semvec/pypi-packages huggingface dataset:
forexy 2026.1.26
EXFOR utility codes
License: MIT
Keywords: EXFOR JSON

# ForEXy
Utility codes for the EXFOR Library

## Requirements
* pylatexenc
* pyspellchecker
* requests

## Input files
The following input files are available on the ForEXy zipped file on the [NRDC Software website](https://nds.iaea.org/nrdc/nrdc_sft/):
* JSON dictionary (e.g., dict_9133.json)
* Supplemental dictionary file for DIC227 (e.g., dict_arc_sup.227)
* CINDA code list for CODUSE (e.g., x4_coduse.cin)

## Installation
```
pip install forexy
```

## Modules 

Module | Purpose
---    | ---
CODDIC | Analyse use of codes in J4 library.
DIC227 | Produce Archive Dictionary 227 from a NUBASE file.
DICA2J | Convert Archive dictionaries to a JSON Dictionary.
DICDIS | Prepare Archive and Backup dictionaries for distribution.
DICJ2A | Convert a JSON Dictionary to Archive dictionaries.
DICJ2T | Convert a JSON Dictionary to a Transmission dictionary.
DIRINI | Split an EXFOR library tape into EXFOR entry files.
DIRUPD | Update the EXFOR entry files with an EXFOR transmission tape.
EXTMUL | Extraction of a dataset from a multiple reaction formalism subentry.
J4TOX4 | Convert a J4 file to an EXFOR file.
MAKCOV | Produce a data table and covariance matrix from a J4 file.
MAKJ4L | Produce and update J4 library.
MAKLIB | Merge EXFOR entry files into a single library tape.
POIPOI | Remove pointers from a J4 file.
REFBIB | Extract bibliography of reference by using DOI.
REFDOI | Obtain DOI for articles registered in CrossRef.
SEQADD | Add record identification to an EXFOR file.
SPELLS | Check English spell in free text in EXFOR format.
X4TOJ4 | Convert an EXROF file to a J4 file.


## References
* Naohiko Otuka, Vidya Devi, Osamu Iwamoto, "EXFOR utility codes (ForEXy) and their application to neutron fission cross section evaluation", [Appl.Radiat.Isot.225(2025)111903](https://doi.org/10.1016/j.apradiso.2025.111903) / [arXiv] ( 	
https://doi.org/10.48550/arXiv.2505.03758).
* Naohiko Otuka, "ForEXy: Utility Codes for EXFOR Library", Report [IAEA-NDS-0244](https://doi.org/10.61092/iaea.hz1z-0dx3), International Atomic Energy Agency, 2025.
pyintime-llm 0.0.1
Defensive package registration.
License: MIT
Requires Python: >=3.6

# pyintime-llm

**NOTICE: Defensive Package Registration**

To provide internal services and prevent new enterprise users from accidentally installing the wrong package, this package is treated as a placeholder package and a friendly reminder is given during installation.

## Contact

For inquiries about this package, please contact the insiders.
domaster 1.0.0
Manage your action list.
License: MIT
Requires Python: >=3.8

# DoMaster 🛠️

**DoMaster** is a robust, lightweight Console Terminal User Interface (TUI) task manager built with Python and SQLite. It is designed for high-performance, keyboard-driven workflows to manage complex projects with task dependencies and detailed reporting.

## 🌟 STATUS
Good to go!

## 🌟 Features

- **Local Persistence**: Powered by an SQLite3 backend for rapid data access and reliability.
- **Project-Centric**: Organize tasks by `project_name` with integrated priority levels.
- **Task Dependencies**: Link tasks together using the `next_task` ID field.
- **Smart Sync**: Import CSV or Tab-Delimited files with automated conflict resolution; existing UUIDs are updated while new ones are inserted.
- **Comprehensive Reporting**: 
  - **Pending Report**: HTML export grouped by project and ordered by priority.
  - **Completion Report**: HTML export grouped by date and ordered by completion time.
- **Database Mobility**: Built-in functions to dump, empty, and load database files with 2026-compliant timestamps.

## 📊 Database Schema (Table: `todo`)

| Field | Type | Description |
| :--- | :--- | :--- |
| `ID` | Integer | Primary Key (Auto-increment). |
| `uuid` | Text | **Immutable** Unique Identifier (Generated on creation). |
| `project_name` | Text | Name of the parent project. |
| `date_created` | Text | ISO-8601 Timestamp of creation. |
| `date_done` | Text | ISO-8601 Timestamp of completion. |
| `task_description`| Text | Full description of the task. |
| `task_priority` | Integer | Numerical priority (Lower = Higher priority). |
| `next_task` | Integer | Reference to the next task `ID` (Defaults to 0). |

## 🚀 Installation

More than one way to enjoy the do-master:

### ✌️Either: 

👉 **Clone the repository**:
```
bash
git clone https://github.com
cd domaster
python ./domaster.py
```

👉 **Otherwise**
```
python -m pip install whatever.whl
python -m domaster
```
drawdata 0.5.0
draw a dataset from inside Jupyter
License: MIT
Requires Python: >=3.10

### drawdata 

> "Just draw some data and get on with your day."

This small Python library contains Jupyter widgets that allow you to draw a dataset in a Jupyter
notebook. This should be very useful when teaching machine learning algorithms.

![CleanShot 2025-05-08 at 17 27 36](https://github.com/user-attachments/assets/ae87e26e-a720-494f-9fd2-ec0374a9f8f3)

The project uses [anywidget](https://anywidget.dev/) under the hood so our tools should work in Jupyter, VSCode and Colab. That also means that you get a proper widget that can interact with [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/) natively. [Here](https://www.youtube.com/watch?v=STPv0jSAQEk) is an example where updating a drawing triggers a new scikit-learn model to train ([code](https://github.com/probabl-ai/youtube-appendix/blob/main/04-drawing-data/notebook.ipynb)).

You can really get creative with this in a notebook, so feel free to give it a spin!

#### Installation 

Installation occurs via pip. 

```
python -m pip install drawdata
```

To read the data, `polars` is useful, but this library also suppots `pandas`:

```
python -m pip install pandas polars
```

#### Usage: `ScatterWidget`

You can load the scatter widget to start drawing immediately. 

```python
from drawdata import ScatterWidget

widget = ScatterWidget()
widget
```

If you want to use the dataset that you've just drawn you can do so via: 

```python
# Get the drawn data as a list of dictionaries
widget.data

# Get the drawn data as a dataframe
widget.data_as_pandas
widget.data_as_polars
```

If you're eager to do scikit-learn stuff with your drawn data you may appreciate this property instead:

```
X, y = widget.data_as_X_y
```

The assumption for this property is that if you've used multiple colors that you're interested in doing classification and if you've only drawn one color you're interested in regression. In the case of regression `y` will refer to the y-axis.

#### Usage: `BarWidget`

```python 
from drawdata import BarWidget

widget = BarWidget(collection_names=["usage", "sunshine"], n_bins=24)
widget
```

![CleanShot_2024-12-01_at_21 19 19](https://github.com/user-attachments/assets/16c936c8-d310-4fa1-9057-6fb870d43db8)