# This script is used to update the CSS ranking. The CSS ranking will affect
# the grouping of CSS properties in Computed Style.
# Usage: Run `python update_css_ranking.py` to update the default
# CSS ranking file and API.
# Run `python update_css_ranking.py <ranking_file>` to update
# the ranking to another file with the default ranking API.
# Run `python update_css_ranking.py <ranking_file> <ranking_api_link>`
# to update the ranking from <ranking_api_link> API to <ranking_file>
import json
import sys
import cluster
import json5_generator
import math
try:
from urllib.request import urlopen
except ImportError:
from urllib2 import urlopen
CSS_RANKING_API = "http://www.chromestatus.com/data/csspopularity"
CSS_RANKING_FILE = "../../core/css/css_properties_ranking.json5"
CSS_PROPERTIES = "../../core/css/css_properties.json5"
CONFIG_FILE = "../../core/css/css_group_config.json5"
def reformat_properties_name(css_properties):
for i in range(len(css_properties)):
if css_properties[i][:5] == "alias":
css_properties[i] = css_properties[i][5:]
if css_properties[i][:6] == "webkit":
css_properties[i] = "-" + css_properties[i]
def update_css_ranking(css_ranking_file, css_ranking_api):
"""Create the css_properties_ranking.json5 for uses in Computed Style grouping
Args:
css_ranking_file: file directory to css_properties_ranking.json5
css_ranking_api: url to CSS ranking api
"""
css_ranking = json.loads(urlopen(css_ranking_api).read())
css_ranking_content = {"properties": {}, "data": []}
css_ranking_content["data"] = [
property_["property_name"] for property_ in sorted(
css_ranking, key=lambda x: -float(x["day_percentage"]))
]
# Pseudo-properties are normally not part of this list, and thus get put fairly far down.
# However, IsStackingContextWithoutContainment is accessed and mutated fairly often
# in the case of e.g. the “transform” property being mutated. If it is put down in rare data,
# incremental computation of inline style will end up being significantly less efficient
# in such cases, as copy-on-write will be invoked on the mutated subobject. (See e.g.
# the MotionMark “multiply” subtest, which does this a lot.) Thus, we give it special treatment
# and push it to the top of the list.
css_ranking_content["data"].insert(0,
"IsStackingContextWithoutContainment")
reformat_properties_name(css_ranking_content["data"])
with open(css_ranking_file, "w") as fw:
fw.write(
"// DO NOT EDIT: This file is generated by manually running:\n"
"// third_party/blink/renderer/build/scripts/update_css_ranking.py\n"
"//\n"
"// The popularity ranking of all css properties the first properties is the most\n"
"// used property according to: https://www.chromestatus.com/metrics/css/popularity\n"
)
json.dump(css_ranking_content, fw, indent=4, sort_keys=False)
def find_partition_rule(css_property_set,
all_properties,
n_cluster,
transform=lambda x: x):
"""Find partition rule for a set of CSS property based on its popularity
Args:
css_property_set: list of CSS properties and their popularity of form
[(css_property_name, popularity_score)..]
n_cluster: number of cluster to divide the set into
all_properties: all CSS properties and its score
transform: data transform function to transform the popularity score,
default value is the identity function
Returns:
partition rule for css_property_set
"""
_, cluster_alloc, _ = cluster.k_means(
[transform(p[1]) for p in css_property_set], n_cluster=n_cluster)
return [
all_properties[css_property_set[i][0]]
for i in range(len(cluster_alloc) - 1)
if cluster_alloc[i] != cluster_alloc[i + 1]
] + [1.0]
def produce_partition_rule(config_file, css_ranking_api):
"""Find the partition rule for the groups and print them to config_file
Args:
config_file: the file to write the parameters to
css_ranking_api: url to CSS ranking api
"""
css_ranking = sorted(json.loads(urlopen(css_ranking_api).read()),
key=lambda x: -x["day_percentage"])
total_css_properties = len(css_ranking)
css_ranking_dictionary = dict(
[(x["property_name"], x["day_percentage"] * 100) for x in css_ranking])
css_ranking_cdf = dict(
zip([x["property_name"] for x in css_ranking], [
float(i) / total_css_properties
for i in range(total_css_properties)
]))
css_properties = json5_generator.Json5File.load_from_files(
[CSS_PROPERTIES]).name_dictionaries
rare_non_inherited_properties = sorted(
[(x["name"].original, css_ranking_dictionary[x["name"].original])
for x in css_properties
if not x["inherited"] and x["field_group"] is not None and "*" in
x["field_group"] and x["name"].original in css_ranking_dictionary],
key=lambda x: -x[1])
rare_inherited_properties = sorted(
[(x["name"].original, css_ranking_dictionary[x["name"].original])
for x in css_properties
if x["inherited"] and x["field_group"] is not None and "*" in
x["field_group"] and x["name"].original in css_ranking_dictionary],
key=lambda x: -x[1])
rni_properties_rule = find_partition_rule(
rare_non_inherited_properties, css_ranking_cdf, n_cluster=3)
ri_properties_rule = find_partition_rule(
rare_inherited_properties,
css_ranking_cdf,
n_cluster=2,
transform=lambda x: math.log(x + 10e-6))
with open(config_file, 'w') as fw:
fw.write(
"// DO NOT EDIT: This file is generated by manually running:\n"
"// third_party/blink/renderer/build/scripts/update_css_ranking.py\n"
"//\n"
"// The grouping parameter is a cumulative distribution "
"over the whole set of ranked\n"
"// CSS properties.\n")
json.dump({
"parameters": {},
"data": [{
"name": "rare_non_inherited_properties_rule",
"cumulative_distribution": rni_properties_rule
},
{
"name": "rare_inherited_properties_rule",
"cumulative_distribution": ri_properties_rule
}]
},
fw,
indent=4)
if __name__ == '__main__':
assert len(sys.argv) < 4, "Too many parameters"
if len(sys.argv) == 1:
update_css_ranking(CSS_RANKING_FILE, CSS_RANKING_API)
produce_partition_rule(CONFIG_FILE, CSS_RANKING_API)
elif len(sys.argv) == 2:
update_css_ranking(sys.argv[1], CSS_RANKING_API)
produce_partition_rule(CONFIG_FILE, CSS_RANKING_API)
elif len(sys.argv) == 3:
update_css_ranking(sys.argv[1], sys.argv[2])
produce_partition_rule(CONFIG_FILE, sys.argv[2])
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