Exemplo n.º 1
0
def corr_circular_linear(alpha, X):
    # Authors:  Jean-Remi King <*****@*****.**>
    #
    # Licence : BSD-simplified
    """

    Parameters
    ----------
        alpha : numpy.array, shape (n_angles,)
            The angular data (if n_dims == 1, repeated across all x dimensions)
        X : numpy.array, shape (n_angles, n_dims)
            The linear data
    Returns
    -------
        R : numpy.array, shape (n_dims)
            R values
        R2 : numpy.array, shape (n_dims)
            R square values
        p_val : numpy.array, shape (n_dims)
            P values

    Adapted from:
        Circular Statistics Toolbox for Matlab
        By Philipp Berens, 2009
        [email protected] - www.kyb.mpg.de/~berens/circStat.html
        Equantion 27.47
    """

    from scipy.stats import chi2
    from jr.utils import pairwise
    import numpy as np

    # computes correlation for sin and cos separately
    # WIP Applies repeated correlation if X is vector
    # TODO: deals with non repeated correlations (X * ALPHA)
    if alpha.ndim > 1:
        rxs = repeated_corr(np.sin(alpha), X)
        rxc = repeated_corr(np.cos(alpha), X)
        rcs = np.zeros_like(alpha[0, :])
        rcs = pairwise(np.sin(alpha), np.cos(alpha), func=_loop_corr,
                       n_jobs=-1)
    else:
        # WIP Applies repeated correlation if alpha is vector
        rxs = repeated_corr(X, np.sin(alpha))
        rxc = repeated_corr(X, np.cos(alpha))
        rcs = repeated_corr(np.sin(alpha), np.cos(alpha))

    # Adapted from equation 27.47
    R = (rxc ** 2 + rxs ** 2 - 2 * rxc * rxs * rcs) / (1 - rcs ** 2)

    # JR adhoc way of having a sign....
    R = np.sign(rxs) * np.sign(rxc) * R
    R2 = np.sqrt(R ** 2)

    # Get degrees of freedom
    n = len(X)
    pval = 1 - chi2.cdf(n * R2, 2)

    return R, R2, pval
Exemplo n.º 2
0
def corr_circular_linear(alpha, X):
    # Authors:  Jean-Remi King <*****@*****.**>
    #
    # Licence : BSD-simplified
    """

    Parameters
    ----------
        alpha : numpy.array, shape (n_angles,)
            The angular data (if n_dims == 1, repeated across all x dimensions)
        X : numpy.array, shape (n_angles, n_dims)
            The linear data
    Returns
    -------
        R : numpy.array, shape (n_dims)
            R values
        R2 : numpy.array, shape (n_dims)
            R square values
        p_val : numpy.array, shape (n_dims)
            P values

    Adapted from:
        Circular Statistics Toolbox for Matlab
        By Philipp Berens, 2009
        [email protected] - www.kyb.mpg.de/~berens/circStat.html
        Equantion 27.47
    """

    from scipy.stats import chi2
    from jr.utils import pairwise
    import numpy as np

    # computes correlation for sin and cos separately
    # WIP Applies repeated correlation if X is vector
    # TODO: deals with non repeated correlations (X * ALPHA)
    if alpha.ndim > 1:
        rxs = repeated_corr(np.sin(alpha), X)
        rxc = repeated_corr(np.cos(alpha), X)
        rcs = np.zeros_like(alpha[0, :])
        rcs = pairwise(np.sin(alpha), np.cos(alpha), func=_loop_corr,
                       n_jobs=-1)
    else:
        # WIP Applies repeated correlation if alpha is vector
        rxs = repeated_corr(X, np.sin(alpha))
        rxc = repeated_corr(X, np.cos(alpha))
        rcs = repeated_corr(np.sin(alpha), np.cos(alpha))

    # Adapted from equation 27.47
    R = (rxc ** 2 + rxs ** 2 - 2 * rxc * rxs * rcs) / (1 - rcs ** 2)

    # JR adhoc way of having a sign....
    R = np.sign(rxs) * np.sign(rxc) * R
    R2 = np.sqrt(R ** 2)

    # Get degrees of freedom
    n = len(X)
    pval = 1 - chi2.cdf(n * R2, 2)

    return R, R2, pval
Exemplo n.º 3
0
def nested_analysis(X,
                    df,
                    condition,
                    function=None,
                    query=None,
                    single_trial=False,
                    y=None,
                    n_jobs=-1):
    """ Apply a nested set of analyses.
    Parameters
    ----------
    X : np.array, shape(n_samples, ...)
        Data array.
    df : pandas.DataFrame
        Condition DataFrame
    condition : str | list
        If string, get the samples for each unique value of df[condition]
        If list, nested call nested_analysis.
    query : str | None, optional
        To select a subset of trial using pandas.DataFrame.query()
    function : function
        Computes across list of evoked. Must be of the form:
        function(X[:], y[:])
    y : np.array, shape(n_conditions)
    n_jobs : int
        Number of core to compute the function. Defaults to -1.

    Returns
    -------
    scores : np.array, shape(...)
        The results of the function
    sub : dict()
        Contains results of sub levels.
    """
    import numpy as np
    from jr.utils import pairwise
    if isinstance(condition, str):
        # Subselect data using pandas.DataFrame queries
        sel = range(len(X)) if query is None else df.query(query).index
        X = X.take(sel, axis=0)
        y = np.array(df[condition][sel])
        # Find unique conditions
        values = list()
        for ii in np.unique(y):
            if (ii is not None) and (ii not in [np.nan]):
                try:
                    if np.isnan(ii):
                        continue
                    else:
                        values.append(ii)
                except TypeError:
                    values.append(ii)
        # Subsubselect for each unique condition
        y_sel = [np.where(y == value)[0] for value in values]
        # Mean condition:
        X_mean = np.zeros(np.hstack((len(y_sel), X.shape[1:])))
        y_mean = np.zeros(len(y_sel))
        for ii, sel_ in enumerate(y_sel):
            X_mean[ii, ...] = np.mean(X[sel_, ...], axis=0)
            if isinstance(y[sel_[0]], str):
                y_mean[ii] = ii
            else:
                y_mean[ii] = y[sel_[0]]
        if single_trial:
            X = X.take(np.hstack(y_sel), axis=0)  # ERROR COME FROM HERE
            y = y.take(np.hstack(y_sel), axis=0)
        else:
            X = X_mean
            y = y_mean
        # Store values to keep track
        sub_list = dict(X=X_mean,
                        y=y_mean,
                        sel=sel,
                        query=query,
                        condition=condition,
                        values=values,
                        single_trial=single_trial)
    elif isinstance(condition, list):
        # If condition is a list, we must recall the function to gather
        # the results of the lower levels
        sub_list = list()
        X_list = list()  # FIXME use numpy array
        for subcondition in condition:
            scores, sub = nested_analysis(
                X,
                df,
                subcondition['condition'],
                n_jobs=n_jobs,
                function=subcondition.get('function', None),
                query=subcondition.get('query', None))
            X_list.append(scores)
            sub_list.append(sub)
        X = np.array(X_list)
        if y is None:
            y = np.arange(len(condition))
        if len(y) != len(X):
            raise ValueError('X and y must be of identical shape: ' +
                             '%s <> %s') % (len(X), len(y))
        sub_list = dict(X=X, y=y, sub=sub_list, condition=condition)

    # Default function
    function = _default_analysis if function is None else function

    scores = pairwise(X, y, function, n_jobs=n_jobs)
    return scores, sub_list
Exemplo n.º 4
0
def nested_analysis(X, df, condition, function=None, query=None,
                    single_trial=False, y=None, n_jobs=-1):
    """ Apply a nested set of analyses.
    Parameters
    ----------
    X : np.array, shape(n_samples, ...)
        Data array.
    df : pandas.DataFrame
        Condition DataFrame
    condition : str | list
        If string, get the samples for each unique value of df[condition]
        If list, nested call nested_analysis.
    query : str | None, optional
        To select a subset of trial using pandas.DataFrame.query()
    function : function
        Computes across list of evoked. Must be of the form:
        function(X[:], y[:])
    y : np.array, shape(n_conditions)
    n_jobs : int
        Number of core to compute the function. Defaults to -1.

    Returns
    -------
    scores : np.array, shape(...)
        The results of the function
    sub : dict()
        Contains results of sub levels.
    """
    import numpy as np
    from jr.utils import pairwise
    if isinstance(condition, str):
        # Subselect data using pandas.DataFrame queries
        sel = range(len(X)) if query is None else df.query(query).index
        X = X.take(sel, axis=0)
        y = np.array(df[condition][sel])
        # Find unique conditions
        values = list()
        for ii in np.unique(y):
            if (ii is not None) and (ii not in [np.nan]):
                try:
                    if np.isnan(ii):
                        continue
                    else:
                        values.append(ii)
                except TypeError:
                    values.append(ii)
        # Subsubselect for each unique condition
        y_sel = [np.where(y == value)[0] for value in values]
        # Mean condition:
        X_mean = np.zeros(np.hstack((len(y_sel), X.shape[1:])))
        y_mean = np.zeros(len(y_sel))
        for ii, sel_ in enumerate(y_sel):
            X_mean[ii, ...] = np.mean(X[sel_, ...], axis=0)
            if isinstance(y[sel_[0]], str):
                y_mean[ii] = ii
            else:
                y_mean[ii] = y[sel_[0]]
        if single_trial:
            X = X.take(np.hstack(y_sel), axis=0)  # ERROR COME FROM HERE
            y = y.take(np.hstack(y_sel), axis=0)
        else:
            X = X_mean
            y = y_mean
        # Store values to keep track
        sub_list = dict(X=X_mean, y=y_mean, sel=sel, query=query,
                        condition=condition, values=values,
                        single_trial=single_trial)
    elif isinstance(condition, list):
        # If condition is a list, we must recall the function to gather
        # the results of the lower levels
        sub_list = list()
        X_list = list()  # FIXME use numpy array
        for subcondition in condition:
            scores, sub = nested_analysis(
                X, df, subcondition['condition'], n_jobs=n_jobs,
                function=subcondition.get('function', None),
                query=subcondition.get('query', None))
            X_list.append(scores)
            sub_list.append(sub)
        X = np.array(X_list)
        if y is None:
            y = np.arange(len(condition))
        if len(y) != len(X):
            raise ValueError('X and y must be of identical shape: ' +
                             '%s <> %s') % (len(X), len(y))
        sub_list = dict(X=X, y=y, sub=sub_list, condition=condition)

    # Default function
    function = _default_analysis if function is None else function

    scores = pairwise(X, y, function, n_jobs=n_jobs)
    return scores, sub_list