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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 catalogue 

 

Input: 

matchList is a lsst::afw::detection::SourceMatch object 

 

Output: 

A dictionary storing the following quanities 

meanOfDiffInPixels Average distance between image and catalogue position (in pixels) 

rmsOfDiffInPixels Root mean square of distribution of distances 

quartilesOfDiffInPixels An array of 5 values giving the boundaries of the quartiles of the 

distribution. 

""" 

 

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