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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np, pandas as pd\n",
"import pickle\n",
"import os\n",
"\n",
"if not os.path.isdir(\"./lut_plot\"):\n",
" os.mkdir(\"./lut_plot\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_meas(path):\n",
" measurements = pickle.load(open(path, \"rb\"))\n",
" df = pd.DataFrame(measurements, columns=[\"ampl\",\"mag_gen_sq\",\"mag_feedback_sq\",\"phase_diff\"])\n",
" df[\"mag_gen\"] = np.sqrt(df[\"mag_gen_sq\"])\n",
" df[\"mag_feedback\"] = np.sqrt(df[\"mag_feedback_sq\"])\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = get_meas(\"./measurements.pkl\")\n",
"df_lut = get_meas(\"./measurements_lut.pkl\")\n",
"df_lut_sq = get_meas(\"./measurements_lut_sq.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_slope(ampl, mag_feedback):\n",
" slope, intersect = np.polyfit(x = ampl[0:20], y = mag_feedback[0:20], deg = 1)\n",
" return slope, intersect\n",
"\n",
"#get_slope(df[\"ampl\"], df[\"mag_feedback\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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",
"def interp(x): return np.interp(x, df[\"ampl\"], fac)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(0.1, 0.5, num = 50)\n",
"\n",
"plt.plot(df[\"ampl\"], df[\"mag_feedback\"], label=\"measurement\")\n",
"plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
"\n",
"plt.legend(loc=0)\n",
"plt.title(\"Original Measurement\")\n",
"\n",
"plt.savefig(\"./lut_plot/original_measurement.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
"\n",
"plt.plot(df_lut[\"ampl\"], df_lut[\"mag_feedback\"], label=\"measurement\")\n",
"plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
"\n",
"plt.legend(loc=0)\n",
"plt.title(\"Lut Measurement\")\n",
"\n",
"plt.savefig(\"./lut_plot/lut_measurement.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"slope, intersect = get_slope(df[\"ampl\"], df[\"mag_feedback\"])\n",
"\n",
"plt.plot(df_lut_sq[\"ampl\"], df_lut_sq[\"mag_feedback\"], label=\"measurement\")\n",
"plt.plot(x, x*slope + intersect, label = \"linear model\")\n",
"\n",
"plt.legend(loc=0)\n",
"plt.title(\"Lut Squared Measurement\")\n",
"\n",
"plt.savefig(\"./lut_plot/lut_sq_measurement.png\")"
]
},
{
"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(x, x*slope + intersect, label = \"linear model\")\n",
"\n",
"plt.legend(loc=0)\n",
"plt.title(\"All Measurements\")\n",
"\n",
"plt.savefig(\"./lut_plot/all_measurement.png\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"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\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": 1
}
|
