Corrected how the effective trap depth is calculated, minor changes to code for plotting of the potentials.
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@ -407,10 +407,13 @@ def computeTrapPotential(w_x, w_z, Power, Polarizability, options):
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TrappingPotential = IdealTrappingPotential
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if not crossed:
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infls = np.where(np.diff(np.sign(np.gradient(np.gradient(TrappingPotential[axis].value)))))[0]
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if TrappingPotential[axis][0] > TrappingPotential[axis][-1]:
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EffectiveTrapDepthInKelvin = TrappingPotential[axis][-1] - min(TrappingPotential[axis])
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EffectiveTrapDepthInKelvin = max(TrappingPotential[axis][infls[1]:-1]) - min(TrappingPotential[axis][infls[0]:infls[1]])
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elif TrappingPotential[axis][0] < TrappingPotential[axis][-1]:
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EffectiveTrapDepthInKelvin = TrappingPotential[axis][0] - min(TrappingPotential[axis])
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EffectiveTrapDepthInKelvin = max(TrappingPotential[axis][0:infls[0]]) - min(TrappingPotential[axis][infls[0]:infls[1]])
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else:
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EffectiveTrapDepthInKelvin = IdealTrapDepthInKelvin
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@ -422,10 +425,23 @@ def computeTrapPotential(w_x, w_z, Power, Polarizability, options):
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CalculatedTrapFrequencies = [v_x, v_y, v_z]
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v, dv, popt, pcov = extractTrapFrequency(Positions, IdealTrappingPotential, axis)
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if np.isinf(v):
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v = np.nan
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if np.isinf(dv):
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dv = np.nan
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IdealTrapFrequency = [v, dv]
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if options['extract_trap_frequencies']:
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v, dv, popt, pcov = extractTrapFrequency(Positions, TrappingPotential, axis)
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if np.isinf(v):
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v = np.nan
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if np.isinf(dv):
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dv = np.nan
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TrapFrequency = [v, dv]
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ExtractedTrapFrequencies = [IdealTrapFrequency, TrapFrequency]
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else:
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ExtractedTrapFrequencies = [IdealTrapFrequency]
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return Positions, IdealTrappingPotential, TrappingPotential, TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies
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@ -582,7 +598,9 @@ def plotGaussianFit(Positions, TrappingPotential, popt, pcov):
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plt.tight_layout()
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plt.show()
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def plotPotential(Positions, ComputedPotentials, axis, Params = [], listToIterateOver = [], save = False):
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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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@ -598,8 +616,12 @@ def plotPotential(Positions, ComputedPotentials, axis, Params = [], listToIterat
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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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@ -874,41 +896,43 @@ def plotCollisionRatesAndPSD(Gamma_elastic, PSD, modulation_depth, new_aspect_ra
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if __name__ == '__main__':
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# Power = 40*u.W
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# Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
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# Wavelength = 1.064*u.um
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# w_x, w_z = 27.5*u.um, 33.8*u.um # Beam Waists in the x and y directions
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Power = 0.420*u.W
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Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
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Wavelength = 1.064*u.um
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w_x, w_z = 30*u.um, 30*u.um # Beam Waists in the x and y directions
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# Power = 11*u.W
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# Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
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# w_x, w_z = 54.0*u.um, 54.0*u.um # Beam Waists in the x and y directions
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"""
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options = {
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'axis': 2, # axis referenced to the beam along which you want the dipole trap potential
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'extent': 3e2, # range of spatial coordinates in one direction to calculate trap potential over
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'axis': 1, # axis referenced to the beam along which you want the dipole trap potential
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'extent': 1e4, # range of spatial coordinates in one direction to calculate trap potential over
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'crossed': False,
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'theta': 0,
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'modulation': True,
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'aspect_ratio': 1,
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'delta': 70, # angle between arms in degrees
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'modulation': False,
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'aspect_ratio': 4, # required aspect ratio of modulated arm
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'gravity': True,
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'tilt_gravity': True,
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'theta': 0.5, # in degrees
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'theta': 0.1, # gravity tilt angle in degrees
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'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam
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'astigmatism': True,
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'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)
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'astigmatism': False,
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'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)
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'extract_trap_frequencies': True
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}
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"""
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"""Plot ideal trap potential resulting for given parameters only"""
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# ComputedPotentials = []
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# Params = []
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# Positions, IdealTrappingPotential, TrappingPotential, TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies = computeTrapPotential(w_x, w_z, Power, Polarizability, options)
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# ComputedPotentials.append(IdealTrappingPotential)
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# ComputedPotentials.append(TrappingPotential)
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# Params.append([TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies])
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# """Plot ideal trap potential resulting for given parameters only"""
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# ComputedPotentials = np.asarray(ComputedPotentials)
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# plotPotential(Positions, ComputedPotentials, options['axis'], Params)
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ComputedPotentials = []
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Params = []
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Positions, IdealTrappingPotential, TrappingPotential, TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies = computeTrapPotential(w_x, w_z, Power, Polarizability, options)
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ComputedPotentials.append(IdealTrappingPotential)
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ComputedPotentials.append(TrappingPotential)
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Params.append([TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies])
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ComputedPotentials = np.asarray(ComputedPotentials)
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plotPotential(Positions, ComputedPotentials, options, Params)
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"""Plot harmonic fit for trap potential resulting for given parameters only"""
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# v, dv, popt, pcov = extractTrapFrequency(Positions, TrappingPotential, options['axis'])
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@ -925,7 +949,7 @@ if __name__ == '__main__':
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# Params.append([TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies])
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# ComputedPotentials = np.asarray(ComputedPotentials)
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# plotPotential(Positions, ComputedPotentials, options['axis'], Params)
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# plotPotential(Positions, ComputedPotentials, options, Params)
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"""Plot transverse intensity profile and trap potential resulting for given parameters only"""
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# options = {
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@ -1059,83 +1083,83 @@ if __name__ == '__main__':
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""" Investigate deviation in alpha"""
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Power = 40*u.W
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Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
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Wavelength = 1.064*u.um
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w_x, w_z = 27.5*u.um, 33.8*u.um
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# Power = 40*u.W
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# Polarizability = 184.4 # in a.u, most precise measured value of Dy polarizability
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# Wavelength = 1.064*u.um
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# w_x, w_z = 27.5*u.um, 33.8*u.um
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options = {
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'axis': 0, # axis referenced to the beam along which you want the dipole trap potential
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'extent': 3e2, # range of spatial coordinates in one direction to calculate trap potential over
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'crossed': False,
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'theta': 0,
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'modulation': False,
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'gravity': True,
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'tilt_gravity': True,
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'theta': 10, # in degrees
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'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam
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'astigmatism': True,
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'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)
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}
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# options = {
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# 'axis': 0, # axis referenced to the beam along which you want the dipole trap potential
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# 'extent': 3e2, # range of spatial coordinates in one direction to calculate trap potential over
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# 'crossed': False,
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# 'theta': 0,
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# 'modulation': False,
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# 'gravity': True,
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# 'tilt_gravity': True,
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# 'theta': 10, # in degrees
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# 'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam
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# 'astigmatism': True,
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# '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)
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# }
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modulation_depth = np.arange(0, 1.1, 0.1)
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Alphas, fin_mod_dep, meas_alpha_x, meas_alpha_z, dalpha_x, dalpha_z = convert_modulation_depth_to_alpha(modulation_depth)
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meas_alpha_deviation = [(g - h) for g, h in zip(meas_alpha_x, meas_alpha_z)]
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sorted_fin_mod_dep, sorted_meas_alpha_deviation = zip(*sorted(zip(fin_mod_dep, meas_alpha_deviation)))
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avg_alpha = [(g + h) / 2 for g, h in zip(meas_alpha_x, meas_alpha_z)]
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sorted_fin_mod_dep, new_aspect_ratio = zip(*sorted(zip(fin_mod_dep, (w_x * avg_alpha) / w_z)))
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# modulation_depth = np.arange(0, 1.1, 0.1)
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# Alphas, fin_mod_dep, meas_alpha_x, meas_alpha_z, dalpha_x, dalpha_z = convert_modulation_depth_to_alpha(modulation_depth)
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# meas_alpha_deviation = [(g - h) for g, h in zip(meas_alpha_x, meas_alpha_z)]
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# sorted_fin_mod_dep, sorted_meas_alpha_deviation = zip(*sorted(zip(fin_mod_dep, meas_alpha_deviation)))
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# avg_alpha = [(g + h) / 2 for g, h in zip(meas_alpha_x, meas_alpha_z)]
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# sorted_fin_mod_dep, new_aspect_ratio = zip(*sorted(zip(fin_mod_dep, (w_x * avg_alpha) / w_z)))
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current_ar = w_x/w_z
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aspect_ratio = np.arange(current_ar, 10*current_ar, 0.8)
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w_x = w_x * (aspect_ratio / current_ar)
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# current_ar = w_x/w_z
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# aspect_ratio = np.arange(current_ar, 10*current_ar, 0.8)
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# w_x = w_x * (aspect_ratio / current_ar)
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v_x = np.zeros(len(w_x))
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#v_y = np.zeros(len(w_x))
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v_z = np.zeros(len(w_x))
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# v_x = np.zeros(len(w_x))
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# #v_y = np.zeros(len(w_x))
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# v_z = np.zeros(len(w_x))
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for i in range(len(w_x)):
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options['axis'] = 0
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ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, Polarizability, options)[5]
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tmp = ExtractedTrapFrequencies[1][0]
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v_x[i] = tmp if not np.isinf(tmp) else np.nan
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#options['axis'] = 1
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# for i in range(len(w_x)):
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# options['axis'] = 0
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# ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, Polarizability, options)[5]
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# tmp = ExtractedTrapFrequencies[1][0]
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#v_y[i] = tmp if not np.isinf(tmp) else np.nan
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options['axis'] = 2
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ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, Polarizability, options)[5]
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tmp = ExtractedTrapFrequencies[1][0]
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v_z[i] = tmp if not np.isinf(tmp) else np.nan
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# v_x[i] = tmp if not np.isinf(tmp) else np.nan
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# #options['axis'] = 1
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# #ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, Polarizability, options)[5]
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# #tmp = ExtractedTrapFrequencies[1][0]
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# #v_y[i] = tmp if not np.isinf(tmp) else np.nan
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# options['axis'] = 2
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# ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, Polarizability, options)[5]
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# tmp = ExtractedTrapFrequencies[1][0]
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# v_z[i] = tmp if not np.isinf(tmp) else np.nan
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#v_x[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'x').value
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#v_y[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'y').value
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#v_z[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'z').value
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# #v_x[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'x').value
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# #v_y[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'y').value
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# #v_z[i] = calculateTrapFrequency(w_x[i], w_z, Power, Polarizability, dir = 'z').value
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alpha_x = [(v_x[0]/v)**(2/3) for v in v_x]
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alpha_z = [(v_z[0]/v)**2 for v in v_z]
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# alpha_x = [(v_x[0]/v)**(2/3) for v in v_x]
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# alpha_z = [(v_z[0]/v)**2 for v in v_z]
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calc_alpha_deviation = [(g - h) for g, h in zip(alpha_x, alpha_z)]
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# calc_alpha_deviation = [(g - h) for g, h in zip(alpha_x, alpha_z)]
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plt.figure()
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plt.plot(aspect_ratio, alpha_x, '-o', label = 'From horz TF')
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plt.plot(aspect_ratio, alpha_z, '-^', label = 'From vert TF')
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plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
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plt.ylabel('$\\alpha$', 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(prop={'size': 12, 'weight': 'bold'})
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plt.show()
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# plt.figure()
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# plt.plot(aspect_ratio, alpha_x, '-o', label = 'From horz TF')
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# plt.plot(aspect_ratio, alpha_z, '-^', label = 'From vert TF')
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# plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
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# plt.ylabel('$\\alpha$', 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(prop={'size': 12, 'weight': 'bold'})
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# plt.show()
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plt.figure()
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plt.plot(aspect_ratio, calc_alpha_deviation, '--ob', label = 'Extracted')
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plt.plot(new_aspect_ratio, sorted_meas_alpha_deviation, '-or', label = 'Measured')
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plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
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plt.ylabel('$\\Delta \\alpha$', fontsize= 12, fontweight='bold')
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plt.ylim([-0.5, 1])
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plt.tight_layout()
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plt.grid(visible=1)
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plt.legend(prop={'size': 12, 'weight': 'bold'})
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plt.show()
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# plt.figure()
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# plt.plot(aspect_ratio, calc_alpha_deviation, '--ob', label = 'Extracted')
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# plt.plot(new_aspect_ratio, sorted_meas_alpha_deviation, '-or', label = 'Measured')
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# plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
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# plt.ylabel('$\\Delta \\alpha$', fontsize= 12, fontweight='bold')
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# plt.ylim([-0.5, 1])
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# plt.tight_layout()
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# plt.grid(visible=1)
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# plt.legend(prop={'size': 12, 'weight': 'bold'})
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# plt.show()
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"""Plot ideal crossed beam trap potential resulting for given parameters only"""
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@ -1181,5 +1205,3 @@ if __name__ == '__main__':
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# plt.ylim([-1800, -200])
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# plt.legend()
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# plt.show()
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