#
# Copyright (C) 2010, 2015, 2016, 2017, 2018, 2019, 2020, 2021
# Smithsonian Astrophysical Observatory
#
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
import logging
import os
import sys
import warnings
import numpy
import sherpa.ui.utils
from sherpa.astro.instrument import create_arf, create_delta_rmf, \
create_non_delta_rmf, has_pha_response
from sherpa.ui.utils import _argument_type_error, _check_type
from sherpa.utils import SherpaInt, SherpaFloat, sao_arange, \
send_to_pager
from sherpa.utils.err import ArgumentErr, ArgumentTypeErr, DataErr, \
IdentifierErr, ImportErr, IOErr, ModelErr
from sherpa.data import Data1D, Data1DAsymmetricErrs
import sherpa.astro.all
import sherpa.astro.plot
from sherpa.astro.ui import serialize
from sherpa.sim import NormalParameterSampleFromScaleMatrix
from sherpa.stats import Cash, CStat, WStat
from sherpa.models.basic import TableModel
from sherpa.astro import fake
from sherpa.astro.data import DataPHA
warning = logging.getLogger(__name__).warning
info = logging.getLogger(__name__).info
string_types = (str, )
__all__ = ('Session',)
[docs]class Session(sherpa.ui.utils.Session):
###########################################################################
# Standard methods
###########################################################################
def __init__(self):
self.clean()
sherpa.ui.utils.Session.__init__(self)
###########################################################################
# High-level utilities
###########################################################################
def _fix_background_id(self, id, bkg_id):
"""Validate the background id.
The identifier has the same restrictions as the dataset
identifier.
Parameters
----------
id : int or str or None
The dataset identifier. This is only used if bkg_id is
None and must refer to a DataPHA dataset.
bkg_id : int or str or None
The identifier to check. If None then the default backround
identifier will be used, taken from the id dataset.
Returns
-------
bkg_id : int or str
The background identifier to use (it will only differ from
the input parameter was set to None).
Raises
------
sherpa.utils.err.ArgumentTypeErr
If the identifier was not a string or an integer.
sherpa.utils.err.IdentifierErr
If the identifier was invalid.
See Also
--------
_fix_id
Notes
-----
Since there is currently no way to set the default background
id of the DataPHA class (e.g. in unpack_pha) we do not use the
_default_id setting here.
"""
if bkg_id is None:
# The assumption here is that if we are asking about a
# background identifier then there must already be a
# loaded PHA dataset.
data = self._get_pha_data(id)
return data.default_background_id
# return self._default_id
# We rely on the validation made by _fix_id
return self._fix_id(bkg_id)
def __setstate__(self, state):
if '_background_sources' not in state:
self.__dict__['_background_sources'] = state.pop(
'_background_models')
sherpa.ui.utils.Session.__setstate__(self, state)
[docs] def clean(self):
"""Clear out the current Sherpa session.
The `clean` function removes all data sets and model
assignments, and restores the default settings for the
optimisation and fit statistic.
See Also
--------
save : Save the current Sherpa session to a file.
restore : Load in a Sherpa session from a file.
save_all : Save the Sherpa session as an ASCII file.
Examples
--------
>>> clean()
"""
self._pileup_models = {}
self._background_models = {}
self._background_sources = {}
self._dataphaplot = sherpa.astro.plot.DataPHAPlot()
self._astrosourceplot = sherpa.astro.plot.SourcePlot()
self._astrocompsrcplot = sherpa.astro.plot.ComponentSourcePlot()
self._astrocompmdlplot = sherpa.astro.plot.ComponentModelPlot()
self._modelhisto = sherpa.astro.plot.ModelHistogram()
self._bkgmodelhisto = sherpa.astro.plot.BkgModelHistogram()
# self._bkgdataplot = sherpa.astro.plot.DataPHAPlot()
self._bkgdataplot = sherpa.astro.plot.BkgDataPlot()
self._bkgmodelplot = sherpa.astro.plot.BkgModelPHAHistogram()
self._bkgfitplot = sherpa.astro.plot.BkgFitPlot()
self._bkgchisqrplot = sherpa.astro.plot.BkgChisqrPlot()
self._bkgdelchiplot = sherpa.astro.plot.BkgDelchiPlot()
self._bkgresidplot = sherpa.astro.plot.BkgResidPlot()
self._bkgratioplot = sherpa.astro.plot.BkgRatioPlot()
self._bkgsourceplot = sherpa.astro.plot.BkgSourcePlot()
self._arfplot = sherpa.astro.plot.ARFPlot()
self._orderplot = sherpa.astro.plot.OrderPlot()
self._energyfluxplot = sherpa.astro.plot.EnergyFluxHistogram()
self._photonfluxplot = sherpa.astro.plot.PhotonFluxHistogram()
# This is a new dictionary of XSPEC module settings. It
# is meant only to be populated by the save function, so
# that the user's XSPEC settings can be saved in the pickle
# file. Then, restore can peel out settings from the
# restored _xspec_state variable, and set abundance,
# cross-section, etc. in the XSPEC module.
#
# TODO: it should probably not be reset by clean since there's
# no way to clear the XSPEC state (we could try and
# unset all the changes but it's not guaranteed we can
# do so).
#
self._xspec_state = None
sherpa.ui.utils.Session.clean(self)
self._pyblocxs = sherpa.astro.sim.MCMC()
self._plot_types['order'] = [self._orderplot]
self._plot_types['energy'] = [self._energyfluxplot]
self._plot_types['photon'] = [self._photonfluxplot]
self._plot_types['compsource'].append(self._astrocompsrcplot)
self._plot_types['compmodel'].append(self._astrocompmdlplot)
self._plot_types['data'].append(self._dataphaplot)
self._plot_types['source'].append(self._astrosourceplot)
self._plot_types['model'].append(self._modelhisto)
self._plot_types['arf'] = [self._arfplot]
self._plot_types['bkg'] = [self._bkgdataplot]
self._plot_types['bkgmodel'] = [self._bkgmodelhisto]
self._plot_types['bkgfit'] = [self._bkgfitplot]
self._plot_types['bkgsource'] = [self._bkgsourceplot]
self._plot_types['bkgratio'] = [self._bkgratioplot]
self._plot_types['bkgresid'] = [self._bkgresidplot]
self._plot_types['bkgdelchi'] = [self._bkgdelchiplot]
self._plot_types['bkgchisqr'] = [self._bkgchisqrplot]
self._plot_type_names['order'] = 'order'
# self._plot_type_names['energy'] = 'energy' - how to do energy/flux plots?
# self._plot_type_names['photon'] = 'photon'
self._plot_type_names['astrocompsource'] = 'source_component'
self._plot_type_names['astrocompmodel'] = 'model_componentl'
self._plot_type_names['astrodata'] = 'data'
self._plot_type_names['astrosource'] = 'source' # is this meaningful anymore
self._plot_type_names['astromodel'] = 'model' # is this meaningful anymore
self._plot_type_names['arf'] = 'arf'
self._plot_type_names['bkg'] = 'bkg'
self._plot_type_names['bkgmodel'] = 'bkg_model'
self._plot_type_names['bkgfit'] = 'bkg_fit'
self._plot_type_names['bkgsource'] = 'bkg_source'
self._plot_type_names['bkgratio'] = 'bkg_ratio'
self._plot_type_names['bkgresid'] = 'bkg_resid'
self._plot_type_names['bkgdelchi'] = 'bkg_delchi'
self._plot_type_names['bkgchisqr'] = 'bkg_chisqr'
# Add ability to save attributes sepcific to the astro package.
# Save XSPEC module settings that need to be restored.
[docs] def save(self, filename='sherpa.save', clobber=False):
"""Save the current Sherpa session to a file.
Parameters
----------
filename : str, optional
The name of the file to write the results to. The default
is 'sherpa.save'.
clobber : bool, optional
This flag controls whether an existing file can be
overwritten (``True``) or if it raises an exception (``False``,
the default setting).
Raises
------
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
clean : Clear all stored session data.
restore : Load in a Sherpa session from a file.
save_all : Save the Sherpa session as an ASCII file.
Notes
-----
The current Sherpa session is saved using the Python `pickle`
module. The output is a binary file, which may not be portable
between versions of Sherpa, but is platform independent, and
contains all the data. This means that files created by `save`
can be sent to collaborators to share results.
Examples
--------
Save the current session to the file 'sherpa.save'.
>>> save()
Save the current session to the file 'bestfit.sherpa',
overwriting any existing version of the file.
>>> save('bestfit.sherpa', clobber=True)
"""
if hasattr(sherpa.astro, "xspec"):
self._xspec_state = sherpa.astro.xspec.get_xsstate()
else:
self._xspec_state = None
sherpa.ui.utils.Session.save(self, filename, clobber)
[docs] def restore(self, filename='sherpa.save'):
"""Load in a Sherpa session from a file.
Parameters
----------
filename : str, optional
The name of the file to read the results from. The default
is 'sherpa.save'.
Raises
------
IOError
If `filename` does not exist.
See Also
--------
clean : Clear all stored session data.
save : Save the current Sherpa session to a file.
Notes
-----
The input to `restore` must have been created with the `save`
command. This is a binary file, which may not be portable
between versions of Sherpa, but is platform independent. A
warning message may be created if a file saved by an older
(or newer) version of Sherpa is loaded. An example of such
a message is::
WARNING: Could not determine whether the model is discrete.
This probably means that you have restored a session saved with a previous version of Sherpa.
Falling back to assuming that the model is continuous.
Examples
--------
Load in the Sherpa session from 'sherpa.save'.
>>> restore()
Load in the session from the given file:
>>> restore('/data/m31/setup.sherpa')
"""
sherpa.ui.utils.Session.restore(self, filename)
if hasattr(sherpa.astro, "xspec"):
if self._xspec_state is not None:
sherpa.astro.xspec.set_xsstate(self._xspec_state)
self._xspec_state = None
def _get_show_data(self, id=None):
data_str = ''
ids = self.list_data_ids()
if id is not None:
ids = [self._fix_id(id)]
for id in ids:
data = self.get_data(id)
data_str += 'Data Set: %s\n' % id
data_str += 'Filter: %s\n' % data.get_filter_expr()
if isinstance(data, sherpa.astro.data.DataPHA):
nbkg = len(data.background_ids)
for bkg_id in data.background_ids:
# Apply grouping/filtering if set
scale = data.get_background_scale(bkg_id)
if scale is None:
continue
data_str += 'Bkg Scale'
if nbkg > 1 or bkg_id != 1:
data_str += ' {}'.format(bkg_id)
data_str += ': '
if numpy.isscalar(scale):
data_str += '{:g}'.format(float(scale))
else:
# would like to use sherpa.utils/print_fields style output
# but not available and I don't feel like it's
# worth it
data_str += '{}[{}]'.format(scale.dtype, scale.size)
data_str += '\n'
data_str += 'Noticed Channels: %s\n' % data.get_noticed_expr()
data_str += data.__str__() + '\n\n'
if isinstance(data, sherpa.astro.data.DataPHA):
for resp_id in data.response_ids:
# ARF or RMF could be None
arf, rmf = data.get_response(resp_id)
if rmf is not None:
data_str += 'RMF Data Set: %s:%s\n' % (id, resp_id)
data_str += rmf.__str__() + '\n\n'
if arf is not None:
data_str += 'ARF Data Set: %s:%s\n' % (id, resp_id)
data_str += arf.__str__() + '\n\n'
data_str += self._get_show_bkg(id)
return data_str
def _get_show_bkg(self, id=None, bkg_id=None):
data_str = ''
ids = self.list_data_ids()
if id is not None:
ids = [self._fix_id(id)]
for id in ids:
data = self.get_data(id)
if not isinstance(data, sherpa.astro.data.DataPHA):
continue
bkg_ids = data.background_ids
if bkg_id is not None:
bkg_ids = [data._fix_background_id(bkg_id)]
for bkg_id in bkg_ids:
bkg = self.get_bkg(id, bkg_id)
data_str += 'Background Data Set: %s:%s\n' % (id, bkg_id)
data_str += 'Filter: %s\n' % bkg.get_filter_expr()
data_str += 'Noticed Channels: %s\n' % bkg.get_noticed_expr()
data_str += bkg.__str__() + '\n\n'
for bk_rp_id in bkg.response_ids:
# ARF or RMF could be None
arf, rmf = bkg.get_response(bk_rp_id)
if rmf is not None:
data_str += ('Background RMF Data Set: %s:%s\n' %
(id, bkg_id))
data_str += rmf.__str__() + '\n\n'
if arf is not None:
data_str += ('Background ARF Data Set: %s:%s\n' %
(id, bkg_id))
data_str += arf.__str__() + '\n\n'
return data_str
def _get_show_bkg_model(self, id=None, bkg_id=None):
model_str = ''
ids = self.list_data_ids()
if id is not None:
ids = [self._fix_id(id)]
for id in ids:
if bkg_id is not None:
bkg_ids = [bkg_id]
else:
bkg_ids = list(self._background_models.get(id, {}).keys())
bkg_ids.extend(self._background_sources.get(id, {}).keys())
bkg_ids = list(set(bkg_ids))
for bkg_id in bkg_ids:
model_str += 'Background Model: %s:%s\n' % (id, bkg_id)
model_str += self.get_bkg_model(id, bkg_id).__str__() + '\n\n'
return model_str
def _get_show_bkg_source(self, id=None, bkg_id=None):
model_str = ''
ids = self.list_data_ids()
if id is not None:
ids = [self._fix_id(id)]
for id in ids:
if bkg_id is not None:
bkg_ids = [bkg_id]
else:
bkg_ids = self._background_sources.get(id, {}).keys()
for bkg_id in bkg_ids:
model_str += 'Background Source: %s:%s\n' % (id, bkg_id)
model_str += self.get_bkg_source(id, bkg_id).__str__() + '\n\n'
return model_str
[docs] def show_bkg(self, id=None, bkg_id=None, outfile=None, clobber=False):
"""Show the details of the PHA background data sets.
This displays information about the background, or
backgrounds, for the loaded data sets. This includes: any
filters, the grouping settings, mission-specific header
keywords, and the details of any associated instrument
responses files (ARF, RMF).
Parameters
----------
id : int or str, optional
The data set. If not given then all background data sets
are displayed.
bkg_id : int or str, optional
The background component to display. The default is all
components.
outfile : str, optional
If not given the results are displayed to the screen,
otherwise it is taken to be the name of the file to
write the results to.
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `outfile` already exists and `clobber` is ``False``.
See Also
--------
list_model_ids : List of all the data sets with a source expression.
load_bkg : Load the background from a file and add it to a PHA data set.
show_all : Report the current state of the Sherpa session.
"""
txt = self._get_show_bkg(id, bkg_id)
send_to_pager(txt, outfile, clobber)
[docs] def show_bkg_source(self, id=None, bkg_id=None, outfile=None, clobber=False):
"""Display the background model expression for a data set.
This displays the background model for a data set, that is,
the expression set by `set_bkg_model` or `set_bkg_source`, as
well as the parameter values for the model. The
`show_bkg_model` function displays the model that is fit to
the data; that is, it includes any instrument responses.
Parameters
----------
id : int or str, optional
The data set. If not given then all background expressions
are displayed.
bkg_id : int or str, optional
The background component to display. The default is all
components.
outfile : str, optional
If not given the results are displayed to the screen,
otherwise it is taken to be the name of the file to
write the results to.
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `outfile` already exists and `clobber` is ``False``.
See Also
--------
list_model_ids : List of all the data sets with a source expression.
set_bkg_model : Set the background model expression for a data set.
show_all : Report the current state of the Sherpa session.
show_model : Display the model expression used to fit a data set.
show_bkg_model : Display the background model expression used to fit a data set.
"""
txt = self._get_show_bkg_source(id, bkg_id)
send_to_pager(txt, outfile, clobber)
[docs] def show_bkg_model(self, id=None, bkg_id=None, outfile=None, clobber=False):
"""Display the background model expression used to fit a data set.
This displays the model used to the the background data set,
that is, the expression set by `set_bkg_model` or
`set_bkg_source` combined with any instrumental responses,
together with the parameter values for the model. The
`show_bkg_source` function displays just the background model,
without the instrument components (if any).
Parameters
----------
id : int or str, optional
The data set. If not given then all background expressions are
displayed.
bkg_id : int or str, optional
The background component to display. The default is all
components.
outfile : str, optional
If not given the results are displayed to the screen,
otherwise it is taken to be the name of the file to
write the results to.
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `outfile` already exists and `clobber` is ``False``.
See Also
--------
list_model_ids : List of all the data sets with a source expression.
set_bkg_model : Set the background model expression for a data set.
show_all : Report the current state of the Sherpa session.
show_model : Display the model expression used to fit a data set.
show_bkg_source : Display the background model expression for a data set.
"""
txt = self._get_show_bkg_model(id, bkg_id)
send_to_pager(txt, outfile, clobber)
###########################################################################
# Data
###########################################################################
# DOC-NOTE: also in sherpa.utils
[docs] def dataspace1d(self, start, stop, step=1, numbins=None,
id=None, bkg_id=None, dstype=sherpa.data.Data1DInt):
"""Create the independent axis for a 1D data set.
Create an "empty" one-dimensional data set by defining the
grid on which the points are defined (the independent axis).
The values are set to 0.
Parameters
----------
start : number
The minimum value of the axis.
stop : number
The maximum value of the axis.
step : number, optional
The separation between each grid point. This is not used if
``numbins`` is set.
numbins : int, optional
The number of grid points. This over-rides the ``step``
setting.
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
If set, the grid is for the background component of the
data set.
dstype : data class to use, optional
What type of data is to be used. Supported values include
`Data1DInt` (the default), `Data1D`, and `DataPHA`.
See Also
--------
dataspace2d : Create the independent axis for a 2D data set.
get_dep : Return the dependent axis of a data set.
get_indep : Return the independent axes of a data set.
set_dep : Set the dependent axis of a data set.
Notes
-----
The meaning of the ``stop`` parameter depends on whether it is a
binned or unbinned data set (as set by the ``dstype``
parameter).
Examples
--------
Create a binned data set, starting at 1 and with a
bin-width of 1.
>>> dataspace1d(1, 5, 1)
>>> print(get_indep())
(array([ 1., 2., 3., 4.]), array([ 2., 3., 4., 5.]))
This time for an un-binned data set:
>>> dataspace1d(1, 5, 1, dstype=Data1D)
>>> print(get_indep())
(array([ 1., 2., 3., 4., 5.]),)
Specify the number of bins rather than the grid spacing:
>>> dataspace1d(1, 5, numbins=5, id=2)
>>> (xlo, xhi) = get_indep(2)
>>> xlo
array([ 1. , 1.8, 2.6, 3.4, 4.2])
>>> xhi
array([ 1.8, 2.6, 3.4, 4.2, 5. ])
>>> dataspace1d(1, 5, numbins=5, id=3, dstype=Data1D)
>>> (x, ) = get_indep(3)
>>> x
array([ 1., 2., 3., 4., 5.])
Create a grid for a PHA data set called 'jet', and
for its background component:
>>> dataspace1d(0.01, 11, 0.01, id='jet', dstype=DataPHA)
>>> dataspace1d(0.01, 11, 0.01, id='jet', bkg_id=1,
... dstype=DataPHA)
"""
# support non-integrated grids with inclusive boundaries
# We do NOT want to use an isinstance check since Data1DInt is
# derived from Data1D.
if dstype in (sherpa.data.Data1D, sherpa.astro.data.DataPHA):
stop += step
xlo, xhi, y = sherpa.utils.dataspace1d(start, stop, step=step,
numbins=numbins)
args = [xlo, xhi, y]
kwargs = {}
if dstype is sherpa.astro.data.DataPHA:
channel = numpy.arange(1, len(xlo) + 1, dtype=float)
args = [channel, y]
# kwargs['bin_lo'] = xlo
# kwargs['bin_hi'] = xhi
elif dstype is not sherpa.data.Data1DInt:
args = [xlo, y]
if bkg_id is not None:
self._get_pha_data(id).set_background(dstype('bkg_dataspace1d',
*args, **kwargs),
bkg_id)
else:
self.set_data(id, dstype('dataspace1d', *args, **kwargs))
# DOC-NOTE: also in sherpa.utils
[docs] def dataspace2d(self, dims, id=None, dstype=sherpa.astro.data.DataIMG):
"""Create the independent axis for a 2D data set.
Create an "empty" two-dimensional data set by defining the
grid on which the points are defined (the independent axis).
The values are set to 0.
Parameters
----------
dims : sequence of 2 number
The dimensions of the grid in ``(width,height)`` order.
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
dstype : data class to use, optional
What type of data is to be used. Supported values include
`DataIMG` (the default), `Data2D`, and `Data2DInt`.
See Also
--------
dataspace1d : Create the independent axis for a 1D data set.
get_dep : Return the dependent axis of a data set.
get_indep : Return the independent axes of a data set.
set_dep : Set the dependent axis of a data set.
Examples
--------
Create a 200 pixel by 150 pixel grid (number of columns by
number of rows) and display it (each pixel has a value of 0):
>>> dataspace2d([200, 150])
>>> image_data()
Create a data space called "fakeimg":
>>> dataspace2d([nx, ny], id="fakeimg")
"""
x0, x1, y, shape = sherpa.utils.dataspace2d(dims)
dataset = None
if issubclass(dstype, (sherpa.astro.data.DataIMGInt,
sherpa.data.Data2DInt)):
dataset = dstype('dataspace2d', x0 - 0.5, x1 - 0.5, x0 + 0.5, x1 + 0.5,
y, shape)
else:
dataset = dstype('dataspace2d', x0, x1, y, shape)
self.set_data(id, dataset)
# DOC-NOTE: also in sherpa.utils
# DOC-TODO: how to describe Crates and/or AstroPy?
[docs] def unpack_arrays(self, *args):
"""Create a sherpa data object from arrays of data.
The object returned by `unpack_arrays` can be used in a
`set_data` call.
Parameters
----------
args : array_like
Arrays of data. The order, and number, is determined by
the `dstype` parameter, and listed in the `load_arrays`
routine.
dstype
The data set type. The default is `Data1D` and values
include: `Data1D`, `Data1DInt`, `Data2D`, `Data2DInt`,
`DataPHA`, and `DataIMG`. The class is expected to
be derived from `sherpa.data.BaseData`.
Returns
-------
instance
The data set object matching the requested `dstype`
parameter.
See Also
--------
get_data : Return the data set by identifier.
load_arrays : Create a data set from array values.
set_data : Set a data set.
unpack_data : Create a sherpa data object from a file.
Examples
--------
Create a 1D (unbinned) data set from the values in
the x and y arrays. Use the returned object to create
a data set labelled "oned":
>>> x = [1, 3, 7, 12]
>>> y = [2.3, 3.2, -5.4, 12.1]
>>> dat = unpack_arrays(x, y)
>>> set_data("oned", dat)
Include statistical errors on the data:
>>> edat = unpack_arrays(x, y, dy)
Create a "binned" 1D data set, giving the low,
and high edges of the independent axis (xlo
and xhi respectively) and the dependent values
for this grid (y):
>>> hdat = unpack_arrays(xlo, xhi, y, Data1DInt)
Create a 3 column by 4 row image:
>>> ivals = np.arange(12)
>>> y, x = np.mgrid[0:3, 0:4]
>>> x = x.flatten()
>>> y = y.flatten()
>>> idat = unpack_arrays(x, y, ivals, (3, 4), DataIMG)
"""
dataset = None
try:
dataset = sherpa.astro.io.read_arrays(*args)
except AttributeError:
# if the astro backend is not set, fall back on io module version.
dataset = sherpa.io.read_arrays(*args)
return dataset
# DOC-NOTE: also in sherpa.utils
# DOC-TODO: rework the Data type notes section (also needed for
# unpack_arrays)
[docs] def load_arrays(self, id, *args):
"""Create a data set from array values.
Parameters
----------
id : int or str
The identifier for the data set to use.
*args
Two or more arrays, followed by the type of data set to
create.
Warnings
--------
Sherpa currently does not support numpy masked arrays. Use the
set_filter function and note that it follows a different convention by
default (a positive value or True for a "bad" channel, 0 or False for
a good channel).
See Also
--------
copy_data : Copy a data set to a new identifier.
delete_data : Delete a data set by identifier.
get_data : Return the data set by identifier.
load_data : Create a data set from a file.
set_data : Set a data set.
unpack_arrays : Create a sherpa data object from arrays of data.
Notes
-----
The data type identifier, which defaults to `Data1D`,
determines the number, and order, of the required inputs.
+------------+-----------------+--------------------+
| Identifier | Required Fields | Optional Fields |
+============+=================+====================+
| Data1D | x, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data1DInt | xlo, xhi, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data2D | x0, x1, y | shape, |
| | | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data2DInt | x0lo, x1lo, | shape, |
| | x0hi, x1hi, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| DataPHA | channel, counts | statistical error, |
| | | systematic error, |
| | | bin_lo, bin_hi, |
| | | grouping, quality |
+------------+-----------------+--------------------+
| DataIMG | x0, x1, y | shape, |
| | | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
The ``shape`` argument should be a tuple giving the size of
the data ``(ny,nx)``, and for the ``DataIMG`` case the arrays
are 1D, not 2D.
Examples
--------
Create a 1D data set with three points:
>>> load_arrays(1, [10, 12, 15], [4.2, 12.1, 8.4])
Create a 1D data set, with the identifier 'prof', from the
arrays ``x`` (independent axis), ``y`` (dependent axis), and
``dy`` (statistical error on the dependent axis):
>>> load_arrays('prof', x, y, dy)
Explicitly define the type of the data set:
>>> load_arrays('prof', x, y, dy, Data1D)
Data set 1 is a histogram, where the bins cover the range
1-3, 3-5, and 5-7 with values 4, 5, and 9 respectively.
>>> load_arrays(1, [1, 3, 5], [3, 5, 7], [4, 5, 9], Data1DInt)
Create an image data set:
>>> ivals = np.arange(12)
>>> y, x = np.mgrid[0:3, 0:4]
>>> x = x.flatten()
>>> y = y.flatten()
>>> load_arrays('img', x, y, ivals, (3, 4), DataIMG)
"""
self.set_data(id, self.unpack_arrays(*args))
# DOC-TODO: should unpack_ascii be merged into this?
[docs] def unpack_table(self, filename, ncols=2, colkeys=None, dstype=Data1D):
"""Unpack a FITS binary file into a data structure.
Parameters
----------
filename
Identify the file to read: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``TABLECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
ncols : int, optional
The number of columns to read in (the first `ncols` columns
in the file). The meaning of the columns is determined by
the `dstype` parameter.
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
dstype : optional
The data class to use. The default is `Data1D` and it
is expected to be derived from `sherpa.data.BaseData`.
Returns
-------
instance
The class of the returned object is controlled by the
`dstype` parameter.
See Also
--------
load_table : Load a FITS binary file as a data set.
set_data : Set a data set.
unpack_ascii : Unpack an ASCII file into a data structure.
Examples
--------
Read in the first two columns of the file, as the independent
(X) and dependent (Y) columns of a data set:
>>> d = unpack_table('sources.fits')
Read in the first three columns (the third column is taken to
be the error on the dependent variable):
>>> d = unpack_table('sources.fits', ncols=3)
Read in from columns 'RMID' and 'SUR_BRI':
>>> d = unpack_table('rprof.fits', colkeys=['RMID', 'SUR_BRI'])
The first three columns are taken to be the two independent
axes of a two-dimensional data set (``x0`` and ``x1``) and
the dependent value (``y``):
>>> d = unpack_table('fields.fits', ncols=3,
... dstype=sherpa.astro.data.Data2D)
When using the Crates I/O library, the file name can include
CIAO Data Model syntax, such as column selection. This can
also be done using the `colkeys` parameter, as shown above:
>>> d = unpack_table('rprof.fits[cols rmid,sur_bri,sur_bri_err]',
... ncols=3)
"""
return sherpa.astro.io.read_table(filename, ncols, colkeys, dstype)
# DOC-TODO: the field listing really should be somewhere else
# as it's needed in multiple places (ideally in the
# DataX class documentation, but users may not find it)
# DOC-TODO: what do the shape arguments for Data2D/Data2DInt mean?
[docs] def load_table(self, id, filename=None, ncols=2, colkeys=None,
dstype=Data1D):
"""Load a FITS binary file as a data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename
Identify the file to read: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``TABLECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
ncols : int, optional
The number of columns to read in (the first ``ncols`` columns
in the file). The meaning of the columns is determined by
the ``dstype`` parameter.
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
dstype : optional
The data class to use. The default is `Data1D`.
See Also
--------
load_arrays : Create a data set from array values.
load_ascii : Load an ASCII file as a data set.
load_image : Load an image as a data set.
set_data : Set a data set.
unpack_table : Unpack a FITS binary table into a data structure.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The column order for the different data types are as follows,
where ``x`` indicates an independent axis and ``y`` the dependent
axis:
+------------+-----------------+--------------------+
| Identifier | Required Fields | Optional Fields |
+============+=================+====================+
| Data1D | x, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data1DInt | xlo, xhi, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data2D | x0, x1, y | shape, |
| | | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data2DInt | x0lo, x1lo, | shape, |
| | x0hi, x1hi, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
Examples
--------
Read in the first two columns of the file, as the independent
(X) and dependent (Y) columns of the default data set:
>>> load_table('sources.fits')
Read in the first three columns (the third column is taken to
be the error on the dependent variable):
>>> load_table('sources.fits', ncols=3)
Read in from columns 'RMID' and 'SUR_BRI' into data set
'prof':
>>> load_table('prof', 'rprof.fits',
... colkeys=['RMID', 'SUR_BRI'])
The first three columns are taken to be the two independent
axes of a two-dimensional data set (``x0`` and ``x1``) and
the dependent value (``y``):
>>> load_table('fields.fits', ncols=3,
... dstype=sherpa.astro.data.Data2D)
When using the Crates I/O library, the file name can include
CIAO Data Model syntax, such as column selection. This can
also be done using the ``colkeys`` parameter, as shown above:
>>> load_table('prof',
... 'rprof.fits[cols rmid,sur_bri,sur_bri_err]',
... ncols=3)
Read in a data set using Crates:
>>> cr = pycrates.read_file('table.fits')
>>> load_table(cr)
Read in a data set using AstroPy:
>>> hdus = astropy.io.fits.open('table.fits')
>>> load_table(hdus)
"""
if filename is None:
id, filename = filename, id
self.set_data(id, self.unpack_table(filename, ncols, colkeys, dstype))
# DOC-TODO: should unpack_ascii be merged into unpack_table?
# DOC-TODO: I am going to ignore the crates support here as
# it is somewhat meaningless, since the crate could
# have been read from a FITS binary table.
[docs] def unpack_ascii(self, filename, ncols=2, colkeys=None,
dstype=Data1D, sep=' ', comment='#'):
"""Unpack an ASCII file into a data structure.
Parameters
----------
filename : str
The name of the file to read in. Selection of the relevant
column depends on the I/O library in use (Crates or
AstroPy).
ncols : int, optional
The number of columns to read in (the first `ncols` columns
in the file). The meaning of the columns is determined by
the `dstype` parameter.
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
sep : str, optional
The separator character. The default is ``' '``.
comment : str, optional
The comment character. The default is ``'#'``.
dstype : optional
The data class to use. The default is `Data1D` and it
is expected to be derived from `sherpa.data.BaseData`.
Returns
-------
instance
The type of the returned object is controlled by the
`dstype` parameter.
See Also
--------
load_ascii : Load an ASCII file as a data set.
set_data : Set a data set.
unpack_table : Unpack a FITS binary file into a data structure.
Examples
--------
Read in the first two columns of the file, as the independent
(X) and dependent (Y) columns of a data set:
>>> d = unpack_ascii('sources.dat')
Read in the first three columns (the third column is taken to
be the error on the dependent variable):
>>> d = unpack_ascii('sources.dat', ncols=3)
Read in from columns 'col2' and 'col3':
>>> d = unpack_ascii('tbl.dat', colkeys=['col2', 'col3'])
The first three columns are taken to be the two independent
axes of a two-dimensional data set (``x0`` and ``x1``) and
the dependent value (``y``):
>>> d = unpack_ascii('fields.dat', ncols=3,
... dstype=sherpa.astro.data.Data2D)
When using the Crates I/O library, the file name can include
CIAO Data Model syntax, such as column selection. This can
also be done using the `colkeys` parameter, as shown above:
>>> d = unpack_ascii('tbl.dat[cols rmid,sur_bri,sur_bri_err]',
... ncols=3)
"""
return sherpa.astro.io.read_ascii(filename, ncols, colkeys, dstype,
sep=sep, comment=comment)
# DOC-TODO: I am going to ignore the crates support here as
# it is somewhat meaningless, since the crate could
# have been read from a FITS binary table.
# DOC-TODO: how best to include datastack support?
# DOC-TODO: what does shape mean here (how is it encoded)?
[docs] def load_ascii(self, id, filename=None, ncols=2, colkeys=None,
dstype=Data1D, sep=' ', comment='#'):
"""Load an ASCII file as a data set.
The standard behavior is to create a single data set, but
multiple data sets can be loaded with this command, as
described in the `sherpa.astro.datastack` module.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to read in. Selection of the relevant
column depends on the I/O library in use (Crates or
AstroPy).
ncols : int, optional
The number of columns to read in (the first ``ncols`` columns
in the file). The meaning of the columns is determined by
the ``dstype`` parameter.
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
sep : str, optional
The separator character. The default is ``' '``.
comment : str, optional
The comment character. The default is ``'#'``.
dstype : optional
The data class to use. The default is `Data1D`.
See Also
--------
load_ascii_with_errors : Load an ASCII file with asymmetric errors as a data set.
load_table : Load a FITS binary file as a data set.
load_image : Load an image as a data set.
set_data : Set a data set.
unpack_ascii : Unpack an ASCII file into a data structure.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The column order for the different data types are as follows,
where ``x`` indicates an independent axis and ``y`` the
dependent axis.
+------------+-----------------+--------------------+
| Identifier | Required Fields | Optional Fields |
+============+=================+====================+
| Data1D | x, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data1DInt | xlo, xhi, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data2D | x0, x1, y | shape, |
| | | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
| Data2DInt | x0lo, x1lo, | shape, |
| | x0hi, x1hi, y | statistical error, |
| | | systematic error |
+------------+-----------------+--------------------+
Examples
--------
Read in the first two columns of the file, as the independent
(X) and dependent (Y) columns of the default data set:
>>> load_ascii('sources.dat')
Read in the first three columns (the third column is taken to
be the error on the dependent variable):
>>> load_ascii('sources.dat', ncols=3)
Read in from columns 'RMID' and 'SUR_BRI' into data set
'prof':
>>> load_ascii('prof', 'rprof.dat',
... colkeys=['RMID', 'SUR_BRI'])
The first three columns are taken to be the two independent
axes of a two-dimensional data set (``x0`` and ``x1``) and
the dependent value (``y``):
>>> load_ascii('fields.txt', ncols=3,
... dstype=sherpa.astro.data.Data2D)
When using the Crates I/O library, the file name can include
CIAO Data Model syntax, such as column selection. This can
also be done using the ``colkeys`` parameter, as shown above:
>>> load_ascii('prof',
... 'rprof.dat[cols rmid,sur_bri,sur_bri_err]',
... ncols=3)
"""
if filename is None:
id, filename = filename, id
self.set_data(id, self.unpack_ascii(filename, ncols=ncols,
colkeys=colkeys, dstype=dstype,
sep=sep, comment=comment))
# DOC-NOTE: also in sherpa.utils
[docs] def unpack_data(self, filename, *args, **kwargs):
"""Create a sherpa data object from a file.
The object returned by `unpack_data` can be used in a
`set_data` call. The data types supported are those
supported by `unpack_pha`, `unpack_image`, `unpack_table`,
and `unpack_ascii`.
Parameters
----------
filename
A file name or a data structure representing the data to
use, as used by the I/O backend in use by Sherpa: e.g. a
``PHACrateDataset``, ``TABLECrate``, or ``IMAGECrate`` for
crates, as used by CIAO, or a list of AstroPy HDU objects.
args
The arguments supported by `unpack_pha`, `unpack_image`,
`unpack_table`, and `unpack_ascii`.
kwargs
The keyword arguments supported by `unpack_pha`, `unpack_image`,
`unpack_table`, and `unpack_ascii`.
Returns
-------
instance
The data set object.
See Also
--------
get_data : Return the data set by identifier.
load_arrays : Create a data set from array values.
set_data : Set a data set.
unpack_arrays : Create a sherpa data object from arrays of data.
unpack_ascii : Unpack an ASCII file into a data structure.
unpack_image : Create an image data structure.
unpack_pha : Create a PHA data structure.
unpack_table : Unpack a FITS binary file into a data structure.
Examples
--------
Create a data object from the contents of the file "src.dat"
and use it to create a Sherpa data set called "src":
>>> dat = unpack_data('src.dat')
>>> set_data('src', dat)
"""
try:
data = self.unpack_pha(filename, *args, **kwargs)
except:
try:
data = self.unpack_image(filename, *args, **kwargs)
except:
try:
data = self.unpack_table(filename, *args, **kwargs)
except:
# If this errors out then so be it
data = self.unpack_ascii(filename, *args, **kwargs)
return data
[docs] def load_ascii_with_errors(self, id, filename=None, colkeys=None, sep=' ',
comment='#', func=numpy.average, delta=False):
"""Load an ASCII file with asymmetric errors as a data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to read in. Selection of the relevant
column depends on the I/O library in use (Crates or
AstroPy).
sep : str, optional
The separator character. The default is ``' '``.
comment : str, optional
The comment character. The default is ``'#'``.
func: python function, optional
The function used to combine the lo and hi values to estimate
an error. The function should take two arguments ``(lo, hi)``
and return a single NumPy array, giving the per-bin error.
The default function used is numpy.average.
delta: boolean, optional
The flag is used to indicate if the asymmetric errors for the
third and fourth columns are delta values from the second (y)
column or not.
The default value is False
See Also
--------
load_ascii: Load an ASCII file as a data set.
load_arrays : Create a data set from array values.
load_table : Load a FITS binary file as a data set.
load_image : Load an image as a data set.
resample_data : Resample data with asymmetric error bars.
set_data : Set a data set.
unpack_ascii : Unpack an ASCII file into a data structure.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The column order for the different data types are as follows,
where ``x`` indicates an independent axis, ``y`` the dependent
axis, the asymmetric errors ``elo`` and ``ehi``.
+----------------------+-----------------+--------------------+
| Identifier | Required Fields | Optional Fields |
+======================+=================+====================+
| Data1DAsymmetricErrs | x, y, elo, ehi | |
+----------------------+-----------------+--------------------+
Examples
--------
Read in the first four columns of the file, as the independent
(X), dependent (Y), error low (ELO) and error high (EHI) columns
of the default data set:
>>> load_ascii_with_errors('sources.dat')
Read in the first four columns (x, y, elo, ehi) where elo and ehi
are of the form y - delta_lo and y + delta_hi, respectively.
>>> load_ascii_with_errors('sources.dat', delta=True)
Read in the first four columns (x, y, elo, ehi) where elo and ehi
are of the form delta_lo and delta_hi, respectively.
>>> def rms(lo, hi):
... return numpy.sqrt(lo * lo + hi * hi)
...
>>> load_ascii_with_errors('sources.dat', func=rms)
Read in the first four columns (x, y, elo, ehi) where elo and ehi
are of the form delta_lo and delta_hi, respectively. The `func`
argument is used to calculate the error based on the elo and ehi
column values.
"""
if filename is None:
id, filename = filename, id
self.set_data(id, self.unpack_ascii(filename, ncols=4,
colkeys=colkeys,
dstype=Data1DAsymmetricErrs,
sep=sep, comment=comment))
data = self.get_data(id)
if not delta:
data.elo = data.y - data.elo
data.ehi = data.ehi - data.y
if func is numpy.average:
staterror = func([data.elo, data.ehi], axis=0)
else:
staterror = func(data.elo, data.ehi)
data.staterror = staterror
def _load_data(self, id, datasets):
"""Load one or more datasets.
Used by load_data and load_pha.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. For multi-dataset
files, currently only PHA2, the id value indicates the
first dataset: if it is an integer then the numbering
starts at id, and if a string then a suffix of 1 to n is
added. If not given then the default identifier is used,
as returned by `get_default_id`.
datasets : Data instance or iterable of Data instances
The data to load, either as a single item or, for
multiple-dataset files, an iterable of them.
"""
if not numpy.iterable(datasets):
self.set_data(id, datasets)
return
# One issue with the following is that if there's
# only one dataset in phasets and id is a string then the
# output will be "foo1" rather than "foo" (when
# id="foo"). DJB thinks we can live with this.
#
if id is None:
id = self.get_default_id()
num = len(datasets)
ids = []
for ctr, data in enumerate(datasets):
try:
idval = id + ctr
except TypeError:
# id is assumed to be a string
idval = id + str(ctr + 1)
self.set_data(idval, data)
ids.append(idval)
if num > 1:
info("Multiple data sets have been input: " +
"{}-{}".format(ids[0], ids[-1]))
else:
info("One data set has been input: {}".format(ids[0]))
# DOC-NOTE: also in sherpa.utils without the support for
# multiple datasets.
#
[docs] def load_data(self, id, filename=None, *args, **kwargs):
# pylint: disable=W1113
"""Load a data set from a file.
This loads a data set from the file, trying in order
`load_pha`, `load_image`, `load_table`, then `load_ascii`.
.. versionchanged:: 4.13.1
The id argument is now used to define the first identifier
when loading in a PHA2 file to match `load_pha` (previously
the range always started at 1).
Parameters
----------
id : int or str, optional
The identifier for the data set to use. For multi-dataset
files, currently only PHA2, the id value indicates the
first dataset: if it is an integer then the numbering
starts at id, and if a string then a suffix of 1 to n is
added. If not given then the default identifier is used,
as returned by `get_default_id`.
filename
A file name or a data structure representing the data to
use, as used by the I/O backend in use by Sherpa: e.g. a
``PHACrateDataset``, ``TABLECrate``, or ``IMAGECrate`` for
crates, as used by CIAO, or a list of AstroPy HDU objects.
args
The arguments supported by `load_pha`, `load_image`,
`load_table`, and `load_ascii`.
kwargs
The keyword arguments supported by `load_pha`, `load_image`,
`load_table`, and `load_ascii`.
See Also
--------
load_arrays : Create a data set from array values.
load_ascii : Load an ASCII file as a data set.
load_image : Load an image as a data set.
load_pha : Load a PHA data set.
load_table : Load a FITS binary file as a data set.
set_data : Set a data set.
unpack_data : Create a sherpa data object from a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments,
then they are interpreted as the `id` and `filename`
parameters, respectively. The remaining parameters are
expected to be given as named arguments.
Examples
--------
>>> load_data('tbl.dat')
>>> load_data('hist.dat', dstype=Data1DInt)
>>> load_data('img', 'img.fits')
>>> load_data('bg', 'img_bg.fits')
>>> cols = ['rmid', 'sur_bri', 'sur_bri_err']
>>> load_data(2, 'profile.fits', colkeys=cols)
"""
if filename is None:
id, filename = filename, id
datasets = self.unpack_data(filename, *args, **kwargs)
self._load_data(id, datasets)
[docs] def unpack_image(self, arg, coord='logical',
dstype=sherpa.astro.data.DataIMG):
"""Create an image data structure.
Parameters
----------
arg
Identify the data: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: an ``IMAGECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
coord : { 'logical', 'image', 'physical', 'world', 'wcs' }, optional
Ensure that the image contains the given coordinate system.
dstype : optional
The image class to use. The default is `DataIMG`.
Returns
-------
img
The class of the returned object is controlled by the
``dstype`` parameter.
Raises
------
sherpa.utils.err.DataErr
If the image does not contain the requested coordinate
system.
See Also
--------
load_image : Load an image as a data set.
set_data : Set a data set.
Examples
--------
>>> img1 = unpack_img("img.fits")
>>> set_data(img1)
>>> img = unpack_img('img.fits', 'physical')
Read in an image using Crates:
>>> cr = pycrates.read_file('broad.img')
>>> idata = unpack_img(cr)
Read in an image using AstroPy:
>>> hdus = astropy.io.fits.open('broad.img')
>>> idata = unpack_img(hdus)
"""
return sherpa.astro.io.read_image(arg, coord, dstype)
[docs] def load_image(self, id, arg=None, coord='logical',
dstype=sherpa.astro.data.DataIMG):
"""Load an image as a data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
arg
Identify the image data: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: an ``IMAGECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
coord : { 'logical', 'image', 'physical', 'world', 'wcs' }
The coordinate system to use. The 'image' option is the
same as 'logical', and 'wcs' the same as 'world'.
dstype : optional
The data class to use. The default is `DataIMG`.
See Also
--------
load_arrays : Create a data set from array values.
load_ascii : Load an ASCII file as a data set.
load_table : Load a FITS binary file as a data set.
set_coord : Set the coordinate system to use for image analysis.
set_data : Set a data set.
unpack_image : Create an image data structure.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `arg` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `arg` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Load the image from the file "img.fits" into the default data
set:
>>> load_image('img.fits')
Set the 'bg' data set to the contents of the file
"img_bg.fits":
>>> load_image('bg', 'img_bg.fits')
"""
if arg is None:
id, arg = arg, id
self.set_data(id, self.unpack_image(arg, coord, dstype))
# DOC-TODO: what does this return when given a PHA2 file?
[docs] def unpack_pha(self, arg, use_errors=False):
"""Create a PHA data structure.
Any instrument or background data sets referenced in the
header of the PHA file - e.g. with the ANCRFILE, RESPFILE,
and BACKFILE keywords - will also be loaded.
Parameters
----------
arg
Identify the PHA file: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``TABLECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
use_errors : bool, optional
If ``True`` then the statistical errors are taken from the
input data, rather than calculated by Sherpa from the
count values. The default is ``False``.
Returns
-------
pha : a `sherpa.astro.data.DataPHA` instance
See Also
--------
load_pha : Load a file as a PHA data set.
pack_pha : Convert a PHA data set into a file structure.
set_data : Set a data set.
Examples
--------
>>> pha1 = unpack_arf("src1.pi")
>>> pha2 = unpack_arf("field.pi")
>>> set_data(1, pha1)
>>> set_bkg(1, pha2)
Read in a PHA file using Crates:
>>> cr = pycrates.read_file("src.fits")
>>> pha = unpack_pha(cr)
Read in a PHA file using AstroPy:
>>> hdus = astropy.io.fits.open("src.fits")
>>> pha = unpack_pha(hdus)
"""
use_errors = sherpa.utils.bool_cast(use_errors)
return sherpa.astro.io.read_pha(arg, use_errors)
# DOC-TODO: what does this return when given a PHA2 file?
[docs] def unpack_bkg(self, arg, use_errors=False):
"""Create a PHA data structure for a background data set.
Any instrument information referenced in the header of the PHA
file - e.g. with the ANCRFILE and RESPFILE, keywords - will
also be loaded. Unlike `unpack_pha`, background files will not
be loaded.
Parameters
----------
arg
Identify the PHA file: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``TABLECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
use_errors : bool, optional
If ``True`` then the statistical errors are taken from the
input data, rather than calculated by Sherpa from the
count values. The default is ``False``.
Returns
-------
pha : a `sherpa.astro.data.DataPHA` instance
See Also
--------
load_bkg : Load the background from a file and add it to a PHA data set.
set_bkg : Set the background for a data set.
Examples
--------
>>> pha1 = unpack_arf("src1.pi")
>>> pha2 = unpack_bkg("field.pi")
>>> set_data(1, pha1)
>>> set_bkg(1, pha2)
Read in a PHA file using Crates:
>>> cr = pycrates.read_file("bg.fits")
>>> pha = unpack_pha(cr)
Read in a PHA file using AstroPy:
>>> hdus = astropy.io.fits.open("bg.fits")
>>> pha = unpack_pha(hdus)
"""
use_errors = sherpa.utils.bool_cast(use_errors)
return sherpa.astro.io.read_pha(arg, use_errors, True)
# DOC-TODO: how best to include datastack support?
[docs] def load_pha(self, id, arg=None, use_errors=False):
"""Load a PHA data set.
This will load the PHA data and any related information, such
as ARF, RMF, and background. The background is loaded but
*not* subtracted. Any grouping information in the file will be
applied to the data. The quality information is read in, but
*not* automatically applied. See `subtract` and `ignore_bad`.
The standard behavior is to create a single data set, but
multiple data sets can be loaded with this command, as
described in the `sherpa.astro.datastack` module.
.. versionchanged:: 4.12.2
The id argument is now used to define the first identifier
when loading in a PHA2 file (previously they always used
the range 1 to number of files).
Parameters
----------
id : int or str, optional
The identifier for the data set to use. For PHA2 files,
that is those that contain multiple datasets, the id value
indicates the first dataset: if it is an integer then the
numbering starts at id, and if a string then a suffix of 1
to n is added. If not given then the default identifier is
used, as returned by `get_default_id`.
arg
Identify the data to read: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``PHACrateDataset`` for crates, as used by
CIAO, or a list of AstroPy HDU objects.
use_errors : bool, optional
If ``True`` then the statistical errors are taken from the
input data, rather than calculated by Sherpa from the
count values. The default is ``False``.
See Also
--------
ignore_bad : Exclude channels marked as bad in a PHA data set.
load_arf : Load an ARF from a file and add it to a PHA data set.
load_bkg : Load the background from a file and add it to a PHA data set.
load_rmf : Load a RMF from a file and add it to a PHA data set.
pack_pha : Convert a PHA data set into a file structure.
save_pha : Save a PHA data set to a file.
subtract : Subtract the background estimate from a data set.
unpack_pha : Create a PHA data structure.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `arg` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `arg` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
ARF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
Load the PHA file 'src.pi' into the default data set, and
automatically load the ARF, RMF, and background from the files
pointed to by the ANCRFILE, RESPFILE, and BACKFILE keywords in
the file. The background is then subtracted and any 'bad
quality' bins are removed:
>>> load_pha('src.pi')
read ARF file src.arf
read RMF file src.rmf
read background file src_bkg.pi
>>> subtract()
>>> ignore_bad()
Load two files into data sets 'src' and 'bg':
>>> load_pha('src', 'x1.fits')
>>> load_pha('bg', 'x2.fits')
If a type II PHA data set is loaded, then multiple data sets
will be created, one for each order. The default behavior is
to use the dataset identifiers 1 to the number of files.
>>> clean()
>>> load_pha('src.pha2')
>>> list_data_ids()
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
If given an identifier as the first argument then this is used
to start the numbering scheme for PHA2 files. If id is an
integer then the numbers go from id:
>>> clean()
>>> load_pha(20, 'src.pha2')
>>> list_data_ids()
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]
If the id is a string then the identifier is formed by adding
the number of the dataset (starting at 1) to the end of
id. Note that the `list_data_ids` routine does not guarantee
an ordering to the output (as shown below):
>>> clean()
>>> load_pha('x', 'src.pha2')
>>> list_data_ids()
['x1', 'x10', 'x11', 'x12', 'x2', 'x3', 'x4', 'x5', 'x6',
'x7', 'x8', 'x9']
Create the data set from the data read in by Crates:
>>> pha = pycrates.read_pha('src.pi')
>>> load_pha(pha)
read ARF file src.arf
read RMF file src.rmf
read background file src_bkg.pi
Create the data set from the data read in by AstroPy:
>>> hdus = astropy.io.fits.open('src.pi')
>>> load_pha(hdus)
read ARF file src.arf
read RMF file src.rmf
read background file src_bkg.pi
The default behavior is to calculate the errors based on the
counts values and the choice of statistic -
e.g. ``chi2gehrels`` or ``chi2datavar`` - but the statistical
errors from the input file can be used instead by setting
``use_errors`` to ``True``:
>>> load_pha('source.fits', use_errors=True)
"""
if arg is None:
id, arg = arg, id
phasets = self.unpack_pha(arg, use_errors)
self._load_data(id, phasets)
def _get_pha_data(self, id):
"""Ensure the dataset is a PHA"""
data = self.get_data(id)
if not isinstance(data, sherpa.astro.data.DataPHA):
raise ArgumentErr('nopha', self._fix_id(id))
return data
def _get_img_data(self, id):
"""Ensure the dataset is an image"""
data = self.get_data(id)
if not isinstance(data, sherpa.astro.data.DataIMG):
raise ArgumentErr('noimg', self._fix_id(id))
return data
# def _read_error(self, filename, *args, **kwargs):
# err = None
# try:
# err = sherpa.astro.io.backend.get_ascii_data(filename, *args,
# **kwargs)[1].pop()
# except:
# try:
# err = sherpa.astro.io.backend.get_table_data(filename, *args,
# **kwargs)[1].pop()
# except:
# try:
# err = sherpa.astro.io.read_image(filename, *args, **kwargs)
# err = err.get_dep()
# except:
# raise
# return err
# DOC-NOTE: also in sherpa.utils
# DOC-TODO: does ncols make sense here? (have removed for now)
#
[docs] def load_filter(self, id, filename=None, bkg_id=None, ignore=False,
ncols=2, *args, **kwargs):
# pylint: disable=W1113
"""Load the filter array from a file and add to a data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file that contains the filter
information. This file can be a FITS table or an ASCII
file. Selection of the relevant column depends on the I/O
library in use (Crates or AstroPy).
bkg_id : int or str, optional
Set if the filter array should be associated with the
background associated with the data set.
ignore : bool, optional
If ``False`` (the default) then include bins with a non-zero
filter value, otherwise exclude these bins.
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
sep : str, optional
The separator character. The default is ``' '``.
comment : str, optional
The comment character. The default is ``'#'``.
See Also
--------
get_filter : Return the filter expression for a data set.
ignore : Exclude data from the fit.
notice : Include data in the fit.
save_filter : Save the filter array to a file.
set_filter : Set the filter array of a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Read in the first column of the file and apply it to the
default data set:
>>> load_filter('filt.dat')
Select the FILTER column of the file:
>>> load_filter(2, 'filt.dat', colkeys=['FILTER'])
When using Crates as the I/O library, the above can
also be written as
>>> load_filter(2, 'filt.dat[cols filter]')
Read in a filter for an image. The image must match the size
of the data and, as ``ignore=True``, pixels with a non-zero
value are excluded (rather than included):
>>> load_filter('img', 'filt.img', ignore=True)
"""
if filename is None:
id, filename = filename, id
self.set_filter(id, self._read_user_model(filename, *args, **kwargs)[1],
bkg_id=bkg_id, ignore=ignore)
# DOC-TODO: does ncols make sense here? (have removed for now)
# DOC-TODO: prob. needs a review as the existing ahelp documentation
# talks about 2 cols, but experimentation suggests 1 col.
#
[docs] def load_grouping(self, id, filename=None, bkg_id=None, *args, **kwargs):
# pylint: disable=W1113
"""Load the grouping scheme from a file and add to a PHA data set.
This function sets the grouping column but does not
automatically group the data, since the quality array may also
need updating. The `group` function will apply the grouping
information.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file that contains the grouping
information. This file can be a FITS table or an ASCII
file. Selection of the relevant column depends on the I/O
library in use (Crates or AstroPy).
bkg_id : int or str, optional
Set if the grouping scheme should be associated with the
background associated with the data set.
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
sep : str, optional
The separator character. The default is ``' '``.
comment : str, optional
The comment character. The default is ``'#'``.
See Also
--------
get_grouping : Return the gouping array for a PHA data set.
group : Turn on the grouping for a PHA data set.
load_quality : Load the quality array from a file and add to a PHA data set.
save_grouping : Save the grouping scheme to a file.
set_grouping : Apply a set of grouping flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
There is no check made to see if the grouping array contains
valid data.
Examples
--------
When using Crates as the I/O library, select the grouping
column from the file 'src.pi', and use it to set the
values in the default data set:
>>> load_grouping('src.pi[cols grouping]')
Use the ``colkeys`` option to define the column in the input
file:
>>> load_grouping('src.pi', colkeys=['grouping'])
Load the first column in 'grp.dat' and use it to populate
the grouping array of the data set called 'core'.
>>> load_grouping('core', 'grp.dat')
Use `group_counts` to calculate a grouping scheme for the
data set labelled 'src1', save this scheme to the file
'grp.dat', and then load this scheme in for data set
'src2'.
>>> group_counts('src1', 10)
>>> save_grouping('src1', 'grp.dat')
>>> load_grouping('src2', 'grp.dat', colkeys=['groups'])
"""
if filename is None:
id, filename = filename, id
grouping = self._read_user_model(filename, *args, **kwargs)[1]
self.set_grouping(id, grouping, bkg_id=bkg_id)
[docs] def load_quality(self, id, filename=None, bkg_id=None, *args, **kwargs):
# pylint: disable=W1113
"""Load the quality array from a file and add to a PHA data set.
This function sets the quality column but does not
automatically ignore any columns marked as "bad". Use the
`ignore_bad` function to apply the new quality information.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file that contains the quality
information. This file can be a FITS table or an ASCII
file. Selection of the relevant column depends on the I/O
library in use (Crates or AstroPy).
bkg_id : int or str, optional
Set if the quality array should be associated with the
background associated with the data set.
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
sep : str, optional
The separator character. The default is ``' '``.
comment : str, optional
The comment character. The default is ``'#'``.
See Also
--------
get_quality : Return the quality array for a PHA data set.
ignore_bad : Exclude channels marked as bad in a PHA data set.
load_grouping : Load the grouping scheme from a file and add to a PHA data set.
save_quality: Save the quality array to a file.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
There is no check made to see if the quality array contains
valid data.
Examples
--------
When using Crates as the I/O library, select the quality
column from the file 'src.pi', and use it to set the
values in the default data set:
>>> load_quality('src.pi[cols quality]')
Use the ``colkeys`` option to define the column in the input
file:
>>> load_quality('src.pi', colkeys=['quality'])
Load the first column in 'grp.dat' and use it to populate
the quality array of the data set called 'core'.
>>> load_quality('core', 'grp.dat')
"""
if filename is None:
id, filename = filename, id
self.set_quality(id,
self._read_user_model(filename, *args, **kwargs)[1], bkg_id=bkg_id)
[docs] def set_filter(self, id, val=None, bkg_id=None, ignore=False):
"""Set the filter array of a data set.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
val : array
The array of filter values (``0`` or ``1``). The size should
match the array returned by `get_dep`.
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
ignore : bool, optional
If ``False`` (the default) then include bins with a non-zero
filter value, otherwise exclude these bins.
See Also
--------
get_dep : Return the dependent axis of a data set.
get_filter : Return the filter expression for a data set.
ignore : Exclude data from the fit.
load_filter : Load the filter array from a file and add to a data set.
notice : Include data in the fit.
save_filter : Save the filter array to a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `val` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `val` parameters,
respectively.
Examples
--------
Ignore those bins with a value less 20.
>>> d = get_dep()
>>> set_filter(d >= 20)
"""
if val is None:
val, id = id, val
filter = numpy.asarray(val, dtype=numpy.bool_)
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
if numpy.iterable(d.mask):
if len(d.mask) == len(filter):
if not ignore:
d.mask |= filter
else:
d.mask &= ~filter
else:
raise DataErr('mismatch', len(d.mask), len(filter))
else:
if len(d.get_y(False)) == len(filter):
if not ignore:
d.mask = filter
else:
d.mask = ~filter
else:
raise DataErr('mismatch', len(d.get_y(False)), len(filter))
# DOC-NOTE: also in sherpa.utils
# DOC-TODO: does ncols make sense here? (have removed for now)
[docs] def load_staterror(self, id, filename=None, bkg_id=None, *args, **kwargs):
# pylint: disable=W1113
"""Load the statistical errors from a file.
Read in a column or image from a file and use the values
as the statistical errors for a data set. This over rides
the errors calculated by any statistic, such as
``chi2gehrels`` or ``chi2datavar``.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to read in. Supported formats depends
on the I/O library in use (Crates or AstroPy) and the
type of data set (e.g. 1D or 2D).
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
sep : str, optional
The separator character. The default is ``' '``.
comment : str, optional
The comment character. The default is ``'#'``.
See Also
--------
get_staterror : Return the statistical error on the dependent axis of a data set.
load_syserror : Load the systematic errors from a file.
set_staterror : Set the statistical errors on the dependent axis of a data set.
set_stat : Set the statistical method.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Read in the first column from 'tbl.dat':
>>> load_staterror('tbl.dat')
Use the column labelled 'col3'
>>> load_staterror('tbl.dat', colkeys=['col3'])
When using the Crates I/O library, the file name can include
CIAO Data Model syntax, such as column selection:
>>> load_staterror('tbl.dat[cols col3]')
Read in the first column from the file 'errors.fits' as the
statistical errors for the 'core' data set:
>>> load_staterror('core', 'errors.fits')
The data set labelled 'img' is loaded from the file
'image.fits' and the statistical errors from 'err.fits'.
The dimensions of the two images must be the same.
>>> load_image('img', 'image.fits')
>>> load_staterror('img', 'err.fits')
"""
if filename is None:
id, filename = filename, id
self.set_staterror(id,
self._read_user_model(filename, *args, **kwargs)[1], bkg_id=bkg_id)
# DOC-NOTE: also in sherpa.utils
# DOC-NOTE: is ncols really 2 here? Does it make sense?
[docs] def load_syserror(self, id, filename=None, bkg_id=None, *args, **kwargs):
# pylint: disable=W1113
"""Load the systematic errors from a file.
Read in a column or image from a file and use the values
as the systematic errors for a data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to read in. Supported formats depends
on the I/O library in use (Crates or AstroPy) and the
type of data set (e.g. 1D or 2D).
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
ncols : int, optional
The number of columns to read in (the first ``ncols`` columns
in the file).
colkeys : array of str, optional
An array of the column name to read in. The default is
``None``.
sep : str, optional
The separator character. The default is ``' '``.
comment : str, optional
The comment character. The default is ``'#'``.
See Also
--------
get_syserror : Return the systematic error on the dependent axis of a data set.
load_staterror : Load the statistical errors from a file.
set_syserror : Set the systematic errors on the dependent axis of a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Read in the first column from 'tbl.dat':
>>> load_syserror('tbl.dat')
Use the column labelled 'col3'
>>> load_syserror('tbl.dat', colkeys=['col3'])
When using the Crates I/O library, the file name can include
CIAO Data Model syntax, such as column selection:
>>> load_syserror('tbl.dat[cols col3]')
Read in the first column from the file 'errors.fits' as the
systematic errors for the 'core' data set:
>>> load_syserror('core', 'errors.fits')
The data set labelled 'img' is loaded from the file
'image.fits' and the systematic errors from 'syserr.fits'.
The dimensions of the two images must be the same.
>>> load_image('img', 'image.fits')
>>> load_syserror('img', 'syserr.fits')
"""
if filename is None:
id, filename = filename, id
self.set_syserror(id,
self._read_user_model(filename, *args, **kwargs)[1], bkg_id=bkg_id)
# also in sherpa.utils
[docs] def set_dep(self, id, val=None, bkg_id=None):
"""Set the dependent axis of a data set.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
val : array
The array of values for the dependent axis.
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
See Also
--------
dataspace1d : Create the independent axis for a 1D data set.
dataspace2d : Create the independent axis for a 2D data set.
get_dep : Return the dependent axis of a data set.
load_arrays : Create a data set from array values.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `val` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `val` parameters,
respectively.
Examples
--------
Create a 1D data set with values at (0,4), (2,10), (4,12),
(6,8), (8,2), and (10,12):
>>> dataspace1d(0, 10, 2, dstype=Data1D)
>>> set_dep([4, 10, 12, 8, 2, 12])
Set the values for the source and background of the data set
'src':
>>> set_dep('src', y1)
>>> set_dep('src', bg1, bkg_id=1)
"""
if val is None:
val, id = id, val
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
dep = None
if isinstance(d, sherpa.astro.data.DataPHA):
if numpy.iterable(val):
dep = numpy.asarray(val, SherpaFloat)
else:
val = SherpaFloat(val)
dep = numpy.array([val] * len(d.channel))
d.counts = dep
else:
if numpy.iterable(val):
dep = numpy.asarray(val, SherpaFloat)
else:
val = SherpaFloat(val)
dep = numpy.array([val] * len(d.get_indep()[0]))
d.y = dep
set_counts = set_dep
# DOC-NOTE: also in sherpa.utils
[docs] def set_staterror(self, id, val=None, fractional=False, bkg_id=None):
"""Set the statistical errors on the dependent axis of a data set.
These values over-ride the errors calculated by any statistic,
such as ``chi2gehrels`` or ``chi2datavar``.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
val : array or scalar
The systematic error.
fractional : bool, optional
If ``False`` (the default value), then the `val` parameter is
the absolute value, otherwise the `val` parameter
represents the fractional error, so the absolute value is
calculated as ``get_dep() * val`` (and `val` must be
a scalar).
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
See Also
--------
load_staterror : Load the statistical errors from a file.
load_syserror : Load the systematic errors from a file.
set_syserror : Set the systematic errors on the dependent axis of a data set.
get_error : Return the errors on the dependent axis of a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `val` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `val` parameters,
respectively.
Examples
--------
Set the statistical error for the default data set to the value
in ``dys`` (a scalar or an array):
>>> set_staterror(dys)
Set the statistical error on the 'core' data set to be 5% of
the data values:
>>> set_staterror('core', 0.05, fractional=True)
"""
if val is None:
val, id = id, val
err = None
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
fractional = sherpa.utils.bool_cast(fractional)
if numpy.iterable(val):
err = numpy.asarray(val, SherpaFloat)
elif val is not None:
val = SherpaFloat(val)
if fractional:
err = val * d.get_dep()
else:
err = numpy.array([val] * len(d.get_dep()))
d.staterror = err
# DOC-NOTE: also in sherpa.utils
[docs] def set_syserror(self, id, val=None, fractional=False, bkg_id=None):
"""Set the systematic errors on the dependent axis of a data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
val : array or scalar
The systematic error.
fractional : bool, optional
If ``False`` (the default value), then the `val` parameter is
the absolute value, otherwise the `val` parameter
represents the fractional error, so the absolute value is
calculated as ``get_dep() * val`` (and `val` must be
a scalar).
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
See Also
--------
load_staterror : Set the statistical errors on the dependent axis of a data set.
load_syserror : Set the systematic errors on the dependent axis of a data set.
set_staterror : Set the statistical errors on the dependent axis of a data set.
get_error : Return the errors on the dependent axis of a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `val` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `val` parameters,
respectively.
Examples
--------
Set the systematic error for the default data set to the value
in ``dys`` (a scalar or an array):
>>> set_syserror(dys)
Set the systematic error on the 'core' data set to be 5% of
the data values:
>>> set_syserror('core', 0.05, fractional=True)
"""
if val is None:
val, id = id, val
err = None
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
fractional = sherpa.utils.bool_cast(fractional)
if numpy.iterable(val):
err = numpy.asarray(val, SherpaFloat)
elif val is not None:
val = SherpaFloat(val)
if fractional:
err = val * d.get_dep()
else:
err = numpy.array([val] * len(d.get_dep()))
d.syserror = err
[docs] def set_exposure(self, id, exptime=None, bkg_id=None):
"""Change the exposure time of a PHA data set.
The exposure time of a PHA data set is taken from the
``EXPOSURE`` keyword in its header, but it can be changed
once the file has been loaded.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
exptime : num
The exposure time, in seconds.
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
See Also
--------
get_exposure : Return the exposure time of a PHA data set.
set_areascal : Change the fractional area factor of a PHA data set.
set_backscal : Change the area scaling of a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `exptime` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `exptime` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Increase the exposure time of the default data set by 5 per
cent.
>>> etime = get_exposure()
>>> set_exposure(etime * 1.05)
Use the EXPOSURE value from the ARF, rather than the
value from the PHA file, for data set 2:
>>> set_exposure(2, get_arf(2).exposure)
Set the exposure time of the second background component
of the 'jet' data set.
>>> set_exposure('jet', 12324.45, bkg_id=2)
"""
if exptime is None:
exptime, id = id, exptime
if exptime is not None:
exptime = SherpaFloat(exptime)
if bkg_id is not None:
self.get_bkg(id, bkg_id).exposure = exptime
else:
self._get_pha_data(id).exposure = exptime
[docs] def set_backscal(self, id, backscale=None, bkg_id=None):
"""Change the area scaling of a PHA data set.
The area scaling factor of a PHA data set is taken from the
BACKSCAL keyword or column, but it can be changed once the
file has been loaded.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
backscale : number or array
The scaling factor.
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
See Also
--------
get_backscal : Return the area scaling of a PHA data set.
set_areascal : Change the fractional area factor of a PHA data set.
set_exposure : Change the exposure time of a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `backscale` parameter. If given two un-named arguments,
then they are interpreted as the `id` and `backscale`
parameters, respectively. The remaining parameters are
expected to be given as named arguments.
"""
if backscale is None:
backscale, id = id, backscale
if numpy.iterable(backscale):
backscale = numpy.asarray(backscale)
elif backscale is not None:
backscale = SherpaFloat(backscale)
if bkg_id is not None:
self.get_bkg(id, bkg_id).backscal = backscale
else:
self._get_pha_data(id).backscal = backscale
# DOC-TODO: the description needs improving.
[docs] def set_areascal(self, id, area=None, bkg_id=None):
"""Change the fractional area factor of a PHA data set.
The area scaling factor of a PHA data set is taken from the
AREASCAL keyword, but it can be changed once the file has been
loaded.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
area : number
The scaling factor.
bkg_id : int or str, optional
Set to identify which background component to set. The
default value (``None``) means that this is for the source
component of the data set.
See Also
--------
get_areascal : Return the fractional area factor of a PHA data set.
set_backscal : Change the area scaling of a PHA data set.
set_exposure : Change the exposure time of a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `area` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `area` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
"""
if area is None:
area, id = id, area
if area is not None:
area = SherpaFloat(area)
if bkg_id is not None:
self.get_bkg(id, bkg_id).areascal = area
else:
self._get_pha_data(id).areascal = area
# DOC-NOTE: also in sherpa.utils, where it does not have
# the bkg_id parameter.
#
[docs] def get_staterror(self, id=None, filter=False, bkg_id=None):
"""Return the statistical error on the dependent axis of a data set.
The function returns the statistical errors on the values
(dependenent axis) of a data set, or its background. These
may have been set explicitly - either when the data set was
created or with a call to `set_staterror` - or as defined by
the chosen fit statistic (such as "chi2gehrels").
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filter : bool, optional
Should the filter attached to the data set be applied to
the return value or not. The default is ``False``.
bkg_id : int or str, optional
Set if the values returned should be from the given
background component, instead of the source data set.
Returns
-------
staterrors : array
The statistical error for each data point. This may be
estimated from the data (e.g. with the ``chi2gehrels``
statistic) or have been set explicitly (`set_staterror`).
For PHA data sets, the return array will match the grouping
scheme applied to the data set. The size of this array
depends on the `filter` argument.
Raises
------
sherpa.utils.err.IdentifierErr
If the data set does not exist.
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.
list_data_ids : List the identifiers for the loaded data sets.
set_staterror : Set the statistical errors on the dependent axis of a data set.
Notes
-----
The default behavior is to not apply any filter defined on the
independent axes to the results, so that the return value is for
all points (or bins) in the data set. Set the `filter` argument
to `True` to apply this filter.
Examples
--------
If not explicitly given, the statistical errors on a data set
may be calculated from the data values (the independent axis),
depending on the chosen statistic:
>>> load_arrays(1, [10, 15, 19], [4, 5, 9])
>>> set_stat('chi2datavar')
>>> get_staterror()
array([ 2. , 2.23606798, 3. ])
>>> set_stat('chi2gehrels')
>>> get_staterror()
array([ 3.17944947, 3.39791576, 4.122499 ])
If the statistical errors are set - either when the data set
is created or with a call to `set_staterror` - then these values
will be used, no matter the statistic:
>>> load_arrays(1, [10, 15, 19], [4, 5, 9], [2, 3, 5])
>>> set_stat('chi2datavar')
>>> get_staterror()
array([2, 3, 5])
>>> set_stat('chi2gehrels')
>>> get_staterror()
array([2, 3, 5])
"""
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
return d.get_staterror(filter, self.get_stat().calc_staterror)
# DOC-NOTE: also in sherpa.utils, where it does not have
# the bkg_id parameter.
#
[docs] def get_syserror(self, id=None, filter=False, bkg_id=None):
"""Return the systematic error on the dependent axis of a data set.
The function returns the systematic errors on the values
(dependenent axis) of a data set, or its background. It is
an error if called on a data set with no systematic errors
(which are set with `set_syserror`).
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filter : bool, optional
Should the filter attached to the data set be applied to
the return value or not. The default is ``False``.
bkg_id : int or str, optional
Set if the values returned should be from the given
background component, instead of the source data set.
Returns
-------
syserrors : array
The systematic error for each data point. The size of this
array depends on the `filter` argument.
Raises
------
sherpa.utils.err.DataErr
If the data set has no systematic errors.
sherpa.utils.err.IdentifierErr
If the data set does not exist.
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.
list_data_ids : List the identifiers for the loaded data sets.
set_syserror : Set the systematic errors on the dependent axis of a data set.
Notes
-----
The default behavior is to not apply any filter defined on the
independent axes to the results, so that the return value is for
all points (or bins) in the data set. Set the `filter` argument
to `True` to apply this filter.
Examples
--------
Return the systematic error for the default data set:
>>> yerr = get_syserror()
Return an array that has been filtered to match the data:
>>> yerr = get_syserror(filter=True)
Return the filtered errors for data set "core":
>>> yerr = get_syserror("core", filter=True)
"""
d = self.get_data(id)
id = self._fix_id(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
err = d.get_syserror(filter)
if err is None or not numpy.iterable(err):
raise DataErr('nosyserr', id)
return err
# DOC-NOTE: also in sherpa.utils, where it does not have
# the bkg_id parameter.
#
[docs] def get_error(self, id=None, filter=False, bkg_id=None):
"""Return the errors on the dependent axis of a data set.
The function returns the total errors (a quadrature addition
of the statistical and systematic errors) on the values
(dependent axis) of a data set or its background. The individual
components can be retrieved with the `get_staterror` and
`get_syserror` functions.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filter : bool, optional
Should the filter attached to the data set be applied to
the return value or not. The default is ``False``.
bkg_id : int or str, optional
Set if the values returned should be from the given
background component, instead of the source data set.
Returns
-------
errors : array
The error for each data point, formed by adding the
statistical and systematic errors in quadrature.
For PHA data sets, the return array will match the grouping
scheme applied to the data set. The size of this array
depends on the `filter` argument.
Raises
------
sherpa.utils.err.IdentifierErr
If the data set does not exist.
See Also
--------
get_dep : Return 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.
list_data_ids : List the identifiers for the loaded data sets.
Notes
-----
The default behavior is to not apply any filter defined on the
independent axes to the results, so that the return value is for
all points (or bins) in the data set. Set the `filter` argument
to `True` to apply this filter.
Examples
--------
Return the error values for the default data set, ignoring any
filter applied to it:
>>> err = get_error()
Ensure that the return values are for the selected (filtered)
points in the default data set (the return array may be smaller
than in the previous example):
>>> err = get_error(filter=True)
Find the errors for the "core" data set and its two background
components:
>>> err = get_error('core', filter=True)
>>> berr1 = get_error('core', bkg_id=1, filter=True)
>>> berr2 = get_error('core', bkg_id=2, filter=True)
"""
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
return d.get_error(filter, self.get_stat().calc_staterror)
# DOC-NOTE: also in sherpa.utils
[docs] def get_indep(self, id=None, filter=False, bkg_id=None):
"""Return the independent axes of a data set.
This function returns the coordinates of each point, or pixel,
in the data set. The `get_axes` function may be be preferred
in some situations.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filter : bool, optional
Should the filter attached to the data set be applied to
the return value or not. The default is ``False``.
bkg_id : int or str, optional
Set if the values returned should be from the given
background component, instead of the source data set.
Returns
-------
axis : tuple of arrays
The independent axis values. These are the values at which
the model is evaluated during fitting. The values returned
depend on the coordinate system in use for the data set (as
set by `set_coord`). For PHA data sets the value returned
is always in channels, whatever the `set_analysis` setting
is, and does not follow any grouping setting for the data
set.
Raises
------
sherpa.utils.err.IdentifierErr
If the data set does not exist.
See Also
--------
get_axes : Return information about the independent axes of a data set.
get_dep : Return the dependent axis of a data set.
list_data_ids : List the identifiers for the loaded data sets.
set_coord : Set the coordinate system to use for image analysis.
Notes
-----
For a two-dimensional image, with size n by m pixels, the
`get_dep` function will return two arrays, each of size n * m,
which contain the coordinate of the center of each pixel. The
`get_axes` function will instead return the coordinates of
each axis separately, i.e. arrays of size n and m.
Examples
--------
For a one-dimensional data set, the X values are returned:
>>> load_arrays(1, [10, 15, 19], [4, 5, 9], Data1D)
>>> get_indep()
(array([10, 15, 19]),)
For a 2D data set the X0 and X1 values are returned:
>>> x0 = [10, 15, 12, 19]
>>> x1 = [12, 14, 10, 17]
>>> y = [4, 5, 9, -2]
>>> load_arrays(2, x0, x1, y, Data2D)
>>> get_indep(2)
(array([10, 15, 12, 19]), array([12, 14, 10, 17]))
For PHA data sets the return value is in channel units:
>>> load_pha('spec', 'src.pi')
>>> set_analysis('spec', 'energy')
>>> (chans,) = get_indep('spec')
>>> chans[0:6]
array([ 1., 2., 3., 4., 5., 6.])
If the ``filter`` flag is set then the return will be limited to
the data that is used in the fit:
>>> notice_id('spec', 0.5, 7)
>>> (nchans,) = get_indep('spec', filter=True)
>>> nchans[0:5]
array([ 35., 36., 37., 38., 39.])
For images the pixel coordinates of each pixel are returned,
as 1D arrays, one value for each pixel:
>>> load_image('img', 'image.fits')
>>> (xvals, yvals) = get_indep('img')
>>> xvals.shape
(65536,)
>>> yvals.shape
(65536,)
>>> xvals[0:5]
array([ 1., 2., 3., 4., 5.])
>>> yvals[0:5]
array([ 1., 1., 1., 1., 1.])
The coordinate system for image axes is determinated by the
`set_coord` setting for the data set:
>>> set_coord('img', 'physical')
>>> (avals, bvals) = get_indep('img')
>>> avals[0:5]
array([ 16.5, 48.5, 80.5, 112.5, 144.5])
"""
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
return d.get_indep(filter=filter)
[docs] def get_axes(self, id=None, bkg_id=None):
"""Return information about the independent axes of a data set.
This function returns the coordinates of each point, or pixel,
in the data set. The `get_indep` function may be be preferred
in some situations.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set if the values returned should be from the given
background component, instead of the source data set.
Returns
-------
axis : tuple of arrays
The independent axis values. The differences to `get_dep`
that this represents the "alternate grid" for the axis. For
PHA data, this is the energy grid (E_MIN and E_MAX). For
image data it is an array for each axis, of the length of
the axis, using the current coordinate system for the data
set.
Raises
------
sherpa.utils.err.IdentifierErr
If the data set does not exist.
See Also
--------
get_indep : Return the independent axis of a data set.
list_data_ids : List the identifiers for the loaded data sets.
Examples
--------
For 1D data sets, the "alternate" view is the same as the
independent axis:
>>> load_arrays(1, [10, 15, 19], [4, 5, 9], Data1D)
>>> get_indep()
array([10, 15, 19])
>>> get_axes()
array([10, 15, 19])
For a PHA data set, the approximate energy grid of the
channels is returned (this is determined by the EBOUNDS
extension of the RMF).
>>> load_pha('core', 'src.pi')
read ARF file src.arf
read RMF file src.rmf
read background file src_bkg.pi
>>> (chans,) = get_indep()
>>> (elo, ehi) = get_axes()
>>> chans[0:5]
array([ 1., 2., 3., 4., 5.])
>>> elo[0:5]
array([ 0.0073, 0.0146, 0.0292, 0.0438, 0.0584])
>>> ehi[0:5]
array([ 0.0146, 0.0292, 0.0438, 0.0584, 0.073 ])
The image has 101 columns by 108 rows. The `get_indep`
function returns one-dimensional arrays, for the full dataset,
whereas `get_axes` returns values for the individual axis:
>>> load_image('img', 'img.fits')
>>> get_data('img').shape
(108, 101)
>>> set_coord('img', 'physical')
>>> (x0, x1) = get_indep('img')
>>> (a0, a1) = get_axes('img')
>>> (x0.size, x1.size)
(10908, 10908)
>>> (a0.size, a1.size)
(101, 108)
>>> np.all(x0[:101] == a0)
True
>>> np.all(x1[::101] == a1)
True
"""
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
if isinstance(d, sherpa.astro.data.DataPHA):
return d._get_ebins(group=False)
if isinstance(d, (sherpa.data.Data2D,
sherpa.astro.data.DataIMG)):
return d.get_axes()
return d.get_indep()
# DOC-NOTE: also in sherpa.utils
[docs] def get_dep(self, id=None, filter=False, bkg_id=None):
"""Return the dependent axis of a data set.
This function returns the data values (the dependent axis)
for each point or pixel in the data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filter : bool, optional
Should the filter attached to the data set be applied to
the return value or not. The default is ``False``.
bkg_id : int or str, optional
Set if the values returned should be from the given
background component, instead of the source data set.
Returns
-------
axis : array
The dependent axis values. The model estimate is compared
to these values during fitting. For PHA data sets, the
return array will match the grouping scheme applied to
the data set. This array is one-dimensional, even for
two dimensional (e.g. image) data.
Raises
------
sherpa.utils.err.IdentifierErr
If the data set does not exist.
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_rate : Return the count rate of a PHA data set.
list_data_ids : List the identifiers for the loaded data sets.
Examples
--------
>>> load_arrays(1, [10, 15, 19], [4, 5, 9], Data1D)
>>> get_dep()
array([4, 5, 9])
>>> x0 = [10, 15, 12, 19]
>>> x1 = [12, 14, 10, 17]
>>> y = [4, 5, 9, -2]
>>> load_arrays(2, x0, x1, y, Data2D)
>>> get_dep(2)
array([4, 5, 9, -2])
If the ``filter`` flag is set then the return will be limited to
the data that is used in the fit:
>>> load_arrays(1, [10, 15, 19], [4, 5, 9])
>>> ignore_id(1, 17, None)
>>> get_dep()
array([4, 5, 9])
>>> get_dep(filter=True)
array([4, 5])
An example with a PHA data set named 'spec':
>>> notice_id('spec', 0.5, 7)
>>> yall = get_dep('spec')
>>> yfilt = get_dep('spec', filter=True)
>>> yall.size
1024
>>> yfilt.size
446
For images, the data is returned as a one-dimensional array:
>>> load_image('img', 'image.fits')
>>> ivals = get_dep('img')
>>> ivals.shape
(65536,)
"""
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
dep = d.get_y(filter)
if isinstance(d, sherpa.astro.data.DataPHA):
old = d._rate
d._rate = False # return predicted counts, not rate for PHA
dep = d.get_y(filter)
d._rate = old
return dep
get_counts = get_dep
[docs] def get_rate(self, id=None, filter=False, bkg_id=None):
"""Return the count rate of a PHA data set.
Return an array of count-rate values for each bin in the
data set. The units of the returned values depends on the
values set by the `set_analysis` rountine for the data
set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filter : bool, optional
Should the filter attached to the data set be applied to
the return value or not. The default is ``False``.
bkg_id : int or str, optional
Set if the rate should be taken from the background
associated with the data set.
Returns
-------
rate : array
The rate array. The output matches the grouping of the data
set. The units are controlled by the `set_analysis` setting
for this data set; that is, the units used in `plot_data`,
except that the `type` argument to `set_analysis` is ignored.
The return array will match the grouping scheme applied to
the data set.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
See Also
--------
get_dep : Return the data for a data set.
ignore : Exclude data from the fit.
notice : Include data in the fit.
plot_data : Plot the data values.
set_analysis : Set the units used when fitting and displaying spectral data.
Examples
--------
Return the count-rate for the default data set. For a PHA
data set, where `set_analysis` has not been called, the return
value will be in units of count/second/keV, and a value for
each group in the data set is returned.
>>> rate = get_rate()
The return value is grouped to match the data, but is not
filtered (with the default `filter` argument). The data
set used here 46 groups in it, but after filtering only has 40
groups, but the call to `get_rate` returns a 46-element array
unless `filter` is explicitly set to `True`:
>>> notice()
>>> get_rate().size
46
>>> ignore(None, 0.5)
>>> ignore(7, None)
>>> get_rate().size
46
>>> get_rate(filter=True).size
40
The rate of data set 2 will be in units of count/s/Angstrom
and only cover the range 20 to 22 Angstroms:
>>> set_analysis(2, 'wave')
>>> notice_id(2, 20, 22)
>>> r2 = get_rate(2, filter=True)
The returned rate is now in units of count/s (the return value
is multiplied by `binwidth^factor`, where `factor` is normally
0):
>>> set_analysis(2, 'wave', factor=1)
>>> r2 = get_rate(2, filter=True)
Return the count rate for the second background component of
data set "grating":
>>> get_rate(id="grating", bkg_id=2)
"""
d = self._get_pha_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
old = d._rate
d._rate = True # return count rate for PHA
rate = d.get_y(filter)
d._rate = old
return rate
# DOC-TODO: how to get the corresponding x bins for this data?
# i.e. what are the X values for these points
[docs] def get_specresp(self, id=None, filter=False, bkg_id=None):
"""Return the effective area values for a PHA data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filter : bool, optional
Should the filter attached to the data set be applied to
the ARF or not. The default is ``False``.
bkg_id : int or str, optional
Set if the ARF should be taken from a background set
associated with the data set.
Returns
-------
arf : array
The effective area values for the data set (or background
component).
Examples
--------
Return the effective-area values for the default data set:
>>> arf = get_specresp()
Return the area for the second background component of the
data set with the id "eclipse":
>>> barf = get_spectresp("eclipse", bkg_id=2)
"""
d = self._get_pha_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
return d.get_specresp(filter)
[docs] def get_exposure(self, id=None, bkg_id=None):
"""Return the exposure time of a PHA data set.
The exposure time of a PHA data set is taken from the
EXPTIME keyword in its header, but it can be changed
once the file has been loaded.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set to identify which background component to use. The
default value (``None``) means that the time is for the
source component of the data set.
Returns
-------
exposure : number
The exposure time, in seconds.
See Also
--------
get_areascal : Return the fractional area factor of a PHA data set.
get_backscal : Return the area scaling of a PHA data set.
set_exposure : Change the exposure time of a PHA data set.
Examples
--------
Return the exposure time for the default data set.
>>> t = get_exposure()
Return the exposure time for the data set with identifier 2:
>>> t2 = get_exposure(2)
Return the exposure time for the first background component
of data set "core":
>>> tbkg = get_exposure('core', bkg_id=1)
"""
if bkg_id is not None:
return self.get_bkg(id, bkg_id).exposure
return self._get_pha_data(id).exposure
[docs] def get_backscal(self, id=None, bkg_id=None):
"""Return the BACKSCAL scaling of a PHA data set.
Return the BACKSCAL setting [1]_ for the source or background
component of a PHA data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set to identify which background component to use. The
default value (``None``) means that the value is for the
source component of the data set.
Returns
-------
backscal : number or ndarray
The BACKSCAL value, which can be a scalar or a 1D array.
See Also
--------
get_areascal : Return the fractional area factor of a PHA data set.
get_bkg_scale : Return the background scaling factor for a PHA data set.
set_backscal : Change the area scaling of a PHA data set.
Notes
-----
The BACKSCAL value can be defined as the ratio of the area of
the source (or background) extraction region in image pixels
to the total number of image pixels. The fact that there is no
ironclad definition for this quantity does not matter so long
as the value for a source dataset and its associated
background dataset are defined in the similar manner, because
only the ratio of source and background BACKSCAL values is
used. It can be a scalar or be an array.
References
----------
.. [1] "The OGIP Spectral File Format", Arnaud, K. & George, I.
http://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Examples
--------
>>> get_backscal()
7.8504301607718007e-06
>>> get_backscal(bkg_id=1)
0.00022745132446289
"""
if bkg_id is not None:
return self.get_bkg(id, bkg_id).backscal
return self._get_pha_data(id).backscal
[docs] def get_bkg_scale(self, id=None, bkg_id=1, units='counts',
group=True, filter=False):
"""Return the background scaling factor for a background data set.
Return the factor applied to the background component to scale
it to match it to the source, either when subtracting the
background (units='counts'), or fitting it simultaneously
(units='rate').
.. versionchanged:: 4.12.2
The bkg_id, counts, group, and filter parameters have been
added and the routine no-longer calculates the average
scaling for all the background components but just for the
given component.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set to identify which background component to use. The
default value is 1.
units : {'counts', 'rate'}, optional
The correction is applied to a model defined as counts, the
default, or a rate. The latter should be used when
calculating the correction factor for adding the background
data to the source aperture.
group : bool, optional
Should the values be grouped to match the data?
filter : bool, optional
Should the values be filtered to match the data?
Returns
-------
ratio : number or array
The scaling factor. The result can vary per channel, in which case
an array is returned.
See Also
--------
get_areascal : Return the fractional area factor of a PHA data set.
get_backscal : Return the area scaling factor for a PHA data set.
set_backscal : Change the area scaling of a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_bkg_full_model : Define the convolved background model expression for a PHA data set.
Notes
-----
The scale factor when units='counts' is::
exp_src * bscale_src * areascal_src /
(exp_bgnd * bscale_bgnd * areascal_ngnd) /
nbkg
where ``exp_x``, ``bscale_x``. and ``areascal_x`` are the
exposure, BACKSCAL, and AREASCAL values for the source
(``x=src``) and background (``x=bgnd``) regions, respectively,
and ``nbkg`` is the number of background datasets associated
with the source aperture. When units='rate', the exposure and
areascal corrections are not included.
Examples
--------
Return the background-scaling factor for the default dataset (this
assumes there's only one background component).
>>> get_bkg_scale()
0.034514770047217924
Return the factor for dataset "pi":
>>> get_bkg_scale('pi')
0.034514770047217924
Calculate the factors for the first two background components
of the default dataset, valid for combining the source
and background models to fit the source aperture:
>>> scale1 = get_bkg_scale(units='rate')
>>> scale2 = get_bkg_scale(units='rate', bkg_id=2)
"""
dset = self._get_pha_data(id)
scale = dset.get_background_scale(bkg_id, units=units,
group=group, filter=filter)
if scale is None:
# TODO: need to add bkg_id?
raise DataErr('nobkg', self._fix_id(id))
return scale
[docs] def get_areascal(self, id=None, bkg_id=None):
"""Return the fractional area factor of a PHA data set.
Return the AREASCAL setting [1]_ for the source or background
component of a PHA data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set to identify which background component to use. The
default value (``None``) means that the value is for the
source component of the data set.
Returns
-------
areascal : number or ndarray
The AREASCAL value, which can be a scalar or a 1D array.
See Also
--------
get_backscal : Return the area scaling of a PHA data set.
set_areascal : Change the fractional area factor of a PHA data set.
Notes
-----
The fractional area scale is normally set to 1, with the ARF used
to scale the model.
References
----------
.. [1] "The OGIP Spectral File Format", Arnaud, K. & George, I.
http://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Examples
--------
Return the AREASCAL value for the default data set:
>>> get_areascal()
Return the AREASCAL value for the first background component
of dataset 2:
>>> get_areascal(id=2, bkg_id=1)
"""
if bkg_id is not None:
return self.get_bkg(id, bkg_id).areascal
return self._get_pha_data(id).areascal
def _save_type(self, objtype, id, filename, bkg_id=None, **kwargs):
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
if isinstance(d, (sherpa.astro.data.DataIMG,
sherpa.astro.data.DataIMGInt,
sherpa.data.Data2D, sherpa.data.Data2DInt)):
backup = d.y
if objtype == 'delchi':
raise AttributeError("save_delchi() does not apply for images")
imgtype = getattr(self, 'get_' + objtype + '_image', None)
if imgtype is None:
raise AttributeError("'get_%s_image()' not found" % objtype)
obj = imgtype(id)
try:
# write out the array using the source dataset,
# include WCS etc.
d.y = obj.y.ravel()
sherpa.astro.io.write_image(filename, d, **kwargs)
finally:
d.y = backup
return
funcname = 'get_'
if bkg_id is not None:
funcname += 'bkg_'
plottype = getattr(self, funcname + objtype + '_plot', None)
if plottype is None:
raise AttributeError("'%s%s_plot()' not found" % (funcname,
objtype))
obj = plottype(id)
if bkg_id is not None:
obj = plottype(id, bkg_id=bkg_id)
args = None
fields = None
# if type(d) in (sherpa.data.Data1DInt, sherpa.astro.data.DataPHA):
# args = [obj.xlo, obj.xhi, obj.y]
# fields = ["XLO", "XHI", str(objtype).upper()]
if isinstance(d, sherpa.astro.data.DataPHA) and \
objtype in ('model', 'source'):
args = [obj.xlo, obj.xhi, obj.y]
fields = ["XLO", "XHI", str(objtype).upper()]
else:
args = [obj.x, obj.y]
fields = ["X", str(objtype).upper()]
sherpa.astro.io.write_arrays(filename, args, fields=fields, **kwargs)
# To fix bug report 13536, save many kinds of data to ASCII by default,
# and let user override if they want FITS (or vice-versa). The new defaults
# as of CIAO 4.6 are:
#
# ascii = False (i.e., write to FITS):
#
# save_pha
# save_image
# save_table
#
# ascii = True (i.e., write to ASCII):
#
# save_arrays
# save_source
# save_model
# save_resid
# save_delchi
# save_filter
# save_staterror
# save_syserror
# save_error
# save_grouping
# save_quality
# save_data
#
# My logic is that in the former group, you are focused on writing to a
# specific kind of file (PHA, image or table) so use FITS by default.
# In the latter group, you are not focused on file type, but on some
# data or model attribute you want to write out and read into some other
# program with ease. ASCII is probably better for that.
# SMD 05/15/13
#
# DOC-NOTE: also in sherpa.utils with a different interface
[docs] def save_arrays(self, filename, args, fields=None, ascii=True,
clobber=False):
"""Write a list of arrays to a file.
Parameters
----------
filename : str
The name of the file to write the array to.
args : array of arrays
The arrays to write out.
fields : array of str
The column names (should match the size of `args`).
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``True``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
This flag controls whether an existing file can be
overwritten (``True``) or if it raises an exception (``False``,
the default setting).
Raises
------
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
save_data : Save the data to a file.
save_image : Save the pixel values of a 2D data set to a file.
save_table : Save a data set to a file as a table.
Examples
--------
Write the x and y columns from the default data set to the
file 'src.dat':
>>> x = get_indep()
>>> y = get_dep()
>>> save_arrays('src.dat', [x, y])
Use the column names "r" and "surbri" for the columns:
>>> save_arrays('prof.fits', [x,y], fields=["r", "surbri"],
... ascii=False, clobber=True)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
sherpa.astro.io.write_arrays(filename, args, fields=fields,
ascii=ascii, clobber=clobber)
# DOC-NOTE: also in sherpa.utils with a different API
[docs] def save_source(self, id, filename=None, bkg_id=None, ascii=False,
clobber=False):
"""Save the model values to a file.
The model is evaluated on the grid of the data set, but does
not include any instrument response (such as a PSF or ARF and
RMF).
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background model should be written out
rather than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``False``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IdentifierErr
If no model has been set for this data set.
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
save_data : Save the data to a file.
save_model : Save the model values to a file.
set_model : Set the source model expression for a data set.
set_full_model : Define the convolved model expression for a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The output file contains the columns ``X`` and ``SOURCE`` for 1D
data sets. The residuals array respects any filter or (for PHA
files), grouping settings.
Examples
--------
Write the model values for the default data set to the file
"model.fits":
>>> save_source('model.fits')
Write the model from the data set 'jet' to the ASCII file
"model.dat":
>>> save_source('jet', "model.dat", ascii=True)
For 2D (image) data sets, the model is written out as an
image:
>>> save_source('img', 'model.img')
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
self._save_type('source', id, filename, ascii=ascii, clobber=clobber,
bkg_id=bkg_id)
# DOC-NOTE: also in sherpa.utils with a different API
[docs] def save_model(self, id, filename=None, bkg_id=None, ascii=False,
clobber=False):
"""Save the model values to a file.
The model is evaluated on the grid of the data set, including
any instrument response (such as a PSF or ARF and RMF).
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background model should be written out
rather than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``False``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IdentifierErr
If no model has been set for this data set.
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
save_data : Save the data to a file.
save_source : Save the model values to a file.
set_model : Set the source model expression for a data set.
set_full_model : Define the convolved model expression for a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The output file contains the columns ``X`` and ``MODEL`` for 1D
data sets. The residuals array respects any filter or (for PHA
files), grouping settings.
Examples
--------
Write the model values for the default data set to the file
"model.fits":
>>> save_model('model.fits')
Write the model from the data set 'jet' to the ASCII file
"model.dat":
>>> save_model('jet', "model.dat", ascii=True)
For 2D (image) data sets, the model is written out as an
image:
>>> save_model('img', 'model.img')
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
self._save_type('model', id, filename, ascii=ascii, clobber=clobber,
bkg_id=bkg_id)
# DOC-NOTE: also in sherpa.utils with a different API
[docs] def save_resid(self, id, filename=None, bkg_id=None, ascii=False,
clobber=False):
"""Save the residuals (data-model) to a file.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background residuals should be written out
rather than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``False``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IdentifierErr
If no model has been set for this data set.
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
save_data : Save the data to a file.
save_delchi : Save the ratio of residuals (data-model) to error to a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The output file contains the columns ``X`` and ``RESID``. The
residuals array respects any filter or (for PHA files),
grouping settings.
Examples
--------
Write the residuals to the file "resid.fits":
>>> save_resid('resid.fits')
Write the residuals from the data set 'jet' to the
ASCII file "resid.dat":
>>> save_resid('jet', "resid.dat", ascii=True)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
self._save_type('resid', id, filename, ascii=ascii, clobber=clobber,
bkg_id=bkg_id)
# DOC-NOTE: also in sherpa.utils with a different API
[docs] def save_delchi(self, id, filename=None, bkg_id=None, ascii=True,
clobber=False):
"""Save the ratio of residuals (data-model) to error to a file.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background residuals should be written out
rather than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``True``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IdentifierErr
If no model has been set for this data set.
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
save_data : Save the data to a file.
save_resid : Save the residuals (data-model) to a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The output file contains the columns ``X`` and ``DELCHI``. The
residuals array respects any filter or (for PHA files),
grouping settings.
Examples
--------
Write the residuals to the file "delchi.dat":
>>> save_delchi('delchi.dat')
Write the residuals from the data set 'jet' to the
FITS file "delchi.fits":
>>> save_delchi('jet', "delchi.fits", ascii=False)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
self._save_type('delchi', id, filename, ascii=ascii, clobber=clobber,
bkg_id=bkg_id)
# DOC-NOTE: also in sherpa.utils with a different interface
[docs] def save_filter(self, id, filename=None, bkg_id=None, ascii=True,
clobber=False):
"""Save the filter array to a file.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background should be written out rather
than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``True``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.DataErr
If the data set has not been filtered.
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
load_filter : Load the filter array from a file and add to a data set.
save_data : Save the data to a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The output file contains the columns ``X`` and ``FILTER``.
Examples
--------
Write the filter from the default data set as an ASCII file:
>>> save_filter('filt.dat')
Write the filter for data set 'src' to a FITS format file:
>>> save_filter('src', 'filter.fits', ascii=False)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
id = self._fix_id(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
else:
d = self.get_data(id)
# Leave this check as d.mask is False since d.mask need not be a boolean
# and we want different errors if mask is True or False (and leave as
# the iterable check to catch 'd.mask' is True or any other value that
# could cause the following code to fall over).
#
if d.mask is False:
raise DataErr('notmask')
if not numpy.iterable(d.mask):
raise DataErr('nomask', id)
if isinstance(d, sherpa.astro.data.DataPHA):
x = d._get_ebins(group=True)[0]
else:
x = d.get_indep(filter=False)[0]
mask = numpy.asarray(d.mask, int)
self.save_arrays(filename, [x, mask], fields=['X', 'FILTER'],
ascii=ascii, clobber=clobber)
# DOC-NOTE: also in sherpa.utils with a different interface
[docs] def save_staterror(self, id, filename=None, bkg_id=None, ascii=True,
clobber=False):
"""Save the statistical errors to a file.
If the statistical errors have not been set explicitly, then
the values calculated by the statistic - such as ``chi2gehrels``
or ``chi2datavar`` - will be used.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background should be written out rather
than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS
format binary table. The default is ``True``. The
exact format of the output file depends on the
I/O library in use (Crates or AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
load_staterror : Load the statistical errors from a file.
save_error : Save the errors to a file.
save_syserror : Save the systematic errors to a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The output file contains the columns ``X`` and ``STAT_ERR``.
Examples
--------
Write out the statistical errors from the default data set to the
file 'errs.dat'.
>>> save_staterror('errs.dat')
Over-write the file it it already exists, and take the data
from the data set "jet":
>>> save_staterror('jet', 'err.out', clobber=True)
Write the data out in FITS format:
>>> save_staterror('staterr.fits', ascii=False)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
id = self._fix_id(id)
if isinstance(d, sherpa.astro.data.DataPHA):
x = d._get_ebins(group=True)[0]
else:
x = d.get_indep(filter=False)[0]
err = self.get_staterror(id, filter=False, bkg_id=bkg_id)
self.save_arrays(filename, [x, err], fields=['X', 'STAT_ERR'],
ascii=ascii, clobber=clobber)
# DOC-NOTE: also in sherpa.utils with a different interface
[docs] def save_syserror(self, id, filename=None, bkg_id=None, ascii=True,
clobber=False):
"""Save the systematic errors to a file.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background should be written out rather
than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS
format binary table. The default is ``True``. The
exact format of the output file depends on the
I/O library in use (Crates or AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If the data set does not contain any systematic errors.
sherpa.utils.err.DataErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
load_syserror : Load the systematic errors from a file.
save_error : Save the errors to a file.
save_staterror : Save the statistical errors to a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The output file contains the columns ``X`` and ``SYS_ERR``.
Examples
--------
Write out the systematic errors from the default data set to the
file 'errs.dat'.
>>> save_syserror('errs.dat')
Over-write the file it it already exists, and take the data
from the data set "jet":
>>> save_syserror('jet', 'err.out', clobber=True)
Write the data out in FITS format:
>>> save_syserror('syserr.fits', ascii=False)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
id = self._fix_id(id)
if isinstance(d, sherpa.astro.data.DataPHA):
x = d._get_ebins(group=True)[0]
else:
x = d.get_indep(filter=False)[0]
err = self.get_syserror(id, filter=False, bkg_id=bkg_id)
self.save_arrays(filename, [x, err], fields=['X', 'SYS_ERR'],
ascii=ascii, clobber=clobber)
# DOC-NOTE: also in sherpa.utils with a different interface
[docs] def save_error(self, id, filename=None, bkg_id=None, ascii=True,
clobber=False):
"""Save the errors to a file.
The total errors for a data set are the quadrature combination
of the statistical and systematic errors. The systematic
errors can be 0. If the statistical errors have not been set
explicitly, then the values calculated by the statistic - such
as ``chi2gehrels`` or ``chi2datavar`` - will be used.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background should be written out rather
than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS
format binary table. The default is ``True``. The
exact format of the output file depends on the
I/O library in use (Crates or AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
get_error : Return the errors on the dependent axis of a data set.
load_staterror : Load the statistical errors from a file.
load_syserror : Load the systematic errors from a file.
save_data : Save the data to a file.
save_staterror : Save the statistical errors to a file.
save_syserror : Save the systematic errors to a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The output file contains the columns ``X`` and ``ERR``.
Examples
--------
Write out the errors from the default data set to the file
'errs.dat'.
>>> save_error('errs.dat')
Over-write the file it it already exists, and take the data
from the data set "jet":
>>> save_error('jet', 'err.out', clobber=True)
Write the data out in FITS format:
>>> save_error('err.fits', ascii=False)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
d = self.get_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
id = self._fix_id(id)
if isinstance(d, sherpa.astro.data.DataPHA):
x = d._get_ebins(group=True)[0]
else:
x = d.get_indep(filter=False)[0]
err = self.get_error(id, filter=False, bkg_id=bkg_id)
self.save_arrays(filename, [x, err], fields=['X', 'ERR'],
ascii=ascii, clobber=clobber)
[docs] def save_pha(self, id, filename=None, bkg_id=None, ascii=False,
clobber=False):
"""Save a PHA data set to a file.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the background should be written out rather
than the source.
ascii : bool, optional
If ``False`` then the data is written as a FITS
format binary table. The default is ``True``. The
exact format of the output file depends on the
I/O library in use (Crates or AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
load_pha : Load a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Write out the PHA data from the default data set to the
file 'src.pi':
>>> save_pha('src.pi')
Over-write the file it it already exists, and take the data
from the data set "jet":
>>> save_pha('jet', 'out.pi', clobber=True)
Write the data out as an ASCII file:
>>> save_pha('pi.dat', ascii=True)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
d = self._get_pha_data(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
sherpa.astro.io.write_pha(filename, d, ascii=ascii, clobber=clobber)
[docs] def save_grouping(self, id, filename=None, bkg_id=None, ascii=True, clobber=False):
"""Save the grouping scheme to a file.
The output is a two-column file, containing the channel and
grouping columns from the data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the grouping array should be taken from the
background associated with the data set.
ascii : bool, optional
If ``False`` then the data is written as a FITS
format binary table. The default is ``True``. The
exact format of the output file depends on the
I/O library in use (Crates or AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
get_grouping : Return the gouping array for a PHA data set.
load_quality : Load the quality array from a file and add to a PHA data set.
set_grouping : Apply a set of grouping flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The column names are 'CHANNEL' and 'GROUPS'.
Examples
--------
Save the channel and grouping columns from the default data
set to the file 'group.dat' as an ASCII file:
>>> save_grouping('group.dat')
Over-write the 'grp.fits' file, if it exists, and write
out the grouping data from the 'jet' data set, as a FITS
format file:
>>> save_grouping('jet', 'grp.fits', ascii=False, clobber=True)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
id = self._fix_id(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
else:
d = self._get_pha_data(id)
if d.grouping is None or not numpy.iterable(d.grouping):
raise DataErr('nogrouping', id)
sherpa.astro.io.write_arrays(filename, [d.channel, d.grouping],
fields=['CHANNEL', 'GROUPS'], ascii=ascii,
clobber=clobber)
[docs] def save_quality(self, id, filename=None, bkg_id=None, ascii=True, clobber=False):
"""Save the quality array to a file.
The output is a two-column file, containing the channel and
quality columns from the data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The format
is determined by the `ascii` argument.
bkg_id : int or str, optional
Set if the quality array should be taken from the
background associated with the data set.
ascii : bool, optional
If ``False`` then the data is written as a FITS
format binary table. The default is ``True``. The
exact format of the output file depends on the
I/O library in use (Crates or AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
get_quality : Return the quality array for a PHA data set.
load_quality : Load the quality array from a file and add to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The column names are 'CHANNEL' and 'QUALITY'.
Examples
--------
Save the channel and quality columns from the default data
set to the file 'quality.dat' as an ASCII file:
>>> save_quality('quality.dat')
Over-write the 'qual.fits' file, if it exists, and write
out the quality array from the 'jet' data set, as a FITS
format file:
>>> save_quality('jet', 'qual.fits', ascii=False, clobber=True)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
id = self._fix_id(id)
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
else:
d = self._get_pha_data(id)
if d.quality is None or not numpy.iterable(d.quality):
raise DataErr('noquality', id)
sherpa.astro.io.write_arrays(filename, [d.channel, d.quality],
fields=['CHANNEL', 'QUALITY'], ascii=ascii,
clobber=clobber)
# DOC-TODO: setting ascii=True is not supported for crates
# and in pyfits it seems to just be a 1D array (needs thinking about)
[docs] def save_image(self, id, filename=None, ascii=False, clobber=False):
"""Save the pixel values of a 2D data set to a file.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the data to. The format
is determined by the `ascii` argument.
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``False``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
If the data set does not contain 2D data.
See Also
--------
save_data : Save the data to a file.
save_model : Save the model values to a file.
save_source : Save the model values to a file.
save_table : Save a data set to a file as a table.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Write the pixel values to the file "img.fits":
>>> save_image('resid.fits')
Write the data from the data set 'jet' to the file "jet.img":
>>> save_image('jet', 'jet.img', clobber=True)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
sherpa.astro.io.write_image(filename, self.get_data(id),
ascii=ascii, clobber=clobber)
# DOC-TODO: the output for an image is "excessive"
[docs] def save_table(self, id, filename=None, ascii=False, clobber=False):
"""Save a data set to a file as a table.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the data to. The format
is determined by the `ascii` argument.
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``False``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
If `outfile` is not ``None``, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
save_data : Save the data to a file.
save_image : Save the pixel values of a 2D data set to a file.
save_pha : Save a PHA data set to a file.
save_model : Save the model values to a file.
save_source : Save the model values to a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Write the data set to the file "table.fits":
>>> save_table('table.fits')
Write the data from the data set 'jet' to the file "jet.dat",
as an ASCII file:
>>> save_table('jet', 'jet.dat', ascii=True, clobber=True)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
id = self._fix_id(id)
_check_type(filename, string_types, 'filename', 'a string')
sherpa.astro.io.write_table(filename, self.get_data(id),
ascii=ascii, clobber=clobber)
# DOC-NOTE: also in sherpa.utils
[docs] def save_data(self, id, filename=None, bkg_id=None, ascii=True, clobber=False):
"""Save the data to a file.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
filename : str
The name of the file to write the array to. The data is
written out as an ASCII file.
bkg_id : int or str, optional
Set if the background should be written out rather
than the source (for a PHA data set).
ascii : bool, optional
If ``False`` then the data is written as a FITS format binary
table. The default is ``True``. The exact format of the
output file depends on the I/O library in use (Crates or
AstroPy).
clobber : bool, optional
This flag controls whether an existing file can be
overwritten (``True``) or if it raises an exception (``False``,
the default setting).
Raises
------
sherpa.utils.err.IdentifierErr
If there is no matching data set.
sherpa.utils.err.IOErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
save_arrays : Write a list of arrays to a file.
save_delchi : Save the ratio of residuals (data-model) to error to a file.
save_error : Save the errors to a file.
save_filter : Save the filter array to a file.
save_grouping : Save the grouping scheme to a file.
save_image : Save the pixel values of a 2D data set to a file.
save_pha : Save a PHA data set to a file.
save_quality : Save the quality array to a file.
save_resid : Save the residuals (data-model) to a file.
save_staterror : Save the statistical errors to a file.
save_syserror : Save the statistical errors to a file.
save_table : Save a data set to a file as a table.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `filename` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `filename` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Write the default data set out to the ASCII file 'src.dat':
>>> save_data('src.dat')
Write the 'rprof' data out to the FITS file 'prof.fits',
over-writing it if it already exists:
>>> save_data('rprof', 'prof.fits', clobber=True, ascii=True)
"""
clobber = sherpa.utils.bool_cast(clobber)
ascii = sherpa.utils.bool_cast(ascii)
if filename is None:
id, filename = filename, id
_check_type(filename, string_types, 'filename', 'a string')
if bkg_id is not None:
d = self.get_bkg(id, bkg_id)
else:
d = self.get_data(id)
try:
sherpa.astro.io.write_pha(filename, d, ascii=ascii,
clobber=clobber)
except IOErr:
try:
sherpa.astro.io.write_image(filename, d, ascii=ascii,
clobber=clobber)
except IOErr:
try:
sherpa.astro.io.write_table(filename, d, ascii=ascii,
clobber=clobber)
except IOErr:
# If this errors out then so be it
sherpa.io.write_data(filename, d, clobber=clobber)
[docs] def pack_pha(self, id=None):
"""Convert a PHA data set into a file structure.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
Returns
-------
pha
The return value depends on the I/O library in use.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
See Also
--------
load_pha : Load a file as a PHA data set.
set_data : Set a data set.
unpack_pha : Create a PHA data structure.
"""
return sherpa.astro.io.pack_pha(self._get_pha_data(id))
[docs] def pack_image(self, id=None):
"""Convert a data set into an image structure.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
Returns
-------
img
The return value depends on the I/O library in use.
See Also
--------
load_image : Load an image as a data set.
set_data : Set a data set.
unpack_image : Create an image data structure.
"""
return sherpa.astro.io.pack_image(self.get_data(id))
[docs] def pack_table(self, id=None):
"""Convert a data set into a table structure.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
Returns
-------
tbl
The return value depends on the I/O library in use and the
type of data (such as `Data1D`, `Data2D`).
See Also
--------
load_table : Load a FITS binary file as a data set.
set_data : Set a data set.
unpack_table : Unpack a FITS binary file into a data structure.
"""
return sherpa.astro.io.pack_table(self.get_data(id))
[docs] @staticmethod
def create_arf(elo, ehi, specresp=None, exposure=None, ethresh=None,
name='test-arf'):
"""Create an ARF.
.. versionadded:: 4.10.1
Parameters
----------
elo, ehi : numpy.ndarray
The energy bins (low and high, in keV) for the ARF. It is
assumed that ehi_i > elo_i, elo_j > 0, the energy bins are
either ascending - so elo_i+1 > elo_i - or descending
(elo_i+1 < elo_i), and that there are no overlaps.
specresp : None or array, optional
The spectral response (in cm^2) for the ARF. It is assumed
to be >= 0. If not given a flat response of 1.0 is used.
exposure : number or None, optional
If not None, the exposure of the ARF in seconds.
ethresh : number or None, optional
Passed through to the DataARF call. It controls whether
zero-energy bins are replaced.
name : str, optional
The name of the ARF data set
Returns
-------
arf : DataARF instance
See Also
--------
create_rmf, get_arf, set_arf, unpack_arf
Examples
--------
Create a flat ARF, with a value of 1.0 cm^2 for each bin,
over the energy range 0.1 to 10 keV, with a bin spacing of
0.01 keV.
>>> egrid = np.arange(0.1, 10, 0.01)
>>> arf = create_arf(egrid[:-1], egrid[1:])
Create an ARF that has 10 percent more area than the ARF
from the default data set::
>>> arf1 = get_arf()
>>> elo = arf1.energ_lo
>>> ehi = arf1.energ_hi
>>> y = 1.1 * arf1.specresp
>>> arf2 = create_arf(elo, ehi, y, exposure=arf1.exposure)
"""
return create_arf(elo, ehi, specresp, exposure, ethresh, name)
[docs] @staticmethod
def create_rmf(rmflo, rmfhi, startchan=1, e_min=None, e_max=None,
ethresh=None, fname=None, name='delta-rmf'):
"""Create an RMF.
If fname is set to `None` then this creats a "perfect" RMF,
which has a delta-function response (so each channel uniquely
maps to a single energy bin), otherwise the RMF is taken from
the image data stored in the file pointed to by `fname`.
.. versionadded:: 4.10.1
Parameters
----------
rmflo, rmfhi : array
The energy bins (low and high, in keV) for the RMF.
It is assumed that emfhi_i > rmflo_i, rmflo_j > 0, that the energy
bins are either ascending, so rmflo_i+1 > rmflo_i or descending
(rmflo_i+1 < rmflo_i), and that there are no overlaps.
These correspond to the Elow and Ehigh columns (represented
by the ENERG_LO and ENERG_HI columns of the MATRIX block) of
the OGIP standard.
startchan : int, optional
The starting channel number: expected to be 0 or 1 but this is
not enforced.
e_min, e_max : None or array, optional
The E_MIN and E_MAX columns of the EBOUNDS block of the
RMF.
ethresh : number or None, optional
Passed through to the DataRMF call. It controls whether
zero-energy bins are replaced.
fname : None or str, optional
If None then a "perfect" RMF is generated, otherwise it gives
the name of the two-dimensional image file which stores the
response information (the format of this file matches that
created by the CIAO tool rmfimg [1]_).
name : str, optional
The name of the RMF data set
Returns
-------
rmf : DataRMF instance
See Also
--------
create_arf, get_rmf, set_rmf, unpack_rmf
References
----------
.. [1] http://cxc.harvard.edu/ciao/ahelp/rmfimg.html
"""
if fname is None:
return create_delta_rmf(rmflo, rmfhi, offset=startchan,
e_min=e_min, e_max=e_max, ethresh=ethresh,
name=name)
return create_non_delta_rmf(rmflo, rmfhi, fname,
offset=startchan, e_min=e_min,
e_max=e_max, ethresh=ethresh,
name=name)
[docs] def get_arf(self, id=None, resp_id=None, bkg_id=None):
"""Return the ARF associated with a PHA data set.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
resp_id : int or str, optional
The identifier for the ARF within this data set, if there
are multiple responses.
bkg_id : int or str, optional
Set this to return the given background component.
Returns
-------
arf : a `sherpa.astro.instrument.ARF1D` instance
This is a reference to the ARF, rather than a copy, so that
changing the fields of the object will change the values in
the data set.
See Also
--------
fake_pha : Simulate a PHA data set from a model.
get_response: Return the respone information applied to a PHA data set.
load_arf : Load an ARF from a file and add it to a PHA data set.
load_pha : Load a file as a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_arf : Set the ARF for use by a PHA data set.
set_rmf : Set the RMF for use by a PHA data set.
unpack_arf : Read in an ARF from a file.
Examples
--------
Return the exposure field of the ARF from the default data
set:
>>> get_arf().exposure
Copy the ARF from the default data set to data set 2:
>>> arf1 = get_arf()
>>> set_arf(2, arf1)
Retrieve the ARF associated to the second background
component of the 'core' data set:
>>> bgarf = get_arf('core', 'bkg.arf', bkg_id=2)
Retrieve the ARF and RMF for the default data set and
use them to create a model expression which includes
a power-law component (pbgnd) that is not convolved by the
response:
>>> arf = get_arf()
>>> rmf = get_rmf()
>>> src_expr = xsphabs.abs1 * powlaw1d.psrc
>>> set_full_model(rmf(arf(src_expr)) + powlaw1d.pbgnd)
>>> print(get_model())
"""
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
arf, rmf = data.get_response(resp_id)
if arf is None:
raise IdentifierErr('getitem', 'ARF data set',
data._fix_response_id(resp_id),
'in PHA data set %s has not been set' %
str(self._fix_id(id)))
if isinstance(arf, sherpa.astro.data.DataARF):
arf = sherpa.astro.instrument.ARF1D(arf, data, rmf)
return arf
# DOC-TODO: add an example of a grating/multiple response
[docs] def set_arf(self, id, arf=None, resp_id=None, bkg_id=None):
"""Set the ARF for use by a PHA data set.
Set the effective area curve for a PHA data set, or its
background.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
arf
An ARF, such as returned by `get_arf` or `unpack_arf`.
resp_id : int or str, optional
The identifier for the ARF within this data set, if there
are multiple responses.
bkg_id : int or str, optional
Set this to identify the ARF as being for use with the
background.
See Also
--------
get_arf : Return the ARF associated with a PHA data set.
load_arf : Load an ARF from a file and add it to a PHA data set.
load_pha : Load a file as a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_rmf : Set the RMF for use by a PHA data set.
unpack_arf : Read in an ARF from a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `arf` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `arf` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
If a PHA data set has an associated ARF - either from when the
data was loaded or explicitly with the `set_arf` function -
then the model fit to the data will include the effect of the
ARF when the model is created with `set_model` or
`set_source`. In this case the `get_source` function returns
the user model, and `get_model` the model that is fit to the
data (i.e. it includes any response information; that is the
ARF and RMF, if set). To include the ARF explicitly, use
`set_full_model`.
Examples
--------
Copy the ARF from the default data set to data set 2:
>>> arf1 = get_arf()
>>> set_arf(2, arf1)
Read in an ARF from the file 'bkg.arf' and set it as the
ARF for the background model of data set "core":
>>> arf = unpack_arf('bkg.arf')
>>> set_arf('core', arf, bkg_id=1)
"""
if arf is None:
id, arf = arf, id
# store only the ARF dataset in the PHA response dict
if type(arf) in (sherpa.astro.instrument.ARF1D,):
arf = arf._arf
_check_type(arf, sherpa.astro.data.DataARF, 'arf', 'an ARF data set')
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.set_arf(arf, resp_id)
# Set units of source dataset from channel to energy
if data.units == 'channel':
data._set_initial_quantity()
[docs] def unpack_arf(self, arg):
"""Create an ARF data structure.
Parameters
----------
arg
Identify the ARF: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``TABLECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
Returns
-------
arf : a `sherpa.astro.instrument.ARF1D` instance
See Also
--------
get_arf : Return the ARF associated with a PHA data set.
load_arf : Load an ARF from a file and add it to a PHA data set.
load_bkg_arf : Load an ARF from a file and add it to the background of a PHA data set.
load_multi_arfs : Load multiple ARFs for a PHA data set.
load_pha : Load a file as a PHA data set.
load_rmf : Load a RMF from a file and add it to a PHA data set.
set_full_model : Define the convolved model expression for a data set.
Notes
-----
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
ARF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
>>> arf1 = unpack_arf("arf1.fits")
>>> arf2 = unpack_arf("arf2.fits")
Read in an ARF using Crates:
>>> acr = pycrates.read_file("src.arf")
>>> arf = unpack_arf(acr)
Read in an ARF using AstroPy:
>>> hdus = astropy.io.fits.open("src.arf")
>>> arf = unpack_arf(hdus)
"""
return sherpa.astro.instrument.ARF1D(sherpa.astro.io.read_arf(arg))
# DOC-TODO: add an example of a grating/multiple response
# DOC-TODO: how to describe I/O backend support?
[docs] def load_arf(self, id, arg=None, resp_id=None, bkg_id=None):
"""Load an ARF from a file and add it to a PHA data set.
Load in the effective area curve for a PHA data set, or its
background. The `load_bkg_arf` function can be used for
setting most background ARFs.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
arg
Identify the ARF: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``TABLECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
resp_id : int or str, optional
The identifier for the ARF within this data set, if there
are multiple responses.
bkg_id : int or str, optional
Set this to identify the ARF as being for use with the
background.
See Also
--------
get_arf : Return the ARF associated with a PHA data set.
load_bkg_arf : Load an ARF from a file and add it to the background of a PHA data set.
load_multi_arfs : Load multiple ARFs for a PHA data set.
load_pha : Load a file as a PHA data set.
load_rmf : Load a RMF from a file and add it to a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_arf : Set the ARF for use by a PHA data set.
unpack_arf : Create an ARF data structure.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `arg` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `arg` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
If a PHA data set has an associated ARF - either from when the
data was loaded or explicitly with the `set_arf` function -
then the model fit to the data will include the effect of the
ARF when the model is created with `set_model` or
`set_source`. In this case the `get_source` function returns
the user model, and `get_model` the model that is fit to the
data (i.e. it includes any response information; that is the
ARF and RMF, if set). To include the ARF explicitly, use
`set_full_model`.
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
ARF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
Use the contents of the file 'src.arf' as the ARF for the
default data set.
>>> load_arf('src.arf')
Read in an ARF from the file 'bkg.arf' and set it as the
ARF for the background model of data set "core":
>>> load_arf('core', 'bkg.arf', bkg_id=1)
"""
if arg is None:
id, arg = arg, id
self.set_arf(id, self.unpack_arf(arg), resp_id, bkg_id)
[docs] def get_bkg_arf(self, id=None):
"""Return the background ARF associated with a PHA data set.
This is for the case when there is only one background
component and one background response. If this does not hold,
use `get_arf` and use the ``bkg_id`` and ``resp_id`` arguments.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
Returns
-------
arf : a `sherpa.astro.instrument.ARF1D` instance
This is a reference to the ARF, rather than a copy, so that
changing the fields of the object will change the values in
the data set.
See Also
--------
fake_pha : Simulate a PHA data set from a model.
load_bkg_arf : Load an ARF from a file and add it to the background of a PHA data set.
load_pha : Load a file as a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_arf : Set the ARF for use by a PHA data set.
set_rmf : Set the RMF for use by a PHA data set.
unpack_arf : Read in an ARF from a file.
Examples
--------
Return the exposure field of the ARF from the background of
the default data set:
>>> get_bkg_arf().exposure
Copy the ARF from the default data set to data set 2,
as the first component:
>>> arf1 = get_bkg_arf()
>>> set_arf(2, arf1, bkg_id=1)
"""
bkg_id = self._get_pha_data(id).default_background_id
resp_id = self._get_pha_data(id).primary_response_id
return self.get_arf(id, resp_id, bkg_id)
# DOC-TODO: how to describe I/O backend support?
[docs] def load_bkg_arf(self, id, arg=None):
"""Load an ARF from a file and add it to the background of a
PHA data set.
Load in the ARF to the background of the given data set. It
is only for use when there is only one background component,
and one response, for the source. For multiple backgrounds
or responses, use `load_arf`.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
arg
Identify the ARF: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``TABLECrate`` for crates, as used by CIAO,
or a list of AstroPy HDU objects.
See Also
--------
load_arf : Load an ARF from a file and add it to a PHA data set.
load_bkg_rmf : Load a RMF from a file and add it to the background of a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `arg` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `arg` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
ARF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
Use the contents of the file 'bkg.arf' as the ARF for the
background of the default data set.
>>> load_bkg_arf('bkg.arf')
Set 'core_bkg.arf' as the ARF for the background of data set
'core':
>>> load_bkg_arf('core', 'core_bkg.arf')
"""
if arg is None:
id, arg = arg, id
bkg_id = self._get_pha_data(id).default_background_id
resp_id = self._get_pha_data(id).primary_response_id
self.set_arf(id, self.unpack_arf(arg), resp_id, bkg_id)
[docs] def load_multi_arfs(self, id, filenames, resp_ids=None):
"""Load multiple ARFs for a PHA data set.
A grating observation - such as a Chandra LETGS data set - may
require multiple responses if the detector has insufficient energy
resolution to sort the photons into orders. In this case, the
extracted spectrum will contain the signal from more than one
diffraction orders.
This function lets the multiple ARFs for such a data set be
loaded with one command. The `load_arf` function can instead
be used to load them in individually.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
filenames : iterable of str
An array of file names.
resp_ids : iterable of int or str
The identifiers for the ARF within this data set.
The length should match the filenames argument.
See Also
--------
load_arf : Load an ARF from a file and add it to a PHA data set.
load_multi_rmfs : Load multiple RMFs for a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with two arguments, they are assumed to be
``filenames`` and ``resp_ids``, and three positional arguments
means `id`, ``filenames``, and ``resp_ids``.
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
ARF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
Load three ARFs into the default data set, using response ids of
1, 2, and 3 for the LETG/HRC-S orders 1, 2, and 3 respectively:
>>> arfs = ['leg_p1.arf', 'leg_p2.arf', 'leg_p3.arf']
>>> load_multi_arfs(arfs, [1, 2, 3])
Load in the ARFs to the data set with the identifier
'lowstate':
>>> load_multi_arfs('lowstate', arfs, [1, 2, 3])
"""
# if type(filenames) not in (list, tuple):
# raise ArgumentError('Filenames must be contained in a list')
# if type(resp_ids) not in (list, tuple):
# raise ArgumentError('Response IDs must be contained in a list')
if resp_ids is None:
id, filenames, resp_ids = resp_ids, id, filenames
filenames = list(filenames)
resp_ids = list(resp_ids)
if len(filenames) != len(resp_ids):
raise ArgumentErr('multirsp')
for filename, resp_id in zip(filenames, resp_ids):
self.load_arf(id, filename, resp_id)
[docs] def get_rmf(self, id=None, resp_id=None, bkg_id=None):
"""Return the RMF associated with a PHA data set.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
resp_id : int or str, optional
The identifier for the RMF within this data set, if there
are multiple responses.
bkg_id : int or str, optional
Set this to return the given background component.
Returns
-------
rmf : a `sherpa.astro.instrument.RMF1D` instance
This is a reference to the RMF, rather than a copy, so that
changing the fields of the object will change the values in
the data set.
See Also
--------
fake_pha : Simulate a PHA data set from a model.
get_response: Return the respone information applied to a PHA data set.
load_pha : Load a file as a PHA data set.
load_rmf : Load a RMF from a file and add it to a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_arf : Set the ARF for use by a PHA data set.
set_rmf : Set the RMF for use by a PHA data set.
unpack_rmf : Read in a RMF from a file.
Examples
--------
Copy the RMF from the default data set to data set 2:
>>> rmf1 = get_rmf()
>>> set_rmf(2, rmf1)
Retrieve the RMF associated to the second background
component of the 'core' data set:
>>> bgrmf = get_rmf('core', 'bkg.rmf', bkg_id=2)
Retrieve the ARF and RMF for the default data set and
use them to create a model expression which includes
a power-law component (pbgnd) that is not convolved by the
response:
>>> arf = get_arf()
>>> rmf = get_rmf()
>>> src_expr = xsphabs.abs1 * powlaw1d.psrc
>>> set_full_model(rmf(arf(src_expr)) + powlaw1d.pbgnd)
>>> print(get_model())
"""
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
arf, rmf = data.get_response(resp_id)
if rmf is None:
raise IdentifierErr('getitem', 'RMF data set',
data._fix_response_id(resp_id),
'in PHA data set %s has not been set' %
str(self._fix_id(id)))
if isinstance(rmf, sherpa.astro.data.DataRMF):
rmf = sherpa.astro.instrument.RMF1D(rmf, data, arf)
return rmf
# DOC-TODO: add an example of a grating/multiple response
[docs] def set_rmf(self, id, rmf=None, resp_id=None, bkg_id=None):
"""Set the RMF for use by a PHA data set.
Set the redistribution matrix for a PHA data set, or its
background.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
rmf
An RMF, such as returned by `get_rmf` or `unpack_rmf`.
resp_id : int or str, optional
The identifier for the RMF within this data set, if there
are multiple responses.
bkg_id : int or str, optional
Set this to identify the RMF as being for use with the
background.
See Also
--------
get_rmf : Return the RMF associated with a PHA data set.
load_pha : Load a file as a PHA data set.
load_rmf : Load a RMF from a file and add it to a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_arf : Set the ARF for use by a PHA data set.
unpack_rmf : Create a RMF data structure.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `rmf` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `rmf` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
If a PHA data set has an associated RMF - either from when the
data was loaded or explicitly with the `set_rmf` function -
then the model fit to the data will include the effect of the
RMF when the model is created with `set_model` or
`set_source`. In this case the `get_source` function returns
the user model, and `get_model` the model that is fit to the
data (i.e. it includes any response information; that is the
ARF and RMF, if set). To include the RMF explicitly, use
`set_full_model`.
Examples
--------
Copy the RMF from the default data set to data set 2:
>>> rmf1 = get_rmf()
>>> set_rmf(2, rmf1)
Read in a RMF from the file 'bkg.rmf' and set it as the
RMF for the background model of data set "core":
>>> rmf = unpack_rmf('bkg.rmf')
>>> set_rmf('core', rmf, bkg_id=1)
"""
if rmf is None:
id, rmf = rmf, id
# store only the RMF dataset in the PHA response dict
if type(rmf) in (sherpa.astro.instrument.RMF1D,):
rmf = rmf._rmf
_check_type(rmf, sherpa.astro.data.DataRMF, 'rmf', 'an RMF data set')
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.set_rmf(rmf, resp_id)
# Set units of source dataset from channel to energy
if data.units == 'channel':
data._set_initial_quantity()
[docs] def unpack_rmf(self, arg):
"""Create a RMF data structure.
Parameters
----------
arg
Identify the RMF: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``RMFCrateDataset`` for crates, as used by
CIAO, or a list of AstroPy HDU objects.
Returns
-------
rmf : a `sherpa.astro.instrument.RMF1D` instance
See Also
--------
get_rmf : Return the RMF associated with a PHA data set.
load_arf : Load a RMF from a file and add it to a PHA data set.
load_bkg_rmf : Load a RMF from a file and add it to the background of a PHA data set.
load_multi_rmfs : Load multiple RMFs for a PHA data set.
load_pha : Load a file as a PHA data set.
load_rmf : Load a RMF from a file and add it to a PHA data set.
set_full_model : Define the convolved model expression for a data set.
Notes
-----
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
RMF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
>>> rmf1 = unpack_rmf("rmf1.fits")
>>> rmf2 = unpack_rmf("rmf2.fits")
Read in a RMF using Crates:
>>> acr = pycrates.read_rmf("src.rmf")
>>> rmf = unpack_rmf(acr)
Read in a RMF using AstroPy:
>>> hdus = astropy.io.fits.open("src.rmf")
>>> rmf = unpack_rmf(hdus)
"""
return sherpa.astro.instrument.RMF1D(sherpa.astro.io.read_rmf(arg))
# DOC-TODO: add an example of a grating/multiple response
# DOC-TODO: how to describe I/O backend support?
[docs] def load_rmf(self, id, arg=None, resp_id=None, bkg_id=None):
"""Load a RMF from a file and add it to a PHA data set.
Load in the redistribution matrix function for a PHA data set,
or its background. The `load_bkg_rmf` function can be used for
setting most background RMFs.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
arg
Identify the RMF: a file name, or a data structure
representing the data to use, as used by the I/O
backend in use by Sherpa: a ``RMFCrateDataset`` for
crates, as used by CIAO, or an AstroPy ``HDUList`` object.
resp_id : int or str, optional
The identifier for the RMF within this data set, if there
are multiple responses.
bkg_id : int or str, optional
Set this to identify the RMF as being for use with the
background.
See Also
--------
get_rmf : Return the RMF associated with a PHA data set.
load_bkg_rmf : Load a RMF from a file and add it to the background of a PHA data set.
load_arf : Load an ARF from a file and add it to a PHA data set.
load_multi_rmfs : Load multiple RMFs for a PHA data set.
load_pha : Load a file as a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_rmf : Load a RMF from a file and add it to a PHA data set.
unpack_rmf : Read in a RMF from a file.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `arg` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `arg` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
If a PHA data set has an associated RMF - either from when the
data was loaded or explicitly with the `set_rmf` function -
then the model fit to the data will include the effect of the
RMF when the model is created with `set_model` or
`set_source`. In this case the `get_source` function returns
the user model, and `get_model` the model that is fit to the
data (i.e. it includes any response information; that is the
ARF and RMF, if set). To include the RMF explicitly, use
`set_full_model`.
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
RMF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
Use the contents of the file 'src.rmf' as the RMF for the
default data set.
>>> load_rmf('src.rmf')
Read in a RMF from the file 'bkg.rmf' and set it as the
RMF for the background model of data set "core":
>>> load_rmf('core', 'bkg.rmf', bkg_id=1)
"""
if arg is None:
id, arg = arg, id
self.set_rmf(id, self.unpack_rmf(arg), resp_id, bkg_id)
[docs] def get_bkg_rmf(self, id=None):
"""Return the background RMF associated with a PHA data set.
This is for the case when there is only one background
component and one background response. If this does not hold,
use `get_rmf` and use the ``bkg_id`` and ``resp_id`` arguments.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
Returns
-------
rmf : a `sherpa.astro.instrument.RMF1D` instance
This is a reference to the RMF, rather than a copy, so that
changing the fields of the object will change the values in
the data set.
See Also
--------
fake_pha : Simulate a PHA data set from a model.
load_bkg_rmf : Load a RMF from a file and add it to the background of a PHA data set.
load_pha : Load a file as a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_arf : Set the ARF for use by a PHA data set.
set_rmf : Set the RMF for use by a PHA data set.
unpack_rmf : Read in a RMF from a file.
Examples
--------
Copy the RMF from the default data set to data set 2,
as the first component:
>>> rmf1 = get_bkg_arf()
>>> set_rmf(2, arf1, bkg_id=1)
"""
bkg_id = self._get_pha_data(id).default_background_id
resp_id = self._get_pha_data(id).primary_response_id
return self.get_rmf(id, resp_id, bkg_id)
# DOC-TODO: how to describe I/O backend support?
[docs] def load_bkg_rmf(self, id, arg=None):
"""Load a RMF from a file and add it to the background of a
PHA data set.
Load in the RMF to the background of the given data set. It
is only for use when there is only one background component,
and one response, for the source. For multiple backgrounds
or responses, use `load_rmf`.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
arg
Identify the RMF: a file name, or a data structure
representing the data to use, as used by the I/O
backend in use by Sherpa: a ``RMFCrateDataset`` for
crates, as used by CIAO, or an AstroPy ``HDUList`` object.
See Also
--------
load_rmf : Load a RMF from a file and add it to a PHA data set.
load_bkg_arf : Load an ARF from a file and add it to the background of a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `arg` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `arg` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
RMF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
Use the contents of the file 'bkg.rmf' as the RMF for the
background of the default data set.
>>> load_bkg_rmf('bkg.rmf')
Set 'core_bkg.rmf' as the RMF for the background of data set
'core':
>>> load_bkg_arf('core', 'core_bkg.rmf')
"""
if arg is None:
id, arg = arg, id
bkg_id = self._get_pha_data(id).default_background_id
resp_id = self._get_pha_data(id).primary_response_id
self.set_rmf(id, self.unpack_rmf(arg), resp_id, bkg_id)
[docs] def load_multi_rmfs(self, id, filenames, resp_ids=None):
"""Load multiple RMFs for a PHA data set.
A grating observation - such as a Chandra LETGS data set - may
require multiple responses if the detector has insufficient energy
resolution to sort the photons into orders. In this case, the
extracted spectrum will contain the signal from more than one
diffraction orders.
This function lets the multiple RMFs for such a data set be loaded
with one command. The `load_rmf` function can instead be used
to load them in individually.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
filenames : iterable of str
An array of file names.
resp_ids : iterable of int or str
The identifiers for the RMF within this data set.
The length should match the filenames argument.
See Also
--------
load_rmf : Load a RMF from a file and add it to a PHA data set.
load_multi_arfs : Load multiple ARFs for a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with two arguments, they are assumed to be
``filenames`` and ``resp_ids``, and three positional arguments
means `id`, ``filenames``, and ``resp_ids``.
The `minimum_energy` setting of the `ogip` section of the
Sherpa configuration file determines the behavior when an
RMF with a minimum energy of 0 is read in. The default is
to replace the 0 by the value 1e-10, which will also cause
a warning message to be displayed.
Examples
--------
Load three ARFs into the default data set, using response ids of
1, 2, and 3 for the LETG/HRC-S orders 1, 2, and 3 respectively:
>>> arfs = ['leg_p1.rmf', 'leg_p2.rmf', 'leg_p3.rmf']
>>> load_multi_rmfs(rmfs, [1, 2, 3])
Load in the RMFs to the data set with the identifier
'lowstate':
>>> load_multi_rmfs('lowstate', rmfs, [1, 2, 3])
"""
# if type(filenames) not in (list, tuple):
# raise ArgumentError('Filenames must be contained in a list')
# if type(resp_ids) not in (list, tuple):
# raise ArgumentError('Response IDs must be contained in a list')
if resp_ids is None:
id, filenames, resp_ids = resp_ids, id, filenames
filenames = list(filenames)
resp_ids = list(resp_ids)
if len(filenames) != len(resp_ids):
raise ArgumentErr('multirsp')
for filename, resp_id in zip(filenames, resp_ids):
self.load_rmf(id, filename, resp_id)
[docs] def get_bkg(self, id=None, bkg_id=None):
"""Return the background for a PHA data set.
Function to return the background for a PHA data set.
The object returned by the call can be used to query and
change properties of the background.
Parameters
----------
id : int or str, optional
The data set. If not given then the default
identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
The identifier for this background, which is needed if
there are multiple background estimates for the source.
Returns
-------
data : a sherpa.astro.data.DataPHA object
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
sherpa.utils.err.IdentifierErr
If no data set is associated with this identifier.
See Also
--------
get_data : Return the data set by identifier.
load_bkg : Load the backgreound from a file and add it to a PHA data set.
set_bkg : Set the background for a PHA data set.
Examples
--------
>>> bg = get_bkg()
>>> bg = get_bkg('flare', 2)
"""
data = self._get_pha_data(id)
bkg = data.get_background(bkg_id)
if bkg is None:
raise IdentifierErr('getitem', 'background data set',
data._fix_background_id(bkg_id),
'in PHA data set %s has not been set' %
str(self._fix_id(id)))
return bkg
[docs] def set_bkg(self, id, bkg=None, bkg_id=None):
"""Set the background for a PHA data set.
The background can either be fit with a model - using
`set_bkg_model` - or removed from the data before fitting,
using `subtract`.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
bkg
A PHA data set, such as returned by `get_data` or
`unpack_pha`.
bkg_id : int or str, optional
The identifier for this background, which is needed if
there are multiple background estimates for the source.
See Also
--------
get_bkg : Return the background for a PHA data set.
load_bkg : Load the background from a file and add it to a PHA data set.
load_pha : Load a file as a PHA data set.
set_bkg_model : Set the background model expression for a data set.
subtract : Subtract the background estimate from a data set.
unpack_pha : Create a PHA data structure.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `bkg` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `bkg` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
If the background has no grouping of quality arrays then they
are copied from the source region. If the background has no
response information (ARF or RMF) then the response is copied
from the source region.
Examples
--------
Copy the background from the default data set to data set 2:
>>> bkg1 = get_bkg()
>>> set_bkg(2, bkg1)
Read in the PHA data from the file 'bkg.pi' and set it as the
second background component of data set "core":
>>> bkg = unpack_pha('bkg.pi')
>>> set_bkg('core', bkg, bkg_id=2)
"""
if bkg is None:
id, bkg = bkg, id
data = self._get_pha_data(id)
_check_type(bkg, sherpa.astro.data.DataPHA, 'bkg', 'a PHA data set')
data.set_background(bkg, bkg_id)
[docs] def list_bkg_ids(self, id=None):
"""List all the background identifiers for a data set.
A PHA data set can contain multiple background datasets, each
identified by an integer or string. This function returns a
list of these identifiers for a data set.
Parameters
----------
id : int or str, optional
The data set to query. If not given then the default
identifier is used, as returned by `get_default_id`.
Returns
-------
ids : array of int or str
The identifiers for the backround data sets for the data
set. In many cases this will just be ``[1]``.
See Also
--------
list_response_ids : List all the response identifiers of a data set.
load_bkg : Load the background of a PHA data set.
"""
return list(self._get_pha_data(id)._backgrounds.keys())
[docs] def list_response_ids(self, id=None, bkg_id=None):
"""List all the response identifiers of a data set.
A PHA data set can contain multiple responses, that is,
pairs of ARF and RMF, each of which has an identifier.
This function returns a list of these identifiers
for a data set.
Parameters
----------
id : int or str, optional
The data set to query. If not given then the default
identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Set this to identify the background component to query.
Returns
-------
ids : array of int or str
The identifiers for the response information for the data
set. In many cases this will just be ``[1]``.
See Also
--------
list_bkg_ids : List all the background identifiers for a data set.
load_arf : Load an ARF from a file and add it to a PHA data set.
load_rmf : Load a RMF from a file and add it to a PHA data set.
"""
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
return list(data._responses.keys())
# DOC-TODO: docs need to be added to sherpa.astro.data.set_analysis
# DOC-TODO: should the arguments be renamed to better match optional
# nature of the routine (e.g. can call set_analysis('energy'))?
[docs] def set_analysis(self, id, quantity=None, type='rate', factor=0):
"""Set the units used when fitting and displaying spectral data.
The set_analysis command sets the units for spectral
analysis. Note that in order to change the units of a data set
from 'channel' to 'energy' or 'wavelength', the appropriate
ARF and RMF instrument response files must be loaded for that
data set. The ``type`` and ``factor`` arguments control how
the data is plotted.
Parameters
----------
id : int or str
If only one argument is given then this is taken to be the
quantity argument (in which case, the change is made to
all data sets). If multiple arguments are given then this
is the identifier for the data set to change.
quantity : { 'channel', 'chan', 'bin', 'energy', 'ener', 'wavelength', 'wave' }
The units to use for the analysis.
type : { 'rate', 'counts' }, optional
The units to use on the Y axis of plots. The default
is 'rate'.
factor : int, optional
The Y axis of plots is multiplied by Energy^factor or
Wavelength^factor before display. The default is 0.
Raises
------
sherpa.utils.err.IdentifierErr
If the `id` argument is not recognized.
See Also
--------
get_analysis : Return the analysis setting for a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `quantity` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `quantity` parameters,
respectively.
Examples
--------
Set all loaded data sets to use wavelength for any future
fitting or display.
>>> set_analysis('wave')
Set the data set with an identifier of 2 to use energy
units.
>>> set_analysis(2, 'energy')
Set data set 1 to use channel units. Plots will use a Y
axis of count/bin rather than the default count/s/bin.
>>> set_analysis(1, 'bin', 'counts')
Set data set 1 to use energy units. Plots of this data set
will display keV on the X axis and counts keV (i.e.
counts/keV * keV^2) in the Y axis.
>>> set_analysis(1, 'energy', 'counts', 2)
"""
if quantity is None:
id, quantity = quantity, id
_check_type(quantity, string_types, 'quantity', 'a string')
_check_type(type, string_types, 'type', 'a string')
ids = self.list_data_ids()
if id is not None:
ids = [id]
for id in ids:
self._get_pha_data(id).set_analysis(quantity, type, factor)
[docs] def get_analysis(self, id=None):
"""Return the units used when fitting spectral data.
Parameters
----------
id : int or str, optional
The data set to query. If not given then the default
identifier is used, as returned by `get_default_id`.
Returns
-------
setting : { 'channel', 'energy', 'wavelength' }
The analysis setting for the data set.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the `id` argument is not recognized.
See Also
--------
get_default_id : Return the default data set identifier.
set_analysis : Change the analysis setting.
Examples
--------
Display the analysis setting for the default data set:
>>> print(get_analysis())
Check whether the data set labelled 'SgrA' is using the
wavelength setting:
>>> is_wave = get_analysis('SgrA') == 'wavelength'
"""
return self._get_pha_data(id).get_analysis()
# DOC-TODO: docs need to be added to sherpa.astro.data.set_coord
# DOC-TODO: how best to document the wcssubs support?
[docs] def set_coord(self, id, coord=None):
"""Set the coordinate system to use for image analysis.
The default coordinate system - that is, the mapping between
pixel position and coordinate value, for images (2D data sets)
is 'logical'. This function can change this setting, so that
model parameters can be fit using other systems. This setting
is also used by the `notice2d` and `ignore2d` series of
commands.
Parameters
----------
id : int or str
The data set to change. If not given then the default
identifier is used, as returned by `get_default_id`.
coord : { 'logical', 'image', 'physical', 'world', 'wcs' }
The coordinate system to use. The 'image' option is the
same as 'logical', and 'wcs' the same as 'world'.
See Also
--------
get_coord : Get the coordinate system used for image analysis.
guess : Estimate the parameter values and ranges given the loaded data.
ignore2d : Exclude a spatial region from an image.
notice2d : Include a spatial region of an image.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `coord` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `coord` parameters,
respectively.
Any limits or values already set for model parameters, such as
those made by `guess`, may need to be changed after changing
the coordinate system.
The 'logical' system is one in which the center of the
lower-left pixel has coordinates ``(1, 1)`` and the center of the
top-right pixel has coordinates ``(nx, ny)``, for a ``nx``
(columns) by ``ny`` (rows) pixel image. The pixels have a side
of length 1, so the first pixel covers the range ``x=0.5`` to
``x=1.5`` and ``y=0.5`` to ``y=1.5``.
The 'physical' and 'world' coordinate systems rely on FITS
World Coordinate System (WCS) standard [1]_. The 'physical'
system refers to a linear transformation, with possible
offset, of the 'logical' system. The 'world' system refers to
the mapping to a celestial coordinate system.
References
----------
.. [1] http://fits.gsfc.nasa.gov/fits_wcs.html
Examples
--------
Change the coordinate system of the default data set to
the world system ('wcs' is a synonym for 'world').
>>> set_coord('wcs')
Change the data set with the id of 'm82' to use the
physical coordinate system.
>>> set_coord('m82', 'physical')
"""
if coord is None:
id, coord = coord, id
_check_type(coord, string_types, 'coord', 'a string')
ids = self.list_data_ids()
if id is not None:
ids = [id]
if len(ids) == 0:
raise IdentifierErr('nodatasets')
for id in ids:
self._get_img_data(id).set_coord(coord)
# DOC-TODO: docs need to be added to sherpa.astro.data.get_coord
[docs] def get_coord(self, id=None):
"""Get the coordinate system used for image analysis.
Parameters
----------
id : int or str, optional
The data set to query. If not given then the default
identifier is used, as returned by `get_default_id`.
Returns
-------
coord : { 'logical', 'physical', 'world' }
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain image data.
sherpa.utils.err.IdentifierErr
If the `id` argument is not recognized.
See Also
--------
get_default_id : Return the default data set identifier.
set_coord : Set the coordinate system to use for image analysis.
"""
return self._get_img_data(id).coord
[docs] def ignore_bad(self, id=None, bkg_id=None):
"""Exclude channels marked as bad in a PHA data set.
Ignore any bin in the PHA data set which has a quality value
that is larger than zero.
Parameters
----------
id : int or str, optional
The data set to change. If not given then the default
identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
The identifier for the background (the default of ``None``
uses the first component).
Raises
------
sherpa.utils.err.DataErr
If the data set has no quality array.
See Also
--------
ignore : Exclude data from the fit.
notice : Include data in the fit.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The `load_pha` command - and others that create a PHA data set
- do not exclude these bad-quality bins automatically.
If the data set has been grouped, then calling `ignore_bad`
will remove any filter applied to the data set. If this
happens a warning message will be displayed.
Examples
--------
Remove any bins that are marked bad in the default data set:
>>> load_pha('src.pi')
>>> ignore_bad()
The data set 'jet' is grouped, and a filter applied. After
ignoring the bad-quality points, the filter has been removed
and will need to be re-applied:
>>> group_counts('jet', 20)
>>> notice_id('jet', 0.5, 7)
>>> get_filter('jet')
'0.496399998665:7.212399959564'
>>> ignore_bad('jet')
WARNING: filtering grouped data with quality flags, previous filters deleted
>>> get_filter('jet')
'0.001460000058:14.950400352478'
"""
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.ignore_bad()
def _notice_warning(self):
quantities = numpy.asarray([data.get_analysis()
for data in self._data.values()
if isinstance(data,
sherpa.astro.data.DataPHA)])
if len(quantities) > 1 and not (quantities == quantities[0]).all():
warning("not all PHA datasets have equal analysis quantities")
[docs] def notice(self, lo=None, hi=None, **kwargs):
if lo is not None or hi is not None:
self._notice_warning()
sherpa.ui.utils.Session.notice(self, lo, hi, **kwargs)
notice.__doc__ = sherpa.ui.utils.Session.notice.__doc__
[docs] def ignore(self, lo=None, hi=None, **kwargs):
if lo is not None or hi is not None:
self._notice_warning()
sherpa.ui.utils.Session.ignore(self, lo, hi, **kwargs)
ignore.__doc__ = sherpa.ui.utils.Session.ignore.__doc__
# DOC-TODO: how best to document the region support?
# DOC-TODO: I have not mentioned the support for radii in arcsec/minutes/degrees
# or sexagessimal formats. Is this supported here?
[docs] def notice2d(self, val=None):
"""Include a spatial region of all data sets.
Select a spatial region to include in the fit. The filter is
applied to all data sets.
Parameters
----------
val : str, optional
A region specification as a string or the name of a file
containing a region filter. The coordinates system of the
filter is taken from the coordinate setting of the data
sets (`set_coord`). If ``None``, then all points are
included.
See Also
--------
ignore2d : Exclude a spatial region from all data sets.
ignore2d_id : Exclude a spatial region from a data set.
ignore2d_image : Select the region to exclude from the image viewer.
notice2d_id : Include a spatial region of a data set.
notice2d_image : Select the region to include from the image viewer.
set_coord : Set the coordinate system to use for image analysis.
Notes
-----
The region syntax support is provided by the CIAO region
library [1]_, and supports the following shapes (the
capitalized parts of the name indicate the minimum length of
the name that is supported):
========= ===================================================
Name Arguments
========= ===================================================
RECTangle (xmin,ymin,xmax,ymax)
BOX (xcenter,ycenter,width,height)
BOX (xcenter,ycenter,width,height,angle)
ROTBOX (xcenter,ycenter,width,height,angle)
CIRcle (xcenter,ycenter,radius)
ANNULUS (xcenter,ycenter,iradius,oradius)
ELLipse (xcenter,ycenter,xradius,yradius,angle)
SECTor (xcenter,ycenter,minangle,maxangle)
PIE (xcenter,ycenter,iradius,oradius,minangle,maxangle)
POLYgon (x1,y1,x2,y2,x3,y3,...)
POInt (xcenter,ycenter)
REGION (file)
FIELD ()
========= ===================================================
Angles are measured in degrees from the X axis, with a
positive value indicating a counter-clockwise direction.
Only simple polygons are supported, which means that a polygon
can not intersect itself. The last point does not need to
equal the first point (i.e. polygons are automatically closed
if necessary).
The shapes can be combined using AND (intersection), OR
(union), or NOT (negation):
intersection::
shape1()*shape2()
shape1()&shape2()
union::
shape1()+shape2()
shape1()|shape2()
shape1()shape2()
negation::
!shape1()
shape1()-shape2()
shape1()*!shape1()
The precedence uses the same rules as the mathematical
operators ``+`` and ``*`` (with ``-`` replaced by ``*!``),
so that::
circle(0,0,10)+rect(10,-10,20,10)-circle(10,0,10)
means that the second circle is only excluded from the
rectangle, and not the first circle. To remove it from both
shapes requires writing::
circle(0,0,10)-circle(10,0,10)+rect(10,-10,20,10)-circle(10,0,10)
A point is included if the center of the pixel lies within
the region. The comparison is done using the selected
coordinate system for the image, so a pixel may not
have a width and height of 1.
The REGION specifier is only supported when using CIAO.
Unfortunately you can not combine region shapes using this
syntax. That is ``region(s1.reg)+region(s2.reg)`` is not
supported.
References
----------
.. [1] http://cxc.harvard.edu/ciao/ahelp/dmregions.html
Examples
--------
Include the data points that lie within a circle centered
at 4324.5,3827.5 with a radius of 300:
>>> set_coord('physical')
>>> notice2d('circle(4324.5,3827.5,430)')
Read in the filter from the file ``ds9.reg``, using either:
>>> notice2d('ds9.reg')
or, when using CIAO,
>>> notice2d('region(ds9.reg)')
Select those points that lie both within the rotated box and
the annulus (i.e. an intersection of the two shapes):
>>> notice2d('rotbox(100,200,50,40,45)*annulus(120,190,20,60)')
Select those points that lie within the rotated box or the
annulus (i.e. a union of the two shapes):
>>> notice2d('rotbox(100,200,50,40,45)+annulus(120,190,20,60)')
All existing spatial filters are removed:
>>> notice2d()
"""
for d in self._data.values():
_check_type(d, sherpa.astro.data.DataIMG, 'img',
'a image data set')
d.notice2d(val, False)
[docs] def ignore2d(self, val=None):
"""Exclude a spatial region from all data sets.
Select a spatial region to exclude in the fit. The filter is
applied to all data sets.
Parameters
----------
val : str, optional
A region specification as a string or the name of a file
containing a region filter. The coordinates system of the
filter is taken from the coordinate setting of the data
sets (`set_coord`). If ``None``, then all points are
included.
See Also
--------
ignore2d_id : Exclude a spatial region from a data set.
ignore2d_image : Select the region to exclude from the image viewer.
notice2d : Include a spatial region from all data sets.
notice2d_id : Include a spatial region of a data set.
notice2d_image : Select the region to include from the image viewer.
set_coord : Set the coordinate system to use for image analysis.
Notes
-----
The region syntax is described in the `notice2d` function.
Examples
--------
Exclude points that fall within the two regions:
>>> ignore2d('ellipse(200,300,40,30,-34)')
>>> ignore2d('box(40,100,30,40)')
Use a region file called 'reg.fits', by using either:
>>> ignore2d('reg.fits')
or
>>> ignore2d('region(reg.fits)')
Exclude all points.
>>> ignore2d()
"""
for d in self._data.values():
_check_type(d, sherpa.astro.data.DataIMG, 'img',
'a image data set')
d.notice2d(val, True)
[docs] def notice2d_id(self, ids, val=None):
"""Include a spatial region of a data set.
Select a spatial region to include in the fit. The filter is
applied to the given data set, or sets.
Parameters
----------
ids : int or str, or array of int or str
The data set, or sets, to use.
val : str, optional
A region specification as a string or the name of a file
containing a region filter. The coordinates system of the
filter is taken from the coordinate setting of the data
sets (`set_coord`). If ``None``, then all points are
included.
See Also
--------
ignore2d : Exclude a spatial region from all data sets.
ignore2d_id : Exclude a spatial region from a data set.
ignore2d_image : Select the region to exclude from the image viewer.
notice2d : Include a spatial region of all data sets.
notice2d_image : Select the region to include from the image viewer.
set_coord : Set the coordinate system to use for image analysis.
Notes
-----
The region syntax is described in the `notice2d` function.
Examples
--------
Select all the pixels in the default data set:
>>> notice2d_id(1)
Select all the pixels in data sets 'i1' and 'i2':
>>> notice2d_id(['i1', 'i2'])
Apply the filter to the 'img' data set:
>>> notice2d_id('img', 'annulus(4324.2,3982.2,40.2,104.3)')
Use the regions in the file `srcs.reg` for data set 1:
>>> notice2d_id(1, 'srcs.reg')
or
>>> notice2d_id(1, 'region(srcs.reg)')
"""
if self._valid_id(ids):
ids = (ids,)
else:
try:
ids = tuple(ids)
except TypeError:
_argument_type_error('ids',
'an identifier or list of identifiers')
for id in ids:
_check_type(self.get_data(id), sherpa.astro.data.DataIMG,
'img', 'a image data set')
self.get_data(id).notice2d(val, False)
[docs] def ignore2d_id(self, ids, val=None):
"""Exclude a spatial region from a data set.
Select a spatial region to exclude in the fit. The filter is
applied to the given data set, or sets.
Parameters
----------
ids : int or str, or array of int or str
The data set, or sets, to use.
val : str, optional
A region specification as a string or the name of a file
containing a region filter. The coordinates system of the
filter is taken from the coordinate setting of the data
sets (`set_coord`). If ``None``, then all points are
included.
See Also
--------
ignore2d : Exclude a spatial region from all data sets.
ignore2d_image : Select the region to exclude from the image viewer.
notice2d : Include a spatial region of all data sets.
notice2d_id : Include a spatial region from a data set.
notice2d_image : Select the region to include from the image viewer.
set_coord : Set the coordinate system to use for image analysis.
Notes
-----
The region syntax is described in the `notice2d` function.
Examples
--------
Ignore the pixels within the rectangle from data set 1:
>>> ignore2d_id(1, 'rect(10,10,20,290)')
Ignore the spatial region in the file `srcs.reg`:
>>> ignore2d_id(1, 'srcs.reg')
or
>>> ignore2d_id(1, 'region(srcs.reg)')
"""
if self._valid_id(ids):
ids = (ids,)
else:
try:
ids = tuple(ids)
except TypeError:
_argument_type_error('ids',
'an identifier or list of identifiers')
for id in ids:
_check_type(self.get_data(id), sherpa.astro.data.DataIMG,
'img', 'a image data set')
self.get_data(id).notice2d(val, True)
[docs] def notice2d_image(self, ids=None):
"""Include pixels using the region defined in the image viewer.
Include points that lie within the region defined in the image
viewer.
Parameters
----------
ids : int or str, or sequence of int or str, optional
The data set, or sets, to use. If ``None`` (the default)
then the default identifier is used, as returned by
`get_default_id`.
See Also
--------
ignore2d : Exclude a spatial region from an image.
ignore2d_image : Exclude pixels using the region defined in the image viewer.
notice2d : Include a spatial region of an image.
set_coord : Set the coordinate system to use for image analysis.
Notes
-----
The region definition is converted into the coordinate system
relevant to the data set before it is applied.
Examples
--------
Use the region in the image viewer to include points from the
default data set.
>>> notice2d_image()
Include points in the data set labelled "2".
>>> notice2d_image(2)
Include points in data sets "src" and "bg".
>>> notice2d_image(["src", "bg"])
"""
if ids is None:
ids = self._default_id
if self._valid_id(ids):
ids = (ids,)
else:
try:
ids = tuple(ids)
except TypeError:
_argument_type_error('ids',
'an identifier or list of identifiers')
for id in ids:
_check_type(self.get_data(id), sherpa.astro.data.DataIMG,
'img', 'a image data set')
coord = self.get_coord(id)
if coord == 'logical':
coord = 'image'
elif coord == 'world':
coord = 'wcs'
regions = self.image_getregion(coord).replace(';', '')
self.notice2d_id(id, regions)
[docs] def ignore2d_image(self, ids=None):
"""Exclude pixels using the region defined in the image viewer.
Exclude points that lie within the region defined in the image
viewer.
Parameters
----------
ids : int or str, or sequence of int or str, optional
The data set, or sets, to ignore. If ``None`` (the default)
then the default identifier is used, as returned by
`get_default_id`.
See Also
--------
ignore2d : Exclude a spatial region from an image.
notice2d : Include a spatial region of an image.
notice2d_image : Include pixels using the region defined in the image viewer.
set_coord : Set the coordinate system to use for image analysis.
Notes
-----
The region definition is converted into the coordinate system
relevant to the data set before it is applied.
Examples
--------
Use the region in the image viewer to ignore points from the
default data set.
>>> ignore2d_image()
Ignore points in the data set labelled "2".
>>> ignore2d_image(2)
Ignore points in data sets "src" and "bg".
>>> ignore2d_image(["src", "bg"])
"""
if ids is None:
ids = self._default_id
if self._valid_id(ids):
ids = (ids,)
else:
try:
ids = tuple(ids)
except TypeError:
_argument_type_error('ids',
'an identifier or list of identifiers')
for id in ids:
_check_type(self.get_data(id), sherpa.astro.data.DataIMG,
'img', 'a image data set')
coord = self.get_coord(id)
if coord == 'logical':
coord = 'image'
elif coord == 'world':
coord = 'wcs'
regions = self.image_getregion(coord).replace(';', '')
self.ignore2d_id(id, regions)
# DOC-TODO: how best to include datastack support? How is it handled here?
[docs] def load_bkg(self, id, arg=None, use_errors=False, bkg_id=None):
"""Load the background from a file and add it to a PHA data set.
This will load the PHA data and any response information - so
ARF and RMF - and add it as a background component to the
PHA data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
arg
Identify the data to read: a file name, or a data structure
representing the data to use, as used by the I/O backend in
use by Sherpa: a ``PHACrateDataset`` for crates, as used by
CIAO, or a list of AstroPy HDU objects.
use_errors : bool, optional
If ``True`` then the statistical errors are taken from the
input data, rather than calculated by Sherpa from the
count values. The default is ``False``.
bkg_id : int or str, optional
The identifier for the background (the default of ``None``
uses the first component).
See Also
--------
load_bkg_arf : Load an ARF from a file and add it to the background of a PHA data set.
load_bkg_rmf : Load a RMF from a file and add it to the background of a PHA data set.
load_pha : Load a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `arg` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `arg` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Examples
--------
Load a source and background data set:
>>> load_pha('src.pi')
read ARF file src.arf
read RMF file src.rmf
>>> load_bkg('src_bkg.pi')
Read in the background via Crates:
>>> bpha = pycrates.read_pha('src_bkg.pi')
>>> load_bkg(bpha)
Create the data set from the data read in by AstroPy:
>>> bhdus = astropy.io.fits.open('src_bkg.pi')
>>> load_bkg(bhdus)
"""
if arg is None:
id, arg = arg, id
bkgsets = self.unpack_bkg(arg, use_errors)
if numpy.iterable(bkgsets):
for bkgid, bkg in enumerate(bkgsets):
self.set_bkg(id, bkg, bkgid + 1)
else:
self.set_bkg(id, bkgsets, bkg_id)
[docs] def group(self, id=None, bkg_id=None):
"""Turn on the grouping for a PHA data set.
A PHA data set can be grouped either because it contains
grouping information [1]_, which is automatically applied when
the data is read in with `load_pha` or `load_data`, or because
the `group` set of routines has been used to dynamically
re-group the data. The `ungroup` function removes this
grouping (however it was created). The `group` function
re-applies this grouping. The grouping scheme can be
changed dynamically, using the ``group_xxx`` series of
routines.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set to group the background associated with the data set.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
fit : Fit one or more data sets.
group_adapt : Adaptively group to a minimum number of counts.
group_adapt_snr : Adaptively group to a minimum signal-to-noise ratio.
group_bins : Group into a fixed number of bins.
group_counts : Group into a minimum number of counts per bin.
group_snr : Group into a minimum signal-to-noise ratio.
group_width : Group into a fixed bin width.
set_grouping : Apply a set of grouping flags to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
ungroup : Turn off the grouping for a PHA data set.
Notes
-----
PHA data is often grouped to improve the signal to noise of
the data, by decreasing the number of bins, so that a
chi-square statistic can be used when fitting the data. After
calling `group`, anything that uses the data set - such as a
plot, fit, or error analysis - will use the grouped data
values. Models should be re-fit if `group` is called; the
increase in the signal of the bins may mean that a chi-square
statistic can now be used.
The grouping is implemented by separate arrays to the main
data - the information is stored in the ``grouping`` and
``quality`` arrays of the PHA data set - so that a data set can
be grouped and ungrouped many times, without losing
information. The `group` command does not create this
information; this is either created by modifying the PHA file
before it is read in, or by using the ``group_xxx`` routines
once the data has been loaded.
The ``grouped`` field of a PHA data set is set to ``True`` when
the data is grouped.
References
----------
.. [1] Arnaud., K. & George, I., "The OGIP Spectral File
Format",
http://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Examples
--------
Group the data in the default data set:
>>> group()
>>> get_data().grouped
True
Group the first background component of the 'core' data set:
>>> group('core', bkg_id=1)
>>> get_bkg('core', bkg_id=1).grouped
True
The data is fit using the ungrouped data, and then plots of
the data and best-fit, and the residuals, are created. The
first plot uses the ungrouped data, and the second plot uses
the grouped data.
>>> ungroup()
>>> fit()
>>> plot_fit_resid()
>>> group()
>>> plot_fit_resid()
"""
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
if bkg_id is None:
# First, group backgrounds associated with the
# data set ID; report if background(s) already grouped.
for bid in data.background_ids:
try:
self.group(id, bid)
except DataErr as e:
info(str(e))
# Now check if data is already grouped, and send error message
# if so
if not data.grouped:
data.group()
[docs] def set_grouping(self, id, val=None, bkg_id=None):
"""Apply a set of grouping flags to a PHA data set.
A group is indicated by a sequence of flag values starting
with ``1`` and then ``-1`` for all the channels in the group,
following [1]_. Setting the grouping column automatically
turns on the grouping flag for that data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
val : array of int
This must be an array of grouping values of the same length
as the data array.
bkg_id : int or str, optional
Set to group the background associated with the data set.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
fit : Fit one or more data sets.
get_grouping : Return the grouping flags for a PHA data set.
group : Turn on the grouping for a PHA data set.
group_adapt : Adaptively group to a minimum number of counts.
group_adapt_snr : Adaptively group to a minimum signal-to-noise ratio.
group_bins : Group into a fixed number of bins.
group_counts : Group into a minimum number of counts per bin.
group_snr : Group into a minimum signal-to-noise ratio.
group_width : Group into a fixed bin width.
load_grouping : Load the grouping scheme from a file and add to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
ungroup : Turn off the grouping for a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `val` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `val` parameters,
respectively.
The meaning of the grouping column is taken from [1]_, which says
that +1 indicates the start of a bin, -1 if the channel is part
of group, and 0 if the data grouping is undefined for all channels.
References
----------
.. [1] "The OGIP Spectral File Format", https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Examples
--------
Copy the grouping array from data set 2 into the default data
set:
>>> grp = get_grouping(2)
>>> set_grouping(grp)
Copy the grouping from data set "src1" to the source and the
first background data set of "src2":
>>> grp = get_grouping("src1")
>>> set_grouping("src2", grp)
>>> set_grouping("src2", grp, bkg_id=1)
"""
if val is None:
id, val = val, id
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
if val is None:
data.grouping = None
else:
if(type(val) in (numpy.ndarray,) and
issubclass(val.dtype.type, numpy.integer)):
data.grouping = numpy.asarray(val)
else:
data.grouping = numpy.asarray(val, SherpaInt)
[docs] def get_grouping(self, id=None, bkg_id=None):
"""Return the grouping array for a PHA data set.
The function returns the grouping value for each channel in
the PHA data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set if the grouping flags should be taken from a background
associated with the data set.
Returns
-------
grouping : ndarray or ``None``
A value of ``1`` indicates the start of a new group, and ``-1``
indicates that the bin is part of the group. This array is
not filtered - that is, there is one element for each channel
in the PHA data set. Changes to the elements of this array will
change the values in the dataset (it is a reference to the values
used to define the quality, not a copy).
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
fit : Fit one or more data sets.
get_quality : Return the quality array for a PHA data set.
ignore_bad : Exclude channels marked as bad in a PHA data set.
load_grouping: Load the grouping scheme from a file and add to a PHA data set.
set_grouping : Apply a set of grouping flags to a PHA data set.
Notes
-----
The meaning of the grouping column is taken from [1]_, which says
that +1 indicates the start of a bin, -1 if the channel is part
of group, and 0 if the data grouping is undefined for all channels.
References
----------
.. [1] "The OGIP Spectral File Format", https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Examples
--------
Copy the grouping array from the default data set to data set 2:
>>> grp1 = get_grouping()
>>> set_grouping(2, grp1)
Return the grouping array of the background component labelled
2 for the 'histate' data set:
>>> grp = get_grouping('histate', bkg_id=2)
"""
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
return data.grouping
[docs] def set_quality(self, id, val=None, bkg_id=None):
"""Apply a set of quality flags to a PHA data set.
A quality value of 0 indicates a good channel,
otherwise (values >=1) the channel is considered bad and can be
excluded using the `ignore_bad` function, as discussed
in [1]_.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
val : array of int
This must be an array of quality values of the same length
as the data array.
bkg_id : int or str, optional
Set if the quality values should be associated with the
background associated with the data set.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
fit : Fit one or more data sets.
get_quality : Return the quality array for a PHA data set.
ignore_bad : Exclude channels marked as bad in a PHA data set.
load_quality : Load the quality array from a file and add to a PHA data set.
set_grouping : Apply a set of grouping flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `val` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `val` parameters,
respectively.
The meaning of the quality column is taken from [1]_, which says
that 0 indicates a "good" channel, 1 and 2 are for channels that
are identified as "bad" or "dubious" (respectively) by software,
5 indicates a "bad" channel set by the user, and values of 3 or 4
are not used.
References
----------
.. [1] "The OGIP Spectral File Format", https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Examples
--------
Copy the quality array from data set 2 into the default data
set, and then ensure that any 'bad' channels are ignored:
>>> qual = get_data(2).quality
>>> set_quality(qual)
>>> ignore_bad()
Copy the quality array from data set "src1" to the source and
background data sets of "src2":
>>> qual = get_data("src1").quality
>>> set_quality("src2", qual)
>>> set_quality("src2", qual, bkg_id=1)
"""
if val is None:
id, val = val, id
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
if val is None:
data.quality = None
else:
if(type(val) in (numpy.ndarray,) and
issubclass(val.dtype.type, numpy.integer)):
data.quality = numpy.asarray(val)
else:
data.quality = numpy.asarray(val, SherpaInt)
# DOC TODO: Need to document that routines like get_quality return
# a reference to the data - so can change the data structure
# - and not a copy
# DOC-TODO: explain that many of these can be done with
# direct object access
# get_data().exposure [= ...]
[docs] def get_quality(self, id=None, bkg_id=None):
"""Return the quality flags for a PHA data set.
The function returns the quality value for each channel in
the PHA data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set if the quality flags should be taken from a background
associated with the data set.
Returns
-------
qual : ndarray or ``None``
The quality value for each channel in the PHA data set.
This array is not grouped or filtered - that is, there
is one element for each channel in the PHA data set. Changes
to the elements of this array will change the values in the
dataset (is is a reference to the values used to define the
quality, not a copy).
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
fit : Fit one or more data sets.
get_grouping : Return the grouping array for a PHA data set.
get_indep : Return the independent axes of a data set.
ignore_bad : Exclude channels marked as bad in a PHA data set.
load_quality : Load the quality array from a file and add to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The meaning of the quality column is taken from [1]_, which says
that 0 indicates a "good" channel, 1 and 2 are for channels that
are identified as "bad" or "dubious" (respectively) by software,
5 indicates a "bad" channel set by the user, and values of 3 or 4
are not used.
References
----------
.. [1] "The OGIP Spectral File Format", https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Examples
--------
Copy the quality array from the default data set to data set 2:
>>> qual1 = get_quality()
>>> set_quality(2, qual1)
Return the quality array of the background component labelled
2 for the 'histate' data set:
>>> qual = get_quality('histate', bkg_id=2)
Change the quality setting for all channels below 30 in the
default data set to 5 (considered bad by the user):
>>> chans, = get_indep()
>>> qual = get_quality()
>>> qual[chans < 30] = 5
"""
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
return data.quality
[docs] def ungroup(self, id=None, bkg_id=None):
"""Turn off the grouping for a PHA data set.
A PHA data set can be grouped either because it contains
grouping information [1]_, which is automatically applied when
the data is read in with `load_pha` or `load_data`, or because
the ``group_xxx`` set of routines has been used to dynamically
re-group the data. The `ungroup` function removes this
grouping (however it was created).
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
Set to ungroup the background associated with the data set.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
fit : Fit one or more data sets.
group : Turn on the grouping for a PHA data set.
Notes
-----
PHA data is often grouped to improve the signal to noise of
the data, by decreasing the number of bins, so that a
chi-square statistic can be used when fitting the data. After
calling `ungroup`, anything that uses the data set - such as a
plot, fit, or error analysis - will use the original data
values. Models should be re-fit if `ungroup` is called; this
may require a change of statistic depending on the counts per
channel in the spectrum.
The grouping is implemented by separate arrays to the main
data - the information is stored in the ``grouping`` and
``quality`` arrays of the PHA data set - so that a data set
can be grouped and ungrouped many times, without losing
information.
The ``grouped`` field of a PHA data set is set to ``False`` when
the data is not grouped.
If subtracting the background estimate from a data set, the
grouping applied to the source data set is used for both
source and background data sets.
References
----------
.. [1] Arnaud., K. & George, I., "The OGIP Spectral File
Format",
http://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html
Examples
--------
Ungroup the data in the default data set:
>>> ungroup()
>>> get_data().grouped
False
Ungroup the first background component of the 'core' data set:
>>> ungroup('core', bkg_id=1)
>>> get_bkg('core', bkg_id=1).grouped
False
"""
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
if bkg_id is None:
# First, ungroup backgrounds associated with the
# data set ID; report if background(s) already ungrouped.
for bid in data.background_ids:
try:
self.ungroup(id, bid)
except DataErr as e:
info(str(e))
# Now check if data is already ungrouped, and send error message
# if so
if data.grouped:
data.ungroup()
# DOC-TODO: need to document somewhere that this ignores existing
# quality flags and how to use tabStops to include
# this information
# DOC-TODO: how to set the quality if using tabstops to indicate
# "bad" channels, rather than ones to ignore
[docs] def group_bins(self, id, num=None, bkg_id=None, tabStops=None):
"""Group into a fixed number of bins.
Combine the data so that there `num` equal-width bins (or
groups). The binning scheme is applied to all the channels,
but any existing filter - created by the `ignore` or `notice`
set of functions - is re-applied after the data has been
grouped.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
num : int
The number of bins in the grouped data set. Each bin
will contain the same number of channels.
bkg_id : int or str, optional
Set to group the background associated with the data set.
When ``bkg_id`` is None (which is the default), the
grouping is applied to all the associated background
data sets as well as the source data set.
tabStops : array of int or bool, optional
If set, indicate one or more ranges of channels that should
not be included in the grouped output. The array should
match the number of channels in the data set and non-zero or
``True`` means that the channel should be ignored from the
grouping (use 0 or ``False`` otherwise).
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
group_adapt : Adaptively group to a minimum number of counts.
group_adapt_snr : Adaptively group to a minimum signal-to-noise ratio.
group_counts : Group into a minimum number of counts per bin.
group_snr : Group into a minimum signal-to-noise ratio.
group_width : Group into a fixed bin width.
set_grouping : Apply a set of grouping flags to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `num` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `num` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Unlike `group`, it is possible to call `group_bins` multiple
times on the same data set without needing to call `ungroup`.
Since the bin width is an integer number of channels, it is
likely that some channels will be "left over". This is even
more likely when the ``tabStops`` parameter is set. If this
happens, a warning message will be displayed to the screen and
the quality value for these channels will be set to 2. This
information can be found with the `get_quality` command.
Examples
--------
Group the default data set so that there are 50 bins.
>>> group_bins(50)
Group the 'jet' data set to 50 bins and plot the result,
then re-bin to 100 bins and overplot the data:
>>> group_bins('jet', 50)
>>> plot_data('jet')
>>> group_bins('jet', 100)
>>> plot_data('jet', overplot=True)
The grouping is applied to the full data set, and then
the filter - in this case defined over the range 0.5
to 8 keV - will be applied. This means that the
noticed data range will likely contain less than
50 bins.
>>> set_analysis('energy')
>>> notice(0.5, 8)
>>> group_bins(50)
>>> plot_data()
Do not group any channels numbered less than 20 or
800 or more. Since there are 780 channels to be
grouped, the width of each bin will be 20 channels
and there are no "left over" channels:
>>> notice()
>>> channels = get_data().channel
>>> ign = (channels <= 20) | (channels >= 800)
>>> group_bins(39, tabStops=ign)
>>> plot_data()
"""
if num is None:
id, num = num, id
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.group_bins(num, tabStops)
# DOC-TODO: should num= be renamed val= to better match
# underlying code/differ from group_bins?
[docs] def group_width(self, id, num=None, bkg_id=None, tabStops=None):
"""Group into a fixed bin width.
Combine the data so that each bin contains `num` channels.
The binning scheme is applied to all the channels, but any
existing filter - created by the `ignore` or `notice` set of
functions - is re-applied after the data has been grouped.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
num : int
The number of channels to combine into a group.
bkg_id : int or str, optional
Set to group the background associated with the data set.
When ``bkg_id`` is None (which is the default), the
grouping is applied to all the associated background
data sets as well as the source data set.
tabStops : array of int or bool, optional
If set, indicate one or more ranges of channels that should
not be included in the grouped output. The array should
match the number of channels in the data set and non-zero or
``True`` means that the channel should be ignored from the
grouping (use 0 or ``False`` otherwise).
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
group_adapt : Adaptively group to a minimum number of counts.
group_adapt_snr : Adaptively group to a minimum signal-to-noise ratio.
group_bins : Group into a fixed number of bins.
group_counts : Group into a minimum number of counts per bin.
group_snr : Group into a minimum signal-to-noise ratio.
set_grouping : Apply a set of grouping flags to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `num` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `num` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Unlike `group`, it is possible to call `group_width` multiple
times on the same data set without needing to call `ungroup`.
Unless the requested bin width is a factor of the number of
channels (and no ``tabStops`` parameter is given), then some
channels will be "left over". If this happens, a warning
message will be displayed to the screen and the quality value
for these channels will be set to 2. This information can be
found with the `get_quality` command.
Examples
--------
Group the default data set so that each bin contains 20
channels:
>>> group_width(20)
Plot two versions of the 'jet' data set: the first uses
20 channels per group and the second is 50 channels per
group:
>>> group_width('jet', 20)
>>> plot_data('jet')
>>> group_width('jet', 50)
>>> plot_data('jet', overplot=True)
The grouping is applied to the full data set, and then
the filter - in this case defined over the range 0.5
to 8 keV - will be applied.
>>> set_analysis('energy')
>>> notice(0.5, 8)
>>> group_width(50)
>>> plot_data()
The grouping is not applied to channels 101 to
149, inclusive:
>>> notice()
>>> channels = get_data().channel
>>> ign = (channels > 100) & (channels < 150)
>>> group_width(40, tabStops=ign)
>>> plot_data()
"""
if num is None:
id, num = num, id
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.group_width(num, tabStops)
[docs] def group_counts(self, id, num=None, bkg_id=None,
maxLength=None, tabStops=None):
"""Group into a minimum number of counts per bin.
Combine the data so that each bin contains `num` or more
counts. The binning scheme is applied to all the channels, but
any existing filter - created by the `ignore` or `notice` set
of functions - is re-applied after the data has been grouped.
The background is *not* included in this calculation; the
calculation is done on the raw data even if `subtract` has
been called on this data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
num : int
The number of channels to combine into a group.
bkg_id : int or str, optional
Set to group the background associated with the data set.
When ``bkg_id`` is None (which is the default), the
grouping is applied to all the associated background
data sets as well as the source data set.
maxLength : int, optional
The maximum number of channels that can be combined into a
single group.
tabStops : array of int or bool, optional
If set, indicate one or more ranges of channels that should
not be included in the grouped output. The array should
match the number of channels in the data set and non-zero or
``True`` means that the channel should be ignored from the
grouping (use 0 or ``False`` otherwise).
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
group_adapt : Adaptively group to a minimum number of counts.
group_adapt_snr : Adaptively group to a minimum signal-to-noise ratio.
group_bins : Group into a fixed number of bins.
group_snr : Group into a minimum signal-to-noise ratio.
group_width : Group into a fixed bin width.
set_grouping : Apply a set of grouping flags to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `num` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `num` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Unlike `group`, it is possible to call `group_counts` multiple
times on the same data set without needing to call `ungroup`.
If channels can not be placed into a "valid" group, then a
warning message will be displayed to the screen and the
quality value for these channels will be set to 2. This
information can be found with the `get_quality` command.
Examples
--------
Group the default data set so that each bin contains at
least 20 counts:
>>> group_counts(20)
Plot two versions of the 'jet' data set: the first uses
20 counts per group and the second is 50:
>>> group_counts('jet', 20)
>>> plot_data('jet')
>>> group_counts('jet', 50)
>>> plot_data('jet', overplot=True)
The grouping is applied to the full data set, and then
the filter - in this case defined over the range 0.5
to 8 keV - will be applied.
>>> set_analysis('energy')
>>> notice(0.5, 8)
>>> group_counts(30)
>>> plot_data()
If a channel has more than 30 counts then do not group,
otherwise group channels so that they contain at least 40
counts. The `group_adapt` and `group_adapt_snr` functions
provide similar functionality to this example. A maximum
length of 10 channels is enforced, to avoid bins getting too
large when the signal is low.
>>> notice()
>>> counts = get_data().counts
>>> ign = counts > 30
>>> group_counts(40, tabStops=ign, maxLength=10)
"""
if num is None:
id, num = num, id
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.group_counts(num, maxLength, tabStops)
# DOC-TODO: check the Poisson stats claim; I'm guessing it means
# gaussian (i.e. sqrt(n))
[docs] def group_snr(self, id, snr=None, bkg_id=None,
maxLength=None, tabStops=None, errorCol=None):
"""Group into a minimum signal-to-noise ratio.
Combine the data so that each bin has a signal-to-noise ratio
of at least `snr`. The binning scheme is applied to all the
channels, but any existing filter - created by the `ignore` or
`notice` set of functions - is re-applied after the data has
been grouped. The background is *not* included in this
calculation; the calculation is done on the raw data even if
`subtract` has been called on this data set.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
snr : number
The minimum signal-to-noise ratio that must be reached
to form a group of channels.
bkg_id : int or str, optional
Set to group the background associated with the data set.
When ``bkg_id`` is None (which is the default), the
grouping is applied to all the associated background
data sets as well as the source data set.
maxLength : int, optional
The maximum number of channels that can be combined into a
single group.
tabStops : array of int or bool, optional
If set, indicate one or more ranges of channels that should
not be included in the grouped output. The array should
match the number of channels in the data set and non-zero or
``True`` means that the channel should be ignored from the
grouping (use 0 or ``False`` otherwise).
errorCol : array of num, optional
If set, the error to use for each channel when calculating
the signal-to-noise ratio. If not given then Poisson
statistics is assumed. A warning is displayed for each
zero-valued error estimate.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
group_adapt : Adaptively group to a minimum number of counts.
group_adapt_snr : Adaptively group to a minimum signal-to-noise ratio.
group_bins : Group into a fixed number of bins.
group_counts : Group into a minimum number of counts per bin.
group_width : Group into a fixed bin width.
set_grouping : Apply a set of grouping flags to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `snr` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `snr` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Unlike `group`, it is possible to call `group_snr` multiple
times on the same data set without needing to call `ungroup`.
If channels can not be placed into a "valid" group, then a
warning message will be displayed to the screen and the
quality value for these channels will be set to 2. This
information can be found with the `get_quality` command.
Examples
--------
Group the default data set so that each bin has a
signal-to-noise ratio of at least 5:
>>> group_snr(20)
Plot two versions of the 'jet' data set: the first uses
a signal-to-noise ratio of 3 and the second 5:
>>> group_snr('jet', 3)
>>> plot_data('jet')
>>> group_snr('jet', 5)
>>> plot_data('jet', overplot=True)
"""
if snr is None:
id, snr = snr, id
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.group_snr(snr, maxLength, tabStops, errorCol)
[docs] def group_adapt(self, id, min=None, bkg_id=None,
maxLength=None, tabStops=None):
"""Adaptively group to a minimum number of counts.
Combine the data so that each bin contains `min` or more
counts. The difference to `group_counts` is that this
algorithm starts with the bins with the largest signal, in
order to avoid over-grouping bright features, rather than at
the first channel of the data. The adaptive nature means that
low-count regions between bright features may not end up in
groups with the minimum number of counts. The binning scheme
is applied to all the channels, but any existing filter -
created by the `ignore` or `notice` set of functions - is
re-applied after the data has been grouped.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
min : int
The number of channels to combine into a group.
bkg_id : int or str, optional
Set to group the background associated with the data set.
When ``bkg_id`` is ``None`` (which is the default), the
grouping is applied to all the associated background
data sets as well as the source data set.
maxLength : int, optional
The maximum number of channels that can be combined into a
single group.
tabStops : array of int or bool, optional
If set, indicate one or more ranges of channels that should
not be included in the grouped output. The array should
match the number of channels in the data set and non-zero or
``True`` means that the channel should be ignored from the
grouping (use 0 or ``False`` otherwise).
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
group_adapt_snr : Adaptively group to a minimum signal-to-noise ratio.
group_bins : Group into a fixed number of bins.
group_counts : Group into a minimum number of counts per bin.
group_snr : Group into a minimum signal-to-noise ratio.
group_width : Group into a fixed bin width.
set_grouping : Apply a set of grouping flags to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `min` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `min` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Unlike `group`, it is possible to call `group_adapt` multiple
times on the same data set without needing to call `ungroup`.
If channels can not be placed into a "valid" group, then a
warning message will be displayed to the screen and the
quality value for these channels will be set to 2. This
information can be found with the `get_quality` command.
Examples
--------
Group the default data set so that each bin contains at
least 20 counts:
>>> group_adapt(20)
Plot two versions of the 'jet' data set: the first uses
an adaptive scheme of 20 counts per bin, the second
the `group_counts` method:
>>> group_adapt('jet', 20)
>>> plot_data('jet')
>>> group_counts('jet', 20)
>>> plot_data('jet', overplot=True)
"""
if min is None:
id, min = min, id
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.group_adapt(min, maxLength, tabStops)
# DOC-TODO: shouldn't this be snr=None rather than min=None
[docs] def group_adapt_snr(self, id, min=None, bkg_id=None,
maxLength=None, tabStops=None, errorCol=None):
"""Adaptively group to a minimum signal-to-noise ratio.
Combine the data so that each bin has a signal-to-noise ratio
of at least `num`. The difference to `group_snr` is that this
algorithm starts with the bins with the largest signal, in
order to avoid over-grouping bright features, rather than at
the first channel of the data. The adaptive nature means that
low-count regions between bright features may not end up in
groups with the minimum number of counts. The binning scheme
is applied to all the channels, but any existing filter -
created by the `ignore` or `notice` set of functions - is
re-applied after the data has been grouped.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
num : number
The minimum signal-to-noise ratio that must be reached
to form a group of channels.
bkg_id : int or str, optional
Set to group the background associated with the data set.
When ``bkg_id`` is ``None`` (which is the default), the
grouping is applied to all the associated background
data sets as well as the source data set.
maxLength : int, optional
The maximum number of channels that can be combined into a
single group.
tabStops : array of int or bool, optional
If set, indicate one or more ranges of channels that should
not be included in the grouped output. The array should
match the number of channels in the data set and non-zero or
``True`` means that the channel should be ignored from the
grouping (use 0 or ``False`` otherwise).
errorCol : array of num, optional
If set, the error to use for each channel when calculating
the signal-to-noise ratio. If not given then Poisson
statistics is assumed. A warning is displayed for each
zero-valued error estimate.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
group_adapt : Adaptively group to a minimum number of counts.
group_bins : Group into a fixed number of bins.
group_counts : Group into a minimum number of counts per bin.
group_snr : Group into a minimum signal-to-noise ratio.
group_width : Group into a fixed bin width.
set_grouping : Apply a set of grouping flags to a PHA data set.
set_quality : Apply a set of quality flags to a PHA data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `num` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `num` parameters,
respectively. The remaining parameters are expected to be
given as named arguments.
Unlike `group`, it is possible to call `group_adapt_snr`
multiple times on the same data set without needing to call
`ungroup`.
If channels can not be placed into a "valid" group, then a
warning message will be displayed to the screen and the
quality value for these channels will be set to 2. This
information can be found with the `get_quality` command.
Examples
--------
Group the default data set so that each bin contains
a signal-to-noise ratio of at least 5:
>>> group_adapt_snr(5)
Plot two versions of the 'jet' data set: the first uses an
adaptive scheme and the second the non-adaptive version:
>>> group_adapt_snr('jet', 4)
>>> plot_data('jet')
>>> group_snr('jet', 4)
>>> plot_data('jet', overplot=True)
"""
if min is None:
id, min = min, id
data = self._get_pha_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
data.group_adapt_snr(min, maxLength, tabStops, errorCol)
[docs] def subtract(self, id=None):
"""Subtract the background estimate from a data set.
The ``subtract`` function performs a channel-by-channel
subtraction of the background estimate from the data. After
this command, anything that uses the data set - such as a
plot, fit, or error analysis - will use the subtracted
data. Models should be re-fit if ``subtract`` is called.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
fit : Fit one or more data sets.
unsubtract : Undo any background subtraction for the data set.
Notes
-----
Unlike X-Spec [1]_, Sherpa does not automatically subtract
the background estimate from the data.
Background subtraction can only be performed when data and
background are of the same length. If the data and background
are ungrouped, both must have same number of channels. If
they are grouped, data and background can start with different
numbers of channels, but must have the same number of groups
after grouping.
The equation for the subtraction is::
src_counts - bg_counts * (src_exposure * src_backscal)
-----------------------------
(bg_exposure * bg_backscal)
where src_exposure and bg_exposure are the source and
background exposure times, and src_backscal and bg_backscal
are the source and background backscales. The backscale, read
from the ``BACKSCAL`` header keyword of the PHA file [2]_, is
the ratio of data extraction area to total detector area.
The ``subtracted`` field of a dataset is set to ``True`` when
the background is subtracted.
References
----------
.. [1] https://heasarc.gsfc.nasa.gov/xanadu/xspec/manual/XspecSpectralFitting.html
.. [2] https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/node5.html
Examples
--------
Background subtract the default data set.
>>> subtract()
>>> get_data().subtracted
True
Remove the background from the data set labelled 'src':
>>> subtract('src')
>>> get_data('src').subtracted
True
Overplot the background-subtracted data on the original
data for the default data set:
>>> plot_data()
>>> subtract()
>>> plot_data(overplot=True)
"""
if not self._get_pha_data(id).subtracted:
self._get_pha_data(id).subtract()
[docs] def unsubtract(self, id=None):
"""Undo any background subtraction for the data set.
The `unsubtract` function undoes any changes made by
`subtract`. After this command, anything that uses the data
set - such as a plot, fit, or error analysis - will use the
original data values. Models should be re-fit if `subtract` is
called.
Parameters
----------
id : int or str, optional
The identifier for the data set to use. If not given then
the default identifier is used, as returned by
`get_default_id`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain a PHA data set.
See Also
--------
fit : Fit one or more data sets.
subtract : Subtract the background estimate from a data set.
Notes
-----
The ``subtracted`` field of a PHA data set is set to ``False``
when the background is not subtracted.
Examples
--------
Remove the background subtraction from the default data set.
>>> subtract()
>>> get_data().subtracted
False
Remove the background subtraction from the data set labelled
'src':
>>> subtract('src')
>>> get_data('src').subtracted
False
"""
if self._get_pha_data(id).subtracted:
self._get_pha_data(id).unsubtract()
[docs] def fake_pha(self, id, arf, rmf, exposure, backscal=None, areascal=None,
grouping=None, grouped=False, quality=None, bkg=None):
"""Simulate a PHA data set from a model.
The function creates a simulated PHA data set based on a source
model, instrument response (given as an ARF and RMF), and exposure
time, along with a Poisson noise term. A background component can
be included.
Parameters
----------
id : int or str
The identifier for the data set to create. If it already
exists then it is assumed to contain a PHA data set and the
counts will be over-written.
arf : filename or ARF object or list of filenames
The name of the ARF, or an ARF data object (e.g. as
returned by `get_arf` or `unpack_arf`). A list of filenames
can be passed in for instruments that require multile ARFs.
Set this to `None` to use any arf that is already set for
the data set given by id.
rmf : filename or RMF object or list of filenames
The name of the RMF, or an RMF data object (e.g. as
returned by `get_arf` or `unpack_arf`). A list of filenames
can be passed in for instruments that require multile RMFs.
Set this to `None` to use any arf that is already set for
the data set given by id.
exposure : number
The exposure time, in seconds.
backscal : number, optional
The 'BACKSCAL' value for the data set.
areascal : number, optional
The 'AREASCAL' value for the data set.
grouping : array, optional
The grouping array for the data (see `set_grouping`).
grouped : bool, optional
Should the simulated data be grouped (see `group`)? The
default is ``False``. This value is only used if the
`grouping` parameter is set.
quality : array, optional
The quality array for the data (see `set_quality`).
bkg : optional
If left empty, then only the source emission is simulated.
If set to a PHA data object, then the counts from this data
set are scaled appropriately and added to the simulated
source signal. To use background model, set ``bkg="model"`. In that
case a background dataset with ``bkg_id=1`` has to be set before
calling ``fake_pha``. That background dataset needs to include
the data itself (not used in this function), the background model,
and the response.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set already exists and does not contain PHA
data.
See Also
--------
fake : Simulate a data set.
get_arf : Return the ARF associated with a PHA data set.
get_rmf : Return the RMF associated with a PHA data set.
get_dep : Return the dependent axis of a data set.
load_arrays : Create a data set from array values.
set_model : Set the source model expression for a data set.
Notes
-----
A model expression is created by using the supplied ARF and RMF
to convolve the source expression for the dataset (the return
value of `get_source` for the supplied `id` parameter). This
expresion is evaluated for each channel to create the expectation
values, which is then passed to a Poisson random number generator
to determine the observed number of counts per channel. Any
background component is scaled by appropriate terms (exposure
time, area scaling, and the backscal value) before it is passed to
a Poisson random number generator. The simulated background is
added to the simulated data.
Examples
--------
Estimate the signal from a 5000 second observation using the
ARF and RMF from "src.arf" and "src.rmf" respectively:
>>> set_source(1, xsphabs.gal * xsapec.clus)
>>> gal.nh = 0.12
>>> clus.kt, clus.abundanc = 4.5, 0.3
>>> clus.redshift = 0.187
>>> clus.norm = 1.2e-3
>>> fake_pha(1, 'src.arf', 'src.rmf', 5000)
Simulate a 1 mega second observation for the data and model
from the default data set. The simulated data will include an
estimated background component based on scaling the existing
background observations for the source. The simulated data
set, which has the same grouping as the default set, for
easier comparison, is created with the 'sim' label and then
written out to the file 'sim.pi':
>>> arf = get_arf()
>>> rmf = get_rmf()
>>> bkg = get_bkg()
>>> bscal = get_backscal()
>>> grp = get_grouping()
>>> qual = get_quality()
>>> texp = 1e6
>>> set_source('sim', get_source())
>>> fake_pha('sim', arf, rmf, texp, backscal=bscal, bkg=bkg,
... grouping=grp, quality=qual, grouped=True)
>>> save_pha('sim', 'sim.pi')
Sometimes, the background dataset is noisy because there are not
enough photons in the background region. In this case, the background
model can be used to generate the photons that the background
contributes to the source spectrum. To do this, a background model
must be passed in. This model is then convolved with the ARF and RMF
(which must be set before) of the default background data set:
>>> set_bkg_source('sim', 'const1d.con1')
>>> load_arf('sim', 'bkg.arf.fits', bkg_id=1)
>>> load_rmf('sim', 'bkg_rmf.fits', bkg_id=1)
>>> fake_pha('sim', arf, rmf, texp, backscal=bscal, bkg='model',
... grouping=grp, quality=qual, grouped=True)
>>> save_pha('sim', 'sim.pi')
"""
id = self._fix_id(id)
if id in self._data:
d = self._get_pha_data(id)
else:
d = sherpa.astro.data.DataPHA('', None, None)
self.set_data(id, d)
if rmf is None and len(d.response_ids) == 0:
raise DataErr('normffake', id)
if type(rmf) in (str, numpy.string_):
if os.path.isfile(rmf):
rmf = self.unpack_rmf(rmf)
else:
raise IOErr("filenotfound", rmf)
if type(arf) in (str, numpy.string_):
if os.path.isfile(arf):
arf = self.unpack_arf(arf)
else:
raise IOErr("filenotfound", arf)
if not (rmf is None and arf is None):
for resp_id in d.response_ids:
d.delete_response(resp_id)
# Get one rmf for testing the channel number
# This would be a lot simpler if I could just raise the
# incombatiblersp error on the OO layer (that happens, but the id
# is not in the error messaage).
if rmf is None:
rmf0 = d.get_rmf()
elif numpy.iterable(rmf):
rmf0 = self.unpack_rmf(rmf[0])
else:
rmf0 = rmf
if d.channel is None:
d.channel = sao_arange(1, rmf0.detchans)
else:
if len(d.channel) != rmf0.detchans:
raise DataErr('incompatibleresp', rmf.name, str(id))
# at this point, we can be sure that arf is not a string, because
# if it was, it would have gone through load_arf already above.
if not (rmf is None and arf is None):
if numpy.iterable(arf):
self.load_multi_arfs(id, arf, range(len(arf)))
else:
self.set_arf(id, arf)
if numpy.iterable(rmf):
self.load_multi_rmfs(id, rmf, range(len(rmf)))
else:
self.set_rmf(id, rmf)
d.exposure = exposure
if backscal is not None:
d.backscal = backscal
if areascal is not None:
d.areascal = areascal
if quality is not None:
d.quality = quality
if grouping is not None:
d.grouping = grouping
if d.grouping is not None:
if sherpa.utils.bool_cast(grouped):
d.group()
else:
d.ungroup()
# Update background here. bkg contains a new background;
# delete the old background (if any) and add the new background
# to the simulated data set, BEFORE simulating data, and BEFORE
# adding scaled background counts to the simulated data.
bkg_models = {}
if bkg is not None:
if bkg == 'model':
bkg_models = {1: self.get_bkg_source(id)}
else:
for bkg_id in d.background_ids:
d.delete_background(bkg_id)
self.set_bkg(id, bkg)
# Calculate the source model, and take a Poisson draw based on
# the source model. That becomes the simulated data.
m = self.get_model(id)
fake.fake_pha(d, m, is_source=False, add_bkgs=bkg is not None,
id=str(id), bkg_models=bkg_models)
d.name = 'faked'
###########################################################################
# PSF
###########################################################################
[docs] def load_psf(self, modelname, filename_or_model, *args, **kwargs):
kernel = filename_or_model
if isinstance(filename_or_model, string_types):
try:
kernel = self._eval_model_expression(filename_or_model)
except:
kernel = self.unpack_data(filename_or_model,
*args, **kwargs)
psf = sherpa.astro.instrument.PSFModel(modelname, kernel)
if isinstance(kernel, sherpa.models.Model):
self.freeze(kernel)
self._add_model_component(psf)
self._psf_models.append(psf)
load_psf.__doc__ = sherpa.ui.utils.Session.load_psf.__doc__
###########################################################################
# Models
###########################################################################
# DOC-NOTE: also in sherpa.utils
[docs] def set_full_model(self, id, model=None):
"""Define the convolved model expression for a data set.
The model expression created by `set_model` can be modified by
"instrumental effects", such as PSF, ARF and RMF for PHA data
sets, or a pile up model. These can be set automatically - for
example, the ARF and RMF can be set up when the source data is
loaded - or explicitly with calls to routines like `set_psf`,
`set_arf`, `set_rmf`, and `set_pileup_model`. The
`set_full_model` function is for when this is not sufficient,
and full control is needed. Examples of when this would be
needed include: if different PSF models should be applied to
different source components; some source components need to
include the ARF and RMF but some do not.
Parameters
----------
id : int or str, optional
The data set containing the source expression. If not given
then the default identifier is used, as returned by
`get_default_id`.
model : str or sherpa.models.Model object
This defines the model used to fit the data. It can be a
Python expression or a string version of it.
See Also
--------
fit : Fit one or more data sets.
set_bkg_full_model : Define the convolved background model expression for a PHA data set.
set_pileup_model : Include a model of the Chandra ACIS pile up when fitting PHA data.
set_psf : Add a PSF model to a data set.
set_model : Set the source model expression for a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `model` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `model` parameters,
respectively.
Some functions - such as `plot_source` and `calc_energy_flux`
- may not work for model expressions created by
`set_full_model`.
Examples
--------
Extract the response - the combined RMF and ARF - for a PHA
data set - and apply it to a model (`xsphabs` * `xsapec`) and
then include a `powlaw1d` component that only includes the
RMF and a gaussian that has no instrumental response:
>>> rsp = get_response()
>>> rmf = get_rmf()
>>> smodel = xsphabs.galabs * xsapec.emiss
>>> bmodel = powlaw1d.pbgnd
>>> set_full_model(rsp(smodel) + rmf(bmodel) + gauss1d.iline)
Apply different PSFs to different components, as well as an
unconvolved component:
>>> load_psf("psf1", "psf1.fits")
>>> load_psf("psf2", "psf2.fits")
>>> smodel = psf1(gauss2d.src1) + psf2(beta2d.src2) + const2d.bgnd
>>> set_full_model("src", smodel)
"""
sherpa.ui.utils.Session.set_full_model(self, id, model)
if model is None:
id, model = model, id
data = self.get_data(id)
if isinstance(data, sherpa.astro.data.DataPHA):
model = self.get_model(id)
if data._responses:
instruments = (sherpa.astro.instrument.RSPModel,
sherpa.astro.instrument.RMFModel,
sherpa.astro.instrument.ARFModel,
sherpa.astro.instrument.MultiResponseSumModel,
sherpa.astro.instrument.PileupRMFModel)
do_warning = True
# if type(model) in instruments:
# if isinstance(model, instruments):
if sherpa.ui.utils._is_subclass(type(model), instruments):
do_warning = False
for part in model:
# if type(part) in instruments:
# if isinstance(part, instruments):
if sherpa.ui.utils._is_subclass(type(part), instruments):
do_warning = False
if do_warning:
warning("PHA source model '%s' \ndoes not" %
model.name +
" have an associated instrument model; " +
"consider using \nset_source() instead of" +
" set_full_model() to include associated " +
"\ninstrument automatically")
set_full_model.__doc__ = sherpa.ui.utils.Session.set_full_model.__doc__
def _add_convolution_models(self, id, data, model, is_source):
"""Add in "hidden" components to the model expression.
This includes PSF and pileup models and, for PHA data sets,
it adds in any background terms and the response function.
Notes
-----
If a background is added to a PHA data set using a vector,
rather than scalar, value, the code has to convert from
the model evaluation grid (e.g. keV or Angstroms) to the
scale array, which will be in channels. The only way to do
this is to apply the instrument response to the background
model separately from the source model, which will fail if
the instrument model is not linear, such as the jdpileup
model.
"""
id = self._fix_id(id)
# Add any convolution components from the sherpa.ui layer
model = super()._add_convolution_models(id, data, model, is_source)
# If we don't need to deal with DataPHA issues we can return
if not isinstance(data, sherpa.astro.data.DataPHA) or not is_source:
return model
return sherpa.astro.background.add_response(self, id, data, model)
def _get_response(self, id, pha):
"""Calculate the response for the dataset.
Parameter
---------
id : int or str
The identifier (this is required to be valid).
pha : sherpa.astro.data.DataPHA
The dataset
Returns
-------
response
The return value depends on whether an ARF, RMF, or pile up
model has been associated with the data set.
"""
pileup_model = self._pileup_models.get(id)
return pha.get_full_response(pileup_model)
[docs] def get_response(self, id=None, bkg_id=None):
"""Return the response information applied to a PHA data set.
For a PHA data set, the source model - created by `set_model`
- is modified by a model representing the instrumental effects
- such as the effective area of the mirror, the energy
resolution of the detector, and any model of pile up - which
is collectively known as the instrument response. The
`get_response` function returns the instrument response model.
Parameters
----------
id : int or str, optional
The data set containing the instrument response. If not given
then the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or str, optional
If given, return the response for the given background
component, rather than the source.
Returns
-------
response
The return value depends on whether an ARF, RMF, or pile up
model has been associated with the data set.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
See Also
--------
get_arf : Return the ARF associated with a PHA data set.
get_pileup_model : Return the pile up model for a data set.
get_rmf : Return the RMF associated with a PHA data set.
set_bkg_full_model : Define the convolved background model expression for a PHA data set.
set_full_model : Define the convolved model expression for a data set.
Examples
--------
Create an empty PHA data set, load in an ARF and RMF, and then
retrieve the response. The response is then used to model the
instrument response applied to a `powlaw1d` model component,
along with a constant component (`bgnd`) that does not
"pass through" the instrument response:
>>> dataspace1d(1, 1024, 1, dstype=DataPHA)
>>> load_arf('src.arf')
>>> load_rmf('src.rmf')
>>> rsp = get_response()
>>> set_full_model(rsp(powlaw1d.pl) + const1d.bgnd)
"""
id = self._fix_id(id)
if bkg_id is not None:
pha = self.get_bkg(id, bkg_id)
else:
pha = self._get_pha_data(id)
return self._get_response(id, pha)
[docs] def get_pileup_model(self, id=None):
"""Return the pile up model for a data set.
Return the pile up model set by a call to `set_pileup_model`.
Parameters
----------
id : int or str, optional
The data set containing the source expression. If not given
then the default identifier is used, as returned by
`get_default_id`.
Returns
-------
model : a `sherpa.astro.models.JDPileup` instance
Raises
------
sherpa.utils.err.IdentifierErr
If no pile up model has been set for the data set.
See Also
--------
delete_pileup_model : Delete the pile up model for a data set.
fit : Fit one or more data sets.
get_model : Return the model expression for a data set.
get_source : Return the source model expression for a data set.
sherpa.astro.models.JDPileup : The ACIS pile up model.
list_pileup_model_ids : List of all the data sets with a pile up model.
set_pileup_model : Include a model of the Chandra ACIS pile up when fitting PHA data.
Examples
--------
>>> jdp1 = get_pileup_model()
>>> jdp2 = get_pileup_model(2)
"""
return self._get_item(id, self._pileup_models, 'pileup model',
'has not been set')
[docs] def delete_pileup_model(self, id=None):
"""Delete the pile up model for a data set.
Remove the pile up model applied to a source model.
.. versionadded:: 4.12.2
Parameters
----------
id : int or str, optional
The data set. If not given then the
default identifier is used, as returned by `get_default_id`.
See Also
--------
get_pileup_model : Return the pile up model for a data set.
list_pileup_model_ids : List of all the data sets with a pile up model.
set_pileup_model : Add a pile up model to a data set.
Examples
--------
>>> delete_pileup_model()
>>> delete_pileup_model('core')
"""
id = self._fix_id(id)
self._pileup_models.pop(id, None)
[docs] def list_pileup_model_ids(self):
"""List of all the data sets with a pile up model.
.. versionadded:: 4.12.2
Returns
-------
ids : list of int or str
The identifiers for all the data sets which have a pile up
model set by `set_pileup_model`.
See Also
--------
list_data_ids : List the identifiers for the loaded data sets.
list_model_ids : List of all the data sets with a source expression.
set_pileup_model : Add a pile up model to a data set.
"""
keys = list(self._pileup_models.keys())
return sorted(keys, key=str)
# DOC-NOTE: should this be made a general function, since it
# presumably does not care about pileup, just adds the
# given model into the expression? Or is it PHA specific?
[docs] def set_pileup_model(self, id, model=None):
"""Include a model of the Chandra ACIS pile up when fitting PHA data.
Chandra observations of bright sources can be affected by
pileup, so that there is a non-linear correlation between
the source model and the predicted counts. This process can
be modelled by including the `jdpileup` model for a
data set, using the `set_pileup_model`.
Parameters
----------
id : int or str, optional
The data set containing the source expression. If not given
then the default identifier is used, as returned by
`get_default_id`.
model : an instance of the `sherpa.astro.models.JDPileup` class
See Also
--------
delete_pileup_model : Delete the pile up model for a data set.
fit : Fit one or more data sets.
get_pileup_model : Return the pile up model for a data set.
sherpa.models.model.JDPileup : The ACIS pile up model.
list_pileup_model_ids : List of all the data sets with a pile up model.
set_full_model : Define the convolved model expression for a data set.
set_model : Set the source model expression for a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `model` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `model` parameters,
respectively.
This is a generic function, and can be used to model other
non-linear detector effects, but at present the only available
model is for the ACIS pile up provided by the jdpileup model.
Examples
--------
Plot up the model (an xsphabs model multiplied by a powlaw1d
component) and then overplot the same expression but including
the effects of pile up in the Chandra ACIS instrument:
>>> load_pha('src.pi')
>>> set_source(xsphabs.gal * powlaw1d.pl)
>>> plot_model()
>>> set_pileup_model(jdpileup.jpd)
>>> plot_model(overplot=True)
"""
if model is None:
id, model = model, id
if isinstance(model, string_types):
model = self._eval_model_expression(model)
self._set_item(id, model, self._pileup_models, sherpa.models.Model,
'model', 'a model object or model expression string')
[docs] def get_bkg_source(self, id=None, bkg_id=None):
"""Return the model expression for the background of a PHA data set.
This returns the model expression created by `set_bkg_model`
or `set_bkg_source`. It does not include any instrument
response.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
Returns
-------
model : a sherpa.models.Model object
This can contain multiple model components. Changing
attributes of this model changes the model used by the data
set.
See Also
--------
delete_bkg_model : Delete the background model expression for a data set.
get_bkg_model : Return the model expression for the background of a PHA data set.
list_model_ids : List of all the data sets with a source expression.
set_bkg_model : Set the background model expression for a PHA data set.
show_bkg_model : Display the background model expression for a data set.
Examples
--------
Return the background model expression for the default data
set:
>>> bkg = get_bkg_source()
>>> len(bkg.pars)
2
"""
id = self._fix_id(id)
bkg_id = self._fix_background_id(id, bkg_id)
model = self._background_sources.get(id, {}).get(bkg_id)
if model is None:
raise ModelErr('nobkg', bkg_id, id)
return model
[docs] def get_bkg_model(self, id=None, bkg_id=None):
"""Return the model expression for the background of a PHA data set.
This returns the model expression for the background of a data
set, including the instrument response (e.g. ARF and RMF),
whether created automatically or explicitly, with
``set_bkg_full_model``.
Parameters
----------
id : int or str, optional
The data set to use. If not given then the default
identifier is used, as returned by ``get_default_id``.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
Returns
-------
instance
This can contain multiple model components and any
instrument response. Changing attributes of this model
changes the model used by the data set.
See Also
--------
delete_bkg_model : Delete the background model expression for a data set.
get_bkg_source : Return the model expression for the background of a PHA data set.
list_model_ids : List of all the data sets with a source expression.
set_bkg_model : Set the background model expression for a PHA data set.
set_bkg_full_model : Define the convolved background model expression for a PHA data set.
show_bkg_model : Display the background model expression for a data set.
Examples
--------
Return the background model expression for the default data
set, including any instrument response:
>>> bkg = get_bkg_model()
"""
id = self._fix_id(id)
bkg_id = self._fix_background_id(id, bkg_id)
mdl = self._background_models.get(id, {}).get(bkg_id)
if mdl is None:
is_source = True
src = self._background_sources.get(id, {}).get(bkg_id)
else:
is_source = False
src = mdl
if src is None:
raise ModelErr('nobkg', bkg_id, id)
if not is_source:
return src
# The background response is set by the DataPHA.set_background
# method (copying one over, if it does not exist), which means
# that the only way to get to this point is if the user has
# explicitly deleted the background response. In this case
# we error out.
#
bkg = self.get_bkg(id, bkg_id)
if len(bkg.response_ids) == 0:
raise DataErr('nobrsp', str(id), str(bkg_id))
resp = sherpa.astro.instrument.Response1D(bkg)
return resp(src)
[docs] def set_bkg_full_model(self, id, model=None, bkg_id=None):
"""Define the convolved background model expression for a PHA data set.
Set a model expression for a background data set in the same
way that `set_full_model` does for a source. This is for when
the background is being fitted simultaneously to the source,
rather than subtracted from it.
Parameters
----------
id : int or str, optional
The data set containing the source expression. If not given
then the default identifier is used, as returned by
`get_default_id`.
model : str or sherpa.models.Model object
This defines the model used to fit the data. It can be a
Python expression or a string version of it.
bkg_id : int or str, optional
The identifier for the background of the data set, in
cases where multiple backgrounds are provided.
See Also
--------
fit : Fit one or more data sets.
set_full_model : Define the convolved model expression for a data set.
set_pileup_model : Include a model of the Chandra ACIS pile up when fitting PHA data.
set_psf : Add a PSF model to a data set.
set_model : Set the source model expression for a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `model` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `model` parameters,
respectively.
Some functions - such as `plot_bkg_source` - may not work for
model expressions created by `set_bkg_full_model`.
Examples
--------
The background is fit by two power laws - one that is passed
through the instrument response (``gbgnd``) and one that is not
(``pbgnd``). The source is modelled by ``xsphabs * galabs``,
together with the background model, scaled by the ratio of
area and time. Note that the background component in the
source expression uses the source response rather than
background response.
>>> rsp = get_response()
>>> bresp = get_response(bkg_id=1)
>>> bscale = get_bkg_scale()
>>> smodel = xsphabs.galabs * xsapec.emiss
>>> bmdl = brsp(powlaw1d.gbdng) + powlaw1d.pbgnd
>>> smdl = rsp(smodel) + bscale*(rsp(gbgnd) + pbgnd)
>>> set_full_model(smdl)
>>> set_bkg_full_model(bmdl)
"""
if model is None:
id, model = model, id
id = self._fix_id(id)
bkg_id = self._fix_background_id(id, bkg_id)
if isinstance(model, string_types):
model = self._eval_model_expression(model)
_check_type(model, sherpa.models.Model, 'model',
'a model object or model expression string')
self._background_models.setdefault(id, {})[bkg_id] = model
data = self.get_bkg(id, bkg_id)
if data.units != 'channel' and data._responses:
instruments = (sherpa.astro.instrument.RSPModel,
sherpa.astro.instrument.RMFModel,
sherpa.astro.instrument.ARFModel,
sherpa.astro.instrument.MultiResponseSumModel,
sherpa.astro.instrument.PileupRMFModel)
do_warning = True
# if type(model) in instruments:
# if isinstance(model, instruments):
if sherpa.ui.utils._is_subclass(type(model), instruments):
do_warning = False
for part in model:
# if type(part) in instruments:
# if isinstance(part, instruments):
if sherpa.ui.utils._is_subclass(type(part), instruments):
do_warning = False
if do_warning:
self.delete_bkg_model(id, bkg_id)
raise TypeError("PHA background source model '%s' \n" % model.name +
" does not have an associated instrument model;" +
" consider using\n set_bkg_source() instead of" +
" set_bkg_model() to include associated\n instrument" +
" automatically")
self._runparamprompt(model.pars)
# DOC-TODO: should probably explain more about how backgrounds are fit?
[docs] def set_bkg_model(self, id, model=None, bkg_id=None):
"""Set the background model expression for a PHA data set.
The background emission can be fit by a model, defined by the
`set_bkg_model` call, rather than subtracted from the data.
If the background is subtracted then the background model is
ignored when fitting the data.
Parameters
----------
id : int or str, optional
The data set containing the source expression. If not given
then the default identifier is used, as returned by
`get_default_id`.
model : str or sherpa.models.Model object
This defines the model used to fit the data. It can be a
Python expression or a string version of it.
bkg_id : int or str, optional
The identifier for the background of the data set, in
cases where multiple backgrounds are provided.
See Also
--------
delete_model : Delete the model expression from a data set.
fit : Fit one or more data sets.
integrate1d : Integrate 1D source expressions.
set_model : Set the model expression for a data set.
set_bkg_full_model : Define the convolved background model expression for a PHA data set.
show_bkg_model : Display the background model expression for a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `model` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `model` parameters,
respectively.
The emission defined by the background model expression is
included in the fit to the source dataset, scaling by exposure
time and area size (given by the ratio of the background to
source BACKSCAL values). That is, if ``src_model`` and
``bkg_model`` represent the source and background model
expressions set by calls to `set_model` and `set_bkg_model`
respectively, the source data is fit by::
src_model + scale * bkg_model
where ``scale`` is the scaling factor.
PHA data sets will automatically apply the instrumental
response (ARF and RMF) to the background expression. For some
cases this is not useful - for example, when different
responses should be applied to different model components - in
which case `set_bkg_full_model` should be used instead.
Examples
--------
The background is model by a gaussian line (``gauss1d`` model
component called ``bline``) together with an absorbed polynomial
(the ``bgnd`` component). The absorbing component (``gal``) is
also used in the source expression.
>>> set_model(xsphabs.gal*powlaw1d.pl)
>>> set_bkg_model(gauss1d.bline + gal*polynom1d.bgnd)
In this example, the default data set has two background
estimates, so models are set for both components. The same
model is applied to both, except that the relative
normalisations are allowed to vary (by inclusion of the
``scale`` component).
>>> bmodel = xsphabs.gabs * powlaw1d.pl
>>> set_bkg_model(2, bmodel)
>>> set_bkg_model(2, bmodel * const1d.scale, bkg_id=2)
"""
if model is None:
id, model = model, id
id = self._fix_id(id)
bkg_id = self._fix_background_id(id, bkg_id)
if isinstance(model, string_types):
model = self._eval_model_expression(model)
_check_type(model, sherpa.models.Model, 'model',
'a model object or model expression string')
self._background_sources.setdefault(id, {})[bkg_id] = model
self._runparamprompt(model.pars)
# Delete any previous model set with set_full_bkg_model()
bkg_mdl = self._background_models.get(id, {}).pop(bkg_id, None)
if bkg_mdl is not None:
warning("Clearing background convolved model\n'%s'\n" %
(bkg_mdl.name) + "for dataset %s background %s" %
(str(id), str(bkg_id)))
set_bkg_source = set_bkg_model
[docs] def delete_bkg_model(self, id=None, bkg_id=None):
"""Delete the background model expression for a data set.
This removes the model expression, created by `set_bkg_model`,
for the background component of a data set. It does not delete
the components of the expression, or remove the models for any
other background components or the source of the data set.
Parameters
----------
id : int or str, optional
The data set containing the source expression. If not given
then the default identifier is used, as returned by
`get_default_id`.
bkg_id : int or string, optional
The identifier for the background component to use.
See Also
--------
clean : Clear all stored session data.
delete_model : Delete the model expression for a data set.
get_default_id : Return the default data set identifier.
list_bkg_ids : List all the background identifiers for a data set.
set_model : Set the source model expression for a data set.
show_model : Display the source model expression for a data set.
Examples
--------
Remove the background model expression for the default data set:
>>> delete_bkg_model()
Remove the model expression for the background component
labelled 'down' for the data set with the identifier 'src':
>>> delete_bkg_model('src', 'down')
"""
id = self._fix_id(id)
bkg_id = self._fix_background_id(id, bkg_id)
# remove dependency of having a loaded PHA dataset at the time
# of bkg model init.
# bkg_id = self._get_pha_data(id)._fix_background_id(bkg_id)
self._background_models.get(id, {}).pop(bkg_id, None)
self._background_sources.get(id, {}).pop(bkg_id, None)
def _read_user_model(self, filename, *args, **kwargs):
x = None
y = None
try:
data = self.unpack_ascii(filename, *args, **kwargs)
x = data.get_x()
y = data.get_y()
# we have to check for the case of a *single* column in an ascii file
# extract the single array from the read and bypass the dataset
except TypeError:
y = sherpa.astro.io.backend.get_ascii_data(filename, *args,
**kwargs)[1].pop()
except:
try:
data = self.unpack_table(filename, *args, **kwargs)
x = data.get_x()
y = data.get_y()
# we have to check for the case of a *single* column in a
# fits table
# extract the single array from the read and bypass the dataset
except TypeError:
y = sherpa.astro.io.backend.get_table_data(filename, *args,
**kwargs)[1].pop()
except:
# unpack_data doesn't include a call to try
# getting data from image, so try that here.
data = self.unpack_image(filename, *args, **kwargs)
# x = data.get_x()
y = data.get_y()
return (x, y)
[docs] def load_xstable_model(self, modelname, filename, etable=False):
"""Load a XSPEC table model.
Create an additive ('atable', [1]_), multiplicative
('mtable', [2]_), or exponential ('etable', [3]_) XSPEC
table model component. These models may have multiple model
parameters.
.. versionchanged:: 4.14.0
The etable argument has been added to allow exponential table
models to be used.
Parameters
----------
modelname : str
The identifier for this model component.
filename : str
The name of the FITS file containing the data, which should
match the XSPEC table model definition [4]_.
etable : bool, optional
Set if this is an etable (as there's no way to determine this
from the file itself). Defaults to False.
Raises
------
sherpa.utils.err.ImportErr
If XSPEC support is not enabled.
See Also
--------
load_conv : Load a 1D convolution model.
load_psf : Create a PSF model
load_template_model : Load a set of templates and use it as a model component.
load_table_model : Load tabular or image data and use it as a model component.
set_model : Set the source model expression for a data set.
set_full_model : Define the convolved model expression for a data set.
Notes
-----
NASA's HEASARC site contains a link to community-provided
XSPEC table models [5]_.
References
----------
.. [1] http://heasarc.gsfc.nasa.gov/docs/xanadu/xspec/manual/XSmodelAtable.html
.. [2] http://heasarc.gsfc.nasa.gov/docs/xanadu/xspec/manual/XSmodelMtable.html
.. [3] http://heasarc.gsfc.nasa.gov/docs/xanadu/xspec/manual/XSmodelEtable.html
.. [4] http://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/general/ogip_92_009/ogip_92_009.html
.. [5] https://heasarc.gsfc.nasa.gov/xanadu/xspec/newmodels.html
Examples
--------
Load in the XSPEC table model from the file 'bbrefl_1xsolar.fits'
and create a model component labelled 'xtbl', which is then
used in a source expression:
>>> load_xstable_model('xtbl', 'bbrefl_1xsolar.fits')
>>> set_source(xsphabs.gal * xtbl)
>>> print(xtbl)
Load in an XSPEC etable model:
>>> load_xstable_model('etbl', 'etable.mod', etable=True)
"""
try:
from sherpa.astro import xspec
except ImportError as exc:
# TODO: what is the best error to raise here?
raise ImportErr('notsupported', 'XSPEC') from exc
tablemodel = xspec.read_xstable_model(modelname, filename, etable=etable)
self._tbl_models.append(tablemodel)
self._add_model_component(tablemodel)
# also in sherpa.utils
# DOC-NOTE: can filename be a crate/hdulist?
# DOC-TODO: how to describe the supported args/kwargs (not just for this function)?
[docs] def load_table_model(self, modelname, filename,
method=sherpa.utils.linear_interp, *args, **kwargs):
# pylint: disable=W1113
"""Load tabular or image data and use it as a model component.
.. note:: Deprecated in Sherpa 4.9
The new `load_xstable_model` routine should be used for
loading XSPEC table model files. Support for these files
will be removed from `load_table_model` in the next
release.
A table model is defined on a grid of points which is
interpolated onto the independent axis of the data set. The
model will have at least one parameter (the amplitude, or
scaling factor to multiply the data by), but may have more
(if X-Spec table models are used).
Parameters
----------
modelname : str
The identifier for this table model.
filename : str
The name of the file containing the data, which should
contain two columns, which are the x and y values for
the data, or be an image.
method : func
The interpolation method to use to map the input data onto
the coordinate grid of the data set. Linear,
nearest-neighbor, and polynomial schemes are provided in
the sherpa.utils module.
args
Arguments for reading in the data.
kwargs
Keyword arguments for reading in the data.
See Also
--------
load_conv : Load a 1D convolution model.
load_psf : Create a PSF model
load_template_model : Load a set of templates and use it as a model component.
load_xstable_model : Load a XSPEC table model.
set_model : Set the source model expression for a data set.
set_full_model : Define the convolved model expression for a data set.
Notes
-----
Examples of interpolation schemes provided by `sherpa.utils`
are: `linear_interp`, `nearest_interp`, `neville`, and
`neville2d`.
Examples
--------
Load in the data from filt.fits and use it to multiply
the source model (a power law and a gaussian). Allow
the amplitude for the table model to vary between 1
and 1e6, starting at 1e3.
>>> load_table_model('filt', 'filt.fits')
>>> set_source(filt * (powlaw1d.pl + gauss1d.gline))
>>> set_par(filt.ampl, 1e3, min=1, max=1e6)
Load in an image ("broad.img") and use the pixel values as a
model component for data set "img":
>>> load_table_model('emap', 'broad.img')
>>> set_source('img', emap * gauss2d)
"""
tablemodel = TableModel(modelname)
# interpolation method
tablemodel.method = method
tablemodel.filename = filename
try:
if not sherpa.utils.is_binary_file(filename):
# TODO: use a Sherpa exception
raise Exception("Not a FITS file")
self.load_xstable_model(modelname, filename)
warnings.warn('Use load_xstable_model to load XSPEC table models',
DeprecationWarning)
return
except Exception:
x = None
y = None
try:
x, y = self._read_user_model(filename, *args, **kwargs)
except:
# Fall back to reading plain ASCII, if no other
# more sophisticated I/O backend loaded (such as
# pyfits or crates) SMD 05/29/13
data = sherpa.io.read_data(filename, ncols=2)
x = data.x
y = data.y
tablemodel.load(x, y)
self._tbl_models.append(tablemodel)
self._add_model_component(tablemodel)
# ## also in sherpa.utils
# DOC-TODO: how to describe *args/**kwargs
# DOC-TODO: how is the _y value used if set
[docs] def load_user_model(self, func, modelname, filename=None, *args, **kwargs):
# pylint: disable=W1113
"""Create a user-defined model.
Assign a name to a function; this name can then be used as any
other name of a model component, either in a source expression
- such as with `set_model` - or to change a parameter
value. The `add_user_pars` function should be called after
`load_user_model` to set up the parameter names and
defaults.
Parameters
----------
func : func
The function that evaluates the model.
modelname : str
The name to use to refer to the model component.
filename : str, optional
Set this to include data from this file in the model. The
file should contain two columns, and the second column is
stored in the ``_y`` attribute of the model.
args
Arguments for reading in the data from `filename`, if set.
See `load_table` and `load_image` for more information.
kwargs
Keyword arguments for reading in the data from `filename`,
if set. See `load_table` and `load_image` for more information.
See Also
--------
add_model : Create a user-defined model class.
add_user_pars : Add parameter information to a user model.
load_image : Load an image as a data set.
load_table : Load a FITS binary file as a data set.
load_table_model : Load tabular data and use it as a model component.
load_template_model : Load a set of templates and use it as a model component.
set_model : Set the source model expression for a data set.
Notes
-----
The `load_user_model` function is designed to make it easy to
add a model, but the interface is not the same as the existing
models (such as having to call both `load_user_model` and
`add_user_pars` for each new instance). The `add_model`
function is used to add a model as a Python class, which is
more work to set up, but then acts the same way as the
existing models.
The function used for the model depends on the dimensions of
the data. For a 1D model, the signature is::
def func1d(pars, x, xhi=None):
where, if xhi is not None, then the dataset is binned and the
x argument is the low edge of each bin. The pars argument is
the parameter array - the names, defaults, and limits can be
set with `add_user_pars` - and should not be changed. The
return value is an array the same size as x.
For 2D models, the signature is::
def func2d(pars, x0, x1, x0hi=None, x1hi=None):
There is no way using this interface to indicate that the
model is for 1D or 2D data.
Examples
--------
Create a two-parameter model of the form "y = mx + c",
where the intercept is the first parameter and the slope the
second, set the parameter names and default values, then
use it in a source expression:
>>> def func1d(pars, x, xhi=None):
... if xhi is not None:
... x = (x + xhi)/2
... return x * pars[1] + pars[0]
...
>>> load_user_model(func1d, "myfunc")
>>> add_user_pars(myfunc, ["c", "m"], [0, 1])
>>> set_source(myfunc + gauss1d.gline)
"""
usermodel = sherpa.models.UserModel(modelname)
usermodel.calc = func
usermodel._file = filename
if filename is not None:
_, usermodel._y = self._read_user_model(filename, *args, **kwargs)
self._add_model_component(usermodel)
###########################################################################
# Fitting
###########################################################################
# TODO: change bkg_ids default to None or some other "less-dangerous" value
def _add_extra_data_and_models(self, ids, datasets, models, bkg_ids={}):
for id, d in zip(ids, datasets):
if isinstance(d, sherpa.astro.data.DataPHA):
bkg_models = self._background_models.get(id, {})
bkg_srcs = self._background_sources.get(id, {})
if d.subtracted:
if (bkg_models or bkg_srcs):
warning(('data set %r is background-subtracted; ' +
'background models will be ignored') % id)
elif not (bkg_models or bkg_srcs):
if d.background_ids and self._current_stat.name != 'wstat':
warning(('data set %r has associated backgrounds, ' +
'but they have not been subtracted, ' +
'nor have background models been set') % id)
else:
bkg_ids[id] = []
for bkg_id in d.background_ids:
if not (bkg_id in bkg_models or bkg_id in bkg_srcs):
raise ModelErr('nobkg', bkg_id, id)
bkg = d.get_background(bkg_id)
datasets.append(bkg)
bkg_data = d
if len(bkg.response_ids) != 0:
bkg_data = bkg
bkg_model = bkg_models.get(bkg_id, None)
bkg_src = bkg_srcs.get(bkg_id, None)
if bkg_model is None and bkg_src is not None:
resp = sherpa.astro.instrument.Response1D(bkg_data)
bkg_model = resp(bkg_src)
models.append(bkg_model)
bkg_ids[id].append(bkg_id)
def _prepare_bkg_fit(self, id, otherids=()):
# prep data ids for fitting
ids = self._get_fit_ids(id, otherids)
# Gather up lists of data objects and models to fit
# to them. Add to lists *only* if there actually is
# a model to fit. E.g., if data sets 1 and 2 exist,
# but only data set 1 has a model, then "fit all" is
# understood to mean "fit 1". If both data sets have
# models, then "fit all" means "fit 1 and 2 together".
datasets = []
models = []
fit_to_ids = []
for i in ids:
# get PHA data and associated background models by id
data = self._get_pha_data(i)
bkg_models = self._background_models.get(i, {})
bkg_sources = self._background_sources.get(i, {})
for bi in data.background_ids:
mod = None
ds = self.get_bkg(i, bi)
if bi in bkg_models or bi in bkg_sources:
mod = self.get_bkg_model(i, bi)
if mod is not None:
datasets.append(ds)
models.append(mod)
fit_to_ids.append(i)
# If no data sets have models assigned to them, stop now.
if len(models) < 1:
raise IdentifierErr("nomodels")
return fit_to_ids, datasets, models
def _get_bkg_fit(self, id, otherids=(), estmethod=None, numcores=1):
fit_to_ids, datasets, models = self._prepare_bkg_fit(id, otherids)
# Do not add backgrounds to backgrounds.
# self._add_extra_data_and_models(fit_to_ids, datasets, models)
fit_to_ids = tuple(fit_to_ids)
f = self._get_fit_obj(datasets, models, estmethod, numcores)
return fit_to_ids, f
# also in sherpa.utils
# DOC-TODO: existing docs suggest that bkg_only can be set, but looking
# at the code it is always set to False.
#
[docs] def fit(self, id=None, *otherids, **kwargs):
# pylint: disable=W1113
"""Fit a model to one or more data sets.
Use forward fitting to find the best-fit model to one or more
data sets, given the chosen statistic and optimization
method. The fit proceeds until the results converge or the
number of iterations exceeds the maximum value (these values
can be changed with `set_method_opt`). An iterative scheme can
be added using `set_iter_method` to try and improve the
fit. The final fit results are displayed to the screen and can
be retrieved with `get_fit_results`.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then
all data sets with an associated model are fit simultaneously.
*otherids : sequence of int or str, optional
Other data sets to use in the calculation.
outfile : str, optional
If set, then the fit results will be written to a file with
this name. The file contains the per-iteration fit results.
clobber : bool, optional
This flag controls whether an existing file can be
overwritten (``True``) or if it raises an exception (``False``,
the default setting).
Raises
------
sherpa.utils.err.FitErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
conf : Estimate the confidence intervals using the confidence method.
contour_fit : Contour the fit to a data set.
covar : Estimate the confidence intervals using the confidence method.
fit_bkg : Fit a model to one or more background PHA data sets.
freeze : Fix model parameters so they are not changed by a fit.
get_fit_results : Return the results of the last fit.
plot_fit : Plot the fit results (data, model) for a data set.
image_fit : Display the data, model, and residuals for a data set in the image viewer.
set_stat : Set the statistical method.
set_method : Change the optimization method.
set_method_opt : Change an option of the current optimization method.
set_bkg_full_model : Define the convolved background model expression for a PHA data set.
set_bkg_model : Set the background model expression for a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_iter_method : Set the iterative-fitting scheme used in the fit.
set_model : Set the model expression for a data set.
show_fit : Summarize the fit results.
thaw : Allow model parameters to be varied during a fit.
Notes
-----
For PHA data sets with background components, the function
will fit any background components for which a background
model has been created (rather than being subtracted). The
`fit_bkg` function can be used to fit models to just the
background data.
Examples
--------
Simultaneously fit all data sets with models and then
store the results in the variable fres:
>>> fit()
>>> fres = get_fit_results()
Fit just the data set 'img':
>>> fit('img')
Simultaneously fit data sets 1, 2, and 3:
>>> fit(1, 2, 3)
Fit data set 'jet' and write the fit results to the text file
'jet.fit', over-writing it if it already exists:
>>> fit('jet', outfile='jet.fit', clobber=True)
"""
kwargs['bkg_only'] = False
self._fit(id, *otherids, **kwargs)
[docs] def fit_bkg(self, id=None, *otherids, **kwargs):
# pylint: disable=W1113
"""Fit a model to one or more background PHA data sets.
Fit only the backgound components of PHA data sets. This can
be used to find the best-fit background parameters, which can
then be frozen before fitting the data, or to ensure that
these parameters are well defined before performing a
simultaneous source and background fit.
Parameters
----------
id : int or str, optional
The data set that provides the background data. If not
given then all data sets with an associated background
model are fit simultaneously.
*otherids : sequence of int or str, optional
Other data sets to use in the calculation.
outfile : str, optional
If set, then the fit results will be written to a file with
this name. The file contains the per-iteration fit results.
clobber : bool, optional
This flag controls whether an existing file can be
overwritten (``True``) or if it raises an exception (``False``,
the default setting).
Raises
------
sherpa.utils.err.FitErr
If `filename` already exists and `clobber` is ``False``.
See Also
--------
conf : Estimate the confidence intervals using the confidence method.
contour_fit : Contour the fit to a data set.
covar : Estimate the confidence intervals using the confidence method.
fit : Fit a model to one or more data sets.
freeze : Fix model parameters so they are not changed by a fit.
get_fit_results : Return the results of the last fit.
plot_fit : Plot the fit results (data, model) for a data set.
image_fit : Display the data, model, and residuals for a data set in the image viewer.
set_stat : Set the statistical method.
set_method : Change the optimization method.
set_method_opt : Change an option of the current optimization method.
set_bkg_full_model : Define the convolved background model expression for a PHA data set.
set_bkg_model : Set the background model expression for a PHA data set.
set_full_model : Define the convolved model expression for a data set.
set_iter_method : Set the iterative-fitting scheme used in the fit.
set_model : Set the model expression for a data set.
show_bkg_source : Display the background model expression for a data set.
show_bkg_model : Display the background model expression used to fit a data set.
show_fit : Summarize the fit results.
thaw : Allow model parameters to be varied during a fit.
Notes
-----
This is only for PHA data sets where the background is being
modelled, rather than subtracted from the data.
Examples
--------
Simultaneously fit all background data sets with models and
then store the results in the variable fres:
>>> fit_bkg()
>>> fres = get_fit_results()
Fit the background for data sets 1 and 2, then do a
simultaneous fit to the source and background data sets:
>>> fit_bkg(1,2)
>>> fit(1,2)
"""
kwargs['bkg_only'] = True
self._fit(id, *otherids, **kwargs)
def _fit(self, id=None, *otherids, **kwargs):
# pylint: disable=W1113
ids = f = None
fit_bkg = False
if 'bkg_only' in kwargs and kwargs.pop('bkg_only'):
fit_bkg = True
# validate the kwds to f.fit() so user typos do not
# result in regular fit
# valid_keys = sherpa.utils.get_keyword_names(sherpa.fit.Fit.fit)
valid_keys = ('outfile', 'clobber', 'filter_nan', 'cache', 'numcores')
for key in kwargs.keys():
if key not in valid_keys:
raise TypeError("unknown keyword argument: '%s'" % key)
numcores = kwargs.get('numcores', 1)
if fit_bkg:
ids, f = self._get_bkg_fit(id, otherids, numcores=numcores)
else:
ids, f = self._get_fit(id, otherids, numcores=numcores)
if 'filter_nan' in kwargs and kwargs.pop('filter_nan'):
for i in ids:
self.get_data(i).mask = self.get_data(
i).mask & numpy.isfinite(self.get_data(i).get_x())
res = f.fit(**kwargs)
res.datasets = ids
self._fit_results = res
info(res.format())
def _get_stat_info(self):
ids, datasets, models = self._prepare_fit(None)
extra_ids = {}
self._add_extra_data_and_models(ids, datasets, models, extra_ids)
output = []
nids = len(ids)
if len(datasets) > 1:
bkg_datasets = datasets[nids:]
bkg_models = models[nids:]
jj = 0
for id, d, m in zip(ids, datasets[:nids], models[:nids]):
f = sherpa.fit.Fit(d, m, self._current_stat)
statinfo = f.calc_stat_info()
statinfo.name = 'Dataset %s' % (str(id))
statinfo.ids = (id,)
output.append(statinfo)
bkg_ids = extra_ids.get(id, ())
nbkg_ids = len(bkg_ids)
idx_lo = jj * nbkg_ids
idx_hi = idx_lo + nbkg_ids
for bkg_id, bkg, bkg_mdl in zip(bkg_ids,
bkg_datasets[idx_lo:idx_hi],
bkg_models[idx_lo:idx_hi]):
bkg_f = sherpa.fit.Fit(bkg, bkg_mdl, self._current_stat)
statinfo = bkg_f.calc_stat_info()
statinfo.name = ("Background %s for Dataset %s" %
(str(bkg_id), str(id)))
statinfo.ids = (id,)
statinfo.bkg_ids = (bkg_id,)
output.append(statinfo)
jj += 1
f = self._get_fit_obj(datasets, models, None)
statinfo = f.calc_stat_info()
if len(ids) == 1:
statinfo.name = 'Dataset %s' % str(ids)
else:
statinfo.name = 'Datasets %s' % str(ids).strip("()")
statinfo.ids = ids
output.append(statinfo)
return output
###########################################################################
# Plotting
###########################################################################
[docs] def get_data_plot(self, id=None, recalc=True):
try:
d = self.get_data(id)
except IdentifierErr:
return super().get_data_plot(id, recalc=recalc)
if isinstance(d, sherpa.astro.data.DataPHA):
plotobj = self._dataphaplot
if recalc:
plotobj.prepare(d, self.get_stat())
return plotobj
return super().get_data_plot(id, recalc=recalc)
get_data_plot.__doc__ = sherpa.ui.utils.Session.get_data_plot.__doc__
[docs] def get_model_plot(self, id=None, recalc=True):
try:
d = self.get_data(id)
except IdentifierErr:
return super().get_model_plot(id, recalc=recalc)
if isinstance(d, sherpa.astro.data.DataPHA):
plotobj = self._modelhisto
if recalc:
plotobj.prepare(d, self.get_model(id), self.get_stat())
return plotobj
return super().get_model_plot(id, recalc=recalc)
get_model_plot.__doc__ = sherpa.ui.utils.Session.get_model_plot.__doc__
# also in sherpa.utils, but without the lo/hi arguments
[docs] def get_source_plot(self, id=None, lo=None, hi=None, recalc=True):
"""Return the data used by plot_source.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
lo : number, optional
The low value to plot (only used for PHA data sets).
hi : number, optional
The high value to plot (only use for PHA data sets).
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_source` (or `get_source_plot`) are returned,
otherwise the data is re-generated.
Returns
-------
instance
An object representing the data used to create the plot by
`plot_source`. The return value depends on the data
set (e.g. PHA, 1D binned, 1D un-binned). If ``lo`` or ``hi``
were set then the ``mask`` attribute of the object can be
used to apply the filter to the ``xlo``, ``xhi``, and ``y``
attributes.
See Also
--------
get_model_plot : Return the data used by plot_model.
plot_model : Plot the model for a data set.
plot_source : Plot the source expression for a data set.
Examples
--------
Retrieve the source plot information for the default data
set and then display it:
>>> splot = get_source_plot()
>>> print(splot)
Return the plot data for data set 2, and then use it to create
a plot:
>>> s2 = get_source_plot(2)
>>> s2.plot()
Retrieve the source plots for the 0.5 to 7 range of the
'jet' and 'core' data sets and display them on the same plot:
>>> splot1 = get_source_plot(id='jet', lo=0.5, hi=7)
>>> splot2 = get_source_plot(id='core', lo=0.5, hi=7)
>>> splot1.plot()
>>> splot2.overplot()
Access the plot data (for a PHA data set) and select only the
bins corresponding to the 2-7 keV range defined in the call:
>>> splot = get_source_plot(lo=2, hi=7)
>>> xlo = splot.xlo[splot.mask]
>>> xhi = splot.xhi[splot.mask]
>>> y = splot.y[splot.mask]
For a PHA data set, the units on both the X and Y axes of the
plot are controlled by the `set_analysis` command. In this
case the Y axis will be in units of photon/s/cm^2/keV x Energy
and the X axis in keV:
>>> set_analysis('energy', factor=1)
>>> splot = get_source_plot()
>>> print(splot)
"""
try:
d = self.get_data(id)
except IdentifierErr as ie:
if recalc:
raise ie
d = None
if isinstance(d, sherpa.astro.data.DataPHA):
plotobj = self._astrosourceplot
if recalc:
plotobj.prepare(d, self.get_source(id), lo=lo, hi=hi)
return plotobj
return super().get_source_plot(id, recalc=recalc)
[docs] def get_fit_plot(self, id=None, recalc=True):
plotobj = self._fitplot
if not recalc:
return plotobj
d = self.get_data(id)
if isinstance(d, sherpa.astro.data.DataPHA):
dataobj = self.get_data_plot(id, recalc=recalc)
# We don't use get_model_plot as that uses the ungrouped data
# modelobj = self.get_model_plot(id)
# but we do want to use a histogram plot, not _modelplot.
# modelobj = self._modelplot
# Should this object be stored in self? There's
# no way to get it by API (apart from get_fit_plot).
#
modelobj = sherpa.astro.plot.ModelPHAHistogram()
modelobj.prepare(d, self.get_model(id),
self.get_stat())
plotobj.prepare(dataobj, modelobj)
return plotobj
return super().get_fit_plot(id, recalc=recalc)
get_fit_plot.__doc__ = sherpa.ui.utils.Session.get_fit_plot.__doc__
[docs] def get_model_component_plot(self, id, model=None, recalc=True):
"""Return the data used to create the model-component plot.
For PHA data, the response model is automatically added by the
routine unless the model contains a response.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
model : str or sherpa.models.model.Model instance
The component to use (the name, if a string).
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_model_component` (or `get_model_component_plot`)
are returned, otherwise the data is re-generated.
Returns
-------
instance
An object representing the data used to create the plot by
`plot_model_component`. The return value depends on the
data set (e.g. PHA, 1D binned, or 1D un-binned).
See Also
--------
get_model_plot : Return the data used to create the model plot.
plot_model : Plot the model for a data set.
plot_model_component : Plot a component of the model for a data set.
Notes
-----
The function does not follow the normal Python standards for
parameter use, since it is designed for easy interactive use.
When called with a single un-named argument, it is taken to be
the `model` parameter. If given two un-named arguments, then
they are interpreted as the `id` and `model` parameters,
respectively.
Examples
--------
Return the plot data for the ``pl`` component used in the
default data set:
>>> cplot = get_model_component_plot(pl)
Return the full source model (``fplot``) and then for the
components ``gal * pl`` and ``gal * gline``, for the data set
'jet':
>>> fmodel = xsphabs.gal * (powlaw1d.pl + gauss1d.gline)
>>> set_source('jet', fmodel)
>>> fit('jet')
>>> fplot = get_model_plot('jet')
>>> plot1 = get_model_component_plot('jet', pl*gal)
>>> plot2 = get_model_component_plot('jet', gline*gal)
For PHA data sets the response is automatically added, but it
can also be manually specified. In the following plot1 and
plot2 contain the same data:
>>> plot1 = get_model_component_plot(pl)
>>> rsp = get_response()
>>> plot2 = get_model_component_plot(rsp(pl))
"""
if model is None:
id, model = model, id
model = self._check_model(model)
try:
d = self.get_data(id)
except IdentifierErr as ie:
if recalc:
raise ie
d = None
if isinstance(d, sherpa.astro.data.DataPHA):
plotobj = self._astrocompmdlplot
if recalc:
if not has_pha_response(model):
try:
rsp = self.get_response(id) # TODO: bkg_id?
model = rsp(model)
except DataErr:
# no response
pass
plotobj.prepare(d, model, self.get_stat())
return plotobj
return super().get_model_component_plot(id, model=model, recalc=recalc)
# copy doc string from sherpa.utils
[docs] def get_source_component_plot(self, id, model=None, recalc=True):
if model is None:
id, model = model, id
model = self._check_model(model)
try:
d = self.get_data(id)
except IdentifierErr as ie:
if recalc:
raise ie
d = None
if isinstance(d, sherpa.astro.data.DataPHA):
plotobj = self._astrocompsrcplot
if recalc:
plotobj.prepare(d, model, self.get_stat())
return plotobj
return super().get_source_component_plot(id, model=model, recalc=recalc)
get_source_component_plot.__doc__ = sherpa.ui.utils.Session.get_source_component_plot.__doc__
[docs] def get_pvalue_plot(self, null_model=None, alt_model=None, conv_model=None,
id=1, otherids=(), num=500, bins=25, numcores=None,
recalc=False):
if recalc and conv_model is None and \
isinstance(self.get_data(id), sherpa.astro.data.DataPHA):
conv_model = self.get_response(id)
return super().get_pvalue_plot(null_model=null_model, alt_model=alt_model,
conv_model=conv_model, id=id, otherids=otherids,
num=num, bins=bins, numcores=numcores,
recalc=recalc)
get_pvalue_plot.__doc__ = sherpa.ui.utils.Session.get_pvalue_plot.__doc__
[docs] def get_order_plot(self, id=None, orders=None, recalc=True):
"""Return the data used by plot_order.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
orders : optional
Which response to use. The argument can be a scalar or
array, in which case multiple curves will be displayed.
The default is to use all orders.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_order` (or `get_order_plot`) are returned, otherwise
the data is re-generated.
Returns
-------
data : a `sherpa.astro.plot.OrderPlot` instance
An object representing the data used to create the plot by
`plot_order`.
See Also
--------
get_default_id : Return the default data set identifier.
plot_order : Plot the model for a data set convolved by the given response.
Examples
--------
Retrieve the plot information for order 1 of the default data set
and then display it:
>>> oplot = get_order_plot(orders=1)
>>> print(oplot)
Return the plot data for orders 1 and 2 of data set 'jet', plot the
first and then overplot the second:
>>> plots = get_order_plot('jet', orders=[1, 2])
>>> plots[0].plot()
>>> plots[1].overplot()
"""
plotobj = self._orderplot
if recalc:
plotobj.prepare(self._get_pha_data(id),
self.get_model(id), orders=orders)
return plotobj
[docs] def get_arf_plot(self, id=None, resp_id=None, recalc=True):
"""Return the data used by plot_arf.
Parameters
----------
id : int or str, optional
The data set with an ARF. If not given then the default
identifier is used, as returned by `get_default_id`.
resp_id : int or str, optional
Which ARF to use in the case that multiple ARFs are
associated with a data set. The default is ``None``,
which means the first one.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_arf` (or `get_arf_plot`) are returned, otherwise
the data is re-generated.
Returns
-------
arf_plot : a `sherpa.astro.plot.ARFPlot` instance
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
See Also
--------
plot : Create one or more plot types.
plot_arf : Plot the ARF associated with a data set.
Examples
--------
Return the ARF plot data for the default data set:
>>> aplot = get_arf_plot()
>>> aplot.y.max()
676.95794677734375
Return the ARF data for the second response of the
data set labelled 'histate', and then plot it:
>>> aplot = get_arf_plot('histate', 2)
>>> aplot.plot()
"""
plotobj = self._arfplot
if not recalc:
return plotobj
id = self._fix_id(id)
arf = self._get_pha_data(id).get_arf(resp_id)
if arf is None:
raise DataErr('noarf', id)
plotobj.prepare(arf, self._get_pha_data(id))
return plotobj
[docs] def get_bkg_fit_plot(self, id=None, bkg_id=None, recalc=True):
"""Return the data used by plot_bkg_fit.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_bkg_fit` (or `get_bkg_fit_plot`) are returned,
otherwise the data is re-generated.
Returns
-------
model : a `sherpa.astro.plot.BkgFitPlot` instance
An object representing the data used to create the plot by
`plot_bkg_fit`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_plot : Return the data used by plot_bkg.
get_bkg_model_plot : Return the data used by plot_bkg_model.
plot_bkg_fit : Plot the fit results (data, model) for the background of a PHA data set.
Examples
--------
Create the data needed to create the "fit plot" for the background
of the default data set and display it:
>>> bplot = get_bkg_fit_plot()
>>> print(bplot)
Return the plot data for data set 2, and then use it to create
a plot:
>>> b2 = get_bkg_fit_plot(2)
>>> b2.plot()
The fit plot consists of a combination of a data plot and a
model plot, which are captured in the `dataplot` and `modelplot`
attributes of the return value. These can be used to display
the plots individually, such as:
>>> b2.dataplot.plot()
>>> b2.modelplot.plot()
or, to combine the two:
>>> b2.dataplot.plot()
>>> b2.modelplot.overplot()
Return the plot data for the second background component to the
"jet" data set:
>>> bplot = get_bkg_fit_plot('jet', bkg_id=2)
"""
plotobj = self._bkgfitplot
if not recalc:
return plotobj
dataobj = self.get_bkg_plot(id, bkg_id, recalc=recalc)
# We don't use get_bkg_model_plot as that uses the ungrouped data
# modelobj = self.get_bkg_model_plot(id, bkg_id, recalc=recalc)
modelobj = self._bkgmodelplot
modelobj.prepare(self.get_bkg(id, bkg_id),
self.get_bkg_model(id, bkg_id),
self.get_stat())
plotobj.prepare(dataobj, modelobj)
return plotobj
[docs] def get_bkg_model_plot(self, id=None, bkg_id=None, recalc=True):
"""Return the data used by plot_bkg_model.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_bkg_model` (or `get_bkg_model_plot`) are returned,
otherwise the data is re-generated.
Returns
-------
model : a `sherpa.astro.plot.BkgModelHistogram` instance
An object representing the data used to create the plot by
`plot_bkg_model`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_source_plot : Return the data used by plot_bkg_source.
plot_bkg_model : Plot the model for the background of a PHA data set.
plot_bkg_source : Plot the model expression for the background of a PHA data set.
Examples
--------
>>> bplot = get_bkg_model_plot()
>>> print(bplot)
>>> get_bkg_model_plot('jet', bkg_id=1).plot()
>>> get_bkg_model_plot('jet', bkg_id=2).overplot()
"""
plotobj = self._bkgmodelhisto
if recalc:
plotobj.prepare(self.get_bkg(id, bkg_id),
self.get_bkg_model(id, bkg_id),
self.get_stat())
return plotobj
[docs] def get_bkg_plot(self, id=None, bkg_id=None, recalc=True):
"""Return the data used by plot_bkg.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_bkg` (or `get_bkg_plot`) are returned, otherwise
the data is re-generated.
Returns
-------
data : a `sherpa.astro.plot.BkgDataPlot` instance
An object representing the data used to create the plot by
`plot_data`. The relationship between the returned values
and the values in the data set depend on the analysis,
filtering, and grouping settings of the data set.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
See Also
--------
get_default_id : Return the default data set identifier.
plot_bkg : Plot the background values for a PHA data set.
Examples
--------
Create the data needed to create the "data plot" for the background
of the default data set and display it:
>>> bplot = get_bkg_plot()
>>> print(bplot)
Return the plot data for data set 2, and then use it to create
a plot:
>>> b2 = get_bkg_plot(2)
>>> b2.plot()
Return the plot data for the second background component to the
"jet" data set:
>>> bplot = get_bkg_plot('jet', bkg_id=2)
"""
plotobj = self._bkgdataplot
if recalc:
plotobj.prepare(self.get_bkg(id, bkg_id),
self.get_stat())
return plotobj
[docs] def get_bkg_source_plot(self, id=None, lo=None, hi=None,
bkg_id=None, recalc=True):
"""Return the data used by plot_bkg_source.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
lo : number, optional
The low value to plot.
hi : number, optional
The high value to plot.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_bkg_source` (or `get_bkg_source_plot`) are returned,
otherwise the data is re-generated.
Returns
-------
source : a `sherpa.astro.plot.BkgSourcePlot` instance
An object representing the data used to create the plot by
`plot_bkg_source`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_model_plot : Return the data used by plot_bkg_model.
plot_bkg_model : Plot the model for the background of a PHA data set.
plot_bkg_source : Plot the model expression for the background of a PHA data set.
Examples
--------
Retrieve the source plot information for the background of
the default data set and display it:
>>> splot = get_bkg_source_plot()
>>> print(splot)
Return the background plot data for data set 2, and then use it
to create a plot:
>>> s2 = get_bkg_source_plot(2)
>>> s2.plot()
Create a plot of the first two background components of the
'histate' data set, overplotting the second on the first:
>>> b1 = get_bkg_source_plot('histate', bkg_id=1)
>>> b2 = get_bkg_source_plot('histate', bkg_id=2)
>>> b1.plot()
>>> b2.overplot()
Retrieve the background source plots for the 0.5 to 7 range of the
'jet' and 'core' data sets and display them on the same plot:
>>> splot1 = get_bkg_source_plot(id='jet', lo=0.5, hi=7)
>>> splot2 = get_bkg_source_plot(id='core', lo=0.5, hi=7)
>>> splot1.plot()
>>> splot2.overplot()
For a PHA data set, the units on both the X and Y axes of the
plot are controlled by the `set_analysis` command. In this
case the Y axis will be in units of photons/s/cm^2 and the X
axis in keV:
>>> set_analysis('energy', factor=1)
>>> splot = get_bkg_source_plot()
>>> print(splot)
"""
plotobj = self._bkgsourceplot
if recalc:
plotobj.prepare(self.get_bkg(id, bkg_id),
self.get_bkg_source(id, bkg_id),
lo=lo, hi=hi)
return plotobj
[docs] def get_bkg_resid_plot(self, id=None, bkg_id=None, recalc=True):
"""Return the data used by plot_bkg_resid.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_bkg_resid` (or `get_bkg_resid_plot`) are returned,
otherwise the data is re-generated.
Returns
-------
resid : a `sherpa.astro.plot.BkgResidPlot` instance
An object representing the data used to create the plot by
`plot_bkg_resid`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_chisqr_plot : Return the data used by plot_bkg_chisqr.
get_bkg_delchi_plot : Return the data used by plot_bkg_delchi.
get_bkg_ratio_plot : Return the data used by plot_bkg_ratio.
plot_bkg_resid : Plot the residual (data-model) values for the background of a PHA data set.
Examples
--------
>>> bplot = get_bkg_resid_plot()
>>> print(bplot)
>>> get_bkg_resid_plot('jet', bkg_id=1).plot()
>>> get_bkg_resid_plot('jet', bkg_id=2).overplot()
"""
plotobj = self._bkgresidplot
if recalc:
plotobj.prepare(self.get_bkg(id, bkg_id),
self.get_bkg_model(id, bkg_id),
self.get_stat())
return plotobj
[docs] def get_bkg_ratio_plot(self, id=None, bkg_id=None, recalc=True):
"""Return the data used by plot_bkg_ratio.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_bkg_ratio` (or `get_bkg_ratio_plot`) are returned,
otherwise the data is re-generated.
Returns
-------
ratio : a `sherpa.astro.plot.BkgRatioPlot` instance
An object representing the data used to create the plot by
`plot_bkg_ratio`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_chisqr_plot : Return the data used by plot_bkg_chisqr.
get_bkg_delchi_plot : Return the data used by plot_bkg_delchi.
get_bkg_resid_plot : Return the data used by plot_bkg_resid.
plot_bkg_ratio : Plot the ratio of data to model values for the background of a PHA data set.
Examples
--------
>>> bplot = get_bkg_ratio_plot()
>>> print(bplot)
>>> get_bkg_ratio_plot('jet', bkg_id=1).plot()
>>> get_bkg_ratio_plot('jet', bkg_id=2).overplot()
"""
plotobj = self._bkgratioplot
if recalc:
plotobj.prepare(self.get_bkg(id, bkg_id),
self.get_bkg_model(id, bkg_id),
self.get_stat())
return plotobj
[docs] def get_bkg_delchi_plot(self, id=None, bkg_id=None, recalc=True):
"""Return the data used by plot_bkg_delchi.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_bkg_delchi` (or `get_bkg_delchi_plot`) are returned,
otherwise the data is re-generated.
Returns
-------
delchi : a `sherpa.astro.plot.BkgDelchiPlot` instance
An object representing the data used to create the plot by
`plot_bkg_delchi`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_chisqr_plot : Return the data used by plot_bkg_chisqr.
get_bkg_ratio_plot : Return the data used by plot_bkg_ratio.
get_bkg_resid_plot : Return the data used by plot_bkg_resid.
plot_bkg_delchi : Plot the ratio of residuals to error for the background of a PHA data set.
Examples
--------
>>> bplot = get_bkg_delchi_plot()
>>> print(bplot)
>>> get_bkg_delchi_plot('jet', bkg_id=1).plot()
>>> get_bkg_delchi_plot('jet', bkg_id=2).overplot()
"""
plotobj = self._bkgdelchiplot
if recalc:
plotobj.prepare(self.get_bkg(id, bkg_id),
self.get_bkg_model(id, bkg_id),
self.get_stat())
return plotobj
[docs] def get_bkg_chisqr_plot(self, id=None, bkg_id=None, recalc=True):
"""Return the data used by plot_bkg_chisqr.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
recalc : bool, optional
If ``False`` then the results from the last call to
`plot_bkg_chisqr` (or `get_bkg_chisqr_plot`) are returned,
otherwise the data is re-generated.
Returns
-------
chisqr : a `sherpa.astro.plot.BkgChisqrPlot` instance
An object representing the data used to create the plot by
`plot_bkg_chisqr`.
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_delchi_plot : Return the data used by plot_bkg_delchi.
get_bkg_ratio_plot : Return the data used by plot_bkg_ratio.
get_bkg_resid_plot : Return the data used by plot_bkg_resid.
plot_bkg_chisqr : Plot the chi-squared value for each point of the background of a PHA data set.
Examples
--------
>>> bplot = get_bkg_chisqr_plot()
>>> print(bplot)
>>> get_bkg_chisqr_plot('jet', bkg_id=1).plot()
>>> get_bkg_chisqr_plot('jet', bkg_id=2).overplot()
"""
plotobj = self._bkgchisqrplot
if recalc:
plotobj.prepare(self.get_bkg(id, bkg_id),
self.get_bkg_model(id, bkg_id),
self.get_stat())
return plotobj
def _prepare_energy_flux_plot(self, plot, lo, hi, id, num, bins,
correlated, numcores, bkg_id,
scales=None, model=None, otherids=(),
clip='hard'):
"""Run sample_energy_flux and convert to a plot.
"""
dist = self.sample_energy_flux(lo, hi, id=id, otherids=otherids,
num=num, scales=scales, model=model,
correlated=correlated, numcores=numcores,
bkg_id=bkg_id, clip=clip)
plot.prepare(dist, bins)
return plot
def _prepare_photon_flux_plot(self, plot, lo, hi, id, num, bins,
correlated, numcores, bkg_id,
scales=None, model=None, otherids=(),
clip='hard'):
"""Run sample_photon_flux and convert to a plot.
"""
dist = self.sample_photon_flux(lo, hi, id=id, otherids=otherids,
num=num, scales=scales, model=model,
correlated=correlated, numcores=numcores,
bkg_id=bkg_id, clip=clip)
plot.prepare(dist, bins)
return plot
[docs] def get_energy_flux_hist(self, lo=None, hi=None, id=None, num=7500, bins=75,
correlated=False, numcores=None, bkg_id=None,
scales=None, model=None, otherids=(), recalc=True,
clip='hard'):
"""Return the data displayed by plot_energy_flux.
The get_energy_flux_hist() function calculates a histogram of
simulated energy flux values representing the energy flux probability
distribution for a model component, accounting for the errors on the
model parameters.
.. versionchanged:: 4.12.2
The scales parameter is no longer ignored when set and the
model and otherids parameters have been added. The clip
argument has been added.
Parameters
----------
lo : number, optional
The lower limit to use when summing up the signal. If not
given then the lower value of the data grid is used.
hi : optional
The upper limit to use when summing up the signal. If not
given then the upper value of the data grid is used.
id : int or string, optional
The identifier of the data set to use. If `None`, the
default value, then all datasets with associated models are
used to calculate the errors and the model evaluation is
done using the default dataset.
num : int, optional
The number of samples to create. The default is 7500.
bins : int, optional
The number of bins to use for the histogram.
correlated : bool, optional
If ``True`` (the default is ``False``) then ``scales`` is the
full covariance matrix, otherwise it is just a 1D array
containing the variances of the parameters (the diagonal
elements of the covariance matrix).
numcores : optional
The number of CPU cores to use. The default is to use all
the cores on the machine.
bkg_id : int or string, optional
The identifier of the background component to use. This
should only be set when the line to be measured is in the
background model.
scales : array, optional
The scales used to define the normal distributions for the
parameters. The size and shape of the array depends on the
number of free parameters in the fit (n) and the value of
the `correlated` parameter. When the parameter is `True`,
scales must be given the covariance matrix for the free
parameters (a n by n matrix that matches the parameter
ordering used by Sherpa). For un-correlated parameters
the covariance matrix can be used, or a one-dimensional
array of n elements can be used, giving the width (specified
as the sigma value of a normal distribution) for each
parameter (e.g. the square root of the diagonal elements
of the covariance matrix). If the scales parameter is not
given then the covariance matrix is evaluated for the
current model and best-fit parameters.
model : model, optional
The model to integrate. If left as `None` then the source
model for the dataset will be used. This can be used to
calculate the unabsorbed flux, as shown in the examples.
The model must be part of the source expression.
otherids : sequence of integer and string ids, optional
The list of other datasets that should be included when
calculating the errors to draw values from.
recalc : bool, optional
If ``True``, the default, then re-calculate the values rather
than use the values from the last time the function was
run.
clip : {'hard', 'soft', 'none'}, optional
What clipping strategy should be applied to the sampled
parameters. The default ('hard') is to fix values at their
hard limits if they exceed them. A value of 'soft' uses
the soft limits instead, and 'none' applies no
clipping.
Returns
-------
hist : a `sherpa.astro.plot.EnergyFluxHistogram` instance
An object representing the data used to create the plot by
`plot_energy_flux`.
See Also
--------
get_photon_flux_hist : Return the data displayed by plot_photon_flux.
plot_energy_flux : Display the energy flux distribution.
plot_photon_flux : Display the photon flux distribution.
sample_energy_flux : Return the energy flux distribution of a model.
sample_flux : Return the flux distribution of a model.
sample_photon_flux : Return the photon flux distribution of a model.
Examples
--------
Get the energy flux distribution for the range 0.5 to 7 for
the default data set:
>>> ehist = get_energy_flux_hist(0.5, 7, num=1000)
>>> print(ehist)
Compare the 0.5 to 2 energy flux distribution from the "core"
data set to the values from the "jet" data set:
>>> ehist1 = get_energy_flux_hist(0.5, 2, id='jet', num=1000)
>>> ehist2 = get_energy_flux_hist(0.5, 2, id='core', num=1000)
Compare the flux distribution for the full source expression
(aflux) to that for just the pl component (uflux); this can be
useful to calculate the unabsorbed flux distribution if the
full source model contains an absorption component:
>>> aflux = get_energy_flux_hist(0.5, 2, num=1000, bins=20)
>>> uflux = get_energy_flux_hist(0.5, 2, model=pl, num=1000, bins=20)
When there are multiple datasets loaded,
`get_energy_flux_hist` uses all datasets to evaluate the
errors when the `id` parameter is left at its default value of
`None`. The `otherids` parameter is used, along with `id`, to
specify exactly what datasets are used:
>>> x = get_energy_flux_hist(2, 10, num=1000, bins=20, model=src)
>>> y = get_energy_flux_hist(2, 10, num=1000, bins=20, model=src,
... id=1, otherids=(2, 3, 4))
"""
if recalc:
self._prepare_energy_flux_plot(self._energyfluxplot, lo, hi, id=id,
num=num, bins=bins, correlated=correlated,
scales=scales, model=model,
otherids=otherids, clip=clip,
numcores=numcores, bkg_id=bkg_id)
return self._energyfluxplot
[docs] def get_photon_flux_hist(self, lo=None, hi=None, id=None, num=7500, bins=75,
correlated=False, numcores=None, bkg_id=None,
scales=None, model=None, otherids=(), recalc=True,
clip='hard'):
"""Return the data displayed by plot_photon_flux.
The get_photon_flux_hist() function calculates a histogram of
simulated photon flux values representing the photon flux probability
distribution for a model component, accounting for the errors on the
model parameters.
.. versionchanged:: 4.12.2
The scales parameter is no longer ignored when set and the
model and otherids parameters have been added.
Parameters
----------
lo : number, optional
The lower limit to use when summing up the signal. If not
given then the lower value of the data grid is used.
hi : optional
The upper limit to use when summing up the signal. If not
given then the upper value of the data grid is used.
id : int or string, optional
The identifier of the data set to use. If `None`, the
default value, then all datasets with associated models are
used to calculate the errors and the model evaluation is
done using the default dataset.
num : int, optional
The number of samples to create. The default is 7500.
bins : int, optional
The number of bins to use for the histogram.
correlated : bool, optional
If ``True`` (the default is ``False``) then ``scales`` is the
full covariance matrix, otherwise it is just a 1D array
containing the variances of the parameters (the diagonal
elements of the covariance matrix).
numcores : optional
The number of CPU cores to use. The default is to use all
the cores on the machine.
bkg_id : int or string, optional
The identifier of the background component to use. This
should only be set when the line to be measured is in the
background model.
scales : array, optional
The scales used to define the normal distributions for the
parameters. The size and shape of the array depends on the
number of free parameters in the fit (n) and the value of
the `correlated` parameter. When the parameter is `True`,
scales must be given the covariance matrix for the free
parameters (a n by n matrix that matches the parameter
ordering used by Sherpa). For un-correlated parameters
the covariance matrix can be used, or a one-dimensional
array of n elements can be used, giving the width (specified
as the sigma value of a normal distribution) for each
parameter (e.g. the square root of the diagonal elements
of the covariance matrix). If the scales parameter is not
given then the covariance matrix is evaluated for the
current model and best-fit parameters.
model : model, optional
The model to integrate. If left as `None` then the source
model for the dataset will be used. This can be used to
calculate the unabsorbed flux, as shown in the examples.
The model must be part of the source expression.
otherids : sequence of integer and string ids, optional
The list of other datasets that should be included when
calculating the errors to draw values from.
recalc : bool, optional
If ``True``, the default, then re-calculate the values rather
than use the values from the last time the function was
run.
clip : {'hard', 'soft', 'none'}, optional
What clipping strategy should be applied to the sampled
parameters. The default ('hard') is to fix values at their
hard limits if they exceed them. A value of 'soft' uses
the soft limits instead, and 'none' applies no
clipping.
Returns
-------
hist : a `sherpa.astro.plot.PhotonFluxHistogram` instance
An object representing the data used to create the plot by
`plot_photon_flux`.
See Also
--------
get_energy_flux_hist : Return the data displayed by plot_energy_flux.
plot_energy_flux : Display the energy flux distribution.
plot_photon_flux : Display the photon flux distribution.
sample_energy_flux : Return the energy flux distribution of a model.
sample_flux : Return the flux distribution of a model.
sample_photon_flux : Return the photon flux distribution of a model.
Examples
--------
Get the photon flux distribution for the range 0.5 to 7 for
the default data set:
>>> phist = get_photon_flux_hist(0.5, 7, num=1000)
>>> print(phist)
Compare the 0.5 to 2 photon flux distribution from the "core"
data set to the values from the "jet" data set:
>>> phist1 = get_photon_flux_hist(0.5, 2, id='jet', num=1000)
>>> phist2 = get_photon_flux_hist(0.5, 2, id='core', num=1000)
Compare the flux distribution for the full source expression
(aflux) to that for just the pl component (uflux); this can be
useful to calculate the unabsorbed flux distribution if the
full source model contains an absorption component:
>>> aflux = get_photon_flux_hist(0.5, 2, num=1000, bins=20)
>>> uflux = get_photon_flux_hist(0.5, 2, model=pl, num=1000, bins=20)
When there are multiple datasets loaded,
`get_photon_flux_hist` uses all datasets to evaluate the
errors when the `id` parameter is left at its default value of
`None`. The `otherids` parameter is used, along with `id`, to
specify exactly what datasets are used:
>>> x = get_photon_flux_hist(2, 10, num=1000, bins=20, model=src)
>>> y = get_photon_flux_hist(2, 10, num=1000, bins=20, model=src,
... id=1, otherids=(2, 3, 4))
"""
if recalc:
self._prepare_photon_flux_plot(self._photonfluxplot, lo, hi, id=id,
num=num, bins=bins, correlated=correlated,
scales=scales, model=model,
otherids=otherids, clip=clip,
numcores=numcores, bkg_id=bkg_id)
return self._photonfluxplot
[docs] def plot_arf(self, id=None, resp_id=None, replot=False, overplot=False,
clearwindow=True, **kwargs):
"""Plot the ARF associated with a data set.
Display the effective area curve from the ARF
component of a PHA data set.
Parameters
----------
id : int or str, optional
The data set with an ARF. If not given then the default
identifier is used, as returned by `get_default_id`.
resp_id : int or str, optional
Which ARF to use in the case that multiple ARFs are
associated with a data set. The default is ``None``,
which means the first one.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_data`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
See Also
--------
get_arf_plot : Return the data used by plot_arf.
plot : Create one or more plot types.
Examples
--------
Plot the ARF for the default data set:
>>> plot_arf()
Plot the ARF from data set 1 and overplot
the ARF from data set 2:
>>> plot_arf(1)
>>> plot_arf(2, overplot=True)
Plot the ARFs labelled "arf1" and "arf2" for the
"src" data set:
>>> plot_arf("src", "arf1")
>>> plot_arf("src", "arf2", overplot=True)
The following example requires that the Matplotlib backend
is selected, since this determines what extra keywords
`plot_arf` accepts. The ARFs from the default and data set
2 are drawn together, but the second curve is drawn with
a dashed line.
>>> plot_arf(ylog=True)
>>> plot_arf(2, overplot=True, linestyle='dashed')
"""
plotobj = self.get_arf_plot(id, resp_id, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
# DOC-NOTE: also in sherpa.utils, but without the lo/hi arguments
[docs] def plot_source(self, id=None, lo=None, hi=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the source expression for a data set.
This function plots the source model for a data set. This does
not include any instrument response (e.g. a convolution
created by `set_psf` or ARF and RMF automatically created for
a PHA data set).
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
lo : number, optional
The low value to plot (only used for PHA data sets).
hi : number, optional
The high value to plot (only use for PHA data sets).
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_source`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
See Also
--------
get_source_plot : Return the data used by plot_source.
get_default_id : Return the default data set identifier.
plot : Create one or more plot types.
plot_model : Plot the model for a data set.
set_analysis : Set the units used when fitting and displaying spectral data.
set_xlinear : New plots will display a linear X axis.
set_xlog : New plots will display a logarithmically-scaled X axis.
set_ylinear : New plots will display a linear Y axis.
set_ylog : New plots will display a logarithmically-scaled Y axis.
Examples
--------
Plot the unconvolved source model for the default data set:
>>> plot_source()
Overplot the source model for data set 2 on data set 1:
>>> plot_source(1)
>>> plot_source(2, overplot=True)
Restrict the plot to values between 0.5 and 7 for the
independent axis:
>>> plot_source(lo=0.5, hi=7)
For a PHA data set, the units on both the X and Y axes of the
plot are controlled by the `set_analysis` command. In this
case the Y axis will be in units of photons/s/cm^2 and the X
axis in keV:
>>> set_analysis('energy', factor=1)
>>> plot_source()
"""
d = self.get_data(id)
if isinstance(d, sherpa.astro.data.DataPHA):
# Note: lo/hi arguments mean we can not just rely on superclass
plotobj = self.get_source_plot(id, lo=lo, hi=hi, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
return
super().plot_source(id=id, replot=replot, overplot=overplot,
clearwindow=clearwindow, **kwargs)
# DOC-TODO: is orders the same as resp_id?
[docs] def plot_order(self, id=None, orders=None, replot=False, overplot=False,
clearwindow=True, **kwargs):
"""Plot the model for a data set convolved by the given response.
Some data sets - such as grating PHA data - can have multiple
responses. The `plot_order` function acts like `plot_model`,
in that it displays the model after passing through a
response, but allows the user to select which response to use.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
orders : optional
Which response to use. The argument can be a scalar or
array, in which case multiple curves will be displayed.
The default is to use all orders.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_model`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
See Also
--------
get_order_plot : Return the data used by plot_order.
plot : Create one or more plot types.
plot_model : Plot the model for a data set.
Examples
--------
Display the source model convolved by the first response
for the default data set:
>>> plot_order(orders=1)
Plot the source convolved through the first and second
responses for the second data set (separate curves for
each response):
>>> plot_order(2, orders=[1, 2])
Add the orders plot to a model plot:
>>> plot_model()
>>> plot_order(orders=[2, 3], overplot=True)
"""
plotobj = self.get_order_plot(id, orders=orders, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg(self, id=None, bkg_id=None, replot=False, overplot=False,
clearwindow=True, **kwargs):
"""Plot the background values for a PHA data set.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
See Also
--------
get_bkg_plot : Return the data used by plot_bkg.
get_default_id : Return the default data set identifier.
plot : Create one or more plot types.
set_analysis : Set the units used when fitting and displaying spectral data.
set_xlinear : New plots will display a linear X axis.
set_xlog : New plots will display a logarithmically-scaled X axis.
set_ylinear : New plots will display a linear Y axis.
set_ylog : New plots will display a logarithmically-scaled Y axis.
Examples
--------
Plot the background from the default data set:
>>> plot_bkg()
Overplot the background from the 'jet' data set on the
data. There is no scaling for differences in aperture or
exposure time:
>>> plot_data('jet')
>>> plot_bkg('jet', overplot=True)
Compare the first two background components of data set 1:
>>> plot_bkg(1, 1)
>>> plot_bkg(1, 2, overplot=True)
"""
plotobj = self.get_bkg_plot(id, bkg_id, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_model(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the model for the background of a PHA data set.
This function plots the model for the background of a PHA data
set, which includes any instrument response (the
ARF and RMF).
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_model`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_model_plot : Return the data used by plot_bkg_model.
plot_bkg_source : Plot the model expression for the background of a PHA data set.
set_bkg_model : Set the background model expression for a PHA data set.
Examples
--------
>>> plot_bkg_model()
>>> plot_bkg('jet')
>>> plot_bkg_model('jet', bkg_id=1, overplot=True)
>>> plot_bkg_model('jet', bkg_id=2, overplot=True)
"""
plotobj = self.get_bkg_model_plot(id, bkg_id, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_resid(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the residual (data-model) values for the background of a PHA data set.
Display the residuals for the background of a PHA data set
when it is being fit, rather than subtracted from the source.
.. versionchanged:: 4.12.0
The Y axis is now always drawn using a linear scale.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_resid`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_resid_plot : Return the data used by plot_bkg_resid.
plot_bkg_chisqr : Plot the chi-squared value for each point of the background of a PHA data set.
plot_bkg_delchi : Plot the ratio of residuals to error for the background of a PHA data set.
plot_bkg_ratio : Plot the ratio of data to model values for the background of a PHA data set.
set_bkg_model : Set the background model expression for a PHA data set.
Notes
-----
The ylog setting is ignored, and the Y axis is drawn using a
linear scale.
Examples
--------
>>> plot_bkg_resid()
>>> plot_bkg('jet')
>>> plot_bkg_resid('jet', bkg_id=1, overplot=True)
>>> plot_bkg_resid('jet', bkg_id=2, overplot=True)
"""
plotobj = self.get_bkg_resid_plot(id, bkg_id, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_ratio(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the ratio of data to model values for the background of a PHA data set.
Display the ratio of data to model values for the background
of a PHA data set when it is being fit, rather than subtracted
from the source.
.. versionchanged:: 4.12.0
The Y axis is now always drawn using a linear scale.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_ratio`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_ratio_plot : Return the data used by plot_bkg_ratio.
plot_bkg_chisqr : Plot the chi-squared value for each point of the background of a PHA data set.
plot_bkg_delchi : Plot the ratio of residuals to error for the background of a PHA data set.
plot_bkg_resid : Plot the residual (data-model) values for the background of a PHA data set.
set_bkg_model : Set the background model expression for a PHA data set.
Notes
-----
The ylog setting is ignored, and the Y axis is drawn using a
linear scale.
Examples
--------
>>> plot_bkg_ratio()
>>> plot_bkg_ratio('jet', bkg_id=1)
>>> plot_bkg_ratio('jet', bkg_id=2, overplot=True)
"""
plotobj = self.get_bkg_ratio_plot(id, bkg_id, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_delchi(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the ratio of residuals to error for the background of a PHA data set.
Display the ratio of the residuals (data-model) to the error
values for the background of a PHA data set when it is being
fit, rather than subtracted from the source.
.. versionchanged:: 4.12.0
The Y axis is now always drawn using a linear scale.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_ratio`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_delchi_plot : Return the data used by plot_bkg_delchi.
plot_bkg_chisqr : Plot the chi-squared value for each point of the background of a PHA data set.
plot_bkg_ratio : Plot the ratio of data to model values for the background of a PHA data set.
plot_bkg_resid : Plot the residual (data-model) values for the background of a PHA data set.
set_bkg_model : Set the background model expression for a PHA data set.
Notes
-----
The ylog setting is ignored, and the Y axis is drawn using a
linear scale.
Examples
--------
>>> plot_bkg_delchi()
>>> plot_bkg_delchi('jet', bkg_id=1)
>>> plot_bkg_delchi('jet', bkg_id=2, overplot=True)
"""
plotobj = self.get_bkg_delchi_plot(id, bkg_id, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_chisqr(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the chi-squared value for each point of the background of a PHA data set.
Display the square of the residuals (data-model) divided by
the error values for the background of a PHA data set when it
is being fit, rather than subtracted from the source.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_chisqr`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_chisqr_plot : Return the data used by plot_bkg_chisqr.
plot_bkg_delchi : Plot the ratio of residuals to error for the background of a PHA data set.
plot_bkg_ratio : Plot the ratio of data to model values for the background of a PHA data set.
plot_bkg_resid : Plot the residual (data-model) values for the background of a PHA data set.
set_bkg_model : Set the background model expression for a PHA data set.
Examples
--------
>>> plot_bkg_chisqr()
>>> plot_bkg_chisqr('jet', bkg_id=1)
>>> plot_bkg_chisqr('jet', bkg_id=2, overplot=True)
"""
plotobj = self.get_bkg_chisqr_plot(id, bkg_id, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_fit(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the fit results (data, model) for the background of a PHA data set.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_fit`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_fit_plot : Return the data used by plot_bkg_fit.
plot : Create one or more plot types.
plot_bkg : Plot the background values for a PHA data set.
plot_bkg_model : Plot the model for the background of a PHA data set.
plot_bkg_fit_delchi : Plot the fit results, and the residuals, for the background of a PHA data set.
plot_bkg_fit_ratio : Plot the fit results, and the data/model ratio, for the background of a PHA data set.
plot_bkg_fit_resid : Plot the fit results, and the residuals, for the background of a PHA data set.
plot_fit : Plot the fit results (data, model) for a data set.
set_analysis : Set the units used when fitting and displaying spectral data.
Examples
--------
Plot the background fit to the default data set:
>>> plot_bkg_fit()
"""
plotobj = self.get_bkg_fit_plot(id, bkg_id, recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_source(self, id=None, lo=None, hi=None, bkg_id=None,
replot=False, overplot=False, clearwindow=True,
**kwargs):
"""Plot the model expression for the background of a PHA data set.
This function plots the model for the background of a PHA data
set. It does not include the instrument response (the ARF and
RMF).
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
lo : number, optional
The low value to plot.
hi : number, optional
The high value to plot.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_model`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_source_plot : Return the data used by plot_bkg_source.
plot_bkg_model : Plot the model for the background of a PHA data set.
set_bkg_model : Set the background model expression for a PHA data set.
Examples
--------
>>> plot_bkg_source()
>>> plot_bkg_source('jet', bkg_id=1)
>>> plot_bkg_source('jet', bkg_id=2, overplot=True)
"""
plotobj = self.get_bkg_source_plot(id, bkg_id=bkg_id, lo=lo, hi=hi,
recalc=not replot)
self._plot(plotobj, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_energy_flux(self, lo=None, hi=None, id=None, num=7500, bins=75,
correlated=False, numcores=None, bkg_id=None,
scales=None, model=None, otherids=(),
recalc=True, clip='hard',
overplot=False, clearwindow=True,
**kwargs):
"""Display the energy flux distribution.
For each iteration, draw the parameter values of the model
from a normal distribution, evaluate the model, and sum the
model over the given range (the flux). Plot up the
distribution of this flux. The units for the flux are as
returned by `calc_energy_flux`. The `sample_energy_flux` and
`get_energy_flux_hist` functions return the data used to
create this plot.
.. versionchanged:: 4.12.2
The scales parameter is no longer ignored when set and the
model and otherids parameters have been added. The clip
argument has been added.
Parameters
----------
lo : number, optional
The lower limit to use when summing up the signal. If not
given then the lower value of the data grid is used.
hi : optional
The upper limit to use when summing up the signal. If not
given then the upper value of the data grid is used.
id : int or string, optional
The identifier of the data set to use. If `None`, the
default value, then all datasets with associated models are
used to calculate the errors and the model evaluation is
done using the default dataset.
num : int, optional
The number of samples to create. The default is 7500.
bins : int, optional
The number of bins to use for the histogram.
correlated : bool, optional
If ``True`` (the default is ``False``) then ``scales`` is the
full covariance matrix, otherwise it is just a 1D array
containing the variances of the parameters (the diagonal
elements of the covariance matrix).
numcores : optional
The number of CPU cores to use. The default is to use all
the cores on the machine.
bkg_id : int or string, optional
The identifier of the background component to use. This
should only be set when the line to be measured is in the
background model.
scales : array, optional
The scales used to define the normal distributions for the
parameters. The size and shape of the array depends on the
number of free parameters in the fit (n) and the value of
the `correlated` parameter. When the parameter is `True`,
scales must be given the covariance matrix for the free
parameters (a n by n matrix that matches the parameter
ordering used by Sherpa). For un-correlated parameters
the covariance matrix can be used, or a one-dimensional
array of n elements can be used, giving the width (specified
as the sigma value of a normal distribution) for each
parameter (e.g. the square root of the diagonal elements
of the covariance matrix). If the scales parameter is not
given then the covariance matrix is evaluated for the
current model and best-fit parameters.
model : model, optional
The model to integrate. If left as `None` then the source
model for the dataset will be used. This can be used to
calculate the unabsorbed flux, as shown in the examples.
The model must be part of the source expression.
otherids : sequence of integer and string ids, optional
The list of other datasets that should be included when
calculating the errors to draw values from.
recalc : bool, optional
If ``True``, the default, then re-calculate the values rather
than use the values from the last time the function was
run.
clip : {'hard', 'soft', 'none'}, optional
What clipping strategy should be applied to the sampled
parameters. The default ('hard') is to fix values at their
hard limits if they exceed them. A value of 'soft' uses
the soft limits instead, and 'none' applies no
clipping.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
See Also
--------
calc_photon_flux : Integrate the unconvolved source model over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
covar : Estimate the confidence intervals using the confidence method.
get_energy_flux_hist : Return the data displayed by plot_energy_flux.
get_photon_flux_hist : Return the data displayed by plot_photon_flux.
plot_cdf : Plot the cumulative density function of an array.
plot_pdf : Plot the probability density function of an array.
plot_photon_flux : Display the photon flux distribution.
plot_trace : Create a trace plot of row number versus value.
sample_energy_flux : Return the energy flux distribution of a model.
sample_flux : Return the flux distribution of a model.
sample_photon_flux : Return the photon flux distribution of a model.
Examples
--------
Plot the energy flux distribution for the range 0.5 to 7 for
the default data set:
>>> plot_energy_flux(0.5, 7, num=1000)
Overplot the 0.5 to 2 energy flux distribution from the "core"
data set on top of the values from the "jet" data set:
>>> plot_energy_flux(0.5, 2, id="jet", num=1000)
>>> plot_energy_flux(0.5, 2, id="core", num=1000, overplot=True)
Overplot the flux distribution for just the pl component (which
must be part of the source expression) on top of the full model.
If the full model was xsphabs.gal * powlaw1d.pl then this will
compare the unabsorbed to absorbed flux distributions:
>>> plot_energy_flux(0.5, 2, num=1000, bins=20)
>>> plot_energy_flux(0.5, 2, model=pl, num=1000, bins=20)
If you have multiple datasets loaded, each with a model, then
all datasets will be used to calculate the errors when the
id parameter is not set. A single dataset can be used by
specifying a dataset (in this example the overplot is just with
dataset 1):
>>> mdl = xsphabs.gal * xsapec.src
>>> set_source(1, mdl)
>>> set_source(2, mdl)
...
>>> plot_energy_flux(0.5, 2, model=src num=1000, bins=20)
>>> plot_energy_flux(0.5, 2, model=src num=1000, bins=20,
... id=1, overplot=True)
If you have multiple datasets then you can use the otherids
argument to specify exactly what set of data is used:
>>> plot_energy_flux(0.5, 2, model=src num=1000, bins=20,
... id=1, otherids=(2, 3, 4))
"""
efplot = self.get_energy_flux_hist(lo=lo, hi=hi, id=id, num=num, bins=bins,
correlated=correlated, numcores=numcores,
bkg_id=bkg_id, scales=scales, model=model,
otherids=otherids, clip=clip, recalc=recalc)
self._plot(efplot, overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_photon_flux(self, lo=None, hi=None, id=None, num=7500, bins=75,
correlated=False, numcores=None, bkg_id=None,
scales=None, model=None, otherids=(),
recalc=True, clip='hard',
overplot=False, clearwindow=True,
**kwargs):
"""Display the photon flux distribution.
For each iteration, draw the parameter values of the model
from a normal distribution, evaluate the model, and sum the
model over the given range (the flux). Plot up the
distribution of this flux. The units for the flux are as
returned by `calc_photon_flux`. The `sample_photon_flux` and
`get_photon_flux_hist` functions return the data used to
create this plot.
.. versionchanged:: 4.12.2
The scales parameter is no longer ignored when set and the
model and otherids parameters have been added. The clip
argument has been added.
Parameters
----------
lo : number, optional
The lower limit to use when summing up the signal. If not
given then the lower value of the data grid is used.
hi : optional
The upper limit to use when summing up the signal. If not
given then the upper value of the data grid is used.
id : int or string, optional
The identifier of the data set to use. If `None`, the
default value, then all datasets with associated models are
used to calculate the errors and the model evaluation is
done using the default dataset.
num : int, optional
The number of samples to create. The default is 7500.
bins : int, optional
The number of bins to use for the histogram.
correlated : bool, optional
If ``True`` (the default is ``False``) then ``scales`` is the
full covariance matrix, otherwise it is just a 1D array
containing the variances of the parameters (the diagonal
elements of the covariance matrix).
numcores : optional
The number of CPU cores to use. The default is to use all
the cores on the machine.
bkg_id : int or string, optional
The identifier of the background component to use. This
should only be set when the line to be measured is in the
background model.
scales : array, optional
The scales used to define the normal distributions for the
parameters. The size and shape of the array depends on the
number of free parameters in the fit (n) and the value of
the `correlated` parameter. When the parameter is `True`,
scales must be given the covariance matrix for the free
parameters (a n by n matrix that matches the parameter
ordering used by Sherpa). For un-correlated parameters
the covariance matrix can be used, or a one-dimensional
array of n elements can be used, giving the width (specified
as the sigma value of a normal distribution) for each
parameter (e.g. the square root of the diagonal elements
of the covariance matrix). If the scales parameter is not
given then the covariance matrix is evaluated for the
current model and best-fit parameters.
model : model, optional
The model to integrate. If left as `None` then the source
model for the dataset will be used. This can be used to
calculate the unabsorbed flux, as shown in the examples.
The model must be part of the source expression.
otherids : sequence of integer and string ids, optional
The list of other datasets that should be included when
calculating the errors to draw values from.
recalc : bool, optional
If ``True``, the default, then re-calculate the values rather
than use the values from the last time the function was
run.
clip : {'hard', 'soft', 'none'}, optional
What clipping strategy should be applied to the sampled
parameters. The default ('hard') is to fix values at their
hard limits if they exceed them. A value of 'soft' uses
the soft limits instead, and 'none' applies no
clipping.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
See Also
--------
calc_photon_flux : Integrate the unconvolved source model over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
covar : Estimate the confidence intervals using the confidence method.
get_energy_flux_hist : Return the data displayed by plot_energy_flux.
get_photon_flux_hist : Return the data displayed by plot_photon_flux.
plot_cdf : Plot the cumulative density function of an array.
plot_pdf : Plot the probability density function of an array.
plot_energy_flux : Display the energy flux distribution.
plot_trace : Create a trace plot of row number versus value.
sample_energy_flux : Return the energy flux distribution of a model.
sample_flux : Return the flux distribution of a model.
sample_photon_flux : Return the photon flux distribution of a model.
Examples
--------
Plot the photon flux distribution for the range 0.5 to 7 for
the default data set:
>>> plot_photon_flux(0.5, 7, num=1000)
Overplot the 0.5 to 2 photon flux distribution from the "core"
data set on top of the values from the "jet" data set:
>>> plot_photon_flux(0.5, 2, id="jet", num=1000)
>>> plot_photon_flux(0.5, 2, id="core", num=1000, overplot=True)
Overplot the flux distribution for just the pl component (which
must be part of the source expression) on top of the full model.
If the full model was xsphabs.gal * powlaw1d.pl then this will
compare the unabsorbed to absorbed flux distributions:
>>> plot_photon_flux(0.5, 2, num=1000, bins=20)
>>> plot_photon_flux(0.5, 2, model=pl, num=1000, bins=20)
If you have multiple datasets loaded, each with a model, then
all datasets will be used to calculate the errors when the
id parameter is not set. A single dataset can be used by
specifying a dataset (in this example the overplot is just with
dataset 1):
>>> mdl = xsphabs.gal * xsapec.src
>>> set_source(1, mdl)
>>> set_source(2, mdl)
...
>>> plot_photon_flux(0.5, 2, model=src num=1000, bins=20)
>>> plot_photon_flux(0.5, 2, model=src num=1000, bins=20,
... id=1, overplot=True)
If you have multiple datasets then you can use the otherids
argument to specify exactly what set of data is used:
>>> plot_photon_flux(0.5, 2, model=src num=1000, bins=20,
... id=1, otherids=(2, 3, 4))
"""
pfplot = self.get_photon_flux_hist(lo=lo, hi=hi, id=id, num=num, bins=bins,
correlated=correlated, numcores=numcores,
bkg_id=bkg_id, scales=scales, model=model,
otherids=otherids, clip=clip, recalc=recalc)
self._plot(pfplot, overplot=overplot, clearwindow=clearwindow,
**kwargs)
def _bkg_jointplot2(self, plot1, plot2, overplot=False,
clearwindow=True, **kwargs):
"""Create a joint plot for bkg, vertically aligned, fit data on the top.
Parameters
----------
plot1 : sherpa.plot.Plot instance
The plot to appear in the top panel.
plot2 : sherpa.plot.Plot instance
The plot to appear in the bottom panel.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
"""
self._jointplot.reset()
try:
sherpa.plot.begin()
self._jointplot.plottop(plot1, overplot=overplot,
clearwindow=clearwindow, **kwargs)
# Unlike the plot version we can assume we are dealing
# with histogram plots here.
#
oldval = plot2.plot_prefs['xlog']
dprefs = plot1.dataplot.histo_prefs
mprefs = plot1.modelplot.histo_prefs
if dprefs['xlog'] or mprefs['xlog']:
plot2.plot_prefs['xlog'] = True
self._jointplot.plotbot(plot2, overplot=overplot, **kwargs)
plot2.plot_prefs['xlog'] = oldval
except:
sherpa.plot.exceptions()
raise
else:
sherpa.plot.end()
[docs] def plot_bkg_fit_ratio(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the fit results, and the data/model ratio, for the background of
a PHA data set.
This creates two plots - the first from `plot_bkg_fit` and the
second from `plot_bkg_ratio` - for a data set.
.. versionchanged:: 4.12.2
The ``overplot`` option now works.
.. versionadded:: 4.12.0
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_fit_ratio`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_fit_plot : Return the data used by plot_bkg_fit.
get_bkg_resid_plot : Return the data used by plot_bkg_resid.
plot : Create one or more plot types.
plot_bkg : Plot the background values for a PHA data set.
plot_bkg_model : Plot the model for the background of a PHA data set.
plot_bkg_fit : Plot the fit results (data, model) for the background of a PHA data set.
plot_bkg_fit_delchi : Plot the fit results, and the residuals, for the background of a PHA data set.
plot_bkg_fit_resid : Plot the fit results, and the residuals, for the background of a PHA data set.
plot_fit : Plot the fit results (data, model) for a data set.
plot_fit_resid : Plot the fit results, and the residuals, for a data set.
set_analysis : Set the units used when fitting and displaying spectral data.
Notes
-----
For the residual plot, the ylog setting is ignored, and the Y axis
is drawn using a linear scale.
Examples
--------
Plot the background fit and the ratio of the background to
this fit for the default data set:
>>> plot_bkg_fit_ratio()
"""
plot1obj = self.get_bkg_fit_plot(id, bkg_id, recalc=not replot)
plot2obj = self.get_bkg_ratio_plot(id, bkg_id, recalc=not replot)
self._bkg_jointplot2(plot1obj, plot2obj,
overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_fit_resid(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the fit results, and the residuals, for the background of
a PHA data set.
This creates two plots - the first from `plot_bkg_fit` and the
second from `plot_bkg_resid` - for a data set.
.. versionchanged:: 4.12.2
The ``overplot`` option now works.
.. versionchanged:: 4.12.0
The Y axis of the residual plot is now always drawn using a
linear scale.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_fit_resid`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_fit_plot : Return the data used by plot_bkg_fit.
get_bkg_resid_plot : Return the data used by plot_bkg_resid.
plot : Create one or more plot types.
plot_bkg : Plot the background values for a PHA data set.
plot_bkg_model : Plot the model for the background of a PHA data set.
plot_bkg_fit : Plot the fit results (data, model) for the background of a PHA data set.
plot_bkg_fit_ratio : Plot the fit results, and the data/model ratio, for the background of a PHA data set.
plot_bkg_fit_delchi : Plot the fit results, and the residuals, for the background of a PHA data set.
plot_fit : Plot the fit results (data, model) for a data set.
plot_fit_resid : Plot the fit results, and the residuals, for a data set.
set_analysis : Set the units used when fitting and displaying spectral data.
Notes
-----
For the residual plot, the ylog setting is ignored, and the Y axis
is drawn using a linear scale.
Examples
--------
Plot the background fit and residuals to the default data set:
>>> plot_bkg_fit_resid()
"""
plot1obj = self.get_bkg_fit_plot(id, bkg_id, recalc=not replot)
plot2obj = self.get_bkg_resid_plot(id, bkg_id, recalc=not replot)
self._bkg_jointplot2(plot1obj, plot2obj,
overplot=overplot, clearwindow=clearwindow,
**kwargs)
[docs] def plot_bkg_fit_delchi(self, id=None, bkg_id=None, replot=False,
overplot=False, clearwindow=True, **kwargs):
"""Plot the fit results, and the residuals, for the background of
a PHA data set.
This creates two plots - the first from `plot_bkg_fit` and the
second from `plot_bkg_delchi` - for a data set.
.. versionchanged:: 4.12.2
The ``overplot`` option now works.
.. versionchanged:: 4.12.0
The Y axis of the residual plot is now always drawn using a
linear scale.
Parameters
----------
id : int or str, optional
The data set that provides the data. If not given then the
default identifier is used, as returned by `get_default_id`.
bkg_id : int or str, optional
Identify the background component to use, if there are
multiple ones associated with the data set.
replot : bool, optional
Set to ``True`` to use the values calculated by the last
call to `plot_bkg_fit_delchi`. The default is ``False``.
overplot : bool, optional
If ``True`` then add the data to an existing plot, otherwise
create a new plot. The default is ``False``.
clearwindow : bool, optional
Should the existing plot area be cleared before creating this
new plot (e.g. for multi-panel plots)?
Raises
------
sherpa.utils.err.ArgumentErr
If the data set does not contain PHA data.
sherpa.utils.err.IdentifierErr
If the ``bkg_id`` parameter is invalid.
sherpa.utils.err.ModelErr
If no model expression has been created for the background
data.
See Also
--------
get_bkg_fit_plot : Return the data used by plot_bkg_fit.
get_bkg_delchi_plot : Return the data used by plot_bkg_delchi.
plot : Create one or more plot types.
plot_bkg : Plot the background values for a PHA data set.
plot_bkg_model : Plot the model for the background of a PHA data set.
plot_bkg_fit : Plot the fit results (data, model) for the background of a PHA data set.
plot_bkg_fit_ratio : Plot the fit results, and the data/model ratio, for the background of a PHA data set.
plot_bkg_fit_resid : Plot the fit results, and the residuals, for the background of a PHA data set.
plot_fit : Plot the fit results (data, model) for a data set.
plot_fit_delchi : Plot the fit results, and the residuals, for a data set.
set_analysis : Set the units used when fitting and displaying spectral data.
Notes
-----
For the residual plot, the ylog setting is ignored, and the Y axis
is drawn using a linear scale.
Examples
--------
Plot the background fit and residuals (normalised by the
error) to the default data set:
>>> plot_bkg_fit_delchi()
"""
plot1obj = self.get_bkg_fit_plot(id, bkg_id, recalc=not replot)
plot2obj = self.get_bkg_delchi_plot(id, bkg_id, recalc=not replot)
self._bkg_jointplot2(plot1obj, plot2obj,
overplot=overplot, clearwindow=clearwindow,
**kwargs)
###########################################################################
# Analysis Functions
###########################################################################
[docs] def resample_data(self, id=None, niter=1000, seed=None):
"""Resample data with asymmetric error bars.
The function performs a parametric bootstrap assuming a skewed
normal distribution centered on the observed data point with
the variance given by the low and high measurement errors. The
function simulates niter realizations of the data and fits
each realization with the assumed model to obtain the best fit
parameters. The function returns the best fit parameters for
each realization, and displays the average and standard
deviation for each parameter.
.. versionadded:: 4.12.2
The samples and statistic keys were added to the return
value and the parameter values are returned as NumPy arrays
rather than as lists.
Parameters
----------
id : int or str, optional
The identifier of the data set to use.
niter : int, optional
The number of iterations to use. The default is ``1000``.
seed : int, optional
The seed for the random number generator. The default is ```None```.
Returns
-------
sampled : dict
The keys are statistic, which contains the best-fit
statistic value for each iteration, samples, which contains
the resampled data used in the fits as a niter by ndata
array, and the free parameters in the fit, containing a
NumPy array containing the fit parameter for each iteration
(of size niter).
See Also
--------
load_ascii_with_errors : Load an ASCII file with asymmetric errors as a data set.
Examples
--------
Account for of asymmetric errors when calculating parameter
uncertainties:
>>> load_ascii_with_errors(1, 'test.dat')
>>> set_model(polynom1d.p0)
>>> thaw(p0.c1)
>>> fit()
Dataset = 1
Method = levmar
Statistic = leastsq
Initial fit statistic = 4322.56
Final fit statistic = 247.768 at function evaluation 6
Data points = 61
Degrees of freedom = 59
Change in statistic = 4074.79
p0.c0 3.2661 +/- 0.193009
p0.c1 2162.19 +/- 65.8445
>>> result = resample_data(1, niter=10)
p0.c0 : avg = 4.159973865314249 , std = 1.0575403309799554
p0.c1 : avg = 1943.5489865678633 , std = 268.64478808013547
>>> print(result['p0.c0'])
[5.856479033432613, 3.8252624107243465, ... 2.8704270612985345]
>>> print(result['p0.c1'])
[1510.049972062868, 1995.4742750432902, ... 2235.9753113309894]
Display the PDF of the parameter values of the p0.c0 component
from a run with 5000 iterations:
>>> sample = resample_data(1, 5000)
p0.c0 : avg = 3.966543284267264 , std = 0.9104639711036427
p0.c1 : avg = 1988.8417667057342 , std = 220.21903089622705
>>> plot_pdf(sample['p0.c0'], bins=40)
The samples used for the analysis are returned as the samples
key (as a 2D NumPy array of size number of iterations by
number of data points), that can be used if further analysis
is desired. In this case, the distribution of the first bin
is shown as a CDF:
>>> sample = resample_data(1, 5000)
>>> samples = sample['samples']
>>> plot_cdf(samples[:, 0])
"""
data = self.get_data(id)
model = self.get_model(id)
resampledata = sherpa.sim.ReSampleData(data, model)
return resampledata(niter=niter, seed=seed)
[docs] def sample_photon_flux(self, lo=None, hi=None, id=None, num=1,
scales=None, correlated=False,
numcores=None, bkg_id=None, model=None,
otherids=(), clip='hard'):
"""Return the photon flux distribution of a model.
For each iteration, draw the parameter values of the model
from a normal distribution, evaluate the model, and sum the
model over the given range (the flux). The return array
contains the flux and parameter values for each iteration.
The units for the flux are as returned by `calc_photon_flux`.
.. versionchanged:: 4.12.2
The model, otherids, and clip parameters were added and
the return value has an extra column.
Parameters
----------
lo : number, optional
The lower limit to use when summing up the signal. If not
given then the lower value of the data grid is used.
hi : optional
The upper limit to use when summing up the signal. If not
given then the upper value of the data grid is used.
id : int or string, optional
The identifier of the data set to use. If `None`, the
default value, then all datasets with associated models are
used to calculate the errors and the model evaluation is
done using the default dataset.
num : int, optional
The number of samples to create. The default is 1.
scales : array, optional
The scales used to define the normal distributions for the
parameters. The size and shape of the array depends on the
number of free parameters in the fit (n) and the value of
the `correlated` parameter. When the parameter is `True`,
scales must be given the covariance matrix for the free
parameters (a n by n matrix that matches the parameter
ordering used by Sherpa). For un-correlated parameters
the covariance matrix can be used, or a one-dimensional
array of n elements can be used, giving the width (specified
as the sigma value of a normal distribution) for each
parameter (e.g. the square root of the diagonal elements
of the covariance matrix). If the scales parameter is not
given then the covariance matrix is evaluated for the
current model and best-fit parameters.
correlated : bool, optional
Should the correlation between the parameters be included
when sampling the parameters? If not, then each parameter
is sampled from independent distributions. In both cases
a normal distribution is used.
numcores : optional
The number of CPU cores to use. The default is to use all
the cores on the machine.
bkg_id : int or string, optional
The identifier of the background component to use. This
should only be set when the line to be measured is in the
background model.
model : model, optional
The model to integrate. If left as `None` then the source
model for the dataset will be used. This can be used to
calculate the unabsorbed flux, as shown in the examples.
The model must be part of the source expression.
otherids : sequence of integer and string ids, optional
The list of other datasets that should be included when
calculating the errors to draw values from.
clip : {'hard', 'soft', 'none'}, optional
What clipping strategy should be applied to the sampled
parameters. The default ('hard') is to fix values at their
hard limits if they exceed them. A value of 'soft' uses
the soft limits instead, and 'none' applies no
clipping. The last column in the returned arrays indicates
if the row had any clipped parameters (even when clip is
set to 'none').
Returns
-------
vals
The return array has the shape ``(num, N+2)``, where ``N``
is the number of free parameters in the fit and num is the
`num` parameter. The rows of this array contain the flux
value, as calculated by `calc_photon_flux`, followed by the
values of the thawed parameters used for that iteration,
and then a flag column indicating if the parameters were
clipped (1) or not (0). The order of the parameters
matches the data returned by `get_fit_results`.
See Also
--------
calc_photon_flux : Integrate the unconvolved source model over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
covar : Estimate the confidence intervals using the confidence method.
plot_cdf : Plot the cumulative density function of an array.
plot_pdf : Plot the probability density function of an array.
plot_energy_flux : Display the energy flux distribution.
plot_photon_flux : Display the photon flux distribution.
plot_trace : Create a trace plot of row number versus value.
sample_energy_flux : Return the energy flux distribution of a model.
sample_flux : Return the flux distribution of a model.
Notes
-----
There are two ways to use this function to calculate fluxes
from multiple sources. The first is to leave the `id` argument
as `None`, in which case all available datasets will be used.
Alternatively, the `id` and `otherids` arguments can be set to
list the exact datasets to use, such as `id=1,
otherids=(2,3,4)`.
The returned value contains all free parameters in the fit,
even if they are not included in the model argument (e.g.
when calculating an unabsorbed flux).
Examples
--------
Calculate the photon flux distribution for the range 0.5 to 7,
and plot up the resulting flux distribution (as a cumulative
distribution):
>>> vals = sample_photon_flux(0.5, 7, num=1000)
>>> plot_cdf(vals[:, 0], name='flux')
Repeat the above, but allowing the parameters to be
correlated, and then calculate the 5, 50, and 95 percent
quantiles of the photon flux distribution:
>>> cvals = sample_photon_flux(0.5, 7, num=1000, correlated=True)
>>> np.percentile(cvals[:, 0], [5, 50, 95])
The photon flux of a component (or sub-set of components) can be
calculated using the model argument. For the following case,
an absorbed power-law was used to fit the data -
`xsphabs.gal * powerlaw.pl` - and then the flux of just the
power-law component is calculated. Note that the returned
array has columns 'flux', 'gal.nh', 'pl.gamma', and 'pl.ampl'
(that is flux and then the free parameters in the full model).
>>> vals = sample_photon_flux(0.5, 7, model=pl, num=1000, correlated=True)
Calculate the 2-10 keV flux for the pl model using a joint fit
to the datasets 1, 2, 3, and 4:
>>> vals = sample_photon_flux(2, 10, model=pl, id=1, otherids=(2,3,4),
... num=1000)
Use the given parameter errors for sampling the parameter distribution.
The fit must have three free parameters, and each parameter is
sampled independently (in this case parerrs gives the sigma
values for each parameter):
>>> parerrs = [0.25, 1.22, 1.04e-4]
>>> vals = sample_photon_flux(2, 10, num=5000, scales=parerrs)
In this case the parameter errors are taken from the covariance
analysis, using the `parmaxes` field since these are positive.
>>> covar()
>>> parerrs = get_covar_results().parmaxes
>>> vals = sample_photon_flux(0.5, 2, num=1000, scales=parerrs)
Run covariance to estimate the parameter errors and then
extract the covariance matrix from the results (as the `cmat`
variable). This matrix is then used to define the parameter
widths - including correlated terms - in the flux sampling,
after being increased by ten percent. This is used to
calculate both the absorbed (`vals1`) and unabsorbed (`vals2`)
fluxes. Both arrays have columns: flux, gal.nh, pl.gamma, and
pl.ampl.
>>> set_source(xsphabs.gal * powlaw1d.pl)
>>> fit()
>>> covar()
>>> cmat = get_covar_results().extra_output
>>> vals1 = sample_photon_flux(2, 10, num=5000, correlated=True,
... scales=1.1 * cmat)
>>> vals2 = sample_photon_flux(2, 10, num=5000, correlated=True,
... model=pl, scales=1.1 * cmat)
Calculate the flux and error distribution using fits
to all datasets:
>>> set_source(xsphabs.gal * xsapec.clus)
>>> set_source(2, gal * clus)
>>> set_source(3, gal * clus)
... fit the data
>>> vals = sample_photon_flux(0.5, 10, model=clus, num=10000)
Calculate the flux and error distribution using fits
to an explicit set of datasets (in this case datasets
1 and 2):
>>> vals = sample_photon_flux(0.5, 10, id=1, otherids=[2],
... model=clus, num=10000)
Generate two sets of parameter values, where the parameter
values in v1 are generated from a random distribution and then
clipped to the hard limits of the parameters, and the values
in v2 use the soft limits of the parameters. The last column
in both v1 and v2 indicates whether the row had any clipped
parameters. The flux1_filt and flux2_filt arrays indicate the
photon-flux distribution after it has been filtered to remove
any row with clipped parameters:
>>> v1 = sample_photon_flux(0.5, 2, num=1000)
>>> v2 = sample_photon_flux(0.5, 2, num=1000, clip='soft')
>>> flux1 = v1[:, 0]
>>> flux2 = v2[:, 0]
>>> flux1_filt = flux1[v1[:, -1] == 0]
>>> flux2_filt = flux2[v2[:, -1] == 0]
"""
_, fit = self._get_fit(id, otherids=otherids)
if bkg_id is None:
data = self.get_data(id)
if model is None:
model = self.get_source(id)
else:
data = self.get_bkg(id, bkg_id)
if model is None:
model = self.get_bkg_source(id, bkg_id)
correlated = sherpa.utils.bool_cast(correlated)
return sherpa.astro.flux.sample_flux(fit, data, model,
method=sherpa.astro.utils.calc_photon_flux,
correlated=correlated,
num=num, lo=lo, hi=hi,
numcores=numcores,
samples=scales, clip=clip)
[docs] def sample_energy_flux(self, lo=None, hi=None, id=None, num=1,
scales=None, correlated=False,
numcores=None, bkg_id=None, model=None,
otherids=(), clip='hard'):
"""Return the energy flux distribution of a model.
For each iteration, draw the parameter values of the model
from a normal distribution, evaluate the model, and sum the
model over the given range (the flux). The return array
contains the flux and parameter values for each iteration.
The units for the flux are as returned by `calc_energy_flux`.
.. versionchanged:: 4.12.2
The model, otherids, and clip parameters were added and
the return value has an extra column.
Parameters
----------
lo : number, optional
The lower limit to use when summing up the signal. If not
given then the lower value of the data grid is used.
hi : optional
The upper limit to use when summing up the signal. If not
given then the upper value of the data grid is used.
id : int or string, optional
The identifier of the data set to use. If `None`, the
default value, then all datasets with associated models are
used to calculate the errors and the model evaluation is
done using the default dataset.
num : int, optional
The number of samples to create. The default is 1.
scales : array, optional
The scales used to define the normal distributions for the
parameters. The size and shape of the array depends on the
number of free parameters in the fit (n) and the value of
the `correlated` parameter. When the parameter is `True`,
scales must be given the covariance matrix for the free
parameters (a n by n matrix that matches the parameter
ordering used by Sherpa). For un-correlated parameters
the covariance matrix can be used, or a one-dimensional
array of n elements can be used, giving the width (specified
as the sigma value of a normal distribution) for each
parameter (e.g. the square root of the diagonal elements
of the covariance matrix). If the scales parameter is not
given then the covariance matrix is evaluated for the
current model and best-fit parameters.
correlated : bool, optional
Should the correlation between the parameters be included
when sampling the parameters? If not, then each parameter
is sampled from independent distributions. In both cases
a normal distribution is used.
numcores : optional
The number of CPU cores to use. The default is to use all
the cores on the machine.
bkg_id : int or string, optional
The identifier of the background component to use. This
should only be set when the line to be measured is in the
background model.
model : model, optional
The model to integrate. If left as `None` then the source
model for the dataset will be used. This can be used to
calculate the unabsorbed flux, as shown in the examples.
The model must be part of the source expression.
otherids : sequence of integer and string ids, optional
The list of other datasets that should be included when
calculating the errors to draw values from.
clip : {'hard', 'soft', 'none'}, optional
What clipping strategy should be applied to the sampled
parameters. The default ('hard') is to fix values at their
hard limits if they exceed them. A value of 'soft' uses
the soft limits instead, and 'none' applies no
clipping. The last column in the returned arrays indicates
if the row had any clipped parameters (even when clip is
set to 'none').
Returns
-------
vals
The return array has the shape ``(num, N+2)``, where ``N``
is the number of free parameters in the fit and num is the
`num` parameter. The rows of this array contain the flux
value, as calculated by `calc_energy_flux`, followed by the
values of the thawed parameters used for that iteration,
and then a flag column indicating if the parameters were
clipped (1) or not (0). The order of the parameters
matches the data returned by `get_fit_results`.
See Also
--------
calc_photon_flux : Integrate the unconvolved source model over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
covar : Estimate the confidence intervals using the confidence method.
plot_cdf : Plot the cumulative density function of an array.
plot_pdf : Plot the probability density function of an array.
plot_energy_flux : Display the energy flux distribution.
plot_photon_flux : Display the photon flux distribution.
plot_trace : Create a trace plot of row number versus value.
sample_photon_flux : Return the flux distribution of a model.
sample_flux : Return the flux distribution of a model.
Notes
-----
There are two ways to use this function to calculate fluxes
from multiple sources. The first is to leave the `id` argument
as `None`, in which case all available datasets will be used.
Alternatively, the `id` and `otherids` arguments can be set to
list the exact datasets to use, such as `id=1,
otherids=(2,3,4)`.
The returned value contains all free parameters in the fit,
even if they are not included in the model argument (e.g.
when calculating an unabsorbed flux).
Examples
--------
Calculate the energy flux distribution for the range 0.5 to 7,
and plot up the resulting flux distribution (as a cumulative
distribution):
>>> vals = sample_energy_flux(0.5, 7, num=1000)
>>> plot_cdf(vals[:, 0], name='flux')
Repeat the above, but allowing the parameters to be
correlated, and then calculate the 5, 50, and 95 percent
quantiles of the energy flux distribution:
>>> cvals = sample_energy_flux(0.5, 7, num=1000, correlated=True)
>>> np.percentile(cvals[:, 0], [5, 50, 95])
The energy flux of a component (or sub-set of components) can be
calculated using the model argument. For the following case,
an absorbed power-law was used to fit the data -
`xsphabs.gal * powerlaw.pl` - and then the flux of just the
power-law component is calculated. Note that the returned
array has columns 'flux', 'gal.nh', 'pl.gamma', and 'pl.ampl'
(that is flux and then the free parameters in the full model).
>>> vals = sample_energy_flux(0.5, 7, model=pl, num=1000, correlated=True)
Calculate the 2-10 keV flux for the pl model using a joint fit
to the datasets 1, 2, 3, and 4:
>>> vals = sample_energy_flux(2, 10, model=pl, id=1, otherids=(2,3,4),
... num=1000)
Use the given parameter errors for sampling the parameter distribution.
The fit must have three free parameters, and each parameter is
sampled independently (in this case parerrs gives the sigma
values for each parameter):
>>> parerrs = [0.25, 1.22, 1.04e-4]
>>> vals = sample_energy_flux(2, 10, num=5000, scales=parerrs)
In this case the parameter errors are taken from the covariance
analysis, using the `parmaxes` field since these are positive.
>>> covar()
>>> parerrs = get_covar_results().parmaxes
>>> vals = sample_energy_flux(0.5, 2, num=1000, scales=parerrs)
Run covariance to estimate the parameter errors and then
extract the covariance matrix from the results (as the `cmat`
variable). This matrix is then used to define the parameter
widths - including correlated terms - in the flux sampling,
after being increased by ten percent. This is used to
calculate both the absorbed (`vals1`) and unabsorbed (`vals2`)
fluxes. Both arrays have columns: flux, gal.nh, pl.gamma, and
pl.ampl.
>>> set_source(xsphabs.gal * powlaw1d.pl)
>>> fit()
>>> covar()
>>> cmat = get_covar_results().extra_output
>>> vals1 = sample_energy_flux(2, 10, num=5000, correlated=True,
... scales=1.1 * cmat)
>>> vals2 = sample_energy_flux(2, 10, num=5000, correlated=True,
... model=pl, scales=1.1 * cmat)
Calculate the flux and error distribution using fits
to all datasets:
>>> set_source(xsphabs.gal * xsapec.clus)
>>> set_source(2, gal * clus)
>>> set_source(3, gal * clus)
... fit the data
>>> vals = sample_energy_flux(0.5, 10, model=clus, num=10000)
Calculate the flux and error distribution using fits
to an explicit set of datasets (in this case datasets
1 and 2):
>>> vals = sample_energy_flux(0.5, 10, id=1, otherids=[2],
... model=clus, num=10000)
Generate two sets of parameter values, where the parameter
values in v1 are generated from a random distribution and then
clipped to the hard limits of the parameters, and the values
in v2 use the soft limits of the parameters. The last column
in both v1 and v2 indicates whether the row had any clipped
parameters. The flux1_filt and flux2_filt arrays indicate the
energy-flux distribution after it has been filtered to remove
any row with clipped parameters:
>>> v1 = sample_energy_flux(0.5, 2, num=1000)
>>> v2 = sample_energy_flux(0.5, 2, num=1000, clip='soft')
>>> flux1 = v1[:, 0]
>>> flux2 = v2[:, 0]
>>> flux1_filt = flux1[v1[:, -1] == 0]
>>> flux2_filt = flux2[v2[:, -1] == 0]
"""
_, fit = self._get_fit(id, otherids=otherids)
if bkg_id is None:
data = self.get_data(id)
if model is None:
model = self.get_source(id)
else:
data = self.get_bkg(id, bkg_id)
if model is None:
model = self.get_bkg_source(id, bkg_id)
correlated = sherpa.utils.bool_cast(correlated)
return sherpa.astro.flux.sample_flux(fit, data, model,
method=sherpa.astro.utils.calc_energy_flux,
correlated=correlated,
num=num, lo=lo, hi=hi,
numcores=numcores,
samples=scales, clip=clip)
[docs] def sample_flux(self, modelcomponent=None, lo=None, hi=None, id=None,
num=1, scales=None, correlated=False,
numcores=None, bkg_id=None, Xrays=True, confidence=68):
"""Return the flux distribution of a model.
For each iteration, draw the parameter values of the model
from a normal distribution, filter out samples that lie
outside the soft limits of the parameters, evaluate the model,
and sum the model over the given range (the flux). Return the
parameter values used, together with the median, upper, and
lower quantiles of the flux distribution.
.. versionchanged:: 4.13.1
The `id` parameter is now used if set (previously the
default dataset was always used). The screen output is now
controlled by the Sherpa logging setup. The flux
calculation no-longer excludes samples at the parameter
soft limits, as this could cause an over-estimation of the
flux when a parameter is only an upper limit. The statistic
value is now returned for each row, even those that were
excluded from the flux calculation. The last-but-one column
of the returned `vals` array now records the rows that were
excluded from the flux calculation.
Parameters
----------
modelcomponent : optional
The model to use. It can be a single component or
a combination. If not given, then the full source
expression for the data set is used.
lo : number, optional
The lower limit to use when summing up the signal. If not
given then the lower value of the data grid is used.
hi : optional
The upper limit to use when summing up the signal. If not
given then the upper value of the data grid is used.
id : int or string, optional
The identifier of the data set to use. The default value
(``None``) means that the default identifier, as returned by
`get_default_id`, is used.
num : int, optional
The number of samples to create. The default is 1.
scales : array, optional
The scales used to define the normal distributions for the
parameters. The form depends on the `correlated`
parameter: when ``True``, the array should be a symmetric
positive semi-definite (N, N) array, otherwise a 1D array
of length N, where N is the number of free parameters.
correlated : bool, optional
If ``True`` (the default is ``False``) then `scales` is the
full covariance matrix, otherwise it is just a 1D array
containing the variances of the parameters (the diagonal
elements of the covariance matrix).
numcores : optional
The number of CPU cores to use. The default is to use all
the cores on the machine.
bkg_id : int or string, optional
The identifier of the background component to use. This
should only be set when the line to be measured is in the
background model.
Xrays : bool, optional
When ``True`` (the default), assume that the model has
units of photon/cm^2/s, and use `calc_energy_flux`
to convert to erg/cm^2/s. This should not be changed from
the default value.
confidence : number, optional
The confidence level for the upper and lower values, as a
percentage (0 to 100). The default is 68, so as to return
the one-sigma range.
Returns
-------
(fullflux, cptflux, vals)
The fullflux and cptflux arrays contain the results for the
full source model and the flux of the `modelcomponent`
argument (they can be the same). They have three elements
and give the median value, the value containing 100 -
confidence/2 of the data, and the fraction containing
confidence/2 of the flux distribution. For the default
confidence argument of 68 this means the last two give the
one-sigma upper and lower bounds. The vals array has a
shape of ``(num+1, N+3)``, where ``N`` is the number of
free parameters and num is the `num` parameter. The rows of
this array contain the flux value for the iteration (for
the full source model), the parameter values, a flag
indicating whether any parameter in that row was clipped
(and so was excluded from the flux calculation), and the
statistic value for this set of parameters.
See Also
--------
calc_photon_flux : Integrate the unconvolved source model over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
covar : Estimate the confidence intervals using the confidence method.
plot_energy_flux : Display the energy flux distribution.
plot_photon_flux : Display the photon flux distribution.
sample_energy_flux : Return the energy flux distribution of a model.
sample_photon_flux : Return the photon flux distribution of a model.
Notes
-----
Setting the Xrays parameter to False is currently unsupported.
The summary output displayed by this routine - giving the
median and confidence ranges - is controlled by the standard
Sherpa logging instance, and can be hidden by changing the
logging to a level greater than "INFO" (e.g. with
`sherpa.utils.logging.SherpaVerbosity`).
This routine can not be used if you have used set_full_model:
the calc_energy_flux routine should be used instead.
Examples
--------
Estimate the flux distribution for the "src" component using
the default data set. The parameters are assumed to be
uncorrelated.
>>> set_source(xsphabs.gal * xsapec.src)
>>> fit()
>>> fflux, cflux, vals = sample_flux(src, 0.5, 2, num=1000)
original model flux = 2.88993e-14, + 1.92575e-15, - 1.81963e-15
model component flux = 7.96865e-14, + 4.65144e-15, - 4.41222e-15
>>> f0, fhi, flo = cflux
>>> print("Flux: {:.2e} {:+.2e} {:+.2e}".format(f0, fhi-f0, flo-f0))
Flux: 7.97e-14 +4.65e-15 -4.41e-15
This time the parameters are assumed to be correlated, using
the covariance matrix for the fit:
>>> ans = sample_flux(src, 0.5, 2, num=1000, correlated=True)
Explicitly send in the parameter widths (sigma values), using
the estimates generated by `covar`:
>>> covar()
>>> errs = get_covar_results().parmaxes
>>> ans = sample_flux(correlated=False, scales=errs, num=500)
Explicitly send in a covariance matrix:
>>> cmatrix = get_covar_results().extra_output
>>> ans = sample_flux(correlated=True, scales=cmatrix, num=500)
Run sample_flux after changing the logging level, so that the
screen output from sample_flux is not displayed. We use the
SherpaVerbosity function from `sherpa.utils.logging` to
only change the logging level while runnng sample_flux:
>>> from sherpa.utils.logging import SherpaVerbosity
>>> with SherpaVerbosity('WARN'):
... ans = sample_flux(num=1000, lo=0.5, hi=7)
"""
if (confidence <= 0) or (confidence > 100):
raise ArgumentErr('bad', 'confidence', 'must be > 0 and <= 100')
if not Xrays:
raise NotImplementedError("sample_flux(Xrays=False) is currently unsupported")
_, fit = self._get_fit(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
else:
data = self.get_data(id)
if (modelcomponent is not None) and \
not isinstance(modelcomponent, sherpa.models.model.Model):
raise ArgumentTypeErr('badarg', 'modelcomponent', 'a model')
# We can not have a "full model" expression so error-out nicely here.
# Thanks to _get_fit we know we have a model so any error below can
# not be because no model is set but must be a "need full model"
# error. TODO: should we have a nicer way to determine this?
# Also note that it appears we have different ways the two code
# paths can error out
#
if bkg_id is not None:
try:
self.get_bkg_source(id, bkg_id)
except (IdentifierErr, ModelErr):
# At present ModelErr is thrown but keep in IdentifierErr
# just in case
raise IdentifierErr('Please use calc_energy_flux as set_bkg_full_model was used') from None
else:
try:
self.get_source(id)
except IdentifierErr:
raise IdentifierErr('Please use calc_energy_flux as set_full_model was used') from None
correlated = sherpa.utils.bool_cast(correlated)
# Why is this +1? The original comment was
# "num+1 cause sample energy flux is under-reporting its result?"
#
niter = num + 1
if not Xrays:
# NOTE: calc_energy_flux only returns a scalar, which means you can
# not pass the results to calc_sample_flux, so this code
# is currently broken
#
samples = self.calc_energy_flux(lo=lo, hi=hi, id=id,
bkg_id=bkg_id)
else:
# NOTE: the samples are drawn from the full model expression
# as this is how it was originally written
#
samples = self.sample_energy_flux(lo=lo, hi=hi, id=id, num=niter,
scales=scales, clip='soft',
correlated=correlated,
numcores=numcores,
bkg_id=bkg_id)
return sherpa.astro.flux.calc_sample_flux(lo=lo, hi=hi,
fit=fit, data=data, samples=samples,
modelcomponent=modelcomponent,
confidence=confidence)
[docs] def eqwidth(self, src, combo, id=None, lo=None, hi=None, bkg_id=None,
error=False, params=None, otherids=(), niter=1000,
covar_matrix=None):
"""Calculate the equivalent width of an emission or absorption line.
The equivalent width [1]_ is calculated in the selected units
for the data set (which can be retrieved with `get_analysis`).
.. versionchanged:: 4.10.1
The `error` parameter was added which controls whether the
return value is a scalar (the calculated equivalent width),
when set to `False`, or the median value, error limits, and
ancillary values.
Parameters
----------
src
The continuum model (this may contain multiple components).
combo
The continuum plus line (absorption or emission) model.
lo : optional
The lower limit for the calculation (the units are set by
`set_analysis` for the data set). The default value (``None``)
means that the lower range of the data set is used.
hi : optional
The upper limit for the calculation (the units are set by
`set_analysis` for the data set). The default value (``None``)
means that the upper range of the data set is used.
id : int or string, optional
The data set that provides the data. If not given then
all data sets with an associated model are used simultaneously.
bkg_id : int or string, optional
The identifier of the background component to use. This
should only be set when the line to be measured is in the
background model.
error : bool, optional
The parameter indicates whether the errors are to be calculated
or not. The default value is False
params : 2D array, optional
The default is None, in which case get_draws shall be called.
The user can input the parameter array (e.g. from running
`sample_flux`).
otherids : sequence of integer or strings, optional
Other data sets to use in the calculation.
niter : int, optional
The number of draws to use. The default is ``1000``.
covar_matrix : 2D array, optional
The covariance matrix to use. If ``None`` then the
result from `get_covar_results().extra_output` is used.
Returns
-------
retval
If ``error`` is ``False``, then returns the equivalent width,
otherwise the median, 1 sigma lower bound, 1 sigma upper
bound, the parameters array, and the array of the equivalent
width values used to determine the errors.
See Also
--------
calc_model_sum : Sum up the fitted model over a pass band.
calc_source_sum : Calculate the un-convolved model signal.
get_default_id : Return the default data set identifier.
set_model : Set the source model expression.
References
----------
.. [1] http://en.wikipedia.org/wiki/Equivalent_width
Examples
--------
Set a source model (a powerlaw for the continuum and a
gaussian for the line), fit it, and then evaluate the
equivalent width of the line. The example assumes that
this is a PHA data set, with an associated response,
so that the analysis can be done in wavelength units.
>>> set_source(powlaw1d.cont + gauss1d.line)
>>> set_analysis('wavelength')
>>> fit()
>>> eqwidth(cont, cont+line)
2.1001988282497308
The calculation is restricted to the range 20 to 20
Angstroms.
>>> eqwidth(cont, cont+line, lo=20, hi=24)
1.9882824973082310
The calculation is done for the background model of
data set 2, over the range 0.5 to 2 (the units of this
are whatever the analysis setting for this data set id).
>>> set_bkg_source(2, const1d.flat + gauss1d.bline)
>>> eqwidth(flat, flat+bline, id=2, bkg_id=1, lo=0.5, hi=2)
0.45494599793003426
With the `error` flag set to `True`, the return value is
enhanced with extra information, such as the median and
one-sigma ranges on the equivalent width::
>>> res = eqwidth(p1, p1 + g1, error=True)
>>> ewidth = res[0] # the median equivalent width
>>> errlo = res[1] # the one-sigma lower limit
>>> errhi = res[2] # the one-sigma upper limit
>>> pars = res[3] # the parameter values used
>>> ews = res[4] # array of eq. width values
which can be used to display the probability density or
cumulative distribution function of the equivalent widths::
>>> plot_pdf(ews)
>>> plot_cdf(ews)
"""
data = self.get_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
####################################################
if error:
def is_numpy_ndarray(arg, name, npars, dim1=None):
if not isinstance(arg, numpy.ndarray):
msg = name + ' must be of type numpy.ndarray'
raise IOErr(msg)
shape = arg.shape
if len(shape) != 2:
msg = name + ' must be 2d numpy.ndarray'
raise IOErr(msg)
if shape[0] != npars:
msg = name + ' must be of dimension (%d, x)' % npars
raise IOErr(msg)
if dim1 is not None:
if shape[1] != npars:
msg = name + ' must be of dimension (%d, %d)' % \
(npars, npars)
raise IOErr(msg)
_, fit = self._get_fit(id)
fit_results = self.get_fit_results()
parnames = fit_results.parnames
npar = len(parnames)
orig_par_vals = numpy.array(fit_results.parvals)
if params is None:
# run get_draws or normal distribution depending on fit stat
if covar_matrix is None:
try:
# check just in case usr has run covar()
covar_results = self.get_covar_results()
covar_matrix = covar_results.extra_output
except sherpa.utils.err.SessionErr:
# usr has not run covar, will have to run it
covar_matrix = fit.est_errors().extra_output
is_numpy_ndarray(covar_matrix, 'covar_matrix', npar, npar)
# Have enough stuff to generate samples
if isinstance(self._current_stat, (Cash, CStat, WStat)):
_, _, params = \
self.get_draws(id, otherids=otherids, niter=niter,
covar_matrix=covar_matrix)
else:
sampler = NormalParameterSampleFromScaleMatrix()
tmp = sampler.get_sample(fit, covar_matrix, niter + 1)
params = tmp.transpose()
else:
is_numpy_ndarray(params, 'params', npar)
mins = fit.model._get_thawed_par_mins()
maxs = fit.model._get_thawed_par_maxes()
eqw = numpy.zeros_like(params[0, :])
for params_index in range(len(params[0, :])):
for parnames_index, parname in enumerate(parnames):
val = params[parnames_index, params_index]
# Note: the normal dist does not respect the soft limits
mymin = mins[parnames_index]
mymax = maxs[parnames_index]
val = max(mymin, min(val, mymax))
self.set_par(parname, val)
eqw[params_index] = \
sherpa.astro.utils.eqwidth(data, src, combo, lo, hi)
median, lower, upper = sherpa.utils.get_error_estimates(eqw)
fit.model.thawedpars = orig_par_vals
return median, lower, upper, params, eqw
####################################################
return sherpa.astro.utils.eqwidth(data, src, combo, lo, hi)
[docs] def calc_photon_flux(self, lo=None, hi=None, id=None, bkg_id=None,
model=None):
"""Integrate the unconvolved source model over a pass band.
Calculate the integral of S(E) over a pass band, where S(E) is
the spectral model evaluated for each bin (that is, the model
without any instrumental responses applied to it).
.. versionchanged:: 4.12.1
The model parameter was added.
Parameters
----------
lo, hi : number, optional
If both are None or both are set then calculate the flux
over the given band. If only one is set then calculate
the flux density at that point. The units for `lo` and `hi`
are given by the current analysis setting.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by `get_default_id`.
bkg_id : int or str, optional
If set, use the model associated with the given background
component rather than the source model.
model : model, optional
The model to integrate. If left as `None` then the source
model for the dataset will be used. This can be used to
calculate the unabsorbed flux, as shown in the examples.
Returns
-------
flux : number
The flux or flux density. For X-Spec style models the
flux units will be photon/cm^2/s and the flux density units
will be either photon/cm^2/s/keV or photon/cm^2/s/Angstrom,
depending on the analysis setting.
See Also
--------
calc_data_sum : Sum up the observed counts over a pass band.
calc_model_sum : Sum up the fitted model over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
calc_source_sum: Sum up the source model over a pass band.
set_analysis : Set the units used when fitting and displaying spectral data
set_model : Set the source model expression for a data set.
Notes
-----
The units of `lo` and `hi` are determined by the analysis
setting for the data set (e.g. `get_analysis`).
Any existing filter on the data set - e.g. as created by
`ignore` or `notice` - is ignored by this function.
The units of the answer depend on the model components used in
the source expression and the axis or axes of the data set.
It is unlikely to give sensible results for 2D data sets.
Examples
--------
Calculate the integral of the unconvolved model over the
full range of the default data set:
>>> calc_photon_flux()
Return the flux for the data set labelled "core":
>>> calc_photon_flux(id='core')
Calculate the photon flux over the ranges 0.5 to 2 and 0.5 to
7 keV, and compared to the energy fluxes for the same bands:
>>> set_analysis('energy')
>>> calc_photon_flux(0.5, 2)
0.35190275
>>> calc_photon_flux(0.5, 7)
0.49050927
>>> calc_energy_flux(0.5, 2)
5.7224906878061796e-10
>>> calc_energy_flux(0.5, 7)
1.3758131915063825e-09
Calculate the photon flux density at 0.5 keV for the source
"core":
>>> calc_photon_flux(0.5, id="core")
0.64978176
Calculate the flux for the model applied to the second background
component of the 'jet' data set, for the wavelength range 20 to 22
Angstroms:
>>> set_analysis('jet', 'wave')
>>> calc_photon_flux(20, 22, id='jet', bkg_id=2)
For the following example, the source model is an absorbed
powerlaw - `xsphabs.gal * powerlaw.pl` - so that the `fabs`
value represents the absorbed flux, and `funabs` the unabsorbed
flux (i.e. just the power-law component):
>>> fabs = calc_photon_flux(0.5, 7)
>>> funabs = calc_photon_flux(0.5, 7, model=pl)
"""
if bkg_id is None:
data = self.get_data(id)
else:
data = self.get_bkg(id, bkg_id)
if model is None:
if bkg_id is None:
model = self.get_source(id)
else:
model = self.get_bkg_source(id, bkg_id)
else:
_check_type(model, sherpa.models.Model, 'model',
'a model object')
return sherpa.astro.utils.calc_photon_flux(data, model, lo, hi)
[docs] def calc_energy_flux(self, lo=None, hi=None, id=None, bkg_id=None,
model=None):
"""Integrate the unconvolved source model over a pass band.
Calculate the integral of E * S(E) over a pass band, where E
is the energy of the bin and S(E) the spectral model evaluated
for that bin (that is, the model without any instrumental
responses applied to it).
.. versionchanged:: 4.12.1
The model parameter was added.
Parameters
----------
lo, hi : number, optional
If both are None or both are set then calculate the flux
over the given band. If only one is set then calculate
the flux density at that point. The units for `lo` and `hi`
are given by the current analysis setting.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by ``get_default_id``.
bkg_id : int or str, optional
If set, use the model associated with the given background
component rather than the source model.
model : model, optional
The model to integrate. If left as `None` then the source
model for the dataset will be used. This can be used to
calculate the unabsorbed flux, as shown in the examples.
Returns
-------
flux : number
The flux or flux density. For X-Spec style models the
flux units will be erg/cm^2/s and the flux density units
will be either erg/cm^2/s/keV or erg/cm^2/s/Angstrom,
depending on the analysis setting.
See Also
--------
calc_data_sum : Sum up the data values over a pass band.
calc_model_sum : Sum up the fitted model over a pass band.
calc_source_sum: Sum up the source model over a pass band.
calc_photon_flux : Integrate the unconvolved source model over a pass band.
set_analysis : Set the units used when fitting and displaying spectral data
set_model : Set the source model expression for a data set.
Notes
-----
The units of ``lo`` and ``hi`` are determined by the analysis
setting for the data set (e.g. ``get_analysis``).
Any existing filter on the data set - e.g. as created by
``ignore`` or ``notice`` - is ignored by this function.
The units of the answer depend on the model components used in
the source expression and the axis or axes of the data set.
It is unlikely to give sensible results for 2D data sets.
Examples
--------
Calculate the integral of the unconvolved model over the
full range of the default data set:
>>> calc_energy_flux()
Return the flux for the data set labelled "core":
>>> calc_energy_flux(id='core')
Calculate the energy flux over the ranges 0.5 to 2 and 0.5 to
7 keV:
>>> set_analysis('energy')
>>> calc_energy_flux(0.5, 2)
5.7224906878061796e-10
>>> calc_energy_flux(0.5, 7)
1.3758131915063825e-09
Calculate the energy flux density at 0.5 keV for the source
"core":
>>> calc_energy_flux(0.5, id="core")
5.2573786652855304e-10
Calculate the flux for the model applied to the second background
component of the 'jet' data set, for the wavelength range 20 to 22
Angstroms:
>>> set_analysis('jet', 'wave')
>>> calc_energy_flux(20, 22, id='jet', bkg_id=2)
For the following example, the source model is an absorbed
powerlaw - `xsphabs.gal * powerlaw.pl` - so that the `fabs`
value represents the absorbed flux, and `funabs` the unabsorbed
flux (i.e. just the power-law component):
>>> fabs = calc_energy_flux(0.5, 7)
>>> funabs = calc_energy_flux(0.5, 7, model=pl)
"""
if bkg_id is None:
data = self.get_data(id)
else:
data = self.get_bkg(id, bkg_id)
if model is None:
if bkg_id is None:
model = self.get_source(id)
else:
model = self.get_bkg_source(id, bkg_id)
else:
_check_type(model, sherpa.models.Model, 'model',
'a model object')
return sherpa.astro.utils.calc_energy_flux(data, model, lo, hi)
# DOC-TODO: how do lo/hi limits interact with bin edges;
# is it all in or partially in or ...
[docs] def calc_data_sum(self, lo=None, hi=None, id=None, bkg_id=None):
"""Sum up the data values over a pass band.
This function is for one-dimensional data sets: use
`calc_data_sum2d` for two-dimensional data sets.
Parameters
----------
lo, hi : number, optional
If both are None or both are set then sum up the data
over the given band. If only one is set then return
the data count in the given bin.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by `get_default_id`.
bkg_id : int or str, optional
If set, use the model associated with the given background
component rather than the source model.
Returns
-------
dsum : number
If a background estimate has been subtracted from the data
set then the calculation will use the background-subtracted
values.
See Also
--------
calc_data_sum2d : Sum up the data values of a 2D data set.
calc_model_sum : Sum up the fitted model over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
calc_photon_flux : Integrate the unconcolved source model over a pass band.
calc_source_sum: Sum up the source model over a pass band.
set_model : Set the source model expression for a data set.
Notes
-----
The units of ``lo`` and ``hi`` are determined by the analysis
setting for the data set (e.g. `get_analysis`). The summation
occurs over those points in the data set that lie within this
range, not the range itself.
Any existing filter on the data set - e.g. as created by
`ignore` or `notice` - is ignored by this function.
If a grouping scheme has been applied to the data set that it
will be used. This can change the results, since the first and
last bins of the selected range may extend outside the
requested range.
Examples
--------
Sum up the data values (the dependent axis) for all points or
bins in the default data set:
>>> dsum = calc_data_sum()
Calculate the number of counts over the ranges 0.5 to 2 and 0.5 to
7 keV for the default data set, first using the observed signal
and then, for the 0.5 to 2 keV band - the background-subtraced
estimate:
>>> set_analysis('energy')
>>> calc_data_sum(0.5, 2)
745.0
>>> calc_data_sum(0.5, 7)
60.0
>>> subtract()
>>> calc_data_sum(0.5, 2)
730.9179738207356
Calculate the data value in the bin containing 0.5 keV for the
source "core":
>>> calc_data_sum(0.5, id="core")
0.0
Calculate the sum of the second background component for data
set 3 over the independent axis range 12 to 45:
>>> calc_data_sum(12, 45, id=3, bkg_id=2)
"""
data = self.get_data(id)
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
return sherpa.astro.utils.calc_data_sum(data, lo, hi)
# DOC-TODO: better comparison of calc_source_sum and calc_model_sum
# needed (e.g. integration or results in PHA case?)
#
# DOC-TODO: add some form of convolution to the last example
# to show the difference between calc_model_sum and
# calc_source_sum
#
[docs] def calc_model_sum(self, lo=None, hi=None, id=None, bkg_id=None):
"""Sum up the fitted model over a pass band.
Sum up M(E) over a range of bins, where M(E) is the per-bin model
value after it has been convolved with any instrumental response
(e.g. RMF and ARF or PSF). This is intended for one-dimensional
data sets: use `calc_model_sum2d` for two-dimensional data sets.
The `calc_source_sum` function is used to calculate the sum of the
model before any instrumental response is applied.
Parameters
----------
lo, hi : number, optional
If both are None or both are set then sum up over the given
band. If only one is set then use the model value in the
selected bin. The units for `lo` and `hi` are given by the
current analysis setting.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by `get_default_id`.
bkg_id : int or str, optional
If set, use the model associated with the given background
component rather than the source model.
Returns
-------
signal : number
The model value (sum or individual bin).
See Also
--------
calc_data_sum : Sum up the observed counts over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
calc_photon_flux : Integrate the unconvolved source model over a pass band.
calc_source_sum: Sum up the source model over a pass band.
set_model : Set the source model expression for a data set.
Notes
-----
The units of ``lo`` and ``hi`` are determined by the analysis
setting for the data set (e.g. `get_analysis`). The summation
occurs over those points in the data set that lie within this
range, not the range itself.
Any existing filter on the data set - e.g. as created by
`ignore` or `notice` - is ignored by this function.
The units of the answer depend on the model components used in
the source expression and the axis or axes of the data set.
Examples
--------
Calculate the model evaluated over the full data set (all points
or pixels of the independent axis) for the default data set,
and compare it to the sum for th first background component:
>>> tsrc = calc_model_sum()
>>> tbkg = calc_model_sum(bkg_id=1)
Sum up the model over the data range 0.5 to 2 for the default
data set, and compared to the data over the same range:
>>> calc_model_sum(0.5, 2)
404.97796489631639
>>> calc_data_sum(0.5, 2)
745.0
Calculate the model sum, evaluated over the range 20 to 22
Angstroms, for the first background component of the "histate"
data set:
>>> set_analysis("histate", "wavelength")
>>> calc_model_sum(20, 22, "histate", bkg_id=1)
In the following example, a small data set is created, covering
the axis range of -5 to 5, and an off-center gaussian model
created (centered at 1). The model is evaluated over the full
data grid and then a subset of pixels. As the summation is done
over those points in the data set that lie within the requested
range, the sum for lo=-2 to hi=1 is the same as that for
lo=-1.5 to hi=1.5:
>>> load_arrays('test', [-5, -2.5, 0, 2.5, 5], [2, 5, 12, 7, 3])
>>> set_source('test', gauss1d.gmdl)
>>> gmdl.pos = 1
>>> gmdl.fwhm = 2.4
>>> gmdl.ampl = 10
>>> calc_model_sum(id='test')
9.597121089731253
>>> calc_model_sum(-2, 1, id='test')
6.179472329646446
>>> calc_model_sum(-1.5, 1.5, id='test')
6.179472329646446
"""
data = self.get_data(id)
model = None
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
model = self.get_bkg_model(id, bkg_id)
else:
model = self.get_model(id)
return sherpa.astro.utils.calc_model_sum(data, model, lo, hi)
[docs] def calc_data_sum2d(self, reg=None, id=None):
"""Sum up the data values of a 2D data set.
This function is for two-dimensional data sets: use
`calc_model_sum` for one-dimensional data sets.
Parameters
----------
reg : str, optional
The spatial filter to use. The default, ``None``, is to
use the whole data set.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by `get_default_id`.
Returns
-------
dsum : number
The sum of the data values that lie within the given
region.
See Also
--------
calc_data_sum : Sum up the data values of a data set.
calc_model_sum2d : Sum up the convolved model for a 2D data set.
calc_source_sum2d: Sum up the unconvolved model for a 2D data set.
set_model : Set the source model expression for a data set.
Notes
-----
The coordinate system of the region filter is determined by
the coordinate setting for the data set (e.g. `get_coord`).
Any existing filter on the data set - e.g. as created by
`ignore2d` or `notice2d` - is ignored by this function.
Examples
--------
The following examples use the data in the default data set
created with the following calls, which sets the y (data)
values to be 0 to 11 in a 3 row by 4 column image:
>>> ivals = np.arange(12)
>>> y, x = np.mgrid[10:13, 20:24]
>>> y = y.flatten()
>>> x = x.flatten()
>>> load_arrays(1, x, y, ivals, (3, 4), DataIMG)
with no argument, the full data set is used:
>>> calc_data_sum2d()
66
>>> ivals.sum()
66
and a spatial filter can be used to restrict the region
used for the summation:
>>> calc_data_sum2d('circle(22,12,1)')
36
>>> calc_data_sum2d('field()-circle(2,2,1)')
30
Apply the spatial filter to the data set labelled "a2142":
>>> calc_data_sum2d('rotbox(4232.3,3876,300,200,43)', 'a2142')
"""
data = self.get_data(id)
return sherpa.astro.utils.calc_data_sum2d(data, reg)
# DOC-TODO: show an example with psf
# and change the model (to a non-flat distribution, otherwise
# the PSF doesn't really help)
# DOC-TODO: this needs testing as doesn't seem to be working for me
[docs] def calc_model_sum2d(self, reg=None, id=None):
"""Sum up the convolved model for a 2D data set.
This function is for two-dimensional data sets: use
`calc_model_sum` for one-dimensional data sets.
Parameters
----------
reg : str, optional
The spatial filter to use. The default, ``None``, is to
use the whole data set.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by `get_default_id`.
Returns
-------
msum : number
The sum of the model values, as fitted to the data, that
lie within the given region. This includes any PSF
included by `set_psf`.
See Also
--------
calc_model_sum : Sum up the fitted model over a pass band.
calc_source_sum2d: Sum up the unconvolved model for a 2D data set.
set_psf : Add a PSF model to a data set.
set_model : Set the source model expression for a data set.
Notes
-----
The coordinate system of the region filter is determined by
the coordinate setting for the data set (e.g. `get_coord`).
Any existing filter on the data set - e.g. as created by
`ignore2d` or `notice2d` - is ignored by this function.
Examples
--------
The following examples use the data in the default data set
created with the following calls, which sets the y (data)
values to be 0 to 11 in a 3 row by 4 column image:
>>> ivals = np.arange(12)
>>> y, x = np.mgrid[10:13, 20:24]
>>> y = y.flatten()
>>> x = x.flatten()
>>> load_arrays(1, x, y, ivals, (3, 4), DataIMG)
>>> set_source(const2d.bgnd)
>>> bgnd.c0 = 2
with no argument, the full data set is used. Since the model
evaluates to 2 per pixel, and there are 12 pixels in the
data set, the result is 24:
>>> calc_model_sum2d()
24.0
and a spatial filter can be used to restrict the region
used for the summation:
>>> calc_model_sum2d('circle(22,12,1)')
8.0
>>> calc_model_sum2d('field()-circle(22,12,1)')
16.0
Apply the spatial filter to the model for the data set
labelled "a2142":
>>> calc_model_sum2d('rotbox(4232.3,3876,300,200,43)', 'a2142')
"""
data = self.get_data(id)
model = self.get_model(id)
return sherpa.astro.utils.calc_model_sum2d(data, model, reg)
[docs] def calc_source_sum2d(self, reg=None, id=None):
"""Sum up the unconvolved model for a 2D data set.
This function is for two-dimensional data sets: use
`calc_source_sum` for one-dimensional data sets.
Parameters
----------
reg : str, optional
The spatial filter to use. The default, ``None``, is to
use the whole data set.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by `get_default_id`.
Returns
-------
msum : number
The sum of the model values that lie within the given
region. This does not include any PSF included by
`set_psf`.
See Also
--------
calc_model_sum2d : Sum up the convolved model for a 2D data set.
calc_source_sum : Sum up the model over a pass band.
set_psf : Add a PSF model to a data set.
set_model : Set the source model expression for a data set.
Notes
-----
The coordinate system of the region filter is determined by
the coordinate setting for the data set (e.g. `get_coord`).
Any existing filter on the data set - e.g. as created by
`ignore2d` or `notice2d` - is ignored by this function.
Examples
--------
The following examples use the data in the default data set
created with the following calls, which sets the y (data)
values to be 0 to 11 in a 3 row by 4 column image:
>>> ivals = np.arange(12)
>>> y, x = np.mgrid[10:13, 20:24]
>>> y = y.flatten()
>>> x = x.flatten()
>>> load_arrays(1, x, y, ivals, (3, 4), DataIMG)
>>> set_source(const2d.bgnd)
>>> bgnd.c0 = 2
with no argument, the full data set is used. Since the model
evaluates to 2 per pixel, and there are 12 pixels in the
data set, the result is 24:
>>> calc_source_sum2d()
24.0
and a spatial filter can be used to restrict the region
used for the summation:
>>> calc_source_sum2d('circle(22,12,1)')
8.0
>>> calc_source_sum2d('field()-circle(22,12,1)')
16.0
Apply the spatial filter to the model for the data set
labelled "a2142":
>>> calc_source_sum2d('rotbox(4232.3,3876,300,200,43)', 'a2142')
"""
data = self.get_data(id)
src = self.get_source(id)
return sherpa.astro.utils.calc_model_sum2d(data, src, reg)
[docs] def calc_source_sum(self, lo=None, hi=None, id=None, bkg_id=None):
"""Sum up the source model over a pass band.
Sum up S(E) over a range of bins, where S(E) is the per-bin model
value before it has been convolved with any instrumental response
(e.g. RMF and ARF or PSF). This is intended for one-dimensional
data sets: use `calc_source_sum2d` for two-dimensional data sets.
The `calc_model_sum` function is used to calculate the sum of the
model after any instrumental response is applied.
Parameters
----------
lo, hi : number, optional
If both are None or both are set then sum up over the given
band. If only one is set then use the model value in the
selected bin. The units for `lo` and `hi` are given by the
current analysis setting.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by `get_default_id`.
bkg_id : int or str, optional
If set, use the model associated with the given background
component rather than the source model.
Returns
-------
signal : number
The model value (sum or individual bin).
See Also
--------
calc_data_sum : Sum up the observed counts over a pass band.
calc_model_sum : Sum up the fitted model over a pass band.
calc_energy_flux : Integrate the unconvolved source model over a pass band.
calc_photon_flux : Integrate the unconvolved source model over a pass band.
set_model : Set the source model expression for a data set.
Notes
-----
The units of ``lo`` and ``hi`` are determined by the analysis
setting for the data set (e.g. `get_analysis`). The summation
occurs over those points in the data set that lie within this
range, not the range itself.
Any existing filter on the data set - e.g. as created by
`ignore` or `notice` - is ignored by this function.
The units of the answer depend on the model components used in
the source expression and the axis or axes of the data set.
Examples
--------
Calculate the model evaluated over the full data set (all points
or pixels of the independent axis) for the default data set,
and compare it to the sum for th first background component:
>>> tsrc = calc_source_sum()
>>> tbkg = calc_source_sum(bkg_id=1)
Sum up the model over the data range 0.5 to 2 for the default
data set:
>>> calc_source_sum(0.5, 2)
139.12819041922018
Compare the output of the `calc_source_sum` and `calc_photon_flux`
routines. A 1099-bin data space is created, with a model which has
a value of 1 for each bin. As the bin width is constant, at 0.01,
the integrated value, calculated by `calc_photon_flux`, is one
hundredth the value returned by `calc_data_sum`:
>>> dataspace1d(0.01, 11, 0.01, id="test")
>>> set_source("test", const1d.bflat)
>>> bflat.c0 = 1
>>> calc_source_sum(id="test")
1099.0
>>> calc_photon_flux(id="test")
10.99
In the following example, a small data set is created, covering
the axis range of -5 to 5, and an off-center gaussian model
created (centered at 1). The model is evaluated over the full
data grid and then a subset of pixels. As the summation is done
over those points in the data set that lie within the requested
range, the sum for lo=-2 to hi=1 is the same as that for
lo=-1.5 to hi=1.5:
>>> load_arrays('test', [-5, -2.5, 0, 2.5, 5], [2, 5, 12, 7, 3])
>>> set_source('test', gauss1d.gmdl)
>>> gmdl.pos = 1
>>> gmdl.fwhm = 2.4
>>> gmdl.ampl = 10
>>> calc_source_sum(id='test')
9.597121089731253
>>> calc_source_sum(-2, 1, id='test')
6.179472329646446
>>> calc_source_sum(-1.5, 1.5, id='test')
6.179472329646446
"""
data = self.get_data(id)
model = None
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
model = self.get_bkg_source(id, bkg_id)
else:
model = self.get_source(id)
return sherpa.astro.utils.calc_source_sum(data, model, lo, hi)
# DOC-TODO: no reason can't k-correct wavelength range,
# but need to work out how to identify the units
[docs] def calc_kcorr(self, z, obslo, obshi, restlo=None, resthi=None,
id=None, bkg_id=None):
"""Calculate the K correction for a model.
The K correction ([1]_, [2]_, [3]_, [4]_) is the numeric
factor applied to measured energy fluxes in an observed
energy band to estimate the flux in a given rest-frame
energy band. It accounts for the change in spectral energy
distribution between the desired rest-frame band and the
rest-frame band corresponding to the observed band. This is
often used when converting a flux into a luminosity.
Parameters
----------
z : number or array, >= 0
The redshift, or redshifts, of the source.
obslo : number
The minimum energy of the observed band.
obshi : number
The maximum energy of the observed band, which must
be larger than ``obslo``.
restlo : number or ``None``
The minimum energy of the rest-frame band. If ``None`` then
use ``obslo``.
resthi : number or ``None``
The maximum energy of the rest-frame band. It must be
larger than ``restlo``. If ``None`` then use ``obshi``.
id : int or str, optional
Use the source expression associated with this data set. If
not given then the default identifier is used, as returned
by `get_default_id`.
bkg_id : int or str, optional
If set, use the model associated with the given background
component rather than the source model.
Returns
-------
kz : number or array of numbers
See Also
--------
calc_energy_flux : Integrate the unconvolved source model over a pass band.
dataspace1d : Create the independent axis for a 1D data set.
Notes
-----
This is only defined when the analysis is in 'energy' units.
If the model contains a redshift parameter then it should
be set to 0, rather than the source redshift.
If the source model is at zero redshift, the observed energy
band is olo to ohi, and the rest frame band is rlo to rhi
(which need not match the observed band), then the K
correction at a redshift z can be calculated as::
frest = calc_energy_flux(rlo, rhi)
fobs = calc_energy_flux(olo*(1+z), ohi*(1+z))
kz = frest / fobs
The energy ranges used - rlo to rhi and olo*(1+z) to ohi*(1+z)
- should be fully covered by the data grid, otherwise the flux
calculation will be truncated at the grid boundaries, leading
to incorrect results.
References
----------
.. [1] "The K correction", Hogg, D.W., et al.
http://arxiv.org/abs/astro-ph/0210394
.. [2] Appendix B of Jones et al. 1998, ApJ, vol 495,
p. 100-114.
http://adsabs.harvard.edu/abs/1998ApJ...495..100J
.. [3] "K and evolutionary corrections from UV to IR",
Poggianti, B.M., A&AS, 1997, vol 122, p. 399-407.
http://adsabs.harvard.edu/abs/1997A%26AS..122..399P
.. [4] "Galactic evolution and cosmology - Probing the
cosmological deceleration parameter", Yoshii, Y. &
Takahara, F., ApJ, 1988, vol 326, p. 1-18.
http://adsabs.harvard.edu/abs/1988ApJ...326....1Y
Examples
--------
Calculate the K correction for an X-Spec apec model, with a
source temperature of 6 keV and abundance of 0.3 solar, for
the energy band of 0.5 to 2 keV:
>>> dataspace1d(0.01, 10, 0.01)
>>> set_source(xsapec.clus)
>>> clus.kt = 6
>>> clus.abundanc = 0.3
>>> calc_kcorr(0.5, 0.5, 2)
0.82799195070436793
Calculate the K correction for a range of redshifts (0 to 2)
using an observed frame of 0.5 to 2 keV and a rest frame of 0.1
to 10 keV (the energy grid is set to ensure that it covers the
full energy range; that is the rest-frame band and the
observed frame band multiplied by the smallest and largest
(1+z) terms):
>>> dataspace1d(0.01, 11, 0.01)
>>> zs = np.linspace(0, 2, 21)
>>> ks = calc_kcorr(zs, 0.5, 2, restlo=0.1, resthi=10)
Calculate the k correction for the background dataset
bkg_id=2 for a redshift of 0.5 over the energy range
0.5 to 2 keV with rest-frame energy limits of 2 to 10 keV.
>>> calc_kcorr(0.5, 0.5, 2, 2, 10, bkg_id=2)
"""
data = self.get_data(id)
model = None
if bkg_id is not None:
data = self.get_bkg(id, bkg_id)
model = self.get_bkg_source(id, bkg_id)
else:
model = self.get_source(id)
return sherpa.astro.utils.calc_kcorr(data, model, z, obslo, obshi,
restlo, resthi)
###########################################################################
# Session Text Save Function
###########################################################################
[docs] def save_all(self, outfile=None, clobber=False):
"""Save the information about the current session to a text file.
This differs to the `save` command in that the output is human
readable. Three consequences are:
1. numeric values may not be recorded to their full precision
2. data sets are not included in the file
3. some settings and values may not be recorded.
Parameters
----------
outfile : str or file-like, optional
If given, the output is written to this file, and the
`clobber` parameter controls what happens if the
file already exists.
`outfile` can be a filename string or a file handle
(or file-like object, such as ``StringIO``) to write
to. If not set then the standard output is used.
clobber : bool, optional
If `outfile` is a filename, then this flag controls
whether an existing file can be overwritten (``True``)
or if it raises an exception (``False``, the default
setting).
Raises
------
sherpa.utils.err.IOErr
If `outfile` already exists and `clobber` is ``False``.
See Also
--------
save : Save the current Sherpa session to a file.
restore : Load in a Sherpa session from a file.
Notes
-----
This command will create a series of commands that restores
the current Sherpa set up. It does not save the set of commands
used. Not all Sherpa settings are saved. Items not fully restored
include:
- data created by calls to `load_arrays`, or changed from the
version on disk - e.g. by calls to `sherpa.astro.ui.set_counts`.
- any optional keywords to comands such as `load_data`
or `load_pha`
- user models may not be restored correctly
- only a subset of Sherpa commands are saved.
Examples
--------
Write the current Sherpa session to the screen:
>>> save_all()
Save the session to the file 'fit.sherpa', overwriting
it if it already exists:
>>> save_all('fit.sherpa', clobber=True)
Write the contents to a StringIO object:
>>> from io import StringIO
>>> store = StringIO()
>>> save_all(store)
"""
if isinstance(outfile, string_types):
if os.path.isfile(outfile):
if sherpa.utils.bool_cast(clobber):
os.remove(outfile)
else:
raise IOErr('filefound', outfile)
with open(outfile, 'w') as fh:
serialize.save_all(self, fh)
else:
if outfile is not None:
fh = outfile
else:
fh = sys.stdout
serialize.save_all(self, fh)