Merge 2d22429 into 1cf01c3

holoviews/core/data/grid.py

@@ -419,6 +419,8 @@ def groupby(cls, dataset, dim_names, container_type, group_type, **kwargs):
 
     @classmethod
     def key_select_mask(cls, dataset, values, ind):
+        if util.pd and values.dtype.kind == 'M':
+            ind = util.parse_datetime_selection(ind)
         if isinstance(ind, tuple):
             ind = slice(*ind)
         if isinstance(ind, get_array_types()):

holoviews/core/data/interface.py

@@ -283,30 +283,35 @@ def select_mask(cls, dataset, selection):
         have been selected.
         """
         mask = np.ones(len(dataset), dtype=np.bool)
-        for dim, k in selection.items():
-            if isinstance(k, tuple):
-                k = slice(*k)
+        for dim, sel in selection.items():
+            if isinstance(sel, tuple):
+                sel = slice(*sel)
             arr = cls.values(dataset, dim)
-            if isinstance(k, slice):
+            if util.isdatetime(arr) and util.pd:
+                try:
+                    sel = util.parse_datetime_selection(sel)
+                except:
+                    pass
+            if isinstance(sel, slice):
                 with warnings.catch_warnings():
                     warnings.filterwarnings('ignore', r'invalid value encountered')
-                    if k.start is not None:
-                        mask &= k.start <= arr
-                    if k.stop is not None:
-                        mask &= arr < k.stop
-            elif isinstance(k, (set, list)):
+                    if sel.start is not None:
+                        mask &= sel.start <= arr
+                    if sel.stop is not None:
+                        mask &= arr < sel.stop
+            elif isinstance(sel, (set, list)):
                 iter_slcs = []
-                for ik in k:
+                for ik in sel:
                     with warnings.catch_warnings():
                         warnings.filterwarnings('ignore', r'invalid value encountered')
                         iter_slcs.append(arr == ik)
                 mask &= np.logical_or.reduce(iter_slcs)
-            elif callable(k):
-                mask &= k(arr)
+            elif callable(sel):
+                mask &= sel(arr)
             else:
-                index_mask = arr == k
+                index_mask = arr == sel
                 if dataset.ndims == 1 and np.sum(index_mask) == 0:
-                    data_index = np.argmin(np.abs(arr - k))
+                    data_index = np.argmin(np.abs(arr - sel))
                     mask = np.zeros(len(dataset), dtype=np.bool)
                     mask[data_index] = True
                 else:

holoviews/core/util.py

@@ -1916,6 +1916,31 @@ def date_range(start, end, length, time_unit='us'):
     return start+step/2.+np.arange(length)*step
 
 
+def parse_datetime(date):
+    """
+    Parses dates specified as string or integer or pandas Timestamp
+    """
+    if pd is None:
+        raise ImportError('Parsing dates from strings requires pandas')
+    return pd.to_datetime(date).to_datetime64()
+
+
+def parse_datetime_selection(sel):
+    """
+    Parses string selection specs as datetimes.
+    """
+    if isinstance(sel, basestring) or isdatetime(sel):
+        sel = parse_datetime(sel)
+    if isinstance(sel, slice):
+        if isinstance(sel.start, basestring) or isdatetime(sel.start):
+            sel = slice(parse_datetime(sel.start), sel.stop)
+        if isinstance(sel.stop, basestring) or isdatetime(sel.stop):
+            sel = slice(sel.start, parse_datetime(sel.stop))
+    if isinstance(sel, (set, list)):
+        sel = [parse_datetime(v) if isinstance(v, basestring) else v for v in sel]
+    return sel
+
+
 def dt_to_int(value, time_unit='us'):
     """
     Converts a datetime type to an integer with the supplied time unit.

holoviews/element/chart.py

@@ -45,21 +45,7 @@ class Chart(Dataset, Element2D):
     __abstract = True
 
     def __getitem__(self, index):
-        sliced = super(Chart, self).__getitem__(index)
-        if not isinstance(sliced, Chart):
-            return sliced
-
-        if not isinstance(index, tuple): index = (index,)
-        ndims = len(self.extents)//2
-        lower_bounds, upper_bounds = [None]*ndims, [None]*ndims
-        for i, slc in enumerate(index[:ndims]):
-            if isinstance(slc, slice):
-                lbound = self.extents[i]
-                ubound = self.extents[ndims:][i]
-                lower_bounds[i] = lbound if slc.start is None else slc.start
-                upper_bounds[i] = ubound if slc.stop is None else slc.stop
-        sliced.extents = tuple(lower_bounds+upper_bounds)
-        return sliced
+        return super(Chart, self).__getitem__(index)
 
 
 class Scatter(Chart):
@@ -69,7 +55,7 @@ class Scatter(Chart):
     location along the x-axis while the first value dimension
     represents the location of the point along the y-axis.
     """
-    
+
     group = param.String(default='Scatter', constant=True)
 
 

holoviews/tests/element/testelementselect.py

@@ -1,8 +1,14 @@
 from itertools import product
+import datetime as dt
 import numpy as np
 
+try:
+    import pandas as pd
+except ImportError:
+    pd = None
+
 from holoviews.core import HoloMap
-from holoviews.element import Image, Contours
+from holoviews.element import Image, Contours, Curve
 from holoviews.element.comparison import ComparisonTestCase
 
 class DimensionedSelectionTest(ComparisonTestCase):
@@ -11,6 +17,11 @@ def setUp(self):
         self.img_fn = lambda: Image(np.random.rand(10, 10))
         self.contour_fn = lambda: Contours([np.random.rand(10, 2)
                                             for i in range(2)])
+        self.datetime_fn = lambda: Curve((
+            [dt.datetime(2000,1,1), dt.datetime(2000,1,2),
+             dt.datetime(2000,1,3)],
+            np.random.rand(3)
+            ), 'time', 'x')
         params = [list(range(3)) for i in range(2)]
         self.sanitized_map = HoloMap({i: Image(i*np.random.rand(10,10))
                                       for i in range(1,10)}, kdims=['A B'])
@@ -85,3 +96,17 @@ def test_duplicate_dim_select(self):
     def test_overlap_select(self):
         selection = self.overlap_layout.select(Default=(6, None))
         self.assertEqual(selection, self.overlap1.clone(shared_data=False) + self.overlap2[6:])
+
+    def test_datetime_select(self):
+        s, e = '1999-12-31', '2000-1-2'
+        curve = self.datetime_fn()
+        overlay = curve * self.datetime_fn()
+        for el in [curve, overlay]:
+            self.assertEqual(el.select(time=(s, e)), el[s:e])
+            self.assertEqual(el.select(time=
+                (dt.datetime(1999, 12, 31), dt.datetime(2000, 1, 2))), el[s:e]
+            )
+            if pd:
+                self.assertEqual(el.select(
+                    time=(pd.Timestamp(s), pd.Timestamp(e))
+                ), el[pd.Timestamp(s):pd.Timestamp(e)])