def parallel_apply(f, array, **kwargs):
    """ Apply a function to an array with openmp parallelisation.

    Equivalent to `[f(x) for x in array]`, but parallelised if required.

    Parameters
    ----------
    f: function
        Univariate function to apply to each element of array

    array: array-like
        Array to apply f to

    parallel: int or bool, optional
        int > 0: number of processes to parallelise over

        int < 0 or bool=True: use OMP_NUM_THREADS to choose parallelisation

        bool=False or int=0: do not parallelise

    tqdm_kwargs: dict, optional
        additional kwargs for tqdm progress bars.

    precurry: tuple, optional
        immutable arguments to pass to f before x,
        i.e. `[f(precurry,x) for x in array]`

    postcurry: tuple, optional
        immutable arguments to pass to f after x
        i.e. `[f(x,postcurry) for x in array]`

    Returns
    -------
    list:
        `[f(precurry,x,postcurry) for x in array]`
        parallelised according to parallel
    """

    precurry = tuple(kwargs.pop('precurry', ()))
    postcurry = tuple(kwargs.pop('postcurry', ()))
    parallel = kwargs.pop('parallel', False)
    tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})
    if kwargs:
        raise TypeError('Unexpected **kwargs: %r' % kwargs)
    try:
        # If running in a jupyter notebook then use tqdm_notebook.
        progress = tqdm_notebook if get_ipython().has_trait('kernel') else tqdm
    except (NameError, AssertionError):
        # Otherwise use regular tqdm progress bar
        progress = tqdm
    if not parallel:
        return [f(*(precurry + (x,) + postcurry)) for x in
                progress(array, **tqdm_kwargs)]
    elif parallel is True:
        parallel = cpu_count()
    elif isinstance(parallel, int):
        if parallel < 0:
            parallel = cpu_count()
        else:
            parallel = parallel
    else:
        raise ValueError("parallel keyword must be an integer or bool")

    if parallel and not PARALLEL:
        warnings.warn("You need to install the package joblib"
                      "if you want to use parallelisation")

    return Parallel(n_jobs=parallel)(delayed(f)(*(precurry + (x,) + postcurry))
                                     for x in progress(array, **tqdm_kwargs))
Apply a function to an array with openmp parallelisation.

    Equivalent to `[f(x) for x in array]`, but parallelised if required.

    Parameters
    ----------
    f: function
        Univariate function to apply to each element of array

    array: array-like
        Array to apply f to

    parallel: int or bool, optional
        int > 0: number of processes to parallelise over

        int < 0 or bool=True: use OMP_NUM_THREADS to choose parallelisation

        bool=False or int=0: do not parallelise

    tqdm_kwargs: dict, optional
        additional kwargs for tqdm progress bars.

    precurry: tuple, optional
        immutable arguments to pass to f before x,
        i.e. `[f(precurry,x) for x in array]`

    postcurry: tuple, optional
        immutable arguments to pass to f after x
        i.e. `[f(x,postcurry) for x in array]`

    Returns
    -------
    list:
        `[f(precurry,x,postcurry) for x in array]`
        parallelised according to parallel