def pre_process(self):
in_pData = self.get_plugin_in_datasets()[0]
data = self.get_in_datasets()[0]
dict_entry = data.get_name() + '_preview_starts'
shift = self.exp.meta_data.get(dict_entry)
det_y = data.get_data_dimension_by_axis_label('detector_y')
det_x = data.get_data_dimension_by_axis_label('detector_x')
# If the data is cropped then the centre of distortion must be shifted
# accordingly, e.g if preview is [:, a:b, c:d] then shift is (a, c)
centre = np.array(
[self.parameters['centre_x'], self.parameters['centre_y']])
centre[0] -= shift[det_y]
centre[1] -= shift[det_x]
# flipping the values
centre = centre[::-1]
# pass two empty arrays of frame chunk size
unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
unwarp.setctr(*centre)
plugin_data_shape = in_pData.get_shape()
temp_array = np.empty(plugin_data_shape, dtype=np.float32)
unwarp.setup(temp_array, temp_array)
self.new_slice = [slice(None)] * 3
orig_shape = self.get_in_datasets()[0].get_shape()
for ddir in self.core_dims:
self.new_slice[ddir] = \
slice(self.crop, orig_shape[ddir]-self.crop)
def pre_process(self):
in_pData = self.get_plugin_in_datasets()[0]
data = self.get_in_datasets()[0]
name = data.get_name()
shift = self.exp.meta_data.get(name + '_preview_starts')
step = self.exp.meta_data.get(name + '_preview_steps')
det_y = data.get_data_dimension_by_axis_label('detector_y')
det_x = data.get_data_dimension_by_axis_label('detector_x')
# check stepping as should not be > 1
self.step_check = \
True if max([step[i] for i in [det_y, det_x]]) > 1 else False
# If the data is cropped then the centre of distortion must be shifted
# accordingly, e.g if preview is [:, a:b, c:d] then shift is (a, c)
centre = np.array([self.parameters['centre_from_left'],
self.parameters['centre_from_top']])
centre[0] -= shift[det_x]
centre[1] -= shift[det_y]
# pass two empty arrays of frame chunk size
unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
unwarp.setctr(*centre)
plugin_data_shape = in_pData.get_shape()
temp_array = np.empty(plugin_data_shape, dtype=np.float32)
unwarp.setup(temp_array, temp_array)
self.new_slice = [slice(None)]*3
orig_shape = self.get_in_datasets()[0].get_shape()
for ddir in self.core_dims:
self.new_slice[ddir] = \
slice(self.crop, orig_shape[ddir]-self.crop)
def pre_process(self):
centre = np.array(self.parameters['centre'])
centre[0] -= self.parameters['shift'][0]
centre[1] -= self.parameters['shift'][1]
# flipping the values
centre = centre[::-1]
#pass two empty arrays of frame chunk size
unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
unwarp.setctr(*centre)
plugin_data_shape = self.get_plugin_in_datasets()[0].get_shape()
temp_array = np.empty(plugin_data_shape, dtype=np.float32)
unwarp.setup(temp_array, temp_array)
self.new_slice = [slice(None)]*3
orig_shape = self.get_in_datasets()[0].get_shape()
for ddir in self.core_dir:
self.new_slice[ddir] = \
slice(self.crop, orig_shape[ddir]-self.crop)
def pre_process(self):
centre = np.array(self.parameters['centre'])
centre[0] -= self.parameters['shift'][0]
centre[1] -= self.parameters['shift'][1]
# flipping the values
centre = centre[::-1]
#pass two empty arrays of frame chunk size
unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
unwarp.setctr(*centre)
plugin_data_shape = self.get_plugin_in_datasets()[0].get_shape()
temp_array = np.empty(plugin_data_shape, dtype=np.float32)
unwarp.setup(temp_array, temp_array)
self.slice_list = [slice(None)]*3
orig_shape = self.get_in_datasets()[0].get_shape()
for ddir in self.core_dir:
self.slice_list[ddir] = \
slice(self.crop, orig_shape[ddir]-self.crop)
def pre_process(self):
in_pData = self.get_plugin_in_datasets()[0]
data = self.get_in_datasets()[0]
name = data.get_name()
shift = self.exp.meta_data.get(name + '_preview_starts')
step = self.exp.meta_data.get(name + '_preview_steps')
det_y = data.get_data_dimension_by_axis_label('detector_y')
det_x = data.get_data_dimension_by_axis_label('detector_x')
# check stepping as should not be > 1
self.step_check = \
True if max([step[i] for i in [det_y, det_x]]) > 1 else False
# If the data is cropped then the centre of distortion must be shifted
# accordingly, e.g if preview is [:, a:b, c:d] then shift is (a, c)
centre = np.array([self.parameters['centre_from_left'],
self.parameters['centre_from_top']])
centre[0] -= shift[det_x]
centre[1] -= shift[det_y]
# pass two empty arrays of frame chunk size
unwarp.setcoeff(*self.parameters['polynomial_coeffs'])
unwarp.setctr(*centre)
plugin_data_shape = in_pData.get_shape()
temp_array = np.empty(plugin_data_shape, dtype=np.float32)
unwarp.setup(temp_array, temp_array)
self.new_slice = [slice(None)] * 3
orig_shape = self.get_in_datasets()[0].get_shape()
for ddir in self.core_dims:
self.new_slice[ddir] = \
slice(self.crop, orig_shape[ddir] - self.crop)
inim=np.expand_dims(tifffile.imread("i32.tif"),0)
inimt=np.tile(inim,(nbatch,1,1))
inim3=np.ascontiguousarray(inimt.astype(np.float32))
print "shape: ",inim3.shape
outim=np.empty_like(inim3)
return(inim3,outim)
#end of get_image_array
(inarray,outarray)=get_image_array()
#set the coefficients -- this coudl be rolled into setup with additional args?
unwarp.setcoeff(1,-1e-3,-6e-7,5e-10,-4e-13)
unwarp.setctr(1000,1000)
#call setup routine
unwarp.setup(inarray,outarray)
unwarp.run(inarray,outarray)
#multiple calls so long as centre and parameters are fixed
#could be different data solong as it is same type/size/etc
unwarp.run(inarray,outarray)
unwarp.cleanup()
print("Writing the files to /dls/i12/data/2015/cm12163-5/tmp/unwarptest/cython_out")
try:
os.makedirs("/dls/i12/data/2015/cm12163-5/tmp/unwarptest/cython_out")
print ("Creating Folder")
except:
print ("Folder already exists")