RaupModel#

class ktch.coiling.RaupModel(r0: float = 1.0, estimator: str = 'ml_2d', n_jobs: int | None = None, verbose: int = 0)[source]#

Raup’s model.

Raup’s logarithmic shell coiling model [Raup_1965] [Raup_1966]. inverse_transform is the generative map Phi: (w_r, t_r, d_r, delta_r, gamma_r) -> form. transform (parameter estimation from measurement data) is not implemented yet.

Parameters:
r0float, default = 1.0

Initial tube radius (scale) used for generation.

estimatorstr, default = “ml_2d”

Fitting method used by transform (not yet implemented).

n_jobsint, optional

Reserved for parallelism.

verboseint, default = 0

Verbosity level.

References

[Raup_1965]

Raup, D.M., Michelson, A., 1965. Theoretical Morphology of the Coiled Shell. Science 147, 1294–1295.

[Raup_1966]

Raup, D.M., 1966. Geometric analysis of shell coiling: general problems. Journal of Paleontology 40, 1178–1190.

fit(X, y=None)[source]#

No-op (stateless). Returns self.

fit_transform(X, y=None, **fit_params)#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters. Pass only if the estimator accepts additional params in its fit method.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None) → ndarray[source]#

Parameter names (w_r, t_r, d_r, delta_r, gamma_r).

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

inverse_transform(X_transformed, theta_range=None, phi_range=None, aperture=None, as_frame=False)[source]#

Generate shell surfaces from Raup’s model parameters.

Parameters:
X_transformedarray-like of shape (n_samples, 5) or (5,)

Rows of (w_r, t_r, d_r, delta_r, gamma_r). A 3-column input (w_r, t_r, d_r) is also accepted, with orientation defaulted to 0.

theta_range, phi_rangearray-like, optional

Sampling grids. See raup().

apertureNone

Aperture shape; only the circular default is supported.

as_framebool, default = False

If True, return a long-format pandas.DataFrame.

Returns:
Xndarray of shape (n_samples, n_theta, n_phi, 3) or pd.DataFrame
set_inverse_transform_request(*, X_transformed: bool | None | str = '$UNCHANGED$', aperture: bool | None | str = '$UNCHANGED$', as_frame: bool | None | str = '$UNCHANGED$', phi_range: bool | None | str = '$UNCHANGED$', theta_range: bool | None | str = '$UNCHANGED$') → RaupModel#

Configure whether metadata should be requested to be passed to the inverse_transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to inverse_transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to inverse_transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
X_transformedstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_transformed parameter in inverse_transform.

aperturestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for aperture parameter in inverse_transform.

as_framestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for as_frame parameter in inverse_transform.

phi_rangestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for phi_range parameter in inverse_transform.

theta_rangestr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for theta_range parameter in inverse_transform.

Returns:
selfobject

The updated object.

set_output(*, transform=None)#

Set output container.

Refer to the user guide for more details and Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • “polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:
selfestimator instance

Estimator instance.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_transform_request(*, b: bool | None | str = '$UNCHANGED$', c: bool | None | str = '$UNCHANGED$', d: bool | None | str = '$UNCHANGED$', f: bool | None | str = '$UNCHANGED$', h: bool | None | str = '$UNCHANGED$') → RaupModel#

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
bstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for b parameter in transform.

cstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for c parameter in transform.

dstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for d parameter in transform.

fstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for f parameter in transform.

hstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for h parameter in transform.

Returns:
selfobject

The updated object.

transform(X, d=None, f=None, h=None, b=None, c=None)[source]#

Estimate Raup parameters from observed shells (not implemented).