in pageload-summary/summarize_testing.py [0:0]
def summarize(data, platforms):
org_data = organize_data(data, platforms)
summary = {}
for platform, apps in org_data.items():
for app, variants in apps.items():
for variant, pl_types in variants.items():
for pl_type, tests in pl_types.items():
platform_summary = {"tests": list(tests.keys()), "values": []}
# Get all the push times
all_push_times = []
for _, info in tests.items():
print(info)
all_push_times.extend(list(info["values"].keys()))
all_push_times = list(set(all_push_times))
all_push_times = temporal_aggregation(all_push_times, 24)
print(all_push_times)
# Get a summary value for each push time
summarized_vals = []
tests_per_val = {}
prev_time = None
prev_test_times = {}
for c, times in enumerate(sorted(all_push_times)):
vals = {}
for time in times:
if not prev_time:
prev_time = time
good = True
testsc = []
testsg = []
for test, info in tests.items():
if time not in info["values"]:
good = False
testsc.append(test)
continue
if test not in prev_test_times:
prev_test_times[test] = time
vals.setdefault(test, []).extend(info["values"][time])
testsg.append(
(
test,
time,
prev_test_times[test],
np.mean(info["values"][time]),
np.mean(info["values"][prev_test_times[test]]),
)
)
prev_test_times[test] = time
if not good:
print(
f"Tests which failed and prevent a summary at time {time}:",
testsc,
)
vals = [np.mean(v) for _, v in vals.items()]
summarized_vals.append((times[-1], gmean(np.asarray(vals))))
tests_per_val[str(c)] = {
"good": testsg,
"bad": testsc,
"vals": vals,
}
prev_time = time
"""
"wikia-firefox-cold-webrender",
"espn-firefox-cold-webrender",
"cnn-firefox-cold-webrender",
"nytimes-firefox-cold-webrender",
"buzzfeed-firefox-cold-webrender",
"expedia-firefox-cold-webrender"
"""
"""
"wikia-firefox-cold-webrender",
"espn-firefox-cold-webrender",
"cnn-firefox-cold-webrender",
"nytimes-firefox-cold-webrender",
"buzzfeed-firefox-cold-webrender",
"expedia-firefox-cold-webrender"
"""
import json
print("hereeee")
print(json.dumps(tests_per_val, indent=4))
# # Get the ratios over time
# prev_test_times = {}
# all_ratios = []
# prev_ratio = np.nan
# for time in sorted(all_push_times):
# ratios = []
# for test, info in tests.items():
# if info["values"].get(time, None):
# if prev_test_times.get(test, None):
# ratios.append(
# np.mean(
# info["values"][time]
# ) / np.mean(
# info["values"][prev_test_times[test]]
# )
# )
# else:
# prev_test_times[test] = time
# else:
# continue
# gmean_ratios = gmean(ratios)
# if np.isnan(gmean_ratios) and not np.isnan(prev_ratio):
# all_ratios.append(prev_ratio)
# continue
# prev_ratio = gmean_ratios
# all_ratios.append(gmean_ratios)
# new_ratios = []
# first_good = None
# for y in all_ratios:
# if not np.isnan(y):
# first_good = y
# break
# for y in all_ratios:
# if np.isnan(y) and first_good:
# new_ratios.append(first_good)
# else:
# new_ratios.append(y)
# first_good = None
# all_ratios = new_ratios
# all_ratios = np.asarray(all_ratios)
# plt.figure()
variant = variant if variant != "None" else "e10s"
plt.title(platform + f"\n{app}-{pl_type}-{variant}")
# plt.plot(list(((all_ratios-min(all_ratios))/(max(all_ratios)-min(all_ratios)))), label="Ratios geomean")
# plt.show()
# plt.figure()
x = np.asarray(
[y for x, y in sorted(summarized_vals, key=lambda x: x[0])]
)
times = np.asarray(
[x for x, y in sorted(summarized_vals, key=lambda x: x[0])]
)
sorted_summary = (x - min(x)) / (max(x) - min(x))
print(sorted_summary)
print(platform)
print(variant)
# break
# plt.plot([i for i in range(len(sorted_summary))], [y for x, y in sorted_summary])
plt.plot(x, label="Geomean")
plt.legend()
plt.show()
summary.setdefault(platform, {}).setdefault(variant, {}).setdefault(
app, {}
)[pl_type] = {
"tests": list(tests.keys()),
"values-gmean": x,
"times": times,
}