Source code for yggdrasil.datatypes

import numpy as np
from yggdrasil import constants, units

[docs]class DataTypeError(TypeError): r"""Error that should be raised when a class encounters a type it cannot handle.""" pass
[docs]def is_default_typedef(typedef): r"""Determine if a type definition is the default type definition. Args: typedef (dict): Type definition to test. Returns: bool: True if typedef is the default, False otherwise. """ return (typedef == constants.DEFAULT_DATATYPE)
[docs]def get_empty_msg(typedef): r"""Get an empty message associated with a type. Args: typedef (dict): Type definition via a JSON schema. Returns: object: Python object representing an empty message for the provided type. """ if typedef['type'] in ['object', 'ply', 'obj']: return {} elif typedef['type'] in ['array']: return [] return b''
[docs]def data2dtype(data): r"""Get numpy data type for an object. Args: data (object): Python object. Returns: np.dtype: Numpy data type. """ data_nounits = units.get_data(data) if isinstance(data_nounits, np.ndarray): dtype = data_nounits.dtype elif isinstance(data_nounits, (list, dict, tuple)): # pragma: debug raise DataTypeError else: dtype = np.array([data_nounits]).dtype return dtype
[docs]def definition2dtype(props): r"""Get numpy data type for a type definition. Args: props (dict): Type definition properties. Returns: np.dtype: Numpy data type. """ typename = props.get('subtype', props.get('type', None)) if typename is None: # pragma: debug raise KeyError('Could not find type in dictionary') if typename in constants.FLEXIBLE_TYPES: nbytes = constants.FIXED_ENCODING_SIZES.get(props.get('encoding', 'ASCII'), 4) if (typename == 'string' and 'subtype' not in props) or nbytes == 4: typename = 'unicode' if 'precision' in props: out = np.dtype((constants.VALID_TYPES[typename], int(props['precision'] // nbytes))) else: out = np.dtype((constants.VALID_TYPES[typename])) elif 'precision' in props: out = np.dtype('%s%d' % (constants.VALID_TYPES[typename], int(props['precision'] * 8))) else: out = np.dtype(constants.VALID_TYPES[typename]) return out
[docs]def type2numpy(typedef): r"""Convert a type definition into a numpy dtype. Args: typedef (dict): Type definition. Returns: np.dtype: Numpy data type. """ out = None if ((isinstance(typedef, dict) and ('type' in typedef) and (typedef['type'] == 'array') and ('items' in typedef))): if isinstance(typedef['items'], dict): as_array = (typedef['items']['type'] in ['1darray', 'ndarray']) if as_array: out = definition2dtype(typedef['items']) elif isinstance(typedef['items'], (list, tuple)): as_array = True dtype_list = [] field_names = [] for i, x in enumerate(typedef['items']): if x['type'] not in ['1darray', 'ndarray']: as_array = False break dtype_list.append(definition2dtype(x)) field_names.append(x.get('title', 'f%d' % i)) if as_array: out = np.dtype(dict(names=field_names, formats=dtype_list)) return out