# matplotlib is quite massive so we're only importing it now.
import matplotlib
import matplotlib.pyplot as plt
bm_file_path, _, _, _ = get_paths()
bm_results_file_path = append_to_filename(bm_file_path, "_results")
# Read results file into rows field.
rows = []
with open(bm_results_file_path) as f:
# Skip CSV header.
next(f)
for line in f:
line = parse_csv_line(line)
line[1] = as_bool(line[1])
line[2] = as_bool(line[2])
for i in range(3, len(line)):
line[i] = float(line[i])
rows.append(line)
# Create basic figure.
fig, axes = plt.subplots(figsize=(6, 3))
if action.startswith("hist_"):
hist_data = action[5:] # Strip hist_ at beginning.
hist_title = ""
# Contains indices of column containing measurements for
blacklisted_urls = []
with open(bl_file_path, "r") as f:
for line in f:
line = line.strip()
# Ignore empty lines and comments.
if line.startswith("#") or len(line) == 0:
continue
blacklisted_urls.append(line)
with open(bm_file_path, "r") as in_file:
# Copy CSV header.
out_file = open(bm_trunc_file_path, "w")
out_file.write(next(in_file))
# Write rows line by line except for the ones that
# belong to a blacklisted URL.
for line in in_file:
if parse_csv_line(line)[columns["url"]] in blacklisted_urls:
continue
out_file.write(line)
out_file.close()
# Copy original file.
shutil.copyfile(bm_file_path, append_to_filename(bm_file_path, "_orig"))
# Overwrite original benchmark file.
shutil.move(bm_trunc_file_path, bm_file_path)
where JS was enabled and one where JS was disabled.
"""
from util import get_paths, parse_csv_line, append_to_filename, as_bool
from util import benchmark_columns as columns
import os
bm_file_path, _, _, _ = get_paths()
bm_js_file_path = append_to_filename(bm_file_path, "_js")
bm_no_js_file_path = append_to_filename(bm_file_path, "_no_js")
with open(bm_file_path, "r") as f:
out_js = open(bm_js_file_path, "w")
out_no_js = open(bm_no_js_file_path, "w")
# Get CSV header and write them to both output files.
csv_header = next(f)
out_js.write(csv_header)
out_no_js.write(csv_header)
for line in f:
# Write to respective file if the jsenabled column
# is either true or false.
if as_bool(parse_csv_line(line)[columns["jsEnabled"]]):
out_js.write(line)
else:
out_no_js.write(line)
out_js.close()
out_no_js.close()
Returns a list of parsed noscript tags from the given
file path, assuming that the file contains nothing but
noscript elements in the document root.
"""
return lxml.html.parse(file_path).find("body").findall("noscript")
with open(bm_file_path, "r") as f:
# Skip CSV header
next(f)
results = {}
pages_scanned = 0
for line in f:
# Scan line for both JS and no JS.
row_js = parse_csv_line(line)
row_no_js = parse_csv_line(next(f))
noscript_tags = []
url = urlparse(row_js[columns["url"]])
# Check if page with JS enabled sent any noscript tags.
if as_bool(row_js[columns["noscript"]]):
file_path = os.path.join(noscript_dir_path, row_js[columns["dataFileName"]] + ".html")
noscript_tags.extend(get_noscript_tags(file_path))
# Check if page with JS disabled sent any noscript tags.
if as_bool(row_no_js[columns["noscript"]]):
file_path = os.path.join(noscript_dir_path, row_no_js[columns["dataFileName"]] + ".html")
noscript_tags.extend(get_noscript_tags(file_path))
)
# List all subdirectory contents.
metrics_dir_list = os.listdir(metrics_dir_path)
noscript_dir_list = os.listdir(noscript_dir_path)
screenshots_dir_list = os.listdir(screenshots_dir_path)
with open(bm_file_path, "r") as f:
# Skip CSV header.
next(f)
# Remove the files from the directory listings that are
# referenced in the main table. In the end, the lists will
# only contain files that need to be deleted.
for line in f:
data_file_name = parse_csv_line(line)[columns["dataFileName"]]
try_remove(metrics_dir_list, data_file_name + ".json")
try_remove(noscript_dir_list, data_file_name + ".html")
try_remove(screenshots_dir_list, data_file_name + ".png")
# Remove unnecessary metrics files.
for x in metrics_dir_list:
os.remove(os.path.join(metrics_dir_path, x))
# Remove unnecessary noscript files.
for x in noscript_dir_list:
os.remove(os.path.join(noscript_dir_path, x))
# Remove unnecessary screenshots.
for x in screenshots_dir_list:
js_file = open(append_to_filename(bm_file_path, "_js"), "r")
nojs_file = open(append_to_filename(bm_file_path, "_no_js"), "r")
# Skip CSV headers.
next(js_file)
next(nojs_file)
while True:
# Both are None if EOF is reached.
js_line = next(js_file, None)
nojs_line = next(nojs_file, None)
if js_line is None or nojs_line is None:
break
js_row = parse_csv_line(js_line)
nojs_row = parse_csv_line(nojs_line)
# 6 = index first median col.
for i in range(6, len(js_row)):
# Parse values into floats.
js_row[i] = float(js_row[i])
nojs_row[i] = float(nojs_row[i])
out_row = [
# col 1: url
js_row[columns["url"]],
# col 2: noscript exists?
as_bool(js_row[columns["noscript"]])
or as_bool(nojs_row[columns["noscript"]]),
# col 3: script exists?