コード例 #1
0
def get_ft_2d(res, obs_key, tmn, tmx):
    if obs_key in ['s']:
        dat = res[obs_key][tmn:tmx, ::]
    if obs_key in ['z', 'x', 'y']:
        dat = ms.get_diag_vecs(res[(obs_key + obs_key)][::])[tmn:tmx, ::]
    ws, ks, amps, iboard, board = make_ft_2d(dat)
    return ws, ks, amps, iboard, board
コード例 #2
0
def get_ft_1d(res, obs_key):
    if obs_key in ['ND', 'CC', 'Y']:
        dat = res[obs_key][::]
    if obs_key in ['s']:
        dat = res[obs_key][::]
        dat = np.mean(dat, axis=1)
    if obs_key in ['z', 'x']:
        dat = ms.get_diag_vecs(res[(obs_key + obs_key)][::])
        dat = np.mean(dat, axis=1)
    freqs, FT, rn = make_ft_1d(dat)
    return freqs, FT, rn
コード例 #3
0
ファイル: fit_grids.py プロジェクト: sbeleidy/aqc_research
    params_list_list = io.make_params_list_list(fixed_params_dict, var_params_dict)

    for params_list in params_list_list:
        for params in params_list:
            res = h5py.File(io.default_file_name(params, 'data', '.hdf5'))
            mode = params['mode']
            S = params['S']
            V = params['V']
            T = params['T']
            L = params['L']
            th = get_th(V)

            n = 2
            sds_frac = 0.6

            grid = ms.get_diag_vecs(res['zz'][::])
            grid = 0.5 * (1.0 - grid)

            impact_t = np.argmax(grid[:,L-1])

            M1_0, M1_1, M2_0, M2_1, intersection = \
                    fit_gmix(grid, sds_frac=sds_frac, n=n)

            im = plt.imshow(grid, origin='lower', aspect=1, interpolation='none')

            #plt.errorbar(M1_0, range(T+1), xerr=M2_0, c='k', lw=1.5)

            plt.errorbar(M1_1, range(T+1), xerr=M2_1, c='r', lw=1.5)

            Bs, chi2, Bs_sd = ft.f_fits(ft.flin, [1.0, 0.0], range(4, cross_t),
                    M1_0[4:cross_t], xerr=M2_0[4:cross_t])
コード例 #4
0
def transport_calc(params,
                   tmax_1,
                   span=[0, 61],
                   n=2,
                   tmin=2,
                   sds_frac=0.6,
                   speed_f=ft.flin,
                   speed_Bs_est=[1.0, 0.0],
                   diff_f=ft.flin,
                   diff_Bs_est=[1.0, 0.0],
                   dirr='R-L',
                   g_fignum=1000):

    transport = {}
    res = h5py.File(io.default_file_name(params, 'data', '.hdf5'))
    print(io.default_file_name(params, 'data', '.hdf5'))
    print([key for key in res.keys()])
    exp = ms.get_diag_vecs(res['zz'][::])
    Ptj = ((1.0 - exp) / 2.0)[span[0]:span[1]]

    if dirr == 'L-R':
        reflect_t = np.argmax(Ptj[:, params['L'] - 1])
    if dirr == 'R-L':
        reflect_t = np.argmax(Ptj[:, 0])

    transport['grid'] = Ptj

    M1_0, M1_1, M2_0, M2_1, intersection = fit_gmix(Ptj,
                                                    params,
                                                    n=n,
                                                    sds_frac=sds_frac,
                                                    g_fignum=g_fignum)

    keys = ['Bs', 'chi2', 'dBs']
    for moment in [1, 2]:
        for peak in [0, 1]:
            moment_name = 'M' + str(moment) + '_' + str(peak)
            mom = eval(moment_name)
            tmax = reflect_t

            if moment == 2:
                tmin = 1
                tmax = np.argmax(M2_0[0:params['L'] - 2])

            if moment == 1 and peak == 1:
                if tmax_1 is None:
                    tmax_1 = tmax
                transport['tmax_1'] = tmax_1
                tmax = tmax_1

            if moment == 1 and peak == 0:
                transport['intersection'] = intersection[tmin:tmax]

            times = np.array(range(tmin, tmax))

            Bs_chi2_dBs = \
                list(ft.f_fits(speed_f, speed_Bs_est, times, mom[tmin:tmax]))
            transport[moment_name] = eval(moment_name)
            transport['times_' + moment_name] = times
            for j, key in enumerate(keys):
                transport_key = '_'.join([moment_name, key])
                transport[transport_key] = Bs_chi2_dBs[j]
    return transport
コード例 #5
0
ファイル: plotting.py プロジェクト: sbeleidy/aqc_research
def plot(params, corrj=None):
    print('Plotting results...')
    results = h5py.File(params['fname'], 'r+')

    # get spin projections along x, y, and z
    x_grid, y_grid, z_grid = [
        measures.get_diag_vecs(results[ab][::]) for ab in ('xx', 'yy', 'zz')
    ]

    proj_grids_stats = results['stats']

    # get g2 correlators at constant row j
    if corrj is None:
        corrj = results['gstats']['corrj'][0]
    x_g2grid, y_g2grid, z_g2grid = [
        measures.get_row_vecs(results[ab][::], j=corrj)
        for ab in ['gxx', 'gyy', 'gzz']
    ]

    g2grids_stats = results['gstats']

    # get mi measure results and place in ordered dict for plotting
    meas_keys = ['ND', 'CC', 'Y']
    meas_list = [results[meas_key][::] for meas_key in meas_keys]
    Fmeas_list = [{
        'amps': results['F' + meas_key][::],
        'RN': results['RN' + meas_key][::]
    } for meas_key in meas_keys]
    meas_dict = OrderedDict(
        (key, data) for key, data in zip(meas_keys, meas_list))
    Fmeas_dict = OrderedDict(
        (key, Fdata) for key, Fdata in zip(meas_keys, Fmeas_list))

    # get local and entropies
    stj = results['s'][::]

    # get mutual information adjacency matrices
    mtjk = results['m'][::]

    # plot spin projections
    proj_titles = [
        r'$\langle \sigma^x_j \rangle$', r'$\langle \sigma^y_j \rangle$',
        r'$\langle \sigma^z_j \rangle$'
    ]

    prob_titles = [
        r'$\frac{\sum_j j P_j(x-)}{\sum_j P_j(x-)}$',
        r'$\frac{\sum_j j P_j(y-)}{\sum_j P_j(y-)}$',
        r'$\frac{\sum_j j P_j(z-)}{\sum_j P_j(z-)}$'
    ]

    plot_grids([x_grid, y_grid, z_grid],
               fignum=0,
               titles=proj_titles,
               suptitle='Spin Projections',
               xlabels=['site', 'site', 'site'],
               wspace=.05)

    plot_grid_time_avg_stats(proj_grids_stats, fignum=1, titles=proj_titles)

    # this makes three figures
    #plot_grid_center_stats(proj_grids_stats, fignum=2, titles=prob_titles)

    # plot two-point correlator w.r.t site corrj
    g2_titles = [
        '$g_2(\sigma^x_{%i},\sigma^x_k;t)$' % corrj,
        '$g_2(\sigma^y_{%i},\sigma^y_k;t)$' % corrj,
        '$g_2(\sigma^z_{%i},\sigma^z_k;t)$' % corrj
    ]

    plot_grids([x_g2grid, y_g2grid, z_g2grid],
               fignum=5,
               titles=g2_titles,
               suptitle='Two Point Correlator',
               xlabels=['site', 'site', 'site'],
               wspace=0.05)

    plot_grid_time_avg_stats(g2grids_stats, fignum=6, titles=g2_titles)

    #plot_grid_space_center_stats(g2grids_stats, fignum=7, titles=g2_titles)

    # plot local and bond entropies
    entropies = [stj]
    if 'sbond' in results:
        stc = results['sbond'][::]
        entropies.append(stc)

    if len(entropies) == 2:
        wspace = 0.088
    elif len(entropies) == 1:
        wspace = -0.23

    plot_grids(entropies,
               titles=[r'$S(j,t)$', r'$S_c(j,t)$'],
               xlabels=['site', 'cut'],
               suptitle='von Neumann entropies',
               wspace=wspace,
               fignum=10)

    # plot probabilities of spin down and space/time averages
    plot_grid_with_avgs(z_grid,
                        fignum=11,
                        suptitle='average probability of measuring 1')

    # plot mi measures
    plot_measures(meas_dict, fignum=12)

    # plot measure Fourier transforms
    plot_measure_fts(Fmeas_dict, fignum=13)

    # plot distribution of mutual information over time
    #plot_edge_strength_contour(mtjk,
    #        bins=60, rng=(0,.1), emax=150, fignum=14)

    # create the full path to where plots will be saved
    fname = params['fname']
    io.base_name(params['output_dir'], 'plots')
    path_list = fname.split('/')
    sub_dir_ind = path_list.index('data')
    path_list[sub_dir_ind] = 'plots'
    path_ext_list = '/'.join(path_list).split('.')
    path_ext_list[-1] = '.pdf'
    out_fname = ''.join(path_ext_list)

    # save all figures to one pdf
    io.multipage(out_fname)
    results.close()
    plt.close('all')
    return out_fname