LSSTApplications  17.0+103,17.0+11,17.0+61,18.0.0+13,18.0.0+25,18.0.0+5,18.0.0+52,18.0.0-4-g68ffd23,18.1.0-1-g0001055+8,18.1.0-1-g03d53ef+1,18.1.0-1-g1349e88+28,18.1.0-1-g2505f39+22,18.1.0-1-g380d4d4+27,18.1.0-1-g5315e5e+1,18.1.0-1-g5e4b7ea+10,18.1.0-1-g7e8fceb+1,18.1.0-1-g85f8cd4+23,18.1.0-1-g9a6769a+13,18.1.0-1-ga1a4c1a+22,18.1.0-1-gd55f500+17,18.1.0-12-g42eabe8e+10,18.1.0-14-gd04256d+15,18.1.0-16-g430f6a53+1,18.1.0-17-gd2166b6e4,18.1.0-18-gb5d19ff+1,18.1.0-2-gfbf3545+7,18.1.0-2-gfefb8b5+16,18.1.0-3-g52aa583+13,18.1.0-3-g62b5e86+14,18.1.0-3-g8f4a2b1+17,18.1.0-3-g9bc06b8+7,18.1.0-3-gb69f684+9,18.1.0-4-g1ee41a7+1,18.1.0-5-g6dbcb01+13,18.1.0-5-gc286bb7+3,18.1.0-6-g48bdcd3+2,18.1.0-6-gd05e160+9,18.1.0-7-gc4d902b+2,18.1.0-7-gebc0338+8,18.1.0-9-gae7190a+10,w.2019.38
LSSTDataManagementBasePackage
cleanBadPoints.py
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1 #
2 # LSST Data Management System
3 # Copyright 2008, 2009, 2010 LSST Corporation.
4 #
5 # This product includes software developed by the
6 # LSST Project (http://www.lsst.org/).
7 #
8 # This program is free software: you can redistribute it and/or modify
9 # it under the terms of the GNU General Public License as published by
10 # the Free Software Foundation, either version 3 of the License, or
11 # (at your option) any later version.
12 #
13 # This program is distributed in the hope that it will be useful,
14 # but WITHOUT ANY WARRANTY; without even the implied warranty of
15 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16 # GNU General Public License for more details.
17 #
18 # You should have received a copy of the LSST License Statement and
19 # the GNU General Public License along with this program. If not,
20 # see <http://www.lsstcorp.org/LegalNotices/>.
21 #
22 
23 __all__ = ["clean"]
24 
25 
26 import numpy as np
27 
28 from lsst.meas.astrom.sip import LeastSqFitter1dPoly
29 
30 
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 
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 
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 
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
def indicesOfGoodPoints(x, y, s, order=1, nsigma=3, maxiter=100)
STL namespace.
def clean(srcMatch, wcs, order=3, nsigma=3)
daf::base::PropertyList * list
Definition: fits.cc:885