Merged master

.travis.yml

@@ -142,7 +142,7 @@ jobs:
     - &doc_build
       <<: *default
       stage: docs_dev
-      env: DESC="docs" CHANS_DEV="-c pyviz/label/dev" HV_DOC_GALLERY='false' HV_REQUIREMENTS="doc"
+      env: DESC="docs" CHANS_DEV="-c pyviz/label/dev" HV_DOC_GALLERY='false' HV_DOC_REF_GALLERY='false' HV_REQUIREMENTS="doc" PANEL_EMBED='true' PANEL_EMBED_JSON='true' PANEL_EMBED_JSON_PREFIX='json'
       script:
         - bokeh sampledata
         - conda install -c conda-forge awscli
@@ -171,7 +171,7 @@ jobs:
 
     - <<: *gallery_build
       stage: gallery_daily
-      env: DESC="gallery" CHANS_DEV="-c pyviz/label/dev" HV_DOC_GALLERY='true' HV_REQUIREMENTS="doc" BUCKET="build."
+      env: DESC="gallery" CHANS_DEV="-c pyviz/label/dev" HV_DOC_GALLERY='false' HV_DOC_REF_GALLERY='false' HV_REQUIREMENTS="doc" BUCKET="build."
 
     - <<: *doc_build
       stage: docs

examples/user_guide/15-Large_Data.ipynb

@@ -159,7 +159,42 @@
    "source": [
     "In all three of the above plots, `rasterize()` is being called to aggregate the data (a large set of x,y locations) into a rectangular grid, with each grid cell counting up the number of points that fall into it.  In the plot on the left, only `rasterize()` is done, and the resulting numeric array of counts is passed to Bokeh for colormapping.  Bokeh can then use dynamic (client-side, browser-based) operations in JavaScript, allowing users to have dynamic control over  even static HTML plots.  For instance, in this case, users can use the Box Select tool and select a range of the histogram shown, dynamically remapping the colors used in the plot to cover the selected range.\n",
     "\n",
-    "The other two plots should be identical.  In both cases, the numerical array output of `rasterize()` is mapped into RGB colors by Datashader itself, in Python (\"server-side\"), which allows special Datashader computations like the histogram-equalization in the above plots and the \"spreading\" discussed below.  The `shade()` and `datashade()` operations accept a `cmap` argument that lets you control the colormap used, which can be selected to match the HoloViews/Bokeh `cmap` option but is strictly independent of it.  See ``hv.help(rasterize)``,  ``hv.help(shade)``, and ``hv.help(datashade)`` for options that can be selected, and the [Datashader web site](http://datashader.org) for all the details. You can also try the lower-level ``hv.aggregate()`` (for points and lines) and ``hv.regrid()` (for image/raster data) operations, which may provide more control."
+    "The other two plots should be identical.  In both cases, the numerical array output of `rasterize()` is mapped into RGB colors by Datashader itself, in Python (\"server-side\"), which allows special Datashader computations like the histogram-equalization in the above plots and the \"spreading\" discussed below.  The `shade()` and `datashade()` operations accept a `cmap` argument that lets you control the colormap used, which can be selected to match the HoloViews/Bokeh `cmap` option but is strictly independent of it.  See ``hv.help(rasterize)``,  ``hv.help(shade)``, and ``hv.help(datashade)`` for options that can be selected, and the [Datashader web site](http://datashader.org) for all the details. The lower-level `aggregate()` and `regrid()` give more control over how the data is aggregated.\n",
+    "\n",
+    "Since datashader only sends the data currently in view to the plotting backend, the default behavior is to rescale colormap to the range of the visible data as the zoom level changes. This behavior may not be desirable when working with images; to instead use a fixed colormap range, the `clim` parameter can be passed to the `bokeh` backend via the `opts()` method. Note that this approach works with `rasterize()` where the colormapping is done by the `bokeh` backend. With `datashade()`, the colormapping is done with the `shade()` function which takes a `clims` parameter directly instead of passing additional parameters to the backend via `opts()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 10_000\n",
+    "\n",
+    "# Strong signal on top\n",
+    "rs = np.random.RandomState(101010)\n",
+    "x = rs.pareto(n, n)\n",
+    "y = x + rs.standard_normal(n)\n",
+    "img1, *_ = np.histogram2d(x, y, bins=60)\n",
+    "\n",
+    "# Weak signal in the middle\n",
+    "x2 = rs.standard_normal(n)\n",
+    "y2 = 5 * x + 10 * rs.standard_normal(n)\n",
+    "img2, *_ = np.histogram2d(x2, y2, bins=60)\n",
+    "\n",
+    "img = img1 + img2\n",
+    "hv_img = hv.Image(img).opts(active_tools=['wheel_zoom'])\n",
+    "auto_scale_grid = rasterize(hv_img).opts(title='Automatic color range rescaling')\n",
+    "fixed_scale_grid = rasterize(hv_img).opts(title='Fixed color range', clim=(img.min(), img.max()))\n",
+    "auto_scale_grid + fixed_scale_grid; # Output supressed and gif shown below instead"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](http://assets.holoviews.org/gifs/guides/user_guide/Large_Data/rasterize_color_range.gif)"
    ]
   },
   {

examples/user_guide/17-Dashboards.ipynb

@@ -155,7 +155,7 @@
     "\n",
     "smoothed = rolling(stock_dmap, rolling_window=rolling_window.param.value)\n",
     "\n",
-    "pn.Row(pn.WidgetBox('## Stock Explorer', symbol, variable, window), smoothed.opts(width=500))"
+    "pn.Row(pn.WidgetBox('## Stock Explorer', symbol, variable, rolling_window), smoothed.opts(width=500))"
    ]
   },
   {

examples/user_guide/Plots_and_Renderers.ipynb

@@ -374,7 +374,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Rendering plots containing ``HoloMap`` and ``DynamicMap`` objects will automatically generate a widget class instance, which you can get a handle on with the ``get_widget`` method:"
+    "Rendering plots containing ``HoloMap`` and ``DynamicMap`` objects will automatically generate a Panel HoloViews pane which can be rendered in the notebook, saved or rendered as a server app:"
    ]
   },
   {
@@ -401,7 +401,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "display_html(renderer.static_html(holomap), raw=True)"
+    "html = renderer.static_html(holomap)"
    ]
   },
   {

holoviews/core/data/dask.py

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

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

@@ -291,30 +291,35 @@ 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

@@ -1830,7 +1830,7 @@ def dt64_to_dt(dt64):
     Safely converts NumPy datetime64 to a datetime object.
     """
     ts = (dt64 - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 's')
-    return dt.datetime.utcfromtimestamp(ts)
+    return dt.datetime(1970,1,1,0,0,0) + dt.timedelta(seconds=ts)
 
 
 def is_nan(x):
@@ -1910,6 +1910,31 @@ 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/plotting/bokeh/element.py

@@ -13,8 +13,7 @@
 from bokeh.models import Renderer, Title, Legend, ColorBar, tools
 from bokeh.models.axes import CategoricalAxis, DatetimeAxis
 from bokeh.models.formatters import (
-    FuncTickFormatter, TickFormatter, PrintfTickFormatter,
-    MercatorTickFormatter)
+    FuncTickFormatter, TickFormatter, MercatorTickFormatter)
 from bokeh.models.mappers import (
     LinearColorMapper, LogColorMapper, CategoricalColorMapper)
 from bokeh.models.ranges import Range1d, DataRange1d, FactorRange
@@ -40,7 +39,7 @@
     TOOL_TYPES, date_to_integer, decode_bytes, get_tab_title,
     glyph_order, py2js_tickformatter, recursive_model_update,
     theme_attr_json, cds_column_replace, hold_policy, match_dim_specs,
-    compute_layout_properties)
+    compute_layout_properties, wrap_formatter)
 
 
 
@@ -631,18 +630,7 @@ def _axis_properties(self, axis, key, plot, dimension=None,
 
         formatter = self.xformatter if axis == 'x' else self.yformatter
         if formatter:
-            if isinstance(formatter, TickFormatter):
-                pass
-            elif isinstance(formatter, FunctionType):
-                msg = ('%sformatter could not be '
-                       'converted to tick formatter. ' % axis)
-                jsfunc = py2js_tickformatter(formatter, msg)
-                if jsfunc:
-                    formatter = FuncTickFormatter(code=jsfunc)
-                else:
-                    formatter = None
-            else:
-                formatter = PrintfTickFormatter(format=formatter)
+            formatter = wrap_formatter(formatter, axis)
             if formatter is not None:
                 axis_props['formatter'] = formatter
         elif FuncTickFormatter is not None and ax_mapping and isinstance(dimension, Dimension):
@@ -1556,6 +1544,10 @@ class ColorbarPlot(ElementPlot):
        User-specified colorbar axis range limits for the plot, as a tuple (low,high).
        If specified, takes precedence over data and dimension ranges.""")
 
+    cformatter = param.ClassSelector(
+        default=None, class_=(util.basestring, TickFormatter, FunctionType), doc="""
+        Formatter for ticks along the colorbar axis.""")
+
     colorbar = param.Boolean(default=False, doc="""
         Whether to display a colorbar.""")
 
@@ -1608,6 +1600,10 @@ def _draw_colorbar(self, plot, color_mapper, prefix=''):
 
         if self.clabel:
             self.colorbar_opts.update({'title': self.clabel})
+
+        if self.cformatter is not None:
+            self.colorbar_opts.update({'formatter': wrap_formatter(self.cformatter, 'c')})
+
         opts = dict(cbar_opts['opts'], color_mapper=color_mapper, ticker=ticker,
                     **self._colorbar_defaults)
         color_bar = ColorBar(**dict(opts, **self.colorbar_opts))
@@ -1871,10 +1867,6 @@ class OverlayPlot(GenericOverlayPlot, LegendPlot):
                           'margin', 'aspect', 'data_aspect', 'frame_width',
                           'frame_height', 'responsive']
 
-    def __init__(self, overlay, **params):
-        super(OverlayPlot, self).__init__(overlay, **params)
-        self.set_root(params.pop('root', None))
-
     def _process_legend(self):
         plot = self.handles['plot']
         subplots = self.traverse(lambda x: x, [lambda x: x is not self])