baron-allowed variable names
- # Change '(' and ')' to '__'
- # This way, for simple variable names like 'x(1_2)' --> 'x__1_2__'
- # FIXME: 7/21/14 This may break if users give variable names
- # with two or more underscores together
- #if symbolic_solver_labels:
- # for key,label in iteritems(object_symbol_dictionary):
- # label = label.replace('(','___')
- # object_symbol_dictionary[key] = label.replace(')','__')
-
#
# EQUATIONS
#
@@ -203,7 +264,6 @@ def _skip_trivial(constraint_data):
sort=sorter,
descend_into=False):
-
if (not constraint_data.has_lb()) and \
(not constraint_data.has_ub()):
assert not constraint_data.equality
@@ -214,13 +274,11 @@ def _skip_trivial(constraint_data):
eqns.append(constraint_data)
- con_symbol = \
- create_symbol_func(symbol_map, constraint_data, labeler)
+ con_symbol = symbol_map.createSymbol(constraint_data, c_labeler)
assert not con_symbol.startswith('.')
assert con_symbol != "c_e_FIX_ONE_VAR_CONST__"
- alias_symbol_func(symbol_map,
- constraint_data,
+ symbol_map.alias(constraint_data,
alias_template % order_counter)
output_file.write(", "+str(con_symbol))
order_counter += 1
@@ -230,11 +288,10 @@ def _skip_trivial(constraint_data):
if n_roeqns > 0:
output_file.write('RELAXATION_ONLY_EQUATIONS ')
for i, constraint_data in enumerate(r_o_eqns):
- con_symbol = create_symbol_func(symbol_map, constraint_data, labeler)
+ con_symbol = symbol_map.createSymbol(constraint_data, c_labeler)
assert not con_symbol.startswith('.')
assert con_symbol != "c_e_FIX_ONE_VAR_CONST__"
- alias_symbol_func(symbol_map,
- constraint_data,
+ symbol_map.alias(constraint_data,
alias_template % order_counter)
if i == n_roeqns-1:
output_file.write(str(con_symbol)+';\n\n')
@@ -245,11 +302,10 @@ def _skip_trivial(constraint_data):
if n_ceqns > 0:
output_file.write('CONVEX_EQUATIONS ')
for i, constraint_data in enumerate(c_eqns):
- con_symbol = create_symbol_func(symbol_map, constraint_data, labeler)
+ con_symbol = symbol_map.createSymbol(constraint_data, c_labeler)
assert not con_symbol.startswith('.')
assert con_symbol != "c_e_FIX_ONE_VAR_CONST__"
- alias_symbol_func(symbol_map,
- constraint_data,
+ symbol_map.alias(constraint_data,
alias_template % order_counter)
if i == n_ceqns-1:
output_file.write(str(con_symbol)+';\n\n')
@@ -260,11 +316,10 @@ def _skip_trivial(constraint_data):
if n_leqns > 0:
output_file.write('LOCAL_EQUATIONS ')
for i, constraint_data in enumerate(l_eqns):
- con_symbol = create_symbol_func(symbol_map, constraint_data, labeler)
+ con_symbol = symbol_map.createSymbol(constraint_data, c_labeler)
assert not con_symbol.startswith('.')
assert con_symbol != "c_e_FIX_ONE_VAR_CONST__"
- alias_symbol_func(symbol_map,
- constraint_data,
+ symbol_map.alias(constraint_data,
alias_template % order_counter)
if i == n_leqns-1:
output_file.write(str(con_symbol)+';\n\n')
@@ -299,33 +354,38 @@ def mutable_param_gen(b):
for param_data in param_data_iter:
yield param_data
- vstring_to_var_dict = {}
- vstring_to_bar_dict = {}
- pstring_to_bar_dict = {}
- _val_template = ' %'+self._precision_string+' '
- for block in all_blocks_list:
- for var_data in active_components_data_var[id(block)]:
- variable_stream = StringIO()
- var_data.to_string(ostream=variable_stream, verbose=False)
- variable_string = variable_stream.getvalue()
- variable_string = ' '+variable_string+' '
- vstring_to_var_dict[variable_string] = var_data
- if output_fixed_variable_bounds or (not var_data.fixed):
- vstring_to_bar_dict[variable_string] = \
- ' '+object_symbol_dictionary[id(var_data)]+' '
- else:
- assert var_data.value is not None
- vstring_to_bar_dict[variable_string] = \
- (_val_template % (var_data.value,))
+ if False:
+ #
+ # This was part of a merge from master that caused
+ # test failures. But commenting this out didn't cause additional failures!?!
+ #
+ vstring_to_var_dict = {}
+ vstring_to_bar_dict = {}
+ pstring_to_bar_dict = {}
+ _val_template = ' %'+self._precision_string+' '
+ for block in all_blocks_list:
+ for var_data in active_components_data_var[id(block)]:
+ variable_stream = StringIO()
+ var_data.to_string(ostream=variable_stream, verbose=False)
+ variable_string = variable_stream.getvalue()
+ variable_string = ' '+variable_string+' '
+ vstring_to_var_dict[variable_string] = var_data
+ if output_fixed_variable_bounds or (not var_data.fixed):
+ vstring_to_bar_dict[variable_string] = \
+ ' '+object_symbol_dictionary[id(var_data)]+' '
+ else:
+ assert var_data.value is not None
+ vstring_to_bar_dict[variable_string] = \
+ (_val_template % (var_data.value,))
- for param_data in mutable_param_gen(block):
- param_stream = StringIO()
- param_data.to_string(ostream=param_stream, verbose=False)
- param_string = param_stream.getvalue()
+ for param_data in mutable_param_gen(block):
+ param_stream = StringIO()
+ param_data.to_string(ostream=param_stream, verbose=False)
+ param_string = param_stream.getvalue()
- param_string = ' '+param_string+' '
- pstring_to_bar_dict[param_string] = \
- (_val_template % (param_data(),))
+ param_string = ' '+param_string+' '
+ pstring_to_bar_dict[param_string] = \
+ (_val_template % (param_data(),))
# Equation Definition
string_template = '%'+self._precision_string
@@ -335,62 +395,15 @@ def mutable_param_gen(b):
c_eqns,
l_eqns):
- #########################
- #CLH: The section below is kind of a hack-y way to use
- # the expr.to_string function to print
- # expressions. A stream is created, writen to, and
- # then the string is recovered and stored in
- # eqn_body. Then the variable names are converted
- # to match the variable names that are used in the
- # bar file.
-
- # Fill in the body of the equation
- body_string_buffer = StringIO()
-
- if constraint_data.body is None:
- continue
- as_numeric(constraint_data.body).to_string(
- ostream=body_string_buffer,
- verbose=False)
- eqn_body = body_string_buffer.getvalue()
-
- # First, pad the equation so that if there is a
- # variable name at the start or end of the equation,
- # it can still be identified as padded with spaces.
-
- # Second, change pyomo's ** to baron's ^, also with
- # padding so that variable can always be found with
- # space around them
-
- # Third, add more padding around multiplication. Pyomo
- # already has spaces between variable on variable
- # multiplication, but not for constants on variables
- eqn_body = ' '+eqn_body+' '
- eqn_body = eqn_body.replace('**',' ^ ')
- eqn_body = eqn_body.replace('*', ' * ')
-
+ variables = set()
+ #print(symbol_map.byObject.keys())
+ eqn_body = expression_to_string(constraint_data.body, variables, smap=symbol_map)
+ #print(symbol_map.byObject.keys())
+ referenced_variable_ids.update(variables)
- #
- # FIXME: The following block of code is extremely inefficient.
- # We are looping through every parameter and variable in
- # the model each time we write a constraint expression.
- #
- ################################################
- vnames = [(variable_string, bar_string)
- for variable_string, bar_string in iteritems(vstring_to_bar_dict)
- if variable_string in eqn_body]
- for variable_string, bar_string in vnames:
- var_data = vstring_to_var_dict[variable_string]
- if output_fixed_variable_bounds or (not var_data.fixed):
- referenced_variable_ids.add(id(var_data))
- eqn_body = eqn_body.replace(variable_string, bar_string)
- for param_string, bar_string in iteritems(pstring_to_bar_dict):
- eqn_body = eqn_body.replace(param_string, bar_string)
- ################################################
-
- if len(vnames) == 0:
+ if len(variables) == 0:
assert not skip_trivial_constraints
- eqn_body += "+ 0 * ONE_VAR_CONST__ "
+ eqn_body += " + 0 * ONE_VAR_CONST__ "
# 7/29/14 CLH:
#FIXME: Baron doesn't handle many of the
@@ -402,7 +415,7 @@ def mutable_param_gen(b):
# useful.
##########################
- con_symbol = object_symbol_dictionary[id(constraint_data)]
+ con_symbol = symbol_map.byObject[id(constraint_data)]
output_file.write(str(con_symbol) + ': ')
# Fill in the left and right hand side (constants) of
@@ -464,46 +477,23 @@ def mutable_param_gen(b):
% (model.name))
# create symbol
- create_symbol_func(symbol_map, objective_data, labeler)
- alias_symbol_func(symbol_map, objective_data, "__default_objective__")
+ symbol_map.createSymbol(objective_data, c_labeler)
+ symbol_map.alias(objective_data, "__default_objective__")
if objective_data.is_minimizing():
output_file.write("minimize ")
else:
output_file.write("maximize ")
- #FIXME 7/18/14 See above, constraint writing
- # section. Will cause problems if there
- # are spaces in variables
- # Similar to the constraints section above, the
- # objective is generated from the expr.to_string
- # function.
- obj_stream = StringIO()
- objective_data.expr.to_string(ostream=obj_stream, verbose=False)
-
- obj_string = ' '+obj_stream.getvalue()+' '
- obj_string = obj_string.replace('**',' ^ ')
- obj_string = obj_string.replace('*', ' * ')
-
- #
- # FIXME: The following block of code is extremely inefficient.
- # We are looping through every parameter and variable in
- # the model each time we write an expression.
- #
- ################################################
- vnames = [(variable_string, bar_string)
- for variable_string, bar_string in iteritems(vstring_to_bar_dict)
- if variable_string in obj_string]
- for variable_string, bar_string in vnames:
- var_data = var_data = vstring_to_var_dict[variable_string]
- if output_fixed_variable_bounds or (not var_data.fixed):
- referenced_variable_ids.add(id(var_data))
- obj_string = obj_string.replace(variable_string, bar_string)
- for param_string, bar_string in iteritems(pstring_to_bar_dict):
- obj_string = obj_string.replace(param_string, bar_string)
- ################################################
+ variables = set()
+ #print(symbol_map.byObject.keys())
+ obj_string = expression_to_string(objective_data.expr, variables, smap=symbol_map)
+ #print(symbol_map.byObject.keys())
+ referenced_variable_ids.update(variables)
+
output_file.write(obj_string+";\n\n")
+ #referenced_variable_ids.update(symbol_map.byObject.keys())
return referenced_variable_ids, branching_priorities_suffixes
@@ -585,17 +575,15 @@ def __call__(self,
output_file.write("}\n\n")
if symbolic_solver_labels:
- labeler = AlphaNumericTextLabeler()
+ v_labeler = AlphaNumericTextLabeler()
+ c_labeler = AlphaNumericTextLabeler()
elif labeler is None:
- labeler = NumericLabeler('x')
+ v_labeler = NumericLabeler('x')
+ c_labeler = NumericLabeler('c')
symbol_map = SymbolMap()
- sm_bySymbol = symbol_map.bySymbol
-
- #cache frequently called functions
- create_symbol_func = SymbolMap.createSymbol
- create_symbols_func = SymbolMap.createSymbols
- alias_symbol_func = SymbolMap.alias
+ symbol_map.default_labeler = v_labeler
+ #sm_bySymbol = symbol_map.bySymbol
# Cache the list of model blocks so we don't have to call
# model.block_data_objects() many many times, which is slow
@@ -604,12 +592,12 @@ def __call__(self,
sort=sorter,
descend_into=True))
active_components_data_var = {}
- for block in all_blocks_list:
- tmp = active_components_data_var[id(block)] = \
- list(obj for obj in block.component_data_objects(Var,
- sort=sorter,
- descend_into=False))
- create_symbols_func(symbol_map, tmp, labeler)
+ #for block in all_blocks_list:
+ # tmp = active_components_data_var[id(block)] = \
+ # list(obj for obj in block.component_data_objects(Var,
+ # sort=sorter,
+ # descend_into=False))
+ # create_symbols_func(symbol_map, tmp, labeler)
# GAH: Not sure this is necessary, and also it would break for
# non-mutable indexed params so I am commenting out for now.
@@ -618,8 +606,8 @@ def __call__(self,
#if not param_data.is_constant():
# create_symbol_func(symbol_map, param_data, labeler)
- symbol_map_variable_ids = set(symbol_map.byObject.keys())
- object_symbol_dictionary = symbol_map.byObject
+ #symbol_map_variable_ids = set(symbol_map.byObject.keys())
+ #object_symbol_dictionary = symbol_map.byObject
#
# Go through the objectives and constraints and generate
@@ -634,11 +622,7 @@ def __call__(self,
all_blocks_list,
active_components_data_var,
symbol_map,
- labeler,
- create_symbol_func,
- create_symbols_func,
- alias_symbol_func,
- object_symbol_dictionary,
+ c_labeler,
output_fixed_variable_bounds,
skip_trivial_constraints,
sorter)
@@ -651,128 +635,113 @@ def __call__(self,
IntVars = []
PosVars = []
Vars = []
- for block in all_blocks_list:
- for var_data in active_components_data_var[id(block)]:
-
- if id(var_data) not in referenced_variable_ids:
- continue
-
- if var_data.is_continuous():
- if var_data.has_lb() and \
- (self._get_bound(var_data.lb) >= 0):
- TypeList = PosVars
- else:
- TypeList = Vars
- elif var_data.is_binary():
- TypeList = BinVars
- elif var_data.is_integer():
- TypeList = IntVars
+ for vid in referenced_variable_ids:
+ name = symbol_map.byObject[vid]
+ var_data = symbol_map.bySymbol[name]()
+
+ if var_data.is_continuous():
+ if var_data.has_lb() and \
+ (self._get_bound(var_data.lb) >= 0):
+ TypeList = PosVars
else:
- assert False
-
- var_name = object_symbol_dictionary[id(var_data)]
- #if len(var_name) > 15:
- # logger.warning(
- # "Variable symbol '%s' for variable %s exceeds maximum "
- # "character limit for BARON. Solver may fail"
- # % (var_name, var_data.name))
-
- TypeList.append(var_name)
+ TypeList = Vars
+ elif var_data.is_binary():
+ TypeList = BinVars
+ elif var_data.is_integer():
+ TypeList = IntVars
+ else:
+ assert False
+ TypeList.append(name)
if len(BinVars) > 0:
+ BinVars.sort()
output_file.write('BINARY_VARIABLES ')
- for var_name in BinVars[:-1]:
- output_file.write(str(var_name)+', ')
- output_file.write(str(BinVars[-1])+';\n\n')
+ output_file.write(", ".join(BinVars))
+ output_file.write(';\n\n')
+
if len(IntVars) > 0:
+ IntVars.sort()
output_file.write('INTEGER_VARIABLES ')
- for var_name in IntVars[:-1]:
- output_file.write(str(var_name)+', ')
- output_file.write(str(IntVars[-1])+';\n\n')
+ output_file.write(", ".join(IntVars))
+ output_file.write(';\n\n')
+ PosVars.append('ONE_VAR_CONST__')
+ PosVars.sort()
output_file.write('POSITIVE_VARIABLES ')
- output_file.write('ONE_VAR_CONST__')
- for var_name in PosVars:
- output_file.write(', '+str(var_name))
+ output_file.write(", ".join(PosVars))
output_file.write(';\n\n')
if len(Vars) > 0:
+ Vars.sort()
output_file.write('VARIABLES ')
- for var_name in Vars[:-1]:
- output_file.write(str(var_name)+', ')
- output_file.write(str(Vars[-1])+';\n\n')
+ output_file.write(", ".join(Vars))
+ output_file.write(';\n\n')
#
# LOWER_BOUNDS
#
- LowerBoundHeader = False
- for block in all_blocks_list:
- for var_data in active_components_data_var[id(block)]:
-
- if id(var_data) not in referenced_variable_ids:
- continue
+ lbounds = {}
+ for vid in referenced_variable_ids:
+ name = symbol_map.byObject[vid]
+ var_data = symbol_map.bySymbol[name]()
- if var_data.fixed:
- if output_fixed_variable_bounds:
- var_data_lb = var_data.value
- else:
- var_data_lb = None
+ if var_data.fixed:
+ if output_fixed_variable_bounds:
+ var_data_lb = var_data.value
else:
var_data_lb = None
- if var_data.has_lb():
- var_data_lb = self._get_bound(var_data.lb)
-
- if var_data_lb is not None:
- if LowerBoundHeader is False:
- output_file.write("LOWER_BOUNDS{\n")
- LowerBoundHeader = True
- name_to_output = object_symbol_dictionary[id(var_data)]
- lb_string_template = '%s: %'+self._precision_string+';\n'
- output_file.write(lb_string_template
- % (name_to_output, var_data_lb))
-
- if LowerBoundHeader:
+ else:
+ var_data_lb = None
+ if var_data.has_lb():
+ var_data_lb = self._get_bound(var_data.lb)
+
+ if var_data_lb is not None:
+ name_to_output = symbol_map.getSymbol(var_data)
+ lb_string_template = '%s: %'+self._precision_string+';\n'
+ lbounds[name_to_output] = lb_string_template % (name_to_output, var_data_lb)
+
+ if len(lbounds) > 0:
+ output_file.write("LOWER_BOUNDS{\n")
+ output_file.write("".join( lbounds[key] for key in sorted(lbounds.keys()) ) )
output_file.write("}\n\n")
+ lbounds = None
#
# UPPER_BOUNDS
#
- UpperBoundHeader = False
- for block in all_blocks_list:
- for var_data in active_components_data_var[id(block)]:
-
- if id(var_data) not in referenced_variable_ids:
- continue
+ ubounds = {}
+ for vid in referenced_variable_ids:
+ name = symbol_map.byObject[vid]
+ var_data = symbol_map.bySymbol[name]()
- if var_data.fixed:
- if output_fixed_variable_bounds:
- var_data_ub = var_data.value
- else:
- var_data_ub = None
+ if var_data.fixed:
+ if output_fixed_variable_bounds:
+ var_data_ub = var_data.value
else:
var_data_ub = None
- if var_data.has_ub():
- var_data_ub = self._get_bound(var_data.ub)
-
- if var_data_ub is not None:
- if UpperBoundHeader is False:
- output_file.write("UPPER_BOUNDS{\n")
- UpperBoundHeader = True
- name_to_output = object_symbol_dictionary[id(var_data)]
- ub_string_template = '%s: %'+self._precision_string+';\n'
- output_file.write(ub_string_template
- % (name_to_output, var_data_ub))
-
- if UpperBoundHeader:
+ else:
+ var_data_ub = None
+ if var_data.has_ub():
+ var_data_ub = self._get_bound(var_data.ub)
+
+ if var_data_ub is not None:
+ name_to_output = symbol_map.getSymbol(var_data)
+ ub_string_template = '%s: %'+self._precision_string+';\n'
+ ubounds[name_to_output] = ub_string_template % (name_to_output, var_data_ub)
+
+ if len(ubounds) > 0:
+ output_file.write("UPPER_BOUNDS{\n")
+ output_file.write("".join( ubounds[key] for key in sorted(ubounds.keys()) ) )
output_file.write("}\n\n")
+ ubounds = None
#
# BRANCHING_PRIORITIES
#
- # Specifyig priorities requires that the pyomo model has established an
+ # Specifying priorities requires that the pyomo model has established an
# EXTERNAL, float suffix called 'branching_priorities' on the model
# object, indexed by the relevant variable
BranchingPriorityHeader = False
@@ -784,7 +753,7 @@ def __call__(self,
if not BranchingPriorityHeader:
output_file.write('BRANCHING_PRIORITIES{\n')
BranchingPriorityHeader = True
- name_to_output = object_symbol_dictionary[id(var_data)]
+ name_to_output = symbol_map.getSymbol(var_data)
output_file.write(name_to_output+': '+str(priority)+';\n')
if BranchingPriorityHeader:
@@ -799,33 +768,21 @@ def __call__(self,
# STARTING_POINT
#
output_file.write('STARTING_POINT{\nONE_VAR_CONST__: 1;\n')
+ tmp = {}
string_template = '%s: %'+self._precision_string+';\n'
- for block in all_blocks_list:
- for var_data in active_components_data_var[id(block)]:
+ for vid in referenced_variable_ids:
+ name = symbol_map.byObject[vid]
+ var_data = symbol_map.bySymbol[name]()
- if id(var_data) not in referenced_variable_ids:
- continue
-
- starting_point = var_data.value
- if starting_point is not None:
- var_name = object_symbol_dictionary[id(var_data)]
- output_file.write(string_template % (var_name, starting_point))
+ starting_point = var_data.value
+ if starting_point is not None:
+ var_name = symbol_map.getSymbol(var_data)
+ tmp[var_name] = string_template % (var_name, starting_point)
+ output_file.write("".join( tmp[key] for key in sorted(tmp.keys()) ))
output_file.write('}\n\n')
output_file.close()
- # Clean up the symbol map to only contain variables referenced
- # in the active constraints
- vars_to_delete = symbol_map_variable_ids - referenced_variable_ids
- sm_byObject = symbol_map.byObject
- for varid in vars_to_delete:
- symbol = sm_byObject[varid]
- del sm_byObject[varid]
- del sm_bySymbol[symbol]
-
- del symbol_map_variable_ids
- del referenced_variable_ids
-
return output_filename, symbol_map
diff --git a/pyomo/repn/plugins/cpxlp.py b/pyomo/repn/plugins/cpxlp.py
index 4acab4bc2a4..7a67a8a1581 100644
--- a/pyomo/repn/plugins/cpxlp.py
+++ b/pyomo/repn/plugins/cpxlp.py
@@ -2,8 +2,8 @@
#
# Pyomo: Python Optimization Modeling Objects
# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
-# Under the terms of Contract DE-NA0003525 with National Technology and
-# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
@@ -29,21 +29,16 @@
Var, value,
SOSConstraint, Objective,
ComponentMap, is_fixed)
-from pyomo.repn import (generate_canonical_repn,
- canonical_degree,
- GeneralCanonicalRepn,
- LinearCanonicalRepn)
+from pyomo.repn import generate_standard_repn
logger = logging.getLogger('pyomo.core')
-
def _no_negative_zero(val):
"""Make sure -0 is never output. Makes diff tests easier."""
if val == 0:
return 0
return val
-
def _get_bound(exp):
if exp is None:
return None
@@ -53,10 +48,8 @@ def _get_bound(exp):
class ProblemWriter_cpxlp(AbstractProblemWriter):
- """Generate the corresponding CPLEX LP file."""
- pyomo.util.plugin.alias('cpxlp',
- 'Generate the corresponding CPLEX LP file')
+ pyomo.util.plugin.alias('cpxlp', 'Generate the corresponding CPLEX LP file')
pyomo.util.plugin.alias('lp', 'Generate the corresponding CPLEX LP file')
def __init__(self):
@@ -135,8 +128,7 @@ def __call__(self,
if len(io_options):
raise ValueError(
"ProblemWriter_cpxlp passed unrecognized io_options:\n\t" +
- "\n\t".join("%s = %s" % (k, v)
- for k, v in iteritems(io_options)))
+ "\n\t".join("%s = %s" % (k,v) for k,v in iteritems(io_options)))
if symbolic_solver_labels and (labeler is not None):
raise ValueError("ProblemWriter_cpxlp: Using both the "
@@ -180,7 +172,6 @@ def __call__(self,
return output_filename, symbol_map
def _print_expr_canonical(self,
- model,
x,
output_file,
object_symbol_dictionary,
@@ -188,6 +179,7 @@ def _print_expr_canonical(self,
is_objective,
column_order,
force_objective_constant=False):
+
"""
Return a expression as a string in LP format.
@@ -200,237 +192,143 @@ def _print_expr_canonical(self,
"""
assert (not force_objective_constant) or (is_objective)
- # cache - this is referenced numerous times.
- if not isinstance(x, GeneralCanonicalRepn):
- var_hashes = None # not needed
- linear_canonical = True
- else:
- var_hashes = x[-1]
- linear_canonical = False
-
+ constant=True
+ linear_coef_string_template = '%+'+self._precision_string+' %s\n'
+ quad_coef_string_template = '%+'+self._precision_string+' '
#
# Linear
#
- linear_coef_string_template = '%+' + self._precision_string + ' %s\n'
- if linear_canonical:
-
- #
- # optimization (these might be generated on the fly)
- #
- coefficients = x.linear
- if (coefficients is not None) and \
- (len(coefficients) > 0):
- variables = x.variables
-
- # the 99% case is when the input instance is a linear
- # canonical expression, so the exception should be rare.
- for vardata in variables:
- self._referenced_variable_ids[id(vardata)] = vardata
-
- try:
- if column_order is None:
- sorted_names = [
- (variable_symbol_dictionary[id(variables[i])],
- coefficients[i])
- for i in xrange(0, len(coefficients))]
- sorted_names.sort()
- else:
- sorted_names = [(variables[i], coefficients[i])
- for i in xrange(0, len(coefficients))]
- sorted_names.sort(key=lambda _x: column_order[_x[0]])
- sorted_names = [
- (variable_symbol_dictionary[id(var)], coef)
- for var, coef in sorted_names]
- except KeyError as err:
- _errors = []
- for v in variables:
- if id(v) in variable_symbol_dictionary:
- continue
- if v.model() is not model.model():
- _errors.append(
- "Variable '%s' is not part of the model "
- "being written out, but appears in an "
- "expression used on this model." % (v.name,))
- else:
- _parent = v.parent_block()
- while _parent is not None and _parent is not model:
- if _parent.type() is not model.type():
- _errors.append(
- "Variable '%s' exists within %s '%s', "
- "but is used by an active "
- "expression. Currently variables "
- "must be reachable through a tree "
- "of active Blocks."
- % (v.name, _parent.type().__name__,
- _parent.name))
- if not _parent.active:
- _errors.append(
- "Variable '%s' exists within "
- "deactivated %s '%s', but is used by "
- "an active expression. Currently "
- "variables must be reachable through "
- "a tree of active Blocks."
- % (v.name, _parent.type().__name__,
- _parent.name))
- _parent = _parent.parent_block()
-
- if _errors:
- for e in _errors:
- logger.error(e)
- err.args = err.args + tuple(_errors)
- raise
-
- for name, coef in sorted_names:
- output_file.write(linear_coef_string_template % (coef, name))
-
- elif not is_objective:
- # If we made it to here we are outputing
- # trivial constraints place 0 *
- # ONE_VAR_CONSTANT on this side of the
- # constraint for the benefit of solvers like
- # Glpk that cannot parse an LP file without
- # a variable on the left hand side.
- output_file.write(linear_coef_string_template
- % (0, 'ONE_VAR_CONSTANT'))
-
- elif 1 in x:
-
- for var_hash in x[1]:
- vardata = var_hashes[var_hash]
+ if len(x.linear_vars) > 0:
+ constant=False
+ for vardata in x.linear_vars:
self._referenced_variable_ids[id(vardata)] = vardata
if column_order is None:
- sorted_names = [(variable_symbol_dictionary[id(var_hashes[var_hash])],
- var_coefficient)
- for var_hash, var_coefficient in iteritems(x[1])]
- sorted_names.sort()
+ #
+ # Order columns by dictionary names
+ #
+ names = [variable_symbol_dictionary[id(var)] for var in x.linear_vars]
+ for i, name in sorted(enumerate(names), key=lambda x: x[1]):
+ output_file.write(linear_coef_string_template % (x.linear_coefs[i], name))
else:
- sorted_names = [(var_hashes[var_hash], var_coefficient)
- for var_hash, var_coefficient in iteritems(x[1])]
- sorted_names.sort(key=lambda _x: column_order[_x[0]])
- sorted_names = [(variable_symbol_dictionary[id(var)], coef)
- for var, coef in sorted_names]
-
- for name, coef in sorted_names:
- output_file.write(linear_coef_string_template % (coef, name))
-
+ #
+ # Order columns by the value of column_order[]
+ #
+ for i, var in sorted(enumerate(x.linear_vars), key=lambda x: column_order[x[1]]):
+ name = variable_symbol_dictionary[id(var)]
+ output_file.write(linear_coef_string_template % (x.linear_coefs[i], name))
#
# Quadratic
#
- quad_coef_string_template = '%+' + self._precision_string + ' '
- if canonical_degree(x) == 2:
-
- # first, make sure there is something to output
- # - it is possible for all terms to have
- # coefficients equal to 0.0, in which case you
- # don't want to get into the bracket notation at
- # all.
- # NOTE: if the coefficient is really 0.0, it
- # should be preprocessed out by the
- # canonial expression generator!
- found_nonzero_term = False # until proven otherwise
- for var_hash, var_coefficient in iteritems(x[2]):
- for var in var_hash:
- vardata = var_hashes[var]
-
- if math.fabs(var_coefficient) != 0.0:
- found_nonzero_term = True
- break
-
- if found_nonzero_term:
-
- output_file.write("+ [\n")
-
- num_output = 0
-
- var_hashes_order = list(iterkeys(x[2]))
- # sort by the sorted tuple of symbols (or column assignments)
- # for the variables appearing in the term
- if column_order is None:
- var_hashes_order.sort(
- key=lambda term:
- sorted(variable_symbol_dictionary[id(var_hashes[vh])]
- for vh in term))
- else:
- var_hashes_order.sort(
- key=lambda term: sorted(column_order[var_hashes[vh]]
- for vh in term))
+ if len(x.quadratic_vars) > 0:
+ constant=False
+ for var1, var2 in x.quadratic_vars:
+ self._referenced_variable_ids[id(var1)] = var1
+ self._referenced_variable_ids[id(var2)] = var2
- for var_hash in var_hashes_order:
-
- coefficient = x[2][var_hash]
+ output_file.write("+ [\n")
+ if column_order is None:
+ #
+ # Order columns by dictionary names
+ #
+ quad = set()
+ names = []
+ i = 0
+ for var1, var2 in x.quadratic_vars:
+ name1 = variable_symbol_dictionary[id(var1)]
+ name2 = variable_symbol_dictionary[id(var2)]
+ if name1 < name2:
+ names.append( (name1,name2) )
+ elif name1 > name2:
+ names.append( (name2,name1) )
+ else:
+ quad.add(i)
+ names.append( (name1,name1) )
+ i += 1
+ for i, names_ in sorted(enumerate(names), key=lambda x: x[1]):
+ #
+ # Times 2 because LP format requires /2 for all the quadratic
+ # terms /of the objective only/. Discovered the last bit thru
+ # trial and error. Obnoxious.
+ # Ref: ILog CPlex 8.0 User's Manual, p197.
+ #
if is_objective:
- coefficient *= 2
-
- # times 2 because LP format requires /2 for all the quadratic
+ tmp = 2*x.quadratic_coefs[i]
+ output_file.write(quad_coef_string_template % tmp)
+ else:
+ output_file.write(quad_coef_string_template % x.quadratic_coefs[i])
+ if i in quad:
+ output_file.write("%s ^ 2\n" % (names_[0]))
+ else:
+ output_file.write("%s * %s\n" % (names_[0], names_[1]))
+ else:
+ #
+ # Order columns by the value of column_order[]
+ #
+ quad = set()
+ cols = []
+ i = 0
+ for var1, var2 in x.quadratic_vars:
+ col1 = column_order[var1]
+ col2 = column_order[var2]
+ if col1 < col2:
+ cols.append( (((col1,col2) , variable_symbol_dictionary[id(var1)], variable_symbol_dictionary[id(var2)])) )
+ elif col1 > col2:
+ cols.append( (((col2,col1) , variable_symbol_dictionary[id(var2)], variable_symbol_dictionary[id(var1)])) )
+ else:
+ quad.add(i)
+ cols.append( ((col1,col1), variable_symbol_dictionary[id(var1)]) )
+ i += 1
+ for i, cols_ in sorted(enumerate(cols), key=lambda x: x[1][0]):
+ #
+ # Times 2 because LP format requires /2 for all the quadratic
# terms /of the objective only/. Discovered the last bit thru
# trial and error. Obnoxious.
# Ref: ILog CPlex 8.0 User's Manual, p197.
-
- output_file.write(quad_coef_string_template % coefficient)
- term_variables = []
-
- var_hash_order = list(iterkeys(var_hash))
- # sort by symbols (or column assignments)
- if column_order is None:
- var_hash_order.sort(
- key=lambda vh:
- variable_symbol_dictionary[id(var_hashes[vh])])
+ #
+ if is_objective:
+ output_file.write(quad_coef_string_template % 2*x.quadratic_coefs[i])
else:
- var_hash_order.sort(
- key=lambda vh: column_order[var_hashes[vh]])
-
- # sort the term for consistent output
- for var in var_hash_order:
- vardata = var_hashes[var]
- self._referenced_variable_ids[id(vardata)] = vardata
- name = variable_symbol_dictionary[id(vardata)]
- term_variables.append(name)
-
- if len(term_variables) == 2:
- output_file.write("%s * %s"
- % (term_variables[0], term_variables[1]))
+ output_file.write(quad_coef_string_template % x.quadratic_coefs[i])
+ if i in quad:
+ output_file.write("%s ^ 2\n" % cols_[1])
else:
- output_file.write("%s ^ 2" % (term_variables[0]))
- output_file.write("\n")
+ output_file.write("%s * %s\n" % (cols_[1], cols_[2]))
- output_file.write("]")
+ output_file.write("]")
- if is_objective:
- output_file.write(' / 2\n')
- # divide by 2 because LP format requires /2 for all the quadratic
- # terms. Weird. Ref: ILog CPlex 8.0 User's Manual, p197
- else:
- output_file.write("\n")
+ if is_objective:
+ output_file.write(' / 2\n')
+ # divide by 2 because LP format requires /2 for all the quadratic
+ # terms. Weird. Ref: ILog CPlex 8.0 User's Manual, p197
+ else:
+ output_file.write("\n")
+
+ if constant and not is_objective:
+ # If we made it to here we are outputing
+ # trivial constraints place 0 *
+ # ONE_VAR_CONSTANT on this side of the
+ # constraint for the benefit of solvers like
+ # Glpk that cannot parse an LP file without
+ # a variable on the left hand side.
+ output_file.write(linear_coef_string_template % (0, 'ONE_VAR_CONSTANT'))
#
# Constant offset
#
- if linear_canonical:
- constant = x.constant
- else:
- if 0 in x:
- constant = x[0][None]
- else:
- constant = None
-
- if constant is not None:
- offset = constant
- else:
- offset = 0.0
-
# Currently, it appears that we only need to print
# the constant offset term for objectives.
- obj_string_template = '%+' + self._precision_string + ' %s\n'
- if is_objective and (force_objective_constant or (offset != 0.0)):
+ #
+ obj_string_template = '%+'+self._precision_string+' %s\n'
+ if is_objective and (force_objective_constant or (x.constant != 0.0)):
output_file.write(obj_string_template
- % (offset, 'ONE_VAR_CONSTANT'))
+ % (x.constant, 'ONE_VAR_CONSTANT'))
#
# Return constant offset
#
- return offset
+ return x.constant
def printSOS(self,
symbol_map,
@@ -453,9 +351,9 @@ def printSOS(self,
level = soscondata.level
output_file.write('%s: S%s::\n'
- % (symbol_map.getSymbol(soscondata, labeler), level))
+ % (symbol_map.getSymbol(soscondata,labeler), level))
- sos_template_string = "%s:%" + self._precision_string + "\n"
+ sos_template_string = "%s:%"+self._precision_string+"\n"
for vardata, weight in sos_items:
weight = _get_bound(weight)
if weight < 0:
@@ -513,13 +411,34 @@ def _print_model_LP(self,
#
# Create variable symbols (and cache the block list)
#
- all_blocks = list( model.block_data_objects(
- active=True, sort=sortOrder) )
- variable_list = list( model.component_data_objects(
- Var, sort=sortOrder) )
- variable_label_pairs = list(
- (vardata, create_symbol_func(symbol_map, vardata, labeler))
- for vardata in variable_list )
+ all_blocks = []
+ variable_list = []
+ for block in model.block_data_objects(active=True,
+ sort=sortOrder):
+
+ all_blocks.append(block)
+
+ for vardata in block.component_data_objects(
+ Var,
+ active=True,
+ sort=sortOrder,
+ descend_into=False):
+
+ variable_list.append(vardata)
+ variable_label_pairs.append(
+ (vardata,create_symbol_func(symbol_map,
+ vardata,
+ labeler)))
+ #
+ # WEH - TODO: See if this is faster
+ #
+ #all_blocks = list( model.block_data_objects(
+ # active=True, sort=sortOrder) )
+ #variable_list = list( model.component_data_objects(
+ # Var, sort=sortOrder) )
+ #variable_label_pairs = list(
+ # (vardata, create_symbol_func(symbol_map, vardata, labeler))
+ # for vardata in variable_list )
variable_symbol_map.addSymbols(variable_label_pairs)
@@ -536,26 +455,24 @@ def _print_model_LP(self,
# NOTE: this *must* use the "\* ... *\" comment format: the GLPK
# LP parser does not correctly handle other formats (notably, "%").
output_file.write(
- "\\* Source Pyomo model name=%s *\\\n\n" % (model.name,))
+ "\\* Source Pyomo model name=%s *\\\n\n" % (model.name,) )
#
# Objective
#
- supports_quadratic_objective = \
- solver_capability('quadratic_objective')
+ supports_quadratic_objective = solver_capability('quadratic_objective')
numObj = 0
onames = []
for block in all_blocks:
- gen_obj_canonical_repn = \
- getattr(block, "_gen_obj_canonical_repn", True)
+ gen_obj_repn = getattr(block, "_gen_obj_repn", True)
# Get/Create the ComponentMap for the repn
- if not hasattr(block, '_canonical_repn'):
- block._canonical_repn = ComponentMap()
- block_canonical_repn = block._canonical_repn
+ if not hasattr(block,'_repn'):
+ block._repn = ComponentMap()
+ block_repn = block._repn
for objective_data in block.component_data_objects(
Objective,
@@ -581,18 +498,17 @@ def _print_model_LP(self,
else:
output_file.write("max \n")
- if gen_obj_canonical_repn:
- canonical_repn = \
- generate_canonical_repn(objective_data.expr)
- block_canonical_repn[objective_data] = canonical_repn
+ if gen_obj_repn:
+ repn = generate_standard_repn(objective_data.expr)
+ block_repn[objective_data] = repn
else:
- canonical_repn = block_canonical_repn[objective_data]
+ repn = block_repn[objective_data]
- degree = canonical_degree(canonical_repn)
+ degree = repn.polynomial_degree()
if degree == 0:
logger.warning("Constant objective detected, replacing "
- "with a placeholder to prevent solver failure.")
+ "with a placeholder to prevent solver failure.")
force_objective_constant = True
elif degree == 2:
if not supports_quadratic_objective:
@@ -601,18 +517,17 @@ def _print_model_LP(self,
"objective functions with quadratic terms. "
"Objective at issue: %s."
% objective_data.name)
- elif degree != 1:
+ elif degree is None:
raise RuntimeError(
"Cannot write legal LP file. Objective '%s' "
"has nonlinear terms that are not quadratic."
% objective_data.name)
output_file.write(
- object_symbol_dictionary[id(objective_data)] + ':\n')
+ object_symbol_dictionary[id(objective_data)]+':\n')
offset = print_expr_canonical(
- model,
- canonical_repn,
+ repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
@@ -644,13 +559,12 @@ def _print_model_LP(self,
def constraint_generator():
for block in all_blocks:
- gen_con_canonical_repn = \
- getattr(block, "_gen_con_canonical_repn", True)
+ gen_con_repn = getattr(block, "_gen_con_repn", True)
# Get/Create the ComponentMap for the repn
- if not hasattr(block, '_canonical_repn'):
- block._canonical_repn = ComponentMap()
- block_canonical_repn = block._canonical_repn
+ if not hasattr(block,'_repn'):
+ block._repn = ComponentMap()
+ block_repn = block._repn
for constraint_data in block.component_data_objects(
Constraint,
@@ -661,39 +575,35 @@ def constraint_generator():
if (not constraint_data.has_lb()) and \
(not constraint_data.has_ub()):
assert not constraint_data.equality
- continue # non-binding, so skip
+ continue # non-binding, so skip
if constraint_data._linear_canonical_form:
- canonical_repn = constraint_data.canonical_form()
- elif isinstance(constraint_data, LinearCanonicalRepn):
- canonical_repn = constraint_data
+ repn = constraint_data.canonical_form()
+ elif gen_con_repn:
+ repn = generate_standard_repn(constraint_data.body)
+ block_repn[constraint_data] = repn
else:
- if gen_con_canonical_repn:
- canonical_repn = generate_canonical_repn(constraint_data.body)
- block_canonical_repn[constraint_data] = canonical_repn
- else:
- canonical_repn = block_canonical_repn[constraint_data]
+ repn = block_repn[constraint_data]
- yield constraint_data, canonical_repn
+ yield constraint_data, repn
if row_order is not None:
sorted_constraint_list = list(constraint_generator())
sorted_constraint_list.sort(key=lambda x: row_order[x[0]])
-
def yield_all_constraints():
- for constraint_data, canonical_repn in sorted_constraint_list:
- yield constraint_data, canonical_repn
+ for data, repn in sorted_constraint_list:
+ yield data, repn
else:
yield_all_constraints = constraint_generator
# FIXME: This is a hack to get nested blocks working...
- eq_string_template = "= %" + self._precision_string + '\n'
- geq_string_template = ">= %" + self._precision_string + '\n\n'
- leq_string_template = "<= %" + self._precision_string + '\n\n'
- for constraint_data, canonical_repn in yield_all_constraints():
+ eq_string_template = "= %"+self._precision_string+'\n'
+ geq_string_template = ">= %"+self._precision_string+'\n\n'
+ leq_string_template = "<= %"+self._precision_string+'\n\n'
+ for constraint_data, repn in yield_all_constraints():
have_nontrivial = True
- degree = canonical_degree(canonical_repn)
+ degree = repn.polynomial_degree()
#
# Write constraint
@@ -713,7 +623,7 @@ def yield_all_constraints():
raise ValueError(
"Solver unable to handle quadratic expressions. Constraint"
" at issue: '%s'" % (constraint_data.name))
- elif degree != 1:
+ elif degree is None:
raise ValueError(
"Cannot write legal LP file. Constraint '%s' has a body "
"with nonlinear terms." % (constraint_data.name))
@@ -726,9 +636,8 @@ def yield_all_constraints():
value(constraint_data.upper)
label = 'c_e_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
- output_file.write(label + ':\n')
- offset = print_expr_canonical(model,
- canonical_repn,
+ output_file.write(label+':\n')
+ offset = print_expr_canonical(repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
@@ -746,9 +655,8 @@ def yield_all_constraints():
else:
label = 'c_l_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
- output_file.write(label + ':\n')
- offset = print_expr_canonical(model,
- canonical_repn,
+ output_file.write(label+':\n')
+ offset = print_expr_canonical(repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
@@ -767,9 +675,8 @@ def yield_all_constraints():
else:
label = 'c_u_' + con_symbol + '_'
alias_symbol_func(symbol_map, constraint_data, label)
- output_file.write(label + ':\n')
- offset = print_expr_canonical(model,
- canonical_repn,
+ output_file.write(label+':\n')
+ offset = print_expr_canonical(repn,
output_file,
object_symbol_dictionary,
variable_symbol_dictionary,
@@ -783,8 +690,8 @@ def yield_all_constraints():
assert constraint_data.has_lb()
if not have_nontrivial:
- logger.warning('Empty constraint block written in LP format '
- '- solver may error')
+ logger.warning('Empty constraint block written in LP format ' \
+ '- solver may error')
# the CPLEX LP format doesn't allow constants in the objective (or
# constraint body), which is a bit silly. To avoid painful
@@ -854,8 +761,8 @@ def yield_all_constraints():
# required because we don't store maps by variable type currently.
# FIXME: This is a hack to get nested blocks working...
- lb_string_template = "%" + self._precision_string + " <= "
- ub_string_template = " <= %" + self._precision_string + "\n"
+ lb_string_template = "%"+self._precision_string+" <= "
+ ub_string_template = " <= %"+self._precision_string+"\n"
# Track the number of integer and binary variables, so you can
# output their status later.
integer_vars = []
@@ -936,6 +843,7 @@ def yield_all_constraints():
for var_name in binary_vars:
output_file.write(' %s\n' % var_name)
+
# Write the SOS section
output_file.write(SOSlines.getvalue())
@@ -949,7 +857,7 @@ def yield_all_constraints():
# rely on this for choosing the set of potential warm start
# variables
vars_to_delete = set(variable_symbol_map.byObject.keys()) - \
- set(self._referenced_variable_ids.keys())
+ set(self._referenced_variable_ids.keys())
sm_byObject = symbol_map.byObject
sm_bySymbol = symbol_map.bySymbol
var_sm_byObject = variable_symbol_map.byObject
diff --git a/pyomo/repn/plugins/gams_writer.py b/pyomo/repn/plugins/gams_writer.py
index 237c3d2f0c0..7b5a8153fbf 100644
--- a/pyomo/repn/plugins/gams_writer.py
+++ b/pyomo/repn/plugins/gams_writer.py
@@ -17,6 +17,8 @@
from pyutilib.misc import PauseGC
+from pyomo.core.expr import current as EXPR
+from pyomo.core.expr.numvalue import is_fixed, value, as_numeric, native_types, native_numeric_types
from pyomo.core.base import (
SymbolMap, AlphaNumericTextLabeler, NumericLabeler,
Block, Constraint, Expression, Objective, Var, Set, RangeSet, Param,
@@ -29,12 +31,78 @@
from pyomo.core.kernel.component_block import IBlockStorage
from pyomo.core.kernel.component_interface import ICategorizedObject
-from pyomo.core.kernel.numvalue import is_fixed, value, as_numeric
import logging
logger = logging.getLogger('pyomo.core')
+#
+# A visitor pattern that creates a string for an expression
+# that is compatible with the GAMS syntax.
+#
+class ToGamsVisitor(EXPR.ExpressionValueVisitor):
+
+ def __init__(self, smap):
+ super(ToGamsVisitor, self).__init__()
+ self.smap = smap
+
+ def visit(self, node, values):
+ """ Visit nodes that have been expanded """
+ tmp = []
+ for i,val in enumerate(values):
+ arg = node._args_[i]
+
+ if arg is None:
+ tmp.append('Undefined')
+ elif arg.__class__ in native_numeric_types:
+ tmp.append(val)
+ elif arg.__class__ in native_types:
+ tmp.append("'{0}'".format(val))
+ elif arg.is_variable_type():
+ tmp.append(val)
+ elif arg.is_expression_type() and node._precedence() < arg._precedence():
+ tmp.append("({0})".format(val))
+ else:
+ tmp.append(val)
+
+ if node.__class__ is EXPR.PowExpression:
+ return "power({0}, {1})".format(tmp[0], tmp[1])
+ else:
+ return node._to_string(tmp, None, self.smap, True)
+
+ def visiting_potential_leaf(self, node):
+ """
+ Visiting a potential leaf.
+
+ Return True if the node is not expanded.
+ """
+ if node is None:
+ return True, None
+
+ if node.__class__ in native_types:
+ return True, str(node)
+
+ if node.is_variable_type():
+ if node.fixed:
+ return True, str(value(node))
+ label = self.smap.getSymbol(node)
+ return True, label
+
+ if not node.is_expression_type():
+ return True, str(value(node))
+
+ return False, None
+
+
+def expression_to_string(expr, labeler=None, smap=None):
+ if labeler is not None:
+ if smap is None:
+ smap = SymbolMap()
+ smap.default_labeler = labeler
+ visitor = ToGamsVisitor(smap)
+ return visitor.dfs_postorder_stack(expr)
+
+
def _get_bound(exp):
if exp is None:
return None
@@ -43,39 +111,6 @@ def _get_bound(exp):
raise ValueError("non-fixed bound or weight: " + str(exp))
-# HACK: Temporary check for Connectors in active constriants.
-# This should be removed after the writer is moved to an
-# explicit GAMS-specific expression walker for generating the
-# constraint strings.
-import pyomo.core.base.expr_coopr3 as coopr3
-from pyomo.core.kernel.numvalue import native_types
-from pyomo.core.base.connector import _ConnectorData
-def _check_for_connectors(con):
- _stack = [ ([con.body], 0, 1) ]
- while _stack:
- _argList, _idx, _len = _stack.pop()
- while _idx < _len:
- _sub = _argList[_idx]
- _idx += 1
- if type(_sub) in native_types:
- pass
- elif _sub.is_expression():
- _stack.append(( _argList, _idx, _len ))
- if type(_sub) is coopr3._ProductExpression:
- if _sub._denominator:
- _stack.append(
- (_sub._denominator, 0, len(_sub._denominator)) )
- _argList = _sub._numerator
- else:
- _argList = _sub._args
- _idx = 0
- _len = len(_argList)
- elif isinstance(_sub, _ConnectorData):
- raise TypeError(
- "Constraint '%s' body contains unexpanded connectors"
- % (con.name,))
-
-
class ProblemWriter_gams(AbstractProblemWriter):
pyomo.util.plugin.alias('gams', 'Generate the corresponding GAMS file')
@@ -92,7 +127,7 @@ def __call__(self,
Name of file to write GAMS model to. Optionally pass a file-like
stream and the model will be written to that instead.
io_options:
- warmstart=False:
+ warmstart=True:
Warmstart by initializing model's variables to their values.
symbolic_solver_labels=False:
Use full Pyomo component names rather than
@@ -158,7 +193,7 @@ def __call__(self,
sort = sorter_map[file_determinism]
# Warmstart by initializing model's variables to their values.
- warmstart = io_options.pop("warmstart", False)
+ warmstart = io_options.pop("warmstart", True)
# Filename for optionally writing solution values and marginals
# Set to True by GAMSSolver
@@ -205,18 +240,23 @@ def __call__(self,
var_labeler = con_labeler = labeler
var_list = []
- symbolMap = SymbolMap()
def var_recorder(obj):
ans = var_labeler(obj)
- var_list.append(ans)
+ try:
+ if obj.is_variable_type():
+ var_list.append(ans)
+ except:
+ pass
return ans
def var_label(obj):
- if obj.is_fixed():
- return str(value(obj))
+ #if obj.is_fixed():
+ # return str(value(obj))
return symbolMap.getSymbol(obj, var_recorder)
+ symbolMap = SymbolMap(var_label)
+
# when sorting, there are a non-trivial number of
# temporary objects created. these all yield
# non-circular references, so disable GC - the
@@ -273,18 +313,23 @@ def _write_model(self,
linear = True
linear_degree = set([0,1])
- # Sanity check: all active components better be things we know
- # how to deal with, plus Suffix if solving
- valid_ctypes = set([
- Block, Constraint, Expression, Objective, Param,
- Set, RangeSet, Var, Suffix, Connector ])
model_ctypes = model.collect_ctypes(active=True)
- if not model_ctypes.issubset(valid_ctypes):
- invalids = [t.__name__ for t in (model_ctypes - valid_ctypes)]
- raise RuntimeError(
- "Unallowable component(s) %s.\nThe GAMS writer cannot "
- "export models with this component type" %
- ", ".join(invalids))
+ if False:
+ #
+ # WEH - Disabling this check. For now, we're allowing
+ # variables defined on non-block objects.
+ #
+ # Sanity check: all active components better be things we know
+ # how to deal with, plus Suffix if solving
+ valid_ctypes = set([
+ Block, Constraint, Expression, Objective, Param,
+ Set, RangeSet, Var, Suffix, Connector ])
+ if not model_ctypes.issubset(valid_ctypes):
+ invalids = [t.__name__ for t in (model_ctypes - valid_ctypes)]
+ raise RuntimeError(
+ "Unallowable component(s) %s.\nThe GAMS writer cannot "
+ "export models with this component type" %
+ ", ".join(invalids))
# HACK: Temporary check for Connectors in active constriants.
# This should be removed after the writer is moved to an
@@ -307,7 +352,8 @@ def _write_model(self,
# HACK: Temporary check for Connectors in active constriants.
if has_Connectors:
- _check_for_connectors(con)
+ raise RuntimeError("Cannot handle connectors right now.")
+ #_check_for_connectors(con)
con_body = as_numeric(con.body)
if skip_trivial_constraints and con_body.is_fixed():
@@ -316,14 +362,12 @@ def _write_model(self,
if con_body.polynomial_degree() not in linear_degree:
linear = False
- body = StringIO()
- con_body.to_string(body, labeler=var_label)
cName = symbolMap.getSymbol(con, con_labeler)
if con.equality:
constraint_names.append('%s' % cName)
ConstraintIO.write('%s.. %s =e= %s ;\n' % (
constraint_names[-1],
- body.getvalue(),
+ expression_to_string(con_body, smap=symbolMap),
_get_bound(con.upper)
))
else:
@@ -332,13 +376,13 @@ def _write_model(self,
ConstraintIO.write('%s.. %s =l= %s ;\n' % (
constraint_names[-1],
_get_bound(con.lower),
- body.getvalue()
+ expression_to_string(con_body, smap=symbolMap)
))
if con.has_ub():
constraint_names.append('%s_hi' % cName)
ConstraintIO.write('%s.. %s =l= %s ;\n' % (
constraint_names[-1],
- body.getvalue(),
+ expression_to_string(con_body, smap=symbolMap),
_get_bound(con.upper)
))
@@ -354,12 +398,10 @@ def _write_model(self,
if obj.expr.polynomial_degree() not in linear_degree:
linear = False
oName = symbolMap.getSymbol(obj, con_labeler)
- body = StringIO()
- obj.expr.to_string(body, labeler=var_label)
constraint_names.append(oName)
ConstraintIO.write('%s.. GAMS_OBJECTIVE =e= %s ;\n' % (
oName,
- body.getvalue()
+ expression_to_string(obj.expr, smap=symbolMap)
))
# Categorize the variables that we found
@@ -385,11 +427,11 @@ def _write_model(self,
output_file.write(";\n\n")
for line in ConstraintIO.getvalue().splitlines():
- if '**' in line:
- # Investigate power functions for an integer exponent, in which
- # case replace with power(x, int) function to improve domain
- # issues.
- line = replace_power(line)
+ #if '**' in line:
+ # # Investigate power functions for an integer exponent, in which
+ # # case replace with power(x, int) function to improve domain
+ # # issues. Skip first term since it's always "con_name.."
+ # line = replace_power(line) + ';'
if len(line) > 80000:
line = split_long_line(line)
output_file.write(line + "\n")
diff --git a/pyomo/repn/plugins/mps.py b/pyomo/repn/plugins/mps.py
index 30eba2f27ee..c78e57efed5 100644
--- a/pyomo/repn/plugins/mps.py
+++ b/pyomo/repn/plugins/mps.py
@@ -29,9 +29,7 @@
Var, value,
SOSConstraint, Objective,
ComponentMap, is_fixed)
-from pyomo.repn import (generate_canonical_repn,
- canonical_degree,
- LinearCanonicalRepn)
+from pyomo.repn import generate_standard_repn
logger = logging.getLogger('pyomo.core')
@@ -182,63 +180,34 @@ def __call__(self,
def _extract_variable_coefficients(
self,
row_label,
- canonical_repn,
+ repn,
column_data,
quadratic_data,
variable_to_column):
- # cache - this is referenced numerous times.
- if isinstance(canonical_repn, LinearCanonicalRepn):
- var_hashes = None # not needed
- else:
- var_hashes = canonical_repn[-1]
-
- constant = None
#
# Linear
#
- if isinstance(canonical_repn, LinearCanonicalRepn):
- constant = canonical_repn.constant
- coefficients = canonical_repn.linear
- if (coefficients is not None) and \
- (len(coefficients) > 0):
- variables = canonical_repn.variables
-
- # the 99% case is when the input instance is a linear
- # canonical expression, so the exception should be rare.
- for vardata, coef in zip(variables, coefficients):
- self._referenced_variable_ids[id(vardata)] = vardata
- column_data[variable_to_column[vardata]].append(
- (row_label, coef))
- else:
- if 0 in canonical_repn:
- constant = canonical_repn[0][None]
- if 1 in canonical_repn:
- for var_hash, coef in iteritems(canonical_repn[1]):
- vardata = var_hashes[var_hash]
- self._referenced_variable_ids[id(vardata)] = vardata
- column_data[variable_to_column[vardata]].append(
- (row_label, coef))
- #
- # Quadratic
- #
- if 2 in canonical_repn:
- quad_terms = []
- for var_hash, coef in iteritems(canonical_repn[2]):
- varlist = [var_hashes[var] for var in var_hash]
- if len(varlist) == 1:
- quad_terms.append(((varlist[0], varlist[0]), coef))
- else:
- quad_terms.append((tuple(varlist), coef))
- quadratic_data.append((row_label, quad_terms))
+ if len(repn.linear_coefs) > 0:
+ for vardata, coef in zip(repn.linear_vars, repn.linear_coefs):
+ self._referenced_variable_ids[id(vardata)] = vardata
+ column_data[variable_to_column[vardata]].append((row_label, coef))
#
- # Return the constant
+ # Quadratic
#
- if constant is None:
- constant = 0.0
+ if len(repn.quadratic_coefs) > 0:
+ quad_terms = []
+ for vardata, coef in zip(repn.quadratic_vars, repn.quadratic_coefs):
+ self._referenced_variable_ids[id(vardata[0])] = vardata[0]
+ self._referenced_variable_ids[id(vardata[1])] = vardata[1]
+ quad_terms.append( (vardata, coef) )
+ quadratic_data.append((row_label, quad_terms))
- return constant
+ #
+ # Return the constant
+ #
+ return repn.constant
def _printSOS(self,
symbol_map,
@@ -375,13 +344,13 @@ def _print_model_MPS(self,
onames = []
for block in all_blocks:
- gen_obj_canonical_repn = \
- getattr(block, "_gen_obj_canonical_repn", True)
+ gen_obj_repn = \
+ getattr(block, "_gen_obj_repn", True)
# Get/Create the ComponentMap for the repn
- if not hasattr(block,'_canonical_repn'):
- block._canonical_repn = ComponentMap()
- block_canonical_repn = block._canonical_repn
+ if not hasattr(block,'_repn'):
+ block._repn = ComponentMap()
+ block_repn = block._repn
for objective_data in block.component_data_objects(
Objective,
active=True,
@@ -414,19 +383,19 @@ def _print_model_MPS(self,
output_file.write("ROWS\n")
output_file.write(" N %s\n" % (objective_label))
- if gen_obj_canonical_repn:
- canonical_repn = \
- generate_canonical_repn(objective_data.expr)
- block_canonical_repn[objective_data] = canonical_repn
+ if gen_obj_repn:
+ repn = \
+ generate_standard_repn(objective_data.expr)
+ block_repn[objective_data] = repn
else:
- canonical_repn = block_canonical_repn[objective_data]
+ repn = block_repn[objective_data]
- degree = canonical_degree(canonical_repn)
+ degree = repn.polynomial_degree()
if degree == 0:
logger.warning("Constant objective detected, replacing "
"with a placeholder to prevent solver failure.")
force_objective_constant = True
- elif (degree != 1) and (degree != 2):
+ elif degree is None:
raise RuntimeError(
"Cannot write legal MPS file. Objective '%s' "
"has nonlinear terms that are not quadratic."
@@ -434,7 +403,7 @@ def _print_model_MPS(self,
constant = extract_variable_coefficients(
objective_label,
- canonical_repn,
+ repn,
column_data,
quadobj_data,
variable_to_column)
@@ -452,13 +421,13 @@ def _print_model_MPS(self,
def constraint_generator():
for block in all_blocks:
- gen_con_canonical_repn = \
- getattr(block, "_gen_con_canonical_repn", True)
+ gen_con_repn = \
+ getattr(block, "_gen_con_repn", True)
# Get/Create the ComponentMap for the repn
- if not hasattr(block,'_canonical_repn'):
- block._canonical_repn = ComponentMap()
- block_canonical_repn = block._canonical_repn
+ if not hasattr(block,'_repn'):
+ block._repn = ComponentMap()
+ block_repn = block._repn
for constraint_data in block.component_data_objects(
Constraint,
@@ -472,36 +441,33 @@ def constraint_generator():
continue # non-binding, so skip
if constraint_data._linear_canonical_form:
- canonical_repn = constraint_data.canonical_form()
- elif isinstance(constraint_data, LinearCanonicalRepn):
- canonical_repn = constraint_data
+ repn = constraint_data.canonical_form()
+ elif gen_con_repn:
+ repn = generate_standard_repn(constraint_data.body)
+ block_repn[constraint_data] = repn
else:
- if gen_con_canonical_repn:
- canonical_repn = generate_canonical_repn(constraint_data.body)
- block_canonical_repn[constraint_data] = canonical_repn
- else:
- canonical_repn = block_canonical_repn[constraint_data]
+ repn = block_repn[constraint_data]
- yield constraint_data, canonical_repn
+ yield constraint_data, repn
if row_order is not None:
sorted_constraint_list = list(constraint_generator())
sorted_constraint_list.sort(key=lambda x: row_order[x[0]])
def yield_all_constraints():
- for constraint_data, canonical_repn in sorted_constraint_list:
- yield constraint_data, canonical_repn
+ for constraint_data, repn in sorted_constraint_list:
+ yield constraint_data, repn
else:
yield_all_constraints = constraint_generator
- for constraint_data, canonical_repn in yield_all_constraints():
+ for constraint_data, repn in yield_all_constraints():
- degree = canonical_degree(canonical_repn)
+ degree = repn.polynomial_degree()
# Write constraint
if degree == 0:
if skip_trivial_constraints:
continue
- elif (degree != 1) and (degree != 2):
+ elif degree is None:
raise RuntimeError(
"Cannot write legal MPS file. Constraint '%s' "
"has nonlinear terms that are not quadratic."
@@ -520,7 +486,7 @@ def yield_all_constraints():
output_file.write(" E %s\n" % (label))
offset = extract_variable_coefficients(
label,
- canonical_repn,
+ repn,
column_data,
quadmatrix_data,
variable_to_column)
@@ -537,7 +503,7 @@ def yield_all_constraints():
output_file.write(" G %s\n" % (label))
offset = extract_variable_coefficients(
label,
- canonical_repn,
+ repn,
column_data,
quadmatrix_data,
variable_to_column)
@@ -556,7 +522,7 @@ def yield_all_constraints():
output_file.write(" L %s\n" % (label))
offset = extract_variable_coefficients(
label,
- canonical_repn,
+ repn,
column_data,
quadmatrix_data,
variable_to_column)
diff --git a/pyomo/repn/plugins/problem_utils.py b/pyomo/repn/plugins/problem_utils.py
index 7d003142249..41db0b03290 100644
--- a/pyomo/repn/plugins/problem_utils.py
+++ b/pyomo/repn/plugins/problem_utils.py
@@ -8,12 +8,13 @@
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
+# TODO: Remove this file. This doesn't seem be used anywhere.
import pyomo.opt
from pyomo.core.base import expr, Var, Constraint, Objective
from pyomo.core.base.var import _VarData
from pyomo.core.base.param import _ParamData
-from pyomo.core.base.expr import *
+from pyomo.core.expr import current as EXPR
from pyomo.core.base.numvalue import *
from pyomo.core.base import _ExpressionData
@@ -55,13 +56,13 @@ def _Collect1(self,exp):
#
# Expression
#
- if isinstance(exp,expr._ExpressionBase):
+ if isinstance(exp,EXPR.ExpressionBase):
#
- # SumExpression
+ # Sum
#
- if isinstance(exp,expr._SumExpression):
- for i in xrange(len(exp._args)):
- self._Collect1(exp._args[i])
+ if isinstance(exp,expr.SumExpressionBase):
+ self._Collect1(exp._args[0])
+ self._Collect1(exp._args[1])
#
# Identity
#
@@ -122,11 +123,11 @@ def _Collect2(self, exp, x, scale=1.0):
#
# Expression
#
- if isinstance(exp,expr._ExpressionBase):
+ if isinstance(exp,EXPR.ExpressionBase):
#
# Sum
#
- if isinstance(exp,expr._SumExpression):
+ if isinstance(exp,expr.SumExpressionBase):
for i in xrange(len(exp._args)):
x = self._Collect2(exp._args[i], x, scale)
#
diff --git a/pyomo/repn/standard_aux.py b/pyomo/repn/standard_aux.py
new file mode 100644
index 00000000000..b6155e69306
--- /dev/null
+++ b/pyomo/repn/standard_aux.py
@@ -0,0 +1,39 @@
+# ___________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# rights in this software.
+# This software is distributed under the 3-clause BSD License.
+# ___________________________________________________________________________
+
+from __future__ import division
+
+__all__ = ['compute_standard_repn']
+
+
+import pyomo.util
+from pyomo.repn.standard_repn import preprocess_block_constraints, preprocess_block_objectives
+
+
+@pyomo.util.pyomo_api(namespace='pyomo.repn')
+def compute_standard_repn(data, model=None):
+ """
+ This plugin computes the standard representation for all objectives
+ and constraints. All results are stored in a ComponentMap named
+ "_repn" at the block level.
+
+ We break out preprocessing of the objectives and constraints
+ in order to avoid redundant and unnecessary work, specifically
+ in contexts where a model is iteratively solved and modified.
+ we don't have finer-grained resolution, but we could easily
+ pass in a Constraint and an Objective if warranted.
+
+ Required:
+ model: A concrete model instance.
+ """
+ idMap = {}
+ for block in model.block_data_objects(active=True):
+ preprocess_block_constraints(block, idMap=idMap)
+ preprocess_block_objectives(block, idMap=idMap)
diff --git a/pyomo/repn/standard_repn.c b/pyomo/repn/standard_repn.c
new file mode 100644
index 00000000000..650572a11f6
--- /dev/null
+++ b/pyomo/repn/standard_repn.c
@@ -0,0 +1,37009 @@
+/* Generated by Cython 0.27.3 */
+
+/* BEGIN: Cython Metadata
+{
+ "distutils": {
+ "name": "pyomo.repn.standard_repn",
+ "sources": [
+ "pyomo/repn/standard_repn.pyx"
+ ]
+ },
+ "module_name": "pyomo.repn.standard_repn"
+}
+END: Cython Metadata */
+
+#define PY_SSIZE_T_CLEAN
+#include "Python.h"
+#ifndef Py_PYTHON_H
+ #error Python headers needed to compile C extensions, please install development version of Python.
+#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
+ #error Cython requires Python 2.6+ or Python 3.3+.
+#else
+#define CYTHON_ABI "0_27_3"
+#define CYTHON_FUTURE_DIVISION 1
+#include
+#ifndef offsetof
+ #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
+#endif
+#if !defined(WIN32) && !defined(MS_WINDOWS)
+ #ifndef __stdcall
+ #define __stdcall
+ #endif
+ #ifndef __cdecl
+ #define __cdecl
+ #endif
+ #ifndef __fastcall
+ #define __fastcall
+ #endif
+#endif
+#ifndef DL_IMPORT
+ #define DL_IMPORT(t) t
+#endif
+#ifndef DL_EXPORT
+ #define DL_EXPORT(t) t
+#endif
+#define __PYX_COMMA ,
+#ifndef HAVE_LONG_LONG
+ #if PY_VERSION_HEX >= 0x02070000
+ #define HAVE_LONG_LONG
+ #endif
+#endif
+#ifndef PY_LONG_LONG
+ #define PY_LONG_LONG LONG_LONG
+#endif
+#ifndef Py_HUGE_VAL
+ #define Py_HUGE_VAL HUGE_VAL
+#endif
+#ifdef PYPY_VERSION
+ #define CYTHON_COMPILING_IN_PYPY 1
+ #define CYTHON_COMPILING_IN_PYSTON 0
+ #define CYTHON_COMPILING_IN_CPYTHON 0
+ #undef CYTHON_USE_TYPE_SLOTS
+ #define CYTHON_USE_TYPE_SLOTS 0
+ #undef CYTHON_USE_PYTYPE_LOOKUP
+ #define CYTHON_USE_PYTYPE_LOOKUP 0
+ #if PY_VERSION_HEX < 0x03050000
+ #undef CYTHON_USE_ASYNC_SLOTS
+ #define CYTHON_USE_ASYNC_SLOTS 0
+ #elif !defined(CYTHON_USE_ASYNC_SLOTS)
+ #define CYTHON_USE_ASYNC_SLOTS 1
+ #endif
+ #undef CYTHON_USE_PYLIST_INTERNALS
+ #define CYTHON_USE_PYLIST_INTERNALS 0
+ #undef CYTHON_USE_UNICODE_INTERNALS
+ #define CYTHON_USE_UNICODE_INTERNALS 0
+ #undef CYTHON_USE_UNICODE_WRITER
+ #define CYTHON_USE_UNICODE_WRITER 0
+ #undef CYTHON_USE_PYLONG_INTERNALS
+ #define CYTHON_USE_PYLONG_INTERNALS 0
+ #undef CYTHON_AVOID_BORROWED_REFS
+ #define CYTHON_AVOID_BORROWED_REFS 1
+ #undef CYTHON_ASSUME_SAFE_MACROS
+ #define CYTHON_ASSUME_SAFE_MACROS 0
+ #undef CYTHON_UNPACK_METHODS
+ #define CYTHON_UNPACK_METHODS 0
+ #undef CYTHON_FAST_THREAD_STATE
+ #define CYTHON_FAST_THREAD_STATE 0
+ #undef CYTHON_FAST_PYCALL
+ #define CYTHON_FAST_PYCALL 0
+ #undef CYTHON_PEP489_MULTI_PHASE_INIT
+ #define CYTHON_PEP489_MULTI_PHASE_INIT 0
+ #undef CYTHON_USE_TP_FINALIZE
+ #define CYTHON_USE_TP_FINALIZE 0
+#elif defined(PYSTON_VERSION)
+ #define CYTHON_COMPILING_IN_PYPY 0
+ #define CYTHON_COMPILING_IN_PYSTON 1
+ #define CYTHON_COMPILING_IN_CPYTHON 0
+ #ifndef CYTHON_USE_TYPE_SLOTS
+ #define CYTHON_USE_TYPE_SLOTS 1
+ #endif
+ #undef CYTHON_USE_PYTYPE_LOOKUP
+ #define CYTHON_USE_PYTYPE_LOOKUP 0
+ #undef CYTHON_USE_ASYNC_SLOTS
+ #define CYTHON_USE_ASYNC_SLOTS 0
+ #undef CYTHON_USE_PYLIST_INTERNALS
+ #define CYTHON_USE_PYLIST_INTERNALS 0
+ #ifndef CYTHON_USE_UNICODE_INTERNALS
+ #define CYTHON_USE_UNICODE_INTERNALS 1
+ #endif
+ #undef CYTHON_USE_UNICODE_WRITER
+ #define CYTHON_USE_UNICODE_WRITER 0
+ #undef CYTHON_USE_PYLONG_INTERNALS
+ #define CYTHON_USE_PYLONG_INTERNALS 0
+ #ifndef CYTHON_AVOID_BORROWED_REFS
+ #define CYTHON_AVOID_BORROWED_REFS 0
+ #endif
+ #ifndef CYTHON_ASSUME_SAFE_MACROS
+ #define CYTHON_ASSUME_SAFE_MACROS 1
+ #endif
+ #ifndef CYTHON_UNPACK_METHODS
+ #define CYTHON_UNPACK_METHODS 1
+ #endif
+ #undef CYTHON_FAST_THREAD_STATE
+ #define CYTHON_FAST_THREAD_STATE 0
+ #undef CYTHON_FAST_PYCALL
+ #define CYTHON_FAST_PYCALL 0
+ #undef CYTHON_PEP489_MULTI_PHASE_INIT
+ #define CYTHON_PEP489_MULTI_PHASE_INIT 0
+ #undef CYTHON_USE_TP_FINALIZE
+ #define CYTHON_USE_TP_FINALIZE 0
+#else
+ #define CYTHON_COMPILING_IN_PYPY 0
+ #define CYTHON_COMPILING_IN_PYSTON 0
+ #define CYTHON_COMPILING_IN_CPYTHON 1
+ #ifndef CYTHON_USE_TYPE_SLOTS
+ #define CYTHON_USE_TYPE_SLOTS 1
+ #endif
+ #if PY_VERSION_HEX < 0x02070000
+ #undef CYTHON_USE_PYTYPE_LOOKUP
+ #define CYTHON_USE_PYTYPE_LOOKUP 0
+ #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)
+ #define CYTHON_USE_PYTYPE_LOOKUP 1
+ #endif
+ #if PY_MAJOR_VERSION < 3
+ #undef CYTHON_USE_ASYNC_SLOTS
+ #define CYTHON_USE_ASYNC_SLOTS 0
+ #elif !defined(CYTHON_USE_ASYNC_SLOTS)
+ #define CYTHON_USE_ASYNC_SLOTS 1
+ #endif
+ #if PY_VERSION_HEX < 0x02070000
+ #undef CYTHON_USE_PYLONG_INTERNALS
+ #define CYTHON_USE_PYLONG_INTERNALS 0
+ #elif !defined(CYTHON_USE_PYLONG_INTERNALS)
+ #define CYTHON_USE_PYLONG_INTERNALS 1
+ #endif
+ #ifndef CYTHON_USE_PYLIST_INTERNALS
+ #define CYTHON_USE_PYLIST_INTERNALS 1
+ #endif
+ #ifndef CYTHON_USE_UNICODE_INTERNALS
+ #define CYTHON_USE_UNICODE_INTERNALS 1
+ #endif
+ #if PY_VERSION_HEX < 0x030300F0
+ #undef CYTHON_USE_UNICODE_WRITER
+ #define CYTHON_USE_UNICODE_WRITER 0
+ #elif !defined(CYTHON_USE_UNICODE_WRITER)
+ #define CYTHON_USE_UNICODE_WRITER 1
+ #endif
+ #ifndef CYTHON_AVOID_BORROWED_REFS
+ #define CYTHON_AVOID_BORROWED_REFS 0
+ #endif
+ #ifndef CYTHON_ASSUME_SAFE_MACROS
+ #define CYTHON_ASSUME_SAFE_MACROS 1
+ #endif
+ #ifndef CYTHON_UNPACK_METHODS
+ #define CYTHON_UNPACK_METHODS 1
+ #endif
+ #ifndef CYTHON_FAST_THREAD_STATE
+ #define CYTHON_FAST_THREAD_STATE 1
+ #endif
+ #ifndef CYTHON_FAST_PYCALL
+ #define CYTHON_FAST_PYCALL 1
+ #endif
+ #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
+ #define CYTHON_PEP489_MULTI_PHASE_INIT (0 && PY_VERSION_HEX >= 0x03050000)
+ #endif
+ #ifndef CYTHON_USE_TP_FINALIZE
+ #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
+ #endif
+#endif
+#if !defined(CYTHON_FAST_PYCCALL)
+#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
+#endif
+#if CYTHON_USE_PYLONG_INTERNALS
+ #include "longintrepr.h"
+ #undef SHIFT
+ #undef BASE
+ #undef MASK
+#endif
+#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
+ #define Py_OptimizeFlag 0
+#endif
+#define __PYX_BUILD_PY_SSIZE_T "n"
+#define CYTHON_FORMAT_SSIZE_T "z"
+#if PY_MAJOR_VERSION < 3
+ #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
+ #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+ PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+ #define __Pyx_DefaultClassType PyClass_Type
+#else
+ #define __Pyx_BUILTIN_MODULE_NAME "builtins"
+ #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+ #define __Pyx_DefaultClassType PyType_Type
+#endif
+#ifndef Py_TPFLAGS_CHECKTYPES
+ #define Py_TPFLAGS_CHECKTYPES 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_INDEX
+ #define Py_TPFLAGS_HAVE_INDEX 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
+ #define Py_TPFLAGS_HAVE_NEWBUFFER 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_FINALIZE
+ #define Py_TPFLAGS_HAVE_FINALIZE 0
+#endif
+#if PY_VERSION_HEX < 0x030700A0 || !defined(METH_FASTCALL)
+ #ifndef METH_FASTCALL
+ #define METH_FASTCALL 0x80
+ #endif
+ typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, Py_ssize_t nargs);
+ typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject **args,
+ Py_ssize_t nargs, PyObject *kwnames);
+#else
+ #define __Pyx_PyCFunctionFast _PyCFunctionFast
+ #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
+#endif
+#if CYTHON_FAST_PYCCALL
+#define __Pyx_PyFastCFunction_Check(func)\
+ ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS)))))
+#else
+#define __Pyx_PyFastCFunction_Check(func) 0
+#endif
+#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
+ #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#elif PY_VERSION_HEX >= 0x03060000
+ #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
+#elif PY_VERSION_HEX >= 0x03000000
+ #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#else
+ #define __Pyx_PyThreadState_Current _PyThreadState_Current
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
+#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
+#else
+#define __Pyx_PyDict_NewPresized(n) PyDict_New()
+#endif
+#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
+ #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y)
+ #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y)
+#else
+ #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y)
+ #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y)
+#endif
+#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
+ #define CYTHON_PEP393_ENABLED 1
+ #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\
+ 0 : _PyUnicode_Ready((PyObject *)(op)))
+ #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u)
+ #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
+ #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u)
+ #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u)
+ #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u)
+ #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i)
+ #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch)
+ #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
+#else
+ #define CYTHON_PEP393_ENABLED 0
+ #define PyUnicode_1BYTE_KIND 1
+ #define PyUnicode_2BYTE_KIND 2
+ #define PyUnicode_4BYTE_KIND 4
+ #define __Pyx_PyUnicode_READY(op) (0)
+ #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u)
+ #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
+ #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
+ #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE))
+ #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u))
+ #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
+ #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
+ #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u))
+#endif
+#if CYTHON_COMPILING_IN_PYPY
+ #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b)
+ #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b)
+#else
+ #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b)
+ #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
+ PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
+ #define PyUnicode_Contains(u, s) PySequence_Contains(u, s)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
+ #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
+ #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
+ #define PyObject_Malloc(s) PyMem_Malloc(s)
+ #define PyObject_Free(p) PyMem_Free(p)
+ #define PyObject_Realloc(p) PyMem_Realloc(p)
+#endif
+#if CYTHON_COMPILING_IN_PYSTON
+ #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co)
+ #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
+#else
+ #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0)
+ #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno)
+#endif
+#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
+#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
+#if PY_MAJOR_VERSION >= 3
+ #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b)
+#else
+ #define __Pyx_PyString_Format(a, b) PyString_Format(a, b)
+#endif
+#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
+ #define PyObject_ASCII(o) PyObject_Repr(o)
+#endif
+#if PY_MAJOR_VERSION >= 3
+ #define PyBaseString_Type PyUnicode_Type
+ #define PyStringObject PyUnicodeObject
+ #define PyString_Type PyUnicode_Type
+ #define PyString_Check PyUnicode_Check
+ #define PyString_CheckExact PyUnicode_CheckExact
+#endif
+#if PY_MAJOR_VERSION >= 3
+ #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
+ #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
+#else
+ #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
+ #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
+#endif
+#ifndef PySet_CheckExact
+ #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type)
+#endif
+#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
+#if PY_MAJOR_VERSION >= 3
+ #define PyIntObject PyLongObject
+ #define PyInt_Type PyLong_Type
+ #define PyInt_Check(op) PyLong_Check(op)
+ #define PyInt_CheckExact(op) PyLong_CheckExact(op)
+ #define PyInt_FromString PyLong_FromString
+ #define PyInt_FromUnicode PyLong_FromUnicode
+ #define PyInt_FromLong PyLong_FromLong
+ #define PyInt_FromSize_t PyLong_FromSize_t
+ #define PyInt_FromSsize_t PyLong_FromSsize_t
+ #define PyInt_AsLong PyLong_AsLong
+ #define PyInt_AS_LONG PyLong_AS_LONG
+ #define PyInt_AsSsize_t PyLong_AsSsize_t
+ #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
+ #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+ #define PyNumber_Int PyNumber_Long
+#endif
+#if PY_MAJOR_VERSION >= 3
+ #define PyBoolObject PyLongObject
+#endif
+#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
+ #ifndef PyUnicode_InternFromString
+ #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
+ #endif
+#endif
+#if PY_VERSION_HEX < 0x030200A4
+ typedef long Py_hash_t;
+ #define __Pyx_PyInt_FromHash_t PyInt_FromLong
+ #define __Pyx_PyInt_AsHash_t PyInt_AsLong
+#else
+ #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
+ #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t
+#endif
+#if PY_MAJOR_VERSION >= 3
+ #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
+#else
+ #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
+#endif
+#ifndef __has_attribute
+ #define __has_attribute(x) 0
+#endif
+#ifndef __has_cpp_attribute
+ #define __has_cpp_attribute(x) 0
+#endif
+#if CYTHON_USE_ASYNC_SLOTS
+ #if PY_VERSION_HEX >= 0x030500B1
+ #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
+ #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
+ #else
+ #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
+ #endif
+#else
+ #define __Pyx_PyType_AsAsync(obj) NULL
+#endif
+#ifndef __Pyx_PyAsyncMethodsStruct
+ typedef struct {
+ unaryfunc am_await;
+ unaryfunc am_aiter;
+ unaryfunc am_anext;
+ } __Pyx_PyAsyncMethodsStruct;
+#endif
+#ifndef CYTHON_RESTRICT
+ #if defined(__GNUC__)
+ #define CYTHON_RESTRICT __restrict__
+ #elif defined(_MSC_VER) && _MSC_VER >= 1400
+ #define CYTHON_RESTRICT __restrict
+ #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+ #define CYTHON_RESTRICT restrict
+ #else
+ #define CYTHON_RESTRICT
+ #endif
+#endif
+#ifndef CYTHON_UNUSED
+# if defined(__GNUC__)
+# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
+# define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+# define CYTHON_UNUSED
+# endif
+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
+# define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+# define CYTHON_UNUSED
+# endif
+#endif
+#ifndef CYTHON_MAYBE_UNUSED_VAR
+# if defined(__cplusplus)
+ template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
+# else
+# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
+# endif
+#endif
+#ifndef CYTHON_NCP_UNUSED
+# if CYTHON_COMPILING_IN_CPYTHON
+# define CYTHON_NCP_UNUSED
+# else
+# define CYTHON_NCP_UNUSED CYTHON_UNUSED
+# endif
+#endif
+#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
+#ifdef _MSC_VER
+ #ifndef _MSC_STDINT_H_
+ #if _MSC_VER < 1300
+ typedef unsigned char uint8_t;
+ typedef unsigned int uint32_t;
+ #else
+ typedef unsigned __int8 uint8_t;
+ typedef unsigned __int32 uint32_t;
+ #endif
+ #endif
+#else
+ #include
+#endif
+#ifndef CYTHON_FALLTHROUGH
+ #if defined(__cplusplus) && __cplusplus >= 201103L
+ #if __has_cpp_attribute(fallthrough)
+ #define CYTHON_FALLTHROUGH [[fallthrough]]
+ #elif __has_cpp_attribute(clang::fallthrough)
+ #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
+ #elif __has_cpp_attribute(gnu::fallthrough)
+ #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
+ #endif
+ #endif
+ #ifndef CYTHON_FALLTHROUGH
+ #if __has_attribute(fallthrough)
+ #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
+ #else
+ #define CYTHON_FALLTHROUGH
+ #endif
+ #endif
+ #if defined(__clang__ ) && defined(__apple_build_version__)
+ #if __apple_build_version__ < 7000000
+ #undef CYTHON_FALLTHROUGH
+ #define CYTHON_FALLTHROUGH
+ #endif
+ #endif
+#endif
+
+#ifndef CYTHON_INLINE
+ #if defined(__clang__)
+ #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
+ #elif defined(__GNUC__)
+ #define CYTHON_INLINE __inline__
+ #elif defined(_MSC_VER)
+ #define CYTHON_INLINE __inline
+ #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+ #define CYTHON_INLINE inline
+ #else
+ #define CYTHON_INLINE
+ #endif
+#endif
+
+#if defined(WIN32) || defined(MS_WINDOWS)
+ #define _USE_MATH_DEFINES
+#endif
+#include
+#ifdef NAN
+#define __PYX_NAN() ((float) NAN)
+#else
+static CYTHON_INLINE float __PYX_NAN() {
+ float value;
+ memset(&value, 0xFF, sizeof(value));
+ return value;
+}
+#endif
+#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
+#define __Pyx_truncl trunc
+#else
+#define __Pyx_truncl truncl
+#endif
+
+
+#define __PYX_ERR(f_index, lineno, Ln_error) \
+{ \
+ __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \
+}
+
+#ifndef __PYX_EXTERN_C
+ #ifdef __cplusplus
+ #define __PYX_EXTERN_C extern "C"
+ #else
+ #define __PYX_EXTERN_C extern
+ #endif
+#endif
+
+#define __PYX_HAVE__pyomo__repn__standard_repn
+#define __PYX_HAVE_API__pyomo__repn__standard_repn
+#ifdef _OPENMP
+#include
+#endif /* _OPENMP */
+
+#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
+#define CYTHON_WITHOUT_ASSERTIONS
+#endif
+
+typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
+ const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
+
+#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
+#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
+#define __PYX_DEFAULT_STRING_ENCODING ""
+#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
+#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#define __Pyx_uchar_cast(c) ((unsigned char)c)
+#define __Pyx_long_cast(x) ((long)x)
+#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\
+ (sizeof(type) < sizeof(Py_ssize_t)) ||\
+ (sizeof(type) > sizeof(Py_ssize_t) &&\
+ likely(v < (type)PY_SSIZE_T_MAX ||\
+ v == (type)PY_SSIZE_T_MAX) &&\
+ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
+ v == (type)PY_SSIZE_T_MIN))) ||\
+ (sizeof(type) == sizeof(Py_ssize_t) &&\
+ (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
+ v == (type)PY_SSIZE_T_MAX))) )
+#if defined (__cplusplus) && __cplusplus >= 201103L
+ #include
+ #define __Pyx_sst_abs(value) std::abs(value)
+#elif SIZEOF_INT >= SIZEOF_SIZE_T
+ #define __Pyx_sst_abs(value) abs(value)
+#elif SIZEOF_LONG >= SIZEOF_SIZE_T
+ #define __Pyx_sst_abs(value) labs(value)
+#elif defined (_MSC_VER)
+ #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
+#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+ #define __Pyx_sst_abs(value) llabs(value)
+#elif defined (__GNUC__)
+ #define __Pyx_sst_abs(value) __builtin_llabs(value)
+#else
+ #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
+#endif
+static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
+static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
+#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
+#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
+#define __Pyx_PyBytes_FromString PyBytes_FromString
+#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
+#if PY_MAJOR_VERSION < 3
+ #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString
+ #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#else
+ #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString
+ #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
+#endif
+#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s)
+#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s)
+#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s)
+#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s)
+#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
+static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
+ const Py_UNICODE *u_end = u;
+ while (*u_end++) ;
+ return (size_t)(u_end - u - 1);
+}
+#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
+#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
+#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode
+#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
+#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
+#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
+#define __Pyx_PySequence_Tuple(obj)\
+ (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
+#if CYTHON_ASSUME_SAFE_MACROS
+#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
+#else
+#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
+#endif
+#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
+#else
+#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
+#endif
+#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+static int __Pyx_sys_getdefaultencoding_not_ascii;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+ PyObject* sys;
+ PyObject* default_encoding = NULL;
+ PyObject* ascii_chars_u = NULL;
+ PyObject* ascii_chars_b = NULL;
+ const char* default_encoding_c;
+ sys = PyImport_ImportModule("sys");
+ if (!sys) goto bad;
+ default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
+ Py_DECREF(sys);
+ if (!default_encoding) goto bad;
+ default_encoding_c = PyBytes_AsString(default_encoding);
+ if (!default_encoding_c) goto bad;
+ if (strcmp(default_encoding_c, "ascii") == 0) {
+ __Pyx_sys_getdefaultencoding_not_ascii = 0;
+ } else {
+ char ascii_chars[128];
+ int c;
+ for (c = 0; c < 128; c++) {
+ ascii_chars[c] = c;
+ }
+ __Pyx_sys_getdefaultencoding_not_ascii = 1;
+ ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
+ if (!ascii_chars_u) goto bad;
+ ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
+ if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
+ PyErr_Format(
+ PyExc_ValueError,
+ "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
+ default_encoding_c);
+ goto bad;
+ }
+ Py_DECREF(ascii_chars_u);
+ Py_DECREF(ascii_chars_b);
+ }
+ Py_DECREF(default_encoding);
+ return 0;
+bad:
+ Py_XDECREF(default_encoding);
+ Py_XDECREF(ascii_chars_u);
+ Py_XDECREF(ascii_chars_b);
+ return -1;
+}
+#endif
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
+#else
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+static char* __PYX_DEFAULT_STRING_ENCODING;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+ PyObject* sys;
+ PyObject* default_encoding = NULL;
+ char* default_encoding_c;
+ sys = PyImport_ImportModule("sys");
+ if (!sys) goto bad;
+ default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+ Py_DECREF(sys);
+ if (!default_encoding) goto bad;
+ default_encoding_c = PyBytes_AsString(default_encoding);
+ if (!default_encoding_c) goto bad;
+ __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
+ if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
+ strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
+ Py_DECREF(default_encoding);
+ return 0;
+bad:
+ Py_XDECREF(default_encoding);
+ return -1;
+}
+#endif
+#endif
+
+
+/* Test for GCC > 2.95 */
+#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
+ #define likely(x) __builtin_expect(!!(x), 1)
+ #define unlikely(x) __builtin_expect(!!(x), 0)
+#else /* !__GNUC__ or GCC < 2.95 */
+ #define likely(x) (x)
+ #define unlikely(x) (x)
+#endif /* __GNUC__ */
+static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
+
+static PyObject *__pyx_m = NULL;
+static PyObject *__pyx_d;
+static PyObject *__pyx_b;
+static PyObject *__pyx_cython_runtime;
+static PyObject *__pyx_empty_tuple;
+static PyObject *__pyx_empty_bytes;
+static PyObject *__pyx_empty_unicode;
+static int __pyx_lineno;
+static int __pyx_clineno = 0;
+static const char * __pyx_cfilenm= __FILE__;
+static const char *__pyx_filename;
+
+
+static const char *__pyx_f[] = {
+ "pyomo/repn/standard_repn.pyx",
+};
+
+/*--- Type declarations ---*/
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct__generate_standard_repn;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_1_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_2_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr;
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr;
+
+/* "pyomo/repn/standard_repn.pyx":267
+ * """
+ * #@profile
+ * def generate_standard_repn(expr, idMap=None, compute_values=True, verbose=False, quadratic=True, repn=None): # <<<<<<<<<<<<<<
+ * #
+ * # Use a custom isclose function
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct__generate_standard_repn {
+ PyObject_HEAD
+ PyObject *__pyx_v_idMap;
+ PyObject *__pyx_v_keys;
+ PyObject *__pyx_v_linear_coefs;
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":350
+ * linear_coefs[key] = c
+ * keys = list(linear_coefs.keys())
+ * repn.linear_vars = tuple(idMap[key] for key in keys) # <<<<<<<<<<<<<<
+ * repn.linear_coefs = tuple(linear_coefs[key] for key in keys)
+ * repn.constant = C_
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_1_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct__generate_standard_repn *__pyx_outer_scope;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":351
+ * keys = list(linear_coefs.keys())
+ * repn.linear_vars = tuple(idMap[key] for key in keys)
+ * repn.linear_coefs = tuple(linear_coefs[key] for key in keys) # <<<<<<<<<<<<<<
+ * repn.constant = C_
+ * return repn
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_2_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct__generate_standard_repn *__pyx_outer_scope;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":457
+ *
+ * #@profile
+ * def _collect_prod(exp, multiplier, idMap, compute_values, verbose, quadratic): # <<<<<<<<<<<<<<
+ * #
+ * # LHS is a numeric value
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod {
+ PyObject_HEAD
+ PyObject *__pyx_v_idMap;
+ PyObject *__pyx_v_lhs;
+ PyObject *__pyx_v_rhs;
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":545
+ * else:
+ * ans.quadratic[rkey,lkey] = multiplier*lcoef*rcoef
+ * el_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(lhs.linear)) # <<<<<<<<<<<<<<
+ * er_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(rhs.linear))
+ * el_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(lhs.quadratic))
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *__pyx_outer_scope;
+ PyObject *__pyx_v_coef;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+ PyObject *(*__pyx_t_2)(PyObject *);
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":546
+ * ans.quadratic[rkey,lkey] = multiplier*lcoef*rcoef
+ * el_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(lhs.linear))
+ * er_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(rhs.linear)) # <<<<<<<<<<<<<<
+ * el_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(lhs.quadratic))
+ * er_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(rhs.quadratic))
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *__pyx_outer_scope;
+ PyObject *__pyx_v_coef;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+ PyObject *(*__pyx_t_2)(PyObject *);
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":547
+ * el_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(lhs.linear))
+ * er_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(rhs.linear))
+ * el_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(lhs.quadratic)) # <<<<<<<<<<<<<<
+ * er_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(rhs.quadratic))
+ * ans.nonl += el_linear*er_quadratic + el_quadratic*er_linear
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *__pyx_outer_scope;
+ PyObject *__pyx_v_coef;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+ PyObject *(*__pyx_t_2)(PyObject *);
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":548
+ * er_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(rhs.linear))
+ * el_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(lhs.quadratic))
+ * er_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(rhs.quadratic)) # <<<<<<<<<<<<<<
+ * ans.nonl += el_linear*er_quadratic + el_quadratic*er_linear
+ * elif len(lhs.linear) + len(rhs.linear) > 1:
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *__pyx_outer_scope;
+ PyObject *__pyx_v_coef;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+ PyObject *(*__pyx_t_2)(PyObject *);
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":551
+ * ans.nonl += el_linear*er_quadratic + el_quadratic*er_linear
+ * elif len(lhs.linear) + len(rhs.linear) > 1:
+ * el_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(lhs.linear)) # <<<<<<<<<<<<<<
+ * er_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(rhs.linear))
+ * ans.nonl += el_linear*er_linear
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *__pyx_outer_scope;
+ PyObject *__pyx_v_coef;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+ PyObject *(*__pyx_t_2)(PyObject *);
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":552
+ * elif len(lhs.linear) + len(rhs.linear) > 1:
+ * el_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(lhs.linear))
+ * er_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(rhs.linear)) # <<<<<<<<<<<<<<
+ * ans.nonl += el_linear*er_linear
+ *
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *__pyx_outer_scope;
+ PyObject *__pyx_v_coef;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+ PyObject *(*__pyx_t_2)(PyObject *);
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":802
+ * raise ValueError( "Unexpected expression (type %s)" % type(exp).__name__)
+ *
+ * def _generate_standard_repn(expr, idMap=None, compute_values=True, verbose=False, quadratic=True, repn=None): # <<<<<<<<<<<<<<
+ * #
+ * # Call recursive logic
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn {
+ PyObject_HEAD
+ PyObject *__pyx_v_ans;
+ PyObject *__pyx_v_idMap;
+ PyObject *__pyx_v_keys;
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":819
+ * #
+ * if compute_values:
+ * keys = list(key for key in ans.linear if not isclose(ans.linear[key],0)) # <<<<<<<<<<<<<<
+ * else:
+ * keys = list(ans.linear.keys())
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *__pyx_outer_scope;
+ PyObject *__pyx_v_key;
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":822
+ * else:
+ * keys = list(ans.linear.keys())
+ * repn.linear_vars = tuple(idMap[key] for key in keys) # <<<<<<<<<<<<<<
+ * repn.linear_coefs = tuple(ans.linear[key] for key in keys)
+ *
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *__pyx_outer_scope;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":823
+ * keys = list(ans.linear.keys())
+ * repn.linear_vars = tuple(idMap[key] for key in keys)
+ * repn.linear_coefs = tuple(ans.linear[key] for key in keys) # <<<<<<<<<<<<<<
+ *
+ * if quadratic:
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *__pyx_outer_scope;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
+ Py_ssize_t __pyx_t_1;
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+
+
+/* "pyomo/repn/standard_repn.pyx":826
+ *
+ * if quadratic:
+ * keys = list(key for key in ans.quadratic if not isclose(ans.quadratic[key],0)) # <<<<<<<<<<<<<<
+ * repn.quadratic_vars = tuple((idMap[key[0]],idMap[key[1]]) for key in keys)
+ * repn.quadratic_coefs = tuple(ans.quadratic[key] for key in keys)
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *__pyx_outer_scope;
+ PyObject *__pyx_v_key;
+};
+
+
+/* "pyomo/repn/standard_repn.pyx":827
+ * if quadratic:
+ * keys = list(key for key in ans.quadratic if not isclose(ans.quadratic[key],0))
+ * repn.quadratic_vars = tuple((idMap[key[0]],idMap[key[1]]) for key in keys) # <<<<<<<<<<<<<<
+ * repn.quadratic_coefs = tuple(ans.quadratic[key] for key in keys)
+ *
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *__pyx_outer_scope;
+ PyObject *__pyx_v_key;
+ PyObject *__pyx_t_0;
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+/* "pyomo/repn/standard_repn.pyx":828
+ * keys = list(key for key in ans.quadratic if not isclose(ans.quadratic[key],0))
+ * repn.quadratic_vars = tuple((idMap[key[0]],idMap[key[1]]) for key in keys)
+ * repn.quadratic_coefs = tuple(ans.quadratic[key] for key in keys) # <<<<<<<<<<<<<<
+ *
+ * if not isclose_const(ans.nonl,0):
+ */
+struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr {
+ PyObject_HEAD
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *__pyx_outer_scope;
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+/* RaiseTooManyValuesToUnpack.proto */
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
+
+/* RaiseNeedMoreValuesToUnpack.proto */
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
+
+/* IterFinish.proto */
+static CYTHON_INLINE int __Pyx_IterFinish(void);
+
+/* UnpackItemEndCheck.proto */
+static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);
+
+/* ListCompAppend.proto */
+#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
+static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
+ PyListObject* L = (PyListObject*) list;
+ Py_ssize_t len = Py_SIZE(list);
+ if (likely(L->allocated > len)) {
+ Py_INCREF(x);
+ PyList_SET_ITEM(list, len, x);
+ Py_SIZE(list) = len+1;
+ return 0;
+ }
+ return PyList_Append(list, x);
+}
+#else
+#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
+#endif
+
+/* GetItemInt.proto */
+#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
+ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\
+ __Pyx_GetItemInt_Generic(o, to_py_func(i))))
+#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
+ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+ int wraparound, int boundscheck);
+#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
+ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+ int wraparound, int boundscheck);
+static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
+ int is_list, int wraparound, int boundscheck);
+
+/* PyThreadStateGet.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate;
+#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current;
+#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type
+#else
+#define __Pyx_PyThreadState_declare
+#define __Pyx_PyThreadState_assign
+#define __Pyx_PyErr_Occurred() PyErr_Occurred()
+#endif
+
+/* SaveResetException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
+#else
+#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb)
+#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb)
+#endif
+
+/* PyErrExceptionMatches.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
+static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
+#else
+#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err)
+#endif
+
+/* GetException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb)
+static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#else
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
+#endif
+
+/* PyIntBinop.proto */
+#if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
+#else
+#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\
+ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
+#endif
+
+/* None.proto */
+static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname);
+
+/* DictGetItem.proto */
+#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
+static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
+ PyObject *value;
+ value = PyDict_GetItemWithError(d, key);
+ if (unlikely(!value)) {
+ if (!PyErr_Occurred()) {
+ PyObject* args = PyTuple_Pack(1, key);
+ if (likely(args))
+ PyErr_SetObject(PyExc_KeyError, args);
+ Py_XDECREF(args);
+ }
+ return NULL;
+ }
+ Py_INCREF(value);
+ return value;
+}
+#else
+ #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
+#endif
+
+/* PyObjectLookupSpecial.proto */
+#if CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PyObject* __Pyx_PyObject_LookupSpecial(PyObject* obj, PyObject* attr_name) {
+ PyObject *res;
+ PyTypeObject *tp = Py_TYPE(obj);
+#if PY_MAJOR_VERSION < 3
+ if (unlikely(PyInstance_Check(obj)))
+ return __Pyx_PyObject_GetAttrStr(obj, attr_name);
+#endif
+ res = _PyType_Lookup(tp, attr_name);
+ if (likely(res)) {
+ descrgetfunc f = Py_TYPE(res)->tp_descr_get;
+ if (!f) {
+ Py_INCREF(res);
+ } else {
+ res = f(res, obj, (PyObject *)tp);
+ }
+ } else {
+ PyErr_SetObject(PyExc_AttributeError, attr_name);
+ }
+ return res;
+}
+#else
+#define __Pyx_PyObject_LookupSpecial(o,n) __Pyx_PyObject_GetAttrStr(o,n)
+#endif
+
+/* ListAppend.proto */
+#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
+static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
+ PyListObject* L = (PyListObject*) list;
+ Py_ssize_t len = Py_SIZE(list);
+ if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
+ Py_INCREF(x);
+ PyList_SET_ITEM(list, len, x);
+ Py_SIZE(list) = len+1;
+ return 0;
+ }
+ return PyList_Append(list, x);
+}
+#else
+#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
+#endif
+
+/* PyDictContains.proto */
+static CYTHON_INLINE int __Pyx_PyDict_ContainsTF(PyObject* item, PyObject* dict, int eq) {
+ int result = PyDict_Contains(dict, item);
+ return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
+}
+
+/* py_dict_keys.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyDict_Keys(PyObject* d);
+
+/* UnpackUnboundCMethod.proto */
+typedef struct {
+ PyObject *type;
+ PyObject **method_name;
+ PyCFunction func;
+ PyObject *method;
+ int flag;
+} __Pyx_CachedCFunction;
+
+/* CallUnboundCMethod0.proto */
+static PyObject* __Pyx__CallUnboundCMethod0(__Pyx_CachedCFunction* cfunc, PyObject* self);
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_CallUnboundCMethod0(cfunc, self)\
+ ((likely((cfunc)->func)) ?\
+ (likely((cfunc)->flag == METH_NOARGS) ? (*((cfunc)->func))(self, NULL) :\
+ (likely((cfunc)->flag == (METH_VARARGS | METH_KEYWORDS)) ? ((*(PyCFunctionWithKeywords)(cfunc)->func)(self, __pyx_empty_tuple, NULL)) :\
+ ((cfunc)->flag == METH_VARARGS ? (*((cfunc)->func))(self, __pyx_empty_tuple) :\
+ (PY_VERSION_HEX >= 0x030600B1 && (cfunc)->flag == METH_FASTCALL ?\
+ (PY_VERSION_HEX >= 0x030700A0 ?\
+ (*(__Pyx_PyCFunctionFast)(cfunc)->func)(self, &PyTuple_GET_ITEM(__pyx_empty_tuple, 0), 0) :\
+ (*(__Pyx_PyCFunctionFastWithKeywords)(cfunc)->func)(self, &PyTuple_GET_ITEM(__pyx_empty_tuple, 0), 0, NULL)) :\
+ (PY_VERSION_HEX >= 0x030700A0 && (cfunc)->flag == (METH_FASTCALL | METH_KEYWORDS) ?\
+ (*(__Pyx_PyCFunctionFastWithKeywords)(cfunc)->func)(self, &PyTuple_GET_ITEM(__pyx_empty_tuple, 0), 0, NULL) :\
+ __Pyx__CallUnboundCMethod0(cfunc, self)))))) :\
+ __Pyx__CallUnboundCMethod0(cfunc, self))
+#else
+#define __Pyx_CallUnboundCMethod0(cfunc, self) __Pyx__CallUnboundCMethod0(cfunc, self)
+#endif
+
+/* PyErrFetchRestore.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
+#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
+#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
+#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
+#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
+#else
+#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
+#endif
+#else
+#define __Pyx_PyErr_Clear() PyErr_Clear()
+#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
+#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb)
+#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb)
+#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb)
+#endif
+
+/* RaiseException.proto */
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
+
+/* PyIntBinop.proto */
+#if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
+#else
+#define __Pyx_PyInt_EqObjC(op1, op2, intval, inplace)\
+ PyObject_RichCompare(op1, op2, Py_EQ)
+ #endif
+
+/* PyObjectCallMethod1.proto */
+static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg);
+static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg);
+
+/* append.proto */
+static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x);
+
+/* GetAttr.proto */
+static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
+
+/* HasAttr.proto */
+static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
+
+/* Import.proto */
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
+
+/* ImportFrom.proto */
+static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
+
+/* IncludeStringH.proto */
+#include
+
+/* CalculateMetaclass.proto */
+static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases);
+
+/* FetchCommonType.proto */
+static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
+
+/* CythonFunction.proto */
+#define __Pyx_CyFunction_USED 1
+#include
+#define __Pyx_CYFUNCTION_STATICMETHOD 0x01
+#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02
+#define __Pyx_CYFUNCTION_CCLASS 0x04
+#define __Pyx_CyFunction_GetClosure(f)\
+ (((__pyx_CyFunctionObject *) (f))->func_closure)
+#define __Pyx_CyFunction_GetClassObj(f)\
+ (((__pyx_CyFunctionObject *) (f))->func_classobj)
+#define __Pyx_CyFunction_Defaults(type, f)\
+ ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
+#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
+ ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
+typedef struct {
+ PyCFunctionObject func;
+#if PY_VERSION_HEX < 0x030500A0
+ PyObject *func_weakreflist;
+#endif
+ PyObject *func_dict;
+ PyObject *func_name;
+ PyObject *func_qualname;
+ PyObject *func_doc;
+ PyObject *func_globals;
+ PyObject *func_code;
+ PyObject *func_closure;
+ PyObject *func_classobj;
+ void *defaults;
+ int defaults_pyobjects;
+ int flags;
+ PyObject *defaults_tuple;
+ PyObject *defaults_kwdict;
+ PyObject *(*defaults_getter)(PyObject *);
+ PyObject *func_annotations;
+} __pyx_CyFunctionObject;
+static PyTypeObject *__pyx_CyFunctionType = 0;
+#define __Pyx_CyFunction_NewEx(ml, flags, qualname, self, module, globals, code)\
+ __Pyx_CyFunction_New(__pyx_CyFunctionType, ml, flags, qualname, self, module, globals, code)
+static PyObject *__Pyx_CyFunction_New(PyTypeObject *, PyMethodDef *ml,
+ int flags, PyObject* qualname,
+ PyObject *self,
+ PyObject *module, PyObject *globals,
+ PyObject* code);
+static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
+ size_t size,
+ int pyobjects);
+static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
+ PyObject *tuple);
+static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
+ PyObject *dict);
+static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
+ PyObject *dict);
+static int __pyx_CyFunction_init(void);
+
+/* Py3ClassCreate.proto */
+static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name, PyObject *qualname,
+ PyObject *mkw, PyObject *modname, PyObject *doc);
+static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases, PyObject *dict,
+ PyObject *mkw, int calculate_metaclass, int allow_py2_metaclass);
+
+/* CLineInTraceback.proto */
+#ifdef CYTHON_CLINE_IN_TRACEBACK
+#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
+#else
+static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
+#endif
+
+/* CodeObjectCache.proto */
+typedef struct {
+ PyCodeObject* code_object;
+ int code_line;
+} __Pyx_CodeObjectCacheEntry;
+struct __Pyx_CodeObjectCache {
+ int count;
+ int max_count;
+ __Pyx_CodeObjectCacheEntry* entries;
+};
+static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
+static PyCodeObject *__pyx_find_code_object(int code_line);
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
+
+/* AddTraceback.proto */
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+ int py_line, const char *filename);
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
+
+/* FastTypeChecks.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
+static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
+#else
+#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
+#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
+#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
+#endif
+
+/* SwapException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#else
+static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
+#endif
+
+/* CoroutineBase.proto */
+typedef PyObject *(*__pyx_coroutine_body_t)(PyObject *, PyThreadState *, PyObject *);
+typedef struct {
+ PyObject_HEAD
+ __pyx_coroutine_body_t body;
+ PyObject *closure;
+ PyObject *exc_type;
+ PyObject *exc_value;
+ PyObject *exc_traceback;
+ PyObject *gi_weakreflist;
+ PyObject *classobj;
+ PyObject *yieldfrom;
+ PyObject *gi_name;
+ PyObject *gi_qualname;
+ PyObject *gi_modulename;
+ int resume_label;
+ char is_running;
+} __pyx_CoroutineObject;
+static __pyx_CoroutineObject *__Pyx__Coroutine_New(
+ PyTypeObject *type, __pyx_coroutine_body_t body, PyObject *closure,
+ PyObject *name, PyObject *qualname, PyObject *module_name);
+static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
+ __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *closure,
+ PyObject *name, PyObject *qualname, PyObject *module_name);
+static int __Pyx_Coroutine_clear(PyObject *self);
+static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value);
+static PyObject *__Pyx_Coroutine_Close(PyObject *self);
+static PyObject *__Pyx_Coroutine_Throw(PyObject *gen, PyObject *args);
+#define __Pyx_Coroutine_SwapException(self) {\
+ __Pyx_ExceptionSwap(&(self)->exc_type, &(self)->exc_value, &(self)->exc_traceback);\
+ __Pyx_Coroutine_ResetFrameBackpointer(self);\
+ }
+#define __Pyx_Coroutine_ResetAndClearException(self) {\
+ __Pyx_ExceptionReset((self)->exc_type, (self)->exc_value, (self)->exc_traceback);\
+ (self)->exc_type = (self)->exc_value = (self)->exc_traceback = NULL;\
+ }
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyGen_FetchStopIterationValue(pvalue)\
+ __Pyx_PyGen__FetchStopIterationValue(__pyx_tstate, pvalue)
+#else
+#define __Pyx_PyGen_FetchStopIterationValue(pvalue)\
+ __Pyx_PyGen__FetchStopIterationValue(__Pyx_PyThreadState_Current, pvalue)
+#endif
+static int __Pyx_PyGen__FetchStopIterationValue(PyThreadState *tstate, PyObject **pvalue);
+static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__pyx_CoroutineObject *self);
+
+/* PatchModuleWithCoroutine.proto */
+static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code);
+
+/* PatchGeneratorABC.proto */
+static int __Pyx_patch_abc(void);
+
+/* Generator.proto */
+#define __Pyx_Generator_USED
+static PyTypeObject *__pyx_GeneratorType = 0;
+#define __Pyx_Generator_CheckExact(obj) (Py_TYPE(obj) == __pyx_GeneratorType)
+#define __Pyx_Generator_New(body, closure, name, qualname, module_name)\
+ __Pyx__Coroutine_New(__pyx_GeneratorType, body, closure, name, qualname, module_name)
+static PyObject *__Pyx_Generator_Next(PyObject *self);
+static int __pyx_Generator_init(void);
+
+/* CheckBinaryVersion.proto */
+static int __Pyx_check_binary_version(void);
+
+/* InitStrings.proto */
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
+
+
+/* Module declarations from 'pyomo.repn.standard_repn' */
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct__generate_standard_repn = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_1_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_2_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr = 0;
+static PyTypeObject *__pyx_ptype_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr = 0;
+#define __Pyx_MODULE_NAME "pyomo.repn.standard_repn"
+extern int __pyx_module_is_main_pyomo__repn__standard_repn;
+int __pyx_module_is_main_pyomo__repn__standard_repn = 0;
+
+/* Implementation of 'pyomo.repn.standard_repn' */
+static PyObject *__pyx_builtin_object;
+static PyObject *__pyx_builtin_id;
+static PyObject *__pyx_builtin_AttributeError;
+static PyObject *__pyx_builtin_enumerate;
+static PyObject *__pyx_builtin_ValueError;
+static PyObject *__pyx_builtin_sum;
+static PyObject *__pyx_builtin_ZeroDivisionError;
+static PyObject *__pyx_builtin_KeyError;
+static const char __pyx_k_[] = "\n";
+static const char __pyx_k_C[] = "C_";
+static const char __pyx_k_a[] = "a";
+static const char __pyx_k_b[] = "b";
+static const char __pyx_k_c[] = "c_";
+static const char __pyx_k_e[] = "e_";
+static const char __pyx_k_i[] = "i";
+static const char __pyx_k_v[] = "v_";
+static const char __pyx_k_id[] = "id";
+static const char __pyx_k_if[] = "_if";
+static const char __pyx_k_PY3[] = "PY3";
+static const char __pyx_k_Sum[] = "Sum";
+static const char __pyx_k_Var[] = "Var";
+static const char __pyx_k_all[] = "__all__";
+static const char __pyx_k_ans[] = "ans";
+static const char __pyx_k_c_2[] = "c";
+static const char __pyx_k_doc[] = "__doc__";
+static const char __pyx_k_err[] = "err";
+static const char __pyx_k_exp[] = "exp";
+static const char __pyx_k_get[] = "get";
+static const char __pyx_k_key[] = "key";
+static const char __pyx_k_lhs[] = "lhs";
+static const char __pyx_k_res[] = "res_";
+static const char __pyx_k_rhs[] = "rhs";
+static const char __pyx_k_six[] = "six";
+static const char __pyx_k_str[] = "__str__";
+static const char __pyx_k_sum[] = "sum";
+static const char __pyx_k_sys[] = "sys";
+static const char __pyx_k_v_2[] = "v";
+static const char __pyx_k_val[] = "val";
+static const char __pyx_k_zip[] = "zip";
+static const char __pyx_k_EXPR[] = "EXPR";
+static const char __pyx_k_args[] = "_args_";
+static const char __pyx_k_body[] = "body";
+static const char __pyx_k_coef[] = "coef";
+static const char __pyx_k_diff[] = "diff";
+static const char __pyx_k_else[] = "_else";
+static const char __pyx_k_exit[] = "__exit__";
+static const char __pyx_k_expr[] = "expr";
+static const char __pyx_k_fabs[] = "fabs";
+static const char __pyx_k_id_2[] = "id_";
+static const char __pyx_k_init[] = "__init__";
+static const char __pyx_k_keys[] = "keys";
+static const char __pyx_k_lkey[] = "lkey";
+static const char __pyx_k_main[] = "__main__";
+static const char __pyx_k_math[] = "math";
+static const char __pyx_k_name[] = "name";
+static const char __pyx_k_nonl[] = "nonl";
+static const char __pyx_k_repn[] = "repn";
+static const char __pyx_k_rkey[] = "rkey";
+static const char __pyx_k_self[] = "self";
+static const char __pyx_k_send[] = "send";
+static const char __pyx_k_slot[] = "__slot__";
+static const char __pyx_k_term[] = "term";
+static const char __pyx_k_test[] = "__test__";
+static const char __pyx_k_then[] = "_then";
+static const char __pyx_k_Bunch[] = "Bunch";
+static const char __pyx_k_block[] = "block";
+static const char __pyx_k_class[] = "__class__";
+static const char __pyx_k_close[] = "close";
+static const char __pyx_k_const[] = "const";
+static const char __pyx_k_denom[] = "denom";
+static const char __pyx_k_enter[] = "__enter__";
+static const char __pyx_k_error[] = "error";
+static const char __pyx_k_fixed[] = "fixed";
+static const char __pyx_k_idMap[] = "idMap";
+static const char __pyx_k_index[] = "index";
+static const char __pyx_k_lcoef[] = "lcoef";
+static const char __pyx_k_nargs[] = "nargs";
+static const char __pyx_k_pyomo[] = "pyomo";
+static const char __pyx_k_rcoef[] = "rcoef";
+static const char __pyx_k_res_2[] = "res";
+static const char __pyx_k_slots[] = "__slots__";
+static const char __pyx_k_state[] = "state";
+static const char __pyx_k_throw[] = "throw";
+static const char __pyx_k_value[] = "value";
+static const char __pyx_k_write[] = "write";
+static const char __pyx_k_active[] = "active";
+static const char __pyx_k_append[] = "append";
+static const char __pyx_k_args_2[] = "args";
+static const char __pyx_k_if_val[] = "if_val";
+static const char __pyx_k_import[] = "__import__";
+static const char __pyx_k_islice[] = "islice";
+static const char __pyx_k_linear[] = "linear";
+static const char __pyx_k_logger[] = "logger";
+static const char __pyx_k_module[] = "__module__";
+static const char __pyx_k_name_2[] = "__name__";
+static const char __pyx_k_object[] = "object";
+static const char __pyx_k_output[] = "output";
+static const char __pyx_k_repn_2[] = "_repn";
+static const char __pyx_k_xrange[] = "xrange";
+static const char __pyx_k_Expr_if[] = "Expr_if";
+static const char __pyx_k_Results[] = "Results";
+static const char __pyx_k_VarData[] = "_VarData";
+static const char __pyx_k_abs_tol[] = "abs_tol";
+static const char __pyx_k_current[] = "current";
+static const char __pyx_k_genexpr[] = "genexpr";
+static const char __pyx_k_isclose[] = "isclose";
+static const char __pyx_k_logging[] = "logging";
+static const char __pyx_k_noclone[] = "noclone";
+static const char __pyx_k_prepare[] = "__prepare__";
+static const char __pyx_k_rel_tol[] = "rel_tol";
+static const char __pyx_k_ret_str[] = "ret_str";
+static const char __pyx_k_varkeys[] = "varkeys";
+static const char __pyx_k_verbose[] = "verbose";
+static const char __pyx_k_KeyError[] = "KeyError";
+static const char __pyx_k_StringIO[] = "StringIO";
+static const char __pyx_k_constant[] = "constant";
+static const char __pyx_k_exc_info[] = "exc_info";
+static const char __pyx_k_exponent[] = "exponent";
+static const char __pyx_k_getstate[] = "__getstate__";
+static const char __pyx_k_getvalue[] = "getvalue";
+static const char __pyx_k_is_fixed[] = "is_fixed";
+static const char __pyx_k_qualname[] = "__qualname__";
+static const char __pyx_k_setstate[] = "__setstate__";
+static const char __pyx_k_variable[] = "variable";
+static const char __pyx_k_IVariable[] = "IVariable";
+static const char __pyx_k_Objective[] = "Objective";
+static const char __pyx_k_ParamData[] = "_ParamData";
+static const char __pyx_k_SimpleVar[] = "SimpleVar";
+static const char __pyx_k_el_linear[] = "el_linear";
+static const char __pyx_k_enumerate[] = "enumerate";
+static const char __pyx_k_er_linear[] = "er_linear";
+static const char __pyx_k_getLogger[] = "getLogger";
+static const char __pyx_k_is_linear[] = "is_linear";
+static const char __pyx_k_iteritems[] = "iteritems";
+static const char __pyx_k_itertools[] = "itertools";
+static const char __pyx_k_metaclass[] = "__metaclass__";
+static const char __pyx_k_objective[] = "objective";
+static const char __pyx_k_quadratic[] = "quadratic";
+static const char __pyx_k_six_moves[] = "six.moves";
+static const char __pyx_k_to_string[] = "to_string";
+static const char __pyx_k_using_py3[] = "using_py3";
+static const char __pyx_k_Constraint[] = "Constraint";
+static const char __pyx_k_Expression[] = "Expression";
+static const char __pyx_k_ValueError[] = "ValueError";
+static const char __pyx_k_basestring[] = "basestring";
+static const char __pyx_k_block_repn[] = "block_repn";
+static const char __pyx_k_constant_2[] = "constant: ";
+static const char __pyx_k_constraint[] = "constraint";
+static const char __pyx_k_expression[] = "expression";
+static const char __pyx_k_itervalues[] = "itervalues";
+static const char __pyx_k_multiplier[] = "multiplier";
+static const char __pyx_k_pyomo_core[] = "pyomo.core";
+static const char __pyx_k_pyomo_util[] = "pyomo.util";
+static const char __pyx_k_setdefault[] = "setdefault";
+static const char __pyx_k_collect_pow[] = "_collect_pow";
+static const char __pyx_k_collect_sum[] = "_collect_sum";
+static const char __pyx_k_collect_var[] = "_collect_var";
+static const char __pyx_k_is_constant[] = "is_constant";
+static const char __pyx_k_linear_coef[] = "linear coef: ";
+static const char __pyx_k_linear_vars[] = "linear_vars";
+static const char __pyx_k_ComponentMap[] = "ComponentMap";
+static const char __pyx_k_StandardRepn[] = "StandardRepn";
+static const char __pyx_k_collect_nonl[] = "_collect_nonl";
+static const char __pyx_k_collect_prod[] = "_collect_prod";
+static const char __pyx_k_descend_into[] = "descend_into";
+static const char __pyx_k_el_quadratic[] = "el_quadratic";
+static const char __pyx_k_er_quadratic[] = "er_quadratic";
+static const char __pyx_k_is_nonlinear[] = "is_nonlinear";
+static const char __pyx_k_is_quadratic[] = "is_quadratic";
+static const char __pyx_k_linear_coefs[] = "linear_coefs";
+static const char __pyx_k_AbsExpression[] = "AbsExpression";
+static const char __pyx_k_PowExpression[] = "PowExpression";
+static const char __pyx_k_Results___str[] = "Results.__str__";
+static const char __pyx_k_include_fixed[] = "include_fixed";
+static const char __pyx_k_isclose_const[] = "isclose_const";
+static const char __pyx_k_lhs_nonl_None[] = "lhs_nonl_None";
+static const char __pyx_k_linear_vars_2[] = "linear vars: ";
+static const char __pyx_k_pyutilib_misc[] = "pyutilib.misc";
+static const char __pyx_k_rhs_nonl_None[] = "rhs_nonl_None";
+static const char __pyx_k_to_expression[] = "to_expression";
+static const char __pyx_k_AttributeError[] = "AttributeError";
+static const char __pyx_k_ExpressionData[] = "_ExpressionData";
+static const char __pyx_k_GeneralVarData[] = "_GeneralVarData";
+static const char __pyx_k_Results___init[] = "Results.__init__";
+static const char __pyx_k_collect_linear[] = "_collect_linear";
+static const char __pyx_k_compute_values[] = "compute_values";
+static const char __pyx_k_linear_var_ids[] = "linear var ids: ";
+static const char __pyx_k_nonlinear_expr[] = "nonlinear_expr";
+static const char __pyx_k_nonlinear_vars[] = "nonlinear_vars";
+static const char __pyx_k_objective_data[] = "objective_data";
+static const char __pyx_k_quadratic_coef[] = "quadratic coef: ";
+static const char __pyx_k_quadratic_vars[] = "quadratic_vars";
+static const char __pyx_k_NumericConstant[] = "NumericConstant";
+static const char __pyx_k_SimpleObjective[] = "SimpleObjective";
+static const char __pyx_k_apply_operation[] = "_apply_operation";
+static const char __pyx_k_constraint_data[] = "constraint_data";
+static const char __pyx_k_isclose_context[] = "isclose_context";
+static const char __pyx_k_isclose_default[] = "isclose_default";
+static const char __pyx_k_pyomo_core_base[] = "pyomo.core.base";
+static const char __pyx_k_pyomo_core_expr[] = "pyomo.core.expr";
+static const char __pyx_k_pyomo_core_util[] = "pyomo.core.util";
+static const char __pyx_k_quadratic_coefs[] = "quadratic_coefs";
+static const char __pyx_k_repn_collectors[] = "_repn_collectors";
+static const char __pyx_k_LinearExpression[] = "LinearExpression";
+static const char __pyx_k_MatrixConstraint[] = "MatrixConstraint";
+static const char __pyx_k_RangedExpression[] = "RangedExpression";
+static const char __pyx_k_SimpleExpression[] = "SimpleExpression";
+static const char __pyx_k_collect_identity[] = "_collect_identity";
+static const char __pyx_k_collect_negation[] = "_collect_negation";
+static const char __pyx_k_is_variable_type[] = "is_variable_type";
+static const char __pyx_k_nonlinear_expr_2[] = "nonlinear expr:\n";
+static const char __pyx_k_nonlinear_vars_2[] = "nonlinear vars: ";
+static const char __pyx_k_quadratic_vars_2[] = "quadratic vars: ";
+static const char __pyx_k_ProductExpression[] = "ProductExpression";
+static const char __pyx_k_ViewSumExpression[] = "ViewSumExpression";
+static const char __pyx_k_ZeroDivisionError[] = "ZeroDivisionError";
+static const char __pyx_k_component_objects[] = "component_objects";
+static const char __pyx_k_polynomial_degree[] = "polynomial_degree";
+static const char __pyx_k_quadratic_var_ids[] = "quadratic var ids: ";
+static const char __pyx_k_EqualityExpression[] = "EqualityExpression";
+static const char __pyx_k_NegationExpression[] = "NegationExpression";
+static const char __pyx_k_StandardRepn___str[] = "StandardRepn.__str__";
+static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
+static const char __pyx_k_collect_comparison[] = "_collect_comparison";
+static const char __pyx_k_collect_linear_sum[] = "_collect_linear_sum";
+static const char __pyx_k_collect_reciprocal[] = "_collect_reciprocal";
+static const char __pyx_k_identify_variables[] = "identify_variables";
+static const char __pyx_k_is_expression_type[] = "is_expression_type";
+static const char __pyx_k_pyutilib_math_util[] = "pyutilib.math.util";
+static const char __pyx_k_IIdentityExpression[] = "IIdentityExpression";
+static const char __pyx_k_StandardRepn___init[] = "StandardRepn.__init__";
+static const char __pyx_k_collect_external_fn[] = "_collect_external_fn";
+static const char __pyx_k_evaluate_expression[] = "evaluate_expression";
+static const char __pyx_k_nonlinear_expr_None[] = "nonlinear expr: None\n";
+static const char __pyx_k_pyomo_core_base_var[] = "pyomo.core.base.var";
+static const char __pyx_k_GeneralObjectiveData[] = "_GeneralObjectiveData";
+static const char __pyx_k_InequalityExpression[] = "InequalityExpression";
+static const char __pyx_k_ReciprocalExpression[] = "ReciprocalExpression";
+static const char __pyx_k_native_numeric_types[] = "native_numeric_types";
+static const char __pyx_k_GeneralExpressionData[] = "_GeneralExpressionData";
+static const char __pyx_k_StandardRepn_is_fixed[] = "StandardRepn.is_fixed";
+static const char __pyx_k_collect_standard_repn[] = "_collect_standard_repn";
+static const char __pyx_k_preprocess_constraint[] = "preprocess_constraint";
+static const char __pyx_k_pyomo_core_base_param[] = "pyomo.core.base.param";
+static const char __pyx_k_StandardRepn_is_linear[] = "StandardRepn.is_linear";
+static const char __pyx_k_collect_branching_expr[] = "_collect_branching_expr";
+static const char __pyx_k_component_data_objects[] = "component_data_objects";
+static const char __pyx_k_generate_standard_repn[] = "generate_standard_repn";
+static const char __pyx_k_isclose_context___exit[] = "isclose_context.__exit__";
+static const char __pyx_k_isclose_context___init[] = "isclose_context.__init__";
+static const char __pyx_k_pyomo_repn_beta_matrix[] = "pyomo.repn.beta.matrix";
+static const char __pyx_k_StandardRepn___getstate[] = "StandardRepn.__getstate__";
+static const char __pyx_k_StandardRepn___setstate[] = "StandardRepn.__setstate__";
+static const char __pyx_k_UnaryFunctionExpression[] = "UnaryFunctionExpression";
+static const char __pyx_k_is_potentially_variable[] = "is_potentially_variable";
+static const char __pyx_k_isclose_context___enter[] = "isclose_context.__enter__";
+static const char __pyx_k_StandardRepn_is_constant[] = "StandardRepn.is_constant";
+static const char __pyx_k_generate_standard_repn_2[] = "_generate_standard_repn";
+static const char __pyx_k_is_named_expression_type[] = "is_named_expression_type";
+static const char __pyx_k_pyomo_core_base_numvalue[] = "pyomo.core.base.numvalue";
+static const char __pyx_k_pyomo_repn_standard_repn[] = "pyomo.repn.standard_repn";
+static const char __pyx_k_StandardRepn_is_nonlinear[] = "StandardRepn.is_nonlinear";
+static const char __pyx_k_StandardRepn_is_quadratic[] = "StandardRepn.is_quadratic";
+static const char __pyx_k_pyomo_core_base_objective[] = "pyomo.core.base.objective";
+static const char __pyx_k_ExternalFunctionExpression[] = "ExternalFunctionExpression";
+static const char __pyx_k_StandardRepn_to_expression[] = "StandardRepn.to_expression";
+static const char __pyx_k_Unexpected_expression_type[] = "Unexpected expression type: ";
+static const char __pyx_k_preprocess_constraint_data[] = "preprocess_constraint_data";
+static const char __pyx_k_pyomo_core_base_expression[] = "pyomo.core.base.expression";
+static const char __pyx_k_collect_prod_locals_genexpr[] = "_collect_prod..genexpr";
+static const char __pyx_k_preprocess_block_objectives[] = "preprocess_block_objectives";
+static const char __pyx_k_Unexpected_expression_type_s[] = "Unexpected expression (type %s)";
+static const char __pyx_k_preprocess_block_constraints[] = "preprocess_block_constraints";
+static const char __pyx_k_pyomo_repn_standard_repn_pyx[] = "pyomo/repn/standard_repn.pyx";
+static const char __pyx_k_StandardRepn_polynomial_degree[] = "StandardRepn.polynomial_degree";
+static const char __pyx_k_This_class_defines_a_standard_c[] = "\n This class defines a standard/common representation for Pyomo expressions\n that provides an efficient interface for writing all models.\n\n TODO: define what \"efficient\" means to us.\n ";
+static const char __pyx_k_exception_generating_a_standard[] = "exception generating a standard representation for objective %s: %s";
+static const char __pyx_k_generate_standard_repn_locals_g[] = "_generate_standard_repn..genexpr";
+static const char __pyx_k_Const_f_Linear_s_Quadratic_s_Non[] = "Const:\t%f\nLinear:\t%s\nQuadratic:\t%s\nNonlinear:\t%s";
+static const char __pyx_k_No_expression_has_been_defined_f[] = "No expression has been defined for objective %s";
+static const char __pyx_k_generate_standard_repn_locals_ge[] = "generate_standard_repn..genexpr";
+static const char __pyx_k_pyomo_core_kernel_component_expr[] = "pyomo.core.kernel.component_expression";
+static const char __pyx_k_pyomo_core_kernel_component_obje[] = "pyomo.core.kernel.component_objective";
+static const char __pyx_k_pyomo_core_kernel_component_vari[] = "pyomo.core.kernel.component_variable";
+static const char __pyx_k_exception_generating_a_standard_2[] = "exception generating a standard representation for constraint %s: %s";
+static const char __pyx_k_No_expression_has_been_defined_f_2[] = "No expression has been defined for the body of constraint %s";
+static PyObject *__pyx_kp_s_;
+static PyObject *__pyx_n_s_AbsExpression;
+static PyObject *__pyx_n_s_AttributeError;
+static PyObject *__pyx_n_s_Bunch;
+static PyObject *__pyx_n_s_C;
+static PyObject *__pyx_n_s_ComponentMap;
+static PyObject *__pyx_kp_s_Const_f_Linear_s_Quadratic_s_Non;
+static PyObject *__pyx_n_s_Constraint;
+static PyObject *__pyx_n_s_EXPR;
+static PyObject *__pyx_n_s_EqualityExpression;
+static PyObject *__pyx_n_s_Expr_if;
+static PyObject *__pyx_n_s_Expression;
+static PyObject *__pyx_n_s_ExpressionData;
+static PyObject *__pyx_n_s_ExternalFunctionExpression;
+static PyObject *__pyx_n_s_GeneralExpressionData;
+static PyObject *__pyx_n_s_GeneralObjectiveData;
+static PyObject *__pyx_n_s_GeneralVarData;
+static PyObject *__pyx_n_s_IIdentityExpression;
+static PyObject *__pyx_n_s_IVariable;
+static PyObject *__pyx_n_s_InequalityExpression;
+static PyObject *__pyx_n_s_KeyError;
+static PyObject *__pyx_n_s_LinearExpression;
+static PyObject *__pyx_n_s_MatrixConstraint;
+static PyObject *__pyx_n_s_NegationExpression;
+static PyObject *__pyx_kp_s_No_expression_has_been_defined_f;
+static PyObject *__pyx_kp_s_No_expression_has_been_defined_f_2;
+static PyObject *__pyx_n_s_NumericConstant;
+static PyObject *__pyx_n_s_Objective;
+static PyObject *__pyx_n_s_PY3;
+static PyObject *__pyx_n_s_ParamData;
+static PyObject *__pyx_n_s_PowExpression;
+static PyObject *__pyx_n_s_ProductExpression;
+static PyObject *__pyx_n_s_RangedExpression;
+static PyObject *__pyx_n_s_ReciprocalExpression;
+static PyObject *__pyx_n_s_Results;
+static PyObject *__pyx_n_s_Results___init;
+static PyObject *__pyx_n_s_Results___str;
+static PyObject *__pyx_n_s_SimpleExpression;
+static PyObject *__pyx_n_s_SimpleObjective;
+static PyObject *__pyx_n_s_SimpleVar;
+static PyObject *__pyx_n_s_StandardRepn;
+static PyObject *__pyx_n_s_StandardRepn___getstate;
+static PyObject *__pyx_n_s_StandardRepn___init;
+static PyObject *__pyx_n_s_StandardRepn___setstate;
+static PyObject *__pyx_n_s_StandardRepn___str;
+static PyObject *__pyx_n_s_StandardRepn_is_constant;
+static PyObject *__pyx_n_s_StandardRepn_is_fixed;
+static PyObject *__pyx_n_s_StandardRepn_is_linear;
+static PyObject *__pyx_n_s_StandardRepn_is_nonlinear;
+static PyObject *__pyx_n_s_StandardRepn_is_quadratic;
+static PyObject *__pyx_n_s_StandardRepn_polynomial_degree;
+static PyObject *__pyx_n_s_StandardRepn_to_expression;
+static PyObject *__pyx_n_s_StringIO;
+static PyObject *__pyx_n_s_Sum;
+static PyObject *__pyx_kp_s_This_class_defines_a_standard_c;
+static PyObject *__pyx_n_s_UnaryFunctionExpression;
+static PyObject *__pyx_kp_s_Unexpected_expression_type;
+static PyObject *__pyx_kp_s_Unexpected_expression_type_s;
+static PyObject *__pyx_n_s_ValueError;
+static PyObject *__pyx_n_s_Var;
+static PyObject *__pyx_n_s_VarData;
+static PyObject *__pyx_n_s_ViewSumExpression;
+static PyObject *__pyx_n_s_ZeroDivisionError;
+static PyObject *__pyx_n_s_a;
+static PyObject *__pyx_n_s_abs_tol;
+static PyObject *__pyx_n_s_active;
+static PyObject *__pyx_n_s_all;
+static PyObject *__pyx_n_s_ans;
+static PyObject *__pyx_n_s_append;
+static PyObject *__pyx_n_s_apply_operation;
+static PyObject *__pyx_n_s_args;
+static PyObject *__pyx_n_s_args_2;
+static PyObject *__pyx_n_s_b;
+static PyObject *__pyx_n_s_basestring;
+static PyObject *__pyx_n_s_block;
+static PyObject *__pyx_n_s_block_repn;
+static PyObject *__pyx_n_s_body;
+static PyObject *__pyx_n_s_c;
+static PyObject *__pyx_n_s_c_2;
+static PyObject *__pyx_n_s_class;
+static PyObject *__pyx_n_s_cline_in_traceback;
+static PyObject *__pyx_n_s_close;
+static PyObject *__pyx_n_s_coef;
+static PyObject *__pyx_n_s_collect_branching_expr;
+static PyObject *__pyx_n_s_collect_comparison;
+static PyObject *__pyx_n_s_collect_external_fn;
+static PyObject *__pyx_n_s_collect_identity;
+static PyObject *__pyx_n_s_collect_linear;
+static PyObject *__pyx_n_s_collect_linear_sum;
+static PyObject *__pyx_n_s_collect_negation;
+static PyObject *__pyx_n_s_collect_nonl;
+static PyObject *__pyx_n_s_collect_pow;
+static PyObject *__pyx_n_s_collect_prod;
+static PyObject *__pyx_n_s_collect_prod_locals_genexpr;
+static PyObject *__pyx_n_s_collect_reciprocal;
+static PyObject *__pyx_n_s_collect_standard_repn;
+static PyObject *__pyx_n_s_collect_sum;
+static PyObject *__pyx_n_s_collect_var;
+static PyObject *__pyx_n_s_component_data_objects;
+static PyObject *__pyx_n_s_component_objects;
+static PyObject *__pyx_n_s_compute_values;
+static PyObject *__pyx_n_s_const;
+static PyObject *__pyx_n_s_constant;
+static PyObject *__pyx_kp_s_constant_2;
+static PyObject *__pyx_n_s_constraint;
+static PyObject *__pyx_n_s_constraint_data;
+static PyObject *__pyx_n_s_current;
+static PyObject *__pyx_n_s_denom;
+static PyObject *__pyx_n_s_descend_into;
+static PyObject *__pyx_n_s_diff;
+static PyObject *__pyx_n_s_doc;
+static PyObject *__pyx_n_s_e;
+static PyObject *__pyx_n_s_el_linear;
+static PyObject *__pyx_n_s_el_quadratic;
+static PyObject *__pyx_n_s_else;
+static PyObject *__pyx_n_s_enter;
+static PyObject *__pyx_n_s_enumerate;
+static PyObject *__pyx_n_s_er_linear;
+static PyObject *__pyx_n_s_er_quadratic;
+static PyObject *__pyx_n_s_err;
+static PyObject *__pyx_n_s_error;
+static PyObject *__pyx_n_s_evaluate_expression;
+static PyObject *__pyx_n_s_exc_info;
+static PyObject *__pyx_kp_s_exception_generating_a_standard;
+static PyObject *__pyx_kp_s_exception_generating_a_standard_2;
+static PyObject *__pyx_n_s_exit;
+static PyObject *__pyx_n_s_exp;
+static PyObject *__pyx_n_s_exponent;
+static PyObject *__pyx_n_s_expr;
+static PyObject *__pyx_n_s_expression;
+static PyObject *__pyx_n_s_fabs;
+static PyObject *__pyx_n_s_fixed;
+static PyObject *__pyx_n_s_generate_standard_repn;
+static PyObject *__pyx_n_s_generate_standard_repn_2;
+static PyObject *__pyx_n_s_generate_standard_repn_locals_g;
+static PyObject *__pyx_n_s_generate_standard_repn_locals_ge;
+static PyObject *__pyx_n_s_genexpr;
+static PyObject *__pyx_n_s_get;
+static PyObject *__pyx_n_s_getLogger;
+static PyObject *__pyx_n_s_getstate;
+static PyObject *__pyx_n_s_getvalue;
+static PyObject *__pyx_n_s_i;
+static PyObject *__pyx_n_s_id;
+static PyObject *__pyx_n_s_idMap;
+static PyObject *__pyx_n_s_id_2;
+static PyObject *__pyx_n_s_identify_variables;
+static PyObject *__pyx_n_s_if;
+static PyObject *__pyx_n_s_if_val;
+static PyObject *__pyx_n_s_import;
+static PyObject *__pyx_n_s_include_fixed;
+static PyObject *__pyx_n_s_index;
+static PyObject *__pyx_n_s_init;
+static PyObject *__pyx_n_s_is_constant;
+static PyObject *__pyx_n_s_is_expression_type;
+static PyObject *__pyx_n_s_is_fixed;
+static PyObject *__pyx_n_s_is_linear;
+static PyObject *__pyx_n_s_is_named_expression_type;
+static PyObject *__pyx_n_s_is_nonlinear;
+static PyObject *__pyx_n_s_is_potentially_variable;
+static PyObject *__pyx_n_s_is_quadratic;
+static PyObject *__pyx_n_s_is_variable_type;
+static PyObject *__pyx_n_s_isclose;
+static PyObject *__pyx_n_s_isclose_const;
+static PyObject *__pyx_n_s_isclose_context;
+static PyObject *__pyx_n_s_isclose_context___enter;
+static PyObject *__pyx_n_s_isclose_context___exit;
+static PyObject *__pyx_n_s_isclose_context___init;
+static PyObject *__pyx_n_s_isclose_default;
+static PyObject *__pyx_n_s_islice;
+static PyObject *__pyx_n_s_iteritems;
+static PyObject *__pyx_n_s_itertools;
+static PyObject *__pyx_n_s_itervalues;
+static PyObject *__pyx_n_s_key;
+static PyObject *__pyx_n_s_keys;
+static PyObject *__pyx_n_s_lcoef;
+static PyObject *__pyx_n_s_lhs;
+static PyObject *__pyx_n_s_lhs_nonl_None;
+static PyObject *__pyx_n_s_linear;
+static PyObject *__pyx_kp_s_linear_coef;
+static PyObject *__pyx_n_s_linear_coefs;
+static PyObject *__pyx_kp_s_linear_var_ids;
+static PyObject *__pyx_n_s_linear_vars;
+static PyObject *__pyx_kp_s_linear_vars_2;
+static PyObject *__pyx_n_s_lkey;
+static PyObject *__pyx_n_s_logger;
+static PyObject *__pyx_n_s_logging;
+static PyObject *__pyx_n_s_main;
+static PyObject *__pyx_n_s_math;
+static PyObject *__pyx_n_s_metaclass;
+static PyObject *__pyx_n_s_module;
+static PyObject *__pyx_n_s_multiplier;
+static PyObject *__pyx_n_s_name;
+static PyObject *__pyx_n_s_name_2;
+static PyObject *__pyx_n_s_nargs;
+static PyObject *__pyx_n_s_native_numeric_types;
+static PyObject *__pyx_n_s_noclone;
+static PyObject *__pyx_n_s_nonl;
+static PyObject *__pyx_n_s_nonlinear_expr;
+static PyObject *__pyx_kp_s_nonlinear_expr_2;
+static PyObject *__pyx_kp_s_nonlinear_expr_None;
+static PyObject *__pyx_n_s_nonlinear_vars;
+static PyObject *__pyx_kp_s_nonlinear_vars_2;
+static PyObject *__pyx_n_s_object;
+static PyObject *__pyx_n_s_objective;
+static PyObject *__pyx_n_s_objective_data;
+static PyObject *__pyx_n_s_output;
+static PyObject *__pyx_n_s_polynomial_degree;
+static PyObject *__pyx_n_s_prepare;
+static PyObject *__pyx_n_s_preprocess_block_constraints;
+static PyObject *__pyx_n_s_preprocess_block_objectives;
+static PyObject *__pyx_n_s_preprocess_constraint;
+static PyObject *__pyx_n_s_preprocess_constraint_data;
+static PyObject *__pyx_n_s_pyomo;
+static PyObject *__pyx_kp_s_pyomo_core;
+static PyObject *__pyx_n_s_pyomo_core_base;
+static PyObject *__pyx_n_s_pyomo_core_base_expression;
+static PyObject *__pyx_n_s_pyomo_core_base_numvalue;
+static PyObject *__pyx_n_s_pyomo_core_base_objective;
+static PyObject *__pyx_n_s_pyomo_core_base_param;
+static PyObject *__pyx_n_s_pyomo_core_base_var;
+static PyObject *__pyx_n_s_pyomo_core_expr;
+static PyObject *__pyx_n_s_pyomo_core_kernel_component_expr;
+static PyObject *__pyx_n_s_pyomo_core_kernel_component_obje;
+static PyObject *__pyx_n_s_pyomo_core_kernel_component_vari;
+static PyObject *__pyx_n_s_pyomo_core_util;
+static PyObject *__pyx_n_s_pyomo_repn_beta_matrix;
+static PyObject *__pyx_n_s_pyomo_repn_standard_repn;
+static PyObject *__pyx_kp_s_pyomo_repn_standard_repn_pyx;
+static PyObject *__pyx_n_s_pyomo_util;
+static PyObject *__pyx_n_s_pyutilib_math_util;
+static PyObject *__pyx_n_s_pyutilib_misc;
+static PyObject *__pyx_n_s_quadratic;
+static PyObject *__pyx_kp_s_quadratic_coef;
+static PyObject *__pyx_n_s_quadratic_coefs;
+static PyObject *__pyx_kp_s_quadratic_var_ids;
+static PyObject *__pyx_n_s_quadratic_vars;
+static PyObject *__pyx_kp_s_quadratic_vars_2;
+static PyObject *__pyx_n_s_qualname;
+static PyObject *__pyx_n_s_rcoef;
+static PyObject *__pyx_n_s_rel_tol;
+static PyObject *__pyx_n_s_repn;
+static PyObject *__pyx_n_s_repn_2;
+static PyObject *__pyx_n_s_repn_collectors;
+static PyObject *__pyx_n_s_res;
+static PyObject *__pyx_n_s_res_2;
+static PyObject *__pyx_n_s_ret_str;
+static PyObject *__pyx_n_s_rhs;
+static PyObject *__pyx_n_s_rhs_nonl_None;
+static PyObject *__pyx_n_s_rkey;
+static PyObject *__pyx_n_s_self;
+static PyObject *__pyx_n_s_send;
+static PyObject *__pyx_n_s_setdefault;
+static PyObject *__pyx_n_s_setstate;
+static PyObject *__pyx_n_s_six;
+static PyObject *__pyx_n_s_six_moves;
+static PyObject *__pyx_n_s_slot;
+static PyObject *__pyx_n_s_slots;
+static PyObject *__pyx_n_s_state;
+static PyObject *__pyx_n_s_str;
+static PyObject *__pyx_n_s_sum;
+static PyObject *__pyx_n_s_sys;
+static PyObject *__pyx_n_s_term;
+static PyObject *__pyx_n_s_test;
+static PyObject *__pyx_n_s_then;
+static PyObject *__pyx_n_s_throw;
+static PyObject *__pyx_n_s_to_expression;
+static PyObject *__pyx_n_s_to_string;
+static PyObject *__pyx_n_s_using_py3;
+static PyObject *__pyx_n_s_v;
+static PyObject *__pyx_n_s_v_2;
+static PyObject *__pyx_n_s_val;
+static PyObject *__pyx_n_s_value;
+static PyObject *__pyx_n_s_variable;
+static PyObject *__pyx_n_s_varkeys;
+static PyObject *__pyx_n_s_verbose;
+static PyObject *__pyx_n_s_write;
+static PyObject *__pyx_n_s_xrange;
+static PyObject *__pyx_n_s_zip;
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_isclose_const(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_rel_tol, PyObject *__pyx_v_abs_tol); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_15isclose_context___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_compute_values); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_15isclose_context_2__enter__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_15isclose_context_4__exit__(CYTHON_UNUSED PyObject *__pyx_self, CYTHON_UNUSED PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_args); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_expr); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_2__getstate__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_4__setstate__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_6__str__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_8is_fixed(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_10polynomial_degree(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_12is_constant(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_14is_linear(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_16is_quadratic(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_18is_nonlinear(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12StandardRepn_20to_expression(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_22generate_standard_repn_genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_22generate_standard_repn_3genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_2generate_standard_repn(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_expr, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic, PyObject *__pyx_v_repn); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_7Results___init__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self, PyObject *__pyx_v_constant, PyObject *__pyx_v_nonl, PyObject *__pyx_v_linear, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_7Results_2__str__(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_4_collect_sum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_13_collect_prod_genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_13_collect_prod_3genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_13_collect_prod_6genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_13_collect_prod_9genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_13_collect_prod_12genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_13_collect_prod_15genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_6_collect_prod(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_8_collect_var(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, CYTHON_UNUSED PyObject *__pyx_v_verbose, CYTHON_UNUSED PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_10_collect_pow(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_12_collect_reciprocal(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_14_collect_branching_expr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_16_collect_nonl(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_18_collect_negation(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_20_collect_identity(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_22_collect_linear(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, CYTHON_UNUSED PyObject *__pyx_v_verbose, CYTHON_UNUSED PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_24_collect_comparison(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, CYTHON_UNUSED PyObject *__pyx_v_idMap, CYTHON_UNUSED PyObject *__pyx_v_compute_values, CYTHON_UNUSED PyObject *__pyx_v_verbose, CYTHON_UNUSED PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_26_collect_external_fn(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, CYTHON_UNUSED PyObject *__pyx_v_idMap, CYTHON_UNUSED PyObject *__pyx_v_compute_values, CYTHON_UNUSED PyObject *__pyx_v_verbose, CYTHON_UNUSED PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_28_collect_linear_sum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, CYTHON_UNUSED PyObject *__pyx_v_verbose, CYTHON_UNUSED PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_30_collect_standard_repn(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_exp, PyObject *__pyx_v_multiplier, PyObject *__pyx_v_idMap, PyObject *__pyx_v_compute_values, PyObject *__pyx_v_verbose, PyObject *__pyx_v_quadratic); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_23_generate_standard_repn_genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_23_generate_standard_repn_3genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_23_generate_standard_repn_6genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_23_generate_standard_repn_9genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_23_generate_standard_repn_12genexpr(PyObject *__pyx_self); /* proto */
+static PyObject *__pyx_pf_5pyomo_4repn_13standard_repn_23_generate_standard_repn_15genexpr(PyObject *__pyx_self); /* proto */
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+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_v_idMap);
+ Py_CLEAR(p->__pyx_v_lhs);
+ Py_CLEAR(p->__pyx_v_rhs);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *)o;
+ if (p->__pyx_v_idMap) {
+ e = (*v)(p->__pyx_v_idMap, a); if (e) return e;
+ }
+ if (p->__pyx_v_lhs) {
+ e = (*v)(p->__pyx_v_lhs, a); if (e) return e;
+ }
+ if (p->__pyx_v_rhs) {
+ e = (*v)(p->__pyx_v_rhs, a); if (e) return e;
+ }
+ return 0;
+}
+
+static int __pyx_tp_clear_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod(PyObject *o) {
+ PyObject* tmp;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod *)o;
+ tmp = ((PyObject*)p->__pyx_v_idMap);
+ p->__pyx_v_idMap = Py_None; Py_INCREF(Py_None);
+ Py_XDECREF(tmp);
+ tmp = ((PyObject*)p->__pyx_v_lhs);
+ p->__pyx_v_lhs = Py_None; Py_INCREF(Py_None);
+ Py_XDECREF(tmp);
+ tmp = ((PyObject*)p->__pyx_v_rhs);
+ p->__pyx_v_rhs = Py_None; Py_INCREF(Py_None);
+ Py_XDECREF(tmp);
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_3__collect_prod", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod, /*tp_traverse*/
+ __pyx_tp_clear_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_3__collect_prod, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_coef);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_coef) {
+ e = (*v)(p->__pyx_v_coef, a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_4_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_4_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_coef);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_coef) {
+ e = (*v)(p->__pyx_v_coef, a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_5_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_5_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_coef);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_coef) {
+ e = (*v)(p->__pyx_v_coef, a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_6_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_6_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_coef);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_coef) {
+ e = (*v)(p->__pyx_v_coef, a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_7_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_7_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_coef);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_coef) {
+ e = (*v)(p->__pyx_v_coef, a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_8_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_8_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_coef);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_coef) {
+ e = (*v)(p->__pyx_v_coef, a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_9_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_9_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_v_ans);
+ Py_CLEAR(p->__pyx_v_idMap);
+ Py_CLEAR(p->__pyx_v_keys);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *)o;
+ if (p->__pyx_v_ans) {
+ e = (*v)(p->__pyx_v_ans, a); if (e) return e;
+ }
+ if (p->__pyx_v_idMap) {
+ e = (*v)(p->__pyx_v_idMap, a); if (e) return e;
+ }
+ if (p->__pyx_v_keys) {
+ e = (*v)(p->__pyx_v_keys, a); if (e) return e;
+ }
+ return 0;
+}
+
+static int __pyx_tp_clear_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn(PyObject *o) {
+ PyObject* tmp;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn *)o;
+ tmp = ((PyObject*)p->__pyx_v_ans);
+ p->__pyx_v_ans = Py_None; Py_INCREF(Py_None);
+ Py_XDECREF(tmp);
+ tmp = ((PyObject*)p->__pyx_v_idMap);
+ p->__pyx_v_idMap = Py_None; Py_INCREF(Py_None);
+ Py_XDECREF(tmp);
+ tmp = ((PyObject*)p->__pyx_v_keys);
+ p->__pyx_v_keys = ((PyObject*)Py_None); Py_INCREF(Py_None);
+ Py_XDECREF(tmp);
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_10__generate_standard_repn", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn, /*tp_traverse*/
+ __pyx_tp_clear_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_10__generate_standard_repn, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_key);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_11_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_11_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_12_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_12_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_13_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
+ 0, /*tp_repr*/
+ 0, /*tp_as_number*/
+ 0, /*tp_as_sequence*/
+ 0, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
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+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_13_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_key);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_14_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
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+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ 0, /*tp_methods*/
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+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_14_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
+};
+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
+
+static PyTypeObject __pyx_type_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "pyomo.repn.standard_repn.__pyx_scope_struct_15_genexpr", /*tp_name*/
+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
+ 0, /*tp_compare*/
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ 0, /*tp_as_async*/
+ #endif
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+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ 0, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ 0, /*tp_doc*/
+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
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+ 0, /*tp_iternext*/
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+ 0, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_15_genexpr, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+ 0, /*tp_del*/
+ 0, /*tp_version_tag*/
+ #if PY_VERSION_HEX >= 0x030400a1
+ 0, /*tp_finalize*/
+ #endif
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+
+static struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr *__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr[8];
+static int __pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr = 0;
+
+static PyObject *__pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+ PyObject *o;
+ if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr)))) {
+ o = (PyObject*)__pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr[--__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr];
+ memset(o, 0, sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr));
+ (void) PyObject_INIT(o, t);
+ PyObject_GC_Track(o);
+ } else {
+ o = (*t->tp_alloc)(t, 0);
+ if (unlikely(!o)) return 0;
+ }
+ return o;
+}
+
+static void __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr(PyObject *o) {
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr *)o;
+ PyObject_GC_UnTrack(o);
+ Py_CLEAR(p->__pyx_outer_scope);
+ Py_CLEAR(p->__pyx_v_key);
+ Py_CLEAR(p->__pyx_t_0);
+ if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr)))) {
+ __pyx_freelist_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr[__pyx_freecount_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr++] = ((struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr *)o);
+ } else {
+ (*Py_TYPE(o)->tp_free)(o);
+ }
+}
+
+static int __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr *p = (struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr *)o;
+ if (p->__pyx_outer_scope) {
+ e = (*v)(((PyObject *)p->__pyx_outer_scope), a); if (e) return e;
+ }
+ if (p->__pyx_v_key) {
+ e = (*v)(p->__pyx_v_key, a); if (e) return e;
+ }
+ if (p->__pyx_t_0) {
+ e = (*v)(p->__pyx_t_0, a); if (e) return e;
+ }
+ return 0;
+}
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+ sizeof(struct __pyx_obj_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr, /*tp_dealloc*/
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+ 0, /*tp_setattr*/
+ #if PY_MAJOR_VERSION < 3
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+ __pyx_tp_traverse_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr, /*tp_traverse*/
+ 0, /*tp_clear*/
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+ __pyx_tp_new_5pyomo_4repn_13standard_repn___pyx_scope_struct_16_genexpr, /*tp_new*/
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+ #if CYTHON_PEP489_MULTI_PHASE_INIT
+ __pyx_moduledef_slots, /* m_slots */
+ #else
+ NULL, /* m_reload */
+ #endif
+ NULL, /* m_traverse */
+ NULL, /* m_clear */
+ NULL /* m_free */
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+}
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+}
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+}
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+#include "frameobject.h"
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+ PyThreadState *tstate = __Pyx_PyThreadState_Current;
+ PyObject **fastlocals;
+ Py_ssize_t i;
+ PyObject *result;
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+ if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
+ return __Pyx_PyObject_CallMethO(func, arg);
+#if CYTHON_FAST_PYCCALL
+ } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
+ return __Pyx_PyCFunction_FastCall(func, &arg, 1);
+#endif
+ }
+ }
+ return __Pyx__PyObject_CallOneArg(func, arg);
+}
+#else
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+ PyObject *result;
+ PyObject *args = PyTuple_Pack(1, arg);
+ if (unlikely(!args)) return NULL;
+ result = __Pyx_PyObject_Call(func, args, NULL);
+ Py_DECREF(args);
+ return result;
+}
+#endif
+
+/* PyObjectCallNoArg */
+ #if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
+#if CYTHON_FAST_PYCALL
+ if (PyFunction_Check(func)) {
+ return __Pyx_PyFunction_FastCall(func, NULL, 0);
+ }
+#endif
+#ifdef __Pyx_CyFunction_USED
+ if (likely(PyCFunction_Check(func) || __Pyx_TypeCheck(func, __pyx_CyFunctionType))) {
+#else
+ if (likely(PyCFunction_Check(func))) {
+#endif
+ if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
+ return __Pyx_PyObject_CallMethO(func, NULL);
+ }
+ }
+ return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
+}
+#endif
+
+/* RaiseTooManyValuesToUnpack */
+ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
+ PyErr_Format(PyExc_ValueError,
+ "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
+}
+
+/* RaiseNeedMoreValuesToUnpack */
+ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
+ PyErr_Format(PyExc_ValueError,
+ "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
+ index, (index == 1) ? "" : "s");
+}
+
+/* IterFinish */
+ static CYTHON_INLINE int __Pyx_IterFinish(void) {
+#if CYTHON_FAST_THREAD_STATE
+ PyThreadState *tstate = __Pyx_PyThreadState_Current;
+ PyObject* exc_type = tstate->curexc_type;
+ if (unlikely(exc_type)) {
+ if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) {
+ PyObject *exc_value, *exc_tb;
+ exc_value = tstate->curexc_value;
+ exc_tb = tstate->curexc_traceback;
+ tstate->curexc_type = 0;
+ tstate->curexc_value = 0;
+ tstate->curexc_traceback = 0;
+ Py_DECREF(exc_type);
+ Py_XDECREF(exc_value);
+ Py_XDECREF(exc_tb);
+ return 0;
+ } else {
+ return -1;
+ }
+ }
+ return 0;
+#else
+ if (unlikely(PyErr_Occurred())) {
+ if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
+ PyErr_Clear();
+ return 0;
+ } else {
+ return -1;
+ }
+ }
+ return 0;
+#endif
+}
+
+/* UnpackItemEndCheck */
+ static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
+ if (unlikely(retval)) {
+ Py_DECREF(retval);
+ __Pyx_RaiseTooManyValuesError(expected);
+ return -1;
+ } else {
+ return __Pyx_IterFinish();
+ }
+ return 0;
+}
+
+/* GetItemInt */
+ static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
+ PyObject *r;
+ if (!j) return NULL;
+ r = PyObject_GetItem(o, j);
+ Py_DECREF(j);
+ return r;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+ CYTHON_NCP_UNUSED int wraparound,
+ CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+ Py_ssize_t wrapped_i = i;
+ if (wraparound & unlikely(i < 0)) {
+ wrapped_i += PyList_GET_SIZE(o);
+ }
+ if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) {
+ PyObject *r = PyList_GET_ITEM(o, wrapped_i);
+ Py_INCREF(r);
+ return r;
+ }
+ return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+ return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+ CYTHON_NCP_UNUSED int wraparound,
+ CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+ Py_ssize_t wrapped_i = i;
+ if (wraparound & unlikely(i < 0)) {
+ wrapped_i += PyTuple_GET_SIZE(o);
+ }
+ if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) {
+ PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
+ Py_INCREF(r);
+ return r;
+ }
+ return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+ return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
+ CYTHON_NCP_UNUSED int wraparound,
+ CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
+ if (is_list || PyList_CheckExact(o)) {
+ Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+ if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
+ PyObject *r = PyList_GET_ITEM(o, n);
+ Py_INCREF(r);
+ return r;
+ }
+ }
+ else if (PyTuple_CheckExact(o)) {
+ Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
+ if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
+ PyObject *r = PyTuple_GET_ITEM(o, n);
+ Py_INCREF(r);
+ return r;
+ }
+ } else {
+ PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+ if (likely(m && m->sq_item)) {
+ if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
+ Py_ssize_t l = m->sq_length(o);
+ if (likely(l >= 0)) {
+ i += l;
+ } else {
+ if (!PyErr_ExceptionMatches(PyExc_OverflowError))
+ return NULL;
+ PyErr_Clear();
+ }
+ }
+ return m->sq_item(o, i);
+ }
+ }
+#else
+ if (is_list || PySequence_Check(o)) {
+ return PySequence_GetItem(o, i);
+ }
+#endif
+ return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+}
+
+/* SaveResetException */
+ #if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+ #if PY_VERSION_HEX >= 0x030700A2
+ *type = tstate->exc_state.exc_type;
+ *value = tstate->exc_state.exc_value;
+ *tb = tstate->exc_state.exc_traceback;
+ #else
+ *type = tstate->exc_type;
+ *value = tstate->exc_value;
+ *tb = tstate->exc_traceback;
+ #endif
+ Py_XINCREF(*type);
+ Py_XINCREF(*value);
+ Py_XINCREF(*tb);
+}
+static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+ PyObject *tmp_type, *tmp_value, *tmp_tb;
+ #if PY_VERSION_HEX >= 0x030700A2
+ tmp_type = tstate->exc_state.exc_type;
+ tmp_value = tstate->exc_state.exc_value;
+ tmp_tb = tstate->exc_state.exc_traceback;
+ tstate->exc_state.exc_type = type;
+ tstate->exc_state.exc_value = value;
+ tstate->exc_state.exc_traceback = tb;
+ #else
+ tmp_type = tstate->exc_type;
+ tmp_value = tstate->exc_value;
+ tmp_tb = tstate->exc_traceback;
+ tstate->exc_type = type;
+ tstate->exc_value = value;
+ tstate->exc_traceback = tb;
+ #endif
+ Py_XDECREF(tmp_type);
+ Py_XDECREF(tmp_value);
+ Py_XDECREF(tmp_tb);
+}
+#endif
+
+/* PyErrExceptionMatches */
+ #if CYTHON_FAST_THREAD_STATE
+static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
+ Py_ssize_t i, n;
+ n = PyTuple_GET_SIZE(tuple);
+#if PY_MAJOR_VERSION >= 3
+ for (i=0; icurexc_type;
+ if (exc_type == err) return 1;
+ if (unlikely(!exc_type)) return 0;
+ if (unlikely(PyTuple_Check(err)))
+ return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
+ return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
+}
+#endif
+
+/* GetException */
+ #if CYTHON_FAST_THREAD_STATE
+static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+#else
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
+#endif
+ PyObject *local_type, *local_value, *local_tb;
+#if CYTHON_FAST_THREAD_STATE
+ PyObject *tmp_type, *tmp_value, *tmp_tb;
+ local_type = tstate->curexc_type;
+ local_value = tstate->curexc_value;
+ local_tb = tstate->curexc_traceback;
+ tstate->curexc_type = 0;
+ tstate->curexc_value = 0;
+ tstate->curexc_traceback = 0;
+#else
+ PyErr_Fetch(&local_type, &local_value, &local_tb);
+#endif
+ PyErr_NormalizeException(&local_type, &local_value, &local_tb);
+#if CYTHON_FAST_THREAD_STATE
+ if (unlikely(tstate->curexc_type))
+#else
+ if (unlikely(PyErr_Occurred()))
+#endif
+ goto bad;
+ #if PY_MAJOR_VERSION >= 3
+ if (local_tb) {
+ if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
+ goto bad;
+ }
+ #endif
+ Py_XINCREF(local_tb);
+ Py_XINCREF(local_type);
+ Py_XINCREF(local_value);
+ *type = local_type;
+ *value = local_value;
+ *tb = local_tb;
+#if CYTHON_FAST_THREAD_STATE
+ #if PY_VERSION_HEX >= 0x030700A2
+ tmp_type = tstate->exc_state.exc_type;
+ tmp_value = tstate->exc_state.exc_value;
+ tmp_tb = tstate->exc_state.exc_traceback;
+ tstate->exc_state.exc_type = local_type;
+ tstate->exc_state.exc_value = local_value;
+ tstate->exc_state.exc_traceback = local_tb;
+ #else
+ tmp_type = tstate->exc_type;
+ tmp_value = tstate->exc_value;
+ tmp_tb = tstate->exc_traceback;
+ tstate->exc_type = local_type;
+ tstate->exc_value = local_value;
+ tstate->exc_traceback = local_tb;
+ #endif
+ Py_XDECREF(tmp_type);
+ Py_XDECREF(tmp_value);
+ Py_XDECREF(tmp_tb);
+#else
+ PyErr_SetExcInfo(local_type, local_value, local_tb);
+#endif
+ return 0;
+bad:
+ *type = 0;
+ *value = 0;
+ *tb = 0;
+ Py_XDECREF(local_type);
+ Py_XDECREF(local_value);
+ Py_XDECREF(local_tb);
+ return -1;
+}
+
+/* PyIntBinop */
+ #if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
+ #if PY_MAJOR_VERSION < 3
+ if (likely(PyInt_CheckExact(op1))) {
+ const long b = intval;
+ long x;
+ long a = PyInt_AS_LONG(op1);
+ x = (long)((unsigned long)a + b);
+ if (likely((x^a) >= 0 || (x^b) >= 0))
+ return PyInt_FromLong(x);
+ return PyLong_Type.tp_as_number->nb_add(op1, op2);
+ }
+ #endif
+ #if CYTHON_USE_PYLONG_INTERNALS
+ if (likely(PyLong_CheckExact(op1))) {
+ const long b = intval;
+ long a, x;
+#ifdef HAVE_LONG_LONG
+ const PY_LONG_LONG llb = intval;
+ PY_LONG_LONG lla, llx;
+#endif
+ const digit* digits = ((PyLongObject*)op1)->ob_digit;
+ const Py_ssize_t size = Py_SIZE(op1);
+ if (likely(__Pyx_sst_abs(size) <= 1)) {
+ a = likely(size) ? digits[0] : 0;
+ if (size == -1) a = -a;
+ } else {
+ switch (size) {
+ case -2:
+ if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+ a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+#ifdef HAVE_LONG_LONG
+ } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
+ lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+ goto long_long;
+#endif
+ }
+ case 2:
+ if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+ a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+#ifdef HAVE_LONG_LONG
+ } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
+ lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+ goto long_long;
+#endif
+ }
+ case -3:
+ if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+ a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+#ifdef HAVE_LONG_LONG
+ } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
+ lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+ goto long_long;
+#endif
+ }
+ case 3:
+ if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+ a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+#ifdef HAVE_LONG_LONG
+ } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
+ lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+ goto long_long;
+#endif
+ }
+ case -4:
+ if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+ a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+#ifdef HAVE_LONG_LONG
+ } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
+ lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+ goto long_long;
+#endif
+ }
+ case 4:
+ if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+ a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+#ifdef HAVE_LONG_LONG
+ } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
+ lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+ goto long_long;
+#endif
+ }
+ default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
+ }
+ }
+ x = a + b;
+ return PyLong_FromLong(x);
+#ifdef HAVE_LONG_LONG
+ long_long:
+ llx = lla + llb;
+ return PyLong_FromLongLong(llx);
+#endif
+
+
+ }
+ #endif
+ if (PyFloat_CheckExact(op1)) {
+ const long b = intval;
+ double a = PyFloat_AS_DOUBLE(op1);
+ double result;
+ PyFPE_START_PROTECT("add", return NULL)
+ result = ((double)a) + (double)b;
+ PyFPE_END_PROTECT(result)
+ return PyFloat_FromDouble(result);
+ }
+ return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
+}
+#endif
+
+/* None */
+ static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname) {
+ PyErr_Format(PyExc_NameError, "free variable '%s' referenced before assignment in enclosing scope", varname);
+}
+
+/* UnpackUnboundCMethod */
+ static int __Pyx_TryUnpackUnboundCMethod(__Pyx_CachedCFunction* target) {
+ PyObject *method;
+ method = __Pyx_PyObject_GetAttrStr(target->type, *target->method_name);
+ if (unlikely(!method))
+ return -1;
+ target->method = method;
+#if CYTHON_COMPILING_IN_CPYTHON
+ #if PY_MAJOR_VERSION >= 3
+ if (likely(__Pyx_TypeCheck(method, &PyMethodDescr_Type)))
+ #endif
+ {
+ PyMethodDescrObject *descr = (PyMethodDescrObject*) method;
+ target->func = descr->d_method->ml_meth;
+ target->flag = descr->d_method->ml_flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST);
+ }
+#endif
+ return 0;
+}
+
+/* CallUnboundCMethod0 */
+ static PyObject* __Pyx__CallUnboundCMethod0(__Pyx_CachedCFunction* cfunc, PyObject* self) {
+ PyObject *args, *result = NULL;
+ if (unlikely(!cfunc->method) && unlikely(__Pyx_TryUnpackUnboundCMethod(cfunc) < 0)) return NULL;
+#if CYTHON_ASSUME_SAFE_MACROS
+ args = PyTuple_New(1);
+ if (unlikely(!args)) goto bad;
+ Py_INCREF(self);
+ PyTuple_SET_ITEM(args, 0, self);
+#else
+ args = PyTuple_Pack(1, self);
+ if (unlikely(!args)) goto bad;
+#endif
+ result = __Pyx_PyObject_Call(cfunc->method, args, NULL);
+ Py_DECREF(args);
+bad:
+ return result;
+}
+
+/* py_dict_keys */
+ static CYTHON_INLINE PyObject* __Pyx_PyDict_Keys(PyObject* d) {
+ if (PY_MAJOR_VERSION >= 3)
+ return __Pyx_CallUnboundCMethod0(&__pyx_umethod_PyDict_Type_keys, d);
+ else
+ return PyDict_Keys(d);
+}
+
+/* PyErrFetchRestore */
+ #if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+ PyObject *tmp_type, *tmp_value, *tmp_tb;
+ tmp_type = tstate->curexc_type;
+ tmp_value = tstate->curexc_value;
+ tmp_tb = tstate->curexc_traceback;
+ tstate->curexc_type = type;
+ tstate->curexc_value = value;
+ tstate->curexc_traceback = tb;
+ Py_XDECREF(tmp_type);
+ Py_XDECREF(tmp_value);
+ Py_XDECREF(tmp_tb);
+}
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+ *type = tstate->curexc_type;
+ *value = tstate->curexc_value;
+ *tb = tstate->curexc_traceback;
+ tstate->curexc_type = 0;
+ tstate->curexc_value = 0;
+ tstate->curexc_traceback = 0;
+}
+#endif
+
+/* RaiseException */
+ #if PY_MAJOR_VERSION < 3
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
+ CYTHON_UNUSED PyObject *cause) {
+ __Pyx_PyThreadState_declare
+ Py_XINCREF(type);
+ if (!value || value == Py_None)
+ value = NULL;
+ else
+ Py_INCREF(value);
+ if (!tb || tb == Py_None)
+ tb = NULL;
+ else {
+ Py_INCREF(tb);
+ if (!PyTraceBack_Check(tb)) {
+ PyErr_SetString(PyExc_TypeError,
+ "raise: arg 3 must be a traceback or None");
+ goto raise_error;
+ }
+ }
+ if (PyType_Check(type)) {
+#if CYTHON_COMPILING_IN_PYPY
+ if (!value) {
+ Py_INCREF(Py_None);
+ value = Py_None;
+ }
+#endif
+ PyErr_NormalizeException(&type, &value, &tb);
+ } else {
+ if (value) {
+ PyErr_SetString(PyExc_TypeError,
+ "instance exception may not have a separate value");
+ goto raise_error;
+ }
+ value = type;
+ type = (PyObject*) Py_TYPE(type);
+ Py_INCREF(type);
+ if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
+ PyErr_SetString(PyExc_TypeError,
+ "raise: exception class must be a subclass of BaseException");
+ goto raise_error;
+ }
+ }
+ __Pyx_PyThreadState_assign
+ __Pyx_ErrRestore(type, value, tb);
+ return;
+raise_error:
+ Py_XDECREF(value);
+ Py_XDECREF(type);
+ Py_XDECREF(tb);
+ return;
+}
+#else
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
+ PyObject* owned_instance = NULL;
+ if (tb == Py_None) {
+ tb = 0;
+ } else if (tb && !PyTraceBack_Check(tb)) {
+ PyErr_SetString(PyExc_TypeError,
+ "raise: arg 3 must be a traceback or None");
+ goto bad;
+ }
+ if (value == Py_None)
+ value = 0;
+ if (PyExceptionInstance_Check(type)) {
+ if (value) {
+ PyErr_SetString(PyExc_TypeError,
+ "instance exception may not have a separate value");
+ goto bad;
+ }
+ value = type;
+ type = (PyObject*) Py_TYPE(value);
+ } else if (PyExceptionClass_Check(type)) {
+ PyObject *instance_class = NULL;
+ if (value && PyExceptionInstance_Check(value)) {
+ instance_class = (PyObject*) Py_TYPE(value);
+ if (instance_class != type) {
+ int is_subclass = PyObject_IsSubclass(instance_class, type);
+ if (!is_subclass) {
+ instance_class = NULL;
+ } else if (unlikely(is_subclass == -1)) {
+ goto bad;
+ } else {
+ type = instance_class;
+ }
+ }
+ }
+ if (!instance_class) {
+ PyObject *args;
+ if (!value)
+ args = PyTuple_New(0);
+ else if (PyTuple_Check(value)) {
+ Py_INCREF(value);
+ args = value;
+ } else
+ args = PyTuple_Pack(1, value);
+ if (!args)
+ goto bad;
+ owned_instance = PyObject_Call(type, args, NULL);
+ Py_DECREF(args);
+ if (!owned_instance)
+ goto bad;
+ value = owned_instance;
+ if (!PyExceptionInstance_Check(value)) {
+ PyErr_Format(PyExc_TypeError,
+ "calling %R should have returned an instance of "
+ "BaseException, not %R",
+ type, Py_TYPE(value));
+ goto bad;
+ }
+ }
+ } else {
+ PyErr_SetString(PyExc_TypeError,
+ "raise: exception class must be a subclass of BaseException");
+ goto bad;
+ }
+ if (cause) {
+ PyObject *fixed_cause;
+ if (cause == Py_None) {
+ fixed_cause = NULL;
+ } else if (PyExceptionClass_Check(cause)) {
+ fixed_cause = PyObject_CallObject(cause, NULL);
+ if (fixed_cause == NULL)
+ goto bad;
+ } else if (PyExceptionInstance_Check(cause)) {
+ fixed_cause = cause;
+ Py_INCREF(fixed_cause);
+ } else {
+ PyErr_SetString(PyExc_TypeError,
+ "exception causes must derive from "
+ "BaseException");
+ goto bad;
+ }
+ PyException_SetCause(value, fixed_cause);
+ }
+ PyErr_SetObject(type, value);
+ if (tb) {
+#if CYTHON_COMPILING_IN_PYPY
+ PyObject *tmp_type, *tmp_value, *tmp_tb;
+ PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
+ Py_INCREF(tb);
+ PyErr_Restore(tmp_type, tmp_value, tb);
+ Py_XDECREF(tmp_tb);
+#else
+ PyThreadState *tstate = __Pyx_PyThreadState_Current;
+ PyObject* tmp_tb = tstate->curexc_traceback;
+ if (tb != tmp_tb) {
+ Py_INCREF(tb);
+ tstate->curexc_traceback = tb;
+ Py_XDECREF(tmp_tb);
+ }
+#endif
+ }
+bad:
+ Py_XDECREF(owned_instance);
+ return;
+}
+#endif
+
+/* PyIntBinop */
+ #if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
+ if (op1 == op2) {
+ Py_RETURN_TRUE;
+ }
+ #if PY_MAJOR_VERSION < 3
+ if (likely(PyInt_CheckExact(op1))) {
+ const long b = intval;
+ long a = PyInt_AS_LONG(op1);
+ if (a == b) {
+ Py_RETURN_TRUE;
+ } else {
+ Py_RETURN_FALSE;
+ }
+ }
+ #endif
+ #if CYTHON_USE_PYLONG_INTERNALS
+ if (likely(PyLong_CheckExact(op1))) {
+ const long b = intval;
+ long a;
+ const digit* digits = ((PyLongObject*)op1)->ob_digit;
+ const Py_ssize_t size = Py_SIZE(op1);
+ if (likely(__Pyx_sst_abs(size) <= 1)) {
+ a = likely(size) ? digits[0] : 0;
+ if (size == -1) a = -a;
+ } else {
+ switch (size) {
+ case -2:
+ if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+ a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+ }
+ case 2:
+ if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+ a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+ }
+ case -3:
+ if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+ a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+ }
+ case 3:
+ if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+ a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+ }
+ case -4:
+ if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+ a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+ }
+ case 4:
+ if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+ a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+ break;
+ }
+ #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15
+ default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ);
+ #else
+ default: Py_RETURN_FALSE;
+ #endif
+ }
+ }
+ if (a == b) {
+ Py_RETURN_TRUE;
+ } else {
+ Py_RETURN_FALSE;
+ }
+ }
+ #endif
+ if (PyFloat_CheckExact(op1)) {
+ const long b = intval;
+ double a = PyFloat_AS_DOUBLE(op1);
+ if ((double)a == (double)b) {
+ Py_RETURN_TRUE;
+ } else {
+ Py_RETURN_FALSE;
+ }
+ }
+ return PyObject_RichCompare(op1, op2, Py_EQ);
+}
+#endif
+
+/* PyObjectCallMethod1 */
+ static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) {
+ PyObject *result = NULL;
+#if CYTHON_UNPACK_METHODS
+ if (likely(PyMethod_Check(method))) {
+ PyObject *self = PyMethod_GET_SELF(method);
+ if (likely(self)) {
+ PyObject *args;
+ PyObject *function = PyMethod_GET_FUNCTION(method);
+ #if CYTHON_FAST_PYCALL
+ if (PyFunction_Check(function)) {
+ PyObject *args[2] = {self, arg};
+ result = __Pyx_PyFunction_FastCall(function, args, 2);
+ goto done;
+ }
+ #endif
+ #if CYTHON_FAST_PYCCALL
+ if (__Pyx_PyFastCFunction_Check(function)) {
+ PyObject *args[2] = {self, arg};
+ result = __Pyx_PyCFunction_FastCall(function, args, 2);
+ goto done;
+ }
+ #endif
+ args = PyTuple_New(2);
+ if (unlikely(!args)) goto done;
+ Py_INCREF(self);
+ PyTuple_SET_ITEM(args, 0, self);
+ Py_INCREF(arg);
+ PyTuple_SET_ITEM(args, 1, arg);
+ Py_INCREF(function);
+ result = __Pyx_PyObject_Call(function, args, NULL);
+ Py_DECREF(args);
+ Py_DECREF(function);
+ return result;
+ }
+ }
+#endif
+ result = __Pyx_PyObject_CallOneArg(method, arg);
+ goto done;
+done:
+ return result;
+}
+static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {
+ PyObject *method, *result = NULL;
+ method = __Pyx_PyObject_GetAttrStr(obj, method_name);
+ if (unlikely(!method)) goto done;
+ result = __Pyx__PyObject_CallMethod1(method, arg);
+done:
+ Py_XDECREF(method);
+ return result;
+}
+
+/* append */
+ static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) {
+ if (likely(PyList_CheckExact(L))) {
+ if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1;
+ } else {
+ PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_n_s_append, x);
+ if (unlikely(!retval))
+ return -1;
+ Py_DECREF(retval);
+ }
+ return 0;
+}
+
+/* GetAttr */
+ static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
+#if CYTHON_USE_TYPE_SLOTS
+#if PY_MAJOR_VERSION >= 3
+ if (likely(PyUnicode_Check(n)))
+#else
+ if (likely(PyString_Check(n)))
+#endif
+ return __Pyx_PyObject_GetAttrStr(o, n);
+#endif
+ return PyObject_GetAttr(o, n);
+}
+
+/* HasAttr */
+ static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
+ PyObject *r;
+ if (unlikely(!__Pyx_PyBaseString_Check(n))) {
+ PyErr_SetString(PyExc_TypeError,
+ "hasattr(): attribute name must be string");
+ return -1;
+ }
+ r = __Pyx_GetAttr(o, n);
+ if (unlikely(!r)) {
+ PyErr_Clear();
+ return 0;
+ } else {
+ Py_DECREF(r);
+ return 1;
+ }
+}
+
+/* Import */
+ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
+ PyObject *empty_list = 0;
+ PyObject *module = 0;
+ PyObject *global_dict = 0;
+ PyObject *empty_dict = 0;
+ PyObject *list;
+ #if PY_MAJOR_VERSION < 3
+ PyObject *py_import;
+ py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
+ if (!py_import)
+ goto bad;
+ #endif
+ if (from_list)
+ list = from_list;
+ else {
+ empty_list = PyList_New(0);
+ if (!empty_list)
+ goto bad;
+ list = empty_list;
+ }
+ global_dict = PyModule_GetDict(__pyx_m);
+ if (!global_dict)
+ goto bad;
+ empty_dict = PyDict_New();
+ if (!empty_dict)
+ goto bad;
+ {
+ #if PY_MAJOR_VERSION >= 3
+ if (level == -1) {
+ if (strchr(__Pyx_MODULE_NAME, '.')) {
+ module = PyImport_ImportModuleLevelObject(
+ name, global_dict, empty_dict, list, 1);
+ if (!module) {
+ if (!PyErr_ExceptionMatches(PyExc_ImportError))
+ goto bad;
+ PyErr_Clear();
+ }
+ }
+ level = 0;
+ }
+ #endif
+ if (!module) {
+ #if PY_MAJOR_VERSION < 3
+ PyObject *py_level = PyInt_FromLong(level);
+ if (!py_level)
+ goto bad;
+ module = PyObject_CallFunctionObjArgs(py_import,
+ name, global_dict, empty_dict, list, py_level, NULL);
+ Py_DECREF(py_level);
+ #else
+ module = PyImport_ImportModuleLevelObject(
+ name, global_dict, empty_dict, list, level);
+ #endif
+ }
+ }
+bad:
+ #if PY_MAJOR_VERSION < 3
+ Py_XDECREF(py_import);
+ #endif
+ Py_XDECREF(empty_list);
+ Py_XDECREF(empty_dict);
+ return module;
+}
+
+/* ImportFrom */
+ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
+ PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
+ if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
+ PyErr_Format(PyExc_ImportError,
+ #if PY_MAJOR_VERSION < 3
+ "cannot import name %.230s", PyString_AS_STRING(name));
+ #else
+ "cannot import name %S", name);
+ #endif
+ }
+ return value;
+}
+
+/* CalculateMetaclass */
+ static PyObject *__Pyx_CalculateMetaclass(PyTypeObject *metaclass, PyObject *bases) {
+ Py_ssize_t i, nbases = PyTuple_GET_SIZE(bases);
+ for (i=0; i < nbases; i++) {
+ PyTypeObject *tmptype;
+ PyObject *tmp = PyTuple_GET_ITEM(bases, i);
+ tmptype = Py_TYPE(tmp);
+#if PY_MAJOR_VERSION < 3
+ if (tmptype == &PyClass_Type)
+ continue;
+#endif
+ if (!metaclass) {
+ metaclass = tmptype;
+ continue;
+ }
+ if (PyType_IsSubtype(metaclass, tmptype))
+ continue;
+ if (PyType_IsSubtype(tmptype, metaclass)) {
+ metaclass = tmptype;
+ continue;
+ }
+ PyErr_SetString(PyExc_TypeError,
+ "metaclass conflict: "
+ "the metaclass of a derived class "
+ "must be a (non-strict) subclass "
+ "of the metaclasses of all its bases");
+ return NULL;
+ }
+ if (!metaclass) {
+#if PY_MAJOR_VERSION < 3
+ metaclass = &PyClass_Type;
+#else
+ metaclass = &PyType_Type;
+#endif
+ }
+ Py_INCREF((PyObject*) metaclass);
+ return (PyObject*) metaclass;
+}
+
+/* FetchCommonType */
+ static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
+ PyObject* fake_module;
+ PyTypeObject* cached_type = NULL;
+ fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI);
+ if (!fake_module) return NULL;
+ Py_INCREF(fake_module);
+ cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name);
+ if (cached_type) {
+ if (!PyType_Check((PyObject*)cached_type)) {
+ PyErr_Format(PyExc_TypeError,
+ "Shared Cython type %.200s is not a type object",
+ type->tp_name);
+ goto bad;
+ }
+ if (cached_type->tp_basicsize != type->tp_basicsize) {
+ PyErr_Format(PyExc_TypeError,
+ "Shared Cython type %.200s has the wrong size, try recompiling",
+ type->tp_name);
+ goto bad;
+ }
+ } else {
+ if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
+ PyErr_Clear();
+ if (PyType_Ready(type) < 0) goto bad;
+ if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0)
+ goto bad;
+ Py_INCREF(type);
+ cached_type = type;
+ }
+done:
+ Py_DECREF(fake_module);
+ return cached_type;
+bad:
+ Py_XDECREF(cached_type);
+ cached_type = NULL;
+ goto done;
+}
+
+/* CythonFunction */
+ static PyObject *
+__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, CYTHON_UNUSED void *closure)
+{
+ if (unlikely(op->func_doc == NULL)) {
+ if (op->func.m_ml->ml_doc) {
+#if PY_MAJOR_VERSION >= 3
+ op->func_doc = PyUnicode_FromString(op->func.m_ml->ml_doc);
+#else
+ op->func_doc = PyString_FromString(op->func.m_ml->ml_doc);
+#endif
+ if (unlikely(op->func_doc == NULL))
+ return NULL;
+ } else {
+ Py_INCREF(Py_None);
+ return Py_None;
+ }
+ }
+ Py_INCREF(op->func_doc);
+ return op->func_doc;
+}
+static int
+__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value)
+{
+ PyObject *tmp = op->func_doc;
+ if (value == NULL) {
+ value = Py_None;
+ }
+ Py_INCREF(value);
+ op->func_doc = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op)
+{
+ if (unlikely(op->func_name == NULL)) {
+#if PY_MAJOR_VERSION >= 3
+ op->func_name = PyUnicode_InternFromString(op->func.m_ml->ml_name);
+#else
+ op->func_name = PyString_InternFromString(op->func.m_ml->ml_name);
+#endif
+ if (unlikely(op->func_name == NULL))
+ return NULL;
+ }
+ Py_INCREF(op->func_name);
+ return op->func_name;
+}
+static int
+__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value)
+{
+ PyObject *tmp;
+#if PY_MAJOR_VERSION >= 3
+ if (unlikely(value == NULL || !PyUnicode_Check(value))) {
+#else
+ if (unlikely(value == NULL || !PyString_Check(value))) {
+#endif
+ PyErr_SetString(PyExc_TypeError,
+ "__name__ must be set to a string object");
+ return -1;
+ }
+ tmp = op->func_name;
+ Py_INCREF(value);
+ op->func_name = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op)
+{
+ Py_INCREF(op->func_qualname);
+ return op->func_qualname;
+}
+static int
+__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value)
+{
+ PyObject *tmp;
+#if PY_MAJOR_VERSION >= 3
+ if (unlikely(value == NULL || !PyUnicode_Check(value))) {
+#else
+ if (unlikely(value == NULL || !PyString_Check(value))) {
+#endif
+ PyErr_SetString(PyExc_TypeError,
+ "__qualname__ must be set to a string object");
+ return -1;
+ }
+ tmp = op->func_qualname;
+ Py_INCREF(value);
+ op->func_qualname = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_self(__pyx_CyFunctionObject *m, CYTHON_UNUSED void *closure)
+{
+ PyObject *self;
+ self = m->func_closure;
+ if (self == NULL)
+ self = Py_None;
+ Py_INCREF(self);
+ return self;
+}
+static PyObject *
+__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op)
+{
+ if (unlikely(op->func_dict == NULL)) {
+ op->func_dict = PyDict_New();
+ if (unlikely(op->func_dict == NULL))
+ return NULL;
+ }
+ Py_INCREF(op->func_dict);
+ return op->func_dict;
+}
+static int
+__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value)
+{
+ PyObject *tmp;
+ if (unlikely(value == NULL)) {
+ PyErr_SetString(PyExc_TypeError,
+ "function's dictionary may not be deleted");
+ return -1;
+ }
+ if (unlikely(!PyDict_Check(value))) {
+ PyErr_SetString(PyExc_TypeError,
+ "setting function's dictionary to a non-dict");
+ return -1;
+ }
+ tmp = op->func_dict;
+ Py_INCREF(value);
+ op->func_dict = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op)
+{
+ Py_INCREF(op->func_globals);
+ return op->func_globals;
+}
+static PyObject *
+__Pyx_CyFunction_get_closure(CYTHON_UNUSED __pyx_CyFunctionObject *op)
+{
+ Py_INCREF(Py_None);
+ return Py_None;
+}
+static PyObject *
+__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op)
+{
+ PyObject* result = (op->func_code) ? op->func_code : Py_None;
+ Py_INCREF(result);
+ return result;
+}
+static int
+__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
+ int result = 0;
+ PyObject *res = op->defaults_getter((PyObject *) op);
+ if (unlikely(!res))
+ return -1;
+ #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+ op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
+ Py_INCREF(op->defaults_tuple);
+ op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
+ Py_INCREF(op->defaults_kwdict);
+ #else
+ op->defaults_tuple = PySequence_ITEM(res, 0);
+ if (unlikely(!op->defaults_tuple)) result = -1;
+ else {
+ op->defaults_kwdict = PySequence_ITEM(res, 1);
+ if (unlikely(!op->defaults_kwdict)) result = -1;
+ }
+ #endif
+ Py_DECREF(res);
+ return result;
+}
+static int
+__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value) {
+ PyObject* tmp;
+ if (!value) {
+ value = Py_None;
+ } else if (value != Py_None && !PyTuple_Check(value)) {
+ PyErr_SetString(PyExc_TypeError,
+ "__defaults__ must be set to a tuple object");
+ return -1;
+ }
+ Py_INCREF(value);
+ tmp = op->defaults_tuple;
+ op->defaults_tuple = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op) {
+ PyObject* result = op->defaults_tuple;
+ if (unlikely(!result)) {
+ if (op->defaults_getter) {
+ if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
+ result = op->defaults_tuple;
+ } else {
+ result = Py_None;
+ }
+ }
+ Py_INCREF(result);
+ return result;
+}
+static int
+__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value) {
+ PyObject* tmp;
+ if (!value) {
+ value = Py_None;
+ } else if (value != Py_None && !PyDict_Check(value)) {
+ PyErr_SetString(PyExc_TypeError,
+ "__kwdefaults__ must be set to a dict object");
+ return -1;
+ }
+ Py_INCREF(value);
+ tmp = op->defaults_kwdict;
+ op->defaults_kwdict = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op) {
+ PyObject* result = op->defaults_kwdict;
+ if (unlikely(!result)) {
+ if (op->defaults_getter) {
+ if (__Pyx_CyFunction_init_defaults(op) < 0) return NULL;
+ result = op->defaults_kwdict;
+ } else {
+ result = Py_None;
+ }
+ }
+ Py_INCREF(result);
+ return result;
+}
+static int
+__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value) {
+ PyObject* tmp;
+ if (!value || value == Py_None) {
+ value = NULL;
+ } else if (!PyDict_Check(value)) {
+ PyErr_SetString(PyExc_TypeError,
+ "__annotations__ must be set to a dict object");
+ return -1;
+ }
+ Py_XINCREF(value);
+ tmp = op->func_annotations;
+ op->func_annotations = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static PyObject *
+__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op) {
+ PyObject* result = op->func_annotations;
+ if (unlikely(!result)) {
+ result = PyDict_New();
+ if (unlikely(!result)) return NULL;
+ op->func_annotations = result;
+ }
+ Py_INCREF(result);
+ return result;
+}
+static PyGetSetDef __pyx_CyFunction_getsets[] = {
+ {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
+ {(char *) "__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
+ {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
+ {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
+ {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
+ {(char *) "__self__", (getter)__Pyx_CyFunction_get_self, 0, 0, 0},
+ {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
+ {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
+ {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
+ {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
+ {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
+ {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
+ {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
+ {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
+ {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
+ {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
+ {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
+ {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
+ {0, 0, 0, 0, 0}
+};
+static PyMemberDef __pyx_CyFunction_members[] = {
+ {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), PY_WRITE_RESTRICTED, 0},
+ {0, 0, 0, 0, 0}
+};
+static PyObject *
+__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, CYTHON_UNUSED PyObject *args)
+{
+#if PY_MAJOR_VERSION >= 3
+ return PyUnicode_FromString(m->func.m_ml->ml_name);
+#else
+ return PyString_FromString(m->func.m_ml->ml_name);
+#endif
+}
+static PyMethodDef __pyx_CyFunction_methods[] = {
+ {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
+ {0, 0, 0, 0}
+};
+#if PY_VERSION_HEX < 0x030500A0
+#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
+#else
+#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func.m_weakreflist)
+#endif
+static PyObject *__Pyx_CyFunction_New(PyTypeObject *type, PyMethodDef *ml, int flags, PyObject* qualname,
+ PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
+ __pyx_CyFunctionObject *op = PyObject_GC_New(__pyx_CyFunctionObject, type);
+ if (op == NULL)
+ return NULL;
+ op->flags = flags;
+ __Pyx_CyFunction_weakreflist(op) = NULL;
+ op->func.m_ml = ml;
+ op->func.m_self = (PyObject *) op;
+ Py_XINCREF(closure);
+ op->func_closure = closure;
+ Py_XINCREF(module);
+ op->func.m_module = module;
+ op->func_dict = NULL;
+ op->func_name = NULL;
+ Py_INCREF(qualname);
+ op->func_qualname = qualname;
+ op->func_doc = NULL;
+ op->func_classobj = NULL;
+ op->func_globals = globals;
+ Py_INCREF(op->func_globals);
+ Py_XINCREF(code);
+ op->func_code = code;
+ op->defaults_pyobjects = 0;
+ op->defaults = NULL;
+ op->defaults_tuple = NULL;
+ op->defaults_kwdict = NULL;
+ op->defaults_getter = NULL;
+ op->func_annotations = NULL;
+ PyObject_GC_Track(op);
+ return (PyObject *) op;
+}
+static int
+__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
+{
+ Py_CLEAR(m->func_closure);
+ Py_CLEAR(m->func.m_module);
+ Py_CLEAR(m->func_dict);
+ Py_CLEAR(m->func_name);
+ Py_CLEAR(m->func_qualname);
+ Py_CLEAR(m->func_doc);
+ Py_CLEAR(m->func_globals);
+ Py_CLEAR(m->func_code);
+ Py_CLEAR(m->func_classobj);
+ Py_CLEAR(m->defaults_tuple);
+ Py_CLEAR(m->defaults_kwdict);
+ Py_CLEAR(m->func_annotations);
+ if (m->defaults) {
+ PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
+ int i;
+ for (i = 0; i < m->defaults_pyobjects; i++)
+ Py_XDECREF(pydefaults[i]);
+ PyObject_Free(m->defaults);
+ m->defaults = NULL;
+ }
+ return 0;
+}
+static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
+{
+ if (__Pyx_CyFunction_weakreflist(m) != NULL)
+ PyObject_ClearWeakRefs((PyObject *) m);
+ __Pyx_CyFunction_clear(m);
+ PyObject_GC_Del(m);
+}
+static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
+{
+ PyObject_GC_UnTrack(m);
+ __Pyx__CyFunction_dealloc(m);
+}
+static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
+{
+ Py_VISIT(m->func_closure);
+ Py_VISIT(m->func.m_module);
+ Py_VISIT(m->func_dict);
+ Py_VISIT(m->func_name);
+ Py_VISIT(m->func_qualname);
+ Py_VISIT(m->func_doc);
+ Py_VISIT(m->func_globals);
+ Py_VISIT(m->func_code);
+ Py_VISIT(m->func_classobj);
+ Py_VISIT(m->defaults_tuple);
+ Py_VISIT(m->defaults_kwdict);
+ if (m->defaults) {
+ PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
+ int i;
+ for (i = 0; i < m->defaults_pyobjects; i++)
+ Py_VISIT(pydefaults[i]);
+ }
+ return 0;
+}
+static PyObject *__Pyx_CyFunction_descr_get(PyObject *func, PyObject *obj, PyObject *type)
+{
+ __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+ if (m->flags & __Pyx_CYFUNCTION_STATICMETHOD) {
+ Py_INCREF(func);
+ return func;
+ }
+ if (m->flags & __Pyx_CYFUNCTION_CLASSMETHOD) {
+ if (type == NULL)
+ type = (PyObject *)(Py_TYPE(obj));
+ return __Pyx_PyMethod_New(func, type, (PyObject *)(Py_TYPE(type)));
+ }
+ if (obj == Py_None)
+ obj = NULL;
+ return __Pyx_PyMethod_New(func, obj, type);
+}
+static PyObject*
+__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
+{
+#if PY_MAJOR_VERSION >= 3
+ return PyUnicode_FromFormat("",
+ op->func_qualname, (void *)op);
+#else
+ return PyString_FromFormat("",
+ PyString_AsString(op->func_qualname), (void *)op);
+#endif
+}
+static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
+ PyCFunctionObject* f = (PyCFunctionObject*)func;
+ PyCFunction meth = f->m_ml->ml_meth;
+ Py_ssize_t size;
+ switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
+ case METH_VARARGS:
+ if (likely(kw == NULL || PyDict_Size(kw) == 0))
+ return (*meth)(self, arg);
+ break;
+ case METH_VARARGS | METH_KEYWORDS:
+ return (*(PyCFunctionWithKeywords)meth)(self, arg, kw);
+ case METH_NOARGS:
+ if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
+ size = PyTuple_GET_SIZE(arg);
+ if (likely(size == 0))
+ return (*meth)(self, NULL);
+ PyErr_Format(PyExc_TypeError,
+ "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
+ f->m_ml->ml_name, size);
+ return NULL;
+ }
+ break;
+ case METH_O:
+ if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
+ size = PyTuple_GET_SIZE(arg);
+ if (likely(size == 1)) {
+ PyObject *result, *arg0;
+ #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+ arg0 = PyTuple_GET_ITEM(arg, 0);
+ #else
+ arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
+ #endif
+ result = (*meth)(self, arg0);
+ #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
+ Py_DECREF(arg0);
+ #endif
+ return result;
+ }
+ PyErr_Format(PyExc_TypeError,
+ "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
+ f->m_ml->ml_name, size);
+ return NULL;
+ }
+ break;
+ default:
+ PyErr_SetString(PyExc_SystemError, "Bad call flags in "
+ "__Pyx_CyFunction_Call. METH_OLDARGS is no "
+ "longer supported!");
+ return NULL;
+ }
+ PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
+ f->m_ml->ml_name);
+ return NULL;
+}
+static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
+ return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
+}
+static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
+ PyObject *result;
+ __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
+ if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
+ Py_ssize_t argc;
+ PyObject *new_args;
+ PyObject *self;
+ argc = PyTuple_GET_SIZE(args);
+ new_args = PyTuple_GetSlice(args, 1, argc);
+ if (unlikely(!new_args))
+ return NULL;
+ self = PyTuple_GetItem(args, 0);
+ if (unlikely(!self)) {
+ Py_DECREF(new_args);
+ return NULL;
+ }
+ result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
+ Py_DECREF(new_args);
+ } else {
+ result = __Pyx_CyFunction_Call(func, args, kw);
+ }
+ return result;
+}
+static PyTypeObject __pyx_CyFunctionType_type = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "cython_function_or_method",
+ sizeof(__pyx_CyFunctionObject),
+ 0,
+ (destructor) __Pyx_CyFunction_dealloc,
+ 0,
+ 0,
+ 0,
+#if PY_MAJOR_VERSION < 3
+ 0,
+#else
+ 0,
+#endif
+ (reprfunc) __Pyx_CyFunction_repr,
+ 0,
+ 0,
+ 0,
+ 0,
+ __Pyx_CyFunction_CallAsMethod,
+ 0,
+ 0,
+ 0,
+ 0,
+ Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC,
+ 0,
+ (traverseproc) __Pyx_CyFunction_traverse,
+ (inquiry) __Pyx_CyFunction_clear,
+ 0,
+#if PY_VERSION_HEX < 0x030500A0
+ offsetof(__pyx_CyFunctionObject, func_weakreflist),
+#else
+ offsetof(PyCFunctionObject, m_weakreflist),
+#endif
+ 0,
+ 0,
+ __pyx_CyFunction_methods,
+ __pyx_CyFunction_members,
+ __pyx_CyFunction_getsets,
+ 0,
+ 0,
+ __Pyx_CyFunction_descr_get,
+ 0,
+ offsetof(__pyx_CyFunctionObject, func_dict),
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+#if PY_VERSION_HEX >= 0x030400a1
+ 0,
+#endif
+};
+static int __pyx_CyFunction_init(void) {
+ __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
+ if (unlikely(__pyx_CyFunctionType == NULL)) {
+ return -1;
+ }
+ return 0;
+}
+static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
+ __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+ m->defaults = PyObject_Malloc(size);
+ if (unlikely(!m->defaults))
+ return PyErr_NoMemory();
+ memset(m->defaults, 0, size);
+ m->defaults_pyobjects = pyobjects;
+ return m->defaults;
+}
+static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
+ __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+ m->defaults_tuple = tuple;
+ Py_INCREF(tuple);
+}
+static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
+ __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+ m->defaults_kwdict = dict;
+ Py_INCREF(dict);
+}
+static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
+ __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
+ m->func_annotations = dict;
+ Py_INCREF(dict);
+}
+
+/* Py3ClassCreate */
+ static PyObject *__Pyx_Py3MetaclassPrepare(PyObject *metaclass, PyObject *bases, PyObject *name,
+ PyObject *qualname, PyObject *mkw, PyObject *modname, PyObject *doc) {
+ PyObject *ns;
+ if (metaclass) {
+ PyObject *prep = __Pyx_PyObject_GetAttrStr(metaclass, __pyx_n_s_prepare);
+ if (prep) {
+ PyObject *pargs = PyTuple_Pack(2, name, bases);
+ if (unlikely(!pargs)) {
+ Py_DECREF(prep);
+ return NULL;
+ }
+ ns = PyObject_Call(prep, pargs, mkw);
+ Py_DECREF(prep);
+ Py_DECREF(pargs);
+ } else {
+ if (unlikely(!PyErr_ExceptionMatches(PyExc_AttributeError)))
+ return NULL;
+ PyErr_Clear();
+ ns = PyDict_New();
+ }
+ } else {
+ ns = PyDict_New();
+ }
+ if (unlikely(!ns))
+ return NULL;
+ if (unlikely(PyObject_SetItem(ns, __pyx_n_s_module, modname) < 0)) goto bad;
+ if (unlikely(PyObject_SetItem(ns, __pyx_n_s_qualname, qualname) < 0)) goto bad;
+ if (unlikely(doc && PyObject_SetItem(ns, __pyx_n_s_doc, doc) < 0)) goto bad;
+ return ns;
+bad:
+ Py_DECREF(ns);
+ return NULL;
+}
+static PyObject *__Pyx_Py3ClassCreate(PyObject *metaclass, PyObject *name, PyObject *bases,
+ PyObject *dict, PyObject *mkw,
+ int calculate_metaclass, int allow_py2_metaclass) {
+ PyObject *result, *margs;
+ PyObject *owned_metaclass = NULL;
+ if (allow_py2_metaclass) {
+ owned_metaclass = PyObject_GetItem(dict, __pyx_n_s_metaclass);
+ if (owned_metaclass) {
+ metaclass = owned_metaclass;
+ } else if (likely(PyErr_ExceptionMatches(PyExc_KeyError))) {
+ PyErr_Clear();
+ } else {
+ return NULL;
+ }
+ }
+ if (calculate_metaclass && (!metaclass || PyType_Check(metaclass))) {
+ metaclass = __Pyx_CalculateMetaclass((PyTypeObject*) metaclass, bases);
+ Py_XDECREF(owned_metaclass);
+ if (unlikely(!metaclass))
+ return NULL;
+ owned_metaclass = metaclass;
+ }
+ margs = PyTuple_Pack(3, name, bases, dict);
+ if (unlikely(!margs)) {
+ result = NULL;
+ } else {
+ result = PyObject_Call(metaclass, margs, mkw);
+ Py_DECREF(margs);
+ }
+ Py_XDECREF(owned_metaclass);
+ return result;
+}
+
+/* CLineInTraceback */
+ #ifndef CYTHON_CLINE_IN_TRACEBACK
+static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) {
+ PyObject *use_cline;
+ PyObject *ptype, *pvalue, *ptraceback;
+#if CYTHON_COMPILING_IN_CPYTHON
+ PyObject **cython_runtime_dict;
+#endif
+ __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
+#if CYTHON_COMPILING_IN_CPYTHON
+ cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
+ if (likely(cython_runtime_dict)) {
+ use_cline = PyDict_GetItem(*cython_runtime_dict, __pyx_n_s_cline_in_traceback);
+ } else
+#endif
+ {
+ PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
+ if (use_cline_obj) {
+ use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
+ Py_DECREF(use_cline_obj);
+ } else {
+ PyErr_Clear();
+ use_cline = NULL;
+ }
+ }
+ if (!use_cline) {
+ c_line = 0;
+ PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
+ }
+ else if (PyObject_Not(use_cline) != 0) {
+ c_line = 0;
+ }
+ __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
+ return c_line;
+}
+#endif
+
+/* CodeObjectCache */
+ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
+ int start = 0, mid = 0, end = count - 1;
+ if (end >= 0 && code_line > entries[end].code_line) {
+ return count;
+ }
+ while (start < end) {
+ mid = start + (end - start) / 2;
+ if (code_line < entries[mid].code_line) {
+ end = mid;
+ } else if (code_line > entries[mid].code_line) {
+ start = mid + 1;
+ } else {
+ return mid;
+ }
+ }
+ if (code_line <= entries[mid].code_line) {
+ return mid;
+ } else {
+ return mid + 1;
+ }
+}
+static PyCodeObject *__pyx_find_code_object(int code_line) {
+ PyCodeObject* code_object;
+ int pos;
+ if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
+ return NULL;
+ }
+ pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+ if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
+ return NULL;
+ }
+ code_object = __pyx_code_cache.entries[pos].code_object;
+ Py_INCREF(code_object);
+ return code_object;
+}
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
+ int pos, i;
+ __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
+ if (unlikely(!code_line)) {
+ return;
+ }
+ if (unlikely(!entries)) {
+ entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
+ if (likely(entries)) {
+ __pyx_code_cache.entries = entries;
+ __pyx_code_cache.max_count = 64;
+ __pyx_code_cache.count = 1;
+ entries[0].code_line = code_line;
+ entries[0].code_object = code_object;
+ Py_INCREF(code_object);
+ }
+ return;
+ }
+ pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+ if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
+ PyCodeObject* tmp = entries[pos].code_object;
+ entries[pos].code_object = code_object;
+ Py_DECREF(tmp);
+ return;
+ }
+ if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
+ int new_max = __pyx_code_cache.max_count + 64;
+ entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
+ __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));
+ if (unlikely(!entries)) {
+ return;
+ }
+ __pyx_code_cache.entries = entries;
+ __pyx_code_cache.max_count = new_max;
+ }
+ for (i=__pyx_code_cache.count; i>pos; i--) {
+ entries[i] = entries[i-1];
+ }
+ entries[pos].code_line = code_line;
+ entries[pos].code_object = code_object;
+ __pyx_code_cache.count++;
+ Py_INCREF(code_object);
+}
+
+/* AddTraceback */
+ #include "compile.h"
+#include "frameobject.h"
+#include "traceback.h"
+static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
+ const char *funcname, int c_line,
+ int py_line, const char *filename) {
+ PyCodeObject *py_code = 0;
+ PyObject *py_srcfile = 0;
+ PyObject *py_funcname = 0;
+ #if PY_MAJOR_VERSION < 3
+ py_srcfile = PyString_FromString(filename);
+ #else
+ py_srcfile = PyUnicode_FromString(filename);
+ #endif
+ if (!py_srcfile) goto bad;
+ if (c_line) {
+ #if PY_MAJOR_VERSION < 3
+ py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+ #else
+ py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+ #endif
+ }
+ else {
+ #if PY_MAJOR_VERSION < 3
+ py_funcname = PyString_FromString(funcname);
+ #else
+ py_funcname = PyUnicode_FromString(funcname);
+ #endif
+ }
+ if (!py_funcname) goto bad;
+ py_code = __Pyx_PyCode_New(
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ __pyx_empty_bytes, /*PyObject *code,*/
+ __pyx_empty_tuple, /*PyObject *consts,*/
+ __pyx_empty_tuple, /*PyObject *names,*/
+ __pyx_empty_tuple, /*PyObject *varnames,*/
+ __pyx_empty_tuple, /*PyObject *freevars,*/
+ __pyx_empty_tuple, /*PyObject *cellvars,*/
+ py_srcfile, /*PyObject *filename,*/
+ py_funcname, /*PyObject *name,*/
+ py_line,
+ __pyx_empty_bytes /*PyObject *lnotab*/
+ );
+ Py_DECREF(py_srcfile);
+ Py_DECREF(py_funcname);
+ return py_code;
+bad:
+ Py_XDECREF(py_srcfile);
+ Py_XDECREF(py_funcname);
+ return NULL;
+}
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+ int py_line, const char *filename) {
+ PyCodeObject *py_code = 0;
+ PyFrameObject *py_frame = 0;
+ PyThreadState *tstate = __Pyx_PyThreadState_Current;
+ if (c_line) {
+ c_line = __Pyx_CLineForTraceback(tstate, c_line);
+ }
+ py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
+ if (!py_code) {
+ py_code = __Pyx_CreateCodeObjectForTraceback(
+ funcname, c_line, py_line, filename);
+ if (!py_code) goto bad;
+ __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
+ }
+ py_frame = PyFrame_New(
+ tstate, /*PyThreadState *tstate,*/
+ py_code, /*PyCodeObject *code,*/
+ __pyx_d, /*PyObject *globals,*/
+ 0 /*PyObject *locals*/
+ );
+ if (!py_frame) goto bad;
+ __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
+ PyTraceBack_Here(py_frame);
+bad:
+ Py_XDECREF(py_code);
+ Py_XDECREF(py_frame);
+}
+
+/* CIntToPy */
+ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+ const long neg_one = (long) -1, const_zero = (long) 0;
+ const int is_unsigned = neg_one > const_zero;
+ if (is_unsigned) {
+ if (sizeof(long) < sizeof(long)) {
+ return PyInt_FromLong((long) value);
+ } else if (sizeof(long) <= sizeof(unsigned long)) {
+ return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+ } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
+ return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+ }
+ } else {
+ if (sizeof(long) <= sizeof(long)) {
+ return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+ } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
+ return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+ }
+ }
+ {
+ int one = 1; int little = (int)*(unsigned char *)&one;
+ unsigned char *bytes = (unsigned char *)&value;
+ return _PyLong_FromByteArray(bytes, sizeof(long),
+ little, !is_unsigned);
+ }
+}
+
+/* CIntFromPyVerify */
+ #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
+ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
+#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
+ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
+#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
+ {\
+ func_type value = func_value;\
+ if (sizeof(target_type) < sizeof(func_type)) {\
+ if (unlikely(value != (func_type) (target_type) value)) {\
+ func_type zero = 0;\
+ if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
+ return (target_type) -1;\
+ if (is_unsigned && unlikely(value < zero))\
+ goto raise_neg_overflow;\
+ else\
+ goto raise_overflow;\
+ }\
+ }\
+ return (target_type) value;\
+ }
+
+/* CIntFromPy */
+ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+ const long neg_one = (long) -1, const_zero = (long) 0;
+ const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+ if (likely(PyInt_Check(x))) {
+ if (sizeof(long) < sizeof(long)) {
+ __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
+ } else {
+ long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ goto raise_neg_overflow;
+ }
+ return (long) val;
+ }
+ } else
+#endif
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+ const digit* digits = ((PyLongObject*)x)->ob_digit;
+ switch (Py_SIZE(x)) {
+ case 0: return (long) 0;
+ case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
+ case 2:
+ if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
+ return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+ }
+ }
+ break;
+ case 3:
+ if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
+ return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+ }
+ }
+ break;
+ case 4:
+ if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
+ return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+ }
+ }
+ break;
+ }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+ if (unlikely(Py_SIZE(x) < 0)) {
+ goto raise_neg_overflow;
+ }
+#else
+ {
+ int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+ if (unlikely(result < 0))
+ return (long) -1;
+ if (unlikely(result == 1))
+ goto raise_neg_overflow;
+ }
+#endif
+ if (sizeof(long) <= sizeof(unsigned long)) {
+ __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+ } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
+ __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+ }
+ } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+ const digit* digits = ((PyLongObject*)x)->ob_digit;
+ switch (Py_SIZE(x)) {
+ case 0: return (long) 0;
+ case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
+ case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0])
+ case -2:
+ if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+ return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+ }
+ }
+ break;
+ case 2:
+ if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+ return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+ }
+ }
+ break;
+ case -3:
+ if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+ return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+ }
+ }
+ break;
+ case 3:
+ if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+ return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+ }
+ }
+ break;
+ case -4:
+ if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+ return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+ }
+ }
+ break;
+ case 4:
+ if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+ return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+ }
+ }
+ break;
+ }
+#endif
+ if (sizeof(long) <= sizeof(long)) {
+ __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+ } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
+ __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+ }
+ }
+ {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+ PyErr_SetString(PyExc_RuntimeError,
+ "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+ long val;
+ PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+ if (likely(v) && !PyLong_Check(v)) {
+ PyObject *tmp = v;
+ v = PyNumber_Long(tmp);
+ Py_DECREF(tmp);
+ }
+ #endif
+ if (likely(v)) {
+ int one = 1; int is_little = (int)*(unsigned char *)&one;
+ unsigned char *bytes = (unsigned char *)&val;
+ int ret = _PyLong_AsByteArray((PyLongObject *)v,
+ bytes, sizeof(val),
+ is_little, !is_unsigned);
+ Py_DECREF(v);
+ if (likely(!ret))
+ return val;
+ }
+#endif
+ return (long) -1;
+ }
+ } else {
+ long val;
+ PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+ if (!tmp) return (long) -1;
+ val = __Pyx_PyInt_As_long(tmp);
+ Py_DECREF(tmp);
+ return val;
+ }
+raise_overflow:
+ PyErr_SetString(PyExc_OverflowError,
+ "value too large to convert to long");
+ return (long) -1;
+raise_neg_overflow:
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to long");
+ return (long) -1;
+}
+
+/* CIntFromPy */
+ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+ const int neg_one = (int) -1, const_zero = (int) 0;
+ const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+ if (likely(PyInt_Check(x))) {
+ if (sizeof(int) < sizeof(long)) {
+ __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
+ } else {
+ long val = PyInt_AS_LONG(x);
+ if (is_unsigned && unlikely(val < 0)) {
+ goto raise_neg_overflow;
+ }
+ return (int) val;
+ }
+ } else
+#endif
+ if (likely(PyLong_Check(x))) {
+ if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+ const digit* digits = ((PyLongObject*)x)->ob_digit;
+ switch (Py_SIZE(x)) {
+ case 0: return (int) 0;
+ case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
+ case 2:
+ if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
+ return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+ }
+ }
+ break;
+ case 3:
+ if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
+ return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+ }
+ }
+ break;
+ case 4:
+ if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
+ return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+ }
+ }
+ break;
+ }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+ if (unlikely(Py_SIZE(x) < 0)) {
+ goto raise_neg_overflow;
+ }
+#else
+ {
+ int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+ if (unlikely(result < 0))
+ return (int) -1;
+ if (unlikely(result == 1))
+ goto raise_neg_overflow;
+ }
+#endif
+ if (sizeof(int) <= sizeof(unsigned long)) {
+ __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+ } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
+ __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+ }
+ } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+ const digit* digits = ((PyLongObject*)x)->ob_digit;
+ switch (Py_SIZE(x)) {
+ case 0: return (int) 0;
+ case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
+ case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0])
+ case -2:
+ if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+ return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+ }
+ }
+ break;
+ case 2:
+ if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+ return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+ }
+ }
+ break;
+ case -3:
+ if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+ return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+ }
+ }
+ break;
+ case 3:
+ if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+ return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+ }
+ }
+ break;
+ case -4:
+ if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
+ return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+ }
+ }
+ break;
+ case 4:
+ if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
+ if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+ __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+ } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
+ return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+ }
+ }
+ break;
+ }
+#endif
+ if (sizeof(int) <= sizeof(long)) {
+ __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+ } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
+ __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+ }
+ }
+ {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+ PyErr_SetString(PyExc_RuntimeError,
+ "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+ int val;
+ PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+ if (likely(v) && !PyLong_Check(v)) {
+ PyObject *tmp = v;
+ v = PyNumber_Long(tmp);
+ Py_DECREF(tmp);
+ }
+ #endif
+ if (likely(v)) {
+ int one = 1; int is_little = (int)*(unsigned char *)&one;
+ unsigned char *bytes = (unsigned char *)&val;
+ int ret = _PyLong_AsByteArray((PyLongObject *)v,
+ bytes, sizeof(val),
+ is_little, !is_unsigned);
+ Py_DECREF(v);
+ if (likely(!ret))
+ return val;
+ }
+#endif
+ return (int) -1;
+ }
+ } else {
+ int val;
+ PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+ if (!tmp) return (int) -1;
+ val = __Pyx_PyInt_As_int(tmp);
+ Py_DECREF(tmp);
+ return val;
+ }
+raise_overflow:
+ PyErr_SetString(PyExc_OverflowError,
+ "value too large to convert to int");
+ return (int) -1;
+raise_neg_overflow:
+ PyErr_SetString(PyExc_OverflowError,
+ "can't convert negative value to int");
+ return (int) -1;
+}
+
+/* FastTypeChecks */
+ #if CYTHON_COMPILING_IN_CPYTHON
+static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
+ while (a) {
+ a = a->tp_base;
+ if (a == b)
+ return 1;
+ }
+ return b == &PyBaseObject_Type;
+}
+static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
+ PyObject *mro;
+ if (a == b) return 1;
+ mro = a->tp_mro;
+ if (likely(mro)) {
+ Py_ssize_t i, n;
+ n = PyTuple_GET_SIZE(mro);
+ for (i = 0; i < n; i++) {
+ if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
+ return 1;
+ }
+ return 0;
+ }
+ return __Pyx_InBases(a, b);
+}
+#if PY_MAJOR_VERSION == 2
+static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
+ PyObject *exception, *value, *tb;
+ int res;
+ __Pyx_PyThreadState_declare
+ __Pyx_PyThreadState_assign
+ __Pyx_ErrFetch(&exception, &value, &tb);
+ res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
+ if (unlikely(res == -1)) {
+ PyErr_WriteUnraisable(err);
+ res = 0;
+ }
+ if (!res) {
+ res = PyObject_IsSubclass(err, exc_type2);
+ if (unlikely(res == -1)) {
+ PyErr_WriteUnraisable(err);
+ res = 0;
+ }
+ }
+ __Pyx_ErrRestore(exception, value, tb);
+ return res;
+}
+#else
+static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
+ int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
+ if (!res) {
+ res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
+ }
+ return res;
+}
+#endif
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
+ if (likely(err == exc_type)) return 1;
+ if (likely(PyExceptionClass_Check(err))) {
+ return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
+ }
+ return PyErr_GivenExceptionMatches(err, exc_type);
+}
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
+ if (likely(err == exc_type1 || err == exc_type2)) return 1;
+ if (likely(PyExceptionClass_Check(err))) {
+ return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
+ }
+ return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
+}
+#endif
+
+/* SwapException */
+ #if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+ PyObject *tmp_type, *tmp_value, *tmp_tb;
+ #if PY_VERSION_HEX >= 0x030700A2
+ tmp_type = tstate->exc_state.exc_type;
+ tmp_value = tstate->exc_state.exc_value;
+ tmp_tb = tstate->exc_state.exc_traceback;
+ tstate->exc_state.exc_type = *type;
+ tstate->exc_state.exc_value = *value;
+ tstate->exc_state.exc_traceback = *tb;
+ #else
+ tmp_type = tstate->exc_type;
+ tmp_value = tstate->exc_value;
+ tmp_tb = tstate->exc_traceback;
+ tstate->exc_type = *type;
+ tstate->exc_value = *value;
+ tstate->exc_traceback = *tb;
+ #endif
+ *type = tmp_type;
+ *value = tmp_value;
+ *tb = tmp_tb;
+}
+#else
+static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
+ PyObject *tmp_type, *tmp_value, *tmp_tb;
+ PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
+ PyErr_SetExcInfo(*type, *value, *tb);
+ *type = tmp_type;
+ *value = tmp_value;
+ *tb = tmp_tb;
+}
+#endif
+
+/* CoroutineBase */
+ #include
+#include
+#define __Pyx_Coroutine_Undelegate(gen) Py_CLEAR((gen)->yieldfrom)
+static int __Pyx_PyGen__FetchStopIterationValue(CYTHON_UNUSED PyThreadState *__pyx_tstate, PyObject **pvalue) {
+ PyObject *et, *ev, *tb;
+ PyObject *value = NULL;
+ __Pyx_ErrFetch(&et, &ev, &tb);
+ if (!et) {
+ Py_XDECREF(tb);
+ Py_XDECREF(ev);
+ Py_INCREF(Py_None);
+ *pvalue = Py_None;
+ return 0;
+ }
+ if (likely(et == PyExc_StopIteration)) {
+ if (!ev) {
+ Py_INCREF(Py_None);
+ value = Py_None;
+ }
+#if PY_VERSION_HEX >= 0x030300A0
+ else if (Py_TYPE(ev) == (PyTypeObject*)PyExc_StopIteration) {
+ value = ((PyStopIterationObject *)ev)->value;
+ Py_INCREF(value);
+ Py_DECREF(ev);
+ }
+#endif
+ else if (unlikely(PyTuple_Check(ev))) {
+ if (PyTuple_GET_SIZE(ev) >= 1) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+ value = PyTuple_GET_ITEM(ev, 0);
+ Py_INCREF(value);
+#else
+ value = PySequence_ITEM(ev, 0);
+#endif
+ } else {
+ Py_INCREF(Py_None);
+ value = Py_None;
+ }
+ Py_DECREF(ev);
+ }
+ else if (!__Pyx_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration)) {
+ value = ev;
+ }
+ if (likely(value)) {
+ Py_XDECREF(tb);
+ Py_DECREF(et);
+ *pvalue = value;
+ return 0;
+ }
+ } else if (!__Pyx_PyErr_GivenExceptionMatches(et, PyExc_StopIteration)) {
+ __Pyx_ErrRestore(et, ev, tb);
+ return -1;
+ }
+ PyErr_NormalizeException(&et, &ev, &tb);
+ if (unlikely(!PyObject_TypeCheck(ev, (PyTypeObject*)PyExc_StopIteration))) {
+ __Pyx_ErrRestore(et, ev, tb);
+ return -1;
+ }
+ Py_XDECREF(tb);
+ Py_DECREF(et);
+#if PY_VERSION_HEX >= 0x030300A0
+ value = ((PyStopIterationObject *)ev)->value;
+ Py_INCREF(value);
+ Py_DECREF(ev);
+#else
+ {
+ PyObject* args = __Pyx_PyObject_GetAttrStr(ev, __pyx_n_s_args_2);
+ Py_DECREF(ev);
+ if (likely(args)) {
+ value = PySequence_GetItem(args, 0);
+ Py_DECREF(args);
+ }
+ if (unlikely(!value)) {
+ __Pyx_ErrRestore(NULL, NULL, NULL);
+ Py_INCREF(Py_None);
+ value = Py_None;
+ }
+ }
+#endif
+ *pvalue = value;
+ return 0;
+}
+static CYTHON_INLINE
+void __Pyx_Coroutine_ExceptionClear(__pyx_CoroutineObject *self) {
+ PyObject *exc_type = self->exc_type;
+ PyObject *exc_value = self->exc_value;
+ PyObject *exc_traceback = self->exc_traceback;
+ self->exc_type = NULL;
+ self->exc_value = NULL;
+ self->exc_traceback = NULL;
+ Py_XDECREF(exc_type);
+ Py_XDECREF(exc_value);
+ Py_XDECREF(exc_traceback);
+}
+#define __Pyx_Coroutine_AlreadyRunningError(gen) (__Pyx__Coroutine_AlreadyRunningError(gen), (PyObject*)NULL)
+static void __Pyx__Coroutine_AlreadyRunningError(CYTHON_UNUSED __pyx_CoroutineObject *gen) {
+ const char *msg;
+ if (0) {
+ #ifdef __Pyx_Coroutine_USED
+ } else if (__Pyx_Coroutine_CheckExact((PyObject*)gen)) {
+ msg = "coroutine already executing";
+ #endif
+ #ifdef __Pyx_AsyncGen_USED
+ } else if (__Pyx_AsyncGen_CheckExact((PyObject*)gen)) {
+ msg = "async generator already executing";
+ #endif
+ } else {
+ msg = "generator already executing";
+ }
+ PyErr_SetString(PyExc_ValueError, msg);
+}
+#define __Pyx_Coroutine_NotStartedError(gen) (__Pyx__Coroutine_NotStartedError(gen), (PyObject*)NULL)
+static void __Pyx__Coroutine_NotStartedError(CYTHON_UNUSED PyObject *gen) {
+ const char *msg;
+ if (0) {
+ #ifdef __Pyx_Coroutine_USED
+ } else if (__Pyx_Coroutine_CheckExact(gen)) {
+ msg = "can't send non-None value to a just-started coroutine";
+ #endif
+ #ifdef __Pyx_AsyncGen_USED
+ } else if (__Pyx_AsyncGen_CheckExact(gen)) {
+ msg = "can't send non-None value to a just-started async generator";
+ #endif
+ } else {
+ msg = "can't send non-None value to a just-started generator";
+ }
+ PyErr_SetString(PyExc_TypeError, msg);
+}
+#define __Pyx_Coroutine_AlreadyTerminatedError(gen, value, closing) (__Pyx__Coroutine_AlreadyTerminatedError(gen, value, closing), (PyObject*)NULL)
+static void __Pyx__Coroutine_AlreadyTerminatedError(CYTHON_UNUSED PyObject *gen, PyObject *value, CYTHON_UNUSED int closing) {
+ #ifdef __Pyx_Coroutine_USED
+ if (!closing && __Pyx_Coroutine_CheckExact(gen)) {
+ PyErr_SetString(PyExc_RuntimeError, "cannot reuse already awaited coroutine");
+ } else
+ #endif
+ if (value) {
+ #ifdef __Pyx_AsyncGen_USED
+ if (__Pyx_AsyncGen_CheckExact(gen))
+ PyErr_SetNone(__Pyx_PyExc_StopAsyncIteration);
+ else
+ #endif
+ PyErr_SetNone(PyExc_StopIteration);
+ }
+}
+static
+PyObject *__Pyx_Coroutine_SendEx(__pyx_CoroutineObject *self, PyObject *value, int closing) {
+ __Pyx_PyThreadState_declare
+ PyThreadState *tstate;
+ PyObject *retval;
+ assert(!self->is_running);
+ if (unlikely(self->resume_label == 0)) {
+ if (unlikely(value && value != Py_None)) {
+ return __Pyx_Coroutine_NotStartedError((PyObject*)self);
+ }
+ }
+ if (unlikely(self->resume_label == -1)) {
+ return __Pyx_Coroutine_AlreadyTerminatedError((PyObject*)self, value, closing);
+ }
+#if CYTHON_FAST_THREAD_STATE
+ __Pyx_PyThreadState_assign
+ tstate = __pyx_tstate;
+#else
+ tstate = __Pyx_PyThreadState_Current;
+#endif
+ if (self->exc_type) {
+#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_PYSTON
+#else
+ if (self->exc_traceback) {
+ PyTracebackObject *tb = (PyTracebackObject *) self->exc_traceback;
+ PyFrameObject *f = tb->tb_frame;
+ Py_XINCREF(tstate->frame);
+ assert(f->f_back == NULL);
+ f->f_back = tstate->frame;
+ }
+#endif
+ __Pyx_ExceptionSwap(&self->exc_type, &self->exc_value,
+ &self->exc_traceback);
+ } else {
+ __Pyx_Coroutine_ExceptionClear(self);
+ __Pyx_ExceptionSave(&self->exc_type, &self->exc_value, &self->exc_traceback);
+ }
+ self->is_running = 1;
+ retval = self->body((PyObject *) self, tstate, value);
+ self->is_running = 0;
+ return retval;
+}
+static CYTHON_INLINE void __Pyx_Coroutine_ResetFrameBackpointer(__pyx_CoroutineObject *self) {
+ if (likely(self->exc_traceback)) {
+#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_PYSTON
+#else
+ PyTracebackObject *tb = (PyTracebackObject *) self->exc_traceback;
+ PyFrameObject *f = tb->tb_frame;
+ Py_CLEAR(f->f_back);
+#endif
+ }
+}
+static CYTHON_INLINE
+PyObject *__Pyx_Coroutine_MethodReturn(CYTHON_UNUSED PyObject* gen, PyObject *retval) {
+ if (unlikely(!retval)) {
+ __Pyx_PyThreadState_declare
+ __Pyx_PyThreadState_assign
+ if (!__Pyx_PyErr_Occurred()) {
+ PyObject *exc = PyExc_StopIteration;
+ #ifdef __Pyx_AsyncGen_USED
+ if (__Pyx_AsyncGen_CheckExact(gen))
+ exc = __Pyx_PyExc_StopAsyncIteration;
+ #endif
+ __Pyx_PyErr_SetNone(exc);
+ }
+ }
+ return retval;
+}
+static CYTHON_INLINE
+PyObject *__Pyx_Coroutine_FinishDelegation(__pyx_CoroutineObject *gen) {
+ PyObject *ret;
+ PyObject *val = NULL;
+ __Pyx_Coroutine_Undelegate(gen);
+ __Pyx_PyGen__FetchStopIterationValue(__Pyx_PyThreadState_Current, &val);
+ ret = __Pyx_Coroutine_SendEx(gen, val, 0);
+ Py_XDECREF(val);
+ return ret;
+}
+static PyObject *__Pyx_Coroutine_Send(PyObject *self, PyObject *value) {
+ PyObject *retval;
+ __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
+ PyObject *yf = gen->yieldfrom;
+ if (unlikely(gen->is_running))
+ return __Pyx_Coroutine_AlreadyRunningError(gen);
+ if (yf) {
+ PyObject *ret;
+ gen->is_running = 1;
+ #ifdef __Pyx_Generator_USED
+ if (__Pyx_Generator_CheckExact(yf)) {
+ ret = __Pyx_Coroutine_Send(yf, value);
+ } else
+ #endif
+ #ifdef __Pyx_Coroutine_USED
+ if (__Pyx_Coroutine_CheckExact(yf)) {
+ ret = __Pyx_Coroutine_Send(yf, value);
+ } else
+ #endif
+ #ifdef __Pyx_AsyncGen_USED
+ if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
+ ret = __Pyx_async_gen_asend_send(yf, value);
+ } else
+ #endif
+ #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
+ if (PyGen_CheckExact(yf)) {
+ ret = _PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
+ } else
+ #endif
+ #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03050000 && defined(PyCoro_CheckExact) && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
+ if (PyCoro_CheckExact(yf)) {
+ ret = _PyGen_Send((PyGenObject*)yf, value == Py_None ? NULL : value);
+ } else
+ #endif
+ {
+ if (value == Py_None)
+ ret = Py_TYPE(yf)->tp_iternext(yf);
+ else
+ ret = __Pyx_PyObject_CallMethod1(yf, __pyx_n_s_send, value);
+ }
+ gen->is_running = 0;
+ if (likely(ret)) {
+ return ret;
+ }
+ retval = __Pyx_Coroutine_FinishDelegation(gen);
+ } else {
+ retval = __Pyx_Coroutine_SendEx(gen, value, 0);
+ }
+ return __Pyx_Coroutine_MethodReturn(self, retval);
+}
+static int __Pyx_Coroutine_CloseIter(__pyx_CoroutineObject *gen, PyObject *yf) {
+ PyObject *retval = NULL;
+ int err = 0;
+ #ifdef __Pyx_Generator_USED
+ if (__Pyx_Generator_CheckExact(yf)) {
+ retval = __Pyx_Coroutine_Close(yf);
+ if (!retval)
+ return -1;
+ } else
+ #endif
+ #ifdef __Pyx_Coroutine_USED
+ if (__Pyx_Coroutine_CheckExact(yf)) {
+ retval = __Pyx_Coroutine_Close(yf);
+ if (!retval)
+ return -1;
+ } else
+ if (__Pyx_CoroutineAwait_CheckExact(yf)) {
+ retval = __Pyx_CoroutineAwait_Close((__pyx_CoroutineAwaitObject*)yf);
+ if (!retval)
+ return -1;
+ } else
+ #endif
+ #ifdef __Pyx_AsyncGen_USED
+ if (__pyx_PyAsyncGenASend_CheckExact(yf)) {
+ retval = __Pyx_async_gen_asend_close(yf, NULL);
+ } else
+ if (__pyx_PyAsyncGenAThrow_CheckExact(yf)) {
+ retval = __Pyx_async_gen_athrow_close(yf, NULL);
+ } else
+ #endif
+ {
+ PyObject *meth;
+ gen->is_running = 1;
+ meth = __Pyx_PyObject_GetAttrStr(yf, __pyx_n_s_close);
+ if (unlikely(!meth)) {
+ if (!PyErr_ExceptionMatches(PyExc_AttributeError)) {
+ PyErr_WriteUnraisable(yf);
+ }
+ PyErr_Clear();
+ } else {
+ retval = PyObject_CallFunction(meth, NULL);
+ Py_DECREF(meth);
+ if (!retval)
+ err = -1;
+ }
+ gen->is_running = 0;
+ }
+ Py_XDECREF(retval);
+ return err;
+}
+static PyObject *__Pyx_Generator_Next(PyObject *self) {
+ __pyx_CoroutineObject *gen = (__pyx_CoroutineObject*) self;
+ PyObject *yf = gen->yieldfrom;
+ if (unlikely(gen->is_running))
+ return __Pyx_Coroutine_AlreadyRunningError(gen);
+ if (yf) {
+ PyObject *ret;
+ gen->is_running = 1;
+ #ifdef __Pyx_Generator_USED
+ if (__Pyx_Generator_CheckExact(yf)) {
+ ret = __Pyx_Generator_Next(yf);
+ } else
+ #endif
+ #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03030000 && (defined(__linux__) || PY_VERSION_HEX >= 0x030600B3)
+ if (PyGen_CheckExact(yf)) {
+ ret = _PyGen_Send((PyGenObject*)yf, NULL);
+ } else
+ #endif
+ ret = Py_TYPE(yf)->tp_iternext(yf);
+ gen->is_running = 0;
+ if (likely(ret)) {
+ return ret;
+ }
+ return __Pyx_Coroutine_FinishDelegation(gen);
+ }
+ return __Pyx_Coroutine_SendEx(gen, Py_None, 0);
+}
+static PyObject *__Pyx_Coroutine_Close(PyObject *self) {
+ __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
+ PyObject *retval, *raised_exception;
+ PyObject *yf = gen->yieldfrom;
+ int err = 0;
+ if (unlikely(gen->is_running))
+ return __Pyx_Coroutine_AlreadyRunningError(gen);
+ if (yf) {
+ Py_INCREF(yf);
+ err = __Pyx_Coroutine_CloseIter(gen, yf);
+ __Pyx_Coroutine_Undelegate(gen);
+ Py_DECREF(yf);
+ }
+ if (err == 0)
+ PyErr_SetNone(PyExc_GeneratorExit);
+ retval = __Pyx_Coroutine_SendEx(gen, NULL, 1);
+ if (unlikely(retval)) {
+ const char *msg;
+ Py_DECREF(retval);
+ if ((0)) {
+ #ifdef __Pyx_Coroutine_USED
+ } else if (__Pyx_Coroutine_CheckExact(self)) {
+ msg = "coroutine ignored GeneratorExit";
+ #endif
+ #ifdef __Pyx_AsyncGen_USED
+ } else if (__Pyx_AsyncGen_CheckExact(self)) {
+#if PY_VERSION_HEX < 0x03060000
+ msg = "async generator ignored GeneratorExit - might require Python 3.6+ finalisation (PEP 525)";
+#else
+ msg = "async generator ignored GeneratorExit";
+#endif
+ #endif
+ } else {
+ msg = "generator ignored GeneratorExit";
+ }
+ PyErr_SetString(PyExc_RuntimeError, msg);
+ return NULL;
+ }
+ raised_exception = PyErr_Occurred();
+ if (likely(!raised_exception || __Pyx_PyErr_GivenExceptionMatches2(raised_exception, PyExc_GeneratorExit, PyExc_StopIteration))) {
+ if (raised_exception) PyErr_Clear();
+ Py_INCREF(Py_None);
+ return Py_None;
+ }
+ return NULL;
+}
+static PyObject *__Pyx__Coroutine_Throw(PyObject *self, PyObject *typ, PyObject *val, PyObject *tb,
+ PyObject *args, int close_on_genexit) {
+ __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
+ PyObject *yf = gen->yieldfrom;
+ if (unlikely(gen->is_running))
+ return __Pyx_Coroutine_AlreadyRunningError(gen);
+ if (yf) {
+ PyObject *ret;
+ Py_INCREF(yf);
+ if (__Pyx_PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit) && close_on_genexit) {
+ int err = __Pyx_Coroutine_CloseIter(gen, yf);
+ Py_DECREF(yf);
+ __Pyx_Coroutine_Undelegate(gen);
+ if (err < 0)
+ return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
+ goto throw_here;
+ }
+ gen->is_running = 1;
+ if (0
+ #ifdef __Pyx_Generator_USED
+ || __Pyx_Generator_CheckExact(yf)
+ #endif
+ #ifdef __Pyx_Coroutine_USED
+ || __Pyx_Coroutine_CheckExact(yf)
+ #endif
+ ) {
+ ret = __Pyx__Coroutine_Throw(yf, typ, val, tb, args, close_on_genexit);
+ #ifdef __Pyx_Coroutine_USED
+ } else if (__Pyx_CoroutineAwait_CheckExact(yf)) {
+ ret = __Pyx__Coroutine_Throw(((__pyx_CoroutineAwaitObject*)yf)->coroutine, typ, val, tb, args, close_on_genexit);
+ #endif
+ } else {
+ PyObject *meth = __Pyx_PyObject_GetAttrStr(yf, __pyx_n_s_throw);
+ if (unlikely(!meth)) {
+ Py_DECREF(yf);
+ if (!PyErr_ExceptionMatches(PyExc_AttributeError)) {
+ gen->is_running = 0;
+ return NULL;
+ }
+ PyErr_Clear();
+ __Pyx_Coroutine_Undelegate(gen);
+ gen->is_running = 0;
+ goto throw_here;
+ }
+ if (likely(args)) {
+ ret = PyObject_CallObject(meth, args);
+ } else {
+ ret = PyObject_CallFunctionObjArgs(meth, typ, val, tb, NULL);
+ }
+ Py_DECREF(meth);
+ }
+ gen->is_running = 0;
+ Py_DECREF(yf);
+ if (!ret) {
+ ret = __Pyx_Coroutine_FinishDelegation(gen);
+ }
+ return __Pyx_Coroutine_MethodReturn(self, ret);
+ }
+throw_here:
+ __Pyx_Raise(typ, val, tb, NULL);
+ return __Pyx_Coroutine_MethodReturn(self, __Pyx_Coroutine_SendEx(gen, NULL, 0));
+}
+static PyObject *__Pyx_Coroutine_Throw(PyObject *self, PyObject *args) {
+ PyObject *typ;
+ PyObject *val = NULL;
+ PyObject *tb = NULL;
+ if (!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb))
+ return NULL;
+ return __Pyx__Coroutine_Throw(self, typ, val, tb, args, 1);
+}
+static int __Pyx_Coroutine_traverse(__pyx_CoroutineObject *gen, visitproc visit, void *arg) {
+ Py_VISIT(gen->closure);
+ Py_VISIT(gen->classobj);
+ Py_VISIT(gen->yieldfrom);
+ Py_VISIT(gen->exc_type);
+ Py_VISIT(gen->exc_value);
+ Py_VISIT(gen->exc_traceback);
+ return 0;
+}
+static int __Pyx_Coroutine_clear(PyObject *self) {
+ __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
+ Py_CLEAR(gen->closure);
+ Py_CLEAR(gen->classobj);
+ Py_CLEAR(gen->yieldfrom);
+ Py_CLEAR(gen->exc_type);
+ Py_CLEAR(gen->exc_value);
+ Py_CLEAR(gen->exc_traceback);
+#ifdef __Pyx_AsyncGen_USED
+ if (__Pyx_AsyncGen_CheckExact(self)) {
+ Py_CLEAR(((__pyx_PyAsyncGenObject*)gen)->ag_finalizer);
+ }
+#endif
+ Py_CLEAR(gen->gi_name);
+ Py_CLEAR(gen->gi_qualname);
+ Py_CLEAR(gen->gi_modulename);
+ return 0;
+}
+static void __Pyx_Coroutine_dealloc(PyObject *self) {
+ __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
+ PyObject_GC_UnTrack(gen);
+ if (gen->gi_weakreflist != NULL)
+ PyObject_ClearWeakRefs(self);
+ if (gen->resume_label >= 0) {
+ PyObject_GC_Track(self);
+#if PY_VERSION_HEX >= 0x030400a1 && CYTHON_USE_TP_FINALIZE
+ if (PyObject_CallFinalizerFromDealloc(self))
+#else
+ Py_TYPE(gen)->tp_del(self);
+ if (self->ob_refcnt > 0)
+#endif
+ {
+ return;
+ }
+ PyObject_GC_UnTrack(self);
+ }
+#ifdef __Pyx_AsyncGen_USED
+ if (__Pyx_AsyncGen_CheckExact(self)) {
+ /* We have to handle this case for asynchronous generators
+ right here, because this code has to be between UNTRACK
+ and GC_Del. */
+ Py_CLEAR(((__pyx_PyAsyncGenObject*)self)->ag_finalizer);
+ }
+#endif
+ __Pyx_Coroutine_clear(self);
+ PyObject_GC_Del(gen);
+}
+static void __Pyx_Coroutine_del(PyObject *self) {
+ PyObject *error_type, *error_value, *error_traceback;
+ __pyx_CoroutineObject *gen = (__pyx_CoroutineObject *) self;
+ __Pyx_PyThreadState_declare
+ if (gen->resume_label < 0) {
+ return;
+ }
+#if !CYTHON_USE_TP_FINALIZE
+ assert(self->ob_refcnt == 0);
+ self->ob_refcnt = 1;
+#endif
+ __Pyx_PyThreadState_assign
+ __Pyx_ErrFetch(&error_type, &error_value, &error_traceback);
+#ifdef __Pyx_AsyncGen_USED
+ if (__Pyx_AsyncGen_CheckExact(self)) {
+ __pyx_PyAsyncGenObject *agen = (__pyx_PyAsyncGenObject*)self;
+ PyObject *finalizer = agen->ag_finalizer;
+ if (finalizer && !agen->ag_closed) {
+ PyObject *res = __Pyx_PyObject_CallOneArg(finalizer, self);
+ if (unlikely(!res)) {
+ PyErr_WriteUnraisable(self);
+ } else {
+ Py_DECREF(res);
+ }
+ __Pyx_ErrRestore(error_type, error_value, error_traceback);
+ return;
+ }
+ }
+#endif
+ if (unlikely(gen->resume_label == 0 && !error_value)) {
+#ifdef __Pyx_Coroutine_USED
+#ifdef __Pyx_Generator_USED
+ if (!__Pyx_Generator_CheckExact(self))
+#endif
+ {
+ PyObject_GC_UnTrack(self);
+#if PY_MAJOR_VERSION >= 3 || defined(PyErr_WarnFormat)
+ if (unlikely(PyErr_WarnFormat(PyExc_RuntimeWarning, 1, "coroutine '%.50S' was never awaited", gen->gi_qualname) < 0))
+ PyErr_WriteUnraisable(self);
+#else
+ {PyObject *msg;
+ char *cmsg;
+ #if CYTHON_COMPILING_IN_PYPY
+ msg = NULL;
+ cmsg = (char*) "coroutine was never awaited";
+ #else
+ char *cname;
+ PyObject *qualname;
+ qualname = gen->gi_qualname;
+ cname = PyString_AS_STRING(qualname);
+ msg = PyString_FromFormat("coroutine '%.50s' was never awaited", cname);
+ if (unlikely(!msg)) {
+ PyErr_Clear();
+ cmsg = (char*) "coroutine was never awaited";
+ } else {
+ cmsg = PyString_AS_STRING(msg);
+ }
+ #endif
+ if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, cmsg, 1) < 0))
+ PyErr_WriteUnraisable(self);
+ Py_XDECREF(msg);}
+#endif
+ PyObject_GC_Track(self);
+ }
+#endif
+ } else {
+ PyObject *res = __Pyx_Coroutine_Close(self);
+ if (unlikely(!res)) {
+ if (PyErr_Occurred())
+ PyErr_WriteUnraisable(self);
+ } else {
+ Py_DECREF(res);
+ }
+ }
+ __Pyx_ErrRestore(error_type, error_value, error_traceback);
+#if !CYTHON_USE_TP_FINALIZE
+ assert(self->ob_refcnt > 0);
+ if (--self->ob_refcnt == 0) {
+ return;
+ }
+ {
+ Py_ssize_t refcnt = self->ob_refcnt;
+ _Py_NewReference(self);
+ self->ob_refcnt = refcnt;
+ }
+#if CYTHON_COMPILING_IN_CPYTHON
+ assert(PyType_IS_GC(self->ob_type) &&
+ _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED);
+ _Py_DEC_REFTOTAL;
+#endif
+#ifdef COUNT_ALLOCS
+ --Py_TYPE(self)->tp_frees;
+ --Py_TYPE(self)->tp_allocs;
+#endif
+#endif
+}
+static PyObject *
+__Pyx_Coroutine_get_name(__pyx_CoroutineObject *self)
+{
+ PyObject *name = self->gi_name;
+ if (unlikely(!name)) name = Py_None;
+ Py_INCREF(name);
+ return name;
+}
+static int
+__Pyx_Coroutine_set_name(__pyx_CoroutineObject *self, PyObject *value)
+{
+ PyObject *tmp;
+#if PY_MAJOR_VERSION >= 3
+ if (unlikely(value == NULL || !PyUnicode_Check(value))) {
+#else
+ if (unlikely(value == NULL || !PyString_Check(value))) {
+#endif
+ PyErr_SetString(PyExc_TypeError,
+ "__name__ must be set to a string object");
+ return -1;
+ }
+ tmp = self->gi_name;
+ Py_INCREF(value);
+ self->gi_name = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static PyObject *
+__Pyx_Coroutine_get_qualname(__pyx_CoroutineObject *self)
+{
+ PyObject *name = self->gi_qualname;
+ if (unlikely(!name)) name = Py_None;
+ Py_INCREF(name);
+ return name;
+}
+static int
+__Pyx_Coroutine_set_qualname(__pyx_CoroutineObject *self, PyObject *value)
+{
+ PyObject *tmp;
+#if PY_MAJOR_VERSION >= 3
+ if (unlikely(value == NULL || !PyUnicode_Check(value))) {
+#else
+ if (unlikely(value == NULL || !PyString_Check(value))) {
+#endif
+ PyErr_SetString(PyExc_TypeError,
+ "__qualname__ must be set to a string object");
+ return -1;
+ }
+ tmp = self->gi_qualname;
+ Py_INCREF(value);
+ self->gi_qualname = value;
+ Py_XDECREF(tmp);
+ return 0;
+}
+static __pyx_CoroutineObject *__Pyx__Coroutine_New(
+ PyTypeObject* type, __pyx_coroutine_body_t body, PyObject *closure,
+ PyObject *name, PyObject *qualname, PyObject *module_name) {
+ __pyx_CoroutineObject *gen = PyObject_GC_New(__pyx_CoroutineObject, type);
+ if (unlikely(!gen))
+ return NULL;
+ return __Pyx__Coroutine_NewInit(gen, body, closure, name, qualname, module_name);
+}
+static __pyx_CoroutineObject *__Pyx__Coroutine_NewInit(
+ __pyx_CoroutineObject *gen, __pyx_coroutine_body_t body, PyObject *closure,
+ PyObject *name, PyObject *qualname, PyObject *module_name) {
+ gen->body = body;
+ gen->closure = closure;
+ Py_XINCREF(closure);
+ gen->is_running = 0;
+ gen->resume_label = 0;
+ gen->classobj = NULL;
+ gen->yieldfrom = NULL;
+ gen->exc_type = NULL;
+ gen->exc_value = NULL;
+ gen->exc_traceback = NULL;
+ gen->gi_weakreflist = NULL;
+ Py_XINCREF(qualname);
+ gen->gi_qualname = qualname;
+ Py_XINCREF(name);
+ gen->gi_name = name;
+ Py_XINCREF(module_name);
+ gen->gi_modulename = module_name;
+ PyObject_GC_Track(gen);
+ return gen;
+}
+
+/* PatchModuleWithCoroutine */
+ static PyObject* __Pyx_Coroutine_patch_module(PyObject* module, const char* py_code) {
+#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
+ int result;
+ PyObject *globals, *result_obj;
+ globals = PyDict_New(); if (unlikely(!globals)) goto ignore;
+ result = PyDict_SetItemString(globals, "_cython_coroutine_type",
+ #ifdef __Pyx_Coroutine_USED
+ (PyObject*)__pyx_CoroutineType);
+ #else
+ Py_None);
+ #endif
+ if (unlikely(result < 0)) goto ignore;
+ result = PyDict_SetItemString(globals, "_cython_generator_type",
+ #ifdef __Pyx_Generator_USED
+ (PyObject*)__pyx_GeneratorType);
+ #else
+ Py_None);
+ #endif
+ if (unlikely(result < 0)) goto ignore;
+ if (unlikely(PyDict_SetItemString(globals, "_module", module) < 0)) goto ignore;
+ if (unlikely(PyDict_SetItemString(globals, "__builtins__", __pyx_b) < 0)) goto ignore;
+ result_obj = PyRun_String(py_code, Py_file_input, globals, globals);
+ if (unlikely(!result_obj)) goto ignore;
+ Py_DECREF(result_obj);
+ Py_DECREF(globals);
+ return module;
+ignore:
+ Py_XDECREF(globals);
+ PyErr_WriteUnraisable(module);
+ if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning, "Cython module failed to patch module with custom type", 1) < 0)) {
+ Py_DECREF(module);
+ module = NULL;
+ }
+#else
+ py_code++;
+#endif
+ return module;
+}
+
+/* PatchGeneratorABC */
+ #ifndef CYTHON_REGISTER_ABCS
+#define CYTHON_REGISTER_ABCS 1
+#endif
+#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
+static PyObject* __Pyx_patch_abc_module(PyObject *module);
+static PyObject* __Pyx_patch_abc_module(PyObject *module) {
+ module = __Pyx_Coroutine_patch_module(
+ module, ""
+"if _cython_generator_type is not None:\n"
+" try: Generator = _module.Generator\n"
+" except AttributeError: pass\n"
+" else: Generator.register(_cython_generator_type)\n"
+"if _cython_coroutine_type is not None:\n"
+" try: Coroutine = _module.Coroutine\n"
+" except AttributeError: pass\n"
+" else: Coroutine.register(_cython_coroutine_type)\n"
+ );
+ return module;
+}
+#endif
+static int __Pyx_patch_abc(void) {
+#if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED)
+ static int abc_patched = 0;
+ if (CYTHON_REGISTER_ABCS && !abc_patched) {
+ PyObject *module;
+ module = PyImport_ImportModule((PY_MAJOR_VERSION >= 3) ? "collections.abc" : "collections");
+ if (!module) {
+ PyErr_WriteUnraisable(NULL);
+ if (unlikely(PyErr_WarnEx(PyExc_RuntimeWarning,
+ ((PY_MAJOR_VERSION >= 3) ?
+ "Cython module failed to register with collections.abc module" :
+ "Cython module failed to register with collections module"), 1) < 0)) {
+ return -1;
+ }
+ } else {
+ module = __Pyx_patch_abc_module(module);
+ abc_patched = 1;
+ if (unlikely(!module))
+ return -1;
+ Py_DECREF(module);
+ }
+ module = PyImport_ImportModule("backports_abc");
+ if (module) {
+ module = __Pyx_patch_abc_module(module);
+ Py_XDECREF(module);
+ }
+ if (!module) {
+ PyErr_Clear();
+ }
+ }
+#else
+ if ((0)) __Pyx_Coroutine_patch_module(NULL, NULL);
+#endif
+ return 0;
+}
+
+/* Generator */
+ static PyMethodDef __pyx_Generator_methods[] = {
+ {"send", (PyCFunction) __Pyx_Coroutine_Send, METH_O,
+ (char*) PyDoc_STR("send(arg) -> send 'arg' into generator,\nreturn next yielded value or raise StopIteration.")},
+ {"throw", (PyCFunction) __Pyx_Coroutine_Throw, METH_VARARGS,
+ (char*) PyDoc_STR("throw(typ[,val[,tb]]) -> raise exception in generator,\nreturn next yielded value or raise StopIteration.")},
+ {"close", (PyCFunction) __Pyx_Coroutine_Close, METH_NOARGS,
+ (char*) PyDoc_STR("close() -> raise GeneratorExit inside generator.")},
+ {0, 0, 0, 0}
+};
+static PyMemberDef __pyx_Generator_memberlist[] = {
+ {(char *) "gi_running", T_BOOL, offsetof(__pyx_CoroutineObject, is_running), READONLY, NULL},
+ {(char*) "gi_yieldfrom", T_OBJECT, offsetof(__pyx_CoroutineObject, yieldfrom), READONLY,
+ (char*) PyDoc_STR("object being iterated by 'yield from', or None")},
+ {0, 0, 0, 0, 0}
+};
+static PyGetSetDef __pyx_Generator_getsets[] = {
+ {(char *) "__name__", (getter)__Pyx_Coroutine_get_name, (setter)__Pyx_Coroutine_set_name,
+ (char*) PyDoc_STR("name of the generator"), 0},
+ {(char *) "__qualname__", (getter)__Pyx_Coroutine_get_qualname, (setter)__Pyx_Coroutine_set_qualname,
+ (char*) PyDoc_STR("qualified name of the generator"), 0},
+ {0, 0, 0, 0, 0}
+};
+static PyTypeObject __pyx_GeneratorType_type = {
+ PyVarObject_HEAD_INIT(0, 0)
+ "generator",
+ sizeof(__pyx_CoroutineObject),
+ 0,
+ (destructor) __Pyx_Coroutine_dealloc,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
+ 0,
+ (traverseproc) __Pyx_Coroutine_traverse,
+ 0,
+ 0,
+ offsetof(__pyx_CoroutineObject, gi_weakreflist),
+ 0,
+ (iternextfunc) __Pyx_Generator_Next,
+ __pyx_Generator_methods,
+ __pyx_Generator_memberlist,
+ __pyx_Generator_getsets,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+ 0,
+#if CYTHON_USE_TP_FINALIZE
+ 0,
+#else
+ __Pyx_Coroutine_del,
+#endif
+ 0,
+#if CYTHON_USE_TP_FINALIZE
+ __Pyx_Coroutine_del,
+#elif PY_VERSION_HEX >= 0x030400a1
+ 0,
+#endif
+};
+static int __pyx_Generator_init(void) {
+ __pyx_GeneratorType_type.tp_getattro = PyObject_GenericGetAttr;
+ __pyx_GeneratorType_type.tp_iter = PyObject_SelfIter;
+ __pyx_GeneratorType = __Pyx_FetchCommonType(&__pyx_GeneratorType_type);
+ if (unlikely(!__pyx_GeneratorType)) {
+ return -1;
+ }
+ return 0;
+}
+
+/* CheckBinaryVersion */
+ static int __Pyx_check_binary_version(void) {
+ char ctversion[4], rtversion[4];
+ PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
+ PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
+ if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
+ char message[200];
+ PyOS_snprintf(message, sizeof(message),
+ "compiletime version %s of module '%.100s' "
+ "does not match runtime version %s",
+ ctversion, __Pyx_MODULE_NAME, rtversion);
+ return PyErr_WarnEx(NULL, message, 1);
+ }
+ return 0;
+}
+
+/* InitStrings */
+ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+ while (t->p) {
+ #if PY_MAJOR_VERSION < 3
+ if (t->is_unicode) {
+ *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
+ } else if (t->intern) {
+ *t->p = PyString_InternFromString(t->s);
+ } else {
+ *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+ }
+ #else
+ if (t->is_unicode | t->is_str) {
+ if (t->intern) {
+ *t->p = PyUnicode_InternFromString(t->s);
+ } else if (t->encoding) {
+ *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
+ } else {
+ *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+ }
+ } else {
+ *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
+ }
+ #endif
+ if (!*t->p)
+ return -1;
+ if (PyObject_Hash(*t->p) == -1)
+ PyErr_Clear();
+ ++t;
+ }
+ return 0;
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
+ return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
+}
+static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
+ Py_ssize_t ignore;
+ return __Pyx_PyObject_AsStringAndSize(o, &ignore);
+}
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+#if !CYTHON_PEP393_ENABLED
+static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+ char* defenc_c;
+ PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
+ if (!defenc) return NULL;
+ defenc_c = PyBytes_AS_STRING(defenc);
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+ {
+ char* end = defenc_c + PyBytes_GET_SIZE(defenc);
+ char* c;
+ for (c = defenc_c; c < end; c++) {
+ if ((unsigned char) (*c) >= 128) {
+ PyUnicode_AsASCIIString(o);
+ return NULL;
+ }
+ }
+ }
+#endif
+ *length = PyBytes_GET_SIZE(defenc);
+ return defenc_c;
+}
+#else
+static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+ if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+ if (likely(PyUnicode_IS_ASCII(o))) {
+ *length = PyUnicode_GET_LENGTH(o);
+ return PyUnicode_AsUTF8(o);
+ } else {
+ PyUnicode_AsASCIIString(o);
+ return NULL;
+ }
+#else
+ return PyUnicode_AsUTF8AndSize(o, length);
+#endif
+}
+#endif
+#endif
+static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+ if (
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+ __Pyx_sys_getdefaultencoding_not_ascii &&
+#endif
+ PyUnicode_Check(o)) {
+ return __Pyx_PyUnicode_AsStringAndSize(o, length);
+ } else
+#endif
+#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
+ if (PyByteArray_Check(o)) {
+ *length = PyByteArray_GET_SIZE(o);
+ return PyByteArray_AS_STRING(o);
+ } else
+#endif
+ {
+ char* result;
+ int r = PyBytes_AsStringAndSize(o, &result, length);
+ if (unlikely(r < 0)) {
+ return NULL;
+ } else {
+ return result;
+ }
+ }
+}
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+ int is_true = x == Py_True;
+ if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
+ else return PyObject_IsTrue(x);
+}
+static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
+#if PY_MAJOR_VERSION >= 3
+ if (PyLong_Check(result)) {
+ if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
+ "__int__ returned non-int (type %.200s). "
+ "The ability to return an instance of a strict subclass of int "
+ "is deprecated, and may be removed in a future version of Python.",
+ Py_TYPE(result)->tp_name)) {
+ Py_DECREF(result);
+ return NULL;
+ }
+ return result;
+ }
+#endif
+ PyErr_Format(PyExc_TypeError,
+ "__%.4s__ returned non-%.4s (type %.200s)",
+ type_name, type_name, Py_TYPE(result)->tp_name);
+ Py_DECREF(result);
+ return NULL;
+}
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
+#if CYTHON_USE_TYPE_SLOTS
+ PyNumberMethods *m;
+#endif
+ const char *name = NULL;
+ PyObject *res = NULL;
+#if PY_MAJOR_VERSION < 3
+ if (likely(PyInt_Check(x) || PyLong_Check(x)))
+#else
+ if (likely(PyLong_Check(x)))
+#endif
+ return __Pyx_NewRef(x);
+#if CYTHON_USE_TYPE_SLOTS
+ m = Py_TYPE(x)->tp_as_number;
+ #if PY_MAJOR_VERSION < 3
+ if (m && m->nb_int) {
+ name = "int";
+ res = m->nb_int(x);
+ }
+ else if (m && m->nb_long) {
+ name = "long";
+ res = m->nb_long(x);
+ }
+ #else
+ if (likely(m && m->nb_int)) {
+ name = "int";
+ res = m->nb_int(x);
+ }
+ #endif
+#else
+ if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
+ res = PyNumber_Int(x);
+ }
+#endif
+ if (likely(res)) {
+#if PY_MAJOR_VERSION < 3
+ if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
+#else
+ if (unlikely(!PyLong_CheckExact(res))) {
+#endif
+ return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
+ }
+ }
+ else if (!PyErr_Occurred()) {
+ PyErr_SetString(PyExc_TypeError,
+ "an integer is required");
+ }
+ return res;
+}
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
+ Py_ssize_t ival;
+ PyObject *x;
+#if PY_MAJOR_VERSION < 3
+ if (likely(PyInt_CheckExact(b))) {
+ if (sizeof(Py_ssize_t) >= sizeof(long))
+ return PyInt_AS_LONG(b);
+ else
+ return PyInt_AsSsize_t(x);
+ }
+#endif
+ if (likely(PyLong_CheckExact(b))) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+ const digit* digits = ((PyLongObject*)b)->ob_digit;
+ const Py_ssize_t size = Py_SIZE(b);
+ if (likely(__Pyx_sst_abs(size) <= 1)) {
+ ival = likely(size) ? digits[0] : 0;
+ if (size == -1) ival = -ival;
+ return ival;
+ } else {
+ switch (size) {
+ case 2:
+ if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
+ return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+ }
+ break;
+ case -2:
+ if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
+ return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+ }
+ break;
+ case 3:
+ if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
+ return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+ }
+ break;
+ case -3:
+ if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
+ return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+ }
+ break;
+ case 4:
+ if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
+ return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+ }
+ break;
+ case -4:
+ if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
+ return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+ }
+ break;
+ }
+ }
+ #endif
+ return PyLong_AsSsize_t(b);
+ }
+ x = PyNumber_Index(b);
+ if (!x) return -1;
+ ival = PyInt_AsSsize_t(x);
+ Py_DECREF(x);
+ return ival;
+}
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
+ return PyInt_FromSize_t(ival);
+}
+
+
+#endif /* Py_PYTHON_H */
diff --git a/pyomo/repn/standard_repn.py b/pyomo/repn/standard_repn.py
new file mode 100644
index 00000000000..ad4624de27d
--- /dev/null
+++ b/pyomo/repn/standard_repn.py
@@ -0,0 +1,1455 @@
+# ___________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# rights in this software.
+# This software is distributed under the 3-clause BSD License.
+# ___________________________________________________________________________
+
+from __future__ import division
+
+__all__ = ['StandardRepn', 'generate_standard_repn']
+
+
+import sys
+import logging
+import math
+import itertools
+
+from pyomo.core.base import (Constraint,
+ Objective,
+ ComponentMap)
+
+import pyomo.util
+from pyutilib.misc import Bunch
+from pyutilib.math.util import isclose as isclose_default
+
+from pyomo.core.expr import current as EXPR
+from pyomo.core.base.objective import (_GeneralObjectiveData,
+ SimpleObjective)
+from pyomo.core.base import _ExpressionData, Expression
+from pyomo.core.base.expression import SimpleExpression, _GeneralExpressionData
+from pyomo.core.base.var import (SimpleVar,
+ Var,
+ _GeneralVarData,
+ _VarData,
+ value)
+from pyomo.core.base.param import _ParamData
+from pyomo.core.base.numvalue import (NumericConstant,
+ native_numeric_types,
+ is_fixed)
+from pyomo.core.kernel.component_expression import IIdentityExpression, expression, noclone
+from pyomo.core.kernel.component_variable import IVariable
+from pyomo.core.kernel.component_objective import objective
+
+import six
+from six import iteritems
+from six import itervalues, iteritems, StringIO
+from six.moves import xrange, zip
+try:
+ basestring
+except:
+ basestring = str
+
+logger = logging.getLogger('pyomo.core')
+
+using_py3 = six.PY3
+
+from pyomo.core.base import _VarData, _GeneralVarData, SimpleVar
+from pyomo.core.kernel.component_variable import IVariable, variable
+
+
+#
+# This checks if the first argument is a numeric value. If not
+# then this is a Pyomo constant expression, and we can only check if its
+# close to 'b' when it is constant.
+#
+def isclose_const(a, b, rel_tol=1e-9, abs_tol=0.0):
+ if not a.__class__ in native_numeric_types:
+ if a.is_constant():
+ a = value(a)
+ else:
+ return False
+ # Copied from pyutilib.math.isclose
+ return abs(a-b) <= max( rel_tol * max(abs(a), abs(b)), abs_tol )
+
+#
+# The global isclose() function used below. This is either isclose_default
+# (defined in pyutilib) or isclose_const
+#
+isclose = isclose_default
+
+
+class StandardRepn(object):
+ """
+ This class defines a standard/common representation for Pyomo expressions
+ that provides an efficient interface for writing all models.
+
+ TODO: define what "efficient" means to us.
+ """
+
+ __slots__ = ('constant', # The constant term
+ 'linear_coefs', # Linear coefficients
+ 'linear_vars', # Linear variables
+ 'quadratic_coefs', # Quadratic coefficients
+ 'quadratic_vars', # Quadratic variables
+ 'nonlinear_expr', # Nonlinear expression
+ 'nonlinear_vars') # Variables that appear in the nonlinear expression
+
+ def __init__(self):
+ self.constant = 0
+ self.linear_vars = tuple()
+ self.linear_coefs = tuple()
+ self.quadratic_vars = tuple()
+ self.quadratic_coefs = tuple()
+ self.nonlinear_expr = None
+ self.nonlinear_vars = tuple()
+
+ def __getstate__(self):
+ """
+ This method is required because this class uses slots.
+ """
+ return (self.constant,
+ self.linear_coefs,
+ self.linear_vars,
+ self.quadratic_coefs,
+ self.quadratic_vars,
+ self.nonlinear_expr,
+ self.nonlinear_vars)
+
+ def __setstate__(self, state):
+ """
+ This method is required because this class uses slots.
+ """
+ self.constant, \
+ self.linear_coefs, \
+ self.linear_vars, \
+ self.quadratic_coefs, \
+ self.quadratic_vars, \
+ self.nonlinear_expr, \
+ self.nonlinear_vars = state
+
+ #
+ # Generate a string representation of the expression
+ #
+ def __str__(self): #pragma: nocover
+ output = StringIO()
+ output.write("\n")
+ output.write("constant: "+str(self.constant)+"\n")
+ output.write("linear vars: "+str([v_.name for v_ in self.linear_vars])+"\n")
+ output.write("linear var ids: "+str([id(v_) for v_ in self.linear_vars])+"\n")
+ output.write("linear coef: "+str(list(self.linear_coefs))+"\n")
+ output.write("quadratic vars: "+str([(v_[0].name,v_[1].name) for v_ in self.quadratic_vars])+"\n")
+ output.write("quadratic var ids: "+str([(id(v_[0]), id(v_[1])) for v_ in self.quadratic_vars])+"\n")
+ output.write("quadratic coef: "+str(list(self.quadratic_coefs))+"\n")
+ if self.nonlinear_expr is None:
+ output.write("nonlinear expr: None\n")
+ else:
+ output.write("nonlinear expr:\n")
+ try:
+ output.write(self.nonlinear_expr.to_string())
+ output.write("\n")
+ except AttributeError:
+ output.write(str(self.nonlinear_expr))
+ output.write("\n")
+ output.write("nonlinear vars: "+str([v_.name for v_ in self.nonlinear_vars])+"\n")
+ output.write("\n")
+
+ ret_str = output.getvalue()
+ output.close()
+ return ret_str
+
+ def is_fixed(self):
+ if len(self.linear_vars) == 0 and len(self.nonlinear_vars) == 0 and len(self.quadratic_vars) == 0:
+ return True
+ return False
+
+ def polynomial_degree(self):
+ if not self.nonlinear_expr is None:
+ return None
+ if len(self.quadratic_coefs) > 0:
+ return 2
+ if len(self.linear_coefs) > 0:
+ return 1
+ return 0
+
+ def is_constant(self):
+ return self.nonlinear_expr is None and len(self.quadratic_coefs) == 0 and len(self.linear_coefs) == 0
+
+ def is_linear(self):
+ return self.nonlinear_expr is None and len(self.quadratic_coefs) == 0
+
+ def is_quadratic(self):
+ return len(self.quadratic_coefs) > 0 and self.nonlinear_expr is None
+
+ def is_nonlinear(self):
+ return not (self.nonlinear_expr is None and len(self.quadratic_coefs) == 0)
+
+ def to_expression(self, sort=True):
+ #
+ # When an standard representation is created, the ordering of the
+ # linear and quadratic terms may not be preserved. Hence, the
+ # sorting option ensures that an expression generated from a
+ # standard representation has a consistent order.
+ #
+ # TODO: Should this replace non-mutable parameters with constants?
+ #
+ expr = self.constant
+
+ lvars = [(i,v) for i,v in enumerate(self.linear_vars)]
+ if sort:
+ lvars = sorted(lvars, key=lambda x: str(x[1]))
+ for i,v in lvars:
+ c = self.linear_coefs[i]
+ if c.__class__ in native_numeric_types:
+ if isclose_const(c, 1.0):
+ expr += v
+ elif isclose_const(c, -1.0):
+ expr -= v
+ elif c < 0.0:
+ expr -= - c*v
+ else:
+ expr += c*v
+ else:
+ expr += c*v
+
+ qvars = [(i,v) for i,v in enumerate(self.quadratic_vars)]
+ if sort:
+ qvars = sorted(qvars, key=lambda x: (str(x[1][0]), str(x[1][1])))
+ for i,v in qvars:
+ if id(v[0]) == id(v[1]):
+ term = v[0]**2
+ else:
+ term = v[0]*v[1]
+ c = self.quadratic_coefs[i]
+ if c.__class__ in native_numeric_types:
+ if isclose_const(c, 1.0):
+ expr += term
+ elif isclose_const(c, -1.0):
+ expr -= term
+ else:
+ expr += c*term
+ else:
+ expr += c*term
+
+ if not self.nonlinear_expr is None:
+ expr += self.nonlinear_expr
+ return expr
+
+
+"""
+
+Note: This function separates linear terms from nonlinear terms.
+Along the way, fixed variable and mutable parameter values *may* be
+replaced with constants. However, that is not guaranteed. Thus,
+the nonlinear expression may contain subexpressions whose value is
+constant. This was done to avoid additional work when a subexpression
+is clearly nonlinear. However, this requires that standard
+representations be temporary. They should be used to interface
+to a solver and then be deleted.
+
+"""
+#@profile
+def generate_standard_repn(expr, idMap=None, compute_values=True, verbose=False, quadratic=True, repn=None):
+ #
+ # Use a custom Results object
+ #
+ global Results
+ if quadratic:
+ Results = ResultsWithQuadratics
+ else:
+ Results = ResultsWithoutQuadratics
+ #
+ # Use a custom isclose function
+ #
+ global isclose
+ if compute_values:
+ isclose = isclose_default
+ else:
+ isclose = isclose_const
+
+ if True:
+ #
+ # Setup
+ #
+ if idMap is None:
+ idMap = {}
+ idMap.setdefault(None, {})
+ if repn is None:
+ repn = StandardRepn()
+ #
+ # The expression is a number or a non-variable constant
+ # expression.
+ #
+ if expr.__class__ in native_numeric_types or not expr.is_potentially_variable():
+ if compute_values:
+ repn.constant = EXPR.evaluate_expression(expr)
+ else:
+ repn.constant = expr
+ return repn
+ #
+ # The expression is a variable
+ #
+ elif expr.is_variable_type():
+ if expr.fixed:
+ if compute_values:
+ repn.constant = value(expr)
+ else:
+ repn.constant = expr
+ return repn
+ repn.linear_coefs = (1,)
+ repn.linear_vars = (expr,)
+ return repn
+ #
+ # The expression is linear
+ #
+ elif expr.__class__ is EXPR.LinearExpression:
+ if compute_values:
+ C_ = EXPR.evaluate_expression(expr.constant)
+ else:
+ C_ = expr.constant
+ if compute_values:
+ linear_coefs = {}
+ for c,v in zip(expr.linear_coefs, expr.linear_vars):
+ if c.__class__ in native_numeric_types:
+ cval = c
+ elif c.is_expression_type():
+ cval = EXPR.evaluate_expression(c)
+ else:
+ cval = value(c)
+ if v.fixed:
+ C_ += cval * v.value
+ else:
+ id_ = id(v)
+ if not id_ in idMap[None]:
+ key = len(idMap) - 1
+ idMap[None][id_] = key
+ idMap[key] = v
+ else:
+ key = idMap[None][id_]
+ if key in linear_coefs:
+ linear_coefs[key] += cval
+ else:
+ linear_coefs[key] = cval
+ keys = list(linear_coefs.keys())
+ repn.linear_vars = tuple(idMap[key] for key in keys)
+ repn.linear_coefs = tuple(linear_coefs[key] for key in keys)
+ else:
+ linear_coefs = {}
+ for c,v in zip(expr.linear_coefs, expr.linear_vars):
+ if v.fixed:
+ C_ += c*v
+ else:
+ id_ = id(v)
+ if not id_ in idMap[None]:
+ key = len(idMap) - 1
+ idMap[None][id_] = key
+ idMap[key] = v
+ else:
+ key = idMap[None][id_]
+ if key in linear_coefs:
+ linear_coefs[key] += c
+ else:
+ linear_coefs[key] = c
+ keys = list(linear_coefs.keys())
+ repn.linear_vars = tuple(idMap[key] for key in keys)
+ repn.linear_coefs = tuple(linear_coefs[key] for key in keys)
+ repn.constant = C_
+ return repn
+
+ #
+ # Unknown expression object
+ #
+ elif not expr.is_expression_type(): #pragma: nocover
+ raise ValueError("Unexpected expression type: "+str(expr))
+
+ #
+ # WEH - Checking the polynomial degree didn't
+ # turn out to be a win. But I'm leaving this
+ # in as a comment for now, since we're not
+ # done tuning this code.
+ #
+ #degree = expr.polynomial_degree()
+ #if degree == 1:
+ # return _generate_linear_standard_repn(expr,
+ # idMap=idMap,
+ # compute_values=compute_values,
+ # verbose=verbose,
+ # repn=repn)
+ #else:
+ return _generate_standard_repn(expr,
+ idMap=idMap,
+ compute_values=compute_values,
+ verbose=verbose,
+ quadratic=quadratic,
+ repn=repn)
+
+##-----------------------------------------------------------------------
+##
+## Logic for _generate_standard_repn
+##
+##-----------------------------------------------------------------------
+
+class ResultsWithQuadratics(object):
+ __slot__ = ('const', 'nonl', 'linear', 'quadratic')
+
+ def __init__(self, constant=0, nonl=0, linear=None, quadratic=None):
+ self.constant = constant
+ self.nonl = nonl
+ self.linear = {}
+ #if linear is None:
+ # self.linear = {}
+ #else:
+ # self.linear = linear
+ self.quadratic = {}
+ #if quadratic is None:
+ # self.quadratic = {}
+ #else:
+ # self.quadratic = quadratic
+
+ def __str__(self): #pragma: nocover
+ return "Const:\t%f\nLinear:\t%s\nQuadratic:\t%s\nNonlinear:\t%s" % (self.constant, str(self.linear), str(self.quadratic), str(self.nonl))
+
+class ResultsWithoutQuadratics(object):
+ __slot__ = ('const', 'nonl', 'linear')
+
+ def __init__(self, constant=0, nonl=0, linear=None):
+ self.constant = constant
+ self.nonl = nonl
+ self.linear = {}
+ #if linear is None:
+ # self.linear = {}
+ #else:
+ # self.linear = linear
+
+ def __str__(self): #pragma: nocover
+ return "Const:\t%f\nLinear:\t%s\nNonlinear:\t%s" % (self.constant, str(self.linear), str(self.nonl))
+
+Results = ResultsWithQuadratics
+
+
+#@profile
+def _collect_sum(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ ans = Results()
+ nonl = []
+ varkeys = idMap[None]
+
+ for e_ in itertools.islice(exp._args_, exp.nargs()):
+ if e_.__class__ is EXPR.MonomialTermExpression:
+ lhs, v = e_._args_
+ if compute_values and not lhs.__class__ in native_numeric_types:
+ lhs = value(lhs)
+ if v.fixed:
+ if compute_values:
+ ans.constant += multiplier*lhs*value(v)
+ else:
+ ans.constant += multiplier*lhs*v
+ else:
+ id_ = id(v)
+ if id_ in varkeys:
+ key = varkeys[id_]
+ else:
+ key = len(idMap) - 1
+ varkeys[id_] = key
+ idMap[key] = v
+ if key in ans.linear:
+ ans.linear[key] += multiplier*lhs
+ else:
+ ans.linear[key] = multiplier*lhs
+ elif e_.__class__ in native_numeric_types:
+ ans.constant += multiplier*e_
+ elif e_.is_variable_type():
+ if e_.fixed:
+ if compute_values:
+ ans.constant += multiplier*e_.value
+ else:
+ ans.constant += multiplier*e_
+ else:
+ id_ = id(e_)
+ if id_ in varkeys:
+ key = varkeys[id_]
+ else:
+ key = len(idMap) - 1
+ varkeys[id_] = key
+ idMap[key] = e_
+ if key in ans.linear:
+ ans.linear[key] += multiplier
+ else:
+ ans.linear[key] = multiplier
+ elif not e_.is_potentially_variable():
+ if compute_values:
+ ans.constant += multiplier * value(e_)
+ else:
+ ans.constant += multiplier * e_
+ else:
+ res_ = _collect_standard_repn(e_, multiplier, idMap,
+ compute_values, verbose, quadratic)
+ #
+ # Add returned from recursion
+ #
+ ans.constant += res_.constant
+ if not (res_.nonl is 0 or res_.nonl.__class__ in native_numeric_types and res_.nonl == 0):
+ nonl.append(res_.nonl)
+ for i in res_.linear:
+ ans.linear[i] = ans.linear.get(i,0) + res_.linear[i]
+ if quadratic:
+ for i in res_.quadratic:
+ ans.quadratic[i] = ans.quadratic.get(i, 0) + res_.quadratic[i]
+
+ if len(nonl) > 0:
+ if len(nonl) == 1:
+ ans.nonl = nonl[0]
+ else:
+ ans.nonl = EXPR.SumExpression(nonl)
+ return ans
+
+#@profile
+def _collect_term(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ #
+ # LHS is a numeric value
+ #
+ if exp._args_[0].__class__ in native_numeric_types:
+ if exp._args_[0] == 0:
+ return Results()
+ return _collect_standard_repn(exp._args_[1], multiplier * exp._args_[0], idMap,
+ compute_values, verbose, quadratic)
+ #
+ # LHS is a non-variable expression
+ #
+ else:
+ if compute_values:
+ val = value(exp._args_[0])
+ if val == 0:
+ return Results()
+ return _collect_standard_repn(exp._args_[1], multiplier * val, idMap,
+ compute_values, verbose, quadratic)
+ else:
+ return _collect_standard_repn(exp._args_[1], multiplier*exp._args_[0], idMap,
+ compute_values, verbose, quadratic)
+
+def _collect_prod(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ #
+ # LHS is a numeric value
+ #
+ if exp._args_[0].__class__ in native_numeric_types:
+ if exp._args_[0] == 0:
+ return Results()
+ return _collect_standard_repn(exp._args_[1], multiplier * exp._args_[0], idMap,
+ compute_values, verbose, quadratic)
+ #
+ # RHS is a numeric value
+ #
+ if exp._args_[1].__class__ in native_numeric_types:
+ if exp._args_[1] == 0:
+ return Results()
+ return _collect_standard_repn(exp._args_[0], multiplier * exp._args_[1], idMap,
+ compute_values, verbose, quadratic)
+ #
+ # LHS is a non-variable expression
+ #
+ elif not exp._args_[0].is_potentially_variable():
+ if compute_values:
+ val = value(exp._args_[0])
+ if val == 0:
+ return Results()
+ return _collect_standard_repn(exp._args_[1], multiplier * val, idMap,
+ compute_values, verbose, quadratic)
+ else:
+ return _collect_standard_repn(exp._args_[1], multiplier*exp._args_[0], idMap,
+ compute_values, verbose, quadratic)
+ #
+ # RHS is a non-variable expression
+ #
+ elif not exp._args_[1].is_potentially_variable():
+ if compute_values:
+ val = value(exp._args_[1])
+ if val == 0:
+ return Results()
+ return _collect_standard_repn(exp._args_[0], multiplier * val, idMap,
+ compute_values, verbose, quadratic)
+ else:
+ return _collect_standard_repn(exp._args_[0], multiplier*exp._args_[1], idMap,
+ compute_values, verbose, quadratic)
+ #
+ # Both the LHS and RHS are potentially variable ...
+ #
+ # Collect LHS
+ #
+ lhs = _collect_standard_repn(exp._args_[0], 1, idMap,
+ compute_values, verbose, quadratic)
+ lhs_nonl_None = lhs.nonl.__class__ in native_numeric_types and lhs.nonl == 0
+ #
+ # LHS is potentially variable, but it turns out to be a constant
+ # because the variables were fixed.
+ #
+ if lhs_nonl_None and len(lhs.linear) == 0 and (not quadratic or len(lhs.quadratic) == 0):
+ if lhs.constant.__class__ in native_numeric_types and lhs.constant == 0:
+ return Results()
+ if compute_values:
+ val = value(lhs.constant)
+ if val == 0:
+ return Results()
+ return _collect_standard_repn(exp._args_[1], multiplier*val, idMap,
+ compute_values, verbose, quadratic)
+ else:
+ return _collect_standard_repn(exp._args_[1], multiplier*lhs.constant, idMap,
+ compute_values, verbose, quadratic)
+ #
+ # Collect RHS
+ #
+ if exp._args_[1].__class__ in native_numeric_types:
+ rhs = Results(constant=exp._args_[1])
+ elif not exp._args_[1].is_potentially_variable():
+ if compute_values:
+ rhs = Results(constant=value(exp._args_[1]))
+ else:
+ rhs = Results(constant=exp._args_[1])
+ else:
+ rhs = _collect_standard_repn(exp._args_[1], 1, idMap,
+ compute_values, verbose, quadratic)
+ rhs_nonl_None = rhs.nonl.__class__ in native_numeric_types and rhs.nonl == 0
+ #
+ # If RHS is zero, then return an empty results
+ #
+ if rhs_nonl_None and len(rhs.linear) == 0 and (not quadratic or len(rhs.quadratic) == 0) and rhs.constant.__class__ in native_numeric_types and rhs.constant == 0:
+ return Results()
+ #
+ # If either the LHS or RHS are nonlinear, then simply return the nonlinear expression
+ #
+ if not lhs_nonl_None or not rhs_nonl_None:
+ return Results(nonl=multiplier*exp)
+ #
+ # If not collecting quadratic terms and both terms are linear, then simply return the nonlinear expression
+ #
+ if not quadratic and len(lhs.linear) > 0 and len(rhs.linear) > 0:
+ # NOTE: We treat a product of linear terms as nonlinear unless quadratic==2
+ return Results(nonl=multiplier*exp)
+
+ ans = Results()
+ ans.constant = multiplier*lhs.constant * rhs.constant
+ if not (lhs.constant.__class__ in native_numeric_types and lhs.constant == 0):
+ for key, coef in six.iteritems(rhs.linear):
+ ans.linear[key] = multiplier*coef*lhs.constant
+ if not (rhs.constant.__class__ in native_numeric_types and rhs.constant == 0):
+ for key, coef in six.iteritems(lhs.linear):
+ if key in ans.linear:
+ ans.linear[key] += multiplier*coef*rhs.constant
+ else:
+ ans.linear[key] = multiplier*coef*rhs.constant
+
+ if quadratic:
+ if not (lhs.constant.__class__ in native_numeric_types and lhs.constant == 0):
+ for key, coef in six.iteritems(rhs.quadratic):
+ ans.quadratic[key] = multiplier*coef*lhs.constant
+ if not (rhs.constant.__class__ in native_numeric_types and rhs.constant == 0):
+ for key, coef in six.iteritems(lhs.quadratic):
+ if key in ans.quadratic:
+ ans.quadratic[key] += multiplier*coef*rhs.constant
+ else:
+ ans.quadratic[key] = multiplier*coef*rhs.constant
+ for lkey, lcoef in six.iteritems(lhs.linear):
+ for rkey, rcoef in six.iteritems(rhs.linear):
+ if lkey <= rkey:
+ ans.quadratic[lkey,rkey] = multiplier*lcoef*rcoef
+ else:
+ ans.quadratic[rkey,lkey] = multiplier*lcoef*rcoef
+ # TODO - Use quicksum here?
+ el_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(lhs.linear))
+ er_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(rhs.linear))
+ el_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(lhs.quadratic))
+ er_quadratic = multiplier*sum(coef*idMap[key[0]]*idMap[key[1]] for key, coef in six.iteritems(rhs.quadratic))
+ ans.nonl += el_linear*er_quadratic + el_quadratic*er_linear
+ elif len(lhs.linear) + len(rhs.linear) > 1:
+ return Results(nonl=multiplier*exp)
+ #el_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(lhs.linear))
+ #er_linear = multiplier*sum(coef*idMap[key] for key, coef in six.iteritems(rhs.linear))
+ #ans.nonl += el_linear*er_linear
+
+ return ans
+
+#@profile
+def _collect_var(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ ans = Results()
+
+ if exp.fixed:
+ if compute_values:
+ ans.constant += multiplier*value(exp)
+ else:
+ ans.constant += multiplier*exp
+ else:
+ id_ = id(exp)
+ if id_ in idMap[None]:
+ key = idMap[None][id_]
+ else:
+ key = len(idMap) - 1
+ idMap[None][id_] = key
+ idMap[key] = exp
+ if key in ans.linear:
+ ans.linear[key] += multiplier
+ else:
+ ans.linear[key] = multiplier
+
+ return ans
+
+def _collect_pow(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ if exp._args_[1].__class__ in native_numeric_types:
+ exponent = exp._args_[1]
+ elif not exp._args_[1].is_potentially_variable():
+ if compute_values:
+ exponent = value(exp._args_[1])
+ else:
+ exponent = exp._args_[1]
+ else:
+ res = _collect_standard_repn(exp._args_[1], 1, idMap, compute_values, verbose, quadratic)
+ if not (res.nonl.__class__ in native_numeric_types and res.nonl == 0) or len(res.linear) > 0 or (quadratic and len(res.quadratic) > 0):
+ # The exponent is variable, so this is a nonlinear expression
+ return Results(nonl=multiplier*exp)
+ exponent = res.constant
+
+ if exponent.__class__ in native_numeric_types:
+ if exponent == 0:
+ return Results(constant=multiplier)
+ elif exponent == 1:
+ return _collect_standard_repn(exp._args_[0], multiplier, idMap, compute_values, verbose, quadratic)
+ # If the exponent is >= 2, then this is a nonlinear expression
+ elif exponent == 2:
+ if quadratic:
+ # NOTE: We treat a product of linear terms as nonlinear unless quadratic==2
+ res =_collect_standard_repn(exp._args_[0], 1, idMap, compute_values, verbose, quadratic)
+ if not (res.nonl.__class__ in native_numeric_types and res.nonl == 0) or len(res.quadratic) > 0:
+ return Results(nonl=multiplier*exp)
+ ans = Results()
+ if not (res.constant.__class__ in native_numeric_types and res.constant == 0):
+ ans.constant = multiplier*res.constant*res.constant
+ for key, coef in six.iteritems(res.linear):
+ ans.linear[key] = 2*multiplier*coef*res.constant
+ for key, coef in six.iteritems(res.linear):
+ ans.quadratic[key,key] = multiplier*coef
+ return ans
+ elif exp._args_[0].is_fixed():
+ if compute_values:
+ return Results(constant=multiplier*value(exp._args_[0])**2)
+ else:
+ return Results(constant=multiplier*exp)
+
+ return Results(nonl=multiplier*exp)
+
+def _collect_reciprocal(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ if exp._args_[0].__class__ in native_numeric_types or not exp._args_[0].is_potentially_variable():
+ if compute_values:
+ denom = 1.0 * value(exp._args_[0])
+ else:
+ denom = 1.0 * exp._args_[0]
+ else:
+ res =_collect_standard_repn(exp._args_[0], 1, idMap, compute_values, verbose, quadratic)
+ if not (res.nonl.__class__ in native_numeric_types and res.nonl == 0) or len(res.linear) > 0 or (quadratic and len(res.quadratic) > 0):
+ return Results(nonl=multiplier*exp)
+ else:
+ denom = 1.0*res.constant
+ if denom.__class__ in native_numeric_types and denom == 0:
+ raise ZeroDivisionError()
+ return Results(constant=multiplier/denom)
+
+def _collect_branching_expr(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ if exp._if.__class__ in native_numeric_types:
+ if_val = exp._if
+ elif not exp._if.is_potentially_variable():
+ if compute_values:
+ if_val = value(exp._if)
+ else:
+ return Results(nonl=multiplier*exp)
+ else:
+ res = _collect_standard_repn(exp._if, 1, idMap, compute_values, verbose, quadratic)
+ if not (res.nonl.__class__ in native_numeric_types and res.nonl == 0) or len(res.linear) > 0 or (quadratic and len(res.quadratic) > 0):
+ return Results(nonl=multiplier*exp)
+ else:
+ if_val = res.constant
+ if if_val:
+ return _collect_standard_repn(exp._then, multiplier, idMap, compute_values, verbose, quadratic)
+ else:
+ return _collect_standard_repn(exp._else, multiplier, idMap, compute_values, verbose, quadratic)
+
+def _collect_nonl(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ res = _collect_standard_repn(exp._args_[0], 1, idMap, compute_values, verbose, quadratic)
+ if not (res.nonl.__class__ in native_numeric_types and res.nonl == 0) or len(res.linear) > 0 or (quadratic and len(res.quadratic) > 0):
+ return Results(nonl=multiplier*exp)
+ if compute_values:
+ return Results(constant=multiplier*exp._apply_operation([res.constant]))
+ else:
+ return Results(constant=multiplier*exp)
+
+def _collect_negation(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ return _collect_standard_repn(exp._args_[0], -1*multiplier, idMap, compute_values, verbose, quadratic)
+
+def _collect_const(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ if compute_values:
+ return Results(constant=value(exp))
+ else:
+ return Results(constant=exp)
+
+def _collect_identity(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ if exp._args_[0].__class__ in native_numeric_types:
+ return Results(constant=exp._args_[0])
+ if not exp._args_[0].is_potentially_variable():
+ if compute_values:
+ return Results(constant=value(exp._args_[0]))
+ else:
+ return Results(constant=exp._args_[0])
+ return _collect_standard_repn(exp.expr, multiplier, idMap, compute_values, verbose, quadratic)
+
+def _collect_linear(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ ans = Results()
+ if compute_values:
+ ans.constant = multiplier*value(exp.constant)
+ else:
+ ans.constant = multiplier*exp.constant
+
+ for c,v in zip(exp.linear_coefs, exp.linear_vars):
+ if v.fixed:
+ if compute_values:
+ ans.constant += multiplier*v.value
+ else:
+ ans.constant += multiplier*v
+ else:
+ id_ = id(v)
+ if id_ in idMap[None]:
+ key = idMap[None][id_]
+ else:
+ key = len(idMap) - 1
+ idMap[None][id_] = key
+ idMap[key] = v
+ if compute_values:
+ if key in ans.linear:
+ ans.linear[key] += multiplier*value(c)
+ else:
+ ans.linear[key] = multiplier*value(c)
+ else:
+ if key in ans.linear:
+ ans.linear[key] += multiplier*c
+ else:
+ ans.linear[key] = multiplier*c
+ return ans
+
+def _collect_comparison(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ return Results(nonl=multiplier*exp)
+
+def _collect_external_fn(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ return Results(nonl=multiplier*exp)
+
+def _collect_linear_sum(exp, multiplier, idMap, compute_values, verbose, quadratic):
+ ans = Results()
+ varkeys = idMap[None]
+
+ for e_ in itertools.islice(exp._args_, exp.nargs()):
+ c,v = e_
+ if not v is None:
+ if v.fixed:
+ if compute_values:
+ ans.constant += multiplier*c*v.value
+ else:
+ ans.constant += multiplier*c*v
+ else:
+ id_ = id(v)
+ if id_ in varkeys:
+ key = varkeys[id_]
+ else:
+ key = len(idMap) - 1
+ varkeys[id_] = key
+ idMap[key] = v
+ if key in ans.linear:
+ ans.linear[key] += multiplier*c
+ else:
+ ans.linear[key] = multiplier*c
+ elif c.__class__ in native_numeric_types:
+ ans.constant += multiplier*c
+ else: # not c.is_potentially_variable()
+ if compute_values:
+ ans.constant += multiplier * value(c)
+ else:
+ ans.constant += multiplier * c
+
+ return ans
+
+
+_repn_collectors = {
+ EXPR.SumExpression : _collect_sum,
+ EXPR.ProductExpression : _collect_prod,
+ EXPR.MonomialTermExpression : _collect_term,
+ EXPR.PowExpression : _collect_pow,
+ EXPR.ReciprocalExpression : _collect_reciprocal,
+ EXPR.Expr_ifExpression : _collect_branching_expr,
+ EXPR.UnaryFunctionExpression : _collect_nonl,
+ EXPR.AbsExpression : _collect_nonl,
+ EXPR.NegationExpression : _collect_negation,
+ EXPR.LinearExpression : _collect_linear,
+ EXPR.InequalityExpression : _collect_comparison,
+ EXPR.RangedExpression : _collect_comparison,
+ EXPR.EqualityExpression : _collect_comparison,
+ EXPR.ExternalFunctionExpression : _collect_external_fn,
+ #EXPR.LinearSumExpression : _collect_linear_sum,
+ #_ConnectorData : _collect_linear_connector,
+ #SimpleConnector : _collect_linear_connector,
+ #param._ParamData : _collect_linear_const,
+ #param.SimpleParam : _collect_linear_const,
+ #param.Param : _collect_linear_const,
+ #parameter : _collect_linear_const,
+ NumericConstant : _collect_const,
+ _GeneralVarData : _collect_var,
+ SimpleVar : _collect_var,
+ Var : _collect_var,
+ variable : _collect_var,
+ IVariable : _collect_var,
+ _GeneralExpressionData : _collect_identity,
+ SimpleExpression : _collect_identity,
+ expression : _collect_identity,
+ noclone : _collect_identity,
+ _ExpressionData : _collect_identity,
+ Expression : _collect_identity,
+ _GeneralObjectiveData : _collect_identity,
+ SimpleObjective : _collect_identity,
+ objective : _collect_identity,
+ }
+
+
+def _collect_standard_repn(exp, multiplier, idMap,
+ compute_values, verbose, quadratic):
+ try:
+ return _repn_collectors[exp.__class__](exp, multiplier, idMap,
+ compute_values, verbose, quadratic)
+ except KeyError:
+ #
+ # These are types that might be extended using duck typing.
+ #
+ if exp.is_variable_type():
+ return _collect_var(exp, multiplier, idMap, compute_values, verbose, quadratic)
+ if exp.is_named_expression_type():
+ return _collect_identity(exp, multiplier, idMap, compute_values, verbose, quadratic)
+ raise ValueError( "Unexpected expression (type %s)" % type(exp).__name__)
+
+
+def _generate_standard_repn(expr, idMap=None, compute_values=True, verbose=False, quadratic=True, repn=None):
+ if expr.__class__ is EXPR.SumExpression:
+ #
+ # This is the common case, so start collecting the sum
+ #
+ ans = _collect_sum(expr, 1, idMap, compute_values, verbose, quadratic)
+ else:
+ #
+ # Call generic recursive logic
+ #
+ ans = _collect_standard_repn(expr, 1, idMap, compute_values, verbose, quadratic)
+ #
+ # Create the final object here from 'ans'
+ #
+ repn.constant = ans.constant
+ #
+ # Create a list (tuple) of the variables and coefficients
+ #
+ v = []
+ c = []
+ for key in ans.linear:
+ val = ans.linear[key]
+ if val.__class__ in native_numeric_types:
+ if val == 0:
+ continue
+ elif val.is_constant():
+ if value(val) == 0:
+ continue
+ v.append(idMap[key])
+ c.append(ans.linear[key])
+ repn.linear_vars = tuple(v)
+ repn.linear_coefs = tuple(c)
+
+ if quadratic:
+ repn.quadratic_vars = []
+ repn.quadratic_coefs = []
+ for key in ans.quadratic:
+ val = ans.quadratic[key]
+ if val.__class__ in native_numeric_types:
+ if val == 0:
+ continue
+ elif val.is_constant():
+ if value(val) == 0:
+ continue
+ repn.quadratic_vars.append( (idMap[key[0]],idMap[key[1]]) )
+ repn.quadratic_coefs.append( val )
+ repn.quadratic_vars = tuple(repn.quadratic_vars)
+ repn.quadratic_coefs = tuple(repn.quadratic_coefs)
+ v = []
+ c = []
+ for key in ans.quadratic:
+ v.append((idMap[key[0]], idMap[key[1]]))
+ c.append(ans.quadratic[key])
+ repn.quadratic_vars = tuple(v)
+ repn.quadratic_coefs = tuple(c)
+
+ if ans.nonl is not None and not isclose_const(ans.nonl,0):
+ repn.nonlinear_expr = ans.nonl
+ repn.nonlinear_vars = []
+ for v_ in EXPR.identify_variables(repn.nonlinear_expr, include_fixed=False):
+ repn.nonlinear_vars.append(v_)
+ #
+ # Update idMap in case we skipped nonlinear sub-expressions
+ #
+ # Q: Should we skip nonlinear sub-expressions?
+ #
+ id_ = id(v_)
+ if id_ in idMap[None]:
+ key = idMap[None][id_]
+ else:
+ key = len(idMap) - 1
+ idMap[None][id_] = key
+ idMap[key] = v_
+ repn.nonlinear_vars = tuple(repn.nonlinear_vars)
+
+ return repn
+
+
+"""
+WEH - This code assumes the expression is linear and fills in a dictionary.
+ This avoids creating temporary Results objects, but in practice that's
+ not a big win. Hence, this is deprecated.
+
+##-----------------------------------------------------------------------
+##
+## Logic for _generate_linear_standard_repn
+##
+##-----------------------------------------------------------------------
+
+def _linear_collect_sum(exp, multiplier, idMap, compute_values, verbose, coef):
+ varkeys = idMap[None]
+
+ for e_ in itertools.islice(exp._args_, exp.nargs()):
+ if e_.__class__ in native_numeric_types:
+ coef[None] += multiplier*e_
+
+ elif e_.is_variable_type():
+ if e_.fixed:
+ if compute_values:
+ coef[None] += multiplier*e_.value
+ else:
+ coef[None] += multiplier*e_
+ else:
+ id_ = id(e_)
+ if id_ in varkeys:
+ key = varkeys[id_]
+ else:
+ key = len(idMap) - 1
+ varkeys[id_] = key
+ idMap[key] = e_
+ if key in coef:
+ coef[key] += multiplier
+ else:
+ coef[key] = multiplier
+
+ elif not e_.is_potentially_variable():
+ if compute_values:
+ coef[None] += multiplier * value(e_)
+ else:
+ coef[None] += multiplier * e_
+
+ elif e_.__class__ is EXPR.NegationExpression:
+ arg = e_._args_[0]
+ if arg.is_variable_type():
+ if arg.fixed:
+ if compute_values:
+ coef[None] -= multiplier*arg.value
+ else:
+ coef[None] -= multiplier*arg
+ else:
+ id_ = id(arg)
+ if id_ in varkeys:
+ key = varkeys[id_]
+ else:
+ key = len(idMap) - 1
+ varkeys[id_] = key
+ idMap[key] = arg
+ if key in coef:
+ coef[key] -= multiplier
+ else:
+ coef[key] = -1 * multiplier
+ else:
+ _collect_linear_standard_repn(arg, -1*multiplier, idMap, compute_values, verbose, coef)
+
+ elif e_.__class__ is EXPR.MonomialTermExpression:
+ if compute_values:
+ lhs = value(e_._args_[0])
+ else:
+ lhs = e_._args_[0]
+ if e_._args_[1].fixed:
+ if compute_values:
+ coef[None] += multiplier*lhs*value(e_._args_[1])
+ else:
+ coef[None] += multiplier*lhs*e_._args_[1]
+ else:
+ id_ = id(e_._args_[1])
+ if id_ in varkeys:
+ key = varkeys[id_]
+ else:
+ key = len(idMap) - 1
+ varkeys[id_] = key
+ idMap[key] = e_._args_[1]
+ if key in coef:
+ coef[key] += multiplier*lhs
+ else:
+ coef[key] = multiplier*lhs
+
+ else:
+ _collect_linear_standard_repn(e_, multiplier, idMap, compute_values, verbose, coef)
+
+def _linear_collect_linear(exp, multiplier, idMap, compute_values, verbose, coef):
+ varkeys = idMap[None]
+
+ if compute_values:
+ coef[None] += multiplier*value(exp.constant)
+ else:
+ coef[None] += multiplier*exp.constant
+
+ for c,v in zip(exp.linear_coefs, exp.linear_vars):
+ if v.fixed:
+ if compute_values:
+ coef[None] += multiplier*v.value
+ else:
+ coef[None] += multiplier*v
+ else:
+ id_ = id(v)
+ if id_ in varkeys:
+ key = varkeys[id_]
+ else:
+ key = len(idMap) - 1
+ varkeys[id_] = key
+ idMap[key] = v
+ if compute_values:
+ if key in coef:
+ coef[key] += multiplier*value(c)
+ else:
+ coef[key] = multiplier*value(c)
+ else:
+ if key in coef:
+ coef[key] += multiplier*c
+ else:
+ coef[key] = multiplier*c
+
+def _linear_collect_term(exp, multiplier, idMap, compute_values, verbose, coef):
+ #
+ # LHS is a numeric value
+ #
+ if exp._args_[0].__class__ in native_numeric_types:
+ if isclose_default(exp._args_[0],0):
+ return
+ _collect_linear_standard_repn(exp._args_[1], multiplier * exp._args_[0], idMap,
+ compute_values, verbose, coef)
+ #
+ # LHS is a non-variable expression
+ #
+ else:
+ if compute_values:
+ val = value(exp._args_[0])
+ if isclose_default(val,0):
+ return
+ _collect_linear_standard_repn(exp._args_[1], multiplier * val, idMap,
+ compute_values, verbose, coef)
+ else:
+ _collect_linear_standard_repn(exp._args_[1], multiplier*exp._args_[0], idMap,
+ compute_values, verbose, coef)
+
+def _linear_collect_prod(exp, multiplier, idMap, compute_values, verbose, coef):
+ #
+ # LHS is a numeric value
+ #
+ if exp._args_[0].__class__ in native_numeric_types:
+ if isclose_default(exp._args_[0],0):
+ return
+ _collect_linear_standard_repn(exp._args_[1], multiplier * exp._args_[0], idMap,
+ compute_values, verbose, coef)
+ #
+ # LHS is a non-variable expression
+ #
+ elif not exp._args_[0].is_potentially_variable():
+ if compute_values:
+ val = value(exp._args_[0])
+ if isclose_default(val,0):
+ return
+ _collect_linear_standard_repn(exp._args_[1], multiplier * val, idMap,
+ compute_values, verbose, coef)
+ else:
+ _collect_linear_standard_repn(exp._args_[1], multiplier*exp._args_[0], idMap,
+ compute_values, verbose, coef)
+ #
+ # The LHS should never be variable
+ #
+ elif exp._args_[0].is_fixed():
+ if compute_values:
+ val = value(exp._args_[0])
+ if isclose_default(val,0):
+ return
+ _collect_linear_standard_repn(exp._args_[1], multiplier * val, idMap,
+ compute_values, verbose, coef)
+ else:
+ _collect_linear_standard_repn(exp._args_[1], multiplier*exp._args_[0], idMap,
+ compute_values, verbose, coef)
+ else:
+ if compute_values:
+ val = value(exp._args_[1])
+ if isclose_default(val,0):
+ return
+ _collect_linear_standard_repn(exp._args_[0], multiplier * val, idMap,
+ compute_values, verbose, coef)
+ else:
+ _collect_linear_standard_repn(exp._args_[0], multiplier*exp._args_[1], idMap,
+ compute_values, verbose, coef)
+
+def _linear_collect_var(exp, multiplier, idMap, compute_values, verbose, coef):
+ if exp.fixed:
+ if compute_values:
+ coef[None] += multiplier*value(exp)
+ else:
+ coef[None] += multiplier*exp
+ else:
+ id_ = id(exp)
+ if id_ in idMap[None]:
+ key = idMap[None][id_]
+ else:
+ key = len(idMap) - 1
+ idMap[None][id_] = key
+ idMap[key] = exp
+ if key in coef:
+ coef[key] += multiplier
+ else:
+ coef[key] = multiplier
+
+def _linear_collect_negation(exp, multiplier, idMap, compute_values, verbose, coef):
+ _collect_linear_standard_repn(exp._args_[0], -1*multiplier, idMap, compute_values, verbose, coef)
+
+def _linear_collect_identity(exp, multiplier, idMap, compute_values, verbose, coef):
+ arg = exp._args_[0]
+ if arg.__class__ in native_numeric_types:
+ coef[None] += arg
+ elif not arg.is_potentially_variable():
+ if compute_values:
+ coef[None] += value(arg)
+ else:
+ coef[None] += arg
+ else:
+ _collect_linear_standard_repn(exp.expr, multiplier, idMap, compute_values, verbose, coef)
+
+def _linear_collect_branching_expr(exp, multiplier, idMap, compute_values, verbose, coef):
+ if exp._if.__class__ in native_numeric_types:
+ if_val = exp._if
+ else:
+ # If this value is not constant, then the expression is nonlinear.
+ if_val = value(exp._if)
+ if if_val:
+ _collect_linear_standard_repn(exp._then, multiplier, idMap, compute_values, verbose, coef)
+ else:
+ _collect_linear_standard_repn(exp._else, multiplier, idMap, compute_values, verbose, coef)
+
+def _linear_collect_pow(exp, multiplier, idMap, compute_values, verbose, coef):
+ if exp._args_[1].__class__ in native_numeric_types:
+ exponent = exp._args_[1]
+ else:
+ # If this value is not constant, then the expression is nonlinear.
+ exponent = value(exp._args_[1])
+
+ if exponent == 0:
+ coef[None] += multiplier
+ else: #exponent == 1
+ _collect_linear_standard_repn(exp._args_[0], multiplier, idMap, compute_values, verbose, coef)
+
+
+_linear_repn_collectors = {
+ EXPR.SumExpression : _linear_collect_sum,
+ EXPR.ProductExpression : _linear_collect_prod,
+ EXPR.MonomialTermExpression : _linear_collect_term,
+ EXPR.PowExpression : _linear_collect_pow,
+ #EXPR.ReciprocalExpression : _linear_collect_reciprocal,
+ EXPR.Expr_ifExpression : _linear_collect_branching_expr,
+ #EXPR.UnaryFunctionExpression : _linear_collect_nonl,
+ #EXPR.AbsExpression : _linear_collect_nonl,
+ EXPR.NegationExpression : _linear_collect_negation,
+ EXPR.LinearExpression : _linear_collect_linear,
+ #EXPR.InequalityExpression : _linear_collect_comparison,
+ #EXPR.RangedExpression : _linear_collect_comparison,
+ #EXPR.EqualityExpression : _linear_collect_comparison,
+ #EXPR.ExternalFunctionExpression : _linear_collect_external_fn,
+ ##EXPR.LinearSumExpression : _collect_linear_sum,
+ ##_ConnectorData : _collect_linear_connector,
+ ##SimpleConnector : _collect_linear_connector,
+ ##param._ParamData : _collect_linear_const,
+ ##param.SimpleParam : _collect_linear_const,
+ ##param.Param : _collect_linear_const,
+ ##parameter : _collect_linear_const,
+ _GeneralVarData : _linear_collect_var,
+ SimpleVar : _linear_collect_var,
+ Var : _linear_collect_var,
+ variable : _linear_collect_var,
+ IVariable : _linear_collect_var,
+ _GeneralExpressionData : _linear_collect_identity,
+ SimpleExpression : _linear_collect_identity,
+ expression : _linear_collect_identity,
+ noclone : _linear_collect_identity,
+ _ExpressionData : _linear_collect_identity,
+ Expression : _linear_collect_identity,
+ _GeneralObjectiveData : _linear_collect_identity,
+ SimpleObjective : _linear_collect_identity,
+ objective : _linear_collect_identity,
+ }
+
+
+def _collect_linear_standard_repn(exp, multiplier, idMap, compute_values, verbose, coefs):
+ try:
+ return _linear_repn_collectors[exp.__class__](exp, multiplier, idMap, compute_values, verbose, coefs)
+ except KeyError:
+ #
+ # These are types that might be extended using duck typing.
+ #
+ if exp.is_variable_type():
+ return _linear_collect_var(exp, multiplier, idMap, compute_values, verbose, coefs)
+ if exp.is_named_expression_type():
+ return _linear_collect_identity(exp, multiplier, idMap, compute_values, verbose, coefs)
+ raise ValueError( "Unexpected expression (type %s)" % type(exp).__name__)
+
+def _generate_linear_standard_repn(expr, idMap=None, compute_values=True, verbose=False, repn=None):
+ coef = {None:0}
+ #
+ # Call recursive logic
+ #
+ ans = _collect_linear_standard_repn(expr, 1, idMap, compute_values, verbose, coef)
+ #
+ # Create the final object here from 'ans'
+ #
+ repn.constant = coef[None]
+ del coef[None]
+ #
+ # Create a list (tuple) of the variables and coefficients
+ #
+ # If we compute the values of constants, then we can skip terms with zero
+ # coefficients
+ #
+ if compute_values:
+ keys = list(key for key in coef if not isclose(coef[key],0))
+ else:
+ keys = list(coef.keys())
+ repn.linear_vars = tuple(idMap[key] for key in keys)
+ repn.linear_coefs = tuple(coef[key] for key in keys)
+
+ return repn
+"""
+
+
+##-----------------------------------------------------------------------
+##
+## Functions to preprocess blocks
+##
+##-----------------------------------------------------------------------
+
+
+def preprocess_block_objectives(block, idMap=None):
+
+ # Get/Create the ComponentMap for the repn
+ if not hasattr(block,'_repn'):
+ block._repn = ComponentMap()
+ block_repn = block._repn
+
+ for objective_data in block.component_data_objects(Objective,
+ active=True,
+ descend_into=False):
+
+ if objective_data.expr is None:
+ raise ValueError("No expression has been defined for objective %s"
+ % (objective_data.name))
+
+ try:
+ repn = generate_standard_repn(objective_data.expr, idMap=idMap)
+ except Exception:
+ err = sys.exc_info()[1]
+ logging.getLogger('pyomo.core').error\
+ ( "exception generating a standard representation for objective %s: %s" \
+ % (objective_data.name, str(err)) )
+ raise
+
+ block_repn[objective_data] = repn
+
+def preprocess_block_constraints(block, idMap=None):
+
+ # Get/Create the ComponentMap for the repn
+ if not hasattr(block,'_repn'):
+ block._repn = ComponentMap()
+ block_repn = block._repn
+
+ for constraint in block.component_objects(Constraint,
+ active=True,
+ descend_into=False):
+
+ preprocess_constraint(block,
+ constraint,
+ idMap=idMap,
+ block_repn=block_repn)
+
+def preprocess_constraint(block,
+ constraint,
+ idMap=None,
+ block_repn=None):
+
+ from pyomo.repn.beta.matrix import MatrixConstraint
+ if isinstance(constraint, MatrixConstraint):
+ return
+
+ # Get/Create the ComponentMap for the repn
+ if not hasattr(block,'_repn'):
+ block._repn = ComponentMap()
+ block_repn = block._repn
+
+ for index, constraint_data in iteritems(constraint):
+
+ if not constraint_data.active:
+ continue
+
+ if constraint_data.body is None:
+ raise ValueError(
+ "No expression has been defined for the body "
+ "of constraint %s" % (constraint_data.name))
+
+ try:
+ repn = generate_standard_repn(constraint_data.body,
+ idMap=idMap)
+ except Exception:
+ err = sys.exc_info()[1]
+ logging.getLogger('pyomo.core').error(
+ "exception generating a standard representation for "
+ "constraint %s: %s"
+ % (constraint_data.name, str(err)))
+ raise
+
+ block_repn[constraint_data] = repn
+
+def preprocess_constraint_data(block,
+ constraint_data,
+ idMap=None,
+ block_repn=None):
+
+ # Get/Create the ComponentMap for the repn
+ if not hasattr(block,'_repn'):
+ block._repn = ComponentMap()
+ block_repn = block._repn
+
+ if constraint_data.body is None:
+ raise ValueError(
+ "No expression has been defined for the body "
+ "of constraint %s" % (constraint_data.name))
+
+ try:
+ repn = generate_standard_repn(constraint_data.body,
+ idMap=idMap)
+ except Exception:
+ err = sys.exc_info()[1]
+ logging.getLogger('pyomo.core').error(
+ "exception generating a standard representation for "
+ "constraint %s: %s"
+ % (constraint_data.name, str(err)))
+ raise
+
+ block_repn[constraint_data] = repn
+
diff --git a/pyomo/repn/tests/ampl/external_expression_fixed.nl.baseline b/pyomo/repn/tests/ampl/external_expression_fixed.nl.baseline
index a8821c32025..03cd8117381 100644
--- a/pyomo/repn/tests/ampl/external_expression_fixed.nl.baseline
+++ b/pyomo/repn/tests/ampl/external_expression_fixed.nl.baseline
@@ -13,10 +13,10 @@ O0 0 #o
o2 #*
o5 #pow
v0 #z
-n2.0
+n2
o5 #pow
n5.0
-n2.0
+n2
x1 # initial guess
0 1
r #0 ranges (rhs's)
diff --git a/pyomo/repn/tests/ampl/external_expression_partial_fixed.nl.baseline b/pyomo/repn/tests/ampl/external_expression_partial_fixed.nl.baseline
index b3d26663dab..7c202d1f368 100644
--- a/pyomo/repn/tests/ampl/external_expression_partial_fixed.nl.baseline
+++ b/pyomo/repn/tests/ampl/external_expression_partial_fixed.nl.baseline
@@ -13,14 +13,14 @@ O0 0 #o
o2 #*
o5 #pow
v1 #z
-n2.0
+n2
o5 #pow
f0 2 #hypot
n3
o0 #+
n1
v0 #y
-n2.0
+n2
x2 # initial guess
0 3
1 1
diff --git a/pyomo/repn/tests/ampl/external_expression_variable.nl.baseline b/pyomo/repn/tests/ampl/external_expression_variable.nl.baseline
index 59402d42a26..dac0370ff75 100644
--- a/pyomo/repn/tests/ampl/external_expression_variable.nl.baseline
+++ b/pyomo/repn/tests/ampl/external_expression_variable.nl.baseline
@@ -13,7 +13,7 @@ O0 0 #o
o2 #*
o5 #pow
v2 #z
-n2.0
+n2
o5 #pow
f0 2 #hypot
o2 #*
@@ -22,7 +22,7 @@ v0 #x
o0 #+
n1
v1 #y
-n2.0
+n2
x3 # initial guess
0 3
1 3
diff --git a/pyomo/repn/tests/ampl/helper.py b/pyomo/repn/tests/ampl/helper.py
index bed102c5b10..219dc6c6e73 100644
--- a/pyomo/repn/tests/ampl/helper.py
+++ b/pyomo/repn/tests/ampl/helper.py
@@ -8,7 +8,8 @@
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
-from pyomo.core.base.numvalue import NumericValue
+from pyomo.core.expr import NumericValue
+
class MockFixedValue(NumericValue):
value = 42
diff --git a/pyomo/repn/tests/ampl/small10_testCase.mod b/pyomo/repn/tests/ampl/small10_testCase.mod
index df7af8ac38a..25d90a20ed8 100644
--- a/pyomo/repn/tests/ampl/small10_testCase.mod
+++ b/pyomo/repn/tests/ampl/small10_testCase.mod
@@ -19,53 +19,53 @@ fix x := 1.0;
fix z := 0.0;
minimize obj: x*y +
- z*y +
- q*y +
- y*y*q +
- p*y +
- y*y*p +
- y*y*z +
- z*(y**2);
+ z*y +
+ q*y +
+ y*y*q +
+ p*y +
+ y*y*p +
+ y*y*z +
+ z*(y**2);
s.t.
- con1:
- x*y == 0;
- con2:
- z*y + y == 0;
- con3:
- q*(y**2) + y == 0;
- con4:
- q*y*x + y == 0;
- con5:
- p*(y**2) + y == 0;
- con6:
- p*y*x + y == 0;
- con7:
- z*(y**2) + y == 0;
- con8:
- z*y*x + y == 0;
+ con1:
+ x*y == 0;
+ con2:
+ z*y + y == 0;
+ con3:
+ q*(y**2) + y == 0;
+ con4:
+ q*y*x + y == 0;
+ con5:
+ p*(y**2) + y == 0;
+ con6:
+ p*y*x + y == 0;
+ con7:
+ z*(y**2) + y == 0;
+ con8:
+ z*y*x + y == 0;
# AMPL differs from Pyomo in these cases that involve immutable params (q).
# These never actually become constraints in Pyomo, and for good reason.
- con9:
- z*y == 0;
-# con10:
-# q*(y**2) == 0;
-# con11:
-# q*y*x == 0;
- con12:
- p*(y**2) == 0;
- con13:
- p*y*x == 0;
- con14:
- z*(y**2) == 0;
- con15:
- z*y*x == 0;
-# con16:
-# q*y == 0;
- con17:
- p*y == 0;
+ con9:
+ z*y == 0;
+# con10:
+# q*(y**2) == 0;
+# con11:
+# q*y*x == 0;
+ con12:
+ p*(y**2) == 0;
+ con13:
+ p*y*x == 0;
+ con14:
+ z*(y**2) == 0;
+ con15:
+ z*y*x == 0;
+# con16:
+# q*y == 0;
+ con17:
+ p*y == 0;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall10.ampl;
diff --git a/pyomo/repn/tests/ampl/small11_testCase.mod b/pyomo/repn/tests/ampl/small11_testCase.mod
index 4f4de9490df..b999a4a2cf8 100644
--- a/pyomo/repn/tests/ampl/small11_testCase.mod
+++ b/pyomo/repn/tests/ampl/small11_testCase.mod
@@ -24,4 +24,4 @@ fix x[1,1] := 1.0;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall11.ampl;
diff --git a/pyomo/repn/tests/ampl/small11_testCase.py b/pyomo/repn/tests/ampl/small11_testCase.py
index adcd78b94b2..34d2b4a3c30 100644
--- a/pyomo/repn/tests/ampl/small11_testCase.py
+++ b/pyomo/repn/tests/ampl/small11_testCase.py
@@ -32,7 +32,7 @@ def obj_rule(model):
model.obj = Objective(rule=obj_rule)
def var_bnd_rule(model,i):
- return -1.0 <= model.x[1,i] <= 1.0
+ return (-1.0, model.x[1,i], 1.0)
model.var_bnd = Constraint(RangeSet(1,n),rule=var_bnd_rule)
model.x[1,1] = 1.0
diff --git a/pyomo/repn/tests/ampl/small12_testCase.mod b/pyomo/repn/tests/ampl/small12_testCase.mod
index 7192a6d57de..b68d7bda625 100644
--- a/pyomo/repn/tests/ampl/small12_testCase.mod
+++ b/pyomo/repn/tests/ampl/small12_testCase.mod
@@ -63,4 +63,4 @@ s.t.
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall12.ampl;
diff --git a/pyomo/repn/tests/ampl/small12_testCase.py b/pyomo/repn/tests/ampl/small12_testCase.py
index 3ce6a7bae48..7dc5e5b980d 100644
--- a/pyomo/repn/tests/ampl/small12_testCase.py
+++ b/pyomo/repn/tests/ampl/small12_testCase.py
@@ -17,7 +17,8 @@
#
from pyomo.environ import *
-from pyomo.core.base.expr import Expr_if
+from pyomo.core.expr.current import Expr_if
+
model = ConcreteModel()
@@ -62,15 +63,15 @@
model.c14 = Constraint(expr= Expr_if(IF=(model.vP1*10.0 > 0), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
# -1 <= x <= 1
-model.c15 = Constraint(expr= Expr_if(IF=(-1 <= model.vN2 <= 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
-model.c16 = Constraint(expr= Expr_if(IF=(-1*model.vP1 <= model.vN1 <= 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
-model.c17 = Constraint(expr= Expr_if(IF=(-1*model.vP1**2 <= model.v0 <= 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
-model.c18 = Constraint(expr= Expr_if(IF=(model.vN1 <= model.vP1 <= 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
-model.c19 = Constraint(expr= Expr_if(IF=(-1 <= model.vP2 <= 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
+model.c15 = Constraint(expr= Expr_if(IF=inequality(-1, model.vN2, 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
+model.c16 = Constraint(expr= Expr_if(IF=inequality(-1*model.vP1, model.vN1, 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
+model.c17 = Constraint(expr= Expr_if(IF=inequality(-1*model.vP1**2, model.v0, 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
+model.c18 = Constraint(expr= Expr_if(IF=inequality(model.vN1, model.vP1, 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
+model.c19 = Constraint(expr= Expr_if(IF=inequality(-1, model.vP2, 1), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
# -1 < x < 1
-model.c20 = Constraint(expr= Expr_if(IF=(-1 < model.vN2 < 1) , THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
-model.c21 = Constraint(expr= Expr_if(IF=(-1 < model.vN1 < 1*model.vP1) , THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
-model.c22 = Constraint(expr= Expr_if(IF=(-1 < model.v0 < 1*model.vP1**2), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
-model.c23 = Constraint(expr= Expr_if(IF=(-1 < model.vP1 < model.vP1) , THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
-model.c24 = Constraint(expr= Expr_if(IF=(-1 < model.vP2 < 1) , THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
+model.c20 = Constraint(expr= Expr_if(IF=inequality(-1, model.vN2, 1, strict=True), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
+model.c21 = Constraint(expr= Expr_if(IF=inequality(-1, model.vN1, 1*model.vP1, strict=True), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
+model.c22 = Constraint(expr= Expr_if(IF=inequality(-1, model.v0, 1*model.vP1**2, strict=True), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pTrue)
+model.c23 = Constraint(expr= Expr_if(IF=inequality(-1, model.vP1, model.vP1, strict=True), THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
+model.c24 = Constraint(expr= Expr_if(IF=inequality(-1, model.vP2, 1, strict=True) , THEN=(model.vTrue), ELSE=(model.vFalse)) == model.pFalse)
diff --git a/pyomo/repn/tests/ampl/small13.pyomo.nl b/pyomo/repn/tests/ampl/small13.pyomo.nl
index 1bf18512207..c285f1b9a70 100644
--- a/pyomo/repn/tests/ampl/small13.pyomo.nl
+++ b/pyomo/repn/tests/ampl/small13.pyomo.nl
@@ -11,27 +11,19 @@ g3 1 1 0 # problem unknown
C0
o5
v0
-n3.0
+n3
C1
o2
n10
-o0
o5
v0
-n3.0
-o2
-n-1
-v0
+n3
C2
o2
n0.1
-o0
o5
v0
-n3.0
-o2
-n-1
-v0
+n3
O0 1
n0
x1
@@ -44,10 +36,10 @@ b
3
k0
J0 1
-0 -1.0
+0 -1
J1 1
-0 0
+0 -10
J2 1
-0 0
+0 -0.1
G0 1
0 1
diff --git a/pyomo/repn/tests/ampl/small14.ampl.col b/pyomo/repn/tests/ampl/small14.ampl.col
new file mode 100644
index 00000000000..bfbe762d708
--- /dev/null
+++ b/pyomo/repn/tests/ampl/small14.ampl.col
@@ -0,0 +1,2 @@
+ONE
+ZERO
diff --git a/pyomo/repn/tests/ampl/small14.ampl.nl b/pyomo/repn/tests/ampl/small14.ampl.nl
new file mode 100644
index 00000000000..1493d25b29b
--- /dev/null
+++ b/pyomo/repn/tests/ampl/small14.ampl.nl
@@ -0,0 +1,142 @@
+g3 0 1 0 # problem small14
+ 2 19 1 0 19 # vars, constraints, objectives, ranges, eqns
+ 19 0 # nonlinear constraints, objectives
+ 0 0 # network constraints: nonlinear, linear
+ 2 0 0 # nonlinear vars in constraints, objectives, both
+ 0 0 0 1 # linear network variables; functions; arith, flags
+ 0 0 0 0 0 # discrete variables: binary, integer, nonlinear (b,c,o)
+ 19 2 # nonzeros in Jacobian, gradients
+ 7 4 # max name lengths: constraints, variables
+ 0 0 0 0 0 # common exprs: b,c,o,c1,o1
+C0
+o43
+v0
+C1
+o42
+v0
+C2
+o41
+v1
+C3
+o46
+v1
+C4
+o38
+v1
+C5
+o40
+v1
+C6
+o45
+v1
+C7
+o37
+v1
+C8
+o51
+v1
+C9
+o53
+v1
+C10
+o49
+v1
+C11
+o50
+v1
+C12
+o52
+o3
+o0
+v0
+n7.3890560989306495
+n5.43656365691809
+C13
+o47
+v1
+C14
+o44
+v1
+C15
+o39
+v0
+C16
+o14
+v0
+C17
+o13
+v0
+C18
+o15
+v0
+O0 0
+n0
+x2
+0 1
+1 0
+r
+4 0
+4 0
+4 0
+4 1
+4 0
+4 0
+4 1
+4 0
+4 0
+4 1.5707963267948966
+4 0
+4 0
+4 0
+4 0
+4 1
+4 1
+4 1
+4 1
+4 1
+b
+3
+3
+k1
+7
+J0 1
+0 0
+J1 1
+0 0
+J2 1
+1 0
+J3 1
+1 0
+J4 1
+1 0
+J5 1
+1 0
+J6 1
+1 0
+J7 1
+1 0
+J8 1
+1 0
+J9 1
+1 0
+J10 1
+1 0
+J11 1
+1 0
+J12 1
+0 0
+J13 1
+1 0
+J14 1
+1 0
+J15 1
+0 0
+J16 1
+0 0
+J17 1
+0 0
+J18 1
+0 0
+G0 2
+0 1
+1 1
diff --git a/pyomo/repn/tests/ampl/small14.ampl.row b/pyomo/repn/tests/ampl/small14.ampl.row
new file mode 100644
index 00000000000..7620a90d360
--- /dev/null
+++ b/pyomo/repn/tests/ampl/small14.ampl.row
@@ -0,0 +1,20 @@
+c_log
+c_log10
+c_sin
+c_cos
+c_tan
+c_sinh
+c_cosh
+c_tanh
+c_asin
+c_acos
+c_atan
+c_asinh
+c_acosh
+c_atanh
+c_exp
+c_sqrt
+c_ceil
+c_floor
+c_abs
+obj
diff --git a/pyomo/repn/tests/ampl/small14.pyomo.nl b/pyomo/repn/tests/ampl/small14.pyomo.nl
new file mode 100644
index 00000000000..c9431b7c408
--- /dev/null
+++ b/pyomo/repn/tests/ampl/small14.pyomo.nl
@@ -0,0 +1,142 @@
+g3 1 1 0 # problem unknown
+ 2 19 1 0 19 # vars, constraints, objectives, ranges, eqns
+ 19 0 0 0 0 0 # nonlinear constrs, objs; ccons: lin, nonlin, nd, nzlb
+ 0 0 # network constraints: nonlinear, linear
+ 2 0 0 # nonlinear vars in constraints, objectives, both
+ 0 0 0 1 # linear network variables; functions; arith, flags
+ 0 0 0 0 0 # discrete variables: binary, integer, nonlinear (b,c,o)
+ 19 2 # nonzeros in Jacobian, obj. gradient
+ 0 0 # max name lengths: constraints, variables
+ 0 0 0 0 0 # common exprs: b,c,o,c1,o1
+C0
+o43
+v0
+C1
+o42
+v0
+C2
+o41
+v1
+C3
+o46
+v1
+C4
+o38
+v1
+C5
+o40
+v1
+C6
+o45
+v1
+C7
+o37
+v1
+C8
+o51
+v1
+C9
+o53
+v1
+C10
+o49
+v1
+C11
+o50
+v1
+C12
+o52
+o2
+o0
+v0
+n7.3890560989306495
+n0.18393972058572117
+C13
+o47
+v1
+C14
+o44
+v1
+C15
+o39
+v0
+C16
+o13
+v0
+C17
+o14
+v0
+C18
+o15
+v0
+O0 0
+n0
+x2
+0 1
+1 0
+r
+4 0.0
+4 0.0
+4 0.0
+4 1.0
+4 0.0
+4 0.0
+4 1.0
+4 0.0
+4 0.0
+4 1.5707963267948966
+4 0.0
+4 0.0
+4 0.0
+4 0.0
+4 1.0
+4 1.0
+4 1.0
+4 1.0
+4 1.0
+b
+3
+3
+k1
+7
+J0 1
+0 0
+J1 1
+0 0
+J2 1
+1 0
+J3 1
+1 0
+J4 1
+1 0
+J5 1
+1 0
+J6 1
+1 0
+J7 1
+1 0
+J8 1
+1 0
+J9 1
+1 0
+J10 1
+1 0
+J11 1
+1 0
+J12 1
+0 0
+J13 1
+1 0
+J14 1
+1 0
+J15 1
+0 0
+J16 1
+0 0
+J17 1
+0 0
+J18 1
+0 0
+G0 2
+0 1
+1 1
diff --git a/pyomo/repn/tests/ampl/small14_testCase.mod b/pyomo/repn/tests/ampl/small14_testCase.mod
new file mode 100644
index 00000000000..83f167085f4
--- /dev/null
+++ b/pyomo/repn/tests/ampl/small14_testCase.mod
@@ -0,0 +1,32 @@
+var ONE := 1;
+var ZERO := 0;
+param pi = 4 * atan(1);
+param e = exp(1);
+
+minimize obj: ONE+ZERO;
+
+s.t.
+ c_log: log(ONE) = 0;
+ c_log10: log10(ONE) = 0;
+ c_sin: sin(ZERO) = 0;
+ c_cos: cos(ZERO) = 1;
+ c_tan: tan(ZERO) = 0;
+ c_sinh: sinh(ZERO) = 0;
+ c_cosh: cosh(ZERO) = 1;
+ c_tanh: tanh(ZERO) = 0;
+ c_asin: asin(ZERO) = 0;
+ c_acos: acos(ZERO) = pi/2;
+ c_atan: atan(ZERO) = 0;
+ c_asinh: asinh(ZERO) = 0;
+ c_acosh: acosh((e^2 + ONE)/(2*e)) = 0;
+ c_atanh: atanh(ZERO) = 0;
+ c_exp: exp(ZERO) = 1;
+ c_sqrt: sqrt(ONE) = 1;
+ c_ceil: ceil(ONE) = 1;
+ c_floor: floor(ONE) = 1;
+ c_abs: abs(ONE) = 1;
+
+option substout 0;
+option presolve 0;
+option auxfiles 'rc';
+write gsmall14.ampl;
diff --git a/pyomo/repn/tests/ampl/small14_testCase.py b/pyomo/repn/tests/ampl/small14_testCase.py
new file mode 100644
index 00000000000..822be0f3f64
--- /dev/null
+++ b/pyomo/repn/tests/ampl/small14_testCase.py
@@ -0,0 +1,46 @@
+# ___________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# rights in this software.
+# This software is distributed under the 3-clause BSD License.
+# ___________________________________________________________________________
+
+from pyomo.environ import *
+from math import e, pi
+
+model = ConcreteModel()
+
+# pick a value in the domain of all of these functions
+model.ONE = Var(initialize=1)
+model.ZERO = Var(initialize=0)
+
+
+model.obj = Objective(expr=model.ONE+model.ZERO)
+
+model.c_log = Constraint(expr=log(model.ONE) == 0)
+model.c_log10 = Constraint(expr=log10(model.ONE) == 0)
+
+model.c_sin = Constraint(expr=sin(model.ZERO) == 0)
+model.c_cos = Constraint(expr=cos(model.ZERO) == 1)
+model.c_tan = Constraint(expr=tan(model.ZERO) == 0)
+
+model.c_sinh = Constraint(expr=sinh(model.ZERO) == 0)
+model.c_cosh = Constraint(expr=cosh(model.ZERO) == 1)
+model.c_tanh = Constraint(expr=tanh(model.ZERO) == 0)
+
+model.c_asin = Constraint(expr=asin(model.ZERO) == 0)
+model.c_acos = Constraint(expr=acos(model.ZERO) == pi/2)
+model.c_atan = Constraint(expr=atan(model.ZERO) == 0)
+
+model.c_asinh = Constraint(expr=asinh(model.ZERO) == 0)
+model.c_acosh = Constraint(expr=acosh((e**2 + model.ONE)/(2*e)) == 0)
+model.c_atanh = Constraint(expr=atanh(model.ZERO) == 0)
+
+model.c_exp = Constraint(expr=exp(model.ZERO) == 1)
+model.c_sqrt = Constraint(expr=sqrt(model.ONE) == 1)
+model.c_ceil = Constraint(expr=ceil(model.ONE) == 1)
+model.c_floor = Constraint(expr=floor(model.ONE) == 1)
+model.c_abs = Constraint(expr=abs(model.ONE) == 1)
diff --git a/pyomo/repn/tests/ampl/small1_testCase.mod b/pyomo/repn/tests/ampl/small1_testCase.mod
index 6b36950477a..31fc5d7d475 100644
--- a/pyomo/repn/tests/ampl/small1_testCase.mod
+++ b/pyomo/repn/tests/ampl/small1_testCase.mod
@@ -12,10 +12,10 @@ var y := 1.0;
minimize OBJ: x^2;
s.t.
- CON1:
- y^2 = 4;
+ CON1:
+ y^2 = 4;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
\ No newline at end of file
+write gsmall1.ampl;
diff --git a/pyomo/repn/tests/ampl/small2_testCase.mod b/pyomo/repn/tests/ampl/small2_testCase.mod
index 66ce9bdc31d..fc4abc1486e 100644
--- a/pyomo/repn/tests/ampl/small2_testCase.mod
+++ b/pyomo/repn/tests/ampl/small2_testCase.mod
@@ -12,10 +12,10 @@ var y := 1.0;
minimize OBJ: x;
s.t.
- CON1:
- y^2 = 4;
+ CON1:
+ y^2 = 4;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall2.ampl;
diff --git a/pyomo/repn/tests/ampl/small3_testCase.mod b/pyomo/repn/tests/ampl/small3_testCase.mod
index 0006a38db11..2c9ff724195 100644
--- a/pyomo/repn/tests/ampl/small3_testCase.mod
+++ b/pyomo/repn/tests/ampl/small3_testCase.mod
@@ -12,10 +12,10 @@ var y := 1.0;
minimize OBJ: x*y;
s.t.
- CON1:
- y^2 = 4;
+ CON1:
+ y^2 = 4;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall3.ampl;
diff --git a/pyomo/repn/tests/ampl/small4_testCase.mod b/pyomo/repn/tests/ampl/small4_testCase.mod
index 764320054ca..45d3e2b05b6 100644
--- a/pyomo/repn/tests/ampl/small4_testCase.mod
+++ b/pyomo/repn/tests/ampl/small4_testCase.mod
@@ -12,10 +12,10 @@ var y := 1.0;
minimize OBJ: y^2;
s.t.
- CON1:
- y*x = 4;
+ CON1:
+ y*x = 4;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall4.ampl;
diff --git a/pyomo/repn/tests/ampl/small5.pyomo.nl b/pyomo/repn/tests/ampl/small5.pyomo.nl
index 0e1d21d6ea4..fee2ed31b4d 100644
--- a/pyomo/repn/tests/ampl/small5.pyomo.nl
+++ b/pyomo/repn/tests/ampl/small5.pyomo.nl
@@ -10,8 +10,8 @@ g3 1 1 0 # problem unknown
0 0 0 0 0 # common exprs: b,c,o,c1,o1
C0
o2
-n0.5
o2
+n0.5
v2
o0
v0
@@ -20,8 +20,8 @@ n-1
v1
C1
o2
-n0.5
o2
+n0.5
v2
o0
v0
@@ -67,27 +67,28 @@ o2
n-1
v1
C6
-o3
o2
+o2
+n0.5
v2
o0
v0
o2
n-1
v1
-n2.0
C7
-o3
o2
+o2
+n0.5
v2
o0
v0
o2
n-1
v1
-n2.0
C8
-o3
+o2
+n0.5
o2
v2
o0
@@ -95,21 +96,19 @@ v0
o2
n-1
v1
-n2.0
C9
o2
v2
o0
-o3
+o2
+n0.5
v0
-n2.0
o2
-n-1
-o3
+n-0.5
v1
-n2.0
C10
-o3
+o2
+n0.5
o2
v2
o0
@@ -117,7 +116,6 @@ v0
o2
n-1
v1
-n2.0
C11
o2
v2
@@ -132,12 +130,12 @@ o2
n0.5
o5
v0
-n2.0
-o3
+n2
+o2
+n0.5
o5
v0
-n2.0
-n2.0
+n2
x3
0 1.0
1 2.0
diff --git a/pyomo/repn/tests/ampl/small5_testCase.mod b/pyomo/repn/tests/ampl/small5_testCase.mod
index b7acd9d0206..6de8dc1973a 100644
--- a/pyomo/repn/tests/ampl/small5_testCase.mod
+++ b/pyomo/repn/tests/ampl/small5_testCase.mod
@@ -32,21 +32,21 @@ s.t.
v*(x/p-y/p) = 2.0;
CON5:
v*(x-y)*(1.0/p) = 2.0;
- CON6:
- v*(x-y) = 2.0*p;
+ CON6:
+ v*(x-y) = 2.0*p;
- CON7:
- 1.0/q*v*(x-y) = 2.0;
- CON8:
- v*1.0/q*(x-y) = 2.0;
- CON9:
- v*(x-y)/q = 2.0;
- CON10:
- v*(x/q-y/q) = 2.0;
- CON11:
- v*(x-y)*(1.0/q) = 2.0;
- CON12:
- v*(x-y) = 2.0*q;
+ CON7:
+ 1.0/q*v*(x-y) = 2.0;
+ CON8:
+ v*1.0/q*(x-y) = 2.0;
+ CON9:
+ v*(x-y)/q = 2.0;
+ CON10:
+ v*(x/q-y/q) = 2.0;
+ CON11:
+ v*(x-y)*(1.0/q) = 2.0;
+ CON12:
+ v*(x-y) = 2.0*q;
data;
@@ -57,4 +57,4 @@ var v := 3.0;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall5.ampl;
diff --git a/pyomo/repn/tests/ampl/small6.pyomo.nl b/pyomo/repn/tests/ampl/small6.pyomo.nl
index bf1130b7278..53579292be5 100644
--- a/pyomo/repn/tests/ampl/small6.pyomo.nl
+++ b/pyomo/repn/tests/ampl/small6.pyomo.nl
@@ -9,27 +9,31 @@ g3 1 1 0 # problem unknown
0 0 # max name lengths: constraints, variables
0 0 0 0 0 # common exprs: b,c,o,c1,o1
C0
-o3
o2
+o2
+o3
+n1.0
+n2.0
v2
o0
v0
o2
n-1
v1
-n2.0
C1
-o3
+o2
o2
v2
+o3
+n1.0
+n2.0
o0
v0
o2
n-1
v1
-n2.0
C2
-o3
+o2
o2
v2
o0
@@ -37,21 +41,26 @@ v0
o2
n-1
v1
+o3
+n1.0
n2.0
C3
o2
v2
o0
-o3
+o2
v0
+o3
+n1.0
n2.0
+o16
o2
-n-1
-o3
v1
+o3
+n1.0
n2.0
C4
-o3
+o2
o2
v2
o0
@@ -59,6 +68,8 @@ v0
o2
n-1
v1
+o3
+n1.0
n2.0
C5
o2
diff --git a/pyomo/repn/tests/ampl/small6_testCase.mod b/pyomo/repn/tests/ampl/small6_testCase.mod
index a50d25116c8..8191967ae07 100644
--- a/pyomo/repn/tests/ampl/small6_testCase.mod
+++ b/pyomo/repn/tests/ampl/small6_testCase.mod
@@ -31,8 +31,8 @@ s.t.
v*(x/p-y/p) = 2.0;
CON5:
v*(x-y)*(1.0/p) = 2.0;
- CON6:
- v*(x-y) = 2.0*p;
+ CON6:
+ v*(x-y) = 2.0*p;
data;
var x := 1.0;
@@ -44,4 +44,4 @@ fix p := 2.0;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall6.ampl;
diff --git a/pyomo/repn/tests/ampl/small6_testCase.py b/pyomo/repn/tests/ampl/small6_testCase.py
index d48026cce8a..7051c24867b 100644
--- a/pyomo/repn/tests/ampl/small6_testCase.py
+++ b/pyomo/repn/tests/ampl/small6_testCase.py
@@ -37,4 +37,4 @@
model.CON3 = Constraint(expr=model.v*(model.x-model.y)/model.p == 2.0)
model.CON4 = Constraint(expr=model.v*(model.x/model.p-model.y/model.p) == 2.0)
model.CON5 = Constraint(expr=model.v*(model.x-model.y)*(1.0/model.p) == 2.0)
-model.CON6 = Constraint(expr=model.v*(model.x-model.y) == 2.0*model.p)
+model.CON6 = Constraint(expr=model.v*(model.x-model.y) - 2.0*model.p == 0)
diff --git a/pyomo/repn/tests/ampl/small7.pyomo.nl b/pyomo/repn/tests/ampl/small7.pyomo.nl
index aaca31f3f53..fd73b76ff43 100644
--- a/pyomo/repn/tests/ampl/small7.pyomo.nl
+++ b/pyomo/repn/tests/ampl/small7.pyomo.nl
@@ -9,61 +9,70 @@ g3 1 1 0 # problem unknown
0 0 # max name lengths: constraints, variables
0 0 0 0 0 # common exprs: b,c,o,c1,o1
C0
-o3
o2
-n0.5
o2
+o2
+o3
+n1.0
+n2.0
+n0.5
v2
o0
v0
o2
n-1
v1
-n2.0
C1
-o3
+o2
+o2
o2
v2
+o3
+n1.0
+n2.0
+o3
+n1.0
+n2.0
o0
v0
o2
n-1
v1
-o2
-n2.0
-n2.0
C2
-o3
o2
n0.5
o2
+o2
v2
o0
v0
o2
n-1
v1
+o3
+n1.0
n2.0
C3
o2
v2
o0
-o3
o2
-n0.5
+o2
v0
+o3
+n1.0
n2.0
+n0.5
+o16
o2
-n-1
-o3
o2
-n0.5
v1
+o3
+n1.0
n2.0
+n0.5
C4
-o3
o2
-n0.5
o2
v2
o0
@@ -71,7 +80,11 @@ v0
o2
n-1
v1
+o2
+o3
+n1.0
n2.0
+n0.5
C5
o2
v2
@@ -81,33 +94,35 @@ o2
n-1
v1
C6
-o3
o2
-n0.5
o2
+o3
+n1.0
+o2
+n2.0
+n2.0
v2
o0
v0
o2
n-1
v1
-n2.0
C7
-o3
+o2
o2
v2
+o3
+n1.0
+o2
+n2.0
+n2.0
o0
v0
o2
n-1
v1
-o2
-n2.0
-n2.0
C8
-o3
o2
-n0.5
o2
v2
o0
@@ -115,27 +130,32 @@ v0
o2
n-1
v1
+o3
+n1.0
+o2
+n2.0
n2.0
C9
o2
v2
o0
-o3
o2
-n0.5
v0
+o3
+n1.0
+o2
n2.0
+n2.0
+o16
o2
-n-1
+v1
o3
+n1.0
o2
-n0.5
-v1
+n2.0
n2.0
C10
-o3
o2
-n0.5
o2
v2
o0
@@ -143,6 +163,10 @@ v0
o2
n-1
v1
+o3
+n1.0
+o2
+n2.0
n2.0
C11
o2
@@ -153,31 +177,35 @@ o2
n-1
v1
C12
-o3
o2
+o2
+o3
+n1.0
+o0
+n2.0
+n2.0
v2
o0
v0
o2
n-1
v1
-o0
-n2.0
-n2.0
C13
-o3
+o2
o2
v2
+o3
+n1.0
+o0
+n2.0
+n2.0
o0
v0
o2
n-1
v1
-o0
-n2.0
-n2.0
C14
-o3
+o2
o2
v2
o0
@@ -185,6 +213,8 @@ v0
o2
n-1
v1
+o3
+n1.0
o0
n2.0
n2.0
@@ -192,20 +222,23 @@ C15
o2
v2
o0
-o3
+o2
v0
+o3
+n1.0
o0
n2.0
n2.0
+o16
o2
-n-1
-o3
v1
+o3
+n1.0
o0
n2.0
n2.0
C16
-o3
+o2
o2
v2
o0
@@ -213,6 +246,8 @@ v0
o2
n-1
v1
+o3
+n1.0
o0
n2.0
n2.0
@@ -225,35 +260,39 @@ o2
n-1
v1
C18
-o3
o2
-v2
-o0
-v0
o2
-n-1
-v1
+o3
+n1.0
o5
o0
n2.0
n2.0
-n2.0
-C19
-o3
-o2
+n2
v2
o0
v0
o2
n-1
v1
+C19
+o2
+o2
+v2
+o3
+n1.0
o5
o0
n2.0
n2.0
-n2.0
+n2
+o0
+v0
+o2
+n-1
+v1
C20
-o3
+o2
o2
v2
o0
@@ -261,33 +300,38 @@ v0
o2
n-1
v1
+o3
+n1.0
o5
o0
n2.0
n2.0
-n2.0
+n2
C21
o2
v2
o0
-o3
+o2
v0
+o3
+n1.0
o5
o0
n2.0
n2.0
-n2.0
+n2
+o16
o2
-n-1
-o3
v1
+o3
+n1.0
o5
o0
n2.0
n2.0
-n2.0
+n2
C22
-o3
+o2
o2
v2
o0
@@ -295,11 +339,13 @@ v0
o2
n-1
v1
+o3
+n1.0
o5
o0
n2.0
n2.0
-n2.0
+n2
C23
o2
v2
diff --git a/pyomo/repn/tests/ampl/small7_testCase.mod b/pyomo/repn/tests/ampl/small7_testCase.mod
index 491867e0934..2931636a5e2 100644
--- a/pyomo/repn/tests/ampl/small7_testCase.mod
+++ b/pyomo/repn/tests/ampl/small7_testCase.mod
@@ -32,21 +32,21 @@ s.t.
v*(x/p/q-y/p/q) = 2.0;
CON5a:
v*(x-y)*(1.0/p/q) = 2.0;
- CON6a:
- v*(x-y) = 2.0*p*q;
+ CON6a:
+ v*(x-y) = 2.0*p*q;
CON1b:
1.0/(p*q)*v*(x-y) = 2.0;
- CON2b:
- v*(1.0/(p*q))*(x-y) = 2.0;
+ CON2b:
+ v*(1.0/(p*q))*(x-y) = 2.0;
CON3b:
v*(x-y)/(p*q) = 2.0;
CON4b:
v*(x/(p*q)-y/(p*q)) = 2.0;
CON5b:
v*(x-y)*(1.0/(p*q)) = 2.0;
- CON6b:
- v*(x-y) = 2.0*(p*q);
+ CON6b:
+ v*(x-y) = 2.0*(p*q);
CON1c:
1.0/(p+q)*v*(x-y) = 2.0;
@@ -58,8 +58,8 @@ s.t.
v*(x/(p+q)-y/(p+q)) = 2.0;
CON5c:
v*(x-y)*(1.0/(p+q)) = 2.0;
- CON6c:
- v*(x-y) = 2.0*(p+q);
+ CON6c:
+ v*(x-y) = 2.0*(p+q);
CON1d:
@@ -72,8 +72,8 @@ s.t.
v*(x/((p+q)^2)-y/((p+q)^2)) = 2.0;
CON5d:
v*(x-y)*(1.0/((p+q)^2)) = 2.0;
- CON6d:
- v*(x-y) = 2.0*((p+q)^2);
+ CON6d:
+ v*(x-y) = 2.0*((p+q)^2);
data;
var x := 1.0;
@@ -85,4 +85,4 @@ fix p := 2.0;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall7.ampl;
diff --git a/pyomo/repn/tests/ampl/small7_testCase.py b/pyomo/repn/tests/ampl/small7_testCase.py
index 49fab1c47b7..33eea2c5f01 100644
--- a/pyomo/repn/tests/ampl/small7_testCase.py
+++ b/pyomo/repn/tests/ampl/small7_testCase.py
@@ -2,8 +2,8 @@
#
# Pyomo: Python Optimization Modeling Objects
# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
-# Under the terms of Contract DE-NA0003525 with National Technology and
-# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
@@ -38,14 +38,14 @@
model.CON3a = Constraint(expr=model.v*(model.x-model.y)/model.p/model.q == 2.0)
model.CON4a = Constraint(expr=model.v*(model.x/model.p/model.q-model.y/model.p/model.q) == 2.0)
model.CON5a = Constraint(expr=model.v*(model.x-model.y)*(1.0/model.p/model.q) == 2.0)
-model.CON6a = Constraint(expr=model.v*(model.x-model.y) == 2.0*model.p*model.q)
+model.CON6a = Constraint(expr=model.v*(model.x-model.y) - 2.0*model.p*model.q == 0)
model.CON1b = Constraint(expr=1.0/(model.p*model.q)*model.v*(model.x-model.y) == 2.0)
model.CON2b = Constraint(expr=model.v*1.0/(model.p*model.p)*(model.x-model.y) == 2.0)
model.CON3b = Constraint(expr=model.v*(model.x-model.y)/(model.p*model.q) == 2.0)
model.CON4b = Constraint(expr=model.v*(model.x/(model.p*model.q)-model.y/(model.p*model.q)) == 2.0)
model.CON5b = Constraint(expr=model.v*(model.x-model.y)*(1.0/(model.p*model.q)) == 2.0)
-model.CON6b = Constraint(expr=model.v*(model.x-model.y) == 2.0*(model.p*model.q))
+model.CON6b = Constraint(expr=model.v*(model.x-model.y) - 2.0*(model.p*model.q) == 0)
model.CON1c = Constraint(expr=1.0/(model.p+model.q)*model.v*(model.x-model.y) == 2.0)
model.CON2c = Constraint(expr=model.v*1.0/(model.p+model.p)*(model.x-model.y) == 2.0)
@@ -59,5 +59,4 @@
model.CON3d = Constraint(expr=model.v*(model.x-model.y)/((model.p+model.q)**2) == 2.0)
model.CON4d = Constraint(expr=model.v*(model.x/((model.p+model.q)**2)-model.y/((model.p+model.q)**2)) == 2.0)
model.CON5d = Constraint(expr=model.v*(model.x-model.y)*(1.0/((model.p+model.q)**2)) == 2.0)
-model.CON6d = Constraint(expr=model.v*(model.x-model.y) == 2.0*((model.p+model.q)**2))
-
+model.CON6d = Constraint(expr=model.v*(model.x-model.y) - 2.0*((model.p+model.q)**2) == 0)
diff --git a/pyomo/repn/tests/ampl/small8.pyomo.nl b/pyomo/repn/tests/ampl/small8.pyomo.nl
index 8ce68c297ce..4780a59e366 100644
--- a/pyomo/repn/tests/ampl/small8.pyomo.nl
+++ b/pyomo/repn/tests/ampl/small8.pyomo.nl
@@ -41,6 +41,6 @@ J2 2
0 -1
2 1
G0 3
-0 1.0
+0 1
1 0
-2 1.0
+2 1
diff --git a/pyomo/repn/tests/ampl/small8_testCase.mod b/pyomo/repn/tests/ampl/small8_testCase.mod
index 022b870d7f8..5330ab12b3f 100644
--- a/pyomo/repn/tests/ampl/small8_testCase.mod
+++ b/pyomo/repn/tests/ampl/small8_testCase.mod
@@ -18,16 +18,16 @@ var z >= 7;
minimize obj: z+x*x+y;
s.t.
- constr:
- y*y >= a;
+ constr:
+ y*y >= a;
- constr2:
- y <= x/a;
+ constr2:
+ y <= x/a;
- constr3:
- z <= y + a;
+ constr3:
+ z <= y + a;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall8.ampl;
diff --git a/pyomo/repn/tests/ampl/small9_testCase.mod b/pyomo/repn/tests/ampl/small9_testCase.mod
index 4e6dc9ec403..0ab8ed3b9f7 100644
--- a/pyomo/repn/tests/ampl/small9_testCase.mod
+++ b/pyomo/repn/tests/ampl/small9_testCase.mod
@@ -20,22 +20,22 @@ fix y := 0.0;
minimize obj: x;
s.t.
- con1:
- x*y*z + x == 1.0;
- con2:
- x*p*z + x == 1.0;
- con3:
- x*q*z + x == 1.0;
+ con1:
+ x*y*z + x == 1.0;
+ con2:
+ x*p*z + x == 1.0;
+ con3:
+ x*q*z + x == 1.0;
# AMPL differs from Pyomo in these cases that involve immutable params (q).
# These never actually become constraints in Pyomo, and for good reason.
- con4:
- x*y*z == 1.0;
- con5:
- x*p*z == 1.0;
-# con6:
-# x*q*z == 1.0;
+ con4:
+ x*y*z == 1.0;
+ con5:
+ x*p*z == 1.0;
+# con6:
+# x*q*z == 1.0;
option substout 0;
option presolve 0;
option auxfiles 'rc';
-write gjunk;
+write gsmall9.ampl;
diff --git a/pyomo/repn/tests/ampl/small9_testCase.py b/pyomo/repn/tests/ampl/small9_testCase.py
index d30fa5da8c0..f93971739b3 100644
--- a/pyomo/repn/tests/ampl/small9_testCase.py
+++ b/pyomo/repn/tests/ampl/small9_testCase.py
@@ -36,7 +36,7 @@
model.con2 = Constraint(expr= model.x*model.p*model.z + model.x == 1.0)
model.con3 = Constraint(expr= model.x*model.q*model.z + model.x == 1.0)
# Pyomo differs from AMPL in these cases that involve immutable params (q).
-# These never actually become constraints in Pyomo, and for good reason.
+# These never actually become constants in Pyomo, and for good reason.
model.con4 = Constraint(expr= model.x*model.y*model.z == 1.0)
model.con5 = Constraint(expr= model.x*model.p*model.z == 1.0)
model.con6 = Constraint(rule= simple_constraint_rule(model.x*model.q*model.z == 0.0) )
diff --git a/pyomo/repn/tests/ampl/test_ampl_comparison.py b/pyomo/repn/tests/ampl/test_ampl_comparison.py
index ec4147237eb..bab15b17d95 100644
--- a/pyomo/repn/tests/ampl/test_ampl_comparison.py
+++ b/pyomo/repn/tests/ampl/test_ampl_comparison.py
@@ -2,8 +2,8 @@
#
# Pyomo: Python Optimization Modeling Objects
# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
-# Under the terms of Contract DE-NA0003525 with National Technology and
-# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
diff --git a/pyomo/repn/tests/ampl/test_ampl_nl.py b/pyomo/repn/tests/ampl/test_ampl_nl.py
index 869069557b1..c81e2043307 100644
--- a/pyomo/repn/tests/ampl/test_ampl_nl.py
+++ b/pyomo/repn/tests/ampl/test_ampl_nl.py
@@ -164,7 +164,7 @@ def test_external_expression_rewrite_fixed(self):
variable_baseline,
delete=True)
- self.assertIsNot(m._ampl_repn, None)
+ self.assertIsNot(m._repn, None)
m.x.fix()
self._cleanup(test_fname)
diff --git a/pyomo/repn/tests/ampl/test_ampl_repn.py b/pyomo/repn/tests/ampl/test_ampl_repn.py
index c290a277305..a80efa54125 100644
--- a/pyomo/repn/tests/ampl/test_ampl_repn.py
+++ b/pyomo/repn/tests/ampl/test_ampl_repn.py
@@ -1,8 +1,7 @@
import pyutilib.th as unittest
from pyomo.core import *
-from pyomo.repn.ampl_repn import _generate_ampl_repn as gar
-from pyomo.repn.ampl_repn import AmplRepn
+from pyomo.repn.standard_repn import generate_standard_repn as gar
class AmplRepnTests(unittest.TestCase):
@@ -18,8 +17,11 @@ def test_divide_by_mutable(self):
m.obj = Objective(expr=m.x**2)
test = gar(m.con.body)
- self.assertEqual(test._constant, 0)
- self.assertEqual(test._linear_vars, {})
- self.assertEqual(test._linear_terms_coef, {})
- self.assertEqual(test._nonlinear_vars, {id(m.x): m.x})
- self.assertIs(test._nonlinear_expr, m.con.body)
+ self.assertEqual(test.constant, 0)
+ self.assertEqual(test.linear_vars, tuple())
+ self.assertEqual(test.linear_coefs, tuple())
+ self.assertEqual(set(id(v) for v in test.nonlinear_vars), set([id(m.x)]))
+ self.assertIs(test.nonlinear_expr, m.con.body)
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/pyomo/repn/tests/ampl/test_cAmpl.py b/pyomo/repn/tests/ampl/test_cAmpl.py
deleted file mode 100644
index 134c67ac04f..00000000000
--- a/pyomo/repn/tests/ampl/test_cAmpl.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# ___________________________________________________________________________
-#
-# Pyomo: Python Optimization Modeling Objects
-# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
-# Under the terms of Contract DE-NA0003525 with National Technology and
-# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
-# rights in this software.
-# This software is distributed under the 3-clause BSD License.
-# ___________________________________________________________________________
-
-import pyutilib.th as unittest
-#from nose.tools import nottest
-import pyomo.repn
-
-_campl_available = False
-try:
- # Disabling these tests
- #import pyomo.repn.plugins.ampl.cAmpl as cAmpl
- _campl_available = False
-except ImportError:
- pass
-
-class MockFixedValue:
- value = 42
- def __init__(self, v = 42):
- self.value = v
- def is_fixed(self):
- return True
-
-@unittest.skipUnless(_campl_available, "C AMPL module required")
-class CAmplBasicTest(unittest.TestCase):
- def testNone(self):
- with self.assertRaises(ValueError) as cm:
- pyomo.repn.generate_ampl_repn(None)
-
- def testVar(self):
- testname = 'testname'
-
- var = pyomo.core.base.var._VarData(testname, None, None)
- var_ar = pyomo.repn.generate_ampl_repn(var)
-
- self.assertIsInstance(var_ar, pyomo.core.ampl.ampl_representation)
-
- self.assertEquals({testname:1.0}, var_ar._linear_terms_coef)
-
- self.assertEquals(1, len(var_ar._linear_terms_var))
- self.assertIsInstance(var_ar._linear_terms_var[testname], pyomo.core.base.var._VarData)
-
- def testExpressionBase(self):
- exp = pyomo.core.base.expr._ExpressionBase('name', 0, [])
- with self.assertRaises(ValueError) as cm:
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- def testSumExpression(self):
- exp = pyomo.core.base.expr._SumExpression()
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- self.assertIsInstance(exp_ar, pyomo.core.ampl.ampl_representation)
-
- def testProductExpression(self):
- x = pyomo.core.base.var.Var()
- y = pyomo.core.base.var.Var()
- exp = x * y
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- self.assertIsInstance(exp_ar, pyomo.core.ampl.ampl_representation)
- self.assertIs(exp_ar._nonlinear_expr, exp)
- self.assertTrue(exp_ar.is_nonlinear())
-
- def testProductExpressionZeroDiv(self):
- exp = pyomo.core.base.expr._ProductExpression()
- exp._numerator = [MockFixedValue(1)]
- exp._denominator = [MockFixedValue(0)]
- with self.assertRaises(ZeroDivisionError) as cm:
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- def testPowExpressionNoneArgs(self):
- exp = pyomo.core.base.expr._PowExpression([None, None])
- with self.assertRaises(ValueError) as cm:
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- def testPowExpressionConstants(self):
- v1 = MockFixedValue(2)
- v2 = MockFixedValue(3)
-
- exp = pyomo.core.base.expr._PowExpression([v1, v2])
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- self.assertIsNotNone(exp_ar)
- self.assertIsInstance(exp_ar, pyomo.core.ampl.ampl_representation)
-
- self.assertEquals(exp_ar._constant, 8)
-
- def testPowExpressionExp1(self):
- v1 = pyomo.core.base.var.Var()
- v2 = MockFixedValue(1)
-
- exp = pyomo.core.base.expr._PowExpression([v1, v2])
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- self.assertIsNotNone(exp_ar)
- self.assertIsInstance(exp_ar, pyomo.core.ampl.ampl_representation)
- self.assertEquals(exp_ar._linear_terms_var, pyomo.repn.generate_ampl_repn(v1)._linear_terms_var)
-
- def testPowExpressionExp0(self):
- v1 = pyomo.core.base.var.Var()
- v2 = MockFixedValue(0)
-
- exp = pyomo.core.base.expr._PowExpression([v1, v2])
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- self.assertIsNotNone(exp_ar)
- self.assertIsInstance(exp_ar, pyomo.core.ampl.ampl_representation)
- self.assertEquals(exp_ar._constant, 1)
-
- def testPowExpressionNonlinear(self):
- v1 = pyomo.core.base.var.Var(initialize=2)
- v2 = pyomo.core.base.var.Var(initialize=3)
-
- exp = pyomo.core.base.expr._PowExpression([v1, v2])
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- self.assertIsNotNone(exp_ar)
- self.assertIsInstance(exp_ar, pyomo.core.ampl.ampl_representation)
- self.assertIsInstance(exp_ar._nonlinear_expr, pyomo.core.base.expr._PowExpression)
-
- def testIntrinsicFunctionExpressionEmpty(self):
- exp = pyomo.core.base.expr._IntrinsicFunctionExpression('testname', 0, [], None)
- with self.assertRaises(AssertionError) as cm:
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- def testIntrinsicFunctionExpressionNoneArg(self):
- exp = pyomo.core.base.expr._IntrinsicFunctionExpression('sum', 1, [None], sum)
- with self.assertRaises(ValueError) as cm:
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- def testAbsFunctionExpression(self):
- exp = pyomo.core.base.expr._AbsExpression([2])
- with self.assertRaises(ValueError) as cm:
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- def testIntrinsicFunctionExpression(self):
- exp = pyomo.core.base.expr._IntrinsicFunctionExpression('sum', 1, [MockFixedValue()], sum)
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- self.assertIsNotNone(exp_ar)
- self.assertIsInstance(exp_ar, pyomo.core.ampl.ampl_representation)
-
- self.assertEquals(type(exp), type(exp_ar._nonlinear_expr))
- self.assertEquals(exp.name, exp_ar._nonlinear_expr.name)
- self.assertEquals(0, len(exp_ar._nonlinear_vars))
-
- def testFixedValue(self):
- val = MockFixedValue()
- val_ar = pyomo.repn.generate_ampl_repn(val)
-
- self.assertIsInstance(val_ar, pyomo.core.ampl.ampl_representation)
- self.assertEquals(MockFixedValue.value, val_ar._constant)
-
- def testCombinedProductSum(self):
- x = pyomo.core.base.var.Var()
- y = pyomo.core.base.var.Var()
- z = pyomo.core.base.var.Var()
- exp = x * y + z
-
- exp_ar = pyomo.repn.generate_ampl_repn(exp)
-
- self.assertIsInstance(exp_ar, pyomo.core.ampl.ampl_representation)
- self.assertTrue(exp_ar.is_nonlinear())
-
-if __name__ == "__main__":
- unittest.main(verbosity=2)
diff --git a/pyomo/repn/tests/ampl/test_equality.py b/pyomo/repn/tests/ampl/test_equality.py
deleted file mode 100644
index 451ee628416..00000000000
--- a/pyomo/repn/tests/ampl/test_equality.py
+++ /dev/null
@@ -1,244 +0,0 @@
-# ___________________________________________________________________________
-#
-# Pyomo: Python Optimization Modeling Objects
-# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
-# Under the terms of Contract DE-NA0003525 with National Technology and
-# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
-# rights in this software.
-# This software is distributed under the 3-clause BSD License.
-# ___________________________________________________________________________
-
-import types
-
-import pyutilib.th as unittest
-
-from pyomo.repn.tests.ampl.helper import MockFixedValue
-from pyomo.core import *
-
-_campl_available = False
-#FIXME: Disabling C AMPL tests until we decide whether to keep C AMPL module
-# around and keep it up to date with nl writer changes. (ZBF)
-#try:
-# import pyomo.core.ampl.cAmpl as cAmpl
-# _campl_available = True
-# cgar = pyomo.core.ampl.cAmpl.generate_ampl_repn
-# gar = pyomo.core.ampl.ampl.py_generate_ampl_repn
-#except ImportError:
-from pyomo.repn.ampl_repn import _generate_ampl_repn as gar
-from pyomo.repn.ampl_repn import AmplRepn
-
-class _GenericAmplRepnEqualityTests(unittest.TestCase):
- def setUp(self):
- # Helper function for param init
- def q_initialize(model, i):
- return [2,3,5,7,11,13,17,19,23,29][i-1]
- # Need a model so that variables can be distinguishable
- self.model = ConcreteModel()
- self.model.s = RangeSet(10)
- self.model.p = Param(default=42)
- self.model.q = Param(self.model.s, initialize=q_initialize)
- self.model.w = Var(self.model.s)
- self.model.x = Var()
- self.model.y = Var()
- self.model.z = Var(self.model.s)
-
- def tearDown(self):
- self.model = None
-
- def assertAmplRepnMatch(self, rep1, rep2):
- self.assertEqual(rep1, rep2)
- self.assertEqual(rep1.is_constant(), rep2.is_constant())
- self.assertEqual(rep1.is_linear(), rep2.is_linear())
- self.assertEqual(rep1.is_nonlinear(), rep2.is_nonlinear())
- self.assertEqual(rep1.needs_sum(), rep2.needs_sum())
-
-class AmplRepnEqualityTests(_GenericAmplRepnEqualityTests):
- """Serves as a test class for the AmplRepn.__eq__ method."""
-
- def testBasicEquality(self):
- self.assertEqual(AmplRepn(), AmplRepn())
-
- def testBasicInequality(self):
- self.assertNotEqual(AmplRepn(), None)
-
- def testVarEquality(self):
- self.assertEqual(gar(self.model.x), gar(self.model.x))
-
- def testVarInequality(self):
- self.assertNotEqual(gar(self.model.x), gar(self.model.y))
-
- def testVarCoefInequality(self):
- self.assertNotEqual(gar(self.model.x), gar(2.0 * self.model.x))
-
- def testFixedValueEquality(self):
- self.assertEqual(gar(MockFixedValue()), gar(MockFixedValue()))
-
- def testFixedValueInequality(self):
- self.assertNotEqual(gar(MockFixedValue(1)), gar(MockFixedValue(2)))
-
- def testProductEquality(self):
- expr = self.model.x * self.model.y
- self.assertEqual(gar(expr), gar(expr))
-
- def testProductInequality(self):
- e1 = self.model.x * self.model.x
- e2 = self.model.y * self.model.y
- self.assertNotEqual(gar(e1), gar(e2))
-
- def testSumEquality(self):
- expr = self.model.x + self.model.y
- self.assertEqual(gar(expr), gar(expr))
-
- def testSumInequality(self):
- e1 = self.model.x + self.model.y
- e2 = self.model.x + self.model.x
- self.assertNotEqual(gar(e1), gar(e2))
-
- def testSumCommutativeEquality(self):
- e1 = self.model.x + self.model.y
- e2 = self.model.y + self.model.x
- self.assertEqual(gar(e1), gar(e2))
-
- def testPowEquality(self):
- expr = self.model.x ** 2
- self.assertEqual(gar(expr), gar(expr))
-
- def testPowInequality(self):
- e1 = self.model.x ** 2
- e2 = self.model.x ** 3
- self.assertNotEqual(gar(e1), gar(e2))
-
- def testIntrinsicEquality(self):
- fns = [sin, cos, tan, sinh, cosh, tanh, asin, acos, atan, asinh, acosh, atanh, log, exp]
- for fn in fns:
- expr = fn(self.model.x)
- self.assertEqual(gar(expr), gar(expr))
-
- def testIntrinsicInequality(self):
- e1 = sin(self.model.x)
- e2 = cos(self.model.x)
- self.assertNotEqual(gar(e1), gar(e2))
-
- def testCompoundEquality(self):
- expr = self.model.x + self.model.y + self.model.x * self.model.y
- self.assertEqual(gar(expr), gar(expr))
-
- def testMoreCompoundEquality(self):
- expr = ((self.model.x + self.model.y) * self.model.x) ** 2
- self.assertEqual(gar(expr), gar(expr))
-
- def testCompoundCoefficientInequality(self):
- e1 = self.model.x * self.model.y
- e2 = 2.0 * self.model.x * self.model.y
- self.assertNotEqual(gar(e1), gar(e2))
-
- def testQuotientEquality(self):
- expr = self.model.x / self.model.y
- self.assertEqual(gar(expr), gar(expr))
-
- def testCompoundQuotientEquality(self):
- expr = self.model.y + self.model.x / self.model.y + self.model.y ** 2
- self.assertEqual(gar(expr), gar(expr))
-
- def testQuotientInequality(self):
- e1 = self.model.x / self.model.y
- e2 = self.model.y / self.model.x
- self.assertNotEqual(gar(e1), gar(e2))
-
- def testSumEquality(self):
- expr = sum(self.model.z[i] for i in self.model.s)
- self.assertEqual(gar(expr), gar(expr))
-
- def testCompoundSumEquality(self):
- expr = sum(self.model.z[i] for i in self.model.s) + self.model.x / self.model.y
- self.assertEqual(gar(expr), gar(expr))
-
-@unittest.skipUnless(_campl_available, "C AMPL module required")
-class CAmplEqualityCompatTests(_GenericAmplRepnEqualityTests):
- """Tests whether the Python and cAmpl implementations produce identical repns."""
-
- def testEnvironment(self):
- # Simply ensure the import environment is working.
- with self.assertRaises(ValueError) as cm:
- cAmpl.generate_ampl_repn(None, None, None)
-
- def testEnvironmentTypes(self):
- self.assertEqual(type(gar), types.FunctionType)
- self.assertEqual(type(cgar), types.BuiltinFunctionType)
-
- def testFixedValue(self):
- expr = MockFixedValue()
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testVar(self):
- expr = self.model.x
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testProduct(self):
- expr = self.model.x * self.model.y
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testSum(self):
- expr = self.model.x + self.model.y
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testSumCommutative(self):
- e1 = self.model.x + self.model.y
- e2 = self.model.y + self.model.x
- self.assertAmplRepnMatch(gar(e1), cgar(e2))
-
- def testPow(self):
- expr = self.model.x ** 2
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testIntrinsic(self):
- fns = [sin, cos, tan, sinh, cosh, tanh, asin, acos, atan, asinh, acosh, atanh, log, exp]
- for fn in fns:
- expr = fn(self.model.x)
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testCompound(self):
- expr = self.model.x + self.model.y + self.model.x * self.model.y
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testMoreCompound(self):
- expr = ((self.model.x + self.model.y) * self.model.x) ** 2
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testQuotient(self):
- expr = self.model.x / self.model.y
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testCompoundQuotient(self):
- expr = self.model.y + self.model.x / self.model.y + self.model.y ** 2
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testSum(self):
- expr = sum(self.model.z[i] for i in self.model.s)
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testCompoundSum(self):
- expr = sum(self.model.z[i] for i in self.model.s) + self.model.x / self.model.y
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testSumExpression(self):
- expr = sum(self.model.w[i] / self.model.z[i] ** 3 for i in self.model.s)
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testSumExpressionParam(self):
- expr = sum(value(self.model.q[i]) / self.model.z[i] ** 3 for i in self.model.s)
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testCantilvrConstraintExpr(self):
- # originally from pyomo.data.cute cantilvr model.
- expr = sum(value(self.model.q[i]) / self.model.z[i] ** 3 for i in self.model.s) - 1.0
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
- def testCantilvrObjective(self):
- # originally from pyomo.data.cute cantilvr model.
- # exposes problem in linear product handling, if present.
- expr = sum(self.model.z[i] for i in self.model.s) * 0.0624
- self.assertAmplRepnMatch(gar(expr), cgar(expr))
-
-if __name__ == "__main__":
- unittest.main(verbosity=2)
diff --git a/pyomo/repn/tests/ampl/test_suffixes.py b/pyomo/repn/tests/ampl/test_suffixes.py
index c99e8f1adab..beb4bc0920f 100644
--- a/pyomo/repn/tests/ampl/test_suffixes.py
+++ b/pyomo/repn/tests/ampl/test_suffixes.py
@@ -42,7 +42,7 @@ def test_EXPORT_suffixes_int(self):
model.junk.set_value(model.y,2)
model.junk_inactive.set_value(model.y,2)
- model.obj = Objective(expr=model.x+summation(model.y))
+ model.obj = Objective(expr=model.x+sum_product(model.y))
model.junk.set_value(model.obj,3)
model.junk_inactive.set_value(model.obj,3)
@@ -83,7 +83,7 @@ def test_EXPORT_suffixes_float(self):
model.junk.set_value(model.y,2)
model.junk_inactive.set_value(model.y,2)
- model.obj = Objective(expr=model.x+summation(model.y))
+ model.obj = Objective(expr=model.x+sum_product(model.y))
model.junk.set_value(model.obj,3)
model.junk_inactive.set_value(model.obj,3)
@@ -115,7 +115,7 @@ def test_EXPORT_suffixes_with_SOSConstraint_duplicateref(self):
model = ConcreteModel()
model.ref = Suffix(direction=Suffix.EXPORT,datatype=Suffix.INT)
model.y = Var([1,2,3])
- model.obj = Objective(expr=summation(model.y))
+ model.obj = Objective(expr=sum_product(model.y))
# The NL writer will convert this constraint to ref and sosno
# suffixes on model.y
@@ -148,7 +148,7 @@ def test_EXPORT_suffixes_with_SOSConstraint_duplicatesosno(self):
model = ConcreteModel()
model.sosno = Suffix(direction=Suffix.EXPORT,datatype=Suffix.INT)
model.y = Var([1,2,3])
- model.obj = Objective(expr=summation(model.y))
+ model.obj = Objective(expr=sum_product(model.y))
# The NL writer will convert this constraint to ref and sosno
# suffixes on model.y
@@ -180,7 +180,7 @@ def test_EXPORT_suffixes_no_datatype(self):
model = ConcreteModel()
model.sosno = Suffix(direction=Suffix.EXPORT,datatype=None)
model.y = Var([1,2,3])
- model.obj = Objective(expr=summation(model.y))
+ model.obj = Objective(expr=sum_product(model.y))
# The NL writer will convert this constraint to ref and sosno
# suffixes on model.y
diff --git a/pyomo/core/base/expr.py b/pyomo/repn/tests/baron/__init__.py
similarity index 87%
rename from pyomo/core/base/expr.py
rename to pyomo/repn/tests/baron/__init__.py
index 1f0b11ca9e9..3719398a540 100644
--- a/pyomo/core/base/expr.py
+++ b/pyomo/repn/tests/baron/__init__.py
@@ -7,7 +7,6 @@
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
-
-import sys
-from pyomo.core.kernel import expr
-sys.modules[__name__] = expr
+#
+# Pyomo BARON plugin tests
+#
diff --git a/pyomo/repn/tests/baron/no_column_ordering_linear.bar.baseline b/pyomo/repn/tests/baron/no_column_ordering_linear.bar.baseline
new file mode 100644
index 00000000000..cfe3329d0be
--- /dev/null
+++ b/pyomo/repn/tests/baron/no_column_ordering_linear.bar.baseline
@@ -0,0 +1,19 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES a, b, c;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, con;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+con: a + b + c <= 1;
+
+OBJ: minimize a + b + c ;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/no_column_ordering_quadratic.bar.baseline b/pyomo/repn/tests/baron/no_column_ordering_quadratic.bar.baseline
new file mode 100644
index 00000000000..bd01176d620
--- /dev/null
+++ b/pyomo/repn/tests/baron/no_column_ordering_quadratic.bar.baseline
@@ -0,0 +1,19 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES a, b, c;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, con;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+con: a + b + c + a * a + b * b + c * c + a * b + a * c + b * c <= 1;
+
+OBJ: minimize a + b + c + a * a + b * b + c * c + a * b + a * c + b * c ;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/no_row_ordering.bar.baseline b/pyomo/repn/tests/baron/no_row_ordering.bar.baseline
new file mode 100644
index 00000000000..db09d211b45
--- /dev/null
+++ b/pyomo/repn/tests/baron/no_row_ordering.bar.baseline
@@ -0,0 +1,23 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES a;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, con1, con2, con3, con4_1_, con4_2_;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+con1: a >= 0;
+con2: a <= 1;
+con3: 0 <= a <= 1;
+con4_1_: a == 1;
+con4_2_: a == 2;
+
+OBJ: minimize a ;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/small1.pyomo.bar b/pyomo/repn/tests/baron/small1.pyomo.bar
new file mode 100644
index 00000000000..e3e8000e805
--- /dev/null
+++ b/pyomo/repn/tests/baron/small1.pyomo.bar
@@ -0,0 +1,21 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: x1 ^ 2 == 4;
+
+OBJ: minimize x2 ^ 2;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 1;
+x2: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/small10.pyomo.bar b/pyomo/repn/tests/baron/small10.pyomo.bar
new file mode 100644
index 00000000000..baefb6dba19
--- /dev/null
+++ b/pyomo/repn/tests/baron/small10.pyomo.bar
@@ -0,0 +1,32 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: 1.0 * x1 == 0;
+c2: 0.0 * x1 + x1 == 0;
+c3: x1 == 0;
+c4: x1 == 0;
+c5: 0.0 * x1 ^ 2 + x1 == 0;
+c6: 0.0 * x1 * 1.0 + x1 == 0;
+c7: 0.0 * x1 ^ 2 + x1 == 0;
+c8: 0.0 * x1 * 1.0 + x1 == 0;
+c9: 0.0 * x1 == 0;
+c10: 0.0 * x1 ^ 2 == 0;
+c11: 0.0 * x1 * 1.0 == 0;
+c12: 0.0 * x1 ^ 2 == 0;
+c13: 0.0 * x1 * 1.0 == 0;
+c14: 0.0 * x1 == 0;
+
+OBJ: minimize 1.0 * x1 + 0.0 * x1 + 0.0 * x1 + x1 * x1 * 0.0 + x1 * x1 * 0.0 + 0.0 * x1 ^ 2;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/small11.pyomo.bar b/pyomo/repn/tests/baron/small11.pyomo.bar
new file mode 100644
index 00000000000..84705f94d75
--- /dev/null
+++ b/pyomo/repn/tests/baron/small11.pyomo.bar
@@ -0,0 +1,21 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2, x3;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: -1 <= 1.0 + 0 * ONE_VAR_CONST__ <= 1;
+c2: -1 <= x1 <= 1;
+c3: -1 <= x2 <= 1;
+
+OBJ: minimize x3;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/small12.pyomo.bar b/pyomo/repn/tests/baron/small12.pyomo.bar
new file mode 100644
index 00000000000..5b76bac4af8
--- /dev/null
+++ b/pyomo/repn/tests/baron/small12.pyomo.bar
@@ -0,0 +1,49 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2, x3, x4, x5, x6, x7;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19, c20, c21, c22, c23, c24;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: Expr_if( ( 0.0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c2: Expr_if( ( 1.0 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c3: Expr_if( ( vN1 <= 0.0 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c4: Expr_if( ( v0 <= 0.0 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c5: Expr_if( ( vP1 <= 0.0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c6: Expr_if( ( vN1 < 0.0 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c7: Expr_if( ( v0 < 0.0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c8: Expr_if( ( vP1 < 0.0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c9: Expr_if( ( 0.0 <= 10.0*vN1 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c10: Expr_if( ( 0.0 <= 10.0*v0 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c11: Expr_if( ( 0.0 <= 10.0*vP1 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c12: Expr_if( ( 0.0 < 10.0*vN1 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c13: Expr_if( ( 0.0 < 10.0*v0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c14: Expr_if( ( 0.0 < 10.0*vP1 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c15: Expr_if( ( -1.0 <= vN2 <= 1.0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c16: Expr_if( ( - vP1 <= vN1 <= 1.0 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c17: Expr_if( ( - vP1**2 <= v0 <= 1.0 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c18: Expr_if( ( vN1 <= vP1 <= 1.0 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c19: Expr_if( ( -1.0 <= vP2 <= 1.0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c20: Expr_if( ( -1.0 < vN2 < 1.0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c21: Expr_if( ( -1.0 < vN1 < vP1 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c22: Expr_if( ( -1.0 < v0 < vP1**2 ), then=( vTrue ), else=( vFalse ) ) == 1;
+c23: Expr_if( ( -1.0 < vP1 < vP1 ), then=( vTrue ), else=( vFalse ) ) == -1;
+c24: Expr_if( ( -1.0 < vP2 < 1.0 ), then=( vTrue ), else=( vFalse ) ) == -1;
+
+OBJ: minimize 10.0 * Expr_if( ( v0 ), then=( vTrue ), else=( vFalse ) );
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 1;
+x2: -1;
+x3: -1;
+x4: 0;
+x5: 1;
+x6: -2;
+x7: 2;
+}
+
diff --git a/pyomo/repn/tests/baron/small13.pyomo.bar b/pyomo/repn/tests/baron/small13.pyomo.bar
new file mode 100644
index 00000000000..ab606879b1b
--- /dev/null
+++ b/pyomo/repn/tests/baron/small13.pyomo.bar
@@ -0,0 +1,22 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: x1 ^ 3 - x1 == 0;
+c2: 10 * (x1 ^ 3 - x1) == 0;
+c3: (x1 ^ 3 - x1) * 0.1 == 0;
+
+OBJ: maximize x1;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 0.5;
+}
+
diff --git a/pyomo/repn/tests/baron/small14.pyomo.bar b/pyomo/repn/tests/baron/small14.pyomo.bar
new file mode 100644
index 00000000000..0c42d05de6f
--- /dev/null
+++ b/pyomo/repn/tests/baron/small14.pyomo.bar
@@ -0,0 +1,39 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: log(x1) == 0;
+c2: log10(x1) == 0;
+c3: sin(x2) == 0;
+c4: cos(x2) == 1;
+c5: tan(x2) == 0;
+c6: sinh(x2) == 0;
+c7: cosh(x2) == 1;
+c8: tanh(x2) == 0;
+c9: asin(x2) == 0;
+c10: acos(x2) == 1.5707963267948966;
+c11: atan(x2) == 0;
+c12: asinh(x2) == 0;
+c13: acosh((7.3890560989306495 + x1) * 0.18393972058572117) == 0;
+c14: atanh(x2) == 0;
+c15: exp(x2) == 1;
+c16: sqrt(x1) == 1;
+c17: ceil(x1) == 1;
+c18: floor(x1) == 1;
+c19: abs(x1) == 1;
+
+OBJ: minimize x1 + x2;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 1;
+x2: 0;
+}
+
diff --git a/pyomo/repn/tests/baron/small2.pyomo.bar b/pyomo/repn/tests/baron/small2.pyomo.bar
new file mode 100644
index 00000000000..5145029d6e5
--- /dev/null
+++ b/pyomo/repn/tests/baron/small2.pyomo.bar
@@ -0,0 +1,21 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: x1 ^ 2 == 4;
+
+OBJ: minimize x2;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 1;
+x2: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/small3.pyomo.bar b/pyomo/repn/tests/baron/small3.pyomo.bar
new file mode 100644
index 00000000000..c3bc07a9aaf
--- /dev/null
+++ b/pyomo/repn/tests/baron/small3.pyomo.bar
@@ -0,0 +1,21 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: x1 ^ 2 == 4;
+
+OBJ: minimize x2 * x1;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 1;
+x2: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/small4.pyomo.bar b/pyomo/repn/tests/baron/small4.pyomo.bar
new file mode 100644
index 00000000000..535e8949669
--- /dev/null
+++ b/pyomo/repn/tests/baron/small4.pyomo.bar
@@ -0,0 +1,21 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: x1 * x2 == 4;
+
+OBJ: minimize x1 ^ 2;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 1;
+x2: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/small5.pyomo.bar b/pyomo/repn/tests/baron/small5.pyomo.bar
new file mode 100644
index 00000000000..84f1650d145
--- /dev/null
+++ b/pyomo/repn/tests/baron/small5.pyomo.bar
@@ -0,0 +1,45 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2, x3;
+
+LOWER_BOUNDS{
+x1: -1;
+x2: -1;
+x3: -1;
+}
+
+UPPER_BOUNDS{
+x1: 1;
+x2: 1;
+x3: 1;
+}
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: 0.5 * x1 * (x2 - x3) == 2;
+c2: 0.5 * x1 * (x2 - x3) == 2;
+c3: x1 * (x2 - x3) * 0.5 == 2;
+c4: x1 * (0.5 * x2 - 0.5 * x3) == 2;
+c5: x1 * (x2 - x3) * 0.5 == 2;
+c6: x1 * (x2 - x3) == 4;
+c7: (1/2.0) * x1 * (x2 - x3) == 2;
+c8: (1/2.0) * x1 * (x2 - x3) == 2;
+c9: x1 * (x2 - x3) * (1/2.0) == 2;
+c10: x1 * ((1/2.0) * x2 - (1/2.0) * x3) == 2;
+c11: x1 * (x2 - x3) * (1/2.0) == 2;
+c12: x1 * (x2 - x3) == 4;
+
+OBJ: minimize x2 ^ 2 * 0.5 + x2 ^ 2 * (1/2.0);
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 3;
+x2: 1;
+x3: 2;
+}
+
diff --git a/pyomo/repn/tests/baron/small6.pyomo.bar b/pyomo/repn/tests/baron/small6.pyomo.bar
new file mode 100644
index 00000000000..d3a372af515
--- /dev/null
+++ b/pyomo/repn/tests/baron/small6.pyomo.bar
@@ -0,0 +1,39 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2, x3;
+
+LOWER_BOUNDS{
+x1: -1;
+x2: -1;
+x3: -1;
+}
+
+UPPER_BOUNDS{
+x1: 1;
+x2: 1;
+x3: 1;
+}
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3, c4, c5, c6;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: (1/2.0) * x1 * (x2 - x3) == 2;
+c2: x1 * (1/2.0) * (x2 - x3) == 2;
+c3: x1 * (x2 - x3) * (1/2.0) == 2;
+c4: x1 * (x2 * (1/2.0) - x3 * (1/2.0)) == 2;
+c5: x1 * (x2 - x3) * (1/2.0) == 2;
+c6: x1 * (x2 - x3) - 2.0 * 2.0 == 0;
+
+OBJ: minimize x2;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 3;
+x2: 1;
+x3: 2;
+}
+
diff --git a/pyomo/repn/tests/baron/small7.pyomo.bar b/pyomo/repn/tests/baron/small7.pyomo.bar
new file mode 100644
index 00000000000..55f442340d2
--- /dev/null
+++ b/pyomo/repn/tests/baron/small7.pyomo.bar
@@ -0,0 +1,57 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2, x3;
+
+LOWER_BOUNDS{
+x1: -1;
+x2: -1;
+x3: -1;
+}
+
+UPPER_BOUNDS{
+x1: 1;
+x2: 1;
+x3: 1;
+}
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19, c20, c21, c22, c23, c24;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: (1/2.0) * 0.5 * x1 * (x2 - x3) == 2;
+c2: x1 * (1/2.0) * (1/2.0) * (x2 - x3) == 2;
+c3: x1 * (x2 - x3) * (1/2.0) * 0.5 == 2;
+c4: x1 * (x2 * (1/2.0) * 0.5 - x3 * (1/2.0) * 0.5) == 2;
+c5: x1 * (x2 - x3) * (1/2.0) * 0.5 == 2;
+c6: x1 * (x2 - x3) - 4.0 * 2.0 == 0;
+c7: (1/(2.0 * 2.0)) * x1 * (x2 - x3) == 2;
+c8: x1 * (1/(2.0 * 2.0)) * (x2 - x3) == 2;
+c9: x1 * (x2 - x3) * (1/(2.0 * 2.0)) == 2;
+c10: x1 * (x2 * (1/(2.0 * 2.0)) - x3 * (1/(2.0 * 2.0))) == 2;
+c11: x1 * (x2 - x3) * (1/(2.0 * 2.0)) == 2;
+c12: x1 * (x2 - x3) - 4.0 * 2.0 == 0;
+c13: (1/(2.0 + 2.0)) * x1 * (x2 - x3) == 2;
+c14: x1 * (1/(2.0 + 2.0)) * (x2 - x3) == 2;
+c15: x1 * (x2 - x3) * (1/(2.0 + 2.0)) == 2;
+c16: x1 * (x2 * (1/(2.0 + 2.0)) - x3 * (1/(2.0 + 2.0))) == 2;
+c17: x1 * (x2 - x3) * (1/(2.0 + 2.0)) == 2;
+c18: x1 * (x2 - x3) - 2.0 * (2.0 + 2.0) == 0;
+c19: (1/(2.0 + 2.0) ^ 2) * x1 * (x2 - x3) == 2;
+c20: x1 * (1/(2.0 + 2.0) ^ 2) * (x2 - x3) == 2;
+c21: x1 * (x2 - x3) * (1/(2.0 + 2.0) ^ 2) == 2;
+c22: x1 * (x2 * (1/(2.0 + 2.0) ^ 2) - x3 * (1/(2.0 + 2.0) ^ 2)) == 2;
+c23: x1 * (x2 - x3) * (1/(2.0 + 2.0) ^ 2) == 2;
+c24: x1 * (x2 - x3) - 2.0 * (2.0 + 2.0) ^ 2 == 0;
+
+OBJ: minimize x2;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+x1: 3;
+x2: 1;
+x3: 2;
+}
+
diff --git a/pyomo/repn/tests/baron/small8.pyomo.bar b/pyomo/repn/tests/baron/small8.pyomo.bar
new file mode 100644
index 00000000000..2657b9f155e
--- /dev/null
+++ b/pyomo/repn/tests/baron/small8.pyomo.bar
@@ -0,0 +1,25 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__, x1, x2, x3;
+
+LOWER_BOUNDS{
+x1: 0;
+x2: 0;
+x3: 7;
+}
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: x1 * x1 >= 2;
+c2: x1 - 0.5 * x2 <= 0;
+c3: x3 - (x1 + 2.0) <= 0;
+
+OBJ: minimize x3 + x2 * x2 + x1;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/small9.pyomo.bar b/pyomo/repn/tests/baron/small9.pyomo.bar
new file mode 100644
index 00000000000..4bf198cb431
--- /dev/null
+++ b/pyomo/repn/tests/baron/small9.pyomo.bar
@@ -0,0 +1,23 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES x1, x2;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c1, c2, c3, c4, c5;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c1: x1 * 0.0 * x2 + x1 == 1;
+c2: 0.0 * x1 * x2 + x1 == 1;
+c3: x1 == 1;
+c4: x1 * 0.0 * x2 == 1;
+c5: 0.0 * x1 * x2 == 1;
+
+OBJ: minimize x1;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/test_baron.py b/pyomo/repn/tests/baron/test_baron.py
new file mode 100644
index 00000000000..4e3f77445b1
--- /dev/null
+++ b/pyomo/repn/tests/baron/test_baron.py
@@ -0,0 +1,207 @@
+# ___________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# rights in this software.
+# This software is distributed under the 3-clause BSD License.
+# ___________________________________________________________________________
+#
+# Test the canonical expressions
+#
+
+import os
+
+import pyutilib.th as unittest
+
+from pyomo.environ import *
+import pyomo.opt
+
+thisdir = os.path.dirname(os.path.abspath(__file__))
+
+
+class Test(unittest.TestCase):
+
+ def _cleanup(self, fname):
+ try:
+ os.remove(fname)
+ except OSError:
+ pass
+
+ def _get_fnames(self):
+ class_name, test_name = self.id().split('.')[-2:]
+ prefix = os.path.join(thisdir, test_name.replace("test_", "", 1))
+ return prefix+".bar.baseline", prefix+".bar.out"
+
+ def _check_baseline(self, model, **kwds):
+ baseline_fname, test_fname = self._get_fnames()
+ self._cleanup(test_fname)
+ io_options = {"symbolic_solver_labels": True}
+ io_options.update(kwds)
+ model.write(test_fname,
+ format="bar",
+ io_options=io_options)
+ self.assertFileEqualsBaseline(
+ test_fname,
+ baseline_fname,
+ delete=True)
+
+ def _gen_expression(self, terms):
+ terms = list(terms)
+ expr = 0.0
+ for term in terms:
+ if type(term) is tuple:
+ prodterms = list(term)
+ prodexpr = 1.0
+ for x in prodterms:
+ prodexpr *= x
+ expr += prodexpr
+ else:
+ expr += term
+ return expr
+
+ def test_no_column_ordering_quadratic(self):
+ model = ConcreteModel()
+ model.a = Var()
+ model.b = Var()
+ model.c = Var()
+
+ terms = [model.a, model.b, model.c,
+ (model.a, model.a), (model.b, model.b), (model.c, model.c),
+ (model.a, model.b), (model.a, model.c), (model.b, model.c)]
+ model.obj = Objective(expr=self._gen_expression(terms))
+ model.con = Constraint(expr=self._gen_expression(terms) <= 1)
+ self._check_baseline(model)
+
+ def test_no_column_ordering_linear(self):
+ model = ConcreteModel()
+ model.a = Var()
+ model.b = Var()
+ model.c = Var()
+
+ terms = [model.a, model.b, model.c]
+ model.obj = Objective(expr=self._gen_expression(terms))
+ model.con = Constraint(expr=self._gen_expression(terms) <= 1)
+ self._check_baseline(model)
+
+ def test_no_row_ordering(self):
+ model = ConcreteModel()
+ model.a = Var()
+
+ components = {}
+ components["obj"] = Objective(expr=model.a)
+ components["con1"] = Constraint(expr=model.a >= 0)
+ components["con2"] = Constraint(expr=model.a <= 1)
+ components["con3"] = Constraint(expr=(0, model.a, 1))
+ components["con4"] = Constraint([1,2], rule=lambda m, i: model.a == i)
+
+ for key in components:
+ model.add_component(key, components[key])
+
+ self._check_baseline(model, file_determinism=2)
+
+ def test_var_on_other_model(self):
+ other = ConcreteModel()
+ other.a = Var()
+
+ model = ConcreteModel()
+ model.x = Var()
+ model.c = Constraint(expr=other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+ self._check_baseline(model)
+
+ def test_var_on_deactivated_block(self):
+ model = ConcreteModel()
+ model.x = Var()
+ model.other = Block()
+ model.other.a = Var()
+ model.other.deactivate()
+ model.c = Constraint(expr=model.other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+ self._check_baseline(model)
+
+ def test_var_on_nonblock(self):
+ class Foo(Block().__class__):
+ def __init__(self, *args, **kwds):
+ kwds.setdefault('ctype',Foo)
+ super(Foo,self).__init__(*args, **kwds)
+
+ model = ConcreteModel()
+ model.x = Var()
+ model.other = Foo()
+ model.other.a = Var()
+ model.c = Constraint(expr=model.other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+ self._check_baseline(model)
+
+
+#class TestBaron_writer(unittest.TestCase):
+class TestBaron_writer(object):
+
+ def _cleanup(self, fname):
+ try:
+ os.remove(fname)
+ except OSError:
+ pass
+
+ def _get_fnames(self):
+ class_name, test_name = self.id().split('.')[-2:]
+ prefix = os.path.join(thisdir, test_name.replace("test_", "", 1))
+ return prefix+".bar.baseline", prefix+".bar.out"
+
+ def test_var_on_other_model(self):
+ other = ConcreteModel()
+ other.a = Var()
+
+ model = ConcreteModel()
+ model.x = Var()
+ model.c = Constraint(expr=other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+
+ baseline_fname, test_fname = self._get_fnames()
+ self._cleanup(test_fname)
+ self.assertRaises(
+ KeyError,
+ model.write, test_fname, format='bar')
+ self._cleanup(test_fname)
+
+ def test_var_on_deactivated_block(self):
+ model = ConcreteModel()
+ model.x = Var()
+ model.other = Block()
+ model.other.a = Var()
+ model.other.deactivate()
+ model.c = Constraint(expr=model.other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+
+ baseline_fname, test_fname = self._get_fnames()
+ self._cleanup(test_fname)
+ self.assertRaises(
+ KeyError,
+ model.write, test_fname, format='bar' )
+ self._cleanup(test_fname)
+
+ def test_var_on_nonblock(self):
+ class Foo(Block().__class__):
+ def __init__(self, *args, **kwds):
+ kwds.setdefault('ctype',Foo)
+ super(Foo,self).__init__(*args, **kwds)
+
+ model = ConcreteModel()
+ model.x = Var()
+ model.other = Foo()
+ model.other.a = Var()
+ model.c = Constraint(expr=model.other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+
+ baseline_fname, test_fname = self._get_fnames()
+ self._cleanup(test_fname)
+ self.assertRaises(
+ KeyError,
+ model.write, test_fname, format='bar')
+ self._cleanup(test_fname)
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/pyomo/repn/tests/baron/test_baron_comparison.py b/pyomo/repn/tests/baron/test_baron_comparison.py
new file mode 100644
index 00000000000..d1812926d79
--- /dev/null
+++ b/pyomo/repn/tests/baron/test_baron_comparison.py
@@ -0,0 +1,73 @@
+# ___________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# rights in this software.
+# This software is distributed under the 3-clause BSD License.
+# ___________________________________________________________________________
+#
+# Test the Pyomo BAR writer
+#
+
+import re
+import glob
+import os
+from os.path import abspath, dirname, join
+currdir = dirname(abspath(__file__))+os.sep
+datadir = abspath(join(currdir, "..", "ampl"))+os.sep
+
+import pyutilib.th as unittest
+import pyutilib.subprocess
+
+import pyomo.scripting.pyomo_main as main
+
+
+class Tests(unittest.TestCase):
+
+ def pyomo(self, cmd):
+ os.chdir(currdir)
+ output = main.main(['convert', '--logging=quiet', '-c']+cmd)
+ return output
+
+class BaselineTests(Tests):
+ def __init__(self, *args, **kwds):
+ Tests.__init__(self, *args, **kwds)
+BaselineTests = unittest.category('smoke', 'nightly','expensive')(BaselineTests)
+
+#
+#The following test generates an nl file for the test case
+#and checks that it matches the current pyomo baseline nl file
+#
+@unittest.nottest
+def barwriter_baseline_test(self, name):
+ if os.path.exists(datadir+name+'.dat'):
+ self.pyomo(['--output='+currdir+name+'.test.bar',
+ datadir+name+'_testCase.py',
+ datadir+name+'.dat'])
+ else:
+ self.pyomo(['--output='+currdir+name+'.test.bar',
+ datadir+name+'_testCase.py'])
+
+ # Check that the pyomo nl file matches its own baseline
+ self.assertFileEqualsBaseline(
+ currdir+name+'.test.bar', currdir+name+'.pyomo.bar',
+ tolerance=(1e-7, False))
+
+
+class ASLTests(Tests):
+
+ def __init__(self, *args, **kwds):
+ Tests.__init__(self, *args, **kwds)
+ASLTests = unittest.category('smoke','nightly','expensive')(ASLTests)
+
+
+# add test methods to classes
+for f in glob.glob(datadir+'*_testCase.py'):
+ name = re.split('[._]',os.path.basename(f))[0]
+ BaselineTests.add_fn_test(fn=barwriter_baseline_test, name=name)
+ #ASLTests.add_fn_test(fn=nlwriter_asl_test, name=name)
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/pyomo/repn/tests/baron/var_on_deactivated_block.bar.baseline b/pyomo/repn/tests/baron/var_on_deactivated_block.bar.baseline
new file mode 100644
index 00000000000..c247cb09c9b
--- /dev/null
+++ b/pyomo/repn/tests/baron/var_on_deactivated_block.bar.baseline
@@ -0,0 +1,19 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES other_a, x;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c: other_a + 2 * x <= 0;
+
+OBJ: minimize x;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/var_on_nonblock.bar.baseline b/pyomo/repn/tests/baron/var_on_nonblock.bar.baseline
new file mode 100644
index 00000000000..c247cb09c9b
--- /dev/null
+++ b/pyomo/repn/tests/baron/var_on_nonblock.bar.baseline
@@ -0,0 +1,19 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES other_a, x;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c: other_a + 2 * x <= 0;
+
+OBJ: minimize x;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/baron/var_on_other_model.bar.baseline b/pyomo/repn/tests/baron/var_on_other_model.bar.baseline
new file mode 100644
index 00000000000..35fa95b7b38
--- /dev/null
+++ b/pyomo/repn/tests/baron/var_on_other_model.bar.baseline
@@ -0,0 +1,19 @@
+OPTIONS {
+Summary: 0;
+}
+
+POSITIVE_VARIABLES ONE_VAR_CONST__;
+
+VARIABLES a, x;
+
+EQUATIONS c_e_FIX_ONE_VAR_CONST__, c;
+
+c_e_FIX_ONE_VAR_CONST__: ONE_VAR_CONST__ == 1;
+c: a + 2 * x <= 0;
+
+OBJ: minimize x;
+
+STARTING_POINT{
+ONE_VAR_CONST__: 1;
+}
+
diff --git a/pyomo/repn/tests/cpxlp/test_cpxlp.py b/pyomo/repn/tests/cpxlp/test_cpxlp.py
index 36ee1d831a8..afdc46a486a 100644
--- a/pyomo/repn/tests/cpxlp/test_cpxlp.py
+++ b/pyomo/repn/tests/cpxlp/test_cpxlp.py
@@ -132,7 +132,7 @@ def test_no_row_ordering(self):
components["obj"] = Objective(expr=model.a)
components["con1"] = Constraint(expr=model.a >= 0)
components["con2"] = Constraint(expr=model.a <= 1)
- components["con3"] = Constraint(expr=0 <= model.a <= 1)
+ components["con3"] = Constraint(expr=(0, model.a, 1))
components["con4"] = Constraint([1,2], rule=lambda m, i: model.a == i)
# add components in random order
@@ -151,7 +151,7 @@ def test_row_ordering(self):
components["obj"] = Objective(expr=model.a)
components["con1"] = Constraint(expr=model.a >= 0)
components["con2"] = Constraint(expr=model.a <= 1)
- components["con3"] = Constraint(expr=0 <= model.a <= 1)
+ components["con3"] = Constraint(expr=(0, model.a, 1))
components["con4"] = Constraint([1,2], rule=lambda m, i: model.a == i)
# add components in random order
@@ -193,9 +193,8 @@ def test_var_on_other_model(self):
baseline_fname, test_fname = self._get_fnames()
self._cleanup(test_fname)
- self.assertRaisesRegexp(
+ self.assertRaises(
KeyError,
- "'a' is not part of the model",
model.write, test_fname, format='lp')
self._cleanup(test_fname)
@@ -210,11 +209,15 @@ def test_var_on_deactivated_block(self):
baseline_fname, test_fname = self._get_fnames()
self._cleanup(test_fname)
- model.write(test_fname, format='lp')
- self.assertFileEqualsBaseline(
- test_fname,
- baseline_fname,
- delete=True)
+ self.assertRaises(
+ KeyError,
+ model.write, test_fname, format='lp' )
+ self._cleanup(test_fname)
+ #model.write(test_fname, format='lp')
+ #self.assertFileEqualsBaseline(
+ # test_fname,
+ # baseline_fname,
+ # delete=True)
def test_var_on_nonblock(self):
class Foo(Block().__class__):
@@ -231,9 +234,8 @@ def __init__(self, *args, **kwds):
baseline_fname, test_fname = self._get_fnames()
self._cleanup(test_fname)
- self.assertRaisesRegexp(
+ self.assertRaises(
KeyError,
- "'other.a' exists within Foo 'other'",
model.write, test_fname, format='lp')
self._cleanup(test_fname)
diff --git a/pyomo/core/base/expr_coopr3.py b/pyomo/repn/tests/gams/__init__.py
similarity index 85%
rename from pyomo/core/base/expr_coopr3.py
rename to pyomo/repn/tests/gams/__init__.py
index f84830428d0..9e692752410 100644
--- a/pyomo/core/base/expr_coopr3.py
+++ b/pyomo/repn/tests/gams/__init__.py
@@ -7,7 +7,6 @@
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
-
-import sys
-from pyomo.core.kernel import expr_coopr3
-sys.modules[__name__] = expr_coopr3
+#
+# Pyomo GAMS plugin tests
+#
diff --git a/pyomo/repn/tests/gams/no_column_ordering_linear.gams.baseline b/pyomo/repn/tests/gams/no_column_ordering_linear.gams.baseline
new file mode 100644
index 00000000000..5357af38f8f
--- /dev/null
+++ b/pyomo/repn/tests/gams/no_column_ordering_linear.gams.baseline
@@ -0,0 +1,42 @@
+$offdigit
+
+EQUATIONS
+ con_hi
+ obj;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ a
+ b
+ c;
+
+con_hi.. a + b + c =l= 1.0 ;
+obj.. GAMS_OBJECTIVE =e= a + b + c ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING lp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/no_column_ordering_quadratic.gams.baseline b/pyomo/repn/tests/gams/no_column_ordering_quadratic.gams.baseline
new file mode 100644
index 00000000000..b1b991595c3
--- /dev/null
+++ b/pyomo/repn/tests/gams/no_column_ordering_quadratic.gams.baseline
@@ -0,0 +1,42 @@
+$offdigit
+
+EQUATIONS
+ con_hi
+ obj;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ a
+ b
+ c;
+
+con_hi.. a + b + c + a*a + b*b + c*c + a*b + a*c + b*c =l= 1.0 ;
+obj.. GAMS_OBJECTIVE =e= a + b + c + a*a + b*b + c*c + a*b + a*c + b*c ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/no_row_ordering.gams.baseline b/pyomo/repn/tests/gams/no_row_ordering.gams.baseline
new file mode 100644
index 00000000000..b6c53875892
--- /dev/null
+++ b/pyomo/repn/tests/gams/no_row_ordering.gams.baseline
@@ -0,0 +1,50 @@
+$offdigit
+
+EQUATIONS
+ con1_lo
+ con2_hi
+ con3_lo
+ con3_hi
+ con4_1_
+ con4_2_
+ obj;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ a;
+
+con1_lo.. 0.0 =l= a ;
+con2_hi.. a =l= 1.0 ;
+con3_lo.. 0.0 =l= a ;
+con3_hi.. a =l= 1.0 ;
+con4_1_.. a =e= 1.0 ;
+con4_2_.. a =e= 2.0 ;
+obj.. GAMS_OBJECTIVE =e= a ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING lp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small1.pyomo.gms b/pyomo/repn/tests/gams/small1.pyomo.gms
new file mode 100644
index 00000000000..90d3c24eff3
--- /dev/null
+++ b/pyomo/repn/tests/gams/small1.pyomo.gms
@@ -0,0 +1,43 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2;
+
+c1.. power(x1, 2) =e= 4.0 ;
+c2.. GAMS_OBJECTIVE =e= power(x2, 2) ;
+
+x1.l = 1.0;
+x2.l = 1.0;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small10.pyomo.gms b/pyomo/repn/tests/gams/small10.pyomo.gms
new file mode 100644
index 00000000000..492d2aff323
--- /dev/null
+++ b/pyomo/repn/tests/gams/small10.pyomo.gms
@@ -0,0 +1,66 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2
+ c3
+ c4
+ c5
+ c6
+ c7
+ c8
+ c9
+ c10
+ c11
+ c12
+ c13
+ c14
+ c15;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1;
+
+c1.. x1 =e= 0.0 ;
+c2.. 0.0*x1 + x1 =e= 0.0 ;
+c3.. x1 =e= 0.0 ;
+c4.. x1 =e= 0.0 ;
+c5.. 0.0*power(x1, 2) + x1 =e= 0.0 ;
+c6.. 0.0*x1*1.0 + x1 =e= 0.0 ;
+c7.. 0.0*power(x1, 2) + x1 =e= 0.0 ;
+c8.. 0.0*x1*1.0 + x1 =e= 0.0 ;
+c9.. 0.0*x1 =e= 0.0 ;
+c10.. 0.0*power(x1, 2) =e= 0.0 ;
+c11.. 0.0*x1*1.0 =e= 0.0 ;
+c12.. 0.0*power(x1, 2) =e= 0.0 ;
+c13.. 0.0*x1*1.0 =e= 0.0 ;
+c14.. 0.0*x1 =e= 0.0 ;
+c15.. GAMS_OBJECTIVE =e= x1 + 0.0*x1 + 0.0*x1 + x1*x1*0.0 + x1*x1*0.0 + 0.0*power(x1, 2) ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small11.pyomo.gms b/pyomo/repn/tests/gams/small11.pyomo.gms
new file mode 100644
index 00000000000..a74c6ea2fa5
--- /dev/null
+++ b/pyomo/repn/tests/gams/small11.pyomo.gms
@@ -0,0 +1,52 @@
+$offdigit
+
+EQUATIONS
+ c1_lo
+ c1_hi
+ c2_lo
+ c2_hi
+ c3_lo
+ c3_hi
+ c4;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2
+ x3;
+
+c1_lo.. -1.0 =l= 1.0 ;
+c1_hi.. 1.0 =l= 1.0 ;
+c2_lo.. -1.0 =l= x1 ;
+c2_hi.. x1 =l= 1.0 ;
+c3_lo.. -1.0 =l= x2 ;
+c3_hi.. x2 =l= 1.0 ;
+c4.. GAMS_OBJECTIVE =e= x3 ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING lp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small12.pyomo.gms b/pyomo/repn/tests/gams/small12.pyomo.gms
new file mode 100644
index 00000000000..252dde13079
--- /dev/null
+++ b/pyomo/repn/tests/gams/small12.pyomo.gms
@@ -0,0 +1,99 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2
+ c3
+ c4
+ c5
+ c6
+ c7
+ c8
+ c9
+ c10
+ c11
+ c12
+ c13
+ c14
+ c15
+ c16
+ c17
+ c18
+ c19
+ c20
+ c21
+ c22
+ c23
+ c24
+ c25;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2
+ x3
+ x4
+ x5
+ x6
+ x7;
+
+c1.. Expr_if( ( 0.0 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c2.. Expr_if( ( 1.0 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c3.. Expr_if( ( vN1 <= 0.0 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c4.. Expr_if( ( v0 <= 0.0 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c5.. Expr_if( ( vP1 <= 0.0 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c6.. Expr_if( ( vN1 < 0.0 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c7.. Expr_if( ( v0 < 0.0 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c8.. Expr_if( ( vP1 < 0.0 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c9.. Expr_if( ( 0.0 <= 10.0*vN1 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c10.. Expr_if( ( 0.0 <= 10.0*v0 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c11.. Expr_if( ( 0.0 <= 10.0*vP1 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c12.. Expr_if( ( 0.0 < 10.0*vN1 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c13.. Expr_if( ( 0.0 < 10.0*v0 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c14.. Expr_if( ( 0.0 < 10.0*vP1 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c15.. Expr_if( ( -1 <= vN2 <= 1 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c16.. Expr_if( ( - vP1 <= vN1 <= 1 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c17.. Expr_if( ( - vP1**2 <= v0 <= 1 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c18.. Expr_if( ( vN1 <= vP1 <= 1 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c19.. Expr_if( ( -1 <= vP2 <= 1 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c20.. Expr_if( ( -1 < vN2 < 1 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c21.. Expr_if( ( -1 < vN1 < vP1 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c22.. Expr_if( ( -1 < v0 < vP1**2 ), then=( vTrue ), else=( vFalse ) ) =e= 1.0 ;
+c23.. Expr_if( ( -1 < vP1 < vP1 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c24.. Expr_if( ( -1 < vP2 < 1 ), then=( vTrue ), else=( vFalse ) ) =e= -1.0 ;
+c25.. GAMS_OBJECTIVE =e= 10.0*Expr_if( ( v0 ), then=( vTrue ), else=( vFalse ) ) ;
+
+x1.l = 1;
+x2.l = -1;
+x3.l = -1;
+x4.l = 0;
+x5.l = 1;
+x6.l = -2;
+x7.l = 2;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small13.pyomo.gms b/pyomo/repn/tests/gams/small13.pyomo.gms
new file mode 100644
index 00000000000..c40e1dcd757
--- /dev/null
+++ b/pyomo/repn/tests/gams/small13.pyomo.gms
@@ -0,0 +1,45 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2
+ c3
+ c4;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1;
+
+c1.. power(x1, 3) - x1 =e= 0.0 ;
+c2.. 10*(power(x1, 3) - x1) =e= 0.0 ;
+c3.. (power(x1, 3) - x1)*0.1 =e= 0.0 ;
+c4.. GAMS_OBJECTIVE =e= x1 ;
+
+x1.l = 0.5;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp maximizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small14.pyomo.gms b/pyomo/repn/tests/gams/small14.pyomo.gms
new file mode 100644
index 00000000000..0e694dd1e1f
--- /dev/null
+++ b/pyomo/repn/tests/gams/small14.pyomo.gms
@@ -0,0 +1,79 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2
+ c3
+ c4
+ c5
+ c6
+ c7
+ c8
+ c9
+ c10
+ c11
+ c12
+ c13
+ c14
+ c15
+ c16
+ c17
+ c18
+ c19
+ c20;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2;
+
+c1.. log(x1) =e= 0.0 ;
+c2.. log10(x1) =e= 0.0 ;
+c3.. sin(x2) =e= 0.0 ;
+c4.. cos(x2) =e= 1.0 ;
+c5.. tan(x2) =e= 0.0 ;
+c6.. sinh(x2) =e= 0.0 ;
+c7.. cosh(x2) =e= 1.0 ;
+c8.. tanh(x2) =e= 0.0 ;
+c9.. asin(x2) =e= 0.0 ;
+c10.. acos(x2) =e= 1.5707963267948966 ;
+c11.. atan(x2) =e= 0.0 ;
+c12.. asinh(x2) =e= 0.0 ;
+c13.. acosh((7.3890560989306495 + x1)*0.18393972058572117) =e= 0.0 ;
+c14.. atanh(x2) =e= 0.0 ;
+c15.. exp(x2) =e= 1.0 ;
+c16.. sqrt(x1) =e= 1.0 ;
+c17.. ceil(x1) =e= 1.0 ;
+c18.. floor(x1) =e= 1.0 ;
+c19.. abs(x1) =e= 1.0 ;
+c20.. GAMS_OBJECTIVE =e= x1 + x2 ;
+
+x1.l = 1;
+x2.l = 0;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small2.pyomo.gms b/pyomo/repn/tests/gams/small2.pyomo.gms
new file mode 100644
index 00000000000..25a5041eea2
--- /dev/null
+++ b/pyomo/repn/tests/gams/small2.pyomo.gms
@@ -0,0 +1,43 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2;
+
+c1.. power(x1, 2) =e= 4.0 ;
+c2.. GAMS_OBJECTIVE =e= x2 ;
+
+x1.l = 1.0;
+x2.l = 1.0;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small3.pyomo.gms b/pyomo/repn/tests/gams/small3.pyomo.gms
new file mode 100644
index 00000000000..55c0c0bc6ce
--- /dev/null
+++ b/pyomo/repn/tests/gams/small3.pyomo.gms
@@ -0,0 +1,43 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2;
+
+c1.. power(x1, 2) =e= 4.0 ;
+c2.. GAMS_OBJECTIVE =e= x2*x1 ;
+
+x1.l = 1.0;
+x2.l = 1.0;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small4.pyomo.gms b/pyomo/repn/tests/gams/small4.pyomo.gms
new file mode 100644
index 00000000000..b2a61c988ab
--- /dev/null
+++ b/pyomo/repn/tests/gams/small4.pyomo.gms
@@ -0,0 +1,43 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2;
+
+c1.. x1*x2 =e= 4.0 ;
+c2.. GAMS_OBJECTIVE =e= power(x1, 2) ;
+
+x1.l = 1.0;
+x2.l = 1.0;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small5.pyomo.gms b/pyomo/repn/tests/gams/small5.pyomo.gms
new file mode 100644
index 00000000000..8fbd323dbcf
--- /dev/null
+++ b/pyomo/repn/tests/gams/small5.pyomo.gms
@@ -0,0 +1,73 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2
+ c3
+ c4
+ c5
+ c6
+ c7
+ c8
+ c9
+ c10
+ c11
+ c12
+ c13;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2
+ x3;
+
+c1.. 0.5*x1*(x2 - x3) =e= 2.0 ;
+c2.. 0.5*x1*(x2 - x3) =e= 2.0 ;
+c3.. x1*(x2 - x3)*0.5 =e= 2.0 ;
+c4.. x1*(0.5*x2 - 0.5*x3) =e= 2.0 ;
+c5.. x1*(x2 - x3)*0.5 =e= 2.0 ;
+c6.. x1*(x2 - x3) =e= 4.0 ;
+c7.. (1/2.0)*x1*(x2 - x3) =e= 2.0 ;
+c8.. (1/2.0)*x1*(x2 - x3) =e= 2.0 ;
+c9.. x1*(x2 - x3)*(1/2.0) =e= 2.0 ;
+c10.. x1*((1/2.0)*x2 - (1/2.0)*x3) =e= 2.0 ;
+c11.. x1*(x2 - x3)*(1/2.0) =e= 2.0 ;
+c12.. x1*(x2 - x3) =e= 4.0 ;
+c13.. GAMS_OBJECTIVE =e= power(x2, 2)*0.5 + power(x2, 2)*(1/2.0) ;
+
+x1.lo = -1.0;
+x1.up = 1.0;
+x1.l = 3.0;
+x2.lo = -1.0;
+x2.up = 1.0;
+x2.l = 1.0;
+x3.lo = -1.0;
+x3.up = 1.0;
+x3.l = 2.0;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small6.pyomo.gms b/pyomo/repn/tests/gams/small6.pyomo.gms
new file mode 100644
index 00000000000..3898ba8440e
--- /dev/null
+++ b/pyomo/repn/tests/gams/small6.pyomo.gms
@@ -0,0 +1,61 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2
+ c3
+ c4
+ c5
+ c6
+ c7;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2
+ x3;
+
+c1.. (1/2.0)*x1*(x2 - x3) =e= 2.0 ;
+c2.. x1*(1/2.0)*(x2 - x3) =e= 2.0 ;
+c3.. x1*(x2 - x3)*(1/2.0) =e= 2.0 ;
+c4.. x1*(x2*(1/2.0) - x3*(1/2.0)) =e= 2.0 ;
+c5.. x1*(x2 - x3)*(1/2.0) =e= 2.0 ;
+c6.. x1*(x2 - x3) - 2.0*2.0 =e= 0.0 ;
+c7.. GAMS_OBJECTIVE =e= x2 ;
+
+x1.lo = -1.0;
+x1.up = 1.0;
+x1.l = 3.0;
+x2.lo = -1.0;
+x2.up = 1.0;
+x2.l = 1.0;
+x3.lo = -1.0;
+x3.up = 1.0;
+x3.l = 2.0;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small7.pyomo.gms b/pyomo/repn/tests/gams/small7.pyomo.gms
new file mode 100644
index 00000000000..df0d9cb4338
--- /dev/null
+++ b/pyomo/repn/tests/gams/small7.pyomo.gms
@@ -0,0 +1,97 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2
+ c3
+ c4
+ c5
+ c6
+ c7
+ c8
+ c9
+ c10
+ c11
+ c12
+ c13
+ c14
+ c15
+ c16
+ c17
+ c18
+ c19
+ c20
+ c21
+ c22
+ c23
+ c24
+ c25;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2
+ x3;
+
+c1.. (1/2.0)*0.5*x1*(x2 - x3) =e= 2.0 ;
+c2.. x1*(1/2.0)*(1/2.0)*(x2 - x3) =e= 2.0 ;
+c3.. x1*(x2 - x3)*(1/2.0)*0.5 =e= 2.0 ;
+c4.. x1*(x2*(1/2.0)*0.5 - x3*(1/2.0)*0.5) =e= 2.0 ;
+c5.. x1*(x2 - x3)*(1/2.0)*0.5 =e= 2.0 ;
+c6.. x1*(x2 - x3) - 4.0*2.0 =e= 0.0 ;
+c7.. (1/(2.0*2.0))*x1*(x2 - x3) =e= 2.0 ;
+c8.. x1*(1/(2.0*2.0))*(x2 - x3) =e= 2.0 ;
+c9.. x1*(x2 - x3)*(1/(2.0*2.0)) =e= 2.0 ;
+c10.. x1*(x2*(1/(2.0*2.0)) - x3*(1/(2.0*2.0))) =e= 2.0 ;
+c11.. x1*(x2 - x3)*(1/(2.0*2.0)) =e= 2.0 ;
+c12.. x1*(x2 - x3) - 4.0*2.0 =e= 0.0 ;
+c13.. (1/(2.0 + 2.0))*x1*(x2 - x3) =e= 2.0 ;
+c14.. x1*(1/(2.0 + 2.0))*(x2 - x3) =e= 2.0 ;
+c15.. x1*(x2 - x3)*(1/(2.0 + 2.0)) =e= 2.0 ;
+c16.. x1*(x2*(1/(2.0 + 2.0)) - x3*(1/(2.0 + 2.0))) =e= 2.0 ;
+c17.. x1*(x2 - x3)*(1/(2.0 + 2.0)) =e= 2.0 ;
+c18.. x1*(x2 - x3) - 2.0*(2.0 + 2.0) =e= 0.0 ;
+c19.. (1/power((2.0 + 2.0), 2))*x1*(x2 - x3) =e= 2.0 ;
+c20.. x1*(1/power((2.0 + 2.0), 2))*(x2 - x3) =e= 2.0 ;
+c21.. x1*(x2 - x3)*(1/power((2.0 + 2.0), 2)) =e= 2.0 ;
+c22.. x1*(x2*(1/power((2.0 + 2.0), 2)) - x3*(1/power((2.0 + 2.0), 2))) =e= 2.0 ;
+c23.. x1*(x2 - x3)*(1/power((2.0 + 2.0), 2)) =e= 2.0 ;
+c24.. x1*(x2 - x3) - 2.0*power((2.0 + 2.0), 2) =e= 0.0 ;
+c25.. GAMS_OBJECTIVE =e= x2 ;
+
+x1.lo = -1.0;
+x1.up = 1.0;
+x1.l = 3.0;
+x2.lo = -1.0;
+x2.up = 1.0;
+x2.l = 1.0;
+x3.lo = -1.0;
+x3.up = 1.0;
+x3.l = 2.0;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small8.pyomo.gms b/pyomo/repn/tests/gams/small8.pyomo.gms
new file mode 100644
index 00000000000..5cffb208b74
--- /dev/null
+++ b/pyomo/repn/tests/gams/small8.pyomo.gms
@@ -0,0 +1,49 @@
+$offdigit
+
+EQUATIONS
+ c1_lo
+ c2_hi
+ c3_hi
+ c4;
+
+POSITIVE VARIABLES
+ x1
+ x2;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x3;
+
+c1_lo.. 2.0 =l= x1*x1 ;
+c2_hi.. x1 - 0.5*x2 =l= 0.0 ;
+c3_hi.. x3 - (x1 + 2.0) =l= 0.0 ;
+c4.. GAMS_OBJECTIVE =e= x3 + x2*x2 + x1 ;
+
+x3.lo = 7;
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/small9.pyomo.gms b/pyomo/repn/tests/gams/small9.pyomo.gms
new file mode 100644
index 00000000000..54ae8cff39b
--- /dev/null
+++ b/pyomo/repn/tests/gams/small9.pyomo.gms
@@ -0,0 +1,49 @@
+$offdigit
+
+EQUATIONS
+ c1
+ c2
+ c3
+ c4
+ c5
+ c6;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ x1
+ x2;
+
+c1.. x1*0.0*x2 + x1 =e= 1.0 ;
+c2.. 0.0*x1*x2 + x1 =e= 1.0 ;
+c3.. x1 =e= 1.0 ;
+c4.. x1*0.0*x2 =e= 1.0 ;
+c5.. 0.0*x1*x2 =e= 1.0 ;
+c6.. GAMS_OBJECTIVE =e= x1 ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING nlp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/test_gams.py b/pyomo/repn/tests/gams/test_gams.py
new file mode 100644
index 00000000000..d0b65416b24
--- /dev/null
+++ b/pyomo/repn/tests/gams/test_gams.py
@@ -0,0 +1,239 @@
+# ___________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# rights in this software.
+# This software is distributed under the 3-clause BSD License.
+# ___________________________________________________________________________
+#
+# Test the canonical expressions
+#
+
+import os
+
+import pyutilib.th as unittest
+
+from pyomo.environ import *
+import pyomo.opt
+
+thisdir = os.path.dirname(os.path.abspath(__file__))
+
+
+class Test(unittest.TestCase):
+
+ def _cleanup(self, fname):
+ try:
+ os.remove(fname)
+ except OSError:
+ pass
+
+ def _get_fnames(self):
+ class_name, test_name = self.id().split('.')[-2:]
+ prefix = os.path.join(thisdir, test_name.replace("test_", "", 1))
+ return prefix+".gams.baseline", prefix+".gams.out"
+
+ def _check_baseline(self, model, **kwds):
+ baseline_fname, test_fname = self._get_fnames()
+ self._cleanup(test_fname)
+ io_options = {"symbolic_solver_labels": True}
+ io_options.update(kwds)
+ model.write(test_fname,
+ format="gams",
+ io_options=io_options)
+ self.assertFileEqualsBaseline(
+ test_fname,
+ baseline_fname,
+ delete=True)
+
+ def _gen_expression(self, terms):
+ terms = list(terms)
+ expr = 0.0
+ for term in terms:
+ if type(term) is tuple:
+ prodterms = list(term)
+ prodexpr = 1.0
+ for x in prodterms:
+ prodexpr *= x
+ expr += prodexpr
+ else:
+ expr += term
+ return expr
+
+ def test_no_column_ordering_quadratic(self):
+ model = ConcreteModel()
+ model.a = Var()
+ model.b = Var()
+ model.c = Var()
+
+ terms = [model.a, model.b, model.c,
+ (model.a, model.a), (model.b, model.b), (model.c, model.c),
+ (model.a, model.b), (model.a, model.c), (model.b, model.c)]
+ model.obj = Objective(expr=self._gen_expression(terms))
+ model.con = Constraint(expr=self._gen_expression(terms) <= 1)
+ self._check_baseline(model)
+
+ def test_no_column_ordering_linear(self):
+ model = ConcreteModel()
+ model.a = Var()
+ model.b = Var()
+ model.c = Var()
+
+ terms = [model.a, model.b, model.c]
+ model.obj = Objective(expr=self._gen_expression(terms))
+ model.con = Constraint(expr=self._gen_expression(terms) <= 1)
+ self._check_baseline(model)
+
+ def test_no_row_ordering(self):
+ model = ConcreteModel()
+ model.a = Var()
+
+ components = {}
+ components["obj"] = Objective(expr=model.a)
+ components["con1"] = Constraint(expr=model.a >= 0)
+ components["con2"] = Constraint(expr=model.a <= 1)
+ components["con3"] = Constraint(expr=(0, model.a, 1))
+ components["con4"] = Constraint([1,2], rule=lambda m, i: model.a == i)
+
+ for key in components:
+ model.add_component(key, components[key])
+
+ self._check_baseline(model, file_determinism=2)
+
+ def test_var_on_other_model(self):
+ other = ConcreteModel()
+ other.a = Var()
+
+ model = ConcreteModel()
+ model.x = Var()
+ model.c = Constraint(expr=other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+ self._check_baseline(model)
+
+ def test_var_on_deactivated_block(self):
+ model = ConcreteModel()
+ model.x = Var()
+ model.other = Block()
+ model.other.a = Var()
+ model.other.deactivate()
+ model.c = Constraint(expr=model.other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+ self._check_baseline(model)
+
+ def test_var_on_nonblock(self):
+ class Foo(Block().__class__):
+ def __init__(self, *args, **kwds):
+ kwds.setdefault('ctype',Foo)
+ super(Foo,self).__init__(*args, **kwds)
+
+ model = ConcreteModel()
+ model.x = Var()
+ model.other = Foo()
+ model.other.a = Var()
+ model.c = Constraint(expr=model.other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+ self._check_baseline(model)
+
+ def test_expr_xfrm(self):
+ from pyomo.repn.plugins.gams_writer import expression_to_string
+ from pyomo.core.expr.symbol_map import SymbolMap
+ M = ConcreteModel()
+ M.abc = Var()
+
+ smap = SymbolMap()
+
+ expr = M.abc**2.0
+ self.assertEqual(str(expr), "abc**2.0")
+ self.assertEqual(expression_to_string(expr, smap=smap), "power(abc, 2.0)")
+
+ expr = log( M.abc**2.0 )
+ self.assertEqual(str(expr), "log(abc**2.0)")
+ self.assertEqual(expression_to_string(expr, smap=smap), "log(power(abc, 2.0))")
+
+ expr = log( M.abc**2.0 ) + 5
+ self.assertEqual(str(expr), "log(abc**2.0) + 5")
+ self.assertEqual(expression_to_string(expr, smap=smap), "log(power(abc, 2.0)) + 5")
+
+ expr = exp( M.abc**2.0 ) + 5
+ self.assertEqual(str(expr), "exp(abc**2.0) + 5")
+ self.assertEqual(expression_to_string(expr, smap=smap), "exp(power(abc, 2.0)) + 5")
+
+ expr = log( M.abc**2.0 )**4
+ self.assertEqual(str(expr), "log(abc**2.0)**4")
+ self.assertEqual(expression_to_string(expr, smap=smap), "power(log(power(abc, 2.0)), 4)")
+
+ expr = log( M.abc**2.0 )**4.5
+ self.assertEqual(str(expr), "log(abc**2.0)**4.5")
+ self.assertEqual(expression_to_string(expr, smap=smap), "power(log(power(abc, 2.0)), 4.5)")
+
+
+
+#class TestGams_writer(unittest.TestCase):
+class TestGams_writer(object):
+
+ def _cleanup(self, fname):
+ try:
+ os.remove(fname)
+ except OSError:
+ pass
+
+ def _get_fnames(self):
+ class_name, test_name = self.id().split('.')[-2:]
+ prefix = os.path.join(thisdir, test_name.replace("test_", "", 1))
+ return prefix+".gams.baseline", prefix+".gams.out"
+
+ def test_var_on_other_model(self):
+ other = ConcreteModel()
+ other.a = Var()
+
+ model = ConcreteModel()
+ model.x = Var()
+ model.c = Constraint(expr=other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+
+ baseline_fname, test_fname = self._get_fnames()
+ self._cleanup(test_fname)
+ self.assertRaises(
+ KeyError,
+ model.write, test_fname, format='gams')
+ self._cleanup(test_fname)
+
+ def test_var_on_deactivated_block(self):
+ model = ConcreteModel()
+ model.x = Var()
+ model.other = Block()
+ model.other.a = Var()
+ model.other.deactivate()
+ model.c = Constraint(expr=model.other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+
+ baseline_fname, test_fname = self._get_fnames()
+ self._cleanup(test_fname)
+ self.assertRaises(
+ KeyError,
+ model.write, test_fname, format='gams' )
+ self._cleanup(test_fname)
+
+ def test_var_on_nonblock(self):
+ class Foo(Block().__class__):
+ def __init__(self, *args, **kwds):
+ kwds.setdefault('ctype',Foo)
+ super(Foo,self).__init__(*args, **kwds)
+
+ model = ConcreteModel()
+ model.x = Var()
+ model.other = Foo()
+ model.other.a = Var()
+ model.c = Constraint(expr=model.other.a + 2*model.x <= 0)
+ model.obj = Objective(expr=model.x)
+
+ baseline_fname, test_fname = self._get_fnames()
+ self._cleanup(test_fname)
+ self.assertRaises(
+ RuntimeError,
+ model.write, test_fname, format='gams')
+ self._cleanup(test_fname)
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/pyomo/repn/tests/gams/test_gams_comparison.py b/pyomo/repn/tests/gams/test_gams_comparison.py
new file mode 100644
index 00000000000..1e935f708c8
--- /dev/null
+++ b/pyomo/repn/tests/gams/test_gams_comparison.py
@@ -0,0 +1,73 @@
+# ___________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# rights in this software.
+# This software is distributed under the 3-clause BSD License.
+# ___________________________________________________________________________
+#
+# Test the Pyomo BAR writer
+#
+
+import re
+import glob
+import os
+from os.path import abspath, dirname, join
+currdir = dirname(abspath(__file__))+os.sep
+datadir = abspath(join(currdir, "..", "ampl"))+os.sep
+
+import pyutilib.th as unittest
+import pyutilib.subprocess
+
+import pyomo.scripting.pyomo_main as main
+
+
+class Tests(unittest.TestCase):
+
+ def pyomo(self, cmd):
+ os.chdir(currdir)
+ output = main.main(['convert', '--logging=quiet', '-c']+cmd)
+ return output
+
+class BaselineTests(Tests):
+ def __init__(self, *args, **kwds):
+ Tests.__init__(self, *args, **kwds)
+BaselineTests = unittest.category('smoke', 'nightly','expensive')(BaselineTests)
+
+#
+#The following test generates an nl file for the test case
+#and checks that it matches the current pyomo baseline nl file
+#
+@unittest.nottest
+def gams_writer_baseline_test(self, name):
+ if os.path.exists(datadir+name+'.dat'):
+ self.pyomo(['--output='+currdir+name+'.test.gms',
+ datadir+name+'_testCase.py',
+ datadir+name+'.dat'])
+ else:
+ self.pyomo(['--output='+currdir+name+'.test.gms',
+ datadir+name+'_testCase.py'])
+
+ # Check that the pyomo nl file matches its own baseline
+ self.assertFileEqualsBaseline(
+ currdir+name+'.test.gms', currdir+name+'.pyomo.gms',
+ tolerance=(1e-7, False))
+
+
+class ASLTests(Tests):
+
+ def __init__(self, *args, **kwds):
+ Tests.__init__(self, *args, **kwds)
+ASLTests = unittest.category('smoke','nightly','expensive')(ASLTests)
+
+
+# add test methods to classes
+for f in glob.glob(datadir+'*_testCase.py'):
+ name = re.split('[._]',os.path.basename(f))[0]
+ BaselineTests.add_fn_test(fn=gams_writer_baseline_test, name=name)
+ #ASLTests.add_fn_test(fn=nlwriter_asl_test, name=name)
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/pyomo/repn/tests/gams/testgmsfile.gms b/pyomo/repn/tests/gams/testgmsfile.gms
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/pyomo/repn/tests/gams/var_on_deactivated_block.gams.baseline b/pyomo/repn/tests/gams/var_on_deactivated_block.gams.baseline
new file mode 100644
index 00000000000..9c7c08d1abe
--- /dev/null
+++ b/pyomo/repn/tests/gams/var_on_deactivated_block.gams.baseline
@@ -0,0 +1,41 @@
+$offdigit
+
+EQUATIONS
+ c_hi
+ obj;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ other_a
+ x;
+
+c_hi.. other_a + 2*x =l= 0.0 ;
+obj.. GAMS_OBJECTIVE =e= x ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING lp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/var_on_nonblock.gams.baseline b/pyomo/repn/tests/gams/var_on_nonblock.gams.baseline
new file mode 100644
index 00000000000..9c7c08d1abe
--- /dev/null
+++ b/pyomo/repn/tests/gams/var_on_nonblock.gams.baseline
@@ -0,0 +1,41 @@
+$offdigit
+
+EQUATIONS
+ c_hi
+ obj;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ other_a
+ x;
+
+c_hi.. other_a + 2*x =l= 0.0 ;
+obj.. GAMS_OBJECTIVE =e= x ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING lp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/gams/var_on_other_model.gams.baseline b/pyomo/repn/tests/gams/var_on_other_model.gams.baseline
new file mode 100644
index 00000000000..18eec5ddc27
--- /dev/null
+++ b/pyomo/repn/tests/gams/var_on_other_model.gams.baseline
@@ -0,0 +1,41 @@
+$offdigit
+
+EQUATIONS
+ c_hi
+ obj;
+
+VARIABLES
+ GAMS_OBJECTIVE
+ a
+ x;
+
+c_hi.. a + 2*x =l= 0.0 ;
+obj.. GAMS_OBJECTIVE =e= x ;
+
+
+MODEL GAMS_MODEL /all/ ;
+SOLVE GAMS_MODEL USING lp minimizing GAMS_OBJECTIVE;
+
+Scalars MODELSTAT 'model status', SOLVESTAT 'solve status';
+MODELSTAT = GAMS_MODEL.modelstat;
+SOLVESTAT = GAMS_MODEL.solvestat;
+
+Scalar OBJEST 'best objective', OBJVAL 'objective value';
+OBJEST = GAMS_MODEL.objest;
+OBJVAL = GAMS_MODEL.objval;
+
+Scalar NUMVAR 'number of variables';
+NUMVAR = GAMS_MODEL.numvar
+
+Scalar NUMEQU 'number of equations';
+NUMEQU = GAMS_MODEL.numequ
+
+Scalar NUMDVAR 'number of discrete variables';
+NUMDVAR = GAMS_MODEL.numdvar
+
+Scalar NUMNZ 'number of nonzeros';
+NUMNZ = GAMS_MODEL.numnz
+
+Scalar ETSOLVE 'time to execute solve statement';
+ETSOLVE = GAMS_MODEL.etsolve
+
diff --git a/pyomo/repn/tests/mps/test_mps.py b/pyomo/repn/tests/mps/test_mps.py
index aa5ae393c06..62403c0fb3f 100644
--- a/pyomo/repn/tests/mps/test_mps.py
+++ b/pyomo/repn/tests/mps/test_mps.py
@@ -132,7 +132,7 @@ def test_no_row_ordering(self):
components["obj"] = Objective(expr=model.a)
components["con1"] = Constraint(expr=model.a >= 0)
components["con2"] = Constraint(expr=model.a <= 1)
- components["con3"] = Constraint(expr=0 <= model.a <= 1)
+ components["con3"] = Constraint(expr=(0, model.a, 1))
components["con4"] = Constraint([1,2], rule=lambda m, i: model.a == i)
# add components in random order
@@ -151,7 +151,7 @@ def test_row_ordering(self):
components["obj"] = Objective(expr=model.a)
components["con1"] = Constraint(expr=model.a >= 0)
components["con2"] = Constraint(expr=model.a <= 1)
- components["con3"] = Constraint(expr=0 <= model.a <= 1)
+ components["con3"] = Constraint(expr=(0, model.a, 1))
components["con4"] = Constraint([1,2], rule=lambda m, i: model.a == i)
# add components in random order
diff --git a/pyomo/repn/tests/test_gams.py b/pyomo/repn/tests/test_gams.py
index 078c284a9d8..94d8e900aaa 100644
--- a/pyomo/repn/tests/test_gams.py
+++ b/pyomo/repn/tests/test_gams.py
@@ -57,7 +57,7 @@ def test_gams_connector_in_active_constraint(self):
m.b2.c.add(m.b2.x)
m.c = Constraint(expr=m.b1.c == m.b2.c)
m.o = Objective(expr=m.b1.x)
- with self.assertRaises(TypeError):
+ with self.assertRaises(RuntimeError):
m.write('testgmsfile.gms')
diff --git a/pyomo/repn/tests/test_standard.py b/pyomo/repn/tests/test_standard.py
new file mode 100644
index 00000000000..46d714c9a13
--- /dev/null
+++ b/pyomo/repn/tests/test_standard.py
@@ -0,0 +1,3249 @@
+# ___________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and
+# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
+# rights in this software.
+# This software is distributed under the 3-clause BSD License.
+# ___________________________________________________________________________
+#
+# Test the standard expressions
+#
+
+import pickle
+import os
+from os.path import abspath, dirname
+currdir = dirname(abspath(__file__))+os.sep
+
+import pyutilib.th as unittest
+import pyutilib.services
+
+from pyomo.core.expr.current import Expr_if
+from pyomo.core.expr import expr_common, current as EXPR
+from pyomo.repn import *
+from pyomo.environ import *
+from pyomo.core.base.numvalue import native_numeric_types
+
+from six import iteritems
+from six.moves import range
+
+class frozendict(dict):
+ __slots__ = ('_hash',)
+ def __hash__(self):
+ rval = getattr(self, '_hash', None)
+ if rval is None:
+ rval = self._hash = hash(frozenset(iteritems(self)))
+ return rval
+
+
+# A utility to facilitate comparison of tuples where we don't care about ordering
+def repn_to_dict(repn):
+ result = {}
+ for i in range(len(repn.linear_vars)):
+ if id(repn.linear_vars[i]) in result:
+ result[id(repn.linear_vars[i])] += repn.linear_coefs[i]
+ else:
+ result[id(repn.linear_vars[i])] = repn.linear_coefs[i]
+ for i in range(len(repn.quadratic_vars)):
+ v1_, v2_ = repn.quadratic_vars[i]
+ if id(v1_) <= id(v2_):
+ result[id(v1_), id(v2_)] = repn.quadratic_coefs[i]
+ else:
+ result[id(v2_), id(v1_)] = repn.quadratic_coefs[i]
+ if not (repn.constant is None or (type(repn.constant) in native_numeric_types and repn.constant == 0)):
+ result[None] = repn.constant
+ return result
+
+
+class TestSimple(unittest.TestCase):
+
+ def test_number(self):
+ # 1.0
+ m = AbstractModel()
+ m.a = Var()
+ e = 1.0
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_var(self):
+ # a
+ m = ConcreteModel()
+ m.a = Var()
+ e = m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ m.a.value = 3
+ m.a.fixed = True
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None: 3 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None: 3 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ self.assertTrue(rep.constant is m.a)
+
+ def test_param(self):
+ # p
+ m = AbstractModel()
+ m.p = Param()
+ e = m.p
+
+ with self.assertRaises(ValueError):
+ rep = generate_standard_repn(e)
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None : m.p }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #s = pickle.dumps(rep)
+ #rep = pickle.loads(s)
+ #baseline = { None : m.p }
+ #self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_simplesum(self):
+ # a + b
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ e = m.a + m.b
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 1, id(m.b) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]) : 1, id(rep.linear_vars[1]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_constsum(self):
+ # a + 5
+ m = AbstractModel()
+ m.a = Var()
+ e = m.a + 5
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:5, id(rep.linear_vars[0]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # 5 + a
+ m = AbstractModel()
+ m.a = Var()
+ e = 5 + m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:5, id(rep.linear_vars[0]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_paramsum(self):
+ # a + 5
+ m = ConcreteModel()
+ m.a = Var()
+ m.p = Param(mutable=True, default=5)
+ e = m.a + m.p
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:5, id(rep.linear_vars[0]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # 5 + a
+ m = ConcreteModel()
+ m.a = Var()
+ m.p = Param(mutable=True, default=5)
+ e = m.p + m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:5, id(rep.linear_vars[0]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ self.assertTrue(rep.constant is m.p)
+
+ def test_paramprod1(self):
+ # p*a
+ m = ConcreteModel()
+ m.a = Var()
+ m.p = Param(mutable=True, default=5)
+ e = m.p*m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]) : 5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0] is m.p)
+
+ def test_paramprod2(self):
+ # p*a
+ m = ConcreteModel()
+ m.a = Var()
+ m.p = Param(mutable=True, default=0)
+ e = m.p*m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 0 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0] is m.p)
+
+ def test_linear_sum1(self):
+ #
+ m = ConcreteModel()
+ m.x = Var()
+ m.y = Var()
+ m.p = Param(mutable=True, default=1)
+ m.q = Param(mutable=True, default=2)
+ e = m.p*m.x + m.q*m.y
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x):1, id(m.y):2 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x):1, id(m.y):2 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0] is m.p)
+ self.assertTrue(rep.linear_coefs[1] is m.q)
+
+ def test_linear_sum2(self):
+ #
+ m = ConcreteModel()
+ m.A = Set(initialize=range(5))
+ m.x = Var(m.A)
+ m.p = Param(m.A, mutable=True, default=1)
+ e = quicksum(m.p[i]*m.x[i] for i in m.A)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 5)
+ self.assertTrue(len(rep.linear_coefs) == 5)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):1, id(m.x[1]):1, id(m.x[2]):1, id(m.x[3]):1, id(m.x[4]):1}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 5)
+ self.assertTrue(len(rep.linear_coefs) == 5)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):1, id(m.x[1]):1, id(m.x[2]):1, id(m.x[3]):1, id(m.x[4]):1}
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0] is m.p[0])
+ self.assertTrue(rep.linear_coefs[1] is m.p[1])
+
+ def test_linear_sum3(self):
+ #
+ m = ConcreteModel()
+ m.A = Set(initialize=range(5))
+ m.x = Var(m.A, initialize=3)
+ m.p = Param(m.A, mutable=True, default=1)
+ e = quicksum((i+1)*m.x[i] for i in m.A)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 5)
+ self.assertTrue(len(rep.linear_coefs) == 5)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):1, id(m.x[1]):2, id(m.x[2]):3, id(m.x[3]):4, id(m.x[4]):5}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ m.x[2].fixed = True
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 4)
+ self.assertTrue(len(rep.linear_coefs) == 4)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):1, id(m.x[1]):2, None:9, id(m.x[3]):4, id(m.x[4]):5}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_linear_sum4(self):
+ #
+ m = ConcreteModel()
+ m.A = Set(initialize=range(5))
+ m.x = Var(m.A, initialize=3)
+ m.p = Param(m.A, mutable=True, default=1)
+ e = quicksum(m.p[i]*m.x[i] for i in m.A)
+
+ m.x[2].fixed = True
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 4)
+ self.assertTrue(len(rep.linear_coefs) == 4)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):1, id(m.x[1]):1, None:3, id(m.x[3]):1, id(m.x[4]):1}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 4)
+ self.assertTrue(len(rep.linear_coefs) == 4)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):1, id(m.x[1]):1, None:3, id(m.x[3]):1, id(m.x[4]):1}
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0] is m.p[0])
+ self.assertTrue(rep.linear_coefs[1] is m.p[1])
+ self.assertTrue(type(rep.constant) is EXPR.MonomialTermExpression)
+
+ def test_linear_sum5(self):
+ #
+ m = ConcreteModel()
+ m.A = Set(initialize=range(5))
+ m.x = Var(m.A, initialize=3)
+ m.p = Param(m.A, mutable=True, default=1)
+ e = quicksum((m.p[i]*m.p[i])*m.x[i] for i in m.A)
+
+ m.x[2].fixed = True
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 4)
+ self.assertTrue(len(rep.linear_coefs) == 4)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):1, id(m.x[1]):1, None:3, id(m.x[3]):1, id(m.x[4]):1}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 4)
+ self.assertTrue(len(rep.linear_coefs) == 4)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):1, id(m.x[1]):1, None:3, id(m.x[3]):1, id(m.x[4]):1}
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0].is_expression_type())
+ self.assertTrue(type(rep.constant) is EXPR.MonomialTermExpression)
+
+ def test_linear_sum6(self):
+ #
+ m = ConcreteModel()
+ m.A = Set(initialize=range(5))
+ m.x = Var(m.A)
+ m.p = Param(m.A, mutable=True, default=1)
+ m.q = Param(m.A, mutable=True, default=2)
+ e = quicksum(m.p[i]*m.x[i] if i < 5 else m.q[i-5]*m.x[i-5] for i in range(10))
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 5)
+ self.assertTrue(len(rep.linear_coefs) == 5)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):3, id(m.x[1]):3, id(m.x[2]):3, id(m.x[3]):3, id(m.x[4]):3}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 5)
+ self.assertTrue(len(rep.linear_coefs) == 5)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):3, id(m.x[1]):3, id(m.x[2]):3, id(m.x[3]):3, id(m.x[4]):3}
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0].is_expression_type())
+
+ def test_general_sum1(self):
+ #
+ m = ConcreteModel()
+ m.A = Set(initialize=range(3))
+ m.x = Var(m.A, initialize=2)
+ m.p = Param(m.A, mutable=True, default=3)
+ e = sum(m.p[i]*m.x[i] for i in range(3))
+ m.x[1].fixed = True
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):3, None:6, id(m.x[2]):3}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):3, None:6, id(m.x[2]):3}
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0] is m.p[0])
+
+ def test_general_sum2(self):
+ #
+ m = ConcreteModel()
+ m.A = Set(initialize=range(3))
+ m.x = Var(m.A, initialize=2)
+ m.p = Param(m.A, mutable=True, default=3)
+ e = sum(m.p[i]*m.x[i] if i!=1 else m.x[i] for i in range(3))
+ m.x[1].fixed = True
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):3, None:2, id(m.x[2]):3}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):3, None:2, id(m.x[2]):3}
+ self.assertEqual(baseline, repn_to_dict(rep))
+ #
+ self.assertTrue(rep.linear_coefs[0] is m.p[0])
+
+ def test_general_sum3(self):
+ #
+ m = ConcreteModel()
+ m.A = Set(initialize=range(3))
+ m.x = Var(m.A, initialize=2)
+ m.p = Param(m.A, mutable=True, default=3)
+ e = sum(m.p[i]*m.x[i] if i<3 else m.x[i-3] for i in range(6))
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):4, id(m.x[1]):4, id(m.x[2]):4}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ rep = generate_standard_repn(e, compute_values=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.x[0]):4, id(m.x[1]):4, id(m.x[2]):4}
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_nestedSum(self):
+ #
+ # Check the structure of nested sums
+ #
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+ m.d = Var()
+
+ # +
+ # / \
+ # + 5
+ # / \
+ # a b
+ e1 = m.a + m.b
+ e = e1 + 5
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a) : 1, id(m.b) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:5, id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # +
+ # / \
+ # 5 +
+ # / \
+ # a b
+ e1 = m.a + m.b
+ e = 5 + e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a) : 1, id(m.b) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:5, id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # +
+ # / \
+ # + c
+ # / \
+ # a b
+ e1 = m.a + m.b
+ e = e1 + m.c
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 1, id(m.b) : 1, id(m.c) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):1, id(rep.linear_vars[2]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # +
+ # / \
+ # c +
+ # / \
+ # a b
+ e1 = m.a + m.b
+ e = m.c + e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 1, id(m.b) : 1, id(m.c) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):1, id(rep.linear_vars[2]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # +
+ # / \
+ # + +
+ # / \ / \
+ # a b c d
+ e1 = m.a + m.b
+ e2 = m.c + m.d
+ e = e1 + e2
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 4)
+ self.assertTrue(len(rep.linear_coefs) == 4)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 1, id(m.b) : 1, id(m.c) : 1, id(m.d) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):1, id(rep.linear_vars[2]):1, id(rep.linear_vars[3]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_sumOf_nestedTrivialProduct(self):
+ #
+ # Check sums with nested products
+ #
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+
+ # +
+ # / \
+ # * b
+ # / \
+ # a 5
+ e1 = m.a * 5
+ e = e1 + m.b
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 5, id(m.b) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]) : 5, id(rep.linear_vars[1]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # +
+ # / \
+ # b *
+ # / \
+ # a 5
+ e = m.b + e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 5, id(m.b) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[1]) : 5, id(rep.linear_vars[0]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # +
+ # / \
+ # * +
+ # / \ / \
+ # a 5 b c
+ e2 = m.b + m.c
+ e = e1 + e2
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 5, id(m.b) : 1, id(m.c) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[2]) : 5, id(rep.linear_vars[0]) : 1, id(rep.linear_vars[1]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # +
+ # / \
+ # + *
+ # / \ / \
+ # b c a 5
+ e2 = m.b + m.c
+ e = e2 + e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 5, id(m.b) : 1, id(m.c) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[2]) : 5, id(rep.linear_vars[0]) : 1, id(rep.linear_vars[1]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # +
+ # / \
+ # * *
+ # / \ / \
+ # a 5 b 5
+ e2 = m.b * 5
+ e = e2 + e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 5, id(m.b) : 5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):5, id(rep.linear_vars[1]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_negation(self):
+ # -
+ # \
+ # a
+ m = AbstractModel()
+ m.a = Var()
+ e = - m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : -1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]) : -1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_simpleDiff(self):
+ # -
+ # / \
+ # a b
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ e = m.a - m.b
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a) : 1, id(m.b) : -1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]) : 1, id(rep.linear_vars[1]) : -1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # a a
+ e = m.a - m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertEqual(len(rep.linear_vars), 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_constDiff(self):
+ # -
+ # / \
+ # a 5
+ m = AbstractModel()
+ m.a = Var()
+ e = m.a - 5
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:-5, id(m.a) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:-5, id(rep.linear_vars[0]) : 1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # 5 a
+ e = 5 - m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a) : -1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:5, id(rep.linear_vars[0]):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_nestedDiff(self):
+ #
+ # Check the structure of nested differences
+ #
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+ m.d = Var()
+
+ # -
+ # / \
+ # - 5
+ # / \
+ # a b
+ e1 = m.a - m.b
+ e = e1 - 5
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:-5, id(m.a):1, id(m.b):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:-5, id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # 5 -
+ # / \
+ # a b
+ e1 = m.a - m.b
+ e = 5 - e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:5, id(m.a):-1, id(m.b):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:5, id(rep.linear_vars[0]):-1, id(rep.linear_vars[1]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # - c
+ # / \
+ # a b
+ e1 = m.a - m.b
+ e = e1 - m.c
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):1, id(m.b):-1, id(m.c):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):-1, id(rep.linear_vars[2]):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # c -
+ # / \
+ # a b
+ e1 = m.a - m.b
+ e = m.c - e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):-1, id(m.b):1, id(m.c):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[1]):-1, id(rep.linear_vars[0]):1, id(rep.linear_vars[2]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # - -
+ # / \ / \
+ # a b c d
+ e1 = m.a - m.b
+ e2 = m.c - m.d
+ e = e1 - e2
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 4)
+ self.assertTrue(len(rep.linear_coefs) == 4)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):1, id(m.b):-1, id(m.c):-1, id(m.d):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):-1, id(rep.linear_vars[2]):-1, id(rep.linear_vars[3]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # - -
+ # / \ / \
+ # c d a b
+ e1 = m.a - m.b
+ e2 = m.c - m.d
+ e = e2 - e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 4)
+ self.assertTrue(len(rep.linear_coefs) == 4)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):-1, id(m.b):1, id(m.c):1, id(m.d):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[2]):-1, id(rep.linear_vars[3]):1, id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_sumOf_nestedTrivialProduct2(self):
+ #
+ # Check the structure of sum of products
+ #
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+
+ # -
+ # / \
+ # * b
+ # / \
+ # a 5
+ e1 = m.a * 5
+ e = e1 - m.b
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):5, id(m.b):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):5, id(rep.linear_vars[1]):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # b *
+ # / \
+ # a 5
+ e1 = m.a * 5
+ e = m.b - e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):-5, id(m.b):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[1]):-5, id(rep.linear_vars[0]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # * -
+ # / \ / \
+ # a 5 b c
+ e1 = m.a * 5
+ e2 = m.b - m.c
+ e = e1 - e2
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):5, id(m.b):-1, id(m.c):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):5, id(rep.linear_vars[1]):-1, id(rep.linear_vars[2]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # / \
+ # - *
+ # / \ / \
+ # b c a 5
+ e1 = m.a * 5
+ e2 = m.b - m.c
+ e = e2 - e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 3)
+ self.assertTrue(len(rep.linear_coefs) == 3)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):-5, id(m.b):1, id(m.c):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[2]):-5, id(rep.linear_vars[0]):1, id(rep.linear_vars[1]):-1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # -
+ # \
+ # -
+ # / \
+ # a b
+ e = - (m.a - m.b)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):-1, id(m.b):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):-1, id(rep.linear_vars[1]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_simpleProduct1(self):
+ m = ConcreteModel()
+ m.a = Var()
+ m.p = Param(default=2)
+ # *
+ # / \
+ # a p
+ e = m.a * m.p
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):2 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):2 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # a 0
+ e = m.a * 0
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_simpleProduct2(self):
+ # *
+ # / \
+ # a 5
+ m = AbstractModel()
+ m.a = Var()
+ e = m.a * 5
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # 5 a
+ e = 5 * m.a
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_nestedProduct(self):
+ # *
+ # / \
+ # * 5
+ # / \
+ # a b
+ m = ConcreteModel()
+ m.a = Var()
+ m.b = Param(default=2)
+ m.c = Param(default=3)
+ m.d = Param(default=7)
+
+ e1 = m.a * m.b
+ e = e1 * 5
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # 5 *
+ # / \
+ # a b
+ e1 = m.a * m.b
+ e = 5 * e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # * *
+ # / \ / \
+ # a b c d
+ e1 = m.a * m.b
+ e2 = m.c * m.d
+ e = e1 * e2
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):42 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):42 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_nestedProduct2(self):
+ #
+ # Check the structure of nested products
+ #
+ m = ConcreteModel()
+ m.a = Param(default=2)
+ m.b = Param(default=3)
+ m.c = Param(default=5)
+ m.d = Var()
+ #
+ # Check the structure of nested products
+ #
+ # *
+ # / \
+ # + +
+ # / \ / \
+ # c + d
+ # / \
+ # a b
+ e1 = m.a + m.b
+ e2 = m.c + e1
+ e3 = e1 + m.d
+ e = e2 * e3
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:50, id(m.d):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { None:50, id(rep.linear_vars[0]):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ #
+ # Check the structure of nested products
+ #
+ # *
+ # / \
+ # * *
+ # / \ / \
+ # c + d
+ # / \
+ # a b
+ e1 = m.a + m.b
+ e2 = m.c * e1
+ e3 = e1 * m.d
+ e = e2 * e3
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.d):125 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):125 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_division(self):
+ #
+ # /
+ # / \
+ # + 2
+ # / \
+ # a b
+ m = ConcreteModel()
+ m.a = Var()
+ m.b = Var()
+ m.y = Var(initialize=2.0)
+ m.y.fixed = True
+
+ e = (m.a + m.b)/2.0
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):0.5, id(m.b):0.5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):0.5, id(rep.linear_vars[1]):0.5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # /
+ # / \
+ # + y
+ # / \
+ # a b
+ e = (m.a + m.b)/m.y
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):0.5, id(m.b):0.5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):0.5, id(rep.linear_vars[1]):0.5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # /
+ # / \
+ # + +
+ # / \ / \
+ # a b y 2
+ e = (m.a + m.b)/(m.y+2)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):0.25, id(m.b):0.25 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):0.25, id(rep.linear_vars[1]):0.25 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_weighted_sum1(self):
+ # *
+ # / \
+ # + 5
+ # / \
+ # a b
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+ m.d = Var()
+
+ e1 = m.a + m.b
+ e = e1 * 5
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):5, id(m.b):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):5, id(rep.linear_vars[1]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # 5 +
+ # / \
+ # a b
+ e1 = m.a + m.b
+ e = 5 * e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):5, id(m.b):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):5, id(rep.linear_vars[1]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # 5 *
+ # / \
+ # 2 +
+ # / \
+ # a b
+ e1 = m.a + m.b
+ e = 5 * 2* e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):10, id(m.b):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):10, id(rep.linear_vars[1]):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # 5(a+2(a+b))
+ e = 5*(m.a+2*(m.a+m.b))
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):15, id(m.b):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):15, id(rep.linear_vars[1]):10 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_quadratic1(self):
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+ m.d = Var()
+
+ ab_key = (id(m.a),id(m.b)) if id(m.a) <= id(m.b) else (id(m.b),id(m.a))
+
+ # *
+ # / \
+ # * b
+ # / \
+ # a 5
+ e1 = m.a * 5
+ e = e1 * m.b
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ if id(rep.quadratic_vars[0][0]) < id(rep.quadratic_vars[0][1]):
+ baseline = { (id(rep.quadratic_vars[0][0]), id(rep.quadratic_vars[0][1])):5 }
+ else:
+ baseline = { (id(rep.quadratic_vars[0][1]), id(rep.quadratic_vars[0][0])):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # * 5
+ # / \
+ # a b
+ e1 = m.a * m.b
+ e = e1 * 5
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ if id(rep.quadratic_vars[0][0]) < id(rep.quadratic_vars[0][1]):
+ baseline = { (id(rep.quadratic_vars[0][0]), id(rep.quadratic_vars[0][1])):5 }
+ else:
+ baseline = { (id(rep.quadratic_vars[0][1]), id(rep.quadratic_vars[0][0])):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # 5 *
+ # / \
+ # a b
+ e1 = m.a * m.b
+ e = 5*e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ if id(rep.quadratic_vars[0][0]) < id(rep.quadratic_vars[0][1]):
+ baseline = { (id(rep.quadratic_vars[0][0]), id(rep.quadratic_vars[0][1])):5 }
+ else:
+ baseline = { (id(rep.quadratic_vars[0][1]), id(rep.quadratic_vars[0][0])):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # b *
+ # / \
+ # a 5
+ e1 = m.a * 5
+ e = m.b*e1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ if id(rep.quadratic_vars[0][0]) < id(rep.quadratic_vars[0][1]):
+ baseline = { (id(rep.quadratic_vars[0][0]), id(rep.quadratic_vars[0][1])):5 }
+ else:
+ baseline = { (id(rep.quadratic_vars[0][1]), id(rep.quadratic_vars[0][0])):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+
+ def test_quadratic2(self):
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+ m.d = Var()
+
+ ab_key = (id(m.a),id(m.b)) if id(m.a) <= id(m.b) else (id(m.b),id(m.a))
+ ac_key = (id(m.a),id(m.c)) if id(m.a) <= id(m.c) else (id(m.c),id(m.a))
+ bc_key = (id(m.b),id(m.c)) if id(m.b) <= id(m.c) else (id(m.c),id(m.b))
+
+ # *
+ # / \
+ # + b
+ # / \
+ # a 5
+ e1 = m.a + 5
+ e = e1 * m.b
+
+ # Collect quadratics
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:1, id(m.b):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ if id(rep.quadratic_vars[0][0]) < id(rep.quadratic_vars[0][1]):
+ baseline = { (id(rep.quadratic_vars[0][0]), id(rep.quadratic_vars[0][1])):1, id(rep.linear_vars[0]):5 }
+ else:
+ baseline = { (id(rep.quadratic_vars[0][1]), id(rep.quadratic_vars[0][0])):1, id(rep.linear_vars[0]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # Do not collect quadratics
+ rep = generate_standard_repn(e, quadratic=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertEqual(len(rep.nonlinear_vars), 2)
+ baseline1 = { }
+ self.assertEqual(baseline1, repn_to_dict(rep))
+ baseline2 = set([ id(m.a), id(m.b) ])
+ self.assertEqual(baseline2, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline1, repn_to_dict(rep))
+
+ # *
+ # / \
+ # b +
+ # / \
+ # a 5
+ e1 = m.a + 5
+ e = m.b * e1
+
+ # Collect quadratics
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:1, id(m.b):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ if id(rep.quadratic_vars[0][0]) < id(rep.quadratic_vars[0][1]):
+ baseline = { (id(rep.quadratic_vars[0][0]), id(rep.quadratic_vars[0][1])):1, id(rep.linear_vars[0]):5 }
+ else:
+ baseline = { (id(rep.quadratic_vars[0][1]), id(rep.quadratic_vars[0][0])):1, id(rep.linear_vars[0]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # Do not collect quadratics
+ rep = generate_standard_repn(e, quadratic=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertEqual(len(rep.nonlinear_vars), 2)
+ baseline1 = { }
+ self.assertEqual(baseline1, repn_to_dict(rep))
+ baseline2 = set([ id(m.a), id(m.b) ])
+ self.assertEqual(baseline2, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline1, repn_to_dict(rep))
+
+ # *
+ # / \
+ # + +
+ # / \ / \
+ # c b a 5
+ e = (m.c + m.b) * (m.a + 5)
+
+ # Collect quadratics
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 2)
+ self.assertTrue(len(rep.quadratic_coefs) == 2)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:1, ac_key:1, id(m.b):5, id(m.c):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ ab_key_ = (id(rep.quadratic_vars[0][0]),id(rep.quadratic_vars[0][1])) if id(rep.quadratic_vars[0][0]) <= id(rep.quadratic_vars[0][1]) else (id(rep.quadratic_vars[0][1]),id(rep.quadratic_vars[0][0]))
+ ac_key_ = (id(rep.quadratic_vars[1][0]),id(rep.quadratic_vars[1][1])) if id(rep.quadratic_vars[1][0]) <= id(rep.quadratic_vars[1][1]) else (id(rep.quadratic_vars[1][1]),id(rep.quadratic_vars[1][0]))
+ baseline = { ab_key_:1, ac_key_:1, id(rep.linear_vars[0]):5, id(rep.linear_vars[1]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # *
+ # / \
+ # + +
+ # / \ / \
+ # a 5 b c
+ e = (m.a + 5) * (m.b + m.c)
+
+ # Collect quadratics
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 2)
+ self.assertTrue(len(rep.linear_coefs) == 2)
+ self.assertTrue(len(rep.quadratic_vars) == 2)
+ self.assertTrue(len(rep.quadratic_coefs) == 2)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:1, ac_key:1, id(m.b):5, id(m.c):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ ab_key_ = (id(rep.quadratic_vars[0][0]),id(rep.quadratic_vars[0][1])) if id(rep.quadratic_vars[0][0]) <= id(rep.quadratic_vars[0][1]) else (id(rep.quadratic_vars[0][1]),id(rep.quadratic_vars[0][0]))
+ ac_key_ = (id(rep.quadratic_vars[1][0]),id(rep.quadratic_vars[1][1])) if id(rep.quadratic_vars[1][0]) <= id(rep.quadratic_vars[1][1]) else (id(rep.quadratic_vars[1][1]),id(rep.quadratic_vars[1][0]))
+ baseline = { ab_key_:1, ac_key_:1, id(rep.linear_vars[0]):5, id(rep.linear_vars[1]):5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # Do not collect quadratics
+ rep = generate_standard_repn(e, quadratic=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertEqual(len(rep.nonlinear_vars), 3)
+ baseline1 = { }
+ self.assertEqual(baseline1, repn_to_dict(rep))
+ baseline2 = set([ id(m.a), id(m.b), id(m.c) ])
+ self.assertEqual(baseline2, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline1, repn_to_dict(rep))
+
+ # *
+ # / \
+ # * +
+ # / \ / \
+ # a 5 b c
+ e = (m.a * 5) * (m.b + m.c)
+
+ # Collect quadratics
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 2)
+ self.assertTrue(len(rep.quadratic_coefs) == 2)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:5, ac_key:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ ab_key_ = (id(rep.quadratic_vars[0][0]),id(rep.quadratic_vars[0][1])) if id(rep.quadratic_vars[0][0]) <= id(rep.quadratic_vars[0][1]) else (id(rep.quadratic_vars[0][1]),id(rep.quadratic_vars[0][0]))
+ ac_key_ = (id(rep.quadratic_vars[1][0]),id(rep.quadratic_vars[1][1])) if id(rep.quadratic_vars[1][0]) <= id(rep.quadratic_vars[1][1]) else (id(rep.quadratic_vars[1][1]),id(rep.quadratic_vars[1][0]))
+ baseline = { ab_key_:5, ac_key_:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # Do not collect quadratics
+ rep = generate_standard_repn(e, quadratic=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertEqual(len(rep.nonlinear_vars), 3)
+ baseline1 = { }
+ self.assertEqual(baseline1, repn_to_dict(rep))
+ baseline2 = set([ id(m.a), id(m.b), id(m.c) ])
+ self.assertEqual(baseline2, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline1, repn_to_dict(rep))
+
+ # *
+ # / \
+ # + *
+ # / \ / \
+ # b c a 5
+ e = (m.b + m.c) * (m.a * 5)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 2)
+ self.assertTrue(len(rep.quadratic_coefs) == 2)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { ab_key:5, ac_key:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ ab_key_ = (id(rep.quadratic_vars[0][0]),id(rep.quadratic_vars[0][1])) if id(rep.quadratic_vars[0][0]) <= id(rep.quadratic_vars[0][1]) else (id(rep.quadratic_vars[0][1]),id(rep.quadratic_vars[0][0]))
+ ac_key_ = (id(rep.quadratic_vars[1][0]),id(rep.quadratic_vars[1][1])) if id(rep.quadratic_vars[1][0]) <= id(rep.quadratic_vars[1][1]) else (id(rep.quadratic_vars[1][1]),id(rep.quadratic_vars[1][0]))
+ baseline = { ab_key_:5, ac_key_:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # Do not collect quadratics
+ rep = generate_standard_repn(e, quadratic=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertEqual(len(rep.nonlinear_vars), 3)
+ baseline1 = { }
+ self.assertEqual(baseline1, repn_to_dict(rep))
+ baseline2 = set([ id(m.a), id(m.b), id(m.c) ])
+ self.assertEqual(baseline2, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline1, repn_to_dict(rep))
+
+ # *
+ # / \
+ # + *
+ # / \ / \
+ # a 5 b c
+ e = (m.a + 5) * (m.b * m.c)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertEqual(len(rep.nonlinear_vars), 3)
+ baseline = { bc_key:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ bc_key_ = (id(rep.quadratic_vars[0][0]),id(rep.quadratic_vars[0][1])) if id(rep.quadratic_vars[0][0]) <= id(rep.quadratic_vars[0][1]) else (id(rep.quadratic_vars[0][1]),id(rep.quadratic_vars[0][0]))
+ baseline = { bc_key_:5 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # Do not collect quadratics
+ rep = generate_standard_repn(e, quadratic=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertEqual(len(rep.nonlinear_vars), 3)
+ baseline1 = { }
+ self.assertEqual(baseline1, repn_to_dict(rep))
+ baseline2 = set([ id(m.a), id(m.b), id(m.c) ])
+ self.assertEqual(baseline2, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline1, repn_to_dict(rep))
+
+ def test_pow(self):
+ # ^
+ # / \
+ # a 0
+ m = ConcreteModel()
+ m.a = Var()
+ m.b = Var()
+ m.p = Param()
+ m.q = Param(default=1)
+ m.r = Param(default=2)
+
+ e = m.a ** 0
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # ^
+ # / \
+ # a 1
+ e = m.a ** 1
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # ^
+ # / \
+ # a 2
+ e = m.a ** 2
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { (id(m.a),id(m.a)):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { (id(rep.quadratic_vars[0][0]),id(rep.quadratic_vars[0][1])):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # ^
+ # / \
+ # a r
+ e = m.a ** m.r
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 2 )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertTrue( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 1)
+ self.assertTrue(len(rep.quadratic_coefs) == 1)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { (id(m.a),id(m.a)):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { (id(rep.quadratic_vars[0][0]),id(rep.quadratic_vars[0][1])):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # ^
+ # / \
+ # a 2
+ e = m.a ** 2
+
+ rep = generate_standard_repn(e, quadratic=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 1)
+ baseline = set([ id(m.a) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = set([ id(rep.nonlinear_vars[0]) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+
+ # ^
+ # / \
+ # a m.r
+ e = m.a ** m.r
+
+ rep = generate_standard_repn(e, quadratic=False)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 1)
+ baseline = set([ id(m.a) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = set([ id(rep.nonlinear_vars[0]) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+
+ # ^
+ # / \
+ # a q
+ e = m.a ** m.q
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_pow2(self):
+ # ^
+ # / \
+ # a 2
+ m = ConcreteModel()
+ m.a = Var(initialize=2)
+ m.p = Param(default=3)
+ m.a.fixed = True
+
+ e = m.p*m.a**2
+
+ rep = generate_standard_repn(e, compute_values=False, quadratic=False)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:12 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_abs(self):
+ # abs
+ # /
+ # a
+ m = ConcreteModel()
+ m.a = Var()
+ m.q = Param(default=-1)
+
+ e = abs(m.a)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 1)
+ baseline = set([ id(m.a) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = set([ id(rep.nonlinear_vars[0]) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+
+ # abs
+ # /
+ # a
+ e = abs(m.a)
+ m.a.set_value(-1)
+ m.a.fixed = True
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # abs
+ # /
+ # q
+ e = abs(m.q)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_cos(self):
+ # cos
+ # /
+ # a
+ m = ConcreteModel()
+ m.a = Var()
+ m.q = Param(default=0)
+
+ e = cos(m.a)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 1)
+ baseline = set([ id(m.a) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = set([ id(rep.nonlinear_vars[0]) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+
+ # cos
+ # /
+ # a
+ e = cos(m.a)
+ m.a.set_value(0)
+ m.a.fixed = True
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:1.0 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # cos
+ # /
+ # q
+ e = cos(m.q)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertTrue( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 0 )
+ self.assertTrue( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { None:1.0 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ def test_ExprIf(self):
+ # ExprIf
+ # / | \
+ # True a b
+ m = AbstractModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+ m.q = Param(default=1)
+
+ e = EXPR.Expr_if(IF=True, THEN=m.a, ELSE=m.b)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # ExprIf
+ # / | \
+ # False a b
+ e = EXPR.Expr_if(IF=False, THEN=m.a, ELSE=m.b)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.b):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # ExprIf
+ # / | \
+ # c a b
+ e = EXPR.Expr_if(IF=m.c, THEN=m.a, ELSE=m.b)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), None )
+ self.assertFalse( rep.is_constant() )
+ self.assertFalse( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertTrue( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 0)
+ self.assertTrue(len(rep.linear_coefs) == 0)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertFalse(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 3)
+ baseline = set([ id(m.a), id(m.b), id(m.c) ])
+ self.assertEqual(baseline, set(id(v_) for v_ in EXPR.identify_variables(rep.nonlinear_expr)))
+ #s = pickle.dumps(rep)
+ #rep = pickle.loads(s)
+ #self.assertEqual(baseline, repn_to_dict(rep))
+
+ m = ConcreteModel()
+ m.a = Var()
+ m.b = Var()
+ m.c = Var()
+ m.q = Param(default=1)
+
+ # ExprIf
+ # / | \
+ # bool a b
+ e = EXPR.Expr_if(IF=m.q, THEN=m.a, ELSE=m.b)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.a):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+ # ExprIf
+ # / | \
+ # c a b
+ e = EXPR.Expr_if(IF=m.c, THEN=m.a, ELSE=m.b)
+ m.c.fixed = True
+ m.c.set_value(0)
+
+ rep = generate_standard_repn(e)
+ #
+ self.assertFalse( rep.is_fixed() )
+ self.assertEqual( rep.polynomial_degree(), 1 )
+ self.assertFalse( rep.is_constant() )
+ self.assertTrue( rep.is_linear() )
+ self.assertFalse( rep.is_quadratic() )
+ self.assertFalse( rep.is_nonlinear() )
+ #
+ self.assertTrue(len(rep.linear_vars) == 1)
+ self.assertTrue(len(rep.linear_coefs) == 1)
+ self.assertTrue(len(rep.quadratic_vars) == 0)
+ self.assertTrue(len(rep.quadratic_coefs) == 0)
+ self.assertTrue(rep.nonlinear_expr is None)
+ self.assertTrue(len(rep.nonlinear_vars) == 0)
+ baseline = { id(m.b):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+ s = pickle.dumps(rep)
+ rep = pickle.loads(s)
+ baseline = { id(rep.linear_vars[0]):1 }
+ self.assertEqual(baseline, repn_to_dict(rep))
+
+
+if __name__ == "__main__":
+ unittest.main()
diff --git a/pyomo/repn/tests/testgmsfile.gms b/pyomo/repn/tests/testgmsfile.gms
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/pyomo/scripting/tests/test_pms.py b/pyomo/scripting/tests/test_pms.py
index faee82d0b82..9bd9b0a88bc 100644
--- a/pyomo/scripting/tests/test_pms.py
+++ b/pyomo/scripting/tests/test_pms.py
@@ -53,7 +53,7 @@ def test_t1(self):
model.A = RangeSet(1,4)
model.x = Var(model.A, bounds=(-1,1))
def obj_rule(model):
- return summation(model.x)
+ return sum_product(model.x)
model.obj = Objective(rule=obj_rule)
def c_rule(model):
expr = 0
diff --git a/pyomo/solvers/plugins/solvers/cplex_direct.py b/pyomo/solvers/plugins/solvers/cplex_direct.py
index fdf14cec686..630cd27dcee 100644
--- a/pyomo/solvers/plugins/solvers/cplex_direct.py
+++ b/pyomo/solvers/plugins/solvers/cplex_direct.py
@@ -14,11 +14,11 @@
import pyutilib.services
from pyutilib.misc import Bunch
from pyomo.util.plugin import alias
-from pyomo.core.kernel.numvalue import is_fixed
-from pyomo.repn import generate_canonical_repn, LinearCanonicalRepn, canonical_degree
+from pyomo.core.expr.numvalue import is_fixed
+from pyomo.core.expr.numvalue import value
+from pyomo.repn import generate_standard_repn
from pyomo.solvers.plugins.solvers.direct_solver import DirectSolver
from pyomo.solvers.plugins.solvers.direct_or_persistent_solver import DirectOrPersistentSolver
-from pyomo.core.kernel.numvalue import value
import pyomo.core.kernel
from pyomo.core.kernel.component_set import ComponentSet
from pyomo.core.kernel.component_map import ComponentMap
@@ -186,60 +186,33 @@ def _process_stream(arg):
def _get_expr_from_pyomo_repn(self, repn, max_degree=2):
referenced_vars = ComponentSet()
- degree = canonical_degree(repn)
+ degree = repn.polynomial_degree()
if (degree is None) or (degree > max_degree):
raise DegreeError('CPLEXDirect does not support expressions of degree {0}.'.format(degree))
- if isinstance(repn, LinearCanonicalRepn):
- new_expr = _CplexExpr()
+ new_expr = _CplexExpr()
+ if len(repn.linear_vars) > 0:
+ referenced_vars.update(repn.linear_vars)
+ new_expr.variables.extend(self._pyomo_var_to_ndx_map[i] for i in repn.linear_vars)
+ new_expr.coefficients.extend(repn.linear_coefs)
- if repn.constant is not None:
- new_expr.offset = repn.constant
+ for i, v in enumerate(repn.quadratic_vars):
+ x, y = v
+ new_expr.q_coefficients.append(repn.quadratic_coefs[i])
+ new_expr.q_variables1.append(self._pyomo_var_to_ndx_map[x])
+ new_expr.q_variables2.append(self._pyomo_var_to_ndx_map[y])
+ referenced_vars.add(x)
+ referenced_vars.add(y)
- if (repn.linear is not None) and (len(repn.linear) > 0):
- list(map(referenced_vars.add, repn.variables))
- new_expr.variables.extend(self._pyomo_var_to_ndx_map[var] for var in repn.variables)
- new_expr.coefficients.extend(coeff for coeff in repn.linear)
-
- else:
- new_expr = _CplexExpr()
- if 0 in repn:
- new_expr.offset = repn[0][None]
-
- if 1 in repn:
- for ndx, coeff in repn[1].items():
- new_expr.coefficients.append(coeff)
- var = repn[-1][ndx]
- new_expr.variables.append(self._pyomo_var_to_ndx_map[var])
- referenced_vars.add(var)
-
- if 2 in repn:
- for key, coeff in repn[2].items():
- new_expr.q_coefficients.append(coeff)
- indices = list(key.keys())
- if len(indices) == 1:
- ndx = indices[0]
- var = repn[-1][ndx]
- referenced_vars.add(var)
- cplex_var = self._pyomo_var_to_ndx_map[var]
- new_expr.q_variables1.append(cplex_var)
- new_expr.q_variables2.append(cplex_var)
- else:
- ndx = indices[0]
- var = repn[-1][ndx]
- referenced_vars.add(var)
- cplex_var = self._pyomo_var_to_ndx_map[var]
- new_expr.q_variables1.append(cplex_var)
- ndx = indices[1]
- var = repn[-1][ndx]
- referenced_vars.add(var)
- cplex_var = self._pyomo_var_to_ndx_map[var]
- new_expr.q_variables2.append(cplex_var)
+ new_expr.offset = repn.constant
return new_expr, referenced_vars
def _get_expr_from_pyomo_expr(self, expr, max_degree=2):
- repn = generate_canonical_repn(expr)
+ if max_degree == 2:
+ repn = generate_standard_repn(expr, quadratic=True)
+ else:
+ repn = generate_standard_repn(expr, quadratic=False)
try:
cplex_expr, referenced_vars = self._get_expr_from_pyomo_repn(repn, max_degree)
@@ -319,10 +292,6 @@ def _add_constraint(self, con):
cplex_expr, referenced_vars = self._get_expr_from_pyomo_repn(
con.canonical_form(),
self._max_constraint_degree)
- elif isinstance(con, LinearCanonicalRepn):
- cplex_expr, referenced_vars = self._get_expr_from_pyomo_repn(
- con,
- self._max_constraint_degree)
else:
cplex_expr, referenced_vars = self._get_expr_from_pyomo_expr(
con.body,
diff --git a/pyomo/solvers/plugins/solvers/cplex_persistent.py b/pyomo/solvers/plugins/solvers/cplex_persistent.py
index 5d60147f332..bd88012d086 100644
--- a/pyomo/solvers/plugins/solvers/cplex_persistent.py
+++ b/pyomo/solvers/plugins/solvers/cplex_persistent.py
@@ -8,14 +8,14 @@
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
+from pyomo.core.expr.numvalue import value
from pyomo.core.base.PyomoModel import ConcreteModel
-from pyomo.solvers.plugins.solvers.cplex_direct import CPLEXDirect
-from pyomo.solvers.plugins.solvers.persistent_solver import PersistentSolver
-from pyomo.util.plugin import alias
from pyomo.core.base.constraint import Constraint
from pyomo.core.base.var import Var
from pyomo.core.base.sos import SOSConstraint
-from pyomo.core.kernel.numvalue import value
+from pyomo.solvers.plugins.solvers.cplex_direct import CPLEXDirect
+from pyomo.solvers.plugins.solvers.persistent_solver import PersistentSolver
+from pyomo.util.plugin import alias
class CPLEXPersistent(PersistentSolver, CPLEXDirect):
diff --git a/pyomo/solvers/plugins/solvers/gurobi_direct.py b/pyomo/solvers/plugins/solvers/gurobi_direct.py
index a6c12114224..4946e036739 100644
--- a/pyomo/solvers/plugins/solvers/gurobi_direct.py
+++ b/pyomo/solvers/plugins/solvers/gurobi_direct.py
@@ -14,11 +14,11 @@
import pyutilib.services
from pyutilib.misc import Bunch
from pyomo.util.plugin import alias
-from pyomo.core.kernel.numvalue import is_fixed
-from pyomo.repn import generate_canonical_repn, LinearCanonicalRepn, canonical_degree
+from pyomo.core.expr.numvalue import is_fixed
+from pyomo.core.expr.numvalue import value
+from pyomo.repn import generate_standard_repn
from pyomo.solvers.plugins.solvers.direct_solver import DirectSolver
from pyomo.solvers.plugins.solvers.direct_or_persistent_solver import DirectOrPersistentSolver
-from pyomo.core.kernel.numvalue import value
import pyomo.core.kernel
from pyomo.core.kernel.component_set import ComponentSet
from pyomo.core.kernel.component_map import ComponentMap
@@ -163,43 +163,31 @@ def _apply_solver(self):
def _get_expr_from_pyomo_repn(self, repn, max_degree=2):
referenced_vars = ComponentSet()
- degree = canonical_degree(repn)
+ degree = repn.polynomial_degree()
if (degree is None) or (degree > max_degree):
raise DegreeError('GurobiDirect does not support expressions of degree {0}.'.format(degree))
- if isinstance(repn, LinearCanonicalRepn):
- if (repn.linear is not None) and (len(repn.linear) > 0):
- list(map(referenced_vars.add, repn.variables))
- new_expr = self._gurobipy.LinExpr(repn.linear, [self._pyomo_var_to_solver_var_map[i] for i in repn.variables])
- else:
- new_expr = 0
+ if len(repn.linear_vars) > 0:
+ referenced_vars.update(repn.linear_vars)
+ new_expr = self._gurobipy.LinExpr(repn.linear_coefs, [self._pyomo_var_to_solver_var_map[i] for i in repn.linear_vars])
+ else:
+ new_expr = 0.0
- if repn.constant is not None:
- new_expr += repn.constant
+ for i,v in enumerate(repn.quadratic_vars):
+ x,y = v
+ new_expr += repn.quadratic_coefs[i] * self._pyomo_var_to_solver_var_map[x] * self._pyomo_var_to_solver_var_map[y]
+ referenced_vars.add(x)
+ referenced_vars.add(y)
- else:
- new_expr = 0
- if 0 in repn:
- new_expr += repn[0][None]
-
- if 1 in repn:
- for ndx, coeff in repn[1].items():
- new_expr += coeff * self._pyomo_var_to_solver_var_map[repn[-1][ndx]]
- referenced_vars.add(repn[-1][ndx])
-
- if 2 in repn:
- for key, coeff in repn[2].items():
- tmp_expr = coeff
- for ndx, power in key.items():
- referenced_vars.add(repn[-1][ndx])
- for i in range(power):
- tmp_expr *= self._pyomo_var_to_solver_var_map[repn[-1][ndx]]
- new_expr += tmp_expr
+ new_expr += repn.constant
return new_expr, referenced_vars
def _get_expr_from_pyomo_expr(self, expr, max_degree=2):
- repn = generate_canonical_repn(expr)
+ if max_degree == 2:
+ repn = generate_standard_repn(expr, quadratic=True)
+ else:
+ repn = generate_standard_repn(expr, quadratic=False)
try:
gurobi_expr, referenced_vars = self._get_expr_from_pyomo_repn(repn, max_degree)
@@ -286,10 +274,10 @@ def _add_constraint(self, con):
gurobi_expr, referenced_vars = self._get_expr_from_pyomo_repn(
con.canonical_form(),
self._max_constraint_degree)
- elif isinstance(con, LinearCanonicalRepn):
- gurobi_expr, referenced_vars = self._get_expr_from_pyomo_repn(
- con,
- self._max_constraint_degree)
+ #elif isinstance(con, LinearCanonicalRepn):
+ # gurobi_expr, referenced_vars = self._get_expr_from_pyomo_repn(
+ # con,
+ # self._max_constraint_degree)
else:
gurobi_expr, referenced_vars = self._get_expr_from_pyomo_expr(
con.body,
diff --git a/pyomo/solvers/plugins/solvers/gurobi_persistent.py b/pyomo/solvers/plugins/solvers/gurobi_persistent.py
index 9e70744720e..6318b284779 100644
--- a/pyomo/solvers/plugins/solvers/gurobi_persistent.py
+++ b/pyomo/solvers/plugins/solvers/gurobi_persistent.py
@@ -12,7 +12,7 @@
from pyomo.solvers.plugins.solvers.gurobi_direct import GurobiDirect
from pyomo.solvers.plugins.solvers.persistent_solver import PersistentSolver
from pyomo.util.plugin import alias
-from pyomo.core.kernel.numvalue import value
+from pyomo.core.expr.numvalue import value
class GurobiPersistent(PersistentSolver, GurobiDirect):
diff --git a/pyomo/solvers/tests/core/performance/bilinear1.py b/pyomo/solvers/tests/core/performance/bilinear1.py
deleted file mode 100644
index 42cf1a5ce84..00000000000
--- a/pyomo/solvers/tests/core/performance/bilinear1.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from pyomo.core import *
-
-def create_model(N):
- model = ConcreteModel()
-
- model.A = RangeSet(N)
- model.x = Var(model.A, bounds=(1,2))
-
- expr=sum(2*model.x[i]*model.x[i+1] for i in model.A if (i+1) in model.A)
- model.obj = Objective(expr=expr)
-
- return model
-
-def pyomo_create_model(options=None, model_options=None):
- return create_model(1000000)
diff --git a/pyomo/solvers/tests/core/performance/bilinearC100000.py b/pyomo/solvers/tests/core/performance/bilinearC100000.py
deleted file mode 100644
index abe278cb3b1..00000000000
--- a/pyomo/solvers/tests/core/performance/bilinearC100000.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from pyomo.core import *
-
-def create_model(N):
- model = ConcreteModel()
-
- model.A = RangeSet(N)
- def domain_rule(model, i):
- return Reals
- model.x = Var(model.A, bounds=(1,2), domain=domain_rule)
-
- expr=sum(2*model.x[i]*model.x[i+1] for i in model.A if (i+1) in model.A)
- model.obj = Objective(expr=expr)
-
- return model
-
-def pyomo_create_model(options=None, model_options=None):
- return create_model(100000)
diff --git a/pyomo/solvers/tests/core/performance/diag1.py b/pyomo/solvers/tests/core/performance/diag1.py
deleted file mode 100644
index 7a9ff20a2ea..00000000000
--- a/pyomo/solvers/tests/core/performance/diag1.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from pyomo.core import *
-
-def create_model(N):
- model = ConcreteModel()
-
- for i in range(1,N+1):
- setattr(model, 'x'+str(i), Var())
-
- expr=sum(i*getattr(model, 'x'+str(i)) for i in range(1,N+1))
- model.obj = Objective(expr=expr)
-
- for i in range(1,N+1):
- setattr(model, 'c'+str(i), Constraint(expr = (N-i+1)*getattr(model, 'x'+str(i)) >= N))
-
- return model
-
-def pyomo_create_model(options=None, model_options=None):
- return create_model(1000000)
diff --git a/pyomo/solvers/tests/core/performance/diagA100000.py b/pyomo/solvers/tests/core/performance/diagA100000.py
deleted file mode 100644
index 5a0121afde3..00000000000
--- a/pyomo/solvers/tests/core/performance/diagA100000.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from pyomo.core import *
-from six.moves import xrange
-
-def create_model(N):
- model = ConcreteModel()
-
- for i in xrange(1,N+1):
- setattr(model, 'x'+str(i), Var())
-
- expr=sum(i*getattr(model, 'x'+str(i)) for i in xrange(1,N+1))
- model.obj = Objective(expr=expr)
-
- for i in xrange(1,N+1):
- setattr(model, 'c'+str(i), Constraint(expr = (N-i+1)*getattr(model, 'x'+str(i)) >= N))
-
- return model
-
-def pyomo_create_model(options=None, model_options=None):
- return create_model(100000)
diff --git a/pyomo/solvers/tests/core/problems/lp_unique1.py b/pyomo/solvers/tests/core/problems/lp_unique1.py
index 372ea6c6d2e..46f3c7ea9ff 100644
--- a/pyomo/solvers/tests/core/problems/lp_unique1.py
+++ b/pyomo/solvers/tests/core/problems/lp_unique1.py
@@ -33,7 +33,7 @@ def A_rule(model, i, j):
model.x = Var(model.N, within=NonNegativeReals)
model.y = Var(model.M, within=NonNegativeReals)
-model.cost = Objective(expr=summation(model.c, model.x))
+model.cost = Objective(expr=sum_product(model.c, model.x))
def primalcon_rule(model, i):
return sum(model.A[i,j]*model.x[j] for j in model.N) >= model.b[i]
diff --git a/pyomo/solvers/tests/core/problems/multidimen_sos1.py b/pyomo/solvers/tests/core/problems/multidimen_sos1.py
index 08650caf5b6..95f6c8cf6a9 100644
--- a/pyomo/solvers/tests/core/problems/multidimen_sos1.py
+++ b/pyomo/solvers/tests/core/problems/multidimen_sos1.py
@@ -26,7 +26,7 @@ def constraint1_rule(model,t1,t2,i):
def constraint2_rule(model,t1,t2):
return sum(model.y[t1,t2,j] for j in xrange(1,len(PIECEWISE_PTS[t1,t2]))) == 1
-model.obj = Objective(expr=summation(model.Fx), sense=maximize)
+model.obj = Objective(expr=sum_product(model.Fx), sense=maximize)
model.constraint1 = Constraint(model.ub_indices,rule=constraint1_rule)
model.constraint2 = Constraint(INDEX_SET1,INDEX_SET2,rule=constraint2_rule)
model.SOS_set_constraint = SOSConstraint(INDEX_SET1,INDEX_SET2, var=model.y, index=model.SOS_indices, sos=1)
diff --git a/pyomo/solvers/tests/core/problems/multidimen_sos2.py b/pyomo/solvers/tests/core/problems/multidimen_sos2.py
index ae3b44fd622..3014b00e56c 100644
--- a/pyomo/solvers/tests/core/problems/multidimen_sos2.py
+++ b/pyomo/solvers/tests/core/problems/multidimen_sos2.py
@@ -28,7 +28,7 @@ def constraint2_rule(model,t1,t2):
def constraint3_rule(model,t1,t2):
return sum(model.y[t1,t2,j] for j in xrange(len(PIECEWISE_PTS[t1,t2]))) == 1
-model.obj = Objective(expr=summation(model.Fx), sense=maximize)
+model.obj = Objective(expr=sum_product(model.Fx), sense=maximize)
model.constraint1 = Constraint(INDEX_SET1,INDEX_SET2,rule=constraint1_rule)
model.constraint2 = Constraint(INDEX_SET1,INDEX_SET2,rule=constraint2_rule)
model.constraint3 = Constraint(INDEX_SET1,INDEX_SET2,rule=constraint3_rule)
diff --git a/pyomo/solvers/tests/core/problems/sos1.py b/pyomo/solvers/tests/core/problems/sos1.py
index abe9bf3d472..fcd2e941756 100644
--- a/pyomo/solvers/tests/core/problems/sos1.py
+++ b/pyomo/solvers/tests/core/problems/sos1.py
@@ -24,7 +24,7 @@ def constraint1_rule(model,t,i):
def constraint2_rule(model,t):
return sum(model.y[t,j] for j in xrange(1,len(PIECEWISE_PTS[t]))) == 1
-model.obj = Objective(expr=summation(model.Fx), sense=maximize)
+model.obj = Objective(expr=sum_product(model.Fx), sense=maximize)
model.constraint1 = Constraint(model.ub_indices,rule=constraint1_rule)
model.constraint2 = Constraint(INDEX_SET,rule=constraint2_rule)
model.SOS_set_constraint = SOSConstraint(INDEX_SET, var=model.y, index=model.SOS_indices, sos=1)
diff --git a/pyomo/solvers/tests/core/problems/sos2.py b/pyomo/solvers/tests/core/problems/sos2.py
index e78c3597935..df87b5464d8 100644
--- a/pyomo/solvers/tests/core/problems/sos2.py
+++ b/pyomo/solvers/tests/core/problems/sos2.py
@@ -26,7 +26,7 @@ def constraint2_rule(model,t):
def constraint3_rule(model,t):
return sum(model.y[t,j] for j in xrange(len(PIECEWISE_PTS[t]))) == 1
-model.obj = Objective(expr=summation(model.Fx), sense=maximize)
+model.obj = Objective(expr=sum_product(model.Fx), sense=maximize)
model.constraint1 = Constraint(INDEX_SET,rule=constraint1_rule)
model.constraint2 = Constraint(INDEX_SET,rule=constraint2_rule)
model.constraint3 = Constraint(INDEX_SET,rule=constraint3_rule)
diff --git a/pyomo/solvers/tests/core/test_component_perf.py b/pyomo/solvers/tests/core/test_component_perf.py
index 36137b9d758..6c3ba8ae644 100644
--- a/pyomo/solvers/tests/core/test_component_perf.py
+++ b/pyomo/solvers/tests/core/test_component_perf.py
@@ -98,7 +98,7 @@ class TestConstraintPerformance(ComponentPerformanceBase, unittest.TestCase):
@classmethod
def _setup(self):
self._create_model(Constraint,
- **{'rule': lambda m,i: 1 <= m.x <= 2})
+ **{'rule': lambda m,i: (1, m.x, 2)})
@unittest.category('performance')
class TestObjectivePerformance(ComponentPerformanceBase, unittest.TestCase):
diff --git a/pyomo/solvers/tests/mip/model.py b/pyomo/solvers/tests/mip/model.py
index efbb2e9d597..7729c312cc8 100644
--- a/pyomo/solvers/tests/mip/model.py
+++ b/pyomo/solvers/tests/mip/model.py
@@ -17,5 +17,5 @@
model.x = Var(model.A)
def obj_rule(model):
- return summation(model.x)
+ return sum_product(model.x)
model.obj = Objective(rule=obj_rule)
diff --git a/pyomo/solvers/tests/models/LP_block.py b/pyomo/solvers/tests/models/LP_block.py
index 73bcf37d46e..527bf44f3f6 100644
--- a/pyomo/solvers/tests/models/LP_block.py
+++ b/pyomo/solvers/tests/models/LP_block.py
@@ -40,7 +40,7 @@ def _generate_model(self):
model.obj.deactivate()
model.B[2].c = Constraint(expr=-model.B[1].x <= -model.a)
model.B[2].obj = Objective(expr=model.b.x + 3.0*model.B[1].x + 2)
- model.B[3].c = Constraint(expr=2.0 <= model.b.x/model.a - model.B[1].x <= 10)
+ model.B[3].c = Constraint(expr=(2.0, model.b.x/model.a - model.B[1].x, 10))
def warmstart_model(self):
assert self.model is not None
@@ -67,5 +67,5 @@ def _generate_model(self):
model.obj.deactivate()
model.B[2].c = pmo.constraint(expr=-model.B[1].x <= -model.a)
model.B[2].obj = pmo.objective(expr=model.b.x + 3.0*model.B[1].x + 2)
- model.B[3].c = pmo.constraint(expr=2.0 <= model.b.x/model.a - model.B[1].x <= 10)
+ model.B[3].c = pmo.constraint(expr=(2.0, model.b.x/model.a - model.B[1].x, 10))
diff --git a/pyomo/solvers/tests/models/LP_simple.py b/pyomo/solvers/tests/models/LP_simple.py
index 289b6631078..daa9f3260d2 100644
--- a/pyomo/solvers/tests/models/LP_simple.py
+++ b/pyomo/solvers/tests/models/LP_simple.py
@@ -48,9 +48,9 @@ def _generate_model(self):
model.obj = Objective(expr=model.p + model.inactive_obj)
model.c1 = Constraint(expr=model.dummy_expr1 <= model.dummy_expr2)
- model.c2 = Constraint(expr=2.0 <= model.x/model.a3 - model.y <= 10)
- model.c3 = Constraint(expr=0 <= model.z1 + 1 <= 10)
- model.c4 = Constraint(expr=-10 <= model.z2 + 1 <= 0)
+ model.c2 = Constraint(expr=(2.0, model.x/model.a3 - model.y, 10))
+ model.c3 = Constraint(expr=(0, model.z1 + 1, 10))
+ model.c4 = Constraint(expr=(-10, model.z2 + 1, 0))
def warmstart_model(self):
assert self.model is not None
@@ -86,6 +86,6 @@ def _generate_model(self):
model.obj = pmo.objective(model.p + model.inactive_obj)
model.c1 = pmo.constraint(model.dummy_expr1 <= pmo.noclone(model.dummy_expr2))
- model.c2 = pmo.constraint(2.0 <= model.x/model.a3 - model.y <= 10)
- model.c3 = pmo.constraint(0 <= model.z1 + 1 <= 10)
- model.c4 = pmo.constraint(-10 <= model.z2 + 1 <= 0)
+ model.c2 = pmo.constraint((2.0, model.x/model.a3 - model.y, 10))
+ model.c3 = pmo.constraint((0, model.z1 + 1, 10))
+ model.c4 = pmo.constraint((-10, model.z2 + 1, 0))
diff --git a/pyomo/solvers/tests/models/LP_unique_duals.py b/pyomo/solvers/tests/models/LP_unique_duals.py
index a86c94681a0..1fd416f358e 100644
--- a/pyomo/solvers/tests/models/LP_unique_duals.py
+++ b/pyomo/solvers/tests/models/LP_unique_duals.py
@@ -65,7 +65,7 @@ def _generate_model(self):
model.x = Var(model.N, within=NonNegativeReals)
model.y = Var(model.M, within=NonNegativeReals)
- model.cost = Objective(expr=summation(model.c, model.x))
+ model.cost = Objective(expr=sum_product(model.c, model.x))
model.primalcon = Constraint(model.M, rule=primalcon_rule)
diff --git a/pyomo/solvers/tests/models/LP_unused_vars.py b/pyomo/solvers/tests/models/LP_unused_vars.py
index aeb7c4a072a..1aab915a5c7 100644
--- a/pyomo/solvers/tests/models/LP_unused_vars.py
+++ b/pyomo/solvers/tests/models/LP_unused_vars.py
@@ -9,7 +9,7 @@
# ___________________________________________________________________________
import pyomo.kernel as pmo
-from pyomo.core import ConcreteModel, Param, Var, Expression, Objective, Constraint, Set, ConstraintList, summation, Block
+from pyomo.core import ConcreteModel, Param, Var, Expression, Objective, Constraint, Set, ConstraintList, sum_product, Block
from pyomo.solvers.tests.models.base import _BaseTestModel, register_model
@register_model
@@ -61,8 +61,8 @@ def _generate_model(self):
model.obj = Objective(expr= model.x + \
model.x_initialy_stale + \
- summation(model.X) + \
- summation(model.X_initialy_stale))
+ sum_product(model.X) + \
+ sum_product(model.X_initialy_stale))
model.c = ConstraintList()
model.c.add( model.x >= 1 )
diff --git a/pyomo/solvers/tests/models/MILP_simple.py b/pyomo/solvers/tests/models/MILP_simple.py
index 1a293c57bdb..cb49de5b613 100644
--- a/pyomo/solvers/tests/models/MILP_simple.py
+++ b/pyomo/solvers/tests/models/MILP_simple.py
@@ -37,7 +37,7 @@ def _generate_model(self):
model.obj = Objective(expr=model.x + 3.0*model.y)
model.c1 = Constraint(expr=model.a <= model.y)
- model.c2 = Constraint(expr=2.0 <= model.x/model.a - model.y <= 10)
+ model.c2 = Constraint(expr=(2.0, model.x/model.a - model.y, 10))
def warmstart_model(self):
assert self.model is not None
@@ -59,4 +59,4 @@ def _generate_model(self):
model.obj = pmo.objective(model.x + 3.0*model.y)
model.c1 = pmo.constraint(model.a <= model.y)
- model.c2 = pmo.constraint(2.0 <= model.x/model.a - model.y <= 10)
+ model.c2 = pmo.constraint((2.0, model.x/model.a - model.y, 10))
diff --git a/pyomo/solvers/tests/models/MILP_unused_vars.py b/pyomo/solvers/tests/models/MILP_unused_vars.py
index 65a26a02bd1..3d024346892 100644
--- a/pyomo/solvers/tests/models/MILP_unused_vars.py
+++ b/pyomo/solvers/tests/models/MILP_unused_vars.py
@@ -9,7 +9,7 @@
# ___________________________________________________________________________
import pyomo.kernel as pmo
-from pyomo.core import ConcreteModel, Param, Var, Expression, Objective, Constraint, ConstraintList, Set, Integers, IntegerInterval, summation, Block
+from pyomo.core import ConcreteModel, Param, Var, Expression, Objective, Constraint, ConstraintList, Set, Integers, IntegerInterval, sum_product, Block
from pyomo.solvers.tests.models.base import _BaseTestModel, register_model
@register_model
@@ -60,8 +60,8 @@ def _generate_model(self):
model.obj = Objective(expr= model.x + \
model.x_initialy_stale + \
- summation(model.X) + \
- summation(model.X_initialy_stale))
+ sum_product(model.X) + \
+ sum_product(model.X_initialy_stale))
model.c = ConstraintList()
model.c.add( model.x >= 1 )
diff --git a/pyomo/solvers/tests/models/MIQP_simple.py b/pyomo/solvers/tests/models/MIQP_simple.py
index 006d6e48673..a621ff1ecfc 100644
--- a/pyomo/solvers/tests/models/MIQP_simple.py
+++ b/pyomo/solvers/tests/models/MIQP_simple.py
@@ -38,7 +38,7 @@ def _generate_model(self):
model.obj = Objective(expr=model.x**2 + 3.0*model.y**2)
model.c1 = Constraint(expr=model.a <= model.y)
- model.c2 = Constraint(expr=2.0 <= model.x/model.a - model.y <= 10)
+ model.c2 = Constraint(expr=(2.0, model.x/model.a - model.y, 10))
def warmstart_model(self):
assert self.model is not None
@@ -70,4 +70,4 @@ def _generate_model(self):
model.obj = pmo.objective(model.x**2 + 3.0*model.y**2)
model.c1 = pmo.constraint(model.a <= model.y)
- model.c2 = pmo.constraint(2.0 <= model.x/model.a - model.y <= 10)
+ model.c2 = pmo.constraint((2.0, model.x/model.a - model.y, 10))
diff --git a/pyomo/solvers/tests/models/QP_simple.py b/pyomo/solvers/tests/models/QP_simple.py
index 41e58549385..86a783a9504 100644
--- a/pyomo/solvers/tests/models/QP_simple.py
+++ b/pyomo/solvers/tests/models/QP_simple.py
@@ -41,7 +41,7 @@ def _generate_model(self):
model.inactive_obj.deactivate()
model.obj = Objective(expr=model.x**2 + 3.0*model.inactive_obj**2 + 1.0)
model.c1 = Constraint(expr=model.a <= model.y)
- model.c2 = Constraint(expr=2.0 <= model.x/model.a - model.y <= 10)
+ model.c2 = Constraint(expr=(2.0, model.x/model.a - model.y, 10))
def warmstart_model(self):
assert self.model is not None
@@ -87,7 +87,7 @@ def _generate_model(self):
model.inactive_obj.deactivate()
model.obj = pmo.objective(model.x**2 + 3.0*model.inactive_obj**2 + 1.0)
model.c1 = pmo.constraint(model.a <= model.y)
- model.c2 = pmo.constraint(2.0 <= model.x/model.a - model.y <= 10)
+ model.c2 = pmo.constraint((2.0, model.x/model.a - model.y, 10))
@register_model
class QP_simple_nosuffixes_kernel(QP_simple_kernel):
diff --git a/pyomo/solvers/tests/models/SOS1_simple.py b/pyomo/solvers/tests/models/SOS1_simple.py
index ff4b9751274..4921e42950b 100644
--- a/pyomo/solvers/tests/models/SOS1_simple.py
+++ b/pyomo/solvers/tests/models/SOS1_simple.py
@@ -9,7 +9,7 @@
# ___________________________________________________________________________
import pyomo.kernel as pmo
-from pyomo.core import ConcreteModel, Param, Var, Expression, Objective, Constraint, NonNegativeReals, SOSConstraint, summation
+from pyomo.core import ConcreteModel, Param, Var, Expression, Objective, Constraint, NonNegativeReals, SOSConstraint, sum_product
from pyomo.solvers.tests.models.base import _BaseTestModel, register_model
@register_model
@@ -37,9 +37,9 @@ def _generate_model(self):
model.obj = Objective(expr=model.x + model.y[1]+2*model.y[2])
model.c1 = Constraint(expr=model.a <= model.y[2])
- model.c2 = Constraint(expr=2.0 <= model.x <= 10.0)
+ model.c2 = Constraint(expr=(2.0, model.x, 10.0))
model.c3 = SOSConstraint(var=model.y, index=[1,2], sos=1)
- model.c4 = Constraint(expr=summation(model.y) == 1)
+ model.c4 = Constraint(expr=sum_product(model.y) == 1)
# Make an empty SOSConstraint
model.c5 = SOSConstraint(var=model.y, index=[1,2], sos=1)
@@ -69,7 +69,7 @@ def _generate_model(self):
model.obj = pmo.objective(model.x + model.y[1]+2*model.y[2])
model.c1 = pmo.constraint(model.a <= model.y[2])
- model.c2 = pmo.constraint(2.0 <= model.x <= 10.0)
+ model.c2 = pmo.constraint((2.0, model.x, 10.0))
model.c3 = pmo.sos1(model.y.values())
model.c4 = pmo.constraint(sum(model.y.values()) == 1)
diff --git a/pyomo/solvers/tests/models/SOS2_simple.py b/pyomo/solvers/tests/models/SOS2_simple.py
index d1a089448bf..61ec6b0bb8d 100644
--- a/pyomo/solvers/tests/models/SOS2_simple.py
+++ b/pyomo/solvers/tests/models/SOS2_simple.py
@@ -9,7 +9,7 @@
# ___________________________________________________________________________
import pyomo.kernel as pmo
-from pyomo.core import ConcreteModel, Param, Var, Expression, Objective, Constraint, SOSConstraint, NonNegativeReals, ConstraintList, summation
+from pyomo.core import ConcreteModel, Param, Var, Expression, Objective, Constraint, SOSConstraint, NonNegativeReals, ConstraintList, sum_product
from pyomo.solvers.tests.models.base import _BaseTestModel, register_model
@register_model
@@ -46,13 +46,13 @@ def _generate_model(self):
model.lmbda = Var([1,2,3])
model.obj = Objective(expr=model.p+model.n)
model.c1 = ConstraintList()
- model.c1.add(0.0 <= model.lmbda[1] <= 1.0)
- model.c1.add(0.0 <= model.lmbda[2] <= 1.0)
+ model.c1.add((0.0, model.lmbda[1], 1.0))
+ model.c1.add((0.0, model.lmbda[2], 1.0))
model.c1.add(0.0 <= model.lmbda[3])
model.c2 = SOSConstraint(var=model.lmbda, index=[1,2,3], sos=2)
- model.c3 = Constraint(expr=summation(model.lmbda) == 1)
- model.c4 = Constraint(expr=model.f==summation(model.fi,model.lmbda))
- model.c5 = Constraint(expr=model.x==summation(model.xi,model.lmbda))
+ model.c3 = Constraint(expr=sum_product(model.lmbda) == 1)
+ model.c4 = Constraint(expr=model.f==sum_product(model.fi,model.lmbda))
+ model.c5 = Constraint(expr=model.x==sum_product(model.xi,model.lmbda))
model.x = 2.75
model.x.fixed = True
@@ -95,8 +95,8 @@ def _generate_model(self):
model.lmbda = pmo.create_variable_dict(range(1,4))
model.obj = pmo.objective(model.p+model.n)
model.c1 = pmo.constraint_dict()
- model.c1[1] = pmo.constraint(0.0 <= model.lmbda[1] <= 1.0)
- model.c1[2] = pmo.constraint(0.0 <= model.lmbda[2] <= 1.0)
+ model.c1[1] = pmo.constraint((0.0, model.lmbda[1], 1.0))
+ model.c1[2] = pmo.constraint((0.0, model.lmbda[2], 1.0))
model.c1[3] = pmo.constraint(0.0 <= model.lmbda[3])
model.c2 = pmo.sos2(model.lmbda.values())
model.c3 = pmo.constraint(sum(model.lmbda.values()) == 1)
diff --git a/pyomo/solvers/tests/piecewise_linear/indexed_nonlinear.nl b/pyomo/solvers/tests/piecewise_linear/indexed_nonlinear.nl
index f4c073899fc..a12b1bdb6f7 100644
--- a/pyomo/solvers/tests/piecewise_linear/indexed_nonlinear.nl
+++ b/pyomo/solvers/tests/piecewise_linear/indexed_nonlinear.nl
@@ -39,10 +39,10 @@ k3 #intermediate Jacobian column lengths
3
J0 2
0 -0.1
-2 1.0
+2 1
J1 2
1 -0.1
-3 1.0
+3 1
G0 2
2 1
3 1
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/concave_multi_vararray1.py b/pyomo/solvers/tests/piecewise_linear/problems/concave_multi_vararray1.py
index c80baeaa5a2..f6ffb43b434 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/concave_multi_vararray1.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/concave_multi_vararray1.py
@@ -36,7 +36,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX_SET1, INDEX_SET2) # range variable
model.p = Param(INDEX_SET1, INDEX_SET2, initialize=1.0)
- model.obj = Objective(expr=summation(model.Fx), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET1,INDEX_SET2,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/concave_multi_vararray2.py b/pyomo/solvers/tests/piecewise_linear/problems/concave_multi_vararray2.py
index d4b014ce18f..070eddd6d68 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/concave_multi_vararray2.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/concave_multi_vararray2.py
@@ -35,7 +35,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX_SET) # range variable
model.p = Param(INDEX_SET, initialize=1.0)
- model.obj = Objective(expr=summation(model.Fx), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/concave_vararray.py b/pyomo/solvers/tests/piecewise_linear/problems/concave_vararray.py
index 62a7128894f..25441bd4013 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/concave_vararray.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/concave_vararray.py
@@ -35,7 +35,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX_SET) # range variable
model.p = Param(INDEX_SET, initialize=1.0)
- model.obj = Objective(expr=summation(model.Fx), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/convex_multi_vararray1.py b/pyomo/solvers/tests/piecewise_linear/problems/convex_multi_vararray1.py
index 6d2dff4d10d..c3f2465c9f6 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/convex_multi_vararray1.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/convex_multi_vararray1.py
@@ -38,7 +38,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX_SET1, INDEX_SET2) # range variable
model.p = Param(INDEX_SET1, INDEX_SET2, initialize=1.0)
- model.obj = Objective(expr=summation(model.Fx), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET1,INDEX_SET2,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/convex_multi_vararray2.py b/pyomo/solvers/tests/piecewise_linear/problems/convex_multi_vararray2.py
index 320f4371635..170ec987a03 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/convex_multi_vararray2.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/convex_multi_vararray2.py
@@ -36,7 +36,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX_SET) # range variable
model.p = Param(INDEX_SET, initialize=1.0)
- model.obj = Objective(expr=summation(model.Fx), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/convex_vararray.py b/pyomo/solvers/tests/piecewise_linear/problems/convex_vararray.py
index 6e2c18e459b..3a9813d0c76 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/convex_vararray.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/convex_vararray.py
@@ -37,7 +37,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX_SET) # range variable
model.p = Param(INDEX_SET,initialize=1.0)
- model.obj = Objective(expr=summation(model.Fx), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/piecewise_multi_vararray.py b/pyomo/solvers/tests/piecewise_linear/problems/piecewise_multi_vararray.py
index 5398b597c77..b6ef0b30ad6 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/piecewise_multi_vararray.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/piecewise_multi_vararray.py
@@ -33,7 +33,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX_SET1, INDEX_SET2) # range variable
model.p = Param(INDEX_SET1, INDEX_SET2, initialize=1.0)
- model.obj = Objective(expr=summation(model.Fx), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET1,INDEX_SET2,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/piecewise_vararray.py b/pyomo/solvers/tests/piecewise_linear/problems/piecewise_vararray.py
index a5d1334251b..dc74878eba0 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/piecewise_vararray.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/piecewise_vararray.py
@@ -32,7 +32,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX_SET) # range variable
model.p = Param(INDEX_SET, initialize=1.0, mutable=True)
- model.obj = Objective(expr=summation(model.Fx), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX_SET,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/pyomo/solvers/tests/piecewise_linear/problems/step_vararray.py b/pyomo/solvers/tests/piecewise_linear/problems/step_vararray.py
index 627719b6e67..ed4c81f8248 100644
--- a/pyomo/solvers/tests/piecewise_linear/problems/step_vararray.py
+++ b/pyomo/solvers/tests/piecewise_linear/problems/step_vararray.py
@@ -30,7 +30,7 @@ def define_model(**kwds):
model.Fx = Var(INDEX) # range variable
- model.obj = Objective(expr=summation(model.Fx)+summation(model.x), sense=kwds.pop('sense',maximize))
+ model.obj = Objective(expr=sum_product(model.Fx)+sum_product(model.x), sense=kwds.pop('sense',maximize))
model.piecewise = Piecewise(INDEX,model.Fx,model.x,
pw_pts=DOMAIN_PTS,
diff --git a/setup.py b/setup.py
index 4eaf47d8f8a..3befe70fde6 100644
--- a/setup.py
+++ b/setup.py
@@ -50,6 +50,35 @@ def read(*rnames):
requires.append('ordereddict')
from setuptools import setup
+import sys
+
+if 'develop' in sys.argv:
+ using_cython = False
+else:
+ using_cython = True
+if '--with-cython' in sys.argv:
+ using_cython = True
+ sys.argv.remove('--with-cython')
+
+ext_modules = []
+if using_cython:
+ try:
+ import platform
+ if not platform.python_implementation() == "CPython":
+ raise RuntimeError()
+ from Cython.Build import cythonize
+ #
+ # Note: The Cython developers recommend that you destribute C source
+ # files to users. But this is fine for evaluating the utility of Cython
+ #
+ import shutil
+ files = ["pyomo/core/expr/expr_pyomo5.pyx", "pyomo/core/expr/numvalue.pyx", "pyomo/core/util.pyx", "pyomo/repn/standard_repn.pyx"]
+ for f in files:
+ shutil.copyfile(f[:-1], f)
+ ext_modules = cythonize(files)
+ except:
+ using_cython = False
+
packages = _find_packages('pyomo')
setup(name='Pyomo',
@@ -94,6 +123,7 @@ def read(*rnames):
packages=packages,
keywords=['optimization'],
install_requires=requires,
+ ext_modules = ext_modules,
python_requires='>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*',
entry_points="""
[console_scripts]