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Adding the new module
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‎.github/workflows/CI.yaml

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channels:
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- uibcdf
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- conda-forge
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- defaults
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channel_priority: strict
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create-args: >-

‎NotePad.md

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## Ideas
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Maybe, if we find different examples of scientific libraries using different unit libraries, we could justify that in a same workflow changing forms is needed.
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# ChatGPT summary
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Aquí tienes el resumen actualizado, eliminando la referencia al factor de impacto de **JOSS** y aclarando su indexación:
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---
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## **📌 Discusión sobre la publicación del artículo de PyUnitWizard**
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### **1️⃣ Objetivo del artículo**
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- Presentar **PyUnitWizard** como una herramienta para manejar unidades en cálculos científicos con Python.
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- Explicar cómo resuelve problemas de interoperabilidad entre bibliotecas (`numpy`, `matplotlib`, `pint`, etc.).
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- Incluir ejemplos de uso, impacto en la comunidad científica y comparaciones con otras herramientas similares.
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---
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### **2️⃣ Revistas consideradas**
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Evaluamos varias opciones con **factor de impacto (FI) e indexadas** en **Scopus y Web of Science**.
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#### **🔹 Opción más viable**
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- **[SoftwareX (Elsevier)](https://www.sciencedirect.com/journal/softwarex)**
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- **FI ≈ 5.0**, indexada en **Scopus y Web of Science**.
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- Publica artículos sobre **software científico de código abierto**.
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- **Alta probabilidad de aceptación** si el artículo presenta bien el software.
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#### **🔹 Alternativa rápida sin FI pero bien reconocida en la comunidad**
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- **[Journal of Open Source Software (JOSS)](https://joss.theoj.org/)**
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- **No tiene factor de impacto (FI) oficial en JCR, pero está indexado en DBLP y DOAJ**.
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- Publicación **rápida y accesible**, enfocada en software de código abierto.
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- **Buena opción si se prioriza visibilidad en la comunidad open-source y menor tiempo de revisión**.
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#### **🔹 Otras opciones viables**
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- **[Computer Physics Communications (Elsevier)](https://www.sciencedirect.com/journal/computer-physics-communications)**
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- **FI ≈ 3.5**, indexada en **Scopus y Web of Science (Q1/Q2)**.
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- Buena opción si se enfoca en aplicaciones en física computacional.
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- **[Journal of Chemical Information and Modeling (ACS)](https://pubs.acs.org/journal/jcisd8)**
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- **FI ≈ 4.0**, ideal si hay aplicaciones en química computacional.
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- **[Journal of Open Research Software (JORS)](https://openresearchsoftware.metajnl.com/)**
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- **Indexada en Scopus**, buena opción si se prioriza rapidez en la publicación.
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#### **🔹 Opciones menos recomendadas (por revisiones exigentes)**
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- *Nature Computational Science*: Muy difícil de publicar sin validaciones extensivas.
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- *Journal of Computational Science*: Buena opción, pero requiere fuerte justificación matemática.
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---
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### **3️⃣ Alternativa de presentar en una conferencia antes de la publicación**
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- Presentar **PyUnitWizard** en una conferencia para obtener feedback antes de enviar a una revista.
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- **Conferencias sugeridas**:
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- [SciPy Conference](https://www.scipy2024.scipy.org/) → Enfocada en Python en ciencia.
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- [EuroSciPy](https://www.euroscipy.org/) → Versión europea, buena para networking.
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- [IEEE e-Science Conference](https://www.escience-conference.org/) → Más formal, revisada por pares.
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---
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### **4️⃣ Estrategia recomendada**
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1. **Escribir el artículo con enfoque en software científico y su impacto**.
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2. **Enviar primero a SoftwareX (Elsevier)** por su balance entre **factor de impacto y probabilidad de aceptación**.
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3. **Si se prioriza rapidez y visibilidad en la comunidad open-source, considerar JOSS (aunque sin FI en JCR)**.
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4. **Explorar la opción de presentar en SciPy o EuroSciPy antes de la publicación**.
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5. **Si el artículo es rechazado en SoftwareX, considerar JORS como alternativa rápida**.
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---
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📢 **Conclusión**: **SoftwareX sigue siendo la mejor opción** para obtener una publicación con impacto y visibilidad.
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🔹 **JOSS es una alternativa excelente si se prioriza rapidez y reconocimiento en la comunidad open-source, aunque sin FI oficial en JCR.**
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🚀 **¿Siguiente paso?** Preparar la estructura del artículo según los requisitos de SoftwareX o JOSS.
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---
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🔹 Si necesitas más detalles o ayuda para estructurar el artículo, dime y podemos trabajarlo juntos. 📄✨

‎README.md

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# PyUnitWizard
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[![GitHub Actions Build Status](https://github.com/uibcdf/PyUnitWizard/workflows/CI/badge.svg)](https://github.com/uibcdf/PyUnitWizard/actions?query=workflow%3ACI)
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[![codecov](https://codecov.io/gh/uibcdf/PyUnitWizard/branch/master/graph/badge.svg)](https://codecov.io/gh/uibcdf/PyUnitWizard/branch/master)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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[![](https://img.shields.io/badge/Python-3.10%20%7C%203.11%20%7C%203.12-blue.svg)](https://www.python.org/downloads/)
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[![Documentation](https://github.com/uibcdf/PyUnitWizard/actions/workflows/sphinx_docs_to_gh_pages.yaml/badge.svg)](https://github.com/uibcdf/PyUnitWizard/actions/workflows/sphinx_docs_to_gh_pages.yaml)
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[![CI](https://github.com/uibcdf/PyUnitWizard/actions/workflows/CI.yaml/badge.svg)](https://github.com/uibcdf/PyUnitWizard/actions/workflows/CI.yaml)
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[![codecov](https://codecov.io/github/uibcdf/PyUnitWizard/graph/badge.svg?token=9ZMA4YZLOR)](https://codecov.io/github/uibcdf/PyUnitWizard)
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[![Install with conda](https://img.shields.io/badge/Install%20with-conda-brightgreen.svg)](https://conda.anaconda.org/uibcdf/pyunitwizard)
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[![Installation on ubuntu-latest](https://github.com/uibcdf/PyUnitWizard/actions/workflows/install_ubuntu_latest.yaml/badge.svg)](https://github.com/uibcdf/PyUnitWizard/actions/workflows/install_ubuntu_latest.yaml)
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[![Installation on macos-latest](https://github.com/uibcdf/PyUnitWizard/actions/workflows/install_macos_latest.yaml/badge.svg)](https://github.com/uibcdf/PyUnitWizard/actions/workflows/install_macos_latest.yaml)
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There are several Python libraries to work with physical quantities in the market, such as pint, unyt or openmm.unit. Imagine that your project or workflow requires the interaction with more than one of these tools, or that you are not sure if you will work with a different quantities library in the future. Wouldn't having a unique API to work with different forms of physical quantities be a relief? PyUnitWizard just does that. It is the wizard you need in your code to change the form of your quantities with no effort.
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There are several Python libraries to work with physical quantities in the
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market, such as pint, unyt or openmm.unit. Imagine that your project or
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workflow requires the interaction with more than one of these tools, or that
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you are not sure if you will work with a different quantities library in the
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future. Wouldn't having a unique API to work with different forms of physical
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quantities be a relief? PyUnitWizard just does that. It is the wizard you need
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in your code to change the form of your quantities with no effort.
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## Example
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‎devtools/requirements.yaml

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setup:
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channels: &setup_channels
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- conda-forge
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- default
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dependencies: &setup_dependencies
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- python
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- pip

‎pyunitwizard/__init__.py

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from ._version import __version__
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# Add imports here
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from .constants import constants as _constants
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from .constants import constants_synonyms as _constants_synonyms
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from .main import unit, quantity, get_form, is_quantity, is_unit
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from .main import get_value, get_unit, get_value_and_unit, change_value
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from .main import convert
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from .main import get_standard_units, standardize, get_dimensionality
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from .main import concatenate, stack, hstack, vstack
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from .main import are_compatible, are_equal, are_close, check
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from .main import get_constant, show_constants
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from . import configure
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from . import kernel as _kernel
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from . import constants
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from . import utils
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_kernel.initialize()
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‎pyunitwizard/configure/configure.py

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from pyunitwizard._private.forms import digest_form
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from pyunitwizard._private.lists_and_tuples import is_list_or_tuple
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from pyunitwizard.main import convert, get_dimensionality
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from pyunitwizard import _constants, _constants_synonyms
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from pyunitwizard.constants import _constants, _constants_synonyms
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import numpy as np
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from importlib.util import find_spec
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from typing import List, Dict, Union

‎pyunitwizard/constants.py

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‎pyunitwizard/constants/__init__.py

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from .constants import get_constant, show_constants
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from .constants import _constants, _constants_synonyms

‎pyunitwizard/constants/constants.py

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from .._private.quantity_or_unit import ArrayLike, QuantityOrUnit, QuantityLike, UnitLike
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from typing import Any, Dict, Optional, Union, Tuple
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_constants = {
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'Avogadro': [6.02214076e+23, '1/mole'],
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'Universal gas': [8.13446261815324, 'J/(kelvin*mole)'], # Avogadro * Boltzmann
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'Boltzmann': [1.380649e-23, 'J/kelvin'],
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}
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_constants_synonyms = {
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'NA': 'Avogadro',
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'R': 'Universal gas',
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'Molar gas': 'Universal gas',
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'KB': 'Boltzmann',
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}
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def get_constant(constant_name: str,
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to_unit: Optional[str]=None,
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to_form: Optional[str]=None,
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standardized: Optional[bool]=False)-> QuantityLike:
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if constant_name in _constants_synonyms:
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constant_name = _constants_synonyms[constant_name]
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try:
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value, unit = _constants[constant_name]
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output = quantity(value, unit, form=to_form, standardized=standardized)
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if to_unit is not None:
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output = convert(output, to_unit=to_unit)
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return output
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except:
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raise ValueError
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def show_constants()-> dict:
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output = {}
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for constant_name, constant_value in _constants.items():
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names = [constant_name]
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value = f'{constant_value[0]} {constant_value[1]}'
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for ii, jj in _constants_synonyms.items():
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if jj==constant_name:
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names.append(ii)
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output[tuple(names)]=value
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return output
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