Convert a CSV from a logging DMM to an Bokeh plot

CSVtoBokehPlot.py

from pathlib import Path
from pprint import pprint

import pandas as pd
import numpy as np

from bokeh.plotting import figure, output_file, show, save
from bokeh.models import ColumnDataSource

for source in Path('.').glob('*.csv'):
    print(source.stem)
    target = Path(source.stem + '.html')
    if target.exists():
        df = pd.read_csv(
            source,
            encoding='utf_16',
            sep=',\t',
            names=['Index', 'Detector', 'Magnitude', 'Unit', 'Time', 'Date'],
            na_values='OL',
            engine='python',  # 
        )
        
        df['Time'] = pd.to_timedelta(df['Time'])  # overload time as string to timestamp
        df['Seconds'] = df['Time'].dt.total_seconds()  # time to seconds into day
        df['Seconds'] = df['Seconds'] - min(df['Seconds'])  # shift to start the values at zero
        # df = df.set_index('Time')[['Magnitude']]  # 

        df = df.where(df['Magnitude'] > 7.5)  # where data less than value change to NA 
        df = df.fillna(method='backfill', limit=25)  # backfill NA values up to 25 counts
        df = df.dropna()  # drop remaining NA rows
        pprint(df.describe())
        print('\n'*2)

        output_file(target)  # set the tartget file to target

        # create a new plot with a title and axis labels
        p = figure(title=source.stem.capitalize(), x_axis_label='Seconds', y_axis_label='Voltage', width=1800, height=900)

        p.line(x='Seconds', y='Magnitude', source=ColumnDataSource(df), line_width=2)  # use Seconds and Mangitude data from df

        save(p, title=source.stem)  # Save p to configured output
        # show(p)  # opens the html output with default browser