9. Data layout and plotting routines

sasmodels.data

SAS data representations.

Plotting functions for data sets:

plot_data() plots the data file.

plot_theory() plots a calculated result from the model.

Wrappers for the sasview data loader and data manipulations:

load_data() loads a sasview data file.

set_beam_stop() masks the beam stop from the data.

set_half() selects the right or left half of the data, which can be useful for shear measurements which have not been properly corrected for path length and reflections.

set_top() cuts the top part off the data.

Empty data sets for evaluating models without data:

empty_data1D() creates an empty dataset, which is useful for plotting a theory function before the data is measured.

empty_data2D() creates an empty 2D dataset.

Note that the empty datasets use a minimal representation of the SasView objects so that models can be run without SasView on the path. You could also use these for your own data loader.

class sasmodels.data.Data1D(x: ndarray | None = None, y: ndarray | None = None, dx: ndarray | None = None, dy: ndarray | None = None)

Bases: object

1D data object.

Note that this definition matches the attributes from sasview, with some generic 1D data vectors and some SAS specific definitions. Some refactoring to allow consistent naming conventions between 1D, 2D and SESANS data would be helpful.

Attributes

x, dx: \(q\) vector and gaussian resolution

y, dy: \(I(q)\) vector and measurement uncertainty

mask: values to include in plotting/analysis

dxl: slit widths for slit smeared data, with dx ignored

qmin, qmax: range of \(q\) values in x

filename: label for the data line

_xaxis, _xunit: label and units for the x axis

_yaxis, _yunit: label and units for the y axis

__annotations__ = {}

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.data', '__doc__': '\n    1D data object.\n\n    Note that this definition matches the attributes from sasview, with\n    some generic 1D data vectors and some SAS specific definitions.  Some\n    refactoring to allow consistent naming conventions between 1D, 2D and\n    SESANS data would be helpful.\n\n    **Attributes**\n\n    *x*, *dx*: $q$ vector and gaussian resolution\n\n    *y*, *dy*: $I(q)$ vector and measurement uncertainty\n\n    *mask*: values to include in plotting/analysis\n\n    *dxl*: slit widths for slit smeared data, with *dx* ignored\n\n    *qmin*, *qmax*: range of $q$ values in *x*\n\n    *filename*: label for the data line\n\n    *_xaxis*, *_xunit*: label and units for the *x* axis\n\n    *_yaxis*, *_yunit*: label and units for the *y* axis\n    ', '__init__': <function Data1D.__init__>, 'xaxis': <function Data1D.xaxis>, 'yaxis': <function Data1D.yaxis>, '__dict__': <attribute '__dict__' of 'Data1D' objects>, '__weakref__': <attribute '__weakref__' of 'Data1D' objects>, '__annotations__': {}})

__dir__()

Default dir() implementation.

__doc__ = '\n    1D data object.\n\n    Note that this definition matches the attributes from sasview, with\n    some generic 1D data vectors and some SAS specific definitions.  Some\n    refactoring to allow consistent naming conventions between 1D, 2D and\n    SESANS data would be helpful.\n\n    **Attributes**\n\n    *x*, *dx*: $q$ vector and gaussian resolution\n\n    *y*, *dy*: $I(q)$ vector and measurement uncertainty\n\n    *mask*: values to include in plotting/analysis\n\n    *dxl*: slit widths for slit smeared data, with *dx* ignored\n\n    *qmin*, *qmax*: range of $q$ values in *x*\n\n    *filename*: label for the data line\n\n    *_xaxis*, *_xunit*: label and units for the *x* axis\n\n    *_yaxis*, *_yunit*: label and units for the *y* axis\n    '

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(x: ndarray | None = None, y: ndarray | None = None, dx: ndarray | None = None, dy: ndarray | None = None) → None

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.data'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

classmethod __subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

xaxis(label: str, unit: str) → None

set the x axis label and unit

yaxis(label: str, unit: str) → None

set the y axis label and unit

class sasmodels.data.Data2D(x: ndarray | None = None, y: ndarray | None = None, z: ndarray | None = None, dx: ndarray | None = None, dy: ndarray | None = None, dz: ndarray | None = None)

Bases: object

2D data object.

Note that this definition matches the attributes from sasview. Some refactoring to allow consistent naming conventions between 1D, 2D and SESANS data would be helpful.

Attributes

qx_data, dqx_data: \(q_x\) matrix and gaussian resolution

qy_data, dqy_data: \(q_y\) matrix and gaussian resolution

data, err_data: \(I(q)\) matrix and measurement uncertainty

mask: values to exclude from plotting/analysis

qmin, qmax: range of \(q\) values in x

filename: label for the data line

_xaxis, _xunit: label and units for the x axis

_yaxis, _yunit: label and units for the y axis

_zaxis, _zunit: label and units for the y axis

Q_unit, I_unit: units for Q and intensity

x_bins, y_bins: grid steps in x and y directions

__annotations__ = {}

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.data', '__doc__': '\n    2D data object.\n\n    Note that this definition matches the attributes from sasview. Some\n    refactoring to allow consistent naming conventions between 1D, 2D and\n    SESANS data would be helpful.\n\n    **Attributes**\n\n    *qx_data*, *dqx_data*: $q_x$ matrix and gaussian resolution\n\n    *qy_data*, *dqy_data*: $q_y$ matrix and gaussian resolution\n\n    *data*, *err_data*: $I(q)$ matrix and measurement uncertainty\n\n    *mask*: values to exclude from plotting/analysis\n\n    *qmin*, *qmax*: range of $q$ values in *x*\n\n    *filename*: label for the data line\n\n    *_xaxis*, *_xunit*: label and units for the *x* axis\n\n    *_yaxis*, *_yunit*: label and units for the *y* axis\n\n    *_zaxis*, *_zunit*: label and units for the *y* axis\n\n    *Q_unit*, *I_unit*: units for Q and intensity\n\n    *x_bins*, *y_bins*: grid steps in *x* and *y* directions\n    ', '__init__': <function Data2D.__init__>, 'xaxis': <function Data2D.xaxis>, 'yaxis': <function Data2D.yaxis>, 'zaxis': <function Data2D.zaxis>, '__dict__': <attribute '__dict__' of 'Data2D' objects>, '__weakref__': <attribute '__weakref__' of 'Data2D' objects>, '__annotations__': {}})

__dir__()

Default dir() implementation.

__doc__ = '\n    2D data object.\n\n    Note that this definition matches the attributes from sasview. Some\n    refactoring to allow consistent naming conventions between 1D, 2D and\n    SESANS data would be helpful.\n\n    **Attributes**\n\n    *qx_data*, *dqx_data*: $q_x$ matrix and gaussian resolution\n\n    *qy_data*, *dqy_data*: $q_y$ matrix and gaussian resolution\n\n    *data*, *err_data*: $I(q)$ matrix and measurement uncertainty\n\n    *mask*: values to exclude from plotting/analysis\n\n    *qmin*, *qmax*: range of $q$ values in *x*\n\n    *filename*: label for the data line\n\n    *_xaxis*, *_xunit*: label and units for the *x* axis\n\n    *_yaxis*, *_yunit*: label and units for the *y* axis\n\n    *_zaxis*, *_zunit*: label and units for the *y* axis\n\n    *Q_unit*, *I_unit*: units for Q and intensity\n\n    *x_bins*, *y_bins*: grid steps in *x* and *y* directions\n    '

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(x: ndarray | None = None, y: ndarray | None = None, z: ndarray | None = None, dx: ndarray | None = None, dy: ndarray | None = None, dz: ndarray | None = None) → None

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.data'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

classmethod __subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

xaxis(label: str, unit: str) → None

set the x axis label and unit

yaxis(label: str, unit: str) → None

set the y axis label and unit

zaxis(label: str, unit: str) → None

set the y axis label and unit

class sasmodels.data.Detector(pixel_size: tuple[float, float] = (None, None), distance: float = None)

Bases: object

Detector attributes.

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.data', '__doc__': '\n    Detector attributes.\n    ', '__init__': <function Detector.__init__>, '__dict__': <attribute '__dict__' of 'Detector' objects>, '__weakref__': <attribute '__weakref__' of 'Detector' objects>, '__annotations__': {}})

__dir__()

Default dir() implementation.

__doc__ = '\n    Detector attributes.\n    '

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(pixel_size: tuple[float, float] = (None, None), distance: float = None) → None

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.data'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

classmethod __subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

class sasmodels.data.Sample

Bases: object

Sample attributes.

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.data', '__doc__': '\n    Sample attributes.\n    ', '__init__': <function Sample.__init__>, '__dict__': <attribute '__dict__' of 'Sample' objects>, '__weakref__': <attribute '__weakref__' of 'Sample' objects>, '__annotations__': {}})

__dir__()

Default dir() implementation.

__doc__ = '\n    Sample attributes.\n    '

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__() → None

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.data'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

classmethod __subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

class sasmodels.data.SesansData(**kw)

Bases: Data1D

SESANS data object.

This is just Data1D with a wavelength parameter.

x is spin echo length and y is polarization (P/P0).

__annotations__ = {}

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.data', '__doc__': '\n    SESANS data object.\n\n    This is just :class:`Data1D` with a wavelength parameter.\n\n    *x* is spin echo length and *y* is polarization (P/P0).\n    ', 'isSesans': True, '__init__': <function SesansData.__init__>, '__annotations__': {'lam': 'OptArray'}})

__dir__()

Default dir() implementation.

__doc__ = '\n    SESANS data object.\n\n    This is just :class:`Data1D` with a wavelength parameter.\n\n    *x* is spin echo length and *y* is polarization (P/P0).\n    '

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(**kw)

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.data'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

classmethod __subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

isSesans = True

xaxis(label: str, unit: str) → None

set the x axis label and unit

yaxis(label: str, unit: str) → None

set the y axis label and unit

class sasmodels.data.Source

Bases: object

Beam attributes.

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.data', '__doc__': '\n    Beam attributes.\n    ', '__init__': <function Source.__init__>, '__dict__': <attribute '__dict__' of 'Source' objects>, '__weakref__': <attribute '__weakref__' of 'Source' objects>, '__annotations__': {}})

__dir__()

Default dir() implementation.

__doc__ = '\n    Beam attributes.\n    '

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__() → None

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.data'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

classmethod __subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

class sasmodels.data.Vector(x: float = None, y: float = None, z: float | None = None)

Bases: object

3-space vector of x, y, z

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.data', '__doc__': '\n    3-space vector of *x*, *y*, *z*\n    ', '__init__': <function Vector.__init__>, '__dict__': <attribute '__dict__' of 'Vector' objects>, '__weakref__': <attribute '__weakref__' of 'Vector' objects>, '__annotations__': {}})

__dir__()

Default dir() implementation.

__doc__ = '\n    3-space vector of *x*, *y*, *z*\n    '

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(x: float = None, y: float = None, z: float | None = None) → None

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.data'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

classmethod __subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

sasmodels.data._as_numpy(data)

sasmodels.data._build_matrix(self, plottable)

Build a matrix for 2d plot from a vector Returns a matrix (image) with ~ square binning Requirement: need 1d array formats of self.data, self.qx_data, and self.qy_data where each one corresponds to z, x, or y axis values

sasmodels.data._fillup_pixels(image=None, weights=None)

Fill z values of the empty cells of 2d image matrix with the average over up-to next nearest neighbor points

Parameters:

image – (2d matrix with some zi = None)

Returns:

image (2d array )

TODO:

Find better way to do for-loop below

sasmodels.data._get_bins(self)

get bins set x_bins and y_bins into self, 1d arrays of the index with ~ square binning Requirement: need 1d array formats of self.qx_data, and self.qy_data where each one corresponds to x, or y axis values

sasmodels.data._plot_2d_signal(data: Data2D, signal: ndarray, vmin: float | None = None, vmax: float | None = None, view: str = None) → tuple[float, float]

Plot the target value for the data. This could be the data itself, the theory calculation, or the residuals.

scale can be ‘log’ for log scale data, or ‘linear’.

sasmodels.data._plot_result1D(data: Data1D, theory: ndarray | None, resid: ndarray | None, view: str, use_data: bool, limits: tuple[float, float] | None = None, Iq_calc: ndarray | None = None) → None

Plot the data and residuals for 1D data.

sasmodels.data._plot_result2D(data: Data2D, theory: ndarray | None, resid: ndarray | None, view: str, use_data: bool, limits: tuple[float, float] | None = None) → None

Plot the data and residuals for 2D data.

sasmodels.data._plot_result_sesans(data: SesansData, theory: ndarray | None, resid: ndarray | None, view: str | None, use_data: bool, limits: tuple[float, float] | None = None) → None

Plot SESANS results.

sasmodels.data.demo() → None

Load and plot a SAS dataset.

sasmodels.data.empty_data1D(q: ndarray, resolution: float = 0.0, L: float = 0.0, dL: float = 0.0) → Data1D

Create empty 1D data using the given q as the x value.

rms resolution \(\Delta q/q\) defaults to 0.0. Note that this is expressed as a fraction rather than a percentage.

If wavelength L and rms wavelength divergence dL are given, then angle theta is infered from \(q = 4\pi/\lambda \sin(\theta)\), and the resolution term applies to rms \(\Delta \theta/\theta\) instead of \(\Delta q/q\). Resolution \(\Delta q\) is then calculated from wavelength and angular resolution.

sasmodels.data.empty_data2D(qx: ndarray, qy: ndarray | None = None, resolution: float = 0.0) → Data2D

Create empty 2D data using the given mesh.

If qy is missing, create a square mesh with qy=qx.

resolution dq/q defaults to 0.0.

sasmodels.data.empty_sesans(z, wavelength=None, zacceptance=None)

sasmodels.data.load_data(filename: str, index: int = 0) → Data1D | Data2D | SesansData

Load data using a sasview loader.

sasmodels.data.plot_data(data: Data1D | Data2D | SesansData, view: str = None, limits: tuple[float, float] | None = None) → None

Plot data loaded by the sasview loader.

data is a sasview data object, either 1D, 2D or SESANS.

view is log, linear or normed.

limits sets the intensity limits on the plot; if None then the limits are inferred from the data.

sasmodels.data.plot_theory(data: Data1D | Data2D | SesansData, theory: ndarray | None, resid: ndarray | None = None, view: str | None = None, use_data: bool = True, limits: tuple[float, float] | None = None, Iq_calc: ndarray | None = None) → None

Plot theory calculation.

data is needed to define the graph properties such as labels and units, and to define the data mask.

theory is a matrix of the same shape as the data.

view is log, linear or normed

use_data is True if the data should be plotted as well as the theory.

limits sets the intensity limits on the plot; if None then the limits are inferred from the data. If (-inf, inf) then use auto limits.

Iq_calc is the raw theory values without resolution smearing

sasmodels.data.protect(func: Callable) → Callable

Decorator to wrap calls in an exception trapper which prints the exception and continues. Keyboard interrupts are ignored.

sasmodels.data.set_beam_stop(data: Data1D | Data2D | SesansData, radius: float, outer: float | None = None) → None

Add a beam stop of the given radius. If outer, make an annulus.

sasmodels.data.set_half(data: Data1D | Data2D | SesansData, half: str) → None

Select half of the data, either “right” or “left”.

sasmodels.data.set_top(data: Data1D | Data2D | SesansData, cutoff: float) → None

Chop the top off the data, above cutoff.