def create_moving_average_model(graph, model, profile, colour):
"""
Rendering the moving average model according to its computed properties.
:param charts.Graph graph: the scatter plot to render new models
:param model: the moving average model which to be rendered to the graph
:param dict profile: the profile to obtains the x-coordinates
:param colour: the color of the current model to distinguish in the case of several models in the graph
:return charts.Graph: the modified graph with new moving average model
"""
# Create legend for the plotted model
legend = '{0}: window={1}, R^2={2:f}'.format(model['moving_method'],
model['window_width'],
model['r_square'])
# Obtains the x-coordinates with the required uid to pair with current model
params = {'of_key': 'amount', 'per_key': model['per_key']}
for x_pts, _, uid in data_provider.data_provider_mapper(profile, **params):
if uid == model['uid']:
# Plot the model
graph.line(x=sorted(x_pts),
y=model['bucket_stats'],
line_color=colour,
line_width=3.5,
legend=legend)
return graph
def postprocess(profile, **configuration):
"""Invoked from perun core, handles the postprocess actions
Arguments:
profile(dict): the profile to analyze
configuration: the perun and options context
"""
# Validate the input configuration
tools.validate_dictionary_keys(configuration,
['method', 'regression_models', 'steps'],
[])
# Perform the regression analysis
analysis = compute(data_provider.data_provider_mapper(profile),
configuration['method'],
configuration['regression_models'],
steps=configuration['steps'])
# Store the results
if 'models' not in profile['global']:
profile['global']['models'] = analysis
else:
profile['global']['models'].extend(analysis)
return PostprocessStatus.OK, "", {'profile': profile}
def postprocess(profile, **configuration):
"""
Invoked from perun core, handles the postprocess actions
:param dict profile: the profile to analyze
:param configuration: the perun and options context
"""
# Perform the non-parametric analysis using the regressogram method
regressogram_models = methods.compute_regressogram(
data_provider.data_provider_mapper(profile, **configuration),
configuration)
# Return the profile after the execution of regressogram method
return PostprocessStatus.OK, '', {
'profile': tools.add_models_to_profile(profile, regressogram_models)
}
def postprocess(profile, **configuration):
"""Invoked from perun core, handles the postprocess actions
:param dict profile: the profile to analyze
:param configuration: the perun and options context
"""
# Validate the input configuration
tools.validate_dictionary_keys(configuration,
['method', 'regression_models', 'steps'],
[])
# Perform the regression analysis
analysis = methods.compute(data_provider.data_provider_mapper(
profile, **configuration),
configuration['method'],
configuration['regression_models'],
steps=configuration['steps'])
# Store the results
profile = tools.add_models_to_profile(profile, analysis)
return PostprocessStatus.OK, "", {'profile': profile}