Example #1
0
def test_rebinner():
    edges = N.array([0.0, 0.1, 1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.0],
                    dtype='d')
    edges_m = N.array([0.1, 1.2, 3.4, 4.5, 7.8], dtype='d')

    ntrue = C.Histogram(edges, N.ones(edges.size - 1))
    rebin = R.Rebin(edges_m.size, edges_m, 3)
    rebin.rebin.histin(ntrue)

    olddata = ntrue.data()
    newdata = rebin.rebin.histout.data()

    mat = convert(rebin.getDenseMatrix(), 'matrix')

    if "pytest" not in sys.modules:
        print(mat)

    prj = mat.sum(axis=0)
    assert ((prj == 1.0) +
            (prj == 0.0)).all(), "Not matching results after rebin"

    #
    # Plot spectra
    #
    if "pytest" not in sys.modules:
        print(((prj == 1.0) + (prj == 0.0)).all() and '\033[32mOK!'
              or '\033[31mFAIL!', '\033[0m')
        fig = P.figure()
        ax = P.subplot(111)
        ax.minorticks_on()
        # ax.grid()
        ax.set_xlabel('X axis')
        ax.set_ylabel('Y axis')
        ax.set_title('Rebinner')

        ax.vlines(edges, 0.0, 4.0, linestyle='--', linewidth=0.5)
        plot_hist(edges, olddata, label='before')
        plot_hist(edges_m, newdata, label='after')

        ax.legend(loc='upper left')

        #
        # Plot matrix
        #
        fig = P.figure()
        ax = P.subplot(111)
        ax.minorticks_on()
        # ax.grid()
        ax.set_xlabel('Source bins')
        ax.set_ylabel('Target bins')
        ax.set_title('Rebinning matrix')

        c = ax.matshow(N.ma.array(mat, mask=mat == 0.0),
                       extent=[edges[0], edges[-1], edges_m[-1], edges_m[0]])
        # add_colorbar( c )

        P.show()
Example #2
0
def plot_ratio(gna, dyboscar, **kwargs):
    title = kwargs.pop('title', '')

    dyb_data, dyb_bins = dyboscar.numpy()
    fig, (axis_spectra, axis_ratio) = plt.subplots(ncols=1,
                                                   nrows=2,
                                                   sharex=True,
                                                   gridspec_kw={'hspace': 0.},
                                                   squeeze=True)
    fig.subplots_adjust(hspace=0.05)
    fig.set_tight_layout(True)

    gna.plot_hist(axis=axis_spectra, label='GNA')
    plot_hist(dyb_bins, dyb_data, label='dybOscar', axis=axis_spectra)
    axis_spectra.legend()
    axis_spectra.set_title("{}".format(title), fontsize=16)
    axis_spectra.minorticks_on()
    axis_spectra.grid('both', alpha=0.5)
    axis_spectra.set_ylabel(r"Entries", fontsize=14)
    axis_spectra.set_ylim(bottom=0.)

    ratio = gna.data() / dyb_data - 1
    try:
        scale = int(kwargs['yscale'])
        ratio *= 10**(scale)
        ylabel = ''.join(("Ratio$-1$, $10^{-", str(scale), "}$"))
        axis_ratio.set_ylabel(r'{}'.format(ylabel), fontsize=14)
    except (KeyError, TypeError):
        axis_ratio.set_ylabel(r"Ratio$-1$", fontsize=14)

    plot_hist(dyb_bins, ratio, axis=axis_ratio, label='GNA/dybOscar')

    ylims = kwargs.pop('ylims')
    if ylims is not None:
        axis_ratio.set_ylim(ylims[0], ylims[1])
    xlims = kwargs.pop('xlims')

    axis_ratio.axhline(y=0.0, linestyle='--', color='grey', alpha=0.5)
    axis_ratio.legend()
    axis_ratio.minorticks_on()
    axis_ratio.grid(alpha=0.5)
    axis_ratio.grid('minor', alpha=0.5)
    axis_ratio.set_xlabel(r"$E_{\mathrm{vis}}$, MeV", fontsize=14)
    plt.setp(axis_ratio.get_yticklabels()[-1], visible=False)
    axis_ratio.get_yaxis().get_major_formatter().set_useOffset(True)
    if xlims is not None:
        axis_spectra.set_xlim(xlims[0], xlims[1])
        axis_ratio.set_xlim(xlims[0], xlims[1])

    printer_to_file = kwargs.get('pp', None)
    if printer_to_file:
        printer_to_file.savefig(fig)
        plt.close('all')
Example #3
0
def plot_hist1(h, *args, **kwargs):
    """Plot 1-dimensinal histogram using pyplot.plot

    executes pyplot.plot(x, y, *args, **kwargs) with first two arguments overridden
    all other arguments passes as is.

    Options:
        autolabel=True guesses plot label with histogram's title

    returns pyplot.plot() result
    """
    if kwargs.pop('autolabel', None):
        kwargs['label'] = h.GetTitle()

    lims = R2N.get_bin_edges_axis(h.GetXaxis())
    height = R2N.get_buffer_hist1(h).copy()

    return helpers.plot_hist(lims, height, *args, **kwargs)
Example #4
0
def plot_hist(output, *args, **kwargs):
    """Plot 1-dimensinal output using pyplot.plot

    executes pyplot.plot(x, y, *args, **kwargs) with first two arguments overridden
    all other arguments are passed as is.

    Options:
        scale=float or 'width' - multiply bin by a scale or divide by bin width

    returns pyplot.plot() result
    """
    scale = kwargs.pop('scale', None)
    height, lims, _ = get_1d_data(output, scale=scale)

    diff = kwargs.pop('diff', None)
    if diff is not None:
        ifSameType(output, diff)
        height1, lims1, _ = get_1d_data(diff, scale)

        height -= height1

    ratio = kwargs.pop('ratio', None)
    if ratio is not None:
        ifSameType(output, ratio)
        height1, lims1, _ = get_1d_data(ratio, scale)

        height /= height1
        height[N.isnan(height)] = 1.0

    offset_ratio = kwargs.pop('offset_ratio', None)
    if offset_ratio is not None:
        ifSameType(output, offset_ratio)
        height1, lims1, _ = get_1d_data(offset_ratio, scale)

        height /= height1
        height -= 1.0
        height[N.isnan(height)] = 0.0

    return helpers.plot_hist(lims, height, *args, **kwargs)
Example #5
0
from mpl_tools.helpers import plot_hist
from gna.labelfmt import formatter as L
axes=()
for i, hist in enumerate(hists_list):
    P.figure(),
    ax = P.subplot( 111 )
    axes+=ax,
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( L.u('evis') )
    ax.set_ylabel( 'Entries' )
    ax.set_title( cfg.detector.detectors[i] )

    data = hist.hist.hist.data()
    print( 'Sum data %i=%i'%( i, data.sum() ) )
    plot_hist(edges, data, label='original')

for bundle in b.bundles.values():
    for i, (oname, out) in enumerate( bundle.outputs.items() ):
        P.sca(axes[i])

        data = out.data()
        print( 'Sum data %s (%s) %i=%f'%( type(bundle).__name__, oname, i, data.sum() ) )

        plot_hist(out.datatype().edges, data, label=type(bundle).__name__)

for i, hist in enumerate(hists_list):
    ax=axes[i]
    P.sca(ax)
    ax.legend( loc='upper right' )
Example #6
0
def test_energyresolution_v01(tmp_path):
    def axes( title, ylabel='' ):
        fig = plt.figure()
        ax = plt.subplot( 111 )
        ax.minorticks_on()
        ax.grid()
        ax.set_xlabel( L.u('evis') )
        ax.set_ylabel( ylabel )
        ax.set_title( title )
        return ax

    def singularities( values, edges ):
        indices = np.digitize( values, edges )-1
        phist = np.zeros( edges.size-1 )
        phist[indices] = 1.0
        return phist

    #
    # Define the parameters in the current namespace
    #
    ns = env.globalns('test_energyresolution_v01')
    weights = [ 'Eres_'+s for s in 'abc' ]
    wvals   = [0.016, 0.081, 0.026]
    percent = 0.01
    ns.defparameter(weights[0],  central=wvals[0], relsigma=30*percent )
    par = ns.defparameter(weights[1],  central=wvals[1], relsigma=30*percent )
    ns.defparameter(weights[2],  central=wvals[2], relsigma=30*percent )
    ns.printparameters()

    values = []
    def pop_value():
        values, par
        par.set(values.pop())

    def push_value(v):
        values, par
        values.append(par.value())
        par.set(v)

    #
    # Define bin edges
    #
    binwidth=0.05
    edges = np.arange( 0.0, 12.0001, binwidth )
    efine = np.arange( edges[0], edges[-1]+1.e-5, 0.005 )

    for eset in [
        [ [1.025], [3.025], [6.025], [9.025] ],
        [ [ 1.025, 5.025, 9.025 ] ],
        [ [ 6.025, 7.025,  8.025, 8.825 ] ],
        ]:
        for i, e in enumerate(eset):
            ax = axes( 'Energy resolution impact' )
            phist = singularities( e, edges )

            hist = C.Histogram( edges, phist )
            edges_o = R.HistEdges(hist)
            with ns:
                eres = C.EnergyResolution(weights, True)
            eres.matrix.Edges( hist )
            eres.smear.Ntrue( hist )

            path = os.path.join(str(tmp_path), 'eres_graph_%i.png'%i)
            savegraph(hist, path)
            allure_attach_file(path)

            smeared = eres.smear.Nrec.data()
            diff = phist.sum()-smeared.sum()
            print( 'Sum check for {} (diff): {}'.format( e, diff ) )
            assert diff<1.e-9

            lines = plot_hist( edges, smeared, label='default' )

            color = lines[0].get_color()
            ax.vlines( e, 0.0, smeared.max(), linestyle='--', color=color )

            if len(e)>1:
                color='green'
            for e in e:
                ax.plot( efine, binwidth*norm.pdf( efine, loc=e, scale=eres.relativeSigma(e)*e ), linestyle='--', color='green' )

            sprev = smeared.copy()
            push_value(0.162)
            assert eres.smear.tainted()
            smeared = eres.smear.Nrec.data()
            assert not np.all(smeared==sprev)
            plot_hist( edges, smeared, label='modified', color='red', alpha=0.5)
            pop_value()

            ax.legend()

            path = os.path.join(str(tmp_path), 'eres_test_{:02d}.png'.format(i))
            savefig(path, density=300)
            allure_attach_file(path)

            plt.close()

    smeared = eres.smear.Nrec.data()

    ax = axes( 'Relative energy uncertainty', ylabel=L.u('eres_sigma_rel') )
    ax.set_ylim(0, 13.0)
    ax.set_xlim(0.5, 12.0)
    x = np.arange( 0.5, 12.0, 0.01 )
    fcn = np.frompyfunc( eres.relativeSigma, 1, 1 )
    y = fcn( x )

    ax.plot( x, y*100. )
    path = os.path.join(str(tmp_path), 'eres_sigma.png')
    savefig(path, density=300)
    allure_attach_file(path)
    plt.close()

    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( '' )
    ax.set_ylabel( '' )
    ax.set_title( 'Energy resolution convertsion matrix (class)' )

    mat = convert(eres.getDenseMatrix(), 'matrix')
    mat = np.ma.array( mat, mask= mat==0.0 )
    c = ax.matshow( mat, extent=[ edges[0], edges[-1], edges[-1], edges[0] ] )
    add_colorbar( c )

    path = os.path.join(str(tmp_path), 'eres_matc.png')
    savefig(path, density=300)
    allure_attach_file(path)
    plt.close()

    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( '' )
    ax.set_ylabel( '' )
    ax.set_title( 'Energy resolution convertsion matrix (trans)' )

    eres.matrix.FakeMatrix.plot_matshow(colorbar=True, mask=0.0, extent=[edges[0], edges[-1], edges[-1], edges[0]])

    path = os.path.join(str(tmp_path), 'eres_mat.png')
    savefig(path, density=300)
    allure_attach_file(path)
    plt.close()
Example #7
0
#
# Plot hists
#
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_title('Non-linearity effect')

smeared = smear.Nrec.data().copy()
print('Sum check for {} (diff): {}'.format(1.0, phist.sum() - smeared.sum()))

plot_hist(edges, phist, label='original')
lines = plot_hist(edges, smeared, label='smeared: nominal')

ax.legend(loc='upper right')
savefig(args.output)

#
# Plot matrix
#
fig = P.figure()
ax1 = P.subplot(111)
ax1.minorticks_on()
ax1.grid()
ax1.set_xlabel('Source bins')
ax1.set_ylabel('Target bins')
ax1.set_title('Daya Bay LSNL matrix')
Example #8
0
print(a.expressions_raw)
print(a.expressions)

a.parse()
a.guessname(lib, save=True)
a.tree.dump(True)
edges = np.linspace(0., 12, 240 + 1)

print()
cfg = NestedDict(snf=NestedDict(
    bundle="reactor_snf_spectra_v02",
    data_path=
    "data/reactor_anu_spectra/SNF/kopeikin_0412.044_spent_fuel_spectrum_smooth.dat",
    edges=edges))
context = ExpressionContext(cfg, ns=env.globalns)
a.build(context)

from gna.bindings import OutputDescriptor
env.globalns.printparameters(labels=True)
print('outputs:')
print(context.outputs)

if args.show:
    import matplotlib.pyplot as plt
    import mpl_tools.helpers as mplh

    snf = context.outputs['snf_ratio']
    bin_centers = (edges[1:] + edges[:-1]) / 2
    mplh.plot_hist(edges, snf.data())
    plt.show()
Example #9
0
rd = R.RenormalizeDiag(ndiag, int(opts.offdiag), int(opts.upper))
rd.renorm.inmat(pmat.points)

esmear = R.HistSmear(opts.upper)
esmear.smear.inputs.SmearMatrix(rd.renorm)
esmear.smear.inputs.Ntrue(hist.hist)

for i, value in enumerate([1.0, 0.5, 2.0]):
    par.set(value)
    smeared = esmear.smear.Nrec.data()
    print('Sum check for {} (diff): {}'.format(value,
                                               phist.sum() - smeared.sum()))

    # bars = P.bar( edges[:-1], phist, binwidth, align='edge' )
    P.sca(ax)
    lines = plot_hist(edges, smeared, label='%.2f' % value)
    color = lines[0].get_color()

    fig = P.figure()
    ax1 = P.subplot(111)
    ax1.minorticks_on()
    ax1.grid()
    ax1.set_xlabel('')
    ax1.set_ylabel('')
    ax1.set_title('Synthetic energy leak matrix (diag scale=%.2f, ndiag=%i)' %
                  (value, ndiag))

    mat = rd.renorm.outmat.data()
    mat = N.ma.array(mat, mask=mat == 0.0)
    c = ax1.matshow(mat, extent=[edges[0], edges[-1], edges[-1], edges[0]])
    add_colorbar(c)
Example #10
0
#
fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( r'$E_\nu$, MeV' )
ax.set_ylabel( 'entries' )
ax.set_title( 'IAV effect' )

smeared = smear.data().copy()
par.set( 2.0 )
smeared2 = smear.data().copy()
print( 'Sum check for {} (diff): {}'.format( 1.0, phist.sum()-smeared.sum() ) )
print( 'Sum check for {} (diff): {}'.format( 2.0, phist.sum()-smeared2.sum() ) )

lines = plot_hist( edges, smeared, label='nominal' )
lines = plot_hist( edges, smeared2, linewidth=1.0, label='diag scale $s=2$' )

ax.legend( loc='upper right' )

if args.xlim:
    ax.set_xlim( *args.xlim )

savefig( args.output )

fig = P.figure()
ax = P.subplot( 111 )
ax.minorticks_on()
ax.grid()
ax.set_xlabel( '' )
ax.set_ylabel( '' )
Example #11
0
def test_energyresolutioninput_v01(tmp_path):
    def axes( title, ylabel='' ):
        fig = plt.figure()
        ax = plt.subplot( 111 )
        ax.minorticks_on()
        ax.grid()
        ax.set_xlabel( L.u('evis') )
        ax.set_ylabel( ylabel )
        ax.set_title( title )
        return ax

    def singularities( values, edges ):
        indices = np.digitize( values, edges )-1
        phist = np.zeros( edges.size-1 )
        phist[indices] = 1.0
        return phist

    #
    # Define the parameters in the current namespace
    #
    wvals = [0.016, 0.081, 0.026]

    #
    # Define bin edges
    #
    binwidth=0.05
    edges = np.arange( 0.0, 12.0001, binwidth )
    efine = np.arange( edges[0], edges[-1]+1.e-5, 0.005 )
    centers = 0.5*(edges[1:]+edges[:-1])

    def RelSigma(e):
        a, b, c = wvals
        return (a**2+ (b**2)/e + (c/e)**2)**0.5
    relsigma = RelSigma(centers)

    for eset in [
        [ [1.025], [3.025], [6.025], [9.025] ],
        [ [ 1.025, 5.025, 9.025 ] ],
        [ [ 6.025, 7.025,  8.025, 8.825 ] ],
        ]:
        for i, e in enumerate(eset):
            ax = axes( 'Energy resolution (input) impact' )
            phist = singularities( e, edges )
            relsigma_i = relsigma.copy()
            relsigma_p = C.Points(relsigma_i)

            hist = C.Histogram( edges, phist )
            edges_o = R.HistEdges(hist)
            eres = C.EnergyResolutionInput(True)
            hist >> eres.matrix.Edges
            relsigma_p >> eres.matrix.RelSigma
            hist >> eres.smear.Ntrue

            path = os.path.join(str(tmp_path), 'eres_graph_%i.png'%i)
            savegraph(hist, path)
            allure_attach_file(path)

            smeared = eres.smear.Nrec.data()
            diff = phist.sum()-smeared.sum()
            print( 'Sum check for {} (diff): {}'.format( e, diff ) )
            assert diff<1.e-9

            lines = plot_hist( edges, smeared, label='default' )

            color = lines[0].get_color()
            ax.vlines( e, 0.0, smeared.max(), linestyle='--', color=color )

            if len(e)>1:
                color='green'
            for e in e:
                ax.plot( efine, binwidth*norm.pdf( efine, loc=e, scale=RelSigma(e)*e ), linestyle='--', color='green' )

            sprev = smeared.copy()

            icut = relsigma_i.size//2
            relsigma_i[icut:]*=2
            relsigma_p.set(relsigma_i, relsigma_i.size)
            smeared = eres.smear.Nrec.data()
            shouldchange = phist[icut:].any()
            assert not np.all(smeared==sprev)==shouldchange
            plot_hist( edges, smeared, label='modified', color='red', alpha=0.5)

            ax.legend()

            path = os.path.join(str(tmp_path), 'eres_test_{:02d}.png'.format(i))
            savefig(path, density=300)
            allure_attach_file(path)
            plt.close()

            relsigma_p.set(relsigma, relsigma.size)

    smeared = eres.smear.Nrec.data()

    ax = axes( 'Relative energy uncertainty', ylabel=L.u('eres_sigma_rel') )
    ax.set_xlim(0.5, 12.0)
    ax.set_ylim(0, 13.0)

    ax.plot( centers, relsigma*100. )
    path = os.path.join(str(tmp_path), 'eres_sigma.png')
    savefig(path, density=300)
    allure_attach_file(path)
    plt.close()

    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( '' )
    ax.set_ylabel( '' )
    ax.set_title( 'Energy resolution convertsion matrix (class)' )

    mat = convert(eres.getDenseMatrix(), 'matrix')
    mat = np.ma.array( mat, mask= mat==0.0 )
    c = ax.matshow( mat, extent=[ edges[0], edges[-1], edges[-1], edges[0] ] )
    add_colorbar( c )

    path = os.path.join(str(tmp_path), 'eres_matc.png')
    savefig(path, density=300)
    allure_attach_file(path)
    plt.close()

    fig = plt.figure()
    ax = plt.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( '' )
    ax.set_ylabel( '' )
    ax.set_title( 'Energy resolution convertsion matrix (trans)' )

    eres.matrix.FakeMatrix.plot_matshow(colorbar=True, mask=0.0, extent=[edges[0], edges[-1], edges[-1], edges[0]])

    path = os.path.join(str(tmp_path), 'eres_mat.png')
    savefig(path, density=300)
    allure_attach_file(path)
    plt.close()
Example #12
0
#
# Test bundle
#
nonlin1.smear.Ntrue(hist.hist)
nonlin2.smear.Ntrue(hist.hist)

#
# Plot hists
#
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_title('Non-linearity effect')

smeared1 = nonlin1.smear.Nrec.data().copy()
smeared2 = nonlin2.smear.Nrec.data().copy()
print('Sum check for {} (diff): {}'.format(1.0, phist.sum() - smeared1.sum()))
print('Sum check for {} (diff): {}'.format(1.0, phist.sum() - smeared2.sum()))

lines = plot_hist(edges, smeared1, label='Smeared 1')
lines = plot_hist(edges, smeared2, label='Smeared 2')
plot_hist(edges, phist, label='original')

ax.legend(loc='upper right')

P.show()
Example #13
0
    ax = axes('Energy resolution impact')
    for i, e in enumerate(eset):
        phist = singularities(e, edges)

        hist = C.Histogram(edges, phist)
        edges_o = R.HistEdges(hist)
        eres = R.EnergyResolution(True)
        eres.matrix.Edges(hist)
        eres.smear.Ntrue(hist)

        smeared = eres.smear.Nrec.data()
        print('Sum check for {} (diff): {}'.format(e,
                                                   phist.sum() -
                                                   smeared.sum()))

        lines = plot_hist(edges, smeared, label='default')

        color = lines[0].get_color()
        ax.vlines(e, 0.0, smeared.max(), linestyle='--', color=color)

        if len(e) > 1:
            color = 'green'
        for e in e:
            ax.plot(efine,
                    binwidth *
                    norm.pdf(efine, loc=e, scale=eres.relativeSigma(e) * e),
                    linestyle='--',
                    color=color)

        push_value(0.162)
        smeared = eres.smear.Nrec.data()
Example #14
0
nl.set(hist.hist, pedges_m.points)

nl.add_input(hist)

smeared = nl.smear.Nrec.data()
print('Sum check (diff): {}'.format(phist.sum() - smeared.sum()))

fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel(L.u('evis'))
ax.set_ylabel('Entries')
ax.set_title('Non-linearity correction')

lines = plot_hist(edges, smeared)
color = lines[0].get_color()
_, ev_m = nlfcn(ev)
heights = smeared[smeared > 0.45]
ax.vlines(ev, 0.0, heights, alpha=0.7, color='red', linestyle='--')
ax.vlines(ev_m, 0.0, heights, alpha=0.7, color='green', linestyle='--')

savefig(opts.output, suffix='_evis')

fig = P.figure()
ax1 = P.subplot(111)
ax1.minorticks_on()
ax1.grid()
ax1.set_xlabel('Source bins')
ax1.set_ylabel('Target bins')
ax1.set_title('Energy non-linearity matrix')
Example #15
0
for bundle in b.bundles.values()+[b, bfn]:
    bkgname=bundle.cfg.get('name', 'total')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'x axis' )
    ax.set_ylabel( 'entries' )
    ax.set_title(bkgname)

    for i, (name, output) in enumerate(bundle.outputs.items()):
        pack=None
        if opts.pack:
            if bkgname=='bkg2':
                group = bundle.groups.get_group(name, 'site')
                pack = (group.index(name), len(group))
            elif bkgname=='bkgw':
                pack = (i, len(bundle.outputs))

        if bkgname=='bkg_fn':
            plot_hist( output.datatype().edges, output.data(), label=name )
        else:
            plot_bar( output.datatype().edges, output.data(), label=name, pack=pack, alpha=0.8 )

    ax.legend( loc='upper right' )

P.show()


Example #16
0
File: iav_2.py Project: gnafit/gna
fig = P.figure()
ax = P.subplot(111)
ax.minorticks_on()
ax.grid()
ax.set_xlabel('')
ax.set_ylabel('')
ax.set_title('IAV effect')

smeared1 = smear1.Nrec.data()
smeared2 = smear2.Nrec.data()
smeared3 = smear3.Nrec.data()
print('Sum check for {} (diff): {}'.format(1.0, phist.sum() - smeared1.sum()))
print('Sum check for {} (diff): {}'.format(2.0, phist.sum() - smeared2.sum()))
print('Sum check for {} (diff): {}'.format(2.0, phist.sum() - smeared3.sum()))

lines = plot_hist(edges, smeared1, linewidth=1.0, label='IAV 1')
lines = plot_hist(edges, smeared2, linewidth=1.0, label='IAV 2')
lines = plot_hist(edges, smeared3, linewidth=1.0, label='IAV 3')

ax.legend(loc='upper right')

#
# Dump graph
#
if args.dot:
    try:
        from gna.graphviz import GNADot

        graph = GNADot(b.transformations_out.values()[0])
        graph.write(args.dot)
        print('Write output to:', args.dot)
Example #17
0
def main(opts):
    global savefig
    cfg = NestedDict(
        bundle = dict(
            name='energy_nonlinearity_birks_cherenkov',
            version='v01',
            nidx=[ ('r', 'reference', ['R1', 'R2']) ],
            major=[],
            ),
        stopping_power='stoppingpower.txt',
        annihilation_electrons=dict(
            file='input/hgamma2e.root',
            histogram='hgamma2e_1KeV',
            scale=1.0/50000 # event simulated
            ),
        pars = uncertaindict(
            [
                ('birks.Kb0',               (1.0, 'fixed')),
                ('birks.Kb1',           (15.2e-3, 0.1776)),
                # ('birks.Kb2',           (0.0, 'fixed')),
                ("cherenkov.E_0",         (0.165, 'fixed')),
                ("cherenkov.p0",  ( -7.26624e+00, 'fixed')),
                ("cherenkov.p1",   ( 1.72463e+01, 'fixed')),
                ("cherenkov.p2",  ( -2.18044e+01, 'fixed')),
                ("cherenkov.p3",   ( 1.44731e+01, 'fixed')),
                ("cherenkov.p4",   ( 3.22121e-02, 'fixed')),
                ("Npescint",            (1341.38, 0.0059)),
                ("kC",                      (0.5, 0.4737)),
                ("normalizationEnergy",   (2.505, 'fixed'))
                # ("normalizationEnergy",   (12.0, 'fixed'))
             ],
            mode='relative'
            ),
        integration_order = 2,
        correlations_pars = [ 'birks.Kb1', 'Npescint', 'kC' ],
        correlations = [ 1.0,   0.94, -0.97,
                         0.94,  1.0,  -0.985,
                        -0.97, -0.985, 1.0   ],
        fill_matrix=True,
        labels = dict(
            normalizationEnergy = '60Co total gamma energy, MeV'
            # normalizationEnergy = 'Pessimistic norm point'
            ),
        )

    ns = env.globalns('energy')
    quench = execute_bundle(cfg, namespace=ns)
    ns.printparameters(labels=True)
    print()
    normE = ns['normalizationEnergy'].value()

    #
    # Input bins
    #
    evis_edges_full_input = N.arange(0.0, 12.0+1.e-6, 0.025)
    evis_edges_full_hist = C.Histogram(evis_edges_full_input, labels='Evis bin edges')
    evis_edges_full_hist >> quench.context.inputs.evis_edges_hist['00']

    #
    # HistNonLinearity transformation
    #
    reference_histogram1_input = N.zeros(evis_edges_full_input.size-1)
    reference_histogram2_input = reference_histogram1_input.copy()
    reference_histogram1_input+=1.0
    # reference_histogram2_input[[10, 20, 50, 100, 200, 300, 400]]=1.0
    reference_histogram2_input[[10, 20]]=1.0
    reference_histogram1 = C.Histogram(evis_edges_full_input, reference_histogram1_input, labels='Reference hist 1')
    reference_histogram2 = C.Histogram(evis_edges_full_input, reference_histogram2_input, labels='Reference hist 2')

    reference_histogram1 >> quench.context.inputs.lsnl.R1.values()
    reference_histogram2 >> quench.context.inputs.lsnl.R2.values()
    reference_smeared1 = quench.context.outputs.lsnl.R1
    reference_smeared2 = quench.context.outputs.lsnl.R2

    #
    # Plots and tests
    #
    if opts.output and opts.output.endswith('.pdf'):
        pdfpages = PdfPages(opts.output)
        pdfpagesfilename=opts.output
        savefig_old=savefig
        pdf=pdfpages.__enter__()
        def savefig(*args, **kwargs):
            if opts.individual and args and args[0]:
                savefig_old(*args, **kwargs)
            pdf.savefig()
    else:
        pdf = None
        pdfpagesfilename = ''

    #
    # Plots and tests
    #
    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'Edep, MeV' )
    ax.set_ylabel( 'dE/dx' )
    ax.set_title( 'Stopping power' )

    quench.birks_quenching_p.points.points.plot_vs(quench.birks_e_p.points.points, '-', markerfacecolor='none', markersize=2.0, label='input')
    ax.legend(loc='upper right')
    savefig(opts.output, suffix='_spower')

    ax.set_xlim(left=0.001)
    ax.set_xscale('log')
    savefig(opts.output, suffix='_spower_log')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'Edep, MeV' )
    ax.set_ylabel( '' )
    ax.set_title( "Birk's integrand" )

    quench.birks_integrand_raw.polyratio.ratio.plot_vs(quench.birks_e_p.points.points, '-o', alpha=0.5, markerfacecolor='none', markersize=2.0, label='raw')
    # birks_integrand_interpolated.plot_vs(integrator_ekin.points.x,   '-', alpha=0.5, markerfacecolor='none', markersize=2.0, label='interpolated')
    lines=quench.birks_integrand_interpolated.plot_vs(quench.integrator_ekin.points.x,   '-', alpha=0.5, markerfacecolor='none', markersize=2.0, label='interpolated')
    quench.birks_integrand_interpolated.plot_vs(quench.integrator_ekin.points.x, 'o', alpha=0.5, color='black', markersize=0.6)
    ax.legend(loc='lower right')

    savefig()

    ax.set_xlim(left=0.0001)
    ax.set_ylim(bottom=0.3)
    # ax.set_yscale('log')
    ax.set_xscale('log')
    savefig(opts.output, suffix='_spower_int')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'Edep, MeV' )
    ax.set_ylabel( '' )
    ax.set_title( "Birk's integral (bin by bin)" )

    quench.birks_integral.plot_hist()

    savefig()

    ax.set_xlim(left=0.0001)
    ax.set_ylim(bottom=0.0)
    # ax.set_yscale('log')
    ax.set_xscale('log')
    ax.set_ylim(auto=True)
    savefig(opts.output, suffix='_spower_intc')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'Edep, MeV' )
    ax.set_ylabel( 'Evis, MeV' )
    ax.set_title( 'Electron energy (Birks)' )
    #  birks_accumulator.reduction.out.plot_vs(integrator_evis.transformations.hist, '-', markerfacecolor='none', markersize=2.0, label='partial sum')
    quench.birks_accumulator.reduction.plot_vs(quench.histoffset.histedges.points_offset)
    ax.plot([0.0, 12.0], [0.0, 12.0], '--', alpha=0.5)

    savefig(opts.output, suffix='_birks_evis')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'Edep, MeV' )
    ax.set_ylabel( 'Evis/Edep' )
    ax.set_title( 'Electron energy (Birks)' )
    #  birks_accumulator.reduction.out.plot_vs(integrator_evis.transformations.hist, '-', markerfacecolor='none', markersize=2.0, label='partial sum')
    quench.histoffset.histedges.points_offset.vs_plot(quench.birks_accumulator.reduction.data()/quench.histoffset.histedges.points_offset.data())
    ax.set_ylim(0.40, 1.0)

    savefig(opts.output, suffix='_birks_evis_rel')

    ax.set_xlim(1.e-3, 2.0)
    ax.set_xscale('log')
    savefig()

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'E, MeV' )
    ax.set_ylabel( 'Npe' )
    ax.set_title( 'Cherenkov photons' )
    quench.cherenkov.cherenkov.ch_npe.plot_vs(quench.histoffset.histedges.points_offset)

    savefig(opts.output, suffix='_cherenkov_npe')

    ax.set_ylim(bottom=0.1)
    ax.set_yscale('log')
    savefig()

    ax.set_xlim(0.0, 2.0)
    # ax.set_ylim(0.0, 200.0)
    savefig()

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'E, MeV' )
    ax.set_ylabel( 'Npe' )
    ax.set_title( 'Electron model' )
    quench.electron_model.single().plot_vs(quench.histoffset.histedges.points_offset)

    savefig(opts.output, suffix='_electron')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'E, MeV' )
    ax.set_ylabel( 'Npe' )
    ax.set_title( 'Electron model (low energy view)' )
    annihilation_gamma_evis = quench.npe_positron_offset.normconvolution.result.data()[0]
    label = 'Annihilation contribution=%.2f Npe'%annihilation_gamma_evis
    quench.electron_model_lowe.plot_vs(quench.ekin_edges_lowe, 'o', markerfacecolor='none', label='data')
    quench.electron_model_lowe_interpolated.single().plot_vs(quench.annihilation_electrons_centers.single(), '-', label='interpolation\n%s'%label)

    ax.legend(loc='upper left')

    savefig(opts.output, suffix='_electron_lowe')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'E, MeV' )
    ax.set_ylabel( 'Npe' )
    ax.set_title( 'Total Npe' )
    quench.positron_model.sum.outputs[0].plot_vs(quench.histoffset.histedges.points_truncated)

    savefig(opts.output, suffix='_total_npe')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'Edep, MeV' )
    ax.set_ylabel( 'Evis, MeV' )
    ax.set_title( 'Positron energy model' )
    quench.positron_model_scaled.plot_vs(quench.histoffset.histedges.points_truncated, label='definition range')
    quench.positron_model_scaled_full.plot_vs(quench.histoffset.histedges.points, '--', linewidth=1., label='full range', zorder=0.5)
    ax.vlines(normE, 0.0, normE, linestyle=':')
    ax.hlines(normE, 0.0, normE, linestyle=':')
    ax.legend(loc='upper left')

    savefig(opts.output, suffix='_total')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'Edep, MeV' )
    ax.set_ylabel( 'Evis/Edep' )
    ax.set_title( 'Positron energy nonlineairty' )
    quench.positron_model_relative.single().plot_vs(quench.histoffset.histedges.points_truncated, label='definition range')
    quench.positron_model_relative_full.plot_vs(quench.histoffset.histedges.points, '--', linewidth=1., label='full range', zorder=0.5)
    ax.vlines(normE, 0.0, 1.0, linestyle=':')

    ax.legend(loc='lower right')
    ax.set_ylim(0.85, 1.1)

    savefig(opts.output, suffix='_total_relative')

    ax.set_xlim(0.75, 3)
    savefig(opts.output, suffix='_total_relative1')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel('Etrue, MeV')
    ax.set_ylabel('Edep, MeV')
    ax.set_title( 'Smearing matrix' )

    quench.pm_histsmear.matrix.FakeMatrix.plot_matshow(mask=0.0, extent=[evis_edges_full_input.min(), evis_edges_full_input.max(), evis_edges_full_input.max(), evis_edges_full_input.min()], colorbar=True)
    ax.plot([0.0, 12.0], [0.0, 12.0], '--', alpha=0.5, linewidth=1.0, color='magenta')
    ax.vlines(normE, 0.0, normE, linestyle=':')
    ax.hlines(normE, 0.0, normE, linestyle=':')

    savefig(opts.output, suffix='_matrix')

    ax.set_xlim(0.8, 3.0)
    ax.set_ylim(3.0, 0.8)
    savefig(opts.output, suffix='_matrix_zoom')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'E, MeV' )
    ax.set_ylabel( '' )
    ax.set_title( 'Reference histogram 1' )

    reference_histogram1.single().plot_hist(linewidth=0.5, alpha=1.0, label='original')
    reference_smeared1.single().plot_hist(  linewidth=0.5, alpha=1.0, label='smeared')

    ax.legend(loc='upper right')

    savefig(opts.output, suffix='_refsmear1')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'E, MeV' )
    ax.set_ylabel( '' )
    ax.set_title( 'Matrix projections' )

    mat = quench.pm_histsmear.matrix.FakeMatrix.data()
    proj0 = mat.sum(axis=0)
    proj1 = mat.sum(axis=1)

    plot_hist(evis_edges_full_input, proj0, alpha=0.7, linewidth=1.0, label='Projection 0: Edep view')
    plot_hist(evis_edges_full_input, proj1, alpha=0.7, linewidth=1.0, label='Projection 1: Evis')

    ax.legend(loc='upper right')

    savefig(opts.output, suffix='_matrix_projections')

    if opts.mapping:
        fig = P.figure()
        ax = P.subplot( 111 )
        ax.minorticks_on()
        ax.grid()
        ax.set_xlabel( 'E, MeV' )
        ax.set_ylabel( '' )
        ax.set_title( 'Mapping' )

        positron_model_scaled_data = quench.positron_model_scaled.single().data()
        for e1, e2 in zip(quench.histoffset.histedges.points_truncated.data(), positron_model_scaled_data):
            if e2>12.0 or e2<1.022:
                alpha = 0.05
            else:
                alpha = 0.7
            ax.plot( [e1, e2], [1.0, 0.0], '-', linewidth=2.0, alpha=alpha )
        ax.axvline(1.022, linestyle='--', linewidth=1.0)
        ax.axvline(12.0, linestyle='--', linewidth=1.0)

        fig = P.figure()
        ax = P.subplot( 111 )
        ax.minorticks_on()
        # ax.grid()
        ax.set_xlabel( 'E, MeV' )
        ax.set_ylabel( '' )
        ax.set_title( 'Mapping' )

        positron_model_scaled_data = quench.positron_model_scaled.single().data()
        for e1, e2 in zip(quench.histoffset.histedges.points_truncated.data(), positron_model_scaled_data):
            if e2>12.0 or e2<1.022:
                alpha = 0.05
            else:
                alpha = 0.7
            ax.plot( [e1, e2], [1.1, 0.9], '-', linewidth=2.0, alpha=alpha )

        for e1 in quench.histoffset.histedges.points.data():
            ax.axvline(e1, linestyle=':', linewidth=1.0, color='black')

        ax.axvline(1.022, linestyle='--', linewidth=1.0)
        ax.axvline(12.0, linestyle='--', linewidth=1.0)

        # ax.legend(loc='upper right')

        savefig(opts.output, suffix='_mapping_bins')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'E, MeV' )
    ax.set_ylabel( '' )
    ax.set_title( 'Reference histogram 2' )

    reference_histogram2.single().plot_hist(linewidth=0.5, alpha=1.0, label='original')
    reference_smeared2.single().plot_hist(  linewidth=0.5, alpha=1.0, label='smeared')
    ax.vlines(normE, 0.0, 1.0, linestyle=':')

    ax.legend(loc='upper right')

    savefig(opts.output, suffix='_refsmear2')

    fig = P.figure()
    ax = P.subplot( 111 )
    ax.minorticks_on()
    ax.grid()
    ax.set_xlabel( 'E, MeV' )
    ax.set_ylabel( 'Entries' )
    ax.set_title( 'Annihilation gamma electrons' )

    plot_hist(quench.annihilation_electrons_edges_input, quench.annihilation_electrons_p_input)
    ax.set_yscale('log')
    savefig(opts.output, suffix='_annihilation_electrons')

    ax.set_yscale('linear')
    ax.set_xlim(0.0, 0.1)
    savefig(opts.output, suffix='_annihilation_electrons_lin')

    if pdfpages:
        pdfpages.__exit__(None,None,None)
        print('Write output figure to', pdfpagesfilename)

    savegraph(quench.histoffset.histedges.points_truncated, opts.graph, namespace=ns)

    if opts.show:
        P.show()