def test_tslib_tz_convert(self):
def compare_utc_to_local(tz_didx, utc_didx):
f = lambda x: tslib.tz_convert_single(x, 'UTC', tz_didx.tz)
result = tslib.tz_convert(tz_didx.asi8, 'UTC', tz_didx.tz)
result_single = np.vectorize(f)(tz_didx.asi8)
self.assert_numpy_array_equal(result, result_single)
def compare_local_to_utc(tz_didx, utc_didx):
f = lambda x: tslib.tz_convert_single(x, tz_didx.tz, 'UTC')
result = tslib.tz_convert(utc_didx.asi8, tz_didx.tz, 'UTC')
result_single = np.vectorize(f)(utc_didx.asi8)
self.assert_numpy_array_equal(result, result_single)
for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern', 'Europe/Moscow']:
# US: 2014-03-09 - 2014-11-11
# MOSCOW: 2014-10-26 / 2014-12-31
tz_didx = date_range('2014-03-01', '2015-01-10', freq='H', tz=tz)
utc_didx = date_range('2014-03-01', '2015-01-10', freq='H')
compare_utc_to_local(tz_didx, utc_didx)
# local tz to UTC can be differ in hourly (or higher) freqs because
# of DST
compare_local_to_utc(tz_didx, utc_didx)
tz_didx = date_range('2000-01-01', '2020-01-01', freq='D', tz=tz)
utc_didx = date_range('2000-01-01', '2020-01-01', freq='D')
compare_utc_to_local(tz_didx, utc_didx)
compare_local_to_utc(tz_didx, utc_didx)
tz_didx = date_range('2000-01-01', '2100-01-01', freq='A', tz=tz)
utc_didx = date_range('2000-01-01', '2100-01-01', freq='A')
compare_utc_to_local(tz_didx, utc_didx)
compare_local_to_utc(tz_didx, utc_didx)
# Check empty array
result = tslib.tz_convert(np.array([], dtype=np.int64),
tslib.maybe_get_tz('US/Eastern'),
tslib.maybe_get_tz('Asia/Tokyo'))
self.assert_numpy_array_equal(result, np.array([], dtype=np.int64))
# Check all-NaT array
result = tslib.tz_convert(np.array([tslib.iNaT], dtype=np.int64),
tslib.maybe_get_tz('US/Eastern'),
tslib.maybe_get_tz('Asia/Tokyo'))
self.assert_numpy_array_equal(result,
np.array([tslib.iNaT], dtype=np.int64))
def test_tslib_tz_convert(self):
def compare_utc_to_local(tz_didx, utc_didx):
f = lambda x: tslib.tz_convert_single(x, 'UTC', tz_didx.tz)
result = tslib.tz_convert(tz_didx.asi8, 'UTC', tz_didx.tz)
result_single = np.vectorize(f)(tz_didx.asi8)
self.assert_numpy_array_equal(result, result_single)
def compare_local_to_utc(tz_didx, utc_didx):
f = lambda x: tslib.tz_convert_single(x, tz_didx.tz, 'UTC')
result = tslib.tz_convert(utc_didx.asi8, tz_didx.tz, 'UTC')
result_single = np.vectorize(f)(utc_didx.asi8)
self.assert_numpy_array_equal(result, result_single)
for tz in ['UTC', 'Asia/Tokyo', 'US/Eastern', 'Europe/Moscow']:
# US: 2014-03-09 - 2014-11-11
# MOSCOW: 2014-10-26 / 2014-12-31
tz_didx = date_range('2014-03-01', '2015-01-10', freq='H', tz=tz)
utc_didx = date_range('2014-03-01', '2015-01-10', freq='H')
compare_utc_to_local(tz_didx, utc_didx)
# local tz to UTC can be differ in hourly (or higher) freqs because
# of DST
compare_local_to_utc(tz_didx, utc_didx)
tz_didx = date_range('2000-01-01', '2020-01-01', freq='D', tz=tz)
utc_didx = date_range('2000-01-01', '2020-01-01', freq='D')
compare_utc_to_local(tz_didx, utc_didx)
compare_local_to_utc(tz_didx, utc_didx)
tz_didx = date_range('2000-01-01', '2100-01-01', freq='A', tz=tz)
utc_didx = date_range('2000-01-01', '2100-01-01', freq='A')
compare_utc_to_local(tz_didx, utc_didx)
compare_local_to_utc(tz_didx, utc_didx)
# Check empty array
result = tslib.tz_convert(np.array([], dtype=np.int64),
tslib.maybe_get_tz('US/Eastern'),
tslib.maybe_get_tz('Asia/Tokyo'))
self.assert_numpy_array_equal(result, np.array([], dtype=np.int64))
# Check all-NaT array
result = tslib.tz_convert(np.array([tslib.iNaT], dtype=np.int64),
tslib.maybe_get_tz('US/Eastern'),
tslib.maybe_get_tz('Asia/Tokyo'))
self.assert_numpy_array_equal(result, np.array(
[tslib.iNaT], dtype=np.int64))
def test_tslib_tz_convert(self):
def compare_utc_to_local(tz_didx, utc_didx):
f = lambda x: tslib.tz_convert_single(x, "UTC", tz_didx.tz)
result = tslib.tz_convert(tz_didx.asi8, "UTC", tz_didx.tz)
result_single = np.vectorize(f)(tz_didx.asi8)
self.assert_numpy_array_equal(result, result_single)
def compare_local_to_utc(tz_didx, utc_didx):
f = lambda x: tslib.tz_convert_single(x, tz_didx.tz, "UTC")
result = tslib.tz_convert(utc_didx.asi8, tz_didx.tz, "UTC")
result_single = np.vectorize(f)(utc_didx.asi8)
self.assert_numpy_array_equal(result, result_single)
for tz in ["UTC", "Asia/Tokyo", "US/Eastern", "Europe/Moscow"]:
# US: 2014-03-09 - 2014-11-11
# MOSCOW: 2014-10-26 / 2014-12-31
tz_didx = date_range("2014-03-01", "2015-01-10", freq="H", tz=tz)
utc_didx = date_range("2014-03-01", "2015-01-10", freq="H")
compare_utc_to_local(tz_didx, utc_didx)
# local tz to UTC can be differ in hourly (or higher) freqs because
# of DST
compare_local_to_utc(tz_didx, utc_didx)
tz_didx = date_range("2000-01-01", "2020-01-01", freq="D", tz=tz)
utc_didx = date_range("2000-01-01", "2020-01-01", freq="D")
compare_utc_to_local(tz_didx, utc_didx)
compare_local_to_utc(tz_didx, utc_didx)
tz_didx = date_range("2000-01-01", "2100-01-01", freq="A", tz=tz)
utc_didx = date_range("2000-01-01", "2100-01-01", freq="A")
compare_utc_to_local(tz_didx, utc_didx)
compare_local_to_utc(tz_didx, utc_didx)
# Check empty array
result = tslib.tz_convert(
np.array([], dtype=np.int64), tslib.maybe_get_tz("US/Eastern"), tslib.maybe_get_tz("Asia/Tokyo")
)
self.assert_numpy_array_equal(result, np.array([], dtype=np.int64))
# Check all-NaT array
result = tslib.tz_convert(
np.array([tslib.iNaT], dtype=np.int64), tslib.maybe_get_tz("US/Eastern"), tslib.maybe_get_tz("Asia/Tokyo")
)
self.assert_numpy_array_equal(result, np.array([tslib.iNaT], dtype=np.int64))
def _tz_convert_with_transitions(values, to_tz, from_tz):
"""
convert i8 values from the specificed timezone to the to_tz zone, taking
into account DST transitions
"""
# vectorization is slow, so tests if we can do this via the faster tz_convert
f = lambda x: tslib.tz_convert_single(x, to_tz, from_tz)
if len(values) > 2:
first_slow, last_slow = f(values[0]),f(values[-1])
first_fast, last_fast = tslib.tz_convert(np.array([values[0],values[-1]],dtype='i8'),to_tz,from_tz)
# don't cross a DST, so ok
if first_fast == first_slow and last_fast == last_slow:
return tslib.tz_convert(values,to_tz,from_tz)
return np.vectorize(f)(values)
def _tz_convert_with_transitions(values, to_tz, from_tz):
"""
convert i8 values from the specificed timezone to the to_tz zone, taking
into account DST transitions
"""
# vectorization is slow, so tests if we can do this via the faster tz_convert
f = lambda x: tslib.tz_convert_single(x, to_tz, from_tz)
if len(values) > 2:
first_slow, last_slow = f(values[0]), f(values[-1])
first_fast, last_fast = tslib.tz_convert(
np.array([values[0], values[-1]], dtype='i8'), to_tz, from_tz)
# don't cross a DST, so ok
if first_fast == first_slow and last_fast == last_slow:
return tslib.tz_convert(values, to_tz, from_tz)
return np.vectorize(f)(values)
def __init__(self, index, warn=True):
self.index = index
self.values = np.asarray(index).view('i8')
if index.tz is not None:
self.values = tslib.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
def __init__(self, index, warn=True):
self.index = index
self.values = np.asarray(index).view('i8')
if index.tz is not None:
self.values = tslib.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
def __init__(self, index, warn=True):
self.index = index
self.values = np.asarray(index).view('i8')
# 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 = tslib.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
def __init__(self, index, warn=True):
self.index = index
self.values = np.asarray(index).view('i8')
# 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 = tslib.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
def __init__(self, index, warn=True):
self.index = index
self.values = np.asarray(index).view("i8")
# 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 = tslib.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 compare_local_to_utc(tz_didx, utc_didx):
f = lambda x: tslib.tz_convert_single(x, tz_didx.tz, 'UTC')
result = tslib.tz_convert(utc_didx.asi8, tz_didx.tz, 'UTC')
result_single = np.vectorize(f)(utc_didx.asi8)
self.assert_numpy_array_equal(result, result_single)
def compare_local_to_utc(tz_didx, utc_didx):
f = lambda x: tslib.tz_convert_single(x, tz_didx.tz, 'UTC')
result = tslib.tz_convert(utc_didx.asi8, tz_didx.tz, 'UTC')
result_single = np.vectorize(f)(utc_didx.asi8)
self.assert_numpy_array_equal(result, result_single)
