Examples/FileExchangeEIS/FileExchangeEIS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remote EIS measurement with file transfer\n",
    "\n",
    "This example shows, when Thales and the Python remote control are executed on different computers, how to transfer the measurement files.\n",
    "\n",
    "The measurement data is automatically exchanged with the [ThalesFileInterface](https://doc.zahner.de/thales_remote/file_interface.html#thales-file-interface) class via TCP/IP.\n",
    "\n",
    "<img src=\"https://doc.zahner.de/github_resources/thales_python.png\" alt=\"Thales Python Setup\" style=\"width: 800px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from thales_remote.connection import ThalesRemoteConnection\n",
    "from thales_remote.script_wrapper import PotentiostatMode,ScanStrategy,ScanDirection,FileNaming,Pad4Mode,ThalesRemoteScriptWrapper\n",
    "from thales_remote.file_interface import ThalesFileInterface\n",
    "from zahner_analysis.file_import.ism_import import IsmImport\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.ticker import EngFormatter\n",
    "import os\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connect Python to the already launched Thales-Software\n",
    "\n",
    "The variable **remoteIP** contains the IP address of the computer on which the Thales software is running and to which the ZENNIUM is connected via USB. This variable is passed to the two constructors for connection establishment via the interface classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    remoteIP = \"192.168.2.66\"\n",
    "    \n",
    "    zenniumConnection = ThalesRemoteConnection()\n",
    "    zenniumConnection.connectToTerm(remoteIP, \"ScriptRemote\")\n",
    "    \n",
    "    zahnerZennium = ThalesRemoteScriptWrapper(zenniumConnection)\n",
    "    zahnerZennium.forceThalesIntoRemoteScript()\n",
    "\n",
    "    zahnerZennium.calibrateOffsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Establish the connection for the transfer of measurement files\n",
    "\n",
    "With the class [ThalesFileInterface](https://doc.zahner.de/thales_remote/file_interface.html#thales-file-interface) a connection to the Term/Thales software via network is established. It provides the methods to control the file transfer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "    fileInterface = ThalesFileInterface(remoteIP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perform an impedance measurement\n",
    "\n",
    "For this example, an impedance measurement is parameterized to show the transfer of the measurement data after the measurement. This is explained in detail in other examples.\n",
    "\n",
    "It is important here that the naming of the files is set to **individual**, then the ism file is named with \"myeis\", without number or time stamp. The path specified is the local path on the computer who runs Thales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    zahnerZennium.setEISOutputPath(r\"C:\\THALES\\temp\")\n",
    "    zahnerZennium.setEISNaming(FileNaming.INDIVIDUAL)\n",
    "    zahnerZennium.setEISOutputFileName(\"myeis\")\n",
    "\n",
    "    zahnerZennium.setPotentiostatMode(PotentiostatMode.POTMODE_POTENTIOSTATIC)\n",
    "    zahnerZennium.setAmplitude(100e-3)\n",
    "    zahnerZennium.setPotential(0)\n",
    "    zahnerZennium.setLowerFrequencyLimit(10)\n",
    "    zahnerZennium.setStartFrequency(1000)\n",
    "    zahnerZennium.setUpperFrequencyLimit(10000)\n",
    "    zahnerZennium.setLowerNumberOfPeriods(5)\n",
    "    zahnerZennium.setLowerStepsPerDecade(5)\n",
    "    zahnerZennium.setUpperNumberOfPeriods(20)\n",
    "    zahnerZennium.setUpperStepsPerDecade(5)\n",
    "    zahnerZennium.setScanDirection(ScanDirection.START_TO_MAX)\n",
    "    zahnerZennium.setScanStrategy(ScanStrategy.SINGLE_SINE)\n",
    "\n",
    "    zahnerZennium.enablePotentiostat()\n",
    "\n",
    "    zahnerZennium.measureEIS()\n",
    "    \n",
    "    zahnerZennium.disablePotentiostat()\n",
    "    zahnerZennium.setAmplitude(0)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtaining a single measurement file\n",
    "\n",
    "After the measurement, the measurement file can be retrieved from the remote computer using [acquireFile](https://doc.zahner.de/thales_remote/file_interface.html#thales_remote.file_interface.ThalesFileInterface.acquireFile).\n",
    "\n",
    "To transfer the file we need to pass as parameter the path of the file on the computer on which Thales is running. The file path is composed of the path and filename specified in the previous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    file = fileInterface.acquireFile(r\"C:\\THALES\\temp\\myeis.ism\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file is returned as a dictionary. This dictionary contains the following keys:\n",
    "\n",
    "**Key** |**Content** |**Example**\n",
    "-----|-----|----- \n",
    "\"name\"|Filename |\"myeis.ism\"\n",
    "\"path\"|Filepath |\"C:\\THALES\\temp\\myeis.ism\"\n",
    "\"binary_data\"|Binary file content |Type: bytearray()\n",
    "\n",
    "As an example, the file is written to the disk with appropriate Python functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    fileHandle = open(r\"D:\\myLocalDirectory\\asdf.ism\",\"wb\")\n",
    "    fileHandle.write(file.binaryData)\n",
    "    fileHandle.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The binary file content as bytearray(), of the dictionary key \"binary_data\" can be passed to the [IsmImport](https://doc.zahner.de/zahner_analysis/file_import/#zahner_analysis.file_import.ism_import.IsmImport) class for further processing. Then the measurement data can be processed directly with Python, for example to plot the data with matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "    ismFile = IsmImport(file.binaryData)\n",
    "    \n",
    "    impedanceFrequencies = ismFile.getFrequencyArray()\n",
    "    \n",
    "    impedanceAbsolute = ismFile.getImpedanceArray()\n",
    "    impedancePhase = ismFile.getPhaseArray()\n",
    "    \n",
    "    figBode, (impedanceAxis, phaseAxis) = plt.subplots(2, 1, sharex=True)\n",
    "    figBode.suptitle(\"Bode\")\n",
    "    \n",
    "    impedanceAxis.loglog(impedanceFrequencies, impedanceAbsolute, marker=\"+\", markersize=5)\n",
    "    impedanceAxis.xaxis.set_major_formatter(EngFormatter(unit=\"Hz\"))\n",
    "    impedanceAxis.yaxis.set_major_formatter(EngFormatter(unit=\"$\\Omega$\"))\n",
    "    impedanceAxis.set_xlabel(r\"$f$\")\n",
    "    impedanceAxis.set_ylabel(r\"$|Z|$\")\n",
    "    impedanceAxis.grid(which=\"both\")\n",
    "    \n",
    "    phaseAxis.semilogx(impedanceFrequencies, np.abs(impedancePhase * (360 / (2 * np.pi))), marker=\"+\", markersize=5)\n",
    "    phaseAxis.xaxis.set_major_formatter(EngFormatter(unit=\"Hz\"))\n",
    "    phaseAxis.yaxis.set_major_formatter(EngFormatter(unit=\"$°$\", sep=\"\"))\n",
    "    phaseAxis.set_xlabel(r\"$f$\")\n",
    "    phaseAxis.set_ylabel(r\"$|Phase|$\")\n",
    "    phaseAxis.grid(which=\"both\")\n",
    "    phaseAxis.set_ylim([0, 90])\n",
    "    figBode.set_size_inches(18, 12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtain all measurement files automatically\n",
    "\n",
    "Instead of calling up each measurement file individually, it is possible to switch on the automatic transfer of all files this is explained below.\n",
    "\n",
    "For the example, the contents of the local directory are first deleted to show what has been received after the measurement. **The user does not need this section.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    localDirectory = r\"D:\\myLocalDirectory\"\n",
    "    \n",
    "    # Delete the entire contents of the directory.\n",
    "    for file in os.listdir(localDirectory):\n",
    "        fileWithPath = os.path.join(localDirectory, file)\n",
    "        try:\n",
    "            if os.path.isfile(fileWithPath) or os.path.islink(fileWithPath):\n",
    "                os.unlink(fileWithPath)\n",
    "            elif os.path.isdir(fileWithPath):\n",
    "                shutil.rmtree(fileWithPath)\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration \n",
    "\n",
    "With [enableSaveReceivedFilesToDisk](https://doc.zahner.de/thales_remote/file_interface.html#thales_remote.file_interface.ThalesFileInterface.enableSaveReceivedFilesToDisk) it is configured that the received files should be saved locally. With the parameter path the path is passed, at which the files are to be stored on the local computer.\n",
    "\n",
    "The method [enableKeepReceivedFilesInObject](https://doc.zahner.de/thales_remote/file_interface.html#thales_remote.file_interface.ThalesFileInterface.enableKeepReceivedFilesInObject), which activates that the measurement data are stored in the Python object, would not be necessary, because this is the default state. If you make a lot of measurements and you don't need them in Python anymore you can disable this with [disableKeepReceivedFilesInObject](https://doc.zahner.de/thales_remote/file_interface.html#thales_remote.file_interface.ThalesFileInterface.disableKeepReceivedFilesInObject).\n",
    "\n",
    "With [enableAutomaticFileExchange](https://doc.zahner.de/thales_remote/file_interface.html#thales_remote.file_interface.ThalesFileInterface.enableAutomaticFileExchange) the automatic transfer is activated. The default parameter *fileExtensions=\"\\*.ism\\*.isc\\*.isw\"* of the method [enableAutomaticFileExchange](https://doc.zahner.de/thales_remote/file_interface.html#thales_remote.file_interface.ThalesFileInterface.enableAutomaticFileExchange) specifies that ism, isc and isw files should be transferred automatically, these are the results of EIS, CV and IE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    fileInterface.enableSaveReceivedFilesToDisk(path = localDirectory)\n",
    "    fileInterface.enableKeepReceivedFilesInObject()\n",
    "    fileInterface.enableAutomaticFileExchange()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the measurement is reconfigured so that the measurements are numbered starting with 13 and the [PAD4](https://zahner.de/products/addon-cards/pad4.html) card is used. For PAD4 measurements the automatic file transfer is also useful, because all PAD4 measurement files are transferred automatically.\n",
    "\n",
    "The configuration of the PAD4 card is explained in another notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    zahnerZennium.setEISNaming(\"counter\")\n",
    "    zahnerZennium.setEISCounter(13)\n",
    "    zahnerZennium.setEISOutputFileName(\"spectra\")\n",
    "    \n",
    "    zahnerZennium.setupPad4Channel(1,1,1,voltageRange=4.0,shuntResistor=10e-3)\n",
    "    zahnerZennium.setupPad4Channel(1,2,1,voltageRange=4.0,shuntResistor=10e-3)\n",
    "    zahnerZennium.setupPad4ModeGlobal(Pad4Mode.VOLTAGE) # or Pad4Mode.CURRENT\n",
    "    zahnerZennium.enablePad4Global()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform the measurements\n",
    "\n",
    "To show which files are transferred and how the process works, a spectrum is measured in the following, then automatic transfer is deactivated, a spectrum is measured again, and a third spectrum is measured when automatic transfer is activated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    zahnerZennium.measureEIS()\n",
    "    fileInterface.disableAutomaticFileExchange()\n",
    "\n",
    "    zahnerZennium.measureEIS()\n",
    "    \n",
    "    fileInterface.enableAutomaticFileExchange()\n",
    "    zahnerZennium.measureEIS()\n",
    "    zahnerZennium.setAmplitude(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further processing of the received data\n",
    "\n",
    "The data can be read from the object with the method [getReceivedFiles](https://doc.zahner.de/thales_remote/file_interface.html#thales_remote.file_interface.ThalesFileInterface.getReceivedFiles). Then as an example the measurement end time of the respective file is read out here.\n",
    "\n",
    "Since two PAD4 channels were active, additional measurement files are created for these channels. The filename is extended by a number depending on the impedance measurement and a additionally number for the PAD4 channels is added. The file with \"_ser00\" is the main channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spectra_0013_ser00.ism measurement finished at 2021-11-18 11:46:05.683000\n",
      "spectra_0013_ser01.ism measurement finished at 2021-11-18 11:46:05.683000\n",
      "spectra_0013_ser02.ism measurement finished at 2021-11-18 11:46:05.683000\n",
      "spectra_0015_ser00.ism measurement finished at 2021-11-18 11:48:18.116000\n",
      "spectra_0015_ser01.ism measurement finished at 2021-11-18 11:48:18.116000\n",
      "spectra_0015_ser02.ism measurement finished at 2021-11-18 11:48:18.116000\n"
     ]
    }
   ],
   "source": [
    "    for file in fileInterface.getReceivedFiles():\n",
    "        ismFile = IsmImport(file.binaryData)\n",
    "        print(f\"{file.name} measurement finished at {ismFile.getMeasurementEndDateTime()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output then shows which files were saved at the specified path.\n",
    "Here the measurement with the number 14 is missing, because the automatic transfer was deactivated before the measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spectra_0013_ser00.ism\n",
      "spectra_0013_ser01.ism\n",
      "spectra_0013_ser02.ism\n",
      "spectra_0015_ser00.ism\n",
      "spectra_0015_ser01.ism\n",
      "spectra_0015_ser02.ism\n"
     ]
    }
   ],
   "source": [
    "    for file in os.listdir(localDirectory):\n",
    "        print(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Close the connections\n",
    "\n",
    "Finally, the two connections are disconnected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "    zenniumConnection.disconnectFromTerm()\n",
    "    fileInterface.close()"
   ]
  }
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