else:
fits_list = readfile(list_name)
if len(argv) == 3:
ecc = argv[1]
ecc = float(ecc)
#----------------------------------------------------------------------
# main code
if VERBOSE > 0: print "number of under processed fits:", len(fits_list)
for name in fits_list:
if VERBOSE > 0: print "--- {0} ---".format(name)
data = pyfits.getdata(name)
imh = pyfits.getheader(name)
exptime = imh['EXPTIME']
exptime = float(exptime)
if VERBOSE > 0: print exptime
paras, cov = curvefit.hist_gaussian_fitting(name, data, shift=-7)
data_mean = paras[0]
data_std = paras[1]
# peak list is a list contain elements with position tuple.
sz = 24
tl = 15
peak_list = []
while len(peak_list) > 500 or len(peak_list) < 3:
sz += 1
peak_list = curvefit.get_peak_filter(data, tall_limit=tl, size=sz)
# star list is a list contain elements with star in this fits
# we want to control the number of stars within 20.
hwl = 3
star_list = []
while len(star_list) > 50 or len(star_list) < 3:
path_of_result = tat_datactrl.get_path("result")
ref_name ="{2}/reference/{1}_{0}.fits".format(date_name[:-4], obj_name, path_of_result)
if VERBOSE>1 :
print " "
print "--- ", ref_name, " ---"
try:
ref_data = pyfits.getdata(ref_name+"pass")
except:
ref_name = fits_list[0]
del fits_list[0]
if VERBOSE>1:
print "No reference, use the first image as reference."
print " "
print "--- ", ref_name, " ---"
ref_data = pyfits.getdata(ref_name)
ref_paras, ref_cov = curvefit.hist_gaussian_fitting("default", ref_data)
ref_data_mean = ref_paras[0]
ref_data_std = ref_paras[1]
if VERBOSE>1:
print "mean: ", ref_data_mean, "std: ", ref_data_std
# test hwo many peak in this figure.
# If too much, raise up the limitation of size
sz = 29
tl = 5
ref_peak_list = []
while len(ref_peak_list) >500 or len(ref_peak_list) < 3:
sz +=1
ref_peak_list = curvefit.get_peak_filter(ref_data, tall_limit = tl, size = sz)
if VERBOSE>3:
print "peak list: "
for peak in ref_peak_list:
os.system(temp)
wrong = True
break
if wrong:
continue
# If the ccd temperature is too high, abandom this fit.
img_temp = darkh['CCDTEMP']
if img_temp >= -29.5:
bad_img_count += 1
temp = "mv {0} X_{0}_X".format(image_list[i])
if VERBOSE > 1: print temp
os.system(temp)
continue
# save the bkg and stdev of each img.
data = pyfits.getdata(image_list[i])
params, cov = curvefit.hist_gaussian_fitting("default", data, shift=-7)
data_mean = params[0]
data_std = params[1]
if VERBOSE > 1: print "mean = {0}, stdev = {1}".format(data_mean, data_std)
bkg_array[i] = data_mean
noise_array[i] = data_std
if VERBOSE > 0: print image_list[i], ",checked"
# check whether the image over exposure or not.
mean_bkg = np.mean(bkg_array[np.nonzero(bkg_array)])
std_bkg = np.std(bkg_array[np.nonzero(bkg_array)])
temp_bkg_array = np.subtract(bkg_array, mean_bkg)
abs_bkg_array = np.absolute(temp_bkg_array)
mean_noise = np.mean(noise_array[np.nonzero(noise_array)])
std_noise = np.std(noise_array[np.nonzero(noise_array)])
#!/usr/bin/env python
'''
try poisson fitting and gaussian
'''
import numpy as np
import curvefit
from sys import argv
import pyfits
imA_name = argv[-1]
imA = pyfits.getdata(imA_name, dtype=float)
paras, cov = curvefit.hist_gaussian_fitting(
"gaussian_fitting :{0}".format(imA_name), imA, VERBOSE=4)