def setUp(self):
self.coefs = np.zeros((12))
self.coefs[0] = 1.
self.coefs[1] = 1.
self.coefs[4] = 2.07
self.coefs[7] = 3
self.coefs[8] = 100
self.coefs[9] = 3.47
self.coefs[11] = 2
self.layer_format = reflect.coefs_to_layer(self.coefs)
theoretical = np.loadtxt(os.path.join(CURDIR, 'theoretical.txt'))
qvals, rvals = np.hsplit(theoretical, 2)
self.qvals = qvals.flatten()
self.rvals = rvals.flatten()
def setUp(self):
self.coefs = np.zeros(12)
self.coefs[0] = 1.
self.coefs[1] = 1.
self.coefs[4] = 2.07
self.coefs[7] = 3
self.coefs[8] = 100
self.coefs[9] = 3.47
self.coefs[11] = 2
self.layer_format = reflect.coefs_to_layer(self.coefs)
theoretical = np.loadtxt(os.path.join(path, 'theoretical.txt'))
qvals, rvals = np.hsplit(theoretical, 2)
self.qvals = qvals.flatten()
self.rvals = rvals.flatten()
# e361 is an older dataset, but well characterised
self.coefs361 = np.zeros(16)
self.coefs361[0] = 2
self.coefs361[1] = 1.
self.coefs361[2] = 2.07
self.coefs361[4] = 6.36
self.coefs361[6] = 2e-5
self.coefs361[7] = 3
self.coefs361[8] = 10
self.coefs361[9] = 3.47
self.coefs361[11] = 4
self.coefs361[12] = 200
self.coefs361[13] = 1
self.coefs361[15] = 3
lowlim = np.zeros(16)
lowlim[1] = 0.1
lowlim[4] = 6.2
hilim = 2 * self.coefs361
bounds = list(zip(lowlim, hilim))
e361 = np.loadtxt(os.path.join(path, 'e361r.txt'))
self.qvals361, self.rvals361, self.evals361 = np.hsplit(e361, 3)
np.seterr(invalid='raise')
self.params361 = curvefitter.to_parameters(self.coefs361,
bounds=bounds,
varies=[False] * 16)
fit = [1, 4, 6, 8, 12, 13]
for p in fit:
self.params361['p%d' % p].vary = True
