Source code for allel.stats.misc

# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, division

import numpy as np

from allel.model.ndarray import SortedIndex

def jackknife(values, statistic):
    """Estimate standard error for `statistic` computed over `values` using
    the jackknife.

    values : array_like or tuple of array_like
        Input array, or tuple of input arrays.
    statistic : function
        The statistic to compute.

    m : float
        Mean of jackknife values.
    se : float
        Estimate of standard error.
    vj : ndarray
        Statistic values computed for each jackknife iteration.


    if isinstance(values, tuple):
        # multiple input arrays
        n = len(values[0])
        masked_values = [ for v in values]
        for m in masked_values:
            assert m.ndim == 1, 'only 1D arrays supported'
            assert m.shape[0] == n, 'input arrays not of equal length'
            m.mask = np.zeros(m.shape, dtype=bool)

        n = len(values)
        masked_values =
        assert masked_values.ndim == 1, 'only 1D arrays supported'
        masked_values.mask = np.zeros(masked_values.shape, dtype=bool)

    # values of the statistic calculated in each jackknife iteration
    vj = list()

    for i in range(n):

        if isinstance(values, tuple):
            # multiple input arrays
            for m in masked_values:
                m.mask[i] = True
            x = statistic(*masked_values)
            for m in masked_values:
                m.mask[i] = False

            masked_values.mask[i] = True
            x = statistic(masked_values)
            masked_values.mask[i] = False


    # convert to array for convenience
    vj = np.array(vj)

    # compute mean of jackknife values
    m = vj.mean()

    # compute standard error
    sv = ((n - 1) / n) * np.sum((vj - m) ** 2)
    se = np.sqrt(sv)

    return m, se, vj

[docs]def plot_variant_locator(pos, step=None, ax=None, start=None, stop=None, flip=False, line_kwargs=None): """ Plot lines indicating the physical genome location of variants from a single chromosome/contig. By default the top x axis is in variant index space, and the bottom x axis is in genome position space. Parameters ---------- pos : array_like A sorted 1-dimensional array of genomic positions from a single chromosome/contig. step : int, optional Plot a line for every `step` variants. ax : axes, optional The axes on which to draw. If not provided, a new figure will be created. start : int, optional The start position for the region to draw. stop : int, optional The stop position for the region to draw. flip : bool, optional Flip the plot upside down. line_kwargs : dict-like Additional keyword arguments passed through to `plt.Line2D`. Returns ------- ax : axes The axes on which the plot was drawn """ import matplotlib.pyplot as plt # check inputs pos = SortedIndex(pos, copy=False) # set up axes if ax is None: x = plt.rcParams['figure.figsize'][0] y = x / 7 fig, ax = plt.subplots(figsize=(x, y)) fig.tight_layout() # determine x axis limits if start is None: start = np.min(pos) if stop is None: stop = np.max(pos) loc = pos.locate_range(start, stop) pos = pos[loc] if step is None: step = len(pos) // 100 ax.set_xlim(start, stop) # plot the lines if line_kwargs is None: line_kwargs = dict() # line_kwargs.setdefault('linewidth', .5) n_variants = len(pos) for i, p in enumerate(pos[::step]): xfrom = p xto = ( start + ((i * step / n_variants) * (stop-start)) ) l = plt.Line2D([xfrom, xto], [0, 1], **line_kwargs) ax.add_line(l) # invert? if flip: ax.invert_yaxis() ax.xaxis.tick_top() else: ax.xaxis.tick_bottom() # tidy up ax.set_yticks([]) ax.xaxis.set_tick_params(direction='out') for l in 'left', 'right': ax.spines[l].set_visible(False) return ax