def map_array(self,
a: tp.ArrayLike,
idx_field: tp.Optional[str] = None,
value_map: tp.Optional[tp.ValueMapLike] = None,
group_by: tp.GroupByLike = None,
**kwargs) -> MappedArray:
"""Convert array to mapped array.
The length of the array should match that of the records."""
if not isinstance(a, np.ndarray):
a = np.asarray(a)
checks.assert_shape_equal(a, self.values)
if idx_field is None:
idx_field = self.idx_field
if idx_field is not None:
idx_arr = self.values[idx_field]
else:
idx_arr = None
return MappedArray(self.wrapper,
a,
self.values['col'],
id_arr=self.values['id'],
idx_arr=idx_arr,
value_map=value_map,
**kwargs).regroup(group_by)
def map_array(self, a, idx_arr=None, group_by=None, **kwargs):
"""Convert array to `MappedArray`.
The length of the array should match that of the records."""
if not isinstance(a, np.ndarray):
a = np.asarray(a)
checks.assert_shape_equal(a, self.records_arr)
if idx_arr is None:
if self.idx_field is not None:
idx_arr = self.records_arr[self.idx_field]
else:
idx_arr = None
if self.wrapper.grouper.is_grouping_changed(group_by=group_by):
self.wrapper.grouper.check_group_by(group_by=group_by)
wrapper = self.wrapper.copy(group_by=group_by)
else:
wrapper = self.wrapper
return MappedArray(
wrapper,
a,
self.records_arr['col'],
idx_arr=idx_arr,
**kwargs
)
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,
mapped_arr: tp.ArrayLike,
col_arr: tp.ArrayLike,
id_arr: tp.Optional[tp.ArrayLike] = None,
idx_arr: tp.Optional[tp.ArrayLike] = None,
mapping: tp.Optional[tp.MappingLike] = None,
col_mapper: tp.Optional[ColumnMapper] = None,
**kwargs) -> None:
Wrapping.__init__(self,
wrapper,
mapped_arr=mapped_arr,
col_arr=col_arr,
id_arr=id_arr,
idx_arr=idx_arr,
mapping=mapping,
col_mapper=col_mapper,
**kwargs)
StatsBuilderMixin.__init__(self)
mapped_arr = np.asarray(mapped_arr)
col_arr = np.asarray(col_arr)
checks.assert_shape_equal(mapped_arr, col_arr, axis=0)
if id_arr is None:
id_arr = np.arange(len(mapped_arr))
else:
id_arr = np.asarray(id_arr)
if idx_arr is not None:
idx_arr = np.asarray(idx_arr)
checks.assert_shape_equal(mapped_arr, idx_arr, axis=0)
if mapping is not None:
if isinstance(mapping, str):
if mapping.lower() == 'index':
mapping = self.wrapper.index
elif mapping.lower() == 'columns':
mapping = self.wrapper.columns
mapping = to_mapping(mapping)
self._mapped_arr = mapped_arr
self._id_arr = id_arr
self._col_arr = col_arr
self._idx_arr = idx_arr
self._mapping = mapping
if col_mapper is None:
col_mapper = ColumnMapper(wrapper, col_arr)
self._col_mapper = col_mapper
def wrap(self,
a: tp.ArrayLike,
index: tp.Optional[tp.IndexLike] = None,
columns: tp.Optional[tp.IndexLike] = None,
dtype: tp.Optional[tp.PandasDTypeLike] = None,
group_by: tp.GroupByLike = None) -> tp.SeriesFrame:
"""Wrap a NumPy array using the stored metadata."""
checks.assert_ndim(a, (1, 2))
_self = self.resolve(group_by=group_by)
if index is None:
index = _self.index
if not isinstance(index, pd.Index):
index = pd.Index(index)
if columns is None:
columns = _self.columns
if not isinstance(columns, pd.Index):
columns = pd.Index(columns)
if len(columns) == 1:
name = columns[0]
if name == 0: # was a Series before
name = None
else:
name = None
arr = np.asarray(a)
arr = reshape_fns.soft_to_ndim(arr, self.ndim)
checks.assert_shape_equal(arr, index, axis=(0, 0))
if arr.ndim == 2:
checks.assert_shape_equal(arr, columns, axis=(1, 0))
if arr.ndim == 1:
return pd.Series(arr, index=index, name=name, dtype=dtype)
if arr.ndim == 2:
if arr.shape[1] == 1 and _self.ndim == 1:
return pd.Series(arr[:, 0],
index=index,
name=name,
dtype=dtype)
return pd.DataFrame(arr, index=index, columns=columns, dtype=dtype)
raise ValueError(f"{arr.ndim}-d input is not supported")
def _wrap(arr):
arr = np.asarray(arr)
checks.assert_ndim(arr, (1, 2))
if fillna is not None:
arr[pd.isnull(arr)] = fillna
arr = reshape_fns.soft_to_ndim(arr, self.ndim)
checks.assert_shape_equal(arr, index, axis=(0, 0))
if arr.ndim == 2:
checks.assert_shape_equal(arr, columns, axis=(1, 0))
if arr.ndim == 1:
return pd.Series(arr, index=index, name=name, dtype=dtype)
if arr.ndim == 2:
if arr.shape[1] == 1 and _self.ndim == 1:
return pd.Series(arr[:, 0],
index=index,
name=name,
dtype=dtype)
return pd.DataFrame(arr,
index=index,
columns=columns,
dtype=dtype)
raise ValueError(f"{arr.ndim}-d input is not supported")
def map_array(self,
a: tp.ArrayLike,
idx_arr: tp.Optional[tp.ArrayLike] = None,
mapping: tp.Optional[tp.MappingLike] = None,
group_by: tp.GroupByLike = None,
**kwargs) -> MappedArray:
"""Convert array to mapped array.
The length of the array should match that of the records."""
if not isinstance(a, np.ndarray):
a = np.asarray(a)
checks.assert_shape_equal(a, self.values)
if idx_arr is None:
idx_arr = self.idx_arr
return MappedArray(self.wrapper,
a,
self.col_arr,
id_arr=self.id_arr,
idx_arr=idx_arr,
mapping=mapping,
col_mapper=self.col_mapper,
**kwargs).regroup(group_by)
def wrap(self,
a,
index=None,
columns=None,
dtype=None,
collapse=None,
**kwargs):
"""Wrap a NumPy array using the stored metadata."""
checks.assert_ndim(a, (1, 2))
group_by = self.grouper.resolve_group_by(**kwargs)
a = np.asarray(a)
a = reshape_fns.soft_to_ndim(a, self.ndim)
if index is None:
index = self.index
if columns is None:
columns = self.grouper.get_columns(**kwargs)
if collapse is None:
collapse = group_by is not None and group_by is not False and self.grouped_ndim == 1
if columns is not None and len(columns) == 1:
name = columns[0]
if name == 0: # was a Series before
name = None
else:
name = None
# Perform checks
if index is not None:
checks.assert_shape_equal(a, index, axis=(0, 0))
if a.ndim == 2 and columns is not None:
checks.assert_shape_equal(a, columns, axis=(1, 0))
if a.ndim == 1:
return pd.Series(a, index=index, name=name, dtype=dtype)
if a.ndim == 2 and a.shape[1] == 1 and collapse:
return pd.Series(a[:, 0], index=index, name=name, dtype=dtype)
return pd.DataFrame(a, index=index, columns=columns, dtype=dtype)
def __init__(self,
data: tp.Optional[tp.ArrayLike] = None,
x_labels: tp.Optional[tp.Labels] = None,
y_labels: tp.Optional[tp.Labels] = None,
z_labels: tp.Optional[tp.Labels] = None,
trace_kwargs: tp.KwargsLike = None,
add_trace_kwargs: tp.KwargsLike = None,
scene_name: str = 'scene',
fig: tp.Optional[tp.BaseFigure] = None,
**layout_kwargs) -> None:
"""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 (Figure or FigureWidget): 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 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:
checks.assert_ndim(data, 3)
data = np.asarray(data)
x_len, y_len, z_len = data.shape
if x_labels is not None:
checks.assert_shape_equal(data, x_labels, (0, 0))
if y_labels is not None:
checks.assert_shape_equal(data, y_labels, (1, 0))
if z_labels is not None:
checks.assert_shape_equal(data, z_labels, (2, 0))
else:
if x_labels is None or y_labels is None or z_labels is None:
raise ValueError("At least data, or 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)
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 = make_figure()
if 'width' in layout_cfg:
# Calculate nice width and height
fig.update_layout(
width=layout_cfg['width'],
height=0.7 * layout_cfg['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: 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)
def test_assert_shape_equal(self):
checks.assert_shape_equal(0, 1)
checks.assert_shape_equal([1, 2, 3], np.asarray([1, 2, 3]))
checks.assert_shape_equal([1, 2, 3], pd.Series([1, 2, 3]))
checks.assert_shape_equal(np.zeros((3, 3)), pd.Series([1, 2, 3]), axis=0)
checks.assert_shape_equal(np.zeros((2, 3)), pd.Series([1, 2, 3]), axis=(1, 0))
with pytest.raises(Exception) as e_info:
checks.assert_shape_equal(np.zeros((2, 3)), pd.Series([1, 2, 3]), axis=(0, 1))
