LennartNaeve_code/merging_tweezer_code/fermions/2025_03_05 (finding power sensitivity).ipynb
2025-04-25 20:52:11 +02:00

4030 lines
207 KiB
Plaintext

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate the possibel range of powers for fermion merging at different power, waist and waist difference:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\naeve\\ferdy-repo\\library_jochim\\fewfermions\\analysis\\calculate\\binding_energies.py:169: SyntaxWarning: invalid escape sequence '\\['\n",
" '''\n",
"c:\\users\\naeve\\ferdy-repo\\library_jochim\\fewfermions\\analysis\\image.py:535: SyntaxWarning: invalid escape sequence '\\s'\n",
" \"%d good shots\\nµ: (%.1f, %.1f)\\n$\\sigma$: (%.1f, %.1f)\\n$\\phi$: %.1f°, ecc.: %.3f\"\n"
]
}
],
"source": [
"from helpers_merging import *\n",
"\n",
"initial_power = 50* si.uW\n",
"initial_waist = 1.1*si.uW\n",
"initial_distance = 3*si.um\n",
"\n",
"trap: DoubleTweezer = DoubleTweezer(\n",
" power=0, # Set pancake laser power to 0, no 2D trap\n",
" grad_z= 0*si.G/si.cm,\n",
" grad_r=0,\n",
" power_tweezer1 = initial_power, #stationary\n",
" power_tweezer2 = initial_power, #transfer tweezer\n",
" waist_tweezer1 = initial_waist, #stationary\n",
" waist_tweezer2 = initial_waist*1.02, #transfer tweezer\n",
" distance_tweezers = initial_distance,\n",
"\n",
" a=180*(4 * np.pi * const.epsilon_0 * const.value(\"Bohr radius\")**3)/(2 * const.epsilon_0 * const.c),\n",
" wvl = 532 * si.nm,\n",
"\n",
" g = 0,\n",
")\n",
"\n",
"x, y, z = trap.x, trap.y, trap.z"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def get_deltaE(power_tweezer1, power_tweezer2, waist_tweezer1, waist_tweezer2,\n",
" n_levels=10,n_pot_steps=2000, initial_distance=4*si.um, plot=True):\n",
" \"\"\"\n",
" Returns the minimal energy gap and occupation numbers of the groundstate when the two tweezers merge.\n",
" \"\"\"\n",
" trap[trap.power_tweezer1] = power_tweezer1\n",
" trap[trap.power_tweezer2] = power_tweezer2\n",
" trap[trap.waist_tweezer1] = waist_tweezer1\n",
" trap[trap.waist_tweezer2] = waist_tweezer2\n",
"\n",
" distances = np.linspace(initial_distance,0*si.um,500) #always has to have smallest distance at last index\n",
" energies, states, potentials = loop_distances(trap, distances,n_levels=n_levels,n_pot_steps=n_pot_steps)\n",
"\n",
" new_energies, new_states, new_potentials, index_top, index, swap_index = swapped_loop_distance(distances, energies, states, potentials)\n",
" energies_left, energies_right, states_left, states_right = find_ass_tweezer(new_energies,new_states, return_deltaE=False)\n",
"\n",
" if len(index)!=0:\n",
" deltaE_min = 0\n",
" print(\"crossover\")\n",
" else:\n",
" deltaE_min = np.inf\n",
" for i in range(energies_left.shape[1]):\n",
" for j in range(energies_right.shape[1]):\n",
" row_diff = np.nanmin(np.abs(energies_left[:,i] - energies_right[:,j]))\n",
" if row_diff<deltaE_min:\n",
" deltaE_min = row_diff\n",
" i_min, j_min = i,j\n",
" print(f\"closest approach between states: left:{i_min}, right:{j_min}\")\n",
"\n",
" if plot:\n",
" # Create the figure with two subplots (1 row, 2 columns)\n",
" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
"\n",
" # Plot the full range on ax1\n",
" \"\"\"\n",
" ax1.plot(distances/si.um, energies_left[:, 0], color=\"red\", label=\"GS left tweezer\")\n",
" ax1.plot(distances/si.um, energies_right[:, 0], color=\"blue\", label=\"GS right tweezer\")\n",
" ax1.plot(distances/si.um, energies_left[:, 1:], color=\"red\", linestyle=\"dotted\", label=\"left tweezer\")\n",
" ax1.plot(distances/si.um, energies_right[:, 1:], color=\"blue\", linestyle=\"dotted\", label=\"right tweezer\")\"\"\"\n",
" ax1.plot(distances/si.um, energies_left[:], color=\"red\", label=\"left tweezer\")\n",
" ax1.plot(distances/si.um, energies_right[:], color=\"blue\", label=\"right tweezer\")\n",
"\n",
" for i, ind in enumerate(index):\n",
" ax1.axvline(distances[ind]/si.um, color=\"k\", alpha=0.7, label=\"energy crossings\")\n",
"\n",
" ax1.set_xlabel(\"Tweezer distance [um]\")\n",
" ax1.set_ylabel(\"Eigenenergy [a.u.]\")\n",
" ax1.set_title(fr\"$p_l={float(trap.subs(trap.power_tweezer1))/si.uW:.1f}$ uW, $p_r={float(trap.subs(trap.power_tweezer2))/si.uW:.1f}$ uW, $w_l={float(trap.subs(trap.waist_tweezer1))/si.um:.3f}$ um, $w_r={float(trap.subs(trap.waist_tweezer2))/si.um:.3f}$ um\")\n",
" ax1.grid()\n",
"\n",
" # Plot the zoomed-in range (1 to 1.5 µm) on ax2\n",
" \"\"\"\n",
" ax2.plot(distances/si.um, energies_left[:, 0], color=\"red\", label=\"GS left tweezer\")\n",
" ax2.plot(distances/si.um, energies_right[:, 0], color=\"blue\", label=\"GS right tweezer\")\n",
" ax2.plot(distances/si.um, energies_left[:, 1:], color=\"red\", linestyle=\"dotted\", label=\"left tweezer\")\n",
" ax2.plot(distances/si.um, energies_right[:, 1:], color=\"blue\", linestyle=\"dotted\", label=\"right tweezer\")\"\"\"\n",
" ax2.plot(distances/si.um, energies_left[:], color=\"red\", label=\"left tweezer\")\n",
" ax2.plot(distances/si.um, energies_right[:], color=\"blue\", label=\"right tweezer\")\n",
"\n",
" for i, ind in enumerate(index):\n",
" ax2.axvline(distances[ind]/si.um, color=\"k\", alpha=0.7, label=\"energy crossings\")\n",
"\n",
" ax2.set_xlabel(\"Tweezer distance [um]\")\n",
" ax2.set_ylabel(\"Eigenenergy [a.u.]\")\n",
" ax2.set_title(\"Zoomed-in Region\")\n",
" ax2.set_xlim(0.7, 1) # Zooming in on the region between 1.0 and 1.5 µm\n",
" ax2.set_ylim(-7e-28,-5e-28)\n",
" ax2.grid()\n",
"\n",
" # Remove duplicates in the legend (labels that appear in both subplots)\n",
" handles, labels = ax1.get_legend_handles_labels()\n",
" unique_labels = dict(zip(labels, handles)) # Remove duplicates\n",
" ax1.legend(unique_labels.values(), unique_labels.keys())\n",
"\n",
" # Show the plots\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
" return deltaE_min"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"def get_power_range(initial_power, initial_waist,factor_waist2, n_spacing=20,n_levels=10):\n",
" \"\"\"Calculates the range of powers where there occurs no level crossing.\"\"\"\n",
" level_spacings = np.linspace(0,1,n_spacing)\n",
" delta_E = np.full(len(level_spacings),np.nan)\n",
"\n",
" trap[trap.power_tweezer1] = initial_power\n",
" trap[trap.waist_tweezer1] = initial_waist\n",
" trap[trap.waist_tweezer2] = initial_waist*factor_waist2\n",
"\n",
" omega_r1 = sp.sqrt(2*trap.power_tweezer1*trap.a/sp.pi/trap.m)*2/trap.waist_tweezer1**2\n",
"\n",
" #offset required for 1 level spacing of the left tweezer\n",
" power2_offset = sp.pi*trap.waist_tweezer2**2*const.hbar*omega_r1/2/trap.a\n",
"\n",
" #factor to match trap depth and offset by half(or another fraction of) the level spacing\n",
" power2_factor = factor_waist2**2 - level_spacings*float(trap.subs(power2_offset))/initial_power\n",
"\n",
" power2s = initial_power*power2_factor\n",
"\n",
" for i, power2 in enumerate(power2s):\n",
" delta_E[i] = get_deltaE(initial_power,power2,initial_waist,initial_waist*factor_waist2,n_levels=n_levels,plot=False)\n",
"\n",
" mask = delta_E != 0\n",
" if np.sum(mask) == 0:\n",
" power_range = 0\n",
" else:\n",
" power_range = np.max(power2s[mask]) - np.min(power2s[mask])\n",
" return power_range, np.nanmax(delta_E)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"crossover\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\naeve\\AppData\\Local\\Temp\\ipykernel_4576\\382801629.py:24: RuntimeWarning: All-NaN slice encountered\n",
" row_diff = np.nanmin(np.abs(energies_left[:,i] - energies_right[:,j]))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"crossover\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:3\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:3\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:3\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:3\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:0, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:3\n"
]
}
],
"source": [
"factor_waist2 = 1.\n",
"\n",
"initial_powers = np.linspace(10,500,10)*si.uW\n",
"initial_waists = np.linspace(0.7,2.2,6)*si.um\n",
"delta_power = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
"delta_E_max = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
"\n",
"for i, pow in enumerate(initial_powers):\n",
" for j, wai in enumerate(initial_waists):\n",
"\n",
" delta_power[i,j], delta_E_max[i,j] = get_power_range(pow,wai,factor_waist2,n_spacing=15)\n",
"\n",
"np.savez('data/waist_ratio_1.npz', initial_powers=initial_powers, initial_waists=initial_waists, delta_power=delta_power, delta_E_max=delta_E_max, factor_waist2=factor_waist2)\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"crossover\n",
"crossover\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\naeve\\AppData\\Local\\Temp\\ipykernel_4576\\382801629.py:24: RuntimeWarning: All-NaN slice encountered\n",
" row_diff = np.nanmin(np.abs(energies_left[:,i] - energies_right[:,j]))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n"
]
}
],
"source": [
"factor_waist2 = 1.025\n",
"\n",
"initial_powers = np.linspace(10,500,10)*si.uW\n",
"initial_waists = np.linspace(0.7,2.2,6)*si.um\n",
"delta_power = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
"delta_E_max = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
"\n",
"for i, pow in enumerate(initial_powers):\n",
" for j, wai in enumerate(initial_waists):\n",
"\n",
" delta_power[i,j], delta_E_max[i,j] = get_power_range(pow,wai,factor_waist2,n_spacing=15)\n",
"\n",
"np.savez('data/waist_ratio_1025.npz', initial_powers=initial_powers, initial_waists=initial_waists, delta_power=delta_power, delta_E_max=delta_E_max, factor_waist2=factor_waist2)\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:21, right:4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\naeve\\AppData\\Local\\Temp\\ipykernel_4576\\382801629.py:24: RuntimeWarning: All-NaN slice encountered\n",
" row_diff = np.nanmin(np.abs(energies_left[:,i] - energies_right[:,j]))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:1, right:1\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:1, right:1\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:2, right:1\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n"
]
}
],
"source": [
"factor_waist2 = 1.05\n",
"\n",
"initial_powers = np.linspace(10,500,10)*si.uW\n",
"initial_waists = np.linspace(0.7,2.2,6)*si.um\n",
"delta_power = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
"delta_E_max = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
"\n",
"for i, pow in enumerate(initial_powers):\n",
" for j, wai in enumerate(initial_waists):\n",
"\n",
" delta_power[i,j], delta_E_max[i,j] = get_power_range(pow,wai,factor_waist2,n_spacing=15)\n",
"\n",
"np.savez('data/waist_ratio_105.npz', initial_powers=initial_powers, initial_waists=initial_waists, delta_power=delta_power, delta_E_max=delta_E_max, factor_waist2=factor_waist2)\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\naeve\\AppData\\Local\\Temp\\ipykernel_4576\\382801629.py:24: RuntimeWarning: All-NaN slice encountered\n",
" row_diff = np.nanmin(np.abs(energies_left[:,i] - energies_right[:,j]))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:2, right:2\n",
"closest approach between states: left:3, right:3\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"closest approach between states: left:4, right:4\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"closest approach between states: left:1, right:0\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n",
"crossover\n"
]
}
],
"source": [
"factor_waist2 = 1.075\n",
"\n",
"initial_powers = np.linspace(10,500,10)*si.uW\n",
"initial_waists = np.linspace(0.7,2.2,6)*si.um\n",
"delta_power = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
"delta_E_max = np.full((len(initial_powers),len(initial_waists)),np.nan)\n",
"\n",
"for i, pow in enumerate(initial_powers):\n",
" for j, wai in enumerate(initial_waists):\n",
"\n",
" delta_power[i,j], delta_E_max[i,j] = get_power_range(pow,wai,factor_waist2,n_spacing=15)\n",
"\n",
"np.savez('data/waist_ratio_1075.npz', initial_powers=initial_powers, initial_waists=initial_waists, delta_power=delta_power, delta_E_max=delta_E_max, factor_waist2=factor_waist2)\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(delta_power/initial_powers[:,np.newaxis],extent=[np.min(initial_waists)/si.uW,np.max(initial_waists)/si.uW,np.min(initial_powers)/si.um,np.max(initial_powers)/si.um],origin=\"lower\",aspect=\"auto\")\n",
"plt.colorbar(label=\"relative power sensitivity\")\n",
"plt.ylabel(\"Power (uW)\")\n",
"plt.xlabel(\"Waist (um)\")\n",
"plt.title(f\"ratio of tweezer waists: {factor_waist2:.2f}\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(delta_E_max,extent=[np.min(initial_waists)/si.uW,np.max(initial_waists)/si.uW,np.min(initial_powers)/si.um,np.max(initial_powers)/si.um],origin=\"lower\",aspect=\"auto\")\n",
"plt.colorbar(label=\"energy gap [a.u.]\")\n",
"plt.ylabel(\"Power (uW)\")\n",
"plt.xlabel(\"Waist (um)\")\n",
"plt.title(f\"ratio of tweezer waists: {factor_waist2:.2f}\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}