LabDataPlot

A flexible Python library for reading and plotting data from laboratory measurement equipment

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LabDataPlot simplifies the process of loading, analyzing, and visualizing data exported from laboratory instruments. Stop wrestling with different file formats and focus on your data.

Features

• Automatic format detection - Just pass the file, the library figures out the format
• Multiple equipment support - 10+ instruments from major manufacturers
• Simple plotting API - Clean wrapper around matplotlib for quick visualizations
• Direct data access - Full pandas DataFrame access for custom analysis
• Time-aware - Automatic detection and parsing of timestamp columns
• Metadata extraction - Access acquisition date, channel info, units, and more

Supported Equipment

Data Acquisition / Dataloggers

Equipment	Parser Name	File Type
Keysight 34970A/34972A	keysight	Excel (.xlsx)
Dewesoft Datalogger	dewesoft	Excel (.xlsx)
Fluke Hydra 2680/2686	fluke	CSV, Excel
Hioki LR8400/LR8401/MR8875	hioki	CSV, Excel

Oscilloscopes

Equipment	Parser Name	File Type
Tektronix TDS/MSO/DPO/MDO	tektronix	CSV
Rigol DS/MSO series	rigol	CSV
Yokogawa DL/SL series	yokogawa	CSV, Excel

Power Analyzers & Source Meters

Equipment	Parser Name	File Type
Yokogawa WT series	yokogawa	CSV, Excel
Keithley 2400/2450 SourceMeter	keithley	CSV, Excel
Keithley DMM6500/DAQ6510	keithley	CSV, Excel

Generic

Format	Parser Name	Description
CSV	csv	Generic CSV with auto-detection

Installation

pip install labdataplot

Requirements

• Python 3.10+
• pandas >= 2.0.0
• matplotlib >= 3.7.0
• openpyxl >= 3.1.0

Quick Start

Basic Usage

from labdataplot import DataLoader, Plotter

# Load data (format auto-detected)
loader = DataLoader('measurement.xlsx')

# View file information
print(loader.info)
# DataInfo(
#   equipment='Keysight 34970A',
#   channels=60,
#   time_column='Time',
#   acquisition_date='2025-11-25 17:37:50'
# )

# Create plots
plotter = Plotter(loader)
plotter.plot(['101 (VDC)', '102 (VDC)', '103 (VDC)'])
plotter.show()

Loading Data

from labdataplot import DataLoader, list_parsers

# See all available parsers
print(list_parsers())
# ['dewesoft', 'keysight', 'fluke', 'hioki', 'tektronix', 'rigol', 'yokogawa', 'keithley', 'csv']

# Auto-detect format
loader = DataLoader('data.xlsx')

# Or specify format explicitly
loader = DataLoader('data.csv', format='tektronix')

# Access data
df = loader.data              # Full pandas DataFrame
time = loader.time            # Time column (if available)
cols = loader.columns         # List of data columns
channel = loader['CH1']       # Direct column access

# Find channels by pattern
voltage_channels = loader.get_channel(r'VDC')  # All channels with "VDC"

Plotting

from labdataplot import DataLoader, Plotter

loader = DataLoader('oscilloscope_capture.csv')
plotter = Plotter(loader)

# Single channel
plotter.plot('CH1', title='Channel 1', ylabel='Voltage (V)')

# Multiple channels
plotter.plot(['CH1', 'CH2', 'CH3', 'CH4'])

# Subplots - one per channel
plotter.subplots(['CH1', 'CH2', 'CH3', 'CH4'], rows=2, cols=2)

# Quick view of first N channels
plotter.quick(10)

# Heatmap visualization
plotter.heatmap(title='All Channels Overview')

# Save figure
plotter.save('output.png', dpi=300)

plotter.show()

Comparing Multiple Files

loader1 = DataLoader('before_test.csv')
loader2 = DataLoader('after_test.csv')

plotter = Plotter(loader1)
plotter.compare(
    loader2,
    column='CH1',
    labels=['Before', 'After'],
    title='Test Comparison'
)
plotter.show()

Examples

Simple Line Plot

plotter.plot(
    ['CH1', 'CH2'],
    title='Oscilloscope Capture',
    ylabel='Voltage (V)',
    xlabel='Time (s)'
)

Subplots

plotter.subplots(
    ['101 (VDC)', '201 (VDC)', '301 (VDC)'],
    rows=3,
    title='One Channel per Slot'
)

Heatmap

plotter.heatmap(
    columns=loader.columns[:15],
    title='First 15 Channels'
)

Adding Support for New Equipment

LabDataPlot is designed to be easily extensible. To add support for new equipment:

1. Create a new parser in labdataplot/parsers/
2. Inherit from BaseParser
3. Implement detect() and parse() methods
4. Register in parsers/__init__.py

from labdataplot.parsers.base import BaseParser, DataInfo
import pandas as pd

class MyEquipmentParser(BaseParser):
    name = "my_equipment"
    description = "Parser for My Equipment"

    def detect(self, filepath: str) -> bool:
        # Return True if file matches this format
        ...

    def parse(self, filepath: str) -> tuple[pd.DataFrame, DataInfo]:
        # Read and return (DataFrame, DataInfo)
        ...

See the documentation for detailed instructions.

API Reference

DataLoader

Property/Method	Description
data	Returns pandas DataFrame with all data
info	Returns DataInfo with metadata
columns	List of data column names
time	Time column as Series (if available)
head(n)	First n rows
describe()	Statistical summary
get_channel(pattern)	Find columns matching regex pattern

Plotter

Method	Description
plot(columns, ...)	Line plot of one or more columns
subplots(columns, rows, cols, ...)	Multiple subplots
compare(*loaders, column, ...)	Compare same column across files
heatmap(columns, ...)	Heatmap visualization
quick(n)	Quick view of first n columns
save(filename, dpi)	Save current figure
show()	Display all plots

Documentation

Full documentation is available in the docs folder:

• Getting Started
• DataLoader API
• Plotter API
• Adding New Parsers
• Examples

Contributing

Contributions are welcome! Whether it's:

• Adding support for new equipment
• Bug fixes
• Documentation improvements
• Feature requests

Please feel free to open an issue or submit a pull request.

Development Setup

# Clone the repository
git clone https://github.com/lgili/LabDataPlot.git
cd LabDataPlot

# Install in development mode with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black labdataplot
ruff check labdataplot

Roadmap

• Keysight 34970A datalogger support
• Dewesoft datalogger support
• Tektronix oscilloscope support
• Rigol oscilloscope support
• Yokogawa instruments support
• Keithley SourceMeter support
• Fluke datalogger support
• Hioki datalogger support
• Generic CSV support
• Interactive plots with Plotly
• Data export functionality
• Report generation
• CLI interface
• NI DAQ support
• LeCroy oscilloscope support
• Rohde & Schwarz support

License

MIT License - see LICENSE file for details.

Acknowledgments

• Built with pandas and matplotlib
• Inspired by the need to quickly visualize test data from various lab equipment