7.Tempurature_varying-5gates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ASRL1::INSTR',\n",
       " 'ASRL2::INSTR',\n",
       " 'ASRL3::INSTR',\n",
       " 'ASRL10::INSTR',\n",
       " 'GPIB0::5::INSTR',\n",
       " 'GPIB0::11::INSTR',\n",
       " 'GPIB0::18::INSTR',\n",
       " 'GPIB0::23::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 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\"))\n",
    "visa.ResourceManager().list_resources()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GOTO_THREAD(K213, step, delay_time, 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, step, delay_time, 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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\pyvisa\\highlevel.py:3355: FutureWarning: get_instrument is deprecated and will be removed in 1.12, use open_resource instead.\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "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\n",
    "\n",
    "#[2,3,4] 如果多P4需要當GATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure(temperature):\n",
    "    min_V = 2    #initial voltage\n",
    "    max_V = 3.2    #final voltage\n",
    "    data_points = 480  #resolution per sweep\n",
    "\n",
    "    gate_voltage_apply_1 = np.append(np.linspace(min_V, max_V,data_points),np.linspace(max_V, min_V,data_points)) # for 213 port 1\n",
    "    gate_voltage_apply_2 = 0.9 # for 213 port 2\n",
    "    gate_voltage_apply_3 = 2.4 # for 213 port 3\n",
    "    gate_voltage_apply_4 = 3.0 # for 213 port 4\n",
    "\n",
    "    measure_wait_time=2  #電壓到時等一段時間穩定後再量測\n",
    "    step = 0.001         #調變電壓每一步的大小\n",
    "    delay_time = 0.05    #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 = 2 #必須至少0.5秒因為830是0.5秒才改變一次\n",
    "\n",
    "    #############################################################\n",
    "    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 = 5e-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)   #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\n",
    "    #############################################################\n",
    "    repeat = 0\n",
    "    folder_path = \"C:/Users/USER/Sean/2020-12-16 QD temp depend/\" ##儲存的地方\n",
    "    file_name = '%sK_repeat%s_at_%s'%(temperature,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':\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",
    "    #畫圖\n",
    "    fontsize = 6\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    \n",
    "\n",
    "    #############################################################\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",
    "\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, step, delay_time,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",
    "    \n",
    "    #############################################################    \n",
    "    \n",
    "    #srs_source.GOTO(0.004, srs_goto_step, srs_goto_delay_time) \n",
    "    srs_source.GOTO(SD_voltage_apply, srs_goto_step, srs_goto_delay_time)       ##### SR-830設定(固定SD)\n",
    "\n",
    "    threads = []\n",
    "    target = [min_V/0.835,gate_voltage_apply_2/0.835,gate_voltage_apply_3/0.835,gate_voltage_apply_4/0.72] #分別目標值\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, step, delay_time,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",
    "\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",
    "    #############################################################\n",
    "    \n",
    "    K213.GOTO(min_V/0.835, step, delay_time, port=1,\\\n",
    "              reset_range=True, auto_range = True, range_m=1)\n",
    "    print(\"Sweeping gate goes to starting value...\")\n",
    "    print(\"Initialize K213...\")\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",
    "    plt.figure(figsize=(3,3), dpi=200)\n",
    "    plt.xlim([min_V,max_V])\n",
    "        #plt.xlim([0,max_I])\n",
    "    start = time.time()\n",
    "\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",
    "    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",
    "    gate_voltage_apply_1 = gate_voltage_apply_1*0.835\n",
    "    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",
    "               \\nresolution_%s(V)\\tDG1and2_at_%sVand%sV\\tDG1and2_at_%sVand%sV\\tfrom%sVto%sV\\tmeasure_wait_time_%s\\tgoto_step_%s\\tdelay_time_goto_%s'\\\n",
    "               %(N_ave,N_ave,\\\n",
    "                 (max_V-min_V)/data_points,gate_voltage_apply_2,gate_voltage_apply_3,gate_voltage_apply_2,gate_voltage_apply_3,min_V,max_V,measure_wait_time,step,delay_time))\n",
    "\n",
    "\n",
    "        ####歸零####\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, step, delay_time,port_list[i], time_shift)))\n",
    "        threads[i].start()\n",
    "\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temp control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymeasure, os\n",
    "import pyvisa as visa\n",
    "from pymeasure.instruments.keithley import Keithley2400, Keithley2000\n",
    "from pymeasure.adapters import VISAAdapter\n",
    "from IPython import display\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "from time import sleep\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "import time\n",
    "from qcodes.instrument_drivers.oxford.triton import Triton\n",
    "from statistics import mean, variance\n",
    "import qcodes.instrument_drivers.oxford.MercuryiPS_VISA as mercury_ips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class TEMPERATURE_CONTROLER(Triton):\n",
    "    def __init__(self):\n",
    "        super(TEMPERATURE_CONTROLER, self).__init__(name = \"self_500\", address=\"10.101.2.69\", port=33576)\n",
    "        self.T_channels={\"T1\":self.T1, \"T2\":self.T2, \"T3\":self.T3, \"T4\":self.T4, \"T5\":self.T5,\\\n",
    "            \"T6\":self.T6, \"T7\":self.T7, \"T8\":self.T8, \"T13\":self.T13}\n",
    "    \n",
    "    def SET_CONTROL_CHANNEL(self, channel):\n",
    "        self.ask_raw(\"SET:DEV:T%d:TEMP:LOOP:HTR:H1\"%channel)\n",
    "    def SET_PID_VALUES(self, channel, PID):\n",
    "        self.ask_raw(\"SET:DEV:T%d:TEMP:LOOP:P:%.6f:I:%.6f:D:%.6f\"%(channel,PID[0], PID[1], PID[2]))\n",
    "    def GET_CURRENT_TEMPERATURE(self, channel):\n",
    "        return self.T_channels[\"T%d\"%channel]()\n",
    "    def SET_HEATER_RANGE(self, channel, range_value):\n",
    "        range_options = [31.6e-6, 100e-6, 316e-6, 1.0e-3, 3.16e-3, 10e-3, 31.6e-3, 100e-3]\n",
    "        if range_value not in range_options:\n",
    "            raise RuntimeError('Wrong heater range!')\n",
    "        else:\n",
    "            range_value *= 1000 # convert to mA\n",
    "            self.ask_raw(\"SET:DEV:T%d:TEMP:LOOP:RANGE:%.6f\"%(channel,range_value))\n",
    "    \n",
    "    def TEMPERATURE_CONTROL(self, channel, set_point, range_value, reading_interval = 60, PID=(10,120,0)):\n",
    "        self.SET_CONTROL_CHANNEL(channel)                  # set the control channel\n",
    "        if control_channel!= self.pid_control_channel():    # check by getting the control channel\n",
    "            raise ValueError(\"Cannot set channel %d as control channel!\"%control_channel)\n",
    "\n",
    "#         self.pid_mode('off')                                # turn off the pid\n",
    "        self.pid_mode('on')                                  # turn of the pid \n",
    "        self.pid_setpoint(set_point)                         # set point temperature\n",
    "        self.SET_HEATER_RANGE(control_channel, range_value)  # set heater range in unit of A\n",
    "        #self.pid_range(heater_range*1e3)                    # set heater range in unit of mA\n",
    "        current_T = self.GET_CURRENT_TEMPERATURE(channel)\n",
    "        criteria = 0.05 ##set_point*0.02 # set criteria\n",
    "        start = time.time()\n",
    "        T_ave_list = [current_T]\n",
    "        T_ave = 0.0\n",
    "        T_variance = 0.0\n",
    "        N_T_ave = 20    # number of points to average to get T_ave\n",
    "\n",
    "        while not (abs(T_ave-set_point)<criteria and len(T_ave_list)==N_T_ave):\n",
    "            for i in range(100):\n",
    "                time.sleep(reading_interval/100.)\n",
    "\n",
    "            end = time.time()\n",
    "            current_T = self.GET_CURRENT_TEMPERATURE(control_channel)\n",
    "\n",
    "            if len(T_ave_list)==N_T_ave:\n",
    "                del T_ave_list[0]\n",
    "\n",
    "            T_ave_list.append(current_T)\n",
    "            T_ave = mean(T_ave_list)\n",
    "            T_variance = (variance(T_ave_list))**0.5\n",
    "            print(\"%.2f second has passed, current temperautre is %.6f%+.6f K.\"%(end-start, T_ave, T_variance) )\n",
    "\n",
    "        print(\"%.6f%+.6f K has been reached.\"%(T_ave,T_variance) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to: OXFORD INSTRUMENTS Triton (serial:N/A, firmware:2.519) in 0.01s\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    triton = TEMPERATURE_CONTROLER()\n",
    "except:\n",
    "    print(\"Triton might have been initialized, or there might be some other problem...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "control_channel = 8            #1.4K以下channel 8 以上channel 5\n",
    "reading_interval = 60                                 # in unit of second\n",
    "set_point_T = np.array([1.0,1.5])  ####np.arange(2,2.01,1)    #np.arange(1.625,1.65001,0.025)       #in unit of K\n",
    "heater_range = 31.6e-3   # in unit of A\n",
    "print(control_channel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[self_500(TEMPERATURE_CONTROLER)] Snapshot: Could not update parameter: Bx\n",
      "[self_500(TEMPERATURE_CONTROLER)] Snapshot: Could not update parameter: By\n",
      "[self_500(TEMPERATURE_CONTROLER)] Snapshot: Could not update parameter: Bz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self_500:\n",
      "\tparameter             value\n",
      "--------------------------------------------------------------------------------\n",
      "B                      :\tINVALID (T)\n",
      "Bx                     :\tNone (T)\n",
      "By                     :\tNone (T)\n",
      "Bz                     :\tNone (T)\n",
      "COOL                   :\t1.2125 (K)\n",
      "IDN                    :\t{'vendor': 'OXFORD INSTRUMENTS', 'model': 'Triton', ...\n",
      "MC                     :\t0.46112 (K)\n",
      "P1                     :\t16236 (bar)\n",
      "P2                     :\t1.2882e+06 (bar)\n",
      "P3                     :\t673 (bar)\n",
      "P4                     :\t7500 (bar)\n",
      "P5                     :\t3.2521e+05 (bar)\n",
      "P6                     :\t0.1 (bar)\n",
      "PT1                    :\t63.657 (K)\n",
      "PT2                    :\t4.4811 (K)\n",
      "SORB                   :\t297.24 (K)\n",
      "STIL                   :\t0.72855 (K)\n",
      "T1                     :\t4.3849 (K)\n",
      "T10                    :\t0 (K)\n",
      "T11                    :\t0 (K)\n",
      "T12                    :\t0 (K)\n",
      "T13                    :\t4.6381 (K)\n",
      "T14                    :\t0 (K)\n",
      "T15                    :\t297.3 (K)\n",
      "T16                    :\t297.24 (K)\n",
      "T2                     :\t4.4811 (K)\n",
      "T3                     :\t0.72855 (K)\n",
      "T4                     :\t0.24246 (K)\n",
      "T5                     :\t1.2125 (K)\n",
      "T6                     :\t41.364 (K)\n",
      "T7                     :\t63.657 (K)\n",
      "T8                     :\t0.46112 (K)\n",
      "T9                     :\t0 (K)\n",
      "action                 :\tCirculating \n",
      "magnet_status          :\tINVALID \n",
      "magnet_sweep_time      :\tINVALID (T/min)\n",
      "magnet_sweeprate       :\tINVALID (T/min)\n",
      "magnet_sweeprate_insta :\tINVALID (T/min)\n",
      "pid_control_channel    :\t8 \n",
      "pid_mode               :\ton \n",
      "pid_ramp               :\toff \n",
      "pid_range              :\t31.6 (mA)\n",
      "pid_rate               :\t0.001 (K/min)\n",
      "pid_setpoint           :\t1 (K)\n",
      "status                 :\tOK \n",
      "time                   :\t11:31:19 \n"
     ]
    }
   ],
   "source": [
    "triton.print_readable_snapshot(update=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### do\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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 362.940854 分鐘\n",
      "Start...\n",
      "Port ID = 1 , target = 0.0000 .Port ID = 2 , target = 0.0000 .\n",
      "\n",
      "Port ID = 3 , target = 0.0000 .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"
    },
    {
     "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": [
    "for temperature in set_point_T:\n",
    "    triton.TEMPERATURE_CONTROL(control_channel, temperature, heater_range)\n",
    "    measure(temperature)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}