Introduce end-of-line normalization (#1443)

Some file were formatted for Windows, which was a problem for #1441

Author: Pierre-Sassoulas

.gitattributes

@@ -0,0 +1,2 @@
+# See http://git-scm.com/docs/gitattributes#_end_of_line_conversion
+* text=auto

astroid/brain/brain_scipy_signal.py

@@ -3,92 +3,92 @@
 # Copyright (c) 2020 Claudiu Popa <[email protected]>
 # Copyright (c) 2021 Pierre Sassoulas <[email protected]>
 # Copyright (c) 2021 Marc Mueller <[email protected]>
-
-# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
-# For details: https://github.com/PyCQA/astroid/blob/main/LICENSE
-
-
-"""Astroid hooks for scipy.signal module."""
-from astroid.brain.helpers import register_module_extender
-from astroid.builder import parse
-from astroid.manager import AstroidManager
-
-
-def scipy_signal():
-    return parse(
-        """
-    # different functions defined in scipy.signals
-
-    def barthann(M, sym=True):
-        return numpy.ndarray([0])
-
-    def bartlett(M, sym=True):
-        return numpy.ndarray([0])
-
-    def blackman(M, sym=True):
-        return numpy.ndarray([0])
-
-    def blackmanharris(M, sym=True):
-        return numpy.ndarray([0])
-
-    def bohman(M, sym=True):
-        return numpy.ndarray([0])
-
-    def boxcar(M, sym=True):
-        return numpy.ndarray([0])
-
-    def chebwin(M, at, sym=True):
-        return numpy.ndarray([0])
-
-    def cosine(M, sym=True):
-        return numpy.ndarray([0])
-
-    def exponential(M, center=None, tau=1.0, sym=True):
-        return numpy.ndarray([0])
-
-    def flattop(M, sym=True):
-        return numpy.ndarray([0])
-
-    def gaussian(M, std, sym=True):
-        return numpy.ndarray([0])
-
-    def general_gaussian(M, p, sig, sym=True):
-        return numpy.ndarray([0])
-
-    def hamming(M, sym=True):
-        return numpy.ndarray([0])
-
-    def hann(M, sym=True):
-        return numpy.ndarray([0])
-
-    def hanning(M, sym=True):
-        return numpy.ndarray([0])
-
-    def impulse2(system, X0=None, T=None, N=None, **kwargs):
-        return numpy.ndarray([0]), numpy.ndarray([0])
-
-    def kaiser(M, beta, sym=True):
-        return numpy.ndarray([0])
-
-    def nuttall(M, sym=True):
-        return numpy.ndarray([0])
-
-    def parzen(M, sym=True):
-        return numpy.ndarray([0])
-
-    def slepian(M, width, sym=True):
-        return numpy.ndarray([0])
-
-    def step2(system, X0=None, T=None, N=None, **kwargs):
-        return numpy.ndarray([0]), numpy.ndarray([0])
-
-    def triang(M, sym=True):
-        return numpy.ndarray([0])
-
-    def tukey(M, alpha=0.5, sym=True):
-        return numpy.ndarray([0])
-        """
-    )
-
-
-register_module_extender(AstroidManager(), "scipy.signal", scipy_signal)
+
+# Licensed under the LGPL: https://www.gnu.org/licenses/old-licenses/lgpl-2.1.en.html
+# For details: https://github.com/PyCQA/astroid/blob/main/LICENSE
+
+
+"""Astroid hooks for scipy.signal module."""
+from astroid.brain.helpers import register_module_extender
+from astroid.builder import parse
+from astroid.manager import AstroidManager
+
+
+def scipy_signal():
+    return parse(
+        """
+    # different functions defined in scipy.signals
+
+    def barthann(M, sym=True):
+        return numpy.ndarray([0])
+
+    def bartlett(M, sym=True):
+        return numpy.ndarray([0])
+
+    def blackman(M, sym=True):
+        return numpy.ndarray([0])
+
+    def blackmanharris(M, sym=True):
+        return numpy.ndarray([0])
+
+    def bohman(M, sym=True):
+        return numpy.ndarray([0])
+
+    def boxcar(M, sym=True):
+        return numpy.ndarray([0])
+
+    def chebwin(M, at, sym=True):
+        return numpy.ndarray([0])
+
+    def cosine(M, sym=True):
+        return numpy.ndarray([0])
+
+    def exponential(M, center=None, tau=1.0, sym=True):
+        return numpy.ndarray([0])
+
+    def flattop(M, sym=True):
+        return numpy.ndarray([0])
+
+    def gaussian(M, std, sym=True):
+        return numpy.ndarray([0])
+
+    def general_gaussian(M, p, sig, sym=True):
+        return numpy.ndarray([0])
+
+    def hamming(M, sym=True):
+        return numpy.ndarray([0])
+
+    def hann(M, sym=True):
+        return numpy.ndarray([0])
+
+    def hanning(M, sym=True):
+        return numpy.ndarray([0])
+
+    def impulse2(system, X0=None, T=None, N=None, **kwargs):
+        return numpy.ndarray([0]), numpy.ndarray([0])
+
+    def kaiser(M, beta, sym=True):
+        return numpy.ndarray([0])
+
+    def nuttall(M, sym=True):
+        return numpy.ndarray([0])
+
+    def parzen(M, sym=True):
+        return numpy.ndarray([0])
+
+    def slepian(M, width, sym=True):
+        return numpy.ndarray([0])
+
+    def step2(system, X0=None, T=None, N=None, **kwargs):
+        return numpy.ndarray([0]), numpy.ndarray([0])
+
+    def triang(M, sym=True):
+        return numpy.ndarray([0])
+
+    def tukey(M, alpha=0.5, sym=True):
+        return numpy.ndarray([0])
+        """
+    )
+
+
+register_module_extender(AstroidManager(), "scipy.signal", scipy_signal)

tests/testdata/python3/data/recursion.py

@@ -1,3 +1,3 @@
-""" For issue #25 """
-class Base(object):
+""" For issue #25 """
+class Base(object):
     pass