def decode(self, buf, out=None):
arr = ndarray_from_buffer(buf, dtype='u1')
expect = arr[:4].view('<u4')[0]
checksum = self.checksum(arr[4:]) & 0xffffffff
if expect != checksum:
raise RuntimeError('checksum failed')
return buffer_copy(arr[4:], out)
def decode(self, buf, out=None):
# do decompression
dec = _lzma.decompress(buf, format=self.format,
filters=self.filters)
# handle destination
return buffer_copy(dec, out)
def decode(self, buf, out=None):
# do decompression
dec = _zlib.decompress(buf)
# handle destination - Python standard library zlib module does not
# support direct decompression into buffer, so we have to copy into
# out if given
return buffer_copy(dec, out)
def decode(self, buf, out=None):
# view encoded data as 1D array
enc = ndarray_from_buffer(buf, self.encode_dtype)
# convert and copy
dec = enc.astype(self.decode_dtype)
# handle output
out = buffer_copy(dec, out)
return out
def decode(self, buf, out=None):
# interpret buffer as 1D array
enc = ndarray_from_buffer(buf, self.astype)
# decode scale offset
dec = (enc / self.scale) + self.offset
# convert dtype
dec = dec.astype(self.dtype, copy=False)
# handle output
return buffer_copy(dec, out)
def decode(self, buf, out=None):
# BZ2 cannot handle ndarray directly at all, coerce everything to
# memoryview
if not isinstance(buf, array.array):
buf = memoryview(buf)
# do decompression
dec = _bz2.decompress(buf)
# handle destination - Python standard library bz2 module does not
# support direct decompression into buffer, so we have to copy into
# out if given
return buffer_copy(dec, out)
def decode(self, buf, out=None):
# view encoded data as ndarray
enc = ndarray_from_buffer(buf, 'u1')
# find out how many bits were padded
n_bits_padded = int(enc[0])
# apply decoding
dec = np.unpackbits(enc[1:])
# remove padded bits
if n_bits_padded:
dec = dec[:-n_bits_padded]
# view as boolean array
dec = dec.view(bool)
# handle destination
return buffer_copy(dec, out)
def decode(self, buf, out=None):
# view encoded data as 1D array
enc = ndarray_from_buffer(buf, self.astype)
# setup decoded output
if isinstance(out, np.ndarray):
# optimization, can decode directly to out
dec = out.reshape(-1, order='A')
copy_needed = False
else:
dec = np.empty_like(enc, dtype=self.dtype)
copy_needed = True
# decode differences
np.cumsum(enc, out=dec)
# handle output
if copy_needed:
out = buffer_copy(dec, out)
return out
def decode(self, buf, out=None):
# view encoded data as ndarray
enc = ndarray_from_buffer(buf, self.astype)
# setup output
if isinstance(out, np.ndarray):
# optimization, decode directly to output
dec = out.reshape(-1, order='A')
copy_needed = False
else:
dec = np.zeros_like(enc, dtype=self.dtype)
copy_needed = True
# apply decoding
for i, l in enumerate(self.labels):
dec[enc == (i + 1)] = l
# handle output
if copy_needed:
dec = buffer_copy(dec, out)
return dec
def decode(self, buf, out=None):
# filter is lossy, decoding is no-op
dec = ndarray_from_buffer(buf, self.astype)
dec = dec.astype(self.dtype, copy=False)
return buffer_copy(dec, out)