def plot_distance(self, results, color, name, size=10, suppress_scatter=False): """Results needs to be a DataFrame or Series object with 'connected' and 'distance' as columns """ connected = results['connected'] distance = results['distance'] dist_win = self.params.value() if self.results is None: self.name = name self.color = color self.results = results if suppress_scatter is True: #suppress scatter plot for all results (takes forever to plot) plots = list(self.plots) plots[1] = None self.dist_plot = distance_plot(connected, distance, plots=plots, color=color, name=name, size=size, window=dist_win, spacing=dist_win) else: self.dist_plot = distance_plot(connected, distance, plots=self.plots, color=color, name=name, size=size, window=dist_win, spacing=dist_win) return self.dist_plot
def plot_distance(self, results, color, name, size=10): """Results needs to be a DataFrame or Series object with 'connected' and 'distance' as columns """ if self.results is None: self.name = name self.color = color self.results = results connected = results['connected'] distance = results['distance'] dist_win = self.params.value() self.dist_plot = distance_plot(connected, distance, plots=self.plots, color=color, name=name, size=size, window=dist_win, spacing=dist_win) self.plots[0].setXRange(0, 200e-6) # return self.dist_plot
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ] nc2 = [] d2 = [] for p, c, d in zip(n_probed, n_connected, dist): nc2.extend([True] * c) nc2.extend([False] * (p - c)) d2.extend([d * 1e-6] * p) plots = distance_plot(nc2, d2, color=(255, 255, 0), name="Brian/Gil Pvalb")[0] # compare to latest results (but note these are from a different layer) expts = cached_experiments() expts.distance_plot('pvalb', 'pvalb', plots=plots)
for i, class_info in enumerate(cell_classes): cell_class, class_name, color = class_info # Add text label label = pg.LabelItem(class_name) label.setFixedWidth(80) label.setParentItem(dist_plots[i].vb) class_results = results[(cell_class, cell_class)] probed_pairs = class_results['probed_pairs'] connected_pairs = class_results['connected_pairs'] probed_distance = [p.distance for p in probed_pairs] connections = [(p in connected_pairs) for p in probed_pairs] plot, xvals, prop, upper, lower = distance_plot(connections, probed_distance, plots=(dist_plots[i], None), color=color, window=40e-6, spacing=40e-6) bins = np.arange(0, 180e-6, 20e-6) hist = np.histogram(probed_distance, bins=bins) hist_plots[i].plot(hist[1], hist[0], stepMode=True, fillLevel=0, brush=color + (80,)) write_csv(csv_file, hist[1], "Figure 4%s, %s histogram values" % (fig_letter, class_name)) write_csv(csv_file, hist[0], "Figure 4%s, %s histogram bin edges" % (fig_letter, class_name)) write_csv(csv_file, xvals, "Figure 4%s, %s distance plot x vals" % (fig_letter, class_name)) write_csv(csv_file, prop, "Figure 4%s, %s distance plot trace" % (fig_letter, class_name)) write_csv(csv_file, upper, "Figure 4%s, %s distance plot upper CI" % (fig_letter, class_name)) write_csv(csv_file, lower, "Figure 4%s, %s distance plot x vals" % (fig_letter, class_name))