#list with names of the fitted files

files=[]

#list with names of the evaluated properties

propNames=[]

for p in prop:

propNames.append(p.__name__)

#list containing lists of every spectrums parameters

params=[]

#list containing lists of every spectrums parameters' errors

paramErrs=[]

#list with lists of every evaluated poperty's values

propVals=[]

#list containing errors of the evaluated properties

propErrs=[]

#list containing the Spectrum objects

specs=[]

for i in xrange(21):

params.append([])

paramErrs.append([])

for i in prop:

propVals.append([])

propErrs.append([])

#index for deciding when to begin a new plot

j=0

#variable to store noise level

noise=0

for datei in dateien:

print datei

if datei.endswith('.npy'):

data=np.load(datei)

elif datei.endswith('.csv') or datei.endswith('.txt'):

try:

data=np.loadtxt(datei)

except:

print 'could not open file '+datei

continue

else:

print 'unknown format '+ datei

continue

s=DFSpec([data[3],data[1]])

s.setAlpha(0.01)

if j==0 and not noise:

s.fit()

s.noisefit1()

noise=s.noise_level

s.setStyle(fitstyle)

s.noise_level=noise

s.fit()

if not s.isValid():

print 'invalid spectrum, skipped: '+datei[-15:]

continue

files.append('...'+datei[-15:-4])

specs.append(s)

for i in xrange(len(s.params)):

params[i].append(s.params[i])

paramErrs[i].append(s.errors[i])

#now get the desired properties

for i in xrange(len(prop)):

#prop[i] is a function or class method taking s as an argument

r=prop[i](s)

#maybe prop[i] returns a tuple of value and error or only one value without error

if type(r)==list or type(r) == tuple:

propVals[i].append(r[0])

propErrs[i].append(r[1])

else:

#there is no error for this value so set it to zero

propVals[i].append(r)

propErrs[i].append(0)

#create a new figure for the spectra if the current is full

if j%20 == 0:

plt.figure(figsize=(20,20))

plt.subplot(5,4,j%20+1)

s.plot()

plt.draw()

j+=1

#get the folder of the files

folder=os.path.dirname(dateien[0])+'/'

filename=folder+'_'.join([str(x) for x in time.localtime()[:6]])+'.log'

#write the results to file

saveLog(files, propNames, params, paramErrs, propVals, propErrs, filename)

''' this function saves the data contained in the arguments. files and propnames must be lists of values,

the other arguments lists of lists of values. listsep and valsep are the seperation characters for lists and values.

if no filename is given, the date and time is taken.'''

#header for the logfile.

log='This is a logfile generated by analyze.py - lines containing the following: 1. filenames of the fitted spectra 2. names of the evaluated parameters 3. fitparameters of the spectra (one line for every parameter, in order of the filenames, this applies to the rest of the file) 4. errors of the fitparameters 5. evaluated properties 6. errors of the properties\n'

#the lists are seperated by listsep, the values are seperated by valsep

for x in files, propNames:

log+=valsep.join([str(y) for y in x])

log+=listsep

for x in params, paramErrs, propVals, propErrs:

for y in x:

log+=valsep.join([str(z) for z in y])

log+=listsep

#remove the last listsep

log=log[:-(len(listsep))]

if not filename:

filename='_'.join([str(x) for x in time.localtime()[:6]])+'.log'

f=open(filename,'w')

f.write(log)

f=open(filename, 'r')

header=f.readline()

print header

raw_data=f.read()

lists=[x.split(valsep) for x in raw_data.split(listsep)]

numlists=len(lists)

files, propNames = lists[:2]

numprops=len(propNames)

numparams=(numlists-2*numprops-2)/2

params=[[float(x) for x in l] for l in lists[2:numparams+2]]

paramErrs=[[float(x) for x in l] for l in lists[numparams+2:2*numparams+2]]

propVals=[[float(x) for x in l] for l in lists[-2*numprops:-numprops]]

propErrs=[[float(x) for x in l] for l in lists[-numprops:]]