{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parsing \n", "Covers examples for parsing of TGA data from multiple manufacturers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## General\n", "To import any type of TGA data, the parse_TGA function can be used for files from Perkin Elmer, Mettler Toledo, TA Instruments (Excel, TRIOS txt, and Q500 txt), and Netzsch." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pyTGA as tga" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Manufacturer: Mettler Toledo\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pyTGA as tga\n", "import os\n", "data_dir = os.path.abspath(os.path.join(os.getcwd(), '..', '..', '..', 'example_data'))\n", "my_exp_MT = tga.parse_TGA(os.path.join(data_dir, 'manufacturers', 'MettlerToledo_example_file.txt'))\n", "my_exp_PE = tga.parse_TGA(os.path.join(data_dir, 'manufacturers', 'PerkinElmer_example_file.txt'))\n", "my_exp_TA = tga.parse_TA_excel(os.path.join(data_dir, 'manufacturers', 'TA_instrument_excel.xls'))\n", "print('Manufacturer: ', my_exp_MT.manufacturer)\n", "my_exp_MT.quickplot()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TimeSample Temp.Reactor Temp.WeightTime(min)
Index
0045.64750.0004.9291500.000000
1145.69950.0834.9291000.016667
2245.77450.1674.9290500.033333
3345.82550.2504.9289900.050000
4445.91150.3334.9288500.066667
..................
1265612656805.929800.0000.028244210.933333
1265712657805.935800.0000.028353210.950000
1265812658805.930800.0000.028464210.966667
1265912659805.929800.0000.028575210.983333
1266012660805.934800.0000.028686211.000000
\n", "

12661 rows × 5 columns

\n", "
" ], "text/plain": [ " Time Sample Temp. Reactor Temp. Weight Time(min)\n", "Index \n", "0 0 45.647 50.000 4.929150 0.000000\n", "1 1 45.699 50.083 4.929100 0.016667\n", "2 2 45.774 50.167 4.929050 0.033333\n", "3 3 45.825 50.250 4.928990 0.050000\n", "4 4 45.911 50.333 4.928850 0.066667\n", "... ... ... ... ... ...\n", "12656 12656 805.929 800.000 0.028244 210.933333\n", "12657 12657 805.935 800.000 0.028353 210.950000\n", "12658 12658 805.930 800.000 0.028464 210.966667\n", "12659 12659 805.929 800.000 0.028575 210.983333\n", "12660 12660 805.934 800.000 0.028686 211.000000\n", "\n", "[12661 rows x 5 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_exp_MT.full" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, its more efficient to use the designated function for each manufacturer:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# for MT\n", "my_exp_MT = tga.parse_MT(data_dir + '/manufacturers/MettlerToledo_example_file.txt')\n", "# for PE\n", "my_exp_PE = tga.parse_PE(data_dir + '/manufacturers/PerkinElmer_example_file.txt')\n", "# for TA Instruments Excel\n", "my_exp_TA = tga.parse_TA_excel(data_dir + '/manufacturers/TA_instrument_excel.xls')\n", "# for TA Instruments TRIOS txt\n", "my_exp_TA_txt = tga.parse_TA_txt(data_dir + '/manufacturers/TAInstruments_TRIOS_example_file.txt')\n", "# for TA Instruments Q500 txt\n", "my_exp_TA_txt_old = tga.parse_TA_txt_old(data_dir + '/manufacturers/TAInstruments_example_file.txt')\n", "#for Netzsch\n", "my_exp_Netzsch = tga.parse_Netzsch(data_dir + '/manufacturers/netzsch_example3.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perking Elmer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Perkin Elmer file contains addtional information with the correct export settings. For example the method and calibration data, which allows to quickly verify both between files." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method: \n", "Start the Run \n", "\tAction occurs Immediately \n", "Switch the Gas to Nitrogen at 45.0 ml/min \n", "\tAction occurs Immediately \n", "1)\tHold for 1.0 min at 50.00°C \n", " \n", "2)\tHeat from 50.00°C to 130.00°C at 5.00°C/min \n", " \n", "3)\tHold for 20.0 min at 130.00°C \n", " \n", "4)\tHeat from 130.00°C to 600.00°C at 5.00°C/min \n", " \n", "5)\tHold for 5.0 min at 600.00°C \n", " \n", "6)\tCool from 600.00°C to 50.00°C at 500.00°C/min \n", " \n", "7)\tHold for 20.0 min at 50.00°C \n", " \n", "8)\tHeat from 50.00°C to 800.00°C at 20.00°C/min \n", "\t\tSwitch the Gas to Oxygen at 45.0 ml/min \n", "\t\t\tAction occurs Immediately \n", " \n", "9)\tHeat from 800.00°C to 1000.00°C at 100.00°C/min \n", " \n", "10)\tHold for 5.0 min at 1000.00°C \n", "\t\tSwitch the Gas to Nitrogen at 45.0 ml/min \n", "\t\t\tAction occurs Immediately \n", " \n", "\n", "Calibration:\n", "Calibration Type: \tMulti-point Calibration \n", " \n", "Ref. Material\tExp. Onset(°C)\tMeas. Onset(°C)(at Rate1)\tMeas. Onset(°C)(at Rate2) \n", "Alumel \t 154.200\t 162.360\t\t 165.650\t\tUsed \n", "Nickel \t 355.300\t 359.690\t\t 360.780\t\tUsed \n", "Perkalloy \t 596.000\t 585.880\t\t 591.810\t\tUsed \n", "Iron \t 770.000\t 775.010\t\t 778.540\t\tUsed \n", "Cobalt \t 1121.000\t 1121.000\t\t 1121.000\t\tNot Used \n", " \n", "Rate 1: 5.0 °C/min \n", "Rate 2: 20.0 °C/min \n", " \n", "NEWPAGE \n", " \n", "\tWEIGHT CALIBRATION INPUTS: \n", "Low Range \n", "Reference Weight = 100.000 mg\tMeasured Weight = 94.910 mg \n", " \n", "WEIGHT CALIBRATION COMPUTED RESULTS: \n", "Medium Range Factor = 1.054\tMedium Range Offset = 48.942 mg\tDate: 03/02/2023 09:17:05 \n", " \n", " \n", "\tTG DRIFT OPTIMIZATION \n", "Minimum: 30.00 °C \n", "Maximum: 1200.00 °C \n", "Scan Rate: 20.0 °C/min \n", " \n", "\tREGISTER READOUTS: \n", "R53: +2.94228638e+002 +0.00000000e+000 +0.00000000e+000 \n", "R64: +9.28329756409e-011 -2.85222508209e-007 +3.14076864519e-004 -1.48984974051e-001 +3.40418892973e+001 -1.13628530634e+005 \n", "R66: -1.050533e-001 +3.186334e+001 -1.135919e+005 +8.990825e-002 -1.927006e+002 -3.274816e+003 3244 14327 200 \n", " \n", " \n", "\tPROFILE VALUES FOR THIS DATA: \n", " \n", "Software Version\t13.3.3.0032 \n", "Firmware Version\tTGA 8000 V2.06 Nov 11 2020 \n", "Instrument Serial Number\t526B20120104 \n", "Load Temperature\t30.0 °C \n", "Go To Temp Rate\t30.0 °C/min \n", "Data taken using the\tLow Range \n", "Ordinate Filter\tOn \n", "\n" ] } ], "source": [ "print(my_exp_PE.method)\n", "print(my_exp_PE.calibration)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The file is also divided into sections, allowing to extract the indivudal stages of the experiment quickly:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['stage1', 'stage2', 'stage3', 'stage4', 'stage5', 'stage6', 'stage7', 'stage8', 'stage9', 'stage10']\n", " Time Unsubtracted weight Baseline weight Program Temp. \\\n", "0 36.900000 6.924197 0.0 130.0 \n", "1 36.916667 6.924231 0.0 130.0 \n", "2 36.933333 6.924265 0.0 130.0 \n", "\n", " Sample Temp. Sample Purge Flow Balance purge flow \n", "0 130.0 44.9 70.0 \n", "1 130.0 44.8 70.0 \n", "2 130.0 44.9 70.0 \n" ] } ], "source": [ "print(my_exp_PE.stage_names())\n", "print(my_exp_PE.stages['stage4'].head(3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mettler Toledo\n", "The Mettler Toledo file does not have the above mentioned features." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n", "None\n", "['stage1']\n" ] } ], "source": [ "print(my_exp_MT.method)\n", "print(my_exp_MT.calibration)\n", "print(my_exp_MT.stage_names())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To split the MT file into stages, a dict or csv file with indices for the individual stages needs to be supplied. The name of the stages can be customized." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "with dict: ['stage1', 'second_stage']\n", "with dict: ['stage1', 'second_stage']\n", "with csv: ['stage1', 'stage2', 'stage3']\n" ] } ], "source": [ "# using a dict\n", "stage_split_dict = {'stage1': {'start_index': 0, 'end_index': 100},'second_stage': {'start_index': 100, 'end_index': 200}}\n", "my_exp_MT_split = tga.parse_MT(data_dir + '/manufacturers/MettlerToledo_example_file.txt', stage_split=stage_split_dict)\n", "print('with dict: ', my_exp_MT_split.stage_names())\n", "\n", "# using a file\n", "my_exp_MT_split = tga.parse_MT(data_dir+ '/manufacturers/MettlerToledo_example_file.txt', stage_split=stage_split_dict)\n", "print('with dict: ', my_exp_MT_split.stage_names())\n", "\n", "# using a file\n", "my_exp_MT_split = tga.parse_MT(data_dir+ '/manufacturers/MettlerToledo_example_file.txt', stage_split=data_dir + '/stage_split_example.csv')\n", "\n", "print('with csv: ',my_exp_MT_split.stage_names())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TA instruments\n", "The TA instrument excel file allows for extraction of stages, but does not contain easily readable details about the method. However, additonal information can be obtained from the 'Details' sheet of the file." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['stage1_Isothermal 60.0 min', 'stage2_Isothermal 5.0 min', 'stage3_Ramp 2.00 °Cmin to 550.00 °C', 'stage4_Isothermal 20.0 min', 'stage5_Ramp 100.00 °Cmin to 30.00 °C', 'full']\n", "None\n", "None\n", "\n", "Detail sheet: value\n", "label \n", "Filename TA_insturments_excel.xls\n", "Instrument name TGA\n", "Operator NaN\n", "rundate 2025-05-03 00:00:00\n", "Sample name Sample 1\n", "... ...\n", "Source Files 2 C:\\XX\\TA Instruments\\TRIOS\\TGA5500\\xx\\xx\\xx..tri\n", "Measured 1 156.0165\n", "Known 1 154.16\n", "Measured 2 346.6409\n", "Known 2 357.22\n", "\n", "[115 rows x 1 columns]\n" ] } ], "source": [ "print(my_exp_TA.stage_names())\n", "print(my_exp_TA.method)\n", "print(my_exp_TA.calibration)\n", "\n", "print('\\nDetail sheet: ', my_exp_TA.details)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Netzsch\n", "Netzsch files contain additional metadate which is also availible via the '.details' method.\n", "Netsch files have stage support when it is included as 'Segment' in the file. The file only provides a relative weight, so the absolute weight is calculated from the sample mass." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Netzsch metadata: \n", " {'EXPORTTYPE': 'DATA ALL', 'FILE': 'CF-A_1000-ash.ngb-ds3', 'FORMAT': 'NETZSCH5', 'FTYPE': 'ASCII', 'IDENTITY': 'CF-A_1000-ash', 'DECIMAL': 'POINT', 'SEPARATOR': 'SEMICOLON', 'MTYPE': 'TG', 'INSTRUMENT': 'NETZSCH STA 449F3', 'PROJECT': '', 'DATE/TIME': '08.10.2024 15:04', 'CORR. FILE': 'blanc_1000_air.ngb-bs3', 'TEMPCAL': 'TCALZERO.TCX', 'SENSITIVITY': '---', 'LABORATORY': 'IJL', 'OPERATOR': 'TS', 'REMARK': '', 'SAMPLE': 'CF-A_1000-ash', 'SAMPLE MASS /mg': '4.28', 'MATERIAL': '', 'REFERENCE': '', 'REFERENCE MASS /mg': '', 'TYPE OF CRUCIBLE': 'DTA/TG crucible Al2O3', 'SAMPLE CRUCIBLE MASS /mg': '0', 'REFERENCE CRUCIBLE MASS /mg': '', 'TG RANGE /mg': '35000', 'TAU-R': '---', 'CORR. CODE': '020', 'EXO': '-1', 'RANGE': '37›C/15,0(K/min)/1000›C/00:00/1000›C/', 'SEGMENT': 'S1-2/2', 'SEG. 1': '37›C/15,0(K/min)/1000›C', 'SEG. 2': '1000›C/00:30/1000›C', 'skiprows': 34}\n", "Netzsch stage names: \n", " ['stage1', 'stage2']\n", " Temp Time Mass/% Gas Flow(purge2)/(ml/min) \\\n", "0 31.061 0.00 100.00000 50.0 \n", "1 31.045 0.01 100.00000 50.0 \n", "2 31.073 0.02 100.00000 50.0 \n", "3 31.057 0.03 99.95327 50.0 \n", "4 31.057 0.04 99.95327 50.0 \n", "\n", " Gas Flow(protective)/(ml/min) Segment Weight \n", "0 40.0 1 4.280 \n", "1 40.0 1 4.280 \n", "2 40.0 1 4.280 \n", "3 40.0 1 4.278 \n", "4 40.0 1 4.278 \n" ] } ], "source": [ "print('Netzsch metadata: \\n', my_exp_Netzsch.details)\n", "print('Netzsch stage names: \\n', my_exp_Netzsch.stage_names())\n", "print(my_exp_Netzsch.full.head(5))" ] } ], "metadata": { "kernelspec": { "display_name": "pytga (3.12.2)", "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 2 }