Exemplo n.º 1
0
 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)
Exemplo n.º 2
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    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)
Exemplo n.º 3
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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)
Exemplo n.º 4
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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,))
Exemplo n.º 5
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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)
Exemplo n.º 6
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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]])
Exemplo n.º 7
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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))
Exemplo n.º 8
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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()
Exemplo n.º 9
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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]
Exemplo n.º 11
0
            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)
Exemplo n.º 12
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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)
Exemplo n.º 13
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        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)
Exemplo n.º 14
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                            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)
Exemplo n.º 16
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                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
Exemplo n.º 18
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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
Exemplo n.º 20
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        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)
Exemplo n.º 22
0
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)
Exemplo n.º 23
0
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