# Specify data and filename
datadir = "/home/bjung/Documents/Leiden_University/brp/data_red/calib_data"
# Specify standard star directories
stddatadir = datadir + "/sorted/STD,IPOL"
std_dirs = [stddatadir + "/Vela1_95/CHIP1", stddatadir + "/WD1615_154/CHIP1"]
teststddata = [std_dirs[0] + "/tpl3/FORS2.2011-05-04T00:05:36.569.fits", # RETA POSANG 45 deg
std_dirs[1] + "/tpl2/FORS2.2011-05-04T05:33:58.533.fits"] # RETA POSANG 45 deg
# Specify science data directories
scidatadir = datadir + "/sorted/NGC4696,IPOL"
sci_dirs = [scidatadir + "/CHIP1"]
testscidata = sci_dirs[0] + "/tpl8/FORS2.2011-05-04T01:31:46.334.fits" # RETA POSANG 45 deg # j=7, k=1
# Combine data dirs in list
testdata_fnames = [teststddata[0], teststddata[1], testscidata]
# Load testdata
headerVELA, dataVELA = polfun.extract_data(teststddata[0])
headerWD, dataWD = polfun.extract_data(teststddata[1])
headerNGC, dataNGC = polfun.extract_data(testscidata)
# Directory for saving plots
plotdir = "/home/bjung/Documents/Leiden_University/brp/data_red/plots"
imdir = "/home/bjung/Documents/Leiden_University/brp/data_red/images"
npsavedir = "/home/bjung/Documents/Leiden_University/brp/data_red/npsaves"
# Specify bias and masterflat
header, Mbias = polfun.extract_data(datadir + "/masterbias.fits")
header, Mflat_norm = polfun.extract_data(datadir + "/masterflats/masterflat_norm_FLAT,LAM_IPOL_CHIP1.fits")
# Aproximate coordinates of selection of stars within CHIP1 of 'Vela1_95' and 'WD1615_154'. Axis 1 specifies the different stars within the std_dir; axis 2 specifies the x, y1, y2 coordinate of the specific star (with y1 specifying the y coordinate on the upper slit and y2 indicating the y coordinate on the lower slit) as well as the aproximate stellar radius and the slit pair number (numbered 1 to 5 from lower to upper slit pair) which the star lies on. NOTE: THE LAST LIST WITHIN AXIS1 IS A SKY APERTURE!!!
star_lst_sci = [[335, 904, 807, 5, 5], [514, 869, 773, 7, 5], [1169, 907, 811, 5, 5],
from scipy import interpolate
from scipy.stats import poisson
from scipy.optimize import curve_fit
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.ticker import FormatStrFormatter
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
# Specify data and filename
datadir = "/home/bjung/Documents/Leiden_University/brp/data_red/calib_data"
scidatadir = datadir + "/sorted/NGC4696,IPOL"
sci_dirs = [scidatadir + "/CHIP1"]
testdata = sci_dirs[
0] + "/tpl8/corrected2/FORS2.2011-05-04T01:31:46.334_COR.fits" # j=7, k=1
# Load testdata
header, data = polfun.extract_data(testdata)
# Directory for saving plots
plotdir = "/home/bjung/Documents/Leiden_University/brp/data_red/plots/"
imdir = "/home/bjung/Documents/Leiden_University/brp/data_red/images/"
tabledir = "/home/bjung/Documents/Leiden_University/brp/data_red/tables/"
# Define grid
tabularasa = np.zeros([90, 90])
xgrid, ygrid = np.meshgrid(np.arange(90), np.arange(90))
# Set test Gaussian
testOgauss = polfun.gaussian2d([xgrid, ygrid], 45, 45, 0, 2, 2,
3).reshape(tabularasa.shape)
testEgauss = polfun.gaussian2d([xgrid, ygrid], 45, 45.3, 0, 2, 2,
3).reshape(tabularasa.shape)
polfun.savefits(testOgauss, imdir + "/gradmethtest", "testO")
polfun.savefits(testEgauss, imdir + "/gradmethtest", "testE")