def grid(path, name, key):
metadata, data = read_nid(path)
height, x, y = load_corrected_image(metadata, data, key=key)
peaks = peak_local_max(
height,
min_distance=6,
).astype('float64')
peaks[:, 0] = peaks[:, 0] * x.max() / height.shape[0]
peaks[:, 1] = peaks[:, 1] * y.max() / height.shape[1]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, aspect=1)
plot = ax.pcolormesh(x, y, height, cmap='inferno')
plot.set_rasterized(True)
ax.plot(peaks[:, 1], peaks[:, 0], 'w.', ms=3)
fig.colorbar(plot, ax=ax, label=r'$z \mathbin{/} \si{\nano\meter}$')
# print(peaks)
d1 = np.array([0, 0])
diagonal_points = []
for a in range(13):
d1 = get_next_point_on_diagonal(d1, peaks)
if len(d1) == 0:
break
diagonal_points.append(d1)
diagonal_distances = np.array([
distance.euclidean(a, b)
for a, b in zip(diagonal_points[:-1], diagonal_points[1:])]
)
gd = ufloat(diagonal_distances.mean(), diagonal_distances.std())
print('Gitterkonstante a_1: {}'.format(gd))
diagonal_points = np.array(diagonal_points)
x_coords = diagonal_points[:, 1]
y_coords = diagonal_points[:, 0]
popt, pcov = curve_fit(line, x_coords, y_coords)
m, b = correlated_values(popt, pcov)
xs = np.linspace(0, 2.44, 50)
ax.plot(xs, line(xs, m.n, b.n), color='lightgray')
angle_diagonal = umath.atan(m)
# print(angle_diagonal)
for d in diagonal_points:
ax.plot(d[1], d[0], 'o', color='#46d7ff', alpha=0.7)
d1 = [0.33, 0]
horizontal_points = []
for a in range(10):
d1 = get_next_point_on_horizontal(d1, peaks)
if len(d1) == 0:
break
horizontal_points.append(d1)
for d in horizontal_points:
ax.plot(d[1], d[0], 'o', color='#dfec56', alpha=0.7)
horizontal_points = np.array(horizontal_points)
x_coords = horizontal_points[:, 1]
y_coords = horizontal_points[:, 0]
popt, pcov = curve_fit(line, x_coords, y_coords)
m, b = correlated_values(popt, pcov)
angle_horizontal = umath.atan(m)
angle = angle_diagonal - angle_horizontal
# should be 60
correction_factor = np.tan(np.pi/3)/umath.tan(angle)
print('Gemessener Winkel ist {}. Soll ist 60. Korrekturfaktor ist {}'.format(
rad2deg(angle), correction_factor
))
ax.plot(xs, line(xs, m.n, b.n), color='lightgray')
horizontal_distances = np.array([
distance.euclidean(a, b)
for a, b in zip(horizontal_points[:-1], horizontal_points[1:])
])
gh = ufloat(horizontal_distances.mean(), horizontal_distances.std())
print('Gitterkonstante a_2: {}'.format(gh))
ax.set_xlim(0, x.max())
ax.set_ylim(0, y.max())
g_string = '\SI{{{:.3f} \pm {:.3f}}}{{\\nano\\meter}}'.format(gh.n, gh.s)
with open('build/grid_constant_horizontal_{}.tex'.format(name), 'w') as f:
f.write(g_string)
g_string = r'$\num{{{:.2f} \pm {:.2f}}}$'.format(correction_factor.n, correction_factor.s)
with open('build/correction_factor_{}.tex'.format(name), 'w') as f:
f.write(g_string)
g_string = '\SI{{{:.3f} \pm {:.3f}}}{{\\nano\\meter}}'.format(gd.n, gd.s)
with open('build/grid_constant_diagonal_{}.tex'.format(name), 'w') as f:
f.write(g_string)
angle = angle_diagonal - angle_horizontal
g_string = '\\ang{{{:.2f} \pm {:.2f}}}'.format(np.rad2deg(angle.n), np.rad2deg(angle.s))
with open('build/grid_angle_{}.tex'.format(name), 'w') as f:
f.write(g_string)
# plt.show()
ax.set_xlabel(r'$x \mathbin{/} \si{\nano\meter}$')
ax.set_ylabel(r'$y \mathbin{/} \si{\nano\meter}$')
fig.tight_layout(pad=0)
fig.savefig('build/plots/hopg_{}.pdf'.format(name))
from scipy.optimize import curve_fit
import uncertainties.unumpy as unp
from uncertainties import correlated_values
from utils import load_corrected_image
from read_nid import read_nid
if __name__ == '__main__':
key = 'DataSet-1:1'
metadata, data = read_nid('data/Noethe_Bruegge_053_gold1.nid')
width = metadata.getfloat(key, 'dim0range') * 1e9
height, y, x = load_corrected_image(
metadata, data, 'DataSet-1:1',
)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, aspect=1)
lower = np.percentile(height, 5)
upper = np.percentile(height, 95)
plot = ax.pcolormesh(x, y, height, cmap='inferno', vmin=lower, vmax=upper)
plot.set_rasterized(True)
fig.colorbar(plot, ax=ax, label=r'$z \mathbin{/} \si{\nano\meter}$')
ax.set_xlim(0, x.max())
ax.set_ylim(0, y.max())
ax.plot(
