def from_ts(cls: tp.Type[RangesT],
ts: tp.ArrayLike,
gap_value: tp.Optional[tp.Scalar] = None,
attach_ts: bool = True,
wrapper_kwargs: tp.KwargsLike = None,
**kwargs) -> RangesT:
"""Build `Ranges` from time series `ts`.
Searches for sequences of
* True values in boolean data (False acts as a gap),
* positive values in integer data (-1 acts as a gap), and
* non-NaN values in any other data (NaN acts as a gap).
`**kwargs` will be passed to `Ranges.__init__`."""
if wrapper_kwargs is None:
wrapper_kwargs = {}
ts_pd = to_pd_array(ts)
ts_arr = to_2d_array(ts_pd)
if gap_value is None:
if np.issubdtype(ts_arr.dtype, np.bool_):
gap_value = False
elif np.issubdtype(ts_arr.dtype, np.integer):
gap_value = -1
else:
gap_value = np.nan
records_arr = nb.find_ranges_nb(ts_arr, gap_value)
wrapper = ArrayWrapper.from_obj(ts_pd, **wrapper_kwargs)
return cls(wrapper,
records_arr,
ts=ts_pd if attach_ts else None,
**kwargs)
def update(self, data: tp.ArrayLike) -> None:
"""Update the trace data."""
data = reshape_fns.to_2d_array(data)
with self.fig.batch_update():
for i, trace in enumerate(self.traces):
trace.y = data[:, i]
def indexing_func(self: OrdersT, pd_indexing_func: tp.PandasIndexingFunc, **kwargs) -> OrdersT:
"""Perform indexing on `Orders`."""
new_wrapper, new_records_arr, group_idxs, col_idxs = \
Records.indexing_func_meta(self, pd_indexing_func, **kwargs)
if self.close is not None:
new_close = new_wrapper.wrap(to_2d_array(self.close)[:, col_idxs], group_by=False)
else:
new_close = None
return self.replace(
wrapper=new_wrapper,
records_arr=new_records_arr,
close=new_close
)
def from_ts(cls: tp.Type[DrawdownsT],
ts: tp.ArrayLike,
attach_ts: bool = True,
wrapper_kwargs: tp.KwargsLike = None,
**kwargs) -> DrawdownsT:
"""Build `Drawdowns` from time series `ts`.
`**kwargs` will be passed to `Drawdowns.__init__`."""
ts_pd = to_pd_array(ts)
records_arr = nb.get_drawdowns_nb(to_2d_array(ts_pd))
wrapper = ArrayWrapper.from_obj(ts_pd, **merge_dicts({},
wrapper_kwargs))
return cls(wrapper,
records_arr,
ts=ts_pd if attach_ts else None,
**kwargs)
def update(self, data: tp.ArrayLike) -> None:
"""Update the trace data.
## Example
```python-repl
>>> bar.update([[2, 1], [4, 3]])
>>> bar.fig
```
"""
data = reshape_fns.to_2d_array(data)
with self.fig.batch_update():
for i, bar in enumerate(self.traces):
bar.y = data[:, i]
if bar.marker.colorscale is not None:
bar.marker.color = data[:, i]
def update(self, data: tp.ArrayLike) -> None:
"""Update the trace data.
Usage:
```pycon
>>> bar.update([[2, 1], [4, 3]])
>>> bar.fig
```
"""
data = reshape_fns.to_2d_array(data)
with self.fig.batch_update():
for i, bar in enumerate(self.traces):
bar.y = data[:, i]
if bar.marker.colorscale is not None:
bar.marker.color = data[:, i]
def update(self, data: tp.ArrayLike) -> None:
"""Update the trace data."""
data = reshape_fns.to_2d_array(data)
with self.fig.batch_update():
for i, trace in enumerate(self.traces):
d = data[:, i]
if self.remove_nan:
d = d[~np.isnan(d)]
mask = np.full(d.shape, True)
if self.from_quantile is not None:
mask &= d >= np.quantile(d, self.from_quantile)
if self.to_quantile is not None:
mask &= d <= np.quantile(d, self.to_quantile)
d = d[mask]
if self.horizontal:
trace.x = d
trace.y = None
else:
trace.x = None
trace.y = d
def plot_func(self,
*args,
_px_func_name: str = px_func_name,
_px_func: tp.Callable = px_func,
**kwargs) -> tp.BaseFigure:
from vectorbt._settings import settings
layout_cfg = settings['plotting']['layout']
layout_kwargs = dict(
template=kwargs.pop('template', layout_cfg['template']),
width=kwargs.pop('width', layout_cfg['width']),
height=kwargs.pop('height', layout_cfg['height']))
# Fix category_orders
if 'color' in kwargs:
if isinstance(kwargs['color'], str):
if isinstance(self.obj, pd.DataFrame):
if kwargs['color'] in self.obj.columns:
category_orders = dict()
category_orders[kwargs['color']] = sorted(
self.obj[kwargs['color']].unique())
kwargs = merge_dicts(
dict(category_orders=category_orders),
kwargs)
# Fix Series name
obj = self.obj.copy(deep=False)
if isinstance(obj, pd.Series):
if obj.name is not None:
obj = obj.rename(str(obj.name))
else:
obj.columns = clean_labels(obj.columns)
obj.index = clean_labels(obj.index)
if _px_func_name == 'imshow':
return make_figure(_px_func(to_2d_array(obj), *args,
**layout_kwargs, **kwargs),
layout=layout_kwargs)
return make_figure(_px_func(obj, *args, **layout_kwargs,
**kwargs),
layout=layout_kwargs)
def to_2d_array(self) -> tp.Array2d:
"""Convert to 2-dim NumPy array.
See `vectorbt.base.reshape_fns.to_2d`."""
return reshape_fns.to_2d_array(self.obj)
def update(self, data: tp.ArrayLike) -> None:
"""Update the trace data."""
data = reshape_fns.to_2d_array(data)
with self.fig.batch_update():
self.traces[0].z = data
def __init__(self,
data: tp.Optional[tp.ArrayLike] = None,
x_labels: tp.Optional[tp.Labels] = None,
y_labels: tp.Optional[tp.Labels] = None,
is_x_category: bool = False,
is_y_category: bool = False,
trace_kwargs: tp.KwargsLike = None,
add_trace_kwargs: tp.KwargsLike = None,
fig: tp.Optional[tp.BaseFigure] = None,
**layout_kwargs) -> None:
"""Create a heatmap plot.
Args:
data (array_like): Data in any format that can be converted to NumPy.
Must be of shape (`y_labels`, `x_labels`).
x_labels (array_like): X-axis labels, corresponding to columns in pandas.
y_labels (array_like): Y-axis labels, corresponding to index in pandas.
is_x_category (bool): Whether X-axis is a categorical axis.
is_y_category (bool): Whether Y-axis is a categorical axis.
trace_kwargs (dict): Keyword arguments passed to `plotly.graph_objects.Heatmap`.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
fig (Figure or FigureWidget): Figure to add traces to.
**layout_kwargs: Keyword arguments for layout.
## Example
```python-repl
>>> import vectorbt as vbt
>>> heatmap = vbt.plotting.Heatmap(
... data=[[1, 2], [3, 4]],
... x_labels=['a', 'b'],
... y_labels=['x', 'y']
... )
>>> heatmap.fig
```
"""
Configured.__init__(
self,
data=data,
x_labels=x_labels,
y_labels=y_labels,
trace_kwargs=trace_kwargs,
add_trace_kwargs=add_trace_kwargs,
fig=fig,
**layout_kwargs
)
from vectorbt._settings import settings
layout_cfg = settings['plotting']['layout']
if trace_kwargs is None:
trace_kwargs = {}
if add_trace_kwargs is None:
add_trace_kwargs = {}
if data is not None:
data = reshape_fns.to_2d_array(data)
if x_labels is not None:
checks.assert_shape_equal(data, x_labels, (1, 0))
if y_labels is not None:
checks.assert_shape_equal(data, y_labels, (0, 0))
else:
if x_labels is None or y_labels is None:
raise ValueError("At least data, or x_labels and y_labels must be passed")
if x_labels is not None:
x_labels = clean_labels(x_labels)
if y_labels is not None:
y_labels = clean_labels(y_labels)
if fig is None:
fig = make_figure()
if 'width' in layout_cfg:
# Calculate nice width and height
max_width = layout_cfg['width']
if data is not None:
x_len = data.shape[1]
y_len = data.shape[0]
else:
x_len = len(x_labels)
y_len = len(y_labels)
width = math.ceil(renormalize(
x_len / (x_len + y_len),
(0, 1),
(0.3 * max_width, max_width)
))
width = min(width + 150, max_width) # account for colorbar
height = math.ceil(renormalize(
y_len / (x_len + y_len),
(0, 1),
(0.3 * max_width, max_width)
))
height = min(height, max_width * 0.7) # limit height
fig.update_layout(
width=width,
height=height
)
heatmap = go.Heatmap(
hoverongaps=False,
colorscale='Plasma',
x=x_labels,
y=y_labels
)
heatmap.update(**trace_kwargs)
fig.add_trace(heatmap, **add_trace_kwargs)
axis_kwargs = dict()
if is_x_category:
if fig.data[-1]['xaxis'] is not None:
axis_kwargs['xaxis' + fig.data[-1]['xaxis'][1:]] = dict(type='category')
else:
axis_kwargs['xaxis'] = dict(type='category')
if is_y_category:
if fig.data[-1]['yaxis'] is not None:
axis_kwargs['yaxis' + fig.data[-1]['yaxis'][1:]] = dict(type='category')
else:
axis_kwargs['yaxis'] = dict(type='category')
fig.update_layout(**axis_kwargs)
fig.update_layout(**layout_kwargs)
TraceUpdater.__init__(self, fig, (fig.data[-1],))
if data is not None:
self.update(data)
def __init__(self,
data: tp.Optional[tp.ArrayLike] = None,
trace_names: tp.TraceNames = None,
horizontal: bool = False,
remove_nan: bool = True,
from_quantile: tp.Optional[float] = None,
to_quantile: tp.Optional[float] = None,
trace_kwargs: tp.KwargsLikeSequence = None,
add_trace_kwargs: tp.KwargsLike = None,
fig: tp.Optional[tp.BaseFigure] = None,
**layout_kwargs) -> None:
"""Create a box plot.
For keyword arguments, see `Histogram`.
## Example
```python-repl
>>> import vectorbt as vbt
>>> box = vbt.plotting.Box(
... data=[[1, 2], [3, 4], [2, 1]],
... trace_names=['a', 'b']
... )
>>> box.fig
```
"""
Configured.__init__(
self,
data=data,
trace_names=trace_names,
horizontal=horizontal,
remove_nan=remove_nan,
from_quantile=from_quantile,
to_quantile=to_quantile,
trace_kwargs=trace_kwargs,
add_trace_kwargs=add_trace_kwargs,
fig=fig,
**layout_kwargs
)
if trace_kwargs is None:
trace_kwargs = {}
if add_trace_kwargs is None:
add_trace_kwargs = {}
if data is not None:
data = reshape_fns.to_2d_array(data)
if trace_names is not None:
checks.assert_shape_equal(data, trace_names, (1, 0))
else:
if trace_names is None:
raise ValueError("At least data or trace_names must be passed")
if trace_names is None:
trace_names = [None] * data.shape[1]
if isinstance(trace_names, str):
trace_names = [trace_names]
if fig is None:
fig = make_figure()
fig.update_layout(**layout_kwargs)
for i, trace_name in enumerate(trace_names):
_trace_kwargs = resolve_dict(trace_kwargs, i=i)
trace_name = _trace_kwargs.pop('name', trace_name)
if trace_name is not None:
trace_name = str(trace_name)
box = go.Box(
name=trace_name,
showlegend=trace_name is not None
)
box.update(**_trace_kwargs)
fig.add_trace(box, **add_trace_kwargs)
TraceUpdater.__init__(self, fig, fig.data[-len(trace_names):])
self._horizontal = horizontal
self._remove_nan = remove_nan
self._from_quantile = from_quantile
self._to_quantile = to_quantile
if data is not None:
self.update(data)
def __init__(self,
data: tp.Optional[tp.ArrayLike] = None,
trace_names: tp.TraceNames = None,
horizontal: bool = False,
remove_nan: bool = True,
from_quantile: tp.Optional[float] = None,
to_quantile: tp.Optional[float] = None,
trace_kwargs: tp.KwargsLikeSequence = None,
add_trace_kwargs: tp.KwargsLike = None,
fig: tp.Optional[tp.BaseFigure] = None,
**layout_kwargs) -> None:
"""Create a histogram plot.
Args:
data (array_like): Data in any format that can be converted to NumPy.
Must be of shape (any, `trace_names`).
trace_names (str or list of str): Trace names, corresponding to columns in pandas.
horizontal (bool): Whether to plot horizontally.
remove_nan (bool): Whether to remove NaN values.
from_quantile (float): Filter out data points before this quantile.
Should be in range `[0, 1]`.
to_quantile (float): Filter out data points after this quantile.
Should be in range `[0, 1]`.
trace_kwargs (dict or list of dict): Keyword arguments passed to `plotly.graph_objects.Histogram`.
Can be specified per trace as a sequence of dicts.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
fig (Figure or FigureWidget): Figure to add traces to.
**layout_kwargs: Keyword arguments for layout.
## Example
```python-repl
>>> import vectorbt as vbt
>>> hist = vbt.plotting.Histogram(
... data=[[1, 2], [3, 4], [2, 1]],
... trace_names=['a', 'b']
... )
>>> hist.fig
```
"""
Configured.__init__(
self,
data=data,
trace_names=trace_names,
horizontal=horizontal,
remove_nan=remove_nan,
from_quantile=from_quantile,
to_quantile=to_quantile,
trace_kwargs=trace_kwargs,
add_trace_kwargs=add_trace_kwargs,
fig=fig,
**layout_kwargs
)
if trace_kwargs is None:
trace_kwargs = {}
if add_trace_kwargs is None:
add_trace_kwargs = {}
if data is not None:
data = reshape_fns.to_2d_array(data)
if trace_names is not None:
checks.assert_shape_equal(data, trace_names, (1, 0))
else:
if trace_names is None:
raise ValueError("At least data or trace_names must be passed")
if trace_names is None:
trace_names = [None] * data.shape[1]
if isinstance(trace_names, str):
trace_names = [trace_names]
if fig is None:
fig = make_figure()
fig.update_layout(barmode='overlay')
fig.update_layout(**layout_kwargs)
for i, trace_name in enumerate(trace_names):
_trace_kwargs = resolve_dict(trace_kwargs, i=i)
trace_name = _trace_kwargs.pop('name', trace_name)
if trace_name is not None:
trace_name = str(trace_name)
hist = go.Histogram(
opacity=0.75 if len(trace_names) > 1 else 1,
name=trace_name,
showlegend=trace_name is not None
)
hist.update(**_trace_kwargs)
fig.add_trace(hist, **add_trace_kwargs)
TraceUpdater.__init__(self, fig, fig.data[-len(trace_names):])
self._horizontal = horizontal
self._remove_nan = remove_nan
self._from_quantile = from_quantile
self._to_quantile = to_quantile
if data is not None:
self.update(data)
def __init__(self,
data: tp.Optional[tp.ArrayLike] = None,
trace_names: tp.TraceNames = None,
x_labels: tp.Optional[tp.Labels] = None,
trace_kwargs: tp.KwargsLikeSequence = None,
add_trace_kwargs: tp.KwargsLike = None,
fig: tp.Optional[tp.BaseFigure] = None,
**layout_kwargs) -> None:
"""Create a scatter plot.
Args:
data (array_like): Data in any format that can be converted to NumPy.
Must be of shape (`x_labels`, `trace_names`).
trace_names (str or list of str): Trace names, corresponding to columns in pandas.
x_labels (array_like): X-axis labels, corresponding to index in pandas.
trace_kwargs (dict or list of dict): Keyword arguments passed to `plotly.graph_objects.Scatter`.
Can be specified per trace as a sequence of dicts.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
fig (Figure or FigureWidget): Figure to add traces to.
**layout_kwargs: Keyword arguments for layout.
## Example
```python-repl
>>> import vectorbt as vbt
>>> scatter = vbt.plotting.Scatter(
... data=[[1, 2], [3, 4]],
... trace_names=['a', 'b'],
... x_labels=['x', 'y']
... )
>>> scatter.fig
```
"""
Configured.__init__(
self,
data=data,
trace_names=trace_names,
x_labels=x_labels,
trace_kwargs=trace_kwargs,
add_trace_kwargs=add_trace_kwargs,
fig=fig,
**layout_kwargs
)
if trace_kwargs is None:
trace_kwargs = {}
if add_trace_kwargs is None:
add_trace_kwargs = {}
if data is not None:
data = reshape_fns.to_2d_array(data)
if trace_names is not None:
checks.assert_shape_equal(data, trace_names, (1, 0))
else:
if trace_names is None:
raise ValueError("At least data or trace_names must be passed")
if trace_names is None:
trace_names = [None] * data.shape[1]
if isinstance(trace_names, str):
trace_names = [trace_names]
if x_labels is not None:
x_labels = clean_labels(x_labels)
if fig is None:
fig = make_figure()
fig.update_layout(**layout_kwargs)
for i, trace_name in enumerate(trace_names):
_trace_kwargs = resolve_dict(trace_kwargs, i=i)
trace_name = _trace_kwargs.pop('name', trace_name)
if trace_name is not None:
trace_name = str(trace_name)
scatter = go.Scatter(
x=x_labels,
name=trace_name,
showlegend=trace_name is not None
)
scatter.update(**_trace_kwargs)
fig.add_trace(scatter, **add_trace_kwargs)
TraceUpdater.__init__(self, fig, fig.data[-len(trace_names):])
if data is not None:
self.update(data)
