How to use the prody.writeArray function in ProDy

.format(len(select)))

    gnm = prody.GNM(pdb.getTitle())
    gnm.buildKirchhoff(select, cutoff, gamma)
    gnm.calcModes(nmodes)
    LOGGER.info('Writing numerical output.')
    if opt.npz:
        prody.saveModel(gnm)
    prody.writeNMD(os.path.join(outdir, prefix + '.nmd'), gnm, 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), 
                         gnm.getArray(), delimiter=delim, format=format)
        prody.writeArray(os.path.join(outdir, prefix + '_evalues'+ext), 
                         gnm.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(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 opt.covar:
        prody.writeArray(os.path.join(outdir, prefix + '_covariance'+ext), 
                         gnm.getCovariance(), delimiter=delim, format=format)
    if outall or opt.ccorr:

extend + '.nmd'), *extended)
        else:
            prody.LOGGER.warn('Model could not be extended, provide a PDB or '
                              'PSF file.')
    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),
                         pca.getArray(), delimiter=delim, format=format)
        prody.writeArray(join(outdir, prefix + '_evalues'+ext),
                         pca.getEigvals(), delimiter=delim, format=format)
    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance'+ext),
                         pca.getCovariance(), delimiter=delim, format=format)
    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(pca)
        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'):
            resnums = select.getResnums()
            hmargs = {} if resnums is None else {'resnums': resnums}
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc, xlabel='Residue', ylabel='Residue',
                               title=pca.getTitle() + ' cross-correlations',
                               **hmargs)

    if outall or kwargs.get('outsf'):
        prody.writeArray(join(outdir, prefix + '_sqfluct'+ext),

pca.getArray(), delimiter=delim, format=format)
        prody.writeArray(os.path.join(outdir, prefix + '_evalues'+ext), 
                         pca.getEigenvalues(), delimiter=delim, format=format)
    if outall or opt.covar:
        prody.writeArray(os.path.join(outdir, prefix + '_covariance'+ext), 
                         pca.getCovariance(), delimiter=delim, format=format)
    if outall or opt.ccorr:
        prody.writeArray(os.path.join(outdir, prefix + '_cross-correlations' + 
                                              ext), prody.calcCrossCorr(pca), 
                         delimiter=delim, format=format)
    if outall or opt.sqflucts:
        prody.writeArray(os.path.join(outdir, prefix + '_sqfluct'+ext), 
                         prody.calcSqFlucts(pca), delimiter=delim, 
                         format=format)
    if outall or opt.proj:
        prody.writeArray(os.path.join(outdir, prefix + '_proj'+ext), 
                         prody.calcProjection(ensemble, pca), delimiter=delim, 
                         format=format)
          
    figall, cc, sf, sp = opt.figures, opt.cc, opt.sf, opt.sp

    if figall or cc or sf or sp: 
        format = format.lower()
        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

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),
                         pca.getArray(), delimiter=delim, format=format)
        prody.writeArray(join(outdir, prefix + '_evalues'+ext),
                         pca.getEigvals(), delimiter=delim, format=format)
    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance'+ext),
                         pca.getCovariance(), delimiter=delim, format=format)
    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(pca)
        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'):
            resnums = select.getResnums()
            hmargs = {} if resnums is None else {'resnums': resnums}
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc, xlabel='Residue', ylabel='Residue',
                               title=pca.getTitle() + ' cross-correlations',
                               **hmargs)

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

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)

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)

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 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(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 opt.covar:
        prody.writeArray(os.path.join(outdir, prefix + '_covariance'+ext), 
                         gnm.getCovariance(), delimiter=delim, format=format)
    if outall or opt.ccorr:
        prody.writeArray(os.path.join(outdir, prefix + '_cross-correlations' 
                                                     + ext), 
                         prody.calcCrossCorr(gnm), delimiter=delim, 
                         format=format)
    if outall or opt.kirchhoff:
        prody.writeArray(os.path.join(outdir, prefix + '_kirchhoff'+ext), 
                         gnm.getKirchhoff(), delimiter=delim, format=format)
    if outall or opt.sqflucts:
        prody.writeArray(os.path.join(outdir, prefix + '_sqfluct'+ext), 
                         prody.calcSqFlucts(gnm), delimiter=delim, 
                         format=format)
          
    figall, cc, sf, bf, cm, modes = \
        opt.figures, opt.cc, opt.sf, opt.bf, opt.cm, opt.modes
    if figall or cc or sf or bf or cm or modes: 
        try:
            import matplotlib.pyplot as plt

extended = prody.extendModel(pca[:nmodes], select, pdb)
            else:
                extended = prody.extendModel(pca[:nmodes], select,
                                             select | pdb.bb)
            prody.writeNMD(join(outdir, prefix + '_extended_' +
                           extend + '.nmd'), *extended)
        else:
            prody.LOGGER.warn('Model could not be extended, provide a PDB or '
                              'PSF file.')
    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),
                         pca.getArray(), delimiter=delim, format=format)
        prody.writeArray(join(outdir, prefix + '_evalues'+ext),
                         pca.getEigvals(), delimiter=delim, format=format)
    if outall or kwargs.get('outcov'):
        prody.writeArray(join(outdir, prefix + '_covariance'+ext),
                         pca.getCovariance(), delimiter=delim, format=format)
    if outall or kwargs.get('outcc') or kwargs.get('outhm'):
        cc = prody.calcCrossCorr(pca)
        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'):
            resnums = select.getResnums()
            hmargs = {} if resnums is None else {'resnums': resnums}
            prody.writeHeatmap(join(outdir, prefix + '_cross-correlations.hm'),
                               cc, xlabel='Residue', ylabel='Residue',