Unlock 30% off on Manual Testing Annual Plans this Holiday Season.

Copied to Clipboard!

How to use rename_attributes method in localstack

Best Python code snippet using localstack_python

replace_func.py

Source:replace_func.py

1import inspect2import chainer3class WrappedFunctionNode(chainer.FunctionNode):4    """Wrap the target function and operate as ``FunctionNode``5    Arguments:6        name (str): name of the function node7        func (func): the target function8        args (list): args for the function9        kwargs (dict): kwargs for the function10        arg_vars (list): list of `chainer.Variable`s in `args` and `kwargs`11        attributes (list): parameters to be set node's attributes12    """13    def __init__(self, name, func, args, kwargs, arg_vars, attributes=None):14        self.custom_function_node_name = name15        self.func = func16        self.args = args17        self.kwargs = kwargs18        self.arg_vars = arg_vars19        self.internal_results = None20        if attributes is not None:21            for k, v in attributes.items():22                setattr(self, k, v)23    def forward(self, xs):24        assert len(xs) == len(self.arg_vars)25        self.xs = xs26        results = self.func(*self.args, **self.kwargs)27        self.skeleton, flattened_results = self._flatten_return_value(results)28        dummy_results = tuple(_unwrap_var(ret) for ret in flattened_results)29        if all([_is_var(ret) for ret in flattened_results]):30            self.internal_results = flattened_results31        if not chainer.is_arrays_compatible(dummy_results):32            raise ValueError(33                'returned values from the function wrapped by \'as_funcnode\' '34                'must consist only array, function name: {}'.format(35                    self.custom_function_node_name))36        return dummy_results37    def backward(self, target_input_indexes, grad_outputs):38        if self.internal_results is None:39            raise ValueError(40                'the target function does not support backward, propagation '41                'is failed')42        grad_inputs = chainer.grad(self.internal_results, self.arg_vars,43                                   grad_outputs=grad_outputs)44        assert len(self.arg_vars) == len(grad_inputs)45        return tuple(grad_input if i in target_input_indexes else None46                     for i, grad_input in enumerate(grad_inputs))47    def _flatten_return_value(self, x):48        outputs = []49        def skeletonize(r):50            if isinstance(r, tuple):51                return tuple(skeletonize(e) for e in r)52            elif isinstance(r, list):53                return [skeletonize(e) for e in r]54            elif isinstance(r, dict):55                return {k: skeletonize(v) for k, v in r.items()}56            else:57                index = len(outputs)58                outputs.append(r)59                return index60        skeleton = skeletonize(x)61        return skeleton, outputs62    def reconstruct_return_value(self, outputs):63        def f(skeleton):64            if isinstance(skeleton, tuple):65                return tuple(f(e) for e in skeleton)66            elif isinstance(skeleton, list):67                return [f(e) for e in skeleton]68            elif isinstance(skeleton, dict):69                return {k: f(v) for k, v in skeleton.items()}70            else:71                return outputs[skeleton]72        return f(self.skeleton)73def fake_as_funcnode(alt_func, name, rename_attributes=None,74                     experimental_warning=True):75    """The target function fakes FunctionNode76    The target function is replaced to the alternative function to connect77    variable node by acting function node. ``alt_func`` must satisfy the78    following restrictions.79    1. Inputs includes one or more ``chainer.Variable`` to trace variables.80    2. Output consists nothing but ``ndarray`` or ``chainer.Variable``81    Even if ``alt_func`` returns ``ndarray``, the value forced to be converted82    to ``chainer.Variable``. A caller of the target function have to care83    both cases, returning ``ndarray`` and ``chainer.Variable``.84    When ``alt_func`` returns ``list`` of variable, the wrapped function will85    also returns multiple variables as ``tuple``. However ``dict`` cannot86    be return, the wrapped function breaks down the returned values as87    ``tuple`` of values, keys will be ignored.88    Arguments of ``alt_func`` except for ``chainer.Variable`` are set as89    function attributes. Attribute names are set ``argN`` (N is index90    number) or keyword on default.91    Example:92       >>> def func(x, a, b, c=1, d=2): pass93       >>> # x is variable94       >>> func = onnx_chainer.replace_func.fake_as_funcnode(95       ...     func, 'CustomNode',96       ...     rename_attributes=[(1, 'value'), ('c', 'y')])97    Then ``func`` will be operated as a function node named "CustomNode", and98    ``'value'``, ``'b'``, ``'y'``, ``'d'`` are set as function's attributes.99    See tests/test_replace_func.py more details.100    Args:101        alt_func (func): actual called function. There are some constrains, see102            the above documentation.103        name (str): function name. This name is used for what ONNX operator104            to be assigned.105        rename_attributes (list or tuple): rename attribute name, set list106            of ``tuple(index_of_args, new_name)`` or107            ``tuple(kwargs_name, new_name)``108        experimental_warning: this function is experimental utility, if set109            ``False``, run without experimental warning.110    Returns:111        func: wrapped function, called on exporting.112    """113    def _wrapper(*args, **kwargs):114        inputs = []115        attributes = {}116        rename_attr_dict = {}117        if rename_attributes is not None:118            rename_attr_dict = {attr[0]: attr[1] for attr in rename_attributes}119        # resolve default value for kwargs120        arg_spec = inspect.signature(alt_func)121        bound = arg_spec.bind(*args, **kwargs)122        bound.apply_defaults()123        # default values are set on `bound.arguments`, but cannot get them124        # from `bound.kwargs`125        for i, (k, v) in enumerate(bound.arguments.items()):126            if i < len(args):127                continue128            kwargs[k] = v129        def set_attr(key, value):130            default_name = key if isinstance(key, str) else 'arg{}'.format(key)131            attributes[rename_attr_dict.get(key, default_name)] = value132        def expand_args(args_iter):133            for i, a in args_iter:134                if _is_var(a):135                    inputs.append(a)136                elif isinstance(a, (tuple, list)):137                    # all elements are variable -> add flatten them to inputs138                    # all elements are not variable -> add them to attributes139                    # mixed variable and other type value -> error140                    flatten_arg = _flatten(a)141                    var_or_not = map(_is_var, flatten_arg)142                    if all(var_or_not):143                        inputs.extend(flatten_arg)144                    elif not any(var_or_not):145                        set_attr(i, a)146                    else:147                        raise ValueError(148                            'arguments mixed variable and other type are not '149                            'supported')150                else:151                    set_attr(i, a)152        expand_args(enumerate(args))153        expand_args(kwargs.items())154        if not inputs:155            raise ValueError(156                'arguments of the function wrapped by \'as_funcnode\' '157                'must include at least one chainer.Variable, function name: '158                '{}'.format(name))159        wrapped = WrappedFunctionNode(160            name, alt_func, args, kwargs, inputs, attributes=attributes)161        ret = wrapped.apply(inputs)162        return wrapped.reconstruct_return_value(ret)163    if experimental_warning:164        chainer.utils.experimental('as_funcnode')165    return _wrapper166def as_funcnode(name, rename_attributes=None):167    """The target function fakes FunctionNode168    The target function is overwrapped to connect variable node by acting169    function node. Expected to be used as decorator. More detail, see170    ``fake_as_funcnode`` documentation.171    Example:172       >>> @onnx_chainer.replace_func.as_funcnode(173       ...     'CustomNode', rename_attributes=[(1, 'value'), ('c', 'y')])174       ... def func(x, a, b, c=1, d=2): pass175    Args:176        name (str): function name. This name is used for what ONNX operator177            to be assigned.178        rename_attributes (list or tuple): rename attribute name, set list179            of ``tuple(index_of_args, new_name)`` or180            ``tuple(kwargs_name, new_name)``181    """182    def _wrapper(fn):183        return fake_as_funcnode(fn, name, rename_attributes=rename_attributes)184    return _wrapper185def _unwrap_var(var):186    return var.array if _is_var(var) else var187def _is_var(array):188    # alias for type checking189    return isinstance(array, chainer.Variable)190def _is_array(v):191    return not isinstance(v, (list, tuple))192def _flatten(xs):193    if _is_array(xs):194        return [xs]195    o = []196    for x in xs:197        if _is_array(x):198            o.append(x)199        else:200            o.extend(_flatten(x))...

main.py

Source:main.py

...107    }108# add neighbourhoods layer109neighbourhoods_layer = layer_management.add_layer_as_reprojected_gpkg(project_folder, geo_layer_folder, NEIGHBOURHOODS['path'], CRS, 'reprojected/{}'.format(NEIGHBOURHOODS['identifier']))110# rename attributes111layer_management.rename_attributes(neighbourhoods_layer, NEIGHBOURHOODS['rename_attributes'])112# clean up neighbourhoods layer113layer_management.delete_all_attributes_except(NEIGHBOURHOODS['attributes_to_keep'], neighbourhoods_layer)114# for each neighbourhood find surrounding neighbourhoods115spatial_analysis.add_adjacent_features(neighbourhoods_layer, 'adjacent_neighbourhoods', 'neighbourhood_code')116# add geo coordinates to neighbourhoods layer117spatial_analysis.add_coordinates(neighbourhoods_layer)118# add municipality layer119municipality_layer = layer_management.add_layer_as_reprojected_gpkg(project_folder, geo_layer_folder, MUNICIPALITIES['path'], CRS, 'reprojected/{}'.format(MUNICIPALITIES['identifier']))120# add corresponding municipality code to each neighbourhood121spatial_analysis.add_overlapping_features(neighbourhoods_layer, municipality_layer, 'municipality_code', MUNICIPALITIES['id_attribute'], area_to_point=True, prefix = MUNICIPALITIES['code_prefix'])122spatial_analysis.add_overlapping_features(neighbourhoods_layer, municipality_layer, 'municipality_name', MUNICIPALITIES['name_attribute'], area_to_point=True)123# loop over heat source files124for NAME, SOURCE in HEAT_SOURCES.items():125    print(NAME)126    # add heat source layers127    heat_layer = layer_management.add_layer_as_reprojected_gpkg(project_folder, geo_layer_folder, SOURCE['path'], CRS, 'reprojected/{}'.format(SOURCE['identifier']))128    # rename attributes129    layer_management.rename_attributes(heat_layer, SOURCE['rename_attributes'])130    #clean up layer131    layer_management.delete_all_attributes_except(SOURCE['attributes_to_keep'], heat_layer)132    # generate unique ID for each heat source133    layer_management.add_unique_id(heat_layer, 'source_id', SOURCE['identifier'])134    # add geo coordinates135    spatial_analysis.add_coordinates(heat_layer)136    # add heat source IDs to neighbourhoods if heat source overlaps with neighbourhood137    spatial_analysis.add_overlapping_features(heat_layer, neighbourhoods_layer, 'neighbourhoods_in_range', 'neighbourhood_code')138    # export to CSV139    layer_management.export_to_csv(heat_layer, project_folder, 'input_data/', SOURCE['output_file'])140# export neighbourhoods layer to CSV...

test_ast.py

Source:test_ast.py

...20        with self.assertRaises(KeyError):21            find_function(self.root, 'bad_name')22class TestAttrRename(AstTestCase):23    def test_load(self):24        root = rename_attributes(ast.parse('var = self.value1'),25                                 load_names={'value1': 'value2'})26        expected = ast.parse('var = self.value2')27        self.assertEqual(root, expected)28    def test_store(self):29        root = rename_attributes(ast.parse('self.var1 = value'),30                                 store_names={'var1': 'var2'})31        expected = ast.parse('self.var2 = value')32        self.assertEqual(root, expected)33    def test_load_and_store(self):34        root = rename_attributes(ast.parse('self.var1 = self.value1'),35                                 load_names={'value1': 'value2'},36                                 store_names={'var1': 'var2'})37        expected = ast.parse('self.var2 = self.value2')38        self.assertEqual(root, expected)39    def test_load_and_store_same_key(self):40        root = rename_attributes(ast.parse('self.foo1 = self.foo1'),41                                 load_names={'foo1': 'load'},42                                 store_names={'foo1': 'store'})43        expected = ast.parse('self.store = self.load')44        self.assertEqual(root, expected)45    def test_ignore_function_call(self):46        root = rename_attributes(ast.parse('self.myfunc(1)'),47                                 load_names={'myfunc': 'load'},48                                 store_names={'myfunc': 'store'})49        expected = ast.parse('self.myfunc(1)')50        self.assertEqual(root, expected)51class TestRemoveFunctionParameters(AstTestCase):52    def setUp(self):53        self.root = parse_source(SimpleClass)54    def test_remove_all_params(self):55        actual = find_function(self.root, 'method_with_params')56        remove_function_parameters(actual)57        root = ast.parse('def method_with_params(): return self, thing')58        expected = find_function(root, 'method_with_params')...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.