Data2D

class sherpa.data.Data2D(name, x0, x1, y, shape=None, staterror=None, syserror=None)[source] [edit on github]

Bases: Data

2D data set

This class represents a 2D data set. It is desigend to work with flattened arrays for coordinates and independent variables, which makes it easy to deal with filters and sparse or irregularly-placed grids.

Of course, the same structure can also be used for regularly-gridded data, it just has to be passed in as a flattened array. However, in this case the more specialized DataIMG class might be more appropriate.

Parameters:
  • name (string) – name of this dataset

  • x0 (array-like) – Independent coordinate values for the first dimension

  • x1 (array-like) – Independent coordinate values for the second dimension

  • y (array-like) – The values of the dependent observable. If this is a numpy masked array, the mask will be used to initialize a mask.

  • shape (tuple) – Shape of the data grid for regularly gridded data (optional). This is used to return the data as an image e.g. for display, but it not needed for fitting and modelling. For irregularly gridded data, shape must be None.

  • staterror (array-like) – the statistical error associated with the data

  • syserror (array-like) – the systematic error associated with the data

Examples

An irregularly-gridded 2D dataset, with points at (-200, -200), (-200, 0), (0, 0), (200, -100), and (200, 150) can be created with:

>>> irreg2d = Data2D("irregular2d",
...                  [-200, -200, 0, 200, 200],
...                  [-200, 0, 0, -100, 150],
...                  [12, 15, 23, 45, -2])

A regularly-gridded 2D dataset can be created, but the arguments must be flattened:

>>> import numpy as np
>>> x1, x0 = np.mgrid[20:30:2, 5:20:2]
>>> datashape = x0.shape
>>> y = np.sqrt((x0 - 10)**2 + (x1 - 31)**2)
>>> x0 = x0.flatten()
>>> x1 = x1.flatten()
>>> y = y.flatten()
>>> reg2d = Data2D("regular2d", x0, x1, y, shape=datashape)

Note

Sherpa provides the DataIMG class to handle regularly-gridded data more easily.

Attributes Summary

dep

Left for compatibility with older versions

indep

The grid of the data space associated with this data set.

mask

Mask array for dependent variable

ndim

The dimensionality of the dataset, if defined, or None.

size

The number of elements in the data set.

staterror

The statistical error on the dependent axis, if set.

syserror

The systematic error on the dependent axis, if set.

x0

kept for compatibility

x1

kept for compatibility

y

The dependent axis.

Methods Summary

apply_filter(data)

eval_model(modelfunc)

Evaluate the model on the independent axis.

eval_model_to_fit(modelfunc)

Evaluate the model on the independent axis after filtering.

get_axes()

get_dep([filter])

Return the dependent axis of a data set.

get_dims([filter])

Return the dimensions of this data space as a tuple of tuples.

get_error([filter, staterrfunc])

Return the total error on the dependent variable.

get_filter()

get_filter_expr()

get_img([yfunc])

Return the dependent axis as a 2D array.

get_imgerr()

get_indep([filter])

Return the independent axes of a data set.

get_max_pos([dep])

Return the coordinates of the maximum value.

get_staterror([filter, staterrfunc])

Return the statistical error on the dependent axis of a data set.

get_syserror([filter])

Return the statistical error on the dependent axis of a data set.

get_x0([filter])

get_x0label()

Return label for first dimension in 2-D view of independent axis/axes.

get_x1([filter])

get_x1label()

Return label for second dimension in 2-D view of independent axis/axes.

get_y([filter, yfunc, use_evaluation_space])

Return dependent axis in N-D view of dependent variable

get_yerr([filter, staterrfunc])

Return errors in dependent axis in N-D view of dependent variable.

get_ylabel([yfunc])

Return label for dependent axis in N-D view of dependent variable.

ignore(*args, **kwargs)

notice([x0lo, x0hi, x1lo, x1hi, ignore])

set_dep(val)

Set the dependent variable values.

set_indep(val)

to_contour([yfunc])

to_fit([staterrfunc])

to_guess()

Attributes Documentation

dep

Left for compatibility with older versions

indep

The grid of the data space associated with this data set.

When set, the field must be set to a tuple, even for a one-dimensional data set. The “related” fields such as the dependent axis and the error fields are set to None if their size does not match.

Changed in version 4.14.1: The filter created by notice and ignore is now cleared when the independent axis is changed.

Return type:

tuple of array_like

mask

Mask array for dependent variable

Returns:

mask

Return type:

bool or numpy.ndarray

ndim = 2

The dimensionality of the dataset, if defined, or None.

size

The number of elements in the data set.

Returns:

size – If the size has not been set then None is returned.

Return type:

int or None

staterror

The statistical error on the dependent axis, if set.

This must match the size of the independent axis.

syserror

The systematic error on the dependent axis, if set.

This must match the size of the independent axis.

x0

kept for compatibility

x1

kept for compatibility

y

The dependent axis.

If set, it must match the size of the independent axes.

Methods Documentation

apply_filter(data) [edit on github]
eval_model(modelfunc) [edit on github]

Evaluate the model on the independent axis.

eval_model_to_fit(modelfunc) [edit on github]

Evaluate the model on the independent axis after filtering.

get_axes()[source] [edit on github]
get_dep(filter=False) [edit on github]

Return the dependent axis of a data set.

Parameters:

filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

axis – The dependent axis values for the data set. This gives the value of each point in the data set.

Return type:

array

See also

get_indep

Return the independent axis of a data set.

get_error

Return the errors on the dependent axis of a data set.

get_staterror

Return the statistical errors on the dependent axis of a data set.

get_syserror

Return the systematic errors on the dependent axis of a data set.

get_dims(filter=False)[source] [edit on github]

Return the dimensions of this data space as a tuple of tuples. The first element in the tuple is a tuple with the dimensions of the data space, while the second element provides the size of the dependent array.

Return type:

tuple

get_error(filter=False, staterrfunc=None) [edit on github]

Return the total error on the dependent variable.

Parameters:
  • filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

  • staterrfunc (function) – If no statistical error has been set, the errors will be calculated by applying this function to the dependent axis of the data set.

Returns:

axis – The error for each data point, formed by adding the statistical and systematic errors in quadrature.

Return type:

array or None

See also

get_dep

Return the independent axis of a data set.

get_staterror

Return the statistical errors on the dependent axis of a data set.

get_syserror

Return the systematic errors on the dependent axis of a data set.

get_filter()[source] [edit on github]
get_filter_expr()[source] [edit on github]
get_img(yfunc=None)[source] [edit on github]

Return the dependent axis as a 2D array.

The data is not filtered.

Parameters:

yfunc (sherpa.models.model.Model instance or None, optional) – If set then it is a model that is evaluated on the data grid and returned along with the dependent axis.

Returns:

img – The data as a 2D array or a pair of 2D arrays when yfunc is set.

Return type:

ndarray or (ndarray, ndarray)

get_imgerr()[source] [edit on github]
get_indep(filter=False) [edit on github]

Return the independent axes of a data set.

Parameters:

filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

axis – The independent axis values for the data set. This gives the coordinates of each point in the data set.

Return type:

tuple of arrays

See also

get_dep

Return the dependent axis of a data set.

get_max_pos(dep=None)[source] [edit on github]

Return the coordinates of the maximum value.

Parameters:

dep (ndarray or None, optional) – The data to search and it must match the current data filter. If not given then the dependent axis is used.

Returns:

coords – The coordinates of the maximum location. The data values match the values returned by get_x0 and get_x1. If there is only one maximum pixel then a pair is returned otherwise a list of pairs is returned.

Return type:

pair or list of pairs

See also

get_dep, get_x0, get_x1

get_staterror(filter=False, staterrfunc=None) [edit on github]

Return the statistical error on the dependent axis of a data set.

Parameters:
  • filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

  • staterrfunc (function) – If no statistical error has been set, the errors will be calculated by applying this function to the dependent axis of the data set.

Returns:

axis – The statistical error for each data point. A value of None is returned if the data set has no statistical error array and staterrfunc is None.

Return type:

array or None

See also

get_error

Return the errors on the dependent axis of a data set.

get_indep

Return the independent axis of a data set.

get_syserror

Return the systematic errors on the dependent axis of a data set.

get_syserror(filter=False) [edit on github]

Return the statistical error on the dependent axis of a data set.

Parameters:

filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

axis – The systematic error for each data point. A value of None is returned if the data set has no systematic errors.

Return type:

array or None

See also

get_error

Return the errors on the dependent axis of a data set.

get_indep

Return the independent axis of a data set.

get_staterror

Return the statistical errors on the dependent axis of a data set.

get_x0(filter=False)[source] [edit on github]
get_x0label()[source] [edit on github]

Return label for first dimension in 2-D view of independent axis/axes.

get_x1(filter=False)[source] [edit on github]
get_x1label()[source] [edit on github]

Return label for second dimension in 2-D view of independent axis/axes.

get_y(filter=False, yfunc=None, use_evaluation_space=False) [edit on github]

Return dependent axis in N-D view of dependent variable

Parameters:
  • filter

  • yfunc

  • use_evaluation_space

get_yerr(filter=False, staterrfunc=None) [edit on github]

Return errors in dependent axis in N-D view of dependent variable.

Parameters:
  • filter

  • staterrfunc

get_ylabel(yfunc=None) [edit on github]

Return label for dependent axis in N-D view of dependent variable.

Parameters:

yfunc

ignore(*args, **kwargs) [edit on github]
notice(x0lo=None, x0hi=None, x1lo=None, x1hi=None, ignore=False)[source] [edit on github]
set_dep(val) [edit on github]

Set the dependent variable values.

Parameters:

val (sequence or number) – If a number then it is used for each element.

set_indep(val) [edit on github]
to_contour(yfunc=None)[source] [edit on github]
to_fit(staterrfunc=None) [edit on github]
to_guess() [edit on github]