def simulate_data(self):
"""
simulate some data from model1
:return:
"""
TC = tasks.TimeCourse(self.mod, end=100, steps=10, intervals=10)
utils.format_timecourse_data(TC.report_name)
return TC
def setUp(self):
self.working_directory = os.path.join(os.path.dirname(__file__),
'viz_tests')
if not os.path.isdir(self.working_directory):
os.makedirs(self.working_directory)
copasi_file = os.path.join(self.working_directory, 'test_model.cps')
with model.BuildAntimony(copasi_file) as loader:
self.mod = loader.load("""
model negative_feedback
compartment cell = 1.0
var A in cell
var B in cell
var Signal in cell
var C in cell
vAProd = 0.1
kADeg = 0.2
kBProd = 0.3
kBDeg = 0.4
kCProd = 0.5
kCDeg = 0.6
A = 0
B = 0
C = 0
Signal = 10
AProd: $Signal => A; cell*vAProd*Signal
ADeg: A =>; cell*kADeg*A*B;
BProd: => B; cell*kBProd*A;
BDeg: B => ; cell*kBDeg*B;
CProd: => C ; cell*kCProd*B;
CDeg: C => ; cell*kCDeg*C
end
""")
## initialize empty dict to store TimeCourse objects
self.TC = tasks.TimeCourse(self.mod,
end=10,
intervals=10,
step_size=1,
global_quantities=[],
y=['A', 'B', 'C'])
tasks
## format the time course data
df = utils.format_timecourse_data(self.TC.report_name)
## write data to file
df.to_csv(self.TC.report_name, sep='\t', index=False)
self.MPE = tasks.MultiParameterEstimation(self.mod,
self.TC.report_name,
method='genetic_algorithm',
population_size=25,
number_of_generations=50,
run_mode=True)
self.MPE.write_config_file()
self.MPE.setup()
self.MPE.run()
def setUp(self):
## create model selection directory
self.dire = os.path.join(os.path.dirname(__file__),
'ChaserEstimationTests')
if not os.path.isdir(self.dire):
os.makedirs(self.dire)
self.copasi_file = os.path.join(self.dire, 'negative_feedback.cps')
with model.BuildAntimony(self.copasi_file) as loader:
self.mod = loader.load("""
model model1
compartment cell = 1.0
var A in cell
var B in cell
vAProd = 0.1
kADeg = 0.2
kBProd = 0.3
kBDeg = 0.4
vBasalAProd = 0.001
A = 0
B = 0
AProd: => A; cell*vAProd*B+vBasalAProd
ADeg: A =>; cell*kADeg*A
BProd: => B; cell*kBProd*A
BDeg: B => ; cell*kBDeg*B
end
""")
self.TC1 = tasks.TimeCourse(self.mod,
end=1000,
step_size=100,
intervals=10,
report_name='report1.txt')
utils.format_timecourse_data(self.TC1.report_name)
## add some noise
data1 = misc.add_noise(self.TC1.report_name)
## remove the data
os.remove(self.TC1.report_name)
## rewrite the data with noise
data1.to_csv(self.TC1.report_name, sep='\t')
self.copy_number = 2
self.pe_number = 3
self.MPE = tasks.MultiParameterEstimation(self.mod,
self.TC1.report_name,
copy_number=self.copy_number,
pe_number=self.pe_number,
method='genetic_algorithm',
population_size=10,
number_of_generations=10,
overwrite_config_file=True,
results_directory='test_mpe',
run_mode=True)
self.list_of_tasks = '{http://www.copasi.org/static/schema}ListOfTasks'
self.MPE.write_config_file()
self.MPE.setup()
self.MPE.run()