semvec/pypi-packages huggingface dataset:
pytomlpp 1.1.0
A python wrapper for toml++
Keywords: toml, parser, serilization, deserialization, serdes

# pytomlpp

[![Build Status](https://github.com/bobfang1992/pytomlpp/workflows/Wheels/badge.svg)](https://github.com/bobfang1992/pytomlpp/actions)
[![Conda Status](https://anaconda.org/conda-forge/pytomlpp/badges/version.svg)](https://anaconda.org/conda-forge/pytomlpp)
[![PyPI version](https://badge.fury.io/py/pytomlpp.svg)](https://badge.fury.io/py/pytomlpp)

This is an python wrapper for `toml++` (https://marzer.github.io/tomlplusplus/).

Some points you may want to know before use:

- Using `toml++` means that this module is fully compatible with TOML [v1.0.0](https://toml.io/en/v1.0.0).
- We convert toml structure to native python data structures (dict/list etc.) when parsing, this is more inline with what `json` module does.
- The binding is using [pybind11](https://github.com/pybind/pybind11).
- The project is tested using [toml-test](https://github.com/BurntSushi/toml-test) and [pytest](https://github.com/pytest-dev/pytest).
- We support all major platforms (Linux, Mac OSX and Windows), for both CPython and Pypy and all recent Python versions. You just need to `pip install` and we have a pre-compiled binaries ready. No need to play with `clang`, `cmake` or any C++ toolchains.

# Example

```
In [1]: import pytomlpp

In [2]: toml_string = 'hello = "世界"'

In [3]: pytomlpp.loads(toml_string)
Out[3]: {'hello': '世界'}

In [4]: type(_)
Out[4]: dict

In [6]: pytomlpp.dumps({"你好": "world"})
Out[6]: '"你好" = "world"'
```

# Why bother?

There are some existing python TOML parsers on the market but from my experience they are implemented purely in python which is a bit slow.

```
Parsing data.toml 1000 times:
     rtoml:   0.540 s
  pytomlpp:   0.542 s ( 1.00x)
     tomli:   2.923 s ( 5.40x)
     qtoml:   8.748 s (16.18x)
   tomlkit:  51.608 s (95.49x)
      toml: Parsing failed. Likely not TOML 1.0.0-compliant.
```

Test it for yourself using [the benchmark script](benchmark/run.py).

# Installing

We recommend you to use `pip` to install this package:

```sh
pip install pytomlpp
```

You can also use `conda` to install this package, on all common platforms & python versions.
If you have an issue with a package from conda-forge, you can raise an issue on the [feedstock](https://github.com/conda-forge/pytomlpp-feedstock)

```sh
conda install -c conda-forge pytomlpp
```

You can also install from source:

```
git clone git@github.com:bobfang1992/pytomlpp.git --recurse-submodules=third_party/tomlplusplus --shallow-submodules
cd pytomlpp
pip install .
```

---

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fleetmanagement-GLOBALITUNU 0.0.1
FLEET MANAGEMent for cpp
Requires Python: >=3.9

         ___        ______     ____ _                 _  ___  
        / \ \      / / ___|   / ___| | ___  _   _  __| |/ _ \ 
       / _ \ \ /\ / /\___ \  | |   | |/ _ \| | | |/ _` | (_) |
      / ___ \ V  V /  ___) | | |___| | (_) | |_| | (_| |\__, |
     /_/   \_\_/\_/  |____/   \____|_|\___/ \__,_|\__,_|  /_/ 
 ----------------------------------------------------------------- 


Hi there! Welcome to AWS Cloud9!

To get started, create some files, play with the terminal,
or visit https://docs.aws.amazon.com/console/cloud9/ for our documentation.

Happy coding!
bssir 0.6.8
Shared data-loading and transformation utilities for Iranian socioeconomic surveys.
Requires Python: >=3.10
Keywords: iran, survey, data, socioeconomic

# BSSIR — Basic Survey Structure for Iran

BSSIR is a foundational Python package developed by Iran-Open-Data that provides shared data-loading and transformation functionality for a variety of socioeconomic surveys from Iran. It serves as the common infrastructure for more specialized data-analysis packages.

## Overview

Working with raw survey data from Iran can be challenging. Datasets often come in complex formats with inconsistencies in naming conventions, value codes, and structures that change over the years. BSSIR tackles these foundational challenges by providing a robust, standardized layer for data access and processing.

This package handles the essential behind-the-scenes work of reading, cleaning, and standardizing data, allowing researchers and developers to build more specific analysis tools on a reliable and consistent foundation.

## Core features

- **Standardized data loading**: Read complex raw survey files from different years without worrying about underlying formats.  
- **Schema standardization**: Apply consistent schemas and naming conventions across survey years.  
- **Metadata integration**: Integrate essential metadata (regional and geographical attributes) into datasets.  
- **Configuration management**: Simple system for managing data paths, settings, and configurations.  
- **Foundational layer**: Acts as the base dependency for other *SIR packages, ensuring code reuse and consistency.

## The *SIR ecosystem

BSSIR is the core component of a larger ecosystem designed to make Iranian socioeconomic data accessible. It provides the base infrastructure for more specialized packages so they can focus on dataset-specific analysis.

- **BSSIR**: Core data-handling infrastructure.  
- **HBSIR**: Builds on BSSIR to provide tools for analyzing the Household Budget Survey.  
- **LFSIR**: Adds provisions for the Labor Force Survey.  
- **CNSIR**: Builds on BSSIR to provide tools for analyzing Iran's Census data.
privatera 0.1.13
Accelerate your drug discovery R&D in private (VPC, on-prem, on-lap) with a RAG. Everything is free and open source to give you a state-of-the-art RAG without the rugpull.
Requires Python: >=3.12

# Private**RA**

Accelerate your reseach with an artificial research assistant that respects your privacy.

- drug discovery R&D
- on-prem, on-lap RAG
- FOSS
- millisecond information retrieval
- thousands of words-per-minute generation (information synthesis)
inventorize 1.2.6
inventory analytics,revenue management and cost calculations for SKUs
Requires Python: >=3.6
Keywords: inventory, pricing, markdowns

Package: inventorize
Title: Inventory Analytics,Markdowns and Pricing.
Version: 1.2.6
Authors person("Haytham", "Omar", email = "haytham@rescaleanalytics.com", role = c("aut", "cre"))
Description: Simulate inventory policies, facilitate inventory analysis calculations such as  stock levels and re-order points,pricing and promotions calculations. 
  The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations.
    The package includes revenue management techniques such as Multi-product optimization,logit model optimization.
  The functions are referenced from :
  1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. <isbn10: 135–136, 152>.
  2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition. <isbn: 0-07- 2231265-3. Chapter 4>.
  3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. <isbn: 978-0471119470>. 
  4-Ballou, R.H. Business Logistics Management. <isbn: 978-0130661845>. Chapter 9.
  5-MIT Micromasters Program.
  6- Columbia University  course for supply and demand analysis.
  8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W
  For further details or correspondence :<www.linkedin.com/in/haythamomar>, <www.rescaleanalytics.com>.

Change Log
==========

1.2.6 (29/11/2025)
Some bug fixes


1.2.3 (26/08/2023)
Some bug fixes

1.2.1 (23/08/2023)
-------------------
- second   Release
1- R.s.S inventory policy is added.
2- Max policy is added (different than Min Max)
2- you can compare forecasting impact on inventory not just relying on the average.
3- the forecasts can be one step like exponential smoothing/croston or multi-steps like machine learning models or seasonal arimas.
4- the plots are automatically added with a TRUE or FALSE arguments.
5- polynomial price optimization is added with the current linear and logit.
6- the markdown model for seasonal merchandizing from (walker 1999) is added to identify the viability and the percentage of the markdown.
7- In static policies, you can can recalculate the min and the max during the simulation horizon. Also user can manually define the min, max, quantity, saftey stock if required.

8- the classification of the demand is added as an output.
9- average flow time is added which is how much time on average a unit stays in stock.
10- initial inventory level can be set manually.
11- also because many have asked the ABC thresholds can be changed and up to two attributes can be added in the function abc_dynamic().
12- negative binomial and gamme distributions are added
13- comparing distributions of demand.



0.0.8 (29/08/2020)
-------------------
- first  Release