# pyerrors.input.openQCD

View Source

    #!/usr/bin/env python
    # coding: utf-8

    import os
    import fnmatch
    import re
    import struct
    import numpy as np  # Thinly-wrapped numpy
    from ..obs import Obs
    from ..fits import fit_lin

    def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
        """Read rwms format from given folder structure. Returns a list of length nrw

        Parameters
        ----------
        version : str
            version of openQCD, default 2.0
        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
        postfix : str
            postfix of the file to read, e.g. '.ms1' for openQCD-files
        """
        known_oqcd_versions = ['1.4', '1.6', '2.0']
        if not (version in known_oqcd_versions):
            raise Exception('Unknown openQCD version defined!')
        print("Working with openQCD version " + version)
        if 'postfix' in kwargs:
            postfix = kwargs.get('postfix')
        else:
            postfix = ''
        ls = []
        for (dirpath, dirnames, filenames) in os.walk(path):
            ls.extend(filenames)
            break

        if not ls:
            raise Exception('Error, directory not found')

        # Exclude files with different names
        for exc in ls:
            if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'):
                ls = list(set(ls) - set([exc]))
        if len(ls) > 1:
            ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
        replica = len(ls)

        if 'r_start' in kwargs:
            r_start = kwargs.get('r_start')
            if len(r_start) != replica:
                raise Exception('r_start does not match number of replicas')
            # Adjust Configuration numbering to python index
            r_start = [o - 1 if o else None for o in r_start]
        else:
            r_start = [None] * replica

        if 'r_stop' in kwargs:
            r_stop = kwargs.get('r_stop')
            if len(r_stop) != replica:
                raise Exception('r_stop does not match number of replicas')
        else:
            r_stop = [None] * replica

        print('Read reweighting factors from', prefix[:-1], ',', replica, 'replica', end='')

        # Adjust replica names to new bookmarking system
        if names is None:
            rep_names = []
            for entry in ls:
                truncated_entry = entry.split('.')[0]
                idx = truncated_entry.index('r')
                rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])

        print_err = 0
        if 'print_err' in kwargs:
            print_err = 1
            print()

        deltas = []

        for rep in range(replica):
            tmp_array = []
            with open(path + '/' + ls[rep], 'rb') as fp:

                # header
                t = fp.read(4)  # number of reweighting factors
                if rep == 0:
                    nrw = struct.unpack('i', t)[0]
                    if version == '2.0':
                        nrw = int(nrw / 2)
                    for k in range(nrw):
                        deltas.append([])
                else:
                    if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):  # little weird if-clause due to the /2 operation needed.
                        raise Exception('Error: different number of reweighting factors for replicum', rep)

                for k in range(nrw):
                    tmp_array.append([])

                # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
                nfct = []
                if version in ['1.6', '2.0']:
                    for i in range(nrw):
                        t = fp.read(4)
                        nfct.append(struct.unpack('i', t)[0])
                    # print('nfct: ', nfct) # Hasenbusch factor, 1 for rat reweighting
                else:
                    for i in range(nrw):
                        nfct.append(1)

                nsrc = []
                for i in range(nrw):
                    t = fp.read(4)
                    nsrc.append(struct.unpack('i', t)[0])
                if version == '2.0':
                    if not struct.unpack('i', fp.read(4))[0] == 0:
                        print('something is wrong!')

                # body
                while 0 < 1:
                    t = fp.read(4)
                    if len(t) < 4:
                        break
                    if print_err:
                        config_no = struct.unpack('i', t)
                    for i in range(nrw):
                        if(version == '2.0'):
                            tmpd = _read_array_openQCD2(fp)
                            tmpd = _read_array_openQCD2(fp)
                            tmp_rw = tmpd['arr']
                            tmp_nfct = 1.0
                            for j in range(tmpd['n'][0]):
                                tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
                                if print_err:
                                    print(config_no, i, j, np.mean(np.exp(-np.asarray(tmp_rw[j]))), np.std(np.exp(-np.asarray(tmp_rw[j]))))
                                    print('Sources:', np.exp(-np.asarray(tmp_rw[j])))
                                    print('Partial factor:', tmp_nfct)
                        elif version == '1.6' or version == '1.4':
                            tmp_nfct = 1.0
                            for j in range(nfct[i]):
                                t = fp.read(8 * nsrc[i])
                                t = fp.read(8 * nsrc[i])
                                tmp_rw = struct.unpack('d' * nsrc[i], t)
                                tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
                                if print_err:
                                    print(config_no, i, j, np.mean(np.exp(-np.asarray(tmp_rw))), np.std(np.exp(-np.asarray(tmp_rw))))
                                    print('Sources:', np.exp(-np.asarray(tmp_rw)))
                                    print('Partial factor:', tmp_nfct)
                        tmp_array[i].append(tmp_nfct)

                for k in range(nrw):
                    deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])

        print(',', nrw, 'reweighting factors with', nsrc, 'sources')
        result = []
        for t in range(nrw):
            if names is None:
                result.append(Obs(deltas[t], rep_names))
            else:
                print(names)
                result.append(Obs(deltas[t], names))
        return result

    def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
        """Extract t0 from given .ms.dat files. Returns t0 as Obs.

        It is assumed that all boundary effects have sufficiently decayed at x0=xmin.
        The data around the zero crossing of t^2<E> - 0.3 is fitted with a linear function
        from which the exact root is extracted.
        Only works with openQCD v 1.2.

        Parameters
        ----------
        path : str
            Path to .ms.dat files
        prefix : str
            Ensemble prefix
        dtr_read : int
            Determines how many trajectories should be skipped when reading the ms.dat files.
            Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
        xmin : int
            First timeslice where the boundary effects have sufficiently decayed.
        spatial_extent : int
            spatial extent of the lattice, required for normalization.
        fit_range : int
            Number of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
        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.
        plaquette : bool
            If true extract the plaquette estimate of t0 instead.
        """

        ls = []
        for (dirpath, dirnames, filenames) in os.walk(path):
            ls.extend(filenames)
            break

        if not ls:
            raise Exception('Error, directory not found')

        # Exclude files with different names
        for exc in ls:
            if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'):
                ls = list(set(ls) - set([exc]))
        if len(ls) > 1:
            ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
        replica = len(ls)

        if 'r_start' in kwargs:
            r_start = kwargs.get('r_start')
            if len(r_start) != replica:
                raise Exception('r_start does not match number of replicas')
            # Adjust Configuration numbering to python index
            r_start = [o - 1 if o else None for o in r_start]
        else:
            r_start = [None] * replica

        if 'r_stop' in kwargs:
            r_stop = kwargs.get('r_stop')
            if len(r_stop) != replica:
                raise Exception('r_stop does not match number of replicas')
        else:
            r_stop = [None] * replica

        print('Extract t0 from', prefix, ',', replica, 'replica')

        Ysum = []

        for rep in range(replica):

            with open(path + '/' + ls[rep], 'rb') as fp:
                # Read header
                t = fp.read(12)
                header = struct.unpack('iii', t)
                if rep == 0:
                    dn = header[0]
                    nn = header[1]
                    tmax = header[2]
                elif dn != header[0] or nn != header[1] or tmax != header[2]:
                    raise Exception('Replica parameters do not match.')

                t = fp.read(8)
                if rep == 0:
                    eps = struct.unpack('d', t)[0]
                    print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
                elif eps != struct.unpack('d', t)[0]:
                    raise Exception('Values for eps do not match among replica.')

                Ysl = []

                # Read body
                while 0 < 1:
                    t = fp.read(4)
                    if(len(t) < 4):
                        break
                    nc = struct.unpack('i', t)[0]

                    t = fp.read(8 * tmax * (nn + 1))
                    if kwargs.get('plaquette'):
                        if nc % dtr_read == 0:
                            Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
                    t = fp.read(8 * tmax * (nn + 1))
                    if not kwargs.get('plaquette'):
                        if nc % dtr_read == 0:
                            Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
                    t = fp.read(8 * tmax * (nn + 1))

            Ysum.append([])
            for i, item in enumerate(Ysl):
                Ysum[-1].append([np.mean(item[current + xmin:current + tmax - xmin]) for current in range(0, len(item), tmax)])

        t2E_dict = {}
        for n in range(nn + 1):
            samples = []
            for nrep, rep in enumerate(Ysum):
                samples.append([])
                for cnfg in rep:
                    samples[-1].append(cnfg[n])
                samples[-1] = samples[-1][r_start[nrep]:r_stop[nrep]]
            new_obs = Obs(samples, [(w.split('.'))[0] for w in ls])
            t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3

        zero_crossing = np.argmax(np.array([o.value for o in t2E_dict.values()]) > 0.0)

        x = list(t2E_dict.keys())[zero_crossing - fit_range: zero_crossing + fit_range]
        y = list(t2E_dict.values())[zero_crossing - fit_range: zero_crossing + fit_range]
        [o.gamma_method() for o in y]

        fit_result = fit_lin(x, y)
        return -fit_result[0] / fit_result[1]

    def _parse_array_openQCD2(d, n, size, wa, quadrupel=False):
        arr = []
        if d == 2:
            tot = 0
            for i in range(n[d - 1] - 1):
                if quadrupel:
                    tmp = wa[tot:n[d - 1]]
                    tmp2 = []
                    for i in range(len(tmp)):
                        if i % 2 == 0:
                            tmp2.append(tmp[i])
                    arr.append(tmp2)
                else:
                    arr.append(np.asarray(wa[tot:n[d - 1]]))
        return arr

    # mimic the read_array routine of openQCD-2.0.
    # fp is the opened file handle
    # returns the dict array
    # at this point we only parse a 2d array
    # d = 2
    # n = [nfct[irw], 2*nsrc[irw]]
    def _read_array_openQCD2(fp):
        t = fp.read(4)
        d = struct.unpack('i', t)[0]
        t = fp.read(4 * d)
        n = struct.unpack('%di' % (d), t)
        t = fp.read(4)
        size = struct.unpack('i', t)[0]
        if size == 4:
            types = 'i'
        elif size == 8:
            types = 'd'
        elif size == 16:
            types = 'dd'
        else:
            print('Type not known!')
        m = n[0]
        for i in range(1, d):
            m *= n[i]

        t = fp.read(m * size)
        tmp = struct.unpack('%d%s' % (m, types), t)

        arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True)
        return {'d': d, 'n': n, 'size': size, 'arr': arr}

def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
View Source

    def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
        """Read rwms format from given folder structure. Returns a list of length nrw

        Parameters
        ----------
        version : str
            version of openQCD, default 2.0
        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
        postfix : str
            postfix of the file to read, e.g. '.ms1' for openQCD-files
        """
        known_oqcd_versions = ['1.4', '1.6', '2.0']
        if not (version in known_oqcd_versions):
            raise Exception('Unknown openQCD version defined!')
        print("Working with openQCD version " + version)
        if 'postfix' in kwargs:
            postfix = kwargs.get('postfix')
        else:
            postfix = ''
        ls = []
        for (dirpath, dirnames, filenames) in os.walk(path):
            ls.extend(filenames)
            break

        if not ls:
            raise Exception('Error, directory not found')

        # Exclude files with different names
        for exc in ls:
            if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'):
                ls = list(set(ls) - set([exc]))
        if len(ls) > 1:
            ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
        replica = len(ls)

        if 'r_start' in kwargs:
            r_start = kwargs.get('r_start')
            if len(r_start) != replica:
                raise Exception('r_start does not match number of replicas')
            # Adjust Configuration numbering to python index
            r_start = [o - 1 if o else None for o in r_start]
        else:
            r_start = [None] * replica

        if 'r_stop' in kwargs:
            r_stop = kwargs.get('r_stop')
            if len(r_stop) != replica:
                raise Exception('r_stop does not match number of replicas')
        else:
            r_stop = [None] * replica

        print('Read reweighting factors from', prefix[:-1], ',', replica, 'replica', end='')

        # Adjust replica names to new bookmarking system
        if names is None:
            rep_names = []
            for entry in ls:
                truncated_entry = entry.split('.')[0]
                idx = truncated_entry.index('r')
                rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])

        print_err = 0
        if 'print_err' in kwargs:
            print_err = 1
            print()

        deltas = []

        for rep in range(replica):
            tmp_array = []
            with open(path + '/' + ls[rep], 'rb') as fp:

                # header
                t = fp.read(4)  # number of reweighting factors
                if rep == 0:
                    nrw = struct.unpack('i', t)[0]
                    if version == '2.0':
                        nrw = int(nrw / 2)
                    for k in range(nrw):
                        deltas.append([])
                else:
                    if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):  # little weird if-clause due to the /2 operation needed.
                        raise Exception('Error: different number of reweighting factors for replicum', rep)

                for k in range(nrw):
                    tmp_array.append([])

                # This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
                nfct = []
                if version in ['1.6', '2.0']:
                    for i in range(nrw):
                        t = fp.read(4)
                        nfct.append(struct.unpack('i', t)[0])
                    # print('nfct: ', nfct) # Hasenbusch factor, 1 for rat reweighting
                else:
                    for i in range(nrw):
                        nfct.append(1)

                nsrc = []
                for i in range(nrw):
                    t = fp.read(4)
                    nsrc.append(struct.unpack('i', t)[0])
                if version == '2.0':
                    if not struct.unpack('i', fp.read(4))[0] == 0:
                        print('something is wrong!')

                # body
                while 0 < 1:
                    t = fp.read(4)
                    if len(t) < 4:
                        break
                    if print_err:
                        config_no = struct.unpack('i', t)
                    for i in range(nrw):
                        if(version == '2.0'):
                            tmpd = _read_array_openQCD2(fp)
                            tmpd = _read_array_openQCD2(fp)
                            tmp_rw = tmpd['arr']
                            tmp_nfct = 1.0
                            for j in range(tmpd['n'][0]):
                                tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
                                if print_err:
                                    print(config_no, i, j, np.mean(np.exp(-np.asarray(tmp_rw[j]))), np.std(np.exp(-np.asarray(tmp_rw[j]))))
                                    print('Sources:', np.exp(-np.asarray(tmp_rw[j])))
                                    print('Partial factor:', tmp_nfct)
                        elif version == '1.6' or version == '1.4':
                            tmp_nfct = 1.0
                            for j in range(nfct[i]):
                                t = fp.read(8 * nsrc[i])
                                t = fp.read(8 * nsrc[i])
                                tmp_rw = struct.unpack('d' * nsrc[i], t)
                                tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
                                if print_err:
                                    print(config_no, i, j, np.mean(np.exp(-np.asarray(tmp_rw))), np.std(np.exp(-np.asarray(tmp_rw))))
                                    print('Sources:', np.exp(-np.asarray(tmp_rw)))
                                    print('Partial factor:', tmp_nfct)
                        tmp_array[i].append(tmp_nfct)

                for k in range(nrw):
                    deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])

        print(',', nrw, 'reweighting factors with', nsrc, 'sources')
        result = []
        for t in range(nrw):
            if names is None:
                result.append(Obs(deltas[t], rep_names))
            else:
                print(names)
                result.append(Obs(deltas[t], names))
        return result

Read rwms format from given folder structure. Returns a list of length nrw

###### Parameters

- version (str): version of openQCD, default 2.0
- 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
- postfix (str): postfix of the file to read, e.g. '.ms1' for openQCD-files

def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
View Source

    def extract_t0(path, prefix, dtr_read, xmin, spatial_extent, fit_range=5, **kwargs):
        """Extract t0 from given .ms.dat files. Returns t0 as Obs.

        It is assumed that all boundary effects have sufficiently decayed at x0=xmin.
        The data around the zero crossing of t^2<E> - 0.3 is fitted with a linear function
        from which the exact root is extracted.
        Only works with openQCD v 1.2.

        Parameters
        ----------
        path : str
            Path to .ms.dat files
        prefix : str
            Ensemble prefix
        dtr_read : int
            Determines how many trajectories should be skipped when reading the ms.dat files.
            Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
        xmin : int
            First timeslice where the boundary effects have sufficiently decayed.
        spatial_extent : int
            spatial extent of the lattice, required for normalization.
        fit_range : int
            Number of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
        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.
        plaquette : bool
            If true extract the plaquette estimate of t0 instead.
        """

        ls = []
        for (dirpath, dirnames, filenames) in os.walk(path):
            ls.extend(filenames)
            break

        if not ls:
            raise Exception('Error, directory not found')

        # Exclude files with different names
        for exc in ls:
            if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'):
                ls = list(set(ls) - set([exc]))
        if len(ls) > 1:
            ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
        replica = len(ls)

        if 'r_start' in kwargs:
            r_start = kwargs.get('r_start')
            if len(r_start) != replica:
                raise Exception('r_start does not match number of replicas')
            # Adjust Configuration numbering to python index
            r_start = [o - 1 if o else None for o in r_start]
        else:
            r_start = [None] * replica

        if 'r_stop' in kwargs:
            r_stop = kwargs.get('r_stop')
            if len(r_stop) != replica:
                raise Exception('r_stop does not match number of replicas')
        else:
            r_stop = [None] * replica

        print('Extract t0 from', prefix, ',', replica, 'replica')

        Ysum = []

        for rep in range(replica):

            with open(path + '/' + ls[rep], 'rb') as fp:
                # Read header
                t = fp.read(12)
                header = struct.unpack('iii', t)
                if rep == 0:
                    dn = header[0]
                    nn = header[1]
                    tmax = header[2]
                elif dn != header[0] or nn != header[1] or tmax != header[2]:
                    raise Exception('Replica parameters do not match.')

                t = fp.read(8)
                if rep == 0:
                    eps = struct.unpack('d', t)[0]
                    print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
                elif eps != struct.unpack('d', t)[0]:
                    raise Exception('Values for eps do not match among replica.')

                Ysl = []

                # Read body
                while 0 < 1:
                    t = fp.read(4)
                    if(len(t) < 4):
                        break
                    nc = struct.unpack('i', t)[0]

                    t = fp.read(8 * tmax * (nn + 1))
                    if kwargs.get('plaquette'):
                        if nc % dtr_read == 0:
                            Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
                    t = fp.read(8 * tmax * (nn + 1))
                    if not kwargs.get('plaquette'):
                        if nc % dtr_read == 0:
                            Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
                    t = fp.read(8 * tmax * (nn + 1))

            Ysum.append([])
            for i, item in enumerate(Ysl):
                Ysum[-1].append([np.mean(item[current + xmin:current + tmax - xmin]) for current in range(0, len(item), tmax)])

        t2E_dict = {}
        for n in range(nn + 1):
            samples = []
            for nrep, rep in enumerate(Ysum):
                samples.append([])
                for cnfg in rep:
                    samples[-1].append(cnfg[n])
                samples[-1] = samples[-1][r_start[nrep]:r_stop[nrep]]
            new_obs = Obs(samples, [(w.split('.'))[0] for w in ls])
            t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3

        zero_crossing = np.argmax(np.array([o.value for o in t2E_dict.values()]) > 0.0)

        x = list(t2E_dict.keys())[zero_crossing - fit_range: zero_crossing + fit_range]
        y = list(t2E_dict.values())[zero_crossing - fit_range: zero_crossing + fit_range]
        [o.gamma_method() for o in y]

        fit_result = fit_lin(x, y)
        return -fit_result[0] / fit_result[1]

Extract t0 from given .ms.dat files. Returns t0 as Obs.

It is assumed that all boundary effects have sufficiently decayed at x0=xmin. The data around the zero crossing of t^2 - 0.3 is fitted with a linear function from which the exact root is extracted. Only works with openQCD v 1.2.

###### Parameters

- path (str): Path to .ms.dat files
- prefix (str): Ensemble prefix
- dtr_read (int): Determines how many trajectories should be skipped when reading the ms.dat files. Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
- xmin (int): First timeslice where the boundary effects have sufficiently decayed.
- spatial_extent (int): spatial extent of the lattice, required for normalization.
- fit_range (int): Number of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
- 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.
- plaquette (bool): If true extract the plaquette estimate of t0 instead.