def loop_teff_logg(data_type,data_spectrum,teff,logg,feh,start_lambda,end_lambda):
teff_space = []
logg_space = []
chisq_space = []
teff_min = teff - 1000
teff_max = teff + 1000
if teff < 4500:
teff_min = int(3500)
if teff > 8000:
teff_max = int(9000)
shift = find_shift(data_type,data_spectrum,teff,logg,feh,start_lambda,end_lambda)
### Create grid
teff_i = teff_min
while teff_i <= teff_max:
teff_space.append(teff_i)
teff_i = teff_i + 250
logg_i = 0.0
while logg_i <= 5.0:
logg_space.append(logg_i)
logg_i = logg_i + 0.5
teff_space = array(teff_space)
logg_space = array(logg_space)
(X,Y) = meshgrid(teff_space,logg_space)
### Create chisq array
teff_i = teff_min
while teff_i <= teff_max:
logg_line = []
logg_i = 0.0
while logg_i <= 5.0:
chisq_i = test_template(data_type,data_spectrum,teff_i,logg_i,0.0,start_lambda,end_lambda,shift)
logg_line.append(chisq_i)
logg_i = logg_i + 0.5
chisq_space.append(logg_line)
teff_i = teff_i + 250
chisq_space = array(chisq_space)
chisq_space = transpose(chisq_space)
# chisq_space = spectype_functions.normalise_array(chisq_space)
# chisq_space = log(chisq_space)
### Find min
teff_min,logg_min = spectype_functions.find_2d_min(chisq_space)
teff_min = teff_space[teff_min]
logg_min = logg_space[logg_min]
# ### Normalise the array
#chisq_space = exp(- chisq_space **2 / 2)
#chisq_space = spectype_functions.normalise_array(chisq_space)
# print chisq_space
### Crop the chisq space immediately surrounding the absolute min
teff_space_cropped,logg_space_cropped,cropped_space =spectype_functions.chop_array(teff_space,logg_space,chisq_space,teff_min,logg_min,500,1.0)
#cropped_space = log(cropped_space)
cropped_space = -1 *(cropped_space - cropped_space.max())
### Use scipy to fit a least sq 2D gaussian to the cropped region
gauss_fit = spectype_functions.fitgaussian(cropped_space)
gauss_height = gauss_fit[0]
gauss_width_x = gauss_fit[3]
gauss_width_y = gauss_fit[4]
### Check if the gaussian fit makes sense
if gauss_height > 100 or gauss_width_x > 20 or gauss_width_y > 20:
gauss_height = 0.00001
gauss_width_x = 99999.
gauss_width_y = 99999.
# plt.clf()
# plt.contourf(teff_space,logg_space,chisq_space,20,cmap=plt.get_cmap("jet"))
# plt.scatter(teff_min,logg_min,s=50,color="r",marker="x")
# plt.xlabel("T_eff")
# plt.ylabel("logg")
# plt.xlim(max(teff_space),min(teff_space))
# plt.ylim(max(logg_space),min(logg_space))
# plt.title(object_name +" " + str(start_lambda) + "-" + str(end_lambda) + "A, Teff=" + str(teff_min) + " logg=" + str(logg_min))
# plt.show()
return teff_space,logg_space,chisq_space,gauss_height,gauss_width_x,gauss_width_y
flux_spectrum = transpose(array(flux_spectrum))
flux_spectrum = spectype_functions.normalise(flux_spectrum,flux_normalise_w1,flux_normalise_w2)
reddening_chisq_list.append(master_norm_array * calculate_chisq_from_input_regions("flux",flux_spectrum,teff_ini,logg_ini,feh_ini,teff_regions))
################################
### Sum the reddening arrays ###
################################
### Find weights!
height_weights = []
x_weights = []
y_weights = []
for chisq_space in reddening_chisq_list:
### Find min
teff_min,logg_min = spectype_functions.find_2d_min(chisq_space)
teff_min = teff_space[teff_min]
logg_min = logg_space[logg_min]
### Crop the chisq space immediately surrounding the absolute min
teff_space_cropped,logg_space_cropped,cropped_space =spectype_functions.chop_array(teff_space,logg_space,chisq_space,teff_min,logg_min,500,1.0)
#cropped_space = log(cropped_space)
cropped_space = -1 *(cropped_space - cropped_space.max())
### Use scipy to fit a least sq 2D gaussian to the cropped region
gauss_fit = spectype_functions.fitgaussian(cropped_space)
gauss_height = gauss_fit[0]
gauss_width_x = gauss_fit[3]
gauss_width_y = gauss_fit[4]
### Check if the gaussian fit makes sense
