def load_xml_data(infile):
"""Read in a file in RFB format and return a list of Beam objects"""
f = open(infile,'rb')
tree = objectify.parse(f)
f.close()
schema_file = open('radpy/plugins/BeamAnalysis/BDML/bdml.xsd','r')
bdml_schema = etree.parse(schema_file)
xmlschema = etree.XMLSchema(bdml_schema)
if not xmlschema.validate(tree):
raise IOError('Not a valid BDML file.')
bdml = tree.getroot()
b = []
for i in bdml.Beam:
xml_class = Beam()
xml_class.importXML(i)
b.append(xml_class)
f.close()
return b
def load_xml_data(infile):
"""Read in a file in RFB format and return a list of Beam objects"""
f = open(infile, 'rb')
tree = objectify.parse(f)
f.close()
schema_file = open('radpy/plugins/BeamAnalysis/BDML/bdml.xsd', 'r')
bdml_schema = etree.parse(schema_file)
xmlschema = etree.XMLSchema(bdml_schema)
if not xmlschema.validate(tree):
raise IOError('Not a valid BDML file.')
bdml = tree.getroot()
b = []
for i in bdml.Beam:
xml_class = Beam()
xml_class.importXML(i)
b.append(xml_class)
f.close()
return b
def editAll(self):
global_beam = Beam()
beams = self.model().asRecord(self.currentIndex())
global_dict = global_beam.trait_get()
for key in global_dict.keys():
if key == 'Data_Abscissa' or key == 'Data_Ordinate':
continue
traits = [getattr(beam[1], key) for beam in beams]
#Test if all traits in the list are the same
if all(traits[0] == trait for trait in traits):
setattr(global_beam, key, traits[0])
#removeRecord returns True if the root node is deleted. This will
#require a reset of the TreeView. (the second parameter of addRecord
#must be True)
root_reset = self.model().removeRecord(self.currentIndex())
#Since traitsUI windows cannot be set to be application modal, the
#tree view must be disabled to prevent the user from making changes
#to the tree model during editing. Allowing changes can lead to
#unexpected behavior.
self.setEnabled(False)
global_beam.edit_traits()
self.setEnabled(True)
global_dict = global_beam.trait_get()
for beam in beams:
for key in global_dict.keys():
if key == 'Data_Abscissa' or key == 'Data_Ordinate':
continue
try:
global_val = getattr(global_beam, key)
trait_val = getattr(beam[1], key)
if global_val != trait_val and not global_beam.is_null(
key):
setattr(beam[1], key, global_val)
except AttributeError:
pass
self.model().addRecord(beam[1], root_reset)
def editAll(self):
global_beam = Beam()
beams = self.model().asRecord(self.currentIndex())
global_dict = global_beam.trait_get()
for key in global_dict.keys():
if key == 'Data_Abscissa' or key == 'Data_Ordinate':
continue
traits = [getattr(beam[1], key) for beam in beams]
#Test if all traits in the list are the same
if all(traits[0] == trait for trait in traits):
setattr(global_beam, key, traits[0])
#removeRecord returns True if the root node is deleted. This will
#require a reset of the TreeView. (the second parameter of addRecord
#must be True)
root_reset = self.model().removeRecord(self.currentIndex())
#Since traitsUI windows cannot be set to be application modal, the
#tree view must be disabled to prevent the user from making changes
#to the tree model during editing. Allowing changes can lead to
#unexpected behavior.
self.setEnabled(False)
global_beam.edit_traits()
self.setEnabled(True)
global_dict = global_beam.trait_get()
for beam in beams:
for key in global_dict.keys():
if key == 'Data_Abscissa' or key == 'Data_Ordinate':
continue
try:
global_val = getattr(global_beam, key)
trait_val = getattr(beam[1], key)
if global_val != trait_val and not global_beam.is_null(key):
setattr(beam[1], key, global_val)
except AttributeError:
pass
self.model().addRecord(beam[1], root_reset)
def get_beam(self, start, stop, axis_len):
"""Given two points, returns a beam object with a profile between them"""
#Currently, this assumes that the profile will be either
#inplane, crossplane or a depth dose (i.e. start and stop positions
#are the same in two coordinates).
#This function needs a good way to determine the abscissa axis values
#in order to generalize to any linear scan.
#Orient start-stop from negative to positive values
scan_range = numpy.array([start[0]-stop[0],start[1]-stop[1],
start[2]-stop[2]])
if len(scan_range.nonzero()[0]) != 1:
raise ValueError("Point to point scans are not able to matched in RadPy.")
axis = scan_range.nonzero()[0][0]
#Always orient the array from negative to positive coordinate values.
if start[axis] > stop[axis]:
start[axis], stop[axis] = stop[axis], start[axis]
dcm_start = [min(self.Data.x_axis), min(self.Data.y_axis),
min(self.Data.z_axis)]
dcm_stop = [max(self.Data.x_axis), max(self.Data.y_axis),
max(self.Data.z_axis)]
for i in range(3):
if not (self.overlap((start[i],stop[i]),(dcm_start[i], dcm_stop[i]))):
raise ValueError("The scan range is outside the Dicom data range.")
#Truncate if scan extends beyond dicom range
if start[i] < dcm_start[i]:
start[i] = dcm_start[i]
if stop[i] > dcm_stop[i]:
stop[i] = dcm_stop[i]
#axis = scan_range.nonzero()[0][0]
# abs_0 = dcm_start[axis]
# abs_1 = dcm_stop[axis]
abscissa = numpy.linspace(start[axis], stop[axis], axis_len)
# abs_0 = start[axis]
# abs_1 = stop[axis]
x_interp = scipy.interpolate.interp1d(self.Data.x_axis,
numpy.arange(len(self.Data.x_axis)))
y_interp = scipy.interpolate.interp1d(self.Data.y_axis,
numpy.arange(len(self.Data.y_axis)))
z_indices = numpy.argsort(self.Data.z_axis)
z_interp = scipy.interpolate.interp1d(self.Data.z_axis[z_indices],
numpy.arange(len(self.Data.z_axis)))
x_values = x_interp(numpy.linspace(start[0],stop[0],axis_len))
y_values = y_interp(numpy.linspace(start[1],stop[1],axis_len))
z_values = z_interp(numpy.linspace(start[2],stop[2],axis_len))
# axes = [x_values, y_values, z_values]
# #abscissa = numpy.linspace(abs_0,abs_1,axis_len)
# abscissa = axes[axis]
# x_ind, y_ind, z_ind = numpy.mgrid[0:axis_len,0:axis_len,0:axis_len]
# x_step = self.Data.x_axis[1] - self.Data.x_axis[0]
# x_values = x_ind*x_step + start[0]
interp_vals = numpy.array([x_values,y_values,z_values])
ordinate = scipy.ndimage.map_coordinates(self.Data.dose, interp_vals)
# abscissa = self.Data.x_axis[27:89]
# ordinate = self.Data.dose[27:89,38,24]
# abscissa = []
# i0 = numpy.sqrt(start[0]**2 + start[1]**2 + start[2]**2)
# i1 = numpy.sqrt((stop[0] - start[0])**2 + (stop[1] - start[1])**2 +
# (stop[2] - start[2])**2)/len(ordinate)
# for i in range(len(ordinate)):
# abscissa.append(i0 + i*i1)
#Create and initialize a new Beam object
xml_beam = Beam()
xml_beam.copy_traits(self)
if axis == 2: #Depth dose
xml_beam.Data_Abscissa = -abscissa[::-1]
xml_beam.Data_Ordinate = ordinate[::-1]
else:
xml_beam.Data_Abscissa = abscissa
xml_beam.Data_Ordinate = ordinate
xml_beam.MeasurementDetails_StartPosition_x = start[0]
xml_beam.MeasurementDetails_StopPosition_x = stop[0]
xml_beam.MeasurementDetails_StartPosition_y = start[1]
xml_beam.MeasurementDetails_StopPosition_y = stop[1]
xml_beam.MeasurementDetails_StartPosition_z = start[2]
xml_beam.MeasurementDetails_StopPosition_z = stop[2]
xml_beam.filename = self.filename
xml_beam.scan_type = xml_beam.get_scan_type()
#xml_beam.set_label()
return xml_beam
