def p_funcall_expr(p):
"""expr : expr LPAREN expr_list RPAREN
| expr LPAREN RPAREN
"""
if (len(p) == 5 and len(p[3]) == 1 and p[3][0].__class__ is node.expr
and p[3][0].op == ":" and not p[3][0].args):
# foo(:) => ravel(foo)
p[0] = node.funcall(func_expr=node.ident("ravel"),
args=node.expr_list([p[1]]))
else:
args = node.expr_list() if len(p) == 4 else p[3]
assert isinstance(args, node.expr_list)
p[0] = node.funcall(func_expr=p[1], args=args)
def p_funcall_expr(p):
"""expr : expr LPAREN expr_list RPAREN
| expr LPAREN RPAREN
"""
if (len(p) == 5 and len(p[3]) == 1 and p[3][0].__class__ is node.expr and
p[3][0].op == ":" and not p[3][0].args):
# foo(:) => ravel(foo)
p[0] = node.funcall(
func_expr=node.ident("ravel"), args=node.expr_list([p[1]]))
else:
args = node.expr_list() if len(p) == 4 else p[3]
assert isinstance(args, node.expr_list)
p[0] = node.funcall(func_expr=p[1], args=args)
def let_statement(u):
"""
If LHS is a plain variable, and RHS is a matrix
enclosed in square brackets, replace the matrix
expr with a funcall.
"""
if u.__class__ is node.let:
if (u.ret.__class__ is node.ident and u.args.__class__ is node.matrix):
u.args = node.funcall(func_expr=node.ident("matlabarray"),
args=node.expr_list([u.args]))
def p_command(p):
"""
command : ident args SEMI
"""
# if p[1].name == "load":
# # "load filename x" ==> "x=load(filename)"
# # "load filename x y z" ==> "(x,y,z)=load(filename)"
# ret=node.expr_list([node.ident(t.value) for t in p[2][1:]])
# p[0] = node.funcall(func_expr=p[1],
# args=node.expr_list(p[2]),
# ret=ret)
# else:
p[0] = node.funcall(p[1],p[2])
def p_command(p):
"""
command : ident args SEMI
"""
# if p[1].name == "load":
# # "load filename x" ==> "x=load(filename)"
# # "load filename x y z" ==> "(x,y,z)=load(filename)"
# ret=node.expr_list([node.ident(t.value) for t in p[2][1:]])
# p[0] = node.funcall(func_expr=p[1],
# args=node.expr_list(p[2]),
# ret=ret)
# else:
p[0] = node.funcall(p[1], p[2])
def resolve(t, symtab=None, fp=None, func_name=None):
if symtab is None:
symtab = {}
do_resolve(t, symtab)
G = as_networkx(t)
#import pdb;pdb.set_trace()
for n in G.nodes():
u = G.node[n]["ident"]
if u.props:
pass
elif G.out_edges(n) and G.in_edges(n):
u.props = "U" # upd
#print u.name, u.lineno, u.column
elif G.in_edges(n):
u.props = "D" # def
elif G.out_edges(n):
u.props = "R" # ref
else:
u.props = "F" # ???
G.node[n]["label"] = "%s\\n%s" % (n, u.props)
for u in node.postorder(t):
#if u.__class__ is node.func_decl:
# u.ident.name += "_"
if u.__class__ is node.funcall:
try:
if u.func_expr.props in "UR": # upd,ref
u.__class__ = node.arrayref
#else:
# u.func_expr.name += "_"
except:
pass
for u in node.postorder(t):
if u.__class__ in (node.arrayref, node.cellarrayref):
for i, v in enumerate(u.args):
if v.__class__ is node.expr and v.op == ":":
v.op = "::"
# for w in node.postorder(v):
# if w.__class__ is node.expr and w.op == "end":
# w.args[0] = u.func_expr
# w.args[1] = node.number(i)
for u in node.postorder(t):
if u.__class__ is node.let:
if (u.ret.__class__ is node.ident
and u.args.__class__ is node.matrix):
u.args = node.funcall(func_expr=node.ident("matlabarray"),
args=node.expr_list([u.args]))
H = nx.connected_components(G.to_undirected())
for i, component in enumerate(H):
for nodename in component:
if G.node[nodename]["ident"].props == "R":
has_update = 1
break
else:
has_update = 0
if has_update:
for nodename in component:
G.node[nodename]["ident"].props += "S" # sparse
#S = G.subgraph(nbunch)
#print S.edges()
return G
def p_expr2(p):
"""expr2 : expr AND expr
| expr ANDAND expr
| expr BACKSLASH expr
| expr COLON expr
| expr DIV expr
| expr DOT expr
| expr DOTDIV expr
| expr DOTDIVEQ expr
| expr DOTEXP expr
| expr DOTMUL expr
| expr DOTMULEQ expr
| expr EQEQ expr
| expr EXP expr
| expr EXPEQ expr
| expr GE expr
| expr GT expr
| expr LE expr
| expr LT expr
| expr MINUS expr
| expr MUL expr
| expr NE expr
| expr OR expr
| expr OROR expr
| expr PLUS expr
| expr EQ expr
| expr MULEQ expr
| expr DIVEQ expr
| expr MINUSEQ expr
| expr PLUSEQ expr
| expr OREQ expr
| expr ANDEQ expr
"""
if p[2] == "=":
if p[1].__class__ is node.let:
raise NotImplementedError("assignment "
"as expression")
# The algorithm, which decides if an
# expression F(X)
# is arrayref or funcall, is implemented in
# resolve.py, except the following lines up
# to XXX. These lines handle the case where
# an undefined array is updated:
# >>> clear a
# >>> a[1:10]=123
# Though legal in matlab, these lines
# confuse the algorithm, which thinks that
# the undefined variable is a function name.
# To prevent the confusion, we mark these
# nodes arrayref as early as during the parse
# phase.
if p[1].__class__ is node.funcall:
# A(B) = C
p[1].__class__ = node.arrayref
elif p[1].__class__ is node.matrix:
# [A1(B1) A2(B2) ...] = C
for e in p[1].args:
if e.__class__ is node.funcall:
e.__class__ = node.arrayref
# XXX
if isinstance(p[1],node.getfield):
#import pdb;pdb.set_trace()
# A.B=C setfield(A,B,C)
p[0] = node.setfield(p[1].args[0],
p[1].args[1],
p[3])
else:
#assert len(p[1].args) > 0
ret = p[1].args if isinstance(p[1],node.matrix) else p[1]
p[0] = node.let(ret=ret,
args=p[3],
lineno=p.lineno(2),
lexpos=p.lexpos(2))
if isinstance(p[1],node.matrix):
# TBD: mark idents as "P" - persistent
if p[3].__class__ not in (node.ident,node.funcall): #, p[3].__class__
raise NotImplementedError("multi-assignment %d" % p[0].lineno)
if p[3].__class__ is node.ident:
# [A1(B1) A2(B2) ...] = F implied F()
# import pdb; pdb.set_trace()
p[3] = node.funcall(func_expr=p[3],
args=node.expr_list())
# [A1(B1) A2(B2) ...] = F(X)
p[3].nargout = len(p[1].args[0])
elif p[2] == ".*":
p[0] = node.dot(p[1],p[3])
# elif p[2] == "." and isinstance(p[3],node.expr) and p[3].op=="parens":
# p[0] = node.getfield(p[1],p[3].args[0])
# raise NotImplementedError(p[3],p.lineno(3),p.lexpos(3))
elif p[2] == ":" and isinstance(p[1],node.expr) and p[1].op==":":
# Colon expression means different things depending on the
# context. As an array subscript, it is a slice; otherwise,
# it is a call to the "range" function, and the parser can't
# tell which is which. So understanding of colon expressions
# is put off until after "resolve".
p[0] = p[1]
p[0].args.insert(1,p[3])
else:
p[0] = node.expr(op=p[2],args=node.expr_list([p[1],p[3]]))
def p_expr2(p):
"""expr2 : expr AND expr
| expr ANDAND expr
| expr BACKSLASH expr
| expr COLON expr
| expr DIV expr
| expr DOT expr
| expr DOTDIV expr
| expr DOTDIVEQ expr
| expr DOTEXP expr
| expr DOTMUL expr
| expr DOTMULEQ expr
| expr EQEQ expr
| expr POW expr
| expr EXP expr
| expr EXPEQ expr
| expr GE expr
| expr GT expr
| expr LE expr
| expr LT expr
| expr MINUS expr
| expr MUL expr
| expr NE expr
| expr OR expr
| expr OROR expr
| expr PLUS expr
| expr EQ expr
| expr MULEQ expr
| expr DIVEQ expr
| expr MINUSEQ expr
| expr PLUSEQ expr
| expr OREQ expr
| expr ANDEQ expr
"""
if p[2] == "=":
if p[1].__class__ is node.let:
raise_exception(SyntaxError,
"Not implemented assignment as expression",
new_lexer)
# The algorithm, which decides if an
# expression F(X)
# is arrayref or funcall, is implemented in
# resolve.py, except the following lines up
# to XXX. These lines handle the case where
# an undefined array is updated:
# >>> clear a
# >>> a[1:10]=123
# Though legal in matlab, these lines
# confuse the algorithm, which thinks that
# the undefined variable is a function name.
# To prevent the confusion, we mark these
# nodes arrayref as early as during the parse
# phase.
if p[1].__class__ is node.funcall:
# A(B) = C
p[1].__class__ = node.arrayref
elif p[1].__class__ is node.matrix:
# [A1(B1) A2(B2) ...] = C
for e in p[1].args:
if e.__class__ is node.funcall:
e.__class__ = node.arrayref
# XXX
if isinstance(p[1], node.getfield):
# import pdb;pdb.set_trace()
# A.B=C setfield(A,B,C)
p[0] = node.setfield(p[1].args[0], p[1].args[1], p[3])
else:
# assert len(p[1].args) > 0
ret = p[1].args if isinstance(p[1], node.matrix) else p[1]
p[0] = node.let(ret=ret,
args=p[3],
lineno=p.lineno(2),
lexpos=p.lexpos(2))
if isinstance(p[1], node.matrix):
# TBD: mark idents as "P" - persistent
if p[3].__class__ not in (node.ident,
node.funcall): #, p[3].__class__
raise_exception(SyntaxError, "multi-assignment", new_lexer)
if p[3].__class__ is node.ident:
# [A1(B1) A2(B2) ...] = F implied F()
# import pdb; pdb.set_trace()
p[3] = node.funcall(func_expr=p[3], args=node.expr_list())
# [A1(B1) A2(B2) ...] = F(X)
p[3].nargout = len(p[1].args[0])
elif p[2] == "*":
p[0] = node.funcall(func_expr=node.ident("dot"),
args=node.expr_list([p[1], p[3]]))
elif p[2] == ".*":
p[0] = node.funcall(func_expr=node.ident("multiply"),
args=node.expr_list([p[1], p[3]]))
# elif p[2] == "." and isinstance(p[3],node.expr) and p[3].op=="parens":
# p[0] = node.getfield(p[1],p[3].args[0])
# raise SyntaxError(p[3],p.lineno(3),p.lexpos(3))
elif p[2] == ":" and isinstance(p[1], node.expr) and p[1].op == ":":
# Colon expression means different things depending on the
# context. As an array subscript, it is a slice; otherwise,
# it is a call to the "range" function, and the parser can't
# tell which is which. So understanding of colon expressions
# is put off until after "resolve".
p[0] = p[1]
p[0].args.insert(1, p[3])
else:
p[0] = node.expr(op=p[2], args=node.expr_list([p[1], p[3]]))
def resolve(t, symtab=None, fp=None, func_name=None):
if symtab is None:
symtab = {}
do_resolve(t, symtab)
G = as_networkx(t)
# import pdb;pdb.set_trace()
for n in G.nodes():
u = G.node[n]["ident"]
if u.props:
pass
elif G.out_edges(n) and G.in_edges(n):
u.props = "U" # upd
# print u.name, u.lineno, u.column
elif G.in_edges(n):
u.props = "D" # def
elif G.out_edges(n):
u.props = "R" # ref
else:
u.props = "F" # ???
G.node[n]["label"] = "%s\\n%s" % (n, u.props)
for u in node.postorder(t):
# if u.__class__ is node.func_decl:
# u.ident.name += "_"
if u.__class__ is node.funcall:
try:
if u.func_expr.props in "UR": # upd,ref
u.__class__ = node.arrayref
# else:
# u.func_expr.name += "_"
except:
pass
for u in node.postorder(t):
if u.__class__ in (node.arrayref, node.cellarrayref):
for i, v in enumerate(u.args):
if v.__class__ is node.expr and v.op == ":":
v.op = "::"
# for w in node.postorder(v):
# if w.__class__ is node.expr and w.op == "end":
# w.args[0] = u.func_expr
# w.args[1] = node.number(i)
for u in node.postorder(t):
if u.__class__ is node.let:
if u.ret.__class__ is node.ident and u.args.__class__ is node.matrix:
u.args = node.funcall(func_expr=node.ident("matlabarray"), args=node.expr_list([u.args]))
H = nx.connected_components(G.to_undirected())
for i, component in enumerate(H):
for nodename in component:
if G.node[nodename]["ident"].props == "R":
has_update = 1
break
else:
has_update = 0
if has_update:
for nodename in component:
G.node[nodename]["ident"].props += "S" # sparse
# S = G.subgraph(nbunch)
# print S.edges()
return G