semvec/pypi-packages huggingface dataset:
bash2py 1.0.0
for my sysadmin tasks
Requires Python: >=3.7

# Bash 2 Python
____
Python bash wrapper that also supports CURL, ETCD2, POSTGRESQL.

Works only in Linux environments.

## Installation
```bash
pip install bash2py
```

#### Bash In Python

```bash
git clone https://github.com/vuhitra-io/bash-from-python.git
```
tvopt 0.2.7
tvopt: A Python Framework for Time-Varying Optimization
Requires Python: >=3.7

![banner](https://github.com/nicola-bastianello/tvopt/blob/main/images/tvopt%201900x600.png)

# Welcome to **tvopt** [![Documentation Status](https://readthedocs.org/projects/tvopt/badge/?version=latest)](https://tvopt.readthedocs.io/en/latest/?badge=latest)
[**Docs**](https://tvopt.readthedocs.io/en/latest/)
| [**Installation**](#installation)
| [**Cite**](#cite)


**tvopt** is a prototyping and benchmarking Python framework for time-varying (or online) optimization. The framework is modular,
and provides different tools for modeling dynamic optimization problems and to solve them with a wide range of well known
algorithms.

The documentation is available [here](https://tvopt.readthedocs.io/en/latest/).
 

## Installation
**tvopt** works on Python 3.7 and depends on: numpy, scipy.

### pip installation
```
pip install tvopt
```

## Cite

```
@inproceedings{bastianello_tvopt_2021,
	title = {tvopt: {A} {Python} {Framework} for {Time}-{Varying} {Optimization}},
	doi = {10.1109/CDC45484.2021.9683695},
	booktitle = {2021 60th {IEEE} {Conference} on {Decision} and {Control} ({CDC})},
	author = {Bastianello, Nicola},
	year = {2021},
	pages = {227--232},
}

```

## Author
**tvopt** is developed by
[Nicola Bastianello](https://nicola-bastianello.it)
systran-align 3.5.0
Alignment tool based on fast_align

# systran-align

**systran-align** is a small alignment tool that is based on https://github.com/clab/fast_align.

## Installation

```bash
pip install systran-align
```

## Usage

```python
import systran_align
```

### Generating alignment probabilities

```python
systran_align.generate_alignment_probabilities(
    input_path: str,
    forward_probs_path: str,
    backward_probs_path: str,
    verbose: bool = False,
    iterations: int = 5,
    favor_diagonal: bool = False,
    beam_threshold: float = -4,
    diagonal_tension: float = 4,
    optimize_tension: bool = False,
    variational_bayes: bool = False,
    alpha: float = 0.01,
    no_null_word: bool = False,
    prob_align_null: float = 0.08,
    thread_buffer_size: int = 10000,
)
```

**Inputs:**

* `input_path`: text file where each line is a source-target example with format:

```text
<source> ||| <target>
```

**Outputs:**

* `forward_probs_path`: binary file containing forward probabilities
* `backward_probs_path`: binary file containing backward probabilities

### Computing alignments

```python
aligner = systran_align.Aligner(
    forward_probs_path: str,
    backward_probs_path: str,
)

# result is a dict with fields:
# * alignments
# * forward_log_prob
# * backward_log_prob
result = aligner.align(
    source: List[str],
    target: List[str],
)

# Batch alternative:
results = aligner.align_batch(
    source: List[List[str]],
    target: List[List[str]],
)
```