如何在不重複的情況下在不同的數據上重複執行相同的任務

Question

我是Python新手，一直在編寫對某些FITS文件執行某些任務的腳本。我目前使用的腳本是這樣的：

# 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 

這個程序有兩種FITS其中有相同的列名，並在列中的數據執行任務的文件。代碼工作正常，我得到了我的結果。

這裏的問題是，由於FITS的列具有相同的名稱，我不得不通過分別將變量分配給每個列來加載它們，並執行任務。

是否可以編寫一個程序來接收FITS文件作爲輸入並執行任務，而無需單獨爲每列分配變量？

Answer 1

你想要的東西，如：

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

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

然後，您可以訪問的變量，比如：

variables1['ra']