From 784f05b6d4fd816e384216da04282aed6870d7d3 Mon Sep 17 00:00:00 2001
From: Michael Schneeberger <michael.schneeberger@fhnw.ch>
Date: Thu, 11 Aug 2022 14:26:02 +0200
Subject: 'to_matrix_repr' function requires 'variables' argument

---
 polymatrix/__init__.py | 53 +++++++++++++++++++++++++-------------------------
 1 file changed, 26 insertions(+), 27 deletions(-)

diff --git a/polymatrix/__init__.py b/polymatrix/__init__.py
index 8ee1488..2863164 100644
--- a/polymatrix/__init__.py
+++ b/polymatrix/__init__.py
@@ -440,38 +440,38 @@ def to_matrix_repr(
             initial=(state, tuple()),
         ))
 
-        if variables is None:
+        # if variables is None:
 
-            def gen_used_variables():
-                def gen_used_auxillary_variables(considered):
-                    monomial_terms = state.auxillary_equations[considered[-1]]
-                    for monomial in monomial_terms.keys():
-                        for variable in monomial:
-                            yield variable
+        #     def gen_used_variables():
+        #         def gen_used_auxillary_variables(considered):
+        #             monomial_terms = state.auxillary_equations[considered[-1]]
+        #             for monomial in monomial_terms.keys():
+        #                 for variable in monomial:
+        #                     yield variable
 
-                            if variable not in considered and variable in state.auxillary_equations:
-                                yield from gen_used_auxillary_variables(considered + (variable,))
+        #                     if variable not in considered and variable in state.auxillary_equations:
+        #                         yield from gen_used_auxillary_variables(considered + (variable,))
 
-                for underlying in underlying_list:
-                    for row in range(underlying.shape[0]):
-                        for col in range(underlying.shape[1]):
+        #         for underlying in underlying_list:
+        #             for row in range(underlying.shape[0]):
+        #                 for col in range(underlying.shape[1]):
 
-                            try:
-                                underlying_terms = underlying.get_poly(row, col)
-                            except KeyError:
-                                continue
+        #                     try:
+        #                         underlying_terms = underlying.get_poly(row, col)
+        #                     except KeyError:
+        #                         continue
 
-                            for monomial in underlying_terms.keys():
-                                for variable, _ in monomial:
-                                    yield variable
+        #                     for monomial in underlying_terms.keys():
+        #                         for variable, _ in monomial:
+        #                             yield variable
 
-                                    if variable in state.auxillary_equations:
-                                        yield from gen_used_auxillary_variables((variable,))
+        #                             if variable in state.auxillary_equations:
+        #                                 yield from gen_used_auxillary_variables((variable,))
 
-            ordered_variable_index = tuple(sorted(set(gen_used_variables())))
+        #     ordered_variable_index = tuple(sorted(set(gen_used_variables())))
 
-        else:
-            state, ordered_variable_index = get_variable_indices(state, variables)
+        # else:
+        state, ordered_variable_index = get_variable_indices(state, variables)
 
         variable_index_map = {old: new for new, old in enumerate(ordered_variable_index)}
 
@@ -557,18 +557,17 @@ def to_matrix_repr(
 
     return init_state_monad(func)
 
-def to_constant_matrix(
+def to_constant_repr(
     expr: Expression,
 ) -> StateMonadMixin[ExpressionState, np.ndarray]:
 
     def func(state: ExpressionState):
         state, underlying = expr.apply(state)
 
-        A = np.zeros(underlying.shape, dtype=np.float32)
+        A = np.zeros(underlying.shape, dtype=np.double)
 
         for (row, col), polynomial in underlying.get_terms():
             for monomial, value in polynomial.items():
-
                 if len(monomial) == 0:
                     A[row, col] = value
 
-- 
cgit v1.2.1