def _parse_maintainer_scope(self, text):
maintainer = {}
maintainer['subsystem'] = ""
maintainer['maintainer'] = []
maintainer['maillist'] = ""
maintainer['status'] = ""
maintainer['file-include-pattern'] = []
maintainer['file-exclude-pattern'] = []
for lnumber, line in enumerate(text):
if line[0] == '[': # subsystem name
maintainer['subsystem'] = line[1:line.rfind(']')].strip()
elif line[0:2] == 'M:': # Maintainer
maintainer['maintainer'].append((line[2:line.find('<')].strip(),
line[line.find('<') + 1:line.find('>')].strip()))
elif line[0:2] == 'L:': # mailinglist
maintainer['maillist'] = line[2:].strip()
elif line[0:2] == 'S:': # Status
maintainer['status'] = line[2:].strip()
elif line[0:2] == 'F:': # File pattern
maintainer['file-include-pattern'].append(self._env_parser.parse(line[2:].strip()))
elif line[0:2] == 'X:': # Exclude pattern
maintainer['file-exclude-pattern'].append(self._env_parser.parse(line[2:].strip()))
else:
logger.warn("Unable to intrpret line %s [must start with one of [MLSFX :[%s]", lnumber, line)
return maintainer
def _parse_maintainer_scope(self, text):
maintainer = {}
maintainer['subsystem'] = ""
maintainer['maintainer'] = []
maintainer['maillist'] = ""
maintainer['status'] = ""
maintainer['file-include-pattern'] = []
maintainer['file-exclude-pattern'] = []
for lnumber, line in enumerate(text):
if line[0] == '[': # subsystem name
maintainer['subsystem'] = line[1:line.rfind(']')].strip()
elif line[0:2] == 'M:': # Maintainer
maintainer['maintainer'].append((line[2:line.find('<')].strip(),
line[line.find('<') + 1:line.find('>')].strip()))
elif line[0:2] == 'L:': # mailinglist
maintainer['maillist'] = line[2:].strip()
elif line[0:2] == 'S:': # Status
maintainer['status'] = line[2:].strip()
elif line[0:2] == 'F:': # File pattern
maintainer['file-include-pattern'].append(self._env_parser.parse(line[2:].strip()))
elif line[0:2] == 'X:': # Exclude pattern
maintainer['file-exclude-pattern'].append(self._env_parser.parse(line[2:].strip()))
else:
logger.warn("Unable to intrpret line %s [must start with one of [MLSFX :[%s]", lnumber, line)
return maintainer
def get_changemetric_for_function(self, session, function_id, metric):
'''
This should be rewritten so that it is more agnostic in how it returns the data to the client. It should
only return the query itself, and not transform it into dictionarys and whatnot.
The view (who is using this function) makes alot of assumptions as to how the data is returned from here
and relies that OrmWrapper transforms data so that it fits the view. Any transformations (for the sake of
clients) should rather instead be done by the views themselves.
Args:
session (sqlalchemy.orm.session)
function_id (int): The function for which the data is gathered
metric (str): The metric we are fetching data for. Usually this is mapped to a
concrete ORM type in the global METRIC_MAPPING dict.
continuous (bool): Indicates wether the metric is continuous or not.
Returns:
dict: A dictionary with the dates and total metric values for the component
'''
total_dict = collections.OrderedDict()
if metric == "defect_modifications":
result = session.query(DefectModification.date, literal_column('1'))\
.filter(DefectModification.function_id == function_id,
DefectModification.date.isnot(None))\
.group_by(DefectModification.version_id).order_by(DefectModification.date)
else:
try:
metric_column = METRIC_MAPPING[metric]
except KeyError:
logger.warn(
"Incorrect metric supplied, beware of SQL injection attacks! metric=%s",
metric)
return total_dict
result = session.query(ChangeMetric.date, metric_column).filter(ChangeMetric.function_id == function_id,
ChangeMetric.date.isnot(None),
metric_column.isnot(None))\
.group_by(ChangeMetric.version_id).order_by(ChangeMetric.date)
for (date_, metric) in result:
total_dict[date_] = metric
return total_dict
def dump_data(data, response_type='application/json', filename=None):
'''
Function used when dumping raw data to the clients. It can handle both csv and json data and dumps the correct
format according to the response_type parameter passed by the client. The HttpResponse is flagged with the content
type and can usually be parsed automatically on the frontend using jquery.ajax() which guesses the correct response
type based on the `content_type` in the `HttpResponse`.
'''
if response_type in ('csv', 'text/csv'):
response = HttpResponse(content_type=response_type)
filename = filename or "file"
response['Content-Disposition'] = 'attachment; filename="{0}.csv"'.format(filename)
response = _toCSV(data, response)
elif response_type in ('json', 'application/json'):
response = HttpResponse(_toJSON(data), content_type=response_type)
else:
logger.warn('Unknown response_type %s', response_type)
return response
def get_changemetric_for_function(self, session, function_id, metric):
'''
This should be rewritten so that it is more agnostic in how it returns the data to the client. It should
only return the query itself, and not transform it into dictionarys and whatnot.
The view (who is using this function) makes alot of assumptions as to how the data is returned from here
and relies that OrmWrapper transforms data so that it fits the view. Any transformations (for the sake of
clients) should rather instead be done by the views themselves.
Args:
session (sqlalchemy.orm.session)
function_id (int): The function for which the data is gathered
metric (str): The metric we are fetching data for. Usually this is mapped to a
concrete ORM type in the global METRIC_MAPPING dict.
continuous (bool): Indicates wether the metric is continuous or not.
Returns:
dict: A dictionary with the dates and total metric values for the component
'''
total_dict = collections.OrderedDict()
if metric == "defect_modifications":
result = session.query(DefectModification.date, literal_column('1'))\
.filter(DefectModification.function_id == function_id,
DefectModification.date.isnot(None))\
.group_by(DefectModification.version_id).order_by(DefectModification.date)
else:
try:
metric_column = METRIC_MAPPING[metric]
except KeyError:
logger.warn("Incorrect metric supplied, beware of SQL injection attacks! metric=%s", metric)
return total_dict
result = session.query(ChangeMetric.date, metric_column).filter(ChangeMetric.function_id == function_id,
ChangeMetric.date.isnot(None),
metric_column.isnot(None))\
.group_by(ChangeMetric.version_id).order_by(ChangeMetric.date)
for (date_, metric) in result:
total_dict[date_] = metric
return total_dict
def dump_data(data, response_type='application/json', filename=None):
'''
Function used when dumping raw data to the clients. It can handle both csv and json data and dumps the correct
format according to the response_type parameter passed by the client. The HttpResponse is flagged with the content
type and can usually be parsed automatically on the frontend using jquery.ajax() which guesses the correct response
type based on the `content_type` in the `HttpResponse`.
'''
if response_type in ('csv', 'text/csv'):
response = HttpResponse(content_type=response_type)
filename = filename or "file"
response[
'Content-Disposition'] = 'attachment; filename="{0}.csv"'.format(
filename)
response = _toCSV(data, response)
elif response_type in ('json', 'application/json'):
response = HttpResponse(_toJSON(data), content_type=response_type)
else:
logger.warn('Unknown response_type %s', response_type)
return response
def get_changemetric_for_file(self,
session,
file_id,
metric,
continuous=None):
'''
This should be rewritten so that it is more agnostic in how it returns the data to the client. It should
only return the query itself, and not transform it into dictionarys and whatnot.
The view (who is using this function) makes alot of assumptions as to how the data is returned from here
and relies that OrmWrapper transforms data so that it fits the view. Any transformations (for the sake of
clients) should rather instead be done by the views themselves.
Args:
session (sqlalchemy.orm.session)
file_id (int): The file for which the data is gathered
metric (str): The metric we are fetching data for. Usually this is mapped to a
concrete ORM type in the global METRIC_MAPPING dict.
continuous (bool): Indicates wether the metric is continuous or not.
Returns:
dict: A dictionary with the dates and total metric values for the component
'''
total_dict = collections.OrderedDict()
if continuous is None:
continuous = METRICS[metric]['continuous']
if metric == "defect_modifications":
result = session.query(DefectModification.date, literal_column('1'))\
.filter(DefectModification.file_id == file_id,
DefectModification.date.isnot(None))\
.group_by(DefectModification.version_id).order_by(DefectModification.date)
for (date_, metric_data) in result:
total_dict[date_] = metric_data
else:
# Handles generic metrics (nloc, complexity, added...) and changerate
try:
metric_column = METRIC_MAPPING[metric]
except KeyError:
logger.warn(
"Incorrect metric supplied, beware of SQL injection attacks! metric=%s",
metric)
return total_dict
change_metrics = session.query(
ChangeMetric.function_id, ChangeMetric.date,
metric_column).filter(ChangeMetric.file_id == file_id)
all_dates = set()
all_functions = {}
all_functions_date = {}
for function_id, date_, metric_total in change_metrics:
if date_ is not None:
all_dates.add(date_)
all_functions.setdefault(function_id, []).append(
(date_, metric_total))
all_functions_date.setdefault(date_, []).append(
(metric_total))
all_dates = sorted(all_dates)
'''
Sort all_entities so that we can assume that dates are *always* in descending
order, this assumption is later used in calculate_continous()
'''
for entrys in all_functions.itervalues():
entrys.sort(key=lambda entry: entry[0], reverse=True)
for date_ in all_dates:
if continuous:
metric = self._calculate_continuous(all_functions, date_)
else:
metric = self._calculate_discontinous(
all_functions_date, date_)
total_dict[date_] = metric
return total_dict
def get_changemetric_for_file(self, session, file_id, metric, continuous=None):
'''
This should be rewritten so that it is more agnostic in how it returns the data to the client. It should
only return the query itself, and not transform it into dictionarys and whatnot.
The view (who is using this function) makes alot of assumptions as to how the data is returned from here
and relies that OrmWrapper transforms data so that it fits the view. Any transformations (for the sake of
clients) should rather instead be done by the views themselves.
Args:
session (sqlalchemy.orm.session)
file_id (int): The file for which the data is gathered
metric (str): The metric we are fetching data for. Usually this is mapped to a
concrete ORM type in the global METRIC_MAPPING dict.
continuous (bool): Indicates wether the metric is continuous or not.
Returns:
dict: A dictionary with the dates and total metric values for the component
'''
total_dict = collections.OrderedDict()
if continuous is None:
continuous = METRICS[metric]['continuous']
if metric == "defect_modifications":
result = session.query(DefectModification.date, literal_column('1'))\
.filter(DefectModification.file_id == file_id,
DefectModification.date.isnot(None))\
.group_by(DefectModification.version_id).order_by(DefectModification.date)
for (date_, metric_data) in result:
total_dict[date_] = metric_data
else:
# Handles generic metrics (nloc, complexity, added...) and changerate
try:
metric_column = METRIC_MAPPING[metric]
except KeyError:
logger.warn("Incorrect metric supplied, beware of SQL injection attacks! metric=%s", metric)
return total_dict
change_metrics = session.query(ChangeMetric.function_id,
ChangeMetric.date,
metric_column).filter(ChangeMetric.file_id == file_id)
all_dates = set()
all_functions = {}
all_functions_date = {}
for function_id, date_, metric_total in change_metrics:
if date_ is not None:
all_dates.add(date_)
all_functions.setdefault(function_id, []).append((date_, metric_total))
all_functions_date.setdefault(date_, []).append((metric_total))
all_dates = sorted(all_dates)
'''
Sort all_entities so that we can assume that dates are *always* in descending
order, this assumption is later used in calculate_continous()
'''
for entrys in all_functions.itervalues():
entrys.sort(key=lambda entry: entry[0], reverse=True)
for date_ in all_dates:
if continuous:
metric = self._calculate_continuous(all_functions, date_)
else:
metric = self._calculate_discontinous(all_functions_date, date_)
total_dict[date_] = metric
return total_dict
