import dtmm
import numpy as np
import matplotlib.pyplot as plt
from dtmm.data import EpsilonCauchy
dtmm.conf.set_verbose(2)
#: pixel size in nm
PIXELSIZE = 200
#: compute box dimensions
NLAYERS, HEIGHT, WIDTH = 60, 96, 96
#: illumination wavelengths in nm
WAVELENGTHS = np.linspace(380, 780, 9)
#: create some experimental data (stack)
d, epsv, epsa = dtmm.nematic_droplet_data((NLAYERS, HEIGHT, WIDTH),
radius=30,
profile="r",
no=1.5,
ne=1.6,
nhost=1.5)
"""
we will be using Cauchy approximation with n = 2 coefficients.
refind = a + b/lambda**2
#e take the epsv values as a reference, so first coefficient is just epv**0.5,
and then w add some small anisotropic dispersion the b coefficient depends on the
anistropy.
units of coefficient b are microns**2
"""
epsc = EpsilonCauchy(shape=(NLAYERS, HEIGHT, WIDTH), n=2)
epsc.coefficients[..., 0] = (epsv)**0.5 # a term, just set to refractive index
epsc.coefficients[..., 0:2,
"""This example shows that calculating 4x4 reflection is less stable and noisier.
"""
import dtmm
import numpy as np
#: pixel size in nm
PIXELSIZE = 200
#: compute box dimensions
NLAYERS, HEIGHT, WIDTH = 60, 96, 96
#: illumination wavelengths in nm
WAVELENGTHS = np.linspace(380, 780, 9)
#: create some experimental data (stack)
optical_data = dtmm.nematic_droplet_data((NLAYERS, HEIGHT, WIDTH),
radius=30,
profile="r",
no=3.5,
ne=3.6,
nhost=3.5)
betamax = np.inf #no need to cutoff in 2x2 mode, in the 4x4 mode, filtering is done automatically
NA = 0.9 #NA of the microscope objective... also this filters out high frequency modes
window = dtmm.aperture((96, 96), 0.95, 0.)
window = None
#: create non-polarized input light
(fin2, w, p) = dtmm.illumination_data((HEIGHT, WIDTH),
WAVELENGTHS,
beta=0.,
pixelsize=PIXELSIZE,
window=window)
(fin4, w, p) = (fin2.copy(), w, p)
import numpy as np
import matplotlib.pyplot as plt
# Change matplotlib backend to an interactive one
import matplotlib
#matplotlib.use("TkAgg")
#: pixel size in nm
PIXELSIZE = 100
#: compute box dimensions
NLAYERS, HEIGHT, WIDTH = 100, 1024, 1024
#: illumination wavelengths in nm
WAVELENGTHS = np.linspace(380, 780, 9)
#: dummy data..
d, e, a = dtmm.nematic_droplet_data((NLAYERS, HEIGHT, WIDTH),
radius=3000,
profile="x",
no=1.5,
ne=1.5,
nhost=1.5)
d[...] = 1
a[...] = 0.
a[..., 1] = np.pi / 2
def eps_periodic(size=1024, period=25, n1=1.7, n2=1.5):
out = []
for i in range(size):
if (i // period) % 2 == 0:
out.append(n1**2)
else:
if isinstance(data, tuple):
d, eps, angles = validate_optical_data(data)
else:
angles = data
director = angles2director(angles)
return plot_director(director, **kwargs)
__all__ = ["plot_material", "plot_angles", "plot_director"]
if __name__ == "__main__":
import dtmm
import dtmm
data = dtmm.nematic_droplet_data((60, 128, 128),
radius=30,
profile="r",
no=1.5,
ne=1.7,
nhost=1.5)
thickness, material_eps, angles = data
fig = plot_material(material_eps,
center=True,
xlim=(-40, -20),
ylim=(-10, 10),
zlim=(-10, 10))
fig = dtmm.plot_angles(angles,
center=True,
xlim=(-3, 3),
ylim=(-3, 3),
zlim=(-3, 3))
