/index_GFF_id/pymodules/python2.7/lib/python/pandas-0.17.1-py2.7-linux-x86_64.egg/pandas/stats/misc.py

from numpy import NaN
from pandas import compat
import numpy as np

from pandas.core.api import Series, DataFrame, isnull, notnull
from pandas.core.series import remove_na
from pandas.compat import zip


def zscore(series):
    return (series - series.mean()) / np.std(series, ddof=0)


def correl_ts(frame1, frame2):
    """
    Pairwise correlation of columns of two DataFrame objects

    Parameters
    ----------

    Returns
    -------
    y : Series
    """
    results = {}
    for col, series in compat.iteritems(frame1):
        if col in frame2:
            other = frame2[col]

            idx1 = series.valid().index
            idx2 = other.valid().index

            common_index = idx1.intersection(idx2)

            seriesStand = zscore(series.reindex(common_index))
            otherStand = zscore(other.reindex(common_index))
            results[col] = (seriesStand * otherStand).mean()

    return Series(results)


def correl_xs(frame1, frame2):
    return correl_ts(frame1.T, frame2.T)

def percentileofscore(a, score, kind='rank'):
    """The percentile rank of a score relative to a list of scores.

    A `percentileofscore` of, for example, 80% means that 80% of the
    scores in `a` are below the given score. In the case of gaps or
    ties, the exact definition depends on the optional keyword, `kind`.

    Parameters
    ----------
    a: array like
        Array of scores to which `score` is compared.
    score: int or float
        Score that is compared to the elements in `a`.
    kind: {'rank', 'weak', 'strict', 'mean'}, optional
        This optional parameter specifies the interpretation of the
        resulting score:

        - "rank": Average percentage ranking of score.  In case of
                  multiple matches, average the percentage rankings of
                  all matching scores.
        - "weak": This kind corresponds to the definition of a cumulative
                  distribution function.  A percentileofscore of 80%
                  means that 80% of values are less than or equal
                  to the provided score.
        - "strict": Similar to "weak", except that only values that are
                    strictly less than the given score are counted.
        - "mean": The average of the "weak" and "strict" scores, often used in
                  testing.  See

                  http://en.wikipedia.org/wiki/Percentile_rank

    Returns
    -------
    pcos : float
        Percentile-position of score (0-100) relative to `a`.

    Examples
    --------
    Three-quarters of the given values lie below a given score:

    >>> percentileofscore([1, 2, 3, 4], 3)
    75.0

    With multiple matches, note how the scores of the two matches, 0.6
    and 0.8 respectively, are averaged:

    >>> percentileofscore([1, 2, 3, 3, 4], 3)
    70.0

    Only 2/5 values are strictly less than 3:

    >>> percentileofscore([1, 2, 3, 3, 4], 3, kind='strict')
    40.0

    But 4/5 values are less than or equal to 3:

    >>> percentileofscore([1, 2, 3, 3, 4], 3, kind='weak')
    80.0

    The average between the weak and the strict scores is

    >>> percentileofscore([1, 2, 3, 3, 4], 3, kind='mean')
    60.0

    """
    a = np.array(a)
    n = len(a)

    if kind == 'rank':
        if not(np.any(a == score)):
            a = np.append(a, score)
            a_len = np.array(lrange(len(a)))
        else:
            a_len = np.array(lrange(len(a))) + 1.0

        a = np.sort(a)
        idx = [a == score]
        pct = (np.mean(a_len[idx]) / n) * 100.0
        return pct

    elif kind == 'strict':
        return sum(a < score) / float(n) * 100
    elif kind == 'weak':
        return sum(a <= score) / float(n) * 100
    elif kind == 'mean':
        return (sum(a < score) + sum(a <= score)) * 50 / float(n)
    else:
        raise ValueError("kind can only be 'rank', 'strict', 'weak' or 'mean'")

def percentileRank(frame, column=None, kind='mean'):
    """
    Return score at percentile for each point in time (cross-section)

    Parameters
    ----------
    frame: DataFrame
    column: string or Series, optional
       Column name or specific Series to compute percentiles for.
       If not provided, percentiles are computed for all values at each
       point in time. Note that this can take a LONG time.
    kind: {'rank', 'weak', 'strict', 'mean'}, optional
        This optional parameter specifies the interpretation of the
        resulting score:

        - "rank": Average percentage ranking of score.  In case of
                  multiple matches, average the percentage rankings of
                  all matching scores.
        - "weak": This kind corresponds to the definition of a cumulative
                  distribution function.  A percentileofscore of 80%
                  means that 80% of values are less than or equal
                  to the provided score.
        - "strict": Similar to "weak", except that only values that are
                    strictly less than the given score are counted.
        - "mean": The average of the "weak" and "strict" scores, often used in
                  testing.  See

                  http://en.wikipedia.org/wiki/Percentile_rank

    Returns
    -------
    TimeSeries or DataFrame, depending on input
    """
    fun = lambda xs, score: percentileofscore(remove_na(xs),
                                              score, kind=kind)

    results = {}
    framet = frame.T
    if column is not None:
        if isinstance(column, Series):
            for date, xs in compat.iteritems(frame.T):
                results[date] = fun(xs, column.get(date, NaN))
        else:
            for date, xs in compat.iteritems(frame.T):
                results[date] = fun(xs, xs[column])
        results = Series(results)
    else:
        for column in frame.columns:
            for date, xs in compat.iteritems(framet):
                results.setdefault(date, {})[column] = fun(xs, xs[column])
        results = DataFrame(results).T
    return results


def bucket(series, k, by=None):
    """
    Produce DataFrame representing quantiles of a Series

    Parameters
    ----------
    series : Series
    k : int
        number of quantiles
    by : Series or same-length array
        bucket by value

    Returns
    -------
    DataFrame
    """
    if by is None:
        by = series
    else:
        by = by.reindex(series.index)

    split = _split_quantile(by, k)
    mat = np.empty((len(series), k), dtype=float) * np.NaN

    for i, v in enumerate(split):
        mat[:, i][v] = series.take(v)

    return DataFrame(mat, index=series.index, columns=np.arange(k) + 1)


def _split_quantile(arr, k):
    arr = np.asarray(arr)
    mask = np.isfinite(arr)
    order = arr[mask].argsort()
    n = len(arr)

    return np.array_split(np.arange(n)[mask].take(order), k)


def bucketcat(series, cats):
    """
    Produce DataFrame representing quantiles of a Series

    Parameters
    ----------
    series : Series
    cat : Series or same-length array
        bucket by category; mutually exclusive with 'by'

    Returns
    -------
    DataFrame
    """
    if not isinstance(series, Series):
        series = Series(series, index=np.arange(len(series)))

    cats = np.asarray(cats)

    unique_labels = np.unique(cats)
    unique_labels = unique_labels[com.notnull(unique_labels)]

    # group by
    data = {}

    for label in unique_labels:
        data[label] = series[cats == label]

    return DataFrame(data, columns=unique_labels)


def bucketpanel(series, bins=None, by=None, cat=None):
    """
    Bucket data by two Series to create summary panel

    Parameters
    ----------
    series : Series
    bins : tuple (length-2)
        e.g. (2, 2)
    by : tuple of Series
        bucket by value
    cat : tuple of Series
        bucket by category; mutually exclusive with 'by'

    Returns
    -------
    DataFrame
    """
    use_by = by is not None
    use_cat = cat is not None

    if use_by and use_cat:
        raise Exception('must specify by or cat, but not both')
    elif use_by:
        if len(by) != 2:
            raise Exception('must provide two bucketing series')

        xby, yby = by
        xbins, ybins = bins

        return _bucketpanel_by(series, xby, yby, xbins, ybins)

    elif use_cat:
        xcat, ycat = cat
        return _bucketpanel_cat(series, xcat, ycat)
    else:
        raise Exception('must specify either values or categories '
                        'to bucket by')


def _bucketpanel_by(series, xby, yby, xbins, ybins):
    xby = xby.reindex(series.index)
    yby = yby.reindex(series.index)

    xlabels = _bucket_labels(xby.reindex(series.index), xbins)
    ylabels = _bucket_labels(yby.reindex(series.index), ybins)

    labels = _uniquify(xlabels, ylabels, xbins, ybins)

    mask = com.isnull(labels)
    labels[mask] = -1

    unique_labels = np.unique(labels)
    bucketed = bucketcat(series, labels)

    _ulist = list(labels)
    index_map = dict((x, _ulist.index(x)) for x in unique_labels)

    def relabel(key):
        pos = index_map[key]

        xlab = xlabels[pos]
        ylab = ylabels[pos]

        return '%sx%s' % (int(xlab) if com.notnull(xlab) else 'NULL',
                          int(ylab) if com.notnull(ylab) else 'NULL')

    return bucketed.rename(columns=relabel)


def _bucketpanel_cat(series, xcat, ycat):
    xlabels, xmapping = _intern(xcat)
    ylabels, ymapping = _intern(ycat)

    shift = 10 ** (np.ceil(np.log10(ylabels.max())))
    labels = xlabels * shift + ylabels

    sorter = labels.argsort()
    sorted_labels = labels.take(sorter)
    sorted_xlabels = xlabels.take(sorter)
    sorted_ylabels = ylabels.take(sorter)

    unique_labels = np.unique(labels)
    unique_labels = unique_labels[com.notnull(unique_labels)]

    locs = sorted_labels.searchsorted(unique_labels)
    xkeys = sorted_xlabels.take(locs)
    ykeys = sorted_ylabels.take(locs)

    stringified = ['(%s, %s)' % arg
                   for arg in zip(xmapping.take(xkeys), ymapping.take(ykeys))]

    result = bucketcat(series, labels)
    result.columns = stringified

    return result


def _intern(values):
    # assumed no NaN values
    values = np.asarray(values)

    uniqued = np.unique(values)
    labels = uniqued.searchsorted(values)
    return labels, uniqued


def _uniquify(xlabels, ylabels, xbins, ybins):
    # encode the stuff, create unique label
    shifter = 10 ** max(xbins, ybins)
    _xpiece = xlabels * shifter
    _ypiece = ylabels

    return _xpiece + _ypiece


def _bucket_labels(series, k):
    arr = np.asarray(series)
    mask = np.isfinite(arr)
    order = arr[mask].argsort()
    n = len(series)

    split = np.array_split(np.arange(n)[mask].take(order), k)

    mat = np.empty(n, dtype=float) * np.NaN
    for i, v in enumerate(split):
        mat[v] = i

    return mat + 1