def test_tz_convert_corner(arr): result = tzconversion.tz_convert(arr, timezones.maybe_get_tz("US/Eastern"), UTC) tm.assert_numpy_array_equal(result, arr) result = tzconversion.tz_convert(arr, UTC, timezones.maybe_get_tz("Asia/Tokyo")) tm.assert_numpy_array_equal(result, arr)
def _compare_local_to_utc(tz_didx, utc_didx): def f(x): return conversion.tz_convert_single(x, tz_didx.tz, UTC) result = tzconversion.tz_convert(utc_didx.asi8, tz_didx.tz, UTC) expected = np.vectorize(f)(utc_didx.asi8) tm.assert_numpy_array_equal(result, expected)
def _compare_utc_to_local(tz_didx): def f(x): return tzconversion.tz_convert_single(x, UTC, tz_didx.tz) result = tzconversion.tz_convert(tz_didx.asi8, UTC, tz_didx.tz) expected = np.vectorize(f)(tz_didx.asi8) tm.assert_numpy_array_equal(result, expected)
def __init__(self, index, warn=True): self.index = index self.values = index.asi8 # This moves the values, which are implicitly in UTC, to the # the timezone so they are in local time if hasattr(index, 'tz'): if index.tz is not None: self.values = tz_convert(self.values, UTC, index.tz) self.warn = warn if len(index) < 3: raise ValueError('Need at least 3 dates to infer frequency') self.is_monotonic = (self.index._is_monotonic_increasing or self.index._is_monotonic_decreasing)
def test_tz_convert_corner(arr): result = tzconversion.tz_convert(arr, timezones.maybe_get_tz("US/Eastern"), timezones.maybe_get_tz("Asia/Tokyo")) tm.assert_numpy_array_equal(result, arr)
def time_tz_convert_from_utc(self, size, tz): # effectively: # dti = DatetimeIndex(self.i8data, tz=tz) # dti.tz_localize(None) tz_convert(self.i8data, UTC, tz)