def do_PUT(self):
self.risk = self.request.survey.restrictedTraverse(
self.context.zodb_path.split('/'))
if self.risk.type in ['top5', 'policy']:
return {'type': 'error',
'message': 'Can not evaluate a %s risk'
% self.risk.type}
try:
if self.risk.evaluation_method == 'direct':
self.context.priority = get_json_token(self.input, 'priority',
IRisk['default_priority'],
default=self.context.priority)
else:
algorithm = evaluation_algorithm(self.risk)
if algorithm == 'french':
self.context.severity = get_json_token(self.input,
'severity', IFrenchEvaluation['default_severity'],
self.context.severity)
self.context.frequency = get_json_token(self.input,
'frequency',
IFrenchEvaluation['default_frequency'],
self.context.frequency)
else: # Kinney
self.context.probability = get_json_token(self.input,
'probability',
IKinneyEvaluation['default_probability'],
self.context.probability)
self.context.frequency = get_json_token(self.input,
'frequency',
IKinneyEvaluation['default_frequency'],
self.context.frequency)
self.context.effect = get_json_token(self.input, 'effect',
IKinneyEvaluation['default_effect'],
self.context.effect)
calculate_priority(self.context, self.risk)
except (KeyError, ValueError) as e:
return {'type': 'error',
'message': str(e)}
return self.do_GET()
def do_GET(self):
self.risk = risk = self.request.survey.restrictedTraverse(
self.context.zodb_path.split('/'))
info = {'id': self.context.id,
'type': 'risk',
'title': risk.title,
'module-title': aq_parent(risk).title,
'problem-description': risk.problem_description,
'show-not-applicable': risk.show_notapplicable,
'evaluation-method': risk.evaluation_method,
'present': self.context.identification,
'priority': self.context.priority,
'comment': self.context.comment,
}
images = filter(None,
[export_image(risk, self.request,
'image%s' % postfix, 'caption%s' % postfix,
width=150, height=500, direction='thumbnail')
for postfix in ['', '2', '3', '4']])
if images:
info['images'] = images
if HasText(risk.description):
info['description'] = risk.description
if HasText(risk.legal_reference):
info['legal-reference'] = risk.legal_reference
if risk.evaluation_method == 'calculated':
algorithm = evaluation_algorithm(risk)
info['evaluation-algorithm'] = algorithm
if algorithm == 'french':
field = IFrenchEvaluation['default_severity']
info['severity'] = vocabulary_token(
field, self.context.severity) \
if self.context.severity else None
info['severity-options'] = vocabulary_options(
field, self.request)
field = IFrenchEvaluation['default_frequency']
info['frequency'] = vocabulary_token(
field, self.context.frequency) \
if self.context.frequency else None
info['frequency-options'] = vocabulary_options(
field, self.request)
else: # Kinney
field = IKinneyEvaluation['default_frequency']
info['frequency'] = vocabulary_token(
field, self.context.frequency) \
if self.context.frequency else None
info['frequency-options'] = vocabulary_options(
field, self.request)
field = IKinneyEvaluation['default_effect']
info['effect'] = vocabulary_token(field, self.context.effect) \
if self.context.effect else None
info['effect-options'] = vocabulary_options(
field, self.request)
field = IKinneyEvaluation['default_probability']
info['probability'] = vocabulary_token(
field, self.context.probability) \
if self.context.probability else None
info['probability-options'] = vocabulary_options(
field, self.request)
solutions = [solution for solution in risk.values()
if ISolution.providedBy(solution)]
if solutions:
info['standard-solutions'] = [
{'description': solution.description,
'action-plan': solution.action_plan,
'prevention-plan': solution.prevention_plan,
'requirements': solution.requirements}
for solution in risk.values()]
return info
def do_GET(self):
self.risk = risk = self.request.survey.restrictedTraverse(
self.context.zodb_path.split('/'))
info = {
'id': self.context.id,
'type': 'risk',
'title': risk.title,
'module-title': aq_parent(risk).title,
'problem-description': risk.problem_description,
'show-not-applicable': risk.show_notapplicable,
'evaluation-method': risk.evaluation_method,
'present': self.context.identification,
'priority': self.context.priority,
'comment': self.context.comment,
}
images = filter(None, [
export_image(risk,
self.request,
'image%s' % postfix,
'caption%s' % postfix,
width=150,
height=500,
direction='thumbnail')
for postfix in ['', '2', '3', '4']
])
if images:
info['images'] = images
if HasText(risk.description):
info['description'] = risk.description
if HasText(risk.legal_reference):
info['legal-reference'] = risk.legal_reference
if risk.evaluation_method == 'calculated':
algorithm = evaluation_algorithm(risk)
info['evaluation-algorithm'] = algorithm
if algorithm == 'french':
field = IFrenchEvaluation['default_severity']
info['severity'] = vocabulary_token(
field, self.context.severity) \
if self.context.severity else None
info['severity-options'] = vocabulary_options(
field, self.request)
field = IFrenchEvaluation['default_frequency']
info['frequency'] = vocabulary_token(
field, self.context.frequency) \
if self.context.frequency else None
info['frequency-options'] = vocabulary_options(
field, self.request)
else: # Kinney
field = IKinneyEvaluation['default_frequency']
info['frequency'] = vocabulary_token(
field, self.context.frequency) \
if self.context.frequency else None
info['frequency-options'] = vocabulary_options(
field, self.request)
field = IKinneyEvaluation['default_effect']
info['effect'] = vocabulary_token(field, self.context.effect) \
if self.context.effect else None
info['effect-options'] = vocabulary_options(
field, self.request)
field = IKinneyEvaluation['default_probability']
info['probability'] = vocabulary_token(
field, self.context.probability) \
if self.context.probability else None
info['probability-options'] = vocabulary_options(
field, self.request)
solutions = [
solution for solution in risk.values()
if ISolution.providedBy(solution)
]
if solutions:
info['standard-solutions'] = [{
'description': solution.description,
'action-plan': solution.action_plan,
'prevention-plan': solution.prevention_plan,
'requirements': solution.requirements
} for solution in risk.values()]
return info
