def test_noisy_background(self):
#Use a fixed random state seed, so unit-test is reproducible:
rstate = numpy.random.RandomState(42)
pixel_noise = 0.5
self.mygauss += rstate.normal(scale=pixel_noise,
size=len(self.mygauss.ravel())).reshape(
self.mygauss.shape)
self.fit = fitgaussian(self.mygauss, self.moments)
self.longMessage = True #Show assertion fail values + given message
# First, let's check we've converged to a reasonable fit in the
# presence of noise:
# print
for param in FIT_PARAMS:
# print param, getattr(self,param), self.fit[param]
self.assertAlmostEqual(
getattr(self, param),
self.fit[param],
places=1,
msg=param + " misfit (bad random noise seed?)",
)
# Now we run the full error-profiling routine and check the chi-sq
# calculations:
self.fit_w_errs, _ = source_profile_and_errors(
data=self.mygauss,
threshold=0.,
noise=pixel_noise,
beam=(self.semimajor, self.semiminor, self.theta),
)
npix = len(self.mygauss.ravel())
# print "CHISQ", npix, self.fit_w_errs.chisq
# NB: this is the calculation for reduced chisq in presence of
# independent pixels, i.e. uncorrelated noise.
# For real data, we try to take the noise-correlation into account.
# print "Reduced chisq", self.fit_w_errs.chisq / npix
self.assertTrue(0.9 < self.fit_w_errs.chisq / npix < 1.1)
def test_noisy_background(self):
#Use a fixed random state seed, so unit-test is reproducible:
rstate = numpy.random.RandomState(42)
pixel_noise = 0.5
self.mygauss += rstate.normal(scale = pixel_noise,
size=len(self.mygauss.ravel())).reshape(self.mygauss.shape)
self.fit = fitgaussian(self.mygauss, self.moments)
self.longMessage = True #Show assertion fail values + given message
# First, let's check we've converged to a reasonable fit in the
# presence of noise:
# print
for param in FIT_PARAMS:
# print param, getattr(self,param), self.fit[param]
self.assertAlmostEqual(getattr(self,param), self.fit[param],
places=1,
msg = param +" misfit (bad random noise seed?)",
)
# Now we run the full error-profiling routine and check the chi-sq
# calculations:
self.fit_w_errs, _ = source_profile_and_errors(
data = self.mygauss,
threshold=0.,
noise=pixel_noise,
beam = (self.semimajor, self.semiminor, self.theta),
)
npix = len(self.mygauss.ravel())
# print "CHISQ", npix, self.fit_w_errs.chisq
# NB: this is the calculation for reduced chisq in presence of
# independent pixels, i.e. uncorrelated noise.
# For real data, we try to take the noise-correlation into account.
# print "Reduced chisq", self.fit_w_errs.chisq / npix
self.assertTrue( 0.9 < self.fit_w_errs.chisq / npix < 1.1)
def fit_to_point(self, x, y, boxsize, threshold, fixed):
"""Fit an elliptical Gaussian to a specified point on the image.
The fit is carried on a square section of the image, of length
*boxsize* & centred at pixel coordinates *x*, *y*. Any data
below *threshold* * rmsmap is not used for fitting. If *fixed*
is set to ``position``, then the pixel coordinates are fixed
in the fit.
Returns an instance of :class:`tkp.sourcefinder.extract.Detection`.
"""
if ((
# Recent NumPy
hasattr(numpy.ma.core, "MaskedConstant") and
isinstance(self.rmsmap, numpy.ma.core.MaskedConstant)
) or (
# Old NumPy
numpy.ma.is_masked(self.rmsmap[x, y])
)):
logger.error("Background is masked: cannot fit")
return None
chunk = ImageData.box_slice_about_pixel(x, y, boxsize/2.0)
if threshold is not None:
# We'll mask out anything below threshold*self.rmsmap from the fit.
labels, num = self.labels.setdefault( #Dictionary mapping threshold -> islands map
threshold,
ndimage.label(
self.clip.setdefault( #Dictionary mapping threshold -> mask
threshold,
numpy.where(
self.data_bgsubbed > threshold * self.rmsmap, 1, 0
)
)
)
)
mylabel = labels[x, y]
if mylabel == 0: # 'Background'
raise ValueError("Fit region is below specified threshold, fit aborted.")
mask = numpy.where(labels[chunk] == mylabel, 0, 1)
fitme = numpy.ma.array(self.data_bgsubbed[chunk], mask=mask)
if len(fitme.compressed()) < 1:
raise IndexError("Fit region too close to edge or too small")
else:
fitme = self.data_bgsubbed[chunk]
if fitme.size < 1:
raise IndexError("Fit region too close to edge or too small")
if not len(fitme.compressed()):
logger.error("All data is masked: cannot fit")
return None
# set argument for fixed parameters based on input string
if fixed == 'position':
fixed = {'xbar': boxsize/2.0, 'ybar': boxsize/2.0}
elif fixed == 'position+shape':
fixed = {'xbar': boxsize/2.0, 'ybar': boxsize/2.0,
'semimajor': self.beam[0],
'semiminor': self.beam[1],
'theta': self.beam[2]}
elif fixed == None:
fixed = {}
else:
raise TypeError("Unkown fixed parameter")
if threshold is not None:
threshold_at_pixel = threshold * self.rmsmap[x, y]
else:
threshold_at_pixel = None
try:
measurement, residuals = extract.source_profile_and_errors(
fitme,
threshold_at_pixel,
self.rmsmap[x, y],
self.beam,
fixed=fixed
)
except ValueError:
# Fit failed to converge
# Moments are not applicable when holding parameters fixed
logger.error("Gaussian fit failed at %f, %f", x, y)
return None
try:
assert(abs(measurement['xbar']) < boxsize)
assert(abs(measurement['ybar']) < boxsize)
except AssertionError:
logger.warn('Fit falls outside of box.')
measurement['xbar'] += x-boxsize/2.0
measurement['ybar'] += y-boxsize/2.0
measurement.sig = (fitme / self.rmsmap[chunk]).max()
return extract.Detection(measurement, self)
def fit_to_point(self, x, y, boxsize, threshold, fixed):
"""Fit an elliptical Gaussian to a specified point on the image.
The fit is carried on a square section of the image, of length
*boxsize* & centred at pixel coordinates *x*, *y*. Any data
below *threshold* * rmsmap is not used for fitting. If *fixed*
is set to ``position``, then the pixel coordinates are fixed
in the fit.
Returns an instance of :class:`tkp.sourcefinder.extract.Detection`.
"""
logger.debug("Force-fitting pixel location ({},{})".format(x, y))
# First, check that x and y are actually valid semi-positive integers.
# Otherwise,
# If they are too high (positive), then indexing will fail
# BUT, if they are negative, then we get wrap-around indexing
# and the fit continues at the wrong position!
if (x < 0 or x >self.xdim
or y < 0 or y >self.ydim ):
logger.warning("Dropping forced fit at ({},{}), "
"pixel position outside image".format(x,y)
)
return None
# Next, check if any of the central pixels (in a 3x3 box about the
# fitted pixel position) have been Masked
# (e.g. if NaNs, or close to image edge) - reject if so.
central_pixels_slice = ImageData.box_slice_about_pixel(x, y, 1)
if self.data.mask[central_pixels_slice].any():
logger.warning(
"Dropping forced fit at ({},{}), "
"Masked pixel in central fitting region".format(x,y))
return None
if ((
# Recent NumPy
hasattr(numpy.ma.core, "MaskedConstant") and
isinstance(self.rmsmap, numpy.ma.core.MaskedConstant)
) or (
# Old NumPy
numpy.ma.is_masked(self.rmsmap[int(x), int(y)])
)):
logger.error("Background is masked: cannot fit")
return None
chunk = ImageData.box_slice_about_pixel(x, y, boxsize/2.0)
if threshold is not None:
# We'll mask out anything below threshold*self.rmsmap from the fit.
labels, num = self.labels.setdefault( #Dictionary mapping threshold -> islands map
threshold,
ndimage.label(
self.clip.setdefault( #Dictionary mapping threshold -> mask
threshold,
numpy.where(
self.data_bgsubbed > threshold * self.rmsmap, 1, 0
)
)
)
)
mylabel = labels[int(x), int(y)]
if mylabel == 0: # 'Background'
raise ValueError("Fit region is below specified threshold, fit aborted.")
mask = numpy.where(labels[chunk] == mylabel, 0, 1)
fitme = numpy.ma.array(self.data_bgsubbed[chunk], mask=mask)
if len(fitme.compressed()) < 1:
raise IndexError("Fit region too close to edge or too small")
else:
fitme = self.data_bgsubbed[chunk]
if fitme.size < 1:
raise IndexError("Fit region too close to edge or too small")
if not len(fitme.compressed()):
logger.error("All data is masked: cannot fit")
return None
# set argument for fixed parameters based on input string
if fixed == 'position':
fixed = {'xbar': boxsize/2.0, 'ybar': boxsize/2.0}
elif fixed == 'position+shape':
fixed = {'xbar': boxsize/2.0, 'ybar': boxsize/2.0,
'semimajor': self.beam[0],
'semiminor': self.beam[1],
'theta': self.beam[2]}
elif fixed == None:
fixed = {}
else:
raise TypeError("Unkown fixed parameter")
if threshold is not None:
threshold_at_pixel = threshold * self.rmsmap[int(x), int(y)]
else:
threshold_at_pixel = None
try:
measurement, residuals = extract.source_profile_and_errors(
fitme,
threshold_at_pixel,
self.rmsmap[int(x), int(y)],
self.beam,
fixed=fixed
)
except ValueError:
# Fit failed to converge
# Moments are not applicable when holding parameters fixed
logger.error("Gaussian fit failed at %f, %f", x, y)
return None
try:
assert(abs(measurement['xbar']) < boxsize)
assert(abs(measurement['ybar']) < boxsize)
except AssertionError:
logger.warn('Fit falls outside of box.')
measurement['xbar'] += x-boxsize/2.0
measurement['ybar'] += y-boxsize/2.0
measurement.sig = (fitme / self.rmsmap[chunk]).max()
return extract.Detection(measurement, self)
def fit_to_point(self, x, y, boxsize, threshold, fixed):
"""Fit an elliptical Gaussian to a specified point on the image.
The fit is carried on a square section of the image, of length
*boxsize* & centred at pixel coordinates *x*, *y*. Any data
below *threshold* * rmsmap is not used for fitting. If *fixed*
is set to ``position``, then the pixel coordinates are fixed
in the fit.
Returns an instance of :class:`tkp.sourcefinder.extract.Detection`.
"""
if ((
# Recent NumPy
hasattr(numpy.ma.core, "MaskedConstant")
and isinstance(self.rmsmap, numpy.ma.core.MaskedConstant)) or (
# Old NumPy
numpy.ma.is_masked(self.rmsmap[x, y]))):
logger.error("Background is masked: cannot fit")
return None
chunk = ImageData.box_slice_about_pixel(x, y, boxsize / 2.0)
if threshold is not None:
# We'll mask out anything below threshold*self.rmsmap from the fit.
labels, num = self.labels.setdefault( #Dictionary mapping threshold -> islands map
threshold,
ndimage.label(
self.clip.
setdefault( #Dictionary mapping threshold -> mask
threshold,
numpy.where(
self.data_bgsubbed > threshold * self.rmsmap, 1,
0))))
mylabel = labels[x, y]
if mylabel == 0: # 'Background'
raise ValueError(
"Fit region is below specified threshold, fit aborted.")
mask = numpy.where(labels[chunk] == mylabel, 0, 1)
fitme = numpy.ma.array(self.data_bgsubbed[chunk], mask=mask)
if len(fitme.compressed()) < 1:
raise IndexError("Fit region too close to edge or too small")
else:
fitme = self.data_bgsubbed[chunk]
if fitme.size < 1:
raise IndexError("Fit region too close to edge or too small")
if not len(fitme.compressed()):
logger.error("All data is masked: cannot fit")
return None
# set argument for fixed parameters based on input string
if fixed == 'position':
fixed = {'xbar': boxsize / 2.0, 'ybar': boxsize / 2.0}
elif fixed == 'position+shape':
fixed = {
'xbar': boxsize / 2.0,
'ybar': boxsize / 2.0,
'semimajor': self.beam[0],
'semiminor': self.beam[1],
'theta': self.beam[2]
}
elif fixed == None:
fixed = {}
else:
raise TypeError("Unkown fixed parameter")
if threshold is not None:
threshold_at_pixel = threshold * self.rmsmap[x, y]
else:
threshold_at_pixel = None
try:
measurement, residuals = extract.source_profile_and_errors(
fitme,
threshold_at_pixel,
self.rmsmap[x, y],
self.beam,
fixed=fixed)
except ValueError:
# Fit failed to converge
# Moments are not applicable when holding parameters fixed
logger.error("Gaussian fit failed at %f, %f", x, y)
return None
try:
assert (abs(measurement['xbar']) < boxsize)
assert (abs(measurement['ybar']) < boxsize)
except AssertionError:
logger.warn('Fit falls outside of box.')
measurement['xbar'] += x - boxsize / 2.0
measurement['ybar'] += y - boxsize / 2.0
measurement.sig = (fitme / self.rmsmap[chunk]).max()
return extract.Detection(measurement, self)
def fit_to_point(self, x, y, boxsize, threshold, fixed):
"""Fit an elliptical Gaussian to a specified point on the image.
The fit is carried on a square section of the image, of length
*boxsize* & centred at pixel coordinates *x*, *y*. Any data
below *threshold* * rmsmap is not used for fitting. If *fixed*
is set to ``position``, then the pixel coordinates are fixed
in the fit.
Returns an instance of :class:`tkp.sourcefinder.extract.Detection`.
"""
logger.debug("Force-fitting pixel location ({},{})".format(x, y))
# First, check that x and y are actually valid semi-positive integers.
# Otherwise,
# If they are too high (positive), then indexing will fail
# BUT, if they are negative, then we get wrap-around indexing
# and the fit continues at the wrong position!
if (x < 0 or x > self.xdim or y < 0 or y > self.ydim):
logger.warning("Dropping forced fit at ({},{}), "
"pixel position outside image".format(x, y))
return None
# Next, check if any of the central pixels (in a 3x3 box about the
# fitted pixel position) have been Masked
# (e.g. if NaNs, or close to image edge) - reject if so.
central_pixels_slice = ImageData.box_slice_about_pixel(x, y, 1)
if self.data.mask[central_pixels_slice].any():
logger.warning("Dropping forced fit at ({},{}), "
"Masked pixel in central fitting region".format(
x, y))
return None
if ((
# Recent NumPy
hasattr(numpy.ma.core, "MaskedConstant")
and isinstance(self.rmsmap, numpy.ma.core.MaskedConstant)) or (
# Old NumPy
numpy.ma.is_masked(self.rmsmap[int(x), int(y)]))):
logger.error("Background is masked: cannot fit")
return None
chunk = ImageData.box_slice_about_pixel(x, y, boxsize / 2.0)
if threshold is not None:
# We'll mask out anything below threshold*self.rmsmap from the fit.
labels, num = self.labels.setdefault( #Dictionary mapping threshold -> islands map
threshold,
ndimage.label(
self.clip.
setdefault( #Dictionary mapping threshold -> mask
threshold,
numpy.where(
self.data_bgsubbed > threshold * self.rmsmap, 1,
0))))
mylabel = labels[int(x), int(y)]
if mylabel == 0: # 'Background'
raise ValueError(
"Fit region is below specified threshold, fit aborted.")
mask = numpy.where(labels[chunk] == mylabel, 0, 1)
fitme = numpy.ma.array(self.data_bgsubbed[chunk], mask=mask)
if len(fitme.compressed()) < 1:
raise IndexError("Fit region too close to edge or too small")
else:
fitme = self.data_bgsubbed[chunk]
if fitme.size < 1:
raise IndexError("Fit region too close to edge or too small")
if not len(fitme.compressed()):
logger.error("All data is masked: cannot fit")
return None
# set argument for fixed parameters based on input string
if fixed == 'position':
fixed = {'xbar': boxsize / 2.0, 'ybar': boxsize / 2.0}
elif fixed == 'position+shape':
fixed = {
'xbar': boxsize / 2.0,
'ybar': boxsize / 2.0,
'semimajor': self.beam[0],
'semiminor': self.beam[1],
'theta': self.beam[2]
}
elif fixed == None:
fixed = {}
else:
raise TypeError("Unkown fixed parameter")
if threshold is not None:
threshold_at_pixel = threshold * self.rmsmap[int(x), int(y)]
else:
threshold_at_pixel = None
try:
measurement, residuals = extract.source_profile_and_errors(
fitme,
threshold_at_pixel,
self.rmsmap[int(x), int(y)],
self.beam,
fixed=fixed)
except ValueError:
# Fit failed to converge
# Moments are not applicable when holding parameters fixed
logger.error("Gaussian fit failed at %f, %f", x, y)
return None
try:
assert (abs(measurement['xbar']) < boxsize)
assert (abs(measurement['ybar']) < boxsize)
except AssertionError:
logger.warn('Fit falls outside of box.')
measurement['xbar'] += x - boxsize / 2.0
measurement['ybar'] += y - boxsize / 2.0
measurement.sig = (fitme / self.rmsmap[chunk]).max()
return extract.Detection(measurement, self)
