Source code for prody.sequence.plotting

# -*- coding: utf-8 -*-
"""This module defines MSA analysis functions."""

__author__ = 'Ahmet Bakan, Chakra Chennubhotla'

from numpy import arange

from .analysis import *
from prody import LOGGER

__all__ = ['showMSAOccupancy', 'showShannonEntropy', 'showMutinfoMatrix', 
           'showDirectInfoMatrix', 'showSCAMatrix']


def pickSequence(msa):
    """Pick a sequence without gaps and deletions and return its residue
    numbers and labels to be used as indices and X-axis label, or a pair
    of **None** at failure."""

    try:
        counts = calcMSAOccupancy(msa, 'row', count=True)
    except TypeError:
        return None, None
    else:
        length = msa.numResidues()
        split, msa.split = msa.split, True
        rows = (counts == length).nonzero()[0]
        for row in rows:
            try:
                label, indices = msa[row].getLabel(), msa[row].getResnums()
            except:
                break
            else:
                return (indices, 'Residue number ({0})'.format(label))
        return None, None


[docs]def showMSAOccupancy(msa, occ='res', indices=None, count=False, **kwargs): """Show a bar plot of occupancy calculated for :class:`.MSA` instance *msa* using :func:`.calcMSAOccupancy`. *occ* may be ``'res'`` or ``'col'``, or a a pre-calculated occupancy array. If x-axis *indices* are not specified, they will be inferred from *msa* or given *label* that may correspond to a sequence in the msa. Occupancy is plotted using :func:`~matplotlib.pyplot.bar` function.""" try: numseq, lenseq = msa.numSequences(), msa.numResidues() except AttributeError: raise TypeError('msa must be an MSA instance') try: length = len(occ) except TypeError: raise TypeError("occ must be 'res', 'col', or an occupancy array") try: sw = occ.startswith except (TypeError, AttributeError): try: ndim = occ.ndim except AttributeError: raise TypeError("occ must be 'res', 'col', or an occupancy array") else: if ndim != 1: raise ValueError('occ must be a 1-dimensional array') else: occ = calcMSAOccupancy(msa, occ, count) length = len(occ) if length == numseq: xlabel = kwargs.pop('xlabel', None) or 'MSA sequence index' if length == lenseq: label = kwargs.pop('label', None) if label is not None: try: indices = msa[label].getResnums() except: LOGGER.info('Specified label not in msa.') xlabel = kwargs.pop('xlabel', None) or label or 'MSA column index' if xlabel is None and indices is None: indices, xlabel = pickSequence(msa) if indices is None: indices = arange(1, length + 1) ylabel = kwargs.pop('ylabel', 'Count' if count else 'Occupancy') title = kwargs.pop('title', None) format = kwargs.pop('format', True) import matplotlib.pyplot as plt show = plt.bar(indices, occ, **kwargs) if format: plt.xlabel(xlabel) plt.ylabel(ylabel) if title is None: title = 'Occupancy: ' + msa.getTitle() plt.title(title) return show
[docs]def showShannonEntropy(entropy, indices=None, **kwargs): """Show a bar plot of Shannon *entropy* array. :class:`.MSA` instances or Numpy character arrays storing sequence alignments are also accepted as *entropy* argument, in which case :func:`.calcShannonEntropy` will be used for calculations. *indices* may be residue numbers, when not specified they will be inferred from *msa* or indices starting from 1 will be used. Entropy is plotted using :func:`~matplotlib.pyplot.bar` function.""" msa = None try: ndim = entropy.ndim except AttributeError: msa = entropy entropy = calcShannonEntropy(msa) ndim = entropy.ndim if ndim != 1: raise ValueError('entropy must be a 1D array') msa = kwargs.pop('msa', msa) xlabel = kwargs.pop('xlabel', None) if indices is None: length = len(entropy) if msa is not None: indices, xlabel = pickSequence(msa) if indices is None: indices = arange(1, length + 1) xlabel = xlabel or 'MSA column index' ylabel = kwargs.pop('ylabel', 'Shannon entropy') title = kwargs.pop('title', None) format = kwargs.pop('format', True) import matplotlib.pyplot as plt show = plt.bar(indices, entropy, **kwargs) if format: plt.xlabel(xlabel) plt.ylabel(ylabel) if title is None: if msa is None: title = 'Entropy' else: title = 'Entropy: ' + str(msa) plt.title(title) return show
[docs]def showMutinfoMatrix(mutinfo, *args, **kwargs): """Show a heatmap of mutual information array. :class:`.MSA` instances or Numpy character arrays storing sequence alignment are also accepted as *mutinfo* argument, in which case :func:`.buildMutinfoMatrix` will be used for calculations. Note that x, y axes contain indices of the matrix starting from 1. Mutual information is plotted using :func:`~matplotlib.pyplot.imshow` function. vmin and vmax values can be set by user to achieve better signals using this function.""" msa = None try: ndim, shape = mutinfo.ndim, mutinfo.shape except AttributeError: msa = mutinfo mutinfo = buildMutinfoMatrix(mutinfo) ndim, shape = mutinfo.ndim, mutinfo.shape msa = kwargs.pop('msa', msa) if ndim != 2: raise ValueError('mutinfo must be a 2D matrix') y, x = shape if x != y: raise ValueError('mutinfo matrix must be a square matrix') kwargs.setdefault('interpolation', 'nearest') kwargs.setdefault('origin', 'lower') if msa is not None: indices, msalabel = pickSequence(msa) if indices is not None: start = indices[0] + 0.5 end = start + x extent = [start, end, start, end] else: extent = [0.5, x + 0.5, 0.5, y + 0.5] else: msalabel = None extent = [0.5, x + 0.5, 0.5, y + 0.5] xlabel = kwargs.pop('xlabel', None) if xlabel is None: xlabel = msalabel or 'MSA column index' title = kwargs.pop('title', None) format = kwargs.pop('format', True) import matplotlib.pyplot as plt show = plt.imshow(mutinfo, extent=extent, *args, **kwargs), plt.colorbar() if format: plt.xlabel(xlabel) plt.ylabel(xlabel) if title is None: if msa is None: title = 'Mutual Information' else: title = 'Mutual Information: ' + str(msa) plt.title(title) return show
[docs]def showDirectInfoMatrix(dirinfo, *args, **kwargs): """Show a heatmap of direct information array. :class:`.MSA` instances or Numpy character arrays storing sequence alignment are also accepted as *dirinfo* argument, in which case :func:`.buildDirectInfoMatrix` will be used for calculations. Note that x, y axes contain indices of the matrix starting from 1. Direct information is plotted using :func:`~matplotlib.pyplot.imshow` function. vmin and vmax values can be set by user to achieve better signals using this function.""" msa = None try: ndim, shape = dirinfo.ndim, dirinfo.shape except AttributeError: msa = dirinfo dirinfo = buildDirectInfoMatrix(dirinfo) ndim, shape = dirinfo.ndim, dirinfo.shape msa = kwargs.pop('msa', msa) if ndim != 2: raise ValueError('dirinfo must be a 2D matrix') y, x = shape if x != y: raise ValueError('dirinfo matrix must be a square matrix') kwargs.setdefault('interpolation', 'nearest') kwargs.setdefault('origin', 'lower') if msa is not None: indices, msalabel = pickSequence(msa) if indices is not None: start = indices[0] + 0.5 end = start + x extent = [start, end, start, end] else: extent = [0.5, x + 0.5, 0.5, y + 0.5] else: msalabel = None extent = [0.5, x + 0.5, 0.5, y + 0.5] xlabel = kwargs.pop('xlabel', None) if xlabel is None: xlabel = msalabel or 'MSA column index' title = kwargs.pop('title', None) format = kwargs.pop('format', True) import matplotlib.pyplot as plt show = plt.imshow(dirinfo, extent=extent, *args, **kwargs), plt.colorbar() if format: plt.xlabel(xlabel) plt.ylabel(xlabel) if title is None: if msa is None: title = 'Direct Information' else: title = 'Direct Information: ' + str(msa) plt.title(title) return show
[docs]def showSCAMatrix(scainfo, *args, **kwargs): """Show a heatmap of SCA (statistical coupling analysis) array. :class:`.MSA` instances. blah or Numpy character arrays storing sequence alignment are also accepted as *scainfo* argument, in which case :func:`.buildSCAMatrix` will be used for calculations. Note that x, y axes contain indices of the matrix starting from 1. SCA information is plotted using :func:`~matplotlib.pyplot.imshow` function. vmin and vmax values can be set by user to achieve better signals using this function.""" msa = None try: ndim, shape = scainfo.ndim, scainfo.shape except AttributeError: msa = scainfo scainfo = buildSCAMatrix(scainfo) ndim, shape = scainfo.ndim, scainfo.shape msa = kwargs.pop('msa', msa) if ndim != 2: raise ValueError('scainfo must be a 2D matrix') y, x = shape if x != y: raise ValueError('scainfo matrix must be a square matrix') kwargs.setdefault('interpolation', 'nearest') kwargs.setdefault('origin', 'lower') if msa is not None: indices, msalabel = pickSequence(msa) if indices is not None: start = indices[0] + 0.5 end = start + x extent = [start, end, start, end] else: extent = [0.5, x + 0.5, 0.5, y + 0.5] else: msalabel = None extent = [0.5, x + 0.5, 0.5, y + 0.5] xlabel = kwargs.pop('xlabel', None) if xlabel is None: xlabel = msalabel or 'MSA column index' title = kwargs.pop('title', None) format = kwargs.pop('format', True) import matplotlib.pyplot as plt show = plt.imshow(scainfo, extent=extent, *args, **kwargs), plt.colorbar() if format: plt.xlabel(xlabel) plt.ylabel(xlabel) if title is None: if msa is None: title = 'SCA Information' else: title = 'SCA Information: ' + str(msa) plt.title(title) return show
if __name__ == '__main__': from prody import * msa = parseMSA('piwi_seed.sth') print(repr(msa)) msa = refineMSA(msa, label=msa[0][0]) print(repr(msa)) print(calcMSAOccupancy(msa, 'row', count=True)) print(pickSequence(msa))