005_d1_demod_dpn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "\n",
    "\n",
    "%autoreload 2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"  \n",
    "GPU = \"0\"\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "from frm_dataset_loader import load_dataset\n",
    "from conf_dataset_1 import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = 'datasets/test_1.dat'\n",
    "\n",
    "(comb_test,carrier_test,clean_test,fading_test,raw_test,\n",
    "         freq_test,timing_offNum_test,timing_step_test,\n",
    "         coeff_test,mod_test,snr_test) = load_dataset(fname,max_sps,len(mod_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:245: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1834: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "(?, ?, ?, ?)\n",
      "(?, 1024, 2)\n",
      "WARNING:tensorflow:From /home/samer/miniconda3/envs/mod_framework/lib/python3.6/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from frm_nn_zoo_01 import create_dualPath\n",
    "\n",
    "pkt_size_net = pkt_size\n",
    "nn_all = create_dualPath(pkt_size = pkt_size_net,n_mods=len(mod_list))\n",
    "# nn_all.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_all.load_weights('models/001.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_all = nn_all.predict(comb_test)\n",
    "pred_freq,pred_noisy,pred_fading,pred_clean,pred_timing_step,pred_timing_off,pred_mod  = predictions_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod_name_test = list(map( lambda x : mod_list[x],np.argmax(mod_test,-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from frm_demod_utils import   demod_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "slc_ber = slice(None,None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "521.5827858448029"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = time.time()\n",
    "test_ser = demod_batch(pred_clean[slc_ber],raw_test[slc_ber],mod_name_test[slc_ber],max_sps,pred_timing_step[slc_ber],\n",
    "                 np.maximum(pred_timing_off[slc_ber],0))\n",
    "time.time()-t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from frm_eval_utils import ecdf,calc_nmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "srt,prob = ecdf(test_ser[np.logical_not(np.isnan(test_ser))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6ca9b182e8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(srt,prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "nmse_all = calc_nmse(pred_fading,fading_test)\n",
    "nmse2_all = calc_nmse(pred_clean,clean_test)\n",
    "nmse2t_all = calc_nmse(pred_fading,clean_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4347847576318815 1.4532892745123818 1.4586687735897244\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(nmse_all),np.mean(nmse2_all),np.mean(nmse2t_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_mod_dec = np.argmax(pred_mod,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.83784\n"
     ]
    }
   ],
   "source": [
    "print(np.mean( np.argmax(mod_test,-1)==pred_mod_dec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "outputs = {}\n",
    "outputs['dataset_params'] = [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",
    "outputs['date'] =  f'{datetime.datetime.now():%Y-%m-%d %H:%M:%S%z}'\n",
    "outputs['dpn'] = (pred_freq,pred_timing_step,pred_timing_off,pred_mod_dec, nmse_all,nmse2_all,nmse2t_all,test_ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "FNAME = '005'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(f'outputs/{FNAME}.pkl','wb') as f:\n",
    "    pickle.dump(outputs,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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