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input submodule refactored
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Fabian Joswig
parent
cc3a734e15
commit
d8437fe122
5 changed files with 744 additions and 734 deletions
337
pyerrors/input/openQCD.py
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337
pyerrors/input/openQCD.py
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#!/usr/bin/env python
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# coding: utf-8
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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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from ..pyerrors 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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Attributes
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-----------------
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version -- version of openQCD, default 2.0
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Keyword arguments
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-----------------
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r_start -- list which contains the first config to be read for each replicum
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r_stop -- list which contains the last config to be read for each replicum
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postfix -- postfix of the file to read, e.g. '.ms1' for openQCD-files
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"""
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#oqcd versions implemented in this method
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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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else: #Set defaults for openQCD Version to be version 1.4, emulate the old behaviour of this method
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# Deprecate this kwarg in version 2.0.
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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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# Exclude files with different names
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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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#ls = fnames
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#print(ls)
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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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# Adjust Configuration numbering to python index
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r_start = [o - 1 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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print('Read reweighting factors from', prefix[:-1], ',', replica, 'replica', end='')
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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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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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#header
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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 verion == '2.0')) or (nrw != struct.unpack('i', t)[0]/2 and version == '2.0')):# little weird if-clause due to the /2 operation needed.
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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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#print('nfct: ', nfct) # Hasenbusch factor, 1 for rat reweighting
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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 is '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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#body
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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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if print_err:
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config_no = struct.unpack('i', t)
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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, np.mean(np.exp(-np.asarray(tmp_rw[j]))), np.std(np.exp(-np.asarray(tmp_rw[j]))))
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print('Sources:', np.exp(-np.asarray(tmp_rw[j])))
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print('Partial factor:', tmp_nfct)
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elif version is '1.6' or version is '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, np.mean(np.exp(-np.asarray(tmp_rw))), 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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for k in range(nrw):
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deltas[k].append(tmp_array[k][r_start[rep]:r_stop[rep]])
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print(',', nrw, 'reweighting factors with', nsrc, 'sources')
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result = []
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for t in range(nrw):
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if names == None:
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result.append(Obs(deltas[t], [w.split(".")[0] for w in ls]))
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else:
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print(names)
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result.append(Obs(deltas[t], names))
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return result
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def extract_t0(path, prefix, dtr_read, xmin, 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 sufficiently decayed at x0=xmin.
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The data around the zero crossing of t^2<E> - 0.3 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 v 1.2.
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Parameters
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----------
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path -- Path to .ms.dat files
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prefix -- Ensemble prefix
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dtr_read -- Determines how many trajectories should be skipped 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 -- First timeslice where the boundary effects have sufficiently decayed.
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spatial_extent -- spatial extent of the lattice, required for normalization.
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fit_range -- Number of data points left and right of the zero crossing to be included in the linear fit. (Default: 5)
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Keyword arguments
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-----------------
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r_start -- list which contains the first config to be read for each replicum.
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r_stop -- list which contains the last config to be read for each replicum.
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plaquette -- If true extract the plaquette estimate of t0 instead.
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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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# Exclude files with different names
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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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# Adjust Configuration numbering to python index
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r_start = [o - 1 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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print('Extract t0 from', prefix, ',', replica, 'replica')
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Ysum = []
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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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# Read header
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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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# Read body
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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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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:current + tmax - xmin]) for current in range(0, len(item), tmax)])
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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[nrep]:r_stop[nrep]]
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new_obs = Obs(samples, [(w.split('.'))[0] for w in ls])
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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([o.value for o in t2E_dict.values()]) > 0.0)
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x = list(t2E_dict.keys())[zero_crossing - fit_range: zero_crossing + fit_range]
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y = list(t2E_dict.values())[zero_crossing - fit_range: 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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return -fit_result[0] / fit_result[1]
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def _parse_array_openQCD2(d, n, size, wa, quadrupel=False):
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arr = []
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if d == 2:
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tot = 0
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for i in range(n[d-1]-1):
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if quadrupel:
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tmp = wa[tot:n[d-1]]
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tmp2 = []
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for i in range(len(tmp)):
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if i % 2 == 0:
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tmp2.append(tmp[i])
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arr.append(tmp2)
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else:
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arr.append(np.asarray(wa[tot:n[d-1]]))
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return arr
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# mimic the read_array routine of openQCD-2.0.
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# fp is the opened file handle
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# returns the dict array
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# at this point we only parse a 2d array
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# d = 2
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# n = [nfct[irw], 2*nsrc[irw]]
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def _read_array_openQCD2(fp):
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t = fp.read(4)
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d = struct.unpack('i', t)[0]
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t = fp.read(4*d)
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n = struct.unpack('%di' % (d), t)
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t = fp.read(4)
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size = struct.unpack('i', t)[0]
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if size == 4:
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types = 'i'
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elif size == 8:
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types = 'd'
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elif size == 16:
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types = 'dd'
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else:
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print('Type not known!')
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m = n[0]
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for i in range(1,d):
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m *= n[i]
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t = fp.read(m*size)
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tmp = struct.unpack('%d%s' % (m, types), t)
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arr = _parse_array_openQCD2(d, n, size, tmp, quadrupel=True)
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return {'d': d, 'n': n, 'size': size, 'arr': arr}
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