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https://github.com/fjosw/pyerrors.git
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747 lines
28 KiB
Python
747 lines
28 KiB
Python
import os
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import fnmatch
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import re
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import struct
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import numpy as np # Thinly-wrapped numpy
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import warnings
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import matplotlib.pyplot as plt
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from matplotlib import gridspec
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from ..obs import Obs
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from ..fits import fit_lin
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def read_rwms(path, prefix, version='2.0', names=None, **kwargs):
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"""Read rwms format from given folder structure. Returns a list of length nrw
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Parameters
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----------
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path : str
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path that contains the data files
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prefix : str
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all files in path that start with prefix are considered as input files.
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May be used together postfix to consider only special file endings.
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Prefix is ignored, if the keyword 'files' is used.
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version : str
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version of openQCD, default 2.0
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names : list
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list of names that is assigned to the data according according
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to the order in the file list. Use careful, if you do not provide file names!
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r_start : list
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list which contains the first config to be read for each replicum
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r_stop : list
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list which contains the last config to be read for each replicum
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r_step : int
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integer that defines a fixed step size between two measurements (in units of configs)
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If not given, r_step=1 is assumed.
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postfix : str
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postfix of the file to read, e.g. '.ms1' for openQCD-files
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files : list
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list which contains the filenames to be read. No automatic detection of
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files performed if given.
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print_err : bool
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Print additional information that is useful for debugging.
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"""
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known_oqcd_versions = ['1.4', '1.6', '2.0']
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if not (version in known_oqcd_versions):
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raise Exception('Unknown openQCD version defined!')
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print("Working with openQCD version " + version)
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if 'postfix' in kwargs:
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postfix = kwargs.get('postfix')
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else:
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postfix = ''
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ls = []
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for (dirpath, dirnames, filenames) in os.walk(path):
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ls.extend(filenames)
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break
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if not ls:
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raise Exception('Error, directory not found')
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if 'files' in kwargs:
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ls = kwargs.get('files')
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else:
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for exc in ls:
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if not fnmatch.fnmatch(exc, prefix + '*' + postfix + '.dat'):
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ls = list(set(ls) - set([exc]))
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if len(ls) > 1:
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ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
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replica = len(ls)
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if 'r_start' in kwargs:
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r_start = kwargs.get('r_start')
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if len(r_start) != replica:
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raise Exception('r_start does not match number of replicas')
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r_start = [o if o else None for o in r_start]
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else:
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r_start = [None] * replica
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if 'r_stop' in kwargs:
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r_stop = kwargs.get('r_stop')
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if len(r_stop) != replica:
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raise Exception('r_stop does not match number of replicas')
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else:
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r_stop = [None] * replica
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if 'r_step' in kwargs:
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r_step = kwargs.get('r_step')
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else:
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r_step = 1
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print('Read reweighting factors from', prefix[:-1], ',',
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replica, 'replica', end='')
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if names is None:
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rep_names = []
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for entry in ls:
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truncated_entry = entry.split('.')[0]
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idx = truncated_entry.index('r')
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rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
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else:
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rep_names = names
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print_err = 0
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if 'print_err' in kwargs:
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print_err = 1
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print()
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deltas = []
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configlist = []
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r_start_index = []
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r_stop_index = []
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for rep in range(replica):
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tmp_array = []
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with open(path + '/' + ls[rep], 'rb') as fp:
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t = fp.read(4) # number of reweighting factors
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if rep == 0:
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nrw = struct.unpack('i', t)[0]
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if version == '2.0':
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nrw = int(nrw / 2)
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for k in range(nrw):
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deltas.append([])
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else:
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if ((nrw != struct.unpack('i', t)[0] and (not version == '2.0')) or (nrw != struct.unpack('i', t)[0] / 2 and version == '2.0')):
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raise Exception('Error: different number of reweighting factors for replicum', rep)
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for k in range(nrw):
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tmp_array.append([])
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# This block is necessary for openQCD1.6 and openQCD2.0 ms1 files
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nfct = []
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if version in ['1.6', '2.0']:
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for i in range(nrw):
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t = fp.read(4)
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nfct.append(struct.unpack('i', t)[0])
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else:
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for i in range(nrw):
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nfct.append(1)
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nsrc = []
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for i in range(nrw):
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t = fp.read(4)
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nsrc.append(struct.unpack('i', t)[0])
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if version == '2.0':
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if not struct.unpack('i', fp.read(4))[0] == 0:
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print('something is wrong!')
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configlist.append([])
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while 0 < 1:
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t = fp.read(4)
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if len(t) < 4:
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break
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config_no = struct.unpack('i', t)[0]
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configlist[-1].append(config_no)
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for i in range(nrw):
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if(version == '2.0'):
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tmpd = _read_array_openQCD2(fp)
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tmpd = _read_array_openQCD2(fp)
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tmp_rw = tmpd['arr']
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tmp_nfct = 1.0
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for j in range(tmpd['n'][0]):
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tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw[j])))
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if print_err:
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print(config_no, i, j,
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np.mean(np.exp(-np.asarray(tmp_rw[j]))),
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np.std(np.exp(-np.asarray(tmp_rw[j]))))
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print('Sources:',
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np.exp(-np.asarray(tmp_rw[j])))
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print('Partial factor:', tmp_nfct)
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elif version == '1.6' or version == '1.4':
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tmp_nfct = 1.0
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for j in range(nfct[i]):
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t = fp.read(8 * nsrc[i])
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t = fp.read(8 * nsrc[i])
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tmp_rw = struct.unpack('d' * nsrc[i], t)
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tmp_nfct *= np.mean(np.exp(-np.asarray(tmp_rw)))
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if print_err:
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print(config_no, i, j,
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np.mean(np.exp(-np.asarray(tmp_rw))),
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np.std(np.exp(-np.asarray(tmp_rw))))
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print('Sources:', np.exp(-np.asarray(tmp_rw)))
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print('Partial factor:', tmp_nfct)
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tmp_array[i].append(tmp_nfct)
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if r_start[rep] is None:
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r_start_index.append(0)
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else:
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try:
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r_start_index.append(configlist[-1].index(r_start[rep]))
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except ValueError:
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raise Exception('Config %d not in file with range [%d, %d]' % (
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r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
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if r_stop[rep] is None:
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r_stop_index.append(len(configlist[-1]) - 1)
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else:
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try:
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r_stop_index.append(configlist[-1].index(r_stop[rep]))
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except ValueError:
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raise Exception('Config %d not in file with range [%d, %d]' % (
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r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
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for k in range(nrw):
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deltas[k].append(tmp_array[k][r_start_index[rep]:r_stop_index[rep]][::r_step])
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if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
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raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
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stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
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if np.any([step != 1 for step in stepsizes]):
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warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
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print(',', nrw, 'reweighting factors with', nsrc, 'sources')
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result = []
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idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]], r_step) for rep in range(replica)]
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for t in range(nrw):
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result.append(Obs(deltas[t], rep_names, idl=idl))
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return result
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def extract_t0(path, prefix, dtr_read, xmin,
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spatial_extent, fit_range=5, **kwargs):
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"""Extract t0 from given .ms.dat files. Returns t0 as Obs.
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It is assumed that all boundary effects have
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sufficiently decayed at x0=xmin.
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The data around the zero crossing of t^2<E> - 0.3
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is fitted with a linear function
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from which the exact root is extracted.
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Only works with openQCD
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It is assumed that one measurement is performed for each config.
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If this is not the case, the resulting idl, as well as the handling
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of r_start, r_stop and r_step is wrong and the user has to correct
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this in the resulting observable.
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Parameters
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----------
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path : str
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Path to .ms.dat files
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prefix : str
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Ensemble prefix
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dtr_read : int
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Determines how many trajectories should be skipped
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when reading the ms.dat files.
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Corresponds to dtr_cnfg / dtr_ms in the openQCD input file.
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xmin : int
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First timeslice where the boundary
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effects have sufficiently decayed.
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spatial_extent : int
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spatial extent of the lattice, required for normalization.
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fit_range : int
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Number of data points left and right of the zero
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crossing to be included in the linear fit. (Default: 5)
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r_start : list
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list which contains the first config to be read for each replicum.
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r_stop : list
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list which contains the last config to be read for each replicum.
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r_step : int
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integer that defines a fixed step size between two measurements (in units of configs)
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If not given, r_step=1 is assumed.
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plaquette : bool
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If true extract the plaquette estimate of t0 instead.
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names : list
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list of names that is assigned to the data according according
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to the order in the file list. Use careful, if you do not provide file names!
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files : list
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list which contains the filenames to be read. No automatic detection of
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files performed if given.
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plot_fit : bool
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If true, the fit for the extraction of t0 is shown together with the data.
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assume_thermalization : bool
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If True: If the first record divided by the distance between two measurements is larger than
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1, it is assumed that this is due to thermalization and the first measurement belongs
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to the first config (default).
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If False: The config numbers are assumed to be traj_number // difference
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"""
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ls = []
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for (dirpath, dirnames, filenames) in os.walk(path):
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ls.extend(filenames)
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break
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if not ls:
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raise Exception('Error, directory not found')
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if 'files' in kwargs:
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ls = kwargs.get('files')
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else:
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for exc in ls:
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if not fnmatch.fnmatch(exc, prefix + '*.ms.dat'):
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ls = list(set(ls) - set([exc]))
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if len(ls) > 1:
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ls.sort(key=lambda x: int(re.findall(r'\d+', x[len(prefix):])[0]))
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replica = len(ls)
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if 'r_start' in kwargs:
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r_start = kwargs.get('r_start')
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if len(r_start) != replica:
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raise Exception('r_start does not match number of replicas')
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r_start = [o if o else None for o in r_start]
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else:
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r_start = [None] * replica
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if 'r_stop' in kwargs:
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r_stop = kwargs.get('r_stop')
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if len(r_stop) != replica:
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raise Exception('r_stop does not match number of replicas')
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else:
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r_stop = [None] * replica
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if 'r_step' in kwargs:
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r_step = kwargs.get('r_step')
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else:
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r_step = 1
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print('Extract t0 from', prefix, ',', replica, 'replica')
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if 'names' in kwargs:
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rep_names = kwargs.get('names')
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else:
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rep_names = []
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for entry in ls:
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truncated_entry = entry.split('.')[0]
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idx = truncated_entry.index('r')
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rep_names.append(truncated_entry[:idx] + '|' + truncated_entry[idx:])
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Ysum = []
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configlist = []
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r_start_index = []
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r_stop_index = []
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for rep in range(replica):
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with open(path + '/' + ls[rep], 'rb') as fp:
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t = fp.read(12)
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header = struct.unpack('iii', t)
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if rep == 0:
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dn = header[0]
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nn = header[1]
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tmax = header[2]
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elif dn != header[0] or nn != header[1] or tmax != header[2]:
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raise Exception('Replica parameters do not match.')
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t = fp.read(8)
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if rep == 0:
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eps = struct.unpack('d', t)[0]
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print('Step size:', eps, ', Maximal t value:', dn * (nn) * eps)
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elif eps != struct.unpack('d', t)[0]:
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raise Exception('Values for eps do not match among replica.')
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Ysl = []
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configlist.append([])
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while 0 < 1:
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t = fp.read(4)
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if(len(t) < 4):
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break
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nc = struct.unpack('i', t)[0]
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configlist[-1].append(nc)
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t = fp.read(8 * tmax * (nn + 1))
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if kwargs.get('plaquette'):
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if nc % dtr_read == 0:
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Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
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t = fp.read(8 * tmax * (nn + 1))
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if not kwargs.get('plaquette'):
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if nc % dtr_read == 0:
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Ysl.append(struct.unpack('d' * tmax * (nn + 1), t))
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t = fp.read(8 * tmax * (nn + 1))
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Ysum.append([])
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for i, item in enumerate(Ysl):
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Ysum[-1].append([np.mean(item[current + xmin:
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current + tmax - xmin])
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for current in range(0, len(item), tmax)])
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diffmeas = configlist[-1][-1] - configlist[-1][-2]
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configlist[-1] = [item // diffmeas for item in configlist[-1]]
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if kwargs.get('assume_thermalization', True) and configlist[-1][0] > 1:
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warnings.warn('Assume thermalization and that the first measurement belongs to the first config.')
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offset = configlist[-1][0] - 1
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configlist[-1] = [item - offset for item in configlist[-1]]
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if r_start[rep] is None:
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r_start_index.append(0)
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else:
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try:
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r_start_index.append(configlist[-1].index(r_start[rep]))
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except ValueError:
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raise Exception('Config %d not in file with range [%d, %d]' % (
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r_start[rep], configlist[-1][0], configlist[-1][-1])) from None
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if r_stop[rep] is None:
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r_stop_index.append(len(configlist[-1]) - 1)
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else:
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try:
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r_stop_index.append(configlist[-1].index(r_stop[rep]))
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except ValueError:
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raise Exception('Config %d not in file with range [%d, %d]' % (
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r_stop[rep], configlist[-1][0], configlist[-1][-1])) from None
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if np.any([len(np.unique(np.diff(cl))) != 1 for cl in configlist]):
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raise Exception('Irregular spaced data in input file!', [len(np.unique(np.diff(cl))) for cl in configlist])
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stepsizes = [list(np.unique(np.diff(cl)))[0] for cl in configlist]
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if np.any([step != 1 for step in stepsizes]):
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warnings.warn('Stepsize between configurations is greater than one!' + str(stepsizes), RuntimeWarning)
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idl = [range(configlist[rep][r_start_index[rep]], configlist[rep][r_stop_index[rep]], r_step) for rep in range(replica)]
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t2E_dict = {}
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for n in range(nn + 1):
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samples = []
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for nrep, rep in enumerate(Ysum):
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samples.append([])
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for cnfg in rep:
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samples[-1].append(cnfg[n])
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samples[-1] = samples[-1][r_start_index[nrep]:r_stop_index[nrep]][::r_step]
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new_obs = Obs(samples, rep_names, idl=idl)
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t2E_dict[n * dn * eps] = (n * dn * eps) ** 2 * new_obs / (spatial_extent ** 3) - 0.3
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zero_crossing = np.argmax(np.array(
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[o.value for o in t2E_dict.values()]) > 0.0)
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x = list(t2E_dict.keys())[zero_crossing - fit_range:
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zero_crossing + fit_range]
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y = list(t2E_dict.values())[zero_crossing - fit_range:
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zero_crossing + fit_range]
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[o.gamma_method() for o in y]
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fit_result = fit_lin(x, y)
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if kwargs.get('plot_fit'):
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plt.figure()
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gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
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ax0 = plt.subplot(gs[0])
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xmore = list(t2E_dict.keys())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
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ymore = list(t2E_dict.values())[zero_crossing - fit_range - 2: zero_crossing + fit_range + 2]
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[o.gamma_method() for o in ymore]
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ax0.errorbar(xmore, [yi.value for yi in ymore], yerr=[yi.dvalue for yi in ymore], fmt='x')
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xplot = np.linspace(np.min(x), np.max(x))
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yplot = [fit_result[0] + fit_result[1] * xi for xi in xplot]
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[yi.gamma_method() for yi in yplot]
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ax0.fill_between(xplot, y1=[yi.value - yi.dvalue for yi in yplot], y2=[yi.value + yi.dvalue for yi in yplot])
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retval = (-fit_result[0] / fit_result[1])
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retval.gamma_method()
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ylim = ax0.get_ylim()
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ax0.fill_betweenx(ylim, x1=retval.value - retval.dvalue, x2=retval.value + retval.dvalue, color='gray', alpha=0.4)
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ax0.set_ylim(ylim)
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ax0.set_ylabel(r'$t^2 \langle E(t) \rangle - 0.3 $')
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|
xlim = ax0.get_xlim()
|
|
|
|
fit_res = [fit_result[0] + fit_result[1] * xi for xi in x]
|
|
residuals = (np.asarray([o.value for o in y]) - [o.value for o in fit_res]) / np.asarray([o.dvalue for o in y])
|
|
ax1 = plt.subplot(gs[1])
|
|
ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
|
|
ax1.tick_params(direction='out')
|
|
ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
|
|
ax1.axhline(y=0.0, ls='--', color='k')
|
|
ax1.fill_between(xlim, -1.0, 1.0, alpha=0.1, facecolor='k')
|
|
ax1.set_xlim(xlim)
|
|
ax1.set_ylabel('Residuals')
|
|
ax1.set_xlabel(r'$t/a^2$')
|
|
|
|
plt.show()
|
|
return -fit_result[0] / fit_result[1]
|
|
|
|
|
|
def _parse_array_openQCD2(d, n, size, wa, quadrupel=False):
|
|
arr = []
|
|
if d == 2:
|
|
for i in range(n[0]):
|
|
tmp = wa[i * n[1]:(i + 1) * n[1]]
|
|
if quadrupel:
|
|
tmp2 = []
|
|
for j in range(0, len(tmp), 2):
|
|
tmp2.append(tmp[j])
|
|
arr.append(tmp2)
|
|
else:
|
|
arr.append(np.asarray(tmp))
|
|
|
|
else:
|
|
raise Exception('Only two-dimensional arrays supported!')
|
|
|
|
return arr
|
|
|
|
|
|
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_qtop(path, prefix, c, dtr_cnfg=1, version="1.2", **kwargs):
|
|
"""Read qtop format from given folder structure.
|
|
|
|
Parameters
|
|
----------
|
|
path : str
|
|
path of the measurement files
|
|
prefix : str
|
|
prefix of the measurement files, e.g. <prefix>_id0_r0.ms.dat
|
|
c : double
|
|
Smearing radius in units of the lattice extent, c = sqrt(8 t0) / L
|
|
dtr_cnfg : int
|
|
(optional) parameter that specifies the number of trajectories
|
|
between two configs.
|
|
if it is not set, the distance between two measurements
|
|
in the file is assumed to be
|
|
the distance between two configurations.
|
|
steps : int
|
|
(optional) (maybe only necessary for openQCD2.0)
|
|
nt step size, guessed if not given
|
|
version : str
|
|
version string of the openQCD (sfqcd) version used to create
|
|
the ensemble
|
|
L : int
|
|
spatial length of the lattice in L/a.
|
|
HAS to be set if version != sfqcd, since openQCD does not provide
|
|
this in the header
|
|
r_start : list
|
|
offset of the first ensemble, making it easier to match
|
|
later on with other Obs
|
|
r_stop : list
|
|
last configurations that need to be read (per replicum)
|
|
files : list
|
|
specify the exact files that need to be read
|
|
from path, practical if e.g. only one replicum is needed
|
|
names : list
|
|
Alternative labeling for replicas/ensembles.
|
|
Has to have the appropriate length
|
|
"""
|
|
known_versions = ["1.0", "1.2", "1.4", "1.6", "2.0", "sfqcd"]
|
|
|
|
if version not in known_versions:
|
|
raise Exception("Unknown openQCD version.")
|
|
if "steps" in kwargs:
|
|
steps = kwargs.get("steps")
|
|
if version == "sfqcd":
|
|
if "L" in kwargs:
|
|
supposed_L = kwargs.get("L")
|
|
else:
|
|
if "L" not in kwargs:
|
|
raise Exception("This version of openQCD needs you to provide the spatial length of the lattice as parameter 'L'.")
|
|
else:
|
|
L = kwargs.get("L")
|
|
r_start = 1
|
|
if "r_start" in kwargs:
|
|
r_start = kwargs.get("r_start")
|
|
if "r_stop" in kwargs:
|
|
r_stop = kwargs.get("r_stop")
|
|
if "files" in kwargs:
|
|
files = kwargs.get("files")
|
|
else:
|
|
found = []
|
|
files = []
|
|
for (dirpath, dirnames, filenames) in os.walk(path + "/"):
|
|
# print(filenames)
|
|
found.extend(filenames)
|
|
break
|
|
for f in found:
|
|
if fnmatch.fnmatch(f, prefix + "*" + ".ms.dat"):
|
|
files.append(f)
|
|
print(files)
|
|
rep_names = []
|
|
|
|
deltas = []
|
|
idl = []
|
|
for rep, file in enumerate(files):
|
|
with open(path + "/" + file, "rb") as fp:
|
|
t = fp.read(12)
|
|
header = struct.unpack('<iii', t)
|
|
# step size in integration steps "dnms"
|
|
dn = header[0]
|
|
# number of measurements, so "ntot"/dn
|
|
nn = header[1]
|
|
# lattice T/a
|
|
tmax = header[2]
|
|
if version == "sfqcd":
|
|
t = fp.read(12)
|
|
Ls = struct.unpack('<iii', t)
|
|
if(Ls[0] == Ls[1] and Ls[1] == Ls[2]):
|
|
L = Ls[0]
|
|
if not (supposed_L == L):
|
|
raise Exception("It seems the length given in the header and by you contradict each other")
|
|
else:
|
|
raise Exception("Found more than one spatial length in header!")
|
|
|
|
print('dnms:', dn)
|
|
print('nn:', nn)
|
|
print('tmax:', tmax)
|
|
t = fp.read(8)
|
|
eps = struct.unpack('d', t)[0]
|
|
print('eps:', eps)
|
|
|
|
Q = []
|
|
ncs = []
|
|
while 0 < 1:
|
|
t = fp.read(4)
|
|
if(len(t) < 4):
|
|
break
|
|
ncs.append(struct.unpack('i', t)[0])
|
|
# Wsl
|
|
t = fp.read(8 * tmax * (nn + 1))
|
|
# Ysl
|
|
t = fp.read(8 * tmax * (nn + 1))
|
|
# Qsl, which is asked for in this method
|
|
t = fp.read(8 * tmax * (nn + 1))
|
|
# unpack the array of Qtops,
|
|
# on each timeslice t=0,...,tmax-1 and the
|
|
# measurement number in = 0...nn (see README.qcd1)
|
|
tmpd = struct.unpack('d' * tmax * (nn + 1), t)
|
|
Q.append(tmpd)
|
|
|
|
if not len(set([ncs[i] - ncs[i - 1] for i in range(1, len(ncs))])):
|
|
raise Exception("Irregularities in stepsize found")
|
|
else:
|
|
if 'steps' in kwargs:
|
|
if steps != ncs[1] - ncs[0]:
|
|
raise Exception("steps and the found stepsize are not the same")
|
|
else:
|
|
steps = ncs[1] - ncs[0]
|
|
|
|
print(len(Q))
|
|
print('max_t:', dn * (nn) * eps)
|
|
|
|
t_aim = (c * L) ** 2 / 8
|
|
|
|
print('t_aim:', t_aim)
|
|
index_aim = round(t_aim / eps / dn)
|
|
print('index_aim:', index_aim)
|
|
|
|
Q_sum = []
|
|
for i, item in enumerate(Q):
|
|
Q_sum.append([sum(item[current:current + tmax])
|
|
for current in range(0, len(item), tmax)])
|
|
print(len(Q_sum))
|
|
print(len(Q_sum[0]))
|
|
Q_round = []
|
|
for i in range(len(Q) // dtr_cnfg):
|
|
Q_round.append(round(Q_sum[dtr_cnfg * i][index_aim]))
|
|
if len(Q_round) != len(ncs) // dtr_cnfg:
|
|
raise Exception("qtops and ncs dont have the same length")
|
|
|
|
truncated_file = file[:-7]
|
|
print(truncated_file)
|
|
idl_start = 1
|
|
|
|
if "r_start" in kwargs:
|
|
Q_round = Q_round[r_start[rep]:]
|
|
idl_start = r_start[rep]
|
|
if "r_stop" in kwargs:
|
|
Q_round = Q_round[:r_stop[rep]]
|
|
idl_stop = idl_start + len(Q_round)
|
|
# keyword "names" prevails over "ens_name"
|
|
if "names" not in kwargs:
|
|
try:
|
|
idx = truncated_file.index('r')
|
|
except Exception:
|
|
if "names" not in kwargs:
|
|
raise Exception("Automatic recognition of replicum failed, please enter the key word 'names'.")
|
|
if "ens_name" in kwargs:
|
|
ens_name = kwargs.get("ens_name")
|
|
else:
|
|
ens_name = truncated_file[:idx]
|
|
rep_names.append(ens_name + '|' + truncated_file[idx:])
|
|
else:
|
|
names = kwargs.get("names")
|
|
rep_names = names
|
|
deltas.append(np.array(Q_round))
|
|
idl.append(range(idl_start, idl_stop))
|
|
result = Obs(deltas, rep_names, idl=idl)
|
|
return result
|
|
|
|
|
|
def read_qtop_sector(target=0, **kwargs):
|
|
"""Constructs reweighting factors to a specified topological sector.
|
|
|
|
Parameters
|
|
----------
|
|
target : int
|
|
Specifies the topological sector to be reweighted to (default 0)
|
|
q_top : Obs
|
|
Alternatively takes args of read_qtop method as kwargs
|
|
"""
|
|
|
|
if not isinstance(target, int):
|
|
raise Exception("'target' has to be an integer.")
|
|
|
|
if "q_top" in kwargs:
|
|
qtop = kwargs.get("q_top")
|
|
else:
|
|
if "path" in kwargs:
|
|
path = kwargs.get("path")
|
|
del kwargs["path"]
|
|
else:
|
|
raise Exception("If you are not providing q_top, please provide path")
|
|
if "prefix" in kwargs:
|
|
prefix = kwargs.get("prefix")
|
|
del kwargs["prefix"]
|
|
else:
|
|
raise Exception("If you are not providing q_top, please provide prefix")
|
|
if "c" in kwargs:
|
|
c = kwargs.get("c")
|
|
del kwargs["c"]
|
|
else:
|
|
raise Exception("If you are not providing q_top, please provide c")
|
|
if "version" in kwargs:
|
|
version = kwargs.get("version")
|
|
del kwargs["version"]
|
|
else:
|
|
version = "1.2"
|
|
if "dtr_cnfg" in kwargs:
|
|
dtr_cnfg = kwargs.get("dtr_cnfg")
|
|
del kwargs["dtr_cnfg"]
|
|
else:
|
|
dtr_cnfg = 1
|
|
qtop = read_qtop(path, prefix, c, dtr_cnfg=dtr_cnfg,
|
|
version=version, **kwargs)
|
|
names = qtop.names
|
|
print(names)
|
|
print(qtop.deltas.keys())
|
|
proj_qtop = []
|
|
for n in qtop.deltas:
|
|
proj_qtop.append(np.array([1 if int(qtop.value + q) == target else 0 for q in qtop.deltas[n]]))
|
|
|
|
result = Obs(proj_qtop, qtop.names)
|
|
return result
|