3.pinch_off_SR830_and_K213_backandforth.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 掃Gate量IV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyvisa as visa\n",
    "import matplotlib.pyplot as plt\n",
    "import os, time, threading, datetime, pymeasure,tqdm\n",
    "from tqdm import trange,tqdm\n",
    "from time import sleep\n",
    "from datetime import date\n",
    "from IPython import display\n",
    "from scipy.optimize import curve_fit\n",
    "from pymeasure.adapters import VISAAdapter\n",
    "from pathlib import Path\n",
    "from pymeasure.instruments.keithley import Keithley2400, Keithley2000\n",
    "import qcodes.instrument_drivers.oxford.MercuryiPS_VISA as mercury_ips\n",
    "current_work_directory = os.getcwd()\n",
    "os.chdir('C:/Users/USER/Device_Library/Device_Library/')\n",
    "from my_SR830 import my_SR830\n",
    "from my_K213 import my_K213\n",
    "import device_query\n",
    "os.chdir(current_work_directory)\n",
    "now = datetime.datetime.now()\n",
    "# print(now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ASRL1::INSTR',\n",
       " 'ASRL2::INSTR',\n",
       " 'ASRL10::INSTR',\n",
       " 'GPIB0::5::INSTR',\n",
       " 'GPIB0::18::INSTR')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visa.ResourceManager().list_resources()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GOTO_THREAD(K213, port_id, target, time_shift):\n",
    "    print(\"Port ID = %d , target = %.4f .\"%(port_id, target))\n",
    "    time.sleep(time_shift)\n",
    "#     K213.GOTO(target, step, delay_time, reset_range=True,auto_range=True, reset_port=True, port=port_id)  old syntax!!\n",
    "    K213.GOTO(target, step, delay_time, port = port_id, reset_range=True,auto_range=True)\n",
    "    K213.GET_OUTPUT(port_id)\n",
    "    time.sleep(1)\n",
    "    \n",
    "def TO_ZERO_THREAD(K213, port_id, time_shift):\n",
    "    print(\"Port ID = %d , target = %.4f .\"%(port_id, 0.0))\n",
    "    time.sleep(time_shift)\n",
    "    K213.ZERO(step, delay_time, port_id)\n",
    "    K213.GET_OUTPUT(port_id)\n",
    "    time.sleep(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讀取830和213"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Port 1 is selected.\n"
     ]
    }
   ],
   "source": [
    "SR830_GPIB_Addr = 5\n",
    "K213_GPIB_Addr = 18\n",
    "srs_source = my_SR830.MY_SR830(SR830_GPIB_Addr)\n",
    "K213 = my_K213.MY_KEITHLEY213(K213_GPIB_Addr, port=1)\n",
    "port_list = [1,2,3,4]  #輸入要固定的port(要同時調整的PORT)\n",
    "## 1 for tuning and 2 is fixed\n",
    "#[1,2,3,4] 如果多P3,P4需要當GATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相關設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "要不要來回掃? Y/N >>> Y\n",
      "(來回算一次，要幾次?(輸入數字 ex. 1, 2, 3, 4, ...) >>> 1\n",
      "\n",
      "掃過的電壓值如下\n",
      "\n",
      "[2.5        2.48995984 2.47991968 2.46987952 2.45983936 2.4497992\n",
      " 2.43975904 2.42971888 2.41967871 2.40963855 2.39959839 2.38955823\n",
      " 2.37951807 2.36947791 2.35943775 2.34939759 2.33935743 2.32931727\n",
      " 2.31927711 2.30923695 2.29919679 2.28915663 2.27911647 2.26907631\n",
      " 2.25903614 2.24899598 2.23895582 2.22891566 2.2188755  2.20883534\n",
      " 2.19879518 2.18875502 2.17871486 2.1686747  2.15863454 2.14859438\n",
      " 2.13855422 2.12851406 2.1184739  2.10843373 2.09839357 2.08835341\n",
      " 2.07831325 2.06827309 2.05823293 2.04819277 2.03815261 2.02811245\n",
      " 2.01807229 2.00803213 1.99799197 1.98795181 1.97791165 1.96787149\n",
      " 1.95783133 1.94779116 1.937751   1.92771084 1.91767068 1.90763052\n",
      " 1.89759036 1.8875502  1.87751004 1.86746988 1.85742972 1.84738956\n",
      " 1.8373494  1.82730924 1.81726908 1.80722892 1.79718876 1.78714859\n",
      " 1.77710843 1.76706827 1.75702811 1.74698795 1.73694779 1.72690763\n",
      " 1.71686747 1.70682731 1.69678715 1.68674699 1.67670683 1.66666667\n",
      " 1.65662651 1.64658635 1.63654618 1.62650602 1.61646586 1.6064257\n",
      " 1.59638554 1.58634538 1.57630522 1.56626506 1.5562249  1.54618474\n",
      " 1.53614458 1.52610442 1.51606426 1.5060241  1.49598394 1.48594378\n",
      " 1.47590361 1.46586345 1.45582329 1.44578313 1.43574297 1.42570281\n",
      " 1.41566265 1.40562249 1.39558233 1.38554217 1.37550201 1.36546185\n",
      " 1.35542169 1.34538153 1.33534137 1.3253012  1.31526104 1.30522088\n",
      " 1.29518072 1.28514056 1.2751004  1.26506024 1.25502008 1.24497992\n",
      " 1.23493976 1.2248996  1.21485944 1.20481928 1.19477912 1.18473896\n",
      " 1.1746988  1.16465863 1.15461847 1.14457831 1.13453815 1.12449799\n",
      " 1.11445783 1.10441767 1.09437751 1.08433735 1.07429719 1.06425703\n",
      " 1.05421687 1.04417671 1.03413655 1.02409639 1.01405622 1.00401606\n",
      " 0.9939759  0.98393574 0.97389558 0.96385542 0.95381526 0.9437751\n",
      " 0.93373494 0.92369478 0.91365462 0.90361446 0.8935743  0.88353414\n",
      " 0.87349398 0.86345382 0.85341365 0.84337349 0.83333333 0.82329317\n",
      " 0.81325301 0.80321285 0.79317269 0.78313253 0.77309237 0.76305221\n",
      " 0.75301205 0.74297189 0.73293173 0.72289157 0.71285141 0.70281124\n",
      " 0.69277108 0.68273092 0.67269076 0.6626506  0.65261044 0.64257028\n",
      " 0.63253012 0.62248996 0.6124498  0.60240964 0.59236948 0.58232932\n",
      " 0.57228916 0.562249   0.55220884 0.54216867 0.53212851 0.52208835\n",
      " 0.51204819 0.50200803 0.49196787 0.48192771 0.47188755 0.46184739\n",
      " 0.45180723 0.44176707 0.43172691 0.42168675 0.41164659 0.40160643\n",
      " 0.39156627 0.3815261  0.37148594 0.36144578 0.35140562 0.34136546\n",
      " 0.3313253  0.32128514 0.31124498 0.30120482 0.29116466 0.2811245\n",
      " 0.27108434 0.26104418 0.25100402 0.24096386 0.23092369 0.22088353\n",
      " 0.21084337 0.20080321 0.19076305 0.18072289 0.17068273 0.16064257\n",
      " 0.15060241 0.14056225 0.13052209 0.12048193 0.11044177 0.10040161\n",
      " 0.09036145 0.08032129 0.07028112 0.06024096 0.0502008  0.04016064\n",
      " 0.03012048 0.02008032 0.01004016 0.         0.         0.01004016\n",
      " 0.02008032 0.03012048 0.04016064 0.0502008  0.06024096 0.07028112\n",
      " 0.08032129 0.09036145 0.10040161 0.11044177 0.12048193 0.13052209\n",
      " 0.14056225 0.15060241 0.16064257 0.17068273 0.18072289 0.19076305\n",
      " 0.20080321 0.21084337 0.22088353 0.23092369 0.24096386 0.25100402\n",
      " 0.26104418 0.27108434 0.2811245  0.29116466 0.30120482 0.31124498\n",
      " 0.32128514 0.3313253  0.34136546 0.35140562 0.36144578 0.37148594\n",
      " 0.3815261  0.39156627 0.40160643 0.41164659 0.42168675 0.43172691\n",
      " 0.44176707 0.45180723 0.46184739 0.47188755 0.48192771 0.49196787\n",
      " 0.50200803 0.51204819 0.52208835 0.53212851 0.54216867 0.55220884\n",
      " 0.562249   0.57228916 0.58232932 0.59236948 0.60240964 0.6124498\n",
      " 0.62248996 0.63253012 0.64257028 0.65261044 0.6626506  0.67269076\n",
      " 0.68273092 0.69277108 0.70281124 0.71285141 0.72289157 0.73293173\n",
      " 0.74297189 0.75301205 0.76305221 0.77309237 0.78313253 0.79317269\n",
      " 0.80321285 0.81325301 0.82329317 0.83333333 0.84337349 0.85341365\n",
      " 0.86345382 0.87349398 0.88353414 0.8935743  0.90361446 0.91365462\n",
      " 0.92369478 0.93373494 0.9437751  0.95381526 0.96385542 0.97389558\n",
      " 0.98393574 0.9939759  1.00401606 1.01405622 1.02409639 1.03413655\n",
      " 1.04417671 1.05421687 1.06425703 1.07429719 1.08433735 1.09437751\n",
      " 1.10441767 1.11445783 1.12449799 1.13453815 1.14457831 1.15461847\n",
      " 1.16465863 1.1746988  1.18473896 1.19477912 1.20481928 1.21485944\n",
      " 1.2248996  1.23493976 1.24497992 1.25502008 1.26506024 1.2751004\n",
      " 1.28514056 1.29518072 1.30522088 1.31526104 1.3253012  1.33534137\n",
      " 1.34538153 1.35542169 1.36546185 1.37550201 1.38554217 1.39558233\n",
      " 1.40562249 1.41566265 1.42570281 1.43574297 1.44578313 1.45582329\n",
      " 1.46586345 1.47590361 1.48594378 1.49598394 1.5060241  1.51606426\n",
      " 1.52610442 1.53614458 1.54618474 1.5562249  1.56626506 1.57630522\n",
      " 1.58634538 1.59638554 1.6064257  1.61646586 1.62650602 1.63654618\n",
      " 1.64658635 1.65662651 1.66666667 1.67670683 1.68674699 1.69678715\n",
      " 1.70682731 1.71686747 1.72690763 1.73694779 1.74698795 1.75702811\n",
      " 1.76706827 1.77710843 1.78714859 1.79718876 1.80722892 1.81726908\n",
      " 1.82730924 1.8373494  1.84738956 1.85742972 1.86746988 1.87751004\n",
      " 1.8875502  1.89759036 1.90763052 1.91767068 1.92771084 1.937751\n",
      " 1.94779116 1.95783133 1.96787149 1.97791165 1.98795181 1.99799197\n",
      " 2.00803213 2.01807229 2.02811245 2.03815261 2.04819277 2.05823293\n",
      " 2.06827309 2.07831325 2.08835341 2.09839357 2.10843373 2.1184739\n",
      " 2.12851406 2.13855422 2.14859438 2.15863454 2.1686747  2.17871486\n",
      " 2.18875502 2.19879518 2.20883534 2.2188755  2.22891566 2.23895582\n",
      " 2.24899598 2.25903614 2.26907631 2.27911647 2.28915663 2.29919679\n",
      " 2.30923695 2.31927711 2.32931727 2.33935743 2.34939759 2.35943775\n",
      " 2.36947791 2.37951807 2.38955823 2.39959839 2.40963855 2.41967871\n",
      " 2.42971888 2.43975904 2.4497992  2.45983936 2.46987952 2.47991968\n",
      " 2.48995984 2.5       ]\n"
     ]
    }
   ],
   "source": [
    "gate_voltage_apply_1,n = [],'no input'\n",
    "\n",
    "min_V = 2.5   #initial voltage\n",
    "max_V = 0    #final voltage\n",
    "data_points = 250  #resolution per sweep\n",
    "\n",
    "forth = np.linspace(min_V, max_V,data_points)\n",
    "back = np.flip(forth,0)\n",
    "gate_voltage_apply_1 = forth\n",
    "\n",
    "flag = input(\"要不要來回掃? Y/N >>> \")\n",
    "if flag == 'Y':\n",
    "    n = eval(input(\"(來回算一次，要幾次?(輸入數字 ex. 1, 2, 3, 4, ...) >>> \"))\n",
    "    for i in range(int(n)-1):\n",
    "        gate_voltage_apply_1 = np.append(gate_voltage_apply_1,back)\n",
    "        gate_voltage_apply_1 = np.append(gate_voltage_apply_1, forth)\n",
    "    gate_voltage_apply_1 = np.append(gate_voltage_apply_1,back)\n",
    "else:\n",
    "    gate_voltage_apply_1 = forth # for 213\n",
    "print(\"\\n掃過的電壓值如下\\n\")\n",
    "print(gate_voltage_apply_1)\n",
    "\n",
    "#gate_voltage_apply_1 = np.linspace(min_V, max_V,data_points) # for 213 port 1\n",
    "gate_voltage_apply_2 = min_V # for 213 port 2,3,4 (other gates, which are kept constant)\n",
    "\n",
    "\n",
    "measure_wait_time=1  #電壓到時等一段時間穩定後再量測\n",
    "step = 0.005         #調變電壓每一步的大小\n",
    "delay_time = 0.1    #每次調變所等待的時間\n",
    "N_ave = 10           #在一個電壓職重複量測幾次\n",
    "ave_wait = 1         #Lock-in 要等0.5秒才能再量\n",
    "\n",
    "#畫圖\n",
    "fontsize = 8\n",
    "drawing_resolution=10\n",
    "\n",
    "#分壓比例(Source Drain SR830)\n",
    "divider_ratio= 1e-5\n",
    "#放大器比例(Source Drain SR830)\n",
    "Amplifier_gain= 1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "print(gate_voltage_apply_1.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 830相關設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 = 1e-3 # 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)   #lockin test ramp\n",
    "# Lock-in 放大率(到K2000需要乘上倍數)\n",
    "# senstivity 2mv = 5000倍\n",
    "# 10V / senstivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 路徑設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "檔名會叫做 >> AG2_pinch_off_backandforth_others_at_2.5V_repeat0_at_2020-11-13\n"
     ]
    }
   ],
   "source": [
    "repeat = 0\n",
    "pin_name = \"AG2\"\n",
    "folder_path = \"C:/Users/USER/Sean/2020-11-10 QD pinch_off/\" ##儲存的地方\n",
    "file_name = '%s_pinch_off_backandforth_others_at_%sV_repeat%s_at_%s'%(pin_name,gate_voltage_apply_2, repeat,date.today())##儲存的檔名\n",
    "\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':\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  !!!!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 213歸零"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start...\n",
      "Port ID = 1 , target = 0.0000 .Port ID = 2 , target = 0.0000 .\n",
      "Port ID = 3 , target = 0.0000 .\n",
      "Port ID = 4 , target = 0.0000 .\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-20:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\USER\\Anaconda3\\lib\\threading.py\", line 926, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"C:\\Users\\USER\\Anaconda3\\lib\\threading.py\", line 870, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-16-ed4b6c856ddf>\", line 13, in TO_ZERO_THREAD\n",
      "    K213.GET_OUTPUT(port_id)\n",
      "  File \"C:\\Users\\USER\\Device_Library\\Device_Library\\my_K213\\my_K213.py\", line 51, in GET_OUTPUT\n",
      "    voltage = eval(voltage)\n",
      "  File \"<string>\", line 1\n",
      "    00.00000V+00.00000\n",
      "            ^\n",
      "SyntaxError: invalid syntax\n",
      "\n",
      "Exception in thread Thread-21:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\USER\\Anaconda3\\lib\\threading.py\", line 926, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"C:\\Users\\USER\\Anaconda3\\lib\\threading.py\", line 870, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"<ipython-input-16-ed4b6c856ddf>\", line 12, in TO_ZERO_THREAD\n",
      "    K213.ZERO(step, delay_time, port_id)\n",
      "  File \"C:\\Users\\USER\\Device_Library\\Device_Library\\my_K213\\my_K213.py\", line 101, in ZERO\n",
      "    self.GOTO(0.0, step, delay_time, port)\n",
      "  File \"C:\\Users\\USER\\Device_Library\\Device_Library\\my_K213\\my_K213.py\", line 85, in GOTO\n",
      "    now_value = self.GET_OUTPUT(port)\n",
      "  File \"C:\\Users\\USER\\Device_Library\\Device_Library\\my_K213\\my_K213.py\", line 51, in GET_OUTPUT\n",
      "    voltage = eval(voltage)\n",
      "  File \"<string>\", line 1\n",
      "    1C0P3R0V+00.00000\n",
      "           ^\n",
      "SyntaxError: invalid syntax\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "P1 is now at 0.0 Volts\n",
      "P2 is now at 0.0 Volts\n",
      "P3 is now at 0.0 Volts\n",
      "P4 is now at 0.0 Volts\n"
     ]
    }
   ],
   "source": [
    "threads = []\n",
    "shift = 4e-2 #同時給兩個port會當機所以要有一個小時間差\n",
    "\n",
    "print(\"Start...\")\n",
    "for i in range(len(port_list)):\n",
    "    time_shift = shift*i\n",
    "    threads.append(threading.Thread(target = TO_ZERO_THREAD, args = (K213, port_list[i], time_shift)))\n",
    "    threads[i].start()\n",
    "\n",
    "for i in range(len(port_list)):\n",
    "    threads[i].join()\n",
    "\n",
    "print(\"Done!\")\n",
    "print('P%d is now at'%port_list[0],K213.GET_OUTPUT(port=1),'Volts')\n",
    "print('P%d is now at'%port_list[1],K213.GET_OUTPUT(port=2),'Volts')\n",
    "print('P%d is now at'%port_list[2],K213.GET_OUTPUT(port=3),'Volts')\n",
    "print('P%d is now at'%port_list[3],K213.GET_OUTPUT(port=4),'Volts')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 將固定電壓先給好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start...\n",
      "Port ID = 1 , target = 2.9940 .\n",
      "Port ID = 2 , target = 2.9940 .Port ID = 3 , target = 2.9940 .\n",
      "Port ID = 4 , target = 3.4722 .\n",
      "\n",
      "Done!\n",
      "P1 is now at 2.99375 Volts\n",
      "P2 is now at 2.99375 Volts\n",
      "P3 is now at 2.99375 Volts\n",
      "P4 is now at 3.47125 Volts\n"
     ]
    }
   ],
   "source": [
    "#srs_source.GOTO(SD_voltage_apply, srs_goto_step, srs_goto_delay_time)       ##### SR-830設定(固定SD)\n",
    "\n",
    "threads = []  ###除上經過濾波器後遞減的電壓\n",
    "target = [min_V/0.835,gate_voltage_apply_2/0.835,gate_voltage_apply_2/0.835,gate_voltage_apply_2/0.72] #分別目標值 (有幾個PORT打幾個)\n",
    "shift = 0.1 #port 和 port之間開始便電壓的時間間隔\n",
    "\n",
    "print(\"Start...\")\n",
    "for i in range(len(port_list)):\n",
    "    time_shift = shift*i\n",
    "    threads.append(threading.Thread(target = GOTO_THREAD, args = (K213, port_list[i],target[i],time_shift)))\n",
    "    threads[i].start()\n",
    "for i in range(len(port_list)):\n",
    "    threads[i].join()\n",
    "print(\"Done!\")\n",
    "print('P%d is now at'%port_list[0],K213.GET_OUTPUT(port=1),'Volts')\n",
    "print('P%d is now at'%port_list[1],K213.GET_OUTPUT(port=2),'Volts')\n",
    "print('P%d is now at'%port_list[2],K213.GET_OUTPUT(port=3),'Volts')\n",
    "print('P%d is now at'%port_list[3],K213.GET_OUTPUT(port=4),'Volts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 到開始量測點後開始量測"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:54: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:62: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq0AAAFxCAYAAABZSJFiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAewgAAHsIBbtB1PgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd7xcVbn/8c+TctIgpBCSkAKEhBoIJQEREAi9CZei9CoozYINFLzoFeEKws8CKtdLUQNRVESpSvHShAAJSCcEAichQCAJgfTy/P5Yezxz5uwpe2ZPO+f7fr32a2bvvdbaaw8T5jlrr2LujoiIiIhII+tW7wqIiIiIiBSjoFVEREREGp6CVhERERFpeApaRURERKThKWgVERERkYanoFVEREREGp6CVhERERFpeApaRURERKThKWgVERERkYanoFVEREREGp6CVhERERFpeApaRURERKThKWgVERERkYanoFVEREREGp6CVhERERFpeApaRURERKThKWgVERERkYanoFVEREREGp6CVhERERFpeApaRURERKThKWgVScDMNjCzQ8zse2Z2t5m9b2YebTfWu35pSeM+zaybmW1lZqeY2bVm9qSZrcgqZ8/q3oWIiHQmPepdAZEm8269K1AjadznicCNKZQjIiKillaRCrQCf6t3JWqg3Pu0rPergBnAc6nUSEREuhwFrSLJfA84FBjm7qOBz9e5PtWSxn2+CHwJ2AXo7+47AH9Kr4oiItKVqHuASALu/p/1rkMtpHGf7j4NmJZCdURERNTSKlIvZtbDzE43s7vM7O1okNL7ZvaQmX3ZzHrXu44iIiKNQi2tInVgZpsCfwG2yjk1GNg92s42s4PdfWat6yciItJoFLSK1JiZDQceBYYCHwHXAfcRRuyvB+xH6As6DrjHzHZw9w/rVF0REZGGoKBVpPauIwSsrcCe7v56zvl/mNmtwMPAGOBrwMW1raKIiEhjUZ9WkRoys/HAIdHuuTEBKwDuPgO4Jto9rRZ1ExERaWQKWkVq67DodSlwZ5G0D0WvG5rZqOpVSUREpPGpe4BIbU2MXvsCq82sUNpswwjdCQAws32Av6dQn03cfXYK5YiIiFSVWlpFamuDMvP1TbUWIiIiTUYtrSK11T16fQP4dIJ8b+TsPwZsmUJ95qZQhoiISNUpaBWprQ+i16HAy+6+upxC3H0p8HJqtRIREWlw6h4gUlszote+wK71rIiIiEgzUdAqUlu3Z73/Rt1qISIi0mQUtIrUkLs/Cfwt2j3IzL5bKL2ZbWxmx1a/ZiIiIo1NfVpFEjCz3YCxWYfWz3o/1sxOyU7v7jfGFHMq8BQwHPiOme0PXA88BywHBgPbAgcAk4E/A7ekcwelSek+yU0HbJf1/gAz2zhr/zV3fyRhVUVEpIswd693HUSahpndCJxcanp3j52I1cw2Am4FJpVQzA3uXtNVsVK8zyT/g7nJ3U9JkF5ERLoQtbSK1IG7v2lmOxNWyPossDNhRoGewCJgJvBP4C/Aw/Wqp4iISKNQS6uIiIiINDwNxBIRERGRhqegVUREREQanoJWEREREWl4ClpFREREpOEpaBURERGRhqcpr0TyMLNewDbR7nxgTR2rIyIi0ky6A0Oi98+5+4pKC1TQKpLfNsCT9a6EiIhIk5tEWAmyIuoeICIiIiINTy2tIvnNz7yZNm0aw4cPr2ddREREmsa8efPYaaedMrvzC6UtlYJWkfz+3Yd1+PDhjBw5sp51ERGRrmLWLDj0UHj3XTjzTLjssnrXqFKpjAlR9wARERGRRnLggfDSS7BgAVx+OTzySL1r1BAUtIqIiIg0io8/hpkz2x/70Y/qU5cGo6BVREREpFHccEPHY88/X3m5110He+wB3/wmLF1aeXl1oKBVUmPBbmZ2hZk9bmaLzGylmb1tZn80s73KKPMSM/Mi2xbVuB8REZGa+93vOh5bsqSyMh98ED7/eXjoIfjhD+GnP62svDrRQCxJ02Tgvuj9WuA1YAkwDjgCOMLMvu/uF5dRdivwVp5zzfkno4iISMbw4fDOO/HnFiyorOwvfan9/rXXhhbXJqOgVdJkhED1KmCquy8EMLMW4BLgQuAiM3vC3e9IWPb17n5JinUVERFpDDNm5A9YAVasgNWroUeCsG3y5NDCGuetfG1AjU3dAyRN04At3f3nmYAVwN1Xuvu3gLujQ2fUpXYiIiKNaIcdiqeZMaP08qZPzx+wZixaVHp5DUJBq6TG3Re7++oCSf4evW5Wi/qIiIh0Gg8/XHraHXcsnua118qvS52oe4DUUu/odVkZefcys62BwcACQqvur929wPOUwsys2GoBw8otW0REJFXTp5eWzqy0dDNnwsSJ5denDhS0Sk2YmQFHR7uPllHEp3L2jwQuMbOz3f3GMqvVWmY+ERGR2nr11XTLO+64sAG4p1t2lah7gNTKGcD2wErg/yXINw/4ATCJ0MraF9iV0D+2D3C9mR2ablVFREQazJw51Sv7iCOqV3aK1NIqVWdmOwA/jnYvcvdZpeZ191/GHH7MzA4G/gj8B3C1md3hnvhPxVFFzg8DnkxYpoiISOmuv760dKVMe9XSUl4dbrstvJ55ZliEoEFZ8t95kdKZ2SaE7gDDgZuBE8oILvOVvRnwSrS7nbs/m0a5WeWPJOpC0NraysiRxbrAioiIJFRqH1SAVasKT3uVpKx8eveGZeUMPWlvzpw5jBr177ahUe5ecVOxugdI1ZjZMMKMAcOBO4FT0gpYAdz9VcKgLICxaZUrIiLSkGbOrP41li+v/jXKpKBVqsLMBhEC1k2B/wOOdvdVVbhUpkx1dRERkc7txRdrc50VK2pznYQUtErqzGwd4C5gPKFP6KHuXvmzho7XWR/YINqtYg91ERGRBlCopTWNrgEZlS4bWyUKWiVVZtYLuB3YGXgBOMDdP6rS5c4nLB37IRowJSIind3s2fHH0wxYAYYPT7e8lCholdSYWXdgKjAZmAXs6+5F/1wzsy+b2Wwzm5pzfGszuzZaVCD7eG8z+xbwzejQf7v7ynTuQkREpEG1Vnl68SuuaOg5W9UPUNL0GeDw6P1a4FaL/+tvnrsfnbU/ANgImJ2TridwFnCWmc0H3oqOb0mYrxXgf4HLK665iIhIrSVtIX333crLyGevveCrX02nrCpR0Cpp6pX1fly0xXmzxPJmAxcDnwS2ADYHWoD3CH1mf+Xu95ZVUxERkWbz/vvVK3v//dPvZpAyBa2dSDQwaQQwhLB61DJgfrS97u5rq3n9aDnVG8vIdwlwSczxRcD3K6yWiIhI57B4cXn5zIo/9p84sbyya0hBaxMzs3WBw4A9gd0pPFfpEjN7HHgYuNPdp1e/hiIiIpKapUuT51l//TDrwIABMHkyvPACvPdex3Q77FB5/apMA7GakJntaGa/Bt4BbgJOJTyKtwLbOsDehBbNJ83sRTM7x8z61f4OREREurhyRuivzBlz/KtfFc9zyy0hYAV44AH45Cc7phk7FgYOTF6fGlNLaxMxsx0Jj8v3yxyKXucRpnx6mtDfcwGwEOgDDAIGApsBk4BtCQOctgB+AlxiZlcAP3b3xpxNWEREpLN5553kedxDF4H+/cP+GWcUz7PNNu33t94a/vzn9seaoGsAKGhtGmZ2A3Aiba3j04EpwB/d/a28GTuW0wJ8CjgO+A9C39fLCCP0T3T3R1KtuIiIiLQf5FTJtFIvvQQ771xa2iFDYOjQ9sfGj++YrkmCVnUPaB4nA6uB/wG2cPeJ7n51koAVwN1Xuvt97n4aMAw4CXiFMOXU5LQrLSIi0uXljsqvZJT+yy+XnjYuQN16647HJkwovz41pKC1efwcGOvun3f3V9Mo0N1XuPtvga2BY4AC68OJiIhI1fXqVbgl9rXXSi8rt2sAwOabwwYbtO337w+f+ETpZdaRgtYm4e7nuPucKpXt7v57d7+lGuWLiIhIidZbr/D5N6OpzktprY0LWlta4Je/DIHroEHh/TrrJK9nHahPq4iIiEi1xAWOhWyxReHzc+dWfu3DDw9bk1FLaxdlZpPM7Kf1roeIiEin9vzzydIfHa1yPmxY/Pm4OVbzieu/2sQUtHYhZjbSzC40sxeBx4Gz610nERERyXL66eF13rz48x98UFo5Eyc2zWP/Uql7QCcXLR5wFGGWgD1om9vVgArm3BAREZFUdesGffoUTrNwYfH+rGPGwI9/nF69GoSC1k7IzAzYhxCoHg70zZyKXucAfwL+WPvaiYiISKxig7AAli8vfH7cuDCXa/fu6dSpgSho7UTMbGvCfK7HAZn14TKB6lvArcAf3P2JOlRPRERECtl888rLGDasUwasoKC16ZnZEOB4QqtqZnbgTKC6CBhA6AbwDXf/fe1rKCIiIiU54ID2+926wdq1ycoYMSK9+jQYBa1NyMx6AYcRAtX9gO60BaorgLuA30avy+pRRxERkS4v6XRXn/98+/01a5KvnrXJJsnSNxEFrU3EzHYjBKpHA/0zhwktqQ8TAtXfu/uHWXlqXU0RERGB5NNd5ZvmKok0uhg0KAWtzeUhQoCaiURfBKYAU9z9rbrVSkRERCqz7rrplNPJ5mbNpqC1OX0EfMndb6x3RURERCQFhx6aTjnjx6dTTgPS4gLNx4B1gP81s3+Z2TfMbGS9KyUiIiIJbbEFtLTApEnw61/Hp/EEU6p36wa9e6dTtwakoLW57A78CviQELyOBy4DZpvZg2Z2mpn1L1SAiIiINIDu3cN8qitWwLRphaepci9tQFavXunVrwEpaG0i7v6ou58JDAM+S5gdYA3hv+OngP8B3jGzW83s02am7h8iIiKNqG/f4mmyDRxYPE1a/WIblILWJuTuK939Vnc/BBgBfBX4F6H1tTdwBHAb8G79aikiIiJ5DR6cLP3ppxdPM2hQeXVpEgpam5y7z3f3q919e2Bb4CrgHUIAO5Aw2wDAVWb2YzPbvU5VFRER6Tqeeabw+TFjkpX3wx/C2WcXTtOJFxYABa2dirs/7+5fA0YBBwFTgeWEAHZD4FzgH2Y2z8yuNbO961dbERGRBmXWfivFmjUweXIYCLXFFrD99oXTT5yYvF7XXAOjR+c/f8QRyctsIgpaOyF3X+vu97j7cYT+r2cSFh+AEMAOBT4P3FunKoqISGfXq1cI+Pr1Ky//CSe0DxxPOKF4nksugR12gFmzyrsmwLPPdjx2zDHF851/Pjz4YBhY9corxdMfeGDyugHsuWf+c6edVl6ZTcI8yVQK0tTMbGPCilonAGMBd/cCwxW7tmgqsVaA1tZWRo7UzGIiIiXp1w+WLm3bHzkSWluTlZGvhTNf3DJ+PLzwQtv+jBmw3XbJrlnOdTOGDYN3EwwlWb268IwB+TzyCOwe09NvxAiYMyd5eVUyZ84cRo0aldkd5e4VV04trV2Iu8929++5+2aE6bP+p951EhGRTig7YIV0g6m4Fte7724fsALstVd614T4FthsH35Y+Hw2s/ICVoDddovPu88+5ZXXRBS0dlHR9FlfqHc9RESkSQ0aBJ/+dMfjBx1U3etOmRJeM90GjjsufjWpRYvSvW6xVtuVK0svq6WlsrrEzRJwzjmVldkEFLQ2CTM7usrljzSzT1bzGiIi0kmYwcKF8Ne/dnycfvfd8XkefDBZ+aWev+WWMAgqTm6LbzWtXVt62vXWq+xap5zSfr9377CqVienoLV5/M7Mnks7eDWz0Wb2c+A1oPM/WxARaVbduoVgbe86T/wSF1Bmjq1YkT9fPVoCazUw6a23kqUfNqyy6/3gB+1bWy+9tLLymoSC1uYxE9gamGpms83sB2a2dTkFmVk/MzvBzO4mBKtnAt2j9yIi0mjM2gYCPfBAfODYt2/bI/Mvfam29YPQ0vfJAg/sZs6sXV0y7rwz3fJy+7XuvHP4vDfaKFk5Y8dWVo8ePWD+fLj11jDA7fzzKyuvSWj2gCYRLcn6ReACYH3aFg2YCTwOPAnMAN4DFkZbH2AQYZGBzYBJwE7R1psw/RXA3cA33f35WtxLs9DsASLSMIqNaD/9dLj++vbn/vUv2Gab2tUFQl/NQn07S405Sp0btRRJ45xC1+7WrX1XhHLr+b3vwcUXl5e3SVRj9gCtTd8k3H01YVWrXwJnA+cAownB6DjgxBKLyvwLWwPcDlzh7k+kXF0REUlLoUfc668P778PN93U8dy22yYP2Iq54orC55MMRqqVWbNg003jz2WCzmeegQkTipe1dm1YfnXu3MpaN/fYo/y8XZhaWpuUmXUD9gU+A+wFbFxCtmXANOBO4GZ3f7tqFewE1NIqIg2hWGuee/40kybBtGm1q0sx99wD++9fOM0JJ7TNEJCG0aPhzTc7Hs+9l0zgf/LJxcvs2RNWrSq/TuXO0dpEqtHSqqC1kzCzEcAngZHAEEK3gOXA/Gh7DnjK3Sv4V9a1KGgVkYZQSdCaOV+ruhQzZAi89151rxEn9zOoxjWS6AKxl7oHSF7uPhe4td71EBGRGluwoN41KN38+fW57oUXwmWXhfc96hz61Pv6TUyzB4iIiDSzI4+szXXKedrULSbM+Na3Kq9LUpdf3jazQr45XWuld+/6Xr+JKWgVERFpVD17Fk/z2GOFzy9blk5d5s5Nniduwv3//u/K69LMBgyodw2aloJWERGRRrV6dfE0xUbsn3tuOnVJS6GVozbcsHb1qJeucI9VoqBVRESkM7vjjtLSnXJKmCy/Fn74w/jj8+bV5vr1tNlm9a5B01LQKiIi0pl98EHxNGZhyqdp09r6fr7wQtv5qVPTrVNmvterr4bf/CbdshvFKafEH99xx5pWozPRlFcieWjKKxGpu7SmZir0W1+v6Z969Gjr/tCvH3z8cf2nokrDoEFtfyjE3c9zz8H48bWtUx1UY8ortbSKiIh0Rd/5TmkDvaolu7/ukiXwwAP1q0s+cSuNFVPs8X8XCFirRUGriIhII3rppeqVfeON8F//VdpAr1rZe+/Kyyj16XHfvsXTDBsGJ52UvA777NP2Pm7KLymbPk0REZFGNGlS9co+7bR0yvn0p9Mpp1z9+sFHH4VgNROwlhK4XnJJ8a4I3/9+eXU644y298cc0/7cOuuUV6YAClpFREQa05Il6ZV1113t99MYz9LSAtdcUzjNwIGVXydbz55hhoEjj4SZM0M/2LhA0D0EtPl8/etw9NGFr3X66eF1//2T1XH06Lb3N9wA664b3pvBT3+arCxpR0FrEzGzY81sS7PO0FNdREQA+PGP20bsZ7a0fec7be9nz06nzCeeKLxKlntYYnbEiHSuByEAHDYM/vAHGDu2cNohQwqf/+1voX//+HPZgeo995Revz592u+3tMDixbB0aZifNt+MAlISLYDbXKYADiw3s+eBZ4AZ0eu/3H1pPSsnIiIJpTk7QKGynnuu7f2+++ZP16sXrFhR/HrdusF22+UvY/nytv05c8q7z+HDO87buskmpeffYov4AD2zjGrPnvDwwzBhQvvzO+3UMVAt9vlmDBoUfzw3mJWyqKW1+RjQB5gIfA64BngUWGxmL5vZVDP7ppntb2ZD61lREREpoFAQlCTIO/XU8JrdrzNX9qpZr72Wv6xSAlZo33Xhu99tf+7DD0sro5jccqH9IKdiDjkk/viWW7a933ZbmDIlLK06dCjcf39oQY7zzDPFr/nhh7BqVel1lEQ0T2sTMbMvANtF23ggd/hj3H/M92jfIvuMu79azXp2FpqnVUSqZvvtSwuCion7DS8U8D7/fGVTLo0aBW+9FX9uxYrQyhqnnJbW/faDv/2t/bF580L3gFIsXRrfr/UPfwh9Yss1ciTMnRt/7oknQkutVGWeVgWtTSrq17o5bUHsdsAEIK51Nfc/8lLgOWCGu59TzXo2MwWtIpKaN96A664LI9K7d6/uogFp9omdMSN/N4AkyqnThhvC22+3P5Y0Zom7bqVxz+rVoa9qbjnf+x5cfHFlZXciWlxA/s2Dl919qrtf4O4HuPtwYDhwEPAt4PfAq4Sg1bK2fsAngC/Up/YiIl3IAQfAmDFw+eVhFajFi9MpN18/yVtvTaf8Xr3SCVjLMWVKGMTViHr0CIO4sl12mQLWGlBLaxdgZn0IrbDZrbLjgT7u3r2edWtkamkVkYoddRT88Y/VKXvvveG+++LPVdLa2tIS+oOmWe+4+rzyCmy+eXx699AivXZtx+NJ9OgBa9a07Q8YAAsXJisjn+XL4Y47wmeVGdwl/1aNllbNHtAFuPsy4PFoA/7dvaDIWnMiIlK2Xr3aD4AqZODA5MFUoSVGTz01zBGa1CabwOuvJ89XjmLLneYGrOUE4t/9Llx0Udv+ww8nLyOf3r3DHyVSM+oe0EVF3QteqXc9RESaSu58qtlb9+6h7+p224X9UgNWCI/C99svWV0KzX96/fWlDbgaOzZMhm8GkyfXLmAFmDo1/7mHHup4LN90UoV8+9swfz7cdlv471HJIDSpO3UPEMlD3QNEBAjB1cknJwtCk8r8FidpTVy7tnj6YudrFQMkua8pU+Cb3wzzu2abOhU++9l06yVVo4FYIiIiSb3ySltraOZRcc+ehVtNM0FW795w7LHVDVjLVcqcqu+8U/16lOIrXyk97XHHdQxYQQGrKGgVEZFOrKUlrIyUcemlISBdvbp4XrPSJ9tPw9IEixr27Vva4J+hQ/O3pq63XunXq9RVV5We9t57Ox5raUmvLtK0FLSKiEhlzjoLBg/OP+l8UmvWhGmb4oKtO+5o3xo6eXL+cszqvzrRqafCBhsUfjyeuc8kQeT8+cnq4R4WFsjo3RsWLUpWRq0cemjHY2plFRS0iojU3jnnhKAh7pF0s61R3tICv/hFGEi00Uawxx7F83z3u2HqoQEDOt7/L38Zpin6zGfC+vaDB7fl6969Y0Dz4INhhaJcrzTIONMbboD33iut72gprb8QWk/75i6IWIKtt25b6nXZsuT5ayXuD41f/ar29ZCGo6BVRKQWXnsNfvSjEJhde23+x87Ll4c0O+5YvMzXX+8Y9GUeo1baB3P5cnjyycJpvvzljgHGQw+F0ej5mMEll4Q12uPWqP9CzponCxa0tVLmToGUMXcuvBqtTn3uufDzn7fvEpCmjTbqeOzjj8sv77vfTZ6nUfqpJpG7slUS2d9r6dIUtIpI85s6NbS4QWj1698//NDtvnvHtIcc0hbg9esHX/xiOq1OxQb1jBsHX/ta6eVNn95+QFDc9TbdtOPxVavCuXxrwMdZtCi+xXenneCll0KLaPbKSIMHhzQ//nF8ea2t4Xx2K2mmzuUqlnfzzUOaa66Bs89OVnam9dEdXn65cLrZs9undw/fo25l/pz+53+G13326Xhuiy06dgO4667yrlNv66xTft5i87lKl6Epr0Ty0JRXRVx2GXzrW/Hn+vQJj2fbpjtJ3/PPh+AkzcnCc73/fsfAC0Lgsskm1btutSxcGALQXGmuVd/Ivv3tMBArW9xvYNzn8cgjsOuuhcsv93P8whdCt4jcuixZUl43gEa1227w6KPJ882cGeaTlaaiKa+kaZjZQWZ2n5ktMLMlZjbdzM4zs7K+c2a2pZlNMbN5ZrbczGaZ2ZVmFvMLLFV14YXhxzlfwAqh5TIzYXm5W8+eYfvZz0KZZ5zR/vw221Q3YAVYf/2OE76bNWfACmHVpXxTO3V2H30E3/9+x1bSXPkGQxULWCF/mcX84hfx+TpTwAoh8I/7o6kYBawSUdAqqTOzC4A7gb2BhcBrwATgJ8BtSQNXM9sLeBo4DugOvAAMA74KPG1mQ9OrfRMYNKgt2IjrXwdw/PGVBYxxyz9mzl1+eXXvL2P16rCdd164br0GYvz979UP8Ar1AZV0ZLeYf/wxXHll+76hW24Z/vsuXtwx78UXJ7tWJnjdYYewucOGG4Zzp5+evO6dycKFyfob13JaLml46h4gqTKzXYBHAQdOcPdbouMTgHuBocDX3f3KEstbF5gFDCEEvV9z91VmNhi4HdgVuNPdD6nCvTRG94C+fRt7pK+Ur1u3ML1Txvrrwwcf1K8+nV2mb3Fm0Fap0v6d3GcfuP/+wmmGDYN589K9bqPr2bPjDArXXhumVJOmo+4B0gwuAgz4VSZgBXD3Z4Hzo90LzKxnieV9gRCwvgSc7+6rovI+ILS8rgYONrMdUqp/9bz9dtvE5ptuCk89lT9ddsueAtbOIfextHv7gBVCH9rMuVmz6lPPRjdwIBx5ZHl53ZMHrH//e3nXKuS++4qnyQws7EpWrWo/YKtfPwWs0o6CVkmNmfUHMkNg/zcmya3AYmAwsFeJxR4Rvd7o7u1+4d39LSDzf/+jktW2Dg45pG0Jyddfh0mT4h/NjxhR33p2dnHBYxqyA840rjFmTHn5Zs3q2A+30WU+o7gpsLJdcUWYAusPf4gfIJe2bt3iR/Wn4emnC5+v1pRdje6jj0IL87x5lU0lJp2SglZJ0/ZAC7AcmJ57MmolzUz8uHOxwsysB5CZrDLfkNPM8aLl1d2MGfWuQXXdf398sPaDHxTOt+227dP37184vRm88ELhANE9zCFaavDoDm+8kfyeBwxoK7daQVSm/NyVn/Ld95gxYRnMzH7ckpj5LFsW/1n16NH2vqUF7r67cDndupX+aLtn1kOX/v3hlls6psnUJ3vKsPffDy1x1ZTbEp6mTF/XzLbttuF49+7w5pvVu24zGDYsbCI5FLRKmsZFr2+5e76lXV7PSVvIxkDmF+31PGmSlNeOmY0stBEGezW3J54oHtxlb3HLJ+bTr1/7vPmW07zwwvhrzZgRJot/9tn26T/8sHAd166FrbYqXr+rry79XgA23jj+ekOjcX7nnRf2990XHnssvF+4MNk1KpH7R0Gp9tuv/We++eYwcWJ4zN69Oxx0UNv53LXsM8dXrWp7v2IFHHBA4f9Ga9aEoKO1tXj9chdBOOaYUMZJJ4XH6IXu9eOPw/lK5gDN1qdP+i3wpXr22XDN1as1ME8kjx7Fk4iUbGD0WuiXPHNuYIE0ueUVKjNJeblK+EVN0UEHVTYx+KpVIciIG8E+d27oJ7vHHsla1nL95S/hdc6ctjlWJ0+Gm2+GXXYJg1iuuKKtVagS2ZPVN2NQ97cAACAASURBVLLc1Yf+9rf61CMN221XePL8tI0cGQLXuPl6DzigcIvtTTeVfp2PPso/s0Mm+Nx+e3jmmbbjmvtTpOkoaJU0ZZppCq0fmVm7spQF1rObffKVmaS8+rrzztDCVawvW7ZSW3tGjAjLbqZl5MiO1349X2O3SAGZ79KLL4bH/9WahcM9tP4vXRpaXl95pW2aKej83XNEugAFrZKmTNRUaJHozNqSpQyJz47CWnL2yykvV7HlmkYAjwPMS2vqmT//OVn6ORXPECLSGDJ9lav5nX7llbb3a9fq349IHeX8bnZPo0wFrZKmUh7Vl9KFILe8TL64yDFJee0UmzPOzP7dp3WnnXZKWryIiIgEQ4CKRxhqIJakaWb0Ojoa+R9nTE7aQmYDq3LyVVKeiIiINCm1tEqaZhCCzN7ADsC07JPRggKTot0nihXm7qvNbDphOqtdiZ/2KrMgeNHyyvAcbfWdD6Qx/80w2qb9mgS8UyCtCOg7I8npOyNJVeM7053Qwgrh97RiClolNe6+2MzuAw4ETicnaAWOBvoDHwD/KLHYPxGC1lPM7EfZCwyY2WjaFjP4YwVVj+XuK4A8y1aVx9qPcH4njWXtpHPTd0aS0ndGkqridybVSYfVPUDSdingwOfM7NjMQTObAFwV7f7Q3VdmnTvKzGab2SMx5f0CeB/YErgqs/yrmQ0Gbib84XW3uycYki8iIiLNRkGrpMrdHwUuJny3bjazWWb2LGGFrKHAncCPcrKtA2wEdJgLx90XA8cQZg74IjDXzJ4C3iJ0DZgNnFaVmxEREZGGoaBVUufulwKHAg8Ag4GxhP4sXwYOy37EX2J59wMTgamEVtxtgHcJLbc7uLv6a4mIiHRy5rVeqk6kC4uWh82sxDVKfc2kGH1nJCl9ZySpZvnOqKVVRERERBqeglYRERERaXgKWkVERESk4alPq4iIiIg0PLW0ioiIiEjDU9AqIiIiIg1PQauIiIiINDwFrSIiIiLS8BS0ioiIiEjDU9AqIiIiIg1PQauIiIiINLwe9a6ASKMys17ANtHufGBNHasjIiLSTLoDQ6L3z7n7ikoLVNAqkt82wJP1roSIiEiTmwQ8VWkh6h4gIiIiIg1PLa0i+c3PvJk2bRrDhw+vZ11ERESaxrx589hpp50yu/MLpS2VglaR/P7dh3X48OGMHDmynnUREZGu4uOP4dRT4fXX4aKL4D/+o941qlQqY0IUtIqIiIg0kv33h8ceC++POAJefRXGjatvnRqA+rSKiIiINIrVq9sC1owrrqhPXRqMglYRERGRRjF1asdj991Xebn/+Aecey789rewpjlncFTQCpjZQWZ2n5ktMLMlZjbdzM4zs7I+HzPb0symmNk8M1tuZrPM7EozG1Ak34Ao3awo37yonC2L5OtmZl80sxlR/RdE93NgCXUt696T3qOZrW9mZ5rZH8zsDTNbYWYfRdf7jpmtV+BaXmSL+RcuIiLShH79647HKg0yn3sODjgArrkGTjwxBK5NyNy93nWoKzO7ALgs2n0d+BgYTwjo/wL8h7uvTVDeXsCdQB/CaLlWYAugb1T+J9393Zh8w4B/AhsDS4GXgVGEiXmXAQe4+0Mx+boDtwMHA2uB54F1gU2iJF939yvTvPdy7tHMHgZ2i3YXAm8Ag4CNAAPeBCa7++sx18t8SR+Nuw/gPne/JM+5spnZSMK90draqoFYIiJSPTNmwA47xJ/r2xeWLCm/7EMOgTvvbNvfY4/Q8lpFc+bMYdSoUZndUe4+p+JC3b3LbsAuhEBvDXBs1vEJwDuAA19LUN66wHtRvh8DPaPjg4FHouN35Ml7T3T+YWBwdKwn8JPo+DtAv5h8F2Sdn5B1/LjovtYCk9K693LvEfgHcCOwM9EfS9Hx7YFXo3xP5vlsPHxVa/79GJm5dmtrq4uIiFQNFN7WrElW3tNPFy6vylpbWz3zGwqM9BR+l7t694CLCK18v3L3WzIH3f1Z4Pxo9wIz61lieV8gtIy+BJzv7qui8j4gBJGrgYPNrN2fUmY2Edg/On98lJ4o/1ei8oYCZ+bkawG+Ee1+Jap35h5uBv43ur+LUrz3su4ROMLdT3H3J9z938377j4DODHanWhm28fUVUREpPMyK57mpZeSlbnjjoXPL12arLwG0GWDVjPrD+wT7f5vTJJbgcWEFsS9Siz2iOj1Rndv1wHF3d8CMj2pj8rJd2T0+vcoXXa+NcBN0e7ROfn2AgZG9fxDTH0y97W/ma2bOVjhvZd1j+6+IOY6mXNPAB9Gu5vlSyciItJlPfJI6WlLCYJfe638utRJlw1aCY+lW4DlwPTck1ELYmbd+Z2LFWZmPYDMnzX5+l5mjueW94kS8+0Y9WHNzTct0+KZ42nC/fUCtss6Xta9V3iPeUX3lGnRXVYg3U/M7G9mdo+ZXRsNIivhX6aIiEiTe+qp0tKV+rPYhEFrV15cIDNL71vuvjpPmteBvbPSFrIxbYFXh8FEOcdzyxuXcz5fvhbCwKXccmLzuftqM2uN0o0j9JfNzpf03jem/Hss5BDCIK41wOMF0p2Xs38W8JCZHeXuiZeIiwZaFTIsaZkiIiJVkbR7QDFHRg95R4+GN99Mt+wq6cotrQOj14UF0mTODSyQJre8QmXmK69YXbKPD4x5n/QeKs1XKG+Szwwz6wdkZk2+yd3fi0l2D/AZYFOgN2GA1HmELgyfAv4atQIn1VpkezJ/VhERkRp6663iacotd3qHh64NqSu3tPaOXlcWSLMieu2ToLxCZeYrr1hdVmS9z85b7j1Umq9Q3iSfGYQ+teOAOcDX4xK4e+58s3OBn5nZE4TuCDsDxwK/KfGaIiIijWHGjNLSvf9+8TRbFpzWPb8ddwxzCjS4rtzSujx6bSmQplf0mrefZUx5hcrMV16xuvTKep+dt9x7qDRfobwlf2ZmdjnwWcL8sIcXGqwVx92fpG0A2hGF0uYxqsg2qYwyRURESpdvbtZcy5bB2iLTxr/8cvn1MAtbsVkH6qgrt7SW8hi7lMfoueVl8s1LUN5CQp/OfHXJ91i+3HuoNF/mfJJ7bMfMvgZ8k9Aye5i7P10ofQH/JLSyjk2a0YtMdKwxXiIi0lDeeAM23bS612jgrgJ1CVrNbH1gBGG+z8GEVrn50fa6J1iBqgIzo9fRZtYjz4CkMTlpC5kNrCIMVBpDfECXr7yZhM9jDPEyx1cSVo7Kzpd9vp2on+fomGuWe++zKf8es+t1BqEf62rgM+7+QL60JcjMmtCV/wATEZGu4KWXqh+0Qugq0IANNzXpHmBm65rZCWb2KzN7BXiXMNXSvcDNwG2E1ZReARZF0xpdHDNBfZpmEAKe3kCH60ST6mceDz9RrLAo8Mv8ebJrnmSZ47nlPZFzPl++p3PmRs3k2ynPAgg7Eh7XrwSeyTpe1r1XeI+Zso8BfkFYjetkd/9LnnJKtXX0WvnycCIiIo2s0DRVaQaZ1Rr0VaGqBq1mtqOZ/ZqwLOhNwKmEQTdWYFuHMNXSJcCTZvaimZ0TjTJPjbsvpm0i/NNjkhwN9Ac+ICxBWoo/Ra+n5MynipmNpm1C/z/mybdvlC47X3fg5Gg3dwGBBwmP4fvTccECaLuve939o8zBCu+93HvEzA4Cfk343p0drdpVNjMbChwf7d5XKK2IiEjTez3PbJNpt4putFG65aWkKkFrFKzeDUwjBBV9CAHpO8BfgP8kzLH5WWA/4DBCQHs+oRVuOuHRsQFbAD8BZpvZN8ysF+m5lLAm7ufM7Nis+k8Arop2f+juK7POHWVms80sbmmKXwDvA1sCV2VaP81sMKFFuQdwd27/TXefBvw9Oj8lSp9p8bw6Ku894LqcfCuAK6Pdq6J6Z+p5HCEg9eg+K773Su7RzHYjBN09gW+4+y9j6tSBmV1mZsebWd+c4xMIn9lAwmdTUnkiIiJNq7W1uuWfd15DzyJgnnLlzOwGwlrymYB4OjAF+GPuEqVFymkhzMF5HPAfwHqEIOst4ER3T7CeWcHrfBv4frT7OmEk+/io/ncSBgmtyUp/CnAD8Ka7bxxT3t7AHYRH7/Oj+m5JGGg1G9jF3d+Jybch8Bhh8YClwEuE/qhDCKP2D3T3f8Tk6wH8FTiA8Mj9eUJrdaZv6YXufnka917JPUbdQjYjDLwqtKzHpe5+d1a+PxP+qFkNvEZY7nVI1v29C3w6CvxTFS0+0ArQ2trKyJHF1iIQERFJYPhweKdDSJDfzjvD4zlr8KTVyrr99qHslkITC5Vuzpw5jBo1KrM7qtjg51JUo6X1ZEKA8T/AFu4+0d2vThKwArj7Sne/z91PI6xMdBKhz+tGwOS0KuvulwKHAg8QBoWNBZ4DvkyeoK1IefcDE4GphCB7G0JgdRWwQ1zAGuV7m7C86tVR+m2i/FOBHeMC1ijfasKKUl+O6j02uo8HgEPyBaxR3rLuvcx77JX1umuBbWhOvp8TWpifBwYR+umuT5j4/xJgfDUCVhERkapLErBCaXO1luuww1ILWKulGi2t1wCXpRFRx5RthP6W3d39lrTLF8mmllYREamqpK2kgwd3DFzTamm94w44+OB0yqJJWlrd/ZxqBKxR2e7uv1fAKiIiIl3OkiXl5Zs5E844o3CaBl5UIKMrr4glIiIiUh9f+ELyPCtzxkaXshDAHXfA2LFw3XVhkNX++3dMM2IEDBuWvD411lBBq5kNNrNCqzSJiIiINL9fljHpzdq17QPXUlpHt9mm/f748R3TTJyYvC51UPeg1cyGmtl1ZvY+Yeqi981soZndmDtnqYiIiEhTMgtbjwoXcHz55dLT9u8Pbf1Kg6237piuCboGQPXmaR1mZm9H21kF0o0BnibMJzqItgUG1iNMmzXDzLarRh1FREREaiJ7sNSaNZUNnnrppdLTjh/f8VpxQeuECR2PNaBqtbTuQZimahDw+wLppgIbEgJVCCO1nwA+io4NBG6J5iIVERER6dx69CjcV3XmzNLLiusKsPXW0KdP++t98pOll1lH1Qpa94xeH3T3D+ISmNkhhLk+HVgAHODuG7n7LoSA94Yo6WbAkVWqp4iIiEjjGDAgTPSfz+zZ4bVXCQuE5vZnBejXD77znbYW2AsvhPXXT1zNeqhWC+YEQjD69wJpjs96/1V3/1tmx92XmdnnCEHteMKKSL+rRkVFREREquYzn0mWfqONCp+fE80qmjuTQJy4llaACy6AY44JA7vGjIlP04Cq1dKaWdXo2QJp9oxePySsWd+Oh1UPrid0E2iOzhYiIiIi2W69NVn6Qw8Nr93yhGjvvlt6WfmCVoCNN26qgBWqF7RuEL3GrjcWDcAaSmiNfdjdV+UpZ0b0umG61RMRERFpQGefHV7X5FlF/oPYXpcdbbJJ0zz2L1W1gtZMt4N8i9junPX+6QLlLIpe+1VcIxEREZFGZgZDhhROs3Bh8dkHevaE738/vXo1iGr1aX2f0Dq6GfBkzPldst4/VaCcdaPX5SnVS0RERKQxrbNO8TQff1z4/PrrwzPPhFWuOplqtbRm+rJ2GPVvZgZEHTZYCzxaoJxMb+QEHThEREREmtDGG1dexrhxnTJgheoFrbcTBlAdZmYn5Zz7OiEYdeB+d/+wQDmZFtlX0q+iiIiISAPZa6/Ky+ikAStUL2idArwZvb/BzJ4wsylmNh24LCvdVfkKiFpkDycEt49XqZ4iIiIi1ZF0uqsvfKH9vnvya6bRWtugqtKn1d2Xmtlngb8B/QnzrU6MTmd6D1+fPTdrjIOAEYSg9b5q1FNERESkapJOd7XllpVfc9y4ystoUNVqacXdpwE7ArcCywjBqhFaYL8GnFmkiIuj13fcXS2tIiIi0nn17JlOOVtvnU45DahaswcA4O6zgM+aWTdgCLDS3ReWmH3v6HV1VSonIiIi0igmTiyephRxS7d2ElUNWjPcfS0JZwBw9yVVqo6IiIhI/fXpA8uWQf/+8Lc8PSbdi8/LmmEWyuqkahK0ioiIiEiWbt1g6dLS0q5ZA927F0/Xq1dldWpwVevTKiIiIiJ59O5detpu3eIXHshtge3XuRcQTT1oNbOj0y4zp/yRZvbJal5DREREpKoGD06WfvLkjsdyp8QaNKj8+jSBarS0/s7Mnks7eDWz0Wb2c+A1YJ80yxYRERFJ1TPPFD6/0UaFz+f64x9h0qTCaYYNS1Zmk6lG0DoT2BqYamazzewHZlbW/Atm1s/MTjCzuwnB6plA9+i9iIiISPomTAiP3s1g6NDS8/3Xf8Ho0XD88fCJTxROu912yerUowdMm1a4C8BBByUrs8lUI2jdmjAP6wfAaOCbwL/M7GUzu9HMzjGzT5rZWDMbbGbdouB0lJlta2ZHmdl/m9mDwHvATcD+hEFj9wDbu/vNVai3iIiIpGWTTULQN2FC+WVkAsdSR8/PmgWXXgqrVpV/TYB//avt/XvvwbPPFs/z05/Cd74Dra1w882wYkXh9PvvX17dCuXLXVGrkzEvZ4mwUgo26wecDZxDCF4hrG5VchHR6xrgduAKd38ivRqKFGZmI4FWgNbWVkaOHFnnGomINIkRI+Dtt9v2d90VHnkkWRlxgeqAAbAwz3TvRx0VHqFnzJ8P66+f7Jr5rgvFl1QdNQrmzCn9OitWQEtL6ekzbr8dDj+84/EhQ0KA3SDmzJnDqFGjMruj3D3BhxOvmitiLXH3K4AxwIHADYTVsKyEbTnwf8A3gI3c/SgFrCIiIk0iO2AFePTRdMpdtCi0YuaaNat9wArJH79XasGCZOnLCVgBDj00zCaQ61OfKq+8JlL1eVqjhQXujTbMbATwSWAkYZWsQYQgdX60PQc85e4Vtu2LiIhIzX35y9Ut//jj4bjjYPPN4bXXwhymcatA5QbOlTIr3Nq6fHnpZZUbsELb9FeLF7c/fsYZ5ZfZJGq+uIC7zwVurfV1RUREJCW5j9Czg7mf/Sw+z4svwlZblVZ+sVWdsq9f6HH+qlXQs2dp16zU2rWlp42bczWJz3wGfvWrtv0ePWDffSsrswlocQEREZFmcPXVpQ9Iqqa4/v3Z9VqzJj7f6aeXfo2PPkpWp3y+8Y10yikmadeAJDMSxLniivattWeeGd9loJPp/HcoIiLS7Mzg/PPb3scFr+PGtZ27667q1WXu3PjjU6fCYYflzzd9enXqU8hNN1X/GmbJFwoYM6ayaw4YAG+8Ebpi/O53YeaCLqDm3QNEREQkBdl9LK+8MvTvzDj44LCufZ8+tavPsccWXpp05cra1SVj0aJ0y+vXD5Ysadsvt+U7rg9uUhtuGFrfuxC1tIqIiDSyP/wh/7m99w6vF17Y8Vyl/SbjPP984fNJBiPVgnsI3vM599xkg6KWLoVddgnvi614Vcjuu5eftwtT0CoiItLIji6wKvoDD4TX1as7nlu7Fs45J926VNpC+OKLxdPETWlViT33jD9uBtdcEwZrZVpMS1lE4PHHQ3eA7bcvv0577VV+3i6saosLiDQ7LS4gIg2h2CNo98Jp0vydr3Qg2Kabtu/GUI1rxJWXO7I/3zWKTWuVli4QezXV4gIiIiJSA8uW1bsGpZs1q/bXdIcpU9r2Cy0rW4tgsnv36l+jk1LQKiIi0sxOPbU21znppHTK+f3v0ykniRNOaJtZ4V//qv31s/XqVd/rNzEFrSIiIo1q+PDiaf7yl8Ln02qJ/c1v0innxBPTKadZFVs4QfJS0CoiItKo3nmneJpiQekVV6RTl7QUmvrqhBNqV496qXRhgS6sakGrmXU3swoW1xUREZGKXX99vWvQ0W23xR/P7nvaWW26ab1r0LSq2dL6ReBjM3vGzDrM12Fm65jZdmamBQ5ERESq5e23i6fJ9PfMbLlTWxWbnzWpzOpenVm++V+326629ehEqhIwRoHoN6PyVwNxHW42AqYDK83sBWBGtD8DeNbdC8wGLCIiIiVZtarw+QEDOh57/vn0p57K9tZbsMEGMH9+2B83Dl59tXrXq6X+/eHDD8P7uM9wjz1qW59OpFotrfsBGwAOfMndVxRI2wJsB5wK/BR4BPjQzF40s5vMrGeV6igiItJ1zZ4No0a1BVi1tHZtW8AKMHMmfPxx7etRzCc+kTzPJpsUPr/rruXVRaoWtB4cvf7T3R8tktYJE7hb1tYd2AI4ATilSnUUERHpmp55JgRXcyqe7z09665beRmlzrPao4QHzd27wz//mbwOxVpSNU9r2aoVtO5ICEb/Wkpid98YGARMBs4Hfg28Tghgv1KdKoqIiDSwMWOqV/akSemUU05LZJrMwryv7m0BaylTc33uc8XTfPWr5dXp9NPb3u+0U/tzPfXwuBLVClozbeMlLOIbuPsid/+Hu/8/dz8FOABYC2xuZltXoY4iIiKN64030isrd+nU1asrL9MMfv7zystJokcPuOoq2HBDuPji0M3g6Jyx3iecEALYPn3yl/OTnxQP3C+9tO2aSWy7bdv7qVPbnzv22GRlSTvVClrXi17fK7cAd38NeCjaPaTiGomIiDSq7JH7gwalX/6557a9X748nTKvv77wSPjs1s+09OsHX/kKzJ0L3/te4bSFPseePeHOO/MHpBtv3Hau2EC2bLmrXW2yCbz5Zgikb7opbFK2agWtmZmD+xVJU2CGYQDuJXQR2DGNSomIiDSUESM6jjBfuDB5OS+8UPj8o1nDSyZPzp8uyWpNp5wSf7xbt/bB6nPPlV5mtriR9yNHlp5/7Nj445lH9EOGxAeR/fuHgWHZSg02Bw/ueGz06NBlIa1lcLuwagWtH0SvI/IlcPeZwDrA9gXKySwQvE2BNCIiIs1n113zz6GaZLopM9hqqxAo5gtes0fmFxpctHhxadd84om29wcf3P7c//1f+/3x40srM9ef/tTx2G67lZ5/333jj2eP7j/uODj55PAZdu8OZ50VZlPIbYE96aQwRVcxixYla5mVRKoVtGaCzTzfmMDdV7v7vwokyXQvGJJKrURERBrBn/4Ejz1WeTnuoV9nxlZb5U+bxryr3bqFZWOzBxjdcUcIlk89NXQ9SBJYFhLXuvnFL5ae/7TT4o+fdVb7/RtvDJ/h6tVw7bX5y3v33eLdHX7yEw22qqJqBa3/IDzWP8LMKpnDIvMvMYV5MEREROrkww/hd79r2z/yyPrUI2ngesMNIVBbuBDmzYM1a6B3747pttoq9HHN7dNZiWeeib9OqYYPjz9+zjnl1Scje37ZbPvt137mAEldtYLW3wDLgf7A/6ugnGHRq1bHEhGR5nTLLWHVqWOOCS2Vacm3TOipp6ZTvllbv9UBA2DYsILJU3X88fBe2WO5C6u0JXT99Tu21u6/P9x7b2XlSlFVCVrd/X3gWkJr6ylmVu4iw5lnDCUsnCwiItJg7rkn9JvMcE/v8fE2eYZ7XH995WWvtx4srXJ70Y9+lP/cb3+bziwHuS3LhabBSuLaa0P3hd13h/vvD/+dpeqq1dIKcBHwMiFwvcLMrjazmGcK8aJuBacRFikotqqWiIhIY9l3XzjwwI7H05gjFcKj+3zKbdHt1SsE1osWxXcDSNP5RdqzsvvqQnl9cnO7YRT6zJI66SR46KHCszFIqqoWtLr7cmA/4DVC4PpF4EUzO9HMCnZ6MbN+wFRgaHRoSrXqKSIiUrJvfattPtU//xmWLIGjjgrTJB1+eEhz003h/H33lV6uO6yzTrK65GtphdD3dEgJY5h79GgLBvv1S28O11I8/3z+c2+91fFYv0KzaOZx662hVfQTnwizJnz2s8nLkIZhnvbEv7kXMBsO3A9sQWg1BVgE3E1oQX2JMEXWKkIf1k8BZwAbRmnvc/f9q1pJkRhmNhJoBWhtbWVkkvkBRaTzmDEDfvrTdFvpcmV+i5O0Jhb7/V6wIH7e0CRlpCXuvnr0iG91Pv54ePbZjkHtf/4nXHJJVaon6ZszZw6jRo3K7I5y9zmVlln1oBXAzPoQugt8Fcj0HC92YQNeBXaL+siK1JSCVpFOpKUlzJ+Z+c076yz4xS8K53GHr38drryy+vUrJ2hdtqz4I/zLL4cLLyx+3Wr79Kfhr38tLa17xwUKMselaVQjaK1mn9Z/c/dl7v5twiIBvyXMLGAFNgduBj6hgFVERMp21lkhEMxM+G4WHqsXC1gzaWsRsJajR4/S+pxecAGsWFH9+hTzl7+Unva11zoGqGnOuiBNq6bfAnef6e4nERYL+DRwKfB74O/APYSpsr4MbObuJ7j7olrWT0REyvThh+mW9/rr+c995zshoGxpgVdfzZ9u4MD44LRQX8q0bbAB/OxnhdO8+GJ4TbKEavaKVMW0tIQgsK3VK2jUlssdY1Zu33XX2tdDGk5NugeINCN1D5Ca6Ns3PObNOPxwuO22+tUnqU02gdmz2/Z/+Us488zS8t51V8clQN3bPyL/61/hkEPC+8mT4cEHO5Zz7bUd582EdFaAqoWkXQNaWhqj9TSpSv57fPRR8oFqUldN2z1AREQimZHnmS07YIUwIt2scEtjtvXXb1/ezJnp1bVY6+kjj7QPWAE+//n4kd8Z2XXNDVgz57MdemhYEx7iA1aAs88uXk41/fnP5edNssJTxsKF5V+vXj74oLL8ClgFBa0i0hk8+mjHIO/9ErrDu4eJwdN44vTAA7Dllh2D0tytVJtuGtJnplHKtf324XxuMLDZZuF40gnsTzwRBg0KgWSmrgMGwMqVHYPQ008P53ffPb6sjTYK53OXtCw3kFy7tnjecj9nCP/9M9sxx5SWLns77LBk18v20kvh9YwzOp4bOhT+9Kf2x446KrTON5tK/o0NHVo8jXQJ6h4gkoe6B5QgLjjo1as2cz0uWwZvvAFbb104XWYAR+5E5fn06hXybL99CIYLufdeOOCA0sqtt622ghdeiD9XapDXrVvpn2PGDjvA9OnJ8lTL6NEdA/C438C4z+MnP4Hzzstf9nvvlR9c3XJL+KMhd/qnxbjDkAAAFR9JREFUl16CLbYI75cubc5gNdtmm5X3JOCee8IyqdJU1D2gSszsIDO7z8wWmNkSM5tuZueZWVmfj5ltaWZTzGyemS03s1lmdqWZDSiSb0CUblaUb15UzpZF8nUzsy+a2Yyo/gui+4lZiqVD3rLuvZnuUaqgUGvWihWFWxqHDw/zMD71VP40+QLB7DR9+xYPWCEEWUkCrRUrQkD82GPhOv/8Z/46NEvACmGwj1l4zFpuq2TSgBUaJ2D92c/gzTc7tpLmGj8+Pn+hgBXCgCv3tpbTJI49Nn6+0kzACs0fsELhQXOFKGCVDHfv0htwAWGKLQdmAc8Ca6L924FuCcvbC1ga5X8PeBpYklX+0Dz5hgFvROmWRPnei/aXAp/Kk687cEeUbk1U/9ez7ulrad97M91jhd+NkZlrtLa2esN44gn39dZz32QT96VL86fL/zCz8PaZz8SXN3Fi+WVq01bvbZddCv+7+v3v3fv2jc+7//7J/53edltb/pdeKq/OnVWSz6BXr3rXVsrU2trqWb/TI91T+F1Oo5Bm3YBdgLVRIHRs1vEJwDtJAyJg3awg7MdAz+j4YOCR6PgdefLeE51/GBgcHesJ/CQ6/g7QLybfBVnnJ2QdPy66r7XApLTuvZnuMYXvR2MErU8/7XX7oe/WrX7X7irbD3/Y9t+63nXp7Nvgwe5XXJE8X9qGDy9+zXXXTf+6jWjwYPcpU9znzYv/HD7/+XrXUMqkoDXlDbgz+jB/GXPuuOjc+5nArITyvh7leRHonnNuNGGpWgd2yDk3MTq+Chidc657VJ4DX8k51wIsiM4dG1Of66Jzt6d17810jyl8P9INWpcvd1+wwP3yy8Prhx/mT1vvH3dt6W+77uo+bpz7jTcW/65sumn969vI24Yb1u5aX/lK5f/2y/k3ftdd1bluI/vgg46fgzStagStXbZPq5n1B/aJdv83JsmtwGJCC+JeJRZ7RPR6o7uvyT7h7m8B90W7R+XkOzJ6/XuULjvfGuCmaPfonHx7AQOjev4hpj6Z+9rfzNbNHKzw3pviHhvSV74SRmdfcEF4XW+9ykeYS3K77QYbbhj6HrqHwVxpOPjgwmHII4+EPn0nn1y8rMyKQO7J63HCCdCnT/J89eQepteaMKFwujFjQtq5c2v37+Sqq6pT7je/Wfj8gV2wu/6gQeG/71FHhfsv5/svnVqXDVqB7QmteMuBDiMF3H0V8GS0u3OxwsysB5BZxiPfkOPM8dzyPlFivh3NrHtMvmlRfXM9Tbi/XsB2WcfLuvcmu8fG8/Of17sG1dWnT3ywVsrUS5m0//VfpV0rE7zk237725Bu+PCO5x5+OAQ9mUEuG28cjmcPekniscdC/jvuKC9/MfmC15aWUPdZs2DnncO0S+7wm9+EkeaZfP36lX6tN98MA9HcQ3Cfz4YbJr6Novr3h2eeiR/cdt99oU6zZrUdK2dQWFLz5lWv7MsvD/d0111h9oAePdrOXXZZ9a7bDG69NXwuIjm6ctA6Lnp9y91jhm0CYbBPdtpCNib0z8zOV2p543LO58vXAmxUar7ovlpjrlnuvW9M89xjUWY2stBGGDjW3Hr2LP4A9MILYd0yG6mHDAk/7O4hUIqzcmX8dW+6CRYtah+QXXRRaQ9ts4OXOMcfH9K9/Xbp95Jpec20vj74IGy7bcdyIUyLlUm7yy6lX6MSuZ/BihWhnmPGwOOP55/g/uOP2/KMHRufZuHCcH706BAMQwju833+c+eG1+uuC8FWZuR87nbLLcXvq7W1/f7dd8OPfhSm19p001DO3nsX/kzSdPvtoVXcHYbV4H8BBx4Y5oZdtartfi64oPrXFWlCXTloHRi9FlpaJHNuYIE0ueUVKjNfecXqkn18YMz7pPdQab5CeRvlHkvRWmR7Mn/WMnSr8J/bN74R5oKMCw423bQt3Zw5bcdXrixe7g9+AIsXty/vqJzeHZlABsLqRO+8E9K99175P+wnnRS6SDSijTeGPfeEZ5/t2ILrXpt5aKth5sz478+AgjPV5XfGGSHYevfd+PPHHAOXXhp/zix0CYib//j882HNmtBNohSFAtfFi+PnBj3ttLb7nzmz7bP59Ke1zr1Ig+pRPEmn1Tt6LfSrnlncuZQOYr2z3ucrM195xeqSvch0dt5y76HSfIXyNso9Np41a5L3w3v77fCIu5hSf9xLdeut6ZYnXde3vgXnnhv+uOnZMyxwUI2FOhYvDl0MMiZMCN0NIDxJKBTY5muBFpGG0pWD1kxTSUuBNL2i12UF0uSWlykzrikmX3nLgb4F6tIr63123nLvodJ8mbyNfI+lGFXk/AjgcYB5afVty34Uev/9+R97ZqxZE1pORZpd9sTy1fpO53Y10L8dkbrJ+d3sni9dEl05aC3lkXIpj6Zzy8vki4ty8pW3kBDQ5atLvsfy5d5Dpfky5xv5HovyIkvKmdm/n3vvtNNOSYoWERGRNkOANystpCv3ac10chodjYqPMyYnbSGzCXOQZucrtbyZOefz5VtJ+//oBfNF9zU65prl3vtsmuceRUREpBPpyi2tMwgBWG9gB2Ba9kkz6wlMinafKFaYu682s+mEqZ52JX5qp0zv/tzyngD2zDqfL9/TOXOjZsrZycx6xkwJtSPh0flK4Jms42Xde5PdYxqeo+1zmE9YfatSw2gb4DWJsMqXSCH6zkhS+s5IUtX4znQntLBC+D2tXBorFDTrBtwFJa0K1VJied+I8hRbLWrHnHM7RceLrRZ1fs65XpS2WtRf0rr3ZrrHRtzIWmWLlFYI0da5N31ntCXd9J3RlnRrlu9MV+4eAHAp4T/Q58zs2MxBM5sAZJZB+aG7r8w6d5SZzTazR2LK+wUh0NsSuCpqscTMBgM3E1q273b3p7Mzufs04O/R+SlR+kyL59VRee8RArTsfCuAK6Pdq6J6Z+p5HHB6dH9xc84kvvcmvEcRERHpLOodNdd7A75N218Xs4BnCY+BHbiDjq2Jp0TnZucpb2/CKHYnBGFPAUui/TeAYXnybUjoM+pR+qei/B6Vt2eefD2Au6N0a6L6z8q6pwvSuvdmvMdG22iSv2a1Nc6m74y2pJu+M9qSbs3ynenqLa24+6XAocADwGBgLKHvxZeBw7x9/8pSyrsfmAhMJfzH3wZ4l9B6uYO7x/YTcfe3CcurXh2l3ybKP5XwqP0fefKtBg6J6vtcVP/B0f0c4u6XF6hrWffeTPcoIiIinYNFEbaI1EC0PGxmMslRXmTaLRF9ZyQpfWckqWb5znT5llYRERERaXwKWkVERESk4SloFREREZGGpz6tIiIiItLw1NIqIiIiIg1PQauIiIiINDwFrSIiIiLS8BS0ioiIiEjDU9AqIiIiIg1PQauIiIiINDwFrSIiIiLS8BS0ioiIiEjDU9AqIiIiIg1PQauIiIiINDwFrSI1YmYHmdl9ZrbAzJaY2XQzO8/M9O9Q2jGzTczsDDP7HzN71sxWm5mb2UX1rps0Jgt2M7MrzOxxM1tkZivN7G0z+6OZ7VXvOkrjMbPDzeyXZva0mc2LvjOLzOwxM/uSmbXUu47/v707jZWsKOMw/rwwCBJANhUERkAWFQQZwiYu7CpRkMBIhiWSuEWCwS0qCBqUxSAqGjSuUbYRGUmUACaKO8oOjoCIyDosyh7WYX39cKq5h+b2vd33nr59Znh+X05Vnzqn6tv93+rqqrrIzFGPQVrqRcTngRNK9WbgUWBzqn8czwX2ycznRjQ8tUxEnAwcPs6tozPz2Jkej9ovInYFLizV54D/AI8BGwMrlc+PzcyjRzA8tVREXATsCDwJ3AXcD6wNrFOaXAnslpkPjWaEL+QMjzRkEbEDcDzVH5IDMvN1mbklMAf4H7AX8KkRDlHtcx9wHvBF4N3AOaMdjpYAQRVUDwXWzMxNM3MOsAZj/zAfFRHvGdUA1Uo/AnYGVs7MDTNzm8xcF9gBuAPYGjhulAOsc6ZVGrKIOB/YE/hBZn60694BwJmU/24z8+kRDFEtFxE/BT6AM63qISJWAR7PzGd63L+A6h+gczNz7xkdnJZIETEXOBu4KzPXmaz9THCmVRqi8odkt1L98ThNFgAPU82GuOZM0pRk5sO9Amvx23LdZCbGo6XCv8p1xZGOosbQKg3XVsDLgMXAVd03y8zq5aW63QyOS9JLywrl+sRIR6ElyQ7l+qK/XaNiaJWGa+NyvX2CWZCbu9pKUmMiIoC5pfrXUY5F7RYRy0bEuhFxKHAS1Y/5jhjxsJ5naJWGa7VyfXCCNp17q03QRpKm6sNU3/o8BZw84rGohSLiExGRwDPAIuA7wO+A7TPzspEOrsbQKg1X5yu5pyZo82S5vnzIY5H0EhMRc4BvlepRmXnTKMej1rqTahb+MqpdbaD6ncW8iFh2ZKPqYmiVhmtxuU60QfPy5epaM0mNiYgNqLZOWwGYT/V1r/QimbkgM9+amdtl5lrA9sCtwJHAKSMdXI2hVRqufr7672cJgST1LSLWotoxYG3gfOCQdI9L9SkzL6XaqvFJ4CMR8doRDwkwtErDdmO5zo6IWT3abNjVVpKmLCJWpwqsrwP+BMx1D2gNKjPvAv5OlRW3HPFwAEOrNGxXA09TfT03p/tmRCwHbFOql87guCQthSJiJeACqmOiLwfem5kuPdJUzeq6jpShVRqizHyYsfPAPzhOk7nAKlQnYv1xhoYlaSkUEcsDv6La8/k64F2Z+choR6UlVUSsz9gM68LRjWSMoVUavuOABD4UEfM6H0bElsA3SvXEzJxohwFJ6qn8wvssYBfgJmD3zHxgtKNSm0XE1hFxTERsOM69dwG/ppphvaAtu06E67Kl4YuILwCdM+NvBh6l+vpuGaofSeydmc+OaHhqmYjYkWrGrGMlql0mHueFu0xslZmLZnJsaqfyD/H8Ur0RuKdH07szc26Pe3oJiYidgD+U6n+BO6h2upkNrFo+vxzYMzPvm/EBjqMVaxSkpV1mHhcRC4FPAlsDawHXAD8BTjGwqstywBrjfL4iLzwHvDX7J2rklq+VN6b3CXu3zcBYtGRYCBwO7ApsBryeKrTeD1wMnA2cMcFpjjPOmVZJkiS1nmtaJUmS1HqGVkmSJLWeoVWSJEmtZ2iVJElS6xlaJUmS1HqGVkmSJLWeoVWSJEmtZ2iVJElS6xlaJUmS1HqGVkmSJLWeoVWSJEmtZ2iVJElS6xlaJUmS1HqGVkmSJLWeoVWSJEmtZ2iVJElS6xlaJUmS1HqGVknSEikilouIGyIiI2L/Iff13dLPqcPsR1JvhlZJ0rRExB9KoHtwCs+eVp7NiNhvwMc/DmwCXA8sqL3z+7V37jzgeHatPXtK7dYJwFPAwRGxzYDjlNQAQ6skabquLddVI+I1/T4UEVsCB5bq3zLzFwM8uxJwRKl+OTOfq90+rVY+uN93FgfVyqd3Cpm5CDgVCODYAd8pqQGGVknSdF1XK79hgOdOZOzv0KcH7PNjwJrAIuDs+o3M/CtwU6nuFxEv7+eFpd2+pXpDZl7a1eTr5bqHs63SzDO0SpKmqx5a39jPAxGxG7BHqf48My/pt7OIWBY4rFR/1jXL2tGZbV0Z2LvPV7+vtIfaLGtHZt4AXFWqh/f5TkkNMbRKkqZroNAaEUE1ywrwJGNf8/drd2B2KZ/Ro83pQJZyv0sEOu1ygveeWa77RsQr+nyvpAYYWiVJ05KZDwB3l2o/ywMOBLYq5W9n5i0Ddvn+cr0xM6/pMaZbgItKdY+IeNVEL4yIV1OFYYA/ZeZtPZqeU64r0P8MrqQGGFolSU3ozLZOONMaEcsDXynV+4Hjp9BXZ0eAyZYUdJYIzALmTdJ2XmlXf+5FSpjtBPSdJnmnpAYZWiVJTeiE1ldGxJoTtDsMWL+Uj8nMhwbpJCLWrT1/+STNzwaeKOXJlgh07j8OTLaLQafft03STlKDDK2SpCZMuoNARKwKHFmq/wa+N4V+3lIrXz1Rw8x8GPhVqW4dEb3G9UZgTqn+MjMfmWQMV5brRpMtO5DUHEOrJKkJ19bKvZYIHAmsXsqfy8ynp9DPurXyPX2072fP1vrnPZcG9Oh3nT7aS2qAoVWS1IQJZ1ojYj2qE6wA/pyZv5xiP6+slfs5ges3jK1BPbDsXFAfVzB2wMHdwIV9vPOBHuORNESGVknStJWv4u8o1fFmWo+l+sV9MvhBAnWr18qThtbMfBaYX6qzgXd0NdkJWK+U55f2k6n3u0Yf7SU1wNAqSWrKuDsIRMQWjB2POj8zr5hGH4tr5b5OuqI6frWje4nAoEsDuvt9omcrSY0ytEqSmtJZ17pORKxS+7xzXOtixn6INVX31sqr92xVU/ZyXViqzx/r2nVs68LM/EefY6j3e2/PVpIaZWiVJDXlRetaI2IX4J3ls29m5u3T7KMeElcb4LnObOsqwF6lvHepQ/+zrN39GlqlGWJolSQ15QXHuZYfOX2t1O8FvtpAH/UTsDYZ4Ln5wDOlfHDXtb7utR+dfh8Dbh7gOUnTYGiVJDXln1Q/tIJqpnUeY/uffqn8WGu6rmBsHek2/T6Umf+j2kkA4J0RsTmwR6n/JjP/O8AYOv1ekpnPTNhSUmMMrZKkRmTmo8Btpfpmqh0DAK4HfthQH08Bl5XqtgM+3lkiMAs4iz6Obe1WjqHdolT/MmD/kqbB0CpJalJnicDuwAal/NmGZyTPL9dtI2LlAZ47F+gcG7tZudZPzerH24HlusYhaQYYWiVJTbquq/77zDyv4T7mU61DXQHYp9+HMnMxsKDr4wWZOci2VQeU6w3T3LpL0oAMrZKkJtVD63PAZ5ruIDPvZGx29MCJ2o7j1K76IEsD6iH5uwP2K2maIjMnbyVJUotExPbAxVQzrhtl5q0z0OdBwOlUx7iun5mPDLtPSWOcaZUkLXEy8xLg18CywBHD7i8ilmHsYISTDKzSzHOmVZK0RIqINwFXUy1D2KiBgwsm6mt/qh0HFgGbDrgOVlIDZk3eRJKk9snMayLiEGAjYDYwtNBKNaN7DNUPywys0gg40ypJkqTWc02rJEmSWs/QKkmSpNYztEqSJKn1DK2SJElqPUOrJEmSWs/QKkmSpNYztEqSJKn1DK2SJElqPUOrJEmSWs/QKkmSpNYztEqSJKn1DK2SJElqPUOrJEmSWs/QKkmSpNYztEqSJKn1DK2SJElqPUOrJEmSWs/QKkmSpNYztEqSJKn1DK2SJElqvf8DdoWoT7Nl87kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "這次量測花費了 97.450044 分鐘\n",
      "Source drain goes to zero...\n",
      "Start...\n",
      "Port ID = 1 , target = 0.0000 .\n",
      "Port ID = 2 , target = 0.0000 .Port ID = 3 , target = 0.0000 .\n",
      "Port ID = 4 , target = 0.0000 .\n",
      "\n",
      "Done!\n",
      "P1 is now at 0.0 Volts\n",
      "P2 is now at 0.0 Volts\n",
      "P3 is now at 0.0 Volts\n",
      "P4 is now at 0.0 Volts\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": [
    "K213.GOTO(min_V/0.835, step, delay_time, port=1,reset_range=True, auto_range = True, range_m=1)\n",
    "print(\"Sweeping gate goes to starting value...\")\n",
    "print(\"Initialize K213...\")\n",
    "\n",
    "current_measure_xx = np.zeros((len(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(gate_voltage_apply_1), N_ave+1))\n",
    "\n",
    "plt.figure(figsize=(3,3), dpi=200)\n",
    "plt.xlim([min_V,max_V])\n",
    "    #plt.xlim([0,max_I])\n",
    "    \n",
    "start = time.time()\n",
    "print(\"Apply Gate voltage...\")\n",
    "gate_voltage_apply_1 = gate_voltage_apply_1/0.835\n",
    "for index_V, voltage in enumerate(tqdm(gate_voltage_apply_1)): #####開始掃GATE(P1)並量測記錄\n",
    "        K213.GOTO(voltage, step, delay_time, port=1,\\\n",
    "                  reset_range=True, auto_range = True, range_m=1)\n",
    "        time.sleep(measure_wait_time)\n",
    "        Sum_of_current, Sum_of_conductance = 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",
    "            Sum_of_current += current_measure_xx[index_V,n]\n",
    "            Sum_of_conductance += conductance_measure_xx[index_V,n]\n",
    "            time.sleep(ave_wait)## Lockin delay 0.5s\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",
    "    \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(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.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(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.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",
    "            display.display(plt.gcf())\n",
    "#             print(\"Current temperature is %.4f K.\"%triton.GET_CURRENT_TEMPERATURE(control_channel))\n",
    "time.sleep(0.01)\n",
    "print(\"Measurement ends.\")\n",
    "\n",
    "display.clear_output(wait=True)\n",
    "\n",
    "plt.subplot(4,1, 1)\n",
    "plt.plot(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(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",
    "stop = time.time()\n",
    "T=(stop-start)/60\n",
    "print('這次量測花費了 %f 分鐘'%T)\n",
    "\n",
    "    ####歸零####\n",
    "print(\"Source drain goes to zero...\")\n",
    "threads = []\n",
    "shift = 4e-2 #同時給兩個port會當機所以要有一個小時間差\n",
    "print(\"Start...\")\n",
    "for i in range(len(port_list)):\n",
    "    time_shift = shift*i\n",
    "    threads.append(threading.Thread(target = TO_ZERO_THREAD, args = (K213, port_list[i], time_shift)))\n",
    "    threads[i].start()\n",
    "for i in range(len(port_list)):\n",
    "    threads[i].join()\n",
    "print(\"Done!\")\n",
    "print('P%d is now at'%port_list[0],K213.GET_OUTPUT(port=1),'Volts')\n",
    "print('P%d is now at'%port_list[1],K213.GET_OUTPUT(port=2),'Volts')\n",
    "print('P%d is now at'%port_list[2],K213.GET_OUTPUT(port=3),'Volts')\n",
    "print('P%d is now at'%port_list[3],K213.GET_OUTPUT(port=4),'Volts')\n",
    "gate_voltage_apply_1 = gate_voltage_apply_1*0.835"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "raw_data = np.hstack((gate_voltage_apply_1.reshape(-1,1), current_measure_xx, conductance_measure_xx))\n",
    "data = np.hstack((gate_voltage_apply_1.reshape(-1,1), current_measure_xx[:,[-1]], conductance_measure_xx[:,[-1]]))\n",
    "np.savetxt(\"%sraw_data%s.txt\" %(folder_path,file_name),raw_data, fmt=\"%.8e\", \\\n",
    "           header='V(V)\\tI(A)*%d\\tI_avg\\tG(S)*%d\\tG_avg(S)'%(N_ave,N_ave))\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\\\n",
    "           \\n(V)\\t(A)\\t(S)\\\n",
    "           \\nfrom%sVto%sV\\tDGs_at_%sV\\t%s\\tresolution_%s(V)\\tmeasure_wait_time_%s\\tgoto_step_%s\\tdelay_time_goto_%s'\\\n",
    "           %(N_ave,N_ave,\\\n",
    "             min_V,max_V,gate_voltage_apply_2,pin_name,(max_V-min_V)/data_points,measure_wait_time,step,delay_time))\n",
    "\n",
    "\n",
    "#\"%s/I_V_G_V%s.txt\" %(folder_path,file_name)\n",
    "# print (now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 其他量測方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#         for n in range(N_ave+1):\n",
    "#             if n == N_ave:\n",
    "#                 current_measure_xx[index_V,n] = Sum_of_current/float(N_ave) #加上平均數值\n",
    "#             else: \n",
    "#                 current_measure_xx[index_V,n] = srs_source.magnitude  ##讀取Lock_in的值\n",
    "#                 Sum_of_current += current_measure_xx[index_V,n]\n",
    "#                 time.sleep(srs_goto_delay_time)## Lockin delay 0.5s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 其他存檔方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = np.concatenate((z_range.reshape(-1,1),resistance_measure_1.reshape(-1,1),resistance_measure_2.reshape(-1,1)), axis=1) #✪measure要改   \n",
    "# np.savetxt(\"%sGraphene_%s.txt\" %(path, file_name), data, fmt=\"%.8e\")\n",
    "\n",
    "# with open(\"%sGraphene_%s.txt\" %(path, annotation), 'ab') as file:\n",
    "#     data = np.concatenate((z_range.reshape(-1,1),voltage_measure_1.reshape(-1,1)), axis=1) #✪measure要改   \n",
    "#     np.savetxt(file, data, fmt=\"%.8e\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K213其他調控方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#法1\n",
    "# gate_volt = k213_source_2.GET_OUTPUT(port=2)\n",
    "# if gate_volt <= min(gate_voltage_apply_2,gate_voltage_apply_3):\n",
    "#     while gate_volt <= min(gate_voltage_apply_2,gate_voltage_apply_3):\n",
    "#         if k213_source_2.GET_OUTPUT(port=2)+step_size > gate_voltage_apply_2:\n",
    "#             break\n",
    "#         gate_volt+=step_size\n",
    "\n",
    "#         k213_source_2.GOTO(gate_volt, step_size, delay_time_goto,\\\n",
    "#                          reset_range=True, auto_range = True, range_m=1, reset_port = True, port=2)  \n",
    "#         k213_source_3.GOTO(gate_volt, step_size, delay_time_goto,\\\n",
    "#                          reset_range=True, auto_range = True, range_m=1, reset_port = True, port=3) \n",
    "\n",
    "# else:\n",
    "#     while gate_volt >= min(gate_voltage_apply_2,gate_voltage_apply_3):\n",
    "#         if k213_source_2.GET_OUTPUT(port=2)-step_size < gate_voltage_apply_2:\n",
    "#             break\n",
    "#         gate_volt+=step_size*-1\n",
    "\n",
    "#         k213_source_2.GOTO(gate_volt, step_size, delay_time_goto,\\\n",
    "#                          reset_range=True, auto_range = True, range_m=1, reset_port = True, port=2)  \n",
    "#         k213_source_3.GOTO(gate_volt, step_size, delay_time_goto,\\\n",
    "#                          reset_range=True, auto_range = True, range_m=1, reset_port = True, port=3) \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#法2\n",
    "##### 213設定(固定P2,P3)\n",
    "# from threading import Thread\n",
    "# def func1():\n",
    "#     return k213_source_2.GOTO(gate_voltage_apply_2, step_size, delay_time_goto,\\\n",
    "#                             reset_range=True, auto_range = True, range_m=1, reset_port = True, port=2)\n",
    "# def func2():\n",
    "#     return k213_source_3.GOTO(gate_voltage_apply_3, step_size, delay_time_goto,\\\n",
    "#                             reset_range=True, auto_range = True, range_m=1, reset_port = True, port=3)\n",
    "# if __name__ == '__main__':\n",
    "#     Thread(target = func1).start()\n",
    "#     Thread(target = func2).start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path = 'C:/Users/LiangLab/Sean/test_data'\n",
    "\n",
    "# #✪位✪置有更正需要改動\n",
    "# try:\n",
    "#     os.makedirs(path)\n",
    "# except:\n",
    "#     print(\"Directory might exists!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}