hartree = 4.36e-18 # J
bohr_radius = 5.29e-11 # m
# plot S vs C3
C3 = np.linspace(0.1, 1.5, 20)
S_max = np.zeros(C3.size)
S_kd = np.zeros(C3.size)
s_result_kd = cs.calc_sensitivity_kd(npv.I_inc_nom, npv.L_nom, npv.v_nom, npv.d_nom)
for i in range(C3.size):
result = cs.calc_sensitivity_vdw(
npv.I_inc_nom, npv.l_nom, npv.L_nom, npv.v_nom, C3[i] * hartree * bohr_radius ** 3, npv.d_nom
)
S_max[i] = result[0]
S_kd[i] = s_result_kd
print result
l1, l2 = plt.plot(C3, S_max, "b-", C3, S_kd, "r-")
plt.xlabel("C3 (atomic units)")
plt.ylabel("sensitivity (rad/s / sqrt(Hz))")
l1_str = "material gratings, optimal open fraction = " + str(cs.f_max(npv.d_nom))
plt.legend(
(l1, l2),
("material gratings, optimal open fraction", "Kapitza-Dirac gratings (open fraction = 0.5)"),
loc="upper right",
)
import nominal_param_values as npv
v = 50.0 # m/s
d = np.linspace(80.0e-9,400.0e-9,30)
d_plot = d*1.0e9
S_vdw = np.zeros(d.size, dtype=np.float)
S_kd = np.zeros(d.size, dtype=np.float)
f1 = np.zeros(d.size, dtype=np.float)
f2 = np.zeros(d.size, dtype=np.float)
f_max = cs.f_max(d)
for i in range(d.size):
print d[i]
result = cs.calc_sensitivity_vdw(npv.I_inc_nom, npv.l_nom, npv.L_nom, npv.v_nom, npv.C3_nom, d[i])
S_vdw[i] = result[0]
f1[i] = result[2]
f2[i] = result[3]
S_kd[i] = cs.calc_sensitivity_kd(npv.I_inc_nom, npv.L_nom, npv.v_nom, d[i])
plt.figure(figsize=plt.figaspect(1.4))
plt.subplot(211)
l1, l2 = plt.plot(d_plot, S_vdw, 'b-', d_plot, S_kd, 'r-')
plt.xlabel('grating period (nm)')
plt.ylabel('sensitivity (rad/s / sqrt(Hz))')
plt.legend((l1, l2), ('material gratings', 'Kapitza-Dirac gratings'), loc='upper right')
plt.subplot(212)
l3, l4, l5 = plt.plot(d_plot, f_max, 'k-', d_plot, f1, 'g-', d_plot, f2, 'm-')
import time
import numpy as np
import matplotlib.pyplot as plt
import calc_sensitivity as cs
import nominal_param_values as npv
# NOTE: unreliable
v = np.linspace(20.0,200.0,20)
S = np.zeros(v.size, dtype=np.float)
d = np.zeros(v.size, dtype=np.float)
f1 = np.zeros(v.size, dtype=np.float)
f2 = np.zeros(v.size, dtype=np.float)
for i in range(v.size):
print v[i]
S_result = cs.calc_sensitivity_vdw(npv.I_inc_nom, npv.l_nom, npv.L_nom, v[i], npv.C3_nom)
S[i] = S_result[0]
d[i] = S_result[1]
f1[i] = S_result[2]
f2[i] = S_result[3]
print S_result
d_plot = d*1.0e9
plt.figure(figsize=(800,1200))
plt.subplot(311)
plt.plot(v, S, 'bo')
plt.ylabel("sensitivity (rad/s / sqrt(Hz))")
plt.xlabel("velocity (m/s)")
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.subplot(312)
# plot S vs d and v
v = np.linspace(20.0, 100.0, 5)
d = np.linspace(80.0e-9, 400.0e-9, 15)
V, D = np.meshgrid(v,d*1.0e9)
S = np.zeros([d.size, v.size], dtype = np.float)
S_kd = np.zeros([d.size, v.size], dtype = np.float)
f1 = np.zeros([d.size, v.size], dtype = np.float)
f2 = np.zeros([d.size, v.size], dtype = np.float)
for i in range(v.size):
for j in range(d.size):
print i,j
result = cs.calc_sensitivity_vdw(npv.I_inc_nom, npv.l_nom, npv.L_nom, v[i], npv.C3_nom, fixed_d = d[j])
S[j][i] = result[0]
f1[j][i] = result[2]
f2[j][i] = result[3]
S_kd[j][i] = cs.calc_sensitivity_kd(npv.I_inc_nom, npv.L_nom, v[i], d[j])
fig = plt.figure(figsize=plt.figaspect(0.8))
ax = fig.add_subplot(2, 2, 1, projection='3d')
surf1 = ax.plot_surface(V, D, S, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=True)
ax.set_xlabel("v (m/s)")
ax.set_ylabel("grating period (um)")
ax.set_zlabel("S (rad/s / sqrt(Hz))")
ax.set_title("a. material gratings")
ax = fig.add_subplot(2, 2, 2, projection='3d')
#!/usr/bin/python
import numpy as np
import cmath
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import calc_sensitivity as cs
import nominal_param_values as npv
# plot S vs C3
f = np.linspace(0.1, 0.99, 20)
S = np.zeros(f.size)
for i in range(f.size):
S[i] = cs.calc_sensitivity_vdw(npv.I_inc_nom, npv.l_nom, npv.L_nom, npv.v_nom, npv.C3_nom, npv.d_nom, f[i])[0]
plt.semilogy(f, S, 'bo')
plt.xlabel("g1 and g2 open fraction")
plt.ylabel("sensitivity (rad/s / sqrt(Hz))")
plt.show()
import time
import numpy as np
import matplotlib.pyplot as plt
import calc_sensitivity as cs
import nominal_param_values as npv
# Replicates Fig 5 of the Cronin 2005 paper, "Limitations on nanotechnology..."
d = np.linspace(10.0e-9,250.0e-9,30)
d_plot = d*1.0e9
S_vdw = np.zeros(d.size, dtype=np.float)
S_kd = np.zeros(d.size, dtype=np.float)
for i in range(d.size):
S_vdw[i] = cs.calc_sensitivity_vdw(npv.I_inc_nom, 150.0e-9, 1.0, 1000.0, 4.8e-49, d[i], 0.5)[0]
S_kd[i] = cs.calc_sensitivity_kd(npv.I_inc_nom, 1.0, 1000.0, d[i])
S_vdw_inv_sq = 1/S_vdw**2
S_kd_inv_sq = 1/S_kd**2
S_vdw_inv_sq_norm = S_vdw_inv_sq / np.linalg.norm(S_vdw_inv_sq)
S_kd_inv_sq_norm = S_kd_inv_sq / np.linalg.norm(S_kd_inv_sq)
plt.plot(d_plot, S_vdw_inv_sq_norm, 'bo', d_plot, S_kd_inv_sq_norm, 'ro')
plt.xlabel('grating period (nm)')
plt.ylabel('normalized inverse-squared sensitivity')
plt.show()
