def test_parse_parameter_estimation_data(self):
"""
:return:
"""
data = viz.Parse(self.pe).data
expected = [4, 6]
actual = list(data['test_model'].shape)
def test_multiple_models(self):
"""
I had a bug that made all models have the same
parameter estimation data. This is the test I used
to fix the bug
Returns:
"""
data = viz.Parse(self.pe)
self.assertNotEqual(
float(data['first']['k1']),
float(data['second']['k1']),
)
def test_compute_x_odd(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'nl2sol')
context.set('tolerance', 1e-1)
context.set('iteration_limit', 5)
context.set('run_mode', True)
context.set('pe_number', 10)
config = context.get_config()
pl = tasks.ParameterEstimation(config)
data = viz.Parse(pl).data
p = viz.PlotProfileLikelihoods(self.pe_mod, pl, 1.2)
print(p.compute_x())
def test_parse(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('tolerance', 1e-1)
context.set('iteration_limit', 5)
context.set('run_mode', True)
context.set('pe_number', 10)
config = context.get_config()
pe = tasks.ParameterEstimation(config)
data = viz.Parse(pe).data['A2B']
expected = (10, 5)
actual = data.shape
self.assertEqual(expected, actual)
def test(self):
"""
Returns:
"""
with tasks.ParameterEstimation.Context(self.model,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('run_mode', True)
config = context.get_config()
pe = tasks.ParameterEstimation(config)
expected = 11
actual = viz.Parse(pe)['A2B'].shape[0]
self.assertEqual(expected, actual)
def test_run(self):
"""
Returns:
"""
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'nl2sol')
context.set('run_mode', True)
context.set('pe_number', 12)
config = context.get_config()
pe = tasks.ParameterEstimation(config)
expected = 12
data = viz.Parse(pe)['A2B']
actual = data.shape[0]
self.assertEqual(expected, actual)
def setUp(self):
super(ProfileLikelihoodTests, self).setUp()
self.fname = os.path.join(os.path.dirname(__file__), 'timecourse.txt')
self.data = self.model.simulate(0, 10, 1, report_name=self.fname)
with tasks.ParameterEstimation.Context(self.model,
self.fname,
context='s',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('run_mode', True)
context.set('randomize_start_values', True)
config = context.get_config()
self.pe = tasks.ParameterEstimation(config)
data = viz.Parse(self.pe).data
self.rss = data.loc[0, 'RSS']
self.pe_mod = self.pe.models['test_model'].model
self.pe_mod.insert_parameters(df=data, index=0, inplace=True)
def test_compute_x(self):
with tasks.ParameterEstimation.Context(self.pe_mod,
self.fname,
context='pl',
parameters='g') as context:
context.set('method', 'nl2sol')
context.set('tolerance', 1e-1)
context.set('iteration_limit', 5)
context.set('run_mode', True)
context.set('pe_number', 10)
config = context.get_config()
pl = tasks.ParameterEstimation(config)
data = viz.Parse(pl).data
p = viz.PlotProfileLikelihoods(self.pe_mod, pl, 1.2)
x = p.compute_x()
expected = (10, 5)
actual = x.shape
self.assertEqual(expected, actual)
def setUp(self):
super(TruncateDataTests, self).setUp()
fname = os.path.join(os.path.dirname(__file__), 'report1.txt')
self.model.simulate(0, 50, 1, report_name=fname)
with tasks.ParameterEstimation.Context(
self.model, fname, context='s', parameters='g',
) as context:
context.set('method', 'genetic_algorithm')
context.set('population_size', 2)
context.set('number_of_generations', 5)
context.set('copy_number', 2)
context.set('pe_number', 2)
context.set('run_mode', True)
config = context.get_config()
self.pe = tasks.ParameterEstimation(config)
self.data = viz.Parse(self.pe).data
def setUp(self):
super(ProfileLikelihoodTests, self).setUp()
ant_str = """
model new_model
R1: A -> B ; _k1*A;
R2: B -> A; k2*B;
R3: C -> D; _k3*C*B;
R4: D -> C; k4*D;
A = 100;
B = 0;
_k1=0.1;
k2 = 0.01
_k3 = 0.01
k4 = 1
end
"""
self.copasi_file = os.path.join(os.path.dirname(__file__),
'test_model.cps')
self.model = model.loada(ant_str, self.copasi_file)
self.fname = os.path.join(os.path.dirname(__file__), 'timecourse.txt')
self.data = self.model.simulate(0, 10, 1, report_name=self.fname)
with tasks.ParameterEstimation.Context(self.model,
self.fname,
context='s',
parameters='g') as context:
context.set('method', 'hooke_jeeves')
context.set('run_mode', True)
context.set('prefix', '_')
context.set('randomize_start_values', True)
config = context.get_config()
self.pe = tasks.ParameterEstimation(config)
data = viz.Parse(self.pe).data['test_model']
self.rss = data.loc[0, 'RSS']
self.pe_mod = self.pe.models['test_model'].model
self.pe_mod.insert_parameters(df=data, index=0, inplace=True)
context.set('pl_lower_bound', 1000)
context.set('pl_upper_bound', 1000)
#context.set('population_size', 50)
#context.set('number_of_generations', 300)
context.set('run_mode', True) ##defaults to False
context.set('pe_number', 3) ## number of repeat items in scan task
#context.set('copy_number', 2) ## number of times to copy model
#context.set('problem', 'Problem1')
#context.set('fit', 3)
#context.set('prefix', 'k')
config = context.get_config()
pe = tasks.ParameterEstimation(config)
#myPctMod.open()
data = viz.Parse(pe)
print(data)
myExMod = model.loada(model_string_ex, copasi_ex_filename)
def my_add_cols(inFName, outFName, sState, i1State, i2State):
myData = pd.read_csv(
os.path.join(os.path.dirname(working_directory), 'data', inFName))
myData.insert(len(myData.columns), 'S_indep', sState)
myData.insert(len(myData.columns), 'I1_indep', i1State)
myData.insert(len(myData.columns), 'I2_indep', i2State)
myData.rename(columns={
"pN": "Np",
"pG": "Gp",
"pD": "Dp",
"pK": "Kp"
# Example SLURM job script for serial (non-parallel) jobs # # # Tell SLURM if you want to be emailed when your job starts, ends, etc. # Currently mail can only be sent to addresses @ncl.ac.uk # #SBATCH --mail-type=ALL #SBATCH [email protected] # """ if rocket: shellString = shellString + "CopasiSE " + copasi_filename shellPath = os.path.join(working_dir, "scratch1part2.sh") f = open(shellPath, 'w') f.write(shellString) f.close() os.system("sbatch " + shellPath) else: os.system("CopasiSE " + copasi_filename) while True: try: myTable = viz.Parse(theTimeCourse) break except: continue print(myTable)
"""
copasi_file = os.path.join(working_directory, 'example_model.cps')
## build model
mod = model.loada(antimony_string, copasi_file)
assert isinstance(mod, model.Model)
## simulate some data, returns a pandas.DataFrame
data = mod.simulate(0, 20, 1)
## write data to file
experiment_filename = os.path.join(working_directory, 'experiment_data.txt')
data.to_csv(experiment_filename)
with tasks.ParameterEstimation.Context(
mod, experiment_filename,
context='pl', parameters='gm'
) as context:
context.set('method', 'hooke_jeeves')
context.set('pl_lower_bound', 1000)
context.set('pl_upper_bound', 1000)
context.set('pe_number', 25) # number of steps in each profile likelihood
context.set('run_mode', True)
config = context.get_config()
myPE = tasks.ParameterEstimation(config)
data = viz.Parse(myPE).data
