PythonCourse-2012/PlotUtils.py at master · petercombs/PythonCourse-2012 · GitHub

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from matplotlib import pyplot as mpl
import numpy as np


def relabel(tick, bp_equiv):
    if tick < 0:
        return "%d bp" % tick
    elif tick == 0:
        return "-0 bp/0%"
    elif tick < bp_equiv:
        return "%d%%" % (float(tick) / bp_equiv * 100)
    elif tick == bp_equiv:
        return '100%/+0 bp'
    else:
        return "+%d bp" % (tick - bp_equiv)


def plot_averaged_genes(upstream, gene, downstream, bp_equiv=200, label=None,
                       normed=False):
    """ Plots the coverage around genes.

    Here, the upstream and downstream regions are assumed to be in base pairs,
    and the gene itself is assumed to cover 100% of the gene length, averaged
    over all the genes included
    """

    retval = []

    fig = mpl.gcf()
    ax = fig.gca()

    if normed:
        normval = np.median(gene)
        upstream = np.array(upstream) / normval
        gene = np.array(gene) / normval
        downstream = np.array(downstream) / normval
    retval.extend(ax.plot(np.arange(-len(upstream), 0),
                           upstream,
                           label=label))
    color = retval[0].get_color()
    retval.extend(ax.plot(np.linspace(0, bp_equiv, len(gene)),
                          gene,
                          color=color))
    retval.extend(ax.plot(np.arange(bp_equiv, len(downstream) + bp_equiv),
                          downstream,
                          color=color))
    ymin, ymax = ax.get_ybound()
    retval.append(ax.vlines([0, bp_equiv], ymin, ymax, linestyles='dashed'))
    ticks = list(ax.get_xticks())
    # Ensure we have at least the full gene labelled
    ticks = np.unique(ticks)
    ticks.extend([0, bp_equiv / 2, bp_equiv])
    ticklabels = [relabel(tick, bp_equiv) for tick in ticks]
    ax.set_xticks(ticks)
    ax.set_xticklabels(ticklabels)
    mpl.show()
    return retval


def plot_average_fft(seqs, label=None):
    avg = np.sum([np.abs(np.fft.fftshift(np.fft.fft(seq)))
                  for seq in seqs if seq], axis=0)
    avg /= len(seqs)
    avg /= np.sqrt(len(seq))
    return mpl.plot(1 / np.fft.fftfreq(len(avg)), avg, label=label)


def plot_average_fft_diff(seqs1, seqs2, label=None):
    avg1 = np.sum([np.abs(np.fft.fftshift(np.fft.fft(seq)))
                   for seq in seqs1 if seq], axis=0)
    avg1 /= len(seqs1)
    avg2 = np.sum([np.abs(np.fft.fftshift(np.fft.fft(seq)))
                   for seq in seqs2 if seq], axis=0)
    avg2 /= len(seqs2)

    return mpl.plot(1 / np.fft.fftfreq(len(avg1)), avg1 - avg2, label=label)