Working on DSSS demodulator sim

1 files changed, 181 insertions, 28 deletions

diff --git a/lab-windows/dsss_experiments-ber.ipynb b/lab-windows/dsss_experiments-ber.ipynb
index a12b5a4..83a4a17 100644
--- a/lab-windows/dsss_experiments-ber.ipynb
+++ b/lab-windows/dsss_experiments-ber.ipynb
@@ -139,6 +139,25 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_duration = 300\n",
+    "test_nbits = 5\n",
+    "test_signal_amplitude=2.0e-3\n",
+    "test_decimation=10\n",
+    "\n",
+    "for test_signal_amplitude in [2.0e-3, 20e-3, 200e-3, 2]:\n",
+    "    test_data = np.random.RandomState(seed=0).randint(0, 2 * (2**test_nbits), test_duration)\n",
+    "    signal = np.repeat(modulate(test_data, test_nbits) * 2.0 - 1, test_decimation) * test_signal_amplitude\n",
+    "    with open(f'dsss_test_signals/dsss_test_noiseless_{test_signal_amplitude*1000:.0f}mHz.bin', 'wb') as f:\n",
+    "        for e in signal:\n",
+    "            f.write(struct.pack('<f', e))"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
    "outputs": [],
@@ -159,7 +178,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5fba7c7cec24f09a4f23f7c7d87eb90",
+       "model_id": "373401133cfe408aa15738e48c58dfaa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -186,6 +205,129 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-44-38927d86fcc7>:1: 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": "2015d1f2951340d49397c6c289096b01",
+       "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": [
+       "Text(0.5, 1.0, 'Ricker wavelet, w=69 a=7.3')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "w = 69\n",
+    "a = 7.3\n",
+    "ax.plot(range(-w//2+1, w//2+1), sig.ricker(w, a))\n",
+    "ax.grid()\n",
+    "ax.axvline(0, color='orange')\n",
+    "ax.set_title(f'Ricker wavelet, w={w} a={a}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-56-8e404350b85f>:1: 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": "3cd0c25b914b41df835005ef6fb51b34",
+       "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",
+    "r = list(range(60, 120))\n",
+    "ax.plot(r, [sum(sig.ricker(w, a)) for w in r])\n",
+    "ax.set_yscale('log')\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-67-2097444ed7d2>:1: 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": "c5d1587fa0c944378aed31941ab66ff6",
+       "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",
+    "sw = 256\n",
+    "w = sig.ricker(sw, a)\n",
+    "r = list(range(1, sw//2 - 10))\n",
+    "d = [-sum(w[:i]) - sum(w[-i:]) for i in r]\n",
+    "ax.plot([sw-2*x for x in r], d)\n",
+    "ax.set_yscale('log')\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
    "outputs": [],
@@ -279,7 +421,7 @@
     "            chain_candidates = []\n",
     "            for chain_score, chain in candidates:\n",
     "                pos, ampl, _idx = chain[-1]\n",
-    "                score_fun = lambda pos, npos, npol: pol_score_factor*abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n",
+    "                score_fun = lambda pos, npos, npol: pol_score_eebfactor*abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n",
     "                next_candidates = sorted([ (score_fun(pos, npos, npol), npos, npol, nidx) for npos, npol, nidx in peak_groups if pos < npos < pos + bit_period*max_lookahead ], reverse=True)\n",
     "\n",
     "                print(f'    candidates for {pos}, {ampl}:')\n",
@@ -366,13 +508,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5106e62521e246a0b0766cce3f9e556f",
+       "model_id": "672fae23dc99473a967c46b4b90d09d7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -386,10 +528,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc200d359d0>]"
+       "[<matplotlib.lines.Line2D at 0x7fc38cb743d0>]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -444,7 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -454,13 +596,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "15e67925b1bf47b5bbde289b353ed201",
+       "model_id": "985b1935796343d19c27e0b0ae31363c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -474,7 +616,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6432dbccc7db43d7a227384bca772716",
+       "model_id": "418a9dd281534ffa8e0aa5f250424127",
        "version_major": 2,
        "version_minor": 0
       },
@@ -491,12 +633,12 @@
      "text": [
       "nbits=5\n",
       "nbits=6\n",
-      "signal_amplitude=0.00046: ser=0.97396 ±0.00737, br=14.64844\n",
       "signal_amplitude=0.00015: ser=0.98958 ±0.00737, br=5.85938\n",
-      "signal_amplitude=0.00022: ser=0.96875 ±0.01276, br=17.57812\n",
-      "signal_amplitude=0.00010: ser=0.97396 ±0.00737, br=14.64844\n",
       "signal_amplitude=0.00032: ser=0.97396 ±0.00737, br=14.64844\n",
+      "signal_amplitude=0.00046: ser=0.97396 ±0.00737, br=14.64844\n",
+      "signal_amplitude=0.00010: ser=0.97396 ±0.00737, br=14.64844\n",
       "signal_amplitude=0.00068: ser=0.94792 ±0.01949, br=29.29688\n",
+      "signal_amplitude=0.00022: ser=0.96875 ±0.01276, br=17.57812\n",
       "signal_amplitude=0.00100: ser=0.78125 ±0.02552, br=123.04688\n",
       "signal_amplitude=0.00147: ser=0.28646 ±0.03683, br=401.36719\n",
       "signal_amplitude=0.00215: ser=0.06250 ±0.03375, br=527.34375\n",
@@ -505,8 +647,8 @@
       "signal_amplitude=0.00010: ser=0.98958 ±0.00737, br=3.51562\n",
       "signal_amplitude=0.00022: ser=0.98438 ±0.00000, br=5.27344\n",
       "signal_amplitude=0.00032: ser=0.98438 ±0.00000, br=5.27344\n",
-      "signal_amplitude=0.00068: ser=0.68229 ±0.09051, br=107.22656\n",
       "signal_amplitude=0.00046: ser=0.97917 ±0.01949, br=7.03125\n",
+      "signal_amplitude=0.00068: ser=0.68229 ±0.09051, br=107.22656\n",
       "signal_amplitude=0.00100: ser=0.15104 ±0.05156, br=286.52344\n",
       "signal_amplitude=0.00147: ser=0.01562 ±0.00000, br=332.22656\n",
       "signal_amplitude=0.00215: ser=0.01562 ±0.00000, br=332.22656\n",
@@ -519,10 +661,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fc23a223550>"
+       "<matplotlib.legend.Legend at 0x7fc382e9b7f0>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -595,13 +737,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bda9f8070b0142928936a7752f318e97",
+       "model_id": "8b9593f48de94ec399fd8e61dc11d1de",
        "version_major": 2,
        "version_minor": 0
       },
@@ -615,10 +757,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fc20046b730>"
+       "<matplotlib.legend.Legend at 0x7fc3c4c54b20>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -651,13 +793,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33b1f777dda149eda3a2f031087372c9",
+       "model_id": "15dc2e1a7cdd460e8cbde0352ea18270",
        "version_major": 2,
        "version_minor": 0
       },
@@ -861,7 +1003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -870,13 +1012,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "406505452bb84597a6c39bc12267dcc6",
+       "model_id": "e381ce02f19d4b85bbbaa9ea99e65623",
        "version_major": 2,
        "version_minor": 0
       },
@@ -891,7 +1033,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-25-50856f807a2a>:20: RuntimeWarning: divide by zero encountered in log10\n",
+      "<ipython-input-19-50856f807a2a>:20: RuntimeWarning: divide by zero encountered in log10\n",
       "  cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n"
      ]
     }
@@ -1011,9 +1153,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "41.6"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "13 * 2**5  / 10"
    ]