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Diffstat (limited to 'dab_tuner.ipynb')
| -rw-r--r-- | dab_tuner.ipynb | 324 |
1 files changed, 324 insertions, 0 deletions
diff --git a/dab_tuner.ipynb b/dab_tuner.ipynb new file mode 100644 index 0000000..635ee35 --- /dev/null +++ b/dab_tuner.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import time\n", + "import src.gen_source as gen_source\n", + "import src.two_tone_lib as tt\n", + "\n", + "import src.tcp_async as tcp_async\n", + "import src.tcp_sync as tcp_sync\n", + "import src.dab_util as du\n", + "import src.dab_tuning_lib as dt\n", + "\n", + "\n", + "from live_analyse_py import live_analyse_py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " __IPYTHON__\n", + " reload(tcp_async)\n", + " reload(tcp_sync)\n", + " reload(gen_source)\n", + " reload(tt)\n", + " reload(du)\n", + " reload(dt)\n", + "except:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sync = tcp_sync.UhdSyncMsg(packet_size=4*8192,\n", + " packet_type=\"\".join([\"f\"]*8192))\n", + "async = tcp_async.UhdAsyncMsg()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top = live_analyse_py()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top.start()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top.set_txgain(86)\n", + "top.set_rxgain(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top.blocks_file_source_0.open(\"./../dab_normalized_c64.dat\", True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sync.has_msg()\n", + "async.has_msg()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tt.gen_two_tone(debug = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "msgs = sync.get_msgs(1)\n", + "msgs = [np.fft.fftshift(msg) for msg in msgs]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def measure(param):\n", + " n_avg = 20\n", + " x2, x3, x4, x5, x6, x7, x8 = param\n", + " \n", + " repeat = True\n", + " while repeat:\n", + " #tt.gen_two_tone(debug = True, predist=tt.predist_poly, par=(x2, x3, x4))\n", + " \n", + " top.dpd_memless_poly_0.set_a1(1)\n", + " top.dpd_memless_poly_0.set_a2(x2)\n", + " top.dpd_memless_poly_0.set_a3(x3)\n", + " top.dpd_memless_poly_0.set_a4(x4)\n", + " top.dpd_memless_poly_0.set_a5(x5)\n", + " top.dpd_memless_poly_0.set_a6(x6)\n", + " top.dpd_memless_poly_0.set_a7(x7)\n", + " top.dpd_memless_poly_0.set_a8(x8)\n", + " \n", + " sync.has_msg()\n", + " np.array(sync.get_msgs(0.8))\n", + " msgs = np.array(sync.get_msgs(n_avg))\n", + " scores = np.zeros(n_avg)\n", + " msgs = [np.fft.fftshift(msg) for msg in msgs]\n", + " \n", + " if async.has_msg():\n", + " print (\"repeat due to async message\")\n", + " continue\n", + " \n", + " a = np.array(msgs)\n", + " mean_msg = a.mean(axis = 0)\n", + " suffix = \"x_2_%.3f_x_3_%.3f_x_4_%.3fx_5_%.3fx_6_%.3fx_7_%.3fx_8_%.3f\" % \\\n", + " (x2, x3, x4, x5, x6, x7, x8)\n", + " #sig_to_noise = tt.analyse_power_spec(mean_msg, debug=True, debug_path=\"/tmp/out\", suffix=suffix)\n", + " for i in range(n_avg):\n", + " if i == 0:\n", + " scores[i] = dt.calc_signal_sholder_ratio(msgs[0], sampling_rate=8000000, debug=True, debug_path=\"/tmp/out\", suffix=suffix)\n", + " else:\n", + " scores[i] = dt.calc_signal_sholder_ratio(msgs[0], sampling_rate=8000000)\n", + " \n", + " score = np.mean(scores)\n", + " print(score, x2, x3, x4, x5, x6, x7, x8)\n", + " repeat = False\n", + " \n", + " return score" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def simple_opt(pars, i, d, func):\n", + " par = pars[i]\n", + " test_pars = []\n", + " for x in [-1, 0, 1]:\n", + " new_par = list(pars)\n", + " new_par[i] = par + x * d \n", + " test_pars.append(new_par)\n", + " res = [func(par_new) for par_new in test_pars]\n", + " sel = np.argmax(res)\n", + " best_par = test_pars[sel]\n", + " return best_par\n", + "\n", + "#pars = [1,1,1]\n", + "#i_rand = np.random.randint(0, len(pars))\n", + "#pars = simple_opt(pars, i_rand, 0.01, lambda x:np.sum(x))\n", + "#pars" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top.set_txgain(86)\n", + "top.set_rxgain(5)\n", + "\n", + "pars = np.zeros(7)\n", + "\n", + "for i in range(10000):\n", + " i_rand = np.random.randint(0, len(pars))\n", + " pars = simple_opt(pars, i_rand, 0.005, measure)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top.set_txgain(85)\n", + "\n", + "params = []\n", + "for x2 in np.linspace(-0.1, 0.1, num = 11):\n", + " for x3 in np.linspace(-0.1, 0.1, num = 11):\n", + " for x4 in np.linspace(-0.1, 0.1, num = 11):\n", + " params.append((x2, x3, x4))\n", + " \n", + "t_start = time.time()\n", + "for idx, param in enumerate(params):\n", + " measure(param)\n", + " time_per_element = (time.time() - t_start) / (idx + 1)\n", + " print (\"Time per Element \" + str(time_per_element) +\n", + " \", total: \" + str(time_per_element * len(params)),\n", + " \", left: \" + str(time_per_element * (len(params) - 1 - idx))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sync.stop()\n", + "async.stop()\n", + "top.stop()\n", + "top.wait()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} |