def __init__(self,
filter_function=lambda md: True,
channels=[],
onlyPlotFailed=True):
TimeseriesAnalyzer.__init__(self ,filter_function=filter_function,
group_function=group_by_name('Config_Name'),
plot_function=plot_lines,
channels=channels,
saveOutput=False)
self.onlyPlotFailed=onlyPlotFailed
self.results = defaultdict(list)
setup = SetupParser()
self.regression_path = os.path.join(setup.get('dll_root'),
'..', '..', 'Regression')
def __init__(self,
filename=os.path.join('output', 'MalariaSummaryReport_AnnualAverage.json'),
filter_function=lambda md: True, # no filtering based on metadata
select_function=lambda ts: pd.Series(ts), # return complete-&-unaltered timeseries
group_function=lambda k, v: k, # group by unique simid-key from parser
plot_function=plot_grouped_lines,
channels=['Annual EIR', 'PfPR_2to10'],
### TODO: plot quantities versus age ###
# ,'Average Population by Age Bin',
# 'PfPR by Age Bin', 'RDT PfPR by Age Bin',
# 'Annual Clinical Incidence by Age Bin',
# 'Annual Severe Incidence by Age Bin'],
saveOutput=False):
TimeseriesAnalyzer.__init__(self, filename,
filter_function, select_function,
group_function, plot_function,
channels, saveOutput)
self.agebins = []
def builtinAnalyzers():
analyzers = {
'time_series':
TimeseriesAnalyzer(select_function=sample_selection(),
group_function=group_by_name('_site_'),
plot_function=plot_grouped_lines),
'vector_species':
VectorSpeciesAnalyzer(select_function=sample_selection(),
group_function=group_by_name('_site_')),
}
return analyzers
def apply(self, parser):
test_channel_data = TimeseriesAnalyzer.apply(self, parser)
reference_path = os.path.join(self.regression_path,
parser.sim_data['Config_Name'],
self.filenames[0])
with open(reference_path) as f:
data_by_channel = json.loads(f.read())['Channels']
ref_channel_data = self.get_channel_data(data_by_channel)
channel_data = pd.concat(dict(test = test_channel_data, reference = ref_channel_data), axis=1)
channel_data.group = test_channel_data.group
channel_data.sim_id = test_channel_data.sim_id
return channel_data
ModFn(DTKConfigBuilder.set_param, 'Run_Number', seed)
for seed in range(5)
],
)
# The run_sim_args is a dictionary informing the command line of:
# - What config builder to use
# - What is the name we want for the experiment
# - Which experiment builder to use
run_sim_args = {
'config_builder': cb,
'exp_name': 'Sample larvicides epxeriment',
'exp_builder': builder
}
# In this file we also decided to include the analyzers.
# When present the `analyzers` variable is an array of Analyzers that will be executed when
# the command `dtk analyze larvicides_campaign.py` is called.
# Here we are using a simple TimeSeriesAnalyzer, grouping the simulations by the larvicide_start tag (averaging
# across the Run_Number). And displaying the results for 3 vector related channels.
analyzers = [
TimeseriesAnalyzer(group_function=group_by_name('larvicide_start'),
channels=['Daily EIR', 'Adult Vectors', 'Infected'])
]
if __name__ == "__main__":
SetupParser.init()
em = ExperimentManagerFactory.init()
em.run_simulations(**run_sim_args)
em.wait_for_finished(verbose=True)
from dtk.utils.analyzers import TimeseriesAnalyzer, VectorSpeciesAnalyzer
from simtools.AnalyzeManager.AnalyzeManager import AnalyzeManager
from simtools.Utilities.Experiments import retrieve_experiment
if __name__ == "__main__":
# Retrieve a couple of test experiments
experiment1 = retrieve_experiment('158cc530-780e-e711-9400-f0921c16849c')
experiment2 = retrieve_experiment('c62aa746-780e-e711-9400-f0921c16849c')
# Create an analyze manager
# Note that we are adding the experiments that we want to analyze
am = AnalyzeManager(exp_list=[experiment1, experiment2])
# Add the TimeSeriesAnalyzer to the manager
am.add_analyzer(TimeseriesAnalyzer())
am.add_analyzer(VectorSpeciesAnalyzer())
# Analyze
am.analyze()
from dtk.utils.analyzers import name_match_filter, group_by_name, plot_grouped_lines, \
TimeseriesAnalyzer, RegressionTestAnalyzer
analyzers = [
TimeseriesAnalyzer(group_function=group_by_name('Config_Name'),
plot_function=plot_grouped_lines),
RegressionTestAnalyzer(filter_function=name_match_filter('Vector'),
channels=[
'Statistical Population', 'Rainfall',
'Adult Vectors', 'Daily EIR', 'Infected',
'Parasite Prevalence'
],
onlyPlotFailed=False)
]
from dtk.utils.builders.sweep import GenericSweepBuilder
from dtk.utils.core.DTKConfigBuilder import DTKConfigBuilder
from dtk.vector.study_sites import configure_site
from simtools.Analysis.AnalyzeManager import AnalyzeManager
from simtools.ExperimentManager.ExperimentManagerFactory import ExperimentManagerFactory
from simtools.SetupParser import SetupParser
# This block will be used unless overridden on the command-line
SetupParser.default_block = 'HPC'
cb = DTKConfigBuilder.from_defaults('VECTOR_SIM')
configure_site(cb, 'Namawala')
cb.set_param('Simulation_Duration',365)
analyzers = (TimeseriesAnalyzer(),
VectorSpeciesAnalyzer())
builder = GenericSweepBuilder.from_dict({'Run_Number': range(5)})
run_sim_args = {
'exp_name': 'testrunandanalyze',
'exp_builder': builder,
'config_builder':cb
}
if __name__ == "__main__":
SetupParser.init(selected_block=SetupParser.default_block)
exp_manager = ExperimentManagerFactory.from_cb(config_builder=cb)
exp_manager.run_simulations(**run_sim_args)
from dtk.utils.analyzers import TimeseriesAnalyzer, VectorSpeciesAnalyzer, sample_selection
from dtk.utils.analyzers.group import group_by_name
from dtk.utils.analyzers.plot import plot_grouped_lines
analyzers = [
TimeseriesAnalyzer(select_function=sample_selection(),
group_function=group_by_name('_site_'),
plot_function=plot_grouped_lines),
VectorSpeciesAnalyzer(
select_function=sample_selection(),
group_function=group_by_name('_site_'),
)
]