-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathbuild_corr.py
131 lines (103 loc) · 4.92 KB
/
build_corr.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
import read_config_time_file as read
import pandas_reader as pr
import eigenvalues as eigen
import correlator
import configtimeobj
import logging
from itertools import product
import pickle
import os
def corr_and_vev_from_pickle(corrfile, srcvevfile=None, snkvevfile=None, cfgs=None, ts=None):
picklefile = corrfile+".pickle"
if os.path.isfile(picklefile):
logging.info("loading file {}".format(picklefile))
return pickle.load( open(picklefile, "rb"))
logging.warn("pickle file {} doesn't exist building and dumping".format(picklefile))
try:
logging.info("reading file {} with pandas".format(corrfile))
c = corr_and_vev_from_files_pandas(corrfile, srcvevfile, snkvevfile)
c.determine_symmetry()
pickle.dump(c, open( picklefile, "wb" ) , protocol=pickle.HIGHEST_PROTOCOL )
return c
except AttributeError:
logging.info("Failed to read with pandas, reading normal")
c = corr_and_vev_from_files(corrfile, srcvevfile, snkvevfile)
c.determine_symmetry()
pickle.dump(c, open( picklefile, "wb" ) , protocol=pickle.HIGHEST_PROTOCOL )
return c
def corr_and_vev_from_files(corrfile, srcvevfile=None, snkvevfile=None, cfgs=None, ts=None):
corrdata = pr.read_datadict_ambiguouscomplex(corrfile)
if(srcvevfile):
vevdata_src = read.read_config_vev(srcvevfile)
else:
vevdata_src = None
if(snkvevfile):
vevdata_snk = read.read_config_vev(snkvevfile)
else:
vevdata_snk = None
return correlator.Correlator.fromDataDicts(corrdata, vevdata_src, vevdata_snk)
def corr_and_vev_from_files_pandas(corrfile, srcvevfile=None, snkvevfile=None, cfgs=None, ts=None):
corrdata = pr.read_datadict_paraenformat_real(corrfile)
if(srcvevfile):
# raise NotImplementedError("Vev currently unsupported in this read mode")
vevdata_src = pr.read_vev_parenformat(srcvevfile)
else:
vevdata_src = {cfg: 0.0 for cfg in corrdata}
if(snkvevfile):
# raise NotImplementedError("Vev currently unsupported in this read mode")
vevdata_snk = pr.read_vev_parenformat(snkvevfile)
else:
vevdata_snk = {cfg: 0.0 for cfg in corrdata}
return correlator.Correlator.fromDataDicts(corrdata, vevdata_src, vevdata_snk)
def from_opfiles(src_opfile, snk_opfile, N=None):
srcdata = read.read_config_time_data_real(src_opfile)
snkdata = read.read_config_time_data_real(snk_opfile)
if N is None:
return correlator.Correlator.fromOpvalCTO(srcdata, snkdata)
else:
return correlator.Correlator.fromOpvalCTO(srcdata, snkdata, dts=list(range(N)))
def diag_from_opfiles(opfile, N=8):
opdata = read.read_config_time_data_real(opfile)
if N is None:
return correlator.Correlator.fromOpvalCTO(opdata, opdata)
else:
return correlator.Correlator.fromOpvalCTO(opdata, opdata, dts=list(range(N)))
def from_eigenvalue_24cubed_opfiles(dirname):
rawdata = eigen.readfile_neigenvalues(dirname, 112)
data = configtimeobj.Cfgtimeobj.fromDataDict(eigen.reduce_to_weighted_trace(rawdata))
data.writeeachconfig(dirname[:-1]+"_weighted/weighted")
return correlator.Correlator.fromOpvalCTO(data, data)
def from_eigenvalue_32cubed_opfiles(dirname):
rawdata = eigen.read_configdir_timeorlevel_evalues(dirname, 264, recall=False, store=False)
data = configtimeobj.Cfgtimeobj.fromDataDict(eigen.reduce_to_trace(rawdata))
return correlator.Correlator.fromOpvalCTO(data, data)
def matrix_from_opfiles(opfile_list):
logging.debug("building matrix of correlators using %s", str(opfile_list))
datas = [read.read_config_time_data_real(op) for op in opfile_list]
matrix = {}
# Product give all possible combinations
for e1, e2 in product(enumerate(datas, start=1), repeat=2):
index1, data1 = e1
index2, data2 = e2
matrix[(index1, index2)] = correlator.Correlator.fromOpvalCTO(data1, data2)
return matrix
def matrix_from_cor_and_vev(directory, cortemplate, vevtemplate, operator_list):
logging.debug("building matrix of correlators using %s", str(operator_list))
matrix = {}
for e1, e2 in product(enumerate(operator_list, start=1), repeat=2):
index1, op1 = e1
index2, op2 = e2
corfile = (directory + cortemplate).format(op1, op2)
srcvevfile = (directory + vevtemplate).format(op1)
snkvevfile = (directory + vevtemplate).format(op2)
matrix[(index1, index2)] = corr_and_vev_from_files(corfile, srcvevfile, snkvevfile)
return matrix
def matrix_from_cor(directory, cortemplate, operator_list):
logging.debug("building matrix of correlators using %s", str(operator_list))
matrix = {}
for e1, e2 in product(enumerate(operator_list, start=1), repeat=2):
index1, op1 = e1
index2, op2 = e2
corfile = (directory + cortemplate).format(op1, op2)
matrix[(index1, index2)] = corr_and_vev_from_files(corfile, None, None)
return matrix