def testAddRowsToCache(self):
self._AddMockData()
rows = []
stored_object.Set(
'externally_visible__num_revisions_ChromiumPerf/win7/dromaeo/dom',
[[10, 2, 3], [15, 4, 5], [100, 6, 7]])
test_key = utils.TestKey('ChromiumPerf/win7/dromaeo/dom')
test_container_key = utils.GetTestContainerKey(test_key)
ts1 = datetime.datetime(2013, 1, 1)
row1 = graph_data.Row(parent=test_container_key,
id=1, value=9, timestamp=ts1)
rows.append(row1)
ts2 = datetime.datetime(2013, 1, 2)
row2 = graph_data.Row(parent=test_container_key,
id=12, value=90, timestamp=ts2)
rows.append(row2)
ts3 = datetime.datetime(2013, 1, 3)
row3 = graph_data.Row(parent=test_container_key,
id=102, value=99, timestamp=ts3)
rows.append(row3)
graph_revisions.AddRowsToCache(rows)
self.assertEqual(
[[1, 9, utils.TimestampMilliseconds(ts1)],
[10, 2, 3],
[12, 90, utils.TimestampMilliseconds(ts2)],
[15, 4, 5], [100, 6, 7],
[102, 99, utils.TimestampMilliseconds(ts3)]],
stored_object.Get('externally_visible__num_revisions_'
'ChromiumPerf/win7/dromaeo/dom'))
def testPost_ReturnsAndCachesCorrectRevisions(self):
self._AddMockData()
response = self.testapp.post(
'/graph_revisions', {'test_path': 'ChromiumPerf/win7/dromaeo/dom'})
cached_rows = stored_object.Get(
'externally_visible__num_revisions_ChromiumPerf/win7/dromaeo/dom')
for index, row in enumerate(json.loads(response.body)):
expected_rev = 15000 + (index * 5)
expected_value = int(expected_rev) * 2.5
expected_timestamp = utils.TimestampMilliseconds(
datetime.datetime(2013, 8, 1))
self.assertEqual([expected_rev, expected_value, expected_timestamp], row)
self.assertEqual([expected_rev, expected_value, expected_timestamp],
cached_rows[index])
def _GetFlotJson(revision_map, tests):
"""Constructs JSON in the format expected by Flot.
Args:
revision_map: A dict which maps revision numbers to data point info.
tests: A list of Test entities.
Returns:
JSON serialization of a dict with line data, annotations, error range data,
(This data may not be passed exactly as-is to the Flot plot funciton, but
it will all be used when plotting.)
"""
# TODO(qyearsley): Break this function into smaller functions.
# Each entry in the following dict is one Flot series object. The actual
# x-y values will be put into the 'data' properties for each object.
cols = {i: _FlotSeries(i) for i in range(len(tests))}
flot_annotations = {}
flot_annotations['series'] = _GetSeriesAnnotations(tests)
# For each Test (which corresponds to a trace line), the shaded error
# region is specified by two series objects. For a demo, see:
# http://www.flotcharts.org/flot/examples/percentiles/index.html
error_bars = {
x: [{
'id': 'bottom_%d' % x,
'data': [],
'color': x,
'clickable': False,
'hoverable': False,
'lines': {
'show': True,
'lineWidth': 0,
'fill': 0.2,
},
'fillBetween': 'line_%d' % x,
}, {
'id': 'top_%d' % x,
'data': [],
'color': x,
'clickable': False,
'hoverable': False,
'lines': {
'show': True,
'lineWidth': 0,
'fill': 0.2,
},
'fillBetween': 'line_%d' % x,
}]
for x, _ in enumerate(tests)
}
test_keys = [t.key.urlsafe() for t in tests]
for revision in sorted(revision_map.keys()):
for series_index, key in enumerate(test_keys):
point_info = revision_map[revision].get(key, None)
if not point_info:
continue
timestamp = point_info.get('timestamp')
if timestamp and type(timestamp) is datetime.datetime:
point_info['timestamp'] = utils.TimestampMilliseconds(
timestamp)
point_list = [revision, point_info['value']]
if 'error' in point_info:
error = point_info['error']
error_bars[series_index][0]['data'].append(
[revision, point_info['value'] - error])
error_bars[series_index][1]['data'].append(
[revision, point_info['value'] + error])
cols[series_index]['data'].append(point_list)
data_index = len(cols[series_index]['data']) - 1
series_dict = flot_annotations.setdefault(series_index, {})
data_dict = copy.deepcopy(point_info)
del data_dict['value']
series_dict.setdefault(data_index, data_dict)
return json.dumps(
{
'data': cols,
'annotations': flot_annotations,
'error_bars': error_bars,
},
allow_nan=False)
def _MakeTriplet(row):
"""Makes a 3-item list of revision, value and timestamp for a Row."""
timestamp = utils.TimestampMilliseconds(row.timestamp)
return [row.revision, row.value, timestamp]
def _GetFlotJson(revision_map, tests, show_old_data_warning):
"""Constructs JSON in the format expected by Flot.
Args:
revision_map: A dict which maps revision numbers to data point info.
tests: A list of Test entities.
show_old_data_warning: Whether to a show a warning to the user that
the graph data is out of date.
Returns:
JSON serialization of a dict with line data, annotations, error range data,
and possibly warning information. (This data may not be passed exactly
as-is to the Flot plot funciton, but it will all be used when plotting.)
"""
# TODO(qyearsley): Break this function into smaller functions.
# Each entry in the following dict is one Flot series object. The actual
# x-y values will be put into the 'data' properties for each object.
cols = {i: _FlotSeries(i) for i in range(len(tests))}
flot_annotations = {}
flot_annotations['series'] = _GetSeriesAnnotations(tests)
# For each Test (which corresponds to a trace line), the shaded error
# region is specified by two series objects. For a demo, see:
# http://www.flotcharts.org/flot/examples/percentiles/index.html
error_bars = {
x: [{
'id': 'bottom_%d' % x,
'data': [],
'color': x,
'clickable': False,
'hoverable': False,
'lines': {
'show': True,
'lineWidth': 0,
'fill': 0.2,
},
'fillBetween': 'line_%d' % x,
}, {
'id': 'top_%d' % x,
'data': [],
'color': x,
'clickable': False,
'hoverable': False,
'lines': {
'show': True,
'lineWidth': 0,
'fill': 0.2,
},
'fillBetween': 'line_%d' % x,
}]
for x, _ in enumerate(tests)
}
test_keys = [t.key.urlsafe() for t in tests]
last_timestamp = None
has_points = False
for revision in sorted(revision_map.keys()):
for series_index, key in enumerate(test_keys):
point_info = revision_map[revision].get(key, None)
if not point_info:
continue
has_points = True
timestamp = point_info.get('timestamp')
if timestamp:
if type(timestamp) is datetime.datetime:
point_info['timestamp'] = utils.TimestampMilliseconds(
timestamp)
if not last_timestamp or point_info[
'timestamp'] > last_timestamp:
last_timestamp = point_info['timestamp']
point_list = [revision, point_info['value']]
if 'error' in point_info:
error = point_info['error']
error_bars[series_index][0]['data'].append(
[revision, point_info['value'] - error])
error_bars[series_index][1]['data'].append(
[revision, point_info['value'] + error])
cols[series_index]['data'].append(point_list)
data_index = len(cols[series_index]['data']) - 1
series_dict = flot_annotations.setdefault(series_index, {})
data_dict = copy.deepcopy(point_info)
del data_dict['value']
series_dict.setdefault(data_index, data_dict)
warning = None
if show_old_data_warning and last_timestamp:
last_timestamp = datetime.datetime.fromtimestamp(last_timestamp / 1000)
if last_timestamp < datetime.datetime.now() - _STALE_DATA_DELTA:
warning = ('Graph out of date! Last data received: %s' %
last_timestamp.strftime('%Y/%m/%d %H:%M'))
elif not has_points:
warning = 'No data available.'
if not utils.IsInternalUser():
warning += ' Note that some data is only available when logged in.'
return json.dumps(
{
'data': cols,
'annotations': flot_annotations,
'error_bars': error_bars,
'warning': warning
},
allow_nan=False)
def _GetLastMondayTimestamp():
"""Get timestamp of 00:00 last Monday in milliseconds as an integer."""
today = datetime.date.today()
monday = today - datetime.timedelta(days=today.weekday())
return utils.TimestampMilliseconds(monday)
