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))