Calculating_Ramp_Profile.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "surrounded-grenada",
   "metadata": {},
   "source": [
    "final ram 0.4ms ramp  \n",
    "Intensity ramp: 0.5 to 0.2, tanh  \n",
    "Bias Ramp: ldumpBias to (1-tunDumpBias), therefore 0.8 to 0.025 or 0.01, exp ramp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "better-lucas",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dramatic-large",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('expDecay_Niawg.txt') as file:\n",
    "    line = file.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "coordinated-spiritual",
   "metadata": {},
   "outputs": [],
   "source": [
    "niawgTimes = np.arange(0,0.4e-3,1/(320e6))[:-1]\n",
    "niawgInit = (1-0.8)\n",
    "niawgFin = 0.01\n",
    "niawgPercents = [float(val) for val in line[0].split(' ')[:-1]]\n",
    "niawgVals = [niawgInit + per * (niawgFin - niawgInit) for per in niawgPercents]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "protecting-ivory",
   "metadata": {},
   "outputs": [],
   "source": [
    "initAg = 0.5\n",
    "finAg = 0.2\n",
    "agPercents = []\n",
    "agTimes = np.arange(0,0.4e-3,1/(500e3))\n",
    "for time in agTimes:\n",
    "    agPercents.append((0.5*(1+np.tanh(-4+8*time/(0.4e-3)))))\n",
    "agVals = [initAg + per * (finAg-initAg) for per in agPercents]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "alternative-screen",
   "metadata": {},
   "outputs": [],
   "source": [
    "totalTimes = [0]\n",
    "niawgCount = 0\n",
    "agCount = 0\n",
    "niawgTime = niawgTimes[0]\n",
    "agTime = agTimes[0]\n",
    "totalIntensity = [niawgVals[niawgCount]*agVals[agCount]]\n",
    "while True:\n",
    "    # determine which time to increment\n",
    "    if niawgCount+1 == len(niawgTimes) and agCount+1 == len(agTimes):\n",
    "        break\n",
    "    if niawgCount+1 < len(niawgTimes) and niawgTimes[niawgCount+1] < agTimes[agCount+1]:\n",
    "        # next change is agilent\n",
    "        niawgCount += 1\n",
    "        totalTimes.append(niawgTimes[niawgCount])\n",
    "    else:\n",
    "        # next change is agilent\n",
    "        agCount += 1\n",
    "        totalTimes.append(agTimes[agCount])\n",
    "    totalIntensity.append(niawgVals[niawgCount]*agVals[agCount])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "premier-begin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128200, 128200)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(totalIntensity), len(totalTimes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "rubber-plate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.547900426854397"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3.5**(3/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "sustainable-deposit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.761904761904763"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.2*0.025/7/3*20e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "false-marketplace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0016666666666666666"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.2*0.0025/6*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "neither-seating",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0006666666666666668"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.2*0.001/6*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "latin-struggle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3335014289482764"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totalIntensity[-1]/6*1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "talented-share",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.547900426854397"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3.5**(3/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "complimentary-lotus",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
      "Invalid limit will be ignored.\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,3, figsize=(15,5))\n",
    "axs[0].plot(niawgTimes*1e3, niawgVals)\n",
    "axs[0].set_ylim(0,0.2)\n",
    "axs[0].set_xlabel('Time (ms)')\n",
    "axs[0].set_ylabel('% Power in Main Tweezers')\n",
    "\n",
    "axs[1].plot(agTimes*1e3, agVals)\n",
    "axs[1].set_ylim(0,0.5)\n",
    "axs[1].set_ylabel(\"Servo Power Setting (mW)\")\n",
    "\n",
    "axs[2].semilogy(np.array(totalTimes)*1e3,np.array(totalIntensity)/6)\n",
    "axs[2].set_ylim(0,0.2*0.5/6)\n",
    "axs[2].set_ylabel(\"Composite Depth (mK)\")\n",
    "\n",
    "for ax in axs:\n",
    "    ax.set_xlim(0,0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "parallel-wednesday",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('ShallowTweezersRamp.txt', 'w') as file:\n",
    "    for time, dp in zip(totalTimes, np.array(totalIntensity)/6):\n",
    "        file.write(str(time) + '\\t' + str(dp) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "informative-warren",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "covered-opening",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}