How to use downcast method in hypothesis

Best Python code snippet using hypothesis

FFIExternalObject.py

Source:FFIExternalObject.py Github

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...126 #exactObject = exactWrapperClass(None)127 exactObject = FFIInstance(exactWrapperClass)128 # Get the downcast pointer that has had all the downcast129 # funcs called130 downcastObject = self.downcast(exactWrapperClass)131 exactObject.this = downcastObject.this132 exactObject.userManagesMemory = downcastObject.userManagesMemory133 # Make sure the original downcast object does not get134 # garbage collected so that the exactObject will not get135 # gc'd thereby transferring ownership of the object to136 # this new exactObject137 downcastObject.userManagesMemory = 0138 return exactObject139 else:140 return self141 def downcast(self, toClass):142 fromClass = self.__class__143 #FFIConstants.notify.debug('downcast: downcasting from %s to %s' % \144 # (fromClass.__name__, toClass.__name__))145 # Check the cache to see if we have looked this up before146 downcastChain = DowncastMap.get((fromClass, toClass))147 if downcastChain == None:148 downcastChain = self.getDowncastFunctions(toClass, fromClass)149 #FFIConstants.notify.debug('downcast: computed downcast chain: ' + repr(downcastChain))150 # Store it for next time151 DowncastMap[(fromClass, toClass)] = downcastChain152 newObject = self153 for downcastFunc in downcastChain:154 #FFIConstants.notify.debug('downcast: downcasting %s using %s' % \155 # (newObject.__class__.__name__, downcastFunc))...

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test_downcast.py

Source:test_downcast.py Github

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...30 np.array([decimal.Decimal(0.0)]),31 ),32 ],33)34def test_downcast(arr, expected, dtype):35 result = maybe_downcast_to_dtype(arr, dtype)36 tm.assert_numpy_array_equal(result, expected)37def test_downcast_booleans():38 # see gh-16875: coercing of booleans.39 ser = Series([True, True, False])40 result = maybe_downcast_to_dtype(ser, np.dtype(np.float64))41 expected = ser42 tm.assert_series_equal(result, expected)43def test_downcast_conversion_no_nan(any_real_dtype):44 dtype = any_real_dtype45 expected = np.array([1, 2])46 arr = np.array([1.0, 2.0], dtype=dtype)47 result = maybe_downcast_to_dtype(arr, "infer")48 tm.assert_almost_equal(result, expected, check_dtype=False)...

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