def fit_correlation(path, mess_array, scan_array, mess_date, maxtau, A_start = [3, 10000, 1, 10], flag_write = False, flag_plot = True, debug = False):
"""
croc.LaserCorrelation.fit_correlation
This method imports the correlation data and fits it two a double exponential.
CHANGELOG:
20120223/RB: collected some scripts into this function
INPUT:
- path (string): the path to the correlation data. Note that this is the same as the path_output in the import_and_process function.
- mess_array (array): the base file names, for example 'correlation_T300'
- scan_array (array): array with the file-endings, for example ['_NR1_1.bin', '_NR1_2.bin', ...]
- mess_date (int): the day of the measurement yyyymmdd
- pixel (int): the pixel that should be used
- maxtau (int): to determine the correct name for the file of the correlation data
- A_start (array, 4 elements): starting guess for the fit of the correlation data
- flag_write (BOOL, False): If True, will write a file with the fitting data
- flag_plot (BOOL, True): If False, it will not plot the result.
- debug (BOOL, False): If true, it will only calculate 1 correlation, not all of them.
OUTPUT:
- a plot of the correlation data using both methods and the corresponding fits.
- a text file with the fitting data
"""
if debug:
i_len = 1
j_len = 1
else:
i_len = len(mess_array)
j_len = len(scan_array)
string = ""
for i in range(i_len):
for j in range(j_len):
if flag_plot:
plt.figure()
for k in range(2):
if k == 0:
sort = "fft"
else:
sort = "jan_" + str(maxtau)
c = Resources.IOMethods.import_data_correlation(path, mess_array, i, scan_array, j, mess_date, sort = sort)
c = c[:1000]
x = numpy.arange(len(c))
A = A_start
A_final = M.fit(x, c, E.double_exp, A)
if flag_plot:
plt.plot(c, ".")
plt.plot(E.double_exp(A_final, x))
plt.title(mess_array[i] + scan_array[j])
s_m = mess_array[i]
s_s = scan_array[j]
s_p = sort
s_a = str(numpy.round(A_final[0],2))
s_t1 = str(numpy.round(A_final[1],1))
s_b = str(numpy.round(A_final[2],2))
s_t2 = str(numpy.round(A_final[3],1))
temp_string = "{0:5} {1:6} {2:8} {3:5} {4:7} {5:5} {6:7}".format(s_m, s_s, s_p, s_a, s_t1, s_b, s_t2)
print(temp_string)
string += temp_string
if flag_plot:
plt.show()
if flag_write:
FILE = open(path + "corr_fitting.txt", "w")
FILE.write(string)
FILE.close()
