def describe(x, reduce='IncrementalPCA', max_dims=None, show=True,
             format_data=True):
    """
    Create plot describing covariance with as a function of number of dimensions

    This function correlates the raw data with reduced data to get a sense
    for how well the data can be summarized with n dimensions.  Useful for
    evaluating quality of dimensionality reduced plots.

    Parameters
    ----------

    x : Numpy array, DataFrame or list of arrays/dfs
        A list of Numpy arrays or Pandas Dataframes

    reduce : str or dict
        Decomposition/manifold learning model to use.  Models supported: PCA,
        IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA,
        FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning,
        TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, and MDS. Can be
        passed as a string, but for finer control of the model parameters, pass
        as a dictionary, e.g. reduce={'model' : 'PCA', 'params' : {'whiten' : True}}.
        See scikit-learn specific model docs for details on parameters supported
        for each model.

    max_dims : int
        Maximum number of dimensions to consider

    show : bool
        Plot the result (default : true)

    format_data : bool
        Whether or not to first call the format_data function (default: True).

    Returns
    ----------

    result : dict
        A dictionary with the analysis results. 'average' is the correlation
        by number of components for all data. 'individual' is a list of lists,
        where each list is a correlation by number of components vector (for each
        input list).

    """

    warnings.warn('When input data is large, this computation can take a long time.')

    def summary(x, max_dims=None):

        # if data is a list, stack it
        if type(x) is list:
            x = np.vstack(x)

        # if max dims is not set, make it the length of the minimum number of columns
        if max_dims is None:
            if x.shape[1]>x.shape[0]:
                max_dims = x.shape[0]
            else:
                max_dims = x.shape[1]

        # correlation matrix for all dimensions
        alldims = get_cdist(x)

        corrs=[]
        for dims in range(2, max_dims):
            reduced = get_cdist(reducer(x, ndims=dims, reduce=reduce))
            corrs.append(get_corr(alldims, reduced))
            del reduced
        return corrs

    # common format
    if format_data:
        x = formatter(x, ppca=True)

    # a dictionary to store results
    result = {}
    result['average'] = summary(x, max_dims)
    result['individual'] = [summary(x_i, max_dims) for x_i in x]

    if max_dims is None:
        max_dims = len(result['average'])

    # if show, plot it
    if show:
        fig, ax = plt.subplots()
        ax = sns.tsplot(data=result['individual'], time=[i for i in range(2, max_dims+2)], err_style="unit_traces")
        ax.set_title('Correlation with raw data by number of components')
        ax.set_ylabel('Correlation')
        ax.set_xlabel('Number of components')
        plt.show()
    return result

    Create plot describing covariance with as a function of number of dimensions

    This function correlates the raw data with reduced data to get a sense
    for how well the data can be summarized with n dimensions.  Useful for
    evaluating quality of dimensionality reduced plots.

    Parameters
    ----------

    x : Numpy array, DataFrame or list of arrays/dfs
        A list of Numpy arrays or Pandas Dataframes

    reduce : str or dict
        Decomposition/manifold learning model to use.  Models supported: PCA,
        IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA,
        FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning,
        TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, and MDS. Can be
        passed as a string, but for finer control of the model parameters, pass
        as a dictionary, e.g. reduce={'model' : 'PCA', 'params' : {'whiten' : True}}.
        See scikit-learn specific model docs for details on parameters supported
        for each model.

    max_dims : int
        Maximum number of dimensions to consider

    show : bool
        Plot the result (default : true)

    format_data : bool
        Whether or not to first call the format_data function (default: True).

    Returns
    ----------

    result : dict
        A dictionary with the analysis results. 'average' is the correlation
        by number of components for all data. 'individual' is a list of lists,
        where each list is a correlation by number of components vector (for each
        input list).

    
hypertools/tools/describe.py