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import numpy as np
from struct import pack, unpack_from
class Vector:
def __init__(self, value):
# asarray still copies if same dtype
if not isinstance(value, np.ndarray) or value.dtype != '>f4':
value = np.asarray(value, dtype='>f4')
if value.ndim != 1:
raise ValueError('expected ndim to be 1')
self._value = value
def __repr__(self):
return f'Vector({self.to_list()})'
def dimensions(self):
return len(self._value)
def to_list(self):
return self._value.tolist()
def to_numpy(self):
return self._value
def to_text(self):
return '[' + ','.join([str(float(v)) for v in self._value]) + ']'
def to_binary(self):
return pack('>HH', self.dimensions(), 0) + self._value.tobytes()
@classmethod
def from_text(cls, value):
return cls([float(v) for v in value[1:-1].split(',')])
@classmethod
def from_binary(cls, value):
dim, unused = unpack_from('>HH', value)
return cls(np.frombuffer(value, dtype='>f4', count=dim, offset=4))
@classmethod
def _to_db(cls, value, dim=None):
if value is None:
return value
if not isinstance(value, cls):
value = cls(value)
if dim is not None and value.dimensions() != dim:
raise ValueError('expected %d dimensions, not %d' % (dim, value.dimensions()))
return value.to_text()
@classmethod
def _to_db_binary(cls, value):
if value is None:
return value
if not isinstance(value, cls):
value = cls(value)
return value.to_binary()
@classmethod
def _from_db(cls, value):
if value is None or isinstance(value, np.ndarray):
return value
return cls.from_text(value).to_numpy().astype(np.float32)
@classmethod
def _from_db_binary(cls, value):
if value is None or isinstance(value, np.ndarray):
return value
return cls.from_binary(value).to_numpy().astype(np.float32)
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