def plot_matrix(dataset_ids):
matrix = []
for dataset_id in dataset_ids:
dataset_values = []
for technique_id in technique_list:
history = Parser.parse_rectangles(technique_id, dataset_id)
avg = compute_location_drift(history)
dataset_values.append(avg)
print(Globals.acronyms[technique_id], dataset_id, avg)
matrix.append(dataset_values)
matrix = np.array(matrix).transpose()
MatrixPlot.plot(matrix,
dataset_ids,
technique_list,
cell_text=True,
title='Location Drift')
def plot_matrix(dataset_ids):
matrix = []
for dataset_id in dataset_ids:
dataset_values = []
for technique_id in technique_list:
history = Parser.parse_rectangles(technique_id, dataset_id)
all_ratios = np.array([])
for revision in range(len(history) - 1):
distances = compute_shneiderman(history[revision],
history[revision + 1])
all_ratios = np.append(all_ratios, distances.values)
dataset_values.append(all_ratios.mean())
print(Globals.acronyms[technique_id], dataset_id,
all_ratios.mean())
matrix.append(dataset_values)
matrix = np.array(matrix).transpose()
MatrixPlot.plot(matrix,
dataset_ids,
technique_list,
cell_text=True,
title='Shneiderman-Wattenberg')
def pearson_matrix(dataset_ids):
matrix = []
for dataset_id in dataset_ids:
dataset_values = []
for technique_id in technique_list:
# print(Globals.acronyms[technique_id], dataset_id)
history = Parser.parse_rectangles(technique_id, dataset_id)
# Compute all delta_vis and delta_data values for a dataset (1 pair per cell)
all_delta_data = np.array([])
all_delta_vis = np.array([])
for revision in range(len(history) - 1):
delta_data = DeltaMetrics.compute_delta_data(history[revision], history[revision + 1])
all_delta_data = np.append(all_delta_data, delta_data)
delta_vis = DeltaMetrics.compute_delta_vis(history[revision], history[revision + 1])
all_delta_vis = np.append(all_delta_vis, delta_vis)
# Compute linear regression statistics
slope, intercept, r_value, p_value, std_err = stats.linregress(all_delta_data, all_delta_vis)
dataset_values.append(r_value)
print(Globals.acronyms[technique_id], dataset_id, r_value)
matrix.append(dataset_values)
matrix = np.array(matrix).transpose()
MatrixPlot.plot(matrix, dataset_ids, technique_list,
shared_cm=False,
cell_text=True,
title='Pearson')
MatrixPlot.plot(matrix, dataset_ids, technique_list,
shared_cm=True,
cell_text=True,
title='Pearson')
MatrixPlot.plot(matrix, dataset_ids, technique_list,
shared_cm=False,
cell_text=False,
title='Pearson')
MatrixPlot.plot(matrix, dataset_ids, technique_list,
shared_cm=True,
cell_text=False,
title='Pearson')
def plot_ar_matrix(dataset_ids):
matrix = []
for dataset_id in dataset_ids:
dataset_values = []
for technique_id in technique_list:
history = Parser.parse_rectangles(technique_id, dataset_id)
all_ratios = np.array([])
for revision in range(len(history) - 1):
ratios = compute_aspect_ratios(history[revision])
all_ratios = np.append(all_ratios, ratios.values)
dataset_values.append(all_ratios.mean())
print(Globals.acronyms[technique_id], dataset_id,
all_ratios.mean())
matrix.append(dataset_values)
matrix = np.array(matrix).transpose()
MatrixPlot.plot(matrix,
dataset_ids,
technique_list,
shared_cm=False,
cell_text=True,
title='Aspect ratio')
MatrixPlot.plot(matrix,
dataset_ids,
technique_list,
shared_cm=True,
cell_text=True,
title='Aspect ratio')
MatrixPlot.plot(matrix,
dataset_ids,
technique_list,
shared_cm=False,
cell_text=False,
title='Aspect ratio')
MatrixPlot.plot(matrix,
dataset_ids,
technique_list,
shared_cm=True,
cell_text=False,
title='Aspect ratio')
def delta_ratio_matrix(dataset_ids):
matrix = []
for dataset_id in dataset_ids:
dataset_values = []
for technique_id in technique_list:
history = Parser.parse_rectangles(technique_id, dataset_id)
all_ratios = np.array([])
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])
ratio = (1 - delta_vis) / (1 - delta_data)
all_ratios = np.append(all_ratios, ratio.values)
dataset_values.append(all_ratios.mean())
print(Globals.acronyms[technique_id], dataset_id, all_ratios.mean())
matrix.append(dataset_values)
matrix = np.array(matrix).transpose()
MatrixPlot.plot(matrix, dataset_ids, technique_list,
shared_cm=False,
cell_text=True,
title='Delta ratio')
MatrixPlot.plot(matrix, dataset_ids, technique_list,
shared_cm=True,
cell_text=True,
title='Delta ratio')
MatrixPlot.plot(matrix, dataset_ids, technique_list,
shared_cm=False,
cell_text=False,
title='Delta ratio')
MatrixPlot.plot(matrix, dataset_ids, technique_list,
shared_cm=True,
cell_text=False,
title='Delta ratio')
dataset_id = sys.argv[2:]
LocationDrift.plot_matrix(dataset_id)
# Sondag's Relative Position Change
elif action == 'rpc-boxplots':
dataset_id = sys.argv[2]
RelativePositionChange.plot_time_boxplot(dataset_id)
elif action == 'rpc-matrix':
dataset_ids = sys.argv[2:]
RelativePositionChange.plot_matrix(dataset_ids)
# Plots any csv, avoiding recomputing a whole metric matrix
elif action == 'generic-matrix':
title = sys.argv[2]
csv_path = sys.argv[3]
MatrixPlot.generic_plot(title, csv_path)
# Create the rank count for the Google Drive document
elif action == 'rank-counter':
csv_paths = sys.argv[2:]
RankCounter.count_to_csv(csv_paths)
# Centroid Trail Visualization
elif action == 'centroid-trail':
dataset_id = sys.argv[2]
CentroidTrail.plot(dataset_id)
else:
print('Invalid command. See the readme file.')