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REFACTORED TESTS
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@venaturum
venaturum committed Sep 10, 2021
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341 changes: 341 additions & 0 deletions tests/test_dates/test_dates_arithmetic.py
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from datetime import datetime

import pandas as pd
import pytest
import pytz

from staircase import Stairs
from staircase.constants import inf


def pytest_generate_tests(metafunc):
if "date_func" in metafunc.fixturenames:
metafunc.parametrize(
"date_func",
["pandas", "pydatetime", "numpy", "pandas_tz", "pydatetime_tz"],
indirect=True,
)


@pytest.fixture
def date_func(request):
# returns a func which takes a pandas timestamp
if request.param == "pandas":
return lambda x: x
elif request.param == "pydatetime":
return pd.Timestamp.to_pydatetime
elif request.param == "numpy":
return pd.Timestamp.to_datetime64
elif request.param == "pandas_tz":
return lambda ts: pd.Timestamp.tz_localize(
ts, pytz.timezone("Australia/Sydney")
)
elif request.param == "pydatetime_tz":
return lambda ts: (
pd.Timestamp.tz_localize(
ts, pytz.timezone("Australia/Sydney")
).to_pydatetime()
)
else:
assert False, "should not happen"


def timestamp(*args, date_func, **kwargs):
ts = pd.Timestamp(*args, **kwargs)
return date_func(ts)


def assert_expected_type(stairs, date_func):
if stairs._data is None:
return
example_type = timestamp(2020, 1, 1, date_func=date_func)
example_type = pd.Timestamp(
example_type
) # pandas natively converts datetimes to timestamps
assert all(
[type(example_type) == type(x) for x in stairs._data.index]
), "Unexpected type in step points"
if isinstance(example_type, (pd.Timestamp, datetime)):
assert all(
[example_type.tzinfo == x.tzinfo for x in stairs._data.index]
), "Unexpected timezone in step points"


def s1(date_func):
int_seq1 = Stairs(initial_value=0)
int_seq1.layer(
timestamp(2020, 1, 1, date_func=date_func),
timestamp(2020, 1, 10, date_func=date_func),
2,
)
int_seq1.layer(
timestamp(2020, 1, 3, date_func=date_func),
timestamp(2020, 1, 5, date_func=date_func),
2.5,
)
int_seq1.layer(
timestamp(2020, 1, 6, date_func=date_func),
timestamp(2020, 1, 7, date_func=date_func),
-2.5,
)
int_seq1.layer(
timestamp(2020, 1, 7, date_func=date_func),
timestamp(2020, 1, 10, date_func=date_func),
-2.5,
)
return int_seq1


def s2(date_func):
int_seq2 = Stairs(initial_value=0)
int_seq2.layer(
timestamp(2020, 1, 1, date_func=date_func),
timestamp(2020, 1, 7, date_func=date_func),
-2.5,
)
int_seq2.layer(
timestamp(2020, 1, 8, date_func=date_func),
timestamp(2020, 1, 10, date_func=date_func),
5,
)
int_seq2.layer(
timestamp(2020, 1, 2, date_func=date_func),
timestamp(2020, 1, 5, date_func=date_func),
4.5,
)
int_seq2.layer(
timestamp(2020, 1, 2, 12, date_func=date_func),
timestamp(2020, 1, 4, date_func=date_func),
-2.5,
)
return int_seq2


def s3(date_func): # boolean
int_seq = Stairs(initial_value=0)
int_seq.layer(
timestamp(2020, 1, 10, date_func=date_func),
timestamp(2020, 1, 30, date_func=date_func),
1,
)
int_seq.layer(
timestamp(2020, 1, 12, date_func=date_func),
timestamp(2020, 1, 13, date_func=date_func),
-1,
)
int_seq.layer(
timestamp(2020, 1, 15, date_func=date_func),
timestamp(2020, 1, 18, date_func=date_func),
-1,
)
int_seq.layer(
timestamp(2020, 1, 20, 12, date_func=date_func),
timestamp(2020, 1, 21, date_func=date_func),
-1,
)
int_seq.layer(
timestamp(2020, 1, 23, date_func=date_func),
timestamp(2020, 1, 23, 12, date_func=date_func),
-1,
)
int_seq.layer(
timestamp(2020, 1, 27, date_func=date_func),
timestamp(2020, 1, 29, 12, date_func=date_func),
-1,
)
return int_seq


def s4(date_func): # boolean
int_seq = Stairs(initial_value=0)
int_seq.layer(
timestamp(2020, 1, 9, date_func=date_func),
timestamp(2020, 1, 29, date_func=date_func),
1,
)
int_seq.layer(
timestamp(2020, 1, 10, 12, date_func=date_func),
timestamp(2020, 1, 12, date_func=date_func),
-1,
)
int_seq.layer(
timestamp(2020, 1, 12, 12, date_func=date_func),
timestamp(2020, 1, 13, date_func=date_func),
-1,
)
int_seq.layer(
timestamp(2020, 1, 20, date_func=date_func),
timestamp(2020, 1, 23, date_func=date_func),
-1,
)
int_seq.layer(
timestamp(2020, 1, 26, date_func=date_func),
timestamp(2020, 1, 26, 12, date_func=date_func),
-1,
)
int_seq.layer(
timestamp(2020, 1, 27, date_func=date_func),
timestamp(2020, 1, 28, 12, date_func=date_func),
-1,
)
return int_seq


@pytest.fixture
def s1_fix():
return s1()


@pytest.fixture
def s2_fix():
return s2()


@pytest.fixture
def s3_fix():
return s3()


@pytest.fixture
def s4_fix():
return s4()


def test_add_dates(date_func):
expected_step_changes = pd.Series(
{
timestamp("2020-01-01 00:00:00", date_func=date_func): -0.5,
timestamp("2020-01-02 00:00:00", date_func=date_func): 4.5,
timestamp("2020-01-02 12:00:00", date_func=date_func): -2.5,
timestamp("2020-01-03 00:00:00", date_func=date_func): 2.5,
timestamp("2020-01-04 00:00:00", date_func=date_func): 2.5,
timestamp("2020-01-05 00:00:00", date_func=date_func): -7.0,
timestamp("2020-01-06 00:00:00", date_func=date_func): -2.5,
timestamp("2020-01-07 00:00:00", date_func=date_func): 2.5,
timestamp("2020-01-08 00:00:00", date_func=date_func): 5,
timestamp("2020-01-10 00:00:00", date_func=date_func): -4.5,
}
)
result = s1(date_func) + s2(date_func)
pd.testing.assert_series_equal(
result.step_changes,
expected_step_changes,
check_names=False,
check_index_type=False,
)
assert_expected_type(result, date_func)


def test_subtract_dates(date_func):
expected_step_changes = pd.Series(
{
timestamp("2020-01-01 00:00:00", date_func=date_func): 4.5,
timestamp("2020-01-02 00:00:00", date_func=date_func): -4.5,
timestamp("2020-01-02 12:00:00", date_func=date_func): 2.5,
timestamp("2020-01-03 00:00:00", date_func=date_func): 2.5,
timestamp("2020-01-04 00:00:00", date_func=date_func): -2.5,
timestamp("2020-01-05 00:00:00", date_func=date_func): 2.0,
timestamp("2020-01-06 00:00:00", date_func=date_func): -2.5,
timestamp("2020-01-07 00:00:00", date_func=date_func): -2.5,
timestamp("2020-01-08 00:00:00", date_func=date_func): -5,
timestamp("2020-01-10 00:00:00", date_func=date_func): 5.5,
}
)
result = s1(date_func) - s2(date_func)
pd.testing.assert_series_equal(
result.step_changes,
expected_step_changes,
check_names=False,
check_index_type=False,
)
assert_expected_type(result, date_func)


def test_multiply_dates(date_func):
expected_step_changes = pd.Series(
{
timestamp("2020-01-01 00:00:00", date_func=date_func): -5.0,
timestamp("2020-01-02 00:00:00", date_func=date_func): 9.0,
timestamp("2020-01-02 12:00:00", date_func=date_func): -5.0,
timestamp("2020-01-03 00:00:00", date_func=date_func): -1.25,
timestamp("2020-01-04 00:00:00", date_func=date_func): 11.25,
timestamp("2020-01-05 00:00:00", date_func=date_func): -14.0,
timestamp("2020-01-06 00:00:00", date_func=date_func): 6.25,
timestamp("2020-01-07 00:00:00", date_func=date_func): -1.25,
timestamp("2020-01-08 00:00:00", date_func=date_func): -2.5,
timestamp("2020-01-10 00:00:00", date_func=date_func): 2.5,
}
)
result = s1(date_func) * s2(date_func)
pd.testing.assert_series_equal(
result.step_changes,
expected_step_changes,
check_names=False,
check_index_type=False,
)
assert_expected_type(result, date_func)


def test_multiply_dates_scalar(date_func):
expected_step_changes = pd.Series(
{
timestamp("2020-01-01 00:00:00", date_func=date_func): 6.0,
timestamp("2020-01-03 00:00:00", date_func=date_func): 7.5,
timestamp("2020-01-05 00:00:00", date_func=date_func): -7.5,
timestamp("2020-01-06 00:00:00", date_func=date_func): -7.5,
timestamp("2020-01-10 00:00:00", date_func=date_func): 1.5,
}
)
result = s1(date_func) * 3
pd.testing.assert_series_equal(
result.step_changes,
expected_step_changes,
check_names=False,
check_index_type=False,
)
assert_expected_type(result, date_func)


def test_divide_dates(date_func):
expected_step_changes = pd.Series(
{
timestamp("2020-01-01 00:00:00", date_func=date_func): -1.3333333333333333,
timestamp("2020-01-02 00:00:00", date_func=date_func): 2.0,
timestamp("2020-01-02 12:00:00", date_func=date_func): 3.3333333333333335,
timestamp("2020-01-03 00:00:00", date_func=date_func): 5.0,
timestamp("2020-01-04 00:00:00", date_func=date_func): -7.5,
timestamp("2020-01-05 00:00:00", date_func=date_func): -2.833333333333333,
timestamp("2020-01-06 00:00:00", date_func=date_func): 1.6666666666666665,
timestamp("2020-01-07 00:00:00", date_func=date_func): -0.8333333333333333,
timestamp("2020-01-08 00:00:00", date_func=date_func): 0.4166666666666667,
timestamp("2020-01-10 00:00:00", date_func=date_func): 0.08333333333333333,
}
)
result = s1(date_func) / (s2(date_func) + 1)
pd.testing.assert_series_equal(
result.step_changes,
expected_step_changes,
check_names=False,
check_index_type=False,
)
assert_expected_type(result, date_func)


def test_divide_dates_scalar(date_func):
expected_step_changes = pd.Series(
{
timestamp("2020-01-01 00:00:00", date_func=date_func): 4.0,
timestamp("2020-01-03 00:00:00", date_func=date_func): 5.0,
timestamp("2020-01-05 00:00:00", date_func=date_func): -5.0,
timestamp("2020-01-06 00:00:00", date_func=date_func): -5.0,
timestamp("2020-01-10 00:00:00", date_func=date_func): 1.0,
}
)
result = s1(date_func) / 0.5
pd.testing.assert_series_equal(
result.step_changes,
expected_step_changes,
check_names=False,
check_index_type=False,
)
assert_expected_type(result, date_func)
0 tests/test_stairs_arrays_dates.py → tests/test_dates/test_dates_arrays.py
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