Linearised by using search for best amplitude

3 files changed, 177 insertions, 8 deletions

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..54b9753
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,35 @@
+.directory
+.ipynb_checkpoints/
+.plot.py.swp
+dual_tone.py
+dual_tone.pyc
+lut.pkl
+lut_plot/
+lut_sq.pkl
+lut_tab.pkl
+measurements.csv
+measurements.pkl
+measurements_lut.pkl
+measurements_lut_sq.pkl
+measurements_tab.pkl
+plots/
+tcp_async.pyc
+usrp_rx
+usrp_tx
+lut_generator.py
+
+
+
+# Created by https://www.gitignore.io/api/vim
+
+### Vim ###
+# swap
+[._]*.s[a-w][a-z]
+[._]s[a-w][a-z]
+# session
+Session.vim
+# temporary
+.netrwhist
+*~
+# auto-generated tag files
+tags
diff --git a/amplitude_ramp.py b/amplitude_ramp.py
index 7ef72e8..fb06d84 100755
--- a/amplitude_ramp.py
+++ b/amplitude_ramp.py
@@ -68,6 +68,9 @@ class RampGenerator(threading.Thread):
 
         if not options.lut is '':
             self.lut_dict = pickle.load(open(options.lut, "rb"))
+            assert(type(self.lut_dict) == dict)
+            assert(len(self.lut_dict["ampl"]) > 2)
+            assert(len(self.lut_dict["fac"])  > 2)
 
         self.tcpa = tcpa
 
@@ -75,7 +78,10 @@ class RampGenerator(threading.Thread):
         if self.lut_dict is None:
             return 1
         else:
-            return np.interp(ampl, self.lut_dict["ampl"], self.lut_dict["fac"])
+            interp = np.interp(ampl, self.lut_dict["ampl"], self.lut_dict["fac"])
+            print("interp " + str(interp))
+            print("ampl " + str(ampl))
+            return interp
 
     def set_source_ampl(self, ampl):
         self.event_queue_.put(ampl)
@@ -107,8 +113,10 @@ class RampGenerator(threading.Thread):
         amplitudes = xrange(self.ampl_start, self.ampl_stop, self.ampl_step)
         measurements = []
 
-        for ampl in amplitudes:
+        for idx, ampl in enumerate(amplitudes):
+            print("run ampl " + str(ampl))
             ampl_lut = self.lut(ampl) * ampl
+            print("run ampl_lut " + str(ampl_lut))
             measurement_correct = False
             max_iter = 10
             while measurement_correct == False and max_iter > 0:
@@ -240,6 +248,8 @@ try:
         elif event == "quit":
             break
         else:
+            print("event")
+            print(event)
             top.set_source_ampl(event)
             rampgen.confirm_source_ampl_updated()
 finally:
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\"))"
    ]
   },
   {