semvec/pypi-packages huggingface dataset:
pyrandvec 1.0.1
Generate random vectors whose components sum up to one
Requires Python: >=3.7

# Generate random vectors whose components sum up to one

This Python3 module implements different approaches [1] to randomly and uniformly generate d-dimensional vectors whose components sum up to one.

[1] Maziero, J. Generating Pseudo-Random Discrete Probability Distributions. Brazilian Journal of Physics 45, 377–382 (2015). https://doi.org/10.1007/s13538-015-0337-8


## Installation

Run the following to install the module:

```bash
pip install pyrandvec
```

## Usage

```python
from pyrandvec import sample

# Generate 10 4-dimensional vectors with the simplex-method
sample(10, 4, method = 'simplex')

# Generate 10 3-dimensional vectors with the trigonometric methdod with subsequent shuffling
sample(10, 3, method = 'trigonometric', shuffle = True)
```

# Developing randvec

To install the **pyrandvec** module along with the tools you need to develop and run test, run the following command in your *virtual environment* (virtualenv):

```bash
pip install -e .[dev]
```
ImageRecovery 0.0.43
Recover images from proximity information.
Keywords: python,node embedding,structural embedding,graph representation learning,manifold learning,DNA sequencing-based microscopy,image reconstruction,image recovery

Staged Image Recovery uses a combination of structural embedding and manifold learning to recover images from proximity information. Such information is encoded in a graph, where edges denote proximity. Although the problem to be solved is quite general, it has special relevancein the context of DNA sequencing-based microscopy.For more information, please read our paper:https://www.biorxiv.org/content/10.1101/2022.09.29.510142v1
randvec 1.0.2
Generate random and uniform vectors whose components sum up to one
Requires Python: >=3.7

# Random vectors with sum one

This Python3 module implements different approaches [1] to randomly and uniformly generate d-dimensional vectors whose components sum up to one.

[1] Maziero, J. Generating Pseudo-Random Discrete Probability Distributions. Brazilian Journal of Physics 45, 377–382 (2015). https://doi.org/10.1007/s13538-015-0337-8


## Installation

Run the following to install the module:

```bash
pip install randvec
```

## Usage

```python
from randvec.methods import sample

# Generate 10 4-dimensional vectors with the simplex-method
sample(10, 4, method = "simplex")

# Generate 10 3-dimensional vectors with the trigonometric methdod with subsequent shuffling
sample(10, 3, method = "trigonometric", shuffle = True)
```

# Developing randvec

To install the **randvec** module along with the tools you need to develop and run test, run the following command in your *virtual environment* (virtualenv):

```bash
pip install -e .[dev]
```
aflight 0.2.4
Arrow Flight simple API service

# Aflight: simple Arrow Flight API builder/runner

Loosely inspired by [Flask](https://github.com/pallets/flask)

### TL;DR

Server:

Create `app.py`

```python
import aflight

app = aflight.Aflight()

@app.command("predict")
def predict(batch):
    result = do_something_with(batch)
    return [result]
```

To Run:

```bash
aflight
```

Client:

```python
from aflight.client import predict

data = some_pandas_data()
print(predict(data))

```

## Why?

Current `kserve`, `torch/serve` etc. APIs are too reliant on HTTP request/response paradigm

Using Arrow as a data format and Arrow Flight as a service API produces a much better interface:

- standard
- has schema
- fast
- columnar
- batch-able