def test_simeval(testdata):
orig = Model(testdata / 'nonmem' / 'pheno.mod')
base = Model(testdata / 'nonmem' / 'qa' / 'pheno_linbase.mod')
simeval_res = read_results(testdata / 'nonmem' / 'qa' /
'simeval_results.json')
cdd_res = read_results(testdata / 'nonmem' / 'qa' / 'cdd_results.json')
calculate_results(orig,
base,
simeval_results=simeval_res,
cdd_results=cdd_res)
def test_bootstrap():
orig = ModelfitResults(ofv=110, parameter_estimates=pd.Series({'TVCL': 0.75, 'TVV': 2.25}))
res1 = ModelfitResults(ofv=100, parameter_estimates=pd.Series({'TVCL': 1.0, 'TVV': 2.0}))
res2 = ModelfitResults(ofv=120, parameter_estimates=pd.Series({'TVCL': 1.5, 'TVV': 3.0}))
class MyModel:
pass
orig_mod = MyModel()
orig_mod.modelfit_results = orig
res1_mod = MyModel()
res1_mod.modelfit_results = res1
res2_mod = MyModel()
res2_mod.modelfit_results = res2
boot = calculate_results([res1_mod, res2_mod], original_model=orig_mod)
correct_statistics = pd.DataFrame(
{
'mean': [1.25, 2.5],
'median': [1.25, 2.50],
'bias': [0.5, 0.25],
'stderr': [0.353553, 0.707107],
'RSE': [0.2828424, 0.2828424],
},
index=['TVCL', 'TVV'],
)
pd.testing.assert_frame_equal(boot.parameter_statistics, correct_statistics)
# json round trip
json = boot.to_json()
obj = read_results(json)
pd.testing.assert_frame_equal(obj.parameter_statistics, correct_statistics)
def results_report(args):
"""Subcommand to generate reports"""
results_path = args.psn_dir / 'results.json'
if not results_path.is_file():
error(FileNotFoundError(results_path))
from pharmpy.results import read_results
res = read_results(results_path)
res.create_report(args.psn_dir)
def results_print(args):
"""Subcommand to print any results"""
if args.dir.is_dir():
path = args.dir / 'results.json'
elif args.dir.is_file():
path = args.dir
else:
error(FileNotFoundError(str(args.dir)))
from pharmpy.results import read_results
res = read_results(path)
print(res)
def test_bootstrap():
orig = ModelfitResults(ofv=110,
parameter_estimates=pd.Series({
'TVCL': 0.75,
'TVV': 2.25
}))
res1 = ModelfitResults(ofv=100,
parameter_estimates=pd.Series({
'TVCL': 1.0,
'TVV': 2.0
}))
res2 = ModelfitResults(ofv=120,
parameter_estimates=pd.Series({
'TVCL': 1.5,
'TVV': 3.0
}))
class MyModel:
pass
orig_mod = MyModel()
orig_mod.modelfit_results = orig
res1_mod = MyModel()
res1_mod.modelfit_results = res1
res2_mod = MyModel()
res2_mod.modelfit_results = res2
boot = BootstrapResults(orig_mod, [res1_mod, res2_mod])
correct_statistics = pd.DataFrame(
{
'mean': [110.0, 1.25, 2.5],
'bias': [0.0, 0.5, 0.25],
'stderr': [14.142136, 0.353553, 0.707107]
},
index=['OFV', 'TVCL', 'TVV'])
pd.testing.assert_frame_equal(
boot.parameter_estimates,
pd.DataFrame({
'TVCL': [1.0, 1.5],
'TVV': [2.0, 3.0]
}))
pd.testing.assert_frame_equal(boot.statistics, correct_statistics)
# json round trip
json = boot.to_json()
obj = read_results(json)
pd.testing.assert_frame_equal(obj.statistics, correct_statistics)
def test_scm(testdata):
orig = Model(testdata / 'nonmem' / 'pheno.mod')
base = Model(testdata / 'nonmem' / 'qa' / 'pheno_linbase.mod')
scm_res = read_results(testdata / 'nonmem' / 'qa' / 'scm_results.json')
res = calculate_results(orig, base, scm_results=scm_res)
correct = """,,dofv,coeff
ETA(1),APGR,2.48792,-0.033334
ETA(1),WGT,0.48218,0.052342
ETA(2),APGR,0.59036,0.008371
ETA(2),WGT,0.00887,-0.003273
"""
correct = pd.read_csv(StringIO(correct), index_col=[0, 1])
correct.index.set_names(['parameter', 'covariate'], inplace=True)
pd.testing.assert_frame_equal(res.covariate_effects, correct, atol=1e-6)
assert res.dofv['dofv']['covariates', 'ET1APGR-2',
np.nan] == pytest.approx(2.48792)
assert res.dofv['df']['covariates', 'ET1APGR-2', np.nan] == 1
def test_resmod(testdata):
orig = Model(testdata / 'nonmem' / 'pheno.mod')
base = Model(testdata / 'nonmem' / 'qa' / 'pheno_linbase.mod')
resmod_res = read_results(testdata / 'nonmem' / 'qa' /
'resmod_results.json')
res = calculate_results(orig, base, resmod_idv_results=resmod_res)
assert list(
res.residual_error['additional_parameters']) == [2, 2, 1, 1, 1, 1]
assert list(
res.residual_error['dOFV']) == [13.91, 8.03, 5.53, 3.34, 1.31, 0.03]
assert res.residual_error.index.tolist() == [
(1, 'dtbs'),
(1, 'time_varying'),
(1, 'tdist'),
(1, 'autocorrelation'),
(1, 'IIV_on_RUV'),
(1, 'power'),
]
assert res.dofv['dofv']['residual_error_model', 'dtbs',
1] == pytest.approx(13.91)
def psn_qa_results(path):
"""Create qa results from a PsN qa run
:param path: Path to PsN qa run directory
:return: A :class:`QAResults` object
"""
path = Path(path)
original_model = Model(path / 'linearize_run' / 'scm_dir1' /
'derivatives.mod')
base_path = list(path.glob('*_linbase.mod'))[0]
base_model = Model(base_path)
fullblock_path = path / 'modelfit_run' / 'fullblock.mod'
if fullblock_path.is_file():
fullblock_model = Model(fullblock_path)
else:
fullblock_model = None
boxcox_path = path / 'modelfit_run' / 'boxcox.mod'
if boxcox_path.is_file():
boxcox_model = Model(boxcox_path)
else:
boxcox_model = None
tdist_path = path / 'modelfit_run' / 'tdist.mod'
if tdist_path.is_file():
tdist_model = Model(tdist_path)
else:
tdist_model = None
addetas_path = path / 'add_etas_run' / 'add_etas_linbase.mod'
if addetas_path.is_file():
addetas_model = Model(addetas_path)
else:
addetas_model = None
iov_path = path / 'modelfit_run' / 'iov.mod'
if iov_path.is_file():
iov_model = Model(iov_path)
else:
iov_model = None
frem_path = path / 'frem_run' / 'results.json'
if frem_path.is_file():
frem_res = read_results(frem_path)
else:
frem_res = None
cdd_path = path / 'cdd_run' / 'results.json'
if cdd_path.is_file():
cdd_res = read_results(cdd_path)
else:
cdd_res = None
scm_path = path / 'scm_run' / 'results.json'
if scm_path.is_file():
scm_res = read_results(scm_path)
else:
scm_res = None
args = psn_helpers.options_from_command(psn_helpers.psn_command(path))
if 'add_etas' not in args:
etas_added_to = None
else:
etas_added_to = args['add_etas'].split(',')
res = calculate_results(
original_model,
base_model,
fullblock_model=fullblock_model,
boxcox_model=boxcox_model,
tdist_model=tdist_model,
add_etas_model=addetas_model,
iov_model=iov_model,
etas_added_to=etas_added_to,
frem_results=frem_res,
cdd_results=cdd_res,
scm_results=scm_res,
)
return res