Code reference¶
Module level aliases¶
For user convenience, the following objects are available at the module level.
- class nanite.Indentation¶
alias of
nanite.indent.Indentation
- class nanite.IndentationGroup¶
alias of
nanite.group.IndentationGroup
- class nanite.IndentationRater¶
alias of
nanite.rate.IndentationRater
- class nanite.QMap¶
alias of
nanite.qmap.QMap
- nanite.load_group()¶
alias of
nanite.group.load_group
Force-indentation data¶
- class nanite.indent.Indentation(data, metadata, diskcache=None)[source]¶
Additional functionalities for afmformats.AFMForceDistance
- apply_preprocessing(preprocessing=None)[source]¶
Perform curve preprocessing steps
- Parameters
preprocessing (list) – A list of preprocessing method names that are stored in the IndentationPreprocessor class. If set to None, self.preprocessing will be used.
- compute_emodulus_mindelta(callback=None)[source]¶
Elastic modulus in dependency of maximum indentation
The fitting interval is varied such that the maximum indentation depth ranges from the lowest tip position to the estimated contact point. For each interval, the current model is fitted and the elastic modulus is extracted.
- Parameters
callback (callable) – A method that is called with the emoduli and indentations as the computation proceeds every five steps.
- Returns
emoduli, indentations – The fitted elastic moduli at the corresponding maximal indentation depths.
- Return type
1d ndarrays
Notes
The information about emodulus and mindelta is also stored in self.fit_properties with the keys “optimal_fit_E_array” and “optimal_fit_delta_array”, if self.fit_model is called with the argument search_optimal_fit set to True.
- estimate_contact_point_index()[source]¶
Estimate the contact point
Contact point (CP) estimation involves a preprocessing step where the force data are transformed into gradient space (to account for a slope in the approach curve) and a subsequent analysis with two different methods to determine when the gradient changes significantly enough to qualify for a CP. Of those two methods, the one which yields the smallest index (measured from the beginning of the approach curve) is returned. If one of the methods fail, then a fit function with a constant and linear part is used to determine the CP.
Preprocessing:
Compute the rolling average of the force (Otherwise the gradient would be too wild)
Compute the gradient (Converting to gradient space gets rid of linear contributions in the approach part)
Compute the rolling average of the gradient (Makes the curve to analyze more smooth so that the methods below don’t hit the alarm too early)
Method 1: baseline deviation
Obtain the baseline (initial 10% of the gradient curve)
Compute average and maximum deviation of the baseline
The CP is the index of the curve where it exceeds twice of the maximum deviation
Method 2: sign of gradient
Apply a median filter to the approach curve
Compute the gradient
Cut off trailing 10 points from the gradient (noise)
The CP is the index of the gradient curve when the sign changes, measured from the point of maximal indentation.
If one of the methods fail, then a combined constant+linear function (max(constant, linear) is fitted to the gradient to determine the contact point. If that fails as well, then the CP defaults to the center of the entire approach curve.
Changed in version 1.6.0: Add the gradient preprocessing step to circumvent issues with tilted baselines. This feature does not significantly affect fitting results.
Changed in version 1.6.1: Added max(constant, linear) fit when the other methods fail.
- estimate_optimal_mindelta()[source]¶
Estimate the optimal indentation depth
This is a convenience function that wraps around compute_emodulus_mindelta and IndentationFitter.compute_opt_mindelta.
- fit_model(**kwargs)[source]¶
Fit the approach-retract data to a model function
- Parameters
model_key (str) – A key referring to a model in nanite.model.models_available
params_initial (instance of lmfit.Parameters or dict) – Parameters for fitting. If not given, default parameters are used.
range_x (tuple of 2) – The range for fitting, see range_type below.
range_type (str) –
One of:
- absolute:
Set the absolute fitting range in values given by the x_axis.
- relative cp:
In some cases it is desired to be able to fit a model only up until a certain indentation depth (tip position) measured from the contact point. Since the contact point is a fit parameter as well, this requires a two-pass fitting.
preprocessing (list of str) – Preprocessing
segment (str) – Segment index (e.g. 0 for approach)
weight_cp (float) – Weight the contact point region which shows artifacts that are difficult to model with e.g. Hertz.
optimal_fit_edelta (bool) – Search for the optimal fit by varying the maximal indentation depth and determining a plateau in the resulting Young’s modulus (fitting parameter “E”).
- get_ancillary_parameters(model_key=None)[source]¶
Compute ancillary parameters for the current model
- get_initial_fit_parameters(model_key=None, common_ancillaries=True, model_ancillaries=True)[source]¶
Return the initial fit parameters
If there are not initial fit parameters set in self.fit_properties, then they are computed.
- Parameters
Notes
global_ancillaries and model_ancillaries only have an effect if self.fit_properties[“params_initial”] is set.
- rate_quality(regressor='Extra Trees', training_set='zef18', names=None, lda=None)[source]¶
Compute the quality of the obtained curve
Uses heuristic approaches to rate a curve.
- Parameters
- Returns
rating – A value between 0 and 10 where 0 is the lowest rating. If no fit has been performed, a rating of -1 is returned.
- Return type
Notes
The rating is cached based on the fitting hash (see IndentationFitter._hash).
- property data¶
- property fit_properties¶
Fitting results, see
Indentation.fit_model()
)
- preprocessing¶
Default preprocessing steps steps, see
Indentation.apply_preprocessing()
.
Groups¶
- class nanite.group.IndentationGroup(path=None, meta_override=None, callback=None)[source]¶
Group of Indentation
- Parameters
path (str or pathlib.Path or None) – The path to the data file. The data format is determined and the file is loaded using index.
meta_override (dict) – if specified, contains key-value pairs of metadata that should be used when loading the files (see
afmformats.meta.META_FIELDS
)callback (callable or None) – A method that accepts a float between 0 and 1 to externally track the process of loading the data.
- nanite.group.load_group(path, callback=None, meta_override=None)[source]¶
Load indentation data from disk
- Parameters
path (path-like) – Path to experimental data
callback (callable) – function for tracking progress; must accept a float in [0, 1] as an argument.
meta_override (dict) – if specified, contains key-value pairs of metadata that should be used when loading the files (see
afmformats.meta.META_FIELDS
)
- Returns
group – Indentation group with force-distance data
- Return type
nanite.IndetationGroup
Loading data¶
- nanite.read.get_data_paths(path)[source]¶
Return list of data paths with force-distance data
DEPRECATED
- nanite.read.get_data_paths_enum(path, skip_errors=False)[source]¶
Return a list with paths and their internal enumeration
- Parameters
path (str or pathlib.Path or list of str or list of pathlib.Path) – path to data files or directory containing data files; if directories are given, they are searched recursively
skip_errors (bool) – skip paths that raise errors
- Returns
path_enum – each entry in the list is a list of [pathlib.Path, int], enumerating all curves in each file
- Return type
list of lists
- nanite.read.get_load_data_modality_kwargs()[source]¶
Return imaging modality kwargs for afmformats.load_data
Uses
DEFAULT_MODALITY
.- Returns
kwargs – keyword arguments for
afmformats.load_data()
- Return type
- nanite.read.load_data(path, callback=None, meta_override=None)[source]¶
Load data and return list of
afmformats.AFMForceDistance
This is essentially a wrapper around
afmformats.formats.find_data()
andafmformats.formats.load_data()
that returns force-distance datasets.- Parameters
path (str or pathlib.Path or list of str or list of pathlib.Path) – path to data files or directory containing data files; if directories are given, they are searched recursively
callback (callable) – function for progress tracking; must accept a float in [0, 1] as an argument.
meta_override (dict) – if specified, contains key-value pairs of metadata that are used when loading the files (see
afmformats.meta.META_FIELDS
)
- nanite.read.DEFAULT_MODALITY = 'force-distance'¶
The default imaging modality when loading AFM data. Set this to None to also be able to load e.g. creep-compliance data. See issue https://github.com/AFM-analysis/nanite/issues/11 for more information. Note that especially the export of rating containers may not work with any imaging modality other than force-distance.
Preprocessing¶
- class nanite.preproc.IndentationPreprocessor[source]¶
- static apply(apret, identifiers=None, preproc_names=None)[source]¶
Perform force-distance preprocessing steps
- Parameters
apret (nanite.Indentation) – The afm data to preprocess
identifiers (list) – A list of preprocessing identifiers that will be applied (in the order given).
preproc_names (list) – Deprecated - use identifiers instead.
Notes
This method is usually called from within the Indentation class instance. If you are using this class directly and apply it more than once, you might need to call apret.reset() before preprocessing a second time.
- static autosort(identifiers)[source]¶
Automatically sort preprocessing identifiers via require_steps
- static compute_tip_position(apret)[source]¶
Perform tip-sample separation
Populate the “tip position” column by adding the force normalized by the spring constant to the cantilever height (“height (measured)”).
This computation correctly reproduces the column “Vertical Tip Position” as it is exported by the JPK analysis software with the checked option “Use Unsmoothed Height”.
- static correct_split_approach_retract(apret)[source]¶
Split the approach and retract curves (farthest point method)
Approach and retract curves are defined by the microscope. When the direction of piezo movement is flipped, the force at the sample tip is still increasing. This can be either due to a time lag in the AFM system or due to a residual force acting on the sample due to the bent cantilever.
To repair this time lag, we append parts of the retract curve to the approach curve, such that the curves are split at the minimum height.
- nanite.preproc.preprocessing_step(identifier, name, require_steps=None)[source]¶
Decorator for Indentation preprocessors
The name and identifier are stored as a property of the wrapped function.
- nanite.preproc.available_preprocessors = ['compute_tip_position', 'correct_force_offset', 'correct_split_approach_retract', 'correct_tip_offset', 'smooth_height']¶
Available preprocessors
Modeling¶
Methods and constants¶
- nanite.model.get_anc_parm_keys(model_key)[source]¶
Return the key names of a model’s ancillary parameters
- nanite.model.get_anc_parms(idnt, model_key)[source]¶
Compute ancillary parameters for a force-distance dataset
Ancillary parameters include parameters that:
are unrelated to fitting: They may just be important parameters to the user.
require the entire dataset: They cannot be extracted during fitting, because they require more than just the approach xor retract curve to compute (e.g. hysteresis, jump of retract curve at maximum indentation). They may, additionally, depend on initial fit parameters set by the user.
require a fit: They are dependent on fitting parameters but are not required during fitting.
Notes
If an ancillary parameter name matches that of a fitting parameter, then it is assumed that it can be used for fitting. Please see
nanite.indent.Indentation.get_initial_fit_parameters()
andnanite.fit.guess_initial_parameters()
.Ancillary parameters are set to np.nan if they cannot be computed.
- Parameters
idnt (nanite.indent.Indentation) – The force-distance data for which to compute the ancillary parameters
model_key (str) – Name of the model
- Returns
ancillaries – key-value dictionary of ancillary parameters
- Return type
- nanite.model.get_model_by_name(name)[source]¶
Convenience function to obtain a model by name instead of by key
- nanite.model.ANCILLARY_COMMON = {'max_indent': ('Maximum indentation', 'm')}¶
Common ancillary parameters
Models¶
Each model is implemented as a submodule in nanite.model. For instance
nanite.model.model_hertz_parabolic
. Each of these modules implements
the following functions (which are not listed for each model in the
subsections below):
- nanite.model.model_submodule.get_parameter_defaults()¶
Return the default parameters of the model.
- nanite.model.model_submodule.model()¶
Wrap the actual model for fitting.
- nanite.model.model_submodule.residual()¶
Compute the residuals during fitting.
In addition, each submodule contains the following attributes:
- nanite.model.model_submodule.model_doc¶
The doc-string of the model function.
- nanite.model.model_submodule.model_key¶
The model key used in the command line interface and during scripting.
- nanite.model.model_submodule.model_name¶
The name of the model.
- nanite.model.model_submodule.parameter_keys¶
Parameter keywords of the model for higher-level applications.
- nanite.model.model_submodule.parameter_names¶
Parameter names of the model for higher-level applications.
- nanite.model.model_submodule.parameter_units¶
Parameter units for higher-level applications.
conical indenter (Hertz)¶
model key |
hertz_cone |
model name |
conical indenter (Hertz) |
model location |
nanite.model.model_conical_indenter |
- nanite.model.model_conical_indenter.hertz_conical(delta, E, alpha, nu, contact_point=0, baseline=0)[source]¶
Hertz model for a conical indenter
\[F = \frac{2\tan\alpha}{\pi} \frac{E}{1-\nu^2} \delta^2\]- Parameters
- Returns
F – Force [N]
- Return type
Notes
These approximations are made by the Hertz model:
The sample is isotropic.
The sample is a linear elastic solid.
The sample is extended infinitely in one half space.
The indenter is not deformable.
There are no additional interactions between sample and indenter.
Additional assumptions:
infinitely sharp probe
References
Love (1939) [Lov39]
parabolic indenter (Hertz)¶
model key |
hertz_para |
model name |
parabolic indenter (Hertz) |
model location |
nanite.model.model_hertz_paraboloidal |
- nanite.model.model_hertz_paraboloidal.hertz_paraboloidal(delta, E, R, nu, contact_point=0, baseline=0)[source]¶
Hertz model for a paraboloidal indenter
\[F = \frac{4}{3} \frac{E}{1-\nu^2} \sqrt{R} \delta^{3/2}\]- Parameters
- Returns
F – Force [N]
- Return type
Notes
The original model reads
\[F = \frac{4}{3} \frac{E}{1-\nu^2} \sqrt{2k} \delta^{3/2},\]where \(k\) is defined by the paraboloid equation
\[\rho^2 = 4kz.\]These approximations are made by the Hertz model:
The sample is isotropic.
The sample is a linear elastic solid.
The sample is extended infinitely in one half space.
The indenter is not deformable.
There are no additional interactions between sample and indenter.
Additional assumptions:
radius of spherical cell is larger than the indentation
References
Sneddon (1965) [Sne65]
pyramidal indenter, three-sided (Hertz)¶
model key |
hertz_pyr3s |
model name |
pyramidal indenter, three-sided (Hertz) |
model location |
nanite.model.model_hertz_three_sided_pyramid |
- nanite.model.model_hertz_three_sided_pyramid.hertz_three_sided_pyramid(delta, E, alpha, nu, contact_point=0, baseline=0)[source]¶
Hertz model for three sided pyramidal indenter
\[F = 0.887 \tan\alpha \cdot \frac{E}{1-\nu^2} \delta^2\]- Parameters
- Returns
F – Force [N]
- Return type
Notes
These approximations are made by the Hertz model:
The sample is isotropic.
The sample is a linear elastic solid.
The sample is extended infinitely in one half space.
The indenter is not deformable.
There are no additional interactions between sample and indenter.
The inclination angle of the pyramidal face (in radians) must be close to zero.
References
Bilodeau et al. 1992 [Bil92]
spherical indenter (Sneddon)¶
model key |
sneddon_spher |
model name |
spherical indenter (Sneddon) |
model location |
nanite.model.model_sneddon_spherical |
- nanite.model.model_sneddon_spherical.delta_of_a(a, R)¶
Compute indentation from contact area radius (wrapper)
- nanite.model.model_sneddon_spherical.get_a(R, delta, accuracy=1e-12)¶
Compute the contact area radius (wrapper)
- nanite.model.model_sneddon_spherical.hertz_spherical(delta, E, R, nu, contact_point=0.0, baseline=0.0)¶
Hertz model for Spherical indenter - modified by Sneddon
\[\begin{split}F &= \frac{E}{1-\nu^2} \left( \frac{R^2+a^2}{2} \ln \! \left( \frac{R+a}{R-a}\right) -aR \right)\\ \delta &= \frac{a}{2} \ln \! \left(\frac{R+a}{R-a}\right)\end{split}\](\(a\) is the radius of the circular contact area between bead and sample.)
- Parameters
- Returns
F – Force [N]
- Return type
Notes
These approximations are made by the Hertz model:
The sample is isotropic.
The sample is a linear elastic solid.
The sample is extended infinitely in one half space.
The indenter is not deformable.
There are no additional interactions between sample and indenter.
Additional assumptions:
no surface forces
References
Sneddon (1965) [Sne65]
spherical indenter (Sneddon, truncated power series)¶
model key |
sneddon_spher_approx |
model name |
spherical indenter (Sneddon, truncated power series) |
model location |
nanite.model.model_sneddon_spherical_approximation |
- nanite.model.model_sneddon_spherical_approximation.hertz_sneddon_spherical_approx(delta, E, R, nu, contact_point=0, baseline=0)[source]¶
Hertz model for Spherical indenter - approximation
\[F = \frac{4}{3} \frac{E}{1-\nu^2} \sqrt{R} \delta^{3/2} \left(1 - \frac{1}{10} \frac{\delta}{R} - \frac{1}{840} \left(\frac{\delta}{R}\right)^2 + \frac{11}{15120} \left(\frac{\delta}{R}\right)^3 + \frac{1357}{6652800} \left(\frac{\delta}{R}\right)^4 \right)\]- Parameters
- Returns
F – Force [N]
- Return type
Notes
These approximations are made by the Hertz model:
The sample is isotropic.
The sample is a linear elastic solid.
The sample is extended infinitely in one half space.
The indenter is not deformable.
There are no additional interactions between sample and indenter.
Additional assumptions:
no surface forces
Truncated power series approximation:
This model is a truncated power series approximation of spherical indenter (Sneddon). The expected error is more than four magnitues lower than the signal (see e.g. Approximating the Hertzian model with a spherical indenter). The Bio-AFM analysis software by JPK/Bruker uses the same model.
References
Sneddon (1965) [Sne65], Dobler (personal communication, 2018) [Dob18]
Fitting¶
- class nanite.fit.FitProperties[source]¶
Fit property manager class
Provide convenient access to fit properties as a dictionary and dynamically manage resets due to new initial parameters.
Dynamic properties include:
set “params_initial” to None if the “model_key” changes
remove all keys except those in FP_DEFAULT if a key that is in FP_DEFAULT changes (All other keys are considered to be obsolete fitting results).
Additional attributes:
- class nanite.fit.IndentationFitter(idnt, **kwargs)[source]¶
Fit force-distance curves
- Parameters
idnt (nanite.indent.Indentation) – The dataset to fit
model_key (str) – A key referring to a model in nanite.model.models_available
params_initial (instance of lmfit.Parameters) – Parameters for fitting. If not given, default parameters are used.
range_x (tuple of 2) – The range for fitting, see range_type below.
range_type (str) –
One of:
- absolute:
Set the absolute fitting range in values given by the x_axis.
- relative cp:
In some cases it is desired to be able to fit a model only up until a certain indentation depth (tip position) measured from the contact point. Since the contact point is a fit parameter as well, this requires a two-pass fitting.
preprocessing (list of str) – Preprocessing
segment (int) – Segment index (e.g. 0 for approach)
weight_cp (float) – Weight the contact point region which shows artifacts that are difficult to model with e.g. Hertz.
optimal_fit_edelta (bool) – Search for the optimal fit by varying the maximal indentation depth and determining a plateau in the resulting Young’s modulus (fitting parameter “E”).
optimal_fit_num_samples (int) – Number of samples to use for searching the optimal fit
- compute_emodulus_vs_mindelta(callback=None)[source]¶
Compute elastic modulus vs. minimal indentation curve
- static compute_opt_mindelta(emoduli, indentations)[source]¶
Determine the plateau of an emodulus-indentation curve
The following procedure is performed:
Smooth the emodulus data with a Butterworth filter
Label sequences that have similar values by binning into ten regions between the min and max.
Ignore sequences with emodulus that is smaller than the binning size.
Determine the longest sequence.
- nanite.fit.guess_initial_parameters(idnt=None, model_key='hertz_para', common_ancillaries=True, model_ancillaries=True)[source]¶
Guess initial fitting parameters
- Parameters
idnt (nanite.indent.Indentation) – The dataset to use for guessing initial fitting parameters using ancillary parameters
model_key (str) – The model key
common_ancillaries (bool) – Guess global ancillary parameters (such as contact point)
model_ancillaries (bool) – Use model-related ancillary parameters
Rating¶
Features¶
- class nanite.rate.features.IndentationFeatures(dataset=None)[source]¶
- static compute_features(idnt, which_type='all', names=None, ret_names=False)[source]¶
Compute the features for a data set
- Parameters
idnt (nanite.Indentation) – A dataset to rate
names (list of str) – The names of the rating methods to use, e.g. [“rate_apr_bumps”, “rate_apr_mon_incr”]. If None (default), all available rating methods are used.
Notes
names may include features that are excluded by which_type. E.g. if a “bool” feature is in names but which_type is “float”, then the “bool” feature will be silently ignored.
- feat_con_apr_flatness()[source]¶
flatness of APR residuals
fraction of the positive-gradient residuals in the approach part
- feat_con_apr_size()[source]¶
relative APR size
length of the approach part relative to the indentation part
- feat_con_bln_slope()[source]¶
slope of BLN
slope obtained from a linear least-squares fit to the baseline
- feat_con_bln_variation()[source]¶
variation in BLN
comparison of the forces at the beginning and at the end of the baseline
- feat_con_cp_curvature()[source]¶
curvature at CP
curvature of the force-distance data at the contact point
- feat_con_cp_magnitude()[source]¶
residuals at CP
mean value of the residuals around the contact point
- feat_con_idt_maxima_75perc()[source]¶
maxima in IDT residuals
sum of the indentation residuals’ maxima in three intervals in-between 25% and 100% relative to the maximum indentation
- feat_con_idt_spike_area()[source]¶
area of IDT spikes
area of spikes appearing in the indentation part
- feat_con_idt_sum_75perc()[source]¶
residuals in 75% IDT
sum of the residuals in the indentation part in-between 25% and 100% relative to the maximum indentation
- classmethod get_feature_funcs(which_type='all', names=None)[source]¶
Return functions that compute features from a dataset
- Parameters
names (list of str) – The names of the rating methods to use, e.g. [“rate_apr_bumps”, “rate_apr_mon_incr”]. If None (default), all available rating methods are returned.
which_type (str) – Which features to return: [“all”, “bool”, “float”].
- Returns
raters – Each item in the list consists contains the name of the rating method and the corresponding rating method.
- Return type
list of tuples (name, callable)
- classmethod get_feature_names(which_type='all', names=None, ret_indices=False)[source]¶
Get features names
- Parameters
- Returns
name_list – List of feature names (callables of this class)
- Return type
list of str
- property contact_point¶
- property datafit_apr¶
- property datares_apr¶
- dataset¶
current dataset from which features are computed
- property datax_apr¶
- property datay_apr¶
- property has_contact_point¶
- property is_fitted¶
- property is_valid¶
- property meta¶
- nanite.rate.features.VALID_FEATURE_TYPES = ['all', 'binary', 'continuous']¶
Valid keyword arguments for feature types
Rater¶
- class nanite.rate.rater.IndentationRater(regressor=None, scale=None, lda=None, training_set=None, names=None, weight=True, sample_weight=None, *args, **kwargs)[source]¶
Rate quality
- Parameters
regressor (sciki-learn RegressorMixin) – The regressor used for rating
scale (bool) – If True, apply a Standard Scaler. If a regressor based on decision trees is used, the Standard Scaler is not used by default, otherwise it is.
lda (bool) – If True, apply a Linear Discriminant Analysis (LDA). If a regressor based on a decision tree is used, LDA is not used by default, otherwise it is.
training_set (tuple of (X, y)) – The training set (samples, response)
names (list of str) – Feature names to use
weight (bool) – Weight the input samples by the number of occurrences or with sample_weight. For tree-based classifiers, set this to True to avoid bias.
sample_weight (list-like) – The sample weights. If set to None sample weights are computed from the training set.
*args (list) – Positional arguments for
IndentationFeatures
**kwargs – Keyword arguments for
IndentationFeatures
See also
sklearn.preprocessing.StandardScaler
Standard scaler
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
Linear discriminant analysis
nanite.rate.regressors.reg_trees
List of regressors that are identified as tree-based
- static get_training_set_path(label='zef18')[source]¶
Return the path to a training set shipped with nanite
Training sets are stored in the nanite.rate module path with
ts_
prepended to label.
- classmethod load_training_set(path=None, names=None, which_type=['continuous'], remove_nan=True, ret_names=False)[source]¶
Load a training set from a directory
By default, only the “continuous” features are imported. The “binary” features are not needed for training; they are used to sort out new force-distance data.
- names¶
feature names used by the regressor pipeline
- pipeline¶
sklearn pipeline with transforms (and regressor if given)
- nanite.rate.rater.get_rater(regressor, training_set='zef18', names=None, lda=None, **reg_kwargs)[source]¶
Convenience method to get a rater
- Parameters
regressor (str or RegressorMixin) – If a string, must be in reg_names.
training_set (str or pathlib.Path or tuple (X, y)) – A string label representing a training set shipped with nanite, the path to a training set, or a tuple representing the training set (samples, response) for use with sklearn.
names (list of str) – Only use these features for rating
lda (bool) – Perform linear discriminant analysis
- Returns
irater – The rating instance.
- Return type
Regressors¶
scikit-learn regressors and their keyword arguments
- nanite.rate.regressors.reg_names = ['AdaBoost', 'Decision Tree', 'Extra Trees', 'Gradient Tree Boosting', 'Random Forest', 'SVR (RBF kernel)', 'SVR (linear kernel)']¶
List of available default regressor names
- nanite.rate.regressors.reg_trees = ['AdaBoostRegressor', 'DecisionTreeRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'RandomForestRegressor']¶
List of tree-based regressor class names (used for keyword defaults in
IndentationRater
)
Manager¶
Save and load user-rated datasets
- class nanite.rate.io.RateManager(path, verbose=0)[source]¶
Manage user-defined rates
- get_cross_validation_score(regressor, training_set=None, n_splits=20, random_state=42)[source]¶
Regressor cross-validation scoring
Cross-validation is used to identify regressors that over-fit the train set by splitting the train set into multiple learn/test sets and quantifying the regressor performance for each split.
- Parameters
regressor (str or RegressorMixin) – If a string, must be in reg_names.
training_set (X, y) – If given, do not use self.samples
Notes
A
skimage.model_selection.KFold
cross validator is used in combination with the mean squared error score.Cross-validation score is computed from samples that are filtered with the binary features and only from samples that do not contain any nan values.
- get_rates(which='user', training_set='zef18')[source]¶
- which: str
Which rating to return: “user” or a regressor name
- get_training_set(which_type='all', prefilter_binary=False, remove_nans=False, transform=False)[source]¶
Return (X, y) training set
- property datasets¶
- path¶
Path to the manual ratings (directory or .h5 file)
- property ratings¶
- property samples¶
The individual sample ratings computed by afmlib
- verbose¶
verbosity level
- nanite.rate.io.hash_file(path, blocksize=65536)[source]¶
Compute sha256 hex-hash of a file
- Parameters
path (str or pathlib.Path) – path to the file
blocksize (int) – block size read from the file
- Returns
hex – The first six characters of the hash
- Return type
- nanite.rate.io.hdf5_rated(h5path, indent)[source]¶
Test whether an indentation has already been rated
- Returns
- Return type
is_rated, rating, comment
- nanite.rate.io.load(path, meta_only=False, verbose=0)[source]¶
Notes
The .fit_properties attribute of each Indentation instance is overridden by a simple dictionary, so its functionalities are not available anymore.
- nanite.rate.io.save_hdf5(h5path, indent, user_rate, user_name, user_comment, h5mode='a')[source]¶
Store all relevant data of a user rating into an hdf5 file
- Parameters
h5path (str or pathlib.Path) – Path to HDF5 rating container where data will be stored
indent (nanite.Indentation) – The experimental data processed and fitted with nanite
user_rate (float) – Rating given by the user
user_name (str) – Name of the rating user
Quantitative maps¶
- class nanite.qmap.QMap(path_or_group, meta_override=None, callback=None)[source]¶
Quantitative force spectroscopy map handling
- Parameters
path_or_group (str or pathlib.Path or afmformats.afm_group.AFMGroup) – The path to the data file or an instance of AFMGroup
meta_override (dict) – Dictionary with metadata that is used when loading the data in path.
callback (callable or None) – A method that accepts a float between 0 and 1 to externally track the process of loading the data.