semvec/pypi-packages huggingface dataset:
ontime 0.7.0
Your library to work with time series
Requires Python: <3.13,>=3.10

[![Continuous Integration](https://github.com/ontime-re/ontime/actions/workflows/ci.yml/badge.svg)](https://github.com/ontime-re/ontime/actions/workflows/ci.yml)
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<div align="center">
    <img src="docs/_static/ontime-logo.png" width="180" title="hover text">
    <h1><i>your library to work with time series</i></h1>
</div>

## Getting Started

Install the library with :

  pip install ontime

Then, take a look at documentation on [ontime.re](https://ontime.re) with : 

- a [Getting Started](https://ontime.re/getting_started) as usual,
- a [User Guide](https://ontime.re/user_guide) with many notebooks as examples.

## Purpose of the library

The purpose of onTime is to make a technological transfer to the partners of the 
DiagnoBat project with respect to their intellectual property. Most parts of 
the library are extendable by using dynamically loaded classes. This
mecanism allows anyone to keep parts of the library private within their own company. 

The library objectives are :

  1. to extend the time series libraries (Darts, GluonTS, Kats, etc.)
  2. to provide benchmarking tools for models and/or detectors
  3. to provide domain specific tools (e.g. for energy consumption, district heating networks, etc.)

In case you have any questions, don't hesitate to ask them by opening an issue or email.

## License

Apache License 2.0
dopy-syntax 0.1.0
Transform Python code using do..end blocks instead of indentation-based scoping
Requires Python: <4.0,>=3.10

# dopy

An experimental Python preprocessor that enables do..end syntax in place of strict indentation

## Requirements

- `python 3.10+`
- `pip`

## Installation

```bash
pip install dopy-syntax
```

## Features

- Converts ruby/lua style `do..end` blocks into indented blocks
- Maintains python's semantics
- Preserves string literals and comments
- Processes .dopy files into pep8 compliant .py files (maintaining the dir structure)
- supports type hints
- Recursively transpiles imports (multithreaded)

## Usage

### Programmatic

```python
from dopy.core import Dopy
dopy = Dopy()

source = '''
def hello_world() do
  print("Hello")
end
hello_world()
'''

processed = dopy.preprocess(source)
exec(processed, namespace={})
```

More examples in the [examples](./examples/) dir

### cli

`dopy my_module.dopy`

Will use the current active python interpreter, can be overridden with `PYTHON_PATH` env var

## Flags

`-h,--help`: Print help text

`-k,--keep`: Keep transpiled python files in place (will overwrite)

`-s,--stdout`: Print the transpiled python code to console and exit

`-c,--check`: Check dopy syntax without transpiling

## Syntax Rules

- Make sure the `do` keyword is on the same line as rest of the block declaration,
- `end` should be on its own line
- all imports at the top of the module
- must create a `__init__.py` file in dirs so that the transpiled python modules can be recognised

## Acknowledgements

This project is hugely inspired by [`mathialo/bython`](https://github.com/mathialo/bython)

### Todo

- [ ] `py2dopy` script
- [ ] function level imports
- [ ] nvim-treesitter support

### License

See [LICENSE](./LICENSE)
data-asset-manager 2.4.1
A Pandas wrapper library that simplifies day-to-day data management tasks
Requires Python: >=3.10

# Data Asset Manager
### data-asset-manager
Data comes in all shapes and sizes. Data Asset Manger does all the heavy lifting when it comes to simple management 
tasks in Pandas. Once a Blob object is instantiated, using a simple dictionary of configuration settings, tasks like 
reading/writing, sorting and deduplicating, can all be performed with a basic command.
Version 2 added the ability to read files in chunks, reducing the memory overhead for the enormous files we're expected 
to handle these days, and version 3 will start to introduce more complex tasks, such as name and address enhancement.
featboostx 0.1.0rc2
A Python package for the FeatBoost-X feature selection algorithm
Requires Python: <4,>=3.10
Keywords: Feature Selection, Gradient Boosting, FeatBoost-X

# FeatBoost-X
Python implementation of FeatBoost-X. See the [paper]() for details.


## Usage
```shell
pip install featboostx
```

### Example
```python
from featboostx import FeatBoostClassifier

clf = FeatBoostClassifier()
clf.fit(X, y)
print(clf.selected_subset_)
```
For a more detailed example, see the [classification example](examples/example_classification.py) or the 
[regression example](examples/example_regression.py).

## Feature selection methods
FeatBoost-X is available classification, regression, and survival problems.
- Classification supports the objectives accuracy (`acc`) and the F1-score (`f1`) through the `FeatBoostClassifier`-class. These can be optimized through the `softmax` or `adaboost` objective.
This implementation originates from the Python implementation of the [original paper](https://github.com/amjams/FeatBoost).
- Regression supports the `mae` objective through the `FeatBoostRegressor`-class and can be optimized through `adaptive` boosting.
- Survival supports the `c_index` objective through the `FeatBoostRegressor`-class and can be optimized through `adaptive` boosting.


# Illustration of FeatBoost-X
![Figure 1](images/Figure_1.png)