recode 0.1.7

Tools to make codecs for time-series serialization and deserialization.

recode

Make codecs for fixed size structured chunks serialization and deserialization of sequences, tabular data, and time-series.

To install: pip install recode

Functionality

his notebook shows various examples of recode and it how it can be used with:

• Single channel numerical streams
• Multi-channel numerical streams
• DataFrames
• Iterators

from recode import (ChunkedEncoder, 
                    ChunkedDecoder, 
                    MetaEncoder, 
                    MetaDecoder, 
                    IterativeDecoder, 
                    StructCodecSpecs, 
                    specs_from_frames,
                    frame_to_meta,
                    meta_to_frame)

Quick run through

First define the frame you want to encode

frame = [1,2,3]

Next define the specifications for that encoding

specs = StructCodecSpecs(chk_format='h')
specs

StructCodecSpecs(chk_format='h', n_channels=1, chk_size_bytes=2)

Next define an encoder and encode your frame to bytes

encoder = ChunkedEncoder(frame_to_chk=specs.frame_to_chk)
b = encoder(frame)
b

b'\x01\x00\x02\x00\x03\x00'

Once you need your original frame again, define a decoder and decode your frame

decoder = ChunkedDecoder(specs.chk_to_frame)
decoded_frames = list(decoder(b))
decoded_frames

[1, 2, 3]

Step-by-step explanation

Define your StructCodecSpecs

StructCodecSpecs is used to define the specs for making codecs for fixed size structured chunks serialization and deserialization of sequences, tabular data, and time-series. This definition is based on format strings of the python struct module.

There are two ways to define StructCodecSpecs, the first being to explicitly define it using the StructCodecSpecs class. StructCodecSpecs takes three arguments: chk_format, n_channels, and chk_size_bytes. Only chk_format is required, as n_channels and chk_size_bytes can be determined based on chk_format. If n_channels is given, then chk_format should only contain one format character, as it will be assumed that all channels have the same data type. If this is not the case, then do not provide an argument for n_channels and instead pass a string with the format character matching the data type for each channel in the frame to chk_format. For example if the first channel contains integers and the second contains floats, then chk_format = 'hd'.

frame = [1,2,3]
specs = StructCodecSpecs(chk_format='h')
specs

StructCodecSpecs(chk_format='h', n_channels=1, chk_size_bytes=2)

The second way to define StructCodecSpecs is to use specs_from_frames which will implictly define StructCodecSpecs based on the frame that is going to be encoded/decoded. This function will return a tuple containing an iterator of the passed frame and the defined StructCodecSpecs. The first element of the tuple can be ignored if the frame passed is not an iterator.

_, specs = specs_from_frames(frame)
specs

StructCodecSpecs(chk_format='h', n_channels=1, chk_size_bytes=2)

If frame is an iterator, then redefine frame as the first argument of the tuple so the first element of frame is not lost for encoding.

frame = iter([[1,2], [3,4]])
frame, specs = specs_from_frames(frame)
specs, list(frame)

(StructCodecSpecs(chk_format='hh', n_channels=2, chk_size_bytes=4),
 [[1, 2], [3, 4]])

Define your Encoder

Your Encoder will allow you to, you guessed it, encode your frames! There are two Encoders currently defined in recode: ChunkedEncoder and MetaEncoder.

ChunkedEncoder should be your goto encoder for sequences, while MetaEncoder works best for tabular data (currently must be in the format of list of dicts).

frame = [1,2,3]
_, specs = specs_from_frames(frame)
print(specs)
encoder = ChunkedEncoder(frame_to_chk=specs.frame_to_chk)
b = encoder(frame)
b

StructCodecSpecs(chk_format='h', n_channels=1, chk_size_bytes=2)





b'\x01\x00\x02\x00\x03\x00'

When using a MetaEncoder, an extra argument named frame_to_meta is required, which can be easily imported from recode!

frame = [{'foo': 1, 'bar': 1}, {'foo': 2, 'bar': 2}, {'foo': 3, 'bar': 4}]
_, specs = specs_from_frames(frame)
print(specs)
encoder = MetaEncoder(frame_to_chk=specs.frame_to_chk, frame_to_meta=frame_to_meta)
b = encoder(frame)
b

StructCodecSpecs(chk_format='hh', n_channels=2, chk_size_bytes=4)





b'\x07\x00foo.bar\x01\x00\x01\x00\x02\x00\x02\x00\x03\x00\x04\x00'

Define your Decoder

Next up your Decoder will allow you to decode your encoded bytes. There are three Encoders currently defined in recode: ChunkedDecoder, IterativeDecoder, and MetaDecoder.

Either ChunkedDecoder or IterativeDecoder will work well for sequences, with the only difference being that IterativeDecoder returns an iterator of decoded chunks while ChunkedDecoder returns a mapping which can be converted to an iterable. MetaDecoder works best for tabular data encoded with MetaEncoder.

frame = [1,2,3]
_, specs = specs_from_frames(frame)
print(specs)
encoder = ChunkedEncoder(frame_to_chk=specs.frame_to_chk)
decoder = ChunkedDecoder(specs.chk_to_frame)
b = encoder(frame)
list(decoder(b))

StructCodecSpecs(chk_format='h', n_channels=1, chk_size_bytes=2)





[1, 2, 3]

As is shown in the following example, an IterativeDecoder will return an unpack_iterator which which can easily be converted to a standard iterable like a list, or be used as an iterator.

frame = [[1,1],[2,2]]
_, specs = specs_from_frames(frame)
print(specs)
encoder = ChunkedEncoder(frame_to_chk=specs.frame_to_chk)
decoder = IterativeDecoder(chk_to_frame=specs.chk_to_frame)
b = encoder(frame)
iter_frames = decoder(b)
print(type(iter_frames))
next(iter_frames), next(iter_frames)

StructCodecSpecs(chk_format='hh', n_channels=2, chk_size_bytes=4)
<class 'unpack_iterator'>





((1, 1), (2, 2))

When using a MetaDecoder, an extra argument named meta_to_frame is required, which can be easily imported from recode!

frame = [{'foo': 1, 'bar': 1}, {'foo': 2, 'bar': 2}, {'foo': 3, 'bar': 4}]
_, specs = specs_from_frames(frame)
print(specs)
encoder = MetaEncoder(frame_to_chk=specs.frame_to_chk, frame_to_meta=frame_to_meta)
decoder = MetaDecoder(chk_to_frame=specs.chk_to_frame, meta_to_frame=meta_to_frame)
b = encoder(frame)
decoded_frames = decoder(b)
decoded_frames

StructCodecSpecs(chk_format='hh', n_channels=2, chk_size_bytes=4)





[{'foo': 1, 'bar': 1}, {'foo': 2, 'bar': 2}, {'foo': 3, 'bar': 4}]

Encoding a waveform

Create a synthetic waveform using hum

from hum.gen.sine_mix import freq_based_stationary_wf
import matplotlib.pyplot as plt
import numpy as np

DFLT_N_SAMPLES = 21 * 2048
DFLT_SR = 44100

wf_mix = freq_based_stationary_wf(freqs=(200, 400, 600, 800), weights=None,
                             n_samples = DFLT_N_SAMPLES, sr = DFLT_SR)
plt.plot(wf_mix[:300]);

wf_mix

array([0.        , 0.07114157, 0.14170624, ..., 0.49480205, 0.54853009,
       0.5979443 ])

Encode the wf

specs = StructCodecSpecs('d')
encoder = ChunkedEncoder(frame_to_chk=specs.frame_to_chk)
decoder = ChunkedDecoder(chk_to_frame=specs.chk_to_frame)
b = encoder(wf_mix)
b[:100]

b'\x00\x00\x00\x00\x00\x00\x00\x00\x1b:\x11\x8dU6\xb2?\xb8!\xa2\x15n#\xc2?\xe6I\xedz\x15\x06\xcb?L;\xe6\xfah\xd8\xd1?}\x8c\x9b\x9a\xf5\x08\xd6?D\xcc\xf1\x897\x0c\xda?\xe3\xc5\xbeb/\xda\xdd?DE\xb6K\xb6\xb5\xe0?\x82|\xfcz\x90\\\xe2?\xae3:$\x99\xde\xe3?\x1e\x95\xbe\xef$9\xe5?\x81\xd7\xdc\xec'

Decode and compare to wf_mix

decoded_wf_mix = decoder(b)
plt.plot(decoded_wf_mix[:300]);

np.all(decoded_wf_mix == wf_mix)

True

Encoding a pandas dataframe

import pandas as pd

Create/import your dataframe

df = pd.DataFrame(data = [[1,2,3],[4,5,6]], columns = ['foo', 'bar', 'set'])
df

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	foo	bar	set
0	1	2	3
1	4	5	6

Prep the dataframe for encoding

frame = df.to_dict('records')
frame

[{'foo': 1, 'bar': 2, 'set': 3}, {'foo': 4, 'bar': 5, 'set': 6}]

Encode the list of dicts

_, specs = specs_from_frames(frame)
encoder = MetaEncoder(frame_to_chk=specs.frame_to_chk, frame_to_meta=frame_to_meta)
decoder = MetaDecoder(chk_to_frame=specs.chk_to_frame, meta_to_frame=meta_to_frame)
b = encoder(frame)
b

b'\x0b\x00foo.bar.set\x01\x00\x02\x00\x03\x00\x04\x00\x05\x00\x06\x00'

decoded_frame = decoder(b)
decoded_frame

[{'foo': 1, 'bar': 2, 'set': 3}, {'foo': 4, 'bar': 5, 'set': 6}]

decoded_df = pd.DataFrame(decoded_frame)
print(np.all(decoded_df == df))
decoded_df

True

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	foo	bar	set
0	1	2	3
1	4	5	6

Miscellaneous information

Byte order, Size, and Alignment

This table provides the characters associated with Byte order, Size, and Alignment. If one of these characters is not given as the first character of the format string, then @ will be assumed. More information about Byte order, Size, and Alignment can be found here.

Character	Byte order	Size	Alignment
@	native	native	native
=	native	standard	none
<	little-endian	standard	none
>	big-endian	standard	none
!	network (=big-endian)	standard	none

Full Examples

Single channel numerical stream

from recode import StructCodecSpecs, ChunkedEncoder, ChunkedDecoder
specs = StructCodecSpecs(chk_format='h')
encoder = ChunkedEncoder(frame_to_chk=specs.frame_to_chk)
decoder = ChunkedDecoder(chk_to_frame=specs.chk_to_frame)
frames = [1, 2, 3]
b = encoder(frames)
assert b == b'\x01\x00\x02\x00\x03\x00'
decoded_frames = list(decoder(b))
assert decoded_frames == frames

Multi-channel numerical stream

from recode import StructCodecSpecs, ChunkedEncoder, ChunkedDecoder
specs = StructCodecSpecs(chk_format='@h', n_channels = 2)
encoder = ChunkedEncoder(frame_to_chk=specs.frame_to_chk)
decoder = ChunkedDecoder(chk_to_frame=specs.chk_to_frame)
frames = [(1, 2), (3, 4), (5, 6)]
b = encoder(frames)
assert b == b'\x01\x00\x02\x00\x03\x00\x04\x00\x05\x00\x06\x00'
decoded_frames = list(decoder(b))
assert decoded_frames == frames

Iterative decoder

from recode import StructCodecSpecs, ChunkedEncoder, IterativeDecoder
specs = StructCodecSpecs(chk_format = 'hdhd')
encoder = ChunkedEncoder(frame_to_chk = specs.frame_to_chk)
decoder = IterativeDecoder(chk_to_frame = specs.chk_to_frame)
frames = [(1,1.1,1,1.1),(2,2.2,2,2.2),(3,3.3,3,3.3)]
b = encoder(frames)
iter_frames = decoder(b)
assert next(iter_frames) == frames[0]
next(iter_frames)

(2, 2.2, 2, 2.2)

DataFrame (as list of dicts) using MetaEncoder/MetaDecoder

from recode import StructCodecSpecs, MetaEncoder, MetaDecoder, frame_to_meta, meta_to_frame
data = [{'foo': 1.1, 'bar': 2.2}, {'foo': 513.23, 'bar': 456.1}, {'foo': 32.0, 'bar': 6.7}]
specs = StructCodecSpecs(chk_format='d', n_channels = 2)
encoder = MetaEncoder(frame_to_chk = specs.frame_to_chk, frame_to_meta = frame_to_meta)
decoder = MetaDecoder(chk_to_frame = specs.chk_to_frame, meta_to_frame = meta_to_frame)
b = encoder(data)
assert decoder(b) == data

Implicitly define codec specs based on frames

from recode import specs_from_frames, ChunkedEncoder, ChunkedDecoder
frames = [1,2,3]
_, specs = specs_from_frames(frames)
encoder = ChunkedEncoder(frame_to_chk = specs.frame_to_chk)
decoder = ChunkedDecoder(chk_to_frame=specs.chk_to_frame)
b = encoder(frames)
assert b == b'\x01\x00\x02\x00\x03\x00'
decoded_frames = list(decoder(b))
decoded_frames

[1, 2, 3]

Implicit definition of iterator

from recode import specs_from_frames, ChunkedEncoder, IterativeDecoder
frames = iter([[1.1,2.2],[3.3,4.4]])
frames, specs = specs_from_frames(frames)
encoder = ChunkedEncoder(frame_to_chk = specs.frame_to_chk)
decoder = ChunkedDecoder(chk_to_frame=specs.chk_to_frame)
b = encoder(frames)
decoded_frames = list(decoder(b))
assert decoded_frames == [(1.1,2.2),(3.3,4.4)]