def beta645_L32_analysis(run_pre_analysis=True,
run_post_analysis=True,
only_generate_data=False,
observables=None,
include_b645x48xx3x96=True,
post_analysis_data_type=["bootstrap"]):
from pre_analysis.pre_analyser import pre_analysis
from post_analysis.post_analyser import post_analysis
from default_analysis_params import get_default_parameters
from tools.folderreadingtools import get_num_observables
import copy
import os
# Different batches
data_batch_folder = "../data/data11"
obs_exlusions = ["w_t_energy", "energy", "topcMC", "topsusMC", "qtq0effMC"]
default_params = get_default_parameters(
data_batch_folder=data_batch_folder, obs_exlusions=obs_exlusions)
# Post analysis figures folder
figures_folder = "figures_b645_32xx4"
if include_b645x48xx3x96:
figures_folder = "figures_b645_32xx4_full"
if not isinstance(observables, type(None)):
default_params["observables"] = observables
# Post analysis parameters
line_fit_interval_points = 20
# topsus_fit_targets = [0.3,0.4,0.5,0.58]
# topsus_fit_targets = [0.3, 0.4, 0.5, 0.6] # tf = sqrt(8*t0)
topsus_fit_targets = [0.5, 0.6]
energy_fit_target = 0.3
# Method of continuum extrapolation.
# Options: plateau, plateau_mean, nearest, interpolate, bootstrap
extrapolation_methods = [
"plateau", "plateau_mean", "nearest", "interpolate", "bootstrap"
]
extrapolation_methods = ["plateau"]
extrapolation_methods = ["bootstrap"]
plot_continuum_fit = False
# Topcr reference value. Options: [float], t0beta, article, t0
topcr_t0 = "t0beta"
# Number of different sectors we will analyse in euclidean time
default_params["numsplits_eucl"] = 4
intervals_eucl = [None, None, None, None]
# Number of different sectors we will analyse in monte carlo time
default_params["MC_time_splits"] = 4
# MC_intervals = [[0, 1000], [500, 1000], [500, 1000], [175, 250]]
MC_intervals = [None, None, None, None]
# Extraction point in flow time a*t_f for q0 in qtq0
q0_flow_times = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6] # [fermi]
# Flow time indexes in percent to plot qtq0 in euclidean time at
euclidean_time_percents = [0, 0.25, 0.50, 0.75, 1.00]
# euclidean_time_percents = [0]
# Blocking
default_params["blocking_analysis"] = True
# Check to only generate data for post-analysis
default_params["only_generate_data"] = only_generate_data
########## Main analysis ##########
databeta60 = copy.deepcopy(default_params)
databeta60["batch_name"] = "beta60"
databeta60["ensemble_name"] = r"$A$"
databeta60["beta"] = 6.0
databeta60["block_size"] = 10 # None
databeta60["topc_y_limits"] = [-9, 9]
databeta60["topc2_y_limits"] = [-81, 81]
databeta60["NCfgs"] = get_num_observables(databeta60["batch_folder"],
databeta60["batch_name"])
databeta60["obs_file"] = "24_6.00"
databeta60["MCInt"] = MC_intervals[0]
databeta60["N"] = 24
databeta60["NT"] = 2 * databeta60["N"]
databeta60["color"] = "#e41a1c"
databeta61 = copy.deepcopy(default_params)
databeta61["batch_name"] = "beta61"
databeta61["ensemble_name"] = r"$B$"
databeta61["beta"] = 6.1
databeta61["block_size"] = 10 # None
databeta61["topc_y_limits"] = [-12, 12]
databeta61["topc2_y_limits"] = [-144, 144]
databeta61["NCfgs"] = get_num_observables(databeta61["batch_folder"],
databeta61["batch_name"])
databeta61["obs_file"] = "28_6.10"
databeta61["MCInt"] = MC_intervals[1]
databeta61["N"] = 28
databeta61["NT"] = 2 * databeta61["N"]
databeta61["color"] = "#377eb8"
databeta62 = copy.deepcopy(default_params)
databeta62["batch_name"] = "beta62"
databeta62["ensemble_name"] = r"$C$"
databeta62["beta"] = 6.2
databeta62["block_size"] = 10 # None
databeta62["topc_y_limits"] = [-12, 12]
databeta62["topc2_y_limits"] = [-196, 196]
databeta62["NCfgs"] = get_num_observables(databeta62["batch_folder"],
databeta62["batch_name"])
databeta62["obs_file"] = "32_6.20"
databeta62["MCInt"] = MC_intervals[2]
databeta62["N"] = 32
databeta62["NT"] = 2 * databeta62["N"]
databeta62["color"] = "#4daf4a"
databeta645 = copy.deepcopy(default_params)
databeta645["flow_epsilon"] = 0.02
databeta645["batch_name"] = "beta645"
databeta645["ensemble_name"] = r"$D_2$"
databeta645["beta"] = 6.45
databeta645["block_size"] = 25 # None
databeta645["topc_y_limits"] = [-15, 15]
databeta645["topc2_y_limits"] = [-300, 300]
databeta645["NCfgs"] = get_num_observables(databeta645["batch_folder"],
databeta645["batch_name"])
databeta645["obs_file"] = "48_6.45"
databeta645["MCInt"] = MC_intervals[3]
databeta645["N"] = 48
databeta645["NT"] = 2 * databeta645["N"]
databeta645["color"] = "#984ea3"
databeta645_32xx4 = copy.deepcopy(default_params)
databeta645_32xx4["flow_epsilon"] = 0.02
databeta645_32xx4["batch_name"] = "beta645-32xx4"
databeta645_32xx4["ensemble_name"] = r"$D_1$"
databeta645_32xx4["beta"] = 6.45
databeta645_32xx4["block_size"] = 40 # None
databeta645_32xx4["topc_y_limits"] = [-15, 15]
databeta645_32xx4["topc2_y_limits"] = [-300, 300]
databeta645_32xx4["NCfgs"] = get_num_observables(
databeta645_32xx4["batch_folder"], databeta645_32xx4["batch_name"])
databeta645_32xx4["obs_file"] = "32_6.45"
databeta645_32xx4["MCInt"] = MC_intervals[3]
databeta645_32xx4["N"] = 32
databeta645_32xx4["NT"] = 32
databeta645_32xx4["color"] = "#98A519"
# Adding relevant batches to args
if include_b645x48xx3x96:
analysis_parameter_list = [
databeta60, databeta61, databeta62, databeta645, databeta645_32xx4
]
else:
analysis_parameter_list = [
databeta60, databeta61, databeta62, databeta645_32xx4
]
# analysis_parameter_list = [databeta645_32xx4, databeta645]
section_seperator = "=" * 160
print section_seperator
print "Observables to be analysed: %s" % ", ".join(
default_params["observables"])
print section_seperator + "\n"
# Submitting main analysis
if run_pre_analysis:
for analysis_parameters in analysis_parameter_list:
pre_analysis(analysis_parameters)
if not analysis_parameter_list[0]["MCInt"] is None:
assert sum(
[len(plist["MCInt"]) - len(analysis_parameter_list[0]["MCInt"])
for plist in analysis_parameter_list]) == 0, \
"unequal amount of MC intervals"
# Submitting post-analysis data
if run_post_analysis:
if len(analysis_parameter_list) >= 3:
post_analysis(analysis_parameter_list,
default_params["observables"],
topsus_fit_targets,
line_fit_interval_points,
energy_fit_target,
q0_flow_times,
euclidean_time_percents,
extrapolation_methods=extrapolation_methods,
plot_continuum_fit=plot_continuum_fit,
post_analysis_data_type=post_analysis_data_type,
figures_folder=figures_folder,
gif_params=default_params["gif"],
verbose=default_params["verbose"])
else:
msg = "Need at least 3 different beta values to run post analysis"
msg += "(%d given)." % len(analysis_parameter_list)
print msg
if not init_file.exists():
initialize_using_FBP(sim)
else:
with h5py.File(init_file, mode='r') as file:
#take the latest sample from the folder
samples_history = file['Layers 1/samples_history'][()]
init_Sym = util.symmetrize(cp.asarray(samples_history[-1]))
del samples_history
u_samples_history = file['Layers 0/samples_history'][()]
u_init_Sym = util.symmetrize(cp.asarray(u_samples_history[-1]))
del u_samples_history
sim.Layers[-1].current_sample_sym = init_Sym
sim.Layers[-1].current_sample = sim.Layers[-1].current_sample_sym[
sim.fourier.basis_number_2D_ravel - 1:]
sim.Layers[-1].record_sample()
sim.Layers[0].current_sample_sym = u_init_Sym
sim.Layers[0].current_sample = sim.Layers[0].current_sample_sym[
sim.fourier.basis_number_2D_ravel - 1:]
sim.Layers[0].record_sample()
print("Used bytes so far, before even running the simulation {}".format(
sim.mempool.used_bytes()))
sim.run()
sim.save(str(simResultPath / 'result.hdf5'))
print('simulation Result path is {}'.format(simResultPath))
#
#do analysis offline
pa.post_analysis(folderName, simResultPath.parent)
def topc_modes_analysis():
"""Analysis for different lattice sizes and their topological charges."""
default_params = get_default_parameters(data_batch_folder="temp")
default_params["blocking_analysis"] = True
default_params["observables"] = ["plaq", "topc", "topc2", "topc4", "topcr",
"topsus", "topsusqtq0", "qtq0e",
"qtq0eff", "topcMC"]
default_params["observables"] = ["topc2", "topc4", "topcr"]
# Check to only generate data for post-analysis
default_params["only_generate_data"] = False
run_pre_analysis = False
run_post_analysis = False
# run_pre_analysis = True
# run_post_analysis = True
default_params["verbose"] = True
########## Post analysis parameters ##########
line_fit_interval_points = 20
topsus_fit_targets = [0.5, 0.6]
energy_fit_target = 0.3
q0_flow_times = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6] # [fermi]
euclidean_time_percents = [0, 0.25, 0.50, 0.75, 1.00]
extrapolation_methods = ["bootstrap"]
plot_continuum_fit = False
post_analysis_data_type = ["bootstrap"]
figures_folder = "figures/topc_modes_analysis"
data_path = "../data/"
if not os.path.isdir(data_path):
data_path = "../" + data_path
########## Smaug data 8x16 analysis ##########
smaug8x16_data_beta60_analysis = copy.deepcopy(default_params)
smaug8x16_data_beta60_analysis["batch_folder"] = data_path
smaug8x16_data_beta60_analysis["batch_name"] = "beta60_8x16_run"
smaug8x16_data_beta60_analysis["ensemble_name"] = r"$E$"
smaug8x16_data_beta60_analysis["beta"] = 6.0
smaug8x16_data_beta60_analysis["block_size"] = 10 # None
smaug8x16_data_beta60_analysis["topc_y_limits"] = [-2, 2]
smaug8x16_data_beta60_analysis["num_bins_per_int"] = 32
smaug8x16_data_beta60_analysis["bin_range"] = [-2.5, 2.5]
smaug8x16_data_beta60_analysis["hist_flow_times"] = [0, 250, 600]
smaug8x16_data_beta60_analysis["NCfgs"] = get_num_observables(
smaug8x16_data_beta60_analysis["batch_folder"],
smaug8x16_data_beta60_analysis["batch_name"])
smaug8x16_data_beta60_analysis["obs_file"] = "8_6.00"
smaug8x16_data_beta60_analysis["N"] = 8
smaug8x16_data_beta60_analysis["NT"] = 16
smaug8x16_data_beta60_analysis["color"] = "#377eb8"
########## Smaug data 12x24 analysis ##########
smaug12x24_data_beta60_analysis = copy.deepcopy(default_params)
smaug12x24_data_beta60_analysis["batch_folder"] = data_path
smaug12x24_data_beta60_analysis["batch_name"] = "beta60_12x24_run"
smaug12x24_data_beta60_analysis["ensemble_name"] = r"$F$"
smaug12x24_data_beta60_analysis["beta"] = 6.0
smaug12x24_data_beta60_analysis["block_size"] = 10 # None
smaug12x24_data_beta60_analysis["topc_y_limits"] = [-4, 4]
smaug12x24_data_beta60_analysis["num_bins_per_int"] = 16
smaug12x24_data_beta60_analysis["bin_range"] = [-4.5, 4.5]
smaug12x24_data_beta60_analysis["hist_flow_times"] = [0, 100, 600]
smaug12x24_data_beta60_analysis["NCfgs"] = get_num_observables(
smaug12x24_data_beta60_analysis["batch_folder"],
smaug12x24_data_beta60_analysis["batch_name"])
smaug12x24_data_beta60_analysis["obs_file"] = "12_6.00"
smaug12x24_data_beta60_analysis["N"] = 12
smaug12x24_data_beta60_analysis["NT"] = 24
smaug12x24_data_beta60_analysis["color"] = "#377eb8"
########## Smaug data 16x32 analysis ##########
smaug16x32_data_beta61_analysis = copy.deepcopy(default_params)
smaug16x32_data_beta61_analysis["batch_folder"] = data_path
smaug16x32_data_beta61_analysis["batch_name"] = "beta61_16x32_run"
smaug16x32_data_beta61_analysis["ensemble_name"] = r"$G$"
smaug16x32_data_beta61_analysis["beta"] = 6.1
smaug16x32_data_beta61_analysis["block_size"] = 10 # None
smaug16x32_data_beta61_analysis["topc_y_limits"] = [-8, 8]
smaug16x32_data_beta61_analysis["num_bins_per_int"] = 16
smaug16x32_data_beta61_analysis["bin_range"] = [-7.5, 7.5]
smaug16x32_data_beta61_analysis["hist_flow_times"] = [0, 100, 400]
smaug16x32_data_beta61_analysis["NCfgs"] = get_num_observables(
smaug16x32_data_beta61_analysis["batch_folder"],
smaug16x32_data_beta61_analysis["batch_name"])
smaug16x32_data_beta61_analysis["obs_file"] = "16_6.10"
smaug16x32_data_beta61_analysis["N"] = 16
smaug16x32_data_beta61_analysis["NT"] = 32
smaug16x32_data_beta61_analysis["color"] = "#377eb8"
param_list = [
smaug8x16_data_beta60_analysis,
smaug12x24_data_beta60_analysis,
smaug16x32_data_beta61_analysis]
if run_pre_analysis:
# Submitting analysis
for analysis_parameters in param_list:
pre_analysis(analysis_parameters)
# Submitting post-analysis data
if run_post_analysis:
if len(param_list) >= 3:
post_analysis(param_list,
default_params["observables"],
topsus_fit_targets, line_fit_interval_points,
energy_fit_target,
q0_flow_times, euclidean_time_percents,
extrapolation_methods=extrapolation_methods,
plot_continuum_fit=plot_continuum_fit,
post_analysis_data_type=post_analysis_data_type,
figures_folder=figures_folder, # "figures/topc_modes_analysis"
verbose=default_params["verbose"])
else:
msg = "Need at least 3 different beta values to run post analysis"
msg += "(%d given)." % len(analysis_parameter_list)
print msg
# Loads topc data
data = []
# N_val = [24, 24, 28]
for i, param in enumerate(param_list):
print "Loading data for: {}".format(param["batch_name"])
data_, p = get_data_parameters(param)
data.append({"data": data_("topc")["obs"].T,
"beta": param["beta"],
"N": param["N"],
"ensemble_name": param["ensemble_name"]})
# Flow time to plots
flow_times = [0, 25, 50, 100, 150, 250, 450, 600]
# Histogram plotting
xlim = 7.5
NBins = np.arange(-xlim, xlim, 0.05)
for t_f in flow_times:
# Adds unanalyzed data
fig, axes = plt.subplots(len(param_list), 1,
sharey=False, sharex=True)
axes = np.atleast_1d(axes)
for i, ax in enumerate(axes):
# lab = r"${0:d}^3\times{1:d}$, $\beta={2:.2f}$".format(
# data[i]["N"], data[i]["N"]*2, data[i]["beta"])
lab = data[i]["ensemble_name"]
weights = np.ones_like(data[i]["data"][t_f])
weights /= len(data[i]["data"][t_f])
ax.hist(data[i]["data"][t_f], bins=NBins,
label=lab, weights=weights)
ax.legend(loc="upper right")
ax.grid(True)
ax.set_xlim(-xlim, xlim)
if i == 1:
ax.set_ylabel(r"Hits(normalized)")
elif i == 2:
ax.set_xlabel(r"$Q$")
# Sets up figure
figpath = figures_folder
if not os.path.isdir(figpath):
figpath = "../" + figpath
check_folder(figpath, verbose=default_params["verbose"])
figpath = os.path.join(figpath, "topc_modes_tf{}.pdf".format(t_f))
fig.savefig(figpath)
print "Figure saved at {0:s}".format(figpath)
plt.close(fig)