def test_with_dataset():
ar = IndentationGroup(jpkfile)[0]
# Initially, fit properties are not set
assert not ar.fit_properties
assert isinstance(ar.fit_properties, dict)
# Prepprocessing
ar.apply_preprocessing(["compute_tip_position",
"correct_tip_offset",
"correct_force_offset"])
# Fitting the data set will populate the dict
ar.fit_model()
assert isinstance(ar.fit_properties, dict)
assert isinstance(ar.fit_properties, FitProperties)
assert "hash" in ar.fit_properties
assert ar.fit_properties["success"]
hash1 = ar.fit_properties["hash"]
# Change something
ar.fit_properties["weight_cp"] = 0
assert "hash" not in ar.fit_properties
ar.fit_model()
hash2 = ar.fit_properties["hash"]
assert hash1 != hash2
# Change it back
ar.fit_properties["weight_cp"] = FP_DEFAULT["weight_cp"]
ar.fit_model()
hash3 = ar.fit_properties["hash"]
assert hash1 == hash3
def test_poc_estimation_via_indent(method, contact_point):
fd = IndentationGroup(data_path / "fmt-jpk-fd_spot3-0192.jpk-force")[0]
fd.apply_preprocessing(
["compute_tip_position", "correct_force_offset", "correct_tip_offset"],
options={"correct_tip_offset": {
"method": method
}})
assert np.argmin(np.abs(fd["tip position"])) == contact_point
def test_unknown_method():
idnt = IndentationGroup(datadir / "spot3-0192.jpk-force")[0]
try:
idnt.apply_preprocessing(["compute_tip_position", "unknown_method"])
except KeyError:
pass
else:
assert False, "Preprocessing with an unknown method must not work."
def test_correct_split_approach_retract():
fd = IndentationGroup(data_path / "fmt-jpk-fd_spot3-0192.jpk-force")[0]
fd.apply_preprocessing(
["compute_tip_position", "correct_force_offset", "correct_tip_offset"])
assert fd.appr["segment"].size == 2000
fd.apply_preprocessing([
"compute_tip_position", "correct_force_offset", "correct_tip_offset",
"correct_split_approach_retract"
])
assert fd.appr["segment"].size == 2006
def test_smooth():
idnt = IndentationGroup(data_path / "fmt-jpk-fd_spot3-0192.jpk-force")[0]
idnt.apply_preprocessing(
["compute_tip_position", "correct_force_offset", "correct_tip_offset"])
orig = np.array(idnt["tip position"], copy=True)
# now apply smoothing filter
idnt.apply_preprocessing([
"compute_tip_position", "correct_force_offset", "correct_tip_offset",
"smooth_height"
])
new = np.array(idnt["tip position"], copy=True)
assert not np.all(orig == new)
def test_change_model_key():
ar = IndentationGroup(jpkfile)[0]
# Prepprocessing
ar.apply_preprocessing(
["compute_tip_position", "correct_tip_offset", "correct_force_offset"])
# Fitting the data set will populate the dict
ar.fit_model(model_key="hertz_para")
# Change the model
assert ar.fit_properties["params_initial"] is not None
ar.fit_properties["model_key"] = "hertz_cone"
# Changing the model key should reset the initial parameters
assert ar.fit_properties["params_initial"] is None
def test_wrong_params_initial():
ar = IndentationGroup(jpkfile)[0]
# Prepprocessing
ar.apply_preprocessing(
["compute_tip_position", "correct_tip_offset", "correct_force_offset"])
md = model.models_available["hertz_para"]
params = md.get_parameter_defaults()
ar.fit_properties["model_key"] = "hertz_cone"
try:
ar.fit_model(params_initial=params)
except FitKeyError:
# We forced the wrong fitting parameters.
pass
else:
raise ValueError("Should not be able to use wrong fit parameters!")
def test_process_flipsign():
# This is a curve extracted from a map file. When loading it
# with nanite, the sign of the force curve was flipped.
flipped = datadir / "flipsign_2015.05.22-15.31.49.352.jpk-force"
idnt = IndentationGroup(flipped)[0]
idnt.apply_preprocessing([
"compute_tip_position", "correct_force_offset", "correct_tip_offset",
"correct_split_approach_retract"
])
# We set the baseline fixed, because this test was written so)
params_initial = idnt.get_initial_fit_parameters(model_key="hertz_para")
params_initial["baseline"].set(vary=False)
idnt.fit_model(model_key="hertz_para",
weight_cp=False,
params_initial=params_initial)
assert np.allclose(idnt.fit_properties["params_fitted"]["E"].value,
5257.047288859021)
def setup_indent():
idnt = IndentationGroup(jpkfile)[0]
idnt.apply_preprocessing(["compute_tip_position"])
inparams = model.model_hertz_paraboloidal.get_parameter_defaults()
inparams["baseline"].vary = True
inparams["contact_point"].set(1.8e-5)
# Fit with absolute full range
idnt.fit_model(model_key="hertz_para",
params_initial=inparams,
range_x=(0, 0),
range_type="absolute",
x_axis="tip position",
y_axis="force",
segment="approach",
weight_cp=False)
return idnt
def test_changed_fit_properties():
ar = IndentationGroup(jpkfile)[0]
# Initially, fit properties are not set
assert not ar.fit_properties
assert isinstance(ar.fit_properties, dict)
# Prepprocessing
ar.apply_preprocessing(
["compute_tip_position", "correct_tip_offset", "correct_force_offset"])
ar.fit_model()
hash1 = ar.fit_properties["hash"]
pinit = copy.deepcopy(ar.fit_properties["params_initial"])
pinit["E"].vary = False
assert "hash" in ar.fit_properties, "make sure we didn't change anything"
ar.fit_properties["params_initial"] = pinit
assert "hash" not in ar.fit_properties
ar.fit_model()
assert hash1 != ar.fit_properties["hash"]
def test_poc_details_fit_line_polynomial():
fd = IndentationGroup(
data_path /
"fmt-jpk-fd_single_tilted-baseline-mitotic_2021-01-29.jpk-force")[0]
details = fd.apply_preprocessing(
["compute_tip_position", "correct_tip_offset"],
options={"correct_tip_offset": {
"method": "fit_line_polynomial"
}},
ret_details=True)
pocd = details["correct_tip_offset"]
for key in ["plot force", "plot fit", "plot poc"]:
assert key in pocd
assert np.allclose(pocd["plot poc"][0][0], 15602, atol=0)
assert np.allclose(
fd["force"][15602],
1.7313584441928315e-09,
atol=0,
)
def test_poc_details_deviation_from_baseline():
fd = IndentationGroup(data_path / "fmt-jpk-fd_spot3-0192.jpk-force")[0]
details = fd.apply_preprocessing(
["compute_tip_position", "correct_force_offset", "correct_tip_offset"],
options={"correct_tip_offset": {
"method": "deviation_from_baseline"
}},
ret_details=True)
pocd = details["correct_tip_offset"]
for key in [
"plot force", "plot baseline mean", "plot baseline threshold",
"plot poc"
]:
assert key in pocd
assert np.allclose(np.mean(pocd["plot baseline threshold"][1]),
8.431890003513514e-11,
atol=0)
assert np.allclose(np.mean(pocd["plot baseline mean"][1]),
-7.573822405258677e-12,
atol=0)
def test_poc_estimation_details(method, contact_point):
fd = IndentationGroup(data_path / "fmt-jpk-fd_spot3-0192.jpk-force")[0]
fd.apply_preprocessing(["compute_tip_position", "correct_force_offset"])
_, details = poc.compute_poc(fd["force"], method, ret_details=True)
assert np.all(np.array(details["plot poc"][0]) == contact_point)
def test_poc_estimation(method, contact_point):
fd = IndentationGroup(data_path / "fmt-jpk-fd_spot3-0192.jpk-force")[0]
fd.apply_preprocessing(["compute_tip_position", "correct_force_offset"])
assert poc.compute_poc(fd["force"], method) == contact_point
def test_wrong_order_2():
idnt = IndentationGroup(data_path / "fmt-jpk-fd_spot3-0192.jpk-force")[0]
with pytest.raises(ValueError, match="requires the steps"):
# order matters
idnt.apply_preprocessing(
["correct_split_approach_retract", "compute_tip_position"])
def test_unknown_method():
idnt = IndentationGroup(data_path / "fmt-jpk-fd_spot3-0192.jpk-force")[0]
with pytest.raises(KeyError, match="unknown_method"):
idnt.apply_preprocessing(["compute_tip_position", "unknown_method"])