demod wip

lab-windows/dsss_experiments-ber.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,11 +14,11 @@
     "from collections import defaultdict\n",
     "import json\n",
     "\n",
-    "\n",
     "from matplotlib import pyplot as plt\n",
     "import matplotlib\n",
     "import numpy as np\n",
     "from scipy import signal as sig\n",
+    "from scipy import fftpack as fftpack\n",
     "import ipywidgets\n",
     "\n",
     "from tqdm.notebook import tqdm\n",
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +162,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4330b0f8ceea4d5d922d2063a81554ca",
+       "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"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.psd(colorednoise.powerlaw_psd_gaussian(1, 1000))\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start 14880 end 24800 rec 29760\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_duration = 32\n",
+    "test_nbits = 5\n",
+    "test_signal_amplitude=2.0e-3\n",
+    "test_decimation=10\n",
+    "test_signal_amplitude = 200e-3\n",
+    "noise_level = 10e-3\n",
+    "\n",
+    "#test_data = np.random.RandomState(seed=0).randint(0, 2 * (2**test_nbits), test_duration)\n",
+    "#test_data = np.array([0, 1, 2, 3] * 50)\n",
+    "test_data = np.array(range(test_duration))\n",
+    "signal = np.repeat(modulate(test_data, test_nbits, pad=False) * 2.0 - 1, test_decimation) * test_signal_amplitude\n",
+    "noise = colorednoise.powerlaw_psd_gaussian(1, len(signal)*3) * noise_level\n",
+    "noise[int(1.5*len(signal)):][:len(signal)] += signal\n",
+    "print('start', int(1.5*len(signal)), 'end', int(1.5*len(signal))+len(signal), 'rec', len(noise))\n",
+    "\n",
+    "with open(f'dsss_test_signals/dsss_test_noiseless_padded.bin', 'wb') as f:\n",
+    "    for e in noise:\n",
+    "        f.write(struct.pack('<f', e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,13 +236,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "373401133cfe408aa15738e48c58dfaa",
+       "model_id": "",
        "version_major": 2,
        "version_minor": 0
       },
@@ -198,6 +258,46 @@
     "plot_distance_func()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-104-abeb28a85dfa>:5: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
+      "  fig, ax = plt.subplots()\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "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"
+    }
+   ],
+   "source": [
+    "#nonlinear_distance_impl = lambda x: np.exp(-np.abs(x)/10) * x**4\n",
+    "nonlinear_distance_impl = lambda x: np.exp(-((x/10 - 0.5)%1 - 0.5)**2 / (2*1.2/10**2))\n",
+    "\n",
+    "def plot_distance_func_impl():\n",
+    "    fig, ax = plt.subplots()\n",
+    "    x = np.linspace(-30, 30, 10000)\n",
+    "    ax.plot(x, nonlinear_distance_impl(x))\n",
+    "\n",
+    "plot_distance_func_impl()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,