def repeat_for_integral_values(values=None):
if not values:
values = {
"integers": 42,
"floats": 3.14,
"booleans": True,
}
return parameterized.expand(list(values.items()))
def repeat_for_integral_values(values=None):
if not values:
values = {
"integers": 42,
"floats": 3.14,
"booleans": True,
}
return parameterized.expand(list(values.items()))
def exist_perms(**kwargs):
all_elems = list(kwargs.keys())
curr_elems = copy.deepcopy(all_elems)
perms = _perms_cycle(curr_elems.pop(), curr_elems, {})
res = []
for name_str, perm in perms:
args = dict([(k, v) for (k, v) in kwargs.items() if perm[k]])
res.append((name_str, args))
return parameterized.expand(res)
def exist_perms(**kwargs):
all_elems = list(kwargs.keys())
curr_elems = copy.deepcopy(all_elems)
perms = _perms_cycle(curr_elems.pop(), curr_elems, {})
res = []
for name_str, perm in perms:
args = dict([(k, v) for (k, v) in kwargs.items() if perm[k]])
res.append((name_str, args))
return parameterized.expand(res)
def repeat_for_values(values=None):
if not values:
values = {
"integers": 42,
"floats": 3.14,
"strings": "Hello you",
"unicode_strings": u"éléphant is the french for elephant",
"booleans": True,
"none": None,
}
return parameterized.expand(list(values.items()))
def repeat_for_values(values=None):
if not values:
values = {
"integers": 42,
"floats": 3.14,
"strings": "Hello you",
"unicode_strings": u"éléphant is the french for elephant",
"booleans": True,
"none": None,
}
return parameterized.expand(list(values.items()))
def test_1_arguments_check(self, msg, argument1, argument2):
assert_equal(self.arg1.get_attribute('text'), 'First argument')
assert_equal(self.arg2.get_attribute('text'), 'Second argument')
self.arg1.send_keys(argument1)
self.arg2.send_keys(argument2)
sleep(2)
assert_equal(self.arg1.get_attribute('text'), argument1, msg)
assert_equal(self.arg2.get_attribute('text'), argument2, msg)
assert_equal(self.res.get_attribute('text'), 'Result', msg) \
\
@parameterized.expand([
('chars', 'a', 'B'),
('symbol', '#', '$'),
# ('symbol cyr', 'Ж', 'й'),
])
class OtherC(Classifier):
dtype = int64_dtype
missing_value = -1
inputs = ()
window_length = 0
class Mask(Filter):
inputs = ()
window_length = 0
for_each_factor_dtype = parameterized.expand([
('datetime64[ns]', datetime64ns_dtype),
('float', float64_dtype),
])
class FactorTestCase(BasePipelineTestCase):
def init_instance_fixtures(self):
super(FactorTestCase, self).init_instance_fixtures()
self.f = F()
def test_bad_input(self):
with self.assertRaises(UnknownRankMethod):
self.f.rank("not a real rank method")
@parameter_space(method_name=['isnan', 'notnan', 'isfinite'])
def test_float64_only_ops(self, method_name):
class NotFloat(Factor):
def true_false_perms(*all_elems_tuple):
all_elems = list(all_elems_tuple)
curr_elems = copy.deepcopy(all_elems)
perms = _perms_cycle(curr_elems.pop(), curr_elems, {})
return parameterized.expand(perms)
class OtherC(Classifier):
dtype = int64_dtype
missing_value = -1
inputs = ()
window_length = 0
class Mask(Filter):
inputs = ()
window_length = 0
for_each_factor_dtype = parameterized.expand([
('datetime64[ns]', datetime64ns_dtype),
('float', float64_dtype),
])
class FactorTestCase(BasePipelineTestCase):
def init_instance_fixtures(self):
super(FactorTestCase, self).init_instance_fixtures()
self.f = F()
def test_bad_input(self):
with self.assertRaises(UnknownRankMethod):
self.f.rank("not a real rank method")
@parameter_space(method_name=['isnan', 'notnan', 'isfinite'])
def test_float64_only_ops(self, method_name):
nameof = op.attrgetter('name')
dtypeof = op.attrgetter('dtype')
asset_infos = (
(make_simple_equity_info(
tuple(map(ord, 'ABC')),
pd.Timestamp(0),
pd.Timestamp('2015'),
), ),
(make_simple_equity_info(
tuple(map(ord, 'ABCD')),
pd.Timestamp(0),
pd.Timestamp('2015'),
), ),
)
with_extra_sid = parameterized.expand(asset_infos)
class BlazeToPipelineTestCase(TestCase):
@classmethod
def setUpClass(cls):
cls.dates = dates = pd.date_range('2014-01-01', '2014-01-03')
dates = cls.dates.repeat(3)
cls.sids = sids = ord('A'), ord('B'), ord('C')
cls.df = df = pd.DataFrame({
'sid': sids * 3,
'value': (0, 1, 2, 1, 2, 3, 2, 3, 4),
'asof_date': dates,
'timestamp': dates,
})
cls.dshape = dshape("""
nameof = op.attrgetter('name')
dtypeof = op.attrgetter('dtype')
asset_infos = (
(make_simple_equity_info(
tuple(map(ord, 'ABC')),
pd.Timestamp(0),
pd.Timestamp('2015'),
),),
(make_simple_equity_info(
tuple(map(ord, 'ABCD')),
pd.Timestamp(0),
pd.Timestamp('2015'),
),),
)
with_extra_sid = parameterized.expand(asset_infos)
class BlazeToPipelineTestCase(TestCase):
@classmethod
def setUpClass(cls):
cls.dates = dates = pd.date_range('2014-01-01', '2014-01-03')
dates = cls.dates.repeat(3)
cls.sids = sids = ord('A'), ord('B'), ord('C')
cls.df = df = pd.DataFrame({
'sid': sids * 3,
'value': (0, 1, 2, 1, 2, 3, 2, 3, 4),
'asof_date': dates,
'timestamp': dates,
})
cls.dshape = dshape("""
def true_false_perms(*all_elems_tuple):
all_elems = list(all_elems_tuple)
curr_elems = copy.deepcopy(all_elems)
perms = _perms_cycle(curr_elems.pop(), curr_elems, {})
return parameterized.expand(perms)
nameof = op.attrgetter('name')
dtypeof = op.attrgetter('dtype')
asset_infos = (
(make_simple_equity_info(
tuple(map(ord, 'ABC')),
pd.Timestamp(0),
pd.Timestamp('2015'),
), ),
(make_simple_equity_info(
tuple(map(ord, 'ABCD')),
pd.Timestamp(0),
pd.Timestamp('2015'),
), ),
)
with_extra_sid = parameterized.expand(asset_infos)
with_ignore_sid = parameterized.expand(
product(chain.from_iterable(asset_infos), [True, False]))
def _utc_localize_index_level_0(df):
"""``tz_localize`` the first level of a multiindexed dataframe to utc.
Mutates df in place.
"""
idx = df.index
df.index = pd.MultiIndex.from_product(
(idx.levels[0].tz_localize('utc'), idx.levels[1]),
names=idx.names,
)
return df
nameof = op.attrgetter('name')
dtypeof = op.attrgetter('dtype')
asset_infos = (
(make_simple_equity_info(
tuple(map(ord, 'ABC')),
pd.Timestamp(0),
pd.Timestamp('2015'),
),),
(make_simple_equity_info(
tuple(map(ord, 'ABCD')),
pd.Timestamp(0),
pd.Timestamp('2015'),
),),
)
with_extra_sid = parameterized.expand(asset_infos)
with_ignore_sid = parameterized.expand(
product(chain.from_iterable(asset_infos), [True, False])
)
def _utc_localize_index_level_0(df):
"""``tz_localize`` the first level of a multiindexed dataframe to utc.
Mutates df in place.
"""
idx = df.index
df.index = pd.MultiIndex.from_product(
(idx.levels[0].tz_localize('utc'), idx.levels[1]),
names=idx.names,
)
