5.Coulomb_SR830_and_QDAC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 掃Gate量IV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\visa.py:23: FutureWarning: The visa module provided by PyVISA is being deprecated. You can replace `import visa` by `import pyvisa as visa` to achieve the same effect.\n",
      "\n",
      "The reason for the deprecation is the possible conflict with the visa package provided by the https://github.com/visa-sdk/visa-python which can result in hard to debug situations.\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-04-04 19:19:12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ASRL1::INSTR',\n",
       " 'ASRL2::INSTR',\n",
       " 'ASRL3::INSTR',\n",
       " 'ASRL10::INSTR',\n",
       " 'GPIB0::8::INSTR')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyvisa as visa\n",
    "import matplotlib.pyplot as plt\n",
    "import my_SR830\n",
    "import os, time, threading, datetime, pymeasure,tqdm\n",
    "\n",
    "from tqdm import trange,tqdm\n",
    "from time import sleep\n",
    "from datetime import date\n",
    "from IPython import display\n",
    "from pymeasure.adapters import VISAAdapter\n",
    "from pathlib import Path\n",
    "\n",
    "import qcodes as qc\n",
    "import qcodes.instrument_drivers.QDevil.QDevil_QDAC as QDac\n",
    "from qcodes.instrument_drivers.QDevil.QDevil_QDAC import Mode\n",
    "\n",
    "print(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "visa.ResourceManager().list_resources()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讀取830和QDac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "QDAC succesfully connected ! :)\n",
      "\n",
      "Number of channels:  24\n"
     ]
    }
   ],
   "source": [
    "SR830_GPIB_Addr = 8  ##請檢察GPIB號碼\n",
    "srs_source = my_SR830.MY_SR830(SR830_GPIB_Addr)\n",
    "\n",
    "qdac = QDac.QDac(name='qdac', address='ASRL3::INSTR', update_currents=False)\n",
    "print('\\nQDAC succesfully connected ! :)\\n')\n",
    "print(\"Number of channels: \",qdac.num_chans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will query voltages of all channels of a 24 channel QDAC \n",
    "# Note that index 0 refer to channel 01, 1 refer to channel 02 and so on \n",
    "def show_now_all_voltage():\n",
    "    np.set_printoptions(linewidth=1000)  ##for pretty printing\n",
    "    print('Channel Voltage : ')\n",
    "    feed = np.around(np.reshape(np.array(qdac.channels[0:24].v()),(3,8)),decimals=3)\n",
    "    print(feed)\n",
    "    return feed\n",
    "# qdac.print_overview(update_currents=False)\n",
    "\n",
    "##For more information see \"QCodes example with QDevil_QDAC\" file\n",
    "\n",
    "\n",
    "def whilegoto(m_gate,volt):\n",
    "    m_gate.v.set(volt)\n",
    "    while abs(volt-m_gate.v.get())>1e-4: \n",
    "        display.clear_output(wait=True)\n",
    "        print(\"Ramping %s voltage to %.6f (V) ...\\ncurrent voltage is %.6f (V)\"%(m_gate,volt,m_gate.v.get()))\n",
    "        m_gate.v.set(volt)\n",
    "        time.sleep(2)\n",
    "    display.clear_output(wait=True)\n",
    "    print(\"Done!\")\n",
    "    print(\"%s is now at %.6f Volts\"%(m_gate,m_gate.v.get()))\n",
    "    return m_gate.v.get()\n",
    "\n",
    "\n",
    "def reshape_for_origin(m_data,m_data_points):\n",
    "    A,B = np.split(m_data,[m_data_points])\n",
    "    C,D = np.split(m_data,[m_data_points-1])\n",
    "    return np.hstack((A,D))\n",
    "\n",
    "\n",
    "def All_Goto(AG=-1,BG_left=-1,BG_right=-1,EG=-1):\n",
    "    preramp_voltage_array = np.around(\n",
    "                                    np.delete(\n",
    "                                        np.array(qdac.channels[0:24].v())\n",
    "                                        ,[0,1,3,5,7,8,10,12,14,16,17,18,19,20,21,22,23])\n",
    "                                    ,decimals=4)\n",
    "\n",
    "    EG3.v.set(EG)\n",
    "    EG4.v.set(EG)\n",
    "    AG45.v.set(EG)\n",
    "    AG56.v.set(AG)\n",
    "    BG4.v.set(EG)\n",
    "    BG5.v.set(BG_left)\n",
    "    BG6.v.set(BG_right)\n",
    "    time.sleep(1)\n",
    "\n",
    "    while 1:\n",
    "        later_voltage_array = np.around(\n",
    "                                    np.delete(\n",
    "                                        np.array(qdac.channels[0:24].v())\n",
    "                                        ,[0,1,3,5,7,8,10,12,14,16,17,18,19,20,21,22,23])\n",
    "                                    ,decimals=4)\n",
    "\n",
    "        if np.size(np.where(preramp_voltage_array == later_voltage_array))>=1:\n",
    "            EG3.v.set(EG)\n",
    "            EG4.v.set(EG)\n",
    "            AG45.v.set(EG)\n",
    "            AG56.v.set(AG)\n",
    "            BG4.v.set(EG)\n",
    "            BG5.v.set(BG_left)\n",
    "            BG6.v.set(BG_right)\n",
    "            time.sleep(1)\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    while abs(BG4.v.get()-EG)>1e-4:\n",
    "        display.clear_output(wait=True)\n",
    "        show_now_all_voltage()\n",
    "        time.sleep(1)\n",
    "    print('Done !')\n",
    "    show_now_all_voltage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QDAC 設定 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "EG3  = qdac.ch10\n",
    "EG4  = qdac.ch16\n",
    "AG45 = qdac.ch12\n",
    "AG56 = qdac.ch14\n",
    "BG4  = qdac.ch03\n",
    "BG5  = qdac.ch05\n",
    "BG6  = qdac.ch07\n",
    "DG2  = qdac.ch02\n",
    "DG3  = qdac.ch08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # gate.slope('Inf')  ## Careful with this one.\n",
    "# gate.v.set(0.0)\n",
    "# gate.mode(Mode.vlow_ilow)  ## 看要檢查什麼模式 (vlow -> +-1.1V)v . !!!!! pls change mode at 0V !!!!! \n",
    "\n",
    "EG3.mode(Mode.vhigh_ilow)\n",
    "EG4.mode(Mode.vhigh_ilow)\n",
    "AG45.mode(Mode.vhigh_ilow)\n",
    "AG56.mode(Mode.vhigh_ilow)\n",
    "BG4.mode(Mode.vhigh_ilow)\n",
    "BG5.mode(Mode.vhigh_ilow)\n",
    "BG6.mode(Mode.vhigh_ilow)\n",
    "DG2.mode(Mode.vhigh_ilow)\n",
    "DG3.mode(Mode.vhigh_ilow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel 2, slope: 0.05 (V/s)\n",
      "Channel 3, slope: 0.05 (V/s)\n",
      "Channel 5, slope: 0.05 (V/s)\n",
      "Channel 7, slope: 0.05 (V/s)\n",
      "Channel 8, slope: 0.05 (V/s)\n",
      "Channel 10, slope: 0.05 (V/s)\n",
      "Channel 12, slope: 0.05 (V/s)\n",
      "Channel 14, slope: 0.05 (V/s)\n",
      "Channel 16, slope: 0.05 (V/s)\n"
     ]
    }
   ],
   "source": [
    "# For smooth voltage changes the maximal voltage change (in V/s) may be set for each channel\n",
    "m = 0.05  #### 設定走多快\n",
    "EG3.slope(m)\n",
    "EG4.slope(m)\n",
    "AG45.slope(m)\n",
    "AG56.slope(m)\n",
    "BG4.slope(m)\n",
    "BG5.slope(m)\n",
    "BG6.slope(m)\n",
    "DG2.slope(m)\n",
    "DG3.slope(m)\n",
    "\n",
    "qdac.print_slopes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sweeping with : <QDacChannel: qdac_chan14 of QDac: qdac>\n",
      "\t\tGate mode : V range high / I range low\n",
      "\n",
      "Barriers with : <QDacChannel: qdac_chan05 of QDac: qdac>  and  <QDacChannel: qdac_chan07 of QDac: qdac>\n"
     ]
    }
   ],
   "source": [
    "##Plunger (Tuned gate)\n",
    "gate = AG56   ##選擇要調控的輸出端子\n",
    "print(\"Sweeping with :\",gate)  ##確認調控的閘極\n",
    "gate.mode(Mode.vhigh_ilow) ### Set mode and slope again just to make sure.\n",
    "gate.slope(m)\n",
    "print(\"\\t\\tGate mode :\",gate.mode.cache().get_label())\n",
    "\n",
    "##Barrier\n",
    "BarrierLeft = BG5  ####Choose the barriers in use\n",
    "BarrierRight = BG6\n",
    "print(\"\\nBarriers with :\",BarrierLeft,\" and \",BarrierRight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 830相關設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "SD_voltage_apply = 1\n",
    "\n",
    "srs_goto_step = 0.05\n",
    "srs_goto_delay_time=0.5\n",
    "srs_source.input_config = 'A'\n",
    "srs_source.input_grounding = 'Float'\n",
    "srs_source.frequency = 87.11 # change the lock-in frequency\n",
    "srs_source.time_constant = 0.3 # change the lock-in time constant\n",
    "srs_source.sensitivity = 2e-5 # change the lock-in sensitivity\n",
    "srs_source.channel1 = 'X'\n",
    "srs_source.channel2 = 'Y'\n",
    "\n",
    "# srs_source.GOTO(SD_voltage_apply, srs_goto_step, srs_goto_delay_time) #給檢查Sensitivity\n",
    "# srs_source.GOTO(0.004, srs_goto_step, srs_goto_delay_time) #歸零\n",
    "# Lock-in 放大率(到2000需要乘上倍數)\n",
    "# senstivity 2mv = 5000倍\n",
    "# 10V / senstivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 包一包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(v_BG5,v_BG6):\n",
    "    ####量測相關設定###############################################################################################\n",
    "    m_gate_voltage_apply_1,n = [],'no input'\n",
    "    \n",
    "    EG = 2.95\n",
    "    \n",
    "    m_min_V = 1.5        #initial voltage\n",
    "    m_max_V = 4.0        #final voltage\n",
    "    m_data_points = 501      #resolution for one sweep (m_min_V to m_max_V)\n",
    "\n",
    "    forth = np.linspace(m_min_V, m_max_V,m_data_points)\n",
    "    back = np.flip(forth,0)\n",
    "    m_gate_voltage_apply_1 = forth\n",
    "    m_gate_voltage_apply_1 = np.append(m_gate_voltage_apply_1[:-1],back)\n",
    "\n",
    "    measure_wait_time = 1  #電壓到時等一段時間穩定後再量測\n",
    "\n",
    "    N_ave = 6 #在一個電壓職重複量測幾次\n",
    "    ave_wait = 2 #Lock-in 要等0.5秒才能再量\n",
    "    fontsize = 8\n",
    "    drawing_resolution = 10\n",
    "    divider_ratio= 1e-5 #分壓比例(Source Drain SR830)\n",
    "    Amplifier_gain= 1e6 #放大器比例(Source Drain SR830)\n",
    "    \n",
    "    ####路徑設定#####################################################################################################\n",
    "    repeat = 0\n",
    "\n",
    "    folder_path = \"C:/Users/USER/Sean/2021-03-31 BGsweep/\" ##儲存的地方\n",
    "    file_name = 'BG5at_%sV_BG6at_%sV_repeat%s_at_%s_Dot_Under_AG56'%(v_BG5,v_BG6,repeat,date.today())##儲存的檔名\n",
    "    print('檔名會叫做 >> %s'%file_name) \n",
    "\n",
    "    overwrite = 'Y'\n",
    "    if os.path.isfile('%s%s.txt'%(folder_path,file_name))==True:\n",
    "        overwrite = input('Do you want to overwtite the existing file? >>> Y/N  ')\n",
    "    if overwrite == 'Y'or 'y':\n",
    "        file_name = file_name\n",
    "    else:\n",
    "        file_name = '##'\n",
    "        print(\"please create a new file\")\n",
    "        raise RuntimeError('!!!!  File exsit, please name a new file  !!!!')\n",
    "\n",
    "    ####調整Barrier和AG電壓##############################################################################################\n",
    "    All_Goto(m_min_V,v_BG5,v_BG6,EG)\n",
    "#     whilegoto(BarrierLeft,v_BG5)\n",
    "#     whilegoto(BarrierRight,v_BG6)\n",
    "    whilegoto(gate,m_gate_voltage_apply_1[0]+0.01)\n",
    "    time.sleep(1)\n",
    "    print('Voltage before measurement was ...')\n",
    "    V_before = show_now_all_voltage()\n",
    "\n",
    "    ####開始量測迴圈######################################################################################################\n",
    "    current_measure_xx = np.zeros((len(m_gate_voltage_apply_1), N_ave+1)) #製造0矩陣以放入量測數據\n",
    "    conductance_measure_xx = np.zeros((len(m_gate_voltage_apply_1), N_ave+1))\n",
    "    current_qdac_measure_xx = np.zeros((len(m_gate_voltage_apply_1), N_ave+1))\n",
    "    voltage_qdac_measure_xx = np.zeros((len(m_gate_voltage_apply_1), N_ave+1))\n",
    "    plt.figure(figsize=(3,3), dpi=200)  ##create empty graph\n",
    "    plt.xlim([m_min_V,m_max_V])\n",
    "\n",
    "    start = time.time()\n",
    "    print(\"Apply Gate voltage...\")\n",
    "    for index_V, voltage in enumerate(tqdm(m_gate_voltage_apply_1)): #####開始掃GATE(P1)並量測記錄\n",
    "        \n",
    "        gate.v.set(voltage)\n",
    "        time.sleep(0.5)\n",
    "        while abs(voltage-gate.v.get())>1e-4: \n",
    "            gate.v.set(voltage)\n",
    "            time.sleep(1)\n",
    "\n",
    "        time.sleep(measure_wait_time)  ## wait a while before measurement\n",
    "        Sum_of_current, Sum_of_conductance, Sum_of_current_qdac,  Sum_of_voltage_qdac = 0, 0, 0, 0\n",
    "\n",
    "        ###量測################################################################\n",
    "        for n in range(N_ave):\n",
    "            current_measure_xx[index_V,n] = srs_source.x/Amplifier_gain   ##讀取Lock_in的值  絕對值:srs_source.magnitude\n",
    "            conductance_measure_xx[index_V,n] = srs_source.x/Amplifier_gain/(SD_voltage_apply*divider_ratio)\n",
    "            current_qdac_measure_xx[index_V,n] = gate.i()\n",
    "            voltage_qdac_measure_xx[index_V,n] = gate.v.get()\n",
    "\n",
    "            Sum_of_current += current_measure_xx[index_V,n]\n",
    "            Sum_of_conductance += conductance_measure_xx[index_V,n]\n",
    "            Sum_of_current_qdac += current_qdac_measure_xx[index_V,n]\n",
    "            Sum_of_voltage_qdac += voltage_qdac_measure_xx[index_V,n]\n",
    "\n",
    "            time.sleep(ave_wait) ## Lockin delay at least 0.5s\n",
    "\n",
    "        current_measure_xx[index_V,N_ave] = Sum_of_current/float(N_ave) #加上平均數值\n",
    "        conductance_measure_xx[index_V,N_ave] = Sum_of_conductance/float(N_ave) #加上平均數值\n",
    "        current_qdac_measure_xx[index_V,N_ave] = Sum_of_current_qdac/float(N_ave)\n",
    "        voltage_qdac_measure_xx[index_V,N_ave] = Sum_of_voltage_qdac/float(N_ave)\n",
    "\n",
    "        ####畫圖################################################################\n",
    "        if (index_V%drawing_resolution==0):\n",
    "            display.clear_output(wait=True)\n",
    "\n",
    "            plt.subplot(4,1, 1)\n",
    "            plt.plot(m_gate_voltage_apply_1[:index_V+1] , current_measure_xx[:index_V+1,N_ave], c='r')#, label='$\\\\rho_{xy} pin 2-11$')\n",
    "            plt.xlabel(\"$V$ (V)\")\n",
    "            plt.xticks(fontsize=fontsize)\n",
    "            plt.ylabel(\"$I$ (A)\")\n",
    "            plt.yticks(fontsize=fontsize)\n",
    "\n",
    "            plt.subplot(4,1, 2)\n",
    "            plt.plot(m_gate_voltage_apply_1[:index_V+1] , conductance_measure_xx[:index_V+1,N_ave], c='r')#, label='$\\\\rho_{xy} pin 2-11$')\n",
    "            plt.xlabel(\"$V$ (V)\")\n",
    "            plt.xticks(fontsize=fontsize)\n",
    "            plt.ylabel(\"$G$ (S)\")\n",
    "            plt.yticks(fontsize=fontsize)\n",
    "            display.display(plt.gcf())\n",
    "\n",
    "    time.sleep(0.01)\n",
    "    print(\"Measurement ends.\")\n",
    "    display.clear_output(wait=True)\n",
    "\n",
    "    plt.subplot(4,1, 1)\n",
    "    plt.plot(m_gate_voltage_apply_1 , current_measure_xx[:,N_ave], c='r')#, label='$\\\\rho_{xy} pin 2-11$')\n",
    "    #plt.ylim([voltage_measure.min(),voltage_measure.max()])\n",
    "    plt.xlabel(\"$V$ (V)\")\n",
    "    plt.xticks(fontsize=fontsize)\n",
    "    plt.ylabel(\"$I$ (A)\")\n",
    "    plt.yticks(fontsize=fontsize)\n",
    "\n",
    "    plt.subplot(4,1, 2)\n",
    "    plt.plot(m_gate_voltage_apply_1 , conductance_measure_xx[:,N_ave], c='r')#, label='$\\\\rho_{xy} pin 2-11$')\n",
    "    #plt.ylim([voltage_measure.min(),voltage_measure.max()])\n",
    "    plt.xlabel(\"$V$ (V)\")\n",
    "    plt.xticks(fontsize=fontsize)\n",
    "    plt.ylabel(\"$G$ (S)\")\n",
    "    plt.yticks(fontsize=fontsize)\n",
    "    # plt.savefig('%s/Rxx.png'%folder_path[index_current],bbox_inches='tight', dpi=200, pad_inches=0.05) ##存圖片\n",
    "    display.display(plt.gcf())\n",
    "    \n",
    "    ####存檔############################################################################################################\n",
    "    voltage_log = np.hstack((m_gate_voltage_apply_1.reshape(-1,1), voltage_qdac_measure_xx[:, -1].reshape(-1,1),\n",
    "                             m_gate_voltage_apply_1.reshape(-1,1)-voltage_qdac_measure_xx[:, -1].reshape(-1,1),\n",
    "                             (m_gate_voltage_apply_1.reshape(-1,1)-voltage_qdac_measure_xx[:, -1].reshape(-1,1))/m_gate_voltage_apply_1.reshape(-1,1)*100))\n",
    "\n",
    "    raw_data = np.hstack((voltage_qdac_measure_xx, current_measure_xx, conductance_measure_xx,\n",
    "                          current_qdac_measure_xx,m_gate_voltage_apply_1.reshape(-1,1)))\n",
    "\n",
    "    data = np.hstack((voltage_qdac_measure_xx[:,[-1]], current_measure_xx[:,[-1]], conductance_measure_xx[:,[-1]], \n",
    "                      current_qdac_measure_xx[:,[-1]], m_gate_voltage_apply_1.reshape(-1,1)))\n",
    "    \n",
    "    data = reshape_for_origin(data, m_data_points)\n",
    "\n",
    "    np.savetxt(\"%svoltage_log%s.txt\" %(folder_path,file_name),voltage_log,comments='', fmt=\"%.8e\", \\\n",
    "               header='v_set\\tv_actual\\terror\\terror_percentage\\\n",
    "               \\n(V)\\t(V)\\t(V)\\t(%)')\n",
    "\n",
    "    np.savetxt(\"%sraw_data%s.txt\" %(folder_path,file_name),raw_data,comments='', fmt=\"%.8e\", \\\n",
    "               header='V\\tI*%d\\tI_avg\\tG*%d\\tG_avg\\tV_set\\\n",
    "               \\n(V)\\t(A)\\t(A)\\t(S)\\t(s)\\t(V)'%(N_ave,N_ave))\n",
    "\n",
    "    np.savetxt('%s%s.txt'%(folder_path,file_name),data, fmt=\"%.8e\",comments='',\\\n",
    "               header='Vag\\tId_avg_of_%s\\tG_avg_of_%s\\tQDACcurrent\\tV_set \\\n",
    "               \\n(V)\\t(A)\\t(S)\\t(A)\\V_set(V)\\\n",
    "               \\nfrom%sVto%sV\\tresolution_%.3f(V)\\tBG=%sV_and_%sV\\tleakage_current\\tset_V\\tback_to_%s\\tback_to_%s\\tback_to_%s\\tback_to_%s\\tback_to_%s\\t%spoints\\tmeasure_wait_time_%s\\tslope_%s'\\\n",
    "               %(N_ave,N_ave,\\\n",
    "                             \\\n",
    "                 m_min_V,m_max_V,(m_max_V-m_min_V)/(m_data_points-1),v_BG5,v_BG6,m_min_V,m_min_V,m_min_V,m_min_V,m_min_V,m_data_points,measure_wait_time,m))\n",
    "    \n",
    "    ################################################################################################################\n",
    "    V_after = show_now_all_voltage()\n",
    "    print('Going back to -1 V ...')\n",
    "    All_Goto()\n",
    "    stop = time.time()\n",
    "    T=(stop-start)/60\n",
    "    display.clear_output(wait=True)\n",
    "    print(\"Measurement ends.\\n這次量測花費了 %f 分鐘\\nDONE!!!\\n\"%T)\n",
    "    print(\"Voltage before measurement : \")\n",
    "    print(V_before)\n",
    "    print(\"Voltage after measurement : \")\n",
    "    print(V_after)\n",
    "    show_now_all_voltage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 輸入需要的 Barrier 電壓  e.g.  run(BG5,BG6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def Everyone_goto(v_set):\n",
    "#     1. read value of each channel for 2 sec and sdd if any channel is stuck (striaght forward but slow)\n",
    "#     2. read sum of all value and compare expected rate (fast but error prone)\n",
    "#     3. read value before ramp and compare value after 2 sec. (probably better?)\n",
    "#     return print(\"all channels ramping correctly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "show_now_all_voltage()\n",
    "display.clear_output(wait=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "All_Goto()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement ends.\n",
      "這次量測花費了 327.871253 分鐘\n",
      "DONE!!!\n",
      "\n",
      "Voltage before measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.35  0.    1.4   0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.51  0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Voltage after measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.35  0.    1.4   0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.5   0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(1.35,1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement ends.\n",
      "這次量測花費了 325.671285 分鐘\n",
      "DONE!!!\n",
      "\n",
      "Voltage before measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.35  0.    1.45  0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.51  0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Voltage after measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.35  0.    1.45  0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.5   0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(1.35,1.45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement ends.\n",
      "這次量測花費了 320.587343 分鐘\n",
      "DONE!!!\n",
      "\n",
      "Voltage before measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.4   0.    1.35  0.  ]\n",
      " [-0.    2.95  0.    2.97  0.    1.51  0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Voltage after measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.4   0.    1.35  0.  ]\n",
      " [-0.    2.95  0.    2.97  0.    1.5   0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(1.4,1.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement ends.\n",
      "這次量測花費了 315.735375 分鐘\n",
      "DONE!!!\n",
      "\n",
      "Voltage before measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.4   0.    1.45  0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.51  0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Voltage after measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.4   0.    1.45  0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.5   0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(1.4,1.45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement ends.\n",
      "這次量測花費了 329.013484 分鐘\n",
      "DONE!!!\n",
      "\n",
      "Voltage before measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.45  0.    1.35  0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.51  0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Voltage after measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.45  0.    1.35  0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.5   0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(1.45,1.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement ends.\n",
      "這次量測花費了 325.631677 分鐘\n",
      "DONE!!!\n",
      "\n",
      "Voltage before measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.45  0.    1.4   0.  ]\n",
      " [-0.    2.95  0.    2.97  0.    1.51  0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Voltage after measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.45  0.    1.4   0.  ]\n",
      " [-0.    2.95  0.    2.97  0.    1.5   0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(1.45,1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement ends.\n",
      "這次量測花費了 321.612669 分鐘\n",
      "DONE!!!\n",
      "\n",
      "Voltage before measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.45  0.    1.45  0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.51  0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Voltage after measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.45  0.    1.45  0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.5   0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(1.45,1.45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement ends.\n",
      "這次量測花費了 325.690800 分鐘\n",
      "DONE!!!\n",
      "\n",
      "Voltage before measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.4   0.    1.4   0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.51  0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Voltage after measurement : \n",
      "[[-0.   -0.    2.95 -0.    1.4   0.    1.4   0.  ]\n",
      " [-0.    2.95  0.    2.95  0.    1.5   0.    2.95]\n",
      " [ 0.   -0.    0.    0.   -0.   -0.    0.    0.  ]]\n",
      "Channel Voltage : \n",
      "[[-0. -0. -1. -0. -1.  0. -1.  0.]\n",
      " [-0. -1.  0. -1.  0. -1.  0. -1.]\n",
      " [ 0. -0.  0.  0. -0. -0.  0.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run(1.4,1.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 如有需要檢查電壓輸出可使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd.set_option('display.max_rows', None)\n",
    "# pd.DataFrame(voltage_log,columns=['v_set', 'v_actual', 'error','error_rate (%)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.set_printoptions(precision=3)\n",
    "# arr = (gate_voltage_apply_1.reshape(-1,1)-voltage_qdac_measure_xx[:, -1].reshape(-1,1))\\\n",
    "# /gate_voltage_apply_1.reshape(-1,1)*100 \n",
    "# #### 調 decimal 當作判斷標準 可以知道有幾組電壓誤差超過此標準\n",
    "# pd.DataFrame(np.around(arr,decimals=3),columns=['error_rate (%)'])  ## Ctrl+F 0.000 for quick find"
   ]
  }
 ],
 "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": "3.7.4"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}