149 lines
25 KiB
Plaintext
149 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.animation as animation\n",
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"import numpy as np\n",
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"import sympy as sp\n",
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"from IPython.display import Math, display\n",
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"from matplotlib.axes import Axes\n",
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"from scipy import constants as const\n",
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"from scipy.integrate import quad\n",
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"from scipy.optimize import root_scalar\n",
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"from scipy.signal import argrelmax,argrelmin,find_peaks\n",
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"from tqdm import tqdm\n",
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"\n",
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"import fewfermions.analysis.units as si\n",
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"from fewfermions.simulate.traps.twod.trap import DoubleTweezer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"initial_power = 50* si.uW\n",
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"initial_waist = 1.1*si.uW\n",
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"initial_distance = 2*si.um\n",
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"\n",
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"trap: DoubleTweezer = DoubleTweezer(\n",
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" power=0, # Set pancake laser power to 0, no 2D trap\n",
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" grad_z= 0*si.G/si.cm,\n",
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" grad_r=0,\n",
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" power_tweezer1 = initial_power, #stationary\n",
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" power_tweezer2 = initial_power*1.1, #transfer tweezer\n",
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" waist_tweezer1 = initial_waist, #stationary\n",
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" waist_tweezer2 = initial_waist, #transfer tweezer\n",
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" distance_tweezers = initial_distance,\n",
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"\n",
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" a=180*(4 * np.pi * const.epsilon_0 * const.value(\"Bohr radius\")**3)/(2 * const.epsilon_0 * const.c),\n",
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" wvl = 532 * si.nm,\n",
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"\n",
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" g = 0,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"n_pot_steps = [40,40,40]\n",
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"n_levels = 4\n",
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"\n",
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"left_cutoff = -0.5*initial_distance-2*np.max([float(trap.subs(trap.waist_tweezer1)),float(trap.subs(trap.waist_tweezer2))])\n",
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"right_cutoff = 0.5*initial_distance+2*np.max([float(trap.subs(trap.waist_tweezer1)),float(trap.subs(trap.waist_tweezer2))])\n",
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"back_cutoff = -2*np.max([float(trap.subs(trap.waist_tweezer1)),float(trap.subs(trap.waist_tweezer2))])\n",
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"front_cutoff = 2*np.max([float(trap.subs(trap.waist_tweezer1)),float(trap.subs(trap.waist_tweezer2))])\n",
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"bottom_cutoff = -2*np.max([float(trap.subs(trap.get_tweezer_rayleigh1())),float(trap.subs(trap.get_tweezer_rayleigh2()))])\n",
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"top_cutoff = 2*np.max([float(trap.subs(trap.get_tweezer_rayleigh1())),float(trap.subs(trap.get_tweezer_rayleigh2()))])\n",
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"\n",
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"extend = [(left_cutoff,right_cutoff),\n",
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" (back_cutoff,front_cutoff),\n",
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" (bottom_cutoff,top_cutoff)]\n",
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"\n",
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"\n",
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"# Save the hamiltonian for external solver\n",
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"energies, states, potential, coords = trap.nstationary_solution(\n",
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" [trap.x,trap.y,trap.z], extend, n_pot_steps, k=n_levels)\n",
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"\n",
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"x = coords[trap.x]\n",
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"y = coords[trap.y]\n",
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"z = coords[trap.z]\n",
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"x3D,y3D,z3D = np.meshgrid(coords[trap.x],coords[trap.y],coords[trap.z],indexing=\"ij\")\n",
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"pot = potential(x3D,y3D,z3D)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x27210f202f0>]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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}
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],
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"source": [
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"#plt.plot(x,states[0,:,int(len(y)/2),int(len(z)/2)])\n",
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"#plt.plot(x,1e29*pot[:,int(len(y)/2),int(len(z)/2)])\n",
|
|
"#plt.plot(y,states[0,int(len(x)/2),:,int(len(z)/2)])\n",
|
|
"#plt.plot(y,1e29*pot[int(len(x)/2),:,int(len(z)/2)])\n",
|
|
"plt.plot(z,states[0,int(len(x)/2),int(len(y)/2),:])\n",
|
|
"#plt.plot(z,1e29*pot[int(len(x)/2),int(len(y)/2),:])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "base",
|
|
"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",
|
|
"version": "3.12.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|