This notebook is one possible overview of how to examine your science data and pull out reasonable photometry using Python and it's available packages.
The materials are put together in order to address the STScI training series on science with Python and will cover these basic areas:
# Import the required modules.
# STDLIB
import os
# THIRD PARTY - PYRAF & IRAF
from pyraf import iraf
from iraf import noao, digiphot, daophot
# THIRD PARTY - ASTROPY
# Make sure you have installed astropy, it's not in Ureka yet.
# After you source your Ureka software, do "easy_install astropy".
from astropy.io import ascii
# THIRD PARTY - OTHER
import matplotlib.pyplot as plt
import numpy as np
import pyfits # Note: pyfits is moving to astropy
# This makes sure USER field is in phot output file
from stsci import tools
tools.irafglobals.userid = "bozo"
# Load the image we are going to use in read-only mode
hdulist = pyfits.open('n8q624e8q_cal.fits')
# Similar to catfits in iraf
hdulist.info()
Filename: n8q624e8q_cal.fits No. Name Type Cards Dimensions Format 0 PRIMARY PrimaryHDU 215 () int16 1 SCI ImageHDU 143 (256, 256) float32 2 ERR ImageHDU 71 (256, 256) float32 3 DQ ImageHDU 71 (256, 256) int16 4 SAMP ImageHDU 71 (256, 256) int16 5 TIME ImageHDU 71 (256, 256) float32
We're going to show you two different ways to do this. The first example is just a quick way to look at your image The second example is more appropriate for a script
image = hdulist[1].data
#display a quick image to see what we are working with
plt.imshow(image, vmin=-1.5, vmax=1.5, cmap=plt.cm.gray, origin='lower')
plt.colorbar() #for reference
<matplotlib.colorbar.Colorbar instance at 0xe214ab8>
# Extract keyword values that we will need.
# Note: Keyword names and units may vary for other instruments. Consult DHB.
header = hdulist[0].header
exptime = header['EXPTIME'] # seconds
gain = header['ADCGAIN'] # e DN^-1; To make sure errors and chi are computed as best as possible
photflam = header["PHOTFLAM"] # ergs cm^-2 ang^-1 DN^-1
photfnu = header["PHOTFNU"] # Jy s DN^-1
readnoise = 26. # electrons
abzpt = -2.5 * log10(photfnu * 1.0 * 1e-23) - 48.6
stzpt = -2.5 * log10(photflam* 1.0) - 21.1
print 'Exposure time:', exptime
print 'Gain:', gain
print 'Readnoise:', readnoise
print 'STMAG zeropoint:', stzpt
print 'ABMAG zeripoint:', abzpt
Exposure time: 1407.892 Gain: 5.4 Readnoise: 26.0 STMAG zeropoint: 25.4467104023 ABMAG zeripoint: 23.1100443446
# It is a good idea to always unlearn the tasks before use.
iraf.unlearn('datapars')
iraf.unlearn('findpars')
iraf.unlearn('centerpars')
iraf.unlearn('fitskypars')
iraf.unlearn('photpars')
iraf.unlearn('daopars')
iraf.unlearn('daofind')
iraf.unlearn('phot')
# This is similar to lpar in iraf.
# You will see that the values you set above are in effect.
iraf.daopars.lParam()
(function = "gauss") Form of analytic component of psf model (varorder = 0) Order of empirical component of psf model (nclean = 0) Number of cleaning iterations for computing psf model (saturated = no) Use wings of saturated stars in psf model computation ? (matchrad = 3.0) Object matching radius in scale units (psfrad = 11.0) Radius of psf model in scale units (fitrad = 3.0) Fitting radius in scale units (recenter = yes) Recenter stars during fit ? (fitsky = no) Recompute group sky value during fit ? (groupsky = yes) Use group rather than individual sky values ? (sannulus = 0.0) Inner radius of sky fitting annulus in scale units (wsannulus = 11.0) Width of sky fitting annulus in scale units (flaterr = 0.75) Flat field error in percent (proferr = 5.0) Profile error in percent (maxiter = 50) Maximum number of fitting iterations (clipexp = 6) Bad data clipping exponent (cliprange = 2.5) Bad data clipping range in sigma (mergerad = INDEF) Critical object merging radius in scale units (critsnratio = 1.0) Critical S/N ratio for group membership (maxnstar = 10000) Maximum number of stars to fit (maxgroup = 60) Maximum number of stars to fit per group (mode = "ql")
# Set up the data and phot parameters for iraf, you can list them like below.
# You can also edit them in an epar interface:
# iraf.datapars.eParam()
# You can omit the params that use default values.
# Notes:
# Values may vary for different images.
# iraf.findpars is used but not modified here.
iraf.datapars.sigma = 1. # default: 0
iraf.datapars.datamin = 0.00001 # default: INDEF
iraf.datapars.datamax = 1000. # default: INDEF
iraf.datapars.gain = 'ADCGAIN' # default: ""
iraf.datapars.readnoi = readnoise # default: 0.0
iraf.datapars.epadu = gain # default: 1.0
iraf.datapars.filter = 'FILTER' # default: ""
iraf.datapars.exposure = 'EXPTIME' # default: ""
iraf.centerpars.calgo = 'centroid' # default: "none"
iraf.centerpars.cbox = 7. # default: 5.0
iraf.fitskypars.salgo = 'centroid' # default: "mode"
iraf.fitskypars.annu = 8 # default: 10.0
iraf.photpars.aperture = 4. # default: "3."
iraf.photpars.zmag = stzpt # default: 25.0
iraf.daopars.varorder = 1 # default: 0
iraf.daopars.psfrad = 15 # default: 11.0
iraf.daopars.fitrad = 4. # default: 3.0
iraf.daopars.fitsky = 'yes' # default: no
iraf.daopars.sannu = 8 # default: 0.0
iraf.daopars.wsann = 5. # default: 11.0
iraf.daopars.proferr = 2.5 # default: 5.0
iraf.daopars.clipexp = 4 # default: 6
iraf.daopars.maxnstar = 1000 # default: 10000
iraf.daopars.maxgroup = 30 # default: 60
Now we are set up to run daofind in daophot. You might also like to try "starfind" in the images package.
Note how I gave daofind the name of the file and not the name of the filehandle that we opened!
inimage = hdulist.filename() + '[1]' # Input will be EXT 1
output = 'test.stars'
outlog = 'daofind.log'
# Another way to do lpar
iraf.lpar('daofind')
image = Input image(s) output = "default" Output coordinate file(s) (default: image.coo.?) (starmap = "") Output density enhancement image(s) (skymap = "") Output sky image(s) (datapars = "") Data dependent parameters (findpars = "") Object detection parameters (boundary = "nearest") Boundary extension (constant|nearest|reflect|wrap) (constant = 0.0) Constant for boundary extension (interactive = no) Interactive mode ? (icommands = "") Image cursor: [x y wcs] key [cmd] (gcommands = "") Graphics cursor: [x y wcs] key [cmd] (wcsout = ")_.wcsout") The output coordinate system (logical,tv,physical) (cache = )_.cache) Cache the image pixels ? (verify = )_.verify) Verify critical daofind parameters ? (update = )_.update) Update critical daofind parameters ? (verbose = )_.verbose) Print daofind messages ? (graphics = ")_.graphics") Graphics device (display = ")_.display") Display device (mode = "ql")
# iraf hates to overwrite output files, so we have to delete them first.
# This is here to make the notebook run repeatedly without error.
# We will cover string operations and looping soon. For now, ignore
for f in (output, outlog):
if os.path.exists(f):
os.remove(f)
# Finally we run daofind in non-interactive mode.
# From lpar above, params not in parenthesis do not need keyword in function call.
# Pipe the screen output to daofind.log -- You can open this file later to see the output.
iraf.daofind(inimage, output, interactive='no', verify='no', update='yes', Stdout=outlog)
# Just like iraf escape sequence for system command line
!ls daofind.log test.stars
daofind.log test.stars
# See last few lines of output
!tail test.stars
196.366 242.903 -1.231 0.696 0.092 -0.047 263 10.566 243.944 -2.241 0.875 -0.342 -0.410 264 142.571 245.396 -0.479 0.668 0.513 -0.003 265 103.852 247.790 -0.637 0.680 -0.126 0.209 266 99.877 250.367 -1.530 0.670 0.101 0.006 267 241.132 249.666 -1.040 0.708 -0.096 0.049 268 38.962 251.130 -0.220 0.735 0.041 0.033 269 141.502 252.582 -0.541 0.648 -0.316 0.062 270 70.044 254.532 -0.590 0.685 0.438 0.141 271 173.314 253.626 -0.424 0.551 -0.103 0.759 272
We will do so in a more involved way so that we can illustrate some standard Python tools along the way. We could use astropy tables instead, but these techniques will be very useful for many things.
Note: We will show you the hard way first, and easier way later in this lecture.
starfile = open(output)
stars = starfile.readlines()
print stars[:45]
['#K IRAF = NOAO/IRAFV2.14EXPORT version %-23s \n', '#K USER = lim name %-23s \n', '#K HOST = bintang.stsci.edu computer %-23s \n', '#K DATE = 2013-01-16 yyy-mm-dd %-23s \n', '#K TIME = 13:56:53 hh:mm:ss %-23s \n', '#K PACKAGE = apphot name %-23s \n', '#K TASK = daofind name %-23s \n', '#\n', '#K SCALE = 1. units %-23.7g \n', '#K FWHMPSF = 2.5 scaleunit %-23.7g \n', '#K EMISSION = yes switch %-23b \n', '#K DATAMIN = 1.000000E-5 counts %-23.7g \n', '#K DATAMAX = 1000. counts %-23.7g \n', '#K EXPOSURE = EXPTIME keyword %-23s \n', '#K AIRMASS = "" keyword %-23s \n', '#K FILTER = FILTER keyword %-23s \n', '#K OBSTIME = "" keyword %-23s \n', '#\n', '#K NOISE = poisson model %-23s \n', '#K SIGMA = 1. counts %-23.7g \n', '#K GAIN = ADCGAIN keyword %-23s \n', '#K EPADU = 5.4 e-/adu %-23.7g \n', '#K CCDREAD = "" keyword %-23s \n', '#K READNOISE = 26. e- %-23.7g \n', '#\n', '#K IMAGE = n8q624e8q_cal.fits[1] imagename %-23s \n', '#K FWHMPSF = 2.5 scaleunit %-23.7g \n', '#K THRESHOLD = 4. sigma %-23.7g \n', '#K NSIGMA = 1.5 sigma %-23.7g \n', '#K RATIO = 1. number %-23.7g \n', '#K THETA = 0. degrees %-23.7g \n', '#\n', '#K SHARPLO = 0.2 number %-23.7g \n', '#K SHARPHI = 1. number %-23.7g \n', '#K ROUNDLO = -1. number %-23.7g \n', '#K ROUNDHI = 1. number %-23.7g \n', '#\n', '#N XCENTER YCENTER MAG SHARPNESS SROUND GROUND ID \\\n', '#U pixels pixels # # # # # \\\n', '#F %-13.3f %-10.3f %-9.3f %-12.3f %-12.3f %-12.3f %-6d \\\n', '#\n', ' 76.102 2.280 -4.065 0.708 0.533 0.089 1 \n', ' 81.730 3.167 -0.597 INDEF -0.591 0.875 2 \n', ' 165.259 3.033 -0.971 0.638 -0.992 -0.023 3 \n', ' 194.855 2.706 -0.172 0.701 0.237 0.014 4 \n']
Now we have an output list of the stellar detections, lets read it in and overplot on our image to see how it did.
But first we have to extract the information from this file in a way that is usable.
We will do this using lower level tools to illustrate some Python basics, but it actually can be done very easily with higher level tools.
# what does readlines return?
print type(stars)
<type 'list'>
What was returned from the file read was a list of strings, one string per line. Lists have been mentioned very briefly in past sessions. Here we go into a little more detail.
Lists are a very fundamental Python data structure used all over. They have some similarities to arrays, but can hold anything, not all the elements have to be the same kind of thing (thought general convention usually has elements of a list sharing some sort of common property). Unlike arrays, lists can be grown, made smaller or inserted into. They are indexed very much like arrays, but only 1 dimensionally. What can be done with lists can done with many other things that have list-like behavior. To illustrate common list functionality;
pets = ['dog', 'cat', 'gerbil'] # creating a list
len(pets)
3
pets2 = ['horse', 'goldfish']
pets + pets2 # list concatenation
['dog', 'cat', 'gerbil', 'horse', 'goldfish']
2*pets # list repetition
['dog', 'cat', 'gerbil', 'dog', 'cat', 'gerbil']
pets[1:2] = pets2 # insert lists by assigning a slice
print pets
['dog', 'horse', 'goldfish', 'gerbil']
print pets[-1], pets[-2]
print pets[-1:0:-2]
gerbil goldfish ['gerbil', 'horse']
pets.insert(2,5) # insert single value
print pets
['dog', 'horse', 5, 'goldfish', 'gerbil']
del pets[2]
print pets
['dog', 'horse', 'goldfish', 'gerbil']
pets.append('python')
print pets
['dog', 'horse', 'goldfish', 'gerbil', 'python']
pets.sort() # Doesn't return any value! Sorts object in place
print pets
['dog', 'gerbil', 'goldfish', 'horse', 'python']
An illustration about python variables
goodpets = pets # Not a copy!
pets[0] = 'moose'
print goodpets
['moose', 'gerbil', 'goldfish', 'horse', 'python']
# Make a copy (option 1)
goodpets = pets[:]
# Make a copy (option 2 -- safer)
import copy
copy_of_pets = copy.deepcopy(pets)
pets[0] = 'elephant'
print 'pets :', pets
print 'goodpets :', goodpets
print 'copy_of_pets:', copy_of_pets
pets : ['elephant', 'gerbil', 'goldfish', 'horse', 'python'] goodpets : ['moose', 'gerbil', 'goldfish', 'horse', 'python'] copy_of_pets: ['moose', 'gerbil', 'goldfish', 'horse', 'python']
back (very briefly) to the data.
columnline = stars[37]
print columnline
dataline = stars[50]
print dataline
#N XCENTER YCENTER MAG SHARPNESS SROUND GROUND ID \ 180.244 10.489 -0.593 0.691 0.435 -0.056 10
We now need to extract information from these line strings. Two kinds of lines are present. There is a header section, the last line of consists of the column names; all that follows is a table of the data. For now I assume we know which line has the column definitions (index 37) and that the data start at index 41. To do that means learning some string manipulation tools.
In some respects, strings are lists of characters. They can be indexed in exactly the same way. But they come with a great deal of built in functionality for manipulating the string. Examples are best, and the rest of the functionality can be discovered in the documentation or iypthon introspection.
len(columnline)
81
columnline[3:7]
'XCEN'
columnline[3]
'X'
'abc'+'xyz'
'abcxyz'
20*'z'
'zzzzzzzzzzzzzzzzzzzz'
# but there are differences from lists
'abc'[1] = 'w'
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-35-ebaf58b71917> in <module>() 1 # but there are differences from lists ----> 2 'abc'[1] = 'w' TypeError: 'str' object does not support item assignment
ERROR: TypeError: 'str' object does not support item assignment [IPython.core.interactiveshell]
Strings are IMMUTABLE. To change one, you must create a new one from the old.
columnline.lower()
'#n xcenter ycenter mag sharpness sround ground id \\\n'
print columnline.find('YCENTER')
print columnline.find('bozo')
13 -1
rowitems = columnline.split()
print rowitems
['#N', 'XCENTER', 'YCENTER', 'MAG', 'SHARPNESS', 'SROUND', 'GROUND', 'ID', '\\']
columnline.split('E')
['#N XC', 'NT', 'R YC', 'NT', 'R MAG SHARPN', 'SS SROUND GROUND ID \\\n']
# how to join lists items? Not intuitively obvious to the newcomer
','.join(rowitems)
'#N,XCENTER,YCENTER,MAG,SHARPNESS,SROUND,GROUND,ID,\\'
In order to work through all the lines, we also need to know how to loop. The most widely used looping construct (by far) is the for
loop.
for pet in pets: # This usage is very alien at first to many scientists!
print pet
# But you will learn to love it. I guarantee it.
elephant gerbil goldfish horse python
Points to remember:
But how do I use an incrementing counter like I'm used to in Fortran or C?
range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
range() returns a list. In a loop, you do not need the list, so you can use xrange() to only return one number at a time, which saves memory for large loops.
for i in xrange(10):
print i
print '\nAnd to make Fortran fans happy:'
for i in xrange(1, 11):
print i
0 1 2 3 4 5 6 7 8 9 And to make Fortran fans happy: 1 2 3 4 5 6 7 8 9 10
What if I need to count things in a list while looping.
This is the way the dinosaurs do it:
i = 0
for pet in pets:
print i, pet
i = i+1
0 elephant 1 gerbil 2 goldfish 3 horse 4 python
To illustrate zip() function:
for i, pet in zip(range(len(pets)), pets):
print i, pet
0 elephant 1 gerbil 2 goldfish 3 horse 4 python
Rodents do it this way (enumerate() also has a "start" keyword if you want to initialize i to non-zero value)
for i, pet in enumerate(pets):
print i, pet
0 elephant 1 gerbil 2 goldfish 3 horse 4 python
# print column names.
print columnline
print dataline
#N XCENTER YCENTER MAG SHARPNESS SROUND GROUND ID \ 180.244 10.489 -0.593 0.691 0.435 -0.056 10
# extract xcenter and ycenter values from a line
vals = dataline.split()
print vals
x = float(vals[0])
y = float(vals[1])
print x,y
['180.244', '10.489', '-0.593', '0.691', '0.435', '-0.056', '10'] 180.244 10.489
# now extract all values in table using a loop
xcen = [] # creating empty lists
ycen = []
for drow in stars[41:]:
items = drow.split()
xcen.append(float(items[0]))
ycen.append(float(items[1]))
print xcen
[76.102, 81.73, 165.259, 194.855, 109.198, 22.607, 175.192, 213.008, 228.56, 180.244, 206.166, 101.506, 172.565, 175.928, 214.8, 225.996, 72.708, 97.662, 209.878, 1.436, 54.408, 36.505, 48.477, 79.886, 137.136, 8.953, 79.361, 30.609, 248.431, 32.776, 20.038, 12.323, 15.664, 23.894, 230.843, 128.621, 12.132, 27.992, 18.801, 117.437, 224.04, 21.896, 52.958, 10.493, 150.459, 20.0, 18.947, 21.059, 12.286, 27.699, 85.851, 232.422, 195.595, 128.655, 16.096, 52.951, 26.08, 28.101, 112.99, 186.219, 20.324, 9.008, 64.014, 211.938, 17.051, 126.761, 199.38, 36.026, 38.605, 235.085, 129.164, 164.555, 206.357, 41.693, 78.943, 173.002, 8.473, 44.856, 50.49, 111.906, 33.673, 91.691, 4.082, 43.204, 118.672, 186.838, 10.587, 250.018, 37.621, 29.811, 226.872, 15.997, 39.162, 40.111, 71.997, 22.463, 27.262, 30.217, 175.377, 18.997, 34.219, 138.128, 23.651, 68.223, 15.579, 17.532, 55.192, 115.122, 26.395, 67.679, 18.875, 33.613, 140.988, 243.038, 11.776, 31.149, 34.335, 217.605, 14.006, 16.67, 26.67, 38.08, 76.175, 253.51, 189.3, 16.339, 23.459, 239.756, 10.352, 60.633, 185.705, 45.311, 170.471, 202.351, 78.395, 48.923, 87.398, 20.017, 47.849, 51.001, 98.391, 200.906, 208.989, 34.334, 145.96, 102.243, 38.956, 168.169, 41.663, 147.553, 234.479, 132.943, 88.895, 164.184, 236.552, 37.029, 184.825, 203.52, 105.76, 213.32, 128.401, 174.648, 233.887, 206.761, 60.098, 192.048, 219.262, 247.062, 250.419, 254.894, 55.562, 236.099, 99.139, 141.451, 201.372, 216.426, 246.878, 238.871, 253.984, 244.811, 253.323, 145.115, 19.477, 249.9, 97.027, 172.996, 182.95, 247.554, 242.84, 251.191, 255.485, 179.012, 188.263, 244.183, 2.601, 46.392, 73.701, 131.103, 190.563, 234.586, 19.906, 237.603, 178.144, 190.817, 206.249, 109.399, 221.26, 117.355, 228.531, 174.599, 194.837, 207.297, 33.387, 86.167, 209.428, 135.1, 180.961, 17.003, 132.221, 117.873, 245.375, 17.225, 93.324, 179.034, 83.45, 150.065, 166.641, 99.477, 253.807, 25.065, 92.677, 27.011, 127.401, 57.373, 75.945, 32.947, 132.291, 238.964, 80.683, 108.706, 143.31, 149.097, 244.056, 126.28, 190.821, 51.447, 155.149, 195.079, 43.985, 144.49, 213.592, 147.656, 165.474, 61.616, 181.507, 98.198, 4.936, 139.891, 39.056, 200.523, 205.251, 129.121, 196.366, 10.566, 142.571, 103.852, 99.877, 241.132, 38.962, 141.502, 70.044, 173.314]
Before long, you need to learn how to test values for certain conditions. Python has all the usual comparison operators such as equality (==, note difference from =) >,< >=, <=, != as well as other tests. And the usual if, else constructs. Some examples.
xcen[0] < 100
True
xcen[0] > 100
False
if 3>1:
print "it's true!"
it's true!
if 0:
print "it's true blue!"
else:
print "it's not true"
it's not true
These tests can be chained. Note that in general, empty or 0 items are considered false, and the converse, true
if []:
print "it's true blue!"
elif 'bozo':
print "hurray for bozo!"
elif 1:
print "one and all"
else:
"everything is false"
hurray for bozo!
# test for membership
'cat' in pets
False
"gerbil" in pets
True
"List Comprehensions" (ugly name) is a powerful way to construct lists while avoiding lenthy text with loops. Basically the for loop and if statements are part of the list creation. A few simple examples
[z**2 for z in range(10)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
[z**2 for z in range(10) if z%2 == 0]
[0, 4, 16, 36, 64]
[pet[0] for pet in pets if len(pet)>5]
['e', 'g', 'g', 'p']
# Plot the image as above.
imshow(image, vmin=-1.5, vmax=1.5, cmap=plt.cm.gray)
# Overlay the stars we found as red points.
plot(xcen, ycen, 'r.',ms=5, label='Star')
legend(numpoints=1)
<matplotlib.legend.Legend at 0xe880910>
# There is an INDEF in one of the rows.
# Let's get rid of all rows with INDEF, and make a new clean starloc listing.
clean_stars = [line for line in stars[41:] if 'INDEF' not in line]
print 'Number of rows thrown out:', len(stars[41:]) - len(clean_stars)
Number of rows thrown out: 10
# Extract x, y (again, since we eliminated some stars), and sharpness, mag.,
# Turn the resulting lists into numpy arrays
x = array([float(line.split()[0]) for line in clean_stars])
y = array([float(line.split()[1]) for line in clean_stars])
sharp = array([float(line.split()[3]) for line in clean_stars])
mag = array([float(line.split()[2]) for line in clean_stars])
# Sanity check
print 'Second original row:', stars[41+1]
print 'Second clean row:', clean_stars[1]
print 'Second clean X Y MAG SHARPNESS:', x[1], y[1], mag[1], sharp[1]
Second original row: 81.730 3.167 -0.597 INDEF -0.591 0.875 2 Second clean row: 165.259 3.033 -0.971 0.638 -0.992 -0.023 3 Second clean X Y MAG SHARPNESS: 165.259 3.033 -0.971 0.638
# Plot of the sharpness versus the mag as blue pluses
plot(mag, sharp, 'b+')
title('Sharpness vs. Mag from daofind')
xlabel('Mag')
ylabel('Sharpness')
# Save the plot as a PDF.
# Matplotlib automatically determines format from given extension.
savefig('sharp_v_mag.pdf')
# Select stars with sharpness greater than 0.6
# use boolean arrays
print (sharp > 0.6)[:20]
# Locations for those stars only
x_high = x[sharp > 0.6]
y_high = y[sharp > 0.6]
# use index array instead (may use less memory), but boolean arrays often faster
indexarr = where(sharp > 0.6)
print indexarr[0][:20]
x_high = x[indexarr]
print 'Number of sharp stars:', x_high.size
[ True True True True True False True True True True True True True True True True True True True True] [ 0 1 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20] Number of sharp stars: 236
# Plot the image again in a different color.
imshow(image, vmin=-3., vmax=3., cmap=plt.cm.jet)
# Only show the high sharpness stars as black circles.
plot(x_high, y_high, 'ko', mfc='None')
[<matplotlib.lines.Line2D at 0xe8b9090>]
I'll use the phot task inside daophot for this example and use the original output coordinates file from above.
# phot output file
outphot = 'test.phot'
photlog = 'phot.log'
# Delete any existing phot output and log
for f in (outphot, photlog):
if os.path.exists(f):
os.remove(f)
# Display phot params
iraf.daophot.phot.lParam()
image = Input image(s) coords = "default" Input coordinate list(s) (default: image.coo.?) output = "default" Output photometry file(s) (default: image.mag.?) skyfile = "" Input sky value file(s) (plotfile = "") Output plot metacode file (datapars = "") Data dependent parameters (centerpars = "") Centering parameters (fitskypars = "") Sky fitting parameters (photpars = "") Photometry parameters (interactive = no) Interactive mode ? (radplots = no) Plot the radial profiles? (icommands = "") Image cursor: [x y wcs] key [cmd] (gcommands = "") Graphics cursor: [x y wcs] key [cmd] (wcsin = ")_.wcsin") The input coordinate system (logical,tv,physical,world) (wcsout = ")_.wcsout") The output coordinate system (logical,tv,physical) (cache = )_.cache) Cache the input image pixels in memory ? (verify = )_.verify) Verify critical phot parameters ? (update = )_.update) Update critical phot parameters ? (verbose = )_.verbose) Print phot messages ? (graphics = ")_.graphics") Graphics device (display = ")_.display") Display device (mode = "ql")
# Perform phot in non-interactive mode.
iraf.daophot.phot(inimage, output, outphot, interactive='no', radplots='no', verify='no', update='no', verbose='yes', Stdout=photlog)
# List working directory again
!ls phot.log test*phot
phot.log test.phot
# Display last 3 lines of phot log file.
!tail -n 3 phot.log
n8q624e8q_cal.fits[1] 141.47 252.78 0.573877 INDEF err n8q624e8q_cal.fits[1] 68.90 253.52 0.231617 INDEF err n8q624e8q_cal.fits[1] 173.37 253.73 0.537419 INDEF err
# Manipulate phot results using the astropy ascii package.
#
# This works if you update the codes with fix in Issue 510.
# The problem is that if your filename extends past the DAOPHOT format limit of 23 characters
# then the name of the file collides with the next column. That isn't an issue in this example because
# we have explicitly set the name of the stellar location and photometry file to be less than 23 characters
#
reader = ascii.Daophot()
photfile = reader.read('test.phot')
photfile
<Table rows=272 names=('IMAGE','XINIT','YINIT','ID','COORDS','LID','XCENTER','YCENTER','XSHIFT','YSHIFT','XERR','YERR','CIER','CERROR','MSKY','STDEV','SSKEW','NSKY','NSREJ','SIER','SERROR','ITIME','XAIRMASS','IFILTER','OTIME','RAPERT','SUM','AREA','FLUX','MAG','MERR','PIER','PERROR')> array([ ('n8q624e8q_cal.fits[1]', 76.102, 2.28, 1, 'test.stars', 1, 76.15, 2.182, 0.048, -0.098, 0.016, 0.014, 108, 'BadPixels', 0.539946, 0.1277227, 0.1022783, 329, 113, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 81.73, 3.167, 2, 'test.stars', 2, 76.15, 2.182, -5.58, -0.985, 0.016, 0.014, 108, 'BadPixels', 0.539946, 0.1277227, 0.1022783, 329, 113, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 165.259, 3.033, 3, 'test.stars', 3, 165.604, 2.463, 0.345, -0.57, 0.068, 0.054, 102, 'EdgeImage', 1.410098, 1.046883, 0.9670413, 297, 151, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 194.855, 2.706, 4, 'test.stars', 4, 194.281, 2.651, -0.574, -0.055, 0.149, 0.07, 102, 'EdgeImage', 0.9178327, 0.7965234, 0.7205291, 340, 114, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 109.198, 4.082, 5, 'test.stars', 5, 109.47, 4.703, 0.272, 0.621, 0.04, 0.037, 108, 'BadPixels', 0.4316305, 0.1230064, 0.09969769, 339, 155, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 65.42598, 50.43528, 43.65657, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 22.607, 5.799, 6, 'test.stars', 6, 22.567, 5.759, -0.04, -0.04, 0.051, 0.056, 108, 'BadPixels', 0.8521596, 0.2649721, 0.238754, 323, 197, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 103.1374, 50.39204, 60.1953, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 175.192, 5.999, 7, 'test.stars', 7, 175.127, 1.0, -0.065, -4.999, 0.019, 0.0, 107, 'BigShift', 2.94417, 1.281157, 1.065699, 347, 71, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 213.008, 6.964, 8, 'test.stars', 8, 214.922, 5.89, 1.914, -1.074, 0.041, 0.042, 108, 'BadPixels', 0.7714115, 0.3063312, 0.2665327, 385, 133, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 113.0904, 50.57268, 74.07806, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 228.56, 10.609, 9, 'test.stars', 9, 228.421, 10.517, -0.139, -0.092, 0.015, 0.015, 0, 'NoError', 0.4657437, 0.1742207, 0.1308682, 501, 143, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 1100.861, 50.66658, 1077.263, '25.737', '0.014', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 180.244, 10.489, 10, 'test.stars', 10, 175.254, 6.101, -4.99, -4.388, 0.033, 0.044, 107, 'BigShift', 2.325989, 1.071207, 0.9031601, 464, 60, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 629.4415, 50.54195, 511.8815, '26.545', '0.027', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 206.166, 10.809, 11, 'test.stars', 11, 206.274, 10.696, 0.108, -0.113, 0.073, 0.072, 0, 'NoError', 0.8042858, 0.3334671, 0.2878907, 480, 173, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 87.36788, 50.54911, 46.71195, '29.145', '0.090', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 101.506, 13.767, 12, 'test.stars', 12, 102.248, 14.097, 0.742, 0.33, 0.168, 0.074, 0, 'NoError', 0.4081183, 0.1285632, 0.1108279, 605, 143, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 97.94621, 50.54932, 77.3161, '28.597', '0.055', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 172.565, 13.476, 13, 'test.stars', 13, 175.127, 1.0, 2.562, -12.476, 0.019, 0.0, 107, 'BigShift', 2.94417, 1.281157, 1.065699, 347, 71, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 175.928, 17.916, 14, 'test.stars', 14, 175.127, 1.0, -0.801, -16.916, 0.019, 0.0, 107, 'BigShift', 2.94417, 1.281157, 1.065699, 347, 71, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 214.8, 17.423, 15, 'test.stars', 15, 214.697, 17.53, -0.103, 0.107, 0.074, 0.082, 0, 'NoError', 0.6412793, 0.2685536, 0.2115871, 594, 216, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 67.06021, 50.4475, 34.70927, '29.467', '0.101', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 225.996, 19.17, 16, 'test.stars', 16, 226.009, 19.213, 0.013, 0.043, 0.062, 0.06, 0, 'NoError', 0.4643444, 0.2026453, 0.1574773, 612, 204, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 102.7537, 50.6481, 79.23551, '28.571', '0.056', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 72.708, 21.352, 17, 'test.stars', 17, 72.682, 21.376, -0.026, 0.024, 0.083, 0.086, 0, 'NoError', 0.6243494, 0.1878592, 0.1409682, 654, 166, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 61.39428, 50.49817, 29.86578, '29.630', '0.099', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 97.662, 20.83, 18, 'test.stars', 18, 97.6, 20.656, -0.062, -0.174, 0.074, 0.071, 0, 'NoError', 0.4330332, 0.1264319, 0.1037977, 692, 128, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 70.24722, 50.52751, 48.36713, '29.107', '0.070', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 209.878, 21.618, 19, 'test.stars', 19, 209.885, 21.773, 0.007, 0.155, 0.082, 0.131, 0, 'NoError', 0.6561824, 0.2267958, 0.1590268, 641, 175, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 89.91817, 50.55596, 56.74424, '28.933', '0.070', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 1.436, 22.979, 20, 'test.stars', 20, 1.603, 23.112, 0.167, 0.133, 0.061, 0.061, 102, 'EdgeImage', 1.027695, 0.9057619, 0.9029217, 281, 150, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 54.408, 23.585, 21, 'test.stars', 21, 54.091, 23.54, -0.317, -0.045, 0.131, 0.056, 108, 'BadPixels', 0.7310712, 0.3854801, 0.3678447, 622, 197, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 88.43876, 50.46838, 51.54279, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 36.505, 24.948, 22, 'test.stars', 22, 35.992, 25.338, -0.513, 0.39, 0.096, 0.078, 0, 'NoError', 1.503833, 1.005731, 0.8320349, 611, 201, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 223.7435, 50.53574, 147.7461, '27.894', '0.067', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 48.477, 26.247, 23, 'test.stars', 23, 48.407, 26.356, -0.07, 0.109, 0.051, 0.048, 0, 'NoError', 0.812016, 0.6503312, 0.5523652, 636, 183, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 152.6701, 50.54945, 111.6231, '28.199', '0.064', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 79.886, 26.319, 24, 'test.stars', 24, 79.696, 26.692, -0.19, 0.373, 0.061, 0.133, 0, 'NoError', 0.567337, 0.1591382, 0.1080677, 711, 109, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 103.8918, 50.50887, 75.23627, '28.627', '0.056', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 137.136, 29.646, 25, 'test.stars', 25, 137.187, 29.494, 0.051, -0.152, 0.059, 0.058, 0, 'NoError', 0.3467353, 0.07688202, 0.06933478, 594, 225, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 98.40217, 50.40742, 80.92414, '28.548', '0.052', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 8.953, 31.368, 26, 'test.stars', 26, 19.977, 41.552, 11.024, 10.184, 0.002, 0.002, 108, 'BadPixels', 5.47311, 2.979768, 2.433932, 664, 152, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 63391.04, 50.47773, 63114.77, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 79.361, 30.116, 27, 'test.stars', 27, 79.459, 29.533, 0.098, -0.583, 0.061, 0.168, 0, 'NoError', 0.556176, 0.1629384, 0.1100632, 690, 122, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 110.7331, 50.70199, 82.53391, '28.527', '0.054', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 30.609, 30.743, 28, 'test.stars', 28, 30.098, 31.016, -0.511, 0.273, 0.045, 0.036, 108, 'BadPixels', 2.202816, 1.193632, 1.038473, 553, 264, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 436.6428, 50.5566, 325.2759, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 248.431, 30.994, 29, 'test.stars', 29, 248.704, 31.004, 0.273, 0.01, 0.143, 0.073, 0, 'NoError', 0.3085558, 0.09237828, 0.06954569, 450, 128, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 70.99669, 50.57892, 55.39027, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 32.776, 31.348, 30, 'test.stars', 30, 30.098, 31.016, -2.678, -0.332, 0.045, 0.036, 108, 'BadPixels', 2.202816, 1.193632, 1.038473, 553, 264, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 436.6428, 50.5566, 325.2759, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 20.038, 33.032, 31, 'test.stars', 31, 19.977, 41.552, -0.061, 8.52, 0.002, 0.002, 108, 'BadPixels', 5.47311, 2.979768, 2.433932, 664, 152, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 63391.04, 50.47773, 63114.77, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 12.323, 34.633, 32, 'test.stars', 32, 19.974, 41.553, 7.651, 6.92, 0.002, 0.002, 108, 'BadPixels', 5.47311, 2.979768, 2.433932, 664, 152, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 63391.03, 50.47748, 63114.76, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 15.664, 33.856, 33, 'test.stars', 33, 19.974, 41.553, 4.31, 7.697, 0.002, 0.002, 108, 'BadPixels', 5.47311, 2.979768, 2.433932, 664, 152, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 63391.03, 50.47748, 63114.76, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 23.894, 33.66, 34, 'test.stars', 34, 19.977, 41.552, -3.917, 7.892, 0.002, 0.002, 108, 'BadPixels', 5.47311, 2.979768, 2.433932, 664, 152, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 63391.04, 50.47773, 63114.77, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 230.843, 35.456, 35, 'test.stars', 35, 230.791, 35.45, -0.052, -0.006, 0.067, 0.066, 108, 'BadPixels', 0.3913002, 0.135236, 0.09888656, 649, 172, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 60.68783, 50.39381, 40.96872, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 128.621, 36.345, 36, 'test.stars', 36, 128.622, 36.372, 0.001, 0.027, 0.053, 0.052, 0, 'NoError', 0.3493724, 0.07714294, 0.0746813, 484, 336, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 112.3641, 50.53769, 94.70767, '28.377', '0.048', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 12.132, 37.093, 37, 'test.stars', 37, 19.974, 41.553, 7.842, 4.46, 0.002, 0.002, 108, 'BadPixels', 5.47311, 2.979768, 2.433932, 664, 152, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 63391.03, 50.47748, 63114.76, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 27.992, 37.621, 38, 'test.stars', 38, 19.974, 41.553, -8.018, 3.932, 0.002, 0.002, 108, 'BadPixels', 5.47311, 2.979768, 2.433932, 664, 152, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 63391.03, 50.47748, 63114.76, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 18.801, 39.885, 39, 'test.stars', 39, 19.974, 41.553, 1.173, 1.668, 0.002, 0.002, 108, 'BadPixels', 5.47311, 2.979768, 2.433932, 664, 152, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 63391.03, 50.47748, 63114.76, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 117.437, 39.4, 40, 'test.stars', 40, 117.391, 39.476, -0.046, 0.076, 0.054, 0.056, 108, 'BadPixels', 0.3625537, 0.1980415, 0.1725099, 564, 252, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 91.99389, 50.61885, 73.64184, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 224.04, 39.378, 41, 'test.stars', 41, 224.023, 39.393, -0.017, 0.015, 0.106, 0.095, 0, 'NoError', 0.4490519, 0.1653501, 0.1312481, 627, 185, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 50.00979, 50.4658, 27.34802, '29.726', '0.102', 0, 'NoError'), 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'BadPixels', 0.7268446, 0.3752657, 0.3356387, 632, 185, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 7166.605, 50.51344, 7129.89, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 149.097, 224.671, 242, 'test.stars', 242, 149.0, 224.596, -0.097, -0.075, 0.0, 0.026, 108, 'BadPixels', 0.6084257, 0.2878399, 0.2450151, 517, 295, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 379.3575, 50.4696, 348.6505, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 244.056, 226.072, 243, 'test.stars', 243, 244.146, 226.175, 0.09, 0.103, 0.083, 0.053, 108, 'BadPixels', 0.2979939, 0.07868048, 0.06042936, 540, 175, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 147.9398, 50.56144, 132.8728, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 126.28, 227.342, 244, 'test.stars', 244, 126.108, 227.302, -0.172, -0.04, 0.022, 0.042, 108, 'BadPixels', 0.4245777, 0.2340178, 0.2086154, 544, 271, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 204.5072, 50.54895, 183.0453, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 190.821, 226.685, 245, 'test.stars', 245, 190.654, 226.611, -0.167, -0.074, 0.054, 0.051, 0, 'NoError', 0.4319761, 0.2167758, 0.1921503, 567, 253, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 146.7641, 50.51867, 124.9412, '28.076', '0.044', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 51.447, 227.786, 246, 'test.stars', 246, 51.431, 227.665, -0.016, -0.121, 0.091, 0.087, 0, 'NoError', 0.2785973, 0.1004618, 0.08524849, 575, 245, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 52.22846, 50.52029, 38.15365, '29.364', '0.079', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 155.149, 228.066, 247, 'test.stars', 247, 147.469, 232.336, -7.68, 4.27, 0.006, 0.006, 108, 'BadPixels', 0.7268446, 0.3752657, 0.3356387, 632, 185, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 7166.605, 50.51344, 7129.89, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 195.079, 227.745, 248, 'test.stars', 248, 190.662, 226.795, -4.417, -0.95, 0.048, 0.056, 107, 'BigShift', 0.4423009, 0.2274344, 0.2025507, 573, 243, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 147.4219, 50.50878, 125.0819, '28.075', '0.044', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 43.985, 230.361, 249, 'test.stars', 249, 43.86, 231.095, -0.125, 0.734, 0.066, 0.165, 0, 'NoError', 0.2896627, 0.1264057, 0.1000825, 622, 194, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 103.7582, 50.56962, 89.1101, '28.443', '0.051', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 144.49, 231.7, 250, 'test.stars', 250, 147.469, 232.336, 2.979, 0.636, 0.006, 0.006, 108, 'BadPixels', 0.7268446, 0.3752657, 0.3356387, 632, 185, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 7166.605, 50.51344, 7129.89, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 213.592, 230.51, 251, 'test.stars', 251, 213.596, 230.446, 0.004, -0.064, 0.067, 0.068, 0, 'NoError', 0.2765534, 0.1107529, 0.08817021, 591, 225, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 67.6531, 50.64243, 53.64777, '28.994', '0.066', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 147.656, 232.386, 252, 'test.stars', 252, 147.469, 232.334, -0.187, -0.052, 0.006, 0.006, 108, 'BadPixels', 0.7268446, 0.3752657, 0.3356387, 632, 185, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 7166.343, 50.50937, 7129.631, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 165.474, 230.1, 253, 'test.stars', 253, 164.782, 232.633, -0.692, 2.533, 0.086, 0.078, 108, 'BadPixels', 0.7147471, 0.2762349, 0.2467831, 544, 273, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 62.14122, 50.49662, 26.0489, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 61.616, 233.16, 254, 'test.stars', 254, 61.606, 233.21, -0.01, 0.05, 0.086, 0.081, 0, 'NoError', 0.2704561, 0.07835079, 0.06537579, 570, 248, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 47.61384, 50.46542, 33.96516, '29.491', '0.082', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 181.507, 233.432, 255, 'test.stars', 255, 181.435, 233.53, -0.072, 0.098, 0.01, 0.01, 0, 'NoError', 0.6508502, 0.2211143, 0.168526, 637, 176, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 2108.977, 50.68242, 2075.99, '25.025', '0.010', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 98.198, 234.868, 256, 'test.stars', 256, 98.205, 234.857, 0.007, -0.011, 0.078, 0.042, 108, 'BadPixels', 0.2620344, 0.06737357, 0.05579652, 618, 199, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 72.2299, 50.55429, 58.98293, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 4.936, 236.165, 257, 'test.stars', 257, 4.892, 236.235, -0.044, 0.07, 0.103, 0.087, 0, 'NoError', 0.3683434, 0.1223619, 0.09178421, 394, 105, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 47.10529, 50.55219, 28.48472, '29.682', '0.094', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 139.891, 236.593, 258, 'test.stars', 258, 147.469, 232.336, 7.578, -4.257, 0.006, 0.006, 108, 'BadPixels', 0.7268446, 0.3752657, 0.3356387, 632, 185, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 7166.605, 50.51344, 7129.89, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 39.056, 236.939, 259, 'test.stars', 259, 41.973, 236.0, 2.917, -0.939, 0.011, 0.0, 108, 'BadPixels', 0.3461087, 0.1311548, 0.1008678, 639, 173, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 260.437, 50.42164, 242.9856, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 200.523, 239.505, 260, 'test.stars', 260, 200.286, 239.549, -0.237, 0.044, 0.059, 0.038, 108, 'BadPixels', 0.3940684, 0.1852265, 0.1661682, 613, 198, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 183.9474, 50.41255, 164.0814, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 205.251, 240.368, 261, 'test.stars', 261, 200.286, 239.549, -4.965, -0.819, 0.059, 0.038, 108, 'BadPixels', 0.3940684, 0.1852265, 0.1661682, 613, 198, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 183.9474, 50.41255, 164.0814, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 129.121, 241.387, 262, 'test.stars', 262, 129.515, 241.291, 0.394, -0.096, 0.061, 0.056, 108, 'BadPixels', 0.3679206, 0.1869231, 0.1679828, 459, 320, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 71.0831, 50.42193, 52.53184, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 196.366, 242.903, 263, 'test.stars', 263, 197.355, 241.938, 0.989, -0.965, 0.047, 0.044, 108, 'BadPixels', 0.4093753, 0.1653378, 0.1491784, 533, 233, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 155.3101, 50.53416, 134.6226, 'INDEF', 'INDEF', 305, 'BadPixels'), ('n8q624e8q_cal.fits[1]', 10.566, 243.944, 264, 'test.stars', 264, 10.488, 243.945, -0.078, 0.001, 0.035, 0.015, 0, 'NoError', 0.3614516, 0.09447963, 0.0726169, 434, 123, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 228.5456, 50.47771, 210.3004, '27.511', '0.032', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 142.571, 245.396, 265, 'test.stars', 265, 142.932, 244.49, 0.361, -0.906, 0.113, 0.178, 0, 'NoError', 0.6159682, 0.2244385, 0.1855494, 435, 272, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 132.7393, 50.47562, 101.6479, '28.300', '0.050', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 103.852, 247.79, 266, 'test.stars', 266, 99.793, 250.369, -4.059, 2.579, 0.036, 0.042, 107, 'BigShift', 0.2689134, 0.06067241, 0.04524283, 409, 129, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 155.6009, 50.48473, 142.0249, '27.937', '0.039', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 99.877, 250.367, 267, 'test.stars', 267, 99.801, 250.44, -0.076, 0.073, 0.04, 0.044, 0, 'NoError', 0.2683258, 0.06073809, 0.04543642, 409, 129, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 155.4877, 50.41766, 141.9593, '27.938', '0.039', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 241.132, 249.666, 268, 'test.stars', 268, 241.213, 249.634, 0.081, -0.032, 0.061, 0.059, 0, 'NoError', 0.286848, 0.08608521, 0.06906061, 400, 120, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 83.72009, 50.49316, 69.23623, '28.717', '0.057', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 38.962, 251.13, 269, 'test.stars', 269, 38.932, 251.183, -0.03, 0.053, 0.094, 0.073, 0, 'NoError', 0.3304959, 0.110926, 0.09449269, 377, 140, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 52.5602, 50.59802, 35.83776, '29.432', '0.082', 0, 'NoError'), ('n8q624e8q_cal.fits[1]', 141.502, 252.582, 270, 'test.stars', 270, 141.469, 252.777, -0.033, 0.195, 0.059, 0.134, 0, 'NoError', 0.5738765, 0.2334989, 0.2082865, 330, 158, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 70.044, 254.532, 271, 'test.stars', 271, 68.905, 253.52, -1.139, -1.012, 0.051, 0.063, 108, 'BadPixels', 0.2316166, 0.05858494, 0.04838339, 360, 111, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage'), ('n8q624e8q_cal.fits[1]', 173.314, 253.626, 272, 'test.stars', 272, 173.375, 253.727, 0.061, 0.101, 0.056, 0.1, 102, 'EdgeImage', 0.5374189, 0.1684188, 0.1130411, 368, 99, 0, 'NoError', 1407.892, 'INDEF', 'F160W', 'INDEF', 4.0, 0.0, 0.0, 0.0, 'INDEF', 'INDEF', 301, 'OffImage')], dtype=[('IMAGE', '|S21'), ('XINIT', '<f8'), ('YINIT', '<f8'), ('ID', '<i8'), ('COORDS', '|S10'), ('LID', '<i8'), ('XCENTER', '<f8'), ('YCENTER', '<f8'), ('XSHIFT', '<f8'), ('YSHIFT', '<f8'), ('XERR', '<f8'), ('YERR', '<f8'), ('CIER', '<i8'), ('CERROR', '|S9'), ('MSKY', '<f8'), ('STDEV', '<f8'), ('SSKEW', '<f8'), ('NSKY', '<i8'), ('NSREJ', '<i8'), ('SIER', '<i8'), ('SERROR', '|S7'), ('ITIME', '<f8'), ('XAIRMASS', '|S5'), ('IFILTER', '|S5'), ('OTIME', '|S5'), ('RAPERT', '<f8'), ('SUM', '<f8'), ('AREA', '<f8'), ('FLUX', '<f8'), ('MAG', '|S6'), ('MERR', '|S5'), ('PIER', '<i8'), ('PERROR', '|S9')])
Let's make a histogram of the recovered magnitudes (no INDEF values included).
# Read in all the magnitudes
mags = photfile['MAG']
# Exclude INDEF values and convert to float.
# AstroPy table column inherits Numpy functionalities.
idx_good = np.where(mags != 'INDEF')
goodmags = mags[idx_good].astype('float')
print type(goodmags)
<class 'astropy.table.table.Column'>
print goodmags
MAG ------- 25.737 26.545 29.145 28.597 29.467 28.571 29.630 29.107 28.933 27.894 28.199 ... 28.443 28.994 29.491 25.025 29.682 27.511 28.300 27.937 27.938 28.717 29.432
hist(goodmags, bins=20)
xlabel('Mag')
ylabel('N')
title('Recovered Magnitudes')
<matplotlib.text.Text at 0xee34510>
# It is a good practice to close any open file pointers.
hdulist.close()