def test_nearest_unequal_elements(self, tz):
dts = safe_tz_localize(
pd.to_datetime(
['2014-01-01', '2014-01-05', '2014-01-06', '2014-01-09']), tz)
def t(s):
return None if s is None else pd.Timestamp(s, tz=tz)
for dt, before, after in (('2013-12-30', None,
'2014-01-01'), ('2013-12-31', None,
'2014-01-01'),
('2014-01-01', None,
'2014-01-05'), ('2014-01-02', '2014-01-01',
'2014-01-05'),
('2014-01-03', '2014-01-01',
'2014-01-05'), ('2014-01-04', '2014-01-01',
'2014-01-05'),
('2014-01-05', '2014-01-01',
'2014-01-06'), ('2014-01-06', '2014-01-05',
'2014-01-09'),
('2014-01-07', '2014-01-06',
'2014-01-09'), ('2014-01-08', '2014-01-06',
'2014-01-09'),
('2014-01-09', '2014-01-06',
None), ('2014-01-10', '2014-01-09',
None), ('2014-01-11', '2014-01-09',
None)):
computed = nearest_unequal_elements(dts, t(dt))
expected = (t(before), t(after))
self.assertEqual(computed, expected)
def test_nearest_unequal_elements(self, tz):
dts = pd.to_datetime(
['2014-01-01', '2014-01-05', '2014-01-06', '2014-01-09'],
).tz_localize(tz)
def t(s):
return None if s is None else pd.Timestamp(s, tz=tz)
for dt, before, after in (('2013-12-30', None, '2014-01-01'),
('2013-12-31', None, '2014-01-01'),
('2014-01-01', None, '2014-01-05'),
('2014-01-02', '2014-01-01', '2014-01-05'),
('2014-01-03', '2014-01-01', '2014-01-05'),
('2014-01-04', '2014-01-01', '2014-01-05'),
('2014-01-05', '2014-01-01', '2014-01-06'),
('2014-01-06', '2014-01-05', '2014-01-09'),
('2014-01-07', '2014-01-06', '2014-01-09'),
('2014-01-08', '2014-01-06', '2014-01-09'),
('2014-01-09', '2014-01-06', None),
('2014-01-10', '2014-01-09', None),
('2014-01-11', '2014-01-09', None)):
computed = nearest_unequal_elements(dts, t(dt))
expected = (t(before), t(after))
self.assertEqual(computed, expected)
def test_nearest_unequal_bad_input(self):
with self.assertRaises(ValueError) as e:
nearest_unequal_elements(
pd.to_datetime(['2014', '2014']),
pd.Timestamp('2014'),
)
self.assertEqual(str(e.exception), 'dts must be unique')
with self.assertRaises(ValueError) as e:
nearest_unequal_elements(
pd.to_datetime(['2014', '2013']),
pd.Timestamp('2014'),
)
self.assertEqual(
str(e.exception),
'dts must be sorted in increasing order',
)
def test_nearest_unequal_bad_input(self):
with self.assertRaises(ValueError) as e:
nearest_unequal_elements(
pd.to_datetime(['2014', '2014']),
pd.Timestamp('2014'),
)
self.assertEqual(str(e.exception), 'dts must be unique')
with self.assertRaises(ValueError) as e:
nearest_unequal_elements(
pd.to_datetime(['2014', '2013']),
pd.Timestamp('2014'),
)
self.assertEqual(
str(e.exception),
'dts must be sorted in increasing order',
)
def test_nearest_unequal_elements_short_dts(self, tz):
# Length 1.
dts = safe_tz_localize(pd.to_datetime(['2014-01-01']), tz)
def t(s):
return None if s is None else pd.Timestamp(s, tz=tz)
for dt, before, after in (('2013-12-31', None,
'2014-01-01'), ('2014-01-01', None, None),
('2014-01-02', '2014-01-01', None)):
computed = nearest_unequal_elements(dts, t(dt))
expected = (t(before), t(after))
self.assertEqual(computed, expected)
# Length 0
dts = safe_tz_localize(pd.to_datetime([]), tz)
for dt, before, after in (('2013-12-31', None, None),
('2014-01-01', None, None), ('2014-01-02',
None, None)):
computed = nearest_unequal_elements(dts, t(dt))
expected = (t(before), t(after))
self.assertEqual(computed, expected)
def test_nearest_unequal_elements_short_dts(self, tz):
# Length 1.
dts = pd.to_datetime(['2014-01-01']).tz_localize(tz)
def t(s):
return None if s is None else pd.Timestamp(s, tz=tz)
for dt, before, after in (('2013-12-31', None, '2014-01-01'),
('2014-01-01', None, None),
('2014-01-02', '2014-01-01', None)):
computed = nearest_unequal_elements(dts, t(dt))
expected = (t(before), t(after))
self.assertEqual(computed, expected)
# Length 0
dts = pd.to_datetime([]).tz_localize(tz)
for dt, before, after in (('2013-12-31', None, None),
('2014-01-01', None, None),
('2014-01-02', None, None)):
computed = nearest_unequal_elements(dts, t(dt))
expected = (t(before), t(after))
self.assertEqual(computed, expected)
def compute_extra_rows(self,
all_dates,
start_date,
end_date,
min_extra_rows):
"""
Ensure that min_extra_rows pushes us back to a computation date.
Parameters
----------
all_dates : pd.DatetimeIndex
The trading sessions against which ``self`` will be computed.
start_date : pd.Timestamp
The first date for which final output is requested.
end_date : pd.Timestamp
The last date for which final output is requested.
min_extra_rows : int
The minimum number of extra rows required of ``self``, as
determined by other terms that depend on ``self``.
Returns
-------
extra_rows : int
The number of extra rows to compute. This will be the minimum
number of rows required to make our computed start_date fall on a
recomputation date.
"""
try:
current_start_pos = all_dates.get_loc(start_date) - min_extra_rows
if current_start_pos < 0:
raise NoFurtherDataError(
initial_message="Insufficient data to compute Pipeline:",
first_date=all_dates[0],
lookback_start=start_date,
lookback_length=min_extra_rows,
)
except KeyError:
before, after = nearest_unequal_elements(all_dates, start_date)
raise ValueError(
"Pipeline start_date {start_date} is not in calendar.\n"
"Latest date before start_date is {before}.\n"
"Earliest date after start_date is {after}.".format(
start_date=start_date,
before=before,
after=after,
)
)
# Our possible target dates are all the dates on or before the current
# starting position.
# TODO: Consider bounding this below by self.window_length
candidates = all_dates[:current_start_pos + 1]
# Choose the latest date in the candidates that is the start of a new
# period at our frequency.
choices = select_sampling_indices(candidates, self._frequency)
# If we have choices, the last choice is the first date if the
# period containing current_start_date. Choose it.
new_start_date = candidates[choices[-1]]
# Add the difference between the new and old start dates to get the
# number of rows for the new start_date.
new_start_pos = all_dates.get_loc(new_start_date)
assert new_start_pos <= current_start_pos, \
"Computed negative extra rows!"
return min_extra_rows + (current_start_pos - new_start_pos)
def compute_extra_rows(self, all_dates, start_date, end_date,
min_extra_rows):
"""
Ensure that min_extra_rows pushes us back to a computation date.
Parameters
----------
all_dates : pd.DatetimeIndex
The trading sessions against which ``self`` will be computed.
start_date : pd.Timestamp
The first date for which final output is requested.
end_date : pd.Timestamp
The last date for which final output is requested.
min_extra_rows : int
The minimum number of extra rows required of ``self``, as
determined by other terms that depend on ``self``.
Returns
-------
extra_rows : int
The number of extra rows to compute. This will be the minimum
number of rows required to make our computed start_date fall on a
recomputation date.
"""
try:
current_start_pos = all_dates.get_loc(start_date) - min_extra_rows
if current_start_pos < 0:
raise NoFurtherDataError(
initial_message="Insufficient data to compute Pipeline:",
first_date=all_dates[0],
lookback_start=start_date,
lookback_length=min_extra_rows,
)
except KeyError:
before, after = nearest_unequal_elements(all_dates, start_date)
raise ValueError(
"Pipeline start_date {start_date} is not in calendar.\n"
"Latest date before start_date is {before}.\n"
"Earliest date after start_date is {after}.".format(
start_date=start_date,
before=before,
after=after,
))
# Our possible target dates are all the dates on or before the current
# starting position.
# TODO: Consider bounding this below by self.window_length
candidates = all_dates[:current_start_pos + 1]
# Choose the latest date in the candidates that is the start of a new
# period at our frequency.
choices = select_sampling_indices(candidates, self._frequency)
# If we have choices, the last choice is the first date if the
# period containing current_start_date. Choose it.
new_start_date = candidates[choices[-1]]
# Add the difference between the new and old start dates to get the
# number of rows for the new start_date.
new_start_pos = all_dates.get_loc(new_start_date)
assert new_start_pos <= current_start_pos, \
"Computed negative extra rows!"
return min_extra_rows + (current_start_pos - new_start_pos)