Merge pull request #14393 from peterbell10/ci-not-failing

azure-pipelines.yml

@@ -142,7 +142,7 @@ stages:
       CXX: g++-4.8
     steps:
     - script: |
-        set -e
+        set -euo pipefail
         sudo apt update -y
         sudo apt install -y g++-4.8 gcc-4.8
       displayName: 'Install GCC 4.8'
@@ -169,11 +169,14 @@ stages:
     - script: >-
         python -m pip install
         pycodestyle==2.5.0
-        pyflakes==2.1.1
+        pyflakes==2.3.1
       displayName: 'Install tools'
       failOnStderr: false
     - script: |
-        PYFLAKES_NODOCTEST=1 pyflakes scipy benchmarks/benchmarks 2>&1 | grep -E -v 'unable to detect undefined names|assigned to but never used|imported but unused|redefinition of unused|may be undefined, or defined from star imports|syntax error in type comment .ignore.import.' > test.out; cat test.out; test \! -s test.out
+        set -euo pipefail
+        PYFLAKES_NODOCTEST=1 pyflakes scipy benchmarks/benchmarks 2>&1 | grep -E -v 'unable to detect undefined names|assigned to but never used|imported but unused|redefinition of unused|may be undefined, or defined from star imports|syntax error in type comment .ignore.import.' > test.out || true
+        cat test.out
+        test \! -s test.out
         pycodestyle scipy benchmarks/benchmarks
         # for Travis CI we used to do: git fetch --unshallow
         # but apparently Azure clones are not as shallow
@@ -193,6 +196,7 @@ stages:
         git submodule update --init
       displayName: 'Fetch submodules'
     - script: |
+            set -euo pipefail
             docker pull i386/ubuntu:bionic
             docker run -v $(pwd):/scipy i386/ubuntu:bionic /usr/bin/linux32 /bin/bash -c "cd scipy && \
             apt-get -y update && \

benchmarks/benchmarks/blas_lapack.py

@@ -2,7 +2,6 @@
 from .common import Benchmark, safe_import
 
 with safe_import():
-    import scipy.linalg.lapack as la
     import scipy.linalg.blas as bla
 
 

benchmarks/benchmarks/interpolate.py

@@ -57,7 +57,7 @@ def setup(self):
         rng = np.random.default_rng(1234)
         m, k = 55, 3
         x = np.sort(rng.random(m+1))
-        c = rng.random((3, m))
+        c = rng.random((k, m))
         self.pp = interpolate.PPoly(c, x)
 
         npts = 100

benchmarks/benchmarks/optimize.py

@@ -16,7 +16,7 @@
     import scipy.optimize
     from scipy.optimize.optimize import rosen, rosen_der, rosen_hess
     from scipy.optimize import (leastsq, basinhopping, differential_evolution,
-                                dual_annealing, OptimizeResult)
+                                dual_annealing)
     from scipy.optimize._minimize import MINIMIZE_METHODS
 
 

benchmarks/benchmarks/signal.py

@@ -92,8 +92,6 @@ class FFTConvolve(Benchmark):
 
     def setup(self, mode, size):
         rng = np.random.default_rng(1234)
-        # sample a bunch of pairs of 2d arrays
-        pairs = []
         self.a = rng.standard_normal(size[0])
         self.b = rng.standard_normal(size[1])
 
@@ -111,8 +109,6 @@ class OAConvolve(Benchmark):
 
     def setup(self, mode, size):
         rng = np.random.default_rng(1234)
-        # sample a bunch of pairs of 2d arrays
-        pairs = []
         self.a = rng.standard_normal(size[0])
         self.b = rng.standard_normal(size[1])
 

benchmarks/benchmarks/sparse.py

@@ -8,7 +8,7 @@
 
 import numpy
 import numpy as np
-from numpy import ones, array, asarray, empty, random
+from numpy import ones, array, asarray, empty
 
 from .common import Benchmark, safe_import
 

benchmarks/benchmarks/stats.py

@@ -50,7 +50,7 @@ def setup(self):
         self.a = rng.random((6,3)) * 10
         self.b = rng.random((6,3)) * 10
         self.c = rng.random((6,3)) * 10
-    
+
     def time_f_oneway(self):
         statistic, pvalue = stats.f_oneway(self.a, self.b, self.c)
         statistic, pvalue = stats.f_oneway(self.a, self.b, self.c, axis=1)
@@ -444,19 +444,19 @@ class DistanceFunctions(Benchmark):
 
     def setup(self, n_size):
         rng = np.random.default_rng(12345678)
-        self.u_values= rng.random(n_size) * 10
+        self.u_values = rng.random(n_size) * 10
         self.u_weights = rng.random(n_size) * 10
         self.v_values = rng.random(n_size // 2) * 10
         self.v_weights = rng.random(n_size // 2) * 10
 
     def time_energy_distance(self, n_size):
         distance = stats.energy_distance(
-                 self.u_values, self.v_values, 
+                 self.u_values, self.v_values,
                  self.u_weights, self.v_weights)
-    
+
     def time_wasserstein_distance(self, n_size):
         distance = stats.wasserstein_distance(
-                 self.u_values, self.v_values, 
+                 self.u_values, self.v_values,
                  self.u_weights, self.v_weights)
 
 
@@ -465,10 +465,11 @@ class Somersd(Benchmark):
     params = [
         [10, 100]
     ]
+
     def setup(self, n_size):
         rng = np.random.default_rng(12345678)
         self.x = rng.choice(n_size, size=n_size)
         self.y = rng.choice(n_size, size=n_size)
 
     def time_somersd(self, n_size):
-        res = stats.somersd(self.x, self.y)
+        res = stats.somersd(self.x, self.y)

ci/azure-travis-template.yaml

@@ -96,12 +96,12 @@ steps:
 
 # Build and test SciPy
 - script: |
-    set -e
+    set -euo pipefail
     python -c 'import numpy as np; print("relaxed strides checking:", np.ones((10,1),order="C").flags.f_contiguous)'
     python -c 'import mpmath.libmp; assert mpmath.libmp.BACKEND == "gmpy"'
 - ${{ if eq(parameters.use_wheel, true) }}:
   - script: |
-      set -e
+      set -euo pipefail
       # Run setup.py build before pip wheel, to build in current directory
       # and make more efficient use of ccache
       echo "setup.py build"
@@ -112,7 +112,7 @@ steps:
     displayName: 'Install SciPy from Wheel'
 - ${{ if eq(parameters.use_sdist, true) }}:
   - script: |
-      set -e
+      set -euo pipefail
       echo "setup.py sdist"
       python tools/suppress_output.py python setup.py sdist
       # Move out of source directory to avoid finding local scipy
@@ -124,16 +124,16 @@ steps:
       #       messes with build isolation.
       pip install $PWD/scipy* -v
     displayName: 'Install SciPy from SDist'
-- script: 'python -u runtests.py -g -j2 --build-only $COVERAGE $USE_WHEEL_BUILD'
+- script: 'python -u runtests.py -g -j2 --build-only ${COVERAGE:-} ${USE_WHEEL_BUILD:-}'
   env:
     ${{ if eq(parameters.coverage, true) }}:
       COVERAGE: '--coverage --gcov'
     ${{ if or(eq(parameters.use_wheel, true), eq(parameters.use_sdist, true))}}:
       USE_WHEEL_BUILD: '--no-build'
   displayName: 'Build SciPy'
 - script: |
-    set -e
-    python -u runtests.py -g -j2 -m ${{ parameters.test_mode }} $COVERAGE $USE_WHEEL_BUILD -- -rfEX --durations=10 2>&1 | tee runtests.log
+    set -euo pipefail
+    python -u runtests.py -g -j2 -m ${{ parameters.test_mode }} ${COVERAGE:-} ${USE_WHEEL_BUILD:-} -- -rfEX --durations=10 2>&1 | tee runtests.log
     tools/validate_runtests_log.py ${{ parameters.test_mode }} < runtests.log
   env:
     ${{ if eq(parameters.coverage, true) }}:
@@ -149,7 +149,7 @@ steps:
     displayName: 'Refguide Check'
 - ${{ if and(ne(variables['Build.SourceBranch'], 'refs/heads/master'), eq(parameters.asv_check, true)) }}:
   - script: |
-      set -e
+      set -euo pipefail
       cd benchmarks
       python -masv check -E existing
       # pyfftw missing should raise an error when running benchmarks with SCIPY_ALLOW_BENCH_IMPORT_ERRORS=0
@@ -160,7 +160,7 @@ steps:
   failOnStderr: true
 - ${{ if eq(parameters.coverage, true) }}:
   - script: |
-      set -e
+      set -euo pipefail
       RUN_DIR=`echo build/testenv/lib/python*/site-packages`
       # Produce gcov output for codecov to find
       find build -name '*.gcno' -type f -exec gcov -pb {} +

scipy/_lib/_util.py

@@ -62,7 +62,7 @@ def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
             raise ValueError("Only one of (fillvalue, f2) can be given.")
 
     args = np.broadcast_arrays(cond, *arrays)
-    cond,  arrays = args[0], args[1:]
+    cond, arrays = args[0], args[1:]
     temp = tuple(np.extract(cond, arr) for arr in arrays)
     tcode = np.mintypecode([a.dtype.char for a in arrays])
     out = np.full(np.shape(arrays[0]), fill_value=fillvalue, dtype=tcode)

scipy/fft/_fftlog.py

@@ -303,12 +303,12 @@ def _fhtq(a, u, inverse=False):
 
     # check for singular transform or singular inverse transform
     if np.isinf(u[0]) and not inverse:
-        warn(f'singular transform; consider changing the bias')
+        warn('singular transform; consider changing the bias')
         # fix coefficient to obtain (potentially correct) transform anyway
         u = u.copy()
         u[0] = 0
     elif u[0] == 0 and inverse:
-        warn(f'singular inverse transform; consider changing the bias')
+        warn('singular inverse transform; consider changing the bias')
         # fix coefficient to obtain (potentially correct) inverse anyway
         u = u.copy()
         u[0] = np.inf

scipy/fft/tests/test_fftlog.py

@@ -26,55 +26,55 @@ def f(r, mu):
 
     # test 1: compute as given
     ours = fht(a, dln, mu, offset=offset, bias=bias)
-    theirs = [ -0.1159922613593045E-02,  0.1625822618458832E-02,
-               -0.1949518286432330E-02,  0.3789220182554077E-02,
-                0.5093959119952945E-03,  0.2785387803618774E-01,
-                0.9944952700848897E-01,  0.4599202164586588    ,
-                0.3157462160881342    , -0.8201236844404755E-03,
-               -0.7834031308271878E-03,  0.3931444945110708E-03,
-               -0.2697710625194777E-03,  0.3568398050238820E-03,
-               -0.5554454827797206E-03,  0.8286331026468585E-03 ]
+    theirs = [-0.1159922613593045E-02, +0.1625822618458832E-02,
+              -0.1949518286432330E-02, +0.3789220182554077E-02,
+              +0.5093959119952945E-03, +0.2785387803618774E-01,
+              +0.9944952700848897E-01, +0.4599202164586588E+00,
+              +0.3157462160881342E+00, -0.8201236844404755E-03,
+              -0.7834031308271878E-03, +0.3931444945110708E-03,
+              -0.2697710625194777E-03, +0.3568398050238820E-03,
+              -0.5554454827797206E-03, +0.8286331026468585E-03]
     assert_allclose(ours, theirs)
 
     # test 2: change to optimal offset
     offset = fhtoffset(dln, mu, bias=bias)
     ours = fht(a, dln, mu, offset=offset, bias=bias)
-    theirs = [  0.4353768523152057E-04, -0.9197045663594285E-05,
-                0.3150140927838524E-03,  0.9149121960963704E-03,
-                0.5808089753959363E-02,  0.2548065256377240E-01,
-                0.1339477692089897    ,  0.4821530509479356    ,
-                0.2659899781579785    , -0.1116475278448113E-01,
-                0.1791441617592385E-02, -0.4181810476548056E-03,
-                0.1314963536765343E-03, -0.5422057743066297E-04,
-                0.3208681804170443E-04, -0.2696849476008234E-04 ]
+    theirs = [+0.4353768523152057E-04, -0.9197045663594285E-05,
+              +0.3150140927838524E-03, +0.9149121960963704E-03,
+              +0.5808089753959363E-02, +0.2548065256377240E-01,
+              +0.1339477692089897E+00, +0.4821530509479356E+00,
+              +0.2659899781579785E+00, -0.1116475278448113E-01,
+              +0.1791441617592385E-02, -0.4181810476548056E-03,
+              +0.1314963536765343E-03, -0.5422057743066297E-04,
+              +0.3208681804170443E-04, -0.2696849476008234E-04]
     assert_allclose(ours, theirs)
 
     # test 3: positive bias
     bias = 0.8
     offset = fhtoffset(dln, mu, bias=bias)
     ours = fht(a, dln, mu, offset=offset, bias=bias)
-    theirs = [ -7.343667355831685     ,  0.1710271207817100    ,
-                0.1065374386206564    , -0.5121739602708132E-01,
-                0.2636649319269470E-01,  0.1697209218849693E-01,
-                0.1250215614723183    ,  0.4739583261486729    ,
-                0.2841149874912028    , -0.8312764741645729E-02,
-                0.1024233505508988E-02, -0.1644902767389120E-03,
-                0.3305775476926270E-04, -0.7786993194882709E-05,
-                0.1962258449520547E-05, -0.8977895734909250E-06 ]
+    theirs = [-7.3436673558316850E+00, +0.1710271207817100E+00,
+              +0.1065374386206564E+00, -0.5121739602708132E-01,
+              +0.2636649319269470E-01, +0.1697209218849693E-01,
+              +0.1250215614723183E+00, +0.4739583261486729E+00,
+              +0.2841149874912028E+00, -0.8312764741645729E-02,
+              +0.1024233505508988E-02, -0.1644902767389120E-03,
+              +0.3305775476926270E-04, -0.7786993194882709E-05,
+              +0.1962258449520547E-05, -0.8977895734909250E-06]
     assert_allclose(ours, theirs)
 
     # test 4: negative bias
     bias = -0.8
     offset = fhtoffset(dln, mu, bias=bias)
     ours = fht(a, dln, mu, offset=offset, bias=bias)
-    theirs = [  0.8985777068568745E-05,  0.4074898209936099E-04,
-                0.2123969254700955E-03,  0.1009558244834628E-02,
-                0.5131386375222176E-02,  0.2461678673516286E-01,
-                0.1235812845384476    ,  0.4719570096404403    ,
-                0.2893487490631317    , -0.1686570611318716E-01,
-                0.2231398155172505E-01, -0.1480742256379873E-01,
-                0.1692387813500801    ,  0.3097490354365797    ,
-                2.759360718240186     , 10.52510750700458       ]
+    theirs = [+0.8985777068568745E-05, +0.4074898209936099E-04,
+              +0.2123969254700955E-03, +0.1009558244834628E-02,
+              +0.5131386375222176E-02, +0.2461678673516286E-01,
+              +0.1235812845384476E+00, +0.4719570096404403E+00,
+              +0.2893487490631317E+00, -0.1686570611318716E-01,
+              +0.2231398155172505E-01, -0.1480742256379873E-01,
+              +0.1692387813500801E+00, +0.3097490354365797E+00,
+              +2.7593607182401860E+00, 10.5251075070045800E+00]
     assert_allclose(ours, theirs)
 
 

scipy/integrate/_quad_vec.py

@@ -267,7 +267,6 @@ def quad_vec(f, a, b, epsabs=1e-200, epsrel=1e-8, norm='2', cache_size=100e6, li
     else:
         norm_func = norm_funcs[norm]
 
-
     parallel_count = 128
     min_intervals = 2
 

scipy/integrate/_quadrature.py

@@ -53,6 +53,7 @@ class AccuracyWarning(Warning):
     # workaround for mypy function attributes see:
     # https://github.com/python/mypy/issues/2087#issuecomment-462726600
     from typing_extensions import Protocol
+
     class CacheAttributes(Protocol):
         cache: Dict[int, Tuple[Any, Any]]
 else:

scipy/linalg/tests/test_basic.py

@@ -578,7 +578,6 @@ def test_simple_her_actuallysym(self):
                 x = solve(a, b, assume_a='her', lower=lower)
                 assert_array_almost_equal(dot(a, x), b)
 
-
     def test_simple_her(self):
         a = [[5, 2+1j], [2-1j, -4]]
         for b in ([1j, 0],
@@ -588,8 +587,6 @@ def test_simple_her(self):
             x = solve(a, b, assume_a='her')
             assert_array_almost_equal(dot(a, x), b)
 
-
-
     def test_nils_20Feb04(self):
         n = 2
         A = random([n, n])+random([n, n])*1j

scipy/linalg/tests/test_build.py

@@ -31,8 +31,8 @@ def get_dependencies(self, file):
     def grep_dependencies(self, file, deps):
         stdout = self.get_dependencies(file)
 
-        rdeps = dict([( dep.encode('latin1'), 
-                        re.compile(dep.encode('latin1'))) for dep in deps])
+        rdeps = dict([(dep.encode('latin1'),
+                       re.compile(dep.encode('latin1'))) for dep in deps])
         founds = []
         for l in stdout.splitlines():
             for k, v in rdeps.items():

scipy/ndimage/interpolation.py

@@ -797,8 +797,7 @@ def zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0,
         if suggest_mode is not None:
             warnings.warn(
                 ("It is recommended to use mode = {} instead of {} when "
-                 "grid_mode is True."
-                ).format(suggest_mode, mode)
+                 "grid_mode is True.").format(suggest_mode, mode)
             )
     mode = _ni_support._extend_mode_to_code(mode)
 

scipy/ndimage/tests/test_interpolation.py

@@ -851,7 +851,6 @@ def test_affine_transform_multi_d_endianness_with_output_parameter(self):
             result = out if returned is None else returned
             assert_array_almost_equal(result, [1])
 
-
     def test_affine_transform_output_shape(self):
         # don't require output_shape when out of a different size is given
         data = numpy.arange(8, dtype=numpy.float64)
@@ -866,7 +865,6 @@ def test_affine_transform_output_shape(self):
             ndimage.affine_transform(
                 data, [[1]], output=out, output_shape=(12,))
 
-
     def test_affine_transform_with_string_output(self):
         data = numpy.array([1])
         out = ndimage.affine_transform(data, [[1]], output='f')

scipy/optimize/_group_columns.py

@@ -10,7 +10,8 @@
 def group_dense(m, n, A):
     B = A.T  # Transposed view for convenience.
 
-    groups = -np.ones(n, dtype=np.intp)  #FIXME: use np.full once pythran supports it
+    # FIXME: use np.full once pythran supports it
+    groups = -np.ones(n, dtype=np.intp)
     current_group = 0
 
     union = np.empty(m, dtype=np.intp)

scipy/optimize/_highs/setup.py

@@ -112,8 +112,8 @@ def configuration(parent_package='', top_path=None):
                      if pathlib.Path(s).parent.name != 'mip']
     ext = config.add_extension(
         '_highs_wrapper',
-        sources=[join('cython', 'src', '_highs_wrapper.cxx')] + \
-                highs_sources + ipx_sources,
+        sources=([join('cython', 'src', '_highs_wrapper.cxx')] +
+                 highs_sources + ipx_sources),
         include_dirs=[
             # highs_wrapper
             'src',