def ValidateROI(self, ROI):
return MI.ValidateROI(ROI, self.ImageShape())
def Compute(self):
sf.CheckCreateFolder(self.outFolder)
logging.info(
'NonAffMaps.Compute() started! Result will be saved in folder ' +
str(self.outFolder))
# Search for correlation map MIfiles, skip autocorrelation maps
fw_mistack = self.cmaps_fw.GetCorrMaps(openMIfiles=True)
bk_mistack = self.cmaps_bk.GetCorrMaps(openMIfiles=True)
common_lags = list(
set(fw_mistack.IdxList).intersection(bk_mistack.IdxList))
if self.lag_range is None:
if 0 in common_lags: common_lags.remove(0)
else:
if self.lag_range[1] < 0:
self.lag_range[1] = np.max(common_lags) + 1
common_lags = [
lag for lag in common_lags
if (lag != 0 and lag >= self.lag_range[0]
and lag <= self.lag_range[1])
]
self.lagList = common_lags
# Export configuration
self.ExportConfiguration()
if self.trans_bk_matrix is not None:
tr_matrix = np.reshape(np.asarray(self.trans_bk_matrix), (2, 2))
logging.debug(
'Backscattered correlation maps will be transformed using matrix '
+ str(tr_matrix) + ' and offset ' + str(self.trans_bk_offset))
# For each couple of correlation maps (with equal lagtime)
for lidx in range(len(self.lagList)):
logging.info('Now working on lagtime ' + str(lidx) + '/' +
str(len(self.lagList)) + ' (d' +
str(self.lagList[lidx]) + ')')
fw_lidx = fw_mistack.IdxList.index(self.lagList[lidx])
bk_lidx = bk_mistack.IdxList.index(self.lagList[lidx])
# eventually compute normalization factors
if self.norm_range is not None:
fw_norm_factor = np.mean(fw_mistack.MIfiles[fw_lidx].Read(
zRange=self.norm_range[:2],
cropROI=self.norm_range[2:],
closeAfter=False))
if self.trans_bk_matrix is None and self.trans_bk_offset is None:
bk_norm_factor = np.mean(bk_mistack.MIfiles[bk_lidx].Read(
zRange=self.norm_range[:2],
cropROI=self.norm_range[2:],
closeAfter=False))
else:
bk_norm_data = bk_mistack.MIfiles[bk_lidx].Read(
zRange=self.norm_range[:2],
cropROI=None,
closeAfter=False)
if len(bk_norm_data.shape) > 2:
bk_norm_data = np.mean(bk_norm_data, axis=0)
logging.debug('shape before transformation: ' +
str(bk_norm_data.shape))
bk_norm_data = sp.ndimage.affine_transform(bk_norm_data, tr_matrix, offset=self.trans_bk_offset,\
output_shape=bk_norm_data.shape, order=1, mode='constant', cval=1.0)
norm_cropROI = MI.ValidateROI(self.norm_range[2:],
bk_norm_data.shape,
replaceNone=True)
logging.debug('shape after transformation: ' +
str(bk_norm_data.shape) +
' will be cropped with ROI ' +
str(norm_cropROI))
bk_norm_factor = np.mean(bk_norm_data[norm_cropROI[1]:norm_cropROI[1]+norm_cropROI[3],\
norm_cropROI[0]:norm_cropROI[0]+norm_cropROI[2]])
bk_norm_data = None
else:
fw_norm_factor, bk_norm_factor = 1, 1
logging.info('Normalization factors: ' + str(fw_norm_factor) +
' (front) and ' + str(bk_norm_factor) + ' (back)')
# load, normalize and eventually smooth correlation maps.
fw_data = np.true_divide(
fw_mistack.MIfiles[fw_lidx].Read(zRange=self.t_range,
cropROI=self.cropROI,
closeAfter=True),
fw_norm_factor)
bk_data = np.true_divide(
bk_mistack.MIfiles[bk_lidx].Read(zRange=self.t_range,
cropROI=self.cropROI,
closeAfter=True),
bk_norm_factor)
if self.smooth_kernel_specs is not None:
Kernel3D = self.LoadKernel(self.smooth_kernel_specs)
logging.debug('Smoothing with kernel with shape ' +
str(Kernel3D.shape))
fw_data = signal.convolve(fw_data, Kernel3D, mode='same')
bk_data = signal.convolve(bk_data, Kernel3D, mode='same')
# transform backscattered images
if self.trans_bk_matrix is not None:
tr_matrix3D = np.asarray(
[[1, 0, 0], [0, tr_matrix[0, 0], tr_matrix[0, 1]],
[0, tr_matrix[1, 0], tr_matrix[1, 1]]])
tr_offset3D = np.asarray(
[0, self.trans_bk_offset[0], self.trans_bk_offset[1]])
bk_data = sp.ndimage.affine_transform(bk_data, tr_matrix3D, offset=tr_offset3D,\
output_shape=fw_data.shape, order=1, mode='constant', cval=1.0)
# sigma2 = ln(forward-scattering corr / backscattering corr) * 6 / (qz_bk^2 - qz_fw^2)
sigma2 = np.log(np.true_divide(
fw_data, bk_data)) * 6.0 / (self.qz_bk**2 - self.qz_fw**2)
# For the first lagtime, generate and export metadata
if (lidx == 0):
out_meta = fw_mistack.MIfiles[fw_lidx].GetMetadata().copy()
out_meta['hdr_len'] = 0
out_meta['gap_bytes'] = 0
out_meta['shape'] = list(sigma2.shape)
if ('fps' in out_meta):
val_tRange = fw_mistack.MIfiles[fw_lidx].Validate_zRange(
self.t_range)
out_meta['fps'] = float(
out_meta['fps']) * 1.0 / val_tRange[2]
exp_config = cf.Config()
exp_config.Import(out_meta, section_name='MIfile')
metadata_fname = os.path.join(self.outFolder,
'NAffMap_metadata.ini')
exp_config.Export(metadata_fname)
logging.info('Metadata exported to file ' +
str(metadata_fname))
# export data
cur_fname = 'NaffMap_d' + str(self.lagList[lidx]).zfill(4) + '.dat'
MI.MIfile(os.path.join(self.outFolder, cur_fname),
metadata_fname).WriteData(sigma2)
logging.info('Result saved to file ' + str(cur_fname))
fw_mistack.MIfiles[fw_lidx].Close()
bk_mistack.MIfiles[bk_lidx].Close()