def analyse_topcMCTime(params):
"""Analysis the topological charge in monte carlo time slices."""
obs_data = params["data"]
MC_interval, interval_size = get_intervals(
obs_data.NCfgs,
numsplits=params["MC_time_splits"],
intervals=params["MCInt"])
bs_index_lists = np.random.randint(interval_size,
size=(params["N_bs"], interval_size))
for MC_int in MC_interval:
analyse_topcMC = TopcMCIntervalAnalyser(obs_data("topc"),
mc_interval=MC_int,
dryrun=params["dryrun"],
parallel=params["parallel"],
numprocs=params["numprocs"],
verbose=params["verbose"])
analyse_default(analyse_topcMC,
params["N_bs"],
bs_index_lists=bs_index_lists)
def analyse_qtq0_effective_mass_mc(params):
"""
Pre-analyser for the effective mass qtq0 with q0 at a fixed flow time in
Monte Carlo intervals.
"""
MC_interval, interval_size = get_intervals(
params["data"].NCfgs,
numsplits=params["MC_time_splits"],
intervals=params["MCInt"])
bs_index_lists = np.random.randint(interval_size,
size=(params["N_bs"], interval_size))
obs_data = params["data"]
if not obs_data.has_observable("topct"):
print("Missing topct data. Returning.")
return
for MC_int in MC_interval:
analyse_eff_mass_MC = QtQ0EffectiveMassMCAnalyser(
obs_data("topct"),
mc_interval=MC_int,
plot_color=params["color"],
dryrun=params["dryrun"],
parallel=params["parallel"],
numprocs=params["numprocs"],
verbose=params["verbose"])
for q0_flow_time in params["q0_flow_times"]:
if q0_flow_time != 0.6: # Only zeroth flow
continue
analyse_eff_mass_MC.set_time(q0_flow_time)
analyse_default(analyse_eff_mass_MC,
params["N_bs"],
bs_index_lists=bs_index_lists,
only_generate_data=params["only_generate_data"])
def analyse_topcrMC(params):
"""
Analysis of the ratio with R=q4c/q2 of the topological charge. Performs an
analysis on Q^2 and Q^4 with the same bootstrap samples, such that an post
analysis can be performed on these explisitly. In a MC interval
"""
MC_interval, interval_size = get_intervals(
params["data"].NCfgs,
numsplits=params["MC_time_splits"],
intervals=params["MCInt"])
N_bs = params["N_bs"]
bs_index_lists = np.random.randint(interval_size,
size=(N_bs, interval_size))
for MC_int in MC_interval:
topc2_analysis = Topc2MCIntervalAnalyser(params["data"]("topc"),
mc_interval=MC_int,
dryrun=params["dryrun"],
parallel=params["parallel"],
numprocs=params["numprocs"],
verbose=params["verbose"])
topc4_analysis = Topc4MCIntervalAnalyser(params["data"]("topc"),
mc_interval=MC_int,
dryrun=params["dryrun"],
parallel=params["parallel"],
numprocs=params["numprocs"],
verbose=params["verbose"])
N_cfgs_topc2 = topc2_analysis.N_configurations
N_cfgs_topc4 = topc4_analysis.N_configurations
assert N_cfgs_topc2 == N_cfgs_topc4, "NCfgs differ in topc2 and topc4."
analyse_default(topc2_analysis,
N_bs,
NBins=150,
bs_index_lists=bs_index_lists)
analyse_default(topc4_analysis, N_bs, bs_index_lists=bs_index_lists)