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forced.py
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forced.py
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from __future__ import print_function
import matplotlib
matplotlib.use('Agg')
import pylab as plt
import numpy as np
from tractor import *
from astrometry.util.plotutils import *
def main():
ps = PlotSequence('forced')
W,H = 50,50
sig1 = 1.
flux = 1000.
tim = Image(data=np.zeros((H,W)), inverr=np.ones((H,W)) / sig1,
psf=NCircularGaussianPSF([0.8], [1.]),
photocal=LinearPhotoCal(1.))
tim.sig1 = sig1
x1 = 20
src1 = PointSource(PixPos(x1, H/2), Flux(flux))
# What happens if we're missing one of the sources in the model?
for x2 in [30, 25, 24, 23, 22, 20]:
src2 = PointSource(PixPos(x2, H/2), Flux(flux))
fitsrc1 = PointSource(PixPos(x1, H/2), Flux(1.))
fitsrc1.freezeParam('pos')
(mod,modx,chix,flux0,fluxes) = runtest([tim], [src1, src2], [fitsrc1], 250)
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower')
plt.title('Model: src2 distance = %i' % (x2 - x1))
ps.savefig()
plt.clf()
plt.imshow(modx, interpolation='nearest', origin='lower')
plt.title('Model: src2 distance = %i' % (x2 - x1))
ps.savefig()
plt.clf()
plt.imshow(chix, interpolation='nearest', origin='lower', vmin=-3, vmax=3)
plt.title('Chi: src2 distance = %i' % (x2 - x1))
ps.savefig()
xm = np.mean(fluxes)
st = np.std(fluxes)
xl = xm - 4.*st
xh = xm + 4.*st
plt.clf()
plt.hist(fluxes, bins=21, range=(xl,xh))
plt.axvline(flux0, color='r')
plt.axvline(flux, color='r', linestyle='--')
plt.xlim(xl,xh)
plt.xlabel('Fit Flux')
plt.title('Fits: src2 distance = %i' % (x2 - x1))
ps.savefig()
# What happens if we have the source position a little wrong?
chislices = []
for fx1 in [20, 21, 22, 23, 24]:
fitsrc1 = PointSource(PixPos(fx1, H/2), Flux(1.))
fitsrc1.freezeParam('pos')
(mod,modx,chix,flux0,fluxes) = runtest([tim], [src1], [fitsrc1], 250)
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower')
plt.title('Model: pos error = %i' % (fx1 - x1))
ps.savefig()
plt.clf()
plt.imshow(modx, interpolation='nearest', origin='lower')
plt.title('Model: pos error = %i' % (fx1 - x1))
ps.savefig()
plt.clf()
plt.imshow(chix, interpolation='nearest', origin='lower', vmin=-3, vmax=3)
plt.title('Chi: pos error = %i' % (fx1 - x1))
ps.savefig()
chislices.append(chix[H/2, :])
xm = np.mean(fluxes)
st = np.std(fluxes)
xl = xm - 4.*st
xh = xm + 4.*st
plt.clf()
plt.hist(fluxes, bins=21, range=(xl,xh))
plt.axvline(flux0, color='r')
plt.axvline(flux, color='r', linestyle='--')
plt.xlim(xl,xh)
plt.xlabel('Fit Flux')
plt.title('Fits: pos error = %i' % (fx1 - x1))
ps.savefig()
plt.clf()
for chi in chislices:
plt.plot(chi)
plt.xlabel('Slice in x direction of image')
plt.ylabel('Chi of best fit')
ps.savefig()
def runtest(tims, realsrcs, fitsrcs, niters):
tr = Tractor(tims, realsrcs)
mod = tr.getModelImage(0)
tr = Tractor(tims, fitsrcs)
tr.freezeParam('images')
#tr.printThawedParams()
fluxes = []
for i in range(niters):
for tim in tims:
if i == 0:
noise = 0.
else:
noise = np.random.normal(scale=tim.sig1, size=tim.shape)
tim.data = mod + noise
if i == 1:
modx = tims[0].data
# while True:
# dlnp,X,alpha = tr.optimize(alphas=[0.1, 0.3, 1.])
# print 'dlnp', dlnp
# if dlnp < 0.1:
# break
tr.optimize_forced_photometry()
if i == 1:
chix = tr.getChiImage(0)
if i > 0:
fluxes.append(tr.getParams()[0])
else:
flux0 = tr.getParams()[0]
print('Noise-free fit flux:', flux0)
return mod, modx, chix, flux0, fluxes
if __name__ == '__main__':
main()