specatalog 1.1.0

Specatalog is a software framework for the systematic management and archival of spectroscopic measurement data, integrating a relational metadata database with structured data storage and analysis workflows.

Specatalog

Management of spectroscopic measurement data

Overview

Specatalog is a software tool for the systematic management and archival of spectroscopic measurement data in research environments. It combines a structured directory-based data storage system with a relational database (PostgreSQL) for metadata management.

The software is designed to ensure reproducibility, traceability, and long-term accessibility of experimental data. In addition to raw data storage, Specatalog supports the integration of processed data and evaluation results within a unified framework.

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Scope and Supported Methods

Specatalog currently supports the following spectroscopic techniques:

• Time-resolved EPR (trEPR)
• Continuous-wave EPR (cwEPR)
• Pulsed EPR (pulseEPR)
• UV/Vis spectroscopy
• Fluorescence spectroscopy
• Transient absorption (TA)

The system is designed to be extensible, allowing the integration of additional spectroscopic methods. Details on extending the data models are provided in the documentation.

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Key Features

• Structured archival system for spectroscopic data
• PostgreSQL-based metadata database
• Integration of raw data, processed data, and evaluation results
• Storage of analysis results within HDF5 files
• Loaders for common spectroscopic data formats
• Programmatic access via Python script
• Graphical user interface (GUI) for interactive data management
• Synchronization tools for remote data storage

A central aspect of Specatalog is the tight coupling between metadata and data storage, enabling consistent and reproducible data handling across projects.

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Graphical User Interface

The graphical user interface provides tools for interactive data management and inspection.

The GUI supports:

• Browsing and searching the database
• Tabular display of entries
• Editing and deletion of existing entries
• Creation of new entries
• Upload of raw measurement data into the archive

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Installation

Clone the repository and install the package locally:

git clone https://github.com/TheresiaQuintes/specatalog.git
cd specatalog
pip install .

Alternatively just run:

pip install specatalog

All dependencies are specified in the pyproject.toml file.

Consult the documentation for the first-time-setup.

Requirements

• Python = 3.11
• PostgreSQL database
• Operating systems: Linux, macOS, Windows

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Command Line Interface

Specatalog provides several command-line tools:

• specatalog-welcome Verifies installation and prints the current working directory

• specatalog-configuration Initial configuration of the system

• specatalog-gui Launches the graphical user interface

• specatalog-sync-download, specatalog-sync-upload Synchronization with remote directories

• specatalog-update-db Updates database models

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Data Model and Storage Concept

Specatalog separates metadata and data storage while maintaining a strict linkage between both.

Directory Structure

base_dir/
├── data/
│   ├── M1/
│   ├── M2/
│   ├── ...
│   └── M{ms_id}/
│       ├── additional_info/
│       ├── figures/
│       ├── literature/
│       ├── raw/
│       ├── scripts/
│       └── measurement_M{ms_id}.h5
├── molecules/
│   ├── MOL1/
│   ├── MOL2/
│   ├── ...
│   └── MOL{mol_id}/
│       ├── file_with_structure.cdxml
│       └── file_with_structure.pdf
└── allowed_values.py

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HDF5 Data Structure

Each measurement is associated with a structured HDF5 file:

measurement_{ms_id}.h5
├── raw_data
│   ├── data
│   ├── data_imag
│   ├── data_real
│   └── xaxis
├── corrected_data
│   └── <user-defined datasets>
└── evaluations
    └── <user-defined datasets>

This structure allows storing both raw data and derived results in a consistent and extendable format.

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Example Usage

Creating a New Measurement Entry

from specatalog.models import creation_pydantic_measurements as ms
from datetime import date
from specatalog.helpers.full_entry import create_full_measurement

new_measurement = ms.TREPRModel(
    molecular_id=1,
    temperature=80,
    solvent="toluene",
    date=date(2025, 12, 24),
    measured_by="your_name",
    device="ELEXSYS",
    frequenc_band="Q",
    attenuation="20dB",
    exciation_wl=530
)

fm = create_full_measurement(new_measurement, BASE_PATH, raw_data, "bruker_bes3t")

print(fm.success)
print(fm.measurement_id)

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Querying the Database

from specatalog.crud_db import read as r

filter_model = r.TREPRFilter(
    molecular_id=1,
    temperature__le=80,
    measured_by="your name"
)

results = r.run_query(filter_model, ordering_model)

print(results)
print(results[0].id)
print(results[0].molecule.name)

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Working with HDF5 Data

from specatalog.data_management.hdf5_reader import load_from_id
import matplotlib.pyplot as plt
import numpy as np

dat, file = load_from_id(1, mode="a")

x = dat.raw_data.xaxis
intensity = dat.raw_data.data

plt.plot(x, np.real(intensity))

offset = 2.3
corrected = x - offset
fit = -2 * (x - 12000)**2 + 2500

dat.corrected_data.set_attr("x-offset", 2.3)
dat.corrected_data.set_dataset("xaxis", corrected)
dat.evaluations.set_dataset("fit1", fit)

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Documentation

Comprehensive documentation, including installation details and instructions for extending the data models, is available at:

https://theresiaquintes.github.io/specatalog/

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Intended Use

Specatalog is intended for the archival and management of spectroscopic data in academic and industrial research environments. It is particularly suited for laboratories that require structured data storage, standardized metadata handling, and reproducible analysis workflows.

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License

This project is licensed under the GNU General Public License v3.0 (GPL-3.0).

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Contributing

Contributions, issue reports, and feature requests are welcome.