Included code snippet to investigate the origin of the deviation of alphas and verified if the potential for the full crossed dipole trap is correct.
This commit is contained in:
parent
ac5aeb4a3e
commit
69ba21b1e6
@ -198,20 +198,20 @@ def convert_modulation_depth_to_alpha(modulation_depth):
|
||||
fin_mod_dep = [0, 0.5, 0.3, 0.7, 0.9, 0.8, 1.0, 0.6, 0.4, 0.2, 0.1]
|
||||
fx = [3.135, 0.28, 0.690, 0.152, 0.102, 0.127, 0.099, 0.205, 0.404, 1.441, 2.813]
|
||||
dfx = [0.016, 0.006, 0.005, 0.006, 0.003, 0.002, 0.002,0.002, 0.003, 0.006, 0.024]
|
||||
fy = [2.746, 1.278, 1.719, 1.058, 0.923, 0.994, 0.911, 1.157, 1.446, 2.191, 2.643]
|
||||
dfy = [0.014, 0.007, 0.009, 0.007, 0.005, 0.004, 0.004, 0.005, 0.007, 0.009, 0.033]
|
||||
fz = [2.746, 1.278, 1.719, 1.058, 0.923, 0.994, 0.911, 1.157, 1.446, 2.191, 2.643]
|
||||
dfz = [0.014, 0.007, 0.009, 0.007, 0.005, 0.004, 0.004, 0.005, 0.007, 0.009, 0.033]
|
||||
|
||||
alpha_x = [(fx[0]/x)**(2/3) for x in fx]
|
||||
dalpha_x = [alpha_x[i] * np.sqrt((dfx[0]/fx[0])**2 + (dfx[i]/fx[i])**2) for i in range(len(fx))]
|
||||
alpha_y = [(fy[0]/y)**2 for y in fy]
|
||||
dalpha_y = [alpha_y[i] * np.sqrt((dfy[0]/fy[0])**2 + (dfy[i]/fy[i])**2) for i in range(len(fy))]
|
||||
alpha_z = [(fz[0]/z)**2 for z in fz]
|
||||
dalpha_z = [alpha_z[i] * np.sqrt((dfz[0]/fz[0])**2 + (dfz[i]/fz[i])**2) for i in range(len(fz))]
|
||||
|
||||
avg_alpha = [(g + h) / 2 for g, h in zip(alpha_x, alpha_y)]
|
||||
avg_alpha = [(g + h) / 2 for g, h in zip(alpha_x, alpha_z)]
|
||||
sorted_fin_mod_dep, sorted_avg_alpha = zip(*sorted(zip(fin_mod_dep, avg_alpha)))
|
||||
|
||||
f = interpolate.interp1d(sorted_fin_mod_dep, sorted_avg_alpha)
|
||||
|
||||
return f(modulation_depth), fin_mod_dep, alpha_x, alpha_y, dalpha_x, dalpha_y
|
||||
return f(modulation_depth), fin_mod_dep, alpha_x, alpha_z, dalpha_x, dalpha_z
|
||||
|
||||
def convert_modulation_depth_to_temperature(modulation_depth):
|
||||
fin_mod_dep = [1.0, 0.8, 0.6, 0.4, 0.2, 0.0, 0.9, 0.7, 0.5, 0.3, 0.1]
|
||||
@ -231,22 +231,22 @@ def convert_modulation_depth_to_temperature(modulation_depth):
|
||||
# POTENTIALS #
|
||||
#####################################################################
|
||||
|
||||
def gravitational_potential(positions: "np.ndarray|u.quantity.Quantity", m:"float|u.quantity.Quantity"):
|
||||
def gravitational_potential(positions, m):
|
||||
return m * ac.g0 * positions
|
||||
|
||||
def single_gaussian_beam_potential(positions: "np.ndarray|u.quantity.Quantity", waists: "np.ndarray|u.quantity.Quantity", alpha:"float|u.quantity.Quantity", P:"float|u.quantity.Quantity"=1, wavelength:"float|u.quantity.Quantity"=1.064*u.um)->np.ndarray:
|
||||
def single_gaussian_beam_potential(positions, waists, alpha, P=1, wavelength=1.064*u.um):
|
||||
A = 2*P/(np.pi*w(positions[1,:], waists[0], wavelength)*w(positions[1,:], waists[1], wavelength))
|
||||
U_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
|
||||
U = - U_tilde * A * np.exp(-2 * ((positions[0,:]/w(positions[1,:], waists[0], wavelength))**2 + (positions[2,:]/w(positions[1,:], waists[1], wavelength))**2))
|
||||
return U
|
||||
|
||||
def astigmatic_single_gaussian_beam_potential(positions: "np.ndarray|u.quantity.Quantity", waists: "np.ndarray|u.quantity.Quantity", del_y:"float|u.quantity.Quantity", alpha:"float|u.quantity.Quantity", P:"float|u.quantity.Quantity"=1, wavelength:"float|u.quantity.Quantity"=1.064*u.um)->np.ndarray:
|
||||
def astigmatic_single_gaussian_beam_potential(positions, waists, del_y, alpha, P=1, wavelength=1.064*u.um):
|
||||
A = 2*P/(np.pi*w(positions[1,:] - (del_y/2), waists[0], wavelength)*w(positions[1,:] + (del_y/2), waists[1], wavelength))
|
||||
U_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
|
||||
U = - U_tilde * A * np.exp(-2 * ((positions[0,:]/w(positions[1,:] - (del_y/2), waists[0], wavelength))**2 + (positions[2,:]/w(positions[1,:] + (del_y/2), waists[1], wavelength))**2))
|
||||
return U
|
||||
|
||||
def modulated_single_gaussian_beam_potential(positions: "np.ndarray|u.quantity.Quantity", waists: "np.ndarray|u.quantity.Quantity", alpha:"float|u.quantity.Quantity", P:"float|u.quantity.Quantity"=1, wavelength:"float|u.quantity.Quantity"=1.064*u.um, mod_amp:"float|u.quantity.Quantity"=1)->np.ndarray:
|
||||
def modulated_single_gaussian_beam_potential(positions, waists, alpha, P=1, wavelength=1.064*u.um, mod_amp=1):
|
||||
mod_amp = mod_amp * waists[0]
|
||||
n_points = len(positions[0,:])
|
||||
dx, x_mod = modulation_function(mod_amp, n_points, func = 'arccos')
|
||||
@ -287,7 +287,7 @@ def crossed_beam_potential(positions, theta, waists, P, alpha, wavelength=1.064*
|
||||
# COMPUTE/EXTRACT TRAP POTENTIAL AND PARAMETERS #
|
||||
#####################################################################
|
||||
|
||||
def trap_depth(w_1:"float|u.quantity.Quantity", w_2:"float|u.quantity.Quantity", P:"float|u.quantity.Quantity", alpha:float)->"float|u.quantity.Quantity":
|
||||
def trap_depth(w_1, w_2, P, alpha):
|
||||
return 2*P/(np.pi*w_1*w_2) * (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
|
||||
|
||||
def calculateTrapFrequency(w_x, w_z, Power, Polarizability, m = 164*u.u, dir = 'x'):
|
||||
@ -306,11 +306,11 @@ def extractTrapFrequency(Positions, TrappingPotential, axis):
|
||||
tmp_pos = Positions[axis, :]
|
||||
tmp_pot = TrappingPotential[axis]
|
||||
center_idx = np.argmin(tmp_pot)
|
||||
lb = int(round(center_idx - len(tmp_pot)/150, 1))
|
||||
ub = int(round(center_idx + len(tmp_pot)/150, 1))
|
||||
lb = int(round(center_idx - len(tmp_pot)/500, 1))
|
||||
ub = int(round(center_idx + len(tmp_pot)/500, 1))
|
||||
xdata = tmp_pos[lb:ub]
|
||||
Potential = tmp_pot[lb:ub]
|
||||
p0=[1e3, tmp_pos[center_idx].value, np.argmin(tmp_pot.value)]
|
||||
p0=[1e3, tmp_pos[center_idx].value, -100]
|
||||
popt, pcov = curve_fit(harmonic_potential, xdata, Potential, p0)
|
||||
v = popt[0]
|
||||
dv = pcov[0][0]**0.5
|
||||
@ -348,9 +348,9 @@ def computeTrapPotential(w_x, w_z, Power, Polarizability, options):
|
||||
else:
|
||||
projection_axis = np.array([1, 1, 1]) # vertical direction (Z-axis)
|
||||
|
||||
x_Positions = np.arange(-extent, extent, 1)*u.um
|
||||
y_Positions = np.arange(-extent, extent, 1)*u.um
|
||||
z_Positions = np.arange(-extent, extent, 1)*u.um
|
||||
x_Positions = np.arange(-extent, extent, 0.05)*u.um
|
||||
y_Positions = np.arange(-extent, extent, 0.05)*u.um
|
||||
z_Positions = np.arange(-extent, extent, 0.05)*u.um
|
||||
Positions = np.vstack((x_Positions, y_Positions, z_Positions)) * projection_axis[:, np.newaxis]
|
||||
|
||||
if not crossed:
|
||||
@ -359,7 +359,7 @@ def computeTrapPotential(w_x, w_z, Power, Polarizability, options):
|
||||
IdealTrappingPotential = (IdealTrappingPotential/ac.k_B).to(u.uK)
|
||||
|
||||
else:
|
||||
theta = options['theta']
|
||||
theta = options['delta']
|
||||
waists = np.vstack((np.asarray([w_x[0].value, w_z[0].value])*u.um, np.asarray([w_x[1].value, w_z[1].value])*u.um))
|
||||
IdealTrappingPotential = crossed_beam_potential(Positions, theta, waists, P = Power, alpha = Polarizability)
|
||||
IdealTrappingPotential = IdealTrappingPotential * (np.ones((3, len(IdealTrappingPotential))) * projection_axis[:, np.newaxis])
|
||||
@ -430,7 +430,7 @@ def computeTrapPotential(w_x, w_z, Power, Polarizability, options):
|
||||
return Positions, IdealTrappingPotential, TrappingPotential, TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies
|
||||
|
||||
else:
|
||||
return TrappingPotential
|
||||
return Positions, TrappingPotential
|
||||
|
||||
def extractWaist(Positions, TrappingPotential):
|
||||
tmp_pos = Positions.value
|
||||
@ -671,13 +671,14 @@ def plotAlphas():
|
||||
Alphas, fin_mod_dep, alpha_x, alpha_y, dalpha_x, dalpha_y = convert_modulation_depth_to_alpha(modulation_depth)
|
||||
|
||||
plt.figure()
|
||||
plt.errorbar(fin_mod_dep, alpha_x, yerr = dalpha_x, fmt= 'ob', markersize=5, capsize=5)
|
||||
plt.errorbar(fin_mod_dep, alpha_y, yerr = dalpha_y, fmt= 'or', markersize=5, capsize=5)
|
||||
plt.errorbar(fin_mod_dep, alpha_x, yerr = dalpha_x, fmt= 'ob', label = 'From Horz TF', markersize=5, capsize=5)
|
||||
plt.errorbar(fin_mod_dep, alpha_y, yerr = dalpha_y, fmt= 'or', label = 'From Vert TF', markersize=5, capsize=5)
|
||||
plt.plot(modulation_depth, Alphas, '--g')
|
||||
plt.xlabel('Modulation depth', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('$\\alpha$', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
def plotTemperatures(w_x, w_z, plot_against_mod_depth = True):
|
||||
@ -690,13 +691,13 @@ def plotTemperatures(w_x, w_z, plot_against_mod_depth = True):
|
||||
|
||||
plt.figure()
|
||||
if plot_against_mod_depth:
|
||||
plt.errorbar(fin_mod_dep, T_x, yerr = dT_x, fmt= 'ob', markersize=5, capsize=5)
|
||||
plt.errorbar(fin_mod_dep, T_y, yerr = dT_y, fmt= 'or', markersize=5, capsize=5)
|
||||
plt.errorbar(fin_mod_dep, T_x, yerr = dT_x, fmt= 'ob', label = 'Horz direction', markersize=5, capsize=5)
|
||||
plt.errorbar(fin_mod_dep, T_y, yerr = dT_y, fmt= 'or', label = 'Vert direction', markersize=5, capsize=5)
|
||||
plt.plot(modulation_depth, Temperatures, '--g')
|
||||
xlabel = 'Modulation depth'
|
||||
else:
|
||||
plt.errorbar(measured_aspect_ratio, T_x, yerr = dT_x, fmt= 'ob', markersize=5, capsize=5)
|
||||
plt.errorbar(measured_aspect_ratio, T_y, yerr = dT_y, fmt= 'or', markersize=5, capsize=5)
|
||||
plt.errorbar(measured_aspect_ratio, T_x, yerr = dT_x, fmt= 'ob', label = 'Horz direction', markersize=5, capsize=5)
|
||||
plt.errorbar(measured_aspect_ratio, T_y, yerr = dT_y, fmt= 'or', label = 'Vert direction', markersize=5, capsize=5)
|
||||
plt.plot(new_aspect_ratio, Temperatures, '--g')
|
||||
xlabel = 'Aspect Ratio'
|
||||
|
||||
@ -704,29 +705,30 @@ def plotTemperatures(w_x, w_z, plot_against_mod_depth = True):
|
||||
plt.ylabel('Temperature (uK)', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
def plotTrapFrequencies(v_x, v_y, v_z, modulation_depth, new_aspect_ratio, plot_against_mod_depth = True):
|
||||
fig, ax3 = plt.subplots(figsize=(8, 6))
|
||||
|
||||
if plot_against_mod_depth:
|
||||
ln1 = ax3.plot(modulation_depth, v_x, '-ob', label = 'v_x')
|
||||
ln1 = ax3.plot(modulation_depth, v_x, '-or', label = 'v_x')
|
||||
ln2 = ax3.plot(modulation_depth, v_z, '-^b', label = 'v_z')
|
||||
ax4 = ax3.twinx()
|
||||
ln3 = ax4.plot(modulation_depth, v_y, '-*r', label = 'v_y')
|
||||
ln3 = ax4.plot(modulation_depth, v_y, '-*g', label = 'v_y')
|
||||
xlabel = 'Modulation depth'
|
||||
else:
|
||||
ln1 = ax3.plot(new_aspect_ratio, v_x, '-ob', label = 'v_x')
|
||||
ln1 = ax3.plot(new_aspect_ratio, v_x, '-or', label = 'v_x')
|
||||
ln2 = ax3.plot(new_aspect_ratio, v_z, '-^b', label = 'v_z')
|
||||
ax4 = ax3.twinx()
|
||||
ln3 = ax4.plot(new_aspect_ratio, v_y, '-*r', label = 'v_y')
|
||||
ln3 = ax4.plot(new_aspect_ratio, v_y, '-*g', label = 'v_y')
|
||||
xlabel = 'Aspect Ratio'
|
||||
|
||||
ax3.set_xlabel(xlabel, fontsize= 12, fontweight='bold')
|
||||
ax3.set_ylabel('Trap Frequency (Hz)', fontsize= 12, fontweight='bold')
|
||||
ax3.tick_params(axis="y", labelcolor='b')
|
||||
ax4.set_ylabel('Trap Frequency (Hz)', fontsize= 12, fontweight='bold')
|
||||
ax4.tick_params(axis="y", labelcolor='r')
|
||||
ax4.tick_params(axis="y", labelcolor='g')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
lns = ln1+ln2+ln3
|
||||
@ -764,10 +766,10 @@ def plotMeasuredTrapFrequencies(w_x, w_z, plot_against_mod_depth = True):
|
||||
ax1.set_ylabel('Trap Frequency (kHz)', fontsize= 12, fontweight='bold')
|
||||
ax1.tick_params(axis="y", labelcolor='b')
|
||||
ax2.set_ylabel('Trap Frequency (Hz)', fontsize= 12, fontweight='bold')
|
||||
ax2.tick_params(axis="y", labelcolor='r')
|
||||
ax2.tick_params(axis="y", labelcolor='g')
|
||||
h1, l1 = ax1.get_legend_handles_labels()
|
||||
h2, l2 = ax2.get_legend_handles_labels()
|
||||
ax1.legend(h1+h2, l1+l2, loc=0)
|
||||
ax1.legend(h1+h2, l1+l2, loc=0, prop={'size': 12, 'weight': 'bold'})
|
||||
else:
|
||||
plt.figure()
|
||||
plt.errorbar(new_aspect_ratio, fx, yerr = dfx, fmt= 'or', label = 'v_x', markersize=5, capsize=5)
|
||||
@ -821,8 +823,8 @@ def plotRatioOfTrapFrequencies(plot_against_mod_depth = True):
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
def plotScatteringLengths():
|
||||
BField = np.arange(0, 2.59, 1e-3) * u.G
|
||||
def plotScatteringLengths(B_range = [0, 2.59]):
|
||||
BField = np.arange(B_range[0], B_range[1], 1e-3) * u.G
|
||||
a_s_array = np.zeros(len(BField)) * ac.a0
|
||||
for idx in range(len(BField)):
|
||||
a_s_array[idx], a_bkg = scatteringLength(BField[idx])
|
||||
@ -872,30 +874,30 @@ def plotCollisionRatesAndPSD(Gamma_elastic, PSD, modulation_depth, new_aspect_ra
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
Power = 40*u.W
|
||||
Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
|
||||
Wavelength = 1.064*u.um
|
||||
w_x, w_z = 27.5*u.um, 33.8*u.um # Beam Waists in the x and y directions
|
||||
# Power = 40*u.W
|
||||
# Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
|
||||
# Wavelength = 1.064*u.um
|
||||
# w_x, w_z = 27.5*u.um, 33.8*u.um # Beam Waists in the x and y directions
|
||||
|
||||
# Power = 11*u.W
|
||||
# Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
|
||||
# w_x, w_z = 54.0*u.um, 54.0*u.um # Beam Waists in the x and y directions
|
||||
|
||||
# options = {
|
||||
# 'axis': 0, # axis referenced to the beam along which you want the dipole trap potential
|
||||
# 'extent': 3e2, # range of spatial coordinates in one direction to calculate trap potential over
|
||||
# 'crossed': False,
|
||||
# 'theta': 0,
|
||||
# 'modulation': True,
|
||||
# 'aspect_ratio': 3.67,
|
||||
# 'gravity': False,
|
||||
# 'tilt_gravity': False,
|
||||
# 'theta': 5, # in degrees
|
||||
# 'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam
|
||||
# 'astigmatism': False,
|
||||
# 'disp_foci': 3 * z_R(w_0 = np.asarray([30]), lamb = 1.064)[0]*u.um # difference in position of the foci along the propagation direction (Astigmatism)
|
||||
# }
|
||||
|
||||
"""
|
||||
options = {
|
||||
'axis': 2, # axis referenced to the beam along which you want the dipole trap potential
|
||||
'extent': 3e2, # range of spatial coordinates in one direction to calculate trap potential over
|
||||
'crossed': False,
|
||||
'theta': 0,
|
||||
'modulation': True,
|
||||
'aspect_ratio': 1,
|
||||
'gravity': True,
|
||||
'tilt_gravity': True,
|
||||
'theta': 0.5, # in degrees
|
||||
'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam
|
||||
'astigmatism': True,
|
||||
'disp_foci': 0.9 * z_R(w_0 = np.asarray([30]), lamb = 1.064)[0]*u.um # difference in position of the foci along the propagation direction (Astigmatism)
|
||||
}
|
||||
"""
|
||||
"""Plot ideal trap potential resulting for given parameters only"""
|
||||
# ComputedPotentials = []
|
||||
# Params = []
|
||||
@ -979,15 +981,15 @@ if __name__ == '__main__':
|
||||
|
||||
"""Calculate relevant parameters for evaporative cooling for different modulation depths, temperatures"""
|
||||
|
||||
AtomNumber = 1.00 * 1e7
|
||||
BField = 1.4 * u.G
|
||||
# modulation_depth = np.arange(0, 1.0, 0.02)
|
||||
# AtomNumber = 1.00 * 1e7
|
||||
# BField = 1.4 * u.G
|
||||
# modulation_depth = np.arange(0, 1.0, 0.08)
|
||||
|
||||
# w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]
|
||||
# new_aspect_ratio = w_xs / w_z
|
||||
# Temperatures = convert_modulation_depth_to_temperature(modulation_depth)[0] * u.uK
|
||||
|
||||
plot_against_mod_depth = True
|
||||
# plot_against_mod_depth = True
|
||||
|
||||
# # n = np.zeros(len(modulation_depth))
|
||||
# Gamma_elastic = np.zeros(len(modulation_depth))
|
||||
@ -1019,11 +1021,11 @@ if __name__ == '__main__':
|
||||
|
||||
# plotMeasuredTrapFrequencies(w_x, w_z, plot_against_mod_depth = plot_against_mod_depth)
|
||||
|
||||
plotRatioOfTrapFrequencies(plot_against_mod_depth = plot_against_mod_depth)
|
||||
# plotRatioOfTrapFrequencies(plot_against_mod_depth = plot_against_mod_depth)
|
||||
|
||||
"""Plot Feshbach Resonances"""
|
||||
|
||||
# plotScatteringLengths()
|
||||
# plotScatteringLengths(B_range = [0, 3.6])
|
||||
|
||||
"""Plot Collision Rates and PSD"""
|
||||
|
||||
@ -1031,43 +1033,129 @@ if __name__ == '__main__':
|
||||
|
||||
"""Plot Collision Rates and PSD from only measured trap frequencies"""
|
||||
|
||||
pd, dpd, T, dT, new_aspect_ratio, modulation_depth = particleDensity(w_x, w_z, Power, Polarizability, AtomNumber, 0, m = 164*u.u, use_measured_tf = True)
|
||||
# pd, dpd, T, dT, new_aspect_ratio, modulation_depth = particleDensity(w_x, w_z, Power, Polarizability, AtomNumber, 0, m = 164*u.u, use_measured_tf = True)
|
||||
|
||||
Gamma_elastic = [(pd[i] * scatteringCrossSection(BField) * meanThermalVelocity(T[i]) / (2 * np.sqrt(2))).decompose() for i in range(len(pd))]
|
||||
Gamma_elastic_values = [(Gamma_elastic[i]).value for i in range(len(Gamma_elastic))]
|
||||
dGamma_elastic = [(Gamma_elastic[i] * ((dpd[i]/pd[i]) + (dT[i]/(2*T[i])))).decompose() for i in range(len(Gamma_elastic))]
|
||||
dGamma_elastic_values = [(dGamma_elastic[i]).value for i in range(len(dGamma_elastic))]
|
||||
# Gamma_elastic = [(pd[i] * scatteringCrossSection(BField) * meanThermalVelocity(T[i]) / (2 * np.sqrt(2))).decompose() for i in range(len(pd))]
|
||||
# Gamma_elastic_values = [(Gamma_elastic[i]).value for i in range(len(Gamma_elastic))]
|
||||
# dGamma_elastic = [(Gamma_elastic[i] * ((dpd[i]/pd[i]) + (dT[i]/(2*T[i])))).decompose() for i in range(len(Gamma_elastic))]
|
||||
# dGamma_elastic_values = [(dGamma_elastic[i]).value for i in range(len(dGamma_elastic))]
|
||||
|
||||
PSD = [((pd[i] * thermaldeBroglieWavelength(T[i])**3).decompose()).value for i in range(len(pd))]
|
||||
dPSD = [((PSD[i] * ((dpd[i]/pd[i]) - (1.5 * dT[i]/T[i]))).decompose()).value for i in range(len(Gamma_elastic))]
|
||||
# PSD = [((pd[i] * thermaldeBroglieWavelength(T[i])**3).decompose()).value for i in range(len(pd))]
|
||||
# dPSD = [((PSD[i] * ((dpd[i]/pd[i]) - (1.5 * dT[i]/T[i]))).decompose()).value for i in range(len(Gamma_elastic))]
|
||||
|
||||
fig, ax1 = plt.subplots(figsize=(8, 6))
|
||||
ax2 = ax1.twinx()
|
||||
ax1.errorbar(modulation_depth, Gamma_elastic_values, yerr = dGamma_elastic_values, fmt = 'ob', markersize=5, capsize=5)
|
||||
ax2.errorbar(modulation_depth, PSD, yerr = dPSD, fmt = '-^r', markersize=5, capsize=5)
|
||||
ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
|
||||
ax1.set_xlabel('Modulation depth', fontsize= 12, fontweight='bold')
|
||||
ax1.set_ylabel('Elastic Collision Rate (' + str(Gamma_elastic[0].unit) + ')', fontsize= 12, fontweight='bold')
|
||||
ax1.tick_params(axis="y", labelcolor='b')
|
||||
ax2.set_ylabel('Phase Space Density', fontsize= 12, fontweight='bold')
|
||||
ax2.tick_params(axis="y", labelcolor='r')
|
||||
# fig, ax1 = plt.subplots(figsize=(8, 6))
|
||||
# ax2 = ax1.twinx()
|
||||
# ax1.errorbar(modulation_depth, Gamma_elastic_values, yerr = dGamma_elastic_values, fmt = 'ob', markersize=5, capsize=5)
|
||||
# ax2.errorbar(modulation_depth, PSD, yerr = dPSD, fmt = '-^r', markersize=5, capsize=5)
|
||||
# ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
|
||||
# ax1.set_xlabel('Modulation depth', fontsize= 12, fontweight='bold')
|
||||
# ax1.set_ylabel('Elastic Collision Rate (' + str(Gamma_elastic[0].unit) + ')', fontsize= 12, fontweight='bold')
|
||||
# ax1.tick_params(axis="y", labelcolor='b')
|
||||
# ax2.set_ylabel('Phase Space Density', fontsize= 12, fontweight='bold')
|
||||
# ax2.tick_params(axis="y", labelcolor='r')
|
||||
# plt.tight_layout()
|
||||
# plt.grid(visible=1)
|
||||
# plt.show()
|
||||
|
||||
""" Investigate deviation in alpha"""
|
||||
|
||||
Power = 40*u.W
|
||||
Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
|
||||
Wavelength = 1.064*u.um
|
||||
w_x, w_z = 27.5*u.um, 33.8*u.um
|
||||
|
||||
options = {
|
||||
'axis': 0, # axis referenced to the beam along which you want the dipole trap potential
|
||||
'extent': 3e2, # range of spatial coordinates in one direction to calculate trap potential over
|
||||
'crossed': False,
|
||||
'theta': 0,
|
||||
'modulation': False,
|
||||
'gravity': True,
|
||||
'tilt_gravity': True,
|
||||
'theta': 10, # in degrees
|
||||
'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam
|
||||
'astigmatism': True,
|
||||
'disp_foci': 0.9 * z_R(w_0 = np.asarray([30]), lamb = 1.064)[0]*u.um # difference in position of the foci along the propagation direction (Astigmatism)
|
||||
}
|
||||
|
||||
modulation_depth = np.arange(0, 1.1, 0.1)
|
||||
Alphas, fin_mod_dep, meas_alpha_x, meas_alpha_z, dalpha_x, dalpha_z = convert_modulation_depth_to_alpha(modulation_depth)
|
||||
meas_alpha_deviation = [(g - h) for g, h in zip(meas_alpha_x, meas_alpha_z)]
|
||||
sorted_fin_mod_dep, sorted_meas_alpha_deviation = zip(*sorted(zip(fin_mod_dep, meas_alpha_deviation)))
|
||||
avg_alpha = [(g + h) / 2 for g, h in zip(meas_alpha_x, meas_alpha_z)]
|
||||
sorted_fin_mod_dep, new_aspect_ratio = zip(*sorted(zip(fin_mod_dep, (w_x * avg_alpha) / w_z)))
|
||||
|
||||
current_ar = w_x/w_z
|
||||
aspect_ratio = np.arange(current_ar, 10*current_ar, 0.8)
|
||||
w_x = w_x * (aspect_ratio / current_ar)
|
||||
|
||||
v_x = np.zeros(len(w_x))
|
||||
#v_y = np.zeros(len(w_x))
|
||||
v_z = np.zeros(len(w_x))
|
||||
|
||||
for i in range(len(w_x)):
|
||||
options['axis'] = 0
|
||||
ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, Polarizability, options)[5]
|
||||
tmp = ExtractedTrapFrequencies[1][0]
|
||||
v_x[i] = tmp if not np.isinf(tmp) else np.nan
|
||||
#options['axis'] = 1
|
||||
#ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, Polarizability, options)[5]
|
||||
#tmp = ExtractedTrapFrequencies[1][0]
|
||||
#v_y[i] = tmp if not np.isinf(tmp) else np.nan
|
||||
options['axis'] = 2
|
||||
ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, Polarizability, options)[5]
|
||||
tmp = ExtractedTrapFrequencies[1][0]
|
||||
v_z[i] = tmp if not np.isinf(tmp) else np.nan
|
||||
|
||||
#v_x[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'x').value
|
||||
#v_y[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'y').value
|
||||
#v_z[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'z').value
|
||||
|
||||
alpha_x = [(v_x[0]/v)**(2/3) for v in v_x]
|
||||
alpha_z = [(v_z[0]/v)**2 for v in v_z]
|
||||
|
||||
calc_alpha_deviation = [(g - h) for g, h in zip(alpha_x, alpha_z)]
|
||||
|
||||
plt.figure()
|
||||
plt.plot(aspect_ratio, alpha_x, '-o', label = 'From horz TF')
|
||||
plt.plot(aspect_ratio, alpha_z, '-^', label = 'From vert TF')
|
||||
plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('$\\alpha$', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
plt.figure()
|
||||
plt.plot(aspect_ratio, calc_alpha_deviation, '--ob', label = 'Extracted')
|
||||
plt.plot(new_aspect_ratio, sorted_meas_alpha_deviation, '-or', label = 'Measured')
|
||||
plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('$\\Delta \\alpha$', fontsize= 12, fontweight='bold')
|
||||
plt.ylim([-0.5, 1])
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
"""Plot ideal crossed beam trap potential resulting for given parameters only"""
|
||||
|
||||
# Powers = [40, 11] * u.W
|
||||
# Powers = [40, 10] * u.W
|
||||
# Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
|
||||
# Wavelength = 1.064*u.um
|
||||
# w_x = [27.5, 54]*u.um # Beam Waists in the x direction
|
||||
# w_z = [33.8, 54]*u.um # Beam Waists in the y direction
|
||||
|
||||
# Powers = [30, 8] * u.W
|
||||
# Polarizability = 136 # in a.u, most precise measured value of Dy polarizability
|
||||
# Wavelength = 1.064*u.um
|
||||
# w_x = [20.5, 101.3]*u.um # Beam Waists in the x direction
|
||||
# w_z = [20.5, 95.0]*u.um # Beam Waists in the y direction
|
||||
|
||||
# options = {
|
||||
# 'axis': 3, # axis referenced to the beam along which you want the dipole trap potential
|
||||
# 'extent': 1e2, # range of spatial coordinates in one direction to calculate trap potential over
|
||||
# 'axis': 1, # axis referenced to the beam along which you want the dipole trap potential
|
||||
# 'extent': 2e3, # range of spatial coordinates in one direction to calculate trap potential over
|
||||
# 'crossed': True,
|
||||
# 'theta': 70,
|
||||
# 'delta': 70,
|
||||
# 'modulation': False,
|
||||
# 'aspect_ratio': 5,
|
||||
# 'gravity': False,
|
||||
@ -1078,19 +1166,20 @@ if __name__ == '__main__':
|
||||
# 'disp_foci': 3 * z_R(w_0 = np.asarray([30]), lamb = 1.064)[0]*u.um # difference in position of the foci along the propagation direction (Astigmatism)
|
||||
# }
|
||||
|
||||
# TrapPotential = computeTrapPotential(w_x, w_z, Powers, Polarizability, options)
|
||||
# Positions, TrapPotential = computeTrapPotential(w_x, w_z, Powers, Polarizability, options)
|
||||
|
||||
# # plt.rcParams["figure.figsize"] = [7.00, 3.50]
|
||||
# # plt.rcParams["figure.autolayout"] = True
|
||||
# # fig = plt.figure()
|
||||
# # ax = fig.add_subplot(111, projection='3d')
|
||||
# # ax.scatter(TrapPotential[0], TrapPotential[1], TrapPotential[2], c=TrapPotential[2], alpha=1)
|
||||
# # plt.show()
|
||||
# plt.rcParams["figure.figsize"] = [7.00, 3.50]
|
||||
# plt.rcParams["figure.autolayout"] = True
|
||||
# fig = plt.figure()
|
||||
# ax = fig.add_subplot(111, projection='3d')
|
||||
# ax.scatter(TrapPotential[0], TrapPotential[1], TrapPotential[2], c=TrapPotential[2], alpha=1)
|
||||
# plt.show()
|
||||
|
||||
# plt.figure()
|
||||
# plt.plot(TrapPotential[0])
|
||||
# plt.plot(Positions[options['axis']], TrapPotential[options['axis']], label = 'Crossed beam potential')
|
||||
# plt.xlim([-500, 500])
|
||||
# plt.ylim([-1800, -200])
|
||||
# plt.legend()
|
||||
# plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user