Script 'mail_helper' called by obssrc
Hello community,
here is the log from the commit of package python-iminuit for openSUSE:Factory
checked in at 2022-03-06 18:15:51
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-iminuit (Old)
and /work/SRC/openSUSE:Factory/.python-iminuit.new.1958 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-iminuit"
Sun Mar 6 18:15:51 2022 rev:19 rq:959729 version:2.10.0
Changes:
--------
--- /work/SRC/openSUSE:Factory/python-iminuit/python-iminuit.changes
2022-01-21 01:26:25.122176468 +0100
+++ /work/SRC/openSUSE:Factory/.python-iminuit.new.1958/python-iminuit.changes
2022-03-06 18:16:09.047829055 +0100
@@ -1,0 +2,10 @@
+Sat Mar 5 15:40:18 UTC 2022 - Atri Bhattacharya <badshah...@gmail.com>
+
+- Update to version 2.10.0:
+ * Minuit.draw_mncontour now works with matplotlib >= 3.5.
+ * Builtin cost functions now work correctly when the mask is set
+ and data is updated on the existing cost function.
+ * Builtin cost functions are now more performant when used with
+ weighted binned data.
+
+-------------------------------------------------------------------
Old:
----
iminuit-2.9.0.tar.gz
New:
----
iminuit-2.10.0.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-iminuit.spec ++++++
--- /var/tmp/diff_new_pack.BKk9Yp/_old 2022-03-06 18:16:09.579829127 +0100
+++ /var/tmp/diff_new_pack.BKk9Yp/_new 2022-03-06 18:16:09.587829128 +0100
@@ -22,7 +22,7 @@
%define skip_python36 1
%define modname iminuit
Name: python-%{modname}
-Version: 2.9.0
+Version: 2.10.0
Release: 0
Summary: Python bindings for MINUIT2
License: MIT
++++++ iminuit-2.9.0.tar.gz -> iminuit-2.10.0.tar.gz ++++++
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/PKG-INFO new/iminuit-2.10.0/PKG-INFO
--- old/iminuit-2.9.0/PKG-INFO 2022-01-09 15:13:19.922676000 +0100
+++ new/iminuit-2.10.0/PKG-INFO 2022-03-04 17:01:36.374506200 +0100
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: iminuit
-Version: 2.9.0
+Version: 2.10.0
Summary: Jupyter-friendly Python frontend for MINUIT2 in C++
Home-page: http://github.com/scikit-hep/iminuit
Author: Piti Ongmongkolkul and the iminuit team
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/pyproject.toml
new/iminuit-2.10.0/pyproject.toml
--- old/iminuit-2.9.0/pyproject.toml 2022-01-09 15:12:36.000000000 +0100
+++ new/iminuit-2.10.0/pyproject.toml 2022-03-04 17:01:07.000000000 +0100
@@ -1,7 +1,6 @@
[build-system]
requires = [
"setuptools>=42",
- "wheel",
"cmake",
]
build-backend = "setuptools.build_meta"
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/fcn.cpp
new/iminuit-2.10.0/src/fcn.cpp
--- old/iminuit-2.9.0/src/fcn.cpp 2022-01-09 15:12:36.000000000 +0100
+++ new/iminuit-2.10.0/src/fcn.cpp 2022-03-04 17:01:07.000000000 +0100
@@ -1,14 +1,14 @@
#include "fcn.hpp"
+#include "type_caster.hpp"
#include <Minuit2/FCNGradientBase.h>
#include <Minuit2/MnPrint.h>
-#include <pybind11/numpy.h>
-#include <pybind11/pybind11.h>
#include <cmath>
#include <cstdint>
#include <limits>
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
#include <sstream>
#include <vector>
-#include "type_caster.hpp"
namespace py = pybind11;
using namespace ROOT::Minuit2;
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/functionminimum.cpp
new/iminuit-2.10.0/src/functionminimum.cpp
--- old/iminuit-2.9.0/src/functionminimum.cpp 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/functionminimum.cpp 2022-03-04 17:01:07.000000000
+0100
@@ -1,3 +1,5 @@
+#include "equal.hpp"
+#include "fcn.hpp"
#include <Minuit2/FunctionMinimum.h>
#include <Minuit2/MinimumSeed.h>
#include <Minuit2/MinimumState.h>
@@ -9,8 +11,6 @@
#include <pybind11/stl.h>
#include <type_traits>
#include <vector>
-#include "equal.hpp"
-#include "fcn.hpp"
namespace py = pybind11;
using namespace ROOT::Minuit2;
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/functionminimum_extra.cpp
new/iminuit-2.10.0/src/functionminimum_extra.cpp
--- old/iminuit-2.9.0/src/functionminimum_extra.cpp 2022-01-09
15:12:36.000000000 +0100
+++ new/iminuit-2.10.0/src/functionminimum_extra.cpp 2022-03-04
17:01:07.000000000 +0100
@@ -1,3 +1,5 @@
+#include "equal.hpp"
+#include "fcn.hpp"
#include <Minuit2/AnalyticalGradientCalculator.h>
#include <Minuit2/FunctionMinimum.h>
#include <Minuit2/MinimumState.h>
@@ -11,8 +13,6 @@
#include <pybind11/pybind11.h>
#include <type_traits>
#include <vector>
-#include "equal.hpp"
-#include "fcn.hpp"
namespace py = pybind11;
using namespace ROOT::Minuit2;
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/iminuit/cost.py
new/iminuit-2.10.0/src/iminuit/cost.py
--- old/iminuit-2.9.0/src/iminuit/cost.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/iminuit/cost.py 2022-03-04 17:01:07.000000000
+0100
@@ -86,23 +86,23 @@
# precision. Fall back to plain numpy for float128 which is not currently
supported
# by numba.
try:
- import numba as nb
- from numba.extending import overload
+ from numba import njit as _njit
+ from numba.extending import overload as _overload
_log_poisson_part_np = _log_poisson_part
- @overload(_log_poisson_part, inline="always")
+ @_overload(_log_poisson_part, inline="always")
def _log_poisson_part_ol(n, mu):
return _log_poisson_part_np # pragma: no cover
_z_squared_np = _z_squared
- @overload(_z_squared, inline="always")
+ @_overload(_z_squared, inline="always")
def _z_squared_ol(y, ye, ym):
return _z_squared_np # pragma: no cover
_sum_log_x_np = _sum_log_x
- _sum_log_x_nb = nb.njit(
+ _sum_log_x_nb = _njit(
nogil=True,
cache=True,
error_model="numpy",
@@ -114,7 +114,7 @@
return _sum_log_x_np(x)
_sum_log_poisson_part_np = _sum_log_poisson_part
- _sum_log_poisson_part_nb = nb.njit(
+ _sum_log_poisson_part_nb = _njit(
nogil=True,
cache=True,
error_model="numpy",
@@ -126,7 +126,7 @@
return _sum_log_poisson_part_np(n, mu)
_sum_log_poisson_np = _sum_log_poisson
- _sum_log_poisson_nb = nb.njit(
+ _sum_log_poisson_nb = _njit(
nogil=True,
cache=True,
error_model="numpy",
@@ -139,7 +139,7 @@
return _sum_log_poisson_np(n, mu)
_sum_z_squared_np = _sum_z_squared
- _sum_z_squared_nb = nb.njit(
+ _sum_z_squared_nb = _njit(
nogil=True,
cache=True,
error_model="numpy",
@@ -152,7 +152,7 @@
return _sum_z_squared_np(y, ye, ym)
_sum_z_squared_soft_l1_np = _sum_z_squared_soft_l1
- _sum_z_squared_soft_l1_nb = nb.njit(
+ _sum_z_squared_soft_l1_nb = _njit(
nogil=True,
cache=True,
error_model="numpy",
@@ -164,8 +164,6 @@
# fallback to numpy for float128
return _sum_z_squared_soft_l1_np(y, ye, ym)
- del nb
- del overload
except ModuleNotFoundError: # pragma: no cover
pass
@@ -403,7 +401,7 @@
class UnbinnedCost(MaskedCost):
"""Base class for unbinned cost functions."""
- __slots__ = "_model"
+ __slots__ = "_model", "_log"
@Cost.ndata.getter
def ndata(self):
@@ -411,9 +409,10 @@
# unbinned likelihoods have infinite degrees of freedom
return np.inf
- def __init__(self, data, model: Callable, verbose):
+ def __init__(self, data, model: Callable, verbose: int, log: bool):
"""For internal use."""
self._model = model
+ self._log = log
super().__init__(describe(model)[1:], _norm(data), verbose)
@@ -431,7 +430,7 @@
"""Get probability density model."""
return self._model
- def __init__(self, data, pdf: Callable, verbose: int = 0):
+ def __init__(self, data, pdf: Callable, verbose: int = 0, log: bool =
False):
"""
Initialize UnbinnedNLL with data and model.
@@ -445,14 +444,22 @@
verbose : int, optional
Verbosity level. 0: is no output (default).
1: print current args and negative log-likelihood value.
+ log : bool, optional
+ Distributions of the exponential family (normal, exponential,
poisson, ...)
+ allow one to compute the logarithm of the pdf directly, which is
more
+ accurate and efficient than effectively doing
``log(exp(logpdf))``. Set this
+ to True, if the model returns the logarithm of the pdf instead of
the pdf.
+ Default is False.
"""
- super().__init__(data, pdf, verbose)
+ super().__init__(data, pdf, verbose, log)
def _call(self, args):
data = self._masked
- pdf = self._model(data, *args)
- pdf = _check_model_output(pdf)
- return -2.0 * _sum_log_x(pdf)
+ x = self._model(data, *args)
+ x = _check_model_output(x)
+ if self._log:
+ return -2.0 * np.sum(x)
+ return -2.0 * _sum_log_x(x)
class ExtendedUnbinnedNLL(UnbinnedCost):
@@ -469,7 +476,7 @@
"""Get density model."""
return self._model
- def __init__(self, data, scaled_pdf: Callable, verbose: int = 0):
+ def __init__(self, data, scaled_pdf: Callable, verbose: int = 0, log: bool
= False):
"""
Initialize cost function with data and model.
@@ -484,16 +491,22 @@
verbose : int, optional
Verbosity level. 0: is no output (default).
1: print current args and negative log-likelihood value.
+ log : bool, optional
+ Distributions of the exponential family (normal, exponential,
poisson, ...)
+ allow one to compute the logarithm of the pdf directly, which is
more
+ accurate and efficient than effectively doing
``log(exp(logpdf))``. Set this
+ to True, if the model returns the logarithm of the density as the
second
+ argument instead of the density. Default is False.
"""
- super().__init__(data, scaled_pdf, verbose)
+ super().__init__(data, scaled_pdf, verbose, log)
def _call(self, args):
data = self._masked
- ns, spdf = self._model(data, *args)
- spdf = _check_model_output(
- spdf, "Model should return numpy array in second position"
- )
- return 2.0 * (ns - _sum_log_x(spdf))
+ ns, x = self._model(data, *args)
+ x = _check_model_output(x, "Model should return numpy array in second
position")
+ if self._log:
+ return 2 * (ns - np.sum(x))
+ return 2 * (ns - _sum_log_x(x))
class BinnedCost(MaskedCost):
@@ -870,7 +883,7 @@
def _call(self, args):
delta = self._value - args
if self._covinv.ndim < 2:
- return np.sum(delta ** 2 * self._covinv)
+ return np.sum(delta**2 * self._covinv)
return np.einsum("i,ij,j", delta, self._covinv, delta)
@Cost.ndata.getter
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/iminuit/minuit.py
new/iminuit-2.10.0/src/iminuit/minuit.py
--- old/iminuit-2.9.0/src/iminuit/minuit.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/iminuit/minuit.py 2022-03-04 17:01:07.000000000
+0100
@@ -1162,7 +1162,7 @@
for p in self.params:
if p.is_fixed:
continue
- matrix[i, i] = p.error ** 2
+ matrix[i, i] = p.error**2
i += 1
if "grad" in r: # trust-constr has "grad" and "jac", but "grad" is
"jac"!
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/iminuit/pdg_format.py
new/iminuit-2.10.0/src/iminuit/pdg_format.py
--- old/iminuit-2.9.0/src/iminuit/pdg_format.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/iminuit/pdg_format.py 2022-03-04
17:01:07.000000000 +0100
@@ -257,10 +257,10 @@
if leader is None:
# invalid leading error, cannot determine digits
- scale = 10 ** -n_exp
+ scale = 10**-n_exp
return ([fmt(v * scale, 4) for v in values], n_exp)
- scale = 10 ** -n_exp
+ scale = 10**-n_exp
digits = round(lerror * scale, 3)
if digits < 0.355:
n_digits = 2
@@ -290,7 +290,7 @@
if i != leader
else math.copysign(digits, values[leader])
)
- * 10 ** shift,
+ * 10**shift,
n_digits - shift,
)
for (i, x) in enumerate(values)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/iminuit/testing.py
new/iminuit-2.10.0/src/iminuit/testing.py
--- old/iminuit-2.9.0/src/iminuit/testing.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/iminuit/testing.py 2022-03-04 17:01:07.000000000
+0100
@@ -11,7 +11,7 @@
https://en.wikipedia.org/wiki/Rosenbrock_function
"""
- return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2
+ return (1 - x) ** 2 + 100 * (y - x**2) ** 2
rosenbrock.errordef = 1
@@ -19,7 +19,7 @@
def rosenbrock_grad(x, y):
"""Gradient of Rosenbrock function."""
- return (-400 * x * (-(x ** 2) + y) + 2 * x - 2, -200 * x ** 2 + 200 * y)
+ return (-400 * x * (-(x**2) + y) + 2 * x - 2, -200 * x**2 + 200 * y)
def ackley(x, y):
@@ -30,7 +30,7 @@
"""
from math import sqrt, exp, cos, pi, e
- term1 = -20 * exp(-0.2 * sqrt(0.5 * (x ** 2 + y ** 2)))
+ term1 = -20 * exp(-0.2 * sqrt(0.5 * (x**2 + y**2)))
term2 = -exp(0.5 * (cos(2 * pi * x) + cos(2 * pi * y)))
return term1 + term2 + 20 + e
@@ -45,8 +45,8 @@
https://en.wikipedia.org/wiki/Test_functions_for_optimization
"""
term1 = 1.5 - x + x * y
- term2 = 2.25 - x + x * y ** 2
- term3 = 2.625 - x + x * y ** 3
+ term2 = 2.25 - x + x * y**2
+ term3 = 2.625 - x + x * y**3
return term1 * term1 + term2 * term2 + term3 * term3
@@ -59,7 +59,7 @@
https://en.wikipedia.org/wiki/Test_functions_for_optimization
"""
- return 0.26 * (x ** 2 + y ** 2) - 0.48 * x * y
+ return 0.26 * (x**2 + y**2) - 0.48 * x * y
matyas.errordef = 1
@@ -73,7 +73,7 @@
"""
import numpy as np
- return np.sum(x ** 2)
+ return np.sum(x**2)
sphere_np.errordef = 1
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/iminuit/version.py
new/iminuit-2.10.0/src/iminuit/version.py
--- old/iminuit-2.9.0/src/iminuit/version.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/iminuit/version.py 2022-03-04 17:01:07.000000000
+0100
@@ -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 = "2.9.0"
+version = "2.10.0"
# 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-2.9.0/src/iminuit.egg-info/PKG-INFO
new/iminuit-2.10.0/src/iminuit.egg-info/PKG-INFO
--- old/iminuit-2.9.0/src/iminuit.egg-info/PKG-INFO 2022-01-09
15:13:19.000000000 +0100
+++ new/iminuit-2.10.0/src/iminuit.egg-info/PKG-INFO 2022-03-04
17:01:36.000000000 +0100
@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: iminuit
-Version: 2.9.0
+Version: 2.10.0
Summary: Jupyter-friendly Python frontend for MINUIT2 in C++
Home-page: http://github.com/scikit-hep/iminuit
Author: Piti Ongmongkolkul and the iminuit team
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/migrad.cpp
new/iminuit-2.10.0/src/migrad.cpp
--- old/iminuit-2.9.0/src/migrad.cpp 2022-01-09 15:12:36.000000000 +0100
+++ new/iminuit-2.10.0/src/migrad.cpp 2022-03-04 17:01:07.000000000 +0100
@@ -1,7 +1,7 @@
+#include "fcn.hpp"
#include <Minuit2/FCNBase.h>
#include <Minuit2/MnMigrad.h>
#include <pybind11/pybind11.h>
-#include "fcn.hpp"
namespace py = pybind11;
using namespace ROOT::Minuit2;
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/minimumstate.cpp
new/iminuit-2.10.0/src/minimumstate.cpp
--- old/iminuit-2.9.0/src/minimumstate.cpp 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/minimumstate.cpp 2022-03-04 17:01:07.000000000
+0100
@@ -1,10 +1,10 @@
+#include "lasymmatrix.hpp"
+#include "lavector.hpp"
#include <Minuit2/MinimumState.h>
+#include <cmath>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
-#include <cmath>
#include <type_traits>
-#include "lasymmatrix.hpp"
-#include "lavector.hpp"
namespace py = pybind11;
using namespace ROOT::Minuit2;
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/minuitparameter.cpp
new/iminuit-2.10.0/src/minuitparameter.cpp
--- old/iminuit-2.9.0/src/minuitparameter.cpp 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/minuitparameter.cpp 2022-03-04 17:01:07.000000000
+0100
@@ -1,7 +1,7 @@
+#include "equal.hpp"
#include <Minuit2/MinuitParameter.h>
#include <pybind11/operators.h>
#include <pybind11/pybind11.h>
-#include "equal.hpp"
namespace ROOT {
namespace Minuit2 {
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/printimpl.cpp
new/iminuit-2.10.0/src/printimpl.cpp
--- old/iminuit-2.9.0/src/printimpl.cpp 2022-01-09 15:12:36.000000000 +0100
+++ new/iminuit-2.10.0/src/printimpl.cpp 2022-03-04 17:01:07.000000000
+0100
@@ -1,5 +1,5 @@
-#include <pybind11/pybind11.h>
#include "Minuit2/MnPrint.h"
+#include <pybind11/pybind11.h>
using ROOT::Minuit2::MnPrint;
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/simplex.cpp
new/iminuit-2.10.0/src/simplex.cpp
--- old/iminuit-2.9.0/src/simplex.cpp 2022-01-09 15:12:36.000000000 +0100
+++ new/iminuit-2.10.0/src/simplex.cpp 2022-03-04 17:01:07.000000000 +0100
@@ -1,7 +1,7 @@
+#include "fcn.hpp"
#include <Minuit2/FCNBase.h>
#include <Minuit2/MnSimplex.h>
#include <pybind11/pybind11.h>
-#include "fcn.hpp"
namespace py = pybind11;
using namespace ROOT::Minuit2;
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/strategy.cpp
new/iminuit-2.10.0/src/strategy.cpp
--- old/iminuit-2.9.0/src/strategy.cpp 2022-01-09 15:12:36.000000000 +0100
+++ new/iminuit-2.10.0/src/strategy.cpp 2022-03-04 17:01:07.000000000 +0100
@@ -1,7 +1,7 @@
+#include "equal.hpp"
#include <Minuit2/MnStrategy.h>
#include <pybind11/operators.h>
#include <pybind11/pybind11.h>
-#include "equal.hpp"
namespace ROOT {
namespace Minuit2 {
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/type_caster.hpp
new/iminuit-2.10.0/src/type_caster.hpp
--- old/iminuit-2.9.0/src/type_caster.hpp 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/type_caster.hpp 2022-03-04 17:01:07.000000000
+0100
@@ -1,6 +1,6 @@
+#include <algorithm>
#include <pybind11/numpy.h>
#include <pybind11/pybind11.h>
-#include <algorithm>
#include <vector>
namespace pybind11 {
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/usercovariance.cpp
new/iminuit-2.10.0/src/usercovariance.cpp
--- old/iminuit-2.9.0/src/usercovariance.cpp 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/src/usercovariance.cpp 2022-03-04 17:01:07.000000000
+0100
@@ -1,9 +1,9 @@
+#include "equal.hpp"
+#include "type_caster.hpp"
#include <Minuit2/MnUserCovariance.h>
#include <pybind11/operators.h>
#include <pybind11/pybind11.h>
#include <vector>
-#include "equal.hpp"
-#include "type_caster.hpp"
namespace ROOT {
namespace Minuit2 {
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/src/userparameterstate.cpp
new/iminuit-2.10.0/src/userparameterstate.cpp
--- old/iminuit-2.9.0/src/userparameterstate.cpp 2022-01-09
15:12:36.000000000 +0100
+++ new/iminuit-2.10.0/src/userparameterstate.cpp 2022-03-04
17:01:07.000000000 +0100
@@ -1,9 +1,9 @@
+#include "equal.hpp"
+#include "type_caster.hpp"
#include <Minuit2/MnUserParameterState.h>
#include <pybind11/operators.h>
#include <pybind11/pybind11.h>
#include <type_traits>
-#include "equal.hpp"
-#include "type_caster.hpp"
namespace ROOT {
namespace Minuit2 {
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_core.py
new/iminuit-2.10.0/tests/test_core.py
--- old/iminuit-2.9.0/tests/test_core.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_core.py 2022-03-04 17:01:07.000000000
+0100
@@ -55,7 +55,7 @@
def fn(x, y):
- return 10 + x ** 2 + ((y - 1) / 2) ** 2
+ return 10 + x**2 + ((y - 1) / 2) ** 2
def fn_grad(x, y):
@@ -104,7 +104,7 @@
def test_MnMigrad_grad():
- fcn = FCN(lambda x: 10 + x ** 2, lambda x: [2 * x], False, 1)
+ fcn = FCN(lambda x: 10 + x**2, lambda x: [2 * x], False, 1)
state = MnUserParameterState()
state.add("x", 5, 0.1)
migrad = MnMigrad(fcn, state, 1)
@@ -176,7 +176,7 @@
def test_MnScan():
- fcn = FCN(lambda x: 10 + x ** 2, None, False, 1)
+ fcn = FCN(lambda x: 10 + x**2, None, False, 1)
state = MnUserParameterState()
state.add("x", 2, 5)
scan = MnScan(fcn, state, 1)
@@ -188,7 +188,7 @@
def test_MnSimplex():
- fcn = FCN(lambda x: 10 + x ** 2, None, False, 1)
+ fcn = FCN(lambda x: 10 + x**2, None, False, 1)
state = MnUserParameterState()
state.add("x", 2, 5)
simplex = MnSimplex(fcn, state, 1)
@@ -200,7 +200,7 @@
def test_FunctionMinimum():
- fcn = FCN(lambda x: 10 + x ** 2, None, False, 1)
+ fcn = FCN(lambda x: 10 + x**2, None, False, 1)
st = MnUserParameterState()
st.add("x", 0.01, 5)
str = MnStrategy(1)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_cost.py
new/iminuit-2.10.0/tests/test_cost.py
--- old/iminuit-2.9.0/tests/test_cost.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_cost.py 2022-03-04 17:01:07.000000000
+0100
@@ -40,6 +40,10 @@
return mle, nx, xe
+def logpdf(x, mu, sigma):
+ return norm(mu, sigma).logpdf(x)
+
+
def pdf(x, mu, sigma):
return norm(mu, sigma).pdf(x)
@@ -59,29 +63,35 @@
@pytest.mark.parametrize("verbose", (0, 1))
-def test_UnbinnedNLL(unbinned, verbose):
+@pytest.mark.parametrize("model", (logpdf, pdf))
+def test_UnbinnedNLL(unbinned, verbose, model):
mle, x = unbinned
- cost = UnbinnedNLL(x, pdf, verbose=verbose)
+ cost = UnbinnedNLL(x, model, verbose=verbose, log=model is logpdf)
assert cost.ndata == np.inf
m = Minuit(cost, mu=0, sigma=1)
m.limits["sigma"] = (0, None)
m.migrad()
assert_allclose(m.values, mle[1:], atol=1e-3)
- assert m.errors["mu"] == pytest.approx(1000 ** -0.5, rel=0.05)
+ assert m.errors["mu"] == pytest.approx(1000**-0.5, rel=0.05)
assert_equal(m.fmin.reduced_chi2, np.nan)
@pytest.mark.parametrize("verbose", (0, 1))
-def test_ExtendedUnbinnedNLL(unbinned, verbose):
+@pytest.mark.parametrize("model", (logpdf, pdf))
+def test_ExtendedUnbinnedNLL(unbinned, verbose, model):
mle, x = unbinned
- def scaled_pdf(x, n, mu, sigma):
- return n, n * norm(mu, sigma).pdf(x)
+ log = model is logpdf
+
+ def density(x, n, mu, sigma):
+ if log:
+ return n, np.log(n) + logpdf(x, mu, sigma)
+ return n, n * pdf(x, mu, sigma)
- cost = ExtendedUnbinnedNLL(x, scaled_pdf, verbose=verbose)
+ cost = ExtendedUnbinnedNLL(x, density, verbose=verbose, log=log)
assert cost.ndata == np.inf
m = Minuit(cost, n=len(x), mu=0, sigma=1)
@@ -89,7 +99,7 @@
m.limits["sigma"] = (0, None)
m.migrad()
assert_allclose(m.values, mle, atol=1e-3)
- assert m.errors["mu"] == pytest.approx(1000 ** -0.5, rel=0.05)
+ assert m.errors["mu"] == pytest.approx(1000**-0.5, rel=0.05)
assert_equal(m.fmin.reduced_chi2, np.nan)
@@ -106,7 +116,7 @@
m.migrad()
# binning loses information compared to unbinned case
assert_allclose(m.values, mle[1:], rtol=0.15)
- assert m.errors["mu"] == pytest.approx(1000 ** -0.5, rel=0.05)
+ assert m.errors["mu"] == pytest.approx(1000**-0.5, rel=0.05)
assert m.ndof == len(nx) - 2
assert_allclose(m.fmin.reduced_chi2, 1, atol=0.15)
@@ -166,7 +176,7 @@
m.migrad()
# binning loses information compared to unbinned case
assert_allclose(m.values, mle, rtol=0.15)
- assert m.errors["mu"] == pytest.approx(1000 ** -0.5, rel=0.05)
+ assert m.errors["mu"] == pytest.approx(1000**-0.5, rel=0.05)
assert m.ndof == len(nx) - 3
assert_allclose(m.fmin.reduced_chi2, 1, 0.1)
@@ -493,7 +503,7 @@
sa = 0.1
sb = 0.02
rho = 0.5
- cov = ((sa ** 2, rho * sa * sb), (rho * sa * sb, sb ** 2))
+ cov = ((sa**2, rho * sa * sb), (rho * sa * sb, sb**2))
lsq3 = lsq1 + NormalConstraint(("a", "b"), (1, 2), cov)
assert lsq1.func_code.co_varnames == ("a", "b")
assert lsq2.func_code.co_varnames == ("a", "b")
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_issue.py
new/iminuit-2.10.0/tests/test_issue.py
--- old/iminuit-2.9.0/tests/test_issue.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_issue.py 2022-03-04 17:01:07.000000000
+0100
@@ -28,7 +28,7 @@
def test_issue_544():
@lsq
def fcn(x, y):
- return x ** 2 + y ** 2
+ return x**2 + y**2
m = Minuit(fcn, x=0, y=0)
m.fixed = True
@@ -45,7 +45,7 @@
if self.first:
assert a == 1.0 and b == 2.0
self.first = False
- return a ** 2 + b ** 2
+ return a**2 + b**2
m = Minuit(F(), a=1, b=2)
m.fixed["a"] = False # this used to change a to b
@@ -75,7 +75,7 @@
def test_issue_669():
@lsq
def fcn(x, y):
- return x ** 2 + (y / 2) ** 2
+ return x**2 + (y / 2) ** 2
m = Minuit(fcn, x=0, y=0)
@@ -96,7 +96,7 @@
@lsq
def fcn(par):
- return np.sum(par ** 2)
+ return np.sum(par**2)
def grad(par):
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_minimize.py
new/iminuit-2.10.0/tests/test_minimize.py
--- old/iminuit-2.9.0/tests/test_minimize.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_minimize.py 2022-03-04 17:01:07.000000000
+0100
@@ -54,7 +54,7 @@
def rosen(par):
x, y = par
- return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2
+ return (1 - x) ** 2 + 100 * (y - x**2) ** 2
r1 = minimize(rosen, (0, 0), tol=1)
r2 = minimize(rosen, (0, 0), tol=1e-6)
@@ -63,9 +63,9 @@
def test_disp(capsys):
- minimize(lambda x: x ** 2, 0)
+ minimize(lambda x: x**2, 0)
assert capsys.readouterr()[0] == ""
- minimize(lambda x: x ** 2, 0, options={"disp": True})
+ minimize(lambda x: x**2, 0, options={"disp": True})
assert capsys.readouterr()[0] != ""
@@ -115,7 +115,7 @@
def __call__(self, x):
self.n += 1
- return x ** 2 + 1e-2 * (self.n % 3)
+ return x**2 + 1e-2 * (self.n % 3)
r = minimize(Fcn(), [1], options={"maxfun": 100000000})
assert not r.success
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_minuit.py
new/iminuit-2.10.0/tests/test_minuit.py
--- old/iminuit-2.9.0/tests/test_minuit.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_minuit.py 2022-03-04 17:01:07.000000000
+0100
@@ -95,7 +95,7 @@
def func5(x, long_variable_name_really_long_why_does_it_has_to_be_this_long,
z):
return (
(x - 1) ** 2
- + long_variable_name_really_long_why_does_it_has_to_be_this_long ** 2
+ + long_variable_name_really_long_why_does_it_has_to_be_this_long**2
+ (z + 1) ** 2
)
@@ -109,7 +109,7 @@
@lsq
def func6(x, m, s, a):
- return a / ((x - m) ** 2 + s ** 2)
+ return a / ((x - m) ** 2 + s**2)
class Correlated:
@@ -119,7 +119,7 @@
sx = 2
sy = 1
corr = 0.5
- cov = (sx ** 2, corr * sx * sy), (corr * sx * sy, sy ** 2)
+ cov = (sx**2, corr * sx * sy), (corr * sx * sy, sy**2)
self.cinv = np.linalg.inv(cov)
def __call__(self, x):
@@ -306,7 +306,7 @@
def test_wrong_use_of_array_init():
- m = Minuit(lambda a, b: a ** 2 + b ** 2, (1, 2))
+ m = Minuit(lambda a, b: a**2 + b**2, (1, 2))
m.errordef = Minuit.LEAST_SQUARES
with pytest.raises(TypeError):
m.migrad()
@@ -452,7 +452,7 @@
assert m.accurate
m.minos(cl=cl)
assert m.values["lambd"] == approx(k)
- assert m.errors["lambd"] == approx(k ** 0.5, abs=2e-3 if limit else None)
+ assert m.errors["lambd"] == approx(k**0.5, abs=2e-3 if limit else None)
assert m.merrors["lambd"].lower == approx(lower, rel=1e-3)
assert m.merrors["lambd"].upper == approx(upper, rel=1e-3)
assert m.merrors[0].lower == m.merrors["lambd"].lower
@@ -850,7 +850,7 @@
def nll(mu, sigma):
z = (data - mu) / sigma
- logp = -0.5 * z ** 2 - np.log(sigma)
+ logp = -0.5 * z**2 - np.log(sigma)
return -np.sum(logp)
m = Minuit(nll, mu=0, sigma=1)
@@ -896,7 +896,7 @@
def __call__(self, x):
self.nfcn += 1
- return np.exp(x ** 2)
+ return np.exp(x**2)
# check that counting is accurate
fcn = Func()
@@ -925,7 +925,7 @@
ngrad = 0
def __call__(self, x):
- return x ** 2
+ return x**2
def grad(self, x):
self.ngrad += 1
@@ -949,7 +949,7 @@
def test_errordef():
- m = Minuit(lambda x: x ** 2, 0)
+ m = Minuit(lambda x: x**2, 0)
m.errordef = 4
assert m.errordef == 4
m.migrad()
@@ -1043,7 +1043,7 @@
def test_function_with_maximum():
def func(a):
- return -(a ** 2)
+ return -(a**2)
m = Minuit(func, a=0)
m.errordef = 1
@@ -1094,11 +1094,11 @@
def test_hesse_without_migrad():
- m = Minuit(lambda x: x ** 2 + x ** 4, x=0)
+ m = Minuit(lambda x: x**2 + x**4, x=0)
m.errordef = 0.5
# second derivative: 12 x^2 + 2
m.hesse()
- assert m.errors["x"] == approx(0.5 ** 0.5, abs=1e-4)
+ assert m.errors["x"] == approx(0.5**0.5, abs=1e-4)
m.values["x"] = 1
m.hesse()
assert m.errors["x"] == approx((1.0 / 14.0) ** 0.5, abs=1e-4)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_repr.py
new/iminuit-2.10.0/tests/test_repr.py
--- old/iminuit-2.9.0/tests/test_repr.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_repr.py 2022-03-04 17:01:07.000000000
+0100
@@ -490,7 +490,7 @@
def test_html_minuit():
- m = Minuit(lambda x, y: x ** 2 + 4 * y ** 2, x=0, y=0)
+ m = Minuit(lambda x, y: x**2 + 4 * y**2, x=0, y=0)
m.errordef = 1
assert m._repr_html_() == m.params._repr_html_()
m.migrad()
@@ -622,7 +622,7 @@
def test_text_minuit():
- m = Minuit(lambda x, y: x ** 2 + 4 * y ** 2, x=0, y=0)
+ m = Minuit(lambda x, y: x**2 + 4 * y**2, x=0, y=0)
m.errordef = 1
assert str(m) == str(m.params)
m.migrad()
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_scipy.py
new/iminuit-2.10.0/tests/test_scipy.py
--- old/iminuit-2.9.0/tests/test_scipy.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_scipy.py 2022-03-04 17:01:07.000000000
+0100
@@ -8,7 +8,7 @@
def fcn(a, b):
- return a ** 2 + ((b - 1) / 2.0) ** 2 + 3
+ return a**2 + ((b - 1) / 2.0) ** 2 + 3
fcn.errordef = 1
@@ -193,7 +193,7 @@
@pytest.mark.parametrize("fixed", (False, True))
def test_scipy_constraints_1(lb, fixed):
def fcn(a, x, y):
- return a + x ** 2 + y ** 2
+ return a + x**2 + y**2
m = Minuit(fcn, a=3, x=1, y=2)
m.fixed["a"] = fixed
@@ -213,7 +213,7 @@
@pytest.mark.parametrize("fixed", (False, True))
def test_scipy_constraints_2(fixed):
def fcn(x, y):
- return x ** 2 + y ** 2
+ return x**2 + y**2
m = Minuit(fcn, x=1, y=2)
m.errordef = 1
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_tabulate.py
new/iminuit-2.10.0/tests/test_tabulate.py
--- old/iminuit-2.9.0/tests/test_tabulate.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_tabulate.py 2022-03-04 17:01:07.000000000
+0100
@@ -9,7 +9,7 @@
def test_params():
- m = Minuit(lambda x, y: x ** 2 + (y / 2) ** 2 + 1, x=0, y=0)
+ m = Minuit(lambda x, y: x**2 + (y / 2) ** 2 + 1, x=0, y=0)
m.errordef = 1
m.limits["y"] = (-1, 1)
m.fixed["x"] = True
@@ -27,7 +27,7 @@
def test_matrix():
- m = Minuit(lambda x, y: x ** 2 + (y / 2) ** 2 + 1, x=0, y=0)
+ m = Minuit(lambda x, y: x**2 + (y / 2) ** 2 + 1, x=0, y=0)
m.errordef = 1
m.migrad()
assert (
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/iminuit-2.9.0/tests/test_util.py
new/iminuit-2.10.0/tests/test_util.py
--- old/iminuit-2.9.0/tests/test_util.py 2022-01-09 15:12:36.000000000
+0100
+++ new/iminuit-2.10.0/tests/test_util.py 2022-03-04 17:01:07.000000000
+0100
@@ -527,14 +527,14 @@
x = np.array([1])
dx = np.array([0])
- y, jac = util._jacobi(lambda x: x ** 2, x, dx, 1e-3)
+ y, jac = util._jacobi(lambda x: x**2, x, dx, 1e-3)
np.testing.assert_equal(y, 1)
np.testing.assert_equal(jac, 0)
x = np.array([np.nan])
dx = np.array([0.1])
- y, jac = util._jacobi(lambda x: x ** 2, x, dx, 1e-3)
+ y, jac = util._jacobi(lambda x: x**2, x, dx, 1e-3)
np.testing.assert_equal(y, np.nan)
np.testing.assert_equal(jac, np.nan)
@@ -543,7 +543,7 @@
dx = np.array([np.nan])
with pytest.raises(AssertionError):
# dx must be >= 0
- util._jacobi(lambda x: x ** 2, x, dx, 1e-3)
+ util._jacobi(lambda x: x**2, x, dx, 1e-3)
@pytest.mark.parametrize("fail", (False, True))
