def setModelFromFile(self, modName, md=None):
"""load the model from file - returns True if the model changed, False if
an existing model was reused"""
#global IntXVals, IntYVals, IntZVals, interpModel, interpModelName, dx, dy, dz
if not modName == self.interpModelName:
#special case for theoretical models
if modName.startswith('ZMODEL:'):
#print modName[7:]
params = eval(modName[7:])
mod, voxelsize = self.genTheoreticalModelZernike(md, **params)
else:
#mf = open(getFullExistingFilename(modName), 'rb')
#mod, voxelsize = load(mf)
#mf.close()
# mf = ImageStack(filename=modName)
# mod = mf.data[:,:,:].astype('f')
# voxelsize = mf.voxelsize
mod, voxelsize_nm = load_psf(modName)
self.setModel(modName, mod, voxelsize_nm)
#print 'model changed'
return True #model changed
else:
return False #model not changed
def GetPSF(self, vshint = None):
import numpy as np
from PYME.IO.load_psf import load_psf
from scipy import stats
PSFMode = self.nb2.GetCurrentPage().PSFMode
#get PSF from file
if PSFMode == 'File':
#psf, vs = np.load(self.GetPSFFilename())
psf, vs = load_psf(self.GetPSFFilename())
psf = np.atleast_3d(psf)
return (self.GetPSFFilename(), psf, vs)
elif (PSFMode == 'Laplace'):
sc = float(self.tLaplaceFWHM.GetValue())/2.0
X, Y = np.mgrid[-30.:31., -30.:31.]
R = np.sqrt(X*X + Y*Y)
if not vshint is None:
vx = vshint*1e3
else:
vx = sc/2.
vs = type('vs', (object,), dict(x=vx/1e3, y=vx/1e3))
psf = np.atleast_3d(stats.cauchy.pdf(vx*R, scale=sc))
return 'Generated Laplacian, FWHM=%f' % (2*sc), psf/psf.sum(), vs
def setModelFromMetadata(self, md):
"""load the model from file - returns True if the model changed, False if
an existing model was reused"""
#global IntXVals, IntYVals, IntZVals, interpModel, interpModelName, dx, dy, dz
modName = md.PSFFile
if not modName == self.interpModelName:
#special case for theoretical models
if modName.startswith('ZMODEL:'):
params = eval(modName[7:])
mod, voxelsize = self.genTheoreticalModelZernike(md, **params)
else:
#try and get psf from task queue
#if not md.taskQueue == None:
try:
mod, voxelsize = md.taskQueue.getQueueData(
md.dataSourceID, 'PSF')
except:
#mf = open(getFullExistingFilename(modName), 'rb')
#mod, voxelsize = load(mf)
#mf.close()
#mf = ImageStack(filename=modName)
#mod = mf.data[:,:,:].astype('f')
#voxelsize = mf.voxelsize
mod, voxelsize = load_psf(modName)
self.setModel(modName, mod, voxelsize)
#print 'model changed'
return True #model changed
else:
return False #model not changed
def preparePSF(md, PSSize):
global PSFFileName, cachedPSF, cachedOTF2, cachedOTFH, autocorr
PSFFilename = md.PSFFile
if (not (PSFFileName == PSFFilename)) or (not (cachedPSF.shape == PSSize)):
try:
ps, vox = md.taskQueue.getQueueData(md.dataSourceID, 'PSF')
except:
#fid = open(getFullExistingFilename(PSFFilename), 'rb')
#ps, vox = pickle.load(fid)
#fid.close()
ps, vox = load_psf(PSFFilename)
ps = ps.max(2)
ps = ps - ps.min()
#ps = ps*(ps > 0)
ps = ps * scipy.signal.hanning(
ps.shape[0])[:, None] * scipy.signal.hanning(ps.shape[1])[None, :]
ps = ps / ps.sum()
PSFFileName = PSFFilename
pw = (numpy.array(PSSize) - ps.shape) / 2.
pw1 = numpy.floor(pw)
pw2 = numpy.ceil(pw)
cachedPSF = pad.with_constant(ps, ((pw2[0], pw1[0]), (pw2[1], pw1[1])),
(0, ))
cachedOTFH = ifftn(cachedPSF) * cachedPSF.size
cachedOTF2 = cachedOTFH * fftn(cachedPSF)
def preparePSF(md, PSSize):
global PSFFileName, PSFSize, FTW
PSFFilename = md.PSFFile
if (not (PSFFileName == PSFFilename)) or (not (PSFSize == PSSize)):
try:
ps, vox = md.taskQueue.getQueueData(md.dataSourceID, 'PSF')
except:
#fid = open(getFullExistingFilename(PSFFilename), 'rb')
#ps, vox = pickle.load(fid)
#fid.close()
load_psf(PSFFilename)
FTW = fftwWeiner(ps, vox, PSSize)
PSFFileName = PSFFilename
PSFSize = PSSize
def setModel(modName, md):
global IntXVals, IntYVals, IntZVals, dx, dy, dz
from PYME.IO import load_psf
mod, vs_nm = load_psf.load_psf(modName)
mod = resizePSF(mod, interpModel().shape)
IntXVals = vs_nm.x * np.mgrid[-(mod.shape[0] / 2.):(mod.shape[0] / 2.)]
IntYVals = vs_nm.y * np.mgrid[-(mod.shape[1] / 2.):(mod.shape[1] / 2.)]
IntZVals = vs_nm.z * np.mgrid[-(mod.shape[2] / 2.):(mod.shape[2] / 2.)]
dx, dy, dz = vs_nm
#interpModel = np.maximum(mod/mod.max(), 0) #normalise to 1
interpModel_by_chan[0] = np.maximum(
mod / mod[:, :, len(IntZVals) / 2].sum(), 0) #normalise to 1 and clip
def getQueueData(self, fieldName, *args):
"""Get data, defined by fieldName and potntially additional arguments, ascociated with queue"""
if fieldName == 'FitResults':
startingAt, = args
#with self.fileResultsLock.rlock:
# if self.h5ResultsFile.__contains__('/FitResults'):
# res = self.h5ResultsFile.root.FitResults[startingAt:]
# else:
# res = []
with h5rFile.openH5R(self.resultsFilename, 'a') as h5f:
res = h5f.getTableData('FitResults', slice(startingAt, None))
return res
elif fieldName == 'PSF':
#from PYME.ParallelTasks.relativeFiles import getFullExistingFilename
from PYME.IO.load_psf import load_psf
res = None
modName = self.metaData.getEntry('PSFFile')
# mf = open(getFullExistingFilename(modName), 'rb')
#res = np.load(mf)
#mf.close()
res = load_psf(getFullExistingFilename(modName))
return res
elif fieldName == 'MAP':
mapName, = args
#from PYME.ParallelTasks.relativeFiles import getFullExistingFilename
from PYME.IO.image import ImageStack
print('Serving map: %s' % mapName)
fn = getFullExistingFilename(mapName)
varmap = ImageStack(
filename=fn, haveGUI=False).data[:, :, 0].squeeze(
) #this should handle .tif, .h5, and a few others
return varmap
else:
return None