def draw_MAP_residuals(objectsA, objectsB, P, scaled='no'):
from pyBA.distortion import compute_displacements, compute_residual
from numpy import array
# Compute displacements between frames for tie objects
xobs, yobs, vxobs, vyobs, sxobs, syobs = compute_displacements(objectsA, objectsB)
# Compute residual
dx, dy = compute_residual(objectsA, objectsB, P)
# Draw residuals
fig = figure(figsize=(16,16))
ax = fig.add_subplot(111, aspect='equal')
if scaled is 'yes':
# Allow relative scaling of arrows
quiver(xobs,yobs,dx,dy)
else:
# Show residuals in absolute size (often very tiny), with uncertainties
# Also plot error ellipses
ellipses = array([ Bivarg( mu = array([xobs[i] + dx[i], yobs[i] + dy[i]]),
sigma = objectsA[i].sigma + objectsB[i].sigma )
for i in range(len(objectsA)) ])
draw_objects(ellipses, replot='yes')
# Residuals
quiver(xobs,yobs,dx,dy,color='r', angles='xy', scale_units='xy', scale=1)
ax.autoscale(enable=None, axis='both', tight=True)
show()
def draw_MAP_residuals(objectsA, objectsB, P, scaled='no'):
from pyBA.distortion import compute_displacements, compute_residual
from numpy import array
# Compute displacements between frames for tie objects
xobs, yobs, vxobs, vyobs, sxobs, syobs = compute_displacements(objectsA, objectsB)
# Compute residual
dx, dy = compute_residual(objectsA, objectsB, P)
# Draw residuals
fig = figure(figsize=(16,16))
ax = fig.add_subplot(111, aspect='equal')
if scaled is 'yes':
# Allow relative scaling of arrows
quiver(xobs,yobs,dx,dy)
else:
# Show residuals in absolute size (often very tiny), with uncertainties
# Also plot error ellipses
ellipses = array([ Bivarg( mu = array([xobs[i] + dx[i], yobs[i] + dy[i]]),
sigma = objectsA[i].sigma + objectsB[i].sigma )
for i in range(len(objectsA)) ])
draw_objects(ellipses, replot='yes')
# Residuals
quiver(xobs,yobs,dx,dy,color='r', angles='xy', scale_units='xy', scale=1)
ax.autoscale(enable=None, axis='both', tight=True)
show()
def draw_realisation(objectsA, objectsB, P, scale, amp, chol, res = 30):
# Grid for regression
x,y = make_grid(objectsA,res=res)
xyarr = np.array([x.flatten(),y.flatten()]).T
# If no cholesky matrix A provided, assume that we are
# drawing realisation on grid without using observed data
if chol == None:
from pyBA.distortion import realise
vx, vy = realise(xyarr, P, scale, amp)
sx, sy = None, None
# Otherwise, use cholesky data to perform regression
else:
from pyBA.distortion import regression
vx, vy, sx, sy = regression(objectsA, objectsB, xyarr, P,
scale, amp, chol)
# Get xy coordinates of base of vectors
from pyBA.distortion import compute_displacements
xobs, yobs, vxobs, vyobs, _, _ = compute_displacements(objectsA, objectsB)
# Matplotlib plotting
fig = figure(figsize=(16,16))
ax = fig.add_subplot(111, aspect='equal')
quiver(x,y,vx,vy,scale_units='width',scale=res*res)
# If uncertainties are provided, plot them as a background image and colour the
# data vectors in white
if sx != None:
quiver(xobs,yobs,vxobs,vyobs,color='w',scale_units='width',scale=res*res)
sarr = np.array(sx + sy).reshape( x.shape )
imshow(np.sqrt(sarr), origin='upper', extent=(x.min(), x.max(), y.min(), y.max()),
interpolation=None)
colorbar()
else:
# Otherwise, no background and data vectors in red
quiver(xobs,yobs,vxobs,vyobs,color='r',scale_units='width',scale=res*res)
ax.autoscale(enable=None, axis='both', tight=True)
show()
return
def draw_realisation(objectsA, objectsB, P, scale, amp, chol, res = 30):
# Grid for regression
x,y = make_grid(objectsA,res=res)
xyarr = np.array([x.flatten(),y.flatten()]).T
# If no cholesky matrix A provided, assume that we are
# drawing realisation on grid without using observed data
if chol == None:
from pyBA.distortion import realise
vx, vy = realise(xyarr, P, scale, amp)
sx, sy = None, None
# Otherwise, use cholesky data to perform regression
else:
from pyBA.distortion import regression
vx, vy, sx, sy = regression(objectsA, objectsB, xyarr, P,
scale, amp, chol)
# Get xy coordinates of base of vectors
from pyBA.distortion import compute_displacements
xobs, yobs, vxobs, vyobs, _, _ = compute_displacements(objectsA, objectsB)
# Matplotlib plotting
fig = figure(figsize=(16,16))
ax = fig.add_subplot(111, aspect='equal')
quiver(x,y,vx,vy,scale_units='width',scale=res*res)
# If uncertainties are provided, plot them as a background image and colour the
# data vectors in white
if sx is not None:
quiver(xobs,yobs,vxobs,vyobs,color='w',scale_units='width',scale=res*res)
sarr = np.array(sx + sy).reshape( x.shape )
imshow(np.sqrt(sarr), origin='upper', extent=(x.min(), x.max(), y.min(), y.max()),
interpolation=None)
colorbar()
else:
# Otherwise, no background and data vectors in red
quiver(xobs,yobs,vxobs,vyobs,color='r',scale_units='width',scale=res*res)
ax.autoscale(enable=None, axis='both', tight=True)
show()
return
def draw_MAP_background(objectsA=np.array([Bivarg()]),
objectsB=np.array([Bivarg()]),
P=Bgmap(),
res=30):
""" Plot the background parametric mapping between frames (the mean function
for the gaussian process) on a grid of given resolution. Overplot observed
displacements from lists of tie objects.
"""
from pyBA.distortion import compute_displacements, astrometry_mean
from numpy import array, sqrt
# Grid for regression
x, y = make_grid(objectsA, res=res)
# Perform evaluation of background function on grid
xy = np.array([x.flatten(), y.flatten()]).T
vxy = astrometry_mean(xy, P)
vx, vy = vxy[:, 0], vxy[:, 1]
# Compute empirical displacements
xobs, yobs, vxobs, vyobs, sxobs, syobs = compute_displacements(
objectsA, objectsB)
# Matplotlib plotting
fig = figure(figsize=(16, 16))
ax = fig.add_subplot(111, aspect='equal')
quiver(x, y, vx, vy, scale_units='width', scale=res * res)
quiver(xobs,
yobs,
vxobs,
vyobs,
color='r',
scale_units='width',
scale=res * res)
ax.autoscale(enable=None, axis='both', tight=True)
# Also plot error ellipses on interpolated points
#ellipses = array([ Bivarg( mu = array([xarr[i,0] + vx[i], xarr[i,1] + vy[i]]),
# sigma = array([ sx[i], sy[i] ]) )
# for i in range(len(xarr)) ])
#draw_objects(ellipses, replot='yes')
show()
return
def draw_MAP_background(objectsA = np.array([ Bivarg() ]),
objectsB = np.array([ Bivarg() ]),
P = Bgmap(),
res = 30):
""" Plot the background parametric mapping between frames (the mean function
for the gaussian process) on a grid of given resolution. Overplot observed
displacements from lists of tie objects.
"""
from pyBA.distortion import compute_displacements, astrometry_mean
from numpy import array, sqrt
# Grid for regression
x,y = make_grid(objectsA,res=res)
# Perform evaluation of background function on grid
xy = np.array([x.flatten(),y.flatten()]).T
vxy = astrometry_mean(xy, P)
vx, vy = vxy[:,0], vxy[:,1]
# Compute empirical displacements
xobs, yobs, vxobs, vyobs, sxobs, syobs = compute_displacements(objectsA, objectsB)
# Matplotlib plotting
fig = figure(figsize=(16,16))
ax = fig.add_subplot(111, aspect='equal')
quiver(x,y,vx,vy,scale_units='width',scale=res*res)
quiver(xobs,yobs,vxobs,vyobs,color='r',scale_units='width',scale=res*res)
ax.autoscale(enable=None, axis='both', tight=True)
# Also plot error ellipses on interpolated points
#ellipses = array([ Bivarg( mu = array([xarr[i,0] + vx[i], xarr[i,1] + vy[i]]),
# sigma = array([ sx[i], sy[i] ]) )
# for i in range(len(xarr)) ])
#draw_objects(ellipses, replot='yes')
show()
return
