Source code for yggdrasil.communication.transforms.IterateTransform

import copy
import numpy as np
from yggdrasil.communication.transforms.TransformBase import TransformBase

[docs]class IterateTransform(TransformBase): r"""Class for iterating over message elements.""" _transformtype = 'iterate'
[docs] def validate_datatype(self, datatype): r"""Assert that the provided datatype is valid for this transformation. Args: datatype (dict): Datatype to validate. Raises: AssertionError: If the datatype is not valid. """ assert datatype.get('type', None) in ['array', 'object', '1darray', 'ndarray']
[docs] def get_elements(self, datatype): r"""Get a list of elements in the datatype for iteration. Args: datatype (dict): Datatype to get elements from. Returns: list: List of datatypes for the elements iterated over. """ if datatype.get('type', None) == 'array': if isinstance(datatype.get('items', None), dict): out = [datatype['items']] else: out = datatype['items'] elif datatype.get('type', None) == 'object': out = [v for v in datatype.get('properties', {}).values()] if 'additionalProperties' in datatype: out.append(datatype['additionalProperties']) elif datatype.get('type', None) == '1darray': out = [dict(datatype, type='scalar')] out[0].pop('length', None) elif datatype.get('type', None) == 'ndarray': if len(datatype['shape']) > 2: out = [dict(datatype, shape=datatype['shape'][1:])] else: out = [dict(datatype, type='1darray', length=datatype['shape'][-1])] out[0].pop('shape', None) else: # pragma: debug raise ValueError("Unsupported datatype: %s" % datatype) return out
[docs] def transform_datatype(self, datatype): r"""Determine the datatype that will result from applying the transform to the supplied datatype. Args: datatype (dict): Datatype to transform. Returns: dict: Transformed datatype. """ elements = self.get_elements(datatype) if all(elements[0] == x for x in elements[1:]): datatype = copy.deepcopy(elements[0]) else: datatype = {'type': 'any'} return datatype
[docs] def evaluate_transform(self, x, no_copy=False): r"""Call transform on the provided message. Args: x (object): Message object to transform. no_copy (bool, optional): If True, the transformation occurs in place. Otherwise a copy is created and transformed. Defaults to False. Returns: object: The transformed message. """ out = x if not no_copy: out = copy.deepcopy(out) if isinstance(out, dict): out = out.values() return iter(out)
[docs] @classmethod def get_testing_options(cls): r"""Get testing options for the transform class. Returns: list: Multiple dictionaries of keywords and messages before/after pairs that will result from the transform created by the provided keywords. """ return [ {'kwargs': { 'original_datatype': { 'type': 'array', 'items': {'type': 'int'}}}, 'in/out': [([0, 1, 2], iter([0, 1, 2]))], 'in/out_t': [ ({'type': 'array', 'items': {'type': 'int'}}, {'type': 'int'})]}, {'kwargs': { 'original_datatype': { 'type': 'array', 'items': [{'type': 'int'}, {'type': 'int'}, {'type': 'int'}]}}, 'in/out': [([0, 1, 2], iter([0, 1, 2]))], 'in/out_t': [ ({'type': 'array', 'items': [{'type': 'int'}, {'type': 'int'}, {'type': 'int'}]}, {'type': 'int'})]}, {'kwargs': { 'original_datatype': { 'type': 'array', 'items': [{'type': 'int'}, {'type': 'string'}]}}, 'in/out': [([0, 'hello'], iter([0, 'hello']))], 'in/out_t': [ ({'type': 'array', 'items': [{'type': 'int'}, {'type': 'string'}]}, {'type': 'any'})]}, {'kwargs': { 'original_datatype': { 'type': 'object', 'properties': {'a': {'type': 'int'}, 'b': {'type': 'int'}}, 'additionalProperties': {'type': 'int'}}}, 'in/out': [({'a': 0, 'b': 1, 'c': 2}, iter([0, 1, 2]))], 'in/out_t': [ ({'type': 'object', 'properties': {'a': {'type': 'int'}, 'b': {'type': 'int'}}, 'additionalProperties': {'type': 'int'}}, {'type': 'int'})]}, {'kwargs': { 'original_datatype': { 'type': '1darray', 'subtype': 'float', 'precision': 64, 'length': 3}}, 'in/out': [(np.ones(3, dtype='float64'), iter(np.ones(3, dtype='float64')))], 'in/out_t': [ ({'type': '1darray', 'subtype': 'float', 'precision': 64, 'length': 3}, {'type': 'scalar', 'subtype': 'float', 'precision': 64})]}, {'kwargs': { 'original_datatype': { 'type': 'ndarray', 'subtype': 'float', 'precision': 64, 'shape': [3, 4]}}, 'in/out': [(np.ones((3, 4), dtype='float64'), iter(np.ones((3, 4), dtype='float64')))], 'in/out_t': [ ({'type': 'ndarray', 'subtype': 'float', 'precision': 64, 'shape': [3, 4]}, {'type': '1darray', 'subtype': 'float', 'precision': 64, 'length': 4})]}, {'kwargs': { 'original_datatype': { 'type': 'ndarray', 'subtype': 'float', 'precision': 64, 'shape': [2, 3, 4]}}, 'in/out': [(np.ones((2, 3, 4), dtype='float64'), iter(np.ones((2, 3, 4), dtype='float64')))], 'in/out_t': [ ({'type': 'ndarray', 'subtype': 'float', 'precision': 64, 'shape': [2, 3, 4]}, {'type': 'ndarray', 'subtype': 'float', 'precision': 64, 'shape': [3, 4]})]}, ]