Handle single pixel Image types (#2794)

holoviews/element/comparison.py

@@ -28,7 +28,7 @@
                     HoloMap, Dimensioned, Layout, NdLayout, NdOverlay,
                     GridSpace, DynamicMap, GridMatrix, OrderedDict)
 from ..core.options import Options, Cycle
-from ..core.util import pd
+from ..core.util import pd, datetime_types, dt_to_int
 
 
 class ComparisonInterface(object):
@@ -265,8 +265,15 @@ def compare_dataframe(cls, df1, df2, msg='DataFrames'):
 
     @classmethod
     def bounds_check(cls, el1, el2, msg=None):
+        lbrt1 = el1.bounds.lbrt()
+        lbrt2 = el2.bounds.lbrt()
         try:
-            cls.assert_array_almost_equal_fn(np.array(el1.bounds.lbrt()), np.array(el2.bounds.lbrt()))
+            for v1, v2 in zip(lbrt1, lbrt2):
+                if isinstance(v1, datetime_types):
+                    v1 = dt_to_int(v1)
+                if isinstance(v2, datetime_types):
+                    v2 = dt_to_int(v2)
+                cls.assert_array_almost_equal_fn(v1, v2)
         except AssertionError:
             raise cls.failureException("BoundingBoxes are mismatched: %s != %s."
                                        % (el1.bounds.lbrt(), el2.bounds.lbrt()))

holoviews/element/raster.py

@@ -279,6 +279,11 @@ def __init__(self, data, kdims=None, vdims=None, bounds=None, extents=None,
         l, b, r, t = bounds.lbrt()
         xdensity = xdensity if xdensity else compute_density(l, r, dim1, self._time_unit)
         ydensity = ydensity if ydensity else compute_density(b, t, dim2, self._time_unit)
+        if not np.isfinite(xdensity) or not np.isfinite(ydensity):
+            raise ValueError('Density along Image axes could not be determined. '
+                             'If the data contains only one coordinate along the '
+                             'x- or y-axis ensure you declare the bounds and/or '
+                             'density.')
         SheetCoordinateSystem.__init__(self, bounds, xdensity, ydensity)
         self._validate(data_bounds, supplied_bounds)
 
@@ -322,8 +327,15 @@ def _validate(self, data_bounds, supplied_bounds):
         else:
             bounds = data_bounds
 
-        if not all(np.isclose(r, c, rtol=self.rtol) for r, c in zip(bounds, self.bounds.lbrt())
-                   if util.isfinite(r) and not isinstance(r, util.datetime_types)):
+        not_close = False
+        for r, c in zip(bounds, self.bounds.lbrt()):
+            if isinstance(r, datetime_types):
+                r = util.dt_to_int(r)
+            if isinstance(c, datetime_types):
+                c = util.dt_to_int(c)
+            if util.isfinite(r) and not np.isclose(r, c, rtol=self.rtol):
+                not_close = True
+        if not_close:
             raise ValueError('Supplied Image bounds do not match the coordinates defined '
                              'in the data. Bounds only have to be declared if no coordinates '
                              'are supplied, otherwise they must match the data. To change '

holoviews/operation/datashader.py

@@ -422,14 +422,23 @@ def _process(self, element, key=None):
             self._precomputed[element._plot_id] = x, y, data, glyph
         (x_range, y_range), (xs, ys), (width, height), (xtype, ytype) = self._get_sampling(element, x, y)
 
+        (x0, x1), (y0, y1) = x_range, y_range
+        if xtype == 'datetime':
+            x0, x1 = (np.array([x0, x1])/10e5).astype('datetime64[us]')
+        if ytype == 'datetime':
+            y0, y1 = (np.array([y0, y1])/10e5).astype('datetime64[us]')
+        bounds = (x0, y0, x1, y1)
+        params = dict(get_param_values(element), kdims=[x, y],
+                      datatype=['xarray'], bounds=bounds)
+
         if x is None or y is None:
             xarray = xr.DataArray(np.full((height, width), np.NaN, dtype=np.float32),
                                   dims=['y', 'x'], coords={'x': xs, 'y': ys})
             return self.p.element_type(xarray)
         elif not len(data):
             xarray = xr.DataArray(np.full((height, width), np.NaN, dtype=np.float32),
                                   dims=[y.name, x.name], coords={x.name: xs, y.name: ys})
-            return self.p.element_type(xarray)
+            return self.p.element_type(xarray, **params)
 
         cvs = ds.Canvas(plot_width=width, plot_height=height,
                         x_range=x_range, y_range=y_range)
@@ -445,8 +454,7 @@ def _process(self, element, key=None):
             vdims = [dims[0](name)]
         else:
             vdims = Dimension('Count')
-        params = dict(get_param_values(element), kdims=[x, y],
-                      datatype=['xarray'], vdims=vdims)
+        params['vdims'] = vdims
 
         dfdata = PandasInterface.as_dframe(data)
         agg = getattr(cvs, glyph)(dfdata, x.name, y.name, agg_fn)

tests/operation/testdatashader.py

@@ -69,8 +69,8 @@ def test_aggregate_curve_datetimes(self):
         dates = pd.date_range(start="2016-01-01", end="2016-01-03", freq='1D')
         curve = Curve((dates, [1, 2, 3]))
         img = aggregate(curve, width=2, height=2, dynamic=False)
-        bounds = (np.datetime64('2015-12-31T23:59:59.723518000'), 1.0,
-                  np.datetime64('2016-01-03T00:00:00.276482000'), 3.0)
+        bounds = (np.datetime64('2016-01-01T00:00:00.000000'), 1.0,
+                  np.datetime64('2016-01-03T00:00:00.000000'), 3.0)
         dates = [np.datetime64('2016-01-01T12:00:00.000000000'),
                  np.datetime64('2016-01-02T12:00:00.000000000')]
         expected = Image((dates, [1.5, 2.5], [[1, 0], [0, 2]]),
@@ -83,8 +83,8 @@ def test_aggregate_curve_datetimes_microsecond_timebase(self):
         xend = np.datetime64('2016-01-03T00:00:00.276482000', 'us')
         curve = Curve((dates, [1, 2, 3]))
         img = aggregate(curve, width=2, height=2, x_range=(xstart, xend), dynamic=False)
-        bounds = (np.datetime64('2015-12-31T23:59:59.585277000'), 1.0,
-                  np.datetime64('2016-01-03T00:00:00.414723000'), 3.0)
+        bounds = (np.datetime64('2015-12-31T23:59:59.723518'), 1.0,
+                  np.datetime64('2016-01-03T00:00:00.276482'), 3.0)
         dates = [np.datetime64('2016-01-01T11:59:59.861759000',),
                  np.datetime64('2016-01-02T12:00:00.138241000')]
         expected = Image((dates, [1.5, 2.5], [[1, 0], [0, 2]]),