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feed_rot_simulation.py
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feed_rot_simulation.py
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import numpy as np
import matplotlib.pylab as plt
from pcapture2 import bl_list
bl2ord = bl_list()
def rot_sim(indict):
''' Simulates effect of non-ideal feed behavior on input data for a single baseline.
If 'unrot' is true, it takes the data as output data and applies the inverse
of the corresponding Mueller matrix to it.
Input is a dictionary (indict) with the following keys (and their defaults if
omitted)
'data' : 4 x ntimes array of data, corresponding to a set of XX, XY, YX, YY,
in that order, for each time. No default (error return if omitted)
'chi1' : Parallactic (or rotation) angle of first antenna (default 0) (scalar or ntimes array)
'chi2' : Parallactic (or rotation) angle of second antenna (default 0) (scalar or ntimes array)
'a1' : Relative amplitude of Y wrt X for first antenna (default 1)
'a2' : Relative amplitude of Y wrt X for second antenna (default 1)
'd1' : Relative cross-talk between X and Y for first antenna (default 0)
'd2' : Relative cross-talk between X and Y for second antenna (default 0)
'unrot': Whether to rotate or unrotate the input data (default False)
'verbose': Print some diagnostic messages
'doplot': Create some plots of the before and after amplitudes and phases
Result is a plot of input and output. Returns the rotated or unrotated data in
the same form as the input data.
'''
# Input is a dictionary, contained needed keys. Any missing
# keys are filled in with defaults:
data = indict.get('data') # No default
if data is None:
print 'Must supply "data" key in input dictionary'
return
chi1 = indict.get('chi1',0)
chi2 = indict.get('chi2',0)
a1 = indict.get('a1',1)
a2 = indict.get('a2',1)
d1 = indict.get('d1',0)
d2 = indict.get('d2',0)
titl = indict.get('title','')
verbose = indict.get('verbose',False)
unrot = indict.get('unrot',False)
doplot = indict.get('doplot',False)
titl = titl+' a1:'+str(a1)+' a2:'+str(a2)+' d1:'+str(d1)+' d2:'+str(d2)
if unrot:
titl += ' Unrot'
# Do some sanity checks. Any or all inputs can have only 1 time (assumed constant at other times)
# but any that do have times must have the same number of times
try:
dn, dnt = data.shape
if dn != 4:
print 'Number of data elements for each time must be 4 [XX, XY, YX, YY].'
return
except:
if len(data) == 4:
dnt = 1
else:
print 'Number of data elements for each time must be 4 [XX, XY, YX, YY].'
return
try:
c1nt, = chi1.shape
except:
c1nt = 1
try:
c2nt, = chi2.shape
except:
c2nt = 1
if dnt > 1 or c1nt > 1 or c2nt > 1:
# Multiple times are requested, so ensure input is compatible
nt = np.max(np.array([dnt,c1nt,c2nt]))
if dnt == 1:
# Expand data to 4 x nt times
data = np.rollaxis(np.tile(data,(nt,1)),1)
if c1nt == 1:
# Expand chi1 to nt times
chi1 = chi1*np.ones(nt)
if c2nt == 1:
# Expand chi2 to nt times
chi2 = chi2*np.ones(nt)
# Now see if they all have the same length
if nt != len(data[0]) or nt != len(chi1) or nt != len(chi2):
print 'Number of times in data, chi1 and chi2 are not compatible.'
return
else:
nt = 1
if verbose:
print 'ntimes=',nt
print 'shapes of data, chi1, chi2:',data.shape,chi1.shape,chi2.shape
# At this point, we should have uniformity of times
# Rotation matrix for first antenna
R1 = np.array([[np.cos(chi1), np.sin(chi1)],[-np.sin(chi1), np.cos(chi1)]])
if verbose:
print 'Rotation matrix R1 shape:',R1.shape,'for first time is'
print R1[:,:,0]
# Amplitude matrix for first antenna
A = np.array([[1,d1],[-d1,a1]])
# Resultant Jones matrix for first antenna
JA = []
for i in range(nt):
JA.append(np.dot(A, R1[:,:,i]))
if verbose:
print 'First element of Jones matrix A:\n',JA[0]
# Rotation matrix for second antenna
R2 = np.array([[np.cos(chi2), np.sin(chi2)],[-np.sin(chi2), np.cos(chi2)]])
if verbose:
print 'Rotation matrix R2 shape:',R2.shape,'for first time is'
print R2[:,:,0]
# Amplitude matrix for second antenna
B = np.array([[1,d2],[-d2,a2]])
# Resultant Jones matrix for second antenna
JB = []
for i in range(nt):
JB.append(np.dot(B, R2[:,:,i]))
if verbose:
print 'First element of Jones matrix B:\n',JB[0]
# Resultant Mueller matrix
M = []
for i in range(nt):
M.append(np.kron(JA[i],np.conj(JB[i])))
if verbose and i == 0:
print 'Mueller matrix at first time is:\n',M[0]
if unrot:
M[i] = np.linalg.inv(M[i])
# Apply matrix to data
out = np.zeros_like(data)
if nt == 1:
out = np.dot(M[0],data)
else:
for i in range(nt):
out[:,i] = np.dot(M[i],data[:,i])
# Now plot a comparison of the input data to output data:
if doplot:
f, ax = plt.subplots(4,2)
f.set_size_inches(5, 6.5)
f.suptitle(titl+' Amp')
pol = ['XX','XY','YX','YY']
dirxn = [' (IN)',' (OUT)']
for i in range(4):
ax[i,0].plot(abs(data[i]),'.')
ax[i,0].set_ylabel('Rel. Amp')
ax[i,1].plot(abs(out[i]),'.')
for j in range(2):
if i == 3:
ax[i,j].set_xlabel('Time index')
ax[i,j].set_ylim(-0.05,2)
ax[i,j].grid()
ax[i,j].text(0.05,0.8,pol[i]+dirxn[j],transform=ax[i,j].transAxes)
f, ax = plt.subplots(4,2)
f.set_size_inches(5, 6.5)
f.suptitle(titl+' Phase')
pol = ['XX','XY','YX','YY']
dirxn = [' (IN)',' (OUT)']
for i in range(4):
ax[i,0].plot(np.angle(data[i]),'.')
ax[i,0].set_ylabel('Phase')
ax[i,1].plot(np.angle(out[i]),'.')
for j in range(2):
if i == 3:
ax[i,j].set_xlabel('Time index')
ax[i,j].set_ylim(-4,4)
ax[i,j].grid()
ax[i,j].text(0.05,0.8,pol[i]+dirxn[j],transform=ax[i,j].transAxes)
return out