input_data.HDXColumns(model,
input_file_lig,
"Lig",
default_sigma=sigma0,
offset=offset,
temp=298.15,
saturation=saturation,
percentD=percentD)
# Initialize a calculation model for each state (Only MultiExponentialModel available now)
for state in model.states:
hdxm = hdx_models.MultiExponentialModel(model=model,
state=state,
sigma=sigma0,
num_exp_bins=num_exp_bins,
init=init)
###############################
### Sampling:
###
# If benchmark is set to true, run a short simulation to estimate runtime
if run_type == "benchmark":
sampling.benchmark(model, sample_sigma=True)
exit()
# Simulated Annealing macro runs high temperature dynamics and relaxes
# to low temperature, followed by an equilibration run of "nsteps"
saturation = 1.0 # Deuterium saturation in experiment
percentD = True # Is the data in percent D (True) or Deuterium units? - Always percentD for Workbench.
###############################
### System Setup:
###############################
# Initialize model (name, FASTA sequence, offset)
model = system_setup.HDXModel("name", inseq, offset=offset)
# Add data to model (model, filename)
input_data.HDXWorkbench(model, workbench_file)
#Initialize a sampling model for each state (Multiexponential in this case)
for state in model.states:
hdxm = hdx_models.MultiExponentialModel(model=model,
state=state,
sigma=sigma0,
init=init)
###############################
### Sampling:
###
# If benchmark is set to true, run a short simulation to estimate runtime
if run_type == "benchmark":
sampling.benchmark(model, sample_sigma=True)
exit()
# Simulated Annealing macro runs high temperature dynamics and relaxes
# to low temperature, followed by an equilibration run of "nsteps"
if run_type == "sampling":
sampling.simulated_annealing(model,
input_data.HDXColumns(model,
input_file_lig,
"Lig",
default_sigma=sigma0,
offset=offset,
temp=298.15,
saturation=saturation,
percentD=percentD)
# Initialize a calculation model for each state (Only MultiExponentialModel available now)
for state in model.states:
hdxm = hdx_models.MultiExponentialModel(model=model,
state=state,
sigma=sigma0,
num_exp_bins=num_exp_bins,
init=init,
first_tp=5,
last_tp=60000)
###############################
### Sampling:
###
# If benchmark is set to true, run a short simulation to estimate runtime
if run_type == "benchmark":
sampling.benchmark(model, sample_sigma=True)
exit()
# Simulated Annealing macro runs high temperature dynamics and relaxes
# to low temperature, followed by an equilibration run of "nsteps"