def volume(self,
x_level=None,
y_level=None,
z_level=None,
x_labels=None,
y_labels=None,
z_labels=None,
slider_level=None,
slider_labels=None,
**kwargs): # pragma: no cover
"""Create a 3D volume figure based on object's multi-index and values.
If multi-index contains more than three levels or you want them in specific order, pass
`x_level`, `y_level`, and `z_level`, each (`int` if index or `str` if name) corresponding
to an axis of the volume. Optionally, pass `slider_level` to use a level as a slider.
See `vectorbt.generic.plotting.create_volume` for other keyword arguments."""
(x_level, y_level, z_level), (slider_level, ) = index_fns.pick_levels(
self.index,
required_levels=(x_level, y_level, z_level),
optional_levels=(slider_level, ))
x_level_vals = self.index.get_level_values(x_level)
y_level_vals = self.index.get_level_values(y_level)
z_level_vals = self.index.get_level_values(z_level)
# Labels are just unique level values
if x_labels is None:
x_labels = np.unique(x_level_vals)
if y_labels is None:
y_labels = np.unique(y_level_vals)
if z_labels is None:
z_labels = np.unique(z_level_vals)
x_name = x_level_vals.name if x_level_vals.name is not None else 'x'
y_name = y_level_vals.name if y_level_vals.name is not None else 'y'
z_name = z_level_vals.name if z_level_vals.name is not None else 'z'
kwargs = merge_kwargs(
dict(trace_kwargs=dict(hovertemplate=f"{x_name}: %{{x}}<br>" +
f"{y_name}: %{{y}}<br>" +
f"{z_name}: %{{z}}<br>" +
"value: %{value}<extra></extra>"),
scene=dict(xaxis_title=x_level_vals.name,
yaxis_title=y_level_vals.name,
zaxis_title=z_level_vals.name)), kwargs)
contains_nans = False
if slider_level is None:
# No grouping
v = self.unstack_to_array(levels=(x_level, y_level, z_level))
if np.isnan(v).any():
contains_nans = True
fig = plotting.create_volume(data=v,
x_labels=x_labels,
y_labels=y_labels,
z_labels=z_labels,
**kwargs)
else:
# Requires grouping
# See https://plotly.com/python/sliders/
fig = None
_slider_labels = []
for i, (name,
group) in enumerate(self._obj.groupby(level=slider_level)):
if slider_labels is not None:
name = slider_labels[i]
_slider_labels.append(name)
v = group.vbt.unstack_to_array(levels=(x_level, y_level,
z_level))
if np.isnan(v).any():
contains_nans = True
_kwargs = merge_kwargs(
dict(
trace_kwargs=dict(
name=str(name) if name is not None else None,
visible=False),
width=700,
height=520,
), kwargs)
fig = plotting.create_volume(data=v,
x_labels=x_labels,
y_labels=y_labels,
z_labels=z_labels,
fig=fig,
**_kwargs)
fig.data[0].visible = True
steps = []
for i in range(len(fig.data)):
step = dict(method="update",
args=[{
"visible": [False] * len(fig.data)
}, {}],
label=str(_slider_labels[i])
if _slider_labels[i] is not None else None)
step["args"][0]["visible"][i] = True
steps.append(step)
prefix = f'{self.index.names[slider_level]}: ' if self.index.names[
slider_level] is not None else None
sliders = [
dict(active=0,
currentvalue={"prefix": prefix},
pad={"t": 50},
steps=steps)
]
fig.update_layout(sliders=sliders)
if contains_nans:
warnings.warn(
"Data contains NaNs. In case of visualization issues, use .show() method on the widget."
)
return fig
def heatmap(self,
x_level=None,
y_level=None,
symmetric=False,
x_labels=None,
y_labels=None,
slider_level=None,
slider_labels=None,
**kwargs): # pragma: no cover
"""Create a heatmap figure based on object's multi-index and values.
If multi-index contains more than two levels or you want them in specific order,
pass `x_level` and `y_level`, each (`int` if index or `str` if name) corresponding
to an axis of the heatmap. Optionally, pass `slider_level` to use a level as a slider.
See `vectorbt.generic.plotting.create_heatmap` for other keyword arguments."""
(x_level, y_level), (slider_level, ) = index_fns.pick_levels(
self.index,
required_levels=(x_level, y_level),
optional_levels=(slider_level, ))
x_level_vals = self.index.get_level_values(x_level)
y_level_vals = self.index.get_level_values(y_level)
x_name = x_level_vals.name if x_level_vals.name is not None else 'x'
y_name = y_level_vals.name if y_level_vals.name is not None else 'y'
kwargs = merge_kwargs(
dict(trace_kwargs=dict(hovertemplate=f"{x_name}: %{{x}}<br>" +
f"{y_name}: %{{y}}<br>" +
"value: %{z}<extra></extra>"),
xaxis_title=x_level_vals.name,
yaxis_title=y_level_vals.name), kwargs)
if slider_level is None:
# No grouping
df = self.unstack_to_df(index_levels=x_level,
column_levels=y_level,
symmetric=symmetric)
fig = df.vbt.heatmap(x_labels=x_labels,
y_labels=y_labels,
**kwargs)
else:
# Requires grouping
# See https://plotly.com/python/sliders/
fig = None
_slider_labels = []
for i, (name,
group) in enumerate(self._obj.groupby(level=slider_level)):
if slider_labels is not None:
name = slider_labels[i]
_slider_labels.append(name)
df = group.vbt.unstack_to_df(index_levels=x_level,
column_levels=y_level,
symmetric=symmetric)
if x_labels is None:
x_labels = df.columns
if y_labels is None:
y_labels = df.index
_kwargs = merge_kwargs(
dict(
trace_kwargs=dict(
name=str(name) if name is not None else None,
visible=False),
width=600,
height=520,
), kwargs)
fig = plotting.create_heatmap(data=df.vbt.to_2d_array(),
x_labels=x_labels,
y_labels=y_labels,
fig=fig,
**_kwargs)
fig.data[0].visible = True
steps = []
for i in range(len(fig.data)):
step = dict(method="update",
args=[{
"visible": [False] * len(fig.data)
}, {}],
label=str(_slider_labels[i])
if _slider_labels[i] is not None else None)
step["args"][0]["visible"][i] = True
steps.append(step)
prefix = f'{self.index.names[slider_level]}: ' if self.index.names[
slider_level] is not None else None
sliders = [
dict(active=0,
currentvalue={"prefix": prefix},
pad={"t": 50},
steps=steps)
]
fig.update_layout(sliders=sliders)
return fig