def _generate(cls, start, end, periods, name, offset,
tz=None, normalize=False):
_normalized = True
if start is not None:
start = Timestamp(start)
if not isinstance(start, Timestamp):
raise ValueError('Failed to convert %s to timestamp'
% start)
if normalize:
start = normalize_date(start)
_normalized = True
else:
_normalized = _normalized and start.time() == _midnight
if end is not None:
end = Timestamp(end)
if not isinstance(end, Timestamp):
raise ValueError('Failed to convert %s to timestamp'
% end)
if normalize:
end = normalize_date(end)
_normalized = True
else:
_normalized = _normalized and end.time() == _midnight
start, end, tz = tools._figure_out_timezone(start, end, tz)
if (offset._should_cache() and
not (offset._normalize_cache and not _normalized) and
_naive_in_cache_range(start, end)):
index = cls._cached_range(start, end, periods=periods,
offset=offset, name=name)
else:
index = _generate_regular_range(start, end, periods, offset)
if tz is not None:
# Convert local to UTC
ints = index.view('i8')
lib.tz_localize_check(ints, tz)
index = lib.tz_convert(ints, tz, _utc())
index = index.view('M8[us]')
index = index.view(cls)
index.name = name
index.offset = offset
index.tz = tz
return index
def onOffset(cls, dt):
if isinstance(dt, np.datetime64):
dt = Timestamp(dt)
return dt.weekday() < 5
def __new__(cls, data=None,
freq=None, start=None, end=None, periods=None,
dtype=None, copy=False, name=None, tz=None,
verify_integrity=True, normalize=False, **kwds):
warn = False
if 'offset' in kwds and kwds['offset']:
freq = kwds['offset']
warn = True
infer_freq = False
if not isinstance(freq, DateOffset):
if freq != 'infer':
freq = to_offset(freq)
else:
infer_freq = True
freq = None
if warn:
import warnings
warnings.warn("parameter 'offset' is deprecated, "
"please use 'freq' instead",
FutureWarning)
if isinstance(freq, basestring):
freq = to_offset(freq)
else:
if isinstance(freq, basestring):
freq = to_offset(freq)
offset = freq
if data is None and offset is None:
raise ValueError("Must provide freq argument if no data is "
"supplied")
if data is None:
_normalized = True
if start is not None:
start = Timestamp(start)
if not isinstance(start, Timestamp):
raise ValueError('Failed to convert %s to timestamp'
% start)
if normalize:
start = normalize_date(start)
_normalized = True
else:
_normalized = _normalized and start.time() == _midnight
if end is not None:
end = Timestamp(end)
if not isinstance(end, Timestamp):
raise ValueError('Failed to convert %s to timestamp'
% end)
if normalize:
end = normalize_date(end)
_normalized = True
else:
_normalized = _normalized and end.time() == _midnight
start, end, tz = tools._figure_out_timezone(start, end, tz)
if (offset._should_cache() and
not (offset._normalize_cache and not _normalized) and
_naive_in_cache_range(start, end)):
index = cls._cached_range(start, end, periods=periods,
offset=offset, name=name)
else:
index = _generate_regular_range(start, end, periods, offset)
index = index.view(cls)
index.name = name
index.offset = offset
index.tz = tz
return index
if not isinstance(data, np.ndarray):
if np.isscalar(data):
raise ValueError('DatetimeIndex() must be called with a '
'collection of some kind, %s was passed'
% repr(data))
if isinstance(data, datetime):
data = [data]
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data, dtype='O')
# try a few ways to make it datetime64
if lib.is_string_array(data):
data = _str_to_dt_array(data)
else:
data = np.asarray(data, dtype='M8[us]')
if issubclass(data.dtype.type, basestring):
subarr = _str_to_dt_array(data)
elif issubclass(data.dtype.type, np.datetime64):
if isinstance(data, DatetimeIndex):
subarr = data.values
offset = data.offset
verify_integrity = False
else:
subarr = np.array(data, dtype='M8[us]', copy=copy)
elif issubclass(data.dtype.type, np.integer):
subarr = np.array(data, dtype='M8[us]', copy=copy)
else:
subarr = np.array(data, dtype='M8[us]', copy=copy)
subarr = subarr.view(cls)
subarr.name = name
subarr.offset = offset
subarr.tz = tz
if verify_integrity:
if offset is not None and not infer_freq:
inferred = subarr.inferred_freq
if inferred != offset.freqstr:
raise ValueError('Dates do not conform to passed '
'frequency')
if infer_freq:
inferred = subarr.inferred_freq
if inferred:
subarr.offset = to_offset(inferred)
return subarr
def __new__(
cls,
data=None,
freq=None,
start=None,
end=None,
periods=None,
dtype=None,
copy=False,
name=None,
tz=None,
verify_integrity=True,
normalize=False,
**kwds
):
warn = False
if "offset" in kwds and kwds["offset"]:
freq = kwds["offset"]
warn = True
if not isinstance(freq, datetools.DateOffset):
freq = datetools.to_offset(freq)
if warn:
import warnings
warnings.warn("parameter 'offset' is deprecated, " "please use 'freq' instead", FutureWarning)
if isinstance(freq, basestring):
freq = datetools.get_offset(freq)
else:
if isinstance(freq, basestring):
freq = datetools.to_offset(freq)
offset = freq
if data is None and offset is None:
raise ValueError("Must provide freq argument if no data is " "supplied")
if data is None:
_normalized = True
if start is not None:
start = Timestamp(start)
if not isinstance(start, Timestamp):
raise ValueError("Failed to convert %s to timestamp" % start)
if normalize:
start = datetools.normalize_date(start)
_normalized = True
else:
_normalized = _normalized and start.time() == _midnight
if end is not None:
end = Timestamp(end)
if not isinstance(end, Timestamp):
raise ValueError("Failed to convert %s to timestamp" % end)
if normalize:
end = datetools.normalize_date(end)
_normalized = True
else:
_normalized = _normalized and end.time() == _midnight
start, end, tz = tools._figure_out_timezone(start, end, tz)
if (
offset._should_cache()
and not (offset._normalize_cache and not _normalized)
and datetools._naive_in_cache_range(start, end)
):
index = cls._cached_range(start, end, periods=periods, offset=offset, name=name)
else:
index = _generate_regular_range(start, end, periods, offset)
index = index.view(cls)
index.name = name
index.offset = offset
index.tz = tz
return index
if not isinstance(data, np.ndarray):
if np.isscalar(data):
raise ValueError(
"DatetimeIndex() must be called with a " "collection of some kind, %s was passed" % repr(data)
)
if isinstance(data, datetime):
data = [data]
# other iterable of some kind
if not isinstance(data, (list, tuple)):
data = list(data)
data = np.asarray(data, dtype="O")
# try a few ways to make it datetime64
if lib.is_string_array(data):
data = _str_to_dt_array(data)
else:
data = np.asarray(data, dtype="M8[us]")
if issubclass(data.dtype.type, basestring):
subarr = _str_to_dt_array(data)
elif issubclass(data.dtype.type, np.integer):
subarr = np.array(data, dtype="M8[us]", copy=copy)
elif issubclass(data.dtype.type, np.datetime64):
subarr = np.array(data, dtype="M8[us]", copy=copy)
else:
subarr = np.array(data, dtype="M8[us]", copy=copy)
# TODO: this is horribly inefficient. If user passes data + offset, we
# need to make sure data points conform. Punting on this
if verify_integrity:
if offset is not None:
for i, ts in enumerate(subarr):
if not offset.onOffset(Timestamp(ts)):
val = Timestamp(offset.rollforward(ts)).value
subarr[i] = val
subarr = subarr.view(cls)
subarr.name = name
subarr.offset = offset
subarr.tz = tz
return subarr