Source code for acopy.utils.plot

# -*- coding: utf-8 -*-
import functools

    import matplotlib.pyplot as plt
    import pandas as pd
except ImportError:

[docs]class Plotter: """Utility for plotting iteration data using matplotlib. This is meant to be used in combination with the :class:`~StatsRecorder` plugin which collects stats about solutions and pheromone levels on each iteration. :param dict stats: map of stats by name """ def __init__(self, stats): self.stats = stats
[docs] def plot(self): """Create and show the plot.""" plt.figure() plt.title('Solutions (stats)') self.plot_solutions() plt.figure() plt.title('Edge Pheromone (levels)') self.plot_pheromone_levels(legend=False) plt.figure() plt.title('Edge Pheromone (stats)') self.plot_edge_pheromone() plt.figure() plt.title('Solutions (uniqueness)') self.plot_unique_solutions() def _plot(self, stat, ax=None, **kwargs): data = self._extract_and_process(stat) data.plot(ax=ax or plt.gca(), **kwargs) def __getattr__(self, name): if name.startswith('plot_'): __, stat = name.split('_', 1) return functools.partial(self._plot, stat) raise AttributeError(name) def _extract_and_process(self, stat): extractor = getattr(self, f'extract_{stat}', lambda: self.stats[stat]) processor = getattr(self, f'process_{stat}', lambda d: pd.DataFrame(d)) return processor(extractor())
[docs] def extract_ant_distances(self): iterations = [] for distances in self.stats['ant_distances']: if all(d is not None for d in distances): distances = list(sorted(distances)) iterations.append(distances)
return iterations