Function test_old_module_example1

tests/python/test_docexamples_f32.py:50–89

()

def test_old_module_example1():
    # Create an empty Asymmetric Numeral Systems (ANS) Coder:
    coder = constriction.stream.stack.AnsCoder()

    # Some made up data and entropy models for demonstration purpose:
    model = constriction.stream.model.QuantizedGaussian(-100, 100)
    symbols = np.array([23, -15, 78, 43, -69], dtype=np.int32)
    means = np.array([35.2, -1.7, 30.1, 71.2, -75.1], dtype=np.float32)
    stds = np.array([10.1, 25.3, 23.8, 35.4, 3.9], dtype=np.float32)

    # Encode the data (in reverse order, since ANS is a stack):
    coder.encode_reverse(symbols, model, means, stds)

    print(f"Compressed size: {coder.num_bits()} bits")
    print(
        f"(without unnecessary trailing zeros: {coder.num_valid_bits()} bits)")

    # Get the compressed bit string, convert it into an architecture-independent
    # byte order, and write it to a binary file:
    compressed = coder.get_compressed()
    if sys.byteorder == "big":
        compressed.byteswap(inplace=True)

    # We won't write it to a file her, let's just directly continue decoding.

    if sys.byteorder == "big":
        compressed.byteswap(inplace=True)

    # Initialize an ANS coder from the compressed bit string:
    coder = constriction.stream.stack.AnsCoder(compressed)

    # Use the same entropy models that we used for encoding:
    min_supported_symbol, max_supported_symbol = -100, 100  # both inclusively
    means = np.array([35.2, -1.7, 30.1, 71.2, -75.1], dtype=np.float32)
    stds = np.array([10.1, 25.3, 23.8, 35.4, 3.9], dtype=np.float32)

    # Decode and print the data:
    reconstructed = coder.decode(model, means, stds)
    assert coder.is_empty()
    assert np.all(reconstructed == symbols)


def test_module_example3():