def plot(self,scatter=None,screen=True,out=None):
""" Plot the VLEP landscape. """
X = np.linspace(0.5,3.2,100)
Y = np.linspace(-2,1.4,100)
Z=np.zeros((100,100))
for i,x in enumerate(X):
for j,y in enumerate(Y):
Z[j,i]=self._function(x,y)
pl.contourf(X,Y,Z,100)
pl.hot()
if scatter!=None:
pl.scatter(scatter[0,:],scatter[1,:],color='b')
#if plot!=None:
#pl.plot(plot[0,:],plot[1,:],color='y')
#pl.scatter(plot[0,:],plot[1,:],color='y')
if screen==True:
pl.show()
else:
assert out!=None
pl.savefig(out)
pl.clf()
self.it+=1
def plot(self, scatter=None, screen=True, out=None):
""" Plot the VLEP landscape. """
X = np.linspace(0.5, 3.2, 100)
Y = np.linspace(-2, 1.4, 100)
Z = np.zeros((100, 100))
for i, x in enumerate(X):
for j, y in enumerate(Y):
Z[j, i] = self._function(x, y)
pl.contourf(X, Y, Z, 100)
pl.hot()
if scatter != None:
pl.scatter(scatter[0, :], scatter[1, :], color='b')
#if plot!=None:
#pl.plot(plot[0,:],plot[1,:],color='y')
#pl.scatter(plot[0,:],plot[1,:],color='y')
if screen == True:
pl.show()
else:
assert out != None
pl.savefig(out)
pl.clf()
self.it += 1
def plothist(x, y, nbins=100, log=False,
doclf=True, docolorbar=True, dohot=True,
plo=None, phi=None,
imshowargs={}, **hist2dargs):
if log:
return loghist(x, y, nbins=nbins, doclf=doclf, docolorbar=docolorbar,
dohot=dohot, imshowargs=imshowargs) #, **kwargs)
if doclf:
plt.clf()
(H,xe,ye) = np.histogram2d(x, y, nbins, **hist2dargs)
myargs = dict(extent=(min(xe), max(xe), min(ye), max(ye)),
aspect='auto',
interpolation='nearest', origin='lower')
vmin = None
if plo is not None:
vmin = np.percentile(H.ravel(), plo)
myargs.update(vmin=vmin)
if phi is not None:
vmin = imshowargs.get('vmin', vmin)
vmax = np.percentile(H.ravel(), phi)
if vmax != vmin:
myargs.update(vmax=vmax)
myargs.update(imshowargs)
plt.imshow(H.T, **myargs)
if dohot:
plt.hot()
if docolorbar:
plt.colorbar()
return H, xe, ye
def S_QE(environ):
'''
A nice plot of S(Q,E)
After loading a sqehist.pkl file, run the following
command will give you a very nice plot ready to
be sent to PRL :)
'''
import pylab
#change color palette
pylab.hot()
#show color bar
pylab.colorbar()
#change axis limits
pylab.xlim(0, 10)
pylab.ylim(-50, 50)
#use latex
pylab.rcParams['text.usetex'] = TexInstalled()
#change labels, title
pylab.xlabel(r"$Q {\rm(\AA^{-1})}$")
pylab.ylabel(r"$E {\rm(eV)}$")
pylab.title(r"$S(Q,E)$")
#save picture
pylab.savefig("sqe.png")
return
def S_QE(environ):
'''
A nice plot of S(Q,E)
After loading a sqehist.pkl file, run the following
command will give you a very nice plot ready to
be sent to PRL :)
'''
import pylab
#change color palette
pylab.hot()
#show color bar
pylab.colorbar()
#change axis limits
pylab.xlim(0,10)
pylab.ylim(-50, 50)
#use latex
pylab.rcParams['text.usetex'] = TexInstalled()
#change labels, title
pylab.xlabel( r"$Q {\rm(\AA^{-1})}$" )
pylab.ylabel( r"$E {\rm(eV)}$" )
pylab.title( r"$S(Q,E)$" )
#save picture
pylab.savefig( "sqe.png" )
return
def viz2D(self, filename, directory_="./", const_dir="z", logplot=False):
"""Visualize a slice."""
directory = fixdir(directory_)
data = load_fortran_unformatted(directory + filename)
if self.param is None:
self.load_param(directory)
x = numpy.linspace(
self.param.dx / 2.0,
self.param.dx * self.param.mx - self.param.dx / 2.0,
self.param.mx,
)
y = numpy.linspace(
self.param.dy / 2.0,
self.param.dy * self.param.my - self.param.dy / 2.0,
self.param.my,
)
z = numpy.linspace(
self.param.dz / 2.0,
self.param.dz * self.param.mz - self.param.dz / 2.0,
self.param.mz,
)
if const_dir == "x":
n1 = self.param.my
n2 = self.param.mz
x1 = y
x2 = z
x1label = "y"
x2label = "z"
elif const_dir == "y":
n1 = self.param.mx
n2 = self.param.mz
x1 = x
x2 = z
x1label = "x"
x2label = "z"
else:
n1 = self.param.mx
n2 = self.param.my
x1 = x
x2 = y
x1label = "x"
x2label = "y"
data = data.reshape((n2, n1))
pylab.clf()
if logplot:
data = 20 * numpy.log10(numpy.abs(data).clip(1e-30, 1e30))
pylab.jet()
else:
pylab.hot()
pylab.contour(x1, x2, data, 64)
pylab.colorbar()
pylab.axis("image")
pylab.xlabel(x1label + " /um")
pylab.ylabel(x2label + " /um")
pylab.show()
def visualize():
'''
Just some ugly plotting to make an animation for the lecture.
'''
import pylab as pp
pp.ion()
def remove_spines(axes):
axes.set_xticks([])
axes.set_yticks([])
axes.spines['right'].set_color('none')
axes.spines['top'].set_color('none')
axes.spines['bottom'].set_color('none')
axes.spines['left'].set_color('none')
pfields, arena, aX, aY, centers = setup()
theta = linspace(0,2*pi,250)
x = cos(theta)*5 + 5
y = sin(theta)*5 + 5
points, estimates_bayes, estimates_direct = [],[], []
for rx, ry in zip(x,y):
handles = []
rates, obs_spikes = simulate_spikes(pfields, rx,ry)
pp.subplot(1,2,1)
a = decode_bayes(pfields, obs_spikes, arena)
pp.contour(aX,aY,a)
pp.plot(centers[:,0],centers[:,1],'k+', alpha=.5)
pp.hot()
ey,ex = unravel_index(argmax(a), a.shape)
estimates_bayes.append((aX[0,ex],aY[ey,0]))
points.append((rx,ry))
pp.plot(array(points)[:,0], array(points)[:,1], 'b-', alpha=0.5)
pp.plot(rx,ry,'bo')
pp.plot(aX[0,ex],aY[ey,0], 'ko')
pp.plot(array(estimates_bayes)[:,0], array(estimates_bayes)[:,1], 'k-', alpha=0.5)
pp.ylim([-.5,10.5])
pp.xlim([-.5,10.5])
remove_spines(pp.gca())
pp.subplot(1,2,2)
a = decode_directbasis(pfields, obs_spikes, arena)
pp.contour(aX,aY,a)
pp.plot(centers[:,0],centers[:,1],'k+', alpha=.5)
pp.hot()
ey,ex = unravel_index(argmax(a), a.shape)
estimates_direct.append((aX[0,ex],aY[ey,0]))
points.append((rx,ry))
pp.plot(array(points)[:,0], array(points)[:,1], 'b-',alpha=.5)
pp.plot(rx,ry,'bo')
pp.plot(aX[0,ex],aY[ey,0], 'ko')
pp.plot(array(estimates_direct)[:,0], array(estimates_direct)[:,1], 'k-', alpha=.5)
pp.ylim([-.5,10.5])
pp.xlim([-.5,10.5])
remove_spines(pp.gca())
yield(handles)
def viz2D(self, filename, directory_='./', const_dir='z', logplot=False):
"""Visualize a slice."""
directory = fixdir(directory_)
data = load_fortran_unformatted(directory + filename)
if self.param is None:
self.load_param(directory)
x = numpy.linspace(self.param.dx / 2.,
self.param.dx * self.param.mx - self.param.dx / 2.,
self.param.mx)
y = numpy.linspace(self.param.dy / 2.,
self.param.dy * self.param.my - self.param.dy / 2.,
self.param.my)
z = numpy.linspace(self.param.dz / 2.,
self.param.dz * self.param.mz - self.param.dz / 2.,
self.param.mz)
if const_dir == 'x':
n1 = self.param.my
n2 = self.param.mz
x1 = y
x2 = z
x1label = 'y'
x2label = 'z'
elif const_dir == 'y':
n1 = self.param.mx
n2 = self.param.mz
x1 = x
x2 = z
x1label = 'x'
x2label = 'z'
else:
n1 = self.param.mx
n2 = self.param.my
x1 = x
x2 = y
x1label = 'x'
x2label = 'y'
data = data.reshape((n2, n1))
pylab.clf()
if logplot:
data = 20 * numpy.log10(numpy.abs(data).clip(1e-30, 1e30))
pylab.jet()
else:
pylab.hot()
pylab.contour(x1, x2, data, 64)
pylab.colorbar()
pylab.axis('image')
pylab.xlabel(x1label + ' /um')
pylab.ylabel(x2label + ' /um')
pylab.show()
def plotCorrespondences2(imgsources, refsources, matches, wcs, W, H, prefix,
saveplot=True, format='png'):
print 'ix,iy'
ix = np.array([s.getXAstrom() for s in imgsources])
iy = np.array([s.getYAstrom() for s in imgsources])
print 'rx,ry'
rx,ry = [],[]
for r in refsources:
xy = wcs.skyToPixel(r.getRaDec())
rx.append(xy[0])
ry.append(xy[1])
rx = np.array(rx)
ry = np.array(ry)
ixy = np.vstack((ix, iy)).T
rxy = np.vstack((rx, ry)).T
print 'plotshift...'
cell = 10
plotshift(ixy, rxy, dcell=cell, ncells=9, W=W, H=H)
fn = prefix + '-shift1.' + format
P1 = _output(fn, format, saveplot)
print 'plotshift 2...'
plt.clf()
plt.hot()
plotshift(ixy, rxy, dcell=cell, ncells=9, W=W, H=H, hist=True, nhistbins=2*cell+1)
fn = prefix + '-shift2.' + format
P2 = _output(fn, format, saveplot)
print 'plotshift 3...'
cell = 2
plotshift(ixy, rxy, dcell=cell, ncells=9, W=W, H=H)
fn = prefix + '-shift3.' + format
P3 = _output(fn, format, saveplot)
print 'plotshift 4...'
plt.clf()
plt.hot()
plotshift(ixy, rxy, dcell=cell, ncells=9, W=W, H=H, hist=True, nhistbins=10*cell+1)
fn = prefix + '-shift4.' + format
P4 = _output(fn, format, saveplot)
if not saveplot:
P1.update(P2)
P1.update(P3)
P1.update(P4)
return P1
def plot(self, nx=100, ny=100, out='screen'):
""" Make 2D contour-color plot of the data. """
b = np.empty((nx, ny))
X = np.linspace(self.xgrid[0], self.xgrid[1], nx)
Y = np.linspace(self.ygrid[0], self.ygrid[1], ny)
for i, x in enumerate(X):
for j, y in enumerate(Y):
b[i, j] = self(x, y)
pl.contourf(b, 100)
pl.hot()
pl.colorbar()
if out == 'screen':
pl.show()
else:
pl.savefig('linter.png')
def plot(self,nx=100,ny=100,out='screen'):
""" Make 2D contour-color plot of the data. """
b=np.empty((nx,ny))
X=np.linspace(self.xgrid[0],self.xgrid[1],nx)
Y=np.linspace(self.ygrid[0],self.ygrid[1],ny)
for i,x in enumerate(X):
for j,y in enumerate(Y):
b[i,j]=self(x,y)
pl.contourf(b,100)
pl.hot()
pl.colorbar()
if out=='screen':
pl.show()
else:
pl.savefig('linter.png')
def plot(self, sumx = 1, nxy = 100, nz = 100, z0 = 0):
if sumx is None: # sum in y
x = self.solver.modes[0].get_y(nxy)
axis = 0
else: # sum in x
x = self.solver.modes[0].get_x(nxy)
axis = 1
z = numpy.linspace(0, self.length, nz)
const_z = numpy.ones_like(z)
f = numpy.zeros((len(x), len(z)), dtype = complex)
for im, (coeffLHS,coeffRHS) in enumerate(zip(self.inputLHS, self.inputRHS)):
m = self.solver.modes[im]
beta = 2 * numpy.pi / self.solver.wl * m.neff
tmp = numpy.sum(m.intensity(x, x), axis = axis)
f += numpy.abs(coeffLHS)**2 * tmp[:, numpy.newaxis] * const_z + numpy.abs(coeffRHS)**2 * tmp[:, numpy.newaxis] * const_z
pylab.hot()
pylab.contourf(x, z0 + z, numpy.abs(f).T, 16)
def plot(self, sumx=1, nxy=100, nz=100, z0=0):
if sumx is None: # sum in y
x = self.solver.modes[0].get_y(nxy)
axis = 0
else: # sum in x
x = self.solver.modes[0].get_x(nxy)
axis = 1
z = numpy.linspace(0, self.length, nz)
const_z = numpy.ones_like(z)
f = numpy.zeros((len(x), len(z)), dtype=complex)
for im, (coeffLHS,coeffRHS) in enumerate(zip(self.inputLHS, self.inputRHS)):
m = self.solver.modes[im]
beta = 2 * numpy.pi / self.solver.wl * m.neff
tmp = numpy.sum(m.intensity(x, x), axis=axis)
f += numpy.abs(coeffLHS)**2 * tmp[:, numpy.newaxis] * const_z + \
numpy.abs(coeffRHS)**2 * tmp[:, numpy.newaxis] * const_z
pylab.hot()
pylab.contourf(x, z0 + z, numpy.abs(f).T, 16)
def viz2D(self, filename, directory_="./", const_dir="z", logplot=False):
"""Visualize a slice."""
directory = fixdir(directory_)
data = load_fortran_unformatted(directory + filename)
if self.param is None:
self.load_param(directory)
x = numpy.linspace(self.param.dx / 2.0, self.param.dx * self.param.mx - self.param.dx / 2.0, self.param.mx)
y = numpy.linspace(self.param.dy / 2.0, self.param.dy * self.param.my - self.param.dy / 2.0, self.param.my)
z = numpy.linspace(self.param.dz / 2.0, self.param.dz * self.param.mz - self.param.dz / 2.0, self.param.mz)
if const_dir == "x":
n1 = self.param.my
n2 = self.param.mz
x1 = y
x2 = z
x1label = "y"
x2label = "z"
elif const_dir == "y":
n1 = self.param.mx
n2 = self.param.mz
x1 = x
x2 = z
x1label = "x"
x2label = "z"
else:
n1 = self.param.mx
n2 = self.param.my
x1 = x
x2 = y
x1label = "x"
x2label = "y"
data = data.reshape((n2, n1))
pylab.clf()
if logplot:
data = 20 * numpy.log10(numpy.abs(data).clip(1e-30, 1e30))
pylab.jet()
else:
pylab.hot()
pylab.contour(x1, x2, data, 64)
pylab.colorbar()
pylab.axis("image")
pylab.xlabel(x1label + " /um")
pylab.ylabel(x2label + " /um")
pylab.show()
def visualiseNormObject(self):
shape = (2 * self.extent, 2 * self.extent)
pylab.ion()
pylab.clf()
#pylab.set_cmap("bone")
pylab.hot()
pylab.title("image: %s" % self.fitsFile)
pylab.imshow(np.reshape(self.signPreserveNorm(), shape, order="F"),
interpolation="nearest")
pylab.plot(np.arange(0, 2 * self.extent),
self.extent * np.ones((2 * self.extent, )), "r--")
pylab.plot(self.extent * np.ones((2 * self.extent, )),
np.arange(0, 2 * self.extent), "r--")
pylab.colorbar()
pylab.ylim(-1, 2 * self.extent)
pylab.xlim(-1, 2 * self.extent)
pylab.xlabel("Pixels")
pylab.ylabel("Pixels")
pylab.show()
def plothist(x, y, nbins=100, log=False,
doclf=True, docolorbar=True, dohot=True,
imshowargs={}, **hist2dargs):
if log:
return loghist(x, y, nbins=nbins, doclf=doclf, docolorbar=docolobar,
dohot=dohit, imshowargs=imshowargs, **kwargs)
if doclf:
plt.clf()
(H,xe,ye) = np.histogram2d(x, y, nbins, **hist2dargs)
myargs = dict(extent=(min(xe), max(xe), min(ye), max(ye)),
aspect='auto',
interpolation='nearest', origin='lower')
myargs.update(imshowargs)
plt.imshow(H.T, **myargs)
if dohot:
plt.hot()
if docolorbar:
plt.colorbar()
return H, xe, ye
def plot(self, n):
"""Plot the sensor's fields."""
pylab.clf()
pylab.hot()
pylab.subplot(2,2,1)
pylab.contour(numpy.abs(self.E1[:,:,n].T), 16)
pylab.axis('image')
pylab.title('E1')
pylab.subplot(2,2,2)
pylab.contour(numpy.abs(self.H1[:,:,n].T), 16)
pylab.axis('image')
pylab.title('H1')
pylab.subplot(2,2,3)
pylab.contour(numpy.abs(self.E2[:,:,n].T), 16)
pylab.axis('image')
pylab.title('E2')
pylab.subplot(2,2,4)
pylab.contour(numpy.abs(self.H2[:,:,n].T), 16)
pylab.axis('image')
pylab.title('H2')
pylab.show()
def plot(self, n):
"""Plot the sensor's fields."""
pylab.clf()
pylab.hot()
pylab.subplot(2, 2, 1)
pylab.contour(numpy.abs(self.E1[:, :, n].T), 16)
pylab.axis("image")
pylab.title("E1")
pylab.subplot(2, 2, 2)
pylab.contour(numpy.abs(self.H1[:, :, n].T), 16)
pylab.axis("image")
pylab.title("H1")
pylab.subplot(2, 2, 3)
pylab.contour(numpy.abs(self.E2[:, :, n].T), 16)
pylab.axis("image")
pylab.title("E2")
pylab.subplot(2, 2, 4)
pylab.contour(numpy.abs(self.H2[:, :, n].T), 16)
pylab.axis("image")
pylab.title("H2")
pylab.show()
def plot(self, n):
"""Plot the sensor's fields."""
pylab.clf()
pylab.hot()
pylab.subplot(2, 2, 1)
pylab.contour(numpy.abs(self.E1[:, :, n].T), 16)
pylab.axis('image')
pylab.title('E1')
pylab.subplot(2, 2, 2)
pylab.contour(numpy.abs(self.H1[:, :, n].T), 16)
pylab.axis('image')
pylab.title('H1')
pylab.subplot(2, 2, 3)
pylab.contour(numpy.abs(self.E2[:, :, n].T), 16)
pylab.axis('image')
pylab.title('E2')
pylab.subplot(2, 2, 4)
pylab.contour(numpy.abs(self.H2[:, :, n].T), 16)
pylab.axis('image')
pylab.title('H2')
pylab.show()
def plot(self, n):
"""Plot the sensor's fields."""
pylab.clf()
pylab.hot()
pylab.subplot(2, 2, 1)
pylab.contour(numpy.abs(self.E1[:, :, n].T), 16)
pylab.axis("image")
pylab.title("E1")
pylab.subplot(2, 2, 2)
pylab.contour(numpy.abs(self.H1[:, :, n].T), 16)
pylab.axis("image")
pylab.title("H1")
pylab.subplot(2, 2, 3)
pylab.contour(numpy.abs(self.E2[:, :, n].T), 16)
pylab.axis("image")
pylab.title("E2")
pylab.subplot(2, 2, 4)
pylab.contour(numpy.abs(self.H2[:, :, n].T), 16)
pylab.axis("image")
pylab.title("H2")
pylab.show()
for i in xrange(nodes-1):
topology.append(('right', i, i+1))
topology.append(('left', i+1, i))
# starts the task
task = start_task(HeatSolver, # name of the task class
cpu = nodes, # use <nodes> CPUs on the local machine
topology = topology,
args=(split_y, dx, dt, iterations))
# Retrieves the result, as a list with one element returned by MonteCarlo.get_result per node
result = task.get_result()
result = np.hstack(result)
return result
if __name__ == '__main__':
result = heat2d(100,2)
pl.hot()
pl.imshow(result)
# x = np.linspace(-1.,1.,98)
# X,Y= np.meshgrid(x,x)
# import matplotlib.pyplot as plt
# from matplotlib import cm
# from mpl_toolkits.mplot3d import Axes3D
# fig = pl.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.plot_wireframe(X,Y,result)
pl.show()
def alignAndPlot(Tme, Tref, rad, ps, doweighted=True, emrad=None, nearest=False, **kwargs):
aliargs = dict(cutrange=emrad)
aliargs.update(kwargs)
A = Alignment(Tme, Tref, searchradius=rad, **aliargs)
if nearest:
# There is something badly wrong with spherematch.nearest().
assert(False)
A.findMatches(nearest=True)
M = A.match
print 'dra,ddec arcsec:', M.dra_arcsec[:100], M.ddec_arcsec[:100]
if A.shift() is None:
print 'Shift not found!'
return None
M = A.match
print 'Shift:', A.arcsecshift()
sr,sd = A.arcsecshift()
sumd2 = np.sum(A.fore * ((M.dra_arcsec [A.subset] - sr)**2 +
(M.ddec_arcsec[A.subset] - sd)**2))
sumw = np.sum(A.fore)
# / 2. to get std per coord.
std = sqrt(sumd2 / (sumw * 2.))
angles = np.linspace(0, 2.*pi, 100)
modstr = ''
if A.cov:
eigs = A.getEllipseSize() * 1000.
if eigs[0] > 100:
modstr = '%.0fx%.0f' % (eigs[0], eigs[1])
else:
modstr = '%.1fx%.1f' % (eigs[0], eigs[1])
else:
modstr = '%.1f' % (1000. * A.sigma)
W = np.zeros_like(A.subset).astype(float)
W[A.subset] = A.fore
rl,rh = Tme.ra.min(), Tme.ra.max()
dl,dh = Tme.dec.min(), Tme.dec.max()
if doweighted:
rounds = [ {}, { 'weights': W } ]
else:
rounds = [ {} ]
for i,args in enumerate(rounds):
tsuf = '' if i == 0 else ' (weighted)'
N = len(M.dra_arcsec) if i == 0 else sumw
plotresids(Tme, M, '%s-%s match residuals%s' % (Tme.cam, Tref.cam, tsuf),
bins=100, **args)
ps.savefig()
dst = 1000. * np.sqrt(M.dra_arcsec ** 2 + M.ddec_arcsec ** 2)
loghist(Tme.mag[M.I], dst, 100, **args)
plt.xlabel(Tme.filter)
plt.ylabel('Match residual (mas)')
ps.savefig()
loghist(Tref.mag[M.J], dst, 100, **args)
plt.xlabel(Tref.filter)
plt.ylabel('Match residual (mas)')
ps.savefig()
H,xe,ye = plotalignment(A)
# show EM circle
ax = plt.axis()
angles = np.linspace(0, 2.*pi, 100)
c = A.cutcenter
r = A.cutrange
plt.plot(c[0] + r * np.cos(angles), c[1] + r * np.sin(angles), 'g--')
plt.axis(ax)
plt.title('%s-%s (%i matches, std %.1f mas, model %s)%s' %
(Tme.cam, Tref.cam, int(sumw), std*1000., modstr, tsuf))
ps.savefig()
bins = 200
edges = np.linspace(-rad, rad, bins)
DR,DD = np.meshgrid(edges, edges)
em = A.getModel(DR.ravel(), DD.ravel()).reshape(DR.shape)
em *= len(M.dra_arcsec) * (edges[1]-edges[0])**2
R2 = DR**2 + DD**2
em[R2 > (A.match.rad)**2] = 0.
plt.clf()
plt.imshow(em, extent=(-rad, rad, -rad, rad),
aspect='auto', interpolation='nearest', origin='lower',
vmin=H.min(), vmax=H.max())
plt.hot()
plt.colorbar()
plt.xlabel('dRA (arcsec)')
plt.ylabel('dDec (arcsec)')
plt.title('EM model')
ps.savefig()
plotfitquality(H, xe, ye, A)
ps.savefig()
rng = ((-5*std, 5*std), (-5*std, 5*std))
myargs = args.copy()
myargs.update({'range':rng})
plothist(M.dra_arcsec - sr, M.ddec_arcsec - sd, 200, **myargs)
ax = plt.axis()
plt.xlabel('dRA (arcsec)')
plt.ylabel('dDec (arcsec)')
for nsig in [1,2]:
X,Y = A.getContours(nsig)
plt.plot(X-sr, Y-sd, 'b-')
plt.axis(ax)
plt.title('%s-%s (matches: %i, std: %.1f mas, model %s)%s' %
(Tme.cam, Tref.cam, int(sumw), std*1000., modstr, tsuf))
ps.savefig()
#plothist(Tme.mag[M.I], Tref.mag[M.J], 100, **args)
#plt.xlabel('%s %s (mag)' % (Tme.cam, Tme.filter))
#plt.ylabel('%s %s (mag)' % (Tref.cam, Tref.filter))
#fn = '%s-%s.png' % (basefn, chr(ploti))
#plt.title('%s-%s%s' % (Tme.cam, Tref.cam, tsuf))
#plt.savefig(fn)
#print 'saved', fn
#ploti += 1
loghist(Tme.mag[M.I], Tref.mag[M.J], 100, **args)
plt.xlabel('%s (mag)' % (Tme.filter))
plt.ylabel('%s (mag)' % (Tref.filter))
plt.title('%s-%s%s' % (Tme.cam, Tref.cam, tsuf))
ps.savefig()
plothist(Tme.ra[M.I], Tme.dec[M.I], 100, range=((rl,rh),(dl,dh)))
setRadecAxes(rl,rh,dl,dh)
plt.title('%s-%s: %i matches%s' % (Tme.cam, Tref.cam, N, tsuf))
ps.savefig()
return A
#Do parameter variations
#import library functions
from pylab import plot, show, figure, xlabel, ylabel, legend, title, autumn,\
hot, cm
from numpy import array, linspace, logspace, hstack
#import the compiled simulation objects
from competition import Case1, Case2, Case3, Case4
lsp = linspace
#Case 3 ----------------------------------------------
#vary the growt rate of organism 1
mo = Case3() #create a simulaton object instance
figure() #create new figure window
hot()
#create a couple of initial values for x and y
r1Vals = linspace(0.5, 3, 10)
col = linspace(0, 1, 10)
#do simulations with the different parameter values, and put the results into a
#phase-plane plot
for i in range(len(r1Vals)):
#initialize the simulation object, override some of the parameter values
mo.initialize('m.r1', r1Vals[i],
'N1_init', 1,
'N2_init', 2,
'solutionParameters.simulationTime', 20,
'showGraph', 0)
mo.simulateDynamic() #solve ODE
res = mo.getResults() #get results as a storage.DictStore object
if r == 0:
return 1 / 4
Lambda = 0.5 # in micrometers
kr = 2 * pi / Lambda * r
return (J(1, kr) / kr)**2
side = 2 # length in micrometers
points = 200 # number of grid points in each direction
spacing = side / points
# Calculate the position of the center
x_center = side / 2
y_center = side / 2
# Make an empty array to store values
intensities = empty([points, points], float)
# Calculate the values in the array
for i in range(points):
y = spacing * i
for j in range(points):
x = spacing * j
dist = r(x - x_center, y - y_center)
intensities[i, j] = I(dist)
imshow(intensities, origin="lower", extent=[0, side, 0, side], vmax=0.01)
hot()
show()
psf.scale = scale
im = psf.instantiateAt(0., 0.)
ny, nx = im.shape
XX, YY = np.meshgrid(np.arange(nx), np.arange(ny))
print('cx', (np.sum(im * XX) / np.sum(im)))
print('cy', (np.sum(im * YY) / np.sum(im)))
plt.clf()
mx = im.max()
plt.imshow(im,
origin='lower',
interpolation='nearest',
vmin=-0.1 * mx,
vmax=mx * 1.1)
plt.hot()
plt.colorbar()
ps.savefig()
print('PSF scale', psf.sampling)
print('1./scale', 1. / psf.sampling)
YY = np.linspace(0, 4096, 5)
XX = np.linspace(0, 2048, 5)
yims = []
for y in YY:
xims = []
for x in XX:
im = psf.instantiateAt(x, y)
im /= im.sum()
def show(im): plt.imshow(np.log10(np.maximum(1e-3, im + 1e-3)), vmin=-3, vmax=0, **ima) plt.hot() plt.colorbar()
def display_maps(Detect, Latencies, fig, inner_grid, expe):
ax = Subplot(fig, inner_grid[0])
St = Detect.Sig_strength
#----------------------------------------
# NEED TO REMAP FOR EXPS 20 22 and 23, and 27!!
if expe <= 22:
aux = copy(St[24])
for w in [24, 23, 22, 21]:
St[w] = copy(St[w - 1])
St[20] = copy(St[11])
St[11] = aux
if expe == 23:
for w in [0, 1, 2, 3]:
St[w] = copy(St[w + 1])
St[4] = [0, 0]
#if exp == 27:
# St[16]== [0,0]
#---------------------------------------------
Stup = np.reshape(St[:, 0], (5, 5)) / St.max()
Stdn = np.reshape(St[:, 1], (5, 5)) / St.max()
# only do if they exist
if St.max() > 0:
im = ax.imshow(Stup, interpolation='none', cmap=pylab.gray())
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.15)
colorbar(im, cax=cax)
ax.set_xticks([0, 1, 2, 3, 4]) # range of values in edges
ax.set_yticks([0, 1, 2, 3, 4]) # range of values in edges
ax.set_xticklabels(['St', '1', '2', '3',
'4']) # range of values in edges
ax.set_yticklabels(['A', 'B ', 'C ', 'D',
'E ']) # range of values in edges
ax.tick_params('both', length=0, width=0)
ax.set_title('Caudal Strength')
fig.add_subplot(ax)
#---------------------------------------------------------------------------------------------
ax = Subplot(fig, inner_grid[1])
im = ax.imshow(Stdn, interpolation='none', cmap=pylab.gray())
ax.set_xticks([0, 1, 2, 3, 4]) # range of values in edges
ax.set_yticks([0, 1, 2, 3, 4]) # range of values in edges
ax.set_xticklabels(['St', '1', '2', '3',
'4']) # range of values in edges
ax.set_yticklabels(['A', 'B ', 'C ', 'D',
'E ']) # range of values in edges
ax.tick_params('both', length=0, width=0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.15)
colorbar(im, cax=cax)
ax.set_title('Rostral Strength')
fig.add_subplot(ax)
#---------------------------------------------------------------------------------------------
ax = Subplot(fig, inner_grid[2])
Stboth = np.nan_to_num((Stup - Stdn) / (Stup + Stdn))
# make a color map of fixed colors
cmap = colors.ListedColormap([
'#00cc00', '#00aa00', '#006600', '#003300', 'black', '#000066',
'#000099', '#0000cc', 'blue'
])
bounds = [-1, -0.75, -0.5, -0.25, -0.01, 0.01, 0.25, 0.5, 0.75, 1]
norm = colors.BoundaryNorm(bounds, cmap.N)
im = ax.imshow(Stboth, interpolation='none', cmap=cmap, norm=norm)
ax.set_xticks([0, 1, 2, 3, 4]) # range of values in edges
ax.set_yticks([0, 1, 2, 3, 4]) # range of values in edges
ax.set_xticklabels(['St', '1', '2', '3',
'4']) # range of values in edges
ax.set_yticklabels(['A', 'B ', 'C ', 'D',
'E ']) # range of values in edges
ax.tick_params('both', length=0, width=0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.15)
colorbar(im, cax=cax)
ax.set_title('Directionality')
fig.add_subplot(ax)
#---------------------------------------------------------------------------------------------
ax = Subplot(fig, inner_grid[3])
Lat = zeros(25)
for i in arange(25):
if Latencies[i, 0] > 5 and Latencies[i, 1] > 5:
Lat[i] = min(Latencies[i, 0], Latencies[i, 1])
elif Latencies[i, 0] > 5:
Lat[i] = Latencies[i, 0]
elif Latencies[i, 1] > 5:
Lat[i] = Latencies[i, 1]
#----------------------------------------
# NEED TO REMAP FOR EXPS 20 22 and 23, and 27!!
if expe <= 22:
aux = copy(Lat[24])
for w in [24, 23, 22, 21]:
Lat[w] = copy(Lat[w - 1])
Lat[20] = copy(Lat[11])
Lat[11] = aux
if expe == 23:
for w in [0, 1, 2, 3]:
Lat[w] = copy(Lat[w + 1])
Lat[4] = 0
#---------------------------------------------
Lat = np.reshape(Lat, (5, 5))
im = ax.imshow(Lat, interpolation='none', cmap=pylab.hot())
ax.set_xticks([0, 1, 2, 3, 4]) # range of values in edges
ax.set_yticks([0, 1, 2, 3, 4]) # range of values in edges
ax.set_xticklabels(['St', '1', '2', '3',
'4']) # range of values in edges
ax.set_yticklabels(['A', 'B ', 'C ', 'D',
'E ']) # range of values in edges
ax.tick_params('both', length=0, width=0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.15)
colorbar(im, cax=cax)
ax.set_title('Latency')
fig.add_subplot(ax)
def test_vs_stars_on(run, camcol, field, band, ps):
bandnum = band_index(band)
datadir = os.path.join(os.path.dirname(__file__), 'testdata')
sdss = DR9(basedir=datadir)
sdss.retrieve('psField', run, camcol, field)
psfield = sdss.readPsField(run, camcol, field)
sdss.retrieve('frame', run, camcol, field, band)
frame = sdss.readFrame(run, camcol, field, band)
img = frame.image
H,W = img.shape
fn = sdss.retrieve('photoObj', run, camcol, field)
T = fits_table(fn)
T.mag = T.get('psfmag')[:,bandnum]
T.flux = T.get('psfflux')[:,bandnum]
# !!
T.x = T.colc[:,bandnum] - 0.5
T.y = T.rowc[:,bandnum] - 0.5
T.cut(T.prob_psf[:,bandnum] == 1)
T.cut(T.nchild == 0)
T.cut(T.parent == -1)
T.cut(T.flux > 1.)
print len(T), 'after flux cut'
#T.cut(T.flux > 10.)
#print len(T), 'after flux cut'
#T.cut(T.flux > 20.)
#print len(T), 'after flux cut'
T.cut(np.argsort(-T.flux)[:25])
# margin
m = 30
T.cut((T.x >= m) * (T.x < (W-m)) * (T.y >= m) * (T.y < (H-m)))
#T.cut(np.argsort(T.mag))
T.cut(np.argsort(-np.abs(T.x - T.y)))
print len(T), 'PSF stars'
#R,C = 5,5
#plt.clf()
eigenpsfs = psfield.getEigenPsfs(bandnum)
eigenpolys = psfield.getEigenPolynomials(bandnum)
RR,CC = 2,2
xx,yy = np.meshgrid(np.linspace(0, W, 12), np.linspace(0, H, 8))
ima = dict(interpolation='nearest', origin='lower')
plt.clf()
mx = None
for i,(psf,poly) in enumerate(zip(eigenpsfs, eigenpolys)):
print
print 'Eigen-PSF', i
XO,YO,C = poly
kk = np.zeros_like(xx)
for xo,yo,c in zip(XO,YO,C):
dk = (xx ** xo) * (yy ** yo) * c
#print 'xo,yo,c', xo,yo,c, '-->', dk
kk += dk
print 'Max k:', kk.max(), 'min', kk.min()
print 'PSF range:', psf.min(), psf.max()
print 'Max effect:', max(np.abs(kk.min()), kk.max()) * max(np.abs(psf.min()), psf.max())
plt.subplot(RR,CC, i+1)
plt.imshow(psf * kk.max(), **ima)
if mx is None:
mx = (psf * kk.max()).max()
else:
plt.clim(-mx * 0.05, mx * 0.05)
plt.colorbar()
ps.savefig()
psfs = psfield.getPsfAtPoints(bandnum, xx,yy)
#print 'PSFs:', psfs
ph,pw = psfs[0].shape
psfs = np.array(psfs).reshape(xx.shape + (ph,pw))
print 'PSFs shape:', psfs.shape
psfs = psfs[:,:,15:36,15:36]
ny,nx,ph,pw = psfs.shape
psfmos = np.concatenate([psfs[i,:,:,:] for i in range(ny)], axis=1)
print 'psfmos', psfmos.shape
psfmos = np.concatenate([psfmos[i,:,:] for i in range(nx)], axis=1)
print 'psfmos', psfmos.shape
plt.clf()
plt.imshow(np.log10(np.maximum(psfmos + 1e-3, 1e-3)), **ima)
ps.savefig()
diffs = None
rmses = None
for i in range(len(T)):
xx = T.x[i]
yy = T.y[i]
ix = int(np.round(xx))
iy = int(np.round(yy))
dx = xx - ix
dy = yy - iy
S = 25
stamp = img[iy-S:iy+S+1, ix-S:ix+S+1]
L = 6
Lx = lanczos_filter(L, np.arange(-L, L+1) - dx)
Ly = lanczos_filter(L, np.arange(-L, L+1) - dy)
sx = correlate1d(stamp, Lx, axis=1, mode='constant')
shim = correlate1d(sx, Ly, axis=0, mode='constant')
shim /= (Lx.sum() * Ly.sum())
psf = psfield.getPsfAtPoints(bandnum, xx, yy)
mod = psf / psf.sum() * T.flux[i]
psf2 = psfield.getPsfAtPoints(bandnum, yy, xx)
mod2 = psf2 / psf2.sum() * T.flux[i]
psf3 = psfield.getPsfAtPoints(bandnum, xx, yy).T
mod3 = psf3 / psf3.sum() * T.flux[i]
mods = [mod, mod2, mod3]
if diffs is None:
diffs = [np.zeros_like(m) for m in mods]
rmses = [np.zeros_like(m) for m in mods]
for m,rms,diff in zip(mods, rmses, diffs):
diff += (m - shim)
rms += (m - shim)**2
if i > 10:
continue
def show(im):
plt.imshow(np.log10(np.maximum(1e-3, im + 1e-3)), vmin=-3, vmax=0, **ima)
plt.hot()
plt.colorbar()
R,C = 3,4
plt.clf()
plt.subplot(R,C,1)
show(shim)
plt.subplot(R,C,2)
show(mods[0])
plt.subplot(R,C,2+C)
plt.imshow(mods[0] - shim, vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.subplot(R,C,3)
show(mods[1])
plt.subplot(R,C,3+C)
plt.imshow(mods[1] - shim, vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.subplot(R,C,4)
show(mods[2])
plt.subplot(R,C,4+C)
plt.imshow(mods[2] - shim, vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.subplot(R,C,3+2*C)
plt.imshow(mods[1] - mods[0], vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.subplot(R,C,4+2*C)
plt.imshow(mods[2] - mods[0], vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.suptitle('%.0f, %.0f' % (xx,yy))
ps.savefig()
for diff in diffs:
diff /= len(T)
rmses = [np.sqrt(rms / len(T)) for rms in rmses]
print 'rms median', np.median(rmses[0]), 'mean', np.mean(rmses[0])
r0,r1 = [np.percentile(rmses[0], p) for p in [10,90]]
R,C = 2,len(diffs)
plt.clf()
for i,(d,r) in enumerate(zip(diffs, rmses)):
plt.subplot(R,C, 1+i)
plt.imshow(d, vmin=-0.05, vmax=0.05, **ima)
plt.colorbar()
plt.hot()
plt.subplot(R,C, 1+i+C)
plt.imshow(r, vmin=r0, vmax=r1, **ima)
plt.colorbar()
plt.hot()
ps.savefig()
def makeplots(tractor, step, suffix):
print 'Making plots'
ds = tractor.getCatalog()[0]
logsa, logt, emis = ds.getArrays()
suff = '.pdf'
#plt.figure(figsize=(8,6))
#plt.figure(figsize=(4.5,4))
#plt.figure(figsize=(4,3.5))
#spa = dict(left=0.01, right=0.99, bottom=0.02, top=0.92)
spa = dict(left=0.005, right=0.995, bottom=0.02, top=0.92)
H = 3.
W1 = 3.5
W2 = (0.90 / 0.99) * H
print 'W2', W2
plt.figure(figsize=(W1,H))
plt.subplots_adjust(**spa)
logsa = np.fliplr(logsa)
logt = np.fliplr(logt)
emis = np.fliplr(emis)
# plt.clf()
# plt.imshow(logsa, interpolation='nearest', origin='lower')
# plt.gray()
# plt.colorbar()
# plt.title('Dust: log(solid angle)')
# plt.savefig('logsa-%02i%s.png' % (step, suffix))
plt.clf()
#plt.imshow(np.exp(logsa), interpolation='nearest', origin='lower')
plt.imshow(np.exp(logsa) * 1000., interpolation='nearest', origin='lower',
vmin=0., vmax=6.)
plt.hot()
plt.colorbar(ticks=[0,2,4,6])
plt.xticks([])
plt.yticks([])
plt.title('Dust Column Density (arb units)') #: solid angle')
plt.savefig(('sa-%02i%s'+suff) % (step, suffix))
# plt.clf()
# plt.imshow(logt, interpolation='nearest', origin='lower')
# plt.gray()
# plt.colorbar()
# plt.title('Dust: log(temperature)')
# plt.savefig('logt-%02i%s'+suff % (step, suffix))
plt.clf()
plt.imshow(np.exp(logt), interpolation='nearest', origin='lower', vmin=0, vmax=30)
plt.hot()
plt.colorbar(ticks=[0,10,20,30])
plt.xticks([])
plt.yticks([])
plt.title('Dust Temperature (K)')
plt.savefig(('t-%02i%s'+suff) % (step, suffix))
plt.clf()
plt.imshow(emis, interpolation='nearest', origin='lower',
vmin=1, vmax=2.5)
plt.gray()
plt.colorbar(ticks=[1.0, 1.5, 2.0, 2.5])
plt.xticks([])
plt.yticks([])
plt.title('Dust Emissivity Index')
plt.savefig(('emis-%02i%s'+suff) % (step, suffix))
#plt.figure(figsize=(4,4))
#plt.figure(figsize=(3,3))
#plt.figure(figsize=(2.5,2.5))
#plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.9)
plt.figure(figsize=(W2,H))
#plt.figure(figsize=(3,3))
plt.subplots_adjust(**spa)
for i,tim in enumerate(tractor.getImages()):
ima = dict(interpolation='nearest', origin='lower',
vmin=tim.zr[0], vmax=tim.zr[1])
plt.clf()
plt.imshow(tim.getImage(), **ima)
plt.gray()
#plt.colorbar()
#plt.title(tim.name)
name = ['PACS 100', 'PACS 160', 'SPIRE 250', 'SPIRE 350', 'SPIRE 500'][i]
plt.title('Data: ' + name)
plt.xticks([])
plt.yticks([])
plt.savefig(('data-%i-%02i%s'+suff) % (i, step, suffix))
mods = []
for i,tim in enumerate(tractor.getImages()):
ima = dict(interpolation='nearest', origin='lower',
vmin=tim.zr[0], vmax=tim.zr[1])
print 'Getting model image', i
mod = tractor.getModelImage(i)
mods.append(mod)
plt.clf()
plt.imshow(mod, **ima)
plt.gray()
name = ['PACS 100', 'PACS 160', 'SPIRE 250', 'SPIRE 350', 'SPIRE 500'][i]
plt.title('Model: ' + name)
#plt.title(name)
plt.xticks([])
plt.yticks([])
plt.savefig(('model-%i-%02i%s'+suff) % (i, step, suffix))
#
# if step == 0:
# plt.clf()
# plt.imshow(tim.getImage(), **ima)
# plt.gray()
# plt.colorbar()
# plt.title(tim.name)
# plt.savefig('data-%i.png' % (i))
#mods = tractor.getModelImages()
# plt.figure(figsize=(8,6))
# for i,mod in enumerate(mods):
# tim = tractor.getImage(i)
# ima = dict(interpolation='nearest', origin='lower',
# vmin=tim.zr[0], vmax=tim.zr[1])
# plt.clf()
# plt.imshow(mod, **ima)
# plt.gray()
# plt.colorbar()
# plt.title(tim.name)
# plt.savefig('model-%i-%02i%s.png' % (i, step, suffix))
#
# if step == 0:
# plt.clf()
# plt.imshow(tim.getImage(), **ima)
# plt.gray()
# plt.colorbar()
# plt.title(tim.name)
# plt.savefig('data-%i.png' % (i))
#
# plt.clf()
# # tractor.getChiImage(i),
# plt.imshow((tim.getImage() - mod) * tim.getInvError(),
# interpolation='nearest', origin='lower',
# vmin=-5, vmax=+5)
# plt.gray()
# plt.colorbar()
# plt.title(tim.name)
# plt.savefig('chi-%i-%02i%s.png' % (i, step, suffix))
plt.figure(figsize=(16,8))
plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.9, wspace=0.1, hspace=0.1)
R,C = 3,len(mods)
plt.clf()
for i,mod in enumerate(mods):
tim = tractor.getImage(i)
print 'Image', tim.name, ': data median', np.median(tim.getImage()),
print 'model median:', np.median(mod)
ima = dict(interpolation='nearest', origin='lower',
vmin=tim.zr[0], vmax=tim.zr[1])
plt.subplot(R, C, i + 1)
plt.imshow(tim.getImage(), **ima)
plt.xticks([])
plt.yticks([])
plt.gray()
plt.colorbar()
plt.title(tim.name)
plt.subplot(R, C, i + 1 + C)
plt.imshow(mod, **ima)
plt.xticks([])
plt.yticks([])
plt.gray()
plt.colorbar()
#plt.title(tim.name)
plt.subplot(R, C, i + 1 + 2*C)
plt.imshow((tim.getImage() - mod) * tim.getInvError(),
interpolation='nearest', origin='lower',
vmin=-5, vmax=+5)
plt.xticks([])
plt.yticks([])
plt.gray()
plt.colorbar()
#plt.title(tim.name)
plt.savefig('all-%02i%s.png' % (step, suffix))
ds = tractor.getCatalog()[0]
logsa, logt, emis = ds.getArrays()
plt.figure(figsize=(16,5))
plt.clf()
plt.subplot(1,3,1)
plt.imshow(np.exp(logsa), interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
plt.title('Dust: solid angle (Sr?)')
plt.subplot(1,3,2)
plt.imshow(np.exp(logt), interpolation='nearest', origin='lower') #, vmin=0)
plt.hot()
plt.colorbar()
plt.title('Dust: temperature (K)')
plt.subplot(1,3,3)
plt.imshow(emis, interpolation='nearest', origin='lower')
plt.gray()
plt.colorbar()
plt.title('Dust: emissivity')
plt.savefig('dust-%02i%s.png' % (step, suffix))
def forced2():
from bigboss_test import radecroi
ps = PlotSequence('forced')
basedir = os.environ.get('BIGBOSS_DATA', '/project/projectdirs/bigboss')
wisedatadir = os.path.join(basedir, 'data', 'wise')
l1bdir = os.path.join(wisedatadir, 'level1b')
wisecat = fits_table(os.path.join(
wisedatadir, 'catalogs', 'wisecat2.fits'))
# CAS PhotoObjAll.resolveStatus bits
sprim = 0x100
#sbad = 0x800
sedge = 0x1000
sbest = 0x200
(ra0, ra1, dec0, dec1) = radecroi
ra = (ra0 + ra1) / 2.
dec = (dec0 + dec1) / 2.
cas = fits_table('sdss-cas-testarea-3.fits')
print('Read', len(cas), 'CAS sources')
cas.cut((cas.resolvestatus & sedge) == 0)
print('Cut to ', len(cas), 'without SURVEY_EDGE set')
# Drop "sbest" sources that have an "sprim" nearby.
Ibest = (cas.resolvestatus & (sprim | sbest)) == sbest
Iprim = (cas.resolvestatus & (sprim | sbest)) == sprim
I, J, d = match_radec(
cas.ra[Ibest], cas.dec[Ibest], cas.ra[Iprim], cas.dec[Iprim], 2. / 3600.)
Ibest[np.flatnonzero(Ibest)[I]] = False
#Ikeep = np.ones(len(Ibest), bool)
#Ikeep[I] = False
cas.cut(np.logical_or(Ibest, Iprim))
print('Cut to', len(cas), 'PRIMARY + BEST-not-near-PRIMARY')
I, J, d = match_radec(cas.ra, cas.dec, cas.ra,
cas.dec, 2. / 3600., notself=True)
plt.clf()
loghist((cas.ra[I] - cas.ra[J]) * 3600.,
(cas.dec[I] - cas.dec[J]) * 3600., 200)
plt.title('CAS self-matches')
ps.savefig()
psf = pyfits.open('wise-psf-w1-500-500.fits')[0].data
S = psf.shape[0]
# number of Gaussian components
K = 3
w, mu, sig = em_init_params(K, None, None, None)
II = psf.copy()
II = np.maximum(II, 0)
II /= II.sum()
xm, ym = -(S / 2), -(S / 2)
res = em_fit_2d(II, xm, ym, w, mu, sig)
if res != 0:
raise RuntimeError('Failed to fit PSF')
print('W1 PSF:')
print(' w', w)
print(' mu', mu)
print(' sigma', sig)
w1psf = GaussianMixturePSF(w, mu, sig)
w1psf.computeRadius()
print('PSF radius:', w1psf.getRadius(), 'pixels')
T = fits_table('wise-roi.fits')
for i in range(len(T)):
basefn = os.path.join(l1bdir, '%s%i-w1' %
(T.scan_id[i], T.frame_num[i]))
fn = basefn + '-int-1b.fits'
print('Looking for', fn)
if not os.path.exists(fn):
continue
print(' -> Found it!')
tim = read_wise_image(basefn, nanomaggies=True)
tim.psf = w1psf
wcs = tim.wcs.wcs
r0, r1, d0, d1 = wcs.radec_bounds()
print('RA,Dec bounds:', r0, r1, d0, d1)
w, h = wcs.imagew, wcs.imageh
rd = np.array([wcs.pixelxy2radec(x, y) for x, y in
[(1, 1), (w, 1), (w, h), (1, h), (1, 1)]])
I = np.flatnonzero((cas.ra > r0) * (cas.ra < r1) *
(cas.dec > d0) * (cas.dec < d1))
J = point_in_poly(cas.ra[I], cas.dec[I], rd)
I = I[J]
cashere = cas[I]
# 10-20k sources...
wbands = ['w1']
sdssband = 'i'
tsrcs = get_tractor_sources_cas_dr9(cashere, nanomaggies=True,
bandname=sdssband, bands=[
sdssband],
extrabands=wbands)
#keepsrcs = []
for src in tsrcs:
for br in src.getBrightnesses():
f = br.getBand(sdssband)
# if f < 0:
# continue
for wb in wbands:
br.setBand(wb, f)
# keepsrcs.append(src)
#tsrcs = keepsrcs
print('Created', len(tsrcs), 'tractor sources in this image')
I = np.flatnonzero((wisecat.ra > r0) * (wisecat.ra < r1) *
(wisecat.dec > d0) * (wisecat.dec < d1))
J = point_in_poly(wisecat.ra[I], wisecat.dec[I], rd)
I = I[J]
print('Found', len(I), 'WISE catalog sources in this image')
wc = wisecat[I]
tra = np.array([src.getPosition().ra for src in tsrcs])
tdec = np.array([src.getPosition().dec for src in tsrcs])
R = 4.
I, J, d = match_radec(wc.ra, wc.dec, tra, tdec,
R / 3600., nearest=True)
# cashere.ra, cashere.dec,
print('Found', len(I), 'SDSS-WISE matches within', R, 'arcsec')
for i, j in zip(I, J):
w1 = wc.w1mpro[i]
w1 = NanoMaggies.magToNanomaggies(w1)
bb = tsrcs[j].getBrightnesses()
for b in bb:
b.setBand('w1', w1 / float(len(bb)))
keepsrcs = []
for src in tsrcs:
# for b in src.getBrightness():
b = src.getBrightness()
if b.getBand(sdssband) > 0 or b.getBand(wbands[0]) > 0:
keepsrcs.append(src)
tsrcs = keepsrcs
print('Keeping', len(tsrcs), 'tractor sources from SDSS')
unmatched = np.ones(len(wc), bool)
unmatched[I] = False
wun = wc[unmatched]
print(len(wun), 'unmatched WISE sources')
for i in range(len(wun)):
pos = RaDecPos(wun.ra[i], wun.dec[i])
nm = NanoMaggies.magToNanomaggies(wun.w1mpro[i])
br = NanoMaggies(i=25., w1=nm)
tsrcs.append(PointSource(pos, br))
plt.clf()
plt.plot(rd[:, 0], rd[:, 1], 'k-')
plt.plot(cashere.ra, cashere.dec, 'r.', alpha=0.1)
plt.plot(wc.ra, wc.dec, 'bx', alpha=0.1)
setRadecAxes(r0, r1, d0, d1)
ps.savefig()
zlo, zhi = tim.zr
ima = dict(interpolation='nearest', origin='lower', vmin=zlo, vmax=zhi)
imchi = dict(interpolation='nearest', origin='lower',
vmin=-5, vmax=5)
plt.clf()
plt.imshow(tim.getImage(), **ima)
plt.hot()
plt.title(tim.name + ': data')
ps.savefig()
wsrcs = []
for i in range(len(wc)):
pos = RaDecPos(wc.ra[i], wc.dec[i])
nm = NanoMaggies.magToNanomaggies(wc.w1mpro[i])
br = NanoMaggies(i=25., w1=nm)
wsrcs.append(PointSource(pos, br))
tr = Tractor([tim], wsrcs)
tr.freezeParam('images')
for jj in range(2):
print('Rendering WISE model image...')
wmod = tr.getModelImage(0)
plt.clf()
plt.imshow(wmod, **ima)
plt.hot()
plt.title(tim.name + ': WISE sources only')
ps.savefig()
assert(np.all(np.isfinite(wmod)))
assert(np.all(np.isfinite(tim.getInvError())))
assert(np.all(np.isfinite(tim.getImage())))
wchi = tr.getChiImage(0)
plt.clf()
plt.imshow(wchi, **imchi)
plt.title(tim.name + ': chi, WISE sources only')
plt.gray()
ps.savefig()
if jj == 1:
break
tr.optimize()
tr = Tractor([tim], tsrcs)
print('Rendering model image...')
mod = tr.getModelImage(0)
plt.clf()
plt.imshow(mod, **ima)
plt.title(tim.name + ': SDSS + WISE sources')
ps.savefig()
print('tim', tim)
print('tim.photocal:', tim.photocal)
wsrcs = []
for i in range(len(wc)):
pos = RaDecPos(wc.ra[i], wc.dec[i])
nm = NanoMaggies.magToNanomaggies(wc.w1mpro[i])
br = NanoMaggies(i=25., w1=nm)
wsrcs.append(PointSource(pos, br))
tr = Tractor([tim], wsrcs)
print('Rendering WISE model image...')
wmod = tr.getModelImage(0)
plt.clf()
plt.imshow(wmod, **ima)
plt.title(tim.name + ': WISE sources only')
ps.savefig()
def plotCorrespondences2(imgsources,
refsources,
matches,
wcs,
W,
H,
prefix,
saveplot=True,
format='png'):
print('ix,iy')
ix = np.array([s.getXAstrom() for s in imgsources])
iy = np.array([s.getYAstrom() for s in imgsources])
print('rx,ry')
rx, ry = [], []
for r in refsources:
xy = wcs.skyToPixel(r.getRaDec())
rx.append(xy[0])
ry.append(xy[1])
rx = np.array(rx)
ry = np.array(ry)
ixy = np.vstack((ix, iy)).T
rxy = np.vstack((rx, ry)).T
print('plotshift...')
cell = 10
plotshift(ixy, rxy, dcell=cell, ncells=9, W=W, H=H)
fn = prefix + '-shift1.' + format
P1 = _output(fn, format, saveplot)
print('plotshift 2...')
plt.clf()
plt.hot()
plotshift(ixy,
rxy,
dcell=cell,
ncells=9,
W=W,
H=H,
hist=True,
nhistbins=2 * cell + 1)
fn = prefix + '-shift2.' + format
P2 = _output(fn, format, saveplot)
print('plotshift 3...')
cell = 2
plotshift(ixy, rxy, dcell=cell, ncells=9, W=W, H=H)
fn = prefix + '-shift3.' + format
P3 = _output(fn, format, saveplot)
print('plotshift 4...')
plt.clf()
plt.hot()
plotshift(ixy,
rxy,
dcell=cell,
ncells=9,
W=W,
H=H,
hist=True,
nhistbins=10 * cell + 1)
fn = prefix + '-shift4.' + format
P4 = _output(fn, format, saveplot)
if not saveplot:
P1.update(P2)
P1.update(P3)
P1.update(P4)
return P1
def main():
if True:
P = pyfits.open('jbf108bzq_flt.fits')
img = P[1].data
err = P[2].data
#dq = P[3].data
P = pyfits.open('jbf108bzq_dq1.fits')
dq = P[0].data
#cut = [slice(900,1200), slice(2300,2600)]
cut = [slice(1400, 1700), slice(2300, 2600)]
img = img[cut]
err = err[cut]
dq = dq[cut]
skyvar = measure_sky_variance(img)
skymed = np.median(img.ravel())
skysig = np.sqrt(skyvar)
print('Estimate sky value', skymed, 'and sigma', skysig)
zrange = np.array([-3., +10.]) * skysig + skymed
invvar = 1. / (err**2)
invvar[dq > 0] = 0
else:
# Dealing with cosmic rays is a PITA so use DRZ for now...
P = pyfits.open('jbf108020_drz.fits')
img = P[1].data
wht = P[2].data
ctx = P[3].data
cut = [slice(1000, 1300), slice(2300, 2600)]
img = img[cut]
wht = wht[cut]
# HACKs abound in what follows...
skyvar = measure_sky_variance(img)
invvar = wht / np.median(wht) / skyvar
skymed = np.median(img.ravel())
skysig = np.sqrt(skyvar)
zrange = np.array([-3., +10.]) * skysig + skymed
# add in source noise to variance map
# problem for the reader: why *divide* by wht?
srcvar = np.maximum(0, (img - skymed) /
np.maximum(wht,
np.median(wht) * 1e-6))
invvar = invvar / (1.0 + invvar * srcvar)
plt.clf()
plt.hist(img.ravel(), bins=np.linspace(zrange[0], zrange[1], 100))
plt.savefig('hist.png')
plt.clf()
plt.imshow(img,
interpolation='nearest',
origin='lower',
vmin=zrange[0],
vmax=zrange[1]) #vmin=50, vmax=500)
plt.hot()
plt.colorbar()
plt.savefig('img.png')
plt.clf()
plt.imshow(invvar,
interpolation='nearest',
origin='lower',
vmin=0.,
vmax=2. / (skysig**2))
plt.hot()
plt.colorbar()
plt.savefig('invvar.png')
plt.clf()
plt.imshow((img - skymed) * np.sqrt(invvar),
interpolation='nearest',
origin='lower')
#vmin=-3, vmax=10.)
plt.hot()
plt.colorbar()
plt.savefig('chi.png')
plt.clf()
plt.imshow((img - skymed) * np.sqrt(invvar),
interpolation='nearest',
origin='lower',
vmin=-3,
vmax=10.)
plt.hot()
plt.colorbar()
plt.savefig('chi2.png')
# Initialize with a totally bogus Gaussian PSF model.
psf = NCircularGaussianPSF([2.0], [1.0])
# test it...
if False:
for i, x in enumerate(np.arange(17, 18.7, 0.1)):
p = psf.getPointSourcePatch(x, x)
img = p.getImage()
print('x', x, '-> sum', img.sum())
plt.clf()
x0, y0 = p.getX0(), p.getY0()
h, w = img.shape
plt.imshow(img,
extent=[x0, x0 + w, y0, y0 + h],
origin='lower',
interpolation='nearest')
plt.axis([10, 25, 10, 25])
plt.title('x=%.1f' % x)
plt.savefig('psf-%02i.png' % i)
# We'll start by working in pixel coords
wcs = NullWCS()
# And counts
photocal = NullPhotoCal()
data = Image(data=img,
invvar=invvar,
psf=psf,
wcs=wcs,
sky=skymed,
photocal=photocal)
tractor = HSTTractor([data])
if False:
X = tractor.getChiImages()
chi = X[0]
plt.clf()
plt.imshow(chi, interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
plt.savefig('chi3.png')
Nsrc = 10
steps = (['plots'] + ['source'] * Nsrc + ['plots'] + ['psf'] + ['plots'] +
['psf2']) * 3 + ['plots'] + ['break']
chiArange = None
for i, step in enumerate(steps):
if step == 'plots':
print('Making plots...')
NS = len(tractor.getCatalog())
chis = tractor.getChiImages()
chi = chis[0]
tt = 'sources: %i, chi^2 = %g' % (NS, np.sum(chi**2))
mods = tractor.getModelImages()
mod = mods[0]
plt.clf()
plt.imshow(mod,
interpolation='nearest',
origin='lower',
vmin=zrange[0],
vmax=zrange[1])
plt.hot()
plt.colorbar()
ax = plt.axis()
img = tractor.getImage(0)
wcs = img.getWcs()
x = []
y = []
for src in tractor.getCatalog():
pos = src.getPosition()
px, py = wcs.positionToPixel(pos)
x.append(px)
y.append(py)
plt.plot(x, y, 'b+')
plt.axis(ax)
plt.title(tt)
plt.savefig('mod-%02i.png' % i)
if chiArange is None:
chiArange = (chi.min(), chi.max())
plt.clf()
plt.imshow(chi,
interpolation='nearest',
origin='lower',
vmin=chiArange[0],
vmax=chiArange[1])
plt.hot()
plt.colorbar()
plt.title(tt)
plt.savefig('chiA-%02i.png' % i)
plt.clf()
plt.imshow(chi,
interpolation='nearest',
origin='lower',
vmin=-3,
vmax=10.)
plt.hot()
plt.colorbar()
plt.title(tt)
plt.savefig('chiB-%02i.png' % i)
#print 'Driving the tractor...'
elif step == 'break':
break
elif step == 'source':
print()
print('Before createSource, catalog is:', end=' ')
tractor.getCatalog().printLong()
print()
rtn = tractor.createSource()
print()
print('After createSource, catalog is:', end=' ')
tractor.getCatalog().printLong()
print()
if False:
(sm, tryxy) = rtn[0]
plt.clf()
plt.imshow(sm, interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
ax = plt.axis()
plt.plot([x for x, y in tryxy], [y for x, y in tryxy], 'b+')
plt.axis(ax)
plt.savefig('create-%02i.png' % i)
elif step == 'psf':
baton = (i, )
tractor.optimizeAllPsfAtFixedComplexityStep(
derivCallback=(psfDerivCallback, baton))
elif step == 'psf2':
tractor.increaseAllPsfComplexity()
print('Tractor cache has', len(tractor.cache), 'entries')
files[k] = os.path.join(dirname, fn)
data[k] = fits.getdata(os.path.join(dirname, fn))
if len(files) == 0:
return
peaks = {}
for ii, (k, fn) in enumerate(files.iteritems()):
d = data[k]
OK = d == d
y, x = np.indices(d.shape, dtype='float')
rr = ((x - d.shape[1] / 2)**2 + (y - d.shape[0] / 2)**2)**0.5
OK *= rr < 10
if OK.sum() == 0:
continue
print k, fn, d[OK].max()
peaks[k] = d[OK].max()
return peaks
if __name__ == "__main__":
pl.hot() # select colormap
peaks = {}
for dirpath, dirnames, filenames in os.walk('./'):
if dirpath != './':
if 'neptune' in dirpath or 'g34.3' in dirpath:
continue
peaks[dirpath] = get_peaks(dirpath, filenames)
def display_maps(Detect, Latencies, fig, inner_grid,expe):
ax= Subplot(fig, inner_grid[0])
St = Detect.Sig_strength
#----------------------------------------
# NEED TO REMAP FOR EXPS 20 22 and 23, and 27!!
if expe<=22:
aux = copy(St[24])
for w in [24,23,22,21]:
St[w] = copy(St[w-1])
St[20] = copy(St[11])
St[11] = aux
if expe == 23:
for w in [0,1,2,3]:
St[w] = copy(St[w+1])
St[4] = [0,0]
#if exp == 27:
# St[16]== [0,0]
#---------------------------------------------
Stup = np.reshape(St[:,0],(5,5)) / St.max()
Stdn = np.reshape(St[:,1],(5,5))/ St.max()
# only do if they exist
if St.max()>0:
im = ax.imshow(Stup,interpolation='none',cmap=pylab.gray())
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.15)
colorbar(im, cax=cax)
ax.set_xticks([0,1,2,3,4]) # range of values in edges
ax.set_yticks([0,1,2,3,4]) # range of values in edges
ax.set_xticklabels(['St','1','2','3','4']) # range of values in edges
ax.set_yticklabels(['A','B ','C ','D','E ']) # range of values in edges
ax.tick_params('both', length=0, width=0)
ax.set_title('Caudal Strength')
fig.add_subplot(ax)
#---------------------------------------------------------------------------------------------
ax= Subplot(fig, inner_grid[1])
im =ax.imshow(Stdn,interpolation='none',cmap=pylab.gray())
ax.set_xticks([0,1,2,3,4]) # range of values in edges
ax.set_yticks([0,1,2,3,4]) # range of values in edges
ax.set_xticklabels(['St','1','2','3','4']) # range of values in edges
ax.set_yticklabels(['A','B ','C ','D','E ']) # range of values in edges
ax.tick_params('both', length=0, width=0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.15)
colorbar(im, cax=cax)
ax.set_title('Rostral Strength')
fig.add_subplot(ax)
#---------------------------------------------------------------------------------------------
ax= Subplot(fig, inner_grid[2])
Stboth = np.nan_to_num((Stup-Stdn)/(Stup+Stdn))
# make a color map of fixed colors
cmap = colors.ListedColormap(['#00cc00','#00aa00','#006600','#003300', 'black','#000066','#000099','#0000cc' ,'blue'])
bounds=[-1,-0.75,-0.5,-0.25,-0.01,0.01,0.25,0.5,0.75,1]
norm = colors.BoundaryNorm(bounds, cmap.N)
im = ax.imshow(Stboth,interpolation='none',cmap=cmap,norm=norm)
ax.set_xticks([0,1,2,3,4]) # range of values in edges
ax.set_yticks([0,1,2,3,4]) # range of values in edges
ax.set_xticklabels(['St','1','2','3','4']) # range of values in edges
ax.set_yticklabels(['A','B ','C ','D','E ']) # range of values in edges
ax.tick_params('both', length=0, width=0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.15)
colorbar(im, cax=cax)
ax.set_title('Directionality')
fig.add_subplot(ax)
#---------------------------------------------------------------------------------------------
ax= Subplot(fig, inner_grid[3])
Lat = zeros(25)
for i in arange(25):
if Latencies[i,0]>5 and Latencies[i,1]>5:
Lat[i] = min(Latencies[i,0],Latencies[i,1])
elif Latencies[i,0]>5:
Lat[i]= Latencies[i,0]
elif Latencies[i,1]>5:
Lat[i]= Latencies[i,1]
#----------------------------------------
# NEED TO REMAP FOR EXPS 20 22 and 23, and 27!!
if expe<=22:
aux = copy(Lat[24])
for w in [24,23,22,21]:
Lat[w] = copy(Lat[w-1])
Lat[20] = copy(Lat[11])
Lat[11] = aux
if expe == 23:
for w in [0,1,2,3]:
Lat[w] = copy(Lat[w+1])
Lat[4] = 0
#---------------------------------------------
Lat = np.reshape(Lat,(5,5))
im = ax.imshow(Lat,interpolation='none',cmap=pylab.hot())
ax.set_xticks([0,1,2,3,4]) # range of values in edges
ax.set_yticks([0,1,2,3,4]) # range of values in edges
ax.set_xticklabels(['St','1','2','3','4']) # range of values in edges
ax.set_yticklabels(['A','B ','C ','D','E ']) # range of values in edges
ax.tick_params('both', length=0, width=0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.15)
colorbar(im, cax=cax)
ax.set_title('Latency')
fig.add_subplot(ax)
def show(im): plt.imshow(np.log10(np.maximum(1e-3, im + 1e-3)), vmin=-3, vmax=0, **ima) plt.hot() plt.colorbar()
states.append(inputs[-1])
refs.append((-1) * np.ones((10, 10)))
for i in range(5):
inputs.append(np.random.random((10, 10)))
#inputs.append(np.diag(np.ones((10))*np.random.random()))
inputs[-1][:] = smooth(inputs[-1], 9) #+0.1
#states.append(np.zeros((10,10)))
states.append(inputs[-1])
refs.append(np.ones((10, 10)) * (1))
ct = CNNTrainer(t_sim=40, errfunc="mean_abs_diff", opt_method="simplex")
#ct = CNNTrainer(t_sim=40,errfunc="sum_sqr_diff", opt_method="powell")
#ct = CNNTrainer(t_sim=40,errfunc="mean_abs_diff", opt_method="anneal")
a, b, z = ct(inputs, states, refs)
#p.ioff()
#print "Test"
p.hot()
p.figure(1)
for i in range(10):
print i,
cnn = CNN2d(inputs[i], states[i], a, b, z)
try:
o = cnn.integrate(np.arange(1, 21, 1))
#p.figure(i+2)
#for i2 in range(20):
# p.subplot(4,5,i2+1)
# #p.axes(frameon=False)
# p.imshow(cnn.sigmoid(o[i2].reshape((10,10))),interpolation="nearest",vmin=-1,vmax=1)
# p.show()
#assert 1==0
time.sleep(1)
p.ioff()
#def apply_action(control, action):
# actions = ((0,0),(0,1),(1,0),(1,1),(0,-1),(-1,0),(-1,-1))
# act = actions[action]
# control.left_engine(act[0])
# control.right_engine(act[1])
#
#def do_next_action(control, image):
# global q_net, action, red_pixels
# splitted_image = split_image(image, 4)
# red_pixels = list(map(get_red_pixels, splitted_image))
# action = q_net.activate(red_pixels)
# arg = np.argmax(action)
# apply_action(control, arg)
pylab.ion()
pylab.hot()
pylab.show()
with CurrentController(3) as control:
environment = ControllerEnvironment(control)
task = MdpRedCubeTask(environment, False)
control.cubes_x = 2
control.cubes_y = 3
control.cubes_size = 4
task.max_samples = 500
actions = len(environment.actions)
actionValueNetwork = ActionValueTable(task.outdim, task.indim)
actionValueNetwork.stdParams = 0.0001
def loghist(x, y, nbins=100,
hot=True, doclf=True, docolorbar=True, lo=0.3,
imshowargs={},
clampxlo=False, clampxlo_val=None, clampxlo_to=None,
clampxhi=False, clampxhi_val=None, clampxhi_to=None,
clampylo=False, clampylo_val=None, clampylo_to=None,
clampyhi=False, clampyhi_val=None, clampyhi_to=None,
clamp=None, clamp_to=None,
**kwargs):
#np.seterr(all='warn')
if doclf:
plt.clf()
myargs = kwargs.copy()
if not 'bins' in myargs:
myargs['bins'] = nbins
rng = kwargs.get('range', None)
x = np.array(x)
y = np.array(y)
if not (np.all(np.isfinite(x)) and np.all(np.isfinite(y))):
K = np.flatnonzero(np.isfinite(x) * np.isfinite(y))
print 'loghist: cutting to', len(K), 'of', len(x), 'finite values'
x = x[K]
y = y[K]
if clamp is True:
clamp = rng
if clamp is not None:
((clampxlo_val, clampxhi_val),(clampylo_val, clampyhi_val)) = clamp
if clamp_to is not None:
((clampxlo_to, clampxhi_to),(clampylo_to, clampyhi_to)) = clamp_to
if clampxlo:
if clampxlo_val is None:
if rng is None:
raise RuntimeError('clampxlo, but no clampxlo_val or range')
clampxlo_val = rng[0][0]
if clampxlo_val is not None:
if clampxlo_to is None:
clampxlo_to = clampxlo_val
x[x < clampxlo_val] = clampxlo_to
if clampxhi:
if clampxhi_val is None:
if rng is None:
raise RuntimeError('clampxhi, but no clampxhi_val or range')
clampxhi_val = rng[0][1]
if clampxhi_val is not None:
if clampxhi_to is None:
clampxhi_to = clampxhi_val
x[x > clampxhi_val] = clampxhi_to
if clampylo:
if clampylo_val is None:
if rng is None:
raise RuntimeError('clampylo, but no clampylo_val or range')
clampylo_val = rng[1][0]
if clampylo_val is not None:
if clampylo_to is None:
clampylo_to = clampylo_val
y[y < clampylo_val] = clampylo_to
if clampyhi:
if clampyhi_val is None:
if rng is None:
raise RuntimeError('clampyhi, but no clampyhi_val or range')
clampyhi_val = rng[1][1]
if clampyhi_val is not None:
if clampyhi_to is None:
clampyhi_to = clampyhi_val
y[y > clampyhi_val] = clampyhi_to
(H,xe,ye) = np.histogram2d(x, y, **myargs)
L = np.log10(np.maximum(lo, H.T))
myargs = dict(extent=(min(xe), max(xe), min(ye), max(ye)),
aspect='auto',
interpolation='nearest', origin='lower')
myargs.update(imshowargs)
plt.imshow(L, **myargs)
if hot:
plt.hot()
if docolorbar:
r = [np.log10(lo)] + range(int(np.ceil(L.max())))
# print 'loghist: L max', L.max(), 'r', r
plt.colorbar(ticks=r, format=FixedFormatter(
['0'] + ['%i'%(10**ri) for ri in r[1:]]))
#set_fp_err()
return H, xe, ye
def show_fractal(self, width=200, height=200, mode="iterations"): data = self.newtons_method(self.make_grid(width, height), mode) pylab.imshow(data) pylab.hot() pylab.show()
def test_vs_stars_on(run, camcol, field, band, ps):
bandnum = band_index(band)
datadir = os.path.join(os.path.dirname(__file__), 'testdata')
sdss = DR9(basedir=datadir)
sdss.retrieve('psField', run, camcol, field)
psfield = sdss.readPsField(run, camcol, field)
sdss.retrieve('frame', run, camcol, field, band)
frame = sdss.readFrame(run, camcol, field, band)
img = frame.image
H,W = img.shape
fn = sdss.retrieve('photoObj', run, camcol, field)
T = fits_table(fn)
T.mag = T.get('psfmag')[:,bandnum]
T.flux = T.get('psfflux')[:,bandnum]
# !!
T.x = T.colc[:,bandnum] - 0.5
T.y = T.rowc[:,bandnum] - 0.5
T.cut(T.prob_psf[:,bandnum] == 1)
T.cut(T.nchild == 0)
T.cut(T.parent == -1)
T.cut(T.flux > 1.)
print len(T), 'after flux cut'
#T.cut(T.flux > 10.)
#print len(T), 'after flux cut'
#T.cut(T.flux > 20.)
#print len(T), 'after flux cut'
T.cut(np.argsort(-T.flux)[:25])
# margin
m = 30
T.cut((T.x >= m) * (T.x < (W-m)) * (T.y >= m) * (T.y < (H-m)))
#T.cut(np.argsort(T.mag))
T.cut(np.argsort(-np.abs(T.x - T.y)))
print len(T), 'PSF stars'
#R,C = 5,5
#plt.clf()
eigenpsfs = psfield.getEigenPsfs(bandnum)
eigenpolys = psfield.getEigenPolynomials(bandnum)
RR,CC = 2,2
xx,yy = np.meshgrid(np.linspace(0, W, 12), np.linspace(0, H, 8))
ima = dict(interpolation='nearest', origin='lower')
plt.clf()
mx = None
for i,(psf,poly) in enumerate(zip(eigenpsfs, eigenpolys)):
print
print 'Eigen-PSF', i
XO,YO,C = poly
kk = np.zeros_like(xx)
for xo,yo,c in zip(XO,YO,C):
dk = (xx ** xo) * (yy ** yo) * c
#print 'xo,yo,c', xo,yo,c, '-->', dk
kk += dk
print 'Max k:', kk.max(), 'min', kk.min()
print 'PSF range:', psf.min(), psf.max()
print 'Max effect:', max(np.abs(kk.min()), kk.max()) * max(np.abs(psf.min()), psf.max())
plt.subplot(RR,CC, i+1)
plt.imshow(psf * kk.max(), **ima)
if mx is None:
mx = (psf * kk.max()).max()
else:
plt.clim(-mx * 0.05, mx * 0.05)
plt.colorbar()
ps.savefig()
psfs = psfield.getPsfAtPoints(bandnum, xx,yy)
#print 'PSFs:', psfs
ph,pw = psfs[0].shape
psfs = np.array(psfs).reshape(xx.shape + (ph,pw))
print 'PSFs shape:', psfs.shape
psfs = psfs[:,:,15:36,15:36]
ny,nx,ph,pw = psfs.shape
psfmos = np.concatenate([psfs[i,:,:,:] for i in range(ny)], axis=1)
print 'psfmos', psfmos.shape
psfmos = np.concatenate([psfmos[i,:,:] for i in range(nx)], axis=1)
print 'psfmos', psfmos.shape
plt.clf()
plt.imshow(np.log10(np.maximum(psfmos + 1e-3, 1e-3)), **ima)
ps.savefig()
diffs = None
rmses = None
for i in range(len(T)):
xx = T.x[i]
yy = T.y[i]
ix = int(np.round(xx))
iy = int(np.round(yy))
dx = xx - ix
dy = yy - iy
S = 25
stamp = img[iy-S:iy+S+1, ix-S:ix+S+1]
L = 6
Lx = lanczos_filter(L, np.arange(-L, L+1) - dx)
Ly = lanczos_filter(L, np.arange(-L, L+1) - dy)
sx = correlate1d(stamp, Lx, axis=1, mode='constant')
shim = correlate1d(sx, Ly, axis=0, mode='constant')
shim /= (Lx.sum() * Ly.sum())
psf = psfield.getPsfAtPoints(bandnum, xx, yy)
mod = psf / psf.sum() * T.flux[i]
psf2 = psfield.getPsfAtPoints(bandnum, yy, xx)
mod2 = psf2 / psf2.sum() * T.flux[i]
psf3 = psfield.getPsfAtPoints(bandnum, xx, yy).T
mod3 = psf3 / psf3.sum() * T.flux[i]
mods = [mod, mod2, mod3]
if diffs is None:
diffs = [np.zeros_like(m) for m in mods]
rmses = [np.zeros_like(m) for m in mods]
for m,rms,diff in zip(mods, rmses, diffs):
diff += (m - shim)
rms += (m - shim)**2
if i > 10:
continue
def show(im):
plt.imshow(np.log10(np.maximum(1e-3, im + 1e-3)), vmin=-3, vmax=0, **ima)
plt.hot()
plt.colorbar()
R,C = 3,4
plt.clf()
plt.subplot(R,C,1)
show(shim)
plt.subplot(R,C,2)
show(mods[0])
plt.subplot(R,C,2+C)
plt.imshow(mods[0] - shim, vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.subplot(R,C,3)
show(mods[1])
plt.subplot(R,C,3+C)
plt.imshow(mods[1] - shim, vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.subplot(R,C,4)
show(mods[2])
plt.subplot(R,C,4+C)
plt.imshow(mods[2] - shim, vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.subplot(R,C,3+2*C)
plt.imshow(mods[1] - mods[0], vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.subplot(R,C,4+2*C)
plt.imshow(mods[2] - mods[0], vmin=-0.05, vmax=0.05, **ima)
plt.hot()
plt.colorbar()
plt.suptitle('%.0f, %.0f' % (xx,yy))
ps.savefig()
for diff in diffs:
diff /= len(T)
rmses = [np.sqrt(rms / len(T)) for rms in rmses]
print 'rms median', np.median(rmses[0]), 'mean', np.mean(rmses[0])
r0,r1 = [np.percentile(rmses[0], p) for p in [10,90]]
R,C = 2,len(diffs)
plt.clf()
for i,(d,r) in enumerate(zip(diffs, rmses)):
plt.subplot(R,C, 1+i)
plt.imshow(d, vmin=-0.05, vmax=0.05, **ima)
plt.colorbar()
plt.hot()
plt.subplot(R,C, 1+i+C)
plt.imshow(r, vmin=r0, vmax=r1, **ima)
plt.colorbar()
plt.hot()
ps.savefig()
continue
files[k] = os.path.join(dirname,fn)
data[k] = fits.getdata(os.path.join(dirname,fn))
if len(files) == 0:
return
peaks = {}
for ii,(k,fn) in enumerate(files.iteritems()):
d = data[k]
OK = d==d
y,x = np.indices(d.shape,dtype='float')
rr = ((x-d.shape[1]/2)**2 + (y-d.shape[0]/2)**2)**0.5
OK *= rr < 10
if OK.sum() == 0:
continue
print k,fn,d[OK].max()
peaks[k] = d[OK].max()
return peaks
if __name__ == "__main__":
pl.hot() # select colormap
peaks = {}
for dirpath, dirnames, filenames in os.walk('./'):
if dirpath != './':
if 'neptune' in dirpath or 'g34.3' in dirpath:
continue
peaks[dirpath] = get_peaks(dirpath,filenames)
# plot the sampled trajectories
f1 = pylab.figure()
path_colours = ['r','g','b']
path_linestyles = ['-','--','-.']
for g in range(num_groups):
pylab.plot(routes[g][:,1],routes[g][:,0],c=path_colours[g],ls=path_linestyles[g],linewidth=2)
ax = pylab.gca()
ax.set_xticks([])
ax.set_yticks([])
if save_figures:
f1.set_figwidth(4)
f1.set_figheight(4)
pylab.savefig('sampled_trajectories.png',bbox_inches='tight')
# plot the sampled mean disease density
pylab.matshow(M)
pylab.colorbar()
pylab.hot()
ax = pylab.gca()
ax.set_xticks([])
ax.set_yticks([])
if save_figures:
f2 = pylab.gcf()
f2.set_figwidth(8)
f2.set_figheight(4)
pylab.savefig('sampled_mean_density.png',bbox_inches='tight')
def alignAndPlot(Tme,
Tref,
rad,
ps,
doweighted=True,
emrad=None,
nearest=False,
**kwargs):
aliargs = dict(cutrange=emrad)
aliargs.update(kwargs)
A = Alignment(Tme, Tref, searchradius=rad, **aliargs)
if nearest:
# There is something badly wrong with spherematch.nearest().
assert (False)
A.findMatches(nearest=True)
M = A.match
print 'dra,ddec arcsec:', M.dra_arcsec[:100], M.ddec_arcsec[:100]
if A.shift() is None:
print 'Shift not found!'
return None
M = A.match
print 'Shift:', A.arcsecshift()
sr, sd = A.arcsecshift()
sumd2 = np.sum(A.fore * ((M.dra_arcsec[A.subset] - sr)**2 +
(M.ddec_arcsec[A.subset] - sd)**2))
sumw = np.sum(A.fore)
# / 2. to get std per coord.
std = sqrt(sumd2 / (sumw * 2.))
angles = np.linspace(0, 2. * pi, 100)
modstr = ''
if A.cov:
eigs = A.getEllipseSize() * 1000.
if eigs[0] > 100:
modstr = '%.0fx%.0f' % (eigs[0], eigs[1])
else:
modstr = '%.1fx%.1f' % (eigs[0], eigs[1])
else:
modstr = '%.1f' % (1000. * A.sigma)
W = np.zeros_like(A.subset).astype(float)
W[A.subset] = A.fore
rl, rh = Tme.ra.min(), Tme.ra.max()
dl, dh = Tme.dec.min(), Tme.dec.max()
if doweighted:
rounds = [{}, {'weights': W}]
else:
rounds = [{}]
for i, args in enumerate(rounds):
tsuf = '' if i == 0 else ' (weighted)'
N = len(M.dra_arcsec) if i == 0 else sumw
plotresids(Tme,
M,
'%s-%s match residuals%s' % (Tme.cam, Tref.cam, tsuf),
bins=100,
**args)
ps.savefig()
dst = 1000. * np.sqrt(M.dra_arcsec**2 + M.ddec_arcsec**2)
loghist(Tme.mag[M.I], dst, 100, **args)
plt.xlabel(Tme.filter)
plt.ylabel('Match residual (mas)')
ps.savefig()
loghist(Tref.mag[M.J], dst, 100, **args)
plt.xlabel(Tref.filter)
plt.ylabel('Match residual (mas)')
ps.savefig()
H, xe, ye = plotalignment(A)
# show EM circle
ax = plt.axis()
angles = np.linspace(0, 2. * pi, 100)
c = A.cutcenter
r = A.cutrange
plt.plot(c[0] + r * np.cos(angles), c[1] + r * np.sin(angles), 'g--')
plt.axis(ax)
plt.title('%s-%s (%i matches, std %.1f mas, model %s)%s' %
(Tme.cam, Tref.cam, int(sumw), std * 1000., modstr, tsuf))
ps.savefig()
bins = 200
edges = np.linspace(-rad, rad, bins)
DR, DD = np.meshgrid(edges, edges)
em = A.getModel(DR.ravel(), DD.ravel()).reshape(DR.shape)
em *= len(M.dra_arcsec) * (edges[1] - edges[0])**2
R2 = DR**2 + DD**2
em[R2 > (A.match.rad)**2] = 0.
plt.clf()
plt.imshow(em,
extent=(-rad, rad, -rad, rad),
aspect='auto',
interpolation='nearest',
origin='lower',
vmin=H.min(),
vmax=H.max())
plt.hot()
plt.colorbar()
plt.xlabel('dRA (arcsec)')
plt.ylabel('dDec (arcsec)')
plt.title('EM model')
ps.savefig()
plotfitquality(H, xe, ye, A)
ps.savefig()
rng = ((-5 * std, 5 * std), (-5 * std, 5 * std))
myargs = args.copy()
myargs.update({'range': rng})
plothist(M.dra_arcsec - sr, M.ddec_arcsec - sd, 200, **myargs)
ax = plt.axis()
plt.xlabel('dRA (arcsec)')
plt.ylabel('dDec (arcsec)')
for nsig in [1, 2]:
X, Y = A.getContours(nsig)
plt.plot(X - sr, Y - sd, 'b-')
plt.axis(ax)
plt.title('%s-%s (matches: %i, std: %.1f mas, model %s)%s' %
(Tme.cam, Tref.cam, int(sumw), std * 1000., modstr, tsuf))
ps.savefig()
#plothist(Tme.mag[M.I], Tref.mag[M.J], 100, **args)
#plt.xlabel('%s %s (mag)' % (Tme.cam, Tme.filter))
#plt.ylabel('%s %s (mag)' % (Tref.cam, Tref.filter))
#fn = '%s-%s.png' % (basefn, chr(ploti))
#plt.title('%s-%s%s' % (Tme.cam, Tref.cam, tsuf))
#plt.savefig(fn)
#print 'saved', fn
#ploti += 1
loghist(Tme.mag[M.I], Tref.mag[M.J], 100, **args)
plt.xlabel('%s (mag)' % (Tme.filter))
plt.ylabel('%s (mag)' % (Tref.filter))
plt.title('%s-%s%s' % (Tme.cam, Tref.cam, tsuf))
ps.savefig()
plothist(Tme.ra[M.I], Tme.dec[M.I], 100, range=((rl, rh), (dl, dh)))
setRadecAxes(rl, rh, dl, dh)
plt.title('%s-%s: %i matches%s' % (Tme.cam, Tref.cam, N, tsuf))
ps.savefig()
return A
def main():
if True:
P = pyfits.open('jbf108bzq_flt.fits')
img = P[1].data
err = P[2].data
#dq = P[3].data
P = pyfits.open('jbf108bzq_dq1.fits')
dq = P[0].data
#cut = [slice(900,1200), slice(2300,2600)]
cut = [slice(1400,1700), slice(2300,2600)]
img = img[cut]
err = err[cut]
dq = dq[cut]
skyvar = measure_sky_variance(img)
skymed = np.median(img.ravel())
skysig = np.sqrt(skyvar)
print 'Estimate sky value', skymed, 'and sigma', skysig
zrange = np.array([-3.,+10.]) * skysig + skymed
invvar = 1. / (err**2)
invvar[dq > 0] = 0
else:
# Dealing with cosmic rays is a PITA so use DRZ for now...
P = pyfits.open('jbf108020_drz.fits')
img = P[1].data
wht = P[2].data
ctx = P[3].data
cut = [slice(1000,1300), slice(2300,2600)]
img = img[cut]
wht = wht[cut]
# HACKs abound in what follows...
skyvar = measure_sky_variance(img)
invvar = wht / np.median(wht) / skyvar
skymed = np.median(img.ravel())
skysig = np.sqrt(skyvar)
zrange = np.array([-3.,+10.]) * skysig + skymed
# add in source noise to variance map
# problem for the reader: why *divide* by wht?
srcvar = np.maximum(0, (img - skymed) / np.maximum(wht, np.median(wht)*1e-6))
invvar = invvar / (1.0 + invvar * srcvar)
plt.clf()
plt.hist(img.ravel(), bins=np.linspace(zrange[0], zrange[1], 100))
plt.savefig('hist.png')
plt.clf()
plt.imshow(img, interpolation='nearest', origin='lower',
vmin=zrange[0], vmax=zrange[1]) #vmin=50, vmax=500)
plt.hot()
plt.colorbar()
plt.savefig('img.png')
plt.clf()
plt.imshow(invvar, interpolation='nearest', origin='lower',
vmin=0., vmax=2./(skysig**2))
plt.hot()
plt.colorbar()
plt.savefig('invvar.png')
plt.clf()
plt.imshow((img-skymed) * np.sqrt(invvar), interpolation='nearest', origin='lower')
#vmin=-3, vmax=10.)
plt.hot()
plt.colorbar()
plt.savefig('chi.png')
plt.clf()
plt.imshow((img-skymed) * np.sqrt(invvar), interpolation='nearest', origin='lower',
vmin=-3, vmax=10.)
plt.hot()
plt.colorbar()
plt.savefig('chi2.png')
# Initialize with a totally bogus Gaussian PSF model.
psf = NCircularGaussianPSF([2.0], [1.0])
# test it...
if False:
for i,x in enumerate(np.arange(17, 18.7, 0.1)):
p = psf.getPointSourcePatch(x, x)
img = p.getImage()
print 'x', x, '-> sum', img.sum()
plt.clf()
x0,y0 = p.getX0(),p.getY0()
h,w = img.shape
plt.imshow(img, extent=[x0, x0+w, y0, y0+h],
origin='lower', interpolation='nearest')
plt.axis([10,25,10,25])
plt.title('x=%.1f' % x)
plt.savefig('psf-%02i.png' % i)
# We'll start by working in pixel coords
wcs = NullWCS()
# And counts
photocal = NullPhotoCal()
data = Image(data=img, invvar=invvar, psf=psf, wcs=wcs, sky=skymed,
photocal=photocal)
tractor = HSTTractor([data])
if False:
X = tractor.getChiImages()
chi = X[0]
plt.clf()
plt.imshow(chi, interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
plt.savefig('chi3.png')
Nsrc = 10
steps = (['plots'] + ['source']*Nsrc + ['plots'] + ['psf'] + ['plots'] + ['psf2'])*3 + ['plots'] + ['break']
chiArange = None
for i,step in enumerate(steps):
if step == 'plots':
print 'Making plots...'
NS = len(tractor.getCatalog())
chis = tractor.getChiImages()
chi = chis[0]
tt = 'sources: %i, chi^2 = %g' % (NS, np.sum(chi**2))
mods = tractor.getModelImages()
mod = mods[0]
plt.clf()
plt.imshow(mod, interpolation='nearest', origin='lower',
vmin=zrange[0], vmax=zrange[1])
plt.hot()
plt.colorbar()
ax = plt.axis()
img = tractor.getImage(0)
wcs = img.getWcs()
x = []
y = []
for src in tractor.getCatalog():
pos = src.getPosition()
px,py = wcs.positionToPixel(pos)
x.append(px)
y.append(py)
plt.plot(x, y, 'b+')
plt.axis(ax)
plt.title(tt)
plt.savefig('mod-%02i.png' % i)
if chiArange is None:
chiArange = (chi.min(), chi.max())
plt.clf()
plt.imshow(chi, interpolation='nearest', origin='lower',
vmin=chiArange[0], vmax=chiArange[1])
plt.hot()
plt.colorbar()
plt.title(tt)
plt.savefig('chiA-%02i.png' % i)
plt.clf()
plt.imshow(chi, interpolation='nearest', origin='lower',
vmin=-3, vmax=10.)
plt.hot()
plt.colorbar()
plt.title(tt)
plt.savefig('chiB-%02i.png' % i)
#print 'Driving the tractor...'
elif step == 'break':
break
elif step == 'source':
print
print 'Before createSource, catalog is:',
tractor.getCatalog().printLong()
print
rtn = tractor.createSource()
print
print 'After createSource, catalog is:',
tractor.getCatalog().printLong()
print
if False:
(sm,tryxy) = rtn[0]
plt.clf()
plt.imshow(sm, interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
ax = plt.axis()
plt.plot([x for x,y in tryxy], [y for x,y in tryxy], 'b+')
plt.axis(ax)
plt.savefig('create-%02i.png' % i)
elif step == 'psf':
baton = (i,)
tractor.optimizeAllPsfAtFixedComplexityStep(
derivCallback=(psfDerivCallback, baton))
elif step == 'psf2':
tractor.increaseAllPsfComplexity()
print 'Tractor cache has', len(tractor.cache), 'entries'
def plot_results(self):
# Plot the cropped picture
plt.subplot(421)
plt.xticks([])
plt.yticks([])
plt.title(self.name)
pylab.imshow(self.image_orig)
pylab.gray()
# Plot thresholded image
pylab.gray()
plt.subplot(422)
plt.xticks([])
plt.yticks([])
plt.title('coverage = %.2f' % self.coverage)
pylab.imshow(self.image > self.thresh)
# Plot distinguished image
pylab.hot()
plt.subplot(423)
plt.xticks([])
plt.yticks([])
plt.title('%d seperate islands' % self.num_islands)
pylab.imshow(self.labeled_by_area)
plt.colorbar()
# Plot distinguished image
plt.subplot(424)
plt.xticks([])
plt.yticks([])
plt.title('%d seperate islands' % self.num_islands)
pylab.imshow(self.labeled_p)
# Plot histogram
plt.subplot(425)
plt.hist(self.size_dist * (1.92**2), normed=True, align='mid')
plt.xlabel('Projected Area (nm$^2$)')
plt.ylabel('Probability')
# Plot histogram
plt.subplot(426)
plt.hist(self.perimeters[1:-1] * 1.92, normed=True, align='mid')
plt.xlabel('Perimeter (nm)')
plt.ylabel('Probability')
# Plot histogram
plt.subplot(427)
plt.hist(self.perimeter_dist, normed=True, align='mid')
plt.xlabel('Fractal Coeffecient')
plt.ylabel('Probability')
plt.subplot(428)
plt.plot(self.perimeters[1:-1] * 1.92, self.size_dist[1:-1] * 1.92**2,
'o')
plt.xlabel('Perimeter [nm]')
plt.ylabel('Area [nm$^2$]')
plt.savefig(self.name + ".png")
plt.show()
"""
self.image_bool = input('Use this image? (True or False)')
self.image_bool = bool(self.image_bool)
if not self.image_bool:
print 'image removed'
os.system('rm %s.tif' % self.name)
"""
return
ps = PlotSequence("ex")
for scale in [False, True]:
# reset fit
psf.scale = scale
im = psf.instantiateAt(0.0, 0.0)
ny, nx = im.shape
XX, YY = np.meshgrid(np.arange(nx), np.arange(ny))
print("cx", (np.sum(im * XX) / np.sum(im)))
print("cy", (np.sum(im * YY) / np.sum(im)))
plt.clf()
mx = im.max()
plt.imshow(im, origin="lower", interpolation="nearest", vmin=-0.1 * mx, vmax=mx * 1.1)
plt.hot()
plt.colorbar()
ps.savefig()
print("PSF scale", psf.sampling)
print("1./scale", 1.0 / psf.sampling)
YY = np.linspace(0, 4096, 5)
XX = np.linspace(0, 2048, 5)
yims = []
for y in YY:
xims = []
for x in XX:
im = psf.instantiateAt(x, y)
im /= im.sum()
#///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
#///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
#Plotting block
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
#Make a beam profile plot in the z-r plane.
py.ion()
py.clf()
py.imshow(P_tot,
norm=colors.LogNorm(vmin=1e-6, vmax=1.),
interpolation='nearest')
ax = py.gca()
py.ylim(ax.get_ylim()[::-1]) #Reverse y-axis
#py.xlim((-floor(z_ap - z_c),50))
py.hot()
py.colorbar(ticks=[1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1.],
label='Power Relative to Optical Axis')
#py.contour(P_tot, [1e-1,1e-2,1e-3,1e-4,1e-5,1e-6], colors='b')
#py.plot([0./resolution,5./resolution],[2.8/resolution,2.8/resolution], 'k')
#py.plot([2.134/resolution,2.134/resolution],[0./resolution,5./resolution], 'k')
locs = zeros(10)
labels = zeros(10)
for i in range(10):
locs[i] = i * length / resolution / 10.
labels[i] = i * length / 10.
#locs = range(0,int(length/resolution),int(length/(resolution*10.)))
#labels = range(0,int(length),int(length/len(locs)))
py.yticks(locs, labels)
py.xticks(locs, labels)
py.xlabel('Distance from horn aperture (mm)')
topology.append(('left', i + 1, i))
# starts the task
task = start_task(
HeatSolver, # name of the task class
cpu=nodes, # use <nodes> CPUs on the local machine
topology=topology,
args=(split_y, dx, dt, iterations))
# Retrieves the result, as a list with one element returned by MonteCarlo.get_result per node
result = task.get_result()
result = np.hstack(result)
return result
if __name__ == '__main__':
result = heat2d(100, 2)
pl.hot()
pl.imshow(result)
# x = np.linspace(-1.,1.,98)
# X,Y= np.meshgrid(x,x)
# import matplotlib.pyplot as plt
# from matplotlib import cm
# from mpl_toolkits.mplot3d import Axes3D
# fig = pl.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.plot_wireframe(X,Y,result)
pl.show()
def visualize():
'''
Just some ugly plotting to make an animation for the lecture.
'''
import pylab as pp
pp.ion()
def remove_spines(axes):
axes.set_xticks([])
axes.set_yticks([])
axes.spines['right'].set_color('none')
axes.spines['top'].set_color('none')
axes.spines['bottom'].set_color('none')
axes.spines['left'].set_color('none')
pfields, arena, aX, aY, centers = setup()
theta = linspace(0, 2 * pi, 250)
x = cos(theta) * 5 + 5
y = sin(theta) * 5 + 5
points, estimates_bayes, estimates_direct = [], [], []
for rx, ry in zip(x, y):
handles = []
rates, obs_spikes = simulate_spikes(pfields, rx, ry)
pp.subplot(1, 2, 1)
a = decode_bayes(pfields, obs_spikes, arena)
pp.contour(aX, aY, a)
pp.plot(centers[:, 0], centers[:, 1], 'k+', alpha=.5)
pp.hot()
ey, ex = unravel_index(argmax(a), a.shape)
estimates_bayes.append((aX[0, ex], aY[ey, 0]))
points.append((rx, ry))
pp.plot(array(points)[:, 0], array(points)[:, 1], 'b-', alpha=0.5)
pp.plot(rx, ry, 'bo')
pp.plot(aX[0, ex], aY[ey, 0], 'ko')
pp.plot(array(estimates_bayes)[:, 0],
array(estimates_bayes)[:, 1],
'k-',
alpha=0.5)
pp.ylim([-.5, 10.5])
pp.xlim([-.5, 10.5])
remove_spines(pp.gca())
pp.subplot(1, 2, 2)
a = decode_directbasis(pfields, obs_spikes, arena)
pp.contour(aX, aY, a)
pp.plot(centers[:, 0], centers[:, 1], 'k+', alpha=.5)
pp.hot()
ey, ex = unravel_index(argmax(a), a.shape)
estimates_direct.append((aX[0, ex], aY[ey, 0]))
points.append((rx, ry))
pp.plot(array(points)[:, 0], array(points)[:, 1], 'b-', alpha=.5)
pp.plot(rx, ry, 'bo')
pp.plot(aX[0, ex], aY[ey, 0], 'ko')
pp.plot(array(estimates_direct)[:, 0],
array(estimates_direct)[:, 1],
'k-',
alpha=.5)
pp.ylim([-.5, 10.5])
pp.xlim([-.5, 10.5])
remove_spines(pp.gca())
yield (handles)
def makeplots(tractor, step, suffix):
print('Making plots')
ds = tractor.getCatalog()[0]
logsa, logt, emis = ds.getArrays()
suff = '.pdf'
#plt.figure(figsize=(8,6))
#plt.figure(figsize=(4.5,4))
#plt.figure(figsize=(4,3.5))
#spa = dict(left=0.01, right=0.99, bottom=0.02, top=0.92)
spa = dict(left=0.005, right=0.995, bottom=0.02, top=0.92)
H = 3.
W1 = 3.5
W2 = (0.90 / 0.99) * H
print('W2', W2)
plt.figure(1, figsize=(W1,H))
plt.subplots_adjust(**spa)
logsa = np.fliplr(logsa)
logt = np.fliplr(logt)
emis = np.fliplr(emis)
# plt.clf()
# plt.imshow(logsa, interpolation='nearest', origin='lower')
# plt.gray()
# plt.colorbar()
# plt.title('Dust: log(solid angle)')
# plt.savefig('logsa-%02i%s.png' % (step, suffix))
plt.clf()
#plt.imshow(np.exp(logsa), interpolation='nearest', origin='lower')
plt.imshow(np.exp(logsa) * 1000., interpolation='nearest', origin='lower',
vmin=0., vmax=6.)
plt.hot()
plt.colorbar(ticks=[0,2,4,6])
plt.xticks([])
plt.yticks([])
plt.title('Dust Column Density (arb units)') #: solid angle')
plt.savefig(('sa-%02i%s'+suff) % (step, suffix))
# plt.clf()
# plt.imshow(logt, interpolation='nearest', origin='lower')
# plt.gray()
# plt.colorbar()
# plt.title('Dust: log(temperature)')
# plt.savefig('logt-%02i%s'+suff % (step, suffix))
plt.clf()
plt.imshow(np.exp(logt), interpolation='nearest', origin='lower', vmin=0, vmax=30)
plt.hot()
plt.colorbar(ticks=[0,10,20,30])
plt.xticks([])
plt.yticks([])
plt.title('Dust Temperature (K)')
plt.savefig(('t-%02i%s'+suff) % (step, suffix))
plt.clf()
plt.imshow(emis, interpolation='nearest', origin='lower',
vmin=1, vmax=2.5)
plt.gray()
plt.colorbar(ticks=[1.0, 1.5, 2.0, 2.5])
plt.xticks([])
plt.yticks([])
plt.title('Dust Emissivity Index')
plt.savefig(('emis-%02i%s'+suff) % (step, suffix))
#plt.figure(figsize=(4,4))
#plt.figure(figsize=(3,3))
#plt.figure(figsize=(2.5,2.5))
#plt.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.9)
plt.figure(2, figsize=(W2,H))
#plt.figure(figsize=(3,3))
plt.subplots_adjust(**spa)
for i,tim in enumerate(tractor.getImages()):
ima = dict(interpolation='nearest', origin='lower',
vmin=tim.zr[0], vmax=tim.zr[1])
plt.clf()
plt.imshow(tim.getImage(), **ima)
plt.gray()
#plt.colorbar()
#plt.title(tim.name)
name = ['PACS 100', 'PACS 160', 'SPIRE 250', 'SPIRE 350', 'SPIRE 500'][i]
plt.title('Data: ' + name)
plt.xticks([])
plt.yticks([])
plt.savefig(('data-%i-%02i%s'+suff) % (i, step, suffix))
mods = []
for i,tim in enumerate(tractor.getImages()):
ima = dict(interpolation='nearest', origin='lower',
vmin=tim.zr[0], vmax=tim.zr[1])
print('Getting model image', i)
mod = tractor.getModelImage(i)
mods.append(mod)
plt.clf()
plt.imshow(mod, **ima)
plt.gray()
plt.title('Model: ' + tim.name)
#plt.title(name)
plt.xticks([])
plt.yticks([])
plt.savefig(('model-%i-%02i%s'+suff) % (i, step, suffix))
#
# if step == 0:
# plt.clf()
# plt.imshow(tim.getImage(), **ima)
# plt.gray()
# plt.colorbar()
# plt.title(tim.name)
# plt.savefig('data-%i.png' % (i))
#mods = tractor.getModelImages()
# plt.figure(figsize=(8,6))
# for i,mod in enumerate(mods):
# tim = tractor.getImage(i)
# ima = dict(interpolation='nearest', origin='lower',
# vmin=tim.zr[0], vmax=tim.zr[1])
# plt.clf()
# plt.imshow(mod, **ima)
# plt.gray()
# plt.colorbar()
# plt.title(tim.name)
# plt.savefig('model-%i-%02i%s.png' % (i, step, suffix))
#
# if step == 0:
# plt.clf()
# plt.imshow(tim.getImage(), **ima)
# plt.gray()
# plt.colorbar()
# plt.title(tim.name)
# plt.savefig('data-%i.png' % (i))
#
# plt.clf()
# # tractor.getChiImage(i),
# plt.imshow((tim.getImage() - mod) * tim.getInvError(),
# interpolation='nearest', origin='lower',
# vmin=-5, vmax=+5)
# plt.gray()
# plt.colorbar()
# plt.title(tim.name)
# plt.savefig('chi-%i-%02i%s.png' % (i, step, suffix))
plt.figure(3, figsize=(16,8))
plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.9, wspace=0.1, hspace=0.1)
R,C = 3,len(mods)
plt.clf()
for i,mod in enumerate(mods):
tim = tractor.getImage(i)
print('Image', tim.name, ': data median', np.median(tim.getImage()), end=' ')
print('model median:', np.median(mod))
ima = dict(interpolation='nearest', origin='lower',
vmin=tim.zr[0], vmax=tim.zr[1])
plt.subplot(R, C, i + 1)
plt.imshow(tim.getImage(), **ima)
plt.xticks([])
plt.yticks([])
plt.gray()
plt.colorbar()
plt.title(tim.name)
plt.subplot(R, C, i + 1 + C)
plt.imshow(mod, **ima)
plt.xticks([])
plt.yticks([])
plt.gray()
plt.colorbar()
#plt.title(tim.name)
plt.subplot(R, C, i + 1 + 2*C)
plt.imshow((tim.getImage() - mod) * tim.getInvError(),
interpolation='nearest', origin='lower',
vmin=-5, vmax=+5)
plt.xticks([])
plt.yticks([])
plt.gray()
plt.colorbar()
#plt.title(tim.name)
plt.savefig('all-%02i%s.png' % (step, suffix))
ds = tractor.getCatalog()[0]
logsa, logt, emis = ds.getArrays()
plt.figure(4, figsize=(16,5))
plt.clf()
plt.subplot(1,3,1)
plt.imshow(np.exp(logsa), interpolation='nearest', origin='lower')
plt.hot()
plt.colorbar()
plt.title('Dust: solid angle (Sr?)')
plt.subplot(1,3,2)
plt.imshow(np.exp(logt), interpolation='nearest', origin='lower') #, vmin=0)
plt.hot()
plt.colorbar()
plt.title('Dust: temperature (K)')
plt.subplot(1,3,3)
plt.imshow(emis, interpolation='nearest', origin='lower')
plt.gray()
plt.colorbar()
plt.title('Dust: emissivity')
plt.savefig('dust-%02i%s.png' % (step, suffix))
states.append(inputs[-1])
refs.append((-1)*np.ones((10,10)))
for i in range(5):
inputs.append(np.random.random((10,10)))
#inputs.append(np.diag(np.ones((10))*np.random.random()))
inputs[-1][:] = smooth(inputs[-1],9)#+0.1
#states.append(np.zeros((10,10)))
states.append(inputs[-1])
refs.append(np.ones((10,10))*(1))
ct = CNNTrainer(t_sim=40,errfunc="mean_abs_diff", opt_method="simplex")
#ct = CNNTrainer(t_sim=40,errfunc="sum_sqr_diff", opt_method="powell")
#ct = CNNTrainer(t_sim=40,errfunc="mean_abs_diff", opt_method="anneal")
a,b,z = ct(inputs,states,refs)
#p.ioff()
#print "Test"
p.hot()
p.figure(1)
for i in range(10):
print i,
cnn = CNN2d(inputs[i],states[i],a,b,z)
try:
o = cnn.integrate(np.arange(1,21,1))
#p.figure(i+2)
#for i2 in range(20):
# p.subplot(4,5,i2+1)
# #p.axes(frameon=False)
# p.imshow(cnn.sigmoid(o[i2].reshape((10,10))),interpolation="nearest",vmin=-1,vmax=1)
# p.show()
#assert 1==0
time.sleep(1)
p.ioff()
