def set_fit_props(name, points, config, remove):
from os import chdir, popen
from os.path import basename, dirname
import json
from lib.utils import (fit_dir, config_dir, point_dir, dir_point,
authorization_request)
if remove:
if points:
print('Warning: points specification not compatible with --remove '
'option.')
return
elif config != 'test':
print('Warning: config specification not compatible with --remove '
'option.')
return
chdir(fit_dir(name))
try:
with open('sims.json', 'r') as file:
sims = json.load(file)
except FileNotFoundError:
sims = []
# SIMS UPDATE
if not remove:
c_dir = config_dir(config)
for Point in points:
p_dir = c_dir + '/' + point_dir(Point)
if p_dir not in sims:
sims += [p_dir]
with open('sims.json', 'w') as file:
json.dump(sims, file, indent=4)
# SIMS REMOTION
else:
new_sims = sims.copy()
for sim in sims:
Point = dir_point(basename(sim))
config = basename(dirname(sim))
what = f"to remove sim from fit '{name}'"
extra = f"\033[38;5;80m config: '{config}'\033[0m"
auth = authorization_request(Point=Point, what_to_do=what,
extra_message=extra)
if auth == 'quit':
print('Nothing done for last sim.')
return
elif auth == 'yes':
new_sims.remove(sim)
with open('sims.json', 'w') as file:
json.dump(new_sims, file, indent=4)
print('Sim removed')
else:
print('Nothing removed.')
def sim_obs(points, config, plot, fit, exclude_torelons, exclude_bootstrap,
fit_name, force):
from os.path import basename, dirname, realpath
import json
from pprint import pprint
from lib.utils import config_dir, dir_point, fit_dir
if fit_name:
f_dir = fit_dir(fit_name)
try:
with open(f_dir + '/sims.json', 'r') as file:
sims = json.load(file)
except FileNotFoundError:
print('No simulation already assigned to this fit.')
points = []
points_configs = {}
for s in sims:
if s[-1] == '/':
s = s[:-1]
Point = dir_point(basename(s))
points += [Point]
points_configs = {**points_configs, Point: realpath(dirname(s))}
c_dir = None
else:
points_configs = None
c_dir = config_dir(config)
col = 216 # color
print(f'Number of selected points: \033[38;5;{col}m{len(points)}\033[0m')
print(f'\033[38;5;{col}m', end='')
pprint(points)
print('\033[0m')
if not force:
i = 0
for Point in points:
args = (Point, points_configs, c_dir, i, force, plot, fit,
exclude_torelons, exclude_bootstrap)
ret = sim_obs_compute(args)
if ret == 'return':
return
elif ret == 'continue':
continue
i += 1
else:
import multiprocessing as mp
i = 0
args = []
for Point in points:
args += [(Point, points_configs, c_dir, i, force, plot, fit,
exclude_torelons, exclude_bootstrap)]
i += 1
with mp.Pool(mp.cpu_count() - 1) as pool:
pool.map(sim_obs_compute, args)
def mean_volumes(configs=None, print_flag=True, path=None):
from pprint import pprint
mvs = {}
for c, sims in sim_paths().items():
if configs and c not in configs:
continue
for path in sims:
from os import chdir
from os.path import basename, isfile
import json
from numpy import loadtxt
from lib.utils import dir_point
chdir(path)
Point = dir_point(basename(path))
if isfile('history/volumes.txt'):
_, volumes = loadtxt('history/volumes.txt', unpack=True)
with open('state.json', 'r') as state_file:
state = json.load(state_file)
try:
cut = state['cut']
except KeyError:
cut = 0
try:
mvs[c] = {**mvs[c], Point: volumes[cut:].mean()}
except KeyError:
mvs[c] = {Point: volumes[cut:].mean()}
if print_flag:
pprint(mvs)
return mvs
def set_cut(p_dir, i=0, force=False):
from os import chdir
from os.path import basename
from math import ceil
from numpy import loadtxt
from pandas import read_csv
from lib.utils import dir_point
chdir(p_dir)
Point = dir_point(basename(p_dir))
vol_file = 'history/volumes.txt'
indices, volumes = read_csv(vol_file, sep=' ').values[:, :2].transpose()
if force:
index = indices[-1] * 0.2
else:
index, _ = select_from_plot(Point, indices, volumes, i)
if index:
return ceil(index)
else:
return index
def divergent_points(configs=None,
conf_plot=False,
save_path=None,
load_path=None):
from os.path import isdir
from numpy import array
from matplotlib.pyplot import plot, show, figure
if load_path and isdir(load_path):
from os import chdir
from numpy import loadtxt
chdir(load_path)
try:
convergent = loadtxt('convergent.csv')
divergent = loadtxt('divergent.csv')
if len(convergent.shape) == 1:
convergent = convergent.reshape(1, convergent.shape[0])
if len(divergent.shape) == 1:
divergent = divergent.reshape(1, divergent.shape[0])
except OSError:
print(f"Invalid path given: '{load_path}'.")
return
try:
fit_line = loadtxt('fit_line.csv')
except OSError:
fit_line = []
else:
convergent = []
divergent = []
fit_line = []
if conf_plot:
from matplotlib.text import Text
ylabel = []
i = 0
for c, sims in sim_paths().items():
if configs and c not in configs:
continue
if conf_plot:
i += 1
print(i, c)
# ylabel += [Text(x=(i-1), y=i, text=c)]
ylabel += [c]
for path in sims:
from os import chdir
from os.path import basename, isfile
from lib.utils import dir_point
chdir(path)
if conf_plot:
Point = [dir_point(basename(path))[0], i]
else:
Point = dir_point(basename(path))
if isfile('max_volume_reached'):
divergent += [Point]
elif isfile('history/volumes.txt'):
convergent += [Point]
fig = figure()
ax = fig.add_subplot(111)
if len(convergent) > 0:
convergent = array(convergent)
# *zip(*convergent)
ax.plot(convergent[:, 0], convergent[:, 1], 'b+')
if len(divergent) > 0:
divergent = array(divergent)
# *zip(*divergent)
ax.plot(divergent[:, 0], divergent[:, 1], 'r+')
if len(fit_line) > 0:
ax.plot(fit_line[:, 0], fit_line[:, 1], 'k')
if save_path and isdir(save_path):
from os import makedirs, chdir, getcwd
from numpy import savetxt, array
chdir(save_path)
makedirs('DivergentPlot', exist_ok=True)
chdir('DivergentPlot')
savetxt('convergent.csv', array(convergent))
savetxt('divergent.csv', array(divergent))
savetxt('fit_line.csv', array(fit_line))
elif save_path:
print(f"Invalid path given: '{path}'.")
if conf_plot:
from numpy import linspace
yticks_location = linspace(1, len(ylabel), len(ylabel))
ax.set_yticks(yticks_location)
ax.set_yticklabels(ylabel)
ax.set_ylim(0, len(ylabel) + 1)
if save_path and isdir(save_path):
from matplotlib.pyplot import savefig
savefig(save_path + '/DivergentPlot/DivergentPlot.pdf')
elif load_path and isdir(load_path):
from matplotlib.pyplot import savefig
savefig(load_path + '/DivergentPlot.pdf')
show()
def export_data(name, unpack):
from os import chdir
from os.path import basename, dirname, isfile
import json
import numpy as np
from lib.utils import fit_dir, dir_point
fit_d = fit_dir(name)
chdir(fit_d)
if not unpack:
try:
with open('sims.json', 'r') as file:
sims = json.load(file)
except FileNotFoundError:
print('No simulation assigned to this fit.')
return
data = []
for s in sims:
if s[-1] == '/':
s = s[:-1]
config = basename(dirname(s))
Point = dir_point(basename(s))
point_data = {}
if isfile(s + '/max_volume_reached'):
print(f'\033[38;5;41m{Point}\033[0m not included in fit, '
'because '
'\033[38;5;80mmax_volume_reached\033[0m is present.')
print(f"\033[38;5;80m config: '{config}'\033[0m")
continue
try:
with open(s + '/measures.json', 'r') as file:
measures = json.load(file)
except FileNotFoundError:
print(f'\033[38;5;41m{Point}\033[0m no measure file present.')
print(f"\033[38;5;80m config: '{config}'\033[0m")
continue
point_data['lambda'] = Point[0]
point_data['beta'] = Point[1]
point_data['config'] = config
point_data.update(measures.copy())
for prop in ['cut', 'block', 'time']:
try:
del point_data[prop]
except KeyError:
pass
data += [point_data]
print(f'\033[38;5;41m{Point}\033[0m collected.')
with open('data.json', 'w') as file:
json.dump(data, file, indent=4)
elif unpack in ['v', 'volumes']:
try:
with open('data.json', 'r') as file:
data = json.load(file)
except FileNotFoundError:
print("No data file (\033[38;5;80m'data.json'\033[0m) to unpack.")
return
vol_data = []
for point_data in data:
Point = (point_data['lambda'], point_data['beta'])
config = point_data['config']
try:
volume = point_data['volume']
except KeyError:
continue
# print(f'\033[38;5;41m{Point}\033[0m, {config}:')
# print('\t', volume)
vol_data += [[Point[0], Point[1], volume[0], volume[1], config]]
with open('volumes.csv', 'w') as file:
sep = ' '
end = '\n'
file.write('# Lambda Beta Volume Error Config' + end)
vol_data = sorted(vol_data)
for point_vol in vol_data:
str_point_vol = []
for x in point_vol:
str_point_vol += [str(x)]
file.write(sep.join(str_point_vol) + end)
print(f"\033[38;5;41m({name})\033[0m volumes from "
"\033[38;5;80m'data.json'\033[0m unpacked to "
"\033[38;5;80m'volumes.csv'\033[0m")
elif unpack in ['g', 'gauge-action']:
try:
with open('data.json', 'r') as file:
data = json.load(file)
except FileNotFoundError:
print("No data file (\033[38;5;80m'data.json'\033[0m) to unpack.")
return
g_action_data = []
for point_data in data:
Point = (point_data['lambda'], point_data['beta'])
config = point_data['config']
try:
g_action = point_data['action']
g_action_density = point_data['action-density']
except KeyError:
continue
# print(f'\033[38;5;41m{Point}\033[0m, {config}:')
# print('\t', volume)
g_action_data += [[Point[0], Point[1], g_action[0], g_action[1],
g_action_density[0], g_action_density[1], config]]
with open('gauge_action.csv', 'w') as file:
sep = ' '
end = '\n'
file.write('# Lambda Beta Action Error ActionDensity Error Config'
+ end)
g_action_data = sorted(g_action_data)
for point_g_action in g_action_data:
str_point_g_action = []
for x in point_g_action:
str_point_g_action += [str(x)]
file.write(sep.join(str_point_g_action) + end)
print(f"\033[38;5;41m({name})\033[0m gauge actions from "
"\033[38;5;80m'data.json'\033[0m unpacked to "
"\033[38;5;80m'gauge_action.csv'\033[0m")
elif unpack in ['p', 'profiles']:
try:
with open('data.json', 'r') as file:
data = json.load(file)
except FileNotFoundError:
print("No data file (\033[38;5;80m'data.json'\033[0m) to unpack.")
return
profile_data = []
for point_data in data:
Point = (point_data['lambda'], point_data['beta'])
config = point_data['config']
try:
profile, errors = point_data['profiles_corr']
except KeyError:
continue
profile_data += [[Point[0], Point[1], config, *profile, *errors]]
with open('profiles.csv', 'w') as file:
sep = ' '
end = '\n'
file.write('# Lambda[0] Beta[1] Config[2] Profile[3:3+t}] ' +
'Errors[3+t:3+2t]' + end)
profile_data = sorted(profile_data)
for point_profile in profile_data:
str_point_profile = []
for x in point_profile:
str_point_profile += [str(x)]
file.write(sep.join(str_point_profile) + end)
print(f"\033[38;5;41m({name})\033[0m profiles from "
"\033[38;5;80m'data.json'\033[0m unpacked to "
"\033[38;5;80m'profiles.csv'\033[0m")
elif unpack in ['pf', 'profiles-fit', 'pf2', 'profiles-fit2']:
try:
with open('data.json', 'r') as file:
data = json.load(file)
except FileNotFoundError:
print("No data file (\033[38;5;80m'data.json'\033[0m) to unpack.")
return
profile_fit_data = []
for point_data in data:
Point = (point_data['lambda'], point_data['beta'])
config = point_data['config']
try:
if unpack[-1] != '2':
fit_data = point_data['profiles_corr_fit']
else:
fit_data = point_data['profiles-corr-fit2']
len_corr = fit_data['par'][0]
err = np.sqrt(fit_data['cov'][0][0])
except KeyError:
continue
profile_fit_data += [[Point[0], Point[1], len_corr, err, config]]
if unpack[-1] != '2':
file_name = 'profiles_length.csv'
else:
file_name = 'profiles_length2.csv'
with open(file_name, 'w') as file:
sep = ' '
end = '\n'
file.write('# Lambda Beta Corr_Length Error Config' + end)
profile_fit_data = sorted(profile_fit_data)
for point_profile in profile_fit_data:
str_point_profile = []
for x in point_profile:
str_point_profile += [str(x)]
file.write(sep.join(str_point_profile) + end)
print(f"\033[38;5;41m({name})\033[0m profiles fit from "
"\033[38;5;80m'data.json'\033[0m unpacked to "
f"\033[38;5;80m'{file_name}'\033[0m")
elif unpack in ['top', 'susc', 'top-susc']:
try:
with open('data.json', 'r') as file:
data = json.load(file)
except FileNotFoundError:
print("No data file (\033[38;5;80m'data.json'\033[0m) to unpack.")
return
susc_data = []
for point_data in data:
Point = (point_data['lambda'], point_data['beta'])
config = point_data['config']
try:
susc = point_data['top-susc']
except KeyError:
continue
susc_data += [[Point[0], Point[1], susc[0], susc[1], config]]
with open('top_susc.csv', 'w') as file:
sep = ' '
end = '\n'
file.write('# Lambda Beta Top-Susc Error Config' + end)
susc_data = sorted(susc_data)
for point_susc in susc_data:
str_point_susc = []
for x in point_susc:
str_point_susc += [str(x)]
file.write(sep.join(str_point_susc) + end)
print(f"\033[38;5;41m({name})\033[0m topological susceptibilities from "
"\033[38;5;80m'data.json'\033[0m unpacked to "
"\033[38;5;80m'top_susc.csv'\033[0m")
elif unpack in ['t', 'torelons']:
try:
with open('data.json', 'r') as file:
data = json.load(file)
except FileNotFoundError:
print("No data file (\033[38;5;80m'data.json'\033[0m) to unpack.")
return
torelon_data = []
for point_data in data:
Point = (point_data['lambda'], point_data['beta'])
config = point_data['config']
try:
# print(Point)
torelon, errors = point_data['torelon-decay']
# print(len(point_data['torelon-decay']))
except KeyError:
continue
torelon_data += [[Point[0], Point[1], config, *torelon, *errors]]
with open('torelons.csv', 'w') as file:
sep = ' '
end = '\n'
file.write('# Lambda[0] Beta[1] Config[2] Torelon[3:3+t}] ' +
'Errors[3+t:3+2t]' + end)
for point_torelon in torelon_data:
str_point_torelon = []
torelon_data = sorted(torelon_data)
for x in point_torelon:
str_point_torelon += [str(x)]
file.write(sep.join(str_point_torelon) + end)
print(f"\033[38;5;41m({name})\033[0m torelons from "
"\033[38;5;80m'data.json'\033[0m unpacked to "
"\033[38;5;80m'torelons.csv'\033[0m")
elif unpack in ['tf', 'torelons-fit', 'tf2', 'torelons-fit2']:
try:
with open('data.json', 'r') as file:
data = json.load(file)
except FileNotFoundError:
print("No data file (\033[38;5;80m'data.json'\033[0m) to unpack.")
return
profile_fit_data = []
for point_data in data:
Point = (point_data['lambda'], point_data['beta'])
config = point_data['config']
try:
if unpack[-1] != '2':
fit_data = point_data['torelon-decay-fit']
else:
fit_data = point_data['torelon-decay-fit2']
len_corr = fit_data['par'][0]
err = np.sqrt(fit_data['cov'][0][0])
except KeyError:
continue
profile_fit_data += [[Point[0], Point[1], len_corr, err, config]]
if unpack[-1] != '2':
file_name = 'torelon_length.csv'
else:
file_name = 'torelon_length2.csv'
with open(file_name, 'w') as file:
sep = ' '
end = '\n'
file.write('# Lambda Beta Corr_Length Error Config' + end)
profile_fit_data = sorted(profile_fit_data)
for point_profile in profile_fit_data:
str_point_profile = []
for x in point_profile:
str_point_profile += [str(x)]
file.write(sep.join(str_point_profile) + end)
print(f"\033[38;5;41m({name})\033[0m profiles fit from "
"\033[38;5;80m'data.json'\033[0m unpacked to "
f"\033[38;5;80m'{file_name}'\033[0m")
def info_fit(name, kind='sims'):
from os import chdir
from os.path import basename, dirname
from pprint import pprint
import json
from lib.utils import fit_dir, config_dir, dir_point
if kind in ['s', 'sims', None]:
kind = 'sims'
elif kind in ['o', 'obs']:
kind = 'obs'
chdir(fit_dir(name))
try:
with open('sims.json', 'r') as file:
sims = json.load(file)
except FileNotFoundError:
print('No simulation already assigned to this fit.')
d = {}
for s in sims:
if s[-1] == '/':
s = s[:-1]
if kind == 'sims':
config = basename(dirname(s))
Point = dir_point(basename(s))
try:
d[config] += [Point]
except KeyError:
d[config] = [Point]
elif kind == 'obs':
try:
with open(s + '/measures.json', 'r') as file:
measures = json.load(file)
except FileNotFoundError:
measures = {}
flags = ''
if 'cut' in measures.keys():
flags += 'C'
if 'block' in measures.keys():
flags += 'B'
if 'volume' in measures.keys():
flags += 'V'
config = basename(dirname(s))
Point = dir_point(basename(s))
try:
d[config] += [[Point, flags]]
except KeyError:
d[config] = [[Point, flags]]
for k in d.keys():
d[k] = sorted(d[k])
if kind == 'sims':
pprint(d)
elif kind == 'obs':
pprint(d)
else:
raise ValueError('info-fit: kind {kind} not recognized')
def fit_divergence(name, kind='volumes', reload=False):
from os import chdir
from os.path import basename, dirname, isfile
from datetime import datetime
import json
from pprint import pprint
from numpy import genfromtxt
from lib.utils import fit_dir, dir_point
from lib.analysis.fit import fit_divergence
if kind in ['v', 'volumes']:
kind = 'volumes'
kind_file = 'volumes'
elif kind in ['p', 'profiles']:
kind = 'profiles'
kind_file = 'profiles_length'
elif kind in ['t', 'torelons']:
kind = 'torelons'
kind_file = 'torelons_length'
elif kind in ['g', 'gauge-action']:
kind = 'gauge-action'
kind_file = 'gauge_action'
elif kind in ['top', 'susc', 'top-susc']:
kind = 'topological-susceptibility'
kind_file = 'top_susc'
else:
raise ValueError(f'{kind} not available for divergence fit.')
fit_d = fit_dir(name)
chdir(fit_d)
try:
with open('sims.json', 'r') as file:
sims = json.load(file)
except FileNotFoundError:
print('No simulation already assigned to this fit.')
# do not return, because if 'kind.csv' is present it can use that
if not isfile(f'{kind_file}.csv') or reload:
d = {}
lambdas = []
betas = []
means = []
errors = []
for s in sims:
if s[-1] == '/':
s = s[:-1]
config = basename(dirname(s))
Point = dir_point(basename(s))
if isfile(s + '/max_volume_reached'):
print(f'\033[38;5;41m{Point}\033[0m not included in fit, '
'because '
'\033[38;5;80mmax_volume_reached\033[0m is present.')
print(f"\033[38;5;80m config: '{config}'\033[0m")
continue
try:
with open(s + '/measures.json', 'r') as file:
measures = json.load(file)
except FileNotFoundError:
measures = {}
with open(s + '/state.json', 'r') as file:
state = json.load(file)
if 'time' in measures.keys():
s_time = datetime.strptime(state['end_time'],
'%d-%m-%Y %H:%M:%S')
m_time = datetime.strptime(measures['time'],
'%d-%m-%Y %H:%M:%S')
else:
print(f'Mising time in {Point}, in config: {config}.')
return
# print(Point)
# print(s_time, type(s_time), '\n' + str(m_time), type(m_time))
if(s_time > m_time):
print('\033[38;5;203mWarning:\033[0m in Point '
f'\033[38;5;41m{Point}\033[0m in '
f"\033[38;5;80mconfig: '{config}'\033[0m measures are "
'\033[38;5;210mnot up to date\033[0m '
'with last simulation\'s data')
print()
d[Point] = {'config': config, **measures,
'time_sim_end': state['end_time']}
k_key = f'{kind}'[:-1]
if k_key in measures.keys():
lambdas += [Point[0]]
betas += [Point[1]]
means += [measures[k_key][0]]
errors += [measures[k_key][1]]
else:
print(f"Missing {k_key} in {Point}, in config: {config}.")
return
with open(f'{kind_file}.csv', 'w') as file:
file.write('# Lambda Beta Volume Error Config\n')
for Point, attr in d.items():
mean, err = attr[f'{kind}'[:-1]]
data = [Point[0], Point[1], mean, err, attr['config']]
line = ' '.join([str(x) for x in data])
file.write(line + '\n')
else:
data = genfromtxt(f'{kind_file}.csv', unpack=True)
lambdas, betas = data[:2]
if kind == 'gauge-action':
means, errors = data[4:6]
else:
means, errors = data[2:4]
fit_divergence(lambdas, means, errors, betas, kind=kind)
def set_block(p_dir, i=0, force=False):
from os import chdir
from os.path import basename
from math import log
import json
import numpy as np
from pandas import read_csv
from lib.utils import dir_point
chdir(p_dir)
Point = dir_point(basename(p_dir))
with open('measures.json', 'r') as file:
measures = json.load(file)
cut = measures['cut']
if not cut:
print(
"No 'cut' found, so it's not possible to go on with the 'block'.")
return None
vol_file = 'history/volumes.txt'
indices, volumes = read_csv(vol_file, sep=' ').values[:, :2].transpose()
imax = indices[-1]
volumes_cut = volumes[indices > cut]
indices_cut = indices[indices > cut]
if volumes_cut.std() < 1e-5:
# use gauge-action as a pseudo-volume if volume is fixed
vol_file = 'history/gauge.txt'
indices, volumes = read_csv(vol_file,
sep=' ').values[:, :2].transpose()
imax = indices[-1]
volumes_cut = volumes[indices > cut]
indices_cut = indices[indices > cut]
ratio = 1.3
block_sizes = [
ratio**k for k in range(15,
int(log(imax - cut, ratio)) - 12)
]
stdevs = []
for bs in block_sizes:
_, stdev = blocked_mean_std(indices_cut, volumes_cut, bs)
stdevs += [stdev]
if force:
block = block_sizes[np.array(stdevs).argmax()]
else:
block, _ = select_from_plot(Point,
block_sizes,
stdevs,
i,
proj_axis='xy',
block=True)
if block == None or block == -1:
print('Nothing done.')
return None
block = int(block)
return block