Metadata-Version: 2.1
Name: beautifuljason
Version: 1.0.3
Summary: A Python Interface for JASON Software by JEOL
Author-email: Nikolay Larin <nikolay.larin@jeoluk.com>
Requires-Python: >=3.10
Description-Content-Type: text/x-rst
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: h5py>=3.11.0
Requires-Dist: numpy
Requires-Dist: colorama
Requires-Dist: pillow

Project description
===================

.. contents:: Table of Contents
   :local:

BeautifulJASON
--------------

BeautifulJASON is a Python API designed to seamlessly interface with NMR data generated by the `JASON desktop application <https://www.jeoljason.com/>`_. Developed by JEOL, JASON represents one of the company's latest additions to its suite of NMR software, available for both Windows and macOS. JASON, an acronym for JEOL Analytical Software Network, showcases JEOL's ongoing dedication to advancing analytical solutions.

With its intuitive design, BeautifulJASON simplifies and enhances the user experience with JASON. It empowers users by automating a range of routine tasks, from loading and saving data to intricate spectral analysis and report generation.

Diving deeper, BeautifulJASON introduces a specialized suite of tools designed for the HDF5-based file format, JJH5. Under the hood, this format leverages the power of the `h5py` library, which is adept at handling NumPy data structures. However, BeautifulJASON abstracts away the complexities of the raw data structures provided by `h5py`. Instead, users interact with high-level, intuitive data structures tailored specifically for handling spectral data and molecular structures. This means that rather than wrestling with generic groups, attributes, and data cubes, users can seamlessly work with familiar and intuitive data structures tailored to their scientific needs. This design choice not only simplifies the user experience but also makes BeautifulJASON especially attractive for AI and ML tasks. Remarkably, these tools have the capability to function independently of the JASON application, even on platforms where JASON isn't available, offering users unparalleled flexibility and extended utility.

Given its comprehensive features and adaptability, BeautifulJASON emerges as an indispensable tool for researchers spanning the fields of chemistry, biochemistry, and materials science. It's particularly beneficial for professionals and enthusiasts navigating NMR data from a variety of vendor formats.

Installation
------------

You can install BeautifulJASON via `pip`. Depending on your setup and environment, you might use different commands:

#. **Using `pip`**:

   For most users, installing via `pip` is the standard method.

   .. code-block:: bash

      pip install beautifuljason

#. **Using `pip` for Python 3**:

   If you have both Python 2 and Python 3 installed, you might need to use `pip3` to ensure the package is installed for Python 3.

   .. code-block:: bash

      pip3 install beautifuljason

#. **Using Python's `-m` option**:

   In some environments, it's beneficial to use Python's `-m` switch.

   .. code-block:: bash

      python -m pip install beautifuljason

#. **Using Python 3's `-m` option**:

   If you want to ensure you're installing for Python 3, use the `-m` switch with `python3`.

   .. code-block:: bash

      python3 -m pip install beautifuljason

Operating System Compatibility
------------------------------

While this package is designed to be OS Independent, it's important to note that the JASON class relies on the JASON program, which is currently built and supported only for Windows and macOS. As a result, while most of the package functions will work on Linux, the JASON class will not.

Configuring the JASON Path
---------------------------

For most users who have installed JASON in a standard manner, there's no need for manual configuration and you can likely skip this section. `BeautifulJASON` will automatically attempt to locate JASON using common default paths.

However, in certain scenarios, such as when:

- JASON is installed in a non-standard location,
- Multiple instances of JASON exist and a specific one needs to be chosen,
- The path to JASON has changed after the installation of `BeautifulJASON`,

You may need to specify or update the path to the JASON application.

The `BeautifulJASON` package is equipped with a tool, `jason_config`, to assist with this:

1. **Display the Current Configuration**:

   To view the current path settings for the JASON application:

   .. code-block:: bash

      jason_config --display

2. **Add a New JASON Path**:

   If the JASON application resides in a different location than the detected default paths:

   .. code-block:: bash

      jason_config --add_path /path/to/your/jason/application

   Be sure to replace `/path/to/your/jason/application` with the actual path to your JASON executable.

3. **For Additional Commands and Options**:

   If you need more details about available commands or want to explore other options:

   .. code-block:: bash

      jason_config --help

Always ensure that the specified path points directly to the JASON executable for `BeautifulJASON` to function correctly.

Running Unit Tests
------------------

To ensure the functionality and correctness of `BeautifulJASON` in your environment, the package comes bundled with a suite of unit tests. These tests provide a way to validate that everything is working as expected.

To run the unit tests, use the following command:

.. code-block:: bash

   python -m unittest discover beautifuljason.tests

This command will discover and run all the tests inside the `beautifuljason.tests` package. If all tests pass, it indicates that `BeautifulJASON` is functioning correctly in your environment. If any tests fail, please review the error messages for insights into potential issues.

For a more detailed output, you can run the tests in verbose mode:

.. code-block:: bash

   python -m unittest discover beautifuljason.tests -v

This will display a more detailed log of each test being run, along with its result.

Quick Start
-----------

For those who prefer diving right into code, a fully runnable example is provided in the `BeautifulJASON` library. You can find the script in the 'examples' subpackage under the name `quick_start.py`.

The example demonstrates:

- Loading a 1H spectral file.
- Applying multiplet analysis on it.
- Customizing the appearance of the spectral data.
- Saving the processed results as an image.
- Finally, viewing the generated image using Python.

Here's a look at what the `quick_start.py` script contains:

.. code-block:: python

   import os
   import tempfile
   import beautifuljason as bjason
   from PIL import Image as PILImage

   # Determine the path to the data directory inside the beautifuljason's tests subpackage
   test_data_dir = os.path.join(os.path.dirname(bjason.__file__), 'tests', 'data')

   # Specify input spectral file and define the path for the output PNG file
   input_1H_file = os.path.join(test_data_dir, "Ethylindanone_Proton-13-1.jdf")
   output_file = os.path.join(tempfile.gettempdir(), "Ethylindanone_Proton-13-1.png")

   # Create an instance of the JASON application interface
   jason = bjason.JASON()

   # Define and customize the default font settings
   font = bjason.base.Font.default_font()
   font['family'] = 'Arial'
   font['point_size'] = 12

   # Load the 1H spectral file, apply multiplet analysis, and customize its visual appearance
   with jason.create_document(input_1H_file, actions=[{'name': 'multiplet_analysis'}]) as doc:
      # Access the first spectral item and adjust its properties
      spec_item = doc.nmr_items[0]
      spec_item.header = 'Ethylindanone'
      spec_item.header_font = font
      spec_item.x_font = font
      spec_item.mult_intg_label_font = font
      spec_item.peak_label_font = font
      spec_item.plot_1d_color = '#3556d8'
      spec_item.show_y_axis = False

      # Save the customized document to an image file
      jason.save(doc, output_file)

   # Display the generated image using the default image viewer
   image = PILImage.open(output_file)
   image.show()

JASON Analyze and Report Script
-------------------------------

Overview
^^^^^^^^

The `analyze_and_report.py` script showcases the capabilities of `BeautifulJASON` by automating various tasks related to NMR data analysis and reporting. This script is designed to:

- Load multiple spectra files.
- Perform automatic analysis of spectra (e.g., multiplet analysis for 1H, peak picking for other types).
- Generate tables of peaks, parameters, and multiplet reports.
- Adjust the layout and graphical properties of spectral items.
- Save the combined results of the input spectra in a specified format, including `.jjh5`, `.jjj`, `.jdx`, and `.pdf`.

While this script serves as an example, it can also be used as a starting point for creating custom scripts tailored to specific needs.

Usage
^^^^^

Execute the script by running:

.. code-block:: bash

   python -m beautifuljason.examples.analyze_and_report [input_files] -output_files [output_file]

Where:
- `input_files`: One or more spectra files you wish to analyze and report on.
- `output_file`: The file where the combined results of the input spectra will be saved. Supported formats include `.jjh5`, `.jjj`, `.jdx`, and `.pdf`.

For example, to analyze and report on `proton.jdf`, `carbon.jdf`, and `hsqc.jdf`, and save the combined results in `combined_results.jjh5`, you would execute:

.. code-block:: bash

   python -m beautifuljason.examples.analyze_and_report proton.jdf carbon.jdf hsqc.jdf -output_files result.jjh5

Notes
^^^^^

- The script is designed to handle multiple file inputs and outputs them as a combined result, making it ideal for batch processing.
- At the end of the operation, if a `.jjh5` file is the output, the script can optionally launch JASON for visual inspection of the results.
- This script serves as a showcase of `BeautifulJASON`'s capabilities. It can be modified or used as a foundation for more complex workflows.

JASON Batch Convert Tool
------------------------

Overview
^^^^^^^^

The `jason_batch_convert` tool provides a convenient way to batch convert files that are openable by JASON into various formats. This utility is especially useful for converting large datasets or for automating repetitive conversion tasks.

Usage
^^^^^

To use the `jason_batch_convert` tool, navigate to the directory containing the tool and run:

.. code-block:: bash

   jason_batch_convert [input_directory] [output_directory] -formats [desired_formats] -extensions [file_extensions]

- `input_directory`: The directory containing the files you wish to convert.
- `output_directory`: The directory where the converted files will be saved.
- `desired_formats`: The formats you wish to convert the files to. Available choices are: `jjh5`, `jjj`, `jdx`, `jdf`, `pdf`, `png`, `jpg`, `svg`.
- `file_extensions`: The extension of the files you wish to convert (e.g., `jdf`, `jdx`, `1`).

For example, to convert all `.jdf` files in the `data` directory to `jjh5` and `pdf`, you would run:

.. code-block:: bash

   jason_batch_convert data converted -formats jjh5 pdf -extensions jdf

Notes
^^^^^

- The tool is designed to handle file-based formats.
- Folder-based formats are not supported in this version.

API Reference
-------------

For a comprehensive API reference, please refer to the `official BeautifulJASON documentation page <https://www.jeoljason.com/beautifuljason/docs>`_ on the JASON project website.

Support & Feedback
------------------

For support, questions, or to provide feedback on BeautifulJASON, please contact `JASON's support <https://www.jeoljason.com/support-and-faqs/>`_ on the JASON project website.

License
-------

BeautifulJASON is provided under the MIT License. For full license details, please refer to the `LICENSE` file included with this distribution or visit `MIT License on OSI <https://opensource.org/licenses/MIT>`_.

Changelog
---------

Version 1.0.3 (2024-07-15)
^^^^^^^^^^^^^^^^^^^^^^^^^^

- Fixed issue where an unintended image item was added to the top-left corner of the first page by `analyze_and_report.py`.
- Addressed BeautifulJASON incompatibility with NumPy 2.0.0.
- Added a `__version__` attribute to BeautifulJASON package.

Version 1.0.2 (2024-04-01)
^^^^^^^^^^^^^^^^^^^^^^^^^^

- Enhanced the JASON class constructor with a `plugins` parameter to manage plugin loading. It defaults to `['off']`, signifying that plugins are not automatically loaded.
- Improved the `Config` class in the `jason.py` module by introducing a `find_path` method. The `add_path` method now returns the index of an existing path instead of triggering an exception. Furthermore, the `add_path_to_config` function in the `jason_config.py` module has been adapted to incorporate the `find_path` method.
- All instances of 'Jason' have been updated to 'JASON' across the application, aligning with case-sensitive naming standards and the name of the binary.

Version 1.0.1 (2023-11-17)
^^^^^^^^^^^^^^^^^^^^^^^^^^

- Added relative path support to the `jason_batch_convert` and `analyze_and_report` scripts for enhanced file path flexibility.

Version 1.0.0 (2023-10-26)
^^^^^^^^^^^^^^^^^^^^^^^^^^

- Initial release: Core `BeautifulJASON` framework for JEOL JASON interactions.

