946 lines
37 KiB
Text
946 lines
37 KiB
Text
{
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"cells": [
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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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"from matplotlib import pyplot as plt\n",
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"import numpy as np\n",
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"from scipy import signal as sig\n",
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"import struct\n",
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"import random\n",
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"import ipywidgets\n",
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"import itertools\n",
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"\n",
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"import colorednoise\n",
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"\n",
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"np.set_printoptions(linewidth=240)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib widget"
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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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"source": [
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"sampling_rate = 10 # sp/s"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"#colorednoise.powerlaw_psd_gaussian(1, int(1e4))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# From https://github.com/mubeta06/python/blob/master/signal_processing/sp/gold.py\n",
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"preferred_pairs = {5:[[2],[1,2,3]], 6:[[5],[1,4,5]], 7:[[4],[4,5,6]],\n",
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" 8:[[1,2,3,6,7],[1,2,7]], 9:[[5],[3,5,6]], \n",
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" 10:[[2,5,9],[3,4,6,8,9]], 11:[[9],[3,6,9]]}\n",
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"\n",
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"def gen_gold(seq1, seq2):\n",
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" print(seq1.shape, seq2.shape)\n",
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" gold = [seq1, seq2]\n",
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" for shift in range(len(seq1)):\n",
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" gold.append(seq1 ^ np.roll(seq2, -shift))\n",
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" return gold\n",
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"\n",
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"def gold(n):\n",
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" n = int(n)\n",
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" if not n in preferred_pairs:\n",
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" raise KeyError('preferred pairs for %s bits unknown' % str(n))\n",
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" t0, t1 = preferred_pairs[n]\n",
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" (seq0, _st0), (seq1, _st1) = sig.max_len_seq(n, taps=t0), sig.max_len_seq(n, taps=t1)\n",
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" return gen_gold(seq0, seq1)"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5081f9508a894fcf810e2d9c92c24a3e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(31,) (31,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7ff8d9616610>"
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]
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},
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"execution_count": 7,
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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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"fig, ax = plt.subplots()\n",
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"ax.matshow(gold(5))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def modulate(data, nbits=5):\n",
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" # 0, 1 -> -1, 1\n",
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" mask = np.array(gold(nbits))*2 - 1\n",
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" \n",
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" sel = mask[data>>1]\n",
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" data_lsb_centered = ((data&1)*2 - 1)\n",
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"\n",
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" return (np.multiply(sel, np.tile(data_lsb_centered, (2**nbits-1, 1)).T).flatten() + 1) // 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": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(31,) (31,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([-1, -1, -1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, 1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1,\n",
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" 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, -1,\n",
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" 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1,\n",
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" 1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1,\n",
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" -1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1,\n",
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" 1, 1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, -1,\n",
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" 1, -1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, 1, 1,\n",
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" -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, -1,\n",
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" -1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, -1, 1, 1])"
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]
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},
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"execution_count": 9,
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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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"data = np.array(list(range(16)))\n",
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"\n",
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"mask = np.array(gold(5))*2 - 1\n",
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" \n",
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"sel = mask[data>>1]\n",
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"data_lsb_centered = ((data&1)*2 - 1)\n",
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"mask.shape, data.shape, sel.shape\n",
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"\n",
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"#fig, ax = plt.subplots()\n",
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"#ax.plot(\n",
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"np.multiply(sel, np.tile(data_lsb_centered, (2**5-1, 1)).T).flatten()"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def correlate(sequence, nbits=5, decimation=1, mask_filter=lambda x: x):\n",
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" mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n",
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"\n",
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" sequence -= np.mean(sequence)\n",
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" \n",
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" return np.array([np.correlate(sequence, row, mode='full') for row in mask])"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(31,) (31,)\n",
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"(31,) (31,)\n",
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"shapes (1240,) (1240,)\n",
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"(31,) (31,)\n",
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"mask (33, 310)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ec8de5680ba541938b7d1843b841c327",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(31,) (31,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
|
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"<matplotlib.image.AxesImage at 0x7ff8d955afa0>"
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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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"nbits = 5\n",
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"decimation = 10\n",
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"\n",
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"foo = np.repeat(modulate(np.array(list(range(4))), nbits).astype(float), decimation)\n",
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"bar = np.repeat(modulate(np.array(list(range(4))), nbits) * 2.0 - 1, decimation) * 1e-3\n",
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"print('shapes', foo.shape, bar.shape)\n",
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"\n",
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"mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n",
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"print('mask', mask.shape)\n",
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"\n",
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"fig, (ax1, ax2) = plt.subplots(2, figsize=(16, 5))\n",
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"fig.tight_layout()\n",
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"corr_m = np.array([np.correlate(foo, row, mode='full') for row in mask])\n",
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"#corr_m = np.array([row for row in mask])\n",
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"ax1.matshow(corr_m, aspect='auto')\n",
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"#ax.matshow(foo.reshape(32, 31)[::2,:])\n",
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"ax2.matshow(correlate(bar, decimation=decimation), aspect='auto')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "bb32b5050ee14ddc8eb64697a8eb774b",
|
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"version_major": 2,
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"version_minor": 0
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},
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|
"text/plain": [
|
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
|
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(31,) (31,)\n",
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"(31,) (31,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
|
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"<matplotlib.image.AxesImage at 0x7ff8d8eb9e20>"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"decimation = 10\n",
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"\n",
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"fig, (ax1, ax2) = plt.subplots(2, figsize=(12, 5))\n",
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"fig.tight_layout()\n",
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"\n",
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"#mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n",
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"#mask_stretched = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, 1)).reshape((2**nbits + 1, (2**nbits-1) * 1))\n",
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"\n",
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"#ax1.matshow(mask)\n",
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"#ax2.matshow(mask_stretched, aspect='auto')\n",
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"\n",
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"foo = np.repeat(modulate(np.array(list(range(4)))).astype(float), 1).reshape((4, 31))\n",
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"foo_stretched = np.repeat(modulate(np.array(list(range(4)))).astype(float), 10).reshape(4, 310)\n",
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"\n",
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"ax1.matshow(foo)\n",
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"ax2.matshow(foo_stretched, aspect='auto')"
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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": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(31,) (31,)\n"
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]
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},
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|
{
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"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "48b3ae259e8046a5b90a82c4e80bab2e",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
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"text": [
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"(31,) (31,)\n",
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"(31,) (31,)\n"
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]
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},
|
|
{
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"data": {
|
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"text/plain": [
|
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"(2.0, 1.0121324810255907)"
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]
|
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},
|
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"execution_count": 13,
|
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"metadata": {},
|
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"output_type": "execute_result"
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}
|
|
],
|
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"source": [
|
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"decimation = 10\n",
|
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"signal_amplitude = 2.0\n",
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"nbits = 5\n",
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"\n",
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"foo = np.repeat(modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0]), nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
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"noise = colorednoise.powerlaw_psd_gaussian(1, len(foo))\n",
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"\n",
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"sosh = sig.butter(4, 0.01, btype='highpass', output='sos', fs=decimation)\n",
|
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"sosl = sig.butter(6, 1.0, btype='lowpass', output='sos', fs=decimation)\n",
|
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"filtered = sig.sosfilt(sosh, sig.sosfilt(sosl, foo + noise))\n",
|
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"#filtered = sig.sosfilt(sosh, foo + noise)\n",
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"\n",
|
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"fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2, figsize=(16, 9))\n",
|
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"fig.tight_layout()\n",
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"\n",
|
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"ax1.plot(foo + noise)\n",
|
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"ax1.plot(foo)\n",
|
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"ax1.set_title('raw')\n",
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"\n",
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"ax2.plot(filtered)\n",
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"ax2.plot(foo)\n",
|
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"ax2.set_title('filtered')\n",
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"\n",
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"ax3.plot(correlate(foo + noise, nbits=nbits, decimation=decimation))\n",
|
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"ax3.set_title('corr raw')\n",
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" \n",
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"ax3.grid()\n",
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"\n",
|
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"ax4.plot(correlate(filtered, nbits=nbits, decimation=decimation))\n",
|
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"ax4.set_title('corr filtered')\n",
|
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"ax4.grid()\n",
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"\n",
|
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"rms = lambda x: np.sqrt(np.mean(np.square(x)))\n",
|
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"rms(foo), rms(noise)"
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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": 14,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
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"mean: 49.98625\n"
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]
|
|
}
|
|
],
|
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"source": [
|
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"with open('/mnt/c/Users/jaseg/shared/raw_freq.bin', 'rb') as f:\n",
|
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" mains_noise = np.copy(np.frombuffer(f.read(), dtype='float32'))\n",
|
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" print('mean:', np.mean(mains_noise))\n",
|
|
" mains_noise -= np.mean(mains_noise)"
|
|
]
|
|
},
|
|
{
|
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"cell_type": "code",
|
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"execution_count": 27,
|
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"metadata": {},
|
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"outputs": [
|
|
{
|
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"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
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"(63,) (63,)\n",
|
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"(63,) (63,)\n"
|
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]
|
|
},
|
|
{
|
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"data": {
|
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"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "73f4caf6a80f448183c41b711412a471",
|
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"version_major": 2,
|
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"version_minor": 0
|
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},
|
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"text/plain": [
|
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
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"output_type": "display_data"
|
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},
|
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{
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"data": {
|
|
"text/plain": [
|
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"(0.0020000000000000005, 0.014544699)"
|
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]
|
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},
|
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"execution_count": 27,
|
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"metadata": {},
|
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"output_type": "execute_result"
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}
|
|
],
|
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"source": [
|
|
"decimation = 10\n",
|
|
"signal_amplitude = 2.0e-3\n",
|
|
"nbits = 6\n",
|
|
"\n",
|
|
"#test_data = np.random.randint(0, 2, 100)\n",
|
|
"#test_data = np.array([0, 1, 0, 0, 1, 1, 1, 0])\n",
|
|
"test_data = np.random.RandomState(seed=0xcbb3b8cf).randint(0, 2 * (2**nbits), 128)\n",
|
|
"#test_data = np.random.RandomState(seed=0).randint(0, 8, 64)\n",
|
|
"#test_data = np.array(list(range(8)) * 8)\n",
|
|
"#test_data = np.array([0, 1] * 32)\n",
|
|
"#test_data = np.array(list(range(64)))\n",
|
|
"\n",
|
|
"foo = np.repeat(modulate(test_data, nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
|
|
"noise = np.resize(mains_noise, len(foo))\n",
|
|
"#noise = 0\n",
|
|
"\n",
|
|
"sosh = sig.butter(3, 0.01, btype='highpass', output='sos', fs=decimation)\n",
|
|
"sosl = sig.butter(3, 0.8, btype='lowpass', output='sos', fs=decimation)\n",
|
|
"#filtered = sig.sosfilt(sosh, sig.sosfilt(sosl, foo + noise))\n",
|
|
"filtered = sig.sosfilt(sosh, foo + noise)\n",
|
|
"\n",
|
|
"cor1 = correlate(foo + noise, nbits=nbits, decimation=decimation)\n",
|
|
"#cor2 = correlate(filtered, nbits=nbits, decimation=decimation)\n",
|
|
"\n",
|
|
"#cor2_pe = correlate(filtered, nbits=nbits, decimation=decimation, mask_filter=lambda mask: sig.sosfilt(sosh, sig.sosfiltfilt(sosl, mask)))\n",
|
|
"\n",
|
|
"sosn = sig.butter(12, 4, btype='highpass', output='sos', fs=decimation)\n",
|
|
"#cor1_flt = sig.sosfilt(sosn, cor1)\n",
|
|
"#cor2_flt = sig.sosfilt(sosn, cor2)\n",
|
|
"#cor1_flt = cor1[1:] - cor1[:-1]\n",
|
|
"#cor2_flt = cor2[1:] - cor2[:-1]\n",
|
|
"\n",
|
|
"fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2, figsize=(16, 9))\n",
|
|
"fig.tight_layout()\n",
|
|
"\n",
|
|
"ax1.plot(foo + noise)\n",
|
|
"ax1.plot(foo)\n",
|
|
"ax1.set_title('raw')\n",
|
|
"ax1.grid(axis='y')\n",
|
|
"\n",
|
|
"ax2.plot(filtered)\n",
|
|
"ax2.plot(foo)\n",
|
|
"ax2.set_title('filtered')\n",
|
|
"ax2.grid(axis='y')\n",
|
|
"\n",
|
|
"for i in range(0, len(foo) + 1, decimation*(2**nbits - 1)):\n",
|
|
" ax1.axvline(i, color='gray', alpha=0.5, lw=1)\n",
|
|
" ax2.axvline(i, color='gray', alpha=0.5, lw=1)\n",
|
|
"\n",
|
|
"for i, (color, trace) in enumerate(zip(plt.cm.winter(np.linspace(0, 1, cor1.shape[0])), cor1.T)):\n",
|
|
" if i%3 == 0:\n",
|
|
" ax3.plot(trace + 0.5 * i, alpha=1.0, color=color)\n",
|
|
"ax3.set_title('corr raw')\n",
|
|
"ax3.grid()\n",
|
|
"\n",
|
|
"#ax4.plot(cor2[:4].T)\n",
|
|
"#ax4.set_title('corr filtered')\n",
|
|
"#ax4.grid()\n",
|
|
"ax4.matshow(cor1, aspect='auto')\n",
|
|
"\n",
|
|
"#ax5.plot(cor1_flt)\n",
|
|
"#ax5.set_title('corr raw (highpass)')\n",
|
|
"#ax5.grid()\n",
|
|
"\n",
|
|
"#ax6.plot(cor2_flt)\n",
|
|
"#ax6.set_title('corr filtered (highpass)')\n",
|
|
"#ax6.grid()\n",
|
|
"\n",
|
|
"#ax6.plot(cor2_pe[:4].T)\n",
|
|
"#ax6.set_title('corr filtered w/ mask preemphasis')\n",
|
|
"#ax6.grid()\n",
|
|
"\n",
|
|
"rms = lambda x: np.sqrt(np.mean(np.square(x)))\n",
|
|
"rms(foo), rms(noise)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "9fa8fa6a6837412da95630c634a12e21",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(63,) (63,)\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x7ff8ad6f3b50>]"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, ax = plt.subplots()\n",
|
|
"\n",
|
|
"seq = np.repeat(gold(6)[29]*2 -1, decimation)\n",
|
|
"sosh = sig.butter(3, 0.01, btype='highpass', output='sos', fs=decimation)\n",
|
|
"sosl = sig.butter(3, 0.8, btype='lowpass', output='sos', fs=decimation)\n",
|
|
"seq_filtered = sig.sosfilt(sosh, sig.sosfiltfilt(sosl, seq))\n",
|
|
"#seq_filtered = sig.sosfilt(sosh, seq)\n",
|
|
"\n",
|
|
"ax.plot(seq)\n",
|
|
"ax.plot(seq_filtered)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "854dec05e45340ae91cf271b2facd7ed",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(63,) (63,)\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'cor2_pe' 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-24-f158dfc14cca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0msosh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbutter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'highpass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sos'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0msosl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbutter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lowpass'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sos'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mcor2_pe_flt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msosfilt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msosh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcor2_pe\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 11\u001b[0m \u001b[0mcor2_pe_flt2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msosfilt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msosh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msosfiltfilt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msosl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcor2_pe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'cor2_pe' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, axs = plt.subplots(3, 1, figsize=(9, 7), sharex=True)\n",
|
|
"fig.tight_layout()\n",
|
|
"axs = axs.flatten()\n",
|
|
"for ax in axs:\n",
|
|
" ax.grid()\n",
|
|
"\n",
|
|
"seq = np.repeat(gold(6)[29]*2 -1, decimation)\n",
|
|
"sosh = sig.butter(3, 0.1, btype='highpass', output='sos', fs=decimation)\n",
|
|
"sosl = sig.butter(3, 0.8, btype='lowpass', output='sos', fs=decimation)\n",
|
|
"cor2_pe_flt = sig.sosfilt(sosh, cor2_pe)\n",
|
|
"cor2_pe_flt2 = sig.sosfilt(sosh, sig.sosfiltfilt(sosl, cor2_pe))\n",
|
|
"\n",
|
|
"axs[0].plot(cor2_pe)\n",
|
|
"axs[1].plot(cor2_pe_flt)\n",
|
|
"axs[2].plot(cor2_pe_flt2)\n",
|
|
"\n",
|
|
"#for idx in np.where(np.abs(cor2_pe_flt2) > 0.5)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "b3f68635d3ad4863b990c0b6e742840b",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x7ff8a9b7a820>]"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, ax = plt.subplots()\n",
|
|
"nonlinear_distance = lambda x: 100**(2*np.abs(0.5-x%1)) / (np.abs(x)+3)**2\n",
|
|
"x = np.linspace(-1.5, 5.5, 10000)\n",
|
|
"ax.plot(x, nonlinear_distance(x))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "a6c4ef5b68a745b9963d3407df2e03fc",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"cor_an (65, 81269)\n",
|
|
"cwt_res (65, 81269)\n",
|
|
"th (65, 81269)\n",
|
|
"[((65,), (65,)), ((65,), (65,)), ((65,), (65,)), ((65,), (65,)), ((65,), (65,))]\n",
|
|
"peaks: 1852\n",
|
|
"avg_peak 1.6610203317347632\n",
|
|
"skipped 3 symbols at 42209.0\n",
|
|
"decoding [ref|dec]:\n",
|
|
" 10| 10 ✔ 69| 69 ✔ 124|124 ✔ 102|102 ✔ 2| 2 ✔ 3| 3 ✔ 78| 78 ✔ 29| 29 ✔ \n",
|
|
"122|123 ✘ 73| 73 ✔ 98| 98 ✔ 34| 34 ✔ -1| -1 ✔ 97| 97 ✔ 7| 7 ✔ 97| 97 ✔ \n",
|
|
" 86| 86 ✔ 120|120 ✔ 95| 95 ✔ 90| 90 ✔ 49| 49 ✔ 89| 89 ✔ 83| 83 ✔ 19| 19 ✔ \n",
|
|
" 84| 84 ✔ 117|117 ✔ 92| 92 ✔ 119|119 ✔ 16| 16 ✔ 45| 45 ✔ 23| 23 ✔ 16| 16 ✔ \n",
|
|
"111|111 ✔ 9| 9 ✔ 89| 89 ✔ 18| 18 ✔ 36| 36 ✔ 2| 2 ✔ 115|115 ✔ 40| 40 ✔ \n",
|
|
"100|100 ✔ 105|105 ✔ 93| 93 ✔ 85| 85 ✔ 107|107 ✔ 90| 90 ✔ 62| 62 ✔ 116|116 ✔ \n",
|
|
" 42| 42 ✔ 123|123 ✔ 40| 40 ✔ -1| -1 ✔ 77| 77 ✔ 40| 40 ✔ 57| 57 ✔ 110|110 ✔ \n",
|
|
" 29| 29 ✔ 94| 94 ✔ 1| 1 ✔ 29| 29 ✔ 71| 71 ✔ 119|119 ✔ 15| 15 ✔ 115|115 ✔ \n",
|
|
"120| -1 70| -1 50| 50 ✔ 71| 71 ✔ 50| 50 ✔ 61| 61 ✔ 38| 38 ✔ 4| 4 ✔ \n",
|
|
" 3| 3 ✔ 124|124 ✔ 95| 95 ✔ 27| 27 ✔ 48| 48 ✔ 116|116 ✔ 3| 3 ✔ 63| 63 ✔ \n",
|
|
" 19| 19 ✔ 79| 79 ✔ 2| 2 ✔ 43| 43 ✔ 92| 92 ✔ 8| 8 ✔ 65| 65 ✔ 35| 35 ✔ \n",
|
|
" 30| 30 ✔ 73| 73 ✔ 73| 73 ✔ 38| 38 ✔ 58| 58 ✔ 49| 49 ✔ 45| 45 ✔ 58| 58 ✔ \n",
|
|
" 46| 46 ✔ 116|116 ✔ 101|101 ✔ 5| 5 ✔ 78| 78 ✔ 126|126 ✔ 105| 76 ✘ 108|108 ✔ \n",
|
|
" 59| 59 ✔ 46| 46 ✔ 27| 27 ✔ 14| 14 ✔ 57| 57 ✔ 81| 81 ✔ 3| 3 ✔ 9| 9 ✔ \n",
|
|
"126|126 ✔ 18| 55 ✘ 76| 76 ✔ 101|101 ✔ 124|124 ✔ 4| 4 ✔ 3| 3 ✔ 102|102 ✔ \n",
|
|
" 79| 79 ✔ 121|121 ✔ 103|103 ✔ 92| 92 ✔ 30| 30 ✔ 4| 4 ✔ 103|103 ✔ 59| 58 ✘ \n",
|
|
"Symbol error rate e=0.046875\n",
|
|
"maximum bitrate r=321.6796875 b/h\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"threshold_factor = 4.0\n",
|
|
"power_avg_width = 1024\n",
|
|
"max_lookahead = 6.5\n",
|
|
"\n",
|
|
"bit_period = (2**nbits) * decimation\n",
|
|
"peak_group_threshold = 0.1 * bit_period\n",
|
|
"\n",
|
|
"cor_an = cor1\n",
|
|
"\n",
|
|
"#fig, (ax1, ax2, ax3) = plt.subplots(3, figsize=(12, 12))\n",
|
|
"fig, (ax1, ax3) = plt.subplots(2, figsize=(12, 5))\n",
|
|
"fig.tight_layout()\n",
|
|
"\n",
|
|
"#ax1.matshow(sig.cwt(cor_an, sig.ricker, np.arange(1, 31)), aspect='auto')\n",
|
|
"\n",
|
|
"#for i in np.linspace(1, 10, 19):\n",
|
|
"# offx = 5*i\n",
|
|
"# ax2.plot(sig.cwt(cor_an, sig.ricker, [i]).flatten() + offx, color='red')\n",
|
|
"#\n",
|
|
"# ax2.text(-50, offx, f'{i:.1f}',\n",
|
|
"# horizontalalignment='right',\n",
|
|
"# verticalalignment='center',\n",
|
|
"# color='black')\n",
|
|
"#ax2.grid()\n",
|
|
"\n",
|
|
"ax3.grid()\n",
|
|
"print('cor_an', cor_an.shape)\n",
|
|
"\n",
|
|
"cwt_res = np.array([ sig.cwt(row, sig.ricker, [0.73 * decimation]).flatten() for row in cor_an ])\n",
|
|
"ax3.plot(cwt_res.T)\n",
|
|
"#def update(w = 1.0 * decimation):\n",
|
|
"# line.set_ydata(sig.cwt(cor_an, sig.ricker, [w]).flatten())\n",
|
|
"# fig.canvas.draw_idle()\n",
|
|
"#ipywidgets.interact(update)\n",
|
|
"\n",
|
|
"print('cwt_res', cwt_res.shape)\n",
|
|
"th = np.array([ np.convolve(np.abs(row), np.ones((power_avg_width,))/power_avg_width, mode='same') for row in cwt_res ])\n",
|
|
"ax1.plot(th.T)\n",
|
|
"print('th', th.shape)\n",
|
|
"\n",
|
|
"def compare_th(elem):\n",
|
|
" idx, (th, val) = elem\n",
|
|
" #print('compare_th:', th.shape, val.shape)\n",
|
|
" return np.any(np.abs(val) > th*threshold_factor)\n",
|
|
"\n",
|
|
"print([ (a.shape, b.shape) for a, b in zip(th.T, cwt_res.T) ][:5])\n",
|
|
"\n",
|
|
"peaks = [ list(group) for val, group in itertools.groupby(enumerate(zip(th.T, cwt_res.T)), compare_th) if val ]\n",
|
|
"print('peaks:', len(peaks))\n",
|
|
"peak_group = []\n",
|
|
"for group in peaks:\n",
|
|
" pos = np.mean([idx for idx, _val in group])\n",
|
|
" pol = np.mean([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group])\n",
|
|
" pol_idx = np.argmax(np.bincount([ np.argmax(np.abs(val)) for _idx, (_th, val) in group ]))\n",
|
|
" #print(f'group', pos, pol, pol_idx)\n",
|
|
" #for pol, (_idx, (_th, val)) in zip([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group], group):\n",
|
|
" # print(' ', pol, val)\n",
|
|
" ax3.axvline(pos, color='cyan', alpha=0.3)\n",
|
|
" \n",
|
|
" if not peak_group or pos - peak_group[-1][1] > peak_group_threshold:\n",
|
|
" if peak_group:\n",
|
|
" peak_pos = peak_group[-1][3]\n",
|
|
" ax3.axvline(peak_pos, color='red', alpha=0.6)\n",
|
|
" #ax3.text(peak_pos-20, 2.0, f'{0 if pol < 0 else 1}', horizontalalignment='right', verticalalignment='center', color='black')\n",
|
|
" \n",
|
|
" peak_group.append((pos, pos, pol, pos, pol_idx))\n",
|
|
" #ax3.axvline(pos, color='cyan', alpha=0.5)\n",
|
|
" \n",
|
|
" else:\n",
|
|
" group_start, last_pos, last_pol, peak_pos, last_pol_idx = peak_group[-1]\n",
|
|
" \n",
|
|
" if abs(pol) > abs(last_pol):\n",
|
|
" #ax3.axvline(pos, color='magenta', alpha=0.5)\n",
|
|
" peak_group[-1] = (group_start, pos, pol, pos, pol_idx)\n",
|
|
" else:\n",
|
|
" #ax3.axvline(pos, color='blue', alpha=0.5)\n",
|
|
" peak_group[-1] = (group_start, pos, last_pol, peak_pos, last_pol_idx)\n",
|
|
"\n",
|
|
"avg_peak = np.mean(np.abs(np.array([last_pol for _1, _2, last_pol, _3, _4 in peak_group])))\n",
|
|
"print('avg_peak', avg_peak)\n",
|
|
"\n",
|
|
"noprint = lambda *args, **kwargs: None\n",
|
|
"def mle_decode(peak_groups, print=print):\n",
|
|
" peak_groups = [ (pos, pol, idx) for _1, _2, pol, pos, idx in peak_groups ]\n",
|
|
" candidates = [ (0, [(pos, pol, idx)]) for pos, pol, idx in peak_groups if pos < bit_period*2.5 ]\n",
|
|
" \n",
|
|
" while candidates:\n",
|
|
" chain_candidates = []\n",
|
|
" for chain_score, chain in candidates:\n",
|
|
" pos, ampl, _idx = chain[-1]\n",
|
|
" score_fun = lambda pos, npos, npol: abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n",
|
|
" next_candidates = sorted([ (score_fun(pos, npos, npol), npos, npol, nidx) for npos, npol, nidx in peak_groups if pos < npos < pos + bit_period*max_lookahead ], reverse=True)\n",
|
|
" \n",
|
|
" print(f' candidates for {pos}, {ampl}:')\n",
|
|
" for score, npos, npol, nidx in next_candidates:\n",
|
|
" print(f' {score:.4f} {npos:.2f} {npol:.2f} {nidx:.2f}')\n",
|
|
" \n",
|
|
" nch, cor_len = cor_an.shape\n",
|
|
" if cor_len - pos < 1.5*bit_period or not next_candidates:\n",
|
|
" score = sum(score_fun(opos, npos, npol) for (opos, _opol, _oidx), (npos, npol, _nidx) in zip(chain[:-1], chain[1:])) / len(chain)\n",
|
|
" yield score, chain\n",
|
|
" \n",
|
|
" else:\n",
|
|
" print('extending')\n",
|
|
" for score, npos, npol, nidx in next_candidates[:3]:\n",
|
|
" if score > 0.5:\n",
|
|
" new_chain_score = chain_score * 0.9 + score * 0.1\n",
|
|
" chain_candidates.append((new_chain_score, chain + [(npos, npol, nidx)]))\n",
|
|
" print('chain candidates:')\n",
|
|
" for score, chain in sorted(chain_candidates, reverse=True):\n",
|
|
" print(' ', [(score, [(f'{pos:.2f}', f'{pol:.2f}') for pos, pol, _idx in chain])])\n",
|
|
" candidates = [ (chain_score, chain) for chain_score, chain in sorted(chain_candidates, reverse=True)[:10] ]\n",
|
|
"\n",
|
|
"res = sorted(mle_decode(peak_group, print=noprint), reverse=True)\n",
|
|
"#for i, (score, chain) in enumerate(res):\n",
|
|
"# print(f'Chain {i}@{score:.4f}: {chain}')\n",
|
|
"(_score, chain), *_ = res\n",
|
|
"\n",
|
|
"def viz(chain):\n",
|
|
" last_pos = None\n",
|
|
" for pos, pol, nidx in chain:\n",
|
|
" if last_pos:\n",
|
|
" delta = int(round((pos - last_pos) / bit_period))\n",
|
|
" if delta > 1:\n",
|
|
" print(f'skipped {delta} symbols at {pos}')\n",
|
|
" for i in range(delta-1):\n",
|
|
" yield None\n",
|
|
" ax3.axvline(pos, color='blue', alpha=0.5)\n",
|
|
" decoded = nidx*2 + (0 if pol < 0 else 1)\n",
|
|
" yield decoded\n",
|
|
" ax3.text(pos-20, 0.0, f'{decoded}', horizontalalignment='right', verticalalignment='center', color='black')\n",
|
|
"\n",
|
|
" last_pos = pos\n",
|
|
"\n",
|
|
"decoded = list(viz(chain))\n",
|
|
"print('decoding [ref|dec]:')\n",
|
|
"failures = 0\n",
|
|
"for i, (ref, found) in enumerate(itertools.zip_longest(test_data, decoded)):\n",
|
|
" print(f'{ref or -1:>3d}|{found or -1:>3d} {\"✔\" if ref==found else \"✘\" if found else \" \"}', end=' ')\n",
|
|
" if ref != found:\n",
|
|
" failures += 1\n",
|
|
" if i%8 == 7:\n",
|
|
" print()\n",
|
|
"print(f'Symbol error rate e={failures/len(test_data)}')\n",
|
|
"print(f'maximum bitrate r={sampling_rate / decimation / (2**nbits) * nbits * (1 - failures/len(test_data)) * 3600} b/h')\n",
|
|
"#ax3.plot(th)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "44d617eb7c5c416f96e16c9221c4fe96",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'cor2_pe_flt2' 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-30-968181501cb1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcor2_pe_flt2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcor2_pe_flt2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\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 8\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcor2_pe_flt2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcor2_pe_flt2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'full'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'cor2_pe_flt2' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, axs = plt.subplots(2, 1, figsize=(9, 7))\n",
|
|
"fig.tight_layout()\n",
|
|
"axs = axs.flatten()\n",
|
|
"for ax in axs:\n",
|
|
" ax.grid()\n",
|
|
" \n",
|
|
"axs[0].plot(cor2_pe_flt2[1::10] - cor2_pe_flt2[:-1:10])\n",
|
|
"a, b = cor2_pe_flt2[1::10] - cor2_pe_flt2[:-1:10], np.array([0.0, -0.5, 1.0, -0.5, 0.0])\n",
|
|
"axs[1].plot(np.convolve(a, b, mode='full'))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "labenv",
|
|
"language": "python",
|
|
"name": "labenv"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|