def test_exception(self):
"""
Forget the trailing 'end' and ensure it produces an error
:return:
"""
exception_message = "Antimony: Error in model string, line 19: syntax error, unexpected end of file, expecting '-' or '+' or => or '<'"
try:
with model.BuildAntimony(self.copasi_file2) as loader:
self.mod2 = loader.load("""
model model2
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
""")
except Exception as E:
self.assertEqual(str(E.message), str(exception_message))
def setUp(self):
## create model selection directory
self.dire = os.path.join(os.path.dirname(__file__), 'AntimonyModels')
if not os.path.isdir(self.dire):
os.makedirs(self.dire)
self.copasi_file1 = os.path.join(self.dire, 'negative_feedback.cps')
with model.BuildAntimony(self.copasi_file1) 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
""")
assert os.path.isfile(self.copasi_file1)
def test_build(self):
"""
Make copasi file from antimony
:return:
"""
with model.BuildAntimony(self.copasi_file1) as loader:
self.mod1 = 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
A = 0
B = 0
AProd: => A; cell*vAProd
ADeg: A =>; cell*kADeg*A*B
BProd: => B; cell*kBProd*A
BDeg: B => ; cell*kBDeg*B
end
""")
self.assertTrue(os.path.isfile(self.copasi_file1))
def setUp(self):
## create model selection directory
self.dire = os.path.join(os.path.dirname(__file__), 'AntimonyModels')
if not os.path.isdir(self.dire):
os.makedirs(self.dire)
self.copasi_file1 = os.path.join(self.dire, 'negative_feedback.cps')
with model.BuildAntimony(self.copasi_file1) 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
""")
if not os.path.isfile(self.copasi_file1):
raise errors.FileDoesNotExistError(
'Copasi File "{}" was not created in '
'setting up the test case'.format(self.copasi_file1))
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__),
'profile_likelihood_latex_tests')
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
A = 0
B = 0
AProd: => A; cell*vAProd
ADeg: A =>; cell*kADeg*A*B
BProd: => B; cell*kBProd*A
BDeg: B => ; cell*kBDeg*B
end
""")
self.TC = self.simulate_data()
self.MPE = self.do_parameter_estimation()
self.pl = self.do_profile_likelihood()
self.filename = os.path.join(self.dire, 'latex_test.pdf')
def test_build_model_to_file_path_which_already_exists(self):
with model.BuildAntimony(self.copasi_file1) as loader:
mod = loader.load("""
model model1
compartment cell = 1.0
var A in cell
var C in cell
vAProd = 0.1
kADeg = 0.2
kCProd = 0.3
kCDeg = 0.4
vBasalAProd = 0.001
A = 0
C = 0
AProd: => A; cell*vAProd*B+vBasalAProd
ADeg: A =>; cell*kADeg*A
CProd: => C; cell*kCProd*A
CDeg: C => ; cell*kCDeg*C
end
""")
self.assertNotIn('B', [i.name for i in mod.metabolites])
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()
def setUp(self):
## create model selection directory
self.dire = os.path.join(os.path.dirname(__file__), 'model_selection')
if not os.path.isdir(self.dire):
os.makedirs(self.dire)
self.copasi_file1 = os.path.join(self.dire, 'negative_feedback.cps')
self.copasi_file2 = os.path.join(self.dire, 'positive_feedback.cps')
self.copasi_file3 = os.path.join(self.dire, 'feedforward.cps')
with model.BuildAntimony(self.copasi_file1) as loader:
self.mod1 = 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
A = 0
B = 0
AProd: => A; cell*vAProd
ADeg: A =>; cell*kADeg*A*B
BProd: => B; cell*kBProd*A
BDeg: B => ; cell*kBDeg*B
end
"""
)
with model.BuildAntimony(self.copasi_file2) as loader:
self.mod2 = loader.load(
"""
model model2
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
"""
)
with model.BuildAntimony(self.copasi_file3) as loader:
self.mod3 = loader.load(
"""
model model3
compartment cell = 1.0
var A in cell
var B in cell
var C in cell
vAProd = 0.1
kADeg = 0.2
kBProd = 0.3
kBDeg = 0.4
kCDeg = 0.5
kCProd = 0.6
A = 0
B = 0
C = 0
AProd: => A; cell*vAProd
ADeg: A =>; cell*kADeg*A
BProd: => B; cell*kBProd*A
BDeg: B => ; cell*kBDeg*B
CProd: => C; cell*kCProd*A*B
CDeg: C => ; cell*kCDeg*C
end
"""
)
self.TC = self.simulate_data()
self.MMF = self.configure_model_selection()
