diff options
Diffstat (limited to 'run.ipynb')
| -rw-r--r-- | run.ipynb | 166 |
1 files changed, 51 insertions, 115 deletions
@@ -42,6 +42,27 @@ "metadata": {}, "outputs": [], "source": [ + "sig = np.fromfile(\"/home/andreas/dab/dab_normalized_c64.dat\", dtype=np.complex64)\n", + "\n", + "#coefs = [0, -0.05, 0, 0, 0.05]\n", + "coefs = [0, 0, 0, 0, 0]\n", + "res = sig\n", + "for idx, coef in enumerate(coefs):\n", + " res += sig * np.abs(sig)**(idx+1) * coef #+1 because first correction term is squared\n", + "\n", + "res = res / np.max(res)\n", + "\n", + "sig.tofile(\"./input.dat\")\n", + "del res\n", + "del sig" + ] + }, + { + "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()" @@ -92,6 +113,15 @@ "metadata": {}, "outputs": [], "source": [ + "top." + ] + }, + { + "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", @@ -157,6 +187,20 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "def meas(i):\n", " import sys\n", @@ -178,7 +222,8 @@ "metadata": {}, "outputs": [], "source": [ - "res = [meas(i) for i in range(89)]" + "top.set_txgain(85)\n", + "mydab.calc_signal_sholder_ratio(fft, fft_size, samp_rate, plot=True)" ] }, { @@ -187,10 +232,7 @@ "metadata": {}, "outputs": [], "source": [ - "plt.plot(res)\n", - "plt.title(\"DAB sholders\")\n", - "plt.xlabel(\"tx gain [db]\")\n", - "plt.ylabel(\"signal - sholder [db]\")" + "res = [meas(i) for i in range(89)]" ] }, { @@ -199,42 +241,10 @@ "metadata": {}, "outputs": [], "source": [ - "top.set_txgain(84)\n", - "for el in l:\n", - " t2 = time.time()\n", - " 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)" + "plt.plot(res)\n", + "plt.title(\"DAB sholders\")#, coefs = [0, -0.05, 0, 0, 0.05]\")\n", + "plt.xlabel(\"tx gain [db]\")\n", + "plt.ylabel(\"signal - sholder [db]\")" ] }, { @@ -261,80 +271,6 @@ "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": [ - "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": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], "source": [] }, { |