201 lines
48 KiB
Plaintext
201 lines
48 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"inputlist_finite=[\n",
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" 'cyl_r30nm_h30nm_p375nmx375nm_40x40_mAg_bg1.52_φ0π_θ(-0.025_0.025)π_ψ0.5π_χ0π_f2.15eV_L2.npz',\n",
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" 'cyl_r30nm_h30nm_p375nmx375nm_50x50_mAg_bg1.52_φ0π_θ(-0.025_0.025)π_ψ0.5π_χ0π_f2.15eV_L2.npz',\n",
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" 'cyl_r30nm_h30nm_p375nmx375nm_70x70_mAg_bg1.52_φ0π_θ(-0.025_0.025)π_ψ0.5π_χ0π_f2.15eV_L2.npz',\n",
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" '100x100.npz'\n",
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" \n",
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"]\n",
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"input_infinite = 'cyl_r30nm_h30nm_p375nmx375nm_mAg_bg1.52_φ0_θ(-0.025_0.025)π_ψ0.5π_χ0π_f2.15eV_L3.npz'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from matplotlib import pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 3 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"%matplotlib inline\n",
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"f, ax = plt.subplots(3,1)\n",
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"u = 1e-12 # inverse micrometer\n",
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"for i, fil in enumerate(inputlist_finite):\n",
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" dat = np.load(inputlist_finite[i], allow_pickle=True)\n",
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" meta = dat['meta'][()]\n",
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" theta = meta['theta']\n",
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" sz = meta['size']\n",
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" N = sz[0]*sz[1]\n",
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" scat_n = dat['σ_scat'][0,:] / N / u\n",
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" abs_n = dat['σ_abs'][0,:] / N / u\n",
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" ext_n = dat['σ_ext'][0,:] / N / u \n",
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" ax[0].plot(theta, scat_n, label='$%d \\\\times %d$' % (sz[0], sz[1]))\n",
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" ax[1].plot(theta, abs_n)\n",
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" ax[2].plot(theta,ext_n)\n",
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"dat = np.load(input_infinite, allow_pickle=True)\n",
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"meta = dat['meta'][()]\n",
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"N = 1\n",
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"scat_n = dat['σ_scat'][0,:] / N / u\n",
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"abs_n = dat['σ_abs'][0,:] / N / u\n",
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"ext_n = dat['σ_ext'][0,:] / N / u\n",
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"ax[0].plot(theta, scat_n, label='$\\\\infty \\\\times \\\\infty$')\n",
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"ax[1].plot(theta, abs_n)\n",
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"ax[2].plot(theta,ext_n)\n",
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"for l in range(3):\n",
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" ax[l].set_xlim([-.05,.05])\n",
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" ax[l].set_ylim([0,0.065])\n",
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"for l in range(2):\n",
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" ax[l].xaxis.set_ticklabels([])\n",
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"ax[1].set_ylabel('$\\\\sigma / \\\\mathrm{\\\\mu m}^{2}$ per particle')\n",
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"ax[2].set_xlabel('incident angle / rad')#('$\\\\theta/\\mathrm{rad}$')\n",
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"for l, txt in enumerate(['$\\\\sigma_\\\\mathrm{scat}$','$\\\\sigma_\\\\mathrm{abs}$','$\\\\sigma_\\\\mathrm{ext}$']):\n",
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" ax[l].text(0.02, 0.95,txt,\n",
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" horizontalalignment='left',\n",
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" verticalalignment='top',\n",
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" transform = ax[l].transAxes)\n",
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"f.legend(title=\"Array size\", loc=\"center right\")\n",
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"plt.savefig('04_scattering_cuts.pdf')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['meta',\n",
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" 'k_sph',\n",
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" 'k_cart',\n",
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" 'E_cart',\n",
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" 'E_sph',\n",
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" 'σ_ext',\n",
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" 'σ_abs',\n",
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" 'σ_scat',\n",
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" 'σ_ext_ir',\n",
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" 'σ_abs_ir',\n",
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" 'σ_scat_ir',\n",
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" 'omega',\n",
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" 'wavenumbers']"
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]
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},
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"execution_count": 31,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dat = np.load(inputlist_finite[0], allow_pickle=True)\n",
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"[k for k in dat.keys()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'period': (3.75e-07, 3.75e-07),\n",
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" 'size': [40, 40],\n",
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" 'background': 1.52,\n",
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" 'material': 'Ag',\n",
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" 'radius': 3e-08,\n",
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" 'height': 3e-08,\n",
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" 'lMax_extend': 6,\n",
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" 'lMax': 2,\n",
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" 'eV': [[2.15]],\n",
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" 'phi': 0.0,\n",
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" 'theta': array([-0.07853982, -0.07696902, -0.07539822, -0.07382743, -0.07225663,\n",
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|
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" -0.07068583, -0.06911504, -0.06754424, -0.06597345, -0.06440265,\n",
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|
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" -0.06283185, -0.06126106, -0.05969026, -0.05811946, -0.05654867,\n",
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|
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" -0.05497787, -0.05340708, -0.05183628, -0.05026548, -0.04869469,\n",
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" -0.04712389, -0.04555309, -0.0439823 , -0.0424115 , -0.0408407 ,\n",
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" -0.03926991, -0.03769911, -0.03612832, -0.03455752, -0.03298672,\n",
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|
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" -0.03141593, -0.02984513, -0.02827433, -0.02670354, -0.02513274,\n",
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|
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" -0.02356194, -0.02199115, -0.02042035, -0.01884956, -0.01727876,\n",
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" -0.01570796, -0.01413717, -0.01256637, -0.01099557, -0.00942478,\n",
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" -0.00785398, -0.00628319, -0.00471239, -0.00314159, -0.0015708 ,\n",
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|
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" 0. , 0.0015708 , 0.00314159, 0.00471239, 0.00628319,\n",
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|
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" 0.00785398, 0.00942478, 0.01099557, 0.01256637, 0.01413717,\n",
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|
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" 0.01570796, 0.01727876, 0.01884956, 0.02042035, 0.02199115,\n",
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|
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" 0.02356194, 0.02513274, 0.02670354, 0.02827433, 0.02984513,\n",
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|
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" 0.03141593, 0.03298672, 0.03455752, 0.03612832, 0.03769911,\n",
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|
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" 0.03926991, 0.0408407 , 0.0424115 , 0.0439823 , 0.04555309,\n",
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" 0.04712389, 0.04869469, 0.05026548, 0.05183628, 0.05340708,\n",
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|
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" 0.05497787, 0.05654867, 0.05811946, 0.05969026, 0.06126106,\n",
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|
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" 0.06283185, 0.06440265, 0.06597345, 0.06754424, 0.06911504,\n",
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" 0.07068583, 0.07225663, 0.07382743, 0.07539822, 0.07696902,\n",
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" 0.07853982]),\n",
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" 'psi': 1.5707963267948966,\n",
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" 'chi': 0.0,\n",
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" 'output': None,\n",
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" 'plot_out': None,\n",
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" 'plot': True,\n",
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" 'save_gradually': False,\n",
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" 'basis_vectors': [(3.75e-07, 0.0), (0.0, 3.75e-07)]}"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dat['meta'][()]"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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|
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.7.3"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 4
|
|||
|
|
}
|