Exemple #1
0
def prody_anm(opt):
    """Perform ANM calculations based on command line arguments."""
    
    outdir = opt.outdir
    if not os.path.isdir(outdir):
        opt.subparser.error('{0:s} is not a valid path'.format(outdir))
        
    import numpy as np
    import prody
    LOGGER = prody.LOGGER


    pdb = opt.pdb
    prefix = opt.prefix
    cutoff, gamma = opt.cutoff, opt.gamma, 
    nmodes, selstr, model = opt.nmodes, opt.select, opt.model
    
    pdb = prody.parsePDB(pdb, model=model)
    if prefix == '_anm':
        prefix = pdb.getTitle() + '_anm'

    select = pdb.select(selstr)
    if select is None:
        opt.subparser('Selection "{0:s}" do not match any atoms.'
                       .format(selstr))
    LOGGER.info('{0:d} atoms will be used for ANM calculations.'
                .format(len(select)))

    anm = prody.ANM(pdb.getTitle())
    anm.buildHessian(select, cutoff, gamma)
    anm.calcModes(nmodes)
    LOGGER.info('Writing numerical output.')
    if opt.npz:
        prody.saveModel(anm)
    prody.writeNMD(os.path.join(outdir, prefix + '.nmd'), anm, select)

    outall = opt.all
    delim, ext, format = opt.delim, opt.ext, opt.numformat

    if outall or opt.eigen:
        prody.writeArray(os.path.join(outdir, prefix + '_evectors'+ext), 
                         anm.getArray(), delimiter=delim, format=format)
        prody.writeArray(os.path.join(outdir, prefix + '_evalues'+ext), 
                         anm.getEigenvalues(), delimiter=delim, format=format)
    if outall or opt.beta:
        fout = prody.openFile(prefix + '_beta.txt', 'w', folder=outdir)
        fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'
                       .format(['C', 'RES', '####', 'Exp.', 'The.']))
        for data in zip(select.getChids(), select.getResnames(), 
                        select.getResnums(), select.getBetas(), 
                        prody.calcTempFactors(anm, select)):
            fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'
                       .format(data))
        fout.close()
    if outall or opt.covar:
        prody.writeArray(os.path.join(outdir, prefix + '_covariance'+ext), 
                         anm.getCovariance(), delimiter=delim, format=format)
    if outall or opt.ccorr:
        prody.writeArray(os.path.join(outdir, prefix + '_cross-correlations' 
                                                     + ext), 
                         prody.calcCrossCorr(anm), delimiter=delim, 
                         format=format)
    if outall or opt.hessian:
        prody.writeArray(os.path.join(outdir, prefix + '_hessian'+ext), 
                         anm.getHessian(), delimiter=delim, format=format)
    if outall or opt.kirchhoff:
        prody.writeArray(os.path.join(outdir, prefix + '_kirchhoff'+ext), 
                         anm.getKirchhoff(), delimiter=delim, format=format)
    if outall or opt.sqflucts:
        prody.writeArray(os.path.join(outdir, prefix + '_sqflucts'+ext), 
                         prody.calcSqFlucts(anm), delimiter=delim, 
                         format=format)
          
    figall, cc, sf, bf, cm = opt.figures, opt.cc, opt.sf, opt.bf, opt.cm

    if figall or cc or sf or bf or cm: 
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            LOGGER.info('Saving graphical output.')
            format, width, height, dpi = \
                opt.figformat, opt.width, opt.height, opt.dpi
            format = format.lower()
            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(anm)
                plt.savefig(os.path.join(outdir, prefix + '_cc.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(anm)
                plt.savefig(os.path.join(outdir, prefix + '_cm.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(anm)
                plt.savefig(os.path.join(outdir, prefix + '_sf.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(anm, select)
                plt.plot(bexp, label='Experimental')
                plt.plot(bcal, label=('Theoretical (R={0:.2f})'
                                        .format(np.corrcoef(bcal, bexp)[0,1])))
                plt.legend(prop={'size': 10})
                plt.xlabel('Node index')
                plt.ylabel('Experimental B-factors')
                plt.title(pdb.getTitle() + ' B-factors')
                plt.savefig(os.path.join(outdir, prefix + '_bf.'+format), 
                    dpi=dpi, format=format)
                plt.close('all')
Exemple #2
0
def prody_anm(pdb, **kwargs):
    """Perform ANM calculations for *pdb*.

    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, join
    outdir = kwargs.get('outdir')
    if not isdir(outdir):
        raise IOError('{0} is not a valid path'.format(repr(outdir)))

    import numpy as np
    import prody
    LOGGER = prody.LOGGER

    selstr = kwargs.get('select')
    prefix = kwargs.get('prefix')
    cutoff = kwargs.get('cutoff')
    gamma = kwargs.get('gamma')
    nmodes = kwargs.get('nmodes')
    selstr = kwargs.get('select')
    model = kwargs.get('model')

    pdb = prody.parsePDB(pdb, model=model)
    if prefix == '_anm':
        prefix = pdb.getTitle() + '_anm'

    select = pdb.select(selstr)
    if select is None:
        LOGGER.warn('Selection {0} did not match any atoms.'
                    .format(repr(selstr)))
        return
    LOGGER.info('{0} atoms will be used for ANM calculations.'
                .format(len(select)))

    anm = prody.ANM(pdb.getTitle())
    anm.buildHessian(select, cutoff, gamma)
    anm.calcModes(nmodes)
    LOGGER.info('Writing numerical output.')
    if kwargs.get('outnpz'):
        prody.saveModel(anm, join(outdir, prefix))
    prody.writeNMD(join(outdir, prefix + '.nmd'), anm, select)

    extend = kwargs.get('extend')
    if extend:
        if extend == 'all':
            extended = prody.extendModel(anm, select, pdb)
        else:
            extended = prody.extendModel(anm, select, select | pdb.bb)
        prody.writeNMD(join(outdir, prefix + '_extended_' +
                       extend + '.nmd'), *extended)

    outall = kwargs.get('outall')
    delim = kwargs.get('numdelim')
    ext = kwargs.get('numext')
    format = kwargs.get('numformat')


    if outall or kwargs.get('outeig'):
        prody.writeArray(join(outdir, prefix + '_evectors'+ext),
                         anm.getArray(), delimiter=delim, format=format)
        prody.writeArray(join(outdir, prefix + '_evalues'+ext),
                         anm.getEigvals(), delimiter=delim, format=format)

    if outall or kwargs.get('outbeta'):
        from prody.utilities import openFile
        fout = openFile(prefix + '_beta.txt', 'w', folder=outdir)
        fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'
                       .format(['C', 'RES', '####', 'Exp.', 'The.']))
        for data in zip(select.getChids(), select.getResnames(),
                        select.getResnums(), select.getBetas(),
                        prody.calcTempFactors(anm, select)):
            fout.write('{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'
                       .format(data))
        fout.close()

    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance' + ext),
                         anm.getCovariance(), delimiter=delim, format=format)

    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(anm)
        if outall or kwargs.get('outcc'):
            prody.writeArray(join(outdir, prefix +
                             '_cross-correlations' + ext),
                             cc, delimiter=delim,  format=format)
        if outall or kwargs.get('outhm'):
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc, resnum=select.getResnums(),
                               xlabel='Residue', ylabel='Residue',
                               title=anm.getTitle() + ' cross-correlations')

    if outall or kwargs.get('hessian'):
        prody.writeArray(join(outdir, prefix + '_hessian'+ext),
                         anm.getHessian(), delimiter=delim, format=format)

    if outall or kwargs.get('kirchhoff'):
        prody.writeArray(join(outdir, prefix + '_kirchhoff'+ext),
                         anm.getKirchhoff(), delimiter=delim, format=format)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqflucts'+ext),
                         prody.calcSqFlucts(anm), delimiter=delim,
                         format=format)

    figall = kwargs.get('figall')
    cc = kwargs.get('figcc')
    sf = kwargs.get('figsf')
    bf = kwargs.get('figbeta')
    cm = kwargs.get('figcmap')


    if figall or cc or sf or bf or cm:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            prody.SETTINGS['auto_show'] = False
            LOGGER.info('Saving graphical output.')
            format = kwargs.get('figformat')
            width = kwargs.get('figwidth')
            height = kwargs.get('figheight')
            dpi = kwargs.get('figdpi')
            format = format.lower()

            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(anm)
                plt.savefig(join(outdir, prefix + '_cc.'+format),
                    dpi=dpi, format=format)
                plt.close('all')

            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(anm)
                plt.savefig(join(outdir, prefix + '_cm.'+format),
                    dpi=dpi, format=format)
                plt.close('all')

            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(anm)
                plt.savefig(join(outdir, prefix + '_sf.'+format),
                    dpi=dpi, format=format)
                plt.close('all')

            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(anm, select)
                plt.plot(bexp, label='Experimental')
                plt.plot(bcal, label=('Theoretical (R={0:.2f})'
                                        .format(np.corrcoef(bcal, bexp)[0,1])))
                plt.legend(prop={'size': 10})
                plt.xlabel('Node index')
                plt.ylabel('Experimental B-factors')
                plt.title(pdb.getTitle() + ' B-factors')
                plt.savefig(join(outdir, prefix + '_bf.'+format),
                    dpi=dpi, format=format)
                plt.close('all')
Exemple #3
0
def prody_gnm(pdb, **kwargs):
    """Perform GNM calculations for *pdb*.
    
    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, splitext, join

    outdir = kwargs.get("outdir")
    if not isdir(outdir):
        raise IOError("{0} is not a valid path".format(repr(outdir)))

    import numpy as np
    import prody

    LOGGER = prody.LOGGER

    selstr = kwargs.get("select")
    prefix = kwargs.get("prefix")
    cutoff = kwargs.get("cutoff")
    gamma = kwargs.get("gamma")
    nmodes = kwargs.get("nmodes")
    selstr = kwargs.get("select")
    model = kwargs.get("model")

    pdb = prody.parsePDB(pdb, model=model)
    if prefix == "_gnm":
        prefix = pdb.getTitle() + "_gnm"

    select = pdb.select(selstr)
    if select is None:
        raise ValueError("selection {0} do not match any atoms".format(repr(selstr)))
    LOGGER.info("{0} atoms will be used for GNM calculations.".format(len(select)))

    gnm = prody.GNM(pdb.getTitle())
    gnm.buildKirchhoff(select, cutoff, gamma)
    gnm.calcModes(nmodes)

    LOGGER.info("Writing numerical output.")

    if kwargs.get("outnpz"):
        prody.saveModel(gnm, join(outdir, prefix))

    prody.writeNMD(join(outdir, prefix + ".nmd"), gnm, select)

    extend = kwargs.get("extend")
    if extend:
        if extend == "all":
            extended = prody.extendModel(gnm, select, pdb)
        else:
            extended = prody.extendModel(gnm, select, select | pdb.bb)
        prody.writeNMD(join(outdir, prefix + "_extended_" + extend + ".nmd"), *extended)

    outall = kwargs.get("outall")
    delim = kwargs.get("numdelim")
    ext = kwargs.get("numext")
    format = kwargs.get("numformat")

    if outall or kwargs.get("outeig"):
        prody.writeArray(join(outdir, prefix + "_evectors" + ext), gnm.getArray(), delimiter=delim, format=format)
        prody.writeArray(join(outdir, prefix + "_evalues" + ext), gnm.getEigvals(), delimiter=delim, format=format)

    if outall or kwargs.get("outbeta"):
        from prody.utilities import openFile

        fout = openFile(prefix + "_beta.txt", "w", folder=outdir)
        fout.write("{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n".format(["C", "RES", "####", "Exp.", "The."]))
        for data in zip(
            select.getChids(),
            select.getResnames(),
            select.getResnums(),
            select.getBetas(),
            prody.calcTempFactors(gnm, select),
        ):
            fout.write("{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n".format(data))
        fout.close()

    if outall or kwargs.get("outcov"):
        prody.writeArray(
            join(outdir, prefix + "_covariance" + ext), gnm.getCovariance(), delimiter=delim, format=format
        )

    if outall or kwargs.get("outcc") or kwargs.get("outhm"):
        cc = prody.calcCrossCorr(gnm)
        if outall or kwargs.get("outcc"):
            prody.writeArray(join(outdir, prefix + "_cross-correlations" + ext), cc, delimiter=delim, format=format)
        if outall or kwargs.get("outhm"):
            prody.writeHeatmap(
                join(outdir, prefix + "_cross-correlations.hm"),
                cc,
                resnum=select.getResnums(),
                xlabel="Residue",
                ylabel="Residue",
                title=gnm.getTitle() + " cross-correlations",
            )

    if outall or kwargs.get("kirchhoff"):
        prody.writeArray(join(outdir, prefix + "_kirchhoff" + ext), gnm.getKirchhoff(), delimiter=delim, format=format)

    if outall or kwargs.get("outsf"):
        prody.writeArray(
            join(outdir, prefix + "_sqfluct" + ext), prody.calcSqFlucts(gnm), delimiter=delim, format=format
        )

    figall = kwargs.get("figall")
    cc = kwargs.get("figcc")
    sf = kwargs.get("figsf")
    bf = kwargs.get("figbeta")
    cm = kwargs.get("figcmap")
    modes = kwargs.get("figmode")

    if figall or cc or sf or bf or cm or modes:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning("Matplotlib could not be imported. " "Figures are not saved.")
        else:
            prody.SETTINGS["auto_show"] = False
            LOGGER.info("Saving graphical output.")
            format = kwargs.get("figformat")
            width = kwargs.get("figwidth")
            height = kwargs.get("figheight")
            dpi = kwargs.get("figdpi")
            format = format.lower()

            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(gnm)
                plt.savefig(join(outdir, prefix + "_cc." + format), dpi=dpi, format=format)
                plt.close("all")

            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(gnm)
                plt.savefig(join(outdir, prefix + "_cm." + format), dpi=dpi, format=format)
                plt.close("all")

            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(gnm)
                plt.savefig(join(outdir, prefix + "_sf." + format), dpi=dpi, format=format)
                plt.close("all")

            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(gnm, select)
                plt.plot(bexp, label="Experimental")
                plt.plot(bcal, label=("Theoretical (corr coef = {0:.2f})".format(np.corrcoef(bcal, bexp)[0, 1])))
                plt.legend(prop={"size": 10})
                plt.xlabel("Node index")
                plt.ylabel("Experimental B-factors")
                plt.title(pdb.getTitle() + " B-factors")
                plt.savefig(join(outdir, prefix + "_bf." + format), dpi=dpi, format=format)
                plt.close("all")

            if modes:
                indices = []
                items = modes.split()
                items = sum([item.split(",") for item in items], [])
                for item in items:
                    try:
                        item = item.split("-")
                        if len(item) == 1:
                            indices.append(int(item[0]) - 1)
                        elif len(item) == 2:
                            indices.extend(range(int(item[0]) - 1, int(item[1])))
                    except:
                        pass
                for index in indices:
                    try:
                        mode = gnm[index]
                    except:
                        pass
                    else:
                        plt.figure(figsize=(width, height))
                        prody.showMode(mode)
                        plt.grid()
                        plt.savefig(
                            join(outdir, prefix + "_mode_" + str(mode.getIndex() + 1) + "." + format),
                            dpi=dpi,
                            format=format,
                        )
                        plt.close("all")
Exemple #4
0
def prody_anm(pdb, **kwargs):
    """Perform ANM calculations for *pdb*.

    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, join
    outdir = kwargs.get('outdir')
    if not isdir(outdir):
        raise IOError('{0} is not a valid path'.format(repr(outdir)))

    import numpy as np
    import prody
    LOGGER = prody.LOGGER

    selstr = kwargs.get('select')
    prefix = kwargs.get('prefix')
    cutoff = kwargs.get('cutoff')
    gamma = kwargs.get('gamma')
    nmodes = kwargs.get('nmodes')
    selstr = kwargs.get('select')
    model = kwargs.get('model')

    pdb = prody.parsePDB(pdb, model=model)
    if prefix == '_anm':
        prefix = pdb.getTitle() + '_anm'

    select = pdb.select(selstr)
    if select is None:
        LOGGER.warn('Selection {0} did not match any atoms.'.format(
            repr(selstr)))
        return
    LOGGER.info('{0} atoms will be used for ANM calculations.'.format(
        len(select)))

    anm = prody.ANM(pdb.getTitle())
    anm.buildHessian(select, cutoff, gamma)
    anm.calcModes(nmodes)
    LOGGER.info('Writing numerical output.')
    if kwargs.get('outnpz'):
        prody.saveModel(anm, join(outdir, prefix))
    prody.writeNMD(join(outdir, prefix + '.nmd'), anm, select)

    extend = kwargs.get('extend')
    if extend:
        if extend == 'all':
            extended = prody.extendModel(anm, select, pdb)
        else:
            extended = prody.extendModel(anm, select, select | pdb.bb)
        prody.writeNMD(join(outdir, prefix + '_extended_' + extend + '.nmd'),
                       *extended)

    outall = kwargs.get('outall')
    delim = kwargs.get('numdelim')
    ext = kwargs.get('numext')
    format = kwargs.get('numformat')

    if outall or kwargs.get('outeig'):
        prody.writeArray(join(outdir, prefix + '_evectors' + ext),
                         anm.getArray(),
                         delimiter=delim,
                         format=format)
        prody.writeArray(join(outdir, prefix + '_evalues' + ext),
                         anm.getEigvals(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outbeta'):
        from prody.utilities import openFile
        fout = openFile(prefix + '_beta.txt', 'w', folder=outdir)
        fout.write(
            '{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'.format(
                ['C', 'RES', '####', 'Exp.', 'The.']))
        for data in zip(select.getChids(), select.getResnames(),
                        select.getResnums(), select.getBetas(),
                        prody.calcTempFactors(anm, select)):
            fout.write(
                '{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'.
                format(data))
        fout.close()

    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance' + ext),
                         anm.getCovariance(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(anm)
        if outall or kwargs.get('outcc'):
            prody.writeArray(join(outdir,
                                  prefix + '_cross-correlations' + ext),
                             cc,
                             delimiter=delim,
                             format=format)
        if outall or kwargs.get('outhm'):
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc,
                               resnum=select.getResnums(),
                               xlabel='Residue',
                               ylabel='Residue',
                               title=anm.getTitle() + ' cross-correlations')

    if outall or kwargs.get('hessian'):
        prody.writeArray(join(outdir, prefix + '_hessian' + ext),
                         anm.getHessian(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('kirchhoff'):
        prody.writeArray(join(outdir, prefix + '_kirchhoff' + ext),
                         anm.getKirchhoff(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqflucts' + ext),
                         prody.calcSqFlucts(anm),
                         delimiter=delim,
                         format=format)

    figall = kwargs.get('figall')
    cc = kwargs.get('figcc')
    sf = kwargs.get('figsf')
    bf = kwargs.get('figbeta')
    cm = kwargs.get('figcmap')

    if figall or cc or sf or bf or cm:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            prody.SETTINGS['auto_show'] = False
            LOGGER.info('Saving graphical output.')
            format = kwargs.get('figformat')
            width = kwargs.get('figwidth')
            height = kwargs.get('figheight')
            dpi = kwargs.get('figdpi')
            format = format.lower()

            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(anm)
                plt.savefig(join(outdir, prefix + '_cc.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(anm)
                plt.savefig(join(outdir, prefix + '_cm.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(anm)
                plt.savefig(join(outdir, prefix + '_sf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(anm, select)
                plt.plot(bexp, label='Experimental')
                plt.plot(bcal,
                         label=('Theoretical (R={0:.2f})'.format(
                             np.corrcoef(bcal, bexp)[0, 1])))
                plt.legend(prop={'size': 10})
                plt.xlabel('Node index')
                plt.ylabel('Experimental B-factors')
                plt.title(pdb.getTitle() + ' B-factors')
                plt.savefig(join(outdir, prefix + '_bf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')
Exemple #5
0
def prody_gnm(pdb, **kwargs):
    """Perform GNM calculations for *pdb*.

    """

    for key in DEFAULTS:
        if not key in kwargs:
            kwargs[key] = DEFAULTS[key]

    from os.path import isdir, splitext, join
    outdir = kwargs.get('outdir')
    if not isdir(outdir):
        raise IOError('{0} is not a valid path'.format(repr(outdir)))

    import numpy as np
    import prody
    LOGGER = prody.LOGGER

    selstr = kwargs.get('select')
    prefix = kwargs.get('prefix')
    cutoff = kwargs.get('cutoff')
    gamma = kwargs.get('gamma')
    nmodes = kwargs.get('nmodes')
    selstr = kwargs.get('select')
    model = kwargs.get('model')
    altloc = kwargs.get('altloc')
    zeros = kwargs.get('zeros')

    pdb = prody.parsePDB(pdb, model=model, altloc=altloc)
    if prefix == '_gnm':
        prefix = pdb.getTitle() + '_gnm'

    select = pdb.select(selstr)
    if select is None:
        raise ValueError('selection {0} do not match any atoms'.format(
            repr(selstr)))
    LOGGER.info('{0} atoms will be used for GNM calculations.'.format(
        len(select)))

    gnm = prody.GNM(pdb.getTitle())

    nproc = kwargs.get('nproc')
    if nproc:
        try:
            from threadpoolctl import threadpool_limits
        except ImportError:
            raise ImportError(
                'Please install threadpoolctl to control threads')

        with threadpool_limits(limits=nproc, user_api="blas"):
            gnm.buildKirchhoff(select, cutoff, gamma)
            gnm.calcModes(nmodes, zeros=zeros)
    else:
        gnm.buildKirchhoff(select, cutoff, gamma)
        gnm.calcModes(nmodes, zeros=zeros)

    LOGGER.info('Writing numerical output.')

    if kwargs.get('outnpz'):
        prody.saveModel(gnm, join(outdir, prefix))

    if kwargs.get('outscipion'):
        prody.writeScipionModes(outdir, gnm)

    prody.writeNMD(join(outdir, prefix + '.nmd'), gnm, select)

    extend = kwargs.get('extend')
    if extend:
        if extend == 'all':
            extended = prody.extendModel(gnm, select, pdb)
        else:
            extended = prody.extendModel(gnm, select, select | pdb.bb)
        prody.writeNMD(join(outdir, prefix + '_extended_' + extend + '.nmd'),
                       *extended)

    outall = kwargs.get('outall')
    delim = kwargs.get('numdelim')
    ext = kwargs.get('numext')
    format = kwargs.get('numformat')

    if outall or kwargs.get('outeig'):
        prody.writeArray(join(outdir, prefix + '_evectors' + ext),
                         gnm.getArray(),
                         delimiter=delim,
                         format=format)
        prody.writeArray(join(outdir, prefix + '_evalues' + ext),
                         gnm.getEigvals(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outbeta'):
        from prody.utilities import openFile
        fout = openFile(prefix + '_beta' + ext, 'w', folder=outdir)
        fout.write(
            '{0[0]:1s} {0[1]:4s} {0[2]:4s} {0[3]:5s} {0[4]:5s}\n'.format(
                ['C', 'RES', '####', 'Exp.', 'The.']))
        for data in zip(select.getChids(), select.getResnames(),
                        select.getResnums(), select.getBetas(),
                        prody.calcTempFactors(gnm, select)):
            fout.write(
                '{0[0]:1s} {0[1]:4s} {0[2]:4d} {0[3]:5.2f} {0[4]:5.2f}\n'.
                format(data))
        fout.close()

    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance' + ext),
                         gnm.getCovariance(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(gnm)
        if outall or kwargs.get('outcc'):
            prody.writeArray(join(outdir,
                                  prefix + '_cross-correlations' + ext),
                             cc,
                             delimiter=delim,
                             format=format)
        if outall or kwargs.get('outhm'):
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc,
                               resnum=select.getResnums(),
                               xlabel='Residue',
                               ylabel='Residue',
                               title=gnm.getTitle() + ' cross-correlations')

    if outall or kwargs.get('kirchhoff'):
        prody.writeArray(join(outdir, prefix + '_kirchhoff' + ext),
                         gnm.getKirchhoff(),
                         delimiter=delim,
                         format=format)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqfluct' + ext),
                         prody.calcSqFlucts(gnm),
                         delimiter=delim,
                         format=format)

    figall = kwargs.get('figall')
    cc = kwargs.get('figcc')
    sf = kwargs.get('figsf')
    bf = kwargs.get('figbeta')
    cm = kwargs.get('figcmap')
    modes = kwargs.get('figmode')

    if figall or cc or sf or bf or cm or modes:
        try:
            import matplotlib.pyplot as plt
        except ImportError:
            LOGGER.warning('Matplotlib could not be imported. '
                           'Figures are not saved.')
        else:
            prody.SETTINGS['auto_show'] = False
            LOGGER.info('Saving graphical output.')
            format = kwargs.get('figformat')
            width = kwargs.get('figwidth')
            height = kwargs.get('figheight')
            dpi = kwargs.get('figdpi')
            format = format.lower()

            if figall or cc:
                plt.figure(figsize=(width, height))
                prody.showCrossCorr(gnm)
                plt.savefig(join(outdir, prefix + '_cc.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or cm:
                plt.figure(figsize=(width, height))
                prody.showContactMap(gnm)
                plt.savefig(join(outdir, prefix + '_cm.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or sf:
                plt.figure(figsize=(width, height))
                prody.showSqFlucts(gnm)
                plt.savefig(join(outdir, prefix + '_sf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if figall or bf:
                plt.figure(figsize=(width, height))
                bexp = select.getBetas()
                bcal = prody.calcTempFactors(gnm, select)
                plt.plot(bexp, label='Experimental')
                plt.plot(bcal,
                         label=('Theoretical (corr coef = {0:.2f})'.format(
                             np.corrcoef(bcal, bexp)[0, 1])))
                plt.legend(prop={'size': 10})
                plt.xlabel('Node index')
                plt.ylabel('Experimental B-factors')
                plt.title(pdb.getTitle() + ' B-factors')
                plt.savefig(join(outdir, prefix + '_bf.' + format),
                            dpi=dpi,
                            format=format)
                plt.close('all')

            if modes:
                indices = []
                items = modes.split()
                items = sum([item.split(',') for item in items], [])
                for item in items:
                    try:
                        item = item.split('-')
                        if len(item) == 1:
                            indices.append(int(item[0]) - 1)
                        elif len(item) == 2:
                            indices.extend(
                                list(range(int(item[0]) - 1, int(item[1]))))
                    except:
                        pass
                for index in indices:
                    try:
                        mode = gnm[index]
                    except:
                        pass
                    else:
                        plt.figure(figsize=(width, height))
                        prody.showMode(mode)
                        plt.grid()
                        plt.savefig(join(
                            outdir, prefix + '_mode_' +
                            str(mode.getIndex() + 1) + '.' + format),
                                    dpi=dpi,
                                    format=format)
                        plt.close('all')