semvec/pypi-packages huggingface dataset:
pii-scanner 0.1.0
A library for scanning Personally Identifiable Information (PII).
Requires Python: >=3.8

# PII Scanner

A library for scanning Personally Identifiable Information (PII) from text.

## Installation

```bash
pip install pii_scanner
```

## Usage 

```bash
from pii_scanner.scanner import PIIScanner

# Create an instance of the scanner
pii_scanner = PIIScanner()

# Sample data containing PII
data = [
    "Ankit Gupta",
    "Lucknow",
    "+9191840562195",
    "Indian",
    "Sofia Rossi",
    "Rome",
    "+390612345678",
    "Italian"
]

# Scan the structured data
results = pii_scanner.scan(data=data, chunk_size=10, sample_size=1.0)

# Print the results
print(results)

```


## Output 

```bash
[
    {
        "text": "Ankit Gupta",
        "entity_detected": [
            {"type": "PERSON", "start": 0, "end": 11, "score": 0.85}
        ]
    },
    {
        "text": "Rome",
        "entity_detected": [
            {"type": "LOCATION", "start": 0, "end": 4, "score": 0.85}
        ]
    },
    {
        "text": "+919140562195",
        "entity_detected": [
            {"type": "PHONE_NUMBER", "start": 0, "end": 13, "score": 0.85}
        ]
    },
    {
        "text": "Sofia Rossi",
        "entity_detected": [
            {"type": "PERSON", "start": 0, "end": 11, "score": 0.85}
        ]
    },
    {
        "text": "Indian",
        "entity_detected": [
            {"type": "NATIONALITY", "start": 0, "end": 6, "score": 0.9}
        ]
    }
]


```
pytest-freezer 0.4.9
Pytest plugin providing a fixture interface for spulec/freezegun
Requires Python: >=3.6

[![actions](https://github.com/pytest-dev/pytest-freezer/actions/workflows/tests.yml/badge.svg)](https://github.com/pytest-dev/pytest-freezer/actions/workflows/tests.yml/)
[![codecov](https://codecov.io/gh/pytest-dev/pytest-freezer/branch/main/graph/badge.svg)](https://codecov.io/gh/pytest-dev/pytest-freezer)
[![pypi](https://img.shields.io/pypi/v/pytest-freezer.svg)](https://pypi.org/project/pytest-freezer)
![womm](https://cdn.rawgit.com/nikku/works-on-my-machine/v0.2.0/badge.svg)

# pytest-freezer

[Pytest][1] plugin providing a fixture interface for [freezegun][2].

## Installation:

``` bash
$ python -m pip install pytest-freezer
```

## Usage:

The fixture name is `freezer`. It is a `freezegun.api.FrozenDateTimeFactory` instance,
so refer to upstream freezegun [usage][3] for the methods.

Time is frozen by default when the fixture is injected:

``` python
def test_frozen_date(freezer):
    now = datetime.now()
    time.sleep(1)
    later = datetime.now()
    assert now == later
```

Time can be controlled within a test by using methods on the fixture:

``` python
def test_freezer_methods(freezer):
    freezer.move_to("2022-10-17")
    assert datetime.now() == datetime(2022, 10, 17)
    freezer.tick()
    assert datetime.now() == datetime(2022, 10, 17, 0, 0, 1)
    freezer.tick(delta=12)
    assert datetime.now() == datetime(2022, 10, 17, 0, 0, 13)
```

## Acknowledgements:

Credit to Tomasz Kontusz for the original [pytest-freezegun][4] plugin.

[1]: https://docs.pytest.org/
[2]: https://github.com/spulec/freezegun
[3]: https://github.com/spulec/freezegun#usage
[4]: https://github.com/ktosiek/pytest-freezegun
segmentedstring 1.0.1
Simple python class that acts like string, but it also provides a way to work with it in segments.
Requires Python: >=3.10
Keywords: string, segments

# segmentedstring
Simple python class that acts like string, but it also provides a way to work with it in segments.


The eq, lt, and gt methods are the same as the ones for ordinary strings.
Therefore, there is no consideration of the segments.

Example usage:
```python
>>> s = SegmentedString(("abc", "def", "ghi"), labels=("first", "second", "third"))
>>> s
'abcdefghi'
>>> s.segments
('abc', 'def', 'ghi')
>>> s.labels
('first', 'second', 'third')
```
decision-rules 1.4.1
Package implementing decision rules. Includes tools for calculations of various measures and indicators, as well as algorithms for filtering rulesets.
Requires Python: >=3.9

# Decision Rules

Package implementing decision rules.
The package allows user to define and process decision rules as Python objects
and perform operations on datasets to which these rules apply.

Three types of problems are supported:

- classification
- regression
- survival

Functionalities includes, but is not limited to:

- serialization and deserialization
- prediction
- summary statistics
- comparison between rules (semantic and syntactic)

## Installation
Package can be installed from [PyPi](https://pypi.org/project/decision-rules/):
```
pip install decision-rules
```

You can also just clone the repository and install the package locally:
```
pip install .
```

## Extras

### Ruleset factories
Module for transforming rule-based models into decision-rules rulesets.

#### Usage
Module expose only a single function `ruleset_factory`, which is used to convert
`RuleKit`-type rulesets into `decision-rules` rulesets.

Example:
```python
from rulekit.classification import RuleClassifier

from decision_rules.ruleset_factories import ruleset_factory
from decision_rules.classification.ruleset import ClassificationRuleSet

rule_classifier = RuleClassifier()
rule_classifier.fit(X, y)

ruleset: ClassificationRuleSet = ruleset_factory(
    model=rule_classifier,
    X_train=X,
    y_train=y,
)
```

A parser for `MLRules`-type rulesets has also been implemented and can be found under
`decision_rules.ruleset_factories._factories.classification.MLRulesRuleSetFactory`.
Its `.make` method takes a list of lines read from MLRules algorithm output file,
the dataset and (optionally) a voting metric to calculate rule weights.

Example:
```python
with open("example_MLRules_output.txt") as file:
    ml_rules_lines = file.readlines()
ruleset: ClassificationRuleSet = MLRulesRuleSetFactory().make(
    ml_rules_lines, X_df, y_df, "Precision"
)
```

## Running tests
To run tests of the base package, please run:
```
python -m unittest discover ./tests/base_tests
```

To run tests of the extras modules, please run:
```
python -m unittest discover ./tests/extras
```

Similarly, you can run tests of the whole package by running:
```
python -m unittest discover ./tests
```

## Documentation
Full documentation along with some usage examples can be found [here](https://ruleminer.github.io/decision-rules/).

## License
The software is licensed under the MIT License. See the LICENSE file for details.
inventory-card-reader 0.0.16
Read, process, and integrate data from inventory cards.
Requires Python: >=3.8

# Inventory Card Reader
This repository contains code to read, process, and integrate data from inventory cards. Currently it uses (pero-ocr)[https://github.com/DCGM/pero-ocr] for text recognition. 

## Installation
1. Install the package from pip using `pip install inventory-card-reader`
2. Or clone the repository and do your thing

## Prepare config files
- Create a config yaml to specify text regions in the inventory card and their mapping to table columns
```
    regions: 
        <<key1>>: [x1,y1,x2,y2]
        <<key2>>: [x1,y1,x2,y2]
        <<...>>: [...]
```
Where key define the column names to be extracted and x1,y1,x2,y2 denote the relative coordinates of the region where the values for the respective columns can be found in the inventory card. (x1,y1) denote the coordinates of the top left corner of the region, and x2,y2 the bottom right. 

For example:
```
    regions:
        Gegenstand: [0.047,0,1,0.077],
        "Inv. Nr.": [0.047,0.077,0.275,0.135],
```
- Download the pero ocr model weights provided by the pero developers (here)[https://nextcloud.fit.vutbr.cz/s/NtAbHTNkZFpapdJ], unzip the file and store the .pt and .pt.cpu files in the pero_resources folder
- Start the extraction by invoking `read_inventory_cards <<config>> <<input_folder>>` where `<<config>>` is the path to the config yaml file described above and `<<input folder>>` the path to a directory of scanned jpgs of inventory cards to be processed.

## Contributing
I'm happy to receive feedback and code contributions. Feel free to open issues or create pull requests.
zfx-multiproc 1.0.0
这是一个用于 多进程 相关功能 Python 工具库,欢迎各路大神反馈意见及提出需求。企鹅号：424491679,企鹅群：961483644


    免责声明:
    本模块是“按原样”提供的，没有任何明示或暗示的保证。在任何情况下，作者或版权持有者均不对因使用本模块而产生的任何索赔、损害或其他责任负责，无论是在合同、侵权或其他情况下。

    使用本模块即表示接受此免责声明。如果您不同意此免责声明的任何部分，请勿使用本模块。

    本模块仅供参考和学习用途，用户需自行承担使用本模块的风险。作者对因使用本模块而造成的任何直接或间接损害不承担任何责任。

    作者保留随时更新本免责声明的权利，恕不另行通知。最新版本的免责声明将在模块的最新版本中提供。
stackexchange_process 0.0.1
This is a template repository for Python projects that use Poetry for their dependency management.
Requires Python: <4.0,>=3.8

# stackexchange-process

[![Release](https://img.shields.io/github/v/release/dosomethingbyme/stackexchange-process)](https://img.shields.io/github/v/release/dosomethingbyme/stackexchange-process)
[![Build status](https://img.shields.io/github/actions/workflow/status/dosomethingbyme/stackexchange-process/main.yml?branch=main)](https://github.com/dosomethingbyme/stackexchange-process/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/dosomethingbyme/stackexchange-process/branch/main/graph/badge.svg)](https://codecov.io/gh/dosomethingbyme/stackexchange-process)
[![Commit activity](https://img.shields.io/github/commit-activity/m/dosomethingbyme/stackexchange-process)](https://img.shields.io/github/commit-activity/m/dosomethingbyme/stackexchange-process)
[![License](https://img.shields.io/github/license/dosomethingbyme/stackexchange-process)](https://img.shields.io/github/license/dosomethingbyme/stackexchange-process)

This is a template repository for Python projects that use Poetry for their dependency management.

- **Github repository**: <https://github.com/dosomethingbyme/stackexchange-process/>
- **Documentation** <https://dosomethingbyme.github.io/stackexchange-process/>

## Getting started with your project

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b main
git add .
git commit -m "init commit"
git remote add origin git@github.com:dosomethingbyme/stackexchange-process.git
git push -u origin main
```

Finally, install the environment and the pre-commit hooks with

```bash
make install
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to main, or when you create a new release.

To finalize the set-up for publishing to PyPI or Artifactory, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-poetry/features/codecov/).

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
sea-starterkit 0.2.0
A module to designed to perform package installations, and verification of install,

# pfse_starterkit
A Python package that installs all required packages for the StructuralPython Python for Structural Engineers (pfse) course.

This installs the following packages:

* streamlit
* numpy
* pandas
* openpyxl
* lxml
* shapely
* matplotlib
* plotly
* sectionproperties
* concreteproperties
* PyNiteFEA
* pycba
* scipy
* handcalcs
* forallpeople
* pytest
* ipytest
* rich
* tqdm
* papermodels
* xlwings
* black
* jupyterlab
* IPython
* ipywidgets
* ipykernel
* jupyterlab-mathjax3 (as an option)
* nbconvert
* flit
* python-docx
* constable
* black