/usr/lib/python3/dist-packages/ginga/util/iqcalc.py is in python3-ginga 2.6.1-2.
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# iqcalc.py -- image quality calculations on FITS data
#
# Eric Jeschke (eric@naoj.org)
#
# Copyright (c) 2011-2012, Eric R. Jeschke. All rights reserved.
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
import math
import logging
import numpy
import threading
try:
import scipy.optimize as optimize
import scipy.ndimage as ndimage
import scipy.ndimage.filters as filters
have_scipy = True
except ImportError:
have_scipy = False
from ginga.misc import Bunch
def get_mean(data_np):
mdata = numpy.ma.masked_array(data_np, numpy.isnan(data_np))
return numpy.mean(mdata)
def get_median(data_np):
mdata = numpy.ma.masked_array(data_np, numpy.isnan(data_np))
return numpy.median(mdata)
class IQCalcError(Exception):
"""Base exception for raising errors in this module."""
pass
class IQCalc(object):
def __init__(self, logger=None):
if not logger:
logger = logging.getLogger('IQCalc')
self.logger = logger
# for mutex around scipy.optimize, which seems to be non-threadsafe
self.lock = threading.RLock()
# for adjustments to background level
self.skylevel_magnification = 1.05
self.skylevel_offset = 40.0
# FWHM CALCULATION
def gaussian(self, x, p):
"""Gaussian fitting function in 1D. Makes a sine function with
amplitude determined by maxv. See calc_fwhm().
p[0]==mean, p[1]==sdev, p[2]=maxv
"""
y = (1.0 / (p[1] * numpy.sqrt(2*numpy.pi)) *
numpy.exp(-(x - p[0])**2 / (2*p[1]**2))) * p[2]
return y
def calc_fwhm(self, arr1d, medv=None, gauss_fn=None):
"""FWHM calculation on a 1D array by using least square fitting of
a gaussian function on the data. arr1d is a 1D array cut in either
X or Y direction on the object.
"""
if not gauss_fn:
gauss_fn = self.gaussian
N = len(arr1d)
X = numpy.array(list(range(N)))
Y = arr1d
# Fitting works more reliably if we do the following
# a. subtract sky background
if medv is None:
medv = numpy.median(Y)
Y = Y - medv
maxv = Y.max()
# b. clamp to 0..max (of the sky subtracted field)
Y = Y.clip(0, maxv)
# Fit a gaussian
p0 = [0, N-1, maxv] # Inital guess
# Distance to the target function
errfunc = lambda p, x, y: gauss_fn(x, p) - y
# Least square fit to the gaussian
with self.lock:
# NOTE: without this mutex, optimize.leastsq causes a fatal error
# sometimes--it appears not to be thread safe.
# The error is:
# "SystemError: null argument to internal routine"
# "Fatal Python error: GC object already tracked"
p1, success = optimize.leastsq(errfunc, p0[:], args=(X, Y))
if not success:
raise IQCalcError("FWHM gaussian fitting failed")
mu, sdev, maxv = p1
self.logger.debug("mu=%f sdev=%f maxv=%f" % (mu, sdev, maxv))
# Now that we have the sdev from fitting, we can calculate FWHM
# (fwhm = sdev * sqrt(8*log(2)) ?)
fwhm = 2.0 * numpy.sqrt(2.0 * numpy.log(2.0)) * sdev
#return (fwhm, mu, sdev, maxv)
return (float(fwhm), float(mu), float(sdev), maxv)
def get_fwhm(self, x, y, radius, data, medv=None):
"""
"""
if medv is None:
medv = numpy.median(data)
# Get two cuts of the data, one in X and one in Y
x0, y0, xarr, yarr = self.cut_cross(x, y, radius, data)
# Calculate FWHM in each direction
fwhm_x, cx, sdx, maxx = self.calc_fwhm(xarr, medv=medv)
fwhm_y, cy, sdy, maxy = self.calc_fwhm(yarr, medv=medv)
ctr_x = x0 + cx
ctr_y = y0 + cy
self.logger.debug("fwhm_x,fwhm_y=%f,%f center=%f,%f" % (
fwhm_x, fwhm_y, ctr_x, ctr_y))
return (fwhm_x, fwhm_y, ctr_x, ctr_y, sdx, sdy, maxx, maxy)
def starsize(self, fwhm_x, deg_pix_x, fwhm_y, deg_pix_y):
cdelta1 = math.fabs(deg_pix_x)
cdelta2 = math.fabs(deg_pix_y)
fwhm = (fwhm_x * cdelta1 + fwhm_y * cdelta2) / 2.0
fwhm = fwhm * 3600.0
return fwhm
def centroid(self, data, xc, yc, radius):
x0, y0, arr = self.cut_region(self, xc, yc, radius, data)
cy, cx = ndimage.center_of_mass(arr)
return (cx, cy)
# FINDING BRIGHT PEAKS
def get_threshold(self, data, sigma=5.0):
median = numpy.median(data)
# NOTE: for this method a good default sigma is 5.0
dist = numpy.fabs(data - median).mean()
threshold = median + sigma * dist
# NOTE: for this method a good default sigma is 2.0
## std = numpy.std(data - median)
## threshold = median + sigma * std
self.logger.debug("calc threshold=%f" % (threshold))
return threshold
def find_bright_peaks(self, data, threshold=None, sigma=5, radius=5):
"""
Find bright peak candidates in (data). (threshold) specifies a
threshold value below which an object is not considered a candidate.
If threshold is blank, a default is calculated using (sigma).
(radius) defines a pixel radius for determining local maxima--if the
desired objects are larger in size, specify a larger radius.
The routine returns a list of candidate object coordinate tuples
(x, y) in data.
"""
if threshold is None:
# set threshold to default if none provided
threshold = self.get_threshold(data, sigma=sigma)
self.logger.debug("threshold defaults to %f (sigma=%f)" % (
threshold, sigma))
data_max = filters.maximum_filter(data, radius)
maxima = (data == data_max)
diff = data_max > threshold
maxima[diff == 0] = 0
labeled, num_objects = ndimage.label(maxima)
slices = ndimage.find_objects(labeled)
peaks = []
for dy, dx in slices:
xc = (dx.start + dx.stop - 1)/2.0
yc = (dy.start + dy.stop - 1)/2.0
# This is only an approximate center; use FWHM or centroid
# calculation to refine further
peaks.append((xc, yc))
return peaks
def cut_region(self, x, y, radius, data):
"""Return a cut region (radius) pixels away from (x, y) in (data).
"""
n = radius
ht, wd = data.shape
x0, x1 = max(0, x-n), min(wd-1, x+n)
y0, y1 = max(0, y-n), min(ht-1, y+n)
arr = data[y0:y1+1, x0:x1+1]
return (x0, y0, arr)
def cut_cross(self, x, y, radius, data):
"""Cut two data subarrays that have a center at (x, y) and with
radius (radius) from (data). Returns the starting pixel (x0, y0)
of each cut and the respective arrays (xarr, yarr).
"""
n = radius
ht, wd = data.shape
x, y = int(round(x)), int(round(y))
x0, x1 = int(max(0, x-n)), int(min(wd-1, x+n))
y0, y1 = int(max(0, y-n)), int(min(ht-1, y+n))
xarr = data[y, x0:x1+1]
yarr = data[y0:y1+1, x]
return (x0, y0, xarr, yarr)
def brightness(self, x, y, radius, medv, data):
"""Return the brightness value found in a region (radius) pixels away
from (x, y) in (data).
"""
x0, y0, arr = self.cut_region(x, y, radius, data)
arr2 = numpy.sort(arr.flat)
idx = int(len(arr2) * 0.8)
res = arr2[idx] - medv
return float(res)
def fwhm_data(self, x, y, data, radius=15):
return self.get_fwhm(x, y, radius, data)
# EVALUATION ON A FIELD
def evaluate_peaks(self, peaks, data, bright_radius=2, fwhm_radius=15,
fwhm_method=1, cb_fn=None, ev_intr=None):
height, width = data.shape
hh = float(height) / 2.0
ht = float(height)
h4 = float(height) * 4.0
wh = float(width) / 2.0
wd = float(width)
w4 = float(width) * 4.0
# Find the median (sky/background) level
median = float(numpy.median(data))
#skylevel = median
# Old SOSS qualsize() applied this calculation to skylevel
skylevel = median * self.skylevel_magnification + self.skylevel_offset
# Form a list of objects and their characteristics
objlist = []
for x, y in peaks:
if ev_intr and ev_intr.isSet():
raise IQCalcError("Evaluation interrupted!")
# Find the fwhm in x and y
try:
if fwhm_method == 1:
(fwhm_x, fwhm_y, ctr_x, ctr_y,
sdx, sdy, maxx, maxy) = self.fwhm_data(x, y, data,
radius=fwhm_radius)
## # Average the X and Y gaussian fitting near the peak
bx = self.gaussian(round(ctr_x), (ctr_x, sdx, maxx))
by = self.gaussian(round(ctr_y), (ctr_y, sdy, maxy))
## ## bx = self.gaussian(ctr_x, (ctr_x, sdx, maxx))
## ## by = self.gaussian(ctr_y, (ctr_y, sdy, maxy))
bright = float((bx + by)/2.0)
else:
raise IQCalcError("Method (%d) not supported for fwhm calculation!" %(
fwhm_method))
except Exception as e:
# Error doing FWHM, skip this object
self.logger.debug("Error doing FWHM on object at %.2f,%.2f: %s" % (
x, y, str(e)))
continue
self.logger.debug("orig=%f,%f ctr=%f,%f fwhm=%f,%f bright=%f" % (
x, y, ctr_x, ctr_y, fwhm_x, fwhm_y, bright))
# overall measure of fwhm as a single value
#fwhm = math.sqrt(fwhm_x*fwhm_x + fwhm_y*fwhm_y)
#fwhm = (math.fabs(fwhm_x) + math.fabs(fwhm_y)) / 2.0
fwhm = (math.sqrt(fwhm_x*fwhm_x + fwhm_y*fwhm_y) *
(1.0 / math.sqrt(2.0)) )
# calculate a measure of ellipticity
elipse = math.fabs(min(fwhm_x, fwhm_y) / max(fwhm_x, fwhm_y))
# calculate a measure of distance from center of image
dx = wh - ctr_x
dy = hh - ctr_y
dx2 = dx*dx / wd / w4
dy2 = dy*dy / ht / h4
if dx2 > dy2:
pos = 1.0 - dx2
else:
pos = 1.0 - dy2
obj = Bunch.Bunch(objx=ctr_x, objy=ctr_y, pos=pos,
fwhm_x=fwhm_x, fwhm_y=fwhm_y,
fwhm=fwhm, fwhm_radius=fwhm_radius,
brightness=bright, elipse=elipse,
x=int(x), y=int(y),
skylevel=skylevel, background=median)
objlist.append(obj)
if cb_fn is not None:
cb_fn(obj)
return objlist
# def _compare(self, obj1, obj2):
# val1 = obj1.brightness * obj1.pos/math.sqrt(obj1.fwhm)
# val2 = obj2.brightness * obj2.pos/math.sqrt(obj2.fwhm)
# if val1 > val2:
# return -1
# elif val2 > val1:
# return 1
# else:
# return 0
def _sortkey(self, obj):
val = obj.brightness * obj.pos/math.sqrt(obj.fwhm)
return val
def objlist_select(self, objlist, width, height,
minfwhm=2.0, maxfwhm=150.0, minelipse=0.5,
edgew=0.01):
results = []
count = 0
for obj in objlist:
count += 1
self.logger.debug("%d obj x,y=%.2f,%.2f fwhm=%.2f bright=%.2f" % (
count, obj.objx, obj.objy, obj.fwhm, obj.brightness))
# If peak has a minfwhm < fwhm < maxfwhm and the object
# is inside the frame by edgew pct
if ((minfwhm < obj.fwhm) and (obj.fwhm < maxfwhm) and
(minelipse < obj.elipse) and (width*edgew < obj.x) and
(height*edgew < obj.y) and (width*(1.0-edgew) > obj.x) and
(height*(1.0-edgew) > obj.y)):
results.append(obj)
#results.sort(cmp=self._compare)
results.sort(key=self._sortkey, reverse=True)
return results
def pick_field(self, data, peak_radius=5, bright_radius=2, fwhm_radius=15,
threshold=None,
minfwhm=2.0, maxfwhm=50.0, minelipse=0.5,
edgew=0.01):
height, width = data.shape
# Find the bright peaks in the image
peaks = self.find_bright_peaks(data, radius=peak_radius,
threshold=threshold)
#print "peaks=", peaks
self.logger.debug("peaks=%s" % str(peaks))
if len(peaks) == 0:
raise IQCalcError("Cannot find bright peaks")
# Evaluate those peaks
objlist = self.evaluate_peaks(peaks, data,
bright_radius=bright_radius,
fwhm_radius=fwhm_radius)
if len(objlist) == 0:
raise IQCalcError("Error evaluating bright peaks")
results = self.objlist_select(objlist, width, height,
minfwhm=minfwhm, maxfwhm=maxfwhm,
minelipse=minelipse, edgew=edgew)
if len(results) == 0:
raise IQCalcError("No object matches selection criteria")
return results[0]
def qualsize(self, image, x1=None, y1=None, x2=None, y2=None,
radius=5, bright_radius=2, fwhm_radius=15, threshold=None,
minfwhm=2.0, maxfwhm=50.0, minelipse=0.5,
edgew=0.01):
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
data = image.cutout_data(x1, y1, x2, y2, astype='float32')
qs = self.pick_field(data, peak_radius=radius,
bright_radius=bright_radius,
fwhm_radius=fwhm_radius,
threshold=threshold,
minfwhm=minfwhm, maxfwhm=maxfwhm,
minelipse=minelipse, edgew=edgew)
# Add back in offsets into image to get correct values with respect
# to the entire image
qs.x += x1
qs.y += y1
qs.objx += x1
qs.objy += y1
self.logger.debug("obj=%f,%f fwhm=%f sky=%f bright=%f" % (
qs.objx, qs.objy, qs.fwhm, qs.skylevel, qs.brightness))
return qs
#END
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