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disc.py
executable file
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/
disc.py
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import numpy as np
from scipy import optimize
from random import random as rnd, randint as rndint, normalvariate as nv
import genetic as g
from utils import cube_convolve, cube_convolve, stripStokes
import pyfits as P
import sys,os
from matplotlib.cm import RdBu_r as cm
import matplotlib
from pylab import plot, imshow, clf, colorbar, figure, draw ,savefig
import pprocess
cm.set_under((0.0,0.0,0.35))
cm.set_over ((0.35,0.0,0.0))
cm.set_bad ((0.0,0.25,0.0))
last_plot=[]
def xyplot(lis):
x,y=apply(zip, lis)
plot(x,y)
def gauss2 (x, params):
"""calculate the sum of 2 gaussians and a scalar
x is a scalar or a numpy array
params = amplitude1, sigma1, mean1, amplitude2, sigma2, mean2, baseline"""
a1,s1,m1,a2,s2,m2,c=params
func1=np.exp(-(x-m1)**2/(2*s1*s1))
func2=np.exp(-(x-m2)**2/(2*s2*s2))
func1*=a1
func2*=a2
func1+=func2
func1+=c
return func1
def gauss (x, params):
a,s,m,c=params
result=np.exp(-(x-m)**2/(2*s*s))
result*=a
result+=c
return result
def almost_eq (a, b, diff=0.01):
if type(a)==type(b)==np.ndarray:
return (abs(a-b) <= diff) | (abs((a-b)/a) <= diff)
else:
return abs(a-b) <= diff or abs((a-b)/a) <= diff
def pvfit (arr, f, init_args_guess):
"""arr: a 2D array
f: a function taking at least 2 parameters, the first of which is the x-cord eg f=lambda x,m,c: m*x+c for straight line fit
init_args_guess: a list of initial guess for the arfs in f eg [1,0] for f=1*x+0
fits the [1:] params of f to minimise the distance between function and pixel weighted by pixel value
"""
pos=np.mgrid[0:arr.shape[0],0:arr.shape[1]]
def func(args):
f2=lambda x: f(x, *args)
return ((f2(pos[0,...])-pos[1,...])**2 * arr).sum()
return optimize.fmin_bfgs(func, init_args_guess, maxiter=100)
def set_mnras_size ():
import pylab
fig_width_pt = 240.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width/1.15
fig_size = [fig_width,fig_height]
params = {'backend': 'ps',
'axes.labelsize': 8,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 7,
'ytick.labelsize': 7,
# 'text.usetex': True,
'figure.figsize': fig_size}
pylab.rcParams.update(params)
def decorate_image(F, pixels=201,imres=0.005):
F.axes[0].set_xticks([round(x*pixels) for x in [0,0.25,0.5,0.75,1]])
F.axes[0].set_xticklabels([(x-pixels/2)*imres for x in F.axes[0].get_xticks()])
F.axes[0].set_yticks([round(y*pixels) for y in [0,0.25,0.5,0.75,1]])
F.axes[0].set_yticklabels([(y-pixels/2)*imres for y in F.axes[0].get_yticks()])
F.axes[0].set_xlabel('x arcsec')
F.axes[0].set_ylabel('y arcsec')
def decorate_pv (F, pixels=201, imres=0.005, nchan=251, velres=100):
F.axes[0].set_xticks([x*nchan for x in [0,0.25,0.5,0.75,1]])
F.axes[0].set_xticklabels([round(10000*(x-0.5)*nchan*velres/1000.)/10000. for x in [0,0.25,0.5,0.75,1]])
F.axes[0].set_yticks([x*pixels for x in [0,0.25,0.5,0.75,1]])
F.axes[0].set_yticklabels([round(10000*(x-0.5)*pixels*imres)/10000. for x in [0,0.25,0.5,0.75,1]])
F.axes[0].set_ylabel('x arcsec')
F.axes[0].set_xlabel('v km/s')
def produce_figs (path, name=''):
if name=='': name=''.join(path.split('.')[:-1])
hdr=P.getheader(path)
im=P.getdata(path)
if len(im.shape)==4 and im.shape[1]==1: im=stripStokes(im)
cube_convolve(im,1.0)
nchan=hdr.get('NAXIS3')
vcentre=nchan/2
pixels=hdr.get('NAXIS2')
pcentre=pixels/2
imres=round(hdr.get('CDELT2')*3600*10000)/10000
if 'STOKES' in hdr.get('CTYPE3'):
velres=hdr.get('CDELT4')
if 'FREQ' in hdr.get('CTYPE4'): velres=velres/hdr.get('CRVAL4')*2.99792458e8
else:
velres=hdr.get('CDELT3')
if 'FREQ' in hdr.get('CTYPE3'): velres=velres/hdr.get('CRVAL3')*2.99792458e8
im_cs=im-im[-1,...]
mom0=im_cs[:round(vcentre-500/velres),...].sum(0)*velres/1000.+im_cs[round(vcentre+500/velres):,...].sum(0)*velres/1000.
extent=sorted(abs(mom0).flat)[mom0.size*999/1000]
F=pylab.figure();pylab.clf();pylab.imshow(mom0, interpolation='nearest', cmap=cm , vmin=-extent, vmax=extent, origin='image');c=pylab.colorbar();c.set_label('K km/s')
decorate_image(F, pixels-1,imres)
pylab.draw()
pylab.savefig(name+'_contSub.eps')
cbar=pv(im_cs[:,pcentre,:],contSub=False, spatRes=imres, velRes=velres/1000., cutFrac=0.003)
cbar.set_label('K')
decorate_pv(F, pixels-1,imres, nchan-1,velres)
draw()
savefig(name+'_PV_centre.eps')
im_cs[round(vcentre-500/velres):round(vcentre+500/velres),...]=(im_cs[round(vcentre-500/velres),...]+im_cs[round(vcentre+500/velres),...])/2
mask=abs(mom0)>abs(mom0).max()/100
cbar=mom1map(im_cs*mask, contSub=False, velwidth=velres/1000.)
cbar.set_label('km/s')
decorate_image(F, pixels-1,imres)
draw()
savefig(name+'_mom1.eps')
def produce_normal_figs (path, name='', pv_plane=100, mom0_centred_on_0=0):
if name=='': name=''.join(path.split('.')[:-1])
hdr=P.getheader(path)
im=P.getdata(path)
cube_convolve(im,1.0)
nchan=hdr.get('NAXIS3')
vcentre=nchan/2
pixels=hdr.get('NAXIS2')
imres=round(hdr.get('CDELT2')*3600*10000)/10000
velres=hdr.get('CDELT3')
mom0=im.sum(0)*velres/1000.
F=figure()
clf()
if mom0_centred_on_0:
extent=sorted(abs(mom0).flat)[mom0.size*999/1000]
imshow(mom0, interpolation='nearest', vmax=extent, vmin=-extent, cmap=cm , origin='image')
else:
imshow(mom0, interpolation='nearest', cmap=cm , origin='image')
c=colorbar();c.set_label('K km/s')
decorate_image(F, pixels,imres)
draw()
savefig(name+'.png')
cbar=pv(im[:,pv_plane,:],contSub=False, spatRes=imres, velRes=velres/1000., cutFrac=0.003)
cbar.set_label('K')
decorate_pv(F, pixels,imres, nchan,velres)
draw()
savefig(name+'_PV_centre.png')
mask=abs(mom0)>abs(mom0).max()/1000
cbar=mom1map(im*mask, contSub=False, velwidth=velres/1000.)
cbar.set_label('km/s')
decorate_image(F, pixels,imres)
draw()
savefig(name+'_mom1.png')
def genetic_double_gaussian_fit(im, verbose=False) :
"fit double gaussian profiles to the each of the spectra in imcube im\nold, dont use"
shape=im.shape
X=np.arange(float(shape[0]))
res=np.empty((7,shape[1],shape[2]))
for i in xrange(shape[1]):
# f=open('log','a')
# f.write(str(i)+'\n')
# f.close()
print i
if not(verbose):
actualstdout = sys.stdout
sys.stdout = open(os.devnull,'w')
for j in xrange(shape[2]):
d=im[:,i,j]
c=sorted(d)[shape[0]/2]
peak =d.argmax()
trough=d.argmin()
mx,mn=d[peak]-c,d[trough]-c
def cons ():
r=rndint(0,2)
if r==0:
return np.array((nv(mx,mx/4), rnd()*15, nv(peak,10),\
nv(-mx,mx/4)*-10,rnd()*15, nv(peak,10),\
c*nv(1, 0.1)))
elif r==1:
return np.array((nv(mx,mx/4)*10, rnd()*15, nv(peak,10),\
nv(mn,mn/4)*-10,rnd()*15, nv(trough,10),\
c*nv(1, 0.1)))
else :
return np.array((nv(mn,mn/4)*10, rnd()*15, nv(trough,10),\
nv(mn,mn/4)*-10,rnd()*15, nv(trough,10),\
c*nv(1, 0.1)))
def foo(params):
#inlined gauss2 (this is the intermost part of the optimisation)
a1,s1,m1,a2,s2,m2,c=params
func1=np.exp(-(X-m1)**2/(2*s1*s1))
func2=np.exp(-(X-m2)**2/(2*s2*s2))
func1*=a1; func2*=a2; func1+=func2; func1+=c; func1-=d; func1*=func1
# blitz('func1=func1*a1+func2*a2+c')#not working for some reason
return func1.sum()
s=g.optimize(foo, cons, satisfactory=0.01*251, tolerance=0.001, its=100, hillWalks=2, verbose=verbose)
r=g.optimize(foo, cons, satisfactory=0.01*251, tolerance=0.001, its=100, hillWalks=2, verbose=verbose)
s.run()
r.run()
final=g.optimize(foo, cons, satisfactory=1.0, tolerance=0.0001, pool=s.pool+r.pool)
final.run()
res[:,i,j]=final.pool[0][1:]
if not(verbose):
sys.stdout.close()
sys.stdout = actualstdout
return res
def double_gaussian_fit(im, verbose=False) :
"fit double gaussian profiles to the each of the spectra in imcube im"
shape=im.shape
X=np.arange(float(shape[0]))
res=np.empty((7,shape[1],shape[2]))
for i in xrange(shape[1]):
# f=open('log','a')
# f.write(str(i)+'\n')
# f.close()
print i
for j in xrange(shape[2]):
d=im[:,i,j]
c=sorted(d)[shape[0]/2]
peak =d.argmax()
trough=d.argmin()
mx,mn=d[peak]-c,d[trough]-c
def cons ():
r=rndint(0,2)
if r==0:
return np.array((nv(mx,mx/4), rnd()*15, nv(peak,10),\
nv(-mx,mx/4)*-10,rnd()*15, nv(peak,10),\
c*nv(1, 0.1)))
elif r==1:
return np.array((nv(mx,mx/4)*10, rnd()*15, nv(peak,10),\
nv(mn,mn/4)*-10,rnd()*15, nv(trough,10),\
c*nv(1, 0.1)))
else :
return np.array((nv(mn,mn/4)*10, rnd()*15, nv(trough,10),\
nv(mn,mn/4)*-10,rnd()*15, nv(trough,10),\
c*nv(1, 0.1)))
def err(params,x,y):
a1,s1,m1,a2,s2,m2=params
func1=np.exp(-(x-m1)**2/(2*s1*s1))
func2=np.exp(-(x-m2)**2/(2*s2*s2))
func1*=a1; func2*=a2; func1+=func2; func1+=c; func1-=y;
return func1
v,success=optimize.leastsq(err, (d[peak]-c, 10, peak , d[trough]-d[peak], 10, trough), args=(X,d))
if success!=1:
def foo(params):
#inlined gauss2 (this is the intermost part of the optimisation)
a1,s1,m1,a2,s2,m2,c=params
func1=np.exp(-(X-m1)**2/(2*s1*s1))
func2=np.exp(-(X-m2)**2/(2*s2*s2))
func1*=a1; func2*=a2; func1+=func2; func1+=c; func1-=d; func1*=func1
return func1.sum()
if not(verbose):
actualstdout = sys.stdout
sys.stdout = open(os.devnull,'w')
#if leastsq doesnt give a goot fit then try to fit with a genetic alg
s=g.optimize(foo, cons, satisfactory=2.51, tolerance=0.001, its=100, hillWalks=2, verbose=verbose, startWalk=True)
r=g.optimize(foo, cons, satisfactory=2.51, tolerance=0.001, its=100, hillWalks=2, verbose=verbose, startWalk=True)
s.run()
r.run()
final=g.optimize(foo, cons, satisfactory=1.0, tolerance=0.0001, pool=s.pool+r.pool, finalWalk=True)
final.run()
if not(verbose):
sys.stdout.close()
sys.stdout = actualstdout
if final.pool[0][0]<=1.5:
res[:,i,j]=final.pool[0][1:]
else:
res[:,i,j]=np.nan
else :
for k in xrange(len (v)): res[k,i,j]=v[k]
res[ -1,i,j]=c
return res
def double_gaussian_fit_wCentral(im, verbose=False) :
"fit double gaussian profiles to the each of the spectra in imcube im"
shape=im.shape
X=np.arange(float(shape[0]))
res=np.empty((7,shape[1],shape[2]))
temp=np.empty(X.shape)
for i in xrange(shape[1]):
# f=open('log','a')
# f.write(str(i)+'\n')
# f.close()
# def blah (i):
print i
for j in xrange(shape[2]):
d=im[:,i,j]
c=sorted(d)[shape[0]/2]
temp=d.copy()
centre=shape[0]/2-8,shape[0]/2+9
temp[centre[0]:centre[1]]=[temp[centre[0]]*(1-x/16.)+temp[centre[1]]*x/16. for x in xrange (17)]
peak =temp.argmax()
trough=temp.argmin()
mx,mn=temp[peak]-c,temp[trough]-c
def cons ():
r=rndint(0,2)
if r==0:
return np.array((nv(mx,mx/4), rnd()*15, nv(peak,10),\
nv(-mx,mx/4)*-10,rnd()*15, nv(peak,10),\
c*nv(1, 0.1)))
elif r==1:
return np.array((nv(mx,mx/4)*10, rnd()*15, nv(peak,10),\
nv(mn,mn/4)*-10,rnd()*15, nv(trough,10),\
c*nv(1, 0.1)))
else :
return np.array((nv(mn,mn/4)*10, rnd()*15, nv(trough,10),\
nv(mn,mn/4)*-10,rnd()*15, nv(trough,10),\
c*nv(1, 0.1)))
def err(params,x,y):
a1,s1,m1,a2,s2,m2=params
func1=np.exp(-(x-m1)**2/(2*s1*s1))
func2=np.exp(-(x-m2)**2/(2*s2*s2))
func1*=a1; func2*=a2; func1+=func2; func1+=c; func1-=y;
return func1
v,success=optimize.leastsq(err, (d[peak]-c, 10, peak , d[trough]-d[peak], 10, trough), args=(X,temp))
if success!=1:
def foo(params):
#inlined gauss2 (this is the intermost part of the optimisation)
a1,s1,m1,a2,s2,m2,c=params
func1=np.exp(-(X-m1)**2/(2*s1*s1))
func2=np.exp(-(X-m2)**2/(2*s2*s2))
func1*=a1; func2*=a2; func1+=func2; func1+=c; func1-=d; func1*=func1
return func1.sum()
if not(verbose):
actualstdout = sys.stdout
sys.stdout = open(os.devnull,'w')
#if leastsq doesnt give a goot fit then try to fit with a genetic alg
s=g.optimize(foo, cons, satisfactory=2.51, tolerance=0.001, its=100, hillWalks=2, verbose=verbose, startWalk=True)
r=g.optimize(foo, cons, satisfactory=2.51, tolerance=0.001, its=100, hillWalks=2, verbose=verbose, startWalk=True)
s.run()
r.run()
final=g.optimize(foo, cons, satisfactory=1.0, tolerance=0.0001, pool=s.pool+r.pool, finalWalk=True)
final.run()
if not(verbose):
sys.stdout.close()
sys.stdout = actualstdout
if final.pool[0][0]<=1.5:
res[:,i,j]=final.pool[0][1:]
else:
res[:,i,j]=np.nan
else :
for k in xrange(len (v)): res[k,i,j]=v[k]
res[ -1,i,j]=c
if verbose :
plot(d)
plot (temp)
plot (gauss2(np.arange(251.), res[:,i,j]))
return res
def disc_analysis (path, threshold=0.1, sigma=4, verbose=False):
"""read in and peform a fitting on the fits file at path containing an imcube and write out
the results to a new fits file called Xfitted.fits of shape [7,x,y] where the 7 planes of the
output are fitting parameters amplitude sigma and mean of the 2 gaussians and the baseline level"""
im=cube_convolve(P.getdata(path), sigma)
print('starting: '+path)
# data=[i for i in xrange(im.shape[1]) if im[:,i,:].sum()/im.shape[0]/im.shape[-1] > np.power(10, -1.5)]
mn,mx=70,130#min(data),max(data)
# res=scipy_double_gaussian_fit(im, verbose=verbose)
res=double_gaussian_fit_wCentral(im[:,mn:mx,:], verbose=verbose)
ans=np.ones(res.shape, dtype=np.float)
ans+=np.nan
shape=res.shape
for i in xrange(shape[1]):
for j in xrange(shape[2]):
ele=res[:,i,j]
if abs(ele[0])>=abs(ele[3]) and not(1.6<abs(ele[1])<2.1 and 122<abs(ele[2])<128 and ele[0]<-4):
ans[:,i,j]=ele
else:
ans[:,i,j]=(ele[3],ele[4],ele[5],ele[0],ele[1],ele[2],ele[6])
try :
print('writting: '+path)
P.writeto(path[:-5]+'_fitted.fits', ans)
except IOError as msg: print msg
return ans
def continuumSubtract(spec, method=lambda x: sorted(x)[int(len(x)/2)]):
return spec-method(spec)
#displays
def pv (im, contSub=True, spatRes=0.625, velRes=0.075, cutFrac=0.01, fractional=False,plane=None,axis=None, cmap=cm, **kwargs):
global last_plot
def contfind(spec):
f=lambda x:x[0]
return f(spec)
# f=lambda x: np.sqrt(np.abs(spec-x)).sum()
# return optimize.fmin(f, spec[0], disp=0)[0]
if (len (im.shape)==3):
if not(plane): plane=float(raw_input("plane number for pv slice: "))
if not(axis): axis=float(raw_input("axis number (y=1, x=2): "))
global last_plot
if axis==1: p=im[:,plane,:].copy()
elif axis==2: p=im[:,:,plane].copy()
if contSub :
for i in xrange(p.shape[1]): p[:,i]=continuumSubtract(p[:,i], contfind)
clf()
if fractional:
#assuming here for fractional that channel 0 is all cont emission
if axis==1: q=im[0,plane,:].reshape((p.shape[1],1))
elif axis==2: q=im[0,:,plane].reshape((p.shape[1],1))
extent=abs(p.transpose()/q).max()
imshow(p.transpose()/q, cmap=cmap, vmax=extent, vmin=-extent, interpolation='nearest', origin='image')
last_plot=p.transpose()/q
return colorbar()
else:
srt=sorted(list(p[:120,:].flatten())+list(p[130:,:].flatten()))
l=len(srt)
extent=max(abs(srt[int(l*cutFrac)]),abs(srt[int((1-cutFrac)*l)]))
imshow(p.transpose(), cmap=cmap, vmax=extent, vmin=-extent, interpolation='nearest', origin='image')
last_plot=p.transpose()
return colorbar()
elif (len (im.shape)==2):
p=im.copy()
if contSub :
for i in xrange(p.shape[1]): p[:,i]=continuumSubtract(p[:,i], contfind)
srt=sorted(list(p[:120,:].flatten())+list(p[130:,:].flatten()))
l=len(srt)
extent=max(abs(srt[int(l*cutFrac)]),abs(srt[int((1-cutFrac)*l)]))
clf()
imshow(p.transpose(), cmap=cmap, vmin=-extent, vmax=extent, interpolation='nearest', origin='image', **kwargs)
last_plot=p.transpose()
return colorbar()
def mom0map (im, velwidth=0.075, logscale=False, centreZero=False, **kwargs):
global last_plot
p=im.sum(0)*velwidth
try :
kwargs['cmap']
except KeyError:
kwargs['cmap']=cm
if centreZero:
m=abs(p).max()
kwargs['vmin']=-m
kwargs['vmax']=m
if logscale: p=np.log10(p)
clf();imshow(p, interpolation='nearest', origin='image', **kwargs);cbar=colorbar()
last_plot=p
cbar.set_label('K km/s')
draw()
return cbar
def mom1map (im, velwidth=0.075, contSub=True, **kwargs):
global last_plot
v=np.linspace(-im.shape[0]/2.0*velwidth, im.shape[0]/2.0*velwidth, im.shape[0]).reshape((im.shape[0],1,1))
mcont=im.copy()
if contSub:
for i,j in [(x,y) for x in xrange(im.shape[1]) for y in xrange(im.shape[2])]:
mcont[:,i,j]=continuumSubtract(mcont[:,i,j])
x=mcont.copy()
mcont=(abs(mcont)*v).sum(0)/abs(mcont).sum(0)
mcont[np.isnan(mcont)]=0
last_plot=mcont
r=max([abs(x) for x in mcont.flatten() if x<1e99])
clf();imshow(mcont, cmap=cm, vmax=r, vmin=-r, interpolation='nearest', origin='image', **kwargs);
cbar=colorbar()
cbar.set_label('km/s')
draw()
return cbar
def mom2map (im, velwidth=75, contsub=1, mom1=0, **kwargs):
global last_plot
v=np.linspace(-im.shape[0]/2.0*velwidth, im.shape[0]/2.0*velwidth, im.shape[0]).reshape((im.shape[0],1,1))
mcont=im.copy()
if contsub:
for i,j in [(x,y) for x in xrange(im.shape[1]) for y in xrange(im.shape[2])]:
mcont[:,i,j]=continuumSubtract(mcont[:,i,j])
if not(mom1):
mom1map(mcont, velwidth,False)
mom1=last_plot
else:
mom1=mom1map.copy()
# mom1*=-1
# mom1+=v
# mom1*=mom1
# mom1*=abs(mcont)
# mcont=np.sqrt(mom1map.sum(0)/abs(mcont).sum(0))
mcont=np.sqrt((abs(mcont)*(v-mom1)**2).sum(0)/abs(mcont).sum(0))
mcont[mcont==np.nan]=0
mcont[im.sum(0)<1e-10]=0
last_plot=mcont
clf();imshow(mcont, cmap=cm, vmin=0, interpolation='nearest', origin='image', **kwargs);cbar=colorbar()
cbar.set_label('km/s')
draw()
return cbar