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sage: fix numpy and scipy integration

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This commit is contained in:
Timo Kaufmann 2018-09-07 13:19:00 +02:00
parent 9466627d36
commit 7521f2863d
2 changed files with 166 additions and 56 deletions
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207
pkgs/applications/science/math/sage/patches/numpy-1.14.3.patch → pkgs/applications/science/math/sage/patches/numpy-1.15.1.patch
View File

@ -1,5 +1,5 @@
diff --git a/src/doc/en/faq/faq-usage.rst b/src/doc/en/faq/faq-usage.rst
index 79b4205fd3..9a89bd2136 100644
index 2347a1190d..f5b0fe71a4 100644
--- a/src/doc/en/faq/faq-usage.rst
+++ b/src/doc/en/faq/faq-usage.rst
@@ -338,7 +338,7 @@ ints. For example::
@ -174,7 +174,7 @@ index 5b89cd75ee..e50b2ea5d4 100644
This creates a random 5x5 matrix ``A``, and solves `Ax=b` where
``b=[0.0,1.0,2.0,3.0,4.0]``. There are many other routines in the :mod:`numpy.linalg`
diff --git a/src/sage/calculus/riemann.pyx b/src/sage/calculus/riemann.pyx
index df85cce43d..34ea164be0 100644
index 60f37f7557..4ac3dedf1d 100644
--- a/src/sage/calculus/riemann.pyx
+++ b/src/sage/calculus/riemann.pyx
@@ -1191,30 +1191,30 @@ cpdef complex_to_spiderweb(np.ndarray[COMPLEX_T, ndim = 2] z_values,
@ -248,7 +248,7 @@ index df85cce43d..34ea164be0 100644
TESTS::
diff --git a/src/sage/combinat/fully_packed_loop.py b/src/sage/combinat/fully_packed_loop.py
index 61b1003002..4baee9cbbd 100644
index 0a9bd61267..d2193cc2d6 100644
--- a/src/sage/combinat/fully_packed_loop.py
+++ b/src/sage/combinat/fully_packed_loop.py
@@ -72,11 +72,11 @@ def _make_color_list(n, colors=None, color_map=None, randomize=False):
@ -269,10 +269,10 @@ index 61b1003002..4baee9cbbd 100644
['blue', 'blue', 'red', 'blue', 'red', 'red', 'red', 'blue']
"""
diff --git a/src/sage/finance/time_series.pyx b/src/sage/finance/time_series.pyx
index c37700d14e..49b7298d0b 100644
index 28779365df..3ab0282861 100644
--- a/src/sage/finance/time_series.pyx
+++ b/src/sage/finance/time_series.pyx
@@ -109,8 +109,8 @@ cdef class TimeSeries:
@@ -111,8 +111,8 @@ cdef class TimeSeries:
sage: import numpy
sage: v = numpy.array([[1,2], [3,4]], dtype=float); v
@ -283,7 +283,7 @@ index c37700d14e..49b7298d0b 100644
sage: finance.TimeSeries(v)
[1.0000, 2.0000, 3.0000, 4.0000]
sage: finance.TimeSeries(v[:,0])
@@ -2098,14 +2098,14 @@ cdef class TimeSeries:
@@ -2100,14 +2100,14 @@ cdef class TimeSeries:
sage: w[0] = 20
sage: w
@ -301,7 +301,7 @@ index c37700d14e..49b7298d0b 100644
sage: v
[20.0000, -3.0000, 4.5000, -2.0000]
diff --git a/src/sage/functions/hyperbolic.py b/src/sage/functions/hyperbolic.py
index 931a4b41e4..bf33fc483d 100644
index aff552f450..7a6df931e7 100644
--- a/src/sage/functions/hyperbolic.py
+++ b/src/sage/functions/hyperbolic.py
@@ -214,7 +214,7 @@ class Function_coth(GinacFunction):
@ -341,7 +341,7 @@ index 931a4b41e4..bf33fc483d 100644
return arctanh(1.0 / x)
diff --git a/src/sage/functions/orthogonal_polys.py b/src/sage/functions/orthogonal_polys.py
index 017c85a96f..33fbb499c5 100644
index ed6365bef4..99b8b04dad 100644
--- a/src/sage/functions/orthogonal_polys.py
+++ b/src/sage/functions/orthogonal_polys.py
@@ -810,12 +810,12 @@ class Func_chebyshev_T(ChebyshevFunction):
@ -379,10 +379,10 @@ index 017c85a96f..33fbb499c5 100644
array([ 0.2 , -0.96])
"""
diff --git a/src/sage/functions/other.py b/src/sage/functions/other.py
index 679384c907..d63b295a4c 100644
index 1883daa3e6..9885222817 100644
--- a/src/sage/functions/other.py
+++ b/src/sage/functions/other.py
@@ -390,7 +390,7 @@ class Function_ceil(BuiltinFunction):
@@ -389,7 +389,7 @@ class Function_ceil(BuiltinFunction):
sage: import numpy
sage: a = numpy.linspace(0,2,6)
sage: ceil(a)
@ -391,7 +391,7 @@ index 679384c907..d63b295a4c 100644
Test pickling::
@@ -539,7 +539,7 @@ class Function_floor(BuiltinFunction):
@@ -553,7 +553,7 @@ class Function_floor(BuiltinFunction):
sage: import numpy
sage: a = numpy.linspace(0,2,6)
sage: floor(a)
@ -400,7 +400,7 @@ index 679384c907..d63b295a4c 100644
sage: floor(x)._sympy_()
floor(x)
@@ -840,7 +840,7 @@ def sqrt(x, *args, **kwds):
@@ -869,7 +869,7 @@ def sqrt(x, *args, **kwds):
sage: import numpy
sage: a = numpy.arange(2,5)
sage: sqrt(a)
@ -409,11 +409,35 @@ index 679384c907..d63b295a4c 100644
"""
if isinstance(x, float):
return math.sqrt(x)
diff --git a/src/sage/functions/spike_function.py b/src/sage/functions/spike_function.py
index 1e021de3fe..56635ca98f 100644
--- a/src/sage/functions/spike_function.py
+++ b/src/sage/functions/spike_function.py
@@ -157,7 +157,7 @@ class SpikeFunction:
sage: S = spike_function([(-3,4),(-1,1),(2,3)]); S
A spike function with spikes at [-3.0, -1.0, 2.0]
sage: P = S.plot_fft_abs(8)
- sage: p = P[0]; p.ydata
+ sage: p = P[0]; p.ydata # abs tol 1e-8
[5.0, 5.0, 3.367958691924177, 3.367958691924177, 4.123105625617661, 4.123105625617661, 4.759921664218055, 4.759921664218055]
"""
w = self.vector(samples = samples, xmin=xmin, xmax=xmax)
@@ -176,8 +176,8 @@ class SpikeFunction:
sage: S = spike_function([(-3,4),(-1,1),(2,3)]); S
A spike function with spikes at [-3.0, -1.0, 2.0]
sage: P = S.plot_fft_arg(8)
- sage: p = P[0]; p.ydata
- [0.0, 0.0, -0.211524990023434..., -0.211524990023434..., 0.244978663126864..., 0.244978663126864..., -0.149106180027477..., -0.149106180027477...]
+ sage: p = P[0]; p.ydata # abs tol 1e-8
+ [0.0, 0.0, -0.211524990023434, -0.211524990023434, 0.244978663126864, 0.244978663126864, -0.149106180027477, -0.149106180027477]
"""
w = self.vector(samples = samples, xmin=xmin, xmax=xmax)
xmin, xmax = self._ranges(xmin, xmax)
diff --git a/src/sage/functions/trig.py b/src/sage/functions/trig.py
index e7e7a311cd..e7ff78a9de 100644
index 501e7ff6b6..5f760912f0 100644
--- a/src/sage/functions/trig.py
+++ b/src/sage/functions/trig.py
@@ -731,7 +731,7 @@ class Function_arccot(GinacFunction):
@@ -724,7 +724,7 @@ class Function_arccot(GinacFunction):
sage: import numpy
sage: a = numpy.arange(2, 5)
sage: arccot(a)
@ -422,7 +446,7 @@ index e7e7a311cd..e7ff78a9de 100644
"""
return math.pi/2 - arctan(x)
@@ -787,7 +787,7 @@ class Function_arccsc(GinacFunction):
@@ -780,7 +780,7 @@ class Function_arccsc(GinacFunction):
sage: import numpy
sage: a = numpy.arange(2, 5)
sage: arccsc(a)
@ -431,7 +455,7 @@ index e7e7a311cd..e7ff78a9de 100644
"""
return arcsin(1.0/x)
@@ -845,7 +845,7 @@ class Function_arcsec(GinacFunction):
@@ -838,7 +838,7 @@ class Function_arcsec(GinacFunction):
sage: import numpy
sage: a = numpy.arange(2, 5)
sage: arcsec(a)
@ -440,7 +464,7 @@ index e7e7a311cd..e7ff78a9de 100644
"""
return arccos(1.0/x)
@@ -920,13 +920,13 @@ class Function_arctan2(GinacFunction):
@@ -913,13 +913,13 @@ class Function_arctan2(GinacFunction):
sage: a = numpy.linspace(1, 3, 3)
sage: b = numpy.linspace(3, 6, 3)
sage: atan2(a, b)
@ -458,10 +482,10 @@ index e7e7a311cd..e7ff78a9de 100644
TESTS::
diff --git a/src/sage/matrix/constructor.pyx b/src/sage/matrix/constructor.pyx
index 19a1d37df0..5780dfae1c 100644
index 12136f1773..491bf22e62 100644
--- a/src/sage/matrix/constructor.pyx
+++ b/src/sage/matrix/constructor.pyx
@@ -494,8 +494,8 @@ class MatrixFactory(object):
@@ -503,8 +503,8 @@ def matrix(*args, **kwds):
[7 8 9]
Full MatrixSpace of 3 by 3 dense matrices over Integer Ring
sage: n = matrix(QQ, 2, 2, [1, 1/2, 1/3, 1/4]).numpy(); n
@ -473,10 +497,31 @@ index 19a1d37df0..5780dfae1c 100644
[ 1 1/2]
[1/3 1/4]
diff --git a/src/sage/matrix/matrix_double_dense.pyx b/src/sage/matrix/matrix_double_dense.pyx
index 48e0a8a97f..1be5d35b19 100644
index 66e54a79a4..0498334f4b 100644
--- a/src/sage/matrix/matrix_double_dense.pyx
+++ b/src/sage/matrix/matrix_double_dense.pyx
@@ -2546,7 +2546,7 @@ cdef class Matrix_double_dense(Matrix_dense):
@@ -606,6 +606,9 @@ cdef class Matrix_double_dense(Matrix_dense):
[ 3.0 + 9.0*I 4.0 + 16.0*I 5.0 + 25.0*I]
[6.0 + 36.0*I 7.0 + 49.0*I 8.0 + 64.0*I]
sage: B.condition()
+ doctest:warning
+ ...
+ ComplexWarning: Casting complex values to real discards the imaginary part
203.851798...
sage: B.condition(p='frob')
203.851798...
@@ -654,9 +657,7 @@ cdef class Matrix_double_dense(Matrix_dense):
True
sage: B = A.change_ring(CDF)
sage: B.condition()
- Traceback (most recent call last):
- ...
- LinAlgError: Singular matrix
+ +Infinity
Improper values of ``p`` are caught. ::
@@ -2519,7 +2520,7 @@ cdef class Matrix_double_dense(Matrix_dense):
sage: P.is_unitary(algorithm='orthonormal')
Traceback (most recent call last):
...
@ -485,7 +530,7 @@ index 48e0a8a97f..1be5d35b19 100644
TESTS::
@@ -3662,8 +3662,8 @@ cdef class Matrix_double_dense(Matrix_dense):
@@ -3635,8 +3636,8 @@ cdef class Matrix_double_dense(Matrix_dense):
[0.0 1.0 2.0]
[3.0 4.0 5.0]
sage: m.numpy()
@ -496,7 +541,7 @@ index 48e0a8a97f..1be5d35b19 100644
Alternatively, numpy automatically calls this function (via
the magic :meth:`__array__` method) to convert Sage matrices
@@ -3674,16 +3674,16 @@ cdef class Matrix_double_dense(Matrix_dense):
@@ -3647,16 +3648,16 @@ cdef class Matrix_double_dense(Matrix_dense):
[0.0 1.0 2.0]
[3.0 4.0 5.0]
sage: numpy.array(m)
@ -518,10 +563,10 @@ index 48e0a8a97f..1be5d35b19 100644
dtype('complex128')
diff --git a/src/sage/matrix/special.py b/src/sage/matrix/special.py
index c698ba5e97..b743bab354 100644
index ccbd208810..c3f9a65093 100644
--- a/src/sage/matrix/special.py
+++ b/src/sage/matrix/special.py
@@ -705,7 +705,7 @@ def diagonal_matrix(arg0=None, arg1=None, arg2=None, sparse=True):
@@ -706,7 +706,7 @@ def diagonal_matrix(arg0=None, arg1=None, arg2=None, sparse=True):
sage: import numpy
sage: entries = numpy.array([1.2, 5.6]); entries
@ -530,7 +575,7 @@ index c698ba5e97..b743bab354 100644
sage: A = diagonal_matrix(3, entries); A
[1.2 0.0 0.0]
[0.0 5.6 0.0]
@@ -715,7 +715,7 @@ def diagonal_matrix(arg0=None, arg1=None, arg2=None, sparse=True):
@@ -716,7 +716,7 @@ def diagonal_matrix(arg0=None, arg1=None, arg2=None, sparse=True):
sage: j = numpy.complex(0,1)
sage: entries = numpy.array([2.0+j, 8.1, 3.4+2.6*j]); entries
@ -540,10 +585,10 @@ index c698ba5e97..b743bab354 100644
[2.0 + 1.0*I 0.0 0.0]
[ 0.0 8.1 0.0]
diff --git a/src/sage/modules/free_module_element.pyx b/src/sage/modules/free_module_element.pyx
index 230f142117..2ab1c0ae68 100644
index 37d92c1282..955d083b34 100644
--- a/src/sage/modules/free_module_element.pyx
+++ b/src/sage/modules/free_module_element.pyx
@@ -982,7 +982,7 @@ cdef class FreeModuleElement(Vector): # abstract base class
@@ -988,7 +988,7 @@ cdef class FreeModuleElement(Vector): # abstract base class
sage: v.numpy()
array([1, 2, 5/6], dtype=object)
sage: v.numpy(dtype=float)
@ -552,7 +597,7 @@ index 230f142117..2ab1c0ae68 100644
sage: v.numpy(dtype=int)
array([1, 2, 0])
sage: import numpy
@@ -993,7 +993,7 @@ cdef class FreeModuleElement(Vector): # abstract base class
@@ -999,7 +999,7 @@ cdef class FreeModuleElement(Vector): # abstract base class
be more efficient but may have unintended consequences::
sage: v.numpy(dtype=None)
@ -596,22 +641,6 @@ index 39fc2970de..2badf98284 100644
"""
if dtype is None or dtype is self._vector_numpy.dtype:
from copy import copy
diff --git a/src/sage/numerical/optimize.py b/src/sage/numerical/optimize.py
index 17b5ebb84b..92ce35c502 100644
--- a/src/sage/numerical/optimize.py
+++ b/src/sage/numerical/optimize.py
@@ -486,9 +486,9 @@ def minimize_constrained(func,cons,x0,gradient=None,algorithm='default', **args)
else:
min = optimize.fmin_tnc(f, x0, approx_grad=True, bounds=cons, messages=0, **args)[0]
elif isinstance(cons[0], function_type) or isinstance(cons[0], Expression):
- min = optimize.fmin_cobyla(f, x0, cons, iprint=0, **args)
+ min = optimize.fmin_cobyla(f, x0, cons, disp=0, **args)
elif isinstance(cons, function_type) or isinstance(cons, Expression):
- min = optimize.fmin_cobyla(f, x0, cons, iprint=0, **args)
+ min = optimize.fmin_cobyla(f, x0, cons, disp=0, **args)
return vector(RDF, min)
diff --git a/src/sage/plot/complex_plot.pyx b/src/sage/plot/complex_plot.pyx
index ad9693da62..758fb709b7 100644
--- a/src/sage/plot/complex_plot.pyx
@ -649,6 +678,76 @@ index ad9693da62..758fb709b7 100644
"""
import numpy
cdef unsigned int i, j, imax, jmax
diff --git a/src/sage/plot/histogram.py b/src/sage/plot/histogram.py
index 5d28473731..fc4b2046c0 100644
--- a/src/sage/plot/histogram.py
+++ b/src/sage/plot/histogram.py
@@ -53,10 +53,17 @@ class Histogram(GraphicPrimitive):
"""
import numpy as np
self.datalist=np.asarray(datalist,dtype=float)
+ if 'normed' in options:
+ from sage.misc.superseded import deprecation
+ deprecation(25260, "the 'normed' option is deprecated. Use 'density' instead.")
if 'linestyle' in options:
from sage.plot.misc import get_matplotlib_linestyle
options['linestyle'] = get_matplotlib_linestyle(
options['linestyle'], return_type='long')
+ if options.get('range', None):
+ # numpy.histogram performs type checks on "range" so this must be
+ # actual floats
+ options['range'] = [float(x) for x in options['range']]
GraphicPrimitive.__init__(self, options)
def get_minmax_data(self):
@@ -80,10 +87,14 @@ class Histogram(GraphicPrimitive):
{'xmax': 4.0, 'xmin': 0, 'ymax': 2, 'ymin': 0}
TESTS::
-
sage: h = histogram([10,3,5], normed=True)[0]
- sage: h.get_minmax_data() # rel tol 1e-15
- {'xmax': 10.0, 'xmin': 3.0, 'ymax': 0.4761904761904765, 'ymin': 0}
+ doctest:warning...:
+ DeprecationWarning: the 'normed' option is deprecated. Use 'density' instead.
+ See https://trac.sagemath.org/25260 for details.
+ sage: h.get_minmax_data()
+ doctest:warning ...:
+ VisibleDeprecationWarning: Passing `normed=True` on non-uniform bins has always been broken, and computes neither the probability density function nor the probability mass function. The result is only correct if the bins are uniform, when density=True will produce the same result anyway. The argument will be removed in a future version of numpy.
+ {'xmax': 10.0, 'xmin': 3.0, 'ymax': 0.476190476190..., 'ymin': 0}
"""
import numpy
@@ -152,7 +163,7 @@ class Histogram(GraphicPrimitive):
'rwidth': 'The relative width of the bars as a fraction of the bin width',
'cumulative': '(True or False) If True, then a histogram is computed in which each bin gives the counts in that bin plus all bins for smaller values. Negative values give a reversed direction of accumulation.',
'range': 'A list [min, max] which define the range of the histogram. Values outside of this range are treated as outliers and omitted from counts.',
- 'normed': 'Deprecated alias for density',
+ 'normed': 'Deprecated. Use density instead.',
'density': '(True or False) If True, the counts are normalized to form a probability density. (n/(len(x)*dbin)',
'weights': 'A sequence of weights the same length as the data list. If supplied, then each value contributes its associated weight to the bin count.',
'stacked': '(True or False) If True, multiple data are stacked on top of each other.',
@@ -199,7 +210,7 @@ class Histogram(GraphicPrimitive):
subplot.hist(self.datalist.transpose(), **options)
-@options(aspect_ratio='automatic',align='mid', weights=None, range=None, bins=10, edgecolor='black')
+@options(aspect_ratio='automatic', align='mid', weights=None, range=None, bins=10, edgecolor='black')
def histogram(datalist, **options):
"""
Computes and draws the histogram for list(s) of numerical data.
@@ -231,8 +242,9 @@ def histogram(datalist, **options):
- ``linewidth`` -- (float) width of the lines defining the bars
- ``linestyle`` -- (default: 'solid') Style of the line. One of 'solid'
or '-', 'dashed' or '--', 'dotted' or ':', 'dashdot' or '-.'
- - ``density`` -- (boolean - default: False) If True, the counts are
- normalized to form a probability density.
+ - ``density`` -- (boolean - default: False) If True, the result is the
+ value of the probability density function at the bin, normalized such
+ that the integral over the range is 1.
- ``range`` -- A list [min, max] which define the range of the
histogram. Values outside of this range are treated as outliers and
omitted from counts
diff --git a/src/sage/plot/line.py b/src/sage/plot/line.py
index 23f5e61446..3b1b51d7cf 100644
--- a/src/sage/plot/line.py
@ -718,7 +817,7 @@ index f3da57c370..3806f4b32f 100644
TESTS:
diff --git a/src/sage/probability/probability_distribution.pyx b/src/sage/probability/probability_distribution.pyx
index f66cd898b9..35995886d5 100644
index 1b119e323f..3290b00695 100644
--- a/src/sage/probability/probability_distribution.pyx
+++ b/src/sage/probability/probability_distribution.pyx
@@ -130,7 +130,17 @@ cdef class ProbabilityDistribution:
@ -741,10 +840,10 @@ index f66cd898b9..35995886d5 100644
import pylab
l = [float(self.get_random_element()) for _ in range(num_samples)]
diff --git a/src/sage/rings/rational.pyx b/src/sage/rings/rational.pyx
index a0bfe080f5..7d95e7a1a8 100644
index 12ca1b222b..9bad7dae0c 100644
--- a/src/sage/rings/rational.pyx
+++ b/src/sage/rings/rational.pyx
@@ -1056,7 +1056,7 @@ cdef class Rational(sage.structure.element.FieldElement):
@@ -1041,7 +1041,7 @@ cdef class Rational(sage.structure.element.FieldElement):
dtype('O')
sage: numpy.array([1, 1/2, 3/4])
@ -754,10 +853,10 @@ index a0bfe080f5..7d95e7a1a8 100644
if mpz_cmp_ui(mpq_denref(self.value), 1) == 0:
if mpz_fits_slong_p(mpq_numref(self.value)):
diff --git a/src/sage/rings/real_mpfr.pyx b/src/sage/rings/real_mpfr.pyx
index 4c630867a4..64e2187f5b 100644
index 9b90c8833e..1ce05b937d 100644
--- a/src/sage/rings/real_mpfr.pyx
+++ b/src/sage/rings/real_mpfr.pyx
@@ -1438,7 +1438,7 @@ cdef class RealNumber(sage.structure.element.RingElement):
@@ -1439,7 +1439,7 @@ cdef class RealNumber(sage.structure.element.RingElement):
sage: import numpy
sage: numpy.arange(10.0)
@ -767,10 +866,10 @@ index 4c630867a4..64e2187f5b 100644
dtype('float64')
sage: numpy.array([1.000000000000000000000000000000000000]).dtype
diff --git a/src/sage/schemes/elliptic_curves/height.py b/src/sage/schemes/elliptic_curves/height.py
index 3d270ebf9d..1144f168e3 100644
index de31fe9883..7a33ea6f5b 100644
--- a/src/sage/schemes/elliptic_curves/height.py
+++ b/src/sage/schemes/elliptic_curves/height.py
@@ -1623,18 +1623,18 @@ class EllipticCurveCanonicalHeight:
@@ -1627,18 +1627,18 @@ class EllipticCurveCanonicalHeight:
even::
sage: H.wp_on_grid(v,4)
@ -798,10 +897,10 @@ index 3d270ebf9d..1144f168e3 100644
tau = self.tau(v)
fk, err = self.fk_intervals(v, 15, CDF)
diff --git a/src/sage/symbolic/ring.pyx b/src/sage/symbolic/ring.pyx
index 2dcb0492b9..2b1a06385c 100644
index 9da38002e8..d61e74bf82 100644
--- a/src/sage/symbolic/ring.pyx
+++ b/src/sage/symbolic/ring.pyx
@@ -1135,7 +1135,7 @@ cdef class NumpyToSRMorphism(Morphism):
@@ -1136,7 +1136,7 @@ cdef class NumpyToSRMorphism(Morphism):
sage: cos(numpy.int('2'))
cos(2)
sage: numpy.cos(numpy.int('2'))

15
pkgs/applications/science/math/sage/sage-src.nix
View File

@ -94,9 +94,20 @@ stdenv.mkDerivation rec {
stripLen = 1;
})
# Only formatting changes.
(fetchpatch {
name = "matplotlib-2.2.2";
url = "https://git.sagemath.org/sage.git/patch?id=0d6244ed53b71aba861ce3d683d33e542c0bf0b0";
sha256 = "15x4cadxxlsdfh2sblgagqjj6ir13fgdzixxnwnvzln60saahb34";
})
(fetchpatch {
name = "scipy-1.1.0";
url = "https://git.sagemath.org/sage.git/patch?id=e0db968a51678b34ebd8d34906c7042900272378";
sha256 = "0kq5zxqphhrmavrmg830wdr7hwp1bkzdqlf3jfqfr8r8xq12qwf7";
})
# https://trac.sagemath.org/ticket/25260
./patches/numpy-1.14.3.patch
./patches/numpy-1.15.1.patch
# https://trac.sagemath.org/ticket/25862
./patches/eclib-20180710.patch