def test_single_moment_analytic():
import numpy as np
# create the data
relax_system()
# read the data from file
data = DataReader('relax.txt')
ts = data['time']
# compare with analytical result
mx, my, mz = single_spin(0.5, 2.21e5, 1e5, ts)
Mx = data['m_x']
My = data['m_y']
Mz = data['m_z']
# check absolute tolerance is < 1e-8
assert np.allclose(mx, Mx, rtol=0, atol=1e-8)
assert np.allclose(my, My, rtol=0, atol=1e-8)
assert np.allclose(mz, Mz, rtol=0, atol=1e-8)
# check absolute tolerance is < 1e-5 for x and y, 1e-9 for z
# (I checked the numbers just now, and this is what passes)
assert np.allclose(mx, Mx, rtol=1e-05, atol=0)
assert np.allclose(my, My, rtol=1e-05, atol=0)
assert np.allclose(mz, Mz, rtol=1e-09, atol=0)
def deal_plot():
data = DataReader('dyn.txt')
ts = data['time'] * 1e9
mx = data['m_x']
my = data['m_y']
mz = data['m_z']
data2 = np.loadtxt('oommf.txt')
ts2 = data2[:, 0] * 1e9
mx2 = data2[:, 1]
my2 = data2[:, 2]
mz2 = data2[:, 3]
plt.plot(ts, mx, '--', label='m_fidimag', dashes=(2, 2))
plt.plot(ts, my, '--', label='', dashes=(2, 2))
plt.plot(ts, mz, '--', label='', dashes=(2, 2))
plt.plot(ts2, mx2, '--', label='m_oommf')
plt.plot(ts2, my2, '--', label='')
plt.plot(ts2, mz2, '--', label='')
plt.title('std4')
plt.legend()
#plt.xlim([0, 0.012])
#plt.ylim([-5, 100])
plt.xlabel(r'Ts (ns)')
# plt.ylabel('Susceptibility')
plt.savefig('cmp.pdf')
def test_energy(do_plot=False):
mesh = CuboidMesh(nx=30)
relax_system(mesh)
if do_plot:
save_plot()
data = DataReader('test_energy.txt')
energy = data['E_total']
for i in range(len(energy) - 1):
assert energy[i] > energy[i + 1]
def deal_plot():
data = DataReader('dyn.txt')
ts = data['time'] * 1e9
mx = data['m_x']
my = data['m_y']
mz = data['m_z']
data2 = np.loadtxt('oommf.txt')
ts2 = data2[:, 0] * 1e9
mx2 = data2[:, 1]
my2 = data2[:, 2]
mz2 = data2[:, 3]
plt.plot(ts, mx, '--', label='m_fidimag', dashes=(2, 2))
plt.plot(ts, my, '--', label='', dashes=(2, 2))
plt.plot(ts, mz, '--', label='', dashes=(2, 2))
plt.plot(ts2, mx2, '--', label='m_oommf')
plt.plot(ts2, my2, '--', label='')
plt.plot(ts2, mz2, '--', label='')
plt.title('std4')
plt.legend()
#plt.xlim([0, 0.012])
#plt.ylim([-5, 100])
plt.xlabel(r'Ts (ns)')
# plt.ylabel('Susceptibility')
plt.savefig('cmp.pdf')
# compute deviation of very last point against reference solution
# from the OOMMF simulation tool
dev_mx = abs(mx[-1] - mx2[-1])
dev_my = abs(my[-1] - my2[-1])
dev_mz = abs(mz[-1] - mz2[-1])
print("Deviations are {}, {} and {} in m_x, m_y and m_z.".format(
dev_mx, dev_my, dev_mz))
expected_devs = 1.8811609559e-06, 1.88438380672e-05, 1.05292548867e-06
print("Expected devs are {}, {} and {} in m_x, m_y and m_z.".format(
*expected_devs))
# and use as simple system test
assert dev_mx < 1.89e-06
assert dev_my < 1.89e-05
assert dev_mz < 1.06e-06
def save_plot():
fig = plt.figure()
data = DataReader('test_energy.txt')
ts = data['time']
#plt.plot(ts, data['E_Dp'], label='E_Dp')
#plt.plot(ts, data['E_Dp'], label='E_Dp')
#plt.plot(ts, data['E_Dx'], label='E_Dx')
plt.plot(ts, data['E_exch'], label='E_exch')
plt.plot(ts, data['E_total'], label='E_total')
plt.ylabel('Energy (J)')
plt.xlabel('Time (s)')
plt.legend(loc=1)
plt.grid()
# plt.ylim([-1.2,1.2])
fig.savefig('energy.pdf')
def plot_all():
font = {
'family': 'serif',
'weight': 'normal',
'size': 12,
}
plt.rc('font', **font)
data = DataReader('relax.txt')
ts = data['time']
mx, my, mz = single_spin(0.5, 2.21e5, 1e5, ts)
ts = ts * 1e9
fig = plt.figure(figsize=(5, 4))
plt.plot(ts, mx, '--', label='m_x', dashes=(2.0, 2.0))
plt.plot(ts, my, '--', label='m_y', dashes=(2.0, 2.0))
plt.plot(ts, mz, '--', label='m_z', dashes=(2.0, 2.0))
ts = ts[::10]
mx = data['m_x'][::10]
my = data['m_y'][::10]
mz = data['m_z'][::10]
plt.plot(ts, mx, '.', color='b')
plt.plot(ts, my, '.', color='g')
plt.plot(ts[::2], mz[::2], '.', color='r')
plt.xlim([0, 1.01])
l1 = plt.legend(bbox_to_anchor=[0.8, 0.8], shadow=True, frameon=True)
custom_legend(l1)
plt.xlabel('Time (ns)')
plt.ylabel('m')
plt.tight_layout()
fig.savefig('m_ts.pdf')
oommf_original = np.loadtxt(OOMMF_DATAFILE)
oommf_data = np.zeros(oommf_original.shape)
oommf_data[1:] = oommf_original[:-1]
# we have zeroes at t = 0 for oommf's data for now
# LaTeX labels
math = lambda txt: "$" + txt + "$" # concatenation because getting {{{}}}
rm = lambda txt: "\mathrm{" + txt + "}" # right in subsequent formats is hard
olbl = math(rm("oommf"))
flbl = math(rm("fidimag"))
lbli = lambda name, mi: math(rm(rm(name) + "\; m_" + rm(mi)))
olbli = lambda i: lbli("oommf", i)
flbli = lambda i: lbli("fidimag", i)
for fi in FIDIMAG_DATAFILES:
data = DataReader(fi)
tol = re.search('dyn_r([0-9]+)_a', fi).groups()[0] # get exponent from filename
difference = np.sqrt((oommf_data[:, 2] - data["m_x"]) ** 2 +
(oommf_data[:, 3] - data["m_y"]) ** 2 +
(oommf_data[:, 4] - data["m_z"]) ** 2)
fig, axes = plt.subplots(3, figsize=(10, 8), sharex= True)
# dynamics of average magnetisation plotted
axes[0].plot(oommf_data[:, 0] * 1e9, oommf_data[:, 2], "b-", label=olbli("x"))
axes[0].plot(oommf_data[:, 0] * 1e9, oommf_data[:, 3], "g-", label=olbli("y"))
axes[0].plot(oommf_data[:, 0] * 1e9, oommf_data[:, 4], "r-", label=olbli("z"))
axes[0].plot(data["time"] * 1e9, data["m_x"], "bx", label=flbli("x"))
axes[0].plot(data["time"] * 1e9, data["m_y"], "gx", label=flbli("y"))
axes[0].plot(data["time"] * 1e9, data["m_z"], "rx", label=flbli("z"))
axes[0].set_ylim((-1.05, 1))
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from fidimag.common.fileio import DataReader
# labels on plot
mathmode = lambda txt: "$" + txt + "$"
rm = lambda txt: "\mathrm{" + txt + "}"
label = lambda name, mi: mathmode(rm(rm(name) + "\; m_" + rm(mi)))
fidi = mathmode(rm("fidimag"))
m = lambda i: label("fidimag", i)
for datafile, descr in (("sim_sundials_J0", "w/o Jacobian"),
("sim_sundials_diag", "Jacobian approx."),
("sim_sundials_J1", "with Jacobian")):
data = DataReader(datafile + ".txt")
total_wall_time = (data["real_time"][-1] - data["real_time"][0]) / 60.0
fig, axes = plt.subplots(2, figsize=(10, 8), sharex=True)
# magnetisation dynamics
axes[0].plot(data["time"] * 1e9, data["m_x"], "bx", label=m("x"))
axes[0].plot(data["time"] * 1e9, data["m_y"], "gx", label=m("y"))
axes[0].plot(data["time"] * 1e9, data["m_z"], "rx", label=m("z"))
axes[0].set_ylim((-0.3, 1.1))
axes[0].set_ylabel("unit magnetisation (1)")
axes[0].legend()
axes[0].set_title("{}, wall time {:.2} minutes".format(
descr, total_wall_time))
# number of RHS evaluations