85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
# This file is part of the micropython-ulab project, https://github.com/v923z/micropython-ulab
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#
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# The MIT License (MIT)
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#
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# Copyright (c) 2022 Zoltán Vörös
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import sys
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use_ulab = False
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try:
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from ubinascii import a2b_base64 as b64decode
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from ubinascii import unhexlify
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import ujson as json
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from ulab import numpy as np
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use_ulab = True
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except:
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from base64 import b64decode
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import json
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import numpy as np
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from numpy.lib.format import descr_to_dtype
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def ulab_descr_to_dtype(descriptor):
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descriptor = descriptor[1:]
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if descriptor == 'u1':
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return np.uint8
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elif descriptor == 'i1':
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return np.int8
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if descriptor == 'u2':
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return np.uint16
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if descriptor == 'i2':
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return np.int16
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elif descriptor == 'f8':
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if np.float != ord('d'):
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raise TypeError('doubles are not supported')
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else:
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return np.float
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elif descriptor == 'f16':
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if np.float != ord('f'):
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raise TypeError('')
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else:
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return np.float
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else:
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raise TypeError('descriptor could not be decoded')
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def json_to_ndarray(json_string, b64=True):
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"""
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Turn a json string into an ndarray
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The string must be the representation of a dictionary with the three keys
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- dtype: a valid numpy dtype string (one of |u1, |i1, <u2, <i2, <f4, <f8, <c8, <c16, >u2, >i2, >f4, >f8, >c8, >c16)
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- array: the hexified, or base64-encoded raw data array
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- shape: the shape of the array (a list or tuple of integers)
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Usage:
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str = '{"dtype": "<f8", "array": "AAAAAAAAAAAAAAAAAADwPwAAAAAAAABAAAAAAAAACEAAAAAAAAAQQAAAAAAAABRAAAAAAAAAGEAAAAAAAAAcQAAAAAAAACBA\n", "shape": [3, 3]}'
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json_to_ndarray(str, b64=True)
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"""
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obj = json.loads(json_string)
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print(obj)
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if not isinstance(obj, dict):
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raise TypeError('input argument must be a dictionary')
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if set(obj.keys()) != {'array', 'dtype', 'shape'}:
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raise ValueError('input must have the keys "array", "dtype", "shape"')
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descriptor = obj['dtype']
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if use_ulab:
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dtype = ulab_descr_to_dtype(descriptor)
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else:
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dtype = descr_to_dtype(descriptor)
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if not b64:
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data = unhexlify(obj['array'])
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else:
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data = b64decode(obj['array'])
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ndarray = np.frombuffer(data, dtype=dtype).reshape(tuple(obj['shape']))
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if dtype in (np.uint16, np.int16, np.float):
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if sys.byteorder != descriptor[1]:
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ndarray.byteswap()
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return ndarray
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