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Diffstat (limited to 'run.ipynb')
| -rw-r--r-- | run.ipynb | 254 |
1 files changed, 254 insertions, 0 deletions
diff --git a/run.ipynb b/run.ipynb new file mode 100644 index 0000000..5c61960 --- /dev/null +++ b/run.ipynb @@ -0,0 +1,254 @@ +{ + "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", + "\n", + "import tcp_async\n", + "import tcp_sync\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", + "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": [ + "samp_rate = 4000000 #samples / second\n", + "frequency_0 = samp_rate/10. #cycles / second\n", + "frequency_1 = samp_rate/9. #cycles / second\n", + "fft_size = 8192" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top.start()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sync.has_msg()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import peakutils\n", + "def score(msg, frequency_0, frequency_1, samp_rate):\n", + " frequency_0_idx = np.round(frequency_0 / samp_rate * fft_size).astype(int)\n", + " frequency_1_idx = np.round(frequency_1 / samp_rate * fft_size).astype(int)\n", + " frequency_idx_diff = frequency_1_idx - frequency_0_idx\n", + " \n", + " start = frequency_0_idx % frequency_idx_diff + frequency_idx_diff / 2.\n", + " start = np.round(start).astype(int)\n", + " end = msg.shape[0] - frequency_idx_diff - (msg.shape[0] - start) % frequency_idx_diff\n", + " \n", + " msg_cut = msg[start:end]\n", + " \n", + " msg_cut = msg_cut.reshape(-1, frequency_idx_diff)\n", + " \n", + " msg_peaks = np.max(msg_cut, axis=1)\n", + " #plt.plot(msg_peaks, \"ro\")\n", + " \n", + " inter_mod = \\\n", + " (0.5 * (msg_peaks[8]/msg_peaks[7] + msg_peaks[9]/msg_peaks[10]),\n", + " 0.5 * (msg_peaks[8]/msg_peaks[6] + msg_peaks[9]/msg_peaks[11]),\n", + " 0.5 * (msg_peaks[8]/msg_peaks[5] + msg_peaks[9]/msg_peaks[12]),\n", + " 0.5 * (msg_peaks[8]/msg_peaks[4] + msg_peaks[9]/msg_peaks[13]),\n", + " 0.5 * (msg_peaks[8]/msg_peaks[3] + msg_peaks[9]/msg_peaks[14]),\n", + " 0.5 * (msg_peaks[8]/msg_peaks[2] + msg_peaks[9]/msg_peaks[15]),\n", + " 0.5 * (msg_peaks[8]/msg_peaks[1] + msg_peaks[9]/msg_peaks[16]))\n", + " \n", + " return(inter_mod)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "top.set_txgain(85)\n", + "for x2 in np.linspace(-0.15, 0.15, num = 15):\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)" + ] + }, + { + "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": [ + "path = gen_source.gen_file(frequency_0=frequency_0,\n", + " frequency_1=frequency_1,\n", + " x1 = 0,\n", + " x2 = 0,\n", + " x3 = 0,\n", + " x4 = 0,\n", + " samp_rate=samp_rate,\n", + " path='./np_twotone')\n", + "a_load = np.fromfile(path, dtype=np.complex64)\n", + "plt.plot(a_load.real[0:100])" + ] + }, + { + "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": [] + }, + { + "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 +} |