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extrudion

Analyzes TRA files for stress and strain

  • 2.1.3
  • PyPI
  • Socket score

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Python Package to extract information about the Stress, Strain and Young Modulus from a set of .TRA files

Set up Instructions

Run the following command in the conda/miniconda terminal to install the package: pip install extrudion

Import the package in your Jupyter project or Python script: import extrudion as ex

Available commands:

Analyze a whole directory ex.analyzeDirectory(folder_path, cut_off = True, sample_thickness = 10) plots all the .TRA files in the given directory.

returns a pandas DataFrame containing

Arguments:

  • folder_path can be left empty to analyze the current working directory (e.g. ex.analyzeDirectory() )

  • folder_path can be a relative path to a folder within the current working directory (e.g. ex.analyzeDirectory('data') )

  • folder_path can be the absolute path (e.g. ex.analyzeDirectory('C:\Users\Desktop\extrusion_data') )

  • cut_off is by default True and must not be specified. If it is true, cuts the data at the max value of Stress

  • sample_thickeness is the value in mm of the size of the sample. The surface of the sample is sample_thickeness * 10 converted in meters squared. If not specified, a value of 10 mm is assumed.

The results are saved in analysis.csv file in the same directory.

Analyze a single file ex.analyzeFile(*file_path*, folder=[], cut_off = True, sample_thickeness = 10)

analyzeFile, analyzes a single file and returns the pandas.Dataframe with the results as shown below. -folder must be specified as a second argument -cut_off and sample_thickness can be specified as a third argument as shown above.

Mathematical Formulas

Stress = Force['N'] / sample_thickness / 10 * 10^3

returns gives the stress in kPa

Strain = ln( length['mm'] / initial length ['mm'] )

Young Modulus = slope of the best line fit for the curve

Intercept = the incercet of the previous fit

Yield Stress and Strain are the point of intersection for the Young modulus line shifted by 0.02 in the Strain and the data.

Example

image

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