PyVisa_Rigol_DP832A_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ca173028-9151-4c4b-9ddf-a2e9aeec679c",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "The aim of this notebook is to introduce the driver class *PyVisa_Rigol_DP832A* utilizing the programmable DC power supply **Rigol DP832A** in my lab.\n",
    "\n",
    "Sources (small selection):\n",
    "\n",
    "- Basic functions for controlling test equipment with PyVisa: [https://github.com/freq0ut/Python-PyVisa](https://github.com/freq0ut/Python-PyVisa)\n",
    "- Controlling a Rigol DP832 using PyVISA: [https://fixturfab.com/articles/controlling-rigol-dp832-using-pyvisa/](https://fixturfab.com/articles/controlling-rigol-dp832-using-pyvisa/)\n",
    "- Controlling Rigol Dp832 with python: [https://juluribk.com/controlling-rigol-dp832-with-python.html](https://juluribk.com/controlling-rigol-dp832-with-python.html)\n",
    "- Automating Test-Equipment with Python: [https://core-electronics.com.au/tutorials/automating-test-equipment-with-python.html](https://core-electronics.com.au/tutorials/automating-test-equipment-with-python.html)\n",
    "- [DP800 Programming Manual, 2021-07-01](https://int.rigol.com/Images/DP800_ProgrammingGuide_EN_tcm7-3044.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf36afa4-d67d-455d-9eaa-3f6707e7004e",
   "metadata": {},
   "source": [
    "## Installation of required Python libraries\n",
    "\n",
    "In this application [PyVisa](https://pypi.org/project/PyVISA/) and [PyVISA-py](https://github.com/pyvisa/pyvisa-py) are used to communicate with the Rigol DP832A via LAN (TCP/IP) interface and SCPI commands. The communication over the USB device port does not work for me at the moment.\n",
    "\n",
    "On Raspberry Pi both Python packages have to be installed first either via *pip* command or using *conda*.\n",
    "\n",
    "Without conda, the installation is sensibly carried out in a Python *virtualenv* environment:\n",
    "```\n",
    "$ source ~/jupyter-env/bin/activate\n",
    "$ pip install pyvisa pyvisa-py\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d9f0742-f79d-46ea-8ca4-dabdc9770a98",
   "metadata": {},
   "source": [
    "## Load globally used libraries and set plot parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "496f544f-263a-4ca9-8072-07f2892c87be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "#import matplotlib.dates as mdates\n",
    "%matplotlib inline\n",
    "\n",
    "# FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. \n",
    "# The converter was registered by pandas on import. \n",
    "# Future versions of pandas will require you to explicitly register matplotlib converters.\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "from IPython.display import set_matplotlib_formats\n",
    "#set_matplotlib_formats('pdf', 'png') # <== deprecated since IPython 7.23\n",
    "%config InlineBackend.figure_formats = ['pdf', 'png']\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 80\n",
    "plt.rcParams['savefig.bbox'] = \"tight\"\n",
    "\n",
    "plt.rcParams['figure.autolayout'] = False\n",
    "plt.rcParams['figure.figsize'] = 10, 6\n",
    "plt.rcParams['axes.labelsize'] = 18\n",
    "plt.rcParams['axes.titlesize'] = 20\n",
    "plt.rcParams['font.size'] = 16\n",
    "plt.rcParams['lines.linewidth'] = 2.0\n",
    "plt.rcParams['lines.markersize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 14\n",
    "\n",
    "# Need to install dependent package first via 'apt install cm-super'\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.family'] = \"serif\"\n",
    "plt.rcParams['font.serif'] = \"cm\"\n",
    "\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a347b3-5c17-4960-9ec0-23d8cce29286",
   "metadata": {},
   "source": [
    "# Short technical overview of the Rigol DP832A\n",
    "\n",
    "The Rigol DP832A is a very high quality programmable laboratory power supply with three switchable outputs (+30 V/3 A, +30 V/3 A and +5 V/3 A) and a maximum power output of 195 W.\n",
    "\n",
    "Following documentation were helpful for further understanding (found [here](https://int.rigol.com/products/DP_detail/DP800)):\n",
    "\n",
    "- [DP800 User Manual, 2016-06-03](https://int.rigol.com/Images/DP800_UserGuide_EN_tcm7-3049.pdf)\n",
    "- [DP800 Programming Manual, 2021-07-01](https://int.rigol.com/Images/DP800_ProgrammingGuide_EN_tcm7-3044.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04cf3fd1-bc8c-4d07-8141-26798c6471b6",
   "metadata": {
    "ipub": {
     "figure": {
      "caption": "Front view of the Rigol DP832A",
      "label": "fig:rigol-dp832a-front",
      "placement": "h!"
     }
    },
    "tags": []
   },
   "outputs": [
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "image/jpeg": {
       "height": 200
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/rigol-dp832a-front.jpg', height=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40457a6-4d1f-49c8-b917-f6c03c314d6b",
   "metadata": {},
   "source": [
    "# Remote control of the Rigol DP832A by SCPI commands\n",
    "\n",
    "The device can be controlled remotely with SCPI commands through a variety of interfaces. The following interfaces are available:\n",
    "\n",
    "- USB\n",
    "- LAN\n",
    "- GPIB (optional)\n",
    "\n",
    "Control of the device can be integrated into your own applications, or by using LabView via these interfaces.\n",
    "\n",
    "In this application [PyVisa](https://pypi.org/project/PyVISA/) and [PyVISA-py](https://github.com/pyvisa/pyvisa-py) are used to communicate with the Rigol DP832A via LAN (TCP/IP) interface and SCPI commands. The communication over the USB device port does not work for me at the moment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3cc48c-bd1c-45ac-b655-9d13ca29956d",
   "metadata": {},
   "source": [
    "## Using the wrapper class 'PyVisa_Rigol_DP832A' manually\n",
    "\n",
    "The new wrapper class **PyVisa_Rigol_DP832A** in the python file *PyVisa_Rigol_DP832A_class.py* implements the communication with the Rigol DP832A via LAN interface and SCPI commands using PyVisa and PyVISA-py.\n",
    "\n",
    "Separate class methods realize the basic control of the power supply, such as setting the current and voltage in the selected channel as well as setting and activating the overvoltage protection (OVP) and overcurrent protection (OCP). In addition, there are methods for measuring the actual values in the selected channel (current, voltage and power)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff816c42-530d-4e10-98d5-37c8e2a76fb2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import wrapper class PyVisa_Rigol_DP832A from python file PyVisa_Rigol_DP832A_class.py\n",
    "from PyVisa_Rigol_DP832A_class import PyVisa_Rigol_DP832A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4370551b-ed20-4f0a-a962-1372da2fc681",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create new device object for the power supply unit Rigol DP832A\n",
    "#psu = PyVisa_Rigol_DP832A(tcp_ip = '192.168.12.201')\n",
    "psu = PyVisa_Rigol_DP832A(tcp_ip = '192.168.10.201')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a06c1de8-3b04-4006-b90e-00e43e0fbe91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Connected'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read connection state of the device\n",
    "psu.status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "66b787e9-a66f-420e-a4d1-f814a17bdc79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'LAN over 192.168.10.201'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read connection path (at the moment there is only TCP/IP implemented; USB does not work for me)\n",
    "psu.connected_with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7eca6556-2c66-4dd7-953f-91563250221d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 'OFF')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# toggle the output of channel 2 to OFF\n",
    "psu.toggleOutput(2, 'OFF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d24e0b6-968a-4795-bcfe-a8fb5ddf8476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the voltage of channel 2 to 20 V\n",
    "psu.setVoltage(2, 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c8f7e459-2311-400c-a39b-5019569d00c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 0.8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the current of channel 2 to 0.8 A\n",
    "psu.setCurrent(2, 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3ce86ce-e501-4a57-a15c-be468ead0db8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 'ON')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# toggle the output of channel 2 to ON\n",
    "psu.toggleOutput(2, 'ON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37964650-cee1-49dc-a3ba-49428d4f3bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 25)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the overvoltage protection of channel 2 to 25 V\n",
    "psu.setOVP(2, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5236d732-a9ce-4492-be16-5a90295d6ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1.5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the overcurrent protection of channel 2 to 1.5 A\n",
    "psu.setOCP(2, 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec345b0b-a970-4bff-8c38-a11511011ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 'ON')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# activate overvoltage protection of channel 2\n",
    "psu.toggleOVP(2, 'ON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "babb77b2-67c8-4814-95a7-6c63da6881cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 'ON')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# activate overcurrent protection of channel 2\n",
    "psu.toggleOCP(2, 'ON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "afc7228c-a727-4a7d-a5b3-c56590e6e8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 30.001)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# measure the actual voltage of channel 2\n",
    "psu.measVolt(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1fd2db53-71af-4d5f-a4ae-9a5f1480f2bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 0.0308)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# measure the actual current of channel 2\n",
    "psu.measCurrent(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2b96f6c3-1318-4ac3-b438-85e0ccf4da74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 0.924)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# measure the actual power of channel 2\n",
    "psu.measPower(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c8ebead-965a-46b9-a5cb-0e9ad1b18026",
   "metadata": {},
   "outputs": [],
   "source": [
    "# close the connection to the device\n",
    "psu.closeConnection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e574577-d643-445d-b398-94d4305b8f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the connection again\n",
    "psu.openConnection('192.168.12.201')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe192ed-0225-4987-bd6a-d2becce20c14",
   "metadata": {},
   "source": [
    "## Automated measurements using the wrapper class 'PyVisa_Rigol_DP832A'\n",
    "\n",
    "In this section, the wrapper class PyVisa_Rigol_DP832A is used to run through a triangular voltage function curve with a rising and falling ramp. At the same time the actual voltage is measured."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78bcc6b0-5f60-4cb9-bd96-c8ca0410081e",
   "metadata": {},
   "source": [
    "### Helper function for handling dataframes\n",
    "\n",
    "First, a dataframe is created and at the same time the column headers are set. The function ```dataframe_add_row()``` is used to add the measured values to the dataframe in the form of new rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5147d72-ac4c-4d4b-b6c6-20a262e295b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_meas_values = pd.DataFrame(columns=['Time [s]', 'Voltage set [V]', 'Voltage measured [V]'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5eac6c2d-46c6-4ec3-8991-92bf40910531",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dataframe_add_row(df=None, row=[]):\n",
    "    if (df is None):\n",
    "        return\n",
    "    \n",
    "    # add a row\n",
    "    df.loc[-1] = row\n",
    "    \n",
    "    # shift the index\n",
    "    df.index = df.index + 1\n",
    "    \n",
    "    # reset the index of dataframe and avoid the old index being added as a column\n",
    "    df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98190900-9f2a-4aa2-bbdd-c8eae5e62b97",
   "metadata": {},
   "source": [
    "### Define a main worker function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cf67c375-21c0-411a-8067-e3f3fd0792eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main_worker_run(tcp_ip='0.0.0.0', chan=1, volt_min=0, volt_max=20, volt_inc=2):\n",
    "    # import wrapper class PyVisa_Rigol_DP832A from python file PyVisa_Rigol_DP832A_class.py\n",
    "    from PyVisa_Rigol_DP832A_class import PyVisa_Rigol_DP832A\n",
    "\n",
    "    # create new device object for the power supply unit Rigol DP832A\n",
    "    psu = PyVisa_Rigol_DP832A(tcp_ip)\n",
    "\n",
    "    # global constants\n",
    "    OUT_CHANNEL   = chan\n",
    "    VOLTAGE_MIN   = volt_min\n",
    "    VOLTAGE_MAX   = volt_max\n",
    "    VOLTAGE_INC   = volt_inc\n",
    "    VOLTAGE_TOL   = 0.5  # allowed tolerance to the set voltage\n",
    "    DELAY_WRITE   = 1.0  # delay for writing the commands in seconds\n",
    "    DELAY_MEASURE = 0.1  # delay for measuring in seconds\n",
    "    DELAY_SAMPLE  = 0.05 # delay for sample interval of the main loop\n",
    "    RAMP_RISING   = True\n",
    "    MEASUREMENT_FINISHED = False\n",
    "\n",
    "    # initialize psu\n",
    "    # toggle the output of selected channel to OFF\n",
    "    psu.toggleOutput(OUT_CHANNEL, 'OFF')\n",
    "    # set the current of selected channel to 0.8 A\n",
    "    psu.setCurrent(OUT_CHANNEL, 0.8)\n",
    "    # set the voltage of selected channel to 0 V\n",
    "    psu.setVoltage(OUT_CHANNEL, VOLTAGE_MIN)\n",
    "    # set the overvoltage protection of selected channel to VOLTAGE_MAX+2 V\n",
    "    psu.setOVP(OUT_CHANNEL, VOLTAGE_MAX+2)\n",
    "    # activate overvoltage protection of selected channel\n",
    "    psu.toggleOVP(OUT_CHANNEL, 'ON')\n",
    "    # toggle the output of selected channel to ON\n",
    "    psu.toggleOutput(OUT_CHANNEL, 'ON')\n",
    "    # init set voltage\n",
    "    voltage_set = VOLTAGE_MIN\n",
    "\n",
    "    # get starting time\n",
    "    time_start = float(\"{:.2f}\".format(time.time()))\n",
    "    timestamp_old_meas = 0.0\n",
    "    timestamp_old_write = 0.0\n",
    "\n",
    "    if (psu.status == 'Connected'):\n",
    "        #while voltage_set <= VOLTAGE_MAX:\n",
    "        while MEASUREMENT_FINISHED != True:\n",
    "            try:\n",
    "                # Get time relative to starting time and round to 2 decimals\n",
    "                timestamp = float(\"{:.2f}\".format(time.time() - time_start))\n",
    "\n",
    "                # execute the measurements in comparatively short measurement intervals \n",
    "                if (timestamp - timestamp_old_meas >= DELAY_MEASURE):\n",
    "                    # measure the actual voltage of channel 2\n",
    "                    chan, voltage_meas = psu.measVolt(OUT_CHANNEL)\n",
    "\n",
    "                    timestamp_old_meas = timestamp\n",
    "\n",
    "                    print(\"Time {} s: \\t chan: {}, set voltage: {} V, measured voltage: {} V\".format(timestamp, OUT_CHANNEL, voltage_set, voltage_meas))\n",
    "\n",
    "                    values_row = [ timestamp,\n",
    "                                   voltage_set,\n",
    "                                   voltage_meas\n",
    "                                 ]\n",
    "\n",
    "                    dataframe_add_row(df_meas_values, values_row)\n",
    "\n",
    "                # set the new voltage setpoints in comparatively long intervals\n",
    "                if (timestamp - timestamp_old_write >= DELAY_WRITE):\n",
    "                    \n",
    "                    timestamp_old_write = timestamp\n",
    "\n",
    "                    # a new voltage setpoint is only incremented or decremented when the \n",
    "                    # actual value has approximately reached the old setpoint (within a tolerance).\n",
    "                    if (abs(voltage_set - voltage_meas) <= VOLTAGE_TOL):\n",
    "                        if (RAMP_RISING == True):\n",
    "                            # increment the set voltage\n",
    "                            voltage_set = voltage_set + VOLTAGE_INC\n",
    "                            \n",
    "                            # switch to falling ramp\n",
    "                            if (voltage_set >= VOLTAGE_MAX): RAMP_RISING = False\n",
    "                            \n",
    "                        elif (RAMP_RISING == False):\n",
    "                            # decrement the set voltage\n",
    "                            voltage_set = voltage_set - VOLTAGE_INC\n",
    "                            \n",
    "                            # stop the measurement\n",
    "                            if (voltage_set < VOLTAGE_MIN): MEASUREMENT_FINISHED = True\n",
    "                                \n",
    "                    # output the current set voltage\n",
    "                    psu.setVoltage(OUT_CHANNEL, voltage_set)\n",
    "\n",
    "                time.sleep(DELAY_SAMPLE)\n",
    "\n",
    "            except:\n",
    "                print(\"Keyboard Interrupt ^C detected.\")\n",
    "                print(\"Bye.\")\n",
    "                break\n",
    "\n",
    "        # toggle the output of channel 2 to OFF\n",
    "        psu.toggleOutput(OUT_CHANNEL, 'OFF')\n",
    "\n",
    "        # close the connection to the device\n",
    "        psu.closeConnection()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd54304f-1a19-4595-be60-1de9fdcce090",
   "metadata": {},
   "source": [
    "### Run the main worker function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "26bd68b0-f387-4365-afc8-8b3e78daab42",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time 0.1 s: \t chan: 2, set voltage: 0 V, measured voltage: 0.0 V\n",
      "Time 0.22 s: \t chan: 2, set voltage: 0 V, measured voltage: 0.0 V\n",
      "Time 0.34 s: \t chan: 2, set voltage: 0 V, measured voltage: 0.0 V\n",
      "Time 0.47 s: \t chan: 2, set voltage: 0 V, measured voltage: 0.0 V\n",
      "Time 0.61 s: \t chan: 2, set voltage: 0 V, measured voltage: 0.0 V\n",
      "Time 0.74 s: \t chan: 2, set voltage: 0 V, measured voltage: 0.0 V\n",
      "Time 0.87 s: \t chan: 2, set voltage: 0 V, measured voltage: 0.0 V\n",
      "Time 1.0 s: \t chan: 2, set voltage: 0 V, measured voltage: 0.0 V\n",
      "Time 1.14 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.0 V\n",
      "Time 1.29 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.0 V\n",
      "Time 1.42 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.0 V\n",
      "Time 1.54 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.0 V\n",
      "Time 1.67 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.0 V\n",
      "Time 1.79 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.5028 V\n",
      "Time 1.9 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.5028 V\n",
      "Time 2.03 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.5028 V\n",
      "Time 2.15 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 0.5028 V\n",
      "Time 2.28 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 0.5028 V\n",
      "Time 2.41 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0032 V\n",
      "Time 2.54 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0032 V\n",
      "Time 2.68 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0032 V\n",
      "Time 2.79 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0032 V\n",
      "Time 2.92 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0035 V\n",
      "Time 3.05 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0035 V\n",
      "Time 3.24 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.0035 V\n",
      "Time 3.37 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.0035 V\n",
      "Time 3.5 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.5057 V\n",
      "Time 3.63 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.5057 V\n",
      "Time 3.76 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.5057 V\n",
      "Time 3.88 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.5057 V\n",
      "Time 4.01 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.5057 V\n",
      "Time 4.2 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 1.506 V\n",
      "Time 4.34 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 1.506 V\n",
      "Time 4.47 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 1.506 V\n",
      "Time 4.61 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 1.506 V\n",
      "Time 4.75 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 1.506 V\n",
      "Time 4.88 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.0058 V\n",
      "Time 4.99 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.0058 V\n",
      "Time 5.12 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.0058 V\n",
      "Time 5.26 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.0058 V\n",
      "Time 5.39 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.006 V\n",
      "Time 5.52 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.006 V\n",
      "Time 5.65 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.006 V\n",
      "Time 5.78 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.006 V\n",
      "Time 5.92 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.5033 V\n",
      "Time 6.06 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.5033 V\n",
      "Time 6.3 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 2.5033 V\n",
      "Time 6.43 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 2.5033 V\n",
      "Time 6.54 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 2.5033 V\n",
      "Time 6.68 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.003 V\n",
      "Time 6.78 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.003 V\n",
      "Time 6.91 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.003 V\n",
      "Time 7.04 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.0036 V\n",
      "Time 7.17 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.0036 V\n",
      "Time 7.29 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.0036 V\n",
      "Time 7.44 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.0036 V\n",
      "Time 7.57 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.0036 V\n",
      "Time 7.71 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.5034 V\n",
      "Time 7.84 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.5034 V\n",
      "Time 7.97 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.5034 V\n",
      "Time 8.09 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.5034 V\n",
      "Time 8.21 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.5034 V\n",
      "Time 8.33 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 3.5034 V\n",
      "Time 8.46 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 3.5037 V\n",
      "Time 8.59 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 3.5037 V\n",
      "Time 8.72 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 3.5037 V\n",
      "Time 8.84 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.0026 V\n",
      "Time 8.98 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.0026 V\n",
      "Time 9.11 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.0026 V\n",
      "Time 9.24 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.0026 V\n",
      "Time 9.41 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.0026 V\n",
      "Time 9.54 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.0027 V\n",
      "Time 9.66 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.0027 V\n",
      "Time 9.79 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.0027 V\n",
      "Time 9.92 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.0027 V\n",
      "Time 10.05 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.0027 V\n",
      "Time 10.17 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.5036 V\n",
      "Time 10.28 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.5036 V\n",
      "Time 10.41 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 4.5036 V\n",
      "Time 10.53 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 4.5036 V\n",
      "Time 10.66 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.0034 V\n",
      "Time 10.79 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.0034 V\n",
      "Time 10.93 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.0034 V\n",
      "Time 11.07 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.0034 V\n",
      "Time 11.2 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.0034 V\n",
      "Time 11.43 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.0038 V\n",
      "Time 11.56 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.0038 V\n",
      "Time 11.67 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.0038 V\n",
      "Time 11.8 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.0038 V\n",
      "Time 11.92 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.5007 V\n",
      "Time 12.05 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.5007 V\n",
      "Time 12.17 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.5007 V\n",
      "Time 12.3 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.5007 V\n",
      "Time 12.43 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 5.5008 V\n",
      "Time 12.57 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 5.5008 V\n",
      "Time 12.7 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 5.5008 V\n",
      "Time 12.84 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 5.5008 V\n",
      "Time 12.97 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 5.5008 V\n",
      "Time 13.1 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.0042 V\n",
      "Time 13.22 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.0042 V\n",
      "Time 13.43 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.0042 V\n",
      "Time 13.57 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5032 V\n",
      "Time 13.7 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5032 V\n",
      "Time 13.83 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5032 V\n",
      "Time 13.95 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5032 V\n",
      "Time 14.08 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5032 V\n",
      "Time 14.21 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5042 V\n",
      "Time 14.43 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 6.5042 V\n",
      "Time 14.57 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 6.5042 V\n",
      "Time 14.69 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 6.5042 V\n",
      "Time 14.82 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.0042 V\n",
      "Time 14.97 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.0042 V\n",
      "Time 15.1 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.0042 V\n",
      "Time 15.24 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.0042 V\n",
      "Time 15.42 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.0044 V\n",
      "Time 15.55 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.0044 V\n",
      "Time 15.69 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.0044 V\n",
      "Time 15.83 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.0044 V\n",
      "Time 15.96 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.0044 V\n",
      "Time 16.09 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.5036 V\n",
      "Time 16.21 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.5036 V\n",
      "Time 16.34 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.5036 V\n",
      "Time 16.47 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 7.5036 V\n",
      "Time 16.57 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 7.7208 V\n",
      "Time 16.67 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 7.7208 V\n",
      "Time 16.8 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 7.7208 V\n",
      "Time 16.94 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 7.7208 V\n",
      "Time 17.07 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 7.7208 V\n",
      "Time 17.21 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.0039 V\n",
      "Time 17.35 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.0039 V\n",
      "Time 17.47 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.0039 V\n",
      "Time 17.6 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.0039 V\n",
      "Time 17.74 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5021 V\n",
      "Time 17.87 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5021 V\n",
      "Time 18.0 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5021 V\n",
      "Time 18.14 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5021 V\n",
      "Time 18.27 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5021 V\n",
      "Time 18.49 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 8.5024 V\n",
      "Time 18.63 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 8.5024 V\n",
      "Time 18.75 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 8.5024 V\n",
      "Time 18.88 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 8.5024 V\n",
      "Time 19.01 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.003 V\n",
      "Time 19.15 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.003 V\n",
      "Time 19.28 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.003 V\n",
      "Time 19.48 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.003 V\n",
      "Time 19.62 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.0032 V\n",
      "Time 19.75 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.0032 V\n",
      "Time 19.89 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.0032 V\n",
      "Time 20.03 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.0032 V\n",
      "Time 20.16 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.5046 V\n",
      "Time 20.28 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.5046 V\n",
      "Time 20.41 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.5046 V\n",
      "Time 20.55 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 9.5046 V\n",
      "Time 20.68 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 9.5046 V\n",
      "Time 20.82 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 9.5046 V\n",
      "Time 20.95 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0019 V\n",
      "Time 21.05 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0019 V\n",
      "Time 21.18 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0019 V\n",
      "Time 21.32 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0035 V\n",
      "Time 21.44 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0035 V\n",
      "Time 21.59 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.0035 V\n",
      "Time 21.75 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.0035 V\n",
      "Time 21.87 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.0035 V\n",
      "Time 22.0 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.5029 V\n",
      "Time 22.13 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.5029 V\n",
      "Time 22.26 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.5029 V\n",
      "Time 22.4 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.5029 V\n",
      "Time 22.59 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 10.5029 V\n",
      "Time 22.72 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 10.5029 V\n",
      "Time 22.83 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 10.5029 V\n",
      "Time 22.96 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 10.5029 V\n",
      "Time 23.09 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.0009 V\n",
      "Time 23.23 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.0009 V\n",
      "Time 23.36 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.0009 V\n",
      "Time 23.5 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.0009 V\n",
      "Time 23.66 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.0009 V\n",
      "Time 23.8 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.0009 V\n",
      "Time 23.91 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.0009 V\n",
      "Time 24.04 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.0009 V\n",
      "Time 24.17 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.0009 V\n",
      "Time 24.31 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.0009 V\n",
      "Time 24.44 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.503 V\n",
      "Time 24.65 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 11.503 V\n",
      "Time 24.79 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 11.503 V\n",
      "Time 24.92 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0025 V\n",
      "Time 25.04 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0025 V\n",
      "Time 25.18 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0025 V\n",
      "Time 25.32 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0025 V\n",
      "Time 25.44 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0025 V\n",
      "Time 25.57 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0032 V\n",
      "Time 25.7 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.0032 V\n",
      "Time 25.84 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.0032 V\n",
      "Time 25.97 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.0032 V\n",
      "Time 26.1 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.0032 V\n",
      "Time 26.23 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.506 V\n",
      "Time 26.38 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.506 V\n",
      "Time 26.51 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.506 V\n",
      "Time 26.69 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 12.5063 V\n",
      "Time 26.84 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 12.5063 V\n",
      "Time 26.98 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 12.5063 V\n",
      "Time 27.11 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 12.5063 V\n",
      "Time 27.21 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 12.5063 V\n",
      "Time 27.34 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.0027 V\n",
      "Time 27.44 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.0027 V\n",
      "Time 27.57 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.0027 V\n",
      "Time 27.76 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.0604 V\n",
      "Time 27.89 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.0604 V\n",
      "Time 28.03 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.0604 V\n",
      "Time 28.16 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.0604 V\n",
      "Time 28.29 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.0604 V\n",
      "Time 28.42 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.5029 V\n",
      "Time 28.53 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.5029 V\n",
      "Time 28.65 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.5029 V\n",
      "Time 28.8 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 13.5029 V\n",
      "Time 28.94 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 13.5029 V\n",
      "Time 29.07 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0032 V\n",
      "Time 29.21 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0032 V\n",
      "Time 29.34 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0032 V\n",
      "Time 29.47 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0032 V\n",
      "Time 29.57 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0032 V\n",
      "Time 29.67 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0032 V\n",
      "Time 29.8 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.0036 V\n",
      "Time 29.94 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.0036 V\n",
      "Time 30.07 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.0036 V\n",
      "Time 30.21 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.5026 V\n",
      "Time 30.34 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.5026 V\n",
      "Time 30.48 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.5026 V\n",
      "Time 30.61 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.5026 V\n",
      "Time 30.84 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 14.5026 V\n",
      "Time 30.97 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 14.503 V\n",
      "Time 31.09 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 14.503 V\n",
      "Time 31.22 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 14.503 V\n",
      "Time 31.36 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.0019 V\n",
      "Time 31.5 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.0019 V\n",
      "Time 31.63 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.0019 V\n",
      "Time 31.82 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.0019 V\n",
      "Time 31.95 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.0019 V\n",
      "Time 32.07 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.4992 V\n",
      "Time 32.17 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.4992 V\n",
      "Time 32.29 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.4992 V\n",
      "Time 32.42 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.4992 V\n",
      "Time 32.54 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.4992 V\n",
      "Time 32.65 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.4992 V\n",
      "Time 32.82 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 15.501 V\n",
      "Time 32.94 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 15.501 V\n",
      "Time 33.07 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 15.501 V\n",
      "Time 33.2 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 15.501 V\n",
      "Time 33.33 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.0016 V\n",
      "Time 33.43 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.0016 V\n",
      "Time 33.57 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.0016 V\n",
      "Time 33.7 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.0016 V\n",
      "Time 33.81 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.0016 V\n",
      "Time 33.93 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.0016 V\n",
      "Time 34.07 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.002 V\n",
      "Time 34.2 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.002 V\n",
      "Time 34.34 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.5029 V\n",
      "Time 34.48 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.5029 V\n",
      "Time 34.61 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.5029 V\n",
      "Time 34.72 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.5029 V\n",
      "Time 34.93 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 16.5029 V\n",
      "Time 35.07 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 16.5031 V\n",
      "Time 35.19 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 16.5031 V\n",
      "Time 35.32 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 16.5031 V\n",
      "Time 35.45 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 16.5031 V\n",
      "Time 35.58 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 16.5031 V\n",
      "Time 35.71 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.0035 V\n",
      "Time 35.82 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.0035 V\n",
      "Time 35.93 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.0035 V\n",
      "Time 36.03 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.0035 V\n",
      "Time 36.16 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.5028 V\n",
      "Time 36.26 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.5028 V\n",
      "Time 36.37 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.5028 V\n",
      "Time 36.5 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.5028 V\n",
      "Time 36.63 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.5028 V\n",
      "Time 36.78 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.5028 V\n",
      "Time 36.95 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 17.5037 V\n",
      "Time 37.09 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 17.5037 V\n",
      "Time 37.21 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 17.5037 V\n",
      "Time 37.31 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 17.5037 V\n",
      "Time 37.43 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.0051 V\n",
      "Time 37.54 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.0051 V\n",
      "Time 37.66 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.0051 V\n",
      "Time 37.79 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.0051 V\n",
      "Time 37.97 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.0051 V\n",
      "Time 38.1 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.0054 V\n",
      "Time 38.24 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.0054 V\n",
      "Time 38.37 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.0054 V\n",
      "Time 38.5 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.5037 V\n",
      "Time 38.64 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.5037 V\n",
      "Time 38.77 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.5037 V\n",
      "Time 38.9 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.5037 V\n",
      "Time 39.08 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 18.5037 V\n",
      "Time 39.22 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 18.5039 V\n",
      "Time 39.35 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 18.5039 V\n",
      "Time 39.48 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 18.5039 V\n",
      "Time 39.61 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 18.5039 V\n",
      "Time 39.74 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.0046 V\n",
      "Time 39.88 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.0046 V\n",
      "Time 40.07 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.0046 V\n",
      "Time 40.2 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.0046 V\n",
      "Time 40.33 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5009 V\n",
      "Time 40.47 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5009 V\n",
      "Time 40.59 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5009 V\n",
      "Time 40.7 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5009 V\n",
      "Time 40.83 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5009 V\n",
      "Time 40.95 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5016 V\n",
      "Time 41.14 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 19.5016 V\n",
      "Time 41.28 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 19.5016 V\n",
      "Time 41.43 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0033 V\n",
      "Time 41.56 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0033 V\n",
      "Time 41.69 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0033 V\n",
      "Time 41.8 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0033 V\n",
      "Time 41.94 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0033 V\n",
      "Time 42.06 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0038 V\n",
      "Time 42.25 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.0038 V\n",
      "Time 42.38 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.0038 V\n",
      "Time 42.52 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.0038 V\n",
      "Time 42.65 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.0038 V\n",
      "Time 42.78 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.5021 V\n",
      "Time 42.89 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.5021 V\n",
      "Time 43.03 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.5021 V\n",
      "Time 43.22 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 20.5021 V\n",
      "Time 43.36 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 20.5021 V\n",
      "Time 43.49 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 20.5024 V\n",
      "Time 43.62 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 20.5024 V\n",
      "Time 43.75 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 20.5024 V\n",
      "Time 43.89 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0014 V\n",
      "Time 43.99 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0014 V\n",
      "Time 44.12 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0014 V\n",
      "Time 44.25 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.0014 V\n",
      "Time 44.39 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.0014 V\n",
      "Time 44.52 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.5003 V\n",
      "Time 44.65 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.5003 V\n",
      "Time 44.79 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.5003 V\n",
      "Time 44.9 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.5003 V\n",
      "Time 45.03 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.5003 V\n",
      "Time 45.16 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.5003 V\n",
      "Time 45.29 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 21.5014 V\n",
      "Time 45.42 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 21.5014 V\n",
      "Time 45.55 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 21.5014 V\n",
      "Time 45.65 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 21.5014 V\n",
      "Time 45.78 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.0025 V\n",
      "Time 45.88 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.0025 V\n",
      "Time 46.01 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.0025 V\n",
      "Time 46.15 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.0025 V\n",
      "Time 46.28 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.0025 V\n",
      "Time 46.39 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.0029 V\n",
      "Time 46.49 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.0029 V\n",
      "Time 46.62 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.0029 V\n",
      "Time 46.76 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.0029 V\n",
      "Time 46.89 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5029 V\n",
      "Time 46.99 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5029 V\n",
      "Time 47.12 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5029 V\n",
      "Time 47.24 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5029 V\n",
      "Time 47.42 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 22.5029 V\n",
      "Time 47.54 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 22.5033 V\n",
      "Time 47.66 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 22.5033 V\n",
      "Time 47.78 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 22.5033 V\n",
      "Time 47.92 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 22.5033 V\n",
      "Time 48.05 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 22.5033 V\n",
      "Time 48.18 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.0039 V\n",
      "Time 48.3 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.0039 V\n",
      "Time 48.51 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.0039 V\n",
      "Time 48.64 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.2463 V\n",
      "Time 48.78 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.2463 V\n",
      "Time 48.91 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.2463 V\n",
      "Time 49.03 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.2463 V\n",
      "Time 49.16 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.2463 V\n",
      "Time 49.29 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.5041 V\n",
      "Time 49.39 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.5041 V\n",
      "Time 49.55 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 23.5041 V\n",
      "Time 49.68 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 23.5041 V\n",
      "Time 49.81 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0024 V\n",
      "Time 49.94 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0024 V\n",
      "Time 50.08 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0024 V\n",
      "Time 50.19 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0024 V\n",
      "Time 50.33 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0024 V\n",
      "Time 50.51 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.0033 V\n",
      "Time 50.65 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.0033 V\n",
      "Time 50.78 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.0033 V\n",
      "Time 50.91 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.0033 V\n",
      "Time 51.02 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.0033 V\n",
      "Time 51.17 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.5023 V\n",
      "Time 51.31 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.5023 V\n",
      "Time 51.44 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.5023 V\n",
      "Time 51.6 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 24.5023 V\n",
      "Time 51.73 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 24.5026 V\n",
      "Time 51.87 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 24.5026 V\n",
      "Time 52.0 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 24.5026 V\n",
      "Time 52.13 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 24.5026 V\n",
      "Time 52.27 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.0034 V\n",
      "Time 52.4 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.0034 V\n",
      "Time 52.57 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.0034 V\n",
      "Time 52.69 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.0034 V\n",
      "Time 52.82 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.501 V\n",
      "Time 52.95 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.501 V\n",
      "Time 53.08 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.501 V\n",
      "Time 53.21 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.501 V\n",
      "Time 53.35 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.501 V\n",
      "Time 53.48 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.5024 V\n",
      "Time 53.61 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 25.5024 V\n",
      "Time 53.74 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 25.5024 V\n",
      "Time 53.87 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 25.5024 V\n",
      "Time 54.0 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 25.5024 V\n",
      "Time 54.14 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0029 V\n",
      "Time 54.25 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0029 V\n",
      "Time 54.39 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0029 V\n",
      "Time 54.52 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0033 V\n",
      "Time 54.7 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.0033 V\n",
      "Time 54.83 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.0033 V\n",
      "Time 54.97 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.0033 V\n",
      "Time 55.1 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.0033 V\n",
      "Time 55.23 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.5029 V\n",
      "Time 55.34 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.5029 V\n",
      "Time 55.47 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.5029 V\n",
      "Time 55.67 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 26.5458 V\n",
      "Time 55.8 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 26.5458 V\n",
      "Time 55.93 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 26.5458 V\n",
      "Time 56.07 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 26.5458 V\n",
      "Time 56.21 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 26.5458 V\n",
      "Time 56.34 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.0016 V\n",
      "Time 56.45 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.0016 V\n",
      "Time 56.59 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.0016 V\n",
      "Time 56.72 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.0016 V\n",
      "Time 56.86 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.0016 V\n",
      "Time 56.99 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.0016 V\n",
      "Time 57.12 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5 V\n",
      "Time 57.22 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5 V\n",
      "Time 57.35 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5 V\n",
      "Time 57.48 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5014 V\n",
      "Time 57.58 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5014 V\n",
      "Time 57.69 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5014 V\n",
      "Time 57.81 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 27.5014 V\n",
      "Time 57.95 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 27.5014 V\n",
      "Time 58.09 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 27.5014 V\n",
      "Time 58.22 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0008 V\n",
      "Time 58.36 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0008 V\n",
      "Time 58.49 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0008 V\n",
      "Time 58.6 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0008 V\n",
      "Time 58.73 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0013 V\n",
      "Time 58.87 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.0013 V\n",
      "Time 59.0 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.0013 V\n",
      "Time 59.13 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.0013 V\n",
      "Time 59.27 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.0013 V\n",
      "Time 59.4 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5005 V\n",
      "Time 59.54 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5005 V\n",
      "Time 59.67 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5005 V\n",
      "Time 59.9 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 28.5005 V\n",
      "Time 60.04 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 28.501 V\n",
      "Time 60.15 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 28.501 V\n",
      "Time 60.29 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 28.501 V\n",
      "Time 60.42 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.0008 V\n",
      "Time 60.56 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.0008 V\n",
      "Time 60.69 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.0008 V\n",
      "Time 60.89 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.0008 V\n",
      "Time 61.03 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.0008 V\n",
      "Time 61.16 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.4995 V\n",
      "Time 61.26 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.4995 V\n",
      "Time 61.39 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.4995 V\n",
      "Time 61.53 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.4995 V\n",
      "Time 61.67 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.4995 V\n",
      "Time 61.8 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.5013 V\n",
      "Time 61.94 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 29.5013 V\n",
      "Time 62.04 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 29.5013 V\n",
      "Time 62.17 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 29.5013 V\n",
      "Time 62.27 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 29.5013 V\n",
      "Time 62.39 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 30.0008 V\n",
      "Time 62.5 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 30.0008 V\n",
      "Time 62.63 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 30.0008 V\n",
      "Time 62.77 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 30.0008 V\n",
      "Time 62.89 s: \t chan: 2, set voltage: 30.0 V, measured voltage: 30.0008 V\n",
      "Time 63.03 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 30.0013 V\n",
      "Time 63.18 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 30.0013 V\n",
      "Time 63.31 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 30.0013 V\n",
      "Time 63.44 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.5017 V\n",
      "Time 63.54 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.5017 V\n",
      "Time 63.67 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.5017 V\n",
      "Time 63.8 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.5017 V\n",
      "Time 63.93 s: \t chan: 2, set voltage: 29.5 V, measured voltage: 29.5017 V\n",
      "Time 64.07 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.5017 V\n",
      "Time 64.21 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.5018 V\n",
      "Time 64.34 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.5018 V\n",
      "Time 64.47 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.5018 V\n",
      "Time 64.6 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.5018 V\n",
      "Time 64.74 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.5018 V\n",
      "Time 64.87 s: \t chan: 2, set voltage: 29.0 V, measured voltage: 29.0014 V\n",
      "Time 65.07 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 29.0014 V\n",
      "Time 65.21 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 29.0014 V\n",
      "Time 65.34 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5032 V\n",
      "Time 65.47 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5032 V\n",
      "Time 65.6 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5032 V\n",
      "Time 65.73 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5032 V\n",
      "Time 65.86 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5032 V\n",
      "Time 65.99 s: \t chan: 2, set voltage: 28.5 V, measured voltage: 28.5012 V\n",
      "Time 66.13 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.5012 V\n",
      "Time 66.26 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.5012 V\n",
      "Time 66.39 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0018 V\n",
      "Time 66.54 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0018 V\n",
      "Time 66.68 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0018 V\n",
      "Time 66.81 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0018 V\n",
      "Time 66.94 s: \t chan: 2, set voltage: 28.0 V, measured voltage: 28.0018 V\n",
      "Time 67.17 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 28.0015 V\n",
      "Time 67.3 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 28.0015 V\n",
      "Time 67.41 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 28.0015 V\n",
      "Time 67.54 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 28.0015 V\n",
      "Time 67.66 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 28.0015 V\n",
      "Time 67.79 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5018 V\n",
      "Time 67.91 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5018 V\n",
      "Time 68.04 s: \t chan: 2, set voltage: 27.5 V, measured voltage: 27.5018 V\n",
      "Time 68.17 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.5018 V\n",
      "Time 68.3 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.5018 V\n",
      "Time 68.43 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.5018 V\n",
      "Time 68.58 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.5018 V\n",
      "Time 68.71 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.5018 V\n",
      "Time 68.85 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.0022 V\n",
      "Time 68.96 s: \t chan: 2, set voltage: 27.0 V, measured voltage: 27.0022 V\n",
      "Time 69.17 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 27.0022 V\n",
      "Time 69.3 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 27.0022 V\n",
      "Time 69.44 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 27.0022 V\n",
      "Time 69.57 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.504 V\n",
      "Time 69.7 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.504 V\n",
      "Time 69.83 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.504 V\n",
      "Time 69.97 s: \t chan: 2, set voltage: 26.5 V, measured voltage: 26.5037 V\n",
      "Time 70.15 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.5037 V\n",
      "Time 70.3 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.5037 V\n",
      "Time 70.44 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.5037 V\n",
      "Time 70.57 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0038 V\n",
      "Time 70.7 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0038 V\n",
      "Time 70.84 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0038 V\n",
      "Time 70.97 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0038 V\n",
      "Time 71.1 s: \t chan: 2, set voltage: 26.0 V, measured voltage: 26.0038 V\n",
      "Time 71.22 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 26.0038 V\n",
      "Time 71.37 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 26.0038 V\n",
      "Time 71.51 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 26.0038 V\n",
      "Time 71.64 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 26.0038 V\n",
      "Time 71.77 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 26.0038 V\n",
      "Time 71.9 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.5028 V\n",
      "Time 72.02 s: \t chan: 2, set voltage: 25.5 V, measured voltage: 25.5028 V\n",
      "Time 72.23 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.5028 V\n",
      "Time 72.37 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.1909 V\n",
      "Time 72.49 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.1909 V\n",
      "Time 72.61 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.1909 V\n",
      "Time 72.74 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.1909 V\n",
      "Time 72.86 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.1909 V\n",
      "Time 73.01 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.0039 V\n",
      "Time 73.13 s: \t chan: 2, set voltage: 25.0 V, measured voltage: 25.0039 V\n",
      "Time 73.29 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 25.0039 V\n",
      "Time 73.42 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 25.0039 V\n",
      "Time 73.55 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.5034 V\n",
      "Time 73.68 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.5034 V\n",
      "Time 73.82 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.5034 V\n",
      "Time 73.93 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.5034 V\n",
      "Time 74.06 s: \t chan: 2, set voltage: 24.5 V, measured voltage: 24.5034 V\n",
      "Time 74.25 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.5029 V\n",
      "Time 74.39 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.5029 V\n",
      "Time 74.52 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.5029 V\n",
      "Time 74.66 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0036 V\n",
      "Time 74.79 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0036 V\n",
      "Time 74.93 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0036 V\n",
      "Time 75.06 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0036 V\n",
      "Time 75.19 s: \t chan: 2, set voltage: 24.0 V, measured voltage: 24.0036 V\n",
      "Time 75.34 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 24.0036 V\n",
      "Time 75.47 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 24.0035 V\n",
      "Time 75.61 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 24.0035 V\n",
      "Time 75.74 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 24.0035 V\n",
      "Time 75.87 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 24.0035 V\n",
      "Time 76.0 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.5048 V\n",
      "Time 76.14 s: \t chan: 2, set voltage: 23.5 V, measured voltage: 23.5048 V\n",
      "Time 76.3 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.5048 V\n",
      "Time 76.4 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.5048 V\n",
      "Time 76.53 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.0053 V\n",
      "Time 76.69 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.0053 V\n",
      "Time 76.82 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.0053 V\n",
      "Time 76.95 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.0053 V\n",
      "Time 77.09 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.0053 V\n",
      "Time 77.22 s: \t chan: 2, set voltage: 23.0 V, measured voltage: 23.0045 V\n",
      "Time 77.36 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 23.0045 V\n",
      "Time 77.5 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 23.0045 V\n",
      "Time 77.63 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5044 V\n",
      "Time 77.76 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5044 V\n",
      "Time 77.89 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5044 V\n",
      "Time 78.01 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5044 V\n",
      "Time 78.14 s: \t chan: 2, set voltage: 22.5 V, measured voltage: 22.5044 V\n",
      "Time 78.33 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.5037 V\n",
      "Time 78.48 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.5037 V\n",
      "Time 78.61 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.5037 V\n",
      "Time 78.74 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.5037 V\n",
      "Time 78.87 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.5037 V\n",
      "Time 79.0 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.0037 V\n",
      "Time 79.15 s: \t chan: 2, set voltage: 22.0 V, measured voltage: 22.0037 V\n",
      "Time 79.35 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 22.0037 V\n",
      "Time 79.48 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.6916 V\n",
      "Time 79.62 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.6916 V\n",
      "Time 79.76 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.6916 V\n",
      "Time 79.91 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.6916 V\n",
      "Time 80.04 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.6916 V\n",
      "Time 80.16 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.5031 V\n",
      "Time 80.26 s: \t chan: 2, set voltage: 21.5 V, measured voltage: 21.5031 V\n",
      "Time 80.39 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.5031 V\n",
      "Time 80.51 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.5031 V\n",
      "Time 80.65 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0035 V\n",
      "Time 80.79 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0035 V\n",
      "Time 80.91 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0035 V\n",
      "Time 81.02 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0035 V\n",
      "Time 81.15 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0035 V\n",
      "Time 81.27 s: \t chan: 2, set voltage: 21.0 V, measured voltage: 21.0028 V\n",
      "Time 81.44 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 21.0028 V\n",
      "Time 81.57 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 21.0028 V\n",
      "Time 81.69 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 21.0028 V\n",
      "Time 81.82 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 21.0028 V\n",
      "Time 81.96 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.5033 V\n",
      "Time 82.1 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.5033 V\n",
      "Time 82.23 s: \t chan: 2, set voltage: 20.5 V, measured voltage: 20.5033 V\n",
      "Time 82.43 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.5029 V\n",
      "Time 82.56 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.5029 V\n",
      "Time 82.69 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.5029 V\n",
      "Time 82.83 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.5029 V\n",
      "Time 82.95 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.5029 V\n",
      "Time 83.08 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0042 V\n",
      "Time 83.2 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0042 V\n",
      "Time 83.33 s: \t chan: 2, set voltage: 20.0 V, measured voltage: 20.0042 V\n",
      "Time 83.46 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 20.0042 V\n",
      "Time 83.59 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 20.0042 V\n",
      "Time 83.73 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 20.0042 V\n",
      "Time 83.87 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5048 V\n",
      "Time 84.02 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5048 V\n",
      "Time 84.15 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5048 V\n",
      "Time 84.28 s: \t chan: 2, set voltage: 19.5 V, measured voltage: 19.5023 V\n",
      "Time 84.46 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.5023 V\n",
      "Time 84.6 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.5023 V\n",
      "Time 84.73 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.0054 V\n",
      "Time 84.86 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.0054 V\n",
      "Time 85.0 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.0054 V\n",
      "Time 85.13 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.0054 V\n",
      "Time 85.27 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.0054 V\n",
      "Time 85.4 s: \t chan: 2, set voltage: 19.0 V, measured voltage: 19.005 V\n",
      "Time 85.59 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 19.005 V\n",
      "Time 85.72 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 19.005 V\n",
      "Time 85.86 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 19.005 V\n",
      "Time 85.99 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 19.005 V\n",
      "Time 86.12 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.5041 V\n",
      "Time 86.23 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.5041 V\n",
      "Time 86.37 s: \t chan: 2, set voltage: 18.5 V, measured voltage: 18.5041 V\n",
      "Time 86.56 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.504 V\n",
      "Time 86.69 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.504 V\n",
      "Time 86.82 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.504 V\n",
      "Time 86.96 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.504 V\n",
      "Time 87.1 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.504 V\n",
      "Time 87.23 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.0059 V\n",
      "Time 87.38 s: \t chan: 2, set voltage: 18.0 V, measured voltage: 18.0059 V\n",
      "Time 87.56 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 18.0059 V\n",
      "Time 87.69 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 18.0059 V\n",
      "Time 87.82 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 18.0059 V\n",
      "Time 87.94 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.505 V\n",
      "Time 88.07 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.505 V\n",
      "Time 88.19 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.505 V\n",
      "Time 88.32 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.505 V\n",
      "Time 88.46 s: \t chan: 2, set voltage: 17.5 V, measured voltage: 17.504 V\n",
      "Time 88.61 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.504 V\n",
      "Time 88.75 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.504 V\n",
      "Time 88.88 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.0044 V\n",
      "Time 89.01 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.0044 V\n",
      "Time 89.15 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.0044 V\n",
      "Time 89.26 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.0044 V\n",
      "Time 89.39 s: \t chan: 2, set voltage: 17.0 V, measured voltage: 17.0044 V\n",
      "Time 89.58 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 17.0041 V\n",
      "Time 89.72 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 17.0041 V\n",
      "Time 89.86 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 17.0041 V\n",
      "Time 90.0 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 17.0041 V\n",
      "Time 90.11 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 17.0041 V\n",
      "Time 90.24 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.5035 V\n",
      "Time 90.35 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.5035 V\n",
      "Time 90.48 s: \t chan: 2, set voltage: 16.5 V, measured voltage: 16.5035 V\n",
      "Time 90.62 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.5035 V\n",
      "Time 90.75 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.1865 V\n",
      "Time 90.89 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.1865 V\n",
      "Time 91.02 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.1865 V\n",
      "Time 91.14 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.1865 V\n",
      "Time 91.27 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.1865 V\n",
      "Time 91.4 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.0022 V\n",
      "Time 91.52 s: \t chan: 2, set voltage: 16.0 V, measured voltage: 16.0022 V\n",
      "Time 91.64 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 16.0022 V\n",
      "Time 91.77 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 16.0022 V\n",
      "Time 91.9 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.5019 V\n",
      "Time 92.03 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.5019 V\n",
      "Time 92.16 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.5019 V\n",
      "Time 92.3 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.5019 V\n",
      "Time 92.42 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.5019 V\n",
      "Time 92.56 s: \t chan: 2, set voltage: 15.5 V, measured voltage: 15.5016 V\n",
      "Time 92.69 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.5016 V\n",
      "Time 92.82 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.5016 V\n",
      "Time 92.94 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.5016 V\n",
      "Time 93.07 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.5016 V\n",
      "Time 93.19 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.0028 V\n",
      "Time 93.31 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.0028 V\n",
      "Time 93.44 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.0028 V\n",
      "Time 93.54 s: \t chan: 2, set voltage: 15.0 V, measured voltage: 15.0028 V\n",
      "Time 93.75 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 15.0028 V\n",
      "Time 93.89 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 15.0023 V\n",
      "Time 94.0 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 15.0023 V\n",
      "Time 94.13 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 15.0023 V\n",
      "Time 94.27 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.5035 V\n",
      "Time 94.41 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.5035 V\n",
      "Time 94.54 s: \t chan: 2, set voltage: 14.5 V, measured voltage: 14.5035 V\n",
      "Time 94.73 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.5035 V\n",
      "Time 94.87 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.5035 V\n",
      "Time 95.02 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0069 V\n",
      "Time 95.15 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0069 V\n",
      "Time 95.28 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0069 V\n",
      "Time 95.41 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0069 V\n",
      "Time 95.55 s: \t chan: 2, set voltage: 14.0 V, measured voltage: 14.0043 V\n",
      "Time 95.73 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 14.0043 V\n",
      "Time 95.88 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 14.0043 V\n",
      "Time 96.01 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 14.0043 V\n",
      "Time 96.14 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.5038 V\n",
      "Time 96.29 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.5038 V\n",
      "Time 96.42 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.5038 V\n",
      "Time 96.55 s: \t chan: 2, set voltage: 13.5 V, measured voltage: 13.5038 V\n",
      "Time 96.74 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.5038 V\n",
      "Time 96.87 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.5034 V\n",
      "Time 97.0 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.5034 V\n",
      "Time 97.14 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.5034 V\n",
      "Time 97.27 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.0033 V\n",
      "Time 97.38 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.0033 V\n",
      "Time 97.51 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.0033 V\n",
      "Time 97.65 s: \t chan: 2, set voltage: 13.0 V, measured voltage: 13.0033 V\n",
      "Time 97.83 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 13.0033 V\n",
      "Time 97.95 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 13.0031 V\n",
      "Time 98.08 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 13.0031 V\n",
      "Time 98.21 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 13.0031 V\n",
      "Time 98.33 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 13.0031 V\n",
      "Time 98.43 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 13.0031 V\n",
      "Time 98.56 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.5068 V\n",
      "Time 98.67 s: \t chan: 2, set voltage: 12.5 V, measured voltage: 12.5068 V\n",
      "Time 98.79 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.5068 V\n",
      "Time 98.93 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.5068 V\n",
      "Time 99.07 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0044 V\n",
      "Time 99.19 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0044 V\n",
      "Time 99.32 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0044 V\n",
      "Time 99.45 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0044 V\n",
      "Time 99.58 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0044 V\n",
      "Time 99.71 s: \t chan: 2, set voltage: 12.0 V, measured voltage: 12.0036 V\n",
      "Time 99.85 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 12.0036 V\n",
      "Time 99.98 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 12.0036 V\n",
      "Time 100.11 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 12.0036 V\n",
      "Time 100.24 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 12.0036 V\n",
      "Time 100.38 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.5037 V\n",
      "Time 100.52 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.5037 V\n",
      "Time 100.66 s: \t chan: 2, set voltage: 11.5 V, measured voltage: 11.5037 V\n",
      "Time 100.89 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.5037 V\n",
      "Time 101.02 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.5034 V\n",
      "Time 101.13 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.5034 V\n",
      "Time 101.26 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.5034 V\n",
      "Time 101.39 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.0027 V\n",
      "Time 101.5 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.0027 V\n",
      "Time 101.63 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.0027 V\n",
      "Time 101.76 s: \t chan: 2, set voltage: 11.0 V, measured voltage: 11.0027 V\n",
      "Time 101.9 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 11.0027 V\n",
      "Time 102.04 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 11.0027 V\n",
      "Time 102.17 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.7546 V\n",
      "Time 102.3 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.7546 V\n",
      "Time 102.44 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.7546 V\n",
      "Time 102.57 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.5034 V\n",
      "Time 102.68 s: \t chan: 2, set voltage: 10.5 V, measured voltage: 10.5034 V\n",
      "Time 102.89 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.5034 V\n",
      "Time 103.03 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.5034 V\n",
      "Time 103.16 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.5034 V\n",
      "Time 103.3 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0046 V\n",
      "Time 103.43 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0046 V\n",
      "Time 103.56 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0046 V\n",
      "Time 103.68 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.0046 V\n",
      "Time 103.81 s: \t chan: 2, set voltage: 10.0 V, measured voltage: 10.004 V\n",
      "Time 103.94 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 10.004 V\n",
      "Time 104.08 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 10.004 V\n",
      "Time 104.21 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 10.004 V\n",
      "Time 104.34 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 10.004 V\n",
      "Time 104.47 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.5053 V\n",
      "Time 104.61 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.5053 V\n",
      "Time 104.74 s: \t chan: 2, set voltage: 9.5 V, measured voltage: 9.5053 V\n",
      "Time 104.95 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.5053 V\n",
      "Time 105.09 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.5053 V\n",
      "Time 105.22 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.5049 V\n",
      "Time 105.36 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.5049 V\n",
      "Time 105.48 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.5049 V\n",
      "Time 105.61 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.0037 V\n",
      "Time 105.72 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.0037 V\n",
      "Time 105.85 s: \t chan: 2, set voltage: 9.0 V, measured voltage: 9.0037 V\n",
      "Time 105.98 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 9.0037 V\n",
      "Time 106.11 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 9.0037 V\n",
      "Time 106.25 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5044 V\n",
      "Time 106.4 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5044 V\n",
      "Time 106.53 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5044 V\n",
      "Time 106.66 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5044 V\n",
      "Time 106.79 s: \t chan: 2, set voltage: 8.5 V, measured voltage: 8.5044 V\n",
      "Time 107.02 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.503 V\n",
      "Time 107.16 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.503 V\n",
      "Time 107.27 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.503 V\n",
      "Time 107.41 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.0048 V\n",
      "Time 107.51 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.0048 V\n",
      "Time 107.64 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.0048 V\n",
      "Time 107.77 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.0048 V\n",
      "Time 107.9 s: \t chan: 2, set voltage: 8.0 V, measured voltage: 8.0048 V\n",
      "Time 108.04 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 8.0043 V\n",
      "Time 108.18 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 8.0043 V\n",
      "Time 108.32 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 8.0043 V\n",
      "Time 108.45 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 8.0043 V\n",
      "Time 108.58 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 8.0043 V\n",
      "Time 108.71 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.5041 V\n",
      "Time 108.83 s: \t chan: 2, set voltage: 7.5 V, measured voltage: 7.5041 V\n",
      "Time 109.04 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.5041 V\n",
      "Time 109.18 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.2759 V\n",
      "Time 109.31 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.2759 V\n",
      "Time 109.44 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.2759 V\n",
      "Time 109.56 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.2759 V\n",
      "Time 109.69 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.2759 V\n",
      "Time 109.83 s: \t chan: 2, set voltage: 7.0 V, measured voltage: 7.005 V\n",
      "Time 110.04 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 7.005 V\n",
      "Time 110.18 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 7.005 V\n",
      "Time 110.3 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 7.005 V\n",
      "Time 110.43 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5054 V\n",
      "Time 110.58 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5054 V\n",
      "Time 110.71 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5054 V\n",
      "Time 110.84 s: \t chan: 2, set voltage: 6.5 V, measured voltage: 6.5054 V\n",
      "Time 111.03 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.5048 V\n",
      "Time 111.16 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.5048 V\n",
      "Time 111.29 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.5048 V\n",
      "Time 111.43 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.0048 V\n",
      "Time 111.55 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.0048 V\n",
      "Time 111.68 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.0048 V\n",
      "Time 111.81 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.0048 V\n",
      "Time 111.93 s: \t chan: 2, set voltage: 6.0 V, measured voltage: 6.0048 V\n",
      "Time 112.07 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 6.0048 V\n",
      "Time 112.2 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 6.0046 V\n",
      "Time 112.33 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 6.0046 V\n",
      "Time 112.46 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 6.0046 V\n",
      "Time 112.59 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 6.0046 V\n",
      "Time 112.73 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.5012 V\n",
      "Time 112.84 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.5012 V\n",
      "Time 112.97 s: \t chan: 2, set voltage: 5.5 V, measured voltage: 5.5012 V\n",
      "Time 113.16 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.5012 V\n",
      "Time 113.28 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.046 V\n",
      "Time 113.41 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.046 V\n",
      "Time 113.53 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.046 V\n",
      "Time 113.67 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.046 V\n",
      "Time 113.8 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.046 V\n",
      "Time 113.93 s: \t chan: 2, set voltage: 5.0 V, measured voltage: 5.0041 V\n",
      "Time 114.12 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 5.0041 V\n",
      "Time 114.26 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 5.0041 V\n",
      "Time 114.39 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 5.0041 V\n",
      "Time 114.53 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 5.0041 V\n",
      "Time 114.66 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.5043 V\n",
      "Time 114.79 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.5043 V\n",
      "Time 114.92 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.5043 V\n",
      "Time 115.05 s: \t chan: 2, set voltage: 4.5 V, measured voltage: 4.5039 V\n",
      "Time 115.23 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.5039 V\n",
      "Time 115.37 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.5039 V\n",
      "Time 115.5 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.5039 V\n",
      "Time 115.64 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.5039 V\n",
      "Time 115.77 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.0031 V\n",
      "Time 115.89 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.0031 V\n",
      "Time 116.02 s: \t chan: 2, set voltage: 4.0 V, measured voltage: 4.0031 V\n",
      "Time 116.22 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 4.0029 V\n",
      "Time 116.35 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 4.0029 V\n",
      "Time 116.48 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 4.0029 V\n",
      "Time 116.63 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 4.0029 V\n",
      "Time 116.76 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 4.0029 V\n",
      "Time 116.9 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.5039 V\n",
      "Time 117.05 s: \t chan: 2, set voltage: 3.5 V, measured voltage: 3.5039 V\n",
      "Time 117.23 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.5039 V\n",
      "Time 117.37 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.5039 V\n",
      "Time 117.5 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.0049 V\n",
      "Time 117.63 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.0049 V\n",
      "Time 117.76 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.0049 V\n",
      "Time 117.88 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.0049 V\n",
      "Time 118.02 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.0049 V\n",
      "Time 118.14 s: \t chan: 2, set voltage: 3.0 V, measured voltage: 3.0039 V\n",
      "Time 118.31 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 3.0039 V\n",
      "Time 118.43 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 3.0039 V\n",
      "Time 118.57 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.504 V\n",
      "Time 118.69 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.504 V\n",
      "Time 118.82 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.504 V\n",
      "Time 118.95 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.504 V\n",
      "Time 119.08 s: \t chan: 2, set voltage: 2.5 V, measured voltage: 2.504 V\n",
      "Time 119.29 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.5036 V\n",
      "Time 119.43 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.5036 V\n",
      "Time 119.56 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.5036 V\n",
      "Time 119.67 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.5036 V\n",
      "Time 119.8 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.5036 V\n",
      "Time 119.93 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.0063 V\n",
      "Time 120.08 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.0063 V\n",
      "Time 120.21 s: \t chan: 2, set voltage: 2.0 V, measured voltage: 2.0063 V\n",
      "Time 120.35 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 2.0063 V\n",
      "Time 120.48 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.9948 V\n",
      "Time 120.61 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.9948 V\n",
      "Time 120.75 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.9948 V\n",
      "Time 120.88 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.9948 V\n",
      "Time 121.01 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.9948 V\n",
      "Time 121.14 s: \t chan: 2, set voltage: 1.5 V, measured voltage: 1.5062 V\n",
      "Time 121.33 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.5062 V\n",
      "Time 121.46 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.5062 V\n",
      "Time 121.59 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0043 V\n",
      "Time 121.71 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0043 V\n",
      "Time 121.84 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0043 V\n",
      "Time 121.97 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0043 V\n",
      "Time 122.1 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0043 V\n",
      "Time 122.23 s: \t chan: 2, set voltage: 1.0 V, measured voltage: 1.0037 V\n",
      "Time 122.4 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 1.0037 V\n",
      "Time 122.54 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 1.0037 V\n",
      "Time 122.66 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 1.0037 V\n",
      "Time 122.79 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.5034 V\n",
      "Time 122.94 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.5034 V\n",
      "Time 123.07 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.5034 V\n",
      "Time 123.2 s: \t chan: 2, set voltage: 0.5 V, measured voltage: 0.5034 V\n",
      "Time 123.37 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.5034 V\n",
      "Time 123.5 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.5031 V\n",
      "Time 123.63 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.5031 V\n",
      "Time 123.74 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.5031 V\n",
      "Time 123.86 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.5031 V\n",
      "Time 124.0 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.0 V\n",
      "Time 124.13 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.0 V\n",
      "Time 124.26 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.0 V\n",
      "Time 124.37 s: \t chan: 2, set voltage: 0.0 V, measured voltage: 0.0 V\n"
     ]
    }
   ],
   "source": [
    "# clear all data in dataframe first\n",
    "df_meas_values = df_meas_values.iloc[0:0]\n",
    "\n",
    "# execute main worker function to carry out the measurements\n",
    "#PSU_IP = '192.168.12.201'\n",
    "PSU_IP = '192.168.10.201'\n",
    "main_worker_run(tcp_ip=PSU_IP, chan=2, volt_min=0, volt_max=30, volt_inc=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9685c355-e2b8-44bb-a89e-06ee2727a27d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time [s]</th>\n",
       "      <th>Voltage set [V]</th>\n",
       "      <th>Voltage measured [V]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>123.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5031</td>\n",
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       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>124.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>124.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>124.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>124.37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>922 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Time [s]  Voltage set [V]  Voltage measured [V]\n",
       "0        0.10              0.0                0.0000\n",
       "1        0.22              0.0                0.0000\n",
       "2        0.34              0.0                0.0000\n",
       "3        0.47              0.0                0.0000\n",
       "4        0.61              0.0                0.0000\n",
       "..        ...              ...                   ...\n",
       "917    123.86              0.0                0.5031\n",
       "918    124.00              0.0                0.0000\n",
       "919    124.13              0.0                0.0000\n",
       "920    124.26              0.0                0.0000\n",
       "921    124.37              0.0                0.0000\n",
       "\n",
       "[922 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_meas_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f21b99cb-14f4-4b42-8374-63404bf7ac16",
   "metadata": {},
   "source": [
    "### Save all measurements to CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "da1956f1-80d0-456e-b233-cf1a72a28878",
   "metadata": {},
   "outputs": [],
   "source": [
    "str_path = r'./data_files/'\n",
    "str_filename = r'PyVisa_Rigol_DP832A_auto_measurement'\n",
    "\n",
    "df_meas_values.to_csv(str_path + str_filename +'.tsv', sep ='\\t', index = False, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b3902b1-e789-4004-a69b-f079527fb71a",
   "metadata": {},
   "source": [
    "## Read in the CSV file and plot the measurements\n",
    "\n",
    "### Read in the CSV file in dataframe\n",
    "\n",
    "This is a helper function for creating a dataframe from CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f2ab530c-65f3-49ff-aba5-4ac5594a3837",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dictionary_from_csv(filename, offset=0, cols_wanted=1):\n",
    "    my_dataframe = pd.read_csv(filename, sep='\\t', index_col=False, decimal='.', header=offset)\n",
    "    \n",
    "    # Delete all cloumns after the desired ones\n",
    "    my_dataframe.drop(my_dataframe.columns[cols_wanted:], axis=1, inplace=True)\n",
    "\n",
    "    return my_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3260b72-421d-487f-9ca3-19ca45cc6582",
   "metadata": {},
   "source": [
    "Read in the desired data record file to a dictionary. The existance of the file has to be proven first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e42764e1-1075-4855-8d0f-ce85bdc91e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "str_path = r'./data_files/'\n",
    "str_filename = r'PyVisa_Rigol_DP832A_auto_measurement'\n",
    "str_filepath = str_path + str_filename +'.tsv'\n",
    "    \n",
    "# prove if filename really exist before reading in to dataframe\n",
    "if os.path.isfile(str_filepath):\n",
    "    df_meas_values_csv = create_dictionary_from_csv(filename=str_filepath, offset=0, cols_wanted=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750322d8-9612-4dca-a669-dc0e474b4378",
   "metadata": {},
   "source": [
    "Whether the dataframe has been filled correctly is checked again here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "131e44e7-7d9d-4f9d-953f-c7bede2d5515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time [s]</th>\n",
       "      <th>Voltage set [V]</th>\n",
       "      <th>Voltage measured [V]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.61</td>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time [s]  Voltage set [V]  Voltage measured [V]\n",
       "0      0.10              0.0                   0.0\n",
       "1      0.22              0.0                   0.0\n",
       "2      0.34              0.0                   0.0\n",
       "3      0.47              0.0                   0.0\n",
       "4      0.61              0.0                   0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Time [s]                float64\n",
       "Voltage set [V]         float64\n",
       "Voltage measured [V]    float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display only filled dataframes\n",
    "if not len(df_meas_values_csv) == 0:\n",
    "    display(df_meas_values_csv.head(5))\n",
    "    display(df_meas_values_csv.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a146a5b3-3fe9-42bc-8646-940f2cbb0914",
   "metadata": {},
   "source": [
    "### Plot the measurement\n",
    "\n",
    "This function is used to plot the the voltage set and the voltage measured from a single measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a07fd408-1294-472b-beaa-8aeed1cd3aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for plotting the measurements\n",
    "def plot_measurement():\n",
    "    \"\"\"Plot the the voltage set and the voltage \n",
    "    measured from a single measurement.\n",
    "    \"\"\"\n",
    "    \n",
    "    _str_title = r'Automatic measurement for Rigol DP832A using PyVisa'\n",
    "    _df_handle = df_meas_values_csv\n",
    "    _str_path = r'./data_files/'\n",
    "    _str_filename = r'PyVisa_Rigol_DP832A_auto_measurement'\n",
    "    \n",
    "    metadata={'ipub': {\n",
    "      'figure': {\n",
    "        'caption': 'Plot caption: {0}'.format(_str_title)}}}\n",
    "    \n",
    "    # Figsize: a tuple (width, height) in inches\n",
    "    # Create figure and axis objects with subplots()\n",
    "    fig, ax1 = plt.subplots(num=0, figsize=(30, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "    axes = plt.gca()\n",
    "\n",
    "    plt.title(_str_title)\n",
    "\n",
    "    # List of named colors: https://matplotlib.org/stable/gallery/color/named_colors.html\n",
    "    line1 = ax1.plot(_df_handle['Time [s]'], _df_handle['Voltage set [V]'], color='navy', label='Voltage set')\n",
    "    line2 = ax1.plot(_df_handle['Time [s]'], _df_handle['Voltage measured [V]'], color='lightseagreen', label='Voltage measured')\n",
    "\n",
    "    # Set x-axis label\n",
    "    ax1.set_xlabel('Time [s]', fontsize=14)\n",
    "    # Set y-axis label\n",
    "    ax1.set_ylabel('Voltage [V]', fontsize=16)\n",
    "    ax1.set_ylim(0, 32)\n",
    "    ax1.grid(True)\n",
    "    plt.xticks(rotation=0)\n",
    "\n",
    "    # Twin object for two different y-axis on the same plot\n",
    "    #ax2 = ax1.twinx()\n",
    "\n",
    "    # Set y-axis label\n",
    "    #ax2.set_ylabel('Voltage measured [V]', fontsize=16)\n",
    "    #ax2.set_ylim(0, 40)\n",
    "    #ax2.grid(True)\n",
    "\n",
    "    # Add all lines to the same legend box\n",
    "    lines_all = line1+line2\n",
    "    labels = [l.get_label() for l in lines_all]\n",
    "    ax1.legend(lines_all, labels, loc='lower center')\n",
    "\n",
    "    # Save plot to PNG and PDF\n",
    "    plt.savefig(_str_path + _str_filename + '.png')\n",
    "    plt.savefig(_str_path + _str_filename + '.pdf')\n",
    "\n",
    "    #plt.show()\n",
    "    \n",
    "    display(fig, metadata=metadata)\n",
    "    plt.close()\n",
    "    \n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8831f9b6-ee45-497f-8cc5-ea4a807aa3ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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\n",
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      "text/plain": [
       "<Figure size 2400x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "ipub": {
       "figure": {
        "caption": "Plot caption: Automatic measurement for Rigol DP832A using PyVisa"
       }
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_measurement()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0cebf2e-4bf3-4522-ac09-52b7c693cfaf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "abstract": "This is a test abstract.",
  "ipub": {
   "bibliography": "bibliographies/example.bib",
   "language": "british",
   "listcode": true,
   "listfigures": true,
   "listtables": false,
   "titlepage": {
    "author": "Björn Kasper",
    "email": "bjoern.kasper@online.de",
    "logo": "images/Cover_image.pdf",
    "title": "Application notebook introducing the driver class 'PyVisa_Rigol_DP832A'"
   },
   "toc": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.3"
  },
  "latex_metadata": {
   "affiliation": "BG ETEM",
   "author": "Björn Kasper",
   "cover_image": "images/Cover_image.pdf",
   "email": "bjoern.kasper@online.de",
   "title": "Application notebook introducing the driver class 'PyVisa_Rigol_DP832A'"
  },
  "toc-autonumbering": true,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}