def test_model_expr_base(qtbot):
"""Expressions sanity check
This test is just to make sure that parameters that have an
expression are not overridden by the UI.
"""
with MockModelModuleExpr() as mod:
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# clear data
war.cb_autosave.setChecked(0)
# perform simple filter
war.tab_preprocess.set_preprocessing(["compute_tip_position"])
# set mock model
idx = war.tab_fit.cb_model.findData(mod.model_key)
war.tab_fit.cb_model.setCurrentIndex(idx)
# Now there should be fit results
itab = war.tab_fit.table_parameters_initial
# set better value for contact point
itab.item(5, 1).setText(str(18000))
fdist = war.data_set[0]
parmsi = fdist.fit_properties["params_initial"]
parmsf = fdist.fit_properties["params_fitted"]
assert parmsi["E1"].expr == "virtual_parameter+E"
assert parmsf["E1"].expr == "virtual_parameter+E"
assert np.allclose(parmsf["E1"].value, 15388.14166)
def test_ancillary_update_nan(qtbot):
with MockModelModule(compute_ancillaries=lambda x: {"E": np.nan},
parameter_anc_keys=["E"],
parameter_anc_names=["ancillary E guess"],
parameter_anc_units=["Pa"],
model_key="test1"):
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# clear data
war.cb_autosave.setChecked(0)
# perform simple filter
war.tab_preprocess.set_preprocessing(["compute_tip_position"])
# disable weighting
war.tab_fit.cb_weight_cp.setCheckState(0)
# set mock model
idx = war.tab_fit.cb_model.findData("test1")
war.tab_fit.cb_model.setCurrentIndex(idx)
# perform fitting with standard parameters
# set initial parameters in user interface
itab = war.tab_fit.table_parameters_initial
atab = war.tab_fit.table_parameters_anc
assert atab.item(0, 1).text() == "nan"
assert itab.item(0, 1).text() == "3000"
def test_clear_and_verify_data(qtbot):
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data())
war = main_window.subwindows[0].widget()
# clear data
tpp = war.tab_preprocess
war.cb_autosave.setChecked(0)
# perform simple filter
tpp.set_preprocessing(["compute_tip_position"])
# perform fitting with standard parameters
# set initial parameters in user interface
itab = war.tab_fit.table_parameters_initial
# disable weighting
war.tab_fit.cb_weight_cp.setCheckState(0)
# enable fitting of force offset
itab.item(4, 0).setCheckState(0)
# set better value for contact point
itab.item(3, 1).setText(str(18000))
apret = war.data_set[0]
war.tab_fit.fit_approach_retract(apret)
# Now check something
ftab = war.tab_fit.table_parameters_fitted
# E
assert np.allclose(apret.fit_properties["params_fitted"]["E"].value,
14741.958242422093,
atol=1e-4,
rtol=0)
assert float(ftab.item(0, 0).text()) == 14742
# contact_point
assert float(ftab.item(1, 0).text()) == 18029
# baseline_offset
assert float(ftab.item(2, 0).text()) == -480.67
def test_fit_all(qtbot):
"""Perform a simple fit with the standard parameters"""
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
war.cb_autosave.setChecked(0)
war.on_fit_all()
a1 = war.data_set[0]
a2 = war.data_set[1]
assert a1.fit_properties == a2.fit_properties
def test_qmap_with_unused_curves(qtbot):
"""Uncheck "use" in the curve list and switch to the qmap tab"""
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data())
war = main_window.subwindows[0].widget()
# uncheck first curve
cl1 = war.list_curves.currentItem()
cl1.setCheckState(3, QtCore.Qt.Unchecked)
war.tabs.setCurrentIndex(5)
QtWidgets.QApplication.processEvents()
def test_preprocessing_poc_estimation(method, contact_point):
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# clear data
war.cb_autosave.setChecked(0)
war.tabs.setCurrentIndex(0)
# perform simple filter
war.tab_preprocess.set_preprocessing(
preprocessing=["compute_tip_position", "correct_tip_offset"],
options={"correct_tip_offset": {"method": method}}
)
war.tabs.setCurrentIndex(1)
fd = war.data_set[0]
assert np.argmin(np.abs(fd["tip position"])) == contact_point
def test_ancillary_update_preproc_change(qtbot):
with MockModelModule(
compute_ancillaries=lambda x: {
# i.e. model works only if there are multiple preproc steps
"E": np.nan if len(x.preprocessing) == 1 else 2345
},
parameter_anc_keys=["E"],
parameter_anc_names=["ancillary E guess"],
parameter_anc_units=["Pa"],
model_key="test1"):
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# clear data
war.cb_autosave.setChecked(0)
# perform simple filter
war.tab_preprocess.set_preprocessing(["compute_tip_position"])
# disable weighting
war.tab_fit.cb_weight_cp.setCheckState(0)
# set mock model
idx = war.tab_fit.cb_model.findData("test1")
war.tab_fit.cb_model.setCurrentIndex(idx)
# perform fitting with standard parameters
# set initial parameters in user interface
itab = war.tab_fit.table_parameters_initial
atab = war.tab_fit.table_parameters_anc
war.on_tab_changed()
assert len(war.data_set[0].preprocessing) == 1
assert len(war.tab_preprocess.current_preprocessing()[0]) == 1
assert atab.item(0, 1).text() == "nan"
assert itab.item(0, 1).text() == "3000"
# up until here this is the same as `test_update_ancillary_nan`
# now change preprocessing
war.tabs.setCurrentIndex(0) # actually switch tabs like a user
# manually check the "correct_tip_offset" widget
for pwid, k in war.tab_preprocess._map_widgets_to_preproc_ids.items():
if k == "correct_tip_offset":
pwid.setChecked(True)
war.tabs.setCurrentIndex(1) # triggers recomputation of anc
assert len(war.data_set[0].preprocessing) == 2
assert len(war.tab_preprocess.current_preprocessing()[0]) == 2
assert atab.item(0, 1).text() == "2345"
assert itab.item(0, 1).text() == "2345"
def test_change_model_keep_parms(qtbot):
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# clear data
war.cb_autosave.setChecked(0)
# perform simple filter
war.tab_preprocess.set_preprocessing(["compute_tip_position"])
# perform fitting with standard parameters
# set initial parameters in user interface
itab = war.tab_fit.table_parameters_initial
# set value for contact point
itab.item(3, 1).setText(str(12345))
# change the model to pyramidal
pyr_name = nmodel.model_hertz_three_sided_pyramid.model_name
pyr_idx = war.tab_fit.cb_model.findText(pyr_name)
war.tab_fit.cb_model.setCurrentIndex(pyr_idx)
# check that contact point is still the same
assert float(itab.item(3, 1).text()) == 12345
def test_ancillary_update_init(qtbot):
with MockModelModule(
compute_ancillaries=lambda x: {
# take initial fit parameter of E
"E": x.get_initial_fit_parameters(model_ancillaries=False)["E"]
.value
},
parameter_anc_keys=["E"],
parameter_anc_names=["ancillary E guess"],
parameter_anc_units=["Pa"],
model_key="test1"):
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# clear data
war.cb_autosave.setChecked(0)
# perform simple filter
war.tab_preprocess.set_preprocessing(["compute_tip_position"])
# disable weighting
war.tab_fit.cb_weight_cp.setCheckState(0)
# set mock model
idx = war.tab_fit.cb_model.findData("test1")
war.tab_fit.cb_model.setCurrentIndex(idx)
# perform fitting with standard parameters
# set initial parameters in user interface
itab = war.tab_fit.table_parameters_initial
atab = war.tab_fit.table_parameters_anc
war.on_tab_changed()
assert len(war.data_set[0].preprocessing) == 1
assert len(war.tab_preprocess.current_preprocessing()[0]) == 1
# The ancillary parameter gets its value from the default parameters
assert atab.item(0, 1).text() == "3000"
assert itab.item(0, 1).text() == "3000"
# Now we change the initial parameter "E" and move on to the next
# curve. Ancillary parameter "F" should also change.
itab.item(0, 1).setText("2000")
assert atab.item(0, 1).text() == "2000"
it = war.list_curves.topLevelItem(1)
war.list_curves.setCurrentItem(it)
assert itab.item(0, 1).text() == "2000"
assert atab.item(0, 1).text() == "2000"
def test_set_indentation_depth_manually_infdoublespinbox(qtbot):
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# perform fitting with standard parameters
# set initial parameters in user interface
itab = war.tab_fit.table_parameters_initial
# set value for contact point
itab.item(3, 1).setText(str(12345))
# change the model to pyramidal
pyr_name = nmodel.model_hertz_three_sided_pyramid.model_name
pyr_idx = war.tab_fit.cb_model.findText(pyr_name)
war.tab_fit.cb_model.setCurrentIndex(pyr_idx)
# set left fitting range
for text_entered, resulting_value in [["-1.40", -1.4], ["1...2", 1.2],
["1.0e-4", 1e-4], ["inf", np.inf],
["1.10201", 1.10201],
["1.001", 1.001], ["-1.04", -1.04]]:
war.tab_fit.sp_range_1.clear()
qtbot.keyClicks(war.tab_fit.sp_range_1, text_entered)
assert war.tab_fit.sp_range_1.value() == resulting_value
def test_hidden_parameters(qtbot):
def get_parameter_defaults():
params = lmfit.Parameters()
params.add("E", value=3e3, min=0)
params.add("R", value=10e-6, min=0, vary=False)
params.add("_nu", value=.5, min=0, max=0.5, vary=False)
params.add("contact_point", value=0)
params.add("baseline", value=0)
return params
def model_func(delta, E, R, _nu, contact_point=0, baseline=0):
return model_hertz_paraboloidal.hertz_paraboloidal(
delta=delta,
E=E,
R=R,
nu=_nu,
contact_point=contact_point,
baseline=baseline)
with MockModelModule(
model_key="petperpan",
parameter_keys=["E", "R", "_nu", "contact_point", "baseline"],
model_func=model_func,
model=nres.get_default_modeling_wrapper(model_func),
residual=nres.get_default_residuals_wrapper(model_func),
get_parameter_defaults=get_parameter_defaults) as mod:
main_window = pyjibe.head.PyJibe()
# disable developer mode
main_window.settings.setValue("advanced/developer mode", 0)
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# clear data
war.cb_autosave.setChecked(0)
# perform simple filter
war.tab_preprocess.set_preprocessing(["compute_tip_position"])
# set mock model
idx = war.tab_fit.cb_model.findData(mod.model_key)
war.tab_fit.cb_model.setCurrentIndex(idx)
# Check visibility of Poisson's ratio
itab = war.tab_fit.table_parameters_initial
assert itab.rowCount() == 4
def test_remember_initial_params(qtbot):
main_window = pyjibe.head.PyJibe()
main_window.load_data(files=make_directory_with_data(2))
war = main_window.subwindows[0].widget()
# clear data
war.cb_autosave.setChecked(0)
# perform simple filter
war.tab_preprocess.set_preprocessing(["compute_tip_position"])
# perform fitting with standard parameters
# set initial parameters in user interface
itab = war.tab_fit.table_parameters_initial
# disable weighting
war.tab_fit.cb_weight_cp.setCheckState(0)
# enable fitting of force offset
itab.item(4, 0).setCheckState(0)
# set better value for contact point
itab.item(3, 1).setText(str(18000))
# change standard tip radius from 10 to 5
assert float(itab.item(1, 1).text()) == 10
itab.item(1, 1).setText(str(5))
cl1 = war.list_curves.currentItem()
cl2 = war.list_curves.itemBelow(cl1)
war.list_curves.setCurrentItem(cl2)
assert float(itab.item(1, 1).text()) == 5
