Function test_chain1

tests/python/test_docexamples_f32.py:121–157

()

def test_chain1():
   # Parameters for a few example Gaussian entropy models:
    leaky_gaussian = constriction.stream.model.QuantizedGaussian(-100, 100)
    means = np.array([3.2, -14.3, 5.7], dtype=np.float32)
    stds = np.array([6.4, 4.2, 3.9], dtype=np.float32)

    def run_encoder_part(side_information):
        # Construct a `ChainCoder` for *decoding*:
        coder = constriction.stream.chain.ChainCoder(
            side_information,    # Provided bit string.
            is_remainders=False, # Bit string is *not* remaining data after decoding.
            seal=True            # Bit string comes from an external source here.
        )
        # Decode side information into a sequence of symbols as usual in bits-back coding:
        symbols = coder.decode(leaky_gaussian, means, stds)
        # Obtain what's *remaining* on the coder after decoding the symbols:
        remaining1, remaining2 = coder.get_remainders()
        return symbols, np.concatenate([remaining1, remaining2])

    def run_decoder_part(symbols, remaining):
        # Construct a `ChainCoder` for *encoding*:
        coder = constriction.stream.chain.ChainCoder(
            remaining,           # Provided bit string.
            is_remainders=True,  # Bit string *is* remaining data after decoding.
            seal=False           # Bit string comes from a `ChainCoder`, no need to seal it.
        )
        # Re-encode the symbols to recover the side information:
        coder.encode_reverse(symbols, leaky_gaussian, means, stds)
        # Obtain the reconstructed data
        data1, data2 = coder.get_data(unseal=True)
        return np.concatenate([data1, data2])

    np.random.seed(123)
    sample_side_information = np.random.randint(2**32, size=10, dtype=np.uint32)
    symbols, remaining = run_encoder_part(sample_side_information)
    recovered = run_decoder_part(symbols, remaining)
    assert np.all(recovered == sample_side_information)


def test_chain2():