A PyQT GUI application for converting InfoLease report outputs into Excel files. Handles parsing and summarizing. Learns where files are meant to be store and compiles monthly and yearly summaries.
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InfoLeaseExtract/venv/Lib/site-packages/pandas/tests/window/test_online.py

104 lines
3.2 KiB

import numpy as np
import pytest
from pandas.compat import (
is_ci_environment,
is_platform_mac,
is_platform_windows,
)
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
# TODO(GH#44584): Mark these as pytest.mark.single_cpu
pytestmark = pytest.mark.skipif(
is_ci_environment() and (is_platform_windows() or is_platform_mac()),
reason="On GHA CI, Windows can fail with "
"'Windows fatal exception: stack overflow' "
"and MacOS can timeout",
)
@td.skip_if_no("numba")
@pytest.mark.filterwarnings("ignore:\n")
class TestEWM:
def test_invalid_update(self):
df = DataFrame({"a": range(5), "b": range(5)})
online_ewm = df.head(2).ewm(0.5).online()
with pytest.raises(
ValueError,
match="Must call mean with update=None first before passing update",
):
online_ewm.mean(update=df.head(1))
@pytest.mark.slow
@pytest.mark.parametrize(
"obj", [DataFrame({"a": range(5), "b": range(5)}), Series(range(5), name="foo")]
)
def test_online_vs_non_online_mean(
self, obj, nogil, parallel, nopython, adjust, ignore_na
):
expected = obj.ewm(0.5, adjust=adjust, ignore_na=ignore_na).mean()
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
online_ewm = (
obj.head(2)
.ewm(0.5, adjust=adjust, ignore_na=ignore_na)
.online(engine_kwargs=engine_kwargs)
)
# Test resetting once
for _ in range(2):
result = online_ewm.mean()
tm.assert_equal(result, expected.head(2))
result = online_ewm.mean(update=obj.tail(3))
tm.assert_equal(result, expected.tail(3))
online_ewm.reset()
@pytest.mark.xfail(raises=NotImplementedError)
@pytest.mark.parametrize(
"obj", [DataFrame({"a": range(5), "b": range(5)}), Series(range(5), name="foo")]
)
def test_update_times_mean(
self, obj, nogil, parallel, nopython, adjust, ignore_na, halflife_with_times
):
times = Series(
np.array(
["2020-01-01", "2020-01-05", "2020-01-07", "2020-01-17", "2020-01-21"],
dtype="datetime64",
)
)
expected = obj.ewm(
0.5,
adjust=adjust,
ignore_na=ignore_na,
times=times,
halflife=halflife_with_times,
).mean()
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
online_ewm = (
obj.head(2)
.ewm(
0.5,
adjust=adjust,
ignore_na=ignore_na,
times=times.head(2),
halflife=halflife_with_times,
)
.online(engine_kwargs=engine_kwargs)
)
# Test resetting once
for _ in range(2):
result = online_ewm.mean()
tm.assert_equal(result, expected.head(2))
result = online_ewm.mean(update=obj.tail(3), update_times=times.tail(3))
tm.assert_equal(result, expected.tail(3))
online_ewm.reset()