Broke up script in to modules.
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@ -1,9 +1,6 @@
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import math
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mtick
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from scipy import signal, interpolate
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from scipy.optimize import curve_fit
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from scipy import interpolate
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from astropy import units as u, constants as ac
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DY_POLARIZABILITY = 184.4 # in a.u, most precise measured value of Dy polarizability
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@ -11,77 +8,7 @@ DY_MASS = 164*u.u
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DY_DIPOLE_MOMENT = 9.93 * ac.muB
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#####################################################################
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# HELPER FUNCTIONS #
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#####################################################################
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def orderOfMagnitude(number):
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return math.floor(math.log(number, 10))
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def rotation_matrix(axis, theta):
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"""
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Return the rotation matrix associated with counterclockwise rotation about
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the given axis by theta radians.
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In 2-D it is just,
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thetaInRadians = np.radians(theta)
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c, s = np.cos(thetaInRadians), np.sin(thetaInRadians)
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R = np.array(((c, -s), (s, c)))
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In 3-D, one way to do it is use the Euler-Rodrigues Formula as is done here
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"""
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axis = np.asarray(axis)
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axis = axis / math.sqrt(np.dot(axis, axis))
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a = math.cos(theta / 2.0)
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b, c, d = -axis * math.sin(theta / 2.0)
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aa, bb, cc, dd = a * a, b * b, c * c, d * d
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bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
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return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
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[2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
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[2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
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def find_nearest(array, value):
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array = np.asarray(array)
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idx = (np.abs(array - value)).argmin()
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return idx
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def modulation_function(mod_amp, n_points, func = 'arccos'):
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if func == 'sin':
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phi = np.linspace(0, 2*np.pi, n_points)
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mod_func = mod_amp * np.sin(phi)
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elif func == 'arccos':
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# phi = np.linspace(0, 2*np.pi, n_points)
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# mod_func = mod_amp * (2/np.pi * np.arccos(phi/np.pi-1) - 1)
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phi = np.linspace(0, 2*np.pi, int(n_points/2))
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tmp_1 = 2/np.pi * np.arccos(phi/np.pi-1) - 1
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tmp_2 = np.flip(tmp_1)
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mod_func = mod_amp * np.concatenate((tmp_1, tmp_2))
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elif func == 'triangle':
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phi = np.linspace(0, 2*np.pi, n_points)
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mod_func = mod_amp * signal.sawtooth(phi, width = 0.5) # width of 0.5 gives symmetric rising triangle ramp
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elif func == 'square':
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phi = np.linspace(0, 1.99*np.pi, n_points)
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mod_func = mod_amp * signal.square(phi, duty = 0.5)
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else:
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mod_func = None
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if mod_func is not None:
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dx = (max(mod_func) - min(mod_func))/(2*n_points)
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return dx, mod_func
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#####################################################################
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# BEAM PARAMETERS #
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#####################################################################
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# Rayleigh length
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def z_R(w_0, lamb)->np.ndarray:
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return np.pi*w_0**2/lamb
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# Beam Radius
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def w(pos, w_0, lamb):
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return w_0*np.sqrt(1+(pos / z_R(w_0, lamb))**2)
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#####################################################################
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# CALCULATIONS
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# AUXILIARY COMPUTATIONS
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#####################################################################
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def calculateHeatingRate(w_x, w_y, P, linewidth, detuning, wavelength = 1.064*u.um):
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@ -218,104 +145,6 @@ def convert_modulation_depth_to_temperature(modulation_depth):
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return f(modulation_depth), fin_mod_dep, T_x, T_y, dT_x, dT_y
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#####################################################################
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# POTENTIALS #
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#####################################################################
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def gravitational_potential(positions, m):
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return m * ac.g0 * positions
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def single_gaussian_beam_potential(positions, waists, alpha = DY_POLARIZABILITY, P=1, wavelength=1.064*u.um):
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A = 2*P/(np.pi*w(positions[1,:], waists[0], wavelength)*w(positions[1,:], waists[1], wavelength))
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U_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
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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))
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return U
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def astigmatic_single_gaussian_beam_potential(positions, waists, del_y, alpha = DY_POLARIZABILITY, P=1, wavelength=1.064*u.um):
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A = 2*P/(np.pi*w(positions[1,:] - (del_y/2), waists[0], wavelength)*w(positions[1,:] + (del_y/2), waists[1], wavelength))
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U_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
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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))
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return U
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def modulated_single_gaussian_beam_potential(positions, waists, alpha = DY_POLARIZABILITY, P=1, wavelength=1.064*u.um, mod_amp=1):
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mod_amp = mod_amp * waists[0]
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n_points = len(positions[0,:])
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dx, x_mod = modulation_function(mod_amp, n_points, func = 'arccos')
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A = 2*P/(np.pi*w(positions[1,:], waists[0], wavelength)*w(positions[1,:], waists[1], wavelength))
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U_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
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dU = np.zeros(2*n_points)
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for i in range(len(x_mod)):
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dU = np.vstack((dU, np.exp(-2 * (np.subtract(x_mod[i], positions[0,:])/w(positions[1,:], waists[0], wavelength))**2)))
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U = - U_tilde * A * 1/(2*mod_amp) * np.trapz(dU, dx = dx, axis = 0)
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return U
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def harmonic_potential(pos, v, xoffset, yoffset, m = DY_MASS):
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U_Harmonic = ((0.5 * m * (2 * np.pi * v*u.Hz)**2 * (pos*u.um - xoffset*u.um)**2)/ac.k_B).to(u.uK) + yoffset*u.uK
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return U_Harmonic.value
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def gaussian_potential(pos, amp, waist, xoffset, yoffset):
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U_Gaussian = amp * np.exp(-2 * ((pos + xoffset) / waist)**2) + yoffset
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return U_Gaussian
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def crossed_beam_potential(positions, waists, P, options, alpha = DY_POLARIZABILITY, wavelength=1.064*u.um):
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delta = options['delta']
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foci_shift = options['foci_shift']
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focus_shift_beam_1 = foci_shift[0]
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focus_shift_beam_2 = foci_shift[1]
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beam_disp = options['beam_disp']
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beam_1_disp = (np.ones(np.shape(positions.T)) * np.array(beam_disp[0])).T * beam_disp[0].unit
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beam_2_disp = (np.ones(np.shape(positions.T)) * np.array(beam_disp[1])).T * beam_disp[1].unit
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beam_1_positions = positions + beam_1_disp
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A_1 = 2*P[0]/(np.pi*w(beam_1_positions[1,:] + focus_shift_beam_1, waists[0][0], wavelength)*w(beam_1_positions[1,:] + focus_shift_beam_1, waists[0][1], wavelength))
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U_1_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
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U_1 = - U_1_tilde * A_1 * np.exp(-2 * ((beam_1_positions[0,:]/w(beam_1_positions[1,:] + focus_shift_beam_1, waists[0][0], wavelength))**2 + (beam_1_positions[2,:]/w(beam_1_positions[1,:] + focus_shift_beam_1, waists[0][1], wavelength))**2))
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R = rotation_matrix([0, 0, 1], np.radians(delta))
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beam_2_positions = np.dot(R, positions + beam_2_disp)
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A_2 = 2*P[1]/(np.pi*w(beam_2_positions[1,:] + focus_shift_beam_2, waists[1][0], wavelength)*w(beam_2_positions[1,:] + focus_shift_beam_2, waists[1][1], wavelength))
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U_2_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
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U_2 = - U_2_tilde * A_2 * np.exp(-2 * ((beam_2_positions[0,:]/w(beam_2_positions[1,:] + focus_shift_beam_2, waists[1][0], wavelength))**2 + (beam_2_positions[2,:]/w(beam_2_positions[1,:] + focus_shift_beam_2, waists[1][1], wavelength))**2))
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U = U_1 + U_2
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return U
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def astigmatic_crossed_beam_potential(positions, waists, P, options, alpha = DY_POLARIZABILITY, wavelength=1.064*u.um):
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delta = options['delta']
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del_y = options['foci_disp_crossed']
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del_y_1 = del_y[0]
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del_y_2 = del_y[1]
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foci_shift = options['foci_shift']
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focus_shift_beam_1 = foci_shift[0]
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focus_shift_beam_2 = foci_shift[1]
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beam_disp = options['beam_disp']
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beam_1_disp = (np.ones(np.shape(positions.T)) * np.array(beam_disp[0])).T * beam_disp[0].unit
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beam_2_disp = (np.ones(np.shape(positions.T)) * np.array(beam_disp[1])).T * beam_disp[1].unit
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beam_1_positions = positions + beam_1_disp
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A_1 = 2*P[0]/(np.pi*w(beam_1_positions[1,:] - (del_y_1/2) + focus_shift_beam_1, waists[0][0], wavelength)*w(beam_1_positions[1,:] + (del_y_1/2) + focus_shift_beam_1, waists[0][1], wavelength))
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U_1_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
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U_1 = - U_1_tilde * A_1 * np.exp(-2 * ((beam_1_positions[0,:]/w(beam_1_positions[1,:] - (del_y_1/2) + focus_shift_beam_1, waists[0][0], wavelength))**2 + (beam_1_positions[2,:]/w(beam_1_positions[1,:] + (del_y_1/2) + focus_shift_beam_1, waists[0][1], wavelength))**2))
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R = rotation_matrix([0, 0, 1], np.radians(delta))
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beam_2_positions = np.dot(R, positions + beam_2_disp)
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A_2 = 2*P[1]/(np.pi*w(beam_2_positions[1,:] - (del_y_2/2) + focus_shift_beam_2, waists[1][0], wavelength)*w(beam_2_positions[1,:] + (del_y_2/2) + focus_shift_beam_2, waists[1][1], wavelength))
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U_2_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
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U_2 = - U_2_tilde * A_2 * np.exp(-2 * ((beam_2_positions[0,:]/w(beam_2_positions[1,:] - (del_y_2/2) + focus_shift_beam_2, waists[1][0], wavelength))**2 + (beam_2_positions[2,:]/w(beam_2_positions[1,:] + (del_y_2/2) + focus_shift_beam_2, waists[1][1], wavelength))**2))
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U = U_1 + U_2
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return U
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#####################################################################
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# COMPUTE/EXTRACT TRAP POTENTIAL AND PARAMETERS #
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#####################################################################
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@ -676,341 +505,3 @@ def computeIntensityProfileAndPotentials(Power, waists, wavelength, options, alp
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dextracted_waist_z = pcovz[1][1]**0.5
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return [x_Positions, z_Positions], [extracted_waist_x, dextracted_waist_x, extracted_waist_z, dextracted_waist_z], I, U, [poptx, pcovx, poptz, pcovz]
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#####################################################################
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# PLOTTING #
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#####################################################################
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def generate_label(v, dv):
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unit = 'Hz'
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if v <= 0.0:
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v = np.nan
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dv = np.nan
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unit = 'Hz'
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elif v > 0.0 and orderOfMagnitude(v) > 2:
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v = v / 1e3 # in kHz
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dv = dv / 1e3 # in kHz
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unit = 'kHz'
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tf_label = '\u03BD = %.1f \u00B1 %.2f %s'% tuple([v,dv,unit])
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return tf_label
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def plotHarmonicFit(Positions, TrappingPotential, TrapDepthsInKelvin, axis, popt, pcov):
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v = popt[0]
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dv = pcov[0][0]**0.5
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happrox = harmonic_potential(Positions[axis, :].value, *popt)
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fig = plt.figure(figsize=(12, 6))
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ax = fig.add_subplot(121)
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ax.set_title('Fit to Potential')
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plt.plot(Positions[axis, :].value, happrox, '-r', label = '\u03BD = %.1f \u00B1 %.2f Hz'% tuple([v,dv]))
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plt.plot(Positions[axis, :], TrappingPotential[axis], 'ob', label = 'Gaussian Potential')
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plt.xlabel('Distance (um)', fontsize= 12, fontweight='bold')
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plt.ylabel('Trap Potential (uK)', fontsize= 12, fontweight='bold')
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plt.ylim([-TrapDepthsInKelvin[0].value, max(TrappingPotential[axis].value)])
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plt.grid(visible=1)
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plt.legend(prop={'size': 12, 'weight': 'bold'})
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bx = fig.add_subplot(122)
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bx.set_title('Fit Residuals')
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plt.plot(Positions[axis, :].value, TrappingPotential[axis].value - happrox, 'ob')
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plt.xlabel('Distance (um)', fontsize= 12, fontweight='bold')
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plt.ylabel('$U_{trap} - U_{Harmonic}$', fontsize= 12, fontweight='bold')
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plt.xlim([-10, 10])
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plt.ylim([-1e-2, 1e-2])
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plt.grid(visible=1)
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plt.tight_layout()
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plt.show()
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def plotGaussianFit(Positions, TrappingPotential, popt, pcov):
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extracted_waist = popt[1]
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dextracted_waist = pcov[1][1]**0.5
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gapprox = gaussian_potential(Positions, *popt)
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fig = plt.figure(figsize=(12, 6))
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ax = fig.add_subplot(121)
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ax.set_title('Fit to Potential')
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plt.plot(Positions, gapprox, '-r', label = 'waist = %.1f \u00B1 %.2f um'% tuple([extracted_waist,dextracted_waist]))
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plt.plot(Positions, TrappingPotential, 'ob', label = 'Gaussian Potential')
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plt.xlabel('Distance (um)', fontsize= 12, fontweight='bold')
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plt.ylabel('Trap Potential (uK)', fontsize= 12, fontweight='bold')
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plt.ylim([min(TrappingPotential), max(TrappingPotential)])
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plt.grid(visible=1)
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plt.legend(prop={'size': 12, 'weight': 'bold'})
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bx = fig.add_subplot(122)
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bx.set_title('Fit Residuals')
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plt.plot(Positions, TrappingPotential - gapprox, 'ob')
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plt.xlabel('Distance (um)', fontsize= 12, fontweight='bold')
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plt.ylabel('$U_{trap} - U_{Gaussian}$', fontsize= 12, fontweight='bold')
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plt.xlim([-10, 10])
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plt.ylim([-1, 1])
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plt.grid(visible=1)
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plt.tight_layout()
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plt.show()
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def plotPotential(Positions, ComputedPotentials, options, Params = [], listToIterateOver = [], save = False):
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axis = options['axis']
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plt.figure(figsize=(9, 7))
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for i in range(np.size(ComputedPotentials, 0)):
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if i % 2 == 0:
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j = int(i / 2)
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else:
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j = int((i - 1) / 2)
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IdealTrapDepthInKelvin = Params[j][0][0]
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EffectiveTrapDepthInKelvin = Params[j][0][1]
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idealv = Params[j][2][0][0]
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idealdv = Params[j][2][0][1]
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if options['extract_trap_frequencies']:
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v = Params[j][2][1][0]
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dv = Params[j][2][1][1]
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else:
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v = np.nan
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dv = np.nan
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if listToIterateOver:
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if np.size(ComputedPotentials, 0) == len(listToIterateOver):
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plt.plot(Positions[axis], ComputedPotentials[i][axis], label = 'Trap Depth = ' + str(round(EffectiveTrapDepthInKelvin.value, 2)) + ' ' + str(EffectiveTrapDepthInKelvin.unit) + '; ' + generate_label(v, dv))
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else:
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if i % 2 == 0:
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plt.plot(Positions[axis], ComputedPotentials[i][axis], '--', label = 'Trap Depth = ' + str(round(IdealTrapDepthInKelvin.value, 2)) + ' ' + str(IdealTrapDepthInKelvin.unit) + '; ' + generate_label(idealv, idealdv))
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elif i % 2 != 0:
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plt.plot(Positions[axis], ComputedPotentials[i][axis], label = 'Effective Trap Depth = ' + str(round(EffectiveTrapDepthInKelvin.value, 2)) + ' ' + str(EffectiveTrapDepthInKelvin.unit) + '; ' + generate_label(v, dv))
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else:
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if i % 2 == 0:
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plt.plot(Positions[axis], ComputedPotentials[i][axis], '--', label = 'Trap Depth = ' + str(round(IdealTrapDepthInKelvin.value, 2)) + ' ' + str(IdealTrapDepthInKelvin.unit) + '; ' + generate_label(idealv, idealdv))
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elif i % 2 != 0:
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plt.plot(Positions[axis], ComputedPotentials[i][axis], label = 'Effective Trap Depth = ' + str(round(EffectiveTrapDepthInKelvin.value, 2)) + ' ' + str(EffectiveTrapDepthInKelvin.unit) + '; ' + generate_label(v, dv))
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if axis == 0:
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dir = 'X - Horizontal'
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elif axis == 1:
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dir = 'Y - Propagation'
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else:
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dir = 'Z - Vertical'
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plt.ylim(top = 0)
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plt.xlabel(dir + ' Direction (um)', fontsize= 12, fontweight='bold')
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plt.ylabel('Trap Potential (uK)', fontsize= 12, fontweight='bold')
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plt.tight_layout()
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plt.grid(visible=1)
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plt.legend(loc=3, prop={'size': 12, 'weight': 'bold'})
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if save:
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plt.savefig('pot_' + dir + '.png')
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plt.show()
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def plotIntensityProfileAndPotentials(positions, waists, I, U):
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x_Positions = positions[0]
|
||||
z_Positions = positions[1]
|
||||
|
||||
w_x = waists[0]
|
||||
dw_x = waists[1]
|
||||
w_z = waists[2]
|
||||
dw_x = waists[3]
|
||||
|
||||
ar = w_x/w_z
|
||||
dar = ar * np.sqrt((dw_x/w_x)**2 + (dw_x/w_z)**2)
|
||||
|
||||
fig = plt.figure(figsize=(12, 6))
|
||||
ax = fig.add_subplot(121)
|
||||
ax.set_title('Intensity Profile ($MW/cm^2$)\n Aspect Ratio = %.2f \u00B1 %.2f um'% tuple([ar,dar]))
|
||||
im = plt.imshow(np.transpose(I.value), cmap="coolwarm", extent=[np.min(x_Positions.value), np.max(x_Positions.value), np.min(z_Positions.value), np.max(z_Positions.value)])
|
||||
plt.xlabel('X - Horizontal (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Z - Vertical (um)', fontsize= 12, fontweight='bold')
|
||||
ax.set_aspect('equal')
|
||||
fig.colorbar(im, fraction=0.046, pad=0.04, orientation='vertical')
|
||||
|
||||
bx = fig.add_subplot(122)
|
||||
bx.set_title('Trap Potential')
|
||||
plt.plot(x_Positions, U[:, np.where(z_Positions==0)[0][0]], label = 'X - Horizontal')
|
||||
plt.plot(z_Positions, U[np.where(x_Positions==0)[0][0], :], label = 'Z - Vertical')
|
||||
plt.ylim(top = 0)
|
||||
plt.xlabel('Extent (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Depth (uK)', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
def plotAlphas():
|
||||
|
||||
modulation_depth = np.arange(0, 1.1, 0.1)
|
||||
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', 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):
|
||||
|
||||
modulation_depth = np.arange(0, 1.1, 0.1)
|
||||
w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]
|
||||
new_aspect_ratio = w_xs / w_z
|
||||
Temperatures, fin_mod_dep, T_x, T_y, dT_x, dT_y = convert_modulation_depth_to_temperature(modulation_depth)
|
||||
measured_aspect_ratio = (w_x * convert_modulation_depth_to_alpha(fin_mod_dep)[0]) / w_z
|
||||
|
||||
plt.figure()
|
||||
if plot_against_mod_depth:
|
||||
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', 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'
|
||||
|
||||
plt.xlabel(xlabel, fontsize= 12, fontweight='bold')
|
||||
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, '-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, '-*g', label = 'v_y')
|
||||
xlabel = 'Modulation depth'
|
||||
else:
|
||||
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, '-*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='g')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
lns = ln1+ln2+ln3
|
||||
labs = [l.get_label() for l in lns]
|
||||
ax3.legend(lns, labs, prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
def plotMeasuredTrapFrequencies(fx, dfx, fy, dfy, fz, dfz, modulation_depth_radial, modulation_depth_axial, w_x, w_z, plot_against_mod_depth = True):
|
||||
|
||||
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))]
|
||||
|
||||
avg_alpha = [(g + h) / 2 for g, h in zip(alpha_x, alpha_y)]
|
||||
new_aspect_ratio = (w_x * avg_alpha) / w_z
|
||||
|
||||
|
||||
if plot_against_mod_depth:
|
||||
fig, ax1 = plt.subplots(figsize=(8, 6))
|
||||
ax2 = ax1.twinx()
|
||||
ax1.errorbar(modulation_depth_radial, fx, yerr = dfx, fmt= 'or', label = 'v_x', markersize=5, capsize=5)
|
||||
ax2.errorbar(modulation_depth_axial, fy, yerr = dfy, fmt= '*g', label = 'v_y', markersize=5, capsize=5)
|
||||
ax1.errorbar(modulation_depth_radial, fz, yerr = dfz, fmt= '^b', label = 'v_z', markersize=5, capsize=5)
|
||||
ax1.set_xlabel('Modulation depth', fontsize= 12, fontweight='bold')
|
||||
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='g')
|
||||
h1, l1 = ax1.get_legend_handles_labels()
|
||||
h2, l2 = ax2.get_legend_handles_labels()
|
||||
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)
|
||||
plt.errorbar(new_aspect_ratio, fz, yerr = dfz, fmt= '^b', label = 'v_z', markersize=5, capsize=5)
|
||||
plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Trap Frequency (kHz)', fontsize= 12, fontweight='bold')
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.show()
|
||||
|
||||
def plotRatioOfTrapFrequencies(fx, fy, fz, dfx, dfy, dfz, v_x, v_y, v_z, modulation_depth, w_x, w_z, plot_against_mod_depth = True):
|
||||
|
||||
w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]
|
||||
new_aspect_ratio = w_xs / w_z
|
||||
|
||||
plt.figure()
|
||||
|
||||
if plot_against_mod_depth:
|
||||
plt.errorbar(modulation_depth, fx/v_x, yerr = dfx/v_x, fmt= 'or', label = 'b/w horz TF', markersize=5, capsize=5)
|
||||
plt.errorbar(modulation_depth, fy/v_y, yerr = dfy/v_y, fmt= '*g', label = 'b/w axial TF', markersize=5, capsize=5)
|
||||
plt.errorbar(modulation_depth, fz/v_z, yerr = dfz/v_z, fmt= '^b', label = 'b/w vert TF', markersize=5, capsize=5)
|
||||
xlabel = 'Modulation depth'
|
||||
else:
|
||||
plt.errorbar(new_aspect_ratio, fx/v_x, yerr = dfx/v_x, fmt= 'or', label = 'b/w horz TF', markersize=5, capsize=5)
|
||||
plt.errorbar(new_aspect_ratio, fy/v_y, yerr = dfy/v_y, fmt= '*g', label = 'b/w axial TF', markersize=5, capsize=5)
|
||||
plt.errorbar(new_aspect_ratio, fz/v_z, yerr = dfz/v_z, fmt= '^b', label = 'b/w vert TF', markersize=5, capsize=5)
|
||||
xlabel = 'Aspect Ratio'
|
||||
|
||||
plt.xlabel(xlabel, fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Ratio', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
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])
|
||||
rmelmIdx = [i for i, x in enumerate(np.isinf(a_s_array.value)) if x]
|
||||
for x in rmelmIdx:
|
||||
a_s_array[x-1] = np.inf * ac.a0
|
||||
|
||||
plt.figure(figsize=(9, 7))
|
||||
plt.plot(BField, a_s_array/ac.a0, '-b')
|
||||
plt.axhline(y = a_bkg/ac.a0, color = 'r', linestyle = '--')
|
||||
plt.text(min(BField.value) + 0.5, (a_bkg/ac.a0).value + 1, '$a_{bkg}$ = %.2f a0' %((a_bkg/ac.a0).value), fontsize=14, fontweight='bold')
|
||||
plt.xlim([min(BField.value), max(BField.value)])
|
||||
plt.ylim([65, 125])
|
||||
plt.xlabel('B field (G)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Scattering length (a0)', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.show()
|
||||
|
||||
def plotCollisionRatesAndPSD(Gamma_elastic, PSD, modulation_depth, new_aspect_ratio, plot_against_mod_depth = True):
|
||||
fig, ax1 = plt.subplots(figsize=(8, 6))
|
||||
ax2 = ax1.twinx()
|
||||
|
||||
if plot_against_mod_depth:
|
||||
ax1.plot(modulation_depth, Gamma_elastic, '-ob')
|
||||
ax2.plot(modulation_depth, PSD, '-*r')
|
||||
ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
|
||||
xlabel = 'Modulation depth'
|
||||
else:
|
||||
ax1.plot(new_aspect_ratio, Gamma_elastic, '-ob')
|
||||
ax2.plot(new_aspect_ratio, PSD, '-*r')
|
||||
ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
|
||||
xlabel = 'Aspect Ratio'
|
||||
|
||||
ax1.set_xlabel(xlabel, fontsize= 12, fontweight='bold')
|
||||
ax1.set_ylabel('Elastic Collision Rate', 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()
|
||||
|
||||
#####################################################################
|
||||
80
ODT Calculator/Helpers.py
Normal file
80
ODT Calculator/Helpers.py
Normal file
@ -0,0 +1,80 @@
|
||||
import math
|
||||
import numpy as np
|
||||
from scipy import signal
|
||||
from astropy import units as u, constants as ac
|
||||
|
||||
DY_POLARIZABILITY = 184.4 # in a.u, most precise measured value of Dy polarizability
|
||||
DY_MASS = 164*u.u
|
||||
DY_DIPOLE_MOMENT = 9.93 * ac.muB
|
||||
|
||||
#####################################################################
|
||||
# HELPER FUNCTIONS #
|
||||
#####################################################################
|
||||
|
||||
def orderOfMagnitude(number):
|
||||
return math.floor(math.log(number, 10))
|
||||
|
||||
def rotation_matrix(axis, theta):
|
||||
"""
|
||||
Return the rotation matrix associated with counterclockwise rotation about
|
||||
the given axis by theta radians.
|
||||
In 2-D it is just,
|
||||
thetaInRadians = np.radians(theta)
|
||||
c, s = np.cos(thetaInRadians), np.sin(thetaInRadians)
|
||||
R = np.array(((c, -s), (s, c)))
|
||||
In 3-D, one way to do it is use the Euler-Rodrigues Formula as is done here
|
||||
"""
|
||||
axis = np.asarray(axis)
|
||||
axis = axis / math.sqrt(np.dot(axis, axis))
|
||||
a = math.cos(theta / 2.0)
|
||||
b, c, d = -axis * math.sin(theta / 2.0)
|
||||
aa, bb, cc, dd = a * a, b * b, c * c, d * d
|
||||
bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
|
||||
return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
|
||||
[2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
|
||||
[2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
|
||||
|
||||
def find_nearest(array, value):
|
||||
array = np.asarray(array)
|
||||
idx = (np.abs(array - value)).argmin()
|
||||
return idx
|
||||
|
||||
def modulation_function(mod_amp, n_points, func = 'arccos'):
|
||||
|
||||
if func == 'sin':
|
||||
phi = np.linspace(0, 2*np.pi, n_points)
|
||||
mod_func = mod_amp * np.sin(phi)
|
||||
elif func == 'arccos':
|
||||
# phi = np.linspace(0, 2*np.pi, n_points)
|
||||
# mod_func = mod_amp * (2/np.pi * np.arccos(phi/np.pi-1) - 1)
|
||||
phi = np.linspace(0, 2*np.pi, int(n_points/2))
|
||||
tmp_1 = 2/np.pi * np.arccos(phi/np.pi-1) - 1
|
||||
tmp_2 = np.flip(tmp_1)
|
||||
mod_func = mod_amp * np.concatenate((tmp_1, tmp_2))
|
||||
elif func == 'triangle':
|
||||
phi = np.linspace(0, 2*np.pi, n_points)
|
||||
mod_func = mod_amp * signal.sawtooth(phi, width = 0.5) # width of 0.5 gives symmetric rising triangle ramp
|
||||
elif func == 'square':
|
||||
phi = np.linspace(0, 1.99*np.pi, n_points)
|
||||
mod_func = mod_amp * signal.square(phi, duty = 0.5)
|
||||
else:
|
||||
mod_func = None
|
||||
|
||||
if mod_func is not None:
|
||||
dx = (max(mod_func) - min(mod_func))/(2*n_points)
|
||||
|
||||
return dx, mod_func
|
||||
|
||||
#####################################################################
|
||||
# BEAM PARAMETERS #
|
||||
#####################################################################
|
||||
|
||||
# Rayleigh length
|
||||
def z_R(w_0, lamb)->np.ndarray:
|
||||
return np.pi*w_0**2/lamb
|
||||
|
||||
# Beam Radius
|
||||
def w(pos, w_0, lamb):
|
||||
return w_0*np.sqrt(1+(pos / z_R(w_0, lamb))**2)
|
||||
|
||||
|
||||
342
ODT Calculator/Plotter.py
Normal file
342
ODT Calculator/Plotter.py
Normal file
@ -0,0 +1,342 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as mtick
|
||||
from astropy import units as u, constants as ac
|
||||
|
||||
#####################################################################
|
||||
# PLOTTING #
|
||||
#####################################################################
|
||||
|
||||
def generate_label(v, dv):
|
||||
unit = 'Hz'
|
||||
if v <= 0.0:
|
||||
v = np.nan
|
||||
dv = np.nan
|
||||
unit = 'Hz'
|
||||
elif v > 0.0 and orderOfMagnitude(v) > 2:
|
||||
v = v / 1e3 # in kHz
|
||||
dv = dv / 1e3 # in kHz
|
||||
unit = 'kHz'
|
||||
tf_label = '\u03BD = %.1f \u00B1 %.2f %s'% tuple([v,dv,unit])
|
||||
return tf_label
|
||||
|
||||
def plotHarmonicFit(Positions, TrappingPotential, TrapDepthsInKelvin, axis, popt, pcov):
|
||||
v = popt[0]
|
||||
dv = pcov[0][0]**0.5
|
||||
happrox = harmonic_potential(Positions[axis, :].value, *popt)
|
||||
fig = plt.figure(figsize=(12, 6))
|
||||
ax = fig.add_subplot(121)
|
||||
ax.set_title('Fit to Potential')
|
||||
plt.plot(Positions[axis, :].value, happrox, '-r', label = '\u03BD = %.1f \u00B1 %.2f Hz'% tuple([v,dv]))
|
||||
plt.plot(Positions[axis, :], TrappingPotential[axis], 'ob', label = 'Gaussian Potential')
|
||||
plt.xlabel('Distance (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Trap Potential (uK)', fontsize= 12, fontweight='bold')
|
||||
plt.ylim([-TrapDepthsInKelvin[0].value, max(TrappingPotential[axis].value)])
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
|
||||
bx = fig.add_subplot(122)
|
||||
bx.set_title('Fit Residuals')
|
||||
plt.plot(Positions[axis, :].value, TrappingPotential[axis].value - happrox, 'ob')
|
||||
plt.xlabel('Distance (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('$U_{trap} - U_{Harmonic}$', fontsize= 12, fontweight='bold')
|
||||
plt.xlim([-10, 10])
|
||||
plt.ylim([-1e-2, 1e-2])
|
||||
plt.grid(visible=1)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
def plotGaussianFit(Positions, TrappingPotential, popt, pcov):
|
||||
extracted_waist = popt[1]
|
||||
dextracted_waist = pcov[1][1]**0.5
|
||||
gapprox = gaussian_potential(Positions, *popt)
|
||||
fig = plt.figure(figsize=(12, 6))
|
||||
ax = fig.add_subplot(121)
|
||||
ax.set_title('Fit to Potential')
|
||||
plt.plot(Positions, gapprox, '-r', label = 'waist = %.1f \u00B1 %.2f um'% tuple([extracted_waist,dextracted_waist]))
|
||||
plt.plot(Positions, TrappingPotential, 'ob', label = 'Gaussian Potential')
|
||||
plt.xlabel('Distance (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Trap Potential (uK)', fontsize= 12, fontweight='bold')
|
||||
plt.ylim([min(TrappingPotential), max(TrappingPotential)])
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
|
||||
bx = fig.add_subplot(122)
|
||||
bx.set_title('Fit Residuals')
|
||||
plt.plot(Positions, TrappingPotential - gapprox, 'ob')
|
||||
plt.xlabel('Distance (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('$U_{trap} - U_{Gaussian}$', fontsize= 12, fontweight='bold')
|
||||
plt.xlim([-10, 10])
|
||||
plt.ylim([-1, 1])
|
||||
plt.grid(visible=1)
|
||||
plt.tight_layout()
|
||||
plt.show()
|
||||
|
||||
def plotPotential(Positions, ComputedPotentials, options, Params = [], listToIterateOver = [], save = False):
|
||||
|
||||
axis = options['axis']
|
||||
|
||||
plt.figure(figsize=(9, 7))
|
||||
for i in range(np.size(ComputedPotentials, 0)):
|
||||
|
||||
if i % 2 == 0:
|
||||
j = int(i / 2)
|
||||
else:
|
||||
j = int((i - 1) / 2)
|
||||
|
||||
IdealTrapDepthInKelvin = Params[j][0][0]
|
||||
EffectiveTrapDepthInKelvin = Params[j][0][1]
|
||||
|
||||
idealv = Params[j][2][0][0]
|
||||
idealdv = Params[j][2][0][1]
|
||||
|
||||
if options['extract_trap_frequencies']:
|
||||
v = Params[j][2][1][0]
|
||||
dv = Params[j][2][1][1]
|
||||
else:
|
||||
v = np.nan
|
||||
dv = np.nan
|
||||
|
||||
if listToIterateOver:
|
||||
if np.size(ComputedPotentials, 0) == len(listToIterateOver):
|
||||
plt.plot(Positions[axis], ComputedPotentials[i][axis], label = 'Trap Depth = ' + str(round(EffectiveTrapDepthInKelvin.value, 2)) + ' ' + str(EffectiveTrapDepthInKelvin.unit) + '; ' + generate_label(v, dv))
|
||||
else:
|
||||
if i % 2 == 0:
|
||||
plt.plot(Positions[axis], ComputedPotentials[i][axis], '--', label = 'Trap Depth = ' + str(round(IdealTrapDepthInKelvin.value, 2)) + ' ' + str(IdealTrapDepthInKelvin.unit) + '; ' + generate_label(idealv, idealdv))
|
||||
elif i % 2 != 0:
|
||||
plt.plot(Positions[axis], ComputedPotentials[i][axis], label = 'Effective Trap Depth = ' + str(round(EffectiveTrapDepthInKelvin.value, 2)) + ' ' + str(EffectiveTrapDepthInKelvin.unit) + '; ' + generate_label(v, dv))
|
||||
else:
|
||||
if i % 2 == 0:
|
||||
plt.plot(Positions[axis], ComputedPotentials[i][axis], '--', label = 'Trap Depth = ' + str(round(IdealTrapDepthInKelvin.value, 2)) + ' ' + str(IdealTrapDepthInKelvin.unit) + '; ' + generate_label(idealv, idealdv))
|
||||
elif i % 2 != 0:
|
||||
plt.plot(Positions[axis], ComputedPotentials[i][axis], label = 'Effective Trap Depth = ' + str(round(EffectiveTrapDepthInKelvin.value, 2)) + ' ' + str(EffectiveTrapDepthInKelvin.unit) + '; ' + generate_label(v, dv))
|
||||
if axis == 0:
|
||||
dir = 'X - Horizontal'
|
||||
elif axis == 1:
|
||||
dir = 'Y - Propagation'
|
||||
else:
|
||||
dir = 'Z - Vertical'
|
||||
|
||||
plt.ylim(top = 0)
|
||||
plt.xlabel(dir + ' Direction (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Trap Potential (uK)', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(loc=3, prop={'size': 12, 'weight': 'bold'})
|
||||
if save:
|
||||
plt.savefig('pot_' + dir + '.png')
|
||||
plt.show()
|
||||
|
||||
def plotIntensityProfileAndPotentials(positions, waists, I, U):
|
||||
|
||||
x_Positions = positions[0]
|
||||
z_Positions = positions[1]
|
||||
|
||||
w_x = waists[0]
|
||||
dw_x = waists[1]
|
||||
w_z = waists[2]
|
||||
dw_x = waists[3]
|
||||
|
||||
ar = w_x/w_z
|
||||
dar = ar * np.sqrt((dw_x/w_x)**2 + (dw_x/w_z)**2)
|
||||
|
||||
fig = plt.figure(figsize=(12, 6))
|
||||
ax = fig.add_subplot(121)
|
||||
ax.set_title('Intensity Profile ($MW/cm^2$)\n Aspect Ratio = %.2f \u00B1 %.2f um'% tuple([ar,dar]))
|
||||
im = plt.imshow(np.transpose(I.value), cmap="coolwarm", extent=[np.min(x_Positions.value), np.max(x_Positions.value), np.min(z_Positions.value), np.max(z_Positions.value)])
|
||||
plt.xlabel('X - Horizontal (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Z - Vertical (um)', fontsize= 12, fontweight='bold')
|
||||
ax.set_aspect('equal')
|
||||
fig.colorbar(im, fraction=0.046, pad=0.04, orientation='vertical')
|
||||
|
||||
bx = fig.add_subplot(122)
|
||||
bx.set_title('Trap Potential')
|
||||
plt.plot(x_Positions, U[:, np.where(z_Positions==0)[0][0]], label = 'X - Horizontal')
|
||||
plt.plot(z_Positions, U[np.where(x_Positions==0)[0][0], :], label = 'Z - Vertical')
|
||||
plt.ylim(top = 0)
|
||||
plt.xlabel('Extent (um)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Depth (uK)', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
def plotAlphas():
|
||||
|
||||
modulation_depth = np.arange(0, 1.1, 0.1)
|
||||
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', 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):
|
||||
|
||||
modulation_depth = np.arange(0, 1.1, 0.1)
|
||||
w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]
|
||||
new_aspect_ratio = w_xs / w_z
|
||||
Temperatures, fin_mod_dep, T_x, T_y, dT_x, dT_y = convert_modulation_depth_to_temperature(modulation_depth)
|
||||
measured_aspect_ratio = (w_x * convert_modulation_depth_to_alpha(fin_mod_dep)[0]) / w_z
|
||||
|
||||
plt.figure()
|
||||
if plot_against_mod_depth:
|
||||
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', 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'
|
||||
|
||||
plt.xlabel(xlabel, fontsize= 12, fontweight='bold')
|
||||
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, '-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, '-*g', label = 'v_y')
|
||||
xlabel = 'Modulation depth'
|
||||
else:
|
||||
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, '-*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='g')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
lns = ln1+ln2+ln3
|
||||
labs = [l.get_label() for l in lns]
|
||||
ax3.legend(lns, labs, prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
def plotMeasuredTrapFrequencies(fx, dfx, fy, dfy, fz, dfz, modulation_depth_radial, modulation_depth_axial, w_x, w_z, plot_against_mod_depth = True):
|
||||
|
||||
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))]
|
||||
|
||||
avg_alpha = [(g + h) / 2 for g, h in zip(alpha_x, alpha_y)]
|
||||
new_aspect_ratio = (w_x * avg_alpha) / w_z
|
||||
|
||||
|
||||
if plot_against_mod_depth:
|
||||
fig, ax1 = plt.subplots(figsize=(8, 6))
|
||||
ax2 = ax1.twinx()
|
||||
ax1.errorbar(modulation_depth_radial, fx, yerr = dfx, fmt= 'or', label = 'v_x', markersize=5, capsize=5)
|
||||
ax2.errorbar(modulation_depth_axial, fy, yerr = dfy, fmt= '*g', label = 'v_y', markersize=5, capsize=5)
|
||||
ax1.errorbar(modulation_depth_radial, fz, yerr = dfz, fmt= '^b', label = 'v_z', markersize=5, capsize=5)
|
||||
ax1.set_xlabel('Modulation depth', fontsize= 12, fontweight='bold')
|
||||
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='g')
|
||||
h1, l1 = ax1.get_legend_handles_labels()
|
||||
h2, l2 = ax2.get_legend_handles_labels()
|
||||
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)
|
||||
plt.errorbar(new_aspect_ratio, fz, yerr = dfz, fmt= '^b', label = 'v_z', markersize=5, capsize=5)
|
||||
plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Trap Frequency (kHz)', fontsize= 12, fontweight='bold')
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.show()
|
||||
|
||||
def plotRatioOfTrapFrequencies(fx, fy, fz, dfx, dfy, dfz, v_x, v_y, v_z, modulation_depth, w_x, w_z, plot_against_mod_depth = True):
|
||||
|
||||
w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]
|
||||
new_aspect_ratio = w_xs / w_z
|
||||
|
||||
plt.figure()
|
||||
|
||||
if plot_against_mod_depth:
|
||||
plt.errorbar(modulation_depth, fx/v_x, yerr = dfx/v_x, fmt= 'or', label = 'b/w horz TF', markersize=5, capsize=5)
|
||||
plt.errorbar(modulation_depth, fy/v_y, yerr = dfy/v_y, fmt= '*g', label = 'b/w axial TF', markersize=5, capsize=5)
|
||||
plt.errorbar(modulation_depth, fz/v_z, yerr = dfz/v_z, fmt= '^b', label = 'b/w vert TF', markersize=5, capsize=5)
|
||||
xlabel = 'Modulation depth'
|
||||
else:
|
||||
plt.errorbar(new_aspect_ratio, fx/v_x, yerr = dfx/v_x, fmt= 'or', label = 'b/w horz TF', markersize=5, capsize=5)
|
||||
plt.errorbar(new_aspect_ratio, fy/v_y, yerr = dfy/v_y, fmt= '*g', label = 'b/w axial TF', markersize=5, capsize=5)
|
||||
plt.errorbar(new_aspect_ratio, fz/v_z, yerr = dfz/v_z, fmt= '^b', label = 'b/w vert TF', markersize=5, capsize=5)
|
||||
xlabel = 'Aspect Ratio'
|
||||
|
||||
plt.xlabel(xlabel, fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Ratio', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.legend(prop={'size': 12, 'weight': 'bold'})
|
||||
plt.show()
|
||||
|
||||
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])
|
||||
rmelmIdx = [i for i, x in enumerate(np.isinf(a_s_array.value)) if x]
|
||||
for x in rmelmIdx:
|
||||
a_s_array[x-1] = np.inf * ac.a0
|
||||
|
||||
plt.figure(figsize=(9, 7))
|
||||
plt.plot(BField, a_s_array/ac.a0, '-b')
|
||||
plt.axhline(y = a_bkg/ac.a0, color = 'r', linestyle = '--')
|
||||
plt.text(min(BField.value) + 0.5, (a_bkg/ac.a0).value + 1, '$a_{bkg}$ = %.2f a0' %((a_bkg/ac.a0).value), fontsize=14, fontweight='bold')
|
||||
plt.xlim([min(BField.value), max(BField.value)])
|
||||
plt.ylim([65, 125])
|
||||
plt.xlabel('B field (G)', fontsize= 12, fontweight='bold')
|
||||
plt.ylabel('Scattering length (a0)', fontsize= 12, fontweight='bold')
|
||||
plt.tight_layout()
|
||||
plt.grid(visible=1)
|
||||
plt.show()
|
||||
|
||||
def plotCollisionRatesAndPSD(Gamma_elastic, PSD, modulation_depth, new_aspect_ratio, plot_against_mod_depth = True):
|
||||
fig, ax1 = plt.subplots(figsize=(8, 6))
|
||||
ax2 = ax1.twinx()
|
||||
|
||||
if plot_against_mod_depth:
|
||||
ax1.plot(modulation_depth, Gamma_elastic, '-ob')
|
||||
ax2.plot(modulation_depth, PSD, '-*r')
|
||||
ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
|
||||
xlabel = 'Modulation depth'
|
||||
else:
|
||||
ax1.plot(new_aspect_ratio, Gamma_elastic, '-ob')
|
||||
ax2.plot(new_aspect_ratio, PSD, '-*r')
|
||||
ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))
|
||||
xlabel = 'Aspect Ratio'
|
||||
|
||||
ax1.set_xlabel(xlabel, fontsize= 12, fontweight='bold')
|
||||
ax1.set_ylabel('Elastic Collision Rate', 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()
|
||||
|
||||
#####################################################################
|
||||
105
ODT Calculator/Potentials.py
Normal file
105
ODT Calculator/Potentials.py
Normal file
@ -0,0 +1,105 @@
|
||||
import numpy as np
|
||||
from astropy import units as u, constants as ac
|
||||
|
||||
DY_POLARIZABILITY = 184.4 # in a.u, most precise measured value of Dy polarizability
|
||||
DY_MASS = 164*u.u
|
||||
DY_DIPOLE_MOMENT = 9.93 * ac.muB
|
||||
|
||||
#####################################################################
|
||||
# POTENTIALS #
|
||||
#####################################################################
|
||||
|
||||
def gravitational_potential(positions, m):
|
||||
return m * ac.g0 * positions
|
||||
|
||||
def single_gaussian_beam_potential(positions, waists, alpha = DY_POLARIZABILITY, 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, waists, del_y, alpha = DY_POLARIZABILITY, 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))
|
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return U
|
||||
|
||||
def modulated_single_gaussian_beam_potential(positions, waists, alpha = DY_POLARIZABILITY, P=1, wavelength=1.064*u.um, mod_amp=1):
|
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mod_amp = mod_amp * waists[0]
|
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n_points = len(positions[0,:])
|
||||
dx, x_mod = modulation_function(mod_amp, n_points, func = 'arccos')
|
||||
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)
|
||||
dU = np.zeros(2*n_points)
|
||||
for i in range(len(x_mod)):
|
||||
dU = np.vstack((dU, np.exp(-2 * (np.subtract(x_mod[i], positions[0,:])/w(positions[1,:], waists[0], wavelength))**2)))
|
||||
U = - U_tilde * A * 1/(2*mod_amp) * np.trapz(dU, dx = dx, axis = 0)
|
||||
return U
|
||||
|
||||
def harmonic_potential(pos, v, xoffset, yoffset, m = DY_MASS):
|
||||
U_Harmonic = ((0.5 * m * (2 * np.pi * v*u.Hz)**2 * (pos*u.um - xoffset*u.um)**2)/ac.k_B).to(u.uK) + yoffset*u.uK
|
||||
return U_Harmonic.value
|
||||
|
||||
def gaussian_potential(pos, amp, waist, xoffset, yoffset):
|
||||
U_Gaussian = amp * np.exp(-2 * ((pos + xoffset) / waist)**2) + yoffset
|
||||
return U_Gaussian
|
||||
|
||||
def crossed_beam_potential(positions, waists, P, options, alpha = DY_POLARIZABILITY, wavelength=1.064*u.um):
|
||||
|
||||
delta = options['delta']
|
||||
|
||||
foci_shift = options['foci_shift']
|
||||
focus_shift_beam_1 = foci_shift[0]
|
||||
focus_shift_beam_2 = foci_shift[1]
|
||||
|
||||
beam_disp = options['beam_disp']
|
||||
beam_1_disp = (np.ones(np.shape(positions.T)) * np.array(beam_disp[0])).T * beam_disp[0].unit
|
||||
beam_2_disp = (np.ones(np.shape(positions.T)) * np.array(beam_disp[1])).T * beam_disp[1].unit
|
||||
|
||||
beam_1_positions = positions + beam_1_disp
|
||||
A_1 = 2*P[0]/(np.pi*w(beam_1_positions[1,:] + focus_shift_beam_1, waists[0][0], wavelength)*w(beam_1_positions[1,:] + focus_shift_beam_1, waists[0][1], wavelength))
|
||||
U_1_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
|
||||
U_1 = - U_1_tilde * A_1 * np.exp(-2 * ((beam_1_positions[0,:]/w(beam_1_positions[1,:] + focus_shift_beam_1, waists[0][0], wavelength))**2 + (beam_1_positions[2,:]/w(beam_1_positions[1,:] + focus_shift_beam_1, waists[0][1], wavelength))**2))
|
||||
|
||||
R = rotation_matrix([0, 0, 1], np.radians(delta))
|
||||
beam_2_positions = np.dot(R, positions + beam_2_disp)
|
||||
A_2 = 2*P[1]/(np.pi*w(beam_2_positions[1,:] + focus_shift_beam_2, waists[1][0], wavelength)*w(beam_2_positions[1,:] + focus_shift_beam_2, waists[1][1], wavelength))
|
||||
U_2_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
|
||||
U_2 = - U_2_tilde * A_2 * np.exp(-2 * ((beam_2_positions[0,:]/w(beam_2_positions[1,:] + focus_shift_beam_2, waists[1][0], wavelength))**2 + (beam_2_positions[2,:]/w(beam_2_positions[1,:] + focus_shift_beam_2, waists[1][1], wavelength))**2))
|
||||
|
||||
U = U_1 + U_2
|
||||
|
||||
return U
|
||||
|
||||
def astigmatic_crossed_beam_potential(positions, waists, P, options, alpha = DY_POLARIZABILITY, wavelength=1.064*u.um):
|
||||
|
||||
delta = options['delta']
|
||||
|
||||
del_y = options['foci_disp_crossed']
|
||||
del_y_1 = del_y[0]
|
||||
del_y_2 = del_y[1]
|
||||
|
||||
|
||||
foci_shift = options['foci_shift']
|
||||
focus_shift_beam_1 = foci_shift[0]
|
||||
focus_shift_beam_2 = foci_shift[1]
|
||||
|
||||
beam_disp = options['beam_disp']
|
||||
beam_1_disp = (np.ones(np.shape(positions.T)) * np.array(beam_disp[0])).T * beam_disp[0].unit
|
||||
beam_2_disp = (np.ones(np.shape(positions.T)) * np.array(beam_disp[1])).T * beam_disp[1].unit
|
||||
|
||||
beam_1_positions = positions + beam_1_disp
|
||||
A_1 = 2*P[0]/(np.pi*w(beam_1_positions[1,:] - (del_y_1/2) + focus_shift_beam_1, waists[0][0], wavelength)*w(beam_1_positions[1,:] + (del_y_1/2) + focus_shift_beam_1, waists[0][1], wavelength))
|
||||
U_1_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
|
||||
U_1 = - U_1_tilde * A_1 * np.exp(-2 * ((beam_1_positions[0,:]/w(beam_1_positions[1,:] - (del_y_1/2) + focus_shift_beam_1, waists[0][0], wavelength))**2 + (beam_1_positions[2,:]/w(beam_1_positions[1,:] + (del_y_1/2) + focus_shift_beam_1, waists[0][1], wavelength))**2))
|
||||
|
||||
R = rotation_matrix([0, 0, 1], np.radians(delta))
|
||||
beam_2_positions = np.dot(R, positions + beam_2_disp)
|
||||
A_2 = 2*P[1]/(np.pi*w(beam_2_positions[1,:] - (del_y_2/2) + focus_shift_beam_2, waists[1][0], wavelength)*w(beam_2_positions[1,:] + (del_y_2/2) + focus_shift_beam_2, waists[1][1], wavelength))
|
||||
U_2_tilde = (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)
|
||||
U_2 = - U_2_tilde * A_2 * np.exp(-2 * ((beam_2_positions[0,:]/w(beam_2_positions[1,:] - (del_y_2/2) + focus_shift_beam_2, waists[1][0], wavelength))**2 + (beam_2_positions[2,:]/w(beam_2_positions[1,:] + (del_y_2/2) + focus_shift_beam_2, waists[1][1], wavelength))**2))
|
||||
|
||||
U = U_1 + U_2
|
||||
|
||||
return U
|
||||
|
||||
Loading…
Reference in New Issue
Block a user