semvec/pypi-packages huggingface dataset:
tsraking 0.1.3
This package implements the TSRAKING procedure developed by Statistics Canada in Python3.
License: MIT
Requires Python: >=3.9

# TSRaking

**TSRaking** is a Python library for generalized time series raking (benchmarking) and constrained adjustment of multi-dimensional data.

It implements a Generalized Least Squares (GLS) raking procedure that reconciles:

- High-frequency predictions (e.g., monthly estimates)
- Low-frequency totals (e.g., annual constraints)
- Cross-sectional constraints (e.g., provincial totals summing to national totals)

TSRaking is designed for statistical agencies, researchers, and practitioners who need to enforce accounting identities or hierarchical consistency in time series data.

---

## Features

- Generalized least squares raking
- Lightweight (NumPy-based)
- Suitable for hierarchical time series benchmarking and frequency table rebalancing

The raking adjustment is computed via:

$$\hat{\theta}
= x + V_e G^\top (G V_e G^\top + V_\varepsilon)^{-1} (g - Gx)$$

If the constraint system is rank-deficient, then Moore–Penrose matrix inversion is used to obtain a unique solution.

---

## System Requirements

### Software Requirements

- Python ≥ 3.8
- NumPy

Optional (for development):
- pytest

### Operating Systems

The development version of TSRaking has been tested on:

- Linux (Ubuntu 20.04+)
- macOS
- Windows 10/11

The package is pure Python and should run on any operating system that supports Python 3.8+.

---

## Installation

### Install from PyPI

Create a virtual environment (recommended):

```bash
python -m venv tsraking-env
source tsraking-env/bin/activate  # macOS/Linux
# or
tsraking-env\Scripts\activate     # Windows

```

For developers or users who want the latest features from the main branch:

```bash
git clone https://github.com/akil-h/tsrakingpy.git
cd tsrakingpy

python -m venv tsraking-env
source tsraking-env/bin/activate  # macOS/Linux
# or
tsraking-env\Scripts\activate     # Windows

pip install -e .
```

## License

This project is licensed under the MIT License.
listo 0.8.0
An improved version of the List built-in
License: MIT
Requires Python: >=3.10

# listo

Listo is an enhanced version of the `list` type that aims to do the following:

- Make behavior more consistent. No more methods with "in-place" changes. Every method call returns a value
- Adds methods that are missing from the `list` type. For example, `listo` has a `first` method that returns `self[0]`
- A list of arguments passed in will be converted to a `listo` object, but a single argument that is an iterator will be unpacked into a `listo` object. This is useful for when you want to pass in a generator or other iterator into a function that expects a list

## Usage

First install it via pip, poetry, or whatever you use

```bash
pip install listo
```

Then use it in your code

```python
from listo import listo

lst = listo(1, 2, 3)
assert lst.first() == 1
assert lst.last() == 2
```

### Usage: How listo handles initial arguments

For arguments passed in, it just converts them to a `listo` object

```python
lst = listo(1, 2, 3)
assert lst == [1, 2, 3]

lst2 = listo([1, 2, 3]) # list is unpacked
assert lst2 == [1, 2, 3]

lst3 = listo((1, 2, 3)) # tuple is converted and unpacked
assert lst3 == [1, 2, 3]

lst4 = listo([1, 2, 3], (1, 2))  # Two args mean nothing is unpacked
assert lst4 == [[1, 2, 3], (1, 2)]
```

This is different from standard Python iterator behavior.

### Contributing to Listo

Have you tried using Listo and found it useful? Do you have ideas for how to make it better? 

We welcome contributions from the community!

See [CONTRIBUTING.md](CONTRIBUTING.md) for details on how to set up your development environment to contribute to this project, run tests, check code quality, release new versions, and more.
pyvip 1.27.1
Python API for Virtualitics Explore
License: MIT
Requires Python: <4.0,>=3.8

## pyVIP - Python API for Virtualitics Explore
Virtualitics is an AI driven visual analytics tool that allows 
users to create immersive data visualizations that can be 
experienced in VR and Desktop. To view the full documentation 
for this API or for Virtualitics Explore, go 
to: [pyVIP Documentation](https://docs.virtualitics.com/hc/en-us/articles/25103039519635-Setting-up-the-Virtualitics-API-pyVIP-with-Virtualitics-Explore)