move period related functions from tslib to period module

Moved many import statement to local scope in period module, similar to
tslib. This fixes circular dependency problems.

Author: blbradley

pandas/period.pyx

@@ -7,6 +7,7 @@
 import pandas.util.testing as tm
 from pandas.compat import range, lrange, zip
 import pandas.lib as lib
+import pandas.period as period
 import pandas.algos as algos
 
 
@@ -731,12 +732,10 @@ def test_to_datetime_bijective(self):
 class TestPeriodField(tm.TestCase):
 
     def test_get_period_field_raises_on_out_of_range(self):
-        from pandas import tslib
-        self.assertRaises(ValueError, tslib.get_period_field, -1, 0, 0)
+        self.assertRaises(ValueError, period.get_period_field, -1, 0, 0)
 
     def test_get_period_field_array_raises_on_out_of_range(self):
-        from pandas import tslib
-        self.assertRaises(ValueError, tslib.get_period_field_arr, -1, np.empty(1), 0)
+        self.assertRaises(ValueError, period.get_period_field_arr, -1, np.empty(1), 0)
 
 if __name__ == '__main__':
     import nose

pandas/tests/test_tseries.py

@@ -12,6 +12,7 @@
 import pandas.core.common as com
 import pandas.lib as lib
 import pandas.tslib as tslib
+import pandas.period as period
 from pandas.tslib import Timedelta
 
 class FreqGroup(object):
@@ -31,12 +32,12 @@ class FreqGroup(object):
 
 class Resolution(object):
 
-    RESO_US = tslib.US_RESO
-    RESO_MS = tslib.MS_RESO
-    RESO_SEC = tslib.S_RESO
-    RESO_MIN = tslib.T_RESO
-    RESO_HR = tslib.H_RESO
-    RESO_DAY = tslib.D_RESO
+    RESO_US = period.US_RESO
+    RESO_MS = period.MS_RESO
+    RESO_SEC = period.S_RESO
+    RESO_MIN = period.T_RESO
+    RESO_HR = period.H_RESO
+    RESO_DAY = period.D_RESO
 
     _reso_str_map = {
     RESO_US: 'microsecond',

pandas/tseries/frequencies.py

@@ -28,6 +28,7 @@
 from pandas.lib import Timestamp
 import pandas.lib as lib
 import pandas.tslib as tslib
+import pandas.period as period
 import pandas.algos as _algos
 import pandas.index as _index
 
@@ -1461,7 +1462,7 @@ def is_normalized(self):
 
     @cache_readonly
     def _resolution(self):
-        return tslib.resolution(self.asi8, self.tz)
+        return period.resolution(self.asi8, self.tz)
 
     def equals(self, other):
         """

pandas/tseries/index.py

@@ -13,7 +13,9 @@
 import pandas.tseries.offsets as offsets
 
 from pandas.period import Period
+import pandas.period as period
 from pandas.period import (
+    get_period_field_arr,
     _validate_end_alias,
     _quarter_to_myear,
 )
@@ -32,7 +34,7 @@
 def _field_accessor(name, alias, docstring=None):
     def f(self):
         base, mult = _gfc(self.freq)
-        return tslib.get_period_field_arr(alias, self.values, base)
+        return get_period_field_arr(alias, self.values, base)
     f.__name__ = name
     f.__doc__ = docstring
     return property(f)
@@ -41,7 +43,7 @@ def f(self):
 def _get_ordinals(data, freq):
     f = lambda x: Period(x, freq=freq).ordinal
     if isinstance(data[0], Period):
-        return tslib.extract_ordinals(data, freq)
+        return period.extract_ordinals(data, freq)
     else:
         return lib.map_infer(data, f)
 
@@ -51,7 +53,7 @@ def dt64arr_to_periodarr(data, freq, tz):
         raise ValueError('Wrong dtype: %s' % data.dtype)
 
     base, mult = _gfc(freq)
-    return tslib.dt64arr_to_periodarr(data.view('i8'), base, tz)
+    return period.dt64arr_to_periodarr(data.view('i8'), base, tz)
 
 # --- Period index sketch
 
@@ -236,7 +238,7 @@ def _from_arraylike(cls, data, freq, tz):
                 else:
                     base1, _ = _gfc(data.freq)
                     base2, _ = _gfc(freq)
-                    data = tslib.period_asfreq_arr(data.values, base1,
+                    data = period.period_asfreq_arr(data.values, base1,
                                                    base2, 1)
             else:
                 if freq is None and len(data) > 0:
@@ -363,7 +365,7 @@ def asfreq(self, freq=None, how='E'):
             raise ValueError('Only mult == 1 supported')
 
         end = how == 'E'
-        new_data = tslib.period_asfreq_arr(self.values, base1, base2, end)
+        new_data = period.period_asfreq_arr(self.values, base1, base2, end)
         return self._simple_new(new_data, self.name, freq=freq)
 
     def to_datetime(self, dayfirst=False):
@@ -431,7 +433,7 @@ def to_timestamp(self, freq=None, how='start'):
         base, mult = _gfc(freq)
         new_data = self.asfreq(freq, how)
 
-        new_data = tslib.periodarr_to_dt64arr(new_data.values, base)
+        new_data = period.periodarr_to_dt64arr(new_data.values, base)
         return DatetimeIndex(new_data, freq='infer', name=self.name)
 
     def _add_delta(self, other):
@@ -881,7 +883,7 @@ def _range_from_fields(year=None, month=None, quarter=None, day=None,
         year, quarter = _make_field_arrays(year, quarter)
         for y, q in zip(year, quarter):
             y, m = _quarter_to_myear(y, q, freq)
-            val = tslib.period_ordinal(y, m, 1, 1, 1, 1, 0, 0, base)
+            val = period.period_ordinal(y, m, 1, 1, 1, 1, 0, 0, base)
             ordinals.append(val)
     else:
         base, mult = _gfc(freq)
@@ -890,7 +892,7 @@ def _range_from_fields(year=None, month=None, quarter=None, day=None,
 
         arrays = _make_field_arrays(year, month, day, hour, minute, second)
         for y, mth, d, h, mn, s in zip(*arrays):
-            ordinals.append(tslib.period_ordinal(y, mth, d, h, mn, s, 0, 0, base))
+            ordinals.append(period.period_ordinal(y, mth, d, h, mn, s, 0, 0, base))
 
     return np.array(ordinals, dtype=np.int64), freq
 

pandas/tseries/period.py

@@ -3,10 +3,12 @@
 import numpy as np
 
 from pandas import tslib
+from pandas import period
 import datetime
 
 from pandas.core.api import Timestamp, Series, Timedelta
-from pandas.tslib import period_asfreq, period_ordinal, get_timezone
+from pandas.tslib import get_timezone
+from pandas.period import period_asfreq, period_ordinal
 from pandas.tseries.index import date_range
 from pandas.tseries.frequencies import get_freq
 import pandas.tseries.offsets as offsets
@@ -764,11 +766,11 @@ def test_addition_subtraction_preserve_frequency(self):
     def test_resolution(self):
 
         for freq, expected in zip(['A', 'Q', 'M', 'D', 'H', 'T', 'S', 'L', 'U'],
-                                  [tslib.D_RESO, tslib.D_RESO, tslib.D_RESO, tslib.D_RESO,
-                                   tslib.H_RESO, tslib.T_RESO,tslib.S_RESO, tslib.MS_RESO, tslib.US_RESO]):
+                                  [period.D_RESO, period.D_RESO, period.D_RESO, period.D_RESO,
+                                   period.H_RESO, period.T_RESO, period.S_RESO, period.MS_RESO, period.US_RESO]):
             for tz in [None, 'Asia/Tokyo', 'US/Eastern', 'dateutil/US/Eastern']:
                 idx = date_range(start='2013-04-01', periods=30, freq=freq, tz=tz)
-                result = tslib.resolution(idx.asi8, idx.tz)
+                result = period.resolution(idx.asi8, idx.tz)
                 self.assertEqual(result, expected)
 
 

pandas/tseries/tests/test_tslib.py

@@ -2,3 +2,7 @@ from numpy cimport ndarray, int64_t
 
 cdef convert_to_tsobject(object, object, object)
 cdef convert_to_timedelta64(object, object, object)
+cpdef object maybe_get_tz(object)
+cdef bint _is_utc(object)
+cdef bint _is_tzlocal(object)
+cdef object _get_dst_info(object)