saving dependency work

2022-12-10 21:17:54 -06:00
parent bf923c3406
commit c2c19e5382
5636 changed files with 666283 additions and 8587 deletions
@@ -0,0 +1,88 @@
+# 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
+# Copyright (c) https://github.com/PyCQA/astroid/blob/main/CONTRIBUTORS.txt
+
+"""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)