def load_inputs(s, p):
""" Load the correct input files for the extra info """
input_files = file_names('input', s, p)
size = ini.get_size(s)
# Load/initialise binned data in the usual way
cuts = ini.file_input(input_files['cu'], size)
accepts = ini.file_input(input_files['ac'], size)
dmu = ini.file_input(input_files['dm'], size)
dmu_acc = ini.file_input(input_files['dma'], size)
dE = ini.file_input(input_files['de'], size)
dE_acc = ini.file_input(input_files['dea'], size)
round_trips = ini.file_input(input_files['rt'], 5)
if int(round_trips[-1] == 0):
round_trips[-1] = 1 # initialise round trip flag
return_dict = {'cu': cuts, 'ac': accepts, \
'dm': dmu, 'dma': dmu_acc, \
'de': dE, 'dea': dE_acc, \
'rt': round_trips}
# Fine-grained matrix only works for TRAP == True (due to indexing issues)
if TRAP == True:
mm_size = (minibins_per_window, size)
minimat = ini.file_input(input_files['mm'], mm_size)
return_dict['mm'] = minimat
return return_dict
def load_inputs(s, p):
""" Load the correct input files for a Wang Landau simulation """
input_files = file_names('input', s, p)
disp = ini.file_input(input_files['d'], (Natoms, 3))
size = ini.get_size(s)
weights = ini.file_input(input_files['w'], size)
hist = ini.file_input(None, size)
return disp, {'w': weights, 'h': hist}
def load_inputs(s, p):
""" Load the correct input files for a Transition Matrix simulation """
input_files = file_names('input', s, p)
disp = ini.file_input(input_files['d'], (Natoms, 3))
size = ini.get_size(s)
weights = ini.file_input(input_files['w'], size)
hist = ini.file_input(input_files['h'], size)
Cmat = ini.file_input(input_files['c'], (size,size) )
return disp, {'w': weights, 'h': hist, 'c': Cmat}
def gen_input_files():
""" If starting a new fixed-weights simulation, empty files will need
to be created to be imported during the first loop iteration. """
if algorithm not in ('multicanonical', 'transition'):
return
print ""
for p in range(Np):
# Map to subdomain index
s = ini.map_proc_to_subdom(p)
# Input files as decided by program
input_files = alg.file_names('input', s, p)
sname, pname = ini.naming_system(s, p)
# User input files from params
params_input_files = {'d': None,
'w': params_weights_file,
'h': params_hist_file,
'c': params_Cmat_file,
's': params_series_file,}
# Check if we need to add '_pY_sX'
params_weights_file_alt = ini.rename_inputs(params_weights_file, sname[0], '')
params_hist_file_alt = ini.rename_inputs(params_hist_file, sname[0], pname[0])
params_Cmat_file_alt = ini.rename_inputs(params_Cmat_file, sname[0], pname[0])
params_series_file_alt = ini.rename_inputs(params_series_file, sname[0], pname[0])
params_input_files_alt = {'d': None,
'w': params_weights_file_alt,
'h': params_hist_file_alt,
'c': params_Cmat_file_alt,
's': params_series_file_alt}
# Correct sizes
size = ini.get_size(s)
start_files = {'d': np.zeros((Natoms, 3)),
'w': np.zeros(size), 'h': np.zeros(size),
'c': np.zeros((size,size)), 's': [np.ones(5)*-1,]}
for key in params_input_files.keys():
# If an input file has been given in params.py
if params_input_files[key] != None:
# If the default input file doesn't yet exist,
# We need to save a copy of the params file to this name
if os.path.exists(input_files[key]) == False:
# First check for the params file modified by '_pY_sX'
if os.path.exists(params_input_files_alt[key]) == True:
tmp = np.loadtxt(params_input_files_alt[key])
print " Copying %s -> %s" %(params_input_files_alt[key], input_files[key])
np.savetxt(input_files[key], tmp)
# Then check for the exact file name given in params.py
elif os.path.exists(params_input_files[key]) == True:
tmp = np.loadtxt(params_input_files[key])
print " Copying %s -> %s" %(params_input_files[key], input_files[key])
np.savetxt(input_files[key], tmp)
# Error if params file hasn't been found
else:
error(this_file, " Neither files %s or %s not found." %(params_input_files_alt[key],params_input_files[key]))
# If the default input file does exist, leave it alone
else:
print " File %s exists and will be read as input" %input_files[key]
print ""
print "File check completed. "
return
fig3, ax3 = plt.subplots()
ax3.set_title("Log of diffusivity")
ax3.set_xlabel(r"$\mu \times \beta/N$")
ax3.set_ylabel("log(Diffusivity)")
ax3.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
if save == True:
global_diffusivity = []
# ------------------------- #
# Iterate over subdomains #
# ------------------------- #
for s in range(Ns_eff):
# Initialise arrays for combined data within subdom
size = ini.get_size(s)
hist = np.zeros(size)
cuts = np.zeros(size)
for p in p_list[s::Ns_eff]:
# Get file names to load
input_files = alg.file_names('output', s, p)
extra_input_files = dyn.file_names('output', s, p)
# Load input files
this_hist = np.loadtxt(input_files['h'])
this_cuts = np.loadtxt(extra_input_files['cu'])
# Add to combined
hist = hist + this_hist
cuts = cuts + this_cuts