Source code for holoviews.interface.seaborn

"""
The HoloViews Seaborn interface wraps around a wide range
of Seaborn plot types including time series, kernel density
estimates, distributions and regression plots.
"""

from __future__ import absolute_import

import numpy as np

import param

from ..core import Dimension, NdMapping, Element2D, HoloMap
from ..element import Chart, Scatter, Curve
from .pandas import DFrame as PandasDFrame


[docs]class TimeSeries(Element2D): """ TimeSeries is a container for any set of curves, which the Seaborn interface combines into a confidence interval, error bar or overlaid plot. The curves should be supplied as an NxM dimensional array, x-values may also be supplied and must be of length N or M. Alternatively a UniformNdMapping or NdOverlay of Curve objects may be supplied. """ kdims = param.List(default=[Dimension('x'), Dimension('n')], bounds=(2, 2)) group = param.String(default='TimeSeries', constant=True) vdims = param.List(default=[Dimension('z')], bounds=(1, 1)) def __init__(self, data, xdata=None, **params): if isinstance(data, NdMapping): self.xdata = data.values()[0].data[:, 0] params = dict(data.values()[0].get_param_values(onlychanged=True), **params) data = np.array([dv.data[:, 1] for dv in data]) else: self.xdata = np.array(range(len(data[0, :]))) if xdata is None\ else xdata super(TimeSeries, self).__init__(data, **params) def dimension_values(self, dimension): dim_idx = self.get_dimension_index(dimension) if dim_idx == 0: return self.xdata elif dim_idx == 1: return self.data.flatten() elif dim_idx == 2: return range(self.data.shape[1]) else: return super(TimeSeries, self).dimension_values(dimension) def sample(self, samples=[], **sample_values): raise NotImplementedError('Cannot sample a TimeSeries.') def reduce(self, dimensions=[], function=None, **reduce_map): raise NotImplementedError('Reduction of TimeSeries not ' 'implemented.')
[docs]class Bivariate(Chart): """ Bivariate Views are containers for two dimensional data, which is to be visualized as a kernel density estimate. The data should be supplied as an Nx2 array, containing the x- and y-data. """ kdims = param.List(default=[Dimension('x'), Dimension('y')]) vdims = param.List(default=[], bounds=(0,1)) group = param.String(default="Bivariate", constant=True)
[docs]class Distribution(Chart): """ Distribution Views provide a container for data to be visualized as a one-dimensional distribution. The data should be supplied as a simple one-dimensional array or list. Internally it uses Seaborn to make all the conversions. """ kdims = param.List(default=[]) group = param.String(default='Distribution', constant=True) vdims = param.List(default=[Dimension('Value')]) _1d = True
[docs]class Regression(Scatter): """ Regression is identical to a Scatter plot but is visualized using the Seaborn regplot interface. This allows it to implement linear regressions, confidence intervals and a lot more. """ group = param.String(default='Regression', constant=True)
[docs]class DFrame(PandasDFrame): """ The SNSFrame is largely the same as a DFrame but can only be visualized via Seaborn plotting functions. Since most Seaborn plots are two dimensional, the x and y dimensions can be set directly on this class to visualize a particular relationship in a multi-dimensional Pandas dframe. """ plot_type = param.ObjectSelector(default=None, objects=['interact', 'regplot', 'lmplot', 'corrplot', 'plot', 'boxplot', 'hist', 'scatter_matrix', 'autocorrelation_plot', 'pairgrid', 'facetgrid', 'pairplot', 'violinplot', 'factorplot', None], doc="""Selects which Pandas or Seaborn plot type to use, when visualizing the plot.""")
__all__ = ['DFrame', 'Bivariate', 'Distribution', 'TimeSeries', 'Regression']