def test_compute_reds_tol(self):
antpos = np.zeros((65,3), dtype=np.float)
antpos[3] = np.array([ 3373240.12832542, 540919.61 , 5674198.83539668])
antpos[49] = np.array([ 3373240.64412165, 540904.7 , 5674198.52887711])
antpos[10] = np.array([ 3373240.90712842, 540889.64 , 5674198.38421393])
antpos[64] = np.array([ 3373240.51728633, 540874.74 , 5674198.51119087])
reds = Oa.filter_reds(Oa.compute_reds(antpos, tol=1.0), ants=(3,10,49,64))
self.assertNotEqual(reds, [[(49, 64), (3, 10)], [(3, 49), (10, 64), (49, 10)]])
reds = Oa.filter_reds(Oa.compute_reds(antpos, tol=1.5), ants=(3,10,49,64))
self.assertEqual(reds, [[(49, 64), (3, 10)], [(3, 49), (10, 64), (49, 10)]])
def saveQuantitiesForArrayComparison(resultsDirectory):
"""Computes and saves interesting quantities for the outrigger comparsion in the same folder as the mapmaking results."""
print "Now computing other interesting mapmaking and array quantities..."
s, times, ps, Dmatrix, PSF, coaddedMap, pointSourcePSF = MapMats.loadAllResults(resultsDirectory)
#Info about calibratability
redundantInfo = oi.RedundantInfo()
reds = oai.compute_reds(s.antennaPositions)
redundantInfo.init_from_reds(reds, s.antennaPositions)
AtransA = redundantInfo.At.dot(redundantInfo.At.T).toarray()
BtransB = redundantInfo.Bt.dot(redundantInfo.Bt.T).toarray()
np.save(resultsDirectory + "omniAtransA",AtransA)
np.save(resultsDirectory + "omniBtransB",BtransB)
gainVariances = np.diag(np.linalg.pinv(AtransA)[0:s.nAntennas,0:s.nAntennas])
np.save(resultsDirectory + "gainVariances",gainVariances)
np.savetxt(resultsDirectory + "nZeroEVs.txt", [np.sum(np.abs(np.linalg.eigvals(XtransX)<1e-10)) for XtransX in [AtransA, BtransB]], fmt='%.d')
coords = Geometry.Coordinates(s)
if s.useAdaptiveHEALPixForPSF: coords.convertToAdaptiveHEALPix(s, times)
print "There are " + str(coords.nPSFPixels) + " pixels in the PSF and " + str(coords.nFacetPixels) + " in the map."
pickle.dump(coords, open(resultsDirectory + "coords.p","wb"), protocol = -1)
mapNoiseCovariance = np.dot(PSF[:,coords.facetIndexLocationsInPSFIndexList],np.transpose(np.diag(Dmatrix)))
AtransNinvA = np.dot(np.diag(Dmatrix**-1),PSF[:,coords.facetIndexLocationsInPSFIndexList])
posVarIndicies = (np.diag(AtransNinvA)>0)
AtransNinvA = AtransNinvA[posVarIndicies,:][:,posVarIndicies] #Deal with the fact that if I map the whole sky, some pixels are not sampled.
mapNoiseCovariance = mapNoiseCovariance[posVarIndicies,:][:,posVarIndicies]
evals, evecs = SortedEigensystem(AtransNinvA)
np.save(resultsDirectory + "mapNoiseCovariance",mapNoiseCovariance)
np.save(resultsDirectory + "AtransNinvA", AtransNinvA)
np.save(resultsDirectory + "noiseEvals", evals)
np.save(resultsDirectory + "noiseEvecs", evecs)
np.save(resultsDirectory + "posVarIndicies", posVarIndicies)
print "Finished with " + resultsDirectory + "\n\n\n"
# plt.xlim(-350,350); plt.ylim(-350,350)
# # plt.colorbar()
# plt.title('Baseline Redundancy')
# #datacursor(display='single',formatter="x={x:.4f}\ny={y:.4f}".format)
# #plt.axis('equal')
# return np.asarray(positions), np.asarray(positions[0:nCore])
# if __name__ == "__main__":
# positions,corePos = HexArray(hexNum = 11, SplitCore = True, SplitCoreOutriggers = True, CutDownTo = None, SpreadOut128 = False)
if True:
import omnical.info as oi
import omnical.arrayinfo as oai
redundantInfo = oi.RedundantInfo()
positions = np.asarray(positions)
reds = oai.compute_reds(positions)
redundantInfo.init_from_reds(reds, positions)
AtransA = redundantInfo.At.dot(redundantInfo.At.T).toarray()
BtransB = redundantInfo.Bt.dot(redundantInfo.Bt.T).toarray()
gainVariances = np.diag(np.linalg.pinv(AtransA)[0:len(positions),0:len(positions)])
print "Gain and phase modes that can't be Omnicaled:"
print [len(XtransX) - np.linalg.matrix_rank(XtransX, tol=1e-10) for XtransX in [AtransA, BtransB]]
plt.figure(3)
plt.clf()
hexScatter = plt.scatter(positions[:,0], positions[:,1], c=np.sqrt(gainVariances), s=100)
plt.colorbar()
plt.title('Antenna Relative Gain Errors')
#%%
if True:
import omnical.info as oi
# plt.xlim(-350,350); plt.ylim(-350,350)
# # plt.colorbar()
# plt.title('Baseline Redundancy')
# #datacursor(display='single',formatter="x={x:.4f}\ny={y:.4f}".format)
# #plt.axis('equal')
# return np.asarray(positions), np.asarray(positions[0:nCore])
# if __name__ == "__main__":
# positions,corePos = HexArray(hexNum = 11, SplitCore = True, SplitCoreOutriggers = True, CutDownTo = None, SpreadOut128 = False)
if True:
import omnical.info as oi
import omnical.arrayinfo as oai
redundantInfo = oi.RedundantInfo()
positions = np.asarray(positions)
reds = oai.compute_reds(positions)
redundantInfo.init_from_reds(reds, positions)
AtransA = redundantInfo.At.dot(redundantInfo.At.T).toarray()
BtransB = redundantInfo.Bt.dot(redundantInfo.Bt.T).toarray()
gainVariances = np.diag(
np.linalg.pinv(AtransA)[0:len(positions), 0:len(positions)])
print "Gain and phase modes that can't be Omnicaled:"
print[
len(XtransX) - np.linalg.matrix_rank(XtransX, tol=1e-10)
for XtransX in [AtransA, BtransB]
]
plt.figure(3)
plt.clf()
hexScatter = plt.scatter(positions[:, 0],
positions[:, 1],
plt.savefig(prefix + 'base.png', dpi=300)
#%%
else:
plt.figure(1, figsize=(8, 8))
plt.clf()
plt.scatter(np.asarray(positions)[:, 0], np.asarray(positions)[:, 1])
plt.xlabel('Position (m)')
plt.ylabel('Position (m)')
plt.axis('square')
if False:
import omnical.info as oi
import omnical.arrayinfo as oai
redundantInfo = oi.RedundantInfo()
positions = np.asarray(positions)
reds = oai.compute_reds(positions)
redundantInfo.init_from_reds(reds, positions)
AtransA = redundantInfo.At.dot(redundantInfo.At.T).toarray()
BtransB = redundantInfo.Bt.dot(redundantInfo.Bt.T).toarray()
gainVariances = np.diag(
np.linalg.pinv(AtransA)[0:len(positions), 0:len(positions)])
print "Gain and phase modes that can't be Omnicaled:"
print[
len(XtransX) - np.linalg.matrix_rank(XtransX, tol=1e-10)
for XtransX in [AtransA, BtransB]
]
#%%
plt.figure(3)
plt.clf()
hexScatter = plt.scatter(positions[:, 0],
return np.asarray(positions)
if __name__ == "__main__":
# positions = HexArray(hexNum = 11, LoadHERAOutriggers = True, fiducialInriggers = True)
# positions = HexArray(hexNum = 11, RedundantOutriggers = False, fullCornerInriggers = False)
# positions = HexArray(hexNum = 7, SplitCore = False, SplitCoreOutriggers = True)
positions = HexArray(hexNum = 11, SplitCore = True, SplitCoreOutriggers = True)
# positions = HexArray(hexNum = 6, SplitCore = False, fullCornerInriggers = False)
if False:
import omnical.info as oi
import omnical.arrayinfo as oai
redundantInfo = oi.RedundantInfo()
reds = oai.compute_reds(positions)
redundantInfo.init_from_reds(reds, positions)
AtransA = redundantInfo.At.dot(redundantInfo.At.T).toarray()
BtransB = redundantInfo.Bt.dot(redundantInfo.Bt.T).toarray()
gainVariances = np.diag(np.linalg.pinv(AtransA)[0:len(positions),0:len(positions)])
# gainVariances = gainVariances/gainVariances.min()
print "Gain and phase modes that can't be Omnicaled:"
# print [np.sum(np.abs(np.linalg.eigvals(XtransX)<1e-10)) for XtransX in [AtransA, BtransB]]
print [len(XtransX) - np.linalg.matrix_rank(XtransX, tol=1e-10) for XtransX in [AtransA, BtransB]]
#%%
plt.figure(3)
plt.clf()
hexScatter = plt.scatter(positions[:,0], positions[:,1], c=np.sqrt(gainVariances), s=100)
plt.colorbar()
plt.title('Antenna Relative Gain Errors')
