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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"id": "a4246751",
"metadata": {},
"outputs": [],
"source": [
"from calculateDipoleTrapPotential import *"
]
},
{
"cell_type": "markdown",
"id": "c68468e4",
"metadata": {},
"source": [
"## Plot ideal trap potential resulting for given parameters only"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"id": "38c770ac",
"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\envs\\py39\\lib\\site-packages\\scipy\\optimize\\minpack.py:833: OptimizeWarning: Covariance of the parameters could not be estimated\n",
" warnings.warn('Covariance of the parameters could not be estimated',\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"Power = 1.07*u.W\n",
"Wavelength = 1.064*u.um\n",
"w_x, w_z = 30*u.um, 30*u.um # Beam Waists in the x and y directions\n",
"\n",
"#Power = 11*u.W\n",
"#w_x, w_z = 67*u.um, 67*u.um # Beam Waists in the x and y directions\n",
"\n",
"options = {\n",
" 'axis': 2, # axis referenced to the beam along which you want the dipole trap potential\n",
" 'extent': 1e2, # range of spatial coordinates in one direction to calculate trap potential over\n",
" 'crossed': False,\n",
" 'delta': 70, # angle between arms in degrees\n",
" 'modulation': False,\n",
" 'aspect_ratio': 4, # required aspect ratio of modulated arm\n",
" 'gravity': True,\n",
" 'tilt_gravity': False,\n",
" 'theta': 0.75, # gravity tilt angle in degrees\n",
" 'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam\n",
" 'astigmatism': False,\n",
" 'disp_foci': 2.5*u.mm, #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)\n",
" 'extract_trap_frequencies': False\n",
"}\n",
"\n",
"ComputedPotentials = [] \n",
"Params = [] \n",
"\n",
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"Positions, IdealTrappingPotential, TrappingPotential, TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies = computeTrapPotential(w_x, w_z, Power, options)\n",
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"ComputedPotentials.append(IdealTrappingPotential)\n",
"ComputedPotentials.append(TrappingPotential)\n",
"Params.append([TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies])\n",
"\n",
"cpots = np.asarray(ComputedPotentials)\n",
"plotPotential(Positions, cpots, options, Params)"
]
},
{
"cell_type": "markdown",
"id": "fc9809de",
"metadata": {},
"source": [
"## Plot harmonic fit for trap potential resulting for given parameters only"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"id": "0f3e80f7",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"v, dv, popt, pcov = extractTrapFrequency(Positions, TrappingPotential, options['axis'])\n",
"plotHarmonicFit(Positions, TrappingPotential, TrapDepthsInKelvin, options['axis'], popt, pcov)"
]
},
{
"cell_type": "markdown",
"id": "37b40607",
"metadata": {},
"source": [
"## Plot trap potential resulting for given parameters (with one parameter being a list of values and the potential being computed for each of these values) only"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"id": "8504f99f",
"metadata": {
"scrolled": false
},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 900x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"Potentials = [] \n",
"Params = [] \n",
"Power = [10, 30, 40]*u.W # Single Beam Power\n",
"for p in Power: \n",
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" Positions, IdealTrappingPotential, TrappingPotential, TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies = computeTrapPotential(w_x, w_z, p, options)\n",
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" Potentials.append(IdealTrappingPotential)\n",
" Potentials.append(TrappingPotential)\n",
" Params.append([TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies])\n",
"\n",
"cpots = np.asarray(Potentials)\n",
"plotPotential(Positions, cpots, options, Params)"
]
},
{
"cell_type": "markdown",
"id": "951010c6",
"metadata": {},
"source": [
"## Plot transverse intensity profile and trap potential resulting for given parameters only"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"id": "f3e4afd9",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
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"Power = 40*u.W\n",
"Wavelength = 1.064*u.um\n",
"w_x, w_z = 30*u.um, 30*u.um # Beam Waists in the x and y directions\n",
"\n",
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"options = {\n",
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" 'axis': 2, # axis referenced to the beam along which you want the dipole trap potential\n",
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" 'extent': 60, # range of spatial coordinates in one direction to calculate trap potential over\n",
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" 'crossed': False,\n",
" 'delta': 70, # angle between arms in degrees\n",
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" 'modulation': True,\n",
" 'modulation_function': 'arccos',\n",
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" 'modulation_amplitude': 2.16,\n",
" 'aspect_ratio': 4, # required aspect ratio of modulated arm\n",
" 'gravity': True,\n",
" 'tilt_gravity': False,\n",
" 'theta': 0.75, # gravity tilt angle in degrees\n",
" 'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam\n",
" 'astigmatism': False,\n",
" 'disp_foci': 2.5*u.mm, #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)\n",
" 'extract_trap_frequencies': False\n",
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"}\n",
"\n",
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"positions, waists, I, U, p = computeIntensityProfileAndPotentials(Power, [w_x, w_z], Wavelength, options)\n",
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"plotIntensityProfileAndPotentials(positions, waists, I, U)"
]
},
{
"cell_type": "markdown",
"id": "db0df307",
"metadata": {},
"source": [
"## Plot gaussian fit for trap potential resulting from modulation for given parameters only"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"id": "7afa7d82",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"x_Positions = positions[0].value\n",
"z_Positions = positions[1].value\n",
"x_Potential = U[:, np.where(z_Positions==0)[0][0]].value\n",
"z_Potential = U[np.where(x_Positions==0)[0][0], :].value\n",
"poptx, pcovx = p[0], p[1]\n",
"poptz, pcovz = p[2], p[3]\n",
"plotGaussianFit(x_Positions, x_Potential, poptx, pcovx)\n",
"plotGaussianFit(z_Positions, z_Potential, poptz, pcovz)"
]
},
{
"cell_type": "markdown",
"id": "5e5b8123",
"metadata": {},
"source": [
"## Calculate relevant parameters for evaporative cooling"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"id": "95ab43bd",
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Particle Density = 4.63E+13 1 / cm3\n",
"Elastic Collision Rate = 2813.75 1 / s\n",
"PSD = 9.00E-05 \n",
"v_x = 2588.17 Hz\n",
"v_y = 20.66 Hz\n",
"v_z = 2588.17 Hz\n",
"a_s = 111.31 \n"
]
}
],
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"source": [
"Power = 40*u.W\n",
"Wavelength = 1.064*u.um\n",
"w_x, w_z = 30*u.um, 30*u.um # Beam Waists in the x and y directions\n",
"\n",
"AtomNumber = 1.00 * 1e7\n",
"BField = 2.5 * u.G\n",
"\n",
"modulation_depth = 0.0\n",
"Temperature = convert_modulation_depth_to_temperature(modulation_depth)[0] * u.uK\n",
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"n = particleDensity(w_x, w_z, Power, N = AtomNumber, T = Temperature).decompose().to(u.cm**(-3))\n",
"Gamma_elastic = calculateElasticCollisionRate(w_x, w_z, Power, N = AtomNumber, T = Temperature, B = BField)\n",
"PSD = calculatePSD(w_x, w_z, Power, N = AtomNumber, T = Temperature).decompose()\n",
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"\n",
"print('Particle Density = %.2E ' % (n.value) + str(n.unit))\n",
"print('Elastic Collision Rate = %.2f ' % (Gamma_elastic.value) + str(Gamma_elastic.unit))\n",
"print('PSD = %.2E ' % (PSD.value))\n",
"\n",
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"v_x = calculateTrapFrequency(w_x, w_z, Power, dir = 'x')\n",
"v_y = calculateTrapFrequency(w_x, w_z, Power, dir = 'y')\n",
"v_z = calculateTrapFrequency(w_x, w_z, Power, dir = 'z')\n",
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"\n",
"print('v_x = %.2f ' %(v_x.value) + str(v_x.unit))\n",
"print('v_y = %.2f ' %(v_y.value) + str(v_y.unit))\n",
"print('v_z = %.2f ' %(v_z.value) + str(v_z.unit))\n",
"\n",
"print('a_s = %.2f ' %(scatteringLength(BField)[0] / ac.a0))"
]
},
{
"cell_type": "markdown",
"id": "ff252fbe",
"metadata": {},
"source": [
"## Plot alphas"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"id": "dd7fc03d",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"plotAlphas()"
]
},
{
"cell_type": "markdown",
"id": "c09cb260",
"metadata": {},
"source": [
"## Plot Temperatures"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"id": "5c79840e",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"plotTemperatures(w_x, w_z, plot_against_mod_depth = True)"
]
},
{
"cell_type": "markdown",
"id": "063879ee",
"metadata": {},
"source": [
"## Calculate and Plot calculated trap frequencies"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"id": "0ddff726",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAJOCAYAAADBIyqKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAADEMklEQVR4nOzdd3hURRfH8e+mEBJ6CyF0kN6kCQgCIgLSi6KACIqgAgpiBQuoYMOuKLygIAKiSJHepEsHQZAinQQJvQdS7/vHkIRQs8lu7ib5fZ7nPtmde3P3JCRkzs7MGYdlWRYiIiIiIiLJ5GV3ACIiIiIikrYpqRARERERkRRRUiEiIiIiIimipEJERERERFJESYWIiIiIiKSIkgoREREREUkRJRUiIiIiIpIiSipERERERCRFfOwOIC2Jjo7mr7/+In/+/Hh5KR8TERERkduLjY3l2LFjVK1aFR+f9Nv1Tr9fmRv89ddf3HPPPXaHISIiIiJpzPr166lZs6bdYbiNkgon5M+fHzA/FAUKFLA5GhERERHxdEePHuWee+6J70emV0oqnBA35alAgQIUKlTI5mhEREREJK1I71Pn0/dXJyIiIiIibqekQkREREREUkRJhYiIiIiIpIiSChERERERSREt1BYRERFJ52JiYoiKirI7jHTF29sbX19fu8PwGEoqRERERNIpy7IICwvj3LlzWJZldzjpjp+fH3nz5iV79ux2h2I7JRUiIiIi6dS5c+c4e/Ys+fLlI0uWLDgcDrtDShcsyyIqKopz585x5MgRgAyfWCipEBEREUmHLMvi+PHjZM+enbx589odTrrj7+9PtmzZCA0N5eTJkxk+qdBCbREREZF0KCYmhpiYmAzf2XUnh8NBjhw5iIiIyPBrVpRUiIiIiKRD0dHRAPj4aGKKO8Ut1o6JibE5EnspqRARERFJx7SOwr30/TWUVIiIiIiISIooqRARERERkRTRJDsRERERSbqYGFi5Eo4ehQIF4L77wNvb7qjEZkoqRERERCRppk2Dfv0gNDShrVAh+PJLaN/evrjEdpr+JCIiIiJ3Nm0aPPxw4oQC4MgR0z5tWqqEMWDAABwOBw6Hg0mTJiU6N2zYsPhz33zzzW3vc/r0aYoUKYLD4SBr1qzs3bsXgIiICCpVqoTD4SBTpkysXbvWbV9LeqKkwtPFxMCyZfDzz+ZjBi9XJiIiIilgWXDpkvPH+fPwwgvm8292TzAjGOfPO3/vm93zNnr16hX/+Keffkp0bsKECYDZmO7xxx+/7X1y587NL7/8go+PD5cuXaJr167ExMTw2muvsX37dgA++OADateu7VR8GZWSCk82bRoUK8aILq+SZf4XjOjyKhQrlmrvBIiIiEg6Ex4OWbM6f+TIYUYkbsWyzAhGjhzO3zs83KkvoWzZstx3330ALFq0iLCwMAA2bNjArl27AOjYsSM5c+a8473q1KnDsGHDAFi7di0dOnTgq6++AqBVq1YMGDDAqdgyMiUVnurqEKMVGsq7VYoTXmI971YujhWaukOMIiIiIp4mbrQiJiYmfgrU+PHjbzifFK+88grNmzcH4Pfff8eyLIoUKcK4ceO0B4UTlFR4opgYDr3Rh01BFp8UuJ/jVRYAcLziMkYUqMWmAhaH3uyrqVAiIiLinIAAuHjR+WPu3KTdf+5c5+8dEOD0l/Hwww+TK1cuwEyBioqKYvLkyQBUrFiRe++9N8n3cjgcvPbaa4naevToQe7cuZ2OKyNT9SdPtHIlxR4Lu/pkaUJ7lhM8/8zxq0+OYq1cCQ0bpnJwIiIikmY5HJAli/Of16SJqfJ05MjN10A4HOZ8kyapUl42c+bMdO3ala+++ootW7YwfPhwTp48CTg3SgEQHh7Oc889l6jtww8/pH379lSsWNFlMad3GqnwREePMmEqeMdcN+TmuPpLHONDmalv8PH/cvLPP06vbxIRERFxjre3KRsLJoG4VtzzL75I1f0qrk0eBg8eDJgF2l27dnXqPr1792bHjh0ADBo0iMyZM3P58mU6duzIpUuXXBdwOqekwhMVKEDnbVBmzLibnx+zht3bhvLaz3dTsSIULw59+8K8eXD5cqpGKiIiIhlF+/bw229QsGDi9kKFTHsq71NRoUKF+GlO0dHRADzyyCNJWqAdZ+zYsfz4448APProowwbNoxPPvkEgJ07d9KnTx/XBp2OKanwRPfdx8K8XdjB1SG32Kv/THEjEvcO5zm/MTRrauHnB4cOwYgR0Lw55MkDrVrBqFEQEmJL9CIiIpJetW8PBw/C0qUwaZL5eOCAbRvfXT/VyZmpTzt27KBv374AFC5cmJEjRwLQp08f2rRpA8CPP/7IuHHjXBNsOuewLE2eSarQ0FAKFy5MSEgIhQoVctvrWBbUKnWajScuYPWsDecLw+YeUO8jyHkAHBD0ZxeONK/J5Z79WLIE5syB2bNvrPZWuTK0aAEtW0KtWqk6KikiIiI2unLlCgcOHKB48eJkzpzZ7nDSrTt9n1Or/2g3LdT2QJGRcPhibqzzueGLgxCTCXDApl5Q81to0ZewuhN5c+bPvL9vL60+/5xWrXywLPj7b5NgzJkDa9aY53//DR98YEYxmjUzSUbTpqCiBiIiIiLiCkoqPJCfH2zYACdOADE+8NcmOHkS8uaFqs8y+VAMw//pxwcNYsnyxze80WY/TJ6MI1s2qlSBKlVg0CDzKfPnmwRj/nw4dQomTjSHlxfUrWsSjBYtoEKFG9ddiYiIiKRFBw8epHjx4re9ZunSpTRUFU2XUVLhoQoXNgd4Q80aic5Vq/kCgcGRvLLoFd58ADIvmMtL991n5j9dM6yWNy88/rg5oqNh9eqEUYx//oGVK83x+utQpEjCNKn77wd//9T9ekVEREQk7dJC7TTq5Xtf5t2G75rHTWFEpq1wzz2wefNNr/fxgfr14aOPYPt2s6bqm2/goYfMyMjhw/DddyaxyJPHJBfffWfaRURERNKSYsWKYVnWbQ+NUriWkoo07M36bzKo3iAA+raA74OOwn33wcyZd/zcYsWgTx+z8eXp0zBrFjz7rBnouHzZjGb07g1Fi5rF3gMHwqpVZsRDRERERORaSirSMIfDwdBGQxlQewAAPVvDhLvCoW1bswFNEgt7BQQkHpnYuhXef9+sufDygm3b4MMPTb6SPz906WKqyJ0+7b6vTURERETSDiUVaZzD4eCTJp/Qu0ZvLAd0a+dgSjkLXnzR7Ijn5NCCw5F4ZOL4cZgwATp1gly5TCIxaZJJLPLlM4nGhx+axEPFiUVEREQyJiUV6YDD4eDr5l/To2oPYh0WnTt68XtZ4NtvoXVrOH8+2ffOkydhZOL4cbOw+7XXoGJFiI01icfAgSYRKVbMTJmaMwfCwxPfZ/FiKF/efBQRERGR9MX2pOKDD6BmTciWDQIDzcyd3bsTX9O9u3kH/dqjdu3E10REwPPPm4pHWbKYvnRoaOJrzpyBrl0hRw5zdO0KZ8+68YtLRV4OL0a1HEWXSl2IJpaOnXyYXz4TzJsH9eq5ZMW1j4+5VdzIxMGDJm9p3hwyZ05Y7N2ypUlGWrQw5w8eNCVud+40HzWiISIiIpK+2J5ULF9uFgyvXQuLFpnZOk2awKVLia9r1gyOHk045s5NfL5/f5g+HSZPNu+eX7xoOrcxMQnXdO4MW7aYPRvmzzePu3Z18xeYiry9vBnXdhwPl3+YSCuado85WFItl8kAatWCjRtd+npFi8Jzz5mRiVOnTEXb554zpXCvXDH/Rn36QPHiZt8NMB8XLnRpGCIiIiJiM4dledb7xidOmBGL5ctNCVQwIxVnz8KMGTf/nHPnzPz+n36CRx81bf/9Zzq3c+ea3aN37jTTb9auNf1rMI/r1IFdu6BMmTvHlla2WY+KieLhKQ8zc/dMAnz8WbC4APWW7TebT0yaZIaD3MiyTNnaOXNMovHnnwnnHA6oXh3Wr9dmeyIiIu505coVDhw4QPHixcmcObPd4aRbd/o+p5X+Y0rZPlJxvXPnzMfcuRO3L1tmko3SpaFnTzO/P86mTRAVZUY44gQHm3n/q1eb52vWmClPcQk
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"AtomNumber = 1.00 * 1e7\n",
"BField = 1.4 * u.G\n",
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"Wavelength = 1.064*u.um\n",
"w_x, w_z = 30*u.um, 30*u.um # Beam Waists in the x and y directions\n",
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"modulation_depth = np.arange(0, 1.0, 0.08)\n",
"\n",
"w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]\n",
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"new_aspect_ratio = w_xs / w_z\n",
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"\n",
"v_x = np.zeros(len(modulation_depth))\n",
"v_y = np.zeros(len(modulation_depth))\n",
"v_z = np.zeros(len(modulation_depth))\n",
"\n",
"for i in range(len(modulation_depth)):\n",
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" v_x[i] = calculateTrapFrequency(w_xs[i], w_z, Power, dir = 'x').value\n",
" v_y[i] = calculateTrapFrequency(w_xs[i], w_z, Power, dir = 'y').value\n",
" v_z[i] = calculateTrapFrequency(w_xs[i], w_z, Power, dir = 'z').value\n",
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"\n",
"plotTrapFrequencies(v_x, v_y, v_z, modulation_depth, new_aspect_ratio, plot_against_mod_depth = True)"
]
},
{
"cell_type": "markdown",
"id": "76ff8301",
"metadata": {},
"source": [
"## Plot measured trap frequencies"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"id": "7a85ec41",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"modulation_depth_radial = [0, 0.5, 0.3, 0.7, 0.9, 0.8, 1.0, 0.6, 0.4, 0.2, 0.1]\n",
"fx = [3.135, 0.28, 0.690, 0.152, 0.102, 0.127, 0.099, 0.205, 0.404, 1.441, 2.813]\n",
"dfx = [0.016, 0.006, 0.005, 0.006, 0.003, 0.002, 0.002,0.002, 0.003, 0.006, 0.024]\n",
"fz = [2.746, 1.278, 1.719, 1.058, 0.923, 0.994, 0.911, 1.157, 1.446, 2.191, 2.643]\n",
"dfz = [0.014, 0.007, 0.009, 0.007, 0.005, 0.004, 0.004, 0.005, 0.007, 0.009, 0.033]\n",
"\n",
"modulation_depth_axial = [1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1]\n",
"fy = [3.08, 3.13, 3.27, 3.46, 3.61, 3.82, 3.51, 3.15, 3.11, 3.02]\n",
"dfy = [0.03, 0.04, 0.04, 0.05, 0.07, 0.06, 0.11, 0.07, 0.1, 1.31]\n",
"\n",
"plotMeasuredTrapFrequencies(fx, dfx, fy, dfy, fz, dfz, modulation_depth_radial, modulation_depth_axial, w_x, w_z, plot_against_mod_depth = True)"
]
},
{
"cell_type": "markdown",
"id": "4a4843d2",
"metadata": {},
"source": [
"## Plot ratio of measured to calculated trap frequencies"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"id": "58cf3f64",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"modulation_depth = [0.5, 0.3, 0.7, 0.9, 0.8, 1.0, 0.6, 0.4, 0.2, 0.1]\n",
"w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]\n",
"\n",
"v_x = np.zeros(len(modulation_depth))\n",
"v_y = np.zeros(len(modulation_depth))\n",
"v_z = np.zeros(len(modulation_depth))\n",
"\n",
"for i in range(len(modulation_depth)):\n",
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" v_x[i] = calculateTrapFrequency(w_xs[i], w_z, Power, dir = 'x').value / 1e3\n",
" v_y[i] = calculateTrapFrequency(w_xs[i], w_z, Power, dir = 'y').value\n",
" v_z[i] = calculateTrapFrequency(w_xs[i], w_z, Power, dir = 'z').value / 1e3\n",
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"\n",
"fx = [0.28, 0.690, 0.152, 0.102, 0.127, 0.099, 0.205, 0.404, 1.441, 2.813]\n",
"dfx = [0.006, 0.005, 0.006, 0.003, 0.002, 0.002,0.002, 0.003, 0.006, 0.024]\n",
"fy = [3.08, 3.13, 3.27, 3.46, 3.61, 3.82, 3.51, 3.15, 3.11, 3.02]\n",
"dfy = [0.03, 0.04, 0.04, 0.05, 0.07, 0.06, 0.11, 0.07, 0.1, 1.31]\n",
"fz = [1.278, 1.719, 1.058, 0.923, 0.994, 0.911, 1.157, 1.446, 2.191, 2.643]\n",
"dfz = [0.007, 0.009, 0.007, 0.005, 0.004, 0.004, 0.005, 0.007, 0.009, 0.033]\n",
"\n",
"plotRatioOfTrapFrequencies(fx, fy, fz, dfx, dfy, dfz, v_x, v_y, v_z, modulation_depth, w_x, w_z, plot_against_mod_depth = True)\n"
]
},
{
"cell_type": "markdown",
"id": "44e92099",
"metadata": {},
"source": [
"## Plot Feshbach Resonances"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"id": "d15205ff",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAKyCAYAAABoqBcWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC3KklEQVR4nOzdd3gU1f4G8HdTCEkIJAQIBAIBpHdRRJQuoFGxYS8oXq+KekXEgl4B9acoKqBgb6BgwWvHiICCSFHpvRs6IZQUUkg2yfz+OEwKKWyZ2XNm5v08D89Mwmby3Wx2su9+z5zj0jRNAxEREREREdlCkOwCiIiIiIiIyDgMeURERERERDbCkEdERERERGQjDHlEREREREQ2wpBHRERERERkIwx5RERERERENsKQR0REREREZCMMeURERERERDYSIrsAVRQXF+PQoUOIioqCy+WSXQ4REREREVE5mqbh5MmTiI+PR1BQ1f06hrzTDh06hISEBNllEBERERERVWv//v1o0qRJlf/PkHdaVFQUACAlJQV169aVXA35wu12Y/78+Rg8eDBCQ0Nll0M+sNNj+OKLwMsvA3ffDUyeLLuawLHTY+hUqj6GEycCL70EjBgBTJkiuxpA04DoaLG/ezdQr57Ucsox4jG8/npg/nxg+nTg9tu9//r0dCAxUewfOQLUrOlTGeUMHQr8/jvwwQeivjPNmwfceCPQvTvw22/ic716AZs3A999B/Tv738NgaLq89Aoq1YBAwcCjRsDW7bIrsYcZj2GWVlZSEhIKMkuVWHIO00fohkVFYXatWtLroZ84Xa7ERERgdq1a9vyhOgEdnoMCwrENjYWcNIpxU6PoVOp+hiGhZVuVXhOFReX7teurUZNOiMew6IisY2J8e2+HT4strVrAw0a+FRCBSGnX7VGRFReU0SE2AYHl/5/cLDYRkaq9RidjarPQ6P4+/tlBWY/hme7vIwTrxARmeDkSbG16x8vokDTNNkVlFe2Hjteyn/qlNj62oE7ckRs4+KMqYfsJTNTbPVuOBmPIY+IyARZWWJ7ltEUROQhPVSpEqicEvLCw337+rQ0sTWqi0f2kpEhtnXqSC3D1hjyiIhMoHfyGPKIjKVKoLJ7yMvLE1tfO3kMeVQdvZPHkGcehjwiIhMw5BEZi8M1A8vf4ZoMeVQdhjzzMeQREZlAH67Ja/KIjMHhmoGld/J8Ha6pyjV5qr05QAKvyTMfQx4RkQnYySMyhyqByu4hT8VOnj+BzY6PkZXxmjzzMeQREZmAIY/IWKp1ZOwe8vzt5Jk5XNOOP2+n4XBN8zHkERGZgLNrEhmLwzUDR9O4hAKZiyHPfAx5REQGKygoXQyd1+QRGUuVQGXnkKefvwC1hmuSffCaPPMx5BERGUwfqgmwk0dkFA7XDBx9qCbg23DN/PzSF/EMeVQZXpNnPoY8IiKD6UM1a9YEQkLk1kJkFxyuGTj6UE2XCwgN9f7rjx4V25AQICbGuLrIPjhc03wMeUREBuPyCUTmUSVQ2TnklZ10xZf7pl+P16CB/J+Nah1gEo8JQ575GPKIiAzGaw2IjKfai3U7hzwVl0/wl90eIyvLzQWKisQ+/06ahyGPiMhg+rUG/ONFZBwO1wwcvZNnp5BH6tD/RgYHA5GRUkuxNYY8IiKDMeQRmUeVQGXnkKd38nxdI8+s5RNU6+aSb/TRLrVr2++5oxKGPCIig/FaAyLjqfYC3wkhT9VOnt1+3k7Dv5GBwZBHRGQwdvKIjMfhmoFTduIVX3C4JlWH160HBkMeEZHBGPKIzKNKoLJzyDOqk2f0cE2yB66RFxgMeUREBmPIIzKeasM1y7JbyPO3k1d2CQXZVP69cSoO1wwMhjwiIoPxDxiReVQJVHYOD3acXVOV3xvi38hAYcgjIjIYO3lExuM1eYGTkyO2vkxvr2kcrknV4zV5gcGQR0RkMIY8IuOp1jlTrR4j6SGvVi3vvzYjAygsFPv16xtWEtkIr8kLDIY8IiKD8V1KIvOo0jVTrbNopOxssfWlk6dfj1enDhAWZlxNZB/p6WIbEyO3DrtjyCMiMhjfpSQynmqhSrV6jORPJ8/M6/Hs3D11khMnxLZuXbl12B1DHhGRgTSNwzWJzKDaC3w7hzy9k+dPyDPzejw7/sydhJ28wGDIIyIyUG5u6fUoDHlExlPlBb6dQ54/E6+otHwCoN6bA8SQFygMeUREBtKvxwsO9u0FEhFVTrVQpVo9RjKik6dKyNPZ8XGyKj3kcbimuRjyiIgMVPZ6PL6oIDKOah0ZO4c8fzp5XD6BqqNp7OQFCkMeEZGBeD0ekblUCVV2Dnl27OSRGk6eBIqKxD5DnrkY8oiIDMSQR2QO1UKVavUYyU7X5JFa9C5eWBgQHi63FrtjyCMiMhDXyCMyB4drBo7qs2uSdenLJ8TE2PO5oxKGPCIiA7GTR2QuVV4Y2jnkcZ08MguvxwschjwiIgNxIXQic6gWqlSrx0h6J8/b4Zr5+aWjGcwcrunNz5zBUC0MeYHDkEdEZCB28ojModqLdbuGvIICwO0W+9528vQuXmioeudAuz1OVsXlEwKHIY+IyEDHj4ttbKzcOojsSpUX63YNefpQTcD7Tp4e8urXt9/PhYxR9po8MhdDHhGRgfQ/YHyXkshYqoUq1eoxij7cMjxcdOS8oc+syUlXqCocrhk4DHlERAbSQx47eUTG4nDNwNBDXu3a3n8tZ9aks+FwzcBhyCMiMpA+XJN/wIjMoUqosnvI82XyKK6RR2fDTl7gMOQRERmIwzWJzKFaqFKtHqP4E/LYyaOz4TV5gcOQR0RkIA7XJHIGhryKVOzkqTbM1+k4XDNwGPKIiAySmwucOiX2+QeMyFiqhirV6vGXESHPrE6eP4HNbo+TVXG4ZuAw5BERGUTv4oWEeL++FBFVT7WQZ9cOkRUmXlHld4C8x+GagcOQR0RkkLJDNfkihMjeVAudRrHbcE1SR3Fx6e8XQ575GPKIiAzCmTWJzKNaqFKtHqP4GvKKi4GjR8U+J16hymRmlj5vGPLMx5BHRGQQTrpCZB7VQpVq9RjF15B3/LgIegBQr56xNZE96NfjRUQAYWFya3EC6SFvyZIluPLKKxEfHw+Xy4Xvvvuu5P/cbjeeeOIJdOrUCZGRkYiPj8cdd9yBQ4cOlTtGfn4+HnroIdSrVw+RkZEYOnQoDhw4EOB7QkROx+UTiJzDriEvK0tsvQ15+vV4sbFAaKixNZE98Hq8wJIe8nJyctClSxdMnz69wv/l5uZizZo1eOaZZ7BmzRp888032LFjB4YOHVrudqNGjcK3336LL774AkuXLkV2djauuOIKFBUVBepuEBFxuCaRiVQLVarVYxS92+JtyFP1ejy7TpBjRVw+IbBCZBdw2WWX4bLLLqv0/+rUqYMFCxaU+9y0adPQo0cP7Nu3D02bNkVmZiY+/PBDfPrpp7jkkksAALNmzUJCQgIWLlyIIUOGmH4fiIgADtckMpNqoUq1eoyiv1nl7XlM9YXQ7fY4WRGXTwgs6SHPW5mZmXC5XIiOjgYArF69Gm63G4MHDy65TXx8PDp27Ijly5dXGfLy8/ORn59f8nHW6fEJbrcbbrfbvDtAptEfNz5+1mX1x/Do0WAAQahTpwhud7HscqSw+mNI6j6GxcXi+VVUpMbzq6AAAELhcmlwuwtll1OOP4/hiRMhAFyoXdsNb7780KEgAMGoV68Ybrc5I6k0TfwOFBYWwu2u2KIrLHQBCIGmldagaeL+VPU1qlL1eeiPo0fF70h0tHm/Iyox6zH09HiWCnmnTp3Ck08+iVtuuQW1Ty/gkpqaiho1aiDmjLcF4uLikJqaWuWxJk6ciGeffbbC5xctWoSIiAhjC6eAOrP7S9Zj1cdw69YeABrh0KFNSE7eI7scqaz6GFIp1R7DAwe6AWiK7du3ITl5l+xysHt3HQD9kJ9/CsnJ82WXUylvH8PiYuDECXFJzPr1v2H//lMef+3y5e0AtEZe3h4kJ2/06vt66vjxiwDUw9q1axAefrjC/69aFQegJzIyMpGcvAQAkJMzAEAUVqxYgaysE6bUZSb
"text/plain": [
"<Figure size 900x700 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"plotScatteringLengths(B_range = [0, 3.6])"
]
},
{
"cell_type": "markdown",
"id": "1a2e113f",
"metadata": {},
"source": [
"## Calculate and Plot Collision Rates and PSD"
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"id": "86e9ba21",
"metadata": {
"scrolled": false
},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"AtomNumber = 1.00 * 1e7\n",
"BField = 1.4 * u.G\n",
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"Power = 40*u.W\n",
"Wavelength = 1.064*u.um\n",
"w_x, w_z = 30*u.um, 30*u.um # Beam Waists in the x and y directions\n",
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"modulation_depth = np.arange(0, 1.0, 0.08)\n",
"\n",
"w_xs = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]\n",
"new_aspect_ratio = w_xs / w_z\n",
"Temperatures = convert_modulation_depth_to_temperature(modulation_depth)[0] * u.uK\n",
"\n",
"# n = np.zeros(len(modulation_depth))\n",
"Gamma_elastic = np.zeros(len(modulation_depth))\n",
"PSD = np.zeros(len(modulation_depth))\n",
"\n",
"for i in range(len(modulation_depth)):\n",
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" # n[i] = particleDensity(w_xs[i], w_z, Power, N = AtomNumber, T = Temperatures[i]).decompose().to(u.cm**(-3))\n",
" Gamma_elastic[i] = calculateElasticCollisionRate(w_xs[i], w_z, Power, N = AtomNumber, T = Temperatures[i], B = BField).value\n",
" PSD[i] = calculatePSD(w_xs[i], w_z, Power, N = AtomNumber, T = Temperatures[i]).decompose().value\n",
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"\n",
"\n",
"plotCollisionRatesAndPSD(Gamma_elastic, PSD, modulation_depth, new_aspect_ratio, plot_against_mod_depth = True)"
]
},
{
"cell_type": "markdown",
"id": "74d353c9",
"metadata": {},
"source": [
"## Plot Collision Rates and PSD from only measured trap frequencies"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"id": "6c81d9da",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
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"AtomNumber = 1.00 * 1e7\n",
"BField = 1.4 * u.G\n",
"Wavelength = 1.064*u.um\n",
"w_x, w_z = 30*u.um, 30*u.um # Beam Waists in the x and y directions\n",
"\n",
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"fin_mod_depth = [0.5, 0.3, 0.7, 0.9, 0.8, 1.0, 0.6, 0.4, 0.2, 0.1]\n",
"v_x = [0.28, 0.690, 0.152, 0.102, 0.127, 0.099, 0.205, 0.404, 1.441, 2.813] * u.kHz\n",
"dv_x = [0.006, 0.005, 0.006, 0.003, 0.002, 0.002,0.002, 0.003, 0.006, 0.024] * u.kHz\n",
"v_z = [1.278, 1.719, 1.058, 0.923, 0.994, 0.911, 1.157, 1.446, 2.191, 2.643] * u.kHz\n",
"dv_z = [0.007, 0.009, 0.007, 0.005, 0.004, 0.004, 0.005, 0.007, 0.009, 0.033] * u.kHz\n",
"sorted_modulation_depth, sorted_v_x = zip(*sorted(zip(fin_mod_depth, v_x)))\n",
"sorted_modulation_depth, sorted_dv_x = zip(*sorted(zip(fin_mod_depth, dv_x)))\n",
"sorted_modulation_depth, sorted_v_z = zip(*sorted(zip(fin_mod_depth, v_z)))\n",
"sorted_modulation_depth, sorted_dv_z = zip(*sorted(zip(fin_mod_depth, dv_z)))\n",
"\n",
"fin_mod_depth = [1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1] \n",
"v_y = [3.08, 3.13, 3.27, 3.46, 3.61, 3.82, 3.51, 3.15, 3.11, 3.02] * u.Hz\n",
"dv_y = [0.03, 0.04, 0.04, 0.05, 0.07, 0.06, 0.11, 0.07, 0.1, 1.31] * u.Hz\n",
"sorted_modulation_depth, sorted_v_y = zip(*sorted(zip(fin_mod_depth, v_y)))\n",
"sorted_modulation_depth, sorted_dv_y = zip(*sorted(zip(fin_mod_depth, dv_y)))\n",
"\n",
"fin_mod_depth = [1.0, 0.8, 0.6, 0.4, 0.2, 0.9, 0.7, 0.5, 0.3, 0.1]\n",
"T_x = [22.1, 27.9, 31.7, 42.2, 145.8, 27.9, 33.8, 42.4, 61.9, 136.1] * u.uK\n",
"dT_x = [1.7, 2.6, 2.4, 3.7, 1.1, 2.2, 3.2, 1.7, 2.2, 1.2] * u.uK\n",
"T_y = [13.13, 14.75, 18.44, 26.31, 52.55, 13.54, 16.11, 21.15, 35.81, 85.8] * u.uK\n",
"dT_y = [0.05, 0.05, 0.07, 0.16, 0.28, 0.04, 0.07, 0.10, 0.21, 0.8] * u.uK\n",
"\n",
"sorted_modulation_depth, sorted_T_x = zip(*sorted(zip(fin_mod_depth, T_x)))\n",
"sorted_modulation_depth, sorted_dT_x = zip(*sorted(zip(fin_mod_depth, dT_x)))\n",
"sorted_modulation_depth, sorted_T_y = zip(*sorted(zip(fin_mod_depth, T_y)))\n",
"sorted_modulation_depth, sorted_dT_y = zip(*sorted(zip(fin_mod_depth, dT_y)))\n",
"\n",
"modulation_depth = sorted_modulation_depth\n",
"\n",
"pd, dpd, T, dT, new_aspect_ratio = calculateParticleDensityFromMeasurements(sorted_v_x, sorted_dv_x, sorted_v_y, sorted_dv_y, sorted_v_z, sorted_dv_z, w_x, w_z, sorted_T_x, sorted_T_y, sorted_dT_x, sorted_dT_y, sorted_modulation_depth, AtomNumber, m = 164*u.u)\n",
"\n",
"Gamma_elastic = [(pd[i] * scatteringCrossSection(BField) * meanThermalVelocity(T[i]) / (2 * np.sqrt(2))).decompose() for i in range(len(pd))]\n",
"Gamma_elastic_values = [(Gamma_elastic[i]).value for i in range(len(Gamma_elastic))]\n",
"dGamma_elastic = [(Gamma_elastic[i] * ((dpd[i]/pd[i]) + (dT[i]/(2*T[i])))).decompose() for i in range(len(Gamma_elastic))]\n",
"dGamma_elastic_values = [(dGamma_elastic[i]).value for i in range(len(dGamma_elastic))]\n",
"\n",
"PSD = [((pd[i] * thermaldeBroglieWavelength(T[i])**3).decompose()).value for i in range(len(pd))]\n",
"dPSD = [((PSD[i] * ((dpd[i]/pd[i]) - (1.5 * dT[i]/T[i]))).decompose()).value for i in range(len(Gamma_elastic))]\n",
"\n",
"fig, ax1 = plt.subplots(figsize=(8, 6))\n",
"ax2 = ax1.twinx()\n",
"ax1.errorbar(modulation_depth, Gamma_elastic_values, yerr = dGamma_elastic_values, fmt = 'ob', markersize=5, capsize=5)\n",
"ax2.errorbar(modulation_depth, PSD, yerr = dPSD, fmt = '-^r', markersize=5, capsize=5)\n",
"ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))\n",
"ax1.set_xlabel('Modulation depth', fontsize= 12, fontweight='bold')\n",
"ax1.set_ylabel('Elastic Collision Rate (' + str(Gamma_elastic[0].unit) + ')', fontsize= 12, fontweight='bold')\n",
"ax1.tick_params(axis=\"y\", labelcolor='b')\n",
"ax2.set_ylabel('Phase Space Density', fontsize= 12, fontweight='bold')\n",
"ax2.tick_params(axis=\"y\", labelcolor='r')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "9329ee37",
"metadata": {},
"source": [
"## Investigate deviation in alpha"
]
},
{
"cell_type": "code",
2023-03-15 11:29:21 +01:00
"execution_count": 17,
2023-03-14 17:13:37 +01:00
"id": "7b8191f2",
"metadata": {},
2023-03-15 11:29:21 +01:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"Power = 40*u.W\n",
"Wavelength = 1.064*u.um\n",
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"w_x, w_z = 30*u.um, 30*u.um\n",
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"\n",
"options = {\n",
" 'axis': 0, # axis referenced to the beam along which you want the dipole trap potential\n",
" 'extent': 3e2, # range of spatial coordinates in one direction to calculate trap potential over\n",
" 'crossed': False,\n",
" 'delta': 70, # angle between arms in degrees\n",
" 'modulation': False,\n",
" 'aspect_ratio': 5, # required aspect ratio of modulated arm\n",
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" 'gravity': True,\n",
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" 'tilt_gravity': False,\n",
" 'theta': 0.75, # gravity tilt angle in degrees\n",
" 'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam\n",
" 'astigmatism': True,\n",
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" 'foci_disp_single': 2.5*u.mm, #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\n",
" 'foci_disp_crossed': [2.5*u.mm, 1.5*u.mm], # astigmatism of each of the two beams in the cODT\n",
" 'foci_shift': [0.0*u.mm, 0.0*u.mm],\n",
" 'beam_disp': [[0.0, 0.0, 0.0]*u.mm, [0, 0, 0.03]*u.mm],\n",
" 'extract_trap_frequencies': False # Flag to extract trap frequencies by fitting the computed potential\n",
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"}\n",
"\n",
"modulation_depth = np.arange(0, 1.1, 0.1)\n",
"Alphas, fin_mod_dep, meas_alpha_x, meas_alpha_z, dalpha_x, dalpha_z = convert_modulation_depth_to_alpha(modulation_depth) \n",
"meas_alpha_deviation = [(g - h) for g, h in zip(meas_alpha_x, meas_alpha_z)]\n",
"sorted_fin_mod_dep, sorted_meas_alpha_deviation = zip(*sorted(zip(fin_mod_dep, meas_alpha_deviation)))\n",
"avg_alpha = [(g + h) / 2 for g, h in zip(meas_alpha_x, meas_alpha_z)]\n",
"sorted_fin_mod_dep, new_aspect_ratio = zip(*sorted(zip(fin_mod_dep, (w_x * avg_alpha) / w_z)))\n",
"\n",
"current_ar = w_x/w_z\n",
"aspect_ratio = np.arange(current_ar, 10*current_ar, 0.8)\n",
"w_x = w_x * (aspect_ratio / current_ar)\n",
"\n",
"v_x = np.zeros(len(w_x))\n",
"#v_y = np.zeros(len(w_x))\n",
"v_z = np.zeros(len(w_x))\n",
"\n",
"for i in range(len(w_x)):\n",
" \n",
" options['axis'] = 0\n",
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" ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, options)[5]\n",
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" tmp = ExtractedTrapFrequencies[0][0]\n",
" v_x[i] = tmp if not np.isinf(tmp) else np.nan\n",
" \n",
" # options['axis'] = 1\n",
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" # ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, options)[5]\n",
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" # tmp = ExtractedTrapFrequencies[1][0]\n",
" # v_y[i] = tmp if not np.isinf(tmp) else np.nan\n",
" \n",
" options['axis'] = 2\n",
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" ExtractedTrapFrequencies = computeTrapPotential(w_x[i], w_z, Power, options)[5]\n",
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" tmp = ExtractedTrapFrequencies[0][0]\n",
" v_z[i] = tmp if not np.isinf(tmp) else np.nan\n",
"\n",
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" #v_x[i] = calculateTrapFrequency(w_x[i], w_z, Power, dir = 'x').value\n",
" #v_y[i] = calculateTrapFrequency(w_x[i], w_z, Power, dir = 'y').value\n",
" #v_z[i] = calculateTrapFrequency(w_x[i], w_z, Power, dir = 'z').value\n",
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"\n",
"alpha_x = [(v_x[0]/v)**(2/3) for v in v_x]\n",
"alpha_z = [(v_z[0]/v)**2 for v in v_z]\n",
"\n",
"calc_alpha_deviation = [(g - h) for g, h in zip(alpha_x, alpha_z)]\n",
"\n",
"plt.figure()\n",
"plt.plot(aspect_ratio, alpha_x, '-o', label = 'From horz TF')\n",
"plt.plot(aspect_ratio, alpha_z, '-^', label = 'From vert TF')\n",
"plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')\n",
"plt.ylabel('$\\\\alpha$', fontsize= 12, fontweight='bold')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.legend(prop={'size': 12, 'weight': 'bold'})\n",
"plt.show()\n",
"\n",
"plt.figure()\n",
"plt.plot(aspect_ratio, calc_alpha_deviation, '--ob', label = 'Extracted')\n",
"plt.plot(new_aspect_ratio, sorted_meas_alpha_deviation, '-or', label = 'Measured')\n",
"plt.xlabel('Aspect Ratio', fontsize= 12, fontweight='bold')\n",
"plt.ylabel('$\\\\Delta \\\\alpha$', fontsize= 12, fontweight='bold')\n",
"plt.ylim([-0.5, 1])\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.legend(prop={'size': 12, 'weight': 'bold'})\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "29a1cf87",
"metadata": {},
"source": [
"## Quick calculation of PSD and elastic collision rate"
]
},
{
"cell_type": "code",
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"execution_count": 18,
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"id": "3950f1b5",
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Particle Density = 1.87E+12 1 / cm3\n",
"Elastic Collision Rate = 37.24 1 / s\n",
"PSD = 2.50E-05 \n"
]
}
],
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"source": [
"Power = 9.5*u.W\n",
"Wavelength = 1.064*u.um\n",
"w_x, w_z = 50*u.um, 45*u.um # Beam Waists in the x and y directions\n",
"\n",
"NCount = 11000\n",
"AtomNumber = calculateAtomNumber(NCount, pixel_size = 3.45 * u.um, magnification = 0.5, eta = 0.5)\n",
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"#AtomNumber = calculateAtomNumber(NCount, pixel_size = 5.86 * u.um, magnification = 0.5, eta = 0.5)\n",
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"\n",
"BField = 3.75 * u.G\n",
"modulation_depth = 0.00\n",
"\n",
"w_x = w_x * convert_modulation_depth_to_alpha(modulation_depth)[0]\n",
"new_aspect_ratio = w_x / w_z\n",
"Temperature = 33 * u.uK\n",
"\n",
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"n = particleDensity(w_x, w_z, Power, N = AtomNumber, T = Temperature).decompose().to(u.cm**(-3))\n",
"Gamma_elastic = calculateElasticCollisionRate(w_x, w_z, Power, N = AtomNumber, T = Temperature, B = BField)\n",
"PSD = calculatePSD(w_x, w_z, Power, N = AtomNumber, T = Temperature).decompose()\n",
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"\n",
"print('Particle Density = %.2E ' % (n.value) + str(n.unit))\n",
"print('Elastic Collision Rate = %.2f ' % (Gamma_elastic.value) + str(Gamma_elastic.unit))\n",
"print('PSD = %.2E ' % (PSD.value))"
]
},
{
"cell_type": "markdown",
"id": "d975586b",
"metadata": {},
"source": [
"## Plot measured PSDs and collision rates"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"id": "b11b6aae",
"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"Gamma_elastic = [96.01, 82.53, 102.95, 143.5, 110.92, 471.77, 908.46]\n",
"PSD = [8.95e-05, 4.19e-04, 2.94e-04, 2.17e-04, 8.98e-04, 5.52e-04, 6.10e-04]\n",
"\n",
"fig, ax1 = plt.subplots(figsize=(8, 6))\n",
"ax2 = ax1.twinx()\n",
"\n",
"ax1.plot(Gamma_elastic, '-ob')\n",
"ax2.plot(PSD, '-*r')\n",
"ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1e'))\n",
"\n",
"ax1.set_xlabel('Optimization Steps', fontsize= 12, fontweight='bold')\n",
"ax1.set_ylabel('Elastic Collision Rate', fontsize= 12, fontweight='bold')\n",
"ax1.tick_params(axis=\"y\", labelcolor='b')\n",
"ax2.set_ylabel('Phase Space Density', fontsize= 12, fontweight='bold')\n",
"ax2.tick_params(axis=\"y\", labelcolor='r')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "da1a3346",
"metadata": {},
"source": [
"## Plot ideal crossed beam trap potential resulting for given parameters only"
]
},
{
"cell_type": "code",
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"execution_count": 40,
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"id": "f17a4d01",
"metadata": {},
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"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x2aa558a3520>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"Powers = [1, 11] * u.W\n",
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"Wavelength = 1.064*u.um\n",
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"w_x = [30, 67]*u.um # Beam Waists in the x direction\n",
"w_z = [30, 67]*u.um # Beam Waists in the y direction\n",
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"\n",
"options = {\n",
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" 'axis':0, # axis referenced to the beam along which you want the dipole trap potential\n",
" 'extent': 1e2, # range of spatial coordinates in one direction to calculate trap potential over\n",
" 'crossed': True, # Flag for either a crossed beam configuration or a single focussed beam\n",
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" 'delta': 70, # angle between arms in degrees\n",
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" 'modulation': False, # Flag for spatial modulation of a single beam\n",
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" 'aspect_ratio': 5, # required aspect ratio of modulated arm\n",
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" 'gravity': False, # Flag for adding levitation/gravitation potential\n",
" 'tilt_gravity': False, # Flag for tilt of the beam wrt to gravity axis\n",
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" 'theta': 0.75, # gravity tilt angle in degrees\n",
" 'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam\n",
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" 'astigmatism': False, # Flag for astigmatism of beam\n",
" 'foci_disp_single': 2.5*u.mm, #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\n",
" 'foci_disp_crossed': [2.5*u.mm, 1.5*u.mm], # astigmatism of each of the two beams in the cODT\n",
" 'foci_shift': [0.0*u.mm, 0.0*u.mm],\n",
" 'beam_disp': [[0.0, 0.0, 0.0]*u.mm, [0.01, 0, 0.023]*u.mm],\n",
" 'extract_trap_frequencies': False # Flag to extract trap frequencies by fitting the computed potential\n",
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"}\n",
"\n",
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"Positions, IdealTrappingPotential, TrappingPotential, TrapDepthsInKelvin, CalculatedTrapFrequencies, ExtractedTrapFrequencies = computeTrapPotential(w_x, w_z, Powers, options)\n",
"\n",
"EffectiveTrapDepthInKelvin = TrapDepthsInKelvin[1]\n",
"v = ExtractedTrapFrequencies[0][0]\n",
"dv = ExtractedTrapFrequencies[0][1]\n",
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"\n",
"plt.figure()\n",
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"plt.plot(Positions[options['axis']], TrappingPotential[options['axis']], label = 'Effective Trap Depth = ' + str(round(EffectiveTrapDepthInKelvin.value, 2)) + ' ' + str(EffectiveTrapDepthInKelvin.unit) + '; ' + generate_label(v, dv)) \n",
"axis = options['axis']\n",
"if axis == 0:\n",
" dir = 'X - Horizontal'\n",
"elif axis == 1:\n",
" dir = 'Y - Propagation'\n",
"else:\n",
" dir = 'Z - Vertical'\n",
" \n",
"plt.ylim(top = 0)\n",
"plt.xlabel(dir + ' Direction (um)', fontsize= 12, fontweight='bold')\n",
"plt.ylabel('Trap Potential (uK)', fontsize= 12, fontweight='bold')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.legend(loc=1, prop={'size': 12, 'weight': 'bold'})"
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]
},
{
"cell_type": "markdown",
"id": "1a28b820",
"metadata": {},
"source": [
"## Calculate trap frequencies in a crossed beam trap for given parameters"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"id": "09149423",
"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"v_x = 419.68 Hz\n",
"v_y = 255.73 Hz\n",
"v_z = 491.44 Hz\n"
]
}
],
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"source": [
"Powers = [1, 11] * u.W\n",
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"w_x = [30, 67]*u.um # Beam Waists in the x direction\n",
"w_z = [30, 67]*u.um # Beam Waists in the y direction\n",
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"\n",
"options = {\n",
" 'axis': 1, # axis referenced to the beam along which you want the dipole trap potential\n",
" 'extent': 1e3, # range of spatial coordinates in one direction to calculate trap potential over\n",
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" 'crossed': True, # Flag for either a crossed beam configuration or a single focussed beam\n",
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" 'delta': 70, # angle between arms in degrees\n",
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" 'modulation': False, # Flag for spatial modulation of a single beam\n",
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" 'aspect_ratio': 5, # required aspect ratio of modulated arm\n",
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" 'gravity': False, # Flag for adding levitation/gravitation potential\n",
" 'tilt_gravity': False, # Flag for tilt of the beam wrt to gravity axis\n",
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" 'theta': 0.75, # gravity tilt angle in degrees\n",
" 'tilt_axis': [1, 0, 0], # lab space coordinates are rotated about x-axis in reference frame of beam\n",
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" 'astigmatism': False, # Flag for astigmatism of beam\n",
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" 'foci_disp_single': 2.5*u.mm, #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\n",
" 'foci_disp_crossed': [2.5*u.mm, 1.5*u.mm], # astigmatism of each of the two beams in the cODT\n",
" 'foci_shift': [0.01*u.mm, 0.20*u.mm],\n",
" 'beam_disp': [[0, 0, 0.5]*u.mm, [0, 0, 0.5]*u.mm],\n",
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" 'extract_trap_frequencies': False # Flag to extract trap frequencies by fitting the computed potential\n",
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"}\n",
"\n",
"v_x = calculateCrossedBeamTrapFrequency(options['delta'], [w_x, w_z], Powers, dir = 'x')\n",
"v_y = calculateCrossedBeamTrapFrequency(options['delta'], [w_x, w_z], Powers, dir = 'y')\n",
"v_z = calculateCrossedBeamTrapFrequency(options['delta'], [w_x, w_z], Powers, dir = 'z')\n",
"\n",
"print('v_x = %.2f ' %(v_x.value) + str(v_x.unit))\n",
"print('v_y = %.2f ' %(v_y.value) + str(v_y.unit))\n",
"print('v_z = %.2f ' %(v_z.value) + str(v_z.unit))"
]
},
{
"cell_type": "code",
"execution_count": null,
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"id": "fc585c24",
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"metadata": {},
"outputs": [],
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"source": []
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}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.9.13"
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}
},
"nbformat": 4,
"nbformat_minor": 5
}