Linearised by using search for best amplitude

1 files changed, 130 insertions, 6 deletions

diff --git a/lut_generator.ipynb b/lut_generator.ipynb
index 489203f..d19ac58 100644
--- a/lut_generator.ipynb
+++ b/lut_generator.ipynb
@@ -38,7 +38,8 @@
    "source": [
     "df = get_meas(\"./measurements.pkl\")\n",
     "df_lut = get_meas(\"./measurements_lut.pkl\")\n",
-    "df_lut_sq = get_meas(\"./measurements_lut_sq.pkl\")"
+    "df_lut_sq = get_meas(\"./measurements_lut_sq.pkl\")\n",
+    "df_tab = get_meas(\"./measurements_tab.pkl\")"
    ]
   },
   {
@@ -46,7 +47,9 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "df.plot()"
+   ]
   },
   {
    "cell_type": "code",
@@ -68,8 +71,8 @@
    "outputs": [],
    "source": [
     "def get_fac(ampl, mag_feedback):\n",
-    "    slope, intersect = get_slope(df[\"ampl\", df[\"mag_feedback\"]])\n",
-    "    return[(x*slope + intersect) / y for (x,y) in zip(df[\"ampl\"], df[\"mag_feedback\"])]\n",
+    "    slope, intersect = get_slope(ampl, mag_feedback)\n",
+    "    return[(x*slope + intersect) / y for (x,y) in zip(ampl, mag_feedback)]\n",
     "def interp(x): return np.interp(x, df[\"ampl\"], fac)"
    ]
   },
@@ -80,6 +83,7 @@
    "outputs": [],
    "source": [
     "x = np.linspace(0.1, 0.5, num = 50)\n",
+    "slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
     "\n",
     "plt.plot(df[\"ampl\"], df[\"mag_feedback\"], label=\"measurement\")\n",
     "plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
@@ -130,11 +134,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "slope, intersect = get_slope(df_tab[\"ampl\"], df_tab[\"mag_feedback\"])\n",
+    "\n",
+    "plt.plot(df_tab[\"ampl\"], df_tab[\"mag_feedback\"], label=\"measurement\")\n",
+    "plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
+    "\n",
+    "plt.legend(loc=0)\n",
+    "plt.title(\"Lut Tab Measurement\")\n",
+    "\n",
+    "plt.savefig(\"./lut_plot/lut_tab_measurement.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
     "\n",
     "plt.plot(df[\"ampl\"], df[\"mag_feedback\"], label=\"measurement\")\n",
     "plt.plot(df_lut[\"ampl\"], df_lut[\"mag_feedback\"], label=\"measurement lut\")\n",
     "plt.plot(df_lut_sq[\"ampl\"], df_lut_sq[\"mag_feedback\"], label=\"measurement lut sq\")\n",
+    "plt.plot(df_tab[\"ampl\"], df_tab[\"mag_feedback\"], label=\"measurement tab sq\")\n",
     "plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
     "\n",
     "plt.legend(loc=0)\n",
@@ -149,8 +178,103 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pickle.dump({\"ampl\":df[\"ampl\"],\"fac\":[f for f in fac]}, open(\"lut.pkl\", \"wb\"))\n",
-    "pickle.dump({\"ampl\":df[\"ampl\"],\"fac\":[f**2 for f in fac]}, open(\"lut_sq.pkl\", \"wb\"))"
+    "fac = get_fac(df[\"ampl\"], df[\"mag_feedback\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump({\"ampl\":list(df[\"ampl\"]),\"fac\":[f for f in fac]}, open(\"lut.pkl\", \"wb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.dump({\"ampl\":list(df[\"ampl\"]),\"fac\":[f**2 for f in fac]}, open(\"lut_sq.pkl\", \"wb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
+    "ampl_corr = []\n",
+    "for ampl in df[\"ampl\"]:\n",
+    "    y = ampl * slope + intersect\n",
+    "    mag_feedback_idx = np.argmin(np.abs(df[\"mag_feedback\"] - y))\n",
+    "    ampl_corr.append(df[\"ampl\"][mag_feedback_idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ampl_corr = np.array(ampl_corr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx_start = np.argmax(ampl_corr != ampl_corr.min()) - 1\n",
+    "idx_end  = np.argmin(ampl_corr != ampl_corr.max()) \n",
+    "start = 1.0 * idx_start / len(ampl_corr)\n",
+    "end   = 1.0 * idx_end / len(ampl_corr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig_range = np.linspace(start,end,num=len(ampl_corr))\n",
+    "new_range  = np.linspace(0,1,num=len(ampl_corr))\n",
+    "\n",
+    "ampl_corr_new = []\n",
+    "for f in new_range:\n",
+    "    ratio = np.interp(f, new_range, orig_range)\n",
+    "    new_ampl = np.interp(ratio, new_range, ampl_corr)\n",
+    "    ampl_corr_new.append(new_ampl)\n",
+    "ampl_corr_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fac = ampl_corr_new / df[\"ampl\"]\n",
+    "pickle.dump({\"ampl\":list(df[\"ampl\"]),\"fac\":[f for f in list(fac)]}, open(\"lut_tab.pkl\", \"wb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pickle.load(open(\"./lut_tab.pkl\"))"
    ]
   },
   {