def parse_dir(directory, default_settings, revision, rep):
"""Parses bench data from bench logs files.
revision can be either svn revision or git commit hash.
"""
revision_data_points = [] # list of BenchDataPoint
file_list = os.listdir(directory)
file_list.sort()
for bench_file in file_list:
scalar_type = None
# Scalar type, if any, is in the bench filename after revision
if (len(revision) > MAX_SVN_REV_LENGTH
and bench_file.startswith('bench_' + revision + '_')):
# The revision is GIT commit hash.
scalar_type = bench_file[len(revision) + len('bench_') + 1:]
elif (bench_file.startswith('bench_r' + revision + '_')
and revision.isdigit()):
# The revision is SVN number
scalar_type = bench_file[len(revision) + len('bench_r') + 1:]
else:
continue
file_handle = open(directory + '/' + bench_file, 'r')
default_settings['scalar'] = scalar_type
revision_data_points.extend(
bench_util.parse(default_settings, file_handle, rep))
file_handle.close()
return revision_data_points
def parse_dir(directory, default_settings, revision, rep):
"""Parses bench data from bench logs files.
revision can be either svn revision or git commit hash.
"""
revision_data_points = [] # list of BenchDataPoint
file_list = os.listdir(directory)
file_list.sort()
for bench_file in file_list:
scalar_type = None
# Scalar type, if any, is in the bench filename after revision
if (len(revision) > MAX_SVN_REV_LENGTH and
bench_file.startswith('bench_' + revision + '_')):
# The revision is GIT commit hash.
scalar_type = bench_file[len(revision) + len('bench_') + 1:]
elif (bench_file.startswith('bench_r' + revision + '_') and
revision.isdigit()):
# The revision is SVN number
scalar_type = bench_file[len(revision) + len('bench_r') + 1:]
else:
continue
file_handle = open(directory + '/' + bench_file, 'r')
default_settings['scalar'] = scalar_type
revision_data_points.extend(
bench_util.parse(default_settings, file_handle, rep))
file_handle.close()
return revision_data_points
def OutputBenchExpectations(bench_type, rev_min, rev_max, representation_alg):
"""Reads bench data from google storage, and outputs expectations.
Ignores data with revisions outside [rev_min, rev_max] integer range. For
bench data with multiple revisions, we use higher revisions to calculate
expected bench values.
bench_type is either 'micro' or 'skp', according to the flag '-b'.
Uses the provided representation_alg for calculating bench representations.
"""
if bench_type not in BENCH_TYPES:
raise Exception('Not valid bench_type! (%s)' % BENCH_TYPES)
expectation_dic = {}
uri = boto.storage_uri(URI_BUCKET, GOOGLE_STORAGE_URI_SCHEME)
for obj in uri.get_bucket():
# Filters out non-bench files.
if ((not obj.name.startswith('perfdata/%s' % BENCH_BUILDER_PREFIX) and
not obj.name.startswith(
'playback/perfdata/%s' % BENCH_BUILDER_PREFIX)) or
obj.name.find('_data') < 0):
continue
if ((bench_type == 'micro' and obj.name.find('_data_skp_') > 0) or
(bench_type == 'skp' and obj.name.find('_skp_') < 0)):
# Skips wrong bench type.
continue
# Ignores uninterested platforms.
platform = obj.name.split('/')[1]
if not platform.startswith(BENCH_BUILDER_PREFIX):
platform = obj.name.split('/')[2]
if not platform.startswith(BENCH_BUILDER_PREFIX):
continue # Ignores non-platform object
if platform not in PLATFORMS:
continue
# Filters by revision.
to_filter = True
for rev in range(rev_min, rev_max + 1):
if '_r%s_' % rev in obj.name:
to_filter = False
break
if to_filter:
continue
contents = cStringIO.StringIO()
obj.get_file(contents)
for point in bench_util.parse('', contents.getvalue().split('\n'),
representation_alg):
if point.config in CONFIGS_TO_FILTER:
continue
key = '%s_%s_%s,%s-%s' % (point.bench, point.config, point.time_type,
platform, representation_alg)
# It is fine to have later revisions overwrite earlier benches, since we
# only use the latest bench within revision range to set expectations.
expectation_dic[key] = point.time
keys = expectation_dic.keys()
keys.sort()
for key in keys:
bench_val = expectation_dic[key]
# Prints out expectation lines.
print '%s,%.3f,%.3f,%.3f' % (key, bench_val,
bench_val * BENCH_LB - BENCH_ALLOWED_NOISE,
bench_val * BENCH_UB + BENCH_ALLOWED_NOISE)
def parse_dir(directory, default_settings, oldest_revision, newest_revision,
rep):
"""Parses bench data from files like bench_r<revision>_<scalar>.
(str, {str, str}, Number, Number) -> {int:[BenchDataPoints]}"""
revision_data_points = {} # {revision : [BenchDataPoints]}
file_list = os.listdir(directory)
file_list.sort()
for bench_file in file_list:
file_name_match = re.match('bench_r(\d+)_(\S+)', bench_file)
if (file_name_match is None):
continue
revision = int(file_name_match.group(1))
scalar_type = file_name_match.group(2)
if (revision < oldest_revision or revision > newest_revision):
continue
file_handle = open(directory + '/' + bench_file, 'r')
if (revision not in revision_data_points):
revision_data_points[revision] = []
default_settings['scalar'] = scalar_type
revision_data_points[revision].extend(
bench_util.parse(default_settings, file_handle, rep))
file_handle.close()
return revision_data_points
def parse_dir(directory, default_settings, oldest_revision, newest_revision,
rep):
"""Parses bench data from files like bench_r<revision>_<scalar>.
(str, {str, str}, Number, Number) -> {int:[BenchDataPoints]}"""
revision_data_points = {} # {revision : [BenchDataPoints]}
for bench_file in os.listdir(directory):
file_name_match = re.match('bench_r(\d+)_(\S+)', bench_file)
if (file_name_match is None):
continue
revision = int(file_name_match.group(1))
scalar_type = file_name_match.group(2)
if (revision < oldest_revision or revision > newest_revision):
continue
file_handle = open(directory + '/' + bench_file, 'r')
if (revision not in revision_data_points):
revision_data_points[revision] = []
default_settings['scalar'] = scalar_type
revision_data_points[revision].extend(
bench_util.parse(default_settings, file_handle, rep))
file_handle.close()
return revision_data_points
def OutputSkpBenchExpectations(rev_min, rev_max, representation_alg):
"""Reads skp bench data from google storage, and outputs expectations.
Ignores data with revisions outside [rev_min, rev_max] integer range. For
bench data with multiple revisions, we use higher revisions to calculate
expected bench values.
Uses the provided representation_alg for calculating bench representations.
"""
expectation_dic = {}
uri = boto.storage_uri(URI_BUCKET, GOOGLE_STORAGE_URI_SCHEME)
for obj in uri.get_bucket():
# Filters out non-skp-bench files.
if ((not obj.name.startswith('perfdata/Skia_')
and not obj.name.startswith('playback/perfdata/Skia_'))
or obj.name.find('_data_skp_') < 0):
continue
# Ignores uninterested platforms.
platform = obj.name.split('/')[1]
if not platform.startswith('Skia_'):
platform = obj.name.split('/')[2]
if not platform.startswith('Skia_'):
continue # Ignores non-platform object
platform = platform[5:] # Removes "Skia_" prefix.
if platform not in PLATFORMS:
continue
# Filters by revision.
to_filter = True
for rev in range(rev_min, rev_max + 1):
if '_r%s_' % rev in obj.name:
to_filter = False
break
if to_filter:
continue
contents = cStringIO.StringIO()
obj.get_file(contents)
for point in bench_util.parse('',
contents.getvalue().split('\n'),
representation_alg):
if point.config in CONFIGS_TO_FILTER:
continue
key = '%s_%s_%s,%s-%s' % (point.bench, point.config,
point.time_type, platform,
representation_alg)
# It is fine to have later revisions overwrite earlier benches, since we
# only use the latest bench within revision range to set expectations.
expectation_dic[key] = point.time
keys = expectation_dic.keys()
keys.sort()
for key in keys:
bench_val = expectation_dic[key]
# Prints out expectation lines.
print '%s,%.3f,%.3f,%.3f' % (
key, bench_val, bench_val * BENCH_LB - BENCH_ALLOWED_NOISE,
bench_val * BENCH_UB + BENCH_ALLOWED_NOISE)
def OutputSkpBenchExpectations(rev_min, rev_max, representation_alg):
"""Reads skp bench data from google storage, and outputs expectations.
Ignores data with revisions outside [rev_min, rev_max] integer range. For
bench data with multiple revisions, we use higher revisions to calculate
expected bench values.
Uses the provided representation_alg for calculating bench representations.
"""
expectation_dic = {}
uri = boto.storage_uri(URI_BUCKET, GOOGLE_STORAGE_URI_SCHEME)
for obj in uri.get_bucket():
# Filters out non-skp-bench files.
if (
not obj.name.startswith("perfdata/Skia_") and not obj.name.startswith("playback/perfdata/Skia_")
) or obj.name.find("_data_skp_") < 0:
continue
# Ignores uninterested platforms.
platform = obj.name.split("/")[1]
if not platform.startswith("Skia_"):
platform = obj.name.split("/")[2]
if not platform.startswith("Skia_"):
continue # Ignores non-platform object
platform = platform[5:] # Removes "Skia_" prefix.
if platform not in PLATFORMS:
continue
# Filters by revision.
to_filter = True
for rev in range(rev_min, rev_max + 1):
if "_r%s_" % rev in obj.name:
to_filter = False
break
if to_filter:
continue
contents = cStringIO.StringIO()
obj.get_file(contents)
for point in bench_util.parse("", contents.getvalue().split("\n"), representation_alg):
if point.config in CONFIGS_TO_FILTER:
continue
key = "%s_%s_%s,%s-%s" % (point.bench, point.config, point.time_type, platform, representation_alg)
# It is fine to have later revisions overwrite earlier benches, since we
# only use the latest bench within revision range to set expectations.
expectation_dic[key] = point.time
keys = expectation_dic.keys()
keys.sort()
for key in keys:
bench_val = expectation_dic[key]
# Prints out expectation lines.
print "%s,%.3f,%.3f,%.3f" % (
key,
bench_val,
bench_val * BENCH_LB - BENCH_ALLOWED_NOISE,
bench_val * BENCH_UB + BENCH_ALLOWED_NOISE,
)
def OutputSkpBenchExpectations(rev_min, rev_max, representation_alg):
"""Reads skp bench data from google storage, and outputs expectations.
Ignores data with revisions outside [rev_min, rev_max] integer range. For
bench data with multiple revisions, we use higher revisions to calculate
expected bench values.
Uses the provided representation_alg for calculating bench representations.
"""
expectation_dic = {}
uri = boto.storage_uri(URI_BUCKET, GOOGLE_STORAGE_URI_SCHEME)
for obj in uri.get_bucket():
# Filters out non-skp-bench files.
if (not obj.name.startswith('perfdata/Skia_')
or obj.name.find('_data_skp_') < 0):
continue
# Ignores uninterested platforms.
platform = obj.name.split('/')[1][5:] # Removes "Skia_" prefix.
if platform not in PLATFORMS:
continue
# Filters by revision.
for rev in range(rev_min, rev_max + 1):
if '_r%s_' % rev not in obj.name:
continue
contents = cStringIO.StringIO()
obj.get_file(contents)
for point in bench_util.parse('',
contents.getvalue().split('\n'),
representation_alg):
if point.config in CONFIGS_TO_FILTER:
continue
# TODO(bensong): the filtering below is only needed during skp generation
# system transitioning. Change it once the new system (bench name starts
# with http) is stable for the switch-over, and delete it once we
# deprecate the old ones.
if point.bench.startswith('http'):
continue
key = '%s_%s_%s,%s-%s' % (point.bench, point.config,
point.time_type, platform,
representation_alg)
# It is fine to have later revisions overwrite earlier benches, since we
# only use the latest bench within revision range to set expectations.
expectation_dic[key] = point.time
keys = expectation_dic.keys()
keys.sort()
for key in keys:
bench_val = expectation_dic[key]
# Prints out expectation lines.
print '%s,%.3f,%.3f,%.3f' % (
key, bench_val, bench_val * BENCH_LB - BENCH_ALLOWED_NOISE,
bench_val * BENCH_UB + BENCH_ALLOWED_NOISE)
def OutputSkpBenchExpectations(rev_min, rev_max, representation_alg):
"""Reads skp bench data from google storage, and outputs expectations.
Ignores data with revisions outside [rev_min, rev_max] integer range. For
bench data with multiple revisions, we use higher revisions to calculate
expected bench values.
Uses the provided representation_alg for calculating bench representations.
"""
expectation_dic = {}
uri = boto.storage_uri(URI_BUCKET, GOOGLE_STORAGE_URI_SCHEME)
for obj in uri.get_bucket():
# Filters out non-skp-bench files.
if (not obj.name.startswith('perfdata/Skia_') or
obj.name.find('_data_skp_') < 0):
continue
# Ignores uninterested platforms.
platform = obj.name.split('/')[1][5:] # Removes "Skia_" prefix.
if platform not in PLATFORMS:
continue
# Filters by revision.
for rev in range(rev_min, rev_max + 1):
if '_r%s_' % rev not in obj.name:
continue
contents = cStringIO.StringIO()
obj.get_file(contents)
for point in bench_util.parse('', contents.getvalue().split('\n'),
representation_alg):
if point.config in CONFIGS_TO_FILTER:
continue
# TODO(bensong): the filtering below is only needed during skp generation
# system transitioning. Change it once the new system (bench name starts
# with http) is stable for the switch-over, and delete it once we
# deprecate the old ones.
if point.bench.startswith('http'):
continue
key = '%s_%s_%s,%s-%s' % (point.bench, point.config, point.time_type,
platform, representation_alg)
# It is fine to have later revisions overwrite earlier benches, since we
# only use the latest bench within revision range to set expectations.
expectation_dic[key] = point.time
keys = expectation_dic.keys()
keys.sort()
for key in keys:
bench_val = expectation_dic[key]
# Prints out expectation lines.
print '%s,%.3f,%.3f,%.3f' % (key, bench_val,
bench_val * BENCH_LB - BENCH_ALLOWED_NOISE,
bench_val * BENCH_UB + BENCH_ALLOWED_NOISE)
else:
usage()
sys.exit(2)
if header:
print header_format.format(
bench='bench'
, config='conf'
, time_type='time'
, old_time='old'
, new_time='new'
, diff='diff'
, diffp='diffP'
)
old_benches = bench_util.parse({}, open(old, 'r'))
new_benches = bench_util.parse({}, open(new, 'r'))
bench_diffs = []
for old_bench in old_benches:
#filter new_benches for benches that match old_bench
new_bench_match = [bench for bench in new_benches
if old_bench.bench == bench.bench and
old_bench.config == bench.config and
old_bench.time_type == bench.time_type
]
if (len(new_bench_match) < 1):
continue
bench_diffs.append(BenchDiff(old_bench, new_bench_match[0]))
bench_diffs.sort(key=lambda d : [d.diffp,
columns = value
elif option == "-s":
stat_type = value
elif option == "-t":
use_tabs = True
elif option == "--match":
match_bench = value
else:
usage()
assert False, "unhandled option"
if old is None or new is None:
usage()
sys.exit(2)
old_benches = bench_util.parse({}, open(old, 'r'), stat_type)
new_benches = bench_util.parse({}, open(new, 'r'), stat_type)
bench_diffs = []
for old_bench in old_benches:
#filter benches by the match criteria
if match_bench and not old_bench.bench.startswith(match_bench):
continue
#filter new_benches for benches that match old_bench
new_bench_match = [bench for bench in new_benches
if old_bench.bench == bench.bench and
old_bench.config == bench.config and
old_bench.time_type == bench.time_type
]
if (len(new_bench_match) < 1):
def OutputTileAnalysis(rev, representation_alg, bench_dir, platform):
"""Reads skp bench data and outputs tile vs. viewport analysis for the given
platform.
Ignores data with revisions other than rev. If bench_dir is not empty, read
from the local directory instead of Google Storage.
Uses the provided representation_alg for calculating bench representations.
Returns (js_codes, body_codes): strings of js/html codes for stats and
visualization.
"""
js_codes = ''
body_codes = ('}</script></head><body>'
'<h3>PLATFORM: %s REVISION: %s</h3><br>' % (platform, rev))
bench_dic = {} # [bench][config] -> [layout, [values]]
file_dic = GetFiles(rev, bench_dir, platform)
for f in file_dic:
for point in bench_util.parse('', file_dic[f].split('\n'),
representation_alg):
if point.time_type: # Ignores non-walltime time_type.
continue
bench = point.bench.replace('.skp', '')
config = point.config.replace('simple_', '')
components = config.split('_')
if components[0] == 'viewport':
bench_dic.setdefault(
bench, {})[config] = [components[1], [point.time]]
else: # Stores per-tile benches.
bench_dic.setdefault(bench, {})[config] = [
point.tile_layout, point.per_tile_values
]
benches = bench_dic.keys()
benches.sort()
for bench in benches:
body_codes += '<h4>%s</h4><br><table><tr>' % bench
heat_plots = '' # For table row of heatmap plots.
table_plots = '' # For table row of data table plots.
# For bar plot legends and values in URL string.
legends = ''
values = ''
keys = bench_dic[bench].keys()
keys.sort()
if not keys[-1].startswith(
'viewport'): # No viewport to analyze; skip.
continue
else:
# Extracts viewport size, which for all viewport configs is the same.
viewport = bench_dic[bench][keys[-1]][0]
for config in keys:
[layout, value_li] = bench_dic[bench][config]
if config.startswith(
'tile_'): # For per-tile data, visualize tiles.
tile_size = config.split('_')[1]
if (not re.search(DIMENSIONS_RE, layout)
or not re.search(DIMENSIONS_RE, tile_size)
or not re.search(DIMENSIONS_RE, viewport)):
continue # Skip unrecognized formats.
[viewport_tile_sum,
matrix] = GetTileMatrix(layout, tile_size, value_li, viewport)
values += '%s|' % viewport_tile_sum
[this_js, row1,
row2] = GetTileVisCodes(config + '_' + bench, matrix)
heat_plots += row1
table_plots += row2
js_codes += this_js
else: # For viewport data, there is only one element in value_li.
values += '%s|' % sum(value_li)
legends += '%s:%s|' % (config, sum(value_li))
body_codes += (heat_plots + '</tr><tr>' + table_plots +
'</tr></table>' + '<br>' + BAR_CHART_TEMPLATE %
(legends[:-1], values[:-1]))
return (js_codes, body_codes)
column_format += column_formats[column_char]
header_format += header_formats[column_char]
else:
usage()
sys.exit(2)
if header:
print header_format.format(bench='bench',
config='conf',
time_type='time',
old_time='old',
new_time='new',
diff='diff',
diffp='diffP')
old_benches = bench_util.parse({}, open(old, 'r'))
new_benches = bench_util.parse({}, open(new, 'r'))
bench_diffs = []
for old_bench in old_benches:
#filter new_benches for benches that match old_bench
new_bench_match = [
bench for bench in new_benches
if old_bench.bench == bench.bench and old_bench.config ==
bench.config and old_bench.time_type == bench.time_type
]
if (len(new_bench_match) < 1):
continue
bench_diffs.append(BenchDiff(old_bench, new_bench_match[0]))
bench_diffs.sort(key=lambda d: [
columns = value
elif option == "-s":
stat_type = value
elif option == "-t":
use_tabs = True
elif option == "--match":
match_bench = value
else:
usage()
assert False, "unhandled option"
if old is None or new is None:
usage()
sys.exit(2)
old_benches = bench_util.parse({}, open(old, 'r'), stat_type)
new_benches = bench_util.parse({}, open(new, 'r'), stat_type)
bench_diffs = []
for old_bench in old_benches:
#filter benches by the match criteria
if match_bench and not old_bench.bench.startswith(match_bench):
continue
#filter new_benches for benches that match old_bench
new_bench_match = [
bench for bench in new_benches
if old_bench.bench == bench.bench and old_bench.config ==
bench.config and old_bench.time_type == bench.time_type
]
if (len(new_bench_match) < 1):
def OutputTileAnalysis(rev, representation_alg, bench_dir, platform):
"""Reads skp bench data and outputs tile vs. viewport analysis for the given
platform.
Ignores data with revisions other than rev. If bench_dir is not empty, read
from the local directory instead of Google Storage.
Uses the provided representation_alg for calculating bench representations.
Returns (js_codes, body_codes): strings of js/html codes for stats and
visualization.
"""
js_codes = ""
body_codes = "}</script></head><body>" "<h3>PLATFORM: %s REVISION: %s</h3><br>" % (platform, rev)
bench_dic = {} # [bench][config] -> [layout, [values]]
file_dic = GetFiles(rev, bench_dir, platform)
for f in file_dic:
for point in bench_util.parse("", file_dic[f].split("\n"), representation_alg):
if point.time_type: # Ignores non-walltime time_type.
continue
bench = point.bench.replace(".skp", "")
config = point.config.replace("simple_", "")
components = config.split("_")
if components[0] == "viewport":
bench_dic.setdefault(bench, {})[config] = [components[1], [point.time]]
else: # Stores per-tile benches.
bench_dic.setdefault(bench, {})[config] = [point.tile_layout, point.per_tile_values]
benches = bench_dic.keys()
benches.sort()
for bench in benches:
body_codes += "<h4>%s</h4><br><table><tr>" % bench
heat_plots = "" # For table row of heatmap plots.
table_plots = "" # For table row of data table plots.
# For bar plot legends and values in URL string.
legends = ""
values = ""
keys = bench_dic[bench].keys()
keys.sort()
if not keys[-1].startswith("viewport"): # No viewport to analyze; skip.
continue
else:
# Extracts viewport size, which for all viewport configs is the same.
viewport = bench_dic[bench][keys[-1]][0]
for config in keys:
[layout, value_li] = bench_dic[bench][config]
if config.startswith("tile_"): # For per-tile data, visualize tiles.
tile_size = config.split("_")[1]
if (
not re.search(DIMENSIONS_RE, layout)
or not re.search(DIMENSIONS_RE, tile_size)
or not re.search(DIMENSIONS_RE, viewport)
):
continue # Skip unrecognized formats.
[viewport_tile_sum, matrix] = GetTileMatrix(layout, tile_size, value_li, viewport)
values += "%s|" % viewport_tile_sum
[this_js, row1, row2] = GetTileVisCodes(config + "_" + bench, matrix)
heat_plots += row1
table_plots += row2
js_codes += this_js
else: # For viewport data, there is only one element in value_li.
values += "%s|" % sum(value_li)
legends += "%s:%s|" % (config, sum(value_li))
body_codes += (
heat_plots
+ "</tr><tr>"
+ table_plots
+ "</tr></table>"
+ "<br>"
+ BAR_CHART_TEMPLATE % (legends[:-1], values[:-1])
)
return (js_codes, body_codes)
sys.exit(2)
for column_char in columns:
if column_formats[column_char]:
column_format += column_formats[column_char]
header_format += header_formats[column_char]
else:
usage()
sys.exit(2)
if header:
print header_format.format(
bench="bench", config="conf", time_type="time", old_time="old", new_time="new", diff="diff", diffp="diffP"
)
old_benches = bench_util.parse({}, open(old, "r"))
new_benches = bench_util.parse({}, open(new, "r"))
bench_diffs = []
for old_bench in old_benches:
# filter new_benches for benches that match old_bench
new_bench_match = [
bench
for bench in new_benches
if old_bench.bench == bench.bench
and old_bench.config == bench.config
and old_bench.time_type == bench.time_type
]
if len(new_bench_match) < 1:
continue
bench_diffs.append(BenchDiff(old_bench, new_bench_match[0]))
def OutputBenchExpectations(bench_type, rev_min, rev_max, representation_alg):
"""Reads bench data from google storage, and outputs expectations.
Ignores data with revisions outside [rev_min, rev_max] integer range. For
bench data with multiple revisions, we use higher revisions to calculate
expected bench values.
bench_type is either 'micro' or 'skp', according to the flag '-b'.
Uses the provided representation_alg for calculating bench representations.
"""
if bench_type not in BENCH_TYPES:
raise Exception('Not valid bench_type! (%s)' % BENCH_TYPES)
expectation_dic = {}
uri = boto.storage_uri(URI_BUCKET, GOOGLE_STORAGE_URI_SCHEME)
for obj in uri.get_bucket():
# Filters out non-bench files.
if ((not obj.name.startswith('perfdata/%s' % BENCH_BUILDER_PREFIX)
and not obj.name.startswith(
'playback/perfdata/%s' % BENCH_BUILDER_PREFIX))
or obj.name.find('_data') < 0):
continue
if ((bench_type == 'micro' and obj.name.find('_data_skp_') > 0)
or (bench_type == 'skp' and obj.name.find('_skp_') < 0)):
# Skips wrong bench type.
continue
# Ignores uninterested platforms.
platform = obj.name.split('/')[1]
if not platform.startswith(BENCH_BUILDER_PREFIX):
platform = obj.name.split('/')[2]
if not platform.startswith(BENCH_BUILDER_PREFIX):
continue # Ignores non-platform object
if platform not in PLATFORMS:
continue
# Filters by revision.
to_filter = True
for rev in range(rev_min, rev_max + 1):
if '_r%s_' % rev in obj.name:
to_filter = False
break
if to_filter:
continue
contents = cStringIO.StringIO()
obj.get_file(contents)
for point in bench_util.parse('',
contents.getvalue().split('\n'),
representation_alg):
if point.config in CONFIGS_TO_FILTER:
continue
key = '%s_%s_%s,%s-%s' % (point.bench, point.config,
point.time_type, platform,
representation_alg)
# It is fine to have later revisions overwrite earlier benches, since we
# only use the latest bench within revision range to set expectations.
expectation_dic[key] = point.time
keys = expectation_dic.keys()
keys.sort()
for key in keys:
bench_val = expectation_dic[key]
# Prints out expectation lines.
print '%s,%.3f,%.3f,%.3f' % (
key, bench_val, bench_val * BENCH_LB - BENCH_ALLOWED_NOISE,
bench_val * BENCH_UB + BENCH_ALLOWED_NOISE)
