Add live analyse for sholders of dab signal

1 files changed, 145 insertions, 31 deletions

diff --git a/run.ipynb b/run.ipynb
index 5c61960..4f243d8 100644
--- a/run.ipynb
+++ b/run.ipynb
@@ -11,6 +11,8 @@
     "import numpy as np\n",
     "import time\n",
     "import src.gen_source as gen_source\n",
+    "from guppy import hpy \n",
+    "import mydab\n",
     "\n",
     "import tcp_async\n",
     "import tcp_sync\n",
@@ -29,6 +31,7 @@
     "    reload(tcp_async)\n",
     "    reload(tcp_sync)\n",
     "    reload(gen_source)\n",
+    "    reload(mydab)\n",
     "except:\n",
     "    pass"
    ]
@@ -124,38 +127,114 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "top.set_txgain(85)\n",
-    "for x2 in np.linspace(-0.15, 0.15, num = 15):\n",
+    "import itertools\n",
+    "x = np.linspace(-0.1, 0.1, num = 5)\n",
+    "l = list(itertools.product(x,x,x,x,x))\n",
+    "mag = [sum(map(lambda x:x*x,el)) for el in l]\n",
+    "idx = list(np.argsort(mag))\n",
+    "l = list(np.array(l)[idx])\n",
+    "len(l)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top.set_txgain(84)\n",
+    "sync.has_msg()\n",
+    "msgs = np.array(sync.get_msgs(2))\n",
+    "fft = np.fft.fftshift(msgs[0])\n",
+    "#plt.plot(fft)\n",
+    "#plt.plot((4000 - f, 4000 -f),(-180,-40))\n",
+    "\n",
+    "mydab.calc_signal_sholder_ratio(fft, fft_size, samp_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def meas(i):\n",
+    "    import sys\n",
+    "    sys.stdout.write(str(i) + \" \")\n",
+    "    top.set_txgain(i)\n",
+    "    sync.has_msg()\n",
+    "    msgs = np.array(sync.get_msgs(10))\n",
+    "    \n",
+    "    res = []\n",
+    "    for msg in msgs:\n",
+    "        fft = np.fft.fftshift(msg)\n",
+    "        res.append(mydab.calc_signal_sholder_ratio(fft, fft_size, samp_rate))\n",
+    "    return np.mean(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = [meas(i) for i in range(89)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(res)\n",
+    "plt.title(\"DAB sholders\")\n",
+    "plt.xlabel(\"tx gain [db]\")\n",
+    "plt.ylabel(\"signal - sholder [db]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top.set_txgain(84)\n",
+    "for el in l:\n",
     "    t2 = time.time()\n",
-    "    for x3 in np.linspace(-0.15, 0.15, num = 14):\n",
-    "        for x4 in np.linspace(-0.15, 0.15, num = 7):\n",
-    "            path = gen_source.gen_file(frequency_0=frequency_0,\n",
-    "                                       frequency_1=frequency_1,\n",
-    "                                       samp_rate=samp_rate,\n",
-    "                                       x2 = x2,\n",
-    "                                       x3 = x3,\n",
-    "                                       x4 = x4,\n",
-    "                                       path='./np_twotone')\n",
-    "            #a_load = np.fromfile(path, dtype=np.complex64)\n",
-    "            #plt.plot(a_load.real[0:100])\n",
-    "            \n",
-    "            sync.has_msg()\n",
-    "            np.array(sync.get_msgs(2))\n",
-    "            msgs = np.array(sync.get_msgs(5))\n",
-    "            inter_mod_avg = (np.max(score(msgs[0], frequency_0, frequency_1, samp_rate)) \n",
-    "                       + np.max(score(msgs[1], frequency_0, frequency_1, samp_rate)) \n",
-    "                       + np.max(score(msgs[2], frequency_0, frequency_1, samp_rate)) \n",
-    "                       + np.max(score(msgs[3], frequency_0, frequency_1, samp_rate)) \n",
-    "                       + np.max(score(msgs[4], frequency_0, frequency_1, samp_rate)))/5\n",
-    "            msg = msgs[0]\n",
-    "            \n",
-    "            \n",
-    "            print(\"inter_mod %.3f, x_2 %.3f, x_3 %.3f\" % (inter_mod_avg, x2, x3))\n",
-    "            \n",
-    "            plt.plot(msg)\n",
-    "            plt.savefig(\"/tmp/out/inter_mod_%.3f_x_2_%.3f_x_3_%.3f_x_4_%.3f.png\" % (inter_mod_avg, x2, x3, x4))\n",
-    "            plt.clf()\n",
-    "        print(time.time() - t2)"
+    "    path = gen_source.gen_file(frequency_0=frequency_0,\n",
+    "                               frequency_1=frequency_1,\n",
+    "                               samp_rate=samp_rate,\n",
+    "                               coefs = el,\n",
+    "                               path='./np_twotone',\n",
+    "                               count = 10\n",
+    "                               )\n",
+    "    #a_load = np.fromfile(path, dtype=np.complex64)\n",
+    "    #plt.plot(a_load.real[0:100])\n",
+    "    \n",
+    "    sync.has_msg()\n",
+    "    np.array(sync.get_msgs(2))\n",
+    "    msgs = np.array(sync.get_msgs(5))\n",
+    "    scores = [np.max(score(msg, frequency_0, frequency_1, samp_rate)) for msg in msgs]\n",
+    "    inter_mod_avg = np.mean(scores)\n",
+    "    inter_mod_var = np.var(scores)\n",
+    "    msg = msgs[0]\n",
+    "    \n",
+    "    \n",
+    "    param_str = \"_\".join([\"%.3f\" % f for f in el])\n",
+    "    print(\"inter_mod %.3f +- %.3f, %s\" % (inter_mod_avg, inter_mod_var, param_str))\n",
+    "    \n",
+    "    plt.plot(msg)\n",
+    "    plt.savefig(\"/tmp/out/inter_mod_%.3f_+-%.3f_%s.png\" % (inter_mod_avg, inter_mod_var, param_str))\n",
+    "    plt.clf()\n",
+    "    \n",
+    "    a = np.fromfile(\"./np_twotone\", dtype=np.complex64)\n",
+    "    plt.plot(a.real)\n",
+    "    plt.savefig(\"/tmp/out/inter_mod_%.3f_+-%.3f_%s_wave.png\" % (inter_mod_avg, inter_mod_var, param_str))\n",
+    "    plt.clf()\n",
+    "    #h = hpy() \n",
+    "    #print h.heap()\n",
+    "    print(time.time() - t2)"
    ]
   },
   {
@@ -207,6 +286,41 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "gen_source.gen_file_d(800000, 1000000, 0, 0.5, 0, 0, 4000000, count = 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a2 = np.fromfile(\"./np_twotone\", dtype=np.complex64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(a.real)\n",
+    "plt.plot(a2.real)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   },
   {