REF/TST: Fix remaining DatetimeArray with DateOffset arithmetic ops

pandas/_libs/tslibs/timestamps.pyx

@@ -376,13 +376,15 @@ cdef class _Timestamp(datetime):
             neg_other = -other
             return self + neg_other
 
+        typ = getattr(other, '_typ', None)
+
         # a Timestamp-DatetimeIndex -> yields a negative TimedeltaIndex
-        elif getattr(other, '_typ', None) == 'datetimeindex':
+        if typ in ('datetimeindex', 'datetimearray'):
             # timezone comparison is performed in DatetimeIndex._sub_datelike
             return -other.__sub__(self)
 
         # a Timestamp-TimedeltaIndex -> yields a negative TimedeltaIndex
-        elif getattr(other, '_typ', None) == 'timedeltaindex':
+        elif typ in ('timedeltaindex', 'timedeltaarray'):
             return (-other).__add__(self)
 
         elif other is NaT:

pandas/core/arrays/timedeltas.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from pandas._libs import tslibs
+from pandas._libs import algos, tslibs
 from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT
 from pandas._libs.tslibs.fields import get_timedelta_field
 from pandas._libs.tslibs.timedeltas import (
@@ -19,7 +19,7 @@
 from pandas.core.dtypes.generic import ABCSeries, ABCTimedeltaIndex
 from pandas.core.dtypes.missing import isna
 
-from pandas.core.algorithms import checked_add_with_arr
+from pandas.core.algorithms import checked_add_with_arr, unique1d
 import pandas.core.common as com
 
 from pandas.tseries.frequencies import to_offset
@@ -133,15 +133,31 @@ def _simple_new(cls, values, freq=None, dtype=_TD_DTYPE):
         result._freq = freq
         return result
 
-    def __new__(cls, values, freq=None, dtype=_TD_DTYPE):
+    def __new__(cls, values, freq=None, dtype=_TD_DTYPE, copy=False):
+        verify_integrity = True
 
         freq, freq_infer = dtl.maybe_infer_freq(freq)
 
-        values = np.array(values, copy=False)
-        if values.dtype == np.object_:
-            values = array_to_timedelta64(values)
+        values, inferred_freq = sequence_to_td64ns(values, copy=copy,
+                                                   unit=None)
+        if inferred_freq is not None:
+            if freq is not None and freq != inferred_freq:
+                raise ValueError('Inferred frequency {inferred} from passed '
+                                 'values does not conform to passed frequency '
+                                 '{passed}'
+                                 .format(inferred=inferred_freq,
+                                         passed=freq.freqstr))
+            elif freq_infer:
+                freq = inferred_freq
+                freq_infer = False
+            verify_integrity = False
 
         result = cls._simple_new(values, freq=freq)
+        # check that we are matching freqs
+        if verify_integrity and len(result) > 0:
+            if freq is not None and not freq_infer:
+                cls._validate_frequency(result, freq)
+
         if freq_infer:
             result.freq = to_offset(result.inferred_freq)
 
@@ -198,6 +214,18 @@ def _validate_fill_value(self, fill_value):
                              "Got '{got}'.".format(got=fill_value))
         return fill_value
 
+    @property
+    def is_monotonic_increasing(self):
+        return algos.is_monotonic(self.asi8, timelike=True)[0]
+
+    @property
+    def is_monotonic_decreasing(self):
+        return algos.is_monotonic(self.asi8, timelike=True)[1]
+
+    @property
+    def is_unique(self):
+        return len(unique1d(self.asi8)) == len(self)
+
     # ----------------------------------------------------------------
     # Arithmetic Methods
 

pandas/core/indexes/timedeltas.py

@@ -227,6 +227,10 @@ def _format_native_types(self, na_rep=u'NaT', date_format=None, **kwargs):
     # -------------------------------------------------------------------
     # Wrapping TimedeltaArray
 
+    is_monotonic_increasing = Index.is_monotonic_increasing
+    is_monotonic_decreasing = Index.is_monotonic_decreasing
+    is_unique = Index.is_unique
+
     days = wrap_field_accessor(TimedeltaArray.days)
     seconds = wrap_field_accessor(TimedeltaArray.seconds)
     microseconds = wrap_field_accessor(TimedeltaArray.microseconds)