Examples/FileExchangeEIS/FileExchangeEIS.ipynb

{
 "cells": [
  {
   "attachments": {},
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from thales_remote.connection import ThalesRemoteConnection\n",
    "from thales_remote.script_wrapper import (\n",
    "    PotentiostatMode,\n",
    "    ScanStrategy,\n",
    "    ScanDirection,\n",
    "    FileNaming,\n",
    "    Pad4Mode,\n",
    "    ThalesRemoteScriptWrapper,\n",
    ")\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"
   ]
  },
  {
   "attachments": {},
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "devel version\n"
     ]
    }
   ],
   "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()"
   ]
  },
  {
   "attachments": {},
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileInterface = ThalesFileInterface(remoteIP)"
   ]
  },
  {
   "attachments": {},
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OK\\r'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)"
   ]
  },
  {
   "attachments": {},
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = fileInterface.acquireFile(r\"C:\\THALES\\temp\\myeis.ism\")"
   ]
  },
  {
   "attachments": {},
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fileHandle = open(r\"C:\\THALES\\temp\\manual_save.ism\", \"wb\")\n",
    "fileHandle.write(file.binaryData)\n",
    "fileHandle.close()"
   ]
  },
  {
   "attachments": {},
   "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": 19,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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(\n",
    "    impedanceFrequencies,\n",
    "    np.abs(impedancePhase * (360 / (2 * np.pi))),\n",
    "    marker=\"+\",\n",
    "    markersize=5,\n",
    ")\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()"
   ]
  },
  {
   "attachments": {},
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "localDirectory = r\"C:\\THALES\\temp\\test\"\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"
   ]
  },
  {
   "attachments": {},
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'128,FileExchange,ON'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fileInterface.enableSaveReceivedFilesToDisk(path=localDirectory)\n",
    "fileInterface.enableKeepReceivedFilesInObject()\n",
    "fileInterface.enableAutomaticFileExchange()"
   ]
  },
  {
   "attachments": {},
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OK\\r'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "attachments": {},
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OK\\r'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zahnerZennium.measureEIS()\n",
    "fileInterface.disableAutomaticFileExchange()\n",
    "\n",
    "zahnerZennium.measureEIS()\n",
    "\n",
    "fileInterface.enableAutomaticFileExchange()\n",
    "zahnerZennium.measureEIS()\n",
    "zahnerZennium.setAmplitude(0)"
   ]
  },
  {
   "attachments": {},
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spectra_0013_ser00.ism measurement finished at 2023-08-08 09:48:56.148500\n",
      "spectra_0013_ser01.ism measurement finished at 2023-08-08 09:48:56.148500\n",
      "spectra_0013_ser02.ism measurement finished at 2023-08-08 09:48:56.148500\n",
      "spectra_0015_ser00.ism measurement finished at 2023-08-08 09:50:43.787500\n",
      "spectra_0015_ser01.ism measurement finished at 2023-08-08 09:50:43.787500\n",
      "spectra_0015_ser02.ism measurement finished at 2023-08-08 09:50:43.787500\n"
     ]
    }
   ],
   "source": [
    "for file in fileInterface.getReceivedFiles():\n",
    "    ismFile = IsmImport(file.binaryData)\n",
    "    print(f\"{file.name} measurement finished at {ismFile.getMeasurementEndDateTime()}\")"
   ]
  },
  {
   "attachments": {},
   "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": 25,
   "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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Close the connections\n",
    "\n",
    "Finally, the two connections are disconnected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "zenniumConnection.disconnectFromTerm()\n",
    "fileInterface.close()"
   ]
  }
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