def update_stats(path_dir, data, ref, tests, metrics, clip, eps=1e-2):
"""Update some entries of data.js; assumes it was already created."""
find_idx = lambda t, d: list(d['stats'][0]['labels']).index(t['name'])
for test in tests:
# Check if entry exists
is_new = test['name'] not in data['stats'][0]['labels']
# Update dictionary
if is_new:
data['imageBoxes'][0]['elements'].append(hdr_to_ldr(
path_dir, test))
data['stats'][0]['labels'].append(test['name'])
else:
t = find_idx(test, data)
hdr_to_ldr(path_dir, test)
# Compute desired metrics
for m, metric in enumerate(metrics):
# Recompute error
err_img = compute_metric(ref, test['data'], metric.lower(), eps)
err_mean = '{:.6f}'.format(np.mean(err_img))
if is_new:
data['stats'][0]['series'][m]['data'].append(err_mean)
else:
data['stats'][0]['series'][m]['data'][t] = err_mean
# Recompute false color heatmap and save to files
fc = falsecolor(err_img, clip, eps)
fc_fname = '{}-{}.png'.format(test['name'], metric.upper())
plt.imsave(os.path.join(path_dir, fc_fname), fc)
if is_new:
fc_entry = {
'title': test['name'],
'version': '-',
'image': fc_fname
}
data['imageBoxes'][m + 1]['elements'].append(fc_entry)
# TODO: Update stats.json
return data
def compute_stats(path_dir, ref, tests, metrics, clip, negpos, eps=1e-2):
"""Generate all false color LDR maps and dictionary for JS.
Assumes tests = {'name': 'my_alg', 'data': ...}
"""
data = {}
data['imageBoxes'] = [{'title': 'Images', 'elements': []}]
data['stats'] = [{'title': 'Stats', 'labels': [], 'series': []}]
ref_entry = hdr_to_ldr(path_dir, {'name': 'Reference', 'data': ref})
data['imageBoxes'][0]['elements'].append(ref_entry)
# Generate images and compute stats
# Couldn't find a way to do it all in only two loops
stats = []
for t, test in enumerate(tests):
# Update dictionary
data['imageBoxes'][0]['elements'].append(hdr_to_ldr(path_dir, test))
data['stats'][0]['labels'].append(test['name'])
# Compute all metrics
stat_entry = {test['name']: {}}
stats.append(stat_entry)
for metric in metrics:
# Compute error
err_img = compute_metric(ref, test['data'], metric, eps)
err_mean = '{:.6f}'.format(np.mean(err_img))
# Compute false color heatmap and save to files
fc = falsecolor(err_img, clip, eps)
fc_fname = '{}-{}.png'.format(test['name'], metric.upper())
plt.imsave(os.path.join(path_dir, fc_fname), fc)
# Save stats, if necessary
stats[t][test['name']][metric.upper()] = {
'val': err_mean,
'fc': fc_fname
}
# Write dictionary
for metric in metrics:
fc_entry = {'title': metric.upper(), 'elements': []}
metric_entry = {
'label': metric.upper(),
'data': [],
'track': {
'x': [],
'y': []
}
}
for t, test in enumerate(tests):
# Add false color filenames to dict
fc_fname = stats[t][test['name']][metric.upper()]['fc']
entry = {'title': test['name'], 'version': '-', 'image': fc_fname}
fc_entry['elements'].append(entry)
# Add metric value to dict
err_mean = stats[t][test['name']][metric.upper()]['val']
metric_entry['data'].append(err_mean)
# Update dictionary with false color filenames and metrics
data['imageBoxes'].append(fc_entry)
data['stats'][0]['series'].append(metric_entry)
# Write negative/positive image if requested
if negpos:
fc_entry = {'title': 'NP SMAPE', 'elements': []}
for t, test in enumerate(tests):
# Compute the N/P false color image
fc = falsecolor_np(ref, test['data'], eps)
fc_fname = '{}-NP.png'.format(test['name'])
plt.imsave(os.path.join(path_dir, fc_fname), fc)
# Save the fcname inside JSON
entry = {'title': test['name'], 'version': '-', 'image': fc_fname}
fc_entry['elements'].append(entry)
# Update dictionary with false color filenames
data['imageBoxes'].append(fc_entry)
generate_thumbnail(path_dir, ref)
return data