tsa_saxs/Minimization/Fit_TSA.py

#!/usr/bin/python

import sys
import os.path
import time
import numpy as np
import os

localtime = time.asctime( time.localtime(time.time()) )
print(localtime)

sys.path.append("/home/semeraro/Programming/PythonStuff/python_tools") 
from Tools import Reading
from Tools import ReadData
from Tools import PlotData
import FitTools
import TSA_algorithm


############################################################
############################################################
############################################################

def X2function(Q,IDATA,I,ERR,N):
	'X^2 calculation'
	X2 = np.sum(  ( ( IDATA-I ) / ERR )**2 )
	return X2/(Q.shape[0]-(N-1))


############################################################

t0=time.time()

############################################################ Read Options & Parameters

############### Read File & Options
print("\n-- Reading File:")
read_par = Reading(sys.argv)
iters = int(sys.argv[2])
print("\n-- Reading Options")
options = read_par.Options()

############### Read Function, from the MODEL parameter
"-- Reading Function"
(function, pltOptions) = FitTools.ChooseFunction( options['function'] )

############### Read Parameters
print("\n-- Reading Parameters\n")
(NAME, PAR, FIX, PRIOR, L_LIM, H_LIM) = read_par.Parameters()


############################################################ Read & Select Data
(Q, IDATA ,ERR) = ReadData(	options['datafile'] ).SelectColumns(	options['qmin'],
																	options['qmax'],
																	options['bing'], 
																	options['err_mul'] )

############################################################ Fit Routine

t0=time.time()	
	
collection = []
collection_X2 = []

if options['plot']==0:
	for it in range(0,iters) :
		print("\n---- Iteration N.", it+1," -----\n")
			
		# Define the inputs for the function
		inputs = [Q, IDATA, ERR, PAR, FIX, PRIOR, L_LIM, H_LIM, function, options['temp'], options['folder']]
				
		(PAR_RES, X2) = TSA_algorithm.SimAnnealing( inputs )
		print("----- Print Results from Iteration N. ",it+1," -----\n")

		print("X^2\t%.7f\n" %X2 )
		for i in range(len(NAME)) :
			print("%d\t%4s\t%.7f" %(i,NAME[i],PAR_RES[i]) )
	
	for it in range(0,iters) :	
		collection.append(PAR_RES)	
		collection_X2.append(X2)	

		#os.rename("./"+options['folder']+"/Trace.dat", "./"+options['folder']+"/Trace_"+str(it)+".dat")	

	t1=time.time()-t0
	(minutes,seconds,cents) = FitTools.TimeCount(t1)
	print ( "%2d:%2d.%2d" %(minutes,seconds,cents) )
	(minutes,seconds,cents) = FitTools.TimeCount(t1/iters)
	print ( "%2d:%2d.%2d per iteration" %(minutes,seconds,cents) )		
			
else:
	intensity_init = function(np.array(Q),PAR)
	I = intensity_init.intensity()		
############################################################ Save Results & Plots
if options['plot']==0:
	collection = np.array(collection).transpose()
	collection_X2 = np.array(collection_X2)
	
	mean = np.mean(collection,axis=1)
	stde = np.std(collection,axis=1)
	median = np.median(collection,axis=1)
	Q1 = np.quantile(collection,0.25,axis=1)
	Q3 = np.quantile(collection,0.75,axis=1)

	intensity_init = function(np.array(Q),mean)
	I = intensity_init.intensity()	
	N_free=collection.shape[0]
	for p in range(collection.shape[0]) : 
		if FIX[p] =="f" : N_free-= 1
	X2_mean = X2function(Q,IDATA,I,ERR,N_free)

	print("\n----- Print statitics -----\n")

	print("\n# Mean\tst.dev.\tX^2 from mean\t",X2_mean,"\n")			
	for p in range(collection.shape[0]) :
		if FIX[p] !="f": print(NAME[p],"\t",mean[p],"\t",stde[p])
		else : print(NAME[p],"\t",mean[p],"\t-")
	print("\n# Median\tQ1\tQ3\n")			
	for p in range(collection.shape[0]) :
		if FIX[p] !="f": print(NAME[p],"\t",median[p],"\t",Q1[p],"\t",Q3[p])
		else : print(NAME[p],"\t",median[p],"\t-")
	
	np.savetxt("./"+options['folder']+"/Results_collection-X2.dat", collection_X2)		
	np.savetxt("./"+options['folder']+"/Results_collection.dat", collection)		

	X2_mean_to_print = np.empty(1,dtype=float)
	X2_mean_to_print[0] = X2_mean
	
	np.savetxt("./"+options['folder']+"/Results_mean-X2.dat", X2_mean_to_print)		
	np.savetxt("./"+options['folder']+"/Results_mean-values.dat", np.column_stack((mean,stde)) )	
	np.savetxt("./"+options['folder']+"/Results_IQR-values.dat", np.column_stack((median,Q1,Q3)) )	
			
print("-- Save Results in ...")
if options['plot'] == 0:
	FitTools.PrintResultsFile(  options['title'], options['folder'], 
								options['datafile'], options['function'], 
								NAME, mean, FIX, X2_mean )

if options['plot'] == 1:
	PAR_RES = PAR.copy()

plotObj = PlotData(	options['title'],
					options['folder'],
					options['qmin'],
					options['qmax'] )

if pltOptions['strucCtr'] == 0:
	if pltOptions['porodCtr'] == 0:
		plotObj.Form(Q, IDATA, ERR, I, mean, pltOptions)
	if pltOptions['porodCtr'] == 1:
		plotObj.Form_Porod(Q, IDATA, ERR, I, mean)
else:
	plotObj.Structure(Q, IDATA, ERR, I, mean, pltOptions)