diffpy.srmise.baselines package

Submodules

diffpy.srmise.baselines.nanospherical module

class diffpy.srmise.baselines.nanospherical.NanoSpherical(Cache=None)

Bases: BaselineFunction

Methods for evaluation of baseline of spherical nanoparticle of uniform density.

Allowed formats are internal: [scale, radius]

Given nanoparticle radius R, the baseline is -scale*r*(1-(3r)/(4R)+(r^3)/(16*R^3)) in the interval (0, abs(R)), and 0 elsewhere. Internally, both scale and radius are unconstrained, but negative values are mapped to their physically meaningful positive equivalents.

The expression in parentheses is gamma_0(r) for a sphere. For a well normalized PDF the scale factor is 4*pi*rho_0, where rho_r is the nanoparticle density.

gamma_0(r) Reference: Guinier et al. (1955). Small-angle Scattering from X-rays. New York: John Wiley & Sons, Inc.

getmodule()

Return ‘diffpy.srmise.basefunction’

diffpy.srmise.baselines.arbitrary module

class diffpy.srmise.baselines.arbitrary.Arbitrary(npars, valuef, jacobianf=None, estimatef=None, Cache=None)

Bases: BaselineFunction

Methods for evaluating a baseline from an arbitrary function.

Supports baseline calculations with arbitrary functions. These functions, if implemented, must have the following signatures and return values: valuef(pars, x) ==> numpy.array of length x if x is a sequence

==> number if x is a number

jacobianf(pars, x, free) ==> list, each element a numpy.array of length x if

x is a sequence or None if value of free for that parameter is False.

==> list, each element a number if x is a number

or None if value of free for that parameter is False

estimatef(x, y) ==> numpy.array of length npars

estimate_parameters(r, y)

Estimate parameters for data baseline.

Parameters:

• r (array-like) – The data along r from which to estimate

• y (array-like) – The data along y from which to estimate

Returns:

• The numpy array of parameters in the default internal format.

• we raise NotImplementedError if no estimation routine is defined, and

• SrMiseEstimationError if parameters cannot be estimated for any other.

getmodule()

Return ‘diffpy.srmise.basefunction’

diffpy.srmise.baselines.fromsequence module

class diffpy.srmise.baselines.fromsequence.FromSequence(*args, **kwds)

Bases: BaselineFunction

Methods for evaluation of a baseline from discrete data via interpolation.

FromSequence uses cubic spline interpolation (no smoothing) on discrete points to approximate the baseline at arbitrary points within the interpolation domain. This baseline function permits no free parameters.

estimate_parameters(r, y)

Return empty numpy array.

A FromSequence object has no free parameters, so there is nothing to estimate.

Parameters:

• r (array-like) – The data along r from which to estimate, Ignored

• y (array-like) – The data along y from which to estimate, Ignored

Returns:

The empty numpy array

Return type:

array-like

getmodule()

Return ‘diffpy.srmise.basefunction’

readxy(filename)

xyrepr(var)

Safe string output of x and y, compatible with eval()

Parameters:

var (array-like) – The sequence or scalar over which to evaluate

Returns:

The x and y values of the given variable

Return type:

str

diffpy.srmise.baselines.polynomial module

class diffpy.srmise.baselines.polynomial.Polynomial(degree, Cache=None)

Bases: BaselineFunction

Methods for evaluation and parameter estimation of a polynomial baseline.

estimate_parameters(r, y)

Estimate parameters for polynomial baseline.

Estimation is currently implemented only for degree < 2. This very rudimentary method assumes the baseline crosses the origin, and y=baseline+signal, where signal is primarily positive.

Parameters:

• r (array-like) – The data along r from which to estimate

• y (array-like) – The data along y from which to estimate

Returns:

The Numpy array of parameters in the default internal format. Raises NotImplementedError if estimation is not implemented for this degree, or SrMiseEstimationError if parameters cannot be estimated for any other reason.

Return type:

array-like

getmodule()

Return ‘diffpy.srmise.basefunction’

diffpy.srmise.baselines.base module

class diffpy.srmise.baselines.base.Baseline(owner, pars, free=None, removable=False, static_owner=False)

Bases: ModelPart

Represents a baseline associated with a BaselineFunction subclass.

static factory(baselinestr, ownerlist)

Instantiate a Peak from a string.

Parameters:

• baselinestr (str) – The string representing Baseline

• ownerlist (array-like) – The list of BaseFunctions that owner is in

class diffpy.srmise.baselines.base.BaselineFunction(parameterdict, parformats, default_formats, metadict, base=None, Cache=None)

Bases: BaseFunction

Base class for functions which represent some data’s baseline term.

Class members

parameterdict: dict

The dictionary mapping string keys to their index in the sequence of parameters. These keys apply only to the default “internal” format.

parformats: array-like

The sequence of strings defining what formats are recognized by a baseline function.

default_formats: dict

The dictionary which maps the strings “default_input” and “default_output” to strings also appearing in parformats. “default_input”-> format used internally within the class “default_output”-> Default format to use when converting parameters for outside use.

Class methods (implemented by inheriting classes)

estimate_parameters() (optional) _jacobianraw() (optional, but strongly recommended) _transform_derivativesraw() (optional, supports propagation of uncertainty for different paramaterizations) _transform_parametersraw() _valueraw()

Class methods

actualize()

Inherited methods

jacobian() value() transform_derivatives() transform_parameters()

actualize(pars, in_format='default_input', free=None, removable=False, static_owner=False)

Create ModelPart instance of self with given parameters. (“Virtual” method)

getmodule()

Return ‘diffpy.srmise.basefunction’