346 lines
16 KiB
Python
346 lines
16 KiB
Python
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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 astropy import units as u, constants as ac
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from Calculator import *
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from Helpers import *
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from Potentials import *
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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]
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z_Positions = positions[1]
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w_x = waists[0]
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dw_x = waists[1]
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w_z = waists[2]
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dw_x = waists[3]
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ar = w_x/w_z
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dar = ar * np.sqrt((dw_x/w_x)**2 + (dw_x/w_z)**2)
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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('Intensity Profile ($MW/cm^2$)\n Aspect Ratio = %.2f \u00B1 %.2f um'% tuple([ar,dar]))
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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)])
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plt.xlabel('X - Horizontal (um)', fontsize= 12, fontweight='bold')
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plt.ylabel('Z - Vertical (um)', fontsize= 12, fontweight='bold')
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ax.set_aspect('equal')
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fig.colorbar(im, fraction=0.046, pad=0.04, orientation='vertical')
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bx = fig.add_subplot(122)
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bx.set_title('Trap Potential')
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plt.plot(x_Positions, U[:, np.where(z_Positions==0)[0][0]], label = 'X - Horizontal')
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plt.plot(z_Positions, U[np.where(x_Positions==0)[0][0], :], label = 'Z - Vertical')
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plt.ylim(top = 0)
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plt.xlabel('Extent (um)', fontsize= 12, fontweight='bold')
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plt.ylabel('Depth (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(prop={'size': 12, 'weight': 'bold'})
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plt.show()
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def plotAlphas():
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modulation_depth = np.arange(0, 1.1, 0.1)
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Alphas, fin_mod_dep, alpha_x, alpha_y, dalpha_x, dalpha_y = convert_modulation_depth_to_alpha(modulation_depth)
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plt.figure()
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plt.errorbar(fin_mod_dep, alpha_x, yerr = dalpha_x, fmt= 'ob', label = 'From Horz TF', markersize=5, capsize=5)
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plt.errorbar(fin_mod_dep, alpha_y, yerr = dalpha_y, fmt= 'or', label = 'From Vert TF', markersize=5, capsize=5)
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plt.plot(modulation_depth, Alphas, '--g')
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plt.xlabel('Modulation depth', 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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def plotTemperatures(w_x, w_z, plot_against_mod_depth = True):
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modulation_depth = np.arange(0, 1.1, 0.1)
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w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]
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new_aspect_ratio = w_xs / w_z
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Temperatures, fin_mod_dep, T_x, T_y, dT_x, dT_y = convert_modulation_depth_to_temperature(modulation_depth)
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measured_aspect_ratio = (w_x * convert_modulation_depth_to_alpha(fin_mod_dep)[0]) / w_z
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plt.figure()
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if plot_against_mod_depth:
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plt.errorbar(fin_mod_dep, T_x, yerr = dT_x, fmt= 'ob', label = 'Horz direction', markersize=5, capsize=5)
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plt.errorbar(fin_mod_dep, T_y, yerr = dT_y, fmt= 'or', label = 'Vert direction', markersize=5, capsize=5)
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plt.plot(modulation_depth, Temperatures, '--g')
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xlabel = 'Modulation depth'
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else:
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plt.errorbar(measured_aspect_ratio, T_x, yerr = dT_x, fmt= 'ob', label = 'Horz direction', markersize=5, capsize=5)
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plt.errorbar(measured_aspect_ratio, T_y, yerr = dT_y, fmt= 'or', label = 'Vert direction', markersize=5, capsize=5)
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plt.plot(new_aspect_ratio, Temperatures, '--g')
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xlabel = 'Aspect Ratio'
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plt.xlabel(xlabel, fontsize= 12, fontweight='bold')
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plt.ylabel('Temperature (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(prop={'size': 12, 'weight': 'bold'})
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plt.show()
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def plotTrapFrequencies(v_x, v_y, v_z, modulation_depth, new_aspect_ratio, plot_against_mod_depth = True):
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fig, ax3 = plt.subplots(figsize=(8, 6))
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if plot_against_mod_depth:
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ln1 = ax3.plot(modulation_depth, v_x, '-or', label = 'v_x')
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ln2 = ax3.plot(modulation_depth, v_z, '-^b', label = 'v_z')
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ax4 = ax3.twinx()
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ln3 = ax4.plot(modulation_depth, v_y, '-*g', label = 'v_y')
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xlabel = 'Modulation depth'
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else:
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ln1 = ax3.plot(new_aspect_ratio, v_x, '-or', label = 'v_x')
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ln2 = ax3.plot(new_aspect_ratio, v_z, '-^b', label = 'v_z')
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ax4 = ax3.twinx()
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ln3 = ax4.plot(new_aspect_ratio, v_y, '-*g', label = 'v_y')
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xlabel = 'Aspect Ratio'
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ax3.set_xlabel(xlabel, fontsize= 12, fontweight='bold')
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ax3.set_ylabel('Trap Frequency (Hz)', fontsize= 12, fontweight='bold')
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ax3.tick_params(axis="y", labelcolor='b')
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ax4.set_ylabel('Trap Frequency (Hz)', fontsize= 12, fontweight='bold')
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ax4.tick_params(axis="y", labelcolor='g')
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plt.tight_layout()
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plt.grid(visible=1)
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lns = ln1+ln2+ln3
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labs = [l.get_label() for l in lns]
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ax3.legend(lns, labs, prop={'size': 12, 'weight': 'bold'})
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plt.show()
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def plotMeasuredTrapFrequencies(fx, dfx, fy, dfy, fz, dfz, modulation_depth_radial, modulation_depth_axial, w_x, w_z, plot_against_mod_depth = True):
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alpha_x = [(fx[0]/x)**(2/3) for x in fx]
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dalpha_x = [alpha_x[i] * np.sqrt((dfx[0]/fx[0])**2 + (dfx[i]/fx[i])**2) for i in range(len(fx))]
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alpha_y = [(fy[0]/y)**2 for y in fy]
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dalpha_y = [alpha_y[i] * np.sqrt((dfy[0]/fy[0])**2 + (dfy[i]/fy[i])**2) for i in range(len(fy))]
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avg_alpha = [(g + h) / 2 for g, h in zip(alpha_x, alpha_y)]
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new_aspect_ratio = (w_x * avg_alpha) / w_z
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if plot_against_mod_depth:
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fig, ax1 = plt.subplots(figsize=(8, 6))
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ax2 = ax1.twinx()
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ax1.errorbar(modulation_depth_radial, fx, yerr = dfx, fmt= 'or', label = 'v_x', markersize=5, capsize=5)
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ax2.errorbar(modulation_depth_axial, fy, yerr = dfy, fmt= '*g', label = 'v_y', markersize=5, capsize=5)
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ax1.errorbar(modulation_depth_radial, fz, yerr = dfz, fmt= '^b', label = 'v_z', markersize=5, capsize=5)
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ax1.set_xlabel('Modulation depth', fontsize= 12, fontweight='bold')
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ax1.set_ylabel('Trap Frequency (kHz)', fontsize= 12, fontweight='bold')
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ax1.tick_params(axis="y", labelcolor='b')
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ax2.set_ylabel('Trap Frequency (Hz)', fontsize= 12, fontweight='bold')
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ax2.tick_params(axis="y", labelcolor='g')
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h1, l1 = ax1.get_legend_handles_labels()
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h2, l2 = ax2.get_legend_handles_labels()
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ax1.legend(h1+h2, l1+l2, loc=0, prop={'size': 12, 'weight': 'bold'})
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else:
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plt.figure()
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plt.errorbar(new_aspect_ratio, fx, yerr = dfx, fmt= 'or', label = 'v_x', markersize=5, capsize=5)
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plt.errorbar(new_aspect_ratio, fz, yerr = dfz, fmt= '^b', label = 'v_z', markersize=5, capsize=5)
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plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')
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plt.ylabel('Trap Frequency (kHz)', fontsize= 12, fontweight='bold')
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plt.legend(prop={'size': 12, 'weight': 'bold'})
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plt.tight_layout()
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plt.grid(visible=1)
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plt.show()
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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):
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w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]
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new_aspect_ratio = w_xs / w_z
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plt.figure()
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if plot_against_mod_depth:
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plt.errorbar(modulation_depth, fx/v_x, yerr = dfx/v_x, fmt= 'or', label = 'b/w horz TF', markersize=5, capsize=5)
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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)
|
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xlabel = 'Modulation depth'
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||
|
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else:
|
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|
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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()
|
||
|
|
|
||
|
|
#####################################################################
|