Fix a bug in is_patched function

.github/workflows/conda-package.yml

@@ -1,6 +1,10 @@
 name: Conda package
 
-on: push
+on:
+    push:
+      branches:
+        - master
+    pull_request:
 
 permissions: read-all
 

mkl_umath/src/_patch.pyx

@@ -27,7 +27,6 @@
 # cython: language_level=3
 
 import mkl_umath._ufuncs as mu
-import numpy.core.umath as nu
 
 cimport numpy as cnp
 import numpy as np
@@ -59,15 +58,15 @@ cdef class patch:
         self.functions_count = 0
         for umath in umaths:
             mkl_umath = getattr(mu, umath)
-            self.functions_count = self.functions_count + mkl_umath.ntypes
+            self.functions_count += mkl_umath.ntypes
 
         self.functions = <function_info *> malloc(self.functions_count * sizeof(function_info))
 
         func_number = 0
         for umath in umaths:
             patch_umath = getattr(mu, umath)
             c_patch_umath = <cnp.ufunc>patch_umath
-            c_orig_umath = <cnp.ufunc>getattr(nu, umath)
+            c_orig_umath = <cnp.ufunc>getattr(np, umath)
             nargs = c_patch_umath.nargs
             for pi in range(c_patch_umath.ntypes):
                 oi = 0
@@ -103,7 +102,7 @@ cdef class patch:
         cdef int* signature
 
         for func in self.functions_dict:
-            np_umath = getattr(nu, func[0])
+            np_umath = getattr(np, func[0])
             index = self.functions_dict[func]
             function = self.functions[index].patch_function
             signature = self.functions[index].signature
@@ -118,7 +117,7 @@ cdef class patch:
         cdef int* signature
 
         for func in self.functions_dict:
-            np_umath = getattr(nu, func[0])
+            np_umath = getattr(np, func[0])
             index = self.functions_dict[func]
             function = self.functions[index].original_function
             signature = self.functions[index].signature
@@ -143,34 +142,97 @@ def _initialize_tls():
 
 
 def use_in_numpy():
-    '''
+    """
     Enables using of mkl_umath in Numpy.
-    '''
+
+    Examples
+    --------
+    >>> import mkl_umath, numpy as np
+    >>> mkl_umath.is_patched()
+    # False
+
+    >>> mkl_umath.use_in_numpy()  # Enable mkl_umath in Numpy
+    >>> mkl_umath.is_patched()
+    # True
+
+    >>> mkl_umath.restore()  # Disable mkl_umath in Numpy
+    >>> mkl_umath.is_patched()
+    # False
+
+    """
     if not _is_tls_initialized():
         _initialize_tls()
     _tls.patch.do_patch()
 
 
 def restore():
-    '''
+    """
     Disables using of mkl_umath in Numpy.
-    '''
+
+    Examples
+    --------
+    >>> import mkl_umath, numpy as np
+    >>> mkl_umath.is_patched()
+    # False
+
+    >>> mkl_umath.use_in_numpy()  # Enable mkl_umath in Numpy
+    >>> mkl_umath.is_patched()
+    # True
+
+    >>> mkl_umath.restore()  # Disable mkl_umath in Numpy
+    >>> mkl_umath.is_patched()
+    # False   
+
+    """
     if not _is_tls_initialized():
         _initialize_tls()
     _tls.patch.do_unpatch()
 
 
 def is_patched():
-    '''
+    """
     Returns whether Numpy has been patched with mkl_umath.
-    '''
+
+    Examples
+    --------
+    >>> import mkl_umath, numpy as np
+    >>> mkl_umath.is_patched()
+    # False
+
+    >>> mkl_umath.use_in_numpy()  # Enable mkl_umath in Numpy
+    >>> mkl_umath.is_patched()
+    # True
+
+    >>> mkl_umath.restore()  # Disable mkl_umath in Numpy
+    >>> mkl_umath.is_patched()
+    # False 
+
+    """
     if not _is_tls_initialized():
         _initialize_tls()
-    _tls.patch.is_patched()
+    return _tls.patch.is_patched()
 
 from contextlib import ContextDecorator
 
 class mkl_umath(ContextDecorator):
+    """
+    Context manager and decorator to temporarily patch NumPy ufuncs 
+    with MKL-based implementations.
+
+    Examples
+    --------
+    >>> import mkl_umath, numpy as np
+    >>> mkl_umath.is_patched()
+    # False
+
+    >>> with mkl_umath.mkl_umath():  # Enable mkl_umath in Numpy
+    >>>     print(mkl_umath.is_patched())
+    # True
+
+    >>> mkl_umath.is_patched()
+    # False  
+
+    """
     def __enter__(self):
         use_in_numpy()
         return self