Added Spikes Element

doc/Tutorials/Elements.ipynb

@@ -33,6 +33,7 @@
     "  <dt>[``Scatter``](#Scatter)</dt><dd>Discontinuous collection of points indexed over a single dimension.</dd>\n",
     "  <dt>[``Points``](#Points)</dt><dd>Discontinuous collection of points indexed over two dimensions.</dd>\n",
     "  <dt>[``VectorField``](#VectorField)</dt><dd>Cyclic variable (and optional auxiliary data) distributed over two-dimensional space.</dd>\n",
+    "  <dt>[``Spikes``](#Spikes)</dt><dd>A collection of horizontal or vertical lines at various locations with fixed height (1D) or variable height (2D).</dd>\n",
     "  <dt>[``SideHistogram``](#SideHistogram)</dt><dd>Histogram binning data contained by some other ``Element``.</dd>\n",
     "  </dl>\n",
     "\n",
@@ -461,6 +462,95 @@
     "points + points[0.3:0.7, 0.3:0.7].hist()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ``Spikes`` <a id='Spikes'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Spikes represent any number of horizontal or vertical line segments with fixed or variable heights. There are a number of uses for this type, first of all they may be used as a rugplot to give an overview of a one-dimensional distribution. They may also be useful in more domain specific cases, such as visualizing spike trains for neurophysiology or spectrograms in physics and chemistry applications.\n",
+    "\n",
+    "In the simplest case a Spikes object therefore represents a 1D distribution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%opts Spikes [spike_height=0.1] (alpha=0.4)\n",
+    "xs = np.random.rand(50)\n",
+    "ys = np.random.rand(50)\n",
+    "hv.Points((xs, ys)) * hv.Spikes(xs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When supplying two dimensions to the Spikes object the second dimension will be mapped onto the line height. Optionally you may also supply a cmap and color_index to map color onto one of the dimensions. This way we can for example plot a mass spectrogram:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%opts Spikes (cmap='Blues')\n",
+    "hv.Spikes(np.random.rand(20, 2), kdims=['Mass'], vdims=['Intensity'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another possibility is to draw a number of spike trains as you would encounter in neuroscience:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%opts Spikes [spike_height=0.1] NdOverlay [show_legend=False]\n",
+    "hv.NdOverlay({i: hv.Spikes(np.random.rand(10, 1))(plot=dict(yposition=0.1*i))\n",
+    "              for i in range(10)})(plot=dict(yticks=[((i+1)*0.1-0.05, i)for i in range(10)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we may use ``Spikes`` to adjoin to a regular plot to visualize marginal distributions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%opts Spikes (alpha=0.4) [bgcolor='w'] AdjointLayout [border_size=0]\n",
+    "points = hv.Points(np.random.randn(100, 2))\n",
+    "points << hv.Spikes(points['y']) << hv.Spikes(points['x'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

holoviews/element/chart.py

@@ -344,3 +344,23 @@ def __init__(self, data, **params):
         if isinstance(data, list) and all(isinstance(d, np.ndarray) for d in data):
             data = np.column_stack([d.flat if d.ndim > 1 else d for d in data])
         super(VectorField, self).__init__(data, **params)
+
+
+
+class Spikes(Chart):
+    """
+    Spikes is a 1D or 2D Element, which represents vertical or
+    horizontal ticks along some dimension.  If an additional dimension
+    is supplied it will be used to specify the height of the
+    lines. The Element may therefore be used to represent 1D
+    distributions, spectrograms or spike trains in electrophysiology.
+    """
+
+    group = param.String(default='Spikes', constant=True)
+
+    kdims = param.List(default=[Dimension('x')])
+
+    vdims = param.List(default=[])
+
+    _1d = True
+

holoviews/plotting/bokeh/__init__.py

@@ -3,7 +3,7 @@
 from ...element import (Curve, Points, Scatter, Image, Raster, Path,
                         RGB, Histogram, Spread, HeatMap, Contours,
                         Path, Box, Bounds, Ellipse, Polygons,
-                        ErrorBars, Text, HLine, VLine, Spline,
+                        ErrorBars, Text, HLine, VLine, Spline, Spikes,
                         Table, ItemTable, Surface, Scatter3D, Trisurface)
 from ...core.options import Options, Cycle, OptionTree
 from ...interface import DFrame
@@ -14,7 +14,7 @@
 from .callbacks import Callbacks
 from .element import OverlayPlot, BokehMPLWrapper, BokehMPLRawWrapper
 from .chart import (PointPlot, CurvePlot, SpreadPlot, ErrorPlot, HistogramPlot,
-                    AdjointHistogramPlot)
+                    AdjointHistogramPlot, SpikesPlot)
 from .path import PathPlot, PolygonPlot
 from .plot import GridPlot, LayoutPlot, AdjointLayoutPlot
 from .raster import RasterPlot, RGBPlot, HeatmapPlot
@@ -37,6 +37,7 @@
                 Scatter: PointPlot,
                 ErrorBars: ErrorPlot,
                 Spread: SpreadPlot,
+                Spikes: SpikesPlot,
 
                 # Rasters
                 Image: RasterPlot,
@@ -81,7 +82,7 @@
 
 
 AdjointLayoutPlot.registry[Histogram] = AdjointHistogramPlot
-
+AdjointLayoutPlot.registry[Spikes] = SpikesPlot
 
 try:
     from ..mpl.seaborn import TimeSeriesPlot, BivariatePlot, DistributionPlot

holoviews/plotting/bokeh/chart.py

@@ -203,3 +203,53 @@ def get_data(self, element, ranges=None):
                 err_xs.append((x, x))
                 err_ys.append((y - neg, y + pos))
         return (dict(xs=err_xs, ys=err_ys), self._mapping)
+
+
+class SpikesPlot(PathPlot):
+
+    color_index = param.Integer(default=1, doc="""
+      Index of the dimension from which the color will the drawn""")
+
+    spike_height = param.Number(default=0.1)
+
+    yposition = param.Number(default=0.)
+
+    style_opts = (['cmap', 'palette'] + line_properties)
+
+    def get_extents(self, element, ranges):
+        l, b, r, t = super(SpikesPlot, self).get_extents(element, ranges)
+        if len(element.dimensions()) == 1:
+            b, t = self.yposition, self.yposition+self.spike_height
+        return l, b, r, t
+
+
+    def get_data(self, element, ranges=None):
+        style = self.style[self.cyclic_index]
+        dims = element.dimensions(label=True)
+
+        ypos = self.yposition
+        if len(dims) > 1:
+            xs, ys = zip(*(((x, x), (ypos, ypos+y))
+                           for x, y in element.array()))
+            mapping = dict(xs=dims[0], ys=dims[1])
+            keys = (dims[0], dims[1])
+        else:
+            height = self.spike_height
+            xs, ys = zip(*(((x[0], x[0]), (ypos, ypos+height))
+                           for x in element.array()))
+            mapping = dict(xs=dims[0], ys='heights')
+            keys = (dims[0], 'heights')
+        if self.invert_axes: keys = keys[::-1]
+        data = dict(zip(keys, (xs, ys)))
+
+        cmap = style.get('palette', style.get('cmap', None))        
+        if self.color_index < len(dims) and cmap:
+            cdim = dims[self.color_index]
+            map_key = 'color_' + cdim
+            mapping['color'] = map_key
+            cmap = get_cmap(cmap)
+            colors = element.dimension_values(cdim)
+            crange = ranges.get(cdim, None)
+            data[map_key] = map_colors(colors, crange, cmap)
+
+        return data, mapping

holoviews/plotting/bokeh/element.py

@@ -58,6 +58,9 @@ class ElementPlot(BokehPlot, GenericElementPlot):
 
           {'ticks': '20pt', 'title': '15pt', 'ylabel': '5px', 'xlabel': '5px'}""")
 
+    invert_axes = param.Boolean(default=False, doc="""
+        Whether to invert the x- and y-axis""")
+
     invert_xaxis = param.Boolean(default=False, doc="""
         Whether to invert the plot x-axis.""")
 
@@ -137,9 +140,7 @@ class ElementPlot(BokehPlot, GenericElementPlot):
     # instance attribute.
     _update_handles = ['source', 'glyph']
 
-    def __init__(self, element, plot=None, invert_axes=False,
-                 show_labels=['x', 'y'], **params):
-        self.invert_axes = invert_axes
+    def __init__(self, element, plot=None, show_labels=['x', 'y'], **params):
         self.show_labels = show_labels
         self.current_ranges = None
         super(ElementPlot, self).__init__(element, **params)

holoviews/plotting/mpl/__init__.py

@@ -108,6 +108,7 @@ def grid_selector(grid):
                 VectorField: VectorFieldPlot,
                 ErrorBars: ErrorPlot,
                 Spread: SpreadPlot,
+                Spikes: SpikesPlot,
 
                 # General plots
                 GridSpace: GridPlot,
@@ -157,7 +158,8 @@ def grid_selector(grid):
 
 
 MPLPlot.sideplots.update({Histogram: SideHistogramPlot,
-                          GridSpace: GridPlot})
+                          GridSpace: GridPlot,
+                          Spikes: MarginalRugPlot})
 
 options = Store.options(backend='matplotlib')