BEAT - plot/scatter/1

Parameters 14

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	X
ylabel	The label of the Y-axis (vertical)	string	Y
title	The title for this plot	string	Scatter plot
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
grid	If we should draw grid lines or not for the plot	bool	False
xaxis_log	If X-axis (horizontal) should be in log-scale	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
width	Width of the resulting image in pixels	uint16	400
height	Height of the resulting image in pixels	uint16	300
content_type	The type of image returned	string	image/png
line_attributes	Scatter/Line attributes passed directly to Matplotlib	string

# Makes sure we won't require an X11 connection
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot

import numpy
import itertools

class Plotter(baselib.Plotter):

def setup(self, parameters):

super(Plotter, self).setup(parameters)
        self.line_attributes = parameters.get('line_attributes', '').split('&')
        self.line_attributes = [k for k in self.line_attributes if k]
        return True

def process(self, inputs):

fig, ax = super(Plotter, self).prepare_canvas()

colormap = matplotlib.pyplot.cm.gist_ncar
        ax.set_color_cycle([colormap(k) for k in numpy.linspace(0, 0.9, len(inputs))])
        
        args = []
        label = []

single_experiment = len(inputs) == 1

for xp_label, xp_data in inputs:
            for scatter in xp_data.data:

# the data
                args.append(scatter.x * self.xaxis_multiplier)
                args.append(scatter.y * self.yaxis_multiplier)

# the label
                if self.label: #gets the label from user overwritten input
                    label.append(self.label[len(label)%len(self.label)])
                else: #make-up label
                    if single_experiment:
                        label.append(scatter.label)
                    else:
                        text = [k for k in (xp_label, scatter.label) if k]
                        label.append('-'.join(text))

# the line attributes
                if self.line_attributes:
                    args.append(self.line_attributes[len(label)%len(self.line_attributes)])

lines = ax.plot(*args)
        if len(lines) > 1:
            ax.legend(lines, label, loc='best', fancybox=True, framealpha=0.5)

super(Plotter, self).apply_parameters(ax)
        return super(Plotter, self).encode_figure(fig)

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

No reports are using this plotter.

plotters plot scatter 1

Parameters 14

plotters

plot

scatter

1