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https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-11-13 01:53:31 +01:00
Simu: 8 moved functions into lib
This commit is contained in:
parent
d629dcc6eb
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1 changed files with 34 additions and 103 deletions
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@ -18,7 +18,13 @@
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import scipy.signal as signal\n",
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"import scipy.fft as ft"
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"\n",
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"import os\n",
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"import sys\n",
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"# Append parent directory to import path so lib can be found\n",
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"sys.path.append(os.path.dirname(os.path.abspath(os.getcwd())))\n",
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"from lib.util import *\n",
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"from lib.plotting import *\n"
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]
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},
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{
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@ -120,89 +126,6 @@
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"### signal generation\n",
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"def fft_bandpass(signal, band, samplerate):\n",
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" \"\"\"\n",
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" Simple bandpassing function employing a FFT.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" signal : arraylike\n",
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" band : tuple(low, high)\n",
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" Frequencies for bandpassing\n",
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" samplerate : float\n",
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" \"\"\"\n",
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" signal = np.asarray(signal)\n",
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"\n",
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" fft = ft.rfft(signal)\n",
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" freqs = ft.rfftfreq(signal.size, 1/samplerate)\n",
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" fft[(freqs < band[0]) | (freqs > band[1])] = 0\n",
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" \n",
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" return ft.irfft(fft, signal.size), (fft, freqs)\n",
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"\n",
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"def deltapeak(timelength=1e3, samplerate=1, offset=None, peaklength=1):\n",
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" N_samples = int(timelength * samplerate)\n",
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" if offset is None:\n",
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" offset = (np.random.random(1)*N_samples).astype(int) % N_samples\n",
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" elif isinstance(offset, (tuple, list)):\n",
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" offset_min = offset[0]\n",
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" offset_max = offset[-1]\n",
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" \n",
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" offset = (np.random.random(1)*(offset_max - offset_min)+offset_min).astype(int) % N_samples\n",
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" \n",
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" position = (offset + np.arange(0, peaklength)).astype(int) % N_samples\n",
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" \n",
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" signal = np.zeros(N_samples)\n",
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" signal[position] = 1\n",
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" \n",
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" return signal, position\n",
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"\n",
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"def sin_delay(f, t, t_delay=0, phase=0):\n",
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" return np.sin(2*np.pi*f * (t + t_delay) + phase)\n",
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"\n",
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"def annotate_width(ax, name, x1, x2, y, text_kw={}, arrow_kw={}):\n",
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" default_arrow_kw = dict(\n",
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" xy = (x1, y),\n",
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" xytext = (x2,y),\n",
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" arrowprops = dict(\n",
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" arrowstyle=\"<->\",\n",
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" shrinkA=False,\n",
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" shrinkB=False\n",
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" ),\n",
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" )\n",
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"\n",
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" default_text_kw = dict(\n",
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" va='bottom',\n",
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" ha='center',\n",
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" xy=((x1+x2)/2, y)\n",
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" )\n",
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"\n",
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" an1 = ax.annotate(\"\", **{**default_arrow_kw, **arrow_kw})\n",
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" an2 = ax.annotate(name, **{**default_text_kw, **text_kw})\n",
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"\n",
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" return [an1, an2]\n",
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"\n",
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"def time2phase(time, frequency=1):\n",
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" return 2*np.pi*frequency*time\n",
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"\n",
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"def phase2time(phase, frequency=1):\n",
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" return phase/(2*np.pi*frequency)\n",
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"\n",
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"def time_roll(a, samplerate, time_shift, *roll_args, **roll_kwargs):\n",
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" \"\"\"\n",
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" Like np.roll, but use samplerate and time_shift to approximate\n",
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" the offset to roll.\n",
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" \"\"\"\n",
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" shift = np.rint(time_shift*samplerate).astype(int)\n",
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" return np.roll(a, shift, *roll_args, **roll_kwargs)"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -278,12 +201,13 @@
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"print(\"Beacon difference [ns]:\", phase2time(beacon_phase_offset, f_beacon)/ns)\n",
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"print(\"Beacon difference [phase]:\", beacon_phase_offset)\n",
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"print(\"Impulse offsets [ns]:\", impulses_offsets[:,0]/ns)\n",
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"print(\"Time difference Impulses [ns]: {}\".format( (impulses_offsets[1,0]-impulses_offsets[0,0])/ns ))"
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"print(\"Time difference Impulses [ns]: {}\".format( (impulses_offsets[1,0]-impulses_offsets[0,0])/ns ))\n",
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"print(\"Time difference Impulses [T]: {}\".format( (impulses_offsets[1,0]-impulses_offsets[0,0])*f_beacon ))"
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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": 5,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -443,18 +367,18 @@
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"\\quad = \\Delta A + (kT) + t_\\phi\n",
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"$\n",
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"\n",
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", where $\\Delta A < T$ and $k \\in \\mathbb{Z}$ and $t_\\phi$ is minimisable.\n",
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", where $\\Delta A < T$ and $k \\in \\mathbb{Z}$ and $t_\\phi$ is minimisable by synchronising the beacons.\n",
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"\n",
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"Then $\\Delta t$ can be determined by iteratively summing the signals, changing $k$, and finding the $k$ belonging to the maximum of the sums."
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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": 8,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def find_best_integer_periods_sum(samplerate, f_beacon, ref_impulse, impulse, k_step=1):\n",
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"def find_beacon_integer_period_sum(samplerate, f_beacon, ref_impulse, impulse, k_step=1):\n",
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" max_k = int( len(ref_impulse)*f_beacon/samplerate )\n",
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" ks = np.arange(-max_k/2, max_k/2, step=k_step)\n",
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" \n",
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@ -470,12 +394,15 @@
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" if maxima[i] > maxima[best_i]:\n",
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" best_i = i\n",
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" \n",
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" return ks[best_i], (ks, maxima)"
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" return ks[best_i], (ks, maxima)\n",
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"\n",
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"def find_beacon_integer_period(samplerate, f_beacon, ref_impulse, impulse, k_step=1):\n",
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" return find_beacon_integer_period_sum(samplerate, f_beacon, ref_impulse, impulse, k_step=k_step)"
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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": 9,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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@ -510,9 +437,9 @@
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" my_impulse = time_roll(my_impulse, samplerate, -t_phi)\n",
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"\n",
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" # $\\Delta A$ offset\n",
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" my_impulse = time_roll(my_impulse, samplerate, Delta_A)\n",
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" my_impulse = time_roll(my_impulse, samplerate, +Delta_A)\n",
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"\n",
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"best_k, (ks, maxima) = find_best_integer_periods_sum(samplerate, f_beacon, ref_impulse, my_impulse)\n",
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"best_k, (ks, maxima) = find_beacon_integer_period(samplerate, f_beacon, ref_impulse, my_impulse)\n",
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"print(\"Best k: {:0g}\".format(best_k))\n",
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"print(\"Maximum: {}\".format(maxima[np.where(ks == best_k)][0]))\n",
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"\n",
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@ -532,7 +459,7 @@
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" axes[i].plot(time/ns, my_impulse, label='impulse')\n",
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" axes[i].legend()\n",
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"\n",
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"axes[-1].set_ylabel(\"Sum\")\n",
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"axes[-1].set_ylabel(\"Coherence Sum\")\n",
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"\n",
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"best_maximum = np.max(maxima)\n",
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"axes[-1].axhline(best_maximum, alpha=0.7)\n",
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@ -544,7 +471,7 @@
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" summed_impulse = ref_impulse + augmented_impulses\n",
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" if True or k%2 == 1:\n",
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" axes[-1].plot(time/ns, summed_impulse, label='k={:.0f}'.format(k),\n",
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" alpha=0.1 + 0.9*1/(1+4*abs(best_maximum-maxima[i]))\n",
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" alpha=0.1 + 0.9*1/(1+2*abs(best_maximum-maxima[i]))\n",
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" )\n",
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" \n",
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"axes[-1].legend()\n",
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@ -558,12 +485,16 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Solve it"
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"## 1. Solve it\n",
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"\n",
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" 1. Find $t_\\phi$\n",
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" 2. Find $A_1$, $A_2$\n",
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" 3. Find $B_1$, $B_2$"
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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": 10,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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@ -596,7 +527,7 @@
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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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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -661,12 +592,12 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### 1.1 Beacon Phase Delays"
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"##### 1.1 Beacon Phase Delay ($t_\\phi$)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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@ -679,7 +610,7 @@
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],
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"source": [
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"beacon_phase_delays = np.array([\n",
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" find_beacon_phase_delay(beacon_samplerate, f_beacon, beacons[0], beacon)\n",
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" find_beacon_phase_delay(beacon_samplerate, f_beacon, ref_beacon, beacon)\n",
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" for beacon in beacons\n",
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"])\n",
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"\n",
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@ -690,7 +621,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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@ -751,7 +682,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### 1.2 Impulse vs beacon delays\n",
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"##### 1.2 Impulse vs beacon delays ($A_1, A_2$)\n",
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"\n",
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"Find the delay within a single beacon period"
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]
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