plot

startorch.plot.matplotlib

Contain functions to plot the generated data with matplotlib.

startorch.plot.matplotlib.hist_feature

hist_feature(
    features: Tensor | ndarray,
    feature_names: Sequence[str] | None = None,
    ncols: int = 2,
    figsize: tuple[float, float] = (6, 4),
    **kwargs: Any
) -> Figure

Plot the distribution of each feature.

If the input has n features, this function returns a figure with n histograms: one for each features.

Parameters:

Name	Type	Description	Default
features	Tensor | ndarray	The features. It must be a tensor of shape (d0, d1, ..., dn).	required
feature_names	Sequence[str] | None	The feature names. If None, the feature names are generated automatically.	None
ncols	int	The number of columns.	2
figsize	tuple[float, float]	The individual figure size in pixels. The first dimension is the width and the second is the height.	(6, 4)
**kwargs	Any	Additional keyword arguments for plt.hist.	{}

Returns:

Type	Description
Figure	matplotlib.pyplot.Figure: The generated figure.

Raises:

Type	Description
RuntimeError	if the features shape is invalid
RuntimeError	if features and feature_names are not consistent

Example usage:

>>> from startorch.plot.matplotlib import hist_feature
>>> import numpy as np
>>> fig = hist_feature(np.random.rand(10, 5))

startorch.plot.matplotlib.hist_sequence

hist_sequence(
    sequence: BaseSequenceGenerator,
    bins: int = 500,
    seq_len: int = 1000,
    batch_size: int = 10000,
    num_batches: int = 1,
    rng: int | Generator = 13683624337160779813,
    figsize: tuple[float, float] = (16, 5),
    scale: str = "identity",
    **kwargs: Any
) -> Figure

Plot the distribution from a sequence generator.

Parameters:

Name	Type	Description	Default
sequence	BaseSequenceGenerator	The sequence generator.	required
bins	int	The number of histogram bins.	500
seq_len	int	The sequence length.	1000
batch_size	int	The batch size.	10000
num_batches	int	The number of batches to generate.	1
rng	int | Generator	A random number generator or a random seed.	13683624337160779813
figsize	tuple[float, float]	The figure size.	(16, 5)
scale	str	The transformation scale of the features.	'identity'
**kwargs	Any	Additional keyword arguments for plt.hist.	{}

Returns:

Type	Description
Figure	The generated figure.

Example usage:

>>> from startorch.plot.matplotlib import hist_sequence
>>> from startorch.sequence import RandUniform
>>> fig = hist_sequence(RandUniform(low=-5, high=5))

startorch.plot.matplotlib.plot_sequence

plot_sequence(
    sequence: BaseSequenceGenerator,
    seq_len: int = 128,
    batch_size: int = 1,
    num_batches: int = 1,
    rng: int | Generator = 13683624337160779813,
    figsize: tuple[float, float] = (16, 5),
    xscale: str = "linear",
    yscale: str = "linear",
    **kwargs: Any
) -> Figure

Plot some sequences generated from a sequence generator.

Parameters:

Name	Type	Description	Default
sequence	BaseSequenceGenerator	The sequence generator.	required
seq_len	int	The sequence length.	128
batch_size	int	The batch size.	1
num_batches	int	The number of batches.	1
rng	int | Generator	A random number generator or a random seed.	13683624337160779813
figsize	tuple[float, float]	The figure size.	(16, 5)
xscale	str	The x-axis scale.	'linear'
yscale	str	The y-axis scale.	'linear'
**kwargs	Any	Additional keyword arguments for plt.plot.	{}

Returns:

Type	Description
Figure	The generated figure.

Example usage:

>>> from startorch.plot.matplotlib import plot_sequence
>>> from startorch.sequence import RandUniform
>>> fig = plot_sequence(RandUniform(low=-5, high=5), batch_size=4)

startorch.plot.plotly

Contain functions to plot the generated data with plotly.

startorch.plot.plotly.hist_feature

hist_feature(
    features: Tensor | ndarray,
    feature_names: Sequence[str] | None = None,
    ncols: int = 2,
    figsize: tuple[int, int] = (250, 200),
    **kwargs: Any
) -> Figure

Plot the distribution of each feature.

If the input has n features, this function returns a figure with n histograms: one for each feature.

Parameters:

Name	Type	Description	Default
features	Tensor | ndarray	The features. It must be a tensor of shape (d0, d1, ..., dn).	required
feature_names	Sequence[str] | None	The feature names. If None, the feature names are generated automatically.	None
ncols	int	The number of columns.	2
figsize	tuple[int, int]	The individual figure size in pixels. The first dimension is the width and the second is the height.	(250, 200)
**kwargs	Any	Additional keyword arguments for plotly.graph_objects.Histogram.	{}

Returns:

Type	Description
Figure	The generated figure.

Raises:

Type	Description
RuntimeError	if the features shape is invalid
RuntimeError	if features and feature_names are not consistent

Example usage:

>>> from startorch.plot.plotly import hist_feature
>>> import numpy as np
>>> fig = hist_feature(np.random.rand(10, 5))

startorch.plot.plotly.hist_sequence

hist_sequence(
    sequence: BaseSequenceGenerator,
    bins: int = 500,
    seq_len: int = 1000,
    batch_size: int = 10000,
    num_batches: int = 1,
    rng: int | Generator = 13683624337160779813,
    figsize: tuple[int, int] = (800, 600),
    scale: str = "identity",
    **kwargs: Any
) -> Figure

Plot the distribution from a sequence generator.

Parameters:

Name	Type	Description	Default
sequence	BaseSequenceGenerator	The sequence generator.	required
bins	int	The number of histogram bins.	500
seq_len	int	The sequence length.	1000
batch_size	int	The batch size.	10000
num_batches	int	The number of batches to generate.	1
rng	int | Generator	A random number generator or a random seed.	13683624337160779813
figsize	tuple[int, int]	The figure size.	(800, 600)
scale	str	A scale transformation of the features.	'identity'
**kwargs	Any	Additional keyword arguments for plotly.graph_objects.Histogram.	{}

Returns:

Type	Description
Figure	The generated figure.

Example usage:

>>> from startorch.plot.plotly import hist_sequence
>>> from startorch.sequence import RandUniform
>>> fig = hist_sequence(RandUniform(low=-5, high=5))

startorch.plot.plotly.plot_sequence

plot_sequence(
    sequence: BaseSequenceGenerator,
    seq_len: int = 128,
    batch_size: int = 1,
    num_batches: int = 1,
    rng: int | Generator = 13683624337160779813,
    **kwargs: Any
) -> Figure

Plot some sequences generated from a sequence generator.

Parameters:

Name	Type	Description	Default
sequence	BaseSequenceGenerator	The sequence generator.	required
seq_len	int	The sequence length.	128
batch_size	int	The batch size.	1
num_batches	int	The number of batches.	1
rng	int | Generator	A random number generator or a random seed.	13683624337160779813
**kwargs	Any	Additional keyword arguments for plt.plot.	{}

Returns:

Type	Description
Figure	The generated figure.

Example usage:

>>> from startorch.plot.plotly import plot_sequence
>>> from startorch.sequence import RandUniform
>>> fig = plot_sequence(RandUniform(low=-5, high=5), batch_size=4)