Merge 1ee50bd into 509d906

holoviews/core/data/__init__.py

@@ -428,7 +428,9 @@ def reindex(self, kdims=None, vdims=None):
         data = self.interface.reindex(self, key_dims, val_dims)
         datatype = self.datatype
         if gridded and dropped:
-            datatype = [dt for dt in datatype if not self.interface.interfaces[dt].gridded]
+            interfaces = self.interface.interfaces
+            datatype = [dt for dt in datatype if not
+                        getattr(interfaces.get(dt, None), 'gridded', True)]
         return self.clone(data, kdims=key_dims, vdims=val_dims,
                           new_type=new_type, datatype=datatype)
 

holoviews/core/data/dask.py

@@ -44,7 +44,7 @@ class DaskInterface(PandasInterface):
 
     @classmethod
     def loaded(cls):
-        return 'dask' in sys.modules
+        return 'dask' in sys.modules and 'pandas' in sys.modules
 
     @classmethod
     def applies(cls, obj):

holoviews/core/data/dictionary.py

@@ -60,7 +60,10 @@ def init(cls, eltype, data, kdims, vdims):
         elif isinstance(data, list) and data == []:
             data = OrderedDict([(d, []) for d in dimensions])
         elif isinstance(data, list) and isscalar(data[0]):
-            data = {dimensions[0]: np.arange(len(data)), dimensions[1]: data}
+            if eltype._auto_indexable_1d:
+                data = {dimensions[0]: np.arange(len(data)), dimensions[1]: data}
+            else:
+                data = {dimensions[0]: data}
         elif (isinstance(data, list) and isinstance(data[0], tuple) and len(data[0]) == 2
               and any(isinstance(v, tuple) for v in data[0])):
             dict_data = zip(*((util.wrap_tuple(k)+util.wrap_tuple(v))

holoviews/core/util.py

@@ -812,7 +812,7 @@ def isscalar(val):
     """
     Value is scalar or None
     """
-    return val is None or np.isscalar(val)
+    return val is None or np.isscalar(val) or isinstance(val, datetime_types)
 
 
 def isnumeric(val):
@@ -965,7 +965,12 @@ def range_pad(lower, upper, padding=None, log=False):
             center = (log_min+log_max) / 2.0
             start, end = np.power(10, center-lspan/2.), np.power(10, center+uspan/2.)
         else:
-            span = (upper-lower)
+            if isinstance(lower, datetime_types) and not isinstance(lower, cftime_types):
+                # Ensure timedelta can be safely divided
+                lower, upper = np.datetime64(lower), np.datetime64(upper)
+                span = (upper-lower).astype('>m8[ns]')
+            else:
+                span = (upper-lower)
             lpad = span*(padding[0])
             upad = span*(padding[1])
             start, end = lower-lpad, upper+upad
@@ -1391,7 +1396,7 @@ def is_dataframe(data):
     Checks whether the supplied data is of DataFrame type.
     """
     dd = None
-    if 'dask' in sys.modules:
+    if 'dask' in sys.modules and 'pandas' in sys.modules:
         import dask.dataframe as dd
     return((pd is not None and isinstance(data, pd.DataFrame)) or
           (dd is not None and isinstance(data, dd.DataFrame)))
@@ -1874,6 +1879,9 @@ def dt_to_int(value, time_unit='us'):
     elif isinstance(value, cftime_types):
         return cftime_to_timestamp(value, time_unit)
 
+    if isinstance(value, dt.date):
+        value = dt.datetime(*value.timetuple()[:6])
+
     # Handle datetime64 separately
     if isinstance(value, np.datetime64):
         try:

holoviews/element/raster.py

@@ -518,7 +518,7 @@ def sample(self, samples=[], **kwargs):
                              (coords, type(self).__name__, self.bounds.lbrt()))
 
         data = self.interface.ndloc(self, (yidx, xidx))
-        return self.clone(data, new_type=Table, datatype=['dataframe', 'dict'])
+        return self.clone(data, new_type=Table, datatype=['dataframe', 'dictionary'])
 
 
     def closest(self, coords=[], **kwargs):

holoviews/operation/element.py

@@ -772,7 +772,7 @@ def _process_layer(self, element, key=None):
         dtype = x.dtype
         is_datetime = dtype.kind == 'M' or isinstance(x[0], datetime_types)
         if is_datetime:
-            dt_type = dtype if dtype.kind == 'M' else 'datetime64[ns]'
+            dt_type = 'datetime64[ns]'
             x = x.astype(dt_type).astype('int64')
         dvals = tuple(element.dimension_values(d) for d in element.dimensions()[1:])
         xs, dvals = INTERPOLATE_FUNCS[self.p.interpolation](x.astype('f'), dvals)

holoviews/plotting/bokeh/element.py

@@ -217,8 +217,10 @@ def _get_hover_data(self, data, element, dimensions=None):
             dim = util.dimension_sanitizer(d.name)
             if dim not in data:
                 data[dim] = element.dimension_values(d)
-            if isinstance(data[dim], np.ndarray) and data[dim].dtype.kind == 'M':
-                data[dim+'_dt_strings'] = [d.pprint_value(v) for v in data[dim]]
+            values = data[dim]
+            if (values.dtype.kind == 'M' or (
+                    len(values) and isinstance(values[0], util.datetime_types))):
+                data[dim+'_dt_strings'] = [d.pprint_value(v) for v in values]
 
         for k, v in self.overlay_dims.items():
             dim = util.dimension_sanitizer(k.name)
@@ -653,6 +655,7 @@ def _update_range(self, axis_range, low, high, factors, invert, shared, log, str
             elif (low == high and low is not None):
                 if isinstance(low, util.datetime_types):
                     offset = np.timedelta64(500, 'ms')
+                    low, high = np.datetime64(low), np.datetime64(high)
                     low -= offset
                     high += offset
                 else:

holoviews/plotting/bokeh/stats.py

@@ -106,7 +106,7 @@ def _apply_transforms(self, element, data, ranges, style, group=None):
             if isinstance(agg, Dimensioned):
                 element = agg
             else:
-                element = element.clone([(element,)])
+                element = element.clone([(agg,)])
         return super(BoxWhiskerPlot, self)._apply_transforms(element, data, ranges, style, group)
 
     def _get_factors(self, element):

holoviews/plotting/mpl/chart.py

@@ -82,7 +82,7 @@ def get_data(self, element, ranges, style):
         xs = element.dimension_values(0)
         ys = element.dimension_values(1)
         dims = element.dimensions()
-        if xs.dtype.kind == 'M':
+        if xs.dtype.kind == 'M' or (len(xs) and isinstance(xs[0], datetime_types)):
             dimtype = element.get_dimension_type(0)
             dt_format = Dimension.type_formatters.get(dimtype, '%Y-%m-%d %H:%M:%S')
             dims[0] = dims[0](value_format=DateFormatter(dt_format))
@@ -91,7 +91,7 @@ def get_data(self, element, ranges, style):
 
     def init_artists(self, ax, plot_args, plot_kwargs):
         xs, ys = plot_args
-        if xs.dtype.kind == 'M':
+        if xs.dtype.kind == 'M' or (len(xs) and isinstance(xs[0], datetime_types)):
             artist = ax.plot_date(xs, ys, '-', **plot_kwargs)[0]
         else:
             artist = ax.plot(xs, ys, **plot_kwargs)[0]
@@ -1189,7 +1189,7 @@ def get_data(self, element, ranges, style):
             cols = []
             for i, vs in enumerate((xs, ys)):
                 vs = np.array(vs)
-                if vs.dtype.kind == 'M' and i < len(dims):
+                if (vs.dtype.kind == 'M' or (len(vs) and isinstance(vs[0], datetime_types))) and i < len(dims):
                     dt_format = Dimension.type_formatters[np.datetime64]
                     dims[i] = dims[i](value_format=DateFormatter(dt_format))
                     vs = np.array([dt_to_int(v, 'D') for v in vs])

holoviews/plotting/util.py

@@ -736,7 +736,7 @@ def list_cmaps(provider=None, records=False, name=None, category=None, source=No
                    # cmap_info stores only non-reversed info, so construct
                    # suitable values for reversed version if appropriate
                    r=r._replace(name=aname)
-                   if aname.endswith('_r') and (r.category is not 'Diverging'):
+                   if aname.endswith('_r') and (r.category != 'Diverging'):
                        if r.bg=='light':
                            r=r._replace(bg='dark')
                        elif r.bg=='dark':

holoviews/tests/core/data/base.py

@@ -3,8 +3,7 @@
 """
 
 import datetime
-from nose.plugins.attrib import attr
-from unittest import SkipTest
+from unittest import SkipTest, skipIf
 
 import numpy as np
 
@@ -21,6 +20,8 @@
 except:
     pd = None
 
+pd_skip = skipIf(pd is None, "pandas is not available")
+
 
 
 class DatatypeContext(object):
@@ -384,11 +385,13 @@ def test_dataset_get_array_by_dimension(self):
         arr = self.dataset_hm.array(['x'])
         self.assertEqual(arr, self.xs[:, np.newaxis])
 
+    @pd_skip
     def test_dataset_get_dframe(self):
         df = self.dataset_hm.dframe()
         self.assertEqual(df.x.values, self.xs)
         self.assertEqual(df.y.values, self.y_ints)
 
+    @pd_skip
     def test_dataset_get_dframe_by_dimension(self):
         df = self.dataset_hm.dframe(['x'])
         self.assertEqual(df, pd.DataFrame({'x': self.xs}, dtype=df.dtypes[0]))
@@ -424,17 +427,15 @@ def init_column_data(self):
     # Test the constructor to be supported by all interfaces supporting
     # heterogeneous column types.
 
+    @pd_skip
     def test_dataset_dataframe_init_ht(self):
         "Tests support for heterogeneous DataFrames"
-        if pd is None:
-            raise SkipTest("Pandas not available")
         dataset = Dataset(pd.DataFrame({'x':self.xs, 'y':self.ys}), kdims=['x'], vdims=['y'])
         self.assertTrue(isinstance(dataset.data, self.data_type))
 
+    @pd_skip
     def test_dataset_dataframe_init_ht_alias(self):
         "Tests support for heterogeneous DataFrames"
-        if pd is None:
-            raise SkipTest("Pandas not available")
         dataset = Dataset(pd.DataFrame({'x':self.xs, 'y':self.ys}),
                           kdims=[('x', 'X')], vdims=[('y', 'Y')])
         self.assertTrue(isinstance(dataset.data, self.data_type))
@@ -496,7 +497,7 @@ def test_dataset_range_with_dimension_range(self):
 
     # Operations
 
-    @attr(optional=1) # Uses pandas
+    @pd_skip
     def test_dataset_redim_with_alias_dframe(self):
         test_df = pd.DataFrame({'x': range(10), 'y': range(0,20,2)})
         dataset = Dataset(test_df, kdims=[('x', 'X-label')], vdims=['y'])

holoviews/tests/core/data/testdaskinterface.py

@@ -1,4 +1,3 @@
-from nose.plugins.attrib import attr
 from unittest import SkipTest
 
 import numpy as np
@@ -14,7 +13,6 @@
 from .testpandasinterface import PandasInterfaceTests
 
 
-@attr(optional=1)
 class DaskDatasetTest(PandasInterfaceTests):
     """
     Test of the pandas DaskDataset interface.

holoviews/tests/core/data/testgridinterface.py

@@ -2,7 +2,7 @@
 
 from collections import OrderedDict
 from itertools import product
-from unittest import SkipTest
+from unittest import SkipTest, skipIf
 
 import numpy as np
 from holoviews.core.data import Dataset
@@ -14,6 +14,9 @@
 except ImportError:
     da = None
 
+pd_skip = skipIf(pd is None, "pandas is not available")
+
+
 from .base import (
     GriddedInterfaceTests, InterfaceTests, HomogeneousColumnTests, DatatypeContext
 )
@@ -28,20 +31,18 @@ class GridInterfaceTests(GriddedInterfaceTests, HomogeneousColumnTests, Interfac
     data_type = (OrderedDict, dict)
     element = Dataset
 
+    @pd_skip
     def test_dataset_dataframe_init_hm(self):
         "Tests support for homogeneous DataFrames"
-        if pd is None:
-            raise SkipTest("Pandas not available")
         exception = "None of the available storage backends "\
          "were able to support the supplied data format."
         with self.assertRaisesRegexp(Exception, exception):
             Dataset(pd.DataFrame({'x':self.xs, 'x2':self.xs_2}),
                     kdims=['x'], vdims=['x2'])
 
+    @pd_skip
     def test_dataset_dataframe_init_hm_alias(self):
         "Tests support for homogeneous DataFrames"
-        if pd is None:
-            raise SkipTest("Pandas not available")
         exception = "None of the available storage backends "\
          "were able to support the supplied data format."
         with self.assertRaisesRegexp(Exception, exception):
@@ -432,6 +433,7 @@ def test_dataset_groupby_drop_dims_dynamic_with_vdim(self):
             partial = ds.to(Dataset, kdims=['Val'], vdims=['Val2'], groupby='y', dynamic=True)
             self.assertEqual(partial[19]['Val'], array[:, -1, :].T.flatten().compute())
 
+    @pd_skip
     def test_dataset_get_dframe(self):
         df = self.dataset_hm.dframe()
         self.assertEqual(df.x.values, self.xs)

holoviews/tests/core/data/testmultiinterface.py

@@ -129,7 +129,7 @@ def test_multi_array_redim(self):
         arrays = [np.column_stack([np.arange(i, i+2), np.arange(i, i+2)]) for i in range(2)]
         mds = Path(arrays, kdims=['x', 'y'], datatype=['multitabular']).redim(x='x2')
         for i, ds in enumerate(mds.split()):
-            self.assertEqual(ds, Path(arrays[i], kdims=['x2', 'y'], datatype=['dask']))
+            self.assertEqual(ds, Path(arrays[i], kdims=['x2', 'y']))
 
     def test_multi_mixed_interface_raises(self):
         arrays = [np.random.rand(10, 2) if j else {'x': range(10), 'y': range(10)}

holoviews/tests/core/data/testpandasinterface.py

@@ -1,4 +1,3 @@
-from nose.plugins.attrib import attr
 from unittest import SkipTest
 
 import numpy as np
@@ -18,7 +17,6 @@
 from .base import HeterogeneousColumnTests, InterfaceTests
 
 
-@attr(optional=1)
 class PandasInterfaceTests(HeterogeneousColumnTests, InterfaceTests):
     """
     Test for the PandasInterface.

holoviews/tests/core/data/testxarrayinterface.py

@@ -1,6 +1,5 @@
 import datetime as dt
 from collections import OrderedDict
-from nose.plugins.attrib import attr
 from unittest import SkipTest
 
 import numpy as np
@@ -21,7 +20,6 @@
 from .testgridinterface import GridInterfaceTests
 
 
-@attr(optional=1)
 class XArrayInterfaceTests(GridInterfaceTests):
     """
     Tests for xarray interface
@@ -233,7 +231,6 @@ def test_dataset_sample_hm_alias(self):
 
 
 
-@attr(optional=1)
 class DaskXArrayInterfaceTest(XArrayInterfaceTests):
     """
     Tests for XArray interface wrapping dask arrays
@@ -290,7 +287,6 @@ def test_xarray_dataset_with_scalar_dim_canonicalize(self):
 
 
 
-@attr(optional=1)
 class Image_XArrayInterfaceTests(Image_ImageInterfaceTests):
 
     datatype = 'xarray'
@@ -358,7 +354,6 @@ def test_dataarray_with_some_coords(self):
             Image(xrarr, kdims=['x', 'y'])
 
 
-@attr(optional=1)
 class RGB_XArrayInterfaceTests(RGB_ImageInterfaceTests):
 
     datatype = 'xarray'
@@ -369,7 +364,6 @@ def init_data(self):
                         self.rgb_array[:, :, 1], self.rgb_array[:, :, 2]))
 
 
-@attr(optional=1)
 class HSV_XArrayInterfaceTest(HSV_ImageInterfaceTests):
 
     datatype = 'xarray'

holoviews/tests/core/testndmapping.py

@@ -132,7 +132,7 @@ def test_idxmapping_unsorted_clone(self):
 
     def test_idxmapping_groupby_unsorted(self):
         data = [(('B', 2), 1), (('C', 2), 2), (('A', 1), 3)]
-        grouped = MultiDimensionalMapping(data, sort=False, kdims=['X', 'Y']).groupby('Y')
+        grouped = NdMapping(data, sort=False, kdims=['X', 'Y']).groupby('Y')
         self.assertEquals(grouped.keys(), [1, 2])
         self.assertEquals(grouped.values()[0].keys(), ['A'])
         self.assertEquals(grouped.last.keys(), ['B', 'C'])