semvec/pypi-packages huggingface dataset:
pytest-canvas 0.1.0
A minimal pytest plugin that streamlines testing for projects using the Canvas SDK.
Requires Python: >=3.11

# pytest-canvas

**pytest-canvas** is a minimal pytest plugin that streamlines testing for projects using the Canvas SDK.

## What it does

- **Auto-loads the Canvas SDK** at the earliest point in the pytest lifecycle.
- **Wraps each test in a Django database transaction** to ensure isolation and consistency.
- **Removes boilerplate**—you no longer need to explicitly depend on `db` or `transactional_db`.
package-app-behavior 1.0.0
Un package Django pour la simulation du comportement des applications

"""
# Django Blog App

Une application Django réutilisable pour créer un blog simple.

## Installation

```bash
pip install django-blog-app
```

## Configuration

1. Ajoutez 'django_blog_app' à vos INSTALLED_APPS :

```python
INSTALLED_APPS = [
    # ... autres apps
    'django_blog_app',
]
```

2. Incluez les URLs dans votre urls.py principal :

```python
from django.urls import path, include

urlpatterns = [
    # ... autres URLs
    path('blog/', include('django_blog_app.urls')),
]
```

3. Exécutez les migrations :

```bash
python manage.py makemigrations django_blog_app
python manage.py migrate
```

## Utilisation

L'application fournit :
- Un modèle Post pour les articles
- Un modèle Comment pour les commentaires
- Des vues pour afficher la liste des posts et le détail
- Une interface d'administration
- Des templates de base

## Personnalisation

Vous pouvez surcharger les templates en créant vos propres templates
dans votre projet Django sous django_blog_app/.
"""
mappings_utility 0.2.7
mapping utility tool for sdmx based partial mappings
Requires Python: <4.0,>=3.12

An SDMX mapping utility to generate partial key maps for referential metadata.
Although not designed to do that, data mappings are also possible.
The package is structured in such a way that it can be used as a package without the FASTAPI overhead.

Three methods are available:
- *map_withFile* GET method (receiving the mapping source and mapping rules both as file references)
- *map_withURN* GET method (receiving the mapping source  as file and the mapping rules as SDMX registry endpoint + mapping ID)
- *map_json_withURN* POST method (receiving the mapping source as the body of the request in json - pandas dataframe dictionary style - and mapping rules as SDMX registry endpoint + mapping ID) (added in version 0.1.1)

A typical usage example in package mode:

```python
    from mappings_utility.sdmx_mapping_utility import SDMXMappingUtility 
    
    mf = Path('to-mapping-artefacts.xml')
    sf = Path('to-file-to-be-mapped.csv')
    mu = SDMXMappingUtility.initialise_with_file(mf, sf, sid_based=False)
    df = mu.generate_mappings_partial_keys(
        includeSourceColumns=False,
        includeAttributesMeasures=False,
        nulledFixedTargets=True,
        writeTargetStructInfo=False,
        dropInvalid=False,
        sid_based=False
        )
    df.to_csv(Path('mapped-file.csv'), index=False)
```
The parameters fine-tune the output file:
- includeSourceColumns: True/False; when set to True the resulting file would include the fields of the source csv file  
- includeAttributesMeasures: True/False; when True it will include the attribute columns and obs_value, otherwise it will suppress those from the output (this is needed for referential metadata partial key mappings)
- nulledFixedTargets: True/False, when True fixed values in the target mapping will be set to Null, also a partial-key mapping motivated feature
- writeTargetStructInfo: True/False, when True it adds or maps columns necessary for the output to be SDMX-CSV compliant
- dropInvalid: True/False, when set to True the output only contains valid mappings (although validity checks are lazy), invalid rows are ommitted; with False the returned dataframe will contain cells with a special string highlighting the positions where the target value could not be determined
- sid_based: True/False, when set to True the resulting file will contain a Series ID (SID) field with the dimensions of the target data model dot-concatenated

# Changelog:
- Version 0.2.3
Introduced validation for implicit maps (target codes are checked whether they are in the referenced codelist - constraints are not yet taken into account)
- Version 0.2.4
Bug-fix for regex substitution (previously substitutions with more than one substitution failed)
- Version 0.2.5
    - Attributes scaffolding for future features (validates coded attributes)
    - Bug-fix for optional attributes missing when all values are null
infj 1.0.1
A Python package that provides the infj complex number literal (0+∞j)
Requires Python: >=3.6
Keywords: infj, complex numbers, infinity, literals, mathematics, mbti

# INFJ

A Python package that provides the `infj` complex number literal (0+∞j).

## Overview

`infj` is a simple yet useful Python package that defines a complex number literal with zero real part and infinite imaginary part. This can be useful in mathematical computations involving complex infinity.

`infj` is also known as the INFJ Personality Type in the Myers-Briggs Type Indicator (MBTI).

## Installation

You can install `infj` using pip:

```bash
pip install infj
```

## Usage

```python
from infj import infj

# Basic usage
print(infj)  # Output: infj
print(type(infj))  # Output: <class 'complex'>

# Mathematical properties
print(infj.real)  # 0.0
print(infj.imag)  # inf

# Comparisons
print(infj == complex(0, float('inf')))  # True

# Mathematical operations
from math import inf

result = inf + infj
print(result)  # (inf+infj)
```

## Requirements

- Python 3.6 or higher
- No external dependencies (uses only standard library)

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

## License

This project is licensed under the WTFPL License - see the [LICENSE](LICENSE) file for details.

## Author

KoolShow - `<koolshowtec@gmail.com>`
apic-tools 0.1.0
A Python package for APIC functionality
Requires Python: >=3.8
Keywords: python, apic

# pyAPIC
[![PyPI](https://img.shields.io/pypi/v/pyAPIC.svg)](https://pypi.org/project/pyAPIC/)
[![Documentation](https://readthedocs.org/projects/pyapic/badge/?version=latest)](https://pyapic.readthedocs.io/)

## Terminology

Recent versions adopt clearer naming for acquisition parameters:

| New name   | Old name      | Description                            |
|------------|---------------|----------------------------------------|
| `illum_px` | `freqXY_calib`| LED positions in pixel coordinates     |
| `illum_na` | `na_calib`    | LED positions expressed in NA units    |
| `system_na`| `na_cal`      | System numerical aperture              |
| `system_na_px` | `na_rp_cal` | System NA in pixel units               |
| `pixel_size` | `dpix_c`    | Camera pixel size                      |

The old attribute names remain available as aliases for backward
compatibility but new code should prefer the terms above.

## Development setup

Install runtime dependencies:

```bash
pip install -r requirements.txt
```

Then install the package with its development extras:

```bash
pip install -e .[dev]
```

This provides tooling for testing and linting the project.

## Public API

The following convenience imports are available from the top-level package:

```python
from pyAPIC import (
    Case,
    ImagingData,
    ReconParams,
    load_mat,
    to_pixel_coords,
)

```

They mirror the underlying modules located in ``pyAPIC.core`` and
``pyAPIC.io`` and may be used in examples and user code.