easy-pues 0.1.0

An easy package for understanding emission spectroscopy.

easy_pues

A lightweight toolkit for processing, cleaning, and interpreting ICP-OES analytical data.

easy_pues provides a set of convenience classes to streamline the workflow around ICP-OES data. It focuses on reading raw exports, cleaning and harmonizing element measurements, applying LOD/LOQ rules, and preparing results for downstream analysis or reporting.

The package is designed to be simple, predictable, and easy to integrate into existing Python data pipelines.

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🌱 Features

• Load Agilent 5800 result files into tidy pandas DataFrames
• Automatic type conversion (strings → floats, numeric cleanup, NA handling)
• LOD/LOQ masking
  • < LOD formatting

  • LOD formatting

  • Optional flagging mode (*, **)
• Element-wise operations with proper alignment of LOD/LOQ tables
• Helpers for MultiIndex structures
• Utility functions for common analytical workflows

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📦 Installation

pip install easy_pues

Or install directly from GitHub:

pip install git+https://github.com/AgentschapPlantentuinMeise/easy_pues

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🚀 Quick Start

import easy_pues as ep
import pandas as pd

# Load results and LOD/LOQ tables
project = ep.ICPOES("results.csv")

# Apply masking
masked = project.apply_lod_loq_mask(project.results, project.lodq)

# Or apply flagging instead
flagged = project.apply_lod_loq_flags(project.results, project.lodq, flag = True)

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🔬 LOD/LOQ Handling

easy_pues implements the standard interpretation rules:

Condition	Output (mask mode)	Output (flag mode)
value < LOD	< LOD	value*
LOD ≤ value < LOQ	> LOD	value**
value ≥ LOQ	numeric	numeric

All operations are vectorized and align element names automatically.

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📁 Typical Workflow

1. Export raw ICPOES data
2. Load into Python using easy_pues
3. Clean and harmonize element columns
4. Apply LOD/LOQ rules
5. Export masked or flagged results

Disclaimer

Developed together with Copilot AI