Add analyze_dab_nonlinearity.ipynb

1 files changed, 451 insertions, 0 deletions

diff --git a/analyze_dab_nonlinearity.ipynb b/analyze_dab_nonlinearity.ipynb
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+++ b/analyze_dab_nonlinearity.ipynb
@@ -0,0 +1,451 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import src.dab_util as du"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Align Original / Recored Files\n",
+    "\n",
+    "Using Txgain 81"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "#path = \"../test_dat/out.iq\"\n",
+    "path = \"./recored.dat\"\n",
+    "frame = 96*8192\n",
+    "offset = 5 * frame + 8192\n",
+    "length  = 2 * frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "sig_rec = np.fromfile(path, dtype=np.complex64, count=offset + length)[offset:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "#sig_rec = sig_rec[30370+0*frame:30370+1*frame]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "i = 0\n",
+    "l = length\n",
+    "plt.plot(np.abs(sig_rec[i:i + l]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "n = sig_rec.shape[0]\n",
+    "max_mean = np.mean(np.sort(np.abs(sig_rec))[int(n*0.90):])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "sym = int(frame / 76.0)\n",
+    "mask = np.ones((int(4*sym)))\n",
+    "mask[int(2*sym) : int(3*sym)] = -1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import scipy.signal as ss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "c = ss.correlate(np.abs(sig_rec), mask, mode='same')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "max1 = np.argmax(c[:frame])\n",
+    "max2 = frame + np.argmax(c[frame:])\n",
+    "max2 - max1 - frame < sym / 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(c)\n",
+    "plt.plot((max1, max1), (0, 1200))\n",
+    "plt.plot((max2, max2), (0, 1200))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "i = max2\n",
+    "l = 2*sym\n",
+    "plt.plot(np.abs(sig_rec[i:i + l]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "sig_rec.tofile(\"./orig_frame_8.iq\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "path = \"../test_dat/out.iq\"\n",
+    "frame = 96*8192\n",
+    "offset = 8 * frame + 8192 + 10500 + 72\n",
+    "length  = 1 * frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "sig_orig = np.fromfile(path, dtype=np.complex64, count=offset + length)[offset:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(sig_orig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "sig_orig.tofile(\"./orig_frame_8.iq\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "sig_rec = sig_rec / sig_rec.std()\n",
+    "sig_orig = sig_orig / sig_orig.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "i = 10000\n",
+    "l = 400\n",
+    "plt.plot(sig_orig[i:i+l], label=\"in\")\n",
+    "plt.plot(sig_rec[i:i+l], label=\"out\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Approximate Amplifier Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(np.abs(sig_orig), np.abs(sig_rec), 'o')\n",
+    "plt.xlabel(\"Amplitude In\")\n",
+    "plt.ylabel(\"Amplitude Out\")\n",
+    "\n",
+    "deg = 8\n",
+    "t = np.linspace(0,5)\n",
+    "model = make_pipeline(PolynomialFeatures(deg), Ridge())\n",
+    "model.fit(np.abs(sig_orig.reshape(-1,1)), np.abs(sig_rec.reshape(-1,1)))\n",
+    "y = model.predict(t.reshape(-1,1))\n",
+    "plt.plot(t, np.abs(y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Approximate Inverse Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(np.abs(sig_orig), np.abs(sig_rec), 'o')\n",
+    "plt.xlabel(\"Amplitude In\")\n",
+    "plt.ylabel(\"Amplitude Out\")\n",
+    "\n",
+    "deg = 8\n",
+    "t = np.linspace(0,3)\n",
+    "model = make_pipeline(PolynomialFeatures(deg), Ridge(alpha=100))\n",
+    "model.fit(np.abs(sig_rec.reshape(-1,1)),np.abs(sig_orig.reshape(-1,1)) )\n",
+    "y = model.predict(t.reshape(-1,1))\n",
+    "plt.plot(t, np.abs(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "# Phase Error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "l = 100000\n",
+    "plt.plot(np.abs(sig_orig[i:i+l]), (np.angle(sig_rec[i:i+l], deg = 1) - np.angle(sig_orig[i:i+l], deg = 1)) % 180, 'o')\n",
+    "plt.xlabel(\"Amplitude In\")\n",
+    "plt.ylabel(\"Phase difference\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "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": 2
+}