def kde(self, x, y, data=None):
data = self.data if data is None else data
plot_opts = dict(self._plot_opts)
invert = self.kwds.get('orientation', False) == 'horizontal'
opts = dict(plot=dict(plot_opts, invert_axes=invert),
style=self._style_opts,
norm=self._norm_opts)
opts = {
'Distribution': opts,
'Area': opts,
'NdOverlay': {
'plot': dict(plot_opts, legend_limit=0)
}
}
if y and self.by:
ds = Dataset(data)
return ds.to(Distribution, y, [], self.by).overlay().opts(opts)
elif y:
return Distribution(data, y, []).opts(opts)
if self.columns:
data = data[self.columns]
df = pd.melt(data,
var_name=self.group_label,
value_name=self.value_label)
ds = Dataset(df)
if len(df):
overlay = ds.to(Distribution, self.value_label).overlay()
else:
vdim = self.value_label + ' Density'
overlay = NdOverlay({0: Area([], self.value_label, vdim)},
[self.group_label])
return overlay.relabel(**self._relabel).opts(opts)
def hist(self, x, y, data=None):
plot_opts = dict(self._plot_opts)
invert = self.kwds.get('orientation', False) == 'horizontal'
opts = dict(plot=dict(plot_opts, labelled=['x'], invert_axes=invert),
style=self._style_opts,
norm=self._norm_opts)
hist_opts = {
'num_bins': self.kwds.get('bins', 10),
'bin_range': self.kwds.get('bin_range', None),
'normed': self.kwds.get('normed', False)
}
data = self.data if data is None else data
ds = Dataset(data)
if y and self.by:
return histogram(ds.to(Dataset, [], y, self.by), **hist_opts).\
overlay().opts({'Histogram': opts})
elif y:
return histogram(ds, dimension=y, **hist_opts).\
opts({'Histogram': opts})
hists = {}
columns = self.columns or data.columns
for col in columns:
hist = histogram(ds, dimension=col, **hist_opts)
ranges = {hist.vdims[0].name: self._dim_ranges['y']}
hists[col] = (hist.redim.range(**ranges).relabel(
**self._relabel).opts(**opts))
return NdOverlay(hists)
def kde(self, x, y, data=None):
data, x, y = self._process_args(data, x, y)
opts = dict(plot=self._plot_opts,
style=self._style_opts,
norm=self._norm_opts)
opts = {
'Distribution': opts,
'Area': opts,
'NdOverlay': {
'plot': dict(self._plot_opts, legend_limit=0)
}
}
if not isinstance(y, (list, tuple)):
ranges = {y: self._dim_ranges['x']}
if self.by:
dists = Dataset(data).to(Distribution, y, [], self.by)
dists = dists.layout() if self.subplots else dists.overlay()
else:
dists = Distribution(data, y, [])
else:
ranges = {self.value_label: self._dim_ranges['x']}
data = data[y]
df = pd.melt(data,
var_name=self.group_label,
value_name=self.value_label)
ds = Dataset(df)
if len(df):
dists = ds.to(Distribution, self.value_label).overlay()
else:
vdim = self.value_label + ' Density'
dists = NdOverlay({0: Area([], self.value_label, vdim)},
[self.group_label])
return dists.redim(**self._redim).redim.range(**ranges).relabel(
**self._relabel).opts(opts)
def test_quadmesh_update_cbar(self):
xs = ys = np.linspace(0, 6, 10)
zs = np.linspace(1, 2, 5)
XS, YS, ZS = np.meshgrid(xs, ys, zs)
values = np.sin(XS) * ZS
ds = Dataset((xs, ys, zs, values.T), ['x', 'y', 'z'], 'values')
hmap = ds.to(QuadMesh).options(colorbar=True, framewise=True)
plot = mpl_renderer.get_plot(hmap)
self.assertEqual(plot.handles['cbar'].get_clim(), (-0.9989549170979283, 0.9719379013633127))
plot.update(3)
self.assertEqual(plot.handles['cbar'].get_clim(), (-1.7481711049213744, 1.7008913273857973))
def test_quadmesh_update_cbar(self):
xs = ys = np.linspace(0, 6, 10)
zs = np.linspace(1, 2, 5)
XS, YS, ZS = np.meshgrid(xs, ys, zs)
values = np.sin(XS) * ZS
ds = Dataset((xs, ys, zs, values.T), ['x', 'y', 'z'], 'values')
hmap = ds.to(QuadMesh).options(colorbar=True, framewise=True)
plot = mpl_renderer.get_plot(hmap)
self.assertEqual(plot.handles['cbar'].get_clim(),
(-0.9989549170979283, 0.9719379013633127))
plot.update(3)
self.assertEqual(plot.handles['cbar'].get_clim(),
(-1.7481711049213744, 1.7008913273857973))
def hist(self, x, y, data=None):
data, x, y = self._process_args(data, x, y)
labelled = ['y'] if self.invert else ['x']
plot_opts = dict(self._plot_opts, labelled=labelled)
opts = dict(plot=plot_opts,
style=self._style_opts,
norm=self._norm_opts)
hist_opts = {
'bin_range': self.kwds.get('bin_range', None),
'normed': self.kwds.get('normed', False),
'cumulative': self.kwds.get('cumulative', False)
}
if 'bins' in self.kwds:
bins = self.kwds['bins']
if isinstance(bins, int):
hist_opts['num_bins'] = bins
else:
hist_opts['bins'] = bins
if not isinstance(y, (list, tuple)):
if self.stacked and not self.subplots and not 'bin_range' in self.kwds:
ys = data[y]
hist_opts['bin_range'] = (ys.min(), ys.max())
ds = Dataset(data, self.by, y)
hist = hists = histogram(ds.to(Dataset, [], y, self.by),
**hist_opts)
if self.by:
hist = hists.last
hists = hists.layout() if self.subplots else hists.overlay()
ranges = {
hist.kdims[0].name: self._dim_ranges['x'],
hist.vdims[0].name: self._dim_ranges['y']
}
return hists.opts({
'Histogram': opts
}).redim(**self._redim).redim.range(**ranges)
ds = Dataset(data)
hists = []
for col in y:
hist = histogram(ds, dimension=col, **hist_opts)
ranges = {
hist.kdims[0].name: self._dim_ranges['x'],
hist.vdims[0].name: self._dim_ranges['y']
}
hists.append((col, hist.redim.range(**ranges).relabel(
**self._relabel).opts(**opts)))
return self._by_type(hists, sort=False).redim(**self._redim)