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generatefits.py
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generatefits.py
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import os
import numpy as np
from astropy.io import fits
folder = "data/"
prihdr = fits.Header()
prihdr['INFO'] = "Lamppost corona intensity profile for an accretion disk of finite thickness"
prihdr['AUTHORS'] = 'A. Abdikamalov, C. Bambi'
prihdr['COMMENTS'] = "Makes a file containing tabulated intensity values."
prihdu = fits.PrimaryHDU(header=prihdr)
aux=[prihdu]
files = os.listdir("data/")
spins = [-0.998, -0.75, -0.5, -0.25, 0.0, 0.2, 0.35, 0.5, 0.6, 0.69, 0.77, 0.8373257, 0.8689509, 0.8939505, 0.91543156, 0.9346257, 0.9521743, 0.9684631, 0.9837458, 0.9982]
rmins = [8.643391, 7.9241004, 7.1784782, 6.402528, 5.6693025, 5.067473, 4.520775, 4.034323, 3.6376512, 3.2537637, 2.8874276, 2.6138475, 2.442025, 2.2888975, 2.1422594, 1.9949745, 1.8398409, 1.6649805, 1.4352659, 1.2274946]
Mdots = [0, 0.05, 0.1, 0.2, 0.3]
norms = [15279.471701493161, 15276.556011507395, 15277.704447002663, 15278.821526714228, 15279.235704605322, 15279.091634291992, 15277.42562267709, 15278.880186568247, 15278.351395483163, 15278.116645095313, 15278.361014946237, 15274.168824563556, 15277.075106446524, 15275.934389911306, 15275.612738793216, 15280.596280508325, 15281.759979442828, 15276.005117296218, 15274.77055351251, 15279.713049336997]
cola = fits.Column(name="spin", format="1E", array = spins)
cola = fits.ColDefs([cola])
tbhdu = fits.BinTableHDU.from_columns(cola)
aux.append(tbhdu)
# hdu = fits.open("rel_lp_table_v0.5b.fits")
# tb = hdu[1].data
# hdu = fits.open("/Users/askarabd/Documents/fudan/FITS/lp_thickness2.fits")
k = 0
# for spin in spins:
for ii in range(len(spins)):
spin = spins[ii]
norm = norms[ii]
hmin1 = 1.7 * (1 + np.sqrt(1 - spin**2))
hgrid1 = np.power(np.arange(40) / 39.0, 2) * (50.0 - hmin1) + hmin1
hmin0 = 1.099 * (1 + np.sqrt(1 - spin**2))
hgrid2 = np.power(np.arange(100) / 99.0, 2.5) * (50.0 - hmin0) + hmin0
for mdot in Mdots:
# for h in hgrid:
Cols = []
l = 0
l2 = 0
# for mdot in Mdots:
for h in hgrid2:
filename = "data/lp_%f_%f_%f.dat" % (spin, h, mdot)
rdisk, intensity, emis_delta, inc_delta = np.loadtxt(filename, unpack=True, skiprows=1)
if not len(Cols):
Cols.append(fits.Column(name='r_%d' % k, format='1E',array=rdisk))
Cols.append(fits.Column(name='intensity_%d_%d' % (k, l), format='1E',array=intensity / norm))
Cols.append(fits.Column(name='emis_delta_%d_%d' % (k, l), format='1E',array=emis_delta))
Cols.append(fits.Column(name='inc_delta_%d_%d' % (k, l), format='1E',array=inc_delta))
# if filename not in files:
# print(spin, h, mdot, filename, " is missing")
# else:
# try:
# data = np.genfromtxt("data/"+filename, filling_values=-100, skiprows=1)
# nans = np.where(data==-100)[0]
# if len(nans):
# print("There are nans in ", spin, h, mdot, filename)
# except:
# print("Problem in loading ", spin, h, mdot, filename)
l+=1
# tb = hdu[k+2].data
# for h in hgrid1:
# col_r = 'r_%d' % k
# col_intens = 'intensity_%d_%d' % (k, l2)
# col_emis = 'emis_delta_%d_%d' % (k, l2)
# col_inc = 'inc_delta_%d_%d' % (k, l2)
# if np.max(np.abs(tb[col_r] - rdisk)) > 10e-3:
# print("Mismatch in ", spin, h, mdot)
# intensity = tb[col_intens]
# emis_delta = tb[col_emis]
# inc_delta = tb[col_inc]
# Cols.append(fits.Column(name='intensity_%d_%d' % (k, l), format='1E',array=intensity / norm))
# Cols.append(fits.Column(name='emis_delta_%d_%d' % (k, l), format='1E',array=emis_delta))
# Cols.append(fits.Column(name='inc_delta_%d_%d' % (k, l), format='1E',array=inc_delta))
# l+=1
# l2+=1
cols = fits.ColDefs(Cols)
tbhdu = fits.BinTableHDU.from_columns(cols)
aux.append(tbhdu)
k+=1
thdulist = fits.HDUList(aux)
thdulist.writeto('lp_thickness_v1.1b.fits')
thdulist.close()
# r_grid = np.arange(1, 25, 25./15.)
# k = 0
# for i in range(len(fits_spin)):
# spin = fits_spin[i]
# rh = 1.75 * (1. + np.sqrt(1.0 - spin * spin))
# hi = np.arange(34)
# h_grid = np.power(hi / 249., 2) * (500. - rh) + rh
# for dp in fits_a13[i]:
# for h in h_grid:
# Cols = []
# l = 0
# for r in r_grid:
# filename = folder + 'a%.05e.h_%.03e_r_%.03e_e_%.03e.a13_%.03e.a22_%.03e.a52_%.03e.dat' % (spin,h,r,0,dp,0,0)
# rbins, emis, intens = np.loadtxt(filename, unpack = True)
# # intens2 = intens
# # pfit1 = np.polyfit(rbins[1:5], intens[1:5], 1)
# # pfit2 = np.poly1d(pfit1)
# # # trff1.append(potrff1e(gs[i]))
# # intens2[0] = pfit2[rbins[0]]
# intens2 = np.zeros(intens.shape)
# pfit1 = np.polyfit(rbins[1:10], intens[1:10], 2)
# pfit2 = np.poly1d(pfit1)
# intens2[0] = pfit2(rbins[0])
# intens2[1:] = intens[1:]
# if not len(Cols):
# Cols.append(fits.Column(name='r_%d' % k, format='1E',array=rbins))
# Cols.append(fits.Column(name='intensity_%d_%d' % (k, l), format='1E',array=intens2))
# l += 1
# # col1 = fits.Column(name='r_%d' % k, format='1E',array=rbins)
# # col2 = fits.Column(name='intensity_%d' % k, format='1E',array=intens2)
# # col3 = fits.Column(name='gmax', format='1E',array=fits_gmaxs)
# cols = fits.ColDefs(Cols)
# tbhdu = fits.BinTableHDU.from_columns(cols)
# aux.append(tbhdu)
# k += 1
# thdulist = fits.HDUList(aux)
# thdulist.writeto('Ring_corona_a13new.fits')
# thdulist.close()
# # thdulist = fits.HDUList(aux)