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ffae400179
This adds SPDX license headers in place of the wide assortment of things currently in our script headers. We default to GPL-2.0-only except for the oeqa code where it was clearly submitted and marked as MIT on the most part or some scripts which had the "or later" GPL versioning. The patch also drops other obsolete bits of file headers where they were encoountered such as editor modelines, obsolete maintainer information or the phrase "All rights reserved" which is now obsolete and not required in copyright headers (in this case its actually confusing for licensing as all rights were not reserved). More work is needed for OE-Core but this takes care of the bulk of the scripts and meta/lib directories. The top level LICENSE files are tweaked to match the new structure and the SPDX naming. (From OE-Core rev: f8c9c511b5f1b7dbd45b77f345cb6c048ae6763e) Signed-off-by: Richard Purdie <richard.purdie@linuxfoundation.org>
339 lines
12 KiB
Python
339 lines
12 KiB
Python
#
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# Copyright (c) 2017, Intel Corporation.
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#
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# SPDX-License-Identifier: GPL-2.0-only
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#
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"""Handling of build perf test reports"""
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from collections import OrderedDict, Mapping, namedtuple
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from datetime import datetime, timezone
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from numbers import Number
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from statistics import mean, stdev, variance
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AggregateTestData = namedtuple('AggregateTestData', ['metadata', 'results'])
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def isofmt_to_timestamp(string):
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"""Convert timestamp string in ISO 8601 format into unix timestamp"""
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if '.' in string:
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dt = datetime.strptime(string, '%Y-%m-%dT%H:%M:%S.%f')
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else:
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dt = datetime.strptime(string, '%Y-%m-%dT%H:%M:%S')
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return dt.replace(tzinfo=timezone.utc).timestamp()
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def metadata_xml_to_json(elem):
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"""Convert metadata xml into JSON format"""
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assert elem.tag == 'metadata', "Invalid metadata file format"
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def _xml_to_json(elem):
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"""Convert xml element to JSON object"""
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out = OrderedDict()
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for child in elem.getchildren():
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key = child.attrib.get('name', child.tag)
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if len(child):
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out[key] = _xml_to_json(child)
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else:
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out[key] = child.text
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return out
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return _xml_to_json(elem)
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def results_xml_to_json(elem):
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"""Convert results xml into JSON format"""
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rusage_fields = ('ru_utime', 'ru_stime', 'ru_maxrss', 'ru_minflt',
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'ru_majflt', 'ru_inblock', 'ru_oublock', 'ru_nvcsw',
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'ru_nivcsw')
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iostat_fields = ('rchar', 'wchar', 'syscr', 'syscw', 'read_bytes',
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'write_bytes', 'cancelled_write_bytes')
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def _read_measurement(elem):
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"""Convert measurement to JSON"""
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data = OrderedDict()
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data['type'] = elem.tag
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data['name'] = elem.attrib['name']
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data['legend'] = elem.attrib['legend']
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values = OrderedDict()
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# SYSRES measurement
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if elem.tag == 'sysres':
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for subel in elem:
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if subel.tag == 'time':
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values['start_time'] = isofmt_to_timestamp(subel.attrib['timestamp'])
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values['elapsed_time'] = float(subel.text)
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elif subel.tag == 'rusage':
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rusage = OrderedDict()
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for field in rusage_fields:
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if 'time' in field:
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rusage[field] = float(subel.attrib[field])
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else:
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rusage[field] = int(subel.attrib[field])
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values['rusage'] = rusage
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elif subel.tag == 'iostat':
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values['iostat'] = OrderedDict([(f, int(subel.attrib[f]))
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for f in iostat_fields])
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elif subel.tag == 'buildstats_file':
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values['buildstats_file'] = subel.text
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else:
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raise TypeError("Unknown sysres value element '{}'".format(subel.tag))
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# DISKUSAGE measurement
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elif elem.tag == 'diskusage':
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values['size'] = int(elem.find('size').text)
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else:
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raise Exception("Unknown measurement tag '{}'".format(elem.tag))
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data['values'] = values
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return data
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def _read_testcase(elem):
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"""Convert testcase into JSON"""
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assert elem.tag == 'testcase', "Expecting 'testcase' element instead of {}".format(elem.tag)
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data = OrderedDict()
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data['name'] = elem.attrib['name']
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data['description'] = elem.attrib['description']
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data['status'] = 'SUCCESS'
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data['start_time'] = isofmt_to_timestamp(elem.attrib['timestamp'])
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data['elapsed_time'] = float(elem.attrib['time'])
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measurements = OrderedDict()
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for subel in elem.getchildren():
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if subel.tag == 'error' or subel.tag == 'failure':
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data['status'] = subel.tag.upper()
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data['message'] = subel.attrib['message']
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data['err_type'] = subel.attrib['type']
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data['err_output'] = subel.text
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elif subel.tag == 'skipped':
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data['status'] = 'SKIPPED'
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data['message'] = subel.text
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else:
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measurements[subel.attrib['name']] = _read_measurement(subel)
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data['measurements'] = measurements
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return data
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def _read_testsuite(elem):
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"""Convert suite to JSON"""
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assert elem.tag == 'testsuite', \
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"Expecting 'testsuite' element instead of {}".format(elem.tag)
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data = OrderedDict()
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if 'hostname' in elem.attrib:
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data['tester_host'] = elem.attrib['hostname']
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data['start_time'] = isofmt_to_timestamp(elem.attrib['timestamp'])
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data['elapsed_time'] = float(elem.attrib['time'])
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tests = OrderedDict()
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for case in elem.getchildren():
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tests[case.attrib['name']] = _read_testcase(case)
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data['tests'] = tests
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return data
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# Main function
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assert elem.tag == 'testsuites', "Invalid test report format"
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assert len(elem) == 1, "Too many testsuites"
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return _read_testsuite(elem.getchildren()[0])
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def aggregate_metadata(metadata):
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"""Aggregate metadata into one, basically a sanity check"""
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mutable_keys = ('pretty_name', 'version_id')
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def aggregate_obj(aggregate, obj, assert_str=True):
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"""Aggregate objects together"""
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assert type(aggregate) is type(obj), \
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"Type mismatch: {} != {}".format(type(aggregate), type(obj))
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if isinstance(obj, Mapping):
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assert set(aggregate.keys()) == set(obj.keys())
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for key, val in obj.items():
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aggregate_obj(aggregate[key], val, key not in mutable_keys)
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elif isinstance(obj, list):
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assert len(aggregate) == len(obj)
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for i, val in enumerate(obj):
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aggregate_obj(aggregate[i], val)
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elif not isinstance(obj, str) or (isinstance(obj, str) and assert_str):
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assert aggregate == obj, "Data mismatch {} != {}".format(aggregate, obj)
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if not metadata:
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return {}
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# Do the aggregation
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aggregate = metadata[0].copy()
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for testrun in metadata[1:]:
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aggregate_obj(aggregate, testrun)
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aggregate['testrun_count'] = len(metadata)
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return aggregate
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def aggregate_data(data):
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"""Aggregate multiple test results JSON structures into one"""
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mutable_keys = ('status', 'message', 'err_type', 'err_output')
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class SampleList(list):
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"""Container for numerical samples"""
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pass
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def new_aggregate_obj(obj):
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"""Create new object for aggregate"""
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if isinstance(obj, Number):
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new_obj = SampleList()
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new_obj.append(obj)
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elif isinstance(obj, str):
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new_obj = obj
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else:
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# Lists and and dicts are kept as is
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new_obj = obj.__class__()
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aggregate_obj(new_obj, obj)
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return new_obj
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def aggregate_obj(aggregate, obj, assert_str=True):
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"""Recursive "aggregation" of JSON objects"""
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if isinstance(obj, Number):
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assert isinstance(aggregate, SampleList)
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aggregate.append(obj)
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return
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assert type(aggregate) == type(obj), \
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"Type mismatch: {} != {}".format(type(aggregate), type(obj))
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if isinstance(obj, Mapping):
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for key, val in obj.items():
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if not key in aggregate:
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aggregate[key] = new_aggregate_obj(val)
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else:
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aggregate_obj(aggregate[key], val, key not in mutable_keys)
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elif isinstance(obj, list):
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for i, val in enumerate(obj):
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if i >= len(aggregate):
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aggregate[key] = new_aggregate_obj(val)
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else:
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aggregate_obj(aggregate[i], val)
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elif isinstance(obj, str):
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# Sanity check for data
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if assert_str:
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assert aggregate == obj, "Data mismatch {} != {}".format(aggregate, obj)
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else:
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raise Exception("BUG: unable to aggregate '{}' ({})".format(type(obj), str(obj)))
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if not data:
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return {}
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# Do the aggregation
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aggregate = data[0].__class__()
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for testrun in data:
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aggregate_obj(aggregate, testrun)
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return aggregate
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class MeasurementVal(float):
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"""Base class representing measurement values"""
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gv_data_type = 'number'
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def gv_value(self):
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"""Value formatting for visualization"""
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if self != self:
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return "null"
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else:
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return self
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class TimeVal(MeasurementVal):
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"""Class representing time values"""
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quantity = 'time'
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gv_title = 'elapsed time'
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gv_data_type = 'timeofday'
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def hms(self):
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"""Split time into hours, minutes and seconeds"""
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hhh = int(abs(self) / 3600)
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mmm = int((abs(self) % 3600) / 60)
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sss = abs(self) % 60
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return hhh, mmm, sss
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def __str__(self):
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if self != self:
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return "nan"
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hh, mm, ss = self.hms()
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sign = '-' if self < 0 else ''
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if hh > 0:
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return '{}{:d}:{:02d}:{:02.0f}'.format(sign, hh, mm, ss)
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elif mm > 0:
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return '{}{:d}:{:04.1f}'.format(sign, mm, ss)
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elif ss > 1:
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return '{}{:.1f} s'.format(sign, ss)
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else:
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return '{}{:.2f} s'.format(sign, ss)
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def gv_value(self):
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"""Value formatting for visualization"""
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if self != self:
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return "null"
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hh, mm, ss = self.hms()
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return [hh, mm, int(ss), int(ss*1000) % 1000]
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class SizeVal(MeasurementVal):
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"""Class representing time values"""
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quantity = 'size'
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gv_title = 'size in MiB'
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gv_data_type = 'number'
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def __str__(self):
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if self != self:
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return "nan"
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if abs(self) < 1024:
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return '{:.1f} kiB'.format(self)
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elif abs(self) < 1048576:
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return '{:.2f} MiB'.format(self / 1024)
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else:
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return '{:.2f} GiB'.format(self / 1048576)
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def gv_value(self):
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"""Value formatting for visualization"""
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if self != self:
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return "null"
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return self / 1024
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def measurement_stats(meas, prefix=''):
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"""Get statistics of a measurement"""
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if not meas:
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return {prefix + 'sample_cnt': 0,
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prefix + 'mean': MeasurementVal('nan'),
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prefix + 'stdev': MeasurementVal('nan'),
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prefix + 'variance': MeasurementVal('nan'),
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prefix + 'min': MeasurementVal('nan'),
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prefix + 'max': MeasurementVal('nan'),
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prefix + 'minus': MeasurementVal('nan'),
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prefix + 'plus': MeasurementVal('nan')}
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stats = {'name': meas['name']}
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if meas['type'] == 'sysres':
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val_cls = TimeVal
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values = meas['values']['elapsed_time']
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elif meas['type'] == 'diskusage':
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val_cls = SizeVal
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values = meas['values']['size']
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else:
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raise Exception("Unknown measurement type '{}'".format(meas['type']))
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stats['val_cls'] = val_cls
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stats['quantity'] = val_cls.quantity
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stats[prefix + 'sample_cnt'] = len(values)
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mean_val = val_cls(mean(values))
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min_val = val_cls(min(values))
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max_val = val_cls(max(values))
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stats[prefix + 'mean'] = mean_val
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if len(values) > 1:
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stats[prefix + 'stdev'] = val_cls(stdev(values))
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stats[prefix + 'variance'] = val_cls(variance(values))
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else:
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stats[prefix + 'stdev'] = float('nan')
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stats[prefix + 'variance'] = float('nan')
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stats[prefix + 'min'] = min_val
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stats[prefix + 'max'] = max_val
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stats[prefix + 'minus'] = val_cls(mean_val - min_val)
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stats[prefix + 'plus'] = val_cls(max_val - mean_val)
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return stats
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