In [14]:
%run prelude.ipy
In [23]:
all_fixes = data.hansen_2012.all_fixations()
all_saccs = data.hansen_2012.all_saccades()
aois = data.hansen_2012.areas_of_interest()
In [41]:
rows = []
i = 10
for (exp_id, trial_id), t_fixes in all_fixes.groupby(["exp_id", "trial_id"]):
num_fixes = len(t_fixes)
avg_fix_duration = metrics.avg_fixation_duration(t_fixes)
# AOIs
t_aois = aois[(aois.exp_id == exp_id) & (aois.trial_id == trial_id)]
t_aois = aoi.combine_aois(t_aois, "line", new_kind="codebbox")
code_bbox = t_aois[t_aois.kind == "codebbox"]
spatial_density = metrics.spatial_density(t_fixes, code_bbox)
convex_hull_area = metrics.convex_hull_area(t_fixes)
# Saccades
t_saccs = all_saccs[(all_saccs.exp_id == exp_id) &
(all_saccs.trial_id == trial_id)]
fix_sacc_proportion = metrics.fixation_saccade_proportion(t_fixes, t_saccs)
# Scanpaths
line_sp = aoi.scanpath_from_fixations(t_fixes, "line", repeats=False)
line_tm = aoi.transition_matrix(line_sp, norm=False)
line_trans_density = metrics.transition_density(line_tm)
block_sp = aoi.scanpath_from_fixations(t_fixes, "block", repeats=False)
block_tm = aoi.transition_matrix(block_sp, norm=False)
block_trans_density = metrics.transition_density(block_tm)
code_out_sp = aoi.scanpath_from_fixations(t_fixes,
{ "interface": ["code box", "output box"] }, repeats=False)
code_out_tm = aoi.transition_matrix(code_out_sp, norm=False)
#code_out_trans_density = metrics.transition_density(code_out_tm)
code_out_transitions = code_out_tm.flatten().sum()
sp_length = metrics.scanpath_length(t_fixes)
rows.append([exp_id, trial_id, num_fixes, avg_fix_duration,
spatial_density, convex_hull_area, fix_sacc_proportion,
line_trans_density, block_trans_density,
code_out_transitions, sp_length])
i = i - 1
if i == 0:
break
cols = ["exp_id", "trial_id", "fix_count", "avg_fix_duration",
"spatial_density", "convex_hull_area", "fix_sacc_proportion",
"line_trans_density", "block_trans_density",
"code_out_transitions", "sp_length"]
trial_metrics = pandas.DataFrame(rows, columns=cols)
trial_metrics[:10]
Out[41]:
In [10]:
base, version = "between", "functions"
fixes = util.filter_program(all_fixations, base, version)
num_lines = len(data.hansen_2012.program_code(base, version))
aoi_names = {
"line" : ["line {0}".format(i) for i in range(1, num_lines+1)],
"interface" : ["output box"]
}
scanpaths = []
for trial_id, t_fixes in fixes.groupby("trial_id"):
t_scanpath, _ = aoi.aoi_scanpath(t_fixes, aoi_names, mixed=True, repeats=False)
scanpaths.append(t_scanpath.values)
In [12]:
results = util.all_pairs(scanpaths, aoi.scanpath_edit_distance)
In [2]:
pandas.Series(results.flatten()).hist().figure
In [30]:
rows = []
for base, version in programs[["base", "version"]].values:
fixes = util.filter_program(all_fixations, base, version)
lines_with_code = data.hansen_2012.program_lines_with_code(base, version)
line_aois = ["line {0}".format(i) for i in lines_with_code]
for trial_id, t_fixes in fixes.groupby("trial_id"):
trial = trials[trials.trial_id == trial_id].iloc[0]
duration_30_percent = trial["duration_ms"] / 3.0
t_fixes = t_fixes[t_fixes.start_ms < duration_30_percent]
percent_fixated = 0.0
if len(t_fixes) > 0:
aoi_names = metrics.first_fixation_ms(t_fixes)
if "line" in aoi_names.index.get_level_values("kind"):
lines_fixated = [name for name in aoi_names.loc["line"].index if name in line_aois]
percent_fixated = len(lines_fixated) / float(len(line_aois))
rows.append([base, version, trial_id, percent_fixated])
df = pandas.DataFrame(rows, columns=["base", "version", "trial_id", "percent_fixated30"])
In [37]:
df.percent_fixated30.mean()
Out[37]:
In [45]:
def base_fun(b_frame, ax):
ax.set_title("{0} ({1} trials)".format(b_frame.iloc[0]["base"], len(b_frame)))
ax.set_xlim(0, 1)
ax.set_xlabel("Percent Lines Fixated")
axes = plot.misc.hist_by_base(df, "percent_fixated30", title="Percent Lines Fixated by Program (30%)",
base_fun=base_fun, figsize=(20, 10))
axes[0].figure
Out[45]:
Other
In [7]:
per_trial = metrics.fixation_ms_per_trial(all_fixations)
print "Mean", per_trial.mean()
print "STD", per_trial.std()