def truncated_gaussian_factor(exp, a, b, sig):
print scipy.special.erf((b - exp) / sig), scipy.special.erf(
(a - exp) / sig), b - exp, a - exp
return 1 / (0.5 * (scipy.special.erf(
(b - exp) / sig * numpy.sqrt(3.1415)) - scipy.special.erf(
(a - exp) / sig * numpy.sqrt(3.1415))))
###############################
### System Setup:
###############################
# Initialize model (name, FASTA sequence, offset)
model = system_setup.HDXModel("simple", inseq, offset=offset)
# Create a fragment
frag = system_setup.Fragment(inseq, 1, 15, 0)
# Add data to the fragment
for t in data:
tp = frag.add_timepoint(t)
for d in data[t]:
tp.add_replicate(d)
# enumerate and score all solutions
best_grid = sampling.enumerate_fragment(frag, exp_grid, sigma0)
###############################
else:
nsteps = args.NSTEPS
num_best_models = 1000 # Number of best models to consider for analysis
# Non user controlled vbl - for now.
annealing_steps = 200 # steps per temperature in annealing - 100-200 sufficient
sigma0 = 5 # Estimate for experimental error in %D Units
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
init = "random" # How to initialize - either "random" or "enumerate". Enumerate is slower but sampling will converge faster
annealing_steps = 100 # steps per temperature in annealing - 100-200 sufficient
nsteps = 2000 # equilibrium steps. 5000 to 10000
num_best_models = 100 # Number of best models to consider for analysis
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
### Here the real work begins....
### You should not have to change anything beneath this line.
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
############################################
### System Setup:
# Initialize model
model = system_setup.HDXModel(
"Simulated", # Name for this system
inseq, # Fasta sequence string
offset=offset) # Offset from FASTA sequence to HDX data
# Add data to model.
input_data.HDXColumns(
model,
input_file_apo,
"Apo",
default_sigma=sigma0,
offset=offset,
temp=
298.15, # temperature of experiment - currently not used, so no need to change.
saturation=saturation,
percentD=percentD)
input_data.HDXColumns(model,