def __call__(self, func): # if key errors occur, fail silently try: self._add_url(func) np.add_docstring(func, self.doc) return func except KeyError: return func
np.can_cast(AR_f8, 1) # E: incompatible type np.vdot(AR_M, AR_M) # E: incompatible type np.copyto(AR_LIKE_f, AR_f8) # E: incompatible type np.putmask(AR_LIKE_f, [True, True, False], 1.5) # E: incompatible type np.packbits(AR_f8) # E: incompatible type np.packbits(AR_u1, bitorder=">") # E: incompatible type np.unpackbits(AR_i8) # E: incompatible type np.unpackbits(AR_u1, bitorder=">") # E: incompatible type np.shares_memory(1, 1, max_work=i8) # E: incompatible type np.may_share_memory(1, 1, max_work=i8) # E: incompatible type np.arange(M) # E: No overload variant np.arange(stop=10) # E: No overload variant np.datetime_data(int) # E: incompatible type np.busday_offset("2012", 10) # E: incompatible type np.datetime_as_string("2012") # E: incompatible type np.compare_chararrays("a", b"a", "==", False) # E: No overload variant np.add_docstring(func, None) # E: incompatible type
def test_add_docstring(self): import numpy as np foo = lambda: None np.add_docstring(foo, "Does a thing") assert foo.__doc__ == "Does a thing"
# -*- coding: utf-8 -*- """ Created on Thu Nov 2 16:07:17 2017 @author: user """ from sklearn import tree features = [[140, 1], [130, 1], [150, 0], [170, 0]] labels = [0, 0, 1, 1] clf = tree.DecisionTreeClassifier() clf = clf.fit(features, labels) print(clf.predict([[140, 1]])) import numpy as np a = np.array([[0, 1, 2, 3, 4], [9, 8, 7, 6, 5]]) a.shape import numpy as np a = np.array([[0, 1, 2, 3, 4], [9, 8, 7, 6, 5]]) a.itemsize b = np.random.shuffle(a) print(b) a = np.add_docstring((3, 4), dtype='int32')
def yu_ye_wang(shape, **kargs): return phantom(shape, yu_ye_wang_parameters, **kargs) # add docstrings to specific phantoms common_docstring = '''Generates a %(name)s phantom with a given shape and dtype Parameters ---------- shape: 3-tuple of ints Shape of the 3d output cube. dtype: data-type Data type of the output cube. Returns ------- cube: ndarray 3-dimensional phantom. ''' modified_shepp_logan_docstring = common_docstring % { 'name': 'Modified Shepp-Logan' } shepp_logan_docstring = common_docstring % {'name': 'Shepp-Logan'} yu_ye_wang_docstring = common_docstring % {'name': 'Yu Ye Wang'} np.add_docstring(modified_shepp_logan, modified_shepp_logan_docstring) np.add_docstring(shepp_logan, shepp_logan_docstring) np.add_docstring(yu_ye_wang, yu_ye_wang_docstring)
["2011-01"], "2011-02", roll="forward")) # E: numpy.ndarray[Any, numpy.dtype[numpy.datetime64]] reveal_type(np.is_busday("2012")) # E: numpy.bool_ reveal_type(np.is_busday( ["2012"])) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(np.datetime_as_string(M)) # E: numpy.str_ reveal_type(np.datetime_as_string( AR_M)) # E: numpy.ndarray[Any, numpy.dtype[numpy.str_]] reveal_type(np.compare_chararrays( "a", "b", "!=", rstrip=False)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(np.compare_chararrays( b"a", b"a", "==", True)) # E: numpy.ndarray[Any, numpy.dtype[numpy.bool_]] reveal_type(np.add_docstring(func, "test")) # E: None reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["c_index"])) # E: tuple[numpy.nditer] reveal_type( np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["readonly", "readonly"]])) # E: tuple[numpy.nditer] reveal_type(np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_dtypes=np.int_)) # E: tuple[numpy.nditer] reveal_type( np.nested_iters([AR_i8, AR_i8], [[0], [1]], order="C", casting="no")) # E: tuple[numpy.nditer]