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python-sherpa/sherpa-pr2188-np2docstrings.patch
Benjamin Greiner de47efe118 - Update to 4.17.0 
* This release of Sherpa includes various enhancements,
    documentation updates, bug fixes, and infrastructure changes.
  ## enhancements:
  * add calc_model and calc_source functions to return an evaluated
    model/source array
  * added wstat to plot_pvalue for the likelihood ratio test
  * changed XSpec interface to use FunctionUtility C++ API instead
    of XSFortran API
  * improved support for PHA data starting at channel 0 and
    handling of STAT_ERR and SYS_ERR PHA columns which are set to 0
  * improved guess for complex models
  * improved filtering handling
  * several updates to enhance plotting capabilities and layout
  ## documentation changes:
  * added paper citations to front page of Sherpa Read the Docs
    documentation
  * cleaned up various typos and URL references
  * added examples such as use of set_x/y_label
  ## infrastructure and testing:
  * improved test coverage
  * many updates to CI
  * drop support for Python 3.9 and numpy <1.24
  * initial/experimental support for Python 3.12
  ## bug fixes:
  * fixed an issue with plotting 1D data with asymmetric errs after
    filter
  * include the default identifier in save_all output if it has
    been changed
- Drop numpy2.patch

OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-sherpa?expand=0&rev=46
2024-12-06 17:18:04 +00:00

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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
""")
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