How to use cytoolz - 10 common examples

@curry
def get_uncle_reward(block_reward: int, block_number: int, uncle: BaseBlock) -> int:
    block_number_delta = block_number - uncle.block_number
    validate_lte(block_number_delta, MAX_UNCLE_DEPTH)
    return (8 - block_number_delta) * block_reward // 8

@curry
def compute_difficulty(
        bomb_delay: int,
        parent_header: BlockHeader,
        timestamp: int) -> int:
    """
    https://github.com/ethereum/EIPs/issues/100
    """
    parent_timestamp = parent_header.timestamp
    validate_gt(timestamp, parent_timestamp, title="Header.timestamp")

    parent_difficulty = parent_header.difficulty
    offset = parent_difficulty // DIFFICULTY_ADJUSTMENT_DENOMINATOR

    has_uncles = parent_header.uncles_hash != EMPTY_UNCLE_HASH
    adj_factor = max(
        (

Returns
    -------
    illum : np.ndarray, float, shape (M, N)
        The estimated illumination over the image field.

    See Also
    --------
    `correct_image_illumination`, `correct_multiimage_illumination`.
    """
    # this function follows the "PyToolz" streaming data model to
    # obtain the illumination estimate.
    # first, define the functions for each individual step:
    in_range = ('image' if input_bitdepth is None
                else (0, 2**input_bitdepth - 1))
    rescale = tz.curry(exposure.rescale_intensity)
    normalize = (tz.partial(stretchlim, bottom=stretch_quantile)
                 if stretch_quantile > 0
                 else skimage.img_as_float)

    # produce a stream of properly-scaled images
    ims = (tz.pipe(fn, io.imread, rescale(in_range=in_range), normalize)
           for fn in fns)

    # take the mean of that stream
    mean_image = mean(ims)

    # return the median filter of that mean
    radius = radius or min(mean_image.shape) // 4
    illum = ndi.percentile_filter(mean_image, percentile=(quantile * 100),
                                  footprint=morphology.disk(radius))
    return illum

def get_branch_indices(node_index: int, depth: int) -> Iterable[int]:
    """
    Get the indices of all ancestors up until the root for a node with a given depth.
    """
    yield from take(depth, iterate(lambda index: index // 2, node_index))

def get_branch_indices(node_index: int, depth: int) -> Sequence[int]:
    """Get the indices of all ancestors up until the root for a node with a given depth."""
    return tuple(take(depth, iterate(lambda index: index // 2, node_index)))

See Also
    --------
    `correct_image_illumination`, `correct_multiimage_illumination`.
    """
    # this function follows the "PyToolz" streaming data model to
    # obtain the illumination estimate.
    # first, define the functions for each individual step:
    in_range = ('image' if input_bitdepth is None
                else (0, 2**input_bitdepth - 1))
    rescale = tz.curry(exposure.rescale_intensity)
    normalize = (tz.partial(stretchlim, bottom=stretch_quantile)
                 if stretch_quantile > 0
                 else skimage.img_as_float)

    # produce a stream of properly-scaled images
    ims = (tz.pipe(fn, io.imread, rescale(in_range=in_range), normalize)
           for fn in fns)

    # take the mean of that stream
    mean_image = mean(ims)

    # return the median filter of that mean
    radius = radius or min(mean_image.shape) // 4
    illum = ndi.percentile_filter(mean_image, percentile=(quantile * 100),
                                  footprint=morphology.disk(radius))
    return illum

def curry_namespace(ns):
        return dict(
            (name, curry(f) if should_curry(f) else f)
            for name, f in ns.items()
            if "__" not in name
        )

def generate_collations():
    explicit_params = {}
    for period in itertools.count():
        default_params = {
            "shard_id": 0,
            "period": period,
            "body": zpad_right(b"body%d" % period, COLLATION_SIZE),
            "proposer_address": zpad_right(b"proposer%d" % period, 20),
        }
        # only calculate chunk root if it wouldn't be replaced anyway
        if "chunk_root" not in explicit_params:
            default_params["chunk_root"] = calc_chunk_root(default_params["body"])

        params = merge(default_params, explicit_params)
        header = CollationHeader(
            shard_id=params["shard_id"],
            chunk_root=params["chunk_root"],
            period=params["period"],
            proposer_address=params["proposer_address"],
        )
        collation = Collation(header, params["body"])
        explicit_params = (yield collation) or {}

def test_call_checks_nonce(vm):
    computation, _ = vm.apply_transaction(SIGNED_DEFAULT_TRANSACTION)
    assert computation.is_success

    computation, _ = vm.apply_transaction(SIGNED_DEFAULT_TRANSACTION)
    assert computation.is_error

    transaction = UnsignedUserAccountTransaction(**merge(DEFAULT_TX_PARAMS, {
        "nonce": 2,
    })).as_signed_transaction(PRIVATE_KEY)
    computation, _ = vm.apply_transaction(transaction)
    assert computation.is_error

def test_from_to_iterable(nums):
    
    nums_pl = nums
    nums_pl = aio.from_iterable(nums_pl)
    nums_pl = cz.partition_all(10, nums_pl)
    nums_pl = aio.map(sum, nums_pl)
    nums_pl = list(nums_pl)

    nums_py = nums
    nums_py = cz.partition_all(10, nums_py)
    nums_py = map(sum, nums_py)
    nums_py = list(nums_py)

    assert nums_py == nums_pl