TST: Add Hypothesis tests for ticks, offsets

These tests are derived from GH18761, by jbrockmendel

Co-authored-by: jbrockmendel <[email protected]>

Author: Zac-HD

pandas/tests/tseries/offsets/test_offsets_properties.py

@@ -0,0 +1,129 @@
+# -*- coding: utf-8 -*-
+"""
+Behavioral based tests for offsets and date_range.
+
+This file is adapted from https://github.com/pandas-dev/pandas/pull/18761 -
+which was more
+
+"""
+import pytest
+from hypothesis import given, assume, strategies as st
+from hypothesis.extra.pytz import timezones as pytz_timezones
+from hypothesis.extra.dateutil import timezones as dateutil_timezones
+
+import pandas as pd
+
+from pandas.tseries.offsets import (
+    Hour, Minute, Second, Milli, Micro, Nano,
+    MonthEnd, MonthBegin, BMonthEnd, BMonthBegin,
+    QuarterEnd, QuarterBegin, BQuarterEnd, BQuarterBegin,
+    YearEnd, YearBegin, BYearEnd, BYearBegin,
+)
+
+
+tick_classes = [Hour, Minute, Second, Milli, Micro, Nano]
+yqm_classes = [MonthBegin, MonthEnd, BMonthBegin, BMonthEnd,
+               QuarterBegin, QuarterEnd, BQuarterBegin, BQuarterEnd,
+               YearBegin, YearEnd, BYearBegin, BYearEnd]
+
+# ----------------------------------------------------------------
+# Helpers for generating random data
+
+gen_date_range = st.builds(
+    pd.date_range,
+    start=st.datetimes(
+        # TODO: Choose the min/max values more systematically
+        min_value=pd.Timestamp(1900, 1, 1).to_pydatetime(),
+        max_value=pd.Timestamp(2100, 1, 1).to_pydatetime()
+    ),
+    periods=st.integers(min_value=2, max_value=100),
+    freq=st.sampled_from('Y Q M D H T s ms us ns'.split()),
+    tz=st.one_of(st.none(), dateutil_timezones(), pytz_timezones()),
+)
+
+gen_random_datetime = st.datetimes(
+    min_value=pd.Timestamp.min.to_pydatetime(),
+    max_value=pd.Timestamp.max.to_pydatetime(),
+    timezones=st.one_of(st.none(), dateutil_timezones(), pytz_timezones())
+)
+
+# Register the various offset classes so st.from_type can create instances.
+# We *could* just append the strategies to a list, but this provides a nice
+# demo and enables future tests to use a simple e.g. `from_type(Hour)`.
+for cls in tick_classes + [MonthBegin, MonthEnd, BMonthBegin, BMonthEnd]:
+    st.register_type_strategy(cls, st.builds(
+        cls,
+        n=st.integers(-99, 99),
+        normalize=st.booleans(),
+    ))
+
+for cls in [YearBegin, YearEnd, BYearBegin, BYearEnd]:
+    st.register_type_strategy(cls, st.builds(
+        cls,
+        n=st.integers(-5, 5),
+        normalize=st.booleans(),
+        month=st.integers(min_value=1, max_value=12),
+    ))
+
+for cls in [QuarterBegin, QuarterEnd, BQuarterBegin, BQuarterEnd]:
+    st.register_type_strategy(cls, st.builds(
+        cls,
+        n=st.integers(-24, 24),
+        normalize=st.booleans(),
+        startingMonth=st.integers(min_value=1, max_value=12)
+    ))
+
+# This strategy can generate any kind of Offset in `tick_classes` or
+# `yqm_classes`, with arguments as specified directly above in registration.
+gen_yqm_offset = st.one_of([st.from_type(cls) for cls in yqm_classes])
+
+
+# ----------------------------------------------------------------
+# Offset-specific behaviour tests
+
+
+@pytest.mark.xfail()
+@given(gen_random_datetime, gen_yqm_offset)
+def test_on_offset_implementations(dt, offset):
+    assume(not offset.normalize)
+    # check that the class-specific implementations of onOffset match
+    # the general case definition:
+    #   (dt + offset) - offset == dt
+    compare = (dt + offset) - offset
+    assert offset.onOffset(dt) == (compare == dt)
+
+
+@pytest.mark.xfail()
+@given(gen_yqm_offset, gen_date_range)
+def test_apply_index_implementations(offset, rng):
+    # offset.apply_index(dti)[i] should match dti[i] + offset
+    assume(offset.n != 0)  # TODO: test for that case separately
+
+    # rng = pd.date_range(start='1/1/2000', periods=100000, freq='T')
+    ser = pd.Series(rng)
+
+    res = rng + offset
+    res_v2 = offset.apply_index(rng)
+    assert (res == res_v2).all()
+
+    assert res[0] == rng[0] + offset
+    assert res[-1] == rng[-1] + offset
+    res2 = ser + offset
+    # apply_index is only for indexes, not series, so no res2_v2
+    assert res2.iloc[0] == ser.iloc[0] + offset
+    assert res2.iloc[-1] == ser.iloc[-1] + offset
+    # TODO: Check randomly assorted entries, not just first/last
+
+
+@pytest.mark.xfail()
+@given(gen_yqm_offset)
+def test_shift_across_dst(offset):
+    # GH#18319 check that 1) timezone is correctly normalized and
+    # 2) that hour is not incorrectly changed by this normalization
+    # Note that dti includes a transition across DST boundary
+    dti = pd.date_range(start='2017-10-30 12:00:00', end='2017-11-06',
+                        freq='D', tz='US/Eastern')
+    assert (dti.hour == 12).all()  # we haven't screwed up yet
+
+    res = dti + offset
+    assert (res.hour == 12).all()

pandas/tests/tseries/offsets/test_ticks.py

@@ -6,6 +6,7 @@
 
 import pytest
 import numpy as np
+from hypothesis import given, assume, strategies as st
 
 from pandas import Timedelta, Timestamp
 from pandas.tseries import offsets
@@ -35,6 +36,39 @@ def test_delta_to_tick():
     assert (tick == offsets.Day(3))
 
 
+@given(cls=st.sampled_from(tick_classes),
+       n=st.integers(-999, 999),
+       m=st.integers(-999, 999))
+def test_tick_add_sub(cls, n, m):
+    # For all Tick subclasses and all integers n, m, we should have
+    # tick(n) + tick(m) == tick(n+m)
+    # tick(n) - tick(m) == tick(n-m)
+    left = cls(n)
+    right = cls(m)
+    expected = cls(n + m)
+
+    assert left + right == expected
+    assert left.apply(right) == expected
+
+    expected = cls(n - m)
+    assert left - right == expected
+
+
+@given(cls=st.sampled_from(tick_classes),
+       n=st.integers(-999, 999), m=st.integers(-999, 999))
+def test_tick_equality(cls, n, m):
+    assume(m != n)
+    # tick == tock iff tick.n == tock.n
+    left = cls(n)
+    right = cls(m)
+    assert left != right
+    assert not (left == right)
+
+    right = cls(n)
+    assert left == right
+    assert not (left != right)
+
+
 # ---------------------------------------------------------------------