def scatter_gather(items, x_val, y_val, c_val='condition_color'):
x, y, c = [], [], []
for i in items:
x.append(get_class_value(i, x_val))
y.append(get_class_value(i, y_val))
c.append(get_class_value(i, c_val))
return x, y, c
def data_matrix(self, prop):
conditions = default_condition_order[:]
values = []
for log in self.all_logs():
val = get_class_value(log, prop)
if val is not None:
values.append((log.condition, log.condition_rank, val))
data = np.ndarray((4, 4))
for i, cond in enumerate(conditions):
for j in range(3):
data[i, j] = np.mean([
val for condition, rank, val in values
if condition == cond and rank == j + 1
])
data[i, 3] = np.mean(
[val for condition, _, val in values if condition == cond])
for j in range(3):
data[3, j] = np.mean(
[val for _, rank, val in values if rank == j + 1])
data[3, 3] = np.mean([val for _, _, val in values])
t = PrettyTable(['', '1', '2', '3', 'M'])
conditions += ['M']
for i, row in enumerate(data):
t.add_row([conditions[i]] + list(row))
t.float_format = ".3"
print(t)
def plot_avg_vs_vp_id(self, prop, show=True):
data = {}
for vp in self.vps:
data[vp.vp_id] = np.mean(
[get_class_value(log, prop) for log in vp.logs])
plt.plot(range(len(data)), [d[1] for d in sorted(data.items())])
show_plot(show)
def boxplot_all(self, prop, show=None, fig=None):
values, data = {}, []
for log in self.all_logs(filter_5=True):
val = get_class_value(log, prop)
if val is not None:
values.setdefault(log.condition, []).append(val)
conditions = EcoSonic_VP.condition_order_from_vp_id(1)
data = [values[cond] for cond in conditions]
boxplot(data, conditions, prop, 'condition', show, fig)
def data_vs_run(self, prop, global_run_counter=False):
max_items = 12 if global_run_counter else 4
data = defaultdict(lambda: [[] for _ in range(max_items)])
for log in self.all_logs():
val = get_class_value(log, prop)
if val is not None:
idx = log.global_run_counter if global_run_counter else log.run
assert idx <= max_items
data[log.condition][idx - 1].append(val)
return data, max_items