def test_contruct_c_matrix_7d_with_xi():
"""Test if 7d second derivative matrix with x=0.1 is constructed correctly"""
c_matrix = np.array([[-0.9, 1, 0, 0, 0, 0, 0], [1, -1.9, 1, 0, 0, 0, 0],
[0, 1, -1.9, 1, 0, 0, 0], [0, 0, 1, -1.9, 1, 0, 0],
[0, 0, 0, 1, -1.9, 1, 0], [0, 0, 0, 0, 1, -1.9, 1],
[0, 0, 0, 0, 0, 1, -0.9]])
assert np.array_equal(c_matrix, helpers.calc_second_deriv_matrix(7, 0.1))
def __init__(self, x_ini, b, A, cov):
"""
x_ini: numpy histogram with the initial Monte Carlo distribution
used to build the response matrix
b: numpy histogram with the measured data distribution that we want to unfold
A: 2d numpy array with the response matrix (n_b x n_x)
cov: 2d numpy array with the covariance matrix
"""
self._b_measured = b
self._x_ini = x_ini
self._response_matrix = A
self._covariance_matrix = cov
self._X_inv = None
self._S = None
self._d = None
n_bins_b = len(self._b_measured[0])
n_bins_x = len(self._x_ini[0])
self._C = helpers.calc_second_deriv_matrix(n_bins_x, 0.01)
self._C_inv = helpers.calc_inverse_second_deriv_matrix(self._C)
assert(self._response_matrix.shape[0] == n_bins_b),\
"Wrong dimensions: bins in b != rows in response matrix"
assert(self._response_matrix.shape[1] == n_bins_x),\
"Wrong dimensions: bins in x_ini != columns in response matrix"
assert helpers.check_symmetric(self._covariance_matrix), \
"Covariance matrix is not symmetric"
assert(n_bins_b > 1 and n_bins_x > 1),\
"Bins should be at least 2"
def test_contruct_c_matrix_7d():
"""Test if 7d second derivative matrix is constructed correctly"""
c_matrix = np.array([[-1, 1, 0, 0, 0, 0, 0], [1, -2, 1, 0, 0, 0, 0],
[0, 1, -2, 1, 0, 0, 0], [0, 0, 1, -2, 1, 0, 0],
[0, 0, 0, 1, -2, 1, 0], [0, 0, 0, 0, 1, -2, 1],
[0, 0, 0, 0, 0, 1, -1]])
assert np.array_equal(c_matrix, helpers.calc_second_deriv_matrix(7, 0))