m-thesis-introduction/simulations/08_beacon_sync.ipynb

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Text

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Beacon Sync\n",
"\n",
"Synchronise two delta peaks, by using an intermediate beacon that was sent out together with it."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import scipy.signal as signal\n",
"\n",
"import os\n",
"import sys\n",
"# Append parent directory to import path so lib can be found\n",
"sys.path.append(os.path.dirname(os.path.abspath(os.getcwd())))\n",
"from lib.util import *\n",
"from lib.plotting import *\n",
"from lib.beacon import *\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Beacon period [ns]: 14.285714285714285\n",
"Beacon initial [ns]: 4.4\n",
"Beacon initial [phase]: 1.9352210746113125\n",
"Beacon initial [idx]: 22.0\n",
"Beacon difference [ns]: 8.571428571428571\n",
"Beacon difference [phase]: 3.7699111843077517\n",
"Impulse offsets [ns]: [ 79. 132.6]\n",
"Time difference Impulses [ns]: 53.599999999999994\n",
"Time difference Impulses [T]: 3.752\n"
]
}
],
"source": [
"us = 1e3 # ns\n",
"ns = 1/us # us\n",
"\n",
"\n",
"band = (30, 80) # MHz\n",
"samplerate = 5000 # MHz\n",
"timelength = 0.2 # us\n",
"\n",
"time = np.arange(0, timelength, 1/samplerate)\n",
"\n",
"# generate beacons\n",
"if True: # in-band\n",
" f_beacon = 70 # MHz\n",
"else: # under band\n",
" f_beacon = 20 # MHz\n",
"\n",
"beacon_amplitude = 0.1\n",
"beacon_init_phase = time2phase(4.4*ns, f_beacon)\n",
"beacon_phase_offset = 1.2*np.pi\n",
"\n",
"beacons = np.array([\n",
" beacon_amplitude * sin_delay(f_beacon, time, t_delay=0, phase=-beacon_init_phase),\n",
" beacon_amplitude * sin_delay(f_beacon, time, t_delay=0, phase=-beacon_init_phase-beacon_phase_offset)\n",
"])\n",
"\n",
"\n",
"# generate impulses\n",
"impulses = []\n",
"impulses_offsets = []\n",
"impulses_def_offsets = [\n",
" (0.3*len(time),0.4*len(time)),\n",
" (0.5*len(time),0.9*len(time)),\n",
" ]# random offsets in interval\n",
"if not True:\n",
" # freeze impulses\n",
" impulses_def_offsets = [\n",
" 0.072*samplerate,\n",
" 0.168*samplerate \n",
" ]\n",
" \n",
"for i in range(2):\n",
" offset = None\n",
" if impulses_def_offsets:\n",
" if len(impulses_def_offsets) == 1:\n",
" offset = impulses_def_offsets[0]\n",
" else:\n",
" offset = impulses_def_offsets[i]\n",
" orig_imp, imp_offset = deltapeak(timelength, samplerate, offset=offset, peaklength=1)\n",
"\n",
" ## Bandpass it\n",
" imp, _ = fft_bandpass(orig_imp, band, samplerate)\n",
" imp /= np.max(imp)\n",
" \n",
" impulses.append(imp)\n",
" impulses_offsets.append(imp_offset/samplerate)\n",
"\n",
"impulses = np.array(impulses)\n",
"impulses_offsets = np.array(impulses_offsets)\n",
"print(\"Beacon period [ns]:\", 1/f_beacon/ns)\n",
"print(\"Beacon initial [ns]:\", phase2time(beacon_init_phase, f_beacon) /ns)\n",
"print(\"Beacon initial [phase]:\", beacon_init_phase)\n",
"print(\"Beacon initial [idx]:\", phase2time(beacon_init_phase, f_beacon)*samplerate)\n",
"print(\"Beacon difference [ns]:\", phase2time(beacon_phase_offset, f_beacon)/ns)\n",
"print(\"Beacon difference [phase]:\", beacon_phase_offset)\n",
"print(\"Impulse offsets [ns]:\", impulses_offsets[:,0]/ns)\n",
"print(\"Time difference Impulses [ns]: {}\".format( (impulses_offsets[1,0]-impulses_offsets[0,0])/ns ))\n",
"print(\"Time difference Impulses [T]: {}\".format( (impulses_offsets[1,0]-impulses_offsets[0,0])*f_beacon ))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"full_signals = impulses + beacons"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x288 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Make a figure showing two signals with a beacon per signal\n",
"colors = ['y','g']\n",
"multiplier_name = ['m','n']\n",
"\n",
"\n",
"fig, axes = plt.subplots(3,1, sharex=True, figsize=(12,4))\n",
"axes[-1].set_xlabel(\"Time [ns]\")\n",
"axes[-1].set_yticks([],[])\n",
"for i in range(0, 2):\n",
" axes[i].set_yticks([],[])\n",
" axes[i].set_ylabel(\"Antenna {:d}\".format(i+1))\n",
" axes[i].plot(time/ns, impulses[i])\n",
" axes[i].plot(time/ns, beacons[i], marker='.')\n",
" if not True:\n",
" axes[i].plot(time/ns, full_signals[i])\n",
"\n",
"\n",
"# indicate timing of pulses\n",
"for i, impulse_offset in enumerate(impulses_offsets):\n",
" kwargs = dict(color=colors[i])\n",
" [ax.axvline(impulse_offset/ns, **kwargs) for ax in (axes[i], axes[-1])]\n",
"\n",
"\n",
"# indicate timing of the beacons\n",
"# and annotate ticks and impulse widths\n",
"tmp_beacon_phases = beacon_init_phase + np.arange(0,2)*beacon_phase_offset\n",
"if True: # mod phases\n",
" tmp_beacon_phases %= 2*np.pi\n",
"tmp_beacon_offsets = phase2time(tmp_beacon_phases, f_beacon)\n",
"\n",
"\n",
"A = np.empty(2)\n",
"B = np.empty(2)\n",
"for i in range(0,2):\n",
" kwargs = dict(color=colors[i], ls=(0, (3,2)))\n",
" tick_kwargs = dict(color='k', alpha=0.2)\n",
"\n",
" # indicate every period of the beacon\n",
" beacon_ticks = tmp_beacon_offsets[i] + [(n)*1/f_beacon for n in range(1+int((time[-1] - time[0]) * f_beacon))]\n",
"\n",
" [axes[i].axvline(tick/ns, **{**kwargs, **tick_kwargs}) for tick in beacon_ticks]\n",
"\n",
" # reference period in beacon\n",
" [ax.axvline(tmp_beacon_offsets[i]/ns, **kwargs) for ax in (axes[i], axes[-1])]\n",
"\n",
" # annotate width between impulse and closest beacon tick\n",
" # and closest beacon tick and reference tick\n",
" closest_beacon_tick_id = np.argmin(np.abs(beacon_ticks-impulses_offsets[i]))\n",
" if closest_beacon_tick_id != 0 and beacon_ticks[closest_beacon_tick_id] > impulses_offsets[i]:\n",
" closest_beacon_tick_id -= 1\n",
" closest_beacon_tick = beacon_ticks[closest_beacon_tick_id]\n",
"\n",
" annotate_width(axes[i], f\"$A_{i+1}$\", closest_beacon_tick/ns, impulses_offsets[i]/ns, 0.7)\n",
" annotate_width(axes[i], f\"$B_{i+1}={multiplier_name[i]}T$\", closest_beacon_tick/ns, tmp_beacon_offsets[i]/ns, 0.4)\n",
"\n",
" A[i] = closest_beacon_tick - impulses_offsets[i]\n",
" B[i] = closest_beacon_tick - tmp_beacon_offsets[i]\n",
"\n",
"# annotate width between beacon reference periods\n",
"annotate_width(axes[-1], \"$t_\\phi$\", tmp_beacon_offsets[0]/ns, tmp_beacon_offsets[-1]/ns, 0.4)\n",
"\n",
"# annotate width between pulses\n",
"annotate_width(axes[-1], \"$\\Delta t$\", impulses_offsets[0]/ns, impulses_offsets[-1]/ns, 0.4)\n",
"\n",
"\n",
"fig.show()\n",
"if False:\n",
" fname = 'figures/08_beacon_sync_timing_outline'\n",
"\n",
" # Dump figure\n",
" fig.savefig(fname +'.pdf')\n",
" \n",
" # Dump information into accompanying file\n",
" with open(fname + '.dat', 'w+') as fp:\n",
" fp.write(\"f_beacon = {}MHz\\n\".format(f_beacon))\n",
" fp.write(\"samplerate = {}\\n\".format(samplerate))\n",
" fp.write(\"band = {}MHz\\n\".format(band))\n",
" fp.write(\"timelength = {}us\\n\".format(timelength))\n",
" \n",
" fp.write(\"-\"*8 + \"\\n\")\n",
" fp.write(\"\\Delta t = {}ns\\n\".format( (impulses_offsets[1][0] - impulses_offsets[0][0])/ns ))\n",
" fp.write(\"t_phi = {}ns\\n\".format( (tmp_beacon_offsets[1]-tmp_beacon_offsets[0])/ns ))\n",
" fp.write(\"\\Delta A = {}ns\\n\".format( (A[1] - A[0])/ns ))\n",
" fp.write(\"kT = {}ns = {}T\\n\".format( (B[1]-B[0])/ns, (B[1]-B[0])*f_beacon ))\n",
" \n",
" fp.write(\"-\"*8 + \"\\n\")\n",
" fp.write(\"A_1 = {}ns\\n\".format( (A[0])/ns ))\n",
" fp.write(\"A_2 = {}ns\\n\".format( (A[1])/ns ))\n",
" fp.write(\"B_1 = {}ns = {}T\\n\".format( (B[0])/ns, (B[0]*f_beacon) ))\n",
" fp.write(\"B_2 = {}ns = {}T\\n\".format( (B[1])/ns, (B[1]*f_beacon) ))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\Delta t = 53.599999999999994ns\n",
"\\Delta A = -2.171428571428574ns\n",
"t_phi = 8.571428571428573ns\n",
"B_1 = 71.42857142857142ns = 5.0T\n",
"B_2 = 114.28571428571426ns = 7.999999999999999T\n",
"kT = 42.85714285714284ns = 2.999999999999999T\n"
]
}
],
"source": [
"t_phi = (tmp_beacon_offsets[1]-tmp_beacon_offsets[0])\n",
"Delta_A = (A[1] - A[0])\n",
"\n",
"print(\"\\Delta t = {}ns\".format( (impulses_offsets[1][0] - impulses_offsets[0][0])/ns ))\n",
"print(\"\\Delta A = {}ns\".format( Delta_A/ns ))\n",
"print(\"t_phi = {}ns\".format( t_phi/ns ))\n",
"print(\"B_1 = {}ns = {}T\".format( (B[0])/ns, (B[0]*f_beacon) ))\n",
"print(\"B_2 = {}ns = {}T\".format( (B[1])/ns, (B[1]*f_beacon) ))\n",
"print(\"kT = {}ns = {}T\".format( (B[1]-B[0])/ns, (B[1]-B[0])*f_beacon ))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\n",
"\\Delta t = (A_2 + B_2) - (A_1 + B_1) + t_\\phi\\\\\n",
"\\quad = (A_2 - A_1) + (B_2 - B_1) + t_\\phi\\\\\n",
"\\quad = (A_2 - A_1) + (nT - mT) + t_\\phi\\\\\n",
"\\quad = \\Delta A + (kT) + t_\\phi\n",
"$\n",
"\n",
", where $\\Delta A < T$ and $k \\in \\mathbb{Z}$ and $t_\\phi$ is minimisable by synchronising the beacons.\n",
"\n",
"Then $\\Delta t$ can be determined by iteratively summing the signals, changing $k$, and finding the $k$ belonging to the maximum of the sums."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def find_beacon_integer_period_sum(samplerate, f_beacon, ref_impulse, impulse, k_step=1):\n",
" max_k = int( len(ref_impulse)*f_beacon/samplerate )\n",
" ks = np.arange(-max_k/2, max_k/2, step=k_step)\n",
" \n",
" maxima = np.empty(len(ks))\n",
" \n",
" best_i = 0\n",
" \n",
" for i,k in enumerate(ks, 0):\n",
" augmented_impulse = time_roll(impulse, samplerate, k/f_beacon)\n",
" \n",
" maxima[i] = max(ref_impulse + augmented_impulse)\n",
" \n",
" if maxima[i] > maxima[best_i]:\n",
" best_i = i\n",
" \n",
" return ks[best_i], (ks, maxima)\n",
"\n",
"def find_beacon_integer_period(samplerate, f_beacon, ref_impulse, impulse, k_step=1):\n",
" return find_beacon_integer_period_sum(samplerate, f_beacon, ref_impulse, impulse, k_step=k_step)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best k: -3\n",
"Maximum: 2.0\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Make figure showing the sums depending on k\n",
"ref_impulse = impulses[0]\n",
"my_impulse = impulses[1]\n",
"\n",
"# remove 'already determined' offsets\n",
"if True:\n",
" # $t_\\phi$ offset\n",
" my_impulse = time_roll(my_impulse, samplerate, -t_phi)\n",
"\n",
" # $\\Delta A$ offset\n",
" my_impulse = time_roll(my_impulse, samplerate, +Delta_A)\n",
"\n",
"best_k, (ks, maxima) = find_beacon_integer_period(samplerate, f_beacon, ref_impulse, my_impulse)\n",
"print(\"Best k: {:0g}\".format(best_k))\n",
"print(\"Maximum: {}\".format(maxima[np.where(ks == best_k)][0]))\n",
"\n",
"\n",
"# Make figure\n",
"fig, axes = plt.subplots(1, 1, sharex=True,figsize=(12,4))\n",
"if not hasattr(axes, 'ndim'):\n",
" axes = [axes]\n",
"\n",
"axes[0].set_title(\"Sum of impulses with $kT$ offsets. Best offset: ${:.0f}*T$\".format(best_k))\n",
"axes[-1].set_xlabel(\"Time [ns]\")\n",
"\n",
"if not True:\n",
" i=0\n",
" axes[i].set_ylabel(\"Reference\")\n",
" axes[i].plot(time/ns, ref_impulse, label=\"reference\")\n",
" axes[i].plot(time/ns, my_impulse, label='impulse')\n",
" axes[i].legend()\n",
"\n",
"axes[-1].set_ylabel(\"Coherence Sum\")\n",
"\n",
"best_maximum = np.max(maxima)\n",
"axes[-1].axhline(best_maximum, alpha=0.7)\n",
"\n",
"for i, k in enumerate(ks, 0):\n",
" sample_offset = int(k*1/f_beacon*samplerate)\n",
" augmented_impulses = np.roll(my_impulse, sample_offset)\n",
" \n",
" summed_impulse = ref_impulse + augmented_impulses\n",
" if True or k%2 == 1:\n",
" axes[-1].plot(time/ns, summed_impulse, label='k={:.0f}'.format(k),\n",
" alpha=0.1 + 0.9*1/(1+2*abs(best_maximum-maxima[i]))\n",
" )\n",
" \n",
"axes[-1].legend()\n",
"fig.show()\n",
"\n",
"del ref_impulse\n",
"del my_impulse"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Solve it\n",
"\n",
" 1. Find $t_\\phi$\n",
" 2. Find $A_1$, $A_2$\n",
" 3. Find $B_1$, $B_2$"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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D28pZ9lJKHb5S1W3OzyxgLtDH9xED30dp2XyxbBu/G9GR1KTa0ceVrzVvGMXjF/Zk9fb9PPn1OrfjGFNruFlAFgEpItJORCLwFIlfXU0lIp2AJsA8r2lNRCTSeR4LnAKsrpHUASQr9yAPTl3FKR2acePgMhtgxjGiazxXnJzIK99v4PuM2nko05ia5loBUdVC4Fbga2ANMEVVV4nIwyLifVXVOOAD/eVlNF2ANBFZBswBHlPVOlVAioqV33+8nIiwECZd3Dtgh6MNJPed3ZUOzetz15Rl7Dl0zO04xgQ9qUuXN6ampmpaWprbMXxi8nfreXTaWp65pDfn9Snz2gNThtXb9nPeCz8yvGtz/nl5P7fjGBMURGSxc875F+xO9CCUmXOAJ2ekc2bXeMb2buV2nKDStVVDfjs8hWkrdjBtxXa34xgT1KyABJnComLu+mg59SJCeeT8HnbJbjVMPD2Z7gkNeeDzley1Q1nGVJsVkCAz+fsslm3Zx8NjuxPXINLtOEEpPDSEv1/Ui32HC3j4P3Xq1JkxPmUFJIik7zzAMzMzOLtHC87tWTsHh6opJd29f7pkK7PX7HQ7jjFByQpIkCguVu7593LqRYby8NjudujKB24Z2oHOLRrwp09X2FC4xlSDFZAg8f6izfy8eR/3ndOV2Pp26MoXIsI8h7J2HTzGo1+ucTuOMUHHCkgQyDmQz+NfreXk5KZc2Ncu2fWlHq0bcd1p7fgwbQuLNu5xO44xQcUKSBD463/WkF9QbFdd+clvh6WQ0Dia+z9dSUFRsdtxjAkaVkAC3HfpuUxdto2bh7anfVx9t+PUSjERYTw4uivrdh7gjR83uB3HmKBhBSSA5RcUcf9nK0mOrcdNQ6yvK386s1sLhndpzjOzMti274jbcYwJClZAAtg/vslk857DPHJ+DyLDQt2OU+s9OLobxao8/IXdG2JMVVgBCVAbdx1i8ndZXNAnoc4PT1tT2jSN4bYzUpi+agdz1ua4HceYgGcFJED99cvVRISFcM+ozm5HqVOuPy2ZDs3r88DUleQX2OBTxlTECkgAmrMuh1lrcrh9WAeaN4xyO06dEhEWwsNju7FlzxFe+S7L7TjGBDQrIAHmWGExD3+xmuTYekwY1M7tOHXSoPaxjOregn/OXc+OvHy34xgTsKyABJg3ftzAhl2HeGB0VyLC7M/jlj+d3YUiVR6fvtbtKMYELFc/oURkpIisE5FMEbmnjPkTRCRXRJY6j+u85o0XkQznMb5mk/tHzv58npudwfAuzRnSqbnbceq0Nk1juP60dny6ZCuLN+11O44xAcm1AiIiocALwCigKzBORLqWseiHqtrbebzqrNsUeBAYAPQHHhSRJjUU3W8em76WgiLl/nPK+jWYmnbzkA40bxDJw1+sori47ozcaUxVudkC6Q9kqmqWqh4DPgDGVnHds4CZqrpHVfcCM4GRfspZI5Zt2ccnP2/l2tPakRRbz+04BqgXGcY9ozqzLDuPT5ZsdTuOMQHHzQKSAGzxep3tTCvtQhFZLiIfi0ib41wXEZkoImkikpabm+uL3D6nqjzy5Rpi60dwy9AObscxXs7rnUCvNo15fPpaDh4tdDuOMQHFzQJSVq+ApY8TfAEkqWpPYBbw1nGs65moOllVU1U1NS4urtph/WnG6p0s3LiHO0d0pH5kmNtxjJeQEOHB0V3JPXCUl+audzuOMQHFzQKSDbTxet0a2Oa9gKruVtWjzstXgH5VXTdYHCss5rGv1pLSvD6XpLapfAVT4/omNmF0r1a8+kOWXdZrjBc3C8giIEVE2olIBHApMNV7ARHxHrd1DFAy6s/XwJki0sQ5eX6mMy3ovLdgExt2HeJPZ3chLNQu2w1UfzirE8XFMGnmOrejGBMwXPvEUtVC4FY8H/xrgCmqukpEHhaRMc5it4vIKhFZBtwOTHDW3QP8H54itAh42JkWVPKOFPDs7AxO6dCMIZ0C8/Ca8WjTNIarBrblo8XZrN2x3+04xgQEUa07lyempqZqWlqa2zH+69Fpa3jl+yz+c9updGvVyO04phL7Dh/j9Cfm0CexCW9d09/tOMbUGBFZrKqppafbMROXbNlzmDd/3MiFfVtb8QgSjWMiuPWMDnybnssPGbvcjmOM66yAuGTSzHRE4K4zO7odxRyHqwYmkdA4mkenrbGbC02dZwXEBWu27+ezpVu5+pR2tGwU7XYccxyiwkP5w8hOrHb+hsbUZVZAXPDE9LU0iAzjpsE2TG0wGt2zFT0SGvHUjHSOFtqYIabusgJSwxZk7WbOulxuGtKBRjHhbscx1RASIvxhZCe27jvCews2ux3HGNdYAalB6nQPHt8wkgmDktyOY07AqR1iGdS+Gf/4JtO6ODF1lhWQGjRz9U5+3ryPO4Z3JDoi1O045gSICL8/qxO7Dx3j9R82uB3HGFdYAakhRcXK379eR3JsPX7Tr7XbcYwP9Elswpld43nluyz2HDrmdhxjapwVkBryyc/ZZOQc5O6zOlmXJbXI3Wd14tCxQl6cm+l2FGNqnH2S1YCjhUU8MyuDnq0bMap7C7fjGB/qGN+A8/u05q15m9ied8TtOMbUKCsgNWDKoi1s3XeEu87shEhZPdGbYHbH8BRQeHZWhttRjKlRVkD87MixIp7/JpP+SU05PSXW7TjGD9o0jeGyAYl8tDibDbsOuR3HmBpjBcTP3pm/iZwDR7nrzI7W+qjFbh7anvBQ4dlZ6W5HMabGWAHxo4NHC3nx2/WclhLLgORmbscxftS8QRTjByXx+bJtZOw84HYcY2qEFRA/euOHDew5dIy7zuzkdhRTA244vT0x4aE8ba0QU0e4WkBEZKSIrBORTBG5p4z5vxOR1SKyXERmi0hbr3lFIrLUeUwtva7b8g4XMPn7LEZ0jad3m8ZuxzE1oGm9CK49tR3TVuxg1bY8t+MY43euFRARCQVeAEYBXYFxItK11GJLgFRV7Ql8DDzhNe+IqvZ2HmMIMJO/X8/Bo4X8boR1116XXHtaMg2jwnh6pl2RZWo/N1sg/YFMVc1S1WPAB8BY7wVUdY6qHnZezgeC4hbu3QeP8saPGzmnR0u6tGzodhxTgxpFhzPx9GRmrdnJ0i373I5jjF+5WUASgC1er7OdaeW5FvjK63WUiKSJyHwROa+8lURkorNcWm5u7oklrqLJ32eRX1DEHcOt9VEXTTilHU1iwpk0086FmNrNzQJS1jWtZQ7xJiJXAKnA370mJzpj9F4GPCMiZQ6uoaqTVTVVVVPj4uJONHOldh08yr9+2sSYXq3o0Ly+3/dnAk/9yDBuGtKe79JzWbRxj9txjPEbNwtINtDG63VrYFvphURkOHAfMEZVj5ZMV9Vtzs8sYC7Qx59hq+rlb9dztLCI24eluB3FuOjKk5OIrR/JM3ZFlqnFKi0gIhIjIn8WkVec1ykicq4P9r0ISBGRdiISAVwK/OJqKhHpA7yMp3jkeE1vIiKRzvNY4BRgtQ8ynZCcA/m8PX8T5/VOIDnOWh91WXREKDcOTubHzN0s3GCtEFM7VaUF8gZwFBjovM4G/nqiO1bVQuBW4GtgDTBFVVeJyMMiUnJV1d+B+sBHpS7X7QKkicgyYA7wmKq6XkBemptFQZFym7U+DHD5gLbWCjG1WlgVlmmvqpeIyDgAVT0iPuqTQ1WnAdNKTXvA6/nwctb7Cejhiwy+snN/Pu8u2MT5fRJoF1vP7TgmAERHhHLTkPb8339WsyBrt/VGYGqdqrRAjolINM4Jbudk9dGKV6l7Xpy7nsJi5bYzOrgdxQSQywckEtcg0u5ON7VSVQrIg8B0oI2IvAvMBv7g11RBZkdePu8t3MyFfRNo28xaH+Z/osJDuWlwe+Zn7WHe+t1uxzHGpyotIKo6E7gAmAC8j+fO8Ln+jRVcXvp2PcXFyq1D7dyH+bXLBiTSvIGdCzG1T7kFRET6ljyAtsB2PJfZJjrTDJ5zH+8t3MwFfRNIbBbjdhwTgKLCPedCFmzYw0/rd7kdxxifqegk+lPOzyg8N/Etw3PzX09gAXCqf6MFhxfnrqfIWh+mEuP6J/LSt+t5dlYGg9rbwGKmdii3BaKqQ1V1KLAJ6Ovczd0Pzw17mTUVMJDl7M/n/YWbuaCPtT5MxaLCQ7lxsKcVMj/LzoWY2qEqJ9E7q+qKkhequhLo7b9IweOlb7MoLFZutSuvTBWM6++5IsvGTje1RVUKyBoReVVEhojIYOeO9DX+Dhbocpz7Ps7rbVdemaopaYXMy7K7003tUJUCcjWwCvgtcAeeLkOu9meoYPDyd1l234c5bpcPSCS2fiTPzrYrskzwq8plvPmq+rSqnu88nlbV/JoIF6hyDnhaH2N7tyLJ7jo3x8HTCvH0kZVmPfWaIFeVzhQ3iEhW6UdNhAtUr3yXxbHCYm4daq0Pc/w8fWRF8OxsOxdigltV+sJK9XoeBfwGaOqfOIFv18GjvDN/M2Otx11TTdERoUw8PZlHp61l8aa99GvbxO1IxlRLVQ5h7fZ6bFXVZ4AzaiBbQHrl+yzyC4vsyitzQq44uS3N6lkrxAS3Slsgpe46D8HTImngt0QBbM+hY7w9bxOje7aivbU+zAmIiQjj+tOTeeyrtSzZvJc+idYKMcGnKldhPeX1+BvQF7jYn6EC1Ws/ZHGkoMiuvDI+ceXJbWkSE87z39h9uSY4VeUcyLXOsLH/JSLt/JQnYO07fIy3ftrE2T1akhJfJxtgxsfqRYZx3WnJ/P3rdazIzqNH60ZuRzLmuFSlBfJxFacdNxEZKSLrRCRTRO4pY36kiHzozF8gIkle8+51pq8TkbN8kacir/+wgYNHC631YXzqqoFtaRQdznPf2LkQE3zKbYGISGegG9BIRC7wmtUQz9VYJ0REQoEXgBF4hsldJCJTSw1Ney2wV1U7iMilwOPAJSLSFc8Y6t2AVsAsEemoqkUnmqsseUcKeOPHjYzs1oLOLRr6YxemjmoQFc61p7Zj0sx0Vm3Lo1sra4WY4FFRC6QTcC7QGBjt9egLXO+DffcHMlU1S1WPAR8AY0stMxZ4y3n+MTDMGU53LPCBqh5V1Q14Onfs74NMZXrzx40cOFrIbcOs9WF8b/ygJBpEhfH8bDsXYoJLuS0QVf0c+FxEBqrqPD/sOwHY4vU6GxhQ3jKqWigieUAzZ/r8UusmlLUTEZkITARITEysVtCt+w5zZtd4+3Zo/KJRdDhXn9KO52ZnsHbHfmvlmqBR0YBSJcPWXiYiz5V++GDfUsY0reIyVVnXM1F1stMVfWpcXNxxRvR44qJevHC5jaFl/OeaU5KoHxlmV2SZoFLRVVglPe6m+Wnf2UAbr9et8Yx4WNYy2SISBjQC9lRxXZ8KD63K9QbGVE/jmAjGD2rLP+euJ2PnAbvSz/iMqnK0sJio8FCfb7uiAaW+cH6+VdbDB/teBKSISDsRicBzUnxqqWWmAuOd5xcB36iqOtMvda7SagekAAt9kMkY11x7ajLR4aHWCjE+9WPmbk557BtWbcvz+bYrugrrC8o5LASgqmNOZMfOOY1bga+BUOB1VV0lIg8Daao6FXgNeFtEMvG0PC511l0lIlPwdC1fCNziryuwjKkpTetFcNXAJF7+bj23D0uhQ3Pr7cCcGFXl2dnphIeG+OXfk3i+0JcxQ2RwJcG+9XkaP0tNTdW0NH8dkTPmxO0+eJRTH5/DyO4tePoSG/jTnJifMndx2asL+MuYbowflFTt7YjIYlVNLT29okNY35Y8gHnAXjytgHnBWDyMCQbN6kdy5cC2fL50K1m5B92OY4Lcs7MziG8YySUntal84Wqoyngg5wDrgeeAfwCZIjLKL2mMMVx/WjIRYSH8Y46dCzHVNz9rNws27OHGwe39cgIdqt6Z4lBVHaKqg4GhwNN+SWOMIa5BJJcPaMvnS7excdcht+OYIPXc7AziGkQyrn/17n+riqoUkBxV9f4qlAXk+CmPMQa44fRkwkKEF6wVYqph0cY9/LR+Nzecnuy31gdUrYCsEpFpIjJBRMYDX+Dpt+qCUn1kGWN8pHnDKMb1T+STJVvZvPuw23FMkHl2Vgax9SO4fEBbv+6nKgUkCtgJDAaGALl4hrQdjaevLGOMH9w0pD2hISwCgO8AABpfSURBVMI/51orxFTd4k17+CFzFxNPTyY6wn+tD6jCeCCqerVfExhjyhTfMIrL+ifyzvxN3DK0A22axrgdyQSBZ2dn0rReBFec7N/WB1TtKqx2IjJJRD4RkaklD78nM8Zw4+D2hNi5EFNFizft5bv0XG44PZmYiKqMF3hiqrKHz/DcEf4FUOzfOMYYby0aRTHupDa8u2CztUJMpZ6dnUHTehFcOdD/rQ+o2jmQfFV9TlXnlLq50BhTA24a0oEQsVaIqVhNtz6gagXkWRF5UEQGikjfkoffkxljAKcV0r8NHy/OZsseuyLLlK2mWx9QtQLSA88IhI/huanwKeBJf4YyxvxSSSvErsgyZXGj9QFVOwdyPpDsDDtrjHFBSSvk3QWbuXmInQsxv+RG6wOq1gJZhmdcdGOMi+xciCmLW60PqFoBiQfWisjXXpfxfu7vYMaYX/I+F2J3p5sSz8xKd6X1AVUrIA/iOYz1KDAJz8h/HfwZyhhTtpuHdiAkRHj+mwy3o5gAkLZxD99n7HKl9QFVKCDOJbt5wDnAm8Aw4KUT2amINBWRmSKS4fxsUsYyvUVknoisEpHlInKJ17w3RWSDiCx1HjbyjqkT4htGccWAtnyyZCsbrKfeOm/SzHRi60dy1cAkV/ZfbgERkY4i8oCIrMEzDsgWPCMYDlXV509wv/cAs1U1BZjtvC7tMHCVqnYDRgLPiIj3uZjfq2pv57H0BPMYEzRuHJJMeKjw/GxrhdRl89bv5qf1u7lpSHu/93lVnopaIGvxtDZGq+qpTtHw1bjjY4G3nOdvAeeVXkBV01U1w3m+DU8X8nE+2r8xQat5gyiuGpjEZ0u3kpljoxbWRarK0zPTiW8YyeUD/DfeR2UqKiAXAjuAOSLyiogMA8RH+41X1e0Azs/mFS0sIv2BCDwjI5Z4xDm09bSIRFaw7kQRSRORtNzcXF9kN8Z1JeM8PGetkDrph8xdLNy4h1uGdvDreB+VqWhM9E9V9RKgMzAXuBOIF5EXReTMyjYsIrNEZGUZj7HHE1BEWgJvA1eraklfXPc6uU7C07X8Hyt4H5NVNVVVU+PirAFjaodm9SOZMCiJL5ZvI33nAbfjmBqkqjw1I51WjaL8NtZ5VVXlJPohVX1XVc8FWgNLKfucRen1hqtq9zIenwM7ncJQUiDKHOFQRBoCXwL3q+p8r21vV4+jwBtA/yq8V2NqletPS6ZeRBjPzEp3O4qpQXPX5bJ0yz5uG5ZCZJh7rQ+o2mW8/6Wqe1T1ZVU94wT3OxUY7zwfD/zqvhIRiQA+Bf6lqh+VmldSfATP+ZOVJ5jHmKDTpF4E15ySxLQVO1i1Lc/tOKYGqCqTZqbTpmk0F/Vr7Xac4ysgPvQYMEJEMoARzmtEJFVEXnWWuRg4HZhQxuW674rICmAFEAv8tWbjGxMYrj0tmYZRYTw1w1ohdcGM1TtZsTWP289IITzUrY/v/6n5O08AVd2N5wqv0tPTgOuc5+8A75Sz/om2gIypFRpFh3PjkPY8MX0dizftoV/bpm5HMn5SVKw8NWMdyXH1OL9PgttxAPdaIMYYH5kwKInY+pE8MX0dqup2HOMnny/dSvrOg9w1ohNhAdD6ACsgxgS9mIgwbh3angUb9vBD5i634xg/OFZYzNOz0unWqiGjurdwO85/WQExphYYNyCRhMbR/P1ra4XURh+mbWHLniPcfVYnQkJ8dTveibMCYkwtEBkWym+HpbA8O48Zq3e6Hcf40JFjRTw/O4OTkpowpGNg3ctmBcSYWuKCvgkkx9bjqRnrKCq2Vkht8a95G8k5cJTfn9UZz50LgcMKiDG1RFhoCHeO6Ej6zoN8tmSr23GMD+zPL+DFb9czuGMc/dsF3hV2VkCMqUXO6dGS7gkNmTQznfwCX/V9atzyyndZ7DtcwN1ndnI7SpmsgBhTi4SECPeM7MLWfUd4Z/4mt+OYE5CzP59Xv9/AOT1b0qN1I7fjlMkKiDG1zKkpsZyWEss/5mSSd6TA7Timmp6elUFBUTG/D9DWB1gBMaZW+uPIzuw7XMDL366vfGETcDJzDjIlbQtXnNyWpNh6bscplxUQY2qh7gmNGNu7Fa//uIEdefluxzHH6Ynpa4kOD+W2Mzq4HaVCVkCMqaXuGtGJomK17t6DTNrGPcxYvZMbTk+mWf1yx8oLCFZAjKmlEpvFcPmAtkxJ20Jmjg06FQxUlb99tZbmDSK59rR2bseplBUQY2qx287oQExEGH+bttbtKKYKZqzeyeJNe7ljeEdiIlzpLP24WAExphZrVj+Sm4e2Z/baHH60jhYDWkFRMY9/tZbkuHpcnOr+YFFV4UoBEZGmIjJTRDKcn03KWa7IazCpqV7T24nIAmf9D53RC40xZbjmlHYkNI7m//6z2ro4CWBvz9tE1q5D3Hd2l4Dprr0ybqW8B5itqinAbMofY/2IqvZ2HmO8pj8OPO2svxe41r9xjQleUeGh3DOqM2t3HOCjtC1uxzFl2HvoGM/OzuC0lFjO6Nzc7ThV5lYBGQu85Tx/C8+45lXijIN+BvBxddY3pi46t2dL+rVtwpMz0jl4tNDtOKaUZ2dncCC/gPvP6RpwHSZWxK0CEq+q2wGcn+WV3CgRSROR+SJSUiSaAftUteR/QTYQGOM7GhOgRIT7z+nCroNHeXFupttxjJfMnAO8PX8T4/on0qlFA7fjHBe/neYXkVlAWUNn3Xccm0lU1W0ikgx8IyIrgP1lLFfugV0RmQhMBEhMTDyOXRtTu/RJbMLY3q145fsNjOufSOsmMW5HMsAjX64hJjyU343o6HaU4+a3FoiqDlfV7mU8Pgd2ikhLAOdnTjnb2Ob8zALmAn2AXUBjESkpfq2BbRXkmKyqqaqaGhcXWIOxGFPT/jCyMwI8MX2d21EM8G16LnPW5XL7sJSAv2mwLG4dwpoKjHeejwc+L72AiDQRkUjneSxwCrBaPeN1zgEuqmh9Y8yvJTSO5obTk5m6bBsLsna7HadOKywq5pEvV9O2WQxXDWrrdpxqcauAPAaMEJEMYITzGhFJFZFXnWW6AGkisgxPwXhMVVc78/4I/E5EMvGcE3mtRtMbE8RuGtKBhMbRPDh1FYVFxW7HqbPemreJ9J0H+dPZXYgMC3U7TrW4cqujqu4GhpUxPQ24znn+E9CjnPWzgP7+zGhMbRUdEcqfz+3Kje8s5u35m7j6lMDvMqO2ydmfz9Mz0xnSKY4zu8a7HafaguNuFWOMT53VLZ7TUmKZNCOd3ANH3Y5T5zw6bQ3HCot5aHS3oLpstzQrIMbUQSLCQ2O6kV9YxOPTrZ+smjQ/azefLd3GjYOTA3qsj6qwAmJMHdU+rj7XnprMx4uzWbxpr9tx6oSComIe+HwlrZtEc9OQwB7royqsgBhTh912RgdaNorigc9XWj9ZNeCtnzaSvvMgD47uRnREcJ4492YFxJg6rF5kGPef05VV2/bz5k8b3Y5Tq+10Tpyf0bk5w7sET39XFbECYkwdd3aPFgztFMdTM9aRvfew23FqrQc/X0VhsfLg6ODq76oiVkCMqeNEhL+e77li/v7PVuK5V9f40vSV25m+agd3DO9I22bBfeLcmxUQYwwJjaO5+8xOzF2Xy9Rl5fYMZKoh70gBf/58FV1bNuS6IBim9nhYATHGADB+UBK92jTm4S9Ws/fQMbfj1Bp/m7aGPYeO8cRFPQkPkoGiqqp2vRtjTLWFhgiPXdCDvCMFPDJtjdtxaoWf1u/ig0VbuO60dnRPaOR2HJ+zAmKM+a8uLRsy8XTPvSE/ZNgY6iciv6CIez9ZQdtmMdwxLPi6aq8KKyDGmF+4fVgKyXH1+P3Hy8g7UuB2nKA1aWY6m3Yf5m8X9KgV93yUxQqIMeYXosJDmXRxb3IOHOUvX6xyO05Qmp+1m1e+z+KyAYkMah/rdhy/sQJijPmV3m0ac8vQDnzy81amr9zudpygsj+/gLumLKNt0xjuO7uL23H8ygqIMaZMt53Rge4JDbn3kxXkHMh3O07QeGjqKnbsz+fpS3pTL9KVETNqjBUQY0yZwkNDePri3hw6VsSfPllhNxhWwbQV2/nk563cMrQDfRKbuB3H71wpICLSVERmikiG8/NXv2kRGSoiS70e+SJynjPvTRHZ4DWvd82/C2Nqv5T4BvxxZGdmrcnhw0Vb3I4T0Hbuz+dPn66gV+tG3HZG8Pe0WxVutUDuAWaragow23n9C6o6R1V7q2pv4AzgMDDDa5Hfl8xX1aU1ktqYOujqQUkMat+Mh75YxbodB9yOE5CKi5W7P1pGfkERky7pXetuGCyPW+9yLPCW8/wt4LxKlr8I+EpVrac3Y2pYSIjwzCW9qR8Zxs3vLubQ0UK3IwWcf87N5PuMXdx/Tlfax9V3O06NcauAxKvqdgDnZ2V9G18KvF9q2iMislxEnhaRyPJWFJGJIpImImm5ubknltqYOqp5wyieu7QPWbsOcd+ndj7E24+Zu5g0M52xvVtx+YBEt+PUKL8VEBGZJSIry3iMPc7ttAR6AF97Tb4X6AycBDQF/lje+qo6WVVTVTU1Li6uGu/EGAMwqEMsdw7vyGdLt/GBnQ8BYEdePre/v4T2cfV59Pwetaab9qry2zVmqjq8vHkislNEWqrqdqdA5FSwqYuBT1X1v7fElrRegKMi8gZwt09CG2MqdOvQDizauIcHp66iZ+tGdGtV+/p3qqqComJufe9njhQU8eIVfWv9JbtlcesQ1lRgvPN8PPB5BcuOo9ThK6foIJ5yfx6w0g8ZjTGllJwPaRoTwc3v/sy+w3W3197Hv1pL2qa9PHZhTzo0b+B2HFe4VUAeA0aISAYwwnmNiKSKyKslC4lIEtAG+LbU+u+KyApgBRAL/LUGMhtjgGb1I3nh8j5s35fPje8s5lhhsduRatxnS7by6g8bGD+wLWN6tXI7jmukLp0MS01N1bS0NLdjGFMrfLokmzs/XMbFqa15/MKedeb4/4Ks3Vz52kL6tm3Mv64ZQERY7b9kV0QWq2pq6el176CdMcYnzu/TmqzcQzz/TSbt4+pzw+D2bkfyu/W5B5n49mJaN43m5StS60TxqIgVEGNMtd05vCNZuw7x2PS1JMXW46xuLdyO5De7Dx7lmjcXERYivDmhP41iwt2O5Lq6XT6NMSckJER46je96Nm6MXd8sJQlm/e6Hckv8guKmPj2Ynbk5fPK+FQSm8W4HSkgWAExxpyQqPBQXrmqH80bRnLV6wtZuTXP7Ug+daywmFvfW8LiTXuZdHFv+taBThKrygqIMeaENW8QxXvXn0zDqHCufG0Ba3fsdzuSTxwrLOaW935m1pqdPDy2G+f0bOl2pIBiBcQY4xMJjaN57/oBRIaFcsWrC8jMOeh2pBNSUFTMbe//zMzVO3lodFeuGpjkdqSAYwXEGOMzbZvV493rBwDCZa/MZ8OuQ25HqpaComJ++8ESvl61kwfO7cqEU9q5HSkgWQExxvhU+7j6vHf9AAqLlYte/ImlW/a5Hem45BcUcdt7S5i2Ygf3n9OFa0614lEeKyDGGJ/rGN+Aj24cSExkKJdOnsfM1TvdjlQluQeOcunk+Xy9egd/Prcr152W7HakgGYFxBjjF+3j6vPpzafQKb4BN7ydxr/mbXQ7UoXSdx7gvBd+ZO2O/bx4eT+utZZHpayAGGP8JrZ+JO9PPJkzOsfzwOer+MsXqwKy76zv0nO58J8/UVBUzEc3DGJk99p7Q6QvWQExxvhVTEQYL1/ZjwmDknjjx41c8OKPAXOF1rHCYp6YvpYJbyykddMYPrvlFHq0rrtd1B8vKyDGGL8LDREeGtONyVf2Y+veI5z7/Pe8u2CTqyMbrtm+n7Ev/Mg/567n4tQ2fHzjQFo1jnYtTzCyvrCMMTXmzG4t6N2mMXd9tIz7Pl3JnLU5PDi6G22a1lzXIIVFxbzy/QYmzVxHo+gIXhufyrAu8TW2/9rEunM3xtS44mLl9R838OSMdRQVK1ec3JZbh3agWf1Iv+7zi+XbeGZWBht2HWJU9xY8cn4PmtaL8Ns+a4vyunO3AmKMcc2OvHyemZXOlLQtxESEcePgZK4cmESjaN/1dKuqzFi9k0kz0lm38wCd4htw15kdGdE1vs6MYXKiAqqAiMhvgIeALkB/VS3zU11ERgLPAqHAq6paMnJhO+ADoCnwM3ClqlY6tqYVEGMCU8bOAzzx9Tpmrt5JRFgII7rGc2HfBE5LiSM8tHqnajN2HuCLZdv4Yvl2Nuw6RHJsPe4Y0ZFze7QkJMQKx/EItALSBSgGXgbuLquAiEgokI5nyNtsYBEwTlVXi8gU4BNV/UBEXgKWqeqLle3XCogxgW3l1jw+XpzN50u3svdwAc3qRTCkU3O6JzSkW6tGdGnZgAZRv26dHC0sYuOuw6TvPMC6HQeYuXon63YeIETg5ORmXNi3NWN7tyKsmsWorguoAvLfnYvMpfwCMhB4SFXPcl7f68x6DMgFWqhqYenlKmIFxJjgcKywmG/Tc/l0STYLN+xh18H/HWCIbxhJWEgIoSFCaIhQVKxs3XeEomLPZ1mIQJ/EJozp1YpRPVrQvEGUW2+j1gjGIW0TgC1er7OBAUAzYJ+qFnpNTyhvIyIyEZgIkJiY6J+kxhifKjmMNaJrPKpKzoGjrNqWx+pt+9m85zCFxUpxsVJYrIgIY3q1IiW+PinNG5AcV4+o8FC330Kd4LcCIiKzgLJu57xPVT+vyibKmKYVTC+Tqk4GJoOnBVKF/RpjAoiIEN8wiviGUZzR2S63DSR+KyCqOvwEN5ENtPF63RrYBuwCGotImNMKKZlujDGmBgXyGaVFQIqItBORCOBSYKp6TtrMAS5ylhsPVKVFY4wxxodcKSAicr6IZAMDgS9F5GtneisRmQbgtC5uBb4G1gBTVHWVs4k/Ar8TkUw850Req+n3YIwxdZ3dSGiMMaZC5V2FFciHsIwxxgQwKyDGGGOqxQqIMcaYarECYowxplrq1El0EckFNlVz9Vg896AEA8vqH5bV94IlJ9TtrG1VNa70xDpVQE6EiKSVdRVCILKs/mFZfS9YcoJlLYsdwjLGGFMtVkCMMcZUixWQqpvsdoDjYFn9w7L6XrDkBMv6K3YOxBhjTLVYC8QYY0y1WAExxhhTLVZAqkBERorIOhHJFJF73M5TFhFpIyJzRGSNiKwSkd+6nakyIhIqIktE5D9uZ6mIiDQWkY9FZK3z+x3odqbyiMidzt9/pYi8LyIBM56riLwuIjkistJrWlMRmSkiGc7PJm5mLFFO1r87/waWi8inItLYzYwlysrqNe9uEVERifXHvq2AVEJEQoEXgFFAV2CciHR1N1WZCoG7VLULcDJwS4Dm9PZbPF31B7pngemq2hnoRYBmFpEE4HYgVVW7A6F4xtEJFG8CI0tNuweYraopwGzndSB4k19nnQl0V9WeQDpwb02HKseb/DorItIGGAFs9teOrYBUrj+QqapZqnoM+AAY63KmX1HV7ar6s/P8AJ4PuXLHinebiLQGzgFedTtLRUSkIXA6zpgzqnpMVfe5m6pCYUC0iIQBMQTQaJ2q+h2wp9TkscBbzvO3gPNqNFQ5ysqqqjOccYoA5uMZDdV15fxeAZ4G/kAFQ36fKCsglUsAtni9ziaAP5gBRCQJ6AMscDdJhZ7B84+72O0glUgGcoE3nMNtr4pIPbdDlUVVtwJP4vnGuR3IU9UZ7qaqVLyqbgfPlyCguct5quoa4Cu3Q5RHRMYAW1V1mT/3YwWkclLGtIC99llE6gP/Bu5Q1f1u5ymLiJwL5KjqYrezVEEY0Bd4UVX7AIcInMMsv+CcPxgLtANaAfVE5Ap3U9U+InIfnkPG77qdpSwiEgPcBzzg731ZAalcNtDG63VrAuiwgDcRCcdTPN5V1U/czlOBU4AxIrIRzyHBM0TkHXcjlSsbyFbVktbcx3gKSiAaDmxQ1VxVLQA+AQa5nKkyO0WkJYDzM8flPBUSkfHAucDlGrg30bXH8yVimfN/rDXws4i08PWOrIBUbhGQIiLtRCQCz0nJqS5n+hURETzH6deo6iS381REVe9V1daqmoTn9/mNqgbkN2VV3QFsEZFOzqRhwGoXI1VkM3CyiMQ4/x6GEaAn/L1MBcY7z8cDn7uYpUIiMhL4IzBGVQ+7nac8qrpCVZurapLzfywb6Ov8W/YpKyCVcE6a3Qp8jec/4xRVXeVuqjKdAlyJ59v8UudxttuhaonbgHdFZDnQG3jU5TxlclpJHwM/Ayvw/P8OmO43ROR9YB7QSUSyReRa4DFghIhk4Lli6DE3M5YoJ+s/gAbATOf/10uuhnSUk7Vm9h24rTBjjDGBzFogxhhjqsUKiDHGmGqxAmKMMaZarIAYY4ypFisgxhhjqsUKiDHGmGqxAmJMFYlIM697bHaIyFav1z/5YX8TRCRXRKrV4aTT/fgOEbnb19mMAU8/P8aYKlDV3XhuJEREHgIOquqTft7th6p6a3VWVNXfi8ghXwcypoS1QIzxARE56PwcIiLfisgUEUkXkcdE5HIRWSgiK0SkvbNcnIj8W0QWOY9TqrCPCSLyiYhMdwZgesKZHioibzqDSK0QkTv9+26N8bAWiDG+1wvogmeMhizgVVXtL55RIm8D7sAzSNXTqvqDiCTi6SqnSxW23RtPV/1HgXUi8jyeLtATnEGkCJSR8kztZwXEGN9bVDLGhYisB0rG5FgBDHWeDwe6evo8BKChiDRwBgOryGxVzXO2vRpoC6wCkp1i8qXX/ozxKysgxvjeUa/nxV6vi/nf/7kQYKCqHjmBbRcBYaq6V0R6AWcBtwAX4xnwyBi/snMgxrhjBp5engEQkd7V3ZCIxAIhqvpv4M8E7nglppaxFogx7rgdeMHpIj4M+A64sZrbSsAz5G7JF8J7fZDPmEpZd+7GBCgRmQCkVvcyXmcbD1EzlxubOsgOYRkTuI4Ao07kRkLgCjzjuBvjc9YCMcYYUy3WAjHGGFMtVkCMMcZUixUQY4wx1WIFxBhjTLX8P85uaaN72d2FAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# single out one period of the beacon\n",
"beacon_samplerate = samplerate # MHz\n",
"beacon_time = np.arange(0, 1/f_beacon, 1/beacon_samplerate)\n",
"ref_beacon = sin_delay(f_beacon, beacon_time, phase=0, t_delay=0)\n",
"\n",
"# .. and show beacon period\n",
"fig, ax = plt.subplots()\n",
"ax.set_title(\"A single beacon period\")\n",
"ax.set_xlabel(\"Time [ns]\")\n",
"ax.set_ylabel(\"Amplitude\")\n",
"ax.plot(beacon_time/ns,ref_beacon)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 1.1 Beacon Phase Delay ($t_\\phi$)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Beacon delays [ns] \\pm k*14.285714285714285ns: [0.0 8.571428571428571]\n"
]
}
],
"source": [
"abs_beacon_time_delays_tmp = np.array([\n",
" beacon_time_delay(beacon_samplerate, beacons[0], beacon)\n",
" for beacon in beacons\n",
"])\n",
"\n",
"abs_beacon_time_delays = abs_beacon_time_delays_tmp[:,0]\n",
"beacon_time_delays = abs_beacon_time_delays % (1/f_beacon)\n",
"beacon_time_delays_err = abs_beacon_time_delays_tmp[:,1]\n",
"\n",
"print(\"Beacon delays [ns] \\pm k*{}ns: {}\".format(1/f_beacon/ns, beacon_time_delays/ns))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Make a figure showing the corrected beacons\n",
"fig, ax = plt.subplots(1,1, sharex=True)\n",
"ax.set_xlabel(\"Time [ns]\")\n",
"ax.set_ylabel(\"Amplitude [au]\")\n",
"ax.set_title(\"Beacon delays [ns] $\\pm$ $k*{}$\\n{}\".format(1/f_beacon/ns, abs_beacon_time_delays/ns))\n",
"\n",
"\n",
"\n",
"\n",
"for i, _ in enumerate(beacons):\n",
" l = ax.plot(\n",
" time/ns, beacons[i],\n",
" label=\"ch {}\".format(i), \n",
" ls ='--', \n",
" alpha=0.5\n",
" )\n",
" \n",
" # indicate start of uncorrected beacons\n",
" ax.axvline(\n",
" beacon_time_delays[i]/ns,\n",
" color=l[0].get_color(),\n",
" ls = '--'\n",
" )\n",
" \n",
" ax.plot(\n",
" (time-beacon_time_delays[i])/ns,\n",
" beacons[i],\n",
" label='ch {} corrected'.format(i),\n",
" color=l[0].get_color(),\n",
" ls=(5*i+2, (20, 20))\n",
" )\n",
" \n",
"ax.legend(ncol=2)\n",
"ax.margins(y=0.3)\n",
"if True:\n",
" ax.set_xlim(time[0]/ns - 1, time[2*samplerate//f_beacon]/ns)\n",
"\n",
"fig.show()\n",
"\n",
"\n",
"#ax.plot((double_signal_time) * ns, signal_2(double_signal_time + calc_shift), 'r--', label='Recovered')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 1.2 Impulse vs beacon delays ($A_1, A_2$)\n",
"\n",
"Find the delay within a single beacon period"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#def find_beacon_impulse_delay(samplerate, f_beacon, impulse, init_phase=0):\n",
"def find_beacon_impulse_phase_delay(samplerate, f_beacon, reference_beacon, impulse, **lag_kwargs):\n",
" \"\"\"\n",
" Return phase delay of `beacon` with respect to `reference_beacon`.\n",
" Note that the returned value can be off by a multiple of $2\\pi$.\n",
" \n",
" Parameters\n",
" ==========\n",
" samplerate : float\n",
" Samplerate of both reference_beacon and delayed_beacon\n",
" f_beacon : float\n",
" Frequency of the beacons\n",
" reference_beacon : ndarray\n",
" The beacon to use as a reference\n",
" beacon : ndarray\n",
" The beacon to find the delay for\n",
" \"\"\"\n",
" \n",
" calc_lag, _ = find_best_lag(reference_beacon, impulse, **lag_kwargs)\n",
" \n",
" return 2*np.pi* f_beacon * calc_lag / samplerate"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'find_best_lag' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-7845dd4cbb3f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m impulse_beacon_phase_delays[i] = find_beacon_impulse_phase_delay(\n\u001b[1;32m 5\u001b[0m \u001b[0mbeacon_samplerate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf_beacon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mref_beacon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimpulses\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m )\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-11-e5130fc29802>\u001b[0m in \u001b[0;36mfind_beacon_impulse_phase_delay\u001b[0;34m(samplerate, f_beacon, reference_beacon, impulse, **lag_kwargs)\u001b[0m\n\u001b[1;32m 17\u001b[0m \"\"\"\n\u001b[1;32m 18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mcalc_lag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_best_lag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreference_beacon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimpulse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mlag_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m \u001b[0mf_beacon\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcalc_lag\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msamplerate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'find_best_lag' is not defined"
]
}
],
"source": [
"impulse_beacon_phase_delays = np.empty( len(impulses) )\n",
"\n",
"for i, _ in enumerate(impulses):\n",
" impulse_beacon_phase_delays[i] = find_beacon_impulse_phase_delay(\n",
" beacon_samplerate, f_beacon, \n",
" ref_beacon, impulses[i]\n",
" )\n",
"\n",
"print(\"Beacon Impuls delays: ${}$ns\".format(impulse_beacon_phase_delays/f_beacon/ns))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Make a figure showing the corrected beacons\n",
"fig, axes = plt.subplots(len(impulses),1, sharex=True)\n",
"axes[-1].set_xlabel(\"Time [us]\")\n",
"ax.set_title(\"Beacon Impuls delays: ${}$ns\".format(impulse_beacon_phase_delays/f_beacon/ns))\n",
"\n",
"for i, beacon in enumerate(beacons):\n",
" ax.set_ylabel(\"Amplitude [au]\")\n",
" ax.plot(time, beacon, label=\"ch {}\".format(i))\n",
"\n",
"ax.plot(beacon_time, corrected_beacon, label='phase corrected (overlaps ch 0)')\n",
"\n",
"ax.legend()\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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