def test_e_BuildMSM(self):
Assignments = io.loadh("Data/Assignments.h5", 'arr_0')
BuildMSM.run(Lagtime, Assignments, Symmetrize="MLE")
# Test mapping
m = np.loadtxt("Data/Mapping.dat")
r_m = np.loadtxt(ReferenceDir + "/Data/Mapping.dat")
npt.assert_array_almost_equal(m, r_m, err_msg="Mapping.dat incorrect")
# Test populations
p = np.loadtxt("Data/Populations.dat")
r_p = np.loadtxt(ReferenceDir + "/Data/Populations.dat")
npt.assert_array_almost_equal(p,
r_p,
err_msg="Populations.dat incorrect")
# Test counts matrix
C = scipy.io.mmread("Data/tCounts.mtx")
r_C = scipy.io.mmread(ReferenceDir + "/Data/tCounts.mtx")
D = (C - r_C).data
Z = 0. * D
D /= r_C.sum(
) #KAB 4-5-2012. We want the normalized counts to agree at 7 decimals
#normalizing makes this test no longer depend on an arbitrary scaling factor (the total number of counts)
#the relative number of counts in the current and reference models DOES matter, however.
npt.assert_array_almost_equal(D, Z, err_msg="tCounts.mtx incorrect")
# Test transition matrix
T = scipy.io.mmread("Data/tProb.mtx")
r_T = scipy.io.mmread(ReferenceDir + "/Data/tProb.mtx")
D = (T - r_T).data
Z = 0. * D
npt.assert_array_almost_equal(D, Z, err_msg="tProb.mtx incorrect")
def test_e_BuildMSM(self):
Assignments = io.loadh("Data/Assignments.h5", 'arr_0')
BuildMSM.run(Lagtime, Assignments, Symmetrize="MLE")
# Test mapping
m = np.loadtxt("Data/Mapping.dat")
r_m = np.loadtxt(ReferenceDir +"/Data/Mapping.dat")
npt.assert_array_almost_equal(m, r_m, err_msg="Mapping.dat incorrect")
# Test populations
p = np.loadtxt("Data/Populations.dat")
r_p = np.loadtxt(ReferenceDir +"/Data/Populations.dat")
npt.assert_array_almost_equal(p, r_p, err_msg="Populations.dat incorrect")
# Test counts matrix
C = scipy.io.mmread("Data/tCounts.mtx")
r_C = scipy.io.mmread(ReferenceDir +"/Data/tCounts.mtx")
D=(C-r_C).data
Z=0.*D
D /= r_C.sum()#KAB 4-5-2012. We want the normalized counts to agree at 7 decimals
#normalizing makes this test no longer depend on an arbitrary scaling factor (the total number of counts)
#the relative number of counts in the current and reference models DOES matter, however.
npt.assert_array_almost_equal(D,Z, err_msg="tCounts.mtx incorrect")
# Test transition matrix
T = scipy.io.mmread("Data/tProb.mtx")
r_T = scipy.io.mmread(ReferenceDir +"/Data/tProb.mtx")
D=(T-r_T).data
Z=0.*D
npt.assert_array_almost_equal(D,Z, err_msg="tProb.mtx incorrect")
def test(self):
BuildMSM.run(lagtime=1, assignments=get('Assignments.h5')['arr_0'], symmetrize='MLE',
out_dir=self.td)
eq(load(pjoin(self.td, 'tProb.mtx')), get('tProb.mtx'), decimal=5)
eq(load(pjoin(self.td, 'tCounts.mtx')), get('tCounts.mtx'), decimal=3)
eq(load(pjoin(self.td, 'Mapping.dat')), get('Mapping.dat'))
eq(load(pjoin(self.td, 'Assignments.Fixed.h5')), get('Assignments.Fixed.h5'))
eq(load(pjoin(self.td, 'Populations.dat')), get('Populations.dat'))
def test(self):
BuildMSM.run(lagtime=1, assignments=get('Assignments.h5')['arr_0'], symmetrize='MLE',
out_dir=self.td)
eq(load(pjoin(self.td, 'tProb.mtx')), get('tProb.mtx'), decimal=5)
eq(load(pjoin(self.td, 'tCounts.mtx')), get('tCounts.mtx'), decimal=3)
eq(load(pjoin(self.td, 'Mapping.dat')), get('Mapping.dat'))
eq(load(pjoin(self.td, 'Assignments.Fixed.h5')), get('Assignments.Fixed.h5'))
eq(load(pjoin(self.td, 'Populations.dat')), get('Populations.dat'))
def test(self):
BuildMSM.run(LagTime=1, assignments=get('Assignments.h5')['arr_0'], Symmetrize='MLE',
OutDir=self.td)
eq(load(pjoin(self.td, 'tProb.mtx')), get('tProb.mtx'))
eq(load(pjoin(self.td, 'tCounts.mtx')), get('tCounts.mtx'))
eq(load(pjoin(self.td, 'Mapping.dat')), get('Mapping.dat'))
eq(load(pjoin(self.td, 'Assignments.Fixed.h5')), get('Assignments.Fixed.h5'))
eq(load(pjoin(self.td, 'Populations.dat')), get('Populations.dat'))
