dataset_creator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ook', 'ask4', 'ask8', 'bpsk', 'qpsk', 'psk8', 'psk16', 'psk32', 'apsk16', 'apsk32', 'apsk64', 'qam16', 'qam32', 'qam64', 'gmsk', 'cpfsk']\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "\n",
    "import sys\n",
    "\n",
    "%autoreload 2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "from frm_modulations import *\n",
    "from frm_dataset_creator import *\n",
    "from frm_dataset_creator2 import *\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.dpi'] = 150\n",
    "\n",
    "\n",
    "from frm_dataset_loader import load_dataset\n",
    "from conf_dataset_1 import *\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"  \n",
    "GPU = \"\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 2000 out of 2500 (80.0%), Elapsed 0:00:08.167872 , estimated 0:00:01.015766"
     ]
    }
   ],
   "source": [
    "fname =  'datasets/valid_1.dat'\n",
    "\n",
    "dataset_valid = generate_dataset_sig2_parallel(10000, pkt_size,max_sps,mod_list,sps_rng,pulse_ebw_list,timing_offset_rng,fading_spread_rng,freq_err_rng,phase_err_rng,snr_rng,\n",
    "    complex_fading=True, freq_in_hz = True,\n",
    "    seed = 0, fname = fname, version = 1,nthreads = 40)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n",
      "/home/samer/Documents/mod_framework_journal/frm_modulations_fast.py:143: NumbaPerformanceWarning: np.dot() is faster on contiguous arrays, called on (array(complex64, 1d, A), array(complex64, 1d, C))\n",
      "  y[k] = np.dot(pulse_vec,symbs_vec)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated 2000 out of 2500 (80.0%), Elapsed 0:00:08.163718 , estimated 0:00:01.002951"
     ]
    }
   ],
   "source": [
    "fname = 'datasets/test_2.dat'\n",
    "\n",
    "dataset_test = generate_dataset_sig2_parallel(100000, pkt_size,max_sps,mod_list,sps_rng,pulse_ebw_list,timing_offset_rng,fading_spread_rng,freq_err_rng,phase_err_rng,snr_list,\n",
    "complex_fading=True, freq_in_hz = True,\n",
    "seed = 1, fname = fname, version = 1,nthreads = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indx = 12\n",
    "for k in dataset_test['sig'].keys():\n",
    "    plt.figure()\n",
    "    plt.plot(dataset_test['sig'][k][indx,:,0])\n",
    "    plt.plot(dataset_test['sig'][k][indx,:,1])\n",
    "    plt.title(k)\n",
    "    plt.figure()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}