Update notebooks
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jaseg
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680
notebooks/Modulation Experiment Calculations.ipynb
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680
notebooks/Modulation Experiment Calculations.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import struct\n",
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"import random\n",
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"import itertools\n",
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"import datetime\n",
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"import multiprocessing\n",
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"from collections import defaultdict\n",
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"import json\n",
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"import traceback\n",
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"import glob\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"import matplotlib\n",
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"from matplotlib import ticker\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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"from scipy import fftpack as fftpack\n",
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"import ipywidgets\n",
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"from IPython.display import set_matplotlib_formats\n",
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"import scipy\n",
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"\n",
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"from tqdm.notebook import tqdm\n",
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"import colorednoise\n",
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"import accupy\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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib widget\n",
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"#%matplotlib inline\n",
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"#set_matplotlib_formats('png', 'pdf')\n",
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"#font = {'family' : 'normal',\n",
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"# 'weight' : 'normal',\n",
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"# 'size' : 6}\n",
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"#matplotlib.rc('font', **font)"
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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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"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": 4,
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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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" 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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" \n",
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" min_key, max_key = min(preferred_pairs.keys()), max(preferred_pairs.keys())\n",
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" \n",
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" if n < min_key:\n",
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" raise KeyError('preferred pairs for %s bits unknown' % str(n))\n",
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" \n",
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" if n > max_key:\n",
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" t0, t1 = preferred_pairs[max_key]\n",
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" (seq0, _st0), (seq1, _st1) = sig.max_len_seq(max_key, taps=t0), sig.max_len_seq(max_key, taps=t1)\n",
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" raw_codes = gen_gold(seq0, seq1)\n",
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" # Hackety hack: This is super-experimental test code and guaranteed sub-optimal!\n",
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" \n",
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" raw_codes = np.array(raw_codes)\n",
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" out = raw_codes[:-1]\n",
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" for i in range(n - max_key):\n",
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" out = np.hstack([out[:out.shape[0]//2], out[out.shape[0]//2:]])\n",
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" return out\n",
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" \n",
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" else:\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 np.array(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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def modulate(data, nbits=5, pad=True):\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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" signal = (np.multiply(sel, np.tile(data_lsb_centered, (sel.shape[1], 1)).T).flatten() + 1) // 2\n",
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" if pad:\n",
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" print('mod: padding by', mask.shape[1])\n",
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" return np.hstack([ np.zeros(mask.shape[1]), signal, np.zeros(mask.shape[1]) ])\n",
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" else:\n",
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" return signal"
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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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"def correlate(sequence, nbits=5, decimation=1, mask_filter=lambda x: x, mask_max=None):\n",
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" codes = gold(nbits)\n",
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" mask = np.tile(np.array(codes)[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((codes.shape[0], codes.shape[1] * decimation))\n",
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" mask = mask.astype(np.float64)\n",
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"\n",
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" # Our input signal has large DC bias. Remove DC bias to reduce numerical errors during correlation.\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='valid') for row in mask[:mask_max]])\n",
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" return np.array([sig.correlate(sequence, row, mode='valid') for row in mask[:mask_max]]) / len(sequence)"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"mean: 50.00341 len: 1946174\n"
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]
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}
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],
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"source": [
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"with open('data/fmeas_export_ocxo_2day.bin', 'rb') as f:\n",
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" meas_data = np.copy(np.frombuffer(f.read(), dtype='float32'))\n",
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" print('mean:', np.mean(meas_data), 'len:', len(meas_data))\n",
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" meas_data -= np.mean(meas_data)\n",
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" meas_data = np.hstack([meas_data, meas_data[::-1]] * 50)\n",
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" def mains_noise_meas(n):\n",
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" last_valid = len(meas_data) - n\n",
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" start = random.randint(0, last_valid)\n",
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" return meas_data[start:start+n]"
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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 load_noise_meas_params(capture_file):\n",
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" with open(capture_file, 'rb') as f:\n",
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" meas_data = np.copy(np.frombuffer(f.read(), dtype='float32'))\n",
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" print('mean:', np.mean(meas_data), 'len:', len(meas_data))\n",
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" meas_data -= np.mean(meas_data)\n",
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" return {'meas_data': meas_data}\n",
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"\n",
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"def mains_noise_measured(n, meas_data):\n",
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" last_valid = len(meas_data) - n\n",
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" start = np.random.randint(last_valid)\n",
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" return meas_data[start:start+n]\n",
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"\n",
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"def load_noise_synth_params(specfile):\n",
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" with open(specfile) as f:\n",
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" d = json.load(f)\n",
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" return {'spl_x': np.linspace(*d['x_spec']),\n",
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" 'spl_N': d['x_spec'][2],\n",
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" 'psd_spl': (d['t'], d['c'], d['k']) }\n",
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" \n",
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"def mains_noise_synthetic(n, psd_spl, spl_N, spl_x):\n",
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" noise = np.random.normal(size=spl_N) * 2\n",
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" spec = scipy.fftpack.fft(noise) **2\n",
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" spec *= np.exp(scipy.interpolate.splev(spl_x, psd_spl))\n",
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"\n",
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" spec **= 1/2\n",
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" \n",
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" renoise = scipy.fftpack.ifft(spec)\n",
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" return np.hstack([renoise[10000:], renoise[10000:][::-1]])[:n]\n",
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"\n",
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"noise_params = load_noise_synth_params('data/grid_freq_psd_spl_108pt.json')\n",
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"mains_noise = lambda n: mains_noise_synthetic(n, **noise_params)"
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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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"source": [
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"def generate_test_signal(duration, nbits=6, signal_amplitude=2.0e-3, decimation=10, seed=0, data=None):\n",
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" test_data = np.random.RandomState(seed=seed).randint(0, 2 * (2**nbits), duration) if data is None else data\n",
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" \n",
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" signal = np.repeat(modulate(test_data, nbits, pad=True) * 2.0 - 1, decimation) * signal_amplitude\n",
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" noise = mains_noise_meas(len(signal))\n",
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" \n",
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" return test_data, signal.astype(np.longdouble) + noise.astype(np.longdouble)"
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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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{
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"data": {
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"text/plain": [
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"array([0.50806058, 0.49242794])"
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]
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},
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"execution_count": 10,
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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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"gold(12)[:2].mean(axis=1)"
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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": 20,
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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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"amplitude @P_mod=10000.0kW: Δf=400.0µHz\n",
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"mod: padding by 1023\n",
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"[0 1 2 3] (49104,)\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": "70c8a0e9c03441fab580b5b6b219e065",
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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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"signal strength (off/on peak): +4.900e-05 | -6.406e-04\n",
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"signal strength (off/on peak): +7.699e-05 | +5.627e-04\n",
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"signal strength (off/on peak): +8.441e-05 | +4.206e-05\n",
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"signal strength (off/on peak): +8.178e-05 | -8.413e-05\n"
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]
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}
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],
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"source": [
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"nbits = 10\n",
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"decimation = 8\n",
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"# working params: decimation=15 nbits=21 power=200kW\n",
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"# working params: decimation=8 nbits=10 power=10MW\n",
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"grid_frequency_characteristic = 25e9 # watt per hertz\n",
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"modulation_power = 10000e3 # watt\n",
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"data = np.array([0, 1, 2, 3])\n",
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"amplitude = modulation_power / grid_frequency_characteristic\n",
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"print(f'amplitude @P_mod={modulation_power/1e3}kW: Δf={amplitude*1e6}µHz')\n",
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"\n",
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"# Generate test data for this simulation run\n",
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"test_data, signal = generate_test_signal(duration=2, nbits=nbits, signal_amplitude=amplitude, decimation=decimation, seed=0, data=data)\n",
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"print(test_data, signal.shape)\n",
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"\n",
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"def correlate_test(sequence, nbits, decimation, mask_max):\n",
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" codes = gold(nbits)\n",
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" mask = np.tile(np.array(codes)[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((codes.shape[0], codes.shape[1] * decimation))\n",
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" mask = mask.astype(np.float64)\n",
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"\n",
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" # Our input signal has large DC bias. Remove DC bias to reduce numerical errors during correlation.\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='valid') for row in mask[:mask_max]])\n",
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" return np.array([accupy.fsum(np.multiply(sequence, row)) for row in mask[:mask_max]]) / len(sequence)\n",
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"\n",
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"signal = signal.astype(np.float64)\n",
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"fig, axs = plt.subplots(len(data), figsize=(12, 5))\n",
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"for i, ax in enumerate(axs.flatten()):\n",
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" gen_per = len(data) + 2 # one period for each symbol, plus one period of padding at the beginning and end\n",
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" symbol_length = signal.shape[0]//gen_per\n",
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" l, r = int(max(0, (i + 1) * symbol_length - 1000)), int((i + 2) * symbol_length + 1000)\n",
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" signal_slice = signal[l:r]\n",
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" cor_an = correlate(signal_slice, nbits=nbits, decimation=decimation, mask_max=2)\n",
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" \n",
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" l, r = int((i + 1) * symbol_length), int((i + 2) * symbol_length)\n",
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" signal_slice = signal[l:r]\n",
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" #print('on peak [numpy]:', correlate(signal_slice, nbits=nbits, decimation=decimation, mask_max=2).flatten() / amplitude)\n",
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" #print('on peak [accupy]:', correlate_test(signal_slice, nbits=nbits, decimation=decimation, mask_max=2).flatten() / amplitude)\n",
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" \n",
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" l, r = int((i + 1 - 0.2) * symbol_length), int((i + 2 - 0.2) * symbol_length)\n",
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" signal_slice = signal[l:r]\n",
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" #print('off peak [numpy]:', correlate(signal_slice, nbits=nbits, decimation=decimation, mask_max=2).flatten() / amplitude)\n",
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" #print('off peak [accupy]:', correlate_test(signal_slice, nbits=nbits, decimation=decimation, mask_max=2).flatten() / amplitude)\n",
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" #ax.plot(cor_an.T)\n",
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"\n",
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" cwt_res = np.array([ sig.cwt(row, sig.ricker, [0.73 * decimation]).flatten() for row in cor_an ])\n",
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" print(f'signal strength (off/on peak): {np.sqrt(np.mean(cwt_res[0,cwt_res.shape[1]//6:cwt_res.shape[1]//3]**2)):+.3e} | {cwt_res[0,cwt_res.shape[1]//2]:+.3e}')\n",
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" ax.plot(cwt_res.T / amplitude)"
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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": 127,
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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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"amplitude @P_mod=10.0kW: Δf=0.39999999999999997µHz\n",
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"mod: padding by 31\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-95-7000bdc82d1b>:7: ComplexWarning: Casting complex values to real discards the imaginary part\n",
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" return test_data, signal.astype(np.longdouble) + noise.astype(np.longdouble)\n"
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]
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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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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"mod: padding by 31\n",
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"[0.00031921745429844274216, 0.0003466253347083378983, 0.00044946868526334726654, 0.00043601785453840703518, 0.00027364766834844621423, 0.00034429714350871021226, 0.00029257480354291570764, 0.00043761373963434835483, 0.00034653346037204395042, 0.00041369145870726819648, 0.00031663491690404936546, 0.0003630671579533060333, 0.0002934121262870590337, 0.00036264881740159871053, 0.00033973548163424448722, 0.00045000162460528714934, 0.00036345615545028226934, 0.00034203880459996953548, 0.0004340491996267533479, 0.00027402160564997715637]\n",
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"mod: padding by 63\n",
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"mod: padding by 63\n",
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"[0.00043183462240684953605, 0.00049995218014044821913, 0.00034320159159449236953, 0.00047574377773946273916, 0.00042495372363872566055, 0.0005340590070278771213, 0.00043128485159190824515, 0.00034867807874549918169, 0.00039072607874113340072, 0.00034884834981040312958, 0.00042730792396368059213, 0.0005043675584057222778, 0.0003499736642140912821, 0.00021263777836169804578, 0.00023831087214244111956, 0.00030008253740133703718, 0.00021268569995328232606, 0.00054148296194613603103, 0.00040150545723533495933, 0.00028661636535881167316]\n",
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"[7.291179023159128505e-05, 6.921653169518604612e-05, 7.0212257862804909805e-05, 7.739735101427627361e-05, 7.05170185815625493e-05, 7.667140441838123699e-05, 6.684245257693558025e-05, 6.7274435477192927474e-05, 7.3140978179672748096e-05, 6.462540379490416508e-05, 7.8575870371873889974e-05, 7.0962264879147295656e-05, 6.887905626735870121e-05, 6.6741332451462875345e-05, 6.774053852476256235e-05, 8.4915545476866071356e-05, 6.755406828145308233e-05, 7.4150959965664274745e-05, 7.289475345040078811e-05, 7.417472792999099321e-05]\n",
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"[3.441001692841763238e-05, 3.9226152369629212293e-05, 3.5174358297006928404e-05, 3.858339666154567386e-05, 3.729609282512620058e-05, 3.8017607168678973304e-05, 3.6432437134237636557e-05, 3.5396590548610446987e-05, 4.2872778372966043538e-05, 3.881954270914244008e-05, 3.6638457035791679994e-05, 4.3987686908835178038e-05, 3.5810535332467513793e-05, 3.669588547824606978e-05, 3.9034162252655188724e-05, 3.686462573963708382e-05, 3.64018808798140893e-05, 4.140879713977473717e-05, 3.601891068378439981e-05, 4.2239933297790315508e-05]\n",
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"mod: padding by 32752\n",
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"[3.0164568033183993325e-05, 3.340002097279428693e-05, 3.1692825762129764792e-05, 3.2341558069057274648e-05, 3.5104645491986034208e-05, 3.3804378516131733095e-05, 3.0847430711483434965e-05, 3.163598515247249634e-05, 3.1877253973163090688e-05, 3.145134169534852806e-05, 2.9469157225219549716e-05, 3.5022547350051354115e-05, 3.185664970143889292e-05, 3.2250533412830754613e-05, 3.2968988013916950676e-05, 3.1167159682343826453e-05, 3.0519324313846599056e-05, 3.6983652848933391153e-05, 3.4137279541673856477e-05, 3.4656056379915872357e-05]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"decimation = 10\n",
|
||||
"grid_frequency_characteristic = 25e9 # watt per hertz\n",
|
||||
"modulation_power = 10e3 # watt\n",
|
||||
"amplitude = modulation_power / grid_frequency_characteristic\n",
|
||||
"print(f'amplitude @P_mod={modulation_power/1e3}kW: Δf={amplitude*1e6}µHz')\n",
|
||||
"\n",
|
||||
"ps_off = []\n",
|
||||
"xs = np.array(list(range(5, 16)))\n",
|
||||
"for nbits in xs:\n",
|
||||
" data = np.array([1])\n",
|
||||
"\n",
|
||||
" def correlate_test(sequence, nbits, decimation, mask_max):\n",
|
||||
" codes = gold(nbits)\n",
|
||||
" mask = np.tile(np.array(codes)[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((codes.shape[0], codes.shape[1] * decimation))\n",
|
||||
" mask = mask.astype(np.float64)\n",
|
||||
"\n",
|
||||
" # Our input signal has large DC bias. Remove DC bias to reduce numerical errors during correlation.\n",
|
||||
" sequence -= np.mean(sequence)\n",
|
||||
"\n",
|
||||
" return np.array([sig.correlate(sequence, row, mode='valid') for row in mask[:mask_max]])\n",
|
||||
" #return np.array([accupy.fsum(np.multiply(sequence, row)) for row in mask[:mask_max]]) / len(sequence)\n",
|
||||
"\n",
|
||||
" vals = []\n",
|
||||
" for i in range(20):\n",
|
||||
" test_data, signal = generate_test_signal(duration=2, nbits=nbits, signal_amplitude=amplitude, decimation=decimation, seed=random.randint(0, 0xffffffff), data=data)\n",
|
||||
" cor_an = correlate(signal, nbits=nbits, decimation=decimation, mask_max=2)\n",
|
||||
" vals.append(np.sqrt(np.mean(cor_an**2)))\n",
|
||||
" ps_off.append((np.mean(vals), np.std(vals)))\n",
|
||||
" print(vals)\n",
|
||||
"\n",
|
||||
"ps_off = np.array(ps_off)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 129,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "3dade90110af4097b123f3de508e843f",
|
||||
"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": [
|
||||
"<ErrorbarContainer object of 3 artists>"
|
||||
]
|
||||
},
|
||||
"execution_count": 129,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(figsize=(12, 5))\n",
|
||||
"ax.errorbar(x=xs, y=ps_off[:,0], yerr=ps_off[:,1])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "ma-thesis-env",
|
||||
"language": "python",
|
||||
"name": "ma-thesis-env"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.2"
|
||||
}
|
||||
},
|
||||
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1
notebooks/data/grid_freq_psd_spl_108pt.json
Normal file
1
notebooks/data/grid_freq_psd_spl_108pt.json
Normal file
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@ -178,7 +120,7 @@
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|
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@ -317,49 +183,9 @@
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@ -399,19 +225,11 @@
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||||
"output_type": "stream",
|
||||
"text": [
|
||||
"mean: 49.98625\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('/mnt/c/Users/jaseg/shared/raw_freq.bin', 'rb') as f:\n",
|
||||
"with open('data/raw_freq.bin', 'rb') as f:\n",
|
||||
" mains_noise = np.copy(np.frombuffer(f.read(), dtype='float32'))\n",
|
||||
" print('mean:', np.mean(mains_noise))\n",
|
||||
" mains_noise -= np.mean(mains_noise)"
|
||||
|
|
@ -419,42 +237,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(63,) (63,)\n",
|
||||
"(63,) (63,)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "73f4caf6a80f448183c41b711412a471",
|
||||
"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": [
|
||||
"(0.0020000000000000005, 0.014544699)"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"decimation = 10\n",
|
||||
"signal_amplitude = 2.0e-3\n",
|
||||
|
|
@ -534,41 +319,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": null,
|
||||
"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"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"\n",
|
||||
|
|
@ -584,42 +337,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": null,
|
||||
"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig, axs = plt.subplots(3, 1, figsize=(9, 7), sharex=True)\n",
|
||||
"fig.tight_layout()\n",
|
||||
|
|
@ -642,34 +362,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": null,
|
||||
"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"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"nonlinear_distance = lambda x: 100**(2*np.abs(0.5-x%1)) / (np.abs(x)+3)**2\n",
|
||||
|
|
@ -679,56 +374,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"execution_count": null,
|
||||
"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"threshold_factor = 4.0\n",
|
||||
"power_avg_width = 1024\n",
|
||||
|
|
@ -880,35 +528,9 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"execution_count": null,
|
||||
"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig, axs = plt.subplots(2, 1, figsize=(9, 7))\n",
|
||||
"fig.tight_layout()\n",
|
||||
|
|
@ -924,9 +546,9 @@
|
|||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "labenv",
|
||||
"display_name": "ma-thesis-env",
|
||||
"language": "python",
|
||||
"name": "labenv"
|
||||
"name": "ma-thesis-env"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
|
@ -938,7 +560,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.1"
|
||||
"version": "3.9.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
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Loading…
Add table
Add a link
Reference in a new issue