Combine different excel files, reformat it, and clean data appropriately for eventual usage in SPSS/R!

Features:

• Use GUI to designate input/output folders, customize settings
• Option to clean data by designating Cadence and HR limits
• Option to obtain how many seconds participants were in negative power
• Option to merge all new bike files into one excel sheet, for SPSS analysis
• Reorganizes all raw bike files into a readable format
• Reorganized files are ready to use with MatLab analysis

To Do:

• Static GUI
• One GUI for all options
• Immediate basic statistical analysis
• Entropy analysis, possibly via MatLab

How to

Open in JupyterLab

http://jupyter.domain.name/hub/user-redirect/git-pull?repo=https://github.com/pomkos/biking&branch=master&subPath=convert/biking2.0.ipynb&app=lab

Start the script:

Install all requirements

pip install -r requirements.txt

Then start the script

python start_biking.py

Compile using pyinstaller

Automatic:

Run pyinstaller on the .spec file.

pyinstaller start_biking.spec -wF

Manual:

pyinstaller start_biking.py -wF

Pyinstaller does not compile pandas in full, the .exe will not work. Edit the start_sbiking.spec and add the following AFTER "block_cipher = None":

def get_pandas_path():
    import pandas
    pandas_path = pandas.__path__[0]
    return pandas_path

Then add the following BEFORE "pyz = PYZ(a.pure, a.zipped_data,":

dict_tree = Tree(get_pandas_path(), prefix='pandas', excludes=["*.pyc"])
a.datas += dict_tree
a.binaries = filter(lambda x: 'pandas' not in x[0], a.binaries)

As of Numpy 1.16.0 you also need to modify:

hiddenimports=['numpy.core._dtype_ctypes']

And then recompile:

pyinstaller start_biking.spec -wF

Output

Before (raw):

After:

Matlab Analysis: