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# 

# LSST Data Management System 

# Copyright 2008, 2009, 2010 LSST Corporation. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program is distributed in the hope that it will be useful, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <http://www.lsstcorp.org/LegalNotices/>. 

# 

 

__all__ = ["sourceMatchStatistics"] 

 

import numpy as np 

 

 

def sourceMatchStatistics(matchList, log=None): 

"""Compute statistics on the accuracy of a wcs solution, using a 

precomputed list of matches between an image and a catalog. 

 

Parameters 

---------- 

matchList : `lsst.afw.detection.SourceMatch` 

List of matches between sources and references to compute statistics 

on. 

 

Returns 

------- 

values : `dict 

Value dictionary with fields: 

 

- diffInPixels_mean : Average distance between image and 

catalog position in pixels (`float`). 

- diffInPixels_std : Root mean square of distribution of distances 

(`float`). 

- diffInPixels_Q25 : 25% quantile boundary of the match dist 

distribution (`float`). 

- diffInPixels_Q50 : 50% quantile boundary of the match dist 

distribution (`float`). 

- diffInPixels_Q75 : 75% quantile boundary of the match 

dist distribution (`float`). 

""" 

 

size = len(matchList) 

if size == 0: 

raise ValueError("matchList contains no elements") 

 

dist = np.zeros(size) 

i = 0 

for match in matchList: 

catObj = match.first 

srcObj = match.second 

 

cx = catObj.getXAstrom() 

cy = catObj.getYAstrom() 

 

sx = srcObj.getXAstrom() 

sy = srcObj.getYAstrom() 

 

dist[i] = np.hypot(cx-sx, cy-sy) 

i = i+1 

 

dist.sort() 

 

quartiles = [] 

for f in (0.25, 0.50, 0.75): 

i = int(f*size + 0.5) 

if i >= size: 

i = size - 1 

quartiles.append(dist[i]) 

 

values = {} 

values['diffInPixels_Q25'] = quartiles[0] 

values['diffInPixels_Q50'] = quartiles[1] 

values['diffInPixels_Q75'] = quartiles[2] 

values['diffInPixels_mean'] = dist.mean() 

values['diffInPixels_std'] = dist.std() 

 

return values