def initFitter(margin, parameters, roi_size, variances):
"""
Create and return a mpFitDaoC.MPFitDao2D object.
"""
sigma = parameters.getAttr("sigma")
mfitter = mpFitDaoC.MPFitDao2D(n_channels=len(variances),
roi_size=roi_size,
sigma_range=[0.5 * sigma, 3.0 * sigma])
# Pass variances to the fitting object.
#
for i in range(len(variances)):
mfitter.setVariance(variances[i], i)
# Load mappings.
#
if parameters.hasAttr("mapping"):
if os.path.exists(parameters.getAttr("mapping")):
mapping_filename = parameters.getAttr("mapping")
else:
raise Exception("Mapping file", parameters.getAttr("mapping"),
"does not exist.")
mfitter.setMapping(*mpUtil.loadMappings(mapping_filename, margin))
# Initialize weights.
#
mfitter.setWeights()
return mfitter
def test_load_mappings_1():
map_test_file = storm_analysis.getPathOutputTest("map.map")
max_ch = 4
mappings = {}
for i in range(1,max_ch):
j = i
mappings[str(i) + "_0_x"] = numpy.arange(j,j+2.5,1.0)
j += 0.1
mappings[str(i) + "_0_y"] = numpy.arange(j,j+2.5,1.0)
j += 0.1
mappings["0_" + str(i) + "_x"] = numpy.arange(j,j+2.5,1.0)
j += 0.1
mappings["0_" + str(i) + "_y"] = numpy.arange(j,j+2.5,1.0)
max_ch -= 1
with open(map_test_file, 'wb') as fp:
pickle.dump(mappings, fp)
mappings = {}
[xt_0toN, yt_0toN, xt_Nto0, yt_Nto0] = mpUtil.loadMappings(map_test_file, 0)
assert(xt_0toN[0,0] == 0.0)
assert(yt_0toN[0,0] == 0.0)
assert(xt_Nto0[0,0] == 0.0)
assert(yt_Nto0[0,0] == 0.0)
assert(abs(xt_0toN[max_ch,2]-5.2) < 1.0e-6)
assert(abs(yt_0toN[max_ch,2]-5.3) < 1.0e-6)
assert(abs(xt_Nto0[max_ch,2]-5.0) < 1.0e-6)
assert(abs(yt_Nto0[max_ch,2]-5.1) < 1.0e-6)
def initFitter(margin, parameters, roi_size, rqes, variances):
"""
Create and return a mpFitDaoC.MPFitDao2D object.
"""
# This fitter only supports 'MLE'
assert (parameters.getAttr("fit_error_model") == 'MLE'
), "Only MLE fitting is supported."
sigma = parameters.getAttr("sigma")
mfitter = mpFitDaoC.MPFitDao2D(n_channels=len(variances),
roi_size=roi_size,
sigma_range=[0.5 * sigma, 3.0 * sigma])
# Initialize fitters for each channel.
#
for i in range(len(variances)):
mfitter.initializeChannel(rqes[i], variances[i], i)
# Load mappings.
#
if parameters.hasAttr("mapping"):
if os.path.exists(parameters.getAttr("mapping")):
mapping_filename = parameters.getAttr("mapping")
else:
raise Exception("Mapping file", parameters.getAttr("mapping"),
"does not exist.")
mfitter.setMapping(*mpUtil.loadMappings(mapping_filename, margin))
# Initialize weights.
#
mfitter.setWeights()
return mfitter
def test_load_mappings_1():
map_test_file = storm_analysis.getPathOutputTest("map.map")
max_ch = 4
mappings = {}
for i in range(1, max_ch):
j = i
mappings[str(i) + "_0_x"] = numpy.arange(j, j + 2.5, 1.0)
j += 0.1
mappings[str(i) + "_0_y"] = numpy.arange(j, j + 2.5, 1.0)
j += 0.1
mappings["0_" + str(i) + "_x"] = numpy.arange(j, j + 2.5, 1.0)
j += 0.1
mappings["0_" + str(i) + "_y"] = numpy.arange(j, j + 2.5, 1.0)
max_ch -= 1
with open(map_test_file, 'wb') as fp:
pickle.dump(mappings, fp)
mappings = {}
[xt_0toN, yt_0toN, xt_Nto0,
yt_Nto0] = mpUtil.loadMappings(map_test_file, 0)
assert (xt_0toN[0, 0] == 0.0)
assert (yt_0toN[0, 0] == 0.0)
assert (xt_Nto0[0, 0] == 0.0)
assert (yt_Nto0[0, 0] == 0.0)
assert (abs(xt_0toN[max_ch, 2] - 5.2) < 1.0e-6)
assert (abs(yt_0toN[max_ch, 2] - 5.3) < 1.0e-6)
assert (abs(xt_Nto0[max_ch, 2] - 5.0) < 1.0e-6)
assert (abs(yt_Nto0[max_ch, 2] - 5.1) < 1.0e-6)
def initFitter(margin, parameters, psf_objects, rqes, variances):
"""
Create and return a mpFitArbC.MPXXFit object.
"""
assert (len(psf_objects) == len(variances))
#
# FIXME: Not sure having two z ranges, one from the spline
# and one in the parameters is a good idea.
#
# Create the fitter object which will do the actual fitting. Unless specified
# the fit for each channel is forced to have the same height.
#
if isinstance(psf_objects[0], psfFn.PSFFn):
mfitter = mpFitArbC.MPPSFFnFit(independent_heights=parameters.getAttr(
"independent_heights", 0),
psf_objects=psf_objects)
elif isinstance(psf_objects[0], pupilFn.PupilFunction):
mfitter = mpFitArbC.MPPupilFnFit(
independent_heights=parameters.getAttr("independent_heights", 0),
psf_objects=psf_objects)
elif isinstance(psf_objects[0], splineToPSF.SplineToPSF3D):
mfitter = mpFitArbC.MPSplineFit(independent_heights=parameters.getAttr(
"independent_heights", 0),
psf_objects=psf_objects)
# Initialize fitters for each channel.
#
for i in range(len(variances)):
mfitter.initializeChannel(rqes[i], variances[i], i)
# Load mappings.
#
if parameters.hasAttr("mapping"):
if os.path.exists(parameters.getAttr("mapping")):
mapping_filename = parameters.getAttr("mapping")
else:
raise Exception("Mapping file", parameters.getAttr("mapping"),
"does not exist.")
mfitter.setMapping(*mpUtil.loadMappings(mapping_filename, margin))
# Load channel Cramer-Rao weights if available.
#
weights = None
if parameters.hasAttr("weights"):
with open(parameters.getAttr("weights"), 'rb') as fp:
weights = pickle.load(fp)
mfitter.setWeights(weights)
return mfitter
def initFitter(margin, parameters, psf_objects, rqes, variances):
"""
Create and return a mpFitArbC.MPXXFit object.
"""
# This fitter only supports 'MLE'
assert (parameters.getAttr("fit_error_model") == 'MLE'), "Only MLE fitting is supported."
assert(len(psf_objects) == len(variances))
#
# FIXME: Not sure having two z ranges, one from the spline
# and one in the parameters is a good idea.
#
# Create the fitter object which will do the actual fitting. Unless specified
# the fit for each channel is forced to have the same height.
#
if isinstance(psf_objects[0], psfFn.PSFFn):
mfitter = mpFitArbC.MPPSFFnFit(independent_heights = parameters.getAttr("independent_heights", 0),
psf_objects = psf_objects)
elif isinstance(psf_objects[0], pupilFn.PupilFunction):
mfitter = mpFitArbC.MPPupilFnFit(independent_heights = parameters.getAttr("independent_heights", 0),
psf_objects = psf_objects)
elif isinstance(psf_objects[0], splineToPSF.SplineToPSF3D):
mfitter = mpFitArbC.MPSplineFit(independent_heights = parameters.getAttr("independent_heights", 0),
psf_objects = psf_objects)
# Initialize fitters for each channel.
#
for i in range(len(variances)):
mfitter.initializeChannel(rqes[i], variances[i], i)
# Load mappings.
#
if parameters.hasAttr("mapping"):
if os.path.exists(parameters.getAttr("mapping")):
mapping_filename = parameters.getAttr("mapping")
else:
raise Exception("Mapping file", parameters.getAttr("mapping"), "does not exist.")
mfitter.setMapping(*mpUtil.loadMappings(mapping_filename, margin))
# Load channel Cramer-Rao weights if available.
#
weights = None
if parameters.hasAttr("weights"):
with open(parameters.getAttr("weights"), 'rb') as fp:
weights = pickle.load(fp)
mfitter.setWeights(weights)
return mfitter
def test_load_mappings_2():
[xt_0toN, yt_0toN, xt_Nto0, yt_Nto0] = mpUtil.loadMappings(None, 0)
assert(xt_0toN.shape[0] == 1)
assert(abs(xt_0toN[0,0]) < 1.0e-6)
assert(abs(xt_0toN[0,1] - 1.0) < 1.0e-6)
assert(abs(xt_0toN[0,2]) < 1.0e-6)
def test_load_mappings_2():
[xt_0toN, yt_0toN, xt_Nto0, yt_Nto0] = mpUtil.loadMappings(None, 0)
assert (xt_0toN.shape[0] == 1)
assert (abs(xt_0toN[0, 0]) < 1.0e-6)
assert (abs(xt_0toN[0, 1] - 1.0) < 1.0e-6)
assert (abs(xt_0toN[0, 2]) < 1.0e-6)