Parameters: | x : Numpy array, DataFrame or list of arrays/dfs
Data for the plot. The form should be samples (rows) by features (cols).
fmt : str or list of strings
A list of format strings. All matplotlib format strings are supported.
linestyle(s) : str or list of str
marker(s) : str or list of str
color(s) : str or list of str
palette : str
A matplotlib or seaborn color palette
group : str/int/float or list
A list of group labels. Length must match the number of rows in your
dataset. If the data type is numerical, the values will be mapped to
rgb values in the specified palette. If the data type is strings,
the points will be labeled categorically. To label a subset of points,
use None (i.e. [‘a’, None, ‘b’,’a’]).
labels : list
A list of labels for each point. Must be dimensionality of data (x).
If no label is wanted for a particular point, input None.
legend : list or bool
If set to True, legend is implicitly computed from data. Passing a
list will add string labels to the legend (one for each list item).
title : str
normalize : str or False
If set to ‘across’, the columns of the input data will be z-scored
across lists (default). If set to ‘within’, the columns will be
z-scored within each list that is passed. If set to ‘row’, each row of
the input data will be z-scored. If set to False, the input data will
be returned (default is False).
reduce : str or dict
Decomposition/manifold learning model to use. Models supported: PCA,
IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA,
FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning,
TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, and MDS. Can be
passed as a string, but for finer control of the model parameters, pass
as a dictionary, e.g. reduce={‘model’ : ‘PCA’, ‘params’ : {‘whiten’ : True}}.
See scikit-learn specific model docs for details on parameters supported
for each model.
ndims : int
An int representing the number of dims to reduce the data x
to. If ndims > 3, will plot in 3 dimensions but return the higher
dimensional data. Default is None, which will plot data in 3
dimensions and return the data with the same number of dimensions
possibly normalized and/or aligned according to normalize/align
kwargs.
align : str or dict or False/None
If str, either ‘hyper’ or ‘SRM’. If ‘hyper’, alignment algorithm will be
hyperalignment. If ‘SRM’, alignment algorithm will be shared response
model. You can also pass a dictionary for finer control, where the ‘model’
key is a string that specifies the model and the params key is a dictionary
of parameter values (default : ‘hyper’).
cluster : str or dict or False/None
Model to use to discover clusters. Support algorithms are: KMeans,
MiniBatchKMeans, AgglomerativeClustering, Birch, FeatureAgglomeration,
SpectralClustering (default: KMeans).Can be passed as a string, but for
finer control of the model parameters, pass as a dictionary, e.g.
reduce={‘model’ : ‘KMeans’, ‘params’ : {‘max_iter’ : 100}}. See
scikit-learn specific model docs for details on parameters supported for
each model.
n_clusters : int
If n_clusters is passed, HyperTools will perform k-means clustering
with the k parameter set to n_clusters. The resulting clusters will
be plotted in different colors according to the color palette.
save_path : str
Path to save the image/movie. Must include the file extension in the
save path (i.e. save_path=’/path/to/file/image.png’). NOTE: If saving
an animation, FFMPEG must be installed (this is a matplotlib req).
FFMPEG can be easily installed on a mac via homebrew brew install
ffmpeg or linux via apt-get apt-get install ffmpeg. If you don’t
have homebrew (mac only), you can install it like this:
/usr/bin/ruby -e “$(curl -fsSL
https://raw.githubusercontent.com/Homebrew/install/master/install)”.
animate : bool, ‘parallel’ or ‘spin’
If True or ‘parallel’, plots the data as an animated trajectory, with
each dataset plotted simultaneously. If ‘spin’, all the data is plotted
at once but the camera spins around the plot (default: False).
duration (animation only) : float
Length of the animation in seconds (default: 30 seconds)
tail_duration (animation only) : float
Sets the length of the tail of the data (default: 2 seconds)
rotations (animation only) : float
Number of rotations around the box (default: 2)
zoom (animation only) : float
How far to zoom into the plot, positive numbers will zoom in (default: 0)
chemtrails (animation only) : bool
A low-opacity trail is left behind the trajectory (default: False).
precog (animation only) : bool
A low-opacity trail is plotted ahead of the trajectory (default: False).
bullettime (animation only) : bool
A low-opacity trail is plotted ahead and behind the trajectory
(default: False).
frame_rate (animation only) : int or float
Frame rate for animation (default: 50)
explore : bool
Displays user defined labels will appear on hover. If no labels are
passed, the point index and coordinate will be plotted. To use,
set explore=True. Note: Explore mode is currently only supported
for 3D static plots, and is an experimental feature (i.e it may not yet
work properly).
show : bool
If set to False, the figure will not be displayed, but the figure,
axis and data objects will still be returned (default: True).
transform : bool
If set to false, skip data transformations (default : True)
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Returns: | fig, ax, data, line_ani : matplotlib.figure.figure, matplotlib.axis.axes, numpy.array, matplotlib.animation.funcanimation
The plot function outputs a figure handle, axis handle, data, and line
animation object. The line animation object is None if animation=False.
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