MAINT: lint: enable line-length check (package-wide) (scipy#19609)

* MAINT: `noqa` for awkward UP007 violation I tried to fix but `mypy` complains along the lines of python/mypy#12393 * MAINT: lint: enable line-length check in `cluster` * MAINT: lint: enable line-length check in `integrate` [lint only] * MAINT: lint: enable line-length check in `interpolate` [lint only] * MAINT: lint: enable line-length check in `io` [lint only] * MAINT: lint: enable line-length check in `ndimage` [lint only] * MAINT: lint: enable line-length check in `signal` [lint only] * MAINT: lint: enable line-length check in `linalg` [lint only] * MAINT: lint: enable line-length check in `spatial` [lint only] * MAINT: lint: enable line-length check in `sparse` [lint only] * MAINT: lint: enable line-length check in `_lib` [lint only] * MAINT: lint: enable line-length check in `stats` [lint only] * MAINT: lint: enable line-length check in `special` [lint only] * MAINT: lint: enable line-length check in `optimize` [lint only] * MAINT: lint: enable line-length check in `benchmarks` [lint only] * MAINT: lint: enable line-length check in `tools` * MAINT: lint: `per-file-ignores` clean-up * MAINT: PR 19609 revisions
xuefeng-xu · Dec 8, 2023 · fa9f13e · fa9f13e
1 parent 8166222
commit fa9f13e
Show file tree

Hide file tree

Showing 177 changed files with 3,320 additions and 1,860 deletions.
diff --git a/benchmarks/benchmarks/go_benchmark_functions/go_funcs_C.py b/benchmarks/benchmarks/go_benchmark_functions/go_funcs_C.py
@@ -551,7 +551,8 @@ class Cube(Benchmark):
         f_{\text{Cube}}(x) = 100(x_2 - x_1^3)^2 + (1 - x1)^2
 
 
-    Here, :math:`n` represents the number of dimensions and :math:`x_i \in [-10, 10]` for :math:`i=1,...,N`.
+    Here, :math:`n` represents the number of dimensions and :math:`x_i \in [-10, 10]`
+    for :math:`i=1,...,N`.
 
     *Global optimum*: :math:`f(x_i) = 0.0` for :math:`x = [1, 1]`
 

diff --git a/benchmarks/benchmarks/go_benchmark_functions/go_funcs_D.py b/benchmarks/benchmarks/go_benchmark_functions/go_funcs_D.py
@@ -403,8 +403,8 @@ class DeVilliersGlasser02(Benchmark):
     r"""
     DeVilliers-Glasser 2 objective function.
 
-    This class defines the DeVilliers-Glasser 2 [1]_ function global optimization problem. This
-    is a multimodal minimization problem defined as follows:
+    This class defines the DeVilliers-Glasser 2 [1]_ function global optimization
+    problem. This is a multimodal minimization problem defined as follows:
 
     .. math::
 

diff --git a/benchmarks/benchmarks/go_benchmark_functions/go_funcs_E.py b/benchmarks/benchmarks/go_benchmark_functions/go_funcs_E.py
@@ -27,7 +27,8 @@ class Easom(Benchmark):
     For Global Optimization Problems Int. Journal of Mathematical Modelling
     and Numerical Optimisation, 2013, 4, 150-194.
 
-    TODO Gavana website disagrees with Jamil, etc. Gavana equation in docstring is totally wrong.
+    TODO Gavana website disagrees with Jamil, etc.
+    Gavana equation in docstring is totally wrong.
     """
 
     def __init__(self, dimensions=2):

diff --git a/benchmarks/benchmarks/go_benchmark_functions/go_funcs_L.py b/benchmarks/benchmarks/go_benchmark_functions/go_funcs_L.py
@@ -203,7 +203,8 @@ class Levy03(Benchmark):
 
     .. math::
 
-        f_{\text{Levy03}}(\mathbf{x}) = \sin^2(\pi y_1)+\sum_{i=1}^{n-1}(y_i-1)^2[1+10\sin^2(\pi y_{i+1})]+(y_n-1)^2
+        f_{\text{Levy03}}(\mathbf{x}) =
+        \sin^2(\pi y_1)+\sum_{i=1}^{n-1}(y_i-1)^2[1+10\sin^2(\pi y_{i+1})]+(y_n-1)^2
 
     Where, in this exercise:
 
@@ -212,7 +213,8 @@ class Levy03(Benchmark):
         y_i=1+\frac{x_i-1}{4}
 
 
-    Here, :math:`n` represents the number of dimensions and :math:`x_i \in [-10, 10]` for :math:`i=1,...,n`.
+    Here, :math:`n` represents the number of dimensions and
+    :math:`x_i \in [-10, 10]` for :math:`i=1,...,n`.
 
     *Global optimum*: :math:`f(x_i) = 0` for :math:`x_i = 1` for :math:`i=1,...,n`
 
@@ -254,11 +256,15 @@ class Levy05(Benchmark):
 
     .. math::
 
-        f_{\text{Levy05}}(\mathbf{x}) = \sum_{i=1}^{5} i \cos \left[(i-1)x_1 + i \right] \times \sum_{j=1}^{5} j \cos \left[(j+1)x_2 + j \right] + (x_1 + 1.42513)^2 + (x_2 + 0.80032)^2
+        f_{\text{Levy05}}(\mathbf{x}) =
+        \sum_{i=1}^{5} i \cos \left[(i-1)x_1 + i \right] \times \sum_{j=1}^{5} j
+        \cos \left[(j+1)x_2 + j \right] + (x_1 + 1.42513)^2 + (x_2 + 0.80032)^2
 
-    Here, :math:`n` represents the number of dimensions and :math:`x_i \in [-10, 10]` for :math:`i=1,...,n`.
+    Here, :math:`n` represents the number of dimensions and
+    :math:`x_i \in [-10, 10]` for :math:`i=1,...,n`.
 
-    *Global optimum*: :math:`f(x_i) = -176.1375779` for :math:`\mathbf{x} = [-1.30685, -1.42485]`.
+    *Global optimum*: :math:`f(x_i) = -176.1375779` for
+    :math:`\mathbf{x} = [-1.30685, -1.42485]`.
 
     .. [1] Mishra, S. Global Optimization by Differential Evolution and
     Particle Swarm Methods: Evaluation on Some Benchmark Functions.

diff --git a/benchmarks/benchmarks/go_benchmark_functions/go_funcs_univariate.py b/benchmarks/benchmarks/go_benchmark_functions/go_funcs_univariate.py
@@ -215,7 +215,8 @@ class Problem07(Benchmark):
 
     .. math::
 
-       f_{\\text{Problem07}}(x) = \\sin(x) + \\sin \\left(\\frac{10}{3}x \\right) + \\log(x) - 0.84x + 3
+       f_{\\text{Problem07}}(x) = \\sin(x) + \\sin \\left(\\frac{10}{3}x
+                                  \\right) + \\log(x) - 0.84x + 3
 
     Bound constraints: :math:`x \\in [2.7, 7.5]`
 
@@ -571,8 +572,9 @@ class Problem18(Benchmark):
 
     .. math::
 
-         f_{\\text{Problem18}}(x) = \\begin{cases}(x-2)^2 & \\textrm{if} \\hspace{5pt} x \\leq 3 \\\\
-                                                  2\\log(x-2)+1&\\textrm{otherwise}\\end{cases}
+         f_{\\text{Problem18}}(x)
+         = \\begin{cases}(x-2)^2 & \\textrm{if} \\hspace{5pt} x
+           \\leq 3 \\\\ 2\\log(x-2)+1&\\textrm{otherwise}\\end{cases}
 
     Bound constraints: :math:`x \\in [0, 6]`
 

diff --git a/benchmarks/benchmarks/integrate.py b/benchmarks/benchmarks/integrate.py
@@ -102,7 +102,8 @@ def setup(self):
             voidp = ctypes.cast(self.f_ctypes, ctypes.c_void_p)
             address = voidp.value
             ffi = cffi.FFI()
-            self.f_cffi = LowLevelCallable(ffi.cast("double (*)(int, double *)", address))
+            self.f_cffi = LowLevelCallable(ffi.cast("double (*)(int, double *)",
+                                                    address))
 
     def time_quad_python(self):
         quad(self.f_python, 0, np.pi)

diff --git a/benchmarks/benchmarks/interpolate.py b/benchmarks/benchmarks/interpolate.py
@@ -81,7 +81,8 @@ def setup(self, n_grids, method):
         self.values = self.func(self.points[:, 0], self.points[:, 1])
 
     def time_evaluation(self, n_grids, method):
-        interpolate.griddata(self.points, self.values, (self.grid_x, self.grid_y), method=method)
+        interpolate.griddata(self.points, self.values, (self.grid_x, self.grid_y),
+                             method=method)
 
 
 class Interpolate1d(Benchmark):
@@ -128,7 +129,8 @@ class Rbf(Benchmark):
     param_names = ['n_samples', 'function']
     params = [
         [10, 50, 100],
-        ['multiquadric', 'inverse', 'gaussian', 'linear', 'cubic', 'quintic', 'thin_plate']
+        ['multiquadric', 'inverse', 'gaussian', 'linear',
+         'cubic', 'quintic', 'thin_plate']
     ]
 
     def setup(self, n_samples, function):
@@ -220,7 +222,8 @@ def time_smooth_bivariate_spline(self, n_samples):
         interpolate.SmoothBivariateSpline(self.x, self.y, self.z)
 
     def time_lsq_bivariate_spline(self, n_samples):
-        interpolate.LSQBivariateSpline(self.x, self.y, self.z, self.xknots.flat, self.yknots.flat)
+        interpolate.LSQBivariateSpline(self.x, self.y, self.z,
+                                       self.xknots.flat, self.yknots.flat)
 
 
 class Interpolate(Benchmark):
@@ -376,14 +379,19 @@ class CloughTocherInterpolatorValues(interpolate.CloughTocher2DInterpolator):
     https://github.com/scipy/scipy/pull/18376 for discussion
     """
     def __init__(self, points, xi, tol=1e-6, maxiter=400, **kwargs):
-        interpolate.CloughTocher2DInterpolator.__init__(self, points, None, tol=tol, maxiter=maxiter)
+        interpolate.CloughTocher2DInterpolator.__init__(self, points, None,
+                                                        tol=tol, maxiter=maxiter)
         self.xi = None
         self._preprocess_xi(*xi)
-        self.simplices, self.c = interpolate.CloughTocher2DInterpolator._find_simplicies(self, self.xi)
+        self.simplices, self.c = (
+            interpolate.CloughTocher2DInterpolator._find_simplicies(self, self.xi)
+        )
 
     def _preprocess_xi(self, *args):
         if self.xi is None:
-            self.xi, self.interpolation_points_shape = interpolate.CloughTocher2DInterpolator._preprocess_xi(self, *args)
+            self.xi, self.interpolation_points_shape = (
+                interpolate.CloughTocher2DInterpolator._preprocess_xi(self, *args)
+            )
         return self.xi, self.interpolation_points_shape
 
     def _find_simplicies(self, xi):

diff --git a/benchmarks/benchmarks/io_mm.py b/benchmarks/benchmarks/io_mm.py
@@ -139,7 +139,7 @@ def generate_dense(size):
 
         a = generate_{matrix_type}({size})
         mmwrite('{self.filename}', a, symmetry='general')
-        """
+        """  # noqa: E501
         time, peak_mem = run_monitored(code)
         return peak_mem / size
 

diff --git a/benchmarks/benchmarks/linalg.py b/benchmarks/benchmarks/linalg.py
@@ -85,11 +85,21 @@ def time_svd(self, size, contig, module):
             sl.svd(self.a)
 
     # Retain old benchmark results (remove this if changing the benchmark)
-    time_det.version = "87e530ee50eb6b6c06c7a8abe51c2168e133d5cbd486f4c1c2b9cedc5a078325"
-    time_eigvals.version = "9d68d3a6b473df9bdda3d3fd25c7f9aeea7d5cee869eec730fb2a2bcd1dfb907"
-    time_inv.version = "20beee193c84a5713da9749246a7c40ef21590186c35ed00a4fe854cce9e153b"
-    time_solve.version = "1fe788070f1c9132cbe78a47fdb4cce58266427fc636d2aa9450e3c7d92c644c"
-    time_svd.version = "0ccbda456d096e459d4a6eefc6c674a815179e215f83931a81cfa8c18e39d6e3"
+    time_det.version = (
+        "87e530ee50eb6b6c06c7a8abe51c2168e133d5cbd486f4c1c2b9cedc5a078325"
+    )
+    time_eigvals.version = (
+        "9d68d3a6b473df9bdda3d3fd25c7f9aeea7d5cee869eec730fb2a2bcd1dfb907"
+    )
+    time_inv.version = (
+        "20beee193c84a5713da9749246a7c40ef21590186c35ed00a4fe854cce9e153b"
+    )
+    time_solve.version = (
+        "1fe788070f1c9132cbe78a47fdb4cce58266427fc636d2aa9450e3c7d92c644c"
+    )
+    time_svd.version = (
+        "0ccbda456d096e459d4a6eefc6c674a815179e215f83931a81cfa8c18e39d6e3"
+    )
 
 
 class Norm(Benchmark):
@@ -178,7 +188,9 @@ def time_lstsq(self, dtype, size, lapack_driver):
                      lapack_driver=lapack_driver)
 
     # Retain old benchmark results (remove this if changing the benchmark)
-    time_lstsq.version = "15ee0be14a0a597c7d1c9a3dab2c39e15c8ac623484410ffefa406bf6b596ebe"
+    time_lstsq.version = (
+        "15ee0be14a0a597c7d1c9a3dab2c39e15c8ac623484410ffefa406bf6b596ebe"
+    )
 
 
 class SpecialMatrices(Benchmark):

diff --git a/benchmarks/benchmarks/optimize.py b/benchmarks/benchmarks/optimize.py
@@ -89,13 +89,15 @@ def print_results(self):
         print("")
         print("=========================================================")
         print("Optimizer benchmark: %s" % (self.function_name))
-        print("dimensions: %d, extra kwargs: %s" % (results[0].ndim, str(self.minimizer_kwargs)))
+        print("dimensions: %d, extra kwargs: %s" %
+              (results[0].ndim, str(self.minimizer_kwargs)))
         print("averaged over %d starting configurations" % (results[0].ntrials))
         print("  Optimizer    nfail   nfev    njev    nhev    time")
         print("---------------------------------------------------------")
         for res in results:
             print("%11s  | %4d  | %4d  | %4d  | %4d  | %.6g" %
-                  (res.name, res.nfail, res.mean_nfev, res.mean_njev, res.mean_nhev, res.mean_time))
+                  (res.name, res.nfail, res.mean_nfev,
+                   res.mean_njev, res.mean_nhev, res.mean_time))
 
     def average_results(self):
         """group the results by minimizer and average over the runs"""
@@ -370,7 +372,8 @@ def run_rosenbrock_tight(self, methods=None):
 
     def run_simple_quadratic(self, methods=None):
         s = funcs.SimpleQuadratic()
-        #    print "checking gradient", scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
+        #    print "checking gradient",
+        #    scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
         b = _BenchOptimizers("simple quadratic function",
                              fun=s.fun, der=s.der, hess=s.hess)
         for i in range(10):
@@ -379,7 +382,8 @@ def run_simple_quadratic(self, methods=None):
 
     def run_asymmetric_quadratic(self, methods=None):
         s = funcs.AsymmetricQuadratic()
-        #    print "checking gradient", scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
+        #    print "checking gradient",
+        #    scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
         b = _BenchOptimizers("function sum(x**2) + x[0]",
                              fun=s.fun, der=s.der, hess=s.hess)
         for i in range(10):
@@ -401,7 +405,8 @@ def der(x):
 
     def run_booth(self, methods=None):
         s = funcs.Booth()
-        #    print "checking gradient", scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
+        #    print "checking gradient",
+        #    scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
         b = _BenchOptimizers("Booth's function",
                              fun=s.fun, der=s.der, hess=None)
         for i in range(10):
@@ -410,7 +415,8 @@ def run_booth(self, methods=None):
 
     def run_beale(self, methods=None):
         s = funcs.Beale()
-        #    print "checking gradient", scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
+        #    print "checking gradient",
+        #    scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
         b = _BenchOptimizers("Beale's function",
                              fun=s.fun, der=s.der, hess=None)
         for i in range(10):
@@ -419,8 +425,9 @@ def run_beale(self, methods=None):
 
     def run_LJ(self, methods=None):
         s = funcs.LJ()
-        # print "checking gradient", scipy.optimize.check_grad(s.get_energy, s.get_gradient,
-        # np.random.uniform(-2,2,3*4))
+        # print "checking gradient",
+        # scipy.optimize.check_grad(s.get_energy, s.get_gradient,
+        #                           np.random.uniform(-2,2,3*4))
         natoms = 4
         b = _BenchOptimizers("%d atom Lennard Jones potential" % (natoms),
                              fun=s.fun, der=s.der, hess=None)
@@ -512,7 +519,9 @@ def __init__(self):
         except (KeyError, ValueError):
             self.numtrials = 100
 
-        self.dump_fn = os.path.join(os.path.dirname(__file__), '..', 'global-bench-results.json')
+        self.dump_fn = os.path.join(os.path.dirname(__file__),
+                                    '..',
+                                    'global-bench-results.json',)
         self.results = {}
 
     def setup(self, name, ret_value, solver):
@@ -536,7 +545,8 @@ def track_all(self, name, ret_value, solver):
             # if so, then just return the ret_value
             av_results = self.results[name]
             if ret_value == 'success%':
-                return 100 * av_results[solver]['nsuccess'] / av_results[solver]['ntrials']
+                return (100 * av_results[solver]['nsuccess']
+                        / av_results[solver]['ntrials'])
             elif ret_value == '<nfev>':
                 return av_results[solver]['mean_nfev']
             else:
@@ -557,7 +567,8 @@ def track_all(self, name, ret_value, solver):
             self.results[name][solver] = av_results[solver]
 
             if ret_value == 'success%':
-                return 100 * av_results[solver]['nsuccess'] / av_results[solver]['ntrials']
+                return (100 * av_results[solver]['nsuccess']
+                        / av_results[solver]['ntrials'])
             elif ret_value == '<nfev>':
                 return av_results[solver]['mean_nfev']
             else:

diff --git a/benchmarks/benchmarks/sparse.py b/benchmarks/benchmarks/sparse.py
@@ -160,7 +160,9 @@ def time_large(self):
             self.matrix1 * self.matrix2
 
     # Retain old benchmark results (remove this if changing the benchmark)
-    time_large.version = "33aee08539377a7cb0fabaf0d9ff9d6d80079a428873f451b378c39f6ead48cb"
+    time_large.version = (
+        "33aee08539377a7cb0fabaf0d9ff9d6d80079a428873f451b378c39f6ead48cb"
+    )
 
 
 class Construction(Benchmark):
@@ -379,12 +381,24 @@ def time_100_cols(self, density, format):
         self.X[:, np.arange(100)]
 
     # Retain old benchmark results (remove this if changing the benchmark)
-    time_10000_rows.version = "dc19210b894d5fd41d4563f85b7459ef5836cddaf77154b539df3ea91c5d5c1c"
-    time_100_cols.version = "8d43ed52084cdab150018eedb289a749a39f35d4dfa31f53280f1ef286a23046"
-    time_3_cols.version = "93e5123910772d62b3f72abff56c2732f83d217221bce409b70e77b89c311d26"
-    time_3_rows.version = "a9eac80863a0b2f4b510269955041930e5fdd15607238257eb78244f891ebfe6"
-    time_getcol.version = "291388763b355f0f3935db9272a29965d14fa3f305d3306059381e15300e638b"
-    time_getrow.version = "edb9e4291560d6ba8dd58ef371b3a343a333bc10744496adb3ff964762d33c68"
+    time_10000_rows.version = (
+        "dc19210b894d5fd41d4563f85b7459ef5836cddaf77154b539df3ea91c5d5c1c"
+    )
+    time_100_cols.version = (
+        "8d43ed52084cdab150018eedb289a749a39f35d4dfa31f53280f1ef286a23046"
+    )
+    time_3_cols.version = (
+        "93e5123910772d62b3f72abff56c2732f83d217221bce409b70e77b89c311d26"
+    )
+    time_3_rows.version = (
+        "a9eac80863a0b2f4b510269955041930e5fdd15607238257eb78244f891ebfe6"
+    )
+    time_getcol.version = (
+        "291388763b355f0f3935db9272a29965d14fa3f305d3306059381e15300e638b"
+    )
+    time_getrow.version = (
+        "edb9e4291560d6ba8dd58ef371b3a343a333bc10744496adb3ff964762d33c68"
+    )
 
 
 class Diagonal(Benchmark):
@@ -404,7 +418,9 @@ def time_diagonal(self, density, format):
         self.X.diagonal()
 
     # Retain old benchmark results (remove this if changing the benchmark)
-    time_diagonal.version = "d84f53fdc6abc208136c8ce48ca156370f6803562f6908eb6bd1424f50310cf1"
+    time_diagonal.version = (
+        "d84f53fdc6abc208136c8ce48ca156370f6803562f6908eb6bd1424f50310cf1"
+    )
 
 
 class Sum(Benchmark):
@@ -429,9 +445,15 @@ def time_sum_axis1(self, density, format):
         self.X.sum(axis=1)
 
     # Retain old benchmark results (remove this if changing the benchmark)
-    time_sum.version = "05c305857e771024535e546360203b17f5aca2b39b023a49ab296bd746d6cdd3"
-    time_sum_axis0.version = "8aca682fd69aa140c69c028679826bdf43c717589b1961b4702d744ed72effc6"
-    time_sum_axis1.version = "1a6e05244b77f857c61f8ee09ca3abd006a10ba07eff10b1c5f9e0ac20f331b2"
+    time_sum.version = (
+        "05c305857e771024535e546360203b17f5aca2b39b023a49ab296bd746d6cdd3"
+    )
+    time_sum_axis0.version = (
+        "8aca682fd69aa140c69c028679826bdf43c717589b1961b4702d744ed72effc6"
+    )
+    time_sum_axis1.version = (
+        "1a6e05244b77f857c61f8ee09ca3abd006a10ba07eff10b1c5f9e0ac20f331b2"
+    )
 
 
 class Iteration(Benchmark):
@@ -463,7 +485,9 @@ def time_toarray(self, format, order):
         self.X.toarray(order=order)
 
     # Retain old benchmark results (remove this if changing the benchmark)
-    time_toarray.version = "2fbf492ec800b982946a62785beda803460b913cc80080043a5d407025893b2b"
+    time_toarray.version = (
+        "2fbf492ec800b982946a62785beda803460b913cc80080043a5d407025893b2b"
+    )
 
 
 class Random(Benchmark):

diff --git a/benchmarks/benchmarks/sparse_csgraph_matching.py b/benchmarks/benchmarks/sparse_csgraph_matching.py
@@ -36,7 +36,8 @@ def random_uniform(shape, rng):
 
 
 def random_uniform_sparse(shape, rng):
-    return scipy.sparse.random(shape[0], shape[1], density=0.1, format='csr', random_state=rng)
+    return scipy.sparse.random(shape[0], shape[1],
+                               density=0.1, format='csr', random_state=rng)
 
 
 def random_uniform_integer(shape, rng):