def categorize(items, prop_func):
"""
:rtype : dict
"""
getter = prop_func if callable(prop_func) else lambda x: rget(x, prop_func, None)
result = { }
for item in items:
key = getter(item)
if key in result:
result[key].append(item)
else:
result[key] = [item]
return result
def categorize(items, prop_func):
"""
:rtype : dict
"""
getter = prop_func if callable(
prop_func) else lambda x: rget(x, prop_func, None)
result = {}
for item in items:
key = getter(item)
if key in result:
result[key].append(item)
else:
result[key] = [item]
return result
def export(results, props):
result = 'name\t' + '\t'.join(pluck(results, 'name')) + '\n'
for p in props:
result += p + '\t' + '\t'.join(['%1.3f' % x for x in pluck(results, p)]) + '\n'
return result
def remove_common_prefixes(items, paths):
paths = list(paths) if type(paths) is not list else paths
return [x for x in items if x['name'] not in paths]
# fetch data and categorize them by version and then by time-frame path
measurements = multi_cat(load_data(), [
lambda x: rget(x, '_id.version', None),
# lambda x: _get_short_name(rget(x, '_id.path', None)),
lambda x: rget(x, '_id.path', None),
])
props = ['d1', 'd2', 'cl']
version_combination = list(get_version_combination(measurements.keys(), history=2))
slow_down_factors = slow_down_factor(measurements, version_combination, props, top=10, limit=10)
clipboard_result = ''
for va, vb, vab in version_combination:
calc_output = export(slow_down_factors[vab], props)
print vab
print calc_output
clipboard_result += calc_output
