def test_init_from_file(self):
fisher_1 = fd.fisher_derived(file_name=test_input +
'/dummy_fisher_matrix_derived.dat')
assert fisher_1.path == test_input + '/dummy_fisher_matrix_derived.dat'
assert fisher_1.name == 'dummy_fisher_matrix_derived'
assert fisher_1.indir == test_input
assert fisher_1.num_params == 28
assert fisher_1.num_derived == 9
assert fisher_1.param_names == [
'omegabh2', 'omegach2', 'omeganuh2', 'h', 'yhe', 'logA', 'ns',
'nrun', 'nt', 'r', 'tau', 'Bias_W_1', 'Bias_W_2', 'Bias_W_3',
'Bias_W_4', 'Bias_W_5', 'Bias_W_6', 'Bias_W_7', 'Bias_W_8',
'Bias_W_9', 'Bias_W_10', 'Bias_W_11', 'Bias_W_12', 'Bias_W_13',
'Bias_W_14', 'alpha_SN', 'beta_SN', 'M0_SN'
]
assert fisher_1.param_names_latex == [
'\\Omega_b h^2', '\\Omega_c h^2', '\\Omega_\\nu h^2', 'h',
'Y_{He}', '{\\rm{ln}}(10^{10} A_s)', 'n_s', 'n_{\\rm run}', 'n_t',
'r', '\\tau', 'b_1', 'b_2', 'b_3', 'b_4', 'b_5', 'b_6', 'b_7',
'b_8', 'b_9', 'b_10', 'b_11', 'b_12', 'b_13', 'b_14',
'\\alpha_{\\rm SN}', '\\beta_{\\rm SN}', 'M_0^{\\rm SN}'
]
assert fisher_1.derived_param_names == [
'omegab', 'omegac', 'omeganu', 'omegav', 'omegak', 'omegam',
'theta', 'mnu', 'z_re'
]
assert fisher_1.derived_param_names_latex == [
'\\Omega_b', '\\Omega_c', '\\Omega_{\\nu}', '\\Omega_{\\Lambda}',
'\\Omega_{K}', '\\Omega_{m}', '100\\theta_{MC}', '\\Sigma m_\\nu',
'z_{\\rm re}'
]
def setup(self):
# generate the Fisher matrix. In this case a simple diagonal matrix.
matrix = np.identity(3)
for i in xrange(3):
matrix[i, i] = i + 1
# Jacobian matrix:
matrix_derived = np.zeros((3, 2))
matrix_derived[0, 0] = 1.0
matrix_derived[1, 1] = 1.0
matrix_derived[2, 0] = 1.0
# parameter names:
param_names_latex = ['m' + str(i) for i in xrange(3)]
derived_param_names_latex = ['md' + str(i) for i in xrange(2)]
# fiducial:
fiducial = [float(i) for i in xrange(3)]
fiducial_derived = [float(i) for i in xrange(2)]
# initialize the Fisher type:
self.fisher_1 = fm.fisher_matrix(fisher_matrix=matrix,
param_names_latex=param_names_latex,
fiducial=fiducial)
# initialize the derived matrix:
self.fisher_der = fd.fisher_derived(
derived_matrix=matrix_derived,
param_names_latex=param_names_latex,
derived_param_names_latex=derived_param_names_latex,
fiducial=fiducial,
fiducial_derived=fiducial_derived)
def test_init_from_python_only_fisher_derived(self):
matrix_derived = np.zeros( (3, 2) )
matrix_derived[0,0] = 1.0
matrix_derived[1,1] = 1.0
matrix_derived[2,0] = 1.0
# parameter names:
param_names_latex = [ 'm'+str(i) for i in xrange(3) ]
derived_param_names_latex = [ 'md'+str(i) for i in xrange(2) ]
# fiducial:
fiducial = [ float(i) for i in xrange(3) ]
fiducial_derived = [ float(i) for i in xrange(2) ]
# initialize the derived matrix:
fisher_1 = fd.fisher_derived( derived_matrix=matrix_derived )
# after calling init all the objects of the class have to be initialized properly:
assert np.allclose( fisher_1.derived_matrix, matrix_derived )
assert fisher_1.path == ''
assert fisher_1.name == ''
assert fisher_1.indir == ''
assert fisher_1.num_params == 3
assert fisher_1.num_derived == 2
assert fisher_1.param_names == ['p1','p2','p3']
assert fisher_1.param_names_latex == fisher_1.param_names
assert np.allclose( fisher_1.param_fiducial, [0.0,0.0,0.0] )
assert fisher_1.derived_param_names == ['p4','p5']
assert fisher_1.derived_param_names_latex == fisher_1.derived_param_names
assert np.allclose( fisher_1.derived_param_fiducial, [0.0,0.0] )
def test_init_from_python_only_fisher_derived(self):
matrix_derived = np.zeros((3, 2))
matrix_derived[0, 0] = 1.0
matrix_derived[1, 1] = 1.0
matrix_derived[2, 0] = 1.0
# parameter names:
param_names_latex = ['m' + str(i) for i in xrange(3)]
derived_param_names_latex = ['md' + str(i) for i in xrange(2)]
# fiducial:
fiducial = [float(i) for i in xrange(3)]
fiducial_derived = [float(i) for i in xrange(2)]
# initialize the derived matrix:
fisher_1 = fd.fisher_derived(derived_matrix=matrix_derived)
# after calling init all the objects of the class have to be initialized properly:
assert np.allclose(fisher_1.derived_matrix, matrix_derived)
assert fisher_1.path == ''
assert fisher_1.name == ''
assert fisher_1.indir == ''
assert fisher_1.num_params == 3
assert fisher_1.num_derived == 2
assert fisher_1.param_names == ['p1', 'p2', 'p3']
assert fisher_1.param_names_latex == fisher_1.param_names
assert np.allclose(fisher_1.param_fiducial, [0.0, 0.0, 0.0])
assert fisher_1.derived_param_names == ['p4', 'p5']
assert fisher_1.derived_param_names_latex == fisher_1.derived_param_names
assert np.allclose(fisher_1.derived_param_fiducial, [0.0, 0.0])
def test_realistic_add(self):
# get the original real Fisher:
fisher = fm.fisher_matrix( file_name=test_input+'/dummy_fisher_matrix.dat' )
# get the derived Fisher:
fisher_1 = fd.fisher_derived( file_name=test_input+'/dummy_fisher_matrix_derived.dat' )
# add them:
new_fisher = fisher_1.add_derived( fisher_matrix=fisher, preserve_input=True )
# check wether the initial spectrum is contained in the final spectrum:
old_eigen = [ i for i in fisher.fisher_eigenvalues if i> 1.1*fisher.fisher_cutoff ] # remove the cutoff modes
assert np.amin(new_fisher.fisher_eigenvalues) < np.amin(old_eigen)
assert np.amax(new_fisher.fisher_eigenvalues) > np.amax(old_eigen)
def test_init_from_file(self):
fisher_1 = fd.fisher_derived( file_name=test_input+'/dummy_fisher_matrix_derived.dat' )
assert fisher_1.path == test_input+'/dummy_fisher_matrix_derived.dat'
assert fisher_1.name == 'dummy_fisher_matrix_derived'
assert fisher_1.indir == test_input
assert fisher_1.num_params == 28
assert fisher_1.num_derived == 9
assert fisher_1.param_names == ['omegabh2', 'omegach2', 'omeganuh2', 'h', 'yhe', 'logA', 'ns', 'nrun', 'nt', 'r', 'tau', 'Bias_W_1', 'Bias_W_2', 'Bias_W_3', 'Bias_W_4', 'Bias_W_5', 'Bias_W_6', 'Bias_W_7', 'Bias_W_8', 'Bias_W_9', 'Bias_W_10', 'Bias_W_11', 'Bias_W_12', 'Bias_W_13', 'Bias_W_14', 'alpha_SN', 'beta_SN', 'M0_SN']
assert fisher_1.param_names_latex == ['\\Omega_b h^2', '\\Omega_c h^2', '\\Omega_\\nu h^2', 'h', 'Y_{He}', '{\\rm{ln}}(10^{10} A_s)', 'n_s', 'n_{\\rm run}', 'n_t', 'r', '\\tau', 'b_1', 'b_2', 'b_3', 'b_4', 'b_5', 'b_6', 'b_7', 'b_8', 'b_9', 'b_10', 'b_11', 'b_12', 'b_13', 'b_14', '\\alpha_{\\rm SN}', '\\beta_{\\rm SN}', 'M_0^{\\rm SN}']
assert fisher_1.derived_param_names == ['omegab', 'omegac', 'omeganu', 'omegav', 'omegak', 'omegam', 'theta', 'mnu', 'z_re']
assert fisher_1.derived_param_names_latex == ['\\Omega_b', '\\Omega_c', '\\Omega_{\\nu}', '\\Omega_{\\Lambda}', '\\Omega_{K}', '\\Omega_{m}', '100\\theta_{MC}', '\\Sigma m_\\nu', 'z_{\\rm re}']
def test_realistic_add(self):
# get the original real Fisher:
fisher = fm.fisher_matrix( file_name=test_input+'/dummy_fisher_matrix.dat' )
# get the derived Fisher:
fisher_1 = fd.fisher_derived( file_name=test_input+'/dummy_fisher_matrix_derived.dat' )
# add them:
new_fisher = fisher_1.add_derived( fisher_matrix=fisher, preserve_input=True )
# check wether the initial spectrum is contained in the final spectrum:
old_eigen = [ i for i in fisher.fisher_eigenvalues if i> 1.1*fisher.fisher_cutoff ] # remove the cutoff modes
assert np.amin(new_fisher.fisher_eigenvalues) < np.amin(old_eigen)
assert np.amax(new_fisher.fisher_eigenvalues) > np.amax(old_eigen)
def test_load_paramnames_invalid_num(self):
matrix_derived = np.zeros( (3, 3) )
matrix_derived[0,0] = 1.0
matrix_derived[1,1] = 1.0
matrix_derived[2,0] = 1.0
param_names = [ 'q'+str(i) for i in xrange(3) ]
param_names_latex = [ 'm'+str(i) for i in xrange(3) ]
derived_param_names = [ 'qd'+str(i) for i in xrange(4) ]
derived_param_names_latex = [ 'md'+str(i) for i in xrange(4) ]
fiducial = [ float(i) for i in xrange(3) ]
fiducial_derived = [ float(i) for i in xrange(4) ]
fisher_1 = fd.fisher_derived( derived_matrix=matrix_derived )
assert_raises( ValueError, fisher_1.load_paramnames_from_file, file_name=test_input+'/dummy_paramnames_5_derived.paramnames' )
def test_load_paramnames_invalid_num(self):
matrix_derived = np.zeros( (3, 3) )
matrix_derived[0,0] = 1.0
matrix_derived[1,1] = 1.0
matrix_derived[2,0] = 1.0
param_names = [ 'q'+str(i) for i in range(3) ]
param_names_latex = [ 'm'+str(i) for i in range(3) ]
derived_param_names = [ 'qd'+str(i) for i in range(4) ]
derived_param_names_latex = [ 'md'+str(i) for i in range(4) ]
fiducial = [ float(i) for i in range(3) ]
fiducial_derived = [ float(i) for i in range(4) ]
fisher_1 = fd.fisher_derived( derived_matrix=matrix_derived )
assert_raises( ValueError, fisher_1.load_paramnames_from_file, file_name=test_input+'/dummy_paramnames_5_derived.paramnames' )
def test_init_1D_fisher_derived(self):
matrix = [10]
fisher_1 = fd.fisher_derived( derived_matrix=matrix )
assert np.allclose( fisher_1.derived_matrix, matrix )
assert fisher_1.path == ''
assert fisher_1.name == ''
assert fisher_1.indir == ''
assert fisher_1.num_params == 1
assert fisher_1.num_derived == 1
assert fisher_1.param_names == ['p1']
assert fisher_1.param_names_latex == fisher_1.param_names
assert np.allclose( fisher_1.param_fiducial, [0.0] )
assert fisher_1.derived_param_names == ['p2']
assert fisher_1.derived_param_names_latex == fisher_1.derived_param_names
assert np.allclose( fisher_1.derived_param_fiducial, [0.0] )
def test_init_1D_fisher_derived(self):
matrix = [10]
fisher_1 = fd.fisher_derived(derived_matrix=matrix)
assert np.allclose(fisher_1.derived_matrix, matrix)
assert fisher_1.path == ''
assert fisher_1.name == ''
assert fisher_1.indir == ''
assert fisher_1.num_params == 1
assert fisher_1.num_derived == 1
assert fisher_1.param_names == ['p1']
assert fisher_1.param_names_latex == fisher_1.param_names
assert np.allclose(fisher_1.param_fiducial, [0.0])
assert fisher_1.derived_param_names == ['p2']
assert fisher_1.derived_param_names_latex == fisher_1.derived_param_names
assert np.allclose(fisher_1.derived_param_fiducial, [0.0])
def test_spectral_change(self):
# redefine the Jacobian matrix:
matrix_derived = np.zeros( (3, 2) )
matrix_derived[0,0] = 1.e10
matrix_derived[2,0] = 1.e-10
# parameter names:
param_names_latex = [ 'm'+str(i) for i in xrange(3) ]
derived_param_names_latex = [ 'md'+str(i) for i in xrange(2) ]
# fiducial:
fiducial = [ float(i) for i in xrange(3) ]
fiducial_derived = [ float(i) for i in xrange(2) ]
# initialize the derived matrix:
fisher_der = fd.fisher_derived( derived_matrix=matrix_derived,
param_names_latex=param_names_latex,
derived_param_names_latex=derived_param_names_latex,
fiducial=fiducial, fiducial_derived=fiducial_derived)
fisher_2 = fisher_der.add_derived( fisher_matrix=self.fisher_1, preserve_input=True )
def test_spectral_change(self):
# redefine the Jacobian matrix:
matrix_derived = np.zeros( (3, 2) )
matrix_derived[0,0] = 1.e10
matrix_derived[2,0] = 1.e-10
# parameter names:
param_names_latex = [ 'm'+str(i) for i in range(3) ]
derived_param_names_latex = [ 'md'+str(i) for i in range(2) ]
# fiducial:
fiducial = [ float(i) for i in range(3) ]
fiducial_derived = [ float(i) for i in range(2) ]
# initialize the derived matrix:
fisher_der = fd.fisher_derived( derived_matrix=matrix_derived,
param_names_latex=param_names_latex,
derived_param_names_latex=derived_param_names_latex,
fiducial=fiducial, fiducial_derived=fiducial_derived)
fisher_2 = fisher_der.add_derived( fisher_matrix=self.fisher_1, preserve_input=True )
def test_load_paramnames_from_file_5(self):
matrix_derived = np.zeros( (3, 4) )
matrix_derived[0,0] = 1.0
matrix_derived[1,1] = 1.0
matrix_derived[2,0] = 1.0
param_names = [ 'q'+str(i) for i in xrange(3) ]
param_names_latex = [ 'm'+str(i) for i in xrange(3) ]
derived_param_names = [ 'qd'+str(i) for i in xrange(4) ]
derived_param_names_latex = [ 'md'+str(i) for i in xrange(4) ]
fiducial = [ float(i) for i in xrange(3) ]
fiducial_derived = [ float(i) for i in xrange(4) ]
fisher_1 = fd.fisher_derived( derived_matrix=matrix_derived )
fisher_1.load_paramnames_from_file( file_name=test_input+'/dummy_paramnames_5_derived.paramnames')
# test if everything is properly initialized:
assert fisher_1.param_names == ['p1', 'p2', 'p3']
assert fisher_1.param_names_latex == ['p_1', 'p_2', 'p_3']
assert np.allclose( fisher_1.param_fiducial, [1.0, 2.0, 3.0] )
assert fisher_1.derived_param_names == ['p4', 'p5', 'p6', 'p7']
assert fisher_1.derived_param_names_latex == ['p_4', 'p_5', 'p_6', 'p7']
assert np.allclose( fisher_1.derived_param_fiducial, [4.0, 5.0,0.0,0.0] )
def test_load_paramnames_from_file_5(self):
matrix_derived = np.zeros( (3, 4) )
matrix_derived[0,0] = 1.0
matrix_derived[1,1] = 1.0
matrix_derived[2,0] = 1.0
param_names = [ 'q'+str(i) for i in range(3) ]
param_names_latex = [ 'm'+str(i) for i in range(3) ]
derived_param_names = [ 'qd'+str(i) for i in range(4) ]
derived_param_names_latex = [ 'md'+str(i) for i in range(4) ]
fiducial = [ float(i) for i in range(3) ]
fiducial_derived = [ float(i) for i in range(4) ]
fisher_1 = fd.fisher_derived( derived_matrix=matrix_derived )
fisher_1.load_paramnames_from_file( file_name=test_input+'/dummy_paramnames_5_derived.paramnames')
# test if everything is properly initialized:
assert fisher_1.param_names == ['p1', 'p2', 'p3']
assert fisher_1.param_names_latex == ['p_1', 'p_2', 'p_3']
assert np.allclose( fisher_1.param_fiducial, [1.0, 2.0, 3.0] )
assert fisher_1.derived_param_names == ['p4', 'p5', 'p6', 'p7']
assert fisher_1.derived_param_names_latex == ['p_4', 'p_5', 'p_6', 'p7']
assert np.allclose( fisher_1.derived_param_fiducial, [4.0, 5.0,0.0,0.0] )
def test_init_from_python_invalid_fiducial_derived(self):
matrix_derived = np.zeros( (3, 2) )
matrix_derived[0,0] = 1.0
matrix_derived[1,1] = 1.0
matrix_derived[2,0] = 1.0
# parameter names:
param_names = [ 'q'+str(i) for i in range(3) ]
param_names_latex = [ 'm'+str(i) for i in range(3) ]
derived_param_names = [ 'qd'+str(i) for i in range(2) ]
derived_param_names_latex = [ 'md'+str(i) for i in range(2) ]
# fiducial:
fiducial = [ float(i) for i in range(3) ]
fiducial_derived = [ float(i) for i in range(3) ]
# initialize the derived matrix:
fisher_1 = fd.fisher_derived( derived_matrix=matrix_derived )
assert_raises( ValueError, fd.fisher_derived, derived_matrix=matrix_derived,
param_names = param_names,
derived_param_names = derived_param_names,
param_names_latex=param_names_latex,
derived_param_names_latex=derived_param_names_latex,
fiducial=fiducial, fiducial_derived=fiducial_derived)
def test_init_from_python_invalid_fiducial_derived(self):
matrix_derived = np.zeros( (3, 2) )
matrix_derived[0,0] = 1.0
matrix_derived[1,1] = 1.0
matrix_derived[2,0] = 1.0
# parameter names:
param_names = [ 'q'+str(i) for i in xrange(3) ]
param_names_latex = [ 'm'+str(i) for i in xrange(3) ]
derived_param_names = [ 'qd'+str(i) for i in xrange(2) ]
derived_param_names_latex = [ 'md'+str(i) for i in xrange(2) ]
# fiducial:
fiducial = [ float(i) for i in xrange(3) ]
fiducial_derived = [ float(i) for i in xrange(3) ]
# initialize the derived matrix:
fisher_1 = fd.fisher_derived( derived_matrix=matrix_derived )
assert_raises( ValueError, fd.fisher_derived, derived_matrix=matrix_derived,
param_names = param_names,
derived_param_names = derived_param_names,
param_names_latex=param_names_latex,
derived_param_names_latex=derived_param_names_latex,
fiducial=fiducial, fiducial_derived=fiducial_derived)
def setup(self):
# generate the Fisher matrix. In this case a simple diagonal matrix.
matrix = np.identity(3)
for i in xrange(3):
matrix[i,i] = i+1
# Jacobian matrix:
matrix_derived = np.zeros( (3, 2) )
matrix_derived[0,0] = 1.0
matrix_derived[1,1] = 1.0
matrix_derived[2,0] = 1.0
# parameter names:
param_names_latex = [ 'm'+str(i) for i in xrange(3) ]
derived_param_names_latex = [ 'md'+str(i) for i in xrange(2) ]
# fiducial:
fiducial = [ float(i) for i in xrange(3) ]
fiducial_derived = [ float(i) for i in xrange(2) ]
# initialize the Fisher type:
self.fisher_1 = fm.fisher_matrix( fisher_matrix=matrix, param_names_latex=param_names_latex, fiducial=fiducial )
# initialize the derived matrix:
self.fisher_der = fd.fisher_derived( derived_matrix=matrix_derived,
param_names_latex=param_names_latex,
derived_param_names_latex=derived_param_names_latex,
fiducial=fiducial, fiducial_derived=fiducial_derived)
