## Functions

def clean (srcMatch, wcs, order=3, nsigma=3)

def indicesOfGoodPoints (x, y, s, order=1, nsigma=3, maxiter=100)

def chooseRx (x, idx, order)

def chooseRy (y, idx, order)

## ◆ chooseRx()

 def lsst.meas.extensions.astrometryNet.cleanBadPoints.chooseRx ( x, idx, order )
Create order+1 values of the ordinate based on the median of groups of elements of x

Definition at line 111 of file cleanBadPoints.py.

111 def chooseRx(x, idx, order):
112  """Create order+1 values of the ordinate based on the median of groups of elements of x"""
113  rSize = len(idx)/float(order+1) # Note, a floating point number
114  rx = np.zeros((order+1))
115
116  for i in range(order+1):
117  rng = list(range(int(rSize*i), int(rSize*(i+1))))
118  rx[i] = np.mean(x[idx[rng]])
119  return rx
120
121
daf::base::PropertyList * list
Definition: fits.cc:885

## ◆ chooseRy()

 def lsst.meas.extensions.astrometryNet.cleanBadPoints.chooseRy ( y, idx, order )
Create order+1 values of the ordinate based on the median of groups of elements of y

Definition at line 122 of file cleanBadPoints.py.

122 def chooseRy(y, idx, order):
123  """Create order+1 values of the ordinate based on the median of groups of elements of y"""
124  rSize = len(idx)/float(order+1) # Note, a floating point number
125  ry = np.zeros((order+1))
126
127  for i in range(order+1):
128  rng = list(range(int(rSize*i), int(rSize*(i+1))))
129  ry[i] = np.median(y[idx[rng]])
130  return ry

## ◆ clean()

 def lsst.meas.extensions.astrometryNet.cleanBadPoints.clean ( srcMatch, wcs, order = 3, nsigma = 3 )
Remove bad points from srcMatch

Input:
srcMatch : list of det::SourceMatch
order:      Order of polynomial to use in robust fitting
nsigma:    Sources more than this far away from the robust best fit
polynomial are removed

Return:
list of det::SourceMatch of the good data points


Definition at line 31 of file cleanBadPoints.py.

31 def clean(srcMatch, wcs, order=3, nsigma=3):
32  """Remove bad points from srcMatch
33
34  Input:
35  srcMatch : list of det::SourceMatch
36  order: Order of polynomial to use in robust fitting
37  nsigma: Sources more than this far away from the robust best fit
38  polynomial are removed
39
40  Return:
41  list of det::SourceMatch of the good data points
42  """
43
44  N = len(srcMatch)
45  catX = np.zeros(N)
46  # catY = np.zeros(N)
47  for i in range(N):
48  x, y = wcs.skyToPixel(srcMatch[i].first.getCoord())
49  catX[i] = x
50  # catY[i] = y
51
52  # TODO -- why does this only use X?
53
54  x = np.array([s.second.getX() for s in srcMatch])
55  dx = x - catX
56  sigma = np.zeros_like(dx) + 0.1
57
58  idx = indicesOfGoodPoints(x, dx, sigma, order=order, nsigma=nsigma)
59
60  clean = []
61  for i in idx:
62  clean.append(srcMatch[i])
63  return clean
64
65
def indicesOfGoodPoints(x, y, s, order=1, nsigma=3, maxiter=100)
def clean(srcMatch, wcs, order=3, nsigma=3)

## ◆ indicesOfGoodPoints()

 def lsst.meas.extensions.astrometryNet.cleanBadPoints.indicesOfGoodPoints ( x, y, s, order = 1, nsigma = 3, maxiter = 100 )
Return a list of indices in the range [0, len(x)]
of points that lie less than nsigma away from the robust
best fit polynomial


Definition at line 66 of file cleanBadPoints.py.

66 def indicesOfGoodPoints(x, y, s, order=1, nsigma=3, maxiter=100):
67  """Return a list of indices in the range [0, len(x)]
68  of points that lie less than nsigma away from the robust
69  best fit polynomial
70  """
71
72  # Indices of elements of x sorted in order of increasing value
73  idx = x.argsort()
74  newidx = []
75  for niter in range(maxiter):
76  rx = chooseRx(x, idx, order)
77  ry = chooseRy(y, idx, order)
78  rs = np.ones((len(rx)))
79
80  lsf = LeastSqFitter1dPoly(list(rx), list(ry), list(rs), order)
81  fit = [lsf.valueAt(value) for value in rx]
82  f = [lsf.valueAt(value) for value in x]
83
84  sigma = (y-f).std()
85  if sigma == 0:
86  # all remaining points are good; short circuit
87  return newidx if newidx else idx
88  deviance = np.fabs((y - f) / sigma)
89  newidx = np.flatnonzero(deviance < nsigma)
90
91  if False:
92  import matplotlib.pyplot as plt
93  plt.plot(x, y, 'ks')
94  plt.plot(rx, ry, 'b-')
95  plt.plot(rx, ry, 'bs')
96  plt.plot(rx, fit, 'ms')
97  plt.plot(rx, fit, 'm-')
98  # plt.plot(x[newidx], y[newidx], 'rs')
99  plt.show()
100
101  # If we haven't culled any points we're finished cleaning
102  if len(newidx) == len(idx):
103  break
104
105  # We get here because we either a) stopped finding bad points
106  # or b) ran out of iterations. Either way, we just return our
107  # list of indices of good points.
108  return newidx
109
110
def indicesOfGoodPoints(x, y, s, order=1, nsigma=3, maxiter=100)
STL namespace.
daf::base::PropertyList * list
Definition: fits.cc:885