semvec/pypi-packages huggingface dataset:
rfc8785 0.1.4
A pure-Python implementation of RFC 8785 (JSON Canonicalization Scheme)
Requires Python: >=3.8

# rfc8785.py

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A pure-Python, no-dependency implementation of [RFC 8785], a.k.a. JSON Canonicalization Scheme or JCS.

This implementation should be behaviorally comparable to
[Andrew Rundgren's reference implementation], with the following added constraints:

1. This implementation does not transparently convert non-`str` dictionary keys into
   strings. Users must explicitly perform this conversion.
1. No support for indentation, pretty-printing, etc. is provided. The output is always
   minimally encoded.
2. All APIs produce UTF-8-encoded `bytes` objects or `bytes` I/O.

## Installation

```bash
python -m pip install rfc8785
```

## Usage

See the full API documentation [here].

```python
import rfc8785

foo = {
    "key": "value",
    "another-key": 2,
    "a-third": [1, 2, 3, [4], (5, 6, "this works too")],
    "more": [None, True, False],
}

rfc8785.dumps(foo)
```

yields:

```python
b'{"a-third":[1,2,3,[4],[5,6,"this works too"]],"another-key":2,"key":"value","more":[null,true,false]}'
```

For direct serialization to an I/O sink, use `rfc8785.dump` instead:

```python
import rfc8785

with open("/some/file", mode="wb") as io:
    rfc8785.dump([1, 2, 3, 4], io)
```

All APIs raise `rfc8785.CanonicalizationError` or a subclass on serialization failures.

## Licensing

Apache License, Version 2.0.

Where noted, parts of this implementation are adapted from [Andrew Rundgren's reference implementation], which is also licensed under the Apache License, Version 2.0.

[RFC 8785]: https://datatracker.ietf.org/doc/html/rfc8785

[Andrew Rundgren's reference implementation]: https://github.com/cyberphone/json-canonicalization/tree/master/python3

[here]: https://trailofbits.github.io/rfc8785.py
sbump 0.1.1
SimpleBump, bump a static project.version in pyproject.toml, no config, no hassle
Requires Python: >=3.11

# SimpleBump

Bump a static project.version in pyproject.toml, no config, no hassle. This tool exists because every other tool requires a config or tries to do too much. This one does not.

- The current version is read from pyproject.toml, only a static version is supported intentionally.
- Without flags the current version is displayed.
- If a semver fragment is provided as an argument, that segment is incremented.
- The new version is written back to pyproject.toml, and nowhere else.
pyimportR 0.9.0
Pandas Code Importer with GUI Interface simplifies the process of adding Pandas code into Python scripts. Through a user-friendly GUI, users can easily select and insert Pandas functions, automating repetitive tasks and enhancing productivity in data analysis projects.
Keywords: python, pandas, gui, importer, pandas importer, import, pyimport, pyimport-0.9.0


PyImport is an innovative tool designed to enhance the coding experience by simplifying the integration of the Pandas library into Python scripts. This powerful program, equipped with an intuitive Graphical User Interface (GUI), enables users to efficiently insert Pandas-related functions and code snippets into their projects without the need for manual coding. By automating this process, PyImport streamlines workflows, reduces human error, and accelerates data manipulation tasks for developers and data analysts alike.

The core feature of PyImport is its user-friendly GUI, which allows both beginners and experienced Python users to easily select from a comprehensive list of Pandas modules. Whether you’re working on basic data cleaning or advanced statistical analyses, PyImport lets you quickly browse and choose the relevant Pandas functions, preview the code, and inject it directly into your Python script. This significantly reduces the time spent on writing repetitive code and ensures accuracy by eliminating potential syntax errors.

In addition to importing Pandas modules, PyImport offers customization options. Users can tailor the imported code to fit their project needs, from adjusting function parameters to modifying data structures. The program is built to enhance flexibility while maintaining simplicity, making it the perfect tool for projects of any scale—whether you're just starting out with Python or managing large datasets.

PyImport not only supports rapid prototyping and testing but also encourages better coding practices by automating repetitive tasks and maintaining consistency in code quality. Its GUI interface is lightweight and easy to navigate, allowing users to focus more on the logic and analysis of their data rather than spending time typing and debugging Pandas imports.

For developers who work extensively with data, PyImport provides a bridge between productivity and precision. It’s the ultimate assistant for data manipulation, empowering users to harness the full capabilities of the Pandas library without the overhead of manual imports.
Integrify 1.0.0
Integrify API inteqrasiyalarını rahatlaşdıran sorğular kitabaxanasıdır.
Requires Python: <4.0,>=3.9

<p align="center">
  <a href="https://integrify.mmzeynalli.dev/"><img width="400" src="docs/assets/integrify.png" alt="Integrify"></a>
</p>
<p align="center">
    <em>Integrify API inteqrasiyalarını rahatlaşdıran sorğular kitabaxanasıdır.</em>
</p>
<p align="center">
  <a href="https://app.netlify.com/sites/integrify-docs/deploys">
    <img src="https://api.netlify.com/api/v1/badges/d8931b6a-80c7-41cb-bdbb-bf6ef5789f80/deploy-status" alt="Netlify Status">
  </a>
</p>

# Integrify

Integrify API inteqrasiyalarını rahatlaşdıran sorğular kitabaxanasıdır.

## Əsas özəlliklər

- Kitabxana həm sync, həm də async sorğu dəyişimini dəstəkləyir.
- Kitabaxanadakı bütün sinif və funksiyalar tamamilə dokumentləşdirilib.
- Kitabaxanadakı bütün sinif və funksiyalar tipləndirildiyindən, "type hinting" aktivdir.
- Sorğuların çoxunun məntiq axını (flowsu) izah edilib.

---

**Dokumentasiya**: [https://integrify.mmzeynalli.dev](https://integrify.mmzeynalli.dev)

**Kod**: [https://github.com/mmzeynalli/integrify](https://github.com/mmzeynalli/integrify)

---

## Kitabxananın yüklənməsi

<div class="termy">

```console
$ pip install integrify
```

</div>

## İstifadəsi

Məsələn, EPoint üçün sorğuları istifadə etmək istərsək:

### Sync

```python
from integrify.epoint import EPointRequest

resp = EPointRequest.pay(amount=100, currency='AZN', order_id='12345678', description='Ödəniş')
print(resp.ok, resp.body)

```

### Async

```python
from integrify.epoint.asyncio import EPointRequest

# Async main loop artıq başlamışdır
resp = await EPointRequest.pay(amount=100, currency='AZN', order_id='12345678', description='Ödəniş')
print(resp.ok, resp.body)

```

### Sorğu cavabı

Yuxarıdakı sorğuların (və ya istənilən sorğunun) cavab formatı `ApiResponse` class-ıdır:

```python
class ApiResponse:
    ok: bool
    """Cavab sorğusunun statusu 400dən kiçikdirsə"""

    status_code: int
    """Cavab sorğusunun status kodu"""

    headers: dict
    """Cavab sorğusunun header-i"""

    body: Dəyişkən
    """Cavab sorğusunun body-si"""
```

![loading](docs/assets/spinner-solid.svg)

## Dəstəklənən API inteqrasiyaları

| Servis  |               Əsas sorğular               |              Bütün sorğular               | Dokumentləşdirilmə | Link                                                                       |
| ------- | :---------------------------------------: | :---------------------------------------: | ------------------ | -------------------------------------------------------------------------- |
| EPoint  |            :heavy_check_mark:             | ![loading](docs/assets/spinner-solid.svg) | Tam                | [Docs](https://github.com/mmzeynalli/integrify/tree/main/integrify/epoint) |
| Payriff | ![loading](docs/assets/spinner-solid.svg) | ![loading](docs/assets/spinner-solid.svg) | Tam                | ![loading](docs/assets/spinner-solid.svg)                                  |
lllqos 1.0.0
Linux PM QoS userspace interface
Requires Python: >=3.7

# `lllqos`: Linux Low-Latency QoS for Python

The Linux kernel provides the [PM Quality Of Service Interface](https://docs.kernel.org/power/pm_qos_interface.html), which allows userspace processes to indicate acceptable wake-up latency. Processes seeking low wake-up latency (e.g., `cyclictest`) use this interface to tell the kernel the acceptable target latency (in microseconds). 

The kernel documentation describes the interface as follows:

> To register the default PM QoS target for the CPU latency QoS, the process must open `/dev/cpu_dma_latency`.
>
> As long as the device node is held open that process has a registered request on the parameter.
>
> To change the requested target value, the process needs to write an `s32` value to the open device node. 

This package provides a simple context-manager API `low_wakeup_latency(target_value)` that automates this. By default, the target value is 0.

On platforms other than Linux (or if the `/dev` filesystem is not mounted in the usual place), this context manager has no effect.

## Example

```python
from lllqos import low_wakeup_latency

# Standard use:
with low_wakeup_latency():
    print('Hi, but quickly!')

# Check whether lease was acquired:
with low_wakeup_latency() as lease:
    print(f'Lease acquired: {lease.acquired()}')
    
# Setting some nonzero goal:
with low_wakeup_latency(1000):
    print('Up to 1ms latency is acceptable here.')
```

A typical use-case is to combine this package with the [`abs-sleep` package](https://pypi.org/project/abs-sleep/) to achieve low-latency periodic activations. 

```python
from lllqos import low_wakeup_latency
from abs_sleep import periodic
import time

# tell Linux to minimize wake-up latency
with low_wakeup_latency():
  	# periodic wake-ups with a period of 500ms
    for t in periodic(0.5, align_secs=1):
      	now = time.monotonic()
      	print(f"periodic activation @ {t}s with {(now - t) * 1000000:.2f}µs latency")
```

NB: To achieve consistently low latency, you must run the Python interpreter with real-time priority (e.g., with the`SCHED_FIFO` or `SCHED_RR` scheduling policies). 

## See Also

- [Linux kernel documentation](https://docs.kernel.org/power/pm_qos_interface.html)
- [`cyclictest` implementation](https://git.kernel.org/pub/scm/utils/rt-tests/rt-tests.git/tree/src/cyclictest/cyclictest.c#n243)
streamlit-custom-checkbox 0.0.1
Streamlit component that allows you to do place checkboxes horizontally, and to have a default checkbox ('All')
Requires Python: >=3.7

# streamlit-custom-component

Streamlit component that allows you to do X

## Installation instructions

```sh
pip install streamlit-custom-component
```

## Usage instructions

```python
import streamlit as st

from my_component import my_component

value = my_component()

st.write(value)
```
CTRF 1.0.9
Please add the appropriate description
Requires Python: >=3.9

## Overview
CTRF is accepted paper in 27th International Conference on Pattern Recognition (ICPR), Kolkata, India. The mission of CTRF is to help users easily and efficiently transform Random Forests into tractable circuits. CTRF exploits bayesian learning and boolean algebra to achieve high efficiency. Key features of CTRF are as follows.
* Works for any number of variables.
* Reduction in variables and logical operations.
* Supported Operating System(s): Linux and Windows.
* Support classification, tested on UCIML datasets.

**Why optimize Random Forests**: A [survey](https://www.kaggle.com/amberthomas/kaggle-2017-survey-results) conducted by Kaggle in 2017 shows that 50%, 46% and 24% of the data mining and machine learning practitioners are users of Decision Trees, Random Forests and GBMs, respectively. 

## Getting Started

### Requirements
1. matplotlib
2. numpy
3. pandas
4. scikit-learn
5. scipy
6. pyeda + related his VS distribution file (https://www.microsoft.com/en-us/download/details.aspx?id=30679)
7. gmm_mml


### Quick Install

    *  pip install CTRF
    *  pip install  github link
    
* Currently only support python3
* After you have installed CTRF, you can import and use the classifier  by:
```python
from CTRF import train ,predictf
clf = train()
clf.predict()
```
### Build from source
```bash
git clone GitHub link
cd CTRF
#under the directory of CTRF we can train and predict the info
```

You will see info about the accuracies of  RF, RF-V,BDS, OBDS after successful running predictf()

### Related papers
* Choi A, Shih A, Goyanka A, Darwiche A. On symbolically encoding the behavior
of random forests. arXiv preprint arXiv:2007.01493. 2020 Jul 3. [pdf](https://arxiv.org/pdf/2007.01493)

* Audemard, Gilles, FrÃ©dÃ©ric Koriche, and Pierre Marquis. On tractable XAI queries based on compiled representations. Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning. Vol. 17. No. 1. 2020. [pdf](https://proceedings.kr.org/2020/86/kr2020-0086-audemard-et-al.pdf)


##Contact

**Principal Investigator**



<a href="https://www.linkedin.com/in/debdoot/">Dr Debdoot Sheet</a> </br>
Department of Electrical Engineering,</br>
Indian Institute of Technology Kharagpur</br>
email: debdoot@ee.iitkgp.ac.in 


Contributors

<a href="https://www.linkedin.com/in/maddimsetti-srinivas-5233801b/"> Maddimsetti Srinivas </a> </br>
Department of Electrical Engineering,</br>
Indian Institute of Technology Kharagpur</br>
email: msrinivas@iitkgp.ac.in



<a href="https://www.linkedin.com/in/sai-hemanth-sattenapalli-3874b010b//">Sattenapalli Sai hemanth </a> 
<br>Scarlet Moose Entertainment</br>
email: saihemanth.s@outlook.com



<a href="https://www.linkedin.com/in/raviteja-garlapati-147a71172/">RaviTeja Garlapati </a> 
<br>Scarlet Moose Entertainment</br>
email: garlapatiravitejag.grr@outlook.com