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我是 Python 新手，一直在编写对某些 FITS 文件执行某些任务的脚本。我目前使用的脚本是这样的：

# General routines
import numpy as np
import math 
import pyfits
import matplotlib.pyplot as plt
import pylab as py
from scipy.optimize import curve_fit

# Load the FITS file into the program
hdulist1 = pyfits.open('/home/ssridhar/mock_test_files/most_massive_halo_density.fits')
hdulist2 = pyfits.open('/home/ssridhar/mock_test_files/less_massive_halo_density.fits')

tbdata1 = hdulist1[1].data
tbdata2 = hdulist2[1].data

# variables for table1
ra_1 = tbdata1.field('ra')
dec_1 = tbdata1.field('dec')
zcosmo_1 = tbdata1.field('zcosmo')
r200_1 = tbdata1.field('halo_r200')
r_1 = tbdata1.field('dist_to_center')
m200_1 = tbdata1.field('halo_mass')
rho_0_1 = tbdata1.field('rho_0')
rho_1 = tbdata1.field('density')

# variables for table2
ra_2 = tbdata2.field('ra')
dec_2 = tbdata2.field('dec')
zcosmo_2 = tbdata2.field('zcosmo')
r200_2 = tbdata2.field('halo_r200')
r_2 = tbdata2.field('dist_to_center')
m200_2 = tbdata2.field('halo_mass')
rho_0_2 = tbdata2.field('rho_0')
rho_2= tbdata2.field('density')

# global variables
pi = math.pi
rad = pi/180 # converting degrees to radians

c_m = 25
delta_c_m = (200*c_m**3)/(3*(math.log(1+c_m)-(c_m/(1+c_m))))
c_l = 5
delta_c_l = (200*c_l**3)/(3*(math.log(1+c_l)-(c_l/(1+c_l))))

r_s_1 = r200_1/c_m
r_s_2 = r200_2/c_l

# finding x = r/r_s
x_1 = np.linspace(0.0,3.5,num=1242)/r_s_1
x_2 = np.linspace(0.0,2.7,num=135)/r_s_2

# splitting values to find sigma(x)

a_11 = (2*delta_c_m*rho_0_1*r_s_1)/(x_1**2-1)
b1_1 = (2/(np.sqrt(1-x_1**2)))
c1_1 = np.arctanh(np.sqrt((1-x_1)/(1+x_1)))
b2_1 = 2/(np.sqrt(x_1**2-1))
c2_1 = np.arctan(np.sqrt((x_1-1)/(1+x_1)))

a_12 = (2*delta_c_l*rho_0_2*r_s_2)/(x_2**2-1)
b1_2 = (2/(np.sqrt(1-x_2**2)))
c1_2 = np.arctanh(np.sqrt((1-x_2)/(1+x_2)))
b2_2 = 2/(np.sqrt(x_2**2-1))
c2_2 = np.arctan(np.sqrt((x_2-1)/(1+x_2)))


# implementing the conditions for x
a_1_1 = a_11[x_1<1]
b_1_1 = b1_1[x_1<1]
c_1_1 = c1_1[x_1<1]

a_2_1 = a_11[x_1>1]
b_2_1 = b2_1[x_1>1]
c_2_1 = c2_1[x_1>1]

a_1_2 = a_12[x_2<1]
b_1_2 = b1_2[x_2<1]
c_1_2 = c1_2[x_2<1]

a_2_2 = a_12[x_2>1]
b_2_2 = b2_2[x_2>1]
c_2_2 = c2_2[x_2>1]


# finding sigma(x), the projected NFW profile

sigma_x_m1 = a_1_1*(1-(b_1_1*c_1_1))
sigma_x_m2 = a_2_1*(1-(b_2_1*c_2_1))
sigma_x_m = np.concatenate((sigma_x_m1,sigma_x_m2))

sigma_x_l1 = a_1_2*(1-(b_1_2*c_1_2))
sigma_x_l2 = a_2_2*(1-(b_2_2*c_2_2))
sigma_x_l = np.concatenate((sigma_x_l1,sigma_x_l2))

# fits

A_m = 400
B_m = (sigma_x_m/m200_1)
sigma_fit_m = A_m*B_m


A_l = 40
B_l = (sigma_x_l/m200_2)
sigma_fit_l = A_l*B_l

# finding the projected number density profile

r_hist_1 = np.histogram(r_1/r200_1, bins=20, range=None, normed=False, weights=None)

r_hist_2 = np.histogram(r_2/r200_2, bins=20, range=None, normed=False, weights=None)


N_1 = r_hist_1[0]  # the array of number of galaxies within specific (r) bins 
dist_1 = r_hist_1[1]
area_1 = math.pi*((dist_1[1:2]-dist_1[0:1])**2)
sigma_num_1 = N_1/area_1

N_2 = r_hist_2[0]  # the array of number of galaxies within specific (r) bins 
dist_2 = r_hist_2[1]
area_2 = math.pi*((dist_2[1:2]-dist_2[0:1])**2)
sigma_num_2 = N_2/area_2

该程序接收两个具有相同列名的 FITS 文件，并对列中的数据执行任务。代码工作正常，我得到了我的结果。

这里的问题是，由于 FITS 的列具有相同的名称，因此我必须通过为每个列分配不同的变量并执行任务来分别加载它们。

是否可以编写一个程序，将 FITS 文件作为输入并执行任务，而不必分别为每一列分配变量？

4

1 回答 1

1

你想要类似的东西：

fields = ['ra', 'dec', 'zcosmo', 'halo_r200', 'dist_to_center', 
          'halo_mass', 'rho_0', 'density']

variables1 = dict((f, tbdata1.field(f)) for f in fields)

然后，您可以访问以下变量：

variables1['ra']
于 2014-02-12T11:00:33.423 回答