Accepting request 1296400 from devel:languages:python:numeric

- Update to 4.17.1: * enhancements: + improved handling for OGIP products that do not follow the standards + Models can now be combined using arbitrary ufuncs + various updates to XSpec interface and support for XSpec 12.15.0 including the additional of 17 new models + added interface to support ArviZ for analysis of Bayesian models * infrastructure and testing: + updated tests to work with python 3.13 * bug fixes: + fixed a bug in the caching code that limited the cache size to 1 + update to use fake_pha for missions which don't use 1 as the first channel - Dropped patches: * sherpa-pr2188-np2docstrings.patch * sherpa-pr2207-mpl.patch * support-pytest-8.3.4.patch - Refresed sherpa-suse-libdir.patch - Add patch support-gcc-15.patch: * Specify function declarations correctly. OBS-URL: https://build.opensuse.org/request/show/1296400 OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-sherpa?expand=0&rev=26
Accepting request 1245410 from devel:languages:python:numeric
2025-07-30 09:45:52 +00:00 · 2025-02-12 20:39:12 +00:00 · 2025-02-12 14:56:16 +00:00 · 2025-01-29 15:16:47 +00:00 · 2025-01-29 05:29:04 +00:00 · 2024-12-08 10:37:24 +00:00
8 changed files with 0 additions and 1608 deletions
--- a/numpy2.patch
+++ b/numpy2.patch
@@ -1,297 +0,0 @@
 From 72028ffe7ce2566a8f1e88c2c06d79cf5f0be9c1 Mon Sep 17 00:00:00 2001
 From: Douglas Burke <dburke.gw@gmail.com>
 Date: Thu, 27 Jun 2024 12:42:52 -0400
 Subject: [PATCH 1/7] root: internal code cleanup
 The root-finding code is not documented well. This adds a small
 wrapper routine to avoid some replicated code, but could we
 just add this to transformed_quad_coef() instead - which is
 not explicitly marked as an external routine?
 Several comments have been added for potential future work.
 ---
 sherpa/utils/__init__.py        | 38 ++++++++++++++++++++++-----------
 sherpa/utils/tests/test_root.py |  5 +++++
 2 files changed, 30 insertions(+), 13 deletions(-)
 Index: sherpa-4.16.1/sherpa/utils/__init__.py
 ===================================================================
 --- sherpa-4.16.1.orig/sherpa/utils/__init__.py
 +++ sherpa-4.16.1/sherpa/utils/__init__.py
@@ -1480,7 +1480,7 @@ def create_expr_integrated(lovals, hival
     delim : str, optional
         The separator for a range.
     eps : number, optional
 -        The tolerance for comparing two numbers with sao_fcmp.
 +        This value is unused.
     Raises
     ------
@@ -3389,6 +3389,7 @@ def bisection(fcn, xa, xb, fa=None, fb=N
         return [[None, None], [[xa, fa], [xb, fb]], nfev[0]]
 +# Is this used at all?
 def quad_coef(x, f):
     """
     p( x ) = f( xc ) + A ( x - xc ) + B ( x - xc ) ( x - xb )
@@ -3461,6 +3462,11 @@ def transformed_quad_coef(x, f):
     xa, xb, xc = x[0], x[1], x[2]
     fa, fb, fc = f[0], f[1], f[2]
 +    # What happens if xb_xa or xc_xa are 0? That is, either
 +    #     xa == xb
 +    #     xc == xa
 +    # Is the assumption that this just never happen?
 +    #
     xc_xb = xc - xb
     fc_fb = fc - fb
     A = fc_fb / xc_xb
@@ -3472,6 +3478,21 @@ def transformed_quad_coef(x, f):
     return [B, C]
 +def _get_discriminant(xa, xb, xc, fa, fb, fc):
 +    """Wrap up code to transformed_quad_coef.
 +
 +    This is common code that could be added to transformed_quad_coef
 +    but is left out at the moment, to make it easier to look back
 +    at code changes. There is no description of the parameters as
 +    the existing code has none.
 +
 +    """
 +
 +    [B, C] = transformed_quad_coef([xa, xb, xc], [fa, fb, fc])
 +    discriminant = max(C * C - 4.0 * fc * B, 0.0)
 +    return B, C, discriminant
 +
 +
 def demuller(fcn, xa, xb, xc, fa=None, fb=None, fc=None, args=(),
              maxfev=32, tol=1.0e-6):
     """A root-finding algorithm using Muller's method.
@@ -3578,10 +3599,7 @@ def demuller(fcn, xa, xb, xc, fa=None, f
         while nfev[0] < maxfev:
 -            [B, C] = transformed_quad_coef([xa, xb, xc], [fa, fb, fc])
 -
 -            discriminant = max(C * C - 4.0 * fc * B, 0.0)
 -
 +            B, C, discriminant = _get_discriminant(xa, xb, xc, fa, fb, fc)
             if is_nan(B) or is_nan(C) or \
                     0.0 == C + mysgn(C) * np.sqrt(discriminant):
                 return [[None, None], [[None, None], [None, None]], nfev[0]]
@@ -3685,11 +3703,7 @@ def new_muller(fcn, xa, xb, fa=None, fb=
             if abs(fc) <= tol:
                 return [[xc, fc], [[xa, fa], [xb, fb]], nfev[0]]
 -            tran = transformed_quad_coef([xa, xb, xc], [fa, fb, fc])
 -            B = tran[0]
 -            C = tran[1]
 -
 -            discriminant = max(C * C - 4.0 * fc * B, 0.0)
 +            B, C, discriminant = _get_discriminant(xa, xb, xc, fa, fb, fc)
             xd = xc - 2.0 * fc / (C + mysgn(C) * np.sqrt(discriminant))
@@ -3827,11 +3841,9 @@ def apache_muller(fcn, xa, xb, fa=None,
         oldx = 1.0e128
         while nfev[0] < maxfev:
 -            tran = transformed_quad_coef([xa, xb, xc], [fa, fb, fc])
 -            B = tran[0]
 -            C = tran[1]
 -            discriminant = max(C * C - 4.0 * fc * B, 0.0)
 -            den = mysgn(C) * np.sqrt(discriminant)
 +
 +            B, C, discriminant = _get_discriminant(xa, xb, xc, fa, fb, fc)
 +            den = np.sign(C) * np.sqrt(discriminant)
             xplus = xc - 2.0 * fc / (C + den)
             if C != den:
                 xminus = xc - 2.0 * fc / (C - den)
@@ -4008,9 +4020,13 @@ def zeroin(fcn, xa, xb, fa=None, fb=None
             warning('%s: %s fa * fb < 0 is not met', __name__, fcn.__name__)
             return [[None, None], [[None, None], [None, None]], nfev[0]]
 +        # With NumPy 2.0 the casting rules changed, leading to some
 +        # behavioural changes in this code. The simplest fix was to
 +        # make sure DBL_EPSILON did not remain a np.float32 value.
 +        #
         xc = xa
         fc = fa
 -        DBL_EPSILON = np.finfo(np.float32).eps
 +        DBL_EPSILON = float(np.finfo(np.float32).eps)
         while nfev[0] < maxfev:
             prev_step = xb - xa
 Index: sherpa-4.16.1/sherpa/utils/tests/test_root.py
 ===================================================================
 --- sherpa-4.16.1.orig/sherpa/utils/tests/test_root.py
 +++ sherpa-4.16.1/sherpa/utils/tests/test_root.py
@@ -1,5 +1,6 @@
 #
 -#  Copyright (C) 2007, 2016, 2018, 2020, 2021  Smithsonian Astrophysical Observatory
 +#  Copyright (C) 2007, 2016, 2018, 2020, 2021, 2024
 +#  Smithsonian Astrophysical Observatory
 #
 #
 #  This program is free software; you can redistribute it and/or modify
@@ -27,7 +28,7 @@ from sherpa.utils import demuller, bisec
     zeroin
 -def sqr(x, *args):
 +def sqr(x):
     return x * x
@@ -177,9 +178,7 @@ def prob34(x, *args):
     return 1.0 / x - numpy.sin(x) + 1.0
 -def prob35(x, *args):
 -    return (x*x - 2.0) * x - 5.0
 -
 +# prob35 was the same as prob16
 def prob36(x, *args):
     return 1.0 / x - 1.0
@@ -288,7 +287,6 @@ def demuller2(fcn, xa, xb, fa=None, fb=N
                           (prob32, 0.1, 0.9),
                           (prob33, 2.8, 3.1),
                           (prob34, -1.3, -0.5),
 -                          (prob35, 2.0, 3.0),
                           (prob36, 0.5, 1.5),
                           (prob37, 0.5, 5.0),
                           (prob38, 1.0, 4.0),
 Index: sherpa-4.16.1/sherpa/estmethods/__init__.py
 ===================================================================
 --- sherpa-4.16.1.orig/sherpa/estmethods/__init__.py
 +++ sherpa-4.16.1/sherpa/estmethods/__init__.py
@@ -380,6 +380,11 @@ def covariance(pars, parmins, parmaxes,
         eflag = est_success
         ubound = diag[num]
         lbound = -diag[num]
 +
 +        # What happens when lbound or ubound is NaN? This is
 +        # presumably why the code is written as it is below (e.g. a
 +        # pass if the values can be added to pars[num]).
 +        #
         if pars[num] + ubound < parhardmaxes[num]:
             pass
         else:
@@ -1093,6 +1098,7 @@ def confidence(pars, parmins, parmaxes,
             print_status(myblog.blogger.info, verbose, status_prefix[dirn],
                          delta_zero, lock)
 +        # This should really set the error flag appropriately.
         error_flags.append(est_success)
         #
 Index: sherpa-4.16.1/sherpa/fit.py
 ===================================================================
 --- sherpa-4.16.1.orig/sherpa/fit.py
 +++ sherpa-4.16.1/sherpa/fit.py
@@ -277,7 +277,7 @@ class FitResults(NoNewAttributesAfterIni
         self.succeeded = results[0]
         self.parnames = tuple(p.fullname for p in fit.model.get_thawed_pars())
 -        self.parvals = tuple(results[1])
 +        self.parvals = tuple(float(r) for r in results[1])
         self.istatval = init_stat
         self.statval = results[2]
         self.dstatval = np.abs(results[2] - init_stat)
@@ -439,25 +439,28 @@ class ErrorEstResults(NoNewAttributesAft
         self.sigma = fit.estmethod.sigma
         self.percent = erf(self.sigma / sqrt(2.0)) * 100.0
         self.parnames = tuple(p.fullname for p in parlist if not p.frozen)
 -        self.parvals = tuple(p.val for p in parlist if not p.frozen)
 +        self.parvals = tuple(float(p.val) for p in parlist if not p.frozen)
         self.parmins = ()
         self.parmaxes = ()
 -        self.nfits = 0
         for i in range(len(parlist)):
             if (results[2][i] == est_hardmin or
 -                    results[2][i] == est_hardminmax):
 +                results[2][i] == est_hardminmax or
 +                results[0][i] is None  # It looks like confidence does not set the flag
 +                ):
                 self.parmins = self.parmins + (None,)
                 warning("hard minimum hit for parameter %s", self.parnames[i])
             else:
 -                self.parmins = self.parmins + (results[0][i],)
 +                self.parmins = self.parmins + (float(results[0][i]),)
             if (results[2][i] == est_hardmax or
 -                    results[2][i] == est_hardminmax):
 +                results[2][i] == est_hardminmax or
 +                results[1][i] is None  # It looks like confidence does not set the flag
 +                ):
                 self.parmaxes = self.parmaxes + (None,)
                 warning("hard maximum hit for parameter %s", self.parnames[i])
             else:
 -                self.parmaxes = self.parmaxes + (results[1][i],)
 +                self.parmaxes = self.parmaxes + (float(results[1][i]),)
         self.nfits = results[3]
         self.extra_output = results[4]
 Index: sherpa-4.16.1/sherpa/astro/tests/test_astro.py
 ===================================================================
 --- sherpa-4.16.1.orig/sherpa/astro/tests/test_astro.py
 +++ sherpa-4.16.1/sherpa/astro/tests/test_astro.py
@@ -206,7 +206,7 @@ def test_sourceandbg(parallel, run_threa
         assert fit_results.numpoints == 1330
         assert fit_results.dof == 1325
 -        assert covarerr[0] == approx(0.012097, rel=1e-3)
 +        assert covarerr[0] == approx(0.012097, rel=1.05e-3)
         assert covarerr[1] == approx(0, rel=1e-3)
         assert covarerr[2] == approx(0.000280678, rel=1e-3)
         assert covarerr[3] == approx(0.00990783, rel=1e-3)
 Index: sherpa-4.16.1/docs/developer/index.rst
 ===================================================================
 --- sherpa-4.16.1.orig/docs/developer/index.rst
 +++ sherpa-4.16.1/docs/developer/index.rst
@@ -100,6 +100,17 @@ files and shows exactly which lines were
 Run doctests locally
 --------------------
 +
 +.. note::
 +   The documentation tests are known to fail if NumPy 2.0 is installed
 +   because the representation of NumPy types such as ``np.float64``
 +   have changed, leading to errors like::
 +
 +       Expected:
 +           2.5264364698914e-06
 +       Got:
 +           np.float64(2.5264364698914e-06)
 +
 If `doctestplus <https://pypi.org/project/pytest-doctestplus/>` is installed
 (and it probably is because it's part of
 `sphinx-astropy <https://pypi.org/project/sphinx-astropy/>`,
 Index: sherpa-4.16.1/docs/install.rst
 ===================================================================
 --- sherpa-4.16.1.orig/docs/install.rst
 +++ sherpa-4.16.1/docs/install.rst
@@ -34,17 +34,14 @@ Requirements
 Sherpa has the following requirements:
 * Python 3.9 to 3.11
 -* NumPy (the exact lower limit has not been determined,
 -  1.21.0 or later will work, earlier version may work)
 +* NumPy (version 2.0 should work but there has been limited testing)
 * Linux or OS-X (patches to add Windows support are welcome)
 Sherpa can take advantage of the following Python packages
 if installed:
 * :term:`Astropy`: for reading and writing files in
 -  :term:`FITS` format. The minimum required version of astropy
 -  is version 1.3, although only versions 2 and higher are used in testing
 -  (version 3.2 is known to cause problems, but version 3.2.1 is okay).
 +  :term:`FITS` format.
 * :term:`matplotlib`: for visualisation of
   one-dimensional data or models, one- or two- dimensional
   error analysis, and the results of Monte-Carlo Markov Chain
--- a/sherpa-4.16.1.tar.gz
+++ b/sherpa-4.16.1.tar.gz
@@ -1,3 +0,0 @@
 version https://git-lfs.github.com/spec/v1
 oid sha256:ed4dfba237c4a6b60b4141e5619b53b25cb9e23be26cbcd7dbf00f8822d4669c
 size 13738921
--- a/sherpa-4.17.0.tar.gz
+++ b/sherpa-4.17.0.tar.gz
@@ -1,3 +0,0 @@
 version https://git-lfs.github.com/spec/v1
 oid sha256:f22f9dcb1b6326508d4142ea88931492972782fd292871ed942d1a212e70091a
 size 13985328
--- a/sherpa-pr2188-np2docstrings.patch
+++ b/sherpa-pr2188-np2docstrings.patch
@@ -1,248 +0,0 @@
 From 388a7b3c87b8068c572041aeb57d2136a583e83b Mon Sep 17 00:00:00 2001
 From: Douglas Burke <dburke.gw@gmail.com>
 Date: Tue, 29 Oct 2024 14:35:02 -0400
 Subject: [PATCH] TEST: address numpy 2 changes to output for docstring tests
 The str/repr output in NumPy 2 is much-more verbose and no-longet
 matches the behaviour of the associated Python types. This breaks
 a number of doctests, which had things like
    >>> (a == b).all()
    True
    >>> x.val
    3.4
 which now fails for "textual" reasons even though the values are
 the same. The simplest approach is just to print the values, so
 we now have
    >>> print((a == b).all())
    True
    >>> print(x.val)
    3.4
 It is not ideal, but is a simple fix that works with versions 1
 and 2 of NumPy.
 ---
 sherpa/astro/data.py       | 14 +++++++-------
 sherpa/data.py             |  8 ++++----
 sherpa/models/basic.py     | 24 ++++++++++++------------
 sherpa/models/model.py     |  2 +-
 sherpa/models/parameter.py | 10 +++++-----
 5 files changed, 29 insertions(+), 29 deletions(-)
 diff --git a/sherpa/astro/data.py b/sherpa/astro/data.py
 index 15f91fad72..87db02f617 100644
 --- a/sherpa/astro/data.py
 +++ b/sherpa/astro/data.py
@@ -2563,9 +2563,9 @@ def _get_ebins(self,
         >>> pha.ungroup()
         >>> pha.units = 'channel'
         >>> clo, chi = pha._get_ebins()
 -        >>> (clo == pha.channel).all()
 +        >>> print((clo == pha.channel).all())
         True
 -        >>> (chi == clo + 1).all()
 +        >>> print((chi == clo + 1).all())
         True
         >>> pha.units = 'energy'
@@ -2574,7 +2574,7 @@ def _get_ebins(self,
         True
         >>> elo[0:5]
         array([0.00146, 0.0146 , 0.0292 , 0.0438 , 0.0584 ])
 -        >>> (elo[1:] == ehi[:-1]).all()
 +        >>> print((elo[1:] == ehi[:-1]).all())
         True
         >>> pha.group()
@@ -2587,7 +2587,7 @@ def _get_ebins(self,
         >>> pha.units = 'wave'
         >>> wlo, whi = pha._get_ebins()
 -        >>> (wlo == glo).all()
 +        >>> print((wlo == glo).all())
         True
         """
@@ -2702,7 +2702,7 @@ def _get_indep(self,
         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()
 +        >>> print((elo[1:] == ehi[:-1]).all())
         True
         >>> pha.units = 'wave'
@@ -2711,7 +2711,7 @@ def _get_indep(self,
         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()
 +        >>> print((wlo[:-1] == whi[1:]).all())
         True
         """
@@ -3425,7 +3425,7 @@ def apply_grouping(self, data, groupfunc=np.sum):
         >>> v1 = pha.apply_grouping(dvals)
         >>> pha.notice(1.2, 4.5)
         >>> v2 = pha.apply_grouping(dvals)
 -        >>> np.all(v1 == v2)
 +        >>> print(np.all(v1 == v2))
         True
         """
 diff --git a/sherpa/data.py b/sherpa/data.py
 index 30fe9dbf0f..daf79d93e2 100644
 --- a/sherpa/data.py
 +++ b/sherpa/data.py
@@ -2042,8 +2042,8 @@ def notice(self,
         >>> x = np.arange(0.4, 2.6, 0.2)
         >>> y = np.ones_like(x)
         >>> d = Data1D('example', x, y)
 -        >>> d.x[0], d.x[-1]
 -        (0.4, 2.4000000000000004)
 +        >>> print(d.x[0], d.x[-1])
 +        0.4 2.4000000000000004
         >>> d.notice()
         >>> d.get_filter(format='%.1f')
         '0.4:2.4'
@@ -2326,8 +2326,8 @@ def notice(self,
         >>> xlo, xhi = edges[:-1], edges[1:]
         >>> y = np.ones_like(xlo)
         >>> d = Data1DInt('example', xlo, xhi, y)
 -        >>> d.xlo[0], d.xhi[-1]
 -        (0.4, 2.400000000000001)
 +        >>> print(d.xlo[0], d.xhi[-1])
 +        0.4 2.400000000000001
         >>> d.get_filter(format='%.1f')
         '0.4:2.4'
         >>> d.notice(0.8, 1.9)
 diff --git a/sherpa/models/basic.py b/sherpa/models/basic.py
 index c1061e033a..d5280eda0f 100644
 --- a/sherpa/models/basic.py
 +++ b/sherpa/models/basic.py
@@ -539,14 +539,14 @@ class Gauss1D(RegriddableModel1D):
     >>> m1.pos, m2.pos = 10, 10
     >>> m1.ampl, m2.ampl = 10, 10
     >>> m1.fwhm, m2.fwhm = 5, 5
 -    >>> m1(10)
 +    >>> print(m1(10))
     10.0
 -    >>> m2(10)
 +    >>> print(m2(10))
     1.8788745573993026
     >>> m1.fwhm, m2.fwhm = 1, 1
 -    >>> m1(10)
 +    >>> print(m1(10))
     10.0
 -    >>> m2(10)
 +    >>> print(m2(10))
     9.394372786996513
     The normalised version will sum to the amplitude when given
@@ -558,9 +558,9 @@ class Gauss1D(RegriddableModel1D):
     >>> m1.fwhm, m2.fwhm = 12.2, 12.2
     >>> grid = np.arange(-90, 110, 0.01)
     >>> glo, ghi = grid[:-1], grid[1:]
 -    >>> m1(glo, ghi).sum()
 +    >>> print(m1(glo, ghi).sum())
     129.86497637060958
 -    >>> m2(glo, ghi).sum()
 +    >>> print(m2(glo, ghi).sum())
     10.000000000000002
     """
@@ -1923,12 +1923,12 @@ class TableModel(ArithmeticModel):
     >>> d.staterror = [.2, .2, .2, .2, .2]
     >>> tm1 = TableModel('tabmodel')
     >>> tm1.load(None, [.6, .2, 1.1, .2, .5])
 -    >>> tm1.ampl.val
 +    >>> print(tm1.ampl.val)
     1.0
     >>> tm1.fold(d)
     >>> fit1 = Fit(d, tm1)
     >>> res1 = fit1.fit()
 -    >>> tm1.ampl.val
 +    >>> print(tm1.ampl.val)
     1.9894736842102083
     In this case the `fold` method is necessary, to ensure that the
@@ -1941,11 +1941,11 @@ class TableModel(ArithmeticModel):
     >>> tm2 = TableModel('tabmodel')
     >>> tm2.load(None, [.6, .2, 1.1, .2, .5])
     >>> tm2.fold(d)
 -    >>> tm2.ampl.val
 +    >>> print(tm2.ampl.val)
     1.0
     >>> fit2 = Fit(d, tm2)
     >>> res2 = fit2.fit()
 -    >>> tm2.ampl.val
 +    >>> print(tm2.ampl.val)
     1.9866666666663104
     The masking also holds if the notice or ignore method has been
@@ -1957,11 +1957,11 @@ class TableModel(ArithmeticModel):
     >>> tm3 = TableModel('tabmodel')
     >>> tm3.load(None, [.6, .2, 1.1, .2, .5])
     >>> tm3.fold(d)
 -    >>> tm3.ampl.val
 +    >>> print(tm3.ampl.val)
     1.0
     >>> fit3 = Fit(d, tm3)
     >>> res = fit3.fit()
 -    >>> tm3.ampl.val
 +    >>> print(tm3.ampl.val)
     1.9866666666663104
     """
 diff --git a/sherpa/models/model.py b/sherpa/models/model.py
 index 5b89181d28..020ef7a30f 100644
 --- a/sherpa/models/model.py
 +++ b/sherpa/models/model.py
@@ -152,7 +152,7 @@
     >>> m1.ampl = 10
     >>> m2.ampl = 12
 -    >>> m3.ampl.val
 +    >>> print(m3.ampl.val)
     11.0
     >>> m3.ampl.link
     <BinaryOpParameter '(gauss1d.ampl + gmdl.ampl) / 2'>
 diff --git a/sherpa/models/parameter.py b/sherpa/models/parameter.py
 index 3e1ee5215a..09af7c9bcf 100644
 --- a/sherpa/models/parameter.py
 +++ b/sherpa/models/parameter.py
@@ -60,7 +60,7 @@
 The `val` attribute is used to retrieve or change the parameter value:
 -    >>> p.val
 +    >>> print(p.val)
     2.0
     >>> p.val = 3
@@ -72,9 +72,9 @@
 for these are the 32-bit floating point maximum value, and it's
 negative:
 -    >>> p.max
 +    >>> print(p.max)
     3.4028234663852886e+38
 -    >>> p.min
 +    >>> print(p.min)
     -3.4028234663852886e+38
 Setting a value outside this range will raise a
@@ -578,9 +578,9 @@ def _set_link(self, link: Optional[Parameter]) -> None:
 >>> a = Parameter("mdl", "a", 2)
 >>> b = Parameter("mdl", "b", 1)
 >>> b.link = 10 - a
 ->>> a.val
 +>>> print(a.val)
 2.0
 ->>> b.val
 +>>> print(b.val)
 8.0
 """)
--- a/sherpa-pr2207-mpl.patch
+++ b/sherpa-pr2207-mpl.patch
@@ -1,86 +0,0 @@
 From 5e3d13df1c09134fe5b1cbb27bfc3559ef0641a0 Mon Sep 17 00:00:00 2001
 From: Doug Burke <dburke@cfa.harvard.edu>
 Date: Fri, 3 Jan 2025 16:46:36 -0500
 Subject: [PATCH] TESTS: avoid matplotlib version differences (fix #2206)
 The matlpotlib get_linestyles method can return
  [(x, None)]
 where x = None, 0.0, or np.float64(0.0) depending on matplotlib
 version (and probably NumPy).
 Since we don't really care too much here, just check the return
 has length of 1, the first element has length of 2, and the
 second element is None.
 ---
 sherpa/astro/ui/tests/test_astro_ui_plot.py | 35 +++++++++++----------
 1 file changed, 18 insertions(+), 17 deletions(-)
 diff --git a/sherpa/astro/ui/tests/test_astro_ui_plot.py b/sherpa/astro/ui/tests/test_astro_ui_plot.py
 index a6b7c07aa8..5a41d3ce4e 100644
 --- a/sherpa/astro/ui/tests/test_astro_ui_plot.py
 +++ b/sherpa/astro/ui/tests/test_astro_ui_plot.py
@@ -1,5 +1,5 @@
 #
 -#  Copyright (C) 2019 - 2024
 +#  Copyright (C) 2019 - 2025
 #  Smithsonian Astrophysical Observatory
 #
 #
@@ -32,6 +32,7 @@
 import copy
 import logging
 +
 import numpy as np
 import pytest
@@ -1843,15 +1844,15 @@ def test_pha1_plot_data_options(caplog, clean_astro_ui, requires_pylab,
     assert len(coll.get_segments()) == 42
 -    # The return value depends on matplotlib version (>= 3.3
 -    # returns something). What has changed? Maybe this should
 -    # not be tested?
 +    # The linestyles return value has changed with matplotib
 +    # and NumPy version, so just check the second element
 +    # and not the first (which can be None, 0.0, np.float64(0)).
     #
 -    expected = [(None, None)]
 -    if matplotlib.__version__ >= '3.3.0':
 -        expected = [(0.0, None)]
 -
 -    assert coll.get_linestyles() == expected
 +    #
 +    ls = coll.get_linestyles()
 +    assert len(ls) == 1
 +    assert len(ls[0]) == 2
 +    assert ls[0][1] is None
     # looks like the color has been converted to individual channels
     # - e.g. floating-point values for R, G, B, and alpha.
@@ -2041,15 +2042,15 @@ def test_pha1_plot_fit_options(clean_astro_ui, requires_pylab, basic_pha1):
     assert len(coll.get_segments()) == 42
 -    # The return value depends on matplotlib version (>= 3.3
 -    # returns something). What has changed? Maybe this should
 -    # not be tested?
 +    # The linestyles return value has changed with matplotib
 +    # and NumPy version, so just check the second element
 +    # and not the first (which can be None, 0.0, np.float64(0)).
     #
 -    expected = [(None, None)]
 -    if matplotlib.__version__ >= '3.3.0':
 -        expected = [(0.0, None)]
 -
 -    assert coll.get_linestyles() == expected
 +    #
 +    ls = coll.get_linestyles()
 +    assert len(ls) == 1
 +    assert len(ls[0]) == 2
 +    assert ls[0][1] is None
     # looks like the color has been converted to individual channels
     # - e.g. floating-point values for R, G, B, and alpha.
--- a/sherpa-test-data-4.16.1.tar.gz
+++ b/sherpa-test-data-4.16.1.tar.gz
@@ -1,3 +0,0 @@
 version https://git-lfs.github.com/spec/v1
 oid sha256:c61443bf3530e42322cec52887cc2f170c2d5d9aaba2822e107da2626c269a1c
 size 137775313
--- a/sherpa-test-data-4.17.0.tar.gz
+++ b/sherpa-test-data-4.17.0.tar.gz
@@ -1,3 +0,0 @@
 version https://git-lfs.github.com/spec/v1
 oid sha256:215d3465c7a9d1b4307ec5eeb1f489a1251de9cee71e75e7162f491ad6037126
 size 137775257
--- a/support-pytest-8.3.4.patch
+++ b/support-pytest-8.3.4.patch
@@ -1,965 +0,0 @@
 From 0fafdd4522408e15561ae79a502b096317bb0d9e Mon Sep 17 00:00:00 2001
 From: Douglas Burke <dburke.gw@gmail.com>
 Date: Fri, 6 Dec 2024 12:06:50 -0500
 Subject: [PATCH 1/2] TEST: handle pytest 8.3.4 change in bool tests with
 approx
 Fix #2202
 In pytest 8.3.3 and earlier
    >>> import numpy as np; import pytest
    >>> pytest.__version__
    '8.3.3'
    >>> np.ones(2, dtype=bool) == pytest.approx([True, True])
    True
 However, pytest 8.3.4 now causes this to fail
    >>> import numpy as np; import pytest
    >>> pytest.__version__
    '8.3.4'
    >>> np.ones(2, dtype=bool) == pytest.approx([True, True])
    False
 This is because of "pytest.approx considers boolean numeric types"
 https://github.com/pytest-dev/pytest/issues/9353
 The solution is to make the "expected" value be a ndarray, and so
    >>> np.ones(2, dtype=bool) == pytest.approx(np.asarray([True, True]))
    True
 holds with both pytest 8.3.3 and 8.3.4.
 So this commit basically goes through and updates the tests so that
 we use a ndarray for boolean arrays.
 An alternative would be to change from
    assert got == pytest.approx(expected)
 to something like
    assert np.all(got == expected)
 However, the error message when the array lengths are different or
 an element is different are a **lot less** useful, and the change would
 be even-more invasive than this change.
 ---
 sherpa/astro/io/tests/test_io_pha.py          |   2 +-
 sherpa/astro/tests/test_astro_data.py         |  43 +++---
 sherpa/astro/tests/test_astro_data2.py        | 131 +++++++++++-------
 sherpa/astro/tests/test_astro_instrument.py   |   3 +-
 sherpa/astro/tests/test_astro_plot.py         |   6 +-
 sherpa/astro/ui/tests/test_astro_ui.py        |   3 +-
 sherpa/astro/ui/tests/test_astro_ui_unit.py   |   9 +-
 sherpa/astro/ui/tests/test_filtering.py       |   8 +-
 .../utils/tests/test_astro_utils_unit.py      |   8 +-
 sherpa/tests/test_data.py                     |   6 +-
 sherpa/ui/tests/test_session.py               |   3 +-
 sherpa/ui/tests/test_ui_unit.py               |  10 +-
 sherpa/utils/tests/test_utils.py              |  10 +-
 13 files changed, 146 insertions(+), 96 deletions(-)
 diff --git a/sherpa/astro/io/tests/test_io_pha.py b/sherpa/astro/io/tests/test_io_pha.py
 index 84dcd4a98c..ceab1609a4 100644
 --- a/sherpa/astro/io/tests/test_io_pha.py
 +++ b/sherpa/astro/io/tests/test_io_pha.py
@@ -1504,7 +1504,7 @@ def test_write_pha_with_bad_quality(tmp_path):
     counts = chans * 2
     group = [1, -1, -1, 1, -1, 1, 1, 1, -1]
     quality = [0, 5, 0, 0, 0, 0, 0, 2, 2]
 -    qfilt = [True, False] + [True] * 5 + [False] * 2
 +    qfilt = np.asarray([True, False] + [True] * 5 + [False] * 2)
     pha0 = DataPHA("qual", chans, counts, grouping=group,
                    quality=quality)
 diff --git a/sherpa/astro/tests/test_astro_data.py b/sherpa/astro/tests/test_astro_data.py
 index b4ab684ce5..9ca381763c 100644
 --- a/sherpa/astro/tests/test_astro_data.py
 +++ b/sherpa/astro/tests/test_astro_data.py
@@ -2367,7 +2367,7 @@ def test_pha_check_limit(ignore, lo, hi, evals):
     pha.units = 'energy'
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 10)
 +    assert pha.get_mask() == pytest.approx(np.ones(10, dtype=bool))
     func = pha.ignore if ignore else pha.notice
     func(lo, hi)
@@ -2380,7 +2380,7 @@ def test_pha_check_limit(ignore, lo, hi, evals):
         vin = True
     c1, c2, c3 = evals
 -    expected = [vout] * c1 + [vin] * c2 + [vout] * c3
 +    expected = np.asarray([vout] * c1 + [vin] * c2 + [vout] * c3)
     assert pha.mask == pytest.approx(pha.get_mask())
     assert pha.mask == pytest.approx(expected)
@@ -2449,7 +2449,7 @@ def test_pha_check_limit_channel(ignore, lo, hi, evals):
     pha.units = 'channel'
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 10)
 +    assert pha.get_mask() == pytest.approx(np.ones(10, dtype=bool))
     func = pha.ignore if ignore else pha.notice
     func(lo, hi)
@@ -2462,7 +2462,7 @@ def test_pha_check_limit_channel(ignore, lo, hi, evals):
         vin = True
     c1, c2, c3 = evals
 -    expected = [vout] * c1 + [vin] * c2 + [vout] * c3
 +    expected = np.asarray([vout] * c1 + [vin] * c2 + [vout] * c3)
     assert pha.mask == pytest.approx(pha.get_mask())
     assert pha.mask == pytest.approx(expected)
@@ -2672,7 +2672,9 @@ def test_is_mask_reset_pha(caplog):
     # Pick a value somewhere within the independent axis
     assert data.mask is True
     data.ignore(None, 2)
 -    assert data.mask == pytest.approx([False, False, True])
 +
 +    mask = np.asarray([False, False, True])
 +    assert data.mask == pytest.approx(mask)
     # Change the independent axis, but to something of the same
     # length.
@@ -2683,7 +2685,7 @@ def test_is_mask_reset_pha(caplog):
     assert len(caplog.records) == 0
     # The mask has *not* been cleared
 -    assert data.mask == pytest.approx([False, False, True])
 +    assert data.mask == pytest.approx(mask)
 def test_is_mask_reset_pha_channel(caplog):
@@ -2703,7 +2705,8 @@ def test_is_mask_reset_pha_channel(caplog):
     assert len(caplog.records) == 0
     # The mask has not been cleared
 -    assert data.mask == pytest.approx([False, False, True])
 +    mask = np.asarray([False, False, True])
 +    assert data.mask == pytest.approx(mask)
 @requires_region
@@ -3376,9 +3379,11 @@ def test_pha_notice_bkg_id_none():
     pha.notice(lo=2, bkg_id=None)  # the default
 -    assert pha.mask == pytest.approx([False, True])
 -    assert b1.mask == pytest.approx([False, True])
 -    assert bup.mask == pytest.approx([False, True])
 +    bfilt = np.asarray([False, True])
 +
 +    assert pha.mask == pytest.approx(bfilt)
 +    assert b1.mask == pytest.approx(bfilt)
 +    assert bup.mask == pytest.approx(bfilt)
 @pytest.mark.parametrize("bkg_id", [1, "up"])
@@ -3394,13 +3399,15 @@ def test_pha_notice_bkg_id_scalar(bkg_id):
     pha.notice(lo=2, bkg_id=bkg_id)
 +    bfilt = np.asarray([False, True])
 +
     assert pha.mask is True
     if bkg_id == 1:
 -        assert b1.mask == pytest.approx([False, True])
 +        assert b1.mask == pytest.approx(bfilt)
         assert bup.mask is True
     else:
         assert b1.mask is True
 -        assert bup.mask == pytest.approx([False, True])
 +        assert bup.mask == pytest.approx(bfilt)
 def test_pha_notice_bkg_id_array_all():
@@ -3415,9 +3422,11 @@ def test_pha_notice_bkg_id_array_all():
     pha.notice(lo=2, bkg_id=["up", 1])
 +    bfilt = np.asarray([False, True])
 +
     assert pha.mask is True
 -    assert b1.mask == pytest.approx([False, True])
 -    assert bup.mask == pytest.approx([False, True])
 +    assert b1.mask == pytest.approx(bfilt)
 +    assert bup.mask == pytest.approx(bfilt)
 @pytest.mark.parametrize("bkg_id", [1, "up"])
@@ -3433,13 +3442,15 @@ def test_pha_notice_bkg_id_array_subset(bkg_id):
     pha.notice(lo=2, bkg_id=[bkg_id])
 +    bfilt = np.asarray([False, True])
 +
     assert pha.mask is True
     if bkg_id == 1:
 -        assert b1.mask == pytest.approx([False, True])
 +        assert b1.mask == pytest.approx(bfilt)
         assert bup.mask is True
     else:
         assert b1.mask is True
 -        assert bup.mask == pytest.approx([False, True])
 +        assert bup.mask == pytest.approx(bfilt)
 def get_img_spatial_mask():
 diff --git a/sherpa/astro/tests/test_astro_data2.py b/sherpa/astro/tests/test_astro_data2.py
 index b5eaf3b598..d6bdb19e49 100644
 --- a/sherpa/astro/tests/test_astro_data2.py
 +++ b/sherpa/astro/tests/test_astro_data2.py
@@ -97,7 +97,7 @@ def test_get_mask_is_none():
     """
     pha = DataPHA('name', [1, 2, 3], [1, 1, 1])
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 3)
 +    assert pha.get_mask() == pytest.approx(np.asarray([True] * 3))
 def test_get_mask_is_none_when_all_filtered():
@@ -497,7 +497,7 @@ def test_288_a():
     # with bools the use of approx is okay (it can tell the
     # difference between 0 and 1, aka False and True).
     #
 -    assert pha.mask == pytest.approx([True, False, True])
 +    assert pha.mask == pytest.approx(np.asarray([True, False, True]))
 def test_288_a_energy():
@@ -525,7 +525,7 @@ def test_288_a_energy():
     # with bools the use of approx is okay (it can tell the
     # difference between 0 and 1, aka False and True).
     #
 -    assert pha.mask == pytest.approx([True, False, True])
 +    assert pha.mask == pytest.approx(np.asarray([True, False, True]))
 def test_288_b():
@@ -565,7 +565,7 @@ def test_288_b_energy():
     assert pha.mask is True
     pha.ignore(3.1, 4)
 -    assert pha.mask == pytest.approx([True, False, True])
 +    assert pha.mask == pytest.approx(np.asarray([True, False, True]))
 @requires_group
@@ -621,8 +621,8 @@ def test_416_a():
     #     grouped)
     #   - pha.get_mask() always returns the ungrouped mask
     #
 -    mask_ungrouped = [False] * 3 + [True] * 3 + [False] * 4
 -    mask_grouped = [False] * 3 + [True] * 2 + [False] * 4
 +    mask_ungrouped = np.asarray([False] * 3 + [True] * 3 + [False] * 4)
 +    mask_grouped = np.asarray([False] * 3 + [True] * 2 + [False] * 4)
     assert pha.mask == pytest.approx(mask_ungrouped)
     assert pha.get_mask() == pytest.approx(mask_ungrouped)
@@ -685,7 +685,7 @@ def test_416_c():
     pha.notice(4.5, 6.5)
     # this should be ~pha.mask
 -    tabstops = [True] * 3 + [False] * 3 + [True] * 4
 +    tabstops = np.asarray([True] * 3 + [False] * 3 + [True] * 4)
     assert ~pha.mask == pytest.approx(tabstops)
     assert pha.grouping is None
@@ -1688,10 +1688,13 @@ def test_pha_quality_all_bad_basic_checks():
     """
 +    all4 = np.ones(4, dtype=bool)
 +    none4 = np.zeros(4, dtype=bool)
 +
     pha = DataPHA("q", [1, 2, 3, 4], [9, 0, 1, 64])
     fvals = [12, 2, 7, 8]
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 4)
 +    assert pha.get_mask() == pytest.approx(all4)
     assert pha.get_filter() == "1:4"
     assert pha.get_x() == pytest.approx([1, 2, 3, 4])
     assert pha.apply_filter(fvals) == pytest.approx(fvals)
@@ -1699,15 +1702,15 @@ def test_pha_quality_all_bad_basic_checks():
     pha.quality = [2, 2, 2, 5]
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 4)
 +    assert pha.get_mask() == pytest.approx(all4)
     assert pha.get_filter() == "1:4"
     assert pha.get_x() == pytest.approx([1, 2, 3, 4])
     assert pha.apply_filter(fvals) == pytest.approx(fvals)
     assert pha.apply_grouping(fvals) == pytest.approx(fvals)
     pha.ignore_bad()
 -    assert pha.mask == pytest.approx([False] * 4)
 -    assert pha.get_mask() == pytest.approx([False] * 4)
 +    assert pha.mask == pytest.approx(none4)
 +    assert pha.get_mask() == pytest.approx(none4)
     assert pha.get_filter() == ""
     assert pha.get_x() == pytest.approx([1, 2, 3, 4])
     assert pha.apply_filter(fvals) == pytest.approx([])
@@ -1742,7 +1745,7 @@ def test_pha_quality_change_mask(make_quality_pha):
     pha.ignore_bad()
     assert pha.mask is True
     pha.mask = [1, 1, 0]
 -    assert pha.mask == pytest.approx([True, True, False])
 +    assert pha.mask == pytest.approx(np.asarray([True, True, False]))
 def test_pha_quality_change_mask_ungrouped(make_quality_pha):
@@ -1753,7 +1756,8 @@ def test_pha_quality_change_mask_ungrouped(make_quality_pha):
     pha.ungroup()
     assert pha.mask is True
     pha.mask = [1, 1, 0, 1, 1, 0, 0, 1, 1]
 -    assert pha.mask == pytest.approx([True, True, False, True, True, False, False, True, True])
 +    mask = np.asarray([True, True, False, True, True, False, False, True, True])
 +    assert pha.mask == pytest.approx(mask)
 def test_pha_quality_change_mask_fullsize(make_quality_pha):
@@ -1972,23 +1976,26 @@ def test_pha_quality_ignore_bad_clear_filter(make_quality_pha):
     pha = make_quality_pha
 +    mask0 = np.ones(9, dtype=bool)
     assert pha.get_filter() == "1:9"
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 9)
 +    assert pha.get_mask() == pytest.approx(mask0)
     assert pha.quality_filter is None
     # channels 2,3 and 7-9 are "bad"
     pha.ignore(hi=3)
 +    mask = np.asarray([False] + [True] * 3)
 +    mask_full = np.asarray([False] * 4 + [True] * 5)
     assert pha.get_filter() == "5:9"
 -    assert pha.mask == pytest.approx([False] + [True] * 3)
 -    assert pha.get_mask() == pytest.approx([False] * 4 + [True] * 5)
 +    assert pha.mask == pytest.approx(mask)
 +    assert pha.get_mask() == pytest.approx(mask_full)
     assert pha.quality_filter is None
     # This resets the previous filters
     pha.ignore_bad()
 -    qflags = [True] * 1 + [False] * 2 + [True] * 3 + [False] * 3
 +    qflags = np.asarray([True] * 1 + [False] * 2 + [True] * 3 + [False] * 3)
     assert pha.get_filter() == "1:9"
     assert pha.mask is True
     assert pha.get_mask() == pytest.approx(qflags)
@@ -1996,16 +2003,19 @@ def test_pha_quality_ignore_bad_clear_filter(make_quality_pha):
     pha.ignore(hi=3)
 +    mask2 = np.asarray([False] + [True] * 2)
 +    mask2_full = np.asarray([False] * 2 + [True] * 2)
 +
     assert pha.get_filter() == "5:6"
 -    assert pha.mask == pytest.approx([False] + [True] * 2)
 -    assert pha.get_mask() == pytest.approx([False] * 2 + [True] * 2)
 +    assert pha.mask == pytest.approx(mask2)
 +    assert pha.get_mask() == pytest.approx(mask2_full)
     assert pha.quality_filter == pytest.approx(qflags)
     pha.ignore(lo=2, hi=4)
     assert pha.get_filter() == "5:6"
 -    assert pha.mask == pytest.approx([False] + [True] * 2)
 -    assert pha.get_mask() == pytest.approx([False] * 2 + [True] * 2)
 +    assert pha.mask == pytest.approx(mask2)
 +    assert pha.get_mask() == pytest.approx(mask2_full)
     assert pha.quality_filter == pytest.approx(qflags)
     # This removes the quality filter!
@@ -2013,7 +2023,7 @@ def test_pha_quality_ignore_bad_clear_filter(make_quality_pha):
     assert pha.get_filter() == "1:9"
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 9)
 +    assert pha.get_mask() == pytest.approx(mask0)
     assert pha.quality_filter is None
@@ -2443,7 +2453,8 @@ def test_pha_change_quality_values(caplog):
     pha.ignore_bad()
     assert len(caplog.records) == 0
 -    assert pha.quality_filter == pytest.approx([True] * 5 + [False] * 2)
 +    qfilt = np.asarray([True] * 5 + [False] * 2)
 +    assert pha.quality_filter == pytest.approx(qfilt)
     assert pha.get_dep(filter=True) == pytest.approx([6])
     assert pha.get_filter() == '1:7'
@@ -2456,7 +2467,7 @@ def test_pha_change_quality_values(caplog):
     assert pha.quality == pytest.approx([0, 0, 0, 2, 2, 0, 0])
     # Should quality filter be reset?
 -    assert pha.quality_filter == pytest.approx([True] * 5 + [False] * 2)
 +    assert pha.quality_filter == pytest.approx(qfilt)
     assert pha.get_dep(filter=True) == pytest.approx([4, 2])
     assert pha.get_filter() == '1:7'
@@ -2499,14 +2510,14 @@ def test_pha_group_ignore_bad_then_filter(caplog):
     assert len(caplog.records) == 0
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 7)
 +    assert pha.get_mask() == pytest.approx(np.ones(7, dtype=bool))
     assert pha.get_filter() == '1:7'
     assert pha.quality_filter is None
     pha.ignore_bad()
     assert len(caplog.records) == 0
 -    qual_mask = [True] * 2 + [False] + [True] * 3 + [False]
 +    qual_mask = np.asarray([True] * 2 + [False] + [True] * 3 + [False])
     assert pha.mask is True
     assert pha.get_mask() == pytest.approx(qual_mask)
     assert pha.get_filter() == '1:7'
@@ -2518,8 +2529,10 @@ def test_pha_group_ignore_bad_then_filter(caplog):
     pha.ignore(4, 5)
     assert len(caplog.records) == 0
 -    assert pha.mask == pytest.approx([True, False, True])
 -    assert pha.get_mask() == pytest.approx([True] * 2 + [False] * 2 + [True])
 +    mask = np.asarray([True, False, True])
 +    mask_full = np.asarray([True] * 2 + [False] * 2 + [True])
 +    assert pha.mask == pytest.approx(mask)
 +    assert pha.get_mask() == pytest.approx(mask_full)
     assert pha.get_filter() == '1:2,6'
     assert pha.quality_filter == pytest.approx(qual_mask)
     assert pha.quality == pytest.approx([0, 0, 2, 0, 0, 0, 2])
@@ -2536,7 +2549,7 @@ def test_pha_group_ignore_bad_then_group(caplog):
     pha.ignore_bad()
     assert len(caplog.records) == 0
 -    qual_mask = [True] * 2 + [False] + [True] * 3 + [False]
 +    qual_mask = np.asarray([True] * 2 + [False] + [True] * 3 + [False])
     assert pha.mask is True
     assert pha.get_mask() == pytest.approx(qual_mask)
     assert pha.quality_filter == pytest.approx(qual_mask)
@@ -2562,7 +2575,7 @@ def test_pha_group_ignore_bad_then_group(caplog):
     pha.ignore_bad()
     assert len(caplog.records) == 0
 -    qual_mask = [True] + [False] + [True] * 5
 +    qual_mask = np.asarray([True] + [False] + [True] * 5)
     assert pha.mask is True
     assert pha.get_mask() == pytest.approx(qual_mask)
     assert pha.get_filter() == '1:7'
@@ -2584,7 +2597,7 @@ def test_pha_filter_ignore_bad_filter(caplog):
     pha.ignore(lo=4, hi=4)
     assert len(caplog.records) == 0
 -    data_mask = [True] * 3 + [False] + [True] * 3
 +    data_mask = np.asarray([True] * 3 + [False] + [True] * 3)
     assert pha.mask == pytest.approx(data_mask)
     assert pha.get_mask() == pytest.approx(data_mask)
     assert pha.get_filter() == '1:3,5:7'
@@ -2596,7 +2609,8 @@ def test_pha_filter_ignore_bad_filter(caplog):
     pha.group_counts(5)
     assert len(caplog.records) == 0
 -    assert pha.mask == pytest.approx([True] * 2 + [False] + [True] * 3)
 +    data_mask2 = np.asarray([True] * 2 + [False] + [True] * 3)
 +    assert pha.mask == pytest.approx(data_mask2)
     assert pha.get_mask() == pytest.approx(data_mask)
     assert pha.get_filter() == '1:3,5:7'
     assert pha.quality_filter is None
@@ -2612,7 +2626,7 @@ def test_pha_filter_ignore_bad_filter(caplog):
     assert r.levelname == "WARNING"
     assert r.getMessage() == "filtering grouped data with quality flags, previous filters deleted"
 -    new_mask = [True] * 2 + [False] + [True] * 4
 +    new_mask = np.asarray([True] * 2 + [False] + [True] * 4)
     assert pha.mask is True
     assert pha.get_mask() == pytest.approx(new_mask)
     assert pha.get_filter() == '1:7'
@@ -2624,8 +2638,10 @@ def test_pha_filter_ignore_bad_filter(caplog):
     pha.ignore(lo=2, hi=2)
     assert len(caplog.records) == 1
 -    assert pha.mask == pytest.approx([False] + [True] * 4)
 -    assert pha.get_mask() == pytest.approx([False] * 2 + [True] * 4)
 +    mask3 = np.asarray([False] + [True] * 4)
 +    mask3_full = np.asarray([False] * 2 + [True] * 4)
 +    assert pha.mask == pytest.approx(mask3)
 +    assert pha.get_mask() == pytest.approx(mask3_full)
     assert pha.get_filter() == '4:7'
     assert pha.quality_filter == pytest.approx(new_mask)
     assert pha.quality == pytest.approx([0, 0, 2, 0, 0, 0, 0])
@@ -2732,7 +2748,7 @@ def test_pha_ignore_bad_group_quality(caplog):
     assert pha.get_filter(format="%.1f") == "3.0:7.0"
     assert pha.get_noticed_channels() == pytest.approx(np.arange(3, 7))
 -    omask = [False] * 2 + [True] * 4 + [False] * 4
 +    omask = np.asarray([False] * 2 + [True] * 4 + [False] * 4)
     assert pha.mask == pytest.approx(omask)
     assert pha.get_mask() == pytest.approx(omask)
@@ -2749,7 +2765,8 @@ def test_pha_ignore_bad_group_quality(caplog):
     assert pha.get_dep(filter=False) == pytest.approx(y)
     assert pha.get_dep(filter=True) == pytest.approx([3, 1])
 -    assert pha.mask == pytest.approx([False] * 2 + [True] * 2 + [False] * 4)
 +    mask = np.asarray([False] * 2 + [True] * 2 + [False] * 4)
 +    assert pha.mask == pytest.approx(mask)
     assert pha.get_mask() == pytest.approx(omask)
     grouping = [0, 0, 1, -1, -1,  1, 0, 0, 0, 0]
@@ -2788,7 +2805,7 @@ def test_pha_ignore_bad_group_quality(caplog):
     # However, get_mask reflects the quality filter, so is all True
     # except for the 6th element.
     #
 -    single_bad = [True] * 5 + [False] + [True] * 4
 +    single_bad = np.asarray([True] * 5 + [False] + [True] * 4)
     assert pha.get_mask() == pytest.approx(single_bad)
     # What about the quality fields?
@@ -2862,7 +2879,7 @@ def test_pha_ignore_bad_quality(groupit, caplog):
     assert pha.get_dep(filter=False) == pytest.approx(y)
     assert pha.get_dep(filter=True) == pytest.approx(y[2:6])
 -    mask = [False] * 2 + [True] * 4 + [False] * 4
 +    mask = np.asarray([False] * 2 + [True] * 4 + [False] * 4)
     assert pha.mask == pytest.approx(mask)
     assert pha.get_mask() == pytest.approx(mask)
@@ -2900,7 +2917,7 @@ def test_pha_ignore_bad_quality(groupit, caplog):
     # The mask changed (the channel=6 value is now filtered out).
     #
 -    mask2 = [False] * 2 + [True] * 3 + [False] * 5
 +    mask2 = np.asarray([False] * 2 + [True] * 3 + [False] * 5)
     assert pha.mask == pytest.approx(mask2)
     assert pha.get_mask() == pytest.approx(mask2)
@@ -3098,7 +3115,7 @@ def test_grouped_pha_mask(make_grouped_pha):
 def test_grouped_pha_get_mask(make_grouped_pha):
     """What is the default get_mask value?"""
     pha = make_grouped_pha
 -    assert pha.get_mask() == pytest.approx([True] * 4 + [False])
 +    assert pha.get_mask() == pytest.approx(np.asarray([True] * 4 + [False]))
 def test_quality_pha_mask(make_quality_pha):
@@ -3115,11 +3132,13 @@ def test_quality_pha_mask(make_quality_pha):
 def test_quality_pha_get_mask(make_quality_pha):
     """What is the default get_mask value?"""
     pha = make_quality_pha
 -    assert pha.get_mask() == pytest.approx([True] * 9)
 +    m1 = np.ones(9, dtype=bool)
 +    assert pha.get_mask() == pytest.approx(m1)
     pha.ignore_bad()
     # This is a regression test
 -    assert pha.get_mask() == pytest.approx([True] + [False] * 2 + [True] * 3 + [False] * 3)
 +    m2 = np.asarray([True] + [False] * 2 + [True] * 3 + [False] * 3)
 +    assert pha.get_mask() == pytest.approx(m2)
 @pytest.mark.parametrize("field,expected",
@@ -3576,8 +3595,10 @@ def test_pha_quality_filtered_apply_filter_invalid_size(vals, make_grouped_pha):
     pha.ignore(hi=1)
     # safety check to make sure we've excluded points
 -    assert pha.mask == pytest.approx([False, True])
 -    assert pha.get_mask() == pytest.approx([False, False, False, True])
 +    m1 = np.asarray([False, True])
 +    m2 = np.asarray([False, False, False, True])
 +    assert pha.mask == pytest.approx(m1)
 +    assert pha.get_mask() == pytest.approx(m2)
     with pytest.raises(DataErr,
                        match="^size mismatch between filtered data and array: 1 vs [28]$"):
@@ -4193,7 +4214,7 @@ def test_rmf_simple_filter_check(startchan, na, nb, nc):
     assert rmf.apply_rmf(mvals) == pytest.approx(mvals)
     selected = rmf.notice([startchan, startchan + 1, startchan + 2])
 -    expected = [False] * na + [True] * nb + [False] * nc
 +    expected = np.asarray([False] * na + [True] * nb + [False] * nc)
     assert selected == pytest.approx(expected)
     # Drop everything but the selected values.
@@ -4295,7 +4316,7 @@ def test_rmf_offset_check_square(offset, caplog):
     nchans = [offset + 2]
     selected = rmf.notice(nchans)
 -    assert selected == pytest.approx([False, True, True, True])
 +    assert selected == pytest.approx(np.asarray([False, True, True, True]))
     expected2 = [0.0 * 0.2 + 0.6 * 0.4,
                  0.4 * 0.4 + 0.5 * 0.2,
@@ -4387,11 +4408,13 @@ def test_rmf_offset_check_rectangular(offset):
     # - drop the last channel
     nchans2 = offset + np.arange(0, 9)
 +    mask = np.asarray([True] * 19 + [False])
 +
     selected2 = rmf.notice(nchans2)
 -    assert selected2 == pytest.approx([True] * 19 + [False])
 +    assert selected2 == pytest.approx(mask)
     selected2 = rmf.notice(offset + np.arange(0, 9))
 -    assert selected2 == pytest.approx([True] * 19 + [False])
 +    assert selected2 == pytest.approx(mask)
     expected2 = mvals[selected2] @ full_matrix[selected2, :]
     got2 = rmf.apply_rmf(mvals[selected2])
@@ -4414,8 +4437,11 @@ def test_rmf_offset_check_rectangular(offset):
     #
     nchans3 = offset + np.asarray([4, 5, 6])
 +    mask3 = np.asarray([True, False] * 2 + [True] * 10 +
 +                       [False] * 4 + [True, False])
 +
     selected3 = rmf.notice(nchans3)
 -    assert selected3 == pytest.approx([True, False] * 2 + [True] * 10 + [False] * 4 + [True, False])
 +    assert selected3 == pytest.approx(mask3)
     # It is not clear what the RMF application does here.
     #
@@ -5970,7 +5996,7 @@ def test_group_xxx_tabtops_not_ndarray(asarray):
     assert pha.get_y() == pytest.approx([2, 3, 4.5, 6])
     assert pha.mask is True
 -    assert pha.get_mask() == pytest.approx([True] * 5)
 +    assert pha.get_mask() == pytest.approx(np.ones(5, dtype=bool))
 @requires_group
@@ -6002,7 +6028,8 @@ def test_group_xxx_tabstops_already_grouped():
     assert pha.get_y(filter=True) == pytest.approx([12, 5.5, 4])
     tstops = ~pha.mask
 -    assert tstops == pytest.approx([False, True, False, False, True])
 +    mask = np.asarray([False, True, False, False, True])
 +    assert tstops == pytest.approx(mask)
     # Apply the mask as the tabStops (after inversion) where
     # len(tstops) < nchannel but does match the number of groups.
 diff --git a/sherpa/astro/tests/test_astro_instrument.py b/sherpa/astro/tests/test_astro_instrument.py
 index b466e3fe5a..584fe371c6 100644
 --- a/sherpa/astro/tests/test_astro_instrument.py
 +++ b/sherpa/astro/tests/test_astro_instrument.py
@@ -712,7 +712,8 @@ def test_rmfmodelpha_delta_no_ebounds(analysis, caplog):
     assert len(caplog.records) == 0
     if analysis == "energy":
 -        assert pha.mask == pytest.approx([False, True, True, True, False])
 +        expected = np.asarray([False, True, True, True, False])
 +        assert pha.mask == pytest.approx(expected)
     else:
         assert not pha.mask.any()
 diff --git a/sherpa/astro/tests/test_astro_plot.py b/sherpa/astro/tests/test_astro_plot.py
 index 3f978728dc..a7df4cb1ee 100644
 --- a/sherpa/astro/tests/test_astro_plot.py
 +++ b/sherpa/astro/tests/test_astro_plot.py
@@ -227,8 +227,8 @@ def test_sourceplot_filtered(caplog, make_basic_datapha):
     # The filtering should probably be this, but let's test the
     # current behavior:
     #
 -    # expected = [False] * 2 + [True] * 6 + [False] * 2
 -    expected = [False] * 3 + [True] * 5 + [False] * 2
 +    # expected = np.asarray([False] * 2 + [True] * 6 + [False] * 2)
 +    expected = np.asarray([False] * 3 + [True] * 5 + [False] * 2)
     assert sp.mask == pytest.approx(expected)
     assert len(caplog.records) == 0
     check_sourceplot_energy(sp)
@@ -365,7 +365,7 @@ def test_sourceplot_wavelength_filtered(caplog, make_basic_datapha):
     # Given the filtering for energy didn't quite match, DJB is
     # slightly surprised this works.
     #
 -    expected = [False] * 2 + [True] * 6 + [False] * 2
 +    expected = np.asarray([False] * 2 + [True] * 6 + [False] * 2)
     assert sp.mask == pytest.approx(expected)
     assert len(caplog.records) == 0
     check_sourceplot_wavelength(sp)
 diff --git a/sherpa/astro/ui/tests/test_astro_ui.py b/sherpa/astro/ui/tests/test_astro_ui.py
 index 26acfb96b0..f6872a3497 100644
 --- a/sherpa/astro/ui/tests/test_astro_ui.py
 +++ b/sherpa/astro/ui/tests/test_astro_ui.py
@@ -1962,7 +1962,8 @@ def check_pha_offset(specresp, matrix, energ_lo, energ_hi,
     ui.notice(0.5, 0.8)
     data = ui.get_data()
 -    assert data.mask == pytest.approx([False] * 3 + [True] * 3 + [False] * 3)
 +    expected = np.asarray([False] * 3 + [True] * 3 + [False] * 3)
 +    assert data.mask == pytest.approx(expected)
     assert ui.get_filter(format="%.2f", delim="-") == "0.45-0.85"
 diff --git a/sherpa/astro/ui/tests/test_astro_ui_unit.py b/sherpa/astro/ui/tests/test_astro_ui_unit.py
 index 5a321fa733..a5c23dca45 100644
 --- a/sherpa/astro/ui/tests/test_astro_ui_unit.py
 +++ b/sherpa/astro/ui/tests/test_astro_ui_unit.py
@@ -2957,7 +2957,7 @@ def test_pha_set_filter_unmasked(simple_pha):
     expected = [True, True, False, True, False]
     ui.set_filter(expected)
 -    assert data.mask == pytest.approx(expected)
 +    assert data.mask == pytest.approx(np.asarray(expected))
 def test_pha_set_filter_unmasked_wrong(simple_pha):
@@ -2973,11 +2973,14 @@ def test_pha_set_filter_masked(simple_pha):
     data = ui.get_data()
 +    mask = np.asarray([True, False, False, False, True])
 +    mask2 = np.asarray([True, False, True, False, True])
 +
     ui.ignore(4, 8)
 -    assert data.mask == pytest.approx([True, False, False, False, True])
 +    assert data.mask == pytest.approx(mask)
     ui.set_filter(np.asarray([True, False, True, False, False]))
 -    assert data.mask == pytest.approx([True, False, True, False, True])
 +    assert data.mask == pytest.approx(mask2)
 def test_pha_set_filter_masked_wrong(simple_pha):
 diff --git a/sherpa/astro/ui/tests/test_filtering.py b/sherpa/astro/ui/tests/test_filtering.py
 index 6e7c331456..eeb41cad3f 100644
 --- a/sherpa/astro/ui/tests/test_filtering.py
 +++ b/sherpa/astro/ui/tests/test_filtering.py
@@ -709,7 +709,7 @@ def test_ignore_bad_simple_comparison(caplog):
         d = s.get_data(idval)
         assert d.mask is True
 -        assert d.get_mask() == pytest.approx([True] * 5)
 +        assert d.get_mask() == pytest.approx(np.ones(5, dtype=bool))
     assert len(caplog.records) == 2
     s.ignore_bad(1)
@@ -733,7 +733,7 @@ def test_ignore_bad_simple_comparison(caplog):
     assert s.get_filter(1) == "1,3:5"
     assert s.get_filter(2) == "1:5"
 -    mask = [True] + [False] + [True] * 3
 +    mask = np.asarray([True] + [False] + [True] * 3)
     d1 = s.get_data(1)
     assert d1.mask == pytest.approx(mask)
     assert d1.get_mask() == pytest.approx(mask)
@@ -762,12 +762,12 @@ def test_ignore_bad_simple_comparison(caplog):
     assert s.get_filter(1) == "1,3"
     assert s.get_filter(2) == "1:3"
 -    mask = [True] + [False] + [True] + [False] * 2
 +    mask = np.asarray([True] + [False] + [True] + [False] * 2)
     d1 = s.get_data(1)
     assert d1.mask == pytest.approx(mask)
     assert d1.get_mask() == pytest.approx(mask)
 -    mask = [True] * 2 + [False] * 2
 +    mask = np.asarray([True] * 2 + [False] * 2)
     d2 = s.get_data(2)
     assert d2.mask == pytest.approx(mask)
     assert d2.get_mask() == pytest.approx(mask)
 diff --git a/sherpa/astro/utils/tests/test_astro_utils_unit.py b/sherpa/astro/utils/tests/test_astro_utils_unit.py
 index 27551d5d22..de8b5e4bf5 100644
 --- a/sherpa/astro/utils/tests/test_astro_utils_unit.py
 +++ b/sherpa/astro/utils/tests/test_astro_utils_unit.py
@@ -194,7 +194,7 @@ def test_filter_resp_basics(offset, selected, ng, fch, nch, mat, msk):
     assert f_chan2 == pytest.approx(np.asarray(fch) + delta)
     assert n_chan2 == pytest.approx(nch)
     assert matrix2 == pytest.approx(mat)
 -    assert mask2 == pytest.approx(msk)
 +    assert mask2 == pytest.approx(np.asarray(msk))
 @pytest.mark.parametrize("lo, hi, expected",
@@ -414,7 +414,8 @@ def test_qual_setting():
     """Regression test."""
     pha = make_data("qual")
 -    assert pha.quality_filter == pytest.approx([True, True, False, True])
 +    expected = np.asarray([True, True, False, True])
 +    assert pha.quality_filter == pytest.approx(expected)
 @pytest.mark.parametrize("data_class", ["1d", "1dint", "pha", "grp", "qual"])
@@ -869,4 +870,5 @@ def test_expand_grouped_mask_ingalid_group():
 def test_expand_grouped_mask(mask, group, expected):
     """Check how test_expand_grouped_mask works."""
 -    assert expand_grouped_mask(mask, group) == pytest.approx(expected)
 +    evals = np.asarray(expected)
 +    assert expand_grouped_mask(mask, group) == pytest.approx(evals)
 diff --git a/sherpa/tests/test_data.py b/sherpa/tests/test_data.py
 index f7a41ebf79..e6744aeb8d 100644
 --- a/sherpa/tests/test_data.py
 +++ b/sherpa/tests/test_data.py
@@ -1321,7 +1321,7 @@ def test_data_filter_invalid_size_scalar():
     x = numpy.asarray([1, 2, 5])
     d = Data1D('x', x, x)
     d.ignore(None, 2)
 -    assert d.mask == pytest.approx([False, False, True])
 +    assert d.mask == pytest.approx(numpy.asarray([False, False, True]))
     with pytest.raises(DataErr,
                        match="Array must be a sequence or None"):
@@ -1567,7 +1567,7 @@ def test_data1dint_check_limit(ignore, lo, hi, evals):
     c1, c2, c3 = evals
     expected = [vout] * c1 + [vin] * c2 + [vout] * c3
 -    assert d.mask == pytest.approx(expected)
 +    assert d.mask == pytest.approx(numpy.asarray(expected))
 def test_filter_apply_none():
@@ -1993,7 +1993,7 @@ def test_mask_sent_array_non_bool():
     expected = [True, False, True, False, True, True, False, True, False, True]
     data.mask = mask
 -    assert data.mask == pytest.approx(expected)
 +    assert data.mask == pytest.approx(numpy.asarray(expected))
 @pytest.mark.parametrize("data", ALL_DATA_CLASSES, indirect=True)
 diff --git a/sherpa/ui/tests/test_session.py b/sherpa/ui/tests/test_session.py
 index 4b352c203b..5723f4a655 100644
 --- a/sherpa/ui/tests/test_session.py
 +++ b/sherpa/ui/tests/test_session.py
@@ -1408,7 +1408,8 @@ def test_load_filter_simple(idval, tmp_path):
     else:
         s.load_filter(idval, str(infile), ncols=1, ignore=True)
 -    assert s.get_data().mask == pytest.approx([True, True, False])
 +    expected = np.asarray([True, True, False])
 +    assert s.get_data().mask == pytest.approx(expected)
 @pytest.mark.parametrize("idval", [None, 1])
 diff --git a/sherpa/ui/tests/test_ui_unit.py b/sherpa/ui/tests/test_ui_unit.py
 index 99cc4e21b5..14f15a78e0 100644
 --- a/sherpa/ui/tests/test_ui_unit.py
 +++ b/sherpa/ui/tests/test_ui_unit.py
@@ -1209,7 +1209,7 @@ def test_set_filter_unmasked(ignore, clean_ui):
     ui.load_arrays(1, x, y)
     data = ui.get_data()
 -    assert data.mask
 +    assert data.mask is True
     if ignore:
         expected = [False, True, False]
@@ -1217,7 +1217,7 @@ def test_set_filter_unmasked(ignore, clean_ui):
         expected = [True, False, True]
     ui.set_filter(np.asarray([True, False, True]), ignore=ignore)
 -    assert data.mask == pytest.approx(expected)
 +    assert data.mask == pytest.approx(np.asarray(expected))
 def test_set_filter_unmasked_wrong(clean_ui):
@@ -1245,7 +1245,9 @@ def test_set_filter_masked(ignore, clean_ui):
     ui.ignore(lo=15, hi=45)
     data = ui.get_data()
 -    assert data.mask == pytest.approx([True, False, False, False, True])
 +
 +    orig = np.asarray([True, False, False, False, True])
 +    assert data.mask == pytest.approx(orig)
     # Unlike test_set_filter_masked the two expected values are not
     # logical inverses, since we need to consider the existing mask.
@@ -1256,7 +1258,7 @@ def test_set_filter_masked(ignore, clean_ui):
         expected = [True, False, True, False, True]
     ui.set_filter(np.asarray([True, False, True, False, False]), ignore=ignore)
 -    assert data.mask == pytest.approx(expected)
 +    assert data.mask == pytest.approx(np.asarray(expected))
 def test_set_filter_masked_wrong(clean_ui):
 diff --git a/sherpa/utils/tests/test_utils.py b/sherpa/utils/tests/test_utils.py
 index a11338d8b8..60e56f82ba 100644
 --- a/sherpa/utils/tests/test_utils.py
 +++ b/sherpa/utils/tests/test_utils.py
@@ -1,5 +1,5 @@
 #
 -#  Copyright (C) 2010, 2016, 2018, 2019, 2020, 2021, 2022, 2023
 +#  Copyright (C) 2010, 2016, 2018 - 2024
 #  Smithsonian Astrophysical Observatory
 #
 #
@@ -374,9 +374,11 @@ def test_filter_bins_one(lo, hi, res):
     lo>hi.
     """
 +    expected = numpy.asarray(res)
 +
     dvals = numpy.asarray([1, 2, 3, 4, 5])
     flags = utils.filter_bins([lo], [hi], [dvals])
 -    assert flags == pytest.approx(res)
 +    assert flags == pytest.approx(expected)
     # We can also check an identity: that
     #    a <= x <= b
@@ -385,7 +387,7 @@ def test_filter_bins_one(lo, hi, res):
     #    x <= b
     #
     flags = utils.filter_bins([lo, None], [None, hi], [dvals, dvals])
 -    assert flags == pytest.approx(res)
 +    assert flags == pytest.approx(expected)
@@ -418,7 +420,7 @@ def test_filter_bins_one_int(lo, hi, res):
     hivals = lovals + 1
     flags = utils.filter_bins([None, lo], [hi, None], [lovals, hivals],
                               integrated=True)
 -    assert flags == pytest.approx(res)
 +    assert flags == pytest.approx(numpy.asarray(res))
 From af47af723c4fb054f037555e9c5fd219871c9695 Mon Sep 17 00:00:00 2001
 From: Douglas Burke <dburke.gw@gmail.com>
 Date: Fri, 6 Dec 2024 12:58:17 -0500
 Subject: [PATCH 2/2] CI: use macos-13 for the macOS Intel run
 This also
 - bumps up the python version from 3.10 to 3.11
 - adds bokeh to the mac test (as it's supposed to be a
  "gull build")
 - adds "Intel" to the name of the test, in preparation for
  adding an ARM build, such as
  https://github.com/sherpa/sherpa/pull/2198/
 ---
 .github/workflows/ci-conda-workflow.yml | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)
 diff --git a/.github/workflows/ci-conda-workflow.yml b/.github/workflows/ci-conda-workflow.yml
 index 6bab3c77ae..507c06efb2 100644
 --- a/.github/workflows/ci-conda-workflow.yml
 +++ b/.github/workflows/ci-conda-workflow.yml
@@ -30,13 +30,14 @@ jobs:
     strategy:
       matrix:
         include:
 -          - name: MacOS Full Build
 -            os: macos-12
 -            python-version: "3.10"
 +          - name: MacOS Intel Full Build
 +            os: macos-13
 +            python-version: "3.11"
             install-type: develop
             fits: astropy
             test-data: submodule
             matplotlib-version: 3
 +            bokeh-version: 3
             xspec-version: 12.14.0i
           - name: Linux Minimum Setup (Python 3.10)