analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "import matplotlib\n",
    "\n",
    "matplotlib.rcParams[\"figure.dpi\"] = 600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = np.array(\n",
    "    [50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500]\n",
    ")  # input values in mV\n",
    "# inputs = np.array([50, 100])\n",
    "results: list[FitInfo] = getFits((1, 16))  # file range 1->16\n",
    "capacitance = 1e-12  # capacitance of test capacitor\n",
    "inputs = inputs * 1e-3 * capacitance * 1e12  # convert mV into charge (C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[FitInfo(\n",
      "mu: 7.415e-08 +- 6e-11\n",
      "std: 5.205e-08 +- 6e-11\n",
      "A: 0.1968 +- 0.0002\n",
      "c: 0.0121 +- 2e-05\n",
      "), FitInfo(\n",
      "mu: 7.782e-08 +- 5e-11\n",
      "std: 5.143e-08 +- 5e-11\n",
      "A: 0.2392 +- 0.0002\n",
      "c: 0.01221 +- 2e-05\n",
      "), FitInfo(\n",
      "mu: 8.082e-08 +- 5e-11\n",
      "std: 5.153e-08 +- 5e-11\n",
      "A: 0.2768 +- 0.0002\n",
      "c: 0.01225 +- 3e-05\n",
      "), FitInfo(\n",
      "mu: 8.275e-08 +- 5e-11\n",
      "std: 5.161e-08 +- 5e-11\n",
      "A: 0.3145 +- 0.0003\n",
      "c: 0.01246 +- 3e-05\n",
      "), FitInfo(\n",
      "mu: 8.473e-08 +- 5e-11\n",
      "std: 5.163e-08 +- 5e-11\n",
      "A: 0.3554 +- 0.0003\n",
      "c: 0.01689 +- 3e-05\n",
      "), FitInfo(\n",
      "mu: 8.694e-08 +- 5e-11\n",
      "std: 5.16e-08 +- 5e-11\n",
      "A: 0.3958 +- 0.0003\n",
      "c: 0.01694 +- 4e-05\n",
      "), FitInfo(\n",
      "mu: 7.915e-08 +- 6e-11\n",
      "std: 5.132e-08 +- 6e-11\n",
      "A: 0.7887 +- 0.0008\n",
      "c: 0.2175 +- 0.0001\n",
      "), FitInfo(\n",
      "mu: 8.651e-08 +- 5e-11\n",
      "std: 5.132e-08 +- 6e-11\n",
      "A: 1.174 +- 0.001\n",
      "c: 0.2202 +- 0.0001\n",
      "), FitInfo(\n",
      "mu: 8.033e-08 +- 5e-11\n",
      "std: 5.144e-08 +- 5e-11\n",
      "A: 1.565 +- 0.001\n",
      "c: 0.08637 +- 0.0002\n",
      "), FitInfo(\n",
      "mu: 8.351e-08 +- 5e-11\n",
      "std: 5.161e-08 +- 5e-11\n",
      "A: 1.951 +- 0.002\n",
      "c: 0.08576 +- 0.0002\n",
      "), FitInfo(\n",
      "mu: 8.735e-08 +- 5e-11\n",
      "std: 5.168e-08 +- 5e-11\n",
      "A: 2.33 +- 0.002\n",
      "c: 0.08764 +- 0.0002\n",
      "), FitInfo(\n",
      "mu: 8.991e-08 +- 5e-11\n",
      "std: 5.178e-08 +- 5e-11\n",
      "A: 2.72 +- 0.002\n",
      "c: 0.09014 +- 0.0002\n",
      "), FitInfo(\n",
      "mu: 9.171e-08 +- 5e-11\n",
      "std: 5.182e-08 +- 5e-11\n",
      "A: 3.102 +- 0.002\n",
      "c: 0.093 +- 0.0003\n",
      "), FitInfo(\n",
      "mu: 9.321e-08 +- 5e-11\n",
      "std: 5.197e-08 +- 5e-11\n",
      "A: 3.472 +- 0.003\n",
      "c: 0.0885 +- 0.0003\n",
      "), FitInfo(\n",
      "mu: 9.494e-08 +- 5e-11\n",
      "std: 5.207e-08 +- 5e-11\n",
      "A: 3.854 +- 0.003\n",
      "c: 0.1374 +- 0.0003\n",
      "), FitInfo(\n",
      "mu: 1.035e-07 +- 4e-11\n",
      "std: 5.365e-08 +- 4e-11\n",
      "A: 5.59 +- 0.004\n",
      "c: 0.1441 +- 0.0004\n",
      ")]\n"
     ]
    }
   ],
   "source": [
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Shaper 1 Calibration\n",
      "Gain 1 & 2 off, Preamp 1\n",
      "3.85 +- 0.02 V/pC\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2880 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "amps = getAmps(results)  # get amplitude values from results\n",
    "amps, amps_std = np.transpose(amps)\n",
    "\n",
    "plt.loglog()\n",
    "plt.errorbar(inputs, amps, amps_std, capsize=7, fmt=\".\")\n",
    "plt.xlabel(\"Input Charge (pC)\")\n",
    "plt.ylabel(\"Output Voltage (V)\")\n",
    "plt.title(\"Shaper Calibration\")\n",
    "\n",
    "linear = lambda x, m, b: m * x + b\n",
    "p, cov = curve_fit(linear, inputs, amps, sigma=amps_std, p0=(3753861894698.39, 0))\n",
    "linerr = np.sqrt(np.diag(cov))\n",
    "plt.plot(inputs, linear(inputs, *p))\n",
    "props = dict(boxstyle=\"round\", facecolor=\"grey\", alpha=0.5)\n",
    "textstr = f\"\"\"\n",
    "Shaper 1 Calibration\\nGain 1 & 2 off, Preamp 1\n",
    "{p[0]:.3g} +- { linerr[0]:.1g} V/pC\n",
    "\"\"\"\n",
    "plt.text(0.05, 3, textstr, fontsize=8, bbox=props)\n",
    "print(textstr)\n",
    "plt.savefig(\"./final_fit.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 9.94\n"
     ]
    }
   ],
   "source": [
    "gain_on = getSlope(np.asarray([50, 100]), (17, 18))\n",
    "print(f\"{gain_on[0]/p[0] : .3g}\")  # gain due to one switch being on. Approximate."
   ]
  }
 ],
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