<jupyter_start><jupyter_text>Spundsteif > Analyse the experimental data of the project Spundsteif.`spundsteif` is a library that allows to clean and analyse experimental data coming from the instrumented site during the project Spundsteif.  Features `spundsteif` offers the following possibilities:  + **Analyse** of mechanical parameters computed from experimental data, with the help of a **dashboard**+ **Provides a solid documented, unit-tested library** that can be reused for the cleaning of various raw data of measurements  Installing > git clone `https://github.com/PAYEUR/spundsteif`>> pip install -e spundsteif*Note that `spundsteif` must be installed into the same python environment that you use for both your Jupyter Server and your workspace.*  How to use  Dashboard usage Open the notbook `core.ipyb` and follow the instructions to analyse your data and get the values of the mechanical parameters.  Library usage For example let us get a cleaned `pandas.dataframe` from a bunch of raw data.> one file in *.txt* format describes the position of each sensor on a *x,y,z* grid  > one file in *.csv* format describes manual measurements performed on sensors  > some files in *.ASC* format describe automatic measurements performed on sensors Import library and desired function<jupyter_code>from spundsteif.sensors import get_data<jupyter_output><empty_output><jupyter_text>Upload raw data as files  in this example we will use test data<jupyter_code># Geometry file
STRUCTURE_FILE = './test/1_structure_file.txt'

# Automatic measurements files
AUTOMATIC_FILES = ['./test/1_data_test_01.ASC',
                   './test/1_data_test_02.txt',
                   './test/1_data_test_03.txt',
                   './test/1_data_test_04.ASC'
                   ]

# Hand measured data
HAND_FILE = './test/df_hand_test.csv'<jupyter_output><empty_output><jupyter_text>Get structured and cleaned `pandas.dataframe`<jupyter_code>df = get_data(list_files_names=AUTOMATIC_FILES,
              structure_data=STRUCTURE_FILE, 
              file_name_df_hand=HAND_FILE)
df<jupyter_output><empty_output>
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '617011', 'license': 'Apache-2.0', 'n_tokens_mistral': 607, 'n_tokens_neox': 592, 'n_words': 220, 'path': 'index.ipynb', 'repo_name': 'PAYEUR/spundsteif', 'size': '2102'}