from __future__ import absolute_import from plotly import exceptions, optional_imports from plotly.figure_factory import utils from plotly.graph_objs import graph_objs # Optional imports, may be None for users that only use our core functionality. np = optional_imports.get_module('numpy') pd = optional_imports.get_module('pandas') scipy = optional_imports.get_module('scipy') scipy_stats = optional_imports.get_module('scipy.stats') DEFAULT_HISTNORM = 'probability density' ALTERNATIVE_HISTNORM = 'probability' def validate_distplot(hist_data, curve_type): """ Distplot-specific validations :raises: (PlotlyError) If hist_data is not a list of lists :raises: (PlotlyError) If curve_type is not valid (i.e. not 'kde' or 'normal'). """ hist_data_types = (list,) if np: hist_data_types += (np.ndarray,) if pd: hist_data_types += (pd.core.series.Series,) if not isinstance(hist_data[0], hist_data_types): raise exceptions.PlotlyError("Oops, this function was written " "to handle multiple datasets, if " "you want to plot just one, make " "sure your hist_data variable is " "still a list of lists, i.e. x = " "[1, 2, 3] -> x = [[1, 2, 3]]") curve_opts = ('kde', 'normal') if curve_type not in curve_opts: raise exceptions.PlotlyError("curve_type must be defined as " "'kde' or 'normal'") if not scipy: raise ImportError("FigureFactory.create_distplot requires scipy") def create_distplot(hist_data, group_labels, bin_size=1., curve_type='kde', colors=None, rug_text=None, histnorm=DEFAULT_HISTNORM, show_hist=True, show_curve=True, show_rug=True): """ BETA function that creates a distplot similar to seaborn.distplot The distplot can be composed of all or any combination of the following 3 components: (1) histogram, (2) curve: (a) kernel density estimation or (b) normal curve, and (3) rug plot. Additionally, multiple distplots (from multiple datasets) can be created in the same plot. :param (list[list]) hist_data: Use list of lists to plot multiple data sets on the same plot. :param (list[str]) group_labels: Names for each data set. :param (list[float]|float) bin_size: Size of histogram bins. Default = 1. :param (str) curve_type: 'kde' or 'normal'. Default = 'kde' :param (str) histnorm: 'probability density' or 'probability' Default = 'probability density' :param (bool) show_hist: Add histogram to distplot? Default = True :param (bool) show_curve: Add curve to distplot? Default = True :param (bool) show_rug: Add rug to distplot? Default = True :param (list[str]) colors: Colors for traces. :param (list[list]) rug_text: Hovertext values for rug_plot, :return (dict): Representation of a distplot figure. Example 1: Simple distplot of 1 data set ``` import plotly.plotly as py from plotly.figure_factory import create_distplot hist_data = [[1.1, 1.1, 2.5, 3.0, 3.5, 3.5, 4.1, 4.4, 4.5, 4.5, 5.0, 5.0, 5.2, 5.5, 5.5, 5.5, 5.5, 5.5, 6.1, 7.0]] group_labels = ['distplot example'] fig = create_distplot(hist_data, group_labels) url = py.plot(fig, filename='Simple distplot', validate=False) ``` Example 2: Two data sets and added rug text ``` import plotly.plotly as py from plotly.figure_factory import create_distplot # Add histogram data hist1_x = [0.8, 1.2, 0.2, 0.6, 1.6, -0.9, -0.07, 1.95, 0.9, -0.2, -0.5, 0.3, 0.4, -0.37, 0.6] hist2_x = [0.8, 1.5, 1.5, 0.6, 0.59, 1.0, 0.8, 1.7, 0.5, 0.8, -0.3, 1.2, 0.56, 0.3, 2.2] # Group data together hist_data = [hist1_x, hist2_x] group_labels = ['2012', '2013'] # Add text rug_text_1 = ['a1', 'b1', 'c1', 'd1', 'e1', 'f1', 'g1', 'h1', 'i1', 'j1', 'k1', 'l1', 'm1', 'n1', 'o1'] rug_text_2 = ['a2', 'b2', 'c2', 'd2', 'e2', 'f2', 'g2', 'h2', 'i2', 'j2', 'k2', 'l2', 'm2', 'n2', 'o2'] # Group text together rug_text_all = [rug_text_1, rug_text_2] # Create distplot fig = create_distplot( hist_data, group_labels, rug_text=rug_text_all, bin_size=.2) # Add title fig['layout'].update(title='Dist Plot') # Plot! url = py.plot(fig, filename='Distplot with rug text', validate=False) ``` Example 3: Plot with normal curve and hide rug plot ``` import plotly.plotly as py from plotly.figure_factory import create_distplot import numpy as np x1 = np.random.randn(190) x2 = np.random.randn(200)+1 x3 = np.random.randn(200)-1 x4 = np.random.randn(210)+2 hist_data = [x1, x2, x3, x4] group_labels = ['2012', '2013', '2014', '2015'] fig = create_distplot( hist_data, group_labels, curve_type='normal', show_rug=False, bin_size=.4) url = py.plot(fig, filename='hist and normal curve', validate=False) Example 4: Distplot with Pandas ``` import plotly.plotly as py from plotly.figure_factory import create_distplot import numpy as np import pandas as pd df = pd.DataFrame({'2012': np.random.randn(200), '2013': np.random.randn(200)+1}) py.iplot(create_distplot([df[c] for c in df.columns], df.columns), filename='examples/distplot with pandas', validate=False) ``` """ if colors is None: colors = [] if rug_text is None: rug_text = [] validate_distplot(hist_data, curve_type) utils.validate_equal_length(hist_data, group_labels) if isinstance(bin_size, (float, int)): bin_size = [bin_size] * len(hist_data) hist = _Distplot( hist_data, histnorm, group_labels, bin_size, curve_type, colors, rug_text, show_hist, show_curve).make_hist() if curve_type == 'normal': curve = _Distplot( hist_data, histnorm, group_labels, bin_size, curve_type, colors, rug_text, show_hist, show_curve).make_normal() else: curve = _Distplot( hist_data, histnorm, group_labels, bin_size, curve_type, colors, rug_text, show_hist, show_curve).make_kde() rug = _Distplot( hist_data, histnorm, group_labels, bin_size, curve_type, colors, rug_text, show_hist, show_curve).make_rug() data = [] if show_hist: data.append(hist) if show_curve: data.append(curve) if show_rug: data.append(rug) layout = graph_objs.Layout( barmode='overlay', hovermode='closest', legend=dict(traceorder='reversed'), xaxis1=dict(domain=[0.0, 1.0], anchor='y2', zeroline=False), yaxis1=dict(domain=[0.35, 1], anchor='free', position=0.0), yaxis2=dict(domain=[0, 0.25], anchor='x1', dtick=1, showticklabels=False)) else: layout = graph_objs.Layout( barmode='overlay', hovermode='closest', legend=dict(traceorder='reversed'), xaxis1=dict(domain=[0.0, 1.0], anchor='y2', zeroline=False), yaxis1=dict(domain=[0., 1], anchor='free', position=0.0)) data = sum(data, []) return graph_objs.Figure(data=data, layout=layout) class _Distplot(object): """ Refer to TraceFactory.create_distplot() for docstring """ def __init__(self, hist_data, histnorm, group_labels, bin_size, curve_type, colors, rug_text, show_hist, show_curve): self.hist_data = hist_data self.histnorm = histnorm self.group_labels = group_labels self.bin_size = bin_size self.show_hist = show_hist self.show_curve = show_curve self.trace_number = len(hist_data) if rug_text: self.rug_text = rug_text else: self.rug_text = [None] * self.trace_number self.start = [] self.end = [] if colors: self.colors = colors else: self.colors = [ "rgb(31, 119, 180)", "rgb(255, 127, 14)", "rgb(44, 160, 44)", "rgb(214, 39, 40)", "rgb(148, 103, 189)", "rgb(140, 86, 75)", "rgb(227, 119, 194)", "rgb(127, 127, 127)", "rgb(188, 189, 34)", "rgb(23, 190, 207)"] self.curve_x = [None] * self.trace_number self.curve_y = [None] * self.trace_number for trace in self.hist_data: self.start.append(min(trace) * 1.) self.end.append(max(trace) * 1.) def make_hist(self): """ Makes the histogram(s) for FigureFactory.create_distplot(). :rtype (list) hist: list of histogram representations """ hist = [None] * self.trace_number for index in range(self.trace_number): hist[index] = dict(type='histogram', x=self.hist_data[index], xaxis='x1', yaxis='y1', histnorm=self.histnorm, name=self.group_labels[index], legendgroup=self.group_labels[index], marker=dict(color=self.colors[index % len(self.colors)]), autobinx=False, xbins=dict(start=self.start[index], end=self.end[index], size=self.bin_size[index]), opacity=.7) return hist def make_kde(self): """ Makes the kernel density estimation(s) for create_distplot(). This is called when curve_type = 'kde' in create_distplot(). :rtype (list) curve: list of kde representations """ curve = [None] * self.trace_number for index in range(self.trace_number): self.curve_x[index] = [self.start[index] + x * (self.end[index] - self.start[index]) / 500 for x in range(500)] self.curve_y[index] = (scipy_stats.gaussian_kde (self.hist_data[index]) (self.curve_x[index])) if self.histnorm == ALTERNATIVE_HISTNORM: self.curve_y[index] *= self.bin_size[index] for index in range(self.trace_number): curve[index] = dict(type='scatter', x=self.curve_x[index], y=self.curve_y[index], xaxis='x1', yaxis='y1', mode='lines', name=self.group_labels[index], legendgroup=self.group_labels[index], showlegend=False if self.show_hist else True, marker=dict(color=self.colors[index % len(self.colors)])) return curve def make_normal(self): """ Makes the normal curve(s) for create_distplot(). This is called when curve_type = 'normal' in create_distplot(). :rtype (list) curve: list of normal curve representations """ curve = [None] * self.trace_number mean = [None] * self.trace_number sd = [None] * self.trace_number for index in range(self.trace_number): mean[index], sd[index] = (scipy_stats.norm.fit (self.hist_data[index])) self.curve_x[index] = [self.start[index] + x * (self.end[index] - self.start[index]) / 500 for x in range(500)] self.curve_y[index] = scipy_stats.norm.pdf( self.curve_x[index], loc=mean[index], scale=sd[index]) if self.histnorm == ALTERNATIVE_HISTNORM: self.curve_y[index] *= self.bin_size[index] for index in range(self.trace_number): curve[index] = dict(type='scatter', x=self.curve_x[index], y=self.curve_y[index], xaxis='x1', yaxis='y1', mode='lines', name=self.group_labels[index], legendgroup=self.group_labels[index], showlegend=False if self.show_hist else True, marker=dict(color=self.colors[index % len(self.colors)])) return curve def make_rug(self): """ Makes the rug plot(s) for create_distplot(). :rtype (list) rug: list of rug plot representations """ rug = [None] * self.trace_number for index in range(self.trace_number): rug[index] = dict(type='scatter', x=self.hist_data[index], y=([self.group_labels[index]] * len(self.hist_data[index])), xaxis='x1', yaxis='y2', mode='markers', name=self.group_labels[index], legendgroup=self.group_labels[index], showlegend=(False if self.show_hist or self.show_curve else True), text=self.rug_text[index], marker=dict(color=self.colors[index % len(self.colors)], symbol='line-ns-open')) return rug