def plot_mean_boxplot_with_pearson(dataset_id):
data = []
pearson = []
for i, technique_id in enumerate(technique_list):
print(Globals.acronyms[technique_id], end=' ', flush=True)
technique_pearson = []
technique_data = []
history = Parser.parse_rectangles(technique_id, dataset_id)
for revision in range(len(history) - 1):
delta_vis = DeltaMetrics.compute_delta_vis(history[revision],
history[revision + 1])
delta_data = DeltaMetrics.compute_delta_data(
history[revision], history[revision + 1])
un_mov = UnavoidableMovement.compute_unavoidable_movement(
history[revision], history[revision + 1])
ratios = (1 - delta_vis) / (1 - delta_data)
diffs = 1 - abs(delta_vis - delta_data)
unavoidable = 1 - (delta_vis - un_mov)
mean = (ratios + diffs + unavoidable) / 3
technique_data.append(mean)
# Compute linear regression statistics
_, _, r_value, _, _ = stats.linregress(delta_data, delta_vis)
technique_pearson.append(r_value if r_value > 0 else 0)
data.append(technique_data)
pearson.append(technique_pearson)
TimeBoxplot.plot_with_pearson(data,
technique_list,
pearson,
title='Mean with Pearson - ' + dataset_id)
def plot_time_boxplot(dataset_id):
data = []
for i, technique_id in enumerate(technique_list):
technique_data = []
history = Parser.parse_rectangles(technique_id, dataset_id)
for revision in range(len(history) - 1):
shneiderman = compute_shneiderman(history[revision],
history[revision + 1])
technique_data.append(shneiderman)
data.append(technique_data)
TimeBoxplot.plot(data, technique_list, title="Shneiderman - " + dataset_id)
def plot_time_boxplot(dataset_id):
data = []
for i, technique_id in enumerate(technique_list):
technique_data = []
history = Parser.parse_rectangles(technique_id, dataset_id)
for revision in range(len(history) - 1):
rpc = relative_position_change_wrapper(history[revision],
history[revision + 1])
technique_data.append(rpc)
data.append(technique_data)
print(Globals.acronyms[technique_id], end=' ', flush=True)
TimeBoxplot.plot(data, technique_list, title="RPC - " + dataset_id)
def delta_ratio_boxplots(dataset_id):
data = []
for i, technique_id in enumerate(technique_list):
technique_data = []
history = Parser.parse_rectangles(technique_id, dataset_id)
for revision in range(len(history) - 1):
delta_vis = compute_delta_vis(history[revision], history[revision + 1])
delta_data = compute_delta_data(history[revision], history[revision + 1])
ratios = (1 - delta_vis) / (1 - delta_data)
technique_data.append(ratios)
data.append(technique_data)
TimeBoxplot.plot(data, technique_list,
title="Delta Ratio - " + dataset_id)
TimeBoxplot.plot(data, technique_list,
median_sorted=True,
title="Delta Ratio - " + dataset_id)
def plot_time_boxplot(dataset_id):
data = []
for i, technique_id in enumerate(technique_list):
technique_data = []
history = Parser.parse_rectangles(technique_id, dataset_id)
for revision in range(len(history)):
ratios = compute_aspect_ratios(history[revision]).tolist()
technique_data.append(ratios)
data.append(technique_data)
TimeBoxplot.plot(data,
technique_list,
title="Aspect Ratios - " + dataset_id)
TimeBoxplot.plot(data,
technique_list,
median_sorted=True,
title="Aspect Ratios - " + dataset_id)
def plot_mean_boxplot(
dataset_id,
metrics='VIS'): # Default case was what was used at VIS18 paper
data = []
for i, technique_id in enumerate(technique_list):
print(Globals.acronyms[technique_id], end=' ', flush=True)
technique_data = []
history = Parser.parse_rectangles(technique_id, dataset_id)
for revision in range(len(history) - 1):
if metrics == 'VIS':
delta_vis = DeltaMetrics.compute_delta_vis(
history[revision], history[revision + 1])
delta_data = DeltaMetrics.compute_delta_data(
history[revision], history[revision + 1])
un_mov = UnavoidableMovement.compute_unavoidable_movement(
history[revision], history[revision + 1])
ratios = (1 - delta_vis) / (1 - delta_data)
diffs = 1 - abs(delta_vis - delta_data)
unavoidable = 1 - (delta_vis - un_mov)
mean = (ratios + diffs + unavoidable) / 3
elif metrics == 'SIBGRAPI':
delta_vis = DeltaMetrics.compute_delta_vis(
history[revision], history[revision + 1])
delta_data = DeltaMetrics.compute_delta_data(
history[revision], history[revision + 1])
ratios = (1 - delta_vis) / (1 - delta_data)
shn = ShneidermanWattenberg.compute_shneiderman(
history[revision], history[revision + 1])
mean = (ratios + shn) / 2
technique_data.append(mean)
data.append(technique_data)
TimeBoxplot.plot(data, technique_list, title='Mean - ' + dataset_id)
TimeBoxplot.plot(data,
technique_list,
median_sorted=True,
title='Mean - ' + dataset_id)