from .. import utils
from .._lazyload import matplotlib as mpl
from .tools import label_axis
from .utils import _get_figure
from .utils import show
from .utils import temp_fontsize
import numpy as np
[docs]@utils._with_pkg(pkg="matplotlib", min_version=3)
def scree_plot(
singular_values,
cumulative=False,
ax=None,
figsize=None,
xlabel="Principal Component",
ylabel="Explained Variance (%)",
fontsize=None,
filename=None,
dpi=None,
**kwargs,
):
"""Plot the explained variance of each principal component.
Parameters
----------
singular_values : list-like, shape=[n_components]
Singular values returned by
`scprep.reduce.pca(data, return_singular_values=True)`
cumulative : bool, optional (default=False)
If True, plot the cumulative explained variance
ax : `matplotlib.Axes` or None, optional (default: None)
Axis to plot on. If None, a new axis will be created.
figsize : tuple or None, optional (default: None)
If not None, sets the figure size (width, height)
{x,y}label : str, optional
Labels to display on the x and y axis.
fontsize : float or None (default: None)
Base font size.
filename : str or None (default: None)
file to which the output is saved
dpi : int or None, optional (default: None)
The resolution in dots per inch. If None it will default to the value
savefig.dpi in the matplotlibrc file. If 'figure' it will set the dpi
to be the value of the figure. Only used if filename is not None.
**kwargs : additional arguments for `matplotlib.pyplot.plot`
Returns
-------
ax : `matplotlib.Axes`
axis on which plot was drawn
Examples
--------
>>> import scprep
>>> import numpy as np
>>> data = np.random.normal(0, 1, [200, 1000])
>>> pca_data, singular_values = scprep.reduce.pca(
data, n_components=100, return_singular_values=True
)
>>> scprep.plot.scree_plot(singular_values)
>>> scprep.plot.scree_plot(singular_values, cumulative=True)
"""
with temp_fontsize(fontsize):
explained_variance = singular_values**2
explained_variance = explained_variance / explained_variance.sum() * 100
if cumulative:
explained_variance = np.cumsum(explained_variance)
fig, ax, show_fig = _get_figure(ax, figsize)
ax.bar(np.arange(len(explained_variance)) + 1, explained_variance, **kwargs)
label_axis(ax.xaxis, label=xlabel)
label_axis(ax.yaxis, label=ylabel)
ax.xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))
ax.set_xlim(0.3, len(explained_variance) + 0.7)
if show_fig:
show(fig)
if filename is not None:
fig.savefig(filename, dpi=dpi)
return ax