def __init__(self, index, columns, ndim, freq=None, column_only_select=None,
group_select=None, grouped_ndim=None, **kwargs):
config = dict(
index=index,
columns=columns,
ndim=ndim,
freq=freq,
column_only_select=column_only_select,
group_select=group_select,
grouped_ndim=grouped_ndim,
)
checks.assert_not_none(index)
checks.assert_not_none(columns)
checks.assert_not_none(ndim)
if not isinstance(index, pd.Index):
index = pd.Index(index)
if not isinstance(columns, pd.Index):
columns = pd.Index(columns)
self._index = index
self._columns = columns
self._ndim = ndim
self._freq = freq
self._column_only_select = column_only_select
self._group_select = group_select
self._grouper = ColumnGrouper(columns, **kwargs)
self._grouped_ndim = grouped_ndim
PandasIndexer.__init__(self)
Configured.__init__(self, **merge_dicts(config, self._grouper._config))
def __init__(self, wrapper, mapped_arr, col_arr, idx_arr=None):
Configured.__init__(
self,
wrapper=wrapper,
mapped_arr=mapped_arr,
col_arr=col_arr,
idx_arr=idx_arr
)
checks.assert_type(wrapper, ArrayWrapper)
if not isinstance(mapped_arr, np.ndarray):
mapped_arr = np.asarray(mapped_arr)
if not isinstance(col_arr, np.ndarray):
col_arr = np.asarray(col_arr)
checks.assert_shape_equal(mapped_arr, col_arr, axis=0)
if idx_arr is not None:
if not isinstance(idx_arr, np.ndarray):
idx_arr = np.asarray(idx_arr)
checks.assert_shape_equal(mapped_arr, idx_arr, axis=0)
self._wrapper = wrapper
self._mapped_arr = mapped_arr
self._col_arr = col_arr
self._idx_arr = idx_arr
PandasIndexer.__init__(self, _mapped_array_indexing_func)
def __init__(self, wrapper: ArrayWrapper, **kwargs) -> None:
checks.assert_instance_of(wrapper, ArrayWrapper)
self._wrapper = wrapper
Configured.__init__(self, wrapper=wrapper, **kwargs)
PandasIndexer.__init__(self)
AttrResolver.__init__(self)
def __init__(self, data, schedule_manager=None, **kwargs):
Configured.__init__(self,
data=data,
schedule_manager=schedule_manager,
**kwargs)
self._data = data
if schedule_manager is None:
schedule_manager = ScheduleManager()
self._schedule_manager = schedule_manager
def __init__(self, giphy_kwargs: tp.KwargsLike = None, **kwargs) -> None:
from vectorbt._settings import settings
telegram_cfg = settings['messaging']['telegram']
giphy_cfg = settings['messaging']['giphy']
Configured.__init__(self, giphy_kwargs=giphy_kwargs, **kwargs)
# Resolve kwargs
giphy_kwargs = merge_dicts(giphy_cfg, giphy_kwargs)
self.giphy_kwargs = giphy_kwargs
default_kwargs = dict()
passed_kwargs = dict()
for k in get_func_kwargs(Updater):
if k in telegram_cfg:
default_kwargs[k] = telegram_cfg[k]
if k in kwargs:
passed_kwargs[k] = kwargs.pop(k)
updater_kwargs = merge_dicts(default_kwargs, passed_kwargs)
persistence = updater_kwargs.pop('persistence', None)
if isinstance(persistence, str):
persistence = PicklePersistence(persistence)
defaults = updater_kwargs.pop('defaults', None)
if isinstance(defaults, dict):
defaults = Defaults(**defaults)
# Create the (persistent) Updater and pass it your bot's token.
logger.info("Initializing bot")
self._updater = Updater(persistence=persistence,
defaults=defaults,
**updater_kwargs)
# Get the dispatcher to register handlers
self._dispatcher = self.updater.dispatcher
# Register handlers
self.dispatcher.add_handler(self.log_handler)
self.dispatcher.add_handler(
CommandHandler('start', self.start_callback))
self.dispatcher.add_handler(CommandHandler("help", self.help_callback))
for handler in self.custom_handlers:
self.dispatcher.add_handler(handler)
self.dispatcher.add_handler(
MessageHandler(Filters.status_update.migrate,
self.chat_migration_callback))
self.dispatcher.add_handler(
MessageHandler(Filters.command, self.unknown_callback))
self.dispatcher.add_error_handler(
self_decorator(self, self.__class__.error_callback))
# Set up data
if 'chat_ids' not in self.dispatcher.bot_data:
self.dispatcher.bot_data['chat_ids'] = []
else:
logger.info("Loaded chat ids %s",
str(self.dispatcher.bot_data['chat_ids']))
def __init__(self,
data: Data,
schedule_manager: tp.Optional[ScheduleManager] = None,
**kwargs) -> None:
Configured.__init__(self,
data=data,
schedule_manager=schedule_manager,
**kwargs)
self._data = data
if schedule_manager is None:
schedule_manager = ScheduleManager()
self._schedule_manager = schedule_manager
def __init__(self,
returns_accessor: ReturnsAccessor,
defaults: tp.KwargsLike = None,
**kwargs) -> None:
checks.assert_instance_of(returns_accessor, ReturnsAccessor)
Configured.__init__(self,
returns_accessor=returns_accessor,
defaults=defaults,
**kwargs)
self._returns_accessor = returns_accessor
self._defaults = defaults
def __init__(self,
index,
columns,
ndim,
freq=None,
column_only_select=None,
group_select=None,
grouped_ndim=None,
group_by=None,
allow_enable=True,
allow_disable=True,
allow_modify=True):
Configured.__init__(self,
index=index,
columns=columns,
ndim=ndim,
freq=freq,
column_only_select=column_only_select,
group_select=group_select,
grouped_ndim=grouped_ndim,
group_by=group_by,
allow_enable=allow_enable,
allow_disable=allow_disable,
allow_modify=allow_modify)
checks.assert_not_none(index)
checks.assert_not_none(columns)
checks.assert_not_none(ndim)
if not isinstance(index, pd.Index):
index = pd.Index(index)
if not isinstance(columns, pd.Index):
columns = pd.Index(columns)
self._index = index
self._columns = columns
self._ndim = ndim
self._freq = freq
self._column_only_select = column_only_select
self._group_select = group_select
self._grouper = ColumnGrouper(columns,
group_by=group_by,
allow_enable=allow_enable,
allow_disable=allow_disable,
allow_modify=allow_modify)
self._grouped_ndim = grouped_ndim
PandasIndexer.__init__(self,
_indexing_func,
column_only_select=column_only_select,
group_select=group_select)
def __init__(self, wrapper, records_arr, ts, idx_field='end_idx'):
Records.__init__(self, wrapper, records_arr, idx_field=idx_field)
Configured.__init__(self,
wrapper=wrapper,
records_arr=records_arr,
ts=ts,
idx_field=idx_field)
self._ts = ts
if not all(field in records_arr.dtype.names
for field in drawdown_dt.names):
raise ValueError(
"Records array must have all fields defined in drawdown_dt")
PandasIndexer.__init__(self, _indexing_func)
def __init__(self, wrapper, records_arr, close, idx_field='exit_idx'):
Records.__init__(self, wrapper, records_arr, idx_field=idx_field)
Configured.__init__(self,
wrapper=wrapper,
records_arr=records_arr,
close=close,
idx_field=idx_field)
self.close = close
if not all(field in records_arr.dtype.names
for field in event_dt.names):
raise ValueError(
"Records array must have all fields defined in event_dt")
PandasIndexer.__init__(self, _indexing_func)
def replace(self: MappedArrayT, **kwargs) -> MappedArrayT:
"""See `vectorbt.utils.config.Configured.replace`.
Also, makes sure that `MappedArray.col_mapper` is not passed to the new instance."""
if self.config.get('col_mapper', None) is not None:
if 'wrapper' in kwargs:
if self.wrapper is not kwargs.get('wrapper'):
kwargs['col_mapper'] = None
if 'col_arr' in kwargs:
if self.col_arr is not kwargs.get('col_arr'):
kwargs['col_mapper'] = None
return Configured.replace(self, **kwargs)
def __init__(self, wrapper, records_arr, idx_field=None):
Configured.__init__(self,
wrapper=wrapper,
records_arr=records_arr,
idx_field=idx_field)
checks.assert_type(wrapper, ArrayWrapper)
if not isinstance(records_arr, np.ndarray):
records_arr = np.asarray(records_arr)
checks.assert_not_none(records_arr.dtype.fields)
checks.assert_in('col', records_arr.dtype.names)
if idx_field is not None:
checks.assert_in(idx_field, records_arr.dtype.names)
else:
if 'idx' in records_arr.dtype.names:
idx_field = 'idx'
self._wrapper = wrapper
self._records_arr = records_arr
self._idx_field = idx_field
PandasIndexer.__init__(self, _records_indexing_func)
def __init__(self, columns, group_by=None, allow_enable=True, allow_disable=True, allow_modify=True):
Configured.__init__(
self,
columns=columns,
group_by=group_by,
allow_enable=allow_enable,
allow_disable=allow_disable,
allow_modify=allow_modify
)
checks.assert_not_none(columns)
self._columns = columns
if group_by is None or isinstance(group_by, bool):
self._group_by = None
else:
self._group_by = group_by_to_index(columns, group_by)
# Everything is allowed by default
self._allow_enable = allow_enable
self._allow_disable = allow_disable
self._allow_modify = allow_modify
def __init__(self, columns: tp.Index, group_by: tp.GroupByLike = None, allow_enable: bool = True,
allow_disable: bool = True, allow_modify: bool = True) -> None:
Configured.__init__(
self,
columns=columns,
group_by=group_by,
allow_enable=allow_enable,
allow_disable=allow_disable,
allow_modify=allow_modify
)
checks.assert_type(columns, pd.Index)
self._columns = columns
if group_by is None or group_by is False:
self._group_by = None
else:
self._group_by = group_by_to_index(columns, group_by)
# Everything is allowed by default
self._allow_enable = allow_enable
self._allow_disable = allow_disable
self._allow_modify = allow_modify
def __init__(self,
index: tp.IndexLike,
columns: tp.IndexLike,
ndim: int,
freq: tp.Optional[tp.FrequencyLike] = None,
column_only_select: tp.Optional[bool] = None,
group_select: tp.Optional[bool] = None,
grouped_ndim: tp.Optional[int] = None,
**kwargs) -> None:
config = dict(
index=index,
columns=columns,
ndim=ndim,
freq=freq,
column_only_select=column_only_select,
group_select=group_select,
grouped_ndim=grouped_ndim,
)
checks.assert_not_none(index)
checks.assert_not_none(columns)
checks.assert_not_none(ndim)
if not isinstance(index, pd.Index):
index = pd.Index(index)
if not isinstance(columns, pd.Index):
columns = pd.Index(columns)
self._index = index
self._columns = columns
self._ndim = ndim
self._freq = freq
self._column_only_select = column_only_select
self._group_select = group_select
self._grouper = ColumnGrouper(columns, **kwargs)
self._grouped_ndim = grouped_ndim
PandasIndexer.__init__(self)
Configured.__init__(self, **merge_dicts(config, self._grouper._config))
def __init__(self,
data=None,
x_labels=None,
y_labels=None,
z_labels=False,
trace_kwargs=None,
add_trace_kwargs=None,
scene_name='scene',
fig=None,
**layout_kwargs):
"""Create a volume plot.
Args:
data (array_like): Data in any format that can be converted to NumPy.
Must be a 3-dim array.
x_labels (array_like): X-axis labels.
y_labels (array_like): Y-axis labels.
z_labels (array_like): Z-axis labels.
trace_kwargs (dict): Keyword arguments passed to `plotly.graph_objects.Volume`.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
scene_name (str): Reference to the 3D scene.
fig (plotly.graph_objects.Figure): Figure to add traces to.
**layout_kwargs: Keyword arguments for layout.
!!! note
Figure widgets have currently problems displaying NaNs.
Use `.show()` method for rendering.
## Example
```python-repl
>>> import vectorbt as vbt
>>> import numpy as np
>>> volume = vbt.plotting.Volume(
... data=np.random.randint(1, 10, size=(3, 3, 3)),
... x_labels=['a', 'b', 'c'],
... y_labels=['d', 'e', 'f'],
... z_labels=['g', 'h', 'i']
... )
>>> volume.fig
```
"""
Configured.__init__(self,
data=data,
x_labels=x_labels,
y_labels=y_labels,
z_labels=z_labels,
trace_kwargs=trace_kwargs,
add_trace_kwargs=add_trace_kwargs,
scene_name=scene_name,
fig=fig,
**layout_kwargs)
from vectorbt.settings import layout
if trace_kwargs is None:
trace_kwargs = {}
if add_trace_kwargs is None:
add_trace_kwargs = {}
if data is None:
if x_labels is None or y_labels is None or z_labels is None:
raise ValueError(
"At least x_labels, y_labels and z_labels must be passed")
x_len = len(x_labels)
y_len = len(y_labels)
z_len = len(z_labels)
else:
checks.assert_ndim(data, 3)
data = np.asarray(data)
x_len, y_len, z_len = data.shape
if x_labels is None:
x_labels = np.arange(x_len)
else:
x_labels = clean_labels(x_labels)
if y_labels is None:
y_labels = np.arange(y_len)
else:
y_labels = clean_labels(y_labels)
if z_labels is None:
z_labels = np.arange(z_len)
else:
z_labels = clean_labels(z_labels)
x_labels = np.asarray(x_labels)
y_labels = np.asarray(y_labels)
z_labels = np.asarray(z_labels)
if fig is None:
fig = FigureWidget()
if 'width' in layout:
# Calculate nice width and height
fig.update_layout(width=layout['width'],
height=0.7 * layout['width'])
# Non-numeric data types are not supported by go.Volume, so use ticktext
# Note: Currently plotly displays the entire tick array, in future versions it will be more sensible
more_layout = dict()
if not np.issubdtype(x_labels.dtype, np.number):
x_ticktext = x_labels
x_labels = np.arange(x_len)
more_layout[scene_name] = dict(xaxis=dict(
ticktext=x_ticktext, tickvals=x_labels, tickmode='array'))
if not np.issubdtype(y_labels.dtype, np.number):
y_ticktext = y_labels
y_labels = np.arange(y_len)
more_layout[scene_name] = dict(yaxis=dict(
ticktext=y_ticktext, tickvals=y_labels, tickmode='array'))
if not np.issubdtype(z_labels.dtype, np.number):
z_ticktext = z_labels
z_labels = np.arange(z_len)
more_layout[scene_name] = dict(zaxis=dict(
ticktext=z_ticktext, tickvals=z_labels, tickmode='array'))
fig.update_layout(**more_layout)
fig.update_layout(**layout_kwargs)
# Arrays must have the same length as the flattened data array
x = np.repeat(x_labels, len(y_labels) * len(z_labels))
y = np.tile(np.repeat(y_labels, len(z_labels)), len(x_labels))
z = np.tile(z_labels, len(x_labels) * len(y_labels))
volume = go.Volume(
x=x,
y=y,
z=z,
opacity=0.2,
surface_count=15, # keep low for big data
colorscale='Plasma')
volume.update(**trace_kwargs)
fig.add_trace(volume, **add_trace_kwargs)
TraceUpdater.__init__(self, fig, [fig.data[-1]])
if data is not None:
self.update(data)
def __init__(self,
data=None,
x_labels=None,
y_labels=None,
trace_kwargs=None,
add_trace_kwargs=None,
fig=None,
**layout_kwargs):
"""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.
trace_kwargs (dict): Keyword arguments passed to `plotly.graph_objects.Heatmap`.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
fig (plotly.graph_objects.Figure): 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 layout
if trace_kwargs is None:
trace_kwargs = {}
if add_trace_kwargs is None:
add_trace_kwargs = {}
if data is None:
if x_labels is None or y_labels is None:
raise ValueError(
"At least x_labels and y_labels must be passed")
else:
data = reshape_fns.to_2d(np.array(data))
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 = FigureWidget()
if 'width' in layout:
# Calculate nice width and height
max_width = layout['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)
fig.update_layout(**layout_kwargs)
heatmap = go.Heatmap(hoverongaps=False,
colorscale='Plasma',
x=x_labels,
y=y_labels)
heatmap.update(**trace_kwargs)
fig.add_trace(heatmap, **add_trace_kwargs)
TraceUpdater.__init__(self, fig, [fig.data[-1]])
if data is not None:
self.update(data)
def __init__(self,
value=None,
label=None,
value_range=None,
cmap_name='Spectral',
trace_kwargs=None,
add_trace_kwargs=None,
fig=None,
**layout_kwargs):
"""Create an indicator plot.
Args:
value (int or float): The value to be displayed.
label (str): The label to be displayed.
value_range (list or tuple of 2 values): The value range of the gauge.
cmap_name (str): A matplotlib-compatible colormap name.
See the [list of available colormaps](https://matplotlib.org/tutorials/colors/colormaps.html).
trace_kwargs (dict): Keyword arguments passed to the `plotly.graph_objects.Indicator`.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
fig (plotly.graph_objects.Figure): Figure to add traces to.
**layout_kwargs: Keyword arguments for layout.
## Example
```python-repl
>>> import vectorbt as vbt
>>> indicator = vbt.plotting.Indicator(
... value=2,
... value_range=(1, 3),
... label='My Indicator'
... )
>>> indicator.fig
```
"""
Configured.__init__(self,
value=value,
label=label,
value_range=value_range,
cmap_name=cmap_name,
trace_kwargs=trace_kwargs,
add_trace_kwargs=add_trace_kwargs,
fig=fig,
**layout_kwargs)
from vectorbt.settings import layout
if trace_kwargs is None:
trace_kwargs = {}
if add_trace_kwargs is None:
add_trace_kwargs = {}
if fig is None:
fig = FigureWidget()
if 'width' in layout:
# Calculate nice width and height
fig.update_layout(width=layout['width'] * 0.7,
height=layout['width'] * 0.5,
margin=dict(t=80))
fig.update_layout(**layout_kwargs)
indicator = go.Indicator(domain=dict(x=[0, 1], y=[0, 1]),
mode="gauge+number+delta",
title=dict(text=label))
indicator.update(**trace_kwargs)
fig.add_trace(indicator, **add_trace_kwargs)
TraceUpdater.__init__(self, fig, [fig.data[-1]])
self.value_range = value_range
self.cmap_name = cmap_name
if value is not None:
self.update(value)
def __init__(self,
data=None,
trace_names=None,
horizontal=False,
remove_nan=True,
from_quantile=None,
to_quantile=None,
trace_kwargs=None,
add_trace_kwargs=None,
fig=None,
**layout_kwargs):
"""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 None:
if trace_names is None:
raise ValueError("At least trace_names must be passed")
if trace_names is None:
data = reshape_fns.to_2d(data)
trace_names = [None] * data.shape[1]
if isinstance(trace_names, str):
trace_names = [trace_names]
if fig is None:
fig = FigureWidget()
fig.update_layout(**layout_kwargs)
for i, trace_name in enumerate(trace_names):
_trace_kwargs = trace_kwargs[i] if isinstance(
trace_kwargs, (list, tuple)) else trace_kwargs
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=None,
trace_names=None,
horizontal=False,
remove_nan=True,
from_quantile=None,
to_quantile=None,
trace_kwargs=None,
add_trace_kwargs=None,
fig=None,
**layout_kwargs):
"""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): 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): Keyword arguments passed to `plotly.graph_objects.Histogram`.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
fig (plotly.graph_objects.Figure): 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 None:
if trace_names is None:
raise ValueError("At least trace_names must be passed")
if trace_names is None:
data = reshape_fns.to_2d(data)
trace_names = [None] * data.shape[1]
if isinstance(trace_names, str):
trace_names = [trace_names]
if fig is None:
fig = FigureWidget()
fig.update_layout(barmode='overlay')
fig.update_layout(**layout_kwargs)
for i, trace_name in enumerate(trace_names):
_trace_kwargs = trace_kwargs[i] if isinstance(
trace_kwargs, (list, tuple)) else trace_kwargs
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=None,
trace_names=None,
x_labels=None,
trace_kwargs=None,
add_trace_kwargs=None,
fig=None,
**layout_kwargs):
"""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): Keyword arguments passed to `plotly.graph_objects.Scatter`.
add_trace_kwargs (dict): Keyword arguments passed to `add_trace`.
fig (plotly.graph_objects.Figure): 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 None:
if trace_names is None:
raise ValueError("At least trace_names must be passed")
if trace_names is None:
data = reshape_fns.to_2d(data)
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 = FigureWidget()
fig.update_layout(**layout_kwargs)
for i, trace_name in enumerate(trace_names):
_trace_kwargs = trace_kwargs[i] if isinstance(
trace_kwargs, (list, tuple)) else trace_kwargs
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)
def __init__(self, wrapper, **kwargs):
checks.assert_type(wrapper, ArrayWrapper)
self._wrapper = wrapper
Configured.__init__(self, wrapper=wrapper, **kwargs)
PandasIndexer.__init__(self)
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.
Usage:
```pycon
>>> 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,
value: tp.Optional[float] = None,
label: tp.Optional[str] = None,
value_range: tp.Optional[tp.Tuple[float, float]] = None,
cmap_name: str = 'Spectral',
trace_kwargs: tp.KwargsLike = None,
add_trace_kwargs: tp.KwargsLike = None,
fig: tp.Optional[tp.BaseFigure] = None,
**layout_kwargs) -> None:
"""Create a gauge plot.
Args:
value (float): The value to be displayed.
label (str): The label to be displayed.
value_range (tuple of float): The value range of the gauge.
cmap_name (str): A matplotlib-compatible colormap name.
See the [list of available colormaps](https://matplotlib.org/tutorials/colors/colormaps.html).
trace_kwargs (dict): Keyword arguments passed to the `plotly.graph_objects.Indicator`.
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
>>> gauge = vbt.plotting.Gauge(
... value=2,
... value_range=(1, 3),
... label='My Gauge'
... )
>>> gauge.fig
```
"""
Configured.__init__(self,
value=value,
label=label,
value_range=value_range,
cmap_name=cmap_name,
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 fig is None:
fig = make_figure()
if 'width' in layout_cfg:
# Calculate nice width and height
fig.update_layout(width=layout_cfg['width'] * 0.7,
height=layout_cfg['width'] * 0.5,
margin=dict(t=80))
fig.update_layout(**layout_kwargs)
indicator = go.Indicator(domain=dict(x=[0, 1], y=[0, 1]),
mode="gauge+number+delta",
title=dict(text=label))
indicator.update(**trace_kwargs)
fig.add_trace(indicator, **add_trace_kwargs)
TraceUpdater.__init__(self, fig, (fig.data[-1], ))
self.value_range = value_range
self.cmap_name = cmap_name
if value is not None:
self.update(value)
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 None:
if x_labels is None or y_labels is None:
raise ValueError(
"At least x_labels and y_labels must be passed")
else:
data = reshape_fns.to_2d(np.asarray(data))
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,
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 bar 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.Bar`.
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
>>> bar = vbt.plotting.Bar(
... data=[[1, 2], [3, 4]],
... trace_names=['a', 'b'],
... x_labels=['x', 'y']
... )
>>> bar.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 None:
if trace_names is None:
raise ValueError("At least trace_names must be passed")
if trace_names is None:
data = reshape_fns.to_2d(np.asarray(data))
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)
bar = go.Bar(x=x_labels,
name=trace_name,
showlegend=trace_name is not None)
bar.update(**_trace_kwargs)
fig.add_trace(bar, **add_trace_kwargs)
TraceUpdater.__init__(self, fig, fig.data[-len(trace_names):])
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)
