def set_sample(domain):
"""
Creating a sample by combining multiple independent domains.
"""
# Then we create a sample based on the domain
# Distribution parameters
N = 102 # number of particules (average)
# xMin = np.log(4)/np.log(10)
# xMax = np.log(30) / np.log(10)
xMin = 4
xMax = 25
nx = 5 # number of domain
# Arithmetic parameters
mu = 8.29 # mean particule diameter
sigma = (2.99) ** 2 # 3nm standard deviation
# LogNormal distribution
# D = np.logspace(xMin, xMax, nx)
D = np.linspace(xMin, xMax, nx)
# D = np.linspace(-np.pi/2, np.pi/2, nx) # flat distribution
X, mu_log, sigma_log = StoneX.lognormale(D, mu, sigma)
# X = np.zeros(D.size) + 1/N
# Information about the distribution
text = """Probability distribution : X
Normale : mu = {}, sigma = {}
Log-normale : m = {}, s = {}
Effective Mean diameter: mean(X) = {}
Number of particules : N = {}
""".format(
mu, sigma, mu_log, sigma_log, np.sum(D * X) / np.sum(X), np.sum(X * N)
)
StoneX.logger.info(text)
# Creating the sample
Density = N * X
sample = StoneX.create_sample(domain, Density)
# Change the domains' parameters according to the distribution
# Domains data
# diameter (nm), Density, V_f(m**3), V_af(m**3), Surface(m**2), Mag. Moment (A/m)
domains_data = np.zeros((D.size, 6))
domains_data[:, 0] = D # Diameter (nm)
R = D / 2 * 1e-9 # Radius (m)
domains_data[:, 1] = Density
domains_data[:, 2] = 4 / 3 * np.pi * R ** 3 # V_f
domains_data[:, 3] = 4 / 3 * np.pi * ((domain.t_af + R) ** 3 - R ** 3) # V_af
domains_data[:, 4] = 4 * np.pi * R ** 2 # S
domains_data[:, 5] = sample.domains[0].Ms # mu_s
for i, d in enumerate(D):
StoneX.logger.debug("Diameter {} : {}nm".format(i, np.round(d, 2)))
sample.domains[i].V_f = domains_data[i, 2]
sample.domains[i].V_af = domains_data[i, 3]
sample.domains[i].S = domains_data[i, 4]
# logger.info(sample.domains[i])
# Backing up the domain's values
np.savetxt(
"{}/domains_data.dat".format(sample.name),
domains_data,
header="diameter (nm), Density, V_f(m**3), V_af(m**3), Surface(m**2), Mag. (A/m)",
)
return sample
nx = 20
mu = 2
sigma = 10
# LogNormal distribution
R = np.logspace(xMin, xMax, nx)
X, m, s = StoneX.lognormale(R, mu, sigma)
logger.info("""Distribution de probabilité
Normale : mu = {}, sigma = {}
Log-normale : m = {}, s = {}""".format(mu, sigma, m, s))
# Creating the sample
Density = N * X
sample = StoneX.create_sample(domain, Density)
if True:
pl.plot(R, np.around(X * N), '-ro')
pl.savefig('{}/distrib.pdf'.format(domain.name), dpi=100)
for i, radius in enumerate(R):
print(i, radius)
sample.domains[i].V_f = 4/3 * np.pi * (radius * 1e-9)**3
sample.domains[i].S = 4 * np.pi * (radius * 1e-9)**2
sample.domains[i].V_af = 4/3 * np.pi * ( ((radius+d_af) * 1e-9)**3 - (radius * 1e-9)**3 )
################################################################################
# MEASUREMENTS
domain.alpha = (2, 'deg')
domain.S = (200e-9)**2
domain.V_f = 10e-9 * domain.S
domain.V_af = 80e-9 * domain.S
domain.K_f = 400
domain.K_af = 110
domain.J_ex = 1e-6
domain.Ms = 400 * 1e-6 * 1e-3 / (1e-4 * 10 * 1e-9)
#sample.M_af = 1/1000 * sample.Ms * sample.V_af / sample.V_f
print(domain.V_af)
# Then we create a sample based on the domain
n = 50
sample = StoneX.create_sample(domain, n)
for i, t in enumerate(np.random.normal(50, 20, n)):
logger.info("N: {}, thickness t = {}nm".format(i, t))
sample.domains[i].V_af = domain.S * t *1e-9
################################################################################
# MEASUREMENTS
################################################################################
# We can measure the sample or one domain only
vsm.load(sample)
vsm.measure()
