Histogram enhancements (#2812)

holoviews/core/element.py

@@ -23,7 +23,7 @@ class Element(ViewableElement, Composable, Overlayable):
     group = param.String(default='Element', constant=True)
 
     def hist(self, dimension=None, num_bins=20, bin_range=None,
-             adjoin=True, individually=True, **kwargs):
+             adjoin=True, **kwargs):
         """
         The hist method generates a histogram to be adjoined to the
         Element in an AdjointLayout. By default the histogram is
@@ -37,7 +37,7 @@ def hist(self, dimension=None, num_bins=20, bin_range=None,
         hists = []
         for d in dimension[::-1]:
             hist = histogram(self, num_bins=num_bins, bin_range=bin_range,
-                             individually=individually, dimension=d, **kwargs)
+                             dimension=d, **kwargs)
             hists.append(hist)
         if adjoin:
             layout = self

holoviews/core/spaces.py

@@ -421,8 +421,7 @@ def hist(self, num_bins=20, bin_range=None, adjoin=True, individually=True, **kw
         if issubclass(self.type, (NdOverlay, Overlay)) and 'index' not in kwargs:
             kwargs['index'] = 0
         for k, v in self.data.items():
-            hists = v.hist(adjoin=False, bin_range=bin_range,
-                           individually=individually, num_bins=num_bins,
+            hists = v.hist(adjoin=False, bin_range=bin_range, num_bins=num_bins,
                            style_prefix=style_prefix, **kwargs)
             if isinstance(hists, Layout):
                 for i, hist in enumerate(hists):
@@ -1421,7 +1420,7 @@ def overlay(self, dimensions=None, **kwargs):
         return self.groupby(dims, group_type=NdOverlay)
 
 
-    def hist(self, num_bins=20, bin_range=None, adjoin=True, individually=True, **kwargs):
+    def hist(self, num_bins=20, bin_range=None, adjoin=True, **kwargs):
         """
         Computes a histogram from the object and adjoins it by
         default.  By default the histogram is computed for the bottom

holoviews/operation/element.py

@@ -486,6 +486,12 @@ class histogram(Operation):
     bin_range = param.NumericTuple(default=None, length=2,  doc="""
       Specifies the range within which to compute the bins.""")
 
+    bins = param.ClassSelector(default=None, class_=(np.ndarray, list, tuple), doc="""
+      An explicit set of bin edges.""")
+
+    cumulative = param.Boolean(default=False, doc="""
+      Whether to compute the cumulative histogram""")
+
     dimension = param.String(default=None, doc="""
       Along which dimension of the Element to compute the histogram.""")
 
@@ -495,9 +501,6 @@ class histogram(Operation):
     groupby = param.ClassSelector(default=None, class_=(basestring, Dimension), doc="""
       Defines a dimension to group the Histogram returning an NdOverlay of Histograms.""")
 
-    individually = param.Boolean(default=True, doc="""
-      Specifies whether the histogram will be rescaled for each Element in a UniformNdMapping.""")
-
     log = param.Boolean(default=False, doc="""
       Whether to use base 10 logarithmic samples for the bin edges.""")
 
@@ -555,15 +558,21 @@ def _process(self, view, key=None):
             hist_range = (0, 1)
 
         datetimes = False
+        bins = None if self.p.bins is None else np.asarray(self.p.bins)
         steps = self.p.num_bins + 1
         start, end = hist_range
         if data.dtype.kind == 'M' or (data.dtype.kind == 'O' and isinstance(data[0], datetime_types)):
             start, end = dt_to_int(start, 'ns'), dt_to_int(end, 'ns')
             datetimes = True
             data = data.astype('datetime64[ns]').astype('int64') * 1000.
-            hist_range = start, end
+            if bins is not None:
+                bins = bins.astype('datetime64[ns]').astype('int64') * 1000.
+            else:
+                hist_range = start, end
 
-        if self.p.log:
+        if self.p.bins:
+            edges = bins
+        elif self.p.log:
             bin_min = max([abs(start), data[data>0].min()])
             edges = np.logspace(np.log10(bin_min), np.log10(end), steps)
         else:
@@ -601,7 +610,11 @@ def _process(self, view, key=None):
         if view.group != view.__class__.__name__:
             params['group'] = view.group
 
-        return Histogram((hist, edges), kdims=[view.get_dimension(selected_dim)],
+        if self.p.cumulative:
+            hist = np.cumsum(hist)
+            if self.p.normed in (True, 'integral'):
+                hist *= edges[1]-edges[0]
+        return Histogram((edges, hist), kdims=[view.get_dimension(selected_dim)],
                          label=view.label, **params)
 
 

tests/operation/testoperation.py

@@ -127,6 +127,35 @@ def test_points_histogram_bin_range(self):
         hist = Histogram(([0.25, 0.25, 0.5], [0., 1., 2., 3.]))
         self.assertEqual(op_hist, hist)
 
+    def test_points_histogram_explicit_bins(self):
+        points = Points([float(i) for i in range(10)])
+        op_hist = histogram(points, bins=[0, 1, 3], normed=False)
+
+        # Make sure that the name and label are as desired
+        op_freq_dim = op_hist.get_dimension('x_frequency')
+        self.assertEqual(op_freq_dim.label, 'x Frequency')
+
+        # Because the operation labels are now different from the
+        #  default Element label, change back before comparing.
+        op_hist = op_hist.redim(x_frequency='Frequency')
+        hist = Histogram(([0, 1, 3], [1, 3]))
+        self.assertEqual(op_hist, hist)
+
+    def test_points_histogram_cumulative(self):
+        arr = np.arange(4)
+        points = Points(arr)
+        op_hist = histogram(points, cumulative=True, num_bins=3, normed=False)
+
+        # Make sure that the name and label are as desired
+        op_freq_dim = op_hist.get_dimension('x_frequency')
+        self.assertEqual(op_freq_dim.label, 'x Frequency')
+
+        # Because the operation labels are now different from the
+        #  default Element label, change back before comparing.
+        op_hist = op_hist.redim(x_frequency='Frequency')
+        hist = Histogram(([0, 1, 2, 3], [1, 2, 4]))
+        self.assertEqual(op_hist, hist)
+
     def test_points_histogram_not_normed(self):
         points = Points([float(i) for i in range(10)])
         op_hist = histogram(points, num_bins=3, normed=False)