semvec/pypi-packages huggingface dataset:
welly 0.5.2
Tools for making and managing well data.
Requires Python: >=3.6

![Welly banner](https://www.dropbox.com/s/a8jg7zomi4wgolb/welly_banner.png?raw=1)

[![Run tests](https://github.com/agile-geoscience/welly/actions/workflows/run-tests.yml/badge.svg)](https://github.com/agile-geoscience/welly/actions/workflows/run-tests.yml)
[![Build docs](https://github.com/agile-geoscience/welly/actions/workflows/build-docs.yml/badge.svg)](https://github.com/agile-geoscience/welly/actions/workflows/build-docs.yml)
[![PyPI version](https://img.shields.io/pypi/v/welly.svg)](https://pypi.python.org/pypi/welly/)
[![PyPI versions](https://img.shields.io/pypi/pyversions/welly.svg)](https://pypi.org/project/welly//)
[![PyPI license](https://img.shields.io/pypi/l/welly.svg)](https://pypi.org/project/welly/)

**`welly` facilitates the loading, processing, and analysis of subsurface wells and well data, such as striplogs, formation tops, well log curves, and synthetic seismograms.**


## Installation

    pip install welly

For developers, there are `pip` options for installing `test`, `docs` or `dev` (docs plus test) dependencies.


## Quick start

```python
from welly import Well, Project

w = Well.from_las('my_wells/my_well.las')  # Load a single well.
p = Project.from_las('my_wells/*.las')     # Load lots of wells.

gr = w.data['GR']  # One log...
gr.plot()          # ...with some superpowers!
```

Next, check out the tutorial notebooks.


## Documentation

[The `welly` documentation](https://code.agilescientific.com/welly) is a work in progress.


## Questions or suggestions?

[![slack](https://img.shields.io/badge/chat-on_slack-808493.svg?longCache=true&style=flat&logo=slack)](https://swung.slack.com/)

**If you'd like to chat about `welly` with us or other users, look for the **#welly-and-lasio** channel in the [Software Underground's Slack](https://softwareunderground.org/slack).**

To report bugs or suggest new features/improvements to the code, please [open an issue](https://github.com/agile-geoscience/welly/issues).


## Contributing

Please see [`CONTRIBUTING.md`](https://github.com/agile-geoscience/redflag/blob/main/CONTRIBUTING.md).


## Philosophy

The [`lasio`](https://github.com/kinverarity1/lasio) project provides a very nice way to read and write [CWLS](http://www.cwls.org/) Log ASCII Standard files. The result is an object that contains all the LAS data — it's more or less analogous to the LAS file.

Sometimes we want a higher-level object, for example to contain methods that have nothing to do with LAS files. We may want to handle other well data, such as deviation surveys, tops (aka picks), engineering data, striplogs, synthetics, and so on. This is where `welly` comes in.

`welly` uses `lasio` for data I/O, but hides much of it from the user. We recommend you look at both projects before deciding if you need the 'well-level' functionality that `welly` provides.
tarski-lapkt 0.1.3
Tarski is a framework for the specification, modeling and manipulation of AI planning problems.
Requires Python: >=3.6
Keywords: planning logic STRIPS RDDL

# Tarski-lapkt - A fork of tarski for lapkt


The tarski-lapkt package is an minor extension of [Tarski](https://github.com/aig-upf/tarski). It includes additional modules to integrate [lapkt](https://github.com/LAPKT-dev/LAPKT-public) and [tarski](https://github.com/aig-upf/tarski)


*Note - Users should follow the copyright and license requirements of [Tarski](https://github.com/aig-upf/tarski) which are as follows.*

## How to Cite

If you find `tarski` useful in your research, you can cite it with the following `bibtex` entry:

```
@misc{tarski:github:18,
  author = {Guillem Franc\'{e}s and Miquel Ramirez and Collaborators},
  title = {Tarski: An {AI} Planning Modeling Framework},
  year = {2018},
  publisher = {{GitHub}},
  journal = {{GitHub} repository},
  howpublished = {\url{https://github.com/aig-upf/tarski}}
}
```

## License
Tarski is licensed under the [Apache-2.0 License](LICENSE).
lenden 0.0.1.1
Import and Export data for Radiomics Workflows.
Requires Python: >=3.6

# lenden

Exchange Workflow Data

- 📙 *lenden for  exchange*

This library allows to express in a Json format the  output of the a [Radiomics](https://radiomicsgroup.github.io/) pipeline.

## Installation

To install execute the command

```bash
pip install lenden
```

## Use examples

An example pipeline with two outputs results.

```python
from lenden.dataexport  import *

result1= DataResults(Description="Do exercitation occaecat Lorem dolore labore culpa quis.", 
                     Help="Aliquip commodo sunt adipisicing officia irure laborum reprehenderit nulla consectetur in minim.",
                     Type=ResultType.image, Path="/path/to/image", Size=ResultSizeType.normal, Format="%20f")

result2= DataResults(Description="Sit pariatur magna nisi do.", 
                     Help="Velit id adipisicing tempor Lorem. Tempor dolore proident ea quis.",
                     Type=ResultType.percent, Path="/path/to/fileresult", Size=ResultSizeType.wide, Format="RGB(2,3,4)")
# Result without Error
data = DataExport(Version=0.1, HasError=False,
                          DisplayType=DisplayType.grid, Results=[result1, result2],CleanPatterns=['*.tmp'], Logs=None)
print(data.getJSON())
```

Then the output is:

```json
{
    "Version": 0.1,
    "HasError": false,
    "DisplayType": "grid",
    "Results": [
        {
            "Description": "Do exercitation occaecat Lorem dolore labore culpa quis.",
            "Help": "Aliquip commodo sunt adipisicing officia irure laborum reprehenderit nulla consectetur in minim.",
            "Type": "image",
            "Path": "/path/to/image",
            "Size": "normal",
            "Format": "%20f"
        },
        {
            "Description": "Sit pariatur magna nisi do.",
            "Help": "Velit id adipisicing tempor Lorem. Tempor dolore proident ea quis.",
            "Type": "percent",
            "Path": "/path/to/fileresult",
            "Size": "wide",
            "Format": "RGB(2,3,4)"
        }
    ],
    "CleanPatterns":["*.tmp"],
    "Logs": {
        "Description": "",
        "Path": []
    }
}
```

If exist an error then

```python

# Result with error
dataWithError = DataExport(Version=0.1, HasError=True,
                          DisplayType=DisplayType.grid, Results=[],CleanPatterns=['*.tmp'],
                          Logs=DataLog("Voluptate sint sint eu aliqua consequat culpa nisi fugiat nostrud aute adipisicing.", 
                                       ["/path/log1","/path/log2","/path/log3"]))
print(dataWithError.getJSON())
```

Then the output is:

```json
{
    "Version": 0.1,
    "HasError": true,
    "DisplayType": "grid",
    "Results": [],
    "CleanPatterns":["*.tmp"],
    "Logs": {
        "Description": "Voluptate sint sint eu aliqua consequat culpa nisi fugiat nostrud aute adipisicing.",
        "Path": [
            "/path/log1",
            "/path/log2",
            "/path/log3"
        ]
    }
}
```