1. plotters
  2. plot
  3. bar
  4. 1

Basic bar plotter for simple histograms

Parameters allow users to change the configuration of an plotter when scheduling an experiment

Name Description Type Default
xlabel The label of the X-axis (horizontal) string
ylabel The label of the Y-axis (vertical) string
title The title for this plot string Bar plot
title-fontsize Controls the title font size uint16 10
xaxis_multiplier The multiplication factor for the X-axis (horizontal) float64 1.0
yaxis_multiplier The multiplication factor for the Y-axis (vertical) float64 1.0
legend Short description of the data, to be added to the plot string
legend-fontsize Controls the font size of the legend uint16 12
legend-loc The location of the legend string best
legend-bbox-to-anchor Specify any arbitrary location for the legend string 1.0&1.0
grid If we should draw grid lines or not for the plot bool False
yaxis_log If Y-axis (vertical) should be in log-scale bool False
dpi Dots-per-inch in raster image formats uint16 60
bar-alpha Value for the alpha effect in the bar plot float64 0.75
bar-norm If set to true will normalize the distribution between 0-1 bool True
width Width of the resulting image in pixels uint16 400
height Height of the resulting image in pixels uint16 300
axis-fontsize Controls the axis font size (labels and values) uint16 10
content_type The type of image returned string image/png
bar_attributes Bar attributes passed directly to Matplotlib string 
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# Makes sure we won't require an X11 connection
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import matplotlib
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matplotlib.use('Agg')
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​
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import numpy
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import itertools
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​
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​
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class Plotter(baselib.Plotter):
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​
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    def setup(self, parameters):
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​
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        super(Plotter, self).setup(parameters)
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        self.loc = parameters.get('legend-loc', '')
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        self.legend_bbox_to_anchor = parameters.get('legend-bbox-to-anchor', '').split('&')
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        self.legend_bbox_to_anchor = [float(self.legend_bbox_to_anchor[k]) for k in range(2) ] #Taking only the two first arguments and cast to float64
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        self.bar_attributes = parameters.get('bar_attributes', '').split('&')
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        self.bar_attributes = [k for k in self.bar_attributes if k]
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        self.bar_default_ratio_width = 1
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        self.bar_alpha       = parameters.get('bar-alpha', '')
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        self.axis_fontsize    = parameters.get('axis-fontsize', '')
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        self.bar_norm = parameters.get('bar-norm', '')
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        self.title_fontsize  = parameters.get('title-fontsize', '')
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        self.legend_fontsize = parameters.get('legend-fontsize', '')
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​
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        # These are the "Tableau 20" colors as RGB.
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        self.tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
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                          (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
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                          (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
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                          (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
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                          (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
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        # Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts.
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        for i in range(len(self.tableau20)):
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            r, g, b = self.tableau20[i]
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            self.tableau20[i] = (r / 255., g / 255., b / 255.)
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​
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        return True
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​
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​
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    def process(self, inputs):
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​
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        fig, ax = super(Plotter, self).prepare_canvas()
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        super(Plotter, self).apply_parameters(ax)
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​
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        ax.set_title(self.title, fontdict={'fontsize':self.title_fontsize})
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        if(self.bar_norm):
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            ax.set_ylabel("Normalized frequency", fontdict={'fontsize':self.axis_fontsize})
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            ax.set_ylim(0,1.1)
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        else:
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            ax.set_ylabel("Frequency", fontdict={'fontsize':self.axis_fontsize})          
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        args = []
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        label = []
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​
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        single_experiment = len(inputs) == 1
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​
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        n_plots = 0
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        bar_lst = []
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​
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        for xp_label, experiment in inputs:
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            for cur_bar in experiment.data:
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                n_plots += 1
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​
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                # Massages the input data
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                x = cur_bar.x * self.xaxis_multiplier
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                y = cur_bar.y * self.yaxis_multiplier
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                #Normalizing if necessary
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                if(self.bar_norm):
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                    y = (y - numpy.min(y))/(numpy.max(y) - numpy.min(y))
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                args = (x,y)
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​
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                # Additional attributes
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                if self.bar_attributes: args = args + (self.bar_attributes,)
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                width = self.bar_default_ratio_width * (x[1] - x[0])
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                kwargs = {'color': self.tableau20[n_plots % len(self.tableau20)], 'width': width, 'alpha':self.bar_alpha, 'linewidth':0.25}
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​
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                # Append labels
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                if self.label: #gets the label from user overwritten input
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                    if single_experiment:
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                        # ignore user label and use only bar labels
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                        label.append(cur_bar.label)
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                    else:
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                        # user-provided labels refer to experiment names
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                        text = [k for k in (self.label[len(label)%len(self.label)], cur_bar.label) if k]
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                        label.append('-'.join(text))                      
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                else: #make-up label
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                    if single_experiment:
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                        label.append(cur_bar.label)
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                    else:
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                        text = [k for k in (xp_label, cur_bar.label) if k]
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                        label.append('-'.join(text))
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​
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                # Plots
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                bar_lst.append(ax.bar(*args, **kwargs))
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​
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        if n_plots > 1:
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            ax.legend(bar_lst, label, loc=self.loc, fontsize=self.legend_fontsize, fancybox=True, framealpha=0.5)
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​
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        return super(Plotter, self).encode_figure(fig)

This is the default plotter for bar plots. It provides sensible defaults in case the user has not selected any.

Reports

Updated Name Actions
Jan. 5, 2017 sbhatta/replay_antispoofing (PAD experiments using ReplayAttack database)
2
July 15, 2016 pkorshunov/MFCC-GMM-based-PAD-system (Performance of MFCC-GMM-based PAD system on presentation attacks)
1
Created with Raphaël 2.1.2[compare]plot/bar/12015May4
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