Source code for sherpa.astro.data

#
#  Copyright (C) 2008, 2015 - 2024
#  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.
#

"""Classes for storing, inspecting, and manipulating astronomical data sets.

The two types of Astronomical data supported in this module are
two-dimensional images (:py:class:`DataIMG`) and X-ray spectra
(:py:class:`DataPHA`), along with associated response information
(:py:class:`DataARF` and :py:class:`DataRMF`). These objects can be
constructed directly or read from :term:`FITS` files using the
:py:mod:`sherpa.astro.io` routines.

Both types of data extend the capabilities of the
:py:class:`sherpa.data.Data` class:

- using geometric shapes (regions) to filter images;

- support different units for filtering images (logical, physical, and
  :term:`WCS`), depending on the available metadata;

- support different analysis units for filtering and display for
  :term:`PHA` files (channels, energy, and wavelengths);

- dynamically re-bin PHA data to improve the signal to noise (grouping
  and quality);

- and automatically support one or more spectra that define the
  background for the observation (for PHA files) that can then be
  subtracted from the data or a background model fit to them.

Design
------

The `DataPHA` class adds support for grouping data - effectively
reducing the number of values in a data set - and adds an extra way
to filter the data with the quality array. The class extends
`~sherpa.data.Data1D`, since the primary data is channels and
counts, but it also has to act like an integrated data set
(`~sherpa.data.Data1DInt`) in some cases. In an extension to
OGIP support, there is limited support for the ``BIN_LO`` and
``BIN_HI`` fields provided with Chandra grating data.

The `DataIMG` class extends 2D support for "gridded" data, with
multiple possible coordinate systems (e.g. ``logical``, ``physical``,
and ``world``).  Along with this, spatial filters can be applied,
using the CIAO region syntax [REGION]_.

Notes
-----

Some functionality depends on the presence of the region and grouping
Sherpa modules, which are optional components of Sherpa.

Notebook support
----------------

The Data objects support the rich display protocol of IPython, with
HTML display of a table of information highlighting the relevant data
and, for some classes, SVG images. Examples can be found at
[AstroNoteBook]_.

References
----------

.. [AstroNoteBook] https://sherpa.readthedocs.io/en/latest/NotebookSupport.html

.. [OGIP_92_007] "The OGIP Spectral File Format", https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/ogip_92_007.html

.. [OGIP_92_007a] "The OGIP Spectral File Format Addendum: Changes log ", https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007a/ogip_92_007a.html

.. [CAL_92_002] "The Calibration Requirements for Spectral Analysis (Definition of RMF and ARF file formats)", https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html

.. [CAL_92_002a] "The Calibration Requirements for Spectral Analysis Addendum: Changes log", https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002a/cal_gen_92_002a.html

.. [PRIVATE_KA] Private communication with Keith Arnaud

.. [REGION] https://cxc.harvard.edu/ciao/ahelp/dmregions.html

Examples
--------

Read in a 2D dataset from the file 'image2.fits' and then filter it to
only use those pixels that lie within 45 units from the physical
coordinate 3150,4515:

>>> from sherpa.astro.io import read_image
>>> img = read_image(data_dir + 'image2.fits')
>>> img.set_coord('physical')
>>> img.notice2d('circle(3150,4515,45)')

Read in a PHA dataset from the file '3c273.pi', subtract the background,
filter to only use the data 0.5 to 7 keV, and re-group the data within
this range to have at least 20 counts per group:

>>> from sherpa.astro.io import read_pha
>>> pha = read_pha(data_3c273 + '3c273.pi')
>>> pha.subtract()
>>> pha.set_analysis('energy')
>>> pha.notice(0.5, 7)
>>> pha.group_counts(20)

"""

import os.path
import logging
import warnings

import numpy

from sherpa.astro import hc
from sherpa.data import Data1DInt, Data2D, Data, Data1D, \
    IntegratedDataSpace2D, _check
from sherpa.models.regrid import EvaluationSpace1D
from sherpa.stats import Chi2XspecVar
from sherpa.utils import pad_bounding_box, interpolate, \
    create_expr, create_expr_integrated, parse_expr, bool_cast, rebin, filter_bins
from sherpa.utils.err import ArgumentTypeErr, DataErr, ImportErr
from sherpa.utils import formatting
from sherpa.utils.numeric_types import SherpaFloat

# There are currently (Sep 2015) no tests that exercise the code that
# uses the compile_energy_grid symbols.
from sherpa.astro.utils import arf_fold, rmf_fold, filter_resp, \
    compile_energy_grid, do_group, expand_grouped_mask

info = logging.getLogger(__name__).info
warning = logging.getLogger(__name__).warning

regstatus = False
try:
    from sherpa.astro.utils._region import Region
    regstatus = True
except ImportError:
    warning('failed to import sherpa.astro.utils._region; Region routines ' +
            'will not be available')

groupstatus = False
try:
    import group as pygroup
    groupstatus = True
except ImportError:
    groupstatus = False
    warning('the group module (from the CIAO tools package) is not ' +
            'installed.\nDynamic grouping functions will not be available.')


__all__ = ('DataARF', 'DataRMF', 'DataPHA', 'DataIMG', 'DataIMGInt', 'DataRosatRMF')


def _notice_resp(chans, arf, rmf):
    """Notice the channel range for the associated responses

    Parameters
    ----------
    chans : ndarray
        The noticed channel values.
    arf : sherpa.astro.data.DataARF or None
    rmf : sherpa.astro.data.DataRMF or None

    """

    bin_mask = None

    if rmf is not None and arf is not None:

        bin_mask = rmf.notice(chans)
        if len(rmf.energ_lo) == len(arf.energ_lo):
            arf.notice(bin_mask)

        # If the response is mis-matched, determine which energy bins in the
        # RMF correspond to energy bins in the ARF and which are noticed.
        # Propagate the noticed RMF energy bins to the ARF energy bins.
        elif len(rmf.energ_lo) < len(arf.energ_lo):
            arf_mask = None
            if bin_mask is not None:
                arf_mask = numpy.zeros(len(arf.energ_lo), dtype=bool)
                for ii, val in enumerate(bin_mask):
                    if val:
                        los = (rmf.energ_lo[ii],)
                        his = (rmf.energ_hi[ii],)
                        grid = (arf.energ_lo, arf.energ_hi)
                        # TODO: should this set integrated=True?
                        #       we only have one test of this code in
                        #       sherpa/astro/tests/test_astro.py:test_missmatch_arf
                        idx = filter_bins(los, his, grid).nonzero()[0]
                        arf_mask[idx] = True
            arf.notice(arf_mask)

    else:
        if rmf is not None:
            bin_mask = rmf.notice(chans)
        if arf is not None:
            arf.notice(bin_mask)


def display_header(header, key):
    """Return the header value for display by _repr_html

    The value is not displayed if it doesn't exist, is None,
    is empty, or is the string 'NONE'. This is intended for
    PHA responses.

    Parameters
    ----------
    header : dict-like
    key : str
        The key to display

    Returns
    -------
    value : None or value
        The value to display, or None.

    Notes
    -----
    It is not clear if the Meta class is intended to only store
    string values or not. Limited protection is provided in case
    the value stored is not a string.
    """

    try:
        val = header[key]
    except KeyError:
        return None

    # Unclear if this can happen
    if val is None:
        return None

    # The metadata value is not guaranteed to be a string
    try:
        val = val.strip()
        if val in ['', 'NONE']:
            return None
    except AttributeError:
        pass

    return val


def make_metadata(header, items):
    """Create the metadata table.

    Parameters
    ----------
    header : dict-like
        The header.
    items : list of (str, str)
        The keys to display (in order), if set. The first element
        is the key name, and the second is the label in the header
        to display.

    Returns
    -------
    meta : list of (str, str) or None
        The two-element table rows to display. If no rows matched
        return None.

    """

    meta = []
    for key, desc in items:
        val = display_header(header, key)
        if val is None:
            continue

        meta.append((desc, val))

    if len(meta) == 0:
        return None

    return meta


def _extract_fields(obj, summary):
    """Extract the "column" fields.

    Write out the _fields values (that are not None) for the Data
    object. The _extra_fields values are ignored, as they are assumed
    to be included in separate "metadata" section.

    Parameters
    ----------
    obj : Data instance
    summary : str
        The label for the details tab.

    Returns
    -------
    html : str
        The HTML for this section.

    """

    meta = []
    fields = [f for f in obj._fields if f != 'name']
    for f in fields:
        v = getattr(obj, f)
        if v is None:
            continue

        meta.append((f.upper(), v))

    return formatting.html_section(meta, summary=summary,
                                   open_block=True)


def html_pha(pha):
    """HTML representation: PHA"""

    from sherpa.astro.plot import DataPHAPlot
    from sherpa import plot

    ls = []

    plotter = DataPHAPlot()
    plotter.prepare(pha)

    try:
        out = plot.backend.as_html_plot(plotter, 'PHA Plot')
    except AttributeError:
        out = None

    if out is None:
        out = _extract_fields(pha, 'PHA Data')

    ls.append(out)

    # Summary properties
    meta = []
    if pha.name is not None and pha.name != '':
        meta.append(('Identifier', pha.name))

    if pha.exposure is not None:
        meta.append(('Exposure', f'{pha.exposure:g} s'))

    meta.append(('Number of bins', len(pha.channel)))
    meta.append(('Channel range', f'{int(pha.channel[0])} - {int(pha.channel[-1])}'))

    # Although assume the counts are integers, do not force this
    cmin = pha.counts.min()
    cmax = pha.counts.max()
    meta.append(('Count range', f'{cmin} - {cmax}'))

    if pha.background_ids != []:
        if pha.subtracted:
            msg = 'Subtracted'
        else:
            msg = 'Not subtracted'

        meta.append(('Background', msg))

    # Make sure show all groups (not just those that are within
    # the filter applied to the object).
    #
    if pha.grouping is not None:
        if pha.grouped:
            ngrp = pha.apply_grouping(pha.counts).size
            msg = f'Applied ({ngrp} groups)'
        else:
            msg = 'Not applied'

        meta.append(('Grouping', msg))

    # Should this only be displayed if a filter has been applied?
    #
    fexpr = pha.get_filter_expr()
    bintype = 'groups' if pha.grouped else 'channels'
    nbins = pha.get_dep(filter=True).size
    meta.append(('Using', f'{fexpr} with {nbins} {bintype}'))

    ls.append(formatting.html_section(meta, summary='Summary',
                                      open_block=True))

    # TODO:
    #   correction factors

    # Display a subset of header values
    # - maybe don't display the FILTER if NONE
    # - how about RESPFILE / PHAFILE
    meta = make_metadata(pha.header,
                         [('TELESCOP', 'Mission or Satellite'),
                          ('INSTRUME', 'Instrument or Detector'),
                          ('GRATING', 'Grating type'),
                          ('ORDER', 'Diffraction order'),
                          ('FILTER', 'Instrument filter'),
                          ('OBJECT', 'Object'),
                          ('TITLE', 'Program description'),
                          ('DATE-OBS', 'Observation date'),
                          ('CREATOR', 'Program that created the PHA'),
                          ('CHANTYPE', 'The channel type'),
                          ('HDUCLAS2', 'Data stored'),
                          ('HDUCLAS3', 'Data format'),
                          ('HDUCLAS4', 'PHA format'),
                          ('XFLT0001', 'XSPEC filter 0001')])

    if meta is not None:
        ls.append(formatting.html_section(meta, summary='Metadata'))

    return formatting.html_from_sections(pha, ls)


def _calc_erange(elo, ehi):
    """Create the energy range information.

    Parameters
    ----------
    elo, ehi - NumPy array
        The low and high energy bins, in keV.

    Returns
    -------
    erange : str
        The string representation of the energy range

    """

    # Have we guaranteed the ordering here or not? Assuming
    # NumPy arrays.
    e1 = elo[0]
    e2 = ehi[-1]
    emin, emax = (e1, e2) if e1 <= e2 else (e2, e1)
    erange = f'{emin:g} - {emax:g} keV'

    # Randomly pick 1% as the cut-off for a constant bin width
    #
    de = numpy.abs(ehi - elo)
    demin = de.min()
    demax = de.max()
    if demin > 0.0:
        dedelta = (demax - demin) / demin
    else:
        dedelta = 1

    if dedelta <= 0.01:
        erange += f', bin size {demax:g} keV'
    else:
        erange += f', bin size {demin:g} - {demax:g} keV'

    return erange


def _calc_wrange(wlo, whi):
    """Create the wavelength range information.

    Parameters
    ----------
    wlo, whi - NumPy array
        The low and high wavelength bins, in Angstroms.

    Returns
    -------
    wrange : str
        The string representation of the wavelength range

    """

    w1 = wlo[0]
    w2 = whi[-1]
    wmin, wmax = (w1, w2) if w1 <= w2 else (w2, w1)
    wrange = f'{wmin:g} - {wmax:g} &#8491;'

    # Randomly pick 1% as the cut-off for a constant bin width
    #
    dw = numpy.abs(whi - wlo)
    dwmin = dw.min()
    dwmax = dw.max()
    if dwmin > 0.0:
        dwdelta = (dwmax - dwmin) / dwmin
    else:
        dwdelta = 1

    if dwdelta <= 0.01:
        wrange += f', bin size {dwmax:g} &#8491;'
    else:
        wrange += f', bin size {dwmin:g} - {dwmax:g} &#8491;'

    return wrange


def html_arf(arf):
    """HTML representation: ARF"""

    # Unlike the string representation, this provides extra
    # information (e.g. energy range covered). Should it include
    # any filters or masks? How about bin_lo/hi values?
    #
    # It also assumes the units are keV/cm^2 which is not
    # guaranteed.

    from sherpa.astro.plot import ARFPlot
    from sherpa import plot

    ls = []

    plotter = ARFPlot()
    plotter.prepare(arf)

    try:
        out = plot.backend.as_html_plot(plotter, 'ARF Plot')
    except AttributeError:
        out = None

    if out is None:
        out = _extract_fields(arf, 'ARF Data')

    ls.append(out)

    # Summary properties
    meta = []
    if arf.name is not None and arf.name != '':
        meta.append(('Identifier', arf.name))

    if arf.exposure is not None:
        meta.append(('Exposure', f'{arf.exposure:g} s'))

    meta.append(('Number of bins', len(arf.specresp)))

    erange = _calc_erange(arf.energ_lo, arf.energ_hi)
    meta.append(('Energy range', erange))

    # repeat for wavelengths (without the energy threshold)
    #
    if arf.bin_lo is not None and arf.bin_hi is not None:
        wrange = _calc_wrange(arf.bin_lo, arf.bin_hi)
        meta.append(('Wavelength range', wrange))

    a1 = numpy.min(arf.specresp)
    a2 = numpy.max(arf.specresp)
    meta.append(('Area range', f'{a1:g} - {a2:g} cm<sup>2</sup>'))

    ls.append(formatting.html_section(meta, summary='Summary',
                                      open_block=True))

    # Display a subset of header values
    # - maybe don't display the FILTER if NONE
    # - how about RESPFILE / PHAFILE
    meta = make_metadata(arf.header,
                         [('TELESCOP', 'Mission or Satellite'),
                          ('INSTRUME', 'Instrument or Detector'),
                          ('GRATING', 'Grating type'),
                          ('ORDER', 'Diffraction order'),
                          ('TG_M', 'Diffraction order'),
                          ('FILTER', 'Instrument filter'),
                          ('OBJECT', 'Object'),
                          ('TITLE', 'Program description'),
                          ('DATE-OBS', 'Observation date'),
                          ('CREATOR', 'Program that created the ARF')])

    if meta is not None:
        ls.append(formatting.html_section(meta, summary='Metadata'))

    return formatting.html_from_sections(arf, ls)


def html_rmf(rmf):
    """HTML representation: RMF"""

    # See _html_arf for general comments

    from sherpa.astro.plot import RMFPlot
    from sherpa import plot

    ls = []

    plotter = RMFPlot()
    plotter.prepare(rmf)

    try:
        out = plot.backend.as_html_plot(plotter, 'RMF Plot')
    except AttributeError:
        out = None

    if out is None:
        out = _extract_fields(rmf, 'RMF Data')


    ls.append(out)

    # Summary properties
    meta = []
    if rmf.name is not None and rmf.name != '':
        meta.append(('Identifier', rmf.name))

    meta.append(('Number of channels', rmf.detchans))
    meta.append(('Number of energies', len(rmf.energ_hi)))

    erange = _calc_erange(rmf.energ_lo, rmf.energ_hi)
    if rmf.ethresh is not None and rmf.energ_lo[0] <= rmf.ethresh:
        # Not entirely happy with the wording of this
        erange += f' (minimum threshold of {rmf.ethresh} was used)'

    meta.append(('Energy range', erange))

    meta.append(('Channel range', f'{int(rmf.offset)} - {int(rmf.offset + rmf.detchans - 1)}'))

    # Could show the energy range as given by e_min/e_max but
    # is this useful?

    ls.append(formatting.html_section(meta, summary='Summary',
                                      open_block=True))

    # Display a subset of header values
    # - how about PHAFILE
    meta = make_metadata(rmf.header,
                         [('TELESCOP', 'Mission or Satellite'),
                          ('INSTRUME', 'Instrument or Detector'),
                          ('GRATING', 'Grating type'),
                          ('ORDER', 'Diffraction order'),
                          ('FILTER', 'Instrument filter'),
                          ('OBJECT', 'Object'),
                          ('TITLE', 'Program description'),
                          ('DATE-OBS', 'Observation date'),
                          ('CREATOR', 'Program that created the RMF'),
                          ('CHANTYPE', 'The channel type'),
                          ('LO_THRES', 'The minimum probability threshold'),
                          ('HDUCLAS3', 'Matrix contents')])

    if meta is not None:
        ls.append(formatting.html_section(meta, summary='Metadata'))

    return formatting.html_from_sections(rmf, ls)


def html_img(img):
    """HTML representation: IMG

    Special-case of the Data2D handling. It would be nice to reuse
    parts of the superclass behavior.
    """
    from sherpa.astro.plot import DataIMGPlot
    from sherpa import plot
    ls = []
    dtype = type(img).__name__

    plotter = DataIMGPlot()
    plotter.prepare(img)

    try:
        out = plot.backend.as_html_image(plotter, f'{dtype} Plot')
    except AttributeError:
        out = None

    if out is None:
        # Only add prefix to summary if there's no plot
        summary = f'{dtype} '

        # Summary properties
        #
        meta = []
        if img.name is not None and img.name != '':
            meta.append(('Identifier', img.name))

        # shape is better defined for DataIMG than Data2D
        meta.append(('Shape',
                     (f'{img.shape[1]} by {img.shape[0]} pixels')))

        meta.append(('Number of bins', len(img.y)))

        # Rely on the _fields ordering, ending at shape
        for f in img._fields[1:]:
            if f == 'shape':
                break

            meta.append((f.upper(), getattr(img, f)))

        if img.staterror is not None:
            meta.append(('Statistical error', img.staterror))

        if img.syserror is not None:
            meta.append(('Systematic error', img.syserror))

        out = formatting.html_section(meta, summary=summary + 'Data',
                                      open_block=True)

    ls.append(out)

    # Add coordinate-system information. The WCS structure in Sherpa
    # is not really sufficient to identify the transform.
    #
    if img.sky is not None:
        meta = []
        meta.append(('Center pixel (logical)', img.sky.crpix))
        meta.append(('Center pixel (physical)', img.sky.crval))
        meta.append(('Pixel size', img.sky.cdelt))

        ls.append(formatting.html_section(meta,
                                          summary=f'Coordinates: {img.sky.name}'))

    if img.eqpos is not None:
        meta = []
        meta.append(('Center pixel (physical)', img.eqpos.crpix))
        # could convert to RA/Dec
        meta.append(('Center pixel (world)', img.eqpos.crval))
        meta.append(('Pixel size', img.eqpos.cdelt))

        meta.append(('Rotation', img.eqpos.crota))
        meta.append(('Epoch', img.eqpos.epoch))
        meta.append(('Equinox', img.eqpos.equinox))

        ls.append(formatting.html_section(meta,
                                          summary=f'Coordinates: {img.eqpos.name}'))

    meta = make_metadata(img.header,
                         [('TELESCOP', 'Mission or Satellite'),
                          ('INSTRUME', 'Instrument or Detector'),
                          ('FILTER', 'Instrument filter'),
                          ('OBJECT', 'Object'),
                          ('TITLE', 'Program description'),
                          ('OBSERVER', 'Observer'),
                          ('EXPOSURE', 'Exposure time'),
                          ('DATE-OBS', 'Observation date'),
                          ('CREATOR', 'Program that created the image')])

    if meta is not None:
        ls.append(formatting.html_section(meta, summary='Metadata'))

    return formatting.html_from_sections(img, ls)


[docs] class DataOgipResponse(Data1DInt): """ Parent class for OGIP responses, in particular ARF and RMF. This class implements some common validation code that inheriting classes can call in their initializers. Inheriting classes should override the protected class field `_ui_name` to provide a more specific label for user messages. """ _ui_name = "OGIP Response" # FIXME For a future time when we'll review this code in a deeper way: we # could have better separation of concerns if the initializers of `DataARF` # and `DataRMF` did not rely on the `Data` initializer, and if the # class hierarchy was better organized (e.g. it looks like children must # not call their super's initializer. Also, I'd expect validation to # happen in individual methods rather than in a large one, and nested ifs # should be avoided if possible. # # The shift to creating a warning message instead of raising an # error has made this messier. # def _validate_energy_ranges(self, label, elo, ehi, ethresh): """Check the lo/hi values are > 0, handling common error case. Several checks are made, to make sure the parameters follow the OGIP standard. At present a failed check can result in either a warning message being logged, or an error raised. It was felt that raising an error in all cases would not be helpful to a user, who can't (easily) change the response files. Parameters ---------- label : str The response file identifier. elo, ehi : numpy.ndarray The input ENERG_LO and ENERG_HI arrays. They are assumed to be one-dimensional and have the same number of elements. ethresh : None or float, optional If None, then elo must be greater than 0. When set, the start bin can have a low-energy edge of 0; it is replaced by ethresh. If set, ethresh must be greater than 0. An error is raised if ethresh is larger than the upper-edge of the first bin (only if the lower edge has been replaced). Returns ------- elo, ehi : numpy arrays The validated energy limits. These can be the input arrays or a copy of them. At present the ehi array is the same as the input array, but this may change in the future. Notes ----- Only some of the constraints provided by the OGIP standard are checked here, since there are issues involving numerical effects (e.g. when checking that two bins do not overlap), as well as uncertainty over what possible behavior is seen in released data products for missions. The current set of checks are: - ehi > elo for each bin - elo is monotonic (ascending or descending) - when emin is set, the lowest value in elo is >= 0, otherwise it is > 0. - ethresh (if set) is less than the minimum value in ENERG_HI """ rtype = self._ui_name if elo.size != ehi.size: raise ValueError(f"The energy arrays must have the same size, not {elo.size} and {ehi.size}") if ethresh is not None and ethresh <= 0.0: raise ValueError("ethresh is None or > 0") if (elo >= ehi).any(): # raise DataErr('ogip-error', rtype, label, # 'has at least one bin with ENERG_HI < ENERG_LO') wmsg = f"The {rtype} '{label}' " + \ 'has at least one bin with ENERG_HI < ENERG_LO' warnings.warn(wmsg) # if elo is monotonically increasing, all elements will be True # decreasing, False # # so the sum will be number of elements or 0 # increasing = numpy.diff(elo, n=1) > 0.0 nincreasing = increasing.sum() if nincreasing > 0 and nincreasing != len(increasing): # raise DataErr('ogip-error', rtype, label, # 'has a non-monotonic ENERG_LO array') wmsg = f"The {rtype} '{label}' " + \ 'has a non-monotonic ENERG_LO array' warnings.warn(wmsg) if nincreasing == 0: startidx = -1 else: startidx = 0 e0 = elo[startidx] if ethresh is None: if e0 <= 0.0: raise DataErr('ogip-error', rtype, label, 'has an ENERG_LO value <= 0') else: # TODO: should this equality be replaced by an approximation test? if e0 == 0.0: if ehi[startidx] <= ethresh: raise DataErr('ogip-error', rtype, label, 'has an ENERG_HI value <= the replacement ' + f'value of {ethresh}') elo = elo.copy() elo[startidx] = ethresh wmsg = "The minimum ENERG_LO in the " + \ f"{rtype} '{label}' was 0 and has been " + \ f"replaced by {ethresh}" warnings.warn(wmsg) elif e0 < 0.0: # raise DataErr('ogip-error', rtype, label, # 'has an ENERG_LO value < 0') wmsg = f"The {rtype} '{label}' " + \ 'has an ENERG_LO value < 0' warnings.warn(wmsg) return elo, ehi def _get_data_space(self, filter=False): # TODO: the class has no _lo/_hi attributes so what is this # meant to do? return EvaluationSpace1D(self._lo, self._hi)
[docs] class DataARF(DataOgipResponse): """ARF data set. The ARF format is described in OGIP documents [CAL_92_002]_ and [CAL_92_002a]_. Parameters ---------- name : str The name of the data set; often set to the name of the file containing the data. energ_lo, energ_hi, specresp : numpy.ndarray The values of the ENERG_LO, ENERG_HI, and SPECRESP columns for the ARF. The ENERG_HI values must be greater than the ENERG_LO values for each bin, and the energy arrays must be in increasing or decreasing order. bin_lo, bin_hi : array or None, optional exposure : number or None, optional The exposure time for the ARF, in seconds. header : dict or None, optional ethresh : number or None, optional If set it must be greater than 0 and is the replacement value to use if the lowest-energy value is 0.0. Raises ------ sherpa.utils.err.DataErr This is raised if the energy arrays do not follow some of the OGIP standards. Notes ----- There is limited checking that the ARF matches the OGIP standard, but as there are cases of released data products that do not follow the standard, these checks can not cover all cases. """ _ui_name = "ARF" _fields = ("name", "energ_lo", "energ_hi", "specresp", "bin_lo", "bin_hi") _extra_fields = ("exposure", "ethresh") def _get_specresp(self): return self._specresp def _set_specresp(self, val): self._specresp = val self._rsp = val specresp = property(_get_specresp, _set_specresp) def __init__(self, name, energ_lo, energ_hi, specresp, bin_lo=None, bin_hi=None, exposure=None, header=None, ethresh=None): self.specresp = specresp self.bin_lo = bin_lo self.bin_hi = bin_hi self.exposure = exposure self.header = {} if header is None else header self.ethresh = ethresh energ_lo, energ_hi = self._validate_energy_ranges(name, energ_lo, energ_hi, ethresh) self._lo, self._hi = energ_lo, energ_hi self.energ_lo = energ_lo self.energ_hi = energ_hi Data1DInt.__init__(self, name, energ_lo, energ_hi, specresp) def _repr_html_(self): """Return a HTML (string) representation of the ARF """ return html_arf(self) def __setstate__(self, state): if 'header' not in state: self.header = {} self.__dict__.update(state) if '_specresp' not in state: self.__dict__['_specresp'] = state.get('specresp', None) self.__dict__['_rsp'] = state.get('specresp', None)
[docs] def apply_arf(self, src, *args, **kwargs): "Fold the source array src through the ARF and return the result" # an external function must be called so all ARFs go through # a single entry point in order for caching to 'work' model = arf_fold(src, self._rsp) # Rebin the high-res source model folded through ARF down to the size # the PHA or RMF expects. if args: (arf, rmf) = args if rmf != () and len(arf[0]) > len(rmf[0]): model = rebin(model, arf[0], arf[1], rmf[0], rmf[1]) return model
[docs] def notice(self, bin_mask=None): self._rsp = self.specresp self._lo = self.energ_lo self._hi = self.energ_hi if bin_mask is not None: self._rsp = self.specresp[bin_mask] self._lo = self.energ_lo[bin_mask] self._hi = self.energ_hi[bin_mask]
[docs] def get_indep(self, filter=False): return (self._lo, self._hi)
[docs] def get_dep(self, filter=False): return self._rsp
[docs] def get_xlabel(self): return 'Energy (keV)'
[docs] def get_ylabel(self): from sherpa import plot return 'cm' + plot.backend.get_latex_for_string('^2')
[docs] class DataRMF(DataOgipResponse): """RMF data set. The RMF format is described in OGIP documents [CAL_92_002]_ and [CAL_92_002a]_. Parameters ---------- name : str The name of the data set; often set to the name of the file containing the data. detchans : int energ_lo, energ_hi : array The values of the ENERG_LO, ENERG_HI, and SPECRESP columns for the ARF. The ENERG_HI values must be greater than the ENERG_LO values for each bin, and the energy arrays must be in increasing or decreasing order. n_grp, f_chan, n_chan, matrix : array-like offset : int, optional e_min, e_max : array-like or None, optional header : dict or None, optional ethresh : number or None, optional If set it must be greater than 0 and is the replacement value to use if the lowest-energy value is 0.0. Notes ----- There is limited checking that the RMF matches the OGIP standard, but as there are cases of released data products that do not follow the standard, these checks can not cover all cases. If a check fails then a warning message is logged. """ _ui_name = "RMF" _fields = ("name", "energ_lo", "energ_hi", "n_grp", "f_chan", "n_chan", "matrix", "e_min", "e_max") _extra_fields = ("detchans", "offset", "ethresh") def __init__(self, name, detchans, energ_lo, energ_hi, n_grp, f_chan, n_chan, matrix, offset=1, e_min=None, e_max=None, header=None, ethresh=None): energ_lo, energ_hi = self._validate(name, energ_lo, energ_hi, ethresh) if offset < 0: raise ValueError(f"offset must be >=0, not {offset}") self.energ_lo = energ_lo self.energ_hi = energ_hi self.offset = offset self.detchans = detchans self.e_min = e_min self.e_max = e_max self.header = {} if header is None else header self.n_grp = n_grp self.f_chan = f_chan self.n_chan = n_chan self.matrix = matrix self.ethresh = ethresh self._fch = f_chan self._nch = n_chan self._grp = n_grp self._rsp = matrix self._lo = energ_lo self._hi = energ_hi # It is assumed, but not yet required, that the RMF components # are set with the __init__ call, and not changed after the # fact (this is to avoid having to have a complex system of # checks as used by the parent Data class for the independent # and dependent axes). It would make sense to make these # fields either read-only or add validation when changed, but # either approach is a large change, so stick with the simple # validation here for now (but allow for them to be set # later). # if (self.e_min is not None) ^ (self.e_max is not None): raise DataErr("e_min/max must both be set or empty") if self.e_min is not None: nelo = len(self.e_min) nehi = len(self.e_max) if nelo != nehi: raise DataErr(f"e_min/max mismatch in size: {nelo} vs {nehi}") if nelo != self.detchans: raise DataErr(f"detchans mismatch with e_min/max: {self.detchans} vs {nelo}") Data1DInt.__init__(self, name, energ_lo, energ_hi, matrix) # Although we have a Data1DInt-like dataset, the dependent axis # does not match the independent axes. So we have to remove the # checks on y. We could enforce the internal constraints but this # is hard to do sensibly, so just do not bother. # @property def y(self): """The dependent axis.""" return self._y @y.setter def y(self, val): self._y = val def _repr_html_(self): """Return a HTML (string) representation of the RMF """ return html_rmf(self) def __setstate__(self, state): if 'header' not in state: self.header = {} self.__dict__.update(state) def _validate(self, name, energy_lo, energy_hi, ethresh): """ Validate energy ranges and, if necessary, make adjustments. Subclasses may override this method to perform different validations or skip validation altogether. Parameters ---------- name : str The name/label of the current file energy_lo, energ_hi : NumPy array The lower bounds of the energy bins. The arrays must have the same size ethresh : float The lowest energy value Returns ------- energy_lo, energ_hi : NumPy array The energy values to use for the bin boundaries """ return self._validate_energy_ranges(name, energy_lo, energy_hi, ethresh)
[docs] def apply_rmf(self, src, *args, **kwargs): "Fold the source array src through the RMF and return the result" # Rebin the high-res source model from the PHA down to the size # the RMF expects. if args: (rmf, pha) = args if pha != () and len(pha[0]) > len(rmf[0]): src = rebin(src, pha[0], pha[1], rmf[0], rmf[1]) if len(src) != len(self._lo): raise TypeError("Mismatched filter between ARF and RMF " + "or PHA and RMF") return rmf_fold(src, self._grp, self._fch, self._nch, self._rsp, self.detchans, self.offset)
[docs] def notice(self, noticed_chans=None): bin_mask = None self._fch = self.f_chan self._nch = self.n_chan self._grp = self.n_grp self._rsp = self.matrix self._lo = self.energ_lo self._hi = self.energ_hi if noticed_chans is not None: (self._grp, self._fch, self._nch, self._rsp, bin_mask) = filter_resp(noticed_chans, self.n_grp, self.f_chan, self.n_chan, self.matrix, self.offset) self._lo = self.energ_lo[bin_mask] self._hi = self.energ_hi[bin_mask] return bin_mask
[docs] def get_indep(self, filter=False): return (self._lo, self._hi)
[docs] def get_dep(self, filter=False): return self.apply_rmf(numpy.ones(self.energ_lo.shape, SherpaFloat))
[docs] def get_xlabel(self): if (self.e_min is not None) and (self.e_max is not None): return 'Energy (keV)' return 'Channel'
[docs] def get_ylabel(self): return 'Counts'
# FIXME There are places in the code that explicitly check if an object is an instance of sherpa.astro.data.DataRMF. # So it's safer to make DataRosatRMF a subclass of the default class, although in principle they should be siblings # and subclasses of the same superclass.
[docs] class DataRosatRMF(DataRMF): ui_name = "ROSAT RMF" def _validate(self, name, energy_lo, energy_hi, ethresh): return energy_lo, energy_hi
def validate_wavelength_limits(wlo, whi, emax): """Check that the wavelength limits are sensible. This is used by DataPHA.notice to ensure that the wavelength limits are meaningful. It deals with converting to energy and handling 0 limits. Parameters ---------- wlo, whi : number or None The wavelength limit for the low and high edges, or None. We have wlo >= 0 and wlo <= whi. emax : number The maximum energy of the response (the upper edge of the last bin). Returns ------- lims : (lo, hi) or None The energy filter or None if it doesn't overlap the response. The lo and hi values can be None. Notes ----- This routine can return a range that doesn't overlap the response, but that is handled downstream (i.e. it only returns None in certain circumstances, not all cases). """ # As we allow wlo and whi to be 0 we need to handle this here, # otherwise we'd have try hc / 0. We can either replace 0 by a # value such as numpy.finfo(numpy.float32).tiny which will result # in a very-large energy, or we can use domain knowledge - the # maximum energy value in the grid and the requested limits. We # try the latter, but it's not as simple # if whi is None: lo = None elif whi == 0.0: lo = -1 else: lo = hc / whi if wlo is None: hi = None elif wlo == 0.0: hi = -1 else: hi = hc / wlo # If either of the arguments were 0 then we need to ensure they # are sensible when combined together. Note that we can't have # whi = 0 and wlo > 0 (ie lo < -1 and hi > 0). # if lo is not None and hi is not None: if lo < 0 and hi < 0: # Both limits were 0 so we can do nothing return None if hi < 0: # The original query was 0 to x which maps to hc/x to None # but we need to know if hc/x is > emax or not if lo < emax: hi = None else: # there is no valid filter here return None elif lo is not None and lo < 0: lo = None elif hi is not None and hi < 0: hi = None return lo, hi def replace_xspecvar_values(src_counts, bkg_counts, staterr, bkg_variances, src_scale, bkg_scales): """Handle error/variances when 0's are present using XSPEC var. Correct the error for background-subtracted data when using the XSPEC variance when at least one of the source or background terms has 0 counts in it. This is to address https://github.com/sherpa/sherpa/issues/356 Parameters ---------- src_counts : ndarray The number of source counts in each group. bkg_counts : list of ndarray The number of background counts in each group, for each background element. staterr : ndarray The error terms for the source counts (expected to be the XSPEC error function ran on src_counts). This may be changed. bkg_variances : list of ndarray The variances for the background counts (expected to be the square of the XSPEC error function ran on the bkg_counts elements). This may be changed. The length of bkg_variances must match bkg_counts. src_scale : number or ndarray The combined exposure, backscal, and areascal for the source. bkg_scales : list of number or ndarray The combined exposure, backscal, and areascal for the background components. Notes ----- The chi2xspecvar error returns 1 when given 0 counts, which is okay when there's no background, but fails - when there's background subtraction, as it doesn't match XSPEC, and is addressed here - is not at all obvious what to do when using chi2xspecvar but are trying to fit the background rather than subtract is, and this is *not* addressed here. When looking at the simple case of a source bin with a single background component then we have to be concerned with a) either the source or the background component has 0 counts but the other one has positive (non-zero) counts b) both source and background have 0 counts. For case a, we can just set the error (or variance) value to 0 for the element with 0 counts. This essentially drops that element from the calculation. For case b then, in order to match XSPEC, we have to clear out the background component and then replace the source error term with a value depending on the value of the relative scaling terms of the source and background components. """ # We care about those elements where either # - all components (source, background_1 .. n) are 0 # - some are 0 # counts = numpy.asarray([src_counts] + bkg_counts) numzero = numpy.sum(counts == 0, axis=0) nbkg = len(bkg_variances) ncpts = 1 + nbkg all_zero = numzero == ncpts some_zero = (numzero > 0) & (numzero < ncpts) # For the some_zero cases we just set them to 0. Unfortunately # identifying them requires another loop as some_zero has # collapsed the data. # idx, = numpy.asarray(src_counts == 0 & some_zero).nonzero() staterr[idx] = 0 for bcnts, bvar in zip(bkg_counts, bkg_variances): idx, = numpy.asarray(bcnts == 0 & some_zero).nonzero() bvar[idx] = 0 # Do we have any bins where all source and background components # are zero? If not, we can return immediately. # nzero = all_zero.sum() if nzero == 0: return # When both source and background are 0, the replacement # depends on how the source and background scaling works. # This is based on a discussion with the XSPEC developers # and the code in Xspec/sc/XSStat/ChiSquare.cxx, in # particular ChiSquare::applyMinVariance. The XSPEC code # only deals with a single background component, so here # we average the scaling factors from multiple components # for the check. # # The XSPEC code uses a value (for the variance) of the # minimum of # # 1 / src_texp^2 # scale^2 / bkg_texp^2 # # where scale = (AREASCAL * BACKSCAL)_src / (AREASCAL * BACKSCAL)_bkg # # However, XSPEC is calculating a rate and we want counts, # so we care about the minimum of (for the variance) # # 1 # scale^2 * src_texp^2 / bkg_texp^2 # # although we actually use the square root of this, as we # change the staterr array (to ensure it doesn't get # re-scaled when calculating statvar below). # # The conversion is per-bin, as the scaling factors are per-bin, # but they may be specified as a scalar. In that case, convert # into an array to simplify the following. The requirement here is # that we have the same channel range in each background component # as the source. # nelem = len(staterr) scales = numpy.zeros((nbkg, nelem)) for idx, bscale in enumerate(bkg_scales): if numpy.isscalar(bscale): scales[idx] = numpy.ones(nelem) * bscale else: scales[idx] = bscale # Use the average background-scaling factor for the background, as # it's not clear what is done in XSPEC for multiple-background # cases. # bkg_scale = scales.sum(axis=0) / nbkg s = numpy.ones(nzero) b = (src_scale / bkg_scale)[all_zero] combined = numpy.asarray([s, b]) minval = numpy.min(combined, axis=0) staterr[all_zero] = minval for bvar in bkg_variances: bvar[all_zero] = 0
[docs] class DataPHA(Data1D): """PHA data set, including any associated instrument and background data. The PHA format is described in an OGIP document [OGIP_92_007]_ and [OGIP_92_007a]_. Parameters ---------- name : str The name of the data set; often set to the name of the file containing the data. channel, counts : array of int The PHA data. staterror, syserror : scalar or array or None, optional The statistical and systematic errors for the data, if defined. bin_lo, bin_hi : array or None, optional The wavelength ranges for the channels. This is intended to support Chandra grating spectra. grouping : array of int or None, optional quality : array of int or None, optional exposure : number or None, optional The exposure time for the PHA data set, in seconds. backscal : scalar or array or None, optional areascal : scalar or array or None, optional header : dict or None, optional If ``None`` the header will be pre-populated with a minimal set of keywords that would be found in an OGIP compliant PHA I file. Attributes ---------- name : str Used to store the file name, for data read from a file. exposure Notes ----- The original data is stored in the attributes - e.g. `counts` - and the data-access methods, such as `get_dep` and `get_staterror`, provide any necessary data manipulation to handle cases such as: background subtraction, filtering, and grouping. There is additional complexity compared to the Data1D case when filtering data because: * although the data uses channel numbers, users will often want to filter the data using derived values (in energy or wavelength units, such as 0.5 to 7.0 keV or 16 to 18 Angstroms); * although derived from the Data1D case, PHA data is more-properly thought about as being an integrated data set, so each channel maps to a range of energy or wavelength values; * the data is often grouped to improve the signal-to-noise, and so requests for values need to determine whether to filter the data or not, whether to group the data or not, and how to combine the data within each group; * and there is also the quality array, which indicates whether or not a channel is trust-worthy or not (and so acts as an additional filtering term). The handling of the AREASCAl value - whether it is a scalar or array - is currently in flux. It is a value that is stored with the PHA file, and the OGIP PHA standard ([OGIP_92_007]_, [OGIP_92_007a]_) describes the observed counts being divided by the area scaling before comparison to the model. However, this is not valid for Poisson-based statistics, and is also not how XSPEC handles AREASCAL ([PRIVATE_KA]_); the AREASCAL values are used to scale the exposure times instead. The aim is to add this logic to the instrument models in `sherpa.astro.instrument`, such as `sherpa.astro.instrument.RMFModelPHA`. The area scaling still has to be applied when calculating the background contribution to a spectrum, as well as when calculating the data and model values used for plots (following XSPEC so as to avoid sharp discontinuities where the area-scaling factor changes strongly). """ _fields = ('name', 'channel', 'counts', 'staterror', 'syserror', 'bin_lo', 'bin_hi', 'grouping', 'quality') _extra_fields = ('exposure', 'backscal', 'areascal', 'grouped', 'subtracted', 'units', 'rate', 'plot_fac', 'response_ids', 'background_ids') _related_fields = Data1D._related_fields + ("bin_lo", "bin_hi", "counts", "grouping", "quality", "backscal", "areascal") def _get_grouped(self): return self._grouped def _set_grouped(self, val): val = bool(val) if val and self.grouping is None: raise DataErr('nogrouping', self.name) # Short cut if the grouping isn't changing. # # This could be dangerous, as there may be times we # would want to generate things, but it is believed # that we have identified all these changes, such # as changing the grouping field. # if val == self._grouped: return if not numpy.iterable(self.mask): self._grouped = val return # As the grouping has changed AND there's a partial filter # then it is important to re-apply the filter so that the mask # matches the new grouping scheme. # old_filter = self.get_filter() self._grouped = val self.ignore() for vals in parse_expr(old_filter): self.notice(*vals) grouped = property(_get_grouped, _set_grouped, doc='Are the data grouped?') def _get_subtracted(self): return self._subtracted def _set_subtracted(self, val): val = bool(val) if len(self._backgrounds) == 0: raise DataErr('nobkg', self.name) self._subtracted = val subtracted = property(_get_subtracted, _set_subtracted, doc='Are the background data subtracted?') def _get_units(self): return self._units def _set_units(self, val): units = str(val).strip().lower() if units == 'bin': units = 'channel' if units.startswith('chan'): # Note: the names of these routines appear confusing because of the # way group values are used self._from_channel = self._group_to_channel units = 'channel' elif units.startswith('ener'): self._from_channel = self._channel_to_energy units = 'energy' elif units.startswith('wave'): self._from_channel = self._channel_to_wavelength units = 'wavelength' else: raise DataErr('bad', 'quantity', val) for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) if bkg.get_response() != (None, None) or \ (bkg.bin_lo is not None and bkg.bin_hi is not None): bkg.units = units self._units = units units = property(_get_units, _set_units, doc="Units of the independent axis: one of 'channel', 'energy', 'wavelength'.") def _get_rate(self): return self._rate def _set_rate(self, val): self._rate = bool_cast(val) for bkg_id in self.background_ids: self.get_background(bkg_id).rate = val rate = property(_get_rate, _set_rate, doc="""Is the Y axis displayed as a rate when plotting data? When True the y axis is normalised by the exposure time to display a rate.""") def _get_plot_fac(self): return self._plot_fac def _set_plot_fac(self, val): # I'd prefer to check whether val is an integer, but there may # be users who have set the value to 2.0 and it doesn't seem # worth breaking that code. We do however want to error out if # val=0.5 (say), hence this slightly-contrived check. # try: ival = int(val) okay = val == ival except (ValueError, TypeError): # For when int() can't convert val, which can raise # different errors. okay = False if not okay: raise DataErr("bad", "plot_fac setting", val) self._plot_fac = ival for bkg_id in self.background_ids: self.get_background(bkg_id).plot_fac = ival plot_fac = property(_get_plot_fac, _set_plot_fac, doc="""How the X axis is used to create the Y axis when plotting data. The Y axis values are multiplied by X^plot_fac. The default value of 0 means the X axis is not used in plots. The value must be an integer.""") def _get_response_ids(self): return self._response_ids def _set_response_ids(self, ids): if not numpy.iterable(ids): raise DataErr('idsnotarray', 'response', str(ids)) keys = list(self._responses.keys()) for resp_id in ids: if resp_id not in keys: raise DataErr('badids', str(resp_id), 'response', str(keys)) self._response_ids = list(ids) response_ids = property(_get_response_ids, _set_response_ids, doc="""IDs of defined instrument responses (ARF/RMF pairs). If set, the identifiers must already exist, and any other responses will be removed. The identifiers can be integers or strings. """) def _get_background_ids(self): return self._background_ids def _set_background_ids(self, ids): if not numpy.iterable(ids): raise DataErr('idsnotarray', 'background', str(ids)) keys = list(self._backgrounds.keys()) for bkg_id in ids: if bkg_id not in keys: raise DataErr('badids', str(bkg_id), 'background', str(keys)) self._background_ids = list(ids) background_ids = property(_get_background_ids, _set_background_ids, doc="""IDs of defined background data sets. If set, the identifiers must already exist, and any other backgrounds will be removed. The identifiers can be integers or strings. """) def __init__(self, name, channel, counts, staterror=None, syserror=None, bin_lo=None, bin_hi=None, grouping=None, quality=None, exposure=None, backscal=None, areascal=None, header=None): # Set the size of the object as soon as we know (it makes it # easier to get usable error messages when checking the # related fields). This is only done for the channel case to # allow the counts field to be set but the channel field # unset. # channel = _check(channel) if channel is not None: self._size = len(channel) counts = _check(counts) self.bin_lo = bin_lo self.bin_hi = bin_hi self.quality = quality self.grouping = grouping self.exposure = exposure self.backscal = backscal self.areascal = areascal if header is None: header = {"HDUCLASS": "OGIP", "HDUCLAS1": "SPECTRUM", "HDUCLAS2": "TOTAL", "HDUCLAS3": "TYPE:I", "HDUCLAS4": "COUNT", "HDUVERS": "1.2.1", "TELESCOP": "none", "INSTRUME": "none", "FILTER": "none", "POISSERR": True} self.header = header self._grouped = grouping is not None # _original_groups is set False if the grouping is changed via # the _dynamic_groups method. This is currently only used by the # serialization code (sherpa.astro.ui.serialize) to determine # whether to write out the grouping data. # self._original_groups = True self._subtracted = False self._response_ids = [] self._background_ids = [] self._responses = {} self._backgrounds = {} self._rate = True self._plot_fac = 0 self.units = "channel" self.quality_filter = None super().__init__(name, channel, counts, staterror, syserror) def _set_related(self, attr, val, check_mask=True, allow_scalar=False): """Set a field that must match the independent axes size. The value can be None, a scalar (if allow_scalar is set), or something with the same length as the independent axis. This is intended to be used from the property setter. """ if val is None: setattr(self, f"_{attr}", None) return if not numpy.iterable(val): if not allow_scalar: raise DataErr("notanarray") setattr(self, f"_{attr}", val) return super()._set_related(attr, val, check_mask=check_mask) # Set up the aliases for channel and counts # @property def channel(self): """The channel array. This is the first, and only, element of the indep attribute. """ return self.indep[0] @channel.setter def channel(self, val): self.indep = (val, ) @property def counts(self): """The counts array. This is an alias for the y attribute. """ return self.y @counts.setter def counts(self, val): self.y = val # Override the mask handling because the mask matches the grouped # data length, not the independent axis. # @Data1D.mask.setter def mask(self, val): # We only need to over-ride the behavior if the data is # grouped and val is a sequence (so we test with isscalar # rather than iterable, to avoid selecting strings). # if self.grouped and val is not None and not numpy.isscalar(val): # The assumption is that if the data is grouped then it contains data. nexp = len(self.get_y(filter=False)) if len(val) != nexp: raise DataErr("mismatchn", "grouped data", "mask", nexp, len(val)) self._data_space.filter.mask = val return # This is a bit messy just to call the original code Data1D.mask.fset(self, val) # Set up the properties for the related fields # @property def bin_lo(self): """The lower edge of each channel, in Angstroms, or None. The values are expected to be in descending order. This is only expected to be set for Chandra grating data. """ return self._bin_lo @bin_lo.setter def bin_lo(self, val): self._set_related("bin_lo", val) @property def bin_hi(self): """The upper edge of each channel, in Angstroms, or None. The values are expected to be in descending order, with the bin_hi value larger than the corresponding bin_lo element. This is only expected to be set for Chandra grating data. """ return self._bin_hi @bin_hi.setter def bin_hi(self, val): self._set_related("bin_hi", val) @property def grouping(self): """The grouping data. A group is indicated by a sequence of flag values starting with ``1`` and then ``-1`` for all the channels in the group, following [OGIP_92_007]_. The grouping array must match the number of channels and it will be converted to an integer type if necessary. .. versionchanged:: 4.15.1 The filter is now re-calculated when the grouping is changed. It is suggested that the filter be checked with `get_filter` to check it is still sensible. If set to None then the group flag is cleared. Returns ------- grouping : numpy.ndarray or None See Also -------- group, grouped, quality """ return self._grouping @grouping.setter def grouping(self, val): # _set_related checks if it's a scalar value, so we just need # to check it's convertible to ndarray. # if val is not None: try: val = numpy.asarray(val, dtype=numpy.int16) except TypeError: raise DataErr("notanintarray") from None # If this is called within __init__ then we do not want to # recreate the filter expression. If the mask is either True # or False then we do not want to recreate filter (as a mask # of True will get converted to [True] * nbins which then # breaks downstream processing, as it looks like notice/ignore # has already been called). # ofilter = None if self._NoNewAttributesAfterInit__initialized and numpy.iterable(self.mask): ofilter = self.get_filter() self._set_related("grouping", val) # If the array has been removed then we need to reset the # group flag. # if val is None and self._NoNewAttributesAfterInit__initialized and self.grouped: self.grouped = False if ofilter is not None: # If the data has been filtered then we want to re-create # the filter (which is needed because the mask size may # have changed). This is not ideal, since the actual # filter is not necessarily the requested filter (thanks # to the width of a group), so this could end up selecting # a surprising range. # self.ignore() for vals in parse_expr(ofilter): self.notice(*vals) @property def quality(self): """The quality data. 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` method, as discussed in [OGIP_92_007]_. The quality array must match the number of channels and it will be converted to an integer type if necessary. Returns ------- quality : numpy.ndarray or None See Also -------- group, grouping """ return self._quality @quality.setter def quality(self, val): # _set_related checks if it's a scalar value, so we just need # to check it's convertible to ndarray. # if val is not None: try: val = numpy.asarray(val, dtype=numpy.int16) except TypeError: raise DataErr("notanintarray") from None # If the quality changes, should we re-create the filter as we # do with grouping? No, because the quality array is currently # not automatically applied. Although perhaps we should reset # quality_filter? # self._set_related("quality", val) @property def areascal(self): """The area scaling value (can be a scalar or array). If this is an array then it must match the length of channel. """ return self._areascal @areascal.setter def areascal(self, val): self._set_related("areascal", val, allow_scalar=True) @property def backscal(self): """The background scaling value (can be a scalar or array). If this is an array then it must match the length of channel. """ return self._backscal @backscal.setter def backscal(self, val): self._set_related("backscal", val, allow_scalar=True) def _repr_html_(self): """Return a HTML (string) representation of the PHA """ return html_pha(self) def __getstate__(self): state = self.__dict__.copy() del state['_from_channel'] return state def __setstate__(self, state): self._background_ids = state['_background_ids'] self._backgrounds = state['_backgrounds'] self._set_units(state['_units']) if 'header' not in state: self.header = {} self.__dict__.update(state) primary_response_id = 1 """The identifier for the response component when not set."""
[docs] def set_analysis(self, quantity, type='rate', factor=0): """Set the units used when fitting and plotting spectral data. Parameters ---------- quantity : {'channel', 'energy', 'wavelength'} The analysis setting. type : {'rate', 'counts'}, optional Do plots display a rate or show counts? 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.DatatErr If the type argument is invalid, the RMF or ARF has the wrong size, or there in no response. See Also -------- get_analysis Examples -------- >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.set_analysis('energy') >>> pha.set_analysis('wave', type='counts', factor=1) >>> pha.units 'wavelength' """ if type not in ["counts", "rate"]: raise DataErr("plottype", type, "'rate' or 'counts'") try: self.plot_fac = factor except DataErr: # Create a slightly-different error message raise DataErr("bad", "factor setting", factor) from None self.rate = type == "rate" arf, rmf = self.get_response() if rmf is not None and rmf.detchans != len(self.channel): raise DataErr("incompatibleresp", rmf.name, self.name) if (rmf is None and arf is None) and \ (self.bin_lo is None and self.bin_hi is None) and \ quantity != "channel": raise DataErr("norsp", self.name) if rmf is None and arf is not None and quantity != "channel" and \ len(arf.energ_lo) != len(self.channel): raise DataErr("incompleteresp", self.name) self.units = quantity
[docs] def get_analysis(self): """Return the units used when fitting spectral data. Returns ------- setting : { 'channel', 'energy', 'wavelength' } The analysis setting. 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 -------- set_analysis Examples -------- >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.set_analysis("wave") >>> is_wave = pha.get_analysis() == 'wavelength' """ return self.units
def _fix_response_id(self, id): if id is not None: return id return self.primary_response_id
[docs] def get_response(self, id=None): """Return the response component. Parameters ---------- id : int or str, optional The identifier of the response component. If it is None then the default response identifier is used. Returns ------- arf, rmf: sherpa.astro.data.DataARF,sherpa.astro.data.DataRMF instances or None The response, as an ARF and RMF. Either, or both, components can be None. See Also -------- delete_response, get_arf, get_rmf, set_response """ id = self._fix_response_id(id) return self._responses.get(id, (None, None))
[docs] def set_response(self, arf=None, rmf=None, id=None): """Add or replace a response component. To remove a response use delete_response(), as setting arf and rmf to None here does nothing. Parameters ---------- arf : sherpa.astro.data.DataARF instance or None, optional The ARF to add if any. rmf : sherpa.astro.data.DataRMF instance or None, optional The RMF to add, if any. id : int or str, optional The identifier of the response component. If it is None then the default response identifier is used. See Also -------- delete_response, get_response, set_arf, set_rmf Notes ----- If the PHA header does not have the TELESCOP, INSTRUME, or FILTER header keywords set (or they are set to "none"), then they are taken from the ARF or RMF, if they are not set to "none". This is to allow simulated data sets to be used with external programs, such as XSPEC. """ if (arf is None) and (rmf is None): return resp_id = self._fix_response_id(id) self._responses[resp_id] = (arf, rmf) if resp_id not in self.response_ids: self.response_ids.append(resp_id) # To support simulated data (e.g. issue #1209) we over-write # the header TELESCOP/INSTRUME/FILTER settings to match the # response if they are at their "default" value (e.g. "none"). # # TODO: should we warn if these values don't match (when not # "none")? # def set_key(key): val = self.header.get(key, "none") if val != "none": return if arf is not None: val = arf.header.get(key, "none") if val != "none": self.header[key] = val return if rmf is None: return val = rmf.header.get(key, "none") if val == "none": return self.header[key] = val set_key("TELESCOP") set_key("INSTRUME") set_key("FILTER")
[docs] def delete_response(self, id=None): """Remove the response component. If the response component does not exist then the method does nothing. Parameters ---------- id : int or str, optional The identifier of the response component. If it is None then the default response identifier is used. See Also -------- set_response """ resp_id = self._fix_response_id(id) self._responses.pop(resp_id, None) self.response_ids.remove(resp_id)
[docs] def get_arf(self, id=None): """Return the ARF from the response. Parameters ---------- id : int or str, optional The identifier of the response component. If it is None then the default response identifier is used. Returns ------- arf: sherpa.astro.data.DataARF instance or None The ARF, if set. See Also -------- get_response, get_rmf, get_full_responses """ return self.get_response(id)[0]
[docs] def get_rmf(self, id=None): """Return the RMF from the response. Parameters ---------- id : int or str, optional The identifier of the response component. If it is None then the default response identifier is used. Returns ------- rmf: sherpa.astro.data.DataRMF instance or None The RMF, if set. See Also -------- get_arf, get_response, get_full_responses """ return self.get_response(id)[1]
[docs] def set_arf(self, arf, id=None): """Add or replace the ARF in a response component. This replaces the existing ARF of the response, keeping the previous RMF (if set). Use the delete_response method to remove the response, rather than setting arf to None. Parameters ---------- arf : sherpa.astro.data.DataARF instance The ARF to add. id : int or str, optional The identifier of the response component. If it is None then the default response identifier is used. See Also -------- delete_response, set_response, set_rmf """ self.set_response(arf, self.get_rmf(id), id)
[docs] def set_rmf(self, rmf, id=None): """Add or replace the RMF in a response component. This replaces the existing RMF of the response, keeping the previous ARF (if set). Use the delete_response method to remove the response, rather than setting rmf to None. Parameters ---------- rmf : sherpa.astro.data.DataRMF instance The RMF to add. id : int or str, optional The identifier of the response component. If it is None then the default response identifier is used. See Also -------- delete_response, set_response, set_arf """ self.set_response(self.get_arf(id), rmf, id)
[docs] def get_specresp(self, filter=False): """Return the effective area values for the data set. Parameters ---------- filter : bool, optional Should the filter attached to the data set be applied to the ARF or not. The default is `False`. Returns ------- arf : array or None The effective area values for the data set (or background component) if set. Notes ----- This will return `None` when a RSP file (a combined ARF and RMF) is used, rather than separate responses. The relationship between RSP, ARF, and RMF is described in `OGIP Calibration Memo CAL/GEN/92-002 <https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html>`_ and `OGIP Calibration Memo CAL/GEN/92-002a <https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002a/cal_gen_92_002a.html>`_. """ self.notice_response(False) arf, rmf = self.get_response() # It's not clear why we do interpolation below, why we replace # with 1 rather than 0, or how it is even meant to work, since # the current code returns different values depending on the # units setting - see issue #1582 # if arf is None or rmf is None: return None specresp = arf.get_dep() elo, ehi = arf.get_indep() lo, hi = self._get_ebins(group=False) newarf = interpolate(lo, elo, specresp) newarf[newarf <= 0] = 1. if bool_cast(filter): newarf = self.apply_filter(newarf, self._middle) return newarf
[docs] def get_full_response(self, pileup_model=None): """Calculate the response for the dataset. Unlike `get_response`, which returns a single response, this function returns all responses for datasets that have multiple responses set and it offers the possibility to include a pile-up model. Parameters ---------- pileup_model : None or a `sherpa.astro.models.JDPileup` instance If a pileup model shall be included in the return, then it needs to be passed in. Returns ------- response The return value depends on whether an ARF, RMF, or pile up model has been associated with the data set. See Also -------- get_response, get_arf, get_rmf """ # import is here because sherpa.astro.instrument depends on # sherpa.astro.data. Importing here instead of on the top # avoids a circular import. from sherpa.astro import instrument if pileup_model is not None: return instrument.PileupResponse1D(self, pileup_model) if len(self._responses) > 1: return instrument.MultipleResponse1D(self) return instrument.Response1D(self)
def _get_ebins(self, response_id=None, group=True): """Return the low and high edges of the independent axis. This method is badly named as it will return values in either channel or energy units, depending on the units setting and the associated response information. When the response includes a RMF then it returns the approximation of the mapping from channel space to energy - that is the E_MIN and E_MAX columns from the RMF EBOUNDS block rather than from the ENERG_LO and ENERG_HI columns from the MATRIX block. Parameters ---------- response_id : int or None, optional The response to use when units are not "channel". The default is to use the default response identifier. group : bool, optional Should the current grouping setting be applied. This is only used if the "grouped" attribute is set. Returns ------- lo, hi : ndarray The low and high edges of each bin, in either channels or keV: energy is used unless the units setting is channel or there is no associated response. If the group flag is set and the data set is grouped then it uses the grouping settings, otherwise the data is for each channel. No filtering is applied. See Also -------- _get_indep Examples -------- >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.ungroup() >>> pha.units = 'channel' >>> clo, chi = pha._get_ebins() >>> (clo == pha.channel).all() True >>> (chi == clo + 1).all() True >>> pha.units = 'energy' >>> elo, ehi = pha._get_ebins() >>> elo.size == pha.channel.size True >>> elo[0:5] array([0.00146, 0.0146 , 0.0292 , 0.0438 , 0.0584 ]) >>> (elo[1:] == ehi[:-1]).all() True >>> pha.group() >>> glo, ghi = pha._get_ebins() >>> glo[0:5] array([0.00146 , 0.2482 , 0.3066 , 0.46720001, 0.56940001]) Note that the returned units are energy even if units is set to "wavelength": >>> pha.units = 'wave' >>> wlo, whi = pha._get_ebins() >>> (wlo == glo).all() True """ group = bool_cast(group) if self.units == 'channel': elo = self.channel ehi = self.channel + 1 elif (self.bin_lo is not None) and (self.bin_hi is not None): elo = self.bin_lo ehi = self.bin_hi if (elo[0] > elo[-1]) and (ehi[0] > ehi[-1]): elo = hc / self.bin_hi ehi = hc / self.bin_lo else: arf, rmf = self.get_response(response_id) if rmf is not None: if (rmf.e_min is None) or (rmf.e_max is None): raise DataErr('noenergybins', 'RMF') elo = rmf.e_min ehi = rmf.e_max elif arf is not None: elo = arf.energ_lo ehi = arf.energ_hi else: elo = self.channel ehi = self.channel + 1 if self.grouped and group: elo = self.apply_grouping(elo, self._min) ehi = self.apply_grouping(ehi, self._max) if len(elo) == 0: raise DataErr('notmask') # apply_grouping applies a quality filter to the output # but if we get here then there is no equivalent. This # is likely confusing, at best, but we don't have good # tests to check what we should be doing. # return (elo, ehi)
[docs] def get_indep(self, filter=True): if filter: return (self.get_noticed_channels(),) return (self.channel,)
def _get_indep(self, filter=False): """Return the low and high edges of the independent axis. Unlike _get_ebins, this returns values in the "native" space of the response - i.e. for a RMF, it returns the bounds from the MATRIX rather than EBOUNDS extension of the RMF - and not the approximation used in _get_ebins. Parameters ---------- filter : bool, optional It is not clear what this option means. Returns ------- lo, hi : ndarray The low and high edges of each bin, in either keV or Angstroms. Raises ------ sherpa.utils.err.DataErr The data set does not contain a response. See Also -------- _get_ebins Notes ----- If the PHA file contains multiple responses then they are combined to create the overall grid. Examples -------- >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.units = 'energy' >>> elo, ehi = pha._get_indep() >>> elo.shape (1090,) >>> pha.channel.shape (1024,) >>> elo[0:5] array([0.1 , 0.11, 0.12, 0.13, 0.14]) >>> ehi[0:5] array([0.11 , 0.12 , 0.13 , 0.14 , 0.15000001]) >>> (elo[1:] == ehi[:-1]).all() True >>> pha.units = 'wave' >>> wlo, whi = pha._get_indep() >>> wlo[0:4] array([112.71289825, 103.32015848, 95.37245534, 88.56013348]) >>> whi[0:4] array([123.98418555, 112.71289825, 103.32015848, 95.37245534]) >>> (wlo[:-1] == whi[1:]).all() True """ if (self.bin_lo is not None) and (self.bin_hi is not None): elo = self.bin_lo ehi = self.bin_hi if (elo[0] > elo[-1]) and (ehi[0] > ehi[-1]): if self.units == 'wavelength': return (elo, ehi) elo = hc / self.bin_hi ehi = hc / self.bin_lo else: energylist = [] for resp_id in self.response_ids: arf, rmf = self.get_response(resp_id) lo = None hi = None if rmf is not None: lo = rmf.energ_lo hi = rmf.energ_hi if filter: lo, hi = rmf.get_indep() elif arf is not None: lo = arf.energ_lo hi = arf.energ_hi if filter: lo, hi = arf.get_indep() energylist.append((lo, hi)) if len(energylist) > 1: # TODO: This is only tested by test_eval_multi_xxx and not with # actual (i.e. real world) data elo, ehi, lookuptable = compile_energy_grid(energylist) elif (not energylist or (len(energylist) == 1 and numpy.equal(energylist[0], None).any())): raise DataErr('noenergybins', 'Response') else: elo, ehi = energylist[0] lo, hi = elo, ehi if self.units == 'wavelength': lo = hc / ehi hi = hc / elo return (lo, hi) def _group_to_channel(self, val, group=True, response_id=None): """Convert group number to channel number. For ungrouped data channel and group numbering are the same. The mid-point of each group is used (rounded down if not an integer). """ if not self.grouped or not group: return val # The middle channel of each group. # mid = self.apply_grouping(self.channel, self._middle) # Convert to an integer (this keeps the channel within # the group). # mid = numpy.floor(mid) val = numpy.asarray(val).astype(numpy.int_) - 1 try: return mid[val] except IndexError: raise DataErr(f'invalid group number: {val}') from None def _channel_to_energy(self, val, group=True, response_id=None): elo, ehi = self._get_ebins(response_id=response_id, group=group) val = numpy.asarray(val).astype(numpy.int_) - 1 try: return (elo[val] + ehi[val]) / 2.0 except IndexError: raise DataErr('invalidchannel', val) from None def _channel_to_wavelength(self, val, group=True, response_id=None): tiny = numpy.finfo(numpy.float32).tiny vals = numpy.asarray(self._channel_to_energy(val, group, response_id)) if vals.shape == (): if vals == 0.0: vals = tiny else: vals[vals == 0.0] = tiny vals = hc / vals return vals default_background_id = 1 """The identifier for the background component when not set. It is an integer or string. """ def _fix_background_id(self, id): """Identify the background identifier. Parameters ---------- id : int, str, or None The background identifier, or None. If None then the default_background_id value will be used. Returns ------- bkg_id : int or str The background identifier. """ if id is not None: return id return self.default_background_id
[docs] def get_background(self, id=None): """Return the background component. Parameters ---------- id : int or str, optional The identifier of the background component. If it is None then the default background identifier is used. Returns ------- bkg : sherpa.astro.data.DataPHA instance or None The background dataset. If there is no component then None is returned. See Also -------- delete_background, set_background """ bkg_id = self._fix_background_id(id) return self._backgrounds.get(bkg_id)
[docs] def set_background(self, bkg, id=None): """Add or replace a background component. 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. Parameters ---------- bkg : sherpa.astro.data.DataPHA instance The background dataset to add. This object may be changed by this method. id : int or str, optional The identifier of the background component. If it is None then the default background identifier is used. See Also -------- delete_background, get_background Notes ----- If the PHA header does not have the TELESCOP, INSTRUME, or FILTER header keywords set (or they are set to "none"), then they are taken from the background, if they are not set to "none". This is to allow simulated data sets to be used with external programs, such as XSPEC. """ if not isinstance(bkg, DataPHA): raise ArgumentTypeErr("badarg", "bkg", "a PHA data set") # Check that the background matches the source (i.e. self) # dataset. For this use case we require that the source has # set up its channel array (ie self.channel cannot be None), # as allowing the background dataset to have channels but not # the source makes tracking the state harder and there is no # obvious use case for that. # if self.channel is None: raise DataErr("The channel field must be set before adding a background") if bkg.channel is None: raise DataErr("The channel field of the background must be set") if len(self.channel) != len(bkg.channel) or \ numpy.any(self.channel != bkg.channel): raise DataErr("The source and background channels differ") bkg_id = self._fix_background_id(id) self._backgrounds[bkg_id] = bkg if bkg_id not in self.background_ids: self.background_ids.append(bkg_id) # Copy over data from the source to the background # if its not present in the background: # - background and grouping # - response information (ONLY THE FIRST TERM) # # The units (only when a response is present), rate, and # plot_fac values are always copied. # if bkg.grouping is None: bkg.grouping = self.grouping bkg.grouped = bkg.grouping is not None if bkg.quality is None: bkg.quality = self.quality if bkg.get_response() == (None, None): bkg.set_response(*self.get_response()) if bkg.get_response() != (None, None): bkg.units = self.units bkg.rate = self.rate bkg.plot_fac = self.plot_fac # To support simulated data (e.g. issue #1209) we over-write # the header TELESCOP/INSTRUME/FILTER settings to match the # background if they are at their "default" value (e.g. "none"). # # TODO: should we warn if these values don't match (when not # "none")? # def set_key(key): val = self.header.get(key, "none") if val != "none": return val = bkg.header.get(key, "none") if val == "none": return self.header[key] = val set_key("TELESCOP") set_key("INSTRUME") set_key("FILTER")
[docs] def delete_background(self, id=None): """Remove the background component. If the background component does not exist then the method does nothing. Parameters ---------- id : int or str, optional The identifier of the background component. If it is None then the default background identifier is used. See Also -------- set_background Notes ----- If this call removes the last of the background components then the subtracted flag is cleared (if set). """ bkg_id = self._fix_background_id(id) if bkg_id not in self.background_ids: return self._backgrounds.pop(bkg_id, None) if len(self._backgrounds) == 0: self._subtracted = False self.background_ids.remove(bkg_id)
[docs] def get_background_scale(self, bkg_id=1, units='counts', group=True, filter=False): """Return the correction factor for the background dataset. .. versionchanged:: 4.12.2 The bkg_id, units, 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 ---------- bkg_id : int or str, optional The background component to use (the default 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 ------- scale : None, number, or NumPy array The scaling factor to correct the background data onto the source data set. If bkg_id is not valid then None is returned. Notes ----- The correction factor when units is 'counts' is:: scale_exposure * scale_backscal * scale_areascal / nbkg where nbkg is the number of background components and scale_x is the source value divided by the background value for the field x. When units is 'rate' the correction is: scale_backscal / nbkg and it is currently uncertain whether it should include the AREASCAL scaling. """ if units not in ['counts', 'rate']: raise ValueError(f"Invalid units argument: {units}") if bkg_id not in self.background_ids: return None nbkg = len(self.background_ids) def correct(obj): """Correction factor for the object""" ans = 1.0 # Should we set 0 values to 1 at this stage? # if obj.backscal is not None: ans *= self._check_scale(obj.backscal, group=False) if obj.areascal is not None and units == 'counts': ans *= self._check_scale(obj.areascal, group=False) if obj.exposure is not None and units == 'counts': ans *= self._check_scale(obj.exposure, group=False) return ans src = correct(self) bkg = correct(self.get_background(bkg_id)) scale = src / bkg / nbkg return self._check_scale(scale, group=group, filter=filter)
def _check_scale(self, scale, group=True, filter=False): """Ensure the scale value is positive and filtered/grouped. Parameters ---------- scale : number or numpy array The scale factor. group : bool, optional Is any grouping applied to the data? This is only relevant for an array. filter : bool, optional Is any filter applied? This is only checked if group is True. Returns ------- scale : number or numpy array Negative values are replaced by 1.0. """ if numpy.isscalar(scale) and scale <= 0.0: return 1.0 if numpy.iterable(scale): scale = numpy.asarray(scale, dtype=SherpaFloat) if group: if filter: scale = self.apply_filter(scale, self._middle) else: scale = self.apply_grouping(scale, self._middle) scale[scale <= 0.0] = 1.0 return scale
[docs] def get_backscal(self, group=True, filter=False): """Return the background scaling of the PHA data set. Return the BACKSCAL setting [OGIP_92_007]_ for the PHA data set. Parameters ---------- 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 ------- backscal : number or ndarray The BACKSCAL value, which can be a scalar or a 1D array. See Also -------- get_areascal, get_background_scale 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 same manner, because only the ratio of source and background BACKSCAL values is used. It can be a scalar or an array. Examples -------- >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.get_backscal() 2.5264364698914e-06 """ if self.backscal is None: return None return self._check_scale(self.backscal, group, filter)
[docs] def get_areascal(self, group=True, filter=False): """Return the fractional area factor of the PHA data set. Return the AREASCAL setting [OGIP_92_007]_ for the PHA data set. Parameters ---------- 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 ------- areascal : number or ndarray The AREASCAL value, which can be a scalar or a 1D array. See Also -------- get_backscal, get_background_scale Notes ----- The fractional area scale is normally set to 1, with the ARF used to scale the model. Examples -------- >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.get_areascal() 1.0 """ if self.areascal is None: return None return self._check_scale(self.areascal, group, filter)
[docs] def apply_filter(self, data, groupfunc=numpy.sum): """Group and filter the supplied data to match the data set. Parameters ---------- data : ndarray or None The data to group, which must match either the number of channels of the data set or the number of filtered channels. groupfunc : function reference The grouping function. See apply_grouping for the supported values. Returns ------- result : ndarray or None The grouped and filtered data, or None if the input was None. Raises ------ sherpa.utils.err.DataErr If the data size does not match the number of channels. ValueError If the name of groupfunc is not supported or the data does not match the filtered data. See Also -------- apply_grouping, ignore, ignore_bad, notice Examples -------- Group and filter the counts array with no filter and then with a filter: >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.grouped True >>> pha.notice() >>> print(pha.apply_filter(pha.counts)) [17. 15. 16. 15. ... >>> pha.notice(0.5, 7) >>> print(pha.apply_filter(pha.counts)) [15. 16. 15. 18. ... As the previous example but with no grouping: >>> pha.ungroup() >>> pha.notice() >>> pha.apply_filter(pha.counts)[0:5] array([0., 0., 0., 0., 0.]) >>> pha.notice(0.5, 7) >>> pha.apply_filter(pha.counts)[0:5] array([4., 3., 0., 1., 1.]) Rather than group the counts, use the channel numbers and return the first and last channel number in each of the filtered groups (for the first five groups): >>> pha.group() >>> pha.notice(0.5, 7.0) >>> pha.apply_filter(pha.channel, pha._min)[0:5] array([33., 40., 45., 49., 52.]) >>> pha.apply_filter(pha.channel, pha._max)[0:5] array([39., 44., 48., 51., 54.]) Find the approximate energy range of each selected group from the RMF EBOUNDS extension: >>> rmf = pha.get_rmf() >>> elo = pha.apply_filter(rmf.e_min, pha._min) >>> ehi = pha.apply_filter(rmf.e_max, pha._max) Calculate the grouped data, after filtering, if the counts were increased by 2 per channel. Note that in this case the data to apply_filter contains the channel counts after applying the current filter: >>> orig = pha.counts[pha.get_mask()] >>> new = orig + 2 >>> cts = pha.apply_filter(new) """ if data is None: return None nelem = self.size if nelem is None: raise DataErr("sizenotset", self.name) data = _check(data) ndata = len(data) # We allow the data to have either (using the un-grouped data) # # - the size of the data object (all channels) # - the size of the filtered object # # This is unlike the other Data classes, where only the "all # channel" case is supported. We need to allow this new # behavior to support model evaluation via eval_model_to_fit # when using a PHA-based instrument model. # if ndata != nelem: mask = self.get_mask() if mask is None: raise DataErr("mismatchn", "data", "array", nelem, ndata) nfiltered = mask.sum() if nfiltered != ndata: # It is hard to get a concise error here: assume that the user # would prefer to know about the filtered length. # raise DataErr("mismatchn", "filtered data", "array", nfiltered, ndata) # Create an array for the full channel range and insert # the user-values into it. # temp = numpy.zeros(nelem, dtype=SherpaFloat) temp[mask] = data data = temp gdata = self.apply_grouping(data, groupfunc) # We can not # # return super().apply_filter(gdata) # # because the super-class does not know that gdata is a # grouped dataset, and so may be smaller than self.size. The # problem is that the size attribute in the PHA case is poorly # defined: does it mean the number of channels or the number # of grouped channels? At the moment it means the former, and # there are places where we need this behavior. Can we add an # "effective size" property? # return self._data_space.filter.apply(gdata)
[docs] def apply_grouping(self, data, groupfunc=numpy.sum): """Apply the grouping scheme of the data set to the supplied data. Parameters ---------- data : ndarray or None The data to group, which must match the number of channels of the data set. groupfunc : function reference The grouping function. Note that what matters is the name of the function, not its code. The supported function names are: "sum", "_sum_sq", "_min", "_max", "_middle", and "_make_groups". Returns ------- grouped : ndarray or None The grouped data, unless the data set is not grouped or the input array was None, when the input data is returned. Raises ------ sherpa.utils.err.DataErr If the data size does not match the number of channels. ValueError If the name of groupfunc is not supported. See Also -------- apply_filter, ignore_bad Notes ----- The supported grouping schemes are: ============ ====================================================== Name Description ============ ====================================================== sum Sum all the values in the group. _min The minimum value in the group. _max The maximum value in the group. _middle The average of the minimum and maximum values. _sum_sq The square root of the sum of the squared values. _make_groups The group number, starting at the first value of data. ============ ====================================================== There are methods of the DataPHA class that can be used for all other than "sum" (the default value). The grouped data is not filtered unless a quality filter has been applied (e.g. by ignore_bad) in which case the quality filter will be applied to the result. In general apply_filter should be used if the data is to be filtered as well as grouped. Examples -------- Sum up the counts in each group (note that the data has not been filtered so using get_dep with the filter argument set to True is generally preferred to using this method): >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> gcounts = pha.apply_grouping(pha.counts) The grouping for an unfiltered PHA data set with 1024 channels is used to calculate the number of channels in each group, the lowest channel number in each group, the highest channel number in each group, and the mid-point between the two: >>> pha.grouped True >>> pha.mask True >>> len(pha.channel) 1024 >>> import numpy as np >>> dvals = np.arange(1, 1025) >>> print(pha.apply_grouping(np.ones(1024))) [ 17. 4. 11. ... >>> print(pha.apply_grouping(dvals, pha._min)) [ 1. 18. 22. ... >>> print(pha.apply_grouping(dvals, pha._max)) [ 17. 21. 32. ... >>> print(pha.apply_grouping(dvals, pha._middle)) [ 9. 19.5 27. ... The grouped data is not filtered (unless ignore_bad has been used): >>> pha.notice() >>> v1 = pha.apply_grouping(dvals) >>> pha.notice(1.2, 4.5) >>> v2 = pha.apply_grouping(dvals) >>> np.all(v1 == v2) True """ if data is None: return None nelem = self.size if nelem is None: raise DataErr("sizenotset", self.name) data = _check(data) ndata = len(data) if ndata != nelem: raise DataErr("mismatchn", "data", "array", nelem, ndata) # TODO: This should probably apply the quality filter whether # grouped or not. # if not self.grouped: return data groups = self.grouping filter = self.quality_filter if filter is None: return do_group(data, groups, groupfunc.__name__) nfilter = len(filter) if len(data) != nfilter or len(groups) != nfilter: raise DataErr("mismatchn", "quality filter", "array", nfilter, len(data)) filtered_data = numpy.asarray(data)[filter] groups = numpy.asarray(groups)[filter] return do_group(filtered_data, groups, groupfunc.__name__)
[docs] def ignore_bad(self): """Exclude channels marked as bad. Ignore any bin in the PHA data set which has a quality value that is not equal to zero. Raises ------ sherpa.utils.err.DataErr If the data set has no quality array. See Also -------- ignore, notice Notes ----- Bins with a non-zero quality setting are not automatically excluded when a data set is created. 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. """ if self.quality is None: raise DataErr("noquality", self.name) qual_flags = ~numpy.asarray(self.quality, bool) if self.grouped and (self.mask is not True): self.notice() warning('filtering grouped data with quality flags,' + ' previous filters deleted') elif not self.grouped: # if ungrouped, create/combine with self.mask if self.mask is not True: self.mask = self.mask & qual_flags return self.mask = qual_flags return # self.quality_filter used for pre-grouping filter self.quality_filter = qual_flags
def _dynamic_group(self, group_func, *args, **kwargs): """Group the data using the given function and arguments. In order to support the grouping module being optional this routine will error out if it is not loaded (each time it is called). To support Sherpa 4.14.0 and earlier group_func can be a callable, but it is expected to be a string which is the name of the callable from the "group" module (which in this module has been renamed to pygroup if it exists). It also allows the user the capability of sending in a callable that they have written without the need for the group library. If group_func is a callable then it must return the grouping and quality arrays for the new scheme. """ if not callable(group_func): if not groupstatus: raise ImportErr('importfailed', 'group', 'dynamic grouping') # The assumption is that the symbol exists so it is # not worth catching the AttributeError if it does not, # because that's a programming error and would have been # caught in testing. # group_func = getattr(pygroup, group_func) keys = list(kwargs.keys())[:] for key in keys: if kwargs[key] is None: kwargs.pop(key) # If tabstops is given then we want to ensure it is an # ndarray. Really this should be done on args as well, in # case the array is sent in as a positional argument, but we # always send it in as a keyword argument. An alternative is # to do the conversion in the C++ code, but that is # significantly harder to orchestrate so this approach has # been taken. # # We also want to ensure that it is the correct size. If a # user tries to call with tabStops=~self.mask, which is the # "obvious" thing to do, then the call will fail if the data # is already grouped, since mask will have less values in it # then required. However, we can identify this case and # convert the mask into a "per-channel" array (in the same way # that get_mask does it). # if "tabStops" in kwargs: ts = numpy.asarray(kwargs["tabStops"]) # We only expand the array if it has the correct size # (expand_grouped_mask does not enforce length checks for # Sherpa ~ 4.15). # # TODO: this probably doesn't work if we have quality_filter # set. # nts = len(ts) nchan = len(self.channel) if self.grouped and nts != nchan and \ numpy.iterable(self.mask) and len(self.mask) == nts: ts = expand_grouped_mask(ts, self.grouping) kwargs["tabStops"] = ts else: # If there is a mask, and it is an array, invert it for # the tabStops. Note that # # a) use get_mask to ensure we have a value for each channel # b) get_mask can return None or an array. # mask = self.get_mask() if numpy.iterable(mask): kwargs["tabStops"] = ~mask self.grouping, self.quality = group_func(*args, **kwargs) self.group() self._original_groups = False
[docs] def group_bins(self, num, 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, by default, applied to only the noticed data range. It is suggested that filtering is done before calling group_bins. .. versionchanged:: 4.16.0 Grouping now defaults to only using the noticed channel range. Parameters ---------- num : int The number of bins in the grouped data set. Each bin will contain the same number of channels. tabStops : array of int or bool, optional If not set then it will be based on the filtering of the data set, so that the grouping only uses the filtered data. If set, it should be an array of booleans where True indicates that the channel should not be used in the grouping (this array must match the number of channels in the 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. Notes ----- 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. """ for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) bkg.group_bins(num, tabStops=tabStops) self._dynamic_group("grpNumBins", len(self.channel), num, tabStops=tabStops)
[docs] def group_width(self, val, tabStops=None): """Group into a fixed bin width. Combine the data so that each bin contains `num` channels. The binning scheme is, by default, applied to only the noticed data range. It is suggested that filtering is done before calling group_width. .. versionchanged:: 4.16.0 Grouping now defaults to only using the noticed channel range. Parameters ---------- val : int The number of channels to combine into a group. tabStops : array of int or bool, optional If not set then it will be based on the filtering of the data set, so that the grouping only uses the filtered data. If set, it should be an array of booleans where True indicates that the channel should not be used in the grouping (this array must match the number of channels in the 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. Notes ----- 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. """ for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) bkg.group_width(val, tabStops=tabStops) self._dynamic_group("grpBinWidth", len(self.channel), val, tabStops=tabStops)
[docs] def group_counts(self, num, 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 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. The binning scheme is, by default, applied to only the noticed data range. It is suggested that filtering is done before calling group_counts. .. versionchanged:: 4.16.0 Grouping now defaults to only using the noticed channel range. Parameters ---------- num : int The number of channels to combine into a group. maxLength : int, optional The maximum number of channels that can be combined into a single group. tabStops : array of int or bool, optional If not set then it will be based on the filtering of the data set, so that the grouping only uses the filtered data. If set, it should be an array of booleans where True indicates that the channel should not be used in the grouping (this array must match the number of channels in the 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. Notes ----- 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. Examples -------- Group by 20 counts within the range 0.5 to 7 keV (this is the default behavior for 4.16 and later): >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.set_analysis("energy") >>> pha.notice() >>> pha.notice(0.5, 7) >>> pha.group_counts(20) Group by 20 but over the whole channel range, but then filtering to the noticed range of 0.5 to 7 keV (this was the default behaviour before 4.16): >>> pha.group_counts(20, tabStops=[0] * pha.size) """ for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) bkg.group_counts(num, maxLength=maxLength, tabStops=tabStops) self._dynamic_group("grpNumCounts", self.counts, num, maxLength=maxLength, tabStops=tabStops)
# DOC-TODO: see discussion in astro.ui.utils regarding errorCol
[docs] def group_snr(self, snr, 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 which exceeds `snr`. 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. The binning scheme is, by default, applied to only the noticed data range. It is suggested that filtering is done before calling group_snr. .. versionchanged:: 4.16.0 Grouping now defaults to only using the noticed channel range. Parameters ---------- snr : number The minimum signal-to-noise ratio that must be exceeded to form a group of channels. maxLength : int, optional The maximum number of channels that can be combined into a single group. tabStops : array of int or bool, optional If not set then it will be based on the filtering of the data set, so that the grouping only uses the filtered data. If set, it should be an array of booleans where True indicates that the channel should not be used in the grouping (this array must match the number of channels in the data set). 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. 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. Notes ----- 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. """ for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) bkg.group_snr(snr, maxLength=maxLength, tabStops=tabStops, errorCol=errorCol) self._dynamic_group("grpSnr", self.counts, snr, maxLength=maxLength, tabStops=tabStops, errorCol=errorCol)
[docs] def group_adapt(self, minimum, maxLength=None, tabStops=None): """Adaptively group to a minimum number of counts. Combine the data so that each bin contains `num` 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, by default, applied to only the noticed data range. It is suggested that filtering is done before calling group_adapt. .. versionchanged:: 4.16.0 Grouping now defaults to only using the noticed channel range. Parameters ---------- minimum : int The number of channels to combine into a group. maxLength : int, optional The maximum number of channels that can be combined into a single group. tabStops : array of int or bool, optional If not set then it will be based on the filtering of the data set, so that the grouping only uses the filtered data. If set, it should be an array of booleans where True indicates that the channel should not be used in the grouping (this array must match the number of channels in the 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. Notes ----- 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. """ for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) bkg.group_adapt(minimum, maxLength=maxLength, tabStops=tabStops) self._dynamic_group("grpAdaptive", self.counts, minimum, maxLength=maxLength, tabStops=tabStops)
# DOC-TODO: see discussion in astro.ui.utils regarding errorCol
[docs] def group_adapt_snr(self, minimum, 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 which exceeds `minimum`. 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, by default, applied to only the noticed data range. It is suggested that filtering is done before calling group_adapt_snr. .. versionchanged:: 4.16.0 Grouping now defaults to only using the noticed channel range. Parameters ---------- minimum : number The minimum signal-to-noise ratio that must be exceeded to form a group of channels. maxLength : int, optional The maximum number of channels that can be combined into a single group. tabStops : array of int or bool, optional If not set then it will be based on the filtering of the data set, so that the grouping only uses the filtered data. If set, it should be an array of booleans where True indicates that the channel should not be used in the grouping (this array must match the number of channels in the data set). 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. 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. Notes ----- 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. """ for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) bkg.group_adapt_snr(minimum, maxLength=maxLength, tabStops=tabStops, errorCol=errorCol) self._dynamic_group("grpAdaptiveSnr", self.counts, minimum, maxLength=maxLength, tabStops=tabStops, errorCol=errorCol)
[docs] def eval_model_to_fit(self, modelfunc): model = super().eval_model_to_fit(modelfunc) return self.apply_filter(model)
[docs] def sum_background_data(self, get_bdata_func=(lambda key, bkg: bkg.counts)): """Sum up data, applying the background correction value. Parameters ---------- get_bdata_func : function, optional What data should be used for each background dataset. The function takes the background identifier and background DataPHA object and returns the data to use. The default is to use the counts array of the background dataset. Returns ------- value : scalar or NumPy array The sum of the data, including any area, background, and exposure-time corrections. Notes ----- For each associated background, the data is retrieved (via the get_bdata_func parameter), and then - divided by its BACKSCAL value (if set) - divided by its AREASCAL value (if set) - divided by its exposure time (if set) The individual background components are then summed together, and then multiplied by the source BACKSCAL (if set), multiplied by the source AREASCAL (if set), and multiplied by the source exposure time (if set). The final step is to divide by the number of background files used. Example ------- Calculate the background counts, per channel, scaled to match the source: >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> bcounts = pha.sum_background_data() Calculate the scaling factor that you need to multiply the background data to match the source data. In this case the background data has been replaced by the value 1 (rather than the per-channel values used with the default argument): >>> bscale = pha.sum_background_data(lambda k, d: 1) """ bdata_list = [] for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) bdata = get_bdata_func(bkg_id, bkg) backscal = bkg.backscal if backscal is not None: backscal = self._check_scale(backscal, group=False) bdata = bdata / backscal areascal = bkg.get_areascal(group=False) if areascal is not None: bdata = bdata / areascal if bkg.exposure is not None: bdata = bdata / bkg.exposure bdata_list.append(bdata) nbkg = len(bdata_list) if nbkg == 0: # do not have a good id to use for the error message raise DataErr('nobkg', self.name) if nbkg == 1: bkgsum = bdata_list[0] else: bkgsum = sum(bdata_list) backscal = self.backscal if backscal is not None: backscal = self._check_scale(backscal, group=False) bkgsum = backscal * bkgsum areascal = self.areascal if areascal is not None: areascal = self._check_scale(areascal, group=False) bkgsum = areascal * bkgsum if self.exposure is not None: bkgsum = self.exposure * bkgsum return bkgsum / SherpaFloat(nbkg)
[docs] def get_dep(self, filter=False): # FIXME: Aneta says we need to group *before* subtracting, but that # won't work (I think) when backscal is an array # if not self.subtracted: # return self.counts # return self.counts - self.sum_background_data() dep = self.counts # The area scaling is not applied to the data, since it # should be being applied to the model via the *PHA # instrument model. Note however that the background # contribution does include the source AREASCAL value # (in the same way that the source BACKSCAL value # is used). # if self.subtracted: bkg = self.sum_background_data() if len(dep) != len(bkg): raise DataErr("subtractlength") dep = dep - bkg if bool_cast(filter): dep = self.apply_filter(dep) return dep
# The code used to re-define set_dep, but the only difference # from the parent class was that it set the counts attribute and # not the y attribute. These are now the same so it is no-longer # needed. # # There was the following comment in the code which we keep here: # # QUS: should this "invert" the areascaling to val # to get the stored values? # # Otherwise, when areascal /= 1 # y1 = d.get_dep() # d.set_dep(y1) # y2 = d.get_dep() # y1 != y2 # # Or perhaps it removes the areascal value in this case? # We already have this split in the API when background data # is available and is subtracted. #
[docs] def get_staterror(self, filter=False, staterrfunc=None): """Return the statistical error. The staterror column is used if defined, otherwise the function provided by the staterrfunc argument is used to calculate the values. Parameters ---------- filter : bool, optional Should the channel filter be applied to the return values? staterrfunc : function reference, optional The function to use to calculate the errors if the staterror field is None. The function takes one argument, the counts (after grouping and filtering), and returns an array of values which represents the one-sigma error for each element of the input array. This argument is designed to work with implementations of the sherpa.stats.Stat.calc_staterror method. Returns ------- staterror : array or None The statistical error. It will be grouped and, if filter=True, filtered. The contribution from any associated background components will be included if the background-subtraction flag is set. Notes ----- There is no scaling by the AREASCAL setting, but background values are scaled by their AREASCAL settings. It is not at all obvious that the current code is doing the right thing, or that this is the right approach. Examples -------- >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi', use_errors=True) >>> dy = pha.get_staterror() Ensure that there is no pre-defined statistical-error column and then use the Chi2DataVar statistic to calculate the errors: >>> from sherpa.stats import Chi2DataVar >>> stat = Chi2DataVar() >>> pha.staterror = None >>> dy = pha.get_staterror(staterrfunc=stat.calc_staterror) """ filter = bool_cast(filter) if filter: filterfunc = self.apply_filter else: filterfunc = self.apply_grouping # Use local definitions for routines that calculate the error # and scaling terms as they use the same logic for the source # and background datasets, including making it obvious that # the filtering and grouping comes from the "source" object # (i.e. the self object). # def get_error(dataobj): """Get the filtered and grouped error. The return value is the error array and then either None or the counts array that was used to calculate the errors. That is, the second term is only set when staterrfunc was called (this is only needed when the XSPEC variance routine is used but this is not checked for here). """ if dataobj.staterror is None: if staterrfunc is None: return None, None cnts = filterfunc(dataobj.counts) return staterrfunc(cnts), cnts return filterfunc(dataobj.staterror, groupfunc=dataobj._sum_sq), None def get_scale(dataobj): """Calculate the scaling value. Returns EXPOSURE * BACKSCAL * AREASCAL (1.0 is used as a replacement for any value is not set). """ scale = 1.0 if dataobj.backscal is not None: scale *= self._check_scale(dataobj.backscal, filter=filter) if dataobj.areascal is not None: scale *= self._check_scale(dataobj.areascal, filter=filter) if dataobj.exposure is not None: scale *= dataobj.exposure return scale staterr, src_counts = get_error(self) if staterr is None: return None if not self.subtracted: return staterr src_scale = get_scale(self) # For each background dataset we filter and/or group the # errors to match the source dataset, and then apply the # various scaling factors (areascal, backscal, and exposure # scaling) to correct them to match the source dataset. The # per-group variance is then stored, so that it can be # combined to create the overall background contribution, # which can then have the "source" side of the scaling values # applied to it before being added to the source error term. # # A complication is if the errors are being calculated with # the datavar method. That is, if the staterror attribute is # None and staterrfunc is using "XSPEC" errors - in this case # Chi2XspecVar.calc_staterror (technically it's really # anything that uses # sherpa.stats._statfcts.calc_chi2xspecvar_errors but do we # want to catch this as well as the more OO version). This # requires storing the scaling factors. # bkg_variances = [] bkg_counts = [] bkg_scales = [] for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) berr, bcounts = get_error(bkg) if berr is None: # We do not know how to generate an error, so # return None. An alternative would be to raise an # error, since we have an error from the source. # return None bscale = get_scale(bkg) # Apply the scaling factors to the variances # bkg_variances.append(1.0 * berr * berr / (bscale * bscale)) # These only need to be stored for the XSPEC variance case. # bkg_scales.append(bscale) bkg_counts.append(bcounts) # For the variance case we need to handle things carefully # when # # 1. we are calculating errors rather than them being # sent in (i.e. both src_counts and bkg_counts are # not None); # # 2. and we are using the XSPEC variance method (we check # only for the Chi2XspecVar method version and not the # low-level # sherpa.stats._statfcts.calc_chi2xspecvar_errors # case, as users are not expected to be using this). # # The reason for point 2 is that calc_chi2xspecvar_errors # returns an error of 1 when the number of counts is 0. This # is okay when there's no background subtraction, but once we # have to worry about the background bins it gets complicated. # For each bin we have src_i and bkg_i_j (for j=1 to N where N # is normally 1), and these refer to the number of counts that # are in this bin (i.e. that are then passed to staterrfct). # # If all the values for the bin are > 0 then we can combine # the variances (and so can ignore them here), otherwise we have # to recalculate the error terms. # if staterrfunc == Chi2XspecVar.calc_staterror and \ src_counts is not None and all(n is not None for n in bkg_counts): replace_xspecvar_values(src_counts, bkg_counts, staterr, bkg_variances, src_scale, bkg_scales) nbkg = len(bkg_variances) if nbkg == 1: bkgvar = bkg_variances[0] else: bkgvar = sum(bkg_variances) / (nbkg * nbkg) statvar = staterr * staterr + bkgvar * src_scale * src_scale return numpy.sqrt(statvar)
[docs] def get_syserror(self, filter=False): """Return any systematic error. Parameters ---------- filter : bool, optional Should the channel filter be applied to the return values? Returns ------- syserror : array or None The systematic error, if set. It will be grouped and, if filter=True, filtered. Notes ----- There is no scaling by the AREASCAL setting. """ syserr = self.syserror if bool_cast(filter): syserr = self.apply_filter(syserr, self._sum_sq) else: syserr = self.apply_grouping(syserr, self._sum_sq) return syserr
[docs] def get_x(self, filter=False, response_id=None): # We want the full channel grid with no grouping. # return self._from_channel(self.channel, group=False, response_id=response_id)
[docs] def get_xlabel(self): xlabel = self.units.capitalize() if self.units == 'energy': xlabel += ' (keV)' elif self.units == 'wavelength': xlabel += ' (Angstrom)' # elif self.units == 'channel' and self.grouped: # xlabel = 'Group Number' return xlabel
def _set_initial_quantity(self): arf, rmf = self.get_response() # Change analysis if ARFs equal or of higher resolution to # allow for high-res model evaluation. if arf is not None and rmf is None: if len(arf.energ_lo) == len(self.channel): self.units = 'energy' # Only change analysis if RMF matches the parent PHA dataset. if rmf is not None: if len(self.channel) != len(rmf.e_min): raise DataErr("incompatibleresp", rmf.name, self.name) self.units = 'energy' def _fix_y_units(self, val, filter=False, response_id=None): """Rescale the data to match the 'y' axis.""" if val is None: return val filter = bool_cast(filter) # make a copy of data for units manipulation val = numpy.array(val, dtype=SherpaFloat) if self.rate and self.exposure is not None: val /= self.exposure # TODO: It is not clear if the areascal should always be applied, # or only if self.rate is set (since it is being considered # a "correction" to the exposure time, but don't we want # to apply it in plots even if the Y axis is in counts?) # if self.areascal is not None: areascal = self._check_scale(self.areascal, filter=filter) val /= areascal # Should this be a user-callable method? # def get_bin_edges(): if self.units != 'channel': xlo, xhi = self._get_ebins(response_id, group=False) else: xlo, xhi = (self.channel, self.channel + 1.) if filter: # If we apply a filter, make sure that # ebins are ungrouped before applying # the filter. xlo = self.apply_filter(xlo, self._min) xhi = self.apply_filter(xhi, self._max) elif self.grouped: xlo = self.apply_grouping(xlo, self._min) xhi = self.apply_grouping(xhi, self._max) return xlo, xhi if self.grouped or self.rate: xlo, xhi = get_bin_edges() if self.units == 'wavelength': dx = hc / xlo - hc / xhi else: # Could be "energy" or "channel" dx = xhi - xlo val /= numpy.abs(dx) # The final step is to multiply by the X axis self.plot_fac # times. if self.plot_fac <= 0: return val # Get the bin edges so we can calculate the center of # each bin. This used to use # # xvals = self.get_x(response_id=response_id) # if filter: # xvals = self.apply_filter(xvals, self._middle) # elif self.grouped: # xvals = self.apply_grouping(xvals, self._middle) # # but this uses the center of each channel and then # averages them, which doesn't quite match the following. # xlo, xhi = get_bin_edges() xmid = (xlo + xhi) / 2 val *= numpy.power(xmid, self.plot_fac) return val
[docs] def get_y(self, filter=False, yfunc=None, response_id=None, use_evaluation_space=False): vallist = Data.get_y(self, yfunc=yfunc) filter = bool_cast(filter) if not isinstance(vallist, tuple): vallist = (vallist,) newvallist = [] for val in vallist: if filter: val = self.apply_filter(val) else: val = self.apply_grouping(val) val = self._fix_y_units(val, filter, response_id) newvallist.append(val) if len(vallist) == 1: vallist = newvallist[0] else: vallist = tuple(newvallist) return vallist
[docs] def get_yerr(self, filter=False, staterrfunc=None, response_id=None): filter = bool_cast(filter) err = self.get_error(filter, staterrfunc) return self._fix_y_units(err, filter, response_id)
[docs] def get_xerr(self, filter=False, response_id=None): """Returns an X "error". The error value for the independent axis is not well defined in Sherpa. .. versionchanged:: 4.16.1 The return value is now half the bin width instead of the full bin width and is now calculated correctly when the analysis is set to "wavelength". Parameters ---------- filter : bool, optional Should the values be filtered to the current notice range? response_id : int or None, optional What response should be used? Returns ------- xerr : ndarray The half-width of each bin (or group) in the current analysis units. """ if bool_cast(filter): # If we apply a filter, make sure that # ebins are ungrouped before applying # the filter. elo, ehi = self._get_ebins(response_id, group=False) elo = self.apply_filter(elo, self._min) ehi = self.apply_filter(ehi, self._max) else: try: elo, ehi = self._get_ebins(response_id=response_id) except DataErr: # What should we do here? This indicates that all bins # have been marked as bad (and grouping is present). # return numpy.asarray([]) # Issue #748 #1817 noted we should return the half-width # Issue #1985 notes that we need to support wavelength. # if self.units != "wavelength": return (ehi - elo) / 2 dlam = hc / elo - hc / ehi return dlam / 2
[docs] def get_ylabel(self): ylabel = 'Counts' if self.rate and self.exposure: ylabel += '/sec' if self.rate or self.grouped: if self.units == 'energy': ylabel += '/keV' elif self.units == 'wavelength': ylabel += '/Angstrom' elif self.units == 'channel': ylabel += '/channel' if self.plot_fac: from sherpa import plot latex = plot.backend.get_latex_for_string( f'^{self.plot_fac}') ylabel += f' X {self.units.capitalize()}{latex}' return ylabel
@staticmethod # Dummy function to tell apply_grouping to construct # an array of groups. def _make_groups(array): pass @staticmethod def _middle(array): array = numpy.asarray(array) return (array.min() + array.max()) / 2.0 @staticmethod def _min(array): array = numpy.asarray(array) return array.min() @staticmethod def _max(array): array = numpy.asarray(array) return array.max() @staticmethod def _sum_sq(array): return numpy.sqrt(numpy.sum(array * array))
[docs] def get_noticed_channels(self): """Return the noticed channels. Returns ------- channels : ndarray The noticed channels (this is independent of the analysis setting). """ chans = self.channel mask = self.get_mask() if mask is None: return chans # This is added to address issue #361 # # If there is a quality filter then the mask may be # smaller than the chans array. It is not clear if this # is the best location for this. If it is, then are there # other locations where this logic is needed? # if self.quality_filter is not None and \ self.quality_filter.size != mask.size: chans = chans[self.quality_filter] return chans[mask]
[docs] def get_mask(self): """Returns the (ungrouped) mask. Returns ------- mask : ndarray or None The mask, in channels, or None. """ groups = self.grouping if self.mask is False: return None if self.mask is True or not self.grouped: if self.quality_filter is not None: return self.quality_filter if numpy.iterable(self.mask): return self.mask return None if self.quality_filter is not None: groups = groups[self.quality_filter] return expand_grouped_mask(self.mask, groups)
[docs] def get_noticed_expr(self): """Returns the current set of noticed channels. The values returned are always in channels, no matter the current analysis setting. Returns ------- expr : str The noticed channel range as a string of comma-separated "low-high" values. As these are channel filters the low and high values are inclusive. If all channels have been filtered out then "No noticed channels" is returned. See Also -------- get_filter, get_noticed_channels """ chans = self.get_noticed_channels() if self.mask is False or len(chans) == 0: return 'No noticed channels' return create_expr(chans, format='%i')
[docs] def get_filter(self, group=True, format='%.12f', delim=':'): """Return the data filter as a string. The filter expression depends on the analysis setting. .. versionchanged:: 4.14.0 Prior to 4.14.0 the filter used the mid-point of the bin, not its low or high value. Parameters ---------- group : bool, optional Should the filter reflect the grouped data? format : str, optional The formatting of the numeric values (this is ignored for channel units, which uses ``format="%i"``). delim : str, optional The string used to mark the low-to-high range. Returns ------- expr : str The noticed channel range as a string of comma-separated ranges, where the low and high values are separated by the `delim` string. The units of the ranges are controlled by the analysis setting. If all bins have been filtered out then "No noticed bins" is returned. See Also -------- get_noticed_channels, get_noticed_expr Examples -------- For a Chandra non-grating dataset which has been grouped: >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.set_analysis('energy') >>> pha.notice(0.5, 7) >>> pha.get_filter(format='%.4f') '0.4672:9.8696' >>> pha.set_analysis('channel') >>> pha.get_filter() '33:676' The filter expression shows the first selected channel to the last one, and so is independent of whether the data is grouped or not: >>> pha.set_analysis('energy') >>> pha.get_filter(format='%.4f') '0.4672:9.8696' >>> pha.get_filter(group=False, format='%.4f') '0.4672:9.8696' Although the `group` argument does not change the output of get_filter, the selected range does depend on whether the data is grouped or not (unless the groups align with the filter edges): >>> pha.ungroup() >>> pha.notice() >>> pha.notice(0.5, 7) >>> pha.get_filter(format='%.3f') '0.496:7.008' >>> pha.group() >>> pha.get_filter(format='%.3f') '0.467:9.870' >>> pha.notice() >>> pha.notice(0.5, 6) >>> pha.ignore(2.1, 2.2) >>> pha.get_filter(format='%.2f', delim='-') '0.47-2.09,2.28-6.57' """ if self.mask is False: return 'No noticed bins' # We use get_noticed_channels since it includes quality # filtering, which the 'self.mask is True' check below does # not make. # chans = self.get_noticed_channels() # Special case all data has been masked. Should it # error out or return either '' or 'No noticed bins'? # if len(chans) == 0: # raise DataErr('notmask') # return 'No noticed bins' return '' # Special case all channels are selected. # if self.mask is True: elo, ehi = self._get_ebins(group=False, response_id=None) if self.units == 'energy': loval = elo[0] hival = ehi[-1] elif self.units == 'wavelength': loval = hc / ehi[-1] hival = hc / elo[0] else: # Assume channel loval = self.channel[0] hival = self.channel[-1] format = '%i' # Check for inversion if loval > hival: loval, hival = hival, loval return f"{format % loval}{delim}{format % hival}" mask = self.get_mask() # Just to check assert mask is not None # What channels are selected - note that it is possible to # get here and have all channels selected (ie all elements # of self.mask are True). # # We handle channel units differently to energy or wavelength, # as channels are treated as Data1D does whereas we want to # use Data1DInt for the units. # # We do everything ungrouped as there is no difference now # (as of Sherpa 4.14.0). # if self.units == 'channel': return create_expr(chans, mask=mask, format='%i', delim=delim) # Unfortunately we don't have a usable API for accessing the # energy or wavelength ranges directly. # xlo, xhi = self._get_ebins(group=False) if self.units == 'wavelength': xlo, xhi = hc / xhi, hc / xlo # Ensure the data is in ascending order for create_expr_integrated. # if xlo[0] > xlo[-1]: xlo = xlo[::-1] xhi = xhi[::-1] mask = mask[::-1] return create_expr_integrated(xlo[mask], xhi[mask], mask=mask, format=format, delim=delim)
[docs] def get_filter_expr(self): return (self.get_filter(format='%.4f', delim='-') + ' ' + self.get_xlabel())
[docs] def notice_response(self, notice_resp=True, noticed_chans=None): notice_resp = bool_cast(notice_resp) if notice_resp and noticed_chans is None: noticed_chans = self.get_noticed_channels() for resp_id in self.response_ids: arf, rmf = self.get_response(resp_id) _notice_resp(noticed_chans, arf, rmf)
[docs] def notice(self, lo=None, hi=None, ignore=False, bkg_id=None): """Notice or ignore the given range. .. versionchanged:: 4.14.0 PHA filtering has been improved to fix a number of corner cases which can result in the same filter now selecting one or two fewer channels that done in earlier versions of Sherpa. The ``lo`` and ``hi`` arguments are now restricted based on the units setting. Parameters ---------- lo, hi : number or None, optional The range to change. A value of None means the minimum or maximum permitted value. The units of ``lo`` and ``hi`` are set by the units field. ignore : bool, optional Set to True if the range should be ignored. The default is to notice the range. bkg_id : int or str, or sequence of int or str, optional If not None then apply the filter to the given background dataset or datasets, otherwise change the object and all its background datasets. See Also -------- get_filter, get_filter_expr, get_mask Notes ----- Calling notice with no arguments selects all points in the dataset (or, if ``ignore=True``, it will remove all points). If no channels have been ignored then a call to `notice` with ``ignore=False`` will select just the ``lo`` to ``hi`` range, and exclude any channels outside this range. If there has been a filter applied then the range ``lo`` to ``hi`` will be added to the range of noticed data (when ``ignore=False``). One consequence to the above is that if the first call to `notice` (with ``ignore=False``) selects a range outside the data set - such as a channel range of 2000 to 3000 when the valid range is 1 to 1024 - then all points will be ignored. When filtering with channel units then: - the ``lo`` and ``hi`` arguments, if set, must be integers, - and the ``lo`` and ``hi`` values are inclusive. For energy and wavelength filters: - the ``lo`` and ``hi`` arguments, if set, must be >= 0, - and the ``lo`` limit is inclusive but the ``hi`` limit is exclusive. Examples -------- So, for an ungrouped PHA file with 1024 channels: >>> from sherpa.astro.io import read_pha >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.ungroup() >>> pha.units = 'channel' >>> pha.get_filter() '1:1024' >>> pha.notice(20, 200) >>> pha.get_filter() '20:200' >>> pha.notice(300, 500) >>> pha.get_filter() '20:200,300:500' Calling `notice` with no arguments removes all the filters: >>> pha.notice() >>> pha.get_filter() '1:1024' Ignore the first 30 channels (this is the same as calling ```pha.ignore(hi=30)``: >>> pha.notice(hi=30, ignore=True) >>> pha.get_filter() '31:1024' When using wavelength or energy units the noticed (or ignored) range will not always match the requested range because each channel has a finite width in these spaces: >>> pha = read_pha(data_3c273 + '3c273.pi') >>> pha.grouped True >>> pha.get_analysis() 'energy' >>> pha.notice() >>> pha.notice(0.5, 7) >>> pha.get_filter(format='%.3f') '0.467:9.870' """ ignore = bool_cast(ignore) # This condition is checked for in the _data_space.filter call # at the end of the method, but it is easier to enforce it # here so we do not need to worry about possible type errors # when comparing string and number values. # for val, label in zip([lo, hi], ['lower', 'upper']): if isinstance(val, str): # match the error seen from other data classes here raise DataErr('typecheck', f'{label} bound') # Validate input # if lo is not None and hi is not None and lo > hi: raise DataErr('bad', 'hi argument', 'must be >= lo') # Ensure the limits are physically meaningful, that is # energy and wavelengths are >= 0. Technically it should be # > but using 0 is a nice value for a minimum. We do not # enforce limits if channels are being used because it's # not clear if channels can technically be negative. # # For channels we just require the numbers are integers. # if self.units == 'channel': if lo is not None and not float(lo).is_integer(): raise DataErr('bad', 'lo argument', 'must be an integer channel value') if hi is not None and not float(hi).is_integer(): raise DataErr('bad', 'hi argument', 'must be an integer channel value') else: if lo is not None and lo < 0: raise DataErr('bad', 'lo argument', 'must be >= 0') if hi is not None and hi < 0: raise DataErr('bad', 'hi argument', 'must be >= 0') # If any background IDs are actually given, then impose # the filter on those backgrounds *only*, and return. Do # *not* impose filter on data itself. # filter_background_only = False if bkg_id is not None: # As bkg_id can be # - int or str # - iterable of int or str # it's a bit awkward to identify what is meant. # if not isinstance(bkg_id, str) and numpy.iterable(bkg_id): bkg_ids = bkg_id else: bkg_ids = [bkg_id] filter_background_only = True else: bkg_ids = self.background_ids # Automatically impose data's filter on background data sets. # Units must agree for this to be meaningful, so temporarily # make data and background units match. # for bid in bkg_ids: bkg = self.get_background(bid) old_bkg_units = bkg.units try: bkg.units = self.units # If the background is all ignored then bkg.notice will # do nothing (other than display an INFO message). # bkg.notice(lo, hi, ignore) finally: bkg.units = old_bkg_units # If we're only supposed to filter backgrounds, return # if filter_background_only: return # Go on if we are also supposed to filter the source data if lo is None and hi is None: self.quality_filter = None self.notice_response(False) # elo and ehi will be in channel (units=channel) or energy # (units=energy or units=wavelength). # try: elo, ehi = self._get_ebins(group=self.grouped) except DataErr as de: info(f"Skipping dataset {self.name}: {de}") return emin = min(elo[[0, -1]]) emax = max(ehi[[0, -1]]) # Convert wavelength limits to energy if necessary. # if self.units == 'wavelength': lims = validate_wavelength_limits(lo, hi, emax) if lims is None: # No useful filter to apply return lo, hi = lims # safety check assert lo is None or hi is None or lo <= hi, (lo, hi, self.name) if self.units == 'channel' and hi is not None: # A channel range lo to hi is read as [lo, hi] rather than # [lo, hi), so we increase the upper limit by 1 to # work around this, as the filter call checks for < hi # and not <= hi. # hi += 1 self._data_space.filter.notice((None, lo), (hi, None), (elo, ehi), ignore=ignore, integrated=True)
[docs] def to_guess(self): elo, ehi = self._get_ebins(group=False) elo = self.apply_filter(elo, self._min) ehi = self.apply_filter(ehi, self._max) if self.units == "wavelength": lo = hc / ehi hi = hc / elo elo = lo ehi = hi cnt = self.get_dep(True) arf = self.get_specresp(filter=True) y = cnt / (ehi - elo) if self.exposure is not None: y /= self.exposure # photons/keV/sec or photons/Ang/sec # y = cnt/arf/self.exposure if arf is not None: y /= arf # photons/keV/cm^2/sec or photons/Ang/cm^2/sec return (y, elo, ehi)
[docs] def to_fit(self, staterrfunc=None): return (self.get_dep(True), self.get_staterror(True, staterrfunc), self.get_syserror(True))
[docs] def to_plot(self, yfunc=None, staterrfunc=None, response_id=None): return (self.apply_filter(self.get_x(response_id=response_id), self._middle), self.get_y(True, yfunc, response_id=response_id), self.get_yerr(True, staterrfunc, response_id=response_id), self.get_xerr(True, response_id=response_id), self.get_xlabel(), self.get_ylabel())
[docs] def group(self): """Group the data according to the data set's grouping scheme. This sets the grouping flag which means that the value of the grouping attribute will be used when accessing data values. This can be called even if the grouping attribute is empty. .. versionchanged:: 4.15.1 The grouping status of any background component is now also changed. See Also -------- ungroup """ self.grouped = True # Ensure any backgrounds are also grouped. # for bkg_id in self.background_ids: bkg = self.get_background(bkg_id) try: bkg.grouped = True except DataErr as exc: info(str(exc))
[docs] def ungroup(self): """Remove any data grouping. This un-sets the grouping flag which means that the grouping attribute will not be used when accessing data values. .. versionchanged:: 4.15.1 The grouping status of any background component is now also changed. See Also -------- group """ self.grouped = False # Ensure any backgrounds are also grouped. # for bkg_id in self.background_ids: # Unlike the group case we do not need to worry about this # failing. bkg = self.get_background(bkg_id) bkg.grouped = False
[docs] def subtract(self): """Subtract the background data. See Also -------- unsubtract """ self.subtracted = True
[docs] def unsubtract(self): """Remove background subtraction. See Also -------- subtract """ self.subtracted = False
[docs] class DataIMG(Data2D): '''Image data set This class builds on `sherpa.data.Data2D` to add support for region filters and the ability to describe the data in different coordinate systems, such as the "logical" coordinates (pixels of the image), the "physical" coordinates (e.g. detector coordinates), and the "world" coordinates (e.g. Ra/Dec on the sky). While this class can also be used for sparse data, much of the functionality added over its parent class will not be useful in that case. Parameters ---------- name : string name of this dataset x0 : array-like Independent coordinate values for the first dimension x1 : array-like Independent coordinate values for the second dimension y : array-like The values of the dependent observable. shape : tuple Shape of the data grid (optional). This is used return the data as an image e.g. for display, but it not needed for fitting and modelling. staterror : array-like the statistical error associated with the data syserror : array-like the systematic error associated with the data sky : `sherpa.astro.io.wcs.WCS` The optional WCS object that converts to the physical coordinate system. eqpos : `sherpa.astro.io.wcs.WCS` The optional WCS object that converts to the world coordinate system. coord : string The coordinate system of the data. This can be one of 'logical', 'physical', or 'world'. 'image' is an alias for 'logical' and 'wcs' is an alias for 'world'. header : dict The FITS header associated with the data to store meta data. Examples -------- In this example, we create an image data set, but do not specify the coordinate system. This means that the coordinates are expressed in the logical coordinates of the image, i.e. in pixels:: >>> from sherpa.astro.data import DataIMG >>> import numpy as np >>> x1, x0 = np.mgrid[20:30, 5:20] >>> datashape = x0.shape >>> y = np.sqrt((x0 - 10)**2 + (x1 - 31)**2) >>> x0 = x0.flatten() >>> x1 = x1.flatten() >>> y = y.flatten() >>> image = DataIMG("bimage", x0=x0, x1=x1, y=y, shape=datashape) Note that in this example, we end up with a "logical" coordinate system in ``image`` and no WCS system to convert it to anything else. On the other hand, in FITS standard terminology, the "logical" coordinate system is the "image", counting pixels starting at 1, while here the ``x0lo``` and ``x1lo`` actually start at 20 and 5, respectively. This behavior works for now, but might be revisited. ''' _extra_fields = ("sky", "eqpos", "coord") sky = None """The optional WCS object that converts to the physical coordinate system.""" eqpos = None """The optional WCS object that converts to the world coordinate system.""" @property def coord(self): """Return the coordinate setting. The attribute is one of 'logical', 'physical', or 'world'. Use `set_coord` to change the setting. """ return self._coord # We do not set this to @coord.setter as the attribute should be # changed with set_coord when outside the methods of this class. # def _set_coord(self, val): coord = str(val).strip().lower() if coord in ('logical', 'image'): coord = 'logical' elif coord == 'physical': self._check_physical_transform() elif coord in ('world', 'wcs'): self._check_world_transform() coord = 'world' else: raise DataErr('bad', 'coordinates', val) self._coord = coord def __init__(self, name, x0, x1, y, shape=None, staterror=None, syserror=None, sky=None, eqpos=None, coord='logical', header=None): self.name = name # needed by transform checks self.sky = sky self.eqpos = eqpos self._set_coord(coord) self.header = {} if header is None else header self._region = None # Store the original axes so we can always recreate the other # systems without having to worry about numerical differences # from switching between systems. This is an explicit decision # to go for repeatable behavior at the expense of using more # memory. See #1380 for more information. # self._orig_indep_axis = (self.coord, x0, x1) super().__init__(name, x0, x1, y, shape, staterror, syserror) def _clear_filter(self): if self._region is None: return # Remove the spatial filter, as if the axis has changed we know # it is going to be wrong. # self.notice2d() warning(f"Region filter has been removed from '{self.name}'") def _repr_html_(self): """Return a HTML (string) representation of the data """ return html_img(self) def __getstate__(self): state = self.__dict__.copy() # Function pointers to methods of the class # (of type 'instancemethod') are NOT picklable # remove them and restore later with a coord init # del state['_get_logical'] # del state['_get_physical'] # del state['_get_world'] # PyRegion objects (of type 'extension') are NOT picklable, yet. # preserve the region string and restore later with constructor # (but correctly handling the "None" case, to avoid #1214) # if state['_region'] is not None: state['_region'] = state['_region'].__str__() return state def __setstate__(self, state): # Populate the function pointers we deleted at pickle time with # no-ops. # self.__dict__['_get_logical']=(lambda : None) # self.__dict__['_get_physical']=(lambda : None) # self.__dict__['_get_world']=(lambda : None) # Unfortunately we can't re-create the original data used to # create the object, but we can fake it using the last-selected # coordinate system (which could lead to some issues for the # world system - see #1380 - but there's little we can do here). # The two-step process is to get around the behavior of the # NoNewAttributesAfterInit parent class. # if "_orig_indep_axis" not in state: state["_orig_indep_axis"] = None self.__dict__.update(state) if 'header' not in state: self.header = {} if self._orig_indep_axis is None: self._orig_indep_axis = (self.coord, self.x0, self.x1) # This may check the data is correct, based on the coord setting, # but is it worth it? It may catch a case when data is loaded into # a system without WCS support. # self._set_coord(state['_coord']) # This used to always use the _region setting to create a # Region filter, but it doesn't make sense if the filter is # _None, so we now skip this case (it lead to #1214). # if self._region is None: return if regstatus: # Should we allow '' to be sent to Region? if self._region == '': self._region = Region() else: self._region = Region(self._region) else: # If the region is "" then str() will produce '' so we want # double quotes about it. warning(f'Unable to restore region="{self._region}" as region module is not available.') self._region = None def _check_physical_transform(self): """Error out if the physical transform does not exist""" if self.sky is None: raise DataErr('nocoord', self.name, 'physical') def _check_world_transform(self): """Error out if the world transform does not exist""" if self.eqpos is None: raise DataErr('nocoord', self.name, 'world') def _logical_to_physical(self, x0=None, x1=None): """Convert logical axes to physical""" if x0 is None or x1 is None: x0, x1 = self.get_indep() self._check_shape() self._check_physical_transform() # logical -> physical x0, x1 = self.sky.apply(x0, x1) return (x0, x1) def _logical_to_world(self, x0=None, x1=None): """Convert logical axes to world""" if x0 is None or x1 is None: x0, x1 = self.get_indep() self._check_shape() self._check_world_transform() # logical -> physical if self.sky is not None: x0, x1 = self.sky.apply(x0, x1) # physical -> world x0, x1 = self.eqpos.apply(x0, x1) return (x0, x1) def _physical_to_logical(self, x0=None, x1=None): """Convert physical axes to logical""" if x0 is None or x1 is None: x0, x1 = self.get_indep() self._check_shape() self._check_physical_transform() # physical -> logical x0, x1 = self.sky.invert(x0, x1) return (x0, x1) def _physical_to_world(self, x0=None, x1=None): """Convert physical axes to world""" if x0 is None or x1 is None: x0, x1 = self.get_indep() self._check_shape() self._check_world_transform() # physical -> world x0, x1 = self.eqpos.apply(x0, x1) return (x0, x1) def _world_to_logical(self, x0=None, x1=None): """Convert world axes to logical""" if x0 is None or x1 is None: x0, x1 = self.get_indep() self._check_shape() self._check_world_transform() # world -> physical x0, x1 = self.eqpos.invert(x0, x1) # physical -> logical if self.sky is not None: x0, x1 = self.sky.invert(x0, x1) return (x0, x1) def _world_to_physical(self, x0=None, x1=None): """Convert world axes to physical""" if x0 is None or x1 is None: x0, x1 = self.get_indep() self._check_shape() self._check_world_transform() # world -> physical x0, x1 = self.eqpos.invert(x0, x1) return (x0, x1) # Convert from the _orig_indep_axis tuple (coord, x0, x1) to the # required data system (if it isn't already set). # def _get_coordsys(self, coord): if self.coord == coord: return self.get_indep() (base, x0, x1) = self._orig_indep_axis x0 = x0.copy() x1 = x1.copy() if base == coord: return (x0, x1) conv = getattr(self, f'_{base}_to_{coord}') return conv(x0, x1)
[docs] def get_logical(self): return self._get_coordsys("logical")
[docs] def get_physical(self): return self._get_coordsys("physical")
[docs] def get_world(self): return self._get_coordsys("world")
# For compatibility with old Sherpa keywords get_image = get_logical get_wcs = get_world
[docs] def set_coord(self, coord): """Change the `coord` attribute. .. versionchanged:: 4.14.1 The filter created by `notice2d` is now cleared when the coordinate system is changed. Parameters ---------- coord : {'logical', 'image', 'physical', 'world', 'wcs'} The coordinate system to use. Note that "image" is a synonym for "logical" and "wcs" is a synomyn for "world". """ coord = str(coord).strip().lower() good = ('logical', 'image', 'physical', 'world', 'wcs') if coord not in good: raise DataErr('badchoices', 'coordinates', coord, ", ".join(good)) if coord == 'wcs': coord = 'world' elif coord == 'image': coord = 'logical' if coord == self.coord: return func = getattr(self, f'get_{coord}') self.indep = func() self._set_coord(coord)
[docs] def get_filter_expr(self): if self._region is None: return "" return str(self._region)
get_filter = get_filter_expr
[docs] def notice2d(self, val=None, ignore=False): """Apply a 2D filter. Parameters ---------- val : str or None, optional The filter to apply. It can be a region string or a filename. ignore : bool, optional If set then the filter should be ignored, not noticed. """ ignore = bool_cast(ignore) # This was originally a bit-more complex, but it has been # simplified. # if val is None: self.mask = not ignore self._region = None return if not regstatus: raise ImportErr('importfailed', 'region', 'notice2d') # Crete the new region # val = str(val).strip() isfile = os.path.isfile(val) reg = Region(val, isfile) # Calculate the mask for this region as an "included" region. mask = reg.mask(self.get_x0(), self.get_x1()) mask = mask.astype(bool) # Apply the new mask to the existing mask. # if not ignore: if self.mask is True: self.mask = mask else: self.mask |= mask else: # Invert the response from region_mask mask = ~mask if self.mask is False: self.mask = mask else: self.mask &= mask # Create the new region shape. # if self._region is None: if ignore: # add an explicit "whole field" constructor to avoid # possible issues with stringification of multiple # ignores. reg = Region('field()').subtract(reg) self._region = reg elif ignore: self._region = self._region.subtract(reg) else: self._region = self._region.union(reg)
[docs] def get_bounding_mask(self): mask = self.mask if not numpy.iterable(self.mask): return mask, None # create bounding box around noticed image regions mask = numpy.array(self.mask).reshape(*self.shape) x0_i, x1_i = numpy.where(mask) x0_lo = x0_i.min() x0_hi = x0_i.max() x1_lo = x1_i.min() x1_hi = x1_i.max() mask = mask[x0_lo:x0_hi + 1, x1_lo:x1_hi + 1] shape = mask.shape mask = mask.ravel() return mask, shape
[docs] def get_img(self, yfunc=None): # FIXME add support for coords to image class -> DS9 self._check_shape() y_img = self.filter_region(self.get_dep(False)) y_img = y_img.reshape(*self.shape) if yfunc is None: return y_img m = self.eval_model_to_fit(yfunc) if numpy.iterable(self.mask): # if filtered, the calculated model must be padded up # to the data size to preserve img shape and WCS coord m = pad_bounding_box(m, self.mask) return (y_img, self.filter_region(m).reshape(*self.shape))
[docs] def get_axes(self): # FIXME: how to filter an axis when self.mask is size of self.y? self._check_shape() # dummy placeholders needed b/c img shape may not be square! axis0 = numpy.arange(self.shape[1], dtype=float) + 1. axis1 = numpy.arange(self.shape[0], dtype=float) + 1. if self.coord == 'logical': return (axis0, axis1) dummy0 = numpy.ones(axis0.size, dtype=float) dummy1 = numpy.ones(axis1.size, dtype=float) if self.coord == 'physical': axis0, dummy = self._logical_to_physical(axis0, dummy0) dummy, axis1 = self._logical_to_physical(dummy1, axis1) else: axis0, dummy = self._logical_to_world(axis0, dummy0) dummy, axis1 = self._logical_to_world(dummy1, axis1) return (axis0, axis1)
[docs] def get_x0label(self): "Return label for first dimension in 2-D view of independent axis/axes" if self.coord == 'physical': return 'x0 (pixels)' if self.coord == 'world': return 'RA (deg)' return 'x0'
[docs] def get_x1label(self): "Return label for second dimension in 2-D view of independent axis/axes." if self.coord == 'physical': return 'x1 (pixels)' if self.coord == 'world': return 'DEC (deg)' return 'x1'
[docs] def to_contour(self, yfunc=None): y = self.filter_region(self.get_dep(False)) if yfunc is not None: m = self.eval_model_to_fit(yfunc) if numpy.iterable(self.mask): # if filtered, the calculated model must be padded up # to the data size to preserve img shape and WCS coord m = self.filter_region(pad_bounding_box(m, self.mask)) y = (y, m) return (self.get_x0(), self.get_x1(), y, self.get_x0label(), self.get_x1label())
[docs] def filter_region(self, data): if data is None or not numpy.iterable(self.mask): return data # We do not want to change the data array hence the # explicit copy: this is done via astype to ensure # we convert to a type that can accept NaN values. # out = data.astype(dtype=SherpaFloat, casting="safe", copy=True) out[~self.mask] = numpy.nan return out
[docs] class DataIMGInt(DataIMG): '''Binned image data set This class is very similar to `sherpa.data.DataIMG`, but it stores integrated data, i.e. the values of the dependent observable are given between bin edges as opposed to at bin centers. This class builds on `sherpa.data.Data2DInt` to add support for region filters and the ability to describe the data in different coordinate systems, such as the "logical" coordinates (pixels of the image), the "physical" coordinates (e.g. detector coordinates), and the "world" coordinates (e.g. Ra/Dec on the sky). While this class can also be used for sparse data, much of the functionality added over its parent class will not be useful in that case. Parameters ---------- name : string name of this dataset x0lo : array-like Lower bounds of the bins in the first dimension of the independent coordinate x1lo : array-like Lower bound of the bins in the second dimension of the independent coordinate x0hi : array-like Upper bound of the bins in the first dimension of the independent coordinate x1hi : array-like Upper bound of the bins in the second dimension of the independent coordinate y : array-like The values of the dependent observable. shape : tuple Shape of the data grid (optional). This is used return the data as an image e.g. for display, but it not needed for fitting and modelling. staterror : array-like the statistical error associated with the data syserror : array-like the systematic error associated with the data sky : `sherpa.astro.io.wcs.WCS` The optional WCS object that converts to the physical coordinate system. eqpos : `sherpa.astro.io.wcs.WCS` The optional WCS object that converts to the world coordinate system. coord : string The coordinate system of the data. This can be one of 'logical', 'physical', or 'world'. 'image' is an alias for 'logical' and 'wcs' is an alias for 'world'. header : dict The FITS header associated with the data to store meta data. Example ------- In this example, we first generate a 1000 (x,y) points from a 2D Gaussian. This could be, e.g., photons observed from a star. In x direction, the center of the Gaussian is at 1.2 and in y direction at 0.0. We then use `~numpy.histogram2d` to bin the data into a 2D histogram. Note that numpy's convention for the ordering of the axis is not the same as Sherpa's. We want to use the bin edges when we create the `DataIMGInt` object, but numpy gives us just one set of edges. We therefore first pick either the upper or lower edges (``x0edges[:-1]`` or ``x0edges[1:]``) and then repeat them to match the number of bins in the flattened data array:: >>> import numpy as np >>> x = np.random.normal(size=1000, loc=1.2) >>> y = np.random.normal(size=1000) >>> xrange = np.arange(-2, 4.1, 0.5) >>> yrange = np.arange(-2, 2.1, 0.5) >>> hist, x0edges, x1edges = np.histogram2d(y, x, bins=(yrange, xrange)) >>> x0lo, x1lo = np.meshgrid(x0edges[:-1], x1edges[:-1]) >>> x0hi, x1hi = np.meshgrid(x0edges[1:], x1edges[1:]) >>> image = DataIMGInt("binned_image", ... x0lo=x0lo.flatten(), x1lo=x1lo.flatten(), ... x0hi=x0hi.flatten(), x1hi=x1hi.flatten(), ... y=hist.flatten(), shape=hist.shape) Note that in this example, we end up with a "logical" coordinate system in ``image`` and no WCS system to convert it to anything else. On the other hand, in FITS standard terminology, the "logical" coordinate system is the "image", counting pixels starting at 1, while here the ``x0lo``` and ``x1lo`` actually start at -2. This behavior works for now, but might be revisited. ''' def __init__(self, name, x0lo, x1lo, x0hi, x1hi, y, shape=None, staterror=None, syserror=None, sky=None, eqpos=None, coord='logical', header=None): self._region = None self.sky = sky self.eqpos = eqpos self._set_coord(coord) self.header = {} if header is None else header self.shape = shape Data.__init__(self, name, (x0lo, x1lo, x0hi, x1hi), y, staterror, syserror) def _init_data_space(self, filter, *data): ndata = len(data) if ndata != 4: raise DataErr("wrongaxiscount", self.name, 4, ndata) ds = IntegratedDataSpace2D(filter, *data) self._check_data_space(ds) return ds
[docs] def get_x0(self, filter=False): if self.size is None: return None indep = self._data_space.get(filter) return (indep.x0lo + indep.x0hi) / 2.0
[docs] def get_x1(self, filter=False): if self.size is None: return None indep = self._data_space.get(filter) return (indep.x1lo + indep.x1hi) / 2.0
@property def x0lo(self): """ Property kept for compatibility """ return self._data_space.x0lo @property def x0hi(self): """ Property kept for compatibility """ return self._data_space.x0hi @property def x1lo(self): """ Property kept for compatibility """ return self._data_space.x1lo @property def x1hi(self): """ Property kept for compatibility """ return self._data_space.x1hi
[docs] def get_logical(self): coord = self.coord x0lo, x1lo, x0hi, x1hi = self.get_indep() if coord == 'logical': return (x0lo, x1lo, x0hi, x1hi) x0lo = x0lo.copy() x1lo = x1lo.copy() convert = getattr(self, f'_{coord}_to_logical') x0lo, x1lo = convert(x0lo, x1lo) x0hi = x0hi.copy() x1hi = x1hi.copy() x0hi, x1hi = convert(x0hi, x1hi) return (x0lo, x1lo, x0hi, x1hi)
[docs] def get_physical(self): coord = self.coord x0lo, x1lo, x0hi, x1hi = self.get_indep() if coord == 'physical': return (x0lo, x1lo, x0hi, x1hi) x0lo = x0lo.copy() x1lo = x1lo.copy() convert = getattr(self, f'_{coord}_to_physical') x0lo, x1lo = convert(x0lo, x1lo) x0hi = x0hi.copy() x1hi = x1hi.copy() x0hi, x1hi = convert(x0hi, x1hi) return (x0lo, x1lo, x0hi, x1hi)
[docs] def get_world(self): coord = self.coord x0lo, x1lo, x0hi, x1hi = self.get_indep() if coord == 'world': return (x0lo, x1lo, x0hi, x1hi) x0lo = x0lo.copy() x1lo = x1lo.copy() convert = getattr(self, f'_{coord}_to_world') x0lo, x1lo = convert(x0lo, x1lo) x0hi = x0hi.copy() x1hi = x1hi.copy() x0hi, x1hi = convert(x0hi, x1hi) return (x0lo, x1lo, x0hi, x1hi)
[docs] def get_axes(self): # FIXME: how to filter an axis when self.mask is size of self.y? self._check_shape() # dummy placeholders needed b/c img shape may not be square! axis0lo = numpy.arange(self.shape[1], dtype=float) - 0.5 axis1lo = numpy.arange(self.shape[0], dtype=float) - 0.5 axis0hi = numpy.arange(self.shape[1], dtype=float) + 0.5 axis1hi = numpy.arange(self.shape[0], dtype=float) + 0.5 if self.coord == 'logical': return (axis0lo, axis1lo, axis0hi, axis1hi) dummy0 = numpy.ones(axis0lo.size, dtype=float) dummy1 = numpy.ones(axis1lo.size, dtype=float) if self.coord == 'physical': axis0lo, dummy = self._logical_to_physical(axis0lo, dummy0) axis0hi, dummy = self._logical_to_physical(axis0hi, dummy0) dummy, axis1lo = self._logical_to_physical(dummy1, axis1lo) dummy, axis1hi = self._logical_to_physical(dummy1, axis1hi) else: axis0lo, dummy = self._logical_to_world(axis0lo, dummy0) axis0hi, dummy = self._logical_to_world(axis0hi, dummy0) dummy, axis1lo = self._logical_to_world(dummy1, axis1lo) dummy, axis1hi = self._logical_to_world(dummy1, axis1hi) return (axis0lo, axis1lo, axis0hi, axis1hi)