# pyerrors.input.misc

View Source

      1import os
      2import fnmatch
      3import re
      4import struct
      5import numpy as np  # Thinly-wrapped numpy
      6import matplotlib.pyplot as plt
      7from matplotlib import gridspec
      8from ..obs import Obs
      9from ..fits import fit_lin
     10
     11
     12def fit_t0(t2E_dict, fit_range, plot_fit=False):
     13    zero_crossing = np.argmax(np.array(
     14        [o.value for o in t2E_dict.values()]) > 0.0)
     15
     16    x = list(t2E_dict.keys())[zero_crossing - fit_range:
     17                              zero_crossing + fit_range]
     18    y = list(t2E_dict.values())[zero_crossing - fit_range:
     19                                zero_crossing + fit_range]
     20    [o.gamma_method() for o in y]
     21
     22    fit_result = fit_lin(x, y)
     23
     24    if plot_fit is True:
     25        plt.figure()
     26        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
     27        ax0 = plt.subplot(gs[0])
     28        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
     29        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
     30        [o.gamma_method() for o in ymore]
     31        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
     32        xplot = np.linspace(np.min(x), np.max(x))
     33        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
     34        [yi.gamma_method() for yi in yplot]
     35        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
     36        retval = (-fit_result[0] / fit_result[1])
     37        retval.gamma_method()
     38        ylim = ax0.get_ylim()
     39        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
     40        ax0.set_ylim(ylim)
     41        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
     42        xlim = ax0.get_xlim()
     43
     44        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
     45        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
     46        ax1 = plt.subplot(gs[1])
     47        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
     48        ax1.tick_params(direction='out')
     49        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
     50        ax1.axhline(y=0.0, ls='--', color='k')
     51        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
     52        ax1.set_xlim(xlim)
     53        ax1.set_ylabel('Residuals')
     54        ax1.set_xlabel(r'$t/a^2$')
     55
     56        plt.draw()
     57    return -fit_result[0] / fit_result[1]
     58
     59
     60def read_pbp(path, prefix, **kwargs):
     61    """Read pbp format from given folder structure.
     62
     63    Parameters
     64    ----------
     65    r_start : list
     66        list which contains the first config to be read for each replicum
     67    r_stop : list
     68        list which contains the last config to be read for each replicum
     69
     70    Returns
     71    -------
     72    result : list[Obs]
     73        list of observables read
     74    """
     75
     76    ls = []
     77    for (dirpath, dirnames, filenames) in os.walk(path):
     78        ls.extend(filenames)
     79        break
     80
     81    if not ls:
     82        raise Exception('Error, directory not found')
     83
     84    # Exclude files with different names
     85    for exc in ls:
     86        if not fnmatch.fnmatch(exc, prefix + '*.dat'):
     87            ls = list(set(ls) - set([exc]))
     88    if len(ls) > 1:
     89        ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
     90    replica = len(ls)
     91
     92    if 'r_start' in kwargs:
     93        r_start = kwargs.get('r_start')
     94        if len(r_start) != replica:
     95            raise Exception('r_start does not match number of replicas')
     96        # Adjust Configuration numbering to python index
     97        r_start = [o - 1 if o else None for o in r_start]
     98    else:
     99        r_start = [None] * replica
    100
    101    if 'r_stop' in kwargs:
    102        r_stop = kwargs.get('r_stop')
    103        if len(r_stop) != replica:
    104            raise Exception('r_stop does not match number of replicas')
    105    else:
    106        r_stop = [None] * replica
    107
    108    print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='')
    109
    110    print_err = 0
    111    if 'print_err' in kwargs:
    112        print_err = 1
    113        print()
    114
    115    deltas = []
    116
    117    for rep in range(replica):
    118        tmp_array = []
    119        with open(path + '/' + ls[rep], 'rb') as fp:
    120
    121            t = fp.read(4)  # number of reweighting factors
    122            if rep == 0:
    123                nrw = struct.unpack('i', t)[0]
    124                for k in range(nrw):
    125                    deltas.append([])
    126            else:
    127                if nrw != struct.unpack('i', t)[0]:
    128                    raise Exception('Error: different number of factors for replicum', rep)
    129
    130            for k in range(nrw):
    131                tmp_array.append([])
    132
    133            # This block is necessary for openQCD1.6 ms1 files
    134            nfct = []
    135            for i in range(nrw):
    136                t = fp.read(4)
    137                nfct.append(struct.unpack('i', t)[0])
    138            print('nfct: ', nfct)  # Hasenbusch factor, 1 for rat reweighting
    139
    140            nsrc = []
    141            for i in range(nrw):
    142                t = fp.read(4)
    143                nsrc.append(struct.unpack('i', t)[0])
    144
    145            # body
    146            while True:
    147                t = fp.read(4)
    148                if len(t) < 4:
    149                    break
    150                if print_err:
    151                    config_no = struct.unpack('i', t)
    152                for i in range(nrw):
    153                    tmp_nfct = 1.0
    154                    for j in range(nfct[i]):
    155                        t = fp.read(8 * nsrc[i])
    156                        t = fp.read(8 * nsrc[i])
    157                        tmp_rw = struct.unpack('d' * nsrc[i], t)
    158                        tmp_nfct *= np.mean(np.asarray(tmp_rw))
    159                        if print_err:
    160                            print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw)))
    161                            print('Sources:', np.asarray(tmp_rw))
    162                            print('Partial factor:', tmp_nfct)
    163                    tmp_array[i].append(tmp_nfct)
    164
    165            for k in range(nrw):
    166                deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
    167
    168    rep_names = []
    169    for entry in ls:
    170        truncated_entry = entry.split('.')[0]
    171        idx = truncated_entry.index('r')
    172        rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
    173    print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources')
    174    result = []
    175    for t in range(nrw):
    176        result.append(Obs(deltas[t], rep_names))
    177
    178    return result

def fit_t0(t2E_dict, fit_range, plot_fit=False): View Source

    13def fit_t0(t2E_dict, fit_range, plot_fit=False):
    14    zero_crossing = np.argmax(np.array(
    15        [o.value for o in t2E_dict.values()]) > 0.0)
    16
    17    x = list(t2E_dict.keys())[zero_crossing - fit_range:
    18                              zero_crossing + fit_range]
    19    y = list(t2E_dict.values())[zero_crossing - fit_range:
    20                                zero_crossing + fit_range]
    21    [o.gamma_method() for o in y]
    22
    23    fit_result = fit_lin(x, y)
    24
    25    if plot_fit is True:
    26        plt.figure()
    27        gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
    28        ax0 = plt.subplot(gs[0])
    29        xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
    30        ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
    31        [o.gamma_method() for o in ymore]
    32        ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
    33        xplot = np.linspace(np.min(x), np.max(x))
    34        yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
    35        [yi.gamma_method() for yi in yplot]
    36        ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
    37        retval = (-fit_result[0] / fit_result[1])
    38        retval.gamma_method()
    39        ylim = ax0.get_ylim()
    40        ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
    41        ax0.set_ylim(ylim)
    42        ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
    43        xlim = ax0.get_xlim()
    44
    45        fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
    46        residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
    47        ax1 = plt.subplot(gs[1])
    48        ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
    49        ax1.tick_params(direction='out')
    50        ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
    51        ax1.axhline(y=0.0, ls='--', color='k')
    52        ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
    53        ax1.set_xlim(xlim)
    54        ax1.set_ylabel('Residuals')
    55        ax1.set_xlabel(r'$t/a^2$')
    56
    57        plt.draw()
    58    return -fit_result[0] / fit_result[1]

def read_pbp(path, prefix, **kwargs): View Source

     61def read_pbp(path, prefix, **kwargs):
     62    """Read pbp format from given folder structure.
     63
     64    Parameters
     65    ----------
     66    r_start : list
     67        list which contains the first config to be read for each replicum
     68    r_stop : list
     69        list which contains the last config to be read for each replicum
     70
     71    Returns
     72    -------
     73    result : list[Obs]
     74        list of observables read
     75    """
     76
     77    ls = []
     78    for (dirpath, dirnames, filenames) in os.walk(path):
     79        ls.extend(filenames)
     80        break
     81
     82    if not ls:
     83        raise Exception('Error, directory not found')
     84
     85    # Exclude files with different names
     86    for exc in ls:
     87        if not fnmatch.fnmatch(exc, prefix + '*.dat'):
     88            ls = list(set(ls) - set([exc]))
     89    if len(ls) > 1:
     90        ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
     91    replica = len(ls)
     92
     93    if 'r_start' in kwargs:
     94        r_start = kwargs.get('r_start')
     95        if len(r_start) != replica:
     96            raise Exception('r_start does not match number of replicas')
     97        # Adjust Configuration numbering to python index
     98        r_start = [o - 1 if o else None for o in r_start]
     99    else:
    100        r_start = [None] * replica
    101
    102    if 'r_stop' in kwargs:
    103        r_stop = kwargs.get('r_stop')
    104        if len(r_stop) != replica:
    105            raise Exception('r_stop does not match number of replicas')
    106    else:
    107        r_stop = [None] * replica
    108
    109    print(r'Read <bar{psi}\psi> from', prefix[:-1], ',', replica, 'replica', end='')
    110
    111    print_err = 0
    112    if 'print_err' in kwargs:
    113        print_err = 1
    114        print()
    115
    116    deltas = []
    117
    118    for rep in range(replica):
    119        tmp_array = []
    120        with open(path + '/' + ls[rep], 'rb') as fp:
    121
    122            t = fp.read(4)  # number of reweighting factors
    123            if rep == 0:
    124                nrw = struct.unpack('i', t)[0]
    125                for k in range(nrw):
    126                    deltas.append([])
    127            else:
    128                if nrw != struct.unpack('i', t)[0]:
    129                    raise Exception('Error: different number of factors for replicum', rep)
    130
    131            for k in range(nrw):
    132                tmp_array.append([])
    133
    134            # This block is necessary for openQCD1.6 ms1 files
    135            nfct = []
    136            for i in range(nrw):
    137                t = fp.read(4)
    138                nfct.append(struct.unpack('i', t)[0])
    139            print('nfct: ', nfct)  # Hasenbusch factor, 1 for rat reweighting
    140
    141            nsrc = []
    142            for i in range(nrw):
    143                t = fp.read(4)
    144                nsrc.append(struct.unpack('i', t)[0])
    145
    146            # body
    147            while True:
    148                t = fp.read(4)
    149                if len(t) < 4:
    150                    break
    151                if print_err:
    152                    config_no = struct.unpack('i', t)
    153                for i in range(nrw):
    154                    tmp_nfct = 1.0
    155                    for j in range(nfct[i]):
    156                        t = fp.read(8 * nsrc[i])
    157                        t = fp.read(8 * nsrc[i])
    158                        tmp_rw = struct.unpack('d' * nsrc[i], t)
    159                        tmp_nfct *= np.mean(np.asarray(tmp_rw))
    160                        if print_err:
    161                            print(config_no, i, j, np.mean(np.asarray(tmp_rw)), np.std(np.asarray(tmp_rw)))
    162                            print('Sources:', np.asarray(tmp_rw))
    163                            print('Partial factor:', tmp_nfct)
    164                    tmp_array[i].append(tmp_nfct)
    165
    166            for k in range(nrw):
    167                deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
    168
    169    rep_names = []
    170    for entry in ls:
    171        truncated_entry = entry.split('.')[0]
    172        idx = truncated_entry.index('r')
    173        rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
    174    print(',', nrw, r'<bar{psi}\psi> with', nsrc, 'sources')
    175    result = []
    176    for t in range(nrw):
    177        result.append(Obs(deltas[t], rep_names))
    178
    179    return result

Read pbp format from given folder structure.

###### Parameters

- r_start (list): list which contains the first config to be read for each replicum
- r_stop (list): list which contains the last config to be read for each replicum

###### Returns

- result (list[Obs]): list of observables read