CI: pycodestyle → ruff (numpy#27300)

* CI: pycodestyle → ruff check * MAINT: Apply ruff/pycodestyle rule E101 E101 Indentation contains mixed spaces and tabs * MAINT: Apply ruff/pycodestyle rule E111 E111 Indentation is not a multiple of 4 * MAINT: Apply ruff/pycodestyle rule E116 E116 Unexpected indentation (comment) * MAINT: Apply ruff/pycodestyle rule E117 E117 Over-indented * MAINT: Apply ruff/pycodestyle rule E201 E201 Whitespace after bracket * MAINT: Apply ruff/pycodestyle rule E202 E202 Whitespace before bracket * MAINT: Apply ruff/pycodestyle rule E203 E203 Whitespace before punctuation * MAINT: Apply ruff/pycodestyle rule E211 E211 Whitespace before '(' * MAINT: Apply ruff/pycodestyle rule E221 E221 Multiple spaces before operator * MAINT: Apply ruff/pycodestyle rule E222 E222 Multiple spaces after operator * MAINT: Apply ruff/pycodestyle rule E225 E225 Missing whitespace around operator * MAINT: Apply ruff/pycodestyle rule E226 E226 Missing whitespace around arithmetic operator * MAINT: Apply ruff/pycodestyle rule E227 E227 Missing whitespace around bitwise or shift operator * MAINT: Apply ruff/pycodestyle rule E228 E228 Missing whitespace around modulo operator * MAINT: Apply ruff/pycodestyle rule E252 E252 Missing whitespace around parameter equals * MAINT: Apply ruff/pycodestyle rule E261 E261 Insert at least two spaces before an inline comment * MAINT: Apply ruff/pycodestyle rule E271 E271 Multiple spaces after keyword * MAINT: Apply ruff/pycodestyle rule E272 E272 Multiple spaces before keyword * MAINT: Apply ruff/pycodestyle rule E275 E275 Missing whitespace after keyword * MAINT: Apply ruff/pycodestyle rule E301 E301 Expected 1 blank line between methods * MAINT: Apply ruff/pycodestyle rule E303 E303 Too many blank lines * MAINT: Apply ruff/pycodestyle rule E304 E304 Blank lines found after function decorator * MAINT: Apply ruff/pycodestyle rule E305 E305 Expected 2 blank lines after class or function definition * MAINT: Apply ruff/pycodestyle rule E306 E306 Expected 1 blank line before a nested definition * MAINT: Apply ruff/pycodestyle rule E501 E501 Line too long * MAINT: Disable ruff/pycodestyle rule E714 in tests E714 Test for object identity should be `is not` * MAINT: Apply ruff/pycodestyle rule E743 E743 Ambiguous function name * MAINT: Disable E501 for now Ruff has found 1080 occurrences of this error. Fixing them sounds excessive.
abhishek-iitmadras · Dec 16, 2024 · c31e599 · c31e599
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diff --git a/.github/workflows/linux.yml b/.github/workflows/linux.yml
@@ -44,11 +44,11 @@ jobs:
     - name: Install linter requirements
       run:
         python -m pip install -r requirements/linter_requirements.txt
-    - name: Run linter on PR diff
+    - name: Run linter on PR
       env:
         BASE_REF: ${{ github.base_ref }}
       run:
-        python tools/linter.py --branch origin/"$BASE_REF"
+        python tools/linter.py
 
   smoke_test:
     # To enable this job on a fork, comment out:

diff --git a/.spin/cmds.py b/.spin/cmds.py
@@ -277,7 +277,7 @@ def _set_mem_rlimit(max_mem=None):
 def _commit_to_sha(commit):
     p = spin.util.run(['git', 'rev-parse', commit], output=False, echo=False)
     if p.returncode != 0:
-        raise(
+        raise (
             click.ClickException(
                 f'Could not find SHA matching commit `{commit}`'
             )

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
@@ -53,7 +53,7 @@ stages:
       # pip 21.1 emits a pile of garbage messages to annoy users :)
       #      failOnStderr: true
     - script: |
-        python tools/linter.py --branch origin/$(System.PullRequest.TargetBranch)
+        python tools/linter.py
       displayName: 'Run Lint Checks'
       failOnStderr: true
 

diff --git a/benchmarks/benchmarks/bench_core.py b/benchmarks/benchmarks/bench_core.py
@@ -137,7 +137,7 @@ class CorrConv(Benchmark):
 
     def setup(self, size1, size2, mode):
         self.x1 = np.linspace(0, 1, num=size1)
-        self.x2 = np.cos(np.linspace(0, 2*np.pi, num=size2))
+        self.x2 = np.cos(np.linspace(0, 2 * np.pi, num=size2))
 
     def time_correlate(self, size1, size2, mode):
         np.correlate(self.x1, self.x2, mode=mode)
@@ -276,10 +276,10 @@ def time_sum(self, dtype, size):
 
 class NumPyChar(Benchmark):
     def setup(self):
-        self.A = np.array([100*'x', 100*'y'])
+        self.A = np.array([100 * 'x', 100 * 'y'])
         self.B = np.array(1000 * ['aa'])
 
-        self.C = np.array([100*'x' + 'z', 100*'y' + 'z' + 'y', 100*'x'])
+        self.C = np.array([100 * 'x' + 'z', 100 * 'y' + 'z' + 'y', 100 * 'x'])
         self.D = np.array(1000 * ['ab'] + 1000 * ['ac'])
 
     def time_isalpha_small_list_big_string(self):

diff --git a/benchmarks/benchmarks/bench_function_base.py b/benchmarks/benchmarks/bench_function_base.py
@@ -35,7 +35,7 @@ def time_fine_binning(self):
 
 class Histogram2D(Benchmark):
     def setup(self):
-        self.d = np.linspace(0, 100, 200000).reshape((-1,2))
+        self.d = np.linspace(0, 100, 200000).reshape((-1, 2))
 
     def time_full_coverage(self):
         np.histogramdd(self.d, (200, 200), ((0, 100), (0, 100)))
@@ -64,7 +64,7 @@ class Mean(Benchmark):
     params = [[1, 10, 100_000]]
 
     def setup(self, size):
-        self.array = np.arange(2*size).reshape(2, size)
+        self.array = np.arange(2 * size).reshape(2, size)
 
     def time_mean(self, size):
         np.mean(self.array)
@@ -136,6 +136,7 @@ def time_select_larger(self):
 
 def memoize(f):
     _memoized = {}
+
     def wrapped(*args):
         if args not in _memoized:
             _memoized[args] = f(*args)
@@ -181,11 +182,11 @@ def reversed(size, dtype, rnd):
         dtype = np.dtype(dtype)
         try:
             with np.errstate(over="raise"):
-                res = dtype.type(size-1)
+                res = dtype.type(size - 1)
         except (OverflowError, FloatingPointError):
             raise SkipNotImplemented("Cannot construct arange for this size.")
 
-        return np.arange(size-1, -1, -1, dtype=dtype)
+        return np.arange(size - 1, -1, -1, dtype=dtype)
 
     @staticmethod
     @memoize

diff --git a/benchmarks/benchmarks/bench_linalg.py b/benchmarks/benchmarks/bench_linalg.py
@@ -123,13 +123,14 @@ def time_numpy_linalg_lstsq_a__b_float64(self):
 class Einsum(Benchmark):
     param_names = ['dtype']
     params = [[np.float32, np.float64]]
+
     def setup(self, dtype):
         self.one_dim_small = np.arange(600, dtype=dtype)
         self.one_dim = np.arange(3000, dtype=dtype)
         self.one_dim_big = np.arange(480000, dtype=dtype)
         self.two_dim_small = np.arange(1200, dtype=dtype).reshape(30, 40)
         self.two_dim = np.arange(240000, dtype=dtype).reshape(400, 600)
-        self.three_dim_small = np.arange(10000, dtype=dtype).reshape(10,100,10)
+        self.three_dim_small = np.arange(10000, dtype=dtype).reshape(10, 100, 10)
         self.three_dim = np.arange(24000, dtype=dtype).reshape(20, 30, 40)
         # non_contiguous arrays
         self.non_contiguous_dim1_small = np.arange(1, 80, 2, dtype=dtype)
@@ -143,7 +144,7 @@ def time_einsum_outer(self, dtype):
 
     # multiply(a, b):trigger sum_of_products_contig_two
     def time_einsum_multiply(self, dtype):
-        np.einsum("..., ...", self.two_dim_small, self.three_dim , optimize=True)
+        np.einsum("..., ...", self.two_dim_small, self.three_dim, optimize=True)
 
     # sum and multiply:trigger sum_of_products_contig_stride0_outstride0_two
     def time_einsum_sum_mul(self, dtype):
@@ -208,8 +209,8 @@ def setup(self, shape, npdtypes):
         self.x2arg = np.random.uniform(-1, 1, np.dot(*shape)).reshape(shape)
         self.x2arg = self.x2arg.astype(npdtypes)
         if npdtypes.startswith('complex'):
-            self.xarg += self.xarg.T*1j
-            self.x2arg += self.x2arg.T*1j
+            self.xarg += self.xarg.T * 1j
+            self.x2arg += self.x2arg.T * 1j
 
     def time_transpose(self, shape, npdtypes):
         np.transpose(self.xarg)

diff --git a/benchmarks/benchmarks/bench_ma.py b/benchmarks/benchmarks/bench_ma.py
@@ -31,17 +31,18 @@ class Indexing(Benchmark):
     params = [[True, False],
               [1, 2],
               [10, 100, 1000]]
+
     def setup(self, masked, ndim, size):
         x = np.arange(size**ndim).reshape(ndim * (size,))
 
         if masked:
-            self.m = np.ma.array(x, mask=x%2 == 0)
+            self.m = np.ma.array(x, mask=x % 2 == 0)
         else:
             self.m = np.ma.array(x)
 
-        self.idx_scalar = (size//2,) * ndim
-        self.idx_0d = (size//2,) * ndim + (Ellipsis,)
-        self.idx_1d = (size//2,) * (ndim - 1)
+        self.idx_scalar = (size // 2,) * ndim
+        self.idx_0d = (size // 2,) * ndim + (Ellipsis,)
+        self.idx_1d = (size // 2,) * (ndim - 1)
 
     def time_scalar(self, masked, ndim, size):
         self.m[self.idx_scalar]
@@ -65,8 +66,8 @@ def setup(self, a_masked, b_masked, size):
         self.a_scalar = np.ma.masked if a_masked else 5
         self.b_scalar = np.ma.masked if b_masked else 3
 
-        self.a_1d = np.ma.array(x, mask=x%2 == 0 if a_masked else np.ma.nomask)
-        self.b_1d = np.ma.array(x, mask=x%3 == 0 if b_masked else np.ma.nomask)
+        self.a_1d = np.ma.array(x, mask=x % 2 == 0 if a_masked else np.ma.nomask)
+        self.b_1d = np.ma.array(x, mask=x % 3 == 0 if b_masked else np.ma.nomask)
 
         self.a_2d = self.a_1d.reshape(1, -1)
         self.b_2d = self.a_1d.reshape(-1, 1)
@@ -130,7 +131,7 @@ class MAFunctions1v(Benchmark):
     def setup(self, mtype, func, msize):
         xs = 2.0 + np.random.uniform(-1, 1, 6).reshape(2, 3)
         m1 = [[True, False, False], [False, False, True]]
-        xl = 2.0 + np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        xl = 2.0 + np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
         maskx = xl > 2.8
         self.nmxs = np.ma.array(xs, mask=m1)
         self.nmxl = np.ma.array(xl, mask=maskx)
@@ -152,7 +153,7 @@ class MAMethod0v(Benchmark):
     def setup(self, method, msize):
         xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
         m1 = [[True, False, False], [False, False, True]]
-        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        xl = np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
         maskx = xl > 0.8
         self.nmxs = np.ma.array(xs, mask=m1)
         self.nmxl = np.ma.array(xl, mask=maskx)
@@ -180,8 +181,8 @@ def setup(self, mtype, func, msize):
         self.nmxs = np.ma.array(xs, mask=m1)
         self.nmys = np.ma.array(ys, mask=m2)
         # Big arrays
-        xl = 2.0 + np.random.uniform(-1, 1, 100*100).reshape(100, 100)
-        yl = 2.0 + np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        xl = 2.0 + np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
+        yl = 2.0 + np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
         maskx = xl > 2.8
         masky = yl < 1.8
         self.nmxl = np.ma.array(xl, mask=maskx)
@@ -203,7 +204,7 @@ class MAMethodGetItem(Benchmark):
     def setup(self, margs, msize):
         xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
         m1 = [[True, False, False], [False, False, True]]
-        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        xl = np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
         maskx = xl > 0.8
         self.nmxs = np.ma.array(xs, mask=m1)
         self.nmxl = np.ma.array(xl, mask=maskx)
@@ -225,7 +226,7 @@ class MAMethodSetItem(Benchmark):
     def setup(self, margs, mset, msize):
         xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
         m1 = [[True, False, False], [False, False, True]]
-        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        xl = np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
         maskx = xl > 0.8
         self.nmxs = np.ma.array(xs, mask=m1)
         self.nmxl = np.ma.array(xl, mask=maskx)
@@ -252,8 +253,8 @@ def setup(self, mtype, msize):
         self.nmxs = np.ma.array(xs, mask=m1)
         self.nmys = np.ma.array(ys, mask=m2)
         # Big arrays
-        xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
-        yl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        xl = np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
+        yl = np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
         maskx = xl > 0.8
         masky = yl < -0.8
         self.nmxl = np.ma.array(xl, mask=maskx)

diff --git a/benchmarks/benchmarks/bench_manipulate.py b/benchmarks/benchmarks/bench_manipulate.py
@@ -10,10 +10,10 @@ class BroadcastArrays(Benchmark):
     timeout = 10
 
     def setup(self, shape, ndtype):
-        self.xarg = np.random.ranf(shape[0]*shape[1]).reshape(shape)
+        self.xarg = np.random.ranf(shape[0] * shape[1]).reshape(shape)
         self.xarg = self.xarg.astype(ndtype)
         if ndtype.startswith('complex'):
-            self.xarg += np.random.ranf(1)*1j
+            self.xarg += np.random.ranf(1) * 1j
 
     def time_broadcast_arrays(self, shape, ndtype):
         np.broadcast_arrays(self.xarg, np.ones(1))
@@ -30,7 +30,7 @@ def setup(self, size, ndtype):
         self.xarg = self.rng.random(size)
         self.xarg = self.xarg.astype(ndtype)
         if ndtype.startswith('complex'):
-            self.xarg += self.rng.random(1)*1j
+            self.xarg += self.rng.random(1) * 1j
 
     def time_broadcast_to(self, size, ndtype):
         np.broadcast_to(self.xarg, (size, size))
@@ -44,11 +44,11 @@ class ConcatenateStackArrays(Benchmark):
     timeout = 10
 
     def setup(self, shape, narrays, ndtype):
-        self.xarg = [np.random.ranf(shape[0]*shape[1]).reshape(shape)
+        self.xarg = [np.random.ranf(shape[0] * shape[1]).reshape(shape)
                      for x in range(narrays)]
         self.xarg = [x.astype(ndtype) for x in self.xarg]
         if ndtype.startswith('complex'):
-            [x + np.random.ranf(1)*1j for x in self.xarg]
+            [x + np.random.ranf(1) * 1j for x in self.xarg]
 
     def time_concatenate_ax0(self, size, narrays, ndtype):
         np.concatenate(self.xarg, axis=0)

diff --git a/benchmarks/benchmarks/bench_random.py b/benchmarks/benchmarks/bench_random.py
@@ -84,6 +84,7 @@ def time_permutation_2d(self):
     def time_permutation_int(self):
         np.random.permutation(self.n)
 
+
 nom_size = 100000
 
 class RNG(Benchmark):
@@ -155,21 +156,21 @@ def setup(self, bitgen, args):
         self.rg.random()
 
     def time_bounded(self, bitgen, args):
-            """
-            Timer for 8-bit bounded values.
-
-            Parameters (packed as args)
-            ----------
-            dt : {uint8, uint16, uint32, unit64}
-                output dtype
-            max : int
-                Upper bound for range. Lower is always 0.  Must be <= 2**bits.
-            """
-            dt, max = args
-            if bitgen == 'numpy':
-                self.rg.randint(0, max + 1, nom_size, dtype=dt)
-            else:
-                self.rg.integers(0, max + 1, nom_size, dtype=dt)
+        """
+        Timer for 8-bit bounded values.
+
+        Parameters (packed as args)
+        ----------
+        dt : {uint8, uint16, uint32, unit64}
+              output dtype
+        max : int
+              Upper bound for range. Lower is always 0.  Must be <= 2**bits.
+        """
+        dt, max = args
+        if bitgen == 'numpy':
+            self.rg.randint(0, max + 1, nom_size, dtype=dt)
+        else:
+            self.rg.integers(0, max + 1, nom_size, dtype=dt)
 
 class Choice(Benchmark):
     params = [1e3, 1e6, 1e8]

diff --git a/benchmarks/benchmarks/bench_reduce.py b/benchmarks/benchmarks/bench_reduce.py
@@ -52,7 +52,7 @@ class StatsReductions(Benchmark):
     def setup(self, dtype):
         self.data = np.ones(200, dtype=dtype)
         if dtype.startswith('complex'):
-            self.data = self.data * self.data.T*1j
+            self.data = self.data * self.data.T * 1j
 
     def time_min(self, dtype):
         np.min(self.data)

diff --git a/benchmarks/benchmarks/bench_scalar.py b/benchmarks/benchmarks/bench_scalar.py
@@ -8,6 +8,7 @@ class ScalarMath(Benchmark):
     # the function call overhead to some degree.
     params = [TYPES1]
     param_names = ["type"]
+
     def setup(self, typename):
         self.num = np.dtype(typename).type(2)
         self.int32 = np.int32(2)

diff --git a/benchmarks/benchmarks/bench_shape_base.py b/benchmarks/benchmarks/bench_shape_base.py
@@ -76,7 +76,7 @@ class Block2D(Benchmark):
     def setup(self, shape, dtype, n_chunks):
 
         self.block_list = [
-             [np.full(shape=[s//n_chunk for s, n_chunk in zip(shape, n_chunks)],
+             [np.full(shape=[s // n_chunk for s, n_chunk in zip(shape, n_chunks)],
                      fill_value=1, dtype=dtype) for _ in range(n_chunks[1])]
             for _ in range(n_chunks[0])
         ]

diff --git a/benchmarks/benchmarks/bench_ufunc.py b/benchmarks/benchmarks/bench_ufunc.py
@@ -251,7 +251,7 @@ class NDArrayGetItem(Benchmark):
 
     def setup(self, margs, msize):
         self.xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
-        self.xl = np.random.uniform(-1, 1, 50*50).reshape(50, 50)
+        self.xl = np.random.uniform(-1, 1, 50 * 50).reshape(50, 50)
 
     def time_methods_getitem(self, margs, msize):
         if msize == 'small':
@@ -268,7 +268,7 @@ class NDArraySetItem(Benchmark):
 
     def setup(self, margs, msize):
         self.xs = np.random.uniform(-1, 1, 6).reshape(2, 3)
-        self.xl = np.random.uniform(-1, 1, 100*100).reshape(100, 100)
+        self.xl = np.random.uniform(-1, 1, 100 * 100).reshape(100, 100)
 
     def time_methods_setitem(self, margs, msize):
         if msize == 'small':
@@ -512,9 +512,11 @@ def time_add_scalar_conv_complex(self):
 
 class ArgPack:
     __slots__ = ['args', 'kwargs']
+
     def __init__(self, *args, **kwargs):
         self.args = args
         self.kwargs = kwargs
+
     def __repr__(self):
         return '({})'.format(', '.join(
             [repr(a) for a in self.args] +