semvec/pypi-packages huggingface dataset:
sysnet-pdffiller 1.3.1
Tools for filling PDF templates with data
Requires Python: >=3.9

# PDF Filler

Knihovna pro plnění připravených PDF šablon daty.


## Verze

### 1.1.0

Přesun z **PyPDF3** v1.0.6 na **pypdf** v3.15.5. 

### 1.2.0

Konfigurační soubor přejmenován ze **settings.py** na **init.py**

### 1.3.0

* Upgrade knihovny **pypdf** na verzi 5.1.0 -> https://pypdf.readthedocs.io/en/stable/ 
* Aktualizace všech ostatních knihoven
* Náhrada zastaralé třídy **PdfFileMerger** za **PdfWriter**

### 1.3.1

Konsolidace závislostí a verzí. 

## Použití

Instalace:

    pip install pip install sysnet-pdffiller


V programu

    from sysnet_pdf.pdf_utils import parse_template_type
    ...
    ...
    template_type = parse_template_type(template.template_first)
SoullessAi-Dem 1.1.4.4
Библиотека для создания демотиваторов
Requires Python: >=3.7
Keywords: demotivator, image processing, meme

Библиотека для создания демотиваторов. Совместима с aiogram, telebot и другими библиотеками для Telegram, также совместима с ВКонтакте.
alglab 1.2.1
A framework for running experiments on algorithms.
Requires Python: >=3.8

# AlgLab
Framework for performing experiments with algorithms in Python.

This package provides a framework for building extendable and reproducible experiments with algorithms in python. This high-level workflow is to

1. Specify a dataset on which to perform an experiment.
2. Implement the algorithms which are to be compared.
3. Implement any evaluation methods by which the algorithm outputs will be scored.
4. Specify the experimental setup, including dataset and algorithm parameters.
5. Run the experiments, and
6. Analyse the results.

This preserves a separation between the dataset, algorithm, evaluation methods, and experimental setup.
This makes it easy to extend and modify experimental setups by changing the datasets, adding new algorithms, or changing parameters.

## Example

Here's a simple example of the workflow using datasets and evaluation functions already provided by alglab.

```python
import alglab

# First, implement the algorithms that you would like to compare.
# Note that the signature of the implemented algorithms should take a dataset as the first argument,
# followed by the algorithm parameters as keyword arguments, with default values.
def kmeans(data: alglab.dataset.PointCloudDataset, k=10):
    sklearn_km = KMeans(n_clusters=k)
    sklearn_km.fit(data.data)
    return sklearn_km.labels_

def spectral_clustering(data: alglab.dataset.PointCloudDataset, k=10):
    sklearn_sc = SpectralClustering(n_clusters=k)
    sklearn_sc.fit(data.data)
    return sklearn_sc.labels_

# Configure the experiments. As well as the algorithms, we specify which dataset class to use,
# and the parameters for the algorithms and dataset.
#
# We also specify any functions which should be used to evaluate the algorithms, and give a
# filename in which to store the results.
experiments = alglab.experiment.ExperimentalSuite(
    [kmeans, spectral_clustering],
    alglab.dataset.TwoMoonsDataset,
    "results/twomoonsresults.csv",
    parameters={
        "k": 2,
        "dataset.n": np.linspace(1000, 5000, 6).astype(int),
    },
    evaluators=[alglab.evaluation.adjusted_rand_index],
)

def main():
    # Run the experiments
    experiments.run_all()

    # Now, we can visualise the results
    results = alglab.results.Results("results/twomoonsresults.csv")
    results.line_plot("n", "running_time_s")
    results.line_plot("n", "adjusted_rand_index")
```
openshift-cluster-management-python-wrapper 2.0.21
Wrapper around https://github.com/openshift/openshift-cluster-management-python-client
Requires Python: ~=3.9
Keywords: OCM, Openshift

# openshift-cluster-management-python-wrapper
Wrapper for [openshift-cluster-management-python client](https://github.com/RedHatQE/openshift-cluster-management-python-client)

## Release new version
### requirements:
* Export GitHub token
```bash
export GITHUB_TOKEN=<your_github_token>
```
* [release-it](https://github.com/release-it/release-it)

Run the following once (execute outside repository dir, for example `~/`):
```bash
sudo npm install --global release-it
npm install --save-dev @release-it/bumper
```
### usage:
* Create a release, run from the relevant branch.  
To create a 1.0 release, run:
```bash
git checkout v1.0
git pull
release-it # Follow the instructions
```

## Installation
From source:
```bash
git clone https://github.com/RedHatQE/openshift-cluster-management-python-client.git
cd openshift-cluster-management-python-client
python setup.py install --user
```

## Examples
### Client
```python
from ocm_python_wrapper.ocm_client import OCMPythonClient
ocm_client = OCMPythonClient(
    token=<ocm api token>>,
    endpoint=<endpoint url>,
    api_host=<production or stage>,
    discard_unknown_keys=True,
)
return ocm_client.client
```
### Cluster
```python
from ocm_python_wrapper.cluster import Cluster
cluster = Cluster(client=client, name=<cluster name>)
cluster_ocp_version = cluster.instance.version.raw_id
```
memcachio 0.5.1
Python async client for Memcached
Requires Python: >=3.10
Keywords: Memcached, asyncio

# memcachio

[![docs](https://readthedocs.org/projects/memcachio/badge/?version=stable)](https://memcachio.readthedocs.org)
[![codecov](https://codecov.io/gh/alisaifee/memcachio/branch/master/graph/badge.svg)](https://codecov.io/gh/alisaifee/memcachio)
[![Latest Version in PyPI](https://img.shields.io/pypi/v/memcachio.svg)](https://pypi.python.org/pypi/memcachio/)
[![ci](https://github.com/alisaifee/memcachio/actions/workflows/main.yml/badge.svg?branch=master)](https://github.com/alisaifee/memcachio/actions?query=branch%3Amaster+workflow%3ACI)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/memcachio.svg)](https://pypi.python.org/pypi/memcachio/)

______________________________________________________________________

A pure python async Memcached client with **0** dependencies
with support for:

- All memcached commands
- Memcached servers serving on TCP or Unix Domain Sockets
- Memcached clusters
- SSL transport
- SASL Authentication
- Connection reuse for multiple concurrent requests
- Dynamically adjusted connection pooling
- Auto discovery with AWS ElastiCache
______________________________________________________________________

## Installation

To install memcachio:

```bash
$ pip install memcachio
```

## Quick start

### Single Node or Cluster client

```python
import asyncio

from memcachio import Client


async def example() -> None:

    #: basic client
    raw_client = Client(("localhost", 11211))
    #: client that decodes the byte responses
    decoding_client = Client(("localhost", 11211), decode_responses=True)
    # or with a cluster
    # cluster_client = Client([("localhost", 11211), ("localhost", 11212)], decode_responses=True)

    await raw_client.flushall()
    await raw_client.set("foo", b"1")
    await raw_client.set("bar", b"2")

    assert 2 == await raw_client.incr("foo", 1)

    # use the raw client to get a value.
    # Note the mapping returned has byte keys
    assert (await raw_client.get("foo")).get(b"foo").value == b"2"

    # get the values with the decoding client and touch their expiry to be 1 second.
    # Note the mapping and the values are strings.
    gat_and_touch_many = await decoding_client.gat("foo", "bar", expiry=1)
    assert ["2", "2"] == [item.value for item in gat_and_touch_many.values()]

    await asyncio.sleep(1)

    assert {} == await decoding_client.get("foo", "bar")


asyncio.run(example())
```

See [Client](https://memcachio.readthedocs.io/en/stable/api.html#memcachio.Client)
for detailed descriptions of available options when constructing a client.

## Compatibility

memcachio is tested against memcached versions `1.6.x`

### Supported python versions

- 3.10
- 3.11
- 3.12
- 3.13
- PyPy 3.10
- PyPy 3.11


## References

- [Documentation (Stable)](http://memcachio.readthedocs.org/en/stable)
- [Documentation (Latest)](http://memcachio.readthedocs.org/en/latest)
- [Changelog](http://memcachio.readthedocs.org/en/stable/release_notes.html)
webscrapehelper 0.1.0
Playwright helper to capture manual browsing sessions and save page HTML snapshots.
Requires Python: >=3.9
Keywords: playwright, web-scraping, automation, browser, session-recording

# Webscrapehelper

A lightweight Playwright helper that opens a headed browser and records the HTML source (plus metadata) for each page you visit until the browser window is closed.

## Features
- Launches Chromium (or another Playwright browser) in headed mode so you can browse manually.
- Collects page HTML, title, URL, HTTP status, and file size whenever the main frame navigates.
- Saves snapshots to disk (`session_log.jsonl` + individual `.html` files) and keeps an in-memory list for immediate post-run use.
- Returns a `SessionResult` with helpers such as `.html_list` and `.html_snapshots`.
- Optional callback hook so your code can react to each captured event in real time.
- Lets you write HTML files to a custom directory via `html_output_dir`.

## Requirements
- Python 3.9+
- Playwright (`pip install playwright` and run `playwright install` once to fetch browser binaries).

## Local installation
```bash
pip install -e .
playwright install
```

## Quick start
```python
import asyncio
from webscrapehelper import SessionRecorder

async def main():
    recorder = SessionRecorder(output_dir="session_data", headless=False)
    result = await recorder.run()
    print(f"Captured {len(result.html_list)} snapshots")
    if result.html_list:
        first_html = result.html_list[0]
        print(first_html[:200])  # preview

if __name__ == "__main__":
    asyncio.run(main())
```

Browse as usual; the script exits after you close the Playwright browser. Captured snapshots live in `session_data/`, and the returned `SessionResult` keeps the HTML in memory. To persist the HTML elsewhere, pass `html_output_dir="C:/some/folder"` when creating the recorder.

See `examples/record_session.py` for a slightly richer example that logs each event.
obsidian-sdk 0.1.1
Add your description here
Requires Python: >=3.13

使用以下方式进行server 和测试 配置对应测试环境 
uv run pytest -s tests/test_main.py::test_get_id_from_name 
uv run python -m clientz.server 80 --prod

docker-compose build --no-cache

# C语言相关使用方式

## 文本内容
├── CMakeLists.txt
├── LICENSE
├── Readme.md
├── conanfile.txt
└── src
    ├── workspace
        ├── main.cpp
        ├── show.cpp
        └── show.h

## build
cd build
conan install .. -of . --build missing
### 构建Cmake
cmake .. -DCMAKE_TOOLCHAIN_FILE=conan_toolchain.cmake -DCMAKE_BUILD_TYPE=Release# or Debug