def test_add_random_variables(pheno_path, rv_new, buf_new):
model = Model(pheno_path)
rvs = model.random_variables
pset = model.parameters
eta = sympy.stats.Normal('eta_new', 0, sympy.sqrt(S(rv_new.name)))
eta.variability_level = VariabilityLevel.IIV
rvs.add(eta)
pset.add(rv_new)
model.random_variables = rvs
model.parameters = pset
model.update_source()
rec_ref = (
f'$OMEGA DIAGONAL(2)\n'
f' 0.0309626 ; IVCL\n'
f' 0.031128 ; IVV\n\n'
f'{buf_new} ; omega\n'
)
rec_mod = ''
for rec in model.control_stream.get_records('OMEGA'):
rec_mod += str(rec)
assert rec_mod == rec_ref
rv = model.random_variables['eta_new']
assert rv.pspace.distribution.mean == 0
assert rv.pspace.distribution.std ** 2 == rv_new.symbol
def test_combined_error_model(testdata):
model = Model(testdata / 'nonmem' / 'pheno.mod')
combined_error(model)
model.update_source()
assert str(model).split('\n')[11] == 'Y = EPS(1)*F + EPS(2) + F'
assert str(model).split('\n')[17] == '$SIGMA 0.09 ; sigma_prop'
assert str(model).split('\n')[18] == '$SIGMA 11.2225 ; sigma_add'
def test_add_parameters_and_statements(pheno_path, param_new, statement_new, buf_new):
model = Model(pheno_path)
pset = model.parameters
pset.append(param_new)
model.parameters = pset
sset = model.statements
# Insert new statement before ODE system.
new_sset = ModelStatements()
for s in sset:
if isinstance(s, ODESystem):
new_sset.append(statement_new)
new_sset.append(s)
model.statements = new_sset
model.update_source()
rec = (
f'$PK\n'
f'IF(AMT.GT.0) BTIME=TIME\n'
f'TAD=TIME-BTIME\n'
f'TVCL=THETA(1)*WGT\n'
f'TVV=THETA(2)*WGT\n'
f'IF(APGR.LT.5) TVV=TVV*(1+THETA(3))\n'
f'CL=TVCL*EXP(ETA(1))\n'
f'V=TVV*EXP(ETA(2))\n'
f'S1=V\n'
f'{buf_new}\n\n'
)
assert str(model.get_pred_pk_record()) == rec
def test_add_parameters(pheno_path, param_new, init_expected, buf_new):
model = Model(pheno_path)
pset = model.parameters
assert len(pset) == 6
pset.append(param_new)
model.parameters = pset
model.update_source()
assert len(pset) == 7
assert model.parameters[param_new.name].init == init_expected
parser = NMTranParser()
stream = parser.parse(str(model))
assert str(model.control_stream) == str(stream)
rec_ref = (
f'$THETA (0,0.00469307) ; PTVCL\n'
f'$THETA (0,1.00916) ; PTVV\n'
f'$THETA (-.99,.1)\n'
f'{buf_new}\n'
)
rec_mod = ''
for rec in model.control_stream.get_records('THETA'):
rec_mod += str(rec)
assert rec_ref == rec_mod
def test_remove_iov(testdata):
model = Model(testdata / 'nonmem/pheno_block.mod')
model_str = str(model)
model_with_iov = re.sub(
r'\$OMEGA 0.031128 ; IVV\n\$OMEGA 0.1',
r'$OMEGA BLOCK(1)\n0.1\n$OMEGA BLOCK(1) SAME\n',
model_str,
)
model.control_stream = NMTranParser().parse(model_with_iov)
remove_iov(model)
model.update_source()
assert (str(model.get_pred_pk_record()) == '$PK\n'
'CL=THETA(1)*EXP(ETA(1))\n'
'V = THETA(2)\n'
'S1 = ETA(2) + ETA(3) + V\n\n')
rec_omega = ''.join(
str(rec) for rec in model.control_stream.get_records('OMEGA'))
assert (rec_omega == '$OMEGA 0.0309626 ; IVCL\n'
'$OMEGA BLOCK(2)\n'
'0.0309626\n'
'0.0005 0.031128\n')
model = Model(testdata / 'nonmem/pheno_block.mod')
with pytest.warns(UserWarning):
remove_iov(model)
def test_michaelis_menten_elimination(testdata):
model = Model(testdata / 'nonmem' / 'pheno.mod')
michaelis_menten_elimination(model)
model.update_source()
correct = """$PROBLEM PHENOBARB SIMPLE MODEL
$DATA pheno.dta IGNORE=@
$INPUT ID TIME AMT WGT APGR DV
$SUBROUTINE ADVAN6 TOL=3
$MODEL COMPARTMENT=(CENTRAL DEFDOSE)
$PK
CLMM = THETA(3)
KM = THETA(2)
V = THETA(1)*EXP(ETA(1))
S1=V
$DES
DADT(1) = -A(1)*CLMM*KM/(V*(A(1)/V + KM))
$ERROR
Y=F+F*EPS(1)
$THETA (0,1.00916) ; TVV
$THETA (0,135.8) ; POP_KM
$THETA (0,0.00469307) ; POP_CLMM
$OMEGA 0.031128 ; IVV
$SIGMA 0.013241
$ESTIMATION METHOD=1 INTERACTION
"""
assert str(model) == correct
def test_add_random_variables(pheno_path, rv_new, buf_new):
model = Model(pheno_path)
rvs = model.random_variables
pset = model.parameters
eta = RandomVariable.normal('eta_new', 'iiv', 0, S(rv_new.name))
rvs.append(eta)
pset.append(rv_new)
model.random_variables = rvs
model.parameters = pset
model.update_source()
rec_ref = (
f'$OMEGA DIAGONAL(2)\n'
f' 0.0309626 ; IVCL\n'
f' 0.031128 ; IVV\n\n'
f'{buf_new} ; omega\n'
)
rec_mod = ''
for rec in model.control_stream.get_records('OMEGA'):
rec_mod += str(rec)
assert rec_mod == rec_ref
rv = model.random_variables['eta_new']
assert rv.sympy_rv.pspace.distribution.mean == 0
assert (rv.sympy_rv.pspace.distribution.std ** 2).name == 'omega'
def test_add_random_variables_and_statements(pheno_path):
model = Model(pheno_path)
rvs = model.random_variables
pset = model.parameters
eta = RandomVariable.normal('ETA_NEW', 'iiv', 0, S('omega'))
rvs.append(eta)
pset.append(Parameter('omega', 0.1))
eps = RandomVariable.normal('EPS_NEW', 'ruv', 0, S('sigma'))
rvs.append(eps)
pset.append(Parameter('sigma', 0.1))
model.random_variables = rvs
model.parameters = pset
sset = model.get_pred_pk_record().statements
statement_new = Assignment(S('X'), 1 + S(eps.name) + S(eta.name))
sset.append(statement_new)
model.get_pred_pk_record().statements = sset
model.update_source()
assert str(model.get_pred_pk_record()).endswith('X = 1 + ETA(3) + EPS(2)\n\n')
def test_iiv_on_ruv(pheno_path, epsilons, same_eta, err_ref, omega_ref):
model = Model(pheno_path)
model_str = str(model)
model_more_eps = re.sub('IPRED=F\nIRES=DV-IPRED',
'IPRED=F+EPS(2)\nIRES=DV-IPRED+EPS(3)', model_str)
model_sigma = re.sub(r'\$SIGMA 0.013241',
'$SIGMA 0.013241\n$SIGMA 0.1\n$SIGMA 0.1',
model_more_eps)
model.control_stream = NMTranParser().parse(model_sigma)
iiv_on_ruv(model, epsilons, same_eta)
model.update_source()
err_rec = model.control_stream.get_records('ERROR')[0]
assert str(
err_rec) == f'$ERROR\n' f'W=F\n' f'{err_ref}' f'IWRES=IRES/W\n\n'
omega_rec = ''.join(
str(rec) for rec in model.control_stream.get_records('OMEGA'))
assert omega_rec == (f'$OMEGA DIAGONAL(2)\n'
f' 0.0309626 ; IVCL\n'
f' 0.031128 ; IVV\n\n'
f'{omega_ref}\n')
def test_remove_eta(pheno_path):
model = Model(pheno_path)
rvs = model.random_variables
eta1 = rvs['ETA(1)']
del rvs[eta1]
model.update_source()
assert str(model).split('\n')[12] == 'V = TVV*EXP(ETA(1))'
def test_set_parameters(pheno_path):
model = Model(pheno_path)
params = {
'THETA(1)': 0.75,
'THETA(2)': 0.5,
'THETA(3)': 0.25,
'OMEGA(1,1)': 0.1,
'OMEGA(2,2)': 0.2,
'SIGMA(1,1)': 0.3,
}
model.parameters = params
assert model.parameters['THETA(1)'] == Parameter('THETA(1)', 0.75, lower=0, upper=1000000)
assert model.parameters['THETA(2)'] == Parameter('THETA(2)', 0.5, lower=0, upper=1000000)
assert model.parameters['THETA(3)'] == Parameter('THETA(3)', 0.25, lower=-0.99, upper=1000000)
assert model.parameters['OMEGA(1,1)'] == Parameter('OMEGA(1,1)', 0.1, lower=0, upper=sympy.oo)
assert model.parameters['OMEGA(2,2)'] == Parameter('OMEGA(2,2)', 0.2, lower=0, upper=sympy.oo)
assert model.parameters['SIGMA(1,1)'] == Parameter('SIGMA(1,1)', 0.3, lower=0, upper=sympy.oo)
model.update_source()
thetas = model.control_stream.get_records('THETA')
assert str(thetas[0]) == '$THETA (0,0.75) ; PTVCL\n'
assert str(thetas[1]) == '$THETA (0,0.5) ; PTVV\n'
assert str(thetas[2]) == '$THETA (-.99,0.25)\n'
omegas = model.control_stream.get_records('OMEGA')
assert str(omegas[0]) == '$OMEGA DIAGONAL(2)\n 0.1 ; IVCL\n 0.2 ; IVV\n\n'
sigmas = model.control_stream.get_records('SIGMA')
assert str(sigmas[0]) == '$SIGMA 0.3\n'
model = Model(pheno_path)
params = model.parameters
params['THETA(1)'].init = 18
model.parameters = params
assert model.parameters['THETA(1)'] == Parameter('THETA(1)', 18, lower=0, upper=1000000)
assert model.parameters['THETA(2)'] == Parameter('THETA(2)', 1.00916, lower=0, upper=1000000)
def test_proportional_error_model_log(testdata):
model = Model(testdata / 'nonmem' / 'pheno.mod')
model.statements[5] = Assignment('Y', 'F')
proportional_error(model, data_trans='log(Y)')
model.update_source()
assert str(model).split('\n')[11] == 'Y = LOG(F) + EPS(1)'
assert str(model).split('\n')[17] == '$SIGMA 0.09 ; sigma'
def test_update_inits(pheno_path):
model = Model(pheno_path)
model.update_inits()
with ConfigurationContext(conf, parameter_names=['comment', 'basic']):
model = Model(pheno_path)
model.update_inits()
model.update_source()
def test_des(testdata, model_path, transformation):
model_ref = Model(testdata / model_path)
transformation(model_ref)
model_ref.update_source()
model_des = Model(StringIO(str(model_ref)))
model_des.source.path = model_ref.source.path # To be able to find dataset
assert model_ref.statements.ode_system == model_des.statements.ode_system
def test_additive_error_model(testdata):
model = Model(testdata / 'nonmem' / 'pheno.mod')
additive_error(model)
model.update_source()
assert str(model).split('\n')[11] == 'Y = F + EPS(1)'
assert str(model).split('\n')[17] == '$SIGMA 11.2225 ; sigma'
before = str(model)
additive_error(model) # One more time and nothing should change
assert before == str(model)
def test_combined_error_model_log(testdata):
model = Model(testdata / 'nonmem' / 'pheno.mod')
combined_error(model, data_trans='log(Y)')
model.update_source()
assert str(model).split('\n')[11] == 'Y = LOG(F) + EPS(2)/F + EPS(1)'
assert str(model).split('\n')[17] == '$SIGMA 0.09 ; sigma_prop'
assert str(model).split('\n')[18] == '$SIGMA 11.2225 ; sigma_add'
before = str(model)
combined_error(model) # One more time and nothing should change
assert before == str(model)
def test_remove_iiv(testdata, etas, pk_ref, omega_ref):
model = Model(testdata / 'nonmem/pheno_block.mod')
remove_iiv(model, etas)
model.update_source()
assert str(model.get_pred_pk_record()) == pk_ref
rec_omega = ''.join(
str(rec) for rec in model.control_stream.get_records('OMEGA'))
assert rec_omega == omega_ref
def test_transit_compartments_added_mdt(testdata):
model = Model(testdata / 'nonmem' / 'modeling' /
'pheno_advan5_nodepot.mod')
set_transit_compartments(model, 2)
transits = model.statements.ode_system.find_transit_compartments(
model.statements)
assert len(transits) == 2
model.update_source()
correct = ("""$PROBLEM PHENOBARB SIMPLE MODEL
$DATA ../pheno.dta IGNORE=@
$INPUT ID TIME AMT WGT APGR DV FA1 FA2
$SUBROUTINE ADVAN5 TRANS1
$MODEL COMPARTMENT=(TRANSIT1 DEFDOSE) COMPARTMENT=(TRANSIT2) COMPARTMENT=(CENTRAL) """
+ """COMPARTMENT=(PERIPHERAL)
$PK
MDT = THETA(6)
IF(AMT.GT.0) BTIME=TIME
TAD=TIME-BTIME
TVCL=THETA(1)*WGT
TVV=THETA(2)*WGT
IF(APGR.LT.5) TVV=TVV*(1+THETA(3))
CL=TVCL*EXP(ETA(1))
V=TVV*EXP(ETA(2))
K30 = CL/V
K34 = THETA(4)
K43 = THETA(5)
S3 = V
K12 = 2/MDT
K23 = 2/MDT
$ERROR
W=F
Y=F+W*EPS(1)
IPRED=F
IRES=DV-IPRED
IWRES=IRES/W
$THETA (0,0.00469307) ; CL
$THETA (0,1.00916) ; V
$THETA (-.99,.1)
$THETA (0,10)
$THETA (0,10)
$THETA (0,0.5) ; POP_MDT
$OMEGA DIAGONAL(2)
0.0309626 ; IVCL
0.031128 ; IVV
$SIGMA 1e-7
$ESTIMATION METHOD=1 INTERACTION
$COVARIANCE UNCONDITIONAL
$TABLE ID TIME DV AMT WGT APGR IPRED PRED RES TAD CWRES NPDE NOAPPEND
NOPRINT ONEHEADER FILE=sdtab1
""")
assert str(model) == correct
def test_block_rvs(testdata, etas, pk_ref, omega_ref):
with ConfigurationContext(conf, parameter_names='comment'):
model = Model(testdata / 'nonmem/pheno_block.mod')
create_rv_block(model, etas)
model.update_source()
assert str(model.get_pred_pk_record()) == pk_ref
rec_omega = ''.join(
str(rec) for rec in model.control_stream.get_records('OMEGA'))
assert rec_omega == omega_ref
def test_proportional_error_model(testdata):
model = Model(testdata / 'nonmem' / 'pheno.mod')
model.statements[5] = Assignment('Y', 'F')
proportional_error(model)
model.update_source()
assert str(model).split('\n')[11] == 'Y=F+F*EPS(1)'
assert str(model).split('\n')[17] == '$SIGMA 0.09 ; sigma'
model = Model(testdata / 'nonmem' / 'pheno.mod')
proportional_error(model)
model.update_source()
assert str(model).split('\n')[11] == 'Y=F+F*EPS(1)'
assert str(model).split('\n')[17] == '$SIGMA 0.013241'
def test_add_depot(testdata):
code = """$PROBLEM PHENOBARB SIMPLE MODEL
$DATA pheno.dta IGNORE=@
$INPUT ID TIME AMT WGT APGR DV
$SUBROUTINE ADVAN1 TRANS2
$PK
CL=THETA(1)*EXP(ETA(1))
V=THETA(2)*EXP(ETA(2))
$ERROR
CONC = A(1)/V
Y = CONC + CONC*EPS(1)
$THETA (0,0.00469307) ; TVCL
$THETA (0,1.00916) ; TVV
$OMEGA 0.0309626 ; IVCL
$OMEGA 0.031128 ; IVV
$SIGMA 0.013241
$ESTIMATION METHOD=1 INTERACTION
"""
model = Model(StringIO(code))
model.source.path = testdata / 'nonmem' / 'pheno.mod' # To be able to find dataset
first_order_absorption(model)
model.update_source()
correct = """$PROBLEM PHENOBARB SIMPLE MODEL
$DATA pheno.dta IGNORE=@
$INPUT ID TIME AMT WGT APGR DV
$SUBROUTINE ADVAN2 TRANS2
$PK
MAT = THETA(3)
CL=THETA(1)*EXP(ETA(1))
V=THETA(2)*EXP(ETA(2))
KA = 1/MAT
$ERROR
CONC = A(2)/V
Y = CONC + CONC*EPS(1)
$THETA (0,0.00469307) ; TVCL
$THETA (0,1.00916) ; TVV
$THETA (0,2.0) ; POP_MAT
$OMEGA 0.0309626 ; IVCL
$OMEGA 0.031128 ; IVV
$SIGMA 0.013241
$ESTIMATION METHOD=1 INTERACTION
"""
assert str(model) == correct
def test_add_covariate_effect_nan(pheno_path):
model = Model(pheno_path)
data = model.dataset
new_col = [np.nan] * 10 + ([1.0] * (len(data.index) - 10))
data['new_col'] = new_col
model.dataset = data
add_covariate_effect(model, 'CL', 'new_col', 'cat')
model.update_source(nofiles=True)
assert not re.search('NaN', str(model))
assert re.search(r'NEW_COL\.EQ\.-99', str(model))
def test_abbr_read_write(pheno_path):
with ConfigurationContext(
conf, parameter_names=['abbr', 'comment', 'basic'], write_etas_in_abbr=True
):
model_write = Model(pheno_path)
add_iiv(model_write, 'S1', 'add')
model_write.update_source()
model_read = Model(StringIO(str(model_write)))
model_read.source.path = pheno_path
assert str(model_read) == str(model_write)
assert model_read.statements == model_write.statements
assert not (
model_read.random_variables - model_write.random_variables
) # Different order due to renaming in read
def test_lag_time(testdata):
model = Model(testdata / 'nonmem' / 'modeling' / 'pheno_advan1.mod')
before = str(model)
add_lag_time(model)
model.update_source()
correct = '''$PROBLEM PHENOBARB SIMPLE MODEL
$DATA ../pheno.dta IGNORE=@
$INPUT ID TIME AMT WGT APGR DV FA1 FA2
$SUBROUTINE ADVAN1 TRANS2
$PK
MDT = THETA(4)
IF(AMT.GT.0) BTIME=TIME
TAD=TIME-BTIME
TVCL=THETA(1)*WGT
TVV=THETA(2)*WGT
IF(APGR.LT.5) TVV=TVV*(1+THETA(3))
CL=TVCL*EXP(ETA(1))
V=TVV*EXP(ETA(2))
S1=V
ALAG1 = MDT
$ERROR
W=F
Y=F+W*EPS(1)
IPRED=F
IRES=DV-IPRED
IWRES=IRES/W
$THETA (0,0.00469307) ; CL
$THETA (0,1.00916) ; V
$THETA (-.99,.1)
$THETA (0,0.5) ; POP_MDT
$OMEGA DIAGONAL(2)
0.0309626 ; IVCL
0.031128 ; IVV
$SIGMA 1e-7
$ESTIMATION METHOD=1 INTERACTION
$COVARIANCE UNCONDITIONAL
$TABLE ID TIME DV AMT WGT APGR IPRED PRED RES TAD CWRES NPDE NOAPPEND
NOPRINT ONEHEADER FILE=sdtab1
'''
assert str(model) == correct
remove_lag_time(model)
model.update_source()
assert str(model) == before
def test_add_two_parameters(pheno_path):
model = Model(pheno_path)
pset = model.parameters
assert len(pset) == 6
param_1 = Parameter('COVEFF1', 0.2)
param_2 = Parameter('COVEFF2', 0.1)
pset.append(param_1)
pset.append(param_2)
model.parameters = pset
model.update_source()
assert len(pset) == 8
assert model.parameters[param_1.name].init == 0.2
assert model.parameters[param_2.name].init == 0.1
def test_to_explicit_odes(pheno_path, testdata):
model = Model(pheno_path)
explicit_odes(model)
model.update_source()
lines = str(model).split('\n')
assert lines[5] == '$MODEL COMPARTMENT=(CENTRAL DEFDOSE)'
assert lines[16] == '$DES'
assert lines[17] == 'DADT(1) = -A(1)*CL/V'
model = Model(testdata / 'nonmem' / 'modeling' /
'pheno_advan1_zero_order.mod')
explicit_odes(model)
model.update_source()
lines = str(model).split('\n')
assert lines[15] == 'D1 = THETA(4)'
def test_remove_estimation_step():
code = '''$PROBLEM base model
$INPUT ID DV TIME
$DATA file.csv IGNORE=@
$PRED
Y = THETA(1) + ETA(1) + ERR(1)
$THETA 0.1
$OMEGA 0.01
$SIGMA 1
$EST METH=COND INTER
'''
model = Model(StringIO(code))
model.remove_estimation_step(0)
assert not model.estimation_steps
model.update_source()
assert str(model).split('\n')[-2] == '$SIGMA 1'
def test_combined_mm_fo_elimination(testdata):
code = """$PROBLEM PHENOBARB SIMPLE MODEL
$DATA pheno.dta IGNORE=@
$INPUT ID TIME AMT WGT APGR DV
$SUBROUTINE ADVAN1 TRANS2
$PK
CL=THETA(1)*EXP(ETA(1))
V=THETA(2)*EXP(ETA(2))
S1=V
$ERROR
Y=F+F*EPS(1)
$THETA (0,0.00469307) ; TVCL
$THETA (0,1.00916) ; TVV
$OMEGA 0.0309626 ; IVCL
$OMEGA 0.031128 ; IVV
$SIGMA 0.013241
$ESTIMATION METHOD=1 INTERACTION
"""
model = Model(StringIO(code))
model.source.path = testdata / 'nonmem' / 'pheno.mod' # To be able to find dataset
mixed_mm_fo_elimination(model)
model.update_source()
correct = """$PROBLEM PHENOBARB SIMPLE MODEL
$DATA pheno.dta IGNORE=@
$INPUT ID TIME AMT WGT APGR DV
$SUBROUTINE ADVAN6 TOL=3
$MODEL COMPARTMENT=(CENTRAL DEFDOSE)
$PK
CLMM = THETA(4)
KM = THETA(3)
CL=THETA(1)*EXP(ETA(1))
V=THETA(2)*EXP(ETA(2))
S1=V
$DES
DADT(1) = -A(1)*(CL + CLMM*KM/(A(1)/V + KM))/V
$ERROR
Y=F+F*EPS(1)
$THETA (0,0.00469307) ; TVCL
$THETA (0,1.00916) ; TVV
$THETA (0,135.8) ; POP_KM
$THETA (0,0.002346535) ; POP_CLMM
$OMEGA 0.0309626 ; IVCL
$OMEGA 0.031128 ; IVV
$SIGMA 0.013241
$ESTIMATION METHOD=1 INTERACTION
"""
assert str(model) == correct
def test_john_draper(pheno_path, etas, etad, buf_new):
model = Model(pheno_path)
john_draper(model, etas)
model.update_source()
rec_ref = (f'$PK\n'
f'{etad}\n'
f'IF(AMT.GT.0) BTIME=TIME\n'
f'TAD=TIME-BTIME\n'
f'TVCL=THETA(1)*WGT\n'
f'TVV=THETA(2)*WGT\n'
f'IF(APGR.LT.5) TVV=TVV*(1+THETA(3))\n'
f'{buf_new}\n'
f'S1=V\n\n')
assert str(model.get_pred_pk_record()) == rec_ref
def test_estimation_steps_setter(estcode, method, inter, cov, rec_ref):
code = '''$PROBLEM base model
$INPUT ID DV TIME
$DATA file.csv IGNORE=@
$PRED
Y = THETA(1) + ETA(1) + ERR(1)
$THETA 0.1
$OMEGA 0.01
$SIGMA 1
'''
code += estcode
model = Model(StringIO(code))
model.estimation_steps[0].method = method
model.estimation_steps[0].interaction = inter
model.estimation_steps[0].cov = cov
model.update_source()
assert str(model).split('\n')[-2] == rec_ref
