def main():
"""
Main function
"""
# make marker coordinates dictionary
markers = {
'markers_1' : (coordinates_file, marker_initial_rows),
'markers_2' : (coordinates_file, marker_final_rows)
}
# establish correlation
corr = Basic()
corr.readPositions(
points=markers, format_=file_format, comments=comments,
skiprows=skiprows, delimiter=delimiter, columns=columns,
indexing=indexing)
corr.establish(type_=transform_type)
# corrtale target coordinates
targets = {
'targets_1' : (coordinates_file, target_initial_rows),
'targets_2' : (coordinates_file, target_final_rows)
}
corr.readPositions(
points=targets, format_=file_format, comments=comments,
skiprows=skiprows, delimiter=delimiter, columns=columns,
indexing=indexing)
corr.correlate()
# write results
write_results(corr=corr, res_file_name=results_file)
def main():
"""
Main function
"""
# establish correlation
corr = Basic()
corr.establish(
markers_1=markers_initial, markers_2=markers_final,
type_=transform_type)
# print transformation parameters
print "Transformation parameters: "
print "Rotation angle: ", corr.transf_1_to_2.phiDeg
print "Scales: ", corr.transf_1_to_2.scale
print "Parity: ", corr.transf_1_to_2.parity
print "Shear: ", corr.transf_1_to_2.shear
print "Translation: ", corr.transf_1_to_2.d
print "RME error: ", corr.transf_1_to_2.rmsError
print "Individual marker errors: "
print corr.transf_1_to_2.error
corr.correlate(targets_1=target_initial)
print " "
print "System 1 targets correlated to system 2: "
print corr.correlated_1_to_2
corr.correlate(targets_2=target_final)
print " "
print "System 2 targets correlated to system 1: "
print corr.correlated_2_to_1
def testReadPositions(self):
"""
Tests readPositions()
"""
corr = Basic()
# indexing 1 (default)
points = {'markers': (self.imagej_file, [3, 4, 5]),
'targets': (self.imagej_file, [6, 7, 8])}
corr.readPositions(points=points, format_='imagej', columns=[8,9])
desired_markers = numpy.array([[120, 114.667],
[103.333, 108],
[126, 130.667]])
np_test.assert_almost_equal(corr.markers, desired_markers)
desired_targets = numpy.array([[221.333, 72.667],
[121.333, 172.667],
[106.667, 278.667]])
np_test.assert_almost_equal(corr.targets, desired_targets)
# indexing 0 (old way)
points = {'markers': (self.imagej_file, [2, 3, 4]),
'targets': (self.imagej_file, [5, 6, 7])}
corr.readPositions(points=points, format_='imagej', columns=[7, 8],
indexing=0)
np_test.assert_almost_equal(corr.markers, desired_markers)
np_test.assert_almost_equal(corr.targets, desired_targets)
def testGetTransformation(self):
"""
Tasts getTransformation()
"""
corr = Basic()
corr.markers_1 = self.x1
corr.markers_2 = self.y1
corr.establish()
np_test.assert_almost_equal(corr.getTransformation(from_=1, to=2).gl,
corr.transf_1_to_2.gl)
np_test.assert_almost_equal(corr.getTransformation(from_=2, to=1).gl,
corr.transf_2_to_1.gl)
def testDecompose(self):
"""
Tests decompose()
"""
corr = Basic()
corr.establish(markers_1=self.x1, markers_2=self.y1)
corr.decompose(order='qpsm')
np_test.assert_almost_equal(corr.transf_1_to_2.phi, numpy.arctan(0.5))
np_test.assert_almost_equal(corr.getTransformation(from_=1, to=2).scale,
[numpy.sqrt(5)] * 2)
np_test.assert_almost_equal(corr.getTransformation(from_=2, to=1).phi,
-numpy.arctan(0.5))
np_test.assert_almost_equal(corr.transf_2_to_1.scale,
[1/numpy.sqrt(5)] * 2)
def testCorrelate(self):
"""
Tests correlate
"""
# establish correlation
corr = Basic()
corr.establish(markers_1=self.x1, markers_2=self.y1)
# test mixed set and passed targets
corr.targets_2 = self.y1[0:2]
corr.correlate(targets_1=self.x1[2:4])
np_test.assert_almost_equal(corr.correlated_1_to_2, self.y1[2:4])
np_test.assert_almost_equal(corr.getTargets(from_=2, to=1),
self.x1[0:2])
# test mixed set and passed targets, set need to be overwritten
corr.targets_1 = self.x1[2:4] * 3.1 # bad
corr.targets_2 = self.y1[0:2]
corr.correlate(targets_1=self.x1[2:4])
np_test.assert_almost_equal(corr.correlated_1_to_2, self.y1[2:4])
np_test.assert_almost_equal(corr.getTargets(from_=2, to=1),
self.x1[0:2])
def testEstablish(self):
"""
Tests establish()
"""
# markers read from a file
corr = Basic()
points = {'markers_1': (self.imagej_file, [3, 4, 5]),
'markers_2': (self.imagej_file, [6, 7, 8])}
corr.establish(points=points, format_='imagej', columns=[8,9])
desired_markers = numpy.array([[120, 114.667],
[103.333, 108],
[126, 130.667]])
np_test.assert_almost_equal(corr.markers_1, desired_markers)
desired_markers = numpy.array([[221.333, 72.667],
[121.333, 172.667],
[106.667, 278.667]])
np_test.assert_almost_equal(corr.markers_2, desired_markers)
# markers given in arguments
corr = Basic()
corr.establish(markers_1=self.x1, markers_2=self.y1)
np_test.assert_equal(isinstance(corr.getTransformation(from_=1, to=2),
Affine2D), True)
np_test.assert_almost_equal(corr.markers_1, self.x1)
np_test.assert_almost_equal(corr.markers_2, self.y1)
np_test.assert_almost_equal(corr.getTransformation(from_=1, to=2).gl,
self.af1_gl)
np_test.assert_almost_equal(corr.getTransformation(from_=1, to=2).d,
self.af1_d)
# markers set
corr = Basic()
corr.markers_1 = self.x1
corr.markers_2 = self.y1
corr.establish()
np_test.assert_almost_equal(corr.markers_1, self.x1)
np_test.assert_almost_equal(corr.markers_2, self.y1)
transf_1_to_2 = corr.getTransformation(from_=1, to=2)
np_test.assert_almost_equal(corr.getTransformation(from_=1, to=2).gl,
self.af1_gl)
np_test.assert_almost_equal(corr.getTransformation(from_=1, to=2).d,
self.af1_d)
transf_2_to_1 = corr.getTransformation(from_=2, to=1)
np_test.assert_almost_equal(transf_2_to_1.gl,
transf_1_to_2.inverse().gl)
np_test.assert_almost_equal(transf_2_to_1.d,
transf_1_to_2.inverse().d)
# markers mixed
corr = Basic()
corr.markers_1 = self.x1 *2.1 # set wrong to check if overwritten
corr.markers_2 = self.y1
corr.establish(markers_1=self.x1)
np_test.assert_almost_equal(corr.getTransformation(from_=1, to=2).gl,
self.af1_gl)
np_test.assert_almost_equal(corr.getTransformation(from_=1, to=2).d,
self.af1_d)