#!/usr/bin/env python
##PACKAGES##
from __future__ import division
import sys
import warnings
import re
import itertools
import seaborn as sns
import pandas as pd
from .._shared.helpers import *
from .static import static_plot
from .animate import animated_plot
from ..tools.cluster import cluster
from ..tools.df2mat import df2mat
from ..tools.reduce import reduce as reduceD
from ..tools.normalize import normalize as normalizer
## MAIN FUNCTION ##
[docs]def plot(x,*args,**kwargs):
"""
Plots dimensionality reduced data and parses plot arguments
Parameters
----------
x : Numpy array, DataFrame or list of arrays/dfs
Data for the plot. The form should be samples (rows) by features (cols).
color(s) (list): A list of colors for each line to be plotted.
Can be named colors, RGB values (e.g. (.3, .4, .1)) or hex codes.
If defined, overrides palette. See here for list of named colors.
Note: must be the same length as X.
group : list of str, floats or ints
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']).
linestyle(s) : list
A list of line styles
marker(s) : list
A list of marker types
palette : str
A matplotlib or seaborn color palette
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
A list of string labels to be plotted in a legend (one for each list
item).
ndims : int
An `int` representing the number of dims to plot in. Must be 1,2, or 3.
NOTE: Currently only works with static plots.
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).
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.
animate : bool
If True, plots the data as an animated trajectory (default: False).
show : bool
If set to False, the figure will not be displayed, but the figure,
axis and data objects will still be returned (see Outputs)
(default: True).
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)".
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 more is currently only supported
for 3D static plots.
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
Zoom, positive numbers will zoom in (default: 0)
chemtrails (animation only) : bool
Added trail with change in opacity (default: False)
Returns
----------
fig, ax, data : Matplotlib.Figure.figure, Matplotlib.Axes.axis, Numpy array
By default, the plot function outputs a figure handle
(matplotlib.figure.Figure), axis handle (matplotlib.axes._axes.Axes)
and data (list of numpy arrays), e.g. fig,axis,data = hyp.plot(x)
If animate=True, the plot function additionally outputs an animation
handle (matplotlib.animation.FuncAnimation)
e.g. fig,axis,data,line_ani = hyp.plot(x, animate=True).
"""
# turn data into common format - a list of arrays
x = format_data(x)
## HYPERTOOLS-SPECIFIC ARG PARSING ##
if 'colors' in kwargs:
kwargs['color'] = kwargs['colors']
del kwargs['colors']
if 'linestyles' in kwargs:
kwargs['linestyle'] = kwargs['linestyles']
del kwargs['linestyles']
if 'markers' in kwargs:
kwargs['marker'] = kwargs['markers']
del kwargs['markers']
if 'normalize' in kwargs:
normalize = kwargs['normalize']
x = normalizer(x, normalize=normalize, internal=True)
del kwargs['normalize']
else:
x = normalizer(x, normalize=False, internal=True)
# reduce dimensionality of the data
if 'ndims' in kwargs:
ndims=kwargs['ndims']
x = reduceD(x,ndims, internal=True)
del kwargs['ndims']
elif x[0].shape[1]>3:
x = reduceD(x,3, internal=True)
ndims=3
else:
ndims=x[0].shape[1]
if 'n_clusters' in kwargs:
n_clusters=kwargs['n_clusters']
cluster_labels = cluster(x, n_clusters=n_clusters, ndims=ndims)
x = reshape_data(x,cluster_labels)
del kwargs['n_clusters']
if 'group' in kwargs:
warnings.warn('n_clusters overrides group, ignoring group.')
del kwargs['group']
if 'group' in kwargs:
group=kwargs['group']
del kwargs['group']
if 'color' in kwargs:
warnings.warn("Using group, color keyword will be ignored.")
del kwargs['color']
# if list of lists, unpack
if any(isinstance(el, list) for el in group):
group = list(itertools.chain(*group))
# if all of the elements are numbers, map them to colors
if all(isinstance(el, int) or isinstance(el, float) for el in group):
group = vals2bins(group)
elif all(isinstance(el, str) for el in group):
group = group_by_category(group)
# reshape the data according to group
x = reshape_data(x,group)
if 'style' in kwargs:
sns.set(style=kwargs['style'])
del kwargs['style']
else:
sns.set(style="whitegrid")
if 'palette' in kwargs:
sns.set_palette(palette=kwargs['palette'], n_colors=len(x))
palette = sns.color_palette(palette=kwargs['palette'], n_colors=len(x))
del kwargs['palette']
else:
sns.set_palette(palette="hls", n_colors=len(x))
palette=sns.color_palette(palette="hls", n_colors=len(x))
if 'animate' in kwargs:
animate=kwargs['animate']
del kwargs['animate']
# if animate mode, pass the color palette via kwargs so we can build a legend
kwargs['color_palette']=palette
else:
animate=False
if animate:
return animated_plot(x,*args,**kwargs)
else:
return static_plot(x,*args,**kwargs)