def test_open_dat_without_spring_constant():
# There are data files without spring constant but with force in nN.
# nanite requires the spring constant for computation of the tip
# position which we check for here.
tmp = tempfile.mkdtemp(prefix="test_nanite_group")
shutil.copy(data_path / "fmt-afm-workshop-fd_single_2021-10-22_14.16.csv",
tmp)
# try without explicit metadata
with pytest.raises(MissingMetaDataError, match="spring constant"):
load_group(tmp)
# try with metadata
grp = load_group(tmp, meta_override={"spring constant": 20})
assert grp[0].metadata["spring constant"] == 20
def test_subgroup():
tmp = tempfile.mkdtemp(prefix="test_nanite_group")
shutil.copy(datadir / "map-data-reference-points.jpk-force-map", tmp)
shutil.copy(datadir / "map2x2_extracted.jpk-force-map", tmp)
shutil.copy(datadir / "flipsign_2015.05.22-15.31.49.352.jpk-force", tmp)
grp = load_group(tmp)
exp = pathlib.Path(tmp) / "map2x2_extracted.jpk-force-map"
subgrp = grp.subgroup_with_path(path=exp)
assert len(grp) == 8
assert len(subgrp) == 4
assert subgrp[0].path == exp
shutil.rmtree(tmp, ignore_errors=True)
def test_plotting():
# use temporary file
_, name = tempfile.mkstemp(suffix=".png", prefix="test_nanite_plotting_")
name = pathlib.Path(name)
grp = load_group(jpkfile)
idnt = grp[0]
idnt.apply_preprocessing(["compute_tip_position", "correct_force_offset"])
idnt.fit_model(model_key="hertz_para",
params_initial=None,
x_axis="tip position",
y_axis="force",
weight_cp=False,
segment="retract")
plotting.plot_data(idnt=idnt, path=name)
assert name.stat().st_size > 90000
"""Fitting and rating
This example uses a force-distance curve of a zebrafish spinal cord
section to illustrate basic data fitting and rating with nanite.
The dataset is part of a study on spinal cord stiffness in zebrafish
:cite:`Moellmert2019`.
"""
import matplotlib.gridspec as gridspec
import matplotlib.pylab as plt
import nanite
# load the data
group = nanite.load_group("data/zebrafish-head-section-gray-matter.jpk-force")
idnt = group[0] # this is an instance of `nanite.Indentation`
# apply preprocessing
idnt.apply_preprocessing(["compute_tip_position",
"correct_force_offset",
"correct_tip_offset"])
# set the fit model ("sneddon_spher_approx" is faster than "sneddon_spher"
# and sufficiently accurate)
idnt.fit_properties["model_key"] = "sneddon_spher_approx"
# get the initial fit parameters
params = idnt.get_initial_fit_parameters()
# set the correct indenter radius
params["R"].value = 18.64e-06
# perform the fit with the edited parameters
idnt.fit_model(params_initial=params)
# obtain the Young's modulus
emod = idnt.fit_properties["params_fitted"]["E"].value
# obtain a rating for the dataset