Basic scatter plotter for simple lines
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 |
xxxxxxxxxx# Makes sure we won't require an X11 connectionimport matplotlibmatplotlib.use('Agg')import matplotlib.pyplotimport numpyimport itertoolsclass 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.