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Hello community,
here is the log from the commit of package python-iminuit for openSUSE:Factory
checked in at 2021-12-09 19:45:35
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-iminuit (Old)
and /work/SRC/openSUSE:Factory/.python-iminuit.new.2520 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-iminuit"
Thu Dec 9 19:45:35 2021 rev:17 rq:937362 version:2.8.4
Changes:
--------
--- /work/SRC/openSUSE:Factory/python-iminuit/python-iminuit.changes
2021-09-16 23:17:54.559999406 +0200
+++ /work/SRC/openSUSE:Factory/.python-iminuit.new.2520/python-iminuit.changes
2021-12-09 19:46:06.981147901 +0100
@@ -1,0 +2,12 @@
+Wed Dec 8 14:38:34 UTC 2021 - pgaj...@suse.com
+
+- version update to 2.8.4
+ Fixes
+ * Pickling of util.Matrix resulted in incomplete state after unpickling,
+ which would cause an exception when you tried to print the matrix
+ Documentation
+ * New tutorial on fitting PDFs that depend on a conditional variable
+ * Fixed JAX tutorial, adapting to change in their interface
+ * Extended documentation of cost functions
+
+-------------------------------------------------------------------
Old:
----
iminuit-2.8.3.tar.gz
New:
----
iminuit-2.8.4.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-iminuit.spec ++++++
--- /var/tmp/diff_new_pack.FfT9Sm/_old 2021-12-09 19:46:07.865148326 +0100
+++ /var/tmp/diff_new_pack.FfT9Sm/_new 2021-12-09 19:46:07.869148328 +0100
@@ -22,7 +22,7 @@
%define skip_python36 1
%define modname iminuit
Name: python-%{modname}
-Version: 2.8.3
+Version: 2.8.4
Release: 0
Summary: Python bindings for MINUIT2
License: MIT
++++++ iminuit-2.8.3.tar.gz -> iminuit-2.8.4.tar.gz ++++++
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/PKG-INFO new/iminuit-2.8.4/PKG-INFO
--- old/iminuit-2.8.3/PKG-INFO 2021-09-03 17:08:02.901465400 +0200
+++ new/iminuit-2.8.4/PKG-INFO 2021-10-11 11:28:35.619011900 +0200
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: iminuit
-Version: 2.8.3
+Version: 2.8.4
Summary: Jupyter-friendly Python frontend for MINUIT2 in C++
Home-page: http://github.com/scikit-hep/iminuit
Author: Piti Ongmongkolkul and the iminuit team
@@ -30,9 +30,11 @@
.. image::
https://readthedocs.org/projects/iminuit/badge/?version=stable
:target: https://iminuit.readthedocs.io/en/stable
.. image:: https://img.shields.io/pypi/l/iminuit
- :alt: License
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3949207.svg
:target: https://doi.org/10.5281/zenodo.3949207
+ .. image::
https://img.shields.io/badge/ascl-2108.024-blue.svg?colorB=262255
+ :target: https://ascl.net/2108.024
+ :alt: ascl:2108.024
*iminuit* is a Jupyter-friendly Python interface for the *Minuit2* C++
library maintained by CERN's ROOT team.
@@ -44,10 +46,15 @@
The iminuit package comes with additional features:
- - Included cost functions for binned and unbinned maximum-likelihood
and (robust)
- least-squares fits
- - Support for SciPy minimisers
- - Numba support (optional)
+ - Builtin cost functions for statistical fits
+
+ - Binned and unbinned maximum-likelihood
+ - Non-linear regression with (optionally robust) weighted
least-squares
+ - Gaussian penalty terms
+ - Cost functions can be combined by adding them: ``total_cost =
cost_1 + cost_2``
+ - Tools for numerical error propagation ``iminuit.util.propagate``
+ - Support for SciPy minimisers as alternatives to Minuit's Migrad
algorithm (optional)
+ - Support for Numba accelerated functions (optional)
Checkout our large and comprehensive list of `tutorials`_ that take
you all the way from beginner to power user. For help and how-to questions,
please use the `discussions`_ on GitHub.
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/README.rst new/iminuit-2.8.4/README.rst
--- old/iminuit-2.8.3/README.rst 2021-09-03 17:07:34.000000000 +0200
+++ new/iminuit-2.8.4/README.rst 2021-10-11 11:28:10.000000000 +0200
@@ -18,9 +18,11 @@
.. image:: https://readthedocs.org/projects/iminuit/badge/?version=stable
:target: https://iminuit.readthedocs.io/en/stable
.. image:: https://img.shields.io/pypi/l/iminuit
- :alt: License
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3949207.svg
:target: https://doi.org/10.5281/zenodo.3949207
+.. image:: https://img.shields.io/badge/ascl-2108.024-blue.svg?colorB=262255
+ :target: https://ascl.net/2108.024
+ :alt: ascl:2108.024
*iminuit* is a Jupyter-friendly Python interface for the *Minuit2* C++ library
maintained by CERN's ROOT team.
@@ -32,10 +34,15 @@
The iminuit package comes with additional features:
-- Included cost functions for binned and unbinned maximum-likelihood and
(robust)
- least-squares fits
-- Support for SciPy minimisers
-- Numba support (optional)
+- Builtin cost functions for statistical fits
+
+ - Binned and unbinned maximum-likelihood
+ - Non-linear regression with (optionally robust) weighted least-squares
+ - Gaussian penalty terms
+ - Cost functions can be combined by adding them: ``total_cost = cost_1 +
cost_2``
+- Tools for numerical error propagation ``iminuit.util.propagate``
+- Support for SciPy minimisers as alternatives to Minuit's Migrad algorithm
(optional)
+- Support for Numba accelerated functions (optional)
Checkout our large and comprehensive list of `tutorials`_ that take you all
the way from beginner to power user. For help and how-to questions, please use
the `discussions`_ on GitHub.
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/setup.cfg new/iminuit-2.8.4/setup.cfg
--- old/iminuit-2.8.3/setup.cfg 2021-09-03 17:08:02.905465400 +0200
+++ new/iminuit-2.8.4/setup.cfg 2021-10-11 11:28:35.619011900 +0200
@@ -70,6 +70,7 @@
sphinx_rtd_theme
tabulate
nbsphinx
+ boost_histogram
doc =
sphinx==4.1
sphinx-rtd-theme
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/src/iminuit/cost.py
new/iminuit-2.8.4/src/iminuit/cost.py
--- old/iminuit-2.8.3/src/iminuit/cost.py 2021-09-03 17:07:34.000000000
+0200
+++ new/iminuit-2.8.4/src/iminuit/cost.py 2021-10-11 11:28:10.000000000
+0200
@@ -1,10 +1,40 @@
"""
-Standard cost functions to minimize.
+Standard cost functions to minimize for statistical fits.
-The cost functions defined here should be preferred over custom
implementations. They
-have been optimized with knowledge about implementation details of Minuit to
give the
-highest accucary and the most robust results. They are partially accelerated
with numba,
-if numba is available.
+We provide these for convenience, so that you do not have to write your own
for standard fits.
+The cost functions optionally use Numba to accelerate some calculations, if
Numba is
+installed.
+
+What to use when
+----------------
+- Fit a normalised probability density to data
+ - Data is not binned: UnbinnedNLL
+ - Data is binned: BinnedNLL, also supports histogram of weighted samples
+- Fit a density to data, density is not normalised
+ - Data is not binned: ExtendedUnbinnedNLL
+ - Data is binned: ExtendedBinnedNLL, also supports histogram of weighted
samples
+- Fit of a function f(x) to (x, y, yerror) pairs with normal-distributed
fluctuations
+ (x can be multi-dimensional)
+ - y values contain no outliers: LeastSquares
+ - y values contain outliers: LeastSquares with "soft_l1" loss function
+
+Combining cost functions
+------------------------
+All cost functions can be added, which generates a new combined cost function.
+Parameters with the same name are shared between component cost functions. Use
this to
+constrain one or several parameters with different data sets and using
different
+statistical models for each data set. Gaussian penalty terms can also be added
to the
+cost function to introduce external knowledge about a parameter.
+
+Notes
+-----
+The cost functions defined here have been optimized with knowledge about
implementation
+details of Minuit to give the highest accucary and the most robust results, so
they
+should perform well. If you have trouble with your own implementations, try
these.
+
+The binned versions of the log-likelihood fits support weighted samples. For
each bin
+of the histogram, the sum of weights and the sum of squared weights is needed
then, see
+class documentation for details.
"""
from .util import describe, make_func_code, merge_signatures,
PerformanceWarning
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/src/iminuit/util.py
new/iminuit-2.8.4/src/iminuit/util.py
--- old/iminuit-2.8.3/src/iminuit/util.py 2021-09-03 17:07:34.000000000
+0200
+++ new/iminuit-2.8.4/src/iminuit/util.py 2021-10-11 11:28:10.000000000
+0200
@@ -1,4 +1,9 @@
-"""Data classes and utilities used by :class:`iminuit.Minuit`."""
+"""
+Data classes and utilities used by :class:`iminuit.Minuit`.
+
+You can look up the interface of data classes that iminuit uses here. Also of
interest
+for users are :func:`propagate` and :func:`make_with_signature`.
+"""
import inspect
from collections import OrderedDict
from argparse import Namespace
@@ -340,6 +345,17 @@
else:
p.text(str(self))
+ # ndarray uses __reduce__ for pickling instead of __getstate__
+ def __reduce__(self):
+ """Get representation for pickling and copying."""
+ restore, args, state = super().__reduce__()
+ return restore, args, (state, self._var2pos)
+
+ def __setstate__(self, state):
+ """Restore from pickled state."""
+ state, self._var2pos = state
+ super().__setstate__(state)
+
class FMin:
"""
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/src/iminuit/version.py
new/iminuit-2.8.4/src/iminuit/version.py
--- old/iminuit-2.8.3/src/iminuit/version.py 2021-09-03 17:07:34.000000000
+0200
+++ new/iminuit-2.8.4/src/iminuit/version.py 2021-10-11 11:28:10.000000000
+0200
@@ -7,7 +7,7 @@
# - Increase MAINTENANCE when fixing bugs without adding features
# - During development, add suffix .devN with N >= 0
# - For release candidates, add suffix .rcN with N >= 0
-version = "2.8.3"
+version = "2.8.4"
# We list the corresponding ROOT version of the C++ Minuit2 library here
root_version = "v6-25-01-1694-g187368db19"
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/src/iminuit.egg-info/PKG-INFO
new/iminuit-2.8.4/src/iminuit.egg-info/PKG-INFO
--- old/iminuit-2.8.3/src/iminuit.egg-info/PKG-INFO 2021-09-03
17:08:02.000000000 +0200
+++ new/iminuit-2.8.4/src/iminuit.egg-info/PKG-INFO 2021-10-11
11:28:35.000000000 +0200
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: iminuit
-Version: 2.8.3
+Version: 2.8.4
Summary: Jupyter-friendly Python frontend for MINUIT2 in C++
Home-page: http://github.com/scikit-hep/iminuit
Author: Piti Ongmongkolkul and the iminuit team
@@ -30,9 +30,11 @@
.. image::
https://readthedocs.org/projects/iminuit/badge/?version=stable
:target: https://iminuit.readthedocs.io/en/stable
.. image:: https://img.shields.io/pypi/l/iminuit
- :alt: License
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3949207.svg
:target: https://doi.org/10.5281/zenodo.3949207
+ .. image::
https://img.shields.io/badge/ascl-2108.024-blue.svg?colorB=262255
+ :target: https://ascl.net/2108.024
+ :alt: ascl:2108.024
*iminuit* is a Jupyter-friendly Python interface for the *Minuit2* C++
library maintained by CERN's ROOT team.
@@ -44,10 +46,15 @@
The iminuit package comes with additional features:
- - Included cost functions for binned and unbinned maximum-likelihood
and (robust)
- least-squares fits
- - Support for SciPy minimisers
- - Numba support (optional)
+ - Builtin cost functions for statistical fits
+
+ - Binned and unbinned maximum-likelihood
+ - Non-linear regression with (optionally robust) weighted
least-squares
+ - Gaussian penalty terms
+ - Cost functions can be combined by adding them: ``total_cost =
cost_1 + cost_2``
+ - Tools for numerical error propagation ``iminuit.util.propagate``
+ - Support for SciPy minimisers as alternatives to Minuit's Migrad
algorithm (optional)
+ - Support for Numba accelerated functions (optional)
Checkout our large and comprehensive list of `tutorials`_ that take
you all the way from beginner to power user. For help and how-to questions,
please use the `discussions`_ on GitHub.
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/src/iminuit.egg-info/SOURCES.txt
new/iminuit-2.8.4/src/iminuit.egg-info/SOURCES.txt
--- old/iminuit-2.8.3/src/iminuit.egg-info/SOURCES.txt 2021-09-03
17:08:02.000000000 +0200
+++ new/iminuit-2.8.4/src/iminuit.egg-info/SOURCES.txt 2021-10-11
11:28:35.000000000 +0200
@@ -293,6 +293,7 @@
tests/test_experimental.py
tests/test_fmin_bad.txt
tests/test_functions.py
+tests/test_issue.py
tests/test_minimize.py
tests/test_minuit.py
tests/test_pdg_format.py
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/src/iminuit.egg-info/requires.txt
new/iminuit-2.8.4/src/iminuit.egg-info/requires.txt
--- old/iminuit-2.8.3/src/iminuit.egg-info/requires.txt 2021-09-03
17:08:02.000000000 +0200
+++ new/iminuit-2.8.4/src/iminuit.egg-info/requires.txt 2021-10-11
11:28:35.000000000 +0200
@@ -32,3 +32,4 @@
sphinx_rtd_theme
tabulate
nbsphinx
+boost_histogram
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/tests/test_issue.py
new/iminuit-2.8.4/tests/test_issue.py
--- old/iminuit-2.8.3/tests/test_issue.py 1970-01-01 01:00:00.000000000
+0100
+++ new/iminuit-2.8.4/tests/test_issue.py 2021-10-11 11:28:10.000000000
+0200
@@ -0,0 +1,118 @@
+from iminuit import Minuit
+from iminuit.util import IMinuitWarning
+import pickle
+import pytest
+import numpy as np
+
+
+def lsq(func):
+ func.errordef = Minuit.LEAST_SQUARES
+ return func
+
+
+def test_issue_424():
+ @lsq
+ def fcn(x, y, z):
+ return (x - 1) ** 2 + (y - 4) ** 2 / 2 + (z - 9) ** 2 / 3
+
+ m = Minuit(fcn, x=0.0, y=0.0, z=0.0)
+ m.migrad()
+
+ m.fixed["x"] = True
+ m.errors["x"] = 2
+ m.hesse() # this used to release x
+ assert m.fixed["x"] is True
+ assert m.errors["x"] == 2
+
+
+def test_issue_544():
+ @lsq
+ def fcn(x, y):
+ return x ** 2 + y ** 2
+
+ m = Minuit(fcn, x=0, y=0)
+ m.fixed = True
+ with pytest.warns(IMinuitWarning):
+ m.hesse() # this used to cause a segfault
+
+
+def test_issue_648():
+ class F:
+ errordef = 1
+ first = True
+
+ def __call__(self, a, b):
+ if self.first:
+ assert a == 1.0 and b == 2.0
+ self.first = False
+ return a ** 2 + b ** 2
+
+ m = Minuit(F(), a=1, b=2)
+ m.fixed["a"] = False # this used to change a to b
+ m.migrad()
+
+
+def test_issue_643():
+ @lsq
+ def fcn(x, y, z):
+ return (x - 2) ** 2 + (y - 3) ** 2 + (z - 4) ** 2
+
+ m = Minuit(fcn, x=2, y=3, z=4)
+ m.migrad()
+
+ m2 = Minuit(fcn, x=m.values["x"], y=m.values["y"], z=m.values["z"])
+ # used to call MnHesse when it was not needed and quickly exhaust call
limit
+ for i in range(10):
+ m2.minos()
+
+ m2.reset()
+ # used to exhaust call limit, because calls to MnHesse did not reset call
count
+ for i in range(10):
+ m2.values = m.values
+ m2.minos()
+
+
+def test_issue_669():
+ @lsq
+ def fcn(x, y):
+ return x ** 2 + (y / 2) ** 2
+
+ m = Minuit(fcn, x=0, y=0)
+
+ m.migrad()
+
+ xy1 = m.mncontour(x="x", y="y", size=10)
+ xy2 = m.mncontour(x="y", y="x", size=10) # used to fail
+
+ # needs better way to compare polygons
+ for x, y in xy1:
+ match = False
+ for (y2, x2) in xy2:
+ if abs(x - x2) < 1e-3 and abs(y - y2) < 1e-3:
+ match = True
+ break
+ assert match
+
+
+@lsq
+def fcn(par):
+ return np.sum(par ** 2)
+
+
+def grad(par):
+ return 2 * par
+
+
+def test_issue_687():
+
+ start = np.zeros(3)
+ m = Minuit(fcn, start)
+
+ m.migrad()
+ s_m = str(m)
+
+ s = pickle.dumps(m)
+ m2 = pickle.loads(s)
+
+ s_m2 = str(m2) # this used to fail
+ assert s_m == s_m2
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/tests/test_minuit.py
new/iminuit-2.8.4/tests/test_minuit.py
--- old/iminuit-2.8.3/tests/test_minuit.py 2021-09-03 17:07:34.000000000
+0200
+++ new/iminuit-2.8.4/tests/test_minuit.py 2021-10-11 11:28:10.000000000
+0200
@@ -1195,21 +1195,6 @@
assert str(expected) in str(excinfo.value)
-def test_issue_424():
- def lsq(x, y, z):
- return (x - 1) ** 2 + (y - 4) ** 2 / 2 + (z - 9) ** 2 / 3
-
- m = Minuit(lsq, x=0.0, y=0.0, z=0.0)
- m.errordef = 1
- m.migrad()
-
- m.fixed["x"] = True
- m.errors["x"] = 2
- m.hesse()
- assert m.fixed["x"] is True
- assert m.errors["x"] == 2
-
-
@pytest.mark.parametrize("sign", (-1, 1))
def test_parameter_at_limit(sign):
m = Minuit(lambda x: (x - sign * 1.2) ** 2, x=0)
@@ -1362,13 +1347,6 @@
m.tol = -1
-def test_issue_544():
- m = Minuit(func0, x=0, y=0)
- m.fixed = True
- with pytest.warns(IMinuitWarning):
- m.hesse() # this used to cause a segfault
-
-
def test_cfunc():
nb = pytest.importorskip("numba")
@@ -1516,64 +1494,9 @@
assert_equal(m.ndof, np.nan)
-def test_issue_648():
- class F:
- errordef = 1
- first = True
-
- def __call__(self, a, b):
- if self.first:
- assert a == 1.0 and b == 2.0
- self.first = False
- return a ** 2 + b ** 2
-
- m = Minuit(F(), a=1, b=2)
- m.fixed["a"] = False # this used to change a to b
- m.migrad()
-
-
def test_call_limit_reached_in_hesse():
m = Minuit(lambda x: ((x - 1.2) ** 4).sum(), np.ones(10) * 10)
m.errordef = 1
m.migrad(ncall=200)
assert m.fmin.has_reached_call_limit
assert m.fmin.nfcn < 205
-
-
-def test_issue_643():
- def fcn(x, y, z):
- return (x - 2) ** 2 + (y - 3) ** 2 + (z - 4) ** 2
-
- fcn.errordef = Minuit.LEAST_SQUARES
-
- m = Minuit(fcn, x=2, y=3, z=4)
- m.migrad()
-
- m2 = Minuit(fcn, x=m.values["x"], y=m.values["y"], z=m.values["z"])
- # used to call MnHesse when it was not needed and quickly exhaust call
limit
- for i in range(10):
- m2.minos()
-
- m2.reset()
- # used to exhaust call limit, because calls to MnHesse did not reset call
count
- for i in range(10):
- m2.values = m.values
- m2.minos()
-
-
-def test_issue_669():
- m = Minuit(func0, x=0, y=0)
-
- m.migrad()
-
- xy1 = m.mncontour(x="x", y="y", size=10)
- xy2 = m.mncontour(x="y", y="x", size=10) # used to fail
-
- # needs better way to compare polygons
- for x, y in xy1:
- match = False
- for (y2, x2) in xy2:
- if abs(x - x2) < 1e-3 and abs(y - y2) < 1e-3:
- match = True
- break
- assert match
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.8.3/tests/test_util.py
new/iminuit-2.8.4/tests/test_util.py
--- old/iminuit-2.8.3/tests/test_util.py 2021-09-03 17:07:34.000000000
+0200
+++ new/iminuit-2.8.4/tests/test_util.py 2021-10-11 11:28:10.000000000
+0200
@@ -4,6 +4,7 @@
from numpy.testing import assert_equal, assert_allclose
import numpy as np
from iminuit._core import MnUserParameterState
+import pickle
def test_ndim():
@@ -112,6 +113,12 @@
with pytest.raises(TypeError):
util.Matrix(1)
+ m2 = pickle.loads(pickle.dumps(m))
+ assert type(m2) is util.Matrix
+
+ assert_equal(m2, m)
+ assert m2._var2pos == m._var2pos
+
def test_Param():
values = 3, "foo", 1.2, 3.4, None, False, False, 42, None
