pixel_extract.py

# -*- coding: utf-8 -*-
"""
Created on Wed Jun 25 15:34:58 2014

@author: Maria
"""

#finding pixel values
from astropy.io import fits
import os
from numpy import *



""" the function med() is not correct """



##########
# DESCRIPTION
#     finds the median sky value for an image and subtracts it from the corresponding
#     image.
# PARAMETERS
#     target_dir - the directory where the files are
#     image      - a combined image 
#     y_images   - the 10X10 sections in the y direction on the image
#     x_images   - the 10X10 sections in the x direction on the image
#     add        - the filter color
#     medx       - the median image for the x direction
#     medy       - the median image for the y direction 
# RETURNS
#    none
##########
def pixel_ext(target_dir,image):
    #change directory    
    os.chdir(target_dir)
    files = os.listdir(target_dir)
    images = sort(files)
    for i in images:
        #creates lists of images 
        y_images,add = Y_sections(image,target_dir)
        x_images = X_sections(image,target_dir)
        medx = median_fits(y_images, "Y" + add + "sky_medain.fits"+ i,add) 
        medy = median_fits(x_images, "X" + add + "sky_medain.fits"+ i,add) 
        med(medx)
        med(medy)
 
##########
# DESCRIPTION
#	Sorts the images in the current folder so that you're left with fits files only
# PARAMETERS
#	images - list of fits files in the current working directory
# RETURNS
#	images
##########   
def sort(files):
    images = []
    for i in files:
        [name, ext] = os.path.splitext(i)
        if ext == '.fits' or ext == '.fit' or ext == '.fts':
            images.append(i)		
    return images	


def med(image):
     f = fits.open(image)
     d = f[0].data
     output = d - median(d)
     hdu = fits.PrimaryHDU(output)
     hdu.writeto("med" +image)
     
###########
# DESCRIPTION
#      locates multiple 10X10 sections of the image and stores them in a list
# PARAMETERS
#      image      - the input combined image
#      path       - the path for the image
#      new_images - list of the 10X10 images
#      k          - the second dimension for the y direction to get a 10X10 image
#      x1         - x low limit
#      x2         - x high limit
#      f          - the opened file
#      x          - the file linked with its path
#      d          - the data in the file
#      section    - the section of the data
#      c          - the filter of the image
# RETURNS
#      new_images back to main
###########     
def Y_sections(image, path):    
    new_images = []    
    for j in range(0,3880,10):
        k = 10+j
        x1 = 2006
        x2 = 2016            
        x = os.path.join(path,image)
        f = fits.open(x)        
        d = f[0].data
        section = d[j:k,x1:x2]
        new_images.append(section)
        c = f[0].header['filter']
        f.close()
        
    return new_images,c


###########
# DESCRIPTION
#      locates multiple 10X10 sections of the image and stores them in a list
# PARAMETERS
#      image      - the input combined image
#      path       - the path for the image
#      new_images - list of the 10X10 images
#      k          - the second dimension for the x direction to get a 10X10 image
#      y1         - y low limit
#      y2         - y high limit
#      f          - the opened file
#      x          - the file linked with its path
#      d          - the data in the file
#      section    - the section of the data
#      c          - the filter of the image
# RETURNS
#      new_images back to main
###########
def X_sections(image, path):    
    new_images = []    
    for j in range(0,3880,10):
        k = 10+j
        y2 = 3550
        y1 = 3540            
        x = os.path.join(path,image)
        f1 = fits.open(x)        
        d1 = f1[0].data
        section = d1[y1:y2,j:k]
        new_images.append(section)
        f1.close()
        
    return new_images
    
    
##########
# DESCRIPTION
#     Creates a new header card called filter and sets it equal to 'Ha 6620'
# PARAMETERS
#     x      - tells function where file is with path joined with filename
#     f      - opened fits file in update mode
#     keyword- card you'd like to add to the header
#     phrase - what you want to be stored in the header card
# RETURNS
#     none
##########       
    
def new_headercard(image,path,keyword ="",phrase=""):
    #for i in images:
    x = os.path.join(path,image)
    f2= fits.open(x, mode ='update')
    f2[0].header[keyword] = phrase
    f2.flush()
    f2.close()

###########
# DESCRIPTION
#     takes a list of arrays and finds the medain element by element
# PARAMETERS
#     dm     - makes an array of the data list
#     med    - the median of the array dm
#     path2  - where you'd like the median images to be sent
#     hdu    - creates a new primary header object
#     hdulist- creates a new primary header
# RETURNS
#     new_image back to main
###########
def median_fits(data_list,newfile_name="",add =""):
    dm = array(data_list)

    #axis 0 is the intensity
    med =  median(dm, axis = 0)
    path2 = '/nfs/home/mcqu4304/maskims/No_obs'
    hdu = fits.PrimaryHDU(med)
    hdulist = fits.HDUList(hdu)
    
    #creates the new file name
    newimage = os.path.join(path2, newfile_name)
    hdulist.writeto(newimage)
    
    
    #new_headercard(newimage,"'/nfs/home/mcqu4304/maskims/No_obs'","OBJECT",add+"sky_median")
    
    return newfile_name
        
pixel_ext('/nfs/home/mcqu4304/maskims/No_obs','noobstrimHacombined_5.fits')