AstroBetter » python

pull request

astropy.wcs
astropy.io.vo
pywcs
vo
astropy.io.fits
print 'I found %10d bugs' % bugCnt
print('I found {:10d} bugs'.format(bugCnt))
def fib_sequence(nth):
	x = [0,1]
	if nth == 1:
		return 1

	for i in xrange(nth-1):
		x.append(x[-1] + x[-2])
		x = [ x[-2], x[-1]]
	return x[-1]
#Laptop time
local = fib_sequence(10000000)

#PiCloud time
import cloud
# This has been anonymized, as the API keys are unique to
# each personal account
cloud.setkey(api_key=something1, api_secretkey='something2')
jid = cloud.call(fib_sequence, 10000000, _high_cpu=True)
remote = cloud.result(jid)
num_parallel = 10
jids = cloud.map(fib_sequence, [10000000]*num_parallel, _high_cpu=True)
remote = cloud.result(jids)
return remote
import pyfits
import numpy as np
import pylab as py
import img_scale

j_img = pyfits.getdata('J.fits')
h_img = pyfits.getdata('H.fits')
k_img = pyfits.getdata('K.fits')
img = np.zeros((j_img.shape[0], j_img.shape[1], 3), dtype=float)
img[:,:,0] = img_scale.sqrt(k_img, scale_min=0, scale_max=10000)
img[:,:,1] = img_scale.sqrt(h_img, scale_min=0, scale_max=10000)
img[:,:,2] = img_scale.sqrt(j_img, scale_min=0, scale_max=10000)
py.clf()
py.imshow(img, aspect='equal')
py.title('Blue = J, Green = H, Red = K')
py.savefig('my_rgb_image.png')
from pyraf import iraf as ir

h_img = pyfits.getdata('H.fits')
k_img = pyfits.getdata('K.fits')
scaleK = 0.1   # arcsec per pixel
scaleJ = 0.15 # arcsec per pixel)
paJ = 30.0  # degrees

ir.unlearn('imlintran')

ir.imlintran.boundary = 'constant'
ir.imlintran.constant = 0
ir.imlintran.interpolant = 'spline3'
ir.imlintran.fluxconserve = 'yes'

# the input pixel position of a reference source
ir.imlintran.xin = 0
ir.imlintran.yin = 0

# the output pixel position of a reference source
ir.imlintran.xout = 10
ir.imlintran.yout = 10

# Set the output size of the final image
ir.imlintran.ncols = k_img.shape[1]
ir.imlintran.nlines = k_img.shape[0]

ir.imlintran('j.fits', 'j_rot_scale.fits', angleJ, angleJ, scaleK/scaleJ, scaleK/scaleJ)

j_img = pyfits.getdata('j_rot_scale.fits')
from scipy.ndimage import interpolation as interp

old_j = pyfits.getdata('j.fits')
new_j = interp.shift(old_j, [shiftY, shiftX])
>>> import atpy
>>> t = atpy.Table('mytable.fits') # FITS table
>>> t = atpy.Table('mytable.hdf5') # HDF5 table
>>> t = atpy.Table('mytable.xml') # VO table
>>> t = atpy.Table('mytable.tbl') # IRSA/IPAC table
>>> t = atpy.Table('sqlite', 'mytable.db') # SQLite database
>>> t = atpy.Table('mysql', db='python', user='bob', \
                   passwd='1(@N(!10))') # MySQL database
>>> t = atpy.Table('postgres', database='python', \
                   user='alice', password='1(@N(!10))') # PostGreSQL database
>>> t.write('mynewtable.fits') # FITS
>>> t.write('mynewtable.hdf5') # HDF5
>>> t.write('mynewtable.xml') # VO
    etc.
>>> import atpy
>>> import numpy as np
>>> ra = np.random.random(5)
>>> dec = np.random.random(5)
>>> flux = np.random.random(5)
>>> t = atpy.Table()
>>> t.add_column('ra', ra, unit='deg')
>>> t.add_column('dec', dec, unit='deg')
>>> t.add_column('flux', flux, unit='mJy')
>>> t['ra']
array([ 0.2331109 ,  0.23474117,  0.62186612,  0.65640893,  0.71596516])
>>> t['ra'][1] = 0.1
>>> t['ra']
array([ 0.2331109 ,  0.1       ,  0.62186612,  0.65640893,  0.71596516])
>>> t.sort('ra')
>>> t.rename_column('ra','ra_j2000')
>>> t.remove_column('flux')
# Just replace this (calling one function twice, in series):
desired_values = [function(args) for args in [args1,args2]]

# ... with this (calling one function twice, in parallel):
import pprocess
nproc = 2  	# maximum number of simultaneous processes desired
results = pprocess.Map(limit=nproc, reuse=1)
parallel_function = results.manage(pprocess.MakeReusable(function))
parallel_function(args1)
parallel_function(args2)
desired_values = results[0:2]
import pprocess
import time
import numpy as np

# Define a function to parallelize:
def takeuptime(ntrials):
    """A function to waste CPU cycles"""
    for ii in range(ntrials):
        junk = np.std(np.random.randn(1e5))
    return junk

list_of_args = [500, 500]

# Serial computation:
tic=time.time()
serial_results = [takeuptime(args) for args in list_of_args]
print "%f s for traditional, serial computation." % (time.time()-tic)

# Parallel computation:
nproc = 2  	# maximum number of simultaneous processes desired
results = pprocess.Map(limit=nproc, reuse=1)
parallel_function = results.manage(pprocess.MakeReusable(takeuptime))
tic=time.time()
[parallel_function(args) for args in list_of_args];  # Start computing things
parallel_results = results[0:3]
print "%f s for parallel computation." % (time.time() - tic)

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