def AutoSave(self):
'''DoAutoSave(self) --> None
Function that conducts an autosave of the model.
'''
io.save_gx(self.parent.model.get_filename(), self.parent.model, \
self.optimizer, self.config)
def AutoSave(self):
'''DoAutoSave(self) --> None
Function that conducts an autosave of the model.
'''
io.save_gx(self.model.get_filename(), self.model, \
self.optimizer, self.config)
def save(frame, event):
'''
save(frame, event) --> None
Event handler for saving a model file ...
'''
frame.model.set_script(frame.script_editor.GetText())
fname = frame.model.get_filename()
# If model hasn't been saved
if fname == '':
# Proceed with calling save as
save_as(frame, event)
else:
# If it has been saved just save it
try:
io.save_gx(fname, frame.model, frame.solver_control.optimizer,\
frame.config)
except modellib.IOError as e:
ShowModelErrorDialog(frame, e.__str__())
except Exception as e:
outp = StringIO()
traceback.print_exc(200, outp)
val = outp.getvalue()
outp.close()
ShowErrorDialog(frame, 'Could not save the file. Python Error:'\
'\n%s'%(val,))
set_title(frame)
frame.main_frame_statusbar.SetStatusText('Model saved to file', 1)
def save(frame, event):
'''
save(frame, event) --> None
Event handler for saving a model file ...
'''
frame.model.set_script(frame.script_editor.GetText())
fname = frame.model.get_filename()
# If model hasn't been saved
if fname == '':
# Proceed with calling save as
save_as(frame, event)
else:
# If it has been saved just save it
try:
io.save_gx(fname, frame.model, frame.solver_control.optimizer,\
frame.config)
except modellib.IOError, e:
ShowModelErrorDialog(frame, e.__str__())
except Exception, e:
outp = StringIO.StringIO()
traceback.print_exc(200, outp)
val = outp.getvalue()
outp.close()
ShowErrorDialog(frame, 'Could not save the file. Python Error:'\
'\n%s'%(val,))
def save_as(frame, event):
'''save_as(frame, event) --> None
Event handler for save as ...
'''
dlg = wx.FileDialog(frame, message="Save As", defaultFile="",\
wildcard="GenX File (*.gx)|*.gx",\
style=wx.SAVE | wx.CHANGE_DIR
)
if dlg.ShowModal() == wx.ID_OK:
frame.model.set_script(frame.script_editor.GetText())
fname = dlg.GetPath()
result = True
if os.path.exists(fname):
filepath, filename = os.path.split(fname)
result = ShowQuestionDialog(frame, \
'The file %s already exists. Do you wish to overwrite it?'%filename\
, 'Overwrite?')
if result:
try:
io.save_gx(fname, frame.model, frame.solver_control.optimizer,\
frame.config)
except modellib.IOError, e:
ShowModelErrorDialog(frame, e.__str__())
except Exception, e:
outp = StringIO.StringIO()
traceback.print_exc(200, outp)
val = outp.getvalue()
outp.close()
ShowErrorDialog(frame, 'Could not save the file. Python Error:'\
'\n%s'%(val,))
set_title(frame)
def make_gx_file():
mod = model.Model()
config = io.Config()
opt = diffev.DiffEv()
par=parameters.Parameters()
make_par_file()
par.set_ascii_input_new(os.path.join(dump_path_locator(),'par_table.tab'))
io.load_gx('temp_DONOT_delete.gx',mod,opt,config)
mod.script=make_script_file()
mod.parameters=par
mod.data,num_ctr=make_data_file()
for i in range(len(mod.data.items)-num_ctr):
mod.data.items[i+num_ctr].use=False
io.save_gx(gx_file_name,mod,opt,config)
print('gx file is saved in the current workdir!\n')
print('##########Basic model info##########')
print('Resonant element: '+RAXR_EL)
print('Number of Gaussian peaks in CTR: '+str(NUMBER_GAUSSIAN_PEAK))
print('Number of Gaussian peaks in RAXR: '+str(NUMBER_GAUSSIAN_PEAK_FREEZE))
print('Number of RAXR datasets: '+str(NUMBER_RAXS_SPECTRA))
print('RAXR data fit mode: '+{'\'MI\''[1:3]:'Model independent fit','\'MD\''[1:3]:'Model dependent fit'}[RAXR_FIT_MODE[1:3]])
def make_gx_file():
mod = model.Model()
config = io.Config()
opt = diffev.DiffEv()
par = parameters.Parameters()
make_par_file()
par.set_ascii_input_new(os.path.join(dump_path_locator(), 'table.tab'))
io.load_gx('temp_DONOT_delete.gx', mod, opt, config)
mod.script = make_script_file()
mod.parameters = par
mod.data, num_ctr = make_data_file()
for i in range(len(mod.data.items) - num_ctr):
mod.data.items[i + num_ctr].use = False
io.save_gx(gx_file_name, mod, opt, config)
print('gx file is saved in the current workdir!\n')
print('##########Basic model info##########')
print('Domain number: ' + str(len(domain_setup_HLT)) +
' half layer domains and ' + str(len(domain_setup_FLT)) +
' full layer domains')
print('sorbate: ' + sorbate)
local_structure_db = eval(LOCAL_STRUCTURE_MATCH_LIB)
for key in local_structure_db.keys():
if sorbate in local_structure_db[key]:
print('local_structure: ' + key)
break
else:
pass
binding_mode_db = []
for i in range(len(binding_mode)):
if binding_mode[i][0].startswith('clean'):
binding_mode_db.append('Clean_surface')
elif binding_mode[i][0].startswith('CS'):
binding_mode_db.append('Corner-sharing')
elif binding_mode[i][0].startswith('ES'):
binding_mode_db.append('Edge-sharing')
elif binding_mode[i][0].startswith('TD'):
binding_mode_db.append('Tridentate-binding')
elif binding_mode[i][0].startswith('OS'):
binding_mode_db.append('Outersphere binding')
print('Binding_mode: ' + ','.join(binding_mode_db))
def save_as(frame, event):
'''save_as(frame, event) --> None
Event handler for save as ...
'''
dlg = wx.FileDialog(frame, message="Save As", defaultFile="",\
wildcard="GenX File (*.gx)|*.gx",\
style=wx.FD_SAVE | wx.FD_CHANGE_DIR
)
if dlg.ShowModal() == wx.ID_OK:
frame.model.set_script(frame.script_editor.GetText())
fname = dlg.GetPath()
base, ext = os.path.splitext(fname)
if ext == '':
ext = '.gx'
# print(base,ext)
fname = base + ext
result = True
if os.path.exists(fname):
filepath, filename = os.path.split(fname)
result = ShowQuestionDialog(frame, \
'The file %s already exists. Do you wish to overwrite it?'%filename\
, 'Overwrite?')
if result:
try:
io.save_gx(fname, frame.model, frame.solver_control.optimizer,\
frame.config)
except modellib.IOError as e:
ShowModelErrorDialog(frame, e.__str__())
except Exception as e:
outp = StringIO()
traceback.print_exc(200, outp)
val = outp.getvalue()
outp.close()
ShowErrorDialog(frame, 'Could not save the file. Python Error:'\
'\n%s'%(val,))
set_title(frame)
dlg.Destroy()
best_fom=mod_raxr_mi.fom
for i in range(len(mod_raxr_mi.parameters.data)):
if mod_raxr_mi.parameters.data[i][2]==True:
print 'scan ',mod_raxr_mi.parameters.data[i][0],' now'
left,right=float(mod_raxr_mi.parameters.data[i][3]),float(mod_raxr_mi.parameters.data[i][4])
values=np.arange(left,right,(right-left)/scan_number)
best=float(mod_raxr_mi.parameters.data[i][1])
fom_container=[]
for value in values:
mod_raxr_mi.parameters.data[i][1]=value
mod_raxr_mi.simulate()
fom_container.append(mod_raxr_mi.fom)
if not np.where(values==value)[0][0]%10:
print 'doing scan ',np.where(values==value)[0][0],'FOM=',mod_raxr_mi.fom
values_sub=np.compress(np.array(fom_container) < best_fom*error_bar_level,np.array(values))
if len(values_sub)==0:
mod_raxr_mi.parameters.data[i][1]=best
print 'the range of this parameter is too large, either make a finer scan or shrink the range'
else:
left_error,right_error=np.min(values_sub)-best,np.max(values_sub)-best
if right_error<0:
right_error=right-best
mod_raxr_mi.parameters.data[i][1]=best
mod_raxr_mi.parameters.data[i][5]='(%3.6f,%3.6f)'%(left_error,right_error)
#mod_raxr_mi.simulate()
print 'after scan fom is ',mod_raxr_mi.fom
print 'the error bar based on FOM scan is: %3.6f(%3.6f,%3.6f)'%(best,left_error,right_error)
io.save_gx(file_path_raxr_mi.replace('.gx','_error_calculated.gx'),mod_raxr_mi,opt_raxr_mi,config_raxr_mi)
error_values = []
for index in range(n_elements):
(error_low, error_high) = opt.calc_error_bar(\
index, error_bar_level)
error_str = '(%.3e, %.3e,)'%(error_low, error_high)
error_values.append(error_str)
mod.parameters.set_error_pars(error_values)
#export table
if do_final_tab:
print 'saving parameter table to file...'
mod.export_table(os.path.join(mainpath,name + '_final.tab'))
#save updated .gx file
print 'saving updated .gx file (this may take a while)...'
filehandling.save_gx(filename, mod, opt, config)
# export atom table (label, element, x, y, z, occ, u)
if do_atom_tab:
print 'saving atom table to file...'
exportfile = os.path.join(mainpath,name + '_atom_tab.dat')
f = open(exportfile,'w')
atomdata = mod.script_module.sample.create_uc_output()
id = atomdata[6]
el = atomdata[5]
x = atomdata[0]
y = atomdata[1]
z = atomdata[2]
oc = atomdata[3]
mod.simulate()
temp_fom.append(mod.fom)
fom_max, fom_min = max(temp_fom), min(temp_fom)
fom_max_index, fom_min_index = temp_fom.index(fom_max), temp_fom.index(
fom_min)
par_max, par_min = np.arange(
left, right, (right - left) / 5)[fom_max_index], np.arange(
left, right, (right - left) / 5)[fom_min_index]
slopes.append(abs((fom_max - fom_min) / (par_max - par_min)))
print mod.parameters.get_value(
i, 0
), "par_max,par_min=", par_max, par_min, "sensitivity=", slopes[-1]
mod.parameters.set_value(i, 1, val_init)
else:
slopes.append(-1000 + i)
indexs = np.array(slopes).argsort()[::-1]
print indexs
for index in indexs:
names.append(mod.parameters.get_value(index, 0))
values.append(mod.parameters.get_value(index, 1))
switch.append(mod.parameters.get_value(index, 2))
low.append(mod.parameters.get_value(index, 3))
high.append(mod.parameters.get_value(index, 4))
for i in range(len(indexs)):
mod.parameters.set_value(i, 0, names[i])
mod.parameters.set_value(i, 1, values[i])
mod.parameters.set_value(i, 2, switch[i])
mod.parameters.set_value(i, 3, low[i])
mod.parameters.set_value(i, 4, high[i])
io.save_gx(sys.argv[2], mod, opt, config)
mod_raxr_mi = model.Model()
config_raxr_mi = io.Config()
opt_raxr_mi = diffev.DiffEv()
mod_raxr_md = model.Model()
config_raxr_md = io.Config()
opt_raxr_md = diffev.DiffEv()
io.load_gx(file_path_raxr_mi,mod_raxr_mi,opt_raxr_mi,config_raxr_mi)
io.load_gx(file_path_raxr_md,mod_raxr_md,opt_raxr_md,config_raxr_md)
first_grid_mi,first_grid_md=None,None
for i in range(len(mod_raxr_mi.parameters.data)):
if mod_raxr_mi.parameters.data[i][0]=='rgh_raxs.setA1':
first_grid_mi=i
break
else:
print mod_raxr_mi.parameters.data[i][0]
for i in range(len(mod_raxr_md.parameters.data)):
if mod_raxr_md.parameters.data[i][0]=='rgh_raxs.setA1':
first_grid_md=i
break
else:
print mod_raxr_md.parameters.data[i][0]
print first_grid_mi,first_grid_md
for i in range(first_grid_md,len(mod_raxr_md.parameters.data)):
for k in range(6):
mod_raxr_md.parameters.set_value(i,k,mod_raxr_mi.parameters.get_value(first_grid_mi+i-first_grid_md,k))
io.save_gx(file_path_raxr_md.replace('.gx','_merged.gx'),mod_raxr_md,opt_raxr_md,config_raxr_md)
for val in np.arange(left, right, (right - left) / 5):
mod.parameters.set_value(i, 1, val)
mod.simulate()
temp_fom.append(mod.fom)
fom_max, fom_min = max(temp_fom), min(temp_fom)
fom_max_index, fom_min_index = temp_fom.index(fom_max), temp_fom.index(fom_min)
par_max, par_min = (
np.arange(left, right, (right - left) / 5)[fom_max_index],
np.arange(left, right, (right - left) / 5)[fom_min_index],
)
slopes.append(abs((fom_max - fom_min) / (par_max - par_min)))
print mod.parameters.get_value(i, 0), "par_max,par_min=", par_max, par_min, "sensitivity=", slopes[-1]
mod.parameters.set_value(i, 1, val_init)
else:
slopes.append(-1000 + i)
indexs = np.array(slopes).argsort()[::-1]
print indexs
for index in indexs:
names.append(mod.parameters.get_value(index, 0))
values.append(mod.parameters.get_value(index, 1))
switch.append(mod.parameters.get_value(index, 2))
low.append(mod.parameters.get_value(index, 3))
high.append(mod.parameters.get_value(index, 4))
for i in range(len(indexs)):
mod.parameters.set_value(i, 0, names[i])
mod.parameters.set_value(i, 1, values[i])
mod.parameters.set_value(i, 2, switch[i])
mod.parameters.set_value(i, 3, low[i])
mod.parameters.set_value(i, 4, high[i])
io.save_gx(sys.argv[2], mod, opt, config)
opt.get_create_trial())
except io.OptionError, e:
print 'Could not write option solver.create trial'
else:
print 'Could not write config to file'
### end of block: save config
# build outfile name
outfile = infile
outfile = outfile.replace('.gx', '_ran.gx')
#outfile = '%s_%s_%s_kr%.2f_km%.2f_pf%.2f_run%d_ran.gx' % (outfile, trial,fom, kr, km, pf,iter)
#outfile=outfile+fom+str(iter)+'_ran.gx'
#outfile = '%s_%s_run%d.gx' % (outfile, TAG, iter)
if rank == 0:
print 'Saving the initial model to %s' % outfile
io.save_gx(outfile, mod, opt, config)
print ''
print 'Settings:'
print '---------'
print 'Number of fit parameters = %s' % len(opt.best_vec)
print 'FOM function = %s' % mod.fom_func.func_name
print ''
print 'opt.km = %s' % opt.km
print 'opt.kr = %s' % opt.kr
print 'opt.create_trial = %s' % opt.create_trial.im_func
print ''
print 'opt.use_parallel_processing = %s' % opt.use_parallel_processing
print ''
print 'opt.use_max_generations = %s' % opt.use_max_generations
sys.path.append('C:\\apps\\genx_pc_qiu')
sys.path.append('C:\\apps\\genx_pc_qiu\\lib')
sys.path.append('/u1/uaf/cqiu/genx_pc_qiu')
import model,diffev,time,fom_funcs
import filehandling as io
import glob
import numpy as np
first_grid=54
grid_gap=20
path='/import/c/w/cqiu/temp_gx_files/'
gx_files=glob.glob(path+'*_May8.gx')#file must be sortable to have the code work correctly
gx_files.sort()
mod = model.Model()
config = io.Config()
opt = diffev.DiffEv()
io.load_gx(gx_files[0],mod,opt,config)
for gx_file in gx_files[1:]:
print "processing ",gx_file
i=gx_files.index(gx_file)
begin_grid=first_grid+grid_gap*i
mod_temp = model.Model()
config_temp = io.Config()
opt_temp = diffev.DiffEv()
io.load_gx(gx_file,mod_temp,opt_temp,config_temp)
for grid_index in range(begin_grid,begin_grid+grid_gap):
for k in range(5):
mod.parameters.set_value(grid_index,k,mod_temp.parameters.get_value(grid_index,k))
io.save_gx(path+"combined_model_file.gx",mod,opt,config)
try:
config.set('solver', 'create trial',\
opt.get_create_trial())
except io.OptionError, e:
print 'Could not write option solver.create trial'
else:
print 'Could not write config to file'
### end of block: save config
# build outfile names
outfile = infile.replace('.gx',
str(rank / (size / split_jobs)) + '_ran.gx')
if rank_group == 0:
print 'Saving the initial model to %s' % outfile
io.save_gx(outfile, mod, opt, config)
if rank == 0:
print ''
print 'Settings:'
print '---------'
print 'Number of fit parameters = %s' % len(opt.best_vec)
print 'FOM function = %s' % mod.fom_func.func_name
print ''
print 'opt.km = %s' % opt.km
print 'opt.kr = %s' % opt.kr
print 'opt.create_trial = %s' % opt.create_trial.im_func
print ''
print 'opt.use_parallel_processing = %s' % opt.use_parallel_processing
print ''
print 'opt.use_max_generations = %s' % opt.use_max_generations
opt.get_create_trial())
except io.OptionError, e:
print 'Could not write option solver.create trial'
else:
print 'Could not write config to file'
### end of block: save config
# build outfile name
outfile = infile
outfile = outfile.replace('.gx','')
#outfile = '%s_%s_%s_kr%.2f_km%.2f_pf%.2f_run%d.gx' % (outfile, trial,fom, kr, km, pf,iter)
outfile=outfile+fom+str(iter)+'_ran.gx'
#outfile = '%s_%s_run%d.gx' % (outfile, TAG, iter)
if rank==0:
print 'Saving the initial model to %s'%outfile
io.save_gx(outfile, mod, opt, config)
print ''
print 'Settings:'
print '---------'
print 'Number of fit parameters = %s' % len(opt.best_vec)
print 'FOM function = %s' % mod.fom_func.func_name
print ''
print 'opt.km = %s' % opt.km
print 'opt.kr = %s' % opt.kr
print 'opt.create_trial = %s' % opt.create_trial.im_func
print ''
print 'opt.use_parallel_processing = %s' % opt.use_parallel_processing
print ''
print 'opt.use_max_generations = %s' % opt.use_max_generations
sys.path.append('C:\\apps\\genx_pc_qiu')
sys.path.append('C:\\apps\\genx_pc_qiu\\lib')
sys.path.append('/u1/uaf/cqiu/genx_pc_qiu')
import model,diffev,time,fom_funcs
import filehandling as io
import glob
import numpy as np
first_grid=54
grid_gap=20
path='/import/c/w/cqiu/temp_gx_files/'
gx_files=glob.glob(path+'*_May8.gx')#file must be sortable to have the code work correctly
gx_files.sort()
mod = model.Model()
config = io.Config()
opt = diffev.DiffEv()
io.load_gx(gx_files[0],mod,opt,config)
for gx_file in gx_files[1:]:
print "processing ",gx_file
i=gx_files.index(gx_file)
begin_grid=first_grid+grid_gap*i
mod_temp = model.Model()
config_temp = io.Config()
opt_temp = diffev.DiffEv()
io.load_gx(gx_file,mod_temp,opt_temp,config_temp)
for grid_index in range(begin_grid,begin_grid+grid_gap):
for k in range(5):
mod.parameters.set_value(grid_index,k,mod_temp.parameters.get_value(grid_index,k))
io.save_gx(path+"combined_model_file.gx",mod,opt,config)
try:
config.set('solver', 'create trial',\
opt.get_create_trial())
except io.OptionError, e:
print 'Could not write option solver.create trial'
else:
print 'Could not write config to file'
### end of block: save config
# build outfile name
outfile = infile
outfile = outfile.replace('.gx','')
outfile = '%s_%s_kr%.2f_km%.2f_run%d.gx' % (outfile, fom, kr, km, iter)
print 'Saving the initial model to %s'%outfile
filehandling.save_gx(outfile, mod, opt, config)
print ''
print 'Settings:'
print '---------'
print 'Number of fit parameters = %s' % len(opt.best_vec)
print 'FOM function = %s' % mod.fom_func.func_name
print ''
print 'opt.km = %s' % opt.km
print 'opt.kr = %s' % opt.kr
print 'opt.create_trial = %s' % opt.create_trial.im_func
print ''
print 'opt.use_parallel_processing = %s' % opt.use_parallel_processing
print 'opt.chunksize = %s' % opt.chunksize
print 'opt.processes = %s' % opt.processes
best = float(mod_raxr_mi.parameters.data[i][1])
fom_container = []
for value in values:
mod_raxr_mi.parameters.data[i][1] = value
mod_raxr_mi.simulate()
fom_container.append(mod_raxr_mi.fom)
if not np.where(values == value)[0][0] % 10:
print 'doing scan ', np.where(
values == value)[0][0], 'FOM=', mod_raxr_mi.fom
values_sub = np.compress(
np.array(fom_container) < best_fom * error_bar_level,
np.array(values))
if len(values_sub) == 0:
mod_raxr_mi.parameters.data[i][1] = best
print 'the range of this parameter is too large, either make a finer scan or shrink the range'
else:
left_error, right_error = np.min(values_sub) - best, np.max(
values_sub) - best
if right_error < 0:
right_error = right - best
mod_raxr_mi.parameters.data[i][1] = best
mod_raxr_mi.parameters.data[i][5] = '(%3.6f,%3.6f)' % (left_error,
right_error)
#mod_raxr_mi.simulate()
print 'after scan fom is ', mod_raxr_mi.fom
print 'the error bar based on FOM scan is: %3.6f(%3.6f,%3.6f)' % (
best, left_error, right_error)
io.save_gx(file_path_raxr_mi.replace('.gx', '_error_calculated.gx'),
mod_raxr_mi, opt_raxr_mi, config_raxr_mi)
def autosave():
#print 'Updating the parameters'
mod.parameters.set_value_pars(opt.best_vec)
io.save_gx(outfile, mod, opt, config)
mod_raxr_mi = model.Model()
config_raxr_mi = io.Config()
opt_raxr_mi = diffev.DiffEv()
mod_raxr_md = model.Model()
config_raxr_md = io.Config()
opt_raxr_md = diffev.DiffEv()
io.load_gx(file_path_raxr_mi,mod_raxr_mi,opt_raxr_mi,config_raxr_mi)
io.load_gx(file_path_raxr_md,mod_raxr_md,opt_raxr_md,config_raxr_md)
first_grid_mi,first_grid_md=None,None
for i in range(len(mod_raxr_mi.parameters.data)):
if mod_raxr_mi.parameters.data[i][0]=='rgh_raxs.setA1':
first_grid_mi=i
break
else:
print mod_raxr_mi.parameters.data[i][0]
for i in range(len(mod_raxr_md.parameters.data)):
if mod_raxr_md.parameters.data[i][0]=='rgh_raxs.setA1':
first_grid_md=i
break
else:
print mod_raxr_md.parameters.data[i][0]
print first_grid_mi,first_grid_md
for i in range(first_grid_md,len(mod_raxr_md.parameters.data)):
for k in range(6):
mod_raxr_md.parameters.set_value(i,k,mod_raxr_mi.parameters.get_value(first_grid_mi+i-first_grid_md,k))
io.save_gx(file_path_raxr_md.replace('.gx','_merged.gx'),mod_raxr_md,opt_raxr_md,config_raxr_md)
def autosave():
#print 'Updating the parameters'
mod.parameters.set_value_pars(opt.best_vec)
io.save_gx(outfile, mod, opt, config)
try:
config.set('solver', 'create trial',\
opt.get_create_trial())
except io.OptionError, e:
print 'Could not write option solver.create trial'
else:
print 'Could not write config to file'
### end of block: save config
# build outfile names
outfile = infile.replace('.gx',str(rank/(size/split_jobs))+'_ran.gx')
if rank_group==0:
print 'Saving the initial model to %s'%outfile
io.save_gx(outfile, mod, opt, config)
if rank==0:
print ''
print 'Settings:'
print '---------'
print 'Number of fit parameters = %s' % len(opt.best_vec)
print 'FOM function = %s' % mod.fom_func.func_name
print ''
print 'opt.km = %s' % opt.km
print 'opt.kr = %s' % opt.kr
print 'opt.create_trial = %s' % opt.create_trial.im_func
print ''
print 'opt.use_parallel_processing = %s' % opt.use_parallel_processing
print ''
print 'opt.use_max_generations = %s' % opt.use_max_generations