patsy/patsy/eval.py at splines · IBMZ-Linux-OSS-Python/patsy · GitHub

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# This file is part of Patsy

# See file COPYING for license information.

# Utilities that require an over-intimate knowledge of Python's execution

# environment.

# These are made available in the patsy.* namespace

__all__ = ["EvalEnvironment", "EvalFactor"]

import sys

import __future__

import inspect

import tokenize

from patsy import PatsyError

from patsy.util import PushbackAdapter

from patsy.tokens import (pretty_untokenize, normalize_token_spacing,

python_tokenize)

from patsy.compat import call_and_wrap_exc

def _all_future_flags():

flags = 0

for feature_name in __future__.all_feature_names:

feature = getattr(__future__, feature_name)

if feature.getMandatoryRelease() > sys.version_info:

flags |= feature.compiler_flag

return flags

_ALL_FUTURE_FLAGS = _all_future_flags()

# This is just a minimal dict-like object that does lookup in a 'stack' of

# dicts -- first it checks the first, then the second, etc. Assignments go

# into an internal, zeroth dict.

class VarLookupDict(object):

def __init__(self, dicts):

self._dicts = [{}] + list(dicts)

def __getitem__(self, key):

for d in self._dicts:

try:

return d[key]

except KeyError:

pass

raise KeyError, key

def __setitem__(self, key, value):

self._dicts[0][key] = value

def __contains__(self, key):

try:

self[key]

except KeyError:

return False

else:

return True

def get(self, key, default=None):

try:

return self[key]

except KeyError:

return default

def __repr__(self):

return "%s(%r)" % (self.__class__.__name__, self._dicts)

def test_VarLookupDict():

d1 = {"a": 1}

d2 = {"a": 2, "b": 3}

ds = VarLookupDict([d1, d2])

assert ds["a"] == 1

assert ds["b"] == 3

assert "a" in ds

assert "c" not in ds

from nose.tools import assert_raises

assert_raises(KeyError, ds.__getitem__, "c")

ds["a"] = 10

assert ds["a"] == 10

assert d1["a"] == 1

class EvalEnvironment(object):

"""Represents a Python execution environment.

Encapsulates a namespace for variable lookup and set of __future__

flags."""

def __init__(self, namespaces, flags=0):

assert not flags & ~_ALL_FUTURE_FLAGS

self._namespaces = list(namespaces)

self.flags = flags

@property

def namespace(self):

"""A dict-like object that can be used to look up variables accessible

from the encapsulated environment."""

return VarLookupDict(self._namespaces)

def add_outer_namespace(self, namespace):

"""Expose the contents of a dict-like object to the encapsulated

environment.

The given namespace will be checked last, after all existing namespace

lookups have failed.

"""

# ModelDesc.from_formula unconditionally calls

# eval_env.add_outer_namespace(builtins)

# which means that if someone uses the same environment for a bunch of

# formulas, our namespace chain will grow without bound, which would

# suck.

if id(namespace) not in self._namespace_ids():

self._namespaces.append(namespace)

def eval(self, expr, source_name="<string>", inner_namespace={}):

"""Evaluate some Python code in the encapsulated environment.

:arg expr: A string containing a Python expression.

:arg source_name: A name for this string, for use in tracebacks.

:arg inner_namespace: A dict-like object that will be checked first

when `expr` attempts to access any variables.

:returns: The value of `expr`.

"""

code = compile(expr, source_name, "eval", self.flags, False)

return eval(code, {}, VarLookupDict([inner_namespace]

+ self._namespaces))

@classmethod

def capture(cls, eval_env=0, reference=0):

"""Capture an execution environment from the stack.

If `eval_env` is already an :class:`EvalEnvironment`, it is returned

unchanged. Otherwise, we walk up the stack by ``eval_env + reference``

steps and capture that function's evaluation environment.

For ``eval_env=0`` and ``reference=0``, the default, this captures the

stack frame of the function that calls :meth:`capture`. If ``eval_env

+ reference`` is 1, then we capture that function's caller, etc.

This somewhat complicated calling convention is designed to be

convenient for functions which want to capture their caller's

environment by default, but also allow explicit environments to be

specified. See the second example.

Example::

x = 1

this_env = EvalEnvironment.capture()

assert this_env["x"] == 1

def child_func():

return EvalEnvironment.capture(1)

this_env_from_child = child_func()

assert this_env_from_child["x"] == 1

Example::

# This function can be used like:

# my_model(formula_like, data)

# -> evaluates formula_like in caller's environment

# my_model(formula_like, data, eval_env=1)

# -> evaluates formula_like in caller's caller's environment

# my_model(formula_like, data, eval_env=my_env)

# -> evaluates formula_like in environment 'my_env'

def my_model(formula_like, data, eval_env=0):

eval_env = EvalEnvironment.capture(eval_env, reference=1)

return model_setup_helper(formula_like, data, eval_env)

This is how :func:`dmatrix` works.

"""

if isinstance(eval_env, cls):

return eval_env

else:

depth = eval_env + reference

frame = inspect.currentframe()

try:

for i in xrange(depth + 1):

if frame is None:

raise ValueError, "call-stack is not that deep!"

frame = frame.f_back

return cls([frame.f_locals, frame.f_globals],

frame.f_code.co_flags & _ALL_FUTURE_FLAGS)

# The try/finally is important to avoid a potential reference cycle --

# any exception traceback will carry a reference to *our* frame, which

# contains a reference to our local variables, which would otherwise

# carry a reference to some parent frame, where the exception was

# caught...:

finally:

del frame

def _namespace_ids(self):

return [id(n) for n in self._namespaces]

def __eq__(self, other):

return (isinstance(other, EvalEnvironment)

and self.flags == other.flags

and self._namespace_ids() == other._namespace_ids())

def __ne__(self, other):

return not self == other

def __hash__(self):

return hash((EvalEnvironment,

self.flags,

tuple(self._namespace_ids())))

def _a(): # pragma: no cover

_a = 1

return _b()

def _b(): # pragma: no cover

_b = 1

return _c()

def _c(): # pragma: no cover

_c = 1

return [EvalEnvironment.capture(),

EvalEnvironment.capture(0),

EvalEnvironment.capture(1),

EvalEnvironment.capture(0, reference=1),

EvalEnvironment.capture(2),

EvalEnvironment.capture(0, 2),

]

def test_EvalEnvironment_capture_namespace():

c0, c, b1, b2, a1, a2 = _a()

assert "test_EvalEnvironment_capture_namespace" in c0.namespace

assert "test_EvalEnvironment_capture_namespace" in c.namespace

assert "test_EvalEnvironment_capture_namespace" in b1.namespace

assert "test_EvalEnvironment_capture_namespace" in b2.namespace

assert "test_EvalEnvironment_capture_namespace" in a1.namespace

assert "test_EvalEnvironment_capture_namespace" in a2.namespace

assert c0.namespace["_c"] == 1

assert c.namespace["_c"] == 1

assert b1.namespace["_b"] == 1

assert b2.namespace["_b"] == 1

assert a1.namespace["_a"] == 1

assert a2.namespace["_a"] == 1

assert b1.namespace["_c"] is _c

assert b2.namespace["_c"] is _c

from nose.tools import assert_raises

assert_raises(ValueError, EvalEnvironment.capture, 10 ** 6)

assert EvalEnvironment.capture(b1) is b1

def test_EvalEnvironment_capture_flags():

if sys.version_info >= (3,):

# This is the only __future__ feature currently usable in Python

# 3... fortunately it is probably not going anywhere.

TEST_FEATURE = "barry_as_FLUFL"

else:

TEST_FEATURE = "division"

test_flag = getattr(__future__, TEST_FEATURE).compiler_flag

assert test_flag & _ALL_FUTURE_FLAGS

source = ("def f():\n"

" in_f = 'hi from f'\n"

" global RETURN_INNER, RETURN_OUTER, RETURN_INNER_FROM_OUTER\n"

" RETURN_INNER = EvalEnvironment.capture(0)\n"

" RETURN_OUTER = call_capture_0()\n"

" RETURN_INNER_FROM_OUTER = call_capture_1()\n"

"f()\n")

code = compile(source, "<test string>", "exec", 0, 1)

env = {"EvalEnvironment": EvalEnvironment,

"call_capture_0": lambda: EvalEnvironment.capture(0),

"call_capture_1": lambda: EvalEnvironment.capture(1),

}

env2 = dict(env)

exec code in env

assert env["RETURN_INNER"].namespace["in_f"] == "hi from f"

assert env["RETURN_INNER_FROM_OUTER"].namespace["in_f"] == "hi from f"

assert "in_f" not in env["RETURN_OUTER"].namespace

assert env["RETURN_INNER"].flags & _ALL_FUTURE_FLAGS == 0

assert env["RETURN_OUTER"].flags & _ALL_FUTURE_FLAGS == 0

assert env["RETURN_INNER_FROM_OUTER"].flags & _ALL_FUTURE_FLAGS == 0

code2 = compile(("from __future__ import %s\n" % (TEST_FEATURE,))

+ source,

"<test string 2>", "exec", 0, 1)

exec code2 in env2

assert env2["RETURN_INNER"].namespace["in_f"] == "hi from f"

assert env2["RETURN_INNER_FROM_OUTER"].namespace["in_f"] == "hi from f"

assert "in_f" not in env2["RETURN_OUTER"].namespace

assert env2["RETURN_INNER"].flags & _ALL_FUTURE_FLAGS == test_flag

assert env2["RETURN_OUTER"].flags & _ALL_FUTURE_FLAGS == 0

assert env2["RETURN_INNER_FROM_OUTER"].flags & _ALL_FUTURE_FLAGS == test_flag

def test_EvalEnvironment_eval_namespace():

env = EvalEnvironment([{"a": 1}])

assert env.eval("2 * a") == 2

assert env.eval("2 * a", inner_namespace={"a": 2}) == 4

from nose.tools import assert_raises

assert_raises(NameError, env.eval, "2 * b")

a = 3

env2 = EvalEnvironment.capture(0)

assert env2.eval("2 * a") == 6

def test_EvalEnvironment_eval_flags():

from nose.tools import assert_raises

if sys.version_info >= (3,):

# This joke __future__ statement replaces "!=" with "<>":

# http://www.python.org/dev/peps/pep-0401/

test_flag = __future__.barry_as_FLUFL.compiler_flag

assert test_flag & _ALL_FUTURE_FLAGS

env = EvalEnvironment([{"a": 11}], flags=0)

assert env.eval("a != 0") == True

assert_raises(SyntaxError, env.eval, "a <> 0")

env2 = EvalEnvironment([{"a": 11}], flags=test_flag)

assert env2.eval("a <> 0") == True

assert_raises(SyntaxError, env2.eval, "a != 0")

else:

test_flag = __future__.division.compiler_flag

assert test_flag & _ALL_FUTURE_FLAGS

env = EvalEnvironment([{"a": 11}], flags=0)

assert env.eval("a / 2") == 11 // 2 == 5

env2 = EvalEnvironment([{"a": 11}], flags=test_flag)

assert env2.eval("a / 2") == 11 * 1. / 2 != 5

def test_EvalEnvironment_eq():

# Two environments are eq only if they refer to exactly the same

# global/local dicts

env1 = EvalEnvironment.capture(0)

env2 = EvalEnvironment.capture(0)

assert env1 == env2

assert hash(env1) == hash(env2)

capture_local_env = lambda: EvalEnvironment.capture(0)

env3 = capture_local_env()

env4 = capture_local_env()

assert env3 != env4

def test_EvalEnvironment_add_outer_namespace():

a = 1

env = EvalEnvironment.capture(0)

env2 = EvalEnvironment.capture(0)

assert env.namespace["a"] == 1

assert "b" not in env.namespace

assert env == env2

env.add_outer_namespace({"a": 10, "b": 2})

assert env.namespace["a"] == 1

assert env.namespace["b"] == 2

assert env != env2

class EvalFactor(object):

def __init__(self, code, eval_env, origin=None):

"""A factor class that executes arbitrary Python code and supports

stateful transforms.

:arg code: A string containing a Python expression, that will be

evaluated to produce this factor's value.

:arg eval_env: The class:`EvalEnvironment` where `code` will be

evaluated.

This is the standard factor class that is used when parsing formula

strings and implements the standard stateful transform processing. See

:ref:`stateful-transforms` and :ref:`expert-model-specification`.

Two EvalFactor's are considered equal (e.g., for purposes of

redundancy detection) if they use the same evaluation environment and

they contain the same token stream. Basically this means that the

source code must be identical except for whitespace::

env = EvalEnvironment.capture()

assert EvalFactor("a + b", env) == EvalFactor("a+b", env)

assert EvalFactor("a + b", env) != EvalFactor("b + a", env)

"""

# For parsed formulas, the code will already have been normalized by

# the parser. But let's normalize anyway, so we can be sure of having

# consistent semantics for __eq__ and __hash__.

self.code = normalize_token_spacing(code)

self._eval_env = eval_env

self.origin = origin

def name(self):

return self.code

def __repr__(self):

return "%s(%r)" % (self.__class__.__name__, self.code)

def __eq__(self, other):

return (isinstance(other, EvalFactor)

and self.code == other.code

and self._eval_env == other._eval_env)

def __ne__(self, other):

return not self == other

def __hash__(self):

return hash((EvalFactor, self.code, self._eval_env))

def memorize_passes_needed(self, state):

# 'state' is just an empty dict which we can do whatever we want with,

# and that will be passed back to later memorize functions

state["transforms"] = {}

# example code: == "2 * center(x)"

i = [0]

def new_name_maker(token):

value = self._eval_env.namespace.get(token)

if hasattr(value, "__patsy_stateful_transform__"):

obj_name = "_patsy_stobj%s__%s__" % (i[0], token)

i[0] += 1

obj = value.__patsy_stateful_transform__()

state["transforms"][obj_name] = obj

return obj_name + ".transform"

else:

return token

# example eval_code: == "2 * _patsy_stobj0__center__.transform(x)"

eval_code = replace_bare_funcalls(self.code, new_name_maker)

state["eval_code"] = eval_code

# paranoia: verify that none of our new names appeared anywhere in the

# original code

if has_bare_variable_reference(state["transforms"], self.code):

raise PatsyError("names of this form are reserved for "

"internal use (%s)" % (token,), token.origin)

# Pull out all the '_patsy_stobj0__center__.transform(x)' pieces

# to make '_patsy_stobj0__center__.memorize_chunk(x)' pieces

state["memorize_code"] = {}

for obj_name in state["transforms"]:

transform_calls = capture_obj_method_calls(obj_name, eval_code)

assert len(transform_calls) == 1

transform_call = transform_calls[0]

transform_call_name, transform_call_code = transform_call

assert transform_call_name == obj_name + ".transform"

assert transform_call_code.startswith(transform_call_name + "(")

memorize_code = (obj_name

+ ".memorize_chunk"

+ transform_call_code[len(transform_call_name):])

state["memorize_code"][obj_name] = memorize_code

# Then sort the codes into bins, so that every item in bin number i

# depends only on items in bin (i-1) or less. (By 'depends', we mean

# that in something like:

# spline(center(x))

# we have to first run:

# center.memorize_chunk(x)

# then

# center.memorize_finish(x)

# and only then can we run:

# spline.memorize_chunk(center.transform(x))

# Since all of our objects have unique names, figuring out who

# depends on who is pretty easy -- we just check whether the

# memorization code for spline:

# spline.memorize_chunk(center.transform(x))

# mentions the variable 'center' (which in the example, of course, it

# does).

pass_bins = []

unsorted = set(state["transforms"])

while unsorted:

pass_bin = set()

for obj_name in unsorted:

other_objs = unsorted.difference([obj_name])

memorize_code = state["memorize_code"][obj_name]

if not has_bare_variable_reference(other_objs, memorize_code):

pass_bin.add(obj_name)

assert pass_bin

unsorted.difference_update(pass_bin)

pass_bins.append(pass_bin)

state["pass_bins"] = pass_bins

return len(pass_bins)

def _eval(self, code, memorize_state, data):

inner_namespace = VarLookupDict([data, memorize_state["transforms"]])

return call_and_wrap_exc("Error evaluating factor",

self,

self._eval_env.eval,

code, inner_namespace=inner_namespace)

def memorize_chunk(self, state, which_pass, data):

for obj_name in state["pass_bins"][which_pass]:

self._eval(state["memorize_code"][obj_name], state, data)

def memorize_finish(self, state, which_pass):

for obj_name in state["pass_bins"][which_pass]:

state["transforms"][obj_name].memorize_finish()

# XX FIXME: consider doing something cleverer with exceptions raised here,

# to make it clearer what's really going on. The new exception chaining

# stuff doesn't appear to be present in any 2.x version of Python, so we

# can't use that, but some other options:

# http://blog.ianbicking.org/2007/09/12/re-raising-exceptions/

# http://nedbatchelder.com/blog/200711/rethrowing_exceptions_in_python.html

def eval(self, memorize_state, data):

return self._eval(memorize_state["eval_code"], memorize_state, data)

def test_EvalFactor_basics():

e = EvalFactor("a+b", EvalEnvironment.capture(0))

assert e.code == "a + b"

assert e.name() == "a + b"

e2 = EvalFactor("a +b", EvalEnvironment.capture(0), origin="asdf")

assert e == e2

assert hash(e) == hash(e2)

assert e.origin is None

assert e2.origin == "asdf"

def test_EvalFactor_memorize_passes_needed():

from patsy.state import stateful_transform

foo = stateful_transform(lambda: "FOO-OBJ")

bar = stateful_transform(lambda: "BAR-OBJ")

quux = stateful_transform(lambda: "QUUX-OBJ")

e = EvalFactor("foo(x) + bar(foo(y)) + quux(z, w)",

EvalEnvironment.capture(0))

state = {}

passes = e.memorize_passes_needed(state)

print passes

print state

assert passes == 2

assert state["transforms"] == {"_patsy_stobj0__foo__": "FOO-OBJ",

"_patsy_stobj1__bar__": "BAR-OBJ",

"_patsy_stobj2__foo__": "FOO-OBJ",

"_patsy_stobj3__quux__": "QUUX-OBJ"}

assert (state["eval_code"]

== "_patsy_stobj0__foo__.transform(x)"

" + _patsy_stobj1__bar__.transform("

"_patsy_stobj2__foo__.transform(y))"

" + _patsy_stobj3__quux__.transform(z, w)")

assert (state["memorize_code"]

== {"_patsy_stobj0__foo__":

"_patsy_stobj0__foo__.memorize_chunk(x)",

"_patsy_stobj1__bar__":

"_patsy_stobj1__bar__.memorize_chunk(_patsy_stobj2__foo__.transform(y))",

"_patsy_stobj2__foo__":

"_patsy_stobj2__foo__.memorize_chunk(y)",

"_patsy_stobj3__quux__":

"_patsy_stobj3__quux__.memorize_chunk(z, w)",

})

assert state["pass_bins"] == [set(["_patsy_stobj0__foo__",

"_patsy_stobj2__foo__",

"_patsy_stobj3__quux__"]),

set(["_patsy_stobj1__bar__"])]

class _MockTransform(object):

# Adds up all memorized data, then subtracts that sum from each datum

def __init__(self):

self._sum = 0

self._memorize_chunk_called = 0

self._memorize_finish_called = 0

def memorize_chunk(self, data):

self._memorize_chunk_called += 1

import numpy as np

self._sum += np.sum(data)

def memorize_finish(self):

self._memorize_finish_called += 1

def transform(self, data):

return data - self._sum

def test_EvalFactor_end_to_end():

from patsy.state import stateful_transform

foo = stateful_transform(_MockTransform)

e = EvalFactor("foo(x) + foo(foo(y))", EvalEnvironment.capture(0))

state = {}

passes = e.memorize_passes_needed(state)

print passes

print state

assert passes == 2

import numpy as np

e.memorize_chunk(state, 0,

{"x": np.array([1, 2]),

"y": np.array([10, 11])})

assert state["transforms"]["_patsy_stobj0__foo__"]._memorize_chunk_called == 1

assert state["transforms"]["_patsy_stobj2__foo__"]._memorize_chunk_called == 1

e.memorize_chunk(state, 0, {"x": np.array([12, -10]),

"y": np.array([100, 3])})

assert state["transforms"]["_patsy_stobj0__foo__"]._memorize_chunk_called == 2

assert state["transforms"]["_patsy_stobj2__foo__"]._memorize_chunk_called == 2

assert state["transforms"]["_patsy_stobj0__foo__"]._memorize_finish_called == 0

assert state["transforms"]["_patsy_stobj2__foo__"]._memorize_finish_called == 0

e.memorize_finish(state, 0)

assert state["transforms"]["_patsy_stobj0__foo__"]._memorize_finish_called == 1

assert state["transforms"]["_patsy_stobj2__foo__"]._memorize_finish_called == 1

assert state["transforms"]["_patsy_stobj1__foo__"]._memorize_chunk_called == 0

assert state["transforms"]["_patsy_stobj1__foo__"]._memorize_finish_called == 0

e.memorize_chunk(state, 1, {"x": np.array([1, 2]),

"y": np.array([10, 11])})

e.memorize_chunk(state, 1, {"x": np.array([12, -10]),

"y": np.array([100, 3])})

e.memorize_finish(state, 1)

for transform in state["transforms"].itervalues():

assert transform._memorize_chunk_called == 2

assert transform._memorize_finish_called == 1

# sums:

# 0: 1 + 2 + 12 + -10 == 5

# 2: 10 + 11 + 100 + 3 == 124

# 1: (10 - 124) + (11 - 124) + (100 - 124) + (3 - 124) == -372

# results:

# 0: -4, -3, 7, -15

# 2: -114, -113, -24, -121

# 1: 258, 259, 348, 251

# 0 + 1: 254, 256, 355, 236

assert np.all(e.eval(state,

{"x": np.array([1, 2, 12, -10]),

"y": np.array([10, 11, 100, 3])})

== [254, 256, 355, 236])

def annotated_tokens(code):

prev_was_dot = False

it = PushbackAdapter(python_tokenize(code))

for (token_type, token, origin) in it:

props = {}

props["bare_ref"] = (not prev_was_dot and token_type == tokenize.NAME)

props["bare_funcall"] = (props["bare_ref"]

and it.has_more() and it.peek()[1] == "(")

yield (token_type, token, origin, props)

prev_was_dot = (token == ".")

def test_annotated_tokens():

tokens_without_origins = [(token_type, token, props)

for (token_type, token, origin, props)

in (annotated_tokens("a(b) + c.d"))]

assert (tokens_without_origins

== [(tokenize.NAME, "a", {"bare_ref": True, "bare_funcall": True}),

(tokenize.OP, "(", {"bare_ref": False, "bare_funcall": False}),

(tokenize.NAME, "b", {"bare_ref": True, "bare_funcall": False}),

(tokenize.OP, ")", {"bare_ref": False, "bare_funcall": False}),

(tokenize.OP, "+", {"bare_ref": False, "bare_funcall": False}),

(tokenize.NAME, "c", {"bare_ref": True, "bare_funcall": False}),

(tokenize.OP, ".", {"bare_ref": False, "bare_funcall": False}),

(tokenize.NAME, "d",

{"bare_ref": False, "bare_funcall": False}),

])

# This was a bug:

assert len(list(annotated_tokens("x"))) == 1

def has_bare_variable_reference(names, code):

for (_, token, _, props) in annotated_tokens(code):

if props["bare_ref"] and token in names:

return True

return False

def replace_bare_funcalls(code, replacer):

tokens = []

for (token_type, token, origin, props) in annotated_tokens(code):

if props["bare_ref"] and props["bare_funcall"]:

token = replacer(token)

tokens.append((token_type, token))

return pretty_untokenize(tokens)

def test_replace_bare_funcalls():

def replacer1(token):

return {"a": "b", "foo": "_internal.foo.process"}.get(token, token)

def t1(code, expected):

replaced = replace_bare_funcalls(code, replacer1)

print "%r -> %r" % (code, replaced)

print "(wanted %r)" % (expected,)

assert replaced == expected

t1("foobar()", "foobar()")

t1("a()", "b()")

t1("foobar.a()", "foobar.a()")

t1("foo()", "_internal.foo.process()")

t1("a + 1", "a + 1")

t1("b() + a() * x[foo(2 ** 3)]",

"b() + b() * x[_internal.foo.process(2 ** 3)]")

class _FuncallCapturer(object):

# captures the next funcall

def __init__(self, start_token_type, start_token):

self.func = [start_token]

self.tokens = [(start_token_type, start_token)]

self.paren_depth = 0

self.started = False

self.done = False

def add_token(self, token_type, token):

if self.done:

return

self.tokens.append((token_type, token))

if token in ["(", "{", "["]:

self.paren_depth += 1

if token in [")", "}", "]"]:

self.paren_depth -= 1

assert self.paren_depth >= 0

if not self.started:

if token == "(":

self.started = True

else:

assert token_type == tokenize.NAME or token == "."

self.func.append(token)

if self.started and self.paren_depth == 0:

self.done = True

# This is not a very general function -- it assumes that all references to the

# given object are of the form '<obj_name>.something(method call)'.

def capture_obj_method_calls(obj_name, code):

capturers = []

for (token_type, token, origin, props) in annotated_tokens(code):

for capturer in capturers:

capturer.add_token(token_type, token)

if props["bare_ref"] and token == obj_name:

capturers.append(_FuncallCapturer(token_type, token))

return [("".join(capturer.func), pretty_untokenize(capturer.tokens))

for capturer in capturers]

def test_capture_obj_method_calls():

assert (capture_obj_method_calls("foo", "a + foo.baz(bar) + b.c(d)")

== [("foo.baz", "foo.baz(bar)")])

assert (capture_obj_method_calls("b", "a + foo.baz(bar) + b.c(d)")

== [("b.c", "b.c(d)")])

assert (capture_obj_method_calls("foo", "foo.bar(foo.baz(quux))")

== [("foo.bar", "foo.bar(foo.baz(quux))"),

("foo.baz", "foo.baz(quux)")])

assert (capture_obj_method_calls("bar", "foo[bar.baz(x(z[asdf])) ** 2]")

== [("bar.baz", "bar.baz(x(z[asdf]))")])

patsy/patsy/eval.py at splines · IBMZ-Linux-OSS-Python/patsy · GitHub

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