diff options
| -rw-r--r-- | .gitignore | 35 | ||||
| -rwxr-xr-x | amplitude_ramp.py | 14 | ||||
| -rw-r--r-- | lut_generator.ipynb | 136 |
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\"))" ] }, { |